Advertisement

An Exposition of the Role of Consideration Sets in a DS/AHP Analysis of Consumer Choice

  • Malcolm J. Beynon
  • Luiz Moutinho
  • Cleopatra Veloutsou
Chapter

Abstract

Consumer behaviour is often perceived through the notion of consideration sets. However, realistic modelling of consumer choice processes identifies impeding factors, including ignorance and non-specificity. In this chapter, the appeasement of these factors and the role of consideration sets are considered through the utilisation of the nascent Dempster-Shafer/Analytic Hierarchy Process (DS/AHP) method of choice analysis. The central element in the DS/AHP analysis is the body of evidence (BOE), with certain BOE constructed at different stages in the analysis, then a number of different sets of results can be found. The chapter is attempting to convey a more realistic approach for the individual consumer to undertake the required judgement making process. The investigation is based on a group of consumers and their preferences on a number of cars over different criteria. The notion of consideration sets is shown to be fundamental within DS/AHP, and a novel approach to the aggregation of the preferences from the consumers is utilised. A notional approach to the identification of awareness, consideration and choice sets is described, based on the levels of belief and plausibility in the best car existing in a group of cars, which could be compared with the algorithm developed by Gensch and Soofi (Int J Res Mark 12: 25–38, 1995).

References

  1. Allenby, G. M., & Ginter, J. L. (1995). The Effects of In-store Displays and Feature Advertising on Consideration Sets. International Journal of Research in Marketing, 12, 67–80.CrossRefGoogle Scholar
  2. Analytis, P., Kothiyal, A., & Katsikopoulos, K. (2014). Multi-Attribute Utility Models as Cognitive Search Engines. Judgment and Decision making, 9(5), 403–419.Google Scholar
  3. Ariely, D., & Levav, J. (2000). Sequential Choice in Group Settings: Taking the Road Less Travelled and Less Enjoyed. Journal of Consumer Research, 27(3), 279–290.CrossRefGoogle Scholar
  4. Arora, N., & Huber, J. (2001). Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments. Journal of Consumer Research, 28(2), 273–283.CrossRefGoogle Scholar
  5. Aurier, P., Jean, S., & Zaichkowsky, J. L. (2000). Consideration Set Size and Familiarity with Usage Context. Advances in Consumer Research, 27, 307–313.Google Scholar
  6. Beaman, C. P. (2013). Inferring the Biggest and Best: A Measurement Model for Applying Recognition to Evoke Consideration Sets and Judge Between Multiple Alternatives. Cognitive Systems Research, 24, 18–25.CrossRefGoogle Scholar
  7. Benitez, J., Delgado-G, X., Izquierdo, J., & Pérez-G, R. (2015). Consistent Completion of Incomplete Judgments in Decision Making Using AHP. Journal of Computational and Applied Mathematics, 290, 412–422.CrossRefGoogle Scholar
  8. Beynon, M. (2002). DS/AHP Method: A Mathematical Analysis, Including an Understanding of Uncertainty. European Journal of Operational Research, 140(1), 149–165.Google Scholar
  9. Beynon, M. J. (2006). The Role of the DS/AHP in Identifying Inter-Group Alliances and Majority Rule Within Group Decision Making. Group Decision and Negotiation, 15(1), 21–42.CrossRefGoogle Scholar
  10. Beynon, M. J., Curry, B., & Morgan, P. H. (2000). The Dempster-Shafer Theory of Evidence: An Alternative Approach to Multicriteria Decision Modelling. Omega, 28(1), 37–50.CrossRefGoogle Scholar
  11. Bloch, B. (1996). Some Aspects of Dempster-Shafer Evidence Theory for Classification of Multi-Modality Images Taking Partial Volume Effect into Account. Pattern Recognition Letters, 17, 905–919.CrossRefGoogle Scholar
