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Article

Artificial Intelligence and Export Performance in Small and Micro-Enterprises: The Roles of Internal Capability and External Tools

by
Mengyang Gu
1 and
Chuyue Jin
2,*
1
Department of Global Management, Kookmin University, Seoul 02707, Republic of Korea
2
Graduate School of Business Administration, Kookmin University, Seoul 02707, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(12), 5846; https://doi.org/10.3390/su18125846
Submission received: 2 March 2026 / Revised: 31 May 2026 / Accepted: 2 June 2026 / Published: 8 June 2026
(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)
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Abstract

Artificial intelligence (AI) is increasingly adopted by small and micro-enterprises to enhance international competitiveness. However, limited research examines how internal AI capability and external AI tool utilization jointly shape export performance. Drawing on the resource-based view and digital resource configuration perspective, this study conceptualizes internal AI capability and external AI tool utilization as distinct but potentially overlapping AI-related resources. Using survey data from 475 exporting small and micro-enterprises in Yiwu International Trade City, we conduct regression analyses to investigate the individual and interactive effects of these two AI-related resources on export performance. The results indicate that both internal AI capability and external AI tool utilization positively affect export performance. Importantly, their interaction is negative and significant, suggesting diminishing marginal returns when both resources are highly developed. This finding indicates that overlapping AI-related investments may reduce each resource’s incremental contribution under resource-constrained conditions. By clarifying how internally developed AI capability and externally accessed AI tools interact in export settings, this study advances understanding of digital resource configuration and provides practical guidance for AI-related investment decisions in small firms.

1. Introduction

Artificial intelligence (AI) has become increasingly embedded in international trade activities [1]. In export contexts, AI applications such as multilingual translation, automated customer communication, and overseas market information retrieval provide structured digital support for firms’ cross-border operations [2]. Empirical studies show that AI-enabled tools can reduce communication costs, mitigate information asymmetry, and improve export performance [3,4,5]. More broadly, research on digitalization reports positive associations between digital resources and export performance, although the magnitude and underlying mechanisms of these effects vary across firms and institutional contexts [6,7].
Small and micro-enterprises provide an important context for examining the relationship between AI utilization and export performance for two main reasons. First, small and micro-enterprises have become increasingly important participants in international trade, particularly in export-oriented economies such as China. For these firms, export performance is not only a financial outcome but also a critical determinant of firm survival, competitiveness, and growth in international markets [8,9]. Despite their relatively limited organizational scale, export-oriented small and micro-enterprises play an important role in regional economic activity and cross-border trade networks.
Second, the growing diffusion of AI technologies has increasingly enabled small firms to adopt digital tools to support export-related activities [10,11]. AI-enabled applications such as multilingual translation, customer communication, content generation, and overseas market information retrieval can help small firms reduce communication barriers and improve operational efficiency in export markets [12]. Recent research further suggests that digital technologies and digital platforms increasingly shape how small firms participate in international markets and improve export-related competitiveness [13]. However, as the availability of AI-related technologies continues to expand, small and micro-enterprises also face increasingly important strategic decisions regarding how to allocate limited financial and managerial resources across different forms of AI-related investments [10,14]. Because these firms are typically unable to invest heavily in every available AI-related technology or capability, questions regarding the most effective configuration of AI-related resources become increasingly important. Addressing this issue is particularly important because small and micro-enterprises must strategically prioritize AI-related investments under conditions of limited organizational resources.
Within this stream of research, two related but conceptually distinct AI-related assets are particularly relevant at the firm level. Internal AI capability captures the firm’s organizational capacity to select, integrate, govern, and routinize AI-related solutions through data readiness, employee skills, process redesign, and managerial practices, so that AI use becomes embedded in export-related workflows. In contrast, external AI tool utilization refers to the intensity with which a firm relies on platform-provided AI applications that are accessed as standardized services (e.g., AI translation, automated customer communication, content generation, and AI-assisted market information search) to support export activities. These tools are typically ready to use and do not require firms to develop algorithms or infrastructure in-house [15]. While learning by using external tools can contribute to capability accumulation over time, the two constructs remain analytically distinct. One reflects reliance on externally sourced AI services, whereas the other reflects an internally embedded ability to deploy AI effectively within organizational routines.
Although prior research has not yet directly examined the interaction between internal AI capability and external AI tool utilization, related studies on digital platforms, digital capabilities, and internal digitalization provide useful theoretical guidance. Research on digital platforms and ecosystems suggests that externally provided digital solutions can help firms access market information, customers, and transaction-support functions without developing all related capabilities internally [8,16]. At the same time, studies grounded in the resource-based view emphasize that internally developed digital capabilities remain important because they shape how effectively firms absorb, integrate, and leverage externally provided digital technologies [17,18]. Taken together, these studies imply that externally accessed digital solutions and internally developed capabilities may operate complementarily in enhancing firm performance. However, prior platform research also suggests that when external digital platforms increasingly provide standardized and ready-to-use solutions, the roles of external tools and internal capabilities may increasingly converge. Under such conditions, the marginal contribution of one capability may decline as the other becomes more developed, resulting in diminishing marginal returns rather than purely additive performance benefits.
Although prior research suggests the possibility of both complementarity and overlap between externally accessed digital solutions and internally developed capabilities, we argue that partial substitution is more likely to emerge in export environments characterized by resource constraints. In such contexts, both internally developed AI capability and externally provided AI tools increasingly support similar export activities, and their functions in export operations may increasingly overlap. Under these conditions, the marginal contribution of one capability is likely to decline as the other becomes more developed. Therefore, rather than generating purely additive performance gains, the interaction between the two may exhibit partial substitution and diminishing marginal returns.
To examine this argument, we focus on exporting small and micro-enterprises in Yiwu International Trade City, a major export cluster where platform-based trade and externally provided AI services are widely accessible. Using survey data from 475 exporting firms, we estimate multiple regression models to assess the direct and interaction effects of external AI tool utilization and internal AI capability on export performance. The results indicate that both external AI tool utilization and internal AI capability are positively associated with export performance. At the same time, their interaction effect is negative, suggesting that when one resource is already highly developed, the incremental contribution of the other becomes smaller. These findings provide evidence of partial substitution between overlapping digital resources in resource-constrained export contexts. Understanding this configuration logic is also relevant to sustainability because inefficient or overlapping digital investments may place additional financial and managerial burdens on small firms, whereas better-aligned AI adoption can support more sustainable export growth.
This study contributes to the literature in three ways. First, this study advances the literature on digital resource configuration by identifying conditions under which internal AI capability and external AI tool utilization exhibit partial substitution rather than complementary effects. Second, by focusing on exporting small and micro-enterprises operating in a highly platformized trade environment, the study extends digital resource configuration research to contexts characterized by limited capital and managerial attention. Third, the findings provide practical implications for sustainable digital investment strategies, suggesting that small and micro-enterprises should carefully balance internal capability development and external tool adoption to avoid inefficient resource duplication and support sustainable export performance improvement.

