Consumer Acceptance of AI-Assisted Online Dispute Resolution in Brazil
The Roles of Hope, Trust, Risk, and Algorithmic Literacy
DOI:
https://doi.org/10.52028/rbadr.v8.i15.ART10.RJKeywords:
Online Dispute Resolution, Artificial Intelligence, Technology Acceptance Model, Hope Theory, Algorithmic Literacy, Consumer Acceptance, BrazilAbstract
The integration of artificial intelligence into online dispute resolution (ODR) reconfigures the psychological foundations on which consumer redress rests, yet empirical evidence on the antecedents of its acceptance remains scarce. This study develops and tests an integrative model that extends the Technology Acceptance Model by incorporating trust in artificial intelligence, perceived risk, hope as a motivational resource, and algorithmic literacy as cognitive competence. The model is examined through covariance-based structural equation modeling on a sample of 4,496 Brazilian adult consumers aged 26 to 65. Measurement properties are strong, and the structural model explains 60.0% of the variance in perceived usefulness and 41.8% of the variance in behavioral intention. Bootstrapped mediation analyses reveal two important findings: algorithmic literacy exerts a substantial total indirect effect on intention via trust and perceived ease of use, and hope exerts its effect on intention primarily via perceived ease of use. The results offer a richer acceptance framework for AI-assisted ODR, with implications for platform design and for the Brazilian Artificial Intelligence Strategy and consumer protection infrastructure.
References
AMARAL, P. C. F.; FERREIRA, J. B. MT17 From anxiety to hope: understanding the adoption of AI counseling tools for anxiety management. Value in Health, v. 28, n. 12, p. S485, 2025. DOI: https://doi.org/10.1016/j.jval.2025.09.2182
ANDERSON, J. C.; GERBING, D. W. Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin, v. 103, n. 3, p. 411-423, 1988. DOI: https://doi.org/10.1037/0033-2909.103.3.411.
BARNETT, J.; TRELEAVEN, P. Algorithmic dispute resolution: the automation of professional dispute resolution using AI and blockchain technologies. SSRN Electronic Journal, v. 6, n. 3, p. 399-408, 2018. DOI: https://doi.org/10.2139/ssrn.4613641.
BARTHOLOMEW, T. T.; SCHEEL, M. J.; COLE, B. P. Development and validation of the Hope for Change Through Counseling Scale. The Counseling Psychologist, v. 43, n. 5, p. 671-702, 2015. DOI: https://doi.org/10.1177/0011000015589190.
BEHMANUSH, H.; AKHTARI, F.; NOORIPOUR, R.; WEBER, I.; KAMATH CANNANURE, V. Hope, aspirations, and the impact of LLMs on female programming learners in Afghanistan. In: DIPACE, A.; LIMONGELLI, C.; MARRAS, M.; PAGLIARA, S. M. (ed.). Artificial Intelligence with and for Learning Sciences: past, present, and future horizons. Cham: Springer, 2026. p. 1-18. (Lecture Notes in Computer Science, v. 16438). DOI: https://doi.org/10.1007/978-3-032-17604-2_14.
CALDER, B. J.; PHILLIPS, L. W.; TYBOUT, A. M. Designing research for application. Journal of Consumer Research, v. 8, n. 2, p. 197-207, 1981. Available at: [suspicious link removed].
CHOI, Y. Using AI in my disputes? Clients’ perception and acceptance of using AI in mediation. Conflict Resolution Quarterly, v. 43, n. 2, p. 223-238, 2025. DOI: https://doi.org/10.1002/crq.21483.
DAVIS, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, v. 13, n. 3, p. 319-340, 1989. DOI: https://doi.org/10.2307/249008.
DOGRUEL, L.; MASUR, P. K.; JOECKEL, S. Development and validation of an algorithm literacy scale for internet users. Communication Methods and Measures, v. 16, n. 2, p. 115-133, 2022. DOI: https://doi.org/10.1080/19312458.2021.1968361.
FEATHERMAN, M. S.; PAVLOU, P. A. Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, v. 59, n. 4, p. 451-474, 2003. DOI: https://doi.org/10.1016/S1071-5819(03)00111-3.
FELSTINER, W. L. F.; ABEL, R. L.; SARAT, A. The emergence and transformation of disputes: naming, blaming, claiming. Law and Society Review, v. 15, n. 3-4, p. 631-654, 1981. DOI: https://doi.org/10.2307/3053505.
FERREIRA, D. B.; GIOVANNINI, C. J.; GROMOVA, E.; SCHMIDT, G. R. Arbitration chambers and trust in technology provider: impacts of trust in technology intermediated dispute resolution proceedings. Technology in Society, v. 68, p. 101872, 2022. DOI: https://doi.org/10.1016/j.techsoc.2022.101872.
