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Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies
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“Hennig-Thurau et al. (2015), show that while garnering positive WOM has little to no effect on consumer preferences, negative WOM can have a negative effect on consumer preferences.”
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Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D. J., & Tversky, A. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511609220.014 · Cited by 32,720
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Organizational Information Requirements, Media Richness and Structural Design
Daft, R. L., & Lengel, R. H. (1986) · Management Science · doi:10.1287/mnsc.32.5.554 · Cited by 9,868
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) · Journal of Interactive Marketing · doi:10.1002/dir.10073 · Cited by 5,916
Self-citationAdvances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501
Research works printed but not linked: 1
- Hayes, D. (2002). Tentpoles fade fast after opening ad blast. Variety Magazine, 4, 2002.
Cited by
Sorted by citations
The future of social media in marketing
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00695-1 · Cited by 1,893
“2019) , as well as how it interacts with other forms of media such as television (e.g., Schweidel 2016, 2019) and affects new product adoption through diffusion of information mechanisms (e.g., Hennig-Thurau et al.”
BackgroundInfluentialBabić Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. A. (2016) · Journal of Marketing Research · doi:10.1509/jmr.14.0380 · Cited by 1,073
“Whereas operationalizations using relative terms, such as the ratio of positive tweets (e.g., Hennig-Thurau, Wiertz, and Feldhaus 2014) or the percentage of one-star ratings (e.g., Chen, Wang, and Xie 2011), are a nonconfounded representation of sentiment (positive or negative valence),…”
BackgroundMethodUniting the Tribes: Using Text for Marketing Insight
Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020) · Journal of Marketing · doi:10.1177/0022242919873106 · Cited by 742
Xia, L., Shin, H., & Burns, A. C. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.04.042 · Cited by 539
Chae, B. (. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.037 · Cited by 481
Rosario, A. B., Valck, K. D., & Sotgiu, F. (2019) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00706-1 · Cited by 590
Comparing automated text classification methods
Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.09.009 · Cited by 399
Marchand, A., & Hennig‐Thurau, T. (2013) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2013.05.001 · Cited by 401
Self-citationSwani, K., Milne, G. R., Brown, B. P., Assaf, A. G., & Donthu, N. (2016) · Industrial Marketing Management · doi:10.1016/j.indmarman.2016.07.006 · Cited by 345
“…their customers – it enables marketers to influence brand outcomes and purchase decisions through fan promotion and popularization of brand content (De Vries, Gensler, and Leeflang, 2012; Kumar and Mirchandani, 2012; Hennig-Thurau, Wiertz, and Feldhaus, 2015; Nelson-Field, Riebe, and Sharp, 2012).”
BackgroundSocial media and the formation of organizational reputation
Etter, M., Ravasi, D., & Colleoni, E. (2019) · Academy of Management Review · doi:10.5465/amr.2014.0280 · Cited by 428
Vocal Minority and Silent Majority: How Do Online Ratings Reflect Population Perceptions of Quality1
Gao, G. G., Greenwood, B. N., Agarwal, R., & McCullough, J. S. (2015) · MIS Quarterly · doi:10.2139/ssrn.2629837 · Cited by 279
Unstructured data in marketing
Balducci, B., & Marinova, D. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0581-x · Cited by 300
Hennig‐Thurau, T., Hofacker, C. F., & Bloching, B. (2013) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2013.09.005 · Cited by 236
Self-citationA multidisciplinary perspective of big data in management research
Sheng, J., Amankwah‐Amoah, J., & Wang, X. (2017) · International Journal of Production Economics · doi:10.1016/j.ijpe.2017.06.006 · Cited by 238
The Digital Divide Among Twitter Users and Its Implications for Social Research
Blank, G. (2016) · Social Science Computer Review · doi:10.1177/0894439316671698 · Cited by 225
“Commercial predictions are typically very short term, for example, opening weekend gross sales (Hennig-Thurau et al., 2015) or next day album sales (Frick et al., 2014).”
