Electronic Commerce Research

, Volume 16, Issue 4, pp 529–551 | Cite as

Information markets over trust networks



Information markets are inefficient. Information products have large upfront development costs, yet their duplication costs are negligibly small; and they are experience goods with high costs of marketing and promotion. As a result, either winner-take-all markets are created through large and expensive promotional campaigns, or artificial monopoly power is conferred by the government through copyright protection, or both, to prevent the collapse of these markets from intense price competition and piracy. Such inefficiency creates opportunities to design more efficient markets by utilizing new technologies. Trust networks provide such an opportunity where the network infrastructure acts not only as a distribution system for information products, but also as an advertising and promotion medium, a payment and pricing mechanism, a guarantee and insurance service, and a copyright enforcement and dispute resolution tool. Such a network-centric market place is proposed to remedy many of the shortcomings of mass markets by relying on peer-to-peer distribution, peer-to-peer payments, and peer-to-peer enforcement of trust and integrity. Analytical models are presented to show that such a market place for information goods can scale up to satisfy large markets without expensive promotions and advertising campaigns, create customized products with dynamic pricing, reduce entry costs by eliminating the distinction between buyers and sellers, and eliminate the need for copyright protection.


Information products Trust networks Social networks Market design Peer-to-peer markets Network distribution 


  1. 1.
    Andersen, R. et al. (2008). Trust-based recommendation systems: An axiomatic approach. Proceedings of WWW conference. pp. 199–208.Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A., & Zheng, R. (2011). REQUEST: A query language for customizing recommendations. Information Systems Research, 22, 1.CrossRefGoogle Scholar
  3. 3.
    Arora, G., Hanneghan, M., & Merabti, M. (2005). P2P commercial digital content exchange. Electronic Commerce Research and Applications, 4(3), 250–263.CrossRefGoogle Scholar
  4. 4.
    Asvanund, A., et al. (2004). An empirical analysis of network effects in peer-to-peer music sharing networks. Information Systems Research, 15(2), 155–174.CrossRefGoogle Scholar
  5. 5.
    Bakos, Y., Brynjolfsson, E., & Lichtman, D. (1999). Shared information goods. Journal of Law and Economics, 42(1), 117–156.CrossRefGoogle Scholar
  6. 6.
    Chesbrough, H., & Spohrer, J. (2006). A research manifesto for services science. Communications of the ACM, 49(7), 35–40.CrossRefGoogle Scholar
  7. 7.
    Conley, J. P., & Yoo, C. S. (2009). Nonrivalry and price discrimination in copyright economics. University of Pennsylvania Law Review.Google Scholar
  8. 8.
    Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.CrossRefGoogle Scholar
  9. 9.
    Dockner, E. J., & Frucghter, G. E. (2004). Dynamic strategic pricing and the speed of diffusion. Journal of Optimization Theory and Applications, 13(2), 331–348.CrossRefGoogle Scholar
  10. 10.
    Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets. Cambridge: Cambridge Press.CrossRefGoogle Scholar
  11. 11.
    Frank, R. (1996). Winner take all society. New York: Penguin Books.Google Scholar
  12. 12.
    Feng, Y., Guo, Z., & Chiang, W. K. (2009). Optimal digital content distribution in the presence of consumer-to-consumer channel. Journal of Management Information Systems, 25(4), 241–270.CrossRefGoogle Scholar
  13. 13.
    Galbreth, M. R., Ghosh, B., & Shor, M. (2012). Social sharing of information goods: Implications for pricing and profits. Marketing Science, 31(4), 603–620.CrossRefGoogle Scholar
  14. 14.
    Goetsch, K. (2014). Ecommerce in the cloud. New York: O’Reilly.Google Scholar
  15. 15.
    Guha, R., Kumar, R., Raghaven, P., & Tomkins, A. (2004). Propagation of trust and distrust. Proceedings of conference on WWW. pp. 403–412.Google Scholar
