Global software development: an exploratory study of challenges of ...

Rev Manag Sci (2016) 10:649–682 DOI 10.1007/s11846-015-0171-y ORIGINAL PAPER

Global software development: an exploratory study of challenges of globalization, HRM practices and process improvement Muhammad Wasim Bhatti1 • Ali Ahsan1

Received: 24 October 2014 / Accepted: 13 April 2015 / Published online: 25 April 2015 Springer-Verlag Berlin Heidelberg 2015

Abstract The globalization of software development industry continues to experience a significant growth. The increased trend of globalization brings new challenges, increases the scope of the core functions of human resource management and impacts the dynamics of process improvement. The aim of this study is to explore the challenges of globalization and indicators of process improvement in distributed teams’ environment. This study also explores the impact of HRM practices on challenges of global software development, and, the impact of HRM practices and challenges of global software development on process improvement. The exploratory mixed method design is adapted as research methodology for this study. In this multi-method approach, study is completed in two phases. In first phase, qualitative data is collected, and analyzed to explore the study variables and their relationships in global software development environment. In second phase, quantitative data is collected, and analyzed to validate the findings of first phase. The findings of this study suggest that the challenges of global software development negatively impact the process improvement; but, effective HRM practices help to minimize the negative impact of challenges and positively impacts the process improvement in global software development environment. Keywords Global software development Challenges of globalization HRM practices Process improvement Mathematics Subject Classification

68N30 62J05 62H25

& Muhammad Wasim Bhatti [email protected]; [email protected] Ali Ahsan [email protected] 1

Engineering Management Department, Center for Advanced Studies in Engineering (CASE), Islamabad, Pakistan

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M. W. Bhatti, A. Ahsan

1 Introduction The globalization impacts every industry in the world by connecting human resources across the globe (Sˇmite et al. 2010). The significant impact of globalization is observed in software industry. Software engineers are teamed-up with globally distributed members to work on the same projects (Colomo-Palacios et al. 2014). Teams are formulated over geographic, temporal, cultural and linguistic distances. These distances are also named as ‘‘global distances’’ (Noll et al. 2010). As a result, the new field of software engineering has emerged as global software development (Oshri et al. 2007). Global Software Development (GSD) is the geographical and temporal dispersion of software development teams across the globe (DeSanctis and Jackson 1994). The business advantages of GSD include low cost of software development, access to highly skilled resources across the world, timely completion of the project and global presence of the organization (Khan et al. 2011). The advent of GSD was initially observed in early 1990s (Symon 1998). Now, large numbers of software development companies are turned into international organizations by adapting GSD as a strategic move (Colomo-Palacios et al. 2014). Carmel and Agarwal (2001) revealed that, large numbers of US based software development organizations are shifted towards GSD. They posited that more than 50 nations are engaged in this process. Bass et al. (2007) revealed that Siemens spends 3 billion Euros annually for globally distributed teams. About 30,000 software engineers work in globally distributed teams for Siemens. Ebert et al. (2008) compared the trend of globalization among five business sectors; including automotive, finance, consumer, ICT and health. They analyzed the offshoring capacity of R&D, Engineering, IT, Finance and HR functions. They observed that R&D and Engineering have significant growth rate, but IT function has highest degree of offshore capacity across five business sectors. In GSD, project teams involved in production of software are dispersed at several distributed sites such as in different cities and countries (Carmel 1999). Its example includes the possibility of teams located in USA, Europe and in Asia. These teams work on same projects using latest technology of Wide Area Network (WAN), Virtual Private Network (VPN) and internet protocols. Such teams normally use configuration management software to maintain the control on their work products and share appropriate version of deliverables with each other (Da Silva et al. 2012). GSD is an emerging methodology in software industry. But, this methodology faces many challenges. For example, week communication and coordination, weak monitoring and control, cultural difference, difference of time-zone, difference of language and geographical distance (Michael and Par 2008). Sabahat et al. (2010) proposed that, trust on other team members, communication, coordination, management of distributed teams, requirements elicitation, difference of language, diversified environment, difference of time zone, distance and cultural differences are major challenges of GSD. Cultural difference causes psychological distance and weak communication among members of distributed teams (Prikladnicki 2012). Different cultures of globally distributed teams impact the overall culture of an organization and hence, as a result, organizational culture keeps on changing

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continuously. Cultural differences, socialization and national stereotypes impacts significantly on information sharing practices of an organization. As a result, sometimes, relevant stakeholders remain unaware from the critical information of a project (Boden et al. 2012). In most of the cases, management remains unaware of the expertise and responsibilities of all resources, therefore, planning, monitoring and controlling of project activities become major challenges of GSD (Da Silva et al. 2012). The difference of processes at different sites of distributed teams results into the development of inconsistent work products. This problem becomes more severe when multiple components of one work product are developed by different teams of a project (Bhat et al. 2006). Considering the challenges of GSD, Ebert et al. (2008) posited that, overall risks involved in GSD are much more than those of collocated teams. The managerial issues of GSD discussed by Mishra and Mishra (2011) include knowledge management, project management, risk management, quality management, process management, requirement management and configuration management. Nidhra et al. (2013) performed systematic literature review and identified 60 different challenges and 79 mitigation strategies for knowledge transfer in GSD environment. The challenges and mitigation strategies identified by Nidhra et al. (2013) do not adequately include management related and process related challenges. The inadequate list of challenges in existing literature, and the dynamic complexities of GSD encouraged us to identify the challenges from the practitioners of GSD, and to establish the categories of challenges for the current study. The core functions of Human Resource Management (HRM) include recruitment, selection, orientation, development, performance evaluation and career path management (Schuler 2001). These functions are operated at multiple levels e.g. philosophy, policy, program, practice and process levels (Schuler 2001). Paul and Anantharaman (2004) believe that HRM practices include the establishment of employee-friendly work environment, career development, development oriented appraisal, and comprehensive training of organizational resources. The dynamic nature of organizational structures and new strategic needs of international business, change the dynamics of human resource management in international organizations (Adler and Bartholomew 1992). To cope with the dynamics of human resource management in international organizations, the field of International Human Resource Management (IHRM) is emerged in early 1990s (Tung 1993). Poole (1990) defined the term IHRM as the management of globally distributed and culturally diverse human resources across the globe. The functions of IHRM are based upon core functions of HRM with additional focus on complexities of international business strategies and complex coordination mechanisms among distributed teams (Adler and Bartholomew 1992). Brewster et al. (2005) discussed the transition of HRM function from domestic to global context. They coined the term of Global Human Resource Management (GHRM), and developed a model of GHRM by establishing relationship between organizational drivers, HR enablers, HR processes and organizational outcomes. They provided strategies to mitigate the issues of human resource management in international organizations. They argue that, International HRM has greater linkage with business agenda, strategic business needs and business processes of an organization. The challenges of globalization

