In the age of artificial intelligence,firms'internal data are increasingly valuable when merged with each other for inter-firm analysis and predictions.However,the inter-firm data transactions represent a novel ch...In the age of artificial intelligence,firms'internal data are increasingly valuable when merged with each other for inter-firm analysis and predictions.However,the inter-firm data transactions represent a novel challenge on pricing due to the complex nature of data,such as quality information asymmetry,lack of pricing standards,and the negligible marginal cost.This paper conducts a case study at Shanghai Data Exchange to explore the factors that can facilitate the data transactions between buyers and providers.We use interview transcripts from 18 participating firms to construct our three theoretical dimensions:increasing the perceived value,mitigating the cost,and improving the market design.We then browse through 18 factors to assess their value for further improvements.The managerial implications are also discussed.展开更多
This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data e...This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.展开更多
The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data.To acquire data for training algorithms and eventually empowering the digital transfo...The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data.To acquire data for training algorithms and eventually empowering the digital transformation,data marketplaces are often required to support and coordinate cross-organizational data transactions.However,the prior industry practices have suggested that the transaction costs in the data marketplaces are severely high,and the supporting infrastructure is far from mature.This paper proposes a data attributes-affected data exchange(DADE)conceptual model to understand the challenges and directions for developing data marketplaces.Specifically,our model framework is built upon two dimensions,data lifecycle maturity and data asset specificity.Based on the DADE model,we propose four approaches for developing data marketplaces and discuss future research directions with an overview of computational methods as potential technical solutions.展开更多
基金the National Natural Science Foundation of China(NSFC)under Grants 71672042,71822201,91746302。
文摘In the age of artificial intelligence,firms'internal data are increasingly valuable when merged with each other for inter-firm analysis and predictions.However,the inter-firm data transactions represent a novel challenge on pricing due to the complex nature of data,such as quality information asymmetry,lack of pricing standards,and the negligible marginal cost.This paper conducts a case study at Shanghai Data Exchange to explore the factors that can facilitate the data transactions between buyers and providers.We use interview transcripts from 18 participating firms to construct our three theoretical dimensions:increasing the perceived value,mitigating the cost,and improving the market design.We then browse through 18 factors to assess their value for further improvements.The managerial implications are also discussed.
基金supported by the National Natural Science Foundation of China[grant numbers 12171158,12371474 and 12571510]Fundamental Research Funds for the Central Universities[grant number 2025ECNU-WLJC006].
文摘This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.
基金support from the National Natural Science Foundations of China(NSFC)[Grants No.91746302 and 71822201]National Engineering Laboratory for Big Data Distribution and Exchange Technologies.
文摘The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data.To acquire data for training algorithms and eventually empowering the digital transformation,data marketplaces are often required to support and coordinate cross-organizational data transactions.However,the prior industry practices have suggested that the transaction costs in the data marketplaces are severely high,and the supporting infrastructure is far from mature.This paper proposes a data attributes-affected data exchange(DADE)conceptual model to understand the challenges and directions for developing data marketplaces.Specifically,our model framework is built upon two dimensions,data lifecycle maturity and data asset specificity.Based on the DADE model,we propose four approaches for developing data marketplaces and discuss future research directions with an overview of computational methods as potential technical solutions.
