Abstract
Purpose
This study aims to investigate the impact of supply chain concentration (SCC) on corporate research and development (R&D) investment and determine the moderating roles of industry concentration and financing constraints on the relationship between SCC and R&D investment.
Design/methodology/approach
The study collected data from Chinese listed companies, used the fixed effects model to test the research hypotheses and further used the two-stage Heckman test and propensity score matching (PSM) to address potential endogeneity issues.
Findings
The result reveals a negative impact of SCC on corporate R&D investment. In addition, industry concentration mitigates the negative impact of SCC on corporate R&D investment, but financing constraints strengthen the negative impact.
Originality/value
This study introduces the concept of SCC and empirically tests its effect on R&D investment, further explaining the lack of corporate innovation. This study inspires companies to strengthen SC management and weigh the level of SCC with environmental factors.
Keywords
Citation
Zhao, S., Fan, X., Shao, D. and Wang, S. (2024), "Does supply chain concentration promote or inhibit corporate R&D investment?", Chinese Management Studies, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/CMS-05-2023-0205
Publisher
:Emerald Publishing Limited
Copyright © 2024, Shukuan Zhao, Xueyuan Fan, Dong Shao and Shuang Wang.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Research and development (R&D) investment is considered one of the most typical forms of learning and a major way of innovation for companies (Koussis et al., 2007; Xiao, 2013). In the current competitive environment, an increasing number of companies recognize the importance of R&D investment in strengthening sustainable competitive advantage and enhancing innovation capability (Qian and Yuan, 2023; Tse et al., 2021). However, in most countries, firms do not invest in R&D at the optimal level proposed by Knott (2012). Why does corporate practical R&D behavior not match their perception? Previous literature has explained the reasons for short-sightedness in R&D activities from different perspectives, such as management compensation gap (Wang et al., 2023), intergenerational power gap (Zhao et al., 2023) and family CEO duality (Lin et al., 2023).
Although these studies provide important insights into corporate R&D underinvestment, there is a shortcoming that they all focus on the organizational internal factors. R&D investment is not only the result of internal management but is also influenced by external factors. Based on the resource dependency theory (RDT), the organization cannot generate all the resources it needs internally and must obtain the necessary resources through other organizations in the environment to survive, but power imbalance caused by dependency relationship between organizations is one of the sources of uncertainty affecting organizational behavior (Pfeffer and Salancik, 1978; Hillman et al., 2009). Supply chain (SC) is a typical dependency structure (Luo et al., 2023). However, the impact of SC structural characteristics on corporate R&D investment has been largely overlooked (Yoo and Seo, 2017), which provides a good entry point for further research.
Therefore, we attempt to address the existing research gap by exploring how the SC dependence structure factor affects R&D investment. Specifically, SC concentration (SCC), as a structural feature of SC relationship, reflects the dependence degree of a firm on its trading partners (Chen et al., 2023; Schwieterman et al., 2017), like a coin with two sides. For example, high SCC implies that firms are highly dependent on their main SC partners, which reduces their bargaining power and thus disrupts their investment strategy (Ahsan et al., 2023; Zhang et al., 2020). However, concentration also encourages firms to collaborate on R&D to build solid SC relationships (Krolikowski and Yuan, 2017). These possibilities drive us to examine whether SCC positively or negatively affects R&D investment. Moreover, conflicting views point to the need to explore the consequences of SCC in the context of additional conditions. As firms are embedded in industry and society, we develop moderators at the industry (industry concentration) and societal (financing constraints) levels. To sum up, we pose two research questions. First, how does SCC affect corporate R&D investment? Second, what are the contingency impacts of industry concentration and financing constraints on the relationship between SCC and R&D investment?
Actually, high SCC is often accompanied by insufficient R&D investment in many firms and industries. For example, Chinese Hygon Information Technology, which is dedicated to R&D of chip products and systems, maintains a high level of SCC but invests less in R&D than the average of other peer companies. Not coincidentally, SunWin (Hubei) Optoelectronic Technology, as a high-tech company, is also in a situation of higher SCC but lower R&D investment. Many scholars showed that high SCC reduced the bargaining power of midstream firms (Ahsan et al., 2023; Hui, 2023), thus resulting in no spare funds in R&D activities because of lower profits. Moreover, high SCC exposes firms to higher risks (Dhaliwal et al., 2016), which makes them cautious about R&D activities, as R&D activities are also high-risk. Therefore, we argue that SCC inhibits firms from investing in R&D. Besides, we hold the view that the above negative impact is weakened in concentrated industry because industry concentration can strengthen the bargaining power of firms (Xu et al., 2019). Conversely, financing constraints strengthen the negative impact, as financing constraints further limit funds available to firms for R&D.
We take a sample of Chinese-listed companies for the following reasons. First, the China Securities Regulatory Commission advises companies to respectively disclose the names and purchase (sale) amounts of their main suppliers (customers), which supports necessary data availability. Second, SCC is more pronounced in Chinese companies than in companies from other countries (Cao et al., 2023). Empirical results support our conjectures. The above findings still hold after controlling for potential endogeneity problems. This study makes the following contributions. First, this study extends the research boundary of RDT in inter-firm dependence by focusing on the impact of SC dependence on R&D investment. Previous research on inter-firm dependence based on RDT mainly emphasized R&D advantages from dependence (as listed in Table 1). This study starts with SC relationship dependence, in contrast to previous points, which reveal the negative impact of inter-firm dependence on firms’ R&D investment. Second, we expand research focusing on SCC. Suppliers and customers together constitute complete resource exchange process of a firm. Therefore, based on previous research focusing on one side of SC (as listed in Table 1), we simultaneously consider supply and demand sides, which highlights overall dependency structure of SC. Third, we introduce industry concentration and financing constraints into research framework to more comprehensively explain the impact of SCC on R&D investment. When considering contingency factors, we emphasize environmental factors, which is an extended study combining the RDT with contingency theory.
The structure of this paper is as follows. Section 2 reviews theory background and sets forth research hypotheses. Section 3 introduces the research model and variable definitions. Section 4 provides descriptive statistics, correlation analysis and empirical results. Section 5 discusses the results. Finally, Section 6 concludes the study, including theoretical contributions, managerial insights and limitations.
2. Theoretical foundation and hypotheses development
2.1 Resource dependency theory
The RDT suggests that an organization primarily depends on resources from other organizations within its environment to survive. SC is an important approach for companies to access critical resources, thus playing an increasingly important role in gaining a competitive advantage for companies (Hofmann, 2010). However, the RBT also points out that power is directly related to resource dependence, which means that the power of one organization over another is equal to the degree of dependence of the latter on the resources held by the former (Pfeffer and Salancik, 1978). In a resource-dependent relationship, there is a tendency for power imbalance between business partners (Hillman et al., 2009). As the proportion of supply (demand) from suppliers (customers) rises, the company’s dependence on them also increases, which increases the power of suppliers or customers over the company, resulting in a power imbalance that exacerbates hindrance problem. Therefore, we doubt that high SCC is detrimental to corporate R&D investment.
2.2 Hypothesis development
2.2.1 Supply chain concentration and research and development investment.
First, when a corporation has high SCC, it is at a disadvantage because of the increased bargaining power of its limited number of suppliers and customers (Dowlatshahi, 1999). Porter’s five forces model considers that the bargaining powers of suppliers and customers, threats of substitutes and potential entrants and competition between existing competitors can affect product price, cost and necessary investment decisions (Porter, 1992). Christopher and Gattorna (2005) also indicated that SC, rather than a corporation’s internal operations, is the last opportunity for companies to cut major costs. However, bargaining power may lead to benefit encroachment. On the one hand, suppliers with strong bargaining power may seek more profit for themselves by requesting early payment and increasing raw material prices, thus increasing the procurement costs of companies (Feng et al., 2023). On the other hand, major customers tend to demand lower product prices and delay payments, which makes it more difficult to raise funds and directly reduces corporate cash flow (Campello and Gao, 2017; Yang, 2017). These may occupy funds and reduce the profitability of corporations, thus decreasing the resources allocated to R&D activities. Therefore, high SCC may reduce the bargaining power of companies, thus resulting in insufficient funds for R&D activities.
