Strategic AI Investment and Sales Mode Selection in Platform Supply Chains with Consumer Privacy Concerns

We construct a game-theoretic model consisting of a single manufacturer and a single platform to examine how the choices made by AI investors regarding implementation and sales models affect supply chain operations in a scenario where artificial intelligence raises consumer privacy concerns. We consider three scenarios in two selling modes: no AI investment, manufacturer-led AI investment, and platform-led AI investment. Game models under different scenarios are established and solved. The results show that: (1) Consumer privacy disutility reduces AI investment in all four investment scenarios. Platform ancillary data revenue strengthens investment incentives under wholesale selling and platform-led agency selling but does not enter the manufacturer’s investment objective under manufacturer-led agency selling. (2) Within the wholesale mode, manufacturer-led investment generates higher firm profits, consumer surplus, and social welfare than platform-led investment. Under agency selling, the preferred investor depends on ancillary data revenue and the stakeholder considered. (3) Under the baseline profit-sharing contract, agency selling yields higher consumer surplus and social welfare without AI investment and with platform-led investment. With manufacturer-led investment, wholesale selling can yield higher welfare over a feasible low-cost interval whose existence depends on residual market potential. A revenue-sharing extension preserves the agency-mode welfare advantage under the selected payoff-matched commissions in both numerical settings, while commission sensitivity reveals regions of wholesale dominance. Further extensions examine AI operating costs, investment cost sharing, and AI-dependent price sensitivity. The results show how investment incentives, contractual payments, and privacy disutility jointly determine supply chain outcomes.