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The dark sides of AI personal assistant: effects of service failure on user continuance intention

Abstract

With the popularization of smart devices and the rapid development of smart voice technology, AI personal assistants (AIPAs) have penetrated deeply into users' lives. Compared with previous years, the accuracy, semantic understanding ability, and wake-up ability of AIPAs have been improved, but the lack of service maturity and the insufficient degree of scene integration have brought users a poor human–computer interaction experience. However, studies have scarcely uncovered the underlying mechanism through which those dark sides of AIPAs exert impacts on users' continuance intention. From the perspective of technostress, the current study proposes a theoretical model for consumers to cope with service failure pressure sources. This article collected 413 questionnaires and conducted an empirical analysis. Results show that negative technical characteristics will affect consumers’ psychological responses and ultimately affect consumers’ technical exhaustion, satisfaction, and two kinds of continuance intentions (general and partial continuance intentions) through cognitive load. Findings open up new avenues for research by exploring the mechanism of how the service failures of these AIPAs affect consumers' continuance intention through the perspective of technostress.

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References

  1. Alge, B. (2001). Effects of Computer Surveillance on Perceptions of Privacy and Procedural Justice. The Journal of Applied Psychology, 86(4), 797–804. https://doi.org/10.1037/0021-9010.86.4.797

    Article  Google Scholar 

  2. Almusaylim, Z. A., & Zaman, N. (2019). A review on smart home present state and challenges: Linked to context-awareness internet of things (IoT). Wireless Networks, 25(6), 3193–3204. https://doi.org/10.1007/s11276-018-1712-5

    Article  Google Scholar 

  3. Ayanso, A., Herath, T. C., & O’Brien, N. (2015). Understanding continuance intentions of physicians with electronic medical records (EMR): An expectancy-confirmation perspective. Decision Support Systems, 77, 112–122. https://doi.org/10.1016/j.dss.2015.06.003

  4. Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–858. https://doi.org/10.2307/41409963

    Article  Google Scholar 

  5. Baier, L., Kühl, N., Schüritz, R., & Satzger, G. (2020). Will the customers be happy? Identifying unsatisfied customers from service encounter data. Journal of Service Management, 32(2), 265–288. https://doi.org/10.1108/JOSM-06-2019-0173

    Article  Google Scholar 

  6. Bala, H., & Venkatesh, V. (2016). Adaptation to information technology: A holistic nomological network from implementation to job outcomes. Management Science, 62(1), 156–179. https://doi.org/10.1287/mnsc.2014.2111

    Article  Google Scholar 

  7. Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248–287. https://doi.org/10.1016/0749-5978(91)90022-L

  8. Bateman, P. J., Pike, J. C., & Butler, B. S. (2011). To disclose or not: Publicness in social networking sites. Information Technology & People, 24(1), 78–100. https://doi.org/10.1108/09593841111109431

    Article  Google Scholar 

  9. Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success. Journal of Service Management, 31(2), 267–289. https://doi.org/10.1108/JOSM-05-2019-0156

    Article  Google Scholar 

  10. Bernard, D., & Arnold, A. (2019). Cognitive interaction with virtual assistants: From philosophical foundations to illustrative examples in aeronautics. Computers in Industry, 107, 33–49. https://doi.org/10.1016/j.compind.2019.01.010

    Article  Google Scholar 

  11. Blascovich, J., & Tomaka, J. (1996). The Biopsychosocial Model of Arousal Regulation. In M. P. Zanna (Ed.), (Vol. 28, pp. 1–51). Academic Press. https://doi.org/10.1016/S0065-2601(08)60235-X

  12. Blöcher, K., & Alt, R. (2020). AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry. Electronic Markets. https://doi.org/10.1007/s12525-020-00443-2

    Article  Google Scholar 

  13. Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15–16), 1401–1436. https://doi.org/10.1080/0267257X.2019.1687571

  14. Brooks, D. J. (2017). A human-centric approach to autonomous robot failures. University of Massachusetts Lowell. ProQuest Dissertations Publishing.

