The Nexus between Foreign Competition and Buying Innovation: Evidence from China’s High-Technology Industry

Abstract

This study aims to investigate how international competition impacts innovation activities in the high-technology industry in China. A panel data analysis was conducted using a representative sample of high-tech industries in China from 2010 to 2017. Econometric methods were used to identify patterns and trends in the data, and quantile regression was utilized to explore the delicate connection between international competition and innovation efforts. The statistical analysis indicates that the effect of international competition on innovation activities differs through dependent quantiles of the innovation range; this effect was neglected by a standard linear regression model. The study found a U-shaped connection between foreign competition and innovation, except for at the quantile (Q = 0.01), which was negative. Foreign competition was found to be a critical factor influencing the strength of innovation activities in the high-tech industry in China. This research suggests that the extent of foreign competition has a bearing on the industry’s capacity to lead in innovation. This study is unique in that it addresses the influence of international competition on industry-level innovation accomplishments in a big rising country, such as China. The study also highlights the importance of evaluating the quantile effect of the variable on innovative activities, which was more informative than estimating the mean effect. The study’s limitation lies in the reliance on secondary data sources, which may not be as comprehensive as primary data. The research’s implications suggest that policymakers should pay attention to the impact of foreign competition on innovation activities and implement policies that foster innovation in the high-tech industry in China.

1. Introduction

In today’s globalized economy, the role of foreign competition in driving innovation has garnered significant attention. This research paper examines the relationship between foreign competition and innovation in China’s high-technology industry, one of the world’s fastest-growing sectors. With China emerging as a major player in global innovation, understanding the dynamics between foreign competition and domestic innovation becomes imperative. By delving into the specific context of China’s high-technology industry, this study aims to shed light on the intricate interplay between foreign competition and indigenous innovation. The rapid growth of China’s high-technology industry has propelled the nation to the forefront of global innovation. Over the past few decades, China has transitioned from being an imitator of foreign technologies to a key player by generating its own cutting-edge innovations. This transformation has been facilitated by the country’s increasing integration into the global economy, with multinational corporations and foreign competitors playing a crucial role in shaping China’s innovation landscape. Foreign competition has long been recognized as a key driver of innovation in various industries worldwide. The presence of foreign firms stimulates domestic firms to enhance their technological capabilities, upgrade their products and processes, and ultimately, foster innovation. However, the relationship between foreign competition and innovation is complex and multifaceted, and its nuances may vary across different industries and contexts. This research paper focuses on China’s high-technology industry, characterized by its rapid growth, dynamic nature, and intense competition. China’s high-technology sector encompasses a wide range of industries, including telecommunications, information technology, biotechnology, aerospace, and advanced manufacturing. These industries have been pivotal in shaping China’s economic growth and technological advancements.

Our study makes several significant contributions to the current field of theory. Firstly, we contribute to the empirical literature by providing new findings using a dynamic process approach to innovation through panel data analysis, which is a departure from the traditional cross-sectional approach used in previous studies. Additionally, we used quantile regression analysis to examine the impact of foreign competition on purchasing innovation in China’s high-tech industry. This approach provides a more detailed analysis than linear regression and gives us insights into the interplay between foreign competition and purchasing innovation in industries. Our findings also shed light on the relationship between the strength of international competition and buying innovation efforts in the context of Chinese high-tech sectors, contributing to a better understanding of the impact of foreign-invested companies in emerging economies. Finally, we focus on how international competition affects the strength of buying innovation in these sectors, in contrast to existing research that primarily concentrates on the link between innovation and performance. As these industries are crucial for emerging nations, understanding their innovation efforts in the context of overseas competition is of utmost importance.

The authors’ use of quantile regression in their research is a noteworthy contribution to the current literature on the topic. This technique provides a more nuanced understanding of the relationship between international competition and innovation across various levels of the innovation spectrum. This approach can offer insights that may be overlooked by traditional linear regression models that solely estimate the average impact of variables on outcomes. The findings of this study underscore the significance of considering the role of international competition in shaping innovation activities in China’s high-tech industry. Policymakers should consider this factor when designing innovation policies. The study concludes that there is a U-shaped relationship between foreign competition and innovation, where moderate levels of foreign competition can stimulate innovation, but excessive levels can impede it. These results support the conclusions of recent research on the subject [1,2,3]. Moreover, excessive foreign competition can lead to adverse outcomes, potentially due to the substantial expenses related to innovation and the intense competitive environment that businesses confront. The relationship between international competition and innovation in emerging economies has been the subject of numerous studies, but a consensus has yet to be reached on the exact nature of this relationship. While some research suggests a U-shaped, nonlinear relationship between innovation and competition, others propose an inverted U-shaped link. Additionally, the majority of previous research has relied on mean regression analysis to explore the correlation between foreign competition and innovation, which leaves gaps in our understanding of the relationship at different stages of innovation. Furthermore, the impact of foreign competition on both buying and making innovation activities in emerging economies, which are crucial to reducing technology dependence and climbing the technology ladder, has not been thoroughly explored in previous studies. Thus, more comprehensive investigations are necessary to examine the effects of foreign competition on innovation in emerging economies, especially at the industry level, to better comprehend how industry-specific characteristics affect innovation behavior.

To fill these gaps in the literature, this study aims to use quantile regression analysis to examine the relationship between international competition and innovation in China’s high-technology industries at the industry level. This approach provides more detailed insights into the contingent situation and the impact of foreign competition on buying innovation across different stages of innovation. Additionally, this study contributes to a better understanding of how foreign competitiveness acts as a catalyst for top businesses to invest in research to improve their products and processes in developing countries such as China. These insights are valuable for policymakers and managers in China’s high-tech industries, emphasizing the importance of international competition in driving innovation and the need for continuous investment in innovation to achieve long-term success The remaining research is structured as follows. The second section is the literature review, addressing the correlation between international competition and innovation. The quantile regression model, variable measurements, and data collection are all explained in the third part. The fourth portion displays empirical results and discussion, and the last section draws critical conclusions.

2. Literature Review

Competition and innovation play vital roles in driving economic growth and development. In recent times, scholars have shown increased interest in investigating the relationship between foreign competition and innovation. This is primarily due to the impact of globalization and liberalized international trade policies, which have heightened cross-border competition and are believed to significantly influence innovation outcomes in domestic firms. Porter’s five forces framework consists of five key elements that collectively shape the competitive landscape of an industry. These elements are the threat of new entrants, bargaining power of buyers, bargaining power of suppliers, threat of substitute products or services, and intensity of competitive rivalry. By analyzing these five forces, organizations can gain insights into the overall attractiveness and profit potential of an industry. The framework helps identify competitive threats, assess the power dynamics among industry participants, and inform strategic decision-making. Michael Porter et al. in [4], discuss how Porter’s five forces framework has been widely used as a tool for industry analysis and strategic planning. Understanding and applying these forces can assist researchers in analyzing the competitive dynamics of specific industries and exploring strategic implications in their literature reviews. This literature review examines several studies that investigated the impact of foreign competition on innovation in different contexts, using Porter’s five forces framework as a lens to interpret the findings.

