The propensity of consumers to talk after a good versus bad experience with a product can differ based on information available from other marketing channels, for example the brand image or advertising. This can result in selection of positive/negative word-of-mouth for reasons outside of product quality. We develop a unifying framework of WOM, brand image, product advertising, and pricing with a focus on the instrumentality motive of word-of-mouth: early adopters talk to inform new buyers’ purchasing decisions. The different marketing channels shape the information sharing behavior of the early adopter as well as the target consumer’s purchase decision. We show that if the brand image is strong, then in equilibrium only negative WOM can arise. In contrast, with a weak brand image, positive WOM must occur. We also show that holding product quality fixed, a positive advertising signal realization leads to a more positive WOM selection. The model can be applied to both one-one informal WOM as well as online reviews. The assumptions and main predictions of our model are consistent with those that we identified from a primary survey and observational Yelp data.
The propensity of consumers to engage in word-of-mouth (WOM) can differ after good versus bad experiences. This can result in positive or negative selection of user-generated reviews. We show how the strength of brand image - determined by the dispersion of consumer beliefs about quality - and the informativeness of good and bad experiences impact the selection of WOM in equilibrium. Our premise is that WOM is costly: Early adopters talk only if their information is instrumental for the receiver’s purchase decision. If the brand image is strong, i.e., consumers have close to homogeneous beliefs about quality, then only negative WOM can arise. With a weak brand image, positive WOM can occur if positive experiences are sufficiently informative. We show that our theoretical predictions are consistent with restaurant review data from Yelp.com. A review rating for a national established chain restaurant is almost 1-star lower (on a 5-star scale) than a review rating for a comparable independent restaurant, controlling for various reviewer and restaurant characteristics. Further, negative chain restaurant reviews have more instances of expectation words, indicating agreement over beliefs about the quality, whereas positive reviews of independent restaurants feature disproportionately many novelty words.
The propensity of consumers to engage in word-of-mouth (WOM) can differ after good versus bad experiences, resulting in positive or negative selection of user-generated reviews. We study how the propensity to engage in WOM depends on information available to customers through different marketing channels. We develop a model of WOM in which a target customer makes a purchase decision based on his private brand association, public product-specific information (e.g. from advertising or past reviews) and WOM content, and an early adopter of the new product engages in WOM only if her information is instrumental to the target customer’s purchase decision. We define brand image to be the distribution of the customers’ brand associations, and strength of the brand image to be the precision of this distribution. We show that if the brand image is strong, then in equilibrium only negative WOM can arise. In contrast, with a weak brand image, positive WOM must occur. Moreover, holding product quality fixed, a positive advertising signal realization leads to a more positive WOM selection. We use restaurant review data from Yelp.com to motivate our model assumptions and validate the predictions. For example, a textual analysis of reviews is consistent with prevalence of an instrumental motive for WOM. Further, a review rating for national established chain restaurant locations, where the brand image is strong, is almost 1-star lower (on a 5-star scale) than a review rating for a comparable independent restaurant, controlling for reviewer and restaurant characteristics.
The propensity of consumers to engage in word-of-mouth (WOM) differs after good versus bad experiences, which can result in positive or negative selection of user-generated reviews. We show how the dispersion of consumer beliefs about quality (brand strength), informativeness of good and bad experiences, and price can affect selection of WOM in equilibrium. WOM is costly: Early adopters talk only if they can affect the receiver’s purchase. Under homogeneous beliefs, only negative WOM can arise. Under heterogeneous beliefs, the type of WOM depends on the informativeness of the experiences. We use data from Yelp.com to validate our predictions.
The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings.
The authors address two significant challenges in using online text reviews to obtain finegrained attribute level sentiment ratings. First, in contrast to methods that rely on word frequency, they develop a deep learning convolutional-LSTM hybrid model to account for language structure. The convolutional layer accounts for spatial structure (adjacent word groups or phrases) and LSTM accounts for sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in constructing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior attribute sentiment scoring accuracy with their model. They find three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Reviewers write to inform and vent/praise, but not based on attribute importance. The heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. More broadly, our results suggest that social science research should pay more attention to reduce measurement error in variables constructed from text.