  16. 16.
    Handy, C. (1995). Trust and the virtual organization. Harvard Business Review, 73(3), 40–50.Google Scholar
  17. 17.
    Hosanagar, K., Han, P., & Tan, Y. (2010). Diffusion models for peer-to-peer (P2P) media distribution: On the impact of decentralized. Constrained Supply. Information Systems Research, 21(2), 271–287.CrossRefGoogle Scholar
  18. 18.
    Josang, A., Ismail, R., & Boyd, C. (2007). A survey of trust and reputation systems for online service provision. Decision Support Systems, 43(2), 618–644.CrossRefGoogle Scholar
  19. 19.
    Khouja, M., et al. (2008). Application of complex adaptive systems to pricing of reproducible information goods. Decision Support Systems, 44, 725–739.CrossRefGoogle Scholar
  20. 20.
    Kim, Y. A. (2015). An enhanced trust propagation approach with expertise and homophily-based trust networks. Knowledge Based Systems, 82, 20–28.CrossRefGoogle Scholar
  21. 21.
    Krishnan, T. V., Bass, F., & Jain, D. C. (1999). Optimal pricing strategy for new products. Management Science, 45(12), 1650–1663.CrossRefGoogle Scholar
  22. 22.
    Lang, K., & Vragov, R. (2005). A pricing mechanism for digital content distribution over peer-to-peer networks. Journal of Management Information Systems, 22(2), 121–139.Google Scholar
  23. 23.
    Lessig, L. (2008). Remix: making art and commerce thrive in a hybrid economy. New York: Penguin Press.CrossRefGoogle Scholar
  24. 24.
    Li, Y. M., Lin, C. H., & Lai, C. Y. (2010). Identifying influential reviewers for word-of-mouth marketing. Electronic Commerce Research Applications, 9, 294–304.CrossRefGoogle Scholar
  25. 25.
    Liu, S., et al. (2015). Identifying effective influencers on trust for electronic word of mouth marketing: A domain aware approach. Information Sciences, 306, 34–52.CrossRefGoogle Scholar
  26. 26.
    Lopez-Pintado, D. (2008). Diffusion in complex social networks. Games and Economic Behavior, 62(2), 573–590.CrossRefGoogle Scholar
  27. 27.
    McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359.CrossRefGoogle Scholar
  28. 28.
    Orman, L. (2013). Bayesian inference in trust networks. ACM Transactions on MIS, 4(2), 1–26.Google Scholar
  29. 29.
    Orman, L. (2011). The potential of virtual institutions. IEEE Technology and Society, 30(1), 56–64.CrossRefGoogle Scholar
  30. 30.
    Postmes, T., & Branscombe, N. (2010). Rediscovering social identity. New York: Psychology Press.Google Scholar
  31. 31.
    Sundarajan, A. (2004). Managing Digital Piracy: pricing and protection. Information Systems Research, 15(3), 287–308.Google Scholar
  32. 32.
    Swaminathan, A., et al. (2010). Relating reputation and money in online markets. ACM Transactions on the Web, 4(4), 17.CrossRefGoogle Scholar
  33. 33.
    Tunca, T., & Wu, Q. (2013). Fighting fire with fire: Commercial piracy and the role file sharing on copyright protection policy for digital goods. Information Systems Research, 24(2), 436–453.CrossRefGoogle Scholar
  34. 34.
    Vega-Redondo, F. (2007). Complex social networks. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  35. 35.
    Wei, W., & Ram, S. (2012). Using a network analysis approach for organizing social bookmarking tags and enabling web content discovery. ACM Transactions on MIS, 3(3), 15.Google Scholar
  36. 36.
    Wei, X., & Nault, B. R. (2013). Experience information goods: Version to upgrade. Decision Support Systems, 56(12), 494–501.CrossRefGoogle Scholar
  37. 37.
    Xiong, L., & Liu, L. (2004). Peertrust: Supporting reputation-based trust for peer to peer electronic communities. IEEE Transactions on Knowledge and Data Engineering, 16(7), 843–857.CrossRefGoogle Scholar
  38. 38.
    Ziegler, C. (2009). On propagating interpersonal trust in social networks. In Computing with Social Trust (pp.133–168). New York: Springer.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Cornell UniversityIthacaUSA

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