TOE Framework: Tech, Org, Environment

• TOE framework is a comprehensive model that categorizes factors influencing technology adoption into three interdependent contexts: Technology, Organization, and Environment.
• Empirical studies operationalize TOE using constructs such as relative advantage, top management support, and regulatory pressure, often validated with high reliability metrics.
• Extensions of TOE across sectors like agribusiness, e-government, and AI-enabled transformation highlight its adaptability in modeling mediation and dynamic feedback effects.

The Technology-Organization-Environment (TOE) framework is a seminal analytical model commonly used to systematically identify and structure the major contexts shaping organizational technology adoption, diffusion, and assimilation. Originally articulated by Tornatzky and Fleischer in 1990, TOE distinguishes three analytically separable yet interdependent contexts—Technology (T), Organization (O), and Environment (E)—each comprising a set of measurable constructs that together determine the propensity, process, and outcome of technological innovation within organizations. Over the past three decades, TOE has been extended, operationalized, and empirically validated across diverse sectors such as trade, agribusiness, e-government, higher education, software development, IS security, and, more recently, AI-driven digital transformation.

1. Formal Structure and Core Dimensions

TOE comprises three major domains:

• Technology Context: The internal and external technologies (current and emergent) relevant to the firm’s operation and innovation activity. Key constructs typically include relative advantage, compatibility, complexity, trialability, observability, and, in AI extensions, features such as analytic capability, data infrastructure maturity, and technical integration (Faqihi et al., 2022, Farhana et al., 11 Dec 2025, Cui, 15 Mar 2025, Abdullahi et al., 2021, Hameed et al., 2016).
• Organization Context: Firm-internal characteristics and resources that mediate technology uptake. Representative constructs include firm size, resource availability, dynamic capabilities, top management support, absorptive capacity, IS readiness, governance mechanisms, internal skill base, and organizational culture (Cui, 15 Mar 2025, Farhana et al., 11 Dec 2025, Abdullahi et al., 2021, Hameed et al., 2016).
• Environment Context: The exogenous setting influencing adoption, including industry structure, regulatory requirements, competitive pressure, institutional support, and ecosystem maturity (Cui, 15 Mar 2025, Abdullahi et al., 2021, Farhana et al., 11 Dec 2025, Mahlangu et al., 2021, Neumann et al., 11 Jan 2026).

The general analytical model takes the additive or moderated form:

$$A = \beta_0 + \beta_1\,T + \beta_2\,O + \beta_3\,E + \varepsilon$$

where $A$ is adoption propensity or performance; $T$, $O$, and $E$ are composite indices from each context (Faqihi et al., 2022, Farhana et al., 11 Dec 2025).

2. Operationalization in Measurement Models

Empirical TOE research typically decomposes each context into latent factors, measured via surveys or coding frameworks and analyzed using SEM or regression models.

Technology: Relative advantage, perceived complexity, and ICT costs (as in Somali agribusiness) are measured via Likert-type multi-item scales with high construct reliability (CR > 0.89) and average variance extracted (AVE > 0.68) (Abdullahi et al., 2021). In AI-adoption contexts, constructs may capture investment in AI (single- or multi-item scales on a Likert continuum), ML integration, system autonomy, or TALENT intelligence module maturity (Cui, 15 Mar 2025, Faqihi et al., 2022, Farhana et al., 11 Dec 2025).

Organization: Measured through categorical variables (e.g., firm size: small, medium, large), numeric indices (resource availability, absorptive capacity), or item sets assessing top management support and employee capabilities (Cui, 15 Mar 2025, Abdullahi et al., 2021). In advanced models, AI-readiness, dynamic capabilities, or survival-oriented innovation mediate or moderate the core T→A relationship (Farhana et al., 11 Dec 2025).

Environment: Measured using indices or clustered categories (e.g., “High”/“Low” technological infrastructure; “Supportive”/“Restrictive” regulatory regime), and item batteries on policy consistency, user involvement, regulatory pressure, and ecosystem AI-maturity (Cui, 15 Mar 2025, Farhana et al., 11 Dec 2025, Mahlangu et al., 2021, Neumann et al., 11 Jan 2026).

3. Model Extensions: Mediation, Moderation, and Integrations

TOE models often introduce:

• Mediators: Firm (or organization) size empirically mediates the effect of technological adoption on performance outcomes such as trade volume (AI→Trade→Firm Size→Trade Performance) (Cui, 15 Mar 2025). Manufacturing performance mediates the pathways from AI-TOE to environmental performance in industrial contexts (Farhana et al., 11 Dec 2025).
• Moderators: Technological infrastructure and regulatory environment moderate the impact of technology on outcomes, with significant effect-size differentials evident in subgroup analyses (e.g., Hedges' g for AI–trade relations in “High-TI” vs “Low-TI” environments) (Cui, 15 Mar 2025).
• Theoretical Integrations: TOE is routinely integrated with Diffusion of Innovations (DOI), Dynamic Capabilities, Transaction Cost Economics (TCE), Resource-Based View (RBV), Institutional Theory, Technology Acceptance Model (TAM), and Theory of Planned Behaviour (TPB), providing a layered, multi-theoretic explanatory scaffold (Cui, 15 Mar 2025, Hameed et al., 2016).

