Entertainment Communication Decisions, Episode 2: “Earned” Channels

  • Thorsten Hennig-ThurauEmail author
  • Mark B. Houston


In addition to paid and owned communication, “earned” communications channels are essential for success in entertainment. We analyze the role of word of mouth and distinguish between three types of such consumer communication: traditional, social media, and other electronic word of mouth, which are more than substitutes for each other when it comes to influencing an entertainment product’s performance. While word of mouth triggers “informed cascades” of information, we show that uninformed information cascades can also be quite influential. We discuss high chart rankings and pre-release buzz as powerful signals, both of which drive the success of new entertainment products, though at different points in time. We further portray automated recommender systems that process information about consumers’ liking of certain products into an information source that is considered to be valuable by others. The cultural nature of entertainment assigns further importance to judgments by professional reviewers and industry peers, as reflected in reviews and awards, such as the Oscar.


  1. Adomavicius, G., Bamshad, M., Francesco, R., & Tuzhilin, A. (2011). Context-aware recommender systems. AI Magazine, 32.Google Scholar
  2. Amatriain, X., & Basilico, J. (2012). Netflix recommendations: Beyond the 5 stars (Part 2). The Netflix Tech Blog, June 20,
  3. Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of Marketing, 4, 291–295.Google Scholar
  4. Asai, S. (2015). Determinants of demand and price for best-selling novels in paperback in Japan. Journal of Cultural Economics, 40, 375–392.Google Scholar
  5. Austin, B. A. (1989). Immediate seating: A look at movie audiences. California: Wadsworth Pub. Co.Google Scholar
  6. Bandura, A. (1977). Social learning theory. Englewood Cliffs, N.J.: Prentice-Hall.Google Scholar
  7. Bart, P. (2017). Peter Bart: Amazon raises bet on movie business, but rivals still baffled about long-term strategy. Deadline, March 10,
  8. Basuroy, S., Chatterjee, S., & Abraham Ravid, S. (2003). How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of Marketing, 67, 103–117.Google Scholar
  9. Berger, J. (2014). Word of mouth and interpersonal communication: A review and directions for future research. Journal of Consumer Psychology, 24, 586–607.Google Scholar
  10. Berger, J., & Schwartz, E. M. (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48, 869–880.Google Scholar
  11. Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49, 192–205.Google Scholar
  12. Bhattacharjee, S., Gopal, R. D., Lertwachara, K., Marsden, J. R., & Telang, R. (2007). The effect of digital sharing technologies on music markets: A survival analysis of albums on ranking charts. Management Science, 53, 1359–1374.Google Scholar
  13. Bialik, C. (2009). The best worst blurbs of 2008. Gelf Magazine, January 2,
  14. Bikhchandani, S., Hirshleifer, D., & Welch, I. (1998). Learning from the behavior of others: Conformity, fads, and informational cascades. Journal of Economic Perspectives, 12, 151–170.Google Scholar
  15. Bishop, B. (2017). How Netflix is trying to rewrite movie marketing with Bright. The Verge, December 19,
  16. Böll, H. (1963). Ansichten eines Clowns. Cologne: Kiepenheuer & Witsch.Google Scholar
  17. Brew, S. (2016). Movie embargoes: What are they, and why do they matter? Den of Geek, July 15,
  18. Brodnig, I. (2015). Netflix-Produktchef Neil Hunt: ‘Ich weiß das alles über Sie’. profil, December 11,
  19. Buli, L., & Hu, V. (2015). Data science and the music industry: What social media has to do with record sales. hypebot, December 6,
  20. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12, 331–370.Google Scholar
  21. Chen, Y., Liu, Y., & Zhang, J. (2012). When do third-party product reviews affect firm value and what can firms do? The case of media critics and professional movie reviews. Journal of Marketing, 76, 116–134.Google Scholar
  22. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43, 345–354.Google Scholar
