Impact of Consumer Behavior Pertaining to Personalization of Price in an E-Commerce Context

Jensolin Abitha Kumari J,Preeti R Gotmare

doi:10.17015/ejbe.2021.028.06

Abstract

The study focuses on developing a conceptual model to explore the factors influencing consumers' judgments in the decision-making process with a prime focus on personalized dynamic pricing (PDP). The study explored the judgmental impact of PDP on customer willingness to pay and mediating role of stickiness to the online store on PDP fairness and customer willingness to pay. The data was collected using a structured questionnaire administered among 256 students at a large university in India. SEM using AMOS software was used to analyze data. Price perception, involvement, product knowledge, and recommendation system positively impact price fairness of PDP, directly and indirectly influencing customer willingness to pay. Results also showed that stickiness to online stores fully mediates the relationship between price fairness of PDP and customer willingness to pay. Theoretically, the study contributes to pricing and marketing literature by identifying the antecedents of price fairness of PDP. For practitioners, this study signifies the importance of a robust recommendation system to stand out from the competition and provide deals to satisfy consumers. Specifically, the results emphasize the need to focus on stickiness to an online store to track consumer characteristics and customer value

Highlights

Personalization of prices is gaining popularity as a viable pricing option in an online context (Priester et al, 2020)
Data assessed for normality assumption using Maximum Likelihood (ML) estimation revealed that kurtosis and skewness of variables in the study were between -1.117 to 1.393 and -1.012 to 0.933 respectively and well within the recommended acceptable range of ± 1.96 for ML estimation (Bollen & Stine, 1992)
Confirmatory factor analysis (CFA) confirmed construct validity of the constructs using the bootstrapping method with n = 2,000 at 90% confidence level, and bias-corrected intervals were executed

Summary

Introduction

Personalization of prices is gaining popularity as a viable pricing option in an online context (Priester et al, 2020). Personalized pricing can benefit more than 60% of customers compared to the firm's uniform pricing (Dubé & Misra, 2019). Personalized dynamic pricing (PDP) is influenced by consumers' prior experience, interpersonal price comparison, date, time of purchase, gender, location, device used (Lastner et al, 2019), buyers’ cultural differences, social norms in setting the price (Garbarino & Maxwell, 2010; Broeder & Wildeman, 2020) and the quantity purchased. Existing literature has studied the impact of PDP on consumer trust, loyalty, seller choice, price-setting mechanism, and competitor prices. Another stream of research focuses on regulatory implications of PDP, such as privacy concerns, legal concerns, societal and consumer welfare implications (Priester et al, 2020)

Methods

Results

Conclusion