Marketing Seminar (2020-09)

Topic:Information and Creativity in Innovation Contests

Speaker:Haosheng Fan, Hong Kong University of Science and Technology

Time: Wednesday,2 December, 13:30-15:00

Microsoft Teams:

https://teams.microsoft.com/l/meetup-join/19%3aade8e46052fa419798afcaf6aad0ad20%40thread.tacv2/1606465928611?context=%7b%22Tid%22%3a%2280b7b804-c47e-4119-8274-0f6835b8e89f%22%2c%22Oid%22%3a%22dae73385-f69d-4688-a153-ff18e3659b00%22%7d

Abstract:

Innovation is often generated from the “wisdom of crowds” and firms therefore often hold contests to solicit innovative or creative ideas in product development and marketing communication from the public. Motivating creativity can be challenging, as it is inherently expensive to explore novel, high-risk directions. In this study, I use logo-design contest data collected from a leading online crowd-sourcing platform to explore how the design of information environments influences an individual’s creative behavior. To measure creative behavior, I propose a machine-learning framework to extract design features from logo images and construct a measure based on contestants’ choice of feature styles. I examine two types of information: feedback on submissions, and the observability of competing submissions. I find that when contestants have received positive ratings from firms, they become less likely to explore novel designs. Enabling contestants to view others’ submissions can alleviate this problem. I explore the underlying mechanism of this phenomenon and demonstrate that positive feedback can reduce individual creative behavior. That is, ratings from a firm reveal its preference and, having learned these, contestants tend to focus on exploiting existing design styles and become less likely to explore new styles. In contrast, allowing contestants to observe others’ work can increase an individual’s creative behavior. That is, contestants observe inspiring and promising design styles used by others and incorporate these into their own work.

Introduction:

Haosheng Fan is a Ph.D. candidate in Marketing at the School of Business and Management, Hong Kong University of Science and Technology (HKUST). His research is in the area of quantitative marketing. His recent works focus on innovation, contest, machine learning and visual marketing. He is scheduled to teach Pricing Strategy. He received a MPhil in Marketing from Hong Kong University of Science and Technology and a BSc in Computer Science from City University of Hong Kong. Prior to joining HKUST, he worked as a research assistant at The Chinese University of Hong Kong (CUHK) for one year.

Your participation is warmly welcomed!