THE GOOD, THE BAD, AND THE UGLY – HOW EMOTIONS AFFECT ONLINE CUSTOMER ENGAGEMENT BEHAVIOR Authors:

During the last years marketing research pays more and more attention to the concept of customer engagement behavior (CEB), which is the customer’s behavioral manifestation toward a firm that goes beyond transactions. A central part of CEB are customer’s word-out-mouth activities, which nowadays mostly take place in online environments. An important part of this online CEB (OCEB) are online user reviews, in which consumers express experiences with products and services. A wide variety of research addresses effects of online reviews and shows that they essentially influence decision making processes of consumers consulting these reviews. Thus, in many areas, online reviews are the most important information source crucially affecting sales numbers. While, consequences of this form of OCEB are well documented, empirical research on determinants motivating customers to engage online is relatively scarce. Nevertheless, existing research indicates that the expression of positive emotions and the venting of negative emotions related to the consumption experience constitute crucial drivers of OCEB. However, it remains widely unclear how the effects of different basic emotions (e.g. joy, anger, or fear) on OCEB differ. Thus, it could be expected that certain emotions are major drivers of OCEB, while others are negligible. This research tackles this question by proposing a theoretical model that distinguishes basic emotions according to their valence and arousal level and proposes varying effects on OCEB. To test the model we develop a text analysis tool that analyzes online reviews and detects basic emotions. Applying this tool 200,000 reviews from Amazon.com are analyzed and coded before applying ordinary least squares (OLS) regressions to test hypotheses. Results entail crucial theoretical and practical implications. INTRODUCTION Although the engagement notion has, in past decades, found far-reaching consideration across a number of academic disciplines, it was applied in marketing research just recently (Brodie et al., 2011, Hollebeek et al., 2014). Thereby, the concept of customer engagement was sought to advance the predictive power and scientific understanding of behavioral outcomes such as customer loyalty (Bowden, 2009, Hollebeek et al., 2014). Customer engagement is assumed to possess cognitive, emotional, and behavioral aspects (Brodie et al., 2011, Hollebeek et al., 2014). This article specifically focuses on the concept’s behavioral dimension, also referred to as customer engagement behavior (CEB), which we define as a customer’s behavioral manifestation toward a firm that goes beyond transactions and results from a diverse set of motivational drivers (Van Doorn et al., 2010). CEB is assumed to take different forms and to be expressed in a multitude of ways (Van Doorn et al., 2010). For the most part, extant literature differentiates between two major types of CEB. While in interactions with a firm or its representatives the concept has regularly been associated with customer co-creation (e.g. in the context of product innovation), CEB in interactions with peer customers are often associated with customer communication activities (e.g. WOM, recommendations) (Jaakkola and Alexander, 2014, Verleye et al., 2013). Nowadays, a great share of interactions between the customer and the firm or other customers are being expressed online, particularly on social media websites, websites of online-retailers or on websites hosting user reviews (Bijmolt et al., 2010, Vivek et al., 2014). This specific kind of CEB, which we refer to as OCEB (i.e. online CEB), is in the center of this article. Extant research shows that OCEB, and particular electronic word-of-mouth, which is distributed through online user reviews, essentially influences decision making processes of consumers consulting these reviews. Thus, online reviews have become the most important information source in many areas, particularly in e-commerce, substituting or complementing offline word-of-mouthcommunication (Chevalier and Mayzlin, 2006, Yin et al., 2014). Accordingly, it is not surprising that online reviews crucially affect sales numbers of products and services (Forman et al., 2008, Ludwig et al., 2013). Furthermore, existing research shows that customers that show positive OCEB have a higher loyalty, while customers that show negative OCEB have low loyalty intentions and often want to harm the responsible company (Brodie et al., 2013). While, the consequences of OCEB are well documented, empirical research on determinants motivating customers to engage online is relatively scarce. Yet, extant research indicates that emotions, which are generally considered an important motivational driver (Oliver et al., 1997), also could be from high relevance for OCEB. Particularly, results from Hennig-Thurau et al. (2004) provide anecdotal evidence that the expression of positive emotions and the venting of