Mass Customization and Guardrails: You Can Not Be All Things to All People

成果类型:
Article
署名作者:
Cil, Eren B.; Pangburn, Michael S.
署名单位:
University of Oregon
刊物名称:
PRODUCTION AND OPERATIONS MANAGEMENT
ISSN/ISSBN:
1059-1478
DOI:
10.1111/poms.12716
发表日期:
2017
页码:
1728-1745
关键词:
mass customization product line design pricing brand dilution
摘要:
A mass customization strategy enables a firm to match its product designs to unique consumer tastes. In a classic horizontal product-differentiation framework, a consumer's utility is a decreasing function of the distance between their ideal taste and the taste defined by the most closely aligned product the firm offers. A consumer thus considers the taste mismatch associated with their purchased product, but otherwise the positioning of the firm's product portfolio (or, brand image) is immaterial. In contrast, self-congruency theory suggests that consumers assess how well both the purchased product and its overall brand image match with their ideal taste. Therefore, we incorporate within the consumer utility function both product-specific and brand-level components. Mass customization has the potential to improve taste alignment with regard to a specific purchased product, but at the risk of increasing brand dilution. Absent brand dilution concerns, a firm will optimally serve all consumers' ideal tastes at a single price. In contrast, by endogenizing dilution costs within the consumer utility model, we prove that a mass-customizing firm optimally uses differential pricing. Moreover, we show that the firm offers reduced prices to consumers with extreme tastes (to stimulate consumer travel), with a higher and fixed price being offered to those consumers having more central (mainstream) tastes. Given that a continuous spectrum of prices will likely not be practical in application, we also consider the more pragmatic approach of augmenting the uniformly priced mass customization range with preset (non-customized) outlying designs, which serve customers at the taste extremes. We prove this practical approach performs close to optimal.