community colleges and this was an external validity concern. He also ..... According to Hinds, Vogel, and Clarke-Steffen (1997) the pilot study should address ...
RELATIONSHIP BETWEEN LEADERSHIP AND MORTGAGE BANKING END-USER COMPUTING EFFICIENCY by Kannan Deivasigamani Copyright 2016
A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Management in Organizational Leadership/Information Systems and Technology
University of Phoenix
ABSTRACT Risk of EUC inefficiency leads to non-compliance and loss to mortgage organizations. The purpose of this quantitative study was an attempt to reveal whether a relationship existed between Leadership End User Computing (EUC) initiatives and mortgage banking EUC efficiency within the United States using a correlational design and a regression model. The study was conducted using a purposive sample with a sample size of 374 participants and with 95% confidence interval and 5% margin of error as indicated by the sample size calculator used in this study preceded by a pilot study. The sample size of the pilot study was 100 participants as a minimum sample size of 30 participants was necessary to establish a robust Cronbach’s alpha. The data analysis tool used was SAS University edition. The findings of this study indicated that there was a direct positive relationship between the two constructs, leadership EUC initiatives and EUC efficiency.
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DEDICATION I dedicate this dissertation to my mom who has always wished me to be a doctor as long as I can remember, and my dad who has encouraged me at every opportunity he had throughout my journey.
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ACKNOWLEDGMENTS I would like to thank my wife, Sumathi and my children Vikram and Meena who have a big part in my success. I would like to thank my chair Dr. Susan Ferebee who has stayed with me from the beginning to the end, guided me through challenges, and directed me along the right path. I would like to thank my committee member Dr. Armando Paladino who stepped up at a difficult time, and was able to rescue me when I was short of a committee member. I would like to thank Dr. Anuj Puri whose EUC expertise was invaluable to my research, right from the moment when I created the instrument, and carried out my analysis and research. I would also like to thank Dr. Keri Heitner who was part of my committee in the beginning and could not continue but had helped me through several remarks and suggestions to improve the quality of my dissertation. I would also like to thank Dr. Upavan Gupta, Dr. Srinivasan, Mrs. Meghala, Mr. Rayaprolu, and Dr. Sandoval who helped with comments on improving my instrument. I would like to thank Dr. Douglas Lunsford, who has helped me in developing my instrument, advised me throughout my journey, and kept me inspired until I reached my finish line. I would like to thank the respondents who participated in my research during my data collection. I would like to thank my boss, Mark Chesta, the management team at HSBC who supported my doctoral education including Wayne Littrell, Michael Banyas, George Okammor, April Routt and Bill Rogers. I would finally like to thank the University of Phoenix for making my dream come true.
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TABLE OF CONTENTS Contents
Page
List of Tables .......................................................................................................................x List of Figures .................................................................................................................... xi Chapter 1: Introduction ........................................................................................................1 Background of the Problem .....................................................................................3 Statement of the Problem .........................................................................................7 Purpose of the Study ................................................................................................8 Significance of the Study .......................................................................................10 Nature of the Study ................................................................................................14 Research design. ........................................................................................14 Research Process ....................................................................................................16 Research Question .................................................................................................17 Null and Alternate Hypotheses ..............................................................................18 Theoretical Framework ..........................................................................................18 Definitions..............................................................................................................19 Assumptions...........................................................................................................21 Scope ......................................................................................................................22 Limitations .............................................................................................................22 Delimitations ..........................................................................................................23 Summary ................................................................................................................23 Chapter 2: Review of the Literature...................................................................................25 Documentation .......................................................................................................33 vi
Historical Perspective ............................................................................................33 IT Leadership and Structure...................................................................................46 CMM Standards .....................................................................................................48 Regulatory Entities.................................................................................................49 Leadership within IT Organizations ......................................................................49 Leadership Challenges ...........................................................................................49 Importance of Optimization to Leadership ............................................................54 Common Methods..................................................................................................58 Gap in Literature ....................................................................................................59 Conclusions ............................................................................................................59 Summary ................................................................................................................59 Chapter 3: Method .............................................................................................................61 Research Method and Design Appropriateness .....................................................62 Research Variables.................................................................................................64 Research Questions and Hypothesis ......................................................................64 Null and Alternate Hypotheses ..............................................................................64 Population ..............................................................................................................64 Sampling ................................................................................................................65 Participants .............................................................................................................68 Informed Consent and Confidentiality...................................................................68 Geographical Location and Limitations.................................................................69 Confidentiality .......................................................................................................69 Data Collection ......................................................................................................70 vii
Instrument ..............................................................................................................71 Demographics Questionnaire .................................................................................74 Instrument Reliability and Validity .......................................................................74 External and Internal Validity of the Study ...........................................................77 Data Analysis .........................................................................................................78 Summary ................................................................................................................79 Chapter 4: Results ..............................................................................................................80 Method ...................................................................................................................80 Sample and procedure. ...............................................................................80 Measures ................................................................................................................81 Results ....................................................................................................................81 Descriptive statistics and reliability coefficients ...................................................82 Validity of Instrumentation ....................................................................................83 Hypothesis Testing.................................................................................................86 Summary ................................................................................................................87 Chapter 5: Summary and Conclusions ...............................................................................89 Discussion of Results .............................................................................................90 Background ............................................................................................................91 Literature that Supports Result Findings ...............................................................92 Implications............................................................................................................93 Limitations and Assumptions ................................................................................94 Recommendations for Future Research .................................................................95 Conclusion .............................................................................................................96 viii
Summary ................................................................................................................97 References ..........................................................................................................................99 Appendix A: Information about End-User Computing (EUC) Efficiency in Mortgage Banking Survey................................................................................................................124 Appendix B: End-User Computing (EUC) Efficiency in Mortgage Banking Survey .....125 Appendix C: Details About Survey Questions ................................................................131 Appendix D: Demographics Questionnaire .....................................................................132 Appendix E: Final End-User Computing (EUC) Efficiency in Mortgage Banking Survey133 Appendix F: Bar Graphs and Scatter Plots ......................................................................134 Appendix G: Linear Regression Equation and Graph .....................................................135 Author Biography ............................................................................................................136
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LIST OF TABLES Table 1: Characteristics of the Current Study ................................................................... 15 Table 2: Mean, Standard Deviation, Skewness, Kurtosis, and Cronbach’s Alpha for Leadership EUC Initiatives and EUC Efficiency for 100 Pilot Study Participants .......... 82 Table 3: Mean, Standard Deviation, Skewness, Kurtosis, and Cronbach’s Alpha for Leadership EUC Initiatives and EUC Efficiency for 374 Study Participants .................. 83 Table 4: Mean, Standard Deviation, Skewness, and Kurtosis for Each of the Scales of SLS and EUC Applications Measures for 374 Study Participants ................................... 84 Table 5: Descriptives and Intercorrelations among Different Dimensions of Leadership Scales ................................................................................................................................ 85 Table 6: Descriptives and Intercorrelations Among the Current EUC and Original EUC Scales for 374 Participants ................................................................................................ 86 Table 7: Descriptives and Intercorrelations among the Leadership EUC Initiatives and Current EUC Scales (Composite Sample) ........................................................................ 86
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LIST OF FIGURES Figure 1: Study map showing the structure of the study. ..................................................17 Figure 2: Business processes in mortgage servicing organizations. ..................................47 Figure 3: The influence of culture on effectiveness of process. ........................................48 Figure 4: Organizational structure in relation to process flow and efficiency. ..................57
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Chapter 1 Introduction Research showed that end user computing (EUC) costs were increasing, EUC needs and requirements were rising (Patton, 1995; Preston, 1994; Schwarzkopf, Burroughs, & Harvey, 1995; Powell & Moore, 2002) as were costs of data mining(Bal, Bal and Demirhan, 2011). If EUC was inefficient with poorly designed systems, the losses could be overwhelming and possibly destroy a mortgage company (MontesinosDelgado, 1992) and as such, EUC efficiency should be a major concern to managers and organizational leaders (Downey, 2004). Unfortunately, there was no measure to assess the efficiency of EUC. The purpose of this study was to examine the relationship between leadership EUC initiatives and EUC efficiency. Because there were no instruments to measure EUC efficiency and leadership EUC initiatives, a step required in this study was to develop and validate the instruments to measure the two constructs. Research showed that the influence of leadership was becoming more critical and significant for an organization’s success (Hatami, Prince, and de Uster, 2013). Again, leadership that lacked direction concerning reports, initiatives and EUC efficiency could be the catalyst that affects risk, intelligence, growth or decline of an organization or system that the EUC activity affects (Al-Azmi, 2013). At the time of the current research, there was no measure specifically designed to assess the initiatives taken by leaders to improve EUC efficiency (leadership EUC initiatives). Therefore, the current measures may guide IT leaders in their actions towards teams to optimize the processes. Because there was no measure for either constructs, it was unknown if a relationship existed between leadership EUC initiatives and EUC efficiency. The current 1
research focused to develop measures of these two constructs and, once established, to determine whether there was a relationship between the two in the mortgage banking industry. End users, technology, and organizations were the three dimensions of EUC (Downey, 2004). End user referred to the user of data who performed computations and could be individuals or groups. Technology referred to the hardware, software, and programming languages used to compute the information. Organizations referred to businesses that perform data processing, have leadership, and that form strategies. Advancement in integrated circuits in the 1980s led to the creation of faster computers, and as expectations concerning their output increased, EUC costs began to increase. As technology and economic growth brought profits to many large corporations such as IBM, Microsoft, and Oracle, overheads incurred from EUC were a major component of technology expenses. In 2008, when the U.S. economy started shrinking, the EUC costs did not shrink in tandem and prompted organizational leaders to look more closely at the EUC expenses with the idea of finding way to reduce the costs. With a reduction in EUC costs an increase in the efficient use of resources became necessary (Guimaraes et al., 1999). Leadership in an organization must improve efficiency and quality, irrespective of the product. Elliot (2010) noted quality was one of the six leadership priorities that resulted in process improvement. According to Standard and Poor’s (2013), the U.S. economy was showing growth and consumer confidence was improving. Subprime lenders and prime lenders were part of the contribution to this growth due to the increase in the loan volume and mortgage
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data processed with EUC. The number of businesses and IT professionals in the banking industry increased at that time, with growth evidenced by Standard and Poor’s. EUC was one aspect of data processing in which central processing unit (CPU) cost contributed to the overall IT expense in an organization. End users were the individuals who interacted with information systems (IS) directly (Moore, Jackson, & Wilkes, 2007). CPU cost was the processing cost incurred when a computer was in a powered state. Costs with technologies based on mainframe architectures such as IBM’s OS/390, AS/400, and ES9000 were more expensive than other non-mainframe technologies such as distributed technologies from Microsoft, Oracle, and other companies. The leaders of some organizations that used mainframe technology for processing were attempting to optimize processes to make them more efficient to reduce processing costs. Mainframe technology played a major role in organizations where storage, speed, and number of users’ criteria for their successes and were an essential part of their business continuity, as the mainframe remained the home for more than 70% of the world’s transactional data (Imhoff, Laurel, & Meyers, 2008). Background of the Problem IT is an integral part of many organizations around the world, and EUC is part of many organizations that use IT, as information needs to get to the end users to stay on track with the organizational strategy or corrective action will be necessary. Organizational leaders try to make a profit, but the effort sometimes undermines operational costs. Large database processing on IBM mainframe systems with OS/390, Z/OS, and AS-400 can involve costs that vary from a few hundred thousand dollars each month to a few million dollars each month. Taylor and Tucker (1989) noted 30% of a 3
$209 million yearly budget was allocated for hardware and software expenses, which was a significant IT budget to ignore any inefficiency in the IT processes, including EUC. Organizations that experienced explosive growth usually had the potential for efficiency improvements and faster data processing. As data volume increased due to business growth and strategy changes, data structures varied and created potential for application tuning and improvement. The current study was intended to establish a measure of the leadership EUC initiatives and EUC efficiency and to reveal whether a relationship existed between IT leaders’ emphases and employees’ perception of how efficient their EUC systems were within the mortgage banking industry. Efficient application development might help save costs and reduce CPU wastage. ISO 14000 can affect organizations in a positive way through increased efficiency and reduced waste (Ford and GM Demand ISO 14000 Compliance, 1999). Inefficient programming resulted in lack of knowledge, accompanied by less-than-perfect technical skills and a lack of exposure to database intricacies and structures. Byrd and Turner (2000) noted 58% of an organization’s IT budget went to IT infrastructure and the expenditure increased 11% every year. The expenditure toward the infrastructure indirectly contributed toward an organization’s EUC expense as the capacity and speed of an infrastructure determined the cost of EUC. Sliet, Al-Mbaideen, Alzabin, Dawood, and Alqarute (2007) noted, “The system must have an intelligent algorithm to select the most suitable server to fulfill a coming request” (p. 311). Lack of communication between database design teams and development teams fostered high data-processing costs. The researcher noticed in his over 18 years of experience in the field of IT development that there was considerable room for improvement in the efficiency of EUC 4
and that leaders seemed to be unaware of leadership EUC initiatives that could help improve the efficiency of their systems. These inefficiencies would likely decrease if steps were taken to improve optimization, improve costs, and eliminate redundancies. Leadership and productivity related positively in both civilian and military settings (Miller & Medalia, 1955). Therefore, it was more likely that these steps were implemented if IT leadership governed its workforce with proper leadership EUC initiatives. The current study might help IT leaders understand the importance of leadership EUC initiatives on improving the efficiency of EUC. The study might also help leaders communicate the importance of EUC efficiency to their subordinates and work to motivate developers and designers to develop and design efficient EUC systems. According to the built-in business model or strategy and the existing data structure in the organization, different ways of optimizing existed. Optimization techniques also depended on the type of access required by the business and the frequency of data updates. Opportunities arose for gaining additional efficiencies and simultaneously reducing costs of data processing. Organizations had ad hoc, daily, weekly, monthly, and yearly processes of reporting to support different operations within the company. Some reports produced daily included the same data more than once (cumulative data). Some historical data did not change, but IT processes might process them daily due to the cumulative processing requirement of EUC reports. As a result, EUC expenses would increase among organizations experiencing growth in IT (Burrows, 1994; Caginalp, 1994; Van Kirk, 1995). As EUC grew globally (Patton, 1995; Preston, 1994; Schwarzkopf, Burroughs, & 5
Harvey, 1995) businesses looked for competitive advantage. This advantage would have been obtained by efficient EUC processes put in place by leadership EUC initiatives. Savings were critical in times of budget crunches, and all savings contributed to the final numbers on financial statements. Companies underwent process cleanups and were usually aggressive in savings during market downturns (Couto, Divakaran, Mani, & Lantz, 2009). Other organizations that conformed to the capability maturity model (CMM) Level III and above tended to have efficient processes. Because many organizations fell at Levels I and II, the potential was great in the IT aspect of their infrastructure to generate savings through efficient processing. Angel (2006) noted the need for innovation within an organization and noted that treating productivity as a proxy for innovation could be a mistake. Efficiency and optimization might not seem as an immediate benefit but can reduce costs in the long run after development phases. The U.S. attorney general is currently pursuing charges against several of the banks and many bank executives are at the time of the study still under deferred prosecution agreement requirements resulting from these issues (Benson et al., 2015). Office of federal housing enterprise oversight raised concerns over end user computing systems of Fannie Mae causing the loss of one billion dollars in just the third quarter of 2003 by Fannie Mae (Alta News, 2004). During the housing crisis, Fannie Mae relied on approximately 70 end user computing accounting systems, which had a high risk of error and had little in the way of controls. Fannie Mae, as one example, was ordered to create a remediation plan that would address all end user financial reporting system issues (McDonald, 2013; National Mortgage News, 2004).