2. From Traditional Trade to Platform-Based AI Adoption: The Yiwu Context

Yiwu provides a representative setting for examining export-related activities among Chinese small and micro-enterprises. Prior research has described Yiwu’s rise as an international trade hub as being rooted in its small-commodity markets, agglomeration economies, and home-grown small trading enterprises rather than the large-firm and foreign-investment model typically associated with export zones [19]. As a result, Yiwu has developed a large concentration of export-oriented small and micro-enterprises that operate through digital trade platforms and international trading networks [20,21,22]. These characteristics make Yiwu particularly suitable for examining export performance in highly platformized small-firm trade environments.
More importantly, Yiwu has moved beyond traditional offline trade and basic digitalization toward platform-based AI adoption. Digital trade platforms such as Chinagoods have introduced AI-enabled services to support small-commodity export activities, including multilingual translation, product-content generation, visual content creation, digital marketing, customer communication, and trade matching. Unlike general-purpose AI tools, these platform-based AI applications are embedded in Yiwu’s small-commodity export context and are directly connected to routine export tasks such as product display, cross-language communication, customer inquiry handling, online promotion, and marketing material preparation. Consequently, AI technologies in Yiwu are not merely experimental digital tools but increasingly constitute operational resources that firms use in their day-to-day export activities.
This context provides a suitable empirical setting for the present study. Fieldwork conducted in July 2024 indicated that small and micro exporting firms in Yiwu varied considerably in their use of platform-provided AI tools. Some firms actively used AI tools for translation, content generation, short-video production, customer communication, and digital marketing, whereas others used them only occasionally or in a limited manner. At the same time, firms also differed in their internal AI capability, including digital infrastructure, employee AI-related skills, organizational learning capacity, and managerial support for integrating AI into export-related workflows [17,22]. Such variation in both external AI tool utilization and internal AI capability makes Yiwu an appropriate context for examining how externally provided AI tools and firms’ internal AI capability jointly shape export performance.