BackgroundFraccastoro, S., Gabrielsson, M., & Pullins, E. B. (2020) · International Business Review · doi:10.1016/j.ibusrev.2020.101776 · Cited by 263
Villarroel Ordenes, F., Ludwig, S., de Ruyter, K., Grewal, D., & Wetzels, M. (2017) · Journal of Consumer Research · doi:10.1093/jcr/ucw070 · Cited by 245
Shankar, V., Grewal, D., Sunder, S., Fossen, B. L., Peters, K., & Agarwal, A. (2021) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2021.09.005 · Cited by 303
Kumar, V., Choi, J., & Greene, M. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0484-7 · Cited by 196
de Vries, L., Gensler, S., & Leeflang, P. S. H. (2017) · Journal of Marketing · doi:10.1509/jm.15.0178 · Cited by 260
“…Sethuraman, Tellis, and Briesch 2011; Srinivasan, Vanhuele, and Pauwels 2 / Journal of Marketing, September 2017 2010), C2C social messages (e.g., Hennig-Thurau, Wiertz, and Feldhaus 2015), and F2C social messages (e.g., Goh, Heng, and Lin 2013; Hutter et al. 2013; Kumar et al. 2016; Rishika et…”
BackgroundA Meta-Analysis of the Effects of Brands’ Owned Social Media on Social Media Engagement and Sales
Liadeli, G., Sotgiu, F., & Verlegh, P. W. J. (2022) · Journal of Marketing · doi:10.1177/00222429221123250 · Cited by 237
“Important distinctions between social networks (e.g., Instagram, Facebook) and microblogs (e.g., Twitter, Sina Weibo) are audience expectations and goals (e.g., Hennig-Thurau, Wiertz, and Feldhaus 2015; Marchand, Hennig-Thurau, and Wiertz 2017).”
BackgroundWhat Are Likes Worth? A Facebook Page Field Experiment
Mochon, D., Johnson, K., Schwartz, J., & Ariely, D. (2017) · Journal of Marketing Research · doi:10.1509/jmr.15.0409 · Cited by 162
“…(Godes and Mayzlin 2004; Stephen andGalak 2012), blogs (Dhar and Chang 2009;Onishi and Manchanda 2012; Stephen and Galak 2012), social networks (Hennig-Thurau, Wiertz, and Feldhaus 2014; Rui, Liu, and Whinston 2013; Trusov, Bucklin, and Pauwels 2009), review sites (Archak, Ghose, and Ipeirotis…”
BackgroundHansen, N., Kupfer, A.-K., & Hennig‐Thurau, T. (2018) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.08.001 · Cited by 164
“…systematic differences between traditional, face-to-face word of mouth and its digital forms, such as articulations on review/retail sites (consumer reviews or electronic word of mouth) and those on social media platforms (social media or microblogging word of mouth) (Hennig-Thurau et al., 2015).”
BackgroundSelf-citationOnline sentiment analysis in marketing research: a review
Rambocas, M., & Pacheco, B. G. (2018) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-05-2017-0030 · Cited by 170
Effects of the intensity of use of social media on brand equity
Stojanović, I., Andreu, L., & Pérez, R. C. (2018) · European Journal of Management and Business Economics · doi:10.1108/ejmbe-11-2017-0049 · Cited by 211
“While social media researchers have focussed primarily on analysing particular social networks in isolation, such as Twitter (Hennig-Thurau et al., 2015), Facebook (De Vries et al.”
BackgroundRabbanee, F. K., Roy, R., & Spence, M. T. (2020) · European Journal of Marketing · doi:10.1108/ejm-03-2018-0221 · Cited by 193
Marchand, A., Hennig‐Thurau, T., & Wiertz, C. (2016) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.09.003 · Cited by 162
Self-citationCasaló, L. V., Flavián, C., & Ibáñez‐Sánchez, S. (2017) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2016.0360 · Cited by 160
Exploring the effect of user engagement in online brand communities: Evidence from Twitter
Ibrahim, N. F., Wang, X., & Bourne, H. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.03.005 · Cited by 148
Song, T., Huang, J., Tan, Y., & Yu, Y. (2019) · Information Systems Research · doi:10.1287/isre.2018.0797 · Cited by 148
Dhaoui, C., & Webster, C. M. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.06.005 · Cited by 138
The power of the “like” button: The impact of social media on box office
Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. J. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.11.002 · Cited by 133
Social media in marketing research: Theoretical bases, methodological aspects, and thematic focus
Li, F., Larimo, J. A., & Leonidou, L. C. (2022) · Psychology and Marketing · doi:10.1002/mar.21746 · Cited by 130
Marketing ecosystem: An outside-in view for sustainable advantage
Zhang, J. Z., & Watson, G. F. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2020.04.023 · Cited by 149
How does brand-related user-generated content differ across social media? Evidence reloaded
Roma, P., & Aloini, D. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.11.055 · Cited by 154
Liu, X. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.02.021 · Cited by 140
Qualitative research in marketing: what can academics do better?
