Ekaterina Svikhnushina
ABSTRACT
We report results of a survey with 2500 US-based users of popular digital assistants (DAs) to understand and prioritize the factors that drive consumer adoption. Using structural equation modelling, we investigated the relationship between respondents’ behavioural intentions to delegate tasks to their DAs and two predictor layers: DA (users’ attitudes and familiarity with them) and task factors (need for control/transparency, subjectivity, risk, self-efficacy, and frequency), mediated by a values layer (trust, perceived ease of use and usefulness). Perceived usefulness and trust were strong direct predictors of willingness to delegate. Both DA-related factors had indirect effects, with a surprising negative influence of familiarity on both trust and usefulness. Our novel findings of task-related effects on the value factors may explain this disparity. These results imply a mismatch between users’ expectations for DAs and their actual experiences. We interpret these findings in light of related work and derive implications for practitioners.
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- Praveen Aggarwal and Tridib Mazumdar. 2008. Decision delegation: A conceptualization and empirical investigation. Psychology & Marketing 25, 1 (2008), 71–93. https://doi.org/10.1002/mar.20201Google Scholar
Cross Ref
- Icek Ajzen 1991. The theory of planned behavior. Organizational behavior and human decision processes 50, 2 (1991), 179–211.Google Scholar
- Tawfiq Ammari, Jofish Kaye, Janice Y. Tsai, and Frank Bentley. 2019. Music, Search, and IoT: How People (Really) Use Voice Assistants. ACM Trans. Comput.-Hum. Interact. 26, 3, Article 17 (apr 2019), 28 pages. https://doi.org/10.1145/3311956Google ScholarDigital Library
- Muhammad Ashfaq, Jiang Yun, Shubin Yu, and Sandra Maria Correia Loureiro. 2020. I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics 54 (2020), 101473. https://doi.org/10.1016/j.tele.2020.101473Google ScholarCross Ref
- Daniel Belanche, Luis V Casaló, and Carlos Flavián. 2019. Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems (2019).Google Scholar
- Izak Benbasat and Weiquan Wang. 2005. Trust in and adoption of online recommendation agents. Journal of the association for information systems 6, 3 (2005), 4.Google ScholarCross Ref
- Anna Bouwer. 2022. Under Which Conditions Are Humans Motivated to Delegate Tasks to AI? A Taxonomy on the Human Emotional State Driving the Motivation for AI Delegation. In Marketing and Smart Technologies, José Luís Reis, Eduardo Parra López, Luiz Moutinho, and José Paulo Marques dos Santos (Eds.). Springer Nature Singapore, Singapore, 37–53.Google Scholar
- Jason W. Burton, Mari-Klara Stein, and Tina Blegind Jensen. 2020. A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making 33, 2 (2020), 220–239. https://doi.org/10.1002/bdm.2155Google ScholarCross Ref
- Cristiano Castelfranchi and Rino Falcone. 1998. Towards a theory of delegation for agent-based systems. Robotics and Autonomous Systems 24, 3 (1998), 141–157. https://doi.org/10.1016/S0921-8890(98)00028-1 Multi-Agent Rationality.Google ScholarCross Ref
- Noah Castelo, Maarten W Bos, and Donald R Lehmann. 2019. Task-dependent algorithm aversion. Journal of Marketing Research 56, 5 (2019), 809–825.Google ScholarCross Ref
- Derwin K. C. Chan, Andreas Ivarsson, Andreas Stenling, Sophie X. Yang, Nikos L. D. Chatzisarantis, and Martin S. Hagger. 2015. Response-order effects in survey methods: A randomized controlled crossover study in the context of sport injury prevention.Journal of Sport & Exercise Psychology 37 (2015), 666–673. https://doi.org/10.1123/jsep.2015-0045Google ScholarCross Ref
- Chih-Yang Chao, Tsai-Chu Chang, Hui-Chun Wu, Yong-Shun Lin, and Po-Chen Chen. 2016. The interrelationship between intelligent agents’ characteristics and users’ intention in a search engine by making beliefs and perceived risks mediators. Computers in Human Behavior 64 (2016), 117–125.Google ScholarDigital Library
