A model develops and validates a model to examine how interface clarity, cognitive-technical alignment, algorithmic reliability, and decision explainability collectively influence behavioral intent to adopt AI-based decision support tools, and offers strong empirical support for applying CFT in AI adoption contexts.
Abstract
As artificial intelligence (AI) systems increasingly support decision-making in the construction sector, understanding the cognitive mechanisms behind user adoption is essential. Based on Cognitive Fit Theory (CFT), the following research develops and validates a model to examine how interface clarity, cognitive-technical alignment, algorithmic reliability, and decision explainability collectively influence behavioral intent to adopt AI-based decision support tools. Data were gathered from 206 construction professionals utilizing a structured questionnaire and assessed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). Results assure that interface clarity and cognitive alignment greatly evolved perceived algorithmic reliability, which then strongly predicts behavioral intent. Decision explainability perception was discovered to mitigate the association among observed reliability and adoption intent, indicating that transparent AI reasoning strengthens the trust-intention link. Furthermore, perceived algorithmic reliability mediates the influence of both interface clarity and cognitive alignment on behavioral intent. The study offers strong empirical support for applying CFT in AI adoption contexts, especially in high-risk, complex environments such as construction. These insights inform the design of cognitively aligned AI interfaces to foster trust, enhance interpretability, and promote sustainable adoption of intelligent systems. Implications for AI interface design, construction technology implementation, and future research in human-AI interaction are discussed.
The study concludes that Explainable AI is not only a technological enhancement but also a strategic tool for promoting employee trust and supporting effective digital transformation and recommends that organizations prioritize explainability, invest in AI literacy and training, and develop transparent AI governance frameworks to encourage successful adoption of AI-driven business process automation.
The results indicate that AI system quality, AI system transparency and AI familiarity significantly enhance AI trust, while AI beliefs have a non-significant effect, and suggest that trust is the main mechanism through which AI-related social and technical factors contribute to improved decision-making outcomes.
Zhaotong Li, Ting-Ong Yan, Kum Fai Yuen· International Trade, Politic...· 0 citations
The rapid global proliferation of AI has created an “AI paradox” where technical adoption fails to yield superior strategic outcomes. Grounded in Organizational Information Processing Theory (OIPT), this study investigates the human-centric “bridge” between capability and decision quality. The authors frame AI as an information-processing capacity and Algorithmic Trust as the essential cognitive processor. Using a sample of 365 managers in China—a global digital laboratory—they employed PLS-SEM to test a moderated-mediation model. Results show that AI Technical Competence (AITC) significantly predicts Algorithmic Trust, which fully mediates the link to Strategic Decision Quality. Crucially, Task Complexity exerts a “dampening effect,” weakening the impact of trust on quality in hyper-complex scenarios. This research contributes to JGIM by shifting focus from “what” AI can do to “how” managers trust it. Practitioners are urged to move toward hybrid-sequential workflows rather than full delegation to navigate the complexities of the global digital economy.
C. C. Liu, C. S. Chen· Journal of Global Informatio...· 0 citations
The findings suggest that perceived AI trustworthiness is positively associated with responsible AI adoption and higher perceived decision efficacy, while decision complexity is an important boundary condition associated with the perceived efficacy of GAI in managerial decision processes.
Guangming Cao, Yanqing Duan, John S. Edwards· Journal of Business Ethics· 0 citations
Assessing the effects of technology reliability (RL), credibility (CR) and technical competence (TEC) on HR professionals’ trust and, subsequently, their intent to deploy AI tools reveals that technology RL, CR and TEC each enhance trust in AI.
R. Arora, Neha Kumari Siradhana· South Asian Journal of Human...· 0 citations
The increasing use of AI has transformed how users perform task-related activities, yet limited research explains how AI creates value through alignment between task-requirements and technological capabilities. This study examines the role of TTF in explaining trust and user satisfaction in AI usage by investigating the effects of TC and TEC on TTF, trust, and satisfaction. A quantitative survey was conducted involving 160 users with experience using AI, and data were analyzed using PLS-SEM. The findings reveal that both TC and TEC significantly influence TTF with TEC showing a stronger effect. TTF significantly enhances trust and users' satisfaction, while trust also positively influences satisfaction. However, task characteristics do not significantly affect trust directly. These findings suggest that trust in AI is shaped more by technology capability than by task complexity, highlighting a technology-driven trust formation mechanism in AI usage. This study extends TTF theory by demonstrating that technology fit serves as a key mechanism linking AI capabilities with user trust and satisfaction, providing a more comprehensive explanation of value creation in AI-supported task environments.
Anggraeni Widya Purwita, R. Bisma, Ghea Sekar Palupi et al.· E3S Web of Conferences· 0 citations
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