262 items - (B2C), suppliers (B2B) and employees (B2E) to transact with the organization from the internet ...... Its products include Marketing Automation, CRM,.
Adoption, Implementation and Usage of Enterprise Systems: An Empirical Study THESIS SUBMITTED FOR THE AWARD OF THE DEGREE OF
Ph. D (BUSINESS ADMINISTRATION)
BY
NAIM AHMAD
Under the Supervision of Dr. ASIF ALI SYED Asst. Professor Department of Business Administration Faculty of Management Studies & Research Aligarh Muslim University Aligarh 202002
Dr. ABID HALEEM Professor Department of Mechanical Engineering Faculty of Engineering and Technology Honorary Director IOCL Jamia Millia Islamia, New Delhi Delhi 110025
DEPARTMENT OF BUSINESS ADMINISTRATION FACULTY OF MANAGEMENT STUDIES & RESEARCH ALIGARH MUSLIM UNIVERSITY ALIGARH (INDIA)
2012
To the Supreme Creator and His righteous servant
DECLERATION I do hereby declare that the thesis titled ‘ADOPTION, IMPLEMENTATION AND USAGE OF ENTERPRISE SYSTEMS: AN EMPIRICAL STUDY’ Submitted to the Faculty of Management Studies and Research, Aligarh Muslim University for the degree of Ph.D. (Business Administration) is a record of original work done by me under the supervision
and guidance of DR ASIF ALI SYED, Assistant Professor, Faculty of
Management Studies and Research, Aligarh Muslim University (Internal Supervisor) and DR ABID HALEEM, Professor, Mechanical Engineering , Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi (External Supervisor) and it has not, previously formed the basis for the award of any degree, diploma, associateship, fellowship or other similar title to any candidate of any university.
Place: New Delhi
Signature of the candidate
Date:
Naim Ahmad
i
CERTIFICATE This is to certify that the thesis titled ‘ADOPTION, IMPLEMENTATION AND USAGE OF ENTERPRISE SYSTEMS: AN EMPIRICAL STUDY’ Submitted to the Faculty of Management Studies and Research, Aligarh Muslim University in partial fulfillment of the requirements for the award of the degree of Ph.D. (Business Administration) is a record of original work done by Mr. Naim Ahmad, during the period of his study in the department of Business Administration, Faculty of Management Studies and Research, Aligarh Muslim University under my supervision and guidance. This thesis has not formed the basis for the award of any degree, diploma, associateship, fellowship or other similar title to any candidate of any university.
Place: Aligarh
Dr. Asif Ali Syed
Date:
Internal Supervisor
ii
CERTIFICATE This is to certify that the thesis titled ‘ADOPTION, IMPLEMENTATION AND USAGE OF ENTERPRISE SYSTEMS: AN EMPIRICAL STUDY’ Submitted to the Faculty of Management Studies and Research, Aligarh Muslim University in partial fulfillment of the requirements for the award of the degree of Ph.D. (Business Administration) is a record of original work done by Mr. Naim Ahmad, during the period of his study in the department of Business Administration, Faculty of Management Studies and Research, Aligarh Muslim University under my supervision and guidance. This thesis has not formed the basis for the award of any degree, diploma, associateship, fellowship or other similar title to any candidate of any university.
Place: New Delhi
Dr. Abid Haleem
Date:
External Supervisor
iii
Acknowledgements This great endeavor is not possible without the support of Almighty Allah and different individuals of the society. So I would like to take this opportunity to thank with the bottom of my heart to Almighty Allah. Moreover I would like to mention names of those who have been instrumental in carrying out this research directly or indirectly.
First and foremost are my two supervisors Dr. Abid Haleem and Dr Asif Ali Syed for their continued and persistent support intellectually, procedurally and morally. They did not allow this work to stagnate in all the difficult times and ensured in all possible ways that this work sees the light of the day. They also made sure that this work progresses in the spirit of creativity and innovation with their valuable inputs.
I would also like to pay my sincere thanks to Dr.Mohammad Israrul Haque, professor & Chairman and Dr. Mohammad Khalid Azam, professor & Dean for sparing their valuable time for this research work. They ensured that I meet every milestone in the available timeframe and meet the international standards of doctoral research.
I would also like to thank professor S. Neelamegham, Director, NIILM CMS, New Delhi. He introduced me to the field of management and allowed me to grow in the discipline by offering numerous opportunities such as participation in the national and international conferences. Moreover I would like to thank my senior colleague professor Prashant Duttagupta at NIILM CMS and former vice president Baan. He would discuss with me information technology management issues at length.
I can easily trace back to my stay at the University of South Alabama where I interacted with Professor Roy J. Daigle, (late) Professor Gene Simmons, Professor iv
David Langan and Professor Bart Longenecker. I would like to thank these great professors for shaping my knowledge in the field of computer and information sciences in multiple ways.
Perseverance and firmness to face the challenges of life and maintaining mental calmness can best be learnt by the religion of Islam. I would like to thank my religious gurus Maulana Salman Awan, Maulana Abdul Hameed Rahmani and Dr Ubaidurrahman Madni for their selfless lecturing and inducing practice of Islam.
Finally how one can forget the all time support of family and friends. Among others my mother, wife and children always allowed me to concentrate on my research and sacrificed their share of time. And also thanks to my friends Aslam, Ateeq, Fahim, Najmul, Ihtisham, Fawwad, Hammad, Khalid, Idrees and Riyaz among others.
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PREFACE The world has gone through various transformations in the recent past. The rate of change is increasing day by day. We have ushered from the industrial revolution to the information age in a century period. Though, throughout the ages the importance of the information cannot be denied. Information used to be at the disposal of rich and ruling class. Due to advancement in the information and communication technology (ICT), it has become a commodity.
Today information is held in all sorts of high capacity, permanent devices. There are numerous high speed medium of information interchange. Increasingly decisions are being backed up by this information rather than intuition. The hyper competitive environment is increasing pressure on to build sound information infrastructure. Information architects have been struggling to connect all the business processes into one seamless mesh. Where, information can flow in real time preserving the accuracy and secrecy.
This information infrastructure is built upon the foundation of information technology (IT) or ICT. Enterprise Systems such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) etc. constitutes the core interface of information infrastructure. Today these systems have become commonplace globally. India though the late entrant has adopted these systems at a rapid pace. And these systems can be seen implemented across industries irrespective of the size of the organization.
Apart from few indigenous Enterprise Systems vendors the market rests in the hands of global players. Among the global vendors such as SAP AG, Microsoft, Oracle, Sage, IFS, Infor Baan, Oracle, LAWSON, ESS, QAD and SSA, SAP AG is a definite vi
leader in the Indian market. These vendors offer packaged software with a variety of parameters to suit the software to the individual organization. The implementation of the systems is normally undertaken by third party IT consultancy firms. The whole journey entails the huge cost and risk.
Academicians and IT consultancy firms have been studying and proposing solution to minimize risks since the early inception of this technology. The research areas range widely from pure organizational issues such as change management to the hardcore technological components of the software and underlying information technology architecture. The process of implementation plays a key role in the success of these systems.
In the information technology area defining success is one of the most challenging issues. The impacts of IT are far reaching. The benefits are not limited to IT department only rather they affect the organizational, strategic, operational and managerial dimensions as well. Some researches assess the success of these systems from the perspective of end users view. Nevertheless today use of IT in business has become highly debatable as to whether it adds strategic value or is merely a commodity.
This study aims to contribute to the Enterprise Systems research in the area of implementation and usage or success of these systems in Indian context. Moreover some parameters of adoption will also be studied. Study in India, being among the fastest growing economy and developing economy will increase in understanding of ES issues in similar context. This work also tries to link the critical success factors of implementation with the actual success of these systems.
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Table of Contents DECLERATION.................................................................................................................. i CERTIFICATE ................................................................................................................... ii CERTIFICATE .................................................................................................................. iii Acknowledgements ............................................................................................................ iv PREFACE .......................................................................................................................... vi List of Tables ..................................................................................................................... xi List of Figures .................................................................................................................. xiv List of Abbreviations ....................................................................................................... xvi Chapter One INTRODUCTION ......................................................................................... 1 1.1 Introduction .......................................................................................................... 1 1.2 Information Technology in Organizations ........................................................... 1 1.3 About Enterprise Systems .................................................................................... 4 1.4 Evolution of Enterprise Systems .......................................................................... 5 1.5 Definitions of Enterprise Systems ....................................................................... 7 1.6 Business Process .................................................................................................. 9 1.7 Components of Enterprise Systems ................................................................... 10 1.8 The architecture of Enterprise Systems ............................................................. 12 1.9 Enterprise Systems Research Directions ........................................................... 13 1.10 Chapter Scheme ............................................................................................... 18 1.11 Conclusion ....................................................................................................... 20 Chapter Two IT INDUSTRY IN INDIA .......................................................................... 22 2.1 Introduction ........................................................................................................ 22 2.2 Government Initiatives....................................................................................... 22 2.3 IT-ITeS Industry ................................................................................................ 25 2.4 Cloud Computing ............................................................................................... 28 2.5 Enterprise Systems Industry .............................................................................. 30 2.6 Enterprise Systems Vendors .............................................................................. 31 2.7 Research Perspective for ES in Indian Organizations ....................................... 37 2.8 Conclusion ......................................................................................................... 40 Chapter Three LITERATURE REVIEW ......................................................................... 42 3.1 Introduction ........................................................................................................ 42 3.2 Lifecycle of Enterprise Systems ........................................................................ 43 3.3 Adoption of Enterprise Systems ........................................................................ 43 3.4 Implementation of Enterprise Systems .............................................................. 53 3.5 Critical Success Factors of ES project ............................................................... 57 viii
3.6 Grouping of Critical Success Factors ................................................................ 74 3.7 Usage of Enterprise Systems ............................................................................. 78 3.8 Relationship between Critical Success Factors and ES Benefits ....................... 84 3.9 Conclusion ......................................................................................................... 87 Chapter Four RESEARCH METHODOLOGY AND DESIGN ...................................... 89 1.1 Introduction ........................................................................................................ 89 4.2 Research Approach ............................................................................................ 89 4.2.1 Structural Equation Modeling ..................................................................... 90 4.2.2 Reflective versus Formative Constructs ..................................................... 91 4.2.3 Partial least squares (PLS) .......................................................................... 92 4.2.4 Construct Reliability and Validity .............................................................. 94 4.2.5 Assessment of inner or structural model ..................................................... 95 4.3 Statement of the Problem ................................................................................... 96 4.4 Objectives of the Research................................................................................. 98 4.5 Research Framework ......................................................................................... 98 4.5 Hypothesis of the Study ................................................................................... 106 4.7 Population, Sampling Frame and Sample ........................................................ 112 4.8 Case Collection Procedure ............................................................................... 112 4.9 Sources of data ................................................................................................. 112 4.10 Case Analysis ................................................................................................. 115 4.11 Scope of the Study ......................................................................................... 119 4.12 Limitation of the Study .................................................................................. 120 4.13 Conclusion ..................................................................................................... 121 Chapter Five Analysis and Interpretation ....................................................................... 123 5.1 Introduction ...................................................................................................... 123 5.2 Flow chart of Research Phases ........................................................................ 123 5.3 Demographic Profile of the cases .................................................................... 124 5.4 Ranking of Adoption Reasons, CSFs and ES Benefits .................................... 129 5.5 Chi-Square Test to Study the Adoption Reasons ............................................. 132 5.6 Chi-Square Test to Study the ES Benefits ....................................................... 133 5.7 Results for PLS Simple Model: Model-1......................................................... 141 5.8 Results for PLS Simple High Item Frequency Model: Model-2 ..................... 153 5.9 Results for PLS Factored Model: Model-3 ...................................................... 159 5.10 Conclusion ..................................................................................................... 169 Chapter Six CONCLUSIONS AND RECOMMENDATIONS ..................................... 172 6.1 Introduction ...................................................................................................... 172 6.2 Foundation and Results of Earlier Research .................................................... 172 ix
6.3 Summary of Findings ....................................................................................... 178 6.4 Discussion ........................................................................................................ 181 6.5 Discussion of PLS Models ............................................................................... 184 6.6 Managerial Implications .................................................................................. 193 6.7 Limitations and Future Research ..................................................................... 196 6.8 Conclusion ....................................................................................................... 197 Bibliography ................................................................................................................... 200 Appendix A: Customer Success Stories ......................................................................... 217 Appendix B Sample Case: SAP Customer Success Story Aditya Birla Group‘s Carbon Black Business ................................................................................................................ 230
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List of Tables Table 1.1 Timeline of Evolution of ES .......................................................................... 8 Table 1.2 The four layers in ERP II ............................................................................. 11 Table 2.1 Performance of Industry between 2006-07 and 2011-12............................. 26 Table 2.2 Segment wise IT-ITeS Exports .................................................................... 27 Table 2.3 Segment wise domestic IT-ITES revenue (INR Bn) ................................... 28 Table 3.1 Literature Survey on Reasons for Adoption for ES ..................................... 44 Table 3.2. Results of final iteration of content analysis ............................................... 48 Table 3.3 Literature Survey on CSF sub factors .......................................................... 58 Table 3.4 CSF sub factors and their presence in research articles ............................... 62 Table 3.5 CFSs framework proposed by Cantu (1999) ............................................... 77 Table 3.6 CSF and their sub factors ............................................................................. 79 Table 4.1 Adoption Reasons Framework..................................................................... 99 Table 4.2 CSF Framework ......................................................................................... 100 Table 4.3 ES benefit framework derived from Shang & Seddon‘s ( 2002) ES benefit framework .................................................................................................................. 103 Table 4.4 Set of hypotheses to be tested for association between adoption reasons and the size of the organization ........................................................................................ 107 Table 4.5 Set of hypotheses to be tested for association between adoption reasons and industry of the organization ....................................................................................... 107 Table 4.6 Set of hypotheses to be tested for association between ES Benefits and the size of the organization .............................................................................................. 108 Table 4.7 Set of hypotheses to be tested for association between ES Benefits and industry of the organization ....................................................................................... 109 Table 4.8 Details of Sources of Cases ....................................................................... 113 xi
Table 4.9 Analyzed Data of a Sample Case ............................................................... 115 Table 5.1 Vendor wise case distribution .................................................................... 126 Table 5.2 Industry and size distribution ..................................................................... 128 Table 5.3. Relative Ranking of Reasons for Adoption .............................................. 129 Table 5.4 Relative Ranking of CSF sub-factors ........................................................ 131 Table 5.5 Relative Ranking of the ES Benefits ......................................................... 132 Table 5.6 Pearson‘s Chi-Square test for Reasons for Adoption versus Size of the Organization ............................................................................................................... 135 Table 5.7 Pearson‘s Chi-Square test for Reasons for Adoption versus Industry of the Organization ............................................................................................................... 136 Table 5.8 Pearson‘s Chi-Square test for ES Benefits versus Size of the Organization .................................................................................................................................... 138 Table 5.9 Pearson‘s Chi-Square test for ES Benefits versus Industry of the Organization ............................................................................................................... 139 Table 5.10 VIF measure to assess multicolinearity for Model-1a ............................. 142 Table 5.11 Bootstrap results to assess weight significance for Model-1a ................. 144 Table 5.12 Structural Model Assessments for Model-1a........................................... 146 Table 5.13 VIF measure to assess multicolinearity for Model-1b ............................. 146 Table 5.14 Bootstrap results to assess weight significance for Model-1b ................. 148 Table 5.15 Structural Model Assessments for Model-1b .......................................... 149 Table 5.16 VIF measure to assess multicolinearity for Model-1c ............................. 151 Table 5.17 Bootstrap results to assess weight significance for Model-1c ................. 151 Table 5.18 Structural Model Assessments for Model-1c........................................... 152 Table 5.19 VIF measure to assess multicolinearity for Model-2a ............................. 154 Table 5.20 Bootstrap results to assess weight significance for Model-2a ................. 156
xii
Table 5.21 Structural Model Assessments for Model-2a........................................... 156 Table 5.22 VIF measure to assess multicolinearity for Model-2b ............................. 157 Table 5.23 Bootstrap results to assess weight significance for Model-2b ................. 158 Table 5.24 Structural Model Assessments for Model-2b .......................................... 159 Table 5.25 VIF measure to assess multicolinearity for Model-3a ............................. 160 Table 5.26 Bootstrap results to assess weight significance for Model-3a ................. 162 Table 5.27 Structural Model Assessments for Model-3a........................................... 164 Table 5.28 VIF measure to assess multicolinearity for Model-3b ............................. 165 Table 5.29 Bootstrap results to assess weight significance for Model-3b ................. 167 Table 5.30 Structural Model Assessments for Model-3b .......................................... 168 Table 6.1 Assessments of Model-1 and Model-2 ...................................................... 186 Table 6.2 Assessments of Model-3a and Model-3b................................................... 188
xiii
List of Figures Figure 1.1 IT Infrastructure Deployed at Multiple Level .............................................. 2 Figure 1.2 An Integrated IT Infrastructure with Ten Capability Clusters ..................... 3 Figure 1.3 Anatomy of an Enterprise Systems .............................................................. 5 Figure 1.4 Process of Sales Order Entry (IDEF3) ....................................................... 10 Figure 1.5 The conceptual framework for ERP II ....................................................... 12 Figure 2.1 Performance of Industry between 2006-07 and 2011-12 ........................... 26 Figure 2.2 Segment wise IT-ITeS Exports revenue (US $ Bn) ................................... 27 Figure 2.3 Segment wise domestic IT-ITES revenue (INR Bn) .................................. 28 Figure 2.4 Service Models of Cloud Computing ......................................................... 30 Figure 2.5 Full Suite ERP (Modules of IFS ES) .......................................................... 35 Figure 2.6 ebizframe ERP Software (Modules of ESS' ES) ........................................ 37 Figure 2.7 The growth of software revenues in India 1984-2002................................ 38 Figure 3.1 Conceptual research model of ERP implementation success in China ...... 85 Figure 3.2 Taxonomy for ERP critical factors ............................................................. 86 Figure 3.3 Theoretical framework for ERP Implementation Management ................. 86 Figure 4.1 Theoretical Framework to Develop Structural Model ................................ 91 Figure 4.2 Reflective Latent Variable .......................................................................... 91 Figure 4.3 Formative Composite Variable................................................................... 91 Figure 4.4 A Two-Step Process of PLS Path Model Assessment................................ 93 Figure 4.5 Framework of ES Success Predictive Model ........................................... 105 Figure 4.6 PLS Simple Model with Hypothesis Label .............................................. 110 Figure 4.7 PLS Factored Model with Hypotheses Labels ......................................... 111 Figure 4.8 Pie chart for sources of cases .................................................................... 114 Figure 5.1 Phase-I Flow chart for ES Adoption Study .............................................. 124 xiv
Figure 5.2 Phase-II Flowchart for Development of Predictive Structural Model...... 125 Figure 5.3 Pie Chart for Vendor wise case distribution ............................................. 126 Figure 5.4 Industry and size distribution ................................................................... 128 Figure 5.5 Structural Model Equation Model-1a ....................................................... 146 Figure 5.6 Structural Model Equation Model-1b ....................................................... 150 Figure 5.7 Structural Model Equation Model-1c ....................................................... 153 Figure 5.8 Structural Model Equation Model-2a ....................................................... 157 Figure 5.9 Structural Model Equation Model-2b ....................................................... 159 Figure 5.10 Impact and contribution of the variables to ES Benefits: Model-3a ...... 165 Figure 5.11 Impact and contribution of the variables to ES Benefits: Model-3b ...... 169 Figure 6.1 Technological and operational drivers (90 SMEs and large enterprises adopting ES system) .................................................................................................. 174 Figure 6.2 CSF sub-factors frequency in literature and Indian Cases ....................... 184 Figure 6.3 Path coefficients for Model-3a and Model-3b between CSF factors and ES Benefits ...................................................................................................................... 190 Figure 6.4 Effect Size of Predictor variables such as CSF factors on ES Benefits for Model-3a and Model-3b ............................................................................................ 190
xv
List of Abbreviations AVE
: Average Variance Extracted
B2B
: Business-to-business
B2C
: Business-to-consumer
B2E
: Business-to-employee
BI
: Business Intelligence
CAGR
: Compound Annual Growth Rate
CMIS
: Content Management Interoperability Services
CPM
: Corporate performance management
CRM
: Customer Relationship Management
CSP
: Cloud Service Provider
EAI
: Enterprise application integration
EAM
: Enterprise Asset Management
EIM
: Enterprise Information Management
ELM
: Employee Lifecycle Management
EPM
: Enterprise Performance Management
ERP
: Enterprise Resource Planning
ES
: Enterprise Systems
GRC
: Governance, Risk and Compliance
GRP
: Government Resource Planning
HCM
: Human Capital Management
IaaS
: Infrastructure as a Service
ICT
: Information and Communication Technology
KPI
: Key Performance Indicators
LE
: Large Enterprises xvi
MRP
: Materials Requirement Planning
NIC
: National Industrial Classification
PaaS
: Platform as a Service
PLM
: Product Lifecycle Management
PLS
: Partial Least Square
SaaS
: Software as a Service
SCM
: Supply Chain Management
SME
: Small and Medium Enterprises
SOA
: Service Oriented Architecture
SOP
: Standard Operating Procedure
SRM
: Supplier Relationship Management
VIF
: Variance Inflation Factor
xvii
Chapter One INTRODUCTION 1.1 Introduction Competition is increasing day by day and companies are operating on raiser thin margins. Sustaining the business requires innovation and proactive approach in anticipation of future issues and problems. It has become mandatory to maintain optimal levels of factors like productivity, customer service and cycle times. Information technology has come out as an invincible tool in the arsenal to ward off competition. This chapter starts off with the introduction of Information Technology, Enterprise Systems, Architecture of Enterprise Systems. Then it discusses the latest trends and research issues addressed in this area. And finally gives the chapter scheme of this thesis and conclusion of the present chapter.
1.2 Information Technology in Organizations Investments in IT are ever increasing and none of the organization wants to miss this band wagon of IT. A sizable portion of 4.2% of annual revenue is spent on IT (Gormoloski, Grigg, & Potter, 2001). On an average organizations‘ 50 percent capital expenditure budget is utilized by IT that has steadily increased every decade from less than 5% in the year 1965 (Carr, 2003).
The core functions of the information technology are data storage, data transport and data processing. The cost to carry out these functions, are decreasing as hypothesized by Moore‘s Law ―Every two years the number of transistors on integrated circuit doubles‖. The information technology can be thought of a bundle of shared services to cater to the need of communication and foundation for implementing business 1
applications (Byrd & Turner, 2000). IT infrastructure is defined as a set of shared IT resources which is a foundation for both communication across the organization and the implementation of present/future business applications (Chanopas, Krairit, & Khang, 2006). IT infrastructure is composed of two components Technical consisting of hardware, software, network, telecommunications, applications and tangible IT resources and Human referring to knowledge and skill required to orchestrate the Technical components (Chanopas, Krairit, & Khang, 2006). Weill, Subramani, & Broadbent (Fall 2002) collected data belonging to the period of 1990 to 2001. They gathered the data for 180 business initiatives from 118 businesses in 89 enterprises. The enterprises selected were top three in their industry. After analysis of the data collected they proposed that the IT infrastructure has to be thought of in terms of services since the agreement level can be made stable whereas underlying technology is more dynamic.
Figure 1.1 IT Infrastructure Deployed at Multiple Level Source: (Weill, Subramani, & Broadbent, Fall 2002) These infrastructural services are deployed at multiple level enterprise wide or business unit level as shown in Figure 1.1. Where to place a capability is a strategic 2
decision pursued by the concerned organization. For instance keeping a single point of customer contact requires capability to be developed at the enterprise wide scale so that the data can be shared and facilitate cross selling.
They further identified 70 IT infrastructural services and grouped them into 10 capability clusters as shown in Figure 1.2. Six layers were defined as the physical layer of IT infrastructure capability namely Channel Management, Security and Risk, Communications, Data Management, Application Infrastructure and IT Facilities Management. And reaming four clusters IT Management, IT Architecture and Standards, IT Education and IT Research and Development were considered as management oriented IT infrastructure capability. Applications like Enterprise Systems such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) fall under the cluster of Application Infrastructure.
Figure 1.2 An Integrated IT Infrastructure with Ten Capability Clusters Source: (Weill, Subramani, & Broadbent, Fall 2002)
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Technical components of IT are readily available and more or less have become commodity input to the whole infrastructure. The distinction lies in skills and abilities to utilize them to gain strategic advantage. Whereas some authors like Carr (2003) have gone to the extent that IT doesn‘t add any strategic value and whole of IT has become an infrastructural technology similar to railroads, telegraph, electricity etc.
1.3 About Enterprise Systems Most organizations have, broadly speaking, following divisions procurement, production and operations inventory management, finance, marketing, and sales and distribution. Each department has got its own processes and procedures. Information and control from one department to another department need to be coordinated in order to execute a business. Traditionally these departments used to work in a very fragmented fashion. That often resulted in the creation of information silos.
Every department used to tackle its own IT initiatives often in isolation with the overall strategic direction of the organization. At times same software was implemented in multiple business units incurring unnecessary cost. As we know software is nothing but a set of complex code written in a programming language. Due to infinite scenarios possible with any simple software, testing the software is a Herculean task. As time passed by these isolated software could not cope on the important parameters of IT like scalability, portability, robustness and integration.
The innovations in the information technology have led to the creation of a perfect network of information interchange that allows the removal of all the hassle in information sharing. Moreover software packages have been developed that can ideally suit to any organization‘s processes. These packages are known as Enterprise Systems that are set of applications that interconnect the different processes and 4
procedures of the organization. They utilize a central database for all the data needs (Davenport T. H., July-August 1998).
Figure 1.3 Anatomy of an Enterprise Systems Source: (Davenport T. H., July-August 1998)
1.4 Evolution of Enterprise Systems Enterprise Systems are also known as enterprise applications or business applications. These systems allow the seamless integration of information flow in the organizations internally and externally. The current ES have a long evolutionary history with them. The promoters of ES have been broadening the canvass of integration from financial and accounting, production and manufacturing, marketing and sales, logistics and distribution, human resource to strategic functions (Davenport T. H., July-August 5
1998). The offerings are increasing day by day along with the complexity and failures of implementations (Xue, Liang, Boulton, & Snyder, Sep 2005). Accordingly researchers and practitioner have been labeling these systems as they are growing.
Inventory management and control processing software were famous in the fifties and sixties. These systems were developed on mainframe platform using third generation programming languages such as Fortran and Cobol. The focus of these systems was on managing and tracking inventory effectively and efficiently by automating inventory management and production schedules.
The next in line were Manufacturing Resource Planning (MRP) software in the seventies. These software were developed on the same technological paradigm as the previous one. The focus of these systems got enlarged to include sales and marketing by linking the planning of product or parts requirements to the master production schedule.
The next version of Manufacturing Resource Planning was termed as MRP II. They were developed on the mainframe legacy platform using fourth generation database software and manufacturing applications. The focus of these systems was further refined to manufacturing strategy and quality control, and included the support for designing production supply chain processes.
In the nineties the most comprehensive software packages, Enterprise Resource Planning (ERP) came into existence. These systems had originated from in MRP and MRP II (Chung & Synder, 2000). They were developed on multiple platform mainframe and client-serever using fourth generation database software and packages software application. The focus of these systems was application integration and
6
customer service by automating and optimizing all the processes sales and distribution, finance and accounts, human resource, procurement etc.
At one end promoters were trying to integrate information flow internally and ended up in developing ERP packages. On the other hand some software vendors were working on to integrate the external information flow from the customer and supplier side and developed customer relationship management (CRM) and supply chain management (SCM) software packages. The ERP vendors integrated the functionalities of SCM and CRM in their packages (Davenport & Brooks, 2004). Hence researchers and practitioners have started using the term ERP II (Moller, 2005). These systems are developed on web based client-server platform and integrated with fifth generation applications like SCM, CRM, SFA etc. The focus of these systems is agility and customer centric global environment by e-enabelment. Following Table 1.1 describes the timeline, system, and platform they utilized.
1.5 Definitions of Enterprise Systems The term ERP in the press was first used in 1992 by Lopes and in the year 1996 Davenport introduced it to IS community at AMCIS ‘96 and called these packages megapackages (Klaus, Rosemann, & Gable, 2000). ERP systems are said to have packaged processes for best business practices as business blueprint that can guide the organization for product engineering, evaluation and analysis, and implementation (Chung & Synder, 2000).
The term Enterprise Systems was in use since 1980s to refer to any enterprise wide integrated systems (Sathisha, Pan, & Raman, 2003). Davenport (1998) used the same term Enterprise Systems instead of ERP in his famous article ―Putting the enterprise
7
Table 1.1 Timeline of Evolution of ES Timeline
System
1960s
Inventory Management and Control
1970s
Material Requirement Planning (MRP)
1980s
1990s
2000s
Platform
Description
Mainframe legacy using third generation software (e.g., Cobol, Fortran)
These systems were designed to manage and track inventory efficiently and help the plant supervisors on purchase orders, alerts, targets, providing replenishment techniques and options, inventory reconciliation, and inventory report. With the focus on sales and marketing, these systems were job shop scheduling processes. MRP generated schedule for production planning, operations control, and inventory management. With a focus on manufacturing strategy and quality control, these systems were designed for helping production managers in designing production chain processes – from production planning, parts purchasing, inventory control, and overhead cost management to product distribution. With a focus on application integration and customer service, these systems were designed for improving the performance of internal business processes across the complete value chain of the organization. They integrate both primary business activities like product planning, purchasing, logistics control, distribution fulfillment and sales; additionally they integrate secondary or support activities like marketing, finance, accounting, and human resource With a focus on agility and customer centric global environment, these systems extended the first generation ERP into interorganizational systems ready for e-business operations. They provide anywhere anytime access to resources of the organization and their partners; additionally, they integrate with newer external business modules such as supply chain management, customer relationship management, sales force automation (SFA), advanced planning and scheduling (APS), etc.
Mainframe legacy using third generation software (e.g., Cobol, Fortran) Material Mainframe Requirement legacy using Planning II fourth generation (MRP II) database software and manufacturing applications Enterprise Mainframe or Resource client server Planning using fourth (ERP) generation database software and package software application to support most organizational functions Extended ERP Client-server or ERP II using the web platform, open source and integrated with fifth generation applications like SCM, CRM, SFA. Also available on Software as a Service (SaaS) environment
Source: (Motiwalla & Thompson, 2009) into the enterprise system‖. Thereafter academic fraternity prefers to use the term Enterprise Systems and includes many other enterprise wide integrated systems such
8
as SCM, CRM, PLM etc. Few of the definition of Enterprise Systems are given in the following paragraphs. ―An, integrated , multi dimensional system for all functions, based on a business model for planning, control, and global (resource) optimization for the entire supply chain, by using the state of the art IS/IT technology that supplies value added services to all internal and external parties.‖ (Slooten & Yap, 1999) American Production and Inventory Control Society (APICS) defines ERP as ‖ a method for the effective planning and controlling of all the resources needed to take, make, ship and account for customer orders in a manufacturing, distribution or service company.‖ ―Enterprise Systems (ES) are typically comprehensive, complex, customizable integrated application software that support core business processes and main administrative areas of enterprises in different industries.‖ (Sathisha, Pan, & Raman, 2003) ―ERP is a standardized software packaged designed to integrate the internal value chain of an enterprise. An ERP system is based on an integrated database and consists of several modules aimed at specific business functions.‖ (Moller, 2005)
1.6 Business Process Enterprise Systems break through the traditional functional boundaries and try to automate a business process such as order fulfillment. Process view is very critical while implementing Enterprise Systems. The process has been defined as ―a set of logically related task performed to achieve a defined business outcome‖ (Davenport & Short, 1990). A process is ―a structured, measured set of activities designed to produce a specified output for a particular customer or market. It implies a strong emphasis on how work is done within an organization.‖ (Davenport T. H., 1993).
There are numerous modeling methods for business process modeling. The following Figure 1.4 shows the diagram of one such method IDEF3 for the Process of Sales Order Entry. IDEF3 is a method to capture the process where domain expert can define a scenario as a set of ordered events along with the participating objects (Shen, Wall, Zaremba, Chen, & Browne,, 2004). 9
Figure 1.4 Process of Sales Order Entry (IDEF3) Source: (Shen, Wall, Zaremba, Chen, & Browne,, 2004)
1.7 Components of Enterprise Systems The first wave of success of ES was more focused on the internal processes of the organization and the packages were termed as ERP. The success of ERPs provided impetus for the ES vendors to venture in the other dimensions. And the major thrust was given to logistics and customer relationship management since both of these processes have to be optimized for success of any enterprise. Therefore these emerging business needs led to the concept of ERP II (Moller, 2005).
10
Table 1.2 The four layers in ERP II Layer
Components
Foundation
Core
Integrated database (DB) Application framework (AF)
Process
Central
Enterprise resource planning (ERP) Business process management (BPM)
Analytical
Corporate
Supply chain management (SCM) Customer relationship management (CRM) Supplier relationship management (SRM) Product Lifecycle management (PLM) Employee Lifecycle management (ELM) Corporate performance management (CPM)
Portal
Collaborative
Business-to-consumer (B2C) Business-to-business (B2B) Business-to-employee (B2E) Enterprise application integration (EAI)
Source: (Moller, 2005) The core components deal with the distributed central database and application framework such as .NET or J2EE. The central components consist of ERP as a transaction processing system with all the traditional sub-components sales and distribution, finance and accounts, human resource, procurement etc. In addition it also contains business process management tools to design, execute and evaluate business processes (Moller, 2005).
The third layer is more analytical in nature and provides the tools for management to answer the challenges in a timely fashion. Supply chain management SCM helps in the planning and production of goods. On the other hand SRM, CRM, ELM and PLM help in maintaining the lifecycle of supplier, customer, employee and product respectively. Corporate performance management (CPM) provides the indices and matrices to see the overall performance of the organization. 11
Figure 1.5 The conceptual framework for ERP II Source: (Moller, 2005) Collaborative layer takes the business online and provide the window to the external and internal world without any bias or control. It provides a portal for customers (B2C), suppliers (B2B) and employees (B2E) to transact with the organization from the internet platform using a simple client like a web browser. In addition it also provides a mechanism for the integration of third party systems via EAI.
1.8 The architecture of Enterprise Systems Enterprise Systems have three distinct characteristics in their architecture data dictionary, middleware and repository (Chung & Synder, 2000). Data dictionary contains thousands of domains along with their fields that can be used in all functions or entire value chain of the organization. Middleware can allow users to set up software routines and databases at different location that can route data intelligently. Repository at the base acts as a business framework containing semantics of business 12
processes, business objects and organizational model (Curran, Keller, & Ladd, 1998). It consists of complete information of application including meta information on models, business objects and technical programming objects (Chung & Synder, 2000).
1.9 Enterprise Systems Research Directions Since 90‘s onward interest in the Enterprise Systems research has appealed to the academia due to the challenges and benefits of these systems. These researches can be broadly classified into general issues, adoption, acquisition, implementation, usage, evolution, retirement, and education (Esteves & Bohorquez, 2007).
There are numerous studies that are trying to define the scope and agendas of ES research (Al-Mashari, 2002; Jacobs & Bendoly, 2003). The effects of these systems on the management decision making has been debated and hence the organizational effectiveness (Carton & Adam, 2005). The study has been undertaken to understand the ES and the stakeholders an organization has to deal with vis-à-vis ES using stakeholder theory (Sathisha, Pan, & Raman, 2003). To understand the computer mediated change initiatives such as ERP an end-to-end narrative approach has been used (Wagner, 2002).
One of the research directions is to understand the business processes of the organization by business modeling. These tools help the organization understand, manage and communicate their business processes (Esteves & Bohorquez, 2007). There are multiple dimensions of business modeling such as modeling language (Becker & Knackstedt, 2003), reference data model (Dorp, 2002) and dynamics of business change or time dimension (Mendes, Mateus, Silva, & Tribolet, 2003). Other researchers are trying to include also the nontechnical components into the business 13
modeling such as management and business context of ICT (Hedman & Kalling, 2002) and structural and behavioral aspects of business domain (Murthy & Wiggins, 2004).
Some researchers have focused on the ERP product itself. What constitutes an ERP product or the essential characteristics of ERP packaged software has been defined as integration, flexibility and transversality. To address the global needs and the need of SMBs new technological paradigms have been studied such as distributed ERP ensures the ACID properties of transactions (Frank, 2001), Web-based object-oriented model (WOOM) (Ng & Ip, 2003), and web-services and peer-to-peer network technology (Brehm, Gomez, & Rautenstrauch, 2005). Some studies challenge the proprietary nature of these software packages in the age of open source software proliferation including ERP packaged software products (Dreiling, Klaus, Rosemann, & Wyssusek, 2005).
Next area under consideration is the decision to adopt ES systems which also marks the first phase of the ES Lifecycle at an organization and majorly deals with developing the sound business case for ES. Some researchers are trying to study the impact of ES using the resource based model of competitive advantage (Beard & Sumner, 2004) while some are doing the longitudinal comparison of adopter to nonadopters (Huntona, Lippincott, & Reck, 2003). Some researchers have done studies of adoption in the developing countries (Arunthari & Hasan, 2005) while some have tried to explain adoption by SMEs (Raymond, Rivard, & Jutras, 2006). The extent of adoption has been studied using Transactional Cost Theory and Institutional theory (Chang, Gold, & Kettinger, 2003). Adoption approaches such as best of breed versus
14
single vendor (Light, Holland, & Wills, 2001) and ERP commercial package versus in house developed ERP have also been studied(Tan, Lim, Pan, & Chan, 2004).
Enablers or reasons for adoption has been studied by many researchers (Markus and Tanis, 2000; Kumar et al., 2002; Olhager and Selldin, 2003; Mabert et al., 2003; Spathis and Constantinides, 2004; Light and Papazafeiropoulou, 2004). Some researchers have also studied the challenges or inhibitors in ES adoptions (He X. , 2004; He & Brown, 2005). The problem of shadow system post ES adoption has been studied by providing a theoretical framework that reveals the causal factors in the categories of technology, organization, business procedures and people (Behrens & Sedera, 2004). Next is acquisition phase, referring to activities like product selection, agreements with vendor and consultant, and ROI calculation or evaluation (Esteves & Bohorquez, 2007). Some researchers have identified factors or criterion affecting selection process such as role of IT management (Axelsson & Avdic, 2001), the effect of emotional reactions from the perspective of various stakeholders (Nelson, 2005) etc. Some studies talk about differences in the selection process between SMBs and Large Enterprises (Bernroider & Koch, 2001).
For better alignment of ES capabilities with organizations‘ requirements, optimization of the latter has been studied using Bunge-Wand-Weber (BWW) ontology (Rosemann, Vessey, Weber, & Wyssusek, 2004). On the other hand some researchers have tried to put forth a framework for the ES acquisition (Verville & Halignten, 2002). Finally the studies of evaluation propose some techniques such as Analytical Hierarchy Process and a method for ex-ante evaluation (Sarkis & Sundarraj, 2001; Sneller, Bots, & Koning, 2004).
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The implementation phase is the most researched area. It deals with topics such as implementation strategies, Critical success factors for implementation, organizational or people centric issues and knowledge management issues. Several researchers have proposed different methodology for implementation process such as an integrated approach (Al-Mudimigh & Y., 2001), a repeatable methodology (Chang S.-I. , 2004), quick iterative process prototyping for project management (Sato & Hori, 2002), AI2M Agile Incremental Implementation Methodology (Stender, 2002) etc. Some studies talk about the techniques such as Enterprise Object Model (EOM) (Arinze & Anandarajan, 2003) and reference model (Rosemann, 2001) that will smooth the transition from requirements to configuration of ES package.
Critical success factors related studies are large in numbers. Some researchers have tried to see the more dynamic picture of implementation by associating CSF to different phases of ES implementation (Nah, Lau, & Kuang, 2001; Somers & Nelson, 2001; Al-Mashari, 2002; Ehie & Madsen, 2005; Yokota, 2007). On the other hand some researchers have defined CFSs at two levels. The top level or category deals with the different dimensions of the ES context or constituents and consists of subfactors at secondary level (Cantu 1999; Zhang, Lee, Zhang, & Banerjee, 2003; Gargeya & Brady, 2005; Motwani, Subramanian, & Gopalakrishna 2005; GarcíaSánchez and Pérez-Bernal 2007; Finney & Corbett 2007; Muscatello & Chen 2008; Ngai, Law, & Wat 2008).
During ES several organizational issues need to be tackled. Change management is one of the most important issues and researched by many scholars (Nandhakumar, Rossi, & Talvinen, 2003; Davenport, Harris, & Cantrell, 2004). Some studies deal specifically with the worker or user resistance (Aladwani, 200; Shang & Su, 2004).
16
On the other hand end-user training and/or satisfaction has also been studied (Rodecker & Hess, 2001; Calvert & Carroll, 2005). Technology is considered culture independent entity but when it comes to implementation of ES it plays a very significant role due to socio-technical nature of the organizations (Reimers, 2002; Davison, 2002, Boersma & Kingma, 2005; Ke & Wei, 2005). Organizational leadership and governance have been studied by some researchers (Dong, 2001; FitzGerald & Carroll, 2003; Chou, Jiang, & Wang, 2004). In the organizational context business process reengineering is most essential to expedite and ensure success (Koch, 2001; Southwick, 2002; Madhavan & Theivananthampillai, 2005). There also exists study that deals with the power shift that this technology brings to the target organization (Wenger, Dhillon, & Caldeira, 2005).
Implementation of the ES is a very knowledge intensive activity due to its complex nature. Therefore competencies to implement ES from different actors have to be managed efficiently. Some studies deal with knowledge integration (Huang, Newell, Galliers, & Pan, 2001; Wan, Ling, & Huang, 2001) and its relationship with social capital (Newell, Tansley, & Huang, 2002). Some studies emphasize on the sharing of this knowledge (Jones, 2001; Jones & Price, 2001). Whereas some studies also talk about organizational learning and/or knowledge transfer to the target organization (Timbrell, Andrews, & Gable, 2001; Ke W. , Wei, Chau, & Deng, 2003; Xu & Cybulski, 2004; Lee & Myers, 2004; Ko, Kirsch, & King, 2005).
After implementation comes the assessment, success or usage of these systems. There are several studies that have attempted to develop ES success measurement instruments (DeLone and McLean 2003; Spathis and Constantinides 2003; Gable, Sedera and Chan 2008). Where as there are studies that have given comprehensive
17
benefit framework (Deloitte-Consulting 1998; Shang and Seddon 2002; Chand, Hachey, Hunton, Owhoso, & Vasudevan, 2005; Eckartz, et al. 2009). Some studies have also given IS success model to assess the success (Gable, Sedera and Chang 2008). There are studies that have validated the ERP success framework proposed in earlier studies for ERP II (Koh, Gunasekaranb and Rajkumar 2008).
Traditionally it is established that more than 50% of the total cost is allocated for the maintenance of any software. Some researchers have given different perspective of maintenance of such integrated software ES (Mookerjee, 2005). The factors for maintenance or upgrade decision have been identified (See Pui Ng, 2001). There are studies that discuss the issues related to outsourcing contracts (Dibbern, Brehm, & Heinzl, 2002; Wu, Ding, & Hitt, 2003). There exist studies that have linked the performance excellence to the post implementation usage of these systems (Laframboise, 2002; Babaian, Lucas, & Topi, 2004).
1.10 Chapter Scheme This thesis has been organized into six chapters. First chapter being introductory gives the overview of the IT adoption in an organization with the special focus on ES. Next it delves into the historical evolution of the ES along with the definitions of the systems. The paradigm shift that ES has brought is process based integration rather than automation of functional divisions. This chapter further talks about different components of ES such as ERP, CRM, SCM etc. Finally the most important section of this chapter is given that brings out the research status of the ES discipline.
As this research has focused on Indian companies, next chapter has tried to shed some light on IT industry and more so ES adoption in India. Indian government‘s policies and initiatives have been summarized to emphasize the commitment of Indian 18
government to this IT and ITES sector that is poised to contribute to the economy at 10% of GDP in the near future. To understand the industry dynamics of ES in India ES market size, significant ES vendors have been mentioned. Finally the research perspective of present study of the ES phenomenon of Indian organizations has been elaborated.
The third chapter being literature review parses through the existing literature with the focus of research perspective defined in the previous chapter. The first section defines the Lifecycle of the ES. Next it illustrates on the ES adoption motivation studies and the content analysis to bring out the framework of ES adoption reasons. It also gives detailed definitions along with the references for these variables. Similarly the literature relevant to implementation with special emphasis on CSF has been analyzed. This results in the generation of the CSF list. Further, they have been grouped into eight categories. Usage study is also essential to complete the significant phases of the Lifecycle of ES. ES success and benefit measurement studies have been analyzed for the selection of ES benefit measure. Finally this chapter gives some empirical and conceptual models that relate the ES success or benefits with CSF.
The fourth chapter is about research methodology. It starts with the research approach taken in this study and gives the brief introduction to the partial least square technique along with reflective versus formative constructs, construct reliability and validity, and assessment of the inner and outer model. Next sections give the clear statement of the problem and objectives of the research. The research framework section gives the brief definition of all the variables involved in the study and ES Success predictive model. Next section establishes all the hypotheses that are to be tested to derive statistical inferences. Following sections define the population, sampling frame and
19
sample considered for the study and case collection procedure. Next sections give details of the sources of data and data from one sample case to illustrate the data extraction method. The chapter ends with defining the scope of the study.
Chapter Five documents the results of the statistical tests. Firstly it gives the flow charts of the two phases loosely defined to carry out the study. Next section gives the demographic profile of the cases. Following section tabulates the ranking of all the variables based on the frequency of presence in the cases. The following sections give the results of the Pearson‘s Chi-square tests for the variation of ES adoption reasons and ES benefits with respect to size and industry of the organizations. Finally the multiple PLS structural models‘ results such as construct reliability and validity measures, inner and outer model assessments and predictive relevance of the model has been given.
