Presenting the Perceived Fairness Model of Dynamic Pricing: Meta-Synthesis Approach

The lack of perceived fairness in pricing can lead to various negative consequences, including diminished trust in the seller and the prices, reduced demand, lower customer satisfaction, negative word-of-mouth advertising, loss of customer loyalty to the company or brand, increased complaints, and a reluctance to make future purchases. Considering all the mentioned destructive consequences, it is necessary to design a perceived fairness model for dynamic pricing. Accordingly, this study to present the perceived fairness model of dynamic pricing using a meta-synthesis approach.

This research is descriptive in terms of its practical purpose, data collection, and qualitative approach. The method used in this study is qualitative. Meta-synthesis involves several steps: searching, evaluating, synthesizing, expressing, and partially interpreting both quantitative and qualitative research. Transcombination can be performed using various methods; in this study, the 7-step model of Sandelowski and Barso was employed. The data collection tool consisted of past documents, including 32 articles. Content analysis was used as the method for data analysis.

The findings indicated the drivers affecting the perceived fairness of dynamic pricing in three categories: customer-related factors (demographic characteristics, price knowledge, price expectation, consumption and behavioral experience, familiarity with dynamic pricing), company-related factors (pricing transparency, communication with customers, trust), and market-related factors (price position, price dispersion). These drivers are the set of factors that affect the perceived fairness of dynamic pricing. The drivers include various factors. Perceived fairness of dynamic pricing includes two dimensions: emotional fairness and cognitive fairness. Customers who expect low prices will be suspicious and unfair about price increases. The actions taken by companies to raise customer awareness about the reasons for price increases, the timing of price changes, and similar actions in past periods significantly influence customers' accurate understanding of the price changes, preventing them from perceiving them as unfair. Perceived fairness consequences of dynamic pricing are categorized into two categories: positive consequences (customer satisfaction, customer loyalty, repurchase intention) and negative consequences (negative feelings, negative behaviors, and negative advertising by customers).

Any price difference should be based on a logical reason and serve as a tool for segmentation. While pricing policies inevitably influence consumers' purchase intentions, they should not undermine the perceived fairness from the customer's perspective. The identified drivers of perceived fairness in dynamic pricing, when applied to pricing the same goods under different conditions, provide a foundation for pricing decision-makers within companies.