3. Theory and Hypotheses

3.1. External AI Tool Utilization and Export Performance

Research on cross-border transactions and firm internationalization has long emphasized the role of information technology in mitigating language barriers and information asymmetry. Prior studies show that information systems and online platforms reduce search costs for overseas customers and trading partners, enhance firms’ awareness of foreign demand and competitive dynamics, and thereby facilitate market entry and expansion [23,24]. Related work suggests that digital technologies can improve export performance by reducing uncertainty and transaction costs and by broadening firms’ export market reach [25,26,27]. In particular, AI-based machine translation, intelligent customer service, and content-generation tools can significantly reduce cross-border communication and search costs, ease frictions stemming from language and cultural differences, and promote export participation and export performance [28,29,30,31].
For exporting small and micro-enterprises, many AI tools are accessed as externally provided digital services that do not require firms to develop underlying algorithms or infrastructure internally. These tools can support multilingual communication, marketing content generation, and overseas market information retrieval [32,33]. Research on cross-border e-commerce and digital platforms further indicates that externally provided platform resources can compensate for SMEs’ resource shortages and facilitate rapid entry into foreign markets [34]. Therefore, broader and more intensive utilization of external AI tools is likely to reduce search and coordination costs and enhance firms’ responsiveness to export opportunities. Accordingly, we hypothesize:
H1: 
External AI tool utilization positively affects firm export performance.

3.2. Internal AI Capability and Export Performance

A growing body of research grounded in the resource-based view suggests that internally developed digital capabilities constitute strategic resources that enhance firm performance [13,35,36,37,38]. Within this perspective, internal AI capability reflects a firm’s ability to integrate AI-related infrastructure, manage data and processes, and embed AI applications into core business routines [17,36,39]. Such capability is accumulated through organizational learning and investment and becomes embedded in firms’ operational systems.
Prior research has linked digital capability to overall firm performance and innovation outcomes. However, relatively limited attention has been paid to export performance, particularly in resource-constrained small and micro-enterprises [23,40,41]. In export contexts, internal AI capability can enhance firms’ ability to integrate AI tools into cross-border communication, customer management, and information processing routines, thereby improving coordination efficiency and decision quality. By strengthening firms’ capacity to embed AI into export-related processes, internal AI capability is expected to contribute directly to superior export performance. Therefore, we hypothesize:
H2: 
Internal AI capability positively affects firm export performance.

3.3. External–Internal Resource Interaction and Export Performance

Prior research suggests that internally developed capabilities can complement externally sourced technologies by improving firms’ ability to recognize, assimilate, and apply external knowledge [18,42]. However, the performance implications of combining internal and external innovation resources are context dependent rather than uniformly additive [43,44]. In Yiwu’s platform-based export environment, external AI tools increasingly support routine export activities, such as translation, content generation, short video production, customer communication, and digital marketing, through standardized digital services. As these services become more capable and more deeply embedded in export workflows, the functional overlap between external AI tool utilization and firms’ internal AI capability may become more pronounced. Under such conditions, the marginal contribution of one resource may decline as the other becomes more developed.
Simultaneous investments in both resources can also generate duplicated task coverage, additional coordination, monitoring, and learning costs, as well as heavier demands on limited managerial attention [45]. This tendency may be especially salient in small exporting firms with constrained financial and organizational resources, since committing scarce resources to both internal AI capability building and extensive external AI tool utilization can crowd out alternative export enhancing investments, such as customer acquisition, product adaptation, logistics improvement, and relationship building in foreign markets [46]. While complementarity may still arise in more customized or nonroutine export activities, we argue that partial substitution is more likely in this context, and thus the interaction effect between external AI tool utilization and internal AI capability on export performance is expected to be negative. Accordingly, we hypothesize:
H3: 
The interaction between external AI tool utilization and internal AI capability negatively affects firm export performance.
Figure 1 presents the research model of the study and summarizes the hypothesized relationships among the key variables. Specifically, the model illustrates the positive effects of external AI tool utilization and internal AI capability on export performance, as well as the negative interaction effect between the two AI-related resources.

