Need Assessment for Implementation of Digital Transformation Practices Through the Capacity Building ^†

Abstract

This study systematically identifies and prioritizes barriers to Industry 4.0 adoption in manufacturing within a developing economy. We used a mixed-methods approach—combining a systematic literature review and PLS-SEM. The research synthesizes 45 critical factors across nine I4.0 pillars, mapped to five sustainability dimensions. Data from 160 professionals show the technological dimension (β = 0.218) to be the most significant broad barrier. Analysis of high outer loadings (≥0.80) highlights key specific barriers: IT infrastructure gaps and poor technological leverage; a lack of organizational and digital readiness; cultural fragmentation and weak knowledge systems; high implementation and cyber threat costs; and low customization demands with absent data standards. The study proposes a maturity model and strategic framework to help policymakers address these barriers and promote sustainable digital transformation.

1. Introduction

Digital transformation, driven by advanced technologies like cloud computing and big data, has become pivotal for industrial progress and competitiveness [1]. While these technologies enhance efficiency and innovation, their adoption faces barriers like technical skill shortages and organizational resistance [2,3]. Capacity building, i.e., developing institutional, technical, and human capabilities, is critical for sustainable DT [4,5]. However, existing research neglects the intersection of DT barriers and sustainability, which encompasses economic, environmental, social, institutional, and technological dimensions [6,7]. Capacity building is essential to address organizational and knowledge barriers in adopting Industry 4.0, requiring leadership development, external collaborations, and structured training to bridge digital leadership gaps and enhance technical competencies [8,9]. Interdisciplinary collaboration challenges necessitate workshops and communication strategies, while digital readiness assessments ensure alignment of technological capabilities with organizational goals [1,10].

Motivation for This Study

Despite its transformative potential for industrial competitiveness and sustainability, Industry 4.0 adoption in developing economies remains critically low due to systemic challenges, which are exacerbated by contextual complexities and fragmented research. Crucially, prior studies often narrowly focus on isolated I4.0 pillars (e.g., cloud computing or IoT) or overlook the interdependencies between barriers and sustainability dimensions. This gap perpetuates a “readiness paradox” identified herein: industries recognize I4.0’s value yet remain paralyzed by multifaceted barriers. Consequently, policymakers and industry leaders lack holistic, actionable frameworks to prioritize interventions that effectively link barrier mitigation to sustainability outcomes. This research addresses these critical voids in the following ways:

• Systematically synthesizing barriers across the nine I4.0 pillars into 45 critical factors.
• Empirically validating barrier interdependencies via PLS-SEM.
• Advancing a sustainability-aligned maturity model for context-specific digital transformation.

2. Literature Review

A review of 39 studies identified barriers to Industry 4.0 adoption across nine pillars, categorized into five sustainability dimensions, with a total of 473 factors (47 economic, 46 environmental, 139 organizational, 54 social, and 187 technological) [3,4]. Key barriers include process management gaps (e.g., a lack of cloud objectives), cybersecurity vulnerabilities, infrastructure deficits, and policy misalignments, necessitating governance frameworks, technical training, and stakeholder collaboration [5,11,12]. Studies highlight the criticality of leadership development [13], IT infrastructure investment [14], and standardized protocols for IoT and cloud integration [15,16]. Challenges such as fragmented workflows [17], industry–academia knowledge gaps [18], and ethical data sharing concerns underscore the need for multidisciplinary education, policy reforms, and modular technical solutions [2,6]. A PRISMA-guided systematic literature review (SLR) synthesizes barriers and extends adoption frameworks [6,19,20]. Partial Least Squares Structural Equation Modeling (PLS-SEM) quantifies barrier interrelationships and prioritizes impacts [1,12,21]. Collectively, our findings advocate capacity building in governance, infrastructure, technical skills, and cross-sector collaboration to enable sustainable digital transformation.

Research Gap

Numerous studies tend to concentrate on individual Industry 4.0 pillar like big data. In contrast, our research establishes connections between obstacles across nine Industry 4.0 pillars, including IoT and big data. Previous investigations seldom associate these barriers with the five dimensions of sustainability, a gap that our mapping seeks to fill. Current frameworks, like the Technology–Organization–Environment (TOE) model, do not provide sufficient detail in linking interventions to sustainability; our Partial Least Squares Structural Equation Modeling (PLS-SEM) approach addresses this deficiency.

3. Research Design

The research design utilizes a sequential mixed-methods strategy to investigate Industry 4.0 adoption barriers and define capacity building needs. Key methodological phases are presented in Figure 1.

