Enhancing SME Export Performance through Export Sharing Communities, Export Market Orientation, and Export Digital Agility: A Mediation Analysis

Introduction

Small and Medium Enterprises (SMEs) play a strategic role in the global economic structure, particularly in developing countries such as Indonesia, through their contribution to employment generation, Gross Domestic Product (GDP) growth, and the strengthening of innovation (Salim et al., 2021; Shelly et al., 2020). In the context of international trade, the involvement of SMEs in export activities not only enhances domestic economic resilience but also serves as a driving force for global economic integration (Triharjanto et al., 2022; Wilujeng, 2021). Empirically, the contribution of SME exports across countries shows significant variation. In India, SMEs contributed approximately 48.1% of total national exports as of 2021 (Rameshbhai & Solanki, 2022), whereas in Indonesia, the equivalent figure remained comparatively low at around 14.37% of total national exports as of 2020 (Wibisono & Sitorus, 2021). Even when benchmarked against other ASEAN economies—including Singapore (approximately 41%) and Thailand (approximately 29%), based on data from Revindo et al. (2019) and (Smesco, 2023)—Indonesia’s SME export contribution lags noticeably behind. It is

important to note that these cross-country comparisons are indicative rather than strictly equivalent, as national definitions of SME size thresholds and measurement bases for export contribution may differ. Nevertheless, the underlying pattern is consistent with a structural gap between Indonesia’s SME potential and its export realization. The relatively low contribution of SME exports signals a gap between potential and actual export performance. Various studies show that limited access to financing, low managerial and international marketing capabilities, and suboptimal policy support are the main factors hindering SME export performance (Nekhoroshkov & Larionov, 2023; Okangi, 2023). In addition, although the issue of SME internationalization has been increasingly examined, attention to the development of sustainable export capabilities remains relatively limited (April & Reddy, 2015). This condition highlights the need for a more comprehensive approach in understanding the determinants of SME export performance, particularly in responding to increasingly complex and digitalized global market dynamics. In responding to these challenges, the literature emphasizes the importance of strengthening both internal and external capabilities of SMEs through adaptive and collaborative strategies (Busarina et al., 2014; Safari et al., 2021). Grounded in dynamic capability theory and social capital theory, the internationalization process of SMEs is not only determined by entrepreneurial orientation but also by the ability to access collaborative networks, interpret global markets, and adapt to technological changes (Singh et al., 2023; Tambunan, 2024). Dynamic capabilities—defined as the firm’s ability to sense, seize, and reconfigure resources in changing environments (Teece et al., 1997)—provide the theoretical logic linking community participation to marketoriented behavior and digital responsiveness. Social capital theory further explains how embeddedness in export communities generates information advantages, trust-based coordination, and collective learning that are otherwise inaccessible to SMEs acting in isolation. Therefore, improving SME export performance requires the integration of social, strategic, and technological aspects as the main foundation for building global competitiveness. In line with this perspective, this study proposes three main constructs—Export Sharing Community (ESC), Export Market Orientation (EMO), and Export Digital Agility (EDA)—as determinants of SME export performance. First, ESC refers to a structured collaborative mechanism through which SMEs share knowledge, resources, and best practices for export activities. ESC is conceptually distinct from general business networks, informal peer groups, or government export assistance programs, in that it specifically encompasses formal and semi-formal export-oriented communities— including government-supported export communities, export associations, and institutional export bodies—whose primary function is to reduce informational barriers and expand international business networks (Haddoud et al., 2017; Sousa & Bradley, 2009). Unlike general social capital, ESC provides export-specific collective learning through structured interactions among business actors (Easmon et al., 2019). Second, Export Market Orientation (EMO) serves as a key factor in aligning SME strategies with the needs and preferences of international markets. EMO reflects the firm’s ability to generate, disseminate, and respond proactively to global market intelligence (Cadogan et al., 2009). Third, in the era of digital transformation, Export Digital Agility (EDA) becomes a critical capability—distinct from general IT adoption—that captures the SME’s ability to rapidly and flexibly deploy digital technologies specifically in support of export activities, including cross-border e-commerce, datadriven decision-making, and real-time coordination with international partners (Chan et al., 2019; Nakabuye et al., 2023). Although ESC, EMO, and EDA have each been examined in prior literature, studies that integrate all three into a comprehensive mediation framework to explain SME export performance remain very limited, particularly in the context of Indonesia. Prior export performance models have largely examined networks (Jraisat et al., 2013), market orientation (Cadogan et al., 2009), or digital capabilities (Al-Khatib, 2023) independently, or at most in two-variable combinations. No study has simultaneously tested whether ESC’s effect on export performance is channeled through both EMO and EDA, nor whether these pathways operate as complementary or independent mediating mechanisms. This dual mediation model represents the primary theoretical contribution of the current study: it extends dynamic capability theory to explain how a socially embedded upstream antecedent (ESC) activates two functionally distinct organizational capabilities (market intelligence responsiveness via EMO; digital speed and flexibility via EDA), both of which are required for competitive export performance in the digital era. Based on this gap, this study addresses the following research questions: (1) Does ESC directly and indirectly influence SME export performance through EMO and EDA? (2) Do EMO and EDA each serve as significant mediating mechanisms in the ESC–export performance relationship? Theoretically, this study contributes by developing an integrative model that links community collaboration, market orientation, and digital agility as a coherent causal system. Practically, the findings are expected to provide actionable guidance for export-support institutions, policymakers, and SME practitioners seeking to strengthen Indonesia’s global trade competitiveness in the digital era.