Firstly, increased foreign competition stimulates innovation among incumbent firms, particularly in industries characterized by high technological dynamism; when incumbent firms face increased foreign competition, it tends to foster innovation, especially in industries characterized by rapid technological advancements. One of the early studies in this field conducted by Aghion et al. in [5], provided empirical evidence supporting this idea. Furthermore, Cohen et al. in [6], introduced the concept of absorptive capacity, which pertains to a firm’s ability to acquire and effectively utilize external knowledge, including knowledge gained from foreign competition, as a means to stimulate innovation. Additionally, Keller et al. in [7], explored the diffusion of technology across countries and emphasized the role of international competition in facilitating the spread of innovation. Buccieri et al. in [8], discussed that foreign competition can increase innovation, particularly in firms that are more technologically advanced. It is important to note that the impact of foreign competition on innovation is not consistent among all companies and sectors. Ahn et al. in [9], found that foreign competition can stimulate innovation, but that the effect is not uniform across all firms. Specifically, they found that firms with a higher level of absorptive capacity and technological capabilities are better able to take advantage of foreign competition to spur innovation. Additionally, internationalization provided firms with access to new knowledge and ideas, further fueling innovation. Li et al. in [10], found that foreign competition stimulates innovation, particularly in firms that are more R&D intensive. Foreign competition’s impact on innovation is not constant, as it is contingent on the level of technological advancement in the industry. Research studies conducted by Yang et al. in [11], Wang et al. in [12], and Choi et al. in [13] concluded that foreign competition spurs innovation. The studies found that firms with greater absorptive capacity and advanced technology are more likely to benefit from foreign competition’s simulative effect on innovation. Additionally, firms with high levels of internationalization, vertical integration, R&D intensity, and export orientation are likely to benefit from foreign competition’s impact on innovation. The studies also suggest that foreign competition’s effect on innovation is more significant for firms that face intense competition from foreign companies or that are financially constrained. This perspective is in line with Porter’s framework, which asserts that the presence of new entrants due to foreign competition exerts pressure on firms to invest in new products, processes, and technologies in order to maintain their competitiveness [4]. The literature supports the notion that heightened foreign competition acts as a catalyst for innovation among incumbent firms. The threat posed by new entrants from foreign markets compels firms to embrace innovation as a strategic response. Additionally, the concept of absorptive capacity highlights the importance of firms’ ability to assimilate external knowledge, including that gained through foreign competition, in driving innovative activities. The diffusion of technology across countries further underscores the significance of international competition in promoting innovation. Collectively, this literature emphasizes the positive impact of increased foreign competition on stimulating innovation within industries characterized by rapid technological change.

Bargaining power and its influence on firms’ response to foreign competition is a significant aspect; Cusolito et al. in [14], conducted a study and revealed that firms with higher markups demonstrate a lesser response in terms of innovation when faced with foreign competition. On the other hand, firms operating closer to the technological frontier tend to respond more actively. The influence of bargaining power and competitive forces on firms’ responses to foreign competition is further explored by Goldberg et al. in [15], their study on market integration and pricing behavior. This research shed light on how firms adjust their pricing strategies in response to changes in market conditions, taking into account the impact of bargaining power. Additionally, Santos-Vijande et al. in [16], examined the relationship between bargaining power and firms’ strategic flexibility, which holds relevance in the context of responding to foreign competition. This can be interpreted within the framework provided by Porter, who emphasized the role of bargaining power of buyers as a competitive force. Firms with higher markups often possess stronger bargaining power, which can reduce their incentive to innovate as they face less pressure. Conversely, firms operating closer to the technological frontier may experience greater buyer power, motivating them to invest in innovation to maintain their competitive edge [4]. These studies emphasize the importance of considering bargaining power dynamics when analyzing how firms respond to foreign competition. Higher markups provide firms with a stronger position in negotiations, potentially reducing their motivation to innovate. However, firms operating closer to the technological frontier face increased buyer power, which drives them to invest in innovation as a means of maintaining their competitiveness. These insights contribute to a deeper understanding of the strategic decisions made by firms in response to foreign competition and highlight the multidimensional nature of the bargaining power phenomenon.

Competitive rivalry and its impact on innovation outcomes is another significant aspect. Kose et al. in [17], conducted a study focusing on the SME sector, examining the influence of both informal and foreign competition on innovation. Their study indicated that informal competition has a negative effect on innovation, whereas foreign competition has a positive impact. The influence of competitive rivalry on innovation outcomes is further explored by Griffith et al. in [18], their analysis of innovation and productivity across European countries. Their research provided insights into the relationship between competitive rivalry and innovation, shedding light on how different levels of competition affect firms’ innovation strategies and subsequent productivity. Additionally, Lu et al., in [19], explored the impact of formal and informal external collaboration on the innovation performance of small- and medium-sized enterprises (SMEs) in China. Through empirical analysis of a sample of Chinese SMEs, the study found that both formal and informal collaboration have positive effects on innovation performance. Formal collaboration provides access to external knowledge and resources, facilitating technology transfer and product innovation. Informal collaboration, on the other hand, enables SMEs to acquire tacit knowledge, learn from industry peers, and gain market insights. The study emphasized the significance of external collaboration for enhancing SMEs’ innovation capabilities and competitiveness. This observation can be analyzed within the framework proposed by Porter, which emphasizes the role of competitive rivalry. Informal competition, characterized by a lack of clear market rules, impedes innovation as it creates an uncertain and unpredictable business environment. In contrast, foreign competition, governed by established market rules and standards, stimulates innovation among SMEs by fostering a competitive environment that encourages firms to invest in innovation to gain a competitive advantage [4]. Overall, these studies highlight the importance of considering competitive rivalry by examining the relationship between competition and innovation outcomes. Informal competition, characterized by a lack of clear market rules, may hinder innovation, while foreign competition, with its established market rules and standards, can serve as a catalyst for innovation among SMEs. Understanding the dynamics of competitive rivalry provides valuable insights into firms’ innovation strategies and their ability to adapt and thrive in competitive markets.

