Abstract
The pricing of data products is a pivotal aspect of the data factor market's construction process, as it holds the key to unlocking the ten-trillion-dollar market potential. Prior studies primarily centered on pricing personal, public, and financial data. In this study, we delve into a novel type of data product, product data, and aim to construct a profit-maximizing pricing model. Specifically, we address the pricing challenge of manufacturing product data and develop a profit-maximizing pricing model tailored to manufacturing product data, considering factors including data quality utility, product characteristics, and the interplay between market supply and demand. To evaluate our findings, we conducted a simulated pricing analysis using a public dataset. Our findings suggest that product competitiveness exerts a more significant influence on pricing than data quality. As such, product data owners must devise strategic pricing plans to achieve optimal profit levels. The model introduced in this study not only supplements the existing theory and methods of data product pricing but also offers novel perspectives on product data pricing strategies.
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Acknowledgement
This research is partly supported by the National Natural Science Foundation of China [72372060, 71972090, 72272066, and 71971101], General Project of Philosophy and Social Science Research in Universities in Jiangsu Province [2022SJYB2238], Postgraduate Research & Practice Innovation Program of Jiangsu Province (Product Data Pricing in Manufacturing Enterprises: Pricing by Data Quality and Product Characteristics), Jiangsu University of Science and Technology Youth Science and Technology Innovation Project [1042922212].
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Li, H., Zhang, M., Jia, Y., Wang, N., Ge, S. (2024). Data Quality, Product Characteristics, and Product Data Pricing in Manufacturing Enterprises. In: Tu, Y.P., Chi, M. (eds) E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future. WHICEB 2024. Lecture Notes in Business Information Processing, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-031-60324-2_1
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DOI: https://doi.org/10.1007/978-3-031-60324-2_1
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