7
AHP-Based Prioritization Framework for Software Outsourcing Human Resource Success Factors in Global Software Development

ABDUL WAHID KHAN1, GHULAM YASEEN2, MUHAMMAD IMRAN KHAN1, FAHEEM KHAN3

1 Department of Computer Science, University of Science & Technology Bannu, KP, Pakistan

2 Department of Computer Science, Qurtuba University of Science & Information Technology D.I.Khan, KP, Pakistan

3 Department of Computer Science, University of Lakki Marwat University, KP, Pakistan Email: [email protected], [email protected], [email protected], [email protected]

Abstract

Software outsourcing enhances the concept of developing a valuable product at a low price in order to continually increase business. The purpose of this study is to assist vendors of software development organizations in the selection of successful human resources from the vendors’ perspective with the aim of accomplishing software development projects effectively. As a methodology, we used a systematic literature review (SLR) and found thirteen critical success factors in all. Next, we conducted a questionnaire survey for the validation of identified success factors. In the last step, an analytical hierarchy process (AHP) approach was adopted for the prioritization of identified success factors and their categories (based on their comparative importance). We grouped the identified success factors into four categories: procurement, organization, reliance, and quality. The category “organization” is more critical than the others based on research findings. Similarly, effective communication, trust development, competence of vendor and good governance, etc., are considered the most critical success factors as compare to others.

Keywords: Analytical hierarchical process (AHP), global software development (GSD), systematic literature review (SLR), human resources (HR), offshore software development outsourcing (OSDO), success factors (SF), critical success factors (CSF)

7.1 Introduction

In this age of science and technology, no field is free from competition. Every vendor wants to lead others in all fields and especially in the software development business. Every vendor struggles to fulfill the needs of their customers in efficient ways and at low cost. Offshore software development outsourcing is a new recently emerging approach adopted by many vendors for the development of software. Outsourcing is the best way to fulfill organization needs according to customer preferences [1]. Software outsourcing development provides the key advantages of easily accessed labor, development of high quality software and fast development [2].

Money assets, physical assets and human resources are considered as core aspects for the fruitful outcomes of all types of businesses [3, 4]. But in this research, we tried to discuss human resource assets that help vendors factors using HR factors effectively [5]. The purpose of this research is to discover HR success apply SLR [6] because of the significant role it has in making an organization viable and competitive in outsourcing. The focus of our research is to highlight which human resource success factors play a vital role in successful outsourcing of software development. Khan et al. [7] described important factors that are necessary for the competitiveness of vendors. They explain in their research that “skilled human resources” are very important in successful software development with client perspective. Fjermestad and Saitta [8] discussed the great importance of “management support” in software development outsourcing. At the time of software development, the vendors select successful HR for the successful outsourcing software development.

The vendor organization concentrates on the selection of different success factors that assist vendors in the successful development of software. Khan et al. [9] declared that the success factors in efficient project management play a vital role in the selection of outsourcing vendors for client. For the competence of vendors and successful outsourcing, it is necessary to select successful HR. Ali and Khan [10] stated that a “collaborative relationship” is an important success factor in successful outsourcing. Khan and Khan [11] showed that a “trustworthy,” relationship management consultation and negotiation are critical success factors for a successful outsourcing organization. Khan and Keung [12] described how “management commitment” and “staff involvement” play vital roles in successful outsourcing software process improvement.

In outsourcing software development, all factors are important but the significance of HR success factor is very vital and unique for successful outsourcing software development from the vendor’s perspective [13-16]. Our research assists vendors to identify and prioritize human resource critical success factors that play a very important role in the success of vendors in software development. We divided our research work into 7 sections. Section 7.2 contains a literature review in which we briefly discuss topic-related issues. In section 7.3, we describe research methodology in which we adopt SLR for the identification of CSFs from the existing literature, and also discuss a questionnaire survey for validation, and AHP prioritizing success factors. In Section 7.4, we discuss the results given. Research limitations are discussed in Section 7.5. In Section 7.6 the implications of the study are discussed. In Section 7.7 conclusions and future work on the study are discussed. Our research work is based on the following three research questions:

  • RQ1. What are the success factors of human resources that keep them on the right track in order to have a positive impact on software outsourcing development from the seller’s point of view?
  • RQ2. What practices are available in the literature to be considered by vendor organizations regarding HR for OSDO?
  • RQ3. How do we allocate values to identify success and how do we categorize and prioritize identified critical success factors?

7.2 Literature Review

Kitchenham recommended that software engineering-related researchers should adopt evidence-based software engineering (EBSE). An evidence-based approach to software engineering is applied to this method in software engineering research and practice. These types of approaches were first applied in medicine but with the passage of time, many other fields adopted this approach, including criminology, sociology, nursing, etc. [17]. Keele presents the guidelines of SLR methodology for existing literature [18]. Many researchers use SLR for the identification of success factors and questionnaire surveys for validation of SLR outputs [19, 20]. Ali et al. [21] used SLR to find the success factors of mutual trust and commitment, which are important for the success of the client and vendor relationship.

