The Effects of Effort-Feedback on Time Spent for Information Processing in Multi-criteria Decision Making

  • Konradin Maier
  • Josef Frysak
  • Edward W. N. Bernroider
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 275)


When making decisions, many people tend to rely on their instinctive choices instead of trusting and adopting those suggested by decision support systems. Feedback interventions can be used to subliminally direct and assist decision makers’ attention to increase the accuracy of the decision. This paper seeks to further develop our understanding of such persuasive decision aids for a preferential choice problem. For this purpose, we applied an experiment design with either enabled or disabled feedback interventions in a self-developed computerized decision aid with varying decision complexities. We applied continuous feedback mechanisms using normative rules to evaluate the participants’ time investments during their information processing stage. Our findings demonstrate that normative effort feedback impacts both time spent on single items and time spent for overall decision processing. We conclude that effort feedback can be a viable feedback option to implement in decision aids.


Decision Aid Decision-making Cognitive decision support multicriteria decision making decision support systems 


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  1. 1.
    Stanovich, K.E., West, R.F.: Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences 23, 645–665 (2000)CrossRefGoogle Scholar
  2. 2.
    Simon, H.A.: A Behavioral Model of Rational Choice. The Quarterly Journal of Economics 69, 99–118 (1955)CrossRefGoogle Scholar
  3. 3.
    Bernroider, E.W.N., Koch, S.: Decision Making for ERP-Investments from the Perspective of Organizational Impact - Preliminary Results from an Empirical Study. In: Proceedings of the Fifth Americas Conference on Information Systems, AMCIS 1999, pp. 773–775. Association for Information Systems AIS, Milwaukee WI (1999)Google Scholar
  4. 4.
    Payne, J.W., Bettman, J.R., Johnson, E.J.: The adaptive decision maker. Cambridge University Press (1993)Google Scholar
  5. 5.
    Tversky, A., Kahneman, D.: Judgment under uncertainty: Heuristics and biases. Science 185, 1124–1131 (1974)CrossRefGoogle Scholar
  6. 6.
    Smith, J.E., von Winterfeldt, D.: Decision Analysis in Management Science. Management Science 50, 561–574 (2004)CrossRefGoogle Scholar
  7. 7.
    Saaty, T.L.: How to make a decision: the analytic hierarchy process. European Journal of Operational Research 48, 9–26 (1990)CrossRefMATHGoogle Scholar
  8. 8.
    Todd, P., Benbasat, I.: An experimental investigation of the impact of computer based decision aids on decision making strategies. Information Systems Research 2, 87–115 (1991)CrossRefGoogle Scholar
  9. 9.
    Bernroider, E., Schmöllerl, P.: A technological, organisational and environmental analysis of decision making methodologies and satisfaction in the context of IT induced business transformations. European Journal of Operational Research 224, 141–153 (2013)CrossRefGoogle Scholar
  10. 10.
    Lu, H.P., Yu, H.J., Lu, S.S.K.: The effects of cognitive style and model type on DSS acceptance: An empirical study. European Journal of Operational Research 131, 649–663 (2001)CrossRefMATHGoogle Scholar
  11. 11.
    Wang, W., Benbasat, I.: Interactive decision aids for consumer decision making in e-commerce: The influence of perceived strategy restrictiveness. MIS Quarterly 33, 293–293 (2009)Google Scholar
  12. 12.
    Davern, M., Shaft, T., Te’eni, D.: Cognition Matters: Enduring Questions in Cognitive IS Research. Journal of the Association for Information Systems 13, 1–1 (2012)Google Scholar
  13. 13.
    Payne, J.W.: Task complexity and contingent processing in decision making: An information search and protocol analysis. Organizational Behavior and Human Performance 16, 366–387 (1976)CrossRefGoogle Scholar
