Exploring the green edge: the role of market orientation and knowledge management in achieving competitive advantage through creativity

The study addresses how firms achieve green competitive advantage (GCA) amid rising environmental concerns by examining two strategic enablers: Green Market Orientation (GMO) and Green Knowledge Management (GKM). GMO reflects a firm’s proactive alignment with eco-conscious market preferences and regulatory expectations, while GKM concerns the acquisition, integration, and application of environmental knowledge to foster innovation and adaptability. The research posits that the effects of GMO and GKM on GCA may be indirect and seeks to clarify internal mechanisms—particularly Green Creativity (GC), defined as the capacity to generate innovative environmental solutions—as a mediator. Grounded in the Knowledge-Based View (KBV), the study investigates how GMO and GKM enhance GC and, in turn, GCA. The research fills gaps in understanding the nuanced dynamics among GMO, GKM, GC, and GCA, especially the mediating role of GC, and provides practical insights given cross-country challenges (regulatory hurdles, consumer perceptions, market readiness) with a focus on the Chinese SME context.

Theoretical framework: Anchored in the Knowledge-Based View (KBV), the paper argues that specialized green knowledge and its integration are central to competitive differentiation. GMO extends KBV by focusing on how firms sense and respond to environmental market dynamics, while GKM comprises processes for cultivating, disseminating, and renewing green knowledge. GC is the transformative stage converting green knowledge into innovative outputs. GCA is the culmination of strategically integrating GMO, GKM, and GC. Hypotheses development (evidence base): - GMO → GCA (H1): Prior work suggests firms with strong GMO differentiate via sustainable offerings, brand reputation, and adaptability; caveats include operational constraints and market saturation. - GMO → GC (H2): GMO provides market insights that spur eco-innovation; relationship may depend on culture, leadership, and resources. - GKM → GCA (H3): Effective green knowledge practices support environmental responsibility, innovation, and market competitiveness; benefits depend on applying knowledge strategically. - GKM → GC (H4): Established GKM frameworks facilitate knowledge sharing and ideation, boosting green creativity. - GC → GCA (H5): GC enhances market positioning via eco-innovation, stakeholder trust, and regulatory compliance; effects can be cumulative and long-term. - Mediation by GC (H6, H7): Mixed findings on direct GMO/GKM links to GCA suggest GC mediates by translating market insights and knowledge into innovative, competitive outcomes.

Research context and sample: The study targets Chinese SMEs operating in green-focused sectors: green tech solutions, sustainable transportation, eco-tourism, sustainable forestry, and bio-based industries. Stratified sampling ensured sectoral and geographic diversity (urban/rural) and consideration of size/ownership structures. A self-administered questionnaire was distributed to 510 firms; 325 complete, credible responses were obtained (63.72% response rate). Data collection occurred from January to May 2023. Measures: Constructs were measured on seven-point Likert scales using validated items adapted from prior studies. GMO: 8 items (Du & Wang, 2022; Narver & Slater, 1990). GKM: 5 items (Mao et al., 2016; Soto-Acosta et al., 2018). GC: 6 items (Barczak et al., 2010; Chen & Chang, 2013). GCA: 4 items (Chen & Chang, 2013). Content validity was established via expert panel review (6 academics, 7 industry professionals), retaining items rated highly and excluding those flagged by any expert. Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS v4 was employed, with bootstrapping (5000 subsamples). The measurement model assessed reliability (Cronbach’s alpha, composite reliability), convergent validity (AVE, loadings), and discriminant validity (HTMT, Fornell-Larcker). Multicollinearity was checked via VIF. Structural model evaluation included R2, Q2 predictive relevance, effect sizes (f2), and direct/indirect (mediation) effects. Non-response bias was assessed (early vs. late respondents) with no significant differences. Common method bias was examined via Harman’s single-factor test (largest factor explained 33.49% < 50%).