Second, high SCC can increase operational risks. If suppliers and customers face financial difficulties or declare bankruptcy, the corporation’s normal operation can be disrupted and suffer losses. Corporations hedge risk for precautionary motives by holding more cash (Zhang et al., 2020). At the same time, the high risks associated with SCC reduce corporate risk-taking. As R&D involves high investment and risk, companies will reduce R&D investment to control absolute risk. Besides, R&D investment is often considered the critical aspect of relational dedicated asset investment in SC relationships (Bowen et al., 1995). The uncertainty in liquidation value of assets increases with specialization, according to the asset specificity theory (Williamson, 1988). Therefore, companies will lose the value of their relationship-specific assets and face high conversion costs if the transaction is suddenly interrupted (Ketokivi and Mahoney, 2020), which reduces the input of relationship-specific assets. Therefore, considering the risks, the corporation tends to be more cautious and reduce its R&D investment when it has high SCC.
Moreover, high SCC means that firms have less access to heterogeneous resources that are necessary for realizing R&D activities. Hence, some scholars have argued that heterogeneous resources positively influence corporate innovation. The number of new products that companies develop increases with partner heterogeneity (Hoskisson et al., 2002). Suppliers are crucial in providing corporations with the necessary raw materials and services. Integrating suppliers into the new product development process directly impacts decisions on manufacturing process design and production configuration (Petersen et al., 2005). Customers boost corporate R&D activities by providing market demand (Christensen et al., 2005). Thus, when the number of suppliers is limited, corporations may have restricted access to knowledge and technology. Similarly, a concentrated customer base limits the focal corporation’s grasp of market demand and increases information asymmetry. Therefore, high SCC, which implies the reduction of heterogeneous resources, inhibits corporate innovation activities, thus reducing R&D investment.
In summary, the following hypothesis is proposed:
SCC inhibits corporate R&D investment.
2.2.2 Moderating role of industry concentration.
Recent studies have found that industry concentration determines the degree of competition in the industry and further influences companies’ business strategies by affecting the distribution of resources and risk (Mithas et al., 2013; Pham, 2018). When industry concentration is low, competition increases due to fragmentation within the industry. The competitive landscape increases business risks of companies, especially those with high SCC. The bargaining power of their suppliers and customers is further enhanced. Because companies with high SCC face greater risks if their procurement and supply models are disrupted (Zu and Zhang, 2023). In this case, companies are more concerned about short-term benefits, such as changes in market share and declining performance, rather than long-term sustainable development. The characteristics of R&D activities, including long lead time and inability to achieve returns in a short time, may put corporations in more serious trouble (McKinley et al., 2014). Therefore, companies in highly competitive industries prefer to adopt cost-leading strategies (Li and Luo, 2021). At the same time, high industry competition heightens business concerns. Corporations respond to the risk of being preyed upon by competitors in a highly competitive market by holding more cash reserves (Haushalter et al., 2007). Under this circumstance, the motivation of companies to reduce R&D investment is further amplified.
On the contrary, companies always have significant market positions when the industry concentration is high (Zu and Zhang, 2023). In this case, the disadvantages for companies with high SCC are eliminated and may even change to favorable conditions. Companies in highly concentrated industries are able to achieve economies of scale and scope and capture greater excess returns (Nieto and González-Álvarez, 2014). Therefore, companies with high SCC have an advantage in limited competition. They have more opportunities to reap more economic benefits, thus having sufficient funds to invest in R&D activities (Schumpeter, 1942). Moreover, they face less uncertainty and are more likely to benefit from R&D investment, which increases their incentive to invest in R&D (Tishler and Milstein, 2009). In addition, when the external environment is stable, the gap between peer companies is obvious. Companies prefer differentiation strategies to maintain competitive advantage. And when market competition is low, it is easier for them to build intellectual property protection barriers and benefit better from R&D. Companies with high SCC are also likely to engage in collaborative R&D to preserve SC relationships.
Based on the discussion, the following hypothesis is proposed:
In the case of higher industry concentration, the impact of SCC on corporate R&D investment is mitigated.
2.2.3 Moderating role of financing constraints.
Prior studies have pointed out that companies typically prioritize endogenous financing over exogenous financing for corporate R&D investment (Himmelberg and Petersen, 1994). Although internal funding is one source of R&D funding, companies must rely on more than endogenous financing. R&D activity is a continuous and cumulative process, and it is difficult to cover the huge investments required for R&D activities by only relying on endogenous financing sources is difficult. Hence, external financing is gradually becoming an important source of R&D investment for corporations (Czarnitzki and Hottenrott, 2011). Therefore, R&D investment is sensitive to the external financing environment (Jones and Williams, 1998). Financing constraints mean that companies are unable to obtain stable and sufficient financial support due to the lack of capital caused by the high cost of external financing.
Investment decisions and risk management become more important when companies face high financing constraints. Because if a company experiences financial difficulties, its SC may also be shaken. Under financing constraints, companies with high SCC face worse financial conditions. At this point, the opportunity cost of corporate investment decision is small. However, R&D activities require large sunk costs and products are difficult to liquidate (Williamson, 1988), which makes it more difficult for struggling companies to accept the risks associated with R&D investment. Moreover, companies with high SCC tend to allocate capital to projects with less risk and stable returns rather than to high-risk R&D activities. Therefore, as financing constraints grow, the negative impact of SCC on R&D investment of companies increases.
Based on the discussion, the following hypothesis is proposed:
In the case of higher financing constraints, the impact of SCC on corporate R&D investment is strengthened.
The theoretical framework is shown in Figure 1.
3. Research methodology
3.1 Data source and sample
To investigate the three previously mentioned hypotheses, we use a sample of Chinese corporations listed on the Shenzhen and Shanghai Stock Exchange from 2006 to 2019. Such corporations are more mature and stable with better transparent data disclosure, which provides convenience and feasibility for research. Besides, 2006 was chosen as the starting year because new accounting standards issued in China in 2006 provided new and clear regulations on corporate R&D expenses and improved information disclosure. Following Zheng et al. (2023), this study selects the initial sample according to the following methods. First, we exclude financial and insurance industries because they have specific objectives and financial indicators that make it impossible to compare data (Yuan and Wen, 2018). Second, we exclude companies indicated as “ST” and “*ST” because of their abnormal operating conditions that would bias the regression results (Ren et al., 2021). Finally, we delete annual observations for companies with missing data. The final sample includes 2,354 companies with a total of 11,990 observations. We collect data from three main sources, the China Stock Market and Accounting Research database, Wind database and corporate annual reports. All continuous variables are winsorized at the 1% and 99% levels in this study to remove the effect of extreme values.
3.2 Measures
3.2.1 Dependent variable.
The widely used indicators to measure R&D investment are R&D investment decisions and R&D intensity. This study follows Gao et al. (2023) using the ratio of R&D expenditure to total assets to measure corporate R&D investment.
3.2.2 Independent variable.
SCC mainly refers to the number of partners upstream and downstream in the SC as well as its degree of business concentration, including supplier and customer concentration (Kwak and Kim, 2020; Zhang et al., 2020). We follow Patatoukas (2012) and Xu et al. (2022) and use the mean of the upstream and downstream concentrations as the overall SCC.
3.2.3 Moderating variables.
In this study, we introduce industry concentration and financing constraints as moderating variables.
