Digital Green Orchestration Capability and Supply Chain Flexibility: Integrating Dynamic Capabilities and Institutional Perspectives

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This paper develops and validates the concept of digital green orchestration capability a meta capability integrating digital, environmental, and governance resources to enhance flexibility and performance. Drawing on the dynamic capabilities view and institutional theory, survey data were collected from 394 manufacturing SMEs across two contrasting institutional environments: Iran (developing, turbulent) and Canada (advanced, stable). Using Partial Least Squares Structural Equation Modelling (PLS-SEM), results reveal that digital green orchestration capability enhances sustainable performance primarily through innovation driven resilience (β = 0.40) and, secondarily, through collaboration (β = 0.25). The institutional pressure moderation was not significant, reflecting a governance saturation effect, while Relational trust significantly strengthened the collaboration resilience relationship (β = 0.18). Findings confirm that digital green orchestration capability manifests as institutionalized flexibility in advanced systems and necessity driven flexibility in developing ones. The paper extends dynamic capabilities view to network level adaptation and offers actionable guidance for policymakers seeking to embed digital green orchestration into supply chain governance. Digital green orchestration supply chain flexibility resilience dynamic capabilities institutional theory green innovation relational trust Figures Figure 1 1. Introduction Global supply chains operate amid escalating turbulence, sustainability mandates, and technological disruption (Kareem et al., 2025 ). Traditional efficiency oriented models are inadequate under these conditions, as firms must simultaneously adapt digitally and act sustainably (Attaran & Attaran, 2007 ). The intersection of digital transformation and Environmental, Social, And Governance (ESG) imperatives thus becomes a decisive source of flexibility and resilience (Li et al., 2024 ). However, the ability to orchestrate these dual agendas differs dramatically between advanced and developing economies. In advanced economies such as Canada, mature digital infrastructures, stable governance, and codified ESG frameworks enable structured, institutionalized adaptation (Evans, 2021 ). In contrast, developing economies such as Iran face volatile regulatory systems, infrastructural constraints, and reliance on informal coordination (Danaeefard, 2025 ). These disparities underscore a theoretical and practical puzzle: How do firms orchestrate digital and green initiatives to remain flexible under radically different institutional realities? Supply chain flexibility the ability to adapt processes rapidly and cost effectively forms the foundation of resilience, which denotes the capacity to recover from disruptions while maintaining performance (Hosseini Shekarabi et al., 2025 ; Shojaei et al., 2025 ). Both constructs derive from the Dynamic Capabilities View (DCV), which posits that firms sustain advantage by sensing change, seizing opportunities, and reconfiguring resources (Farrukh & Sajjad, 2025 ; Teece, 2007 ). Yet, most prior research has examined dynamic capabilities at the firm level and within homogeneous, stable contexts. Extending DCV to inter organizational networks and institutional asymmetries remains a crucial theoretical gap. To address this gap, this study introduces Digital Green Orchestration Capability (DGOC) a meta capability that aligns and reconfigures digital and sustainability resources across supply chain tiers to generate adaptive flexibility. DGOC bridges the efficiency logic of digitalization with the legitimacy logic of ESG compliance. The framework is tested across two contrasting institutional contexts Iran and Canada to capture how governance quality alters capability outcomes. Three research questions guide the investigation: How does environmental turbulence stimulate DGOC formation? Through which mechanisms green innovation and/or network collaboration does DGOC enhance resilience and sustainable performance? How do formal (institutional pressure) and informal (relational trust) governance mechanisms moderate these effects? By addressing these questions, this paper extends DCV into a network level, context sensitive theory of capability orchestration and advances institutional theory by explaining how governance maturity shapes flexibility. The rest of the paper is structured as follows. Section 2 reviews the literature and develops hypotheses; Section 3 presents methodology; Section 4 reports results; Section 5 discusses theoretical and managerial implications; Section 6 concludes. Insert Fig. 1. Conceptual framework 2. Literature Review and Hypotheses Development 2.1 Flexibility, Resilience, and the Dynamic Capabilities View Modern supply chains confront frequent disruption, resource scarcity, and ESG pressures that challenge efficiency-based paradigms (Yadav et al., 2025 ). Supply chain flexibility the ability to reconfigure processes, structures, and relationships quickly and economically has become a prerequisite for resilience, defined as the capacity to absorb shocks and recover performance during crises (Hosseini Shekarabi et al., 2025 ; Shojaei et al., 2025 ). Both stem from the Dynamic Capabilities View, which explains sustainable advantage through sensing, seizing, and reconfiguring routines (Farrukh & Sajjad, 2025 ; Teece, 2007 ). Yet the majority of DCV research is firm centric and assumes relatively stable institutions. Adaptation occurs within multi-tier networks where diverse actors operate under unequal governance quality. Flexibility, therefore, is not merely a firm capability but a network level orchestration processes the alignment of partners, data, and technologies that allows collective reconfiguration (Awwad et al., 2022 ). This study builds on that logic, extending DCV beyond single organizations toward inter organizational dynamic capabilities responsive to institutional asymmetry. 2.2 Digital Green Orchestration Capability The accelerating convergence of digital transformation and sustainability has created a pressing managerial dilemma: how to align efficiency driven technologies with legitimacy driven environmental responsibilities (Calantone et al., 2003 ; Yadav et al., 2025 ). To address this paradox, the present study introduces Digital Green Orchestration Capability a meta capability that integrates digital infrastructures and ESG routines into a unified mechanism of adaptive coordination. DGOC is distinct from existing constructs such as integration capability , coordination capability , or sustainability orientation . Integration capability typically optimizes internal efficiency, and coordination capability stabilizes dyadic transactions; DGOC transcends these boundaries by orchestrating a multi-tier ecosystem of digital platforms, sustainability standards, and governance relationships. It transforms fragmented digital and environmental initiatives into a coherent system of interdependent routines that promote cross-tier adaptability. From a Dynamic Capabilities View, DGOC embodies higher order routines that reconfigure first order processes (sensing, seizing, reconfiguring). From an Institutional Theory perspective, it serves as an institutional bridge, translating external ESG expectations into digital operating procedures. The capability manifests through three reinforcing sub-dimensions Integration Alignment, Cross-Tier Synchronization, and Reconfiguration Agility later empirically validated (see Table 4 in Section 3.6.1 ). In advanced economies like Canada, DGOC operates within codified ESG systems, benefiting from standardized reporting, digital traceability, and formal audits. In developing economies such as Iran, DGOC arises more improvisationally: managers compensate for regulatory voids by relying on trust, informal agreements, and small-scale technological improvisation. Hence, DGOC is both an institutionalized capability and an adaptive improvisation depending on context. H1. Environmental turbulence positively influences Digital Green Orchestration Capability. 2.3 DGOC and Green Innovation Green Innovation (GI) refers to novel products, processes, or management practices that reduce environmental impact while sustaining competitiveness (Xin et al., 2023 ; Zhou et al., 2024 ). Within the DCV, GI functions as a seizing capability: it converts the opportunities sensed and structured by digital green orchestration capability into actionable eco-technological outcomes (Xue et al., 2025 ). digital green orchestration capability stimulates green innovation through three intertwined mechanisms. First, digital visibility: data integration exposes inefficiencies and carbon hotspots, guiding targeted eco-innovation. Second, knowledge recombination: orchestration connects partners across tiers, enabling cross fertilization of sustainability ideas and resource pooling. Third, institutional legitimation: digital green orchestration capability embeds environmental metrics into decision routines, making green innovation both operationally feasible and socially acceptable (Wamba et al., 2023 ). However, the effectiveness of these mechanisms depends on institutional maturity. In Canada, stable policies and abundant digital infrastructure foster formal R&D programs, standardized life cycle assessments, and systematic green redesigns. In Iran, limited capital and weak enforcement compel firms to innovate incrementally and adaptively for example, modifying machinery to save energy or locally sourcing recyclable materials. Thus, digital green orchestration capability becomes a necessity driven innovation platform in developing economies and a strategic innovation system in advanced ones. H2. Digital Green Orchestration Capability positively influences Green Innovation. 2.4 DGOC and Network Collaboration Whereas GI captures technological transformation, Network Collaboration (NC) embodies relational transformation the joint planning, problem solving, and information exchange that enable collective response to turbulence (Di et al., 2024 ; El Baz & Ruel, 2024 ; Shojaei et al., 2025 ). From an Information Processing Theory (IPT) standpoint (Liang et al., 2020 ), collaboration expands the network’s information processing capacity, crucial under high uncertainty. DGOC reinforces collaboration through three complementary levers. Technological enablement: shared digital platforms synchronize ESG metrics, giving all partners a common data language. Governance alignment: orchestration clarifies accountability, thus lowering transaction and monitoring costs. Relational reinforcement: transparency generated by digital systems builds credibility and reciprocity. However, collaboration can be double edged. When over formalized, it slows decision making and burdens coordination (Gulati & Singh, 1998 ; Zhai et al., 2021 ). Optimal collaboration balances information richness with autonomy “loose tight coupling.” Institutional context shapes where this balance lies. In Canada, NC is platform enabled and contractually managed; in Iran, it is person centered and trust based. Regardless of form, DGOC should improve NC by creating structural and cognitive alignment across tiers. H3. Digital Green Orchestration Capability positively influences Network Collaboration. H4. Network Collaboration positively influences Supply Chain Resilience. 2.5 Green Innovation, Resilience, and Sustainable Performance Resilience (SCR) denotes the ability of a supply chain to absorb shocks and restore operations with minimal performance loss (Ameer et al., 2024 ). It represents the realized outcome of dynamic flexibility (Mehmood et al., 2025 ). Green Innovation enhances resilience by introducing modularity, efficiency, and adaptive routines that allow firms to reconfigure processes swiftly under regulatory, environmental, or market shocks (Hasan Al-Obaidy et al., 2025 ). In weak governance contexts such as Iran, GI acts as a functional substitute for missing institutional support firms innovate frugally to buffer volatility (Afshar Jahanshahi et al., 2020 ; Ebrahimi & Mirbargkar, 2017 ). In advanced economies, GI complements regulatory systems by reinforcing formal sustainability commitments (Chen & Xing, 2025 ). Accordingly, the GI and Resilience link is expected to dominate the NC and Resilience path because technological adaptation delivers direct, hard resource flexibility, while collaboration offers softer, information-based resilience. Resilient networks then translate adaptive capability into Sustainable Performance (SP) the balanced achievement of environmental and financial objectives. Resilience sustains operations during crises, safeguards customer trust, and strengthens corporate legitimacy. Therefore, DGOC is expected to influence SP indirectly via the dual pathways of innovation and collaboration, culminating in the mediation model tested later. H5. Green Innovation positively influences Resilience. H6. Resilience positively influences Sustainable Performance. H7. Green Innovation and Network Collaboration jointly mediate the DGOC Sustainable Performance relationship. 2.6 Institutional Moderators: Formal versus Informal Governance The conversion of DGOC into tangible outcomes is contingent upon institutional mechanisms that regulate or enable inter firm coordination. Two complementary forms dominate: formal governance (Institutional Pressure) and informal governance (Relational Trust). Their relative salience defines whether flexibility becomes institutionalized or personalized . Formal Governance and Institutional Pressure (IP) Institutional pressure arises from coercive and normative forces such as regulations, standards, and stakeholder expectations (Mustikasari & Ciptono, 2025 ; Sharma et al., 2025 ). In advanced contexts, strong ESG requirements amplify the DGOC and GI link because firms with high orchestration can more efficiently comply and innovate under regulatory mandates (Borsatto et al., 2020 ). However, once most organizations meet these standards, marginal returns from additional pressure decline a governance saturation effect. This plateau occurs in mature systems like Canada, where ESG integration is mainstream. In developing contexts like Iran, weak enforcement and policy inconsistency blunt formal influence altogether. Hence, IP’s moderating power is asymmetric across contexts. Informal Governance and Relational Trust (RT) Relational trust reflects confidence in partners’ reliability, fairness, and benevolence (Frederiksen, 2014 ; Yang & Lim, 2009 ). In fragile institutions, trust substitutes for regulation by ensuring collaboration even when contracts are weak. It lowers transaction uncertainty, fosters candid communication, and promotes collective problem solving all vital to resilience. In strong institutions, trust complements contracts, enhancing efficiency through goodwill and social capital. Thus, RT is predicted to reinforce the NC and SCR link more powerfully in Iran than in Canada, where formal mechanisms dominate. Taken together, formal pressure and relational trust represent competing yet complementary governance logics: the former institutionalizes flexibility through compliance, the latter personalizes flexibility through commitment. Exploring both within a single model illuminates how governance maturity shapes orchestration outcomes across contexts. H8. Institutional Pressure positively moderates the DGOC Green Innovation relationship, strengthening it under higher formal governance. H9. Relational Trust positively moderates the Network Collaboration Resilience relationship, strengthening it under higher informal governance. 