Leveraging online reviews to decode quality-induced customer dissatisfaction: From perception to product discouragement

Abstract

E-commerce practitioners and researchers recognize that quality concerns are the primary drivers of customer dissatisfaction with products or services. While dissatisfaction can arise from various factors, little is known about quality and its components, specifically from the perspective of dissatisfied customers. Grounded in the foundational principles of expectancy conformance theory and emotional regulation theory, our study investigates the key characteristics driving quality-induced customer dissatisfaction and their influence on consumers’ response behaviors. We further examine how ways of expressions and feelings underlying reviews nudge future recommendations. By combining natural language processing and statistical modeling for around a million online reviews, we uncover and identify the characteristics underlying the sources of quality-induced customer dissatisfaction. Our findings highlight the intermediary role of negative sentiments and emotions, shifting the focus from regular defects or design-related stand-alone issues for the practice. Rather, it is the customers’ affective states, escalating from mild dissatisfaction to strong frustration, which mediate the impact on future recommendations and can lead to extreme reactions such as product discouragement. Therefore, portal managers can apply our findings to enhance decision-making in complex situations by developing coping strategies to regulate affective states of disappointed customers and thereby curb negative word-of-mouth.