3.1. Phase I (Factor Selection for Study)

This phase includes the systematic literature review (SLR), which aims to identify barriers associated with nine different Industry 4.0 technologies. The sustainability dimension mapping process is performed in this phase. This process categorizes the identified barriers into five dimensions: economic, environmental, organizational, social, and technological. L. Seghezzo states that the five-dimensional model functions as a versatile analytical and policymaking instrument, enhancing existing frameworks rather than displacing them [22].

Following the data mapping for each pillar, we identify the most critical factors from the current research with the assistance of an expert. We choose five factors for each Industry 4.0 pillar that align with the sustainability dimension, as illustrated in

Table 1. Selected factors.

Industry 4.0 Pillar	Sustainability Dimension
	Economic	Environmental	Organizational	Social	Technological
Cloud Computing	Operational Costs	External Pressure	Lack of Management Support	Power Distance	High Complexity
Cybersecurity	Cyber Threat Costs	Advanced Cyber Threats	Organizational Flexibility	Privacy Awareness	Performance Barriers
System Integration	Implementation Costs	Operational Disruption Risk	Consultant Availability Issues	Cultural Fragmentation	Process Metrics Issues
Big Data	Financial Assistance Uncertainty	External Support Issues	Organizational Readiness	Traceability Challenges	Technological Advantage
IoT	Extended Payback	Regulatory Barriers	Collaboration Issues	Transparency Issues	IT Infrastructure Gaps
Additive Manufacturing	Machine/Material Costs	Low Customization Demand	Managerial Challenges	Lack of Knowledge Systems	Printing System Size
Simulation	Simulation Time Issues	Project Uniqueness	Management Vision Gaps	Learning Curve Barriers	Simulation Cycle Time Issues
Autonomous Robots	High Costs	Data Sharing Standards Lack	Digital Readiness Issues	Robot Technology Reservations	Human–Robot Interaction Issues
Augmented Reality	Capital Investment Needs	Tech Approval Barriers	Leadership in Digitalization	Tech Familiarization Challenges	Interface Consistency Issues

below.

3.2. Phase II (Questionnaire Development and Response Collection)

This phase includes the questionnaire development for assessment of capacity building needs. Following the selection of barriers, we create a questionnaire comprising four sections. The first section collects demographic information about the respondents to outline their profiles. The second section focuses on willingness to adopt, aimed at evaluating the existing strategies for Industry 4.0 adoption. The third section, titled “Barriers,” is designed to rank these obstacles using a five-point Likert scale. The fourth section includes open-ended questions, inviting respondents to provide suggestions for capacity building strategies. To ensure the validity of the survey questionnaire content, we conducted a pilot test with a sample size of n = 20, and the results indicated that our content is indeed valid.

3.3. Phase III (Data Analysis)

To establish data reliability, we assessed internal consistency using Cronbach’s alpha and validated constructs via Pearson’s correlation tests. Quantitative analysis employed structural equation modeling (SEM) to identify key Industry 4.0 adoption barriers [1]. Our measurement model validation followed established psychometric standards, such as the values of Cronbach’s alpha and composite reliability exceeding 0.7 and the value of average variance extracted being greater than 0.5 [22].

Proposed Hypotheses for the Research

The hypotheses (H1–H5) posit that economic (ECO), environmental (ENV), organizational (ORG), social (SOC), and technological (TECH) factors act as prominent barriers to adoption. These hypotheses are grounded in a structural equation model (SEM) that employs a first-order construct (I4.0 Adoption Barriers) and a second-order constructs (ECO, ENV, ORG, SOC, TECH). Each hypothesis is described in

Table 2. Proposed hypotheses.

Factors	Abb	Relationship	Hypothesis
Economic	ECO	ECO → I4.0 Adop. Barriers	H1: Economic constraints significantly hinder I4.0 implementation.
Environmental	ENV	ENV → I4.0 Adop. Barriers	H2: Environmental concerns create substantial adoption barriers.
Organizational	ORG	ORG → I4.0 Adop. Barriers	H3: Organizational limitations impede I4.0 technology integration.
Social	SOC	SOC → I4.0 Adop. Barriers	H4: Social factors present critical obstacles to adoption.
Technological	TECH	TECH → I4.0 Adop. Barriers	H5: Technological challenges significantly restrict I4.0 deployment.

.

RQ1. What are the factors (economic, environmental, organizational, social, or technological) that impact Industry 4.0 adoption?

RQ2. What strategies can be implemented to address the barriers to adoption?

4. Results and Discussion

This study involved 159 industry practitioners (140 males, 19 female), predominantly aged 25–34 (63.8%) and holding bachelor’s degrees (67.5%). Middle management constituted the largest role (70%), with textiles (44.7%), services (18%), and automotive (13.7%) as dominant sectors. Most respondents (60.2%) expressed readiness for Industry 4.0 adoption.