  14. 14.
    Lerner, J.S., Tetlock, P.E.: Accounting for the effects of accountability. Psychological Bulletin 125, 255–255 (1999)CrossRefGoogle Scholar
  15. 15.
    Speier, C.: The influence of information presentation formats on complex task decision-making performance. International Journal of Human-Computer Studies 64, 1115–1131 (2006)CrossRefGoogle Scholar
  16. 16.
    Tetlock, P.E., Skitka, L., Boettger, R.: Social and cognitive strategies for coping with accountability: Conformity, complexity, and bolstering. Journal of Personality and Social Psychology 57, 632–632 (1989)CrossRefGoogle Scholar
  17. 17.
    Todd, P., Benbasat, I.: Evaluating the impact of DSS, cognitive effort, and incentives on strategy selection. Information Systems Research 10, 356–356 (1999)CrossRefGoogle Scholar
  18. 18.
    Kluger, A.N., DeNisi, A.: The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin 119, 254–254 (1996)CrossRefGoogle Scholar
  19. 19.
    Agostinelli, G., Brown, J.M., Miller, W.R.: Effects of normative feedback on consumption among heavy drinking college students. Journal of Drug Education 25, 31–40 (1995)CrossRefGoogle Scholar
  20. 20.
    Becker, L.J.: Joint effect of feedback and goal setting on performance: A field study of residential energy conservation. Journal of Applied Psychology 63, 428–428 (1978)CrossRefGoogle Scholar
  21. 21.
    Froehlich, J., Findlater, L., Landay, J.: The design of eco-feedback technology. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems, pp. 1999–2008. ACM, New York (2010)Google Scholar
  22. 22.
    Loock, C.M., Staake, T., Landwehr, J.: Green IS Design and Energy Conservation: An Empirical Investigation of Social Normative Feedback. In: ICIS 2011 Proceedings, pp. 1–15. Curran Associates Inc., New York (2011)Google Scholar
  23. 23.
    Simonson, I., Nye, P.: The effect of accountability on susceptibility to decision errors. Organizational Behavior and Human Decision Processes 51, 416–446 (1992)CrossRefGoogle Scholar
  24. 24.
    Oinas-Kukkonen, H., Harjumaa, M.: Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems 24, 28–28 (2009)Google Scholar
  25. 25.
    Neighbors, C., Larimer, M.E., Lewis, M.A.: Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered personalized normative feedback intervention. Journal of Consulting and Clinical Psychology 72, 434–434 (2004)CrossRefGoogle Scholar
  26. 26.
    Bernroider, E.W.N., Mitlöhner, J.: Social Choice Aggregation Methods for Multiple Attribute Business Information System Selection. In: Ninth International Conference on Business Information Systems, BIS, pp. 267–276. Gesellschaft für Informatik, Bonn Germany (2006)Google Scholar
  27. 27.
    Creyer, E.H., Bettman, J.R., Payne, J.W.: The impact of accuracy and effort feedback and goals on adaptive decision behavior. Journal of Behavioral Decision Making 3, 1–16 (1990)CrossRefGoogle Scholar
  28. 28.
    Ashton, R.H.: Pressure and performance in accounting decision settings: Paradoxical effects of incentives, feedback, and justification. Journal of Accounting Research 28, 148–180 (1990)CrossRefGoogle Scholar
  29. 29.
    Murphy, A.H., Winkler, R.L.: Reliability of subjective probability forecasts of precipitation and temperature. Applied Statistics 26, 41–47 (1977)CrossRefGoogle Scholar
  30. 30.
    Fennema, M.G., Kleinmuntz, D.N.: Anticipations of effort and accuracy in multiattribute choice. Organizational Behavior and Human Decision Processes 63, 21–32 (1995)CrossRefGoogle Scholar
  31. 31.
    Chenoweth, T., Dowling, K.L., St Louis, R.D.: Convincing DSS users that complex models are worth the effort. Decision Support Systems 37, 71–82 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Konradin Maier
    • 1
  • Josef Frysak
    • 1
  • Edward W. N. Bernroider
    • 1
  1. 1.Vienna University of Economics and BusinessViennaAustria

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