  15. Brünken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38(1), 53–61. https://doi.org/10.1207/S15326985EP3801_7

    Article  Google Scholar 

  16. Cacioppo, J., & Berntson, G. (1994). Relationship between attitudes and evaluative space: A critical review, with emphasis on the separability of positive and negative substrates. Psychological Bulletin, 115(3), 401–423.https://doi.org/10.1037/0033-2909.115.3.401

  17. Califf, C. B., Sarker, S., & Sarker, S. (2020). The Bright and Dark Sides of Technostress: A Mixed Methods Study Involving Healthcare IT. MIS Quarterly, 44(2), 809–856. https://doi.org/10.25300/MISQ/2020/14818

  18. Cenfetelli, R. T., & Schwarz, A. (2011). Identifying and testing the inhibitors of technology usage intentions. Information Systems Research, 22(4), 808–823. https://doi.org/10.1287/isre.1100.0295

    Article  Google Scholar 

  19. Cheng, X., Fu, S., Vreede, T. D., Vreede, G. D., Maier, R., & Weber, B. (2020). Idea Convergence Quality in Open Innovation Crowdsourcing : A Cognitive Idea Convergence Quality in Open Innovation Crowdsourcing : A Cognitive Load Perspective. Journal of Management Information Systems, 37(2), 349–376. https://doi.org/10.1080/07421222.2020.1759344

    Article  Google Scholar 

  20. Cherubini, M., Gutierrez, A., De Oliveira, R., & Oliver, N. (2010). Social tagging revamped: Supporting the users’ need of self-promotion through persuasive techniques. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2, 985–994. https://doi.org/10.1145/1753326.1753473

    Article  Google Scholar 

  21. Cho, J., Ramgolam, D. I., Schaefer, K. M., & Sandlin, A. N. (2011). The Rate and Delay in Overload: An Investigation of Communication Overload and Channel Synchronicity on Identification and Job Satisfaction. Journal of Applied Communication Research, 39(1), 38–54. https://doi.org/10.1080/00909882.2010.536847

    Article  Google Scholar 

  22. Chuang, A., Shen, C.-T., & Judge, T. A. (2016). Development of a Multidimensional Instrument of Person-Environment Fit: The Perceived Person-Environment Fit Scale (PPEFS). Applied Psychology, 65(1), 66–98. https://doi.org/10.1111/apps.12036

    Article  Google Scholar 

  23. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–210. https://doi.org/10.2307/249688

    Article  Google Scholar 

  24. Cooper, C. L., Dewe, P. J., & O’Driscoll, M. P. (2001). Organizational Stress. Sage Publications.

    Google Scholar 

  25. Costa, P. (2018). Conversing with Personal Digital Assistants: On Gender and Artificial Intelligence. Journal of Science and Technology of the Arts, 10, 2. https://doi.org/10.7559/citarj.v10i3.563

    Article  Google Scholar 

  26. de Guinea, A. O., & Markus, M. L. (2009). Why Break the Habit of a Lifetime? Rethinking the Roles of Intention, Habit, and Emotion in Continuing Information Technology Use. MIS Quarterly, 33(3), 433–444. https://doi.org/10.2307/20650303

    Article  Google Scholar 

  27. Derks, D., Bakker, A. B., Peters, P., & van Wingerden, P. (2016). Work-related smartphone use, work-family conflict and family role performance: The role of segmentation preference. Human Relations, 69(5), 1045–1068. https://doi.org/10.1177/0018726715601890

    Article  Google Scholar 

  28. Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C channel satisfaction and preference: Validating e-commerce metrics. Information Systems Research, 13(3), 316–333. https://doi.org/10.1287/isre.13.3.316.77

    Article  Google Scholar 

  29. Dinev, T., Hart, P., Dinev, T., & Hart, P. (2006). An Extended Privacy Calculus Model for E-Commerce Transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080

    Article  Google Scholar 

  30. Doherty, W. J., & Kelisky, R. P. (1979). Managing Vm/Cms Systems for User Effectiveness. IBM Systems Journal, 18(1), 143–163. https://doi.org/10.1147/sj.181.0143

    Article  Google Scholar 

  31. Dunin-Underwood, A. (2020). Alexa, can you keep a secret? Applicability of the third-party doctrine to information collected in the home by virtual assistants. Information and Communications Technology Law, 29(1), 101–119. https://doi.org/10.1080/13600834.2020.1676956

    Article  Google Scholar 

  32. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedig, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, V., Janssen, M., Jones, P., Kumar Kar, A., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L. C., Misra, S., Mogaji, E., Kumar Sharma, S., Bahadur Singhs, J., Raghavan, V., Raman, R., P. Rana, N., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Waltony, P., & D. Williams, M. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

  33. Ebbers, F., Zibuschka, J., Zimmermann, C., & Hinz, O. (2020). User preferences for privacy features in digital assistants. Electronic Markets. https://doi.org/10.1007/s12525-020-00447-y

