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Article

Strategic Capabilities Integration for Competitive Advantage: Evidence from Thailand’s Freight Forwarding Industry

by
Nattakorn Pinyanitikorn
*,
Rawida Wiriyakitjar
and
Aannicha Thunyachairat
Business School, University of the Thai Chamber of Commerce, Bangkok 10400, Thailand
*
Author to whom correspondence should be addressed.
Logistics 2026, 10(6), 119; https://doi.org/10.3390/logistics10060119
Submission received: 11 January 2026 / Revised: 17 February 2026 / Accepted: 20 March 2026 / Published: 29 May 2026
(This article belongs to the Special Issue Advancing Circular Supply Chains: Integrating Logistics, Supply Chain Management and Circular Economy Practices)
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Abstract

Background: Thailand is considered a logistics hub in Southeast Asia where the freight forwarding sector is essential for international trade and economic growth. This study aims to explore the relationships between logistics resources, strategic capabilities, competitive advantage, and organizational performance in Thailand’s freight forwarding sector. Methods: A quantitative cross-sectional survey was conducted with 250 management-level respondents from Thai freight forwarding companies. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine direct and mediating relationships among constructs. Results: Logistics resources significantly influenced competitive advantage, while strategic capabilities that integrate dynamic capabilities and green supply chain management exerted a stronger effect. Competitive advantage strongly predicted organizational performance, explaining 53.4% of its variance. Mediation analysis confirmed that competitive advantage mediates the effects of both logistics resources and strategic capabilities on organizational performance. Conclusions: Strategic capabilities exert greater impact on competitive advantage than static logistics resources, underscoring that organizational adaptability is more critical than resource possession alone. Freight forwarding firms should prioritize capability development and embed environmental management as a core competency rather than a compliance obligation.

1. Introduction

The global logistics industry has seen remarkable growth, with a market value of $12.2 trillion in 2023 and a projected $18.7 trillion by 2030, reflecting a compound annual growth rate of 6.2% [1]. Within this context, the freight forwarding sector is essential for international trade and economic growth. Thailand, as a logistics hub in Southeast Asia, is a significant case for analyzing competitive advantage development, with its freight forwarding industry contributing about 14.2% to the country’s Gross Domestic Product [2]. However, Singapore dominates the Southeast Asian market with a 31% share, followed by Malaysia (19%), Thailand (16%), Vietnam (14%), and Indonesia (12%) [3]. This competitive scenario demands strategic differentiation through enhanced service quality, operational excellence, and sustainable practices. Additionally, the industry encounters several concurrent challenges, including digital transformation needs, environmental sustainability mandates, and the necessity for ongoing adaptation to rapidly evolving market conditions [4,5].
While existing literature has extensively analyzed the role of third-party logistics (3PL) providers in improving supply chain efficiency in Western contexts [6,7], notable gaps persist regarding their applicability in Asian business environments. Few studies have specifically examined how freight forwarders in emerging Asian economies cultivate competitive advantage through the strategic integration of resource-based capabilities, dynamic adaptation strategies, and environmental management practices [8,9,10]. Most prior research has focused on general logistics performance and operational efficiencies without adequately addressing their interconnections within the distinct institutional and cultural frameworks of Southeast Asian markets.
This research was conducted to address the critical need for understanding how freight forwarding firms in emerging Asian economies can develop sustainable competitive advantages amid increasing market pressures and environmental demands. The study is guided by the following research questions: (1) How do logistics resources influence competitive advantage in Thailand’s freight forwarding industry? (2) What role do strategic capabilities encompassing dynamic capabilities and green supply chain management (GSCM) capabilities play in facilitating competitive advantage? (3) How does competitive advantage translate into organizational performance? (4) What mediating mechanisms connect resources and capabilities to performance outcomes?
This study employs a quantitative research approach using structural equation modeling (SEM) with data collected from 250 management-level respondents in Thai freight forwarding companies. SEM was selected as the analytical method because it allows for simultaneous examination of multiple relationships among latent constructs, effectively testing both direct and mediating effects while accounting for measurement error [11]. This methodological approach is particularly suitable for validating complex theoretical models involving resources, capabilities, and performance outcomes.
The originality of this study lies in its integrative framework that combines re-source-based view (RBV), dynamic capabilities theory, and GSCM within a single model, specifically contextualized for Southeast Asian emerging markets. Unlike previous studies that examined these constructs in isolation or within Western contexts, this research contributes to the literature by: (1) empirically testing the relative importance of static resources versus dynamic capabilities in generating competitive advantage; (2) incorporating environmental sustainability as a core strategic capability rather than a peripheral compliance requirement; (3) examining the mediating role of competitive advantage in an Asian business environment characterized by relationship-centric practices; and (4) providing practical guidance for freight forwarding firms seeking sustainable competitive positioning in rapidly evolving markets.

2. Literature Review

2.1. Empirical Studies on Competitive Advantage in Logistics and Freight Forwarding

The relationship between organizational resources, capabilities, and competitive advantage in logistics has been extensively examined in the literature. However, most studies have been conducted in Western contexts, with limited attention to emerging Asian markets. This literature review was conducted through a systematic search of academic databases and industry sources to identify relevant studies on logistics resources, dynamic capabilities, green supply chain management, and competitive advantage in the logistics sector. The search strategy employed the databases from Google Scholar, Scopus, Web of Science, Emerald Insight, and MDPI to capture both peer-reviewed academic literature and conference proceedings. Publications from 2010 to 2025 were prioritized to ensure currency of findings, with seminal works from earlier periods included when they established foundational theoretical frameworks. Studies were included if they: (1) examined relationships between organizational resources, capabilities, and performance outcomes; (2) focused on logistics or supply chain contexts; (3) provided empirical evidence through quantitative or qualitative methods; or (4) addressed freight forwarding, third-party logistics, or related service industries. The key findings from the reviewed studies are summarized in Table 1.

