A Roadmap Toward Building Trust in AI Environment: Research Agenda
DOI:
https://doi.org/10.14738/bjhr.1201.18209Keywords:
Trust, Interpersonal relationship, AI Technology, AI Deployment, AI Environments, OxytocinAbstract
This paper explores the critical interplay between trust in AI technology and trust in its deployment, focusing on the gaps in interpersonal trust often overlooked in AI adoption. While AI enhances operational efficiency and decision-making, it struggles to replicate the emotional and psychological foundations of human trust. Drawing on interdisciplinary literature, the study highlights three trust dimensions—technological, organizational, and user-centric—and their misalignment, which fuels skepticism about AI reliability and ethical behavior. Despite AI's potential to transform industries, trust deficits persist due to concerns about transparency, accountability, and the emotional resonance of AI. The findings underscore that trust in AI must evolve as a complementary construct, bridging human and machine interactions without displacing interpersonal trust. The study proposes a framework emphasizing cultural and organizational adaptation, regulatory oversight, and user engagement to foster a balanced, sustainable AI.
References
Hengstler, M., Enkel, E., and 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.
Weisz, E., Herold, D.M., Ostern, N.K., Payne, R. and Kummer, S. (2024). Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust. Online Information Review, 49(1); 164-181. Available at: http://www.emeraldinsight.com/doi/10.1108/OIR-02-2024-0083
Kwon, Ik-Whan and Kim, Sung-Ho (2024). Relationship between economics of trust and transaction cost: A brief exposition. International Journal of Business & Management Studies, 5(2). Available at: DOI: 10.56734/ijbms.v5n2a5.
Kwon, Ik-Whan, Hamilton, John and Hong, Seock-Jin (2012). Trust and Transaction Cost in Supply Chain Cost Optimization: An Exploratory Study in Inter-Organizational Information Systems and Business Management: Theories for Researchers. Kishor Vaidya edited IGI Global Publication: 70-82. Available at: DOI: 10.4018/978-1-60960-768-5.ch005.
Carter, Sue (2014). Oxytocin pathways and the evolution of human behavior. Annual Review of Psychology, 65; 17-39. Available at: DOI: 10.1146/annurev-psych-010213-115110.
Thompson, Stuart A. (2024). Which Parts of These Images Are A.I.-Generated? The New York Times, December 27, Available at: https://www.nytimes.com/interactive/2024/12/27/technology/artificialintelligence-generative-fill-photoshop-openai.html.
Zak, Paul J. (2017). The Neuroscience of Trust; Management behaviors that foster employee engagement. Harvard Business Review, January-February; 84-90.
Simatupang, T. and Sridharan, R. (2004). Benchmarking Supply Chain Collaboration: An Empirical Study. Benchmarking: An International Journal, 484-503.
Baryannis, G., Validi, S., Dani, S. and Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7); 2179-2202. DOI: 10.1080/00207543.2018.1530476. 260
Mikl, J., Herold, D.M., Cwiklicki, M. and Kummer, S. (2021). The impact of digital logistics start-ups on incumbent firms: a business model perspective. The International Journal of Logistics Management, 32(4); 1461-1480. Available at: DOI: 10.1108/ijlm-04-2020-0155.
Pournader, M., Ghaderi, H., Hassanzadegan, A. and Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, Available at: DOI: 10.1016/j.ijpe.2021.108250.
Collins, Banjamin (2024). Contextualizing Ethics and Equity of Healthcare Artificial Intelligence Through a Lifecycle. The Conference on Bridging disparities in health care using AI, Saint Louis University, December 6.
Subasi, Abdulhamit (2020). Use of artificial intelligence in Alzheimer’s disease detection. In Artificial intelligence in precision health, London: Academic Press, 257-278. Available at: DOI: 10.1016/B978-0-12-817133-2.00011-2.
Lainjo, Bongs (2024). Integrating artificial intelligence into healthcare systems: opportunities and challenges. Academia of Medicine, Available at: DOI.org/10.20935/AcadMed7382:1-13.
Chen, R.R., Chen, K. and Ou, C.X. (2023). Facilitating interorganizational trust in strategic alliances by leveraging blockchain-based systems: case studies of two eastern banks. International Journal of Information Management, 68; 102521. Available at: DOI: 10.1016/j.ijinfomgt.2022.102521.
Shin, Donghee (2021). The effects of explain ability and causality on perception, trust, and acceptance: implications for explainable AI. International Journal of Human-Computer Studies, 146; 102551. Available at: DOI: 10.1016/j.ijhcs.2020.102551.
Makariusa, E.E., Mukherjeea, D., Foxa, J.D., FoxbRising, A.K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial. Journal of Business Research, 120; 262-273.
Lukyanenko, R., Maass, W. and Storey, V. (2022). Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities. Electronic Markets, 32; 1993-2020.
Montag, C., Becker, B. and Li, B. (2024). On trust in humans and trust in artificial intelligence: A study with samples from Singapore and Germany extending recent research. Computers in Human Behavior: Artificial Humans, 2(2); 1-7.
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. and Aroyo, L.M. (2021). Everyone wants to do the model work, not the data work: Data Cascades in High-Stakes AI. ACM Digital Library, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems; 1-15.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. and Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6); 1-35.
Henrique, B.M. and Santo Jr., E. (2016). Trust in artificial intelligence: Literature review and main path analysis. Computers in Human Behavior: Artificial Humans, 2(1); 2-13.
Noor Al-Ma’aitah (2024). Impact of organizational culture on healthcare supply chain resilience in Jordan: Moderating role of technology integration. Problems and Perspectives in Management, 22(4); 68-82. Available at: DOI:10.21511/ppm.22(4).2024.06.
Vardi, M.Y. (2022). ACM, ethics, and corporate behavior. Communications of the ACM, 65(3); 5-5. Available at: DOI: 10.1145/3516423.
The Council of State Governments (2023). State Leaders Convene, Collaborate at the 2023 CSG National Conference in Raleigh. State Leaders Convene, Collaborate at the 2023 CSG National Conference in Raleigh, December 6.
European Parliament (2023). EU AI Act: first regulation on artificial intelligence. August 6.
Risk and Insurance (2024). Product Recalls Reached Five-Year High in 2023, Sedgwick Report Says. March 14. Available at: https://riskandinsurance.com/product-recalls-reached-five-year-high-in-2023-sedgwickreport-says/.
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Copyright (c) 2025 Ik-Whan G. Kwon, Sung-Ho Kim
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