The RBV [8][15] posits that sustainable competitive advantage derives from resources and capabilities that are valuable, rare, imperfectly imitable, and non-substitutable. While influential, the RBV has been criticised for its emphasis on resource stocks at the expense of dynamic processes of resource renewal [16], its potentially tautological core propositions [17], and its inability to explain how firms sustain advantages amid continuous environmental upheaval [18]. These limitations motivated the development of the dynamic capabilities framework.

Dynamic capabilities, defined as the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments [19] p. 516, were subsequently disaggregated into three micro-foundations by Teece [10]: sensing scanning and interpreting environmental information; seizing decision-making structures and investment commitments to exploit opportunities; and reconfiguring realigning organisational structures and asset configurations. In SME, sensing reflects the owner-manager’s cognitive alertness rather than formalised intelligence systems [14]; seizing is constrained by limited capital and informal decision-making; and reconfiguring may involve bricolage creative reassembly of existing resources [20].

A central theoretical challenge concerns the boundary between dynamic capabilities and strategic outcomes. Following Helfat and Peteraf [1] and Winter [21], this study conceptualises dynamic capabilities as latent organisational processes the routines, cognitive capacities, and governance mechanisms enabling detection, commitment, and transformation. Strategic adaptation, by contrast, is defined as the observable, purposive recalibration of a firm’s competitive posture in response to environmental changes [22][23]. This distinction parallels the relationship between cognitive ability and academic performance: the latent capacity enables but does not automatically produce the observable outcome [23]. Modelling strategic adaptation as a separate mediating construct thus enables more precise theorisation of how dynamic capabilities generate competitive effects.

Institutional voids the absence of reliable legal enforcement, transparent regulation, and efficient information intermediaries impose additional demands on firm-level adaptive capacities [2][3]. Fainshmidt et al. [24] argue that dynamic capabilities assume greater strategic importance in institutionally weak environments. Empirical research in African contexts includes work by Nkongolo-Bakenda and Chrysostome on Congolese SMEs, Acquaah on Ghanaian firms, and Adomako et al. on dynamic managerial capabilities in Ghana. In Cameroon, Ndjanyou [25] and Fotso Kouam [6] have examined related dimensions, but no prior study has tested the full Teece framework in this circumstance.

Competitive advantage for SMEs is typically manifested through customer intimacy, localised knowledge, and niche specialisation rather than scale-based cost leadership [26]. In Cameroon, relational embeddedness within community-based networks provides additional sources of advantage [27]. Environmental dynamism the rate and unpredictability of environmental change [28] is theorised as a boundary condition amplifying the relevance of dynamic capabilities [29][30]. Cameroon’s environment exhibits substantial dynamism driven by commodity price volatility, currency rigidity, regional insecurity, and digital transformation [7][31].

A positivist, cross-sectional survey design was employed to collect quantitative data from SME owner-managers across three economically significant regions of Cameroon: Littoral Douala, Centre Yaoundé, and West Bafoussam, which collectively account for approximately 68% of registered SMEs [4]. The target population comprised owner-managers of formally registered SMEs employing 5 - 250 employees per the APME classification. A stratified random sampling procedure was used, with strata defined by region and sector. The sampling frame was constructed from the APME enterprise registry and regional Chamber of Commerce membership lists. Stratum sample sizes were allocated proportionally to each region’s share of the frame, yielding target quotas of 214 firms for Littoral, 171 for Centre, and 109 for West. Within each region, firms were further stratified across seven sectors agriculture and agri-food, retail and commerce, manufacturing, services, construction, information and communication technology, and hospitality in proportion to the sector composition of the regional sub-frame. Target sector counts within each region were obtained by multiplying that region’s overall quota by the sector’s share of the regional sub-frame, producing one target count per region sector cell. Within each region sector cell, firms were selected by simple random sampling: each eligible firm was assigned a sequence number and draws were generated using the RAND function in Microsoft Excel against the merged APME Chamber of Commerce list, with replacement draws used where selected firms declined or could not be contacted, until the target cell quota was reached. The realised regional distribution of the 387 analysed responses Littoral 43.4%, Centre 34.6%, West 22.0% closely matches the planned allocation. In absolute terms, the achieved regional samples comprised 168 firms in Littoral, 134 in Centre, and 85 in West; the realised sectoral composition of the achieved sample is reported in Section 5.1.

A total of 494 questionnaires were distributed between March and July 2025, of which 401 were returned response rate: 81.2%. After excluding 14 responses with excessive missing data, the final analytical sample comprised 387 valid responses effective response rate: 78.4%. Non-response bias was assessed using independent samples t-tests comparing early and late respondents, revealing no statistically significant differences [32].

All constructs were measured using multi-item scales adapted from validated instruments on five-point Likert scales 1 = strongly disagree to 5 = strongly agree. Dynamic capabilities were operationalised using a 15-item scale adapted from Wilden et al. [30] and Pavlou and El Sawy [33], with five items each for sensing, seizing, and reconfiguring. Strategic adaptation was measured using eight items adapted from Zajac et al. [22] and Zhou and Li [31]. Competitive advantage was assessed using seven items adapted from Man et al. [26] and Newbert [34]. Environmental dynamism used five items adapted from Dess and Beard [28] and Miller and Friesen [35]. The questionnaire was developed in English, translated into French, and back-translated to ensure semantic equivalence. A pilot study with 35 SME owner-managers informed minor revisions. Consistent with the source instruments and the conceptualisation of each construct as a set of interchangeable indicators reflecting a common underlying latent variable, all six constructs sensing, seizing, reconfiguring, strategic adaptation, competitive advantage, and environmental dynamism were modelled reflectively. Standardised outer loadings were inspected against the .70 threshold [36]: all 35 retained indicators loaded between 0.712 and 0.879 on their assigned construct, and no item was dropped during measurement purification.

