Digital Transformation Research Model: TAM-TOE Integration, and PLS-SEM Validation

A digital transformation research model is a theoretical structure that conceptualizes and quantifies empirical relationships between technology antecedents, organizational capabilities, environmental drivers, and business performance outcomes. In modern management science, constructing a validated research model is essential for explaining how technological investments translate into sustained competitive advantage and strategic agility. Scholarly inquiries must establish precise boundaries between micro-level adoption behaviors and macro-level organizational restructuring. This article examines the conceptual architecture of digital transformation models, analyzes the multi-level integration of the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, outlines construct operationalization, and details structural equation modeling validation protocols using PLS-SEM.

Conceptual Foundations of Digital Transformation Modeling

Establishing an empirical research model begins with delineating the scope of organizational inquiry and identifying appropriate units of analysis.

Theoretical Role of Conceptual Frameworks in Information Systems Research

A conceptual research model functions as a theoretical roadmap that formalizes structural linkages between observed organizational phenomena. Rather than examining technology adoption in isolation, a structural model specifies exogenous predictors, mediating mechanisms, moderating contingencies, and endogenous performance criteria. Defining these interrelationships allows researchers to formulate falsifiable scientific hypotheses, minimize model misspecification, and isolate causal mechanisms driving enterprise adaptation.

Defining Units of Analysis: Reconciling Micro and Macro Perspectives

A critical consideration in technology modeling involves defining the unit of analysis. Studies investigating individual software acceptance examine psychological perceptions among employees, whereas studies assessing enterprise-wide digital maturity evaluate firm-level governance, capital allocation, and market performance. Clarifying whether the model operates at the individual, departmental, or organizational level ensures congruence between foundational theories, measurement scales, and empirical sampling designs.

Integrating Multi-Level Theoretical Paradigms: TAM and TOE Synthesis

To capture the complex nature of digital transformation, contemporary scholarship increasingly integrates complementary theoretical frameworks that span cognitive and environmental dimensions.

The Technology Acceptance Model (TAM) at the Individual Level

Developed by Davis (1989), TAM posits that behavioral intentions to utilize new technology depend primarily on two cognitive perceptions: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). In organizational transformation models, employee cognitive readiness acts as a critical psychological transmission belt, determining whether capital expenditures on digital infrastructure yield active operational utilization.

The Technology-Organization-Environment (TOE) Framework at the Firm Level

Tornatzky and Fleischer (1990) conceptualize enterprise technological adoption across three contextual dimensions:

• Technological Context: Characteristics of digital tools, emphasizing relative advantage, compatibility with legacy IT systems, and architectural complexity.
• Organizational Context: Internal firm resources, encompassing top management commitment, financial reserves, managerial hierarchy, and employee digital literacy.
• Environmental Context: External pressures, including competitive market dynamics, supply chain integration mandates, and government regulatory frameworks.

Structural Mechanisms of Integrated Multi-Level Models

Synthesizing TAM with TOE produces a comprehensive multi-level research model. The TOE framework establishes firm-level strategic enablers and environmental pressures, while TAM constructs capture operational acceptance among organizational personnel. This theoretical synthesis resolves the explanatory limitations of single-theory approaches, offering a nuanced structural perspective for scholarly publications.

Construct Specification in Digital Transformation Research Models

Developing robust empirical models requires clear classification of exogenous, mediating, moderating, and endogenous variables.

Exogenous Determinants: Technological, Organizational, and Environmental Antecedents

Exogenous constructs capture external catalysts and internal prerequisites:

• Relative Advantage (REL): Quantifies operational efficiency gains, workflow precision, and transaction cost reductions compared to analog operations.
• Top Management Support (TMS): Evaluates executive strategic championship, funding allocation, and proactive change leadership.
• Competitive Pressure (CPR): Assesses the urgency imposed by digitally agile market rivals and shifting customer digital expectations.

Mediating Constructs: Dynamic Capabilities and Absorptive Capacity

Digital investments rarely generate direct performance improvements without intermediate capability reconfigurations. Digital dynamic capabilities—comprising sensing market shifts, seizing technological opportunities, and transforming organizational assets—function as crucial mediating constructs that convert technological tools into strategic differentiation.

