Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies

Hennig‐Thurau, Thorsten; Wiertz, Caroline; Feldhaus, Fabian

doi:10.1007/s11747-014-0388-3

Cited by 333 publications

(266 citation statements)

References 44 publications

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“…Extant literature often compares the valence (i.e., overall positivity or negativity) of new product reviews (Ludwig et al, 2013;You, Vadakkepatt, and Joshi, 2015), yet empirical findings linking the valence to innovation diffusion have been mixed. Some studies suggest that negative online reviews affect new product evaluation by potential adopters more significantly and predict sales more accurately than positive online reviews (Floyd, Freling, Alhoqail, Cho, and Freling, 2014;Hennig-Thurau, Wiertz, and Feldhaus, 2015). Others, however, document that negative reviews are not damaging to a potential adopter's evaluation of new products (Kim and Gupta, 2012) and under certain circumstances even increase sales by boosting potential adopters' awareness of an unknown brand (Berger, Sorensen, and Rasmussen, 2010).…”

Section: Introductionmentioning

confidence: 99%

Social Functions of Anger: A Competitive Mediation Model of New Product Reviews

Xiao¹,

Zhang²,

Cervone³

2017

J of Product Innov Manag

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Negative online product reviews generated by current users can influence potential adopters' new product evaluation and set up diffusion barriers. Integrating the social functions of emotion theory with the innovation diffusion context, this research proposes that the emotional intensity expressed in angry online reviews influences potential adopters' new product evaluation through two competitive routes (i.e., in an opposite direction): perceived problem seriousness in the review and perceived rationality of the reviewer, thus establishing two simultaneous mediations in the conceptual framework. Furthermore, this research suggests that the two mediations are moderated by product innovativeness. This investigation was completed in three experiments with consumers recruited via Amazon Mechanical Turk (MTurk). Study 1 manipulated anger intensity in two levels expressed in a single negative review, using a smartphone as the reviewed new product. Study 2 employed a multi‐review setting, including negative, neutral, and positive reviews, using an E‐reader as the reviewed new product. The multi‐review setting was also adopted in Study 3, where three levels of anger were manipulated. Three studies adopted an incrementally versus really new product version to manipulate innovativeness. Combined findings of the three studies support the proposed dual mediation processes. In addition, results show that the mediation effect of problem seriousness in the review is stronger for a really new product than an incrementally new product. This research extends the underpinning of innovation diffusion literature and provides managerial implications for new product launch management. First, it identifies that anger intensity expressed in a new product review socially influences a potential adopter's concurrent appraisals of the reviewed product and the reviewer. The two appraisal routes mediate the impact of anger intensity on new product evaluation in opposite directions. Second, the research explores and identifies which mediation route is more influential on new product evaluation when product innovativeness varies.

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Section: Introductionmentioning

confidence: 99%

Social Functions of Anger: A Competitive Mediation Model of New Product Reviews

Xiao¹,

Zhang²,

Cervone³

2017

J of Product Innov Manag

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“…According to Gopaldas (2014), sentiment fuels market dynamics, institutional changes, and economic transformations. In big data settings, consumer research that draws on psycholinguistic concepts (Pennebaker, Mehl, and Niederhoffer 2003) has assessed the impact of valence words on behaviors (Berger, Sorensen, and Rasmussen 2010;Hennig-Thurau, Wiertz and Feldhaus 2014;Ludwig et al 2013). However, we posit that these valenced words mask the effects of further language granularities, such as the strength with which consumers express their sentiment (Thelwall et al 2010).…”

Section: Conceptual Foundationsmentioning

confidence: 93%

Unveiling What Is Written in the Stars: Analyzing Explicit, Implicit, and Discourse Patterns of Sentiment in Social Media

Ordenes

Ludwig

Ruyter

et al. 2017

Journal of Consumer Research

194

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Deciphering consumers’ sentiment expressions from big data (e.g., online reviews) has become a managerial priority to monitor product and service evaluations. However, sentiment analysis, the process of automatically distilling sentiment from text, provides little insight regarding the language granularities beyond the use of positive and negative words. Drawing on speech act theory, this study provides a fine-grained analysis of the implicit and explicit language used by consumers to express sentiment in text. An empirical text-mining study using more than 45,000 consumer reviews demonstrates the differential impacts of activation levels (e.g., tentative language), implicit sentiment expressions (e.g., commissive language), and discourse patterns (e.g., incoherence) on overall consumer sentiment (i.e., star ratings). In two follow-up studies, we demonstrate that these speech act features also influence the readers’ behavior and are generalizable to other social media contexts, such as Twitter and Facebook. We contribute to research on consumer sentiment analysis by offering a more nuanced understanding of consumer sentiments and their implications.

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“…A brand or product ad can become trendy in the blink of an eye on Twitter, which has a huge impact on consumer attitudes and purchase decisions (Hennig-Thurau et al, 2015;Jin & Phua, 2014). As a 2016 report by Matejic highlights, "when to communicate on social media is as important as what [emphasis added] you communicate."…”

Section: Methods Objectivementioning

confidence: 99%

“…For example, more than 1.5 million businesses have developed Facebook fan pages for brand communication purposes (Xie & Lee, 2015). Twitter, used as a form of microblogging word of mouth, also greatly affects early product adoption behaviors (Hennig-Thurau, Wiertz & Feldhaus, 2015). In order to retrieve valuable information generated from consumers' social media use, advertisers are becoming more interested in investing in social media and marketing analytics.…”

Section: Overview Social Media Advertising and Analyticsmentioning

confidence: 99%

Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study

Lou

2017

Journal of Advertising Education

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This paper presents a class case study of an assignment that asked students to use a Twitter follower report to design a Twitter advertising campaign. The purpose of this case study is to immerse students in a real social media environment and help them become familiar with analyzing social media data to develop advertising campaigns. Students' interview responses suggest that incorporating a project that requires social media analytics techniques in an advertising class can help them better understand the role of secondary research and database analysis in developing consumer profiles and making campaign decisions. The findings also suggest that students have a strong desire to work with secondary data in designing social media advertising campaigns. The advantages of data analytics should be further explored in advertising campaign classes to help students become successful campaign designers. Limitations and future research direction are also discussed.

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Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link:

Cited by 333 publications

References 44 publications

Social Functions of Anger: A Competitive Mediation Model of New Product Reviews

Social Functions of Anger: A Competitive Mediation Model of New Product Reviews

Unveiling What Is Written in the Stars: Analyzing Explicit, Implicit, and Discourse Patterns of Sentiment in Social Media

Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study

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