4. Data and Method

4.1. Sample and Research Context

This study examines exporting small and micro-enterprises operating in Yiwu International Trade City, one of China’s largest clusters of export-oriented small-commodity businesses [19,20]. In recent years, AI-based applications tailored to small merchants, such as AI translation tools, digital trade assistants, and automated content-generation services, have been increasingly promoted in Yiwu. These externally provided tools are designed to facilitate cross-border trade by supporting routine export-related tasks such as translation, product-content generation, customer communication, and online promotion. At the same time, firms differ substantially in their ability to use and integrate these tools into daily export activities. The coexistence of widespread external tool availability and heterogeneous internal capability conditions makes Yiwu an appropriate setting for examining how external AI tool utilization and internal AI capability jointly influence export performance.
To support questionnaire development and better understand the research context, we conducted preliminary field visits and interviews with exporting merchants. The interviews suggested that merchants frequently relied on platform-provided external AI tools for routine export-related tasks, such as translation, product-description generation, and customer inquiry handling, while firms differed in their ability to integrate these tools into daily operations. Interviewees also expressed differing views on whether these external AI tools substituted for or strengthened internal AI capability development. These insights helped refine the questionnaire items and informed the distinction between external AI tool utilization and internal AI capability.
The main survey was conducted between May and August 2025. The questionnaire was administered in Chinese and distributed through face-to-face surveys to merchants operating in Yiwu. The respondents were firm owners or managers directly involved in export decision-making and daily operations. Only one respondent from each firm participated in the survey. This respondent selection is consistent with upper echelons theory proposed by Hambrick and Mason (1984), which argues that organizational outcomes and strategic choices reflect the characteristics, values, and cognitive bases of top managers [47]. Since AI adoption and export operations involve firm-level strategic and operational decisions, respondents with actual decision-making authority were considered appropriate key informants for providing information on export operations and AI utilization [47,48].
Prior to the survey, all respondents were fully informed of the academic purpose, voluntary participation, and confidentiality of the study both orally and in writing. This study was conducted in strict compliance with academic ethical norms and the principles of informed consent. Respondents were assured that their answers would be used solely for academic research and reported only in aggregate form, and no personally identifiable information was disclosed throughout the research process to protect participants’ legitimate rights and privacy.
A total of 521 questionnaires were distributed. After excluding responses from firms primarily focused on domestic sales and questionnaires with substantial missing values on key variables, 475 valid observations were retained for analysis.
Table 1 summarizes the characteristics of the sampled firms and respondents. Overall, the sample generally reflects the characteristics of small and micro-enterprises operating in Yiwu’s export environment. Most firms were relatively small in size, with 65.69% employing 50 or fewer employees. In particular, firms with one employee accounted for 2.32% of the sample and refer to very small owner-managed exporting businesses in which the owner manager is the only full-time worker. Such firms are commonly observed in Yiwu’s small commodity export ecosystem and were retained because they independently conduct export related business activities and fall within the scope of small and micro enterprises examined in this study [20]. At the same time, a smaller proportion of firms employed more than 200 workers (8.21%), reflecting the presence of export oriented firms that also operate production facilities within Yiwu’s industrial cluster [49]. Consistent with this explanation, 61.68% of the sampled firms reported operating their own production facilities. In addition, 73.47% of the firms were family owned, and all sampled firms were actively engaged in export activities. Respondents were relatively balanced in gender distribution, and most possessed at least a college level education.

