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Marketing pp 145-161 | Cite as

Explaining Consumer Decision Making through Evaluation Process Models

Chapter

Abstract

In what manner do consumers combine information in making decisions? Stimulated by the seminal contribution of researchers in decision theory and psychologyl, the past decade has witnessed an explosion of research interest in answering this question2.

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Notes

  1. 1.
    Fishbein, M., An investigation of the relationship between beliefs about an object and the attitude toward that object, in: Human Relations, 1963, 16, 233–239.Google Scholar
  2. Hammond, K. R., Probabilistic functionning and the clinical method, in: Psychological Review, 1955, 62, 255–262.Google Scholar
  3. Hoffman, P. J., The paramorphic letin, 1960, 57, 116–131.Google Scholar
  4. Rosenberg, M. J., Cognitive structure and attitudinal effect, in: Journal of Abnormal and Social Psychology, 1956, 53, 337–340.Google Scholar
  5. Simon, H. A., A behavioral model of rational choice, in: Quarterly Journal of Economics, 1955, 69, 99–118.Google Scholar
  6. 2.
    Bass, F. M.; Talarzyk, W. W., An attitude model for the study of brand preference, in: Journal of Marketing Research, 1972, 9, 93–96.Google Scholar
  7. Bettman, J. R., Behavioral simulation models in marketing systems, unpunlished doctoral dissertation, New Haven: Yale University, 1969.Google Scholar
  8. Cohen, J. B.; Fishbein, M.; Ahtola, O. T., The nature and uses of expectancy value models in consumer attitude research, in: Journal of Marketing Research, 1972, 9, 456–460.CrossRefGoogle Scholar
  9. Day, G. S., Evaluating models of attitude structure, in: Journal of Marketing Research, 1972, 9, 279–286.CrossRefGoogle Scholar
  10. Ginter, J. L., An experimental study of attitude change and choice of new brands, submitted to the American Marketing Association, Spring Conference, 1972, New YorkGoogle Scholar
  11. Lehman, D. R., Television show preference: an application of a choice model, in: Journal of Marketing Research, 1971, 8, 45–55.CrossRefGoogle Scholar
  12. Nakanishi, M.; Bett-Man, J. R., Attitude models revisited: an individual level analysis, in: Journal of Consumer Research, 1974, 16–21.Google Scholar
  13. Russ, F. A., Evaluation process models and the prediction of preferences, in: Proceedings, 2nd Annual Conference, Association for Consumer Research, 1971, 346–350.Google Scholar
  14. Sheth, J. N., Reply to comments on the nature and uses of expectancy value models in consumer attitude research, in: Journal of Marketing Research, 1972, 9, 462–465.CrossRefGoogle Scholar
  15. Sheth, J. N.; Talarzyk, W. W., Perveived instrumentality and value importance as determinants of attitudes, in: Journal of Marketing Research, 1955, 9, 6–9.CrossRefGoogle Scholar
  16. Wilkie, W. L.; Pessemier, E. A., Issues in marketing’s use of multi-attribute attitude models, in: Journal of Marketing Research, 1973, 10, 428–441.CrossRefGoogle Scholar
  17. Winter, F. W., Mathematical considerations in the use of linear attitude models, in: Working paper, University of Illinois, 1972.Google Scholar
  18. Wright, P. L., Use of consumer judgment models in promotion planning, in: Journal of Marketing, 1973, 37, 27–33.CrossRefGoogle Scholar
  19. 3.
    It should be noted that this article focused on the evaluation process models, that is, on the composition-approach. The decomposition approach also deals with multiattribute choice theory and typically employs multidimensional scaling analysis in order to discern the relevant dimensions of brand choice. The study of the decomposition approach would constitute by itself another topic. However, it can be said that when compared to the composition approach, the multidimensional scaling technique was less good a predictor of preferences.Google Scholar
  20. 4.
    Tversky, A., Intransitivity of preferences, in: Psychological Review, 1969, 76, 31–48.CrossRefGoogle Scholar
  21. 5.
    Morrison, H. W., Testable conditions for trials of paired comparison choices, in: Psychometrika, 1963, 28, 369–390.CrossRefGoogle Scholar
  22. 6.
    Tversky, A., op. cit. footn. 4.Google Scholar
  23. 7.
    Adams, E. W.; Fagot, R., A model of riskless choice, in: Behavioral Science, 1959, 4, 1–10.CrossRefGoogle Scholar
  24. 8.
    Tversky, A., Additivity, utility and subjective probability, in: Journal of Mathematical Psychology, 1967, 4, 175–202.CrossRefGoogle Scholar
  25. 9.
    Miller, J. R. III, The assessment of worth: a systematic procedure and its experimental validation. Unpublished doctoral dissertation, Massachusetts, Institute of Technology, 1966.Google Scholar
  26. 10.
    Miller, J. R. III, Assessing alternative transportation systems, Rand Corporation Memorandum, RM-5865-DOT, 1969.Google Scholar