negative feelings related to either a very positive or negative consumption experience constitute crucial drivers of OCEB. However, there is no empirical evidence that specifies the role emotions play with regard to OCEB. Thus, it remains unclear if effects of positive and negative emotions on OCEB differ. Furthermore, it has not been investigated if the different distinct emotions within these categories vary in their relevance. Thus, for instance, it could be expected that different negative emotions, like anger and sadness, are not equally important for OCEB. In addition to that, existing research indicates that the effects of emotions on consumer behavior strongly depend on the respective service/product category (e.g., Chitturi et al., 2008). Thus, for example, an emotion like joy could play a different role for OCEB in a product category in which utilitarian benefits are central compared to a product category in which hedonic benefits are in the center of attention. Hence, in spite of the outlined research gaps and the elevated relevance and wide-reaching consequences of nowadays’ online interactions, an in-depth empirical examination of the relative importance of customer emotions in driving OCEB appears to be essential. Accordingly this paper strives to contribute to this stream of research (1) by determining the relative importance of all basic emotions for OCEB, (2) by providing a theoretical framework that explains the varying effects of the different basic emotions and allows for an integration of findings into existing research on emotions and OCEB, and (3) by accounting for differences in product/service categories that allow for more precise predictions to which extend the different basic emotions foster OCEB. To pursue these contributions this paper proceeds as follows. First, we develop a theoretical framework that makes predictions on the relevance of different emotions for OCEB. Building on Plutchik’s (1980,1982) wheel of emotions, we identify eight basic emotions and use the dimensions of valence and arousal to explain varying effects on OCEB. Second, the framework is extended by integrating moderating effects of the respective product/service category (i.e. utilitarian vs. hedonic). Third, for our empirical investigation we develop a text analysis tool that analyzes online reviews and automatically detects distinct basic emotions. Applying this tool a large sample of 200,000 online reviews of eight product/service categories from Amazon.com were analyzed and coded before applying ordinary least squares (OLS) regressions to test the research hypotheses. Fourth, building on our theoretical development and empirical analysis, theoretical and practical implications are derived and directions for future research on emotions and OCEB are provided. CONCEPTUAL DEVELOPMENT Classification of Emotions A lot of work has been conducted in the field of emotion research, highlighting the emotion concept as vital element and important driver of human behavior and, more specifically of customer behavior (Gaur et al., 2014). Thereby, emotions are often regarded as “mental state of readiness that arises from cognitive appraisals of events or thoughts”, that “[. ] is often expressed physically (e.g., in gestures, posture, facial features) and may result in specific actions to affirm or cope with the emotion [. ]” (Bagozzi et al., 1999; p. 184). There is a great number of emotions that can be distinguished. Academic literature has mostly utilized two major approaches for categorizing emotions (Havlena and Holbrook, 1986): basic emotional categories (e.g., Izard, 1977, Plutchik, 1980) and underlying dimensions of affective states (e.g., Mehrabian and Russell, 1974, Russell, 1980). One of the most prominent emotional typologies was introduced by Robert Plutchik (1980) and has provided the conceptual framework for a multitude of empirical investigations into the subject (e.g., Havlena and Holbrook, 1986). Based in evolutionary processes, Plutchick (1980; 1982) defines emotions in terms of a complex stimulus-reaction sequence, and focuses on only eight basic emotional categories. Building the foundation for all more complex secondary emotions, the array of basic or primary emotions include anger, joy, trust, surprise, fear, sadness, disgust and anticipation (Plutchik, 2001). An overview is provided in Table 1. Plutchik (1982; 2001) arranges the emotions in a circumplex manner with bipolar pairs allocated opposite to each other. He argues that different kinds of dyads (primary, secondary, or tertiary) may result from the combination of any two either directly or indirectly adjoined emotions. Assumed to involve different levels of intensity or arousal, emotions, further, may as a result eventually lead to dissimilar behavioral manifestations (Plutchik, 1991). This is also in line with basic assumptions involved in affect theory, claiming affective states to be best represented in terms of continuous underlying dimensions of valence and arousal (or activation) (e.