Résumés
Résumé
L’intelligence artificielle (IA) est associée à plusieurs bénéfices pour les travailleurs et les organisations. Toutefois, ses capacités inédites sont propices à engendrer chez les humains de la crainte pour la pérennité de leur emploi, et de la réticence à utiliser l’IA. Dans la présente étude, nous explorons le rôle de la confiance envers l’utilisation de l’IA chez les travailleurs, ainsi que la capacité de l’explicabilité de l’algorithme à promouvoir la confiance. À cet effet, un devis expérimental à répartition aléatoire a été utilisé. Les résultats révèlent que la confiance favorise l’intention d’utiliser l’IA, mais que l’explicabilité ne contribue pas au développement de la confiance. De plus, l’explicabilité a eu un effet inattendu délétère sur l’intention d’utiliser l’IA.
Mots-clés :
- Intelligence artificielle,
- confiance,
- explicabilité,
- travail,
- intention d'utilisation
Abstract
Artificial intelligence (AI) is associated with numerous benefits for workers and organizations. However, its novel capabilities are likely to generate fears for the sustainability of their jobs and reluctance to use AI among humans. In this study, the role of trust is studied in the use of AI among workers, as well as the ability of the explainability of the algorithm to promote trust. To achieve this, a randomized experimental design was used. The results reveal that trust promotes the intention to use AI, but that explainability does not contribute to the development of trust. In addition, explainability had an unexpectedly deleterious effect on the intention to use AI.
Veuillez télécharger l’article en PDF pour le lire.
Télécharger
Parties annexes
Bibliographie
- Agrawal, A., Gans, J. S. et Goldfarb, A. (2019). Exploring the impact of artificial intelligence: Prediction versus judgment. Information Economics and Policy, 47, 1-6. https://doi.org/10.1016/j.infoecopol.2019.05.001
- Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R. et Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
- Asan, O., Bayrak, A. E. et Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of Medical Internet Research, 22(6). https://doi.org/10.2196/15154
- Ashoori, M. et Weisz, J. D. (2019). In AI we trust? Factors that influence trustworthiness of AI-infused decision-making processes. arXiv, 1-10. https://doi.org/10.48550/arXiv.1912.02675
- Atzmüller, C. et Steiner, P. M. (2010). Experimental vignette studies in survey research. Methodology European Journal of Research Methods for the Behavioral and Social Sciences, 6(3), 128-138. https://doi.org/10.1027/1614-2241/a000014
- Bedué, P. et Fritzsche, A. (2022). Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), 530-549. https://doi.org/10.1108/JEIM-06-2020-0233
- Benbasat, I. et Wang, W. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 4. https://doi.org/10.17705/1jais.00065
- Black, J. S. et van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager use it?. Business Horizons, 63(2), 215-226. https://doi.org/10.1016/j.bushor.2019.12.001
- Boon, S. et Holmes, J. (1991). The dynamics of interpersonal trust: Resolving uncertainity in the face of risk. Dans R. Hinde et J. Gorebel (dir.), Cooperation and prosocial behaviour (190-211). Cambridge University Press.