Vizcaino et al. [22] describe the HR success factors that affect the success of GSD vendors. Due to these factors, the vendors or suppliers can win market competencies to achieve company goals [23-25]. Niazi et al. [26] discuss the HR success factors that are important for the success of software project management in GSD environment. Rashid and Khan [27] tried to identify the HR success factors that are affected at outsourcing software development. Various researchers described success factors in their research that are affected at successful outsourcing [28]. Abdulkader [29] identified and analyzed the success factors and issues faced by GSD outsourcing organizations.

Ali et al. [21] claimed in their research that HR success factors like “mutual trust,” “effective communication,” “mutual interdependence and shared values,” and “3Cs” are important for successful outsourcing. Khan et al. [30] identified success factors and developed a SPIIMM model in their research that assists vendors in the selection of successful outsourcing. Wibisono et al. [31] illustrated how some outsourcing organizations are successful and some are not successful, and how the HR interaction coordination and cooperation factor is important for a successful IT outsourcing vendor.

In our research, we make an effort to sum up human resource critical success factors that assist GSD vendors for the selection of successful HR, which plays a vital role in the success of outsourcing vendors. In past studies, researchers attempted to draw attention to GSD outsourcing vendors’ HR factors. Our contribution is not only to include some new critical success factors through consistent methodology SLR but also validate these CSFs through a questionnaire survey with different practitioners. Moreover, we prioritized the identified success factors by using AHP methodology. We showed which HR success factors are critical compared to others and determined their criticality level locally and globally as well. The AHP and Fuzzy AHP prioritization approach was previously adopted by various other researchers in the same research domain [53-55].

7.3 Research Methodology

A systematic and scientific way of data compilation, analysis, verification, and validation of problem is called a research method. Methodology is an approach used to solve a specified problem. SLR is a method of searching out or solving an occurring research problem. It provides guidelines for solving research issues with specific mechanisms like systematic process, task, method, tools, and techniques. In research methodology, according to the problems, different researchers use different criteria for solving problems and getting outcomes. Research methodology is classified into different types, which describe purpose of study, research design and nature of study. However, in our work we apply methodology in three phases. In phase-1 we use SLR for identification of CSFs that assist vendors in the selection of successful HR factors in outsourcing. In phase-2 we apply a questionnaire survey to validate SLR results. In phase-3 we apply AHP methodology for categorizing and prioritizing CSFs and finding their criticality level.

7.3.1 Systematic Literature Review

In online digital libraries, SLR is the best methodology for extracting data from existing literature. The main purpose of SLR is recognizing, estimating and understanding all available studies particular to the research questions, field and experience of significance [32]. However, a SLR is a secondary study [33, 34]. Khan et al. [35] adopted SLR methodology for extracting data. Khan et al. [36] used SLR in their research for extracting critical success factors and critical challenges. The advantage of using SLR over other traditional data extracting methodologies is that its results are perfect compared to others [37]. Khan and Keung [12] used SLR methodology for extraction of data in their research. Kitchenham et al. [38] used SLR methodology in software engineering on existing literature for data extraction. The main steps involved in the construction of SLR are planning, conducting and reporting [39]. Using the same SLR, we tried to sum up the related data for the numerical judgment about our results. We also tuned up these solutions to design an empirical study for the evaluation of SLR. We exactly followed the process and steps of a SLR procedure discussed by different researchers.

7.3.2 Search String Process

We followed the search string formation process of different researchers in [12, 24, 40].

  • Research Question I:
    • – Success factors: “success factors” OR “winner factors” OR “important factors” OR “sensational factors” OR “key factors”.
    • – Vendors: “Vendors” OR “suppliers” OR “contractors” OR “sellers” OR “organization” OR “associations” OR “company”.
    • – Offshore software development outsourcing: “offshore software development sourcing” OR “contractor software development outsourcing” OR “offshore software development.
  • Research Question II:
    • – Vendor: “vendor” OR “supplier” OR “contractor” OR “brokers” OR “sellers”
    • – Practices: “practices” OR “outcomes” OR “Outputs” OR “manners” OR “results”

Research Question I: ((“outsourcing” OR “subcontracting”) AND (“software outsourcing development”) AND (“outsourcing model”) AND (“success factor” OR “winner factor” OR “good factor” OR “important factor”))

Research Question I and II: ((“Software outsourcing” OR “software outsourcing development” OR “Offshore software development outsourcing” OR “software subcontracting”) AND (Vendor OR supplier OR Seller) AND (“success factor” OR “success reason” OR “Winner Factor”) AND (Solution OR Solution OR Practice OR Practices OR advice) AND (“Human Resource”))

7.3.3 Search String Development

According to the research questions, we developed a search term and applied different types of digital libraries and online databases. We constructed a search string for the RQ1 and RQ2. Two libraries, i.e., Google Scholar and Science Direct, do not execute long search string, so we used a substring of this final search string. The second reason for a two-search string is that a single search string it is a very tiresome and time-consuming task. The search strings for research question 1 and 2 follow.