Theoretical Background and Hypothesis

Export Sharing Community

Export Sharing Community (ESC) is conceptualized as a structured collaborative mechanism that facilitates the sharing of knowledge, resources, and best practices among SMEs specifically to enhance export capabilities and decision-making quality (Kumar et al., 2020). Drawing on social capital theory, ESC generates the relational resources—information access, network ties, and trust-based coordination—that individual SMEs cannot readily accumulate in isolation. It is important to distinguish ESC from broader constructs such as general business networks, social capital, or government export assistance: ESC refers to organized export-oriented communities, ranging from formally registered export associations and government-supported export groups to institutional bodies whose explicit mandate is facilitating members’ engagement with international markets. Through networks and strategic partnerships, ESC strengthens access to market information, customer preferences, and global trends that form the basis for developing Export Market Orientation (EMO) (Babakus et al., 2006; Liao et al., 2007). The literature indicates that information sharing and network integration within ESC encourage the generation, dissemination, and responsiveness to export market intelligence, thereby strengthening EMO (Cadogan et al., 2009; Imran et al., 2019; İpek & Bıçakcıoğlu‐ Peynirci, 2020; Jraisat et al., 2013; Nguyen et al., 2022). Collaborative participation also enhances organizational adaptability and learning (Birru et al., 2019) and access to effective marketing strategies (Okangi, 2023), collectively confirming that ESC positively influences EMO. H1: Export Sharing Community (ESC) positively influences Export Market Orientation (EMO) in SMEs in Indonesia. ESC also enhances Export Digital Agility (EDA) through information sharing and network integration that accelerate coordination, efficiency, and responsiveness to changes in digital export markets (Inouye et al., 2019; Jraisat et al., 2013).

Collaboration within ESC enables the adoption of digital technologies, real-time information exchange, and enhanced customer interaction, which form the foundation of digital agility (Nakabuye et al., 2023; Zahoor et al., 2024). Furthermore, strategic partnerships with foreign buyers Revindo & Gan (2017) and the adoption of innovative digital practices (Okangi, 2023) further strengthen SMEs’ digital adaptation capabilities. Thus, ESC consistently drives the improvement of EDA through cross-actor coordination, collaborative learning, and technology utilization in export activities. H2: Export Sharing Community (ESC) positively influences Export Digital Agility (EDA) in SMEs in Indonesia. ESC directly contributes to improving export performance through strengthening networks, information sharing, and collaborative practices that enhance operational efficiency and global competitiveness (Jraisat et al., 2013; Kumar et al., 2020). Network integration and trust-based relationships enable better coordination, reduced transaction costs, and increased market access (Babakus et al., 2006). Collective learning and relational capabilities within export communities strengthen firms’ ability to respond to international market dynamics (Escandon-Barbosa et al., 2019), while adaptive export strategies further enhance performance sustainably (Safari & Saleh, 2020). Overall, ESC acts as a strategic catalyst that directly improves SME export performance. H3: Export Sharing Community (ESC) positively influences SME Export Performance in Indonesia.

Export Market Orientation

Export Market Orientation (EMO) is a strategic approach based on the generation, dissemination, and responsiveness to international market intelligence, enabling SMEs to adapt their strategies and business processes to global market conditions (Akyol & Akehurst, 2003; Julian et al., 2013; Kayabaşı & Mtetwa, 2016). EMO strengthens firms’ ability to access market information, identify opportunities, and respond rapidly to changes, thereby fostering the development of digital capabilities and agility in export activities (Harrison & Pooe, 2022; Lengler et al., 2016). The mechanism connecting EMO to EDA operates through the sensing-seizing-reconfiguring logic of dynamic capabilities: market intelligence generation (sensing) triggers awareness of digital opportunities; dissemination of that intelligence across functional teams (seizing) enables coordinated digital responses; and responsiveness involves reconfiguring digital platforms and tools to match evolving export market demands. The EMO dimensions—information generation, dissemination, and responsiveness—thus directly support digital adaptation, export platform reconfiguration, and continuous learning, which form the foundation of EDA (Cadogan et al., 2009; Garcia et al., 2024; Kazemi et al., 2022; Nguyen et al., 2022). Prior studies have examined market orientation and digital capability as largely independent constructs (Liu et al., 2025); this study tests their direct linkage as a novel empirical contribution. H4: Export Market Orientation (EMO) positively influences Export Digital Agility (EDA) in SMEs in Indonesia. EMO directly contributes to improving export performance through firms’ ability to generate, disseminate, and respond to market intelligence effectively, enabling marketing strategy adjustments, faster market penetration, and increased export sales (Cadogan et al., 2009; Chung, 2012; Kayabaşı & Mtetwa, 2016; Kumara & Samarakoon, 2019). EMO also enhances innovation and the effectiveness of market-based strategies, strengthening export performance across various industrial and environmental contexts (Cadogan et al., 2012; Kazemi et al., 2022). Empirical and meta-analytic evidence confirms EMO as a strong predictor of export performance (Abdul-Talib et al., 2023; Cadogan et al., 2003), although its strength may be moderated by firm size, external networks, and market turbulence. H5: Export Market Orientation (EMO) positively influences SME Export Performance in Indonesia.

Export Digital Agility

Export Digital Agility (EDA) reflects the ability of SMEs to respond rapidly and flexibly to international market dynamics through the utilization of digital technologies, encompassing supply chain optimization, customer interaction, and operational efficiency (Lee et al., 2015; Nakabuye et al., 2023; Rubino et al., 2023). EDA is conceptually distinct from general IT adoption in that it captures the speed and flexibility with which digital tools are deployed in export-specific contexts, rather than mere technological possession. EDA enables SMEs to enhance adaptability to changing customer preferences and competitive pressures, and to strengthen the effectiveness of data-driven marketing and operational strategies (Elo & Silva, 2022; Okangi, 2023). Digital-based sensing, processing, and responding capabilities accelerate market penetration, product adaptation, and export sales growth, thereby directly contributing to improved export performance (Asaad et al., 2013; Chung, 2012; İpek & Bıçakcıoğlu‐Peynirci, 2020; Lin et al., 2014). Thus, EDA becomes a key capability that strengthens competitive advantage and SME export performance in global markets. H6: Export Digital Agility (EDA) positively influences SME Export Performance in Indonesia.

