Making new products go viral and succeed

Why are some new product introductions more viral and successful than others? This research integrates theories of interpersonal communication and consumer learning to explore this question. Analyzing a unique data set of millions of consumer word-of-mouth transmissions (eWOM) on social media regarding 345 new automobile products introduced during 2008–2015, we find that more innovative products generate more eWOM volume but surprisingly less positive sentiment. These effects vary in magnitude across eWOM channels. However, the use of rich-content communication, pre-announcement, and cobranding strengthens (weakens) the positive (negative) effect of product innovativeness on eWOM volume (sentiment). The results further indicate that eWOM sentiment is a stronger predictor of new product success than eWOM volume. Experimental results reveal more insights into how product innovativeness influences eWOM metrics in several product categories and shed light on the role of excitement and perceived risk as mechanisms underlying these effects. The research offers useful implications for firms to design effective viral marketing campaigns to enhance new product success.     

eWOM across channels: comparing the impact of self-enhancement,positivity bias and vengeance on Facebook and Twitter

Abstract

When sharing personal details, versus talking about others, consumers tend to accentuate the positive experiences they have with brands in order to self-enhance amongst friends. Consumers also take vengeance on brands in public venues. Turning to social network sites (SNSs), it is unclear which is dominant. Here historical Facebook and Twitter eWOM (n = 47,907) is analyzed for a static group of 783 active US consumers. Self-enhancement is found on both SNSs. The majority of eWOM is positive Vengeance was rare, occurring only in 10.3% of all eWOM. eWOM appears to mirror a consumer's non-eWOM sentiment valence across SNSs. In this cross-platform comparison we suggest that SNS affordances alter eWOM creation Facebook has privacy expectations and limits direct brand interactions. Twitter is a public platform with less privacy expectations and a larger customer service component. Here eWOM is more prevalent on Twitter but contrary to our expectations is more positive.     

A Proposed Model for Consumer-Based Brand Equity Analysis on Social Media Using Data Mining and Social Network Analysis

Online social networks have expanded their “virtual borders,” making the Internet more like an environment of social interaction than a business tool. However, even before the emergence and expansion of social media, marketing professionals were interested in identifying consumers' perceptions about brands. Thus, operational models have been proposed to facilitate such a task. Those models, however, can be expensive and inconvenient, since the models use questionnaires for data collection. To help overcome this problem, this article proposes a model for brand equity analysis from the consumer perspective expressed in social networks using opinion mining techniques and social network analysis. The application of the proposed model on data collected from Twitter made it possible to analyze five brand equity dimensions: brand awareness, brand loyalty, perceived sentiment, perceived quality, and brand associations. The results reached by the application of the model show that brand equity can be analyzed from data retrieved from virtual social networks, disclosing how consumers perceive brands in such an environment, without using questionnaires, enabling different brands in different contexts. Those data can be analyzed under both objective and replicable criteria for each of the brand equity elements that make up the model.     

Drivers of eWOM engagement on social media for luxury consumers: Analysis,implications, and future research directions

The rapid industrialization and growth across the world have fostered the consumption of luxury fashion brands. Electronic word-of- mouth on social media (eWOM) is fast becoming an effective and germane strategy to engage luxury consumers through posting pictures, sharing reviews, and communicating information on platforms like Facebook, Instagram, and TikTok. Extant research has not examined the antecedents and drivers that lead to eWOM behavior. We leverage self-congruity theory and through its focal lens, our study addresses this research gap through a survey conducted with 453 consumers in Mexico, Latin America's fastest growing market. Our results indicate that need for status, susceptibility to normative influence, and luxury brand involvement, moderated by authentic pride and social media influencers lead to eWOM behavior on social media. We also demonstrate that luxury brand involvement and susceptibility to normative influence mediate the relationship between need for status and eWOM behavior on social media. The study provides important implications to managers and researchers by suggesting long-term actionable strategies for growth that can help luxury firms develop a sustainable competitive advantage over rivals and competitors.     

Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?

User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment.     

eWOM and the importance of capturing consumer attention within social media

Word-of-mouth (WOM) is widely regarded as one of the most influential factors impacting consumer behavior, yet traditional models were constructed oblivious to the potential of electronic word-of-mouth (eWOM) and social media. In particular, very little is known about the dynamics affecting consumer attention within two-way many-to-many mediated environments even though attention constitutes a critical step. Thus, we construct a conceptual framework that grounds consumer attention within a larger progression of behavioral responses to eWOM. We then conduct an experiment (n = 28) that isolates contextual antecedents of attention to positive, negative, and neutral WOM for both luxury and non-luxury brands within a social media platform. Using behavioral eye-tracking, we find that WOM message valence interacts with brand type to affect attention differently. Implications of these findings for facilitating eWOM are discussed with future research directions suggested.     