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The effects of misreported EUC information were still being experienced in 2015 by several of the huge banks that were paying settlements and increased deferred prosecution agreements (DPA). These banks were working on these numbers in the form of remediation plans and establishing new controls as in the case of Barclays, Bank of America, and Goldman Sachs are pursued by U.S. Attorney General, Loretta Lynch, who inherited several mortgage related cases (Benson et al., 2015). Justice department settled about $39 billion from the nation’s largest banks such as JP Morgan Chase, Bank of America, Hong Kong Shanghai Banking Corporation (HSBC), Citi and Suntrust Banks (Benson et al., 2015). The inaccuracies and misreporting of mortgage banking data are far from complete, as evidence presented above has indicated. Without any way to measure these systems’ efficiency, and with no understanding of the relationship between leadership EUC initiatives and EUC efficiency, leaders in mortgage banking industry had difficulty knowing what improvements to make and how to make them. Statement of the Problem Currently there is no measure to determine if EUC processes are efficient. Therefore unidentified inefficiencies result in financial loss to organizations, risk of breaking SLAs, risk of non-compliance, risk of errors that my lead the organizations to regulatory and legal implications as demonstrated by several litigations by some of the major financial corporations in the 21st century (Apptio, 2014; EMC, 2014; Deloitte, 2011; Gammage, 2013; Panko & Port, 2013) . Because no measure of EUC efficiency has previously existed, no research has been carried out to ascertain which if any variables are related to the construct of EUC efficiency. Experience of the current researcher has brought to light the observation that systems that appear to perform more 7
efficiently are supervised by leaders who use specific EUC initiatives. Unfortunately, there is no current measure of the construct of leadership EUC initiatives, so no correlational studies could be conducted to reveal what if any relationship exists between these constructs. This gap may mean that there is a specific problem that organizations that have inefficiencies that are prone and currently bear the different types of risks mentioned above (Alta News, 2004; Benson, Schoenberg, & Smythe, 2015). The general problem is that, to corporations, EUC is of high importance; EUC inefficiency is costly and increases risk due to errors resulting in non-compliance with regulations (Apptio, 2014; EMC, 2014; Deloitte, 2011; Gammage, 2013; Panko & Port, 2013). Hill (2011) emphasized the need for greater control over EUC application development and use. Purpose of the Study The purpose of the current study was to examine the relationship between leadership EUC initiatives and EUC efficiency. Because there were no instruments to measure EUC efficiency and leadership EUC initiatives, a needed step was to develop and validate these instruments. An existence of a relationship between the two constructs would help the leaders take appropriate action to improve EUC efficiency accordingly in order to reduce the EUC risks referred to in the problem. The value of having the instruments and knowing the association between them was that leadership may use the measured leadership EUC initiatives to alter their behavior regarding efficiency gains of their EUC processes and measure the outcome. If the instrument developed in this research provided even a partial solution to the EUC efficiency problems identified in the prime and subprime mortgage industry, then the leaders would be able to make better 8
decisions to improve their EUC efficiency that would result in financial gain. The industry would benefit from easily implementable instruments measuring these constructs. Creswell (2003) explained that a correlational study would best determine relationships between different factors. A non-experimental correlational research design with a regression model would serve the purpose of the current study. An experimental study would not have been a right choice as the current study would not involve manipulating the predictor variable, and the intent was not to establish a cause-and-effect relationship between leadership EUC initiatives and EUC efficiency. The study involved capturing both predictor and criterion variables at one point in time and did not involve any interference or manipulation by the researcher. Panko and Port (2013) stated that EUC was pivotal to critical decision-making (Panko & Port, 2013), it seemed logical that the research revealed the existence or the non-existence of a relationship between the two variables of interest. In spite of the growth and importance of EUC, many aspects of EUC remained unanswered and invisible (Panko & Port, 2013). Panko and Port (2013) also noted that EUC supported critical gains in decision making in every corner of the firm. Moore et al. (2007) noted IT leaders within companies have struggled to formulate an efficient EUC strategy that was critical for the IT workforce. Panko and Port (2013) compared EUC with unexplained concept in physics referred as dark matter within corporate IT in organizations. The findings from the current study might add to the literature on EUC and might be helpful to IT leaders’ EUC initiatives.
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In keeping with the purpose of the study, a valid and reliable measure needed to be developed of IT leadership initiative in terms of optimization, training, uncovering data patterns, and working processes within mortgage banking organizations with respect to prime or subprime loan servicing and EUC efficiency. It was also necessary to develop a measure of EUC efficiency in terms of reliance, new EUC requirements, eliminating obsoleteness and duplication, process and design modification, resource allocation, and historical process review within mortgage banking organizations dealing with prime or subprime loan servicing. Both measures needed to demonstrate satisfactory reliability and validity. The next phase involved finding out whether a significant relationship existed between leadership EUC initiatives and EUC efficiency in the mortgage banking industry. To determine the type regression analysis, a residual plot was created to determine linearity. The last step in the study was to carry out a regression analysis to examine the nature of the relationship using scores from the Leadership EUC Initiatives scale and scores on the EUC Efficiency scale in the End-User-Computing (EUC) Efficiency in Mortgage Banking Survey (see Appendices A and B). Additional details about the study and the instrument appear in the Nature of the Study section in Chapter 1 and the Instrument section in Chapter 3. Significance of the Study The study might be important to IT leaders managing subprime and prime mortgage banking data because the knowledge obtained from the survey could help reduce spending in processing data. IT leaders might benefit from the current study by learning how they might influence the efficiency of IT systems and the infrastructure they 10
controlled. Leaders and managers required operational data regularly in order to monitor the performance of their businesses and to forecast and adjust strategies. As an example, Arend (1992) noted that, by switching to a cooperative data processing environment, Union National Bank leaders projected a savings of up to $600,000 over a 5-year period. Organizations merge with the expectation of saving data processing costs as well as gaining synergy. Efficient programming and optimization could increase savings among organizations as a part of mergers and acquisitions. Contract offer bids and annual expenses can also affect efficiency in data processing. Even and Shankaranarayanan (2009) described the dilemma that organizational leaders face given their need to examine the growing costs of managing data and associated cost–benefit trade-offs with respect to optimizing IT processes. Some research studies indicated that leaders were leaning towards emphasizing optimization and efficiency. Boisvert (2010) indicated most organizational leaders focus on optimizing various processes to build efficient products. Obreja (2009) observed the importance of analyzing strategic-level changes due to information system costs. Organizational leaders analyze their systems to explore the possibilities of optimizing to save costs (Obreja, 2009). Organizational leaders might be limited with their options due to unoptimized data structures that were constraints to optimization in the production and operational systems that might be optimizable otherwise. Advanced processors increased the speed of data processing while information systems flooded with more data became part of the database or repository and grew daily. Several research studies (Barroso, Dean & Hölzle, 2003; Bradski, Kozyrakis, Penmetsa, Raghuraman, & Ranger, 2007; Dean & Ghemawat, 2008; Vasco & Mundani, 11
2011) examining processing techniques omitted any special guidance toward EUC efficiency or optimization in the mortgage industry. The research studies did not reveal any specific insights into operational aspects of mortgage data EUC that can help in improving efficiency. Research literature lacked pertaining to EUC optimization that could assist in the efficient processing of mortgage data structures. The current study included an original approach to the problem with respect to mortgage banking data, as no research was available with regard to EUC efficiency and optimization. The researcher hoped to reveal valuable insights into the EUC efficiency and optimization potential within mortgage data. A survey that indicated how optimization related to leadership policies and standards would have been beneficial in the setting of goals and objectives. Leaders might use the information obtained to create training programs for other leaders and to train their technology workers, programmers, and analysts to take advantage of the optimization potential within the individual systems. The approach might also allow leaders to modify their estimates and project proposals by taking cost and timesavings into consideration (S. Subramaniam, personal communication, October 25, 2015). The survey results might help IT leadership to provide competitive SLAs with lower costs and could enable their organizations to win new projects among industry competitors. The study could help leaders in the software industry to provide deliverables with better performance, thereby helping them to understand the relationship between leadership EUC initiatives and EUC efficiency. The study might provide technology and industry leaders with critical information related to the potential to improve mortgage data processing EUC efficiency, and leaders might feel encouraged to 12
take advantage of the identified potential (S. Rayaprolu, personal communication, October 26, 2015). It was a common knowledge among industry professionals that a typical mainframe involved the computation of data on the order of millions of entries daily. Given the nature of the financial and mortgage industries, transactions, customer behavior, payment patterns, and industry competition mandated statistical computations of data to provide information on the data to upper management. Detailed analysis and report generation involved EUC and added to the cost. At the end of every month, it was common in the industry that technology departments usually charged the analysis and EUC costs back to the business and accumulated toward yearly servicing costs. CPU consumption in organizations using mainframes was usually in the range of millions of dollars every year (Kershaw, 2009). Leaders strived to reduce the cost of producing analytical and operational reports, but the challenge of increased EUC expenses remained in many organizations. As processes continued to undergo redesigning and redevelopment through software and technologies, there is no easy solution or algorithm that organizational leaders used while developing expensive reports or programs. The survey for the current study might help leaders adopt a standard approach by offering guidelines for maintaining savings in operational and analytical reporting in the subprime mortgage industry, as well as other industries that have similar data patterns. The literature did not reveal any special guidance toward EUC efficiency in the subprime mortgage industry. A search of the literature showed there was a paucity of literature on the subject. The results of this survey study might help the leaders to 13
develop goals and steps to optimize their processes by using a best method or a combination of methods that would result in EUC efficiency in the mortgage industry and perhaps in other industries as well. The survey might help to determine if leadership EUC initiatives would help improve efficiency in EUC systems within the mortgage banking business. The study might contribute to proper goal setting for IT leaders within their organizations. The results might also reveal the potential for improving efficiency among mortgage banking EUC processes. The findings might help designing groups whose members can create efficient designs of which EUC processes can take advantage Nature of the Study Research design. The study included the stages of development of two measures after which these measures were used to carry out a descriptive correlational design to describe the relationship between leadership EUC initiatives and EUC efficiency within the mortgage industry. Given (2007) noted descriptive studies included details about social setting, a group, a community, a situation, or a phenomenon and involved exploring events that would have happened even in the absence of the researcher. The current correlational study included the data collected from the survey technique for the two variables: leadership EUC initiatives and EUC efficiency. The predictor variable in this study was scores from the leadership EUC initiatives measure and the criterion variable was scores from EUC efficiency measure. During the pilot study, it was determined that the responses from the two measures were normally distributed with satisfactory psychometric properties, so that the predictor variable in this study, leadership EUC initiatives, and the criterion variable, 14
EUC efficiency, qualified for a correlational design (Correlational Research Design, 2005). As there was no manipulation of the predictor variable, the study was a nonexperimental study (Spector, 1981). The current study involved an attempt to reveal whether a positive relationship existed between leadership EUC initiatives and EUC efficiency. The intent of the study was not to claim any causality between the two variables but rather was descriptive and associative; hence, a correlational design using a regression model was appropriate (Sheskin, 2010). The various aspects of the current study including the type, design, method, and other key characteristics are encapsulated in Table 1. Table 1 Characteristics of the Current Study Serial no. Name 1 Type of study 2 Design 3 Method 4 Model 5 Data collection technique
Chosen method for the study Correlational study Correlational design Quantitative method Regression model Cross-sectional survey technique
The reason for carrying out a quantitative design was that the researcher, after having done qualitative research with several managers and subject matter experts for over 18 years, found value in conducting quantitative research to come up with a tool to measure leadership EUC initiatives and EUC efficiency. A qualitative study would have been more time consuming, would have required a relatively large administrative process, and would have required extensive analysis before the outcome can emerge. Developing a tool that was easy for leaders to administer and use was a valuable component of this study. The use of a quantitative measure was more valuable for 15
corporations than the use of a tool with qualitative responses. Less time would be necessary to score and analyze data from a quantitative measure. Researchers could measure and analyze responses from a larger numbers of respondents using quantitative approach. For these reasons, the quantitative approach was the choice in this research. One of the intents of the study was to examine if the two variables of interest, EUC efficiency and leadership EUC initiatives, have a relationship and acceptable reliability. Once it was established that the measures had an acceptable level of reliability and demonstrated some degree of validity, correlational analysis was the most appropriate way to determine if such a relationship existed. The reason for selecting a survey method was because it was a method that could be easily replicated by even untrained researchers. Research Process The current quantitative study involved developing a measure of leadership EUC initiatives and a measure of EUC efficiency employing a 4-point summated rating scale. After the two constructs (leadership EUC initiatives and EUC efficiency) obtained a level of reliability and validity (discussed in Chapter 3), the researcher administered the survey to measure both constructs to a sample of respondents identified through purposive sampling. Face validity, construct validity, content validity, domain relevance, and domain representation (Sireci, 2007) are some of the validity types, along with domainrelated factors that would undergo evaluation for this research study that are discussed in more detail in Chapter 3. From the reliability perspective, test–retest reliability and internal consistency reliability were the two types considered for the current study, as
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described later in Chapter 3. The Visio flowchart below in Figure 1 is the logical representation of the current study as a study map. CURRENT STUDY EUC efficiency and Leadership EUC Initiatives
QUANTITATIVE
IV Leadership EUC Initiatives
DV EUC efficiency
Manipulate IV ?
NO
NON-Experimental
CORRELATIONAL
REGRESSION
Figure 1. Study map showing the structure of the study. Research Question The research question and hypotheses for the current study are as follows: 17
Research Question: What is the relationship between leadership EUC initiatives and EUC efficiency? Developing valid and reliable measures of leadership EUC initiatives and EUC efficiency was necessary in advance of determining if the correlation between the two constructs exists. The current study presented the development of these measures with appropriate reliability and validity findings. Null and Alternate Hypotheses The null and alternate hypotheses for Research Question 1 were as follows: H0: There is no relationship between the leadership EUC initiatives measure and EUC efficiency. Ha: There is a relationship between the leadership EUC initiatives measure and EUC efficiency. Even though the researcher’s professional experience indicated that there may be a positive correlation between the predictor and criterion variables, no prior research data indicated having a directional hypothesis. However, completion of this research revealed that a positive relationship existed between the two constructs. Theoretical Framework The theoretical framework referred to different definitions, interrelated concepts, and propositions related to a research problem. In the current study, the theoretical framework would be that a link existed between leadership EUC initiatives and EUC from an efficiency perspective. Researchers have published several leadership journal articles in fields such as health care, administration, defense, and others. As Kuhn (1996) stated, several theories in relation to workplace efficiency such as Frederick Taylor’s 18
scientific management and many other theories factored in different aspects in a labor workforce by way of time and motion studies, motivational factors between men and women, competition, and optimizing any sequence of functions that a worker may perform in a normal workday. General systems theory included an expanded view of information systems in organizations with three different designs: a designer-oriented or American approach, a user-oriented or European approach, and a business-centric approach (Garrity, 2001). Garrity (2001) also noted that several reasons such as lack of support from leadership, inadequate resources, training, participation, and lack of attention to design issues caused project failures. These constraints created a challenge that leaders needed to consider and operate. The lack of literature on mortgage EUC efficiency in relation to leadership in an organization was a gap the current study might start to fill. Definitions The criterion variable for the study was EUC efficiency as measured by an instrument created with a focus on the level of optimization, redundancy, cost effectiveness, and speed of code execution within mortgage banking organizations dealing with prime or subprime loan servicing (see Appendix B). As stated by Ilias, Suki, Yasoa, & Razak (2008), different researchers have defined the term EUC according to their studies. As explained by Delligatta and Umbaugh (1993), end user computing was the process of obtaining information that a line organization needs as quickly as possible. The operational definition of EUC efficiency was the sum of the numerical values of a participant’s responses to the questions in the EUC efficiency portion of the
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survey. More precisely, EUC efficiency in this study referred to evaluations of those surveyed as to the degree of behaviors by the people to
prevent redundant EUC
design EUC processes efficiently so the processes use minimal CPU time and run in least time, where CPU time translated to cost in mortgage business.
eliminate duplicate EUC reports or processes from the past.
eliminate obsolete EUC reports from automation that may not be necessary due to evolution or change of strategy.
The predictor variable for the current study was leadership EUC initiatives, as measured by an instrument created with a focus on the guidance and directives through which leaders in the industry steer their teams toward the vision of the organization within mortgage banking organizations dealing with prime or subprime loan servicing (see Appendix B). Williams (2016) and Jablokow, Jablokow, and Seasock (2010 have revealed that effective IT leaders are involved in leading corporate innovations, workplace transformation, workforce transformation, revenue transformation and wholesale transformation. One of the responsibilities as pointed out by Williams (2016) is revenue transformation, which can source from IT savings by increasing EUC efficiency. The operational definition of leadership EUC initiatives was the sum of the numerical values of a participant’s responses to the questions in the leadership portion of the survey (see Appendix B, part A). The emphasis in the current study is the efficiency of that process that obtains the necessary information to the line organization. More
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precisely, leadership EUC initiatives in this study referred to respondents’ views as to the degree their IT leader took steps to:
ensure the EUC processes remain optimized.
ensure the technical employees involved in everyday EUC development work design and build optimized systems.
avoid redundancy in EUC operations.
provide required training to technology workers in positions capable of optimizing EUC processes.
allocate time for technology workers to review and optimize EUC processes periodically.
allocate time for technology workers to review automated reports and EUC processes to improve optimization.
optimize EUC systems.
