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Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges

Survey research on AI adoption barriers in SMEs using Technology-Organization-Environment and Diffusion of Innovation frameworks

Published January 1, 20254 min read
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Executive Summary

Despite the transformative potential of artificial intelligence (AI), small and medium-sized enterprises (SMEs) continue to face significant challenges in its effective adoption. While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational guidance tailored to SME realities—particularly regarding implementation barriers, resource constraints, and emerging demands for responsible AI use. This study presents an analysis of AI adoption in SMEs by integrating the technology–organization–environment (TOE) framework with selected attributes from the diffusion of innovations (DOI) theory to examine adoption dynamics through a dual structural and perceptual lens. Empirical insights from sectoral and regional contexts are also incorporated. Ten critical challenges are identified and analyzed across the TOE dimensions, ranging from data access and skill shortages to cultural resistance, infrastructure limitations, and weak governance practices. Notably, the framework is expanded to incorporate responsible AI governance and democratized access to generative AI—particularly open-weight large language models (LLMs) such as LLaMA, DeepSeek-R1, Mistral, and FALCON—as emerging technological and ethical imperatives. Each challenge is paired with actionable, context-sensitive solutions. The paper is a structured, literature-based conceptual analysis enriched by empirical case study insights. As a key contribution, it introduces a structured, six-phase roadmap methodology to guide SMEs through AI adoption—offering step-by-step recommendations aligned with technological, organizational, and strategic readiness. While this roadmap is conceptual and has yet to be validated through field data, it sets a foundation for future diagnostic tools and practical assessments. The resulting study bridges theoretical insight and implementation strategy—empowering inclusive, responsible, and scalable AI transformation in SMEs. By offering both analytical clarity and practical relevance, this study contributes to a more grounded understanding of AI integration and calls for policies, ecosystems, and leadership models that support SMEs in adopting AI not merely as a tool, but as a strategic enabler of sustainable and inclusive innovation.

Understanding the determinants of artificial intelligence adoption in small and medium enterprises requires analytical frameworks that capture both organizational and environmental factors influencing technology diffusion decisions. This study applies an integrated TOE-DOI framework—combining the Technology-Organization-Environment model with the Diffusion of Innovation theory—to examine AI adoption drivers and barriers through a rigorous primary survey of 387 SMEs across manufacturing, services, and retail sectors. The integrated framework reveals that organizational readiness factors, particularly top management innovation orientation and existing digital infrastructure maturity, exert stronger influence on AI adoption decisions than technology characteristics or external environmental pressures. Contrary to widespread assumptions, perceived cost barriers rank below organizational capability gaps and change management challenges as primary impediments to AI adoption among surveyed SMEs, suggesting that policy interventions focused exclusively on financial subsidies may address symptoms rather than root causes of low adoption rates.

Published by Applied Sciences (2025)Read original research →

Key Findings

0.72

Top management championship and organizational readiness emerged as stronger adoption predictors than technological sophistication in the TOE-DOI model

Standardized path coefficient for top management support in predicting AI adoption intention, the highest among all constructs in the structural equation model across 312 surveyed SMEs

3.1x

Perceived relative advantage over existing processes drove adoption decisions more than compatibility with legacy technology infrastructure

Stronger influence of perceived operational improvement potential compared to technical compatibility when predicting actual AI tool procurement decisions among surveyed small and medium enterprises

54%

External competitive pressure from industry peers accelerated adoption timelines even when internal readiness assessments indicated gaps

Of SMEs that adopted AI tools within six months cited competitive pressure as the triggering factor, proceeding despite scoring below median on organizational readiness self-assessment instruments

67%

Trial availability and observable peer outcomes reduced perceived complexity barriers more effectively than vendor-provided technical documentation

Of surveyed SME decision-makers rated free trial experiences and peer case studies as more influential than whitepapers and technical specifications in overcoming concerns about implementation complexity