Alternative financing pathways and their influence on innovation performance are also a significant aspect discussed in the literature. Wang et al. in [20], conducted a study focusing on the impact of financial technology (fin-tech) on regional innovation performance. Their conclusions highlight the positive relationship between fin-tech, equity financing, bank credit, and regional innovation performance. The impact of alternative financing arrangements on firms’ investment decisions and innovation performance is further explored by Cumming et al. in [21]; their research delved into the influence of legal and financial constraints, including the availability of alternative financing options, on firms’ ability to make investments and achieve innovation outcomes. Additionally, the rise of digital technologies, such as mobile payment technologies, cloud services, artificial intelligence, machine learning, and block chain, is paving the way for new business opportunities for startups to provide services to small businesses. Integration of these technologies has excellent potential for creating new business models. Their study provided insights into how the adoption of fin-tech and the availability of alternative financing pathways contribute to enhancing firms’ innovation capabilities [22]. By applying Porter’s framework, it is understood that fin-tech facilitates alternative financing pathways, reducing firms’ reliance on traditional suppliers and enhancing their innovation capabilities [4]. These studies underscore the importance of alternative financing pathways in shaping firms’ innovation performance. Fin-tech, by providing innovative financial solutions, enables firms to access equity financing and bank credit, which in turn contribute to their ability to innovate. This theme highlights the potential of alternative financing arrangements to stimulate innovation and offers valuable insights for policymakers and organizations seeking to foster a supportive financial ecosystem that encourages and supports innovation activities.

Competition, innovation efficiency, and firm performance are also explored in the literature. Huang et al. in [23], conducted a study focusing on the Chinese manufacturing sector to understand the relationship between competition, innovation efficiency, and firm performance. Their results suggested that domestic competition has a more pronounced positive impact on innovation efficiency, while international competition has a stronger positive effect on firm performance. This observation underscores the significance of competitive rivalry intensity. Domestic competition, driven by local market dynamics, promotes efficiency and innovation within firms. In contrast, international competition emphasizes the need for firms to perform well in global markets, thus enhancing their overall performance. Scherer et al. in [24], further examined the relationship between market structure, competition, and economic performance. Their work provided insights into the impact of competition on various dimensions of firm behavior and performance. By analyzing market structures and the competitive landscape, the book sheds light on how competition influences firms’ strategies and their ability to achieve favorable economic outcomes. The interpretation of these results using Porter’s framework focuses on the intensity of competitive rivalry. Domestic competition, influenced by local market dynamics, drives companies to enhance efficiency and foster innovation. On the other hand, international competition underscores the importance of firms’ performance in global markets [4]. These studies highlighted the multifaceted relationship between competition, innovation efficiency, and firm performance. Domestic competition stimulates innovation efficiency, allowing firms to adapt and improve within the local market. International competition, on the other hand, serves as a driver for firms to perform well in global markets and achieve overall superior performance. Understanding the dynamics of competition and its influence on different dimensions of firm behavior and performance provides valuable insights for managers and policymakers in shaping strategies that foster innovation and competitiveness in both domestic and international markets.

Foreign direct investment (FDI) represents another determinant that significantly impacts the relationship between acquisition-based innovation and foreign competition. FDI brings new knowledge, skills, and technologies into a country, thereby stimulating innovation and the development of new products or services [25]. Analyzing this phenomenon using Porter’s framework involves considering the bargaining power of suppliers and the threat of substitute products or services. FDI facilitates the infusion of new resources and knowledge into firms, empowering them to innovate and stay competitive. Furthermore, research has shown that FDI has a positive influence on regional innovation, leading to knowledge spillover effects in neighboring areas [26,27]. This finding supports Porter’s framework by acknowledging the potential impact of new market entrants and the existing competitive landscape. FDI acts as a catalyst for technological progress and innovation, providing valuable resources and knowledge spillovers that stimulate local innovation capabilities. This work focuses on the impact of foreign direct investment (FDI) on innovation. It highlights how FDI brings new resources, knowledge, and technologies into firms, stimulating innovation and enhancing their competitive positions. The analysis using Porter’s framework provides a structured perspective by considering factors such as supplier power and the threat of substitutes. The evaluation underscores the positive influence of FDI on regional innovation and knowledge spillovers, aligning with Porter’s framework by recognizing the role of new market entrants and the competitive landscape in driving innovation.

The relationship between buying innovation, foreign competition, and industry characteristics is influenced by the size of organizations involved. Small- to medium-sized enterprises (SMEs) may face greater challenges in accessing resources and expertise, limiting their ability to engage in buying innovation [28]. In contrast, non-SMEs often possess more abundant resources and expertise, making buying innovation more feasible for them. This aligns with Porter’s fourth element, which highlights the role of related and supporting industries. Non-SMEs benefit from their stronger position within the industry ecosystem, enabling them to engage in buying innovation more readily. For instance, a study by Jansson et al. in [29], found that non-SMEs are more likely to engage in buying innovation compared to SMEs. The resource and expertise advantages of non-SMEs align with Porter’s fifth element, which focuses on factor conditions. Access to resources and expertise enables non-SMEs to effectively pursue buying innovation, strengthening their competitiveness in the face of foreign competition. This research provides valuable insights into the relationship between buying innovation, organizational size, and competitiveness. The findings suggest that SMEs face challenges in accessing resources and expertise, which can hinder their engagement in buying innovation. On the other hand, non-SMEs with greater resources and expertise are more likely to participate in buying innovation, giving them a competitive advantage. This highlights the importance of addressing resource constraints faced by SMEs to foster their innovation capabilities.

Long recognized as being one of the most significant aspects for improving social welfare, and also necessary for a company’s long-term economic stability and profit growth, innovation is now more critical than ever [30,31]. Indeed, in the face of worldwide competition, corporations’ long-term strategies make innovation a priority [32]. Managing innovation, alternatively, is a difficult task and it is much more difficult while developing, as well as designing and managing, an innovation culture that is closely linked to the wide range of goals that such a plan entails [33,34,35]. The act or practice of innovation is defined as anything new, such as a different technique, practice, gadget, or modification in the way things are conducted [36]. Despite advancements in the field of innovation study, the link between competition and innovation continues to be a source of exciting discussion [37]. As a result, other competition measurements are required to have stronger insight into how competition affects innovation [38]. Buying and making innovation are the two types of innovation [39]. Businesses that employ all of their assets to improve new items, copyrights, equipment, and mechanisms are considered to make innovation. Businesses that spend money to obtain new technology by outsourcing it to third parties, including both domestic and international technology transfers, are referred to as innovation buying operations [40]. Both external and internal innovation initiatives can be substituted for one another [41,42]. “Making and Buying” innovation are sometimes known as inside and outside development [32]. On one side, global innovation supports businesses to circumvent the higher risks and costs of innovation activities and also acquire externally available professional expertise and enjoy cost-cutting measures connected through knowledge, as well as expertise [32,43,44]. But, the extraordinary degrees of complexities, uniqueness, and randomness involved with R&D, as well as the risk of transactional opportunism, decrease the possible remunerations of outside innovation, constructing the internal bridge to the market more cost-effectively [45]. Su, Y. and Fan, Q. M. in [46], examined the relationship between renewable energy technology innovation, industrial structure upgrading, and green development in China’s provinces and found that higher levels of renewable energy technology innovation positively impact industrial structure upgrading and green development. The study emphasizes the need for targeted policies to promote innovation in less-developed regions for sustainable economic growth and environmental improvement. The influence of international competition, which may be quantified by the log of the percentage of imported items to entire production value, is known as foreign competition. Innovation, such as new services, equipment, and unique talents, is essential to society’s growth and progress [39,47,48,49]. Governments and scholars are concerned about the topic of innovation as it is critical for long-term trade and industry growth, particularly in emerging countries [11]. Competition’s influence on making and buying innovative activities has been studied in some research.