2.2. Research Gap

Despite the growing body of literature on logistics resources, dynamic capabilities, and green supply chain management, several significant gaps remain that this study seeks to address. First, the majority of existing research has been conducted within Western business contexts, with limited empirical investigation of how these constructs operate within Asian institutional and cultural frameworks [9,10]. The applicability of Western-developed theories to emerging Asian economies, where relationship-based practices and collectivistic decision-making prevail, remains underexplored. Second, while individual constructs such as RBV, dynamic capabilities, and GSCM have been studied extensively in isolation, few studies have integrated these perspectives within a unified framework to examine their relative and combined effects on competitive advantage [16,23]. This fragmented approach limits our understanding of how resources and capabilities interact to generate sustainable competitive positioning. Third, the freight forwarding sector in Southeast Asia has received insufficient scholarly attention despite its economic significance. Most prior research has focused on general logistics performance and third-party logistics providers without adequately addressing the unique characteristics and challenges of freight forwarding operations in emerging markets [7,8]. Fourth, the mediating mechanisms through which resources and capabilities translate into organizational performance remain inadequately examined. While competitive advantage is theoretically positioned as an intervening variable, empirical validation of this mediation relationship particularly in Asian contexts is limited [24,25]. Finally, environmental sustainability has predominantly been treated as a peripheral compliance requirement rather than a core strategic capability. The integration of GSCM as a fundamental component of strategic capabilities that drives competitive advantage warrants further empirical investigation [26,27].

3. Theoretical Framework and Hypotheses Development

3.1. Resource-Based View and Logistics Resources

The Resource-Based View (RBV) is a foundational framework in strategic management for analyzing competitive advantage, particularly in knowledge-intensive sectors such as freight forwarding [10]. RBV posits that superior performance stems from resources that are Valuable, Rare, Inimitable, and Non-substitutable (VRIN) [12]. This framework suggests that recognizing and acquiring such resources enhances organizational performance and sustains competitive advantages [12]. In the freight forwarding context, success largely depends on intangible assets such as relationships, expertise, and reputation that are challenging for competitors to replicate [10]. Following Wong and Karia [12], this study delineates five logistics resource components: technology resources (capabilities for information management and digital systems), physical resources (logistics equipment and infrastructure), management expertise resources (skilled personnel and logistics knowledge), relational resources (stakeholder relationships and network connections), and organizational resources (systems and processes for strategy implementation).
In Asian contexts, RBV applications have adapted to emphasize relationship-based resources, which are often paramount in collectivistic cultures [28,29]. Thai freight forwarding firms must leverage these resources within the context of local cost dynamics, regulatory environments, and cultural practices that differ from Western norms [30].
Given that logistics resources meeting VRIN criteria should enable firms to establish superior market positions, this study hypothesizes:
H1. 
Logistics resources positively influence competitive advantage.

3.2. Dynamic Capabilities Theory

Dynamic Capabilities Theory, introduced by Teece et al. [31], extends strategic management understanding by explaining how firms adapt competencies in volatile environments. Dynamic capabilities are defined as the firm’s proficiency in integrating, building, and reconfiguring internal and external competencies to address rapidly changing environments [31]. This theory is particularly relevant to Asian business contexts facing digital transformation and global disruptions [22,32]. Teece [31] delineates dynamic capabilities into three dimensions. Sensing entails evaluating opportunities and threats in dynamic environments; in Thai freight forwarding, this includes detecting regulatory shifts, customer expectation changes, and market opportunities [20]. Seizing denotes the capacity to identify pertinent information and adapt business models while judiciously allocating resources, requiring integration of rapid adaptation with relationship maintenance central to local organizational cultures [28,31]. Transforming encompasses reorganizing business structures and investing in new capabilities, requiring culturally sensitive and relationship-preserving strategies [28,31].
Recent studies distinguish dynamic capabilities from operational and ordinary capabilities, emphasizing that higher-order capabilities reflect a firm’s ability to adapt and develop resources in response to environmental changes [33,34]. The ongoing integration of technologies such as Artificial Intelligence, blockchain, and Internet of Things enhances operational efficiency, yet effective adaptation requires both technological investment and organizational transformation [35]. Given that dynamic capabilities enable firms to continuously adapt to market changes and develop sustainable competitive positions, this study hypothesizes:
H2. 
Dynamic capabilities positively influence competitive advantage.

3.3. Green Supply Chain Management Capabilities

Green Supply Chain Management (GSCM) represents a vital framework for integrating environmental considerations into supply chain operations while maintaining competitive edge [36]. In Southeast Asia, GSCM is particularly significant due to rapid industrialization and environmental challenges necessitating sustainable practices [37]. Researchers increasingly investigate sustainability-oriented capabilities that facilitate firms’ green transformation, especially amid evolving Asian environmental regulations [38]. Lee et al. [13] identified four foundational constructs of GSCM practices. Eco-design involves integrating environmental considerations into service design, emphasizing that design decisions significantly influence environmental impacts through material use, energy consumption, and lifecycle assessments [39,40]. Customer cooperation focuses on collaborative environmental initiatives with supply chain partners, recognizing cultural nuances in Thai business practices [37,41]. Green purchasing emphasizes integrating environmental criteria into procurement and supplier relations, balancing relationship-based practices with environmental considerations [23,28]. Internal environmental management focuses on systems and capabilities enabling organizations to meet environmental objectives through monitoring systems, commitment mechanisms, and cross-functional cooperation [42].
In Thailand, sustainability is emerging as a core component of supply chain strategies, providing freight forwarders opportunities to differentiate through environmental performance [43]. As Asian firms face increasing international and regulatory pressures regarding environmental performance, GSCM capabilities enable creation of customer value propositions focused on sustainability [26,27]. Given that GSCM capabilities enable firms to meet environmental demands while creating differentiation opportunities, this study hypothesizes:
H3. 
GSCM capabilities positively influence competitive advantage.