FERREIRA, D. B.; GROMOVA, E.; TITOVA, E. V. The principle of a trial within a reasonable time and JustTech: benefits and risks. Human Rights Review, v. 25, n. 1, p. 47-66, 2024. DOI: https://doi.org/10.1007/s12142-024-00715-w.
FERREIRA, F. R. N.; FERREIRA, J. B.; AMARAL, P. C. F.; RAMOS, F. L. MT25 Hope and acceptance of AI-guided psychotherapy: patient adoption of conversational AI in mental health care. Value in Health, v. 28, n. 12, p. S487, 2025. DOI: https://doi.org/10.1016/j.jval.2025.09.2190.
FERREIRA, J. B. MT22 Hope as a catalyst: AI-based conversational agents in mental health support. Value in Health, v. 28, n. 6, p. S270, 2025. DOI: https://doi.org/10.1016/j.jval.2025.04.1132.
FORNELL, C.; LARCKER, D. F. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, v. 18, n. 1, p. 39-50, 1981. DOI: https://doi.org/10.2307/3151312.
GEFEN, D.; KARAHANNA, E.; STRAUB, D. W. Trust and TAM in online shopping: an integrated model. MIS Quarterly, v. 27, n. 1, p. 51-90, 2003. DOI: https://doi.org/10.2307/30036519.
GROMOVA, E. A.; FERREIRA, D. B. Regulatory sandbox: bridging the gaps by designing a dispute resolution system. Conflict Resolution Quarterly, v. 43, n. 1, p. 91-98, 2025. DOI: https://doi.org/10.1002/crq.21482.
HENSELER, J.; RINGLE, C. M.; SARSTEDT, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, v. 43, n. 1, p. 115-135, 2015. DOI: https://doi.org/10.1007/s11747-014-0403-8.
HU, L.; BENTLER, P. M. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, v. 6, n. 1, p. 1-55, 1999. DOI: https://doi.org/10.1080/10705519909540118.
JUNTUNEN, C. L.; WETTERSTEN, K. B. Work hope: development and initial validation of a measure. Journal of Counseling Psychology, v. 53, n. 1, p. 94-106, 2006. DOI: https://doi.org/10.1037/0022-0167.53.1.94.
KATSH, E.; RIFKIN, J. Online dispute resolution: resolving conflicts in cyberspace. Jossey-Bass, 2001. https://www.wiley.com/en-us/Online+Dispute+Resolution%3A+Resolving+Conflicts+in+Cy cyberspace-p-9780787956769.
KATSH, E.; SCHWARTZ-MAOR, T.; RABINOVICH-EINY, O. AI and the future of disputing: naming, blaming, claiming, and preventing. Conflict Resolution Quarterly, v. 43, n. 1, p. 79-89, 2025. DOI: https://doi.org/10.1002/crq.21477.
KLINE, R. B. Principles and practice of structural equation modeling. 4. ed. Guilford Press, 2016. https://psycnet.apa.org/record/2015-56948-000.
KOCK, N. Common method bias in PLS-SEM: a full collinearity assessment approach. International Journal of e-Collaboration, v. 11, n. 4, p. 1-10, 2015. DOI: https://doi.org/10.4018/ijec.2015100101.
LIU, Y.; WAN, Y. Consumer satisfaction with the online dispute resolution on a second-hand goods-trading platform. Sustainability, v. 15, n. 4, p. 3182, 2023. DOI: https://doi.org/10.3390/su15043182.
MACKINNON, D. P. Introduction to statistical mediation analysis. New York: Routledge, 2008. DOI: https://doi.org/10.4324/9780203809556.
MAIA, A.; FLóRIO, R. A. Online Dispute Resolution (ODR): mediação de conflitos on-line rumo à singularidade tecnológica? Revista Brasileira de Alternative Dispute Resolution, v. 5, n. 10, p. 39-51, 2023. https://rbadr.emnuvens.com.br/rbadr/article/view/217.
MAYER, R. C.; DAVIS, J. H.; SCHOORMAN, F. D. An integrative model of organizational trust. Academy of Management Review, v. 20, n. 3, p. 709-734, 1995. DOI: https://doi.org/10.2307/258792.
MCKNIGHT, D. H.; CHOUDHURY, V.; KACMAR, C. Developing and validating trust measures for e-commerce: an integrative typology. Information Systems Research, v. 13, n. 3, p. 334-359, 2002. DOI: https://doi.org/10.1287/isre.13.3.334.81.
MEERS, J.; HALLIDAY, S.; TOMLINSON, J. Why we need to rethink procedural fairness for the digital age and how we should do it. In: RESEARCH HANDBOOK ON LAW AND TECHNOLOGY. [S.l.]: [s.n.], 2023. p. 468-482. DOI: https://doi.org/10.4337/9781803921327.00038.
MOTTA, I.; LIMA, M. J.; AMBROSIO, T. Towards designing Artificial Intelligence (AI)-based Online Dispute Resolution (ODR) services: the case of +Acordo. Linköping: Linköping University Electronic Press, 2023. Available at: https://ecp.ep.liu.se/index.php/servdes/article/view/853/758.