4.2. Measures

The dependent variable is export performance. Following Sinkovics et al. ([50]) and adapting the items to the export context, export performance is measured using four items reflecting firms’ perceived export outcomes: export sales ratio, export sales growth, export profit contribution, and overall export performance. All items are measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The scale demonstrates satisfactory internal consistency (Cronbach’s α = 0.86).
The first independent variable is external AI tool utilization. Drawing on prior research on digital technology adoption intensity and AI capability development [17], and informed by preliminary field interviews, we developed five items to capture the frequency and scope of AI tool utilization across major export-related activities, including language processing, content generation, marketing support, and information search. This construct is measured using a multi-item five-point Likert scale and demonstrates strong internal consistency (Cronbach’s α = 0.89).
The second independent variable is internal AI capability. This measure is adapted from the IT and e-business readiness scale developed by Lin and Lin ([51]) and tailored to the AI application context. Internal AI capability is measured using four items that capture firms’ capability to integrate AI-related infrastructure, manage data and processes, and embed AI applications into export-related business routines. The construct is measured using a multi-item five-point Likert scale and demonstrates good internal consistency (Cronbach’s α = 0.86).
We included several firm-level controls that are likely to be associated with export performance. All of these variables were collected through the questionnaire and coded for use in the regression analysis. Firm age was coded as 1 for firms operating for six years or less and 0 for firms operating for more than six years. Firm size was coded as 1 for firms with 50 or fewer employees and 0 for firms with more than 50 employees. These variables were included to account for differences in operating experience and resource endowment, two factors commonly considered in export performance research. Family ownership was coded as 1 for family-owned firms and 0 otherwise, given that ownership type has been linked to SMEs’ international involvement. We also controlled for whether firms had their own production facilities, coded as 1 if they did and 0 otherwise, to capture differences in production autonomy and operational capability. Finally, main business model was coded as 1 for firms mainly engaged in export sales and 0 for firms combining domestic and export sales, as these firms may differ in export orientation and resource commitment.
We also included two respondent-level controls: gender and educational attainment. Gender was coded as 1 for female respondents and 0 for male respondents. Educational attainment was coded as 1 for respondents with a bachelor’s degree or above and 0 otherwise. These controls were also collected through the questionnaire and were included because owner or manager characteristics can shape digitalization and export-related decisions in small and micro-enterprises.

4.3. Common Method Bias

Before conducting hypothesis testing, we assessed potential common method bias using both Harman’s single-factor test and a common latent factor approach. The Harman single-factor test showed that the first unrotated factor explained 43.11% of the total variance, below the commonly used threshold of 50%, suggesting that common method bias is unlikely to be severe. In addition, a common latent factor was introduced into the CFA model to further assess the potential influence of common method variance. The inclusion of the common latent factor did not significantly improve model fit (Δχ2 = 0.05, p = 0.822), and the common-method loadings were not statistically significant. These results suggest that common method bias is unlikely to be a serious concern in this study.

4.4. Analytical Strategy

This study employs ordinary least squares (OLS) regression to test the proposed hypotheses. The dependent variable and the two focal explanatory variables were operationalized as composite measures based on multiple five-point Likert scale items. OLS regression was adopted because the study aims to examine both the direct effects of external AI tool utilization and internal AI capability on export performance, as well as the interaction effect between these two variables. Since the analysis focuses on testing direct and interactive relationships among composite constructs rather than estimating a full latent variable structural model, OLS provides a parsimonious and interpretable analytical approach. To construct the interaction term, external AI tool utilization and internal AI capability were mean-centered prior to multiplication. Mean-centering also facilitates coefficient interpretation and helps reduce nonessential multicollinearity in interaction models.
In OLS regression, the normality assumption primarily concerns the residuals rather than the raw survey variables themselves. To assess this assumption, we examined the standardized residuals from the full regression model. The residual distribution was centered close to zero and approximated normality without evidence of serious distortion. Visual inspection of the histogram and normal Q-Q plot further indicated only minor deviations from normality. Given the composite nature of the scale measures, the sample size of 475 firms, and the study’s focus on direct and interaction effects, OLS regression provides an appropriate and interpretable method for analyzing the survey data.

5. Results

5.1. Descriptive Statistics and Correlations

Table 2 reports the descriptive statistics and correlation matrix for all variables. External AI tool utilization is positively correlated with export performance (r = 0.388, p < 0.05), and internal AI capability is also positively correlated with export performance (r = 0.439, p < 0.05). In addition, external AI tool utilization and internal AI capability are positively correlated with each other (r = 0.431, p < 0.05). To assess potential multicollinearity, variance inflation factor (VIF) tests were conducted. The VIF values range from 1.02 to 1.28, with a mean of 1.08, well below conventional thresholds, indicating that multicollinearity is not a concern.

