Product Pricing and Design Strategy in Platform-Based Collaborative Innovation With Cognitive Bias. Issue 2 (3rd April 2023) - Zhu, Siyuan; Du, Shaofu; Nie, Tengfei; Zhu, Yangguang
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.