  23. Chintagunta, P., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29, 944–957.Google Scholar
  24. Clement, M., Proppe, D., & Rott, A. (2007a). Do critics make bestsellers? Opinion leaders and the success of books. Journal of Media Economics, 20, 77–105.Google Scholar
  25. Clement, M., Christensen, B., Albers, S., & Guldner, S. (2007b). Was bringt ein Oscar im Filmgeschäft? Eine empirische Analyse unter Berücksichtigung des Selektionseffekts. Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 59, 198–220.Google Scholar
  26. Clement, M., Hille, A., Lucke, B., Schmidt-Stölting, C., & Sambeth, F. (2008). Der Einfluss von Rankings auf den Absatz – Eine empirische Analyse der Wirkung von Bestsellerlisten und Rangpositionen auf den Erfolg von Büchern. Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 60, 746–777.Google Scholar
  27. Clement, M., Wu, S., & Fischer, M. (2014). Empirical generalization of demand and supply dynamics for movies. International Journal of Research in Marketing, 31, 207–223.Google Scholar
  28. Corliss, R. (2009). Box-office weekend: Brüno a one-day wonder? Time Magazine, July 13,
  29. Cox, J., & Kaimann, D. (2015). How do reviews from professional critics interact with other signals of product quality? Evidence from the video game industry. Journal of Consumer Behavior, 14, 366–377.Google Scholar
  30. Craig, C. S., Greene, W. H., & Versaci, A. (2015a). E-word of mouth: Early predictor of audience engagement—How pre-release ‘E-WOM’ drives box-office outcomes of movies. Journal of Advertising Research, 55, 62–72.Google Scholar
  31. D’Alessandro, A. (2015b). Tom Hardy Soviet drama ‘Child 44’ bombs at box office: What the hell happened? Deadline, May 1,
  32. D’Alessandro, A. (2017). How ‘Pirates’ & ‘Baywatch’ are casualties of summer franchise fatigue at the domestic B.O. Deadline, May 29,
  33. D’Astous, A., & Colbert, F. (2002). Moviegoers’ consultation of critical reviews: Psychological antecedents and consequences. International Journal of Arts Management, 5, 24–35.Google Scholar
  34. D’Astous, A., Carù, A., Koll, O., & Sigué, S. P. (2005). Moviegoers’ consultation of film reviews in the search for information: A multi-country study. International Journal of Arts Management, 7, 32–45.Google Scholar
  35. De Vany, A., & Lee, C. (2001). Quality signals in information cascades and the dynamics of the distribution of motion picture box office revenues. Journal of Economic Dynamics & Control, 25, 593–614.Google Scholar
  36. Dewan, S., & Ramaprasad, J. (2012). Music blogging, online sampling, and the long tail. Information Systems Research, 23, 1056–1067.Google Scholar
  37. Dhar, T., & Weinberg, C. B. (2015). Measurement of interactions in non-linear marketing models: The effect of critics’ ratings and consumer sentiment on movie demand. International Journal of Research in Marketing, 33, 392–408.Google Scholar
  38. Divakaran, P. K. P., Palmer, A., Alsted Søndergaard, H., & Matkovskyy, R. (2017). Pre-launch prediction of market performance for short lifecycle products using online community data. Journal of Interactive Marketing, 38, 12–28.Google Scholar
  39. Dowd, A. A. (2015). No, I didn’t call your shitty movie a “comedic masterstroke”. A.V. Club, July 27,
  40. Duan, W., Bin, G., & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. Journal of Retailing, 84, 233–242.Google Scholar
  41. Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2010). Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction, 4, 81–173.Google Scholar
  42. Elberse, A., & Eliashberg, J. (2003). Demand and supply dynamics for sequentially released products in international markets: The case of motion pictures. Marketing Science, 22, 329–354.Google Scholar
  43. Eliashberg, J., & Shugan, S. M. (1997). Film critics: Influencers or predictors? Journal of Marketing, 61, 68–78.Google Scholar
  44. Fischer, S. F., Hammerschmidt, M., & Weiger, W. H. (2017). Signals from the echoverse – the informational value of brand buzz dispersion. Proceedings of Winter AMA Conference, 28.Google Scholar
  45. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. [An online version of the book can be found at Mr. Ajzen’s website at].