Crick, J. M. (2021) · Journal of Strategic Marketing · doi:10.1080/0965254x.2020.1743738 · Cited by 172
“If scholars choose to manually-code their data, they are recommended to use a robust system, in which they code their key and sub-themes, such as using numbers, letters, or colours – whatever is clearest for them (Hennig-Thurau et al., 2015; Miles & Huberman, 1994; Walker et al., 2017).”
MethodSantana, A. A., & Gil, S. M. (2017) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2017.02.003 · Cited by 137
Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?
Kübler, R. V., Colicev, A., & Pauwels, K. H. (2019) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2019.08.001 · Cited by 121
Shen, Z. (2021) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-10-2019-0161 · Cited by 151
“Thus, eWOM is defined as “any positive or negative statement is made available to a multitude of people and institutions via the Internet” (Hennig et al., 2015, p. 39).”
BackgroundAleti, T., Pallant, J. I., Tuan, A., & van Laer, T. (2019) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2019.03.003 · Cited by 109
Creators, multipliers, and lurkers: who contributes and who benefits at online review sites
Munzel, A., & Kunz, W. H. (2014) · Journal of service management · doi:10.1108/josm-04-2013-0115 · Cited by 106
Vilnai‐Yavetz, I., & Levina, O. A. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.10.034 · Cited by 99
“Extrinsic and Intrinsic Motives for Sharing E-Business Content: Self-Reports versus Experimental Findings As noted earlier, previous findings (Hennig-Thurau et al., 2014) support the notion that when share buttons for e-business content are available, some proportion of users will click them.”
BackgroundResultInfluentialSay what? How the interplay of tweet readability and brand hedonism affects consumer engagement
Davis, S. W., Horváth, C., Gretry, A., & Belei, N. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.01.071 · Cited by 99
Battle of the Brand Fans: Impact of Brand Attack and Defense on Social Media
Ilhan, B. E., Kübler, R. V., & Pauwels, K. H. (2018) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2018.01.003 · Cited by 105
Online to Offline: The Impact of Social Media on Offline Sales in the Automobile Industry
Wang, Y.-Y., Guo, C., Susarla, A., & Sambamurthy, V. (2017) · Information Systems Research · doi:10.1287/isre.2020.0984 · Cited by 96
Fetais, A. H. M. A., Algharabat, R., Aljafari, A. M., & Rana, N. P. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10264-7 · Cited by 95
Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry
Mariani, M. M., & Borghi, M. (2020) · International Journal of Electronic Commerce · doi:10.1080/10864415.2020.1806464 · Cited by 94
“The latter ones enable potential, actual, or former consumers to articulate and share their opinions and perceptions about products, services, experiences, brands, and companies on the Internet [62].”
BackgroundInfluentialWu, L., Dodoo, N. A., Wen, T. J., & Li, K. (2021) · International Journal of Advertising · doi:10.1080/02650487.2021.1920218 · Cited by 88
#IHateYourBrand: adaptive service recovery strategies on Twitter
Abney, A. K., Pelletier, M. J., Ford, T.-R. S., & Horky, A. B. (2017) · Journal of Services Marketing · doi:10.1108/jsm-02-2016-0079 · Cited by 85
Governing social media: the emergence of hybridised boundary objects
Arnaboldi, M., AZZONE, G. F., & Sidorova, Y. (2017) · Accounting Auditing & Accountability Journal · doi:10.1108/aaaj-07-2015-2132 · Cited by 87
“Social media started to spread through personal use, but now have become an instrument at business level (Richardson, 2009; Jin, 2012; Chikandiwa et al., 2013; Guinan et al., 2014), and was first used in the fields of communication and marketing (Gopinath et al., 2014; Hennig-Thurau et al., 2015).”
BackgroundMethodInfluentialSchivinski, B., Muntinga, D. G., Pontes, H. M., & Łukasik, P. (2019) · Journal of Strategic Marketing · doi:10.1080/0965254x.2019.1572641 · Cited by 105
The effect of social media interactions on customer relationship management
Maecker, O., Barrot, C., & Becker, J. U. (2016) · BuR - Business Research · doi:10.1007/s40685-016-0027-6 · Cited by 108
“For instance, consumers can like, rate, review, or comment on the brand (Hennig-Thurau et al. 2004, 2015).”