    Article  Google Scholar 

  34. Edmunds, A., & Morris, A. (2000). Problem of information overload in business organizations: A review of the literature. International Journal of Information Management, 20(1), 17–28. https://doi.org/10.1016/S0268-4012(99)00051-1

    Article  Google Scholar 

  35. Edu, J. S., Such, J. M., & Suarez-Tangil, G. (2021). Smart Home Personal Assistants: A Security and Privacy Review. ACM Computing Surveys, 53(6). https://doi.org/10.1145/3412383

  36. Fan, A., Wu, L., Laurie, & Mattila, A. S. (2016). Does anthropomorphism influence customers’ switching intentions in the self-service technology failure context? Journal of Services Marketing, 30(7), 713–723. https://doi.org/10.1108/JSM-07-2015-0225

  37. Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press. https://doi.org/10.4324/9780203838020

  38. Fox, M., Dwyer, D., & Ganster, D. (1993). Effects of stressful job demands and control on physiological and attitudinal outcomes in a hospital setting. Academy of Management, 36, 289–318. https://doi.org/10.2307/256524

  39. Gaudioso, F., Turel, O., & Galimberti, C. (2015). Explaining Work Exhaustion From a Coping Theory Perspective: Roles of Techno-Stressors and Technology-Specific Coping Strategies. Studies in Health Technology and Informatics, 219, 14–20.

    Google Scholar 

  40. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis. Pearson Education Limited.

  41. Han, S., & Yang, H. (2018). Understanding adoption of intelligent personal assistants : A parasocial relationship perspective. Industrial Management & Data Systems, 118(3), 618–636. https://doi.org/10.1108/IMDS-05-2017-0214

  42. Hess, R. L., Ganesan, S., & Klein, N. M. (2003). Service failure and recovery: The impact of relationship factors on customer satisfaction. Journal of the Academy of Marketing Science, 31(2), 127–145. https://doi.org/10.1177/0092070302250898

    Article  Google Scholar 

  43. Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed Cognition: Toward a New Foundation for Human-Computer Interaction Research. ACM Transactions on Computer-Human Interaction, 7(2), 174–196. https://doi.org/10.1145/353485.353487

    Article  Google Scholar 

  44. Hsieh, P. J., & Lin, W. S. (2018). Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model. Information and Management, 55(1), 51–63. https://doi.org/10.1016/j.im.2017.03.008

    Article  Google Scholar 

  45. Hsu, C.-L., & Lin, J.C.-C. (2020). Understanding continuance intention to use online to offline (O2O) apps. Electronic Markets, 30(4), 883–897. https://doi.org/10.1007/s12525-019-00354-x

    Article  Google Scholar 

  46. Hu, P. J. H., Hu, H. F., & Fang, X. (2017). Examining the mediating roles of cognitive load and performance outcomes in user satisfaction with a website: A field quasi-experiment. MIS Quarterly, 41(3), 975–987. https://doi.org/10.25300/MISQ/2017/41.3.14

  47. Hu, Q., Lu, Y., Pan, Z., Gong, Y., & Yang, Z. (2021). Can AI artifacts influence human cognition? The effects of artificial autonomy in intelligent personal assistants. International Journal of Information Management, 56, 102250. https://doi.org/10.1016/j.ijinfomgt.2020.102250

  48. Huang, B., Philp, M. (2020). When AI-based services fail: Examining the effect of the self-AI connection on willingness to share negative word-of-mouth after service failures. The Service Industries Journal, 1–23. https://doi.org/10.1080/02642069.2020.1748014

  49. IResearch. (2018). iResearch consulting: 2018 China Intelligent voice assistant enterprise Case Study Report. http://www.199it.com/archives/737587.html.