NUNNALLY, J. C. Psychometric theory. 2. ed. McGraw-Hill, 1978. https://archive.org/details/dli.scoerat.1556psychometrictheorysecondedition.
OELDORF-HIRSCH, A.; NEUBAUM, G. Attitudinal and behavioral correlates of algorithmic awareness among German and U.S. social media users. Journal of Computer-Mediated Communication, v. 28, n. 5, p. zmad035, 2023. DOI: https://doi.org/10.1093/jcmc/zmad035.
PODSAKOFF, P. M.; MACKENZIE, S. B.; LEE, J. Y.; PODSAKOFF, N. P. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, v. 88, n. 5, p. 879-903, 2003. DOI: https://doi.org/10.1037/0021-9010.88.5.879.
PREACHER, K. J.; HAYES, A. F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, v. 40, n. 3, p. 879-891, 2008. DOI: https://doi.org/10.3758/BRM.40.3.879.
ROBINSON, C.; ROSE, S. Predictive, construct, and convergent validity of general and domain-specific measures of hope for college student academic achievement. Research in the Schools, v. 17, n. 1, p. 38-52, 2010. Available at: https://psycnet.apa.org/record/2011-23901-006.
SCHMITZ, A. J.; ZELEZNIKOW, J. Intelligent legal tech to empower self-represented litigants. Science and Technology Law Review, v. 23, n. 1, p. 142-191, 2021. DOI: https://doi.org/10.52214/stlr.v23i1.9391.
SCHOEFFER, J.; KUEHL, N.; MACHOWSKI, Y. There is not enough information: on the effects of explanations on perceptions of informational fairness and trustworthiness in automated decision-making. In: ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, 2022. Proceedings [...]. New York: ACM, 2022. p. 1616-1628. DOI: https://doi.org/10.1145/3531146.3533218.
SELA, A. Can computers be fair? How automated and human-powered online dispute resolution affect procedural justice in mediation and arbitration. Ohio State Journal on Dispute Resolution, v. 33, p. 91, 2018. Available at: https://ssrn.com/abstract=3074311.
SNYDER, C. R. Hope theory: rainbows in the mind. Psychological Inquiry, v. 13, n. 4, p. 249-275, 2002. DOI: https://doi.org/10.1207/S15327965PLI1304_01.
SNYDER, C. R.; HARRIS, C.; ANDERSON, J. R.; HOLLERAN, S. A.; IRVING, L. M.; SIGMON, S. T.; YOSHINOBU, L.; GIBB, J.; LANGELLE, C.; HARNEY, P. The will and the ways: development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, v. 60, n. 4, p. 570-585, 1991. DOI: https://doi.org/10.1037/0022-3514.60.4.570.
VAN DEURSEN, A. J. A. M.; HELSPER, E. J.; EYNON, R. Development and validation of the Internet Skills Scale (ISS). Information, Communication and Society, v. 19, n. 6, p. 804-823, 2016. DOI: https://doi.org/10.1080/1369118X.2015.1078834.
VENKATESH, V.; MORRIS, M. G.; DAVIS, G. B.; DAVIS, F. D. User acceptance of information technology: toward a unified view. MIS Quarterly, v. 27, n. 3, p. 425-478, 2003. DOI: https://doi.org/10.2307/30036540.
VENKATESH, V.; THONG, J. Y. L.; XU, X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, v. 36, n. 1, p. 157-178, 2012. DOI: https://doi.org/10.2307/41410412.
WING, L. Ethical principles for online dispute resolution: a GPS device for the field. International Journal of Online Dispute Resolution, v. 3, n. 1, p. 12-29, 2016. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2973278.
WONG, L.-W.; TAN, G. W.-H.; OOI, K.-B.; DWIVEDI, Y. The role of institutional and self in the formation of trust in artificial intelligence technologies. Internet Research, v. 34, n. 2, p. 343-370, 2024. DOI: https://doi.org/10.1108/INTR-07-2021-0446.
WU, X.; ZHOU, Z.; CHEN, S. A mixed-methods investigation of the factors affecting the use of facial recognition as a threatening AI application. Internet Research, v. 34, n. 5, p. 1872-1897, 2024. DOI: https://doi.org/10.1108/INTR-11-2022-0894.
ZHAO, X.; YOU, W.; ZHENG, Z. How do consumers trust and accept AI agents? An extended theoretical framework and empirical evidence. Behavioral Sciences, v. 15, n. 3, p. 337, 2025. DOI: https://doi.org/10.3390/bs15030337.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Revista Brasileira de Alternative Dispute Resolution - Brazilian Journal of Alternative Dispute Resolution - RBADR
This work is licensed under a Creative Commons Attribution 4.0 International License.
No royalties or other compensation shall be due for the publication of the works.
The opinions expressed by the authors of the articles and reviews are their sole responsibility.
