Government support versus international knowledge: Investigating innovations from emerging-market small and medium enterprises

We compare how non-financial government support, versus knowledge gained through exporting, supports innovation in Vietnamese small and medium enterprises (SMEs). Analysis of panel data of 1,018 SMEs shows that government support for staff training and technological development quality increases the likelihood of product and process innovation. Resource slack mediates the relationship between these non-financial government support measures and innovation, thus pointing to the importance of underutilized resources to enable the use of external sources of knowledge. Therefore, these policy measures are more efficient for emerging-market SMEs that can incorporate them. The negative moderation effect of exporting in the relationship between non-financial government support and process innovation suggests that policy-makers should limit the use of non-financial support when they design exporting policies for emerging-market SMEs.

Journal of Business Research 154 (2023) 113305 Contents lists available at ScienceDirect Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres Government support versus international knowledge: Investigating innovations from emerging-market small and medium enterprises Tam Nguyen a, Martie-Louise Verreynne a, John Steen b, Rui Torres de Oliveira c, * a The University of Queensland, Business School, Australia University of British Columbia, Canada c Queensland University of Technology, Business School, Australian Center for Entrepreneurship Research, Australia b A R T I C L E I N F O A B S T R A C T Keywords: Government support Innovation Slack Exporting Emerging markets Global value chain SMEs We compare how non-financial government support, versus knowledge gained through exporting, supports innovation in Vietnamese small and medium enterprises (SMEs). Analysis of panel data of 1,018 SMEs shows that government support for staff training and technological development quality increases the likelihood of product and process innovation. Resource slack mediates the relationship between these non-financial government support measures and innovation, thus pointing to the importance of underutilized resources to enable the use of external sources of knowledge. Therefore, these policy measures are more efficient for emerging-market SMEs that can incorporate them. The negative moderation effect of exporting in the relationship between non-financial government support and process innovation suggests that policy-makers should limit the use of non-financial support when they design exporting policies for emerging-market SMEs. 1 Introduction Small and medium enterprises (SMEs) are critical to emerging markets (e.g., Smallbone & Welter, 2001). Emerging-market SMEs are also important to global value chains (GVCs), where they often contribute as low-cost manufacturers (Gereffi, 2018; Sturgeon, 2009). However, these SMEs face many challenges, such as a scarcity of capital, qualified staff, and modern technology (Gentile-Lüdecke, Torres de Oliveira, & Paul, 2020). Because governments are aware of these constraints, they often attempt to implement measures to encourage SMEs to innovate and improve their competitiveness (McCarthy, Oliver, & Verreynne, 2015). Through research and development (R&D) subsidies or tax credits, there is evidence that financial support motivates firms to invest more in the R&D needed to launch new products (Battisti, Deakins, & Roxas, 2010). The rationale for support is that business innovation will help emergingmarket SMEs add value, increase market share (Oura, Zilber, & Lopes, 2016; Rothaermel & Hess, 2007), improve their positions within GVCs (Humphrey & Schmitz, 2000), and be more sustainable (Ren, Eisingerich, & Tsai, 2015; Teece, 2017). However, in cases of non-high-tech firms in emerging markets, the worth of financial support measures is questioned (Chen & Gupta, 2017). This has led emerging-market governments to introduce nonfinancial measures to support capability development and growth in SMEs (Nguyen, Verreynne, & Steen, 2014). These measures can also serve as sources of ideas for innovation, for example, when staff attend training courses. However, to fully take advantage of institutional support, firms need latent capacity, also known as resource slack (Cyert & March 1963) or excess resources (Penrose, 1959). These same emerging-market SMEs are often suppliers in GVCs (Park, Lee, & Kim, 2020). Through their interaction with other firms in GVCs, they are exposed to ideas that can lead them to innovate (Torres de Oliveira, Nguyen, Liesch, Verreynne, & Indulska, 2021). However, emerging-market SMEs must also make allocation choices because of their restricted resources and capabilities for innovation (Temouri, Shen, Pereira, & Xie, 2020). Thus, participation in GVCs may not necessarily result in more innovation. Therefore, we ask the following research questions: What is the relationship between government support for staff training, as well as quality assurance and technology improvement, and innovation outcomes? How is * Corresponding author. E-mail addresses: tam.nguyen@business.uq.edu.au (T. Nguyen), m.verreynne@business.uq.edu.au (M.-L. Verreynne), john.steen@ubc.ca (J. Steen), rui. torresdeoliveira@qut.edu.au (R. Torres de Oliveira). https://doi.org/10.1016/j.jbusres.2022.113305 Received 20 March 2021; Received in revised form 2 September 2022; Accepted 6 September 2022 0148-2963/© 2022 Elsevier Inc. All rights reserved. T. Nguyen et al. Journal of Business Research 154 (2023) 113305 explains that such upgrading of capabilities depends on the relationships between the lead and supplier firms regarding governance, contract, and power (Gereffi et al., 2005). Moreover, the GVC literature points to the difficult choices that SMEs from emerging markets need to make as international suppliers. For example, those from emerging markets must navigate in a local environment with few resources and capabilities (Temouri et al., 2020) while at the same time engaging with firms and some orchestrators that have broader capabilities and resources from which they can learn (Torres de Oliveira et al., 2021). These SMEs see any institutional support as a boon since it potentially addresses scarce resources (Oriaifo et al., 2020). Yet, when SMEs try to tap into institutional support, they expect it to stress their resources and capabilities (Simon, 1947). Thus, one way that engaging in GVC activities can support upgrading is by exposing emerging-market firms to learning opportunities by linking them to the successful innovation systems of developed markets (Amendolagine et al., 2019; Pietrobelli & Rabellotti, 2011). However, Simona and Axele (2012) explain that knowledge transfer is a precondition for any such upgrading to occur. Indeed, organizational learning theory (Fiol & Lyles, 1985) explains that for these firms to learn from the available institutional support or GVCs interaction, they need to absorb and internalize such knowledge, as we explain next. this relationship affected by resource slack and exporting? These questions are interconnected when there are several potential sources of new knowledge (Fletcher & Harris, 2012), as is the case with emerging-market SMEs operating in GVCs (De Marchi, Di Maria, Golini, & Perri, 2020). While access to such sources of knowledge may sound beneficial to these SMEs’ development, internal constraints in the form of few resources and capabilities may restrict their capacity to assimilate this knowledge (Oriaifo, Torres de Oliveira, & Ellis, 2020). The GVC literature supports the importance of emerging-market SMEs as efficient contributors to the front end of the value chain (Torres de Oliveira, Sahasranamam, Figueira, & Paul, 2020). Therefore, these SMEs need to use their slack resources effectively to assimilate ideas for innovation that can lead to these outcomes (Sui & Baum, 2014). Thus, for the GVC literature, it is vital to understand the ramifications of institutional support for innovation, and how resource slack improves that relationship, as it signals their capabilities to orchestrators. We draw on organizational learning theory (Fiol & Lyles, 1985) to clarify the GVC literature’s ambiguity regarding how firms use new knowledge from different sources and what resources and capabilities are needed to absorb, internalize and translate ideas (Grillitsch & Trippl, 2014). To test our hypotheses, we used a unique and robust longitudinal dataset from Vietnam that was as a collaboration between Copenhagen