- Braun, M., Bleher, H. et Hummel, P. (2021). A leap of faith: is there a formula for “Trustworthy” AI?. Hastings Center Report, 51(3), 17-22. https://doi.org/10.1002/hast.1207
- Braganza, A., Chen, W., Canhoto, A. et Sap, S. (2021). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. Journal of Business Research, 131, 485-494. https://doi.org/10.1016/j.jbusres.2020.08.018
- Bruhn, J. et Anderer, M. (2019). Implementing artificial intelligence in organizations and the special role of trust. Media Trust in a Digital World: Communication at Crossroads, 191-205. https://doi.org/10.1007/978-3-030-30774-5_14
- Burton, J. W., Stein, M. K. et Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220-239. https://doi.org/10.1002/bdm.2155
- Cabiddu, F., Moi, L., Patriotta, G. et Allen, D. G. (2022). Why do users trust algorithms? A review and conceptualization of initial trust and trust over time. European Management Journal, 40(5), 685-706. https://doi.org/10.1016/j.emj.2022.06.001
- Carton, S., Mei, Q. et Resnick, P. (2020). Feature-based explanations don't help people detect misclassifications of online toxicity. Proceedings of the International AAAI Conference on Web and Social Media, 14, 95-106. https://doi.org/10.1609/icwsm.v14i1.7282
- Castelvecchi, D. (2016). Can we open the black box of AI?. Nature News, 538(7623), 20-23. https://www.nature.com/news/can-we-open-the-black-box-of-ai-1.20731
- Choung, H., David, P. et Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(3), 1–13. https://doi.org/10.1080/10447318.2022.2050543
- Chaudhry, I. S., Paquibut, R. Y. et Chabchoub, H. (2022). Factors influencing employees trust in AI & its adoption at work: Evidence from United Arab Emirates. Proceedings of the International Arab Conference on Information Technology, 1-7. https://doi.org/10.1109/acit57182.2022.9994226
- Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A. et Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100899
- Confalonieri, R., Coba, L., Wagner, B et Besold, T. R. (2021). A historical perspective of explainable Artificial Intelligence. WIREs Data Mining and Knowledge Discovery, 11(1). https://doi.org/10.1002/widm.1391
- Costello, A. B. et Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(7). https://doi.org/10.7275/jyj1-4868
- Custers, B. (2022). AI in criminal law: An overview of AI applications in substantive and procedural criminal law. Dans B. Custers et E. Fosch-Villaronga (dir.), Law and artificial intelligence. Information technology and law series (vol. 35). T.M.C. Asser Press. https://doi.org/10.1007/978-94-6265-523-2_11
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
- de Vet, H. C., Mokkink, L. B., Mosmuller, D. G. et Terwee, C. B. (2017). Spearman–Brown prophecy formula and Cronbach's alpha: different faces of reliability and opportunities for new applications. Journal of Clinical Epidemiology, 85, 45-49. https://doi.org/10.1016/j.jclinepi.2017.01.013
- Dietz, G. et Hartog, D. N. (2006). Measuring trust inside organisations. Personnel Review, 35(5), 557-588. https://doi.org/10.1108/00483480610682299
- Dimensional Research. (2019, mai). Artificial intelligence and machine learning projects are obstructed by data issues global survey of data scientists, global survey of data scientists, AI experts and stakeholders. https://t.ly/FGo54
- Eisinga, R., Grotenhuis, M. T. et Pelzer, B. (2013). The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown?. International Journal of Public Health, 58(4), 637-642. https://doi.org/10.1007/s00038-012-0416-3
- Emons, W. H. M., Sijtsma, K. et Meteijer, R. R. (2007). On the consistency of individual classification using short scales. Psychological Methods, 12(1), 105-120. https://doi.org/10.1037/1082-989X.12.1.105
- Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. et Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989X.4.3.272
- Fan, W., Liu, J., Zhu, S. et Pardalos, P. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294, 567-592. https://doi.org/10.1007/s10479-018-2818-y
- Ferrario, A. et Loi, M. (2022). The robustness of counterfactual explanations over time. IEEE Access, 10, 82736-82750. https://doi.org/10.1109/ACCESS.2022.3196917
- Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725-737. https://doi.org/10.1016/S0305-0483(00)00021-9
- Gefen, D., Karahanna, E. et Straub D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
- Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U. et Redzepi, A. (2022). The dark sides of people analytics: Reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410-435. https://doi.org/10.1080/0960085X.2021.1927213
- Gillath, O., Ai, T., Branicky, M. S., Keshmiri, S., Davison, R. B. et Spaulding, R. (2021). Attachment and trust in artificial intelligence. Computers in Human Behavior, 115. https://doi.org/10.1016/j.chb.2020.106607
- Glikson, E. et Woolley, A. W. (2020). Human trust in artificial intelligence: review of empirical research. Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
- Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F. et Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1-42. https://doi.org/10.1145/3236009
- Gulati, R. (1995). Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. Academy of Management Journal, 38(1), 85-112. https://doi.org/10.5465/256729
- Gulati, S., Sousa, S. et Lamas, D. (2019). Towards an empirically developed scale for measuring trust. Proceedings of the 31st European Conference on Cognitive Ergonomics, 45-48. https://doi.org/10.1145/3335082.3335116
- Gunning, D. et Aha, D. W. (2019). DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine, 40(2), 44-58. https://doi.org/10.1609/aimag.v40i2.2850
- Hair, J. F., Black, W., Babin, B., Andeson, R. et Tatham, R. (2006). Multivariate data analysis (6e éd.). Pearson-Prentice Hall.
