Skip to main content

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

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-67431-5_9
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-67431-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.99
Price excludes VAT (USA)
Fig. 1

References

  1. Department of Economic and Social Affairs: Concise Report on the World Population Situation in 2014. New York (2014)

    Google Scholar 

  2. Department of Economic and Social Affairs: Profiles of Ageing (2015)

    Google Scholar 

  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. 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. 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)

    CrossRef  Google Scholar 

  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. 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

    CrossRef  Google Scholar 

  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

    CrossRef  Google Scholar 

  9. National Institute on Aging, and National Library of Medicine: Making Your Web Site Senior Friendly (2002)

    Google Scholar 

  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

    CrossRef  Google Scholar 

  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

    CrossRef  Google Scholar 

  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

    CrossRef  Google Scholar 

  13. Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69, 99–118 (1955)

    CrossRef  Google Scholar 

  14. Tversky, A., Kahneman, D.: Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974)

    CrossRef  Google Scholar 

  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

    CrossRef  Google Scholar 

  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

    CrossRef  Google Scholar 

  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. Sproles, George B., Kendall, Elizabeth L.: A Methodology for profiling consumers’ decision-making styles. J. Consum. Aff. 20, 267–279 (1986)

    CrossRef  Google Scholar 

  19. Carlson, John G.: Recent assessments of the Myers-Briggs type indicator. J. Pers. Assess. 49, 356–365 (1985)

    CrossRef  Google Scholar 

  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. 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

    CrossRef  Google Scholar 

  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. 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. 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. 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

    CrossRef  Google Scholar 

  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. Arnott, D.: Cognitive biases and decision support systems development: a design science approach. Inf. Syst. J. 16, 55–78 (2006)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  29. Duchowski, A.T.: Eye Tracking Methodology: Theory and Practice. Vasa. Second edn. (2007). doi:10.1145/1117309.1117356

  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

    CrossRef  Google Scholar 

  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. 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. Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31, 137–209 (2007)

    Google Scholar 

  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:Article

    Google Scholar 

  35. Vessey, I., Galletta, D.: Cognitive fit: an empirical study of information acquisition. Inf. Syst. Res. 2, 63–84 (1991)

    CrossRef  Google Scholar 

  36. Yates, J.F., Curley, S.P.: Contingency judgement: primacy effects and attention decrement. Acta Physiol. (Oxf) 62, 293–302 (1986)

    Google Scholar 

  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. 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. 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

    CrossRef  Google Scholar 

  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. 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. 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

    CrossRef  Google Scholar 

  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. Riedl, R., Léger, P.M.: Fundamentals of NeuroIS. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Berlin, Heidelberg (2016)

    Google Scholar 

  45. Just, M.A., Carpenter, P.A.: A theory of reading: from eye fixations to comprehension. Psychol. Rev. 87, 329–354 (1980)

    CrossRef  Google Scholar 

  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

    CrossRef  Google Scholar 

  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. 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. 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

    CrossRef  Google Scholar 

  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

    CrossRef  Google Scholar 

  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. Thunholm, P.: Decision-making style: habit, style or both? Pers. Individ. Differ. 36, 931–944 (2004). doi:10.1016/S0191-8869(03)00162-4

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  54. Gilovich, T., Griffin, D., Kahneman, D.: Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge University Press, New York (2002)

    CrossRef  Google Scholar 

  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. Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)

    Google Scholar 

  57. Bazerman, M.H., Moore, D.: Judgment in Managerial Decision Making, 7th edn. John Wiley & Sons, Inc, USA (2009)

    Google Scholar 

  58. Häubl, G., Trifts, V.: Consumer decision making in online shopping environments: the effects of interactive decision aids. Mark. Sci. 19, 4–21 (2000)

    CrossRef  Google Scholar 

  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. 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

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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

    CrossRef  Google Scholar 

  64. DiChristopher, T.: Your holiday gift returns cost retailers billions. CNBC (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nour El Shamy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

El Shamy, N., Hassanein, K. (2018). The Impact of Age and Cognitive Style on E-Commerce Decisions: The Role of Cognitive Bias Susceptibility. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-67431-5_9

Download citation