  27. 11.
    Raiffa, H., Preferences for multi-attributed alternatives. Rand Corporation Memorandum, RM-5868-DOT RC, 1969.Google Scholar
  28. 12.
    Meehl, P. E., Clinical versus statistical prediction. Minneapolis: University of Minnesota Press, 1954.CrossRefGoogle Scholar
  29. 13.
    Shepard, R. N., On subjectively optimum selection among multiattribute alternatives, in: Human judgments and optimality, 1964.Google Scholar
  30. 14.
    Abelson, R. P,; Aronson, E.; McGuire, W.; Newcomb, T.; Rosenberg, M. J.; Tannenbaun, P., Cognitive consistency: a sourcebook, 1968.Google Scholar
  31. 15.
    Anderson, N, H., A simple model for information integration, in: Cognitive consistency: a sourcebook, 1968.Google Scholar
  32. 16.
    Feldman, S., What do you think of a cruel, wise man? in: Cognitive consistency: a source-book, 1968.Google Scholar
  33. 17.
    Rosenberg, M. J., Impression processing and the evaluation of new and old objects, in: Cognitive consistency: a sourcebook, 1968.Google Scholar
  34. 18.
    Einhorn, H. J., The use of nonlinear, noncompensatory models in decision making, in: Psychological Bulletin, 1970, 73, 221–230.CrossRefGoogle Scholar
  35. 19.
    Rosenberg, M. J., op. cit. footn. 1.Google Scholar
  36. 20.
    Rosenberg, M. J., A structural theory of attitude dynamics, in: Public Opinion Quarterly, 1960, 24, 319–340.CrossRefGoogle Scholar
  37. 21.
    Fishbein, M., Readings in attitude theory and measurement, 1967.Google Scholar
  38. 22.
    Sheth, J. N., Talarzyk, W. W., Relative contribution of perceived instrumentality and value importance components in determining attitudes, Faculty Working Paper nr. 15, College of Commerce and Business Administration, University of Illinois, 1971.Google Scholar
  39. 23.
    Sheth, J. N., Talarzyk, W. W. Perceived instrumentality and value importance as determinants of attitudes, in: Journal of Marketing Research, 1972, 9, 6–9.CrossRefGoogle Scholar
  40. 24.
    Bass, F. M., Talarzyk, W. W., A study of attitude theory and brand preference, Paper presented at the American Marketing Association,Educators’ Conference, Cincinatti, 1969.Google Scholar
  41. 25.
    Bass, F. M., Talarzyk, W. W., op. cit. footn. 2.Google Scholar
  42. 26.
    Cohen, J. B.; Fishbein, M.; Ahtola, O. T., op. cit. footn. 2 Bass, F. M., Fishbein and brand preference: a reply, in: Journal of Marketing Research, 1972, 9, 461.Google Scholar
  43. Talarzyk, W. W., A reply to the response to Bass, Talarzyk, and Sheth, in: Journal of Marketing Research, 1972, 9, 465–467.. — Sheth, J. N., op. cit. footn. 2.Google Scholar
  44. 27.
    Myers, J. H.; Alpert, M. I., Determinant buying attitudes: meaning and measurements, in: Journal of Marketing, 1968, 32 (4), 14.Google Scholar
  45. 28.
    Coombs, C. H.; Kao, R. C., Nonmetric factor analysis, Research Bulletin nr. 38, University of Michigan Engineering Research Institute, 1955.Google Scholar
  46. 29.
    Coombs, C. H., A theory of data, 1964.Google Scholar
  47. 30.
    Dawes, R. M., Social selection based on multi-dimensional criteria, in: Journal of Abnormal Psychology, 1964 (a), 68, 104–109.CrossRefGoogle Scholar
  48. 31.
    Dawes, R. M., Toward a general framework for evaluation, University of Michigan, Department of Psychology, 1964 (b).Google Scholar
  49. 32.
    Russ, F. A., op. cit. footn. 2.Google Scholar
  50. 33.
    Russ, F. A., op. cit. footn. 2.Google Scholar
  51. 34.
    Einhorn, H. J., op. cit. footn. 18.Google Scholar
  52. 35.
    Einhorn, H. J., Use of nonlinear, noncompensatory models as a function of task and amount of information, in: Organizational Behavior and Human Performance, 1971, 6, 1–27Google Scholar
  53. 36.
    Goldberg, L. R., Five models of clinical judgment: an empirical comparison between linear and nonlinear representations of the human inference process, in: Organizational Behavior and Human Performance, 1971, 6, 458–479.Google Scholar
  54. 37.
    Einhorn, H. J., op. cit. footn. 18. — Einhorn, H. J., op. cit. footn. 35. — Komorita, S. S., A model for decision making under risk, in: American Jorunal of Psychology, 1964, 77, 429–436.Google Scholar
  55. 38.
    Details of the procedure are in Einhorn (39).Google Scholar
  56. 39.
    Einhorn, H. J., The use of nonlinear, noncompensatory models in decision making, unpublished doctoral dissertation, Wayne State University, 1969.Google Scholar
  57. 40.
    Variations between individuals are characterized by variations in parameters. Consider a two-characteristics case: U= x, α1 x2 α2 with a, + a2 = k (constant). At the extremes, a consumer for whom a, = 0 is interested only in x2 one for whom a, = k is interested only in x,.Google Scholar
  58. 41.
    Stevens, S. S., Measurement, statistics and the schematic view, in: Science, 1968, 161, 849–856.Google Scholar
  59. 42.
