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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
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
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
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
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–858. https://doi.org/10.2307/41409963
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
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
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
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
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
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
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
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
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
Brooks, D. J. (2017). A human-centric approach to autonomous robot failures. University of Massachusetts Lowell. ProQuest Dissertations Publishing.
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
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
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
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
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
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
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
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
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
Cooper, C. L., Dewe, P. J., & O’Driscoll, M. P. (2001). Organizational Stress. Sage Publications.
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
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
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
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
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
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
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
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
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
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
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
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
Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press. https://doi.org/10.4324/9780203838020
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
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.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis. Pearson Education Limited.
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
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
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
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
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
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
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
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
IResearch. (2018). iResearch consulting: 2018 China Intelligent voice assistant enterprise Case Study Report. http://www.199it.com/archives/737587.html.
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
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
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
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
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
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
Kim, K., & Park, H. (2018). The effects of technostress on information technology acceptance. Journal of Theoretical and Applied Information Technology, 96(24), 8300–8312.
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
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
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
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
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
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
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
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
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
Lefcourt, H. M. (1976). Locus of control: Current trends in theory and research. Psychology Press.
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
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
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
Locke, E. (1976). The nature and causes of job satisfaction. Handbook of Industrial and Organizational Psychology, 1, 1297–1343.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
Shneiderman, B. (1998). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley.
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
Weil, M., & Rosen, L. (1997). TechnoStress: Coping with technology. John Wiley & Sons.
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
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
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.
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.
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
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
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).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Responsible Editor: Jian Mou
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sun, Y., Li, S. & Yu, L. The dark sides of AI personal assistant: effects of service failure on user continuance intention. Electron Markets 32, 17–39 (2022). https://doi.org/10.1007/s12525-021-00483-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12525-021-00483-2