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effect processes at industry-level, firm-level and function-level (Kim et al. 2003). At Industry level, the global processes are related to international trade, product standardization, cost reduction and value-addition (Makhija et al. 1997). At firmlevel, global processes are related to foreign asset management, sales management of foreign subsidiaries, and dispersion of responsibilities and involvement of top management (Ramaswamy et al. 1996). At function level, global resources are related to management of people, information sharing, integration and social orientation (Kim et al. 2003). According to Scullion and Linehan (2005), the global HRM leverages the human resources of an international organization to gain competitive advantages at local levels and at global level. Sheehan and Sparrow (2012) believe that the global HRM helps to manage diversified cultures, deals with geographical and organizational challenges and helps to manage globally distributed human resources. The impact of HRM practices on organizational performance, resource development and organization behavior is an important topic in the field of human resource management (Paul and Anantharaman 2004; Brewster and Scullion 1997). Literature reveals that, effective practices of HRM improve the knowledge, awareness, abilities and performance of an organization; ensure legitimacy and efficiency across organization; and help to reduce the negative impact of challenges and problems of distributed teams (Brewster et al. 2005; Jones and Wright 1992). In existing literature, HRM practices are discussed with internationalization (Adler and Bartholomew 1992), staffing, management of distributed subsidiaries, and additional factors of globalization (Brewster et al. 2005). HRM practices are found to have positive relationship with productivity (Huselid 1995), organizational performance (Becker and Gerhart 1996), resource involvement (Batt 2002), social climate, trust development, cooperation, understanding of language (Collins 2006) and employee retention (Huselid 1995). In software industry, HRM policies are linked with business strategy, organizational performance (Paul and Anantharaman 2002) and overall success of an organization (Caligiuri and Stroh 1995). Studies reveal that IHRM has significant relationship with socialization (Caligiuri and Stroh 1995), organizational characteristics, technology, organizational changes, regional characteristics, political, economic and socio-cultural conditions, headquarter orientation in global subsidiaries, competitive strategy and management of international operations (Schuler 2001). It is found that, effective human resource management helps to avoid uncertainty (Yao 2014), manages diversity and handles challenges of distributed teams in international organizations (Shen et al. 2009). Human resources are considered an important factor in software development process (Curtis et al. 2009). Galinec (2010) believe that the experienced and qualified human resources play important role in the success of a software project. Considering the importance of human resources in the process of software development, Software Engineering Institute (SEI) introduced a process maturity framework (e.g. People-CMM) to manage human resources in software development organizations (Curtis et al. 2009). The process maturity framework of PeopleCMM contains five levels of capability and maturity to manage human resources in software industry. The five maturity levels are called (1) Initial, (2) Managed, (3) Defined, (4) Predictable and (5) Optimizing (Curtis et al. 2009). Initial level

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contains no process areas. It is the default level of maturity of each software development organization (Colomo-Palacios et al. 2012). All other levels contain a set of process areas to deal with different aspects of human resource management (Curtis et al. 2009). The managed level contains compensation, training and development, performance management, work environment, communication and coordination and staffing process areas (Colomo-Palacios et al. 2012). The defined level contains the participatory culture, workgroup development, competency-based practices, career development, competency development, workforce planning and competency analysis process areas (Colomo-Palacios et al. 2012). The predictable level contains mentoring, organizational capability management, quantitative performance management, competency-based assets, empowered workgroups and competency integration process areas (Colomo-Palacios et al. 2012). The optimizing level contains continuous workforce innovation, organizational performance alignment and continuous capability improvement process areas (Colomo-Palacios et al. 2012). A detailed discussion on the process areas and practices of PeopleCMM is presented by Curtis et al. (2009) and Colomo-Palacios et al. (2012). Richardson et al. (2012) proposed a process framework to establish teams in global software development environment. The proposed framework contains one process area i.e. global teaming. The proposed process area contains five specific practices i.e. global task management, knowledge and skills management, global project management, operating procedures and collaboration between locations (Richardson et al. 2012). In existing literature, the role of HRM practices to mitigate the challenges of GSD is not adequately addressed. In current study, it is therefore important to investigate the impact of HRM practices on challenges of GSD. A process contains a set of activities to accomplish a task (SEI 2010). Process improvement is the optimized sequence and combination of activities to accomplish a task more effectively (SEI 2010). Software process improvement improves scalability in software development activities and ensures better quality of work products and services (SEI 2010). Process improvement is very important in GSD environment. It helps to reduce negative impact of challenges and issues of GSD (Noll et al. 2010). The consistent processes across all locations are necessary to develop consistent work products from all distributed teams (Bhat et al. 2006). The processes can be consistent by establishing standardized policies and procedures across all sites (Sparrow 2007), and, by providing trainings on process elements to all members of distributed teams (Noll et al. 2010). Software development organizations that focus on process improvement are likely to achieve more quality in their products and services (Martıńez-Costa et al. 2009). Process improvement in an organization ensures speedy improvement towards capability and maturity of processes and products; and serves as an umbrella for overall quality improvement (Wilson et al. 2001). Software specific process improvement provides a roadmap for the selection of right processes of software development; and ensures successful completion of projects under the umbrella of standardized processes (Wilson et al. 2001). Considering the importance of process improvement in software development, current study is focused to identify the indicators of successful process improvement in GSD environment. Ebert et al. (2008) believe that, in global software development, process improvement becomes a major challenge, and

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requires additional attention for significant improvement. Therefore, it is worth questioning whether the HRM practices have positive impact and challenges of GSD have negative impact on process improvement in GSD environment. Several authors focused on different aspects of HRM in software engineering, GSD and process improvement contexts. Some of the relevant studies include implementation of People-CMM in GSD environment (Colomo-Palacios et al. 2012), a process framework for global software engineering teams (Richardson et al. 2012), establishment of team structures for a software project (Andre´ et al. 2010), identification of skills and technical competencies of human resources in a software project (Colomo-Palacios et al. 2012) and skills enhancements of software engineers in a software project (Soto-Acosta et al. 2010). But, the role of HMR practices to mitigate the challenges of GSD and their impact on process improvement is not adequately addressed in existing literature. The combined analysis of these variables is not found in the referred literature. Therefore, this study is designed to bridge the gap of existing body of knowledge by exploring the challenges of GSD; indicators of process improvement; the impact of HRM practices on challenges of GSD; and the impact of HRM practices and challenges of GSD on process improvement in distributed teams’ environment. Following research questions are designed for this exploratory study; Research Question #1 Research Question #2 Research Question #3 Research Question #4 Research Question #5

What challenges are faced by globally distributed software development teams, and, how these challenges can be grouped to develop more robust categories of challenges of GSD? What are indicators of successful process improvement in GSD environment? What is impact of HRM practices on challenges of GSD? What is impact of challenges of GSD on process improvement in GSD environment? What is impact of HRM practices on process improvement in GSD environment?

Research Question #1 is addressed by identification and categorization of challenges of GSD in Sect. 3.1.1. Research Question #2 is answered by identification of indicators of successful process improvement in GSD environment in Sect. 3.1.2 Research Questions #3, 4 and 5 are answered by exploring a negative relationship between HRM practices and challenges of GSD; a negative relationship between challenges of GSD and process improvement; and a positive relationship between HRM practices and process improvement in Sects. 3.1.4, 3.1.3, 3.1.5, 3.2.7 and 3.2.8.