- Chih-Yang Chao, Tsai-Chu Chang, Hui-Chun Wu, Yong-Shun Lin, and Po-Chen Chen. 2016. The interrelationship between intelligent agents’ characteristics and users’ intention in a search engine by making beliefs and perceived risks mediators. Computers in Human Behavior 64 (2016), 117–125. https://doi.org/10.1016/j.chb.2016.06.031Google ScholarDigital Library
- Benjamin R. Cowan, Nadia Pantidi, David Coyle, Kellie Morrissey, Peter Clarke, Sara Al-Shehri, David Earley, and Natasha Bandeira. 2017. "What Can i Help You with?": Infrequent Users’ Experiences of Intelligent Personal Assistants. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (Vienna, Austria) (MobileHCI ’17). Association for Computing Machinery, New York, NY, USA, Article 43, 12 pages. https://doi.org/10.1145/3098279.3098539Google ScholarDigital Library
- Fred D. Davis. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13, 3 (1989), 319–340. http://www.jstor.org/stable/249008Google ScholarDigital Library
- Alireza Fereidunian, Mohammad Ali Zamani, Bahar Fatah, Hamid Lesani, Caro Lucas, Payam Kharazmi, and Habib Torabi. 2010. Investigation of performance shaping factors in a practical human-automation interaction system. In 2010 IEEE International Conference on Systems, Man and Cybernetics. 1825–1831. https://doi.org/10.1109/ICSMC.2010.5642287Google ScholarCross Ref
- Claes Fornell and David F. Larcker. 1981. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research 18, 1 (1981), 39–50. http://www.jstor.org/stable/3151312Google ScholarCross Ref
- Dogan Gursoy, Oscar Hengxuan Chi, Lu Lu, and Robin Nunkoo. 2019. Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management 49 (2019), 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008Google ScholarDigital Library
- J.F. Hair, W.C. Black, R.E. Anderson, and B.J. Babin. 2018. Multivariate Data Analysis (8th ed.). Cengage.Google Scholar
- Paige Hayes and Jason Wagner. 2018. Prepare for the voice revolution. https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/voice-assistants.html.Google Scholar
- Li‐tze Hu and Peter M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6, 1 (1999), 1–55. https://doi.org/10.1080/10705519909540118Google ScholarCross Ref
- Dharun Lingam Kasilingam. 2020. Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society 62 (2020), 101280. https://doi.org/10.1016/j.techsoc.2020.101280Google ScholarCross Ref
- Timothy Z Keith. 2019. Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. Routledge. 62–63 pages.Google Scholar
- Hee-Woong Kim, Hock Chuan Chan, and Sumeet Gupta. 2007. Value-based Adoption of Mobile Internet: An empirical investigation. Decision Support Systems 43, 1 (2007), 111–126. https://doi.org/10.1016/j.dss.2005.05.009 Mobile Commerce: Strategies, Technologies, and Applications.Google ScholarDigital Library
- Rex B Kline. 2015. Principles and practice of structural equation modeling. Guilford publications.Google Scholar
- Sherrie Y. X. Komiak and Izak Benbasat. 2006. The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents. MIS Quarterly 30, 4 (2006), 941–960. http://www.jstor.org/stable/25148760Google ScholarCross Ref
- Sherrie Y. X. Komiak and Izak Benbasat. 2006. The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly (2006), 941–960.Google Scholar
- John D. Lee and Neville Moray. 1994. Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human-Computer Studies 40, 1 (1994), 153–184. https://doi.org/10.1006/ijhc.1994.1007Google ScholarDigital Library
- Michael Leyer and Sabrina Schneider. 2019. ME, YOU OR AI? HOW DO WE FEEL ABOUT DELEGATION. In Proceedings of the 27th European Conference on Information Systems (ECIS). Stockholm & Uppsala, Sweden, 1825–1831. https://aisel.aisnet.org/ecis2019_rp/36Google Scholar
