Analysing Incomplete Consumer Web Data Using the Classification and Ranking Belief Simplex (Probabilistic Reasoning and Evolutionary Computation)

  • Malcolm J. Beynon
  • Kelly Page
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 258)


Consumer attitudes, involvement and motives have long been identified as important determinates of decision making in classic models of consumer behaviour. Online consumer attitudes may differ depending on the level of web experience of the intended consumer. This chapter considers Classification and Ranking Belief Simplex (CaRBS) analyses of consumer web data, considering attitudes from consumers with different levels of web experience. The CaRBS technique is based on Probabilistic Reasoning (Dempster-Shafer theory) and Evolutionary Computation (Trignometric-Differential Evolution), two known components of soft computing. An important facet of the presented analyses is the ability of the CaRBS technique to analyse incomplete data, without the need for the missing values present to be managed in anyway. The chapter allows a pertinent demonstration of how soft computing, here using CaRBS, can offer the opportunity for realistic analysis, more realistic than traditional techniques.


Differential Evolution Survey Question Internet Survey Consumer Attitude Focal Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Babin, B.J., Darden, W.R., Griffin, M.: Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research 20, 644–656 (1994)CrossRefGoogle Scholar
  2. Ballantine, P.W.: Effects of Interactivity and Product Information on Consumer Satisfaction in an Online Retailing Setting. International Journal of Retailing and Distribution Management 33(6), 461–471 (2005)CrossRefGoogle Scholar
  3. Bellenger, D.N., Korgaonkar, P.K.: Profiling the Recreational Shopper. Journal of Retailing 56, 77–91 (1980)Google Scholar
  4. Beynon, M.J.: A Novel Technique of Object Ranking and Classification under Ignorance: An Application to the Corporate Failure Risk Problem. European Journal of Operational Research 167, 493–517 (2005a)zbMATHCrossRefGoogle Scholar
  5. Beynon, M.J.: A Novel Approach to the Credit Rating Problem: Object Classification Under Ignorance. International Journal of Intelligent Systems in Accounting, Finance and Management 13, 113–130 (2005b)CrossRefGoogle Scholar
  6. Beynon, M.J.: Optimising Object Classification: Uncertain Reasoning based Analysis using CaRBS Systematic Search Algorithms. In: Vrakas, D., Vlahavas, I. (eds.) Artificial Intelligence for Advanced Problem Solving, pp. 234–253. IDEA Group Inc., PA (2008)Google Scholar
  7. Brengman, M., Geuens, M., Weijters, B., et al.: Segmenting Internet Shoppers Based On Their Web-Usage-Related Lifestyle: A Cross-Cultural Validation. Journal of Business Research 58(1), 79–88 (2005)CrossRefGoogle Scholar
  8. Chang, S.: Internet segmentation: state-of-the-art marketing applications. Journal of Segmentation Marketing 2(1), 19–34 (1998)CrossRefGoogle Scholar
  9. Chen, Z.: Data Mining and Uncertain Reasoning: An Integrated Approach. John Wiley, New York (2001)Google Scholar
  10. Christian, L.M., Dillman, D.A., Smyth, J.D.: Helping Respondents Get it Right the First Time: The Influence of Words, Symbols, and Graphics in Web Surveys. Public Opinion Quarterly 71(1), 113–125 (2007)CrossRefGoogle Scholar
  11. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: A comparison of two theoretical models. Management Science 35(8), 982–1003 (1989)CrossRefGoogle Scholar
  12. Dempster, A.P.: Upper and lower probabilities induced by a multiple valued mapping. Ann. Math. Statistics 38, 325–339 (1967)zbMATHCrossRefMathSciNetGoogle Scholar
  13. Diaz, A.N., Hammond, K., McWilliam, G.: A Study of Web Use and Attitudes Amongst Novices, Moderate Users and Heavy Users. In: Paper presented at the 25th EMAC Conference Proceedings (1997)Google Scholar
  14. Dillman, D.A., Christian, L.M.: Survey Mode as a Source of Instability in Responses Across Surveys. Field Methods 17, 30–52 (2005)CrossRefGoogle Scholar
  15. Donthu, N., Garcia, A.: The Internet Shopper. Journal of Advertising Research, 52–58 (May/June 1999)Google Scholar
  16. ESOMAR, Global MR Trends ESOMAR Report (2006), (September 5, 2009),
  17. Experian, Online to Rescue Britain’s Retail Sector from Recession, Experian Report Accessed (June 24, 2009),
  18. Fan, H.-Y., Lampinen, J.A.: Trigonometric Mutation Operation to Differential Evolution. Journal of Global Optimization 27, 105–129 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  19. Forrester, European Online Retail Consumer (December 20, 2004),,1769,973,00.html (retrieved September 20, 2005)
  20. Forsythe, S., Liu, C., Shannon, D., et al.: Development of a Scale to Measure the Perceived Benefits and Risks of Online Shopping. Journal of Interactive Marketing 20(2), 55–75 (2006)CrossRefGoogle Scholar
  21. Francis, J.E.: Internet Retailing Quality: One Size Does Not Fit All. Managing Service Quality 17(3), 341–355 (2007)CrossRefMathSciNetGoogle Scholar
  22. Handzic, M., Low, G.C.: The role of experience in user perceptions of information technology: An empirical examination. South African Computer Journal 24, 194–200 (1999)Google Scholar