  50. Jonathon, R. B., Halbesleben, M. W., & Psychology, B. (2007). Emotional exhaustion and job performance: The mediating role of motivation. Journal of Applied Psychology, 92(1), 93–106. https://doi.org/10.1037/0021-9010.92.1.93

  51. Kannampallil, T., Smyth, J. M., Jones, S., Payne, P. R. O., & Ma, J. (2020). Cognitive plausibility in voice-based AI health counselors. NPJ Digital Medicine, 3(1). https://doi.org/10.1038/s41746-020-0278-7

  52. Karadal, H., & Abubakar, A. M. (2021). Internet of things skills and needs satisfaction: Do generational cohorts’ variations matter? Online Information Review. https://doi.org/10.1108/OIR-04-2020-0144

    Article  Google Scholar 

  53. Karr-wisniewski, P., & Lu, Y. (2010). When more is too much : Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061–1072. https://doi.org/10.1016/j.chb.2010.03.008

    Article  Google Scholar 

  54. Keller, K. L. (2012). Understanding the richness of brand relationships: Research dialogue on brands as intentional agents. Journal of Consumer Psychology, 22(2), 186–190. https://doi.org/10.1016/j.jcps.2011.11.011

    Article  Google Scholar 

  55.  Kangsoo, K., De Melo, C. M., Norouzi, N., Bruder, G., & Welch, G. F. (2020). Reducing Task Load with an Embodied Intelligent Virtual Assistant for Improved Performance in Collaborative Decision Making. IEEE Conference on Virtual Reality and 3D User Interfaces, 529–538. https://doi.org/10.1109/VR46266.2020.1581084624004

  56. Kim, K., & Park, H. (2018). The effects of technostress on information technology acceptance. Journal of Theoretical and Applied Information Technology, 96(24), 8300–8312.

    Google Scholar 

  57. Kim, S. Y., & Lim, Y. J. (2001). Consumers’ Perceived Importance of and Satisfaction with Internet Shopping. Electronic Markets, 11(3), 148–154. https://doi.org/10.1080/101967801681007988

    Article  Google Scholar 

  58. Kiseleva, J., Crook, A. C., Williams, K., Zitouni, I., Awadallah, A. H., & Anastasakos, T. (2016). Predicting user satisfaction with intelligent assistants. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 45–54. https://doi.org/10.1145/2911451.2911521

  59. Klapp, O. E. (1987). Overload and Boredom : Essays on the Quality of Life in the Information Society. American Sociological Association, 16(4), 580–581. https://doi.org/10.1086/601886

    Article  Google Scholar 

  60. Kuo, F. Y., Hsu, C. W., & Day, R. F. (2009). An exploratory study of cognitive effort involved in decision under Framing-an application of the eye-tracking technology. Decision Support Systems, 48(1), 81–91. https://doi.org/10.1016/j.dss.2009.06.011

    Article  Google Scholar 

  61. Lee, S., & Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies, 103, 95–105. https://doi.org/10.1016/j.ijhcs.2017.02.005

    Article  Google Scholar 

  62. Lee, M., & Cunningham, L. F. (2001). A cost/benefit approach to understanding service loyalty. Journal of Services Marketing, 15(2), 113–130. https://doi.org/10.1108/08876040110387917

    Article  Google Scholar 

  63. Lee, K., Forlizzi, S., Srinivasa J. S., & Rybski, P. (2010). Gracefully mitigating breakdowns in robotic services. IEEE International Conference on Human-Robot Interaction, 203–210. https://doi.org/10.1145/1734454.1734544

  64. Lee, Y.-H., Hsieh, Y.-C., & Chen, Y.-H. (2013). An investigation of employees’ use of e-learning systems: Applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173–189. https://doi.org/10.1080/0144929X.2011.577190

    Article  Google Scholar 

  65. Lee, A. R., Son, S. M., & Kim, K. K. (2016). Information and communication technology overload and social networking service fatigue: A stress perspective. Computers in Human Behavior, 55, 51–61. https://doi.org/10.1016/j.chb.2015.08.011

    Article  Google Scholar 

  66. Lefcourt, H. M. (1976). Locus of control: Current trends in theory and research. Psychology Press.

  67. Li, H., Gupta, A., Zhang, J., & Flor, N. (2020). Who will use augmented reality ? An integrated approach based on text analytics and field survey. European Journal of Operational Research, 281(3), 502–516. https://doi.org/10.1016/j.ejor.2018.10.019

    Article  Google Scholar 

  68. Lin, J., Lin, S., Turel, O., & Xu, F. (2020). The buffering effect of flow experience on the relationship between overload and social media users’ discontinuance intentions. Telematics and Informatics, 49. https://doi.org/10.1016/j.tele.2020.101374

  69. Little, T. D. (1997). Mean and covariance structures (MACS), analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32(1), 53–76. https://doi.org/10.1207/s15327906mbr3201_3

  70. Locke, E. (1976). The nature and causes of job satisfaction. Handbook of Industrial and Organizational Psychology, 1, 1297–1343.