Assumptions The current study included a few assumptions related to the sample and the survey respondents. Results of several studies (Tobey, Yamamoto & Robertson, 2014; Ha & Nguyen, 2014) conducted in other environments lead to the first assumption that the relationship in the current study between the predictor and the criterion exists and may be linear or curvilinear and consistent across the range of scores of the predictor variable. The results of the analysis in the later chapters reveal a linear relationship supporting the assumption. Tourangeau and Yan (2007) revealed that the rate of misreporting increased as the amount of embarrassing information to report increased. Consequently, the current 21
survey did not include any questions of a personal nature that might lead to personal embarrassment so it was assumed that the survey respondents provided truthful information in their survey responses. Scope The scope of the study included four survey measures totaling 55 questions, which were given to each respondent. Question one of the EUC efficiency measure, which was a screening question, was given to eliminate the participants who did not qualify the purposive sample because of insignificant levels of EUC activity. Those in the population who were not reachable to take the survey did not take part in the study. Limitations The first limitation was the quality of data available. The measures were selfreport measures and therefore the responses might be from a biased point of view or the respondents may misremember. Making the survey anonymous encouraged the respondents to answer in an unbiased manner. Another limitation was that the validity of the descriptive correlational study depended on the reliability of the survey instrument used to gather and interpret research data. Another limitation was that this was a purposive sample and therefore may not have been representative of the population. This assumption was addressed to a fair extent by expanding the respondents to several professional contacts of the researcher employed by multiple organizations and multiple geographical locations which increased the randomness of the sample. Even though a certain level of reliability and validity was demonstrated on the measures created in this study, the scope of the study did not allow for a confirmatory factor analysis. It was considered that the level of construct validity provided by concurrent validity evidence 22
would be sufficient to establish support for the hypothesis, which is the main purpose of the study. Social desirability may cause respondents to give responses that present their corporation in good light. Any participants feeling negative toward their corporation might respond with negatively biased answers. It would be impossible to establish a cause and effect relationship from these survey data or from the correlations demonstrated. The impact of this limitation was minimized to a fair extent by the anonymity aspect of the survey that encouraged unbiased answers. Measurement error may occur if the wordings of the survey items are such that the respondents answer in a way not entirely in accord with their true views. Delimitations Delimitations are factors a researcher can control or decides not to include in a study. These factors limit the ability to generalize the results of the study to the actual population. For example, the sample was not selected randomly from the population of IT professionals. The current study included selected IT professionals with experience in the mortgage industry. The study involved using only data obtained from survey findings, and thus the quality of findings is only as good as the quality of the data obtained. It might be possible to use this study to create other studies so that findings would eventually generalize to different companies whose employees service and support businesses other than prime and subprime mortgage businesses. Summary This chapter contained an introduction to the problem of EUC inefficiency leading to non-compliance and loss to mortgage organizations. The background 23
information included a historical view of EUC processing and the associated costs. The theoretical framework of the quantitative correlational study showed the leadership theories relating to efficiency in different organizations. Different leadership studies in professions such as nursing, education, sports, production, and others have shown how leadership has played a role in affecting efficiency. A gap existed due to the lack of a tool that would measure EUC efficiency in the mortgage industry. It was worthwhile looking into the efficiency of current mainframe systems before investing in newer technology and software. The research results from the survey might show the factors that IT leaders might consider while developing goals and strategies for their designs in the future. The study showed the relationship between the predictor and the criterion variables that might help IT leadership with the strategies for different project designs. IT leadership required multiple levels of expertise, including technology and management. These two specific requirements make IT leadership unique. Leaders also need to work on efficient workforce development and developing a culture that nourishes constant improvement and encourages individuals to search for opportunities to improve and to design processes efficiently. Although the study has limitations and delimitations, the correlational study might contribute a way to improve efficiency in mortgage application systems and reduce EUC costs. The next chapter includes the literature review and an expanded theoretical framework with a comparison of leadership attributes and employee perceptions related to efficiency within organizations. Chapter 2 also includes a comprehensive literature review of IT processes and existing research.
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Chapter 2 Review of the Literature The literature review includes research and literature related to EUC process efficiency related and any relationship it may show to leadership emphasis. The literature review for the current study includes sources from the University of Phoenix online electronic library, ProQuest, EBSCOhost, Gale Power Search databases, journals, books, online search engine Google for current news. Although most searches were constrained to peer-reviewed, empirical journal articles, the keywords were also searched without these constraints in case some article could lead to relevant valuable information. Theoretical framework search strings included data processing, optimization, IT efficiency, process improvement, performance issues, EUC, EUC efficiency, End User Computing, correlational study, peer-reviewed, CPU cost, leadership, IT leadership initiatives, organizational culture, human performance improvement, inefficient programming, skilled resources, and CMM (Capability Maturity Model). Also included are the description and justification of descriptive versus experimental research, correlational design versus other descriptive designs, and the reasoning behind the selected choice of using a descriptive correlational design for the current study. It also includes information from research articles related to optimization, inefficiencies in programming, EUC, and management initiatives affecting efficiency. Regarding the study technique of EUC efficiency, because no researchers have addressed EUC efficiency, the selected process has no precedence; however, the use of surveys to examine constructs in exploring leadership and criterion variables related to leadership has been widespread (i.e., Servant Leadership Survey, Measuring Information 25
Technology Performance survey). However, this study is not about leadership styles but rather is focused on the functionality of the leaders in improving the level of EUC in their organizations. This aspect has not been researched in any previous literature. The general focus of leadership has been on styles rather than on responsibilities or functions of leaders in the IT industry geared towards EUC efficiency. Therefore, this study did not include in depth analysis of styles of leadership and theories. The leaders of many companies have difficulty justifying their investments in IT and EUC efficiency (Irani et al., 1998). EUC efficiency is a topic that, for many, may be hard to justify because an IT professional may not be able to highlight unknown benefits due to an optimization attempt not being in place that can affect service-level agreements (SLAs). Goo et al. (2009) defined an SLA as a formal contract between two parties containing change and governance details. In the IT industry, SLAs define the level of service such as maximum duration of failures, time between system failures, delivery time, and many more aspects as agreed between a business and a service provider. SLAs may also help service providers and receivers to plan their resources and funds and to process timelines to serve their customers. A banking organization with several departments can even have SLAs that relate to technology-related services by the serviceproviding department to support the service-receiving department within an agreed upon timeline, accuracy, performance, and cost in a secure fashion. Efficiency or inefficiency of EUCs may affect SLAs directly in many organizations. Montesinos-Delgado (1992) indicated poorly designed systems and cumbersome reporting processes are an issue in developing countries such as those in Latin America. This finding was based on the study using data collected from 1050 26
survey questionnaires in Costa Rica, Ecuador, and Guatemala that resulted in 414 responses. The data was analyzed using general linear data model methods that supported the conclusion. Poor designs can logically result in long running processes affecting SLAs. SLAs can impact the profitability and daily operations of different businesses; if the service providers do not meet benchmark efficiency standards according to, SLAs, the business can incur a loss of reputation, profit, increase risk or customer base as a result (Aia, 2009). Organizational leaders therefore own and use online and batch processes that might range from less than perfect to highly inefficient systems (Aia, 2009). The reason for this inefficiency may be the lack of motivation by the vendors because they would receive no additional financial gain for any optimization efforts beyond designing systems that barely meet the SLAs. Powell and Moore (2002) revealed the importance of EUC growth alongside growth in technology. The revelation was based on the literature review from the early 1980s in comparison with the IT revolution in the 1990s. Because performance measurement of EUC processes is not a mandatory function of all teams, the procedures developed might involve inefficient EUC processes that result in wasted time and money. Organizational leaders may have maintained inefficient applications that remain unnoticed for several years. Therefore, leaders need to encourage teams to locate inefficient processes and improvise application performance and speed with appropriate targets as money saved annually affects company profits. It is common knowledge in the IT industry that during the life of an organization, managements change, market dynamics change, and strategies change. An application platform originally built for one purpose therefore may serve many purposes, and the original design might not be the best 27
design suited for growth because of strategy changes over the years. EUC requirements might change accordingly and need to stay in sync with the strategy. In a journal article about the convergence of organizational computing and EUC, McLean and Kappelman (1993) explained the importance of EUC and of how end users need to work with IT professionals for successful business operations. The explanation was based on a survey conducted among senior information systems executives across different consumer-goods manufacturing firms on their patterns of EUC that revealed an expansion of EUC definition to a broader context and that EUC was successful among organizations. After the 2008 economic downturn, the banking industry gained enormous exposure on different levels from ethical aspects and financial irregularities in relation to data accuracy and timely information delivery that may have resulted in changes to existing EUC and new EUC requirements through data warehouses. Data warehouse, by definition is the massive database that serves as a repository from which useful information can be retrieved by means of a process called mining (Business Dictionary, 2015). Data retrieval and computation are all part of EUC operation within an organization. Several of the changes might have originated from large amounts of EUC data and processes. The obsolete processes might still be using resources without anyone even knowing that the processes still exist. Because the relationship between IT leadership EUC initiatives and EUC efficiency has not been shown, it logically follows that leaders would not necessarily see a need to emphasize efficiency as part of the standard EUC design and would not see the requirement to measure and calibrate it in a standard form. Because poor efficiency impacts data intensive computing with issues in cost, reliability and energy, technology 28
leaders are forced to rethink the direction of future research (Anderson & Tucek, 2010). Many software projects operate under a limited buffer or no buffer at all, which means that project teams do not spend significant resources on analyzing performance. Close-up media Inc (CMI) who published an article in 2014 about some organizations’ applications that waste resources, therefore increasing carbon footprint, discussed this. CMI reported that a company named Cast had developed the Green IT index, with which they analyzed about 1800 enterprise applications finding that almost 97.5% of the applications’ efficiency was less than 3.9 in a scale of 1 to 4 with 4 being most environmentally sustainable. Cast Leadership measured this as an essential element for process optimization. They emphasized that leaders need to motivate their employees with incentives or awards for new and beneficial ideas and that those who would only follow recommendations on a periodic basis may improve the efficiency of their EUCs (Kendrick, 2011). Organizational leaders face pressure and challenges to form winning strategies, to harness technology, to manage resources efficiently, to maintain information security, to manage data, and to plan for succession (Hoving, 2007). Organizational leaders also need to keep a close eye on performance of data processing to run applications as defined in the SLA contract. Service-level contracts are performance-based contracts with penalties for underperformance (Liang & Atkins, 2013). Adding processing or enhancements might risk extending SLA and may mean penalties and losses for the organization. This may be instrumental in the lack of research on the topic of encouraging measurement of EUC efficiency.
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Leadership is one of the most researched topics in any discipline of study. A research article published by Giltinane (2013) reveals three common types of leadership styles; transactional leadership, transformational leadership and situational leadership. While Giltinane recognizes transformational leadership style as the most popular style, the need for a situational leadership style is appropriate with constantly changing health services needs. Further, the research study recommends a flexible leadership style based on constantly changing needs and suggests that situational leadership might work well for the nursing discipline. Transformational leadership empowers employees although it has a certain level of risk that employees may take advantage of the freedom that the leadership style offers (Emerald Insight, 2015). In addition, the article revealed the other attributes of transformational leadership that included leading by example as a role model, generating commitment through a shared vision, enabling employees to question the organizational assumptions, and empowering employees to come up with alternate solutions to a problem. Information on these leadership styles was valuable in the development of a pool of items for the IT leadership initiative measure. Manktelow (2015) presented another way of grouping different leadership theories: (a) Trait theories (b) Behavioral theories (c) Contingency theories and (d) Power and Influence theories. While there may not be one solution to different types of teams and organizations, awareness of different characteristics of leadership can help one choose the right style depending on the situation. Manktelow (2015) also stated that trait theories are based on different traits and qualities that different leaders share (e.g, integrity, empathy, likability, good decision-making skills). Behavioral theories are 30
based on three style of leadership behavior: (a) Autocratic, (b) Democratic, and (c) Laissez-faire leaders (Fowler, 2016). These three styles show different levels of employee support of a leader’s decisions. Contingency theories present styles based on different situations: (a) Path-goal theory and (b) Fiedler’s Contingency Model. Power and influence theories are based on how a leader uses power and influence to get results. Different theories have strengths and weaknesses. Leadership in a health organization pointed out that an authoritative approach can be perceived as bullying, a paternalistic approach can allude to an appearance of favoritism and a transformational leadership can have a negative effect if a leader leaves an organization weakening the setup (Fowler, 2016). Therefore, one needs to weigh in on both the strengths and weaknesses of each style while trying to motivate a team of technology workers to improve EUC efficiency of their systems. While there are several leadership theories available and were discussed above, several organizations have incentives and motivational strategies pointing to the Transformation leadership style (Manktelow, 2015). The second construct in this study is based on efficiency and optimization. Researchers have conducted optimization research on databases and systems but have not explored optimization as it relates to EUC efficiency in the mortgage industry with IT leadership EUC initiatives within an IT entity. This has left a gap in the literature. Based on the data from the U.S. Census Bureau (2011), more than 131 million houses are in the United States, and approximately 51 million of those houses are mortgaged. Approximately 67% of the homes in the United States have either a prime or a subprime mortgage. 31
The number of homes with mortgages in the United States translates to the volume of prime and subprime data within the mortgage databases. If companies that process this information handled the mortgages for these homes, the data-flow would go through a series of steps, updates, and refinements before they appeared in a report. Mortgage data analysis involved EUC, as leaders required operational intelligence to develop their business strategies. Optimization involves selecting the best element from a set of options available. As stated in Newswire (2014), the media company Nielsen found that efficient reach of their customers was based on optimizing Nielsen’s advertisements in the media. Optimization and efficiency of IT systems requires an allocation of time that leadership might or might not support. Several large organizations have programming teams as part of EUC to satisfy the data needs of the operational teams that maintain the daily operational functions in a business. Marslof, Gallivan, and Wijshoff (1999) found that optimization occurred with mathematical computations involving scalars, vectors, and dense matrices through identifying structures within sparse matrices and by customizing their solutions accordingly. The finding was based on efforts of increasing efficiency of software called Falcon developed from prototyping software called Matlab (Marslof et al., 1999). The mortgage data was the focus in the current study and bears some of the unique data structure described by Marslof et al (1999). Fitzgerald and O’Kane (1999) noticed that organizing and attaining a certain optimization level is a time-consuming process and noted that moving from one level to another can take years. They also noted that leaders do not attempt the optimization due to pending projects in the pipeline taking a higher priority. The finding was based on a case study on the progression of Motorola 32
cellular group to CMM level four and a dimension of optimization of its software development process. IT organizations’ cultures include optimization and EUC efficiency. This study might help identify the level of emphasis that different IT professionals have provided while developing and supporting EUC systems within the mortgage industry in the United States. It is the proposal of this author that the information gathered through a survey should reveal a relationship between the efficiency of different EUC systems and the leadership emphasis levels within different organizations that process mortgage data. Documentation Historical Perspective EUC has been a critical component of the IT industry since the 1980s and has gained increasing importance with the advent of analytic and data needs expected from IT leaders. Mortgage data have received significant exposure from several organizations such as the U.S. Securities and Exchange Commission (SEC, 2012), Fannie Mae, and Freddie Mac. Increasing analytics and data transformations brought the need for efficiency and optimization. Different researchers provided ideas to improve efficiency of IT processes (Abu-Alkheil, Burghof, & Khan, 2012; Jalics, 1989). As EUC continues to grow, several organizations are making sizeable investments in this area (Guimaraes & Igbaria, 1997). A field study among 187 end-users investigating EUC effectiveness followed by development and testing of a conceptual path analytic model revealed that end-user computer experience and attitudes were closely related to the general system usage (Guimaraes & Igbaria, 1997).