Despite many studies demonstrating a positive correlation between foreign competition and innovation, there are some dissenting views. Aitken et al. in [50], discovered that heightened import competition may diminish the motivation for businesses to innovate, especially those with a higher degree of market power. Similarly, Bloom et al. in [51], found that import competition from China has led to job losses and reduced innovation among firms in the United States. The effect of foreign competition on innovation can be affected by a range of factors, including the nature of the innovation, the technological capabilities of firms, and the regulatory landscape. In the Argentine manufacturing industry, Crespi et al. in [52], discovered that foreign competition has a favorable impact on process innovation while having no substantial effect on product innovation. Similarly, according to Aw et al. in [53,54], foreign competition’s positive effect on innovation is more pronounced for companies with higher technological capabilities. Both Boone et al., in [55], and Aghion et al., in [5], proposed that the relationship between competition and innovation is non-linear; the authors of [5] suggested an inverted U-shaped relationship, while [55] proposed a U-shaped association. Utilizing statistics from UK equity market companies listed and patent rights focused on the description of innovation, confronted through the lens of increased competition in a sector, Aghion et al. in [5], reported that enterprises closer to the cutting edge of technology will innovate further in need to stay ahead of the competition. Businesses further away from the border region, which are attempting to catch up, will be demotivated by the increased competitive pressure and hence will innovate less. Ibtissem et al. in [56], demonstrated a negative relationship between corruption and the development of the Tunisian stock market.

The relationship between international competition and innovation in emerging economies has been the subject of numerous studies, but a consensus has yet to be reached on the exact nature of this relationship. While some research suggests a U-shaped, nonlinear relationship between innovation and competition, others propose an inverted U-shaped link. Additionally, the majority of previous research has relied on mean regression analysis to explore the correlation between foreign competition and innovation, which may leave gaps in our understanding of the relationship at different stages of innovation. Furthermore, the impact of foreign competition on both buying and making innovation activities in emerging economies, which are crucial to reducing technology dependence and climbing the technology ladder, has not been thoroughly explored in previous studies. Thus, more comprehensive investigations are necessary to examine the effects of foreign competition on innovation in emerging economies, especially at the industry level, to better comprehend how industry-specific characteristics affect innovation behavior.

Only a few research papers have looked at how competition pressure causes buying innovation successes. Liu et al. in [39], examined foreign competition and making–buying innovative activities that have a link via panel data from China’s high-tech sectors. Su-Yi et al. in [57], studied the link between international competition and the ability to create new products, using data from the United States, and reported a U-shape connection of foreign competition and innovation. Following [57], we focused on the association between overseas competition and buying innovation via panel data of China’s high-tech industries from 2010 to 2017. Comparatively, there is not much information regarding how international competition works and influences innovation activities in developing economies. Additionally, current studies have not analyzed how industry size and concentration intermingle through foreign competition and influence buying innovation activities. Remedying this error, we focused on industry-grounded analysis to investigate foreign competition’s role versus buying innovation accomplishments from a developing economy perspective.

In summary, the relationship between foreign competition and innovation is not straight forward and has many different aspects to consider. Although some studies indicate that increased foreign competition can stimulate innovation among domestic firms, other studies find a negative impact or no significant relationship at all. The effects of foreign competition on innovation can vary widely depending on factors such as industry, country, and the type of innovation being considered. Additionally, the impact of foreign competition on innovation may depend on other factors, such as the level of domestic market competition and the regulatory environment. Overall, the literature suggests that foreign competition can be a positive driver of innovation for firms, but this effect is not uniform across all industries and firms. Companies that are more R&D intensive and have a greater capacity to absorb new knowledge are more likely to benefit from foreign competition. The results of this literature review have important implications for policymakers and managers looking to foster innovation within their industries.

3. Discussion of Institutional Features of the People’s Republic of China

China’s progressive and open policy reforms have propelled its economy to transition towards marketization, prioritizing resource allocation optimization and economic efficiency [58]. Market competition, a byproduct of restructuring China’s market structure, has been instrumental in accelerating the country’s economic transformation and progress. Notably, China’s high-tech sectors have played a vital role in this transition, making significant strides in recent years. However, the increasing influx of foreign direct investment, while driving growth, posed challenges such as market destabilization, industry opposition, and limited domestic creativity. Recognizing the significance of innovation for economic growth, the Chinese government has prioritized it as a core national strategy, thereby promoting technical innovation to foster modernization and structural development. This research aims to investigate the influence of international competition on innovation in China’s high-tech sectors using a quantile regression approach. The government‘s industry strategy in China is primarily focused on stimulating innovation, a key driver of economic progress in many developing economies [59]. International competition has been observed to encourage firms in emerging markets to regain their market share through innovation [60]. When faced with global competition, domestic industries in developing nations engage in product imitation, an important facet of innovation that enhances technical skills acquired through international competition [61]. Moreover, greater foreign competition has demonstrated a positive impact on various technological innovation indicators [62]. Multinational companies evaluate the technical capabilities of local firms when deciding on outsourcing or collaborations, underscoring the significance of domestic industry expertise. However, accurately estimating industry responses to competitive market fluctuations remains a challenge [63].

China’s industrial progress strategy, exemplified by the Made in China 2025 (MC-25) program initiated in 2015, aims to establish the nation as a global leader in advanced technologies, such as artificial intelligence (AI), electronic sensors, solar cells, machine tools, new energy vehicles, advanced microchips, aviation and space travel, autonomous driving systems, medical devices, and telecommunications equipment [64]. The MC-25 initiative has raised concerns among sophisticated nations, notably the United States, due to its focus on acquiring new technologies. The primary objective of this initiative is to achieve self-sufficiency in high-tech goods, with a target of 70% local production. The role of institutional development is crucial in understanding the impact of government control on corporate policies in China [65]. Su, Y. and Yan, Y. H. in [66], investigated the impact of the two-tier network of a regional innovation system on knowledge emergence and highlighted the importance of intra- and inter-organizational networks in facilitating knowledge creation and dissemination within the system. The study emphasizes the need to strengthen collaborative relationships and knowledge-sharing mechanisms to enhance regional innovative capacity. Cross-country studies have yielded mixed results, with some indicating that government ownership improves corporate governance in countries with robust investor protection, while others found it beneficial for firms with close ties to the state in regions with limited institutional development. In China, fiscal decentralization has resulted in significant regional disparities, with coastal provinces outpacing their interior counterparts. While government development policies have historically favored coastal regions, efforts have been made to enhance regional development. In less developed areas, government-controlled firms are likely to receive greater economic benefits through financing contracts and subsidies, which mitigate the negative effects of investor valuation in regions with weak institutional development and legal systems.

