Jul 2026· Cureus Journal of Business and Economics· Vol 3· 0 citations· 40 references
TL;DR
High implementation costs, resistance to cultural change, and lack of skilled expertise emerged as significant barriers to AI adoption among Nigeria's construction SMEs.
Abstract
Artificial Intelligence (AI) is increasingly recognized as a transformative tool for improving efficiency, accuracy, and decision-making in construction project management. Despite its potential, the level of AI adoption among small and medium-sized construction businesses (SMEs) in developing economies remains uneven and poorly understood. This study investigates the key determinants influencing AI adoption among construction SMEs in Nigeria. A quantitative research design was employed, informed by Innovation Diffusion Theory and underpinned by the Technology-Organisation-Environment (TOE) framework and the Technology Acceptance Model (TAM). Data were collected through structured questionnaires administered to 382 randomly selected registered construction SMEs, of which 360 valid responses were analysed. Exploratory factor analysis reduced 18 AI adoption variables into three core dimensions: AI functions, AI utilisation, and perceived AI effectiveness. Multiple regression analysis revealed that Technological Infrastructure Readiness was the strongest positive predictor of AI adoption (β = 0.471, p < 0.001). In contrast, Workforce Training and Skills, Industry Collaboration, Regulatory/Institutional Support, and Top Management Support did not significantly influence AI adoption. High implementation costs, resistance to cultural change, and lack of skilled expertise emerged as significant barriers to AI adoption among Nigeria's construction SMEs. The study contributes empirical evidence from a developing-country construction context. It provides practical insights for policymakers, industry regulators, and SME managers seeking to accelerate digital transformation in Nigeria's construction sector.
Digital accounting with artificial intelligence technology represents a crucial means for facilitating the productivity and competitiveness of small and medium enterprises in developing countries. In regard to the state of Pakistan, the implementation and use of accounting software with artificial intelligence technology in SMEs has yet to reach satisfactory standards despite the crucial part played by SMEs regarding economic resilience in the country. This research aims to explore the crucial factors of the implementation process of accounting software with artificial intelligence technology from a quantitative research perspective. In order to discuss the research objectives and achieve its aims, the research employs the technology acceptance model and organization environment theory. A cross-sectional research design method was adopted to investigate 250 owners/managers of SMEs in the main Pakistani cities. To determine the values of perceptual variables, a 5-point Likert scale was adopted; yet, adoption is a binary variable. By using logistic regression analysis, hypotheses were tested. The results indicate that perceived ease of use and competitive pressure are significant positive predictors, yet perceived cost and security concerns are significant negative predictors. A positive relationship between the owner’s level of education and adoption is marginally significant. The Nagelkerke R² is 58%. The results indicate that, apart from using the basic applications developed through this technology, the adoption mechanism is itself a complex process that is fueled by cost and data security concerns. Implications of this research study would include developing cost-effective models by software companies regarding data security or designing a subsidy program by governments, according to the cost and/or data security concerns of Pakistani SME businessmen.
Khuram Shahzad· Revista Academicus: Revista...· 0 citations
Examining the impact of Artificial Intelligence adoption on the economic, environmental, and social dimensions of sustainable business performance among Indian MSMEs is expected to demonstrate that AI adoption significantly improves operational efficiency, innovation capability, environmental responsibility, and financial performance among MSMEs.
