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The Impact of Age and Cognitive Style on E-Commerce Decisions: The Role of Cognitive Bias Susceptibility

  • Nour El ShamyEmail author
  • Khaled Hassanein
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 25)

Abstract

The aging associated declines in cognitive abilities could render older adults more susceptible to cognitive biases that are detrimental to their e-commerce decisions’ quality. Additionally, certain cognitive styles can lead online consumers to rely on decision heuristics which makes them less meticulous and more prone to bias. In this research-in-progress paper we introduce cognitive bias susceptibility as a potential mediator between age and cognitive style on one end, and decisional outcomes on the other. An experimental design to validate our proposed model is outlined. Both psychometric and eye-tracking methodologies are utilized to achieve a more holistic understanding of the relationships in the proposed model. Potential contributions and implications for future research are outlined.

Keywords

Aging Older adults Cognitive style Cognitive bias Order bias Vividness bias Eye-tracking Decision quality Decision effort 

References

  1. 1.
    Department of Economic and Social Affairs: Concise Report on the World Population Situation in 2014. New York (2014)Google Scholar
  2. 2.
    Department of Economic and Social Affairs: Profiles of Ageing (2015)Google Scholar
  3. 3.
    Wagner, N., Hassanein, K., Head, M.: Computer use by older adults: a multi-disciplinary review. Comput. Hum. Behav. 26, 870–882 (2010)Google Scholar
  4. 4.
    Statista: Distribution of internet users in North America as of November 2014, by age group. North America: age distribution of internet users 2014 (2017)Google Scholar
  5. 5.
    Lian, J.-W., Yen, D.C.: Online shopping drivers and barriers for older adults: age and gender differences. Comput. Hum. Behav. 37, 133–143 (2014)CrossRefGoogle Scholar
  6. 6.
    El Shamy, N., Hassanein, K.: The influence of cognitive biases and decision making styles of older adults in e-commerce tasks: an exploratory study. In: Proceedings of the Fourteenth Pre-ICIS SIG-HCI Workshop, Fort Worth, TX (2015)Google Scholar
  7. 7.
    Prieto, T.E., Myklebust, J.B., Hoffmann, R.G., Lovett, E.G., Myklebust, B.M.: Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Trans. Biomed. Eng. 43, 956–966 (1996). doi: 10.1109/10.532130 CrossRefGoogle Scholar
  8. 8.
    Czaja, Sara J., Charness, Neil, Fisk, Arthur D., Hertzog, Christopher, Nair, Sankaran N., Rogers, Wendy A., Sharit, Joseph: Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychol. Aging 21, 333–352 (2006). doi: 10.1055/s-0029-1237430.Imprinting CrossRefGoogle Scholar
  9. 9.
    National Institute on Aging, and National Library of Medicine: Making Your Web Site Senior Friendly (2002)Google Scholar
  10. 10.
    Salthouse, Timothy A., Babcock, Renee L.: Decomposing adult age differences in working memory. Dev. Psychol. 27, 763–776 (1991). doi: 10.1037/0012-1649.27.5.763 CrossRefGoogle Scholar
  11. 11.
    Plude, D.J., Doussard-Roosevelt, J.A.: Aging, selective attention, and feature integration. Psychol. Aging 4, 98–105 (1989). doi: 10.1037/0882-7974.4.1.98 CrossRefGoogle Scholar
  12. 12.
    Wan, Y., Menon, S., Ramaprasad, A.: The paradoxical nature of electronic decision aids on comparison-shopping: the experiments and analysis. J. Theor. Appl. Electron. Commer. Res. 4, 80–96 (2009). doi: 10.4067/S0718-18762009000300008 CrossRefGoogle Scholar
