Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Google Scholar | Crossref | ISI Ajzen, I. (2011). The theory of planned behaviour: reactions and reflections. Psychology & Health, 26, 1113–1127.
Google Scholar | Crossref | Medline | ISI Ajzen, I., Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall, Englewood Cliffs, NJ.
Google Scholar Alam, M. S., Dhar, S. S., Munira, K. S. (2020). HR Professionals’ intention to adopt and use of artificial intelligence in recruiting talents. Business Perspective Review, 2, 15–30.
Google Scholar | Crossref Amershi, S., Weld, D., Vorvoreanu, M., et al. (2019). Guidelines for human-AI interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, Scotland, 3, 1–13. https://doi.org/10.1145/3290605.3300233
Google Scholar | Crossref Bach, M. P., Čeljo, A., & Zoroja, J. (2016). Technology acceptance model for business intelligence systems: Preliminary research. Procedia Computer Science, 100, 995–1001.
Google Scholar | Crossref Bamberg, S., Möser, G. (2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology, 27, 14–25.
Google Scholar | Crossref | ISI Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4, 359–373.
Google Scholar | Crossref | ISI Bartneck, C., Kulić, D., Croft, E., et al. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1, 71–81.
Google Scholar | Crossref | ISI Bendig, E., Erb, B., Schulze-Thuesing, L., et al. (2019). The next generation: Chatbots in clinical psychology and psychotherapy to foster mental health – A scoping review. Verhaltenstherapie, 29, 266–280. https://doi.org/10.1159/000501812
Google Scholar | Crossref Boomsma, A., Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In: Cudeck, R., du Toit, S., Sörbom, D. (Eds.), Structural equation modeling: Present and future. A Festschrift in honor of Karl Jöreskog (pp. 139–168). Scientific Software International, Inc, Lincolnwood, IL.
Google Scholar Cassidy, S., Eachus, P. (2002). Developing the computer user self-efficacy (CUSE) scale: Investigating the relationship between computer self-efficacy, gender and experience with computers. Journal of Educational Computing Research, 26, 133–153.
Google Scholar | SAGE Journals Cave, S., Dihal, K. (2019). Hopes and fears for intelligent machines in fiction and reality. Nature Machine Intelligence, 1, 74–78.
Google Scholar | Crossref Dang, J., King, K. M., Inzlicht, M. (2020). Why are self-report and behavioral measures weakly correlated? Trends in Cognitive Sciences, 24, 267–269.
Google Scholar | Crossref | Medline Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems [Doctoral dissertation, Massachusetts Institute of Technology] [Cambridge]. Massachusetts.
Google Scholar Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
Google Scholar | Crossref | ISI Davis, F. D., Bagozzi, R. P., Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.
Google Scholar | Crossref | ISI Deci, E. L., Ryan, R. M. (1980). Self-determination theory: When mind mediates behavior. The Journal of Mind and Behavior, 1, 33–43.
Google Scholar Duyck, P., Pynoo, B., Devolder, P., et al. (2008). Möchten Krankenhausärzte wirklich auf digitale Systeme umsteigen? Rofo, 180, 631–638.
Google Scholar | Crossref | Medline Dwivedi, Y. K., Rana, N. P., Chen, H., et al. (2011). A meta-analysis of the unified theory of acceptance and use of technology (UTAUT). In: Nüttgens, M., Gadatsch, A., Kautz, K., Schirmer, I., Blinn, N. (Eds.), Governance and sustainability in information systems. Managing the transfer and diffusion of IT: vol. 366. IFIP advances in information and communication technology (pp. 155–170). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24148-2_10
Google Scholar | Crossref Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., et al. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 719–734.
Google Scholar | Crossref Dwyer, D. B., Falkai, P., Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14, 91–118.
Google Scholar | Crossref | Medline Edmunds, R., Thorpe, M., Conole, G. (2012). Student attitudes towards and use of ICT in course study, work and social activity: A technology acceptance model approach. British Journal of Educational Technology, 43, 71–84.
Google Scholar | Crossref Eickhoff, S. (2020, January). Frühere und sicherere Diagnosen durch Künstliche Intelligenz [Interview]. Report Psychologie; Berufsverband Deutscher Psychologinnen und Psychologen (BDP) e.V.
Google Scholar Fast, E., Horvitz, E. (2017). Long-term trends in the public perception of artificial intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 31, 963–969. https://ojs.aaai.org/index.php/AAAI/article/view/10635
Google Scholar Fischbach, J. (2019). Determinanten der Technologie- und Prozessakzeptanz im Kontext kooperativer Arbeit. Zeitschrift für Arbeitswissenschaft, 73, 35–44.
Google Scholar | Crossref Fishbein, M., Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research (Vol. 27). Addison-Wesley, Reading, MA.
Google Scholar Fishbein, M., Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press, New York, NY. https://doi.org/10.4324/9780203838020
Google Scholar Haas, P., Scheiner, C., Witt, M., et al. (2013). Der Einfluss von Gamification auf die empfundene Selbstwirksamkeit von Teilnehmern von Online-Ideengenerierungswettbewerben über die Zeit. In: M. Horbach (Ed.), INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt (pp. 2321–2335). Gesellschaft für Informatik e.V., Bonn. https://dl.gi.de/handle/20.500.12116/20660
Google Scholar Hagger, M. S., Polet, J., Lintunen, T. (2018). The reasoned action approach applied to health behavior: Role of past behavior and tests of some key moderators using meta-analytic structural equation modeling. Social Science & Medicine, 213, 85–94.