University, Denmark; the United Nations University World Institute for Development Economics Research, Finland; the Central Institute for Economic Management, Vietnam; and the Institute of Labour Science and Social Affairs, Vietnam, which had 1,018 firms in a panel survey running between 2005 and 2015. Our analysis shows that government non-financial support increases the chance of introducing new products and business processes. We also found that the presence of resource slack mediates the positive benefits of staff-training support for both product and process innovation. Further, the results show a negative effect of exporting on the relationship between government nonfinancial support and process innovation, which contradicts the standard view of exporting as an enabler of innovation. These findings allow us to advance theory in three established bodies of literature. First, we speak to the core of organizational learning and the GVC literature for emerging-market SMEs in establishing the relationship between exporting and local support activities to enable process and product innovation. While organizational learning theory frequently focuses on the process of learning by emphasizing the human element, this research extends this theory to include external institutional support for improving knowledge from an organizational perspective and for explaining that context matters (Rottig, Muscarella, & Torres de Oliveira, 2019). Thus, we add to the organizational learning literature (Kiss, Fernhaber, & McDougal-Covin, 2018) by focusing on emerging markets and advancing the recent literature on GVC (Zhou, Yan, & Sun, 2022) that addresses the potential of emerging-market SMEs to upgrade. Second, we contribute to the GVC literature by revealing the tensions absorbing non-financial support for innovation provided by governments to SMEs in emerging markets and their engagement with international supply chains, which adds to Gereffi and colleagues (2021) recent discussion. We thirdly expand organizational learning theory (Kimjeon & Davidsson, 2021) by showing the impact of institutional non-financial support on emerging-market SMEs, as an external enabler, which without resource slack, is unlikely to enhance innovation. 2.1 Innovation and organizational learning theory Innovation has been studied widely since Schumpeter’s introduction into mainstream economic theory (e.g., Freeman, 1982; Hall & Rosenberg, 2010). Defined as the application or adoption of an invention to create value, Schumpeter (1939) argues that innovation presents in several forms, such as a new product or service, a new production method, a new market, or new sources of supply. These types are categorized broadly as product or process innovations (Freeman, 1982). Innovation results from generating, selecting, and developing innovative ideas and delivering new products/services to markets (Birkinshaw, Hamel, & Mol, 2008). It draws on knowledge, capabilities and resources to meet the demands of each stage of that process (Hogan, Soutar, McColl-Kennedy, & Sweeney, 2011). As organizational learning theory explains, innovation follows a process whereby knowledge and ideas are creatively transformed into new products or business methods (Argyris & Schön, 1997; Lawson & Samson, 2001; Schiuma, 2013). Furthermore, innovation requires knowledge, capabilities and resources (Barney, 1991; Nason & Wiklund, 2018; Pitelis & Runde, 2017) that are essential for firms to formulate products to sell in the market for profit (Pitelis, 2007). For instance, human and intellectual capital resources allow firms to generate and execute innovation, while financial resources pay for R&D activities that produce innovation (Forés & Camison, 2016; Lai, Hsu, Lin, Chen, & Lin, 2014). However, these resources are not always fully used. Unused or under-deployed resources remain available as slack because of the indivisibility of resources (Penrose, 1959; Dolmans, van Burg, Reymen, & Romme, 2014; Steen & Liesch, 2007; Suzuki, 2018). Resource slack is internally generated through business activity, and resources can be presented as available, recoverable, and potential (Bourgeois & Singh, 1983). Innovation policy research has traditionally focused on how innovation relates positively to subsidies for R&D and other forms of government financial support (Clausen, 2009; Lu, Liu, Wright, & Filatotchev, 2014). Receiving such support provides more funds for firms to finance capital-intensive innovation activities (Schneider & Veugelers, 2010). Nevertheless, governments also provide other nonfinancial supporting measures, such as employee professional development and consultancy services (MPI, 2014). These programs improve knowledge and skills that support innovation activity (Laursen & Foss, 2014). However, little is known about the internal mechanisms that allow SMEs to incorporate the knowledge gained from these programs to support innovation and the role that available resource slack plays. For 2. Literature review and hypotheses The GVC literature explains that by engaging in GVCs, emergingmarket firms will be able to upgrade (Gereffi et al., 2005). This upgrading could follow a process, product, or functional approach (Humphrey & Schmitz, 2000). From an institutional perspective, the advice to emerging-market governments and their firms from interorganizational agencies over the last decades has been to “export your products to developed markets, and your industries will upgrade to global standards” (Navas-Aleman, 2011, p. 1386). The GVC literature 2 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 instance, Cano-Kollmann, Hamilton and Mudambi (2017) explore the positive impact of non-financial support on the degree of innovation openness of individual firms by connecting different players within the innovation system without arguing the role of improving knowledge. Furthermore, Bong, Park and Park (2020) explain that non-financial government assistance improves employee performance by raising their knowledge and skills but did not expand on how it enhances innovation. Therefore, while these authors do not focus on enhancing innovation, their research inspired us to argue that knowledge improvement and slack are important to the relationship between government non-financial support and innovation. From the perspective of organizational learning theory (Argyris & Schön, 1997; Nonaka, 1994), slack resources that firms hold, combined with government support measures, may provide an impetus for firms to innovate. Therefore, understanding how support programs for SMEs interact with resource slack is, for example, essential to policy-makers who devise better programs as firms’ strategic choices may impact them. We also know that exporting can redirect resources and capabilities previously directed towards innovation, with firms making strategic choices (Zhang, 2018). Such attention (Simon, 1947) is significant for firms with few resources and capabilities, such as emerging-market SMEs (GentileLüdecke et al., 2020; Nguyen et al., 2014). Exporting is especially exacerbated by resource and capability scarcity (Hitt, Dacin, Levitas, Arregle, & Borza, 2000; Mathews & Cho, 1999) and differences because of liability of foreignness (Johanson & Vahlne, 2009). This makes it challenging for emerging-market SMEs to absorb external knowledge (Nonaka, Toyama, & Nagata, 2000). Seen against their roles as international suppliers at the bottom of the GVC (World Trade Organization, IDE-JETRO, OECD, UIBE, & Group, 2019), GVC orchestrators are less inclined to invest in these SMEs’ knowledge bases (Morrison, Pietrobelli, & Rabellotti, 2008). Therefore, we expect that a firm’s exporting strategy will also impact the relationship between institutional support and innovation outcomes. Oliveira, 2020). For example, marketing skills are equally necessary for understanding products, services and users (Athuene-Gima, 1993; Prabhu, 2014). These skills require more advanced commercial knowledge than that provided by staff-training support. Furthermore, training supports the workforce individually by developing employee skills and capabilities and enriching market understanding. Because product innovation depends on testing and prototyping skills, planning and modelling capabilities should improve the success rate of new products (Chandy, Hopstaken, Narasimhan, & Prabhu, 2006; Johne & Snelson, 1988). Planning, scheduling and communicating changes to other employees are also important for successfully introducing process innovations that require more advanced skills, particularly from managers (Frishammar, Kurkkio, Abrahamsson, & Lichtenthaler, 