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Jalics (1989) presented ideas about efficiency potential while building COBOL applications. In his article, he noted that management does not create performance goals pertaining to program efficiency and this can result in unoptimized and inefficient EUC systems. Management had little knowledge about how fast programs should run and therefore did not build the performance as part of their goals (Jalics, 1989). Abu-Alkheil, et al. (2012) conducted a study on relative efficiency performance of the Islamic Bank of Britain using a two stage Data Development Analysis model to determine the impact of internal and external factors on efficiency. In his research article, Abu-Alkheil et al. (2012) indicated that management issues are the reason for inefficiencies in the Islamic Bank of Britain. Because of the downturn of the mortgage industry in 2008 and with new government regulations such as Dodd-Frank certification and the Housing and Economic Recovery Act of 2008, EUC activity in the banking industry increased. These regulations required data miners in mortgage banking companies to be more analytical, thereby increasing EUC activity. The regulation requires leaders of mortgage banking and financial firms to assess their risk levels on a periodic basis and to report to the government while maintaining certain thresholds and other limits. The various details required by different regulations involve meeting requirements that require EUC processing. The government regulations affect both prime and subprime mortgage loans that increase EUC. Prime mortgages refer to mortgage loans for customers with good credit and a qualifying score required by mortgage bankers (Amromin & Paulson, 2009). Subprime mortgages refer to loans to customers with less than perfect credit that require them to purchase loans at higher interest rates. Subprime data processing, while 34
necessary for enhancing business operations and analytics, is increasingly costly to manage and requires more EUC. The cost of data processing for report generation usually increases the chargeback to the technology group and reduces profitability of the subprime business unit. Profitability can be impacted by increased EUC activity caused by erroneous processing if not prevented in a timely fashion (Deivasigamani, 2015). The article by Deivasigamani (2015) also indicated that timely alerts and prompt treatment of errors can reduce EUC costs and analyst time. Subprime mortgage bankers, in an effort to collect the premiums from subprime customers, have programs and processes to support that effort. It is well known in the industry that these processes, programs, and reports require daily processing of data through EUC to tune the strategies and operational activities to meet the organizational goals. The volume of subprime data increases the cost of CPU use, the number of management information system programmers, the IT infrastructure, and the IT expenditure. Managers do not always understand the level of efficiency of the developed software programs (Jalics, 1989). The current study was a quantitative correlational study. A relationship between leadership EUC initiatives and EUC efficiency within mortgage banking organizations who deal with prime or subprime loans is hypothesized. Leadership EUC initiatives will be quantified by determining (a) requirements for reporting and efficiency-review frequency, (b) requirements for new reporting, and (c) requirements for timely pruning of obsolescence and redundancy. EUC efficiency will be measured using a survey created for the purpose of determining (a) optimization, (b)
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training, (c) researching improvements and redundancies, (d) quality control, and (e) technology. It is the objective of the author to introduce measurement tools to measure Leadership EUC initiatives and EUC efficiency measures that might serve to encourage leaders in the IT industry. Leaders may then emphasize EUC efficiency and motivate their subordinates to save costs and CPU resources. The study also revealed that the mortgage banking EUC has the potential to improve efficiency due to the nature of the mortgage loan collection data. One loan might create multiple collection records due to the phone conversations, payment activities, and loan treatments occurring simultaneously. The experiences of many IT users reveal that data- increase in the form of files, tables, and databases is notable in the mortgage banking industry involving prime and especially subprime mortgage banking that contributes to inefficiencies in EUC. The current study included surveys of leadership EUC Initiatives and End-User Computing Efficiency with the purpose of measuring these two constructs. The first construct concerns the leadership EUC initiatives taken by company leaders and the second concerns the level of efficiency experienced by end users in the mainframe industry. The respondents included IT professionals with experience working in mainframe mortgage banking organizations. Organizations with efficient data-processing systems will have a competitive edge over other companies that are less efficient. Cooke and Bunt (1975) noted that cumulative effects of simple programming errors contributed vastly to the maintenance of operational programs increasing EUC cost. Programmers spent most of their time identifying and correcting errors (Cooke &
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Bunt, 1975). Programming errors might result in erroneous data that could consume additional time to correct the errors and delay resumption of normal business functions. In 1979, McLean wrote an article about demand for computer based information systems and the capacity of data processing organizations to meet demand. In the article, McLean (1979) stated that demand for growing end-user-based computing requirements led to a new category of programmers and application developers whose existence eased the data processing department’s capacity. McLean (1979) also found that the focus of application developers was on managerial efficiency and not application efficiency and noticed the existence of a disorganized way of installing applications resulting in inefficiency of the end user system. Benbasat and Vessey (1980) in their research article about programmer and analyst time and cost estimation presented several methods of estimation techniques to measure programmer efficiency. They presented different methods to estimate cost of programmer and analyst time of which some were simple and some were complex based on parametric equations. Some of the methods discussed in this article are based on personal experience, analogy, work factors, standards such as number of lines of code, and parametric equations derived using multiple regression techniques based on multiple parameters such as man-months, number of sub-programs, mathematical instructions, programming language, and experience levels of the programmer. Benbassat & Vessey (1980) made it clear that managers were missing an important factor which was, the allocation of resources did not include exclusively assessing and improving the efficiency of developed programs. A different method proposed by Chrysler (1978) recommended the study of variables within a framework that affected programmer performance. He 37
developed a multiple regression equation based on five different variables related to programming that resulted in a correlation coefficient value of 0.836 which was very significant. He included the programming experience, number of input files, number of control break totals, number of input edits, and the number of input fields. While programming languages and technology has changed over the years, many of the attributes he used may still be applicable at this time. Alavi and Weiss (1985) showed EUC is a rapidly growing phenomenon within organizations, and concern about potential organizational risks of EUC is increasing, which indicates that leadership might find it beneficial to allocate more resources to mitigate EUC risks and foster favorable growth. Their research article included six categories of risks and recommendations referred to as controls. Those controls were used to mitigate the risks that the operational teams experienced in organizations. Leadership was presented with a choice of controls so that appropriate steps were taken. EUC systems may benefit from audits to ensure process optimization and efficiency. According to Alavi and Weiss (1985), improving optimization using multiprocessing systems is essential in both small-scale and large-scale data-processing centers. Zhang and Qin (1991) presented a study that resulted in a design for large multiprocessor systems. The study included several analytical models to predict and evaluate non-uniform memory access multiprocessor operations. Some of the measurements specified by Zhang and Qin might affect EUC processing and pertain to process scheduling, process synchronization, remote memory access, interprocessor communication, and memory contention. IT professionals can determine these
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measurements from the hardware capacity and can determine the efficiency of hardware performance and system execution logs. In a different study, Ghaemi, Fard, Tabatabaee, and Sadeghizadeh (2008) presented that complexities of relational database queries increased due to distributed applications. They also recognized the need for better algorithms to optimize the process. They recommended semi-optimal and faster approaches to database access by a multiagent mechanism to increase efficiency. Grun, Rauber, and Rohrig (1998) presented a study that detailed the issues of compiler and operating system issues in the programming environment called P4 where developers can program efficient code without being concerned with the architectural details of the underlying system. The study further revealed optimization through parallel programming with shared memory through the use of a special type of shared memory computer, SB-PRAM, which might improve EUC performance by reading and writing while each processor shares memory at the same time. Grun et al. (1998) recommended that the suggested parallel programming would be an optimal choice for several challenging environments. In addition to the different approaches to optimization, Bauer, Dengler, Paul, and Meyer (2000) observed software agents were often not able to identify the root cause of problems and therefore offer solutions. Bauer et al (2000) suggested that an agent who was unable to resolve a problem should reach out to another agent with a better skill for help and upon resolution, learn the solution using programming-by-demonstration framework (PBD). If these problems are not resolved, SLAs in EUC processes may be affected (Bauer et al, 2000). Leadership in organizations must meet SLAs, and any breach can affect business operations. Efficiency becomes a concern as older techniques 39
from original designs might not work with refinements to the business logic. Grossman et al. (2005) mentioned the existence of prevalent inefficiencies among data mining and data analysis applications. Ho and Pepyne (2002) explained an impossibility theorem called No-Free-Lunch Theorem of optimization (NFLT). The theorem clarified that an optimization method can be superior to another optimization method only if the technique is customized. Several analyses over the years pertaining to optimization, efficiency, and integration revealed a general purpose solution is impossible and cannot outperform another if IT developers customize and specialize it to the structure of a specific problem (Ho & Pepyne, 2002). Matloff (2004) noted that companies took several measures to minimize costs. Companies spent 15% to 40% less money on projects that were off-shored and spent 15% to 33% less on workers brought from offshore on H1-B visas into the United States. The companies also saved money by taking advantage of tax loopholes and L-1 visas. Researchers at the U.S. Government Accountability Office conducted a study of major challenges facing federal agency chief information officers and revealed two major challenges: finding effective management and obtaining the resources required for their projects (Powner, 2004). Further analysis by Powner (2004) on agency chief information officers revealed additional challenges were in capital planning and management, architecture, information security, workforce planning, acquisition, development and integration, and information dissemination. The focus of the current research relates to some of the challenges noted by Powner regarding workforce planning that can affect efficiency and optimization in IT processes.
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Kamal (2005) questioned the preparedness of U.S. businesses regarding the demand faced for IT workers. He noted the shortage of highly skilled workers in the IT industry in the United States and recognized the projected increasing trend. Students newly graduated from college did not consider the computer profession to be their primary career but worked in IT contributing toward EUC inefficiency until trained (Kamal, 2005). Dorner (2009) used the focus-dominance model to determine the value of IT personnel in small and medium sized enterprises. The lack of highly skilled technical resources can result in a workforce that might not be able to recognize the potential for improving efficiency or any opportunities for saving CPU time as part of EUC. Organizational culture and process management that foster objective measurement and organizational learning are essential elements for systematic improvements (Slovensky, 2007). Slovensky (2007) also revealed organizational culture plays a role in creating a sustainable process, and leadership plays a role in forming that organizational culture. It follows logically that organizational culture and leadership will impact EUC processes in an organization. Milliken (2002) discussed a proposal initiative for the development of a qualification for headship in Northern Ireland. Leadership is defined in this article as a process by which a leader’s goals are achieved in an organization. In another research by Iheriohanma (2009), the importance between knowledge leadership and employee productivity was demonstrated. A survey from 180 respondents showed that knowledge leadership can be a catalyst to improve productivity. This relates to having the right leadership to influence appropriate initiatives.
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Studies within the past ten years also show that leadership development programs provide opportunities to improve quality and efficiency in health care (McAlearney, Scheck, & Butler, 2008). They used three qualitative studies of leadership development to determine the programs that would result in improving quality and efficiency in health organizations. Osseo-Asare, Longbottom, and Chourides (2007) conducted a study on education in the United Kingdom with an emphasis on adopting a certain style of leadership to sustain management and best practices for total quality management in higher studies institutions in the UK. The study involved surveys, interviews, and inductive analysis from 42 higher education institutions (HEI) in the UK between 2000 and 2005. The result of the study revealed a weak association between management efficiency and effectiveness on total quality management. Management efficiency and effectiveness on total quality management was measured by a survey of 42 HEIs in the UK and these were not efficient enough measures to be able to adequately show the relationship. In a different study, Jing, Liu, and Liu (2008) found that leadership capabilities have influence over enterprise efficiency using a regression analysis performed on the data collected from 1500 Chinese firms from five cities in 2001. The study revealed that the results were different for state owned and non-state-owned firms. The differences were mainly due to incentive differences and issues related to property rights. Pitt and Bunamo (2008) examined leadership excellence among top-performing units in the U.S. Air Force Office of Special Investigations (OSI), and reverse engineered performance excellence to identify effective leadership. The study revealed a positive relationship between effective leadership and performance excellence. The research involved 42
interviews and statistical analysis of data from 150 OSI field detachments over the period of 2001 to 2004. Thorpe (2008) revealed that leadership role was pivotal to the efficiency and quality of nursing services. Thorpe (2008) presented the importance of leadership’s planning, their formal and informal roles, different management and leadership theories and summarizes with their importance towards a efficient nursing environment. Amagoh’s (2009) article focused on identifying leadership development programs based on different approaches such as integrated solutions approach, experience-based approach, and other miscellaneous approaches based on personal growth strategies. Amagoh (2009) noted organizational effectiveness and survival were dependent on the selection and development of current and future leaders. Lubowe, Cipollari, and Antoine (2009), Altholz and Frese (2009), and Torrelas et al. (2009) showed culture is a major factor that can affect efficiency within an organization. Leadership within an organization has an influence on culture. Altholz and Frese (2009) noted the cultural influences of an organization determine organizational structures and organizational processes, which mean organizational culture, can affect processes and can make a difference in the efficiency of processes. Altholz and Frese (2009) indicated organizational culture has an influence on process efficiency. Murugan (2009) studied the performance of employees in the IT industry in Chennai, India, and revealed organizational culture remained a major factor. The study was descriptive and used a survey with 495 completed questionnaires from a multi-staged random sample. Among a number of findings, the results indicated a strong relationship between leadership styles and worker participation.
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Research by Von Urff Kaufeld, Chari, and Freeme (2009) showed growth in the IT industry included several unique challenges to organizations and technology workers, resulting in elevated pressure on IT professionals and executives to achieve unrealistic results. Von Urff Kaufeld et al. (2009) noted poor leadership in the IT industry leads to increased pressure on IT workers, improper workload distribution, and unrealistic targets. They also recommended models that help develop the right leadership skills by shifting responsibilities from primary leadership to individual business units, which might help leaders understand processes better, lead IT teams, and understand the efforts of IT professionals. Altholz and Frese (2009) noted organizational culture can create a competitive advantage over other companies and can help to reduce cost by efficient processing, which indicates leadership can influence and form the foundation of a culture that values efficiency. Cohn et al. (2009) found health care organizations whose leaders welcome innovation are in a better position to reap the benefits of IT. The research article was written by several healthcare professionals with their experience from medical institutions. Cohn et al. (2009) also noted the adoption of health care IT greatly depends on the culture within the health care organization. The potential benefits of and the case for optimization and improving the efficiency of the operational process depend on the open-mindedness of the culture influenced by physicians and other leaders of the organizational teams (Cohn et al., 2009). Vanourek and Vanourek (2010) presented a few turnarounds of organizations that were in deep trouble financially due to trust issues where the entire governing board was replaced. In the article, the author demonstrated how the leaders emerged and created a 44
positive impact. Leaders need to take steps to create a positive culture to build efficient and optimized processes as part of every project developed. Quality, efficiency, and optimization within organizations are values that employees can acquire from organizational culture. The power to develop positive culture lies in the hands of effective leaders. Leadership is a critical part of an organization in IT projects and the quality of products created by employees. Leidner and Kayworth (2006) reviewed the research literature about culture and its relationship to IT. The study revealed that culture within an organization can affect the managerial process and IT in a direct or indirect fashion and can ultimately influence the efficient implementation and use of IT. Organizational culture therefore can influence the standards and practices within a group setup and leadership can influence culture within the organization. Periodic or frequent acquisitions may pose a challenge to the efficiency the acquired firms may have their own systems that also need to be managed by the acquiring firm. Leaders need to realize that efficiencies are crucial to increasing profits in an IT business unit. Rondeau, Ragu-Nathan, and Vonderembse (2010) conducted a study on a firm’s information systems (IS) strategy on responsiveness, end-user training effectiveness, and development of EUC skill. The research included a structural equation model of data collected from 265 senior manufacturing managers. The study revealed business leaders who failed to plan their information systems might limit effectiveness and end-user competence. Liu, Chen, Klein, and Jiang (2009) conducted a study of conflict during development and used survey data collected from 85 respondents and the results of their analysis revealed that team conflicts could affect the quality of the software product. Liu 45
et al. (2009) noted other factors such as conflict within programming teams could also affect efficiency, scheduling, budgets, and overall quality of the product. This is a clear example of how leadership impacts product quality. IT Leadership and Structure Jablokow, Jablokow, and Seasock (2010) recognized the need to investigate IT leadership. The research revealed that identifying problems is one of the main challenges faced by leadership. Figure 2 shows how leadership in IT organizations influences both IT processes, strategies based on the requirements and needs of IT. The figure clarifies that leadership forms short-term and long-term strategies that are also subject to business and compliance requirements through various IT projects that are developed and implemented. Each of these projects may include complex EUC calculations and ultimately will affect the cost of EUC. Leadership’s response to this cost would determine their decisions on their next iteration shown by the dotted line.
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LEADERSHIP
SHORT-TERM & LONG-TERM STRATEGIES
BUSINESS / LEGAL REQUIREMENTS
TECHNICAL REQUIREMENTS
IT PROJECT-1
IT PROJECT-2
IT PROJECT-3
IT PROJECT n
EUC-1
EUC-2
EUC-3
EUC n
EUC
BUSINESS LEADERS
Figure 2. Business processes in mortgage servicing organizations. A balance exists between the needs of the users and business strategies based on organizational goals. The illustration in figure 3 shows the EUC impact of corporate culture that consists of the compound impact of financial status, culture and work habits within an organization. The representation may be extended to businesses that are even different from mortgage banking and in other countries as well.
