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Neuro-Adaptive Interface System to Evaluate Product Recommendations in the Context of E-Commerce

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Design Science Research for a New Society: Society 5.0 (DESRIST 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13873))

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Abstract

Personalized product recommendations are widely used by online retailers to combat choice overload, a phenomenon where excessive product information adversely increases the cognitive workload of the consumer, thereby degrading their decision quality and shopping experience. However, scientific evidence on the benefits of personalized recommendations remains inconsistent, giving rise to the idea that their effects may be muted unless the consumer is actually experiencing choice overload. The ability to test this idea is thus an important goal for marketing researchers, but challenging to achieve using conventional approaches. To overcome this challenge, the present study followed a design science approach while leveraging cognitive neuroscience to develop a real-time neuro-adaptive interface for e-commerce tasks. The function of the neuro-adaptive interface was to induce choice overload and permit comparisons of cognitive load and decision quality associated with personalized recommendations, which were presented according to the following three conditions: (a) not presented (control), (b) perpetually presented, or (c) presented only when a real-time neurophysiological index indicated that cognitive workload was high. Formative testing cycles produced a neuro-adaptive system in which the personalization of recommendations and neuro-adaptivity function as intended. The artifact is now ready for use in summative testing regarding the effects of personalized recommendations on cognitive workload and decision quality.

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References

  1. Kodali, S.: The State of Retailing Online 2019. In: Editor (Ed.)^(Eds.): Book The State of Retail-ing Online 2019 (Forrester, 2019, edn.), p. 25 (2019)

    Google Scholar 

  2. Iyengar, S.S., Lepper, M.R.: When choice is demotivating: can one desire too much of a good thing? J. Pers. Soc. Psychol. 79(6), 995–1006 (2000)

    Article  Google Scholar 

  3. Scheibehenne, B., Greifeneder, R., Todd, P.: Can there ever be too many options? a meta-analytic review of choice overload. J. Consum. Res. 37, 409–425 (2010)

    Article  Google Scholar 

  4. Özkan, E., Tolon, M.: The effects of information overload on consumer confusion: an examination on user generated content. Bogazici J. 29, 27–51 (2015)

    Article  Google Scholar 

  5. Bawden, D., Robinson, L.: Information Overload: An Overview: Oxford Encyclopedia of Political Decision Making. Oxford University Press, Oxford (2020)

    Google Scholar 

  6. Fehrenbacher, D.D., Djamasbi, S.: Information systems and task demand: an exploratory pupillometry study of computerized decision making. Decis. Support Syst. 97, 1–11 (2017)

    Article  Google Scholar 

  7. Deck, C., Jahedi, S.: The effect of cognitive load on economic decision making: a survey and new experiments. Eur. Econ. Rev. 78, 97–119 (2015)

    Article  Google Scholar 

  8. Peng, M., Xu, Z., Huang, H.: How does information overload affect consumers’ online decision process? An event-related potentials study. Front. Neurosci. 15, 695852 (2021)

    Google Scholar 

  9. Chernev, A., Böckenholt, U., Goodman, J.: Choice overload: a conceptual review and meta-analysis. J. Consum. Psychol. 25(2), 333–358 (2015)

    Article  Google Scholar 

  10. Chen, Y.-C., Shang, R.-A., Kao, C.-Y.: The effects of information overload on consumers’ subjective state towards buying decision in the internet shopping environment. Electron. Commer. Res. Appl. 8(11), 48–58 (2009)

    Article  Google Scholar 

  11. Eppler, M.J., Mengis, J.: The concept of information overload: a review of literature from organization science, accounting, marketing, mis, and related disciplines. Inf. Soc. 20(5), 325–344 (2004)

    Article  Google Scholar 

  12. Calvo, L., Christel, I., Terrado, M., Cucchietti, F., Pérez-Montoro, M.: Users’ cognitive load: a key aspect to successfully communicate visual climate information. Bull. Am. Meteor. Soc. 103(1), E1–E16 (2022)