3.4. Competitive Advantage and Organizational Performance

Porter’s competitive advantage theory outlines how firms achieve sustainable advantages through strategic industry positioning [44]. Firms can pursue competitive advantage through three generic strategies: cost leadership (achieving lowest industry service costs), differentiation (emphasizing unique service attributes), and focus (targeting specific customer segments or niches) [44,45]. In Thailand’s freight forwarding sector, cost leadership requires understanding local cost dynamics, regulations, and operational efficiencies distinct from Western norms, exploiting geographic advantages and governmental logistics incentives [30,43]. Differentiation is often based on expertise in Asian trade networks, cultural awareness, and regulatory navigation across Southeast Asian countries, where personalized service respecting hierarchical structures is crucial [10]. Focus strategies are pertinent due to diverse industry needs and trade routes, including specialized sectors like cold chain logistics or hazardous materials [6].
Strategic management literature clarifies that organizational capabilities must be transformed into market positions for superior performance [31,44,46]. While valuable, capabilities alone do not yield performance outcomes; they must be utilized to forge competitive advantages that favorably position the organization against competitors, thereby enhancing financial, operational, market, and customer performance [18]. Given that competitive advantage represents the mechanism through which superior market positioning translates into tangible performance outcomes, this study hypothesizes:
H4. 
Competitive advantage positively influences organizational performance.

3.5. Mediating Role of Competitive Advantage

The mediation relationship between organizational inputs and performance outcomes is central to understanding how resources and capabilities generate value [18]. Barney [46] argued that competitive advantage serves as the essential link between organizational resources and capabilities with performance outcomes. Resources and capabilities, regardless of their value, must be deployed to create competitive positions that distinguish the firm from rivals before performance benefits can be realized. This mediation logic suggests that logistics resources (while necessary) are insufficient for performance enhancement unless they are leveraged to establish competitive differentiation. Similarly, strategic capabilities including dynamic capabilities and GSCM capabilities generate performance outcomes not directly, but through their contribution to competitive positioning [18,22,38].
Given that resources and capabilities require translation into competitive positions before generating performance outcomes, this study hypothesizes:
H5. 
Competitive advantage mediates the relationship between logistics resources and organizational performance.
H6. 
Competitive advantage mediates the relationship between strategic capabilities (dynamic capabilities and GSCM capabilities) and organizational performance.
The proposed research framework integrating logistics resources, strategic capabilities, competitive advantage, and organizational performance is illustrated in Figure 1.

4. Methodology

4.1. Research Methods

This study utilized a deductive methodology for hypothesis testing based on empirical data. A cross-sectional survey design was employed, with an online questionnaire as the primary data instrument. The study population comprised management personnel from Thai freight forwarding companies engaged in strategic, operational, or customer management roles, including senior executives and middle managers with relevant experience. As the precise population of registered freight forwarding firms in Thailand is not definitively established, with estimates of approximately 3,200 firms reported by the Department of Business Development [47]. Based on industry estimates suggesting an average of 3–5 management-level personnel per firm meeting the study criteria, the estimated total population of eligible respondents ranges from 9600 to 16,000 individuals.
Purposive sampling was employed, selecting respondents based on criteria pertinent to the research objectives [48,49]. This non-probability sampling technique was chosen because of no comprehensive database of management personnel in Thai freight forwarding exists; and the study required respondents with specific characteristics and expertise with direct knowledge of organizational resources, capabilities, and performance. Respondents were required to possess a minimum of two years in the freight forwarding industry and one year in their current management position. While purposive sampling limits generalizability compared to probability sampling [48,49], the diversity achieved across geographic regions, firm sizes, service types, and management levels supports reasonable representativeness of the Thai freight forwarding management population. The recruitment in this study involved professional networks, industry events such as seminars, direct emails, and referrals.
The target sample size of 250 respondents was determined based on several considerations. First, following Hair et al. [11], structural equation modeling requires a minimum sample size of 10 times the number of indicators for the most complex construct, or 10 times the largest number of structural paths directed at a particular construct. Given that the most complex construct in this study contains 15 indicators, a minimum of 150 respondents was required. Second, to ensure adequate statistical power (0.80) for detecting medium effect sizes at α = 0.05 in SEM analysis, a sample of 200–250 respondents is recommended [11]. Third, anticipating potential incomplete responses and data quality issues, a target of 250 usable responses was established to maintain sufficient analytical power after data cleaning.

4.2. Research Instrument

An online questionnaire was developed and administered using Google Forms with a three-month period for data collection, starting from May–July 2025. Measurement instruments were adapted from established scales to fit the Thai logistics context. Logistics Resources were measured using Wong and Karia’s five-dimensional framework [12], while Dynamic Capabilities were assessed according to Teece et al.’s framework [31]. GSCM Capabilities were evaluated based on Lee et al.’s four-dimensional framework [13], and Competitive Advantage followed Porter’s generic strategies [12,18,45]. Organizational Performance utilized Hamann and Schiemann’s multidimensional framework encompassing various performance aspects [50].
All measurement items employed five-point Likert scales from 1 (Strongly Disagree) to 5 (Strongly Agree). The questionnaire was available in Thai and English to accommodate linguistic diversity and ensure comprehension. Pre-testing was conducted with experts and researchers to validate content relevance.

