Learning by Exporting in Emerging Economies: How Business Group Structure Shapes Firm Innovation

Learning by Exporting in Emerging Economies: How Business Group Structure Shapes Firm Innovation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Learning by Exporting in Emerging Economies: How Business Group Structure Shapes Firm Innovation Syed Azhar Husain Jafri, Snehal Suyash Awate This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9237636/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Firms’ participation in international markets can create opportunities for learning, technological catch-up, and improved global competitiveness. However, empirical evidence on learning by exporting remains mixed, suggesting that the innovation benefits of exporting may depend on the organizational context in which firms operate. In many emerging economies, firms are embedded within business groups that provide internal markets for resources and knowledge sharing. This study examines how business group affiliation and group characteristics shape firms’ ability to translate export exposure into innovation outcomes. Using a longitudinal dataset of 276 Indian manufacturing firms spanning 2003–2020, we investigate the relationship between export intensity and firm innovation. The results show that export intensity positively influences firm innovation. We further find that business group affiliation strengthens the innovation benefits of exporting, indicating that internal group networks facilitate knowledge acquisition and recombination. However, the characteristics of business groups play an important moderating role. In particular, business group size reduces the marginal benefits of exporting for firm innovation. In contrast, export-driven innovation is stronger in technologically focused groups where affiliates operate in related industries. These findings highlight the importance of organizational structures in shaping firms’ ability to learn from international markets. JEL Codes: F14, O31, O32, L25 Learning by Exporting Firm Innovation Business Groups Export Intensity Technological Relatedness Figures Figure 1 1. Introduction Exporting is widely regarded as an important channel through which firms enhance their technological capabilities and global competitiveness (MacGarvie, 2006 ; Salomon & Jin, 2007 ; Bhat & Momaya, 2020 ). Participation in international markets exposes firms to advanced technologies, demanding customers, and more intense competition, creating opportunities for learning and capability development (Aw et al., 2000 ; Blalock & Gertler, 2004 ; Golovko & Valentini, 2014 ). Through interactions with foreign buyers and competitors, firms can acquire knowledge about production processes, product design, and market preferences that can subsequently be incorporated into innovation activities. This mechanism is commonly described as learning by exporting , whereby firms improve their technological capabilities as a consequence of engaging in international markets (Salomon & Shaver, 2005 ). Understanding whether and how such learning effects emerge is particularly important for firms in emerging economies, where export participation is frequently viewed as a pathway for technological upgrading, capability catch-up, and improved international competitiveness (Lall, 2001 ; Momaya, 2001 ; Awate et al., 2014 ). However, empirical evidence on learning-by-exporting remains mixed, and the literature broadly falls into three streams. The first stream supports the learning-by-exporting hypothesis, arguing that participation in foreign markets exposes firms to new knowledge and technologies that can stimulate innovation. Empirical studies aligned with this perspective report positive effects of exporting on innovation and firm growth (Salomon & Jin, 2010; Cassiman & Golovko, 2010 ; Love & Ganotakis, 2012 ; Golovko & Valentini, 2014 ). A second line of research finds little evidence that exporting itself generates productivity or innovation improvements (Delgado et al., 2002 ; Wagner, 2007 ; Monreal-Pérez et al., 2011 ). The third perspective attributes the export–performance relationship largely to self-selection, whereby more productive firms are more likely to enter export markets (Clerides et al., 1998 ; Bernard & Jensen, 1999 ; Wagner, 2007 ). Overall, these findings suggest that exporting does not automatically lead to innovation gains, and that the benefits of export participation may depend on firms’ ability to absorb and utilize knowledge from foreign markets. Researchers increasingly attribute these divergent findings to contextual and organizational factors that shape firms’ ability to absorb knowledge from international markets. The innovation benefits of exporting may depend on the technological knowledge accessed through foreign markets, such as new product designs and manufacturing processes (Salomon & Shaver, 2005 ; Greenaway & Kneller, 2007 ). They may also depend on environmental and institutional conditions, including host country knowledge levels, the strength of institutional environments, and the cultural and knowledge distance between the home and host countries (Smith, 2014 ; Wu et al., 2015 ; Thakur-Wernz & Samant, 2017 ). Finally, firm-level characteristics influence how effectively export exposure translates into innovation, including firm size, internal R&D intensity, and ownership structure (Tsao & Lien, 2013; Damijan et al., 2017 ). However, despite the importance of ownership structures in shaping firm behavior, existing research has paid limited attention to how organizational forms such as business group affiliation influence firms’ ability to leverage export participation for innovation. Among these ownership structures, business groups represent a particularly important organizational form in emerging economies. Business groups consist of networks of legally independent firms connected through ownership and strategic ties and are widely prevalent in many emerging markets (Chang & Hong, 2000 ; Khanna & Palepu, 2000 ). Such groups often provide internal markets for capital, labor, and knowledge, enabling affiliated firms to access resources and capabilities that may be difficult to obtain through external markets (Mahmood & Mitchell, 2004 ; Douma et al., 2006 ). Through these internal networks, business groups may facilitate the sharing and recombination of knowledge obtained from export markets, allowing affiliated firms to benefit from the experiences of other group members (Belenzon & Berkovitz, 2010 ; Chittoor et al., 2014 ). Such intra-group knowledge exchanges may enable affiliated firms to more effectively absorb and utilize knowledge generated through international activities, strengthening their ability to translate export exposure into innovation outcomes relative to standalone firms (Iona et al., 2013 ). However, the effectiveness of these internal markets and knowledge networks may depend on the characteristics of the business group itself. In particular, two structural characteristics may influence how effectively export exposure translates into innovation. First, business group size may shape the efficiency of knowledge diffusion across affiliated firms. Larger groups often provide broader internal markets for capital, managerial talent, and scientific expertise, as well as access to a wider pool of technological knowledge that can support innovation activities (Guillén, 2000; Mahmood & Mitchell, 2004 ; Belenzon & Berkovitz, 2010 ). At the same time, however, larger groups may face coordination challenges, agency problems, and risks of resource misallocation or tunnelling that can hinder effective knowledge sharing and innovation outcomes (Chakrabarti et al., 2006 ; Khanna & Yafeh, 2007 ; Carney et al., 2011 ). Second, the technological relatedness among group affiliates may determine how effectively firms recombine knowledge acquired from international markets. When firms within a group operate in technologically related industries, knowledge spillovers are more likely to occur, facilitating innovation (Cohen & Levinthal, 1990 ; Fleming & Sorenson, 2004 ). In contrast, highly diversified groups spanning unrelated industries may experience weaker knowledge transfer due to limited technological overlap (Dosi, 1988; Tanriverdi & Venkatraman, 2004 ). These arguments suggest that the size and technological composition of business groups may play a critical role in shaping firms’ ability to learn from export activities. Against this backdrop, this study examines how business group structures shape firms’ ability to learn from exporting and translate export exposure into innovation outcomes. Using a longitudinal dataset of 276 Indian manufacturing firms spanning 2003 to 2020, we address three related questions: Does export intensity enhance firm innovation outcomes, measured through patent applications? Does business group affiliation strengthen the innovation benefits of exporting? How do business group characteristics, specifically group size and technological relatedness among affiliates, moderate the relationship between exporting and innovation? Our results show three major findings. First, consistent with the learning-by-exporting hypothesis, export intensity positively influences firm-level innovation. Second, firms affiliated with business groups are better able to translate export exposure into innovation outcomes, suggesting that internal group networks facilitate knowledge acquisition and capability recombination. Third, the structure of business groups plays an important moderating role. While moderate business group size enhances export learning through expanded internal networks, the marginal benefits of learning-by-exporting decline as group size increases, likely reflecting coordination challenges and internal inefficiencies. Importantly, the technological composition of business groups also shapes export learning. Export-driven innovation is significantly stronger in technologically focused groups, where affiliates operate in related industries and are therefore better positioned to share and recombine knowledge obtained from international markets. This study contributes to two strands of literature. First, it advances research on learning-by-exporting by identifying the organizational conditions under which export participation leads to innovation. While prior studies have largely focused on the direct relationship between exporting and innovation, we show that firms’ internal organizational structures shape their ability to absorb and recombine knowledge obtained from international markets. Second, the study contributes to research on business groups by demonstrating how group structures influence firms’ capacity to convert export exposure into innovation outcomes. Although business groups represent a dominant organizational form in many emerging economies, their role in shaping firms’ ability to learn from exporting has received limited attention in prior research. By focusing on this organizational context, we show that structural characteristics of business groups, such as group size and technological relatedness among affiliates, play a critical role in shaping firms’ ability to learn from exporting in emerging market contexts. 