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User acceptance on content optimization algorithms: predicting filter bubbles in conversational AI services

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Abstract

As the role of artificial intelligence (AI) agents in information curation has emerged with recent advancements in AI technologies, the present study explored which users would potentially be susceptible to the filter bubble phenomenon. First, a large-scale analysis of conversational agent users in South Korea (N = 2808) was conducted to investigate the relative importance of content optimization algorithms in shaping positive user experience. Five user clusters were identified based on their information technology proficiency and demographics, and a multiple-group path analysis was performed to compare the influences of content optimization algorithms across the user groups. The results indicated that the personalization algorithm generally exhibited a stronger impact on evaluations of an AI agent’s usefulness than the diversity algorithm. In addition, increased user age and greater Internet usage were found to decrease the importance of objectivity in shaping trust in AI agents. This study improves the understanding of the social influence of AI technology and suggests the necessity of segmented approaches in the development of AI technology.

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References

  1. Hoy, M.B.: Alexa, Siri, Cortana, and more: an introduction to voice assistants. Med. Ref. Serv. Q. 37, 81–88 (2018). https://doi.org/10.1080/02763869.2018.1404391

    Article  Google Scholar 

  2. Nass, C.I., Brave, S.: Wired for speech: how voice activates and advances the human-computer relationship. MIT press, Cambridge (2005)

    Google Scholar 

  3. Pinker, S.: The language instinct: how the mind creates language. Penguin, New York (2003)

    Google Scholar 

  4. Le Bigot, L., Caroux, L., Ros, C., Lacroix, A., Botherel, V.: Investigating memory constraints on recall of options in interactive voice response system messages. Behav. Inf. Technol. 32(2), 106–116 (2013)

    Article  Google Scholar 

  5. Sunstein, C.R.: Infotopia: how many minds produce knowledge. Oxford University Press, Oxford (2006)

    Google Scholar 

  6. Sunstein, C.R.: Republic.Com 2.0. Princeton University Press, Princeton, NJ (2009). https://www.amazon.com/Republic-com-2-0-Cass-R-Sunstein/dp/0691143285

  7. Haim, M., Graefe, A., Brosius, H.-B.: Burst of the filter bubble?. Effects of personalization on the diversity of google news. Digit. J. 6, 1–14 (2018). https://doi.org/10.1080/21670811.2017.1338145

    Article  Google Scholar 

  8. Smyth, B., McClave, P.: Similarity vs. diversity. ICCBR 2080, 347–361 (2001)

    MATH  Google Scholar 

  9. Di Noia, T., Rosati, J., Tomeo, P., Di Sciascio, E.: Adaptive multi-attribute diversity for recommender systems. Inf. Sci. 382–383, 234–253 (2017)

    Article  Google Scholar 

  10. Jordan, M.I.: Artificial intelligence—the revolution hasn’t happened yet. Harv. Data Sci. Rev. (2019). https://doi.org/10.1162/99608f92.f06c6e61

    Article  Google Scholar 

  11. Castañeda, J.A., Muñoz-Leiva, F., Luque, T.: Web acceptance model (WAM)—moderating effects of user experience. Inf. Manag. 44, 384–396 (2007)

    Article  Google Scholar 

  12. Sundar, S.S., Marathe, S.S.: Personalization versus customization: the importance of agency, privacy, and power usage. Hum. Commun. Res. 36, 298–322 (2010). https://doi.org/10.1111/j.1468-2958.2010.01377.x

    Article  Google Scholar 

  13. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q 27(3), 425 (2003)

    Article  Google Scholar 

  14. Kendall, L., Chaudhuri, B., Bhalla, A.: Understanding technology as situated practice: everyday use of voice user interfaces among diverse groups of users in Urban India. Inf. Syst. Front. 22, 585–605 (2020). https://doi.org/10.1007/s10796-020-10015-6

    Article  Google Scholar 

  15. Sundar, S.S.: Rise of machine agency: a framework for studying the psychology of human–AI interaction (HAII). J. Comp. Mediat. Comm. 52, 286–315 (2020). https://doi.org/10.1093/jcmc/zmz026

    Article  Google Scholar 

  16. Chen, T.-W., Sundar, S.S.: This app would like to use your current location to better serve you - importance of user assent and system transparency in personalized mobile services. CHI 6, 1–13 (2018)

    Google Scholar 

  17. Zhang, B., Wu, M., Kang, H., et al. Effects of security warnings and instant gratification cues on attitudes toward mobile websites. CHI 111–114 (2014)