2 Research methodology In this study, exploratory mixed method research (Creswell 2007, 2009) is adapted to investigate the answers of research questions. Mixed method research is the combination of methodologies to study the same phenomenon with multiple

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viewpoints (Bryman 2006). In this approach, qualitative research is used to explore the meaning and understanding of constructs, while quantitative research is used to assess magnitude and frequency of constructs (Jick 1979). The combination of qualitative and quantitative research neutralizes the weaknesses and exploits the strengths of each method (Rohner 1977). A detailed discussion on the strengths and weaknesses of each method is presented by Johnson and Onwuegbuzie (2004). The inductive process of qualitative research helps to construct a new theory, which can be effectively grounded and generalized in relevant cases (Fitzgerald 1998). While, quantitative methodology helps to analyze numeric data (Sjøberg et al. 2007), and validates the study variables and their relationships (Creswell 2009). Jick (1979) believes that mixed-method design is best approach to uncover and to explore the new and deeper dimensions of a phenomenon. He proposed that qualitative data should be used to build and pre-test a survey instrument, and quantitative analysis should be used to validate the findings of qualitative results. Bouchard (1976) believe that the consistent results from multiple methods of research increases the validity of results, and ensures that the consistent findings from multiple methods are not a methodological artifact. Many researchers (e.g. Creswell and Plano Clark 2011; Creswell 2009; Teddlie and Tashakkori 2009; Greene 2008; Sjøberg et al. 2007; Johnson et al. 2007; Rossman and Wilson 1985; Jick 1979; Rohner 1977) advocate the usage of mixed method research to achieve better accuracy, precision and confidence in research results. This study is completed in two phases. In first phase, the inductive process of qualitative research is adapted by using grounded theory methodology to explore the study variables and their relationships (Adolph et al. 2011; Fitzgerald 1998; Strauss and Corbin 1994). In second phase, the quantitative research is used to validate the findings of qualitative research for better accuracy and precision of the results (Creswell 2009). The qualitative and quantitative methods are integrated by connecting the results from the qualitative research with the data collection of the quantitative research (Creswell and Plano Clark 2011). The approach we selected is conceptually similar to the exploratory sequential design as described by Creswell (2009), to the sequential model as described by Sandelowski (2000), to the sequential mixed model as described by Tashakkori and Teddlie (1998), and to the sequential triangulation design as described by Morse (1991). To investigate the Research Questions #1 and 2, this approach helped to develop (Greene 2008; Jick 1979) and validate (Creswell 2009) the survey instruments of ‘Challenges of GSD’ and ‘Process Improvement’. To investigate the Research Questions #3, 4 and 5, the relationships of the study variables are explored by using qualitative research (Adolph et al. 2011), and validated by using quantitative research (Creswell 2009). The consistent findings from multiple methods ensured that the results are due to the traits of the variables and not due to any specific method of research (Bouchard 1976). The detailed methodology of qualitative research is presented in Sect. 2.1; and of quantitative research is presented in Sect. 2.2.

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2.1 Phase 1: methodology of qualitative data analysis The main methodology adapted at first phase of the study is qualitative data analysis (Strauss and Corbin 1994). The practitioners of GSD, members of process improvement and members of HRM group in international organizations are interviewed; and grounded theory methodology (Strauss and Corbin 1994) is applied to identify the important themes to explore the answers of all research questions of this study. Grounded theory is the methodology of generation of a new theory from the analysis of qualitative data (Glaser and Strauss 1967). In this methodology, data is collected, coded and analyzed to generate a new theory (Strauss and Corbin 1994). This process of data collection, coding and analysis continues until theoretical saturation is reached (Draucker et al. 2007). The coding of data involves open coding, axial coding and selective coding (Strauss and Corbin 1994). In open coding phase, data is analyzed line-by-line, and appropriate codes are extracted from the text. In axial coding, categories are associated with related and sub-categories. In selective coding, a central category is introduced, and related categories are refined to establish an emerging theory (Strauss and Corbin 1994). The detailed methodology of this phase is given below; 2.1.1 Study participants Qualitative data is collected from 65 participants (45 males and 20 females). Indepth qualitative semi-structured individual interviews are conducted from 25 participants; and four focus groups are administered from 40 participants (10 participants in each focus group). The participants include the general practitioners of GSD (20 participants), members of process improvement (25 participants), and members of HRM group (20 participants). 2.1.2 Sampling In mixed method research, purposive sampling strategies are mainly used to collect and analyze the qualitative data (Teddlie and Yu 2007). In purposive sampling strategies, units or cases (e.g. individuals, groups or institutions) are purposely selected from a population to get the specific information to answer the study’s research questions (Maxwell 1998). The detailed discussion about mixed method sampling strategies and the guidelines to select appropriate sampling techniques are elaborated by Teddlie and Yu (2007); and Collins et al. (2007). In this study, we adapted theoretical sampling technique (Draucker et al. 2007; Collins et al. 2007) under purposive sampling strategy to collect the qualitative data, and to identify the emerging themes from all interviews and focus groups. 2.1.3 Data collection The process of data collection was initiated by conducting first interview from one practitioner of GSD. The discussion started by asking the participant about the challenges of GSD, indicators of process improvement and role of HRM practices.

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All participants shared their views and explained their opinion about the study variables and their dynamics in GSD environment. After each focus group and interview, notes were prepared, external resources were studied, data were uploaded and analyzed in NVIVO 8, and semi-structured interview questions were revisited for next interviews. Themes identified during first interview started recurring during subsequent interviews and focus groups. New themes also emerged during subsequent interviews and focus groups. But, theoretical saturation occurred, when data of 65 participants were analyzed. The process of data collection therefore was discontinued after data collection from 65 participants. At theoretical saturation, complete lists of challenges of GSD and indicators of process improvement are identified. The significant themes about the impact of HRM practices on challenges of GSD; and the impact of HRM practices and challenges of GSD on process improvement are also emerged. All focus group discussions are audio taped, and all individual interviews are recorded as field notes. Complete data set comprises of audio recordings lasting approx. 12 h and field notes of approx. 50 pages. 2.1.4 Data analysis Grounded Theory methodology (Strauss and Corbin 1994) is used to analyze the qualitative data. NVIVO 8 is employed for data management and to assist data analysis process. All audio recordings were transcribed verbatim. All data (transcribed verbatim and interview notes) were uploaded into NVIVO’s Sources section. Analysis was started by analyzing text, identifying emerging themes and by developing concept nodes and their relationships. Similarities and differences between emerging themes are analyzed by using the method of Constant Comparison (Glaser and Strauss 1967). By using the inductive process of grounded theory, concept nodes are grouped, relevant categories are established and emerging themes are linked iteratively with nodes and categories. All nodes and relationships are reviewed to perform a more analytical ordering and correction of relationships. Concept nodes and their relationships are visualized by generating models from the nodes and relationships. Models are discussed by analyzing themes of relevant nodes and inter-node relationships of each model. Therefore, all phases of grounded theory methodology (open coding, axial coding and selective coding) are adapted to achieve the research objectives of this study (Strauss and Corbin 1994). The detailed results of analysis process are discussed in Sect. 3.1. 2.2 Phase 2: methodology of quantitative data analysis Quantitative research is the method of collecting and analyzing numeric data to measure study variables and their relationships (Creswell 2009). Sjoberg et al. (2007) advocate the usage of empirical methods of research in the field of software engineering. They proposed face validity, internal validity, external validity, construct validity, and reliability analysis for the studies related to software engineering. Kitchenham et al. (2002) provide comprehensive guidelines for the usage of quantitative and empirical methods in the field of software engineering. They support the usage of reliability analysis, validity analysis and regression