    Article  Google Scholar 

  13. Kurien, R., Paila, A.R., Nagendra, A.: Application of paralysis analysis syndrome in customer decision making. Procedia Econ. Finance 11, 323–334 (2014)

    Article  Google Scholar 

  14. Deng, L., Poole, M.S.: Affect in web interfaces: a study of the impacts of web page visual complexity and order. MIS Q. 34(4), 711–730 (2010)

    Article  Google Scholar 

  15. Aljukhadar, M., Senecal, S., Daoust, C.-E.: Using recommendation agents to cope with information overload. Int. J. Electron. Commer. 17(2), 41–70 (2012)

    Article  Google Scholar 

  16. Liang, T.-P., Lai, H.-J., Ku, Y.-C.: Personalized content recommendation and user satisfaction: theoretical synthesis and empirical findings. J. Manag. Inf. Syst. 23(3), 45–70 (2006)

    Article  Google Scholar 

  17. Zhang, H., Zhao, L., Gupta, S.: The role of online product recommendations on customer decision making and loyalty in social shopping communities. Int. J. Inf. Manage. 38, 150–166 (2018)

    Article  Google Scholar 

  18. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1), 101–123 (2012)

    Article  Google Scholar 

  19. Wertenbroch, K., et al.: Autonomy in consumer choice. Mark. Lett. 31(4), 429–439 (2020). https://doi.org/10.1007/s11002-020-09521-z

    Article  Google Scholar 

  20. Chen, C.C., Shih, S.-Y., Lee, M.: Who should you follow? Combining learning to rank with social influence for informative friend recommendation. Decis. Support Syst. 90, 33–45 (2016)

    Article  Google Scholar 

  21. Wang, W., Benbasat, I.: Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. J. Manage. Inf. Syst. 23, 217–246 (2007)

    Article  Google Scholar 

  22. Rose, J.M., Roberts, F.D., Rose, A.M.: Affective responses to financial data and multimedia: the effects of information load and cognitive load. Int. J. Account. Inf. Syst. 5(1), 5–24 (2004)

    Article  Google Scholar 

  23. Sia, C., Shi, Y., Yan, J., Chen, H.: Web personalization to build trust in E-commerce: a design science approach. World Acad. Sci. Eng. Technol. 64, 325–329 (2010)

    Google Scholar 

  24. Ball, N.L.: Design science II: the impact of design science on e-commerce research and practice. Communications of the Association for Information Systems 7, 2 (2001)

    Google Scholar 

  25. Karmokar, S., Singh, H.: Improving the website design process for SMEs: a design science perspective (2012)

    Google Scholar 

  26. van der Merwe, A., Gerber, A., Smuts, H.: Guidelines for conducting design science research in information systems. In: ICT Education, pp. 163–178 (2020). https://doi.org/10.1007/978-3-030-35629-3_11

  27. McKenny, J.L., Keen, P.G.W.: How managers’ minds work. In: Editor (Ed.)^(Eds.): Book How Managers’ Minds Work (1974, edn.), pp. 79–90 (1974)

    Google Scholar 

  28. Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37(2), 337–355 (2013)

    Article  Google Scholar 

  29. Fernandez Rojas, R., et al.: Electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding a swarm of unmanned ground vehicles in contested environments. Front. Neurosci. 14, 40 (2020)

    Article  Google Scholar 

  30. Antonenko, P.P., Paas, F., Grabner, R., Gog, T.: Using electroencephalography to measure cognitive load. Educ. Psychol. Rev. 22, 425–438 (2010)

    Article  Google Scholar 

  31. Gredin, N.V., Broadbent, D.P., Findon, J.L., Williams, A.M., Bishop, D.T.: The impact of task load on the integration of explicit contextual priors and visual information during anticipation. Psychophysiology 57(6), 1–13 (2020)

    Article  Google Scholar 

  32. Guan, K., Zhang, Z., Chai, X., Tian, Z., Liu, T., Niu, H.: EEG based dynamic functional connectivity analysis in mental workload tasks with different types of information. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 632–642 (2022)

