Electronic Markets

, Volume 28, Issue 1, pp 93–109 | Cite as

Commerce-oriented revenue models for content providers: an experimental study of commerciality’s effect on credibility

  • Benedikt BergerEmail author
Research Paper


If content providers want to build successful businesses on the Internet, they have to establish viable revenue models online. Because selling content or ads is less profitable online than offline, content providers have begun to generate revenues by selling products or services related to their content. However, this incentivizes content providers to increase sales by manipulating their content and thus may harm the content’s credibility. We conducted a vignette-based online experiment to test the effect of content providers’ revenue models on the credibility of two different types of content. Although our results revealed significant differences between revenue models for one of the content types, we did not find evidence that users distrust content providers employing commerce-oriented revenue models. Our findings shed light on the relationship between credibility and monetization of content on the Internet and provide helpful insights for practitioners in the media industry regarding optimal revenue generation strategies.


Content credibility Content providers Revenue models Affiliate marketing Content-driven commerce 

JEL classification

M15 M31 



This article is based on the third essay of the author’s dissertation. The author thanks the editors and two anonymous reviewers for their helpful comments during the review process. He is also grateful to Thu Mai Nguyen for first insights into credibility research and to PACIS 2016 participants for valuable feedback on an earlier version of this manuscript.


  1. Abnett, K. (2015). Condé Nast to transform Style.Com into global e-commerce player. Accessed 11 Nov 2015.
  2. Aguinis, H., & Bradley, K. J. (2014). Best practice recommendations for designing and implementing experimental vignette methodology studies. Organizational Research Methods, 17(4), 351–371.CrossRefGoogle Scholar
  3. Alexander, J., & Parsehian, S. (2014). Content-driven commerce: Differentiating and driving sales with content in commerce. Short Hills: FitForCommerce.Google Scholar
  4. Amit, R., & Zott, C. (2001). Value creation in e-business. Strategic Management Journal, 22(6–7), 493–520.CrossRefGoogle Scholar
  5. Appelman, A., & Sundar, S. S. (2016). Measuring message credibility: Construction and validation of an exclusive scale. Journalism and Mass Communication Quarterly, 93(1), 59–79.CrossRefGoogle Scholar
  6. Berger, B. (2016). To believe or not to believe - Investigating the effect of commerce-oriented media revenue models on content credibility. In 20th Pacific Asia Conference on Information Systems, Chiayi, Taiwan, June 27–July 1, 2016 (Paper 257).Google Scholar
  7. Berger, B., & Hess, T. (2015). The convergence of content and commerce: Exploring a new type of business model. In 21st Americas Conference on Information Systems, Fajardo, Puerto Rico, August 13–15, 2015 (Paper 16).Google Scholar
  8. Berger, B., Matt, C., Steininger, D. M., & Hess, T. (2015). It is not just about competition with “Free”: Differences between content formats in consumer preferences and willingness to pay. Journal of Management Information Systems, 32(3), 105–128.CrossRefGoogle Scholar
  9. Cacioppo, J. T., & Petty, R. E. (1984). The elaboration likelihood model of persuasion. Advances in Consumer Research, 11(1), 673–675.Google Scholar
  10. Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766.CrossRefGoogle Scholar
  11. Charness, G., Gneezy, U., & Kuhn, M. A. (2012). Experimental methods: Between-subject and within-subject design. Journal of Economic Behavior & Organization, 81(1), 1–8.CrossRefGoogle Scholar
  12. Cheng, T., Brisson, H., & Hay, M. (2014). The role of content in the consumer decision making process. New York: The Nielsen Company.Google Scholar
  13. Chung, C. J., Nam, Y., & Stefanone, M. A. (2012). Exploring online news credibility: The relative influence of traditional and technological factors. Journal of Computer-Mediated Communication, 17(2), 171–186.CrossRefGoogle Scholar
