The rise of smart and connected products in the last decade has led to unprecedented data richness and the ever-increasing product connectivity, which give rise to the emergence of the data network effect, and open new opportunities to advance product innovation. This paper asks two relevant questions: (1) whether and how can the connected (physical) product providers leverage the data network effect by optimally forming the critical mass under a non-negligible marginal cost? (2) how do the value chain of the physical product and the innovation on the product connectivity interact with each other in the presence of the data network effects? To address these questions, we develop a game-theoretic model to study the value chain of a connected product, in which an upstream manufacturer and a downstream retailer choose their seeding and pricing strategies sequentially and independently. We find that, surprisingly, under the classic wholesale-price contract, the value chain can achieve efficient strategic coordination when the product connectivity is relatively high. Then, we turn to a long-run analysis by considering the innovation of product connectivity. We find that, it is possible that the decentralized value chain can achieve greater social welfare than the centralized planner, because the manufacturer with a high innovation capability could invest more in product connectivity in a decentralized value chain. This implies that the classic double marginalization problem has an unexpected beneficial effect by enhancing productive investments. Our model thus sheds new light on value chain management and its welfare implication.
About the speaker
About the speaker
D. J. Wu is the Ernest Scheller Jr. Chair in Innovation, Entrepreneurship and Commercialization, Professor of IT Management, and Area Coordinator in IT Management at the Scheller College of Business, Georgia Institute of Technology. He graduated from the Computer Science and Technology Department of Tsinghua University and received his Ph.D. from the Wharton School, University of Pennsylvania.
Dr. Wu's current research interests include economics of digital innovation and transformation, digital business model innovations, platform ecosystems, enterprise information technology, IT contracting, online auctions, the economics of cloud computing, and machine learning. Dr. Wu's recent work has been published in academic journals, including Management Science, Information Systems Research, Manufacturing and Service Operations Management, and MIS Quarterly.
Prof. Wu serves as a Department Editor of Information Systems, Management Science. He also serves as a Co-Editor for the Management Science Special Issue on Human-Algorithm Connection. In addition, he has served as a Senior Editor for Information Systems Research (2018 -2020) and as President of INFORMS Information Systems Society (2019-2021).
D. J. Wu is the Ernest Scheller Jr. Chair in Innovation, Entrepreneurship and Commercialization, Professor of IT Management, and Area Coordinator in IT Management at the Scheller College of Business, Georgia Institute of Technology. He graduated from the Computer Science and Technology Department of Tsinghua University and received his Ph.D. from the Wharton School, University of Pennsylvania.
Dr. Wu's current research interests include economics of digital innovation and transformation, digital business model innovations, platform ecosystems, enterprise information technology, IT contracting, online auctions, the economics of cloud computing, and machine learning. Dr. Wu's recent work has been published in academic journals, including Management Science, Information Systems Research, Manufacturing and Service Operations Management, and MIS Quarterly.
Prof. Wu serves as a Department Editor of Information Systems, Management Science. He also serves as a Co-Editor for the Management Science Special Issue on Human-Algorithm Connection. In addition, he has served as a Senior Editor for Information Systems Research (2018 -2020) and as President of INFORMS Information Systems Society (2019-2021).
