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The components of perceived quality and their influence on online re-purchase intention

业务 营销 质量(理念) 广告 消费者行为 感知质量 采购 认识论 哲学
作者
Rahul Kumar,Varsha Jain,Jacqueline K. Eastman,Anupama Ambika
出处
期刊:Journal of Consumer Marketing [Emerald Publishing Limited]
卷期号:42 (1): 38-55 被引量:10
标识
DOI:10.1108/jcm-04-2024-6798
摘要

Purpose This study aims to understand holistic consumer perceptions of quality and their effect on re-purchase intentions by measuring the latent characteristics of online Amazon reviews. Design/methodology/approach Data was collected from entries in the Amazon customer review data set, which explicitly mentions quality, economic evaluation and future purchase intention. The analyses included natural language processing, structural topic modeling and econometric analysis. The study used real-time customer reviews to determine the overall perceived quality, the impact of perceived quality on re-purchase intention and the mediating roles of price consciousness and customer satisfaction. Findings Consumers’ perception of overall quality includes product- and service-related dimensions. Perceived quality influences re-purchase intentions through the mediating role of customer satisfaction. While price consciousness impacts the link between perceived quality and customer satisfaction, it does not affect re-purchase intention. Practical implications The managerial implications emphasize multiple dimensions of quality in the online environment and the role of customer satisfaction in consumers’ online re-purchase intentions. The results also illustrate that price effects are insignificant in influencing re-purchase intentions. Thus, while price cuts may encourage initial purchases, quality and customer satisfaction are vital to stimulate re-purchase. Originality/value The e-commerce literature lacks a comprehensive and rigorous understanding of the components of consumers’ perceived quality. This research develops a thorough understanding of what impacts overall e-commerce quality based on real-time customer reviews, avoiding the biases arising from traditional methods, including surveys.
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