  12. Brown, C. L., & Carpenter, G. S. (2000). Why Is the Trivial Important? A Reasons-Based Account for the Effects of Trivial Attributes on Choice. Journal of Consumer Research, 26(4), 372–385.CrossRefGoogle Scholar
  13. Bryson, N., & Mobolurin, A. (1999). A Process for Generating Quantitative Belief Functions. European Journal of Operational Research, 115(3), 624–633.CrossRefGoogle Scholar
  14. Butler, L. T., & Berry, D. C. (2001). Transfer Effects in Implicit Memory and Consumer Choice. Applied Cognitive Psychology, 15(6), 587–601.CrossRefGoogle Scholar
  15. Carson, R., & Louviere, J. (2014). Statistical Properties of Consideration Sets. Journal of Choice Modelling, 13, 37–48.CrossRefGoogle Scholar
  16. Chakravarti, A., & Janiszewski, C. (2003). The Influence of Macro-Level Motives on Consideration Set Composition in Novel Purchase Situations. Journal of Consumer Research, 30(September), 244–258.CrossRefGoogle Scholar
  17. Chase, W. G., & Simon, H. K. (1973). Perception in Chess. Cognitive Psychology, 4, 55–81.CrossRefGoogle Scholar
  18. Cherenev, A., & Carpenter, G. S. (2001). The Role of Market Efficiency Intuitions in Consumer Choice: A Case of Compensatory Inferences. Journal of Marketing Research, 38(3), 349–361.CrossRefGoogle Scholar
  19. Chiang, J., Chib, S., & Narasimhan, C. (1998). Markov Chain Monte Carlo and Models of Consideration Set and Parameter Heterogeneity. Journal of Econometrics, 89(1–2), 223–248.CrossRefGoogle Scholar
  20. Dede, G., Kamalakis, T., & Sphicopoulos, T. (2016). Theoretical Estimation of the Probability of Weight Rank Reversal in Pairwise Comparisons. European Journal of Operational Research, 252(2), 587–600.CrossRefGoogle Scholar
  21. Dempster, A. P. (1968). A Generalization of Bayesian Inference (with Discussion). Journal of the Royal Statistical Society. Series B, 30(2), 205–247.Google Scholar
  22. Desai, K. K., & Hoyer, W. D. (2000). Descriptive Characteristics of Memory-Based Consideration Sets: Influence of Usage Occasion Frequency and Sage Location Familiarity. Journal of Consumer Research, 27(3), 309–323.CrossRefGoogle Scholar
  23. Diehl, K. (2004,August). When Two Rights Make a Wrong: Searching Too Much in Ordered Environments. Journal of Marketing Research, XLII, 213–322.Google Scholar
  24. Dubois, D., & Prade, H. (1985). A Note on Measures of Specificity for Fuzzy Sets. International Journal of General Systems, 10(4), 279–283.CrossRefGoogle Scholar
  25. Ducey, M. J. (2001). Representing Uncertainty in Silvicultural Decisions: An Application of the Dempster-Shafer Theory of Evidence. Forest Ecology and Management, 150, 199–211.CrossRefGoogle Scholar
  26. Eliaz, K., & Spiegler, R. (2011). Consideration Sets and Competitive Marketing. Review of Economic Studies, 78(1), 235–262.CrossRefGoogle Scholar
  27. Erdem, T., Imai, S., & Keane, M. P. (2003). Brand and Quantity Choice Dynamics Under Price Uncertainty. Quantitative Marketing and Economics, 1(1 March), 5–64.CrossRefGoogle Scholar
  28. Gensch, D. H., & Soofi, E. S. (1995). Information-Theoretic Estimation of Consideration Sets. International Journal of Research in Marketing, 12, 25–38.CrossRefGoogle Scholar
  29. Gilbride, T., & Allenby, G. (2004). A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules. Marketing Science, 23(3), 391–406.CrossRefGoogle Scholar
  30. Gobet, F., & Simon, H. A. (1998a). Expert Chess Memory: Revisiting the Chunking Hypothesis. Memory, 6(3), 225–255.CrossRefGoogle Scholar