  18. Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity, pp. 257–260. ACM Press, New York (2010)

    Google Scholar 

  19. Wang, W., Xu, J.D., Wang, M.: Effects of recommendation neutrality and sponsorship disclosure on trust vs. distrust in online recommendation agents—moderating role of explanations for organic recommendations. Manag. Sci. 64, 5198–5219 (2018)

    Article  Google Scholar 

  20. Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., et al.: Explaining the user experience of recommender systems. User Model. User-Adap. Inter. 22, 441–504 (2012). https://doi.org/10.1007/s11257-011-9118-4

    Article  Google Scholar 

  21. Pariser, E.: The filter bubble: what the Internet is hiding from you. Penguin, New York (2011)

    Google Scholar 

  22. Rhodes, S.C.: Filter bubbles, echo chambers, and fake news: how social media conditions individuals to be less critical of political misinformation. Polit. Commun. 39(1), 1–22 (2022). https://doi.org/10.1080/10584609.2021.1910887

    Article  Google Scholar 

  23. Corbett, C.F., Wright, P.J., Jones, K., Parmer, M.: Voice-activated virtual home assistant use and social isolation and loneliness among older adults: mini review. Front. Public Health 9 (2021). https://doi.org/10.3389/fpubh.2021.742012

  24. Nilashi, M., Jannach, D., Bin Ibrahim, O., et al.: Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electron. Commer. Res. Appl. 19, 70–84 (2016)

    Article  Google Scholar 

  25. Karimi, M., Jannach, D., Jugovac, M.: News recommender systems—survey and roads ahead. Inf. Process. Manag. 54, 1203–1227 (2018). https://doi.org/10.1016/j.ipm.2018.04.008

    Article  Google Scholar 

  26. Wang, W., Benbasat, I.: Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. J. Manag. Inf. Syst. 23, 217–246 (2007). https://doi.org/10.2753/MIS0742-1222230410

    Article  Google Scholar 

  27. Sohn, K., Kwon, O.: Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telemat. Inform. 47, 101324 (2020)

    Article  Google Scholar 

  28. Chung, S., Wo, K.P.: Using consumer perceptions of a voice-activated speaker device as an educational tool. JMIR Med. Educ. 6(1), e17336 (2020)

    Article  Google Scholar 

  29. Vargas, S., Baltrunas, L., Karatzoglou, A., Castells, P.: Coverage, redundancy and size-awareness in genre diversity for recommender systems. Rec. Sys. 209–216 (2014). https://doi.org/10.1145/2645710.2645743

  30. 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, 45–70 (2014). https://doi.org/10.2753/MIS0742-1222230303

    Article  Google Scholar 

  31. Wang, W., Qiu, L., Kim, D., Benbasat, I.: Effects of rational and social appeals of online recommendation agents on cognition- and affect-based trust. Decis. Support Syst. 86, 48–60 (2016). https://doi.org/10.1016/j.dss.2016.03.007

    Article  Google Scholar 

  32. Burke, R.D.: Hybrid recommender systems—survey and experiments. User Model. User Adapt. Interact. 12, 331–370 (2002)

    Article  MATH  Google Scholar 

  33. Carlson, M.S., Desai, M., Drury, J.L., et al. Identifying factors that influence trust. In: Proceedings of the workshop on Etiquette

  34. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: an introduction. Cambridge University Press, Cambridge (2010)

    Book  Google Scholar 

  35. Hong, T., Kim, E.: Segmenting customers in online stores based on factors that affect the customer’s intention to purchase. Expert Syst. Appl. 39, 2127–2131 (2012). https://doi.org/10.1016/j.eswa.2011.07.114

    Article  Google Scholar 

  36. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319 (1989)

    Article  Google Scholar 

  37. Komiak, S.Y., Benbasat, I.: The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Q. 30(4), 941–960 (2006)

    Article  Google Scholar 

  38. Nguyen, T.T., Maxwell Harper, F., Terveen, L., Konstan, J.A.: User personality and user satisfaction with recommender systems. Inf. Syst. Front. 20, 1173–1189 (2017). https://doi.org/10.1007/s10796-017-9782-y

    Article  Google Scholar 

  39. Pasquale, F.: The black box society: the secret algorithms that control money and information. Harvard University Press, Cambridge (2015)

    Book  Google Scholar 

  40. Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender systems handbook, pp. 257–297. Springer, Boston (2011)