  46. Follows, S. (2015). How much do Hollywood campaigns for an Oscar cost? January 12,
  47. Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19, 291–313.Google Scholar
  48. Foutz, N. Z., & Jank, W. (2010). Prerelease demand forecasting for motion pictures using functional shape analysis of virtual stock markets. Marketing Science, 29, 568–579.Google Scholar
  49. Freedman, N. (2015). How social media is changing Hollywood. Digital America, April 6,
  50. Fry, Stephen (2010). Two million reasons to be cheerful, November 30,
  51. Gemser, G., Van Oostrum, M., & Leenders, M. A. A. M. (2007). The impact of film reviews on the box office performance of art house versus mainstream motion pictures. Journal of Cultural Economics, 31, 43–63.Google Scholar
  52. Gemser, G., Leenders, Mark A. A. M., & Wijnberg, N. M. (2008). Why some awards are more effective signals of quality than others: A study of movie awards. Journal of Management, 34, 25–54.Google Scholar
  53. Golder, D. (2015). 14 movies that were ‘Not Screened For Critics’, August 5,
  54. Gomez-Uribe, C. A., & Hunt, N. (2015). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6, 13–19.Google Scholar
  55. Goode, L. (2017). Netflix is ditching five-star ratings in favor of a thumbs-up. The Verge, March 16,
  56. Gopinath, S., Chintagunta, P. K., & Venkataraman, S. (2013). Blogs, advertising, and local-market movie box office performance. Management Science, 59, 2635–2654.Google Scholar
  57. Gower, S. (2014). Netflix Prize and SVD. Working Paper.Google Scholar
  58. Hennig-Thurau, T., Walsh, G., & Bode, M. (2003). Exporting media products: Understanding the success and failure of Hollywood movies in Germany. Working Paper, Bauhaus-University of Weimar.Google Scholar
  59. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18, 38–52.Google Scholar
  60. Hennig-Thurau, T., Houston, M. B., & Sridhar, S. (2006a). Can good marketing carry a bad product? Evidence from the motion picture industry. Marketing Letters, 17, 205–219.Google Scholar
  61. Hennig-Thurau, T., Houston, M. B., & Walsh, G. (2006b). The differing roles of success drivers across sequential channels: An application to the motion picture industry. Journal of the Academy of Marketing Science, 34, 559–575.Google Scholar
  62. Hennig-Thurau, T., Marchand, A., & Marx, P. (2012a). Can automated group recommender systems help consumers make better choices? Journal of Marketing, 76, 89–109.Google Scholar
  63. Hennig-Thurau, T., Marchand, A., & Hiller, B. (2012b). The relationship between reviewer judgments and motion picture success: Re-analysis and extension. Journal of Cultural Economics, 36, 249–283.Google Scholar
  64. Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. Journal of the Academy of Marketing Science, 43, 375–394.Google Scholar
  65. (2014). Dave Manning.
  66. Horn, J. (2009). ComicCon’s buzzmakers. Los Angeles Times, July 27,
  67. Houston, M. B., Kupfer, A., Hennig-Thurau, T., & Spann, M. (2018). Pre-release new product consumer buzz. Journal of the Academy of Marketing Science, 46, 338–360.Google Scholar
  68. Hsu, G. (2006a). Evaluative schemas and the attention of critics in the US film industry. Industrial & Corporate Change, 15, 467–496.Google Scholar
  69. Jabr, W., & Zhiqiang, Z. (Eric) (2014). Know yourself and know your enemy: An analysis of firm recommendations and consumer reviews in a competitive environment. MIS Quarterly, 38, 635–654.Google Scholar
  70. Jain, K., & Srinivasan, N. (1991). An empirical assessment of multiple operationalizations of involvement. Advances in Consumer Research, 17, 594–602.Google Scholar
  71. Jannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016). Recommender systems–beyond matrix completion. Communications of the ACM, 59, 94–102.Google Scholar
  72. Jozefowicz, J., Kelley, J., & Brewer, S. (2008). New release: An empirical analysis of VHS/DVD rental success. Atlantic Economic Journal, 36, 139–151.Google Scholar
  73. Karniouchina, E. V. (2011). Impact of star and movie buzz on motion picture distribution and box office revenue. International Journal of Research in Marketing, 28, 62–74.Google Scholar
  74. Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communication. New York: The Free Press.Google Scholar
  75. Kaye, D. (2012). 4 films that failed to live up to their blockbuster Comic-Con buzz. Syfy Wire, December 14,
  76. Kim, H., & Hanssens. D. M. (2017). Advertising and word-of-mouth effects on pre-launch consumer interest and initial sales of experience products. Journal of Interactive Marketing, 37, 57–74.Google Scholar
  77. Kirkham, E. (2015). Hollywood spends $150 million on Oscar campaigns each year. The Philadelphia Inquirer, January 15,
