Adoption of fourth industrial revolution 4.0 among Malaysian small and medium enterprises (SMEs)

The study addresses how Malaysian SMEs are adopting Industry 4.0 (IR4.0) and what factors drive or hinder adoption within a developing economy context where manufacturing is central to GDP growth. Despite national initiatives (Industry4WRD) and the sector’s importance, adoption remains low, with many SMEs only at Industry 3.0 levels and facing capability gaps. The research is grounded in Diffusion of Innovation (DOI) theory and the Technology-Organization-Environment (TOE) framework to examine technological (relative advantage, compatibility), organizational (organizational readiness, top management support), and environmental (competitive pressure, governmental regulation) determinants of IR4.0 adoption. Research questions: (a) How can IR4.0 adoption among Malaysian SMEs be defined and measured, including technological factors such as relative advantage and compatibility? (b) What organizational aspects (organizational readiness and top management support) influence IR4.0 adoption? (c) What environmental factors (competitive pressure and governmental regulation) influence IR4.0 adoption? The study develops and empirically validates a conceptual model using survey data from over 120 Malaysian SMEs.

IR4.0 entails the advanced digitalization of manufacturing with core technologies including big data analytics, Internet of Things (IoT/IIoT), cloud computing, autonomous robots, additive manufacturing, simulation, augmented reality, horizontal/vertical system integration, and cybersecurity. These enable capabilities such as real-time data analysis, traceability, customization, agility, and preventive maintenance, promising productivity and efficiency gains, especially for SMEs. In Malaysia, adoption lags: only ~30% of manufacturing SMEs are at Industry 3.0, and around 10% adopt ICT elements compared to ~50% in developed Asian peers (Singapore, Japan, South Korea). Awareness and access to government programs (e.g., Industry4WRD Readiness Assessment) are limited. The conceptual model draws from DOI (Rogers, 2003) and the TOE framework (Tornatzky et al., 1990), which categorize influences into technological, organizational, and environmental contexts. Prior studies applying TOE across technologies (EDI, e-business, RFID, cloud) show the salience of factors like relative advantage, compatibility, top management support, competitive pressure, and regulatory support. Hypotheses are proposed linking six predictors—relative advantage, compatibility, organizational readiness, top management support, competitive pressure, and governmental regulation—to IR4.0 adoption.

Design: A structured questionnaire was developed by adapting validated scales to the IR4.0 context. Dependent variable: IR4.0 adoption measured with 14 items (e.g., responsiveness to change, competitive advantage, customer relations, cost reductions, employee productivity, data access/accuracy) from Ghobakhloo et al. (2011) and Raguseo and Vitari (2018). Independent variables: relative advantage (6 items), compatibility (3 items), top management support (4 items), organizational readiness (4 items), competitive pressure (3 items), governmental regulation (3 items) adapted from prior work (Chen et al., 2015; Ghobakhloo et al., 2011; Lai et al., 2018; Agrawal, 2015). Sampling and data collection: From a population of 1000 SMEs registered with MIDF Berhad, 300 SMEs across manufacturing, non-manufacturing, and other sectors were sampled nationwide. Data were collected via Google Forms. Returned questionnaires: 123 (response rate 41%), during the COVID-19 period. Respondent and firm profile: <2/3 top management, <1/3 middle management. ~80% manufacturing; remainder in other sectors. Geographic distribution led by Selangor (>1/3), Kuala Lumpur (>20%), Penang (>12%). Firm age: ~80% operating 11–30+ years. Firm size: >40% with 5–29 employees; >30% with 30–74; ~15% >200; ~10% 75–200; <3% <5. Annual sales: nearly half RM3–<15m; >18% RM15–50m; >17% RM0.3–<3m; ~10% >RM50m; ~5% <RM0.3m. IR4.0 knowledge: >60% medium, <1/3 high, ~10% low, <2% none. Analysis: Data analyzed using SPSS 25 and SmartPLS 3.0. A two-step PLS-SEM procedure assessed the reflective measurement model (reliability, convergent and discriminant validity) and the structural model. Reliability and validity: All constructs showed composite reliability above thresholds (CR > 0.60), AVE > 0.50 (IR4.0 = 0.902; relative advantage = 0.890; compatibility = 0.935; organizational readiness = 0.896; top management support = 0.879; competitive pressure = 0.898; government regulation = 0.785). Discriminant validity via Fornell-Larcker criterion was satisfied (square roots of AVE exceeded inter-construct correlations). Structural paths were evaluated with bootstrapped t-values and p-values.

