Expectation hurts? A dynamic model of the exogenous and endogenous role of eWOM and its effect on post-purchase responses
Supervisor: Guda van Noort
Building on the Expectation-Confirmation Theory (ECT), this study aims to: (1) investigate electronic word-of-mouth (eWOM) as an exogenous variable by comprehensively examining the effects of eWOM dimensions (valence, volume and quality) on post-purchase responses as well as the underlying cognitive mechanisms; (2) investigate eWOM as an endogenous variable predicted by exposure to previous eWOM; and (3) build a dynamic model to explain the temporal evolution of eWOM dimensions. A mixed-method approach, combining an experimental design and a large-scale automated content analysis, was adopted to examine the hypothesized model.
Sub-hypotheses of H1 propose the relationships between eWOM dimensions and purchase responses, where eWOM valence and quality are supposed to have a positive effect while eWOM volume has a negative effect. Sub-hypotheses of H2 presume the relationships between eWOM dimensions and expectation, where eWOM valence and volume have a positive effect on expectation, while eWOM quality has a negative effect on expectation. Sub-hypotheses of H3 propose the relationship between eWOM dimensions and confirmation, where eWOM valence and volume have a negative effect on confirmation, while eWOM quality has a positive effect on confirmation. Sub-hypotheses of H4 expect chronological evolution of eWOM dimensions.
Study 1: Experiment
Study 1 applied experimentation to test the sub-hypotheses of H1, H2 and H3, where we investigated the effects of eWOM valence, volume and quality on post-purchase responses as well as the underlying cognitive mechanisms. We found that: (1) eWOM quality is the strongest predictor of consumers’ post-purchase satisfaction, attitude and repurchase intention, which can be explained by confirmation; (2) the positive effect of eWOM valence on post-purchase responses is counteracted with its negative effect on confirmation; and (3) the effect of eWOM volume on post-purchase responses is not supported.
Study 2: Automatic content analysis
Study 2 applied automatic content analysis to test the sub-hypotheses of H4, where we inspected the chronological evolution of eWOM dimensions in a large-scale data set of restaurant reviews and revealed that: (1) eWOM valence becomes more balanced over time, such that eWOM valences tends to decline when it starts high, while increase when it starts low; (2) eWOM volume encounters a steady increase until it reaches a peak and starts to decrease; and (3) eWOM quality tends to decline over time.
This study calls attention to three under-investigated perspectives of eWOM research. First, among the limited amount of studies focusing on consumer responses in the post-purchase stage, few have simultaneously inspected the impacts of all the three dimensions of eWOM. Meanwhile, mixed findings are present in literatures. Study 1 demonstrated that different eWOM dimensions have different cognitive patterns of impact on post-purchase affective, attitudinal and behavioral consequences. Second, this study applies the ECT model to eWOM effects, which helps to extend the current knowledge on the cognitive processes in the effect of eWOM on post-purchase responses, as well as explaining the mixed findings in the literature. Coherent with the ECT, the findings confirm the effects of expectation and confirmation on post-purchase responses where confirmation plays a more dominant role in the explanatory power. Third, most research on eWOM effects have treated eWOM as an input, while the dynamic nature of eWOM as an output generating from post-purchase responses are largely neglected. Therefore, it becomes critical to investigate how eWOM dimensions evolve chronologically.
This study aims to shed light on the underexplored topic of customer retention through illuminating the relationship between eWOM and post-purchase responses. This study provides marketing practitioners and vendors with a comprehensive model of eWOM effects on post-purchase responses and their evolution over time, which helps to draw two useful tactics at an enterprise level. First, we propose that a product or service with higher early ratings should focus more on maintaining their product or service quality to meet consumers’ expectation, while a product or service with lower early ratings should motivate consumers to post online reviews by reminding them with emails or messages in order to reduce bias reviews. Second, for the long term, marketers should set strategies to encourage online reviews with higher quality, such as setting a minimum word count to improve the length of reviews, providing customers with review templates to improve the objectivity level of online reviews, etc.