How differences in eWOM platforms impact consumers’ perceptions and decision-making

ABSTRACT

This article aims to examine the effects of different features of various eWOM (electronic word-of-mouth) platforms on consumers’ perceived credibility of eWOM regarding the product-related risks. Based on the stimuli–organism–response (S–O–R) theory, this study establishes a model to explore the relationships among eWOM platforms, tie strength, social cues, and perceived eWOM credibility. A mixed design of 2 (eWOM platforms) × 2 (product-related risks) conditions experiment and a survey method is applied to verify the model. The results show that tie strength between eWOM publishers and recipients positively influences the perceived eWOM credibility. The volume of social cues in eWOM platforms positively influences the perceived credibility of a female, while the opposite is true for a male. Both tie strength and volume of social cues in social media are greater than those in e-commerce websites. For products with low risks, eWOM in e-commerce websites is perceived to be more credible. Findings implicate that interactive functions should be added to the product comment area to enhance communications between the reviewers and potential consumers. Findings also encourage the cooperation between e-commerce websites and social media and suggest that managers should develop proper strategies in different situations.     

The effects of eWOM volume and valence on product sales – an empirical examination of the movie industry

The present research examines how quantitative and qualitative aspects – volume and valence – of eWOM on various social media platforms in the movie industry, along with other forms of marketing communications (e.g. advertising expenditure), predict box office and DVD sales for pre- and post-periods of movie releases. The analysis of aggregated big data from multiple sources – 65,665,859 social media postings from blogs, forums, news, and Twitter, Nielsen’s ad expenditure data, and Rotten Tomatoes ratings for 170 movies—revealed that the explanatory power of the model for box office revenue and DVD sales mostly comes from the volume, rather than valence, of eWOM.     

Detection of potential customers’ empathy behavior towards customers' reviews

The aim of the current study is to identify potential customers' empathy behavior and their behavioral reactions based on appraisal and stimulus–organism–response (SOR) theories to customers' reviews of financial services firms using lexicon-based unsupervised learning techniques. After filtering, we obtained 30263 reviews from the Yelp dataset of financial service companies. We examined the connections between several sorts of emotional dimensions and different types of behavioral reactions of potential consumers using lexicon-based unsupervised machine learning methods. Our findings show that the various types of customer sentiment have a significant impact on potential customers' emotional experiences on social media platforms, prompting them to behave differently. Furthermore, potential consumers' reactions to the customers' reviews varied according to their seven emotional aspects. The study is the first to examine the impact of potential customers' empathetic behavioral reactions on customers' evaluations using lexicon-based unsupervised learning techniques.     

Community crosstalk: an exploratory analysis of destination and festival eWOM on Twitter

Abstract

Research suggests that festivals can promote a destination via online word-of-mouth (eWOM) on social media, even though the nature of this effect is not yet fully understood. Using a combination of Social Network Analysis and text analysis (qualitative and quantitative), this article examines eWOM at a tourism destination (Bournemouth) when a festival (Bournemouth Air Show 2013) is staged. The Communities of Interest of eWOM interactions on Twitter were captured and analysed to understand the structure and content of eWOM. Findings indicate that key users are usually already prominent individuals and that festivals act as both a direct generator as well as an online animator of eWOM. Finally, network size, span and scope may be useful indicators when comparing eWOM networks.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Word-of-mouth rhetorics in social media talk

Although researchers and practitioners have access to a growing body of evidence on the effects of electronic word-of-mouth (eWOM) frequency and valence, a more detailed understanding of eWOM content is needed in order to better influence these social media-enabled conversations. Based on an ethnomethodological analysis of community conversations in a popular parenting online forum, we reveal that eWOM is a powerful social act in which consumers use a broad repertoire of eWOM rhetorical methods. The repertoire for advice-seeking talk includes five components: seeker's legitimacy, topic legitimacy, request formulation, solicitation of responders, and requested response framing. The repertoire for advice-giving includes four components: foundation of authority, advice framing, advice focus, and advice scheme. For each of these components in the two repertoires, we identify corresponding rhetorical methods that can be used in the conversation (over 30 methods in total). Preliminary evidence also suggests that rhetorical alignment between advice-seekers and givers drives effectiveness of eWOM advice. The proposed rhetorical framework has relevance and implication for the many areas where social media and marketing meet.     