    Goldberg, L. R., op. cit. footn. 36.Google Scholar
  60. 43.
    Bettman, J. R., Perceived risk and its components: a model and empirical test, Paper presented at the Annual Conference of the Association for Consumer Research, 1972.Google Scholar
  61. 44.
    Goldberg, L. R., op. cit. footn. 36.Google Scholar
  62. 45.
    Russ, F. A., op. cit. footn. 2.Google Scholar
  63. 46.
    They indicate that, what Einhorn may have shown is not that individuals have nonlinear evaluation functions for alternatives, but that they have nonlinear preference functions for attributes.Google Scholar
  64. 47.
    Dawes, R. M., op. cit. footn. 31.Google Scholar
  65. 48.
    These types of models were introduced by Russ in his research; he showed that they do not necessary downgrade alternatives with poor values on any attribute; they especially downgrade alternatives with poor values on the most important attributes.Google Scholar
  66. 49.
    If two or more alternatives become acceptable at the same time, either choose a smaller arbitrary value or if this small value corresponds to a j.n.d. (just noticeable difference), give the same rank to these alternatives.Google Scholar
  67. 50.
    Russ, F. A., op. cit. footn. 2.Google Scholar
  68. 51.
    Pras, B.; Summers, J. O., A comparison of linear and nonlinear evaluation process models, in: Journal of Marketing Research, 12, 1975, 276–281.CrossRefGoogle Scholar
  69. 52.
    Debreu, G., Representation of a preference ordering by a numerical function, in: Decision processes, 1954.Google Scholar
  70. 53.
    Alexis, Haines, G. H., Jr.; Simon, L., Consumer information processing: the case of women’s clothing, in: Marketing and the science of planning, American Marketing Association, 1968.Google Scholar
  71. 54.
    Bettman, J. R., op. cit. footn. 2.Google Scholar
  72. 55.
    Russ, F. A., op. cit. footn. 2.Google Scholar
  73. 56.
    Russ, F. A., op. cit. footn. 2.Google Scholar
  74. 57.
    Yntema, D. B.; Torgerson, W..S., Man-Computer cooperation in decisions requiring common sense. IRE transactions on human factors in electronics HFE-2, 1961, 20–26.Google Scholar
  75. 58.
    For example see: Luce, R. D., Semiorders and a theory of utility discrimination, in: Econometrica, 1956, 24, 178–191. — Tversky, A., op. cit. footn. 4.Google Scholar
  76. 59.
    For example see:Luce, R. D., op. cit. footn. 58. — Pras, B.; Summers, J. O., op. cit. 51. — Russ, F. A., op. cit. footn. 4. — Tversky, A., op. cit. footn. 4. — Yntema, D. B.; Torgerson, W. S.. op. cit. footn. 57.Google Scholar
  77. 60.
    The jsut noticeable difference depends on the scale used in the study. We will try several values and use the one which gives the best fit.Google Scholar
  78. 61.
    Russ, F. A., op. cit. footn. 2.Google Scholar
  79. 62.
    Pras, B., Predictive qualities of linear and nonlinear evaluation process models, unpublished doctoral dissertation, Indiana University, 1973.Google Scholar
  80. 63.
    Boyds, H. W., Hr.; Ray, M. L.; Strong, E. C., An attitudinal framework for advertising strategy, in: Journal of Marketing, 1972, 36, 27–33.Google Scholar
  81. 64.
    For example see: Cohen, J. B.; Houston, M., The structure of consumer attitudes: the use of attribute possession and importance scores, Faculty Working paper nr. 2, College of Commerce and Business Administration, University of Illinois, 1971. — Sheth, J. N.; Talarzyk, W. W., op. cit. footn. 22.Google Scholar
  82. 65.
    Settle, R. B.; Golden, L. L., Attribution theory and advertiser credibility, in: Journal of Marketing Research, 1974, 11, 181–185.CrossRefGoogle Scholar
  83. 66.
    Pras, B., Factors affecting the predictive qualities of consumer evaluation process models, in: Der Markt, 1975, 53, 5–8.Google Scholar
  84. 67.
    Angelmar, R.; Pras, B., Advertising strategy implications of consumer evaluation process, submitted for publication, 1975.Google Scholar
  85. 68.
    Can the study of these models also help the manager to understand his own decision process? — In order to make decisions, a manager must evaluate a set of alternatives with respect to certain criteria. Knowledge of the different types of process models would help him to identity the one he is rising and his competitors’. This might be particularly useful in domains such as conflict resolution or bargaining. It is the hypothesis of this author that managers use a conjunctive approach when some of the alternatives they consider are unacceptable to them. They switch to a lexicographic model when the alternatives are all acceptable.Google Scholar
  86. 69.
    Wilkie, W. L.; Pessemier, E. A., op. cit. footn. 2.Google Scholar

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© Betriebswirschaftlicher Verlag Dr. Th. Gabler KG, Wiesbaden 1978

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