The concluding chapter starts with the foundation and results of earlier research. Next it summarizes the findings of this study. Following section discusses the findings of this study in the light of results of earlier research. The ES success predictive model has been summarized in the next section. Managerial implications of the results of the study have been elaborated to help the practitioners. Finally the limitations of the study along with the future scope of the research have been given.
1.11 Conclusion The major portion of capital investment goes into IT that does nothing but store, transport and process data. Though the hardware cost is decreasing day by day but systems cost is increasing due to addition of innovative, invaluable brains that utilize the core commodity inputs of IT and bring out distinctive solutions or services. Some studies count them up to 70 in the organizational context. One such solution or 20
system happens to be Enterprise Systems. These systems have been evolving since the 1960s from very simple systems such as Inventory control and management to ERP II. These systems in the present avatar along with all the components are capable to seamlessly integrate internal and external processes to run business more effectively and efficiently. To study the processes various methods are used such as IDEF3, integrated definition method. Architecturally they have data dictionary, middleware and repository. In order to make ES adoptable researchers have been carrying out studies in defining the research scope and agenda, understanding the business processes using business modeling tool, ES product, adoption decision and reasons for adoption, acquisition phase, implementation phase, assessment or usage phase, and last but not the least the maintenance phase.
21
Chapter Two IT INDUSTRY IN INDIA 2.1 Introduction The India being one of the BRIC (Brazil, Russia, India and China) nations is very visible on the global economic map. India joined the race of developed nation in the late nineties and in the beginning of this century. When, the government of India adopted the policies of liberalization and privatization. India started getting integrated into the world economy. And India has seen for almost a decade the GDP growth rate between 5 and 10. Information Technology has given a big boost to the Indian economy. IT industry is one of the most robust industries offering direct employment to 2.5 million in fiscal year 2011 and indirect job opportunities to 8.3million, and generating an annual revenue of USD 100 billion in FY2012 (NASSCOM, 2012). This chapter discusses the Government Initiatives, IT-ITeS industry, Cloud Computing, ES industry, ES vendors, and research perspective for ES in Indian organizations.
2.2 Government Initiatives Indian government has well addressed the issues and challenges of Information Technology area. It has set up Department of Information Technology (DIT) under the Ministry of Communication and Information Technology. Recently it has released a Twelfth Five Year Plan (2012 – 17) on Information Technology Sector. In order for complete participation from Industry Sectors, Industry Associations and Academia, Experts and other Intellectuals to come up with a comprehensive report, it constituted seven Sub-Groups. They are mentioned in the following paragraphs along with their
22
key objectives. It will shed some light towards the level of participation of Indian government in the IT sector (see (Working-Group, 2011) for more details). 1.
e-Government To evolve a pan India Enterprise Framework for services delivery on the basis of matured projects of NeGP (National e-Governance Programme ) and MMPs (Mission Mode Projects ) and to leverage the emerging technology trends in Cloud Computing and linking up with UIDAI for online ID services To extend the accessibility of e-enable services by linking the Banking correspondents and financial institutions to the citizens through mobile and to reposition CSCs as Bharat Nirman Common Service Centre in order to cover all Panchayats of the Country To follow a holistic approach in order to secure Indian Cyber Space by including legal framework, R&D in different aspects of cyber security, security practices compliance and assurance, international cooperation and training To assess the impact of IT sector and Suggest measure to improve the use of IT in various fields for increasing productivity, bringing in socioeconomic development and services like e-medicine, e-education, e-entertainment especially in the rural areas.
2.
e-Learning
The e-Learning sub-group will cover the topics of HRD and Skill Development, Technology Development in Indian Languages, e-Learning Technologies and Digital Library Initiatives, e-Infrastructure, Internet Governance, National Knowledge Network, Activities of DOEACC, ERNET and NIXI. To evolve strategies to become world leaders in providing highly skilled manpower for Information, Electronics, and Communication Technology (IECT) sectors. Strategy for collaboration between the industry and the academia would be worked out; this collaboration would not only look into the changing needs of the industry but also focus on R&D by harnessing the possibilities of the synergy between resources of industry and academia. The possibilities of Public Private Partnership (PPP) would be explored in detail. To study the role of new educational technologies, e.g., Distance Education, Multimedia etc. and to recommend modalities for their integration in the present educational/training system. To suggest measures necessary to improve teaching of non- IECT subjects by using computers and the Internet for all students. To assess the impact of IECT sector and suggest measures to improve the use of IECT in various fields for increasing productivity, bringing in socioeconomic development and services like e-medicine, e-education, eentertainment especially in the rural areas.
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3. e-Security Define the scope of cyber security initiatives Identify potential strategies and initiatives (Action Plan) to address cyber security issues in a holistic manner to become Global leader (Legal framework, R&D, Security practices compliance and assurance, international cooperation, awareness, skill development and training). 4. e-Industry (Electronics Hardware Industry) To evolve a policy and make specific recommendations for establishing ten world class manufacturing facilities in collaboration with MNCs in the country during the Twelfth Plan period. To promote the domestic manufacturing in electronic hardware sector and formulate Special Incentive Package Scheme II (SIPS II) in order to provide competitive edge to IT-ITeS and electronics industry by pursuing the recommendations of the Task Force 2009. To examine and put in place the policy package and incentives for setting up of semiconductor wafer fabs in the country and work out the implementation strategy. To suggest measures for creating a world class eco system and infrastructure for Electronics System Design and Manufacturing (ESDM) Sector. To suggest ways to create a globally recognizable ―Made in India‖ brand for ESDM and communication strategies aligned with overall sectoral priorities. To suggest ways and measures to promote development of electronics hardware products/devices suitable for unique Indian requirements (low cost, local language support, low power consumption, ruggedized and suitable for prevailing climatic conditions). To suggest measures to leverage international collaboration for technology acquisition and attracting investments in high tech areas and leverage the Indian Diaspora in this regard. 5. e-Industry (IT-ITeS Industry)
To create one million jobs for the entire sector including Software. To make India as a leading hub of innovation in cost effective technology and services. To suggest steps required fostering enabling IT ecosystems relevant to the domestic sector including e-Services, e-Health and e-Commerce. To review competitiveness of the IT-ITES industry and suggest appropriate strategies for ensuring sustenance of SMEs. To review and suggest strategy to encourage growth of IT-ITES industry in existing locations and to other urban areas for ensuring balanced regional growth.
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6. e-Innovation / R&D To suggest mechanism/strategies to promote R&D for design led manufacturing of products, packages and services; and also to suggest mechanisms to widen the R&D base in the country. To devise strategies to support technology start-up companies and to promote innovation in academic and R&D institutes leading to eco-system for product development. To identify a few user-specific large technology development/demonstration projects in the country during the 12th Plan period to establish and demonstrate the country‘s real technological capabilities. To arrive at an estimate of R&D funds required in the Electronics and IT Sector with break-ups under major technology areas. 7. e-Inclusion To suggest measures for digital literacy of target groups. To suggest a broad outline for National Policy on e-Inclusion for the identified target groups namely SCs, STs, Minorities, Women, differently abled, etc. To identify various schemes of DIT which are suitable for implementing SCSP, TSP and gender budgeting. To identify the institutions and domain areas to establish R&D centers for eInclusion.
2.3 IT-ITeS Industry India is poised to grow its offshore IT-ITeS industries at an annual rate of 13 -14 percent, provide direct jobs to 10 million people and generate exports of USD 175 billion by 2020. India adds 3.5 million professionals including 500,000 IT and Electronics and Communication Engineering graduates annually to its talent pool (Working-Group, 2011). Indian companies have moved up in the value chain and have grown its delivery centers globally from 340 centers in 184 cities across 48 countries in 2007 to 500 centers in 200 cities across 60 countries (Working-Group, 2011). The Indian IT-ITeS industry truly has become not just a growth catalyst for the economy but also for improving the standard of living.
Government has played an excellent role to sustain the growth of this sector by providing tax schemes incentives, creation of the STPI (software technology parks of 25
India), and zero import duty on software. The industry is showing maturity in terms of cost reduction, new market focus, investment in S&D, domain expertise, operational excellence and customer orientation. India has maintained double digit growth with the exception of the year 2009. In this year only the growth rate was in single digit due to decline in worldwide IT spending (see Table 2.1and Figure 2.1).
Table 2.1 Performance of Industry between 2006-07 and 2011-12 US $ Bn
2006-07 2007-08 2008-09 2009-10 2010-11 (E) 2011-12 (P) CAGR
Exports Domestic Total
31.2 8.2 39.4
40.4 11.7 52.1
47.1 12.8 59.9
49.7 14.2 63.9
59 17.2 76.2
69 17.00% 20 19.50% 89 17.70%
Source: (NASSCOM, 2012) 100 90 80
20
70
17.2
60 US $ Bn
40 30 20 10
14.2
12.8
50
11.7 8.2
31.2
47.1
49.7
2008-09
2009-10
40.4
69
59
0 2006-07
2007-08
Exports
2010-11 (E)
2011-12 (P)
Domestic
Figure 2.1 Performance of Industry between 2006-07 and 2011-12 India has emerged as a global leader in offshore IT-ITeS market with a total share of 55% in 2010 that has increased from 49% in the year 2005. Its 90% exports go to the developed nations (Working-Group, 2011). India has maintained CAGR of 17% in dollar terms. Total exports are majorly classified into three categories IT Services,
26
BPO and Software Products/Engineering. IT Services is the major contributor to the total revenue (see Table 2.2 and Figure 2.2).
Table 2.2 Segment wise IT-ITeS Exports SERVICE LINES
2007-08
2008-09
2009-10
2010-11 (E)
2011-12 (P)
IT Services BPO Software products/engineering
22.2 9.9 8.3
25.8 11.7 9.6
27.3 12.4 10
33.5 14.1 11.4
39.2 16.5 13.3
Total
40.4
47.1
49.7
59
69
Source: (NASSCOM, 2012) 80 70 13.3
60 11.4
50 US $ Bn 40 30
10
9.6 8.3 11.7
12.4
22.2
25.8
27.3
2007-08
2008-09
2009-10
14.1
9.9
20 10
16.5
33.5
39.2
0
IT Services
BPO
2010-11 (E)
2011-12 (P)
Software products/engineering
Figure 2.2 Segment wise IT-ITeS Exports revenue (US $ Bn) The Domestic IT market has grown at a faster rate than exports at a CAGR of 19.5% in dollar terms. It is expected to generate revenues of US $ 20 billion (Rs. 90,000 crores) in the year of 2011-12 (see Table 2.3 and Figure 2.3). Government is supposed to help maintain strong domestic demands through spending for National egovernance Plans (NeGP). To this end, Indian firms, MNCs and SMEs are gearing up (Working-Group, 2011). 27
Table 2.3 Segment wise domestic IT-ITES revenue (INR Bn) SERVICE LINES
2007-08
2008-09
2009-10
2010-11 (E)
2011-12 (P)
IT Services BPO Software products/engineering
318 64 88
378 89 123
429 109 140
501 127 159
574 145 184
Total
470
590
678
787
900
Source: (NASSCOM, 2012) 1000 900 184
800 159
700 140
600 INR Bn
123
500 400 300 200
88 64
318
145 127
109
89
378
429
2008-09
2009-10
501
574
100 0 2007-08 IT Services
BPO
2010-11 (E)
2011-12 (P)
Software products/engineering
Figure 2.3 Segment wise domestic IT-ITES revenue (INR Bn)
2.4 Cloud Computing Cloud computing has been on the horizon for almost a decade. This seems to have gained momentum in the recent past in India. Cloud computing utilizes the same IT building blocks but presents a whole new paradigm. It promises to remove the age old digital divide at a macroscopic level as mobile computing has done at the microscopic level. Any country developed or developing, any organization large or small and any
28
individual belonging to any socioeconomic strata can implement any application present on cloud and utilize any platform. Characteristics of the Cloud In the simplest terms Cloud refers to Internet-based data access and exchange plus internet-based access to low cost computing and applications (CII, 2012). In addition it should posses following characteristics: On-Demand Self-Services, Internet Accessibility, Pooled Resources, Elastic Capacity and Usage Based Billing (CII, 2012).
There are three cloud service models SaaS, PaaS and IaaS. SaaS, software as a Service, is a software delivery model where data and application is hosted on the cloud. Almost all the ES vendors are offering their product through this model. PaaS, Platform as a Service, is platform delivery model where CSP, Cloud Service Provider, offers all the resources such as database, IDE, web servers etc. to develop, deploy and maintain applications. IaaS, Infrastructure as a Service, is an infrastructure delivery model where CSP offers hardware resources in the form of virtual machines or others.
Cloud deployment is also possible in multiple ways such as private, public, community and hybrid. Private clouds are operated by the single organization. Public clouds are meant to be used by general masses and are owned by a CSP. Community cloud serves the interest of the specific community and is shared by several organizations. Hybrid option allows for application and data portability by binding two or more clouds technologically.
29
Challenges in India The core component of Cloud computing infrastructure is a data center. There are only about 36 data centers in India whereas more than 100 data centers available in the UK and Germany (CII, 2012). United States is the undisputed leader with more than thousand data centers (CII, 2012). India needs to evolve its cloud policy and project it as US says Cloud-first policy to encourage its disparate organization to adopt it. In doing so it needs to look carefully at the issues such as Service prerequisites, data and network sovereignty, privacy and security of the data.
Figure 2.4 Service Models of Cloud Computing Source: http://en.wikipedia.org/wiki/Infrastructure_as_a_service#Service_models
2.5 Enterprise Systems Industry Growing economies like India is adopting technology very rapidly and shifting its focus towards IT enabled solutions. Adoption of ES Systems by Indian organizations started from the mid 1990, grew at a phenomenal rate of 70% CAGR for the period of 1995 to 2001, and 800 organizations had implemented ES by 2001 (Tarafdar, 2005). 30
As per the recent studies Indian ERP market is estimated to be at Rs 40,000 crores and is expected to grow at 25% CAGR for years 2012-14 (Desai, 2012). The demand lies at the bottom of the pyramid the SMBs and Micro Enterprises. It is estimated that out of 4.1 million SMBs with PC penetration 1 million organizations would consider implementing ERP in the next four years (Desai, 2012).
Another study by Gartner (2012) projects the enterprise software market to grow at 13.7 % as revenue reaches $3.45 billion USD in 2012. It also forecasts a CAGR of 14.6 percent for the period of 2011 to 2016 the highest growth rate in the world.
2.6 Enterprise Systems Vendors Enterprise Systems are the packaged software like operating system and RDBMS. The difference lies in the power of configurability or parameterization that these packages come with. They can simulate the environment of any organization irrespective of industry, size and organizational design. Moreover they can be parameterized to process, product or service based organization. The history of most of these packages tells that they started off to automate the processes of one organization. Later on they were made more and more flexible to suit any organization and acquired by bigger brands. Therefore ES Systems industry is witnessing consolidation through mergers and acquisition. Following paragraphs discuss about the vendors which are significant in Indian marketplace. i.
Microsoft
Microsoft an omnipresent name associated with operating systems also has a remarkable presence in India. It offers business solutions such as ERP and CRM under the brand name Microsoft Dynamics. Microsoft ranks highest when it comes to ease of use since familiar look and feel with MS Office. Like other vendors Microsoft 31
also offers on premise and on cloud solutions. Dynamics offers ease to import and export data to MS Excel. Microsoft Dynamics solutions can be extended for industry specific functionality with the help of third party out-of-the-box plug-ins. Moreover Microsoft also offers pre-configured industry specific solutions to reduce implementation time. ii.
SAP India
SAP India headquartered in Bangaluru and subsidiary of SAP AG, started its operation in India in 1996. As of now it has more than 3000 implementations in India. It has adopted the cloud infrastructure and offers its software as SaaS (Software as a Service) and pay-as-you-use model. SAP offers solutions based on processes such as CRM, SCM, ERP, HCM, EAM, PLM, Procurement and Sustainability. It also offers solutions for business analytics such as BI, Data Warehousing, EIM, EPM and GRP. Most importantly it also offers industry pre-configured solutions also for almost all verticals. iii. Oracle Oracle a trusted name in the IT domain, offers end to end services in all the stacks from hardware to software. It has a customer base of more than 390,000. More than 80,000 customers worldwide rely on complete portfolio of enterprise and industry applications. It has acquired JD Edwards, Seibal and Peoplesoft to strengthen its applications portfolio in ERP, CRM and HCM areas. Oracle maintains one of the largest clouds Oracle Cloud, visited by more than 25 million users every day. It offers the widest range of choice for the deployment of applications on-premise, public cloud, private cloud or the hybrid platform.
Oracle Accelerate are midsized
companies‘ specific simple-to-deploy, packaged, enterprise-class solutions offered in
32
association with a network of expert partners. It also offers Oracle Business One ERP application for SMBs. iv. Ramco Established in the year 1999 headquartered in Chennai is an Indian ES vendor. Over 1000 implementations across more than 40 verticals has forayed into the cloud computing platform. Ramco defines its product to be web architectured, scalable and modular and comes as pre-built solution to expedite the implementation. Ramco uses its own proprietary platform Ramco VirtualWorks® and Ramco DecisionWorks™ to deliver all the solutions. Ramco‘s ERP suite contains Manufacturing, FM, SCM, HCM, CRM, EAM, Project Management, Process Control, Analytics, Advanced Planning & Optimization, and Connectors. Analytics suite gives KPIs to measure, monitor and manage the business. Aviation suite developed ground up specifically contains M&E and MRO modules. Ramco also offers GRP to cater to the needs of the government organizations and automates processes like FM, Budget Planning & Execution, Debt Management, Project Management & Accounting, Procurement, HCM, Citizen Services and Analytics. v.
Sage Software India (P) Ltd
Sage Software India (P) Ltd is a subsidiary of The Sage Group plc. Sage specializes in business solution to the SMB sector. Sage has installations at over 1000 organizations in India for its CRM, Accpac ERP, Payroll and Human Resource Management. CRM comes in Sage ACT!, Sage CRM Cloud, Sage CRM Sage SalesLogix to cater for different size of the organization. Similarly payroll software Sage Pocket, comes in Standard (single user, up to 100 employees), Professional (single user, up to 300
33
employees), Premium (multiuser, multi-company, up to 1000 employees) and online versions. In addition, Sage also offers IDMsys ERP specific to the Hospital. vi. IFS India IFS Founded in 1983, headquarters in Linköping, Sweden, develops, supplies and implements IFS Applications™, an ERP built on component based SOA. The company boasts on 2000 customers and presence in 50 countries. It doesn‘t project itself as a pure-play technology vendor and hence capable of integrating evolutionary innovations. IFS Applications is a single product with four core modules Service Asset Management, Manufacturing Management, Project Management and SCM. In addition it also offers FM, CRM etc. to make it a complete package Figure 2.5. vii. Infor Infor claims to be the third largest provider of enterprise applications and services. It has presence in 194 countries with implementations in 70,000 organizations. Infor offers industry specific solutions with flexible deployment options like in cloud, on premise or hybrid option. Its products include Marketing Automation, CRM, Community Development and Regulations, Discrete Manufacturing, Process Manufacturing, SCM, Warehouse Management, FM, HCM, PLM, EAM, EPM. Infor has had cloud presence for more than a decade and supports all the core processes through its cloud. It offers two payment options SaaS mode meaning pay-as-you go and Cloud License with the flexibility to take solution on-premise. viii. 3i Infotech 3i Infotech an Indian IT organization with a global presence in more 50 countries has made 1500 customers. Company is ISO certified. It offers more than 20 IP based
34
Figure 2.5 Full Suite ERP (Modules of IFS ES) Source: http://www.ifsworld.com/hi-in/solutions/product/ 35
software solutions. Its ERP suite comes with a brand name of Orion. Orion Enterprise with built-in CRM, SCM, FM and HR capabilities caters to large enterprises. Whereas Orion Lite caters to the SMBs and Orion Advantage is ready-to-deploy, singlewindow, micro-vertical-specific ERP. In addition it also offers Fleet Management Software. ix. LAWSON LAWSON Software global provider of ERP solutions and applications headquartered in St. Paul, Minnesota, U.S.A., opened its office in New Delhi, India in 2008. It offers solutions to the targeted industries such as fashion, food and beverage, and manufacturing. It has 4,000 customers across 40 countries. Lawson‘s solution includes EPM, SCM, ERP, CRM, MRP, EAM and also the industry-tailored applications. Lawson Software acquired Enwisen to improve its HCM package. x.
QAD MFG/PRO
QAD founded in 1979, develops software focused for manufacturing organizations. It has more than 5,500 installations in 90 countries. From the very beginning its software is based on open system architecture. In all these years QAD has remained committed to select verticals in manufacturing: automotive, consumer products, food and beverage, discrete products, configured products, and life sciences. QAD Enterprise Applications contains all the modules to support key processes. xi. Eastern Software Systems ESS, an ISO certified and CMM Level 5 IT company, is an Indian organization offering its ERP in India and Africa. Its ERP is known with the brand name ebizframe. It has been successfully implemented in more than 750 organizations and
36
more than 20 industry verticals. The ebizframe automates all the business processes as shown in Figure 2.6.
Figure 2.6 ebizframe ERP Software (Modules of ESS' ES) Source: http://www.essindia.com/ebizframe-erp-software
2.7 Research Perspective for ES in Indian Organizations India being the developing country, historically has been a late entrant to Technology. Though in the recent past data speaks about the manifold growth in IT-ITeS industry Figure 2.7. Though the revenue is largely contributed by the exports and is about 80%. The size of the domestic market is not small. As has been mentioned earlier in this chapter that the domestic IT is expected to generate US $ 20 billion (Rs. 90,000 crores) in the year 2011-12.
Moreover the recent studies estimate the Indian ERP market to be at Rs 40,000 crores and expect it to grow at 25% CAGR for years 2012-14 (Desai, 2012). And
37
considering the data reported on the ES vendors‘ websites then thousands of Indian organizations have adopted ES. Therefore it gives good impetus to study the ES phenomenon in India.
14000 12000
$ million
10000 8000 6000 4000 2000 0 exports total
1984 1985 1986 1987 1989 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 22
26
38
54
105
330
485
734
1085 1759 2600 3962 6217 7647 9875
558
835
1224 1755 2936 4011 5539 8298 9958 1245
Figure 2.7 The growth of software revenues in India 1984-2002 Source: (Athreye, 2005) The implementation of ES is a risky endeavor. Many failures in terms of cost and time overruns, realization of benefits less than the projected, and the worst outcome, mismanaged ES led to bankruptcy of the business are reported in literature (Xue, Liang, Boulton, & Snyder, Sep 2005). Academicians and IT consultancy firms have been studying and proposing solution to minimize risks since the early inception of this technology. The research areas range widely from pure organizational issues such as change management to the hardcore technological components of the software and underlying information technology architecture. The process of implementation plays a key role in the success of these systems.
38
The literature review shows that there are a number of studies that talk about the adoption phenomenon of the Enterprise Systems packaged software. (Markus and Tanis, 2000; Kumar et al., 2002; Olhager and Selldin, 2003; Mabert et al., 2003; Spathis and Constantinides, 2004; Light and Papazafeiropoulou, 2004). However there is a dearth of research in Indian context. Therefore there is a clear gap in the literature about the ES phenomenon in India.
This study has taken a broader exploratory approach to study the various facets of ES study. As has been mentioned in the research directions section in the previous chapter that research on ES can be taken in numerous areas. These are defining the research scope and agenda, understanding the business processes using business modeling tool, ES product, adoption decision and reasons for adoption, acquisition phase, implementation phase, assessment or usage phase, and finally the maintenance phase.
This study has focused on three phases of ES projects. Firstly the motives for adoption of ES among Indian organization will be studied. Next the implementation phase has been studied. The implementation of ES is said to be one of the most complicated parts of ES Lifecycle. There are several approaches to study the implementation phase. Some researchers have taken the path of critical success factors. Some are trying to analyze the implementation as a process. Process based approach gives more insight into the implementation. On the other hand some researchers are trying to combine both aspects of implementation (Nah, Lau, & Kuang, 2001; Somers & Nelson, 2001; Al-Mashari, 2002; Ehie & Madsen, 2005; Yokota, 2007). In this research work critical success factors have been identified.
39
That would help the practitioners to manage the implementation of these systems more efficiently and effectively.
Thirdly the usage or success of ES has been studied. In the information technology area defining success is one of the one of the most challenging issues. The impacts of IT are far reaching. The benefits are not limited to IT department only rather they affect the organizational, strategic, operational and managerial dimensions as well. Some researches assess the success of these systems from the perspective of end users view. Nevertheless today use of IT in business has become highly debatable as to whether it adds strategic value or is merely a commodity.
The critical success factors approach has been long debated over its effectiveness in understanding implementation process. But the simplicity of this method gives an edge over process oriented research. In this research the relationship between the benefits of the ES and critical success factors have been explored. The research perspective has been further defined in the statement of the problem and objectives of the research sections in chapter 4.
2.8 Conclusion IT-ITeS industry in India is generating revenues of 100 US$B that is about 7.5% of the nation‘s GDP. The sector is well supported by the Department of Information Technology under the Ministry of Communication and Information Technology, Government of India. In order to sustain growth and utilization it focuses on seven areas e-Government, e-Learning, e-Security, e-Industry (electronics hardware), eIndustry (IT-ITeS), e-Innovation and e-Inclusion. For the last six years the sector has maintained the CAGR of 17%. Cloud computing offers three service models SaaS, PaaS and IaaS and is being adopted rapidly in India. But India lags far behind to 40
house CSPs. It needs to address issues such as service pre-requisites, data and network sovereignty, privacy and security of the data in addition to installation of Data Centers. Coming on to ES adoption in India, with thousands of Indian organizations already implemented ES and market size of $3.45 billion USD in 2012, the growth lies with SMBs. All the global major ES vendors along with leading indigenous players claim to have implemented ES in thousands of Indian organizations. And most of them are offering ES through cloud computing platform. Given the market size of India, the complexity involved in the adoption of ES, and sparsely researched phenomenon in Indian context at least in terms of reporting in the literature makes it a good research problem for the present study.
41
Chapter Three LITERATURE REVIEW 3.1 Introduction ES research frontiers are very wide, truly interdisciplinary, nicely interwoven into different disciplines such as management studies, social or behavioral studies, information technology etc. Numerous theoretical models have been used by researchers to conceptualize and theories fundamental concepts in this area. Moreover, in terms of theory testing and building it is relatively in advanced stage. Though there are still lots of gaps to be addressed.
Academia has aptly helped the industry to promote and improve the success with these systems in the organizational context. The research directions section in the previous chapter has shed some light at this end. This chapter takes that effort further in a more focused manner in the direction of the intended scope of this research. The objective is to build the theoretical framework that is needed to carry forward the research.
Firstly the ES lifecycle studies have been discussed, followed by the adoption phase and identification of adoption motivations or reasons studies. Next section delves into the literature of the implementation phase, followed by critical success factors studies and grouping of these CSFs. Next the literature of usage phase with reference to the ES success measure or ES benefit framework is put forth. Finally various models depicting relationships between CSF and ES success measure and ES benefit framework have been discussed.
42
3.2 Lifecycle of Enterprise Systems The literature contains numerous ES Lifecycle models. Markus and Tenis (2000) have identified four phases in the ERP life cycle: chartering, project, shakedown and onward and upward. These phases refers to defining a business case, getting system and end user up and running, stabilizing, and maintain and extensions respectively. Parr and Shanks (2000) have divided it into three phases planning, project and enhancement. Further project phase contains five sub-phases setup, re-engineer, design, configuration and testing, and installation.
Yokota (2007) simply defines it in three stages pre-implementation, implementation and post-implementation. These stages contain phases of preparation, business process design and scheduling, implementation, system design, system building, user support and system conversion, and go-live, maintenance, improvement and enhancement respectively. Esteves and Bohorquez (2007) propose six phase ERP lifecycle model adoption decision phase, acquisition phase, implementation phase, use and maintenance phase, evolution phase and retirement phase.
Analysis of various models shows some common characteristics and broadly speaking they contain four phases namely adoption, implementation, usage and maintenance, and integration. India is developing nation and ES implementations are relatively in its early stages. There is an acute dearth of research reporting in Indian context among refereed international journals. Therefore only first three phases adoption, implementation and usage will be studied in this research work.
3.3 Adoption of Enterprise Systems A decade ago decision to adopt ES was considered to be a very risky endeavor due to numerous failures cited in the literature (Xue, Liang, Boulton, & Snyder, Sep 2005; 43
Barker & Frolick, Fall2003). Today most large organizations across the globe have already adopted ES and smaller ones are following suit. ES adoption is not a choice in today‘s hyper competitive world but a necessity. Managing ES is an ongoing and continuous process to integrate, optimize and informate to maximize the value of ES (Davenport, Harris, & Cantrell, 2004). The adoption phase includes the ―definition of system requirements, its goals and benefits, and an analysis of impact of adoption at a business and organisational level‖ (Esteves & Bohorquez, 2007). During this phase detailed cost benefit analysis or return on investment calculation should also be done. Some of the parameters of ES adoption study are role of external influences in ES selection, resource and adaptability constraints on ES adoption, efficiency improvement versus more strategic business development objectives of ES adoption, and scope of integration pursued through ES adoption (Laukkanen, Sarapola, & Hallikainen, 2007).
Identifying the reasons of adoption has been extensively researched in the academic literature. Table 3.1 gives the details of twelve such studies along with reasons for adoption and country of research.
Table 3.1 Literature Survey on Reasons for Adoption for ES S. No. 1
Authors
Country
Reasons for Adoption
(DeloitteConsulting, 1998)
Global
2
(Markus Tanis, 2000)
Global
1: Systems Not Y2K Compliant 2: Disparate Systems 3: Poor Quality/Visibility of Information 4: Business Processes or Systems not Integrated 5: Difficult to Integrate Acquisitions 6: Obsolete Systems 7: Unable to Support Growth 8: Poor/Uncompetitive Business Performance 9: Cost Structure Too High 10: Not Responsive Enough to Customers 11: Complex, Ineffective Business Processes 12: Unable to Support New Business Strategies 13: Business Becoming Global 14: Inconsistent Business Processes 1: Solve Y2K and similar problems 2: Integrate applications cross-functionally 3: Consolidate multiple different systems of the same type (e.g., general ledger packages) 4: Replace hard-to-maintain interfaces 5: Reduce software maintenance
&
44
ContinuedSurvey on Reasons for Adoption for ES Table 3.1 Literature S. No.
Authors
Country
3
(Kumar, Maheshwari , & Kumar, 2002)
Canada
4
(Olhager & Selldin, 2003)
Sweden
5
(Mabert, Soni, & Venkataram anan, 2003)
US
6
(Spathis and Constantini des,2004)
Greece
7
(Light & Papazafeiro poulou, 2004)
Global
8
(Arunthari & Hasan,
Thailand
Reasons for Adoption burden through outsourcing 6: Eliminate redundant data entry and concomitant errors and difficulty analyzing data 7: Improve IT architecture 8: Ease technology capacity constraints 9: Decrease computer operating costs 10: Accommodate business growth 11: Acquire multilanguage and multicurrency IT support 12: Provide integrated IT support 13: Improve informal and/or inefficient business processes 14: Standardize different numbering, naming, and coding schemes 15: Clean up data and records through standardization 16: Standardize procedures across different locations 17: Reduce business operating and administrative expenses 18: Present a single face to the customer 19: Reduce inventory carrying costs and stockouts 20: Acquire worldwide “available to promise” capability 21: Eliminate delays and errors in filling customers‟ orders for merged businesses 22: Streamline financial consolidations 23: Improve companywide decision support 1: Integrated and better quality of information 2: Y2K readiness 3: Multiple systems, interfaces, vendors were difficult to manage 4: Low reliability of older system 5: Lowering costs of operations (low maintenance, higher efficiencies 6: Systems used or endorsed by a significant partner 7: Higher functionality 8: Improved decision making 9: E-business enablement 10: Standardizing business processes across the organization 1: Replace legacy systems 2: Simplify and standardize systems 3: Gain strategic advantage 4: Improve interactions and communication with suppliers and customers 5: Ease of upgrading systems 6: Link to global activities 7: Restructure company organization 8: Solve the Y2K problem 9: Pressure to keep up with competitors 1: Replace legacy systems 2: Solve the Y2K Problem 3: Ease of upgrading systems 4: Simplify and standardize systems 5: Pressure to keep up with competitors 6: Improve interactions and communications with suppliers and customers 7: Restructure company organization 8: Gain strategic advantage 9: Link to global activities 1: Increased demand for real-time information 2: Information generation for decision making 3: Integration of applications 4: BPR 5: Cost reduction 6: Taxation requirements 7: Introduction of Euro 8: Increase sales 9: Application of new business plan 10: Development of activities into new areas with business contacts 11: Competition 12: Internet development 13: Integration of IS 14: Stock exchange requirements 15: Government funding – subsidization 16: Year 2000 problem 1: The Desire for Standardization 2: To 'Overcome' IS Legacy Problems 3: To Deal with an Applications Backlog 4: The Role of Selling 5: Cost 6: The Perception of a „Tried and Tested‟ Product 7: The Availability of a Broader Knowledge and Skills Base 8: To „Free up‟ the information systems Function 9: To Implement Change 10: To Attain Best Practices 11: Bravado 12: Policy 1: One Integrated System 2: Y2K compliance 3: Unwanted
45
ContinuedSurvey on Reasons for Adoption for ES Table 3.1 Literature S. No.
Authors
Country
2005)
9
(Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005)
Italy
10
(Katerattan akul, Hong, & Lee, 2006)
Korea
11
(Laukkanen , Sarapola, & Hallikainen, 2007)
Finland
12
(Kamhawi, 2008)
Bahrain
Reasons for Adoption Legacy Systems 4: best business practices 5: Central Control 6: Top-down Focused Financial Strategy 7: Real-time Sharing of Data 1: HW/SW obsolescence 2: Euro issue 3: Y2K issue 4: Unsatisfying process integration 5: Unsatisfying order management 6: Data redundancy and/or inconsistency 7: Limited support to decisions 8: Lack of flexibility 9: Forced decision (by a controlling company) 10: Logistics and transportation issues 11: High cost of data distribution 12: Over-dimensioning of stock 13: CRM issues 14: Unsatisfying time-to-market 15: Dissimilarity of procedures (i.e. rules on quality management) 1: Simplify and standardize systems 2: Gain strategic advantage 3: Ease of upgrading systems 4: Link to global activities 5: Improve interactions and communication with suppliers and customers 6: Replace legacy systems 7: Restructure company organization 8: Pressure to keep up with competitors 1: Importance of improvements in competitive position 2: Importance of cost reductions 3: Importance of improvements in operational efficiency 4: Replacement of outdated information system 5: Importance of the intra-organizational integration capabilities of ERP system 6: Importance of the inter-organizational integration capabilities of ERP system 7: Importance of new ways of conducting business enabled by the system 8: Importance of the development of electronic commerce capabilities 1: Enhance managers‟ individual decision-making abilities 2: Support collaborative decision making inside the organization 3: Enhance cooperation with people outside the organization (such as buyers or suppliers . . . etc.) 4: Improve productivity 5: Optimize inventory 6: Optimize supply 7: Reduce cost structures 8: Integrate operations 9: Standardize our processes 10: Reduce cycle times (such as financial cycles) 11: Empower our users 12: Re-engineer our processes 13: Reduce number of employees 14: Increase business flexibility 15: Respond to competitive pressures 16: Enhance responsiveness to customers 17: Support sales growth 18: Reduce time to market 19: Boost our poor performance 20: Support globalization strategy 21: Integrate data 22: Standardize our databases 23: Integrate our disparate systems 24: Update our obsolete systems 25: Enhance our poor quality of data 26: Replace mainframes with client-server architecture 27: Solve the year 2000 (Y2K) problem
The analysis has shown that 158 items have been mentioned in these studies. All these items were printed on a single piece of paper and categorized in three iterations. The first iteration yielded 57 categories and second iteration summed up items in 13 categories. In second iteration data and information related issues, and improved 46
decision making were kept as separate categories. Therefore frequency count placed them into 8th and 12th position. In the final iteration two categories were clubbed together into one and frequency count boosted this category into 4th position. Therefore finally list ended up in categorizing these variables into 12 categories as shown in the following paragraphs.
i. ii. iii. iv. v. vi. vii. viii. ix. x. xi. xii.
Operational Improvements (cost, employee, cycle time reductions) Legacy System Replacement or IT Architectural Improvements Business Growth or Extensions Data or Information issues Regulatory and Compliance Issues Organizational Change Integration of Systems or processes Standardization and best practices Globalization Support Competition Customer and Supplier Intimacy External Forces
Table 3.2 gives the details of the final iteration and these categories have been explained in the following section. i.
Operational Improvements (cost, employee, cycle time reductions)
Operational improvements has come out to be one the most important factors. This factor has been supported by 8 research articles and received 22 items same as that of the second factor. This has been chosen to be ranked in the first position due to its direct relation with business performance. Various cost reductions have been cited to be the reasons to go for these systems such as computer operating cost, business operating cost, administrative expense, inventory carrying cost (Markus & Tanis, 2000). Though some studies have mentioned generally the motive of cost reduction (Deloitte Consulting, 2000; Kumar, Maheshwari and Kumar, 2002; Spathis and Constantinides, 2004; Laukkanen, Sarapola, & Hallikainen, 2007). The employee reduction has also been cited by one paper (Kamhawi, 2008). The efficiency of the 47
cycle times is also one of the major parameters to gauge the excellence of operations. Therefore unsatisfying time-to-market, financial cycle times were some of the reasons cited for ERP adoption (Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005; Kamhawi, 2008).
Table 3.2. Results of final iteration of content analysis Authors (Deloitte-Consulting, 1998) (Markus & Tanis, 2000) (Kumar, Maheshwari, & Kumar, 2002) (Olhager & Selldin, 2003) (Mabert, Soni, & Venkataramanan, 2003) (Spathis and Constantinides,2004) (Light & Papazafeiropoulou, 2004) (Arunthari & Hasan, 2005) (Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005) (Katerattanakul, Hong, & Lee, 2006) (Laukkanen, Sarapola, & Hallikainen, 2007) (Kamhawi, 2008)
ii.
Country
1
2
3
4
5
6
7
Global
1
1
3
1
1
2
2
Global
4
4
1
3
1
2
2
Canada
1
3
1
2
1
Sweden
2
1
1
1
1
1
1
1
9
US
2
1
1
1
1
1
1
1
9
4
1
Greece
1
Global
2
Thailand Italy
4
Korea
4 3
2
1
2
1
1
2
2
2
1
2
9
10
11
1
1
1
14
1
23
3
1
2
1 2
1
1
1
1
1
1
12
1
2
1
1
8
16
3
12
7 1
2
10
1
1
1
Sum
1
1
1
Finland
2
1
2
Bahrain
7
2
2
5
1
2
2
1
1
2
2
Sum
22
22
17
17
13
13
12
11
9
8
8
15
8
1
8
27 6
158
Legacy System Replacement or IT Architectural Improvements
Legacy system replacement or IT architectural improvement has been supported by 11 out of 12 research articles. Decades old systems developed in-house and on older and at times obsolete technological paradigms were becoming hard to upgrade and maintain (Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005; Deloitte Consulting, 2000). These systems more known as legacy system often grew in complexity with time to address the changing needs of the organizations. Multiple 48
vendors were supporting these systems hence managing them was a difficult task (Kumar, Maheshwari, & Kumar, 2002). Moreover the client server architecture supported through the public network, internet, was the need of the hour (Kamhawi, 2008). These systems had become so cumbersome that tweaking them was more expensive and riskier than replacing them. Though the Enterprise Systems‘ architecture promises the ease of upgrading (Olhager and Selldin, 2003; Mabert, Soni, & Venkataramanan, 2003; Katerattanakul, Hong, & Lee, 2006). iii. Business Growth or Extensions Organizations through different means such as mergers and acquisitions, vertical integration etc. keep on growing. Therefore flexibility and scalability are very essential to sustain the growth (Deloitte Consulting, 2000; Markus and Tanis, 2000; Kamhawi, 2008). E-business or e-commerce is also an integral part of most businesses therefore Enterprise Systems must support them (Kumar, Maheshwari, & Kumar, 2002; Laukkanen, Sarapola, & Hallikainen, 2007). Organizations also want to pursue strategic objectives through their IT investments (Olhager and Selldin, 2003; Mabert, Soni, & Venkataramanan, 2003; Katerattanakul, Hong, & Lee, 2006). It has also been reported that organizations adopt these systems to show bravado or pioneer in technology adoption (Light & Papazafeiropoulou, 2004). iv. Data or Information issues Legacy systems also had one major drawback of data or information integration (Kumar, Maheshwari, & Kumar, 2002; Kamhawi, 2008). These systems often created redundant and duplicate data, and islands of information (Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005). This severely limited the decision making and timely or real-time information access (Arunthari & Hasan, 2005). This didn‘t only
49
hamper individual but also the collaborative decision making (Kamhawi, 2008). Therefore to gain improvement in visibility and quality of information organization adopted Enterprise Systems (Deloitte Consulting, 2000). v.
Regulatory and Compliance Issues
Enterprise Systems‘ vendors have been constantly adding features to accommodate environmental changes of respective countries. These systems facilitate flexibility in information access formats to suit to different needs of the governmental and regulatory organizations (Spathis and Constantinides, 2004; Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005). Which otherwise is a cost incurring and time consuming activity with zero value addition what so ever. Moreover they also claim to make their systems Y2K compliant (Deloitte Consulting, 2000; Markus and Tanis, 2000). These issues prove to be a great impetus to go for these systems. vi. Organizational Change Lean, agile and learning organizations are best suited to shear off competition and lead the segment. But it takes great risk to transform large, static and bureaucratic organization into the desired structure. Enterprise Systems are seen as a tool to bring those changes required to sustain the growth (Light & Papazafeiropoulou, 2004). These systems look at the organization as a bundle of processes traversing through different departments and connecting the producers and consumers without revealing the complexity beneath them. Therefore these systems are adopted to re-engineer and remove inefficiencies from these business processes (Deloitte Consulting, 2000; Markus and Tanis, 2000; Kamhawi, 2008).
50
vii. Integration of Systems or processes Fragmented and disparate systems were not capable of supporting the integrated business processes (Deloitte Consulting, 2000; Kamhawi, 2008). Since newer processes didn‘t look at organization divided into different departments. Therefore they required a whole new approach of automation. Enterprise Systems working on integrated database and integrated applications principals offered to solve this problem (Markus & Tanis, 2000). And served the need of integration at intraorganizational and inter-organizational levels (Laukkanen, Sarapola, & Hallikainen, 2007). viii. Standardization and best practices There are certain set of activities common across industries, more known as support activities. These activities don‘t add value to the core business activities. There are standardized methods to carry out these activities that are captured in these software packages (Katerattanakul, Hong, & Lee, 2006). In other words software vendors over the years have learned the best practices and embedded them in their ES (Arunthari & Hasan, 2005). Therefore implementing these systems saves cost that otherwise would have been incurred in the inefficient methods. Moreover processes are not even standardized within the organization and these systems dictate uniformity (Markus and Tanis, 2000; Kumar, Maheshwari, & Kumar, 2002) ix. Globalization Support With rapid communication and transportation advancement, and liberalization organizations are not limited to the parent countries to optimize resources and access to the markets. As a matter of fact if one doesn‘t explore the globalized world chances of sustenance are bleak. Businesses operating in different countries require systems to
51
support their operations internationally. Multi-language and multi-currency support is very basic and essential (Markus & Tanis, 2000). However the Enterprise Systems must support the globalization strategy at large (Kamhawi, 2008). x.
Competition
Organizations are facing fierce competition due to changing preferences, innovation in products and globally integrated market. Some businesses operate on razor thin profit margins. Countries like China are flooding markets with very cheap products. In this scenario it‘s paramount to keep up the business performance with competitors (Mabert, Soni, & Venkataramanan, 2003; Olhager and Selldin, 2003). Enterprise Systems are said to boost poor business performance, hence improve the competitiveness (Deloitte Consulting, 2000; Laukkanen, Sarapola, & Hallikainen, 2007; Spathis and Constantinides, 2004). xi. Customer and Supplier Intimacy Enterprise Systems provides a good foundation for business value chain connectivity. ERP, SCM, PLM and CRM software packages are used to cooperate with people outside the organization (Kamhawi, 2008). SCM software is essential to improve inventory management and timely delivery of products. CRM is adopted to provide responsive and single interface to the customers (Deloitte Consulting, 2000; Markus and Tanis, 2000). Organisations in countries like Sweden, US and Korea had motivation to improve interactions and communication with suppliers and customers through their ES (Olhager and Selldin, 2003; Mabert, Soni, & Venkataramanan, 2003; Katerattanakul, Hong, & Lee, 2006).
52
xii. External Forces The decision to implement an Enterprise Systems didn‘t originate within the organization in some cases. Looking at the benefits of these systems government of some countries such as Greece offer subsidies or funding to implement these systems (Spathis & Constantinides, 2004). Business conglomerates and large corporations want to integrate business information to increase inter business unit cooperation. Therefore force the business units to adopt Enterprise Systems (Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005). Organizations have also implemented these systems endorsed or used by significant partner (Kumar, Maheshwari, & Kumar, 2002).
3.4 Implementation of Enterprise Systems Implementation is one of the most tedious of all phases. In this phase organization‘s routine business is severely affected. Employees are overburdened and challenged on their established ways of doing work. Processes are re-engineered and organizations are restructured. In this phase ―customization or parameterization and adaptation of the ES package is done as per the need of the organization‖ (Esteves & Bohorquez, 2007). Therefore this phase is the most studied by the researchers and practitioners and some are as follows.
Al-Mashari and Zairi (2000) have proposed an SAP R/3 specific model of implementation. Model has 7 elements in it: business case, benchmarking, implementation strategy, project management infrastructure, change management, BPR and SAP R/3 implementation. Moreover any organization intending to implement SAP R/3 on a corporate level needs to establish its competencies in five core areas. These are: change strategy development and deployment, enterprise-wide
53
project management, change management techniques and tools, BPR integration with IT, and strategic, architectural and technical aspects of SAP installation.
