Paper Number
ICIS2025-1882
Paper Type
Complete
Abstract
Generative Artificial Intelligence (GenAI) has emerged as a transformative force in digital markets, offering substantial cost-reduction effects through efficient transfer learning. While this accelerates the proliferation of digital products, it may intensify market competition and influence innovation incentives to explore new markets. In particular, innovators choose strategies between concentrating on a high-demand(popular) market or exploring a low-demand(niche) market, driven by the trade-off between market size and competition intensity. In this paper, we develop a two-stage Stackelberg market entry game to study GenAI-driven cost asymmetries on market competition dynamics. We further investigate how platform-enforced GenAI transparency policies moderate these dynamics by safeguarding consumer preferences for originality. Our equilibrium analysis reveals a reverse U-shaped effect of GenAI’s transfer learning ability on innovation incentives: moderate ability encourages innovators to explore niche markets most, while low/high ability drives concentration in popular markets. We identify this as an innovation trap, where equilibrium choices deter exploration despite higher potential payoffs and welfare gains. Transparency policies significantly amplify exploration incentives and mitigate this trap. These findings offer actionable guidance for managers seeking to balance efficiency and innovation, and for policymakers aiming to design governance mechanisms that preserve originality in the GenAI era.
Recommended Citation
Bao, Kaiwen and Li, Xiaofan, "Cost Reduction at the cost of Innovation Incentives? Market entry and competition under Generative AI in digital worlds" (2025). ICIS 2025 Proceedings. 17.
https://aisel.aisnet.org/icis2025/gen_ai/gen_ai/17
Cost Reduction at the cost of Innovation Incentives? Market entry and competition under Generative AI in digital worlds
Generative Artificial Intelligence (GenAI) has emerged as a transformative force in digital markets, offering substantial cost-reduction effects through efficient transfer learning. While this accelerates the proliferation of digital products, it may intensify market competition and influence innovation incentives to explore new markets. In particular, innovators choose strategies between concentrating on a high-demand(popular) market or exploring a low-demand(niche) market, driven by the trade-off between market size and competition intensity. In this paper, we develop a two-stage Stackelberg market entry game to study GenAI-driven cost asymmetries on market competition dynamics. We further investigate how platform-enforced GenAI transparency policies moderate these dynamics by safeguarding consumer preferences for originality. Our equilibrium analysis reveals a reverse U-shaped effect of GenAI’s transfer learning ability on innovation incentives: moderate ability encourages innovators to explore niche markets most, while low/high ability drives concentration in popular markets. We identify this as an innovation trap, where equilibrium choices deter exploration despite higher potential payoffs and welfare gains. Transparency policies significantly amplify exploration incentives and mitigate this trap. These findings offer actionable guidance for managers seeking to balance efficiency and innovation, and for policymakers aiming to design governance mechanisms that preserve originality in the GenAI era.
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12-GenAI