- Hair, J. F., Page, M. et Brunsveld, N. (2019). Essentials of business research methods (4e éd.). Routeledge-Taylor & Francis.
- Hasija, A. et Esper, T. L. (2022). In artificial intelligence (AI) we trust: A qualitative investigation of AI technology acceptance. Journal of Business Logistics, 43(3), 388-412. https://doi.org/10.1111/jbl.12301
- Hengstler, M., Enkel, E. et Duelli, S. (2016). Applied artificial intelligence and trust: The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120. https://doi.org/10.1016/j.techfore.2015.12.014
- Hmoud, B. et Laszlo, V. (2019). Will artificial intelligence take over human resources recruitment and selection. Network Intelligence Studies, 7(13), 21-30. https://dea.lib.unideb.hu/server/api/core/bitstreams/cff8e1b9-7db6-47e9-9642-6fe8e19f565d/content
- Hmoud, B. I. et Várallyai, L. (2020). Artificial intelligence in human resources information systems: Investigating its trust and adoption determinants. International Journal of Engineering and Management Sciences, 5(1), 749-765. https://doi.org/10.21791/IJEMS.2020.1.65
- Höddinghaus, M., Sondern, D. et Hertel, G. (2021). The automation of leadership functions: Would people trust decision algorithms?. Computers in Human Behavior, 116. https://doi.org/10.1016/j.chb.2020.106635
- Hoff, K. A. et Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407-434. https://doi.org/10.1177/0018720814547570
- Hughes, C., Robert, L., Frady, K. et Arroyos, A. (2019). Artificial intelligence, employee engagement, fairness, and job outcomes: Managing technology and middle and low skilled employees. Dans The changing context of managing people (p. 61-68). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78973-077-720191005
- Kaiser, H. F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20(1). https://doi.org/10.1177/001316446002000116
- Kaplan, A. D., Kessler, T. T., Brill, J. C. et Hancock, P. A. (2023). Trust in artificial intelligence: Meta-analytic findings. Human Factors, 65(2), 337-359. https://doi.org/10.1177/00187208211013988
- Kim, J. Y. et Heo, W. (2022). Artificial intelligence video interviewing for employment: perspectives from applicants, companies, developer and academicians. Information Technology & People, 35(3), 861-878. https://doi.org/10.1108/ITP-04-2019-0173
- Knowles, B. et Hanson, V. L. (2018). The wisdom of older technology (non) users. Communications of the ACM, 61(3), 72-77. https://eprints.lancs.ac.uk/id/eprint/88296/1/CACM_Pure.pdf
- Koufaris, M. et Hampton-Sosa, W. (2004). The development of initial trust in an online company by new customers. Information & Management, 41(3), 377-397. https://doi.org/10.1016/ j.im.2003.08.004
- Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1). https://doi.org/10.1525/collabra.33267
- Lane, M., Williams, M. et Broecke, S. (2023). The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers (publication no 288). https://doi.org/10.1787/ea0a0fe1-en
- Lankton, N. K., McKnight, D. H. et Tripp, J. (2015). Technology, humanness, and trust: Rethinking trust in technology. Journal of the Association for Information Systems, 16(10). https://doi.org/10.17705/1jais.00411
- Langer, M. et König, C. J. (2023). Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2021.100881
- Lapatin, S., Gonçalves, M., Nillni, A., Chavez, L., Quinn, R. L., Green, A. et Alegría, M. (2012). Lessons from the use of vignettes in the study of mental health service disparities. Health Services Research, 47(3.2), 1345-1362. https://doi.org/10.1111/j.1475-6773.2011.01360.x
- Larasati, R., De Liddo, A. et Motta, E. (2020, mars). The effect of explanation styles on user's trust. Dans Workshop on explainable smart systems for algorithmic transparency in emerging technologies. https://oro.open.ac.uk/70421/1/70421.pdf