Parr and Shanks (2000) have proposed a project phase model (PPM) for the ERP implementation coupled with phase specific critical success factors. Process of implementation was derived from the studies of Bancroft et al., Ross and, Markus and Tanis. They have reported: large-scale ERP implementation projects are high risk and difficult to implement on time and within budget, need to understand the nature of the champion‘s role and previous experience, and ‗vanilla‘ ERP implementation may be considered a best-practice ERP implementation.
Robey, Ross and Boudreau (Summer 2002) have said that antecedents or CSF study lacks the theoretical framework to explain to the variety of implementation results. On the other hand process model is similar to the Van de Ven and Poole‘s life cycle mechanism for organizational change. The process model is also more descriptive than explanatory. Therefore authors have adopted a process theory perspective with dialectic assumption about the mechanisms that generate change. Through data analysis organizational learning, emerged as the dialectic between old knowledge and new knowledge. It identifies two categories of knowledge barriers configuration and assimilation knowledge barrier. The configuration knowledge barrier can be overcome by the core team and effectively managed consulting relationship. Assimilation knowledge barriers can be overcome by employee education and incremental pace of implementation. A hybrid approach of the piecemeal and concerted approach to implementation should be followed to overcome the assimilation knowledge barrier.
54
Ho, Wu and Tai (2004) employed the technology perspective of adaptation to examine the three dimensions of misalignment namely technology, delivery system and performance criteria. The authors have adopted the model of Leonard-Barton which says ―deviation from the damage caused by the initial design of ERP is less than the extent of the misalignment triggered by its subsequent extension‖. The authors have prepared the critical factors for these dimensions from the literature review. It is said that the effective linkage between technology, delivery system and performance criteria is the responsibility of the management. Based on case analyses adaptation framework for ERP implementation has been proposed. This framework contains three dimensions: ERP system, process re-engineering and synergy or process integration.
Burn and Ash (2005) have taken three strategic theories for e-business implementation namely: Virtual Organization, e-Business Change (eBC) and Benefits of B2B. The main theme is ―benefits of e-business implementations derived from virtual organizing through eBC management‖. Through the triangulation of three theories authors have proposed a model for e-Business Transformation (eBT) and governance, and a dynamic strategic planning model for progress through a cycle of innovation. They have proposed some key enabler for managers: sense of urgency, leadership from top and initiatives from employees, vision for change should be embraced by all levels of the organization, and numerous measurements.
Yu (2005) has taken the process oriented approach that would help define a framework for success from the very beginning. The methodology ―belief-attitudesbehavior-performance‖ is adopted from social and cognitive psychology. This methodology is also contrasted with the concept of four stages of implementation:
55
concept, development, implementation and operation given by Weston, and Markus and Tanis. Five belief variables, seven attitude variables, twelve behavior variables and five effective variables have been taken through the literature review and some through Taiwanese relevance. Results show that ―CEO commitment and involvement‖, ―professional management knowledge of MIS leaders‖ and ―top and middle management commitment and involvement‖ parameters of belief or concept phase have strong causation for the post implementation effectiveness.
Karimi, Somers and Bhattacherjee (Fall 2007) have taken the perspective of resource based view of the firm to analyze the business process outcome of the ERP implementation. The information systems resource is supposed to be composed of knowledge resource, relationship resource and IT infrastructure resource. Partial Least Square (PLS) was used to test the research model. Hypothesis have been proposed to see the individual and synergistic effects of IS resources on the ERP capability, ERP capabilities‘ effects on business process outcome and, complimentary effect of IS resources and ERP capabilities on business process outcome. Direct association has been found between relation resources and building ERP capabilities. It has been established that the co-presence of IS resources (synergistic effect) tends to supersede the effects of each IS resource alone. It has also been found that the building ERP capabilities has positive association with business process outcome, and IS resources can intervene to strengthen the relationship.
Analysis of the studies has shown that most researchers are trying to see the moving picture of implementation and emphasizing on to the process based approach. Pure antecedent or critical success factor (CSF) research fall short to explain the causes of the outcome of implementation. But the approach does ensure the positive outcome or
56
the success of implementation. In addition to being simple, it also has a good foundation in the literature that can be seen in the next section. Therefore in this research CSF approach has been employed to study the implementation process.
3.5 Critical Success Factors of ES project The critical success factors are defined as ―the limited number of areas in which results, if they are satisfactory, will ensure successful competitive performance for the organization‖ (Rockart, 1979). Several studies are present in the literature regarding CSFs of ES projects. These studies can be grouped in two categories: studies that give lists of CSFs and studies which try to group CSFs using different techniques. Holland and Light (1999) have given an analysis framework of CSFs, which are classified into strategic factors and tactical factors. Ho, Wu and Tai (2004) have classified CSFs into technology, delivery system and performance criteria.
In this study twenty three research articles have been considered for the compilation of the critical success factors identified in the previous studies. They are listed in the Table 3.3 along with the authors and the country where study has been carried out. In some cases it has been difficult to identify the country and in some studies factors are not empirical rather theoretical.
Number of CFSs reported in these studies has varied from 4 to 28. Finney & Corbett (2007) have identified 26 CSFs through content analysis and inductive coding technique after the analysis of 45 research articles. CSFs found in their study has been made the basis to further explore the CSFs. Many research articles have grouped the CSFs in fewer categories and defined them with multiple items (Motwani, Subramanian, & Gopalakrishna, 2005; Jarrar, Al-Mudimigh, & Zairi, 2000). Some
57
articles have listed CFSs in much broader and exhaustive fashion (Bajwa, Garcia, & Mooney, 2004).
Table 3.3 Literature Survey on CSF sub factors S. No
Authors
Country
Critical Success Factors
1
(Dembla, 1999)
Global
2
(Jarrar, AlMudimigh, & Zairi, 2000) (Nah, Lau, & Kuang, 2001)
Global
1: Corporate leadership and direction 2: Definition of overall business model and operating standard 3: Program management and ongoing implementation of master plan coordination 4: Selection and management of III party support relationships 5: Consistent and high quality education & training 6: Tight schedules and deadlines 7: Proper data 8: Full commitment. 1: Top Management Commitment 2: Business Process Reengineering 3: IT Infrastructure 4: Change Management
4
(Somers & Nelson, 2001)
Global
5
(Zhang, Lee, Zhang, & Banerjee, 2003) (AlMashari, AlMudimigh, & Zairi, 2003) (Umble, Haft, & Umble, 2003)
China
(Bajwa, Garcia, & Mooney, 2004)
Theoretical
3
6
7
8
Theoretical
Theoretical
USA
1: ERP teamwork and composition 2: Change management program and culture 3: Top management support 4: Business plan and vision 5: BPR and minimum customization 6: Effective communication 7: Project management 8: Software development testing and troubleshooting 9: Monitoring and evaluation of performance 10: Project champion 11: Appropriate business and IT legacy systems 1: Top management support 2: Project team competence 3: Interdepartmental cooperation 4: Clear goals and objectives 5: Project management 6: Interdepartmental communication 7: Management of expectations 8: Project champion 9: Vendor support 10: Careful package selection 11: Data analysis & conversion 12: Dedicated resources 13: Use of steering committee 14: User training on software 15: Education on new business processes 16: Business Process Reengineering 17: Minimal customization 18: Architecture choices 19: Change management 20: Partnership with vendor 21: Use of vendors‘ tools 22: Use of consultants 1: Top Management Support 2: Re-engineering Business Process 3: Effective Project Management 4: Company-Wide Commitment 5: Education and Training 6: User Involvement 7: Suitability of Software and Hardware 8: Data Accuracy 9: Vendor Support 10: Chinese Organizational Culture 1: Management and leadership 2: Visioning and planning 3: ERP package selection 4: Communication 5: Process management 6: Training and education 7: Project management 8: Legacy systems management 9: System integration 10: System testing 11: Cultural and structural changes 12: Performance evaluation and management 1: Clear understanding of strategic goals 2: Commitment by top management 3: Excellent project management 4: Organizational change management 5: A great implementation team 6: Data accuracy 7: Extensive education and training 8: Focused performance measures 9: Multi-site issues 1: Stakeholder Pressures 2: Industry Trends 3: Information Quality 4: Business Performance 5: Industry Norm 6: Industry Shakedown 7: Unique Needs 8: Resources 9: Enabling Constraints 10: Management Support 11: Stakeholder needs 12: Project Management 13: Change Management 14: Training 15:
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ContinuedSurvey on CSF sub factors Table 3.3 Literature S. No
9
Authors
Country
(Sun, Yazdani, & Overend, 2005) (Gargeya & Brady, 2005)
USA
(Motwani, Subramania n, & Gopalakris hna, 2005) (Ehie & Madsen, 2005)
USA
(Jafari, Osman, Yusuff, & Tang, 2006) (Woo, 2007)
Malaysia
15
(GarcíaSánchez & PérezBernal, 2007)
Mexico
16
(Finney & Corbett, 2007)
Theoretical
10
11
12
13
14
China
Critical Success Factors User Groups 16: Commitment 17: Perceived work benefits 18: IT provider and Integrator Push 19: Technical IT Quality 20: IT Integration 21: Compatibility/ Sophistication of Application 22: IT Providers and Integrator Profiles 23: Value Chain integration 24: IT Architecture requirements 25: IT provider and Integrator Support 26: IT Unit Competence 27: Value Chain Connectivity 28: IT Support Structure 1: Management/organization 2: Process 3: Technology 4: Data 5: People
1: Worked with SAP functionality/maintained scope 2: Project team/management support/consultants 3: Internal readiness/training 4: Deal with organizational diversity 5: Planning/development/budgeting 6: Adequate testing 1: strategic initiatives 2: learning capacity 3: cultural readiness 4: information technology 5: network relationships 6: change management practice 7: process management practice 1: Project management principles 2: Feasibility/evaluation of ERP project 3: Human resource development 4: Process re-engineering 5: Top management support 6: Cost/budget 7: It infrastructure 8: Consulting services 1: top management support 2: clear goals and objectives 3: communication 4: effective project management 5: business process reengineering 6: data accuracy and integrity 7: suitability of software and hardware 8: vendor support 9: education and training, and 10: user involvement 1: Top management 2: Project team 3: Project management 4: Process change 5: Education and training 6: Communication 1: Top management support 2: Project management 3: Teamwork composition for the ERP project 4: Communication 5: Business process reengineering 6: ERP system selection 7: Having external consultants 8: Training and support for users 9: Project champion 10: End users involvement 11: Change management plan 12: Tests and problem solution 13: To facilitate changes in the organizational structure, in the “legacy systems” and in the IT infrastructure 14: Vision statement and adequate business plan 1: Top management commitment and support 2: Change management 3: BPR and software configuration 4: Training and job redesign 5: Project team: the best and brightest 6: Implementation strategy and timeframe 7: Consultant selection and relationship 8: Visioning and planning 9: Balanced team 10: Project champion 11: Communication plan 12: IT infrastructure 13: Managing cultural change 14: Post-implementation evaluation 15: Selection of ERP 16: Team morale and motivation 17: Vanilla ERP 18: Project management 19: Troubleshooting/crises management 20: Legacy system consideration 21: Data conversion and integrity 22: System testing 23: Client consultation 24: Project cost planning and management 25: Build a
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ContinuedSurvey on CSF sub factors Table 3.3 Literature S. No
Authors
Country
Critical Success Factors business case 26: Empowered decision makers
17
(Yokota, 2007)
Japan
18
(Ngai, Law, & Wat, 2008)
Theoretical
19
(Muscatello & Chen, 2008)
USA
20
(Seidel & Back, 2009)
Global
21
(Snider, da Silveira, & Balakrishna n, 2009) (Sumner & Bradley, 2009) (Sammon, Adam, & Carlsson, 2009)
Canada
22 23
USA
USA
1: Project Mission 2: Champion 3: Top Management Support and Leadership 4: Business Process Reengineering 5: Project Scheduling and Management 6: Team Formation 7: Communication Network 8: Minimal Customization 9: Trouble Shooting/ Risk Management 10: Change Management 11: User Education and Training 12: Technical Task and Tools 1: Appropriate business and IT legacy systems 2: Business plan/vision/goals/justification 3: Business process reengineering 4: Change management culture and programme 5: Communication 6: Data management 7: ERP strategy and implementation methodology 8: ERP teamwork and composition 9: ERP vendor 10: Monitoring and evaluation of performance 11: Organizational characteristics 12: Project champion 13: Project management 14: Software development, testing, and troubleshooting 15: Top management support 16: Fit between ERP and business/process 17: National culture 18: Country-related functional requirements 1: Strategic Initiatives 2: Executive Commitment 3: Human resources 4: Project Management 5: Information technology 6: business Process 7: training 8: Project support & communications 9: Software Selection and Support 1: Change Management Approach 2: Funding Model 3: Governance Model 4: Human Resources 5: Inability to Change 6: Management Attention 7: Market & Business Cultures 8: Method Selection 9: Necessary Preconditions 10: Technical Factors 11: Tools 12: Unwillingness to Change 1: Operational process discipline 2: Small internal team 3: Project management capabilities 4: External end user training 5: Management support 6: Qualified consultant 1: Alignment 2: Full Time Project Manager 3: Project Manager Reporting to Mgmt 4: Project Manager Experience 5: Training 6: CEO Involvement 7: Champion 1: Existence of Actual Strategic Business Need informing Specific Project Goals and Objectives 2: Top Management Commitment and Support 3: Prioritized Business Requirements and Required System Functionality 4: Allocation of Best Internal Business Personnel 5: Effective Communication 6: Definitive Project Scope 7: Accurate Project Timeframe and Costing 8: Required Organizational Buy-In and Project Ownership
Therefore a total of 258 items found in 23 articles result in 262 items. Analysis of these articles has led to the identification of 37 distinct items Table 3.4. The rationale behind coming up with a large number of items was to get expansive knowledge of 60
the phenomenon and capture the nuances between these items. Managing this long list per say is very difficult. Therefore they have been grouped into fewer categories and hence form the CSF categories for the current study with 37 items explaining them in more details. Following paragraphs explain the CSF items in detail. i.
Top management commitment and support.
Top management commitment and support has been cited maximum number of times. It refers to the utmost attention that an organization‘s decision making body has to give to this project. It ensures that all the resources are provided, and the project has taken the right direction. It doesn‘t end with initialization rather last for the whole lifecycle of the project (Al-Mashari, Al-Mudimigh, & Zairi, 2003; Jarrar, AlMudimigh, & Zairi, 2000). Top management asserts the Enterprise Systems implementation to be a key mission statement and is able to sell it to the whole organization (Dembla, 1999). It is top management‘s responsibility to iron out any dispute or doubt for smooth implementation (Zhang, Lee, Zhang, & Banerjee, 2003). It has to ensure the best and brightest people of the organization are spared for the project (Sammon, Adam, & Carlsson, 2009). Also the project is headed by the project champion (Umble, Haft, & Umble, 2003). ii.
Training and job redesign
Training need and cost are very often underestimated in the Enterprise Systems project and hinder achieving the desired success. Therefore training need and associated costs have to be identified from the very onset of the project (Nah, Lau, & Kuang, 2001). The implementation team and IT staff need very specialized training. Whereas users need to be trained as to how they have to perform their task in the new environment of Enterprise Systems. Last but not the least staff restructuring and
61
Table 3.4 CSF sub factors and their presence in research articles
Authors
1
1
1 1 1 1
1 1 1 1 1 1 1 1 1 1 1
62
1
1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1
1 1
1 1 1 1 1 1 1
1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1
1 1
1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1
1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1
(Dembla, 1999) (Jarrar, Al-Mudimigh, & Zairi, 2000) (Nah, Lau, & Kuang, 2001) (Somers & Nelson, 2001) (Zhang, Lee, Zhang, & Banerjee, 2003) (Al-Mashari, Al-Mudimigh, & Zairi, 2003) (Umble, Haft, & Umble, 2003) (Bajwa, Garcia, & Mooney, 2004) (Sun, Yazdani, & Overend, 2005) (Gargeya & Brady, 2005) (Motwani, Subramanian, & Gopalakrishna, 2005) (Ehie & Madsen, 2005)
Stakeholder Pressures IT provider and Integrator Push Value Chain Connectivity Country-related functional requirements Multi-site issues Team morale and motivation System Integration Empowered decision makers Troubleshooting/crises management Technical Task and Tools Interdepartmental Cooperation Vanilla ERP Project cost planning and management Client consultation Expectation Management Vendor Support System testing Post-implementation evaluation Organizational characteristics Legacy system consideration Managing cultural change Project champion Implementation strategy and timeframe Build a business case Data conversion and integrity Selection of ERP Project team: the best and brightest Consultant selection and relationship IT infrastructure Visioning and planning Change management Communication plan Balanced team Project management BPR and software configuration Training and job redesign Top management commitment and support
CSF Sub-factors
Continued Table 3.4 CSF sub factors and their presence in research articles
Authors
1 1 1 1 1 1 2 2 2 3 3 4 5 5 6 6 6 6 7 7 7 7 7 8 8 8 8 9 10 11 11 12 12 15 16 20 23
63
1 1
1
1
1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1
1
1
1 1
1
1 1
1 1
1
1
1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1
1 1
1 1 1
1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 (Jafari, Osman, Yusuff, & Tang, 2006) (Woo, 2007) (García-Sánchez & Pérez-Bernal, 2007) (Finney & Corbett, 2007) (Yokota, 2007) (Ngai, Law, & Wat, 2008) (Muscatello & Chen, 2008) (Seidel & Back, 2009) (Snider, da Silveira, & Balakrishnan, 2009) (Sumner & Bradley, 2009) (Sammon, Adam, & Carlsson, 2009)
Stakeholder Pressures IT provider and Integrator Push Value Chain Connectivity Country-related functional requirements Multi-site issues Team morale and motivation System Integration Empowered decision makers Troubleshooting/crises management Technical Task and Tools Interdepartmental Cooperation Vanilla ERP Project cost planning and management Client consultation Expectation Management Vendor Support System testing Post-implementation evaluation Organizational characteristics Legacy system consideration Managing cultural change Project champion Implementation strategy and timeframe Build a business case Data conversion and integrity Selection of ERP Project team: the best and brightest Consultant selection and relationship IT infrastructure Visioning and planning Change management Communication plan Balanced team Project management BPR and software configuration Training and job redesign Top management commitment and support
CSF Sub-factors
compensation redesign should also be considered. Organizations can not realize the complete benefits unless end users are trained because the frustrated users will try to bypass the system and develop their own processes (Umble, Haft, & Umble, 2003). iii. BPR and software configuration This factor stood at number 3 in terms of frequency of appearance in agreement with Finney & Corbett (2007). BPR is essential to bring existing processes closest to the one defined in ES in order to reduce errors of customization and making the solution ready to accept newer versions and release (Nah, Lau, & Kuang, 2001). BPR exercise uses a process modeling tool or vendor development tools (Finney & Corbett, 2007). BPR also results in to-be-model from the as-is-model of the processes (Yokota, 2007). Most companies either re-engineer their processes to fit to application or chose application that fits well to their business and only a fraction of the organizations go for customization (Somers & Nelson, 2001). Cantu (1999) has defined process as one of the five factors with four dimensions: Alignment, Documentation, Integration and Process Redesign. iv. Project management ―Project management refers to the management of the implementation plan‖ (Finney & Corbett, 2007) so as to avoid cost or time overruns. It involves planning, allocating responsibilities, defining milestones and critical paths, human resource planning and training, and defining the measures of success (Finney & Corbett, 2007). Some researchers specifically talk about scope, cost and time planning (Nah, Lau, & Kuang, 2001). It also refers to the development of work plan and resource plan, and project progress tracking (Umble, Haft, & Umble, 2003; Yokota, 2007). Zhang, Lee, Zhang, & Banerjee (2003) have included five parts in project management: formal
64
implementation plan, time-frame, periodic meetings, project leader and team members from among the stakeholders. v.
Balanced team
The individuals of the team should come from diverse areas having sound knowledge of business and IT skills (Finney & Corbett, 2007). As Somers & Nelson (2001) put it, team members should not only be technologically competent but also understand the company and its business requirement. Moreover multiple team formation has also been ascertained in some cases such as strategic thinking team, a business analyst group, and an operations group (Motwani, Subramanian, & Gopalakrishna, 2005). vi. Communication plan Planned open communication should take place between the various functions especially business and IT personnel, shop-floor employees, suppliers and customers (Finney & Corbett, 2007; Yokota, 2007). ―Communication is the oil that keeps everything working properly‖ (Somers & Nelson, 2001). Communication should clearly tell in advance that change will take place and the scope of it along with objectives and activities (Nah, Lau, & Kuang, 2001; Al-Mashari, Al-Mudimigh, & Zairi, 2003). It‘s advisable to have open information policy using systems such as emailer system to avoid communication failures (Al-Mashari, Al-Mudimigh, & Zairi, 2003). Communication is also essential for the acceptance of the system (Kraemmerand, Moller, & Boer, 2003). vii. Change management Organizations traditionally have been operating in hierarchical and bureaucratic style. People heading the departments used to hold information and access to it used to take place with complicated procedures set by the de facto protocols. Enterprise Systems 65
allow the seamless integration of processes and allow the information sharing without any human intervention. Magnitude of change is so huge that it can cause resistance, confusion, redundancies, and errors (Somers, Nelson, & Karimi, Summer 2003). Making this shift requires a rigorous change management program to be in place. Yokota ( 2007) defines change management as ―To change organizational structure, end users‘ business processes to fit the rules and procedures for the implemented ERP systems‖. These systems dictate its own logic on a company‘s strategy, organization, and culture (Umble, Haft, & Umble, 2003). One important task in a change management program is to ensure the user acceptance by getting the endorsement and support of opinion leaders (Aladwani, 2001). viii. Visioning and planning Articulating business vision, and linking project‘s goals and objectives to IS strategy and planning incorporating risk, quality, tasks and benchmarking of internal and external best practices of ES implementation is essential (Finney & Corbett, 2007). The business plan should list the benefits expected and resources, risk and cost involved (Nah, Lau, & Kuang, 2001; Ngai, Law, & Wat, 2008). ES project vision and strategy definition and their congruence with business vision is also critical (Yokota, 2007; Jafari, Osman, Yusuff, & Tang, 2006). The three competing goals scope, time and cost must be defined and met (Somers & Nelson, 2001). ix. IT infrastructure IT infrastructure including skills and architecture must be appropriate to implement the ES or else should be upgraded (Finney & Corbett, 2007). Jarrar, Al-Mudimigh, & Zairi (2000) stress on the appropriate hardware, networking and software configuration. Cantu (1999) has taken four dimensions of Technology: hardware,
66
software, systems management and interfaces. Muscatello & Chen (2008) in their survey find the willingness of organizations to supplement hardware and software expertise externally if not up to the mark. x.
Consultant selection and relationship
The consultant should be made part of the implementation team and efforts should be made to transfer the knowledge to the company (Finney & Corbett, 2007). The quality of consultant is assessed by business understanding, software knowledge and soft skills (Snider, da Silveira, & Balakrishnan, 2009). Consultants offer multiple services (Jarrar, Al-Mudimigh, & Zairi, 2000) therefore may be involved in multiple stages (Somers & Nelson, 2001). Consultants may have financial ties with the ES vendor they recommend, therefore their veracity need to be ascertained (Piturro, 1999). xi. Project team: the best and brightest This factor talks about the people who are result oriented and top performers should be assigned to the ES project. In other words these are highly motivated, disciplined and dedicated people. Therefore sparing the best and brightest people with proven reputation for full-time is essential (Finney & Corbett, 2007). xii. Selection of ERP ES package selection must be led by match with overall business strategy and best fit with the existing business procedures (Al-Mashari, Al-Mudimigh, & Zairi, 2003). At the microscopic level selection should be governed by the budgets, time frames, goals, and deliverables (Somers & Nelson, 2001). In this process if the organization lacks the expertise then they can also take the help of external consultants (Muscatello & Chen, 2008). For SMBs Rao (2000) have proposed five criteria affordability,
67
domain knowledge of suppliers, level of local support, software upgradability and use of latest technology for the selection of ES package. xiii. Data conversion and integrity Data might exist in multiple repositories and on papers. Moreover the formats and definitions may not be consistent as well. Therefore at times this turns out to be a Herculean task and pivotal for the success of the ES project. Due to centralized database mistake at one place can cause problems at multiple places like dominos effect (Umble, Haft, & Umble, 2003). Cantu (1999) has defined this factor with four subfactors master files, transaction files, data structures and maintenance and integrity. xiv. Build a business case The ES project should be considered as a business initiative. Therefore an economic and strategic justification is essential (Finney & Corbett, 2007). And this project like any other IT project must be aligned with business needs, goals and strategies (Sumner & Bradley, 2009). Though the stimuli to implement an ES may arise from business needs or technology needs (Motwani, Subramanian, & Gopalakrishna, 2005). xv. Implementation strategy and timeframe There are numerous implementation strategies for transition to the ES. These are big bang, phased, parallel, process line and hybrid (Leon, 2009). Many researchers have supported phased approach (Finney & Corbett, 2007). It has been found that companies not considering their peak seasons disruptions incur losses (Motwani, Subramanian, & Gopalakrishna, 2005). Therefore the implementation strategy and timeframe has to be well thought out. 68
xvi. Project champion The project leader is also very important factor for the success of ES implementation. Project champion should be good at leadership and, business, technical and personal managerial traits (Finney & Corbett, 2007). The champion should persistently strive to resolve conflicts and manage resistance (Nah, Lau, & Kuang, 2001). This person should be placed high in authority to muster the necessary resources to lead the transformation (Somers & Nelson, 2001). xvii. Managing cultural change Finney & Corbett (2007) have considered it a separate category from change management due to the excessive citation of this factor. As per the Davison R. (2002) culture varies by organization and geographic location. One of the important difference lies in the culture of sharing information (Motwani, Subramanian, & Gopalakrishna, 2005) that greatly influences the success of ES. Therefore transformation has to be well planned and based on adequate strategy and well established methodology (Al-Mashari, Al-Mudimigh, & Zairi, 2003). xviii. Legacy system consideration Existing IT infrastructure, business processes, organizational structure and culture are all part of legacy systems (Al-Mashari, Al-Mudimigh, & Zairi, 2003). Systematic Study of legacy system must be a starting point. It can bring out lots of potential problems and gaps that are unresolved. It also reveals the anxiety or comforts that users face. The poorer the legacy system the larger is going to be the effort to implement the ES. Therefore this factor has been supported by many studies (Finney & Corbett, 2007; Nah, Lau, & Kuang, 2001; Bajwa, Garcia, & Mooney, 2004)
69
xix. Organizational characteristics Organizational characteristics such as maturity of business processes, willingness to adopt standard operating procedures, Technology affinity and experience with large scale change (Ngai, Law, & Wat, 2008) make it easier to implement ES project. xx. Post-implementation evaluation Evaluation of ES is important to retain the continued support for maintenance (Ross & Vitale, 2000), usage and upgradation. For assessment metrics and yardsticks have to be defined priori (Ross & Vitale, 2000). This also allows for the feedback mechanism (Mandal & Gunasekaran, 2003) to further improve the performance of ES. Therefore Finney & Corbett (2007) assert that the project is incomplete without post-implementation evaluation and consider it critical for success. xxi. System testing Rigorous testing of ES solution is very essential after customization or parameterization. Inadequate testing can reverse the fate of the ES project. For instance Gillette Company and Eastman Kodak attribute their success to rigorous testing (Gargeya & Brady, 2005). On the other hand Whirlpool Corporation attributes their failure to inadequate testing (Gargeya & Brady, 2005). xxii. Vendor Support Like with any software, ES also requires continuous support from its vendor. With evolving business needs the implementing organization needs extended technical assistance, emergency maintenance, updates, and special user training (Somers & Nelson, 2001). Zhang, Lee, Zhang, & Banerjee (2003) also mention this to be critical for success and specifically mention in the context of different business processes of Chinese organization. 70
xxiii. Expectation Management Benefits of ES could be short term or long term, tangible or intangible, financially quantifiable or non-quantifiable. Moreover some of the promised benefits are never realized especially in the case of oversold system by vendors (Somers & Nelson, 2001). Unrealistic expectation has also been reported to cause failure of ES (Gargeya & Brady, 2005). Therefore communication of benefits and hence expectation management is critical for the success of the project. xxiv. Client consultation User involvement is essential at two stages need definition and implementation (Jafari, Osman, Yusuff, & Tang, 2006). This can greatly reduce the resistance towards the new system since they realize they have been an integral part for adoption decision (Zhang, Lee, Zhang, & Banerjee, 2003). Finney & Corbett (2007) also consider keeping clients apprised about the system is essential for the success. xxv. Project cost planning and management Detailed cost analysis is very critical to avoid cost overrun surprises that may result in the abrupt termination of the project. Like Waste Management, Inc. decided to abort ES implementation due to cost overrun (Gargeya & Brady, 2005). Similarly Dell also abandoned the SAP implementation due to cost overrun (Ehie & Madsen, 2005). Seidel & Back (2009) discuss about this factor by the name of funding model that should support the effective and efficient ERP implementation. xxvi. Vanilla ERP Vanilla ERP means an organization is committed to implement ES with zero or the least amount of customization (Finney & Corbett, 2007). Yokota (2007) also considers minimal customization to be a success factor. Many researchers have talked 71
about modifying the business process to fit the ES rather than altering the ES (Holland & Light, 1999; Roberts & Barrar, 1992). xxvii. Interdepartmental Cooperation This factor depends upon the nature of the organization. An organization that has a culture of sharing goals, and values trust between partners, employees, managers and corporation scores high on this factor (Somers & Nelson, 2001). In order to gain the potential of ES there has to be strong coordination of goals and efforts between business and IT personnel (Willcocks & Sykes, 2000). xxviii. Technical Task and Tools Yokota (2007) considers selection, usage and training of accelerator tools to be essential for the success of ES implementation. Usage of CASE tools and simulation for process analysis and design has also been ascertained in the study of Motwani, Subramanian, & Gopalakrishna (2005). xxix. Troubleshooting/crises management It refers to the identification of problems and contingent solution to address them (Yokota, 2007). Scott & Vessey (2000) using Sitkin's theory of intelligent failure to ERP implementations conclude that it is essential to foresee the trouble areas and learn from failures. xxx. Empowered decision makers This factor refers to the sufficient authority conferred upon the team members to meet the landmarks of the project (Finney & Corbett, 2007). As pointed out by Motwani, Subramanian, & Gopalakrishna (2005) that the autocratic style of top management led
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to serious consequences by one of the companies considered in their study. The top management overlooked the team and signaled the ES to go live. xxxi. System Integration System integration can be thought of in two dimensions one talking about integrating different applications from different vendors through middleware that deals more with the value chain connectivity and defined separately. The other dimension concerned here is the integration of ES with other organizational systems such as legacy systems (Bingi, Sharma, & Godla, 1999). xxxii. Team morale and motivation Moral boost and motivation drills are essential to maintain the pace of the project (Finney & Corbett, 2007). In order to retain the staff involved in the project company need to acknowledge the efforts of the members (Barker & Frolick, Fall2003) and create a stimulating work environment (Mandal & Gunasekaran, 2003) . xxxiii. Multi-site issues Multi-site issues CSF deals with issues such as the individual site autonomy, difference of culture between sites and the decision for the cut over strategy (Umble, Haft, & Umble, 2003). These issues contend between remote versus centralized control, corporate standardization versus local optimization, and quick return versus smoother implementation respectively (Umble, Haft, & Umble, 2003; Allen, 1997). xxxiv. Country-related functional requirements This factor deals with the discrepancies arising due to political boundaries. Different countries may have different business practices, and legal and governmental requirements (Ngai, Law, & Wat, 2008). For instance the failure for western packages
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to address the Chinese government mandated user interface and report formats (Xue, Liang, Boulton, & Snyder, Sep 2005). xxxv. Value Chain Connectivity Bajwa, Garcia, & Mooney (2004) have proposed five stage ES assimilation model. For each stage they have considered technical and business construct and classified into external and internal antecedents or CSF. Value chain consideration has been considered an internal technical construct influencing the outcome in two stages namely selection and implementation stages. xxxvi. IT provider and Integrator Push IT provider and Integrator influence such as aggressive marketing, due diligence, knowledge, is considered an external factor (Bajwa, Garcia, & Mooney, 2004). As per the model of Bajwa, Garcia, & Mooney (2004) IT provider and Integrator push or support in each stage greatly influences the outcome of the assimilation of ES. This factor is only cited by them. xxxvii. Stakeholder Pressures Stakeholders such as suppliers, customers and business partners also play an important role in the adoption of ES. The model of Bajwa, Garcia, & Mooney (2004) takes into account this factor in first four stages awareness, selection, preparation and implementation. This factor is part of external, business construct and also only considered by them.
3.6 Grouping of Critical Success Factors Al-Mashari (2002) has identified 12 CSFs based on the analysis of ERP literature consisting of research studies and organizational experiences. These factors have been 74
grouped into three phases setting-up (Management and leadership, Visioning and planning), implementation (ERP package selection, Communication, Process management, Training and education, Project management, Legacy systems management, System integration, System testing, Cultural and structural changes), and Evaluation (Performance evaluation and management).
Ehie & Madsen (2005) have defined the five stage implementation process. They have identified 38 items that are critical for success. Using Principal Component Analysis they have grouped these items into eight factors Project management principles, Feasibility/evaluation of ERP projects, Human resource development, Process re-engineering, Top management support, Cost/budget, IT Infrastructure, Consulting services.
The above studies and other similar studies (Nah, Lau, & Kuang, 2001; Yokota, 2007; Somers & Nelson, 2001) have tried to club the CSFs on the basis of stages or phases of the implementation process. The focus of this research is to group the CSFs on the basis of themes and sub themes, meaning on the basis of the distinct phenomenon and categorization of similar phenomenon into the super category. Following are some of the studies which have adopted similar approach and have identified CSFs and explained them with related items or sub-factors.
Zhang, Lee, Zhang, & Banerjee (2003) extracted five dimensions to group CSFs from the previous researches. These groups are Organizational Environment (Top Management
Support,
Re-engineering
Business
Process,
Effective
Project
Management, Company-Wide Commitment), People Characteristics (Education and Training, User Involvement), Technical Problems (Suitability of Software and
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Hardware, Data Accuracy) ERP Vendor Support (Vendor Support), and Cultural Impacts (Chinese Organizational Culture).
Gargeya & Brady (2005) have given a very compressed list of only six CSFs with the help of content analysis of 44 published articles on the SAP implementations. These factors
are
Worked
team/management
with
SAP
support/consultants,
functionality/maintained Internal
scope,
readiness/training,
Project
Deal
with
organizational diversity, Planning/development/budgeting, and Adequate testing.
Motwani, Subramanian, & Gopalakrishna (2005) have categorized the critical factors into seven constructs consisting of 24 items after analyzing four case studies based on business process change theory. These constructs are strategic initiatives (Stimuli, Formulation scope, Formulation scope, Strategy led) learning capacity (Adaptation, Improved efficiency, Declarative knowledge, External information use, Learning type), cultural readiness (Change agents and leadership, Risk aversion, Open communications,
Cross-training),
information
technology
leveragability
and
knowledge-sharing capability (IT role, Use of communication technology), Network relationships (Inter-organizational linkages, Cross-functional cooperation), change management practice (Pattern of change, Management readiness to change, Scope of change,
Management
of
change),
process
management
practice
(Process
measurement, Tools and techniques, Team based).
García-Sánchez and Pérez-Bernal (2007) have done an empirical research on the 48 large and medium Mexican enterprises. 14 Critical Success Factors were identified through the 9 previous researches. Authors have also grouped the factors as per the management tasks into Human Factors (Teamwork composition, Communication, Project Champion, End users involvement), Technological Factors (Project 76
Management, ERP system selection, Training and Support for Users, Tests and problem solutions, To facilitate of changes in the organization structure in the ―legacy systems‖ and in the IT infrastructure) and Organizational Factors (Top Management Support, Business Process Reengineering,
Having External Consultants, Change
Management Plan, Vision Statement and Adequate Business Plan).
Table 3.5 CFSs framework proposed by Cantu (1999) Critical success factor CSF attributes 1. Management/organization 1. Commitment 2. Education 3. Involvement 4. Project team selection 5. Training 6. Roles and responsibility 7. Alignment 2. Process 8. Documentation 9. Integration 10. Process redesign 11. Hardware 3. Technology 12. Software 13. Systems management 14. Interface 15. Master files 4. Data 16. Transactional files 17. Data structure 18. Maintenance and integrity 19. Education 5. People 20. Training 21. Skills development 22. Knowledge management
Finney & Corbett (2007) have grouped all the 26 CSFs into two categories strategic and tactical. Muscatello & Chen (2008) have identified 9 constructs of CSFs and have used more than four items to measure them. Ngai, Law, & Wat (2008) have identified 18 CFSs consisting of 80 sub-factors. They have done the analysis of 48 research articles belonging to 10 different geographic regions. Sun, Yazdani, & Overend
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(2005) have used Forrester simulation approach to study how cost, schedule and achievement and CFSs priorities together can predict success of Enterprise Systems. They have used the CFSs framework proposed by Cantu (1999) shown in the Table 3.5.
Working along the lines of these articles this research has attempted to classify the CFSs into eight categories along with 37 sub-factors identified from the 23 research articles listed in Table 3.4. CSFs along with their sub-factors are listed in the following Table 3.6.
3.7 Usage of Enterprise Systems Every investment needs to be assessed so as to sustain organizational growth. IT budget has been burgeoning since its utility in the organizational context. It has been debated whether IT is a commodity technology or a differentiating technology (Carr, 2003). In this context it has become paramount to assess the returns on the IT investments. Enterprise Systems software is a packaged software solution promising to fulfill information needs and run business processes internally and externally. They help in executing day to day operations, decision making, organizational performance and developing competitive strengths.
Since IT investments affect a series of variables in conjunction with other factors (Legare, 2002). Success cannot be gauged with a simple measure like return on investment (ROI). Rather, success is considered to be multidimensional dependant variable (DeLone and McLean 1992, DeLone and McLean 2003, Gable, Sedera and Chan 2008). Efforts have been going on to come up with a comprehensive measurement instrument. Whereas some studies are trying to give guidelines to
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Table 3.6 CSF and their sub factors Critical Success Factor 1. ES Ground work
2. 3.
4.
5.
6.
7.
8.
Sub-Factors 1.1 Organizational characteristics 1.2 Legacy system consideration 1.3 Client consultation 1.4 Country-related functional requirements 1.5 Value Chain Connectivity 1.6 System Integration 1.7 Build a business case 2.1 Vanilla ERP Minimal Customization 2.2 BPR and software configuration 2.3 Selection of ERP 3.1 Visioning and planning Project Visioning, Planning and Management 3.2 Implementation strategy and timeframe 3.3 Project cost planning and management 3.4 Communication plan 3.5 Project management 4.1 Top management commitment and People and Organizational Support support 4.2 Empowered decision makers 4.3 Project champion 4.4 Project team: the best and brightest 4.5 Balanced team 4.6 Interdepartmental Cooperation 5.1 IT infrastructure Technical Issues and Resources 5.2 Data conversion and integrity 5.3 Multi-site issues 5.4 Technical Task and Tools 6.1 Vendor Support External Pressure and Support 6.2 Consultant selection and relationship 6.3 Stakeholder Pressures 6.4 IT provider and Integrator Push 7.1 Change management Change Management 7.2 Training and job redesign 7.3 Managing cultural change 7.4 Team morale and motivation 7.5 Expectation Management 8.1 System testing Testing and Assessment 8.2 Troubleshooting/crises management 8.3 Post-implementation evaluation
develop such instruments (Chand, Hachey, Hunton, Owhoso, & Vasudevan, 2005). Following paragraphs give a brief descriptionon of some of the important studies. 79
DeLone and McLean (1992) have done a very comprehensive study in defining the dependent variable success. They proposed success to be a multidimensional variable. There are six dependent variables system quality, information quality, use, user satisfaction, individual impact and organizational impact. They further argued that these components of success are interdependent and interrelated and form an I/S success model. DeLone and McLean (2003) did review of literature ten years post of their original model proposition (DeLone & McLean, 1992). They added one variable service quality and merged individual impact and organizational impact into net benefits.
Deloitte-Consulting (1998) surveyed 164 individuals at 62 Fortune 500 companies. Based on the responses this study first mentions the expected capabilities upon completion of ERP program and anticipated benefits of ERP program. Organizations are expected to improve efficiency, effectiveness and transformational capabilities. Anticipated benefits largely fall under cost reductions and faster time cycles. Then study has tried to seek out the expected timeline of the benefits realization. Actual benefits realized are classified into tangible and intangible benefits contrasted with the anticipated benefits.
Murphy and Simon (2002) bring out the important issue on assessment of IT investments. They point out IT investments are poised to be strategic investments and often assessed using operational measures. Moreover many success measuring instruments involve difficult to quantify intangible benefits. Therefore are making it difficult to apply traditional project evaluation techniques. Using literature review they have classified IS benefits with the help benefit frameworks proposed by different researchers. They have employed a case study and reported that benefits that
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are long term strategic and organizationally oriented are difficult to quantify. Whereas benefits that are short term and operational, and IT infrastructure are easier to quantify.
Shang and Seddon (2002) have developed Enterprise Systems Benefit Framework by extending the work of Anthony (1965). They added two more dimensions IT Infrastructure and Organizational Benefits to the Anthony‘s (1965) Operational, Managerial and Strategic benefits. This framework was developed using literature review, analysis of vendor reported case studies and interview with managers. In addition they also provided perceived net benefit flows (PNBFs) graphs to suggest the occurrences of benefits timeline.
Spathis and Constantinides (2003) have studied the impact of ERP on accounting information and management processes. They have developed 15 items instrument to measure the benefits derived from the ERP system. This study has been done on Greek companies. Highly rated items have been increased flexibility in information generation, improved quality of reports-financial statements, increased integration of applications and easy maintenance of databases. Factor analysis has revealed five independent subsets of these benefits: Effective logistics, Effective communication function, Effective decision-making process, Efficient data processing and Effective information systems.
Chand, Hachey, Hunton, Owhoso, & Vasudevan (2005) have developed a holistic framework for assessing benefits called ERP scorecard. They have used Kaplan and Norton‘s four dimensions of balanced scorecard process, customer, finance, and innovation and learning, and Zuboff‘s three levels automate, informate and transformate. In turn they have developed a 12 cell matrix of benefits. 81
Where
automate, informate and tranformate levels look into operational, tactical and strategic benefits of ERP systems respectively. Staehr (2007) has used Shang and Seddon‘s (2000) ES benefit framework on four Australian manufacturing organizations. He has confirmed all dimensions and categories of Shang and Seddon‘s (2000) ES benefit framework. He has further extended two categories and one partial category into it. User accountability has been added in operational benefit dimension and standardization has been added in organizational dimension. The partial benefit category is service differentiation. It has also been found that benefits are affected by the contextual variables of the organizations such as motivation, time, functional area, site and scope.
Gable, Sedera and Chan (2008) proposed an IS success model in terms of IS-impact model. They defined it as ―IS-Impact of an Information System (IS) as “a measure at a point in time, of the stream of net benefits from the IS, to date and anticipated, as perceived by all key-user-groups”. Their model has two halves impact half and quality half. The impact half has two dimensions individual and organizational and represents the benefits till date. The quality portion also has two dimensions information quality and system quality and a predictor of future benefits. They have first reconciled Benbasat and Zmud‘s (2003) IS-Net model with DeLone and McLean‘s (1992) I/S Success Model. Resulting IS-Net has been reconciled with their IS-impact model and the recursive nature of the net benefits has been pointed out. Finally they assert that the net benefits are realized in cycles where impact precedes the quality. Therefore when measuring quality one has to measure impact retrospectively to get the snapshot of benefits. Their research goes through three
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stages literature review, exploratory and confirmatory analysis that reduces 119 measures of IS success to 27 measures in four dimensions.
Koh, Gunasekaranb and Rajkumar (2008) have done research on ERP II, their business benefits and impediments to success. They statistically tested 21 categories of Shang and Seddon‘s (2000) ES benefit framework. Results show that all categories falling under operational, managerial and IT infrastructure benefits dimensions can be carried forward to ERP II arena. Whereas only support for business growth, building business innovations and building cost leadership under strategic benefit dimension, and only changing work pattern, empowerment and building common vision under organizational benefits dimension can be carried forward to ERP II arena.
Eckartz, Daneva, Wieringa, & Hillegersberg (2009) has done a structured literature review (SLR) to spot the gaps in the research in the ERP benefits area. They point out towards the incompleteness in the previous studies in terms of ES benefits identification, realization and assessment. Studies either helped in benefits identification or realization and assessment. They have proposed a comprehensive three dimensional benefit framework model. The first dimension is derived from Antony‘s work (1965) and Shang and Seddon‘s (2000) operational, managerial and strategic dimensions. Second dimension is based on Kaplan and Norton‘s (1996) balance scorecard categories process, customer, finance and innovation and one additional category of HR. The third dimension is based on the remaining two dimensions of Shang and Seddon (2000): IT infrastructure and organizational benefits. They hope this framework will help in exploring the relationship between benefits and cost to realize them.
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Schubert and Williams (2009) firstly have identified four gaps in the current state of ES benefits research. These are lack of consideration of the variations of motivation of the organization undertaking ERP projects, temporal modulation of benefits, reach and scope of the ERP projects, and quality and richness of research data. They aimed at providing an extended benefit framework that can relate between motivations to implement ERP and expected and realized benefits. They are using cases from the eXperience database (www.experience-online.eu). In the current stage of their research they have identified five main categories, 17 elements and 30 criteria and benefit codes have been given three dimensions accordingly.
Analysis of the above studies suggests that the I/S success model (DeLone & McLean, 1992), IS-Impact model (Gable, Sedera, & Chan, 2008) and ERP benefit framework (Shang & Seddon, 2002) have been developed more comprehensively. These works also have made often basis for other studies. Whereas exp-ben-taxonomy (Schubert & Williams, 2009) and three dimensional benefit framework (Eckartz, Daneva, Wieringa, & Hillegersberg, 2009) seems most promising ongoing efforts in this area. In this research work Shang and Seddon‘s (2002) ERP benefit framework has been chosen.