((“Software outsourcing” OR “software outsourcing development” OR “Offshore software development outsourcing” OR “software subcontracting”) AND (Vendor OR supplier OR Seller) AND (“success factor” OR “success reason” OR “Winner Factor”) AND (Solution OR Solution OR Practice OR Practices OR advice) AND (“Human Resource”))

7.3.4 Selection of Publications

Basically, the initial selection of journal and article or research papers of publications takes place based on the abstract of the paper, keywords of paper, and the title of the paper. The result of this basic selection of papers are displayed in Table 7.1.

Table 7.1: Search outcomes of different resources/libraries and databases.

Schematic illustration of search outcomes of different resources/libraries and databases.

After the initial selection, we read out all selected papers by following inclusion criteria and exclusion criteria for the final selection of papers.

7.3.4.1 Inclusion Techniques

Inclusion techniques identify which part of the data extraction process should be included in the existing literature. We use criteria based on the study of the following main inclusion criteria to define which part of the literature will be used for the data extraction process. Our criteria for inclusion is base on the study of the following topics of the software engineering field:

  • Offshore outsourcing
  • Human resource offshore outsourcing
  • Human resource outsourcing software development
  • HR success factor in outsourcing
  • HR success factors in outsourcing for vendors’ perspective
  • Global software development success factor related to our research question
  • Confirm success factor related to HR outsourcing from vendors perspectives

Therefore, we include the research paper that consists of the English language and title, abstract, and keywords that are the same as our search string.

7.3.4.2 Exclusion Criteria

The reason for the exclusion process is that it ignores part of the literature which is not used for data extraction. Our process for exclusion is based on the study of the following:

  • Is not related to outsourcing
  • Does not fulfill our research question issues
  • Is not the same title as our search string
  • Is not the same abstract as our search string
  • Is not the same keyword as our search string
  • Does not fulfill the criteria of HR outsourcing success factors
  • Research paper consists of other languages; not in the English language

7.3.4.3 Secondary Reviewer Support

As far as the initial or primary sources of choice are concerned, it’s entirely based on only the assessment of the title, summary, and reserve words of different literature and research papers. For the final choice of a research paper, it only checks the results on specified defined criteria of inclusion and exclusion. If there are sometimes unconvinced conditions about inclusion and exclusion criteria then a secondary assessor calls for a review of the selected data.

7.3.5 Commencement of Data Extraction

After studying the primary selection of research papers data extraction phase, this part entirely studies the purpose of filling out the research questions. The following results were collected during this phase. The data extraction phase will start after studying primary selected publications and it will totally emphasize filling out our research questions. The following data will be collected during the data extraction phase. The details are displayed in the following Table 7.2.

Table 7.2: Details of extracted data.

Schematic illustration of details of extracted data. images

7.3.6 Result Generated for Research Questions through SLR by Applying Final Search String

By applying the final search string on five digital libraries, we collected the different number of research papers. Total search results contained 2165 research papers, total accessed 2108, primary selection 117 and final selection contain just 45 papers that are related to our research work. The details of the final selections of research papers and publications are mentioned above in Table 7.2.

We find 45 research papers in the final selection. In the first phase, we found 20 SFs after analyzing and synthesizing some of them merged the thirteen critical success factors that remain in our final selected success factors described in Table 7.3. By using the SPSS tool, we found percentage and frequency of every CSF cited in the table. The details of SFs are described as under. To answer RQ1 we found the results shown in Table 7.3. The 1st critical success factor (CSF) in our findings is “competence of vendor,” its frequency is 23 out of 45, which means that the success factor repeats in the 45 papers 23 times. Its percentage is about 57% so we declare it a critical success factor because we include success factor as a critical when its percentage is greater than or equal to 36%.

The 2nd critical success factors in our research work are “well trained” and “technical capability”; the frequency of these success factors is 28 and percentage is 63%. The 3rd CSF is “good governance”; the frequency of this success factor is 17 and percentage is 38%. The 4th CSF is “proper procurement”; the frequency of this success factor is 32 and percentage is 72%. The 5th CSFs are “collaboration coordination” and “cooperation 3Cs”; the frequency of these success factors is 19 and percentage is 43%.

The 6th CSF is “effective communication”; the frequency of this success factor is 24 and percentage is 54%. The 7th CSF is “bidirectional transfer of knowledge (BTK) and exchange of knowledge”; the frequency of this success factor is 16 and percentage is 36%. The 8th CSF is “relationships enhancement”; the frequency of this success factor is 26 and percentage is 58%. The 9th CSF is “trust development”; the frequency of this success factor is 17 and percentage is 38%. The 10th CSF is “quality management”; the frequency of this success factor is 22 and percentage is 49%. The 11th CSF is “quality management”; the frequency of this success factor is 17 and percentage is 38%. The 12th CSF is “aware of standard”; the frequency of this success factor is 22 and percentage is 49%. The 13th CSF is “performance-based evaluation”; the frequency of this success factor is 22 and percentage is 49%. All the CSFs are explained below in Table 7.3.