We use the Herfindahl–Hirschman index (HHI) to measure the degree of industry concentration. A lower HHI value indicates a less concentrated and more fragmented industry, whereas a higher HHI value indicates a more concentrated market and a stronger monopoly of companies in the industry. The HHI is a common measure of industry concentration and is calculated as the sum of the squares of each corporation’s revenue as a percentage of the industry’s total revenue, which is calculated as follows:
In this study, we use the WW (Whited and Wu, 2006) index because it considers both external industry characteristics and internal financial characteristics, which gives it a broader economic significance. The WW index is calculated as follows:
3.2.4 Control variables.
Consistent with the existing literature on exploring factors affecting R&D investment, we control for many corporate characteristics to eliminate alternative explanations. First, we calculate the current ratio (Lev) by using the total current assets to the total current liabilities ratio. The increase in corporate liabilities prompts firms to increase R&D expenditures (Aaboen et al., 2006). Large corporations have larger market shares and greater risk management abilities than smaller ones. Therefore, corporate size has a significant positive effect on R&D investment (Muhammad et al., 2022; Shefer and Frenkel, 2005). We use the log of a corporation’s total assets to measure corporate size (Size). We add corporate age to the control variables because younger corporations are more interested in R&D ideas (Coad et al., 2016). Meanwhile, corporations with better financial performance are more likely to engage in R&D activities because they can afford such investment. Corporate performances, indicated by return on equity (ROE) and increased rate of main business revenue (Growth), are thus controlled. We also control the nature of property rights of companies based on the Chinese institutional context.
Second, we control for board characteristics because good board characteristics can impact corporate investments (Menshawy et al., 2023). Therefore, we control for board size (Board), independent directors ratio (Indep) and the duality of chairman and general manager (Dual). The checks and balances among shareholders facilitate the management to make investment decisions that are consistent with the corporation’s continued growth (Gomes and Novaes, 2001). Thus, we include power balance with shareholder structure (Balance) as a control variable.
Finally, we control for industry and year. Notably, the industry codes are referred from China’s two-digit industrial classification and code. Table 2 presents the description of the variables in detail.
3.3 Regression model
This study adopts the following models to examine our hypotheses:
Model 3 is used to test H1. Based on Model 3, we added industry concentration, financing constraints and their interaction terms with SCC to test H2 to H3 in Models 4 and 5.
4. Results
4.1 Descriptive statistics
Table 3 shows descriptive statistics. The maximum value of R&D is 0.102, indicating that R&D expenditure intensity among sample companies is relatively low. The mean value of SCC is 0.052. The maximum and minimum values of SCC are 0.354 and 0.001, respectively, showing a significant difference in corporate SCC.
4.2 Correlations between variables
We perform a correlation analysis on the study variables. Table 4 shows the Pearson correlation coefficients between all variables. The coefficient between SCC and R&D investment is significantly negative, which initially validates the main hypothesis of this study. Most other correlation coefficients are low. We also check the variance inflation factor (VIF) values for each regression, with a maximum VIF of 5.33, which is significantly less than 10. This implies that no serious multicollinearity problem exists in this study’s regression analysis.
4.3 Baseline regression results and analysis
According to Cameron and Trivedi (2009), when using panel data, we can use either the fixed effects model or the random effects model regression. We perform Hausman specification test to determine the best regression method for our panel data analysis. Based on the Hausman test results, we finally apply the fixed effects model. Table 5 presents the regression results. H1 predicts that SCC inhibits R&D investment of companies. Column 1 includes SCC and all control variables to test H1. The regression coefficient of SCC is significantly at the 1% level, suggesting that high SCC can significantly discourage companies from investing in R&D activities. H1 is supported. The coefficient estimate of SCC suggests that ceteris paribus, SCC is associated, on average, with a decrease of –0.008 in R&D investment. Column 2 adds HHI and the interaction between SCC and HHI to test H2. The regression coefficient of SCC × HHI is 0.075 and is significant at the 5% level, thus supporting H2. Column 3 reports the moderating effect of financing constraints on the relationship between SCC and R&D investment. The regression coefficient of SCC×WW is –0.062 and is significant at the 5% level, suggesting that the negative impact of SCC on R&D investment is strengthened in the case of higher financing constraints. The result supports H3.
Figures 2 and 3 illustrate the regulatory effect diagrams to demonstrate the regulatory effects better. Figure 2 shows that when the industry concentration is low, the relationship between SCC and R&D investment is negative and the slope is steep. Conversely, when industry concentration is high, this relationship becomes less negative and the slope is flatter. The results show that industry concentration weakens the negative role of SCC in inhibiting corporate R&D investment, which is consistent with the theoretical expectations. Figure 3 shows that when the financing constraints are low, the relationship between SCC and R&D investment is negative and the slope is flat. Conversely, when financing constraints are high, this relationship becomes more negative and the slope is steeper. The results show that financing constraints strengthen the negative role of SCC in inhibiting corporate R&D investment, which is consistent with the theoretical expectations.
The empirical analysis framework is shown in Figure 4.
4.4 Robustness checks
4.4.1 Alternative measure of independent variable.
We re-measure SCC using the average of the percentage of sales revenue to the top five customers and the percentage of purchases from the top five suppliers for robustness testing to conduct the regression analysis. Table 6 presents the regression analysis results of robustness tests. Column 4 shows that SCC has a negative impact on R&D investment (β = −0.007, p < 0.01). Moreover, Column 5 shows that the industry concentration weakens the negative effect of SCC on R&D investment (β = 0.032, p < 0.05). Column 6 shows that financing constraints strengthen the negative effect of SCC on R&D investment (β = −0.025, p < 0.01). The above results demonstrate the robustness of this study’s main empirical findings.
4.4.2 Alternative measure of dependent variable.
We use the R&D expenditure to operating revenue ratio to measure R&D investment intensity, referencing Zhao et al. (2023). Table 7 shows the shows the results of the robustness test. After the above tests, the conclusions of this study do not change substantially.
4.5 Endogeneity tests
4.5.1 Two-stage Heckman method.
The decision to disclose R&D investment costs is a corporate choice that may lead to possible sample selection bias in our study (Li et al., 2022). We apply the Heckman two-stage method for re-testing to alleviate the potential sample selection bias (Heckman, 1979). In the first stage, we use the Probit model to calculate the inverse Mills ratio (IMR). We further include IMR as a control variable into the Heckman second-stage model for regression analysis. Table 8 represents the results, and these results indicate that while the coefficients of IMR are significant, our findings are robust.
4.5.2 Propensity score matching.
Propensity score matching (PSM) and Heckman two-step methods are not interchangeable because they attempt to solve different endogeneity problems (Tucker, 2010). We further use PSM to correct the bias related to self-selection problem. First, data matching and the balance test are carried out. The companies are divided into two groups. Specifically, we treat companies with SCC greater than the sample median as the treatment group. The rest of the sample companies are treated as the control group. We match the treatment and control groups with the above control variables acting as covariates and using the radius matching method. Subsequently, we perform a balance test to test whether there is a significant difference between the treatment and the control groups after matching. The balance test results are shown in Table 9. The absolute value of the standard deviation after matching is less than 10%, indicating a good matching result. We further re-implement the regression analysis based on the samples after PSM. Table 10 represents the regression results. The results demonstrate that the main empirical results of this study are robust.
5. Discussion
This study explores the impact of SCC on corporate R&D investment. We discuss our main findings in the following sections.
Although SCC is regarded as an important structural feature in SC relationship and is widely focused on its impacts on various aspects of a company, there is limited research on the relationship between SCC and corporate R&D behavior after reviewing previous studies. We reveal the negative impact of SCC on corporate R&D investment from the RDT perspective. This result is due to an increase in bargaining power of suppliers and customers, which reduces profitability and risk-taking capacity of focal companies (Cao et al., 2021; Gu et al., 2022), thus investing less in R&D activities. Additionally, companies with high SCC tend to focus more on these key partners, which limits companies’ search breadth and access to resources and information (Zhong et al., 2021), thus reducing new product development. These findings echo the view that concentration implies high dependence of focal company on its SC, which fosters investment of the focal company (Hui, 2023).