2. Theory and Hypothesis 2.1 Learning by exporting and firm innovation Participation in international markets allows firms to access valuable sources of knowledge that may stimulate innovation. Exporting firms interact with foreign buyers, competitors, and suppliers, allowing them to observe new technologies, production techniques, and evolving market requirements (Aw et al., 2000 ; Blalock & Gertler, 2004 ; Keller, 2010 ). Such exposure allows firms to acquire external knowledge that can subsequently be integrated into innovation activities. This mechanism is commonly referred to as learning-by-exporting, whereby firms develop new capabilities through engagement with foreign markets (Salomon & Shaver, 2005 ). Foreign markets often impose more demanding technological and quality standards, pushing firms to upgrade their capabilities. Firms supplying to international customers frequently adapt their products to meet foreign specifications and regulatory requirements, which can stimulate innovation and technological improvement (Verhoogen, 2008 ; Love & Ganotakis, 2012 ). In addition, exposure to international competition may increase pressure on firms to innovate in order to remain competitive (Porter, 1990 ; Lileeva & Trefler, 2010 ). For firms in emerging economies, such interactions can also facilitate technological catch-up by enabling them to access advanced knowledge and capabilities embedded in global markets (Fu et al., 2011 ; Awate et al., 2014 ). Through these mechanisms, export participation can contribute to improvements in technological capabilities and international competitiveness (Momaya, 2001 ; Bhat & Momaya, 2020 ). Empirical studies provide evidence consistent with learning-by-exporting, showing that export participation can stimulate innovation and knowledge creation (Salomon & Shaver, 2005 ; Cassiman & Golovko, 2011). While some studies attribute the export–innovation relationship to self-selection, whereby more productive or innovative firms are more likely to enter export markets (Clerides et al., 1998 ; Bernard & Jensen, 1999 ; Wagner, 2007 ), the learning-by-exporting perspective suggests that participation in foreign markets can itself stimulate innovation by exposing firms to new knowledge, technologies, and competitive pressures. Collectively, these arguments suggest that firms participating more intensively in export markets are more likely to generate innovation outcomes. H1: Export intensity positively influences subsequent firm innovation. 2.2 Business Group affiliation and export learning While exporting can provide firms with opportunities to acquire knowledge from international markets, the extent to which this knowledge translates into innovation depends on the organizational environment in which firms operate (Wu et al., 2015 ). In many emerging economies, firms are embedded within business groups, networks of legally independent firms connected through ownership and strategic ties (Khanna & Palepu, 2000 ). These organizational structures often emerge in contexts characterized by institutional voids, where underdeveloped markets for capital, labor, and technology limit firms’ ability to access critical resources. In such environments, business groups can serve as important organizational mechanisms through which affiliated firms gain access to resources, capabilities, and knowledge. One mechanism through which business groups may enhance export learning is through internal markets for capital, labor, and managerial expertise (Guillén, 2000; Khanna & Yafeh, 2007 ). These internal markets can help affiliated firms overcome risks and resource constraints that often limit investment in innovation activities, enabling them to finance projects that exploit knowledge obtained from international markets (Mahmood & Mitchell, 2004 ; Belenzon & Berkovitz, 2010 ). Business groups may also facilitate the diffusion of knowledge across affiliated firms. Interactions among affiliates through shared ownership structures, managerial networks, and internal collaboration can promote the transfer of technological knowledge within the group (Iona et al., 2013 ; Chittoor et al., 2014 ). As a result, export experiences gained by one affiliate may benefit other firms within the group by expanding the collective knowledge base and enabling broader learning. These arguments suggest that firms affiliated with business groups may be better positioned to benefit from exporting. By providing access to shared resources and internal knowledge networks, business groups can enhance firms’ ability to translate export exposure into innovation outcomes. H2: Business group affiliation strengthens the positive relationship between export intensity and firm innovation. 2.3 Business Group Characteristics and Export Learning Although business group affiliation may facilitate knowledge sharing and resource access, the effectiveness of these internal networks depends on the structural characteristics of the business group itself. In particular, business group size and the technological relatedness among affiliated firms may influence how effectively knowledge obtained from export markets can be shared and recombined. Business group size can shape firms’ access to internal resources and knowledge networks. Larger groups typically encompass a greater number of affiliated firms, expanding opportunities for internal collaboration, knowledge exchange, and access to diverse technological capabilities and managerial expertise (Chang & Hong, 2000 ; Khanna & Rivkin, 2001 ; Belenzon & Berkovitz, 2010 ). Such internal diversity may enhance firms’ ability to exploit knowledge obtained from international markets by providing complementary capabilities within the group. However, as business groups grow larger, coordination challenges and organizational complexity may reduce the efficiency of knowledge diffusion across affiliates. Increasing numbers of affiliates may slow information flows and complicate collaboration, making it more difficult for firms to effectively integrate knowledge gained from export activities (Carney et al., 2011 ; Meyer & Peng, 2015 ). Consequently, while moderate group size may enhance export learning, the marginal benefits of exporting for innovation may decline as group size increases. Another important structural characteristic is the technological relatedness among affiliated firms. Knowledge spillovers are more likely when firms operate in technologically related industries because they share similar knowledge bases and capabilities (Jaffe, 1986 ; Dosi, 2000 ). When affiliates possess related technological expertise, knowledge obtained through exporting can be more easily transferred, interpreted, and recombined across firms. This compatibility facilitates learning by allowing firms to leverage complementary capabilities within the group (Fleming & Sorenson, 2004 ; Tanriverdi & Venkatraman, 2004 ). In contrast, highly diversified business groups spanning unrelated industries may experience weaker knowledge transfer because limited technological overlap reduces opportunities for effective knowledge sharing (Ning & Guo, 2021 ). The concept of absorptive capacity further supports this argument, suggesting that firms are better able to assimilate and apply external knowledge when it is related to their existing knowledge base (Cohen & Levinthal, 1990 ). Greater technological relatedness among affiliates may therefore strengthen the innovation benefits derived from export participation. H3: The positive relationship between export intensity and firm innovation weakens as business group size increases. H4: Technological relatedness within business groups strengthens the positive relationship between export intensity and firm innovation. Figure 1 summarizes the conceptual framework and hypotheses examined in this study. 