2012; Phillips, 2014). Therefore, we argue that: Hypothesis 1a. Government staff-training support positively relates to SMEs’ new product and process innovation. Technological government support comes in different forms and aims to improve technology and production efficiency at the firm, industrial, regional and national levels. This support may involve three types: assisting firms in implementing cleaner production technology (and thus reducing the negative impacts on the environment); providing information to connect firms needing technological innovation; and advising firms on how to choose appropriate production technology that suits their investment in operational capacity (MPI, 2014). Such programs can increase firms’ ability to develop new products. For example, a program that supports cleaner production capabilities may reduce resource use and waste generation (Severo, Guimarães, & Dorion, 2017). Such a program would also allow firms to increase efficiency when using raw materials, energy and other resources, thus benefitting economically (Boons, Montalvo, Quist, & Wagner, 2013). This type of support program positively influences new product development in such a context. Technological assistance in the form of quality assurance programs helps firms eliminate problems and inefficiencies in new products before and after their launch, thus enabling a new or improved product to be introduced (Kim, Kumar, & Kumar, 2012; Prajogo & Sohal, 2004). Additionally, technological support to introduce quality assurance programs, such as Six Sigma, ISO 9000, or ISO 14000, can help identify and rectify production-process problems and thus contribute feasible inputs to generate innovative ideas. Improving a firm’s ability to solve problems introduces the prospect of process innovations. Successfully introducing quality assurance initiatives also makes it more likely to add a new process because it can be refined and must be adjusted once operational (Prajogo & Sohal, 2004). Therefore, it is hypothesized that: 2.2 Government support and innovation The rationale behind government support for innovation is to help a heterogeneous group of firms improve their innovation processes, outcomes, and other management practices (Dodgson & Staggs, 2012). These programs are not always successful at supporting SMEs to introduce innovation, most likely because resources and other constraints absorb the benefits of those programs (Cohen & Levinthal, 1990; Malik & Kotabe, 2009). In addition, these programs have a broad remit beyond innovation. In Vietnam, for example, a range of supporting programs exists for operating and developing firms (MPI, 2014), covering management, marketing, finance, tax, and industrial communication, irrespective of the location and size of firms. Some support measures target innovation by supporting cleaner production in manufacturing industries, assisting SMEs operating in supporting industries, and informing about technological innovation (MPI, 2014). These support measures can be both financial and non-financial. To illustrate, the two types of non-financial government support programs that we investigate — staff training and quality and technology improvement assistance — develop the overall skills of the labor force and improve product quality and technological competence. Programs that focus on improving firms’ human capital, called stafftraining support, are found in diverse forms. They include management training for owners/managers, employee professional development, and consultancy services that help firms develop managerial, operational, financial, and marketing skills (MPI, 2014). These support measures also improve the capability of staff to deliver innovation. For example, skilled employers have greater capabilities to develop, reconfigure, or modify production. Specifically, government staff-training support increases the skills of the workforce so that employees, to some extent, can be more creative and accept the risk caused by uncertainty (Chen & Huang, 2009; Laursen & Foss, 2003; Ribeiro, Duarte, Filipe, & Torres de Hypothesis 1b. Government quality and technology improvement support positively relate to SMEs’ new product and process innovation. 2.3 The mediating role of slack resources Management researchers focus much on how slack resources enable firms to engage in a range of activities that may improve their performance (e.g., Bourgeois, 1981; Cheng & Kesner, 1997; Cyert & March 1963; Huang & Li, 2012). Slack is an exploitable resource that allows firms to engage in activities not necessarily seen as essential to day-today survival (Cyert & March 1963). It is the collection of excess resources left after these business-as-usual outputs are produced (Nohria & Gulati, 1996). Especially important is that slack resources improve firms’ capability to respond to external shocks (George, 2005), to introduce innovations (Geiger & Makri, 2006), or to enter new markets (Lin, Cheng, & Liu, 2009). For example, product innovation frequently entails uncertainty and variability (Huang & Li, 2012), and slack resources allow the relaxation of internal controls and enable risk-taking (George, 2005). 3 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 Examples of slack include underutilized facilities, staff time, or excess production capacity, which can enhance innovation by providing the context for creative learning and thinking (Greve, 2003). In contrast, slack resources can also be used to preserve organizational stability when firms face uncertainty (Singh, 1986). Firms need slack to capture and integrate new knowledge and practices from training and other support measures (Bourgeois, 1981), including that by government. Slack does this in two ways. First, it isolates from pressure those staff involved in innovation, allowing them the time and space to focus on creating new products and processes (Huang & Li, 2012). This means they can then attend government support training, engage with the ideas presented there, and bring them back into the firm to innovate. Second, the availability of slack resources allows managers to pursue new strategies (Kiss et al., 2018) while mitigating risks associated with innovation (Adomako & Nguyen, 2020). Learning can be prioritized, and new technologies introduced through support programs seeking to, for example, focus on R&D projects or develop a better understanding of markets for new products and services (Geiger & Makri, 2006). Thus, we hypothesize that: which does not bring immediate knowledge benefits (Torres de Oliveira et al., 2021), instead of focusing on local-learning government support programs. Thus, we hypothesize that: Hypothesis 3a. Exporting negatively moderates the relationship between government staff-training support and new product and process innovation. Hypothesis 3b. Exporting negatively moderates the relationship between government quality and technology improvement support and new product and process innovation. Fig. 1 represents the hypothesized relationships in this study. 3 Method 3.1 Sample The hypotheses are tested using data from the small- and mediumscale enterprise survey in Vietnam, conducted in 2005 and repeated every two years in 2007, 2009, 2011 and 2013. The survey was a collaborative effort undertaken by the Department of Economics, University of Copenhagen, Denmark; the Central Institute for Economic Management, Vietnam; and the Institute of Labour and Social Affairs, Vietnam; with financial support from the Danish Development Agency and United Nations University. The respondents were encouraged to participate in face-to-face interviews to provide recommendations to design better business development policies, which is typical in Vietnam. This database covers around 2,500 manufacturing firms in three cities and seven rural provinces across Vietnam (CIEM, DoE, & ILSSA, 2014). Approximately 95% of the enterprise population are registered as household enterprises (Rand & Finn, 2007). The sample covers firms with<250 employees, the accepted definition of an SME in Vietnam (MPI, 2014). These surveys had a high response rate of 98%, thus eliminating nonresponse bias (Armstrong & Overton, 1977). To check for common method bias, we applied Harman’s single-factor test and marker variable correlation (Lindell & Whitney, 2001; Podsakoff & Organ, 1986). The Harman test showed that the slight variance was explained by the first factor (17%). There were no substantive correlations between the marker variable (a binary variable saying whether a firm selects its suppliers freely) and variables in the study. This result implies that common method bias is not a problem (Crowston, Sawyer, & Wigand, 2015). Furthermore, interviewing managers and owners with intimate knowledge of these SMEs’ operations and performance improved the chance of collecting accurate responses (Healey & Rawlinson, 1993). Another motivation behind choosing these interviewees was to provide correct answers that policy-makers could use to develop policies, which also helps avoid common source bias from investigating only one data source (MacKenzie & Podsakoff, 2012). Hypothesis 2a. Resource slack mediates the relationship between government staff-training support and new product and process innovation. Hypothesis 2b. Resource slack mediates the relationship between government quality and technology-improvement support and new product and process innovation. 