In addition, we further examine whether contingent factors (industry concentration and financing constraints) could affect the impact of SCC. Our study finds that industry concentration weakens the negative impact of SCC on corporate R&D investment. Uneven industry markets generate more complex dependence relationship (Cheng and Nault, 2012). Industry concentration reflects the intensity of competition within an industry (Ali et al., 2009). Companies operating in low industry concentration environment face greater power from suppliers and customers as well as greater operational risk because of larger numbers of competitors in a limited market, leading them to prefer conservative investment strategies. In contrast, high industry concentration implies that the focal company has a degree of dominance. Thus, when companies with high SCC operate in a more concentrated industry market, their disadvantage in reducing R&D investment is weakened. We indirectly confirm the view that companies in more concentrated industries are less risky and thus tend to innovate (Gallagher et al., 2015). In addition, as R&D investment requires significant financial support, financing environment may have a contingent effect. Our study finds that financing constraints strengthen the negative effect of SCC on corporate R&D investment. This is because increased financing constraints further limit available funds for companies. At this point, companies may prioritize maintaining normal SC operations while further reducing R&D investment. Our study thus complements the previous view that financing constraints are detrimental to corporate R&D investment.
6. Conclusions
Based on Chinese listed companies, we empirically investigate the impact of SCC on corporate R&D investment and further determine boundary conditions of this impact. Guided by the RDT, we find the negative of SCC on R&D investment. Besides, industry concentration mitigates the impact, but financing constraints strengthen it.
Our study contributes to the literature in several ways. First, it contributes to the R&D literature by focusing on SC relationship structure. For example, Huang et al. (2022) explained the impact of SC structure on R&D investment in terms of both network power and network cohesion. Although they do not consider the linkage strength factor, they emphasize the necessity to explore the impact of SC relationship on R&D investment from the structural embeddedness perspective. As previous studies have argued that resource dependence leads to power imbalance problems, we demonstrate that firms with high SCC are reluctant to invest in R&D activities for some reasons (e.g. low bargaining power). We complement previous research from SC structural feature. Second, we add to the SC literature by emphasizing the impact of overall SCC on firms’ behavior. Previous research on SCC focused on one side of SC relationship. For example, a concentrated customer base affects firms’ financial position and performance (Han et al., 2023; Xin et al., 2022). However, suppliers and customers together form a complete SC relationship for midstream organizations (Hui, 2023), so it is necessary for firms to consider both supply and demand side factors. In view of this, we focus on the overall concentration of both upstream and downstream components, which allows for a comprehensive measure of all instances of a firm’s SC concentration. Third, we also consider the moderating roles of industry concentration and financing constraints in the relationship between SCC and R&D investment. Although exploring the consequences of SCC extends the research scope of RDT, considering environmental factors could more systematically understand the consequences of SC linkage strength. We introduce industry concentration because industry environment may affect discourse and dependence (Fu, 2023; Han et al., 2023). In addition, financing constraints are taken into account as external financing situation affects funding constraints of firms (Calabrese et al., 2023). We reveal that industry concentration (financing constraints) may weaken (strengthen) the impact of SCC concentration on R&D investment. These results suggest that SC dependence’s consequences need to be considered in a broader context.
Our findings provide the following management insights for companies. R&D is critical for companies to grow and compete. With deeper specialization and division of labor, many firms’ SCC is increasing. However, our main result finds that SCC leads firms to reduce their R&D investment. In light of this, firms should strengthen SC management. On the one hand, firms should raise awareness of SC risks, be alert to SC and capital chain breaks that may be caused by a high degree of SCC and prevent asymmetric dependence from becoming passive. On the other hand, firms should strive to enhance their voice and improve the autonomy and flexibility of SC. For example, firms can strengthen coordination and mutual trust between upstream and downstream partners and adjust dependency structure by forming equal, interdependent and risk-sharing R&D alliances to enhance core competitiveness. In addition, firms can also seek diversified SC partners and build a stable and reliable SC system to reduce dependence on a few partners. Second, from the perspective of organizational–environmental coupling, firms need to weigh the level of SCC in relation to environment and own situation. Specifically, for firms in concentrated industries, they should emphasize advantages of market position to balance disadvantages of SCC. And firms with financing constraints should shape broader SC network relationships to avoid double binds.
Like any other research, this study has some limitations. First, our findings are based on Chinese-listed companies. Whether the results can be extended to Chinese small- and medium-sized enterprises or companies in other countries deserves a future investigation. Moreover, beyond our focus on the boundary effects of industry concentration and financing constraints, future research could further explore situational factors that may affect the impact of SCC on corporate R&D behavior. Finally, our research methodology is singular. Specifically, due to limitations on research length, we only briefly clarify with cases. Future research could combine qualitative and quantitative methods to enhance the comprehensiveness and credibility of main findings.
Figures
A review of literature
| Research stream | Research content | Exemplar studies | Research gaps |
|---|---|---|---|
| Inter-firm dependence | Interlocking firms | Lin et al. (2023) | Emphasizing cooperative dependence while ignoring transactional dependence |
