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Lost in Online Stores? Agent-Based Modeling of Cognitive Limitations of Elderly Online Consumers

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11549))

Abstract

We have developed an agent-based model of e-commerce platform users’ behavior with emphasis on reflecting decision-making characteristics of elderly adults. The model has been used to verify how cognitive deficits of older customers influence the effectiveness of collaborative filtering and content-based recommender systems. The results from our simulation suggest that the effectiveness of recommender systems in improving quality of elderly consumers choices is low for population of agents with strong cognitive deficits.

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Notes

  1. 1.

    The source code is available on request.

  2. 2.

    Agents referred to as ‘elderly’ have a specific level of cognitive deficiencies that can appear with very different intensity also at a different age.

  3. 3.

    Attributes ‘brand’ = 1, ‘negative review’ = 1 or both, in which case only ‘negative review’ triggers the heuristc.

  4. 4.

    Agents with WM = 10 and h = 0.

  5. 5.

    Random reduced: results of simulation where items are presented in a random order, assuming that agent put a filter on the items set first. The items with attribute values below the reservation levels are excluded if given attribute is kept in agent’s WM.

  6. 6.

    Popularity voting recommender system.

References

  1. 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)

    Article  Google Scholar 

  2. Besedeš, T., Deck, C., Sarangi, S., Shor, M.: Decision-making strategies and performance among seniors. J. Econ. Behav. Organ. 81, 524–533 (2012)

    Article  Google Scholar 

  3. Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–34. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_1

    Chapter  MATH  Google Scholar 

  4. Belleville, S.: Cognitive training for persons with mild cognitive impairment. Int. Psychogeriatr. 20(1), 57–66 (2008)

    Article  Google Scholar 

  5. Hess, T.M., Strough, J., Löckenhoff, C. (eds.): Aging and Decision Making: Empirical and Applied Perspectives. Academic Press, Cambridge (2015)

    Google Scholar 

  6. Tauringana, M., Good, A., Fitch, C.J.: E-commerce adoption among the elderly people. J. Electron. Commer. Organ. (2015)

    Google Scholar 

  7. Wagner, N., Hassanein, K., Head, M.: Computer use by older adults: a multi-disciplinary review. Comput. Hum. Behav. 26(5), 870–882 (2010)

    Article  Google Scholar 

  8. Savvopoulos, A., Virvou, M.: Tutoring the elderly on the use of recommending systems. Campus-Wide Inf. Syst. 27(3), 162–172 (2010)

    Article  Google Scholar 

  9. Beel, J., Langer, S., Nürnberger, A., Genzmehr, M.: The impact of demographics (age and gender) and other user-characteristics on evaluating recommender systems. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, Charles J. (eds.) TPDL 2013. LNCS, vol. 8092, pp. 396–400. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40501-3_45

    Chapter  Google Scholar 

  10. Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)

    Article  Google Scholar 

  11. Wierzbicki, A.P.: Reference point approaches. In: Gal, T., Stewart, T.J., Hanne, T. (eds.) Multicriteria Decision Making, vol. 21, pp. 237–275. Springer, Boston (1999). https://doi.org/10.1007/978-1-4615-5025-9_9

    Chapter  Google Scholar 

  12. Mikels, J.A., Shuster, M.M., Thai, S.T.: Aging, emotion, and decision making. In: Aging and Decision Making, pp. 169–188. Elsevier (2015)

    Google Scholar 

  13. von Helversen, B., Abramczuk, K., Kopeć, W., Nielek, R.: Influence of consumer reviews on online purchasing decisions in older and younger adults. Decis. Support Syst. 113, 1–10 (2018)

    Article  Google Scholar 

  14. Lambert-Pandraud, R., Laurent, G.: Why do older consumers buy older brands? The role of attachment and declining innovativeness. J. Mark. 74, 104–121 (2010)

    Article  Google Scholar 

  15. Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)

    Article  Google Scholar 

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Acknowledgements

This research was supported by the Polish National Science Center grants 2015/19/B/ST6/03179 and 2018/29/B/HS6/02604.

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Authors and Affiliations

  1. Polish-Japanese Institute of Information Technology, Warsaw, Poland

    Justyna Pawlowska, Radoslaw Nielek & Adam Wierzbicki

Authors
  1. Justyna Pawlowska
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  2. Radoslaw Nielek
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  3. Adam Wierzbicki
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Corresponding author

Correspondence to Justyna Pawlowska .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, NY, USA

    Robert Thomson

  2. University of Michigan–Flint, Flint, MI, USA

    Halil Bisgin

  3. Bucknell University, Lewisburg, PA, USA

    Christopher Dancy

  4. The Ohio State University, Columbus, OH, USA

    Ayaz Hyder

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Pawlowska, J., Nielek, R., Wierzbicki, A. (2019). Lost in Online Stores? Agent-Based Modeling of Cognitive Limitations of Elderly Online Consumers. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_21

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  • DOI: https://doi.org/10.1007/978-3-030-21741-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21740-2

  • Online ISBN: 978-3-030-21741-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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