Abstract

The study investigates the role of dynamic capabilities in facilitating strategic adaptation and generating sustainable competitive advantage among Small and Medium Enterprises (SMEs) in Cameroon’s emerging market. Grounded in the dynamic capabilities framework and the resource-based view, the research examines how sensing, seizing, and reconfiguring capabilities influence strategic adaptation and market performance. The study distinguishes dynamic capabilities as latent organisational processes from strategic adaptation as the observable recalibration of competitive posture, arguing that the latter constitutes the behavioural channel through which dynamic capabilities produce competitive effects. A cross-sectional survey of 387 SME owner-managers across three regions of Cameroon was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results indicate that sensing (β = 0.342, p < 0.001), seizing (β = 0.287, p < 0.001), and reconfiguring (β = 0.219, p < 0.001) significantly predict strategic adaptation. Strategic adaptation strongly predicts competitive advantage (β = 0.461, p < 0.001), and environmental dynamism positively moderates this relationship (β = 0.156, p < 0.01). Alternative model comparisons confirm full mediation as the preferred specification. As one of the first empirical examinations of dynamic capabilities in Central Africa’s SME sector, the study extends dynamic capabilities theory into contexts characterised by institutional voids.

Keywords

Dynamic Capabilities, Competitive Advantage, Strategic Adaptation, SMEs, Cameroon, Emerging Markets, PLS-SEM

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1. Introduction

The global business landscape has undergone unprecedented transformations driven by technological change, economic integration, and intensifying competitive pressures [1]. For small and medium enterprises (SMEs) in emerging markets, these developments present both challenges and opportunities. Cameroon, a lower-middle-income economy in the Gulf of Guinea, exemplifies the complex conditions under which African SMEs compete, characterised by institutional voids, infrastructural deficits, regulatory unpredictability, and informational asymmetries [2] [3].

SMEs constitute approximately 99.2% of formally registered enterprises in Cameroon [4] and employ an estimated 72% of the non-agricultural labour force [5]. Yet nearly 60% of newly registered SMEs cease operations within three years, and only 15% survive beyond five years [6] [7]. Traditional strategic management theories, particularly the resource-based view (RBV; [8]), offer valuable frameworks but have been criticised for their static orientation and limited ability to explain how firms adapt resource configurations amid environmental turbulence.

The dynamic capabilities framework [9] [10] addresses this limitation by theorising how firms sense opportunities and threats, seize those opportunities through strategic investments, and reconfigure asset bases to sustain evolutionary fitness. However, fewer than 4% of empirical studies on dynamic capabilities have been conducted in Sub-Saharan Africa [11], and none in Central Africa’s SME sector specifically. Cameroon provides a particularly instructive environment given its bilingual institutional heritage, commodity-dependent economic structure, and the competitive pressures arising from AfCFTA implementation, for which Cameroon’s readiness is assessed at only 42% [12].

The study pursues two objectives first, to examine the extent to which sensing, seizing, and reconfiguring capabilities contribute to strategic adaptation among Cameroonian SMEs and second, to evaluate whether strategic adaptation mediates the relationship between dynamic capabilities and competitive advantage, with environmental dynamism as a moderator. The study represents the first empirical test of Teece’s [10] tripartite framework among SMEs in a Central African emerging economy, responding to calls for contextualised investigations of dynamic capabilities in institutionally diverse settings [13] [14].

2. Literature Review

2.1. The Resource-Based View and Its Limitations

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.

2.2. Dynamic Capabilities: Conceptual Foundations

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].

2.3. Distinguishing Dynamic Capabilities from Strategic Adaptation

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.

2.4. Dynamic Capabilities in Emerging Markets and African Contexts

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.

2.5. Competitive Advantage and Environmental Dynamism

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].

3. Conceptual Framework and Hypotheses

Building on the preceding review, the study proposes an integrative framework (Figure 1) positioning the three dimensions of dynamic capabilities as independent variables, strategic adaptation as a mediating variable, competitive advantage as the dependent variable, and environmental dynamism as a moderator. Firm size, firm age, sector, and owner-manager education serve as controls.

Figure 1. Dynamic capabilities, strategic adaptation, and competitive advantage.

The framework specifies full mediation through strategic adaptation based on three theoretical grounds: dynamic capabilities are higher-order processes whose competitive effects are indirect [21]; the link between dynamic capabilities and performance is mediated by substantive capability changes [23]; and prior empirical work supports full mediation [30]. An alternative partial mediation model is tested for robustness (see Section 5.4).