  13. 13.
    Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69, 99–118 (1955)CrossRefGoogle Scholar
  14. 14.
    Tversky, A., Kahneman, D.: Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974)CrossRefGoogle Scholar
  15. 15.
    Chu, P.C., Spires, Eric E.: The joint effects of effort and quality on decision strategy choice with computerized decision aids. Decis. Sci. 31, 259–292 (2000). doi: 10.1111/j.1540-5915.2000.tb01624.x CrossRefGoogle Scholar
  16. 16.
    Johnson, E.J., Payne, J.W.: Effort and accuracy in choice. Manage. Sci. 31, 395–414 (1985). doi: 10.1287/mnsc.31.4.395 CrossRefGoogle Scholar
  17. 17.
    Davern, M., Shaft, T., Te’eni, D.: Cognition matters: enduring questions in cognitive IS research. J. Assoc. Inf. Syst. 13, 273–314 (2012)Google Scholar
  18. 18.
    Sproles, George B., Kendall, Elizabeth L.: A Methodology for profiling consumers’ decision-making styles. J. Consum. Aff. 20, 267–279 (1986)CrossRefGoogle Scholar
  19. 19.
    Carlson, John G.: Recent assessments of the Myers-Briggs type indicator. J. Pers. Assess. 49, 356–365 (1985)CrossRefGoogle Scholar
  20. 20.
    Barkhi, R.: Cognitive style may mitigate the impact of communication mode. Inf. Manage. 39, 677–688. (2002). doi: 10.1016/S0378-7206(01)00114-8
  21. 21.
    Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., Lehman, D.R.: Maximizing versus satisficing: happiness is a matter of choice. J. Pers. Soc. Psychol. 83, 1178–1197 (2002). doi: 10.1037/0022-3514.83.5.1178 CrossRefGoogle Scholar
  22. 22.
    Karimi, S., Papamichail, K.N., Holland, C.P.: The effect of prior knowledge and decision-making style on the online purchase decision-making process: a typology of consumer shopping behaviour. Decis. Support Syst. 77, 137–147 (2015)Google Scholar
  23. 23.
    Tams, S., Grover, V., Thatcher, J.: Modern information technology in an old workforce: toward a strategic research agenda. J. Strateg. Inf. Syst. 23, 284–304. (2014). doi: 10.1016/j.jsis.2014.10.001
  24. 24.
    Fleischmann, M., Amirpur, M., Benlian, A., Hess, T.: Cognitive biases in information systems research: a scientometric analysis. In: European Conference on Information Systems, pp. 1–21. Tel Aviv (2014)Google Scholar
  25. 25.
    Arnott, D., Pervan, G.: Eight key issues for the decision support systems discipline. Decis. Support Syst. 44, 657–672 (2008). doi: 10.1016/j.dss.2007.09.003 CrossRefGoogle Scholar
  26. 26.
    Orquin, J.L., Loose, S.M.: Attention and choice: a review on eye movements in decision making. Acta Psychol. 144, 190–206. (2013). doi: 10.1016/j.actpsy.2013.06.003
  27. 27.
    Arnott, D.: Cognitive biases and decision support systems development: a design science approach. Inf. Syst. J. 16, 55–78 (2006)CrossRefGoogle Scholar
  28. 28.
    Todd, P., Benbasat, I.: The use of information in decision making: an experimental investigation of the impact of computer-based decision aids. MIS Q. 16, 373–393 (1992)CrossRefGoogle Scholar
  29. 29.
    Duchowski, A.T.: Eye Tracking Methodology: Theory and Practice. Vasa. Second edn. (2007). doi: 10.1145/1117309.1117356
  30. 30.
    Glaholt, M.G., Reingold, E.M.: Eye movement monitoring as a process tracing methodology in decision making research. J Neurosci. Psychol. Econ. 4, 125–146 (2011). doi: 10.1037/a0020692 CrossRefGoogle Scholar
  31. 31.
    Wang, Q., Yang, S., Liu, M., Cao, Z., Ma, Q.: An eye-tracking study of website complexity from cognitive load perspective. Decis. Support Syst. 62, 1–10. (2014). doi: 10.1016/j.dss.2014.02.007
  32. 32.