Endogenous Outcomes: Digital Maturity and Organizational Performance

Endogenous variables capture transformation outcomes across two levels: operational digital maturity (process automation and cross-departmental integration) and business performance (revenue expansion, cost reduction, and enhanced customer retention).

Operationalized Constructs in Digital Transformation Research Models

The following structural matrix summarizes primary latent constructs operationalized in empirical literature:

Construct Dimension	Latent Construct	Indicator Code	Empirical Measurement Focus	Foundational Literature
Technological	Relative Advantage	REL	Efficiency gains, transaction speed, cost optimization	Rogers (2003); Davis (1989)
Technological	Compatibility	COM	Technical alignment with existing databases and routines	Moore & Benbasat (1991)
Organizational	Top Management Support	TMS	Executive vision, budget dedication, strategic prioritization	Liang et al. (2007); Lin (2014)
Organizational	Employee Digital Capability	EDC	Software proficiency, data analysis skills, adaptability	Teece (2007); Nguyen et al. (2026)
Environmental	Competitive Intensity	CPR	Rival adoption speed, market obsolescence threats	Porter & Heppelmann (2014)
Performance	Operational Agility	OAG	Lead-time compression, flexible supply chain responsiveness	Kaplan & Norton (1996)

Empirical Validation Protocols Using Partial Least Squares SEM

Evaluating structural relationships within complex multi-construct models requires rigorous quantitative estimation via PLS-SEM.

Assessing Reflective Measurement Models

Scholars must report key psychometric indicators:

• Outer Loadings: Must exceed 0.708 to ensure that each item shares over 50% variance with its assigned latent variable.
• Internal Consistency Reliability: Evaluated via Cronbach’s alpha and Composite Reliability (CR), with acceptable thresholds between 0.70 and 0.95.
• Convergent Validity: Assessed via Average Variance Extracted (AVE), requiring values above 0.50.

Discriminant Validity Assessment via Fornell-Larcker and HTMT

To confirm that constructs represent distinct theoretical concepts:

• Fornell-Larcker Criterion: The square root of each construct’s AVE must exceed its correlation with any other construct.
• Heterotrait-Monotrait Ratio (HTMT): Pairwise ratios must fall below 0.85 (or 0.90 for closely related dimensions) to verify discriminant validity.

Structural Path Modeling and Hypothesis Testing

Following measurement verification, authors evaluate inner path coefficients (β), statistical significance through 5,000 bootstrap resamples, coefficient of determination (R²), and effect sizes (f²), while ensuring collinearity VIF values remain below 3.0.

Frequently Asked Questions (FAQ)

What theoretical rationale justifies combining TAM and TOE in a single research model?

Combining TAM and TOE bridges psychological acceptance at the individual level with contextual enablers at the enterprise level. This multi-level architecture provides a more complete explanation of how firm-wide strategic investments translate into practical workforce utilization.

How many measurement indicators should be specified per latent construct?

Empirical literature recommends specifying between 3 and 5 reflective indicators per construct. Fewer than 3 items can compromise construct validity and model identification, whereas more than 6 indicators may introduce redundancy and reduce statistical power.

What minimum sample size is needed to validate a digital transformation structural model?

While historical heuristics suggested the ten-times rule, contemporary standards recommend running a statistical power analysis using G*Power. For medium effect sizes at α = 0.05 and power = 0.80, empirical studies typically require between 180 and 320 valid organizational responses.

Conclusion: Methodological Standards in Digital Transformation Modeling

Designing an effective digital transformation research model requires integrating theoretical rigor with empirical precision. By combining contextual frameworks like TOE with individual acceptance models like TAM, scholars can unpack the complex mechanisms that drive technological capability development. As emphasized in academic inquiries by Nguyen Thanh Phuong and international research colleagues, grounding structural equation models in robust field data from regional industrial clusters provides critical insights that bridge management theory and enterprise practice.