3. Data and Methods This study uses firm-level panel data of Indian manufacturing firms from the period 2003 to 2020. The financials and firm ownership details were obtained from the Centre for Monitoring Indian Economy (CMIE) Prowess database. Patent application data was collected from records of the Indian Patent Office, allowing us to capture firm-level innovation outcomes. The sample focuses on manufacturing firms because these industries are more likely to engage in export activities and technological innovation. We restricted our sample to those firms which had at least 5 applications over the study period. After applying this exclusion criteria, the final dataset consisted of 276 firms. The dependent variable is firm innovation , measured as the number of patent applications filed by a firm in a given year. Patent applications are widely used as an indicator of innovative output because they reflect firms’ efforts to develop new technologies and protect intellectual property. The main independent variable is export intensity , measured as the ratio of export sales to total firm sales, capturing the degree of firms’ exposure to international markets. To examine the role of business groups, we include several moderating variables. Business group affiliation ( BG affiliation ) is measured using a dummy variable indicating whether a firm belongs to a business group. Business group size ( BG Size ) is measured as the logarithm of the number of affiliated firms within the group. Business group technological relatedness ( BG Tech Relatedness) is measured using a Herfindahl index based on affiliate sales across industries, where higher values indicate greater concentration in related activities. Several control variables are included to account for firm characteristics that may influence innovation outcomes, including R&D expenditure , firm size, firm age , leverage, liquidity, patent stock , and industry concentration . Besides these, we also control for industry and time effects. The empirical analysis uses Poisson pseudo-maximum likelihood (PPML) estimation with high-dimensional fixed effects with clustered standard errors, which is appropriate for count-dependent variables such as patent applications (Correia et al., 2020 ; Hou et al., 2022 ). To mitigate potential reverse causality and allow time for export learning to influence innovation, all independent variables and moderators are lagged by one year relative to the dependent variables. 4. Descriptive statistics Table 1 presents the descriptive statistics and pairwise correlations for the variables used in the analysis. Prior to estimation, all continuous variables were winsorized at the 1st and 99th percentiles to reduce the influence of extreme observations. The correlation matrix indicates that the maximum pairwise correlation among the explanatory variables is below 0.6, suggesting that multicollinearity is unlikely to bias the estimates. Table 1 – Summary statistics and correlation Variables Mean SD (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (1) Firm Innovation 5.61 17.99 1.00 (2) Export Intensity 2.03 1.57 0.004 1.00 (3) BG Affiliation 0.57 0.49 0.03*** 0.06*** 1.00 (4) BG Size 2.40 1.48 0.17*** -0.12*** 1.00 (5) BG Tech Relatedness 0.50 0.20 -0.04*** 0.12*** -0.79*** 1.00 (6) R&D Expenditure 4.3 1.97 0.38*** -0.02 0.12*** 0.17*** 0.06*** 1.00 (7) Firm Size 9.1 2.05 0.33*** -0.08*** 0.13*** 0.40*** -0.20*** 0.65*** 1.00 (8) Firm Age 3.25 0.75 0.18*** -0.08*** 0.14*** 0.12*** -0.11*** 0.18*** 0.42*** 1.00 (9) Leverage 0.47 0.48 -0.15*** 0.10*** 0.04*** -0.03* -0.03 -0.24*** -0.14*** -0.23*** 1.00 (10) Liquidity 0.65 0.36 0.08*** 0.02 -0.09*** -0.05*** 0.11*** 0.09*** 0.01 0.03** -0.42*** 1.00 (11) Patent Stock 1.769 1.442 0.59*** -0.05*** 0.05*** 0.19*** -0.04* 0.53*** 0.47*** 0.24*** -0.16*** 0.07*** 1.00 (12) Industry concentration 5.887 .59 -0.02* -0.03*** 0.02** 0.21*** -0.19*** -0.15*** 0.09*** 0.01 -0.07*** 0.04*** -0.06*** To further assess potential multicollinearity concerns, we compute variance inflation factors (VIFs) for the regression variables. The VIF diagnostics suggest that multicollinearity is not a major concern. The maximum VIF among the explanatory variables is 8.58, which remains below commonly used thresholds (Hair et al., 2013). 5. Results Table 2 presents the baseline results and the moderating effect of business group affiliation. In Model 3, export intensity has a positive and statistically significant effect on firm innovation (β = 0.0698, p < 0.05), providing support for H1. In Model 4, the interaction between export intensity and business group affiliation is positive and significant (β = 0.378, p < 0.01), indicating that group-affiliated firms derive stronger innovation benefits from exporting. This finding supports H2. Table 2 – Export intensity, Business group affiliation and Firm innovation (ppmlhdfe model) DV: Firm Innovation Model 1 Model 2 Model 3 Model 4 b/se b/se b/se b/se Export Intensity (t−1) 0.0698 ** -0.299 (0.0321) (0.123) BG Affiliation # Export Intensity (t−1) 0.378 *** (0.136) R&D expenditure (t−1) -0.0307 0.102 0.0944 (0.0229) (0.0725) (0.0689) Firm Size (t−1) 0.107 *** -0.0737 0.179 0.222 (0.0285) (0.0607) (0.145) (0.143) Firm Age (t−1) 0.0484 0.0753 -0.254 -0.191 (0.0479) (0.165) (0.499) (0.512) Leverage (t−1) -0.0261 -0.0119 -0.414 ** -0.409 ** (0.0173) (0.0433) (0.184) (0.184) Liquidity (t−1) 0.0352 -0.173 * 0.286 0.305 * (0.0261) (0.0991) (0.184) (0.182) Patent Stock (t−1) 0.0359 *** 0.0306 0.533 *** 0.523 *** (0.0136) (0.0328) (0.0861) (0.0875) Industry concentration (t−1) -0.0476 0.0438 -0.0713 -0.0456 (0.0318) (0.110) (0.199) (0.195) Industry effects Yes Yes Yes Yes Year effects Yes Yes Yes Yes Constant 0.545 * 1.392 -0.277 -1.035 (0.308) (1.009) (2.137) (2.178) Observations 2393 2345 2219 2170 Clustered standard errors at the firm level in parentheses. Firm and year fixed effects included in all models. * p < 0.10, ** p < 0.05, *** p < 0.01 Table 3 examines the role of business group structure. In Model 1, the interaction between export intensity and business group size is negative and statistically significant (β = − 0.0328, p < 0.05), suggesting that the innovation gains from exporting decline as group size increases. This is in line with H3. Table 3 – Export intensity and Firm innovation – Moderating effect of BG Size and BG Technology relatedness (ppmlhdfe model) DV: Firm Innovation Model 1 Model 2 b/se b/se Export Intensity (t−1) 0.196 *** 0.012 (0.0610) (0.045) BG Size (t−1) 0.611 (0.315) Export Intensity (t−1) # BG Size (t−1) -0.0328 ** (0.0141) BG Tech Relatedness -0.171 (0.920) Export Intensity (t−1) # BG Tech Relatedness (t−1) 0.203 ** (0.089) R&D expenditure (t−1) 0.084 0.088 (0.072) (0.076) Firm Size (t−1) 0.166 0.210 (0.137) (0.137) Firm Age (t−1) -0.127 -0.248 (0.502) (0.521) Leverage (t−1) -0.464 ** -0.417 ** (0.200) (0.193) Liquidity (t−1) 0.260 0.270 (0.195) (0.204) Patent Stock (t−1) 0.554 *** 0.537 *** (0.096) (0.097) Industry concentration (t−1) -0.036 -0.076 (0.237) (0.240) Industry effects Yes Yes Year effects Yes Yes Constant -2.774 -0.459 (2.645) (2.458) Observations 1904 1903 Clustered standard errors at the firm level in parentheses. Firm and year fixed effects included in all models. * p < 0.10, ** p < 0.05, *** p < 0.01 In Model 2, the interaction between export intensity and technological relatedness is positive and significant (β = 0.203, p < 0.05), indicating that export-driven innovation is stronger in technologically related business groups. This finding supports H4. Overall, the results consistently show that while exporting enhances innovation, the magnitude of this effect depends on business group characteristics, particularly affiliation, size, and technological relatedness. 6. Robustness Several additional analyses were conducted to ensure the robustness of the results. First, all the models were re-estimated using a two-year lag of export intensity, and the findings remain qualitatively unchanged. This shows that the results are persistent and not sensitive to the choice of lag structure. Second, we address potential endogeneity concerns by estimating dynamic panel models using the systems GMM estimator. The results are consistent with the baseline models, and the diagnostic tests confirm the validity of the instruments, with the Hansen test indicating instrument validity and no evidence of second-order serial correlation. Third, to further assess reverse causality, we estimate models using future export intensity as a predictor of current innovation. The coefficient is not statistically significant, suggesting that innovation does not drive future exporting, thereby supporting the learning-by-exporting interpretation. The results of these robustness tests are available in the appendix file. 7. Discussion and Implications This study shows that exporting enhances firm innovation, reinforcing the view that international market participation improves firm competitiveness through knowledge acquisition. However, the findings also highlight that the ability to convert export exposure into innovation is not uniform but depends on organizational context. Business group affiliation strengthens export learning, suggesting that internal capital and knowledge networks help firms recombine external knowledge more effectively. At the same time, business group size weakens export-driven innovation because excessive scale introduces coordination costs that dilute learning benefits. In contrast, technological relatedness within business groups improves the effectiveness of export learning by facilitating knowledge transfer and recombination across affiliates. Together, these findings suggest that competitiveness gains from exporting are shaped not only by market exposure but also by how firms are embedded within organizational structures that enable or constrain knowledge utilization. For managers, the results emphasize that exporting alone is insufficient for sustained competitiveness. Firms must build internal mechanisms to absorb and deploy external knowledge. Business group affiliated firms should actively leverage internal networks through structured knowledge sharing routines, cross-affiliate collaboration, and coordinated R&D efforts. However, managers must be cautious of excessive group size, which can reduce agility and slow knowledge diffusion. Streamlining coordination, decentralizing decision-making, and fostering focused technological domains can improve learning efficiency. Strategically aligning affiliates around related technologies can further enhance the recombination of export-derived knowledge, strengthening innovation-led competitiveness. For policymakers, the findings suggest that export promotion policies should move beyond increasing export participation toward enhancing firms’ learning capabilities. Policies should support collaborative innovation within business groups through targeted R&D incentives, cluster-based initiatives, and technology-sharing platforms. Encouraging technological specialization and industry clustering can improve knowledge spillovers and collective competitiveness. Additionally, strengthening absorptive capacity through skill development, managerial training, and industry–academia linkages can help firms better utilize export-driven knowledge. Overall, competitiveness gains from exporting depend not only on market access but on institutional support that enables effective knowledge diffusion and recombination. 