Table 7.3: Synthesis of details of success factors.

Schematic illustration of synthesis of details of success factors.

7.3.7 Categorization of Identified Success Factors

We listed the identified success factors in four groups. The investigation of success factors performed according to their group, which are shown in detail in Table 7.4. The questionnaire survey is based on the abovementioned success factors in the field of software development organizations. The identified success factors by SLR provide knowledge to the survey participants. The category-wise grouping of the success factors creates a strong structure that assists practitioners in the most critical area of the field and also assists vendors in the selection of successful HR factors.

Table 7.4: Success factors for software outsourcing human resource.

Schematic illustration of success factors for software outsourcing human resource.

7.3.8 Analytical Hierarchical Process (AHP)

The most recent popular decision-making technique used is AHP. Saaty [41] is the developer of AHP and was the first to use this technique. After that, AHP has been used by various researchers in various fields for solving different complex decision-making problems [42]. We studied different research papers that used AHP methodology for analysis and prioritizing of SLR findings [43, 44]. The AHP methodology consists of the following three phases:

  • Divide complex problem into a hierarchical structure shown in Figure 7.1.
  • Find out the priority weight of each factor and its subfactor by using pairwise matrix comparison.
  • Check the consistency of judgment.

The details of the above three phases are given below.

Schematic illustration of division of complex problem into a hierarchical structure.

Figure 7.1: Division of complex problem into a hierarchical structure.

Phase-1: Decompose a complex decision problem into a hierarchical structure. In this phase, the complex problem is divided into hierarchical interconnected components [45]. The hierarchy of every success factor category contains a minimum of three phases, which are shown in Figure 7.1. The first phase describes the goal of the problem, the second phase shows the factors, and the third phase shows the subfactors of phase two.

Phase-2: Determine the priority weight of each factor and subfactor with the help of pairwise comparisons [46]. After performing the first phase in the second phase, the priority and weight are calculated by pairwise comparison decision matrix. The 9-point standardized comparison scaled in AHP is cited in Table 7.5. The pairwise comparison matrix is developed for every factor and its related subfactors.

Table 7.5: Description of 9-point scale for intensity of importance.

DefinitionIntensity of Importance
Significant1
Moderate significant3
Very significant5
Very strongly significant7
Very extremely significant9
Medium value2,4,6,8

Suppose C = {Cj|j = 1, 2. . . n} where n is an evaluation factor and every element of the evaluation matrix A, i.e., aij(i, j = 1, 2, .., n) represents its normalized relative weight, as illustrated in Equation (7.1).

(7.1)image

where aij = 1aij, aij > 0.

The weight vector w is identified using the characteristic equation, as shown in Equation

(7.2)image

where A is the pairwise comparison matrix for the factor, w is the weight vector, and max is the largest Eigen value.

Phase-3: Test the consistency of the pairwise comparison matrix. In phase three, the pairwise matrix is consistent. The pairwise matrix consistency is found through the consistency index (CI) and consistency ratio (CR) with the help of Equation (7.3) and (7.4).

(7.3)image
(7.4)image

where λ max is the maximum Eigen value of matrix A and n denotes the order of the factors. RI is the value of a random index of consistency, which has different values based on the number of factors, as listed in Table 7.6. The accepted value of CR is up to 0.10. If the calculated value of CR must be less than 0.10, then the priority vector (weight) of the factor is acceptable and we can conclude that matrix A has sufficient consistency. Otherwise, to improve the consistency, we repeat the evaluation procedure from Phase-1.

Table 7.6: Relationship between size of matrix and random consistency index.

Size of matrix1234
Random consistency index000.0250.087

7.4 Proposed Methodology

The final goal of this study is to find the success factors through SLR and prioritize them through AHP based on their significance [47, 48]. In our study, we prepared three phases. In the first phase we collected success factors using SLR; in the second phase the success factors were validated through a questionnaire survey; and in the third phase the success factors were prioritized through AHP.

Schematic illustration of proposed research design of the analytic hierarchy process (AHP) consistency ratio.

Figure 7.2: Schematic diagram of proposed research design of the analytic hierarchy process (AHP) consistency ratio.

We discarded many random expert selections and selected top expert software organizations, including DigiLynx, NESPAK, Arfa Karim Software House, Three C Technology, etc., from Pakistan. We identified the 13 success factors that are important for software outsourcing vendors and categorized them into 4 levels: Procurement, Organization, Reliance, and Quality. These categories of success factors are validated through a questionnaire survey by collecting the responses of 25 practitioners and their experience in the field of software outsourcing human resource environment. Finally, the method used for prioritizing was AHP.

The success factors were prioritized after performing pairwise matrix comparison of success factors. The following subsection describes the application of the proposed AHP methodology, on questionnaire survey findings for validating and prioritizing success factors.

