Jul 2026· International Journal For Multidisciplinary Research· Vol 8· 0 citations· 70 references
TL;DR
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.
Abstract
Artificial Intelligence (AI) is increasingly recognized as a strategic tool for enhancing organizational efficiency, innovation, and decision-making. This study examined the factors influencing the behavioral intention to use AI among decision-makers of multipurpose cooperatives in the Davao Region, focusing on the Unified Theory of Acceptance and Use of Technology (UTAUT) Model, Personal Concern, Perceived Risk, and Top Management Support. Using a quantitative approach through Pearson correlation, ordinary least squares regression, and partial least squares structural equation modeling (PLS-SEM), the study found that all constructs were rated high and significantly related to behavioral intention. The results revealed that 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. The model demonstrated strong explanatory and predictive capability, confirming that AI adoption among cooperatives is primarily driven by perceived usefulness, ease of use, social influence, organizational support, and personal considerations. The findings reinforce the applicability of the UTAUT framework in explaining AI acceptance and provide insights for promoting AI adoption and digital transformation in cooperative organizations.
This study examines the role of AI self-efficacy in enhancing employees' actual use of artificial intelligence (AI) tools through the mediating roles of perceived ease of use, perceived usefulness, and behavioral intention within the Technology Acceptance Model (TAM). As AI technologies become increasingly integrated into workplace activities, understanding the factors that drive employees' actual AI usage has become an important issue, particularly in the banking sector. A quantitative research design was employed using survey data collected from 100 employees of commercial banks in Banyumas Raya, Indonesia, who had experience using AI tools such as ChatGPT, Google Gemini, and Microsoft Copilot. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings reveal that AI self-efficacy has a positive and significant effect on perceived ease of use and perceived usefulness. Furthermore, perceived ease of use and perceived usefulness significantly influence behavioral intention, which subsequently has a positive effect on the actual use of AI tools. The findings also confirm the mediating roles of perceived ease of use and perceived usefulness in linking AI self-efficacy to behavioral intention. This study extends the Technology Acceptance Model by incorporating AI self-efficacy as an antecedent of technology acceptance and provides practical implications for commercial banks in strengthening employees' AI capabilities to encourage effective and sustainable AI adoption.
Alfionita Mariska Saputri, R. Kurniasih, Siti Zulaikha Wulandari et al.· The International Conference...· 0 citations
This study aims to analyze the factors that affect the use of Artificial Intelligence (AI) in accounting learning using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study used an explanatory quantitative approach with a survey method on 327 students of the Under Graduate of Accounting Program at Public Universities and Private Universities in Jakarta and Depok City. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the help of SmartPLS 4.0. The results of the study show that performance expectancy, effort expectancy, and social influence have a positive and significant effect on behavioral intention in using AI. In addition, facilitating conditions and behavioral intention have a positive and significant effect on use behavior. The greatest influence was found on the relationship of behavioral intention to use behavior, which suggests that intent of use is a major factor driving the actual use of AI in accounting learning. The research model also had good predictive ability with an R² value of 0.676 for behavioral intention and 0.758 for use behavior. These findings strengthen the relevance of the UTAUT model in explaining the adoption of AI in accounting education and provide implications for universities in optimizing the use of AI to improve the quality of learning.
Budi Prijanto, Revalina Putri Widiyatmoko, A. Sari· International journal of res...· 0 citations
To adopt artificial intelligence (AI) technology or not has become crucial for e-commerce SMEs seeking to enhance their marketing capabilities and competitive advantages in the increasingly digitalized business arena. This study aims to elucidate how organizational and individual determinants converge to influence AI adoption within e-commerce SMEs, and subsequently, how this adoption process orchestrates multi-dimensional marketing agility.
Integrating technology acceptance model (TAM) and dynamic capabilities theory (DCT), we developed a multi-level framework examining the relationships between adoption antecedents, usage behavior and strategic outcomes. Using data collected from 317 marketing managers in Chinese e-commerce SMEs, we leveraged a dual-stage analytical approach, combining PLS-SEM (partial least squares structural equation modeling) and artificial neural networks (ANN) analysis, to test our theoretical framework and reveal linear and non-linear relationships that may be obscured by traditional methods. Marketing agility was operationalized as a second-order construct comprising four dimensions and all measurement scales were adapted from established literature.
The results reveal that technology readiness and organizational support play dominant roles in shaping AI adoption intentions, while the impact of perceived usefulness and ease of use is relatively smaller. Furthermore, AI integration is demonstrated to be critical driver for our marketing agility construct. This study demonstrates the joint influencing mechanism of organizational and individual factors, providing empirical evidence to engage with the micro-foundations debates within the theoretical framework of dynamic capabilities. In practice, it provides an actionable roadmap for SMEs to transform AI potential into tangible marketing capabilities.
Upon the investigation, our contribution lies in empirically testing how individual psychological acceptance scales into organizational strategic agility, providing a non-linear and micro-foundational perspective that best reconciles TAM and DCT for the AI-driven marketing landscape of SMEs. Unlike traditional adoption models that focus solely on the “if” and “when” questions of technology use, this study reveals the “how” roadmap of strategic transformation.
Luoxi Pu, R. Radics, Muhammad Umar et al.· International Journal of Eme...· 0 citations
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.
Shiau-Yoon Choong, Zahir Osman· International journal of res...· 0 citations
This study examines the determinants of artificial intelligence adoption intention in Qatar’s financial sector by integrating individual, organizational, and regulatory perspectives within a mixed-methods sequential explanatory design. Drawing on survey data from 215 validated responses collected from financial professionals across conventional, Islamic, foreign, and FinTech institutions, the analysis employs partial least squares structural equation modeling (PLS-SEM) to test the effects of technology readiness, organizational readiness, regulatory environment, perceived trust, and fairness perception on intention to adopt AI. The results show that organizational readiness is the strongest predictor of adoption intention, followed by technology readiness and the regulatory environment, while perceived trust partially mediates the relationship between readiness and intention. Multi-group analysis further reveals heterogeneity across bank types and levels of AI exposure, with stronger effects observed in FinTech and digitally oriented institutions. The findings suggest that AI adoption in Qatar depends not only on technical capability, but also on institutional preparedness and ethical legitimacy. The study contributes to the literature on financial innovation by extending readiness-based models to a Gulf context shaped by state-led modernization, regulated experimentation, and the growing need for responsible AI governance.
Mashael Hamad K.H. Al-Thani· International Journal of inn...· 0 citations
The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness and suggests that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and strengthen organizational adaptability.
Mark Ian C. Abrias, Nerissa M. Revilla· World Journal of Advanced Re...· 0 citations