Identifying Influencing Factors for Data Transactions: A Case Study from Shanghai Data Exchange

Abstract

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.

Keywords

Big data / two-sided market / pricing information goods / case study

References

[1] Akerlof G A (1978). The market for lemons: Quality uncertainty and the market mechanism. In Uncertainty in Economics: 235–251. Academic Press.

[2] Anderson S P, de Palma A. The logit as a model of product differentiation. Oxford Economic Papers, 1992, 44(1): 51-67.

[3] Anderson C (2009). Free: The future of a radical price. Random House.

[4] Armstrong M. Competition in two sided markets. The RAND Journal of Economics, 2006, 37(3): 668-691.

[5] August T, Niculescu M F. The influence of software process maturity and customer error reporting on software release and pricing. Management Science, 2013, 59(12): 2702-2726.

[6] Bakos Y, Brynjolfsson E. Bundling information goods: Pricing, profits, and efficiency. Management Science, 1999, 45(12): 1613-1630.

[7] Chen Y, Xie J. Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 2008, 54(3): 477-491.

[8] Cheng H K, Li S, Liu Y. Optimal software free trial strategy: Limited version, time locked, or hybrid?. Production and Operations Management, 2015, 24(3): 504-517.

[9] Cong L W, He Z. Blockchain disruption and smart contracts. The Review of Financial Studies, 2019, 32(5): 1754-1797.

[10] Dou Y, Niculescu M F, Wu D J. Engineering optimal network effects via social media features and seeding in markets for digital goods and services. Information Systems Research, 2013, 24(1): 164-185.

[11] Dhebar A, Oren S S. Optimal dynamic pricing for expanding networks. Marketing Science, 1985, 4(4): 336-351.

[12] Edelman B, Ostrovsky M, Schwarz M. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review, 2007, 97(1): 242-259.

[13] Edelman B, Hu P (2016). Disintermediation in Two-Sided Marketplaces. Harvard Business Case.

[14] Gioia D A, Thomas J B, Clark S M, Chittipeddi K. Symbolism and strategic change in academia: The dynamics of sensemaking and influence. Organization Science, 1994, 5(3): 363-383.

[15] Hagiu A (2009). Quantity vs. quality and exclusion by two-sided platforms. Harvard Business School Strategy Unit Working Paper, 09-094.

[16] Kleindorfer P R, Wu D J. Integrating long-and short-term contracting via business-to-business exchanges for capital-intensive industries. Management Science, 2003, 49(11): 1597-1615.

[17] Mehta S, Dawande M, Janakiraman G, Mookerjee V (2019). How to sell a dataset? Pricing policies for data monetization. INFORMS CIST at Seattle.

[18] Mittendorf C (2018). Trust and distrust in two-sided markets: an example in the sharing economy. In Proceedings of the 51st Hawaii International Conference on System Sciences.

[19] Niculescu M F, Wu D J. Economics of free under perpetual licensing: Implications for the software industry. Information Systems Research, 2014, 25(1): 173-199.

[20] Parker G G, Van Alstyne M W. Two-sided network effects: A theory of information product design. Management Science, 2005, 51(10): 1494-1504.

[21] Ray J, Menon S, Mookerjee V. Bargaining over Data: When Does Making the Buyer More Informed Help?. Information Systems Research, 2020, 31(1): 1-15.

[22] Rochet J C, Tirole J. Platform competition in two-sided markets. Journal of the European Economic Association, 2003, 1(4): 990-1029.

[23] Shapiro C, Varian H R, Becker W E. Information rules: A strategic guide to the network economy. Journal of Economic Education, 1999, 30: 189-190.

[24] Simester D, Timoshenko A, Zoumpoulis S I. Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Science, 2020, 66(6): 2495-2522.

[25] Song P, Xue L, Rai A, Zhang C. The ecosystem of software platform: A study of asymmetric cross-side network effects and platform governance. MIS Quarterly, 2018, 42(1): 121-142.

[26] Strauss A, Corbin J (1990). Basics of qualitative research. Sage Publications.

[27] Sundararajan A. Nonlinear pricing of information goods. Management Science, 2004, 50(12): 1660-1673.

[28] Varian H R (1997). Versioning information goods. Unpublished Manuscript.

[29] Walker R (2015). From Big Data to Big Profits. Oxford University Press.

[30] Wei X D, Nault B R. Experience information goods: Version-to-upgrade. Decision Support Systems, 2013, 56: 494-501.

[31] Wise R, Morrison D. Beyond the exchange — the future of B2B. Harvard Business Review, 2000, 78(6): 86-96.

[32] Yin R K (2017). Case Study Research and Applications: Design and Methods. Sage Publications.