  78. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42, 30–37.Google Scholar
  79. Körte, P. (2009). Filmkritik ist, wenn man von der Brücke in den Fluss spuckt. In T. Hennig-Thurau & V. Henning (Eds.), Guru Talk – Die deutsche Filmindustrie im 21. Jahrhundert (pp. 192–197). Marburg: Schüren.Google Scholar
  80. Krauss, J., Nann, S., Simon, D., Fischbach, K., & Gloor, P. (2008). Predicting movie success and Academy Awards through sentiment and social network analysis. In Proceedings of 16th European Conference on Information Systems (pp. 2026–2037). Galway, Ireland.Google Scholar
  81. Krishnan, V., Narayanashetty, P. K., Nathan, M., Davies, R. T., & Konstan, J. A. (2008). Who predicts better?—Results from an online study comparing humans and an online recommender system. In Proceedings of the 2008 ACM Conference on Recommender Systems (pp. 211–218). New York: ACM.Google Scholar
  82. Kupfer, A.-K., Pähler vor der Holte, N., Kübler, R., & Hennig-Thurau, T. (2018). The role of the partner brand’s social media power in brand alliances. Journal of Marketing, 82, 25–44.Google Scholar
  83. Lampel, J., & Shamsie, J. (2000). Critical push: Strategies for creating momentum in the motion picture industry. Journal of Management, 26, 233–257.Google Scholar
  84. Lasar, M. (2011). Digging into Pandora’s music genome with musicologist Nolan Gasser. Ars Technica, January 13,
  85. Lee, J., Boatwright, P., & Kamakura, W. A. (2003). A Bayesian model for prelaunch sales forecasting of recorded music. Management Science, 49, 179–196.Google Scholar
  86. Legoux, R., Larocque, D., Laporte, S., Belmati, S., & Boquet, T. (2015). The effect of critical reviews on exhibitors’ decisions: Do reviews affect the survival of a movie on screen? International Journal of Research in Marketing, 33, 357–374.Google Scholar
  87. Li, X., & Hitt, L. M. (2008). Self-selection and information role of online product reviews. Information Systems Research, 19, 456–474.Google Scholar
  88. (2017). The award is important in order to bring people to the movie theater. That’s the only principle meaning of any award.
  89. Littleton, C., & Holloway, D. (2017). Jeff Bezos mandates programming shift at Amazon Studios. Variety, September 8,
  90. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70, 74–89.Google Scholar
  91. Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35, 363–388.Google Scholar
  92. Lops, P., de Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender systems handbook (pp. 73–104). New York: Springer.Google Scholar
  93. Lovett, M. J., & Staelin, R. (2016). The role of paid, earned, and owned media in building entertainment brands: Reminding, informing, and enhancing enjoyment. Marketing Science, 35, 142–157.Google Scholar
  94. Luan, Y. J., & Sudhir, K. (2010). Forecasting marketing-mix responsiveness for new products. Journal of Marketing Research, 47, 444–457.Google Scholar
  95. Lynn, F. B., Walker, M. H., & Peterson, C. (2016). Is popular more likeable? Choice status by intrinsic appeal in an experimental music market. Social Psychology Quarterly, 79, 168–180.Google Scholar
  96. Madrigal, A. C. (2014). How Netflix reverse engineered Hollywood. The Atlantic, January 2,
  97. Malhotra, N., & Helmer, E. (2012). Inflation in weekend box office estimates. Applied Economics Letters, 19, 1411–1415.Google Scholar
  98. Marchand, A. (2016). The power of an installed base to combat lifecycle decline: The case of video games. International Journal of Research in Marketing, 33, 140–154.Google Scholar
  99. Marchand, A., Hennig-Thurau, T., & Wiertz, C. (2016). Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success. International Journal of Research in Marketing, 34, 336–354.Google Scholar
  100. Marx, P., Hennig-Thurau, T., & Marchand, A. (2010). Increasing consumers’ understanding of recommender results: A preference-based hybrid algorithm with strong explanatory power. In Proceedings of the fourth ACM Conference on Recommender Systems (pp. 297–300). New York: ACM.Google Scholar
  101. McKenzie, J. (2010). How do theatrical box office revenues affect DVD retail sales? Australian empirical evidence. Journal of Cultural Economics, 34, 159–179.Google Scholar
  102. Meiseberg, B. (2016). The effectiveness of e-tailers’ communication practices in stimulating sales of niche versus popular products. Journal of Retailing, 92, 319–332.Google Scholar
  103. Nelson, R. A., Donihue, M. R., Waldman, D. M., & Wheaton, G. (2001). What’s an Oscar worth? Economic Inquiry, 39, 1–16.Google Scholar
  104. Netflix (2017). Decoding the defenders: Netflix unveils the gateway shows that lead to a heroic binge. Press Release, August 22,