7.4.1 Questionnaire Development

We found success factors through SLR and identified those success factors by questionnaire survey. Different researchers also preferred to use a questionnaire survey based on empirical study. Khan et al. [49] used this method for collecting responses of experts. The questionnaire survey contains 13 success factors and for every success factor five options are mentioned like strongly agree, agree, neutral, disagree, and strongly disagree. In the past, most of the researchers only used four options, but now they also use the option “neutral.” Neutral plays a significant role in collecting responses of experts [50, 51]. When we requested a response on the questionnaire survey the participant guaranteed that the collected data would not be shared with any third party at any cost and that the survey raw data would only be used for research purposes.

7.4.2 Data Sources

The purpose of our study is to prioritize and evaluate the identifying success factors. It is very important in our research that we collect expert responses from different global software development (GSD) outsourcing organizations that have different experiences and designations. We collect questionnaire survey responses from different experts mostly through email and some responses were received through the post. Data was collected from November 2019 to December 2020. Our data collection process was completed six months later; data was received from a total of 25 experts and we managed the questionnaire survey responses manually. The responsibilities of the experts in the organizations included developer, manager, engineer and others in the last mixed category.

7.4.3 Validation of Identified Success Factors

The success factors were collected through SLR and validated through a questionnaire survey. The success factors are cited below in Table 7.7. The table is divided into three columns labeled positive, negative and neutral: the positive column contains the success factors that belong to the strongly agree and agree options; the negative column contains the success factors that belong to the disagree and strongly disagree options; and the neutral category contains the neutral results.

The category of positive option in Table 7.7 describes the percentage of responses of questionnaire survey experts who agree with the success factors identified through SLR. The negative category contains the expert responses of those who do not agree with our identified success factors. The neutral category contains the expert responses of those whose feelings are neither negative or positive. In the positive category in Table 7.7, the success factors contain a greater than or equal to 45% score in the positive category. These success factors are applicable for further analysis.

Table 7.7: Success factors identified in the questionnaire study.

Schematic illustration of success factors identified in the questionnaire study.

7.4.4 Application of AHP to Prioritize Success Factors

For prioritizing identified success factors, we use the AHP method described below in detail.

  • Step 1. The grouping of success factors and their subfactors are identified in this first step.
  • Step 2. In this step, we divided our problem into pictorial and hierarchical structures, level one, level two, and level three, respectively.
  • Step 3. The comparison of each success factor and each category was conducted in this step. Table 7.5 displays the scale values and Tables 7.8, 7.10, 7.12 and 7.14 present comparisons and display results. The priority vector of each success factor is listed group-wise in Tables 7.9, 7.11, 7.13, and 7.15. All priority vector sums are equal to -1. These vectors show the relative weight of success subfactors.
Schematic illustration of hierarchical structure of the present study for the analytic hierarchy process (AHP) method.

Figure 7.3: Hierarchical structure of the present study for the analytic hierarchy process (AHP) method.

Table 7.8: Pairwise comparison matrix between the success factors of the “procurement” category.

SF- NoSF-1SF-2SF-3SF-4
SF-1150.252
SF-20.33140.25
SF-30.5216
SF-40.250.330.51

Table 7.9: Synthesized or normalized matrix of the “procurement” category.

images

Used short forms:

  • – CI: consistency index (CI = 0.078);
  • – CR: consistency ratio (RI = 0.9);
  • – RI: random consistency index (CR = 0.09 < 0.1 [Consistency is OK]).
  • – λmax = 4.175;

Table 7.10: Pairwise comparison matrix between the success factors of the “organization” category.

images

Table 7.11: Synthesized or normalized matrix of the “organization” category.

images

Used short forms:

  • – CI: consistency index (CI = 0.015);
  • – CR: consistency ratio (RI = 0.58);
  • – RI: random consistency index (CR = 0.03 < 0.1 [Consistency is OK]).
  • – λmax = 3.029;

Table 7.12: Pairwise comparison matrix between the success factors of the “reliance” category.

images

Table 7.13: Synthesized or normalized matrix of the “reliance” category.

images

Used short forms:

  • – CI: consistency index (CI = 0.016);
  • – CR: consistency ratio (RI = 0.58);
  • – RI: random consistency index (CR = 0.03 < 0.1 [Consistency is OK]).
  • – λmax = 3.033;

Table 7.14: Pairwise comparison matrix between the success factors of the “reliance” category.

images

Table 7.15: Synthesized or normalized matrix of the “quality” category.

images

Used short forms:

  • – CI: consistency index (CI = 0.031);
  • – CR: consistency ratio (RI = 0.58);
  • – RI: random consistency index (CR = 0.05 < 0.1 [Consistency is OK]).
  • – λmax = 3.062;

Table 7.16: Pairwise comparison matrix between the categories of success factors.

images

Table 7.17: Synthesized or normalized matrixes of the categories of success factors.

images

Used short forms:

  • – CI: consistency index (CI = 0.084);
  • – CR: consistency ratio (RI = 0.9);
  • – RI: random consistency index (CR = 0.09 < 0.1 [Consistency is OK]).
  • – λmax = 4.24;

In Table 7.17 above, we identified the value of consistency of ratio (CR) that is less than 0.1 and declared it to be an accepted priority of the success factors. Likewise, we also identified the priority of vector for all remaining success factors. The detail of these priority vector results are shown in Tables 7.9, 7.11, 7.13, 7.15, and 7.17.