Bartschat, M., Cziehso, G. P., & Hennig‐Thurau, T. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.11.035 · Cited by 105
Self-citationOnline fashion shopping paradox: The role of customer reviews and facebook marketing
Kawaf, F., & Istanbulluoglu, D. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.02.017 · Cited by 80
Dimitriu, R., & Guesalaga, R. (2017) · Psychology and Marketing · doi:10.1002/mar.21007 · Cited by 79
Hussain, S., Song, X., & Niu, B. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03055 · Cited by 116
The impact of online movie word-of-mouth on consumer choice
Chiu, Y.-L., Chen, K., Wang, J., & Hsu, Y. (2019) · International Marketing Review · doi:10.1108/imr-06-2018-0190 · Cited by 86
Consumer-based strategy: using multiple methods to generate consumer insights that inform strategy
Hamilton, R. W. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0476-7 · Cited by 77
“Observational data (such as publicly available online data; Hennig-Thurau et al. 2015; Thompson et al. 2015), experiments (Heidenreich et al. 2015; Xie et al. 2015), surveys (Brocato et al. 2015; Gallan et al. 2013), customer purchase data (Carter and Curry 2013; Papies et al. 2011), and…”
MethodMarolt, M., Zimmermann, H., Žnidaršič, A., & Pucihar, A. (2020) · Journal of theoretical and applied electronic commerce research · doi:10.4067/s0718-18762020000200104 · Cited by 89
“In this context, relationships with customers became more challenging to control and manage [49].”
BackgroundTechnology in the 21st century: New challenges and opportunities
Sheng, J., Amankwah‐Amoah, J., & Wang, X. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.06.009 · Cited by 73
Making sense of consumers’ tweets
Pantano, E., Giglio, S., & Dennis, C. (2018) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-07-2018-0127 · Cited by 73
A roadmap for driving customer word-of-mouth
Keiningham, T. L., Rust, R. T., Larivière, B., Aksoy, L., & Williams, L. (2018) · Journal of service management · doi:10.1108/josm-03-2017-0077 · Cited by 79
Cheng, M., Liu, J., Qi, J., & Wan, F. (2021) · Industrial Marketing Management · doi:10.1016/j.indmarman.2021.07.001 · Cited by 82
Empirical generalizations on the impact of stars on the economic success of movies
Hofmann, J., Clément, M., Völckner, F., & Hennig‐Thurau, T. (2016) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.08.006 · Cited by 69
Self-citationTopaloglu, O., & Dass, M. (2019) · Decision Sciences · doi:10.1111/deci.12378 · Cited by 70
The economics of movies (revisited): A survey of recent literature
McKenzie, J. (2022) · Journal of Economic Surveys · doi:10.1111/joes.12498 · Cited by 69
Debates and assumptions about motion picture performance: a meta-analysis
Carrillat, F. A., Legoux, R., & Hadida, A. L. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0561-6 · Cited by 78
“…likely apply to other experiential products, such as books, music, video games, live shows, fine dining, travels, or education (e.g., Eliashberg and Sawhney 1994; Hennig-Thurau et al. 2015), for which ingredient brands, opinions of experts, and reviews by other consumers provide important cues.”
BackgroundArtificial intelligence and empirical consumer research: A topic modeling analysis
Vaid, S., Puntoni, S., & Khodr, A. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114110 · Cited by 77
Harnessing stakeholder input on Twitter: A case study of short breaks in Spanish tourist cities
Bigné, E., Oltra, E., & Andreu, L. (2019) · Tourism Management · doi:10.1016/j.tourman.2018.10.013 · Cited by 56
Attitude toward social media reviews and restaurant visit intention: a Bangladeshi perspective
Popy, N. N., & Bappy, T. A. (2020) · South Asian Journal of Business Studies · doi:10.1108/sajbs-03-2020-0077 · Cited by 76
Effects of online brand communities on millennials' brand loyalty in the fashion industry
Ozuem, W., Willis, M., Howell, K. E., Helal, G., Ranfagni, S., & Lancaster, G. (2021) · Psychology and Marketing · doi:10.1002/mar.21466 · Cited by 74
The bright and dark sides of humorous response to online customer complaint
Shin, H., & Larson, L. R. L. (2020) · European Journal of Marketing · doi:10.1108/ejm-08-2018-0522 · Cited by 65
Talk Bursts: The Role of Spikes in Prerelease Word-of-Mouth Dynamics
Gelper, S., Peres, R., & Eliashberg, J. (2018) · Journal of Marketing Research · doi:10.1177/0022243718817007 · Cited by 63
Creating identification with brand communities on Twitter
López, M., Piñero, M. S., & Moyeda-Carabaza, A. A. (2017) · Internet Research · doi:10.1108/intr-12-2013-0258 · Cited by 60
Jung, S. H., & Jeong, Y. J. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101409 · Cited by 70
Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales
Yu, Y., Yang, Y., Huang, J., & Tan, Y. (2023) · MIS Quarterly · doi:10.25300/misq/2022/16600 · Cited by 55
“Consumers tend to search the review for diagnostic information that they have not considered (Hennig-Thurau et al. 2015), which requires a higher level of cognitive effort (Yin et al.”