2.7 Conceptual Framework The full conceptual model (see Fig. 1 ) integrates the above hypotheses: Environmental Turbulence, DGOC, Green Innovation, Network Collaboration, Resilience, and Sustainable Performance, moderated by Institutional Pressure and Relational Trust. This framework captures the theoretical tension between institutionalized orchestration and improvised orchestration , addressing how flexibility is achieved under both governance abundance and scarcity. 3 Research Methodology 3.1 Research Design To empirically test the conceptual framework shown in Fig. 1 , this study adopted a two wave, multi-tier survey design that introduced temporal separation to mitigate bias and capture causal ordering. Wave 1 measured the antecedent constructs environmental turbulence, digital green orchestration capability, institutional pressure, and relational trust while wave 2 (six weeks later) measured the outcome constructs green innovation, network collaboration, supply chain resilience, and sustainable performance. This temporal separation reduced common method bias and allowed partial causal inference. The data were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4 . PLS-SEM was appropriate because DGOC is a hierarchical reflective–reflective construct with multiple latent dimensions and because the model includes both mediation and moderation paths (Hair & Alamer, 2022 ). The approach also accommodates moderate sample sizes and non-normal data distributions typical of international SME surveys. 3.2 Sampling and Data Collection 3.2.1 Sampling Frame and Context The population comprised manufacturing small and medium sized enterprises (SMEs) in Iran and Canada, representing two contrasting institutional contexts: weak versus strong governance maturity. Eligible firms maintained at least one upstream supplier and one downstream distributor to ensure multi-tier network relevance. Sampling lists were drawn from national industry directories and chambers of commerce. In total, 1 100 firms (550 per country) were invited; 394 valid responses were obtained 197 from each country yielding a 37% response rate. Respondents were senior managers responsible for operations, sustainability, or digital transformation. Table 1 Sample Comparability Across Countries Characteristic Iran (%) Canada (%) χ²/t (p) Interpretation Electronics / Machinery 24 22 χ² = 0.80 (p > 0.10) Similar sector mix Automotive / Transport 18 21 χ² = 0.33 (p > 0.10) Comparable structure Food / Chemicals 34 32 χ² = 0.20 (p > 0.10) Balanced sample Mean firm size (employees) 186 192 t = 0.47 (p > 0.10) Comparable scale Mean firm age (years) 14.6 13.9 t = 0.62 (p > 0.10) No significant difference Source: Authors’ survey data. As shown in Table 1 , the two subsamples are statistically comparable in sectoral and structural terms, enabling meaningful cross-country analysis. 3.2.2 Power and Sample Adequacy A priori power analysis ( G Power 3.1; α = 0.05, f² = 0.15, power = 0.95) recommended at least 146 cases per group. The achieved 197 per country provided power = 0.96, exceeding this requirement and ensuring sufficient sensitivity for multi group comparisons. 3.2.3 Contextual Contrast Descriptive contrasts revealed strong institutional asymmetry. Iranian firms reported higher environmental turbulence (M = 4.7 ± 1.2) and lower DGOC maturity (M = 4.5 ± 1.1) than Canadian firms (ET M = 3.6 ± 0.9; DGOC M = 5.1 ± 0.9). Institutional Pressure was lower (M = 3.8 ± 1.0) in Iran but Relational Trust was higher (M = 5.0 ± 1.2). These differences validated the theoretical logic of testing DGOC under contrasting governance environments. 3.3 Measurement Instrument All variables were measured on seven-point Likert scales (1 = strongly disagree to 7 = strongly agree). Scale sources were: DGOC and ET (Farrukh & Sajjad, 2025 ; Teece, 2007 ); GI (Zhou et al., 2024 ); NC (Di et al., 2024 ); SCR (Ameer et al., 2024 ); SP (Hasan Al-Obaidy et al., 2025 ); IP (Sharma et al., 2025 ); (Mustikasari & Ciptono, 2025 ); RT (Frederiksen, 2014 ; Yang & Lim, 2009 ). Translation followed a back translation protocol (Persian ↔ English ↔ French). A pilot test of 60 managers yielded α > 0.70 across constructs. DGOC was modeled as a second order reflective–reflective construct comprising Integration Alignment, Cross-Tier Synchronization, and Reconfiguration Agility, consistent with (Jarvis et al., 2003 ). All measurement items are listed in Appendix A, and summary reliability and validity statistics appear in Appendix B. 3.4 Control Variables Four firm level controls firm size (log employees), firm age, industry type (dummy coded), and ownership (0 = domestic, 1 = foreign) were linked to Sustainable Performance (SP) to adjust for possible structural bias. 3.5 Common Method and Multicollinearity Diagnostics Multiple procedural and statistical checks were employed. Table 2 summarizes the results. Table 2 Bias and Multicollinearity Diagnostics Test Criterion Result Interpretation Harman’s one factor < 50% variance 27% No common method bias Latent common factor VIF < 3.3 1.21 Negligible bias Full collinearity VIF < 3.3 1.10–2.60 No multicollinearity Source: Authors’ SmartPLS analysis. These metrics confirm that neither common method nor multicollinearity biases materially affected the dataset. Detailed diagnostics are presented in Appendix C. 3.6 Measurement Model Evaluation Reliability and validity were tested via Confirmatory Composite Analysis (CCA). All loadings exceeded 0.70 (p < 0.001); Cronbach’s α = 0.82–0.94; Composite Reliability = 0.86–0.95; AVE = 0.55–0.79; and HTMT ratios < 0.85, demonstrating discriminant validity. Global fit indices (SRMR = 0.052; NFI = 0.92) met recommended thresholds (Henseler et al., 2016 ; Jarvis et al., 2003 ). Table 3 Measurement Model Reliability and Validity Construct α CR AVE HTMT (max) Status ET 0.84 0.88 0.63 0.71 Valid DGOC 0.93 0.95 0.77 0.80 Valid GI 0.89 0.91 0.66 0.74 Valid NC 0.87 0.90 0.61 0.70 Valid SCR 0.88 0.91 0.67 0.78 Valid SP 0.90 0.92 0.70 0.73 Valid IP 0.86 0.89 0.62 0.68 Valid RT 0.85 0.88 0.60 0.69 Valid Source: Authors’ PLS-SEM analysis. All constructs satisfied the minimum reliability (α ≥ 0.80) and convergent validity criteria (AVE > 0.50). 3.6.1 Second Order DGOC Validation The hierarchical model verified DGOC’s three-dimensional structure. All outer weights were significant (p < 0.001), confirming a unified meta capability. Details appear in Table 4 . Table 4 Second Order DGOC Validation Dimension Outer Weight (β) t p VIF R² Interpretation Integration Alignment 0.74 11.40 < 0.001 2.1 0.68 Significant Cross-Tier Synchronization 0.83 13.80 < 0.001 1.9 0.72 Significant Reconfiguration Agility 0.86 15.20 < 0.001 2.3 0.74 Significant Source: Authors’ SmartPLS analysis. These findings confirm DGOC as a robust, higher order orchestration capability that integrates digital and sustainability routines into a single adaptive system. 3.7 Measurement Invariance and Model Specification Cross national equivalence was verified through the MICOM procedure (Measurement Invariance of Composites) (Henseler et al., 2016 ). Configural and compositional invariance were achieved, and partial scalar invariance (p > 0.05) indicated that observed path differences reflect genuine contextual variation rather than measurement artifacts. The full structural model was then estimated using 5 000 bootstrap resamples (two tailed α = 0.05) under consistent-PLS settings. 3.8 Ethical Considerations Both surveys complied with institutional research ethics protocols in Iran and Canada. Participation was voluntary, anonymous, and uncompensated; informed consent was obtained from all respondents. No identifying data were collected, ensuring adherence to the Tri-Council Policy Statement (Canada) and equivalent Iranian standards. Survey administration details (two wave procedure, languages, consent) are summarized in Appendix E. 4 Results and Analysis 4.1 Descriptive Statistics and Contextual Differences Descriptive results establish the contextual asymmetry between the two national samples. As shown in Table 5 , Iranian firms reported higher environmental turbulence (M = 4.7, SD = 1.20) and lower DGOC maturity (M = 4.5) compared with Canadian firms (M = 3.6 and 5.1 respectively). Likewise, green innovation and sustainable performance were stronger in Canada (M = 4.9 and 5.2) than in Iran (M = 4.2 and 4.6). Conversely, institutional pressure was weaker in Iran (M = 3.8) while relational trust was higher (M = 5.0 vs. 4.8). These variations confirm the theorized institutional contrast formal regulation dominance in Canada versus informal trust reliance in Iran. Table 5 Descriptive Statistics and Correlations Variable Mean (IR) SD (IR) Mean (CA) SD (CA) 1 2 3 4 5 6 7 1 ET 4.7 1.20 3.6 0.90 – 2 DGOC 4.5 1.10 5.1 0.90 0.44** – 3 GI 4.2 1.20 4.9 0.90 0.36** 0.59** – 4 NC 4.6 1.05 5.0 0.85 0.31** 0.52** 0.47** – 5 SCR 4.5 1.15 5.1 0.90 0.29** 0.48** 0.46** 0.30** – 6 SP 4.6 1.30 5.2 0.90 0.27* 0.42** 0.49** 0.38** 0.49** – 7 IP 3.8 1.00 4.9 0.90 0.28** 0.39** 0.43** 0.33** 0.24* 0.23* – 8 RT 5.0 1.20 4.8 0.85 0.20* 0.34** 0.35** 0.30** 0.45** 0.44** 0.22* *p < 0.05; * p < 0.01. IR = Iran; CA = Canada. Source: Authors’ survey data. The moderate intercorrelations among variables (r < 0.60) indicate distinct yet related constructs, justifying subsequent SEM analysis. 4.2 Measurement Model Confirmation Confirmatory Composite Analysis reaffirmed construct reliability (Cronbach’s α = 0.82–0.94; CR = 0.86–0.95; AVE = 0.55–0.79). Model fit remained acceptable (SRMR = 0.052; NFI = 0.92), aligning with (Henseler et al., 2016 ). These results validate the measurement model and confirm DGOC’s second order structure as previously established in Table 4 . 4.3 Structural Model Results (Pooled Sample) Hypotheses were tested via bootstrapping with 5 000 resamples (two tailed α = 0.05). Table 6 summarizes the standardized path coefficients, t-values, and effect sizes. All relationships were significant except the moderation by Institutional Pressure (H8). Notably, Green Innovation (β = 0.40) exerted a stronger effect on resilience than Network Collaboration (β = 0.25). Table 6 Structural Model Results (Pooled Sample) Path β t p f² Result H1 ET → DGOC 0.43 8.80 < 0.001 0.15 Supported H2 DGOC → GI 0.55 12.10 < 0.001 0.22 Supported H3 DGOC → NC 0.47 9.90 < 0.001 0.18 Supported H4 NC → SCR 0.25 3.28 0.001 0.09 Supported H5 GI → SCR 0.40 7.12 < 0.001 0.14 Supported H6 SCR → SP 0.44 9.00 < 0.001 0.15 Supported H7 DGOC → (GI + NC) → SCR → SP 0.15 3.96 < 0.001 – Supported H8 DGOC × IP → GI 0.10 1.25 0.21 – Not supported H9 NC × RT → SCR 0.18 3.02 0.003 – Supported R² = DGOC 0.33; GI 0.41; NC 0.37; SCR 0.48; SP 0.56. Source: Authors’ SmartPLS analysis. Innovation’s stronger contribution to resilience demonstrates its role as the dominant conduit translating DGOC into adaptive performance. The insignificant H8 corroborates the proposed governance saturation phenomenon. 4.4 Cross Country Multi Group Analysis (MGA) Comparative multi group analysis verified that the underlying mechanisms differ significantly across institutional contexts. Results summarized in Table 7 show that environmental turbulence had a stronger influence on DGOC in Iran (β = 0.47) than in Canada (β = 0.32), indicating that orchestration there arises from necessity. Conversely, the DGOC and GI path was stronger in Canada (β = 0.58 vs. 0.44), confirming institutionalized innovation. The moderation of trust (H9) was markedly stronger in Iran (β = 0.22 vs. 0.09). A comprehensive version of the multi group comparison is provided in Appendix D. Table 7 Multi Group Path Comparison (Iran vs Canada) Path β (IR) β (CA) Δβ p (MGA) Interpretation ET → DGOC 0.47 0.32 0.15 0.04* Turbulence drives orchestration in Iran DGOC → GI 0.44 0.58 –0.14 0.02* Innovation path stronger in Canada GI → SCR 0.39 0.30 0.09 0.05* Innovation compensates weak governance DGOC × IP → GI 0.05 0.10 –0.05 0.34 ns Formal moderation absent NC × RT → SCR 0.22 0.09 0.13 0.02* Trust based collaboration critical in Iran p < 0.05; ns = non-significant. Source: Authors’ MGA analysis. These results empirically substantiate the theoretical tension between formal governance (institutionalized innovation) and informal governance (trust-based resilience). 4.5 Mediation, Moderation, and Predictive Validation To examine indirect and moderating mechanisms simultaneously, a bootstrapped specific indirect effect test was conducted. As displayed in Table 8 , the DGOC, GI, SCR, and SP chain produced the strongest indirect effect (β ≈ 0.16–0.18, p < 0.001), roughly twice the collaboration-based mediation (β ≈ 0.07). The NC × RT moderation was significant (p < 0.01), confirming the resilience enhancing power of relational trust in weak institutional environments. Predictive relevance (Q² ≈ 0.24–0.26) and R² values (0.33–0.56) indicate good explanatory capability without over fit. Table 8 Indirect, Moderating, and Predictive Effects Effect β (IR) β (CA) 95% CI Significance Q² Interpretation DGOC → GI → SCR → SP 0.16 0.18 [0.07, 0.25] *** 0.26 Innovation mediation dominant DGOC → NC → SCR → SP 0.07 0.05 [0.01, 0.10] ** 0.24 Collaboration mediation modest DGOC × IP → GI – 0.10 [–0.04, 0.20] ns – Policy moderation non-significant NC × RT → SCR 0.18 – [0.05, 0.22] ** – Trust moderation significant Model fit – – – – SRMR = 0.055; NFI = 0.90 Adequate fit ***p < 0.001; * p < 0.01. Source: Authors’ SmartPLS analysis. The results collectively demonstrate that innovation driven adaptation is the principal engine of DGOC’s performance impact, while trust based collaboration acts as a critical amplifier in weaker institutional settings. 