Journal of Business and Psychology

, Volume 31, Issue 1, pp 87–102 | Cite as

The Technology Effect: How Perceptions of Technology Drive Excessive Optimism

  • Brent B. Clark
  • Christopher Robert
  • Stephen A. Hampton
Original Paper



We propose that constant exposure to advances in technology has resulted in an implicit association between technology and success that has conditioned decision makers to be overly optimistic about the potential for technology to drive successful outcomes. Three studies examine this phenomenon and explore the boundaries of this “technology effect.”


In Study 1, participants (N = 147) made simulated investment decisions where the information about technology was systematically varied. In Study 2 (N = 143), participants made decisions in a resource dilemma where technology was implicated in determining the amount of a resource available for harvest. Study 3 (N = 53 and N = 60) used two implicit association tests to examine the assumption that people associate technology with success.


Results supported our assumption about an implicit association between technology and success, as well as a “technology effect” bias in decision making. Signals of high performance trigger the effect, and the effect is more likely when the technology invoked is unfamiliar.


Excessive optimism that technology will result in success can have negative consequences. Individual investment decisions, organizational decisions to invest in R&D, and societal decisions to explore energy and climate change solutions might all be impacted by biased beliefs about the promise of technology.


We are the first to systematically examine the optimistic bias in the technology effect, its scope, and boundaries. This research raises decision makers’ awareness and initiates research examining how the abstract notion of technology can influence perceptions of technological advances.


Technology Decision making Optimism Diagnostic cue Resource dilemma Implicit association test 


