Electronic Marketing
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ItemUse of clustering for consideration set modeling in recommender systems( 2021-01-05)The cold-start problem has become a significant challenge in recommender systems. To solve this problem, most approaches use various user-side data and combine them with item-side information in their systems design. However, when such user data is not available, those methods become unfeasible. We provide a novel recommender system design approach which is based on two-stage decision heuristics. By utilizing only the item-side characteristics we first identify the structure of the final choice set and then generate it using stochastic and deterministic approaches.
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ItemPsychological Factors Predicting Organic Food Consumption in Social Commerce( 2021-01-05)This paper examines consumer perceptions of the purchase of organic food in social commerce. We extend the Theory of Planned Behavior to perceived information usefulness and the perceived consequences of s-commerce use. We empirically test our hypothesized conceptual model among 261 consumers in market conditions with limited access to organic food products via conventional grocery stores. The results show that the perceived usefulness of organic food information in s-commerce has a highly significant effect both on the consumer attitude to using s-commerce and on the subjective norm. Perceived consequences influence the subjective norm and perceived behavioral control, but not on attitude. Attitude and the subjective norm significantly predict the consumer’s intention to use s-commerce for organic food purchases; perceived behavioral control does not. Our results highlight the importance of information in social commerce as a driver of purchasing, especially in markets offering little product information and availability in conventional channels.
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ItemLook What I’m Interested in! Toward a Better Understanding of How Personalization and Self-Reference Drive News Sharing( 2021-01-05)Along with the continuing shift from traditional to digital news consumption, many news consumers share news via social networks and messaging services. Hence, news providers benefit from an increase in user involvement and a growing awareness of their news offerings. Although personalizing digital news offerings has become common practice, we know little about how personalization affects news sharing. Building on the stimulus-organism-response model, we propose a comprehensive framework to investigate how personalization and self-referential cues impact users’ sharing intention mediated by their cognitive and affective reactions. To test our research model, we conduct an experiment with a fictitious news application and analyze the results using partial least squares structural equation modeling. The results reveal that personalization and self-reference impact users’ perceived preference fit and perceived enjoyment, which in turn drive news sharing. The findings have important implications for researchers and news providers.
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ItemIn the Eye of the Reviewer: An Application of Unsupervised Clustering to User Generated Imagery in Online Reviews( 2021-01-05)Mining opinions from online reviews has been shown to be extremely valuable in the past decades. There has been a surge of research focused on understanding consumer brand perceptions from the textual content of online reviews using text mining methods. With the increase in smartphone usage and ease of posting images, these reviews now often contain visual content. We propose an unsupervised cluster method to understand the user-generated imagery (UGI) of online reviews in the travel industry. Using the deep embedded clustering model we group together similar UGI and examine the average review ratings of these clusters to identify imagery associated with positive and negative reviews. After training the method on the entire dataset, we map out individual hotels and their corresponding UGI to show how hotel managers can use the method to understand their performance in particular areas of customer service based on UGI. The performance in a cluster relative to the population can be a clear indicator of areas that need improvement or areas that should be highlighted in the hotel's marketing efforts. Overall, we present a useful application using visual analytics for mining consumer opinions and perceptions directly from image data.
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ItemFactors Affecting Negative E-WOM: A Literature Review and Merged Model( 2021-01-05)This study aims to understand the motivations, firms, systems, and customer-related factors that drive negative electronic word-of-mouth (eWOM) communications. We attempt to understand why and how negative eWOM is formed because studies have suggested that negative eWOM may influence customers’ purchase behavior more than positive eWOM does. We collected 45 journal articles from 2012-2020 and identified factors and theories based on negative eWOM. A merged model and 21 propositions were developed based on the literature and results of meta research. The effect of negative eWOM is increasing because of the widespread use of social media. Our results shed light on the importance of the intrinsic motivations of negative eWOM and provide business ideas regarding how negative eWOM can be managed with a holistic view that includes multiple levels of factors. Future eWOM research can build on theories as well as our results and findings to ensure continuous development.
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ItemIntroduction to the Minitrack on Electronic Marketing( 2021-01-05)