  71. Loideain, N. N., & Adams, R. (2020). From Alexa to Siri and the GDPR: The gendering of Virtual Personal Assistants and the role of Data Protection Impact Assessments. Computer Law & Security Review, 36, 105366. https://doi.org/10.1016/j.clsr.2019.105366

  72. Lowry, P. B., D’Arcy, J., Hammer, B., & Moody, G. D. (2016). “Cargo Cult” science in traditional organization and information systems survey research: A case for using nontraditional methods of data collection, including Mechanical Turk and online panels. The Journal of Strategic Information Systems, 25(3), 232–240. https://doi.org/10.1016/j.jsis.2016.06.002

  73. Lyu, Q., Zheng, N., Liu, H., Gao, C., Chen, S., & Liu, J. (2019). Remotely access “My” smart home in private: An anti-tracking authentication and key agreement scheme. IEEE Access, 7, 41835–41851. https://doi.org/10.1109/ACCESS.2019.2907602

  74. Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015a). Giving too much social support: Social overload on social networking sites. European Journal of Information Systems, 24(5), 447–464. https://doi.org/10.1057/ejis.2014.3

    Article  Google Scholar 

  75. Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015b). The effects of technostress and switching stress on discontinued use of social networking services: A study of Facebook use. Information Systems Journal, 25(3), 275–308. https://doi.org/10.1111/isj.12068

    Article  Google Scholar 

  76. Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4),336–355. https://doi.org/10.1287/isre.1040.0032

  77. Mani, Z., & Chouk, I. (2017). Drivers of consumers’ resistance to smart products. Journal of Marketing Management, 33(1–2), 76–97. https://doi.org/10.1080/0267257X.2016.1245212

    Article  Google Scholar 

  78. Maslach, C., & Jackson, S. E. (1981a). The measurement of experienced burnout. Journal of Occupational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205

    Article  Google Scholar 

  79. Maslach, C., & Jackson, S. E. (1981b). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205

    Article  Google Scholar 

  80. Mazmanian, M., Orlikowski, W. J., & Yates, J. (2013). The Autonomy Paradox: The Implications of Mobile Email Devices for Knowledge Professionals. Organization Science, 24(5), 1337–1357. https://doi.org/10.1287/orsc.1120.0806

    Article  Google Scholar 

  81. McFarlane, D. C., & Latorella, K. A. (2002). The Scope and Importance of Human Interruption in Human-Computer Interaction Design. Human Computer Interaction, 17(1), 1–61. https://doi.org/10.1207/S15327051HCI1701_1

    Article  Google Scholar 

  82. McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa … examine the variables influencing the use of artificial intelligent in-home voice assistants. Computers in Human Behavior, 99, 28–37. https://doi.org/10.1016/j.chb.2019.05.009

    Article  Google Scholar 

  83. Meadow, C. T., & Yuan, W. (1997). Measuring the impact of information: Defining the concepts. Information Processing and Management, 33(6), 697–714. https://doi.org/10.1016/S0306-4573(97)00042-3

    Article  Google Scholar 

  84. Moon, Y. (2000). Intimate exchanges: Using computers to elicit self‐disclosure from consumers. Journal of Consumer Research, 26(4), 323–339. https://doi.org/10.1086/209566

  85. Moore, J. E. (2000). One road to turnover: An examination of work exhaustion in technology professionals. MIS Quarterly, 24(1), 141–168. https://doi.org/10.2307/3250982

  86. Moussawi, S., Koufaris, M., & Benbunan-Fich, R. (2020). How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electronic Markets. https://doi.org/10.1007/s12525-020-00411-w

    Article  Google Scholar 

  87. Nath, A. K., & Singh, R. (2010). Evaluating the performance and quality of web services in electronic marketplace. e-ServiceJournal, 7(1), 43–59. https://doi.org/10.2979/esj.2010.7.1.43

  88. Novak, T. P., & Hoffman, D. L. (2019). Relationship journeys in the internet of things : a new framework for understanding interactions between consumers and smart objects. Journal of the Academy of Marketing Science, 47(2), 216–237.https://doi.org/10.1007/s11747-018-0608-3

  89. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1

    Article  Google Scholar 

  90. Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive Load Theory: Instructional Implications of the Interaction between Information Structures and Cognitive Architecture. Instructional Science, 32(1), 1–8. https://doi.org/10.1023/B:TRUC.0000021806.17516.d0