4.3. Data Analysis Method

Several structural equation modeling approaches were considered for this study, including Covariance-Based SEM (CB-SEM), and Partial Least Squares (PLS-SEM) using SmartPLS. CB-SEM employs maximum likelihood estimation to minimize the discrepancy between the observed and model-implied covariance matrices, requiring multivariate normality assumptions and typically larger sample sizes (n > 200–500) [11]. In contrast, PLS-SEM utilizes an iterative algorithm based on ordinary least squares (OLS) regression, making it distribution-free and suitable for smaller samples.
PLS-SEM was selected over CB-SEM for this study based on the following considerations: (1) the research objective emphasizes prediction and explanation of variance in endogenous constructs rather than theory confirmation; (2) the model complexity involves multiple constructs with numerous indicators; (3) preliminary analysis indicated non-normal data distribution, violating CB-SEM assumptions; and (4) the sample size of 250, while adequate, benefits from PLS-SEM’s efficiency with moderate samples [11]. Alternative computational techniques such as regression-based mediation analysis (e.g., PROCESS macro) and factor-based SEM were also considered but deemed less suitable due to their inability to simultaneously estimate complex measurement and structural models with multiple mediating pathways.
Data analysis utilized SPSS version 28.0 for preliminary insights and SmartPLS version 4.0 for PLS-SEM analysis. SmartPLS version 4.0 was chosen for its efficacy in complex models, adeptness at managing non-normal distributions, and capability to yield reliable outcomes with moderate sample sizes [11]. The analysis incorporated a two-step methodology. In Stage 1 (outer model estimation), the algorithm iteratively calculates outer weights connecting indicators to their latent constructs using Mode A (correlation weights) for reflective constructs and Mode B (regression weights) for formative constructs. This study employed Mode A for all constructs as they were specified as reflective. The iteration continues until convergence is achieved, defined as when the change in outer weights between iterations falls below a specified threshold (10−7 in SmartPLS version 4.0). In Stage 2 (inner model estimation), path coefficients between latent variables are calculated using OLS regression. For each endogenous construct, the algorithm regresses the construct scores on the scores of all constructs with paths directed toward it. The path coefficient (β) represents the standardized regression weight indicating the strength and direction of relationships. The coefficient of determination (R2) is computed as the proportion of variance in endogenous constructs explained by their predictors.

5. Results

5.1. Respondents’ Personal and Organizational Profiles

The final sample consisted of 250 senior management respondents from Thai freight forwarding firms. The respondents’ profiles are presented in Table 2. A significant proportion of the respondents were from senior management, with Chief Executive Officers (CEOs) and Managing Directors at 56.4%, General Managers at 13.2%, and Operations Managers at 18.0%. This high-level executive involvement affirms the study’s strategic focus and guarantees access to critical insights. The experience profile showed that 34.4% of them having over 20 years in the industry, 21.6% with 16–20 years, and 16.4% with 11–15 years. Collectively, those with over 11 years of experience constituted 72.4% of the sample, providing valuable insights from professionals with extensive industry exposure.
Analysis of company size in regard to employment showed a predominance of small operations, with 71.2% employing fewer than 50 staffs. Service focus analysis indicated that ocean freight predominated at 42.8%, followed by air freight at 19.6% and land transport at 16.0%. Multimodal services comprised 11.6%, while warehousing and customs brokerage were smaller segments. The organizations’ ownership structure revealed significant domestic control with 72.8% Thai-owned, 17.6% foreign-owned, and 9.6% joint ventures. International engagement was notably high, with 86.4% of firms sourcing at least 25% of business from international freight operations, highlighting strong integration with global supply networks. Key differentiation factors were industry expertise and specialization (38.8%), technology and innovation (33.6%), geographic coverage (30.8%), and sustainability practices (30.4%).
Annual shipment volume analysis showed 42.0% managing 1000–5000 shipments, 27.2% managing 5001–10,000, 26.0% with fewer than 1000, and 4.8% with over 10,000 shipments.
Barriers to green implementation encompassed lack of government incentives (55.0%), regulatory uncertainty (43.4%), and high costs (41.4%). Nonetheless, 94.0% viewed environmental sustainability as crucial for future competitiveness. Strategic initiatives over the past three years encompassed digital transformation and automation (56.4%), staff training (55.6%), strategic partnerships (54.8%), and supply chain visibility systems (48.0%).

5.2. Measurement Model Assessment

The measurement model was analyzed via Smart-PLS for reliability and validity. Constructs displayed strong reliability, with composite reliability values from 0.869 to 0.960, exceeding the 0.70 benchmark. Average Variance Extracted (AVE) values were between 0.627 and 0.856, all above the 0.50 minimum. Cronbach’s alpha values ranged from 0.798 to 0.944, indicating robust internal consistency.
Initial discriminant validity assessment using the Heterotrait–Monotrait (HTMT) ratio indicated issues, particularly between GSCM Capabilities and Dynamic Capabilities (HTMT = 1.009), surpassing the threshold of 1.0. Elevated values were also noted for GSCM-Competitive Advantage (0.913) and GSCM-Organizational Performance (0.915), implying a lack of empirical distinction between environmental management and dynamic organizational capabilities. To resolve these discriminant validity concerns, a revised model was created, merging Dynamic Capabilities and GSCM Capabilities into a single higher-order construct termed “Strategic Capabilities.” This modification is theoretically substantiated as both capability sets share foundational concepts in organizational adaptability and strategic responsiveness. The integrated Strategic Capabilities construct encompasses seven dimensions: sensing (0.836), seizing (0.848), transforming (0.819), eco-design (0.647), cooperation with customers (0.769), green purchasing (0.840), and internal environmental management (0.778).
Post-modification, discriminant validity showed significant improvement. The updated HTMT matrix reflected all values within acceptable limits: Strategic Capabilities–Competitive Advantage (0.876), Logistics Resources–Competitive Advantage (0.829), Strategic Capabilities–Logistics Resources (0.801), Organizational Performance–Competitive Advantage (0.828), Organizational Performance–Logistics Resources (0.835), and Strategic Capabilities–Organizational Performance (0.903). While the latter marginally exceeded the strict 0.90 limit, it remained under the more lenient 0.95 criterion, suggesting acceptable discriminant validity.
Collinearity analysis via Variance Inflation Factor (VIF) indicated most values were within acceptable ranges of less than 5. Organizational Performance dimensions had the highest VIF values, with Operational Performance (5.911) slightly exceeding the preferred threshold of 5.0 while remaining below the conservative threshold of 10.0, indicating acceptable collinearity levels.