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analysis to conduct the studies of software engineering. Jørgensen (1999) performed the construct validity analysis to establish and measure the software quality construct for software projects. Perry et al. (2000) support the quantitative methods to study process improvement in software engineering. They proposed the usage of internal validity, external validity, construct validity and reliability analysis for the studies of software engineering and process improvement. In this study, the quantitative data analysis is performed to validate the findings of first phase of the study. The study variables (‘Challenges of GSD’, ‘HRM Practices’ and ‘Process Improvement’) and their measurement items are validated through face validity, content validity, construct validity, convergent validity and discriminant validity. The relationship among the study variables is analyzed through the correlation analysis and linear regression analysis. It has been validated that challenges of GSD have negative impact on process improvement. HRM practices have negative impact on challenges and positive impact on the process improvement in GSD environment. 2.2.1 Sample In mixed method research, probability sampling strategies are mainly used to collect and analyze the quantitative data (Teddlie and Yu 2007). In probability sampling techniques, units or cases are randomly selected from a population or group, where probability samples represent the complete population (Tashakkori and Teddlie 2003). In this study, we adapted simple random sampling technique in probability sampling to collect and analyze quantitative data (Teddlie and Yu 2007; Collins et al. 2007). The questionnaire survey is administered in 35 organizations, which are engaged in the phenomena of global software development. The data (for measurement items) is collected by using a 5-point Likert-type scale, where 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree. As a whole 500 potential respondents are approached in selected organizations, research project is explained to them and they are requested to fill the questionnaire. The basic criterion for the selection of respondents for data collection is decided as ‘‘a professional working in GSD environment (with any technical, managerial, process improvement or human resource management role)’’. The resulting sample comprised of 210 (n = 210) usable filled questionnaires, representing a 42 % response rate. The sample respondents represented the diverse roles and designations in globally distributed software development teams. They are classified as HR consultants (9.5 %), HR executives (10 %), HR managers (10.47), software programmers (11.90 %), managers (10.47 %), quality assurance engineers (16.66 %), requirement engineers (10.95 %), and quality control engineers (9.5 %). 2.2.2 Measurements To measure the variable ‘Process Improvement’, measurement items are generated from qualitative data analysis. These measurement items are enlisted in Table 1. In questionnaire, the respondents are asked questions like ‘‘To what extent are process improvement goals aligned with organization’s business goals’’ etc. To measure the

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Table 1 Indicators of successful process improvement in GSD environment Sr. no.

Success indicator

1

Process improvement goals are aligned with organization’s business goals

2

Members of all distributed teams are trained on processes and procedures of the organization

3

Processes are consistent in all distributed teams of an organization

4

Process adherence is observed in all distributed teams by reducing the non-compliances under standardized threshold limits

5

Process related activities don’t delay the project activities

Fig. 1 Challenges of GSD

variable ‘Challenges of GSD’, measurement items are generated from the list of challenges presented in Fig. 1. These measurement items are grouped into five dimensions (Management related Challenges, Process related Challenges, Social Challenges, Technical challenges, and Environment related challenges). All the measurement items of ‘Challenges of GSD’ are presented in Table 2. The HRM practices (Compensation, Trainings, Employment Security, Social Interaction, Communication and Performance Appraisal) are grouped as dimensions of ‘HRM Practices’ variable. Items for these dimensions are collected from previously developed measurement scales. These measurement scales are modified under the context of current study. To measure the ‘Compensation’, one question is adapted from Smeenk et al. (2006) and it is modified to generate two questions under the context of our study. These questions are about the ‘competitive salary’ and ‘fringe benefits’ of employee with respect to the other organizations of software industry. ‘Training’ is measured by adapting the items from the instrument of Arthur (1994) and by modifying them as ‘I often receive off-the-job trainings, away from my work place’, ‘I often receive off-the-job trainings on my work place’ and ‘I often receive on-the-job trainings on my work place’. To measure the perceived ‘Employment Security’ one item is adapted from Gaertner and Nollen (1989), and it is modified to

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Table 2 Challenges of GSD Sr. no.

Measurement items

Management related challenges 1

I often face difficulties in communication and coordination with other distributed teams

2

I often face difficulties to monitor and control the tasks of distributed teams

3

I often face difficulties to manage a project in GSD environment

4

I often face difficulties in planning tasks of distributed teams

5

I often face difficulties to manage plans of distributed teams

6

I often face problem in team management in distributed teams’ environment

7

I often feel that the distributed teams exhibit loose bindings with each other

8

I often face difficulties to work as one team, with members of other locations of the organization

9

I often feel that my productivity decreases in GSD environment

10

I often feel that risks and issues increase in GSD environment

11

I often face difficulties in understanding my role and responsibilities in GSD environment

12

I often feel that the number of uncertainties increase in GSD environment

13

I often feel that the skills of distributed team members differ from each other

Process related challenges 1

I often feel that processes are not consistent in distributed teams’ environment

2

I often feel that the implementation of processes in distributed teams’ environment is difficult

3

I often feel that the level of processes’ adherence is not equal in different distributed team

4

I often face difficulties during tailoring of processes for different distributed teams

5

I often feel the level of understanding about the procedures of software development is not equal in different distributed teams

6

I often feel that the quality of work is not equal in different distributed teams

7

I often feel that different distributed teams have different perspective about the maturity of processes

Social challenges 1

Different distributed teams exhibit different cultures

2

I often feel that the distributed teams exhibit loose social bindings with each other

3

Different distributed teams speak different languages

4

I often feel that in distributed teams’ environment, resources exhibit lack of trust on other team members

5

I often feel that in distributed teams’ environment, resources feel fear from other team members

6

I often feel that in distributed teams’ environment, resources don’t share important information with other team members

Technical challenges 1

I often face problems when I integrate components developed by other distributed teams

2

I often face the problems related to configuration management in distributed teams’ environment

3

I often face problems when I synchronize work products with other distributed teams

4

I often face problems to establish and maintain a consistent technical environment in all distributed teams

Environment related challenges 1

In our organization, different distributed teams belong to different time-zones

2

In our organization, different distributed teams have different weather conditions

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Table 2 continued Sr. no.

Measurement items

3

In our organization, different distributed teams have different geo-political situations in their regions

4

In our organization, different distributed teams exist at long distance from each other

Table 3 HRM practices Sr. no.

Measurement items

Compensation 1

I receive competitive salary as compare to the other organizations of software industry.

2

I receive competitive fringe benefits as compare to the other organizations of software industry

Training 1

I often receive off-the-job trainings, away from my work place

2

I often receive off-the-job trainings on my work place’ and ‘I often receive on-the-job trainings on my work place

Employment security 1

HR department does all it can do to avoid layoffs

2

Sr. management of my site does all it can do to avoid layoffs

Social interaction 1

I frequently have off-the-job contacts with my work colleagues

2

I feel very much a part of my work group

3

I feel very much a part of all distributed teams of my organization

Communication 1

I am adequately informed about what is currently going on in the organization

2

I am adequately informed about changes that affect my job

Performance appraisal 1

My performance is assessed on the basis of goals of my job

2

The goals of my job include the goals related to process improvement

generate two items under the context of current study. The perceived ‘Social Interaction’ is measured by adapting three items from Sheldon’s instrument (1971). The perceived ‘Communication’ and ‘Performance Appraisal’ are measured by adapting two items for each from Smeenk et al. (2006). The items of ‘Social Interaction’, ‘Communication’ and ‘Performance Appraisal’ are also modified under the context of current study. All the measurement items of ‘HRM Practices’ are presented in Table 3. 2.2.3 Method The validation procedure suggested by DeVellis (2003) is applied to validate the items of study variables (i.e. ‘Challenges of GSD’, ‘HRM Practices’ and ‘Process Improvement’). Face validity, content validity, construct validity, convergent and discriminant validity are computed. The construct validity of the study variables is

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assessed by adapting the method suggested by Clark and Watson (1995). Confirmatory Factor Analysis (CFA) is performed by using Lisrel 8.8 software package; and hypothesized model of each study variable is analyzed by computing Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Comparative Fit Index (CFI), Goodness-of-Fit Index (GFI), and maximum likelihood Chi-square (v2). Reliability analysis is performed by computing the value of Cronbach’s alpha for all variables. Correlation and linear regression analysis is performed to assess the relationship between independent and dependent variables and to validate the findings of qualitative data analysis. The detailed results are presented in Sect. 3.2.