    Article  Google Scholar 

  91. Palmer, J. W., & Palmer, J. W. (2002). Web Site Usability, Design, and Performance Metrics. Information Systems Research, 13(2), 151–167. https://doi.org/10.1287/isre.13.2.151.88

    Article  Google Scholar 

  92. Parkes, A. (2013). The effect of task-individual-technology fit on user attitude and performance: An experimental investigation. Decision Support Systems, 54(2), 997–1009. https://doi.org/10.1016/j.dss.2012.10.025

    Article  Google Scholar 

  93. Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information Systems Research, 9(4), 362–379. https://doi.org/10.1287/isre.9.4.362

  94. Pennington, R., & Tuttle, B. (2007). The effects of information overload on software project risk assessment. Decision Sciences, 38(3), 489–526. https://doi.org/10.1111/j.1540-5915.2007.00167.x

    Article  Google Scholar 

  95. Pirkkalainen, H., Salo, M., Tarafdar, M., & Makkonen, M. (2019). Deliberate or Instinctive? Proactive and Reactive Coping for Technostress. Journal of Management Information Systems, 36(4), 1179–1212. https://doi.org/10.1080/07421222.2019.1661092

    Article  Google Scholar 

  96. Player, D., Youngs, P., Perrone, F., & Grogan, E. (2017). How principal leadership and person-job fit are associated with teacher mobility and attrition. Teaching and Teacher Education, 67, 330–339. https://doi.org/10.1016/j.tate.2017.06.017

    Article  Google Scholar 

  97. Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86. https://doi.org/10.1016/S0959-4752(01)00016-0

    Article  Google Scholar 

  98. Pridmore, J., & Mols, A. (2020). Personal choices and situated data: Privacy negotiations and the acceptance of household Intelligent Personal Assistants. Big Data and Society, 7(1). https://doi.org/10.1177/2053951719891748

  99. Pridmore, J., Vitak, J., Trottier, D., Liao, Y., Zimmer, M., Mols, A., & Kumar, P. C. (2019). Intelligent personal assistants and the intercultural negotiations of dataveillance in platformed households. Surveillance and Society, 17(1–2), 125–131. https://doi.org/10.24908/ss.v17i1/2.12936

  100. Pullins, E., Tarafdar, M., & Pham, P. (2020). The dark side of sales technologies: How technostress affects sales professionals. Journal of Organizational Effectiveness, 7(3), 297–320. https://doi.org/10.1108/JOEPP-04-2020-0045

    Article  Google Scholar 

  101. Ragni, M., Rudenko, A., Kuhnert, B., & Arras, K. O. (2016). Errare humanum est: Erroneous robots in human-robot interaction. 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 501–506. https://doi.org/10.1109/ROMAN.2016.7745164

  102. Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and validation. Information Systems Research, 19(4), 417–433. https://doi.org/10.1287/isre.1070.0165

    Article  Google Scholar 

  103. Ramadan, Z., Farah, M., & Essrawi, L. (2020). From to: How Alexa is redefining companionship and interdependence for people with special needs. Psychology & Marketing, 38(4), 596–609. https://doi.org/10.1002/mar.21441

  104. Ravindran, T., Kuan, A. C. Y., & Lian, D. G. H. (2014). Antecedents and Effects of Social Network Fatigue. Journal of the Association for Information Science and Technology, 65(11), 2306–2320. https://doi.org/10.1002/asi.23122

    Article  Google Scholar 

  105. Reinig, B. A. (2003). Toward an understanding of satisfaction with the process and outcomes of teamwork. Journal of Management Information Systems, 19(4), 65–83. https://doi.org/10.1080/07421222.2003.11045750

  106. Reis, A., Paulino, D., Paredes, H., & Barroso, J. (2017). Using intelligent personal assistants to strengthen the elderlies’ social bonds a preliminary evaluation of Amazon Alexa, Google Assistant, Microsoft Cortana, and Apple Siri.  International Conference on Universal Access in Human-Computer Interaction, 10279, 593–602. https://doi.org/10.1007/978-3-319-58700-4_48

  107. Rose, G., & Straub, D. (2001). The Effect of Download Time on Consumer Attitude Toward the e-Service Retailer. e-Service Journal, 1, 55–76. https://doi.org/10.1353/esj.2001.0005

  108. Sacks E. (2018, May 26). Alexa privacy fail highlights risks of smart speakers. NBC News, p. 7. https://www.nbcnews.com/tech/innovation/alexa-privacy-fail-highlights-risks-smart-speakers-n877671