InfluentialZhao, K., Zhang, P., & Lee, H.-M. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113684 · Cited by 64
Huang, V., & Yeo, T. E. D. (2017) · Internet Research · doi:10.1108/intr-08-2016-0248 · Cited by 55
Making new products go viral and succeed
Nguyen, H. T., & Chaudhuri, M. (2018) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2018.09.007 · Cited by 63
Bazi, S., Hajli, A., Hajli, N., Shanmugam, M., & Lin, X. (2019) · Information Technology and People · doi:10.1108/itp-09-2018-0415 · Cited by 69
The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets
Xu, P., Dredze, M., & Broniatowski, D. A. (2020) · Journal of Medical Internet Research · doi:10.2196/21499 · Cited by 53
Strategic Decision Making in the Digital Age: Expert Sentiment and Corporate Capital Allocation
Nauhaus, S., Luger, J., & Raisch, S. (2021) · Journal of Management Studies · doi:10.1111/joms.12742 · Cited by 67
Self-presentation, privacy and electronic word-of-mouth in social media
Pasternak, O., Veloutsou, C. A., & Morgan‐Thomas, A. (2017) · Journal of Product & Brand Management · doi:10.1108/jpbm-04-2016-1150 · Cited by 56
Data properties and the performance of sentiment classification for electronic commerce applications
Choi, Y. T., & Lee, H. (2017) · Information Systems Frontiers · doi:10.1007/s10796-017-9741-7 · Cited by 53
Iyer, P., Yazdanparast, A., & Strutton, D. R. (2017) · Journal of Consumer Marketing · doi:10.1108/jcm-11-2015-1605 · Cited by 53
Predicting information diffusion on Twitter – Analysis of predictive features
Hoang, T. B. N., & Mothe, J. (2017) · Journal of Computational Science · doi:10.1016/j.jocs.2017.10.010 · Cited by 53
“Other examples could be found in marketing [2].”
BackgroundHouston, M. B., Kupfer, A.-K., Hennig‐Thurau, T., & Spann, M. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0572-3 · Cited by 51
Self-citationMai, E. S., & Liao, Y. (2021) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-05-2021-0269 · Cited by 50
Ryoo, J. H., Wang, X., & Lu, S. (2020) · Journal of Marketing · doi:10.1177/0022242920937703 · Cited by 49
Ruiz‐Equihua, D., Romero, J., & Casaló, L. V. (2019) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2019.1630698 · Cited by 53
Azer, J., & Ranaweera, C. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.068 · Cited by 51
Bernabé-Moreno, J., Tejeda-Lorente, Á., Porcel, C., Fujita, H., & Herrera‐Viedma, E. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.12.033 · Cited by 53
A Temporal Study of the Effects of Online Opinions: Information Sources Matter
Huang, J., Boh, W. F., & Goh, K. H. (2017) · Journal of Management Information Systems · doi:10.1080/07421222.2017.1394079 · Cited by 49
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Guan, J., Lau, Y., Yang, H., & Ren, L. (2021) · Young Consumers Insight and Ideas for Responsible Marketers · doi:10.1108/yc-12-2020-1272 · Cited by 46
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Metadata
- Authors
- Hennig-Thurau, T., Wiertz, C., & Feldhaus, F.
- Affiliations
- City, University of London (GB)
- University of Münster (DE)
- Corresponding author
- Wiertz, Caroline
- Venue
- Journal of the Academy of Marketing Science
- Published
- 2014-06-01
- Volume
- 43
- Issue
- 3
- Pages
- 375-394
- ISSN
- 0092-0703 (ISSN-L)
- 1552-7824
- OpenAlex
- W3124914435
- Cited by
- 434
- References
- 54
- Citations by year
- 2026: 13 · 2025: 28 · 2024: 33 · 2023: 38 · 2022: 47 · 2021: 44 · 2020: 38 · 2019: 48 · 2018: 54 · 2017: 49 · 2016: 19 · 2015: 10 · 2014: 4
- FWCI
- 89.02
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of the Academy of Marketing Science (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- City Research Online (City University London) (acceptedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Social Sciences › Communication › Media and Digital Communication
- Keywords
- negativity bias, purchase decision
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