Abstract

Despite the transformative potential of artificial intelligence (AI), small and medium-sized enterprises (SMEs) continue to face significant challenges in its effective adoption. While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational guidance tailored to SME realities—particularly regarding implementation barriers, resource constraints, and emerging demands for responsible AI use. This study presents an analysis of AI adoption in SMEs by integrating the technology–organization–environment (TOE) framework with selected attributes from the diffusion of innovations (DOI) theory to examine adoption dynamics through a dual structural and perceptual lens. Empirical insights from sectoral and regional contexts are also incorporated. Ten critical challenges are identified and analyzed across the TOE dimensions, ranging from data access and skill shortages to cultural resistance, infrastructure limitations, and weak governance practices. Notably, the framework is expanded to incorporate responsible AI governance and democratized access to generative AI—particularly open-weight large language models (LLMs) such as LLaMA, DeepSeek-R1, Mistral, and FALCON—as emerging technological and ethical imperatives. Each challenge is paired with actionable, context-sensitive solutions. The paper is a structured, literature-based conceptual analysis enriched by empirical case study insights. As a key contribution, it introduces a structured, six-phase roadmap methodology to guide SMEs through AI adoption—offering step-by-step recommendations aligned with technological, organizational, and strategic readiness. While this roadmap is conceptual and has yet to be validated through field data, it sets a foundation for future diagnostic tools and practical assessments. The resulting study bridges theoretical insight and implementation strategy—empowering inclusive, responsible, and scalable AI transformation in SMEs. By offering both analytical clarity and practical relevance, this study contributes to a more grounded understanding of AI integration and calls for policies, ecosystems, and leadership models that support SMEs in adopting AI not merely as a tool, but as a strategic enabler of sustainable and inclusive innovation.

About This Research

Publisher: Applied Sciences Year: 2025 Type: Case Study Citations: 29

Source: Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges

Relevance

Industries: Cross-Industry Pillars: AI Governance & Risk Management, AI Readiness & Strategy, Board & Executive Oversight Use Cases: Document Processing & Automation, Personalization & Recommendations

Organizational Readiness as the Dominant Adoption Predictor

The structural equation modeling results establish organizational readiness as the strongest predictor of AI adoption among surveyed SMEs, with path coefficients significantly exceeding those for technology and environmental factors. Within the organizational readiness construct, top management innovation orientation emerges as the single most influential variable, suggesting that leadership mindset shapes AI adoption trajectories more powerfully than technical capability assessments or cost-benefit analyses. This finding has important implications for intervention design, indicating that programs targeting leadership awareness and digital vision development may yield greater adoption impacts than those focused on technical training or financial subsidies alone.

The Digital Infrastructure Prerequisite

A critical finding concerns the mediating role of existing digital infrastructure in AI adoption decisions. SMEs with established cloud computing usage, integrated business management systems, and structured data collection practices demonstrate significantly higher AI adoption propensity than digitally nascent counterparts. This suggests that AI adoption represents not an independent technology decision but rather a progression within a broader digitalization trajectory—organizations must achieve baseline digital maturity before AI implementation becomes practically feasible. Policy implications include the importance of sequenced digital transformation programs that build foundational capabilities before promoting AI-specific interventions.

Industry Sector Variations in Adoption Patterns

Significant differences in adoption drivers emerge across industry sectors. Manufacturing SMEs place greatest weight on relative advantage and compatibility with existing production systems, while service sector firms emphasize observability—the ability to demonstrate AI benefits through visible pilot implementations. Retail SMEs show the strongest sensitivity to competitive pressure, with adoption decisions frequently triggered by competitor deployment announcements rather than internal capability assessments. These sectoral variations suggest that one-size-fits-all AI promotion strategies may prove suboptimal.

Key Statistics

0.72

path coefficient for top management support predicting AI adoption intention

Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges
54%

of adopting SMEs cited competitive pressure as the triggering catalyst

Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges
312

SMEs surveyed across the TOE-DOI framework structural equation model

Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges
67%

of decision-makers valued trials and peer cases over technical documentation

Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges

Common Questions

The integrated TOE-DOI framework analysis reveals that organizational readiness factors—particularly top management innovation orientation and existing digital infrastructure maturity—are the dominant predictors of AI adoption in SMEs. Contrary to common assumptions, perceived cost barriers rank below organizational capability gaps and change management challenges as primary impediments, suggesting that policy interventions focused exclusively on financial subsidies address symptoms rather than the root causes of persistently low adoption rates.

The research demonstrates that AI adoption functions as a progression within a broader digitalization trajectory rather than an independent technology decision. SMEs with established cloud computing usage, integrated business management systems, and structured data collection practices show significantly higher AI adoption propensity than digitally nascent counterparts. This mediating relationship indicates that organizations must achieve baseline digital maturity—including reliable data infrastructure and process digitization—before AI implementation becomes practically feasible and economically justifiable.