Strengthening SMEs competitiveness and performance via industrial internet: Technological, organizational, and environmental pathways

The paper addresses how SMEs in manufacturing adopt and leverage the industrial internet to enhance performance and competitive advantage. Despite rapid digitalization, SMEs face barriers such as immature technologies, resource constraints, and managerial challenges across transformation stages. The study extends the Technology-Organization-Environment (TOE) framework beyond adoption to cover application, driven, and performance stages, and applies fsQCA to capture configurational pathways. Research questions: (RQ1) whether technological, organizational, and environmental factors affect industrial internet adoption among manufacturing SMEs; (RQ2) how industrial internet adoption enhances SMEs’ performance and competitive advantage. Objectives: to develop a conceptual digital transformation paths chain; identify key factors prompting SMEs to adopt the industrial internet; and reveal pathways from industrial internet to performance and competitiveness.

The study integrates the TOE framework and Innovation Diffusion Theory (IDT). TOE posits that technology (IT capabilities, data handling, infrastructure), organization (top management support, culture, readiness), and environment (government support, standards, partner and competitive pressures) shape innovation adoption. Prior studies confirm TOE’s relevance across cloud, AI, blockchain, and other digital technologies. IDT describes innovation diffusion stages—knowledge, persuasion, decision, implementation, confirmation—and has been combined with TOE to explain organizational adoption. Building on these, the authors propose the Digital Transformation Paths Chain comprising four stages: Stage 1 Adoption (driven by TOE), Stage 2 Application (integration of digital tech into operations), Stage 3 Driven (data-driven operations and decisions), Stage 4 Performance (realizing competitive advantage and performance benefits). Ten hypotheses are proposed: H1–H3 (IIT, IIO, IIE → IIA), H4 (IIA → III), H5 (IIA → IIAP), H6 (III → IID), H7 (IIAP → IID), H8 (IID → EP), H9 (IID → ECD), H10 (EP → ECD). The research model links TOE antecedents to adoption, then to integration/application, to driven, and ultimately to enterprise performance and competitive advantage.

Design: Mixed-method analytical approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to capture linear and configurational effects. Measures: Questionnaire with 9 latent variables and 39 items measured on 5-point Likert scales, plus a marker variable (perceived habit) for CMV testing. Constructs adapted from prior literature: Industrial Internet Technology (IIT), Organization (IIO), Environment (IIE), Adoption (IIA), Integration (III), Application (IIAP), Driven (IID), Enterprise Performance (EP), Enterprise Competitive Advantage (ECD), and marker variable. Pretesting: Expert panel (5 scholars/entrepreneurs) reviewed and refined items (definitions, wording, ordering). A pilot with 30 managers confirmed clarity and reliability (Cronbach’s alpha > 0.7). Sampling and data collection: Purposeful sampling of Chinese manufacturing SMEs. Online survey via Questionnaire Star; 401 distributed, 314 valid responses (response rate 78.3%). Demographics: enterprise age (≤3 years: 17.8%; 4–8: 52.9%; 9–15: 21.0%; ≥16: 8.3%) and size (≤20 employees: 38.5%; 21–299: 47.8%; 300–999: 11.5%; ≥1000: 2.2%). CMV control: Ex-ante design steps, confidentiality assurance, and post hoc tests (single-factor test, marker variable technique) showed negligible CMV; marker variable unrelated to model constructs. PLS-SEM procedures: Assessed reliability (Cronbach’s alpha, CR > 0.8), convergent validity (AVE ≥ 0.594), discriminant validity (Fornell-Larcker, cross-loadings, HTMT < 0.663). Model fit SRMR = 0.046 (< 0.08). R²: IID 42.5%, EP 37.1%, ECD 46.4%. Predictive relevance Q² for ECD = 0.293 (> 0.15). Bootstrapping supported H1–H10. Cross-stage tests showed no significant direct effects from IIA to IID (p > 0.5), IIA to ECD (p > 0.5), and IIAP to ECD (p > 0.5), supporting the staged process logic. fsQCA procedures: Calibration to fuzzy sets (5%, 50%, 95% anchors reported). Necessity analysis found no single necessary condition for high ECD. Sufficiency analysis used raw consistency ≥ 0.80, PRI ≥ 0.50, case frequency ≥ 2; yielded 12 high-ECD configurations with solution consistency 0.877 and coverage 0.519. Robustness checks with stricter thresholds (PRI 0.60, case frequency 3) produced similar configurations.

- TOE antecedents significantly predict industrial internet adoption among SMEs: H1 (IIT → IIA), H2 (IIO → IIA), H3 (IIE → IIA) supported. - Adoption enables subsequent stages: H4 (IIA → III) and H5 (IIA → IIAP) supported. - Integration and application drive digital operations: H6 (III → IID) and H7 (IIAP → IID) supported. - Driven stage improves outcomes: H8 (IID → EP) and H9 (IID → ECD) supported; performance further enhances competitive advantage: H10 (EP → ECD) supported. - Cross-stage analysis indicates no significant direct paths from adoption to driven (IIA → IID, p > 0.5) or competitive advantage (IIA → ECD, p > 0.5), nor from application to competitive advantage (IIAP → ECD, p > 0.5), confirming the multi-stage chain. - Model quality: R² = 42.5% (IID), 37.1% (EP), 46.4% (ECD); SRMR = 0.046; Q² (ECD) = 0.293. - Measurement quality: Cronbach’s alpha and CR > 0.8 for all constructs; AVE ≥ 0.594; HTMT < 0.663; strong discriminant validity. - fsQCA: No single necessary condition for high ECD; 12 sufficient configurational pathways with solution consistency 0.877 and coverage 0.519. Many configurations include elements from TOE and cover all four transformation stages, showing multiple routes to competitive advantage. - Control variables (enterprise age, size, development stage) were not significant predictors of competitive advantage. - CMV diagnostics indicated minimal bias.

Findings address RQ1 by confirming that technological, organizational, and environmental factors (TOE) significantly drive SMEs’ adoption of the industrial internet. Addressing RQ2, the study demonstrates a staged mechanism whereby adoption contributes to integration and application, which in turn drive data-informed operations (driven stage), improving enterprise performance and ultimately competitive advantage. The staged, non-direct effects from adoption to later outcomes validate the proposed digital transformation paths chain. The results extend TOE’s applicability beyond adoption into later transformation stages and show, via fsQCA, that multiple combinational pathways can yield high competitive advantage—underscoring equifinality and the importance of contextual configurations. This enriches literature on industrial internet and SME digital transformation by clarifying how and through which stages benefits materialize, offering evidence from Chinese manufacturing SMEs.

The study proposes and empirically validates a Digital Transformation Paths Chain—adoption, application, driven, performance—to explain how SMEs leverage the industrial internet to enhance performance and competitive advantage. TOE factors are critical antecedents of adoption, and adoption’s benefits unfold through integration and application to a driven stage that elevates performance and competitiveness. Using PLS-SEM and fsQCA, the research provides robust linear and configurational evidence, offering a practical roadmap for SMEs and insights for policymakers on enabling conditions and staged strategies for digital transformation.

- Cross-sectional design limits causal inference and pre/post transformation comparisons; future longitudinal or multi-group tracking is recommended. - Sample is limited to Chinese manufacturing SMEs; generalizability to other countries, sectors, and institutional contexts should be tested via cross-cultural and cross-industry studies. - Potential heterogeneity across enterprise development stages and industries was not deeply explored; future work could examine moderating effects and stage-specific contingencies.