Google Scholar | Crossref | Medline Han, Y., Hansen, H. (2012). Determinants of sustainable food consumption: A meta-analysis using a traditional and a structural equation modelling approach. International Journal of Psychological Studies, 4, 22–45.
Google Scholar | Crossref Harman, H. H. (1967). Modern factor analysis. University Press of Chicago, Chicago, IL.
Google Scholar Holden, R. J., Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43, 159–172.
Google Scholar | Crossref | Medline | ISI Hoque, R., Sorwar, G. (2017). Understanding factors influencing the adoption of mhealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84.
Google Scholar | Crossref | Medline Hoyle, R. H., Panter, A. T. (1995). Writing about structural equation models. In: Hoyle, R. H. (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 158–176). Sage Publications, Thousand Oaks, CA.
Google Scholar Hu, L. t., Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.
Google Scholar | Crossref | ISI Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., et al. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16, 91–112.
Google Scholar | Crossref | ISI Johansson, R., Skantze, G., Jönsson, A. (2017a). A psychotherapy training environment with virtual patients implemented using the Furhat robot platform. In: J. Beskow, C. Peters, G. Castellano, C. O'Sullivan, I. Leite, & S. Kopp (Eds.), Lecture Notes in Computer Science: vol. 10498. Intelligent Virtual Agents, IVA 2017 (pp. 184–187). Springer, Cham. https://doi.org/10.1007/978-3-319-67401-8_20
Google Scholar Johansson, R., Thellman, S., Skantze, G., et al.  (2017b). Psychotherapists’ interest in using the furhat social robot for clinical training. Proceedings of the 13th SweCog Conference, Uppsala, Sweden, 46–48.
Google Scholar Kaliyaperumal, K. (2004). Guideline for conducting a knowledge, attitude and practice (KAP) study. AECS Illumination, 4, 7–9.
Google Scholar Kersting, M. (2020). Big data und KI in der Personalarbeit. Personalführung, 4, 30–34.
Google Scholar Khalilzadeh, J., Ozturk, A. B., Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474.
Google Scholar | Crossref Khechine, H., Lakhal, S., Ndjambou, P. (2016). A meta-analysis of the UTAUT model: Eleven years later. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration, 33, 138–152.
Google Scholar | Crossref Kim, J.-M. (2017). Study on intention and attitude of using artificial intelligence technology in healthcare. Journal of Convergence for Information Technology, 7, 53–60.
Google Scholar King, W. R., He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43, 740–755.
Google Scholar | Crossref | ISI Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd Ed.). Guilford Press, New York, NY.
Google Scholar Landers, R. N. (2019, September 25). An I-O Perspective on Machine Learning in HR. Society for Industrial and Organizational Psychology (SIOP). https://www.siop.org/Media-Resources/Press-Releases/ArtMID/28305/ArticleID/3155/An-I-O-Perspective-on-Machine-Learning-in-HR
Google Scholar Lee, Y., Kozar, K. A., Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12, 752–780.
Google Scholar | Crossref Lichtenthaler, U. (2020). Extremes of acceptance: Employee attitudes toward artificial intelligence. Journal of Business Strategy, 41, 39–45.
Google Scholar | Crossref Liu, L., Miguel Cruz, A., Rios Rincon, A., et al. (2015). What factors determine therapists’ acceptance of new technologies for rehabilitation – A study using the unified theory of acceptance and use of technology (UTAUT). Disability and Rehabilitation, 37, 447–455.
Google Scholar | Crossref | Medline | ISI Luxton, D. D. (2014). Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice, 45, 332–339.
Google Scholar | Crossref Madigan, R., Louw, T., Dziennus, M., et al. (2016). Acceptance of automated road transport systems (ARTS): An adaptation of the UTAUT model. Transportation Research Procedia, 14, 2217–2226.
Google Scholar | Crossref Mayerl, J., Giehl, C. (2018). A closer look at attitude scales with positive and negative items. Response latency perspectives on measurement quality. Survey Research Methods, 12, 193–209.
Google Scholar McEachan, R., Taylor, N., Harrison, R., et al. (2016). Meta-analysis of the reasoned action approach (RAA) to understanding health behaviors. Annals of Behavioral Medicine, 50, 592–612. https://doi.org/10.1007/s12160-016-9798-4
Google Scholar | Crossref | Medline | ISI Mishra, D., Akman, I., Mishra, A. (2014). Theory of reasoned action application for green information technology acceptance. Computers in Human Behavior, 36, 29–40.
Google Scholar | Crossref | ISI Mohiuddin, M., Al Mamun, A., Syed, F. A., et al. (2018). Environmental knowledge, awareness, and business school students’ intentions to purchase green vehicles in emerging countries. Sustainability, 10, 1534. https://doi.org/10.3390/su10051534
Google Scholar | Crossref Mruk, C. J. (1987). The interface between computers and psychology: Toward a psychology of computerization. Computers in Human Behavior, 3, 167–179.
Google Scholar | Crossref Nadarzynski, T., Miles, O., Cowie, A., et al. (2019). Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital Health, 5, 1–12.
Google Scholar | SAGE Journals Neath, A. A., Cavanaugh, J. E. (2012). The Bayesian information criterion: Background, derivation, and applications. WIREs Comp Stat, 4, 199–203.