Product Pricing and Design Strategy in Platform-Based Collaborative Innovation With Cognitive Bias

ABSTRACT

Collaborative innovation, in which multiple companies try to incorporate content generated by consumers into new product development, is becoming increasingly popular. The fact that overconfidence usually increases with more data means that companies integrating large amounts of consumer-generated content in collaborative innovation have a strong tendency to be overconfident. We study the effects associated with overconfidence in collaborative innovation, where overconfidence is defined as a decision maker’s cognitive bias that leads to an overestimation of the precision of an uncertain event. In our collaborative innovation model, an online shopping platform collects and assimilates content (such as online reviews) generated by consumers to generate a product design and then sells that design to the manufacturer, after which the manufacturer produces a corresponding new product and sets a retail price. In this article, we mainly focus on how overconfidence impacts the product design strategy, pricing strategies, and decision makers’ equilibrium profit levels. We demonstrate that overconfidence can be a positive force for collaborative innovation and even lead to a win-win-win situation for the platform, manufacturer, and consumer. We show that overconfidence can make the platform change its product design strategy from aesthetic-oriented in the unbiased scenario to functionality-oriented in the biased scenario. Furthermore, we show that each of the two product design strategies has its own scope of application; neither is universally dominant.

KEY WORDS AND PHRASES:

• Product design strategy
• collaborative innovation
• online pricing
• game theory
• new product development
• overconfidence online
• online platforms

Acknowledgments

Our research was supported by the National Natural Science Foundation of China (grant nos. 71971201, 72071193, 72271078, 72188101), National Social Science Fund of China (no. 21&ZD129), Top-Notch Young Talents Program of China, and the Fundamental Research Funds for the Central Universities (grant nos. KY2040000049). The authors also thank the Young Taishan Scholars Program of Shandong Province (no. tsqn202103024) and the Qilu Young Scholars of Shandong University for financial support.

Disclosure statement

No potential conflict of interest was reported by the author.

Supplementary Information

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10864415.2023.2184240

Notes

¹ https://tech.sina.com.cn/roll/2020-01-02/doc-iihnzhfz9916000.shtml (in Chinese, accessed on 09.05.2022). Note: this website can be translated directly from Chinese to English by clicking the right mouse button “Translate” in Microsoft Edge or Google Chrome.

² https://www.sohu.com/a/231669974_114930 (in Chinese, accessed on 09.05.2022).

³ https://www.psychologicalscience.org/news/minds-business/collaboration-can-breed-overconfidence.html (accessed on 09.05.2022).

⁴ In our setting, the platform provides a channel for the manufacturer to sell the product without an additional fee for each sale. The following reasons make the platform serve the manufacturer to sell the product for free. First, because the expected sales volume directly determines the platform profit, the platform has an incentive to service the manufacturer without an additional fee to achieve higher sales volume. In such a case, the platform will obtain a higher profit by expanding market demand. Second, if the platform serves the manufacturer to sell the product with an additional fee for each sale, its expected profit may decrease, because the manufacturer will raise the retail price to make a profit resulting in lower market demand. In such a case, the platform profit will decrease when the increase in marginal profit cannot offset the decrease in profit caused by lower market demand. Consequently, the platform does not have a strong incentive to charge an additional fee. Third, the platform is a perfectly rational player, and its profit achieved by charging twice can be realized by charging once. Since the decision of the platform is already happening before the consumer’s purchase, the platform can replace two lower charges with one higher charge. Nevertheless, we also explore the situation that the platform provides a retail channel for the manufacturer with an additional fee for each sale in online Appendix B.

⁵ https://www.chargedretail.co.uk/2022/07/26/tmall-innovation-centre (accessed on 02.08.2022).

⁶ A question may exist as to why there is not a one-time charge for product design. As mentioned before, the product design is often considered a product patent, so it is common for a product patent to be charged per unit of sales. In addition, considering the high uncertainty inherent in new product development, the manufacturer is willing to let the upstream platform use unit pricing based on the expected sales volume to reduce risk.

⁷ https://www.cnbc.com/2020/02/21/why-you-should-wait-to-buy-a-phone-with-a-folding-screen.html (accessed on 09.05.2022).

⁸ Although we mainly study the vertical differences between the APD and FPD products when given the horizontal differentiations in our core model, we also consider the impact of both horizontal and vertical differences on the main results in the subsection (i.e., partial consumers contributing content to collaboration). We find that the main insights are robust when simultaneously considering both horizontal and vertical differentiation.

⁹ https://www.bears.com.cn/news/newsinfo.html?ctype=26$\&$id=110 (in Chinese, accessed on 09.05.2022).

¹⁰ Because the equilibrium outcomes of the platform and manufacturer are qualitatively the same and differ only quantitatively, we focus on analyzing the platform.

¹¹ Given that the assumption about the taste preference heterogeneity is already adequately complex in our core model, we relax this assumption and consider $x_n\sim U\left[0,1\right]$ and $x_p\sim U\left[0,\mathbf{\Lambda }\right]$ ($0<\mathbf{\Lambda }<1$) to capture the situation where consumers have a higher expected misfit for an APD product than for an FPD product.

Additional information

Notes on contributors

Siyuan Zhu

Siyuan Zhu is a Ph.D. candidate at the School of Management, University of Science and Technology of China. Hefei, P. R. China His research interests include game theory, behavioral operations management, and platform operations. He has published in Computers & Industrial Engineering. Email: [email protected]

Shaofu Du

Shaofu Du is a professor at the School of Management, University of Science and Technology of China, Hefei, P. R. China. His research interests include supply chain management, game theory, crowdfunding, behavioral operations, and low-carbon operations management. Dr. Du has published in European Journal of Operational Research, International Journal of Production Research, Transportation Research Part E: Logistics and Transportation Review, Annals of Operations Research, International Journal of Production Economics, Omega, and other journals. Email: [email protected]

Tengfei Nie

Tengfei Nie is a professor at the School of Management, Shandong University, Jinan, P. R. China. His research interests include supply chain management, game theory, behavioral operations management, crowdfunding, and co-creation. Dr. Nie has published in European Journal of Operational Research, International Journal of Production Research, Transportation Research Part E: Logistics and Transportation Review, International Transactions in Operational Research, and other journals. Email: [email protected]

Yangguang Zhu

Yangguang Zhu is a lecturer at the School of Economics, Hefei University of Technology, Hefei, P. R. China. Her research interests include financial risk management, financial crisis contagion studies, and supply chain finance. Dr. Zhu has published in International Journal of Production Research, Transportation Research Part E: Logistics and Transportation Review, Annals of Operations Research, International Transactions in Operational Research, and other journals. Email: [email protected]