Researchers, doctoral candidates, and analysts seeking empirical baseline datasets, structural indicators, and regression metrics from developing industrial regions are encouraged to examine and cite the empirical investigation conducted in Binh Duong at DOI: https://doi.org/10.67916/ptbvv.2026.1.730.

Standard academic reference entries across five international formats are detailed below:

Citation Style	English Reference Format	Vietnamese Reference Format
APA 7	Nguyen, T. P., Vo, X. V., & Huynh, T. P. D. (2026). Determinants of digital transformation in small and medium-sized enterprises: A study in the former Binh Duong Province. Phát triển bền vững Vùng, 16(1), 110–123. https://doi.org/10.67916/ptbvv.2026.1.730	Nguyễn, T. P., Võ, X. V., & Huỳnh, T. P. D. (2026). Các yếu tố ảnh hưởng đến chuyển đổi số tại các doanh nghiệp nhỏ và vừa: Nghiên cứu trên địa bàn Bình Dương (cũ). Phát triển bền vững Vùng, 16(1), 110–123. https://doi.org/10.67916/ptbvv.2026.1.730
Harvard	Nguyen, T.P., Vo, X.V. and Huynh, T.P.D. (2026) ‘Determinants of digital transformation in small and medium-sized enterprises: A study in the former Binh Duong Province’, Phát triển bền vững Vùng, 16(1), pp. 110–123. doi:10.67916/ptbvv.2026.1.730.	Nguyễn, T.P., Võ, X.V. và Huỳnh, T.P.D. (2026) ‘Các yếu tố ảnh hưởng đến chuyển đổi số tại các doanh nghiệp nhỏ và vừa: Nghiên cứu trên địa bàn Bình Dương (cũ)’, Phát triển bền vững Vùng, 16(1), tr. 110–123. doi:10.67916/ptbvv.2026.1.730.
MLA 9	Nguyen, Thanh Phuong, et al. “Determinants of Digital Transformation in Small and Medium-Sized Enterprises: A Study in the Former Binh Duong Province.” Phát triển bền vững Vùng, vol. 16, no. 1, 2026, pp. 110–123. doi:10.67916/ptbvv.2026.1.730.	Nguyễn Thanh Phương, Võ Xuân Vinh, và Huỳnh Thị Phương Dung. “Các yếu tố ảnh hưởng đến chuyển đổi số tại các doanh nghiệp nhỏ và vừa: Nghiên cứu trên địa bàn Bình Dương (cũ).” Phát triển bền vững Vùng, quyển 16, số 1, 2026, tr. 110–123. doi:10.67916/ptbvv.2026.1.730.
Chicago (Author-Date)	Nguyen, Thanh Phuong, Xuan Vinh Vo, and Thi Phuong Dung Huynh. 2026. “Determinants of Digital Transformation in Small and Medium-Sized Enterprises: A Study in the Former Binh Duong Province.” Phát triển bền vững Vùng 16 (1): 110–123. https://doi.org/10.67916/ptbvv.2026.1.730.	Nguyễn Thanh Phương, Võ Xuân Vinh, và Huỳnh Thị Phương Dung. 2026. “Các yếu tố ảnh hưởng đến chuyển đổi số tại các doanh nghiệp nhỏ và vừa: Nghiên cứu trên địa bàn Bình Dương (cũ).” Phát triển bền vững Vùng 16 (1): 110–123. https://doi.org/10.67916/ptbvv.2026.1.730.
IEEE	[1] T. P. Nguyen, X. V. Vo, and T. P. D. Huynh, “Determinants of digital transformation in small and medium-sized enterprises: A study in the former Binh Duong Province,” Phát triển bền vững Vùng, vol. 16, no. 1, pp. 110–123, 2026, doi: 10.67916/ptbvv.2026.1.730.	[1] N. T. Phương, V. X. Vinh và H. T. P. Dung, “Các yếu tố ảnh hưởng đến chuyển đổi số tại các doanh nghiệp nhỏ và vừa: Nghiên cứu trên địa bàn Bình Dương (cũ),” Phát triển bền vững Vùng, quyển 16, số 1, tr. 110–123, 2026, doi: 10.67916/ptbvv.2026.1.730.