Export Performance

SME export performance reflects the effectiveness and efficiency of SMEs in international market activities, measured through indicators such as export sales volume, profitability, market share, and export sustainability, encompassing both quantitative and qualitative dimensions, including the ability to build long-term relationships and adapt products to global market needs (Esteve‐Pérez et al., 2007). This performance is influenced by internal and external factors including organizational capabilities, market orientation, export commitment, and resource access (Sinkovics et al., 2018), where export-oriented SMEs tend to exhibit higher financial growth and innovation capabilities (Safari et al., 2021). Improving export performance not only strengthens firm competitiveness but also contributes to economic development through job creation and enhanced national trade balance (Malca et al., 2019). Strategically, export activities increase revenue and profitability, expand market reach, reduce dependence on domestic markets, and diversify risk (Safari & Saleh, 2020), and engagement in global markets enables the transfer of best practices and innovation (Chidoko, 2018; Serrano et al., 2016).

Mediating Roles of Export Market Orientation and Export Digital Agility

Beyond their direct effects on export performance, EMO and EDA are expected to function as mediating mechanisms through which ESC contributes to SME export performance. Social capital theory suggests that collaborative networks provide access to information, trust, and relational resources, while dynamic capability theory explains how these resources can be transformed into organizational capabilities that support superior performance outcomes. EMO also acts as a mediating variable that bridges the influence of ESC on export performance by translating information flows, collaborative learning, and trust-based interactions within the community into responsive marketoriented behaviors (Cadogan et al., 2012; Sarıhan, 2024). Through this process, ESC strengthens EMO’s ability to generate and utilize market intelligence, which subsequently

improves targeting strategies and the exploitation of export opportunities (Kazemi et al., 2022; Safari & Saleh, 2020). Network collaboration and information sharing not only have a direct impact on performance but also operate through EMO as a primary mechanism enhancing market responsiveness (Cadogan et al., 2009; Sitabutr & Pimdee, 2017). H7: Export Market Orientation (EMO) mediates the relationship between Export Sharing Community and SME Export Performance in Indonesia. EDA also acts as a mediating variable linking ESC to export performance by translating collaboration, information sharing, and network learning into fast and effective digital responses (Al-Khatib, 2023; Giudice et al., 2022; Nakabuye et al., 2023; Onjewu et al., 2022). The collaborative environment within ESC encourages the utilization of digital platforms, data analytics, and cross-actor coordination that enhance market responsiveness and export strategy effectiveness. Dynamic capability theory and digital marketing perspectives confirm that EDA serves as a key mechanism that transforms collective resources and knowledge within export communities into improved export performance (AlKhatib, 2023; Pfajfar et al., 2024; Wang, 2020). This mediation operates through a complementary pathway alongside EMO’s mediation: where EMO channels ESC’s effect through intelligence-driven strategy adaptation, EDA channels it through digital speed and operational flexibility. Together, they represent two functionally distinct yet theoretically coherent mechanisms linking collaborative export networks to export outcomes. H8: Export Digital Agility (EDA) mediates the relationship between Export Sharing Community and SME Export Performance in Indonesia.

Methods

Research Design

This study adopts a quantitative approach using a crosssectional survey design. The cross-sectional design is appropriate for the stated research objectives because the study seeks to assess the simultaneous structural relationships among ESC, EMO, EDA, and export performance rather than to track individual firms’ trajectories over time. Structural Equation Modeling (SEM) is employed as the primary analytical technique because it uniquely enables simultaneous testing of both measurement models (reliability and validity of latent constructs) and structural models (direct and indirect path relationships), while explicitly controlling for measurement error. SEM is particularly well-suited for testing the proposed mediation model, in which multiple latent variables are hypothesized to operate as both outcomes of antecedent constructs and predictors of downstream performance, a complexity that cannot be adequately addressed through conventional regression approaches (Hair et al., 2013). Data were collected in 2025 using structured questionnaires and analyzed using AMOS version 24.

Population and Sample

The population of this study includes all SMEs in Indonesia engaged in export activities. Sampling was conducted using purposive sampling based on the following eligibility criteria: (1) domiciled in Indonesia; (2) operating for at least one year; (3) actively engaged in export activities; (4) members of an export sharing community; and (5) having adopted digital technologies in their export operations. These criteria were verified through a screening section at the start of the questionnaire, in which respondents self-reported their compliance with each criterion; incomplete or ineligible responses were excluded prior to analysis. A total of 215 complete and valid responses were obtained. Because purposive sampling is a non-probability method, the term 'representativeness' is used here in the statistical rather than inferential sense. The sample size of 215 meets the absolute minimum threshold of N ≥ 200 recommended for SEM models with complex structures involving multiple latent constructs and mediating paths (Hair et al., 2013), and is further supported by the acceptable model fit indices obtained across all evaluated criteria. Readers should note that the use of purposive non-probability sampling limits formal generalizability beyond the SME population meeting the stated eligibility criteria.

Research Location and Data Collection

Data collection was conducted through an online survey distributed between January and June 2025. Questionnaires were distributed to SMEs in major Indonesian cities that serve as centers of SME export activities, including Jakarta, Surabaya, Bandung, Medan, and Makassar. These cities were selected because they represent diverse economic conditions, levels of industrial development, and degrees of digital readiness, providing a contextually relevant environment for analyzing the roles of ESC, EMO, and EDA. Respondents were recruited through export community administrators and export association platforms, which also served as a verification mechanism ensuring that respondents were active exportcommunity members. Prior to completing the questionnaire, respondents were provided with an explanation of the research objectives, a data confidentiality assurance, and a consent form. The response rate was approximately 78% of questionnaires distributed.