  109. Salanova, M., Llorens, S., & Cifre, E. (2013). The dark side of technologies: Technostress among users of information and communication technologies. International Journal of Psychology, 48(3), 422–436. https://doi.org/10.1080/00207594.2012.680460

    Article  Google Scholar 

  110. Salo, M., & Frank, L. (2017). User behaviours after critical mobile application incidents: The relationship with situational context. Information Systems Journal, 27(1), 5–30. https://doi.org/10.1111/isj.12081

    Article  Google Scholar 

  111. Salo, M., Makkonen, M., & Hekkala, R. (2020). The Interplay of IT Users’ Coping Strategies: Uncovering Momentary Emotional Load, Routes, and Sequences. MIS Quarterly, 44(3), 1143–1175. https://doi.org/10.25300/MISQ/2020/15610

  112. Santos, J., Rodrigues, J. J. P. C., Silva, B. M. C., Casal, J., Saleem, K., & Denisov, V. (2016). An IoT-based mobile gateway for intelligent personal assistants on mobile health environments. Journal of Network and Computer Applications, 71, 194–204. https://doi.org/10.1016/j.jnca.2016.03.014

  113. Saunders, C., Wiener, M., Klett, S., & Sprenger, S. (2017). The Impact of Mental Representations on ICT-Related Overload in the Use of Mobile Phones. Journal of Management Information Systems, 34(3), 803–825. https://doi.org/10.1080/07421222.2017.1373010

    Article  Google Scholar 

  114. Sellberg, C., & Susi, T. (2014). Technostress in the office: A distributed cognition perspective on human–technology interaction. Cognition, Technology & Work, 16(2), 187–201. https://doi.org/10.1007/s10111-013-0256-9

    Article  Google Scholar 

  115. SeoYoung Lee, J. C. (2017). Enhancing user experience with conversational agent for movie recommendation Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies, 103, 95–105. https://doi.org/10.1016/j.ijhcs.2017.02.005

  116. Shimon Dolan & Aharon. (1988). Implementing Computer-Based Automation in the Office : A Study of Experienced Stress. Journal of Organizational Behavior, 9(2), 183–187. https://doi.org/10.1002/job.4030090209.

    Article  Google Scholar 

  117. Shneiderman, B. (1998). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley.

    Google Scholar 

  118. Sprecher, S., Treger, S., Wondra, J. D., Hilaire, N., & Wallpe, K. (2013). Taking turns : Reciprocal self-disclosure promotes liking in initial interactions. Journal of Experimental Social Psychology, 49(5), 860–866. https://doi.org/10.1016/j.jesp.2013.03.017

    Article  Google Scholar 

  119. Srivastava, S. C., Chandra, S., & Shirish, A. (2015). Technostress creators and job outcomes: Theorising the moderating influence of personality traits. Information Systems Journal, 25(4), 355–401. https://doi.org/10.1111/isj.12067

    Article  Google Scholar 

  120. Suh, A., & Lee, J. (2017). Understanding teleworkers’ technostress and its influence on job satisfaction. Internet Research, 27, 140–159. https://doi.org/10.1108/IntR-06-2015-0181

    Article  Google Scholar 

  121. Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Quarterly, 37(4), 1013–1041. https://doi.org/10.25300/MISQ/2013/37.4.02

  122. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7

    Article  Google Scholar 

  123. Tan, C. W., Benbasat, I., & Cenfetelli, R. T. (2016). An exploratory study of the formation and impact of electronic service failures. MIS Quarterly, 40(1), 1–29. https://doi.org/10.25300/MISQ/2016/40.1.01

  124. Tarafdar, M., & Ragu-Nathan, B. S. (2008). The Consequences of Technostress for End Users in Organizations : Conceptual Development and Empirical Validation, 19(4), 417–433. https://doi.org/10.1287/isre.1070.0165

    Article  Google Scholar 

  125. Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328. https://doi.org/10.2753/MIS0742-1222240109

    Article  Google Scholar 

  126. Tarafdar, M., Tu, Q., Ragu-Nathan, T. S., & Ragu-Nathan, B. S. (2011). Crossing to the Dark Side: Examining Creators, Outcomes, and Inhibitors of Technostress. Communications of the ACM, 54(9), 113–120. https://doi.org/10.1145/1995376.1995403