4.6 Robustness Checks Several robustness tests were executed to ensure stability: Alternative causal order (GI ↔ NC reversal): Model fit deteriorated (SRMR = 0.078; NFI = 0.83), validating the hypothesized direction. Reverse causality (SP and DGOC): β = 0.05, ns, confirming non-recursiveness. Endogeneity (Gaussian-copula test): p > 0.10; no bias detected. Non-response bias (early late comparison): p > 0.10; response timing neutral. These diagnostics confirm internal validity and strengthen confidence in the findings. 4.7 Hypothesis Summary A concise overview of all hypothesis tests is presented in Table 9 . Table 9 Summary of Hypotheses and Findings Hypothesis Statement Supported? Interpretation H1 ET → DGOC Yes Adaptive formation stronger in Iran H2 DGOC → GI Yes Policy reinforced innovation in Canada H3 DGOC → NC Yes Positive in both contexts H4 NC → SCR Yes Collaboration complements innovation H5 GI → SCR Yes Strongest path (β = 0.40) H6 SCR → SP Yes Resilience drives performance H7 (GI + NC) Mediation Yes Innovation dominant mediator H8 Institutional Pressure Moderation No Governance saturation effect H9 Relational Trust Moderation Yes Trust reinforces resilience in Iran 5 Discussion and Implications 5.1 Overview of Findings The findings reported in Tables 6 to 9 demonstrate that the proposed framework performs robustly across contexts while exposing meaningful institutional contrasts. Digital–Green Orchestration Capability exerts a strong and consistent influence on both green innovation and network collaboration, validating its position as a higher order capability that fuses digital transformation with sustainability governance. The analysis confirms that green innovation (β = 0.40) is the primary conduit through which DGOC enhances supply chain resilience, whereas network collaboration (β = 0.25) offers secondary but still significant support. Resilience itself emerges as the most decisive driver of sustainable performance (β = 0.44), establishing the central role of adaptability in linking dynamic capabilities to long term outcomes. The moderation tests further refine these insights. Institutional pressure (H8) proved non-significant, indicating that once ESG compliance becomes institutionalized, additional policy or regulatory enforcement yields diminishing returns a governance saturation effect. In contrast, relational trust (H9) significantly strengthened the network collaboration resilience link (β = 0.18), confirming that informal social capital continues to shape adaptive coordination in fragile institutional environments. Collectively, these findings show that DGOC is not a static construct but a context responsive orchestration system: it becomes institutionalized and standardized in high governance settings such as Canada, while emerging as a trust driven, necessity-based process in developing contexts such as Iran. 5.2 Theoretical Contributions This research contributes to the literature in four major ways. First, it extends the Dynamic Capabilities View beyond the firm level by framing DGOC as an inter-organizational orchestration capability. Rather than focusing on how individual firms reconfigure internal resources, this study demonstrates that flexibility arises through the coordinated sensing, seizing, and reconfiguring of multiple partners. DGOC functions as a meta routine that synchronizes digital infrastructures and sustainability objectives, transforming individual learning into collective adaptability. The results presented in Table 6 confirm that these orchestration routines explain a substantial share of variance in resilience and performance, thereby scaling DCV from the micro to the network level. Second, the study integrates DCV with Institutional Theory, illustrating that dynamic capabilities are contextually activated . In Canada, DGOC is embedded within stable regulatory and technological architectures, yielding institutionalized flexibility characterized by structured ESG dashboards, codified procedures, and formal reporting. In Iran, where institutions are volatile and enforcement limited, DGOC operates improvisationally; managers rely on relational trust, reputation, and informal agreements to align digital and green initiatives. This dual manifestation demonstrates that the same capability can generate equivalent adaptive outcomes through different governance mechanisms, enriching theoretical understanding of how institutional quality shapes the activation of dynamic capabilities. Third, the insignificance of H8 introduces the notion of institutional saturation a boundary condition for Institutional Theory. Once coercive and normative pressures achieve near universal compliance, they cease to differentiate performance. Competitive advantage then depends on how well firms orchestrate and internalize ESG routines rather than on how closely they conform to them. This finding adds nuance to the literature on institutional isomorphism by showing that maturity may neutralize the marginal utility of additional formal pressure. Finally, the significance of H9 validates relational trust as a functional substitute for formal governance. Trust facilitates honest communication, encourages knowledge sharing, and enables rapid collaborative response when rules are ambiguous or enforcement is weak. This outcome extends prior research on institutional voids by offering quantitative evidence that social capital operationalizes flexibility and resilience in developing economies. Together, these contributions articulate a cohesive theoretical bridge linking dynamic capability micro foundations with macro institutional logics. 5.3 Managerial Implications From a managerial standpoint, DGOC should be recognized as the cornerstone of sustained adaptability. Managers in both advanced and emerging economies can operationalize DGOC through a continuous cycle of alignment, synchronization, innovation, and reconfiguration. Alignment requires embedding environmental and social metrics directly into digital investment decisions so that technological progress inherently advances sustainability performance. Synchronization demands that firms establish interoperable digital platforms through which ESG data are exchanged transparently with suppliers and distributors, thereby creating a shared basis for decision making. Innovation then builds on these digital and informational foundations to identify process inefficiencies and generate cleaner, more resource efficient technologies. Finally, reconfiguration ensures that these digital green systems can be redeployed quickly in response to disruption, closing the loop between sensing and seizing. The empirical results underscore the payoff of this orchestration: a one standard deviation increase in DGOC corresponds to approximately a 0.40 SD gain in resilience and a 0.44 SD gain in sustainable performance (Table 6 ). Managers in advanced economies such as Canada should concentrate on institutionalizing DGOC within existing ESG frameworks through supplier audits, digital traceability systems, and formal cross-tier collaboration agreements so that flexibility becomes an embedded organizational routine. In developing contexts such as Iran, where regulatory support is weaker, firms should nurture DGOC through relational strategies building trust-based clusters, leveraging personal credibility, and adopting incremental digital tools that enhance coordination even in the absence of formal mandates. In both environments, the orchestration of digital and green resources converts short term efficiency into long term resilience. 5.4 Policy Implications At the policy level, the results challenge the assumption that regulation alone can sustain innovation. The evidence from Tables 7 and 8 shows that excessive institutional pressure produces negligible additional impact once ESG compliance becomes standard practice. Policymakers should therefore redirect attention from rulemaking toward capability building initiatives. Investments in national digital ESG platforms, inter firm learning consortia, and open data infrastructures can amplify the diffusion of orchestration capabilities across supply chains. Such initiatives enable small and medium sized firms to access digital tools and sustainability expertise that would otherwise remain beyond their reach, translating public governance into private capability enhancement. In developing economies, policy should prioritize mechanisms that formalize trust without bureaucratizing it. Transparent procurement systems, public private sustainability partnerships, and joint certification programs can transform interpersonal trust into institutional credibility. Over time, these initiatives help create a hybrid governance ecosystem where informal commitment and formal accountability coexist, fostering resilience not by imposing rules but by enabling orchestrated collaboration. 5.5 Synthesis: Two Logics of Flexibility The comparative analysis summarized in Table 9 reveals that flexibility is not a uniform construct, but a plural phenomenon governed by two distinct logics. In advanced economies, flexibility is institutionalized: it is embedded in digital infrastructures, standardized ESG procedures, and predictable policy frameworks that together support systematic innovation and stable resilience. In developing economies, flexibility is personalized: it emerges through social coordination, relational trust, and improvisational learning that collectively sustain adaptation when formal mechanisms are absent. Both logics produce viable resilience, but through different routes the first by codifying behavior, the second by humanizing it. DGOC bridges these logics by serving as a meta capability that operates across them. It institutionalizes flexibility when governance is abundant and personalizes it when governance is scarce. This insight redefines resilience as an orchestrated equilibrium between structure and agency, showing that dynamic capabilities derive their power not solely from technological sophistication but from their alignment with prevailing governance architectures. 6 Conclusion This research set out to explain how Digital Green Orchestration Capability enables supply chain flexibility, resilience, and sustainable performance across sharply contrasting institutional environments. Drawing on the Dynamic Capabilities View and Institutional Theory, the study proposed that DGOC acts as a meta capability linking digital transformation and sustainability imperatives into a single, adaptive framework. Using two wave survey data from 394 manufacturing SMEs in Iran and Canada, analyzed through PLS-SEM, the results confirmed that DGOC improves performance primarily through green innovation (β = 0.40) and secondarily through network collaboration (β = 0.25). Resilience, in turn, proved to be the pivotal mediator translating these effects into sustainable performance (β = 0.44). The findings reveal that DGOC behaves differently under distinct governance logics. In advanced economies such as Canada, where formal institutions, digital infrastructures, and ESG frameworks are mature, orchestration is institutionalized embedded in standardized processes, transparent reporting, and policy compliance. In developing economies such as Iran, DGOC becomes adaptive and trust driven, functioning through social coordination, informal reciprocity, and improvisational learning. The non-significant moderation by Institutional Pressure (H8) reflects a governance saturation effect, indicating that once ESG norms reach institutional maturity, further regulatory tightening adds little incremental value. Conversely, the significant moderation by Relational Trust (H9) demonstrates that social capital remains a vital enabler of resilience when formal enforcement mechanisms are weak. Collectively, these patterns affirm that flexibility is not merely a structural characteristic but an orchestrated outcome a product of how digital and environmental resources are aligned, synchronized, and reconfigured within prevailing institutional constraints. DGOC thus emerges as a unifying construct capable of institutionalizing flexibility where governance is strong and personalizing it where governance is weak. In both scenarios, the orchestration of technology, sustainability, and governance represents the essential pathway to long term adaptability. 6.1 Theoretical Contributions This study advances theory in three major respects. First, it scales the Dynamic Capabilities View to the network level, showing that adaptability arises not only from internal routines but also from the orchestration of inter organizational systems. DGOC serves as a higher order capability that coordinates sensing and seizing across partners, thereby generating collective resilience. Second, it enriches Institutional Theory by demonstrating that the activation of dynamic capabilities depends on governance maturity. Formal institutions institutionalize flexibility through compliance, while informal systems sustain it through trust. Third, it introduces the concept of institutional saturation as a theoretical boundary: when coercive and normative forces are fully diffused, additional regulation ceases to produce differentiation, and competitive advantage shifts toward orchestration quality. These insights collectively contribute to building a context sensitive theory of dynamic capabilities under institutional asymmetry. 6.2 Managerial and Policy Insights From a managerial perspective, the results emphasize that firms should treat DGOC as an integrated system rather than a series of discrete projects. Effective orchestration requires embedding environmental metrics in digital decision making, synchronizing ESG data across tiers, and maintaining agility through continuous reconfiguration. In mature institutional contexts, managers should formalize DGOC through codified digital sustainability programs and structured ESG partnerships. In less developed contexts, managerial attention should focus on cultivating relational trust and micro level collaboration that compensate for the absence of formal support. For policymakers, the evidence suggests a strategic pivot from rule proliferation toward capability enablement. Governments can strengthen systemic resilience by funding shared digital infrastructures, facilitating inter firm knowledge platforms, and supporting trust building initiatives that bridge the public and private sectors. These interventions transform regulation from a compliance mechanism into a catalyst for capability development, allowing national supply chains to respond more dynamically to environmental and technological disruption. 