- Lee, P., Fyffe, S., Son, M., Jia, Z. et Yao, Z. (2023). A paradigm shift from “human writing” to “machine generation” in personality test development: An application of state-of-the-art natural language processing. Journal of Business and Psychology, 38(1), 163-190. https://doi.org/10.1007/s10869-022-09864-6
- Leppink, J. (2018). Analysis of covariance (ANCOVA) vs. moderated regression (MODREG): Why the interaction matters. Health Professions Education, 4(3), 225-232. https://doi.org/10.1016/j.hpe.2018.04.001
- Liao, Q. V. et Sundar, S. S. (2022). Designing for responsible trust in AI systems: a communication perspective. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 1257-1268. https://doi.org/10.1145/3531146.3533182
- Lichtenthaler, U. (2020). Five maturity levels of managing AI: from isolated ignorance to integrated intelligence. Journal of Innovation Management, 8(1), 39-50. https://doi.org/10.24840/2183-0606_008.001_0005
- Lin, H. C., Ho, C. F. et Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: An empirical study of UTAUT model. International Journal of Mobile Learning and Organisation, 16(1), 74-94. https://doi.org/10.1504/IJMLO.2022.119966
- Liu, X., He, X., Wang, M. et Shen, H. (2022). What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society, 70. https://doi.org/10.1016/j.techsoc.2022.101996
- Lu, L., Cai, R. et Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51. https://doi.org/10.1016/j.ijhm.2019.01.005
- Madsen, M. et Gregor, S. (2000). Measuring human-computer trust. Proceedings of the 11th Australasian conference on information systems, 6-8. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b8eda9593fbcb63b7ced1866853d9622737533a2
- Mahmud, H., Islam, A. N., Ahmed, S. I. et Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175. https://doi.org/10.1016/j.techfore.2021.121390
- Makarius, E. E., Mukherjee, D., Fox, J. D. et Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262-273. https://doi.org/10.1016/j.jbusres.2020.07.045
- Mayer, R. C., Davis, J. H. et Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734. https://doi.org/10.5465/amr.1995.9508080335
- Mcknight, D. H., Carter, M., Thatcher, J. B. et Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems (TMIS), 2(2), 1-25. https://doi.org/10.1145/1985347.1985353
- McKnight, D. H., Choudhury, V. et Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems, 11(3-4), 297-323. https://doi.org/10.1016/S0963-8687(02)00020-3
- McKnight, D. H., Cummings, L. L. et Chervany, N. L. (1998). Initial trust formation in new organizational relationships. Academy of Management Review, 23(3), 473-490. https://doi.org/10.5465/amr.1998.926622
- Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1-38. https://doi.org/10.1016/j.artint.2018.07.007
- Mirbabaie, M., Brünker, F., Möllmann, N. R. et Stieglitz, S. (2022). The rise of artificial intelligence–understanding the AI identity threat at the workplace. Electronic Markets, 32, 73-99. https://doi.org/10.1007/s12525-021-00496-x
- Misztal, B. A. (2001). Trust and cooperation: the democratic public sphere. Journal of Sociology, 37(4), 371-386. https://doi.org/10.1177/144078301128756409
- Moray, N. et Inagaki, T. (1999). Laboratory studies of trust between humans and machines in automated systems. Transactions of the Institute of Measurement and Control, 21(4-5), 203-211. https://doi.org/10.1177/0142331299021004
- Ore, O. et Sposato, M. (2022). Opportunities and risks of artificial intelligence in recruitment and selection. International Journal of Organizational Analysis, 30(6), 1771-1782. https://doi.org/10.1108/IJOA-07-2020-2291
- Pasmore, W., Winby, S., Mohrman, S. A. et Vanasse, R. (2019). Reflections: sociotechnical systems design and organization change. Journal of Change Management, 19(2), 67-85. https://doi.org/10.1080/14697017.2018.1553761
- Pereira, V., Hadjielias, E., Christofi, M. et Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2021.100857
- Pett, M. A., Lackey, N. R. et Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Sage.