3.8 Relationship between Critical Success Factors and ES Benefits There are several studies in the literature that have shown the relationship between critical success factors and ES implementation success, outcome or benefits. Some of these studies and their models are discussed here. First one to discuss is the research work of Zhang, Lee, Zhang, & Banerjee (2003). They have identified 10 CSF from literature review and grouped them in five categories. They took these factors as independent variables. For measuring the ES implementation success they considered
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Figure 3.1 Conceptual research model of ERP implementation success in China Source: (Zhang, Lee, Zhang, & Banerjee, 2003)
85
Figure 3.2 Taxonomy for ERP critical factors Source: (Al-Mashari, Al-Mudimigh, & Zairi, 2003)
Figure 3.3 Theoretical framework for ERP Implementation Management Source: (Motwani, Subramanian, & Gopalakrishna, 2005) 86
two measures ABCD classification (Wight, 1981) and end user satisfaction adapted from Doll & Torkzadeh (1988). They have studied the relationship between CSFs and implementation success using structural equation modeling of PLS Graph in Chinese organizations Figure 3.1.
Al-Mashari, Al-Mudimigh, & Zairi (2003) have developed a theoretical and practical grounded taxonomy of ES CFSs Figure 3.2. They proposed a tight link between implementation approach and performance measures that in turn will maximize the ES benefits. In other words they propose relationships between ES CSFs, ES success and ES benefits. They have taken the definition of failure or success from the work of Lyytinen & Hirschheim (1987) in terms of Correspondence success, Process success, Interaction success and Expectation success. And for ES benefits, they have taken the benefit framework of Shang & Seddon (2002).
Motwani, Subramanian, & Gopalakrishna (2005), using the case study methodology grounded in business process change theory, have tried to establish the relationship of CSFs with the success or failure of the ES Figure 3.3. Most of the models cited in the literature are conceptual. This research develops a similar model shown in the next chapter and tests it using partial least square method of structural equation modeling.
3.9 Conclusion The Lifecycle of ES in a broader sense has four phases adoption, implementation, usage and maintenance, and integration. The last phase is more concerned with the upgrade, or integration of more components that resuscitates life into the system and outside the scope of the present study. The adoption phase assesses or defines system requirements, goals and benefits, business and organizational level impact, and ROI. There have been 12 motivational factors to adopt these systems such as Operational 87
Improvements, Legacy System Replacement or IT Architectural Improvements, Business Growth or Extensions, Data or Information issues, Regulatory and Compliance Issues, Organizational Change, Integration of Systems or processes, Standardization and best practices, Globalization Support, Competition, Customer and Supplier Intimacy, and External Forces.
The implementation phase deals with the customization or parameterization of the ES package. Various theories and models have been proposed to understand, expedite and make impeccable the implementation. The CSF approach has a good foundation in the literature and is being used for the present study. Eight major CSF categories have been identified consisting of 37 subcategories. These are ES Ground work, Minimal Customization, Project Visioning, Planning and Management, People and Organizational Support, Technical Issues and Resources, External Pressure and Support, Change Management, and Testing and Assessment.
In order to sustain any project organizations need to assess its utility. There are three broadly speaking measures to assess the usage of the ES namely that I/S success model (DeLone & McLean, 1992), IS-Impact model (Gable, Sedera, & Chan, 2008) and ERP benefit framework (Shang & Seddon, 2002) and they also make the foundation for other measures. The Shang & Seddon‘s (2002) ES benefit framework has been adopted for the present study. Moreover various researchers have tried to correlate the CSF with the usage, benefits or the success of the ES. Therefore this study has also tried to establish the relationship between them using structured equation modeling.
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Chapter Four RESEARCH METHODOLOGY AND DESIGN 1.1 Introduction Research methodology and design gives the blueprint essential to carry out research (Malhotra, 2007). This chapter defines the research problem, objectives, the research approach, research framework and hypotheses. In the research approach section brief introduction to the partial least square technique along with reflective versus formative constructs, construct reliability and validity, and assessment of the inner and outer model has been given. The research framework section gives the brief definition of all the variables involved in the study and ES Benefits predictive model. The chapter further defines the population, sampling frame and sample considered for the study and case collection procedure. Finally the sources of data, data from one sample case and scope of the study has been provided.
4.2 Research Approach The exploratory research design has been used for studying the adoption and success of these systems in Indian context. Extensive literature review has helped identify adoption reasons, CSFs and their sub factors, and ES benefits. This study has employed structural equation modeling, the partial least square method to study the relationship between CSFs and the actual success of these systems, using the ES benefit framework proposed by (Shang & Seddon, 2002). Following paragraphs will illustrate the structural equation modeling, reflexive versus formative constructs and PLS method. Moreover to test the hypotheses secondary data derived from the cases or customer success stories have been utilized. The case study method has foundation
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in many IT studies (Yin, 2003) and considered appropriate wherthe goalal is theory testing and theory building (Benbasat, Goldstein, & Mead, 1987). 4.2.1 Structural Equation Modeling First generation statistical techniques such regression, factor analysis or cluster analysis uses empirical data to confirm or identify the theoretical hypothesis. These techniques have certain limitation or make unrealistic assumption such as multiple independent variables regress to one dependent variable, variables to be observable, and the error free measurements of variables (Haenlein & Kaplan, 2004).
Structural equation modeling defeats the limitations of first generation statistical techniques and allows for the modeling of multiple independent constructs and multiple dependent construct in a holistic, systematic and unified way (Gefen, Straub, & Boudreau, 2000). Every variable is considered either exogenous (independent) or endogenous (dependent). And the latter being explained by the relationships postulated in the model (Diamantopoulos, 1994). SEM employs two techniques covariance or variance to analyze the model. Partial least square regression uses a variance based approach.
In order to develop a model profound understating of theory is mandatory. So that the model structure is in sync with the theoretical structures available in the theory. The theory consists of theoretical concepts and derived concepts both defined as latent variables and an empirical concept defined as an indicator variable. Moreover these concepts are linked with non-observational hypothesis (linking theoretical concepts with theoretical concepts), theoretical definitions (linking theoretical concepts with derived concepts), and correspondence rules (linking theoretical or derived concepts with empirical concepts) (Bagozzi & Phillips, 1982) Figure 4.1. 90
Figure 4.1 Theoretical Framework to Develop Structural Model Source: (Haenlein & Kaplan, 2004) 4.2.2 Reflective versus Formative Constructs Ref lective
constructs cause the items or measurements whereas formative constructs
are caused by the items or measurements (Freeze & Raschke, 2007) Figure 4.1 and Figure 4.2. In the case of reflective constructs any change in the latent construct is reflected in the measurement and measurement error is associated with the measurements (Freeze & Raschke, 2007). On the other hand formative constructs are derived from measurements and the measurement error is introduced at the level of construct (Freeze & Raschke, 2007).
Figure 4.2 Reflective Latent Variable
Figure 4.3 Formative Composite Variable
Source: (Freeze & Raschke, 2007)
Source: (Freeze & Raschke, 2007)
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It‘s easy to understand in the case of CSFs that they are formative constructs hence composite or combination of the items. It‘s difficult to define the ES success construct. Is it that since the implementation was successful therefore the benefit items are realized? The answer seems true and hence reflective construct. But one may also ask ―What if benefits are not realized, would the implementation be considered successful?‖ Negative answer to this question will make it formative construct. In other words debate is between a successful implementation leads to ES benefits versus ES benefits defines the success of ES. Though the ES benefits itself is a formative construct composite or combinations of items of benefits. Moreover formative constructs helps in predicting (Esteves, Casanovas, & Pastor, 2003). LISREL, EQS and AMOS, covariance based SEM cannot be used since they don‘t support formative constructs (Gefen, Straub, & Boudreau, 2000). Whereas partial least square based SEM supports both formative and reflective constructs (Gefen, Straub, & Boudreau, 2000). Moreover PLS doesn‘t make any assumption about the population or scale of measurement (Fornell & Bookstein, 1982). Therefore works with nominal and higher scale and with no assumptions of the distribution (Haenlein & Kaplan, 2004). 4.2.3 Partial least squares (PLS) PLS utilizes the concept of maximization of variance of the dependent variables explained by the independent variables in contrast to the reconstructing of the covariance matrix (Haenlein & Kaplan, 2004). The PLS model consists of a structure showing all the latent or unobservable constructs along with their measurements, items or indicators. Secondly it consists of paths showing the relationships among
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them. Finally it has an additional component that defines the weight relation used to estimate the unobservable variables (Haenlein & Kaplan, 2004).
The order of computation goes as follows first the weight relations are calculated, next the case values the composite of weighted average are estimated, finally the structural relations are calculated with the help of a set of regression equations (Fornell & Bookstein, 1982). Therefore the most important step is the estimation of weight relations since all successive steps depend on it. In a more simplistic approach one may give equal weights to all the indicators but it will be difficult to get theoretical support for the same(Haenlein & Kaplan, 2004). Moreover it will also undermine the more reliable indicators supposed to get higher weights (Chin, Marcolin, & Newsted, 1996).
Figure 4.4 A Two-Step Process of PLS Path Model Assessment. Source: (Henseler, Ringle, & Sinkovics, 2009) PLS employs two-step outside and inside approximation, iteratively until the case values converge Figure 4.4. For outside approximation the latent variable is estimated with their respective indicators with the help of regression for formative constructs as 93
proposed in this research (Haenlein & Kaplan, 2004). And for inside approximation the case value is weighted with the help of neighboring latent variables using centroid, factor or path weighing scheme (Haenlein & Kaplan, 2004). Moreover the problem of consistency is removed if the sample size and the number of indicators approach infinity. 4.2.4 Construct Reliability and Validity Outside approximation more known as an outer model or measurement model provides the reliability and validity of blocks of manifest variables (binti Ismail, binti Abd Hamid, & Idris, 2012). There are five criteria for the assessment of outer model for the reflexive constructs namely indicator reliability, internal consistency reliability, convergent validity, and discriminant validity at indicator and construct levels (binti Ismail, binti Abd Hamid, & Idris, 2012).
Indicator reliability or the reliability of the manifest variable is achieved when factor loading is more than 0.7 (Henseler, Ringle, & Sinkovics, 2009). Composite reliability or Cronbach‘s alpha more than 0.6 is required for the internal consistency reliability or reliability of block of manifest variable (Henseler, Ringle, & Sinkovics, 2009). Convergent validity, a measure indicating manifest variables represent the underlying construct, is assessed by average variance extracted (AVE) and should be more than 0.5 (Henseler, Ringle, & Sinkovics, 2009). Discriminant validity at the construct level is a measure of the extent to which constructs don‘t correlate with other constructs. It is estimated with the Fornell-Larcker criterion that is construct‘s AVE should be higher that its squared correlation with other constructs (Hair Jr., Money, Samouel, & Page, 2007). Whereas the indicator level discriminant validity is established when manifest variable loads highest on the mapped construct.
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For the formative constructs the indicator reliability is meaningless due to the assumption of error free measures (Henseler, Ringle, & Sinkovics, 2009). Three criteria are used to assess the measurement model for the formative constructs namely indicators
relative
contribution
to
the
construct,
significance
of
weight,
multicolinearity (binti Ismail, binti Abd Hamid, & Idris, 2012). Of these last two are more important to establish since they decide as to which indicator will enter the model. Bootstrapping method is used to assess the significance of the estimated indicators weight (Henseler, Ringle, & Sinkovics, 2009). The multicolinearity among formative constructs is estimated with the help of variance inflation factor (VIF) and a value more than 10 shows critical multicolinearity (Henseler, Ringle, & Sinkovics, 2009). However the value of VIF less than 3.3 is considered excellent (Diamantopoulos & Siguaw, 2006). Moreover the value of condition number for the construct below 30 signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995). 4.2.5 Assessment of inner or structural model Once the reliable and valid outer model is achieved the inner model is estimated. The coefficient of determination R2 value of endogenous variable is an essential criterion (Henseler, Ringle, & Sinkovics, 2009). The values of R2 0.67, 0.33, or 0.19 defines the endogenous latent variable to be substantial, moderate, or weak respectively (Chin, 1998). The lesser value of R2 is acceptable where one or two exogenous variable explain endogenous variable. The second criterion is the path coefficient assessed for value, sign and significance. The significance is estimated with the help of bootstrapping method.
95
The third criterion is the Effect size f2. The values of f2 0.02, 0.15, or 0.35 can be considered as a measure of weak, medium, or large effect at the structural level (Henseler, Ringle, & Sinkovics, 2009). Last but not the least measure is the predictive capability of the structural model. This is estimated with the help of predictive relevance Q2, that measures how well observed values are reconstructed by model and its parameters (binti Ismail, binti Abd Hamid, & Idris, 2012). The value of Q2 is calculated by blindfolding procedure. A value above zero indicates that the observed values are close to predicted values and proves the predictive relevance of the model (Henseler, Ringle, & Sinkovics, 2009).
4.3 Statement of the Problem The literature review shows that there are a number of studies that talk about the adoption phenomenon of the Enterprise Systems packaged software. (Markus and Tanis, 2000; Kumar et al., 2002; Olhager and Selldin, 2003; Mabert et al., 2003; Spathis and Constantinides, 2004; Light and Papazafeiropoulou, 2004). However there is a dearth of research in Indian context. The primary question in the area of adoption would be the objectives of ES adoption. Whether companies are adopting ES for efficiency improvement, strategic business development or building integration capabilities. Therefore this research has addressed the following questions.
Research Question 1: What are the motivations or objectives for the adoption of ES and their presence in Indian organizations? Research Question 2: Is there any association between these adoption reasons and size or industry of the organizations? An implementation is said to be one of the most complicated parts of ES Lifecycle. Several instances of implementation failure have been reported in the literature (Xue,
96
Liang, Boulton, & Snyder, Sep 2005). There are several approaches to study the implementation phase. Some researchers have taken the path of critical success factors. Some are trying to analyze the implementation as a process. Process based approach gives more insight into the implementation. On the other hand some researchers are trying to combine both aspects of implementation (Nah, Lau, & Kuang, 2001; Somers & Nelson, 2001; Al-Mashari, 2002; Ehie & Madsen, 2005; Yokota, 2007). In this research work critical success factors would be identified. That will help the practitioners manage the implementation of these systems more efficiently and effectively. Moreover there is a need to study about their presence in Indian organizations. Therefore research has addressed the following questions.
Research Question 3: What are the critical success factors of ES implementation and their relative importance in Indian context? Thirdly the success of ES would be studied using an ES benefit framework proposed by Shang & Seddon (2002). The critical success factors approach has been long debated over its effectiveness in understanding implementation process. But the simplicity of this method gives an edge over process oriented research. In this research the relationship between the benefits of the ES and critical success factors will be explored. In essence this research has addressed following research questions.
Research Question 4: What are the benefits of ES implementation and their presence in Indian organizations?
Research Question 5: Is there any association between the ES Benefits and size or industry of the organizations? Research Question 6: How are CSFs related to the benefits of ES?
97
Figure 4.5 shows the research models that would help answer the research question 6.
4.4 Objectives of the Research Literature review in the area of Enterprise Systems has assisted in identifying the research gaps and has led to the problem statement. To present a crisper view of the study the objectives are being highlighted in the following paragraphs.
1) To study the adoption reasons or motivations for ES, and their presence in Indian Context. 2) To study the variation of adoption reasons in Indian organizations of different size and industry. 3) To study the CSFs for the ES implementation and their presence in Indian organizations. 4) To study the benefits derived from ES and their presence in Indian organizations. 5) To study the variation of benefits derived from ES in Indian organizations of different size and industry 6) To study the relationships between CSFs and ES benefits using structural equation modeling and arriving at the success predictive model using CSFs as independent variables.
4.5 Research Framework This research work has been carried out to understand the ES adoption, implementation and usage in Indian organizations. For the adoption study the reasons of adoptions have been identified through the literature review as discussed in the literature review section. For the implementation study critical success factors approach has been adopted. Literature review section again has got the details of the CSF framework developed for this study. To understand the sustained usage the benefits realized from the ES has been studied using the Shang & Seddon‘s ( 2002) ES benefit framework. Finally the relationship between the CSF and the ES benefits 98
or the success of the ES has been studied with the help of the PLS Structural modeling technique. Following passages give details of frameworks utilized in this study. ES Adoption Study The ES adoption study has used the adoption reasons framework developed in the literature review section. The variables have been summarized in the following Table 4.1. The case studies have been scanned for the presence of these variables keeping in mind the brief definitions of these variables. The resultant data have been analyzed for the development of the frequency list of these variables. Moreover the variables are tested using Pearson‘s Chi-square test for the variation with respect to size and industry of the organization.
Table 4.1 Adoption Reasons Framework S. Variable Name No. Operational 1 Improvements (cost, employee, cycle time reductions) Legacy System 2 Replacement or IT Architectural Improvements Business Growth or 3 Extensions 4 5
Data or Information issues Regulatory and Compliance Issues
6
Organizational Change
7
Integration of Systems or processes
8
Standardization and best practices Globalization Support
9
Variable Accounts for Code A1 Reductions in computer operating, inventory carrying and business operating costs, administrative expense, Employees, time-tomarket, and financial cycle times A2 Replacement of difficult to sustain software and architecture developed on obsolete technological paradigm due to the complexity and cost involved A3 Sustenance of growth using IT infrastructure having scalability, flexibility and internet portability A4 Gaining real-time visibility, quality and integrity of information A5 Accommodation of global and local environmental changes such as flexibility in report formats A6 The support of the innovative organizational designs that are based on integrated business processes rather than fragmented departments A7 Need of inter and intra organizational integration and supporting newer formats of business processes A8 The automation of non-unique standard tasks offered by ES vendors as SOPs A9 The support of a globalization strategy including
99
Continued Table 4.1 Adoption Reasons Framework S. No.
Variable Name
Variable Code
10
Competition
A10
11
Customer and Supplier Intimacy External Forces
A11
12
A12
Accounts for multi-language and multi-currency support to sustain business The improvement in competitiveness by boosting performance at various fronts The responsiveness towards customer and supplier relationships Government support, Parent organization‘s push or partners‘ endorsement
Critical Success Factor Study To understand the implementation process CSF study has been carried out. The literature review section has listed 37 CSF identified and their detailed definition. Further, they have been categorized into eight constructs. The CSF framework along with their brief description has been given in Table 4.2. The case studies have been scanned for the presence of these variables keeping in mind the brief definitions of these variables. The resultant data have been analyzed for the development of the frequency list of these variables.
Table 4.2 CSF Framework Variable Category
ES Ground work
Variable Variable Code
Accounts for
CSF1.1
Organizational characteristics
CSF1.2
Legacy system consideration
CSF1.3 CSF1.4
CSF1.6
Client consultation Country-related functional requirements Value Chain Connectivity System Integration
Maturity of business processes, willingness to adopt SOPs, technology loving and experience with large scale change The consideration of existing IT infrastructure, business processes, organizational structure and culture Involvement of users at multiple stages Country specific requirements
CSF1.7
Build a business
CSF1.5
100
Inter-organizational integration Organizational systems integration including legacy system integration Business initiative rather than technical
Continued Table 4.2 CSF Framework Variable Category
Minimal Customization
Project Visioning, Planning and Management
Variable Variable Code CSF2.1 CSF2.2 CSF2.3
Visioning and planning
CSF3.2
Implementation strategy and timeframe Project cost planning and management Communication plan Project management Top management commitment and support Empowered decision makers
CSF3.3
CSF3.5 CSF4.1
CSF4.2
CSF4.3
Project champion
CSF4.4
Project team: the best and brightest Balanced team
CSF4.5 CSF4.6 CSF5.1 Technical Issues and Resources
CSF5.2 CSF5.3 CSF5.4
External Pressure and Support
case Vanilla ERP BPR and software configuration Selection of ERP
CSF3.1
CSF3.4
People and Organizational Support
Accounts for
CSF6.1 CSF6.2
CSF6.3
Interdepartmental Cooperation IT infrastructure Data conversion and integrity Multi-site issues
initiative Least customization Business process re-engineering approach Selection process to identify ES closely fitting with organization Linking the project‘s goal and objectives with business and defining scope, resource, risk and time Selection of appropriate transition strategy and timeframe Detailed cost analysis to avoid cost overrun Planned open communication to convey information proactively Development of work plan, resource plan and project progress tracking The involvement of top management and prioritized allocation of best resources The sufficient authority conferred upon the team members to meet the landmarks Project owner with leadership, business, technical and personal managerial traits The allocation of highly motivated, disciplined and dedicated people Team members with business and IT skills The culture of sharing goals with stakeholders internally and externally. Readiness of IT infrastructure including skills and architecture Development of unified data dictionary
Issues such as site autonomy, variation in culture and cutover strategy Technical Task and Accelerator tools to ease the Tools implementation Vendor Support Support from the vendor of ES Consultant Selection of appropriate consultant selection and with no questionable financial ties with relationship ES vendor and knowledge transfer Stakeholder Influence of suppliers, customers and
101
Continued Table 4.2 CSF Framework Variable Category
Variable Variable Code CSF6.4
CSF7.1 CSF7.2
Change Management
CSF7.3 CSF7.4
CSF7.5 CSF8.1 Testing and Assessment
CSF8.2 CSF8.3
Accounts for
Pressures IT provider and Integrator Push Change management Training and job redesign Managing cultural change Team morale and motivation
Expectation Management System testing Troubleshooting/cr ises management Postimplementation evaluation
business partners Aggressive marketing, due diligence, knowledge of IT provider and Integrator Change in organizational structure and process to match the ES Training of implementation team, IT staff and users in addition to job redesign The variation of culture at organizational and geographical level Moral boost and motivation drills along with acknowledgement of efforts and creation of stimulating work environment Communication of benefits Rigorous testing of the ES solution after customization or parameterization The identification of problems and contingent solutions Establishment of post-implementation criteria priori and evaluation
ES Benefits Study In order to assess the usage or success of ES benefits derived from these systems has been studied using the Shang & Seddon‘s ( 2002) ES benefit framework. The brief description of this framework has been derived from Shang & Seddon‘s ( 2002) ES benefit framework Table 4.3. Similarly, the case studies have been checked for the presence of these variables keeping in mind the brief definitions of these variables. The resultant data have been analyzed for the development of the frequency list of these variables. Moreover the variables have been tested using Pearson‘s Chi-square test for the variation with respect to size and industry of the organization.
102
Table 4.3 ES benefit framework derived from Shang & Seddon’s ( 2002) ES benefit framework Variable Category
Vari- Variable able Code B1.1 Cost reduction B1.2
Operational benefits
B1.3 B1.4 B1.5 B2.1
Managerial benefits
B2.2
B2.3
B3.1
B3.2
B3.3 B3.4 Strategic benefits
B3.5
B3.6
B3.7 B3.8
IT Infrastructural
B4.1
Cycle time reduction Productivity improvement Quality improvement Customer service improvement Better resource management Improved decision making and planning Performance improvements
Accounts for
Labor, inventory and warehousing, and administrative cost reductions Cycle time reductions for activities that support customer, employees and suppliers Gains in production with existing workforce Reduction in error rate and improvement in data reliability and data accuracy Ease of data access and inquiries Better asset, inventory, production and workforce management Improved strategic, management and customer decisions
Financial and manufacturing performance improvement, and operational efficiency and effectiveness management Support business Transaction scalability, support for new growth markets, new business unit, products and services, increased employees Support business Effective and efficient consolidation, alliance consistent IT architecture , Selling model update, transition to corporate systems and integration of resources for new business units and acquired entities Building business Support for new market strategy, process innovation chains and products or services Building cost Building lean structure with streamlines leadership processes, reaching economies of scale and shared services Generating Providing customized products and services, product and lean production with market-to-order differentiation capability Enabling Centralized global operation, global resource worldwide management, multi-currency support, global expansion market penetration and global solution deployment Enabling eB2B, B2C, B2E support worldwide on internet commerce technologies Generating or Maintaining competitiveness with quick sustaining decision making and better internal support competitiveness and utilizing opportunities generated by ES Building business Rapid response to internal and external flexibility at changes with multiple options at lower lower cost
103
Continued Table 4.3 ES benefit framework derived from Shang & Seddon’s ( 2002) ES benefit framework Variable Category
Vari- Variable able Code B4.2 B4.3
IT cost reduction Increase IT infrastructure capability
B5.1
Changing work pattern with shifted focus Facilitating business learning and broaden employee skills Empowerment
B5.2
B5.3 Organizational benefits B5.4 B5.5 B5.6
Building common vision Shifting work focus Increase employee morale and satisfaction
Accounts for
cost Lower maintenance cost Stable and flexible support for the current and future business changes in process and structure Co-ordination between interdisciplinary matters and harmonization of interdepartmental processes Motivation to learn the process by entire workforce in shorter time that result in broadened employee skill Employees with accountability and responsibility, proactive problem solver, owner of the system, autonomous working style, and greater role in business management Consistent vision and working as a single unit Focus on customer and market, business process and overall performance Satisfied users with better decision making tools, work efficiency, problem solving efficiency, systems skill and business knowledge, and employee services and moral boost due to better business performance
Relationship between CSF and ES benefits (ES Success or Usage) This study also has explored the relationship between the CSF and the success of the ES. As explained in the literature review section there are numerous studies that have proposed such models (Zhang, Lee, Zhang, & Banerjee, 2003; Al-Mashari, AlMudimigh, & Zairi, 2003; Motwani, Subramanian, & Gopalakrishna, 2005). The objective has been to develop an ES success predictive model using CSF as predictors Figure 4.5. The partial least square structural equation modeling technique has been utilized to explore the relationships and study the hypothesis given in the next section.
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CSF
ES Benefits
Composite of all 37 CSF sub-factors
Composite of 25 items
ES Ground work Organizational characteristics Legacy system consideration Client consultation Country-related functional requirements Value Chain Connectivity System Integration Build a business case
Minimal Customization Vanilla ERP BPR and software configuration Selection of ERP
Project Visioning, Planning and Management Visioning and planning Implementation strategy and timeframe Project cost planning and management Communication plan Project management Build a business case
People and Organizational Support Top management commitment and support Empowered decision makers Project champion Project team: the best and brightest Balanced team Interdepartmental Cooperation
ES Benefits Composite of 25 items
Technical Issues and Resources IT infrastructure Data conversion and integrity Multi-site issues Technical Task and Tools
External Pressure and Support Vendor Support Consultant selection and relationship Stakeholder Pressures IT provider and Integrator Push
Change Management Change management Training and job redesign Managing cultural change Team morale and motivation Expectation Management
Assessment and Testing System testing Troubleshooting/crises management Post-implementation evaluation
Figure 4.5 Framework of ES Success Predictive Model 105
4.5 Hypothesis of the Study There are multiple sets of hypotheses that have been tested statistically using Person‘s Chi square statistic. First two sets deal with hypotheses to test the significance of the association between adoption reasons and size Table 4.4, and adoption reasons and industry of the organization Table 4.5. The next two sets deal with the significance of association between benefits of ES and size Table 4.6, and benefits of ES and industry of the organization Table 4.7.
In order to answer research question 6, how are CSF related to the benefits of the ES, following hypotheses have been proposed. Moreover these hypotheses have been shown in the Figure 4.6 and Figure 4.7.
Hypothesis C: CSF is positively related with the ES Benefits. Hypothesis C1: ES Ground Work is positively related with the ES Benefits. Hypothesis C2: Minimal Customization is positively related with the ES Benefits. Hypothesis C3: Project Visioning, Planning and Management is positively related with the ES Benefits. Hypothesis C4: People and Organizational Support is positively related with the ES Benefits. Hypothesis C5: Technical Issues and Resources is positively related with the ES Benefits. Hypothesis C6: External Pressure and Support is positively related with the ES Benefits. Hypothesis C7: Change Management is positively related with the ES Benefits. Hypothesis C8: Assessment and Testing is positively related with the ES Benefits.
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Table 4.4 Set of hypotheses to be tested for association between adoption reasons and the size of the organization S. No. 1 2 3 4 5 6 7 8 9 10 11 12
Hypothesis Hypothesis A1s: There is a significant association between Operational Improvements (cost, employee, cycle time reductions) and size of the organization. Hypothesis A2s: There is a significant association between Legacy System Replacement or IT Architectural Improvements and size of the organization. Hypothesis A3s: There is a significant association between Business Growth or Extensions and size of the organization. Hypothesis A4s: There is a significant association between Data or Information issues and size of the organization. Hypothesis A5s: There is a significant association between Regulatory and Compliance Issues and size of the organization. Hypothesis A6s: There is a significant association between Organizational Change and size of the organization. Hypothesis A7s: There is a significant association between Integration of Systems or processes and size of the organization. Hypothesis A8s: There is a significant association between Standardization and best practices and size of the organization. Hypothesis A9s: There is a significant association between Globalization Support and size of the organization. Hypothesis A10s: There is a significant association between Competition and size of the organization. Hypothesis A11s: There is a significant association between Customer and Supplier Intimacy and size of the organization. Hypothesis A12s: There is a significant association between External Forces and size of the organization.
Table 4.5 Set of hypotheses to be tested for association between adoption reasons and industry of the organization S. No.
1 2 3 4 5 6 7 8 9
10 11 12
Hypothesis Hypothesis A1i: There is a significant association between Operational Improvements (cost, employee, cycle time reductions) and industry of the organization. Hypothesis A2i: There is a significant association between Legacy System Replacement or IT Architectural Improvements and industry of the organization. Hypothesis A3i: There is a significant association between Business Growth or Extensions and industry of the organization. Hypothesis A4i: There is a significant association between Data or Information issues and industry of the organization. Hypothesis A5i: There is a significant association between Regulatory and Compliance Issues and industry of the organization. Hypothesis A6i: There is a significant association between Organizational Change and industry of the organization. Hypothesis A7i: There is a significant association between Integration of Systems or processes and industry of the organization. Hypothesis A8i: There is a significant association between Standardization and best practices and industry of the organization. Hypothesis A9i: There is a significant association between Globalization Support and industry of the organization. Hypothesis A10i: There is a significant association between Competition and industry of the organization. Hypothesis A11i: There is a significant association between Customer and Supplier Intimacy and industry of the organization. Hypothesis A12i: There is a significant association between External Forces and industry of the organization.
107
Table 4.6 Set of hypotheses to be tested for association between ES Benefits and the size of the organization S. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Hypothesis Hypothesis B1s: There is a significant association between Cost reduction and size of the organization. Hypothesis B2s: There is a significant association between Cycle time reduction and size of the organization. Hypothesis B3s: There is a significant association between Productivity improvement and size of the organization. Hypothesis B4s: There is a significant association between Quality improvement and size of the organization. Hypothesis B5s: There is a significant association between Customer service improvement and size of the organization. Hypothesis B6s: There is a significant association between Better resource management and size of the organization. Hypothesis B7s: There is a significant association between Improved decision making and planning and size of the organization. Hypothesis B8s: There is a significant association between Performance improvements and size of the organization. Hypothesis B9s: There is a significant association between Supporting business growth and size of the organization. Hypothesis B10s: There is a significant association between Supporting business alliance and size of the organization. Hypothesis B11s: There is a significant association between Building business innovation and size of the organization. Hypothesis B12s: There is a significant association between Building cost leadership and size of the organization. Hypothesis B13s: There is a significant association between Generating product differentiation and size of the organization. Hypothesis B14s: There is a significant association between Enabling worldwide expansion and size of the organization. Hypothesis B15s: There is a significant association between Web enablement of business and size of the organization. Hypothesis B16s: There is a significant association between Generating or sustaining competitiveness and size of the organization. Hypothesis B17s: There is a significant association between Building business flexibility by rapid response to change at lower cost and size of the organization. Hypothesis B18s: There is a significant association between IT cost reduction and size of the organization. Hypothesis B19s: There is a significant association between Increase IT infrastructure capability and size of the organization. Hypothesis B20s: There is a significant association between Changing work pattern with shifted focus and size of the organization. Hypothesis B21s: There is a significant association between Facilitating business learning and broaden employee skills and size of the organization. Hypothesis B22s: There is a significant association between Empowerment and size of the organization. Hypothesis B23s: There is a significant association between Building common vision and size of the organization. Hypothesis B24s: There is a significant association between Shifting work focus and size of the organization. Hypothesis B25s: There is a significant association between Increase employee morale and satisfaction and size of the organization.
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Table 4.7 Set of hypotheses to be tested for association between ES Benefits and industry of the organization S. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Hypothesis Hypothesis B1i: There is a significant association between Cost reduction and industry of the organization. Hypothesis B2i: There is a significant association between Cycle time reduction and industry of the organization. Hypothesis B3i: There is a significant association between Productivity improvement and industry of the organization. Hypothesis B4i: There is a significant association between Quality improvement and industry of the organization. Hypothesis B5i: There is a significant association between Customer service improvement and industry of the organization. Hypothesis B6i: There is a significant association between Better resource management and industry of the organization. Hypothesis B7i: There is a significant association between Improved decision making and planning and industry of the organization. Hypothesis B8i: There is a significant association between Performance improvements and industry of the organization. Hypothesis B9i: There is a significant association between Supporting business growth and industry of the organization. Hypothesis B10i: There is a significant association between Supporting business alliance and industry of the organization. Hypothesis B11i: There is a significant association between Building business innovation and industry of the organization. Hypothesis B12i: There is a significant association between Building cost leadership and industry of the organization. Hypothesis B13i: There is a significant association between Generating product differentiation and industry of the organization. Hypothesis B14i: There is a significant association between Enabling worldwide expansion and industry of the organization. Hypothesis B15i: There is a significant association between Web enablement of business and industry of the organization. Hypothesis B16i: There is a significant association between Generating or sustaining competitiveness and industry of the organization. Hypothesis B17i: There is a significant association between Building business flexibility by rapid response to change at lower cost and industry of the organization. Hypothesis B18i: There is a significant association between IT cost reduction and industry of the organization. Hypothesis B19i: There is a significant association between Increase IT infrastructure capability and industry of the organization. Hypothesis B20i: There is a significant association between Changing work pattern with shifted focus and industry of the organization. Hypothesis B21i: There is a significant association between Facilitating business learning and broaden employee skills and industry of the organization. Hypothesis B22i: There is a significant association between Empowerment and industry of the organization. Hypothesis B23i: There is a significant association between Building common vision and industry of the organization. Hypothesis B24i: There is a significant association between Shifting work focus and industry of the organization. Hypothesis B25i: There is a significant association between Increase employee morale and satisfaction and industry of the organization.
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C
Figure 4.6 PLS Simple Model with Hypothesis Label
110
C1 C2 C3 C4
C5
C6
C7 C8
Figure 4.7 PLS Factored Model with Hypotheses Labels
111
4.7 Population, Sampling Frame and Sample Population contains all the companies that have implemented any of the business applications or ES and are Indian. Whereas sampling frame defines companies whose experience with ES have been published in the form of customer success stories by vendors, clients, consultants or IT magazines or news. Moreover they are available on line. The sample contains 300 such stories sequentially searched on the internet.
4.8 Case Collection Procedure It took one month to collect 300 cases from the internet. All the cases have been downloaded between the periods of 12 June, 2008 to 14th July, 2008. Search started from major ES vendors‘ website such as SAP, Microsoft and Oracle. It was easy to get cases from Microsoft‘s website since they have listed Indian cases in one place. Whereas SAP and Oracle didn‘t have cases divided on the basis of the country. Therefore cases were checked for Indian origin.
After major industry players, websites of Infor Baan, IFS India, Sage, SSA, QAD MFG/PRO, and LAWSON. Finally the websites of Indian vendors such as Ramco Systems, 3i Infotech and ESS were considered. This exhaustive search on the vendor‘s site did reach to the initial intended sample size of 300. Therefore general search started on the Google for Indian cases for ES implementation. This led to the various IT magazines and consultant‘s website. Next section has given the details of the sources of the cases.
4.9 Sources of data Sources of cases are classified into three categories ES vendors, IT Magazines and News and Consultants. Figure 4.8 shows the percent distribution of the three sources. 61% of the total cases have been downloaded directly from the vendor‘s sites. Of 112
which Microsoft tops the list with 126 cases Table 4.8. Though, the number of cases belonging to SAP is none less than 115, the majority of cases have been downloaded either from the IT Magazines‘ and News‘, or Consultants‘ websites.
Table 4.8 Details of Sources of Cases S.No.
Case Sources
Details
1
ES Vendors
2
IT Magazines and News
3
Consultants
www.microsoft.com www.oracle.com www.sap.com www.ramcoerp.com www.sagesoftware.co.in www.ifsworld.com www.3i-infotech.com www.lawson.com www.expresscomputeronline.com www.networkmagazineindia.com www.financialexpress.com www.biztech2.in.com www.cxotoday.com www.dqindia.ciol.com www.ciol.com www.constructionweekonline.in www.hinduonnet.com www-01.ibm.com www.silvertouch.com www.enteg.com www.wipro.in www4.hp.com www.ibacnet.org www.in.sun.com rhcustomers.files.wordpress.com www.accenture.com www.arteriatech.com www.groupsoft.in www.intelligroupasia.com www.patni.com www.comm-it.in www.mobileone.in itti.com
Among
IT
magazines
Count
and
news, and
(www.expresscomputeronline.com)
113
Express Financial
126 19 17 7 6 4 2 2 60 10 4 2 2 1 1 1 1 10 8 3 2 1 1 1 1 1 1 1 1 1 1 1 1
Count Sum 183
82
35
Computer Express
(www.financialexpress.com) both belonging to The Indian Express Ltd together have contributed maximum number of cases, 64. On the other hand IBM and Silver Touch have contributed a large number of cases 10 and 8 respectively among the consultants category.
Case Sources 12%
ES Vendors IT Mangazines and News
27% 61%
Counsultants
Figure 4.8 Pie chart for sources of cases The websites of the implementing companies or clients were also considered. This was due to ambiguous or incomplete information on the industry and the size of the organization in some cases.
Moreover, it was also to ascertain the veracity of the
cases.
In addition industry reports and government reports were considered to understand the macroscopic view of IT industry in general and more specifically ES industry in India. Industry classification was done with the help of National Industrial Classification 2008 (NIC-2008) data issued by Central Statistical Organization, India. More details of which is available in the subsequent chapters. 114
4.10 Case Analysis There are a total of 74 variables, 12 adoption reasons, 37 CSF sub factors and 25 benefit variables. All these variables have been studied very thoroughly. The definitions of adoption reasons and CSF sub factors have been given in the literature review section whereas benefit items have been explained in the article of Shang & Seddon (2002). Keeping the above descriptions in mind the customer success stories have been classified for the presence or absence of these parameters.
One sample customer success story for Aditya Birla Group from Wipro, a consulting partner for SAP, along with parameter labels has been provided in Appendix B. The following Table 4.9 gives the textual excerpts from the case to show the presence of the variable. The keywords in the excerpts have been highlighted as well. This case has witnessed 38 out of 74 variables. It is the second largest story in terms of the variable presence count, along with Wockhardt Ltd. The customer success stories of Oil and Natural Gas Corporation Ltd. and Wockhardt Ltd stood at the first and second position with variable presence count at 43 and 38 respectively and are published by SAP. Wipro‘s story was chosen because it was in an editable pdf format, whereas SAP‘s story was in pdf format with only print rights.
1
A1 Adoption Reasons
2
A2
Variable
Excerpts from Case
Operational Improvements (cost, employee, cycle time reductions) Legacy System Replacement or IT Architectural Improvements
―Yet another problem was the high cost of maintaining and upgrading disparate systems.‖ ―The Group company was using different custom-developed applications at its Carbon Black
115
1:Present 0:Absent
Variable Code
Variable Category
S. No.
Table 4.9 Analyzed Data of a Sample Case
1
1
Variable
Excerpts from Case
1:Present 0:Absent
Variable Code
Variable Category
S. No.
Continued Table 4.9 Analyzed Data of a Sample Case
business units in Egypt, Thailand and India.‖ A3
4
A4
5
A5
Regulatory and Compliance Issues
6 7
A6 A7
Organizational Change Integration of Systems or processes
8
A8
Standardization and best practices
9
A9
Globalization Support
10 11
A10 A11
12
A12
Competition Customer and Supplier Intimacy External Forces
1
CSF1.1
Organizational characteristics
CSF1.2
Legacy system consideration
CSF1.3 CSF1.4
Client consultation Country-related functional requirements
CSF1.5 CSF1.6
Value Chain Connectivity System Integration
2 3 4 5 6
ES Ground work
3
Business Growth or Extensions Data or Information issues
116
0 ―Data for each function was available, but in a scattered form, requiring manual consolidation.‖
1
―Country-specific statutory compliances‖
1
―For better control and analysis, it was essential that all business processes across business units be standardized and optimized to enable integrated information availability.‖ ―For better control and analysis, it was essential that all business processes across business units be standardized and optimized to enable integrated information availability.‖ ―Multi location, multi-language, multiple currency implementation‖
0 1
1
1 0 0
―Since the Group company was already using SAP across all its businesses i.e., Cement, Chemical, Garment, Textile, Sponge Iron, SAP R/3 was the preferred choice for its Carbon Black business.‖ ―Wipro undertook complete review of the Group's existing systems to understand its pain areas‖ ―Wipro undertook complete review of the Group's existing systems to understand its pain areas‖ ―Remaining 150 reports & layouts customized to meet country specific requirements‖
1
1
1
0 1
0 0
14 15 16 17 18 19 20 21 22 23 24 25 26 27
28
29
Variable
CSF1.7 CSF2.1 CSF2.2
Build a business case Vanilla ERP BPR and software configuration
CSF2.3 CSF3.1 CSF3.2
Selection of ERP Visioning and planning Implementation strategy and timeframe
CSF3.3
Project cost planning and management Communication plan Project management Top management commitment and support Empowered decision makers Project champion Project team: the best and brightest Balanced team
CSF3.4 CSF3.5 CSF4.1 CSF4.2 CSF4.3 CSF4.4 CSF4.5
CSF4.6 CSF5.1 CSF5.2 CSF5.3 CSF5.4
Interdepartmental Cooperation IT infrastructure Data conversion and integrity Multi-site issues Technical Task and Tools
CSF6.1 CSF6.2
Vendor Support Consultant selection and relationship
CSF6.3
Stakeholder Pressures
CSF6.4
IT provider and Integrator Push
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Excerpts from Case
―Preparation of multi-country Business Blue Prints for processes from a single location‖
―The entire project spanning three countries was completed in a record time of six months‖
1:Present 0:Absent
Variable Code
Variable Category Minimal Customization Project Visioning, Planning and Management
13
People and Organizational Support
10 11 12
Technical Issues & Resources
7 8 9
External Pressure and Support
S. No.
Continued Table 4.9 Analyzed Data of a Sample Case
0 0 1
0 0 1
0 0 0 0 0 0 1 ―a team of 16 functional consultants and 6 technical consultants from Wipro‖
1
0
―fast track implementation‖ ―The client partnered with Wipro for fast track implementation of 14 SAP modules across three independent global locations‖ ―Since the Group company was already using SAP across all its businesses i.e., Cement, Chemical, Garment, Textile, Sponge Iron, SAP R/3 was the preferred choice for its Carbon Black business.‖
0 0 0 1 0 1
1
0
CSF7.2
Training and job redesign
CSF7.3
Managing cultural change
―Adopting Change Management methodology during all phases of Implementation‖ ―Training more than 350+ users across the business‖ ―Team formation of different nationalities and cultures‖
CSF7.4 CSF7.5 CSF8.1
Team morale and motivation Expectation Management System testing
CSF8.2
Troubleshooting/crises management
37
CSF8.3
1
B1.1
Post-implementation evaluation Cost reduction
2
B1.2
Cycle time reduction
B1.3 B1.4
Productivity improvement Quality improvement
5
B1.5
Customer service improvement
6
Managerial benefits
B2.1
Better resource management
B2.2 B2.3
Improved decision making and planning Performance improvements
B3.1 B3.2
Support business growth Support business alliance
36
3 4
7 8
9 10
Change Management
33 34 35
Testing and Assessment
32
Operational benefits
31
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―Wipro's solution comprising global business processes was validated by SAP India Consultants‖ All locations achieved stability within 10 days of Post Go-Live operations
1:Present 0:Absent
Change management
Variable Code CSF7.1
30
Variable Category
Excerpts from Case
S. No.
Variable
Strategic benefits
Continued Table 4.9 Analyzed Data of a Sample Case
1
1 1 0 0 1
1
0 ―Minimum paper, only 1 printer. Very low STD usage‖ ―Order fulfillment ( Sales order to invoicing lead time) 2.5 days to 1 day‖ ―Provisioning of common systems and consistent information across the entire group - instead of fragmented systems and scattered data‖ ―Ease of data exchange and communication across all locations‖ ―Working Capital High to Negative‖ ―Better analysis of operations and faster decision-making process‖ ―Moving from breakdown maintenance to preventive maintenance based on quality checks on products‖ ―Complex CMIS requirement met successfully through SAP-BW for the first time in the Group‖
1 1
0 1
1 1 1 1
0 1
B3.3 B3.4 B3.5
14
B3.6
15 16
B3.7 B3.8
Enabling e-commerce Generating or sustaining competitiveness
17
B4.1
Building business flexibility at lower cost
B4.2
IT cost reduction
B4.3
Increase IT infrastructure capability
20
B5.1
21
B5.2
Changing work pattern with shifted focus Facilitating business learning and broaden employee skills Empowerment Building common vision
19
22 23
24 25
Organizational benefits
18
IT infrastructure benefits
11 12 13
B5.3 B5.4
B5.5 B5.6
Building business innovation Building cost leadership Generating product differentiation Enabling worldwide expansion
Excerpts from Case
0 0 0 ―Consolidation at a global level with a common fiscal year and common currency‖ ―Improved responsiveness to changing global market scenario‖
1
0 1
―Weigh Bridge and Bar Code interface across all locations‖ ―Cost savings from fast track implementation across three independent Global locations‖
1
―Solution capable of supporting concurrent access demands of the company and becoming central to internal communications in a short time span‖
1
1
0 ―Helped in right sizing people and upgrading their skills‖ ―Solution capable of supporting concurrent access demands of the company and becoming central to internal communications in a short time span‖
Shifting work focus Increase employee morale and satisfaction
4.11 Scope of the Study As with any doctoral research program this study also suffers from the several limitations. Even though all due precautions has been observed so as to arrive at as
119
1:Present 0:Absent
Variable
Variable Code
Variable Category
S. No.
Continued Table 4.9 Analyzed Data of a Sample Case
1 0 1
0 0
representative outcome as possible. This study has set certain boundaries to make this research feasible in the available timeframe and resources.
