This paper examines multiple contract design choices in the context of transaction and relational attributes and consequent ex ante and ex post transaction costs. It focuses on two understudied themes in the IT outsourcing literature. First, while the literature is predominantly concerned with opportunism and consequent ex post hazard costs that contracts can safeguard against, parties to a contract also economize on ex ante transaction costs by their choice of contract type and contract extensiveness. Second, the literature studies the aggregate extensiveness of contracts rather than of distinct contract functions: safeguarding, coordination, and adaptability. Against this backdrop, our research model portrays a nuanced picture that is anchored in the following theoretical interpretation: transaction and relational attributes have implications on specific ex ante and ex post transaction costs, and these implications can be balanced by respective choices in both contract type and the extensiveness of specific contract functions. These two contract design choices complement and substitute for each other in their ability to economize on specific transaction costs. Our analysis of 210 software development outsourcing contracts finds that explanatory power increases when analyzing the extensiveness of individual contract functions rather than the aggregate contract extensiveness, highlighting subtle competing influences that are otherwise masked by an aggregate measure. Our analysis also shows that a preference for time-and-material contracts counteracts the effect of certain transaction attributes on contract extensiveness, and even cancels it out in the case of transaction uncertainty.
The emergence of new service science approaches to business problems in information technology (IT) services offers new, unusually relevant insights for the senior management of vendors in this business area. This research examines how service-level agreement contract flexibility should be designed when the technological and business market environments result in volatility of demand, based on an understanding of related changes in the cost drivers that underlie IT services contracts. Our approach draws on a blend of well-known methods from financial economics--the real option pricing method and the contingent claims analysis method. In particular, our research examines a setting in which a vendor provides IT services to a client according to a prenegotiated IT services contract in the presence of demand volatility. We analyze the motivation of and value consequences for a vendor that offers the client the flexibility to opt out of the contract. For example, the client might switch to another vendor, or backsource and provide its own services internally. Our core results offer important foundational thinking for how to specify various forms of IT service-related flexibility in terms of put and call options from the point of view of an IT services vendor, so that their value and exercise timing can be estimated. We show that the client firm's demand trigger value for deciding when to backsource its IT services varies, and it depends on the degree of demand volatility as well as the usage-based fees charged by the vendor. Working from our modeling approach, we also are able to characterize the extent to which a vendor can benefit from bearing the costs of making a backsourcing flexibility option available to its client.
This field study research evaluates the viability of applying an option-based risk management (OBRiM) framework, and its accompanying theoretical perspective and methodology, to real-world sequential information technology (IT) investment problems. These problems involve alternative investment structures that bear different risk profiles for the firm, and also may improve the payoffs of the associated projects and the organization's performance. We sought to surface the costs, benefits, and risks associated with a complex sequential investment setting that has the key features that OBRiM treats. We combine traditional, purchased real options that subsequently create strategic flexibility for the decision maker, with implicit or embedded real options that are available with no specific investment required provided the decision maker recognizes them. This combination helps the decision maker to both (1) explicitly surface all of his or her strategic choices and (2) accurately value those choices, including ones that require prior enabling investments. The latter permits senior managers to adjust a project's investment trajectory in the face of revealed risk. This normally is important when there are uncertain organizational, technological, competitive, and market conditions. The context of our research is a data mart consolidation project, which was conducted by a major airline firm in association with a data warehousing systems vendor. Field study inquiry and data collection were essential elements in the retrospective analysis of the efficacy of OBRiM as a means to control risk in a large-scale project. We learned that OBRiM's main benefits are (1) the ability to generate meaningful option-bearing investment structures, (2) simplification of the complexities of real options for the business context, (3) accuracy in analyzing the risks of IT investments, and (4) support for more proactive planning. These issues, which we show are more effectively addressed by OBRiM.
As real options analysis (ROA) is being applied to increasingly complex information technology (IT) investment problems, a concern arises over the use of heuristic ROA models that are simpler to apply but can produce overvaluations. A good example is the application of a heuristic nested variation of the Black-Scholes (BS) model to the evaluation of interrelated IT investments as nested options. This particular heuristic BS model could overvalue by more than 100 percent. Using a binomial model that is custom-tailored to a generic IT investment embedding nested options as the "baseline," we identify conditions under which the degree of overvaluation of this heuristic BS model is severe and unpredictable. Moreover, upon examining the structure of the custom-tailored binomial model, we identify the reason for overvaluation and derive a more accurate nested variation of the BS model. These findings should serve as a cautionary message about the use of untested heuristic ROA models.