2.4 The moderating role of exporting The international business literature explains that internationalizing can enhance firms’ knowledge as they learn about new practices in the markets with which they engage (Autio, Sapienza, & Almeida, 2000; Fletcher & Harris, 2012). Emerging-market firms do, however, have a different knowledge base than that of their international customers (Petersen, Pedersen, & Lyles, 2008), in addition to other liabilities of foreignness (Zaheer, 1995), such as religion (Richardson & Rammal, 2018), language barriers, different norms and values, communication pattern differences, or just different operational logic. This presents absorptive-capacity constraints when assimilating such knowledge (Cohen & Levinthal, 1990). Furthermore, these emerging-market SMEs are typically international suppliers and are more likely to export (Filatotchev, Liu, Buck, & Wright, 2009), since the GVC literature (Gereffi, Humphrey, & Sturgeon, 2005) shows their likelihood to produce simple, repetitive and lowadded-value products (Sturgeon & Lester, 2004). Recent studies (e.g., Torres de Oliveira et al., 2021) further explain that innovation capabilities remain with the GVC orchestrator and implicitly assume that those orchestrators are not incentivized to transfer knowledge to firms within their supply chain because another emerging-market SME can easily replicate their needs. Assimilating new knowledge is an essential aspect of innovation (Cohen & Levinthal, 1990). Here, we argue that firms can learn from exporting through their opportunities to collaborate with others in the supply chain (Salomon & Shaver, 2005). They also learn from their engagement in government support programs through training and exposure to new technologies. However, engaging in both types of learning simultaneously may be beyond the learning capacity of SMEs. This means that focusing on exporting will most likely reduce the effect of the learning capabilities acquired from governmental support programs because the span of attention and the capabilities and resources are known to be restricted1 (Nguyen et al., 2014). Therefore, we argue that these firms might become distracted by their exporting process, 3.2 Measures 3.2.1. Dependent variables The dependent variables in this study were new product innovation and new process innovation widely used in innovation studies (e.g., Jin, García, & Salomon, 2018). New product innovation is defined as whether firms have introduced new products. New process innovation reflects the introduction of new production processes. These definitions are consistent with how the OECD (2005) defines innovation. The two dependent variables are binary: firms either reported each type of innovation or did not during the past two years. 3.2.2. Explanatory variables 1 We recognise that this focus on exporting might, in future, enhance firm resources and capabilities. We thank one of the anonymous reviewers for pointing this out to us. The explanatory variables used were government support for staff training and quality and technology improvement, which are forms of 4 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 Fig. 1. Hypothesized relationships. government non-financial assistance. The survey asked, “Did you receive any of the following forms (as listed above) of government assistance in the preceding year?” with the list including activities such as training provided in business start-ups, business management, or employee training. Government support for technology included quality and technology improvement programs, such as training to implement quality assurance programs or technology transfer assistance. All these variables were coded as binary variables (see Table 1). The resource slack variable was measured by the ability to increase production from present levels using only existing equipment/machinery. This measure is, first, an indicator of resource slack, demonstrating that a firm could mobilize unused resources to increase its production capacity (Cyert & March 1963). However, this measure may go beyond resource slack. It also captures the firm’s flexibility in how it uses current tangible resources for activities (Eisenhardt & Galunic, 2000). To increase production, the firm needs a new combination of tangible and intangible resources (Nonaka, 1994). In other words, human resources need to be reconfigured to create a new setup with current equipment/ machinery, which could only be done if there is slack in the firm’s human resources. In the survey, firms were asked, “By how much would you be able to increase your production from the present level using existing equipment/machinery only?” The answers were classified into six categories: 1 = “Not at all, operating at maximum capacity”; 2 = “By no more than 10%”; 3 = “By between 10% and 25%”; 4 = “By between 25% and 50%”; 5 = “By between 50% and 100%”; and 6 = “By more than 100%”. The exporting moderator was operationalized through a binary variable of defining whether or not firms are involved in exporting their products to overseas markets (e.g., D’Angelo, Ganotakis, & Love, 2020). 3.2.3. Control variables Eight control variables important for analysis (namely, firm size, age, location, legal form, industries, owner qualification, access to government financial support, and year) were included in the study. Firm size was calculated by the number of full-time employees (log) (Grönum, Verreynne, & Kastelle, 2012). Firm age was measured by the number of years in business (log) (Guan, Yam, Tang, & Lau, 2009; Robson, Haugh, & Obeng, 2009). Firm size and firm age were analyzed in the form of logarithm transformation to reduce the skewness of these two control variables (from 4.08 to 0.68 and 1.96 to –0.41 for firm size and firm age, respectively), which are adequate for the regressions (Tabachnick & Fidell, 2013). Firm location was categorized as urban or rural, and legal form was classified into household and non-household firms (Battisti et al., 2010; Fernandes, Ferreira, & Marques, 2015). Industries were categorized into: (1) low technology; (2) medium–low technology; (3) medium–high technology; and (4) high technology, based on OECD definitions for manufacturing industries (Hall, Lotti, & Mairesse, 2009; OECD, 2009). The possession of a technical certificate measures owner qualification (Pickernell, Packham, Brooksbank, & Jones, 2010). We also included access to government financial support as one control variable because access to such support increases the firm’s operating capability through funds for their innovation activities (Wei, Zheng, Liu, & Lu, 2014). Year dummy variables were also included in the analysis to control for the variance between years. Table 1 Measures used in the study. Variables Measures New product innovation Firms introduced new product/s during the past two years (0/1) Firms introduced a new production process during the past two years (0/1) Firms received government support to implement quality assurance programs or technology transfer assistance (0/1) Firms received government support to improve human resource quality (0/1) The level of increased production from the present level using existing equipment/machinery only. 1 = “Not at all, operating at maximum capacity”; 2 = “By no more than 10%”; 3 = “By between 10% and 25%”; 4 = “By between 25% and 50%”; 5 = “By between 50% and 100%”; and 6 = “By more than 100%” Firms exporting products or not (0/1) Number of full-time employees Number of years trading Firms operating in low-technology industries (0/1) Firms operating in medium–low technology industries (0/1) Firms operating in medium–high technology industries (0/1) Firms located in urban areas (0/1) Firms registered as household businesses (0/1) Owners with at least a vocational training degree (0/1) Member of Communist Party of Vietnam (0/1) New process innovation Government quality and technological support Government staff-training support Resource slack Exporting Firm size (Log) Firm age (Log) Low technology Medium-low technology Medium-high technology Location Household firms Owner qualification Communist Party member 3.3 Statistical method The dependent variables were binary, so the study applied probit regression to test our hypotheses. The model is described as: Φ−1 (pi ) = k=n ∑ βk xik k=0 where (pi ) is the probability of innovation introduction, and xik is a set of explanatory and control variables. As the dependent variables were binary, maximum likelihood estimation was applied (Wooldridge, 2010). 