First-mover and late-mover advantages in AI deployment under platform preannouncement behavior

Purpose In pursuit of market prospects stemming from new technology, platforms have been rolling out artificial intelligence (AI)-related applications. Simultaneously, these platforms often employ preannouncement as a low-commitment tactic to prelaunch related information about AI applications, thus accelerating the market penetration of new technology. The main purpose of this paper is to analyze how a weak commitment signal affects platforms' market performance under different timings of AI deployment (early or late). Design/methodology/approach This study develops a continuous-timing game model to examine the interaction between the AI deployment sequence and preannouncement strategies, including four strategy combinations: strategy DF (platform does not preannounce and acts as the pioneer to deploy AI), strategy DL (platform does not pre-announce and acts as the follower to deploy AI), strategy RF (platform chooses to preannounce and acts as the pioneer to deploy AI) and strategy RL (platform chooses to preannounce and acts as the follower to deploy AI). Findings (1) A substitution relationship exists between preannouncement influence and the first-mover advantage, enabling platforms to gain greater profits under RL in the preannouncement scenario and under the DF strategy in the non-preannouncement scenario. (2) As shown in the equilibrium outcome, the RL strategy is the platform's optimal strategy by leveraging preannouncement information and the second-mover advantage. (3) The consumer growth rate will drive the platform to deploy AI earlier by shortening the market entry time, which will have a negative effect. (4) The rapid growth period of the AI market lifecycle can encourage the platform to preannounce more information and delay the timing of AI deployment. Originality/value (1) This study frames preannouncement behavior as a corporate strategy and explores preannouncement strategies for deploying AI applications on platforms; (2) Based on the background of market opportunities and challenges brought by AI deployment on online platforms, this study explores the entry timing of AI deployment to realize advantages of different deployment orders; (3) This study considers the impact of preannouncement behavior on the market perception of information related to AI applications, and explores how different preannouncement strategies influence the optimal sequence for deploying AI.