Technology Investment Decision-Making under Uncertainty

• Main Authors: KAUFFMAN, Robert J., LIU, Jun, MA, Dan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Decision-making under uncertainty; Economic analysis; IT investment; Mean reversion; process Mobile payments; Real option analysis; Technology valuation; Computer Sciences; Management Information Systems; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/sis_research/2473; https://ink.library.smu.edu.sg/context/sis_research/article/3472/viewcontent/TechnologyInvestmentDecision_2015_av.pdf
• Institution: Singapore Management University

Innovations involving information technology (IT) provide potentially valuable investment opportunities for industry and government organizations. Significant uncertainties are associated with decision-making for IT investment though, a problem that senior executives have been concerned about for a long time. The uncertainties include consumer, market and regulatory responses, IT-driven changes in operational and transactional performance, technology standards and competition, and future market conditions. All these things have an impact on organizations’ willingness to adopt. As a result, traditional capital budgeting, investment experience, and intuition have not been very effective in IT investment decision-making. We propose a new option-based stochastic valuation modeling approach for IT investment under uncertainty that incorporates a mean reversion process to capture cost and benefit flow variations over time. We apply the proposed approach in two industry settings: to a largescale IT investment in the consolidation of data marts at a major airline, and to a mobile payment system infrastructure investment on the part of a start-up. The applications supported the evaluation of the proposed methods, and offered some illustrations about the kinds of managerial insights that can be obtained. We also report on several extensions that demonstrate how the creation of useful management findings from the modeling approach can be supplemented with project value sensitivity analysis and the use of simulation-based least-squares Monte Carlo valuation. The findings are useful to assess the power and value of the approach.