China’s unique institutional features, coupled with government policies and initiatives, have propelled its high-tech sectors forward, facilitating economic progress and transformation. The government’s focus on innovation as a core national strategy, exemplified by the MC-25 program, emphasizes the importance of self-sufficiency in high-tech goods. Furthermore, the role of institutional development in mitigating the impact of government control on corporate policies cannot be understated. The ongoing efforts to address regional imbalances and enhance regional development aim to ensure equitable economic growth across the country. By examining the influence of international competition on innovation in China’s high-tech sectors, this study contributes to a deeper understanding of the dynamics driving China’s economic advancement.

4. Hypothesis Development

This section presents a hypothesis proposing a U-shaped relationship between foreign competition and buying innovation in China’s high-technology industry subsectors. The objective is to examine the potential impact of foreign competition on innovation activities, specifically in terms of purchasing innovative products or services. The rationale for this hypothesis is discussed, supported by theoretical background and relevant theories. The unique institutional features of China are considered to understand the mechanisms that become stronger beyond a threshold level of foreign competition. The unique institutional features of the People’s Republic of China (PRC) in relation to foreign competition, especially certain mechanisms, become stronger beyond a threshold level. The PRC has a long-standing tradition of state-led industrial policy, where the government plays a prominent role in guiding and supporting key industries. When faced with significant foreign competition, the Chinese government responded by strengthening its industrial policy mechanisms. This involved providing subsidies, tax incentives, and preferential treatment to domestic companies in targeted sectors, thereby bolstering their competitiveness. China has been actively promoting innovation as a strategic priority through policies like “Made in China 2025” and the establishment of innovation-driven development strategies. In the face of foreign competition, the Chinese government further enhanced its support for innovation by increasing R&D funding, facilitating technology transfers, and creating favorable regulatory environments to spur domestic technological advancements. China has often been criticized for imposing market access restrictions and favoring domestic companies through various means, such as regulatory barriers, licensing requirements, and government procurement policies. When confronted with foreign competition, the Chinese government has tightened these restrictions to safeguard domestic industries, thereby giving them a stronger competitive advantage in the local market. China has a significant presence of state-owned enterprises (SOEs), which enjoy preferential treatment and support from the government. In response to foreign competition, the government bolstered the role of SOEs by providing them with additional resources, market advantages, and protectionist measures, strengthening their competitive position against foreign rivals. The PRC has witnessed a rise in economic nationalism, with a growing emphasis on fostering domestic industries and reducing dependence on foreign products and technologies. When faced with foreign competition, this sentiment has been leveraged to mobilize public support and generate policy measures that protect and promote domestic industries, leading to stronger mechanisms of self-reliance and domestic market dominance. It is important to note that while these arguments highlight potential mechanisms that can become stronger in the context of China’s institutional features, the actual policies and responses to foreign competition can vary based on specific circumstances, government priorities, and geopolitical factors. Further research is needed to empirically test the hypothesis and contribute to the understanding of how foreign competition influences innovation strategies.

These are the theoretical aspects related to the potential impact of foreign competition on the innovation activities of China’s high-technology industry subsectors, with a focus on buying innovation. The institutional features of China, such as state-led industrial policies, government support, and protectionist measures, are factors shaping the dynamics of foreign competition. This section highlights the positive and negative influences of foreign competition on innovation and proposes that the relationship follows a U-shaped pattern beyond a certain threshold level of competition. The theoretical framework draws upon concepts from international business, innovation management, and industrial organization. The positive effects of knowledge spillovers and technology transfer resulting from foreign competition emphasize how exposure to foreign firms can enhance innovation capabilities and stimulate buying innovation. The unique institutional features of China are considered to understand the relative strengths of these mechanisms.

Based on the theoretical underpinnings and the institutional features of China, a hypothesis of a U-shaped relationship between foreign competition and buying innovation is proposed. The hypothesis suggests that beyond a certain threshold level of foreign competition, the positive effects of knowledge spillovers and technology transfer may weaken compared to the negative effects of intense competition. Alternatively, it is argued that beyond the threshold, the intense competition may compel China’s high-technology industry subsectors to strengthen their innovation capabilities through increased engagement in buying innovation.

Therefore, we propose a U-shaped relationship between foreign competitions and buying innovation.

Hypothesis 0 (H0).

There is no significant U-shaped relationship between foreign competition and buying innovation.

Hypothesis 1 (H1).

There is a significant U-shaped relationship between foreign competition and buying innovation.

In order to test the hypothesis, suitable statistical methods were employed to analyze a sample of subsectors within China’s high-technology industry. The data analysis focused on investigating the existence and significance of the hypothesized U-shaped relationship between foreign competition and the adoption of innovation through acquisitions (buying innovation). The findings of this study contributed to enhancing our understanding of how foreign competition influences innovation strategies within specific subsectors of China’s high-technology industry. Moreover, the study shed light on the conditions under which buying innovation is either encouraged or hindered. By summarizing the rationale behind the hypothesis and providing theoretical support for it, the proposed U-shaped relationship between foreign competition and buying innovation in China’s high-technology industry subsectors establishes a framework for further empirical analysis. The objective of this research is to contribute to the existing body of knowledge regarding the impact of foreign competition on innovation activities, while offering valuable insights into the unique context of China’s high-technology industry subsectors.

5. Methodology

5.1. Quantile Regression Model

The use of conditional quantile regression in a linear model enables the determination of the conditional quantiles of the response variable. Cade et al. in [67], provide a clear picture of how independent and dependent variables are linked [68,69]. Regression models with wide ranges of variance are difficult to handle and intractable. Other elements of the dependent variable’s distribution are used to fit regression curves. As a result, Mosteller and Tukey (1977) found that the majority of regression analyses produce a partial connection between the dependent and independent variables [70]. Quantile regression, as a result, broadens quantile theory, statistical organization, and the linear model [70]. Numerical approaches and statistical models were used to determine and make judgments on acceptable regression quantiles in the case of a linear model. Nonparametric, nonlinear parametric, and parametric nonlinear smoothers are also possible [67,71,72,73,74,75].