3 Results 3.1 Phase 1: results of qualitative data analysis 3.1.1 Challenges faced by globally distributed software development teams The qualitative data analysis depicted that the practitioners of GSD face many challenges and problems in global software development environment. These challenges are categorized into five different categories. These categories include; (1) Management related challenges, (2) Process related challenges, (3) Social Challenges, (4) Technical challenges, and (5) Environment related challenges. The detailed list of challenges is presented in Fig. 1. 3.1.1.1 Management related challenges The dynamics of GSD bring several challenges and problems at management level. One of the common problems faced by distributed teams is weak communication and coordination. The effective communication and coordination is very important for the execution of project activities, but, in GSD environment, it becomes a major problem for all members of distributed teams. Members of distributed teams have minimum clarity about their roles, responsibilities and tasks. This increases uncertainty, and negatively impacts the productivity of the resources. The productivity of the resources is also affected by insufficient trainings and inappropriate skills of distributed resources. The main cause of all of these problems is weak communication and coordination and unavailability of sufficient details about the resources of remote sites. Project management becomes more difficult in GSD environment. Project planning requires detailed knowledge about the availability and skills of distributed resource. It also requires strong bindings of distributed teams and effective management of distributed plans. But, the overall management becomes difficult due to weak monitoring and controlling of project progress and distributed resources. In distributed teams, the major problem is communication gap… Risks, issues and uncertainty increases in distributed environment… (A. Shashikanth, personal communication, June 28, 2014).

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Core problems of GSD are communication and monitoring and controlling… Assignment of common tasks to different teams of different locations is a major problem… (A. Chand, personal communication, June 28, 2014). Among all, communication is very important… (A.S. Koch, personal communication, June 28, 2014). Teams don’t realize that resource from remote location is part of their team… (S. Anwer, personal communication, July 4, 2014). Project management, integration of plans and monitoring and control are common problems of GSD… (T. Iqbal, personal communication, July 4, 2014). 3.1.1.2 Process related challenges It is important to devise a process implementation strategy for distributed software development teams. Selection of appropriate processes for different distributed teams, process tailoring, and selection of an appropriate Software Development Life Cycle (SDLC) model are the challenges of GSD. The mismatch of processes at distributed sites occurs when different teams follow different processes to achieve similar goals. This difference of processes at different sites of a project causes poor quality of work products. It becomes difficult to ensure the consistent processes and unanimous quality across all sites of a project, because, different teams have different perspective of process maturity and different level of understandings of product development procedures. Mismatch occurs when we follow different processes… (A. Chand, personal communication, June 28, 2014). Mismatch of processes becomes a real challenge to achieve unanimous quality from all work products of project… (A. Shashikanth, personal communication, June 28, 2014). You need to decide at the beginning about the processes for locations, teams and projects… Careful process tailoring is necessary… (A.S. Koch, personal communication, June 28, 2014). Resources have different level of understanding about processes and procedures and about quality… (R. Sheikh, personal communication, July 4, 2014). 3.1.1.3 Social challenges Member of globally distributed teams speak different languages and exhibit different cultures. These differences cause several fears (including the fear of information sharing), low level of mutual trust and weak social bindings among globally distributed software development teams. We have different cultures and different languages… (R. Sheikh, personal communication, July 4, 2014). It is difficult to trust a person who sits thousands miles away… (A. Shah, personal communication, July 8, 2014). We can’t share important information of project with other distributed teams… (S. Rehman, personal communication, July 8, 2014).

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3.1.1.4 Technical challenges It becomes difficult to develop and maintain a technical environment that supports building and integrating product components in distributed teams’ environment. Configuration management is one the major challenges of GSD. Poor management of configuration items causes incomplete synchronization of common work products among distributed teams in GSD environment. Technical activities would be complicated since those require integration of say two modules with one developed at one site and the other developed on other… (R. Sheikh, personal communication, July 4, 2014). In configuration management synchronization of data becomes difficult … (N. Potter, personal communication, July 8, 2014). 3.1.1.5 Environment related challenges The physical distance and difference of time-zone among distributed teams are important challenges of GSD. These challenges cause weak communication and coordination, and poor management of distributed teams. The difference of weather and the different geopolitical situations in different countries negatively impact the availability of resources and progress of the project. Different time-zone of different locations causes weak communication and poor controlling of issues… (A. Shashikanth, personal communication, July 8, 2014). Different weather and different political situations of distributed teams cause problems for resources to become available for work… (A.S. Koch, personal communication, June 28, 2014). 3.1.2 Indicators of successful process improvement Following themes (shown in Table 1) are emerged as success indicators of process improvement in GSD environment; Measurements of process non-compliances should be defined at organization level. All sites should meet the threshold values to reduce non-compliances. Resources should be well aware of all processes and mismatch of processes should not occur in distributed sites…. (N. Potter, personal communication, June 28, 2014) Processes should be part of culture of organization; this will help avoid delays in projects due to process related activities… (A. Shashikanth, personal communication, June 28, 2014) Processes should be defined by considering the broader scope of organizational and business goals…. (A.S. Koch, personal communication, June 28, 2014) 3.1.3 Challenges of GSD and process improvement The impact of challenges of GSD on process improvement in GSD environment is analyzed by using NVIVO software package; and, the model diagram is presented in

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Fig. 2 Challenges of GSD and process improvement

Fig. 2. The results of analysis depict that the different perspective of process maturity at different sites of distributed teams have negative impact on the consistency of processes in GSD environment. However, by ensuring common understanding of procedures, encouraging process adherence and by performing regular process audits, consistency of processes can be improved in distributed teams’ environment. The implementation of a process improvement framework requires process tailoring with respect to different teams and sites of the organization. However, the implementation of any suitable process improvement framework and consistency of processes positively impact the process improvement, and, help to achieve unanimous quality of products and services from all distributed teams in GSD environment. Distributed resources face difficulty in developing understanding about procedures…. (T. Iqbal, personal communication, July 4, 2014). Quality audits ensure that there are no non-conformances… (A. Shashikanth, personal communication, June 28, 2014). Process following is important for consistent processes…. (S. Anwer, personal communication, July 4, 2014). For me the challenge is the perspective of process maturity… (T. Schweigert, personal communication, July 4, 2014). You may need to modify/change/tailor the organizational set of standard processes… (A.U. Malik, Shashikanth, personal communication, June 28, 2014). 3.1.4 HRM practices and challenges of GSD The role of HRM practices (Compensation, Trainings, Employment Security, Social Interaction, Communication and Performance Appraisal) to solve the challenges of GSD is analyzed by using NVIVO software package; and, the model diagrams are presented in Figs. 3, 4, 5, 6, 7, 8. The results of analysis depict that the

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Fig. 3 Compensation and challenges of GSD

Fig. 4 Trainings and challenges of GSD

Fig. 5 Employment security and challenges of GSD

Compensation function of HRM helps to develop trust among members of distributed teams; and positively impacts the productivity of employees in GSD environment.

