2024· International Journal of Innovative Research in Humanities & Technology· Vol 7, pp. 01-16· 0 citations
TL;DR
The study concludes that trust is a multidimensional concept involving technical performance, ethical considerations, and user experience, highlighting the need for trustworthy AI systems to support effective and responsible digital communication.
Abstract
Artificial Intelligence (AI) has transformed modern communication systems by enabling intelligent interactions, automated content generation, personalized recommendations, real-time translation, and conversational support. The widespread adoption of AI in areas such as social media, healthcare, education, customer service, and enterprise communication depends largely on user trust. Trust in AI communication systems is influenced by factors such as transparency, reliability, explainability, privacy, fairness, and accuracy. However, concerns regarding bias, misinformation, data privacy, and lack of explainability can reduce user confidence. This study examines technological, psychological, and social factors affecting trust in AI-based communication systems. The findings reveal that transparency, reliability, privacy protection, and explainability are critical for building long-term user trust. The study concludes that trust is a multidimensional concept involving technical performance, ethical considerations, and user experience, highlighting the need for trustworthy AI systems to support effective and responsible digital communication.
The growing complexity of digital crisis communication has led organizations to adopt artificial intelligence (AI) systems and social media influencers as strategic instruments. However, limited empirical research has examined their combined impact on public trust. This study investigates the effects of AI transparency, responsiveness, and influencer credibility on trust during digital crisis communication. Data collected from 350 Vietnamese social media users were analyzed using structural equation modeling (SEM). The results indicate that AI transparency and responsiveness significantly enhance public trust, while influencer credibility exerts the strongest influence. Moreover, the integrated AI–influencer approach yields significantly higher trust levels than independent deployment. The study contributes to crisis communication literature by proposing a hybrid framework that integrates technological efficiency with human relational influence and offers managerial implications for designing transparent AI systems and strategic influencer collaboration.
Tuan Tai Nguyen· Hoa Binh University Journal...· 0 citations
Artificial intelligence (AI)-based decision support systems are increasingly shaping organizational decision-making by influencing how humans engage in cognitive tasks. While prior research has largely emphasized the performance benefits of AI adoption, less attention has been given to its association with human judgment. Drawing on Automation Bias theory and Human–AI collaboration research, this study examines the relationships among trust in AI-based decision support systems (AI-DSS), reliance intention, judgment attenuation—defined here as a perceived reduction in independent evaluative effort—and accountability pressure. Survey data from 400 organizational employees were analyzed using covariance-based structural equation modeling. The results show that trust in AI-DSS is positively associated with users’ reliance intention, which in turn is associated with greater self-reported judgment attenuation. Trust also shows a direct association with judgment attenuation, indicating that AI use co-occurs with both behavioral and cognitive correlates of reduced independent evaluation. Furthermore, the positive association between trust and judgment attenuation is weaker under conditions of high accountability pressure, a pattern consistent with, though not a direct test of, greater cognitive engagement. Because judgment attenuation is measured by self-report at a single time point without a performance criterion, these findings should be interpreted as evidence of perceived rather than behaviorally verified erosion of judgment, and the cross-sectional design precludes strong causal claims. This study extends Automation Bias theory to contemporary Human–AI collaboration by showing that trust in AI is associated with both the facilitation and the perceived constraint of organizational decision-making, and it highlights accountability as a candidate governance mechanism for preserving self-reported human judgment in AI-assisted environments, pending behavioral validation.
Youngkeun Choi· Human Systems Management· 0 citations
Analysis of consumers' trust in AI-generated recommendations under conditions of AI-assisted decision-making shows that emotional trust may be a mediator in the intention to delegate decision-making to AI agents and proposes strategies to build more transparent and trustworthy AI recommendation systems that can improve the user experience.
Ya-Yi Liu· Frontiers in Humanities and...· 0 citations
This conceptual and normative paper links together research on anthropomorphism, mental models, trust calibration and AI-assisted decision-making into a single end-to-end chain, proposing a conceptual model with propositions for empirical testing.
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
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
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