Measurement model: All constructs demonstrated strong reliability and convergent validity (composite reliability generally ≥0.915; AVE ≥0.785). Discriminant validity was established (e.g., square root of AVE: IR4.0 0.950; relative advantage 0.944; compatibility 0.967; organizational readiness 0.946; top management support 0.938; competitive pressure 0.948; government regulation 0.886). Structural model: R² for IR4.0 adoption = 0.775, indicating substantial explanatory power. Path coefficients (Table 5):

• H1 Relative advantage → IR4.0: β = 0.739, t = 9.734, p < 0.001 (supported). Strong positive effect.
• H2 Compatibility → IR4.0: β = -0.181, t = 2.624, p = 0.004 (reported as supported). Coefficient negative though interpreted as positive in text.
• H4 Top management support → IR4.0: β = 0.269, t = 3.247, p = 0.001 (supported). Positive effect.
• H5 Competitive pressure → IR4.0: β = 0.167, t = 1.870, p = 0.031 (supported). Positive effect.
• H3 Organizational readiness → IR4.0: β = -0.020, t = 0.366, p = 0.357 (not supported). No significant effect.
• H6 Government regulation → IR4.0: β = 0.001, t = 0.016, p = 0.494 (not supported). No significant effect. Overall, relative advantage, compatibility (as reported), top management support, and competitive pressure are significant predictors, while organizational readiness and government regulation are not significant in this sample.

The results address the research questions by confirming the primacy of technological factors in IR4.0 adoption among Malaysian SMEs: perceived relative advantage and compatibility are central determinants, consistent with DOI and TOE perspectives. Organizationally, top management support significantly facilitates adoption, underscoring the importance of leadership commitment, resource allocation, and strategic prioritization of IR4.0 initiatives. Environmental competitive pressure also spurs adoption, indicating SMEs respond to market dynamics and peers’ actions to maintain competitiveness. In contrast, organizational readiness and government regulation did not significantly predict adoption in the model, suggesting that, despite capability gaps (capital, IT infrastructure, analytics, skills) and available incentives, these factors may not directly translate into adoption decisions or may operate indirectly. The findings imply that enhancing perceived benefits, ensuring fit with existing processes and culture, securing executive sponsorship, and acknowledging competitive dynamics are pivotal for successful IR4.0 uptake. For policymakers, the limited influence of regulation indicates a need to refine policy instruments, improve awareness, streamline support channels, and strengthen regulatory frameworks around security and privacy to make them more impactful.

Using the TOE framework (with DOI underpinnings), the study shows that technological factors (relative advantage and compatibility), organizational leadership (top management support), and environmental dynamics (competitive pressure) significantly shape IR4.0 adoption among Malaysian SMEs, explaining 77.5% of variance. Practical implications include prioritizing leadership buy-in, aligning IR4.0 initiatives with existing practices and culture, and leveraging demonstrated benefits to drive adoption. Policymakers should strengthen and better communicate support mechanisms (technical assistance, training, funding) and enhance regulatory clarity on security and privacy to encourage uptake. The study contributes empirical evidence to the Malaysian IR4.0 context, addressing a noted gap, and extends TOE-based understanding of SME technology adoption. Future research should focus on larger and more representative samples across all Malaysian states, emphasize manufacturing and related services per Industry4WRD, and consider firms with comparable IR4.0 exposure for precise comparisons.

• Sampling limited to SMEs in Peninsular Malaysia from a frame of 1000, excluding Sabah and Sarawak; may limit generalizability.
• Cross-sector sample rather than a focus on manufacturing, where IR4.0 is most applicable.
• Lower exposure to new technology among some respondents may affect measurement.
• Data collection during COVID-19 (MCO) constrained outreach and likely reduced response rate due to survival priorities, introducing potential nonresponse bias.
• Overall response rate of 41% (123/300) and pandemic-era negative business sentiment could influence results and limit broader applicability.