    Article  Google Scholar 

  127. Tarafdar, M., Tu, Q. A., & Ragu-Nathan, T. S. (2014). Impact of technostress on end-user satisfaction and performance. Journal of Management Information Systems, 27(3), 303–334. https://doi.org/10.2753/MIS0742-1222270311

  128. Tarafdar, M., Pirkkalainen, H., Salo, M., & Makkonen, M. (2020). Taking on the “Dark Side” - Coping with Technostress. IT Professional, 22(6), 82–89. https://doi.org/10.1109/MITP.2020.2977343

    Article  Google Scholar 

  129. Turel, O. (2015). Quitting the use of a habituated hedonic information system: A theoretical model and empirical examination of Facebook users. European Journal of Information Systems, 24(4), 431–446. https://doi.org/10.1057/ejis.2014.19

    Article  Google Scholar 

  130. Um, T., Kim, T., & Chung, N. (2020). How does an intelligence chatbot affect customers compared with self-service technology for sustainable services? Sustainability, 12(12), 5119. https://doi.org/10.3390/su12125119

  131. Van Mulken, S., André, E., & Müller, J. (1999). An empirical study on the trustworthiness of life-like interface agents. Proceedings of HCI International 99 (the 8th International Conference on Human-Computer Interaction), Munich, Germany, 2, 152–156.

  132. Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872

    Article  Google Scholar 

  133. Venkatesh, V., & Goyal, S. (2010). Expectation Disconfirmation and Technology Adoption: Polynomial Modeling and Response Surface Analysis. MIS Quarterly, 34(2), 281–303. https://doi.org/10.2307/20721428

    Article  Google Scholar 

  134. Waite, K., & Harrison, T. (2002). Consumer expectations of online information provided by bank websites. Journal of Financial Services Marketing, 6, 309–322. https://doi.org/10.1057/palgrave.fsm.4770061

    Article  Google Scholar 

  135. Wang, X., & Li, B., (2019). Technostress among teachers in higher education: An investigation from multidimensional person-environment misfit theory. Frontiers in Psychology, 10, 1791. https://doi.org/10.3389/fpsyg.2019.01791

  136. Wang, K., Shu, Q., & Tu, Q. (2008). Technostress under different organizational environments: An empirical investigation. Computers in Human Behavior, 24(6), 3002–3013. https://doi.org/10.1016/j.chb.2008.05.007

    Article  Google Scholar 

  137. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology. https://doi.org/10.1037/0022-3514.54.6.1063

    Article  Google Scholar 

  138. Weil, M., & Rosen, L. (1997). TechnoStress: Coping with technology. John Wiley & Sons.

  139. Whang, C., & Im, H. (2021). I Like Your Suggestion! the role of humanlikeness and parasocial relationship on the website versus voice shopper’s perception of recommendations. Psychology & Marketing, 38(4), 581–595. https://doi.org/10.1002/mar.21437

    Article  Google Scholar 

  140. Wixom, B., & Watson, H. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25, 17–41. https://doi.org/10.2307/3250957

  141. Yuan, L. I., & Dennis, A. R. (2019). Acting Like Humans? Anthropomorphism and Consumer’s Willingness to Pay in Electronic Commerce. Journal of Management Information Systems, 36(2), 450–477. https://doi.org/10.1080/07421222.2019.1598691.

  142. Zhang, Y., Narayanan, V., Chakraborti, T., & Kambhampati, S. (2015). A human factors analysis of proactive support in human-robot teaming. IEEE/RSJ International Conference on Intelligent Robots and Systems, 3586–3593.

  143. Zhang, S., Zhao, L., Lu, Y., & Yang, J. (2016). Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services. Information & Management, 53(7), 904–914. https://doi.org/10.1016/j.im.2016.03.006

    Article  Google Scholar 

  144. Zolfagharian, M., & Yazdanparast, A. (2017). The dark side of consumer life in the age of virtual and mobile technology. Journal of Marketing Management, 33(15–16), 1304–1335. https://doi.org/10.1080/0267257X.2017.1369143

    Article  Google Scholar 

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Acknowledgements

This study was supported by grants from the National Natural Science Foundation of China (NSFC) (71802126), and a grant from the Shanghai Pujiang Program (18PJC060).

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Sun, Y., Li, S. & Yu, L. The dark sides of AI personal assistant: effects of service failure on user continuance intention. Electron Markets (2021). https://doi.org/10.1007/s12525-021-00483-2

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Keywords

  • AI personal assistants
  • Dark side
  • Service failure
  • Technostress

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