| Firms in the innovation ecosystems | Mei et al. (2019) | ||
| R&D consortium | Yang (2022) | ||
| SC structure | Supply side | Feng et al. (2023) Cheng et al. (2022) Dong et al. (2022) |
Separately focusing on one side of SC |
| Demand side | Fu (2023) Cao et al. (2023) Zhu et al. (2022) |
Source: Table created by authors
Variable definition
| Variable name | Variable symbol | Variable description |
|---|---|---|
| R&D Investment | R&D | The ratio of R&D investment of total asset |
| SC Concentration | SCC | The average of purchases from TOP5 suppliers to total purchases and sales to TOP5 customers to total sales |
| Leverage | Lev | The proportion of total corporate liabilities to total corporate assets |
| Firm size | Size | The natural logarithm of the total asset at the end of the year |
| Firm age | Age | The logarithm of the current year minus the year the corporation was founded plus one |
| Firm growth | Growth | The sales growth rate |
| Nature of ownership | SOE | If the enterprise is a state-owned enterprise, it is represented by 1, otherwise it is equal to 0 |
| Return on equity | ROE | The ratio of net profits divided by book value of equity |
| Board size | Board | The natural logarithm of the number of board members |
| Percentage of independent directors | Indep | The ratio of the number of independent directors divided by the number of directors |
| Dual role of chairman and general manage | Dual | If the chairman and the general manager are one and the same person, it is denoted by 1 and equals 0 otherwise |
| Shareholding checks and balances | Balance | The ratio of the sum of the shareholdings of the second to fifth largest shareholders to that of the first largest shareholder |
| Industry concentration | HHI | The sum of the squared operating revenues of each corporation in the industry as a percentage of total industry operating revenues |
| Financing constraints | WW | The WW index proposed by White and Wu |
Source: Table created by authors
Descriptive statistics
| Variable | Obs | Mean | Std. | Min. | Max. |
|---|---|---|---|---|---|
| R&D | 11,990 | 0.023 | 0.019 | 0.000 | 0.102 |
| SCC | 11,990 | 0.051 | 0.064 | 0.001 | 0.354 |
| HHI | 11,990 | 0.085 | 0.078 | 0.015 | 0.445 |
| WW | 11,990 | −1.003 | 0.061 | −1.171 | −0.865 |
| Lev | 11,990 | 0.396 | 0.194 | 0.053 | 0.859 |
| Size | 11,990 | 21.976 | 1.106 | 19.969 | 25.391 |
| Age | 11,990 | 2.802 | 0.342 | 1.792 | 3.466 |
| Growth | 11,990 | 0.184 | 0.388 | −0.486 | 2.343 |
| SOE | 11,990 | 0.229 | 0.420 | 0.000 | 1.000 |
| ROE | 11,990 | 0.059 | 0.128 | −0.659 | 0.334 |
| Board | 11,990 | 2.110 | 0.190 | 1.609 | 2.565 |
| Indep | 11,990 | 0.376 | 0.053 | 0.333 | 0.571 |
| Dual | 11,990 | 0.320 | 0.467 | 0.000 | 1.000 |
| Balance | 11,990 | 0.797 | 0.626 | 0.037 | 2.895 |
Source: Table created by authors
Correlation of all variables
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | VIF | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| R&D | 1.000 | ||||||||||||||
| SCC | −0.059 *** | 1.000 | 1.02 | ||||||||||||
| HHI | −0.234 *** | 0.083 *** | 1.000 | 1.03 | |||||||||||
| WW | 0.086 *** | 0.074 *** | −0.056 *** | 1.000 | 5.21 | ||||||||||
| Lev | −0.202 *** | −0.009 | 0.097 *** | −0.292 *** | 1.000 | 1.57 | |||||||||
| Size | −0.205 *** | −0.057 *** | 0.093 *** | −0.828 *** | 0.505 *** | 1.000 | 5.33 | ||||||||
| Age | −0.057 *** | 0.003 | −0.007 | −0.083 *** | 0.116 *** | 0.159 *** | 1.000 | 1.06 | |||||||
| Growth | 0.023 ** | 0.019 ** | −0.022 ** | −0.305 *** | 0.014 | 0.058 *** | −0.040 *** | 1.000 | 1.32 | ||||||
| ROE | 0.095 *** | −0.038 *** | −0.031 *** | −0.356 *** | −0.213 *** | 0.090 *** | −0.033 *** | 0.274 *** | 1.000 | 1.42 | |||||
| Board | −0.075 *** | −0.018 * | 0.071 *** | −0.191 *** | 0.123 *** | 0.220 *** | 0.024 *** | −0.008 | 0.053 *** | 1.000 | 1.71 | ||||
| Indep | 0.035 *** | −0.000 | −0.042 *** | 0.041 *** | −0.013 | −0.027 *** | 0.010 | −0.014 | −0.030 *** | −0.591 *** | 1.000 | 1.58 | |||
| Dual | 0.090 *** | −0.025 *** | −0.056 *** | 0.099 *** | −0.096 *** | −0.147 *** | −0.073 *** | 0.033 *** | 0.012 | −0.169 *** | 0.130 *** | 1.000 | 1.09 | ||
| Balance | 0.096 *** | −0.006 | −0.037 *** | 0.066 *** | −0.140 *** | −0.101 *** | −0.006 | 0.073 *** | −0.019 ** | 0.024 *** | −0.035 *** | 0.021 ** | 1.000 | 1.08 | |
| SOE | −0.121 *** | 0.061*** | 0.083*** | −0.217 *** | 0.274*** | 0.332*** | 0.191*** | −0.080 *** | −0.026 *** | 0.243*** | −0.079 *** | −0.250 *** | −0.223 *** | 1.000 | 1.33 |
The coefficients with *, ** and ***represent significance at 10, 5 and 1% level
Source: Table created by authors
The result of SC concentration on R&D investment
| Model 1 | Model 2 | Model 3 | |
|---|---|---|---|
| R&D | R&D | R&D | |
| Lev | 0.0002 (0.23) | 0.0004 (0.39) | 0.0003 (0.31) |
| Size | −0.004*** (−17.07) | −0.004*** (−17.08) | −0.005*** (−14.74) |
| Age | 0.001 (0.51) | 0.001 (0.50) | 0.001 (0.50) |
| Growth | 0.001** (2.08) | 0.001** (2.16) | 0.0004 (1.42) |
| ROE | −0.001* (−1.67) | −0.001 (−1.63) | −0.002* (−1.77) |
| Board | 0.003*** (2.74) | 0.003*** (2.70) | 0.003*** (2.74) |
| Indep | −0.002 (−0.57) | −0.002 (−0.62) | −0.002 (−0.60) |
| Dual | 0.0002 (0.69) | 0.0002 (0.66) | 0.0002 (0.67) |
| Balance | 0.001** (2.46) | 0.001** (2.53) | 0.001** (2.44) |
| SOE | 0.001 (0.77) | 0.001 (0.74) | 0.001 (0.76) |
| SCC | −0.008*** (−3.46) | −0.015*** (−4.05) | −0.070** (−2.26) |
| HHI | 0.006 (1.51) | ||
| SCC × HHI | 0.075** (2.30) | ||
| WW | 0.001 (0.16) | ||
| SCC × WW | −0.062** (−2.00) | ||
| _cons | 0.101*** (9.86) | 0.102*** (9.92) | 0.105*** (10.06) |
| Year | Yes | Yes | Yes |
| Industry | Yes | Yes | Yes |
| N | 11,990 | 11,990 | 11,990 |
| R2 | 0.103 | 0.104 | 0.103 |
t-statistics are reported in parentheses. The coefficients with *, ** and ***represent significance at 10, 5 and 1% levels, respectively
Source: Table created by authors
Results of robustness test: alternative measure of independent variable
| Model 4 | Model 5 | Model 6 | |
|---|---|---|---|
| R&D | R&D | R&D | |
| Lev | 0.0002 (0.25) | 0.0004 (0.42) | 0.0004 (0.38) |
| Size | −0.005*** (−17.67) | −0.005*** (−17.71) | −0.005*** (−15.30) |
| Age | 0.001 (0.39) | 0.001 (0.38) | 0.001 (0.38) |
| Growth | 0.001** (2.18) | 0.001** (2.29) | 0.0004 (1.43) |
| ROE | −0.001 (−1.56) | −0.001 (−1.50) | −0.001 (−1.62) |
| Board | 0.003*** (2.71) | 0.003*** (2.69) | 0.003*** (2.71) |
| Indep | −0.002 (−0.62) | −0.002 (−0.64) | −0.002 (−0.60) |
| Dual | 0.0002 (0.73) | 0.0002 (0.71) | 0.0002 (0.76) |
| Balance | 0.001** (2.52) | 0.001*** (2.60) | 0.001** (2.48) |
| SOE | 0.001 (0.84) | 0.001 (0.85) | 0.001 (0.82) |
| SCC | −0.007*** (−6.33) | −0.010*** (−6.25) | −0.032*** (−2.69) |