  • Step 4. Table 7.18, column 4, shows the local weight of success factors and their corresponding category ranking, and the particular success factors and category ranking are shown in Table 7.18, column 5.

Table 7.18: Summary of local and global weights issues and their rankings.

Schematic illustration of summary of local and global weights issues and their rankings.
  • Step 5. In this step we identified the global weight of success factors for final ranking of success factors by using AHP; for example, the weight of success factor SF-01 (0.0901), SF-02 (0.0675), SF-03 (0.0876), etc., cited in Table 7.18, column 6.
  • Step 6. In this final step, a total of 13 success factors are prioritized and categorized according to weight. In this step, we find which success factor is very important among all of the 13 success factors of the software outsourcing human resource model. Table 7.18, column 6, shows that the success factor SF-06 is extremely important.

7.4.5 Comparison of Proposed Framework

The identified success factors of AHP-based prioritization framework for software outsourcing human resource are compared in Table 7.13. Shrivastava and Rathod [52] identified risk factors and compared them in their research; therefore, like these, we compare success factors of the proposed framework. In their research, they identified the factors that negatively affect software development. However, in our research, we identified success factors that affect positively on software outsourcing development activities.

Table 7.19: Prioritizing the success factors.

Sr. No.Name of Success FactorsPriority
SF-06Effective communication1.00
SF-09Trust development2.00
SF-01Competence of vendors3.00
SF-03Good Governance4.00
SF-02Well trained and technical capability5.00
SF-08Relationship enhancement6.00
SF-05Coordination, Cooperation and Collaboration 3Cs7.00
SF-07Bidirectional Transfer of Knowledge (BTK)’Exchange of Knowledge8.00
SF-11Quality management9.00
SF-12Aware of standards10.00
SF-10Innovative skill11.00
SF-04Proper Procurement12.00
SF-13Performance based evaluation13.00

7.5 Limitations

This study is conducted using the SLR approach and the two major study limitations are given below.

  • Internal validity: Some authors may not give the valid reasons behind critical success factors that arise during software outsourcing human resources.
  • External validity: In the questionnaire survey and case study, there may be a lack of interest of participants due to their busy schedules or responsibilities. There is also a chance that we have missed some of the key points in the literature review process.

7.6 Implications of the Study

Implementation of our research work is very useful for both practitioners and researchers. Our study proposed a framework based on 13 success factors identified through SLR and validated by a questionnaire survey, prioritizing through the AHP methodology. This research may help researchers imitate the AHP method in their research work for evaluating the ranking of issues based on their importance. The findings of this study have real-world industrial implications for vendor organizations. Similarly, the researchers could replicate the study findings and conduct the future studies in the same research domain. Moreover, AHP methodology can be used for complex and group decision-making problems that will help researchers evaluate the success factors ranking based on their importance.

7.7 Conclusions and Future Work

Mostly offshore software development vendors are faced with many problems when selecting successful HR for successful software development projects. We identified 13 critical success factors through SLR to evaluate these success factors with the input from 25 practitioners who participated in the questionnaire survey. Next, the ranking of success factors was performed through AHP on the responses of experts. The results of this study show that the “effective communication” success factor belongs to the most critical category, “organization” the second most critical success factor, and “trust development” belongs to “reliance,” which assists vendors in selecting successful HR. Many researchers in the past have identified HR success factors and HR challenges; however, no such model like our model assists vendors for the selection of HR success factors from the vendor’s perspective through AHP methodology.

In future work our research aims to develop a model that may assist GSD vendors for the selection of successful HR. To enhance our study, researchers need to identify additional success factors for software outsourcing human resources by conducting the SLR to get different results by the selection of a large sample size in the questionnaire survey. Our work is based on critical success factors; for future work, researchers need to take the critical challenges and best practices from these challenges and success factors.

References

1. Ahmad, J., Khan, A. W., & Qasim, I. (2018). Software Outsourcing Cost Estimation Model (SOCEM). A Systematic Literature Review Protocol. University of Sindh Journal of Information and Communication Technology, 2(1), 25-30.

2. Azeem, M. I., & Khan, S. U. (2011, December). Intercultural challenges in offshore software development outsourcing relationships: A systematic literature review protocol. In 2011 Malaysian Conference in Software Engineering (pp. 475-480). IEEE.

3. Gautam, D. K. (2015). Strategic integration of HRM for organizational performance: Nepalese reality. South Asian Journal of Global Business Research.

4. Abdul-Halim, H., Ee, E., Ramayah, T., & Ahmad, N. H. (2014). Human resource outsourcing success: leveraging on partnership and service quality. Sage Open, 4(3), 2158244014545475.

5. Kodwani, A. D. (2007). Human resource outsourcing: Issues and challenges. Journal of Nepalese Business Studies, 4(1), 38-46.