8. Key Questions Reflecting Applicability in Real Life? How can exporting help firms actually improve their products, processes, and overall competitiveness? Do firms that are part of business groups benefit more from exporting than standalone firms, and why? Does being part of a very large business group make it harder for firms to turn export experience into innovation? Can firms learn more effectively from exports when their group companies operate in related industries? Declarations Conflict of Interest: The authors confirm that there is no conflict of interest. Ethics approval: Not Applicable. Consent Not Applicable Funding: This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Author Contributions Syed Azhar Husain Jafri created the dataset, did econometric analysis, conducted literature review and wrote the manuscript. Dr. Snehal Awate contributed to the supervision of the study and assisted in the design and review of the manuscript and findings. Data Availability Datasets were created by combining Prowess and Indian Patent Office data. The analysis was done by Stata. Both the dataset and Stata files are available on request. References Aw, B. 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The Economic Journal , 117 (517), F134–F161. https://doi.org/10.1111/j.1468-0297.2007.02018.x Hou, Y., Png, I., & Xiong, X. (2022). When stronger patent law reduces patenting: Empirical evidence. Strategic Management Journal , 44 (4), 977–1012. https://doi.org/10.1002/smj.3464 Iona, A., Leonida, L., & Navarra, P. (2013). Business Group Affiliation, Innovation, Internationalization, and Performance: A SemiParametric Analysis. Global Strategy Journal , 3 (3), 244–261. https://doi.org/10.1111/j.2042-5805.2013.01060.x Jaffe, A. B. (1986). Technological Opportunity and Spillovers of R&D: Evidence from Firms’ Patents, Profits and Market Value. SSRN Electronic Journal . https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1506329 Keller, W. (2010). International trade, foreign direct investment, and technology spillovers. In Handbook of the economics of innovation (pp. 793–829). https://doi.org/10.1016/s0169-7218(10)02003-4 Khanna, T., & Palepu, K. (2000). Is group affiliation profitable in emerging markets? An analysis of diversified Indian Business groups. The Journal of Finance , 55 (2), 867–891. https://doi.org/10.1111/0022-1082.00229 Khanna, T., & Rivkin, J. W. (2001). Estimating the performance effects of business groups in emerging markets. Strategic Management Journal , 22 (1), 45–74. https://doi.org/10.1002/1097-0266(200101)22:1 Khanna, T., & Yafeh, Y. (2007). Business groups in emerging markets: paragons or parasites? Journal of Economic Literature , 45 (2), 331–372. https://doi.org/10.1257/jel.45.2.331 Lall, S. (2001). Technological change and industrialization in the Asian Newly industrializing economies: Achievements and challenges. Edward Elgar Publishing eBooks . https://doi.org/10.4337/9781781950555.00014 Lileeva, A., & Trefler, D. (2010). Improved access to foreign markets raises Plant-Level productivity.. for some plants*. The Quarterly Journal of Economics , 125 (3), 1051–1099. https://doi.org/10.1162/qjec.2010.125.3.1051 Love, J. H., & Ganotakis, P. (2012). Learning by exporting: Lessons from high-technology SMEs. International Business Review , 22 (1), 1–17. https://doi.org/10.1016/j.ibusrev.2012.01.006 MacGarvie, M. (2006). Do Firms Learn from International Trade? The Review of Economics and Statistics , 88 (1), 46–60. https://doi.org/10.1162/rest.2006.88.1.46 Mahmood, I. P., & Mitchell, W. (2004). Two faces: Effects of business groups on innovation in emerging economies. Management Science , 50 (10), 1348–1365. https://doi.org/10.1287/mnsc.1040.0259 Meyer, K. E., & Peng, M. W. (2015). Theoretical foundations of emerging economy business research. Journal of International Business Studies , 47 (1), 3–22. https://doi.org/10.1057/jibs.2015.34 Momaya, K. S. (2001). International competitiveness: evaluation and enhancement. In Hindustan Pub. Corp. (India) eBooks . https://ci.nii.ac.jp/ncid/BA54527889 Monreal-Pérez, J., Aragón-Sánchez, A., & Sánchez-Marín, G. (2011). A longitudinal study of the relationship between export activity and innovation in the Spanish firm: The moderating role of productivity. International Business Review , 21 (5), 862–877. https://doi.org/10.1016/j.ibusrev.2011.09.010 Ning, L., & Guo, R. (2021). Technological diversification to green domains: technological relatedness, invention impact and knowledge integration capabilities. Research Policy , 51 (1), 104406. https://doi.org/10.1016/j.respol.2021.104406 Porter, M. E. (1990). The competitive advantage of nations . Salomon, R., & Jin, B. (2007). Does knowledge spill to leaders or laggards? Exploring industry heterogeneity in learning by exporting. Journal of International Business Studies , 39 (1), 132–150. https://doi.org/10.1057/palgrave.jibs.8400320 Salomon, R. M., & Shaver, J. M. (2005). Learning by Exporting: New Insights from Examining Firm Innovation. Journal of Economics & Management Strategy , 14 (2), 431–460. https://doi.org/10.1111/j.1530-9134.2005.00047.x Smith, S. W. (2014). Follow me to the innovation frontier? Leaders, laggards, and the differential effects of imports and exports on technological innovation. Journal of International Business Studies , 45 (3), 248–274. https://doi.org/10.1057/jibs.2013.57 Tanriverdi, H., & Venkatraman, N. (2004). Knowledge relatedness and the performance of multibusiness firms. Strategic Management Journal , 26 (2), 97–119. https://doi.org/10.1002/smj.435 Thakur-Wernz, P., & Samant, S. (2017). Relationship between international experience and innovation performance: The importance of organizational learning for EMNEs. Global Strategy Journal , 9 (3), 378–404. https://doi.org/10.1002/gsj.1183 Tsao, S., & Lien, W. (2011). Family Management and Internationalization: The impact on Firm Performance and innovation. Management International Review , 53 (2), 189–213. https://doi.org/10.1007/s11575-011-0125-9 Verhoogen, E. A. (2008). Trade, quality upgrading, and wage inequality in the Mexican manufacturing sector*. The Quarterly Journal of Economics , 123 (2), 489–530. https://doi.org/10.1162/qjec.2008.123.2.489 Wagner, J. (2007). Exports and Productivity: A Survey of the Evidence from Firm-level Data. World Economy , 30 (1), 60–82. https://doi.org/10.1111/j.1467-9701.2007.00872.x Wu, J., Wu, Z., & Zhuo, S. (2015). The effects of institutional quality and diversity of foreign markets on exporting firms’ innovation. International Business Review , 24 (6), 1095–1106. https://doi.org/10.1016/j.ibusrev.2015.05.001 Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eExporting is widely regarded as an important channel through which firms enhance their technological capabilities and global competitiveness (MacGarvie, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Salomon \u0026amp; Jin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bhat \u0026amp; Momaya, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Participation in international markets exposes firms to advanced technologies, demanding customers, and more intense competition, creating opportunities for learning and capability development (Aw et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Blalock \u0026amp; Gertler, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Golovko \u0026amp; Valentini, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Through interactions with foreign buyers and competitors, firms can acquire knowledge about production processes, product design, and market preferences that can subsequently be incorporated into innovation activities. This mechanism is commonly described as \u003cem\u003elearning by exporting\u003c/em\u003e, whereby firms improve their technological capabilities as a consequence of engaging in international markets (Salomon \u0026amp; Shaver, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Understanding whether and how such learning effects emerge is particularly important for firms in emerging economies, where export participation is frequently viewed as a pathway for technological upgrading, capability catch-up, and improved international competitiveness (Lall, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Momaya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Awate et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, empirical evidence on learning-by-exporting remains mixed, and the literature broadly falls into three streams. The first stream supports the learning-by-exporting hypothesis, arguing that participation in foreign markets exposes firms to new knowledge and technologies that can stimulate innovation. Empirical studies aligned with this perspective report positive effects of exporting on innovation and firm growth (Salomon \u0026amp; Jin, 2010; Cassiman \u0026amp; Golovko, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Love \u0026amp; Ganotakis, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Golovko \u0026amp; Valentini, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A second line of research finds little evidence that exporting itself generates productivity or innovation improvements (Delgado et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wagner, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Monreal-P\u0026eacute;rez et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The third perspective attributes the export\u0026ndash;performance relationship largely to self-selection, whereby more productive firms are more likely to enter export markets (Clerides et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Bernard \u0026amp; Jensen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Wagner, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Overall, these findings suggest that exporting does not automatically lead to innovation gains, and that the benefits of export participation may depend on firms\u0026rsquo; ability to absorb and utilize knowledge from foreign markets.