5 T. Nguyen et al. 0.103 0.185 3.05 0.002 0.066 0.303 0.039 –2.12 0.034 –0.075 –0.003 0.533 23.31 <0.001 0.488 0.578 6 −0.223**** 0.050**** –0.010 0.080**** 0.021 –0.720*** –0.445*** 0.019 –0.007 –0.069*** –0.016 0.094*** –0.053*** –0.039** –0.012 0.090*** –0.042*** 0.027 –0.066*** –0.029* 0.009 –0.009 0.368*** –0.631*** –0.018 0.044** 0.266*** –0.057*** 0.041** –0.042** –0.014 0.082*** –0.254*** 0.002 0.015 0.021 –0.008 0.019 0.014 –0.028* 0.026 –0.063*** –0.045** 0.020 0.036* 0.023 0.120*** 0.132*** –0.003 0.023 –0.027 0.009 0.010 –0.133*** 0.028 0.061*** 0.210*** 0.022 0.064*** 0.100*** –0.035* 0.024 –0.014 –0.018 0.003 –0.089*** –0.003 0.042** 0.131*** 0.106*** 0.059*** 0.142*** 0.279*** –0.105*** 0.020 –0.033* –0.017 0.109*** –0.228*** 0.002 0.037** 0.288*** 0.072*** 0.048*** 0.022 0.061*** 0.149*** –0.161*** –0.100*** 0.032* 0.036* 0.047*** –0.087*** 0.007 0.014 0.152 1.217 0.182 1.120 0.588 0.492 0.441 0.327 0.483 0.457 0.292 0.291 SD Notes: Propensity scored matching with psmatch command in Stata 16.0. Note: Spearman correlation matrix, with associated p-values denoted by * (p < 0.05), ** (p < 0.01), and *** (p < 0.001). 0.001 0.024 2.972 0.034 1.858 2.605 0.589 0.265 0.121 0.369 0.704 0.906 0.094 0.046 4 5 6 7 8 9 10 11 12 13 14 15 2.00 –0.124*** 0.048*** –0.010 –0.042** 12 11 10 9 8 7 6 5 4 3 2 0.052 0.314 0.373 0.124 1 95% confident interval Mean p-value Table 3 Descriptive statistics and correlation matrix. Government staff-training support => New product innovation Government staff-training support => New process innovation Government quality and technological support => New product innovation Government quality and technological support => New process innovation Zscore 0.111 0.167 0.016 Table 2 Average treatment effect of government staff-training support and quality and technology support on innovation. New product innovation New process innovation Government quality and technological support Government staff-training support Resource slack Exporting Firm size (log) Firm age (log) Low technology Medium-low technology Medium-high technology Location Household firms Owner qualification Communist Party member Table 3 presents the descriptive statistics and correlation matrix for 1 2 3 4 Results 4.1 Descriptive analysis Coefficient –0.323*** –0.046** –-0.095*** 13 Because a firm would initially have been better endowed with innovation — for instance, larger firms with a higher number of qualified staff might gain advantages in conducting innovation activities — the estimation of influence on innovation may be overstated (Forés & Camison, 2016). In addition, government assistance could be a function of firm characteristics; for example, a larger firm may be more likely to have access to subsidies, raising concerns about endogeneity leading to estimation bias (Almus & Czarnitzki, 2003). We dealt with this issue by undertaking several techniques. First, we applied the propensity score method (Austin, 2011). The propensity score method’s application can help avoid the problem of selection bias by adjusting the covariates between the treated and controlled groups (Li, 2013). Rosenbaum bounds were estimated for the sensitivity tests for matched one-to-one observations (DiPrete & Gangl, 2004). The results implied that the matching estimators are robust. Second, we applied the instrumental variable method to control endogeneity issues (Bascle, 2008). Firm size, location, legal form, and political connection were added as instrumental variables (Table 2). They were included because larger firms tend to be more visible to those offering support programs and are more likely to turn government subsidies into outputs (Li, Andries, Pellens, & Xu, 2021). Firms operating in less urbanized areas are also favoured by program providers (Crowley, 2017). In addition, governments tend to support formal firms (non-household firms in our study) because of their ability to provide returns through taxation (Prado, 2011). Finally, firms with owners/managers with political connections may have greater access to support programs (Li, Song, & Wu, 2015). We conducted Stock and Yogo’s tests, and the results implied that we could conclude our instruments were appropriate (Stock & Yogo, 2005). The regressions were run following a two-step approach. We estimated the propensity scores for accessing government support in the first step. For this stage, six variables, firm size, age, location, legal forms, owner’s qualifications, and industry, were included in the estimation. In later stages, ordered probit and instrumental variable probit regressions using the estimated propensity scores were used to estimate the relationship between government support, slack, and innovation. Furthermore, to test the mediation effect, the study followed the procedure suggested by Baron and Kenny (1986). First, we predicted the direct impact of government support on innovation. Second, we ran a regression on the relationship between government support and resource slack. Finally, we regressed the influence of resource slack on innovation, controlling for the impact of accessing government support. –0.014 0.048*** Journal of Business Research 154 (2023) 113305 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 all variables used in the analysis. The results show that only 11% of firms introduced new products, while the number of firms applying new technology in production was slightly higher, reaching nearly 17%. Regarding access to government support, around 2% of firms were granted technological and staff-training assistance. Almost 4% of firms exported products or services to international markets, which aligned with the Vietnam census figure (GSO, 2018). The results also show significant correlations between government technological support, staff-training support, slack, firm size and age, and new product and process innovation. and both product (β = 9.63, p < 0.001) and process innovation (β = 15.02, p < 0.001) (see Table 4). By accessing this type of support, a firm is likely to have a 9.63% and 15.02% higher chance of introducing product and process innovation, respectively. Therefore, H1a was supported. Then, to test the mediation effect, we ran a regression between this type of support and resource slack (β = 1.35, p = 0.003) and resource slack and innovation (β = 0.06, p = 0.008 for resource slack and new product innovation, and β = 0.06, p < 0.001 for resource slack and new process innovation) when controlling for staff-training support. The statistical outputs demonstrate that the relationships between government staff-training support for new product innovation and new process innovation is significantly positively mediated by resource slack. In both models is an increase in the effect (total effects are greater than direct effect), thus showing that the indirect effect coefficient of government staff-training support on new product innovation is 0.12 (=9.62–9.50) and 0.09 (=15.02–14.93) for new process innovation. This indirect effect is tested using the Sobel Z-scores that are statistically significant. Therefore, H2a was supported. The positive relationship also holds for technological assistance and both new product (β = 14.23, p < 0.001) and process innovation (β = 23.91, p < 0.001) (see Table 4); thus confirming H1b. This finding indicates that, when a firm is granted government quality and technology improvement assistance, the possibility of introducing innovation increases by 14.23% for new products and 23.91% for new processes. Resource slack also significantly mediates the relationship between technological support and process innovation (Z-score = 2.20, p = 0.028). However, the Sobel Z-scores denote that the mediation effect of resource slack on the relationship between government technological support and new product innovation is not statistically significant (Zscore = 1.92, p = 0.055). Therefore, H2b was only partially supported. We then tested the moderation impact of exporting on the relationship between government support and innovation. We found that the moderation impact does not hold for new product innovation for both forms of non-financial support (β = -2.446, p = 0.287, and β = -1.481, p = 0.630). However, the positive relationship between these support types and process innovation is negatively moderated by exporting (β = -5.397, p = 0.003; and β = -7.435, p = 0.012), which means that exporting reduces the positive impact of government staff training and quality, and technological assistance on introducing new production processes but not product innovation (see Tables 5 and 6). Therefore, H3a and H3b were partially supported. We also found that larger firms are more likely to introduce a new product or process innovations. Firms operating with higher levels of technology and located in urban areas are more likely to have product innovation rather than process innovation, which requires more effort and resources. However, there is no statistical difference between young and mature businesses and firms with different levels of owners’ qualifications regarding their having product or process innovations. 