  31. Gobet, F., & Simon, H. A. (1998b). Pattern Recognition Makes Search Possible: Comments on Holding (1992). Psychological Research, 61(3), 204–209.CrossRefGoogle Scholar
  32. Goodman, J., Broniarczyk, S., Griffin, J., & McAlister, L. (2013). Help or Hinder? When Recommendation Signage Expands Consideration Sets and Heightens Decision Difficulty. Journal of Consumer Psychology, 23(2), 165–174.CrossRefGoogle Scholar
  33. Guest, D., Estes, Z., Gibbert, M., & Mazunrsky, D. (2016). Brand Suicide? Memory and Linking of Negative Brand Names. PloS One, 11(3), e0151628.CrossRefGoogle Scholar
  34. Hamilton, R. (2003). Why Do People Suggest What They Don Not Want? Using Context Effects to Influence Others’ Choices. Journal of Consumer Research, 29, 492–506.CrossRefGoogle Scholar
  35. Han, D., Han, C., & Deng, Y. (2013). Novel Approaches for the Transformation of Fuzzy Membership Function into Basic Probability Assignment Based on Uncertainty Optimization. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 21(2), 289–322.CrossRefGoogle Scholar
  36. Hastak, M., & Mitra, A. (1996). Facilitating and Inhibiting Effects of Brand Cues on Recall, Consideration Set and Choice. Journal of Business Research, 37(2), 121–127.CrossRefGoogle Scholar
  37. Hauser, J. R., & Wernerfelt, B. (1990). An Evaluation Cost Model of Evoked Sets. Journal of Consumer Research, 16(March), 383–408.Google Scholar
  38. Hauser, J., Toubia, O., Evgeniou, T., Befurt, R., & Dzyaburra, D. (2010). Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets. Journal of Marketing Research, 47(3), 485–496.CrossRefGoogle Scholar
  39. Hogarth, R. M. (1980). Judgement and Choice (2nd ed.). New York: Wiley.Google Scholar
  40. Horowitz, J. L., & Louviere, J. J. (1995). What Is the Role of Consideration Sets in Choice Modeling? International Journal of Research in Marketing, 12(1), 39–54.CrossRefGoogle Scholar
  41. Jeongwen, C., & Chib, S. (1999). Markov Chain, Monte Carlo and Models of Consideration Set and Parameter Heterogeneity. Journal of Econometrics, 89(1/2), 223–249.Google Scholar
  42. Johnson, M. D., & Lehmann, D. R. (1997). Consumer Experiences and Consideration Sets for Brands and Product Categories. Advances in Consumer Research, 24, 295–301.CrossRefGoogle Scholar
  43. Kivetz, R., & Simonson, I. (2000). The Effects of Incomplete Information on Consumer Choice. Journal of Marketing Research, 37(4), 427–448.CrossRefGoogle Scholar
  44. Klir, G. J., & Wierman, M. J. (1998). Uncertainty-Based Information: Elements of Generalized Information Theory. Heidelberg: Physica-Verlag.Google Scholar
  45. Lapersonne, E., Laurent, G., & Le Goff, J.-J. (1995). Consideration Sets of Size One: An Empirical Investigation of Automobile Purchases. International Journal of Research in Marketing, 12(1), 55–66.CrossRefGoogle Scholar
  46. Laroche, M., Kim, C., & Marsui, T. (2003). Which Decision Heuristivs Are Used in Consideration Set Formation? Journal of Consumer Marketing, 20(3), 192–209.CrossRefGoogle Scholar
  47. Lipshitz, R., & Strauss, O. (1997). Coping with Uncertainty: A Naturalistic Decision-Making Analysis. Organisational Behaviour and Human Decision Processes, 69(2), 149–163.CrossRefGoogle Scholar
  48. Lock, A. R., & Thomas, H. (1979). Appraisal of Multi-Attribute Utility Models in Marketing. European Journal of Marketing, 13(5), 294–307.CrossRefGoogle Scholar
  49. Lootsma, F. A. (1993). Scale Sensitivity in the Multiplicative AHP and SMART. Journal of Multi-Criteria Decision Analysis, 2, 87–110.CrossRefGoogle Scholar
  50. Luce, M. F., Payne, J. W., & Bettman, J. R. (1999). Emotional Trade-off Difficulty and Choice. Journal of Marketing Research, 36(2), 143–159.CrossRefGoogle Scholar