  14. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.CrossRefGoogle Scholar
  15. Dennis, A. R., Robert, L. P., Curtis, A. M., Kowalczyk, S. T., & Hasty, B. K. (2011). Research note—Trust is in the eye of the beholder: A vignette study of postevent behavioral controls’ effects on individual trust in virtual teams. Information Systems Research, 23(2), 546–558.CrossRefGoogle Scholar
  16. Dörr, J., Wagner, T., Benlian, A., & Hess, T. (2013). Music as a service as an alternative to music piracy? - an empirical investigation of the intention to use music streaming services. Business & Information Systems Engineering, 5(6), 383–396.CrossRefGoogle Scholar
  17. Du, H. S. (2014). The role of media-embedded heuristics in achieving online readership popularity. Journal of the Association for Information Science and Technology, 65(2), 302–312.CrossRefGoogle Scholar
  18. Escalas, J. E. (2007). Self-referencing and persuasion: Narrative transportation versus analytical elaboration. Journal of Consumer Research, 33(4), 421–429.CrossRefGoogle Scholar
  19. Firnkes, M. (2014). Online shop trend narrative retailing: How content marketing is said to optimize shopping. Accessed 6 Aug 2014.
  20. Flanagin, A. J., & Metzger, M. J. (2007). The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New Media & Society, 9(2), 319–342.CrossRefGoogle Scholar
  21. Fogg, B. J. (2003). Prominence-interpretation theory: Explaining how people assess credibility online. In CHI '03 Extended Abstracts on Human Factors in Computing Systems, Ft. Lauderdale, Forida, April 5-10, 2003 (722-723).Google Scholar
  22. Folkes, V. S. (1988). Recent attribution research in consumer behavior: A review and new directions. Journal of Consumer Research, 14(4), 548–565.CrossRefGoogle Scholar
  23. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  24. Forrester Consulting (2012). Using ecommerce to monetize digital content in the media industry. Thought Leadership Papers. Cambridge: Forrester Research.Google Scholar
  25. Gallaugher, J. M., Auger, P., & BarNir, A. (2001). Revenue streams and digital content providers: An empirical investigation. Information Management, 38(7), 473–485.CrossRefGoogle Scholar
  26. Goh, K.-Y., Heng, C.-S., & Lin, Z. (2013). Social media brand community and consumer behavior: Quantifying the relative impact of user- and marketer-generated content. Information Systems Research, 24(1), 88–107.CrossRefGoogle Scholar
  27. Ha, L., & Ganahl, R. (2004). Webcasting business models of clicks-and-bricks and pure-play media: A comparative study of leading webcasters in South Korea and the United States. JMM: The International Journal on Media Management, 6(1/2), 74–87.Google Scholar
  28. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.CrossRefGoogle Scholar
  29. Hess, T. (2014). What is a media company? A reconceptualization for the online world. JMM: The International Journal on Media Management, 16(1), 3–8.Google Scholar
  30. Hilligoss, B., & Rieh, S. Y. (2008). Developing a unifying framework of credibility assessment: Construct, heuristics, and interaction in context. Information Processing and Management, 44(4), 1467–1484.CrossRefGoogle Scholar
  31. Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion - psychological studies of opinion change. New Haven: Yale University Press.Google Scholar
  32. Jain, S. P., & Posavac, S. S. (2001). Prepurchase attribute verifiability, source credibility, and persuasion. Journal of Consumer Psychology, 11(3), 169–180.CrossRefGoogle Scholar
  33. Jeong, H. J., & Kwon, K.-N. (2012). The effectiveness of two online persuasion claims: Limited product availability and product popularity. Journal of Promotion Management, 18(1), 83–99.CrossRefGoogle Scholar
  34. Karande, K., Almurshidee, K. A., & Al-Olayan, F. S. (2006). Advertising standardisation in culturally similar markets. International Journal of Advertising, 25(4), 489–511.CrossRefGoogle Scholar
  35. Kohring, M., & Matthes, J. (2007). Trust in news media: Development and validation of a multidimensional scale. Communication Research, 34(2), 231–252.CrossRefGoogle Scholar
  36. Kursad, A., Nanda, K., & Varghese, S. J. (2012). Pricing models for online advertising: CPM vs. CPC. Information Systems Research, 23(3), 804–822.Google Scholar
