Shuai Ling

Non-technical firms (NTFs) accumulate rich proprietary operational data but lack the algorithmic capability to convert it into AI, and increasingly delegate this transformation to external AI technology service providers (TSPs). Unlike IT outsourcing, AI outsourcing requires transferring data across organizational boundaries, and is therefore constrained by data-protection regulations such as the GDPR, CCPA, and PIPL.We develop a four-stage Hotelling duopoly game to examine how data circulation regulation shapes NTFs' transformation strategies, market outcomes, and welfare. We show that under asymmetric competition, more data is not uniformly better: the effect of data scale on AI capability, price, and market share is non-monotonic. We further show that asymmetric transformation configurations cannot be sustained as Nash equilibria, so equilibrium strategies take the form of either dual in-house development or dual outsourcing. At intermediate circulation levels, both equilibria coexist; since outsourcing Pareto-dominates, firms may be locked into the inferior outcome absent coordination.AI outsourcing generally raises social welfare but may reduce user surplus because of double marginalization. Only when the data circulation level is sufficiently high do firms, users, and society gain simultaneously. Extensions involving interfirm data sharing, fixed development costs, and training-related carbon emissions establish the boundary conditions of these results.Our results suggest that NTFs should tailor transformation strategies to the type and sensitivity of the data underpinning their operations, while governments should adopt differentiated compliance frameworks balancing data security against data usability.

Data infrastructure serves as a core solution to the trust dilemma between data suppliers and consumers and supports the secure and efficient circulation of data. This paper establishes a supply chain competition system consisting of two data suppliers, two data trading platforms and shared demand market, and explores how differentiated data infrastructure construction decisions affect supply chain participants, alongside the equilibrium strategy of competing data trading platforms. The study finds that data infrastructure delivers both vertical effect of cost saving and trust enhancing, as well as horizontal effect of market competition, thereby granting a competitive advantage to the investor while placing the non-investor at a competitive disadvantage. The equilibrium strategy is jointly determined by the vertical effect and the construction cost. When the construction cost remains at a moderate level, both platforms may fall into a prisoner's dilemma. In the scenario where independent incentive is inadequate yet supply chain profitability can be promoted, the cost-sharing contract can effectively drive coordinated construction of data infrastructure. This paper provides theoretical references and managerial implications for data infrastructure construction and supply chain collaboration within competitive data market.

Data infrastructure serves as a core solution to the trust dilemma between data suppliers and consumers and supports the secure and efficient circulation of data. This paper establishes a supply chain competition system consisting of two data suppliers, two data trading platforms and shared demand market, and explores how differentiated data infrastructure construction decisions affect supply chain participants, alongside the equilibrium strategy of competing data trading platforms. The study finds that data infrastructure delivers both vertical effect of cost saving and trust enhancing, as well as horizontal effect of market competition, thereby granting a competitive advantage to the investor while placing the non-investor at a competitive disadvantage. The equilibrium strategy is jointly determined by the vertical effect and the construction cost. When the construction cost remains at a moderate level, both platforms may fall into a prisoner's dilemma. In the scenario where independent incentive is inadequate yet supply chain profitability can be promoted, the cost-sharing contract can effectively drive coordinated construction of data infrastructure. This paper provides theoretical references and managerial implications for data infrastructure construction and supply chain collaboration within competitive data market.

This study investigates the spatiotemporal distribution of electric vehicle (EV) charging demands and operating efficiency of the charging system. The travel behavior of EV drivers is analyzed by considering the heterogeneity of range anxiety and bound rationality. Building on existing charging choice models, our more holistic perspective of charging demand distribution is obtained through multiagent system (MAS) modeling. In our study, the charging demand distribution in different areas is compared by considering two charging price schemes. The performance of the charging system is then evaluated based on three indicators, the charging request rejection rate, charging pile utilization rate, and charging load deviation, thereby verifying the effectiveness of our charging demand prediction model. This is done using multiagent-based simulations applied to the National Household Travel Survey (NHTS) 2017 dataset. The results are analyzed according to different key indicators, mainly indicating (a) the limitations of the time-of-use (TOU) pricing strategy in reducing the peak-valley difference, (b) the transferability of charging demand among different functional areas, and (c) that there is less demand for charging piles in the workplace, as users mainly rely on home charging. These results facilitate further analyses to help the design and operation of the EV charging infrastructure.