4.2 Regression analysis Table 4 estimates the relationship between government support, resource slack, and innovation using Stata 16.0. First, results from the instrumental variable probit regression results show a significant relationship between the propensity for government staff-training support Table 4 The relationship between government support, resource slack, and innovation. Government stafftraining support => New product innovation Government stafftraining support => Resource slack Resource slack=> New product innovation Sobel Z-score Government stafftraining support => New process innovation Government stafftraining support => Resource slack Resource slack=> New process innovation Sobel Z-score Government quality and technological support => New product innovation Government quality and technological support=> Resource slack Resource slack=> New product innovation Sobel Z-score Government quality and technological support=> New process innovation Government quality and technological support => Resource slack Resource slack=> New process innovation Sobel Z-score Number of observations Coefficient p-value Confident intervals (95%) Hypothesis 9.63 <0.001 7.56 11.69 H1a supported 1.35 0.003 0.46 2.23 H2a supported 0.06 0.008 0.016 0.102 1.99 15.02 0.023 <0.001 13.37 16.68 H1a supported 1.35 0.003 0.46 2.23 H2a supported 0.06 <0.001 0.028 0.096 2.30 14.23 0.011 <0.001 10.59 17.86 H1b supported 1.86 0.003 0.64 3.08 H2b partially supported 0.06 0.012 0.012 0.099 1.92 23.91 0.055 <0.001 21.10 26.72 H1b supported 1.86 0.003 0.64 3.08 H2b partially supported 0.05 0.001 0.022 0.088 2.20 5090 0.028 4.3 Robustness tests To check whether our results were robust, we estimated seemingly unrelated bivariate probit regressions, within which the first equation illustrates that firms could access government support. In contrast, the second equation shows the relationship between government support, slack, and innovation. In the first equation, firm size, location, and legal form were considered additional determinants of access to government support. The new results are consistent with the main findings. Fixedeffect logistic regressions were also deployed to examine the relationship. In addition, the Hayes Macro program was applied to test the mediation effect by considering the uneven six-level categorical variable as a continuous mediator (Hayes, 2018). The results show that little difference was found between the models used in the main findings and those used for robustness tests. Therefore, the results can be considered robust. Note: Year dummies were included but not reported. Instrumental variables were government financial support, firm size, location, and legal form. High technology and 2005 were references. 7 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 Table 5 The moderation of exporting in the relationship between government staff-training support and innovation. New product innovation Coefficient p-value New process innovation Confident intervals (95%) Coefficient p-value Confident intervals (95%) Government staff-training support 2.589 0.166 –1.077 6.255 3.968 0.005 1.212 6.724 Exporting 0.313 0.143 –0.105 0.732 0.53 0.003 0.18 0.88 Government staff-training support* Exporting –2.446 0.287 –6.943 2.052 –5.397 0.003 –8.917 −1.877 Firm size (log) 0.158 <0.001 0.072 0.243 0.257 <0.001 0.195 0.318 Firm age (log) –0.021 0.645 –0.111 0.069 –0.026 0.537 –0.109 0.057 Location 0.075 0.254 –0.054 0.203 0.098 0.036 0.006 0.189 Legal form –0.062 0.536 –0.257 0.134 –0.12 0.056 –0.243 0.003 Low-technology industry –0.581 <0.001 –0.827 −0.334 –0.077 0.608 –0.372 0.218 Medium-low-technology industry –0.204 0.173 –0.496 0.089 –0.169 0.19 –0.422 0.084 Medium-high-technology industry –0.19 0.218 –0.493 0.113 –0.153 0.285 –0.434 0.127 Owner qualification 0.014 0.884 –0.18 0.209 –0.034 0.663 –0.186 0.118 Number of observations 5090 5090 Note: Year dummies were included but not reported. Instrumental variables were government financial support, firm size, location, and legal form. High technology and 2005 were references. Table 6 The moderation of exporting in the relationship between government quality and technological support and innovation. New product innovation Coefficient p-value New process innovation Confident intervals (95%) Coefficient p-value Confident intervals (95%) Government quality and technological support 3.304 0.366 –3.864 10.472 7.46 0.021 1.103 13.817 Exporting 0.233 0.309 –0.216 0.682 0.457 0.005 0.14 0.773 Government quality and technological support * Exporting –1.481 0.63 –7.509 4.547 –7.435 0.012 –13.212 –1.658 Firm size (log) 0.136 0.003 0.046 0.225 0.233 <0.001 0.149 0.317 Firm age (log) –0.026 0.592 –0.121 0.069 –0.033 0.385 –0.108 0.042 Location 0.093 0.342 –0.099 0.286 0.12 0.044 0.003 0.236 Legal form –0.059 0.482 –0.223 0.105 –0.128 0.071 –0.266 0.011 Low-technology industry –0.62 0 –0.926 –0.314 –0.153 0.344 –0.471 0.164 Medium-low-technology industry –0.236 0.137 –0.546 0.075 –0.239 0.122 –0.542 0.064 Medium-high-technology industry –0.164 0.328 –0.492 0.164 –0.149 0.38 –0.482 0.184 Owner qualification 0.063 0.506 –0.123 0.249 0.024 0.778 –0.141 0.189 Number of observations 5090 5090 Note: Year dummies were included but not reported. Instrumental variables were government financial support, firm size, location, and legal form. High technology and 2005 were references. 5 Discussion We also identify a mediating role for research slack in the relationship between staff training and quality and technology assistance programs and innovation. When given government support, even if not financial, Vietnamese firms with higher levels of resource slack are more likely to benefit from these support measures. While these unused resources might not be noticeable, they still positively impact the probability of innovating, as shown through the mediation effect of resource slack. It is difficult for SMEs to access government support, as governments must carefully consider whom they should target in their programs. Therefore, it seems that these firms try to maximize the benefit of supporting measures once they are granted, to increase their knowledge, skills, and business ideas for innovation. However, the data analysis shows that, while resource slack mediates the relationship between training support and both process and product innovation, and mediates technological support and process innovation, this mediation impact is not significant for technological support and product innovation. This may be because the manufacturing SMEs in our sample are constrained by what the GVC to which they belong expects. It may also be because Vietnamese SMEs, like those in many other emerging economies, have lower levels of education and technology skills, curbing externally sourced knowledge for product innovation (MPI, 2014). Thus, even if these SMEs have resource slack, they may not have the required capabilities to use it, or they may have to abide by what the GVC orchestrator expects. Finally, we found an overall negative moderation effect of exporting on the relationship between government support for staff training and quality, and technological assistance and process innovation and product innovation, being significant only for process innovation. Specifically, when local non-financial government support is present in a firm, Innovation is widely recognized for its importance in helping firms enlarge their markets, attain competitive positions, and