1. The scope of study is limited to the Indian organizations that have implemented one of the ES packaged software. 2. The sampling frame includes organizations whose information has been disclosed by the ES vendors, consultants or e-magazines, therefore limiting the sample to the said companies only. 3. For analysis purposes size of the organization has been classified only into two categories SME and LE. 4. For analysis purposes industry of the organization has also been classified into two categories manufacturing and non-manufacturing. 5. All the manifest variables have been marked with only two possible values 0 for absence and 1 for presence while omitting the information on relative strength. 6. The majority of the cases included are taken from the ES vendors‘ websites as success stories. Therefore there is potential possibility of bias towards ES implementation benefits and relative comfort in implementing the target vendor‘s product.
4.12 Limitation of the Study 1. This study only has included the success stories and hasn‘t included the failure cases. Therefore limiting the model‘s prediction accuracy towards relative success or benefits only and not to the failure. 2. The data classification from these cases has been done by a single researcher (the PhD student) therefore it contains the possibility of misinterpretation.
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3. Cases were selected on the basis of sequential search that forms a sort of convenience sampling. And the cases are not the good representation of the population therefore care must be taken before generalizing the results to the population. 4. Variables are measured on binary scale and hence decreasing the precision of measurement and hence the outcome of the analysis. 5. This research has utilized secondary data that might not have been created to address the current problem therefore may be limited in relevancy and accuracy (Malhotra, 2007).
4.13 Conclusion The research design of the study is exploratory based on secondary data derived from the customer success stories reported on the internet either by the ES vendors, consultants or e-magazines. The first order statistical technique such as Pearson‘s Chisquare test is used for the study of variation of reasons for ES adoption and ES Benefits with respect to size and industry of the organization. Structural equation modeling, a second order statistical technique that allows for the modeling of multiple independent constructs and multiple dependent constructs in a holistic, systematic and unified way has been explored. PLS method of SEM is used to study the relationship between CSF and ES Benefits since it can incorporate both reflective and formative constructs. Moreover it can work with the nominal data with less stringent assumption about the distributions. For reliability and validity assessment of measurement model weight significance with the help of bootstrapping method and multicolinearity among formative constructs by VIF indices is evaluated. The inner model is assessed with the help of coefficient of determination R2 value of endogenous variable, the
121
path coefficient for value, sign and significance, Effect size f2, and value of StoneGeisser‘s Q2 by blindfolding procedure.
This study first has identified the frameworks for reasons for adoptions, CSFs, and ES Benefits measure, and the relationship between CSF and ES Benefits via literature review. Secondly it has utilized theses frameworks to identify their presence in the Indian cases. The population of the study is all the Indian companies that have implemented Enterprise Systems. A total of 300 cases have been downloaded and the contents of the cases have been analyzed. The data from 1 sample case have been given in Table 4.9 and full case with variable flags is provided in the Appendix B. To simplify the analysis the companies have been classified only into large and SMB, and manufacturing and non-manufacturing. Among others major limitation is the nature of case studies that have been written without keeping in mind the objectives of this research.
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Chapter Five Analysis and Interpretation 5.1 Introduction This chapter gives all the results that have come out from the analysis of the data using different statistical techniques. The first section gives the flow charts of the two phases loosely defined to carry out the study. Next section gives the demographic profile of the cases. Following section tabulates the ranking of all the variables based on the frequency of presence in the cases. The following sections give the results of the Pearson‘s Chi-square tests for the variation of ES adoption reasons and ES benefits with respect to size and industry of the organizations. Finally the results of multiple PLS structural models such as construct reliability and validity measures, inner and outer model assessments and predictive relevance of the model has been given.
5.2 Flow chart of Research Phases This research has proceeded in two phases. In the first phase study of adoption reasons, ES benefits and development of the customer success stories dataset has been carried out. The variation of adoption reasons and ES benefits with respect to size and industry is studied with the help of Chi-Square test in addition to the development of the frequency list of these factors. In the second phase detailed literature review of CSF followed by frequency ordering of these factors among Indian companies has been carried out. And a structural model to assess the relationship between CSF and ES Benefits has been developed. The model has been tested for the prediction relevancy of the success of Enterprise Systems using CSF as predictors. The tasks of
123
each phase are shown in the form of flow chart in Figure 5.1and Figure 5.2 respectively.
Collection of research articles related to ES adoption reasons and ES Benefits
Compilation of exhaustive list of all the adoption reasons and Selection of ES Benefit Framework
Categorization of adoption resaon in a multiple iterative process
Development of Customer Success Stories Database from the vendors'websites and online IT magazines
Identification of 12 adoption reasons' and 25 benefit items' presence or absence from the database
Statistical analysis using Chi-square statitic for Hypothesis Testing
Discussions and Conlusions
Figure 5.1 Phase-I Flow chart for ES Adoption Study
5.3 Demographic Profile of the cases Three hundred Indian cases have been downloaded from the vendors‘ websites, different IT magazines and consultants‘ websites, are listed in Appendix A. Out of these twelve cases hasn‘t fit the Enterprise Systems implementation category. A total of 288 cases has been analyzed to see the presence of twelve adoption reasons identified in the literature review. Two demographic variables industry and size of the organization have also been estimated. Vendor distribution of these cases is shown in Table 5.1 and Figure 5.3. Microsoft and Oracle cases are approximately equal and 124
Collection of Research Articles Related to CSF
Compilation of CSF subfactors by content analysis and inductive coding
Clustering of CSF subfactors on the basis of literature review and expert judgement
Literature review and slection of ES success measure
Identification of presence or absence of 37 CSF subfactors and 25 ES sucess items from customer success stories database developed in the Phase -I
Development of theoritical structural model for predicting ES success using CSF as predictors
Entry of model into the software XLSTAT consisting of latent variables, indicator variables and direction arrows
Testing of Mode using PLS routine
No
Model Fit Satisfaction Yes ModelDiscussion and Conlusion
Figure 5.2 Phase-II Flowchart for Development of Predictive Structural Model
125
they together with Oracle account for almost 89% of the cases. Whereas Indian ES vendors Ramco Systems, 3i Infotech, and ESS together account for 4.5% of cases.
Table 5.1 Vendor wise case distribution S.N. 1 2 3
Count 126 115 25
4 5 6 7 8 9 10
Vendor Microsoft SAP Oracle, JD Edwards, People Soft, Siebel Ramco Sage IFS India Infor Baan 3i Infotech LAWSON ESS Ebiz frame
11
QAD MFG/PRO
2
12
SSA
2
0.7%
1.3%
0.7% 1.7% 3.0%
0.7% 0.7%
Vendors
9 6 5 4 2 2 2
Microsoft SAP
0.7%
Oracle, JD Edwards, People Soft, Siebel Ramco
2.0%
Sage
8.3% 42.0%
IFS India Infor Baan 3i Infotech LAWSON
38.3%
ESS Ebiz frame QAD MFG/PRO SSA
Figure 5.3 Pie Chart for Vendor wise case distribution
126
Industry classification has been done with the help of National Industrial Classification 2008 (NIC-2008) data issued by Central Statistical Organization, India. NIC-2008 employs hierarchical five layered architecture to classify all economic activities into sections, divisions, groups, classes, and sub-classes. It contains 21 sections, 88 divisions, 238 groups, 403 classes, and 1304 sub-classes. NIC-2008 is comparable to ISIC Rev.4 (International Standard Industrial Classification) till classes in totality. In this research work only top level classification, sections, is utilized and other layers are used to resolve appropriate sections. Further they are grouped into manufacturing and non-manufacturing industries.
The size of the organization is categorized into two categories Small and large enterprises (SME) and large enterprises (LE). Evaluation of size has been done in two steps. Firstly it has been checked whether the number of employees are less than 500 to be classified as SME as per US convention (Ebrahim, Ahmed, & Taha, 2009). In some cases number of users‘ licenses purchased is more than 500 users so they have been classified as large enterprises (LE). Secondly if number of employee data was not available then annual turnover less than Euro 50 million has been used to classify the size as SME as per European Commission convention (Ebrahim, Ahmed, & Taha, 2009). In some cases, number of employees and annual turnover data have been directly extracted from the company‘s website. It is difficult to use Indian convention because it requires data on capital investment rarely available in these cases or on the company‘s website.
Industry and size distribution of these cases is shown in Table 5.2 and Figure 5.4. Maximum number of cases, 179 out of 288, belongs to manufacturing industry. That is 62% of the total cases. Out of 288, 155 cases belong to the large enterprises
127
whereas 133 cases belong to SMEs that makes 53% composition of the former and the rest of the latter.
Table 5.2 Industry and size distribution NIC
Size SME LE
Agriculture, forestry and fishing Mining and quarrying Manufacturing Electricity, gas, steam and air conditioning supply Construction Wholesale and retail trade; repair of motor vehicles and motorcycles Transportation and storage Accommodation and Food service activities Information and communication Financial and insurance activities Real estate activities Administrative and support service activities Education Total SME
3 6 101 9 7 3 5 1 14 4 1 1 0 155
8 6 179 11 8 22 8 1 28 8 4 1 4 288
LE
Figure 5.4 Industry and size distribution 128
1 3
1 0
0 4 Education
4 4
Administrative and support service activities
14 14
Real estate activities
3
1 0
Financial and insurance activities
5
Information and communication
3 19
Wholesale and retail trade; repair of motor vehicles and motorcycles
7 1
Construction
Electricity, gas, steam and air conditioning supply
0
Manufacturing
5
9 2
6 Mining and quarrying
3
Transportation and storage
78
Accommodation and Food service activities
101
Agriculture, forestry and fishing
200 180 160 140 120 100 80 60 40 20 0
5 0 78 2 1 19 3 0 14 4 3 0 4 133
Total
5.4 Ranking of Adoption Reasons, CSFs and ES Benefits All the cases have been analyzed to see the presence of twelve adoption motivation factor in Indian context. The results are shown in Table 5.3 and are sorted as per their percent occurrence in the 288 cases. All reasons of adoption are present in Indian companies. Legacy System Replacement or IT Architectural Improvements and Integration of Systems or processes happens to appear in almost 90 percent of the cases. Data or Information issues and Operational Improvements have been also a strong reason to go for these systems. Regulatory and Compliance Issues, Globalization Support, and Organizational Change have been sited only by 16, 15 and 13 percent of the companies respectively. External forces hasn‘t provided much impetus for adoption in Indian companies and is found only in 5 percent of total companies analyzed. Remaining factors have the presence between 75 to 25 percent.
Table 5.3. Relative Ranking of Reasons for Adoption Rank
Code
1
A2
2 3 4
A7 A4 A1
5 6 7 8 9 10 11 12
A8 A3 A11 A10 A5 A9 A6 A12
Reasons for Adoption Legacy System Replacement or IT Architectural Improvements Integration of Systems or processes Data or Information issues Operational Improvements (cost, employee, cycle time reductions) Standardization and best practices Business Growth or Extensions Customer and Supplier Intimacy Competition Regulatory and Compliance Issues Globalization Support Organizational Change External Forces
% of Occurrences 89.9% 88.9% 86.5% 78.8% 66.0% 56.9% 34.4% 23.6% 16.0% 14.6% 12.5% 4.9%
Marking the presence of CSF has been very intricate because of the slight nuances in their meaning. The idea to take all the variables is to bring the complete picture of the
129
implementation process in Indian companies. Moreover cases haven‘t provided complete information of the implementation process and hence accounted for less number of CSFs. Nevertheless the presence of all the CSFs has been ascertained Table 5.4. Though only 5 out of 37 CSFs have been found in more than 50% of the cases namely, consultant selection and relationship, implementation strategy and timeframe, system integration, selection of ES and, technical task and tools. Whereas 8 CSFs Managing cultural change, Team morale and motivation, Post-implementation evaluation, Project champion, Empowered decision makers, Interdepartmental Cooperation, Multi-site issues, and IT provider and Integrator Push have been found in less than 5% of the cases.
Shang & Seddon (2002) ES benefit framework seems very relevant in Indian context Table 5.5 Most of the benefits have significant presence in the Indian companies . All the items of operational benefits quality improvement, customer service improvement, cycle time reduction, cost reduction and productivity improvement have made it to the top ten list. Improved decision making and planning, and better resource management items of managerial benefits are also among the top ten benefits. Gaining strategic benefits is considered very debatable. Building cost leadership, the strategic benefit has been cited by 70% of the cases. Increase IT infrastructure capability, IT infrastructure benefit and Empowerment, organizational benefit are also among the top ten benefits. Only two items generating product differentiation and Building business innovation, both belonging to strategic benefits have been cited less than 10% of all the cases.
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Table 5.4 Relative Ranking of CSF sub-factors Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Code CSF6.2 CSF3.2 CSF1.6 CSF2.3 CSF5.4 CSF5.2 CSF7.2 CSF5.1 CSF2.2 CSF1.4 CSF4.1 CSF6.1 CSF2.1 CSF3.3 CSF4.4 CSF1.7 CSF1.5 CSF3.1 CSF4.5 CSF1.3 CSF8.1 CSF7.1 CSF3.5 CSF8.2 CSF6.3 CSF1.1 CSF1.2 CSF7.5 CSF3.4 CSF7.3 CSF7.4 CSF8.3 CSF4.3 CSF4.2 CSF4.6 CSF5.3 CSF6.4
CSF Sub-factors Consultant selection and relationship Implementation strategy and timeframe System Integration Selection of ERP Technical Task and Tools Data conversion and integrity Training and job redesign IT infrastructure BPR and software configuration Country-related functional requirements Top management commitment and support Vendor Support Vanilla ERP Project cost planning and management Project team: the best and brightest Build a business case Value Chain Connectivity Visioning and planning Balanced team Client consultation System testing Change management Project management Troubleshooting/crises management Stakeholder Pressures Organizational characteristics Legacy system consideration Expectation Management Communication plan Managing cultural change Team morale and motivation Post-implementation evaluation Project champion Empowered decision makers Interdepartmental Cooperation Multi-site issues IT provider and Integrator Push
131
% of Occurrences 79.2% 76.7% 61.8% 60.1% 54.2% 40.3% 38.9% 38.5% 33.0% 25.7% 24.0% 23.6% 21.9% 20.1% 19.8% 17.4% 15.6% 15.3% 14.9% 14.6% 14.6% 14.2% 9.4% 8.3% 8.3% 8.3% 8.3% 8.0% 5.2% 4.5% 4.5% 4.5% 4.2% 3.5% 3.1% 1.4% 1.0%
Table 5.5 Relative Ranking of the ES Benefits Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Code
ES Benefits
B1.4 B1.5 B2.2 B3.4 B1.2 B1.1 B1.3 B2.1 B4.3 B5.3 B3.7 B3.1 B3.2 B4.1 B4.2 B3.6 B5.2 B5.5 B3.8 B5.1 B2.3 B5.4 B5.6 B3.5 B3.3
Quality improvement Customer service improvement Improved decision making and planning Building cost leadership Cycle time reduction Cost reduction Productivity improvement Better resource management Increase IT infrastructure capability Empowerment Enabling e-commerce Support business growth Support business alliance Building business flexibility at lower cost IT cost reduction Enabling worldwide expansion Facilitating business learning and broaden employee skills Shifting work focus Generating or sustaining competitiveness Changing work pattern with shifted focus Performance improvements Building common vision Increase employee morale and satisfaction Generating product differentiation Building business innovation
% of Occurrences 80.9% 80.9% 79.9% 69.8% 67.7% 59.4% 59.0% 57.6% 56.6% 42.7% 40.6% 35.8% 35.4% 31.3% 30.6% 27.8% 24.3% 23.6% 22.2% 20.8% 18.1% 16.3% 15.3% 8.3% 7.3%
5.5 Chi-Square Test to Study the Adoption Reasons The analysis of the cases yielded dichotomous data. Therefore Pearson‘s chi-square test of statistical significance ha been used to see the association of reason of adoption with the size of the organization Table 5.6. For the hypotheses A1s – A5s and A7s – A12s, p >.05 therefore these hypotheses are rejected. All but one reason has been found to be indifferent to the size of the organizations. In other words the motivations to adopt ES system are uniform across all sizes of the organization except the organizational change. Organizational Change is found to be significantly related to
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the size: Pearson‘s χ2 = 4.041 (DF = 1, p = .044, N = 288). Out of 288 merely 12.5 percent organization quoted organizational change to be reason to adopt Enterprise Systems. Among them 8.3 percent are SMEs and 16.1 percent are large enterprises. Similarly Pearson‘s chi-square test of statistical significance has been used to see the relationship of reason of adoption with the industry of the organization. Here all reasons without any exception were found indifferent to the industry of the organizations as shown in Table 5.7. For the hypotheses A1i – A12i, p >.05 therefore these hypotheses are rejected. In other words the reasons for adoption don‘t vary with respect to the industry of the organization. Manufacturing and Non-Manufacturing organization adopt these systems for the same reasons.
5.6 Chi-Square Test to Study the ES Benefits All the 25 ES benefits were estimated for the presence or absence in the cases and yielded in the form of dichotomous data. Therefore Pearson‘s chi-square test of statistical significance was used to see the association of ES benefits with the size of the organization Table 5.8. For the hypotheses B1s – B6s, B8s, B9s, B11s – B15s and B17s – B25s p >.05 therefore these hypotheses are rejected. Improved decision making and planning is found to be significantly related to the size of the organization: Pearson‘s χ2 = 4.522 (df = 1, p = 0.033, N = 288). Out of 288, 79.9% organizations witnessed the benefit of improved decision making and planning with slight variation vis-à-vis size such that 74.4% SMEs and 84.5% large enterprises. Support business alliance benefit is also significantly related to the size of the organization: Pearson‘s χ2 = 7.531 (DF = 1, p = 0.006, N = 288). Out of 288, 35.4% organizations have cited the benefit of support business alliance with large variation with respect to size of the organization such that 27.1% SMEs and 42.6% LEs. 133
Generating or sustaining competitiveness benefit is also significantly related to the size of the organization: Pearson‘s χ2 = 7.380 (DF = 1, p = 0.007, N = 288).Out of 288, merely 22.2% organizations have shown the presence for the benefit of generating or sustaining competitiveness with large variation between SMEs and LEs such that 15.0% and 28.4% respectively. Similarly Pearson‘s chi-square test of statistical significance has been used to see the relationship of ES benefits with the industry of the organization. The results of the test are summarized in Table 5.9. For the hypotheses B1i – B3i, B5i – B11i, B13i and B15i – B25i p >.05 therefore these hypotheses are rejected. Quality improvement is found to be significantly related to the industry of the organization: Pearson‘s χ2 = 4.438 (DF = 1, p = 0.035, N = 288). Out of 288, a large percentage 80.9% of organizations witnessed the benefit of quality improvement with small difference vis-à-vis industry such that 87.2% Manufacturing and 77.1% NonManufacturing organizations. Building cost leadership benefit is also significantly related to the industry of the organization: Pearson‘s χ2 = 9.96
(df = 1, p = 0.002,
N = 288). Out of 288, 69.8% organizations sited the benefit of building cost leadership with large variation with respect to industry of the organization such that 80.7% Manufacturing and 63.1% others. Enabling worldwide expansion benefit is also significantly related to the industry of the organization: Pearson‘s χ2 = 3.897 (DF = 1, p = 0.048, N = 288). Out of 288, merely 27.8% organizations have shown the presence for the benefit of enabling worldwide expansion with small variation between Manufacturing and Non-Manufacturing such that 21.1% and 31.8% respectively.
134
Table 5.6 Pearson’s Chi-Square test for Reasons for Adoption versus Size of the Organization Pearson Chi-Square Test S. No.
1 2
Hypothesis1
Reasons for Adoption
SMS
LE
Total
Value2
df
75.2%
81.9%
78.8%
1.952
91.0%
89.0%
89.9%
54.9%
58.7%
Statistical Significance
1
Asymp. Sig. (2sided) 0.162
No Difference
0.299
1
0.584
No Difference
56.9%
0.427
1
0.514
No Difference
1
A1s
2
A2s
3
A3s
Operational Improvements (cost, employee, cycle time reductions) Legacy System Replacement or IT Architectural Improvements Business Growth or Extensions
4
A4s
Data or Information issues
90.2%
83.2%
86.5%
2.996
1
0.083
No Difference
5
A5s
Regulatory and Compliance Issues
16.5%
15.5%
16.0%
0.060
1
0.807
No Difference
6
A6s
Organizational Change
8.3%
16.1%
12.5%
4.041
1
0.044
Significant
7
A7s
Integration of Systems or processes
89.5%
88.4%
88.9%
0.086
1
0.770
No Difference
8
A8s
Standardization and best practices
61.7%
69.7%
66.0%
2.053
1
0.152
No Difference
9
A9s
Globalization Support
16.5%
12.9%
14.6%
0.761
1
0.383
No Difference
10
A10s
Competition
21.1%
25.8%
23.6%
0.897
1
0.344
No Difference
11
A11s
Customer and Supplier Intimacy
13.9%
20.5%
34.4%
2.025
1
0.155
No Difference
12
A12s
External Forces
6.0%
3.9%
4.9%
0.712
1
0.399
No Difference
See Table 4.4 Set of hypotheses to be tested for association between adoption reasons and the size of the organization 0 cells (.0%) have expected count less than 5.
135
Table 5.7 Pearson’s Chi-Square test for Reasons for Adoption versus Industry of the Organization
S. No.
1 2
Pearson Chi-Square Test 1
Hypothesis
Reasons for Adoption
Manufacturing
Others
Total
78.80%
77.80%
78.80%
0.001
91.10%
88.10%
89.90%
55.90%
58.70%
Value2
df
Statistical Significance
1
Asymp. Sig. (2-sided) 0.979
No Difference
0.668
1
0.414
No Difference
56.90%
0.224
1
0.636
No Difference
1
A1i
2
A2i
3
A3i
Operational Improvements (cost, employee, cycle time reductions) Legacy System Replacement or IT Architectural Improvements Business Growth or Extensions
4
A4i
Data or Information issues
87.70%
84.40%
86.50%
0.632
1
0.426
No Difference
5
A5i
Regulatory and Compliance Issues
15.10%
17.40%
16.00%
0.278
1
0.598
No Difference
6
A6i
Organizational Change
10.60%
15.60%
12.50%
1.537
1
0.215
No Difference
7
A7i
Integration of Systems or processes
89.40%
88.10%
88.90%
0.118
1
0.731
No Difference
8
A8i
Standardization and best practices
69.80%
59.60%
66.00%
3.139
1
0.076
No Difference
9
A9i
Globalization Support
14.50%
14.70%
14.60%
0.001
1
0.971
No Difference
10
A10i
Competition
21.80%
26.60%
23.60%
0.872
1
0.350
No Difference
11
A11i
Customer and Supplier Intimacy
37.40%
29.40%
34.40%
1.957
1
0.162
No Difference
12
A12i
External Forces
5.60%
3.70%
4.90%
0.538
1
0.463
No Difference
See Table 4.5 Set of hypotheses to be tested for association between adoption reasons and industry of the organization 0 cells (.0%) have expected count less than 5.
136
Table 5.8 Pearson’s Chi-Square test for ES Benefits versus Size of the Organization
S. No.
Pearson Chi-Square Test Hypothesis1
ES Benefits
SME
LE
Total
Value2
Asymp. Sig. (2-sided)
df
Statistical Significance
1
B1s
Cost reduction
54.9%
63.2%
59.4%
2.063
1
0.151
No Difference
2
B2s
Cycle time reduction
66.9%
68.4%
67.7%
0.071
1
0.790
No Difference
3
B3s
Productivity improvement
61.7%
56.8%
59.0%
0.705
1
0.401
No Difference
4
B4s
Quality improvement
82.7%
79.4%
80.9%
0.521
1
0.471
No Difference
5
B5s
Customer service improvement
82.0%
80.0%
80.9%
0.177
1
0.674
No Difference
6
B6s
Better resource management Improved decision making and planning
57.9%
57.4%
57.6%
0.007
1
0.935
No Difference
74.4%
84.5%
79.9%
4.522
1
0.033
Significant
8
B8s
Performance improvements
17.3%
18.7%
18.1%
0.097
1
0.755
No Difference
9
B9s
Support business growth
34.6%
36.8%
35.8%
0.149
1
0.699
No Difference
10
B10s
Support business alliance
27.1%
42.6%
35.4%
7.531
1
0.006
Significant
11
B11s
Building business innovation
4.5%
9.7%
7.3%
2.826
1
0.093
No Difference
12
B12s
Building cost leadership
68.4%
71.0%
69.8%
0.220
1
0.639
No Difference
13
B13s
Generating product differentiation
6.8%
9.7%
8.3%
0.794
1
0.373
No Difference
14
B14s
Enabling worldwide expansion
27.8%
27.7%
27.8%
0.000
1
0.988
No Difference
15
B15s
Enabling e-commerce Generating or sustaining competitiveness
39.1%
41.9%
40.6%
0.239
1
0.625
No Difference
15.0%
28.4%
22.2%
7.380
1
0.007
Significant
B7s 7
B16s 16
137
Continued Table 5.8 Pearson’s Chi-Square test for ES Benefits versus Size of the Organization
S. No.
Pearson Chi-Square Test Hypothesis1
LE
Total
Value2
Asymp. Sig. (2-sided)
df
Statistical Significance
B17s
Building business flexibility at lower cost
25.6%
36.1%
31.3%
3.719
1
0.054
No Difference
18
B18s
IT cost reduction
27.8%
32.9%
30.6%
0.872
1
0.350
No Difference
19
B19s
Increase IT infrastructure capability
59.4%
54.2%
56.6%
0.789
1
0.374
No Difference
20
B20s
Changing work pattern with shifted focus Facilitating business learning and broaden employee skills
21.8%
20.0%
20.8%
0.141
1
0.707
No Difference
21.8%
26.5%
24.3%
0.840
1
0.359
No Difference
B21s 22
B22s
Empowerment
40.6%
44.5%
42.7%
0.448
1
0.503
No Difference
23
B23s
Building common vision
15.8%
16.8%
16.3%
0.051
1
0.822
No Difference
24
B24s
Shifting work focus Increase employee morale and satisfaction
23.3%
23.9%
23.6%
0.013
1
0.911
No Difference
13.5%
16.8%
15.3%
0.581
1
0.446
No Difference
B25s 25
2
SME
17
21
1
ES Benefits
See Table 4.6 Set of hypotheses to be tested for association between ES Benefits and the size of the organization 0 cells (.0%) have expected count less than 5.
138
Table 5.9 Pearson’s Chi-Square test for ES Benefits versus Industry of the Organization Pearson Chi-Square Test
S. No.
Hypothesis1
1
B1i
Cost reduction
57.8%
60.3%
59.4%
0.181
1
0.671 No Difference
2
B2i
Cycle time reduction
69.7%
66.5%
67.7%
0.326
1
0.568 No Difference
3
B3i
Productivity improvement
56.0%
60.9%
59.0%
0.681
1
0.409 No Difference
4
B4i
Quality improvement
87.2%
77.1%
80.9%
4.438
1
0.035 Significant
5
B5i
Customer service improvement
84.4%
78.8%
80.9%
1.391
1
0.238 No Difference
6
B6i
53.2%
60.3%
57.6%
1.408
1
0.235 No Difference
7
B7i
Better resource management Improved decision making and planning
79.8%
79.9%
79.9%
0
1
0.988 No Difference
8
B8i
Performance improvements
16.5%
19.0%
18.1%
0.282
1
0.596 No Difference
9
B9i
Support business growth
37.6%
34.6%
35.8%
0.261
1
0.609 No Difference
10
B10i
Support business alliance
31.2%
38.0%
35.4%
1.368
1
0.242 No Difference
11
B11i
Building business innovation
7.3%
7.3%
7.3%
0.001
1
0.981 No Difference
12
B12i
Building cost leadership
80.7%
63.1%
69.8%
9.96
1
0.002 Significant
13
B13i
Generating product differentiation
10.1%
7.3%
8.3%
0.71
1
0.399 No Difference
14
B14i
Enabling worldwide expansion
21.1%
31.8%
27.8%
3.897
1
0.048 Significant
15
B15i
41.3%
40.2%
40.6%
0.032
1
0.859 No Difference
16
B16i
Enabling e-commerce Generating or sustaining competitiveness
18.3%
24.6%
22.2%
1.522
1
0.217 No Difference
ES Benefits
Manufacturing
139
Others
Total
Value2
df
Asymp. Sig. (2-sided)
Statistical Significance
ContinuedChi-Square test for ES Benefits versus Industry of the Organization Table 5.9 Pearson’s
1 2
Pearson Chi-Square Test
S. No.
Hypothesis1
ES Benefits
17
B17i
Building business flexibility at lower cost
31.2%
31.3%
31.3%
0
1
0.987 No Difference
18
B18i
IT cost reduction
29.4%
31.3%
30.6%
0.119
1
0.731 No Difference
19
B19i
58.7%
55.3%
56.6%
0.32
1
0.571 No Difference
20
B20i
23.9%
19.0%
20.8%
0.97
1
0.325 No Difference
21
B21i
Increase IT infrastructure capability Changing work pattern with shifted focus Facilitating business learning and broaden employee skills
29.4%
21.2%
24.3%
2.433
1
0.119 No Difference
22
B22i
Empowerment
49.5%
38.5%
42.7%
3.346
1
0.067 No Difference
23
B23i
Building common vision
12.8%
18.4%
16.3%
1.551
1
0.213 No Difference
24
B24i
27.5%
21.2%
23.6%
1.488
1
0.223 No Difference
25
B25i
Shifting work focus Increase employee morale and satisfaction
16.5%
14.5%
15.3%
0.207
1
0.649 No Difference
Manufacturing
Others
Total
See Table 4.7 Set of hypotheses to be tested for association between ES Benefits and industry of the organization 0 cells (.0%) have expected count less than 5.
140
Value2
df
Asymp. Sig. (2-sided)
Statistical Significance
5.7 Results for PLS Simple Model: Model-1 This Model is most simplified considering all the CSF indicators and ES Benefits indicators and mapping them to CSF and ES Benefits constructs respectively Figure 4.6. XLSTAT Version 2012.6.02 has been used for all the calculation related to PLS Structural Model. For VIF index calculation XLSTAT‘s Multicolinearity statistics routine from Describing Data menu has been utilized. For running the PLS Graph following options has been selected
Treatment of the manifest variables: Standardized, weights on raw MV Initial weights: Values of the first eigenvector Internal estimation: Centroid Regression: OLS Stop conditions: Iterations = 100 / Convergence = 0.0001 Confidence intervals: 95 / Bootstrap / Resamplings = 100 / Sample size = 288 Blindfolding: 30 Latent variable scores: Standardized Seed (random numbers): 4206008 Results of First Iteration for the PLS Simple Model: Model-1a As discussed earlier there are two important criteria for the assessment of the outer or the measurement model namely multicolinearity test and significance of weight. VIF results have shown that all the values are below 3.3 except for B1.4 and B1.5. Although VIF values for B1.4 and B1.5 are well below 10 a number that signifies critical colinearity. Nonetheless these two B1.4 (Quality improvement) and B1.5 (Customer service improvement) items are theoretically related as well. First deals with error rate, data reliability and accuracy and second deals with ease of data access 141
(Shang & Seddon, 2002). Also the condition numbers for both constructs are well below 30 that signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995).
Table 5.10 VIF measure to assess multicolinearity for Model-1a Latent Variable CSF (Condition Number = 2.874)
Manifest Variable CSF1.1
0.244
0.756
1.322
CSF1.2
0.261
0.739
1.353
CSF1.3
0.314
0.686
1.458
CSF1.4
0.220
0.780
1.282
CSF1.5
0.287
0.713
1.403
CSF1.6
0.260
0.740
1.351
CSF1.7
0.266
0.734
1.362
CSF2.1
0.248
0.752
1.329
CSF2.2
0.314
0.686
1.459
CSF2.3
0.284
0.716
1.397
CSF3.1
0.297
0.703
1.423
CSF3.2
0.282
0.718
1.393
CSF3.3
0.222
0.778
1.285
CSF3.4
0.334
0.666
1.502
CSF3.5
0.323
0.677
1.478
CSF4.1
0.344
0.656
1.524
CSF4.2
0.356
0.644
1.552
CSF4.3
0.265
0.735
1.361
CSF4.4
0.414
0.586
1.705
CSF4.5
0.330
0.670
1.492
CSF4.6
0.370
0.630
1.588
CSF5.1
0.368
0.632
1.583
CSF5.2
0.271
0.729
1.373
CSF5.3
0.232
0.768
1.302
CSF5.4
0.331
0.669
1.495
CSF6.1
0.290
0.710
1.408
CSF6.2
0.264
0.736
1.359
CSF6.3
0.301
0.699
1.430
CSF6.4
0.140
0.860
1.163
CSF7.1
0.343
0.657
1.523
CSF7.2
0.354
0.646
1.549
CSF7.3
0.344
0.656
1.525
142
R²
Tolerance
VIF
Continued Table 5.10 VIF measure to assess multicolinearity for Model-1a Latent Variable
ES Benefits (Condition Number = 3.902)
Manifest Variable CSF7.4
0.255
0.745
1.342
CSF7.5
0.265
0.735
1.361
CSF8.1
0.311
0.689
1.451
CSF8.2
0.351
0.649
1.541
CSF8.3
0.326
0.674
1.483
B1.1
0.261
0.739
1.353
B1.2
0.246
0.754
1.327
B1.3
0.230
0.770
1.298
B1.4
0.708
0.292
3.425
B1.5
0.699
0.301
3.320
B2.1
0.261
0.739
1.353
B2.2
0.255
0.745
1.342
B2.3
0.247
0.753
1.329
B3.1
0.298
0.702
1.423
B3.2
0.350
0.650
1.539
B3.3
0.281
0.719
1.390
B3.4
0.251
0.749
1.336
B3.5
0.331
0.669
1.495
B3.6
0.346
0.654
1.529
B3.7
0.501
0.499
2.005
B3.8
0.318
0.682
1.467
B4.1
0.280
0.720
1.389
B4.2
0.221
0.779
1.284
B4.3
0.289
0.711
1.407
B5.1
0.293
0.707
1.415
B5.2
0.335
0.665
1.505
B5.3
0.426
0.574
1.741
B5.4
0.273
0.727
1.375
B5.5
0.280
0.720
1.388
B5.6
0.269
0.731
1.367
R²
Tolerance
VIF
Bootstrap procedure is used to assess the significance of weight. The results are shown in Table 5.11. Large numbers of indicators are showing the critical ratio between the lower bound and upper bound hence making them significant. 10 Manifest variables of CSF, CSF1.3, CSF1.6, CSF2.3, CSF3.3, CSF4.1, CSF5.1, 143
CSF5.2, CSF5.4, CSF6.1 and CSF6.2 are found non-significant. Similarly 7 manifest variables of ES benefits construct B1.3, B3.2, B3.6, B4.2, B5.1, B5.5 and B5.6 are found non-significant. These non-significant indicators has led to second iteration and creation of Model-1b discussed in the next section.
Table 5.11 Bootstrap results to assess weight significance for Model-1a Latent variable CSF
Manifest variables
CSF1.1 CSF1.2 CSF1.3 CSF1.4 CSF1.5 CSF1.6 CSF1.7 CSF2.1 CSF2.2 CSF2.3 CSF3.1 CSF3.2 CSF3.3 CSF3.4 CSF3.5 CSF4.1 CSF4.2 CSF4.3 CSF4.4 CSF4.5 CSF4.6 CSF5.1 CSF5.2 CSF5.3 CSF5.4 CSF6.1 CSF6.2 CSF6.3 CSF6.4 CSF7.1 CSF7.2 CSF7.3 CSF7.4 CSF7.5
Outer weight -0.589 -0.140 0.466 -0.083 0.239 0.698 0.003 -0.212 -0.129 0.543 0.228 0.019 0.421 -0.878 -0.180 -0.375 1.050 0.176 -0.542 -0.128 1.681 0.385 0.495 -0.197 0.849 0.330 0.524 -0.291 -0.218 -0.126 0.274 -0.285 -0.104 0.732
Outer weight (Bootstrap) -0.173 -0.164 0.283 -0.050 0.207 0.340 0.050 -0.049 -0.108 0.240 0.217 0.035 0.278 -0.206 -0.076 -0.143 0.534 0.155 -0.275 -0.095 0.700 0.185 0.232 -0.129 0.472 0.149 0.234 -0.080 0.034 -0.019 0.177 -0.118 0.037 0.358
144
Standard error
Critical ratio (CR)
0.702 0.670 0.483 0.268 0.717 0.306 0.425 0.314 0.310 0.330 0.465 0.346 0.345 0.682 0.489 0.418 0.847 0.682 0.538 0.341 1.049 0.331 0.232 1.053 0.293 0.316 0.316 0.704 0.921 0.414 0.319 0.859 0.683 0.582
-0.861 -0.232 1.043 -0.300 0.359 2.229 0.007 -0.684 -0.432 1.663 0.499 0.054 1.250 -1.121 -0.389 -1.007 1.267 0.245 -1.027 -0.381 1.635 1.181 1.984 -0.202 2.699 1.069 1.882 -0.555 -0.272 -0.346 0.818 -0.415 -0.169 1.160
Lower bound (95%) -1.438 -1.588 -0.725 -0.762 -1.307 -0.343 -0.982 -0.761 -0.976 -0.408 -0.846 -0.814 -0.635 -1.382 -1.072 -1.173 -1.480 -1.294 -1.213 -0.839 -1.441 -0.656 -0.337 -3.054 -0.087 -0.517 -0.413 -1.557 -2.008 -0.853 -0.501 -1.859 -1.631 -0.774
Upper bound (95%) 1.280 1.608 1.242 0.427 1.509 1.000 0.905 0.540 0.542 0.896 1.090 0.819 1.080 1.687 0.975 0.794 2.122 1.380 1.116 0.536 2.806 0.876 0.642 1.762 1.060 0.956 1.004 1.898 1.670 1.124 0.812 1.723 1.454 1.667
Continuedresults to assess weight significance for Model-1a Table 5.11 Bootstrap Latent variable
ES Benefits
Manifest variables
CSF8.1 CSF8.2 CSF8.3 B1.1 B1.2 B1.3 B1.4 B1.5 B2.1 B2.2 B2.3 B3.1 B3.2 B3.3 B3.4 B3.5 B3.6 B3.7 B3.8 B4.1 B4.2 B4.3 B5.1 B5.2 B5.3 B5.4 B5.5 B5.6
Outer weight 0.005 0.399 -0.370 0.091 -0.191 -0.304 -0.868 0.053 -0.002 0.060 0.112 0.041 0.589 -0.629 -0.181 0.183 0.961 0.084 0.358 -0.187 -0.519 0.247 -0.952 -0.368 -0.396 -0.291 -0.367 -0.630
Outer weight (Bootstrap) 0.001 0.186 -0.208 0.062 -0.183 -0.067 -0.364 0.044 0.045 -0.103 -0.054 0.027 0.218 -0.327 -0.029 0.019 0.423 -0.158 0.160 -0.037 -0.261 0.181 -0.535 -0.291 -0.110 -0.126 -0.217 -0.344
Standard error
Critical ratio (CR)
0.429 0.558 1.049 0.295 0.339 0.385 0.641 0.540 0.336 0.539 0.474 0.436 0.409 0.567 0.342 0.760 0.470 0.688 0.439 0.350 0.313 0.340 0.439 0.380 0.442 0.462 0.368 0.472
0.011 0.661 -0.336 0.309 -0.550 -0.844 -1.459 0.095 -0.005 0.115 0.264 0.104 1.420 -0.977 -0.549 0.265 1.991 0.128 0.985 -0.566 -1.868 0.748 -2.276 -0.888 -0.822 -0.651 -1.098 -1.309
Lower bound (95%) -1.014 -0.969 -2.217 -0.609 -0.930 -0.826 -1.759 -1.061 -0.772 -1.174 -1.014 -0.714 -0.665 -1.388 -0.781 -1.444 -0.641 -1.422 -0.874 -0.727 -0.865 -0.508 -1.311 -1.044 -1.008 -1.425 -0.959 -1.310
Upper bound (95%) 0.807 1.360 2.437 0.573 0.550 0.782 1.186 1.624 0.789 0.964 0.801 0.966 0.963 1.072 0.877 1.532 1.321 1.123 0.979 0.757 0.451 0.769 0.468 0.802 0.772 0.805 0.649 0.718
Assessment of inner or structural model is based on four criteria. Firstly the R2 value of the endogenous variable, ES Benefit that is 0.419 between 0.33 and 0.67. It puts ES benefit, a latent variable between moderate to substantial category (Chin, 1998). Secondly the path coefficient that is -0.647. Magnitude is slightly on the lower side but significant at 0.05 level. Still, the incongruity lies in its negative sign that establishes the negative relationship for the hypothesis C. Thirdly the f2 is larger than 0.35 therefore the predictor latent variable CSF has a large effect at structural level. 145
Finally Stone-Geisser‘s Q2 measure for predictive relevance, is below zero therefore disproves the predictive relevance of the model.
Table 5.12 Structural Model Assessments for Model-1a Path
Path Coefficient
t
Pr > |t|
CSF ES Benefits
-0.647
-14.363
0.000
f² (Effect Size) 0.721
-0.647**
CSF
R
2
Q2 (Redundancies)
0.419
-0.0123
ES Benefit R2 = 0.419
ESBenefit = -0.647 * CSF
** significant at the 0.05 level Figure 5.5 Structural Model Equation Model-1a Results of Second Iteration for the PLS Simple Model: Model-1b VIF results have shown that all the values are below 3.3 Table 5.13. Also the condition numbers for both constructs are well below 30 that signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995).
Table 5.13 VIF measure to assess multicolinearity for Model-1b Latent Variable
Manifest Variable
CSF (Condition Number = 2.5933)
CSF1.1
0.167
0.833
1.200
CSF1.2
0.193
0.807
1.239
CSF1.4
0.174
0.826
1.210
CSF1.5
0.182
0.818
1.223
CSF1.7
0.223
0.777
1.287
CSF2.1
0.176
0.824
1.213
CSF2.2
0.261
0.739
1.354
CSF3.1
0.227
0.773
1.294
CSF3.2
0.176
0.824
1.214
CSF3.4
0.284
0.716
1.396
146
R²
Tolerance
VIF
Continued Table 5.13 VIF measure to assess multicolinearity for Model-1b Latent Variable
ES Benefits (Condition Number = 3.5)
Manifest Variable
R²
Tolerance
VIF
CSF3.5
0.266
0.734
1.363
CSF4.2
0.254
0.746
1.340
CSF4.3
0.242
0.758
1.320
CSF4.4
0.383
0.617
1.621
CSF4.5
0.288
0.712
1.404
CSF4.6
0.307
0.693
1.443
CSF5.3
0.172
0.828
1.208
CSF6.3
0.198
0.802
1.247
CSF6.4
0.092
0.908
1.102
CSF7.1
0.307
0.693
1.442
CSF7.2
0.292
0.708
1.412
CSF7.3
0.290
0.710
1.408
CSF7.4
0.204
0.796
1.256
CSF7.5
0.224
0.776
1.288
CSF8.1
0.225
0.775
1.291
CSF8.2
0.260
0.740
1.351
CSF8.3
0.275
0.725
1.378
B1.1
0.213
0.787
1.270
B1.2
0.197
0.803
1.245
B1.4
0.690
0.310
3.228
B1.5
0.684
0.316
3.161
B2.1
0.221
0.779
1.284
B2.2
0.214
0.786
1.272
B2.3
0.154
0.846
1.183
B3.1
0.219
0.781
1.280
B3.3
0.206
0.794
1.259
B3.4
0.213
0.787
1.271
B3.5
0.266
0.734
1.362
B3.7
0.404
0.596
1.678
B3.8
0.262
0.738
1.355
B4.1
0.210
0.790
1.265
B4.3
0.231
0.769
1.300
B5.2
0.228
0.772
1.295
B5.3
0.376
0.624
1.601
B5.4
0.221
0.779
1.283
147
Bootstrap procedure is used to assess the significance of weight. The results are shown in Table 5.14. Large numbers of indicators are showing the critical ratio between the lower bound and upper bound hence making them significant. In the second iteration only 4 Manifest variables of CSF, CSF1.2, CSF1.5, CSF2.2 and CSF8.1 are found non-significant. Similarly again 6 manifest variables of ES Benefits construct B1.1, B2.1, B2.2, B3.7, B5.2 and B5.3 are found non-significant. These non-significant indicators has led to third iteration and creation of Model-1c discussed in the next section.