  1. Alter, A. L., & Oppenheimer, D. M. (2006). Predicting short-term stock fluctuations by using processing fluency. Proceedings of the National Academy of Sciences, 103(24), 9369.CrossRefGoogle Scholar
  2. Baca, S. P., Garbe, B. L., & Weiss, R. A. (2000). The rise of sector effects in major equity markets. Financial Analysts Journal, 56(5), 34–40.CrossRefGoogle Scholar
  3. Bain, R. (1937). Technology and state government. American Sociological Review, 2(6), 860–874.CrossRefGoogle Scholar
  4. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261–292.CrossRefGoogle Scholar
  5. Borges, B., Goldstein, D. G., Ortmann, A., & Gigerenzer, G. (1999). Can ignorance beat the stock market. In G. Gigerenzer, P. M. Todd, & the ABC Research Group (Eds.), Simple heuristicsthat make us smart (pp. 59–72). New York: Oxford University.Google Scholar
  6. Burrill, S. G. (2002). Biotech 2002: The 16th Annual Report on the Industry. San Francisco: Burrill & Company.Google Scholar
  7. Burrill, S. G. (2011). Biotech 2011 life sciences: 25 years : Looking back to see ahead. San Francisco: Burrill & Company.Google Scholar
  8. Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367.CrossRefGoogle Scholar
  9. Camerer, C., & Lovallo, D. (1999). Overconfidence and excess entry: An experimental approach. The American Economic Review, 89(1), 306–318.CrossRefGoogle 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. Chaiken, S., Liberman, A., & Eagly, A. H. (1989). Heuristic and systematic information processing within and beyond the persuasion context. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp. 212–252). New York: Guilford Press.Google Scholar
  12. Chandy, R. K., Prabhu, J. C., & Antia, K. D. (2003). What will the future bring? Dominance, technology expectations, and radical innovation. Journal of Marketing, 67(3), 1–18.CrossRefGoogle Scholar
  13. Costa-Font, J., Mossialos, E., & Rudisill, C. (2009). Optimism and the perceptions of new risks. Journal of Risk Research, 12(1), 27–41.CrossRefGoogle Scholar
  14. Einhorn, H. J., & Hogarth, R. M. (1975). Unit weighting schemes for decision making. Organizational Behavior and Human Performance, 13(2), 171–192.CrossRefGoogle Scholar
  15. Evans, J. S. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278.CrossRefPubMedGoogle Scholar
  16. Feldman, J. M., & Lynch, J. G. (1988). Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. Journal of Applied Psychology, 73(3), 421.CrossRefGoogle Scholar
  17. Golder, P. N., & Tellis, G. J. (1993). Pioneer advantage: Marketing logic or marketing legend? Journal of Marketing Research, 30(2), 158–170.CrossRefGoogle Scholar
  18. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 4–27.CrossRefPubMedGoogle Scholar
  19. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480.CrossRefPubMedGoogle Scholar
  20. Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the implicit association test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197–216.CrossRefPubMedGoogle Scholar
  21. Greenwald, A. G., Poelman, T. A., Ulmann, E. L., & Banaji, M. R. (2009). Understanding and using the implicit association test: III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology, 97(1), 17–41.CrossRefPubMedGoogle Scholar
  22. Griffin, R., & Kacmar, K. M. (1991). Laboratory research in management: Misconceptions and missed opportunities. Journal of Organizational Behavior, 12(4), 301–311.CrossRefGoogle Scholar
  23. Hamilton, D. P. (2004). Biotech’s dismal bottom line: More than $40 billion in losses. Wall Street Journal, 20, A1–A8. Retrieved from
  24. Hekman, D., Aquino, K., Owens, B., Mitchell, T., Schilpzand, P., & Leavitt, K. (2010). An examination of whether and how racial and gender biases influence customer satisfaction ratings. Academy of Management Journal, 53(2), 238–264.CrossRefGoogle Scholar
  25. Herman, J. L., Stevens, M. J., Bird, A., Mendenhall, M., & Oddou, G. (2010). The tolerance for ambiguity scale: Towards a more refined measure for international management research. International Journal of Intercultural Relations, 34(1), 58–65.CrossRefGoogle Scholar
  26. Hoeffler, S. (2003). Measuring preferences for really new products. Journal of Marketing Research, 40(4), 406–420.CrossRefGoogle Scholar
  27. Hossain, T., & Morgan, J. (2006). …plus shipping and handling: Revenue (non) equivalence in field experiments on ebay. The B.E. Journal of Economic Analysis & Policy, 5(2), 1–27.Google Scholar
  28. Huberman, G. (2001). Familiarity breeds investment. Review of Financial Studies, 14(3), 659–680.CrossRefGoogle Scholar
  29. Ilgen, D. R. (1986). Laboratory research: A question of when, not if. In E. A. Locke (Ed.), Generalizing from laboratory to field settings (pp. 257–267). Lexington: Lexington Books.Google Scholar
  30. Jackson, D. N. (1994). Jackson personality inventory—Revised manual. Port Heron: Sigma Assessment Systems Inc.Google Scholar
  31. Jager, W., Janssen, M. A., & Vlek, C. A. J. (2002). How uncertainty stimulates over-harvesting in a resource dilemma: Three Process Explanations. Journal of Environmental Psychology, 22(3), 247–263.CrossRefGoogle Scholar
  32. John, L., Acquisti, A., & Loewenstein, G. (2009). The best of strangers: Context dependent willingness to divulge personal information. Available at Accessed 1 Jan 2011.