Recently, an option-based risk management (OBRiM) framework has been proposed to control risk and maximize value in information technology investment decisions. While the framework is prescriptive in nature, its core logic rests on a set of normative risk-option mappings for choosing which particular real options to embed in an investment in order to control specific risks. This study tests empirically whether these mappings are observed in practice. The research site is a large Irish financial services organization with well established IT risk management practices not tied to any real options framework. Our analysis of the risk management plans developed for a broad portfolio of 50 IT investments finds ample empirical support for OBRiM's risk-option mappings. This shows that IT managers follow the logic of option-based risk management, although purely based on intuition. Unfortunately, reliance on this logic based on intuition alone could lead to suboptimal or counterproductive risk management practices. We therefore argue that managerial intuition ought to be supplemented with the use of formal real option models, which allow for better quantitative insights into which risk mitigations to pursue and combine in order to effectively address the risks most worth controlling.
Past information systems research on real options has focused mainly on evaluating information technology (IT) investments that embed a single, a priori known option (such as, deferral option, prototype option). In other words, only once a specific isolated option is identified as being embedded in a target IT investment, does this research call upon using real options analysis to evaluate the option. In effect, however, because real options are not inherent in any IT investment, they usually must be planned and intentionally embedded in a target IT investment in order to control various investment-specific risks, just like financial risk management uses carefully chosen options to actively manage investment risks. Moreover, when an IT investment involves multiple risks, there could be numerous ways to reconfigure the investment using different series of cascading (compound) options. In this light, we present an approach for managing IT investment risk that helps to rationally choose which options to deliberately embed in an investment so as to optimally control the balance between risk and reward. We also illustrate how the approach is applied to an IT investment entailing the establishment of an Internet sales channel.
The application of real options analysis to information technology investment evaluation problems recently has been proposed in the IS literature (Chalasani et al. 1997; Dos Santos 1991; Kambil et al. 1993; Kumar 1996; Taudes 1998). The research reported on in this paper illustrates the value of applying real options analysis in the context of a case study involving the deployment of point-of-sale (POS) debit services by the Yankee 24 shared electronic banking network of New England. In the course of so doing, the paper also attempts to operationalize real options analysis concepts by examining claimed strengths of this analysis approach and balancing them against methodological difficulties that this approach is believed to involve. The research employs a version of the Black-Scholes option pricing model that is adjusted for risk-averse investors, showing how it is possible to obtain reliable values for Yankee 24's 'investment timing option,' even in the absence of a market to price it. To gather evidence for the existence of the timing option, basic scenario assumptions, and the parameters of the adjusted Black-Scholes model, a structured interview format was developed. The results obtained using real options analysis enabled the network's senior management to identify conditions for which entry into the POS debit market would be profitable. These results also indicated that, in the absence of formal evaluation of the timing option, traditional approaches for evaluating information technology investments would have produced the wrong recommendations.
The application of fundamental option pricing models (OPMs), such as the binomial and the Black-Scholes models, to problems in information technology (IT) investment decision making have been the subject of some debate in the last few years. Prior research, for example, has made the case that pricing "real options" in real world operational and strategic settings offers the potential for useful insights in the evaluation of irreversible investments under uncertainty. However, most authors in the IS literature have made their cases using illustrative, rather than actual real world examples, and have always concluded with caveats and questions for future research about the applicability of such methods in practice. This paper makes three important contributions in this context: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes option pricing model in the context of the spectrum of capital budgeting methods that might be employed to assess IT investments; (2) it shows why the assumptions of both the Black-Scholes and the binomial option pricing models place constraints on the range of IT investment situations that one can evaluate that are similar to those implied by traditional capital budgeting methods such as discounted cash flow analysis; and (3) it presents the first application of the Black-Scholes model that uses a real world business situation involving IT as its test bed. Our application focuses on an analysis of the timing of the deployment of point-of-sale (POS) debit services by the Yankee 24 shared electronic banking network of New England. This application enables us to make the case for the generalizability of the approach we discuss to four IT investment settings.