| HHI | −0.001 (−0.09) | −0.001 (−0.09) | |
| SCC × HHI | 0.032** (2.43) | 0.032** (2.42) | |
| WW | 0.006 (1.12) | ||
| SCC × WW | −0.025** (−2.11) | ||
| _cons | 0.108*** (10.45) | 0.109*** (10.57) | 0.117*** (10.48) |
| Year | Yes | Yes | Yes |
| Industry | Yes | Yes | Yes |
| N | 11,990 | 11,990 | 11,990 |
| R2 | 0.105 | 0.106 | 0.106 |
t-statistics are reported in parentheses. The coefficients with *, ** and ***represent significance at 10, 5 and 1% levels, respectively
Source: Table created by authors
Results of robustness test: alternative measure of dependent variable
| Model 7 | Model 8 | Model 9 | |
|---|---|---|---|
| R&D | R&D | R&D | |
| Lev | −0.019*** (−8.42) | −0.019*** (−8.32) | −0.020*** (−8.84) |
| Size | 0.003*** (4.52) | 0.003*** (4.53) | 0.004*** (6.13) |
| Age | −0.005 (−1.34) | −0.005 (−1.35) | −0.006 (−1.48) |
| Growth | −0.008*** (−14.58) | −0.008*** (−14.52) | −0.006*** (−10.57) |
| ROE | −0.026*** (−13.97) | −0.026*** (−13.95) | −0.024*** (−12.40) |
| Board | −0.0002 (−0.09) | −0.0003 (−0.11) | −0.0003 (−0.12) |
| Indep | −0.010 (−1.44) | −0.011 (−1.47) | −0.012 (−1.61) |
| Dual | 0.001 (0.71) | 0.001 (0.70) | 0.0004 (0.66) |
| Balance | 0.002*** (3.42) | 0.002*** (3.44) | 0.002*** (3.34) |
| SOE | 0.00004 (0.02) | 0.00001 0.00 | −0.00004 (−0.02) |
| SCC | −0.042*** (−7.65) | −0.055*** (−6.57) | −0.209*** (−2.93) |
| HHI | 0.0040 (0.43) | −0.001 (−0.09) | |
| SCC × HHI | 0.154** (2.04) | 0.032** (2.42) | |
| WW | 0.044*** (4.89) | ||
| SCC × WW | −0.167** (−2.35) | ||
| _cons | 0.010 (0.40) | 0.010 (0.43) | 0.020 (0.81) |
| Year | Yes | Yes | Yes |
| Industry | Yes | Yes | Yes |
| N | 11,990 | 11,990 | 11,990 |
| R2 | 0.134 | 0.135 | 0.137 |
t-statistics are reported in parentheses. The coefficients with *, ** and ***represent significance at 10, 5 and 1% levels, respectively
Source: Table created by authors
Results of the two-stage Heckman method
| Model 10 | Model 11 | Model 12 | |
|---|---|---|---|
| R&D | R&D | R&D | |
| Lev | −0.001 (−0.62) | 0.000 (−0.47) | −0.001 (−0.55) |
| Size | −0.005*** (−14.08) | −0.005*** (−14.06) | −0.005*** (−13.30) |
| Age | −0.0004 (−0.25) | −0.0004 (−0.26) | −0.0004 (−0.25) |
| Growth | −0.0002 (−0.55) | −0.0001 (−0.45) | −0.0003 (−0.74) |
| ROE | 0.007** (2.41) | 0.007** (2.35) | 0.006** (2.32) |
| Board | 0.003*** (3.13) | 0.003*** (3.11) | 0.003*** (3.12) |
| Indep | −0.003 (−0.83) | −0.003 (−0.85) | −0.003 (−0.86) |
| Dual | 0.001* (1.72) | 0.001* (1.67) | 0.001* (1.70) |
| Balance | 0.001** (2.29) | 0.001** (2.36) | 0.001** (2.26) |
| SOE | 0.001 (1.01) | 0.001 (0.99) | 0.001 (1.00) |
| IMR | 0.007*** (3.15) | 0.007*** (3.08) | 0.007*** (3.12) |
| SCC | −0.010*** (−4.15) | −0.015*** (−4.15) | −0.079** (−2.53) |
| HHI | 0.007 (1.63) | ||
| SCC × HHI | 0.061* (1.83) | ||
| WW | 0.001 (0.37) | ||
| SCC × WW | −0.070** (−2.21) | ||
| _cons | 0.123*** (12.60) | 0.122*** (12.51) | 0.127*** (12.77) |
| Year | Yes | Yes | Yes |
| Industry | Yes | Yes | Yes |
| N | 11,803 | 11,803 | 11,803 |
| R2 | 0.103 | 0.104 | 0.103 |
t-statistics are reported in parentheses. The coefficients with *, ** and ***represent significance at 10, 5 and 1% levels, respectively
Source: Table created by authors
Equilibrium test results
| Unmatched | Mean | %reduc | t-test | |||
|---|---|---|---|---|---|---|
| Variable | Matched | Treated | Control | %bias | t | p > t |
| Lev | U | 0.387 | 0.405 | −9.1 | −4.97 | 0.000 |
| M | 0.387 | 0.388 | −0.6 | −0.33 | 0.740 | |
| Size | U | 21.825 | 22.121 | −27 | −14.78 | 0.000 |
| M | 21.826 | 21.815 | 1.1 | 0.61 | 0.544 | |
| Age | U | 2.797 | 2.807 | −3 | −1.62 | 0.105 |
| M | 2.797 | 2.797 | 0 | 0.02 | 0.986 | |
| Growth | U | 0.195 | 0.172 | 5.8 | 3.18 | 0.001 |
| M | 0.194 | 0.202 | −2.1 | −1.05 | 0.295 | |
| ROE | U | 0.052 | 0.066 | −11.2 | −6.1 | 0.000 |
| M | 0.052 | 0.050 | 1.2 | 0.59 | 0.554 | |
| Board | U | 2.098 | 2.122 | −12.7 | −6.96 | 0.000 |
| M | 2.098 | 2.093 | 2.7 | 1.4 | 0.162 | |
| Indep | U | 0.376 | 0.375 | 2.5 | 1.36 | 0.173 |
| M | 0.376 | 0.377 | −1.2 | −0.64 | 0.522 | |
| Dual | U | 0.330 | 0.308 | 4.7 | 2.57 | 0.010 |
| M | 0.330 | 0.337 | −1.4 | −0.77 | 0.439 | |
| Balance | U | 0.787 | 0.808 | −3.4 | −1.85 | 0.064 |
| M | 0.787 | 0.788 | −0.1 | −0.08 | 0.935 | |
| SOE | U | 0.229 | 0.230 | −0.1 | −0.08 | 0.936 |
| M | 0.229 | 0.224 | 1 | 0.56 | 0.574 | |
Source: Table created by authors
Results of PSM test
| Model 13 | Model 14 | Model 15 | |
|---|---|---|---|
| Variable | R&D | R&D | R&D |
| Lev | 0.0003 (0.31) | 0.0005 (0.47) | 0.0004 (0.39) |
| Size | −0.004*** (−17.12) | −0.004*** (−17.14) | −0.005*** (−14.81) |
| Age | 0.001 (0.39) | 0.001 (0.39) | 0.001 (0.39) |
| Growth | 0.001** (2.16) | 0.001** (2.24) | 0.0004 (1.49) |
| ROE | −0.001 (−1.64) | −0.001 (−1.60) | −0.001* (−1.75) |
| Board | 0.003*** (2.75) | 0.003*** (2.71) | 0.003*** (2.74) |
| Indep | −0.002 (−0.51) | −0.002 (−0.55) | −0.002 (−0.54) |
| Dual | 0.0002 (0.69) | 0.0002 (0.66) | 0.0002 (0.67) |
| Balance | 0.001** (2.42) | 0.001** (2.49) | 0.001** (2.40) |
| SOE | 0.001 (0.77) | 0.001 (0.75) | 0.001 (0.77) |
| SCC | −0.008*** (−3.43) | −0.015*** (−4.03) | −0.071** (−2.28) |
| HHI | 0.006 (1.54) | ||
| SCC × HHI | 0.076** (2.31) | ||
| WW | 0.001 (0.15) | ||
| SCC × WW | −0.063** (−2.02) | ||
| _cons | 0.102*** (9.95) | 0.103*** (10.01) | 0.106*** (10.15) |
| Year | Yes | Yes | Yes |
| Industry | Yes | Yes | Yes |
| N | 11,929 | 11,929 | 11,929 |
| R2 | 0.101 | 0.102 | 0.101 |
t-statistics are reported in parentheses. The coefficients with *, ** and ***represent significance at 10, 5 and 1% levels, respectively
Source: Table created by authors
References
Aaboen, L., Lindelof, P., von Koch, C. and Lofsten, H. (2006), “Corporate governance and performance of small high-tech firms in Sweden”, Technovation, Vol. 26 No. 8, pp. 955-968.
Ahsan, T., Mirza, S.S., Gull, A.A. and Majeed, M.A. (2023), “How to deal with customer and supplier concentration to attain sustainable financial growth? The role of business strategy”, Business Strategy and the Environment, Vol. 32 No. 7, doi: 10.1002/bse.3383.
Ali, A., Klasa, S. and Yeung, E. (2009), “The limitations of industry concentration measures constructed with compustat data: implications for finance research”, Review of Financial Studies, Vol. 22 No. 10, pp. 3839-3871.
Bowen, R.M., DuCharme, L. and Shores, D. (1995), “Stakeholders' implicit claims and accounting method choice”, Journal of Accounting and Economics, Vol. 20 No. 3, pp. 255-295.
Calabrese, G.G., Falavigna, G. and Ippoliti, R. (2023), “Financial constraints, R&D investment and uncertainty: new evidence from the Italian automotive supply chain”, Technology Analysis and Strategic Management.