4. Sectoral and Contextual Applications

TOE’s explanatory power and adaptability have been demonstrated in varied domains:

• Cross-Border Trade: AI adoption enhances trade volume, with effect sizes contingent on infrastructure and regulatory moderation; large firms enjoy an additive benefit via mediation (Cui, 15 Mar 2025).
• Software Engineering and Agile Contexts: The framework explains misalignments between regulatory environments, organizational governance, and technological affordances, highlighting the consequences of policy–practice gaps (e.g., shadow IT precipitated by overly restrictive policies) (Neumann et al., 11 Jan 2026).
• Agribusiness: In low-infrastructure settings (Somalia), challenge-oriented complexity, top management support, and competitive pressure drive ICT adoption, while cost and vendor support are insignificant (Abdullahi et al., 2021).
• Higher Education: Extended with Innovation Resistance Theory (IRT), TOE elucidates institutional and environmental drivers of educators’ resistance to GenAI adoption; organization and environment are explicitly modeled as distinct resistance factors (Kalmus et al., 2024).
• E-Government: TOE classification enables a comprehensive grouping of infrastructural, organizational, and policy-related barriers that create persistent service gaps in developing countries (Mahlangu et al., 2021).
• IS Security Implementation: TOE factors display stage-specific influence, with technology and environment dominating pre-adoption, organization determining acquisition and assimilation, and user-level models (TAM/TPB) required to explain integration (Hameed et al., 2016).
• AI-Enabled Digital Transformation: Extensions such as the “AI-enhanced TOE” (AI-TOE) introduce high-dimensional, context-specific constructs (e.g., survival-oriented innovation, institutional leapfrogging, AI elasticity coefficient), tailored to explain adoption and sustainability in fragile economies (Farhana et al., 11 Dec 2025). AI-driven moderation and dynamic feedback loops are formalized (e.g., interaction terms in regression/SEM; see also AI-moderated TOE in talent intelligence (Faqihi et al., 2022)).

5. Principal Findings and Empirical Insights

Empirical results consistently establish:

• Effect Sizes and Pathways: Moderate-to-large direct effects for technology adoption on organizational outcomes (e.g., Hedges’ g up to 0.40 for AI–trade in high-TI clusters) (Cui, 15 Mar 2025). Substantial explained variance (R² up to 0.71) in PLS-SEM models for ICT adoption (Abdullahi et al., 2021) and over 60% in manufacturing/environmental performance when using AI-TOE (Farhana et al., 11 Dec 2025).
• Contextual Amplification and Constraints: Infrastructure maturity, regulatory flexibility, and organizational readiness each amplify technological impact. Conversely, lack of alignment, resource constraints, and policy inconsistency systematically dampen adoption or create service gaps (Neumann et al., 11 Jan 2026, Mahlangu et al., 2021).
• Feedback Loops and Dynamic Moderation: Regulatory pressure translates into organizational policies that can either facilitate or inhibit technology use, with practitioners developing informal workarounds under constrained regimes (“shadow IT,” bypassing official tool sanctions) (Neumann et al., 11 Jan 2026).

6. Methodological Approaches and Evaluation

Researchers employ a spectrum of qualitative (template analysis, thematic coding), quantitative (SEM, PLS-SEM, moderated regression), and mixed-methods approaches to operationalize and validate the TOE framework.

• Measurement Model Rigor: Construct reliability and validity are established using CR and AVE metrics. Bootstrapped t-values and p-values assess hypothesis support for path coefficients (e.g., β for relative advantage, top management support, etc.) (Abdullahi et al., 2021, Farhana et al., 11 Dec 2025).
• Structural Equation Models: Mediation, moderation, and indirect pathways are formalized, and subgroup (multigroup) analyses enable context-sensitive comparison (e.g., Yemen vs. KSA for AI-TOE) (Farhana et al., 11 Dec 2025).

7. Implications and Model Evolution

Findings from TOE research inform both theory and practice:

• Policy Guidance: Investment in digital infrastructure, policy harmonization, capacity-building for SMEs, and targeted interventions to address organizational and institutional barriers are actionable recommendations (Cui, 15 Mar 2025, Farhana et al., 11 Dec 2025).
• AI-Specific Enhancements: TOE now accommodates recursive feedback, dynamic capacity building, and the imperative of resilience, particularly via AI-moderated relationships and multi-context indicators (Institutional Ductility Index, AI Elasticity Coefficient, Digital Yield Point) (Farhana et al., 11 Dec 2025, Faqihi et al., 2022).
• Generalizability and Context Sensitivity: TOE’s core value lies in its extensibility; it adapts to high-variance settings—whether stable, fragile, or rapidly transforming—by refactoring its primary constructs while retaining its triadic schema. Empirical evidence demonstrates that the balance of influence among T, O, and E is sector- and context-contingent (Cui, 15 Mar 2025, Abdullahi et al., 2021, Farhana et al., 11 Dec 2025).

Paper/Context	Technology Dimension	Organization Dimension	Environment Dimension
AI in Trade (SEA) (Cui, 15 Mar 2025)	AI adoption c/w DOI, TCE, Network	Firm size, dynamic capabilities	Technological infrastructure, regulation
GenAI in Agile Teams (Neumann et al., 11 Jan 2026)	GenAI affordances, integration	Governance, training, shadow IT	GDPR, EU AI Act, data-sovereignty
ICT in Agribusiness (Abdullahi et al., 2021)	Relative advantage, complexity, cost	Leadership, capability	Competition, vendor support
AI in Industry (Farhana et al., 11 Dec 2025)	ML, data maturity, system autonomy	Survival, flexibility, AI skills	Institutional support, partnerships
E-Gov in Zimbabwe (Mahlangu et al., 2021)	Infrastructure, access, compatibility	Funding, coordination, skills	Policy consistency, user input

TOE remains a foundational, evolving framework and a unifying schema for cross-contextual studies of technology adoption, accommodating both fine-grained (factor-level) and systemic (dynamic, multi-stage) explanations in organizational innovation research.