- Pettersen, L. (2019). Why artificial intelligence will not outsmart complex knowledge work. Work, Employment and Society, 33(6), 1058-1067. https://doi.org/10.1177/0950017018817489
- Rajpurkar, P., Chen, E., Banerjee, O. et Topol, E. J. (2022). AI in health and medicine. Nature medicine, 28(1), 31-38. https://doi.org/10.1038/s41591-021-01614-0
- Ramachandran, K. K., Mary, A. A. S., Hawladar, S., Asokk, D., Bhaskar, B. et Pitroda, J. R. (2022). Machine learning and role of artificial intelligence in optimizing work performance and employee behavior. Materials Today: Proceedings, 51(8), 2327-2331. https://doi.org/10.1016/j.matpr.2021.11.544
- Ransbotham, S., Kiron, D., Candelon, F., Khodabandeh, S. et Chu, M. (2022). Achieving individual and organizational value with AI. MIT Sloan Management Review. https://sloanreview.mit.edu/projects/achieving-individual-and-organizational-value-with-ai/
- Robinson, M. D. et Clore, G. L. (2001). Simulation, scenarios, and emotional appraisal: Testing the convergence of real and imagined reactions to emotional stimuli. Personality and Social Psychology Bulletin, 27(11), 1520-1532. https://doi.org/10.1177/01461672012711012
- Rossi, F. (2018). Building trust in artificial intelligence. Journal of International Affairs, 72(1), 127-134. https://www.jstor.org/stable/26588348
- Rotter, J. B. (1967). A new scale for the measurement of interpersonal trust. Journal of Personality, 35(4), 651–665. https://doi.org/10.1111/j.1467-6494.1967.tb01454.x
- Rotter, J. B. (1971). Generalized expectancies for interpersonal trust. American Psychologist, 26(5), 443–452. https://doi.org/10.1037/h0031464
- Russel, S. et Susskind, D. (2021, octobre). Positive AI Economic Futures. World Economic Forum. https://www3.weforum.org/docs/WEF_Positive_AI_Economic_Futures_2021.pdf
- Ryan, M. (2020). In AI we trust: ethics, artificial intelligence, and reliability. Science and Engineering Ethics, 26(5), 2749-2767. https://doi.org/10.1007/s11948-020-00228-y
- Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8(4), 350–353. https://doi.org/10.1037/1040-3590.8.4.350
- Schneider, B. A., Avivi-Reich, M. et Mozuraitis, M. (2015). A cautionary note on the use of the Analysis of Covariance (ANCOVA) in classification designs with and without within-subject factors. Frontiers in Psychology, 6(474), 1-12. https://doi.org/10.3389/fpsyg.2015.00474
- Shi, S., Gong, Y. et Gursoy, D. (2021). Antecedents of trust and adoption intention toward artificially intelligent recommendation systems in travel planning: a heuristic–systematic model. Journal of Travel Research, 60(8), 1714-1734. https://doi.org/10.1177/0047287520966395
- Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI. International Journal of Human-Computer Studies, 146. https://doi.org/10.1016/j.ijhcs.2020.102551
- Shulner-Tal, A., Kuflik, T. et Kliger, D. (2023). Enhancing fairness perception–Towards human-centred AI and personalized explanations understanding the factors influencing laypeople’s fairness perceptions of algorithmic decisions. International Journal of Human–Computer Interaction, 39(7), 1455-1482. https://doi.org/10.1080/10447318.2022.2095705
- Siau, K. et Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47-53. https://www.researchgate.net/publication/324006061_Building_Trust_in_Artificial_Intelligence_Machine_Learning_and_Robotics
- Söllner, M., Hoffmann, A., Hoffmann, H., Wacker, A. et Leimeister, J. M. (2012). Understanding the formation of trust in IT artifacts. Proceedings of the International Conference on Information Systems (ICIS 2012). https://doi.org/10.1007/978-3-319-05044-7__3
- Söllner, M., Hoffmann, A. et Leimeister, J. M. (2016). Why different trust relationships matter for information systems users. European Journal of Information Systems, 25(3), 274-287. https://doi.org/10.1057/ejis.2015.17
- Stevens, J. P. (2009). Applied multivariate statistics for the social sciences (5e éd.). Routeledge-Taylor & Francis.
- Streiner, D. L. (2003). Starting at the beginning: an introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99-103. https://doi.org/10.1207/S15327752JPA8001_18
- Tabachnick, B. G. et Fidell, L. S. (2019). Using multivariate statistics (7e éd.). Pearson Education.
- Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(1), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
- Tarafdar, M., Tu, Q. et Ragu-Nathan, T. S. (2010). 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
- Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T., Nguyen, S. H., Tran, B. X., Latkin, C. A., Ho, R. C. M. et Ho, C. S. (2021). Determinants of intention to use artificial intelligence-based diagnosis support system among prospective physicians. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.755644
- Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K., Zelaya, C. G. et Van Moorsel, A. (2020). The relationship between trust in AI and trustworthy machine learning technologies. Proceedings of the 2020 conference on fairness, accountability, and transparency (272-283). https://doi.org/10.1145/3351095.3372834
- Trist, E. L. et Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting: an examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Human Relations, 4(1), 3-38. https://doi.org/10.1177/001872675100400101
- Tsiakas, K. et Murray-Rust, D. (2022). Using human-in-the-loop and explainable AI to envisage new future work practices. Proceedings of the 15th International Conference on Pervasive Technologies Related to Assistive Environments. https://doi.org/10.1145/3529190.3534779
- Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308, 1-12. https://doi.org/10.1007/s10479-020-03918-9
- Venkatesh, V., Morris, M. G. et Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60. https://doi.org/10.1006/obhd.2000.2896
- Venkatesh, V., Morris, M. G., Davis, G. B. et Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
- Vereschak, O., Bailly, G. et Caramiaux, B. (2021). How to evaluate trust in AI-assisted decision making? A survey of empirical methodologies. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1-39. https://doi.org/10.1145/3476068
- von Eschenbach, W. J. (2021). Transparency and the black box problem: why we do not trust AI. Philosophy & Technology, 34(4), 1607-1622. https://doi.org/10.1007/s13347-021-00477-0
- Wadden, J. J. (2022). Defining the undefinable: the black box problem in healthcare artificial intelligence. Journal of Medical Ethics, 48(10), 764-768. https://doi.org/10.1136/medethics-2021-107529
- Wang, X. et Yin, M. (2021). Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making. Proceeding of the 26th International Conference on Intelligent User Interfaces. https://doi.org/10.1145/3397481.3450650
- Waung, M., McAuslan, P. et Lakshmanan, S. (2021). Trust and intention to use autonomous vehicles: manufacturer focus and passenger control. Transportation Research Part F: Traffic Psychology and Behaviour, 80, 328-340. https://doi.org/10.1016/j.trf.2021.05.004
- Westphal, M., Vössing, M., Satzger, G., Yom-Tov, G. B. et Rafaeli, A. (2023). Decision control and explanations in human-AI collaboration: improving user perceptions and compliance. Computers in Human Behavior, 144. https://doi.org/10.1016/j.chb.2023.107714
- Wright, S. A. et Schultz, A. E. (2018). The rising tide of artificial intelligence and business automation: developing an ethical framework. Business Horizons, 61(6), 823-832. https://doi.org/10.1016/j.bushor.2018.07.001
- Xiang, Y., Zhao, L., Liu, Z., Wu, X., Chen, J., Long, E., Lin, D., Chen, C., Lin, Z. et Lin, H. (2020). Implementation of artificial intelligence in medicine: status analysis and development suggestions. Artificial Intelligence in Medicine, 102. https://doi.org/10.1016/j.artmed.2019.101780
- Yang, R. et Wibowo, S. (2022). User trust in artificial intelligence: A comprehensive conceptual framework. Electronic Markets, 32(4), 2053-2077. https://doi.org/10.1007/s12525-022-00592-6
- Yu, X., Xu, S. et Ashton, M. (2023). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: the socio-technical system theory perspective. Information Technology & People, 36(1), 454-474. https://doi.org/10.1108/ITP-04-2021-0254
- Zhang, B., Zhu, Y., Deng, J., Zheng, W., Liu, Y., Wang, C. et Zeng, R. (2023). “I am here to assist your tourism”: predicting continuance intention to use AI-based chatbots for tourism. Does gender really matter?. International Journal of Human–Computer Interaction, 39(9), 1887-1903. https://doi.org/10.1080/10447318.2022.2124345
- Zielonka, J. T. (2022). The impact of trust in technology on the appraisal of technostress creators in a work-related context. Proceedings of the 55th Hawaii International Conference on System Sciences. http://hdl.handle.net/10125/80055