  37. Lee, C., Kim, J., & Chan-Olmsted, S. M. (2011). Branded product information search on the web: The role of brand trust and credibility of online information sources. Journal of Marketing Communications, 17(5), 355–374.CrossRefGoogle Scholar
  38. Levine, T. R. (2005). Confirmatory factor analysis and scale validation in communication research. Communication Research Reports, 22(4), 335–338.CrossRefGoogle Scholar
  39. Li, H., Edwards, S. M., & Lee, J.-H. (2002). Measuring the intrusiveness of advertisements: Scale development and validation. Journal of Advertising, 31(2), 37–47.CrossRefGoogle Scholar
  40. Libai, B., Biyalogorsky, E., & Gerstner, E. (2003). Setting referral fees in affiliate marketing. Journal of Service Research, 5(4), 303.CrossRefGoogle Scholar
  41. Lord, K. R., & Putrevu, S. (1998). Communicating in print: A comparison of consumer responses to different promotional formats. Journal of Current Issues and Research in Advertising, 20(2), 1–18.CrossRefGoogle Scholar
  42. Lord, K. R., & Putrevu, S. (2009). Informational and transformational responses to celebrity endorsements. Journal of Current Issues and Research in Advertising, 31(1), 1–13.CrossRefGoogle Scholar
  43. Lowry, P. B., Wilson, D. W., & Haig, W. L. (2014). A picture is worth a thousand words: Source credibility theory applied to logo and website design for heightened credibility and consumer trust. International Journal of Human Computer Interaction, 30(1), 63–93.CrossRefGoogle Scholar
  44. Lucassen, T., & Schraagen, J. M. (2012). Propensity to trust and the influence of source and medium cues in credibility evaluation. Journal of Information Science, 38(6), 566–577.CrossRefGoogle Scholar
  45. Luo, C., Luo, X., Schatzberg, L., & Sia, C. L. (2013). Impact of informational factors on online recommendation credibility: The moderating role of source credibility. Decision Support Systems, 56, 92–102.CrossRefGoogle Scholar
  46. Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84(1), 123–136.CrossRefGoogle Scholar
  47. McCroskey, J. C., & Teven, J. J. (1999). Goodwill: A reexamination of the construct and its measurement. Communication Monographs, 66(1), 90–103.CrossRefGoogle Scholar
  48. Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59(Part B), 210–220.CrossRefGoogle Scholar
  49. Metzger, M. J., Flanagin, A. J., Eyal, K., Lemus, D. R., & McCann, R. M. (2003). Credibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment. In P. J. Kalbfleisch (Ed.), Communication Yearbook 27 (pp. 295-335, Communication Yearbook, Vol. 27). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  50. Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413–439.CrossRefGoogle Scholar
  51. Moore, D. J., Reardon, R., & Mowen, J. C. (1989). Source independence in multiple source advertising appeals: The confederate effect. Advances in Consumer Research, 16(1), 719–722.Google Scholar
  52. Niedzwiadek, N. (2016). Vox to join other media companies in e-commerce push. Accessed 15 April 2016.
  53. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed., McGraw-Hill Series in Psychology). New York: McGraw-Hill.Google Scholar
  54. Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39–52.CrossRefGoogle Scholar
  55. O’Keefe, D. J. (2002). Persuasion: Theory & research (2nd ed., Current Communication: An Advanced Text Series). Thousand Oaks: Sage Publications.Google Scholar
  56. Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16, 1–25.Google Scholar
  57. Pauwels, K., & Weiss, A. (2008). Moving from free to fee: How online firms market to change their business model successfully. Journal of Marketing, 72(3), 14–31.CrossRefGoogle Scholar
  58. Petty, R. E., & Cacioppo, J. T. (1979). Effects of forwarning of persuasive intent and involvement on cognitive responses and persuasion. Personality and Social Psychology Bulletin, 5(2), 173–176.CrossRefGoogle Scholar
  59. Pew Research Center. (2015). State of the news media 2015. Washinton, DC: Pew Research Center.Google Scholar
  60. Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34(2), 243–281.CrossRefGoogle Scholar
  61. Rieh, S. Y., & Danielson, D. R. (2007). Credibility: A multidisciplinary framework. Annual Review of Information Science and Technology, 41(1), 307–364.CrossRefGoogle Scholar