5.3. Structural Model Assessment

Model fit evaluation indicated mixed but satisfactory outcomes. Table 3 presents the goodness-of-fit indices for the structural model. The structural path results are visually presented in Figure 2.
The Standardized Root Mean Square Residual (SRMR) value for the estimated model (0.114) exceeds the conservative threshold of 0.08 suggested by Hu and Bentler 1999 [51]. However, several methodological considerations support the adequacy of model fit in this study.
First, the 0.08 SRMR threshold was originally developed for covariance-based SEM (CB-SEM) and may be overly stringent for PLS-SEM applications. Henseler et al., 2016 [52] argue that this threshold should not be applied rigidly in PLS-SEM contexts, where values up to 0.10 or even slightly higher may be acceptable for complex models with numerous indicators. Hair et al. [11] further emphasize that PLS-SEM prioritizes predictive accuracy over exact model fit, distinguishing it from CB-SEM approaches.
Second, the saturated model demonstrates excellent fit (SRMR = 0.047), well below the 0.08 threshold. The discrepancy between the saturated and estimated models reflects the constraints imposed by the hypothesized structural paths, which is expected in theory-driven research. The saturated model’s strong fit confirms that the measurement model adequately represents the data.
Third, regarding the Geodesic Discrepancy (d_G) and unweighted least squares discrepancy (d_ULS) values, these should be interpreted through bootstrap-based confidence intervals rather than absolute thresholds. While the estimated model shows higher d_ULS (2.487) and d_G (0.493) values compared to the saturated model, these discrepancy measures were suggested to be evaluated against bootstrap confidence intervals to determine statistical significance, in which these values fall within the 95% bootstrap confidence interval (CI) and thus the model fit is considered adequate.
Fourth, the Normed Fit Index (NFI) value for the estimated model (0.861) approaches the conventional threshold of 0.90. While slightly below this benchmark, Hair et al. [11] note that NFI tends to underestimate fit in smaller samples and should be interpreted alongside other indicators. The saturated model NFI of 0.893 further supports the adequacy of the measurement structure.
Further, the substantial R2 values (63.8% and 53.4%) indicate strong explanatory power, which is the primary evaluation criterion in PLS-SEM; and positive Q2 values confirm predictive relevance. PLS-SEM methodology also explicitly prioritizes prediction and explanation over exact model fit. Collectively, these considerations justify proceeding with hypothesis testing and interpretation of structural relationships.
Hypothesis H1 (Logistics Resources → Competitive Advantage): β = 0.377, t = 4.987, p < 0.001, was supported. This finding affirms the critical role of organizational assets in competitive positioning, validating that the five resource dimensions collectively aid freight forwarding companies in establishing competitive advantages.
Hypothesis H2 & H3 (Strategic Capabilities → Competitive Advantage): β = 0.478, t = 5.813, p < 0.001, was supported. This represents the most substantial direct effect on competitive advantage, illustrating that adaptive capabilities related to market responsiveness and environmental sustainability are vital for superior positioning, with dynamic capabilities appearing more significant than static resources.
Hypothesis H4 (Competitive Advantage → Organizational Performance): β = 0.731, t = 25.985, p < 0.001, was supported. This robust relationship highlights that effective competitive positioning through various strategies leads to enhanced financial, operational, market, and customer performance outcomes.
Hypothesis H5 (Logistics Resources → Competitive Advantage → Organizational Performance) Mediation analysis revealed notable indirect effects. Logistics Resources exhibited a significant indirect effect on Organizational Performance via Competitive Advantage (β = 0.276, 95% CI [0.174, 0.392], p < 0.001).
Hypothesis H6 (Strategic Capabilities → Competitive Advantage → Organizational Performance) Strategic Capabilities displayed a more substantial indirect effect (β = 0.349, 95% CI [0.221, 0.474], p < 0.001). These results suggest that both resources and capabilities impact performance primarily through competitive advantage creation, with capabilities demonstrating stronger mediation effects than resources.