\u003c/p\u003e \u003cp\u003eResearchers increasingly attribute these divergent findings to contextual and organizational factors that shape firms\u0026rsquo; ability to absorb knowledge from international markets. The innovation benefits of exporting may depend on the technological knowledge accessed through foreign markets, such as new product designs and manufacturing processes (Salomon \u0026amp; Shaver, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Greenaway \u0026amp; Kneller, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). They may also depend on environmental and institutional conditions, including host country knowledge levels, the strength of institutional environments, and the cultural and knowledge distance between the home and host countries (Smith, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thakur-Wernz \u0026amp; Samant, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Finally, firm-level characteristics influence how effectively export exposure translates into innovation, including firm size, internal R\u0026amp;D intensity, and ownership structure (Tsao \u0026amp; Lien, 2013; Damijan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, despite the importance of ownership structures in shaping firm behavior, existing research has paid limited attention to how organizational forms such as business group affiliation influence firms\u0026rsquo; ability to leverage export participation for innovation.\u003c/p\u003e \u003cp\u003eAmong these ownership structures, business groups represent a particularly important organizational form in emerging economies. Business groups consist of networks of legally independent firms connected through ownership and strategic ties and are widely prevalent in many emerging markets (Chang \u0026amp; Hong, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Khanna \u0026amp; Palepu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Such groups often provide internal markets for capital, labor, and knowledge, enabling affiliated firms to access resources and capabilities that may be difficult to obtain through external markets (Mahmood \u0026amp; Mitchell, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Douma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Through these internal networks, business groups may facilitate the sharing and recombination of knowledge obtained from export markets, allowing affiliated firms to benefit from the experiences of other group members (Belenzon \u0026amp; Berkovitz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chittoor et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such intra-group knowledge exchanges may enable affiliated firms to more effectively absorb and utilize knowledge generated through international activities, strengthening their ability to translate export exposure into innovation outcomes relative to standalone firms (Iona et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, the effectiveness of these internal markets and knowledge networks may depend on the characteristics of the business group itself.\u003c/p\u003e \u003cp\u003eIn particular, two structural characteristics may influence how effectively export exposure translates into innovation. First, business group size may shape the efficiency of knowledge diffusion across affiliated firms. Larger groups often provide broader internal markets for capital, managerial talent, and scientific expertise, as well as access to a wider pool of technological knowledge that can support innovation activities (Guill\u0026eacute;n, 2000; Mahmood \u0026amp; Mitchell, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Belenzon \u0026amp; Berkovitz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). At the same time, however, larger groups may face coordination challenges, agency problems, and risks of resource misallocation or tunnelling that can hinder effective knowledge sharing and innovation outcomes (Chakrabarti et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Khanna \u0026amp; Yafeh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Carney et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Second, the technological relatedness among group affiliates may determine how effectively firms recombine knowledge acquired from international markets. When firms within a group operate in technologically related industries, knowledge spillovers are more likely to occur, facilitating innovation (Cohen \u0026amp; Levinthal, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Fleming \u0026amp; Sorenson, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In contrast, highly diversified groups spanning unrelated industries may experience weaker knowledge transfer due to limited technological overlap (Dosi, 1988; Tanriverdi \u0026amp; Venkatraman, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These arguments suggest that the size and technological composition of business groups may play a critical role in shaping firms\u0026rsquo; ability to learn from export activities.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, this study examines how business group structures shape firms\u0026rsquo; ability to learn from exporting and translate export exposure into innovation outcomes. Using a longitudinal dataset of 276 Indian manufacturing firms spanning 2003 to 2020, we address three related questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes export intensity enhance firm innovation outcomes, measured through patent applications?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes business group affiliation strengthen the innovation benefits of exporting?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do business group characteristics, specifically group size and technological relatedness among affiliates, moderate the relationship between exporting and innovation?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eOur results show three major findings. First, consistent with the learning-by-exporting hypothesis, export intensity positively influences firm-level innovation. Second, firms affiliated with business groups are better able to translate export exposure into innovation outcomes, suggesting that internal group networks facilitate knowledge acquisition and capability recombination. Third, the structure of business groups plays an important moderating role. While moderate business group size enhances export learning through expanded internal networks, the marginal benefits of learning-by-exporting decline as group size increases, likely reflecting coordination challenges and internal inefficiencies. Importantly, the technological composition of business groups also shapes export learning. Export-driven innovation is significantly stronger in technologically focused groups, where affiliates operate in related industries and are therefore better positioned to share and recombine knowledge obtained from international markets.\u003c/p\u003e \u003cp\u003eThis study contributes to two strands of literature. First, it advances research on learning-by-exporting by identifying the organizational conditions under which export participation leads to innovation. While prior studies have largely focused on the direct relationship between exporting and innovation, we show that firms\u0026rsquo; internal organizational structures shape their ability to absorb and recombine knowledge obtained from international markets. Second, the study contributes to research on business groups by demonstrating how group structures influence firms\u0026rsquo; capacity to convert export exposure into innovation outcomes. Although business groups represent a dominant organizational form in many emerging economies, their role in shaping firms\u0026rsquo; ability to learn from exporting has received limited attention in prior research. By focusing on this organizational context, we show that structural characteristics of business groups, such as group size and technological relatedness among affiliates, play a critical role in shaping firms\u0026rsquo; ability to learn from exporting in emerging market contexts.