Instrumentation

The study uses a structured questionnaire as the primary data collection instrument. All items were measured on a fivepoint Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Measurement items for each construct were adapted from validated scales in the export management and digital agility literature, as summarized in Table 1. Specifically, Table 1. Construct Operationalization and Measurement Sources Construct Source/ Adaptation Number of Items Sample Item Validation Basis Export Sharing Community (ESC) Kumar et al. (2020); Jraisat et al. (2013a) 5 "Our export community facilitates effective sharing of market knowledge among members." CFA; CR > 0.70; AVE > 0.50 Export Market Orientation (EMO) Cadogan et al. (2009b) 6 "We systematically collect information about our export customers’ needs." CFA; CR > 0.70; AVE > 0.50

Export Digital Agility

(EDA) Nakabuye et al. (2023); Chan et al. (2019) 5 "We rapidly adapt our digital tools in response to export market changes." CFA; CR > 0.70; AVE > 0.50

Export Performance

(EP) Cadogan et al. (2009); Safari & Saleh (2020) 6 "Our export sales volume has grown significantly in recent years." CFA; CR > 0.70; AVE > 0.50

items for Export Sharing Community (ESC) were adapted from Kumar et al. (2020) and Jraisat et al. (2013a); items for Export Market Orientation (EMO) were adapted from Cadogan et al. (2009); items for Export Digital Agility (EDA) were adapted from Nakabuye et al. (2023) and Chan et al. (2019); and items for Export Performance were adapted from Cadogan et al. (2009b) and Safari & Saleh (2020). The questionnaire was originally constructed in English and translated into Indonesian using a forward-backward translation procedure; minor wording discrepancies identified during back-translation were resolved through discussion among the research team. A pilot test involving 30 SME respondents not included in the final sample was conducted prior to full data collection to assess item clarity and preliminary reliability. The questionnaire also includes demographic questions covering business sector, type of export community joined, length of business operation, type of digital platforms used, duration of involvement in export activities, and firm size.

Data Analysis

This study employs Structural Equation Modeling (SEM) using AMOS version 24 as a multivariate statistical technique capable of simultaneously analyzing relationships between latent variables and observed indicators while controlling for measurement errors. Prior to SEM estimation, data screening was conducted to assess normality (Mardia’s multivariate kurtosis), outliers (Mahalanobis D²), and common method bias (Harman’s single-factor test). Discriminant validity was evaluated using the Fornell-Larcker criterion (square root of AVE compared against inter-construct correlations) and the Heterotrait-Monotrait ratio (HTMT); results are reported in Table 4. No single factor accounted for a majority of variance in the Harman test, indicating that common method bias is unlikely to substantially distort the findings. The SEM analysis proceeds in two stages. First, a Confirmatory Factor Analysis (CFA) is conducted to evaluate the measurement model: convergent validity is assessed based on standardized loading factor values (≥ 0.50), Construct Reliability (CR ≥ 0.70), and Average Variance Extracted (AVE ≥ 0.50). Discriminant validity is assessed via the Fornell-Larcker criterion and HTMT (≤ 0.85). Model fit is evaluated using multiple indices: CMIN/DF ≤ 3.00, RMSEA ≤ 0.08, TLI ≥ 0.90, IFI ≥ 0.90, CFI ≥ 0.90, and NFI ≥ 0.90. The model is considered acceptable when at least four indices meet the recommended criteria (Hair et al., 2013). In the present study, no post-hoc model modifications were applied; the structural model was estimated as theoretically specified, without any respecification based on modification indices, thereby preserving the confirmatory intent of the analysis. Second, the structural model is tested to examine the eight research hypotheses via standardized path coefficients and critical ratios (CR ≥ 1.96 at α = 0.05). Mediation significance is assessed by reporting standardized indirect effects derived directly from the AMOS Indirect Effects matrix. The study additionally uses the Sobel test as a supplementary significance check for indirect effects, while acknowledging its assumption of normally distributed indirect effects. Future studies are encouraged to confirm these results via bootstrapped confidence intervals, which provide more robust inference for complex mediation structures.

Ethical Approval

This study was conducted in accordance with ethical principles in survey-based social science research. Respondent participation was voluntary, and informed consent was obtained prior to data collection. The study did not collect personally identifiable information, ensuring respondent anonymity. All data were stored securely and used solely for academic purposes. Ethical clearance for this study was reviewed at the institutional level in accordance with the research ethics guidelines of Universitas Tanjungpura; the study was determined to meet ethical standards for non-clinical social science research involving anonymous survey participants.

Result and Discussion

Sample Characteristics

A total of 215 respondents were included in the final sample. Table 2 presents the distribution of respondents by sector, export community type, operational tenure, digital platform type, export experience, and firm size, including both frequencies and percentages. Several patterns deserve attention. The sample is dominated by small firms (79.9%), the Wood Products, Forestry & Handicrafts sector (34.0%), and government-supported export communities (52.1%)—reflecting Indonesia’s actual export community landscape and implying that findings are most directly applicable to small manufacturing SMEs embedded in formal government-linked programs. The majority of respondents (54.4%) report export experience of four to seven years. Micro enterprises (16.7%) and medium-sized firms (3.4%) are represented but in smaller proportions; findings should be interpreted cautiously for these size categories.