Retailers,don't ignore me on social media! The importance of consumer-brand interactions in raising purchase intention - Privacy the Achilles heel

Effective interactions are essential for retail brands to progressively nudge consumers towards purchase. While social media provides the platform for brands to directly connect with consumers, it is critical that brands take privacy concerns seriously. This paper address common questions retailers ask: How do brands develop effective interactions with consumers on social media? Do consumer-brand interactions impact purchase intention? Does privacy matter? Through 541 UK participant responses and using social exchange theory, this research examines consumer-brand interactions on social media, focusing on how social media activities, attitudes towards social media advertising, and privacy, impact upon purchase intention. Our results show that brands must establish strong relationships through high-quality consumer-brand interactions to significantly raise purchase intentions, while also carefully managing consumers' privacy expectations. Effective privacy management positively mediates the link between social media and purchase intention but ignore privacy, and it becomes the Achilles heel of the relationship.     

Brand Actions on Social Media: Direct Effects on Electronic Word of Mouth (eWOM) and Moderating Effects of Brand Loyalty and Social Media Usage Intensity

Most existing social media research has been user focused. This study looks at social media from the brand's perspective by testing (1) theoretical links between brand-action antecedents and positive electronic word of mouth (eWOM); and (2) how brand loyalty and social media usage intensity moderate the relationships between the antecedents and eWOM. Using structural equational modelling from 290 Facebook users, we find empirical support for three brand action constructs (personalization, responsiveness, and transparency) to eWOM. We also find the moderating effects of social media usage intensity and brand loyalty on the relationship between responsiveness and eWOM. Theoretical and managerial implications for these findings are discussed.     

What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature

Electronic word-of mouth (eWOM) has attracted considerable interest from researchers in the past decade. Although the extant research has helped us to develop a good understanding of a number of the issues pertaining to eWOM, several research and managerial questions remain. Furthermore, no attempt has been made to consolidate and synthesize this stream of research. With consumers' increasing reliance on online retailing and information seeking, as well as the continued growth of social media, the importance of eWOM cannot be overstated. Based on a systematic review of 190 studies, we conduct a multi-dimensional analysis of eWOM communication. We present the key issues in current and emerging literature and propose important questions for future research.     

Trust in online recommendations: an evolutionary psychology perspective

Electronic word-of-mouth (eWOM) is playing an increasingly important role in influencing consumer behavior, and it represents another opportunity for marketers to build support for their brands. This study explores how the gender of the reader influences how eWOM is perceived. An experiment grounded in evolutionary psychology examines how males and females differ in their perceived trust of eWOM of positive, negative, or mixed valence. The results indicate that females place greater trust than males in eWOM messages. But both genders find eWOM of mixed valence more trustworthy than entirely positive or negative eWOM. A follow-up phenomenological study of male and female consumers who had experienced eWOM shed more light on the experimental findings and offers additional support for their evolutionary basis. Theoretical and practical implications are discussed as well as recommendations for further research.     

How advertising in offline media drives reach of and engagement with brands on Facebook

ABSTRACT

Research investigating the drivers of consumers’ engagement with brands on social media is proliferating. However, little is known about how advertising outside social media drives engagement with brands on social media. This study aims to explore the relation between advertising spend in different offline media (TV, radio, newspapers, magazines, out of home), and reach of and engagement with brand pages on Facebook. The study uses a unique real-life data-set containing information about the Facebook pages of 45 brands for approximately three years combined with Nielsen Advertising Spend data. Results showed that while advertising in offline media influenced organic and viral reach, the number of page likes was directly influenced by advertising on Facebook only. It can be concluded that offline advertising is relevant in driving consumers’ online brand engagement; however, there is a unique set of drivers for organic reach, viral reach and likes.     

Starbucks’ marketing communications strategy on Twitter

Many scholars have found that social media are extraordinarily useful platforms for marketers to engage their stakeholders. Much research has developed frameworks for and proposed empirical guidelines on how brands should communicate and interact with audiences on social media platforms. However, a comprehensive study that is based on a successful and reputable brand is still lacking. The objective of this research was to categorise the types of content used by one of the most admired brands in the world, Starbucks, and evaluate its effectiveness on Twitter. In total, the author studied 565 tweets and retweets and 1392 replies. This research found that Starbucks’ marketing communications strategy includes three types of original and retweeted content: information-sharing, emotion-evoking, and action-inducing content. The author also found six types of replies: information, Apology and suppot, positive comment, question and enquiry, chit-chat, and gratitude. To be successful on the social media platforms, this research suggests that brands should use various types of content, focus on visual content, and prepare for and manage customer interactions appropriately. The findings present useful and practical information for brands that seek to engage audiences using an inexpensive but compelling marketing communications strategy on social media.