For the quantitative analysis, structural equation modeling techniques were employed utilizing SmartPLS4^® software (version 4.1.1.1). Various statistical tests were conducted on the collected data. The data exhibited a normal distribution, as the skewness and kurtosis values for the variables fell within the ranges of ±1.96 and ±1, respectively [22]. Given that the data was collected using a Likert scale questionnaire, it was assumed to be normally distributed, and the values within the specified range further supported this assumption. After confirming the normality of the data, several reliability and validity assessments of the proposed model were conducted. Construct reliability was evaluated through item loading analysis, confirming the internal consistency of measurement scales [22].

Table 3. Measurement model results.

Construct	Cronbach’s Alpha (0.81–0.83)	Composite Reliability > 0.80	Average Variance Extracted > 0.5
Eco	0.83	0.84	0.65
Env	0.81	0.82	0.59
Org	0.82	0.84	0.63
Soc	0.81	0.83	0.63
Tech	0.83	0.84	0.64

provides a detailed evaluation of the measurement model concerning five constructs, defined as economic (eco), environmental (env), organizational (org), social (soc), and technological (tech) factors, by assessing their reliability and validity through essential metrics. All constructs exhibit high reliability, with Cronbach’s alpha values between 0.81 and 0.83 and composite reliability scores surpassing the 0.82 mark (ranging from 0.82 to 0.84), which confirms strong internal consistency. Convergent validity is evidenced by elevated indicator loadings (predominantly greater than 0.70) and average variance extracted (AVE) values exceeding 0.50 (between 0.59 and 0.65), signifying that each construct accounts for a considerable amount of variance in its indicators.

Hypothesis testing assessed the significance of each proposed relationship, as shown in

Table 4. Predicted hypotheses for SEM-Model.

Hypothesis	Relationship	Beta-Value	Std-Error	T-Value	p-Value	Decision	CI-2.5%	CI-97.5%
H1	eco → I4.0 Adoption Barriers	0.200	0.007	4.15	0.00	Supported	0.188	0.213
H2	env → I4.0 Adoption Barriers	0.200	0.004	5.36	0.00	Supported	0.194	0.207
H3	org → I4.0 Adoption Barriers	0.215	0.004	2.71	0.00	Supported	0.208	0.225
H4	soc → I4.0 Adoption Barriers	0.215	0.004	9.83	0.00	Supported	0.207	0.224
H5	tech → I4.0 Adoption Barriers	0.218	0.004	15.69	0.00	Supported	0.209	0.226

, using path loading t-values. All five hypotheses regarding Industry 4.0 adoption barriers were supported (t > 1.645, α = 0.05), with no rejections being observed [23].

The structural equation model confirms that all hypothesized factors—economic (eco), environmental (env), organizational (org), social (soc), and technological (tech)—exert statistically significant influences on Industry 4.0 adoption barriers (p = 0.00), with technological factors emerging as the most impactful (β = 0.218; T = 15.69). Economic and environmental factors exhibit moderate but significant effects (β = 0.200), although environmental barriers show marginally stronger statistical confidence (T = 5.36 vs. T = 4.15). Organizational and social factors both demonstrate identical Beta-values (β = 0.215), but social barriers (e.g., workforce resistance, privacy concerns) are far more robustly supported (T = 9.83) compared to organizational challenges like leadership gaps (T = 2.71), suggesting potential measurement overlap or multicollinearity in the latter. The narrow confidence intervals (e.g., tech: 0.209–0.226; soc: 0.207–0.224) reflect high precision in estimates, reinforcing the reliability of these relationships. Based on the coefficients of respective independent latent variables scores, the equation of the structural model is represented as follows:

Y = a + 0.200x1 + 0.200x2 + 0.215x3 + 0.215x4 + 0.218x5 + ζ

• Y = I4.0 Adoption Barriers
• a = Constant
• x1 = Economic Barriers
• x2 = Environmental Barriers
• x3 = Organizational Barriers
• x4 = Social Barriers
• x5 = Technological Barriers
• ζ = Error term

4.1. Most Significant Barriers

Of the barriers identified through analysis of outer loading values (≥0.80 from Figure 2), the most significant are distributed across various pillars and their dimensions. Within the economic Pillar, the significant barriers are the high costs associated with cyber threats (CSeco) and the substantial financial outlay required for implementation (SIeco). For the environmental dimension, the primary obstacles are a low market demand for customization (AMenv) and a critical lack of unified data sharing standards (ROBenv). Organizationally, the most pressing issues are a lack of overall organizational readiness (BDorg) and specific challenges related to digital readiness (ROBorg). From a social perspective, significant barriers include a lack of established knowledge systems (AMsoc) and issues arising from cultural fragmentation (SIsoc). Finally, within the technological pillar, the key barriers identified are the inability to leverage a technological advantage (BDtech) and significant gaps in IT infrastructure (IoTtech).

