Sequential IT Investment: Can the Risk of IT
Implementation Failure be Your Friend?
An extensive literature has studied the benefits for a firm to be the first to invest in innovative technologies such as Information Technologies (ITs). However, investment in innovative technologies faces high levels of uncertainty. How would such uncertainty affect a pioneering firm’s incentive to invest? Do late adopters benefit from information about the pioneer’s investment? In this paper, we investigate these questions in a context where firms engage in sequential investment in an innovative IT. This paper differs from prior literature in two aspects: First, IT adoption is nonexclusive and available to all client firms. Second, IT implementation can fail. In this case, a late adopter may have an information advantage since he can make investment decisions contingent on knowledge about the early adopter’s IT investment and implementation outcome. We use a standard Hotelling model of duopoly competition to examine firms’ incentives to sequentially invest in IT given the risk of IT implementation failure.
Our results show that the probability of IT implementation failure impacts firms’ investment incentives and profit through three distinct effects: the first-mover advantage mitigation effect, competition mitigation effect, and uncertainty-driven cost-based differentiation effect, although these three effects may drive the firms’ investment and profit in opposite directions. The follower’s knowledge about the leader’s IT investment level before making his own IT investment decision gives the leader a first-mover advantage and the follower a disadvantage. In contrast, the follower’s knowledge about the leader’s IT implementation outcome can benefit both the leader and the follower. Finally, we find that a spaced-out IT investment schedule in which the follower makes his investment decision after the Leader’s IT investment level and implementation outcome are known leads to the highest industry-wide IT investment and social surplus. We conduct a field experiment with the B2B retailer, providing salespeople with their own model’s price recommendations in real-time through the retailer’s CRM system, and allowing them to adjust their original pricing accordingly. We find that despite the loss of non-codeable information available to the salesperson but not to the model, providing the model’s price to the salesperson increases profits for treated quotes by 10% relative to a control condition. Using a counterfactual analysis, we show that while in most of the cases the model’s pricing leads to higher profitability, the salesperson generates higher profits when pricing for quotes or clients with unique or complex characteristics.
Mingdi Xin is an Assistant Professor of Information Systems at UCI Paul Merage School of Business.