Elsevier

Decision Support Systems

Volume 102, October 2017, Pages 42-56
Decision Support Systems

A systematic literature review and critical assessment of model-driven decision support for IT outsourcing

https://doi.org/10.1016/j.dss.2017.07.002Get rights and content

Highlights

  • IT sourcing decision support researchers adopted diverse decision analysis methods.

  • Use of naturalistic evaluation & reference theories is limited in IT sourcing research.

  • Recommendation for development of IT sourcing decision support artifacts presented.

Abstract

Information technology outsourcing (ITO) is a widely-adopted strategy for IT governance. The decisions involved in IT outsourcing are complicated. Empirical research confirms that a rational and formalized decision-making process results in better decision outcomes. However, formal and systematic approaches for making ITO decisions appear to be scarce in practice. To support organizational decision-makers involved in IT outsourcing (including cloud sourcing), researchers have suggested several decision support methods. To date there is no comprehensive review and assessment of the research in this domain. In this study 133 model-driven decision support research articles for IT outsourcing and cloud sourcing were identified through a systematic literature review and assessed based on a highly-regarded research framework. An analysis of these 133 research articles suggested a range of Multiple Criteria Decision Making (MCDM), optimization and simulation methods to support different IT outsourcing decisions. Our findings raise concerns about the limited use of reference design theories, and the lack of validation and naturalistic evaluation of the decision support artifacts reported in ITO decision support literature. Based on the review, we provide future research directions, as well as a number of recommendations to enhance the rigor and relevance of ITO Decision Support Systems research.

Introduction

IT outsourcing (ITO) is an established IT governance strategy and IT outsourcing decisions are vital for organizations. The ITO industry is expanding continuously [1], shaped by intricate multi-sourced environments and disruptive technologies such as cloud computing. In practice, despite the widespread adoption of ITO, not all organizations are satisfied with their ITO initiatives. There are numerous cases of ITO failure or dissatisfaction reported in the literature (e.g. [2], [3], [4]). In addition, some organizations that adopted ITO, later decide to abandon their ITO initiative and bring their IT back in-house due to dissatisfaction with ITO outcomes or due to internal or external organizational changes. The following instance exemplifies a case in which an organization changed its IT sourcing model over time: “Kellwood's multimillion dollar IT outsourcing deal with EDS served it well for many years. But after significant organizational changes and intense investigation of the 13-year deal, it became clear that insourcing was the best way for the apparel maker to save money moving forward … Analysis revealed that … insourcing IT would not only streamline IT services and provide greater flexibility than outsourcing; it would also generate even more cost savings” [5]. As another example, in 2002 JPMorgan announced its seven-year, five billion dollars outsourcing arrangement with IBM, which was at the time the largest outsourcing deal on record. However, in 2005, the company decided to end the contract with IBM and bring its IT back in-house [6]. These examples clearly show the complexity and the risks involved in ITO decisions and highlight the importance of a comprehensive and prudent ITO decision-making process for organizations. Moreover, the complex nature of ITO decision-making is a well-recognized and agreed upon fact among academic ITO researchers [7], [8], [9]. In addition, empirical research confirms that a rational and formalized decision-making process results in better decision outcomes [10], and the lack of a structured and systematic approach to IT outsourcing (ITO) decision making in practice is frequently highlighted in the literature [11], [12], [13], [14], [15].

The research into IT outsourcing is extensive and IT outsourcing decisions have been the subject of both descriptive and normative research for nearly three decades. The descriptive strand, with adoption of various theories from different disciplines, seeks to understand the processes by which organizations make ITO decisions and also to understand the outcomes of those decisions [16]. The normative strand, which includes model-driven Decision Support Systems (DSS) research, is concerned more with how organizations can make effective ITO decisions. The increase in adoption, volume and complexity of ITO has prompted academic researchers to develop various model-driven decision artifacts to support practitioners in their ITO decision making. However, these decision-support artifacts have not been identified and assessed in one place, e.g. in a literature review paper, and have not been critically assessed for rigor and relevance. Moreover, the normative ITO research should exploit the findings of descriptive ITO research to develop rigorous and scientific-grounded DSS for ITO initiatives. However, to the best of authors' knowledge there is no study that investigates whether research-based decision support artifacts are built on descriptive ITO research findings or not. Furthermore, previous empirical studies have raised concerns about the limited impact of ITO research on decision making in practice (e.g. [10], [13], [17]). For instance, Westphal and Sohal [10] noted: “ITO decisions seem to be made without the use of any of the decision models [proposed by researchers]”.

To address these research problems, a literature review approach was adopted in this study. The need for and importance of literature reviews in the IS discipline has been recently highlighted (e.g. [18], [19]) because such papers provide reflection on prior research and provide a foundation for future studies and can be used to raise practitioners' awareness of extant research. Although there are several journal articles that provide reviews of the descriptive ITO literature (e.g. [16], [20], [21], [22], [23]), to date, to the best of our knowledge, there is little in the way of a comprehensive review of normative ITO literature, i.e. ITO decision support models/tools. Thus, there is no assessment of this body of literature available to provide a comprehensive account to practitioners who may be in search of a decision support tool for their ITO decisions or to researchers who wish to expand the depth and breadth of the field. To address this gap, we focused our study on the following research questions:

RQ1: What decision analysis methods have been suggested in the literature to support organizational IT outsourcing decisions?