4.2. Capacity Building Strategies

We identified the primary themes from the suggestions of respondents that inform the development of technology adoption strategies across various dimensions. Each dimension encompasses specific strategies and themes, aimed at promoting effective adoption. From an economic standpoint, the focus is on fostering investment while addressing and minimizing associated risks. Financial incentives such as tax breaks and grants facilitate the adoption of advanced technologies by small and medium-sized enterprises (SMEs), with public–private partnerships aiding in the sharing of infrastructure costs (for instance, retrofitting IoT sensors rather than complete replacements). From an environmental perspective, IoT-enabled energy monitoring and practices that are aligned with the circular economy promote sustainability, while green certifications connect ecological objectives with economic advantages. Achieving organizational success necessitates leadership development, process alignment, and upskilling initiatives to effectively incorporate Industry 4.0 tools. On a social level, human-centered automation strikes a balance between productivity and job preservation, alongside initiatives for digital inclusion and ethical AI guidelines to build trust. From a technological standpoint, strong cybersecurity measures, interoperable systems, and investments in cloud-edge computing or 5G technology ensure scalability, with open standards preventing dependency on specific vendors. By harmonizing financial practicality, environmental responsibility, organizational flexibility, social fairness, and technological robustness, both businesses and governments can leverage the potential of Industry 4.0 while addressing risks across sectors.

4.3. Proposed Maturity Model

The maturity model outlines progression across five key dimensions. Each dimension progresses through five maturity levels, Developing, Defined, Managed, Optimized, and Transformative, reflecting a journey from foundational efforts to advanced leadership [24].

In the technological dimension, overcoming barriers like technological advantage (BDtech) and IT infrastructure gaps (IoTtech) involves implementing cybersecurity, IoT retrofitting, interoperability standards, and scalable platforms. Organizations advance from ad hoc technology use (Developing) through piloting (Defined), measurable management (Managed), and optimization (Optimized) to a Transformative state, where technology drives innovation and new markets. The organizational dimension addresses organizational readiness (BDorg) and digital readiness issues (ROBorg) via leadership support, process optimization, and workforce training. Maturity evolves from resistance and low digital literacy (Developing) through strategic awareness (Defined) and active management (Managed) to optimized agility and, ultimately, effortless predictive adaptation (Transformative). The social dimension tackles cultural fragmentation (SIsoc) and lack of knowledge systems (AMsoc) through collaboration, digital inclusion, and ethical data guidelines. It progresses from silos and tribal knowledge (Developing) to defined and managed initiatives (Defined/Managed), seamless collaboration (Optimized), and finally, AI-driven ethical leadership (Transformative). The economic dimension addresses implementation costs (SIeco) and cyber threat costs (CSeco) using financial incentives, ROI pilots, and partnerships. It moves from prohibitive costs and reactive security (Developing) through business case development (Defined) and proactive cost management (Managed) to streamlined investment (Optimized) and innovation-driven, security-by-design growth (Transformative).

The environmental dimension focuses on low customization demand (AMenv) and the lack of data sharing standards (ROBenv) via sustainable manufacturing, circular economy practices, and green certifications. Organizations progress from standardized, non-sustainable offerings (Developing) through piloting (Defined) and formal management (Managed) to market differentiation (Optimized) and, ultimately, driving demand with innovative products and industry leadership in standards (Transformative).

5. Conclusions

This study demonstrates that Industry 4.0 adoption in developing economies is hindered by interconnected barriers across technological, organizational, social, economic, and environmental dimensions. Empirical analysis using PLS-SEM identified technological readiness—specifically IT infrastructure gaps, cybersecurity vulnerabilities, and an inability to leverage technological advantages—as the most significant hurdle (β = 0.218). This is compounded by organizational inertia, including digital readiness issues, and social resistance, such as cultural fragmentation.

A key finding is the “readiness paradox,” where the industries that are most willing to adopt I4.0 technologies are also the most aware of these barriers, highlighting a gap between intent and actionable capability. To address this, the study proposes a sustainability-aligned maturity model that enables organizations to self-assess their status and implement targeted interventions. For example, high-intent sectors like textiles and automotive should prioritize foundational technological integration, while nascent technologies like augmented reality require demystification strategies.

Theoretically, this work enriches the TOE framework by integrating sustainability as a diagnostic lens. Practically, it offers policymakers a replicable methodology for fostering equitable and sustainable digital transformation through targeted capacity building in finance, leadership, ethics, and infrastructure.