Measurement model: - Reliability (Cronbach’s alpha): GMO = 0.882; GKM = 0.760; GC = 0.832; GCA = 0.763. Composite reliability (all > 0.7). - Convergent validity: AVE values exceeded 0.5 (GMO = 0.544; GKM = 0.508; GC = 0.544; GCA = 0.585); item loadings > 0.6 (lowest GKM4 = 0.668). - Discriminant validity: HTMT range 0.372–0.823 (< 0.85); Fornell-Larcker criteria satisfied. - Multicollinearity: VIFs below 5 (e.g., GCA VIF = 1.877; GC VIF = 1.117). Model fit and predictive power: - R2: GC = 0.467; GCA = 0.488. - Q2: GC = 0.25; GCA = 0.274 (both > 0), indicating predictive relevance. Effect sizes (f2): - On GC: GMO = 0.116 (small); GKM = 0.511 (large). - On GCA: GMO = 0.043 (small); GKM = 0.044 (small); GC = 0.226 (medium). Structural paths (Table 6; all p < 0.001): - H1 GMO → GCA: β = 0.166, SE = 0.047, t = 3.550 (Supported). - H2 GMO → GC: β = 0.262, SE = 0.043, t = 6.047 (Supported). - H3 GKM → GCA: β = 0.194, SE = 0.052, t = 3.695 (Supported). - H4 GKM → GC: β = 0.552, SE = 0.034, t = 16.405 (Supported). - H5 GC → GCA: β = 0.468, SE = 0.056, t = 8.326 (Supported). Mediation effects: - H6 GMO → GC → GCA: indirect β = 0.123, SE = 0.025, t = 4.928 (Supported). - H7 GKM → GC → GCA: indirect β = 0.258, SE = 0.035, t = 7.396 (Supported). Overall, GMO and GKM both positively influence GC and GCA, with GC playing a significant mediating role in translating GMO and GKM into GCA.

The results confirm the KBV perspective that market-oriented sensing of green demand (GMO) and effective green knowledge processes (GKM) are critical inputs to eco-innovation (GC) and, ultimately, green competitive advantage (GCA). GMO directly enhances GCA and stimulates GC, indicating that aligning with and shaping green market needs fosters creative eco-solutions that differentiate firms. GKM substantially boosts GC and contributes to GCA, highlighting that merely possessing green knowledge is insufficient; firms must diffuse and apply it creatively. The findings reconcile mixed evidence on direct GMO/GKM–GCA links by showing that GC is a key mechanism converting insights and knowledge into competitive outcomes. This is salient in contexts with limited green demand or risks of greenwashing, where creativity transforms orientation and knowledge into credible offerings, processes, and positioning that earn stakeholder trust and regulatory goodwill. Thus, cultivating GC helps firms navigate operational constraints, market saturation, and credibility challenges, strengthening the pathway from GMO and GKM to GCA.

This study demonstrates, within a KBV framework, that GMO and GKM positively affect GC and GCA among Chinese green-oriented SMEs, with GC significantly mediating the effects of both GMO and GKM on GCA. Contributions include integrating GC as a central mechanism linking market orientation and knowledge practices to competitive advantage, and providing robust empirical support via PLS-SEM. Managerially, firms should jointly invest in market sensing of green needs, build systems for green knowledge sharing and application, and purposefully develop green creativity to translate insights into differentiating, sustainable innovations. Future research should deepen causal inference, explore contextual moderators, and extend to varied sectors and regions.

Key limitations include: cross-sectional design limiting causal claims; potential endogeneity (simultaneity, omitted variables) not fully resolved by SEM; voluntary sampling of Chinese SMEs may introduce selection bias toward greener firms; generalizability beyond the Chinese SME context and selected sectors may be constrained. Future research should employ longitudinal designs and causal methods (e.g., instrumental variables), test contextual factors (regulatory environment, culture, stakeholder pressures), and examine trade-offs and barriers in embedding GC. Broader, probabilistic sampling could mitigate selection bias and enhance external validity.