Dr.K. Surendran· International Journal of Dru...· 0 citations
Background: The adoption of Business Intelligence (BI) practices has gained attention across enterprises as rapid advances in digital technologies enable the automation of business processes and enhance organisational competitiveness. BI tools offer SMEs the ability to generate insights that support informed decision-making. Despite these benefits, SMEs often encounter challenges in adopting and implementing BI, particularly in relation to financial constraints, limited staff skills, and organisational culture. Objectives: This study investigates the adoption of BI practices in small and medium-sized enterprises (SMEs) to examine the extent of BI integration and its impact on decision-making, competitiveness, and the critical success factors influencing effective BI implementation. Method: A qualitative research approach aligned with an interpretivist paradigm was employed. The Technology Acceptance Model (TAM) was adopted as the lens to guide the study. Data were collected through in-depth interviews with nine (9) participants, including owners, a director and co-founder, and employees from three purposively selected SMEs. Participants were chosen to reflect diverse organisational roles and experiences with BI tools. The interview data were analysed thematically to identify patterns related to BI adoption, utilisation, and effectiveness. Results: The findings indicate that BI adoption among South African SMEs remains limited, with many enterprises relying primarily on basic BI tools. Nevertheless, SMEs that have adopted BI reported improvements in decision-making speed and overall competitiveness. Key challenges identified include data integration issues, financial limitations, and a lack of technical skills. Successful BI adoption was found to depend on leadership support, appropriate training, strategic alignment, and the use of tailored, cloud-based BI solutions. Conclusion: The study concludes that while BI adoption offers notable performance benefits for South African SMEs, its success is contingent upon addressing financial, skills-related, and cultural barriers. Enhanced funding support, practical training initiatives, and effective change management are essential. The study contributes practical insights into the enablers and challenges of BI adoption in a developing economy context and extends the discourse on BI adoption in emerging economies, offering guidance for policymakers, SME owners, and technology providers seeking to enhance SME competitiveness through data-driven decision-making.
Mirano Jansen, K. Ohei, Sam Lubbe· Veredas do Direito· 0 citations
The research provides a comprehensive overview of AI adoption patterns among SMEs in the Kathmandu Valley, highlighting both the opportunities for digital transformation and the challenges impeding widespread implementation.
Aman Lama· SAIM Journal of Social Scien...· 0 citations
Generative artificial intelligence (GenAI) is transforming the accounting sector, yet small and medium-sized enterprises (SMEs) in developing economies face persistent challenges regarding inconsistent governance and compliance risks. Existing literature primarily examines technology acceptance from a consumer perspective while overlooking the psychological adoption behavior of employees who directly interact with intelligent systems. This research investigates the factors influencing Artificial Intelligence Adoption (AIA) among accounting employees in Ho Chi Minh City, specifically evaluating the mediating role of Trust and the moderating effects of Organizational support (OS) and AI Literacy (AIL). The researchers collected empirical data through an online survey of 286 accounting professionals working in small and medium-sized enterprises. The data were subsequently analyzed utilizing Partial Least Squares Structural Equation Modeling. The structural model reveals that Perceived Risk (PR) and Perceived Intelligence (PI) significantly foster trust in artificial intelligence, demonstrating respective path coefficients of 0.365 and 0.237. Trust subsequently drives adoption behavior with a path coefficient of 0.182. Notably, perceived intelligence demonstrates no direct effect on adoption. Conversely, perceived risk exerts a direct positive influence with a path coefficient of 0.190. Furthermore, high organizational support negatively moderates the relationship between trust and adoption with a coefficient of negative 0.151. These findings establish that trust serves as a critical psychological mechanism for translating technological perceptions into concrete adoption behaviors. Managers should prioritize building transparent systems and framing artificial intelligence as a tool for risk mitigation to ensure successful technology integration in high-accountability professional environments.
Thi Mai Anh Hoang, Nhi Ngoc Bao Do, H. Nguyen et al.· Tạp chí Khoa học Đại học Côn...· 0 citations
Small and Medium-sized Enterprises (SMEs) in Hong Kong face intense pressure to adopt Artificial Intelligence (AI) for business development, yet many fail to achieve anticipated outcomes. This study proposes and theoretically validates a readiness-based model explaining AI adoption pathways. Grounded in the Resource-Based View (RBV), the model posits that three independent variables—Talent Readiness, Financial Access, and Change Management—influence the dependent variable of Expected Outcomes in Business Development. Using an explanatory-correlational quantitative design with a target sample of 150 Hong Kong SMEs, this study details how Pearson and partial correlation analyses can be used to test the proposed relationships. Power analysis confirms that N=150 is sufficient to detect SMEs at α = 0.05. The study delineates distinct adoption pathways (transformational, incremental, futile, and stagnant) and assesses the SME's effectiveness in market expansion, revenue growth, and process innovation. The research indicates that SMEs in Hong Kong are recommended to adopt a stepwise approach. This approach involves first enhancing talent and change management capabilities, followed by securing funding, and finally implementing advanced AI. This readiness-first strategy is intended to position AI as a catalyst for business growth rather than a financial liability.
Jacky Mong, Kwan Watt· The International Journal of...· 0 citations