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Fig. 6 Social interaction and challenges of GSD

Fig. 7 Communication and challenges of GSD

Through compensation and reward, we encourage resources to perform better and to trust organization and other members of organization…. (J. Israr, personal communication, July 8, 2014). The productivity of employees increases with high level of compensation… (S. Saleem, personal communication, July 8, 2014). The distributed teams with competitive compensation perform better… (H. Aslam, personal communication, July 8, 2014). The Training function of HRM helps to ensure consistent processes across all locations in GSD environment. It also helps resources to effectively integrate the components of a product developed by dsitributed team members.

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Fig. 8 Performance appraisal and challenges of GSD

HRM department arrange proper trainings for consistent processes and integration of work products… (B. Khan, personal communication, July 8, 2014). The Employment Security function of HRM eliminates the fear of layoff and reduces the uncertainty about job security. It increases employees’ retention and ensures team bindings in GSD environment. Job security is important to eliminate the fear and uncertainty… (S. Saleem, personal communication, July 8, 2014). Employment security increases team bindings… (B. Khan, personal communication, July 8, 2014). The Social Interaction function of HRM helps to solve the social challenges in GSD environment. The social challenges include the lack of trust and fear among members of distributed teams; difference of language and culture; poor information sharing and weak social bindings among each other. The social interaction eliminates the social problems of GSD team members…. (H. Aslam, personal communication, July 8, 2014). The Communication function of HRM is very important in GSD environment. It helps to solve and mitigate several challenges of distributed teams. The communication function of HRM helps to ensure a common understanding of organizational procedures among all distributed teams. It eliminates the factor of

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uncertainity and increases the clarity about roles and responsibilities and capabilities of the resources. It solves the problem of weak social bindings and improves the process of information sharing among distributed team members. HRM ensures the true picture of capabilities of resources through the function of communication…. (H. Aslam, personal communication, July 8, 2014). Communication can increase social bindings among teams and increases the involvement of resrouces…. (B. Khan, personal communication, July 8, 2014). The procedures and other organizational information can be shared through effective communication from HR department… (J. Israr, personal communication, July 8, 2014). The uncertainity can be removed through proper comunication… (M. Wahab, personal communication, July 8, 2014). Resources can understand their job descriptions by communicating with HR department… (J. Israr, personal communication, July 8, 2014). The Performance Appraisal function of HRM ensures the proper information about the capabilities of resources from distributed teams. It also increases the resource involvement in GSD environment. This function has positive impact on the development of clarity abour roles and responsibilities of resources; and it increases productivity of employees in GSD environment. Performance appraisal is a tool to judge the capabilties of resources and to motivate the productive resources… (H. Aslam, personal communication, July 8, 2014). Performance appraisal increases the clarity about the responsibilites of employees and it increases their commitment towards their tasks… (N. Ahmed, personal communication, July 4, 2014). 3.1.5 HRM practices and process improvement HRM practices have positive impact on process improvement in GSD environment. The communication function is important to ensure the awareness of processes among members of distributed teams. It helps to achieve consistency of processes across all locations of an organization. Trainings bridge the gap of skills and techniques. It helps to achieve unanimous quality of products and services. Social interaction encourges the informal communication and helps to achieve process awareness. The appraisal of process related goals and encourgement of resources have positive impact on process imrpovement in GSD environment. The model diagram of HRM practices and process improvement is presented in Fig. 9. HRM should be engaged in process improvement…. (H. Aslam, personal communication, July 8, 2014). HR department can solve the problems of weak communication and inconsistencies… (M. Wahab, personal communication, July 8, 2014). Through trainings skills can be enhanced… (N. Ahmed, personal communication, July 4, 2014).

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Fig. 9 HRM practices and process improvement

Process related goals should be part of performance appraisal… (B. Khan, personal communication, July 8, 2014). 3.2 Phase 2: results of quantitative data analysis The measurement items of ‘Process Improvement’ are presented in Table 1, ‘Challenges of GSD’ in Table 2, and ‘HRM Practices’ in Table 3. ‘Process Improvement’ has 5 items; ‘Challenges of GSD’ has 34 items in five dimensions; and ‘HRM Practices’ has 13 items in six dimensions. These study variables (‘Challenges of GSD’, ‘HRM Practices’ and ‘Process Improvement’) are computed by taking averages of their respective items. 3.2.1 Face validity Face validity is the validity of appropriate appearance of a scale. It is the degree of transparency and relevance of a measure with the concept of interest. A measure is said have face validity, if it seems to measure what it is supposed to measure (Mosier 1947). A questionnaire survey is prepared to quantitatively measure the face validity of the study variables (i.e. ‘Challenges of GSD’, ‘HRM Practices’, and ‘Process Improvement’). Eight criteria (Relevant, Clear, Concise, Concrete, Correct, Coherent, Complete and Practical) are introduced to assess each measurement item of all the study variables. Data is collected from ten participants, and average indexes for each criterion are calculated for each study variable. The overall Face Validity Index (FVI) of each study variable is computed by taking average of all respective indexes. The FVI of ‘Challenges of GSD’ is 8.03, ‘HRM Practices’ is 8.12 and ‘Process Improvement’ is 8.25. All of these values represent a good face validity index of each study variable. The average indexes of ‘Challenges of GSD’ are presented in Fig. 10, ‘HRM Practices’ in Fig. 11 and ‘Process Improvement in Fig. 12.

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Fig. 10 Average indexes of ‘challenges of GSD’

Fig. 11 Average indexes of ‘HRM practices’

Fig. 12 Average indexes of ‘process improvement’

3.2.2 Content validity Content validity is the degree to which a scale appropriately and completely measures the content domain (Haynes et al. 1995). Content validity index (Polit and Beck 2006) is the most popular method to measure the content validity of a scale. The content validity of the framework is performed by using the method suggested by Bolino and Turnley (1999). Twelve experts of process improvement and HRM in GSD environment are engaged in the process of content validity. A questionnaire survey is prepared by establishing two sections; (1) Variables and (2) Items. All the study variables are listed in random order in ‘‘Variables Section’’; and all measurement items are listed in random order in ‘‘Items Section’’. Participants are requested to match items with variables. Data is analyzed by computing averages of

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Table 4 Construct validity of the study variables Challenges of GSD 2

HRM practices 2

Process improvement

Chi square

v (372, N = 210) = 718.31, p \ 0.001

v (356, N = 210) = 690.2, p \ 0.001

v2 (332, N = 210) = 480.34, p \ 0.001

RMSEA

0.071

0.079

0.083

SRMR

0.066

0.072

0.069

CFI

0.95

0.95

0.95

GFI

0.79

0.77

0.81

correct and incorrect matching. Results depicted that overall 80.76 % are correct matching of measurement items with their respective study variables. This value shows a significant content validity of the study variables and their items. 3.2.3 Construct validity The construct validity of the study variables is assessed by adapting the method suggested by Clark and Watson (1995). On sample data (n = 210), Confirmatory Factor Analysis (CFA) is performed and hypothesized model (as shown in Tables 1, 2, 3) of each study variable is analyzed by computing Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Comparative Fit Index (CFI), Goodness-of-Fit Index (GFI), and maximum likelihood Chi-square (v2). According to Xu (2011), for a good model fit, the value of Chi square should be non-significant. He also suggests that RMSEA = {0.06 to 0.1}; CFI [ 0.95 and SRMR \ 0.08 are indications of a good model fit. Bagozzi and Yi (1988) and Hu and Bentler (1999) propose that the values of RMSEA and SRMR should be \0.09; and the value of CFI should be [0.9. The detailed results of construct validity of the study variables are presented in Table 4. Hair et al. (2006) believes that, the standardized factor loadings of measurement items should be [0.5. All the items of three study variables are significantly loaded into their respective hypothesized factors (with loadings [0.5). Clark and Watson (1995) suggest that the items with strong loadings should be retained into their respective factors of a variable. The detailed results of CFA show a significant construct validity of the study variables. 3.2.4 Convergent validity The convergent validity is the degree to which the theoretically related components of a variable, model or framework are in fact related (Ballard and Seibold 2004). The convergent validity of the study variables is analyzed to assess the model adequacy of each variable. The method suggested by Ballard and Seibold (2004) is adapted to analyze convergent validity of ‘Challenges of GSD’ and ‘HRM Practices’. All the items of five factors of ‘Challenges of GSD’ are grouped into one factor, and, all the items of six factors of ‘HRM Practices’ are also grouped into one