6. Discussion

The positive correlation between Logistics Resources and Competitive Advantage (β = 0.377, p < 0.001) supports Barney’s Resource-Based View asserting that valuable, rare, inimitable, and non-substitutable resources are essential for competitive advantage, which in turn plays a crucial mediating role in transforming organizational inputs into performance outcomes [24]. Wong and Karia’s five-dimensional resource framework is validated [12], highlighting the roles of technology resources (λ = 0.864), physical resources (λ = 0.890), management expertise resources (λ = 0.903), relational resources (λ = 0.912), and organizational resources (λ = 0.924) in freight forwarding. Notably, organizational and relational resources demonstrated the highest loadings, indicating their paramount importance in the Thai context where relationship-based business practices prevail. However, the moderate path coefficient (β = 0.377) indicates that mere resource possession is inadequate for explaining competitive advantage. This aligns with RBV critiques that static frameworks fail to account for the dynamic development and reconfiguration of resources over time [14,28,31]. The finding reinforces the notion that resources are necessary yet insufficient for competitive advantage, necessitating complementary capabilities for effective resource application. This supports the view that resources in Asia necessitate advanced deployment capabilities to effectively translate into market positions, as relationship-based assets require organizational competencies for effective leverage [17,46].
The significant correlation between Strategic Capabilities and Competitive Advantage (β = 0.478, p < 0.001) substantiates dynamic capabilities theory while offering critical insights on capability integration in modern business contexts. This effect is the most substantial in the structural model, asserting that organizational capabilities have a more pronounced impact on competitive positioning than static resource allocations. Among the Strategic Capabilities dimensions, seizing (λ = 0.848), sensing (λ = 0.836), and green purchasing (λ = 0.840) demonstrated the highest loadings, while eco-design showed the lowest (λ = 0.647), suggesting that Thai freight forwarders have developed stronger market responsiveness capabilities than environmental design capabilities. The findings reaffirm Teece’s fundamental assertion that competitive advantage arises chiefly from organizational competence in identifying, mobilizing resources, and adapting operations to align with shifting market needs [31]. The observation that Strategic Capabilities exert approximately 27% more influence than Logistics Resources (β = 0.478 vs. β = 0.377) serves as vital corroboration of dynamic capabilities theory’s essential claim that organizational achievement relies more on capabilities to utilize resources than on the resources themselves [14,18].
The strong relationship between Competitive Advantage and Organizational Performance (β = 0.731, p < 0.001) represents the most substantial effect in the model, with competitive advantage explaining 53.4% of variance in organizational performance. Among the Competitive Advantage dimensions, differentiation (λ = 0.861) and cost leadership (λ = 0.860) showed nearly equal importance, while focus strategy (λ = 0.857) was marginally lower. This balanced loading pattern suggests that Thai freight forwarders pursue hybrid competitive strategies rather than singular approaches. The Organizational Performance construct revealed that operational performance (λ = 0.948) and market performance (λ = 0.934) had the highest loadings, followed by financial performance (λ = 0.917) and customer performance (λ = 0.902), indicating that competitive advantages translate most directly into operational and market outcomes.
The effective amalgamation of Dynamic Capabilities and GSCM Capabilities into a cohesive Strategic Capabilities construct (R2 = 0.638) holds substantial theoretical ramifications that transcend mere measurement model concerns. The discriminant validity issues (HTMT = 1.009) that prompted this integration yield empirical support for an evolving theoretical perspective: environmental sustainability and market responsiveness capabilities are complementary dimensions of organizational adaptability. The theoretical integration of this framework is consistent with contemporary literature advocating for the “integration of sustainability considerations into core strategic processes” [38] and upholds the natural-resource-based view that “sustainability-oriented dynamic capabilities are essential for minimizing environmental impact while ensuring organizational viability” [38].
The findings of this study extend beyond freight forwarding and offer valuable implications for other logistics sectors operating in similar emerging market contexts. For third-party logistics (3PL) providers, the demonstrated importance of strategic capabilities over static resources suggests prioritizing capability development in sensing market opportunities, seizing partnerships, and transforming service offerings, while the strong loading of relational resources (λ = 0.912) indicates that 3PL providers in Asian markets should invest heavily in relationship management systems. In warehousing and distribution, the finding that organizational resources (λ = 0.924) and operational performance (λ = 0.948) are critical success factors implies that warehouse operators should develop dynamic capabilities for sensing inventory optimization opportunities and seizing automation technologies, while the eco-design dimension represents a growth opportunity for differentiation through green building certifications. For last-mile delivery services, the moderate performance of technology resources suggests accelerating digital transformation investments in route optimization, real-time tracking, and customer communication platforms, with strategic capabilities enabling providers to anticipate consumer preferences and rapidly deploy innovative solutions. Cold chain logistics operators can leverage the strategic capabilities framework to simultaneously address sustainability mandates and competitive positioning, transforming environmental management from a cost center into a source of differentiation, given that environmental considerations directly impact operational costs and regulatory compliance. Finally, for cross-border logistics providers operating across ASEAN markets, this study’s validation in the Thai context provides a foundation for understanding resource-capability-performance relationships in similar institutional environments; however, the emphasis on relational resources and cultural adaptation suggests that cross-border operators must develop context-specific capabilities for each market rather than applying standardized approaches. Collectively, these implications demonstrate that the integrated strategic capabilities framework offers a transferable model for logistics sector participants seeking to enhance competitive positioning through balanced resource deployment and capability development.

