算法价格歧视和顾客感知背叛

Algorithmic Price Discrimination and Customer Perceived Betrayal

近年来,算法价格歧视(如大数据杀熟)现象屡屡出现,为人所诟病。本文基于归因理论和关系质量理论,运用实地实验方法检验算法价格歧视对顾客感知背叛的影响机理,揭示蓄意性归因的中介变量作用,以及关系质量和价格敏感度的调节作用。研究结果表明,高算法价格歧视对消费者产生更强的蓄意归因,进而产生更强烈的感知背叛;相较于低关系质量,当企业与消费者的关系质量越高时,算法价格歧视对消费者产生更高的蓄意归因,进而产生更强烈的感知背叛;相较于低价格敏感度的消费者,高价格敏感度的消费者面对高算法价格歧视容易产生更强烈的感知背叛。感知背叛是大数据杀熟式算法价格歧视的负面效果。企业在营销中应尽可能地减少大数据杀熟式的价格歧视,并把算法细节公布与众,这样可以增强消费者对企业的信任感。同时,应该采用其他算法或助推的方式进行定价。由于动态定价不针对老用户等用户群体,是根据需求关系而变动价格,这一定程度上可以消除顾客被“杀熟”的感知,还常常会让顾客觉得性价比高。

In recent years, along with the development of data analytics and collection of large personal data, algorithmic price discrimination is increasingly draw attention from the public. Drawing on the attribution theory and relationship quality theory, we proposed a research model on the impacts of algorithmic price discrimination on consumer perceived betrayal. Data was collected on three different platform services using field experiment methods. Through analysis of variance and mediation analysis in Process, we empirically examined the mechanism of the influence of algorithmic price discrimination on customer perceived betrayal, the role of mediating variables of deliberate attribution, and the roles of two moderating variables of relationship quality and price sensitivity. The results of Study 1 show that when regular customers pay a higher price than new customers, regular customer are more likely to think that this is a deliberate profit-making behavior of the company, and therefore they exhibit higher perceived betrayal. The results of Study 2 show that when the relationship quality between the company and customers is high, the deliberate attribution and therefore perceived betrayal due to algorithmic price discrimination is stronger. The results of Study 3 show that when the consumer is more sensitive to prices, the influence of algorithmic price discrimination on perceived betrayal gets stronger. This research finds perceived betrayal as an important consequence of algorithmic price discrimination from the perspective of consumers, extending the application of perceptual betrayal in the field of artificial intelligence. Therefore, our findings provide a reference point for other scholars to explain consumers’ responses to algorithmic price discrimination. Practically, this research can provide a guidance to the marketing communication and service quality management for network platform companies.