enhance their survival and growth (Beck, Demirguc-Kunt, & Levine, 2005; Lukacs, 2005). To encourage SMEs to undertake innovation activities, governments in both developed and emerging markets provide the support that involves financial and non-financial measures (Michael & Pearce, 2009). In this paper, we focused on non-financial assistance measures, which we argued may impact innovation through their positive influence on staff knowledge development. We considered the effect of exporting and resource slack in emerging economies. Our findings confirm that receiving government non-financial support improves the probability of emerging-market SMEs introducing innovation, which matters for product and process innovation. This government non-financial support exists for firms that wish to develop skilled workers or gain market and technological knowledge. Consequently, this more broadly impacts firms’ activities and performance, arguably more relevantly than financial support (Doh & Kim, 2014). These training programs enhance employee knowledge and skills, which is essential for innovation activities (Phillips, 2014). Learning increases business opportunities being identified and the capability to solve business problems (Asif, de Vries, & Ahmad, 2013; Schiuma, 2013), leading to a higher chance of introducing new products and processes. The positive impact of quality and technological support can be seen in introducing quality assurance programs through which employees can lift their knowledge of quality management, especially in the aspect of continual improvement, thereby enabling firms to diagnose and solve technical issues in innovative ways (Asif et al., 2013). 8 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 product innovation is not impacted by the presence of exports, but it does matter for process innovation. While this supports our initial hypothesis, it is contrary to the international business literature. International business scholars argue that exporting increases firms’ performance (Pangarkar, 2008) and the innovation outcomes for large and small to medium firms (Cassiman & Golovko, 2011). Supported by organizational learning theory (Fiol & Lyles, 1985), international business academics reason that exporting is particularly important for innovation owing to the knowledge transferred between the local firm and its foreign customers (Petersen et al., 2008). We alternatively argue that a significant knowledge gap occurs between the emerging-market supplier and its international customers, in that the former struggles to understand, absorb, and assimilate foreign knowledge (Clark & Fujimoto, 1991; Torres de Oliveira et al., 2021), even if they might perceive it as beneficial and aspirational. While the international business literature is largely silent about the experiences of firms at the low end of GVCs, focusing instead on the GVC orchestrators (Buckley, 2009), the GVC literature (Gereffi et al., 2005) has been analyzing those chains from a bottom-up perspective. Linking the GVC literature and the recent advances in emerging economies’ learning capabilities (Torres de Oliveira et al., 2021), points to the importance of managerial attention span constraints (Nguyen et al., 2014; Ocasio, 1997) inflicted by juggling the exporting process with innovation in emerging-market SMEs. The exception is process innovation, which is supported through knowledge transfer by GVC orchestrators. Therefore, the lack of a moderation effect between government support and product innovation is somehow unsurprising, as product innovation remains with the orchestrator, who is interested in improving process innovation from their suppliers but not product innovation (Torres de Oliveira et al., 2021). However, those suppliers might not be prepared to make sense of such knowledge (Cohen & Levinthal, 1990), which impacts the firms’ innovation activities in the presence of government support activities. because the classic international business literature (Dunning & Lundan, 2008) assumes that internationalization is always favorable to firms as they open new markets, enlarge their customer base, and diminish overall firm risk exposure to other economic and political systems. Therefore, we advance past research that looked at slack in SMEs from developed economies (Kiss et al., 2018) by showing that context matters, in that SMEs from emerging markets face different resource and capability constraints but also externalities. Our results expand the GVC and international business literature in emerging markets (e.g., Luo & Tung, 2007) by explaining that managers and researchers need to consider the non-financial government support to which focal firms are exposed when implementing an internationalization strategy. To them, juggling it with innovation, and incorporating government support simultaneously into the firm is outside the scope of most emergingmarket SMEs. Taken together, we contribute to the recent discussion on the importance of institutional support for GVC and emerging-market firms’ upgrading objectives (Gereffi, Lim, & Lee, 2021). Third, we enrich organizational learning theory with new work in entrepreneurship on external enablers for small and new firms by illuminating the role of government support as an external enabler (Kimjeon & Davidsson, 2021) of knowledge absorption and innovation. In particular, we show two things. The first is that, while organizational learning theory frequently focuses on the process of learning with an emphasis on the human element (Drejer, 2000), this research extends this theory by using resource slack to include external institutional support for improving firm knowledge from an organizational perspective. The second focuses on the trade-off between knowledge gained through exporting versus by engaging with government support. Despite requiring time and experience to develop knowledge, other ways can help firms improve knowledge and boost this generation process (Hotho, Lyles, & Easterby-Smith, 2015). We show that, while firms can gather it through external sources, they have to make choices when they are resource constrained. Excess resources are found within firms because of the indivisibility of resources and the creation of knowledge that allows firms to reduce the time and any form of resources in implementing business activities without influencing the production outputs (Kor, Mahoney, Siemsen, & Tan, 2016; Penrose, 1959). The analysis of resource slack contributes further to developing that surplus because it enables firms to deploy resources more efficiently and demonstrates their ability to recombine resources to meet the demands of their innovation activities. 6 Implications 6.1 Theoretical implications The findings presented in this paper contribute to theory in several ways. First, we add to the core of organizational learning and the GVC literature in establishing the relationship between exporting and government support activities to enable process and product innovation. Emerging-market SMEs can foresee substantial knowledge gains from engaging with foreign customers (Villar, Alegre, & Pla-Barber, 2014), mainly because they are usually orchestrators from developed economies and are modern, well-resourced, and frequently innovative enterprises (Buckley, 2009). Therefore, emerging-market SMEs may see those foreign customers as aspirational. Furthermore, those customers may signal their intention to improve the focal firm’s innovation process. However, our results suggest that cross-border knowledge transfer either does not happen nor positively impact innovation when in the presence of non-financial support. Indeed, this cross-border knowledge transfer impact is prejudicial for process innovation, which we attribute to a lack of attention span in our small, resource-poor SMEs. We thus show that the knowledge gap between the focal firm and the GVC orchestrator is over the threshold where knowledge assimilation and translation do not occur (Clark & Fujimoto, 1991), thus challenging existing arguments (Humphrey & Schmitz, 2000) that GVC orchestrators, or lead firms, are key to upgrading to occur in emerging-market SMEs. In contrast, it is non-financial support from governments that positively impacts both product and process innovation. These latter arguments illuminate how we contribute to the GVC literature, where second, we show that upgrading (Gereffi, et al. 2021) should be supported through alternative pathways. For our focal firms, focusing on government support as well as knowledge absorption from the GVC becomes an additional attention burden that takes attention away