    Article  Google Scholar 

  33. Al-Samarraie, H., Eldenfria, A., Zaqout, F., Price, M.L.: How reading in single- and multiple-column types influence our cognitive load: an EEG study. Electron. Libr. 37(4), 593–606 (2019)

    Article  Google Scholar 

  34. Andreessen, L.M., Gerjets, P., Meurers, D., Zander, T.O.: Toward neuroadaptive support technologies for improving digital reading: a passive BCI-based assessment of mental workload imposed by text difficulty and presentation speed during reading. User Model. User-Adap. Inter. 31(1), 75–104 (2020). https://doi.org/10.1007/s11257-020-09273-5

    Article  Google Scholar 

  35. Krol, L.R., Zander, T.O.: Passive BCI-based neu-roadaptive systems. In: Editor (Ed.)^(Eds.): Book Passive BCI-Based Neuroadaptive Systems (2017, edn.), pp. (2017)

    Google Scholar 

  36. Wolpaw, J.R., Millán, J.d.R., Ramsey, N.F.: Chapter 2 - brain-computer interfaces: definitions and principles. In: Ramsey, N.F., Millán, J.d.R. (eds.): Handbook of Clinical Neurology, pp. 15–23. Elsevier (2020)

    Google Scholar 

  37. Eldenfria, A., Al-Samarraie, H.: Towards an online continuous adaptation mechanism (OCAM) for enhanced engagement: an EEG study. Int. J. Hum.-Comput. Interact. 35(20), 1960–1974 (2019)

    Article  Google Scholar 

  38. Di Flumeri, G., et al.: Brain-computer interface-based adaptive automation to prevent out-of-the-loop phenomenon in air traffic controllers dealing with highly automated systems. Front. Hum. Neurosci. 13, 296 (2019)

    Article  Google Scholar 

  39. Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31(1), 137–209 (2007)

    Article  Google Scholar 

  40. Wheeler, P., Arunachalam, V.: The effects of multimedia on cognitive aspects of decision-making. Int. J. Account. Inf. Syst. 10(2), 97–116 (2009)

    Article  Google Scholar 

  41. Appiah Kusi, G., Azmira Rumki, Z., Hammond Quarcoo, F., Otchere, E., Fu, G.: The role of information overload on consumers online shopping behavior. J. Bus. Manage. Stud. 4(4), 162–178 (2022)

    Article  Google Scholar 

  42. Lee, B.-K., Lee, W.-N.: The effect of information overload on consumer choice quality in an on-line environment. Psychol. Mark. 21(3), 159–183 (2004)

    Article  Google Scholar 

  43. Ho, E.H., Hagmann, D., Loewenstein, G.: Measuring information preferences. Manage. Sci. 67(1), 126–145 (2021)

    Article  Google Scholar 

  44. Lurie, N.H.: Decision making in information-rich environments: the role of information structure. J. Consum. Res. 30(4), 473–486 (2004)

    Article  Google Scholar 

  45. Greifeneder, R., Scheibehenne, B., Kleber, N.: Less may be more when choosing is difficult: choice complexity and too much choice. Acta Physiol. (Oxf) 133, 45–50 (2009)

    Google Scholar 

  46. Chen, Z., Jin, J., Daly, I., Zuo, C., Wang, X., Cichocki, A.: Effects of visual attention on tactile P300 BCI. Computat. Intell. Neurosci., 1–11 (2020)

    Google Scholar 

  47. Khorshidtalab, A., Salami, M.J.E.: EEG signal classification for real-time brain-computer inter-face applications: a review. In: Editor (Ed.)^(Eds.): Book EEG signal classification for real-time brain-computer interface applications: A review (2011, edn.), pp. 1–7 (2011)

    Google Scholar 

  48. Guarnieri, R., Zhao, M., Taberna, G.A., Ganzetti, M., Swinnen, S.P., Mantini, D.: RT-NET: real-time reconstruction of neural activity using high-density electroencephalography. Neuroinformatics 19(2), 251–266 (2020). https://doi.org/10.1007/s12021-020-09479-3