5.2. Results of Hypothesis Testing

Table 3 reports the results of the OLS regression analyses. Model (1) includes only the control variables. Models (2) and (3) separately introduce external AI tool utilization and internal AI capability, respectively, to examine their individual associations with export performance. In Model (2), external AI tool utilization shows a positive and significant effect on export performance (β = 0.364, p < 0.01). Similarly, Model (3) indicates that internal AI capability is positively and significantly associated with export performance (β = 0.447, p < 0.01). Model (4) includes both external AI tool utilization and internal AI capability simultaneously. Both variables remain positive and significant (β = 0.228, p < 0.01; β = 0.345, p < 0.01, respectively), providing support for H1 and H2. These results suggest that both external AI tool utilization and internal AI capability independently contribute to export performance.
Model (5) introduces the interaction term between external AI tool utilization and internal AI capability to test H3. The interaction coefficient is negative and statistically significant (β = –0.107, p < 0.01), supporting H3. This finding suggests that the positive effect of one digital resource on export performance diminishes as the level of the other resource increases. Figure 2 further illustrates this interaction. When internal AI capability is low, external AI tool utilization exhibits a stronger positive association with export performance. In contrast, when internal AI capability is high, the positive effect of external AI tool utilization on export performance becomes weaker. This finding suggests that firms possessing stronger internal AI capability may rely less on external AI tools to achieve export performance gains. The interaction pattern therefore indicates a partial substitution relationship between the two digital resources rather than a purely complementary effect.

5.3. Robustness Checks

First, given that a small number of firms in the sample reported more than 200 employees, the analyses were re-estimated after excluding these firms, and the results remained substantively unchanged. In addition, the models were re-run after excluding firms with more than 50 employees to focus more strictly on small and micro-enterprises. The main and interaction effects remained consistent in both direction and statistical significance. Second, several control variables were operationalized as dummy variables to preserve degrees of freedom. To ensure robustness, the regressions were re-estimated using their original categorical classifications, and the results remained substantively unchanged.

6. Discussion

6.1. Theoretical Implications

Using survey data from 475 exporting small and micro-enterprises in Yiwu International Trade City, this study examined how external AI tool utilization and internal AI capability jointly shape export performance. The results show that both external AI tool utilization and internal AI capability are positively associated with export performance. This finding is broadly consistent with prior studies suggesting that digitalization and digital resources can improve firm performance and internationalization outcomes [6,52]. Research on digital platforms and SME internationalization has similarly suggested that externally provided digital resources can help small firms overcome resource constraints and participate more effectively in international markets [8,16,53]. Recent studies have further argued that digital platforms can reshape organizational capabilities and facilitate capability reconfiguration among SMEs [54]. Consistent with this view, our findings suggest that both externally accessed AI tools and internally developed AI capability represent important AI-related resources through which small and micro-enterprises can enhance export performance.
The findings also clarify the roles of external AI tools and internal AI capability in export settings. External AI tools provide firms with access to ready-made AI functionalities through platforms and service providers, whereas internal AI capability reflects a firm’s ability to integrate and utilize AI within its own operations and routines. Although these two resources differ in how AI-related support is accessed and deployed, both can be applied to similar export-related activities, such as translation, customer communication, content generation, and market information processing.
More importantly, this study identifies partial substitution as a boundary condition in digital resource configuration. Prior research has frequently emphasized the complementarity between internal capabilities and external knowledge sources [18,42]. However, recent studies also suggest that the value of combining internal and external resources depends on contextual conditions such as resource constraints, coordination costs, and functional overlap among resources [43,44,55,56]. Our findings are more consistent with this contingent perspective. In the Yiwu context, external AI tools and internal AI capability often support the same routine export activities. As a result, their benefits may partially overlap, reducing the marginal contribution of one resource when the other is already highly developed. This pattern is particularly likely in platform-intensive export environments where standardized AI tools already provide support for many of the activities that firms might otherwise improve through internal capability development. By highlighting functional overlap and resource constraints as underlying mechanisms, the study advances understanding of how internal and external AI-related resources interact in shaping export performance.

6.2. Practical Implications

From a practical standpoint, the findings suggest that exporting small and micro-enterprises should approach digital investment decisions with greater strategic alignment. While both internal AI capability development and external AI tool utilization independently enhance export performance, simultaneously intensifying both may not always be efficient. Firms with limited resources should carefully evaluate how internally developed AI capability and externally provided AI tools can be balanced. While external AI tools may provide accessible and cost-efficient support for export activities, firms with stronger internal AI capability may derive less incremental benefit from extensive reliance on standardized external solutions. Careful calibration between internal capability accumulation and external tool adoption may therefore support more efficient and sustainable export performance improvement by reducing redundant digital investment and improving the long-term viability of AI-enabled exporting.