Table 5.14 Bootstrap results to assess weight significance for Model-1b Latent variable CSF
ES
Manifest variables CSF1.1 CSF1.2 CSF1.4 CSF1.5 CSF1.7 CSF2.1 CSF2.2 CSF3.1 CSF3.2 CSF3.4 CSF3.5 CSF4.2 CSF4.3 CSF4.4 CSF4.5 CSF4.6 CSF5.3 CSF6.3 CSF6.4 CSF7.1 CSF7.2 CSF7.3 CSF7.4 CSF7.5 CSF8.1 CSF8.2 CSF8.3 B1.1
Outer weight -0.963 1.258 -0.183 -1.172 -0.447 -0.350 0.471 0.058 0.073 -1.188 -0.513 0.496 -0.179 -0.143 0.142 -1.566 0.092 0.510 -0.865 -0.373 -0.452 0.611 0.171 -0.704 -0.645 -0.653 2.489 0.091
Outer weight (Bootstrap) -0.449 0.644 -0.124 -0.589 -0.258 -0.141 0.199 -0.041 0.035 -0.703 -0.256 0.201 -0.248 -0.050 0.023 -0.823 0.118 0.086 -0.484 -0.162 -0.274 0.257 0.067 -0.448 -0.275 -0.294 1.378 0.062
148
Standard error 0.539 0.584 0.382 0.517 0.504 0.412 0.368 0.650 0.372 0.683 0.479 0.905 0.873 0.657 0.430 1.396 1.112 0.721 1.268 0.477 0.444 1.011 0.786 0.881 0.383 0.559 1.331 0.295
Critical ratio (CR) -1.785 2.155 -0.480 -2.268 -0.885 -0.849 1.279 0.089 0.195 -1.739 -1.072 0.548 -0.205 -0.218 0.329 -1.122 0.083 0.707 -0.682 -0.782 -1.017 0.604 0.218 -0.799 -1.682 -1.167 1.870 1.053
Lower bound (95%) -1.893 -0.434 -1.107 -1.714 -1.185 -1.021 -0.658 -1.388 -0.810 -2.324 -1.176 -2.124 -2.053 -1.351 -0.723 -3.043 -3.167 -1.532 -3.911 -1.051 -1.041 -2.461 -1.766 -2.310 -1.098 -1.240 -2.180 -0.609
Upper bound (95%) 0.700 2.018 0.649 0.795 0.874 0.883 0.920 1.178 0.701 0.785 0.728 1.878 1.528 1.315 0.993 3.134 3.261 1.657 2.103 0.876 0.797 2.457 1.977 1.633 0.423 0.867 3.746 0.573
Continuedresults to assess weight significance for Model-1b Table 5.14 Bootstrap Latent variable Benefits
Manifest variables B1.2 B1.4 B1.5 B2.1 B2.2 B2.3 B3.1 B3.3 B3.4 B3.5 B3.7 B3.8 B4.1 B4.3 B5.2 B5.3 B5.4
Outer weight -0.191 -0.304 -0.868 0.053 -0.002 0.060 0.112 0.041 0.589 -0.629 -0.181 0.183 0.961 0.084 0.358 -0.187 -0.519
Outer weight (Bootstrap) -0.183 -0.067 -0.364 0.044 0.045 -0.103 -0.054 0.027 0.218 -0.327 -0.029 0.019 0.423 -0.158 0.160 -0.037 -0.261
Standard error 0.339 0.385 0.641 0.540 0.336 0.539 0.474 0.436 0.409 0.567 0.342 0.760 0.470 0.688 0.439 0.350 0.313
Critical ratio (CR) -0.048 0.678 0.231 1.978 -2.302 -0.630 0.468 -1.199 0.132 1.161 -2.128 0.345 -0.269 -0.503 -1.873 1.316 -0.941
Lower bound (95%) -0.930 -0.826 -1.759 -1.061 -0.772 -1.174 -1.014 -0.714 -0.665 -1.388 -0.781 -1.444 -0.641 -1.422 -0.874 -0.727 -0.865
Upper bound (95%) 0.550 0.782 1.186 1.624 0.789 0.964 0.801 0.966 0.963 1.072 0.877 1.532 1.321 1.123 0.979 0.757 0.451
Assessment of inner or structural model is based on four criteria. Firstly the R2 value of the endogenous variable, ES Benefits that is 0.301 close to 0.33. It puts ES Benefits, a latent variable in the moderate category (Chin, 1998). Secondly the path coefficient that is 0.548. Magnitude is slightly on the lower side but significant at 0.05 level. In contrast to first Model1-a, its sign is positive that establishes the positive relationship and proves the hypothesis C to be true. Thirdly the f2 is larger than 0.35 therefore the predictor latent variable CSF has a large effect at structural level. Finally Stone-Geisser‘s Q2 measure for predictive relevance, is below zero therefore disproves the predictive relevance of the model.
Table 5.15 Structural Model Assessments for Model-1b Path
Path Coefficient
t
Pr > |t|
CSF ES Benefits
0.548
11.086
0.000
149
f² (Effect Size) 0.430
R
2
0.301
2
Q (Redundancies) -0.018
0.548**
CSF
ES Benefit R2 = 0.301
ES Benefits = 0.548*CSF
** significant at the 0.05 level
Figure 5.6 Structural Model Equation Model-1b
Results of Third Iteration for the PLS Simple Model: Model-1c VIF results have shown that all the values are below 3.3 Table 5.16. Also the condition numbers for both construct is well below 30 that signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995).
Bootstrap procedure is used to assess the significance of weight. The results are shown in Table 5.17. The nonsignificant factor count has considerably decreased. Only 3 Manifest variables of CSF, CSF3.2, CSF6.3 and CSF7.2, and 2 manifest variables of ES benefit construct B3.1 and B4.3 are found non-significant. Assessment of inner or structural model is based on four criteria. Firstly the R2 value of the endogenous variable, ES Benefit that is 0.229 close to 0.33. It puts ES Benefits, a latent variable in the moderate category (Chin, 1998). Secondly the path coefficient that is 0.478. Magnitude is slightly on the lower side but significant at 0.05 level. Again the sign is positive that establishes the positive relationship and proves the hypothesis C to be true. Thirdly the f2 is lower than 0.35 therefore the predictor latent variable CSF has got reduced effect at the structural level in comparison to Model1-b. Finally Stone-Geisser‘s Q2 measure for predictive relevance, is below zero therefore
150
Table 5.16 VIF measure to assess multicolinearity for Model-1c Latent Variable
Manifest Variable
CSF (Condition Number = 2.428)
CSF1.1
0.136
0.864
1.157
CSF1.4
0.161
0.839
1.192
CSF1.7
0.169
0.831
1.203
CSF2.1
0.158
0.842
1.188
CSF3.1
0.182
0.818
1.222
CSF3.2
0.165
0.835
1.198
CSF3.4
0.253
0.747
1.338
CSF3.5
0.234
0.766
1.305
CSF4.2
0.233
0.767
1.304
CSF4.3
0.228
0.772
1.295
CSF4.4
0.350
0.650
1.539
CSF4.5
0.236
0.764
1.308
CSF4.6
0.244
0.756
1.323
CSF5.3
0.143
0.857
1.167
CSF6.3
0.140
0.860
1.163
CSF6.4
0.077
0.923
1.083
CSF7.1
0.270
0.730
1.371
CSF7.2
0.277
0.723
1.383
CSF7.3
0.268
0.732
1.367
CSF7.4
0.178
0.822
1.217
CSF7.5
0.184
0.816
1.225
CSF8.2
0.215
0.785
1.275
CSF8.3
0.198
0.802
1.247
B1.2
0.176
0.824
1.213
B1.4
0.671
0.329
3.044
B1.5
0.675
0.325
3.076
B2.3
0.113
0.887
1.128
B3.1
0.183
0.817
1.223
B3.3
0.158
0.842
1.187
B3.4
0.147
0.853
1.173
B3.5
0.222
0.778
1.285
B3.8
0.223
0.777
1.288
B4.1
0.181
0.819
1.221
B4.3
0.200
0.800
1.250
B5.4
0.196
0.804
1.244
ES Benefits (Condition Number = 3.188)
R²
Tolerance
VIF
Table 5.17 Bootstrap results to assess weight significance for Model-1c Latent variable CSF
Manifest variables CSF1.1
Outer weight 0.376
Outer weight (Bootstrap) 0.153
151
Standard error 0.790
Critical ratio (CR) 0.476
Lower bound (95%) -1.586
Upper bound (95%) 1.731
Latent variable
ES Benefits
Manifest variables CSF1.4 CSF1.7 CSF2.1 CSF3.1 CSF3.2 CSF3.4 CSF3.5 CSF4.2 CSF4.3 CSF4.4 CSF4.5 CSF4.6 CSF5.3 CSF6.3 CSF6.4 CSF7.1 CSF7.2 CSF7.3 CSF7.4 CSF7.5 CSF8.2 CSF8.3 B1.2 B1.4 B1.5 B2.3 B3.1 B3.3 B3.4 B3.5 B3.8 B4.1 B4.3 B5.4
Outer weight -0.443 0.311 0.460 0.602 0.490 -0.724 -0.164 0.128 -0.690 1.045 0.441 -1.984 -0.467 -0.820 -0.936 0.145 -0.993 1.644 -0.321 -1.435 0.035 2.063 -0.003 -0.105 -0.786 0.733 0.598 -0.427 -0.456 2.219 0.918 -0.137 -0.934 -0.772
Outer weight (Bootstrap) -0.180 0.251 0.244 0.389 0.242 -0.450 -0.090 0.280 -0.469 0.663 0.294 -1.062 -0.172 -0.438 -0.342 0.075 -0.541 0.902 -0.255 -0.784 -0.107 1.289 0.013 -0.122 -0.494 0.524 0.305 -0.190 -0.270 1.428 0.484 -0.032 -0.506 -0.366
Standard error 0.474 0.491 0.523 0.690 0.407 0.747 0.564 1.059 1.098 0.676 0.417 1.078 1.194 0.471 1.380 0.492 0.477 0.887 0.915 0.762 0.603 1.167 0.579 0.711 0.888 0.534 0.442 0.773 0.486 0.997 0.789 0.438 0.469 0.690
Critical ratio (CR) -0.935 0.633 0.879 0.872 1.204 -0.968 -0.291 0.121 -0.629 1.546 1.056 -1.842 -0.391 -1.741 -0.678 0.295 -2.082 1.852 -0.350 -1.882 0.058 1.768 -0.005 -0.147 -0.886 1.373 1.353 -0.553 -0.939 2.225 1.164 -0.312 -1.991 -1.119
Lower bound (95%) -1.045 -0.807 -0.798 -1.458 -0.808 -2.316 -1.295 -2.015 -3.198 -1.173 -0.620 -3.429 -2.423 -1.386 -2.788 -1.070 -1.399 -1.475 -2.135 -1.969 -1.450 -2.204 -1.231 -1.546 -2.250 -0.676 -0.662 -1.554 -1.279 -1.323 -1.713 -1.054 -1.360 -1.757
Upper bound (95%) 1.023 1.207 1.355 1.886 0.925 1.149 1.277 2.688 1.833 1.724 1.200 1.812 3.469 0.603 3.021 1.020 0.696 2.596 1.851 1.108 1.180 3.481 1.310 1.482 1.668 1.647 1.225 1.679 0.914 2.898 1.796 0.869 0.600 1.002
disproves the predictive relevance of the model. The results of this model has shown that Model1-b is better than Model1-c, therefore further iteration are not needed.
Table 5.18 Structural Model Assessments for Model-1c Path
Path Coefficient
t
Pr > |t|
CSF ES Benefits
0.478
9.205
0.000
152
f² (Effect Size) 0.296
R
2
0.229
2
Q (Redundancies) -0.052
0.478**
CSF
ES Benefit R2 = 0.229
ES Benefits = 0.478*CSF
** significant at the 0.05 level Figure 5.7 Structural Model Equation Model-1c
5.8 Results for PLS Simple High Item Frequency Model: Model-2 This model is developed using only the items that have a frequency of the presence of 20% or more in the 288 cases. XLSTAT Version 2012.6.02 has been used for all the calculation related to PLS Structural Model. For VIF index calculation XLSTAT‘s Multicolinearity statistics routine from Describing Data menu has been utilized. For running the PLS Graph following options have been selected
Treatment of the manifest variables: Standardized, weights on raw MV Initial weights: Values of the first eigenvector Internal estimation: Centroid Regression: OLS Stop conditions: Iterations = 100 / Convergence = 0.0001 Confidence intervals: 95 / Bootstrap / Resamplings = 100 / Sample size = 288 Blindfolding: 30 Latent variable scores: Standardized Seed (random numbers): 4206008
153
Table 5.19 VIF measure to assess multicolinearity for Model-2a Latent Variable
Manifest Variable
CSF (Condition Number = 1.784)
CSF1.4
0.126
0.874
1.145
CSF1.6
0.167
0.833
1.200
CSF2.1
0.166
0.834
1.198
CSF2.2
0.214
0.786
1.272
CSF2.3
0.172
0.828
1.208
CSF3.2
0.209
0.791
1.264
CSF3.3
0.152
0.848
1.179
CSF4.1
0.174
0.826
1.210
CSF4.4
0.200
0.800
1.250
CSF5.1
0.233
0.767
1.304
CSF5.2
0.184
0.816
1.225
CSF5.4
0.246
0.754
1.327
CSF6.1
0.162
0.838
1.193
CSF6.2
0.130
0.870
1.149
CSF7.2
0.202
0.798
1.253
B1.1
0.185
0.815
1.228
B1.2
0.159
0.841
1.188
B1.3
0.129
0.871
1.148
B1.4
0.692
0.308
3.252
B1.5
0.682
0.318
3.142
B2.1
0.176
0.824
1.214
B2.2
0.129
0.871
1.148
B3.1
0.205
0.795
1.257
B3.2
0.198
0.802
1.248
B3.4
0.155
0.845
1.184
B3.6
0.238
0.762
1.313
B3.7
0.378
0.622
1.608
B3.8
0.206
0.794
1.259
B4.1
0.221
0.779
1.284
B4.2
0.159
0.841
1.189
B4.3
0.209
0.791
1.264
B5.1
0.226
0.774
1.292
B5.2
0.264
0.736
1.359
B5.3
0.350
0.650
1.538
B5.5
0.204
0.796
1.256
ES Benefits (Condition Number = 3.694)
154
R²
Tolerance
VIF
Results of First Iteration for the PLS Simple High Item Frequency Model: Model-2a VIF results have shown that all the values are well below 3.3 except for B1.4 and B1.5. The values of these items are also below 3.3 but close to it. Also the condition numbers for both construct are well below 30 that signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995).
Bootstrap procedure has been used to assess the significance of weight. The results are shown in Table 5.20. Almost half of the items of CSF are showing nonsignificance. These are CSF1.6, CSF2.3, CSF3.3, CSF5.2, CSF5.4, CSF6.1 and CSF6. Similarly 9 manifest variables of ES Benefits construct B1.3, B2.1, B3.2, B3.4, B3.6, B4.2, B5.1, B5.2 and B5.3 are found non-significant. These non-significant indicators has led to second iteration and creation of Model-2b discussed in the next section. Assessment of inner or structural model is based on four criteria. Firstly the R2 value of the endogenous variable, ES Benefits that is 0.314 slightly lower than 0.33. It puts ES Benefits, a latent variable slightly lower than moderate category (Chin, 1998). Secondly the path coefficient that is 0.560. Magnitude is slightly on the lower side but significant at 0.05 level. Therefore proves the hypothesis C to be true. Thirdly the f2 is larger than 0.35 therefore the predictor latent variable CSF has a large effect at structural level. Finally Stone-Geisser‘s Q2 measure for predictive relevance, is below zero therefore disproves the predictive relevance of the model.
155
Table 5.20 Bootstrap results to assess weight significance for Model-2a Latent variable CSF
ES Benefits
Manifest variables CSF1.4 CSF1.6 CSF2.1 CSF2.2 CSF2.3 CSF3.2 CSF3.3 CSF4.1 CSF4.4 CSF5.1 CSF5.2 CSF5.4 CSF6.1 CSF6.2 CSF7.2 B1.1 B1.2 B1.3 B1.4 B1.5 B2.1 B2.2 B3.1 B3.2 B3.4 B3.6 B3.7 B3.8 B4.1 B4.2 B4.3 B5.1 B5.2 B5.3 B5.5
Outer weight -0.077 0.717 -0.214 -0.020 0.602 0.166 0.608 -0.566 -0.404 0.372 0.538 1.225 0.464 0.470 0.012 -0.138 0.161 0.334 0.827 0.092 0.304 -0.307 -0.034 -0.634 0.299 -1.025 -0.171 -0.022 0.305 0.584 -0.300 1.090 0.420 0.441 0.258
Outer weight (Bootstrap) -0.049 0.573 -0.182 -0.117 0.498 0.105 0.424 -0.475 -0.319 0.290 0.385 0.914 0.343 0.344 0.032 -0.121 0.103 0.223 0.565 0.093 0.176 -0.255 -0.031 -0.479 0.211 -0.824 -0.134 -0.053 0.242 0.414 -0.197 0.779 0.316 0.335 0.142
Standard error 0.334 0.340 0.332 0.371 0.331 0.399 0.431 0.445 0.461 0.463 0.300 0.386 0.318 0.331 0.340 0.268 0.335 0.262 0.656 0.664 0.278 0.346 0.431 0.247 0.285 0.427 0.382 0.435 0.366 0.374 0.374 0.366 0.316 0.328 0.330
Critical ratio (CR) -0.230 2.110 -0.646 -0.055 1.820 0.415 1.411 -1.272 -0.876 0.803 1.792 3.175 1.456 1.419 0.036 -0.513 0.482 1.274 1.261 0.139 1.092 -0.885 -0.078 -2.566 1.049 -2.401 -0.448 -0.050 0.833 1.564 -0.802 2.980 1.331 1.346 0.783
Lower bound (95%) -0.781 -0.248 -0.895 -1.112 -0.396 -0.726 -0.545 -1.295 -1.147 -0.870 -0.326 -0.249 -0.275 -0.458 -0.831 -0.676 -0.542 -0.384 -1.160 -1.476 -0.503 -0.912 -1.013 -0.970 -0.485 -1.564 -0.813 -1.005 -0.683 -0.540 -0.983 -0.430 -0.448 -0.564 -0.578
Upper bound (95%) 0.681 1.250 0.619 0.634 1.089 1.189 1.248 0.901 0.867 1.069 0.980 1.499 1.068 0.941 0.713 0.546 0.885 0.857 1.646 1.629 0.704 0.632 0.898 0.225 0.769 0.669 0.851 0.796 0.946 1.151 0.540 1.312 0.960 0.945 1.009
Table 5.21 Structural Model Assessments for Model-2a Path
Path Coefficient
t
Pr > |t|
CSF ES Benefits
0.560
11.445
0.000
156
f² (Effect Size) 0.458
R
2
0.314
2
Q (Redundancies) -0.006
0.560**
CSF
ES Benefit R2 = 0.314
ESBenefit = 0.560 * CSF
** significant at the 0.05 level
Figure 5.8 Structural Model Equation Model-2a Results of Second Iteration for the PLS Simple High Item Frequency Model: Model-2b VIF results have shown that all the values are below 3.3 Table 5.22. Also the condition numbers for both constructs are well below 30 that signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995).
Table 5.22 VIF measure to assess multicolinearity for Model-2b Latent Variable
Manifest Variable
CSF (Condition Number = 1.496)
CSF1.4
0.107
0.893
1.119
CSF2.1
0.075
0.925
1.081
CSF2.2
0.150
0.850
1.177
CSF3.2
0.132
0.868
1.152
CSF4.1
0.097
0.903
1.107
CSF4.4
0.145
0.855
1.170
CSF5.1
0.135
0.865
1.156
CSF7.2
0.115
0.885
1.130
B1.1
0.095
0.905
1.104
B1.2
0.105
0.895
1.118
B1.4
0.671
0.329
3.035
B1.5
0.663
0.337
2.968
B2.2
0.078
0.922
1.085
B3.1
0.104
0.896
1.116
B3.7
0.074
0.926
1.080
B3.8
0.131
0.869
1.151
B4.1
0.171
0.829
1.206
B4.3
0.147
0.853
1.173
B5.5
0.099
0.901
1.110
ES Benefits (Condition Number = 3.166)
157
R²
Tolerance
VIF
Bootstrap procedure is used to assess the significance of weight. The results are shown in Table 5.23. CSF items have almost stabilized with only one item CSF5.1 showing non-significance. But again 6 manifest variables of ES Benefits construct B1.2, B2.2, B3.1, B3.7, B4.1 and B5.5 are found non-significant.
Table 5.23 Bootstrap results to assess weight significance for Model-2b Latent variable CSF
ES Benefits
Manifest variables CSF1.4 CSF2.1 CSF2.2 CSF3.2 CSF4.1 CSF4.4 CSF5.1 CSF7.2 B1.1 B1.2 B1.4 B1.5 B2.2 B3.1 B3.7 B3.8 B4.1 B4.3 B5.5
Outer weight 0.518 -0.062 -0.644 -1.058 -0.940 0.272 1.425 -0.636 0.190 0.932 0.835 -0.220 0.959 -0.697 0.338 0.201 -0.821 -0.184 0.941
Outer weight (Bootstrap) 0.309 -0.154 -0.473 -0.764 -0.350 0.153 0.799 -0.252 0.130 0.516 0.475 -0.124 0.446 -0.411 0.185 0.236 -0.404 -0.127 0.541
Standard error 0.692 0.690 0.680 0.667 0.704 0.737 0.625 0.641 0.443 0.549 1.118 1.190 0.612 0.429 0.352 0.920 0.543 0.667 0.504
Critical ratio (CR) 0.750 -0.090 -0.947 -1.586 -1.334 0.368 2.281 -0.992 0.430 1.698 0.748 -0.185 1.568 -1.623 0.960 0.219 -1.513 -0.276 1.866
Lower bound (95%) -1.135 -1.658 -1.631 -2.004 -1.795 -1.329 -0.577 -1.467 -0.628 -0.540 -2.218 -2.246 -1.020 -1.257 -0.668 -1.430 -1.512 -1.349 -0.728
Upper bound (95%) 1.595 1.203 1.291 0.676 1.060 1.727 1.714 0.991 1.193 1.660 2.710 2.425 1.473 0.607 0.916 1.797 0.843 1.102 1.479
In the second iteration the R2 value of the endogenous variable ES Benefits has reduced to 0.129 that is less than 0.19. It puts ES Benefits, a latent variable in the below weak category (Chin, 1998). Secondly the path coefficient that is -0.359. Magnitude has gone down from the Model-2a but significant at 0.05 level. In contrast to first Model-2a, its sign is negative that establishes the negative relationship for the hypothesis C. Thirdly the f2 is 0.148 that is almost equal to 0.15 therefore the predictor latent variable CSF has medium effect at structural level. Finally StoneGeisser‘s Q2 measure for predictive relevance, is below zero therefore disproves the 158
predictive relevance of the model. Overall the results of Model-2b are inferior to Model-2a therefore have eliminated the need to go to the next iteration.
Table 5.24 Structural Model Assessments for Model-2b Path
Path Coefficient
t
Pr > |t|
CSF ES Benefits
-0.359
-6.496
0.000
f² (Effect Size) 0.148
-0.359**
CSF
R
2
2
Q (Redundancies)
0.129
-0.049
ES Benefit R2 = 0.129
ES Benefits = -0.359*CSF
** significant at the 0.05 level
Figure 5.9 Structural Model Equation Model-2b
5.9 Results for PLS Factored Model: Model-3 This model has been developed for studying the relationship of eight CSF subgroups formed in the literature review and has been discussed in research framework and hypothesis section Figure 4.5 and Figure 4.7. XLSTAT Version 2012.6.02 has been used for all the calculation related to PLS Structural Model. For VIF index calculation XLSTAT‘s Multicolinearity statistics routine from Describing Data menu has been utilized. For running the PLS Graph following options have been selected
Treatment of the manifest variables: Standardized, weights on raw MV Initial weights: Values of the first eigenvector Internal estimation: Centroid Regression: OLS Stop conditions: Iterations = 100 / Convergence = 0.0001 159
Confidence intervals: 95 / Bootstrap / Resamplings = 100 / Sample size = 288 Blindfolding: 30 Latent variable scores: Standardized Seed (random numbers): 4206008 Results of First Iteration for the PLS Factored Model: Model-3a VIF results have shown that all the values are below 3.3 except for B1.4 and B1.5 Table 5.25. Nevertheless VIF values for B1.4 and B1.5 are also well below 10 a number that signifies critical colinearity. Also the condition numbers for all the eight CSF constructs and ES Benefits are well below 30 that signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995).
Table 5.25 VIF measure to assess multicolinearity for Model-3a Latent variable ES Ground work
(Condition Number = 1.316)
Minimal Customization
(Condition Number = 1.191) Project Visioning, Planning and Management (Condition Number = 1.283)
People and Organizational Support (Condition Number = 1.727)
Technical Issues and Resources (Condition Number = 1.294)
Manifest variables CSF1.1 CSF1.2 CSF1.3 CSF1.4 CSF1.5 CSF1.6 CSF1.7 CSF2.1 CSF2.2 CSF2.3 CSF3.1 CSF3.2 CSF3.3 CSF3.4 CSF3.5 CSF4.1 CSF4.2 CSF4.3 CSF4.4 CSF4.5 CSF4.6 CSF5.1 CSF5.2 CSF5.3
160
R² 0.244 0.261 0.314 0.220 0.287 0.260 0.266 0.248 0.314 0.284 0.297 0.282 0.222 0.334 0.323 0.344 0.356 0.265 0.414 0.330 0.370 0.368 0.271 0.232
Tolerance 0.756 0.739 0.686 0.780 0.713 0.740 0.734 0.752 0.686 0.716 0.703 0.718 0.778 0.666 0.677 0.656 0.644 0.735 0.586 0.670 0.630 0.632 0.729 0.768
VIF 1.322 1.353 1.458 1.282 1.403 1.351 1.362 1.329 1.459 1.397 1.423 1.393 1.285 1.502 1.478 1.524 1.552 1.361 1.705 1.492 1.588 1.583 1.373 1.302
Table 5.25 VIF measure to assess multicolinearity for Model-3a Continued Latent variable
External Pressure and Support (Condition Number = 1.238)
Change Management
(Condition Number = 1.599)
Assessment and Testing
(Condition Number = 1.191) ES Benefits
(Condition Number = 3.902)
Manifest variables CSF5.4 CSF6.1 CSF6.2 CSF6.3 CSF6.4 CSF7.1 CSF7.2 CSF7.3 CSF7.4 CSF7.5 CSF8.1 CSF8.2 CSF8.3 B1.1 B1.2 B1.3 B1.4 B1.5 B2.1 B2.2 B2.3 B3.1 B3.2 B3.3 B3.4 B3.5 B3.6 B3.7 B3.8 B4.1 B4.2 B4.3 B5.1 B5.2 B5.3 B5.4 B5.5 B5.6
R² 0.331 0.290 0.264 0.301 0.140 0.343 0.354 0.344 0.255 0.265 0.311 0.351 0.326 0.261 0.246 0.230 0.708 0.699 0.261 0.255 0.247 0.298 0.350 0.281 0.251 0.331 0.346 0.501 0.318 0.280 0.221 0.289 0.293 0.335 0.426 0.273 0.280 0.269
Tolerance 0.669 0.710 0.736 0.699 0.860 0.657 0.646 0.656 0.745 0.735 0.689 0.649 0.674 0.739 0.754 0.770 0.292 0.301 0.739 0.745 0.753 0.702 0.650 0.719 0.749 0.669 0.654 0.499 0.682 0.720 0.779 0.711 0.707 0.665 0.574 0.727 0.720 0.731
VIF 1.495 1.408 1.359 1.430 1.163 1.523 1.549 1.525 1.342 1.361 1.451 1.541 1.483 1.353 1.327 1.298 3.425 3.320 1.353 1.342 1.329 1.423 1.539 1.390 1.336 1.495 1.529 2.005 1.467 1.389 1.284 1.407 1.415 1.505 1.741 1.375 1.388 1.367
Bootstrap procedure has been used to assess the significance of weight. The results are shown in Table 5.26. Large numbers of indicators are showing the critical ratio between the lower bound and upper bound hence making them significant. CSF1.6 of ES Ground work, CFS2.3 of Minimal customization, CSF3.2 of Project Visioning, 161
Planning and Management, and CSF5.4 of Technical Issues and Resources are found non-significant. Similarly 9 manifest variables of ES benefit construct B1.3, B1.4, B3.2, B3.3, B3.6, B4.1, B4.2, B5.1 and B5.5 are found non-significant. These nonsignificant indicators has led to second iteration and creation of Model-3b discussed in the next section.
Table 5.26 Bootstrap results to assess weight significance for Model-3a Latent variable
ES Ground work
Minimal Customization Project Visioning, Planning and Management
People and Organizational Support
Technical Issues and Resources
External Pressure and Support Change Management
Manifest variables
CSF1.1 CSF1.2 CSF1.3 CSF1.4 CSF1.5 CSF1.6 CSF1.7 CSF2.1 CSF2.2 CSF2.3 CSF3.1 CSF3.2 CSF3.3 CSF3.4 CSF3.5 CSF4.1 CSF4.2 CSF4.3 CSF4.4 CSF4.5 CSF4.6 CSF5.1 CSF5.2 CSF5.3 CSF5.4 CSF6.1 CSF6.2 CSF6.3 CSF6.4 CSF7.1 CSF7.2 CSF7.3 CSF7.4
Outer weight
-0.053 -0.544 1.575 0.215 0.406 1.384 0.526 -1.060 -0.279 1.869 -0.453 -1.666 -0.480 1.555 2.405 -1.674 1.609 0.339 -0.867 -0.866 3.351 0.071 0.831 0.481 1.807 -0.720 -1.683 1.732 4.605 -0.797 1.112 -0.987 -0.012
Outer weight (Bootstrap) 0.066 -0.365 1.132 0.117 0.237 0.769 0.663 -0.752 -0.400 1.336 -0.305 -1.090 -0.184 0.850 1.408 -0.943 0.276 -0.140 -0.542 -0.691 1.131 -0.046 0.633 0.557 1.416 -0.305 -0.958 1.189 2.511 -0.492 0.769 0.087 0.498
162
Standard error
Critical ratio (CR)
Lower bound (95%)
Upper bound (95%)
1.250 0.930 0.677 0.509 0.969 0.653 0.651 0.821 1.076 0.626 0.975 0.753 0.826 1.907 0.931 0.806 1.549 1.693 0.947 0.783 2.538 0.726 0.632 2.584 0.556 1.262 0.903 1.495 2.996 1.079 0.716 1.578 2.053
-0.042 -0.585 2.327 0.422 0.419 2.121 0.808 -1.292 -0.259 2.984 -0.465 -2.213 -0.581 0.815 2.582 -2.075 1.038 0.200 -0.915 -1.106 1.320 0.098 1.314 0.186 3.252 -0.571 -1.864 1.159 1.537 -0.739 1.552 -0.625 -0.006
-2.731 -1.937 -0.972 -1.008 -2.018 -1.115 -0.927 -2.309 -2.144 -0.648 -1.915 -2.193 -1.778 -2.538 -0.483 -2.294 -3.788 -3.548 -2.205 -2.370 -3.637 -1.723 -1.334 -6.503 -1.009 -2.278 -2.392 -2.348 -3.240 -2.764 -1.249 -3.624 -4.602
2.968 1.979 2.411 1.227 1.835 1.879 2.107 1.369 1.769 2.055 1.582 0.716 1.719 4.123 3.117 0.730 2.830 4.070 1.352 1.094 6.660 1.665 1.760 6.893 2.025 2.221 1.540 3.899 7.881 1.973 1.907 3.569 4.526
Continuedresults to assess weight significance for Model-3a Table 5.26 Bootstrap Latent variable
Assessment and Testing ES Benefits
Manifest variables
CSF7.5 CSF8.1 CSF8.2 CSF8.3 B1.1 B1.2 B1.3 B1.4 B1.5 B2.1 B2.2 B2.3 B3.1 B3.2 B3.3 B3.4 B3.5 B3.6 B3.7 B3.8 B4.1 B4.2 B4.3 B5.1 B5.2 B5.3 B5.4 B5.5 B5.6
Outer weight
3.062 1.026 -2.437 3.461 0.003 0.077 0.254 0.886 -0.245 -0.159 0.141 -0.172 -0.178 -0.648 0.710 0.196 -0.423 -0.747 -0.139 -0.216 0.281 0.309 -0.037 1.121 0.303 0.580 0.326 0.494 0.313
Outer weight (Bootstrap) 1.697 0.651 -1.431 1.984 0.010 0.170 0.042 0.415 -0.031 -0.157 0.131 -0.072 -0.192 -0.417 0.344 0.030 -0.032 -0.362 0.042 -0.084 0.140 0.155 -0.086 0.660 0.273 0.265 0.166 0.286 0.335
Standard error
Critical ratio (CR)
Lower bound (95%)
Upper bound (95%)
1.219 1.236 1.916 1.989 0.301 0.515 0.336 0.534 0.542 0.324 0.443 0.385 0.323 0.464 0.507 0.316 0.623 0.459 0.587 0.370 0.310 0.325 0.375 0.405 0.455 0.551 0.341 0.344 0.520
2.513 0.830 -1.272 1.740 0.011 0.149 0.756 1.660 -0.452 -0.490 0.317 -0.446 -0.551 -1.396 1.400 0.623 -0.678 -1.628 -0.236 -0.582 0.904 0.952 -0.098 2.766 0.666 1.054 0.955 1.435 0.603
-1.547 -2.432 -3.984 -3.194 -0.652 -0.989 -0.681 -0.572 -0.998 -0.920 -0.747 -0.929 -0.850 -1.194 -0.808 -0.846 -1.475 -1.238 -1.160 -0.892 -0.576 -0.555 -0.824 -0.437 -0.903 -0.968 -0.613 -0.440 -0.840
3.921 2.862 3.440 5.993 0.623 1.139 0.595 1.486 1.176 0.497 0.970 0.881 0.541 0.808 1.240 0.636 1.323 0.738 1.279 0.709 0.892 0.744 0.697 1.402 1.197 1.087 0.980 1.077 1.320
The R2 value of the endogenous variable, ES Benefits is 0.365 slightly higher than 0.33. It puts ES Benefits, a latent variable slightly higher than moderate category (Chin, 1998). Secondly all path coefficients except Project Visioning, Planning and Management ES Benefits (hypothesis C3 not supported) are significant at 0.05 level Table 5.30. But out of 7 significant paths only 5 are having positive signs meaning Hypotheses C1, C2, C4, C5 and C7 are fully supported Figure 4.7. And negative relationship is established for External Pressure and Support ES Benefits 163
(C6) and Assessment and Testing ES Benefits (C8). Thirdly the f2 is varying between 0.019 and 0.100 that is between almost 0.02 and 0.15 and making the effect size of all the predictor variables weak to medium at structural level. Finally StoneGeisser‘s Q2 measure for predictive relevance, is below zero therefore disproves the predictive relevance of the model.
Table 5.27 Structural Model Assessments for Model-3a Path (Hypothesis)
ES Ground work ES Benefits (C1) Minimal Customization ES Benefits (C2) Project Visioning, Planning and Management ES Benefits (C3) People and Organizational Support ES Benefits (C4) Technical Issues and Resources ES Benefits (C5) External Pressure and Support ES Benefits (C6) Change Management ES Benefits (C7) Assessment and Testing ES Benefits (C8)
Path Coefficient
t
Pr > |t|
f² (Effect Size)
0.231
4.711
0.000
0.080
0.133
2.657
0.008
0.025
-0.093
-1.856
0.064
0.012
0.197
4.016
0.000
0.058
0.263
5.288
0.000
0.100
-0.146
-2.989
0.003
0.032
0.141
2.893
0.004
0.030
-0.112
-2.297
0.022
0.019
R
2
0.365
2
Q (Redund ancies)
-0.001
Results of Second Iteration for the PLS Factored Model: Model-3b VIF results have shown that all the values are below 3.3 Table 5.28. Moreover all VIF values are also well below 10 a number that signifies critical colinearity. Also the condition numbers for all the eight CSF constructs and ES Benefits are well below 30 that signifies the absence of any colinearity (Hair, Anderson, Tatham, & Black, 1995).
164
Impact and contribution of the variables to ES Benefits 0.3
100
0.25 80
Path coefficients
0.15 0.1
60
0.05 0
40
-0.05 -0.1
Contribution to R² (%)
0.2
20
-0.15 -0.2
0
Latent variable Path coefficient
Cumulative %
Figure 5.10 Impact and contribution of the variables to ES Benefits: Model-3a Table 5.28 VIF measure to assess multicolinearity for Model-3b Latent variable ES Ground work
(Condition Number = 1.242)
Minimal Customization
(Condition Number = 1.159) Project Visioning, Planning and Management (Condition Number = 1.226) People and Organizational Support (Condition Number = 1.727)
Technical Issues and Resources (Condition Number = 1.107) External Pressure and Support (Condition Number = 1.238)
Manifest variables CSF1.1 CSF1.2 CSF1.3 CSF1.4 CSF1.5 CSF1.7 CSF2.1 CSF2.2 CSF3.1 CSF3.3 CSF3.4 CSF3.5 CSF4.1 CSF4.2 CSF4.3 CSF4.4 CSF4.5 CSF4.6 CSF5.1 CSF5.2 CSF5.3 CSF6.1 CSF6.2
165
R² 0.230 0.229 0.287 0.171 0.198 0.215 0.203 0.264 0.276 0.185 0.307 0.286 0.309 0.332 0.249 0.393 0.312 0.342 0.313 0.239 0.201 0.261 0.224
Tolerance 0.770 0.771 0.713 0.829 0.802 0.785 0.797 0.736 0.724 0.815 0.693 0.714 0.691 0.668 0.751 0.607 0.688 0.658 0.687 0.761 0.799 0.739 0.776
VIF 1.298 1.298 1.403 1.206 1.246 1.273 1.254 1.360 1.382 1.227 1.443 1.400 1.447 1.496 1.331 1.649 1.454 1.521 1.455 1.313 1.252 1.352 1.288
Continued Table 5.28 VIF measure to assess multicolinearity for Model-3b Latent variable
Change Management
(Condition Number = 1.599)
Assessment and Testing
(Condition Number = 1.191) ES Benefits
(Condition Number = 2.193)
Manifest variables CSF6.3 CSF6.4 CSF7.1 CSF7.2 CSF7.3 CSF7.4 CSF7.5 CSF8.1 CSF8.2 CSF8.3 B1.1 B1.2 B1.5 B2.1 B2.2 B2.3 B3.1 B3.4 B3.5 B3.6 B3.7 B3.8 B4.3 B5.2 B5.3 B5.4 B5.6
R² 0.249 0.115 0.303 0.329 0.312 0.243 0.238 0.257 0.329 0.280 0.223 0.229 0.187 0.224 0.236 0.201 0.249 0.220 0.238 0.279 0.456 0.252 0.202 0.254 0.394 0.235 0.241
Tolerance 0.751 0.885 0.697 0.671 0.688 0.757 0.762 0.743 0.671 0.720 0.777 0.771 0.813 0.776 0.764 0.799 0.751 0.780 0.762 0.721 0.544 0.748 0.798 0.746 0.606 0.765 0.759
VIF 1.332 1.130 1.435 1.490 1.454 1.321 1.312 1.345 1.490 1.388 1.286 1.296 1.229 1.289 1.309 1.251 1.331 1.282 1.312 1.387 1.837 1.336 1.253 1.341 1.651 1.307 1.318
Bootstrap procedure has been used to assess the significance of weight. The results are shown in Table 5.29. Large numbers of indicators are showing the critical ratio between the lower bound and upper bound hence making them significant. CFS2.2 of Minimal Customization, and CSF5.1 of Technical Issues and Resources are found non-significant. Similarly 4 manifest variables of ES Benefits construct B1.2, B2.2, B3.7 and B5.2 are found non-significant.
166
Table 5.29 Bootstrap results to assess weight significance for Model-3b Latent variable
ES Ground work
Minimal Customization Project Visioning, Planning and Management People and Organizational Support
Technical Issues and Resources External Pressure and Support Change Management
Assessment and Testing ES Benefits
Manifest variables
CSF1.1 CSF1.2 CSF1.3 CSF1.4 CSF1.5 CSF1.7 CSF2.1 CSF2.2 CSF3.1 CSF3.3 CSF3.4 CSF3.5 CSF4.1 CSF4.2 CSF4.3 CSF4.4 CSF4.5 CSF4.6 CSF5.1 CSF5.2 CSF5.3 CSF6.1 CSF6.2 CSF6.3 CSF6.4 CSF7.1 CSF7.2 CSF7.3 CSF7.4 CSF7.5 CSF8.1 CSF8.2 CSF8.3 B1.1 B1.2 B1.5 B2.1 B2.2 B2.3 B3.1 B3.4 B3.5 B3.6 B3.7
Outer weight
1.380 -0.852 1.860 0.369 1.065 1.020 0.190 2.092 2.037 0.560 2.317 0.564 1.329 -0.309 1.385 0.767 0.476 2.726 -1.852 0.602 2.137 1.750 1.308 0.316 4.281 0.046 1.093 1.035 2.921 1.540 0.502 3.419 0.425 0.193 1.115 0.379 -0.235 0.786 0.223 -0.435 -0.119 -0.015 0.662 1.126
Outer weight (Bootstrap) 1.072 -0.775 1.308 0.189 0.585 0.619 0.675 1.503 1.366 0.601 1.592 0.569 0.635 0.198 0.644 0.467 0.629 1.234 -1.157 0.709 1.547 1.230 0.840 0.705 0.988 0.351 0.716 0.311 1.118 0.800 0.498 2.067 -0.234 0.040 0.478 0.198 -0.211 0.425 0.156 -0.277 -0.028 -0.115 0.175 0.560
167
Standard error
Critical ratio (CR)
Lower bound (95%)
Upper bound (95%)
1.115 1.065 0.872 0.674 0.788 0.930 1.273 0.728 0.800 0.898 1.795 1.221 0.758 1.914 1.687 1.099 0.715 2.524 0.854 0.969 3.139 0.890 0.901 1.671 2.924 0.956 0.726 1.540 2.151 1.775 1.038 1.287 2.933 0.381 0.545 0.436 0.374 0.481 0.472 0.501 0.432 0.769 0.579 0.561
1.238 -0.800 2.134 0.547 1.352 1.098 0.149 2.876 2.546 0.624 1.291 0.462 1.753 -0.161 0.821 0.698 0.666 1.080 -2.168 0.622 0.681 1.966 1.453 0.189 1.464 0.048 1.505 0.672 1.358 0.868 0.484 2.656 0.145 0.506 2.045 0.871 -0.628 1.633 0.472 -0.869 -0.275 -0.019 1.143 2.007
-1.407 -2.973 -1.300 -1.349 -1.044 -1.717 -2.220 -0.561 -0.554 -1.420 -2.961 -2.315 -1.280 -3.330 -3.228 -1.879 -0.918 -4.013 -2.071 -1.868 -6.690 -1.267 -1.758 -2.787 -7.427 -1.854 -1.055 -2.730 -4.475 -3.086 -1.642 -1.732 -5.244 -0.833 -0.799 -0.574 -0.886 -0.765 -0.817 -1.125 -0.869 -1.459 -0.956 -0.721
2.843 1.839 2.712 1.788 2.060 2.230 2.471 2.170 2.705 2.336 4.471 2.936 1.838 4.244 4.158 2.372 2.301 5.595 1.014 1.993 8.860 2.388 2.461 3.669 8.599 2.240 1.903 3.805 4.978 3.787 2.841 4.039 4.569 1.055 1.304 1.310 0.583 1.387 1.101 0.944 0.904 1.447 1.328 1.636
Table 5.29 Bootstrap Continuedresults to assess weight significance for Model-3b Latent variable
Manifest variables
B3.8 B4.3 B5.2 B5.3 B5.4
Outer weight
0.101 -0.218 0.906 -0.487 0.008
Outer weight (Bootstrap) 0.051 -0.043 0.560 -0.167 0.053
Standard error
Critical ratio (CR)
Lower bound (95%)
Upper bound (95%)
0.495 0.580 0.443 0.498 0.407
0.204 -0.375 2.046 -0.978 0.021
-1.077 -1.192 -0.582 -1.094 -0.744
0.993 1.214 1.455 0.878 0.787
The R2 value of the endogenous variable, ES Benefits is 0.274 lower than 0.33. It puts ES Benefits, a latent variable lower than moderate category (Chin, 1998). Secondly 5 path coefficients are significant at 0.05 level Table 5.30. All the significant paths are having positive signs that meaning Hypotheses C1, C3, C4, C6 and C8 are fully supported Figure 4.7. And hypotheses C2, C5 and C7 are not supported. The relative Table 5.30 Structural Model Assessments for Model-3b Path (Hypothesis)
ES Ground work ES Benefits (C1) Minimal Customization ES Benefits (C2) Project Visioning, Planning and Management ES Benefits (C3) People and Organizational Support ES Benefits (C4) Technical Issues and Resources ES Benefits (C5) External Pressure and Support ES Benefits (C6) Change Management ES Benefits (C7) Assessment and Testing ES Benefits (C8)
Path Coefficient
t
Pr > |t|
f² (Effect Size)
0.292
5.485
0.000
0.108
0.034
0.626
0.532
0.001
0.148
2.675
0.008
0.026
0.196
3.516
0.001
0.044
0.093
1.732
0.084
0.011
0.139
2.663
0.008
0.025
0.066
1.205
0.229
0.005
0.143
2.611
0.010
0.024
R
2
0.274
2
Q (Redund ancies)
-0.011
contribution of these variables can be seen from the Figure 5.11. Thirdly the f2 is varying between 0.024 and 0.108 that is between almost 0.02 and 0.15 and making the 168
effect size of all the predictor variables weak to medium at structural level. Finally Stone-Geisser‘s Q2 measure for predictive relevance, is below zero therefore disproves the predictive relevance of the model. The value of R2 has gone down therefore need to go for further iteration has been eliminated.
Impact and contribution of the variables to ES Benefits 100
Path coefficients
0.3
80
0.25 0.2
60
0.15
40
0.1 20
0.05 0
Contribution to R² (%)
0.35
0
Latent variable Path coefficient
Cumulative %
Figure 5.11 Impact and contribution of the variables to ES Benefits: Model-3b
5.10 Conclusion In order for the better understanding the two phases first dealing with ES adoption reasons and ES Benefits study, and the second dealing with CSF and its relationship with the ES Benefits study have been defined. Demographic of the cases tells that among others only Microsoft and SAP together account for 84% of the cases and Indian ES vendors Ramco Systems, 3i Infotech, and ESS together account for only 4.5% of cases. Moreover 62% organizations are manufacturing and 54% organizations are large in size. Legacy System Replacement or IT Architectural Improvements, Integration of Systems or processes, Data or Information issues, and Operational Improvements (cost, employee, cycle time reductions) are top ranking 169
adoption reasons in the order given. Whereas Globalization Support, Organizational Change, and External Forces happen to fall in the lower ranks and last being the External Forces.