  51. Maheswaran, D., Mackie, D. M., & Chaiken, S. (1992). Brand Name as a Heuristic Cue: The Effects of Task Importance and Expectancy Confirmation on Consumer Judgments. Journal of Consumer Psychology, 1(4), 317–336.CrossRefGoogle Scholar
  52. Manrai, A. K. (1995). Mathematical Models of Brand Choice Behaviour. European Journal of Operational Research, 82, 1–17.CrossRefGoogle Scholar
  53. Mattila, A. (1998). An Examination of Consumers’ Use of Heuristic Cues in Making Satisfaction Judgments. Psychology and Marketing, 15(5), 477–501.CrossRefGoogle Scholar
  54. Mehta, N., Rajiv, S., & Srinivasan, K. (2003). Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation. Marketing Science, 22(1), 58–84.CrossRefGoogle Scholar
  55. Miller, G. A. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review, 63, 81–97.CrossRefGoogle Scholar
  56. Mitra, A. (1995). Advertising and the Stability of Consideration Sets Over Multiple Purchase Occasions. International Journal of Research in Marketing, 12(1), 81–94.CrossRefGoogle Scholar
  57. Murphy, C. K. (2000). Combining Belief Functions When Evidence Conflicts. Decision Support Systems, 29, 1–9.CrossRefGoogle Scholar
  58. Nowlis, S. M., & Simonson, I. (2000). Sales Promotions and the Choice Context as Competing Influences on Consumer Decision Making. Journal of Consumer Psychology, 9(1), 1–16.CrossRefGoogle Scholar
  59. Park, C., & Lessig, V. P. (1981). Familiarity and Its Impact on Consumer Decision Biases and Heuristics. Journal of Consumer Research, 8(12), 223–331.CrossRefGoogle Scholar
  60. Park, C. W., Jun, S. Y., & MacInnis, D. J. (2000). Choosing What I Want Versus Rejecting What I Do Not Want: An Application of Decision Framing to Product Option Choice Decisions. Journal of Marketing Research, XXXVII, 187–202.CrossRefGoogle Scholar
  61. Park, D., Kim, Y., Um, M. J., & Choi, S. U. (2015). Robust Priority for Strategic Environmental Assessment with Incomplete Information Using Multi-Criteria Decision Making Analysis. Sustainability, 7(8), 10233–10249.CrossRefGoogle Scholar
  62. Pires, T. (2016, September). Costly Search and Consideration Sets in Storable Goods Markets. Quantitative Marketing and Economics, 14(3), 157–193.CrossRefGoogle Scholar
  63. Prabhaker, P. R., & Sauer, P. (1994). Hierarchical Heuristics in Evaluation of Competitive Brands Based on Multiple Cues. Psychology and Marketing, 11(3), 217–235.CrossRefGoogle Scholar
  64. Punj, G., & Brookes, R. (2001). Decision Constraints and Consideration-Set Formation in Consumer Durables. Psychology and Marketing, 18(8), 843–863.CrossRefGoogle Scholar
  65. Punj, G., & Brookes, R. (2002). The Influence of Pre-decisional Constraints on Information Search and Consideration Set Formation in New Automobile Purchases. International Journal of Research in Marketing, 19, 383–400.CrossRefGoogle Scholar
  66. Racioppi, V., Marcarelli, G., & Squillante, M. (2015). Modelling a Sustainable Requalification Problem by Analytic Hierarchy Process. Quality and Quantity, 49(4), 1661–1677.CrossRefGoogle Scholar
  67. Raffone, A., & Wolters, G. (2001). A Cortical Mechanism for Binding in Visual Working Memory. Journal of Cognitive Neuroscience, 13(6), 766–785.CrossRefGoogle Scholar
  68. Roberts, J. H., & Lattin, J. M. (1997). Consideration: Review of Research and Prospects for Future Insights. Journal of Marketing Research, XXXIV, 406–410.CrossRefGoogle Scholar
  69. Roberts, J., & Nedungadi, P. (1995). Studying Consideration in the Consumer Decision Process: Progress and Challenges. International Journal of Research in Marketing, 12, 3–7.CrossRefGoogle Scholar