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CORPORATE CULTURE
ORGANIZATIONAL FINANCIAL STATUS
STRUCTURE
WORK HABITS
RESULTING IMPACT
EUC EFFICIENCY
Figure 3. The influence of culture on effectiveness of process. CMM Standards Organizational leaders seek to attain top CMM levels which would indicate a decrease in their information systems costs (Nelson, Armstrong, Buche, & Ghods, 2000). Various methods that organizations may achieve cost reduction is by eliminating obsolete processes, demising unnecessary systems, improvising to better technology and software that may be less expensive and less expensive to maintain where support personnel are easily available. The authors examined the experience of a CMM Level 3 IS organization within a large manufacturing firm. Level 3 indicates that the organization has the manufacturing and software engineering processes well documented and benefited by their CMM standard. The study shows that organizations achieve their cost goals by following CMM standards. Organizations fit into one of the five levels of CMM based on their maturity. Several factors determine the CMM level of an organization, such as documentation, standardization, and integration. According to Nelson et al. (2000), only 12 organizations in the world attained CMM Level 5, which indicates most IT organizations still have a great potential to reduce costs by improving efficiency. 48
Regulatory Entities Policies set up by regulatory agencies in different counties, states, and countries can affect leadership outlooks and strategies. Some of these entities are the SEC, BASEL (a global banking regulatory agency in Switzerland), the U.S. federal government, and the U.S. Department of the Treasury. In 2012, a U.S. federal agency filed a lawsuit against Goldman Sachs with allegations of fraudulent activities. Davidoff, Morrison, and Wilhelm (2012) noted the results of the litigation brought structural changes in the investment banking industry, and revealed that regulations affecting the investments can also affect mortgage-backed securities. EUC inefficiencies might exist in systems related to such regulatory reporting processes. Leadership within IT Organizations Leadership and teamwork are necessary for success in IT projects, and IT project leaders must display leadership characteristics that can improve technical project performance (Randeree & Ninan, 2011). These authors conducted a research in United Arab Emirates (UAE) to determine the effectiveness of leadership and team processes in IT using data collected from 42 project teams across different sectors. The research revealed that leadership involved getting results and building goal oriented teams. An IT leader therefore needs to be able to influence people, be technically sound, and bring in a diverse workforce to work together to improve efficiency despite their differences (Randeree & Ninan, 2011). Leadership Challenges Common challenges of modern leaders other than human factor and culture, are (a) lack of knowledge of the EUC optimization benefit, (b) lack of efficiency level of 49
EUC reporting processes, (c) lack of availability of skilled resources, (d) lack of business knowledge to optimize systems, (e) lack of clear requirements for developing reporting systems, (f) inability to police performance properly, and (h) lack of knowledge of longterm benefits of other processes that are tangible and challenging to measure. Haneefa (2007) investigated the application of information and communication technologies in special libraries in Kerala, India. The research revealed the challenges that organizational leaders face in finding skilled resources to automate library management activities. Haneefa (2007) also noted that outsourcing work from businesses resulted in a loss of control of the efficiencies of outsourced processes. The outsourcing challenges can also affect the ability of a leader to position a strong team that can advise on appropriate changes. Graham (2005) published an article about maximizing the investment in IT training and referenced the heavy demand in Europe and UK for IT skills. Graham (2005) found Europe needed more than 1 million skilled workers. Graham also revealed IT leaders suffered from budget cuts that resulted in untrained employees and therefore decreased EUC efficiency. The research also included the perceived notion that retraining the existing workforce was expensive and time consuming and therefore not pursued, which meant a continuation of the inefficiency. In another study, Hunter (2008) showed declining science and engineering graduates in Europe and a challenge to retain students to continue higher education in certain science disciplines. The findings also indicated that the German Ministry of Economy and Technology had a 30% increase in vacancies for engineers. Hunter (2008) also mentioned the United States fared well in attracting skilled foreign workers, which resulted in approximately 30% of science 50
doctorates and 50% of engineering doctorates coming from foreign countries. The pattern resulted in a dependency on foreign workers. As foreign workers departed with expiring travel-visas, gaps resulted. IT leadership subsequently hired a domestic workforce that did not have the required skills to improve efficiency, which resulted in inefficient IT processes. Blank and Barratt (1988) published a journal article about the challenges that institutions face to find a good business analyst possessing all ideal qualities. They revealed a demand for skilled designers and developers because of a new development and design workload available. They expressed the importance of having skilled analysts and explored the expectation that analysts and designers should be up-to-date in mainframe technology and other technologies to construct efficient systems. Also mentioned was the lack of confidence that business users would have if incompetent professionals built the system. Information Technology Association of America showed that leaders of most companies felt dissatisfied with the preparation of technology students in educational institutions, and there was a major shortage for skilled technology workers in several organizations (Jo-Ann, 1988). Jo-Ann (1988) also revealed competition existed for hiring a skilled workforce among technology companies, even among top industry leaders such as Microsoft. Jo-Ann (1988) pointed to a bidding war among companies competing for similar skills and how the layoff from one organization resulted in a hiring event in another organization for skilled technology workers. Jo-Ann (1998) revealed that technology companies required high skills and learning ability. The demand for skilled workers was high in the late 1990s. A series of layoffs after the downturn of the economy in 2008 resulted in decreased manpower in 51
organizations that has gone through several phases of workforce trimming that resulted in the loss of a skilled workforce that can help improve efficiency. The studies by Jo-Ann (1998), Graham (2005), Hunter (2008), and Blank and and Barratt (1988) clearly reveal the burden that lies on leadership to provide the required training in their organizations to improve the skill levels of their workforce to bring an optimum performance and to build efficient systems. Employers might also face the challenge of keeping skilled and motivated employees challenged so they do not start looking for more interesting opportunities available outside their current organization. In a slow economy, leaders might have to assure their employees that resources will be available in the future. Even though the economy and hiring have slowed due to increased outsourcing, strategic and key skilled positions still exist in the United States. Human resources personnel are unable to fill all vacancies, and Congress eased policies on H1-B visas to encourage skilled foreign workers to come into the United States (Gershon, 2000). Most of the jobs outsourced are low-skill, maintenance, and routine jobs that do not require high proficiency in technological skills. If organizational leaders have trouble finding skilled resources in automating manual processes, advanced skills to optimize applications and EUC reporting will also be hard to find. In the absence of advanced skills, leadership in organizations will have little or no help from existing, limited expertise to perform advanced functions in improvising the performance of their systems. Malone and Belady (2008) researched data center efficiency in companies with power use efficiency and computer power efficiency. The research involved analysis of data from two surveys and considered factors such as power consumption, readings at 52
different points during the year and hardware costs. Leaders had the challenge of selecting efficient cooling mechanisms and considering different models so they would not ignore critical performance factors. Rao, Babu, Damodaram, and Chaparala (2010) conducted a study to identify modules that can be optimized for efficiency based on data mining using a software called Weka (open source software from New Zealand). The data for the research was obtained from Halstead metrics on KC1 dataset available in the public domain. Rao et al. (2010) stated that several billion lines of code that could be examined for optimization are running using legacy systems and are critical for several businesses. Rao et al. (2010) also revealed IT managers have an important responsibility to maintain the existing legacy system and optimize modules as required while there might be a need to use different technologies in order to attain increased efficiency. Organizational leaders constantly look for opportunities to be profitable and might have to spot available opportunities to optimize their existing operational processes. Technologies change every few years in terms of advanced features and presentation methods of data. These changes call for a redefinition of these areas in IT and include the power of computing, Internet infrastructure, telecommunications, new features with emerging trends, security and disaster recovery, and environmentally green IT (Xing, Wang, & Peterson, 2011). Switching to new technologies might be an opportunity for leaders to improvise organizational processes, redesign, take a step back, and look at the wider impact of the organization. The extant literature in optimization showed that various optimization techniques exist. These techniques include indexing (Chang et al., 2000), vectorization (Wolfe, 1988), parallel processing (Buneman, Davidson, Hillebrand,
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& Suciu, 1996), and optimization by using cache memory and hash tables to improve efficiency (Rao et al., 2010). Importance of Optimization to Leadership The Toyota Motor Corporation emerged as the world’s leading automobile manufacturer and focused on efficiency in its vehicles (Rapp, 2007). Rapp (2007) attributed the efficiencies of Toyota automobiles to IT activities embedded in the wellresearched production system, smart design, and global ordering methods that made Toyota the industry’s technology innovation leader and continued cost leader. Rapp (2007) found leadership strategy is part of a long-term vision. Lubowe et al. (2009) found leaders face immense pressure to cut costs and optimize resources and capital productivity. Performance improvement needs to be part of organizational goals to encourage IT leaders to find ways to cut costs by optimizing existing processes and applying best practices. Leaders may device incentive programs to encourage efficiencies within processes built. Based on the no-free-lunch theorem (Ho and Pepyne, 2002), one of the challenges with EUC files and processes is that customization is greater for each reporting system and analytical system and therefore no standardized solution can exist to improve the efficiency of all analytical systems. Sakalauskas and Felinskas (2006) showed that IT-related applications are often subject to time and resource constraints. They also found these constraints might not leave the developing crew sufficient time to spend on optimization efforts. Resource constraints can put pressure on development teams to focus more on the guidelines for completion and less on optimization (Sakalauskas & Felinskas, 2006). When leaders prepare project estimates and 54
development contracts, management does not provide sufficient weight on the calibration or optimization of the performance of the application due to a lack of scale (Sakalauskas & Felinskas, 2006). Taylor (2006) explored the consequences of promising unrealistic timelines and overselling results to clients. Taylor (2006) revealed unrealistic timelines are likely to result in rushed efforts in development and can end up with little or no effort pertaining to efficiency or optimization. Acquisitions and integration efforts in the case of large industries include an approach with an interest in how soon the vendor managing the integration project can integrate the data into the existing infrastructure with little or no emphasis on how efficient that design will be in terms of processing cost and optimization in the long term (Taylor, 2006). Project managers can meet minimum standards as required by their SLAs. The unknown component of efficiency remains an area to explore. Fris (1989) mentioned investments in productivity helped improve different types of data-processing operations. Investments in improving efficiency, programming style, and culture can return investments made to train the workforce to learn and adapt to specific technology and coding standards. Several factors such as organizational culture, skill, infrastructure, resource constraints, time constraints, motivation, incentive, and many other factors as shown in Figure 4 can have an impact on EUC efficiency. Agrawal et al. (2009) found the availability and use of data in databases have multiplied. Due to deadlines and business pressures, EUC teams might choose to develop and complete many designs treating a problem instead of addressing the root
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cause of an issue. The pile of code might become complicated due to the attrition of the workforce and loss of undocumented knowledge. Velicanu, Litan, and Mocanu (2010) noted optimizing applications through databases has been one method of improving efficiency in the past. Monitoring application performance is a gray area with a lack of tools due to the level of complexity, customization, and design that is specific to the business and its requirements. Industry leaders need a tangible way of measuring potential efficiency gains and evidence of tangible projected savings in a given year. No tool was available to measure the return on investment for a chief information officer or organizational leader to use when making a decision.
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Skill Level
Organizational Culture
Motivation
Incentives
EUC Processes & Efficiency
Technical Leadership
Resource Constraints
Time Constraints
IT Governance & Best Practices
Figure 4. Organizational structure in relation to process flow and efficiency. 57
Common Methods Qualitative and quantitative research methods are the two most common research methods used by researchers, and these methods have certain advantages and disadvantages. Lehning (2013) conducted a qualitative study about servant leadership in community colleges and showed that the results could not be generalized to other community colleges and this was an external validity concern. He also recognized that the sample was not big enough. He pointed out that a disadvantage of conducting a qualitative study was that it was difficult to make quantitative predictions, as in the case of a quantitative study. Several qualitative studies showed more time was necessary to administer and analyze the results. It was commonly known that researchers’ personal bias may play a larger role in qualitative research and, hence, lead to less credible outcomes. While studying the efficiency of cyber-security in a quantitative study, Jung (2005) mentioned that a data collection environment was a challenge as it might have led to a less accurate outcome and the need to create a more controlled environment to have a more natural response from the participants. One of the advantage of conducting a quantitative study include the possibility of generalizing research findings when the samples are large and randomly selected. Another advantage is the ability to obtain precise quantitative, numerical data and the fact that less time is needed to administer the instrument for measuring the constructs. The basis of quantitative research is using probability to determine statistical significance, and these types of studies, by the nature of typical data collection, include less researcher bias. Some of the disadvantages of quantitative analysis might include not being able to query a subject in depth, as in a 58
qualitative study, the quality of data collected might impact the study, and the sample used in the study might not represent the true population. Gap in Literature The focus of research related to the optimization of processes is generally on streamlining or fine-tuning the performance of a database in a relational setting such as Oracle, Sybase, DB2, Access, or SQL Server. An unexplored area of data processing that is considerable in size but seldom noticed is the EUC. As businesses become larger and as mergers and acquisitions increase, costs increase proportionally in many instances. A lack of literature exists in scholarly journals and databases to mitigate such EUC expenses. The current study involved an attempt to address the gap that existed in literature on EUC reporting optimization. Conclusions Organizational leadership does not have defined responsibilities or incentives to build efficient systems, optimize current systems, or save CPU time or processing costs (Powner, 2004). The literature by Taylor (2006) also revealed project managers and leaders outsource projects and make unrealistic promises to customers. The promises result in insufficient time for development and no time for maintenance, efficiency, or optimization of the current project (Taylor, 2006). The integration of systems because of mergers and acquisitions and new business development in support of business strategies leave the IT leaders with no time for efficiency improvement of existing systems. Summary The literature review revealed the limited research conducted in the area of EUC efficiency in prime and subprime data processing. Organizational leaders tend to look at 59
optimization, efficiency, and improvement only in times of crisis. The literature review included only the area of optimization and efficiency that can impact subprime and prime parts of the mortgage business. Tichy and Jones (2002) revealed outsourcing companies promise benefits to clients such as reduced fixed costs, elimination of origination costs, and superior service. A focus on outsourcing takes away the focus on efficiency and the optimization of EUC processes. The findings of the research might help industry leaders understand how to apply concepts to other businesses and data types.
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Chapter 3 Method Researchers have studied different types of data using various methods such as qualitative analysis, quantitative analysis, and mixed methods. Qualitative research methods helped researchers explore human elements and answer questions related to research regarding what, when, why, and how (Given, 2008). Qualitative research depends on the researcher’s way of interpreting data, whereas the basis of quantitative research methods relates to counts or scored responses assigning numerical values (Sandelowski, 2009). The design of the current study was descriptive correlational and the data collection technique was through surveys. The current quantitative study revealed whether reliable and constructs valid measures for leadership EUC initiatives and for EUC efficiency can be developed. The study involved investigating whether reliable and valid measures could be created to measure the two constructs. The author examined the strength and direction of the relationship between responses of these two measures. The intent for creating the measures in this study was to enable leaders to evaluate their EUC systems’ level of IT leadership initiative and efficiency, thereby enabling them to take steps for improvement. Previous quantitative research studies did not indicate the relationship between EUC efficiency and leadership EUC initiatives. The studies included specific scenarios and sub domains as their focus, and therefore the results did not generalize to broader situations regarding the overall EUC efficiency. The results from the current study might be generalizable to overall EUC efficiency for all organizations that use data that fit the pattern in subprime and prime mortgage businesses because the survey respondents were 61
from the banking industry, which had exposure to this type of data. The tools developed for the study might help leaders find out if a relationship exists between their leadership EUC initiatives and EUC system efficiency in prime and subprime mortgage data processing. This chapter contains four major components: (a) explanation on selecting an appropriate research method and design; (b) population, sampling, and data collection procedures and the rationale behind the procedure; (c) survey validation measures; and (d) the plan for data analysis. Research Method and Design Appropriateness Vogt (2007) defined a research design as a plan for obtaining evidence to answer a research question and further went on to list seven types of research designs based on levels of intrusion into people’s lives. Other approaches as indicated by Black (1999) are Qualitative descriptive, Descriptive, Normative and Ex post facto. Black (1999) stated that correlational approach was used to examine relationships as pairs of numeric variables to see how they vary with respect to each other. Black (1999) also stated that correlational studies could provide the rationale for experimental studies. A different explanation by Christensen, Johnson, and Turner (2014) revealed correlational studies as measuring two variables and the degree of relationship that exists between them (p. 41). Christensen et al. (2014) also stated that the correlational approach was very effective and helped in description and prediction. The first step was to identify the relationship between leadership EUC initiatives and EUC efficiency. Because the ultimate objective of determining this relationship would be to provide a model that any company might use to predict the level of EUC efficiency by examining their level of leadership EUC
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initiatives and then, as there is one predictor variable and one criterion variable, a regression analysis was the appropriate next step. Researchers considered cost, time constraints, ethics, and other issues before selecting a method (Cozby, 2009). As a qualitative research study would take a longer time to research and implement within organizations, a quantitative method was chosen for the current study. The current study was not an experimental study because the scope of the research does not allow for the manipulation of the independent variable. Therefore, the advantages of gaining internal validity and establishing evidence of causality by running an experiment would not apply here. The current study is an attempt to describe the data collected and to find out the extent of the relationship between the predictor and criterion variables. As there was no prior research done on this topic, there were no prerequisites establishing causality giving sufficient grounds to conduct an experimental study. As there was no manipulation of the predictor variable, the study was a nonexperimental study (Spector, 1981). A causal comparative study was not appropriate because the research objective was not to search backwards in time for causal explanations of current differences. The study involved an attempt to reveal whether a relationship existed between leadership EUC initiatives and EUC efficiency. The study did not involve an attempt to claim any causality between the two variables; it was descriptive and associative, and thus a descriptive correlational design using a regression model was appropriate (Sheskin, 2010).
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Research Variables The predictor variable in the study was IT leadership, for which a constitutive definition was the guidance and directives through which leaders in the industry steered their teams toward the vision of the organization. The operational definition of IT leadership initiative was the sum of responses across questions 1 through 6 from part A (Appendix B). The researcher developed the survey instrument for the current study (see Appendix B). The criterion variable in the study was EUC efficiency, for which a constitutive definition was the level of code optimization, code redundancy, and code execution speed and cost effectiveness. The operational definition of EUC efficiency was the sum of responses across questions 1 through 6 from part B (Appendix B). The level of measurement for each item was ordinal with options of “Never (1)” to “Always (4).” Research Questions and Hypothesis Research Question: What is the relationship between the leadership EUC initiatives and EUC efficiency? Null and Alternate Hypotheses The null and alternate hypotheses for Research Question 1 are as follows: H0: There is no relationship between scores on the leadership EUC initiatives measure and the EUC efficiency measure. Ha: There is a relationship between scores on the leadership EUC initiatives measure and the EUC efficiency measure. Population The population for the current study included all IT professionals in the mortgage industry with the specific mortgage banking experience within the United States. The 64
respondents included (a) senior vice presidents, (b) vice presidents, (c) assistant vice presidents, (d) developers, I doctoral degree holders with a specialization in information systems, (f) senior developers, (g) programmers, (h) analysts, (i) end-users, and (j) testers. The population also included professionals in the financial industry who affected EUC as a EUC requestor or a business leader or user in the banking environment whose business activity created or lead to IT development work adding or changing the EUC. The experiences of the professionals normally range between 1 and 40 years in the mortgage banking industry within the United States. Sampling The study necessitated a sample that met specific criteria within the Information processing industry, a purposive sampling that served the needs of the current study. The population for the current study included all IT professionals in the mortgage industry with the specific mortgage banking experience within the United States. The sampling frame was the respondents who met the criteria and in my reach from where I recruited the sample for the current study. This necessity of sample respondents with exposure to mortgage banking data processes limited the availability of respondents. The purposive sample chosen for this study included characteristics of different levels of experience, different designations, and different leadership positions and was from different companies that dealt with prime and subprime mortgage businesses. Therefore, convenience sampling, quota sampling, or a random sampling would not have provided the data required to conduct this study. The current study required a purposive sample that included respondents matching specific criteria.