Cameron, A.C. and Trivedi, P.K. (2009), Microeconometrics Using Stata, Stata Press.
Campello, M. and Gao, J. (2017), “Customer concentration and loan contract terms”, Journal of Financial Economics, Vol. 123 No. 1, pp. 108-136.
Cao, Y., Dong, Y.Z., Ma, D. and Sun, L. (2021), “Customer concentration and corporate risk-taking”, Journal of Financial Stability, Vol. 54, p. 100890.
Cao, X.Y., Ni, J., Wang, F. and Xu, Y. (2023), “Does customer concentration affect corporate risk-taking? Evidence from China”, Finance Research Letters, Vol. 58, p. 104297.
Chen, M., Tang, X.L., Liu, H.F. and Gu, J.B. (2023), “The impact of supply chain concentration on integration and business performance”, International Journal of Production Economics, Vol. 257, p. 108781.
Cheng, Z. and Nault, B.R. (2012), “Relative industry concentration and customer-driven IT spillovers”, Information Systems Research, Vol. 23 No. 2, pp. 340-355.
Cheng, L.T.W., Poon, J.S.C., Tang, S.L. and Wang, J.W.J. (2022), “Does supplier concentration matter to investors during the COV1D-19 crisis: evidence from China?”, Financial Innovation, Vol. 8 No. 1, p. 85.
Christopher, M. and Gattorna, J. (2005), “Supply chain cost management and value-based pricing”, Industrial Marketing Management, Vol. 34 No. 2, pp. 115-121.
Christensen, J.F., Olesen, M.H. and Kjær, J.S. (2005), “The industrial dynamics of open innovation—evidence from the transformation of consumer electronics”, Research Policy, Vol. 34 No. 10, pp. 1533-1549.
Coad, A., Segarra, A. and Teruel, M. (2016), “Innovation and firm growth: does firm age play a role?”, Research Policy, Vol. 45 No. 2, pp. 387-400.
Czarnitzki, D. and Hottenrott, H. (2011), “R&D investment and financing constraints of small and medium-sized firms”, Small Business Economics, Vol. 36 No. 1, pp. 65-83.
Dhaliwal, D., Judd, J.S., Serfling, M. and Shaikh, S. (2016), “Customer concentration risk and the cost of equity capital”, Journal of Accounting and Economics, Vol. 61 No. 1, pp. 23-48.
Dong, C.T., Li, X. and Chang, X.Z. (2022), “Interdependence with suppliers in the innovation ecosystem: the effects of supplier concentration on firm innovation”, Chinese Management Studies, Vol. 16 No. 5, pp. 1145-1160.
Dowlatshahi, S. (1999), “Bargaining power in buyer-supplier relationships”, Production and Inventory Management Journal, Vol. 40 No. 1, pp. 27-35.
Feng, C.B., Khurram, M.U., Safdar, R., Mirza, S.S. and Iqbal, A. (2023), “Corporate strategic responses, supplier concentration and sustainable growth of Chinese listed firms”, Operations Management Research, Vol. 16 No. 3, pp. 1413-1427.
Fu, J.Y. (2023), “Customer concentration and corporate charitable donations: evidence from China”, Managerial and Decision Economics, Vol. 44 No. 1, pp. 545-561.
Gallagher, D.R., Ignatieva, K. and McCulloch, J. (2015), “Industry concentration, excess returns and innovation in Australia”, Accounting and Finance, Vol. 55 No. 2, pp. 443-466.
Gao, F.Y., Tao, Y.Q., Chen, D. and Cao, Y.Y. (2023), “Stringent financial regulation and corporate R&D investment: evidence from a quasi-natural experiment in China”, Finance Research Letters, Vol. 55, p. 103933.
Gomes, A. and Novaes, W. (2001), Sharing of Control as a Corporate Governance Mechanism, Penn Caress Working Papers.
Gu, J., Shi, X.Y., Wang, P.N. and Xu, X. (2022), “Examining the impact of upstream and downstream relationship stability and concentration on firms' financial performance”, Journal of Business Research, Vol. 141, pp. 229-242.
Han, Z.J., Yu, C.H., Li, Y.X., Shi, J.Y. and Liu, Y.Y. (2023), “Customer concentration and supplier financial performance: an inverted U-shaped relationship”, Industrial Marketing Management, Vol. 113, pp. 138-152.
Haushalter, D., Klasa, S. and Maxwell, W.F. (2007), “The influence of product market dynamics on a firm's cash holdings and hedging behavior”, Journal of Financial Economics, Vol. 84 No. 3, pp. 797-825.
Heckman, J.J. (1979), “Sample selection bias as a specification error”, Econometrica, Vol. 47 No. 1, pp. 153-161.
Hillman, A.J., Withers, M.C. and Collins, B.J. (2009), “Resource dependence theory: a review”, Journal of Management, Vol. 35 No. 6, pp. 1404-1427.
Himmelberg, C.P. and Petersen, B.C. (1994), “R&D and internal finance: a panel study of small firms in high-tech industries”, The Review of Economics and Statistics, Vol. 76 No. 1, p. 38.
Hofmann, E. (2010), “Linking corporate strategy and supply chain management”, International Journal of Physical Distribution and Logistics Management, Vol. 40 No. 4, pp. 256-276.
Hoskisson, R.E., Hitt, M.A., Johnson, R.A. and Grossman, W. (2002), “Conflicting voices: the effects of institutional ownership heterogeneity and internal governance on corporate innovation strategies”, Academy of Management Journal, Vol. 45 No. 4, pp. 697-716.
Huang, H.S., Zhang, J.P., Yan, J., Gong, Y. and Wang, L.K. (2022), “Supply chain network structure and firm's R&D investments: empirical evidence from Chinese manufacturing firms”, European Journal of Innovation Management, Vol. 27 No. 2, doi: 10.1108/EJIM-03-2022-0139.
Hui, Z.Y. (2023), “Supply-Chain concentration and inefficient investment”, Emerging Markets Finance and Trade, Vol. 59 No. 7, pp. 2129-2144.
Jones, C.I. and Williams, J.C. (1998), “Measuring the social return to R&D”, The Quarterly Journal of Economics, Vol. 113 No. 4, pp. 1119-1135.
Ketokivi, M. and Mahoney, J.T. (2020), “Transaction cost economics as a theory of supply chain efficiency”, Production and Operations Management, Vol. 29 No. 4, pp. 1011-1031.
Knott, A.M. (2012), “The Trillion-Dollar R&D fix”, Harvard Business Review, Vol. 90 No. 5, p. 76.
Koussis, N., Martzoukos, S.H. and Trigeorgis, L. (2007), “Real R&D options with time-to-learn and learning-by-doing”, Annals of Operations Research, Vol. 151 No. 1, pp. 29-55.
Krolikowski, M. and Yuan, X.J. (2017), “Friend or foe: customer-supplier relationships and innovation”, Journal of Business Research, Vol. 78, pp. 53-68.
Kwak, K. and Kim, N. (2020), “Concentrate or disperse? The relationship between major customer concentration and supplier profitability and the moderating role of insider ownership”, Journal of Business Research, Vol. 109, pp. 648-658.
Li, J. and Luo, Z. (2021), “Product market competition and cost stickiness: evidence from China”, Managerial and Decision Economics, Vol. 42 No. 7, pp. 1808-1821.
Li, X., Cheng, J., Wan, S. and Zhao, Z. (2022), “Does institutional fragility affect innovation investment?”, Chinese Management Studies, Vol. 16 No. 3, pp. 589-608.
Lin, H.E., Yu, A.Y., Stambaugh, J., Tsao, C.W., Wang, R.J.H. and Hsu, I.C. (2023), “Family CEO duality and research and development intensity in public family enterprises: temporality as a model boundary”, Journal of Business Research, Vol. 158, p. 113572.
Luo, P., Ngai, E.W.T. and Cheng, T.C.E. (2023), “Supply chain network structures and firm financial performance: the moderating role of international relations”, International Journal of Operations and Production Management, Vol. 44 No. 1, doi: 10.1108/IJOPM-07-2022-0434.
