Strategic price bundling for online retail platforms considering consumer behavior of valuation discount

Abstract

Bundling has become an important operational tool for online retail platforms to attract consumers. Nevertheless, published empirical results on consumer behavior show that a bundle discount negatively influences the consumers’ perceived value of individual component products. We investigate how valuation discounts like these influence the online retail platforms’ decision optimization in bundling. Optimization models for mixed-joint and mixed-leader bundling are developed to analyze this effect. Based on the benchmark model of no-bundling, insightful implications are provided by comparing these three models. It is found that, in specified conditions, mixed-leader bundling outperforms mixed-joint bundling due to the negative effects of valuation discounts. Moreover, when the potential consumer pools of individual components are both large, mixed-joint bundling, which common knowledge considers the most profitable strategy, performs worse than the no-bundling strategy when valuation discounts are taken into account.

Keywords:

• Pricing
• retailing
• behavioural OR

Acknowledgements

The authors would like to express heartfelt gratitude to the Associate Editor and three anonymous referees for their constructive comments and valuable suggestions that allowed us to substantially improve the original manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China (72001066, 72271225, 71922008, 71991464/71991460, 71921001); Anhui Provincial Natural Science Foundation (2208085J06); Philosophy and Social Science Planning Project of Anhui Province (AHSKQ2020D27); Key Support Project for University Outstanding Youth Talent of Anhui Province (gxyqZD2022069); Key Research Project of Humanities and Sociology Science in Anhui Universities (SK2021A0504); and the USTC Research Funds of the Double First-Class Initiative (YD2040002017).