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Suen, Huang, and Lee (2014) stated that a researcher chose purposive sampling techniques by purposely selecting samples carefully with the expectation that the samples provided rich and useful information for the study. Therefore, purposive sampling was the most appropriate technique for the current study. The participants were IT professionals, leaders, or subject matter experts who had exposure to EUC in the financial industry and who meet the criteria of the population noted above. In the current research, technology workers and leaders with experience in either the contributing or the consuming end of the EUC were necessary, so purposive sampling was an essential method of sampling. Professionals who did not meet the experience requirements did not add any value to the research. The study included a sample size of 374 (for H1 and H2) based on the sample size calculator (SurveyMonkey, 2015) and sample size table (Research-Advisors, 2015). As there was little or no research available in the literature as it related to the area of study (Cohen, 1992), an effect size of 0.5 was estimated. The alpha or Type I error probability used in the calculation was .05 and the power used was .95. Even though a power level of 0.8 would be acceptable, using a Power of 0.95 would have increased the strength of the results. The Federal Deposit Insurance Corporation (2015) reported the existence of 6415 financial institutions that were FDIC insured. Even if there was one employee performing the EUC functions in each of the organization, there would have been 6415 individuals reporting to a manager, the approximate minimum population size would have been 12830 (6415 times 2). With 95% confidence interval and 5% margin of error, the sample size calculator mentioned above showed 374 as the appropriate sample size. The statistical sample size calculator for Cronbach’s alpha returned 14 as the number 66
required as a sample size for 12 items, probability of type-I error of 0.05, Power at 0.80, and expected Cronbach’s alpha at 0.7 (Chang, 2014) used for the pilot was 10. The size of the larger sample was 374 in the actual study after the pilot. Yurdugul (2008) recommended a minimum of 30 participants and indicated not more than a 100 would be necessary for a robust Cronbach’s alpha, as long as the eigenvalue was sufficient. Therefore, a pilot sample size of 100 representing the sample was used for the pilot study. The survey contained questions related to the two variables with multiple choices from which the respondents selected their responses. The survey instrument was field tested with 5 survey experts. The participants of the field test had expertise in different areas. Their expertise included psychometric measurement development, information technology solutions, PhD in computer science, PhD in information technology, and methodology and their feedback was incorporated into the survey instrument to ensure clarity, ease of understanding, elimination of steering towards specific responses, elimination of double barreled questions, and improvement of grammar, readability, and content. The respondents had experience in mortgage banking within the United States. The variables measured in the current study were of interval in nature, as shown in Appendices A and B. The process of selecting the sample involved using the researcher’s professional and personal network contacts in the United States. Participants were from companies like HSBC, Citi, Wells Fargo, and JP Morgan who serviced customers throughout the United States. The sample included members of the active IT workforce or those who managed mortgage processes involved with EUC in their daily work, who held senior leadership positions affecting EUCs. 67
Participants Participants had characteristics of interest (Guarte & Barrios, 2006) with the overall population of IT professionals. The age group of the participants ranged from the 20s to the low 60s. Their job responsibilities ranged from gathering specifications and developing new IT processes to supervising low-level managers who executed the projects and specifications. The experience of the IT professionals ranged from junior or entry-level programmers to experienced senior architect level or senior IT managers with a designation of senior vice president. The participants’ educational levels ranged from a bachelor’s degree to a doctoral degree. The participants’ work experience ranged from 1 to 44 years in the IT industry and the mortgage banking industry. The sample included members of both genders with approximately 49% females and 51% males. There were about 63% of the individuals who were in some form of a leadership role and 36% of the participants were in nonleadership roles. The researcher recruited the purposive sample by starting with personal and professional contacts and ensured all recruited primary and secondary participants contacted through the researchers personal and professional contacts met the necessary criteria for the study. All responses were received through an online survey. The participants received a URL that they accessed to provide their survey responses. Informed Consent and Confidentiality Informed consent should not only be a disclosure document but rather need to help the readers understand the intention of the process (Flory & Emmanuel, 2004). The participants received a cover letter and an informed consent form on which they read before providing any responses. If an individual did not volunteer to participate, the 68
person was allowed to exit the survey at any point by clicking the close button of the browser. By proceeding with the survey, the participants acknowledged that they were IT professionals and had experience in the mortgage banking industry with prime or subprime loans; they understood the purpose of the study, the potential risks as a participant, the means by which the survey responses would remain confidential and safe, and proceeded to the survey. To avoid anomalies, the data from surveys that were incomplete were not part of the study. Geographical Location and Limitations The participants were from organizations within the United States or their experiences were based on experience within the United States. The location is one of the items defining the scope of the study. Subprime and prime mortgage banking organizations were the prime focus in the current study. Confidentiality According to Ogden (2008), confidentiality helped to enhance both quality and validity of data collected through surveys. The participants received information before the survey began that their identity would remain confidential and was kept confidential. Pilot surveys had reference numbers P001, P002, P003, etc. where the 3 digit number referred to the survey number that was mapped to the individual. This mapping reference remained confidential with the researcher. The participants had a choice to opt out of volunteering after proceeding with the confidentiality agreement mentioned in the informed consent. The organizations the individuals worked for also remained confidential, so no link existed between respondents and specific organizations. The dataset is locked in a filing cabinet and will be kept for three years (U.S. Department of 69
Health and Human Services, 2015). Reports or extracts used for analysis did not contain personally identifiable information. Data Collection Given (2007) stated that descriptive researches provided details about social setting, a group, a community, a situation or a phenomenon and exploration of events that would have happened even in the absence of the researcher. The current descriptive correlational study included the data collected for the two variables, leadership EUC initiatives and EUC efficiency. The data collection process for the quantitative study involved a survey instrument. The industry under study is large and findings may be applicable to many thousands of companies worldwide. One of the reasons for using surveys as the data collection technique within the quantitative descriptive correlational study was so that future researchers can use the same surveys in their organizations. The survey was conducted online. Informed consent was presented as part of the pilot and the main surveys. Details about the survey questions are in Appendix C. As the data were collected at one time, the current study was a cross-sectional study (Cramer & Howitt, 2004). If the participants or subjects were followed for a long time and data were captured at different points then a longitudinal study would have been appropriate. Conducting a longitudinal survey study would have been more time consuming and the amount of data collected available for analysis would have depended more on several associated factors including the size of the sample, frequency of collection, and response levels. To improve the process of collecting data and provide options to the respondents to exit out of the survey, there were reminders in the beginning and throughout the survey. Participants received information that all participants must be 70
IT professionals with mortgage banking experience within the United States even though their employers might have businesses in other countries. Cooper and Schindler (2008) described three main types of surveys: selfadministered surveys, telephone surveys, and personal interview surveys. Researcheradministered survey is a type of survey in which a researcher can clarify questions and ensure completion of the questionnaire with a higher response rate and greater control of the environment. One reason a self-administered survey instrument was appropriate for this research was that such a survey would provide information specific to the topic. It was also important to give participants time to think over their responses rather than putting them under pressure as in personal interviews. The self-administered survey was less expensive, did not require interviews, was suitable for a large number of respondents, and avoided any interviewer bias. The results of the survey were also easier to code and analyze. An electronic survey was suitable as it was easier to transmit electronically. The survey included the survey questions that were part of the quantitative study. The survey instrument had multiple-choice response options. An electronic survey was also easy to complete regardless of respondents’ location. Instrument Having defined the two constructs of leadership EUC initiatives and EUC efficiency, the researcher developed a pool of items for the current study for each construct based on domains of interest of these definitions. The researcher formulated these items with the definitions of the constructs in mind. The researcher developed the survey instrument listed in Appendix B for the current study. The intent for the design of the measure was to create one score to reflect the EUC efficiency construct by summing 71
all the choices after reverse scoring was implemented for the first six questions of part A (Appendix B). A single score was obtained for each of the participants for the constructs. The score for leadership EUC initiatives construct was obtained by summing the responses across the questions for the first six questions of part A (Appendix B). This measure served to assess two constructs. The first construct measured the leadership EUC initiatives by company leaders and the second measured the level of EUC efficiency within the mortgage banking industry. The researcher created the measures because no one has developed a way to measure leadership EUC initiatives or EUC efficiency. The items created to measure the leadership aspect of the survey followed the traits of the transformation leadership theory that empowered the individual to perform at one's best while providing the needed support to accomplish respective goals (Manktelow, 2015). The theory supported by use of tools such as profilers, tracers and program status recorders while software programs execute as stated by Wong (2001(, formed the basis of this high level data collection for the measure of EUC efficiency. The researcher worked under G. Douglas Lunsford, an expert in the field of measurement development, to improve grammar, readability, and content issues. The researcher implemented comments from Lunsford (personal communication, February 2, 2014( and developed the instrument to be used in a pilot study to obtain content validity of the measure using comments from a group of participants representative of the population of interest. The instruments were used to measure the above mentioned constructs within the mortgage banking industry. The survey began with a pilot survey (see Appendix A) to obtain information about reliability and content validity of the measure. The pilot study participants 72
provided responses to indicate whether the pilot study items relate to the constructs of the leadership EUC initiatives as defined. The first part of the survey (six questions) had four possible responses with numeric values 1, 2, 3, and 4, respectively. If a respondent answered all six questions with a valid answer, then the possible range of the score for leadership was a sum that ranged from six to 24 for the IT leadership initiative measurement. Similarly, the second portion of the survey (with six questions) corresponded to EUC with a possible score (sum of individual numeric response equivalents) that ranged from six to 24. If the results showed that a higher leadership value correlated with a higher EUC value and a lower leadership score correlated with lower EUC values, then the results showed a positive relationship between the two constructs demonstrated with a significant Pearson’s product-moment correlation coefficient followed up with a regression. All items in the first construct and the first 6 items of the second construct in the current survey instrument (see Appendix A) had the following choices: 1 = never, 2 = rarely, 3 = often, and 4 = always. The items from Banna’s (2001) survey used 5 scale responses ranging from 1= Extremely False, 2= Slightly False, 3= Neither False nor True, 4= Slightly True, to 5= Extremely True. The respondents were able to choose one response. The resulting summed ordinal scores to become interval scores ranged from 6 (low) to 24 (high), with low scores indicating poor leadership EUC initiatives. The intent for the design of the measure was to create one score to reflect the IT leadership initiative construct by summing all the choices indicated above.
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Demographics Questionnaire A set of demographics questionnaire (see Appendix D) was included at the time of administering the survey to the participants so that there is some information about the participants available for future studies. If a researcher liked to reproduce the survey, the descriptive statistics of the current demographics data might be utilized to replicate the study. As no descriptive information was available about the population, a comparison of the sample population was not possible. Instrument Reliability and Validity According to Creswell (2005), validity requires scores from an instrument to be meaningful and conclusions drawn from the sample should lead to an understanding of the construct. Messick (1995) noted there are three main types of validity: content, criterion, and construct. According to Vogt (2013), construct validity was the extent to which variables accurately measured the constructs of interest. The construct validity under the current study was the content validity and concurrent validity obtained during the pilot study. Expert judges determined face validity in a specialized area in order to obtain content validity (Vogt, 2005). Concurrent validity was the extent to which a new measure related to another measure administered at the same time (Mislevy & Rupp, 2010; Mowbray, 2003). A measure of leadership EUC initiatives and EUC efficiency was unavailable from any prior research. However, even though it was not possible to demonstrate concurrent validity using exactly the same constructs, constructs similar to the new measures in the current study were used to show some evidence of concurrent validity. A measure developed by Dierendonck (2010) was used to show evidence of some level of 74
concurrent validity with leadership EUC initiatives. The scales called Empowerment, Accountability and Stewardship were used in regard to concurrent validity of IT leadership efficiency measure and scales called Courage and Forgiveness were used to show evidence of discriminant validity. The Cronbach’s alphas for the Empowerment, Accountability and Stewardship scales are .89, .81, and .74, respectively. Dierendonck (2010) carried out research demonstrating factorial, content, incremental, and criterionrelated validity for the scales used in the current study (Dierendonck, 2010). A measure called Organizational benefits of EUC projects, developed by Banna (2001), represented some relevance to the EUC efficiency construct. The 25 items survey instrument measuring organizational benefits of EUC showed acceptable reliability with a Cronbach’s alpha of .921, and criterion-related validity (Banna, 2001). Therefore, the scale from Banna (2001) was used to show evidence of concurrent validity with the EUC efficiency measure. Four reliable and validated scales with 18 questions (7 through 24) in the first part of the instrument (Appendix B) were from Dierendonk’s inventory called Servant Leadership Survey (2010). These were included and the scores were obtained according to Dierendonk’s (2010) scoring rubric. Correlations with these validated scales were part of the data analysis with the objective of demonstrating concurrent validity. No scale items from Dierendonk (2010) and Banna (2001) were been modified. The scales Courage and Forgiveness (Dierendonck, 2010) helped to provide evidence of divergent validity for the EUC efficiency construct. The extent to which a test appears to represent the domain intended to measure is content validity (Sireci, 2007). As described by Sireci (2007), domain definition, domain representation, domain relevance, and appropriateness of the test construction are the four 75
different aspects of content validation. After the development of the pools for both constructs in the current study, an expert in the field of measurement development reviewed each item and helped to remove poorly constructed items and to reword items to create simple and single-idea items for each measure to provide a degree of content validity (Litwin, 1995). The researcher then placed the items on a test form (see Appendix A) and developed instructions to establish a measure for the pilot study. The instructions asked respondents to react to each item and to indicate the relevance of each item regarding the constructs described in the instructions. The researcher presented the survey to subject matter experts and measurement experts and confirmed the content validity from their responses. According to Hinds, Vogel, and Clarke-Steffen (1997) the pilot study should address the issue of reliability through analysis of the responses from the participants. Responses underwent statistical reliability analysis to obtain Cronbach’s alpha, a measure of internal consistency. Because the pilot study was designed to address the issues of content validity and internal consistency in the form of Cronbach’s alpha, the measures related to concurrent validity were not administered. After demonstrating the level of content validity and internal consistency of the measure of leadership EUC initiatives and EUC efficiency in the pilot study, the researcher then administered the measures to the larger sample along with the measures related to establishing concurrent validity. The next step was to calculate another reliability coefficient (Cronbach’s alpha) using the larger sample. As proposed, had the data from either measure turned out to be other than normally distributed, a Spearman’s correlation would have been appropriate for the next step in the analysis. Because both measures returned statistics supporting normality a 76
Pearson’s product-moment correlation was considered more appropriate. Analysis of data from second test administered to the same participants after an interval, through correlation will reveal test-retest reliability (Cramer & Howitt, 2004). External and Internal Validity of the Study External validity refers to the generalizability of the findings of the study to the general population referred to by the study (Calder, Phillips, & Tybout, 1983; Creswell, 2005). Internal validity is the degree to which the researcher can indicate that leadership EUC initiatives as measured can cause EUC efficiency. One of the threats to internal validity is that the measuring instruments may introduce unwanted variables, may be more relevant when taking more than one measurement of a group, and may affect the study (Black, 1999). Another threat is having members drop out of the study before its completion (Black, 1999). Attrition might affect the current study. History and differential history are the threats that might occur in an experimental setting that includes two groups, where one group experiences a history event while the other group does not (Christensen et al., 2014 p.168-170). This is not likely in the current study due to a nonexperimental setting. The other factors that might affect the internal validity are the quality of data, possibility of biased responses referred as rater bias (Hoyt, 2002), misremembered responses or rater errors (Downing, 2005), reliability of the survey instrument, and the fact that the sample will be a purposive sample and therefore may not be representative of the population. A CFA analysis was not carried out and therefore the measures of both the predictor and the criterion variables may not be valid. Social desirability bias (Chung & Monroe, 2003) might cause respondents to give responses that present their 77
corporation in a good light. It was not possible to establish a cause and effect relationship from survey data or from correlations. Well known was the limitation that self-reports on survey items might be such that the respondents might have answered in a way that was not entirely in accord with their true views. Another limitation is the inability to control for other variables that might explain or contribute to the relationship of interest and the inability to measure or infer causality from correlation, covariation, or statistical effect in a regression equation, given the descriptive nature of the study. Some of the threats to external validity stated by Marczyk, DeMatteo, and Festinger (2005) in the current study are sample characteristics, reactivity of assessment, and timing of measurement. Experimental arrangements, multiple-treatment interference, novelty effects, and test sensitization are threats that may be less relevant to the current study due to a nonexperimental design. Because this is a purposive sample, the external validity was not as high as desired. An attempt to increase the ecological validity involves collecting the data via e-mail. Although this sampling method is a concern to population validity, the study involved an attempt to include participants from different demographics to help increase the population validity. Data Analysis In order to assess the normality of the data statistically, tests of skew and kurtosis were presented as part of the analysis. The first step of data analysis included the pilot study responses. The study involved calculating Cronbach’s alpha on the responses to establish the level of internal consistency of the items. After obtaining the responses from the larger sample of the main study, data analysis again provided Cronbach’s alpha for each measure to confirm the reliability of these measures (Cronbach’s alpha, 2010). 78
SPSS Version 22 (IBM, 2014) was suitable on both administrations for calculating Cronbach’s alpha, the correlation coefficient, the coefficient of determination, and the regression coefficients. In that the scores were normally distributed, 𝑥, s, skew, kurtosis were used to describe the sample characteristics. The current study included both predictor and criterion variables that were measures obtained from the survey instrument. The first set of questions helped to derive the predictor variable (leadership EUC initiatives). The second set of questions in the survey instrument helped to derive the criterion variable (EUC efficiency). Both are measures of continuous latent variables and are therefore considered to be interval measures. The process involved deriving these measures for each participant in the study and Pearson’s product-moment correlation revealed the relationship between the predictor variable and the criterion. The value of the coefficient indicated the strength and direction of the relationship between the predictor and the criterion variables. Because no previous research was available, the tests in this study were two-tailed, and the alpha level was .05 (Williams, Jones & Tukey, 1999). Summary Other researchers might expand the current research to include other industries and data patterns to determine whether IT processes have an impact. The current research will influence organizations positively as measuring EUC efficiency will be easy if they use the tool from the current study. The next chapter includes a summary of the data collected and the steps followed to analyze the collected data.