6.3. Limitations and Future Research

This study has several limitations that offer directions for future research. First, the data are drawn from exporting small and micro-enterprises located in Yiwu, China. Although Yiwu represents a highly relevant platform-based export environment, the regional and industry concentration may limit the generalizability of the findings. In addition, this study adopts a context-specific operationalization of external AI tool utilization and internal AI capability. The external AI tools examined mainly refer to platform-based and externally provided AI applications commonly used by small exporting merchants in Yiwu, such as AI translation, product content generation, customer communication, and online promotion support. While this bounded operationalization improves construct clarity and respondent interpretability, the findings should be interpreted within the specific context of Yiwu’s export ecosystem. Accordingly, the negative interaction observed in this study should not be interpreted as evidence that external AI systems and internal AI capability are universally substitutive. Rather, the findings suggest that partial substitution may emerge when standardized external AI tools and internally developed AI capability overlap in supporting routine export-related activities. Future research could examine broader categories of AI systems and develop more generalizable measurement frameworks to further investigate the conditions under which different AI-related resources function as complements or substitutes. In addition, future studies could examine firms across different countries, industries, and digital-platform ecosystems to assess the external validity of the partial substitution effect.
Second, this study focuses exclusively on small and micro-enterprises engaged in export activities. Whether similar patterns emerge among larger firms, non-exporting firms, or organizations at different stages of digital transformation remains an open question. Extending the analysis to diverse organizational contexts would help clarify the scope conditions of the proposed framework.
Third, although the reliability tests indicate satisfactory internal consistency, the key variables are measured using self-reported survey data, which may raise concerns regarding common method bias or social desirability effects. In addition, the cross-sectional design does not allow us to fully rule out reverse causality. Firms with stronger export performance may have more resources and incentives to adopt external AI tools or develop internal AI capability. Future studies could incorporate objective performance indicators, platform transaction records, financial data, longitudinal designs, panel data, or instrumental-variable approaches to strengthen causal inference and better identify the direction of causality. Longitudinal research would also be particularly useful for examining the dynamic relationship between external AI tool utilization and internal AI capability. While repeated use of external AI tools may facilitate learning-by-using and contribute to internal capability accumulation, excessive reliance on standardized external tools may also reduce firms’ incentives to develop internal AI capability. Future research is therefore needed to clarify whether external AI tool utilization strengthens, weakens, or reshapes internal AI capability over time.

7. Conclusions

In conclusion, this study shows that both external AI tool utilization and internal AI capability contribute positively to export performance among exporting small and micro-enterprises. However, their significant negative interaction indicates that these two AI-related resources do not always generate purely additive benefits. Instead, when one resource is already highly developed, the marginal contribution of the other may decline, suggesting partial substitution and diminishing marginal returns.
These findings provide useful implications for both researchers and practitioners. For researchers, the study highlights the need to examine not only the independent effects of AI-related resources, but also how different digital resources interact under resource-constrained conditions. For practitioners, especially owner-managers of exporting small and micro-enterprises, the results suggest that AI-related investment decisions should be aligned with firms’ existing resource conditions and operational needs. Rather than assuming that greater investment in both internal AI capability and external AI tools will always improve performance, small firms should carefully balance internal capability development and external tool adoption. Such strategic alignment can help reduce redundant digital investment and support more sustainable export performance improvement.

Author Contributions

Conceptualization, M.G. and C.J.; methodology, M.G. and C.J.; formal analysis, M.G.; investigation, M.G.; data curation, M.G.; writing—original draft preparation, M.G.; writing—review and editing, M.G. and C.J.; visualization, M.G.; supervision, C.J.; project administration, C.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study is waived for ethical review according to the Bioethics and Safety Act in Korea and institutional guidelines governing social science research. Therefore, Ethics Committee approval was not required for this study. All procedures performed in this research were conducted in accordance with the ethical standards of research.

Informed Consent Statement

Verbal informed consent was obtained from the participants. Verbal consent was obtained rather than written because the data used in this study were collected through an anonymous survey of firms. The survey targeted firms rather than individual human subjects, and no personally identifiable or sensitive personal information was collected. Before completing the survey, respondents were informed about the purpose of the research and that their participation was entirely voluntary. By choosing to complete the questionnaire, respondents provided their informed consent to participate in the study.