  70. Saaty, T. L. (1977). A Scaling Method for Priorities in Hierarchical Structures. Journal of Mathematical Psychology, 15, 59–62.CrossRefGoogle Scholar
  71. Seiler, S. (2013, June). The Impact of Search Costs on Consumer Behavior: A Dynamic Approach. Quantitative Marketing and Economics, 11(2), 155–203.Google Scholar
  72. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton: Princeton University Press.Google Scholar
  73. Shafer, G. (1990). Perspectives in the Theory of Belief Functions. International Journal of Approximate Reasoning, 4, 323–362.CrossRefGoogle Scholar
  74. Shafir, E. (1993). Choosing Versus Rejecting: Why Some Options are Better and Worse Than Others. Memory and Cognition, 21(4), 546–556.CrossRefGoogle Scholar
  75. Shapiro, S., MacInnis, D. J., & Heckler, S. E. (1997). The Effects of Incidental ad Exposure on the Formation of Consideration Sets. Journal of Consumer Research, 24(1), 94–104.CrossRefGoogle Scholar
  76. Sharpanskykh, A., & Zia, K. (2012). Emotional Decision Making in Large Crowds. In Y. Demazeau, J. Müller, J. Rodríguez, & J. Pérez (Eds.), Advances on Practical Applications of Agents and Multi-Agent Systems (pp. 191–200). Heidelberg: Springer.CrossRefGoogle Scholar
  77. Simon, H. A. (1955). A Behavioral Model of Rational Choice. Quarterly Journal of Economics, 69(1), 99–118.CrossRefGoogle Scholar
  78. Smets, P. (1994). What Is Dempster-Shafer’s Model? In R. R. Yager, M. Fedrizzi, & J. Kacprzyk (Eds.), Advances in the Dempster-Shafer Theory of Evidence (pp. 5–34). New York: Wiley.Google Scholar
  79. Swait, J., & Adamowicz, W. (2001). The Influence of Task Complexity on Consumer Choice: A Latent Class Model of Decision Strategy Switching. Journal of Consumer Research, 28(1), 135–148.CrossRefGoogle Scholar
  80. Taroun, A., & Yang, J.-B. (2013). A DST-Based Approach for Construction Project Risk Analysis. Journal of the Operational Research Society, 64(8), 1221–1230.CrossRefGoogle Scholar
  81. Verwey, W. B. (2003). Effect of Sequence Length on the Execution of Familiar Keying Sequences: Lasting Segmentation and Preparation? Journal of Motor Behavior, 35(4), 343–354.CrossRefGoogle Scholar
  82. Verwey, W., Groen, E., & Wright, D. (2016). The Stuff that Motor Chunks Are Made of: Spatial Instead of Motor Representations? Experimental Brain Research, 234(2), 353–366.CrossRefGoogle Scholar
  83. Vroomen, B., van Nierop, E., & Franses, P. H. (2003). Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks. European Journal of Operational Research, 154, 206–217.CrossRefGoogle Scholar
  84. Wang, J., Hu, Y., Xiao, F., Deng, X., & Deng, Y. (2016). A Novel Method to Use Fuzzy Soft Sets in Decision Making Based on Ambiguity Measure and Dempster-Shafer Theory of Evidence: An Application in Medical Diagnosis. Artificial Intelligence in Medicine, 69, 1–11.CrossRefGoogle Scholar
  85. Wedel, M., & Pieters, R. (2000). Eye Fixations on Advertisements and Memory for Brands: A Model and Findings. Marketing Science, 19(4), 297–312.CrossRefGoogle Scholar
  86. Wright, D., Rhee, J., & Vaculin, A. (2010). Offline Improvement During Motor Sequence Learning Is Not Restricted to Developing Motor Chunks. Journal of Motor Behavior, 42(5), 317–324.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Malcolm J. Beynon
    • 1
  • Luiz Moutinho
    • 2
    • 3
  • Cleopatra Veloutsou
    • 4
  1. 1.Cardiff Business SchoolCardiff UniversityCardiffUK
  2. 2.University of SuffolkSuffolk, EnglandUK
  3. 3.The University of the South PacificSuvaFiji
  4. 4.Adam Smith Business SchoolUniversity of GlasgowGlasgowUK

Personalised recommendations