Validity and Reliability

Model fit

Hypothesis Testing: Direct Effects

The inner model test is presented in Figure 1. Table 6 presents the SEM path coefficients and critical ratios for all six directly hypothesized relationships, including both unstandardized (β) and standardized (β) estimates,

standard errors, critical ratios, p-values, and Cohen’s f² effect sizes. A path is considered statistically significant when CR ≥ 1.96 (p < 0.05); all six direct paths meet this criterion at p < 0.001. Effect sizes are interpreted using (Cohen, 2013) benchmarks: f² ≥ 0.02 (small), f² ≥ 0.15 (medium), f² ≥ 0.35 Table 2. Summary of respondents’ characteristics Characteristic Criteria Frequency Percentage (%) Sector Food & Beverages 44 20.5% Basic Metal (Iron & Steel) 4 1.9% Textiles, Leather Goods & Footwear 59 27.4% Wood Products, Forestry & Handicrafts 73 34.0% Chemicals and Chemical Products 5 2.3% Cement & Non-Metallic Mineral Products 28 13.0% Machinery & Equipment 2 0.9% Total Sector 215 100% Export Community Type Government-supported community 112 52.1% Export association 85 39.5% Institutional export community 18 8.4% Total 215 100% Length of Operation 3–5 years 8 3.7% 6–10 years 71 33.0% > 10 years 136 63.3% Total 215 100% Digital Platform Used Global marketplace 107 49.8% Government platform 80 37.2% Company website 5 2.3% Social media 23 10.7% Total 215 100% Export Experience < 1 year 2 0.9% 1–3 years 30 14.0% 4–7 years 117 54.4% > 7 years 66 30.7% Total 215 100% Firm Size Micro (≤ IDR 1B assets & ≤ IDR 2B revenue) 36 16.7% Small (IDR 1–5B assets & IDR 2–15B revenue) 172 79.9% Medium (IDR 5–10B assets & IDR 15–50B revenue) 7 3.4% Total 215 100% Source: Primary Data Table 3. Standardized Loading Factor, Construct Reliability (CR) and Average Variance Extracted (AVE) for Overall Model Fit Variable Items SLF CR AVE Export Sharing Community Knowledge Sharing 0.922 0.94 0.85 Resource Sharing 0.911 Network Collaboration 0.918 Joint Marketing Efforts 0.913 Access to Export Logistics 0.937 Innovative Practices 0.927 Feedback Mechanisms 0.937 Export Market Orientation Market Intelligence Generation 0.940 0.94 0.84 Market Intelligence Dissemination 0.923 Market Intelligence Responsiveness 0.915 Customer Orientation 0.895 Competitor Orientation 0.893 Interfunctional Coordination 0.937 Export Digital Agility Response Time to Market Changes 0.933 0.95 0.87 Digital Capability 0.913 Integration of Digital Tools 0.941 Data-Driven Decision Making 0.911 Collaboration and Communication 0.924 Innovation Capacity 0.947 Customer Engagement and Feedback Mechanisms 0.945 Export Performance Export Market Share 0.940 0.93 0.86 Export Sales Volume 0.950 Export Profitability 0.929 Sales Growth 0.943 Export Financial Liquidity 0.911 New Market Penetration 0.898 Source: Data Processing Results (AMOS 24)

(large). H1 is supported: ESC exerts a positive and significant effect on EMO (Unstd. β = 0.415; Std. β = 0.450; CR = 6.939; p < 0.001; f² = 0.254, medium), indicating that active participation in export sharing communities substantially enhances SMEs’ market intelligence orientation. H2 is supported: ESC positively influences EDA (Unstd. β = 0.338; Std. β = 0.342; CR = 5.278; p < 0.001; f² = 0.182, medium), confirming that community participation strengthens digital agility in export activities. H3 is supported: ESC has a positive and significant direct effect on export performance (Unstd. β = 0.321; Std. β = 0.298; CR = 6.236; p < 0.001; f² = 0.317, medium). H4 is supported: EMO positively influences EDA (Unstd. β = 0.386; Std. β = 0.360; CR = 5.538; p < 0.001; f² = 0.202, medium), indicating that market intelligence orientation activates digital adaptability. H5 is supported: EMO positively influences export performance (Unstd. β = 0.315; Std. β = 0.270; CR = 5.614; p < 0.001; f² = 0.260, medium). H6 is supported: EDA is the strongest direct predictor of export performance (Unstd. β = 0.506; Std. β = 0.465; CR = 9.128; p < 0.001; f² = 0.772, large), underscoring the critical role of digital agility. Table 7 presents R² values derived from AMOS squared multiple correlations. ESC explains 20.2% of variance in EMO; ESC and EMO jointly explain 35.7% of variance in EDA; and ESC, EMO, and EDA collectively explain 72.0% of variance in export performance, a notably high proportion indicating strong explanatory power of the integrated model.

Mediation Analysis

export performance relationship, with a standardized indirect effect of 0.159 (path: ESC→EDA→EP), also constituting partial complementary mediation. The Sobel test further confirms significance (z = 4.511; p < 0.001). An additional serial indirect path (ESC→EMO→EDA→EP) yields a standardized indirect effect of 0.075, capturing a further pathway through which market intelligence development activates digital agility before improving export performance. The total standardized indirect effect of ESC on export performance across all pathways is 0.356, while the direct effect is 0.298, yielding a total effect of 0.654. The simultaneous significance of all mediation paths, alongside a retained direct ESC→EP effect, confirms a complementary partial mediation structure (Zhao et al., 2010). This indicates that ESC’s influence on export performance is realized through multiple distinct channels— intelligence-driven market adaptation (via EMO) and digital operational speed and flexibility (via EDA)—that are additive rather than substitutive. In this study, mediation significance was assessed using standardized indirect effects from the AMOS Indirect Effects matrix, with the Sobel test employed as a supplementary significance check. The Sobel test assumes an approximately normal sampling distribution for indirect effects, which may not hold in all conditions; future research is therefore encouraged to employ bias-corrected bootstrapped confidence intervals, which provide distributionfree inference and greater statistical power for asymmetric indirect effect distributions. The integrated model tested in this study reveals a coherent structural pattern in which structured export community participation functions as an upstream social mechanism theoretically specified to activate two distinct organizational capabilities—market intelligence orientation (EMO) and digital agility (EDA)—each of which is associated with superior export performance in the structural model. The central theoretical insight is not merely that these three variables each improve performance, but that the mechanism through which community embeddedness is associated with competitive export outcomes appears to be capability-driven and dual-channeled: ESC's total indirect effect (Std. indirect = 0.356) exceeds its direct effect (Std. β = 0.298), suggesting that a substantial portion of community-derived export advantage is realized through the development of organizational capability rather than through direct relational leverage alone. The dominance of EDA as the proximate predictor (Std. β = 0.465; f² = 0.772, large)—substantially exceeding the direct effects of EMO (0.270) and ESC (0.298)— further suggests that in the contemporary digitalized export landscape, strategic market intelligence may be a necessary but insufficient condition: the speed and flexibility with which intelligence is operationalized through digital means appears to be a key differentiating factor in SME export competitiveness. Given the cross-sectional design, these patterns are consistent with the proposed theoretical model but do not constitute proof of causal directionality.