H1: Sensing capabilities have a significant positive effect on strategic adaptation among Cameroonian SMEs.

H2: Seizing capabilities have a significant positive effect on strategic adaptation among Cameroonian SMEs.

H3: Reconfiguring capabilities have a significant positive effect on strategic adaptation among Cameroonian SMEs.

H4: Strategic adaptation positively mediates the relationship between dynamic capabilities and competitive advantage.

H5: Environmental dynamism positively moderates the relationship between strategic adaptation and competitive advantage.

4. Research Methodology

4.1. Research Design, Sampling, and Data Collection

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].

4.2. Measurement Instruments

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.

4.3. Common Method Bias Mitigation

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.

4.4. Data Analysis

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.

5. Results

5.1. Respondent Profile

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.

5.2. Measurement Model Assessment

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.

5.3. Discriminant Validity

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) = $\sqrt{\text{AVE}}$ . All off-diagonal correlations significant at p < 0.01. SENS = Sensing; SEIZ = Seizing; RECON = Reconfiguring; SA = Strategic Adaptation; CA = Competitive Advantage; ED = Environmental Dynamism.

5.4. Structural Model and Hypothesis Testing

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 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.

6. Discussion

The findings provide robust empirical support for Teece’s dynamic capabilities framework in a Central African SME context. All three capability dimensions sensing, seizing, and reconfiguring significantly predict strategic adaptation, suggesting that owner-managers’ alertness, investment commitment, and resource realignment are positively associated with purposeful strategic shifts. This underscores the claim that dynamic capabilities operate as higher-order processes that are indirectly linked with performance outcomes [10]. Strategic adaptation, in turn, strongly predicts competitive advantage, indicating that recalibrating competitive posture is a key channel through which dynamic capabilities are linked with market success. The positive moderation by environmental dynamism supports the theoretical expectation that dynamic capabilities are more valuable under conditions of rapid change [29] [30].

The full mediation effect suggests that dynamic capabilities are associated with competitive advantage predominantly through their link with adaptive strategy execution, rather than through any direct association with performance. This finding aligns with recent theorising that emphasises the intervening role of actions and routines [1] [16]. In practice, it suggests that, for SMEs in weak institutional settings, translating sensing and seizing into observable strategic changes (e.g. new products, markets, or alliances) may be an important route to advantage. Policy-wise, support measures (e.g. training, networks) that enhance SMEs’ ability to adapt strategically could amplify the returns to entrepreneurial alertness and resource reconfiguration.

The study contributes to the literature by extending the dynamic capabilities paradigm to an under-researched setting. It complements prior African studies [6] [25] by linking dynamic capabilities to competitive advantage via an explicit strategy channel. The results also support the robustness of Teece’s [10] tripartite model across environments. While much of dynamic capabilities research focuses on large firms in advanced economies, our findings show that owner-managers of SMEs can exercise analogous processes, albeit informally [14].

7. Conclusions

The study suggests that dynamic capabilities are closely associated with SME strategic outcomes in Cameroon’s turbulent environment. Sensing, seizing, and reconfiguring capabilities are positively associated with strategic adaptation, which in turn shows the strongest association with competitive advantage among the variables examined. The moderating role of environmental dynamism highlights that in rapidly changing contexts, the capacity to adapt is even more critical. For managers, the implication is clear: develop routines and mindsets that emphasise opportunity recognition, flexible decision-making, and resource redeployment. For policymakers, improving institutional support (e.g. information systems, market access) can strengthen these firm-level capabilities indirectly. Two scope boundaries should be kept in view when interpreting these conclusions. First, because the research design is cross-sectional and relies on self-reported measures from a single informant per firm, the reported relationships are associations rather than established causal effects, and the directional language used throughout the Discussion and Conclusion should be read as theory-consistent interpretation rather than as causal demonstration. Second, the findings apply to formally registered SMEs employing 5 - 250 workers in three economically significant regions Littoral, Centre, and West and should not be extrapolated to the full Cameroonian SME population, which includes informal micro-enterprises, firms in other regions, and rural operators whose institutional environment, digital access, and adaptive repertoires may differ materially from those captured in the present sample.

The study’s limitations include its cross-sectional design and reliance on self-reported measures. Future research could employ longitudinal or qualitative approaches to unpack the microprocesses of adaptation in African SMEs. Comparative studies across different African countries could also examine how national institutions shape the development and efficacy of dynamic capabilities.

Conflicts of Interest

The authors declare no conflicts of interest.

References