    Dimoka, A., Banker, R.D., Benbasat, I., Davis, F.D., Dennis, A.R., Gefen, D., Gupta, A., et al.: On the use of neurophysiological tools in IS research: developing a research agenda for NeuroIS. MIS Q. 36, 679–702 (2012)Google Scholar
  33. 33.
    Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31, 137–209 (2007)Google Scholar
  34. 34.
    Wang, W., Benbasat, I.: Interactive decision aids for consumer decision making in e-commerce: the influence of perceived strategy restrictiveness. MIS Q. 33, 293–320. (2009). doi:ArticleGoogle Scholar
  35. 35.
    Vessey, I., Galletta, D.: Cognitive fit: an empirical study of information acquisition. Inf. Syst. Res. 2, 63–84 (1991)CrossRefGoogle Scholar
  36. 36.
    Yates, J.F., Curley, S.P.: Contingency judgement: primacy effects and attention decrement. Acta Physiol. (Oxf) 62, 293–302 (1986)Google Scholar
  37. 37.
    Xu, Y.C., Kim, H.W.: Order effect and vendor inspection in online comparison shopping. J. Retail. 84, 477–486 (2008). doi: 10.1016/j.jretai.2008.09.007
  38. 38.
    Pan, B., Hembrooke, H., Gay, G.K., Granka, L., Feusner, M.K., Newman, J.K.: The determinants of web page viewing behavior: an eye-tracking study. In: Proceedings of the ETRA ’04 Symposium on Eye Tracking Research and Applications, vol. 1, pp. 147–154 (2004). doi: 10.1145/968363.968391
  39. 39.
    Scott, L.M., Vargas, P.: Writing with pictures: toward a unifying theory of consumer response to images. J. Consum. Res. 34, 341–356 (2007). doi: 10.1086/519145 CrossRefGoogle Scholar
  40. 40.
    Lim, K.H., Benbasat, I.: The effect of multimedia on perceived equivocality and perceived usefulness of information systems. Mis Q. 24, 449–471 (2000). doi: 10.2307/3250969
  41. 41.
    Cyr, D., Head, M., Larios, H., Pan, B.: Exploring human images in website design: a multi-method approach. MIS Q. 33, 539–566 (2009)Google Scholar
  42. 42.
    Pronin, E., Lin, D.Y., Ross, L.: The bias blind spot: perceptions of bias in self versus others. Pers. Soc. Psychol. Bull. 28, 369–381 (2002). doi: 10.1177/0146167202286008 CrossRefGoogle Scholar
  43. 43.
    Riedl, R., Davis, F.D., Hevner, A.R.: Towards a NeuroIS research methodology: intensifying the discussion on methods, tools, and measurement. J. Assoc. Inf. Syst. 15, i–xxxv (2014)Google Scholar
  44. 44.
    Riedl, R., Léger, P.M.: Fundamentals of NeuroIS. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Berlin, Heidelberg (2016)Google Scholar
  45. 45.
    Just, M.A., Carpenter, P.A.: A theory of reading: from eye fixations to comprehension. Psychol. Rev. 87, 329–354 (1980)CrossRefGoogle Scholar
  46. 46.
    Ghisletta, P., Rabbitt, P., Lunn, M., Lindenberger, U.: Two thirds of the age-based changes in fluid and crystallized intelligence, perceptual speed, and memory in adulthood are shared. Intelligence 40, 260–268 (2012). doi: 10.1016/j.intell.2012.02.008 CrossRefGoogle Scholar
  47. 47.
    Finucane, M.L., Slovic, P., Hibbard, J.H., Peters, E., Mertz, C.K., Macgregor, D.G.: Aging and decision-making competence: an analysis of comprehension and consistency skills in older versus younger adults considering health-plan options. J. Behav. Decis. Mak. 15, 141–164 (2002). doi: 10.1002/bdm.407
  48. 48.
    Tams, S.: A refined examination of worker age and stress: explaining how, and why, older workers are especially techno-stressed in the interruption age. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.M., Randolph, A.B. (eds.) Information Systems and Neuroscience, pp. 175–183 (2017). doi: 10.1007/978-3-319-41402-7_22