Interpretation of Key Findings

Three findings in this study are particularly significant from a theoretical standpoint and warrant focused interpretive attention. First, the ESC→EMO pathway (Std. β = 0.450; f² = 0.254) demonstrates that structured export community participation functions as an institutional substitute for market intelligence infrastructure that resource-constrained SMEs cannot develop independently. Within dynamic capability theory (Teece et al., 1997), this pathway represents the sensing mechanism: by embedding SMEs in networks of shared information flows, export communities collectively lower the cost of market intelligence acquisition and raise the quality of intelligence to which individual members have access. This is theoretically significant because it positions ESC not as a social capital construct (which would predict performance effects through relational trust and access to resources) but as a sensing-capability enhancer that strengthens the firm's ability to interpret and respond to environmental signals—a distinction with direct implications for how export promotion programs should be designed. Second, the EMO→EDA path (Std. β = 0.360; f² = 0.202) represents the most theoretically novel contribution of this study. Prior research has treated market orientation and digital capabilities as parallel, independent predictors of export performance (Cadogan et al., 2006; Liu et al., 2025). The present finding that EMO significantly precedes and activates EDA extends the sensing-seizing-reconfiguring framework (Teece et al., 1997) into a sequential capability-building logic: firms that have internalized systematic market intelligence routines are better equipped to identify which digital tools are strategically valuable, to sequence digital investments according to market-detected priorities, and to reconfigure digital platforms in real time as market signals shift. In the Indonesian context, this sequential dynamic is plausibly reinforced by the institutional environment: governmentsupported export communities provide structured market intelligence briefings that inform member SMEs' digital investment decisions, creating a community-mediated pathway from market sensing to digital reconfiguration. Third, the large EDA→EP effect size (f² = 0.772) and the R² of 0.720 for export performance together signal that the model captures a substantial proportion of the variance that determines export success in this context. The magnitude of the EDA effect is noteworthy because it exceeds what would be expected if digital capability were merely a hygiene factor or a competitive threshold. Instead, its dominant coefficient suggests that in Indonesia's SME export ecosystem— characterized by high global marketplace platform adoption (49.8%) and significant digital platform heterogeneity— Table 7. R² Values (Squared Multiple Correlations from AMOS) Endogenous Construct R² (Squared Multiple Correlation) Variance Explained EMO 0.202 20.2% EDA 0.357 35.7% EP 0.720 72.0% Source: Data Processing Results (AMOS 24) Table 8. Standardized Indirect, Direct, and Total Effects:

Mediation Analysis

Mediation Path Std. Direct Std. Indirect Std. Total Mediation Type H7: ESC → EMO → EP 0.298 0.122 0.654 Partial (complementary) H8: ESC → EDA → EP 0.298 0.159 0.654 Partial (complementary) ESC → EMO → EDA → EP (serial) 0.298 0.075 0.654 Sequential indirect EMO → EDA → EP 0.270 0.167 0.438 Partial (complementary) Total indirect: ESC → EP — 0.356 — Sum of all indirect paths Note: Std. indirect effects from AMOS Indirect Effects matrix. Sobel test supplementary: z(ESC→EMO→EP) = 4.055, p = .000049; z(ESC→EDA→EP) = 4.511, p = .000064. Direct effect shown is ESC→EP. Total effects from AMOS Standardized Total Effects matrix Source: Data Processing Results (AMOS 24)

variation in digital agility translates directly into meaningful performance differentiation. This finding implies that EDA func tions not merely as an enabler of export participation but as an active source of competitive advantage for SMEs that surpass threshold levels of platform capability.

Comparison with Previous Studies

The findings of this study are generally consistent with prior research emphasizing the importance of collaboration, market orientation, and digital capabilities in enhancing export performance. Specifically, the positive association between Export Sharing Community (ESC) and Export Market Orientation (EMO) (Std. β = 0.450) and the positive direct association between ESC and Export Performance (Std. β = 0.298) are aligned with Kim & Kim (2023) and Olabode et al. (2018), which demonstrate that knowledge sharing and a market-oriented culture strengthen the effectiveness of export strategies. These findings are further consistent with Kayabaşı & Mtetwa (2016) and Cadogan et al. (2006), who indicate that market orientation contributes significantly to success in international markets. The ESC→EMO→performance pathway is similarly consistent with Jraisat et al. (2013), who found that information sharing within agri-food export supply chains is positively linked to both market responsiveness and export outcomes. However, whereas prior studies position network participation primarily as a relational capital mechanism that transfers resources or reduces uncertainty (Babakus et al., 2006; Easmon et al., 2019), the present study indicates that ESC’s performance association is predominantly transmitted through capability development (total indirect = 0.356) rather than direct relational leverage alone (direct = 0.298), suggesting a complementary rather than purely relational mechanism. Moreover, the findings regarding the role of Export Digital Agility (EDA) in improving Export Performance (Std. β = 0.465; f² = 0.772, large) reinforce the results of Abeyruwan et al. (2024) and Hyun (2024), who highlight the importance of digital transformation in enhancing global competitiveness, and are consistent with Al-Khatib (2023), who found digital transformation to be among the strongest predictors of export performance in a multi-country Middle Eastern sample. This is also consistent with Naglič et al. (2020) and Li et al. (2022) concerning the integration of technology in export activities. The large effect size observed in the present study may reflect the particularly high concentration of global marketplace platform users in the sample (49.8%), pointing to global marketplace platform penetration as a theoretically important contextual moderator for future cross-national research. However, some differences emerge when compared to previous studies, particularly regarding the relationship between EMO and digital capabilities, which has yielded mixed results in earlier research (Akyol & Akehurst, 2003; Kumara & Samarakoon, 2019). In contrast, this study provides more robust empirical evidence supporting the positive association between EMO and EDA (Std. β = 0.360; f² = 0.202, medium)— a path not previously modeled in the export management literature (Cadogan et al., 2006; Liu et al., 2025). This discrepancy may be attributable to the research context, which focuses on SMEs in Indonesia that are currently undergoing a phase of digital acceleration, thereby strengthening the interaction between market orientation and digital capabilities. Relatedly, the EMO→export performance association observed here (Std. β = 0.270) is somewhat more modest than the effects reported by Abdul-Talib et al. (2023), Cadogan et al. (2003) and Kayabaşı & Mtetwa (2016), where standardized effects typically range from approximately 0.30 to 0.45; this attenuation is theoretically interpretable, since EMO’s total effect on export performance is substantially larger (Std. total Figure 1. Full Model Analysis