    Article  Google Scholar 

  49. Zanetti, R., Arza, A., Aminifar, A., Atienza, D.: Real-time EEG-based cognitive workload monitoring on wearable devices. IEEE Trans. Biomed. Eng. 69(1), 265–277 (2022)

    Article  Google Scholar 

  50. Aricò, P., Borghini, G., Di Flumeri, G., Sciaraffa, N., and Babiloni, F.: Passive BCI beyond the lab: current trends and future directions. Physiol. Meas. 39(8), 08tr02 (2018)

    Google Scholar 

  51. Yangyang Miao, M.C., et al.: BCI-based rehabilitation on the stroke in sequela stage. Neural Plasticity, 2020 (2020)

    Google Scholar 

  52. Ron-Angevin, R., Garcia, L., Fernández-Rodríguez, Á., Saracco, J., André, J.M., Lespinet-Najib, V.: Impact of speller size on a visual P300 brain-computer interface (BCI) system under two conditions of constraint for eye movement. Computational Intelligence & Neuroscience, 1–16 (2019)

    Google Scholar 

  53. Velasco-Álvarez, F., Fernández-Rodríguez, Á., Vizcaíno-Martín, F.-J., Díaz-Estrella, A., Ron-Angevin, R.: Brain–computer interface (BCI) control of a virtual assistant in a smartphone to manage messaging applications. Sensors (14248220) 21(11), 3716 (2021)

    Google Scholar 

  54. Perry, N.C., Wiggins, M.W., Childs, M., Fogarty, G.: Can reduced processing decision support interfaces improve the decision-making of less-experienced incident commanders? Decis. Support Syst. 52(2), 497–504 (2012)

    Article  Google Scholar 

  55. Linden, G., Smith, B., York, J.: Amazon.com recommendations. In: Editor (Ed.)^(Eds.): Book Amazon.com Recommendations (IEEE Computer Society, 2003, edn.), pp. 76–80 (2003)

    Google Scholar 

  56. Sharma, J., Sharma, K., Garg, K., Sharma, A.K.: Product recommendation system a comprehensive review. IOP Conf. Ser. Mater. Sci. Eng. 1022(1), 12–21 (2021)

    Article  Google Scholar 

  57. Huang, Z., Zeng, D., Chen, H.: A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intell. Syst. 22(5), 68–78 (2007)

    Article  Google Scholar 

  58. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Editor (Ed.)^(Eds.): Book Analysis of Recommendation Algorithms for E-Commerce (University of Minnesota, 2000, edn.), pp. 158–167 (2000)

    Google Scholar 

  59. Pandey, S., Kumar, T.S.: Customization of recommendation system using collaborative filtering algorithm on cloud using mahout. IJRET: Int. J. Res. Eng. Technol. 3(7), 39–43 (2014)

    Google Scholar 

  60. Yuan, Z.-m, Huang, C., Sun, X.-y, Li, X.-x, Xu, D.-r: A microblog recommendation algorithm based on social tagging and a temporal interest evolution model. Front. Inf. Technol. Electron. Eng. 16(7), 532–540 (2015). https://doi.org/10.1631/FITEE.1400368

    Article  Google Scholar 

  61. Adabi, A., de Alfaro, L.: Toward a social graph recommendation algorithm: do we trust our friends in movie recommendations? In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds.) OTM 2012. LNCS, vol. 7567, pp. 637–647. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33618-8_83

    Chapter  Google Scholar 

  62. Adriyendi, M.: Multi-attribute decision making using simple additive weighting and weighted product in food choice. Int. J. Inf. Eng. Electron. Bus. 7(6), 8–14 (2015)

    Google Scholar 

  63. Sun, P., Yang, J., Zhi, Y.: Multi-attribute decision-making method based on Taylor expansion. Int. J. Distrib. Sens. Netw. 15(3), 1550147719836078 (2019)