RQ2: What level of rigor is evident in the model-driven artifacts developed to support organizational IT outsourcing decisions?

This article provides a systematic review of the model-driven ITO decision support literature in order to provide a critical assessment of work to date. Model-driven DSSs (also called model-oriented DSS or computationally-oriented DSS) use quantitative models including algebraic, decision analytic, financial, simulation, and optimization models to provide decision support functionality [24], [25].

As a result of this systematic literature review, we identify that model-driven decision support for IT outsourcing can be categorized according to the type of decision being supported and the type of decision-making method used.

This study is significant in its comprehensive assessment of the body of knowledge pertaining to the ITO decision-support field, identification of its weaknesses, and suggestions of a rigorous foundation for future designs of ITO decision support systems.

This paper is organized as follows. In Section 2, prior research with regard to evaluation and assessment of DSS research, decision analysis methods, and descriptive ITO decision-making research is briefly discussed to provide the background to the study. In Section 3, the literature review survey method and sample are described. In Section 4, the results of the literature survey are provided. Finally, we identify key issues remaining to be solved in ITO DSS research and make recommendations for improvement and suggest further research directions.

Section snippets

Approaches used for assessment of DSS

Classic assessment frameworks and models (e.g. [26], [27], [28]) have been criticized for their lack of “a holistic worldview that considers jointly the organizational, user, designer and builder criteria of interest” ([29], p.643). Several studies (e.g. [30], [31], [32], [33]) have reviewed and assessed the DSS literature using a design science-based approach as a superior strategy for assessment of DSS research, since it takes the entire range of development activities into consideration [34]

Methodology

In this paper we adopted a systematic literature review methodology [94], [95], to identify peer reviewed articles that developed model-driven decision-making artifacts to support ITO, ASP, net-sourcing and cloud sourcing decisions. The articles were identified by querying six academic publication indexing databases: EBSCOhost Business Source Complete, Science Direct, Scopus, Emerald Insight, AIS Electronic Library (AISeL) and IEEE Xplorer. We consider our choice of the six databases was

Literature survey findings

This section reports the results of the analysis of the surveyed articles. The section is structured according to the IS research framework (Fig. 2). In total, we identified 133 articles (73 journal articles and 60 conference papers) that applied single or hybrid decision-making methods to IT outsourcing decisions. Publication dates of these articles ranged from 1995 to 2016.

Discussion and recommendations

In this section we discuss the results of our analysis of model-driven ITO decision support literature in relation to each of research questions investigated in this study and suggest recommendations for improvement.

RQ1: What decision analysis methods have been suggested in the literature to support organizational IT outsourcing decisions?

The review identified the potential of various decision analysis methods to support different decisions involved in the process of ITO and cloud sourcing.

Conclusion, contributions, limitations and future research

This paper provided a critical assessment of model-driven decision support for IT outsourcing in academic research through a systematic literature review and document analysis of a total of 133 peer-reviewed articles published between 1995 and 2016.

Our review identified the potential of various decision analysis methods to support different decisions required in the process of ITO and cloud sourcing. These methods included MCDM methods, optimization, system dynamics, real options and other

Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Mohammad Mehdi Rajaeian is a casual academic staff at the University of Southern Queensland (USQ), Australia. He received a BSc in Industrial Engineering and a MBA both from Sharif University of Technology, Iran, and a PhD in Information Systems from USQ. His research interest include: IT outsourcing, Decision Support Systems, System Theory, Diffusion of Innovation and research-practice gap. Mehdi was a lecturer at Sadjad University of Technology, Iran before he joins USQ. Prior to his academic

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    Mohammad Mehdi Rajaeian is a casual academic staff at the University of Southern Queensland (USQ), Australia. He received a BSc in Industrial Engineering and a MBA both from Sharif University of Technology, Iran, and a PhD in Information Systems from USQ. His research interest include: IT outsourcing, Decision Support Systems, System Theory, Diffusion of Innovation and research-practice gap. Mehdi was a lecturer at Sadjad University of Technology, Iran before he joins USQ. Prior to his academic career, he worked as system analyst, programmer and IT Manager in different organizations.

    Aileen Cater-Steel's research interests include IT Service Management (ITSM), IT Standards and Governance, e-Learning systems, and IT outsourcing. She was Lead Chief Investigator on two ITSM projects that achieved funding from the Australian Research Council. She has published in top journals and co-edited three research books. Her work has been recognized with a citation from the Australian Learning & Teaching Council for outstanding contribution to student learning. Prior to her academic appointment, Aileen worked in the private sector and government organizations where her career progressed from programmer to IT Manager. She is a Fellow of the Australian Computer Society (ACS) and member of the ACS Professional Standards Board.

    Michael Lane is a senior lecturer in Information Systems, within the School of Management and Enterprise. He holds a PhD in Information Systems from the University of Southern Queensland. He has a strong managerial and technical background in ICT. Michael Lane has extensive experience in IT Outsourcing from research and industry perspectives. His research in information outsourcing includes Impact of Partnership and Service quality on the IT Outsourcing Relationship and IT Outsourcing Success, and the Governance of Risks in IT Sourcing Models including IT Outsourcing and Offshoring.

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