6.3 Limitations and Future Research Despite its methodological rigor including a two-wave design, measurement invariance testing, and extensive bias diagnostics this study has limitations that future research could address. The cross-sectional structure, though temporally separated, cannot fully establish causal evolution of DGOC. Longitudinal or panel studies tracking capability reconfiguration over multiple disruption cycles would clarify how DGOC matures and stabilizes over time. The focus on manufacturing SMEs in Iran and Canada limits generalizability to other sectors; extending the analysis to services, logistics, or digital platform ecosystems could reveal whether the same orchestration patterns apply in knowledge intensive settings. Future research might also integrate objective digital trace data, such as ESG reporting records or real time supply chain analytics, to triangulate perceptual measures and enhance validity. Moreover, combining PLS-SEM with configurational methods could uncover alternative causal pathways leading to resilience, thereby deepening understanding of capability combinations under varying institutional conditions. Finally, future investigations should explore the micro foundations of DGOC in greater depth how leadership cognition, inter organizational learning, and data governance routines jointly produce orchestration quality. Such multi level designs would connect individual managerial actions to network level outcomes, completing the theoretical bridge between micro capability formation and macro institutional adaptation. 6.4 Closing Remark In conclusion, this study demonstrates that supply chain resilience is neither spontaneous nor purely regulatory it is strategically orchestrated. DGOC provides the missing integrative mechanism through which digital transformation and sustainability objectives co evolve to produce long term adaptability. Whether institutionalized through regulation or improvised through trust, orchestration remains the cornerstone of flexible and sustainable supply chains. The implication for both scholars and practitioners is clear: the future of competitiveness lies not merely in adopting digital tools or meeting environmental targets, but in orchestrating them intelligently within the institutional realities that govern organizational life. All supplementary materials including full measurement items, diagnostic tests, and cross context results are available in Appendices A–E. Declarations Funding No funds, grants, or other support were received during the preparation of this manuscript. References Afshar Jahanshahi, A., Al‐Gamrh, B., & Gharleghi, B. (2020). Sustainable development in Iran post‐sanction: Embracing green innovation by small and medium‐sized enterprises. Sustainable Development , 28 (4), 781-790. Ameer, F., Khan, M. R., Khan, I., Khan, N. R., & Keoy, K. H. (2024). Organizational resilience and green innovation for environmental performance: evidence from manufacturing organizations. Vision , 09722629241254955. Attaran, M., & Attaran, S. (2007). Collaborative supply chain management: the most promising practice for building efficient and sustainable supply chains. Business process management journal , 13 (3), 390-404. Awwad, A. S., Ababneh, O. M. A., & Karasneh, M. (2022). The mediating impact of IT capabilities on the association between dynamic capabilities and organizational agility: the case of the Jordanian IT sector. Global Journal of Flexible Systems Management , 23 (3), 315-330. Borsatto, J. M. L. S., Bazani, C., & Amui, L. (2020). Environmental regulations, green innovation and performance: An analysis of industrial sector companies from developed countries and emerging countries. BBR. Brazilian Business Review , 17 (5), 559-578. Calantone, R., Garcia, R., & Dröge, C. (2003). The effects of environmental turbulence on new product development strategy planning. Journal of product innovation management , 20 (2), 90-103. Chen, Z., & Xing, R. (2025). 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Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics , 1 (3), 100027. Hasan Al-Obaidy, O. F., Abdullah Alshammary, I. S., & Al-Dulaimi, M. I. J. (2025). Resilience and Environmental Performance of SMEs: The Mediating Role of Ambidextrous Green Innovation. Journal of Advanced Transportation , 2025 (1), 9999874. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems , 116 (1), 2-20. Hosseini Shekarabi, S. A., Kiani Mavi, R., & Romero Macau, F. (2025). Supply chain resilience: A critical review of risk mitigation, robust optimisation, and technological solutions and future research directions. Global Journal of Flexible Systems Management , 1-55. Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of consumer research , 30 (2), 199-218. Kareem, S., Fehrer, J. A., Shalpegin, T., & Stringer, C. (2025). Navigating tensions of sustainable supply chains in times of multiple crises: A systematic literature review. Business Strategy and the Environment , 34 (1), 316-337. Li, Z., Miao, S., & Xu, L. (2024). Digital transformation and environmental, social, and governance greenwashing: Evidence from China. Journal of Environmental Management , 365 , 121460. Liang, X., Lu, W., & Wu, Z. (2020). Effects of collaboration networks on technology innovation in the solar photovoltaic (pv) sector: A case study of China. Journal of Green Building , 15 (3), 139-157. Mehmood, S., Nazir, S., Fan, J., & Nazir, Z. (2025). Achieving supply chain sustainability: enhancing supply chain resilience, organizational performance, innovation and information sharing: empirical evidence from Chinese SMEs. Modern Supply Chain Research and Applications , 7 (1), 2-29. Mustikasari, A., & Ciptono, W. S. (2025). The Microfoundations of Managing Adaptive Tension as Dynamic Capability in Innovation Management. Global Journal of Flexible Systems Management , 1-31. Sharma, D., Virmani, N., Mangla, S. K., Kumar, P., & Mohanty, R. (2025). Leveraging green innovation, organizational learning, and institutional pressure to enhance supply chain flexibility: A petroleum refinery perspective using SAP-LAP and e-IRP. Global Journal of Flexible Systems Management , 1-27. Shojaei, P., Rashidi Alavije, A., & Askarifar, K. (2025). Supply Chain Resilience Improvement in SMEs: The Role of Industry 4.0 Technologies. Global Journal of Flexible Systems Management , 1-17. Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal , 28 (13), 1319-1350. Wamba, S. F., Queiroz, M. M., Jabbour, C. J. C., & Shi, C. V. (2023). Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence? International journal of production economics , 265 , 109015. Xin, X., Miao, X., & Cui, R. (2023). Enhancing sustainable development: Innovation ecosystem coopetition, environmental resource orchestration, and disruptive green innovation. Business Strategy and the Environment , 32 (4), 1388-1402. Xue, C., Wang, J., & Torres de Oliveira, R. (2025). Digital Capability and Radical Green Innovation: The Role of Organizational Resilience and Environmental Dynamism. Corporate Social Responsibility and Environmental Management . Yadav, M. P., Kushwah, S. V., Sehgal, V., Saradhi, V. R., & Shore, A. P. (2025). Is Quantile Connectedness Flexible or Uniform Under Catastrophic Tenure? Insights into ESG Investing and Financial Markets in the Quad Nations. Global Journal of Flexible Systems Management , 1-16. Yang, S.-U., & Lim, J. S. (2009). The effects of blog-mediated public relations (BMPR) on relational trust. Journal of public relations research , 21 (3), 341-359. Zhai, J., Xu, X., Xu, J., & Lyu, X. (2021). Research on green collaborative innovation mechanism of cloud manufacturing enterprises under government supervision. Mathematical Problems in Engineering , 2021 (1), 8820791. Zhou, Q., Wang, S., Ma, X., & Xu, W. (2024). Digital technologies and corporate green innovation: opening the “black box” of resource orchestration mechanisms. Sustainability Accounting, Management and Policy Journal , 15 (4), 884-912. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":20748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8065826/v1/9e07f644e08e0e2aa56476b6.png"},{"id":98748314,"identity":"c49390c4-a367-42ea-9352-6cbbf63b02b5","added_by":"auto","created_at":"2025-12-22 08:54:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1571417,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8065826/v1/1b47450c-084b-4894-98bd-e3b25906dc79.pdf"},{"id":96722007,"identity":"594d1ba1-9246-49e2-a794-c5ca0e57950f","added_by":"auto","created_at":"2025-11-25 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Introduction","content":"\u003cp\u003eGlobal supply chains operate amid escalating turbulence, sustainability mandates, and technological disruption (Kareem et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Traditional efficiency oriented models are inadequate under these conditions, as firms must simultaneously adapt digitally and act sustainably (Attaran \u0026amp; Attaran, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The intersection of digital transformation and Environmental, Social, And Governance (ESG) imperatives thus becomes a decisive source of flexibility and resilience (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the ability to orchestrate these dual agendas differs dramatically between advanced and developing economies.\u003c/p\u003e\u003cp\u003eIn advanced economies such as Canada, mature digital infrastructures, stable governance, and codified ESG frameworks enable structured, institutionalized adaptation (Evans, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, developing economies such as Iran face volatile regulatory systems, infrastructural constraints, and reliance on informal coordination (Danaeefard, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These disparities underscore a theoretical and practical puzzle: \u003cem\u003eHow do firms orchestrate digital and green initiatives to remain flexible under radically different institutional realities?\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSupply chain flexibility the ability to adapt processes rapidly and cost effectively forms the foundation of resilience, which denotes the capacity to recover from disruptions while maintaining performance (Hosseini Shekarabi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shojaei et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Both constructs derive from the Dynamic Capabilities View (DCV), which posits that firms sustain advantage by sensing change, seizing opportunities, and reconfiguring resources (Farrukh \u0026amp; Sajjad, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Teece, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Yet, most prior research has examined dynamic capabilities at the firm level and within homogeneous, stable contexts. Extending DCV to inter organizational networks and institutional asymmetries remains a crucial theoretical gap.\u003c/p\u003e\u003cp\u003eTo address this gap, this study introduces Digital Green Orchestration Capability (DGOC) a meta capability that aligns and reconfigures digital and sustainability resources across supply chain tiers to generate adaptive flexibility. DGOC bridges the efficiency logic of digitalization with the legitimacy logic of ESG compliance. The framework is tested across two contrasting institutional contexts Iran and Canada to capture how governance quality alters capability outcomes.\u003c/p\u003e\u003cp\u003eThree research questions guide the investigation:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow does environmental turbulence stimulate DGOC formation?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThrough which mechanisms green innovation and/or network collaboration does DGOC enhance resilience and sustainable performance?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow do formal (institutional pressure) and informal (relational trust) governance mechanisms moderate these effects?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBy addressing these questions, this paper extends DCV into a network level, context sensitive theory of capability orchestration and advances institutional theory by explaining how governance maturity shapes flexibility.\u003c/p\u003e\u003cp\u003eThe rest of the paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the literature and develops hypotheses; Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents methodology; Section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports results; Section \u003cspan refid=\"Sec31\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses theoretical and managerial implications; Section \u003cspan refid=\"Sec37\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert Fig.\u0026nbsp;1. Conceptual framework\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Literature Review and Hypotheses Development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Flexibility, Resilience, and the Dynamic Capabilities View\u003c/h2\u003e\u003cp\u003eModern supply chains confront frequent disruption, resource scarcity, and ESG pressures that challenge efficiency-based paradigms (Yadav et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Supply chain flexibility the ability to reconfigure processes, structures, and relationships quickly and economically has become a prerequisite for resilience, defined as the capacity to absorb shocks and recover performance during crises (Hosseini Shekarabi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shojaei et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Both stem from the Dynamic Capabilities View, which explains sustainable advantage through sensing, seizing, and reconfiguring routines (Farrukh \u0026amp; Sajjad, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Teece, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eYet the majority of DCV research is firm centric and assumes relatively stable institutions. Adaptation occurs within multi-tier networks where diverse actors operate under unequal governance quality. Flexibility, therefore, is not merely a firm capability but a network level orchestration processes the alignment of partners, data, and technologies that allows collective reconfiguration (Awwad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study builds on that logic, extending DCV beyond single organizations toward inter organizational dynamic capabilities responsive to institutional asymmetry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Digital Green Orchestration Capability\u003c/h2\u003e\u003cp\u003eThe accelerating convergence of digital transformation and sustainability has created a pressing managerial dilemma: how to align efficiency driven technologies with legitimacy driven environmental responsibilities (Calantone et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To address this paradox, the present study introduces Digital Green Orchestration Capability a \u003cem\u003emeta capability\u003c/em\u003e that integrates digital infrastructures and ESG routines into a unified mechanism of adaptive coordination.