  23. Hansen, T.: Consumer Adoption of Online Grocery Buying: A Discriminant Analysis. International Journal of Retail and Distribution Management 33, 101–121 (2005)CrossRefGoogle Scholar
  24. Huang, X., Zhu, Q.: A pseudo-nearest-neighbour approach for missing data on Gaussian random data sets. Pattern Recognition Letters 23, 613–1622 (2002)CrossRefMathSciNetGoogle Scholar
  25. Huisman, M.: Imputation of Missing Item Responses: Some Simple Techniques. Quality & Quantity 34, 331–351 (2000)CrossRefGoogle Scholar
  26. Jain, K., Srinivasan, N.: An Empirical Assessment of Multiple Operationalisation of Involvement. In: Paper presented at the Advances in Consumer Research, Provo, UT (1990)Google Scholar
  27. Korgaonkar, P.K., Wolin, L.D.: A Multivariate Analysis of Web Usage. Journal of Advertising Research, 53–68 (March/April 1999)Google Scholar
  28. Koslowsky, S.: The case of missing data. Journal of Database Marketing 9(4), 312–318 (2002)CrossRefGoogle Scholar
  29. Lee, G.-G., Lin, H.-F.: Customer Perceptions of e-Service Quality in Online Shopping. International Journal of Retailing and Distribution Management 33(2), 161–176 (2005)CrossRefGoogle Scholar
  30. Liao, Z., Tow-Cheung, M.: Internet-based E-shopping and consumer attitudes: An empirical study. Information and Management 38, 299–306 (2001)CrossRefGoogle Scholar
  31. Lucas, C., Araabi, B.N.: Generalisation of the Dempster-Shafer Theory: A Fuzzy-Valued Measure. IEEE Transactions on Fuzzy Systems 7(3), 255–270 (1999)CrossRefGoogle Scholar
  32. McQuarrie, E.F., Munson, J.M.: The Zaichkowsky Personal Involvement Inventory: Modification and Extension. In: Paper presented at the Advances in Consumer Research, Provo, UT (1986)Google Scholar
  33. Manfreda, K.L., Bosnjak, M., Berzelak, J., et al.: Web Surveys versus Other Survey Modes: A Meta-analysis Comparing Response Rates. International Journal of Marketing Research 50(1), 79–104 (2008)Google Scholar
  34. Mantores, R.L.: De Approximate Reasoning Models. Ellis Horwood, West Sussex (1990)Google Scholar
  35. Mittal, B.: Measuring Purchase-Decision Involvement. Psychology & Marketing 6, 147–162 (Summer 1989)CrossRefGoogle Scholar
  36. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 2(3), 192–222 (1991)CrossRefGoogle Scholar
  37. Page-Thomas, K.L., Moss, G., Chelly, D., et al.: The provision of service delivery information prior to purchase: A missed opportunity. International Journal of Retailing & Distribution Management 34(4/5), 258–277 (2006)CrossRefGoogle Scholar
  38. Page, K.L.: World Wide Web Perceptions and Use: Investigating the Role of Web Knowledge. Unpublished Doctoral Dissertation, UNSW, Sydney (2003)Google Scholar
  39. Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann, Los altos (1999)Google Scholar
  40. Roesmer, C.: Nonstandard Analysis and Dempster-Shafer Theory. International Journal of Intelligent Systems 15, 117–127 (2000)zbMATHCrossRefGoogle Scholar
  41. Rohm, A.J., Swaminathan, V.: A typology of online shoppers based on shopping motivations. Journal of Business Research 57(7), 748–757 (2004)CrossRefGoogle Scholar
  42. Roth, P.: Missing Data: A Conceptual Review for Applied Psychologists. Personnel Psychology 47, 537–560 (1994)CrossRefGoogle Scholar
  43. Safranek, R.J., Gottschlich, S., Kak, A.C.: Evidence Accumulation Using Binary Frames of Discernment for Verification Vision. IEEE Transactions on Robotics and Automation 6, 405–417 (1990)CrossRefGoogle Scholar
  44. Schafer, J.L., Graham, J.W.: Missing Data: Our View of the State of the Art. Psychological Methods 7(2), 147–177 (2002)CrossRefGoogle Scholar
  45. Shafer, G.A.: Mathematical theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  46. Slama, M.E., Tashchian, A.: Selected Socioeconomic and Demographic Characteristics Associated with Purchasing Involvement. Journal of Marketing 49, 72–82 (Winter 1985)CrossRefGoogle Scholar
  47. Smith, C.: Casting the Net: Surveying an Internet population. Journal of Computer Mediated Communication 3(1) (1997)Google Scholar
  48. Smith, M.F., Carsky, M.L.: Grocery Shopping Behaviour. Journal of Retailing and Consumer Services 3(3), 73–80 (1996)CrossRefGoogle Scholar
  49. Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimisation 11, 341–359 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  50. Swoboda, B.S.: Conditions of Consumer Information Seeking: Theoretical Foundations and Empirical Results of Using Interactive Multimedia Systems. The International Review of Retail, Distribution, and Consumer Research 8(4), 361–381 (1998)CrossRefGoogle Scholar
  51. Taylor, S., Todd, P.: Assessing IT usage: The role of prior experience. MIS Quarterly 19, 561–570 (1995)CrossRefGoogle Scholar
  52. Yang, J.-B., Liu, J., Wang, J., et al.: Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach—RIMER. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 36(2), 266–285 (2006)CrossRefGoogle Scholar
  53. Zadeh, L.A.: Fuzzy Logic and Approximate Reasoning (In Memory of Grigore Moisel). Synthese 30, 407–428 (1975)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Malcolm J. Beynon
    • 1
  • Kelly Page
    • 1
  1. 1.Cardiff Business SchoolCardiff UniversityCardiffUK

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