= 0.438) once the sequential EMO→EDA→EP indirect pathway (Std. indirect = 0.167) is accounted for, suggesting that in digitally intensive export environments, market orientation’s performance association is increasingly realized through digital agility rather than through direct strategy adaptation alone. The dual complementary mediation structure confirmed in this study (H7 and H8) further advances beyond prior mediation models in the export literature, which have predominantly tested single mediators—including learning orientation (Jin & Cho, 2018), marketing capability (Zahoor & Lew, 2023), and market orientation alone (Assadinia et al., 2019). A particularly noteworthy finding is the serial indirect path ESC→EMO→EDA→EP (Std. indirect = 0.075), which suggests the two parallel mediating pathways are not fully independent; this serial mechanism has not been theorized or tested in prior literature and represents an emergent finding with implications for understanding the sequential logic of SME export capability development. Accordingly, these findings suggest that the relationships among ESC, EMO, EDA, and Export Performance are contextdependent and influenced by factors such as the level of technology adoption, market dynamics, and organizational characteristics. As such, this study contributes to a more nuanced understanding of the determinants of export performance across different industrial and geographical settings.

Limitations and Cautions

Several important limitations should be taken into account when interpreting the findings of this study. First, the cross-sectional survey design, while appropriate for testing the hypothesized structural relationships at a single point in time, fundamentally precludes causal inference. Although the directional paths specified in the model are grounded in dynamic capability theory and social capital theory, the observed associations are consistent with but do not prove the proposed causal sequence. In particular, reverse causation cannot be ruled out: it is conceivable, for example, that SMEs with higher export performance are more likely to invest in export community participation, or that digitally agile firms self-select into export communities precisely because they already possess superior market intelligence capabilities. Longitudinal panel designs or quasi-experimental approaches exploiting exogenous variation in export community access would be necessary to establish causal directionality with greater confidence. Second, all four constructs—ESC, EMO, EDA, and export performance—are measured through respondent self-reports using perceptual Likert-scale items. Export performance in particular captures SME owners' subjective assessments of their relative export success across dimensions such as market share, sales growth, and profitability, rather than independently verified financial records. While perceptual performance measures are widely employed in the export management literature and exhibit acceptable convergent validity (CR = 0.93; AVE = 0.86), they may be susceptible to social desirability bias, halo effects, and imprecise retrospective estimation. Future research should triangulate perceptual measures with objective indicators drawn from export customs records, administrative trade databases, or audited financial statements where available. Third, notwithstanding the use of Harman's single-factor test and the absence of a dominant single factor, common method variance (CMV) remains a potential concern given that all variables were collected from the same respondent at the same time using the same survey instrument. Although procedural remedies—including scale anchoring, reversecoded items, and temporal separation of predictor and outcome measurement—were not systematically applied in this study, the high discriminant validity evidenced by HTMT ratios below 0.85 and Fornell-Larcker criterion compliance reduces the likelihood that CMV is substantially inflating interconstruct relationships. Future studies should employ full procedural and statistical remedies for CMV, including marker variable techniques and confirmatory factor analysis-based CMV tests. Fourth, the mediation significance assessments rely on the Sobel test as a supplementary analytical tool. While the Sobel z-statistics are large and their associated p-values highly significant (z = 4.055 and 4.511 respectively), the Sobel test assumes that the sampling distribution of indirect effects is approximately normal—an assumption that may not hold in small-to-moderate samples or when indirect effects involve small component paths. Bootstrapped confidence intervals for indirect effects, which are distribution-free and more powerful for asymmetric indirect effect distributions, would provide stronger inferential support and are recommended for future replications. Fifth, the sample is drawn exclusively from purposively selected SMEs in five major Indonesian cities that serve as centers of export activity, with a notable concentration in small firms (79.9%), Wood Products and Handicrafts (34.0%), and government-supported export communities (52.1%). This composition reflects the structural realities of Indonesia's SME export ecosystem but limits the generalizability of the findings in several respects. The results may not be directly transferable to micro-enterprises with fewer organizational resources, to sectors with different digital adoption profiles, to privately organized or informally networked export communities, or to other developing economy contexts with different institutional arrangements for export promotion. Cross-national replication studies are needed to establish the boundary conditions of the proposed model.