\u003c/p\u003e\u003cp\u003eDGOC is distinct from existing constructs such as \u003cem\u003eintegration capability\u003c/em\u003e, \u003cem\u003ecoordination capability\u003c/em\u003e, or \u003cem\u003esustainability orientation\u003c/em\u003e. Integration capability typically optimizes internal efficiency, and coordination capability stabilizes dyadic transactions; DGOC transcends these boundaries by orchestrating a multi-tier ecosystem of digital platforms, sustainability standards, and governance relationships. It transforms fragmented digital and environmental initiatives into a coherent system of interdependent routines that promote cross-tier adaptability.\u003c/p\u003e\u003cp\u003eFrom a Dynamic Capabilities View, DGOC embodies higher order routines that reconfigure first order processes (sensing, seizing, reconfiguring). From an Institutional Theory perspective, it serves as an institutional bridge, translating external ESG expectations into digital operating procedures. The capability manifests through three reinforcing sub-dimensions Integration Alignment, Cross-Tier Synchronization, and Reconfiguration Agility later empirically validated (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e in Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e3.6.1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn advanced economies like Canada, DGOC operates within codified ESG systems, benefiting from standardized reporting, digital traceability, and formal audits. In developing economies such as Iran, DGOC arises more improvisationally: managers compensate for regulatory voids by relying on trust, informal agreements, and small-scale technological improvisation. Hence, DGOC is both an institutionalized capability and an adaptive improvisation depending on context.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH1.\u003c/b\u003e Environmental turbulence positively influences Digital Green Orchestration Capability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 DGOC and Green Innovation\u003c/h2\u003e\u003cp\u003eGreen Innovation (GI) refers to novel products, processes, or management practices that reduce environmental impact while sustaining competitiveness (Xin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within the DCV, GI functions as a \u003cem\u003eseizing\u003c/em\u003e capability: it converts the opportunities sensed and structured by digital green orchestration capability into actionable eco-technological outcomes (Xue et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003edigital green orchestration capability stimulates green innovation through three intertwined mechanisms. First, digital visibility: data integration exposes inefficiencies and carbon hotspots, guiding targeted eco-innovation. Second, knowledge recombination: orchestration connects partners across tiers, enabling cross fertilization of sustainability ideas and resource pooling. Third, institutional legitimation: digital green orchestration capability embeds environmental metrics into decision routines, making green innovation both operationally feasible and socially acceptable (Wamba et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the effectiveness of these mechanisms depends on institutional maturity. In Canada, stable policies and abundant digital infrastructure foster formal R\u0026amp;D programs, standardized life cycle assessments, and systematic green redesigns. In Iran, limited capital and weak enforcement compel firms to innovate incrementally and adaptively for example, modifying machinery to save energy or locally sourcing recyclable materials. Thus, digital green orchestration capability becomes a \u003cem\u003enecessity driven innovation platform\u003c/em\u003e in developing economies and a \u003cem\u003estrategic innovation system\u003c/em\u003e in advanced ones.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH2.\u003c/b\u003e Digital Green Orchestration Capability positively influences Green Innovation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 DGOC and Network Collaboration\u003c/h2\u003e\u003cp\u003eWhereas GI captures technological transformation, Network Collaboration (NC) embodies relational transformation the joint planning, problem solving, and information exchange that enable collective response to turbulence (Di et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; El Baz \u0026amp; Ruel, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shojaei et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). From an Information Processing Theory (IPT) standpoint (Liang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), collaboration expands the network\u0026rsquo;s information processing capacity, crucial under high uncertainty.\u003c/p\u003e\u003cp\u003eDGOC reinforces collaboration through three complementary levers.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTechnological enablement: shared digital platforms synchronize ESG metrics, giving all partners a common data language.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGovernance alignment: orchestration clarifies accountability, thus lowering transaction and monitoring costs.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRelational reinforcement: transparency generated by digital systems builds credibility and reciprocity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eHowever, collaboration can be double edged. When over formalized, it slows decision making and burdens coordination (Gulati \u0026amp; Singh, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zhai et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Optimal collaboration balances information richness with autonomy \u0026ldquo;loose tight coupling.\u0026rdquo; Institutional context shapes where this balance lies. In Canada, NC is platform enabled and contractually managed; in Iran, it is person centered and trust based. Regardless of form, DGOC should improve NC by creating structural and cognitive alignment across tiers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH3.\u003c/b\u003e Digital Green Orchestration Capability positively influences Network Collaboration.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH4.\u003c/b\u003e Network Collaboration positively influences Supply Chain Resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Green Innovation, Resilience, and Sustainable Performance\u003c/h2\u003e\u003cp\u003eResilience (SCR) denotes the ability of a supply chain to absorb shocks and restore operations with minimal performance loss (Ameer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It represents the realized outcome of dynamic flexibility (Mehmood et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Green Innovation enhances resilience by introducing modularity, efficiency, and adaptive routines that allow firms to reconfigure processes swiftly under regulatory, environmental, or market shocks (Hasan Al-Obaidy et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn weak governance contexts such as Iran, GI acts as a functional substitute for missing institutional support firms innovate frugally to buffer volatility (Afshar Jahanshahi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ebrahimi \u0026amp; Mirbargkar, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In advanced economies, GI complements regulatory systems by reinforcing formal sustainability commitments (Chen \u0026amp; Xing, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Accordingly, the GI and Resilience link is expected to dominate the NC and Resilience path because technological adaptation delivers direct, hard resource flexibility, while collaboration offers softer, information-based resilience.\u003c/p\u003e\u003cp\u003eResilient networks then translate adaptive capability into Sustainable Performance (SP) the balanced achievement of environmental and financial objectives. Resilience sustains operations during crises, safeguards customer trust, and strengthens corporate legitimacy. Therefore, DGOC is expected to influence SP indirectly via the dual pathways of innovation and collaboration, culminating in the mediation model tested later.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH5.\u003c/b\u003e Green Innovation positively influences Resilience.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH6.\u003c/b\u003e Resilience positively influences Sustainable Performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH7.\u003c/b\u003e Green Innovation and Network Collaboration jointly mediate the DGOC Sustainable Performance relationship.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Institutional Moderators: Formal versus Informal Governance\u003c/h2\u003e\u003cp\u003eThe conversion of DGOC into tangible outcomes is contingent upon institutional mechanisms that regulate or enable inter firm coordination. Two complementary forms dominate: formal governance (Institutional Pressure) and informal governance (Relational Trust). Their relative salience defines whether flexibility becomes \u003cem\u003einstitutionalized\u003c/em\u003e or \u003cem\u003epersonalized\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFormal Governance and Institutional Pressure (IP)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInstitutional pressure arises from coercive and normative forces such as regulations, standards, and stakeholder expectations (Mustikasari \u0026amp; Ciptono, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In advanced contexts, strong ESG requirements amplify the DGOC and GI link because firms with high orchestration can more efficiently comply and innovate under regulatory mandates (Borsatto et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, once most organizations meet these standards, marginal returns from additional pressure decline a governance saturation effect. This plateau occurs in mature systems like Canada, where ESG integration is mainstream. In developing contexts like Iran, weak enforcement and policy inconsistency blunt formal influence altogether. Hence, IP\u0026rsquo;s moderating power is asymmetric across contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInformal Governance and Relational Trust (RT)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRelational trust reflects confidence in partners\u0026rsquo; reliability, fairness, and benevolence (Frederiksen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang \u0026amp; Lim, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In fragile institutions, trust substitutes for regulation by ensuring collaboration even when contracts are weak. It lowers transaction uncertainty, fosters candid communication, and promotes collective problem solving all vital to resilience. In strong institutions, trust complements contracts, enhancing efficiency through goodwill and social capital. Thus, RT is predicted to reinforce the NC and SCR link more powerfully in Iran than in Canada, where formal mechanisms dominate.\u003c/p\u003e\u003cp\u003eTaken together, formal pressure and relational trust represent \u003cem\u003ecompeting yet complementary\u003c/em\u003e governance logics: the former institutionalizes flexibility through compliance, the latter personalizes flexibility through commitment. Exploring both within a single model illuminates how governance maturity shapes orchestration outcomes across contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH8.\u003c/b\u003e Institutional Pressure positively moderates the DGOC Green Innovation relationship, strengthening it under higher formal governance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH9.\u003c/b\u003e Relational Trust positively moderates the Network Collaboration Resilience relationship, strengthening it under higher informal governance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Conceptual Framework\u003c/h2\u003e\u003cp\u003eThe full conceptual model (see \u003cem\u003eFig.\u0026nbsp;1\u003c/em\u003e) integrates the above hypotheses: Environmental Turbulence, DGOC, Green Innovation, Network Collaboration, Resilience, and Sustainable Performance, moderated by Institutional Pressure and Relational Trust. This framework captures the theoretical tension between \u003cem\u003einstitutionalized orchestration\u003c/em\u003e and \u003cem\u003eimprovised orchestration\u003c/em\u003e, addressing how flexibility is achieved under both governance abundance and scarcity.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Research Methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Research Design\u003c/h2\u003e\u003cp\u003eTo empirically test the conceptual framework shown in \u003cem\u003eFig.\u0026nbsp;1\u003c/em\u003e, this study adopted a two wave, multi-tier survey design that introduced temporal separation to mitigate bias and capture causal ordering. Wave 1 measured the antecedent constructs environmental turbulence, digital green orchestration capability, institutional pressure, and relational trust while wave 2 (six weeks later) measured the outcome constructs green innovation, network collaboration, supply chain resilience, and sustainable performance. This temporal separation reduced common method bias and allowed partial causal inference.\u003c/p\u003e\u003cp\u003eThe data were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in \u003cem\u003eSmartPLS 4\u003c/em\u003e. PLS-SEM was appropriate because DGOC is a hierarchical reflective\u0026ndash;reflective construct with multiple latent dimensions and because the model includes both mediation and moderation paths (Hair \u0026amp; Alamer, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The approach also accommodates moderate sample sizes and non-normal data distributions typical of international SME surveys.