Price and quality optimization with the intertemporal reference price effect
When making purchasing decisions for a certain product, consumers normally take the historical price as a reference. Focusing on the intertemporal reference price effect (IRPE), this study investigates the joint optimization problem of price and quality from a new product releaser's perspective. More specifically, we establish a choice model based on the multinomial logit model to deal with consumers' behaviour when facing a newly released product and an existing alternative. Our analyses start from a short-term case, where the optimization is only about determining the best price, and then move to a long-term case, in which the firm can adjust both the price and quality. The consumers' attitudes toward the IRPE are examined respectively in the asymmetric format (i.e., the loss-averse and gain-seeking consumers) and the symmetric format (the loss-neutral consumer). The analytical results with and without the reference price are compared regarding reference price sensitivity parameters, from which managerial insights are outlined to facilitate the firm's pricing and quality decisions. Finally, the intertemporal reference quality effect (IRQE) is studied on top of the IRPE. Such an extension sheds light on the additional impact of IRQE and the importance of jointly considering IRQE and IRPE while optimizing price and quality.
Authors
- Lianmin Zhang (ORCID: https://orcid.org/0000-0002-8626-1847)
- Ginger Y. Ke (ORCID: https://orcid.org/0000-0001-5951-0580)
- Lixiang Li (ORCID: https://orcid.org/0000-0001-8541-308X)
- Min Li (ORCID: https://orcid.org/0000-0002-0072-5912)
Institutions
- Memorial University of Newfoundland (CA)
- Shenzhen Research Institute of Big Data (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Journal of Industrial and Management Optimization
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3934/jimo.2025085
- Primary Topic
- Consumer Market Behavior and Pricing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province
- Natural Science Foundation of Jiangsu Province
- Shenzhen Research Institute of Big Data
- Shenzhen Research Institute, City University of Hong Kong
- Natural Sciences and Engineering Research Council of Canada