Cognition, Technology & Work

, Volume 7, Issue 1, pp 63–68

Increasing productivity through framing effects for interactive consumer choice

Original Article

Abstract

Framing effects have a profound impact on most areas of human psychology. These effects have been studied extensively, but have not been applied broadly to how people interact with information on the Internet. The present paper provides evidence that frames influence people’s productivity on the Internet across a variety of tasks. Experiment 1 demonstrates that satisfaction is increased by presenting information in a list for tasks that involve simple processing of information. For more complex information, hierarchical navigation was shown to increase satisfaction significantly. Experiment 2 provides evidence that abstract information, such as monetary values, must be presented in terms of concrete frames. Specifically, we demonstrate that framing discounts in terms of dollars off, instead of additional goods received, tends to be less effective given the complexity of processing abstract information. In both cases, the type of information presented affects people’s levels of satisfaction and performance.

Keywords

Framing effects Decision making Internet Behavioral economics Purchase behavior 

References

  1. Ayton P, Wright G (1994) Subjective probability: what should we believe? In: Wright G, Ayton P (eds) Subjective probability Wiley, Chichester, pp 163–183Google Scholar
  2. Casscells W, Schoenberger A, Graboys TB (1978) Interpretation by physicians of clinical laboratory results. New Engl J Med 299:999–1001Google Scholar
  3. Cosmides L, Tooby J (1996) Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition 58:1–73CrossRefGoogle Scholar
  4. Gigerenzer G (1996) On narrow norms and vague heuristics: a reply to Kahneman and Tversky. Psychol Rev 103(3):592–596CrossRefGoogle Scholar
  5. Kahneman D, Tversky A (1996) On the reality of cognitive illusions. Psychol Rev 103(3):582–591CrossRefGoogle Scholar
  6. Kahneman D, Slovic P, Tversky A (1982) Judgement under uncertainty: heuristics and biases. Cambridge University Press, CambridgeGoogle Scholar
  7. Klein G, Kaempf G, Wolf S, Thorsden M, Miller T (1997) Applying decision requirements to user-centered design. Int J Hum Comput Stud 46:1–15CrossRefGoogle Scholar
  8. Kotovsky K, Hayes JR, Simon HA (1985) Why are some problems hard? Evidence from the tower of Hanoi. Cogn Psychol 17:248–294CrossRefGoogle Scholar
  9. Kumar H, Plaisant C, Schneiderman B (1997) Browsing hierarchical data with multi-level dynamic queries and pruning. Int J Hum Comp Stud 46:103–124CrossRefGoogle Scholar
  10. Lowrie T (1996) The use of visual imagery as a problem-solving tool: classroom implementation. J Ment Imagery 20(3–4):127–140Google Scholar
  11. Lyman P, Varian H (2003) How much information. Information management and systems. Berkeley University of California, BerkeleyGoogle Scholar
  12. Norman D (1993) Things that make us smart. Addison-Wesley, ReadingGoogle Scholar
  13. Schotter A, Weigelt K, Wilson C (1994) A laboratory investigation of multi-person rationality and presentation effects. Games Econ Behav 6:445–468CrossRefGoogle Scholar
  14. Shneiderman B (1998) Designing the user interface. Addison-Wesley, ReadingGoogle Scholar
  15. Simon HA (1978) Information processing theory of human problem solving. In: Estes WK (ed) Handbook of learning and cognitive processes, vol 5. Hillsdale, ErlbaumGoogle Scholar
  16. Simonson I, Carmon Z, O’Curry S (1994) “Experimental evidence on the negative effect of product features & sales promotions on brand choice,”. Market Sci 13:23–40Google Scholar
  17. Sloman SA, Over D, Slovak L, Stibel JM (2003) Frequency Illusions and Other Fallacies. Organ Behav Hum Decis Process 91:296–309CrossRefGoogle Scholar
  18. Stibel JM (1999a) Consumer choice, decision making, and the Internet. Internetworking 2.1Google Scholar
  19. Stibel JM (1999b) The role of explanatory-based feature relations among artifact and biological kind categorization, Graduate Dissertation, Brown University, Dissertation Abstracts InternationalGoogle Scholar
  20. Stibel JM (2005) Mental models and online consumer behaviour. Behav Inform TechnolGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2005

Authors and Affiliations

  1. 1.United Online Inc.MalibuUSA

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