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Sampling and Data Collection\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Sampling Frame and Context\u003c/h2\u003e\u003cp\u003eThe population comprised manufacturing small and medium sized enterprises (SMEs) in Iran and Canada, representing two contrasting institutional contexts: weak versus strong governance maturity. Eligible firms maintained at least one upstream supplier and one downstream distributor to ensure multi-tier network relevance. Sampling lists were drawn from national industry directories and chambers of commerce. In total, 1 100 firms (550 per country) were invited; 394 valid responses were obtained 197 from each country yielding a 37% response rate. Respondents were senior managers responsible for operations, sustainability, or digital transformation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample Comparability Across Countries\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIran (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCanada (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;/t (p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElectronics / Machinery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.80 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimilar sector mix\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutomotive / Transport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.33 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eComparable structure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood / Chemicals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2; = 0.20 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBalanced sample\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean firm size (employees)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.47 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eComparable scale\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean firm age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.62 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo significant difference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; survey data.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the two subsamples are statistically comparable in sectoral and structural terms, enabling meaningful cross-country analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Power and Sample Adequacy\u003c/h2\u003e\u003cp\u003eA priori power analysis (\u003cem\u003eG\u003c/em\u003ePower 3.1; α\u0026thinsp;=\u0026thinsp;0.05, f\u0026sup2; = 0.15, power\u0026thinsp;=\u0026thinsp;0.95) recommended at least 146 cases per group. The achieved 197 per country provided power\u0026thinsp;=\u0026thinsp;0.96, exceeding this requirement and ensuring sufficient sensitivity for multi group comparisons.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Contextual Contrast\u003c/h2\u003e\u003cp\u003eDescriptive contrasts revealed strong institutional asymmetry. Iranian firms reported higher environmental turbulence (M\u0026thinsp;=\u0026thinsp;4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2) and lower DGOC maturity (M\u0026thinsp;=\u0026thinsp;4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1) than Canadian firms (ET M\u0026thinsp;=\u0026thinsp;3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9; DGOC M\u0026thinsp;=\u0026thinsp;5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9). Institutional Pressure was lower (M\u0026thinsp;=\u0026thinsp;3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0) in Iran but Relational Trust was higher (M\u0026thinsp;=\u0026thinsp;5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2). These differences validated the theoretical logic of testing DGOC under contrasting governance environments.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Measurement Instrument\u003c/h2\u003e\u003cp\u003eAll variables were measured on seven-point Likert scales (1\u0026thinsp;=\u0026thinsp;strongly disagree to 7\u0026thinsp;=\u0026thinsp;strongly agree). Scale sources were: DGOC and ET (Farrukh \u0026amp; Sajjad, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Teece, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); GI (Zhou et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); NC (Di et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); SCR (Ameer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); SP (Hasan Al-Obaidy et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); IP (Sharma et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); (Mustikasari \u0026amp; Ciptono, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); RT (Frederiksen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang \u0026amp; Lim, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Translation followed a back translation protocol (Persian \u0026harr; English \u0026harr; French). A pilot test of 60 managers yielded α\u0026thinsp;\u0026gt;\u0026thinsp;0.70 across constructs. DGOC was modeled as a second order reflective\u0026ndash;reflective construct comprising Integration Alignment, Cross-Tier Synchronization, and Reconfiguration Agility, consistent with (Jarvis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). All measurement items are listed in Appendix A, and summary reliability and validity statistics appear in Appendix B.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Control Variables\u003c/h2\u003e\u003cp\u003eFour firm level controls firm size (log employees), firm age, industry type (dummy coded), and ownership (0\u0026thinsp;=\u0026thinsp;domestic, 1\u0026thinsp;=\u0026thinsp;foreign) were linked to Sustainable Performance (SP) to adjust for possible structural bias.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Common Method and Multicollinearity Diagnostics\u003c/h2\u003e\u003cp\u003eMultiple procedural and statistical checks were employed. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBias and Multicollinearity Diagnostics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHarman\u0026rsquo;s one factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50% variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo common method bias\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatent common factor VIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegligible bias\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFull collinearity VIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10\u0026ndash;2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo multicollinearity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; SmartPLS analysis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThese metrics confirm that neither common method nor multicollinearity biases materially affected the dataset. Detailed diagnostics are presented in Appendix C.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Measurement Model Evaluation\u003c/h2\u003e\u003cp\u003eReliability and validity were tested via Confirmatory Composite Analysis (CCA). All loadings exceeded 0.70 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.82\u0026ndash;0.94; Composite Reliability\u0026thinsp;=\u0026thinsp;0.86\u0026ndash;0.95; AVE\u0026thinsp;=\u0026thinsp;0.55\u0026ndash;0.79; and HTMT ratios\u0026thinsp;\u0026lt;\u0026thinsp;0.85, demonstrating discriminant validity. Global fit indices (SRMR\u0026thinsp;=\u0026thinsp;0.052; NFI\u0026thinsp;=\u0026thinsp;0.92) met recommended thresholds (Henseler et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jarvis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurement Model Reliability and Validity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eα\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHTMT (max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStatus\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDGOC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; PLS-SEM analysis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAll constructs satisfied the minimum reliability (α\u0026thinsp;\u0026ge;\u0026thinsp;0.80) and convergent validity criteria (AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50).\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.6.1 Second Order DGOC Validation\u003c/h2\u003e\u003cp\u003eThe hierarchical model verified DGOC\u0026rsquo;s three-dimensional structure. All outer weights were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming a unified meta capability. Details appear in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSecond Order DGOC Validation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOuter Weight (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntegration Alignment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-Tier Synchronization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReconfiguration Agility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSignificant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; SmartPLS analysis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThese findings confirm DGOC as a robust, higher order orchestration capability that integrates digital and sustainability routines into a single adaptive system.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Measurement Invariance and Model Specification\u003c/h2\u003e\u003cp\u003eCross national equivalence was verified through the MICOM procedure (Measurement Invariance of Composites) (Henseler et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Configural and compositional invariance were achieved, and partial scalar invariance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicated that observed path differences reflect genuine contextual variation rather than measurement artifacts. The full structural model was then estimated using 5 000 bootstrap resamples (two tailed α\u0026thinsp;=\u0026thinsp;0.05) under consistent-PLS settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Ethical Considerations\u003c/h2\u003e\u003cp\u003eBoth surveys complied with institutional research ethics protocols in Iran and Canada. Participation was voluntary, anonymous, and uncompensated; informed consent was obtained from all respondents. No identifying data were collected, ensuring adherence to the \u003cem\u003eTri-Council Policy Statement (Canada)\u003c/em\u003e and equivalent Iranian standards. Survey administration details (two wave procedure, languages, consent) are summarized in Appendix E.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results and Analysis","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive Statistics and Contextual Differences\u003c/h2\u003e\u003cp\u003eDescriptive results establish the contextual asymmetry between the two national samples. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Iranian firms reported higher \u003cem\u003eenvironmental turbulence\u003c/em\u003e (M\u0026thinsp;=\u0026thinsp;4.7, SD\u0026thinsp;=\u0026thinsp;1.20) and lower \u003cem\u003eDGOC maturity\u003c/em\u003e (M\u0026thinsp;=\u0026thinsp;4.5) compared with Canadian firms (M\u0026thinsp;=\u0026thinsp;3.6 and 5.1 respectively). Likewise, \u003cem\u003egreen innovation\u003c/em\u003e and \u003cem\u003esustainable performance\u003c/em\u003e were stronger in Canada (M\u0026thinsp;=\u0026thinsp;4.9 and 5.2) than in Iran (M\u0026thinsp;=\u0026thinsp;4.2 and 4.6). Conversely, \u003cem\u003einstitutional pressure\u003c/em\u003e was weaker in Iran (M\u0026thinsp;=\u0026thinsp;3.8) while \u003cem\u003erelational trust\u003c/em\u003e was higher (M\u0026thinsp;=\u0026thinsp;5.0 vs. 4.8). These variations confirm the theorized institutional contrast \u003cem\u003eformal regulation dominance\u003c/em\u003e in Canada versus \u003cem\u003einformal trust reliance\u003c/em\u003e in Iran.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics and Correlations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (IR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD (IR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean (CA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD (CA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 ET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 DGOC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.44**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 GI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.36**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.59**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 NC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.31**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.52**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.47**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.29**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.48**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.46**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.30**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 SP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.27*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.49**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.38**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.49**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7 IP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.28**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.43**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.33**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.24*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.23*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8 RT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.34**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.35**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.30**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.45**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.44**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.22*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01. IR\u0026thinsp;=\u0026thinsp;Iran; CA\u0026thinsp;=\u0026thinsp;Canada.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; survey data.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe moderate intercorrelations among variables (r\u0026thinsp;\u0026lt;\u0026thinsp;0.60) indicate distinct yet related constructs, justifying subsequent SEM analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Measurement Model Confirmation\u003c/h2\u003e\u003cp\u003eConfirmatory Composite Analysis reaffirmed construct reliability (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.82\u0026ndash;0.94; CR\u0026thinsp;=\u0026thinsp;0.86\u0026ndash;0.95; AVE\u0026thinsp;=\u0026thinsp;0.55\u0026ndash;0.79). Model fit remained acceptable (SRMR\u0026thinsp;=\u0026thinsp;0.052; NFI\u0026thinsp;=\u0026thinsp;0.92), aligning with (Henseler et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These results validate the measurement model and confirm DGOC\u0026rsquo;s second order structure as previously established in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Structural Model Results (Pooled Sample)\u003c/h2\u003e\u003cp\u003eHypotheses were tested via bootstrapping with 5 000 resamples (two tailed α\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the standardized path coefficients, t-values, and effect sizes. All relationships were significant except the moderation by Institutional Pressure (H8). Notably, \u003cem\u003eGreen Innovation\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;0.40) exerted a stronger effect on resilience than \u003cem\u003eNetwork Collaboration\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;0.25).