Felix EggersFrank T. BekePeter C. VerhoefJaap E. Wieringa...
341-360页
查看更多>>摘要：In recent years, firms' privacy practices have received increasing attention from consumers. While firms largely see this development as a threat, as consumers might prohibit collection or use of data, we suggest that it can also represent an opportunity for firms. On the "market for privacy," firms can gain a competitive advantage by differentiating and actively promoting preferred privacy practices. In this context, the authors study how consumers trade off five privacy elements, three relating to distributive fairness (i.e., information collection, storage, use) and two relating to procedural fairness (i.e., transparency, control). Moreover, they analyze how the impact of these elements differs among four industries that vary in information sensitivity and interaction intensity. By using discrete choice experiments, the authors show that all privacy elements matter to consumers, even when in a trade-off with price. In highly sensitive industries, differences in information collection and use matter more, while storage matters less, for differentiation. When consumers have less frequent interactions with companies, they require more transparency about their privacy practices. The authors demonstrate empirically that optimizing privacy practices can lead to robust changes in market shares (Study Ⅰ) and higher revenues in equilibrium (Study 2) when firms embrace the market for privacy.
查看更多>>摘要：Online consumer reviews, as a major source of information and influence, are of great interest to marketing researchers and practitioners. This study investigates the effects of linguistic coordination on perceived review quality. Drawing on the elaboration likelihood model, the authors theorize that two types of linguistic coordination-topic matching (semantic component) and language style matching (lexical component)-have profound effects on perceived review quality. Utilizing natural language processing tools and a novel clustering technique to measure matching, empirical analyses based on an IMDb data set support the positive direct effects of both types of matching. Moreover, the authors find that there is a negative interaction between topic matching and language style matching in affecting perceived review quality. The findings contribute to the understanding of online review quality, and the application of natural language processing enriches the methodological tool kit available to researchers.
查看更多>>摘要：In service settings, chatbots frequently are associated with substandard care, depersonalization, and linguistic misunderstandings. Drawing on assemblage theory (i.e., the examination of how heterogeneous parts, through their ongoing interaction, create an emergent whole with new capacities that the parts themselves do not have), the authors investigate how chatbots' language concreteness-the specificity of words used during interactions with consumers-can help improve satisfaction, willingness to use the chatbot, and perceived shopping efficiency. Across three experiments, the findings reveal a psychological mechanism driven by concrete chatbot language that makes chatbots seem competent and reinforces consumer self-competence, in turn boosting satisfaction, willingness to use the chatbot, and perceived shopping efficiency. This pattern of results contributes to consumer behavior by providing evidence of the chatbot language concreteness effect on consumer-chatbot interactions. For practitioners, the authors outline conversational designs that could help optimize implementation of chatbots in customer service.
查看更多>>摘要：The enormous growth of social media has increased interest in this platform among marketers and marketing academics. However, the previous literature has not provided a clear consensus regarding the influence of social media content on consumers' brand loyalty. The meta-analysis presented in this article integrates results from 223 independent samples, with a total of 97,709 respondents. The study synthesizes previous research to develop a conceptual framework around the dimensions of brand loyalty (cognitive, affective, and conative loyalty), user-generated and firm-generated social media content attributes, and the moderating effects of contextual characteristics and control variables. Selected content attributes (information quality, information credibility, information usefulness, positive emotions, interactivity, and self-congruity) emerged as triggers in social media for dimensions of brand loyalty. Specifically, the authors show that the impact of the attributes on the brand loyalty dimension is stronger for firm-generated content than for user-generated content for most of the relationships. The results indicate that these effects are dependent on contextual characteristics (e.g., low involvement vs. high involvement, hedonic vs. utilitarian, nondurable vs. durable, Human Development Index, and social media platform). The contributions to theory and managerial implications of these findings are discussed, and future research directions are developed.
Steven HolidayJameson L. HayesHaseon ParkYuanwei Lyu...
414-439页
查看更多>>摘要：Social media influencers rely on emotional connection to maintain and grow their fallowings and have value for brands. To date, however, no research has quantitatively examined the impact of emotion in the facial expressions and caption text that influencers use in their video posts on consumer engagement through likes, comments, and views of posts. Grounded in consumer brand engagement, psychological sense of community, and the behavior ecology view of facial displays, this study uses social media analytics, facial expression analysis, and computational linguistic analysis to assess the emotional substance of 402 video posts by prominent micro-, macro-, and mega-influencer mothers, known as InstaMoms, as exemplars of Instagram influencers. The study identifies that the amount of emotion used and specific discrete emotions have a meaningful influence on engagement, and both follower count and presence of branding saliently contribute to a more robust understanding of the relationship. Theoretical and practical implications are identified and discussed.
查看更多>>摘要：Marketers and researchers recognize the importance and impact on consumer behavior of marketer-generated content (MGC) in social media channels. In this study, the authors present a method to classify MGC using a combination of unsupervised and supervised machine learning. They gather a large data set of posts from Facebook, Instagram, and Twitter and use a time-series model (panel-data vector autoregression) to demonstrate how MGC can be used to explain average toxicity on the part of users. They contribute to the field by examining what types of MGC lead to toxic comments and how these toxic comments impact product usage. The authors find that MGC that demonstrates the quality of products and MGC that is aimed at creating a sense of belonging to a group are more likely to increase average toxicity. Furthermore, the authors find that higher average toxicity in social media communities leads to an increase in usage of the focal product. Finally, the results contribute to the literature by providing insights on the impact of MGC on product usage.
NETL
NSTL
Sage