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Chapter 4 Results The purpose of the current quantitative correlational study was to determine the nature of the relationship between leadership EUC initiatives and EUC efficiency constructs. In keeping with the purpose of this quantitative study, it was necessary to determine whether it was possible to establish a measure of leadership EUC initiatives and a measure of EUC efficiency as constructs and measure them reliably with a certain amount of construct validity. Following the establishment of the relationship expected, regression analysis followed to report a trend line formula and a coefficient of determination. Vogt (2005) noted that researchers use regression to answer questions of the predictability of the criterion variable using the predictor variable. Later in this chapter, a trend line equation will show the relationship between the two measures. Method Sample and procedure. In keeping with the objectives stated above, the study involved developing a pool of items reflecting leadership EUC initiatives and EUC efficiency as described in the sampling section and piloting it on 100 participants. A post hoc power analysis using G*Power 3.1.9.2 tool indicated that a sample size of 34 was sufficient to produce a power of 0.9886 and an n of 100 produced a power of 1.0. Data collection occurred through an online survey. The pilot study served to establish that the distribution of the summed raw scores was normal and that the internal consistency of items in each of the two measures was sufficient to use the measures in the planned research of a further 374 participants. The participants’ educational levels varied from bachelor’s degree to doctoral degree or Ph.D. The participants had work experiences that 80
varied between 1 to 44 years in the mortgage banking industry. The sample included almost equal proportion of the genders with approximately 49% female participants and 51% male participants. There were approximately 63% of the individuals who held a leadership role and approximately 36% of the participants held in non-leadership roles in the industry. Measures The data analysis involved entering the responses of the 100 participants into the SAS software and calculating univariate statistics. Skewness for leadership EUC initiatives was -0.66 and for EUC efficiency was -0.50. Both leadership EUC initiatives and EUC efficiency returned internal consistency coefficients that were acceptable (Cronbach’s α = .89 and .79, respectively). The EUC efficiency alpha was much lower than the leadership EUC initiatives alpha, and a reexamination of the items revealed that the first item did not sufficiently relate to the construct to remain as part of the study. In addition, Item 6 was an item that could not reveal valid information because the respondent would need to guess to provide a response to that item. After removing Items 1 and 6 from the measure, the analysis took place again. The final version of the survey with Items 1 and 6 removed from EUC efficiency measure appears in Appendix E. Results Table 2 shows the descriptive statistics of means, standard deviations, skewness, kurtoses, with reliability coefficient indices (Cronbach’s alphas) for leadership EUC initiatives and EUC efficiency from the pilot study involving 100 participants. Table 3 shows the descriptive statistics of means, standard deviations, skewnesses, kurtoses, with reliability coefficient indices (Cronbach’s alphas) for leadership EUC initiatives and EUC 81
efficiency from the current study involving 374 participants. Table 4 shows the descriptive statistics of means, standard deviations, skewness, kurtoses, with reliability coefficient indices (Cronbach’s alphas) for Dierendonk’s (2010) Servant Leadership Survey (SLS) from the current study involving 374 participants. Table 5 provides concurrent validity coefficients that includes the L_scale and E_scale correlation results for the 374 participants. Finally, a regression formula with adjusted r2 is provided. Descriptive statistics and reliability coefficients The following table presents the descriptive statistics and reliability coefficients returned from an analysis carried out in the SAS® statistical software. These statistics resulted from analysis of responses from 100 participants in the pilot study to establish the usability of these measures in the current study. Table 2 Mean, Standard Deviation, Skewness, Kurtosis, and Cronbach’s Alpha for Leadership EUC Initiatives and EUC Efficiency for 100 Pilot Study Participants
Leadership EUC initiatives ECU efficiency
Mean 17.57 11.6
SD 4.23 2.66
Skew -0.86 -0.74
Kurtosis 0.69 0.54
Alpha .89 .79
Histograms of raw scale scores served as support for acceptable findings of both skewness and kurtosis and returned values between -1 and 1 (see Appendix F). The graphs demonstrate the normality assumption was tenable. These findings supported the idea that it was possible to develop a valid and reliable measure of both IT leadership EUC initiatives and EUC efficiency. As planned, the next step was to obtain a further sample of 374 participants, again obtained by promoting the study through an online survey. Table 3 shows that the pilot study results were a reliable estimate of the 82
univariate statistics. Internal consistency coefficients (Cronbach’s alpha) the pilot and the current studies indicated a sufficient level of reliability. Table 3 Mean, Standard Deviation, Skewness, Kurtosis, and Cronbach’s Alpha for Leadership EUC Initiatives and EUC Efficiency for 374 Study Participants
Leadership EUC initiatives ECU efficiency
Mean 16.96 11.21
SD 4.21 2.54
Skew -0.66 -0.13
Kurtosis 0.26 -0.3
Alpha .89 .75
Validity of Instrumentation Examining the further validation of the leadership EUC initiatives involved collecting data from the same participants on five scales from a measure of leadership using SLS (Dierendonck, 2010). The process involved calculating skewness, kurtosis, and Cronbach’s alpha on the responses to these scales and establishing the assumptions of normality and internal consistency (reliability) for each of the scales of the SLS (see Table 4). The correlation coefficient showing the relationship between the Empowerment, Accountability, Courage, Forgiveness, and Stewardship scales of the SLS measure and the leadership EUC initiatives measure (r = .44, .24, .40, .26, and .50, respectively) established a certain level of construct validity from the pilot study using 100 participants to a further degree by providing concurrent validity in the form of a significant correlation with an established measure of similar constructs. The corresponding correlation coefficient showing the relationship between the EUC efficiency scale of the current measure and the EUC applications measure (Banna, 2001) (r = .43) established construct validity from the pilot study.
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The correlation coefficient showing the relationship between the Empowerment, Accountability, Courage, Forgiveness, and Stewardship scales of the SLS measure and the leadership EUC initiatives measure (r = .45, .22, .26, .17, and .44, respectively) established construct validity from the pilot study using 374 participants to a further degree by providing concurrent validity in the form of a significant correlation with an established measure of similar constructs. The corresponding correlation coefficient (r = .46) showing the relationship between the EUC efficiency scale of the current measure and the EUC applications measure (Banna, 2001) established construct validity from the current study. The size of the correlations reflected the degree of relationship between leadership EUC initiatives and these leadership constructs. The Empowerment and Stewardship scales showed the strongest correlations with the leadership EUC initiatives measure. Table 4 Mean, Standard Deviation, Skewness, and Kurtosis for Each of the Scales of SLS and EUC Applications Measures for 374 Study Participants
Empowerment Accountability Courage Forgiveness Stewardship EUC applications
M 22.32 10.46 5.38 6.89 9.29 92.76
SD 4.05 1.58 1.45 2.30 1.98 16.79
Skewness -0.64 -1.23 -0.32 0.49 -0.63 -0.32
Kurtosis -0.05 1.49 -0.93 -0.62 -0.02 0.64
Although no one had created a measure of EUC efficiency prior to this study, the study involved exploring concurrent validity for this construct using a measure of efficiency called EUC applications measure (Banna, 2001). The study involved calculating skewness, kurtosis, and Cronbach’s alpha on the responses to this measure 84
and establishing the assumptions of normality and internal consistency (reliability) for this second measure of efficiency (see Table 4 and Table 5). Table 5 shows the means, standard deviations, and Pearson’s correlation coefficients for the leadership scales used in the study. The Empowerment and Stewardship scales were highly correlated, and although there were moderate to strong relationships between the other scales, they appeared to be measuring different dimensions of leadership. The Forgiveness scale appeared to have the smallest correlation coefficient when related to the Accountability scale. The leadership EUC initiatives scale created for this study had a statistically significant relationship with each of the leadership scales used to establish concurrent validity. Table 5 Descriptives and Intercorrelations among Different Dimensions of Leadership Scales M SD 1 2 3 4 5 6 1. Empowerment 22.32 4.05 .74 2. Accountability 10.46 1.58 .45*** .78 3. Courage 5.38 1.45 .35*** .13* .77 4. Forgiveness 6.89 2.30 -.18*** -.03 .27*** .82 5. Stewardship 9.29 1.98 .79*** .43*** .39*** -.10* .73 6. L_Scale 16.96 4.22 .45*** .22*** .26*** .17*** .44*** .75 Note. N = 374. Diagonal contains Cronbach’s alpha for each scale. *p < .05. **p < .01. ***p < .001. Table 6 provides the means, standard deviations, and Pearson’s correlation coefficients for the EUC efficiency scales developed for the study. The E_Scale and AE_Scale showed a statistically significant relationship (r=.46). The relationship demonstrated herein between the leadership scales and the L_Scale, the AE_Scale, and the E_Scale supports the hypothesis that developing measures for leadership initiative and EUC efficiency is possible. 85
Table 6 Descriptives and Intercorrelations Among the Current EUC and Original EUC Scales for 374 Participants M SD 1 2 1. E_Scale 11.21 2.54 .75 2. AE_Scale 92.76 16.78 .43*** .76 Note. Diagonal contains Cronbach’s alpha for each scale. E_Scale = End User Computing efficiency scale. AE_Scale = Organizational benefits of EUC projects. *p < .05. **p < .01. ***p < .001. Hypothesis Testing In keeping with the hypothesis of the study, after obtaining evidence that the scores for both measures came from normally distributed populations and had acceptable levels of validity and reliability, it was appropriate to carry out a Pearson’s product– moment correlation analysis on the scores obtained from the leadership EUC initiatives and the EUC efficiency measures. The outcome of this analysis showed that a statistically moderate to strong positive relationship existed between these two constructs (r = .60, p < .001). A scatter plot of this relationship appears in Appendix F. This result specifically addresses the research question, “What is the relationship between the leadership EUC initiatives and EUC efficiency?” Table 7 Descriptives and Intercorrelations among the Leadership EUC Initiatives and Current EUC Scales (Composite Sample) M SD r L_Scale 17.09 4.22 .62 E_Scale 11.29 2.57 Note. N = 474. Diagonal contains Cronbach’s alpha for each scale. L_Scale = Leadership EUC initiatives scale. E_Scale = EUC initiatives scale. *p < .05. **p < .01. ***p < .001. 86
Following the statistically significant findings of the analysis of correlation, a regression analysis was carried out to determine the predictability of leadership EUC efficiency given different levels of leadership EUC initiatives. The variable to be predicted are the total score on the E_Scale, the predictor variable was the total score on the L_Scale. The regression analysis yielded an adjusted r2 of 0.36 [F( 1,372) = 198.76 , p < .0001]. The positive regression coefficients provided evidence that as the total score on the L_Scale increased, the total score on the E_Scale also increased and vice versa. The percentage of variation and scores from the E_Scale based on knowledge of the variation in the L_Scale was calculated at 36% (r2 = .36). On a residual plot, no discernible pattern was observed, and the plotted points appeared random around the horizontal axis making the linear regression model the most appropriate for this data. The result was a trend line formula of y = 0.36x + 5.07 where x represents the L_Scale score and y represents an estimated value of the E_Scale score (see Appendix G). This formula should be used with caution bearing in mind an adjusted r2 of 0.36. Summary Chapter 4 included an analysis of the data collected from survey responses from 374 participants in the mortgage banking industry. Tables 2 and 3 and information in related appendices supported the rejection of the null hypothesis in the study. Evidence supported the proposal that measures of leadership EUC initiatives and EUC efficiency are constructs and that the L_Scale and E_Scale are adequate measures of these 87
constructs. Summary tables represented useful information about the descriptive statistics of the population and provided a comprehensive presentation of the significant findings concerning the data collected. Further analysis indicated a statistically significant moderate to strong positive relationship existed between leadership EUC initiatives and EUC efficiency, and the researcher formulated a regression equation to examine trends. Chapter 5 follows with the conclusions, implications, limitations, and recommendations for future research that can enrich the findings of the current study.
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Chapter 5 Summary and Conclusions The objective of the current quantitative correlational study was to determine the nature of the relationship between leadership EUC initiatives and EUC efficiency in the mortgage banking industry. Evidence gathered supports the reliability and validity of these measures as well as the significant relationship between these two constructs. Findings of this research provide the foundation for the inferences discussed in this chapter. The general problem the current study addressed was the need for greater control over EUC application development and use because EUC was of high importance to corporations, EUC inefficiency is costly and increases risk due to errors resulting in noncompliance with regulations (Apptio, 2014; EMC, 2014; Deloitte, 2011; Gammage, 2013; Hill, 2011; Panko & Port, 2013). By using the developed EUC efficiency measure, organizations can have better control over their EUC applications’ efficiency. The specific problem reported is that there is a risk of EUC inefficiency leading to noncompliance and loss to mortgage organizations (Alta News, 2004; Benson et al., 2015). The developed measure will help IT leaders to assess and improve their leadership EUC initiatives and therefore increase their EUC efficiency levels to better manage these risks. Manktelow (2015) enhanced the understanding of the concepts of leadership by examining Trait theories, Behavioral theories, Contingency theories, Power and Influence theories, Path-goal theory, Fiedler’s Contingency Model and Transformational leadership style. In the current study the construct of leadership was measured with EUC initiatives as the focus. The leadership EUC initiatives construct was strongly correlated with the 89
EUC efficiency construct. A discussion about the leadership EUC initiatives construct was provided in chapter four. Data collection procedures, descriptive statistics and demographics, data analysis for both reliability and validity, and a full summary of the research findings can also be found in chapter four. This chapter restates the research question, problem and purpose, and includes a discussion of the findings as they relate to the previous research and solutions to business leaders’ EUC efficiency problems as presented in the introduction, and the literature review. Limitations and delimitations of this research are covered as well as inferences concerning the use of these measures by IT leaders are included as well as suggestions for future research. Discussion of Results The intent of the pilot study was to determine whether it was possible to demonstrate the reliability and validity of the newly developed measures using 100 participants. Responses from these participants were analyzed and both measures obtained internal consistency coefficients that were satisfactory, and both correlated statistically significantly with measures of similar constructs. An online survey was used to gather the data for this study and to gather data to support the concurrent validity of the measures using Servant Leadership Survey by Dierendonk (2010) and Organizational Benefits of EUC Projects by Banna (2001). As no previous research on EUC efficiency existed, the study involved using an available EUC measure to obtain responses and to demonstrate some level of concurrent validity. After eliminating data based on the screening question, reliability and validity of the instrumentation was assessed.
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Background EUC efficiency is one of the unnoticed elements of IT processing based on the literature review presented in Chapter 2, which has resulted in no instrument developed in the mortgage banking industry. This gap in the literature and in available measures has resulted in a deep financial impact in the mortgage banking industry and has legal implications and lawsuits so that the attorney general is still pursuing several financial organizations in deferred prosecution agreements (Benson et al., 2015). These organizations must add to their EUC framework to comply with the legal requirements. Although the measurement of EUC efficiency was not clear, the equally significant construct in this research study was leadership EUC initiatives, which also had no specific measure available. Several researchers have examined leadership and different dimensions of leadership, but there was no research available to measure leadership EUC efficiency specifically. The initial stage of the current study was to develop an instrument to measure the two constructs so organizational leaders will be able to administer such a survey to measure both leadership EUC initiatives and EUC efficiency inexpensively. Jalics (1989) noted that management does not create performance goals pertaining to program efficiency, which will be possible through the new measure that can measure EUC efficiency. Abu-Alkheil et al. (2012) found that management issues were the reason for the inefficiencies in the Islamic Bank of Britain and could use the current tool to measure both leadership EUC initiatives and EUC efficiency.