6. Usman, A., & Khan, A. W. (2018). Software outsourcing quality challenges model systematic literature review (SLR) protocol. University of Sindh Journal of Information and Communication Technology, 2(4), 196-201.

7. Khan, S. U., Niazi, M., & Ahmad, R. (2011). Factors influencing clients in the selection of offshore software outsourcing vendors: An exploratory study using a systematic literature review. Journal of Systems and Software, 84(4), 686-699.

8. Fjermestad, J., & Saitta, J. A. (2005). A strategic management framework for IT outsourcing: A review of the literature and the development of a success factors model. Journal of Information Technology Case and Application Research, 7(3), 42-60.

9. Khan, S. U., Niazi, M., & Ahmad, R. (2010, June). Critical success factors for offshore software development outsourcing vendors: an empirical study. In International Conference on Product Focused Software Process Improvement (pp. 146-160). Springer, Berlin, Heidelberg.

10. Ali, S., & Khan, S. U. (2016). Software outsourcing partnership model: An evaluation framework for vendor organizations. Journal of Systems and Software, 117, 402-425.

11. Khan, A. W., & Khan, S. U. (2013). Critical success factors for offshore software outsourcing contract management from vendors’ perspective: an exploratory study using a systematic literature review. IET Software, 7(6), 327-338.

12. Khan, A. A., & Keung, J. (2016). Systematic review of success factors and barriers for software process improvement in global software development. IET Software, 10(5), 125-135.

13. Khan, A. A., Keung, J., Hussain, S., Niazi, M., & Tamimy, M. M. I. (2017). Understanding software process improvement in global software development: a theoretical framework of human factors. ACM SIGAPP Applied Computing Review, 17(2), 5-15.

14. Alam, A. U., Khan, S. U., & Ali, I. (2012). Knowledge sharing management risks in outsourcing from various continents perspective: a systematic literature review. International Journal of Digital Content Technology and Its Applications, 6(21), 27.

15. McCracken, M., & McIvor, R. (2013). Transforming the HR function through outsourced shared services: insights from the public sector. The International Journal of Human Resource Management, 24(8), 1685-1707.

16. Keegan, A., & Francis, H. (2010). Practitioner talk: the changing textscape of HRM and emergence of HR business partnership. The International Journal of Human Resource Management, 21(6), 873-898.

17. Savolainen, P., Ahonen, J. J., & Richardson, I. (2012). Software development project success and failure from the supplier’s perspective: A systematic literature review. International Journal of Project Management, 30(4), 458-469.

18. Keele, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Vol. 5). Technical Report, Ver. 2.3 EBSE Technical Report. EBSE. Software Engineering Group School of Computer Science and Mathematics Keele University Keele, Staffs ST5 5BG, UK. p. 65.

19. Noroz, A. K., Khan, S. U., Alam, A. U., & Alam, S. U. Multi-Sourcing Human Resource Management from Vendor’s Perspective: A Systematic Literature Review Protocol.

20. Khan, S. U., Niazi, M., & Ahmad, R. (2009, July). Critical success factors for offshore software development outsourcing vendors: A systematic literature review. In 2009 Fourth IEEE International Conference on Global Software Engineering (pp. 207-216). IEEE.

21. Ali, S., Hongqi, L., Khan, S. U., Zhongguo, Y., & Liping, Z. (2017). Success factors for software outsourcing partnership management: An exploratory study using systematic literature review. IEEE Access, 5, 23589-23612.

22. Vizcaíno, A., García, F., Villar, J. C., Piattini, M., & Portillo, J. (2013). Applying Q-methodology to analyse the success factors in GSD. Information and Software Technology, 55(7), 1200-1211.

23. Khan, S. U., Niazi, M., & Ahmad, R. (2012). Empirical investigation of success factors for offshore software development outsourcing vendors. IET Software, 6(1), 1-15.

24. Ahmad, J., Khan, A. W., & Qasim, I. (2018). Software Outsourcing Cost Estimation Model (SOCEM). A Systematic Literature Review Protocol. University of Sindh Journal of Information and Communication Technology, 2(1), 25-30.

25. J. D. Lilly, M. V., D. A. Gray (2015). Investigation of success factors for offshore software development outsourcing vendors. San Jose State University SJSU ScholarWorks, January, p.20.

26. Niazi, M., Mahmood, S., Alshayeb, M., Qureshi, A. M., Faisal, K., & Cerpa, N. (2016). Toward successful project management in global software development. International Journal of Project Management, 34(8), 1553-1567.

27. Rashid, N., & Khan, S. U. (2015). Green agility for global software development vendors: A systematic literature review protocol. Proceedings of the Pakistan Academy of Sciences, 52(4), 301-313.

28. Yang, Y. H., & Tamir, G. (2015). Offshore software project management: mapping project success factors. International Journal of Project Organisation and Management, 7(2), 111-131.

29. Abdulkader, Z. (2016). A study on how outsourcing creates challenges and issues to the human resource in an organisation, a case study on Dell Ireland (Doctoral dissertation, Human Resources Management, Dublin Business School).

