2026· IEEE transactions on engineering management· Vol 73, pp. 4357-4373· 0 citations· 130 references
Computer Science
Abstract
The rapid adoption of artificial intelligence (AI) in labor-intensive manufacturing raises concerns about how trust between humans and AI develops under production pressure. This study examines the erosion and consequences of human–AI trust in garment factories, where workers must quickly adapt to AI-driven systems in highly monitored environments. Drawing on the Swift Trust Theory and the Job Demands–Resources model, we propose a framework that considers relationships among constructs, such as compressed trust formation, trust fragility, sacrificial compliance, perceived organizational support, and workplace techno-pressure. We employed a two-phase mixed-methods design. An exploratory qualitative study informed construct development, followed by a quantitative study for scale validation and hypothesis testing. Results show that compressed trust formation is positively associated with trust fragility, and both are positively linked to sacrificial compliance. Trust fragility partially mediates the relationship between compressed trust formation and sacrificial compliance. Perceived organizational support weakens the relationship between compressed trust formation and trust fragility, whereas workplace techno-pressure strengthens the relationship between trust fragility and sacrificial compliance. The findings suggest that trust formed rapidly under techno-pressure can enable short-term coordination but remains structurally fragile and may convert into self-sacrificial work behaviors. The study extends Swift Trust Theory to human–AI collaboration and embeds trust dynamics within the Job Demands–Resources model, highlighting how organizational support and techno-pressure management shape whether digital transformation supports sustainable or harmful forms of adaptation.
The findings suggest that trustworthy AI integration requires addressing pre-existing structural failures before introducing algorithmic decision support, and offer design recommendations for AI-supported appraisal systems, with implications for HCI research and organizational practice.
Andreas Harnindito, Akshat Sawarni· Proceedings of the 2026 Euro...· 0 citations
A trust-based mechanism connecting institutional trust, risk perception, AI service trust, and behavioral intention in China's digital government context is examined to indicate that perceived risk constrains acceptance of AI-enabled public services.
Huihui Wang, Shixin Zhu· Frontiers in Psychology· 0 citations
As more and more AI-powered tools and platforms are adopted in organizations to automate routine tasks, support decision-making, and improve efficiency, adoption is often lopsided, with employees embracing the system while also wondering whether it can effectively perform work-critical tasks. This study examines how organizations adopt AI platforms by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) in the context of user experience (UX) and competence trust, two AI-salient concepts. Competence trust is the extent to which employees believe an AI platform will reliably produce accurate, dependable, and work-relevant outputs. An exploratory sequential mixed-methods design was employed to generate and confirm inductive analysis-level explanations of trust formation and acceptance, grounded in insights gained through observation. The first phase involves semi-structured interviews with 15–25 organizational users. In this phase, the study maps the path from the UX stage to trust or distrust and acceptance, identifies important incidents that affect people's confidence in the platform, and gathers users’ trust-related language to help fine-tune constructs and measurement criteria. In Phase 2, a survey instrument is developed from issues identified in Phase 1 and established scales. CFA and SEM test a longer UTAUT model in which factors affecting performance expectancy, effort expectancy, social influence, and facilitating conditions are used to quantify levels of competence, trust and behavioral intention to use the AI platform. Where the sample size allows, multi-group comparisons can be made by user intensity or job function. Phase 3 combines qualitative themes and quantitative path findings through a combined display to extract converging data, identify contradictions, and provide expanded interpretations, ultimately deriving actionable suggestions that will be applied. Contribution of the study. The study contributes theoretically by framing competence trust as an integral mechanism linking UX to adoption within a broader UTAUT framework, and by providing a better explanation of why perceiving usefulness and ease of use alone may be inadequate in AI settings. Moreover, it has practical design and governance implications for organizations to foster sustainable adoption by incorporating UX features that signal reliability (e.g., stability, clear guidance, robust error handling) and by reinforcing social and organizational support to enhance trust.
Wittika Thangchan, W. P. Wall· Decision Science Letters· 0 citations
AI is being increasingly integrated into HRM, modifying recruitment, performance analysis, learning and development, and managerial decisions. This paper uses eighteen recent organizational psychology studies and focuses on information systems and human-computer interaction. It provides a synthesis on what triggers trust between employees and AI, the human-algorithm workload balance, and fairness concerns regarding AI in people management and assessment. The review concludes that trust in AI is separate from trust in human colleagues, that the process shapes trust and the delegation of trust can improve employees’ performance and overall satisfaction, even when the trust is not complete. This review also notes that trust, explainability, accountability, and the varying perceptions of fairness limit the deployment of AI. Task Division, trust construction, varying perceptions of fairness, and the organizational structure are the main components of the Human-AI and HRM collaboration system, which this review seeks to examine. It also identifies research gaps.
Nidhi Goel· International Journal For Mu...· 0 citations
Purpose: This paper examines perceived quality of Artificial Intelligence (AI)-supported recruitment by testing how explanation, confidence information, practical AI experience, trust, willingness to use AI, perceived fairness and human control shape evaluations of AI candidate-ranking recommendations.
Methodology/Approach: The study uses an anonymous survey of Human Resources (HR)-related respondents (N = 145) and a randomised scenario comparing AI ranking without explanation with ranking supported by brief reasoning and confidence information. We use reliability checks, regression models, mediation and automation tests.
Findings: Practical AI experience significantly moderated the effect of explanation on trust and showed a weaker pattern for willingness to use AI. The moderation was not significant for perceived fairness. Intention to use AI was associated with trust, perceived usefulness, human control and prior AI experience.
Research Limitation/Implication: The non-probabilistic sample has a strong Central European component. The results measure perceptions rather than behavioural outcomes or audited fairness.
Originality/Value of paper: The paper shows that explanation and confidence strengthen trust mainly among AI-experienced respondents but do not significantly improve perceived fairness. It distinguishes between trusting AI as decision support and being convinced that AI-supported ranking is fair to candidates.
Coi Tran, R. Delina· Kvalita Inovácia Prosperita· 0 citations
The proliferation of artificial intelligence (AI) in complex decision environments has intensified scholarly and practical interest in how humans calibrate trust when collaborating with intelligent systems. This paper presents a cross-domain empirical investigation of human-AI collaborative decision-making across climate intelligence and organisational governance contexts. Drawing on a mixed-methods synthesis of 68 primary studies, original survey data from 200 industry professionals, and 30 semi-structured interviews, we examine trust calibration dynamics, performance outcomes, and implementation barriers. Our findings reveal a systematic trust calibration paradox: while 75% of organisations reported efficiency gains from AI integration, only 45% perceived their systems as transparent and explainable, and 38% identified persistent algorithmic bias. Effect sizes varied substantially across applications, reaching g = 1.65 for extreme-weather detection, with considerable between-study heterogeneity. Critically, we find that trust exhibited a non-linear association with AI autonomy, with higher observed trust at intermediate than at maximal levels of delegation; turning regions varied by decision context and should not be interpreted as universal autonomy thresholds. These findings advance human-intelligent systems integration theory by demonstrating that effective collaboration depends less on algorithmic performance alone and more on calibrated trust, institutional capacity, and governance frameworks that sustain human agency within hybrid decision architectures.
Elizabeth I. Igbodor, I. Mordi, N. B. Umoru et al.· Human-Intelligent Systems In...· 0 citations
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