Homo Connectus pp 103-119 | Cite as

Are Users All the Same? – A Comparative International Analysis of Digital Technology Adoption

  • Stefan Hopf
  • Arnold Picot


Rapid technological advances have resulted in an array of new digital consumer technologies in the last decade. While there is a continuous uptake in digital technology adoption, certain groups of users are lagging behind, fueling fears of an increasing digital divide. This article attempts to open the “black box” of users by exploring user-centric antecedents of digital technology adoption. Based on an online survey dataset of 7.231 representative Internet users from Germany, Sweden, United States, Brazil, China, and South-Korea, the empirical analysis aims at comparing user-specific demographic, attitudinal and behavioral factors related to digital technology adoption across countries. While we find that users differ significantly in their digital technology adoption among some factors (i.e. age, people in household, education, and being a student or pupil) across countries, we also identify factors with similar impacts across countries (i.e. net income, gender, children in household, and being unemployed). Counteracting tendencies towards an increasing digital divide, this paper pledges for an extensive differentiation of policy measures based on country specific determinants of digital technology adoption. Gaining a better understanding of (dis)similarities among users of digital technologies, these results shall ultimately promote evidence-based business decisions and policymaking.


  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.CrossRefGoogle Scholar
  2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.Google Scholar
  3. Awad, N. F., & Kirshnan, M. S. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quaterly, 30(1), 13–28.CrossRefGoogle Scholar
  4. Baldwin, C. Y. & Clark, K. B. (2000). Design rules: The power of modularity. Cambridge, MIT Press.Google Scholar
  5. Beatty, S. E., & Talpade, S. (1994). Adolescent influence in family decision making: A replication with extension. Journal of Consumer Research, 21(2), 332–341.CrossRefGoogle Scholar
  6. Bélanger, F., & Crossler, R. E. (2011). Privacy in the digital age: A review of information privacy research in information systems. MIS Quaterly, 45(4), 1017–1041.CrossRefGoogle Scholar
  7. Belch, G. A., Belch, M. A., & Ceresino, G. (1985). Parental and teenage child influence in family decision making. Journal of Business Research, 13(2), 163–176.CrossRefGoogle Scholar
  8. Caselli, F., & Coleman, W., II. (2011). Cross-country technology diffusion: The case of computers. American Economic Review, 91(2), 328–335.CrossRefGoogle Scholar
  9. Chellappa, R. K., & Shivendu, S. (2007). An economic model of privacy: A property rights approach to regulatory choices for online personalization. Journal of Management Information Systems, 24(3), 193–225.CrossRefGoogle Scholar
  10. Chellappa, R. K., & Sin, R. G. (2005). Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information Technology and Management, 6(2), 181–202.CrossRefGoogle Scholar
  11. Chinn, M., & Fairlie, R. (2007). The determinants of the global digital divide: A cross-country analysis of computer and internet penetration. Oxford Economic Papers, 59(1), 16–44.CrossRefGoogle Scholar
  12. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah: Routledge.Google Scholar
  13. Davis, F. A. (1985). Technology acceptance Model for empirically testing new end-user information systems: Theory and results. Unpublished Ph.D. Dissertation, Massachusetts Institute of Technology, Cambridge.Google Scholar
  14. Dijk, J. van, & Hacker, K. (2003). The digital divide as a complex and dynamic phenomenon. The Information Society, 19(4), 315–326.CrossRefGoogle Scholar
  15. Foxman, E., Tahsuhaj, P., & Ekstrom, K. M. (1989). Adolescents’ influence in family purchase decisions: A socialization perspective. Journal of Business Research, 15(2), 482–491.Google Scholar
  16. Hofstede, G. (1980). Culture’s consequences: International differences in work – Related values. Newbury Park: Sage.Google Scholar
  17. Hong, W., & Thong, J. Y. L. (2013). Internet privacy concerns: An integrated conceptualization and four empirical studies. MIS Quarterly, 37(1), 275–298.CrossRefGoogle Scholar
  18. Immelt, J. R., Govindarajan, V., & Trimble, C. (2009). How GE is disrupting itself. Harvard Business Review, 87(10), 56–65.Google Scholar
  19. ITU. (2013). Percentage of individuals using the internet. Accessed 10 February 2014.
  20. Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: An empirical study. MIS Quarterly, 34(3), 549–566.CrossRefGoogle Scholar
  21. Korupp, S. E., & Szydlik, M. (2005). Causes and trends of the digital divide. European Sociological Review, 21(4), 409–422.CrossRefGoogle Scholar
  22. Korzaan, M. L., & Boswell, K. T. (2008). The influence of personality traits and information privacy concerns on behavioral intentions. The Journal of Computer Information Systems, 48(4), 15–24.Google Scholar
  23. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 48(3), 191–204.CrossRefGoogle Scholar
  24. Leidner, D. E., & Kayworth, T. (2006). Review: A review of culture in information systems research: Toward a theory of information technology culture conflict. MIS Quarterly, 30(2), 357–399.CrossRefGoogle Scholar
  25. Miller, R. G. Jr. (1981). Simultaneous statistical inference. New York, Springer.Google Scholar
  26. Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoptions decisions: Implications for a changing workforce. Personal Psychology, 52(2), 375–403.CrossRefGoogle Scholar
  27. Münchner, Kreis, EICT, Siemens, Telekom, TNS Infrastest, & Zdf. (2011). Pictures of the future in a digital world – An international comparison of user perspectives. Accessed 10 February 2014.
  28. Palan, K. M., & Wilkes, R. E. (1997). Adolescent-parent interaction in family decision making. Journal of Consumer Research, 24(2), 159–169.CrossRefGoogle Scholar
  29. Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105–136.CrossRefGoogle Scholar
  30. Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007.CrossRefGoogle Scholar
  31. Rosenberg, N. (1972). Factors affecting the diffusion of technology. Explorations in Economic History, 10(1), 3–33.CrossRefGoogle Scholar
  32. Srite, M., & Karahanna, E. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly, 30(3), 679–704.CrossRefGoogle Scholar
  33. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, and emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 343–365.CrossRefGoogle Scholar
  34. Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop and ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 21(1), 115–140.CrossRefGoogle Scholar
  35. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Towards a unified view. MIS Quarterly, 27(3), 425–478.CrossRefGoogle Scholar
  36. Wagner, J., & Hanna, S. (1983). The effectiveness of family life cycle variables in consumer expenditure research. Journal of Consumer Research, 10(3), 281–291.CrossRefGoogle Scholar

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© Springer Fachmedien Wiesbaden GmbH 2018

Authors and Affiliations

  1. 1.MünchenDeutschland

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