Data Availability Statement

The data presented in this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 18 05846 g001
Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Interaction effects of external AI tool utilization and internal AI capability on export performance.
Figure 2. Interaction effects of external AI tool utilization and internal AI capability on export performance.
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Table 1. Sample characteristics (N = 475).
Table 1. Sample characteristics (N = 475).
Firm Characteristics
Firm age (years)%Firm size (number of employees)%
1–311.3712.32
4–641.892–57.16
7–1028.006–1015.16
10+18.7411–5041.05
51–20026.11
200+8.21
Family owned%Production facility%
Yes73.47Yes61.68
No26.53No38.32
Main business model%
Export-only40.84
Export-and-domestic59.16
Respondent Characteristics
Gender%Education level%
Female45.05Junior high or below5.89
Male54.95High school13.05
College20.84
Bachelor45.47
Master or above14.74
Table 2. Descriptive statistics and correlations.
Table 2. Descriptive statistics and correlations.
Variables(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)
(1) Export performance1.000
(2) External AI tool utilization0.388 *1.000
(3) Internal AI capability0.439 *0.431 *1.000
(4) Firm age−0.075−0.056−0.0411.000
(5) Firm size−0.034−0.030−0.0160.0431.000
(6) Family-owned−0.019−0.030−0.001−0.0080.0581.000
(7) Production facility0.0700.0740.149 *−0.009−0.0040.125 *1.000
(8) Main business model−0.018−0.072−0.0800.117 *0.0320.0720.100 *1.000
(9) Education0.0620.0880.010−0.003−0.089−0.0010.0140.0281.000
(10) Gender−0.023−0.030−0.142 *−0.008−0.059−0.002−0.0350.0220.0361.000
Mean3.7263.6143.6890.5330.6570.7350.6170.4080.6020.451
Std. Dev.0.9270.9660.9160.4990.4750.4420.4870.4920.4900.498
Note: * p < 0.05.
Table 3. Regression results for AI tool utilization, AI capability, and export performance (n = 475).
Table 3. Regression results for AI tool utilization, AI capability, and export performance (n = 475).
Variable(1)(2)(3)(4)(5)
Firm age−0.134−0.101−0.109−0.094−0.109
(0.086)(0.080)(0.077)(0.075)(0.075)
Firm size−0.048−0.036−0.033−0.029−0.036
(0.090)(0.084)(0.081)(0.079)(0.078)
Family owned0.0550.0280.0430.0290.042
(0.097)(0.090)(0.088)(0.085)(0.085)
Production facility−0.138−0.077−0.0080.000−0.009
(0.089)(0.082)(0.081)(0.079)(0.078)
Main business model−0.0290.0240.0440.0600.058
(0.088)(0.082)(0.079)(0.077)(0.077)
Education0.1130.0490.1010.0640.041
(0.087)(0.081)(0.079)(0.077)(0.077)
Gender−0.045−0.0230.0680.0560.055
(0.086)(0.079)(0.078)(0.076)(0.075)
External AI tool utilization 0.364 *** 0.228 ***0.198 ***
(0.041) (0.043)(0.044)
Internal AI capability 0.447 ***0.345 ***0.286 ***
(0.043)(0.046)(0.050)
External AI tool utilization × Internal AI capability −0.107 ***
(0.036)
Constant3.831 ***3.797 ***3.688 ***3.699 ***3.768 ***
(0.121)(0.112)(0.110)(0.107)(0.109)
N475475475475475
R-squared0.0160.1570.2020.2470.262
Adj. R-squared0.0020.1420.1880.2330.246
F-statistic1.1110.8414.7516.9916.46
Root MSE0.9260.8580.8350.8120.805
Standard errors in parentheses.*** p < 0.01.
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Gu, M.; Jin, C. Artificial Intelligence and Export Performance in Small and Micro-Enterprises: The Roles of Internal Capability and External Tools. Sustainability 2026, 18, 5846. https://doi.org/10.3390/su18125846

AMA Style

Gu M, Jin C. Artificial Intelligence and Export Performance in Small and Micro-Enterprises: The Roles of Internal Capability and External Tools. Sustainability. 2026; 18(12):5846. https://doi.org/10.3390/su18125846

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Gu, Mengyang, and Chuyue Jin. 2026. "Artificial Intelligence and Export Performance in Small and Micro-Enterprises: The Roles of Internal Capability and External Tools" Sustainability 18, no. 12: 5846. https://doi.org/10.3390/su18125846

APA Style

Gu, M., & Jin, C. (2026). Artificial Intelligence and Export Performance in Small and Micro-Enterprises: The Roles of Internal Capability and External Tools. Sustainability, 18(12), 5846. https://doi.org/10.3390/su18125846

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