IS

Ding, Min

Topic Weight Topic Terms
0.156 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present
0.150 contract contracts incentives incentive outsourcing hazard moral contracting agency contractual asymmetry incomplete set cost client
0.111 systems information management development presented function article discussed model personnel general organization described presents finally

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Hitt, Lorin M. 1 Wu, D. J. 1
analytical modeling 1 business value of IT 1 economics of IS 1 enterprise systems 1
laboratory experiments 1 management of IS projects 1

Articles (1)

IT Implementation Contract Design: Analytical and Experimental Investigation of IT Value, Learning, and Contract Structure. (Information Systems Research, 2013)
Authors: Abstract:
    This article analytically and experimentally investigates how firms can best capture the business value of information technology (IT) investments through IT contract design. Using a small sample of outsourcing contracts for enterprise information technology (EIT) projects in several industries-coupled with reviews of contracts used by a major enterprise software maker-the authors determine the common provisions and structural characteristics of EIT contracts. The authors use these characteristics to develop an analytical model of optimal contract design with principal-agent techniques. The model captures a set of key characteristics of EIT contracts, including a staged, multiperiod project structure; learning; probabilistic binary outcomes; variable fee structures; possibly risk-averse agents; and implementation risks. The model characterizes conditions under which multistage contracts enable clients to create and capture greater project value than single-stage projects, and how project staging enables firms to reduce project risks, capture learning benefits, and increase development effort. Finally, the authors use controlled laboratory experiments to complement their analytical approaches and demonstrate robustness of their key findings.