  62. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH.Google Scholar
  63. Salmon, C. T., Reid, L. N., Pokrywczynski, J., & Willett, R. W. (1985). The effectiveness of advocacy advertising relative to news coverage. Communication Research, 12(4), 546–567.CrossRefGoogle Scholar
  64. Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of Retailing, 80(2), 159–169.CrossRefGoogle Scholar
  65. Shaw, J. C., Wild, E., & Colquitt, J. A. (2003). To justify or excuse?: A meta-analytic review of the effects of explanations. Journal of Applied Psychology, 88(3), 444–458.CrossRefGoogle Scholar
  66. Smith, D. C., & Park, C. W. (1992). The effects of brand extensions on market share and advertising efficiency. Journal of Marketing Research, 29(3), 296–313.CrossRefGoogle Scholar
  67. Smith, M. D., & Telang, R. (2009). Competing with free: The impact of movie broadcasts on DVD sales and internet piracy. MIS Quarterly, 33(2), 321–338.CrossRefGoogle Scholar
  68. Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. J. Metzger & A. J. Flanagin (Eds.), Digital media, youth, and credibility (pp. 73–100). Cambridge: The MIT Press.Google Scholar
  69. Till, B. D., & Busler, M. (1998). Matching products with endorsers: Attractiveness versus expertise. Journal of Consumer Marketing, 15(6), 576–586.CrossRefGoogle Scholar
  70. Trenz, M., & Berger, B. (2013). Analyzing online customer reviews - An interdisciplinary literature review and research agenda. In 21st European Conference on Information Systems, Utrecht, Netherlands, June 5–8, 2013 (Paper 83).Google Scholar
  71. Tseng, S., & Fogg, B. J. (1999). Credibility and computing technology. Communications of the ACM, 42(5), 39–44.CrossRefGoogle Scholar
  72. Tutaj, K., & van Reijmersdal, E. A. (2012). Effects of online advertising format and persuasion knowledge on audience reactions. Journal of Marketing Communications, 18(1), 5–18.CrossRefGoogle Scholar
  73. van Reijmersdal, E. A., Neijens, P. C., & Smit, E. G. (2005). Readers’ reactions to mixtures of advertising and editorial content in magazines. Journal of Current Issues and Research in Advertising, 27(2), 39–53.CrossRefGoogle Scholar
  74. Wang, A. (2005). Integrating and comparing others’ opinions. Journal of Website Promotion, 1(1), 105–129.CrossRefGoogle Scholar
  75. Wathen, C. N., & Burkell, J. (2002). Believe it or not: Factors influencing credibility on the web. Journal of the American Society for Information Science and Technology, 53(2), 134–144.CrossRefGoogle Scholar
  76. Watts Sussman, S., & Schneier Siegal, W. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.CrossRefGoogle Scholar
  77. Willemsen, L. M., Neijens, P. C., & Bronner, F. (2012). The ironic effect of source identification on the perceived credibility of online product reviewers. Journal of Computer-Mediated Communication, 18(1), 16–31.CrossRefGoogle Scholar
  78. Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.CrossRefGoogle Scholar
  79. Xu, Q. (2013). Social recommendation, source credibility, and cecency: Effects of news cues in a social bookmarking website. Journalism and Mass Communication Quarterly, 90(4), 757–775.CrossRefGoogle Scholar
  80. Zaichkowsky, J. L. (1994). The personal involvement inventory: Reduction, revision, and application to advertising. Journal of Advertising, 23(4), 59–70.CrossRefGoogle Scholar
  81. Zerdick, A., Picot, A., Schrape, K., Artopé, A., Goldhammer, K., Lange, U. T., et al. (2000). E-conomics: Strategies for the digital marketplace (European communication council report). Berlin & Heidelberg: Springer.CrossRefGoogle Scholar
  82. Zhao, X., Lynch Jr., J. G., & Chen, Q. (2010). Reconsidering baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206.CrossRefGoogle Scholar
  83. Zhu, M., Billeter, D. M., & Inman, J. J. (2012). The double-edged sword of signaling effectiveness: When salient cues curb postpurchase consumption. Journal of Marketing Research, 49(1), 26–38.CrossRefGoogle Scholar

Copyright information

© Institute of Applied Informatics at University of Leipzig 2017

Authors and Affiliations

  1. 1.Institute for Information Systems and New MediaLudwig-Maximilians-Universität MünchenMunichGermany

Personalised recommendations