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural Model Results (Pooled Sample)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ef\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1 ET \u0026rarr; DGOC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2 DGOC \u0026rarr; GI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3 DGOC \u0026rarr; NC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4 NC \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5 GI \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6 SCR \u0026rarr; SP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7 DGOC \u0026rarr; (GI\u0026thinsp;+\u0026thinsp;NC) \u0026rarr; SCR \u0026rarr; SP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8 DGOC \u0026times; IP \u0026rarr; GI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot supported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH9 NC \u0026times; RT \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eR\u0026sup2; = DGOC 0.33; GI 0.41; NC 0.37; SCR 0.48; SP 0.56. Source: Authors\u0026rsquo; SmartPLS analysis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eInnovation\u0026rsquo;s stronger contribution to resilience demonstrates its role as the dominant conduit translating DGOC into adaptive performance. The insignificant H8 corroborates the proposed governance saturation phenomenon.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Cross Country Multi Group Analysis (MGA)\u003c/h2\u003e\u003cp\u003eComparative multi group analysis verified that the underlying mechanisms differ significantly across institutional contexts. Results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that environmental turbulence had a stronger influence on DGOC in Iran (β\u0026thinsp;=\u0026thinsp;0.47) than in Canada (β\u0026thinsp;=\u0026thinsp;0.32), indicating that orchestration there arises from necessity. Conversely, the DGOC and GI path was stronger in Canada (β\u0026thinsp;=\u0026thinsp;0.58 vs. 0.44), confirming institutionalized innovation. The moderation of trust (H9) was markedly stronger in Iran (β\u0026thinsp;=\u0026thinsp;0.22 vs. 0.09). A comprehensive version of the multi group comparison is provided in Appendix D.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti Group Path Comparison (Iran vs Canada)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (IR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ (CA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep (MGA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eET \u0026rarr; DGOC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTurbulence drives orchestration in Iran\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDGOC \u0026rarr; GI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInnovation path stronger in Canada\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInnovation compensates weak governance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDGOC \u0026times; IP \u0026rarr; GI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34 ns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFormal moderation absent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNC \u0026times; RT \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTrust based collaboration critical in Iran\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ns\u0026thinsp;=\u0026thinsp;non-significant.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; MGA analysis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThese results empirically substantiate the theoretical tension between formal governance (institutionalized innovation) and informal governance (trust-based resilience).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Mediation, Moderation, and Predictive Validation\u003c/h2\u003e\u003cp\u003eTo examine indirect and moderating mechanisms simultaneously, a bootstrapped \u003cem\u003especific indirect effect\u003c/em\u003e test was conducted. As displayed in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the DGOC, GI, SCR, and SP chain produced the strongest indirect effect (β\u0026thinsp;\u0026asymp;\u0026thinsp;0.16\u0026ndash;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), roughly twice the collaboration-based mediation (β\u0026thinsp;\u0026asymp;\u0026thinsp;0.07). The NC \u0026times; RT moderation was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming the resilience enhancing power of relational trust in weak institutional environments. Predictive relevance (Q\u0026sup2; \u0026asymp; 0.24\u0026ndash;0.26) and R\u0026sup2; values (0.33\u0026ndash;0.56) indicate good explanatory capability without over fit.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndirect, Moderating, and Predictive Effects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (IR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ (CA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDGOC \u0026rarr; GI \u0026rarr; SCR \u0026rarr; SP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.07, 0.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInnovation mediation dominant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDGOC \u0026rarr; NC \u0026rarr; SCR \u0026rarr; SP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.01, 0.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCollaboration mediation modest\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDGOC \u0026times; IP \u0026rarr; GI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u0026ndash;0.04, 0.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePolicy moderation non-significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNC \u0026times; RT \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.05, 0.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTrust moderation significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel fit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSRMR\u0026thinsp;=\u0026thinsp;0.055; NFI\u0026thinsp;=\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdequate fit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; *\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Authors\u0026rsquo; SmartPLS analysis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe results collectively demonstrate that innovation driven adaptation is the principal engine of DGOC\u0026rsquo;s performance impact, while trust based collaboration acts as a critical amplifier in weaker institutional settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Robustness Checks\u003c/h2\u003e\u003cp\u003eSeveral robustness tests were executed to ensure stability:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAlternative causal order (GI \u0026harr; NC reversal): Model fit deteriorated (SRMR\u0026thinsp;=\u0026thinsp;0.078; NFI\u0026thinsp;=\u0026thinsp;0.83), validating the hypothesized direction.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReverse causality (SP and DGOC): β\u0026thinsp;=\u0026thinsp;0.05, ns, confirming non-recursiveness.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEndogeneity (Gaussian-copula test): p\u0026thinsp;\u0026gt;\u0026thinsp;0.10; no bias detected.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNon-response bias (early late comparison): p\u0026thinsp;\u0026gt;\u0026thinsp;0.10; response timing neutral.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese diagnostics confirm internal validity and strengthen confidence in the findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Hypothesis Summary\u003c/h2\u003e\u003cp\u003eA concise overview of all hypothesis tests is presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Hypotheses and Findings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSupported?\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eET \u0026rarr; DGOC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdaptive formation stronger in Iran\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDGOC \u0026rarr; GI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePolicy reinforced innovation in Canada\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDGOC \u0026rarr; NC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive in both contexts\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNC \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCollaboration complements innovation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGI \u0026rarr; SCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStrongest path (β\u0026thinsp;=\u0026thinsp;0.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSCR \u0026rarr; SP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResilience drives performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(GI\u0026thinsp;+\u0026thinsp;NC) Mediation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnovation dominant mediator\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInstitutional Pressure Moderation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGovernance saturation effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRelational Trust Moderation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTrust reinforces resilience in Iran\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Discussion and Implications","content":"\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Overview of Findings\u003c/h2\u003e\u003cp\u003eThe findings reported in Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e to \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e demonstrate that the proposed framework performs robustly across contexts while exposing meaningful institutional contrasts. Digital\u0026ndash;Green Orchestration Capability exerts a strong and consistent influence on both green innovation and network collaboration, validating its position as a higher order capability that fuses digital transformation with sustainability governance. The analysis confirms that green innovation (β\u0026thinsp;=\u0026thinsp;0.40) is the primary conduit through which DGOC enhances supply chain resilience, whereas network collaboration (β\u0026thinsp;=\u0026thinsp;0.25) offers secondary but still significant support. Resilience itself emerges as the most decisive driver of sustainable performance (β\u0026thinsp;=\u0026thinsp;0.44), establishing the central role of adaptability in linking dynamic capabilities to long term outcomes.\u003c/p\u003e\u003cp\u003eThe moderation tests further refine these insights. Institutional pressure (H8) proved non-significant, indicating that once ESG compliance becomes institutionalized, additional policy or regulatory enforcement yields diminishing returns a governance saturation effect. In contrast, relational trust (H9) significantly strengthened the network collaboration resilience link (β\u0026thinsp;=\u0026thinsp;0.18), confirming that informal social capital continues to shape adaptive coordination in fragile institutional environments. Collectively, these findings show that DGOC is not a static construct but a context responsive orchestration system: it becomes institutionalized and standardized in high governance settings such as Canada, while emerging as a trust driven, necessity-based process in developing contexts such as Iran.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Theoretical Contributions\u003c/h2\u003e\u003cp\u003eThis research contributes to the literature in four major ways. First, it extends the Dynamic Capabilities View beyond the firm level by framing DGOC as an inter-organizational orchestration capability. Rather than focusing on how individual firms reconfigure internal resources, this study demonstrates that flexibility arises through the coordinated sensing, seizing, and reconfiguring of multiple partners. DGOC functions as a meta routine that synchronizes digital infrastructures and sustainability objectives, transforming individual learning into collective adaptability. The results presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e confirm that these orchestration routines explain a substantial share of variance in resilience and performance, thereby scaling DCV from the micro to the network level.\u003c/p\u003e\u003cp\u003eSecond, the study integrates DCV with Institutional Theory, illustrating that dynamic capabilities are \u003cem\u003econtextually activated\u003c/em\u003e. In Canada, DGOC is embedded within stable regulatory and technological architectures, yielding institutionalized flexibility characterized by structured ESG dashboards, codified procedures, and formal reporting. In Iran, where institutions are volatile and enforcement limited, DGOC operates improvisationally; managers rely on relational trust, reputation, and informal agreements to align digital and green initiatives. This dual manifestation demonstrates that the same capability can generate equivalent adaptive outcomes through different governance mechanisms, enriching theoretical understanding of how institutional quality shapes the activation of dynamic capabilities.\u003c/p\u003e\u003cp\u003eThird, the insignificance of H8 introduces the notion of institutional saturation a boundary condition for Institutional Theory. Once coercive and normative pressures achieve near universal compliance, they cease to differentiate performance. Competitive advantage then depends on how well firms orchestrate and internalize ESG routines rather than on how closely they conform to them. This finding adds nuance to the literature on institutional isomorphism by showing that maturity may neutralize the marginal utility of additional formal pressure.