  49. 49.
    Peters, E., Hess, T.M., Västfjäll, D., Auman, C.: Adult age differences in dual information processes: implications for the role of affective and deliberative processes in older adults’ decision making. Perspect. Psychol. Sci. J. Assoc. Psychol. Sci. 2, 1–23 (2007). doi: 10.1111/j.1745-6916.2007.00025.x CrossRefGoogle Scholar
  50. 50.
    Bergstrom, R., Jennifer, C., Olmsted-Hawala, E.L., Jans, M.E.: Age-related differences in eye tracking and usability performance: web site usability for older adults. Int. J. Hum. Comput. Interact. 29, 541–548 (2013). doi: 10.1080/10447318.2012.728493 CrossRefGoogle Scholar
  51. 51.
    Gudigantala, N., Song, J., Jones, D.R.: Transforming consumer decision making in e-commerce: a case for compensatory decision aids. In: Lee (ed.) Transforming E-Business Practices and Applications: Emerging Technologies and Concepts: Emerging Technologies and Concepts, pp. 72–88 (2010). doi: 10.4018/978-1-60566-910-6.ch005
  52. 52.
    Thunholm, P.: Decision-making style: habit, style or both? Pers. Individ. Differ. 36, 931–944 (2004). doi: 10.1016/S0191-8869(03)00162-4 CrossRefGoogle Scholar
  53. 53.
    Allinson, C.W., Hayes, J.: The cognitive style index: a measure of intuition-analysis for organizational research. J. Manage. Stud. 33, 119–135 (1996)CrossRefGoogle Scholar
  54. 54.
    Gilovich, T., Griffin, D., Kahneman, D.: Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge University Press, New York (2002)CrossRefGoogle Scholar
  55. 55.
    Gigerenzer, G., Todd, P.M., The ABC Research Group: Simple Heuristics That Make Us Smart. Oxford University Press, New York (2014)Google Scholar
  56. 56.
    Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)Google Scholar
  57. 57.
    Bazerman, M.H., Moore, D.: Judgment in Managerial Decision Making, 7th edn. John Wiley & Sons, Inc, USA (2009)Google Scholar
  58. 58.
    Häubl, G., Trifts, V.: Consumer decision making in online shopping environments: the effects of interactive decision aids. Mark. Sci. 19, 4–21 (2000)CrossRefGoogle Scholar
  59. 59.
    Buettner, R.: The Relationship between visual website complexity and a user’s mental workload: a NeuroIS perspective. In: Davis, F., Riedl, R., vom Brocke, J., Léger, P.M., Randolph, A.B. (eds.) Information Systems and Neuroscience, pp. 107–113. Springer, Berlin (2017). doi: 10.1007/978-3-319-41402-7_14
  60. 60.
    Drolet, A., Luce, M.F.: The rationalizing effects of cognitive load on emotion based tradeoff avoidance. J. Consum. Res. 31, 63–77 (2004). doi: 10.1086/383424 CrossRefGoogle Scholar
  61. 61.
    Dimoka, A., Pavlou, P., Davis, F.D.: Neuro IS: the potential of cognitive neuroscience for information systems research. Inf. Syst. Res. 22, 687–702 (2011)CrossRefGoogle Scholar
  62. 62.
    Tams, S., Hill, K., Thatcher, J.: Neuro-IS—Alternative or complement to existing methods? illustrating the holistic effects of neuroscience and self-reported data in the context of technostress research. J. Assoc. Inf. Syst. 15, 723–753 (2014)Google Scholar
  63. 63.
    Mao, J.Y., Benbasat, I.: The use of explanations in knowledge-based systems: cognitive perspectives and a process-tracing analysis. J. Manage. Inf. Syst. 17, 153–179 (2000). doi: 10.1080/07421222.2000.11045646 CrossRefGoogle Scholar
  64. 64.
    DiChristopher, T.: Your holiday gift returns cost retailers billions. CNBC (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.DeGroote School of BusinessMcMaster UniversityHamiltonCanada

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