2026· International journal of research and innovation in social science· Vol 10, pp. 5224-5236· 0 citations
TL;DR
Investigating the factors influencing behavioral intention to use AI-enabled BIM systems in Malaysia's construction sector by extending the UTAUT-1 framework with initial trust and individual difference variables shows that performance expectancy, effort expectancy, social influence, and initial trust significantly influence behavioral intention.
Abstract
The integration of artificial intelligence (AI) with Building Information Modeling (BIM) has strong potential to transform the construction industry, yet adoption remains limited, especially in developing contexts. This study investigates the factors influencing behavioral intention to use AI-enabled BIM systems in Malaysia’s construction sector by extending the UTAUT-1 framework with initial trust and individual difference variables. A quantitative research design was used, based on survey data collected from G7 construction firms. The proposed model examined performance expectancy, effort expectancy, social influence, self-efficacy, personal innovativeness, and initial trust and assessed the mediating role of trust. Structural equation modeling was applied to test the hypothesized relationships. The findings show that performance expectancy, effort expectancy, social influence, and initial trust significantly influence behavioral intention, with effort expectancy emerging as the strongest predictor. By contrast, self-efficacy and personal innovativeness do not have significant direct effects. Initial trust also partially mediates the relationships between performance expectancy, social influence, and self-efficacy with behavioral intention, but not those involving effort expectancy or personal innovativeness. However, even though self-efficacy does not have a significant direct effect on behavioral intention, it has a meaningful indirect effect through initial trust, indicating that confidence in one’s abilities helps build trust in AI-enabled BIM systems. The study contributes to theory by integrating UTAUT-1, trust theory, and individual difference perspectives, while also challenging the view that trust always mediates adoption relationships. Practically, the findings highlight the importance of system usability, performance benefits, organizational support, and trust-building measures. Overall, the study deepens understanding of technology adoption in AI-driven construction environments and provides a useful basis for future research and industry implementation.
The UTAUT Model was the strongest predictor of behavioral intention, followed by Personal Concern and Top Management Support, while Perceived Risk had no significant influence and the model demonstrated strong explanatory and predictive capability.
Roselyn Ann Quijano, Jimnanie Manigo· International Journal For Mu...· 0 citations
This study examines how technology readiness shapes logistics professionals’ intention to adopt generative artificial intelligence (AI), with particular attention to the roles of trust, intrinsic motivation, and anthropomorphism. Drawing on the Technology Readiness Index and Self-Determination Theory, the study proposes that optimism, discomfort, insecurity, and service awareness influence adoption intention primarily through trust-based motivational and perceptual pathways. A cross-sectional survey was conducted among 203 logistics practitioners in Oman, Saudi Arabia, and Iraq, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings show that technology readiness dimensions do not directly predict generative AI adoption intention but significantly shape trust in AI. Trust emerged as a central mechanism, exerting a strong direct effect on adoption intention while also positively influencing intrinsic motivation and anthropomorphism. In turn, both intrinsic motivation and anthropomorphism significantly enhance intention to adopt generative AI. The indirect results further indicate that technology readiness operates mainly through trust-driven pathways rather than as a direct behavioral driver. The study extends AI adoption research by repositioning technology readiness as an upstream psychological resource and by highlighting the importance of trust, motivation, and perceptions of human-like AI in the logistics context.
A. Hamid, Mohammed Salah, Adam Y. A. Hamad et al.· Emerging Science Journal· 0 citations
This study provides a novel integration of UTAUT2 with collaboration frameworks, emphasizing the theoretical link between AI adoption, trust, risk and collaboration levels, and contributes to collaboration theory by empirically showing how trust enables, and risk constrains, effective collaborative engagement in remote work.
