Credibility of Algorithm Based Decentralized Computer Networks Governing Personal Finances: The Case of Cryptocurrency

  • Sapumal AhangamaEmail author
  • Danny Chiang Choon Poo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9751)


In spite of the virtual nature and the system operating purely based on pre-formulated computer algorithms, cryptocurrency networks have reached greater popularity with a significant follower base with people placing trust on the system operation. As credibility is an important factor for systems facilitating financial transactions, in this study we will be presenting a simple model facilitating identification of relevant important factors to be considered by users and a methodology for assessing the credibility of cryptocurrency networks. We identify two routes, systems and the psychological perspective in the credibility assessment process which varies with the user expertise.


Credibility Cryptocurrency Information systems in finance Bitcoins 


  1. 1.
    Nakamoto, S., Bitcoin: A peer-to-peer electronic cash system (2008)Google Scholar
  2. 2.
    Vigna, P., Casey, M.J.: The Age of Cryptocurrency: How Bitcoin and Digital Money are Challenging the Global Economic Order. St. Martin’s Press, New York (2015)Google Scholar
  3. 3.
    Yermack, D., Is Bitcoin a real currency? An economic appraisal. National Bureau of Economic Research (2013)Google Scholar
  4. 4.
    Hilligoss, B., Rieh, S.Y.: Developing a unifying framework of credibility assessment: construct, heuristics, and interaction in context. Inf. Process. Manage. 44(4), 1467–1484 (2008)CrossRefGoogle Scholar
  5. 5.
    Fogg, B.J. and H. Tseng. The elements of computer credibility. ACM (1999)Google Scholar
  6. 6.
    Ba, S., Pavlou, P.A.: Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior. MIS Q. 26(3), 243–268 (2002)CrossRefGoogle Scholar
  7. 7.
    Dimoka, A.: What does the brain tell us about trust and distrust? evidence from a functional neuroimaging study. MIS Q. 34(2), 373–396 (2010)Google Scholar
  8. 8.
    Sirdeshmukh, D., Singh, J., Sabol, B.: Consumer trust, value, and loyalty in relational exchanges. J. Mark. 66(1), 15–37 (2002)CrossRefGoogle Scholar
  9. 9.
    Garbarino, E., Lee, O.F.: Dynamic pricing in internet retail: effects on consumer trust. Psychol. Mark. 20(6), 495–513 (2003)CrossRefGoogle Scholar
  10. 10.
    Goodhue, D.L.: Understanding user evaluations of information systems. Manage. Sci. 41(12), 1827–1844 (1995)CrossRefGoogle Scholar
  11. 11.
    Goodhue, D.L., Thompson, R.L.: Task-technology fit and individual performance. MIS Q. 19(2), 213–236 (1995)CrossRefGoogle Scholar
  12. 12.
    Tseng, S., Fogg, B.J.: Credibility and computing technology. Commun. ACM 42(5), 39–44 (1999)CrossRefGoogle Scholar
  13. 13.
    Petty, R.E., Cacioppo, J.T.: The elaboration likelihood model of persuasion. Springer, New York (1986)Google Scholar
  14. 14.
    Petty, R.E., Cacioppo, J.T.X.: Attitudes and persuasion: Classic and contemporary approaches. Westview Press, Boulder (1996)Google Scholar
  15. 15.
    Wathen, C.N., Burkell, J.: Believe it or not: factors influencing credibility on the web. J. Am. soc. Inf. Sci. Technol. 53(2), 134–144 (2002)CrossRefGoogle Scholar
  16. 16.
    Sundar, S.S.: Technology and credibility: cognitive heuristics cued by modality, agency, interactivity and navigability. In: Metzge, M.J., Flanagin, A.J. (eds.) Digital Media, Youth, and Credibility. MacArthur Foundation Series on Digital Media and Learning, pp. 73–100. MIT Press, Cambridge (2007)Google Scholar
  17. 17.
    Eastin, M.S., Yang, M.-S., Nathanson, A.I.: Children of the net: an empirical exploration into the evaluation of Internet content. J. Broadcast. Electron. Media 50(2), 211–230 (2006)CrossRefGoogle Scholar
  18. 18.
    Bhattacherjee, A., Sanford, C.: Influence processes for information technology acceptance: an elaboration likelihood model. MIS Q. 30(4), 805–825 (2006)Google Scholar
  19. 19.
    Yang, S.C., et al.: Investigating initial trust toward e-tailers from the elaboration likelihood model perspective. Psychol. Mark. 23(5), 429–445 (2006)CrossRefGoogle Scholar
  20. 20.
    Klopping, I.M., McKinney, E.: Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Inf. Technol. Learn. Perform. J. 22, 35–48 (2004)Google Scholar
  21. 21.
    Lee, C.-C., Cheng, H.K., Cheng, H.-H.: An empirical study of mobile commerce in insurance industry: task–technology fit and individual differences. Decis. Support Syst. 43(1), 95–110 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zhou, T., Lu, Y., Wang, B.: Integrating TTF and UTAUT to explain mobile banking user adoption. Comput. Hum. Behav. 26(4), 760–767 (2010)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Shang, R.-A., Y.-C. Chen, and C.-M. Chen, Why people blog? An empirical investigations of the task technology fit model. In: Proceedings of PACIS, p. 5 (2007)Google Scholar
  24. 24.
    Zhou, T.: Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Comput. Hum. Behav. 28(4), 1518–1525 (2012)CrossRefGoogle Scholar
  25. 25.
    Beldad, A., De Jong, M., Steehouder, M.: How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust. Comput. Hum. Behav. 26(5), 857–869 (2010)CrossRefGoogle Scholar
  26. 26.
    Ingram, C., M. Morisse, and R. Teigland. ‘A Bad Apple Went Away’: Exploring Resilience Among Bitcoin Entrepreneurs. In: Twenty-Third European Conference on Information Systems (ECIS), Münster, Germany (2015)Google Scholar
  27. 27.
    Pavlou, P.A., Gefen, D.: Building effective online marketplaces with institution-based trust. Inf. Syst. Res. 15(1), 37–59 (2004)CrossRefGoogle Scholar
  28. 28.
    Newell, S.J., Goldsmith, R.E.: The development of a scale to measure perceived corporate credibility. J. Bus. Res. 52(3), 235–247 (2001). ISSN: 0148-2963CrossRefGoogle Scholar
  29. 29.
    Lin, T.-C., Huang, C.-C.: Understanding knowledge management system usage antecedents: an integration of social cognitive theory and task technology fit. Inf. Manage. 45(6), 410–417 (2008)CrossRefGoogle Scholar
  30. 30.
    Gefen, D., Straub, D.W., Rigdon, E.E.: An update and extension to SEM guidelines for admnistrative and social science research. Manage. Inf. Syst. Q. 35(2), iii–xiv (2011)Google Scholar
  31. 31.
    Kankanhalli, A., Lee, O.-K.D., Lim, K.H.: Knowledge reuse through electronic repositories: a study in the context of customer service support. Inf. Manage. 48(2), 106–113 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Information Systems, School of ComputingNational University of SingaporeSingaporeSingapore

Personalised recommendations