Recommendations for Future Research

The findings and limitations of this study point to several productive directions for future inquiry. First, longitudinal panel designs are needed to establish the directionality of the ESC→EMO, ESC→EDA, and EMO→EDA structural associations. A natural experiment approach—exploiting exogenous variation in SMEs’ access to export communities arising from government program expansions or geographic rollouts—would offer a particularly strong basis for causal inference. Second, replacing perceptual export performance measures with objective financial data—such as customs export records, tax administrative databases, or audited financial statements—would address construct validity limitations and allow verification of whether the observed structural patterns persist against independent benchmarks. Third, the model’s boundary conditions warrant systematic examination through the inclusion of theoretically motivated moderating variables, including SME digital maturity, export community governance quality, sector-level digital intensity, and host-country digital infrastructure. Fourth, the serial indirect path ESC→EMO→EDA→EP identified in this study has not been hypothesized or tested in prior literature; future research should formally theorize and test this sequential capability development mechanism as a third distinct mediation pathway alongside the two parallel mediators confirmed here. Fifth, cross-national replication is essential to establish the external validity of the proposed model. The institutional specificity of Indonesia’s government-linked export community ecosystem may amplify the ESC→EMO and ESC→EDA pathways relative to contexts where export communities are more informally organized; replication in other developing economies, including Vietnam, Bangladesh, Nigeria, and Peru, would clarify whether the model’s pathways are generalizable or context-bound. Sixth, future studies should assess mediation effects using bias-corrected bootstrapped

confidence intervals rather than the Sobel test, to provide distribution-free inference for the indirect effects identified in this study.

Conclusion

This study developed and empirically tested an integrative model examining whether Export Sharing Community (ESC) is positively associated with SME export performance both directly and through two complementary mediated pathways— Export Market Orientation (EMO) and Export Digital Agility (EDA). Using SEM–AMOS 24 with a purposive sample of 215 Indonesian exporting SMEs, all eight hypothesized paths are supported, and the model accounts for 72.0% of variance in export performance (R² = 0.720). Subject to the crosssectional design, self-reported measures, and purposive sampling limitations noted above, the findings suggest three principal contributions to the export management and SME internationalization literatures. ESC appears to function as a distinct antecedent construct—differentiated from general social capital or business networks—as an institutional capability-building mechanism: community participation is positively associated with market intelligence orientation (Std. β = 0.450) and digital agility (Std. β = 0.342) as upstream organizational capabilities. The direct EMO→EDA path (Std. β = 0.360) provides initial empirical evidence consistent with a sensingto-reconfiguring dynamic capability sequence in which market orientation is positively associated with digital reconfiguration—an inter-construct relationship largely absent from prior export literature. The dual complementary mediation structure, in which ESC’s total indirect association with export performance (Std. indirect = 0.356) operates through both EMO and EDA simultaneously, suggests that community-derived export advantage may be primarily capability-mediated rather than direct. From a practical standpoint, the findings offer differentiated guidance across three stakeholder groups. For SME managers, the results suggest that sustained participation in structured export communities is positively linked to performance benefits through two functionally distinct capability channels; SMEs are encouraged to engage proactively with the intelligence and digital adoption support functions these communities provide. For government export agencies, the dual mediation pattern underscores the potential value of designing export communities as integrated capability platforms simultaneously delivering market intelligence services and digital technology adoption support. For cross-border digital platform providers, the large EDA association with performance (f² = 0.772) suggests that enabling SMEs to deploy digital features with speed and flexibility—rather than providing platform access alone—may generate greater performance returns.

Author contributions

This article reports the collective intellectual output of three authors whose contributions are described below in accordance with the CRediT (Contributor Roles Taxonomy) framework. Bintoro Bagus Purmono (Corresponding Author) led the conceptualization of the research framework, including the theoretical integration of Export Sharing Community, Export Market Orientation, and Export Digital Agility as a dual-mediation model grounded in dynamic capability theory and social capital theory. He was responsible for the research design, hypothesis development, instrument construction and pilot testing, full data collection, structural equation model specification and estimation using AMOS 24, and the primary drafting of the manuscript across all sections. He also led the iterative revision process in response to peer review feedback and is accountable for the integrity of the data and the accuracy of all reported statistical outputs. Ramadania contributed to the theoretical grounding of the Export Market Orientation construct, provided critical input on the export management and international marketing literatures, participated in the interpretation of the structural model findings, reviewed and substantively revised the Discussion and Conclusion sections, and contributed to the identification of theoretical contributions and policy implications. Wenny Pebrianti contributed to the operationalization of the Export Digital Agility construct, provided input on the digital transformation and SME internationalization literatures, participated in data analysis and verification of measurement model results, and reviewed the Methods and Results sections for methodological accuracy and consistency with reported AMOS outputs. All three authors have read and approved the final version of the manuscript for submission. There are no conflicts of interest to declare. The study did not receive external funding. The principal intellectual contributions of this study are: (1) the introduction of Export Sharing Community as a theoretically bounded antecedent construct in the export performance literature, distinct from general social capital and business network constructs; (2) the first empirical demonstration of a direct Export Market Orientation → Export Digital Agility pathway within a dynamic capability framework for export management; (3) the development and confirmation of a dual complementary mediation model in which a single upstream social mechanism (ESC) activates two functionally distinct organizational capabilities (EMO and EDA) that each independently mediate export performance outcomes; and (4) the identification of a serial indirect pathway (ESC→EMO→EDA→EP) as an additional mechanism through which sequential capability development enhances export competitiveness.

Funding

This research was supported by the Faculty of Economics and Business, Universitas Tanjungpura. The support provided was facilitative in nature to assist the implementation of the study. However, the funding body had no involvement in the research design, data collection and analysis, interpretation of results, or manuscript preparation. This statement confirms that the research was conducted independently, thereby ensuring the integrity and objectivity of the findings.

Acknowledgements

The authors would like to express their sincere appreciation to the Faculty of Economics and Business, Universitas Tanjungpura for the support provided in conducting this research. The authors also extend their gratitude to all SME respondents who generously contributed their time and valuable information. In addition, the authors thank colleagues and other parties who provided both technical and non-technical assistance, which contributed to the successful completion of this study.