\u003c/p\u003e"},{"header":"2. Theory and Hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Learning by exporting and firm innovation\u003c/h2\u003e \u003cp\u003eParticipation in international markets allows firms to access valuable sources of knowledge that may stimulate innovation. Exporting firms interact with foreign buyers, competitors, and suppliers, allowing them to observe new technologies, production techniques, and evolving market requirements (Aw et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Blalock \u0026amp; Gertler, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Keller, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Such exposure allows firms to acquire external knowledge that can subsequently be integrated into innovation activities. This mechanism is commonly referred to as learning-by-exporting, whereby firms develop new capabilities through engagement with foreign markets (Salomon \u0026amp; Shaver, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eForeign markets often impose more demanding technological and quality standards, pushing firms to upgrade their capabilities. Firms supplying to international customers frequently adapt their products to meet foreign specifications and regulatory requirements, which can stimulate innovation and technological improvement (Verhoogen, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Love \u0026amp; Ganotakis, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition, exposure to international competition may increase pressure on firms to innovate in order to remain competitive (Porter, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Lileeva \u0026amp; Trefler, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For firms in emerging economies, such interactions can also facilitate technological catch-up by enabling them to access advanced knowledge and capabilities embedded in global markets (Fu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Awate et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Through these mechanisms, export participation can contribute to improvements in technological capabilities and international competitiveness (Momaya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bhat \u0026amp; Momaya, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical studies provide evidence consistent with learning-by-exporting, showing that export participation can stimulate innovation and knowledge creation (Salomon \u0026amp; Shaver, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Cassiman \u0026amp; Golovko, 2011). While some studies attribute the export\u0026ndash;innovation relationship to self-selection, whereby more productive or innovative firms are more likely to enter export markets (Clerides et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Bernard \u0026amp; Jensen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Wagner, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the learning-by-exporting perspective suggests that participation in foreign markets can itself stimulate innovation by exposing firms to new knowledge, technologies, and competitive pressures. Collectively, these arguments suggest that firms participating more intensively in export markets are more likely to generate innovation outcomes.\u003c/p\u003e \u003cp\u003eH1: Export intensity positively influences subsequent firm innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Business Group affiliation and export learning\u003c/h2\u003e \u003cp\u003eWhile exporting can provide firms with opportunities to acquire knowledge from international markets, the extent to which this knowledge translates into innovation depends on the organizational environment in which firms operate (Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In many emerging economies, firms are embedded within business groups, networks of legally independent firms connected through ownership and strategic ties (Khanna \u0026amp; Palepu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These organizational structures often emerge in contexts characterized by institutional voids, where underdeveloped markets for capital, labor, and technology limit firms\u0026rsquo; ability to access critical resources. In such environments, business groups can serve as important organizational mechanisms through which affiliated firms gain access to resources, capabilities, and knowledge.\u003c/p\u003e \u003cp\u003eOne mechanism through which business groups may enhance export learning is through internal markets for capital, labor, and managerial expertise (Guill\u0026eacute;n, 2000; Khanna \u0026amp; Yafeh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These internal markets can help affiliated firms overcome risks and resource constraints that often limit investment in innovation activities, enabling them to finance projects that exploit knowledge obtained from international markets (Mahmood \u0026amp; Mitchell, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Belenzon \u0026amp; Berkovitz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBusiness groups may also facilitate the diffusion of knowledge across affiliated firms. Interactions among affiliates through shared ownership structures, managerial networks, and internal collaboration can promote the transfer of technological knowledge within the group (Iona et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chittoor et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As a result, export experiences gained by one affiliate may benefit other firms within the group by expanding the collective knowledge base and enabling broader learning.\u003c/p\u003e \u003cp\u003eThese arguments suggest that firms affiliated with business groups may be better positioned to benefit from exporting. By providing access to shared resources and internal knowledge networks, business groups can enhance firms\u0026rsquo; ability to translate export exposure into innovation outcomes.\u003c/p\u003e \u003cp\u003eH2: Business group affiliation strengthens the positive relationship between export intensity and firm innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Business Group Characteristics and Export Learning\u003c/h2\u003e \u003cp\u003eAlthough business group affiliation may facilitate knowledge sharing and resource access, the effectiveness of these internal networks depends on the structural characteristics of the business group itself. In particular, business group size and the technological relatedness among affiliated firms may influence how effectively knowledge obtained from export markets can be shared and recombined.\u003c/p\u003e \u003cp\u003eBusiness group size can shape firms\u0026rsquo; access to internal resources and knowledge networks. Larger groups typically encompass a greater number of affiliated firms, expanding opportunities for internal collaboration, knowledge exchange, and access to diverse technological capabilities and managerial expertise (Chang \u0026amp; Hong, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Khanna \u0026amp; Rivkin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Belenzon \u0026amp; Berkovitz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Such internal diversity may enhance firms\u0026rsquo; ability to exploit knowledge obtained from international markets by providing complementary capabilities within the group. However, as business groups grow larger, coordination challenges and organizational complexity may reduce the efficiency of knowledge diffusion across affiliates. Increasing numbers of affiliates may slow information flows and complicate collaboration, making it more difficult for firms to effectively integrate knowledge gained from export activities (Carney et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Meyer \u0026amp; Peng, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, while moderate group size may enhance export learning, the marginal benefits of exporting for innovation may decline as group size increases.\u003c/p\u003e \u003cp\u003eAnother important structural characteristic is the technological relatedness among affiliated firms. Knowledge spillovers are more likely when firms operate in technologically related industries because they share similar knowledge bases and capabilities (Jaffe, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Dosi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). When affiliates possess related technological expertise, knowledge obtained through exporting can be more easily transferred, interpreted, and recombined across firms. This compatibility facilitates learning by allowing firms to leverage complementary capabilities within the group (Fleming \u0026amp; Sorenson, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tanriverdi \u0026amp; Venkatraman, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In contrast, highly diversified business groups spanning unrelated industries may experience weaker knowledge transfer because limited technological overlap reduces opportunities for effective knowledge sharing (Ning \u0026amp; Guo, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The concept of absorptive capacity further supports this argument, suggesting that firms are better able to assimilate and apply external knowledge when it is related to their existing knowledge base (Cohen \u0026amp; Levinthal, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Greater technological relatedness among affiliates may therefore strengthen the innovation benefits derived from export participation.\u003c/p\u003e \u003cp\u003eH3: The positive relationship between export intensity and firm innovation weakens as business group size increases.\u003c/p\u003e \u003cp\u003eH4: Technological relatedness within business groups strengthens the positive relationship between export intensity and firm innovation.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the conceptual framework and hypotheses examined in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026lt;Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e\u0026gt;\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Methods","content":"\u003cp\u003eThis study uses firm-level panel data of Indian manufacturing firms from the period 2003 to 2020. The financials and firm ownership details were obtained from the Centre for Monitoring Indian Economy (CMIE) Prowess database. Patent application data was collected from records of the Indian Patent Office, allowing us to capture firm-level innovation outcomes. The sample focuses on manufacturing firms because these industries are more likely to engage in export activities and technological innovation. We restricted our sample to those firms which had at least 5 applications over the study period. After applying this exclusion criteria, the final dataset consisted of 276 firms.\u003c/p\u003e \u003cp\u003eThe dependent variable is \u003cem\u003efirm innovation\u003c/em\u003e, measured as the number of patent applications filed by a firm in a given year. Patent applications are widely used as an indicator of innovative output because they reflect firms\u0026rsquo; efforts to develop new technologies and protect intellectual property. The main independent variable is \u003cem\u003eexport intensity\u003c/em\u003e, measured as the ratio of export sales to total firm sales, capturing the degree of firms\u0026rsquo; exposure to international markets.