30. Khan, A. A., Keung, J. W., & Abdullah-Al-Wadud, M. (2017). SPIIMM: toward a model for software process improvement implementation and management in global software development. IEEE Access, 5, 13720-13741.

31. Wibisono, Y. W., Govindaraju, R., Irianto, D., & Sudirman, I. (2016, December). Interaction capability, process quality, and outsourcing success: A vendor perspective in offshore IT outsourcing. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1780-1784). IEEE.

32. Siddaway, A. (2014). What is a systematic literature review and how do I do one. University of Stirling, 1(1): p. 1-13.

33. Keele, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Vol. 5). Technical Report, Ver. 2.3 EBSE Technical Report. EBSE.

34. Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering–a tertiary study. Information and Software Technology, 52(8), 792-805.

35. Khan, A. W., & Imran, M. (2017, August). A Comparative Study of Critical Challenges of Outsourcing Contract Management Identified through SLR and Empirical Study. In Proceedings of the International Conference on Advances in Image Processing (pp. 161-164).

36. Khan, A. A., Keung, J., Hussain, S., Niazi, M., & Kieffer, S. (2018). Systematic literature study for dimensional classification of success factors affecting process improvement in global software development: client–vendor perspective. IET Software, 12(4), 333-344.

37. Fernández-Alemán, J. L., Se˜nor, I. C., Lozoya, P. Á. O., & Toval, A. (2013). Security and privacy in electronic health records: A systematic literature review. Journal of Biomedical Informatics, 46(3), 541-562.

38. Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering–a systematic literature review. Information and Software Technology, 51(1), 7-15.

39. Okoli, C., & Schabram, K. (2010). A guide to conducting a systematic literature review of information systems research.

40. Salam, M., & Khan, S. U. (2016, August). Developing green and sustainable software: Success factors for vendors. In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 1059-1062). IEEE.

41. Saaty, T.L. (1988). What is the analytic hierarchy process?, in Mathematical models for decision support. Springer. p. 109-121.

42. Chatzimouratidis, A., Theotokas, I., & Lagoudis, I. N. (2012). Decision support systems for human resource training and development. The International Journal of Human Resource Management, 23(4), 662-693.

43. Nurhayati, S. (2019, November). Application of Computer-assisted Analytic Hierarchy Process Method to Evaluate Employee Performance. In IOP Conference Series: Materials Science and Engineering (Vol. 662, No. 2, p. 022033). IOP Publishing.

44. Hussain, M., Khan, H. U., Khan, A. W., & Khan, S. U. (2021). Prioritizing the Issues extracted for Getting Right People on Right Project in Software Project Management From Vendors’ Perspective. IEEE Access, 9, 8718-8732.

45. Badri, M. A. (2001). A combined AHP-GP model for quality control systems. International Journal of Production Economics, 72(1), 27-40.

46. Albayrak, E., & Erensal, Y. C. (2004). Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision making problem. Journal of Intelligent Manufacturing, 15(4), 491-503.

47. Bozbura, F. T., Beskese, A., & Kahraman, C. (2007). Prioritization of human capital measurement indicators using fuzzy AHP. Expert Systems with Applications, 32(4), 1100-1112.

48. Akbar, M. A., Khan, A. A., Khan, A. W., & Mahmood, S. (2020). Requirement change management challenges in GSD: An analytical hierarchy process approach. Journal of Software: Evolution and Process, 32(7), e2246.

49. Khan, A. A., Keung, J., Niazi, M., Hussain, S., & Ahmad, A. (2017). Systematic literature review and empirical investigation of barriers to process improvement in global software development: Client–vendor perspective. Information and Software Technology, 87, 180-205.

50. Niazi, M., Mahmood, S., Alshayeb, M., Riaz, M. R., Faisal, K., Cerpa, N., ... & Richardson, I. (2016). Challenges of project management in global software development: A client-vendor analysis. Information and Software Technology, 80, 1-19.

51. Khan, A. W., Hussain, I., & Zamir, M. (2020). Analytic hierarchy process-based prioritization framework for vendor’s reliability challenges in global software development. Journal of Software: Evolution and Process, e2310.

52. Shrivastava, S. V., & Rathod, U. (2015). Categorization of risk factors for distributed agile projects. Information and Software Technology, 58, 373-387.

53. Khan, A. A., Shameem, M., Nadeem, M., & Akbar, M. A. (2021). Agile trends in Chinese global software development industry: Fuzzy AHP based conceptual mapping. Applied Soft Computing, 102, 107090.

54. Khan, A. A., & Shameem, M. (2020). Multicriteria decision-making taxonomy for DevOps challenging factors using analytical hierarchy process. Journal of Software: Evolution and Process, 32(10), e2263. DOI: 10.1002/smr.2263

55. Khan, A. A., Shameem, M., Kumar, R. R., Hussain, S., & Yan, X. (2019). Fuzzy AHP based prioritization and taxonomy of software process improvement success factors in global software development. Applied Soft Computing, 83, 105648.

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