    Chapter  Google Scholar 

  41. Ekstrand, M.D., Harper, F.M., Willemsen, M.C., Konstan, J.A.: User perception of differences in recommender algorithms. Rec. Sys.161–168 (2014). https://doi.org/10.1145/2645710.2645737

  42. McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems, pp. 1097–1101. ACM Press, New York (2006)

    Google Scholar 

  43. Yi, C., Jiang, Z.J., Benbasat, I.: Designing for diagnosticity and serendipity—an investigation of social product-search mechanisms. Inf. Syst. Res. 28, 413–429 (2017)

    Article  Google Scholar 

  44. Gunning, D., Aha, W.: DARPA’s explainable artificial intelligence program. AI Mag. 40, 44–58 (2019)

    Google Scholar 

  45. Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors 46, 50–80 (2004). https://doi.org/10.1518/hfes.46.1.50_30392

    Article  Google Scholar 

  46. Benbasat, I., Wang, W.: Trust in and adoption of online recommendation agents. J. AIS 6(3), 72–101 (2005)

    Google Scholar 

  47. Gefen, D., Karahanna, E., Straub, D.W.: Trust and TAM in online shopping—an integrated model. MIS Q. 27, 51–90 (2003)

    Article  Google Scholar 

  48. Qiu, L., Benbasat, I.: Evaluating anthropomorphic product recommendation agents: a social relationship perspective to designing information systems. J. Manag. Inf. Syst. 25, 145–182 (2014). https://doi.org/10.2753/MIS0742-1222250405

    Article  Google Scholar 

  49. Martínez-López, F.J., Esteban-Millat, I., Cabal, C.C., Gengler, C.: Psychological factors explaining consumer adoption of an e-vendor’s recommender. Industr. Manag. Data Syst. 115, 284–310 (2015). https://doi.org/10.1108/IMDS-10-2014-0306

    Article  Google Scholar 

  50. Lee, S., Choi, J.: Enhancing user experience with conversational agent for movie recommendation: effects of self-disclosure and reciprocity. Int. J. Hum. Comput. Stud. 103, 95–105 (2017)

    Article  Google Scholar 

  51. Kowalczuk, P.: Consumer acceptance of smart speakers: a mixed methods approach. J. Res. Interact. Mark. 12, 418–431 (2018). https://doi.org/10.1108/JRIM-01-2018-0022

    Article  Google Scholar 

  52. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39, 273–315 (2008)

    Article  Google Scholar 

  53. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204 (2000). https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  54. Albashrawi, M., Motiwalla, L.: Privacy and personalization in continued usage intention of mobile banking—an integrative perspective. Inf. Syst. Front. 21(5), 1031–1043 (2019)

    Article  Google Scholar 

  55. Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31, 137–209 (2007). https://doi.org/10.5555/2017327.2017335

    Article  Google Scholar 

  56. Koufaris, M., Kambil, A., Labarbera, P.A.: Consumer behavior in web-based commerce: an empirical study. Int. J. Electron. Commer. 6, 115–138 (2001)

    Article  Google Scholar 

  57. Ong, C.-S., Lai, J.-Y.: Gender differences in perceptions and relationships among dominants of e-learning acceptance. Comput. Hum. Behav. 22, 816–829 (2006)

    Article  Google Scholar 

  58. Guner, H., Acarturk, C.: The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ. Access Inf. Soc. 19, 311–330 (2018). https://doi.org/10.1007/s10209-018-0642-4

    Article  Google Scholar 

  59. IMD (2021) IMD world digital competitiveness ranking. IMD world competitiveness center, pp. 110–111

  60. Salih, W. K.: Does auditor objectivity impact on the relationship between information technology and efficiency and effectiveness of auditing: evidence from Iraq. J. Rev. Glob. Econ. 8, 226–238 (2019). https://doi.org/10.6000/1929-7092.2019.08.20

    Article  Google Scholar 

  61. Jauhari, H., Singh, S.: Perceived diversity climate and employees’ organizational loyalty. Equality Divers. Incl. Int. J. 32, 262–276 (2013). https://doi.org/10.1108/EDI-12-2012-0119

    Article  Google Scholar 

  62. Kim, N., Park, Y., Lee, D.: Differences in consumer intention to use on-demand automobile-related services in accordance with the degree of face-to-face interactions. Tech Fore Soc Change 139, 277–286 (2019). https://doi.org/10.1016/j.techfore.2018.11.014