  105. Niraj, R., & Singh, J. (2015). Impact of user-generated and professional critics reviews on Bollywood movie success. Australasian Marketing Journal, 23, 179–187.Google Scholar
  106. O’Connor, M., Cosley, D., Konstan, J. A., & Riedl, J. (2001). PolyLens: A recommender system for groups of users. In Proceedings of the 7th European Conference on Computer-Supported Cooperative Work (pp. 199–218). Dodrecht: Kluwer.Google Scholar
  107. Panaligan, R., & Chen, A. (2013). Quantifying movie magic with Google Search. White Paper, Google.Google Scholar
  108. Papies, D., & van Heerde, H. (2015). How the Internet has changed the marketing of entertainment goods: The case of the music industry. Working Paper, Marketing Science Institute.Google Scholar
  109. Pardoe, I., & Simonton, D. K. (2008). Applying discrete choice models to predict academy award winners. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171, 375–394.Google Scholar
  110. Phipps, K. (2005). The ghost of David Manning will have his revenge. AV Club, August 3,
  111. Rao, V. R., Ravid, A. S., Gretz, R.T., Chen, J., & Basuroy, S. (2017). The impact of advertising content on movie revenues. Marketing Letters, 28, 341–355.Google Scholar
  112. Redelmeier, D. A., & Singh, S. M. (2001). Survival in Academy Award-winning actors and actresses. Annals of Internal Medicine, 134, 955–962.Google Scholar
  113. Reinstein, D. A., & Snyder, C. M. (2004). The influence of expert reviews on consumer demand for experience goods: A case study of movie critics. The Journal of Industrial Economics, 53, 27–51.Google Scholar
  114. Rosario, A. B., Sotgiu, F., De Valck, K., & Bijmolt, T. H. A. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Research in Marketing, 53, 297–318.Google Scholar
  115. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854–856.Google Scholar
  116. Salganik, M. J., & Watts, D. J. (2008). Leading the herd astray: An experimental study of self-fulfilling prophecies in an artificial cultural market. Social Psychology Quarterly, 71, 338–355.Google Scholar
  117. Schmidt-Stölting, C., Blömeke, E., & Clement, M. (2011). Success drivers of fiction books: An empirical analysis of hardcover and paperback editions in Germany. Journal of Media Economics, 24, 24–47.Google Scholar
  118. Shone, T. (2004). Blockbuster. New York: Free Press.Google Scholar
  119. Smith, S. P., & Smith, V. K. (1986). Successful movies: A preliminary empirical analysis. Applied Economics, 18, 501–507.Google Scholar
  120. Soderstrom, S. B., Uzzi, B., Rucker, D. D., Fowler, J. H., & Diermeier, D. (2016). Timing matters: How social influence affects adoption pre- and post-product release. Sociological Science, 3, 915–939.Google Scholar
  121. Sorensen, A. T. (2007). Bestseller lists and product variety: The case of book sales. Working Paper, Stanford GSB Research Paper No. 1878.Google Scholar
  122. Sorensen, A. T., & Rasmussen, S. J. (2004). Is any publicity good publicity? A note on the impact of book reviews. Working Paper, Stanford University.Google Scholar
  123. Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing, 66, 15–37.Google Scholar
  124. Strause, J. (2016). How ‘My Big Fat Greek Wedding’ became an indie phenomenon. Hollywood Reporter, March 25,
  125. Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58, 696–707.Google Scholar
  126. The Economist (2017). How to devise the perfect recommendation algorithm.
  127. tickld (2016). 31 fascinating things most people don’t know about Netflix.
  128. TV Tropes (2013). Not screened for critics. TV Tropes,
  129. Wohlfeil, M., & Whelan, S. (2008). Confessions of a movie-fan: Introspection into a consumer’s experiential consumption of ‘Pride and Prejudice’. Proceedings of European ACR Conference (pp. 137–143).Google Scholar
  130. Wyatt, R. O., & Badger, D. P. (1984). How reviews affect interest in and evaluation of films. Journalism Quarterly, 61, 874–878.Google Scholar
  131. Wyatt, R. O., & Badger, D. P. (1990). Effects of information and evaluation in film criticism. Journalism Quarterly, 67, 359–368.Google Scholar
  132. Xiong, G., & Bharadwaj, S. (2014). Prerelease buzz evolution patterns and new product performance. Marketing Science, 33, 401–421.Google Scholar
  133. You, Y., Vadakkepatt, G. G., & Joshi, A. M. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing, 79, 19–39.Google Scholar
  134. Zhu, F., & Zhang, X. M. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74, 133–148.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of MünsterMünsterGermany
  2. 2.The Neeley School of BusinessTexas Christian UniversityFort WorthUSA

Personalised recommendations