Suman Kumar, M. Moslehpour, A. Walawalkar et al.· Journal of Information, Comm...· 0 citations
Business administration education relies on case analysis, strategic simulation, and complex decision-making, all of which generative artificial intelligence (GenAI) can substantially support. However, limited research has examined the mechanisms driving business students’ intention to use GenAI tools to support their learning. To address this gap, this study aims to investigate business administration students’ intention to use GenAI by drawing on an integrated Technology-Organization-Environment (TOE) framework and an extended Technology Acceptance Model (TAM) incorporating perceived trust and perceived risk. Using survey data from 385 Chinese business administration students, a hybrid analytical approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) is adopted to examine both the net effects and the configurational mechanisms underlying behavioral intention. The results show that perceived ease of use and perceived trust are the two strongest direct predictors of intention to use GenAI, jointly outweighing perceived usefulness, and that perceived trust serves as the sole significant mediator linking cognitive evaluations to intention to use. Perceived risk, in contrast, exhibits neither a direct nor a mediating effect, suggesting a risk tolerance pattern among business students. Among the TOE dimensions, environmental support (peer influence) emerges as the most potent contextual driver, whereas organizational support facilitates perceived ease of use but does not directly enhance perceived usefulness. The fsQCA findings further identify five typical pathways to high intention to use, namely TAM-driven, trust-compensated, environment-driven, organization-driven risk-tolerant, and comprehensive external-support configurations. These findings provide guidance for designing targeted GenAI-supported strategies and peer-driven interventions in higher business education.
The rapid integration of artificial intelligence (AI) into organizational practices has intensified scholarly interest in employees’ readiness and behavioral intentions toward AI adoption, particularly in emerging economies such as Vietnam. While prior studies have largely emphasized technological and organizational determinants, relatively limited attention has been paid to individual-level and contextual factors shaping AI adoption within developing digital environments. This study examines the determinants of employees’ behavioral intentions to adopt AI tools in Vietnamese technology firms, drawing on an integrated framework that combines the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, with the inclusion of personality traits and social influence. A quantitative research design was employed, using survey data collected from 231 employees working in technology-related roles across Vietnam. Structural Equation Modeling (SEM) was applied to test the proposed relationships among key constructs. The findings reveal that perceived ease of use plays a pivotal mediating role, significantly influenced by organizational support, environmental factors, and individual personality traits. Moreover, perceived usefulness and attitude toward using AI emerge as the strongest direct predictors of behavioral intention. Notably, and contrary to much of the existing literature, technological characteristics and social influence do not exhibit statistically significant effects on employees’ adoption intentions in this context. These findings contribute empirical evidence from an emerging market and suggest that internal organizational readiness and individual psychological factors may outweigh purely technological considerations in shaping AI adoption. From a practical perspective, the study highlights the importance of employee-centered training, supportive organizational cultures, and targeted change management strategies for enhancing AI adoption in Vietnamese technology firms.
Nhung Thi Hong Tran, T. Nguyen, Hong Duyen· Tạp chí Khoa học Đại học Côn...· 0 citations
As AIGC is increasingly integrated into design education and professional practice, understanding the factors that drive design majors’ intention to use these tools has become an important issue. Existing studies have primarily explained technology adoption from cognitive and utilitarian perspectives, while paying limited attention to the role of designer identity.
Drawing on Social Identity Theory (SIT), Innovation Diffusion Theory (IDT), and Innovation Resistance Theory (IRT), this study proposes an integrated theoretical model to examine how designer identity influences design majors’ intention to use AIGC tools. Survey data were collected from 615 design majors in China and analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).
The results show that designer identity positively predicts intention to use AIGC tools both directly and indirectly through innovativeness and self-efficacy. In addition, perceived ease of use negatively moderates the relationship between designer identity and the intention to use AIGC tools, suggesting that highly accessible tools may sometimes raise concerns about professional distinctiveness among design majors with a strong designer identity. The fsQCA results further reveal three pathways associated with high intention to use AIGC tools: the Identity-Driven Innovation Pattern, the Identity-Driven Exploration Pattern, and the Tool-Empowered Pattern.
This study contributes to the literature by introducing a professional identity-based lens to AIGC tool adoption research and demonstrating how designer identity, innovativeness, self-efficacy, and perceived ease of use jointly shape design majors’ engagement with emerging design technologies. The findings also provide practical implications for design education seeking to balance technological competence with the development of designer identity.
Yao-Sheng Zhang, Yi-Ping Luo· Frontiers in Psychology· 0 citations
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