7. Conclusions

This study empirically analyzed logistics resources, strategic capabilities, competitive advantage, and organizational performance in Thailand’s freight forwarding industry using structural equation modeling with 250 management-level respondents. The findings substantiate the integration of Resource-Based View and Dynamic Capabilities Theory for understanding superior performance in Southeast Asian freight forwarding.
Structural model analysis confirmed all hypothesized direct relationships. Logistics Resources positively influence Competitive Advantage (H1: β = 0.377, p < 0.001), with organizational resources (λ = 0.924) and relational resources (λ = 0.912) demonstrating the strongest contributions. Strategic Capabilities exert a stronger positive influence on Competitive Advantage (H2: β = 0.478, p < 0.001), with seizing (λ = 0.848) and green purchasing (λ = 0.840) as the most influential dimensions. Competitive Advantage strongly predicts Organizational Performance (H4: β = 0.731, p < 0.001), explaining 53.4% of performance variance. Mediation analysis confirmed that Competitive Advantage mediates the relationship between Logistics Resources and Organizational Performance (H5: β = 0.276, 95% CI [0.174, 0.392]) and between Strategic Capabilities and Organizational Performance (H6: β = 0.349, 95% CI [0.221, 0.474]), with Strategic Capabilities exhibiting stronger mediation effects.
A key theoretical contribution emerged from the empirical integration of Dynamic Capabilities and GSCM Capabilities into a unified Strategic Capabilities construct, supported by discriminant validity analysis. This integration suggests that environmental sustainability capabilities and market responsiveness capabilities are complementary dimensions of organizational adaptability rather than distinct constructs, advancing theoretical understanding of capability configurations in modern business contexts.

8. Implications

8.1. Theoretical Implications

This research significantly enhances strategic management theory. First, it offers empirical support for merging dynamic capabilities with environmental sustainability capabilities into a cohesive strategic capability framework, advocating for their reconceptualization as fundamental components of dynamic capabilities. This synthesis creates new research opportunities to explore the interplay of sensing, seizing, and transforming capabilities within both market and environmental contexts. Second, the varying effect sizes between resources and capabilities substantiate that capabilities are more crucial than resources for achieving competitive advantage, with capabilities yielding approximately 27% greater impact. Third, the validation of a comprehensive theoretical framework that integrates RBV, dynamic capabilities, competitive strategy, and GSCM theories underscores the importance of multi-theoretical perspectives in comprehending intricate organizational phenomena. This implies that strategic management research should increasingly favor integrative theoretical frameworks over isolated single-theory analyses.

8.2. Practical Implications

The research findings offer insights for freight forwarding firms to improve competitiveness and performance. The stronger influence of capabilities over resources highlights the need for strategic resource allocation. Companies should invest in capability development alongside resource acquisition, prioritizing initiatives that optimize resource use. Investments should focus on enhancing organizational capabilities for market opportunity recognition, opportunistic strategic actions, and operational transformation for competitive alignment. Formal sensing mechanisms such as customer feedback, market research, technology scouting, and regulatory monitoring should be established by companies. These investments should target market opportunities and sustainability challenges for competitive advantage. Companies need to create frameworks for investment decisions, partnership developments, and resource mobilization to exploit opportunities swiftly. Additionally, organizations should cultivate flexibility, change management skills, and partner portfolio management for ongoing adaptation. Further, the incorporation of GSCM capabilities into strategic capabilities signifies that environmental management should be a core competency rather than a compliance issue. Companies should execute comprehensive GSCM initiatives that include eco-design, customer cooperation on environmental projects, green purchasing, and internal environmental management systems.

9. Suggestions for Future Study

This study has several limitations that should be acknowledged. First, the cross-sectional research design precludes establishing definitive causal relationships and cannot capture how resources and capabilities evolve over time. Second, the exclusive focus on Thailand’s freight forwarding industry limits generalizability to other national or regional contexts where institutional, cultural, and regulatory environments differ. Third, while GSCM capabilities were incorporated, the study did not directly measure environmental performance outcomes such as carbon emissions reduction or energy efficiency improvements. Fourth, the sample predominantly comprised small-to-medium enterprises, raising concerns about applicability to larger multinational logistics firms. Finally, reliance on self-reported data from single respondents introduces potential common method bias, despite statistical and procedural safeguards.
These limitations suggest several promising avenues for future research. Longitudinal studies tracking capability development over 3–5 years could elucidate causal sequences and examine firm responses to environmental disruptions. Comparative cross-national research across Southeast Asian markets (e.g., Singapore, Malaysia, Vietnam, Indonesia) would assess how cultural and regulatory factors influence resource-capability-advantage dynamics. Future studies should also incorporate objective environmental performance metrics to examine whether GSCM capabilities translate into measurable environmental improvements and whether environmental performance mediates the link to financial outcomes. Additionally, research deliberately stratifying samples by firm size could reveal whether resource-capability-performance relationships operate differently between small and medium enterprises and large enterprises. Mixed-methods approaches combining surveys with qualitative case studies would provide richer insights into dynamic capability processes and help triangulate findings.

Author Contributions

Conceptualization, N.P. and R.W.; methodology, N.P., R.W. and A.T.; validation, N.P. and R.W.; formal analysis, N.P.; investigation, N.P.; writing—original draft preparation, N.P.; writing—review and editing, N.P., R.W. and A.T.; visualization, R.W. and R.W.; supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with Graduate School, University of the Thai Chamber of Commerce, and the protocol was approved by the Ethics Committee of UTCCEC/Exemp118/2025 on 18 October 2025.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to ethical restrictions.

Acknowledgments

The researchers are grateful to the participants and freight forwarding companies for their interest in and support of this research.

Conflicts of Interest

The authors declare no conflicts of interest.