Similarly the top five CSF sub-factors are Consultant selection and relationship, Implementation strategy and timeframe, System Integration, Selection of ERP, and Technical Task and Tools. Whereas Project champion, Empowered decision makers, Interdepartmental Cooperation, Multi-site issues, and IT provider and Integrator Push happens to be lowest ranking factors, last being IT provider and Integrator Push. Similarly the top five ES Benefits are Quality improvement, Customer service improvement, Improved decision making and planning, Building cost leadership, and Cycle time reduction. Whereas bottom five ES Benefits are Performance improvements, Building common vision, Increase employee morale and satisfaction, Generating product differentiation, and Building business innovation and last being Building business innovation. Nevertheless all the 74 variables identified in literature review have marked the presence in Indian organizations.
Only Organizational Change has been found to be significantly more important for large organizations among all 12 adoption reason and all of them are indifferent to the industry. Improved decision making and planning, Support business alliance, and Generating or sustaining competitiveness are significantly more visible in large organizations. Whereas Quality improvement, and Building cost leadership benefits are more relevant in manufacturing organizations and Enabling worldwide expansion is significantly relevant in non-manufacturing organizations.
Three sets of models have been developed to study the relationship between CSF and ES Benefits and their predictive relevance. Model-1a, Model-1b and Model-1c are the 170
three iterations of the most simplified model where all the CSF sub-categories have been mapped to single CSF construct. Model-2a and Model-2b are the iterations of same conceptual models as the first one with the exception that only high frequency items having frequency more than 20% in the cases have been considered. Model-3a and Model-3b have been developed using multiple constructs of CSF as categorized in the literature review section and mapped with their CSF sub-categories. The first two sets of models developed to establish the hypothesis C, have yielded the positive results and the third set of models developed to establish hypotheses C1 to C8, have also yielded positive results. Hence they established the relationships between CSF and ES Benefits. Although none of the models have established the predictive relevance.
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Chapter Six CONCLUSIONS AND RECOMMENDATIONS 6.1 Introduction This is a most essential chapter that summarizes the results of this research and put it in the form so that future studies can take benefit from it. It starts with laying the foundation from the results of earlier research. Next it summarizes the findings of this study and follows it with a discussion of the findings in the light of the results of earlier research. PLS Models have been summarized in the next section. Managerial implications of the results of the study have been elaborated to help the practitioners make use of this study. Finally the limitations of the study along with the future scope of the research and conclusion have been given.
6.2 Foundation and Results of Earlier Research Adoption Reasons This research has studied the motives for adoption present in different countries through literature review. The literature review section through content analysis has listed all 12 motivational factors and defined them. Operational improvement and legacy system replacement have had equal occurrences of 22 and ranked at position one and two. Similarly business growth or extension and data or information issues have had equal occurrences of 17 and placed in third and fourth position. Regulatory and compliance issues and organizational change have happened to appear 13 times ranked accordingly. Integration of Systems or processes, standardization and best practices and globalization support have been quoted 12, 11 and 9 times respectively. Competition and customer and supplier intimacy was seen 8 times and finally the external forces factor has been spotted 6 times Table 3.2.
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Three research papers out of twelve are global in selection of samples for their study. And other papers are limited to single country. These countries are Canada, Sweden, US, Greece, Global, Thailand, Italy, Korea, Finland, Bahrain. Therefore all these factors can be said more or less present in many the countries.
Adoptions reasons for smaller organizations are generally the subset of larger organizations (Markus & Tanis, 2000). Mabert, Soni, & Venkataramanan (2003) have studied the variation of adoption reasons with respect to small, medium and large sizes. Larger firms are more concerned about adoption reasons such as Ease of upgrading systems, Simplify and standardize systems, Gain strategic advantage, and Link to global activities and less about Solve the Y2K Problem.
Laukkanen, Sarapola, & Hallikainen (2007) have found that large and medium sized organizations are interested in pursuing the value chain integrations through the ES adoption than the smaller organizations. Other adoption reasons identified are more or less uniform across all sizes. Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini (2005) have studied the variation of adoption drivers with respect to SMEs and large companies Figure 6.1. Five factors are showing large variation among SMEs and large companies. HW/SW obsolescence and Unsatisfying order management drivers are more prevalent in SMEs. On the other hand Unsatisfying process integration, Forced decision (by a controlling company), and Dissimilarity of procedures (i.e. rules on quality management) drivers are more relevant in the case of large companies.
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50 45 40 35 30 25 20 15 10 5
Percentage of cases (SMEs) Dissimilarity of procedures
Unsatisfying time-to-market
CRM issues
Over-dimensioning of stock
Other reasons
High cost of data distribution
Logistics and transportation issues
Forced decision (by a controlling company)
Lack of flexibility
Limited support to decisions
Data redundancy and/or inconsistency
Unsatisfying order management
Y2K issue
Unsatisfying process integration
Euro issue
HW/SW obsolescence
0 Percentage of cases (large compaies)
Figure 6.1 Technological and operational drivers (90 SMEs and large enterprises adopting ES system) Source: (Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini, 2005) ES Benefits In the literature review section the analysis of articles has shown that I/S success model (DeLone & McLean, 1992), IS-Impact model (Gable, Sedera, & Chan, 2008) and ERP benefit framework (Shang & Seddon, 2002) have been developed more comprehensively. These works also have made often basis for other studies. Whereas exp-ben-taxonomy (Schubert & Williams, 2009) and three dimensional benefit framework (Eckartz, Daneva, Wieringa, & Hillegersberg, 2009) seems most
174
promising ongoing efforts in this area. In this research work Shang and Seddon‘s (2002) ERP benefit framework has been chosen.
Koh, Gunasekaranb, & Rajkumar (2008) in their study have used the same ES benefit framework in the context of ERP II. They have used 21 benefit items that is 4 less than the originally proposed by Shang and Seddon (2002). They reduced two items enabling worldwide expansion and generating or sustaining competitiveness from strategic benefits, and shifting workfocus and increased employee morale and work satisfaction from organizational benefit. They have confirmed that the same benefits can be carried forward in the ERPII and their intensity also increases. They have also found that there exists a significant difference among the implementers, parent users and suppliers on the perception of benefits. Staehr (2007) has used Shang and Seddon‘s (2000) benefit framework on four Australian manufacturing organizations. He has confirmed all dimensions and categories of Shang and Seddon‘s (2000) benefit framework. Benefits realization has varied from extensive for one, substantial for one and limited for two organizations. There is time ordering of benefits such as IT infrastructure, followed by operational and managerial and followed by organizational and strategic benefits. Among all the IT infrastructure benefits, IT cost reduction hasn‘t shown early results and has been very illusive in most of the organizations. Financial time reductions an operational benefit has been achieved earlier. Managerial benefits due to standard reports have been achieved relatively earlier than the one that required ad hoc reports. Organizational benefits have varied widely across four organizations. Companies have achieved strategic benefits after some passage of time and only two of the four have reported substantial strategic benefits.
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Deloitte-Consulting (1998) surveyed 164 individuals at 62 Fortune 500 companies. Based on the responses this study mentions the expected capabilities upon completion of ERP program and anticipated benefits of ERP program. Organizations had been expecting to improve efficiency, effectiveness and transformational capabilities. Anticipated benefits largely fall under cost reductions and faster time cycles. Tangible benefits such as inventory reduction, personal reduction, productivity improvement, Order Management/Improvements, Financial close cycle reduction, IT cost reduction, procurement cost reduction, cash management improvement, revenue/profit increase, logistics cost reduction, maintenance reduction, and on-time delivery improvement are realized in the decreasing order of strength once the system is live. The order of strength of intangible benefits post go-live is information/visibility, new improved processes, customer responsiveness, cost reduction, integration, standardization, flexibility, globalization, Y2K, business performance and Supply/Demand Chain.
O'Leary (2004) study has made the basis of study of Deloitte-Consulting (1998). His results are consistent with the study of Deloitte-Consulting (1998) and all tangible and intangible benefits shown in the previous paragraph are supported. In addition he finds additional intangible benefits Acquisitions, New Reports/Reporting Capability, Sales Automation Change Business Model/Competitive Advantage, Growth, Financial Controls, Better Decisions, Leverage size, Increased Time for Analysis, NO Redundant Data Entry, Reduced Training with Transfer, and Speed. He has also statistically tested the difference of these benefits vis-à-vis manufacturing and SW/HW (Software/Hardware) industry. All the tangible benefits have been uniform across industry except Inventory Reduction that has relevance to manufacturing firms. Whereas many intangible benefit categories have varied. New Processes, Cost Reduction, Integration, and Y2K are more emphasized by the manufacturing firms, 176
and Flexibility and Globalization were given more emphasis by SW/HW firms. For additional intangible categories Better Decision, No Redundant Data and Speed were more realized in manufacturing firms, and Acquisitions and New Reports/Reporting Capability were realized more in SW/HW firms. CSF of ES projects The literature review section gives the details of 23 research articles that have been considered for the compilation of the CSF sub-factors identified in the previous studies Table 3.3. These studies can be classified as theoretical and empirical. Among the empirical studies some studies are global and some are country specific belonging to Canada, China, Japan, Malaysia, Mexico and USA. Maximum number of studies belongs to USA that is 5. Number of CFSs reported in these studies varies from 4 to 28. Total of 262 items found in these studies has led to the identification of 37 disticnt CSF sub-factors. Their ranking is is given in table Table 3.4.
Top management commitment and support , Training and job redesign, BPR
and
software configuration, Project management, Balanced Team, Communication plan, Change management , Visioning and planning, and IT are among the top ranking factors and having frequency count more than 10. Team morale and motivation, Multi-site issues, Country-related functional requirements, Value
Chain
Connectivity, IT provider and Integrator Push, and Stakeholder Pressures have had frequency count of only one. Troubleshooting/crises management, Empowered decision makers, and System Integration have had the frequency of 2 and Interdepartmental Cooperation, and Technical Task and Tools have had the frequency of 3 only. Study has been carried retaining all the CSF sub-factors in order to maintain
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the holistic nature of the study. Though, study has also been carried on high frequency items found in Indian cases.
Managing this long list per say is very difficult. Therefore they have been grouped into fewer categories and hence forming the 8 CSF catgories for the current study with 37 items explaining them in more details Table 3.6. These categories are ES Ground work, Minimal Customization, Project Visioning, Planning and Management, People and Organizational Support, Technical Issues and Resources, External Pressure and Support, Change Management, and Assessment and Testing.
6.3 Summary of Findings Adoption Reasons Four factors Legacy System Replacement or IT Architectural Improvements, Integration of Systems or processes, Data or Information issues, and Operational Improvements have been cited by more than 75% of the total companies in India. Almost two thirds of the companies have expressed Standardization and best practices to be one of the adoption reasons. Business Growth or Extensions has also been cited by more than 50% of the organizations. Customer and Supplier Intimacy, Competition, Regulatory and Compliance Issues, Globalization Support and Organizational Change has seemed to be less important motivators. External factors such as government support, push from parent organization and endorsement from partners hasn‘t influenced much in the adoption decision. The Pearson‘s chi-square statistical test has been applied to see the significance of variation of these factors among Indian companies vis-à-vis size and industry. Results have shown no relationship between all 12 factors and size and industry except one in case of size of the organizations. In case of SMEs organizational change has been 178
significantly less important motivational factor than in case of large enterprises. The number of large enterprises showing this factor has been almost double of SMEs. This research further has asserted that all these factors are globally present. ES Benefits After detailed literature review the ES benefit framework of Shang and Seddon (2002) has been chosen for the comprehensiveness, simplicity and nature of the data (Customer Success Stories). The fact cannot be denied that IT helps in operational improvements and has been found in this study as well. All the items of operational improvements have shown presence in almost more than 60% of the cases. Managerial benefit, improved decision making and planning have been present in 80% of the cases and better resource management has also been present in more than 50 percent of the cases. On other hand performance improvement has been present only in 18% percent of the cases.
Among the strategic benefits, only building cost leadership has scored high with a frequency of 69.8%. The rest of the 7 strategic benefits have shown frequency of 40% or below. Among the IT infrastructure benefit, increased capability has been cited by 56.6% of the organizations. And the other two benefits building business flexibility at lower cost and IT cost reduction have shown presence of almost 30%. Organizational benefits have had the weakest presence with highest frequency of only 42.7% for empowerment and the rest of the organizational benefits have had the frequency below 25%. The Pearson‘s chi-square statistical test has been applied to see the significance of variation of these benefits among Indian companies vis-à-vis size and industry. Improved decision making and planning has been found to be significantly related 179
with the size of the organization. Out of 288, 79.9% organizations have witnessed the benefit of improved decision making and planning with slight variation vis-à-vis size such that 74.4% SMEs and 84.5% large enterprises. Support business alliance benefit has also been significantly related with size of the organization. Out of 288, 35.4% organizations cited the benefit of support business alliance with large variation with respect to size of the organization such that 27.1% SMEs and 42.6% large enterprises. Generating or sustaining competitiveness benefit has also been significantly related with size of the organization. Out of 288, merely 22.2% organizations have shown the presence for the benefit of generating or sustaining competitiveness with large variation between SMEs and large enterprises such that 15.0% and 28.4% respectively. Remaining 22 benefits have been uniform across both the sizes of organizations.
Quality improvement has been found to be significantly related with the industry of the organization. Out of 288, a large percentage 80.9% of organizations have witnessed the benefit of quality improvement with small difference vis-à-vis industry such that 87.2% Manufacturing and 77.1% Non-Manufacturing organizations. Building cost leadership benefit has also been significantly related with industry of the organization. Out of 288, 69.8% organizations have cited the benefit of building cost leadership with large variation with respect to industry of the organization such that 80.7% Manufacturing and 63.1% others. Enabling worldwide expansion benefit has also been significantly related with industry of the organization. Out of 288, merely 27.8% organizations have shown the presence for the benefit of enabling worldwide expansion with small variation between Manufacturing and NonManufacturing such that 21.1% and 31.8% respectively. Remaining 22 benefits have been uniform across manufacturing and non-manufacturing organizations. 180
CSF of ES projects Marking the presence of CSF has been very intricate because of the slight nuances in their meanings. The idea to take all the variables has been to bring out the complete picture of the implementation process in the Indian companies. Though cases haven‘t provided the complete information of the implementation process and hence accounted for less number of CSFs. Nevertheless the presence of all the CSFs have been ascertained Table 5.4. Though only 5 out of 37 CSFs have been found in more than 50% of the cases namely, consultant selection and relationship, implementation strategy and timeframe, system integration, selection of ES and, technical task and tools. Whereas 8 CSFs Managing cultural change, Team morale and motivation, Postimplementation evaluation, Project champion, Empowered decision makers, Interdepartmental Cooperation, Multi-site issues, and IT provider and Integrator Push have been found in less than 5% of the cases.
6.4 Discussion ES Adoption Reasons Three of the top four motivations to adopt ES, Legacy System Replacement or IT Architectural
Improvements,
Data or
Information issues, and
Operational
Improvements (cost, employee, cycle time reductions) identified in this study have been consistent with the literature. And motivations such as globalization support, and external forces have shown weakest presence that is consistent with the literature. Indian organizations have shown stronger motivation for Integration of Systems or processes, and Customer and Supplier Intimacy through their ES than their global counterparts. Moreover Indian organizations have shown weaker motivation to pursue Business Growth or Extensions, and Globalization Support through their ES.
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The motivations to adopt ES haven‘t varied with respect to size or industry of the organizations. Every organization irrespective of their size and industry has the uniform aspirations from their ES project. Though there has been one exception to this and that is motivation for the organizational change through the ES with respect to size. Smaller organizations have been less concerned about organizational change and hence have shown weaker motivation to adopt ES for the same and this has been consistent with the results of Buonanno, Faverio, Pigni, Ravarini, Sciuto, & Tagliavini (2005). ES Benefits All the benefits of Shang and Seddon‘s (2002) ES benefit framework have been verified that is consistent with the previous studies of Staehr (2007) and Koh, Gunasekaranb, & Rajkumar (2008). Koh, Gunasekaranb, & Rajkumar (2008) have asserted usage of the ES benefit framework for the ERP II environment that further has supported the present study since the cases selected have had both ERP and ERPII implementations.
Murphy & Simon (2002) have defined the tangibility and quantifiability of the Shang and Seddon‘s (2002) ES benefit framework with Full, Most, Some and Low categories. Organizational benefits have been classified with low tangibility and quantifiability that is consistent with this study and have shown the weakest presence in the Indian organizations. Managerial benefit, Improved decision making and planning has been way ahead than resource management and performance improvements. Which has been consistent with results of Staehr (2007) and those require ad-hoc reporting. IT cost reduction has also been proved to be illusive in the present study and has been consistent with the previous studies (Staehr, 2007).
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Operational benefits have the strongest presence as expected and consistent with the study of Staehr (2007). Finally among the strategic benefits only building cost leadership has shown the strongest presence and rest have been very weak. The study of Staehr (2007) has asserted late realization of strategic benefits.
ES Benefits variations with respect to the size of the organization and industry of the organization is non-significant for the majority of the benefit items. Only Improved decision making and planning, Support business alliance, and Generating or sustaining competitiveness benefits have been more realized by the large organization in comparison to the SMEs. The first one is managerial benefit and more realized by large organizations. And the last two benefits are strategic benefit meaning larger organizations are realizing more strategic benefits than the SMEs.
Variation with respect to industry has also been limited to only three benefit categories. Here building cost leadership and quality improvement have shown more presence in the case of manufacturing organizations. That is consistent with the manufacturing industry norm to offer best quality at the lowest cost. Nonmanufacturing organizations have realized more benefit of enabling worldwide expansion from their ES implementations. CSF of ES projects From Figure 6.2, it is clear that most of the CSF sub-categories have shown weaker presence in comparison to the literature. This is due to the fact that Customer Success Stories have focused more on ES Benefits and Adoption reasons. They have talked less about the process of implementation. 11 CSF sub-categories Consultant selection and relationship (10: CSF6.2), Selection of ERP (12: CSF2.3), Data conversion and integrity (13: CSF5.2), Implementation strategy and timeframe (15: CSF3.2), Vanilla 183
ERP (26: CSF2.1), Technical Task and Tools (28: CSF5.4), System Integration (31: CSF1.6), Team morale and motivation (32: CSF7.4), Country-related functional requirements (34: CSF1.4), Value Chain Connectivity (35: CSF1.5), and Stakeholder Pressures (37: CSF6.3) have shown stronger presence in the Indian cases in comparison to global results reported in the literature.
1: CSF4.1 37: CSF6.3 2: CSF7.2 100.0% 36: CSF6.4 3: CSF2.2 35: CSF1.5 4: CSF3.5 90.0% 34: CSF1.4 5: CSF4.5 80.0% 33: CSF5.3
70.0%
6: CSF3.4
60.0%
32: CSF7.4
7: CSF7.1
50.0% 40.0%
31: CSF1.6
8: CSF3.1
30.0% 30: CSF4.2
9: CSF5.1
20.0% 10.0%
29: CSF8.2
10: CSF6.2
0.0%
28: CSF5.4
11: CSF4.4
27: CSF4.6
12: CSF2.3
26: CSF2.1
13: CSF5.2
25: CSF3.3
14: CSF1.7
24: CSF1.3
15: CSF3.2
23: CSF7.5 22: CSF6.1 21: CSF8.1 20: CSF8.3
16: CSF4.3 17: CSF7.3 18: CSF1.2 19: CSF1.1
CSF frequency in literature
CSF frequency in Indian Cases
Figure 6.2 CSF sub-factors frequency in literature and Indian Cases
6.5 Discussion of PLS Models One of the distinct features of this research has been to analyze the relationship between CSF and ES Benefits utilizing a very simplified approach PLS structural modeling technique. It is remarkable in the sense that it relieves the data with 184
stringent statistical assumptions about distributions such as multivariate normal. And it can handle data of nominal nature such as binary data.
Three models have been developed Model-1, Model-2 and Model-3. Model-1 is the simplest model and maps all the 37 CSF sub-factors to the single CSF latent variable and all the 25 ES benefits dimensions to ES Benefits and hence named as a PLS Simple Model. Model-2 takes only those CSF sub-factors and ES benefits into consideration that have the presence frequency more than 80% in the 288 cases and hence named as a PLS High Item Frequency Model. Therefore only 15 CSF subfactors and 20 ES benefits dimensions have been associated with latent constructs CSF and ES Benefits.
Model-3 has mapped the path between the factored CSFs to the ES Benefits. In the literature review section the CSF sub-factors have been categorized into 8 factors. Therefore the 37 CSF sub-factors have been loaded onto one of the eight CSF factors and all the 25 ES benefit dimensions have been loaded onto single latent dependant variable ES Benefits. Model-1 has been iterated three times resulting in Model-1a, Model-1b and Model1-c. Similarly Model-2 and Model-3 have been iterated twice each and have resulted in Model-2a and Model-2b, and Model-3a and Model-3b respectively.
All the constructs have been formative in this research. Formative constructs are assessed differently for reliability and validity in comparison to reflective constructs. For formative constructs, at the abstract level theoretical support (nomological validity) is mandatory, and at an empirical level item weight significance and assessment of multicolinearity among the indicators of latent constructs is sufficient. In PLS the coefficient of determination R2 is measured to see whether model is 185
capable of explaining the exogenous or dependent variable. The paths are verified for strengths, signs and statistical significance and effect size of the independent variable. And redundancies Q2 are checked for the predictive relevance of the model. PLS Simple Model (Model-1) and PLS Simple High Item Frequency Model (Model-2) All the five models given in Table 6.1 have been assessed for VIF and haven‘t shown any significant sign of multiclinearity. But all the models do have non-significant items being loaded onto the latent variables. The Table 6.1 shows the total items counts and non-significant items counts that have been associated with a latent variable in the parenthesis under the path column.
Table 6.1 Assessments of Model-1 and Model-2 PLS Models Model-1a Model-1b Model-1c Model-2a Model-2b
Path (no. of nonsignificant items / no. of total items) CSF(10/37) ES Benefits(7/25) CSF(4/27) ES Benefits(6/18) CSF(3/23) ES Benefits(2/12) CSF(7/15) ES Benefits(9/20) CSF(1/8) ES Benefits(6/11)
Path Coeffic ient -0.647
t
Pr > |t|
-14.363
0.548
R
2
2
0.000
f² (Effect Size) 0.721
0.419
Q (Redund ancies) -0.0123
11.086
0.000
0.430
0.301
-0.018
0.478
9.205
0.000
0.296
0.229
-0.052
0.560
11.445
0.000
0.458
0.314
-0.006
-0.359
-6.496
0.000
0.148
0.129
-0.049
The 17 non-significant items (10 for CSF and 7 for ES Benefits) in Model-1a has distorted the theoretically supported model and has given the negative path coefficient. And Model-1c hasn‘t improved from Model-1b after removing the 10 (4 for CSF and 6 for ES Benefits) non-significant items. Rather the strength of path coefficient, effect size and R2 has deteriorated. Moreover the redundancies (Q2) statistic to gauge the predictive relevance has also become more negative. Therefore
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Model-1b is optimum model that has supported significantly the hypothesis C that the CSF is positively related with ES Benefits.
Now speaking about the simple high item frequency models Model-2a and Model-2b. Model-2a has been clearly better than Model-2b. Since the sign of the path has become negative, strength has gone down, effect size of the independent variable has also decreased and coefficient of determination R2 has also reduced. Moreover the redundancies measure Q2 has become more negative and hence has further reduced the relevancy for the prediction of model.
Therefore Model-1b and Model-2a have been optimum model and proving the hypothesis that CSF is positively related with ES Benefits. Surprisingly the results of both models have been pretty closed for all the measurements though the numbers of items associated with CSF and ES Benefits have been different in each case. Six CSF sub-factors Country-related functional requirements, Vanilla ERP, BPR and software configuration, Implementation strategy and timeframe, Project team: the best and brightest and, Training and job redesign have been common indicators for the CSF construct in both the models out of 27 and 15 indicators respectively.
Similarly 14 items of ES Benefits Cost reduction, Cycle time reduction, Quality improvement, Customer service improvement, Better resource management, Improved decision making and planning, Support business growth, Building cost leadership, Enabling e-commerce, Generating or sustaining competitiveness, Building business flexibility at lower cost, Increase IT infrastructure capability, Facilitating business learning and broaden employee skills, Empowerment have been common indicators for ES Benefits construct out of 18 and 20 indictors respectively.
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PLS Factored Model: Model-3 To understand better the CSF phenomenon and its effects on ES Benefits, CSF subfactors are categorized into 8 categories. These categories represent diverse dimension of the context of ES project. These categories are ES Ground work, Minimal Customization, Project Visioning, Planning and Management, People and Organizational Support, Technical Issues and Resources, External Pressure and Support, Change Management, and Assessment and Testing. These exogenous latent variables have been linked to ES Benefits an endogenous variable Figure 4.5 and Figure 4.7. Two iterations have been run on XLSTAT software that have resulted in Model-3a and Model-3b.
Table 6.2 Assessments of Model-3a and Model-3b Path Coeffi cient
t
Pr > |t|
f² (Effect Size)
ES Ground work ES Benefits (C1)
a: 1/7 b: 0/6 a: 1/3 b: 1/2 a: 1/5 b: 0/4
0.231 0.292 0.133 0.034 -0.093 0.148
4.711 5.485 2.657 0.626 -1.856 2.675
0.000 0.000 0.008 0.532 0.064 0.008
0.080 0.108 0.025 0.001 0.012 0.026
a: 0/6 b: 0/6
0.197 0.196
4.016 3.516
0.000 0.001
0.058 0.044
0.263 0.093
5.288 1.732
0.000 0.084
0.100 0.011
a: 0/4 b: 0/4
-0.146 0.139
-2.989 2.663
0.003 0.008
0.032 0.025
a: 0/5 b: 0/5 a: 0/3 b: 0/3
0.141 0.066 -0.112 0.143
2.893 1.205 -2.297 2.611
0.004 0.229 0.022 0.010
0.030 0.005 0.019 0.024
Minimal Customization ES Benefits (C2) Project Visioning, Planning and Management ES Benefits (C3) People and Organizational Support ES Benefits (C4) Technical Issues and Resources ES Benefits (C5) External Pressure and Support ES Benefits (C6) Change Management ES Benefits (C7) Assessment and Testing ES Benefits (C8)
a: 1/4 b: 1/3
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R
2
Q
2
a: 0.365 b: 0.274 a: -0.001 b: -0.011
non-significant item count / total items count IV DV
a: 9/25 b: 4/16
Path (Hypothesis)
Both models given in Table 6.2 have been assessed for VIF and haven‘t shown any significant sign of multiclinearity. But both the models have had non-significant items being loaded on the latent variables. The Table 6.2 shows the total item counts and non-significant item counts that are associated with a latent variable.
The non-significant factors have been very less in both the models for CSF factors 4 and 2 respectively. ES Ground work, Minimal Customization, Project Visioning, Planning and Management, and Technical Issues and Resources have had one nonsignificant factor each in Model-3a. In Model-3b Minimal Customization, and Technical Issues and Resources have had one non-significant indicator each. But the dependent variable ES Benefits has had 9 and 4 non-significant indicators in Model3a and Model-3b respectively. Overall Model-3a has been better than Model-3b since the coefficient of determination R2 is larger for the first model and also the redundancies measure Q2 is also less negative than the second model. In Model-3a 7 hypotheses have been supported at .05 level though three have shown negative association. On the other hand Model-3b has supported 5 hypotheses fully.
ES Ground work formatively defined by CSF sub-categories Organizational characteristics, Legacy system consideration, Client consultation, Country-related functional requirements, Value Chain Connectivity, System Integration, and Build a business case has been supported for positive association in both models Table 6.2. Though, the strength and effect size have been more in second model Figure 6.3 and Figure 6.4.
Minimal Customization formatively defined by CSF sub-categories Vanilla ERP, BPR and software configuration, and Selection of ERP has been supported for
189
positive association in Model-3a only Table 6.2. Though, the strength and effect size have been more or less medium comparing other factors Figure 6.3 and Figure 6.4.
0.35 0.3 0.25
0.292
0.263
0.231
0.2 0.133
0.15
0.197 0.196 0.148
0.139 0.141 0.093
0.1
0.066
0.034
0.05
0.143
Model-3a Testing and Assessment
-0.146
Change Management
External Pressure and Support
Technical Issues and Resources
-0.2
-0.093
People and Organizational Support
-0.15
Project Visioning, Planning and Management
-0.1
Minimal Customization
-0.05
ES Ground work
0
Model-3b -0.112
Figure 6.3 Path coefficients for Model-3a and Model-3b between CSF factors and ES Benefits 0.12 0.1
0.108
0.1
0.08
0.08
0.058 0.044
0.06 0.04
0.025
0.02
0.001
0.032 0.025 0.03
0.026 0.012
0.011
0.019 0.024
0.005 Model-3a Testing and Assessment
Change Management
External Pressure and Support
Technical Issues and Resources
People and Organizational Support
Project Visioning, Planning and Management
Minimal Customization
ES Ground work
0
Model-3b
Figure 6.4 Effect Size of Predictor variables such as CSF factors on ES Benefits for Model-3a and Model-3b 190
Project Visioning, Planning and Management formatively defined by CSF subcategories Visioning and planning, Implementation strategy and timeframe, Project cost planning and management, Communication plan, and Project management has been supported for positive association in Model-3b Table 6.2. Though, the strength and effect size have been more or less medium comparing other factors Figure 6.3 and Figure 6.4.
People and Organizational Support formatively defined by CSF sub-categories Top management commitment and support, Empowered decision makers, Project champion, Project team: the best and brightest, Balanced team, and Interdepartmental Cooperation has been supported for positive association in both models with same strength Table 6.2. Though, the effect size has been relatively larger in first model Figure 6.3 and Figure 6.4.
Technical Issues and Resources formatively defined by CSF sub-categories IT infrastructure, Data conversion and integrity, Multi-site issues, and Technical Task and Tools has been supported for positive association in Model-3a only Table 6.2. The strength and effect size have been maximum for this path in Model-3a Figure 6.3 and Figure 6.4.
External Pressure and Support formatively defined by CSF sub-categories Vendor Support, Consultant selection and relationship, Stakeholder Pressures, and IT provider and Integrator Push has been supported for negative association in Model-3a and positive association in Model-3b Table 6.2. The strength of the path has been on the medium side comparing other factors but the effect size has been on the lower side for both the models Figure 6.3 and Figure 6.4.
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Change
Management
formatively defined
by CSF
sub-categories
Change
management, Training and job redesign, Managing cultural change, Team morale and motivation, and Expectation Management has been supported for positive association by Model-3a. The strength of the path has been medium comparing other factors and the effect size has been relatively on the lower side.
Assessment and Testing formatively defined by CSF sub-categories System testing, Troubleshooting/crises management, and Post-implementation evaluation has been supported for negative association in Model-3a and positive association in Model-3b Table 6.2. The strength of the path has been on the medium side comparing other factors but the effect size has been on the lower side for both the models Figure 6.3 and Figure 6.4.
Model-3a is superior to the Model-3b as has been stated earlier. But results from both the models have given interesting outcome. ES Ground work and People and Organizational Support are supported for positive association by both the models. Minimal Customization, Technical Issues and Resources and Change Management are supported for positive association by Model-3a. And remaining factors Project Visioning, Planning and Management, External Pressure and Support, and Assessment and Testing are supported by Model-3b. In other words looking at the theoretical base of all the 8 factors and results of these two models, it can be said that all the CSF factors are positively related with the ES Benefits.
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6.6 Managerial Implications Adoption Motivations Adoption motivations are a good starting point to see the need for the ES. Presence of adoption motivations should also ring the alarm for the change. Following 12 motivations for the adoption of ES have been identified in the literature review.
i. ii. iii. iv. v. vi. vii. viii. ix. x. xi. xii.
Operational Improvements (cost, employee, cycle time reductions) Legacy System Replacement or IT Architectural Improvements Business Growth or Extensions Data or Information issues Regulatory and Compliance Issues Organizational Change Integration of Systems or processes Standardization and best practices Globalization Support Competition Customer and Supplier Intimacy External Forces
All these factors have been found to have relevance in Indian context. Moreover they haven‘t varied much with respect to the size and industry of the organizations. However some factors such as Legacy System Replacement or IT Architectural Improvements, Data or Information issues, and Operational Improvements (cost, employee, cycle time reductions) have been more frequent. And motivations such as globalization support, and external forces have been less frequent. Moreover Indian organizations have shown stronger motivation for Integration of Systems or processes, and Customer and Supplier Intimacy through their ES than their global counterparts and weaker motivation to pursue Business Growth or Extensions, and Globalization Support through their ES than their global counterparts.
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ES Benefits Success of the ES is dependent upon the benefits realized from them. Therefore metrics have to be defined from the very onset of the project and assessed periodically. For the purpose of this study Shang & Seddon‘s (2002) ES Benefit Framework has been chosen. ES literature has shown that ES benefits vary among various dimensions such motives of ES adoption, business functions, sites, time, industry and size of the organization. The operational benefits that address the automate dimension of ES have been more tangible and quantifiable and show up relatively earlier. The managerial benefits which correspond to informate dimension of ES have been less tangible and quantifiable than the first and show up more or less along with operational benefits. The strategic benefits that represent the transformate dimension of the ES have been even lesser tangible and quantifiable than the first two and show up much later post-implementation of ES may take years to be realized. It‘s been increasingly observed that the IT cost reductions though projected and perceived have not been realized. Nevertheless ES has brought in many other IT infrastructure benefits such as flexibility to support change in business dynamics and increase in IT infrastructure capability. The organizational benefits have been least tangible in nature and most difficult to quantify and have found lowest frequency of occurrences among Indian Organizations.
This study has also gauged the variation of the benefits with respect to size such as large and SMB, and industry such as manufacturing and non-manufacturing. Larger organizations have benefited more in the dimensions of improved decision making and planning, support for business alliance, and generating or sustaining competitiveness. On the other hand manufacturing organizations have benefited more
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in the dimensions of quality improvement, building cost leadership, and nonmanufacturing organizations have benefited more in enablement of worldwide expansion. Critical Success Factors The critical success factors are defined as ―the limited number of areas in which results, if they are satisfactory, will ensure successful competitive performance for the organization‖ (Rockart, 1979). The implementation of the ES has been considered very resource draining and risky endeavor. Literature have shown if not managed properly it has led to drastic consequences. Therefore researchers have tried to study the implementation process with the help of CSF approach and have emphasized the observance of these factors for the positive outcome of the ES Project. 37 CSF subfactors have been identified from the content analysis of 23 research articles and have been categorized into eight categories.
Using PLS structural modeling technique this research has proved the hypothesis that CSF is positively related with ES Benefits. The results from simple models have shown the presence of six common CSF sub-factors Country-related functional requirements, Vanilla ERP, BPR and software configuration, Implementation strategy and timeframe, Project team: the best and brightest and, Training and job therefore have made them more important than the others.
In addition to the simplified model this study has also factored the CSF sub-factors into fewer categories relevant in the context of ES implementation so as to help the practitioners to monitor them more efficiently and effectively. Following is the list of those factors and for details of their sub-factors refer to Table 3.6
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i. ii. iii. iv. v. vi. vii. viii.
ES Ground work Minimal Customization Project Visioning, Planning and Management People and Organizational Support Technical Issues and Resources External Pressure and Support Change Management Testing and Assessment
ES Ground work and People and Organizational Support have been supported for positive association with ES Benefits by both factored models. Minimal Customization, Project Visioning, Planning and Management, Technical Issues and Resources, External Pressure and Support, and Change Management, and Testing and Assessment have been supported for positive relationship with the ES Benefits by one or the other model. Therefore the observance of these factors should lead to a positive outcome of ES project. Nevertheless none of the models have established the predictive relevance for the ES benefits using CSF or factored CSFs as predictor variable.
6.7 Limitations and Future Research This research has relied on the data reported by the software vendors or e-magazines in other words secondary data. Moreover simply near exhaustive listing of all the cases has been done whatsoever available on the internet. Future research may develop a questionnaire on the basis of these factors and collect primary data on scale of ratio or interval data.
Moreover the cases reported by the vendors, consultant or E-magazines haven‘t illustrated the process of implementation in detail. Therefore has limited the study of implementation process or identification of CFS. Therefore future research may make use of more uniform and balanced cases such as eXperience database
196
(www.experience-online.eu) utilized by Schubert and Williams (2009) for better results.
The size of the organization may further be classified as micro, mini, small, medium and large to understand more closely the motivation of Enterprise Systems adoption and benefits realization in Indian companies. For the assessment of ES benefits Shang and Seddon‘s (2000) benefit framework has been used that doesn‘t consider the variation of benefits with motivation for ES, time, business functions and sites (Staehr, 2007). Therefore future studies may take these dimensions into consideration.
6.8 Conclusion The content analysis and inductive coding of 12 research articles has yielded 12 adoption motivations for ES. Similarly the clustering of CSF sub-factors from 23 research article has yielded 37 CSF sub-factors. These sub-factors have been grouped into 8 CSF categories. Further the literature review on ES usage, benefits or success has ended up in identifying the ES Benefit Framework of Shang and Seddon (2002) to measure the benefits of ES which contains 25 benefit items grouped into five categories. Therefore together they make a total of 74 variables.
The analysis of the 288 Indian cases written as success stories has resulted into the binary data showing the presence and absence of 74 variables. As for motivations to adopt the ES more than half of the 12 variables have shown consistency with the global results. The inconsistent ones are Integration of Systems or processes, and Customer and Supplier Intimacy, higher on ranking, and Business Growth or Extensions, and Globalization Support, lower on ranking in comparison to their global
197
counterparts. The variations of these factors vis-à-vis size and industry has not been significant except for organizational change and that is consistent with previous studies. Only organizational change has been more concern area for large organizations than the SMBs.
All the ES Benefits of Shang and Seddon (2002) have been verified in the Indian organizations. Operational benefits, and managerial benefits that don‘t require ad-hoc reporting have the strongest presence in the Indian companies. IT infrastructure benefits has also been achieved with the exception of sizable IT cost reduction. Organizational benefits have been least tangible and have shown weakest presence in Indian organization. Apart from Building cost leadership, all the strategic benefits have shown weaker presence. Only Improved decision making and planning, Support business alliance, and Generating or sustaining competitiveness benefits have been more realized by the large organization in comparison to the SMEs. Building cost leadership and quality improvement has shown more presence in the case of manufacturing organizations. Non-manufacturing organizations have realized more benefit of enabling worldwide expansion from the ES. The results of ES Benefits have been in agreement with the previous studies.
Most of the CSF sub-categories have shown weaker presence in comparison to the literature. This has been due to the fact that Customer Success Stories focus more on ES Benefits and Adoption reasons. They talk less about the process of implementation. 11 CSF sub-categories Consultant selection and, Selection of ERP, Data conversion and integrity, Implementation strategy and timeframe, Vanilla ERP, Technical Task and Tools, System Integration, Team morale and motivation, Country-related functional requirements, Value Chain Connectivity, and Stakeholder
198
Pressures have shown stronger presence or higher frequency in Indian cases in comparison to global results reported in literature review.
PLS path modeling method has been utilized to study the association between CSF and ES Benefits. PLS method allows for formative constructs and works with nominal data with less distributional assumption. As has been anticipated that the more the CSF observed better would be the chance to gain benefits from the ES that in turn may make the ES project successful. The results of the PLS structural Models have shown the positive results for all the hypotheses C and C1 – C8. Three sets of models been generated refer to Figure 4.6 for first and second set of models, and Figure 4.7 for third set of models. Second set is same as first except that the items that have frequency less than 20% have been dropped. Optimum models from the first two sets have shown the presence of six common sub-factors Country-related functional requirements, Vanilla ERP, BPR and software configuration, Implementation strategy and timeframe, Project team: the best and brightest and, Training and job therefore have made them more important than the others.
Among the categorized CSF ES Ground work, and People and Organizational Support have been supported for positive association with ES benefits by both factored models, the third set of models. Minimal Customization, Project Visioning, Planning and Management, Technical Issues and Resources, External Pressure and Support, and Change Management, and Testing and Assessment have been supported for positive relationship with the ES benefits by one or the other model. Therefore observance of these factors should lead to positive outcome of ES project. Nevertheless none of the models have established the predictive relevance for the ES Benefits using CSF or factored CSFs as predictor variable.
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Bibliography
Aladwani, A. (2001). Change management strategies for successful ERP implementation. Business Process Management Journal , 7, 266. Allen, D. (1997). Multisite implementation: Special strategies. APICS 1997 International Conference Proceedings, (pp. 551–555). Church, VA. Al-Mashari, M. (2002). Enterprise resource planning (ERP) systems: a reserch agenda. Industrial Management & Data Systems , 102 (3), 165-170. Al-Mashari, M., & Zairi, M. (2000). The effective application of SAP R/3: a proposed model of best practice. Logistics Information Management , 13 (3), 156166. Al-Mashari, M., Al-Mudimigh, A., & Zairi, M. (2003). Enterprise resource planning: A taxonomy of critical factors. European Journal of Operational Research , 146, 352–364. Al-Mudimigh, A., & Y., J. (2001). An Integrated Approach to ERP Implementation – A Proposed Methodology. European Conference on Information Technology Evaluation, (pp. 277-286). Arinze, B., & Anandarajan, M. (2003). A Framework for using OO mapping methods to rapidly configure ERP systems. Communications of the ACM , 46 (2), 61-65. Arunthari, S., & Hasan, H. (2005). A Grounded Study of ERP Adoption and Vendor Selection in Thailand. Proceeding of 16th Australasian Conference on Information Systems (ACIS 2005 Proceedings), (p. Paper 3). Sydney. Arunthari, S., & Hasan, H. (2005). A Grounded Study of ERP Adoption and Vendor Selection in Thailand. Australasian Conference on Information Systems (ACIS). Sydney. Athreye, S. S. (2005). The Indian software industry and its evolving service capability. Industrial and Corporate Change , 14 (3), 393-418. Axelsson, K., & Avdic, A. (2001). IT Management during Acquisition of ERPsystems – Experiences from Goal Dialogues at two Companies. 8th European Conference on IT Evaluation, (pp. 293-301). Oxford, United Kingdom. Babaian, T., Lucas, W., & Topi, H. (2004). Collaborating to Improve ERP Usability. Proceedings of ICEIS, (pp. 164-168). 200
Bagozzi, R. P., & Phillips, L. W. (1982). Representing and Testing Organizational Theories: A Holistic Construal. Administrative Science Quarterly , 27 (3), 459489. Bajwa, D. S., Garcia, J. E., & Mooney, T. (2004). An Integrative framework for the assimilation of Enterprise Resource Planning Systems: Phases, Antecedents and Outcomes. Journal f Computer Information Systems , 44 (3), 81-90. Barker, T., & Frolick, M. N. (Fall2003). ERP implementation failure: a case study. Information Systems Management Journal , 20 (4), 43-49. Beard, J. W., & Sumner, M. (2004). Seeking strategic advantage in the post-net era: viewing ERP systems from the resource-based perspective. Journal of Strategic Information Systems , 13 (2), 129-150. Becker, J., & Knackstedt, R. (2003). Requirements Definition for Enterprise Information Portals: An Integrated Method for Specifying Quantitative and Qualitative Information Needs. Ninth Americas Conference on Information Systems. Behrens, S., & Sedera, W. (2004). Why do shadow systems exist after an ERP implementation? lessons from a case study. proceedings of the Eighth Pacific Asia Conference on Information Systems, (pp. 153-166). Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case researh strategy in studies of Information Systems. MIS Quarterly , 369-386. Bernroider, E., & Koch, S. (2001). ERP selection process in midsize and large organizations. Business Process Management Journa , 7 (3), 251-257. Bingi, P., Sharma, M. K., & Godla, J. K. (1999). Critical Issues Affecting an ERP Implementation. Information Systems Management , 16 (3), 7-14. binti Ismail, R. I., binti Abd Hamid, R., & Idris, F. (2012). PLS Application in Journal of Operations Management: A Review. Global Conference on Operations and Supply Chain Management Proceeding. Bandung, Indonasia. Boersma, K., & Kingma, S. (2005). Developing a cultural perspective on ERP. Business Process Management Journal , 11 (2), 123-136. Brehm, N., Gomez, J. M., & Rautenstrauch, C. (2005). An ERP solution based on web services and peer-to-peer networks for small and medium enterprises. International Journal of Information Systems and Change Management , 99-111.
201
Buonanno, G., Faverio, P., Pigni, F., Ravarini, A., Sciuto, D., & Tagliavini, M. (2005). Factors affecting ERP system adoption: A comparative analysis between SMEs and large companies. Journal of Enterprise Information Management , 18 (4), 384-426. Burn, J., & Ash, C. (2005). A dynamic model of e-business strategies for ERP enabled organistions. Industrial Management & Data Systems , 105 (8), 10841095. Byrd, T. A., & Turner, D. E. (2000). Measuring the Flexibility of Information Technology Infrastructure: Exploratory Analysis of a Construct. Journal of Management Information Systems , 17 (1), 167 - 208. Calvert, C., & Carroll, J. (2005). Training for ERP: Does the IS Training Literature Have Value? Australasian Conference on Information Systems (ACIS). Cantu, R. (1999). A framework for implementing enterprise resource planning systems in small manufacturing companies. San Antonio: Master‘s Thesis, St. Mary‘s University. Carr, N. G. (2003). IT Doesn't Matter. Harvard Business Review , 81 (5), 41-49. Carton, F., & Adam, F. (2005). Understanding the Impact of Enterprise Systems on Management Decision Making: An Agenda for Future Research. The Electronic Journal of Information Systems Evaluation , 8 (2), 99-106. Chand, D., Hachey, G., Hunton, J., Owhoso, V., & Vasudevan, S. (2005). A balanced scorecard based framework for assessing the strategic impacts of ERP systems. Computers in Industry , 56, 558–572. Chang, K.-c., Gold, A., & Kettinger, W. (2003). The Extent of Enterprise System Adoption in Companies: A Multiple Theoretical Perspective. Americas Conference on Information Systems (AMCIS). Chang, S.-I. (2004). ERP Life Cycle Implementation, Management and Support: Practice and Research. Proceedings of the 37th Hawaii International Conference on System Sciences. Chanopas, A., Krairit, D., & Khang, D. B. (2006). Managing information technology infrastructure: a new flexibility framework. Management Research News , 29 (10), 632-651.