\u003c/p\u003e \u003cp\u003eTo examine the role of business groups, we include several moderating variables. Business group affiliation (\u003cem\u003eBG affiliation\u003c/em\u003e) is measured using a dummy variable indicating whether a firm belongs to a business group. Business group size (\u003cem\u003eBG Size\u003c/em\u003e) is measured as the logarithm of the number of affiliated firms within the group. Business group technological relatedness \u003cb\u003e(\u003c/b\u003e\u003cem\u003eBG Tech Relatedness)\u003c/em\u003e is measured using a Herfindahl index based on affiliate sales across industries, where higher values indicate greater concentration in related activities. Several control variables are included to account for firm characteristics that may influence innovation outcomes, including \u003cem\u003eR\u0026amp;D expenditure\u003c/em\u003e, \u003cem\u003efirm size, firm age\u003c/em\u003e, \u003cem\u003eleverage, liquidity, patent stock\u003c/em\u003e, and \u003cem\u003eindustry concentration\u003c/em\u003e. Besides these, we also control for industry and time effects. The empirical analysis uses Poisson pseudo-maximum likelihood (PPML) estimation with high-dimensional fixed effects with clustered standard errors, which is appropriate for count-dependent variables such as patent applications (Correia et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hou et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To mitigate potential reverse causality and allow time for export learning to influence innovation, all independent variables and moderators are lagged by one year relative to the dependent variables.\u003c/p\u003e"},{"header":"4. Descriptive statistics","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics and pairwise correlations for the variables used in the analysis. Prior to estimation, all continuous variables were winsorized at the 1st and 99th percentiles to reduce the influence of extreme observations. The correlation matrix indicates that the maximum pairwise correlation among the explanatory variables is below 0.6, suggesting that multicollinearity is unlikely to bias the estimates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Summary statistics and correlation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) Firm Innovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(2) Export Intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(3) BG Affiliation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(4) BG Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(5) BG Tech Relatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.04***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(6) R\u0026amp;D Expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(7) Firm Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.20***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.65***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(8) Firm Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.11***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(9) Leverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(10) Liquidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.09***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.09***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(11) Patent Stock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.47***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.07***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(12) Industry concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.15***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.09***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.07***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.04***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.06***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003e\u0026lt;Insert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e\u0026gt;\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further assess potential multicollinearity concerns, we compute variance inflation factors (VIFs) for the regression variables. The VIF diagnostics suggest that multicollinearity is not a major concern. The maximum VIF among the explanatory variables is 8.58, which remains below commonly used thresholds (Hair et al., 2013).\u003c/p\u003e"},{"header":"5. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the baseline results and the moderating effect of business group affiliation. In Model 3, export intensity has a positive and statistically significant effect on firm innovation (β\u0026thinsp;=\u0026thinsp;0.0698, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), providing support for H1. In Model 4, the interaction between export intensity and business group affiliation is positive and significant (β\u0026thinsp;=\u0026thinsp;0.378, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that group-affiliated firms derive stronger innovation benefits from exporting. This finding supports H2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Export intensity, Business group affiliation and Firm innovation (ppmlhdfe model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDV: Firm Innovation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb/se\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb/se\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb/se\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eb/se\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExport Intensity \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0698\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.123)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG Affiliation # Export Intensity \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.136)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026amp;D expenditure \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0689)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm Size \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.107\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.143)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm Age \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0479)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.512)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.414\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.409\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.184)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidity \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.173\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.182)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatent Stock \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0359\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.533\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0875)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry concentration \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.195)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.545\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.178)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eClustered standard errors at the firm level in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFirm and year fixed effects included in all models.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e examines the role of business group structure. In Model 1, the interaction between export intensity and business group size is negative and statistically significant (β = \u0026minus;\u0026thinsp;0.0328, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the innovation gains from exporting decline as group size increases. This is in line with H3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Export intensity and Firm innovation \u0026ndash; Moderating effect of BG Size and BG Technology relatedness (ppmlhdfe model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDV: Firm Innovation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb/se\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb/se\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExport Intensity \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG Size \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExport Intensity \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e # BG Size \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0328\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG Tech Relatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.920)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExport Intensity \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e # BG Tech Relatedness \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026amp;D expenditure \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.076)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm Size \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.137)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm Age \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.521)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeverage \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.464\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.417\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.193)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiquidity \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.204)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatent Stock \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.554\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.537\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.097)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry concentration \u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.240)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.458)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eClustered standard errors at the firm level in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFirm and year fixed effects included in all models.