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Logistics 10 00119 g001
Figure 1. Research Conceptual Model.
Figure 1. Research Conceptual Model.
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Figure 2. SEM Analysis. Note: Logistics Resources (LR): TER = Technology Resources; PHR = Physical Resources; MER = Management Expertise Resources; RLR = Relational Resources; OZR = Organizational Resources. Strategic Capabilities (SC): SEN = Sensing; SEI = Seizing; TRA = Transforming; EOD = Eco-Design; COC = Cooperation with Customers; GRP = Green Purchasing; IEM = Internal Environmental Management. Competitive Advantage (CA): CCL = Cost Leadership; CDI = Differentiation; CFS = Focus Strategy. Organizational Performance (OP): FP = Financial Performance; OP = Operational Performance; MP = Market Performance; CP = Customer Performance. Values inside circles represent R2 (variance explained); values on arrows represent standardized path coefficients (β) and outer loadings.
Figure 2. SEM Analysis. Note: Logistics Resources (LR): TER = Technology Resources; PHR = Physical Resources; MER = Management Expertise Resources; RLR = Relational Resources; OZR = Organizational Resources. Strategic Capabilities (SC): SEN = Sensing; SEI = Seizing; TRA = Transforming; EOD = Eco-Design; COC = Cooperation with Customers; GRP = Green Purchasing; IEM = Internal Environmental Management. Competitive Advantage (CA): CCL = Cost Leadership; CDI = Differentiation; CFS = Focus Strategy. Organizational Performance (OP): FP = Financial Performance; OP = Operational Performance; MP = Market Performance; CP = Customer Performance. Values inside circles represent R2 (variance explained); values on arrows represent standardized path coefficients (β) and outer loadings.
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Table 1. Summary of Empirical Studies on Resources, Capabilities, and Competitive Advantage in Logistics.
Table 1. Summary of Empirical Studies on Resources, Capabilities, and Competitive Advantage in Logistics.
Author(s)PurposeMethodsKey Findings
Wong & Karia [12]Examine logistics resources and their impact on 3PL provider performanceSurvey-based quantitative studyIdentified five resource dimensions (technology, physical, management expertise, relational, organizational) that positively influence logistics performance
Lee et al. [13]Investigate green supply chain management practices and their effectsSurvey, structural equation modelingFour GSCM dimensions (eco-design, customer cooperation, green purchasing, internal environmental management) significantly impact environmental and operational performance
Bleady et al. [14]Review dynamic capabilities theory evolution and applicationSystematic literature reviewDynamic capabilities enable firms to adapt to changing environments; application varies across cultural contexts
Wang et al. [15]Examine drivers of GSCM practices and environmental performanceSurvey, SEM with 256 manufacturing firmsCustomer pressure and cost drivers significantly influence GSCM adoption and subsequent environmental performance
Zhang et al. [16]Investigate dynamic capabilities in Asian business environmentsMixed-methods studyDynamic capabilities require adaptation for collective decision-making and relationship-centric practices prevalent in Asian contexts
Peng et al. [17]Analyze resource deployment capabilities in Asian marketsSystematic literature reviewResource deployment effectiveness varies based on institutional and cultural factors in Asian markets
Lin & Tsai [18]Validate distinctions between capabilities and resourcesEmpirical study using SEMConfirms that dynamic capabilities have stronger effects on competitive positioning than static resource possession
Tan et al. [19]Examine dynamic capabilities in Asian organizational frameworksSystematic literature reviewAsian firms demonstrate distinct patterns of capability development influenced by collectivistic cultural values
Bonyasuwon & Thanaiudompat [20]Examine dynamic capabilities in supply chain management from organizational learning perspectiveEmpirical study with mixed methodsOrganizational learning mechanisms facilitate dynamic capability development; knowledge acquisition, distribution, and interpretation processes enable supply chain adaptation and reconfiguration
Dovbischuk [21]Examine innovation-oriented dynamic capabilities and resilience during COVID-19Longitudinal study of logistics service providersInnovation-oriented dynamic capabilities and dynamic resilience significantly impact firm performance during disruptions
Bag et al. [22]Examine Logistics 4.0 enabled dynamic capabilities and firm performanceSurvey-based SEM with 230 automotive manufacturersTechnological, organizational, and environmental capabilities significantly affect Logistics 4.0 capabilities, which in turn enhance firm performance
Table 2. Current Position in the Company.
Table 2. Current Position in the Company.
Frequency (n)%
Current Position
CEO/Managing Director14156.4
General Manager3313.2
Operations Manager4518.0
Supply Chain Manager83.2
Logistics Manager114.4
Sales Manager41.6
Import–Export Manager83.2
Freight-Related Working Experience
Less than 5 years114.4
5–10 years5823.2
11–15 years4116.4
16–20 years5421.6
More than 20 years8634.4
Table 3. Model Fit Indices.
Table 3. Model Fit Indices.
Fit IndexSaturated ModelEstimated Model
SRMR0.0470.114
d_ULS0.4172.487
d_G0.3290.493
Chi-square468.838608.276
NFI0.8930.861
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MDPI and ACS Style

Pinyanitikorn, N.; Wiriyakitjar, R.; Thunyachairat, A. Strategic Capabilities Integration for Competitive Advantage: Evidence from Thailand’s Freight Forwarding Industry. Logistics 2026, 10, 119. https://doi.org/10.3390/logistics10060119

AMA Style

Pinyanitikorn N, Wiriyakitjar R, Thunyachairat A. Strategic Capabilities Integration for Competitive Advantage: Evidence from Thailand’s Freight Forwarding Industry. Logistics. 2026; 10(6):119. https://doi.org/10.3390/logistics10060119

Chicago/Turabian Style

Pinyanitikorn, Nattakorn, Rawida Wiriyakitjar, and Aannicha Thunyachairat. 2026. "Strategic Capabilities Integration for Competitive Advantage: Evidence from Thailand’s Freight Forwarding Industry" Logistics 10, no. 6: 119. https://doi.org/10.3390/logistics10060119

APA Style

Pinyanitikorn, N., Wiriyakitjar, R., & Thunyachairat, A. (2026). Strategic Capabilities Integration for Competitive Advantage: Evidence from Thailand’s Freight Forwarding Industry. Logistics, 10(6), 119. https://doi.org/10.3390/logistics10060119

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