from process innovation. This implication is interesting to the GVC 6.2 Policy and managerial implications The findings also suggest several managerial and economic policy implications. First, managers from emerging-market SMEs can explore and deploy the benefits of non-financial government support. While financial support from governments is always important to boosting performance in general, and innovation in particular, our results show that accessing non-financial support measures also plays a role in upgrading or using hidden capabilities and resources of these businesses, thereby providing additional engines for innovation. In other words, having non-financial support is an efficient alternative for emergingmarket SMEs struggling to access financial subsidies to support their innovation. Moreover, the positive mediation impact of resource slack suggests another avenue for SMEs’ managers to use non-financial institutional support to increase the availability and flexibility of resources in their business to build new process and product innovations. This also aligns with the apparent importance to focus on innovation and elevate SMEs’ knowledge bases before engaging in internationalization strategies. When non-financial government support is available, managers need to realize that an internationalization strategy (Fernandes, Ferreira, Lobo, & Raposo, 2020) is not in the firm’s best interest for product innovation, and is even detrimental to process innovation. For policy-makers, these results indicate that emerging-market SMEs must be careful when adapting successful innovation support strategies 9 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 References from developed economies. Government officials should consider implementing non-financial government assistance during industry downturns since government staff training supports innovation through resource slack. Providing this support will help firms innovate and overcome challenges when the business environment is unfavorable. 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H. (2020). Learning by exporting under fast, shortterm changes: The moderating role of absorptive capacity and foreign collaborative agreements. International Business Review, 101687. 6.3 Limitations and future research This study has some limitations that could be addressed in future studies. First, socioeconomic and institutional factors may influence the application of government support. To partially address this, several variables, such as legal form, location, and owner qualifications, which partly reflect the impact of these factors, were included in the analysis, but future studies should consider other variables. Second, the research uses only binary innovation and government support variables. We have applied several strategies to deal with this potential measurement issue. First, the survey started with a pilot, which was used to eliminate or adjust potentially misunderstood questions. Second, interviewees were provided with a manual to explain key concepts to the respondents, ensuring the accuracy and consistency of the collected responses. Third, we cross-checked the list of products/ services to ensure the responses to the innovation question were aligned with the differences in the list between the two survey rounds. In each survey, firms were asked to list products/services produced with associated International Standard of Industrial Classification codes. We used the list to check whether a firm introduced a product that differed from their previous survey list. Fourth, we applied the propensity scorematching method to calculate the propensity scores for accessing government support. That said, future research could use other innovation measurements. Furthermore, this paper only investigated manufacturing firms. On the one hand, this is positive because in this way we mitigate causality and noise between different sectors. On the other hand, future research should investigate services and other sectors of activities in emerging markets. Finally, future studies should try to understand in more detail, through qualitative, quantitative, or mixed methods, the mechanisms in place related to absorptive capacity and knowledge-related capability use in innovation and other firm processes. What we know about emerging-market firms, particularly SMEs, which do not belong to the BRICS (Brazil, Russia, India, China, and South Africa) nations, is still very low and deserves the attention of future studies. CRediT authorship contribution statement Tam Nguyen: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. Martie-Louise Verreynne: Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation. John Steen: Writing – review & editing, Writing – original draft, Funding acquisition, Conceptualization. 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Climbing the Ladder: Inward Sourcing as an Upgrading Capability in Global Value Chains. Research Policy, 51(3), Article 104439. Tam Nguyen is a Senior Research Assistant at the UQ Business School, The University of Queensland, Australia and holds a permanent position as Senior Data Officer at the PhN Corporation. He researches on the strategy and innovation topics. Tam has a background in Statistics and Strategy, having obtained his PhD from the University of Queensland in Strategy and Innovation in 2019. He has published in journal as the Journal of World Business, Entrepreneurship and Regional Development, among others. 12 T. Nguyen et al. Journal of Business Research 154 (2023) 113305 Martie-Louise Verreynne is Deputy PVC (Research and Innovation) and a Professor of Innovation at RMIT University. She has held roles such as Interim Head of School, Deputy Head of School, Head of Strategy and International Business, and Program Leader for several programs during her tenure at the University of Queensland. Martie-Louise is a recipient of and Australian national citation for contribution to student learning based on her work in the area of commercialisation of high-tech start-ups. She developed and led the MicroMasters in Corporate Innovation delivered through EdX. Martie-Louise is internationally recognised for her research on innovation, strategy and entrepreneurship in small business, is an associate editor with the Journal of Small Business Management and publishes widely in top tiered journals. She has been successful at winning ARC Discovery, ARC ITTC and several other Category 1, 2 and 3 grants. She actively works with industry to create research impact and has received several awards in recognition of this work. She holds a PhD from Massey University. She has published more than 100 peer-review academic journals in top journals as Journal of world Business, Journal of Business Research, Industrial and Corporate Change, Journal of Management & Organization, Small Business Economic, Journal of Cleaner Production, Journal of Small Business Management, R&D Management, Tourism Management, among others. University of Queensland and was later head of the entrepreneurship and strategy department at UQ Business School. He joined the University of British Columbia in 2019 as the EY Distinguished Scholar in Global Mining Futures and is Director at the Bradshaw Research Initiative in Mining and Minerals at UBC. Professor Steen has more than a 100 publications in top journals as JIBS, MIR, Journal of Cleaner Production, IJPM, JIM, Strategic Organization, among others. Rui Torres de Oliveira is a Senior Lecturer at the Management School and International Business cluster and a Chief Investigator at the renown Australian Centre for Entrepreneurship. He is also a Chief Investigator at the Centre for Future Enterprise. His academic interest relates to strategy, digital capabilities, innovation, and international business. Rui Torres de Oliveira’s work has been published in top journals as: Journal of World Business, Global Strategic Journal, Strategic Entrepreneurship Journal, Journal of Business Research, Small Business Economics, Social Indicator Research, Journal of Retailing and Consumer Services, Journal of Leadership and Organizational Studies, International Journal of Emerging Markets, among other academic papers, case studies and book chapters. Rui holds a Master Degree in Civil Engineering, an MBA, and a Doctorate on International Business and Strategy from Manchester Business School in the UK. Professor Steen became interested in science and technology at the University of Tasmania and completed a PhD in biochemistry. He then finished a PhD in innovation strategy at the 13

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