    Article  Google Scholar 

  64. Pratiwi, D., Putri, J., Agushinta, D.: Decision support system to majoring high school student using simple additive weighting method. Int. J. Comput. Trends Technol. 10, 153–159 (2014)

    Article  Google Scholar 

  65. Aminudin, N., et al.: Higher education selection using simple additive weighting. Int. J. Eng. Technol. (UAE) 7(2.27), 211–217 (2018)

    Article  Google Scholar 

  66. Santoso, P.A., Wibawa, A.P., Pujianto, U.: Internship recommendation system using simple additive weighting. Bull. Soc. Inform. Theory Appl. 2(1), 15–21 (2018)

    Article  Google Scholar 

  67. Hdioud, F., Frikh, B., Ouhbi, B.: Multi-criteria recommender systems based on multi-attribute decision making. In: Proceedings of the International Conference on Information Integration and Web-based Applications & Services (2013)

    Google Scholar 

  68. Engel, M.M., Utomo, W.H., Purnomo, H.D.: Fuzzy multi attribute decision making simple additive weighting (MADM SAW) for information retrieval (IR) in E commerce recommendation. Int. J. Comput. Sci. Softw. Eng. 6(6), 136–145 (2017)

    Google Scholar 

  69. Gregor, S.: The nature of theory in information systems. MIS Q. 30(3), 611–642 (2006)

    Article  Google Scholar 

  70. Kuechler, W., Vaishnavi, V.: On theory development in design science research: anatomy of a research project. EJIS 17, 489–504 (2008)

    Google Scholar 

  71. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24, 45 (2008)

    Article  Google Scholar 

  72. Hevner, A.: A three cycle view of design science research. Scandinavian J. Inf. Syst. 19, 4 (2007)

    Google Scholar 

  73. Simon, H.A.: The Sciences of the Artificial. The MIT Press (1996)

    Google Scholar 

  74. Okfalisa, O., et al.: Decision support system for smartphone recommendation: the comparison of fuzzy Ahp and fuzzy Anp in multi-attribute decision making. Sinergi 25(1), 101–110 (2020)

    Article  Google Scholar 

  75. Hevner, A., Park, J., March, S.T.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)

    Article  Google Scholar 

  76. Wang, S., Gwizdka, J., Chaovalitwongse, W.A.: Using wireless EEG signals to assess memory workload in the N-Back task. IEEE Trans. Hum.-Mach. Syst. 46(3), 424–435 (2016)

    Article  Google Scholar 

  77. Kirchner, W.K.: Age differences in short-term retention of rapidly changing information. J. Exp. Psychol. 55(4), 352–358 (1958)

    Article  Google Scholar 

  78. Karran, A.J., et al.: Toward a hybrid passive BCI for the modulation of sustained attention using EEG and fNIRS. Front. Hum. Neurosci. 13, 393 (2019)

    Article  Google Scholar 

  79. Jones, D., Gregor, S.: The anatomy of a design theory. J. Assoc. Inf. Syst. 8(5), 312–335 (2007)

    Google Scholar 

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Authors and Affiliations

  1. Tech3Lab, HEC Montréal, Montréal, Canada

    Bella Tadson, Jared Boasen, François Courtemanche, Noémie Beauchemin, Alexander-John Karran, Pierre-Majorique Léger & Sylvain Sénécal

  2. Faculty of Health Sciences, Hokkaido University, Sapporo, Japan

    Jared Boasen

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  1. Bella Tadson
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  1. University of the Western Cape, Cape Town, South Africa

    Aurona Gerber

  2. Georgia State University, Atlanta, GA, USA

    Richard Baskerville

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Tadson, B. et al. (2023). Neuro-Adaptive Interface System to Evaluate Product Recommendations in the Context of E-Commerce. In: Gerber, A., Baskerville, R. (eds) Design Science Research for a New Society: Society 5.0. DESRIST 2023. Lecture Notes in Computer Science, vol 13873. Springer, Cham. https://doi.org/10.1007/978-3-031-32808-4_4

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