Abstract
Customized product design has gained significant attention in various industries given its capacity to better satisfy customers' needs and increase the profit of customizers. Online product configurators, consisting of a set of predefined product attributes for customers to choose from, serve as a critical interface that bridges the gap between customers' needs and companies' offerings. To reduce customers' decision-making burden in the product configuration process, some configurators highlight bestsellers that serve as decision-making shortcuts and increase customers' likelihood of choose bestseller options, in a phenomenon known as observational learning. Although some strategic customers are aware of the observational learning effect, it remains unclear whether such awareness influences their selection of bestseller options. To elucidate that phenomenon, we conducted an empirical study to answer our research questions and develop different versions of online configurators for participants to customize sandwiches. According to our results, awareness of the observational learning effect does not significantly affect customers' selection of bestseller options, whereas other factors significant affect their selection of bestseller options when using product configurators. Copyright © 2023 IEEE.
Original language | English |
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Title of host publication | Proceedings of 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023 |
Place of Publication | USA |
Publisher | IEEE |
Pages | 123-127 |
ISBN (Electronic) | 9798350323153 |
DOIs | |
Publication status | Published - 2023 |
Citation
Cheng, Y. T., Choi, H. L., & Wang, Y. (2023). How awareness of the observational learning effect influences consumers' decisions in the online configuration process. In Proceedings of 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023 (pp. 123-127). IEEE. https://doi.org/10.1109/IEEM58616.2023.10406909Keywords
- E-commerce
- Configurator
- Customization
- Observational learning