- Leonardo Liberman and Dirk Boehe. 2011. Worldwide willingness to delegate and country labor quality. The International Journal of Human Resource Management 22, 17 (2011), 3477–3495. https://doi.org/10.1080/09585192.2011.610943Google ScholarCross Ref
- Brian Lubars and Chenhao Tan. 2019. Ask not what AI can do, but what AI should do: Towards a framework of task delegability. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.). Vol. 32. Curran Associates, Inc.https://proceedings.neurips.cc/paper/2019/file/d67d8ab4f4c10bf22aa353e27879133c-Paper.pdfGoogle Scholar
- Ewa Luger and Abigail Sellen. 2016. "Like Having a Really Bad PA": The Gulf between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 5286–5297. https://doi.org/10.1145/2858036.2858288Google ScholarDigital Library
- Hasan Mahmud, A. K. M. Najmul Islam, Ranjan Kumar Mitra, and Ahmed Rizvan Hasan. 2022. The Impact of Functional and Psychological Barriers on Algorithm Aversion – An IRT Perspective. In The Role of Digital Technologies in Shaping the Post-Pandemic World, Savvas Papagiannidis, Eleftherios Alamanos, Suraksha Gupta, Yogesh K. Dwivedi, Matti Mäntymäki, and Ilias O. Pappas (Eds.). Springer International Publishing, Cham, 95–108.Google Scholar
- Hasan Mahmud, A.K.M. Najmul Islam, Syed Ishtiaque Ahmed, and Kari Smolander. 2022. What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change 175 (2022), 121390. https://doi.org/10.1016/j.techfore.2021.121390Google ScholarCross Ref
- Roger C. Mayer, James H. Davis, and F. David Schoorman. 1995. An Integrative Model of Organizational Trust. The Academy of Management Review 20, 3 (1995), 709–734. http://www.jstor.org/stable/258792Google ScholarCross Ref
- Brian K. Miller and Marcia Simmering. 2020. Impact of Survey Design Features on Score Reliability. Collabra: Psychology 6, 1 (11 2020). https://doi.org/10.1525/collabra.17975 17975.Google ScholarCross Ref
- Jum C. Nunnally and Ira H. Bernstein. 1994. Psychometric Theory. McGraw-Hill Companies,Incorporated.Google Scholar
- R. Parasuraman, T.B. Sheridan, and C.D. Wickens. 2000. A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, 3 (2000), 286–297. https://doi.org/10.1109/3468.844354Google ScholarDigital Library
- Robert A. Peterson. 1994. A Meta-analysis of Cronbach’s Coefficient Alpha. Journal of Consumer Research 21, 2 (09 1994), 381–391.Google ScholarCross Ref
- Rajasshrie Pillai and Brijesh Sivathanu. 2020. Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management (2020).Google ScholarCross Ref
- Yves Rosseel. 2012. lavaan: a brief user’s guide. https://users.ugent.be/ yrosseel/lavaan/lavaan2.pdf.Google Scholar
- Albert Satorra and Peter M Bentler. 2001. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 66, 4 (2001), 507–514.Google ScholarCross Ref
- Justina Sidlauskiene. 2022. What Drives Consumers’ Decisions to Use Intelligent Agent Technologies? A Systematic Review. Journal of Internet Commerce 21, 4 (2022), 438–475. https://doi.org/10.1080/15332861.2021.1961192Google ScholarCross Ref
- Bergur Thormundsson. 2022. Virtual Assistant Technology – Statistics & Facts. https://www.statista.com/topics/5572/virtual-assistants/##topicOverview.Google Scholar
- Milka Trajkova and Aqueasha Martin-Hammond. 2020. "Alexa is a Toy": Exploring Older Adults’ Reasons for Using, Limiting, and Abandoning Echo. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376760Google ScholarDigital Library
- Brahim Zarouali, Evert Van den Broeck, Michel Walrave, and Karolien Poels. 2018. Predicting Consumer Responses to a Chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking 21, 8 (2018), 491–497. https://doi.org/10.1089/cyber.2017.0518 PMID: 30036074.Google ScholarCross Ref
Index Terms
- Expectation vs Reality in Users’ Willingness to Delegate to Digital Assistants
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