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Literature that Supports Result Findings Leadership literature references from different chapters point to various styles narrowing down to the popular transformational leadership that is based on motivational, empowerment and incentive strategies (Fowler, 2016; Giltinane, 2013; Manktelow, 2015; Pitt & Bunamo, 2008). The current measure of leadership was developed mainly using characteristics of transformational leadership style with some items directly gravitating towards motivational and empowerment attributes. This supports the findings of the current study that a positive increase of leadership EUC initiatives will influence the EUC efficiency in a positive manner. A business centric approach (Garrity, 2001) of general systems theory revealed that lack of leadership support might lead to system failures. The current research supports the theory by showing that proper leadership initiatives can lead to efficient EUC systems resulting in cost savings to an organization. The finding of the current study closes an enormous gap in the literature concerning the specific leadership EUC initiatives that should be implemented in the field of IT end user computing. Too often, authors assume that knowledge in the readers as to what is expected in order to obtain the success in leadership and in company growth. This study provides guidelines that are more specific as to how to implement the construct of leadership EUC initiatives. The findings of the current study, that leadership EUC initiatives correlate with EUC efficiency supports the findings of Jing et al. (2008) that leadership capabilities have influence over enterprise efficiency. Another study by Pitt and Bunamo (2008) that revealed a positive relationship between leadership and performance excellence is in line with the findings of the current study that shows the relationship between the constructs Leadership EUC initiatives and EUC efficiency. The 92
relationship demonstrated in the current study is also supported by the findings presented by other research studies by Altholz and Frese (2009), Amagoh (2009), Lubowe et al. (2009), Torrelas et al. (2009), Randeree and Ninan (2011), Rondeau et al. (2010), and Thorpe (2008). Implications Research Question: What is the relationship between leadership EUC initiatives and EUC efficiency? The findings obtained from both the pilot study (N = 100) and the main study (N = 374) supported the supposition that leadership EUC initiatives and EUC efficiency are measurable constructs, and the hypothesis that they are statistically significantly positively related to one another, which leads to the inference that an understanding of leadership EUC initiatives may lead to improvements in EUC efficiency. A correlation of .60 indicates that as leadership initiative scores improve, EUC efficiency scores also improve. Seminal literature by Cohen (1988) indicates that a correlation coefficient r ≥ .5 can be characterized as large. Samuel and Okey (2015) who indicated that a correlation of 0.8 is considered a strong correlation and a 0.5 is weak support the current finding. Thus, the correlation coefficient of r = .60 is moderate to strong. The percentage of change in EUC efficiency that can be predicted by leadership initiatives is 36% (R2 = 0.36) which for a single variable is notable. It is possible that the level of reliability of both measures account for some of the remaining 64% and that there are other variables that need to be considered when looking for the other correlates that will improve the predictability. In the future it is hoped that more efficient measures of EUC efficiency and Leadership initiatives will be developed and that, now a measure of EUC efficiency has 93
been created, that other variables may be found to increase the predictability of EUC efficiency. This finding suggests that applying leadership EUC initiatives would improve EUC efficiency, which for most companies could amount to millions of dollars. This inference is beyond the scope of this study because the approach was correlational. Therefore, to be able to establish causality, future true experiments would need to be conducted. Although the focus of these measures was the mortgage banking industry, any relationships found may be applicable to other industries and businesses. The study took place within the United States, but the findings are not geographically limited and therefore may be applicable in other countries. Limitations and Assumptions The main limitation of the study was that the findings were correlational, and although the findings might seem to be causal, there was no such inference made. One limitation of the study was the quality of the data. The study involved collecting the current data through online survey using a purposive sample rather than a random sample. Obtaining information via surveys was a major limitation with respect to the lack of depth of the subject in the survey. It was assumed that each respondent gave honest answers to the items on the measures. However, responses may have contained biases, which could lead to inaccuracy. Respondents may have felt inclined to respond inaccurately because of reactance, lack of memory, or boredom Respondents might have wanted to give a false impression of those in leadership positions and of the efficiency of EUC and hence may have responded in a biased manner. Finally, different respondents may have interpreted survey questions differently which could lead to data anomaly.
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Another limitation was using purposive sampling. The respondents may or may not have been who they said they were, and this was not verifiable due to the anonymity of the survey. The researcher bore this in mind when interpreting and making inferences based on the results of the study. Based on the post hoc power analysis, a n value of 100 was sufficient to produce adequate power. Although a more accurate way of obtaining information about leadership EUC initiatives and EUC efficiency would be to observe the companies directly, this process would include prohibitive costs and more time than was available. A level of proprietary aspect would prohibit obtaining information in any other way than survey anonymously. Thus, the disadvantages of the survey technique would be unavoidable. Recommendations for Future Research The recommendations from this study may have an impact on the IT leadership and the EUC development community in all business lines. One of the recommendations is to expand this study to other lines of businesses to see if these results would have a similar impact if they are replicated for other types of business. Another recommendation is to develop a calibration to indicate a level of efficiency in terms of the number of records or accounts processed to measure an efficiency level. Such a calibration would allow the leaders to know more specifically their levels of EUC efficiency. This information will enable them to make decisions that will improve that efficiency level. The measure of the efficiency can help the business leaders to form the appropriate strategy to mitigate situations with dynamic businesses. Further research from banks in countries other than the U.S. and other product lines will supplement the findings of the current study. Future researchers may examine how to use these measures 95
in agile and new technologies such as Hadoop and Cloudera. As the current study was correlational and could not infer causality, future researchers could remedy this by obtaining internal validity by using a sample of volunteers to participate in an experimental setup using the leadership EUC initiatives as a treatment option against a control group. Such an experiment may lead to causal inferences of leadership EUC initiatives effecting EUC efficiency. Conclusion Measures of leadership EUC initiatives and EUC efficiency were created and a degree of reliability and validity was established. Following this, a relationship was found between them through a correlational analysis. This adds to the body of literature in that little research has considered leadership in this specific way and no research has considered leadership in relation to the IT industry’s end-user computing. Discovering and empirical relationship between the level of leadership EUC initiatives and EUC efficiency makes a fundamental contribution to the study of this relationship in the mortgage banking industry. These findings reinforce the researcher’s own personal experience concerning the two constructs. Results have indicated a significant relationship between leadership EUC initiatives and the leadership measure with which concurrent validity was established. Results have also indicated between the measure of EUC efficiency and the only other measure that was close to the EUC construct. The confirmation of the reliability and validity of these measures lend credibility to the statistically significant findings to the relationship between leadership EUC initiatives and EUC efficiency. The results imply that those organizations who would score low on the leadership EUC initiatives measure 96
would score low on the EUC efficiency measure. Any mortgage banking IT leader upon examining the results of this study would find compelling the notion that implementing leadership EUC initiatives would have positive effects on EUC efficiency. By encouraging IT leaders within the mortgage banking industry to optimize the processes that are developed, train their employees to improve EUC efficiency, have the employees spend time to identify potential for improving efficiency, provide required technology and software to employees to improve efficiency, the EUC efficiency for the organization should proportionately increase. This would of course not necessarily be causal by design of this study. IT leaders should use the leadership EUC initiatives measure as part of their leadership checklist. Summary This study adds to the body of knowledge on the functionality of leadership geared toward EUC initiatives and EUC efficiency. The current study not only addressed the body of knowledge with respect to measuring leadership EUC initiatives and EUC efficiency, but also made an instrument available for industry leaders to measure their own leadership EUC initiatives and EUC efficiency levels. This study is important; as it is the first time a researcher has developed a survey instrument to measure both the leadership EUC initiatives and EUC efficiency. For both of these measures, reliability was satisfactory and there was a degree of concurrent validity. As hypothesized, the results of the study showed a positive relationship between the two constructs. The instrument may be suitable for measuring EUC efficiency in organizations, and leadership will be able to use the measure as a guide to make necessary steps to improve the EUC efficiency of their systems. The participants were from the mortgage banking 97
industry, but leaders in other industries may find this study helpful in measuring their EUC efficiencies. The study included a discussion on the limitations of survey use, as well as the decision to use the correlational design. Future researchers may expand this study to other industries to confirm or reveal more findings that may help industry leaders improve their EUC efficiencies. The EUC efficiency measure is new to IT leaders and business organizations. The knowledge gained from this study can affect the understanding of EUC efficiency and the ways IT leadership can influence the efficiency directly. Developing a tool to measure the two constructs can help save money by making the EUC systems efficient. As the need for EUC increases, a proportional increase in savings may be possible. Based on this study, researchers may identify additional opportunities by expanding the survey instrument to other dimensions of IT developers’ functions or from a leader’s perspective. As EUC becomes more critical to organizations in the future, EUC efficiency will be an element that leaders need to monitor.
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Appendix A Information about End-User Computing (EUC) Efficiency in Mortgage Banking Survey A. I am measuring the level of emphasis IT and business leadership has on efficiency/optimization. Please respond to these items and alongside your response, please indicate your opinion of the relevance of the item in my quest to find that level by ranking the item on a scale of 0 (indicating not relevant) to 4 (indicating very relevant). Please indicate the option by shading over the choice, a check mark to communicate your choice for the survey item. If you are leader or a manager or a developer or a user driving EUC in an organization or a member of an organization impacting a EUC statistic, or have a say in the development of ad-hoc or scheduled periodic reports containing business data or data infrastructure or a staff member who uses reports as part of your work or recommends or requests changes to existing reports, or requests new reports, please continue with the survey.
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Appendix B End-User Computing (EUC) Efficiency in Mortgage Banking Survey
Part A. Items related to leadership EUC initiatives construct Never Rarely Often 1
2
3
4 5
6
7
IT Leadership has encouraged me to optimize the processes I develop or manage. IT Leadership has provided me the required training to improve efficiency of the processes I develop or manage. IT Leadership has asked me to spend time to identify potential for improving efficiency. IT Leadership has instructed me to review automated reports to improve efficiency. IT Leadership has asked developers in my organization to analyze the efficiency of reports prior to automation IT Leadership has provided me the required technology facilities to improve efficiency of the processes I develop or manage. Subtotals Totals
My manager gives me the information I need to do my work well. 8 My manager encourages me to use my talents. 9 My manager helps me to further develop myself. 10 My manager encourages his/her staff to come up with new ideas. 11 My manager gives me the authority to take decisions which make work easier for me.
Always
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12 My manager enables me to
13
14
15 16 17
18 19
20
21
22
23
24
solve problems myself instead of telling me what to do. My manager offers me abundant opportunities to learn new skills. My manager emphasizes the importance of focusing on the good of the whole. My manager has long-term vision. My manager emphasizes the societal responsibility of work. My manager holds me responsible for the work that I carry out. I am held accountable for my performance by my manager. My manager holds me and my colleagues responsible for the way we handle a job My manager keeps criticizing people for the mistakes they have made in their work. My manager maintains a hard attitude towards people who have offended him/her at work My manager finds it difficult to forget things that went wrong in the past. My manager takes risks even when he/she is not certain of the support from his/her own manager. My manager takes risks and does what needs to be done in his/her view.
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Part 1 2 3
4
5
6
B. Items related to EUC construct. Business leadership requires reports to be generated daily. Leadership team requests IT developers to build new reports on a daily basis. Leadership team requests unwanted reports to be eliminated from automated reporting systems. Leadership team, before requesting a new report, looks at the existing reports to ensure they are not duplicating their requests. IT leadership mandates all developers to have their report programs analyzed for efficiency. IT leadership waits until internal or external pressure urges them to remove unwanted reports in their systems. Subtotals Total
7
The subject EUC project will enhance competitiveness or create strategic advantage
8
The subject EUC project will enable the organization to catch up with competitors
9
The subject EUC project will align well with stated organizational goals
10
The subject EUC project will help establish useful linkages with other organizations
Never
Rarely Often
Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True
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Always
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
11 The subject EUC project
will enable the organization to respond more quickly to change 12 The subject EUC project
will improve customer relations
13 The subject EUC project
will provide new products or services to customers
14 The subject EUC process
will provide better products or services to customers 15 The subject EUC project
will enable faster retrieval or delivery of information or reports. 16 The subject EUC project
will enable easier access to information
17 The subject EUC project
will improve management information for strategic planning 18 The subject EUC project
will improve the accuracy or reliability of information 19 The subject EUC project
will improve information for operational control
Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True Extremely Slightly Neither False False False nor True 128
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
20 The subject EUC process
Slightly True
Extremely True
21
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
Slightly True
Extremely True
22
23
24
25
26
27
28
will present information in Extremely Slightly Neither False False False a more concise manner or nor better format True The subject EUC project will increase the flexibility Extremely Slightly Neither False False False of information requests nor True The subject EUC project Extremely Slightly Neither will save money by False False False reducing travel costs nor True The subject EUC project Extremely Slightly Neither will save money by False False False reducing communication nor costs True The subject EUC project Extremely Slightly Neither will save money by False False False reducing system nor modification or True enhancement costs. The subject EUC project Extremely Slightly Neither will allow other False False False applications to be nor developed faster True The subject EUC project Extremely Slightly Neither will allow previously False False False infeasible applications to nor be implemented True The subject EUC project Extremely Slightly Neither will provide the ability to False False False perform maintenance nor faster True The subject EUC project Extremely Slightly Neither will save money by False False False avoiding the need to nor increase the workforce True 129
29 The subject EUC project
will speed up transactions or shorten product cycles
30 The subject EUC project
will increase return on financial assets
31 The subject EUC project
will enhance employee productivity or business efficiency.
Extremely Slightly Neither Slightly False False False True nor True Extremely Slightly Neither Slightly False False False True nor True Extremely Slightly Neither Slightly False False False True nor True
130
Extremely True Extremely True Extremely True
Appendix C Details About Survey Questions (Part A) 1) Shows about the encouragement from management received to optimize the EUC processes developed or managed in an organization. 2) Shows the training received to improve EUC efficiency. 3) Shows if management has asked to identify potential and improve EUC efficiency. 4) Shows if management has requested to review automated reports to improve efficiency. 5) Shows if management has asked to analyze efficiency of reports prior to automation. 6) Shows if internal or external pressure forces to improve efficiency. Overall 1-6 will help reveal the level of initiatives by management resulting in EUC process efficiency.
(Part B) 1) 2) 3) 4) 5) 6)
Shows if developers create efficient code. Shows if code for common logic is shared for new reports developed. Shows if unwanted reports are eliminated from automation. Shows if new report requests are examined to avoid redundancy. Shows if developers analyze their programs for efficiency. Shows if developers look for efficiency consistently.
Overall 1-6 will help reveal the level of efficiency in the EUC systems.
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Appendix D Demographics Questionnaire The following questions will help revealing some demographics information about the survey participants. Years of Experience in the mortgage banking EUC processing: ____ Age: ____ Developer (Yes/No): _____ User (Yes/No): _____ Leader (Yes/No): ______ Manager (Yes/No) _____
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Appendix E Final End-User Computing (EUC) Efficiency in Mortgage Banking Survey Part A. Items related to leadership EUC initiatives construct Never Rarely Often 1
2
3
4 5
6
IT Leadership has encouraged me to optimize the processes I develop or manage. IT Leadership has provided me the required training to improve efficiency of the processes I develop or manage. IT Leadership has asked me to spend time to identify potential for improving efficiency. IT Leadership has instructed me to review automated reports to improve efficiency. IT Leadership has asked developers in my organization to analyze the efficiency of reports prior to automation IT Leadership has provided me the required technology facilities to improve efficiency of the processes I develop or manage. Subtotals
Part B 1 2
3
4
Always
Items related to EUC construct. Leadership team requests IT developers to build new reports on a daily basis. Leadership team requests unwanted reports to be eliminated from automated reporting systems. Leadership team, before requesting a new report, looks at the existing reports to ensure they are not duplicating their requests. IT leadership mandates all developers to have their report programs analyzed for efficiency.
Subtotals
133
Never
Rarely Often
Always
Appendix F Bar Graphs and Scatter Plots
134
Appendix G Linear Regression Equation and Graph
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Author Biography I am Kannan Deivasigamani, born and raised in a small town called Ranipet, Tamilnadu, in the peninsular part of India. I received my Bachelor’s degree from Vellore Institute of Technology, majored in Electronics and Communication, and MBA from Seton Hall University, majored in Management Information systems. I have been in the Information Technology industry for over 20 years and have consulted for several organizations including MAARS Systems, Mastech, American Management Systems, IRS, Paragon, IBM, Prudential, Aetna, Mellon, Readers Digest, BMW, Experian, AOL, and have been employed with HSBC for over a decade. I hold Base & Advanced SAS certifications. I currently manage the SAS infrastructure in UNIX & mainframe platforms supporting the sub-prime mortgage portfolio of HSBC consumer and mortgage lending services in Tampa. I enjoy arts, sports, games, technology and astronomy. I am a licensed HAM Radio operator and enthusiast. I served in Boy Scouts several years ago, earned my Wood Badge (“Go Beavers”) and helped my son. I am also a certified photographer and I dream of making my own movie some day. I consider myself lucky to be a Floridian living with the company of my wife, children & parents.
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