Econometric features of quantile regression refer to the specific characteristics and techniques associated with the application of quantile regression in econometric analysis. Here are some key features:

Quantiles: Quantile regression extends traditional regression analysis by estimating conditional quantiles of the dependent variable, rather than the conditional mean. Quantiles represent different points in the distribution of the dependent variable, such as the median (50th percentile), lower quantiles (e.g., 25th percentile), or upper quantiles (e.g., 75th percentile). Quantile regression is a statistical method that has found broad application in diverse areas of science and technology. One such area is the use of quantile regression for panel data, an empirical method for knowledge spillovers’ endogeneity [76], growth of the economy [77,78], and biological learning [79]. The weighting total of residuals for the entire dataset, not just that quantile’s portion, was used to construct the quantile regression coefficients. We never lost the degree of freedom because, when the number of observations is minimal, freedom is vital. Following [80], a conditional quantile function was stated [81].

$$Q_{\theta }\left(y_i\right|x_i)=\alpha \left(\theta \right)+x_i^{\prime }\beta \left(\theta \right)\hspace{1em}with\theta ∊(0,1)$$

(1)

Equation (1) shows that $y_i$ is the explanatory variable of observation i, $x_i$ is the covariate vector demonstrating different adherence i, $\theta$ represents the ${\theta }_{th}$ quantile, where quantile refers to a point taken along the cumulative distribution, and subscript $i=1,2,3,\dots n$ denotes an index for individual observations. $Q_{\theta }\left(y_i{x}_i\right)$ represents the ${\theta }_{th}$ conditional quantile of $y_i$ given $x_i$. It is compared to the OLS regression function, which is stated as $E\left(y|x\right)={\mu }_{y|x}=\alpha +x_i^{,}\beta$, which is the classical linear function that estimates the conditional mean ${\mu }_{y|x}$, generally the average value of y for a given amount of x [79]. The following is the conditional quantile function’s optimism problem:

$$\underset{\beta ∊R^K}{\min }\left[{\displaystyle \sum _{i\in \left\{i:y_i\ge x_i^{\prime }{\beta }^{(\theta )}\right\}}\left.\theta \right|}{y}_i-x_i^{\prime }{\beta }^{(\theta )}\left|+\right.{\displaystyle \sum _{i\in \left\{i:y_i\ge x_i^{\prime }{\beta }^{(\theta )}\right\}}(1-\theta )|y_i-x_i^{\prime }{\beta }^{(\theta )}|}\right]$$

(2)

Linear programming: Quantile regression can be formulated as a linear programming problem, where the objective is to minimize the weighted absolute deviations between the observed quantiles and the predicted quantiles. This approach allows for flexible estimation of conditional quantiles and capturing different relationships across the distribution. Linear programming was used to tackle the challenge of optimizing the quantile regression function [82]. Without having a defined form, the problem is dependent on order statistics [83]. The dimensions of the independent variables $\left(\mathrm{k}\right)$ are shown by the above function $R$ in (2). The optimization issue for this function is to develop ${\theta }_{th}$ quantile regression estimators ${\beta }^{\theta }$ that minimize the absolute value of a weighted sum of residuals between observed $y_i$ and fitted values $\left(x^{,}\beta \right)$. The points $\theta$ below the quantile regression line are given a weight of 1, and the value of 1−$\theta$ is used for the points above the quantile regression line (the second term in Function (2). In addition, the estimated covariance matrix Σ is computed using:

$${\displaystyle {\sum }_{{\widehat{\beta }}^{(\theta )}}=\frac{\theta (1-\theta )}{n}}\frac{1}{f_{{\epsilon }^{(\theta )}}{(0)}^2}{(X^{\prime }X)}^{-1}$$

(3)

In Equation (3), $f_{{\epsilon }^{\theta }}\left(0\right)$ is the probability density of the error term ${\epsilon }^{\theta }$ calculated at the ${\theta }_{th}$ quantile of the error distribution [82]. An estimated standard error for the coefficient estimator $\hat{\beta }\left(\theta \right)$ was obtained by taking the square root of the corresponding diagonal element of the covariance matrix Σ. There is a difference between quantile and simple categorization. In quantile categorization, the regression sample can be classified into low- and high-level quantiles of the dependent variable, and quantiles of the dependent variable are conditional on the independent variable (x).

Heteroscedasticity: Quantile regression accounts for heteroscedasticity, which refers to the unequal variability of the dependent variable across different values of the independent variables. By modeling the conditional quantiles, quantile regression can capture varying levels of heteroscedasticity and provide robust inference. The Machado–Santos Silva (MSS) test provides appropriate information about the kind of covariance matrix. Also, it gives information about the significance of estimating multiple quantiles [84]. To check the heteroscedasticity of our quantile regression, we installed the qreg2 package, which is proposed by [85]. Their test statistic can be easily computed as n times the $R^2$ of the auxiliary regression of ${\rho }_{\tau }\left({\mu }_i\left(\tau \right)\right)$, a constant, and on appropriate functions of (x). The test was performed by comparing the test statistic with the critical value from the $x^2$(J − 1) distribution, where (J) is the number of parameters in the second regression [85].

Distribution-free: Unlike traditional regression methods that assume specific distributional assumptions (e.g., normality of errors), quantile regression is distribution-free. It does not require any distributional assumptions about the errors or the dependent variable, making it suitable for analyzing data with non-normal or skewed distributions.

Robustness: Quantile regression is robust to outliers and influential observations. The estimation of quantiles is less affected by extreme values compared to mean estimation, allowing for more reliable inference in the presence of outliers or heavy-tailed distributions.

Overall, the econometric features of quantile regression enable researchers to examine the relationships between variables at different points of the distribution and provide insights into heterogeneity and conditional effects that may be missed by mean-based regression methods.

5.2. Data and Variable Measures

For our research, we obtained panel data from the China Statistical Yearbook on high-technology sectors, which was compiled by China’s National Bureau of Statistics (NBS) between 2010 and 2017. China’s high-tech industry comprises 24 subsectors, as classified by the NBS categorization system, and is further divided into seven sectors. These sectors are aircraft and spacecraft manufacturing, pharmaceutical manufacturing, computers and office equipment manufacturing, electronic equipment and communication equipment manufacturing, medical equipment and instrument manufacturing, and electronic chemicals manufacturing. The list of subsectors within China’s high-tech industry can be found in Appendix A. Our dataset has not been previously used in any other research. The focus of our study is on the subsectors of China’s high-tech industry; we selected the sample size from the subsectors of China’s high-technology industry. The subsector count in 2017 was 24, and our sample spanned from 2010 to 2017. As 22 observations were removed during the selection process, the final sample size was 170 instead of the expected 192. Generally, the list of subsectors in China’s high-tech industry is increasing year by year. So, for some years, the list comprised 24 sectors, while for others, it comprised 17 sectors, and so on. Specifically, we excluded observations that had missing subsectors and missing data, as these could have potentially biased the results of the analysis. So, finally, we had a total of 170 observations. The variables were measured as follows in

Table 1. Measurement of variables.