Multiple procedural and statistical measures addressed common method bias CMB, [37]. Procedurally, respondent anonymity was assured, independent and dependent variable items were separated, and the verbal anchors of the response scales were varied across blocks agreement, frequency, and evaluative anchors while a common five-point response width was retained across all constructs to preserve scale comparability. Statistically, Harman’s single-factor test showed the first factor explained only 31.4% of variance, full collinearity VIF values ranged from 1.42 to 2.38 below 3.3, [38] and a measured latent marker variable exhibited negligible correlations with substantive constructs (r = 0.03 to 0.09, all p > 0.10). These measures provide reasonable confidence that CMB does not substantially bias the results.

Data were analysed using PLS-SEM in SmartPLS 4.0 [39], selected for its prediction-oriented emphasis, suitability for complex models with mediating and moderating pathways, and less restrictive distributional assumptions [36]. Control variables included firm size log-transformed employee count, firm age, sector, and owner-manager education level. None of the control variables exerted a statistically significant effect on either strategic adaptation or competitive advantage firm size: β = 0.041, p = 0.28; firm age: β = 0.028, p = 0.46, sector dummies all |β| < 0.050, p > 0.18; owner-manager education: β = 0.034, p = 0.37. Comparison of the focal model with and without the controls showed that their inclusion did not materially alter the magnitude or significance of the hypothesised paths largest observed change: |Δβ| < 0.012. For parsimony, control estimates are not tabulated in Table 1 but are available from the authors on request.

Table 1. PLS-SEM structural model results.

β = standardised path coefficient; SE = standard error; ED = Environmental Dynamism; SA = Strategic Adaptation. Bootstrap samples = 5000.

The sample spans seven sectors, with agriculture and agri-food (28.4%; n = 110) and retail/commerce (22.6%; n = 87) as the largest segments. Small enterprises (10 - 49 employees) represent 42.4% (n = 164), medium (50 - 250) 31.3% (n = 121), and micro (5 - 9) 26.3% (n = 102). The majority of owner-managers are male (67.7%), hold at least a bachelor’s degree (58.1%), and have more than five years of experience (63.8%). Firms are distributed across Littoral (43.4%; n = 168), Centre (34.6%; n = 134), and West (22.0%; n = 85) regions.

The measurement model demonstrates satisfactory psychometric properties (Table 2). All Cronbach’s alpha values range from .841 to 0.912 (exceeding 0.70), composite reliability values from 0.888 to 0.929 (exceeding 0.70), and AVE values from .614 to .689 (exceeding 0.50), confirming convergent validity [36][40]. VIF values (1.42 - 2.13) indicate no multicollinearity concern.

Table 2.Reliability and convergent validity of constructs.

CR = Composite Reliability; AVE = Average Variance Extracted; VIF = Variance Inflation Factor.

Discriminant validity was confirmed using the Fornell-Larcker criterion (Table 3) and HTMT ratios (0.418 - 0.672, all below 0.85).

Table 3. Fornell-larcker discriminant validity matrix.

Diagonal values (bold) = AVE . All off-diagonal correlations significant at p < 0.01. SENS = Sensing; SEIZ = Seizing; RECON = Reconfiguring; SA = Strategic Adaptation; CA = Competitive Advantage; ED = Environmental Dynamism.

Path coefficients were estimated using bootstrapping with 5000 subsamples [36]. Table 1 presents the results.

The structural model with path coefficients (β values) is illustrated in Figure 2Figure 2. Mediation analysis using bootstrapping with 5000 bias-corrected and accelerated resamples confirmed that strategic adaptation mediates the relationship between each dynamic capability dimension and competitive advantage. The three indirect effects through strategic adaptation were all positive and statistically significant: sensing → strategic adaptation → competitive advantage (β = 0.158, SE = 0.027, 95% BCa CI [0.107, 0.213], p < 0.001); seizing → strategic adaptation → competitive advantage (β = 0.132, SE = 0.026, 95% BCa CI [.084, .185], p < 0.001); and reconfiguring → strategic adaptation → competitive advantage (β = 0.101, SE = 0.025, 95% BCa CI [0.055, 0.152], p < 0.001). The full mediation and partial mediation models were compared using the PLS-SEM mediation decision procedure recommended by Hair et al. [36]. In the partial mediation model, direct paths from each capability to competitive advantage were estimated alongside the mediated paths: none of these direct paths reached significance (sensing → CA: β = 0.042, p = 0.31; seizing → CA: β = 0.031, p = 0.47; reconfiguring → CA: β = 0.018, p = 0.68), while the three indirect effects remained significant at the magnitudes reported above. The partial model produced no meaningful gain in explained variance for competitive advantage (ΔR² = 0.003) and a higher BIC value, indicating a deterioration in parsimony-adjusted predictive performance. Because the additional direct paths were jointly nonsignificant, the indirect effects remained substantively unchanged, and the partial specification was less parsimonious, the full mediation model was retained as the preferred specification.

Figure 2. PLS-SEM path model with coefficients.