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factor. Model fit for both study variables is analyzed by performing CFA. The detailed results are presented in Table 5. All the results in Table 5 indicate a poor model fit of both study variables. These results suggest that the items of each study variable don’t group significantly into one factor. This therefore supports the convergent validity of both variables. ‘Process Improvement’ is a uni-dimensional variable, the method suggested by Ballard and Seibold (2004) is not applicable on it. Therefore, the method proposed by Aubert et al. (1996) is adapted to assess the convergent validity of this study variable. Same method is applied to triangulate the results of ‘Challenges of GSD’ and ‘HRM Practices’. Averaged Variance Extracted (AVE) is computed to analyze the convergent validity of all study variables. The value AVE of all factors of each study variable is computed by using the formula proposed by Fornell and Larcker (1981). To support the convergent validity, Aubert et al. (1996) believe that, the value of AVE of a construct should be [0.5. Table 6 shows that the AVE of all components of ‘Challenges of GSD’ ranges from 0.52 to 0.61. Table 7 shows that Table 5 Convergent validity of ‘challenges of GSD’ and ‘HRM practices’ Challenges of GSD

HRM practices

Chi square

v2 (230, N = 210) = 2130.36, p \ 0.001

v2 (196, N = 210) = 1870.22, p \ 0.001

RMSEA

0.207

0.17

SRMR

0.13

0.14

CFI

0.83

0.87

Table 6 Average variance extracted of ‘challenges of GSD’

Table 7 Average variance extracted of ‘HRM practices’

Factor

Variance

Management related challenges

0.54

Process related challenges

0.61

Social challenges

0.53

Technical challenges

0.56

Environment related Challenges

0.52

Average variance extracted (AVE)

0.552

Factor

Variance

Compensation

0.57

Training

0.51

Employment security

0.54

Social interaction

0.59

Communication

0.62

Performance appraisal

0.53

Average variance extracted (AVE)

0.56

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Table 8 Average variance extracted of ‘process improvement’ Average variance extracted (AVE)

0.57

the AVE of all components of ‘HRM Practices’ ranges from 0.51 to 0.62. Table 8 shows that the AVE of ‘Process Improvement’ is 0.57. All of these values are [0.5, which indicate that the latent construct indicators of all study variables have significant amount of common variance (Xu 2011; Aubert et al. 1996). This shows that the variance captured by the constructs is greater than the variance occurred due to measurement errors. These results support the convergent validity of all study variables (Aubert et al. 1996; Fornell and Larcker 1981). 3.2.5 Discriminant validity The discriminant validity is the degree to which the theoretically unrelated components of a construct, model or framework are in fact unrelated (Clark and Watson 1995). The method suggested by Xu (2011) is adapted to assess the discriminant validity of the study variables. The correlation between five factors of ‘Challenges of GSD’; six factors of ‘HRM Practices’; and five items of ‘Process Improvement’ is computed. The value of coefficient of correlation for the factors of ‘Challenges of GSD’, factors of ‘HRM Practices’ and items of ‘Process Improvement’ ranges from 0.35 to 0.66, 0.41 to 0.68, and 0.38 to 0.62. These results indicate that the factor/item correlations are \0.9. Thus, each factor/item of each study variable is significantly distinct from other factor/item. These findings support the discriminant validity of all study variables. 3.2.6 Reliability analysis The value of Cronbach’s alpha for all the study variables is computed. Results show that value of Cronbach’s alpha of ‘Challenges of GSD’ is 0.76; ‘Process Improvement’ is 0.82; and ‘HRM Practices’ is 0.79. These results indicate a good reliability and internal consistency of all study variables. 3.2.7 Correlation analysis Correlation matrix is established to analyze the relationship between the study variables. Table 9 shows the coefficient of correlations among all the study variables. The significant positive relationship between ‘HRM Practices’ and ‘Process Improvement’ shows that both the variables are positively correlated with each other. The significant negative relationship of ‘HRM Practices’ and ‘Process Improvement’ with ‘Challenges of GSD’ shows that ‘HRM Practices’ and ‘Process Improvement’ are negatively correlated with ‘Challenges of GSD’.

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Table 9 Correlation matrix of the study variables Challenges of GSD

HRM practices

Process improvement

Challenges of GSD HRM practices

-0.759

–

–

Process improvement

-0.674

0.771

–

Bivariate correlation Correlation coefficient: pearson Correlation is significant at the 0.05 level

Table 10 Linear regression analysis

HRM practices

Beta

T

Sig. t

-0.759

-3.08

0.01

Predictor: HRM practices Dependent variable: challenges of GSD General multiple regression equation: F = 9.51, p \ 0.01; R = 0.759; R2 = 0.576, p \ 0.001 Method: enter

3.2.8 Regression analysis To investigate the impact of HRM practices on the challenges of GSD, linear regression analysis is performed by specifying ‘HRM Practices’ as independent variable and ‘Challenge of GSD’ as dependent variable. The value of R2 (0.576) shows that 57.6 % change in dependent variable is accounted for by the independent variable. The significant results of linear regression analysis (in Table 10) show that ‘HRM Practices’ significantly predict the ‘Challenges of GSD’. This finding therefore answers the 4th research question of our study. To analyze the impact of HRM practices and challenges of GSD on the process improvement in GSD environment, multiple regression analysis is performed by specifying ‘HRM Practices’ and ‘Challenges of GSD’ as independent variables and ‘Process Improvement’ as dependent variable. The value of R2 (0.614) shows that 61.4 % change in dependent variable is accounted for by the independent variables. The significant results of multiple regression analysis (in Table 11) show that ‘HRM Practices’ and ‘Challenges of GSD’ significantly predict the ‘Process Improvement’. This finding therefore answers the 3rd and 5th research questions of our study.