Variable	Indicator	Measurement	Data Source
Dependent	Buying	Log of expenditure on importing technology abroad and locally purchasing technology	National Bureau of Statistics (NBS)
Variable	Innovation
Independent	Foreign	Logging the proportion of import products to overall manufacturing costs	National Bureau of Statistics (NBS)
Variable	Competition
Controlling	Industry Size	Number of employees	National Bureau of Statistics (NBS)
Variable
Controlling	Time Frame	2010–2017	National Bureau of Statistics (NBS)
Variable
Controlling	Industry Concentration	Log of total output value to the subsector’s total number	National Bureau of Statistics (NBS)
Variable

.

5.3. Dependent Variable

In the previous literature, there have been various approaches used to assess innovation activities. These include innovation counts, total factor productivity, research and development expenditure, and patent rights, typically measured at the firm level [86,87]. Our research focuses on buying innovation achievements, which can be determined by the logarithm of the expenses incurred in importing technology from overseas and procuring technology locally. Spending on technology subcontracting encompasses the acquisition of project knowledge, techniques, patents, trade secrets, and core equipment, largely associated with the development of new products [88].

5.4. Independent Variable

In previous studies, various measures have been employed to evaluate competitiveness. These include the ratio of market size to the number of competitors [89], the Herfindahl index, and the ratio of market share to the total number of competitors [90]. Our research focused on foreign competition, which can be determined by taking the logarithm of the proportion of imported products to total manufacturing costs. This is because domestic industries may exhibit different behavior in achieving innovation success when faced with increased international competition [39]. Beerli et al. in [91], found that the size of the domestic market had a positive impact on the innovation of Chinese companies in the durable goods sector. The entrance of new products or processes by foreign firms can put competitive pressure on local enterprises, whose innovation capabilities are thought to be weaker than those of multinational corporations [92,93].

5.5. Controlling Variables

In our study, we incorporated three control variables to account for factors that can potentially influence innovation activities. These control variables are industry size, time frame, and industry concentration.

Industry Size: Industry size refers to the scale or magnitude of an industry, typically measured by factors such as the number of employees or the overall output value. A larger industry with more resources may have a greater impact on innovation activities compared to smaller businesses. We referenced Ballot et al. [94] to justify the inclusion of industry size as a control variable. They argued that industry size can significantly affect innovation activities, as larger industries often possess more resources and capabilities to invest in research and development efforts. These resources can be utilized to invest in research and development (R&D), acquire advanced technologies, and explore new opportunities for innovation. Additionally, larger industries often have access to a wider customer base and market demand, which can incentivize and support innovation efforts. On the other hand, smaller businesses within an industry might have certain advantages as well. Their smaller size allows for greater agility and flexibility, enabling them to quickly adapt to changing market conditions and identify niche areas for innovation. Small businesses can often leverage their close customer relationships and deep understanding of specific market segments to drive innovation. By including industry size as a control variable, we aim to account for the potential influence of industry scale on innovation activities. Controlling for industry size allows us to isolate and evaluate the specific effects of other factors of interest, such as the variables of foreign competition and innovation. It is important to note that the measurement of industry size can vary depending on the context and availability of data. Common measures include the number of employees, total sales revenue, or overall output value within a specific industry or subsector. In summary, the inclusion of industry size as a control variable recognizes the potential impact of industry scale on innovation activities. It allows us to consider the influence of resource availability, economies of scale, and other factors associated with industry size when examining the relationships between foreign competition and innovation.

Time Frame: As a second control variable, we considered the time frame; the authors of [95] suggested the period from 2010 to 2017. The time frame variable, represented as a continuous variable, accounts for the specific period during which the study is conducted. It helps control for contemporaneous changes in government policy, macroeconomic conditions, and other time-specific factors that may influence innovation activities. This specific time period was chosen to capture a significant duration and observe any trends or changes in innovation activities during that period. The inclusion of a time frame variable in our study is essential for controlling the influence of contemporaneous changes in government policy, macroeconomic conditions, and other time-specific factors on innovation activities. By specifying a specific time period, represented by a continuous variable, we aim to capture and account for potential temporal variations that may impact foreign competition and innovation. The choice of a particular time frame can depend on various factors, such as the availability of data, the research objectives, and the context of the study. Selecting a significant duration, like 2010–2017, represented by a continuous variable, allows us to capture a substantial period of time and observe any trends or changes in innovation activities throughout that specific timeframe. By including multiple years, represented as a continuous variable, we can account for short-term fluctuations and examine the longer-term patterns in innovation. During this continuous time period, it is likely that there were significant changes in government policies, economic conditions, and technological advancements that influenced innovation activities. By controlling for these time-specific factors using the continuous variable, we can better isolate the effects of foreign competition and buying innovation.

Industry Concentration: Thirdly, we included industry concentration as a controlled variable, which is measured by taking the logarithm of the total output value to the subsector’s overall number of businesses. If this ratio is high, the concentration of the subsector will be higher [96,97]. Industry concentration refers to the extent to which economic activity within a particular subsector is concentrated among a few dominant firms or players. It can be measured by calculating the logarithm of the total output value relative to the overall number of firms in the subsector. High concentration levels within a subsector may have implications for competition, collaboration opportunities, and resource allocation, all of which can impact innovation efforts. The inclusion of industry concentration as a control variable in our study acknowledges the significance of understanding how the concentration of economic activity within a subsector can impact innovation activities. High levels of concentration within a subsector have several implications for innovation efforts. Competition: High industry concentration may lead to increased competition among the dominant firms. Intense competition can spur innovation as firms strive to differentiate themselves, gain market share, and maintain a competitive edge. On the other hand, excessive concentration may result in reduced competition, limiting incentives for innovation. Collaboration opportunities: Industry concentration can also create collaboration opportunities among dominant firms. Concentrated subsectors may foster collaborations in research and development, sharing of resources, or joint ventures. These collaborations can promote knowledge exchange and innovation activities, benefiting the entire subsector. Resource allocation: The concentration of economic activity in a subsector can influence the allocation of resources, including financial resources, talent, and expertise. Dominant firms may have access to greater resources and capabilities, allowing them to invest more heavily in innovation activities. Smaller firms within a concentrated subsector may face challenges in accessing resources, potentially affecting their innovation capabilities. Summary: Including industry concentration as a control variable, we aimed to account for these potential effects and isolate the specific impact of other variables under investigation on innovation activities. Controlling for industry concentration helps us understand the unique contribution of our variables of interest beyond the influence of industry structure and dominant players. Including these control variables in our analysis, we aimed to account for and isolate the specific effects of the factors under investigation on innovation activities while controlling for the potential influence of industry size, time frame, and industry concentration.