\u003c/p\u003e\u003cp\u003eFinally, the significance of H9 validates relational trust as a functional substitute for formal governance. Trust facilitates honest communication, encourages knowledge sharing, and enables rapid collaborative response when rules are ambiguous or enforcement is weak. This outcome extends prior research on institutional voids by offering quantitative evidence that social capital operationalizes flexibility and resilience in developing economies. Together, these contributions articulate a cohesive theoretical bridge linking dynamic capability micro foundations with macro institutional logics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Managerial Implications\u003c/h2\u003e\u003cp\u003eFrom a managerial standpoint, DGOC should be recognized as the cornerstone of sustained adaptability. Managers in both advanced and emerging economies can operationalize DGOC through a continuous cycle of alignment, synchronization, innovation, and reconfiguration. Alignment requires embedding environmental and social metrics directly into digital investment decisions so that technological progress inherently advances sustainability performance. Synchronization demands that firms establish interoperable digital platforms through which ESG data are exchanged transparently with suppliers and distributors, thereby creating a shared basis for decision making. Innovation then builds on these digital and informational foundations to identify process inefficiencies and generate cleaner, more resource efficient technologies. Finally, reconfiguration ensures that these digital green systems can be redeployed quickly in response to disruption, closing the loop between sensing and seizing.\u003c/p\u003e\u003cp\u003eThe empirical results underscore the payoff of this orchestration: a one standard deviation increase in DGOC corresponds to approximately a 0.40 SD gain in resilience and a 0.44 SD gain in sustainable performance (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Managers in advanced economies such as Canada should concentrate on institutionalizing DGOC within existing ESG frameworks through supplier audits, digital traceability systems, and formal cross-tier collaboration agreements so that flexibility becomes an embedded organizational routine. In developing contexts such as Iran, where regulatory support is weaker, firms should nurture DGOC through relational strategies building trust-based clusters, leveraging personal credibility, and adopting incremental digital tools that enhance coordination even in the absence of formal mandates. In both environments, the orchestration of digital and green resources converts short term efficiency into long term resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Policy Implications\u003c/h2\u003e\u003cp\u003eAt the policy level, the results challenge the assumption that regulation alone can sustain innovation. The evidence from Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that excessive institutional pressure produces negligible additional impact once ESG compliance becomes standard practice. Policymakers should therefore redirect attention from rulemaking toward capability building initiatives. Investments in national digital ESG platforms, inter firm learning consortia, and open data infrastructures can amplify the diffusion of orchestration capabilities across supply chains. Such initiatives enable small and medium sized firms to access digital tools and sustainability expertise that would otherwise remain beyond their reach, translating public governance into private capability enhancement.\u003c/p\u003e\u003cp\u003eIn developing economies, policy should prioritize mechanisms that formalize trust without bureaucratizing it. Transparent procurement systems, public private sustainability partnerships, and joint certification programs can transform interpersonal trust into institutional credibility. Over time, these initiatives help create a hybrid governance ecosystem where informal commitment and formal accountability coexist, fostering resilience not by imposing rules but by enabling orchestrated collaboration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Synthesis: Two Logics of Flexibility\u003c/h2\u003e\u003cp\u003eThe comparative analysis summarized in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e reveals that flexibility is not a uniform construct, but a plural phenomenon governed by two distinct logics. In advanced economies, flexibility is institutionalized: it is embedded in digital infrastructures, standardized ESG procedures, and predictable policy frameworks that together support systematic innovation and stable resilience. In developing economies, flexibility is personalized: it emerges through social coordination, relational trust, and improvisational learning that collectively sustain adaptation when formal mechanisms are absent. Both logics produce viable resilience, but through different routes the first by codifying behavior, the second by humanizing it.\u003c/p\u003e\u003cp\u003eDGOC bridges these logics by serving as a meta capability that operates across them. It institutionalizes flexibility when governance is abundant and personalizes it when governance is scarce. This insight redefines resilience as an orchestrated equilibrium between structure and agency, showing that dynamic capabilities derive their power not solely from technological sophistication but from their alignment with prevailing governance architectures.\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis research set out to explain how Digital Green Orchestration Capability enables supply chain flexibility, resilience, and sustainable performance across sharply contrasting institutional environments. Drawing on the Dynamic Capabilities View and Institutional Theory, the study proposed that DGOC acts as a meta capability linking digital transformation and sustainability imperatives into a single, adaptive framework. Using two wave survey data from 394 manufacturing SMEs in Iran and Canada, analyzed through PLS-SEM, the results confirmed that DGOC improves performance primarily through green innovation (β\u0026thinsp;=\u0026thinsp;0.40) and secondarily through network collaboration (β\u0026thinsp;=\u0026thinsp;0.25). Resilience, in turn, proved to be the pivotal mediator translating these effects into sustainable performance (β\u0026thinsp;=\u0026thinsp;0.44).\u003c/p\u003e\u003cp\u003eThe findings reveal that DGOC behaves differently under distinct governance logics. In advanced economies such as Canada, where formal institutions, digital infrastructures, and ESG frameworks are mature, orchestration is institutionalized embedded in standardized processes, transparent reporting, and policy compliance. In developing economies such as Iran, DGOC becomes adaptive and trust driven, functioning through social coordination, informal reciprocity, and improvisational learning. The non-significant moderation by Institutional Pressure (H8) reflects a governance saturation effect, indicating that once ESG norms reach institutional maturity, further regulatory tightening adds little incremental value. Conversely, the significant moderation by Relational Trust (H9) demonstrates that social capital remains a vital enabler of resilience when formal enforcement mechanisms are weak.\u003c/p\u003e\u003cp\u003eCollectively, these patterns affirm that flexibility is not merely a structural characteristic but an orchestrated outcome a product of how digital and environmental resources are aligned, synchronized, and reconfigured within prevailing institutional constraints. DGOC thus emerges as a unifying construct capable of institutionalizing flexibility where governance is strong and personalizing it where governance is weak. In both scenarios, the orchestration of technology, sustainability, and governance represents the essential pathway to long term adaptability.\u003c/p\u003e\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Theoretical Contributions\u003c/h2\u003e\u003cp\u003eThis study advances theory in three major respects. First, it scales the Dynamic Capabilities View to the network level, showing that adaptability arises not only from internal routines but also from the orchestration of inter organizational systems. DGOC serves as a higher order capability that coordinates sensing and seizing across partners, thereby generating collective resilience. Second, it enriches Institutional Theory by demonstrating that the activation of dynamic capabilities depends on governance maturity. Formal institutions institutionalize flexibility through compliance, while informal systems sustain it through trust. Third, it introduces the concept of institutional saturation as a theoretical boundary: when coercive and normative forces are fully diffused, additional regulation ceases to produce differentiation, and competitive advantage shifts toward orchestration quality. These insights collectively contribute to building a context sensitive theory of dynamic capabilities under institutional asymmetry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Managerial and Policy Insights\u003c/h2\u003e\u003cp\u003eFrom a managerial perspective, the results emphasize that firms should treat DGOC as an integrated system rather than a series of discrete projects. Effective orchestration requires embedding environmental metrics in digital decision making, synchronizing ESG data across tiers, and maintaining agility through continuous reconfiguration. In mature institutional contexts, managers should formalize DGOC through codified digital sustainability programs and structured ESG partnerships. In less developed contexts, managerial attention should focus on cultivating relational trust and micro level collaboration that compensate for the absence of formal support.\u003c/p\u003e\u003cp\u003eFor policymakers, the evidence suggests a strategic pivot from rule proliferation toward capability enablement. Governments can strengthen systemic resilience by funding shared digital infrastructures, facilitating inter firm knowledge platforms, and supporting trust building initiatives that bridge the public and private sectors. These interventions transform regulation from a compliance mechanism into a catalyst for capability development, allowing national supply chains to respond more dynamically to environmental and technological disruption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec40\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Limitations and Future Research\u003c/h2\u003e\u003cp\u003eDespite its methodological rigor including a two-wave design, measurement invariance testing, and extensive bias diagnostics this study has limitations that future research could address. The cross-sectional structure, though temporally separated, cannot fully establish causal evolution of DGOC. Longitudinal or panel studies tracking capability reconfiguration over multiple disruption cycles would clarify how DGOC matures and stabilizes over time. The focus on manufacturing SMEs in Iran and Canada limits generalizability to other sectors; extending the analysis to services, logistics, or digital platform ecosystems could reveal whether the same orchestration patterns apply in knowledge intensive settings. Future research might also integrate objective digital trace data, such as ESG reporting records or real time supply chain analytics, to triangulate perceptual measures and enhance validity. Moreover, combining PLS-SEM with configurational methods could uncover alternative causal pathways leading to resilience, thereby deepening understanding of capability combinations under varying institutional conditions.\u003c/p\u003e\u003cp\u003eFinally, future investigations should explore the micro foundations of DGOC in greater depth how leadership cognition, inter organizational learning, and data governance routines jointly produce orchestration quality. Such multi level designs would connect individual managerial actions to network level outcomes, completing the theoretical bridge between micro capability formation and macro institutional adaptation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec41\" class=\"Section2\"\u003e\u003ch2\u003e6.4 Closing Remark\u003c/h2\u003e\u003cp\u003eIn conclusion, this study demonstrates that supply chain resilience is neither spontaneous nor purely regulatory it is strategically orchestrated. DGOC provides the missing integrative mechanism through which digital transformation and sustainability objectives co evolve to produce long term adaptability. Whether institutionalized through regulation or improvised through trust, orchestration remains the cornerstone of flexible and sustainable supply chains. The implication for both scholars and practitioners is clear: the future of competitiveness lies not merely in adopting digital tools or meeting environmental targets, but in orchestrating them intelligently within the institutional realities that govern organizational life. All supplementary materials including full measurement items, diagnostic tests, and cross context results are available in Appendices A\u0026ndash;E.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAfshar Jahanshahi, A., Al‐Gamrh, B., \u0026amp; Gharleghi, B. (2020). 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This paper develops and validates the concept of digital green orchestration capability a meta capability integrating digital, environmental, and governance resources to enhance flexibility and performance. Drawing on the dynamic capabilities view and institutional theory, survey data were collected from 394 manufacturing SMEs across two contrasting institutional environments: Iran (developing, turbulent) and Canada (advanced, stable). Using Partial Least Squares Structural Equation Modelling (PLS-SEM), results reveal that digital green orchestration capability enhances sustainable performance primarily through innovation driven resilience (β\u0026thinsp;=\u0026thinsp;0.40) and, secondarily, through collaboration (β\u0026thinsp;=\u0026thinsp;0.25). The institutional pressure moderation was not significant, reflecting a governance saturation effect, while Relational trust significantly strengthened the collaboration resilience relationship (β\u0026thinsp;=\u0026thinsp;0.18). Findings confirm that digital green orchestration capability manifests as institutionalized flexibility in advanced systems and necessity driven flexibility in developing ones. The paper extends dynamic capabilities view to network level adaptation and offers actionable guidance for policymakers seeking to embed digital green orchestration into supply chain governance.\u003c/p\u003e","manuscriptTitle":"Digital Green Orchestration Capability and Supply Chain Flexibility: Integrating Dynamic Capabilities and Institutional Perspectives","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 11:39:25","doi":"10.21203/rs.3.rs-8065826/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7bb6881-0593-4bbb-a752-af5aab3dbdfd","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T08:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 11:39:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8065826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8065826","identity":"rs-8065826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}