Real options, learning cost and timing software upgrades: Towards an integrative model for enterprise software upgrade decision analysis
Introduction
Enterprise software infrastructure is a general purpose information technology which supports not only the productive capabilities of an organization but also its strategic core capabilities as well (Bresnahan and Greenstein, 2001, Brynjolfsson and Hitt, 1995, Yen and Sheu, 2004). As such, enterprise software infrastructure requires continuous upgrading to improve productivity and sustain the organization׳s competitiveness (Voulgaris et al., 2015, Hawking and Sellitto, 2015). Deciding when to upgrade critical enterprise software infrastructure is a challenging problem for senior management (Choi et al., 2013, Ngwenyama et al., 2007, Olson and Zhao, 2007, Khoo and Robey, 2007, Ng, 2001). Upgrading enterprise software infrastructure is a significant investment that may impact the efficiency and competitiveness of the enterprise; furthermore, it is costly in terms of downtime, implementation and learning (Gebauer and Schober, 2006, Ngwenyama et al., 2007, Ashurst et al., 2008, Dempsey and Liam Sheehan, 2013). In addition, vendor charges for software infrastructure upgrades and maintenance are relatively high, and their software upgrade release life cycle is often excessive (Gable et al., 2001, Irani et al., 2006; Sahin and Zahedi, 2001a; Ellison and Fudenberg, 2000, Jansen and Brinkkemper, 2006). But in our hyper-competitive global economy, enterprises are dependent on software infrastructure for providing timely service to clients, continually improving their operating efficiency and effectiveness, and managing supply networks that extend across geography and time zones. Consequently, billions of dollars are spent each year on new software infrastructure with the expectation of high returns on productivity and competitiveness (Osei-Bryson and Ko, 2004, Jurison, 1996a, Jurison, 1996b, Dempsey and Liam Sheehan, 2013).
In the United States (U.S.) alone, spending on information technology (IT) for 2014 has reached US$1.03 trillion (Bartels et al., 2014). Much of this spending is on upgrading existing software infrastructure, such as enterprise resource planning (ERP) system upgrades, which the Gartner group forecasted to be close to US$4 billion in 2014 (Gartner, 2013). However, studies have shown that these IT investments do not necessarily meet the expectations of senior managers (Doherty et al., 2012, Cecez-Kecmanovic et al., 2014), and those IT projects that ultimately fail can be very costly for firms in terms of competitiveness and lost market value (Bharadwaj et al., 2009). A key challenge that IT managers face is how to carefully analyze the decision options available to them when considering software upgrades geared toward improving their cost-performance. Some researchers have raised concerns that IT upgrade decisions are not well researched (Khoo and Robey, 2007; Light, 2001), and others have raised concerns about the limitations of existing decision models to support a comprehensive analysis of the range of IT decision-making problems that managers face (Ngwenyama et al., 2007, Osei-Bryson and Ngwenyama, 2008, Plaza and Rohlf, 2008). Other researchers have repeatedly pointed out that decision models developed by academics are often inaccessible to practicing managers, and advise a focus on accessibility and applicability (Ball, 1985, Little, 1986, Little, 2004, Kasanen et al., 2000, Čibej, 2002, Lilien, 2010, Jarzabkowski and Kaplan, 2015).
In this paper, we respond to the call for decision support models which are accessible to practicing managers and applicable to real-world IT management problems. We contribute to the literature by presenting an illustrative case that sheds new light on the software-upgrade timing problem. A major challenge that arises in developing accessible decision analysis tools is how to strike a balance between simplicity to ensure tractability, and realism to accurately convey the complexity and difficulty of real-world software upgrade decisions. We focus on an analytically manageable, but non-trivial choice between the following two alternatives: (1) the retention of an existing ERP system under which maintenance costs increase annually after the vendor discontinues user support, and (2) timing the adoption of one of two versions (technical versus functional) of a newly released ERP system that contain maintenance costs, but require substantial upfront expenditures at a time when estimates of the benefits to be had are not reliable.
We not only recognize that prior research has sought to enhance ERP cost estimation through the development of the COnstructive COst MOdel (COCOMO), among others (Kotb et al., 2011), but also that the application of real options analysis (RoA) to IT-related investment problems is not new to the production economics and information system (IS) literatures (Brynjolfsson and Hitt, 1995, Dimakopoulou et al., 2014, Fichman, 2004, Kyläheiko et al., 2002, Yen and Sheu, 2004). Our primary focus is on providing a decision model accessible to IT managers, and illustrating its usefulness to a strategic analysis of the software upgrade problem. Our research objective differs from other studies that emphasize more conceptually complicated approaches to real options analysis which may limit the discourse to academics (Datar and Mathews, 2004). Our research program seeks to advance production economics and IS/IT management research by opening up a wider discussion and research on the conceptualization, measurement and tracking of uncertainty in the context of real options applications (Collan et al., 2003, Collan et al., 2009).
The remainder of this paper is organized as follows. In Section 2, we briefly discuss the relevant literature on the software upgrade problem. In Section 3, we present an options-based framework for the analysis of the option to wait. Drawing on this framework, we present and analyze an illustrative case example in Section 4. Section 5 concludes with a discussion of the main practical implications, and directions for future research.
Section snippets
The software upgrade problem
Deciding when to upgrade an enterprise׳s software infrastructure is a dilemma that most IT managers face (Dempsey and Liam Sheehan, 2013, Ellison and Fudenberg, 2000, Khoo and Robey, 2007, Ngwenyama et al., 2007). The timing of software infrastructure upgrades is often driven by the software vendor׳s product life cycle and profit maximization goals, and not by the implementing enterprise׳s strategic objectives (Light, 2001, Khoo and Robey, 2007, Dempsey and Liam Sheehan, 2013). Increasingly, IT
Framework for analysis
In this section, we present a RoA decision model that incorporates insights from the LcA approach. In particular, we consider the case where a vendor approaches the firm with the opportunity to invest in a new software infrastructure. On the one hand, managers may be attracted to the new technology because it has the potential to generate substantial cost savings, and thereby contributes to productivity growth and competitiveness. On the other hand, they may be unwilling to commit substantial
The illustrative example
Let us consider the case of CT, a large Canadian retail company that is heavily dependent on a specific enterprise software infrastructure for operating its business. CT has retail outlets distributed across Canada and suppliers across the globe. In early 2008, CT launched a strategic initiative to integrate its value chain activities with a new highly regarded ERP software infrastructure. The firm׳s objectives were to replace legacy systems, and integrate various e-commerce applications with
Discussion and concluding comments
Enterprise software infrastructures are general purpose technologies which contribute to the productive and competitive capabilities of economic organizations, and as such decision making about their implementation and use are of interest not only to management scientists, and practicing managers but to production economists (Bresnahan and Greenstein, 2001, Brynjolfsson and Hitt, 1995, Yen and Sheu, 2004). The enterprise software infrastructure upgrade is one of the largest IT capital
Acknowledgment
The authors thank the editor and two anonymous reviewers for their valuable and insightful comments on earlier versions of this paper. They also thank Maral Karimi for editorial assistance.
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