  33. Keh, H. T., Foo, M. D., & Lim, B. C. (2002). Opportunity evaluation under risky conditions: The cognitive processes of entrepreneurs. Entrepreneurship Theory and Practice, 27(2), 125–148.CrossRefGoogle Scholar
  34. Klayman, J., Soll, J. B., González-Vallejo, C., & Barlas, S. (1999). Overconfidence: It depends on how, what, and whom you ask. Organizational Behavior and Human Decision Processes, 79(3), 216–247.CrossRefPubMedGoogle Scholar
  35. Lane, K. A., Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2007). Understanding and using the Implicit Association Test: IV: What we know (so far) about the method. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp. 59–102). New York: Guilford.Google Scholar
  36. Leavitt, K., Fong, C. T., & Greenwald, A. G. (2011). Asking about well-being gets you half an answer: Intraindividual processes of implicit and explicit job attitudes. Journal of Organizational Behavior, 32(4), 672–687.CrossRefGoogle Scholar
  37. Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14(S2), 95–112.CrossRefGoogle Scholar
  38. Lowe, R. A., & Ziedonis, A. A. (2006). Overoptimism and the performance of entrepreneurial firms. Management Science, 52(2), 173–186.CrossRefGoogle Scholar
  39. Malmendier, U., & Tate, G. (2005). CEO overconfidence and corporate investment. The Journal of Finance, 60(6), 2661–2700.CrossRefGoogle Scholar
  40. Moore, G. E. (1965). Cramming more components onto integrated circuits. Proceedings of the IEEE, 86(1), 82–85.CrossRefGoogle Scholar
  41. Muthitcharoen, A., Palvia, P. C., & Grover, V. (2011). Building a model of technology preference: The case of channel choices. Decision Sciences, 42(1), 205–237.CrossRefGoogle Scholar
  42. Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge: Belknap Press.Google Scholar
  43. Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the implicit association test: II. Method variables and construct validity. Personality and Social Psychology Bulletin, 31, 166–180.CrossRefPubMedGoogle Scholar
  44. Pinch, T. J., & Bijker, W. (1987). The social construction of facts and artifacts. In D. G. Johnson & J. M. Wetmore (Eds.), Technology and society: Building our sociotechnical future (pp. 107–140). Cambridge: The MIT Press.Google Scholar
  45. Pisano, G. P. (2006). Science business: The promise, the reality, and the future of biotech. Boston: Harvard Business School Press.Google Scholar
  46. Reynolds, S., Leavitt, K., & Decelles, K. (2010). Automatic ethics: The effects of implicit assumptions and contextual cues on moral behavior. Journal of Applied Psychology, 95, 752–760.CrossRefPubMedGoogle Scholar
  47. Rindova, V. P., & Petkova, A. P. (2007). When is a new thing a good thing? Technological change, product form design, and perceptions of value for product innovations. Organization Science, 18(2), 217–232.CrossRefGoogle Scholar
  48. Rumelt, R. P. (1974). Strategy, structure, and economic performance. Cambridge: Harvard University Business School Press.Google Scholar
  49. Silverstein, S. (2009). Health care information technology, hospital responsibilities, and joint commission standards. JAMA: The Journal of the American Medical Association, 302(4), 382.CrossRefPubMedGoogle Scholar
  50. Simon, M., & Houghton, S. M. (2003). The relationship between overconfidence and the introduction of risky products: Evidence from a field study. Academy of Management Journal, 46(2), 139–150.CrossRefGoogle Scholar
  51. Soll, J. B. (1996). Determinants of overconfidence and miscalibration: The roles of random error and ecological structure. Organizational Behavior and Human Decision Processes, 65(2), 117–137.CrossRefGoogle Scholar
  52. Stiegler, B. (1998). Technics and time: The fault of epimetheus. Stanford: Stanford University Press.Google Scholar
  53. Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. New York: Basic Books.Google Scholar
  54. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.CrossRefGoogle Scholar
  55. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124.CrossRefPubMedGoogle Scholar
  56. Uhlmann, E. L., Leavitt, K., Menges, J. I., Koopman, J., Howe, M., & Johnson, R. E. (2012). Getting explicit about the implicit: A taxonomy of implicit measures and guide for their use in organizational research. Organizational Research Methods, 15(4), 553–601.CrossRefGoogle Scholar
  57. Van Kleef, G. A., Homan, A. C., Beersma, B., van Knippenberg, D., van Knippenberg, B., & Damen, F. (2009). Searing sentiment or cold calculation? The effects of leader emotional displays on team performance depend on follower epistemic motivation. Academy of Management Journal, 52(3), 562–580.CrossRefGoogle Scholar
  58. Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215.CrossRefGoogle Scholar
  59. Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20(3), 470–477.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Brent B. Clark
    • 1
  • Christopher Robert
    • 2
  • Stephen A. Hampton
    • 3
  1. 1.Department of ManagementUniversity of South DakotaVermillionUSA
  2. 2.Department of ManagementUniversity of MissouriColumbiaUSA
  3. 3.Department of MarketingUniversity of MissouriColumbiaUSA

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