202
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides, Modern Methods for Business Research (pp. 295–358). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (1996). A partial least squares latent variable modelling approach for measuring interaction effects: Results from a Monte Carlo simulation study and voice mail emotion/adoption study. 17th International Conference on Information. Cleveland, OH. Chou, H.-W., Jiang, J., & Wang, E. (2004). Charismatic Leadership Style, Intragroup Conflict, And ERP Implementation Team Performance. Pacific Asia Conference on Information Systems (PACIS). Chung, S. H., & Synder, C. A. (2000). ERP adoption: a technological evolution approach. International Journal of Agile Management Systems , 2 (1), 24-32. CII. (2012). The Indian Cloud Revolution. Confederation of Indian Industries. Curran, T. A., Keller, G., & Ladd, A. (1998). SAP R/3 Business Blueprint: Understanding the Business Process Reference Model. Englewood Cliffs, NJ: Prentice-Hall. Davenport, T. H. (1993). Process Innovation: Reengineering Work Through Information Technology. Boston, MA: Harward Business School Press. Davenport, T. H. (July-August 1998). Putting the enterprise into the enterprise system. Havard Business Review , 76 (4), 121-131. Davenport, T. H., & Brooks, J. D. (2004). Enterprise systems and the supply chain. Journal of Enterprise Information Mangement , 17 (1), 8-19. Davenport, T. H., & Short, J. E. (1990). The New Industrial Engineering: Information Technology and Business Process Redesign. Sloan Management Review , 11-27. Davenport, T. H., Harris, J. G., & Cantrell, S. (2004). Enterprise systems and ongoing process change. Business Process Management Journal , 10 (1), 16-26. Davison, R. (2002). Cultural complications of ERP. Communications of the ACM , 7, 109. Davison, R. (2002). Cultural Complications of ERP. Communications of the ACM , 45 (7), 109-111.
203
Deloitte-Consulting. (1998). ERP's Second Wave-Maximizing the Value of ERPEnabled Processes. New York: Deloitte Consulting. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research , 3 (1), 60-95. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems , 19 (4), 9-30. Dembla, P. (1999). Enterprise resource planning: business drivers and critical success factors. Singapore: SAP Asia Pacafic. Desai, S. (2012, May 7). Opportunities For Partners In ERP. Retrieved Oct 6, 2012, from Computer Reseller News: http://www.crn.in/Software-007May012Opportunities-For-Partners-In-ERP.aspx Diamantopoulos, A. (1994). Modelling with LISREL:Aguide for the uninitiated. Journal of Marketing Management , 10, 105–136. Diamantopoulos, A., & Siguaw, J. A. (2006). Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. British Journal of Management , 17 (4), 263–282. Dibbern, J., Brehm, L., & Heinzl, A. (2002). Rethinking ERP-Outsourcing Decisions for Leveraging Technological and Preserving Business Knowledge. Proceedings of the 35th Hawaii International Conference on System Sciences. Doll, W. J., & Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction. MIS Quarterly , 259-273. Dong, L. (2001). Modeling top management influence on ES implementation. Business Process Management Journal, , 7 (3), 243-250. Dorp, C. v. (2002). Extending ERP with Recipe and Material Traceability. Eighth Americas Conference on Information Systems. Dorp, C. v. (2002). Induction of a reference data model for tracking and tracing. Eighth Americas Conference on Information Systems. Dreiling, A., Klaus, H., Rosemann, M., & Wyssusek, B. (2005). Open Source Enterprise Systems: Towards a Viable Alternative. Proceedings of the 38th Hawaii International Conference on System Sciences.
204
Ebrahim, N. A., Ahmed, S., & Taha, Z. (2009). Virtual R & D teams in small and medium enterprises: A literature review. Scientific Research and Essays , 4 (13), 1575-1590. Eckartz, S., Daneva, M., Wieringa, R., & Hillegersberg, J. v. (2009). A Conceptual Framework for ERP Benefit Classification: A Literature Review. Centre for Telematics and Information Technology, University of Twente. Ehie, I. C., & Madsen, M. (2005). Identifying critical issues in enterprise resource planning (ERP) implementation. Computers in Industry , 56, 545–557. ERP implementation in the Mid Market Segment. PriceWaterHouseCoopers. ERP's Second Wave-Maximizing the Value of ERP-Enabled Processes. (2000). Retrieved June 10, 2009, from www.dc.com/Insights/research Esteves, J., & Bohorquez, V. (2007). An updated ERP systems annotated bibliography: 2001-2005. Communication of the Association for Information Systems , 19, 386-446. Esteves, J., Casanovas, J., & Pastor, J. (2003). Modeling with Partial Least Squares Critical Success Factors Interrelationships in ERP Implementations. Ninth Americas Conference on Information Systems, (pp. 446-451). Finney, S., & Corbett, M. (2007). ERP implementation: a compilation and analysis of critical success factors. Business Process Management Journal , 13 (3), 329-347. Fitz-Gerald, L., & Carroll, J. (2003). The Role of Governance in ERP System Implementation. Australasian Conference on Information Systems (ACIS). Fornell, C., & Bookstein, F. L. (1982). Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory. Journal of Marketing Research , 19 (4), 440-452. Frank, L. (2001). Electronic Commerce Using Distributed ERP-Systems with Approximated ACID Properties. Proceedings of the 34th Hawaii International Conference on System Sciences. Freeze, R. D., & Raschke, R. L. (2007). An assessment of formative and reflective constructs in IS research. Proceedings of the 15th European Conference on Information Systems (ECIS2007), (pp. 1481-1492). St. Gallen.
205
Gable, G. G., Sedera, D., & Chan, T. (2008). Re-conceptualizing information system success: the IS-Impact Measurement Model. Journal of the Association for Information Systems , 9 (7), 377-408. García-Sánchez, N., & Pérez-Bernal, L. E. (2007). Determination of critical success factors in implementing an ERP system: A field study in Mexican enterprises: View From Practice. Information Technology for Development , 13 (3), 293-309. Gargeya, V. B., & Brady, C. (2005). Success and failure factors of adopting SAP in ERP system implementation. Business Process Management Journal , 11 (5), 501 - 516. Gartner. (2012). Gartner Says India Will Be The Worlds Fastest Growing Enterprise Software Market through 2016. Retrieved 10 13, 2012, from http://www.gartner.com/it/page.jsp?id=2180615 Gefen, D., Straub, D. W., & Boudreau, M.-C. (2000). Structural Equation Modeling and Regression: Guideline for research practice. Communication of the Association for Information Systems , 4, 1-79. Gormoloski, S. B., Grigg, T., & Potter, K. (2001). 2001 IT Spending and Staffing Survey Results. Stamford, Connecticut: Gartner. Haenlein, M., & Kaplan, A. M. (2004). A beginner‘s guide to partial least squares analysis. Understanding Statistics , 3 (4), 283–297. Hair Jr., J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research Methods for Business. West Sussex, UK: Wiley. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis : with readings. Englewood Cliffs, New Jersey: Prentice-Hall. Hakkinen, L., & Hilmola, O.-P. (2008). Life after ERP implementation: long term development of user perceptions of system success in an after-sales environment. Journal of Enterprise Information Management , 21 (3), 285-309. Hawking, P. (2007). Implementing ERP systems globally: Challenges and lessons. Journal of Business systems, Governance and Ethics , 2 (1), 21-32. He, L., & Brown, D. (2005). The Adoption of ERP Applications in China. Americas Conference on Information Systems (AMCIS). He, X. (2004). The ERP challenge in China: a resource-based perspective. Information Systems Journal , 14 (2), 153–167. 206
Hedman, J., & Kalling, T. (2002). The Business Model: A Means to Comprehend the Management and Business Context of Information and Communication Technology. European Conference on Information Systems (ECIS), (pp. 148-162). Gdańsk, Poland. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In Advances in International Marketing (pp. 277-319). Bingley: Emerald JAI Press. Ho, C.-F., Wu, W.-h., & Tai, Y.-M. (2004). Strategies for adaptation of ERP systems. Industrial Management & Data Systems , 104 (3), 234-251. Holland, C. P., & Light, B. (1999). A critical success factor model for ERP implementation. IEEE Software , 16 (3), 30-36. Huang, J., Newell, S., Galliers, R., & Pan, S. (2001). ERP and Knowledge Management Systems: Managerial Panaceas orSynergetic Solutions? Americas Conference on Information Systems. Huntona, J. E., Lippincott, B., & Reck, J. L. (2003). Enterprise resource planning systems: comparing firm performance of adopters and nonadopters. International Journal of Accounting Information Systems , 4, 165–184. Jacobs, R., & Bendoly, E. (2003). Enterprise resource planning: Developments and directions for operations management research. European Journal of Operational Research , 146 (2), 5-12. Jafari, S., Osman, M., Yusuff, R., & Tang, S. (2006). ERP systems implementation in Malasiya: The importance of critical success factors. International Journal of Engineering and Technology , 3 (1), 125-131. Jarrar, Y. F., Al-Mudimigh, A., & Zairi, M. (2000). ERP implementation critical success factors - the role and impact of business process management. Proceedings of the 2000 IEEE International Conference on Management of Innovation and Technology. Singapore. Jones, M. (2001). Organizational Knowledge Sharing and ERP: An Exploratory Assessment. Americas Conference on Information Systems (AMCIS). Jones, M., & Price, R. L. (2001). Organizational Knowledge Sharing in ERP Implementation: A Multiple Case Study Analysis. International Conference on Information Systems (ICIS), (pp. 551-554).
207
Kamhawi, E. M. (2008). Enterprise resource-planning systems adoption in Bahrain: motives, benefits, and barriers. Journal of Enterprise Information Management , 21 (3), 310-334. Kaplan, R. S., & Norton, D. P. (1993, September-October). Putting the Balanced Scorecard to Work. Harvard Business Review , 134-147. Karimi, j., Somers, T. M., & Bhattacherjee, A. (Fall 2007). The role of information systems resources in ERP capability building and business process outcomes. Journal of Management Information Systems , 24 (2), 221-260. Katerattanakul, P., Hong, S., & Lee, J. (2006). Enterprise resource planning survey of Korean manufacturing firms. Management Research News , 29 (12), 820-837. Ke, W., & Wei, K. K. (2005). Organizational Culture and Leadership in ERP Implementation. Pacific Asia Conference on Information Systems (PACIS). Ke, W., Wei, K.-K., Chau, P., & Deng, Z. (2003). Organizational Learning in ERP Implementation: An Exploratory Study of Strategic Renewal. Americas Conference on Information Systems (AMCIS). Klaus, H., Rosemann, M., & Gable, G. G. (2000). What is ERP? Information Systems Frontiers , 2 (2), 141-162. Ko, D.-G., Kirsch, L. J., & King, W. R. (2005). Antecedents of Knowledge Transfer from Consultants to Clients in Enterprise System Implementations. Management Information Systems Quarterly , 29 (1), 59-85. Koch, C. (2001). BPR and ERP: realizing a vision of process with IT. Business Process Management Journal , 7 (3), 258-265. Koh, S., Gunasekaranb, A., & Rajkumar, D. (2008). ERP II: The involvement, benefits and impediments of collaborative information sharing. International Journal of Production Economics , 113, 245–268. Koh, S., Simpson, M., Padmore, J., Dimitriadis, N., & Misopoulos, F. (2006). An exploratory study of enterprise resource planning adoption in Greek companies. Industrial Management & Data Systems , 106 (7), 1033-1059. Kraemmerand, P., Moller, C., & Boer, H. (2003). ERP implementation: An integrated process of radical change and continuous learning. Production Planning & Control , 14 (4), 228-248.
208
Kumar, V., Maheshwari, B., & Kumar, U. (2002). Enterprise resource planning systems adoption process: a survey of Canadian organizations. International Journal of Production Research , 40 (3), 509-523. Laframboise, K. (2002). Business Performance and Enterprise Resource Planning. European Conference on Information Systems (ECIS). Laukkanen, S., Sarapola, S., & Hallikainen, P. (2007). Enterprise size matters: objectives and constraints of ERP adoption. Journal of Enterprise Information Management , 20 (3), 319-334. Lee, J.-C., & Myers, M. (2004). Enterprise Systems Implementation Failure: The Role of Organizational Defensive Routines. Pacific Asia Conference on Information Systems (PACIS). Legare, T. L. (2002). The role of organizational factors in realizing ERP benefits. Information Systems Management , 19 (4), 21-42. Leon, A. (2009). Enterprise Resource Planning. New Delhi, India: Tata McGraw Hill. Light, B., & Papazafeiropoulou, A. (2004). Reasona Behind ERP Package Adoption: A Diffusion of Innovations Perspective. ECIS 2004 Proceedings, (p. Paper 80). Light, B., Holland, C. P., & Wills, K. (2001). ERP and best of breed: a comparative analysis. Business Process Management Journal , 7 (3), 216 - 224. Lyytinen, K., & Hirschheim, R. (1987). Information systems failures––a survey and classification of the empirical literature. In Oxford Surveys in Information Technology (pp. 257-309). New York, NY: Oxford University Press. Mabert, V. A., Soni, A., & Venkataramanan, M. (2003). The impact of organizationsize onen terprise resource planning (ERP) implementations in the US manufacturing sector. Omega , 31 (3), 235– 246. Madhavan, T., & Theivananthampillai, P. (2005). Business Process Re-design in Enterprise Systems Projects: Radical and Evolutionary Change. Americas Conference on Information Systems (AMCIS). Malhotra, N. K. (2007). Marketing Research: An Applied Orientation (5th ed.). Delhi, India: Pearson Education, Inc. Mandal, P., & Gunasekaran, A. (2003). Issues in implementing ERP: A case study. European Journal of Operational Research , 146, 274–283. 209
Markus, M. L., & Tanis, C. (2000). The Enterprise System Experience—From Adoption to Success. In R. W. Zmud (Ed.), Framing the Domains of IT Management : Projecting the Future Through the Past (pp. 173-207). Cincinnatti, OH: Pinnaflex Educational Resources, Inc. Mendes, R., Mateus, J., Silva, E., & Tribolet, J. (2003). Applying Business Process Modeling to Organizational Change. Seventh Americas Conference on Information Systems. Tampa, FL. Moller, C. (2005). ERP II: a conceptual framework for next-generation enterprise systems? Journla of Enterprize Information Management , 18 (4), 483-497. Mookerjee, R. (2005). Maintaining Enterprise Communications of the ACM , 48 (11), 75-79.
Software
Applications.
Motiwalla, L. F., & Thompson, J. (2009). Enterprise Systems for Management. Delhi: Pearson Education. Motwani, J., Subramanian, R., & Gopalakrishna, P. (2005). Critical factors for successful ERP implementation: Exploratory findings from four case studies. Computers in Industry , 56, 529–544. Murphy, K. E., & Simon, S. J. (2002). Intangible Benefits Valuation in ERP Projects. Information Systems Journal , 12 (4), 301-320. Murthy, U., & Wiggins, J. C. (2004). OOREA: An object-oriented resources, events, agents model for enterprise systems design. International Conference on Information Systems (ICIS). Muscatello, J. R., & Chen, I. J. (2008). Enterprise resource Planning (ERP) Implementaitons: Theory and Practice. International Journal of Enterprise Information Systems , 4 (1), 63-78. Nah, F. F.-H., Lau, J. L.-S., & Kuang, J. (2001). Critical factors for successful implementation of enterprise systems. Business Process Management , 7 (3), 285296. Nandhakumar, J., Rossi, M., & Talvinen, J. (2003). Planning for ‗drift‘?: Implementation process of enterprise resource planning systems. Proceedings of the 36th Hawaii International Conference on System Sciences, (pp. 1-10). NASSCOM. (2012). The IT-BPO Sector in India : Strategic Review. Nelson, K. (2005). Exploring Emotions during ERP Adoption: A Stakeholder Analysis . Hawaii International Conference on System Sciences (HICSS). 210
Newell, S., Tansley, C., & Huang, J. (2002). Knowledge Integration in a Temporary ERP Project Team: The Unexpected Debilitating Impact of Social Capital. Americas Conference on Information Systems (AMCIS). Ng, J. K., & Ip, W. (2003). Web-ERP: the new generation of enterprise resources planning. Journal of Materials Processing Technology , 138, 590-593. Ngai, E., Law, C., & Wat, F. (2008). Examining the critical success factors in the adoption of enterprise resource planning. Computers in Industry , 59, 548-564. O'Leary, D. E. (2004). Enterprise Resource Planning (ERP) Systems: An Emperical Analysis of Benefits. Journal of Emerging Technologies in Accounting , 1, 63-72. Olhager, J., & Selldin, E. (2003). Enterprise resource planning survey of Swedish manufacturing firms. European Journal of Operational Research , 146, 365–373. Parr, A., & Shanks, G. (2000). A model of ERP project implementation. Journal of Information Technology , 15 (4), 289-303. Piturro, M. (1999). How Midsize Companies are Buying ERP. Journal of Accountancy , 188 (3), 41-48. Rao, S. S. (2000). Enterprise resource planning: business needs and technologies. Industrial Management & Data Systems , 100 (2), 81-88. Raymond, L., Rivard, S., & Jutras, D. (2006). Evaluating Readiness for ERP Adoption in Manufacturing SMEs. International Journal of Enterprise Information Systems (IJEIS) , 2 (4), 1-17. Reimers, k. (2002). Implementing ERP Systems in China. Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS). Roberts, H., & Barrar, P. (1992). MRPII implementation: key factors for success. Computer Integrated Manufacturing Systems , 5 (1), 31-38. Robey, D., Ross, J. W., & Boudreau, M.-C. (Summer 2002). Learning to implement enterprise systems: An exploratory study of the dialectics of change. Journal of Management Information Systems , 19 (1), 17-46. Rockart, J. (1979). Chief executives define their own data needs. Harvard Business Review , 57 (2), 81-93.
211
Rodecker, R., & Hess, T. (2001). User Satisfaction with Enterprise Resource Planning Systems: The Effect of User Knowledge and Involvement, Training, and Locus of Control. Americas Conference on Information Systems (AMCIS). Rosemann, M. (2001). Requirements Engineering for Enterprise Systems. Americas Conference on Information Systems (AMCIS). Rosemann, M., Vessey, I., Weber, R., & Wyssusek, B. (2004). On the Applicability of the Bunge–Wand–Weber Ontology to Enterprise Systems Requirements. Australasian Conference on Information Systems (ACIS). Ross, J., & Vitale, M. (2000). The ERP revolution: surviving vs thriving. Information Systems Frontiers , 2, 233. Sammon, D., Adam, F., & Carlsson, S. (2009). CSF Relationships in ERP Project Implementations: A Collective Case Study. Proceedings of the Fifteenth Americas Conference on Information Systems. San Francisco. Sarkis, J., & Sundarraj, R. P. (2001). A Decision Model for Strategic Evaluation of Enterprise Information Technologies. Information Systems Management , 18 (3), 62-72. Sathisha, S., Pan, S. L., & Raman, K. S. (2003). A Stakeholder Perspective of Enterprise Systems. 7th Pacific Asia Conference on Information Systems, (pp. 669-682). Adelaide, South Australia. Sato, R., & Hori, K. (2002). Developing quick Iterative Process Prototyping for Project. 4th International Conference On Enterprise Information Systems , (pp. 851-854). Ciudad Real - Spain. Schubert, P., & Williams, S. P. (2009). An Extended Framework for Comparing Expectations and Realized Benefits of Enterprise Systems Implementations. Proceedings of the 15th Americas Conference on Information Systems (AMCIS 2009 Proceedings), (p. Article 751). San Francisco. Scott, J. E., & Vessey, I. (2000). Implementing Enterprise Resource Planning Systems: The Role of Learning from Failure. Information Systems Frontiers , 2 (2), 213-232. See Pui Ng, C. (2001). A Framework for Enterprise Resource Planning Maintenance and Upgrade Decisions. Americas Conference on Information Systems (AMCIS).
212
Seidel, G., & Back, A. (2009). Success Factor Validation for Global ERP Programmes. 17th European Conference on Information Systems , 1-13. Shang, S., & Seddon, P. B. (2002). Assessing and managing the benefits of enterprise systems: the business manager's perspective. Informatio Systems Journal , 12 (4), 271-299. Shang, S., & Seddon, P. B. (2007). Managing process deficiencies with enterprise systems. Business Process Management , 13 (3), 405-416. Shang, S., & Su, T. (2004). Managing User Resistance in Enterprise Systems Implementation. Americas Conference on Information Systems (AMCIS). Shen, H., Wall, B., Zaremba, M., Chen, Y., & B. J. (2004). Integration of business modelling methods for enterprise information system analysis and user requirements gathering. Computers in Industry , 54, 307–323. Slooten, K. v., & Yap, L. (1999). Implementing ERP information systems using SAP. AMCIS 1999 Proceedings. Milwaukee. Sneller, L., Bots, J., & Koning, F. d. (2004). Ex-ante evaluation of ERP systems: Towards a more systematic development of methods in IT evaluation. European Conference on Information Technology Evaluation, (pp. 417-426). Snider, B., da Silveira, G. J., & Balakrishnan, J. (2009). ERP implementation at SMEs: analysis of five Canadian cases. International Journal of Operations and Production Management, , 29 (1), 4-29. Somers, T. M., & Nelson, K. (2001). The Impact of Critical Success Factors across the Stages of Enterprise Resource Planning Implementations. Proceedings of the 34th Hawaii International Conference on System Sciences, (pp. 1-10). Somers, T. M., Nelson, K., & Karimi, J. (Summer 2003). Confirmatory Factor Analysis of the End-User Computing Satisfaction Instrument: Replication within an ERP Domain. Decision Sciences , 34 (3), 595-621. Southwick, R. (2002). Reengineering Identities? Examining the Cultural Aspects of Business Process Redesign in a University ERP Implementation. Americas Conference on Information Systems (AMCIS). Spathis, C., & Constantinides, S. (2004). Enterprise resource planning systems‘ impact on accounting processes. Business Process Management Journal , 10 (2), 234-247.
213
Spathis, C., & Constantinides, S. (2003). The usefulness of ERP systems for effective Management. Industrial management & Data Systems , 103 (9), 677685. Staehr, L. (2007). Assessing Business Benefits from ERP Systems: An Improved ERP Benefits Framework. Proceeding of the 28th International Conference on Information Systems (ICIS 2007 Proceedings), (p. Article 36). Montreal. Stender, M. (2002). Outline of an Agile Incremental Implementation Methodology for Enterprise Systems. Americas Conference on Information Systems (AMCIS). Sumner, M. R., & Bradley, J. (2009). CSF's for Implementing ERP within SME's. Proceedings of the Fifteenth Americas Conference on Information Systems. San Francisco. Sun, A. Y., Yazdani, A., & Overend, J. D. (2005). Achievement assessment for enterprise resource planning (ERP) system implementations based on critical success factors (CSFs). International Journal of Production Economics , 98, 189– 203. Tan, C. W., Lim, E. T., Pan, S. L., & Chan, C. M. (2004). Enterprise System as an Orchestrator of Dynamic Capability Development: A Case Study of the IRAS and TechCo. IFIP International Federation for Information Processing , 143, 515534. Tarafdar, M. (2005). ERP Adoption in Indian Organizations. In Encyclopedia of Information Science and Technology (pp. 1102-1107). IGI Global. Timbrell, G. T., Andrews, N. M., & Gable, G. G. (2001). Impediments to Interfirm Transfer of Best Practice in an enterprise systems context. Proceedings of 7th Americas Conference on Information Systems (AMCIS), (pp. 1084-1090). Boston, MA. Umble, E. J., Haft, R. R., & Umble, M. M. (2003). Enterprise resource planning: Implementation procedures and critical success factors. European Journal of Operational Research , 146, 241–257. Verville, J., & Halignten, A. (2002). An investigation of the Decision process for selecting an ERP software: the case of ESC. Management Decision , 40 (3), 206210. Wagner, E. L. (2002). Interconnecting Information Systems Narrative Research: An End-to-End Approach for Process-Oriented Field Studies. IFIP WG 8.2 Working Conference, (pp. 419-435). Barcelona. 214
Wan, A., Ling, P.-S., & Huang, J. (2001). Barriers to ERP Implementation: An Action Research. Pacific Asia Conference on Information Systems (PACIS). Weill, P., Subramani, M., & Broadbent, M. (Fall 2002). Building IT infrastructure for strategic agility. MIT Sloan Management Review , 44 (1), 57-65. Wenger, M. R., Dhillon, G., & Caldeira, M. (2005). ERP Implementation in Portugal: Interpreting Dimensions of Power. Americas Conference on Information Systems (AMCIS). Wight, O. W. (1981). MRP II unlocking America's productivity potential. Boston: CBI Publishing. Willcocks, L. P., & Sykes, R. (2000). The Role of the CIO and IT Function in ERP. Communications of the ACM , 43 (4), 33-38. Woo, H. S. (2007). Critical success factors implementing ERP: the cause of a Chinese electronics manufacturer. Journal of Manufacturing Technology Management , 18 (4), 431-442. Working-Group. (2011). Infommation Technology Sector, Twelfth Five Year Plan (2012 - 17). Department of Informatoin Technology,Ministry of Communicatoins and Information Technology, Government of India. Wu, D., Ding, M., & Hitt, L. M. (2003). Learning in ERP Contracting: A Principal-Agent Analysis. Hawaii International Conference on System Sciences (HICSS). Wu, J.-H., & Wang, Y.-M. (2006). Measuring ERP success: the ultimate user's view. International Journal of Operations & Production Management , 26 (8), 882-903. Xu, B., & Cybulski, J. (2004). ERP Implementation: A Technological Diffusion and Knowledge Transfer Perspective. Pacific Asia Conference on Information Systems (PACIS). Xue, Y., Liang, H., Boulton, W. R., & Snyder, C. A. (Sep 2005). ERP implementation failures in China: Case studies with implications for ERP. International Journal of Production Economics , 97 (3), 279-295. Yin, R. K. (2003). Case Study Research: Design and Methods (3rd ed.). Thousand Oaks, CA: Sage Publications. Yokota, A. (2007). Statistical Analysis of critical success factors for successful ERP implementation. Bulletin of Yonezawa Women's Junior College , 42, 65-79. 215
Yu, C.-S. (2005). Causes influencing the effectiveness of post-implementation ERP system. Industrial Management & Data Systems , 105 (1), 115-132. Zhang, L., Lee, M. K., Zhang, Z., & Banerjee, P. (2003). Critical Success Factors of Enterprise Resource Planning Systems Implementation Success in China. Proceedings of the 36th Hawaii International Conference on System Sciences. IEEE.
216
Appendix A: Customer Success Stories S.N .
Client
Vendor
Nation
Web Address (URL)
1
Shrachi Housing Development Limited
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=3&type=C
2
Net4Barter
Microsoft
India
3
AIMIL Limited
Microsoft
India
4
GISIL
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
5 6 7
Alpex Exports Pvt. Ltd. Munjal Auto Components Tricolite Electrical Industries Pvt. Ltd.
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=4&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=20&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=30&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=44&type=V http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=77&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=79&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=83&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=114&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=149&type=V http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=154&type=V
8
Tyresoles
Microsoft
India
9
In Trading Pvt. Ltd.
Microsoft
India
10
Satyam Auto Components Ltd
Microsoft
India
11
Hero Mindmine
Microsoft
India
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=178&type=V
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=185&type=V
12
13
Deepak International Limited Sigma Freudenberg NOK Private Limited
14
eSys Distribution
Microsoft
India
15
Marico Limited
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
16 17 18 19 20 21
Balzers India Limited Seasons Furnishings Ltd. Sigma Computer Casio India Co. Pvt. Ltd. Orient Ceramics and Industries Limited Pooja Forge Limited
22
Soex Flora
Microsoft
India
23
EFD Induction India
Microsoft
India
24
Essel Propack
Microsoft
India
25
Coventry Matic
Microsoft
India
Coil-o-
217
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=191&type=V http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=214&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=226&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=250&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=251&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=253&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=254&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=255&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=256&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=276&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=277&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=283&type=C
26 27
28
BLA Group of Companies Sankeshwar Packaging Private Limited HSBC Software Development (India) Private Limited
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=292&type=V
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=298&type=V
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=299&type=C
29
Nordson India
Microsoft
India
30
Malana LNJ
Microsoft
India
31
Ammini Solar
Microsoft
India
32
Forgewell Limited
Microsoft
India
33
Uttam Air Products Private Limited
Microsoft
India
34
Swastik Pipes Ltd
Microsoft
India
35
Punjab Chemicals and Crop Protection Ltd.
Microsoft
India
36
Asian Paints
Microsoft
India
37
Autofit (P) Limited
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
38 39 40 41 42 43 44 45 46 47 48 49
TACO MobiAapps Telematics Limited Monnet Ispat Limited Vallabh Steel Limited Ideal Movers Pvt. Ltd. Patel Alloy Steel Pvt. Ltd. U-Turn Housing Private Limited Thacker Dairy Products Pvt. Ltd. Sadolin Paints Salveo Life Sciences Limited. Brand Alloys Limited Big Boss Infotech Limited United Nanotech Products
50
Print Sales Limited
Microsoft
India
51
Super Shopping
Microsoft
India
52
TAI Industries Ltd.
Microsoft
India
53
Linc Retail
Microsoft
India
218
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=306&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=319&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=322&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=335&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=336&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=338&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=340&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=341&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=352&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=353&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=357&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=358&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=368&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=369&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=389&type=V http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=390&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=407&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=426&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=460&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=464&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=469&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=470&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=471&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=475&type=C http://www.microsoft.com/india/CustomerEvid
54 55 56 57
MPS Aqua Marine Products Ltd. Lilliput Kidswear Limited MPS Greenery Developers Tessitura Monti India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
ence/details.aspx?casestudyid=477&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=478&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=482&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=483&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=495&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=496&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=497&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=498&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=500&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=502&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=507&type=C http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=518&type=C
58
Control Print India
Microsoft
India
59
Anokhi
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=523&type=C
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=530&type=C
Microsoft
India
60 61 62 63 64
65
66 67
JK Helene Curtis Limited Connexios Life Sciences MPS Food Products. Fashion Knits Deki Electronics Limited Southern Power Equipment Company Private Limited Venkatesh Logistics Private Limited LNV Technology Pvt. Ltd.
68
John Energy Ltd.
Microsoft
India
69
Telsima India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
70 71 72 73 74 75 76 77 78
Kilburn Office Automation Limited. Tenughat Vidyut Nigam Limited. M K Agrotech Private Limited Premier Tissues CAG Equipments Pvt. Ltd. Sunrise Kitchens Ltd. SFO Technologies IDA Trading Foundation Pvt. Ltd. Star Hitech Systems Pvt., Ltd.
79
T.T. Limited
Microsoft
India
80
ICOMM
Microsoft
India
219
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Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
86
Metamorph
Microsoft
India
87
MakeMyTrip.com
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
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88 89 90 91 92 93 94
Thai Summit Neel Auto Pvt. Ltd. Neptune Readymix Concrete Pvt. Ltd. Deluxe Silk Traders Sharda Spuntex Pvt. Ltd. Dhanuka Agritech Ltd. KORTEK Electronics. St. Jude‟s Public School and Junior College
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95
Sakthi Masala
Microsoft
India
96
Ritika Book House
Microsoft
India
97
Musashi Auto Parts India Private Limited
Microsoft
India
98
Odyssey India Ltd.
Microsoft
India
Microsoft
India
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=663&type=C
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=664&type=C
Microsoft
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http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=665&type=C
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=171&type=V
Microsoft
India
http://www.microsoft.com/india/CustomerEvid
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The American College Industrial Forge and Engineering Co. Ltd. (IFECL) ICLEAN Maya Global Private Limited (MGPL) SREI Infrastructure Finance Limited LM Glasfiber (India) Pvt. Ltd. HeroITES Chempharm Industries (India) Limited Mfar Holdings
220
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108 109 110 111
LifeCell Bartronics India Ltd. Shemaroo Entertainment Pvt. Ltd. Aizant Drug and Research Solutions
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
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112
Rinac India Limited
Microsoft
India
113
SemanticSpace Technologies.
Microsoft
India
114
Bharat Group
Microsoft
India
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=334&type=C
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=366&type=C
Microsoft
India
http://www.microsoft.com/india/CustomerEvid ence/details.aspx?casestudyid=404&type=C
Microsoft
India
Microsoft
India
Microsoft
India
115
116 117 118 119 120
Associated Instruments Manufacturers India Limited Sai Supreme Textiles Cranes Software International Limited Silver Lining Integra Global Solutions Sushil Financial Services
121
Fame (I) Limited
Microsoft
India
122
Machwan Communication & Research Pvt. Ltd.
Microsoft
India
123
AEGON Religare
Microsoft
India
Microsoft
India
Microsoft
India
Microsoft
India
124 125 126
Lavasa Corporation Limited. Proteans Software Solutions Pvt. Ltd. The Structural Waterproofing Company Limited
127
Apollo Tyres
SAP
India
128
Hero Honda
SAP
India
129
Mahindra Mahindra Ltd.
SAP
India
130
MRF
SAP
India
131
Tata Motors Ltd
SAP
India
132
Hindustan Unilever Limited
SAP
India
133
GMR Group
SAP
India
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221
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134
Hindustan Construction Company
SAP
India
135
Wockhardt Ltd
SAP
India
SAP
India
SAP
India
SAP
India
136 137 138
Jindal Stainless Ltd. Oil and Natural Gas Corporation Ltd. Bharat Oman Refineries Limited
139
Reliance Infocomm Limited
SAP
India
140
Suzlon Energy
SAP
India
141
Pidilite
142
Kirby India
143
AEGON Religare Life Insurance Company
Oracle
India
144
Air India
Oracle
India
145
AVTEC Limited
Oracle
India
146
Birlasoft
Oracle
India
147
Cummins India Ltd
Oracle
India
148
Cummins Turbo Technologies
Oracle
India
Oracle
India
Oracle
India
Oracle
India
Oracle
India
Oracle
India
Oracle
India
Oracle
India
Oracle
India
149 150 151 152 153 154 155
Indu Projects Limited Kirloskar Oil Engines Ltd Maruti Suzuki RDC Concrete (India) Pvt Ltd Sify Technologies Limited South Asian Petrochem LG Electronics India Pvt. Ltd
156
Mercury Travels
157
L&T LTM
158 159
Leroy Somer and Controls India Pvt Limited Godrej Industries
3i Infotech 3i Infotech
Oracle JD Edwards Oracle JD Edwards Oracle
India India
India
India India
222
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160
UTI Asset Management Company
PeopleS oft Oracle PeopleS oft
161
Tikona Networks
Oracle Siebel
India
162
DE Controls
Ramco
India
163
Ramco
India
Ramco
India
Case Study
Ramco
India
166
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167
SIP Academy India
Ramco
India
168
Sujana Group Companies
Ramco
India
164 165
169
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SAP
170
Brakes India
171
Britannia Industries Limited
172
Godrej Sara Limited
173
SAP
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SpiceJet
174
Subros
175
Sutlej Textiles and Industries Limited
176
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Usha International
SAP
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178
VIP Industries Ltd
SAP
India
179
Aditya Birla Group Carbon Black
SAP
India
180
VST Industries
SAP
India
181
Dabur India
SAP
India
182
WELSPUN GROUP
SAP
India
183
Reliance Capital
SAP
India
184
Kirloskar Limited
SAP
India
185
KLG Systel Limited
SAP
India
186
MRF Ltd.
Oracle JD Edwards
India
http://www.intelligroupasia.com/casestudies/ MRFLtd.pdf
SAP
India
http://www.patni.com/media/5479/cs_erp_sa p_grrp.pdf
Sage
India
Sage
India
Sage
India
Sage
India
187 188 189 190 191
Brothers
Gujarat Reclaim & Rubber Products Ltd Agarwal Fasteners Pvt. Ltd Bolhoff Fastenings India Pvt. Ltd Confident Sales India Pvt. Ltd Cybage Software Private Limited
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192
Ecolab India
Sage
India
193
M. P Acctech Solutions Pvt. Ltd
Sage
India
194
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IFS India
India
IFS India
India
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India
http://www.ifsworld.com/in/customers/hal.asp
195 196
Gateway Terminals India Private Ltd Hindustan Aeronautics Ltd
197
NHPC
IFS India
India
198
GINI & JONY
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India
224
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200
Global Pharmatech Private Limited
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201
Pantaloon
SAP
India
202
CavinKare Pvt. Ltd
SAP
India
203
Tata Power
SAP
India
Hindustan Petroleum Corporation Limited Oriental Structural Engineers Pvt Ltd (OSE)
Oracle JD Edwards
India
SAP
India
206
Indiabulls Retail
SAP
India
207
Osram Limited
SAP
India
208
TISCO
SAP
India
IFS India
India
SAP
India
SAP
India
204
205
209 210 211
India
Global Vectra Helicorp Limited Jupiter Knitting Company (JKC) Network Clothing Company (NCC)
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212
Carnation Auto
SAP
India
213
Madras Limited
Oracle Siebel
India
214
ABB India
SAP
India
215
ELGI
IFS India
India
216
C. M. Smith & Sons
SAP
India
217
Dhruvi Equipments Ltd.
SAP
India
http://www.silvertouch.com/downloads/Dhruvi _Road_Equipment_STTL_Case_Study.pdf
218
Divine Tree Ltd.
SAP
India
http://www.silvertouch.com/downloads/Divine _Tree_STTL_Case_Study.pdf
219
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SAP
India
http://www.silvertouch.com/downloads/Hofm ann_Engineering_STTL_Case_Study.pdf
220
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SAP
India
SAP
India
SAP
India
SAP
India
221 222 223
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225
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224 225 226 227 228
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SAP
India
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India
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India
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India
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229
Bajaj Auto
SAP
India
230
Daikin Shriram
SAP
India
231
Dimexon Diamonds Ltd
SAP
India
232
Ankit Forgings
SAP
India
233
Greenply Industries
SAP
India
234
Grupo Antolin Pune Private Ltd
SAP
India
235
The Park Hotels
SAP
India
236
Indian Express Newspapers (Mumbai) Ltd
SAP
India
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237
Oil India Limited
SAP
India
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SAP
India
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SAP
India
http://www.expresscomputeronline.com/2008 0225/management02.shtml
238
239
Ranbaxy Fine Chemicals Limited(RFCL) University of Petroleum and Energy Studies
240
Tata Ryerson
SAP
India
241
Sundaram-Clayton Limited
SAP
India
242
United Breweries
SAP
India
243
Aircel Limited
SAP
India
244
Alstom India
SAP
India
245
Bharat Petrolium
SAP
India
SAP
India
SAP
India
SAP
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246 247 248
Power
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226
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249
Kores India Ltd
SAP
India
250
L&T E&C
SAP
India
251
L&TEBG
SAP
India
252
Macmillan India
SAP
India
253
Maestro Engineering
SAP
India
254
MTR Foods
SAP
India
255
Phillips Black Ltd
SAP
India
256
Rallis India Limited
SAP
India
257
Reliance Industries
SAP
India
SAP
India
SAP
India
258 259
Carbon
Sakaal Group of Publications SHV Energy SUPER Gas
260
Tantia Construction
SAP
India
261
Tatra Motors
SAP
India
262
Thomas Cook
SAP
India
263
Adeep Locks
SAP
India
264
Apar Industries
SAP
India
265
DCM Engineering
SAP
India
266
Eicher Motors
SAP
India
267
Kemwell
SAP
India
268
Panacea Biotec
SAP
India
269
VISA Steel Limited
SAP
India
270
H&R Johnson India
SAP
India
271
Lupin
SAP
India
272
N R Agarwal Industries
SAP
India
273
Radico Khaitan
SAP
India
274
Shri Sant Gajanan Maharaj Sansthan temple trust
SAP
India
275
Hariyali Kisaan Bazaar (Hariyali)
SAP
India
276
Madura Garments
SAP
India
Vectra
227
0414/management01.shtml http://www.expresscomputeronline.com/2007 0813/management01.shtml http://www.expresscomputeronline.com/2006 1113/management03.shtml http://www.expresscomputeronline.com/2002 0617/ebiz1.shtml http://www.expresscomputeronline.com/2004 1129/management01.shtml http://www.expresscomputeronline.com/2006 0605/management01.shtml http://www.expresscomputeronline.com/2005 0124/management01.shtml http://www.expresscomputeronline.com/2006 0102/management01.shtml http://www.expresscomputeronline.com/2007 0820/management02.shtml http://www.expresscomputeronline.com/2007 0101/industrialproduction01.shtml http://www.expresscomputeronline.com/2006 1211/management02.shtml http://www.expresscomputeronline.com/2007 0416/management02.shtml http://www.expresscomputeronline.com/2007 0409/management02.shtml http://www.expresscomputeronline.com/2006 0724/management03.shtml http://www.expresscomputeronline.com/2003 0113/ebiz1.shtml http://www.expresscomputeronline.com/2006 0123/management01.shtml http://www.expresscomputeronline.com/2009 0216/casestudyspecial07.shtml http://www.expresscomputeronline.com/2006 0703/management01.shtml http://www.expresscomputeronline.com/2005 1107/management04.shtml http://www.expresscomputeronline.com/2005 0606/management01.shtml http://www.expresscomputeronline.com/2006 1016/management01.shtml http://www.expresscomputeronline.com/2007 0319/management01.shtml http://www.expresscomputeronline.com/2003 1124/appsspecial11.shtml http://www.expresscomputeronline.com/2002 0923/ebiz1.shtml http://www.expresscomputeronline.com/2006 0116/management03.shtml http://www.expresscomputeronline.com/2006 1106/management01.shtml http://www.expresscomputeronline.com/2008 1020/management02.shtml http://www.expresscomputeronline.com/2006 0313/management01.shtml http://www.expresscomputeronline.com/2005 0221/management01.shtml
http://www.expresscomputeronline.com/2005 1219/management01.shtml http://www.financialexpress.com/news/vardh man-group-puts-erp-network-cloud-inplace/69724/0# http://www.commit.in/download/Case%20Study%20ERP%20Ji ndalSaw.pdf http://www.mobileone.in/pdfs/Dr-ReddysLaboratories.pdf http://www.financialexpress.com/news/usingerp-to-meet-the-challenges-of-running-anorganisation/164647/0# http://www.financialexpress.com/news/anerp-package-should-serve-allfunctionalities/65541/#
277
KSL and Industries
SAP
India
278
Vardhman Spinning and General Mills Ltd
Infor
India
279
Jindal SAW Ltd
IFS India
India
280
Dr. Reddy‟s Laboratories Ltd.
SAP
India
281
Alpha Pneumatics
3i Infotech
India
282
Wesman Ipsen Furnaces Pvt Ltd
3i Infotech
India
283
Nagarjuna Fertilizers Chemicals (NFCL)
SAP
India
284
Ambuja Cements
SAP
India
SAP
India
IFS India
India
IFS India
India
Oracle
India
http://www.networkmagazineindia.com/20030 8/case1.shtml
Oracle Siebel
India
http://www.networkmagazineindia.com/20021 0/case7.shtml
Oracle Siebel
India
SAP
India
SAP
India
http://www.networkmagazineindia.com/20020 5/case2.shtml
India
http://www.networkmagazineindia.com/20040 8/casestudy02.shtml
285 286 287 288
289 290 291
292
and Ltd
Videocon International Advantec Coils Private Limited TEI Technologies Escorts Limited's Agri Machinery Group (EL-AMG) Godrej Consumer Products Limited (GCPL) Gestetner India Limited (GIL) TVS Motor Company Ltd Orchid Chemicals and Pharmaceuticals Limited (OCPL)
Oracle PeopleS oft Oracle Siebel
293
Inter Gold
294
Moser Baer
295
Grasim Industries
SAP
India
296
Café Coffee Day
SAP
India
297
AstraZeneca Pharma India
Oracle
India
298
Shopper‟s Stop
Oracle
India
SAP
India
SAP
India
299 300
MIRC Electronics Onida ACG Worldwide
India
228
http://www.financialexpress.com/news/chang e-and-continuity/99850/0# http://dqindia.ciol.com/content/casestudy/200 9/109022102.asp http://www.hinduonnet.com/businessline/ew/ 2001/10/03/stories/0303c058.htm http://www.networkmagazineindia.com/20050 1/casestudy02.shtml http://www.networkmagazineindia.com/20031 0/casestudy02.shtml
http://www.networkmagazineindia.com/20030 9/casestudy03.shtml http://www.networkmagazineindia.com/20030 9/casestudy01.shtml
http://www.expresscomputeronline.com/2004 0510/casestudyspecial02.shtml http://www.expresscomputeronline.com/2009 1116/casestudy09.shtml http://www.expresscomputeronline.com/2009 1116/casestudy13.shtml http://www.expresscomputeronline.com/2003 1027/sme08.shtml http://www.expresscomputeronline.com/2009 0216/casestudyspecial06.shtml http://www.expresscomputeronline.com/2009 0824/expressintelligententerprise13.shtml http://h41131.www4.hp.com/in/en/stories/acg
-teams-up-with-hp-to-make-internalbusiness-processes-more-easier-to-operateand-transparent.html
229
Appendix B Sample Case: SAP Customer Success Story Aditya Birla Group’s Carbon Black Business
A2
A8 A7 CSF6.2
CSF5.4 A9
A5 CSF7.3
CSF2.2 A4
A1
A12
CSF6.3
230
CSF7.2 CSF1.4
CSF7.1
CSF1.2
CSF4.5
CSF1.1
CSF3.2
CSF8.2 CSF8.1
231
B1.2
B2.1 B1.1
B2.3 B5.2
B1.5 B4.1 B4.2 B4.3 B5.4 B1.4
B2.2 B3.8 B3.6 \m]p
232
B3.2