McKinley, W., Latham, S. and Braun, M. (2014), “Organizational decline and innovation: turnarounds and downward spirals”, Academy of Management Review, Vol. 39 No. 1, pp. 88-110.
Mei, L., Zhang, T. and Chen, J. (2019), “Exploring the effects of inter-firm linkages on SMEs' open innovation from an ecosystem perspective: an empirical study of Chinese manufacturing SMEs”, Technological Forecasting and Social Change, Vol. 144, pp. 118-128.
Menshawy, I.M., Basiruddin, R., Mohd-Zamil, N.A. and Hussainey, K. (2023), “Strive towards investment efficiency among Egyptian companies: do board characteristics and information asymmetry matter?”, International Journal of Finance and Economics, Vol. 28 No. 3, pp. 2382-2403.
Mithas, S., Tafti, A., Mitchell, W., University of Illinois At, U.-C., Duke, U., University Of, T. and University Of, M. (2013), “How a firm's competitive environment and digital strategic posture influence digital business strategy”, MIS Quarterly, Vol. 37 No. 2, pp. 511-536.
Muhammad, H., Migliori, S. and Consorti, A. (2022), “Corporate governance and R&D investment: does firm size matter?”, Technology Analysis and Strategic Management, Vol. 36 No. 3, doi: 10.1080/09537325.2022.2042508.
Nieto, M. and González-Álvarez, N. (2014), “Product innovation: testing the relative influence of industry, institutional context and firm factors”, Technology Analysis and Strategic Management, Vol. 26 No. 9, pp. 1023-1036.
Patatoukas, P.N. (2012), “Customer-base concentration: implications for firm performance and capital markets”, The Accounting Review, Vol. 87 No. 2, pp. 363-392.
Petersen, K.J., Handfield, R.B. and Ragatz, G.L. (2005), “Supplier integration into new product development: coordinating product, process and supply chain design”, Journal of Operations Management, Vol. 23 Nos 3/4, pp. 371-388.
Pfeffer, J. and Salancik, G.R. (1978), The External Control of Organizations: A Resource Dependence Perspective, Harper and Row, New York, NY.
Pham, T.A.T. (2018), “Industry concentration, firm efficiency and average stock returns: evidence from Australia”, Asia-Pacific Financial Markets, Vol. 25 No. 3, pp. 221-247.
Porter, M.E. (1992), “Capital disadvantage: America's failing capital investment system”, Harvard Business Review, Vol. 70 No. 5, pp. 65-82.
Qian, F.R. and Yuan, X.Y. (2023), “Ownership concentration and R&D investment: the moderating role of stock options”, Management Decision, Vol. 61 No. 11, doi: 10.1108/MD-07-2022-0885.
Ren, S., Sun, H. and Zhang, T. (2021), “Do environmental subsidies spur environmental innovation? Empirical evidence from Chinese listed firms”, Technological Forecasting and Social Change, Vol. 173, p. 121123.
Schumpeter, J.A. (1942), “Capitalism, socialism, and democracy”, American Economic Review, Vol. 3 No. 4, pp. 594-602.
Schwieterman, M., Goldsby, T. and Knemeyer, A.M. (2017), “Advocating C in supply chain research: a systematic literature review and research agenda”, Transportation Journal, Vol. 56 No. 4, pp. 429-476.
Shefer, D. and Frenkel, A. (2005), “R&D, firm size and innovation: an empirical analysis”, Technovation, Vol. 25 No. 1, pp. 25-32.
Tishler, A. and Milstein, I. (2009), “R&D wars and the effects of innovation on the success and survivability of firms in oligopoly markets”, International Journal of Industrial Organization, Vol. 27 No. 4, pp. 519-531.
Tse, C.H., Yim, C.K.B., Yin, E., Wan, F. and Jiao, H. (2021), “R&D activities and innovation performance of MNE subsidiaries: the moderating effects of government support and entry mode”, Technological Forecasting and Social Change, Vol. 166, p. 120603.
Tucker, J.W. (2010), “Selection bias and econometric remedies in accounting and finance research”, Journal of Accounting Literature, Vol. 29, pp. 31-57.
Wang, L., Liu, Z.Y., Wang, Y.X., Ai, W. and An, Y.B. (2023), “Executive compensation incentives and corporate R&D investments: an analysis based on the moderating effect of managerial power”, Emerging Markets Finance and Trade, Vol. 59 No. 8, pp. 2664-2693.
Whited, T.M. and Wu, G.J. (2006), “Financial constraints risk”, Review of Financial Studies, Vol. 19 No. 2, pp. 531-559.
Williamson, O.E. (1988), “Corporate finance and corporate governance”, The Journal of Finance (New York, Vol. 43 No. 3, pp. 567-591.
Xiao, G. (2013), “Legal shareholder protection and corporate R&D investment”, Journal of Corporate Finance, Vol. 23, pp. 240-266.
Xin, Y.Y., Zeng, X. and Luo, Z.Y. (2022), “Customers' annual report tone and suppliers' innovation decisions: evidence from China”, European Journal of Innovation Management, Vol. 26 No. 6, doi: 10.1108/EJIM-12-2021-0585.
Xu, D., Zhou, K.Z. and Du, F. (2019), “Deviant versus aspirational risk taking: the effects of performance feedback on bribery expenditure and R&D intensity”, Academy of Management Journal, Vol. 62 No. 4, pp. 1226-1251.
Xu, L., Li, B., Ma, C. and Liu, J.Y. (2022), “Supply chain finance and firm diversification: evidence from China”, Australian Journal of Management, Vol. 48 No. 2, doi: 10.1177/03128962221075366.
Yang, X.T. (2022), “Coopetition for innovation in R&D consortia: moderating roles of size disparity and formal interaction”, Asia Pacific Journal of Management, Vol. 39 No. 1, pp. 79-102.
Yang, Z. (2017), “Customer concentration, relationship, and debt contracting”, Journal of Applied Accounting Research, Vol. 18 No. 2, pp. 185-207.
Yoo, S.H. and Seo, Y.W. (2017), “Effect of supply chain structure and power dynamics on R&D and market performances”, Journal of Business Economics and Management, Vol. 18 No. 4, pp. 801-801.
Yuan, R. and Wen, W. (2018), “Managerial foreign experience and corporate innovation”, Journal of Corporate Finance (Amsterdam, Netherlands), Vol. 48, pp. 752-770.
Zhang, X.D., Zou, M.F., Liu, W.M. and Zhang, Y.F. (2020), “Does a firm's supplier concentration affect its cash holding?”, Economic Modelling, Vol. 90, pp. 527-535.
Zhao, Y., Yang, X., Xin, D.Q., Zhou, W.C., Zhang, S.J. and Wang, L.Y. (2023), “Intergenerational power gap and R&D investment: evidence from China”, Journal of Innovation and Knowledge, Vol. 8 No. 2, p. 100359.
Zheng, S.Y., Zhang, Q.H. and Zhang, P.D. (2023), “Can customer concentration affect corporate ESG performance?”, Finance Research Letters, Vol. 58, p. 104432.
Zhong, W., Ma, Z., Tong, T.W., Zhang, Y. and Xie, L. (2021), “Customer concentration, executive attention, and firm search behavior”, Academy of Management Journal, Vol. 64 No. 5, pp. 1625-1647.
Zhu, L., Chen, W.Y. and Zheng, Q.W. (2022), “Competitive or collaborative? Customer concentration and real earnings management”, International Journal of Emerging Markets, doi: 10.1108/IJOEM-09-2021-1493.
Zu, Y.F. and Zhang, R.N. (2023), “Supplier change, market competition and enterprise innovation”, European Journal of Innovation Management, Vol. 26 No. 4, pp. 1034-1053.
Acknowledgements
Funding: This research was supported by the National Natural Science Foundation of China [Grant number 72174073].