    Article  Google Scholar 

  63. Lee, J., Ryu, M. H., Lee, D.: A study on the reciprocal relationship between user perception and retailer perception on platform-based mobile payment service. J Retail. and Cons. Serv. 48, 7–15 (2019). https://doi.org/10.1016/j.jretconser.2019.01.007

    Article  Google Scholar 

  64. Yeon, J., Park, I., Lee, D.: What creates trust and who gets loyalty in social commerce? J. of Retail. and Cons. Serv. 50, 138-144 (2019). https://doi.org/10.1016/j.jretconser.2019.05.009

    Article  Google Scholar 

  65. Yen, C-H., Lu, H-P.: Effects of e‐service quality on loyalty intention: an empirical study in online auction. Managing Serv. Qual. Int. J. 18, 127–146 (2008). https://doi.org/10.1108/09604520810859193

    Article  Google Scholar 

  66. Von Luxburg, U., Belkin, M., Bousquet, O.: Consistency of spectral clustering. Ann. Stat. 36, 555–586 (2008)

    MathSciNet  MATH  Google Scholar 

  67. Marsden, A.: Eigenvalues of the Laplacian and their relationship to the connectedness of a graph. University of Chicago, REU (2013)

    Google Scholar 

  68. von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17, 395–416 (2007). https://doi.org/10.1007/s11222-007-9033-z

    Article  MathSciNet  Google Scholar 

  69. James, G., Witten, D., Hastie, T., Tibshirani, R.: An introduction to statistical learning. Springer, New York (2013)

    Book  MATH  Google Scholar 

  70. Hooper, D., Coughlan, J., Mullen, M.R.: Structural equation modelling: guidelines for determining model fit. Electron. J. Bus. Res. Methods 6, 53–60 (2008)

    Google Scholar 

  71. Hu, L.T., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6, 1–55 (1999). https://doi.org/10.1080/10705519909540118

    Article  Google Scholar 

  72. Byrne, B.M.: Structural equation modeling with AMOS: basic concepts, applications, and programming. Routledge, New York, NY (2016)

    Book  Google Scholar 

  73. Shardanand, U., Maes, P. Social information filtering—algorithms for automating “word of mouth”. CHI 210–217 (1995)

  74. Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. ICUIMC (2008). https://doi.org/10.1145/1352793.1352837

    Article  Google Scholar 

  75. Bellaachia, A., Alathel, D.: Improving the recommendation accuracy for cold start users in trust-based recommender systems. Int. J. Comput. Commun. Eng. 5, 206–214 (2016). https://doi.org/10.1145/223904.223931

    Article  Google Scholar 

  76. Yoo, K.-H., Gretzel, U.: Creating more credible and persuasive recommender systems—the influence of source characteristics on recommender system evaluations. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender systems handbook, pp. 455–477. Springer, Boston (2011)

    Chapter  Google Scholar 

  77. Cramer, H., Evers, V., Ramlal, S., et al.: The effects of transparency on trust in and acceptance of a content-based art recommender. User Model. User Adap. Inter. 18, 455–496 (2008). https://doi.org/10.1007/s11257-008-9051-3

    Article  Google Scholar 

  78. von Hippel, W., Silver, L.A., Lynch, M.E.: Stereotyping against your will: the role of inhibitory ability in stereotyping and prejudice among the elderly. Pers. Soc. Psychol. Bull. 26, 523–532 (2016). https://doi.org/10.1177/0146167200267001

    Article  Google Scholar 

  79. Heid, A.R., Zarit, S.H., Fingerman, K.L.: “My parent is so stubborn!”—perceptions of aging parents’ persistence, insistence, and resistance. GERONB 71, 602–612 (2016). https://doi.org/10.1093/geronb/gbu177

    Article  Google Scholar 

  80. Lee, M.-C.: Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation-confirmation model. Comput. Educ. 54, 506–516 (2010)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5B5A16077452)

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Authors and Affiliations

  1. Technology Management, Economics and Policy Program, Seoul National University, Seoul, Republic of Korea

    Hosoo Cho

  2. Department of Interaction Science, Sungkyunkwan University, Seoul, Republic of Korea

    Daeho Lee

  3. Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, Republic of Korea

    Daeho Lee

  4. Media School, Hallym University, 1, Hallymdaehak-gil, Chuncheon-si, Gangwon-do, Republic of Korea

    Jae-Gil Lee

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Cho, H., Lee, D. & Lee, JG. User acceptance on content optimization algorithms: predicting filter bubbles in conversational AI services. Univ Access Inf Soc 22, 1325–1338 (2023). https://doi.org/10.1007/s10209-022-00913-8

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