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Model 2, the interaction between export intensity and technological relatedness is positive and significant (β\u0026thinsp;=\u0026thinsp;0.203, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that export-driven innovation is stronger in technologically related business groups. This finding supports H4. Overall, the results consistently show that while exporting enhances innovation, the magnitude of this effect depends on business group characteristics, particularly affiliation, size, and technological relatedness.\u003c/p\u003e"},{"header":"6. Robustness","content":"\u003cp\u003eSeveral additional analyses were conducted to ensure the robustness of the results. First, all the models were re-estimated using a two-year lag of export intensity, and the findings remain qualitatively unchanged. This shows that the results are persistent and not sensitive to the choice of lag structure. Second, we address potential endogeneity concerns by estimating dynamic panel models using the systems GMM estimator. The results are consistent with the baseline models, and the diagnostic tests confirm the validity of the instruments, with the Hansen test indicating instrument validity and no evidence of second-order serial correlation. Third, to further assess reverse causality, we estimate models using future export intensity as a predictor of current innovation. The coefficient is not statistically significant, suggesting that innovation does not drive future exporting, thereby supporting the learning-by-exporting interpretation. The results of these robustness tests are available in the appendix file.\u003c/p\u003e"},{"header":"7. Discussion and Implications","content":"\u003cp\u003eThis study shows that exporting enhances firm innovation, reinforcing the view that international market participation improves firm competitiveness through knowledge acquisition. However, the findings also highlight that the ability to convert export exposure into innovation is not uniform but depends on organizational context. Business group affiliation strengthens export learning, suggesting that internal capital and knowledge networks help firms recombine external knowledge more effectively. At the same time, business group size weakens export-driven innovation because excessive scale introduces coordination costs that dilute learning benefits. In contrast, technological relatedness within business groups improves the effectiveness of export learning by facilitating knowledge transfer and recombination across affiliates. Together, these findings suggest that competitiveness gains from exporting are shaped not only by market exposure but also by how firms are embedded within organizational structures that enable or constrain knowledge utilization.\u003c/p\u003e \u003cp\u003eFor managers, the results emphasize that exporting alone is insufficient for sustained competitiveness. Firms must build internal mechanisms to absorb and deploy external knowledge. Business group affiliated firms should actively leverage internal networks through structured knowledge sharing routines, cross-affiliate collaboration, and coordinated R\u0026amp;D efforts. However, managers must be cautious of excessive group size, which can reduce agility and slow knowledge diffusion. Streamlining coordination, decentralizing decision-making, and fostering focused technological domains can improve learning efficiency. Strategically aligning affiliates around related technologies can further enhance the recombination of export-derived knowledge, strengthening innovation-led competitiveness.\u003c/p\u003e \u003cp\u003eFor policymakers, the findings suggest that export promotion policies should move beyond increasing export participation toward enhancing firms\u0026rsquo; learning capabilities. Policies should support collaborative innovation within business groups through targeted R\u0026amp;D incentives, cluster-based initiatives, and technology-sharing platforms. Encouraging technological specialization and industry clustering can improve knowledge spillovers and collective competitiveness. Additionally, strengthening absorptive capacity through skill development, managerial training, and industry\u0026ndash;academia linkages can help firms better utilize export-driven knowledge. Overall, competitiveness gains from exporting depend not only on market access but on institutional support that enables effective knowledge diffusion and recombination.\u003c/p\u003e \u003cp\u003e \u003cb\u003e8. Key Questions Reflecting Applicability in Real Life?\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow can exporting help firms actually improve their products, processes, and overall competitiveness?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDo firms that are part of business groups benefit more from exporting than standalone firms, and why?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes being part of a very large business group make it harder for firms to turn export experience into innovation?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCan firms learn more effectively from exports when their group companies operate in related industries?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eThe authors confirm that there is no conflict of interest.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval:\u003c/strong\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions Syed Azhar Husain Jafri created the dataset, did econometric analysis, conducted literature review and wrote the manuscript. Dr. Snehal Awate contributed to the supervision of the study and assisted in the design and review of the manuscript and findings.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDatasets were created by combining Prowess and Indian Patent Office data. The analysis was done by Stata. Both the dataset and Stata files are available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAw, B. Y., Chung, S., \u0026amp; Roberts, M. J. (2000). Productivity and Turnover in the Export Market: Micro-level Evidence from the Republic of Korea and Taiwan (China). \u003cem\u003eThe World Bank Economic Review\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 65\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/wber/14.1.65\u003c/span\u003e\u003cspan address=\"10.1093/wber/14.1.65\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwate, S., Larsen, M. M., \u0026amp; Mudambi, R. (2014). 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However, empirical evidence on learning by exporting remains mixed, suggesting that the innovation benefits of exporting may depend on the organizational context in which firms operate. In many emerging economies, firms are embedded within business groups that provide internal markets for resources and knowledge sharing. This study examines how business group affiliation and group characteristics shape firms’ ability to translate export exposure into innovation outcomes. Using a longitudinal dataset of 276 Indian manufacturing firms spanning 2003–2020, we investigate the relationship between export intensity and firm innovation. The results show that export intensity positively influences firm innovation. We further find that business group affiliation strengthens the innovation benefits of exporting, indicating that internal group networks facilitate knowledge acquisition and recombination. However, the characteristics of business groups play an important moderating role. In particular, business group size reduces the marginal benefits of exporting for firm innovation. In contrast, export-driven innovation is stronger in technologically focused groups where affiliates operate in related industries. These findings highlight the importance of organizational structures in shaping firms’ ability to learn from international markets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes:\u003c/strong\u003e F14, O31, O32, L25\u003c/p\u003e","manuscriptTitle":"Learning by Exporting in Emerging Economies: How Business Group Structure Shapes Firm Innovation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 17:12:34","doi":"10.21203/rs.3.rs-9237636/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T09:12:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T07:12:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T04:55:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124283508514579178132908572461073078465","date":"2026-04-08T05:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128504992111893594573234681614859733057","date":"2026-04-07T06:05:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-05T05:48:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T07:45:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T07:44:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Global Business and Competitiveness","date":"2026-03-26T19:32:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-global-business-and-competitiveness","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jgbc","sideBox":"Learn more about [International Journal of Global Business and Competitiveness](https://www.springer.com/journal/42943)","snPcode":"42943","submissionUrl":"https://submission.springernature.com/new-submission/42943/3","title":"International Journal of Global Business and Competitiveness","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e03bfbcf-d393-4173-9a9b-fdeb4f78be41","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T11:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 17:12:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9237636","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9237636","identity":"rs-9237636","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}