4 Discussion The present study is conducted to explore the challenges of GSD, HRM practices and process improvement in GSD environment. The results indicate that distributed teams face five types of challenges; (1) Management related Challenges, (2) Process

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Table 11 Multiple regression analysis Beta HRM practices Challenges of GSD

0.612 -0.21

T

Sig. t 1.571

-0.538

0.167 0.61

Predictors: HRM practices, challenges of GSD Dependent variable: process improvement General multiple regression equation: F = 4.76, p \ 0.05; R = 0.783; R2 = 0.614, p \ 0.001 Method: enter

related Challenges, (3) Social Challenges, (4) Technical Challenges, and (5) Environment related challenges. The management related challenges include weak communication and coordination (Prikladnicki 2012), weak monitoring and control (Michael and Par 2008), poor project planning and management (Da Silva et al. 2012), ineffective team management (Sabahat et al. 2010), poor productivity, risks and issues (Ebert et al. 2008), unclear roles and responsibilities, uncertainties (Boden et al. 2012), and inappropriate skills and trainings of distributed team members (Da Silva et al. 2012). Colomo-Palacios et al. (2014) believe that the intrinsic complexity of management of distributed teams and weak communication and coordination causes delay in completion of tasks in GSD environment. Poor management and delay in completion of tasks impacts employees’ productivity negatively. However, upright skills, appropriate expertise and additional efforts of project managers are helpful to manage distributed teams and to ensure competitive levels of productivity of distributed team members (Colomo-Palacios et al. 2014). The process related challenges include inconsistent processes (Da Silva et al. 2012), difficulties in implementation and tailoring of processes (Sparrow 2007), poor adherence to the processes (Da Silva et al. 2012), diversified level of understandings about the maturity of the processes and procedures, and inconsistent quality of work products produced by different distributed teams (Bhat et al. 2006). ColomoPalacios et al. (2014) found that the quality of a work product produced by distributed teams is lower than the quality of a work product produced by co-located teams. They believe that the inconsistent quality of work products is because of inconsistent adherence of processes in distributed teams’ environment. The social challenges include the difference of language and culture (Sabahat et al. 2010), weak social bindings, fear and‘ lack of trust, and information hiding from members of distributed teams (Boden et al. 2012). Søderberg et al. (2013) believe that, building mutual trust among distributed team members, establishing transparent working environment and ensuring cultural understanding of distributed teams are very important to establish and execute the strategic partnership among vendors and clients in GSD environment. The technical challenges include difficulties in components integration and data synchronization (Bhat et al. 2006), and establishment of configuration and technical environment for distributed teams. The environment related challenges include the difference of time-zone, weather and geopolitical situations and the physical distance (Prikladnicki 2012) among different sites in distributed teams’ environment (Michael and Par 2008). Nidhra et al. (2013)

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identified challenges of GSD and mitigation strategies through literature review and interviews of industrial experts. They found 60 different challenges and 79 mitigation strategies for GSD settings. They grouped the challenges and mitigation strategies into three factors; (1) Personnel, (2) Project, and (3) Technology. The sub categories of personnel related challenges include language barriers, cultural differences, trust, personal attributes and staffing. The sub categories of project related challenges include inadequate infrastructure, problems in requirements engineering and documentation, temporal distance, changing vendor, additional costs, meeting project deadlines, coping with novelty and communication challenges. The sub categories of technology related challenges include challenges with tool support and challenges with transactive memory system (Nidhra et al. 2013). The indicators of successful process improvement in GSD environment include the alignment of process goals with business goals of the organization (Prikladnicki et al. 2010), implementation and adherence of consistent processes (Richardson et al. 2012), training of distributed team members on standardized processes and procedures and smooth execution of process activities without delaying project tasks in distributed teams’ environment (Gotel et al. 2012). The HRM practices considered for the analysis include compensation (Smeenk et al. 2006), trainings (Arthur 1994), employment security (Gaertner and Nollen 1989), social interaction (Sheldon 1971), communication, and performance appraisal (Smeenk et al. 2006). This study explores the relationship between HRM practices and challenges of GSD. Results indicate that effective HRM practices can help to minimize the negative impact of challenges of GSD. The compensation function of HRM practices helps to develop trust among employees and increases productivity of the distributed team members (Huselid 1995). Colomo-Palacios et al. (2012) analyzed the impact and level of adaption of process areas of People-CMM in GSD environment. They found that compensation has medium impact in GSD, and, it is easy to be adapted in distributed teams’ environment. In ‘high performance work systems’ compensation schemes are provided to high performing workforce (US Department of Labor 1993). Go´mez-Meija and Balkin (1992) found a positive relationship between compensation and firm performance. Sheehan and Sparrow (2012) found that foreign direct investment increases wages, and better wages increase productivity of employees. In China, they found 69 % increased salaries of employees between 2005 and 2010; and they observed 4 % annual growth in productivity. Proper trainings ensure consistency of processes and integration of disintegrated components (Arthur, 1994). Training and development has medium impact in GSD, and, it is easy to be adapted in distributed teams’ environment (Colomo-Palacios et al. 2012). Employment security increases confidence of employees and reduces several uncertainties about job, projects and processes (Gaertner and Nollen 1989). Work force planning has high impact, but, it is difficult to be adapted in GSD environment (Colomo-Palacios et al. 2012). The social interaction is very important to resolve the social challenges of GSD (Boden et al. 2012). Participatory culture has very high impact, but, it is very difficult to be adapted in distributed teams’ environment (Colomo-Palacios et al. 2012). Communication is the core function of HRM for the resolution of several important

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challenges including the challenges of information sharing, weak social bindings of employees, and involvement of resources from distributed teams (Michael and Par 2008). Communication and coordination have very high impact, but, it is difficult to be adapted in GSD environment (Colomo-Palacios et al. 2012). Performance appraisal function boosts the productivity and increase the involvement of distributed team members (Smeenk et al. 2006). Performance management and quantitative performance management have high impact in GSD, but, it is difficult to be adapted in distributed teams’ environment (Colomo-Palacios et al. 2012). This study also aims to explore the impact of challenges of GSD and HRM practices on process improvement in distributed teams’ environment. The results indicate that the challenges of GSD have negative impact on process improvement (Da Silva et al. 2012). The implementation and tailoring of processes at each site is a major challenge in GSD environment (Prikladnicki 2012). The diversified level of process maturity at each site negatively impacts the consistency of processes (Martıńez-Costa et al. 2009). However, to ensure process improvement a common understanding of procedures should be established, process adherence should be ensured and regular process audits should be conducted in all distributed sites in GSD environment (Da Silva et al. 2012). The results indicate that the HRM practices have positive impact on process improvement. The communication function helps to ensure the awareness of processes and increases consistency by ensuring consistent process adherence among distributed team members (Sparrow 2007). Trainings, social interaction, compensation, employment security, and performance appraisals have positive impact on process improvement in GSD environment (Brewster et al. 2005).

5 Limitations In this study, only six HRM practices (i.e. compensation, training, employment security, social interaction, communication, and performance appraisal) are included in analysis. Although, these practices are most relevant for this study, but, People-CMM have some additional process areas and practices of HRM. Therefore, there can be possibility that we might not have included some HRM practices those are important and relevant in the context of GSD and process improvement. Small sample of data in quantitative part of the study is also a limitation of this study. Large sample could result into more supporting findings of this study. The scales for ‘Challenges of GSD’ and ‘Process Improvement’ are developed in this study. More studies are needed to validate these instruments with large sample of data.

6 Future research GSD is an emerging methodology in software industry. This methodology is adapted to access globalized resource pool and to reduce the cost of software development. However, this methodology faces many challenges (e.g. management

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related challenges, process related challenges, social challenges, technical challenges and environment related challenges). In future research, the challenges of GSD and their categorization should be used to develop a scale (with large sample of data) for the measurement of challenges of GSD in distributed teams’ environment. The individual challenges can be used as items and categories can be used as factors of the scale. Future research can also be directed to develop management methodologies and frameworks to deal with the challenges of GSD and to manage projects successfully in distributed teams’ environment. In this study, process improvement indicators are identified. In future research, these indicators can be used to develop a scale (with large sample of data) for the measurement of process improvement in GSD environment. These indicators can also be used to develop a process improvement framework for globally distributed software development teams. HRM practices have significant role to reduce the negative impact of challenges of GSD. Future research can be directed to study the impact of HRM practices on the success of a software project in GSD environment.

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