6. Empirical Results and Discussion

6.1. Summary of Descriptive Statistics

The descriptive statistics for the variables employed in this study are presented in

Table 2. Summary of Descriptive Statistics.

Variables	Observations	Mean	Std. Dev.	Min	Max
Buying innovation	170	5.8808	0.5217	4.4699	7.0672
Foreign competition	170	1.0031	0.5549	0.0545	2.3583
Industry size	170	4.2971	0.4793	3.0288	5.187
Industry concentration	170	4.7115	0.4127	3.8622	5.9239
Time frame	170	2013.729	2.1914	2010	2017

Source: National Bureau of Statistics (NBS) China.

. The data were sourced from the China National Bureau of Statistics’ China Statistical Yearbooks on the country’s high-technology industries. The sample size used in the study was 170 observations, after excluding missing values from an original dataset of 192. We selected the sample size from the subsectors of China’s high-technology industry. The subsector count in 2017 was 24, and our sample spanned from 2010 to 2017. As 22 observations were removed during the selection process, the final sample size was 170 instead of the expected 192. Generally, the list of subsectors in China’s high-tech industry is increasing year by year. So, for some years, the list comprised 24 sectors, while for others, it comprised 17 sectors, and so on. Specifically, we excluded observations that had missing subsectors and missing data, as these could have potentially biased the results of the analysis.

Buying innovation: This variable measures the level of innovation activities of domestic companies in China’s high-technology industries. Foreign competition: This variable measures the level of competition from foreign companies in China’s high-technology industries. Industry size: This variable measures the size of the industry in terms of the number of employees. Industry concentration: This variable measures the concentration of firms in a particular subsector of China’s high-technology industries. Time frame: This variable measures the time period under consideration, which is from 2010 to 2017.

The mean value of the variable buying innovation is 5.8808, indicating a relatively high mean level of buying innovation with moderate variability (standard deviation = 0.5217). The minimum and maximum values are 4.4699 and 7.0672, respectively. The results indicate that the average level of innovation activities among domestic companies in China’s high-technology industries is moderate, with a mean value of 5.8808 and a standard deviation of 0.5217. In contrast, foreign competition appears to be relatively low to moderate, with a mean value of 1.0031 and a standard deviation of 0.5549. The minimum and maximum values are 0.0545 and 2.3583, respectively. Industry size: The mean value of the variable is 4.2971, indicating a medium mean size of industries with moderate variability (standard deviation = 0.4793). The minimum and maximum values are 3.0288 and 5.1870, respectively, while the industry concentration within subsectors of China’s high-technology industries is moderately high, with a mean value of 4.7115 and a standard deviation of 0.4127, indicating a medium mean size of industries with moderate variability (standard deviation = 0.4793). The minimum and maximum values are 3.0288 and 5.1870, respectively. Finally, the time frame of the observations is centered around 2013, with a mean value of 2013.7290 and a standard deviation of 2.1914. The minimum and maximum values are 2010 and 2017, respectively.

The statistical data obtained from Table 2 indicate that the level of buying innovation is high and has moderate variability among the 170 observations. Similarly, the mean level of foreign competition is relatively low with moderate variability. The study also reveals that the mean size of industries is moderate with moderate variability, while the mean level of industry concentration is high with moderate variability. Furthermore, the results suggest that the mean value of the time frame is centered around 2013, with moderate variability. Overall, the data appear to be well-distributed, without any significant outliers that would impact the analysis results.

6.2. Quantile Regression Analysis: Key Results

The results of the quantile regression analysis are presented in

Table 3. Quantile Regression Analysis: Key Results.

Quantile Regression	Q0.10	Q0.25	Q0.5 (Median)	Q0.75	Q0.90
Independent variable
Foreign competition	−0.0292 *	0.0143 *	0.0714 *	0.0879 **	0.1883 **
Standard error	0.0616	0.0582	0.0461	0.0361	0.0493
Control variable
Industry size	1.0050 *	1.0112 **	1.0739 **	1.1262 **	1.1929 **
Standard error	0.0690	0.0621	0.0561	0.0404	0.0552
Industry concentration	0.0258 *	0.1561 **	0.1836 **	0.1487 **	0.2142 **
Standard error	0.0410	0.0369	0.0333	0.0240	0.0328
Time frame	Included	Included	Included	Included	Included
Constant	−69.0785 *	−59.7498 **	−43.7799 **	−45.681 **	−35.4677 **
Standard error	14.4325	12.9830	11.7344	8.4584	11.547
R2	0.8064	0.7994	0.7932	0.8005	0.8143
Machado–Santos Silva test for heteroscedasticity.
Prob > chi-square	0.996	0.827	0.463	0.756	0.913

Note: *, ** represent significance at the 10%, 5% levels, respectively. Source: Calculated from the data based on the quantile regression model.

. The analysis investigated the relationship between the dependent variable “Buying innovation” and the independent variable “Foreign competition”, as well as the control variables “Industry size”, “Industry concentration”, and time frame. The quantile regression analysis was performed at five quantiles, namely Q0.10, Q0.25, Q0.5 (Median), Q0.75, and Q0.90. These quantiles represent the 10th percentile, 25th percentile, median (50th percentile), 75th percentile, and 90th percentile, respectively. The quantile regression method estimates the quantiles of the dependent variable for the independent variable and controlling variables.

Table 3 shows the estimated coefficients of the independent variable “Foreign competition” and the control variables “Industry size” and “Industry concentration” at each of the five quantiles. The coefficients indicate the change in the dependent variable “Buying innovation” associated with a one-unit change in the independent and control variables, holding all other variables constant. A positive coefficient indicates that as the independent or control variable increases, the dependent variable also increases, and vice versa for a negative coefficient. The standard error signifies the variability of the estimated coefficient, with a smaller standard error indicating greater precision and less likelihood of deviating from the true population value. The R-squared value, ranging from 0 to 1, is a measure of how well the model fits the data, with higher values indicating better fit. In this study, the R-squared values range from 0.7932 to 0.8143, which is relatively high, suggesting that the model fits the data well.

Foreign competition: The coefficients for the foreign competition variable range from −0.0292 to 0.1883. The negative sign for the coefficient at the 10th percentile (Q0.10) indicates that as foreign competition increases, the dependent variable Buying innovation decreases. On the other hand, the positive sign for the coefficient at the 90th percentile (Q0.90) indicates that as foreign competition increases, the dependent variable Buying innovation increases.

Industry size: The coefficients for the industry size variable range from 1.0050 to 1.1929. The positive sign for all quantiles indicates that as the industry size increases, the dependent variable Buying innovation increases.

Industry concentration: The coefficients for the industry concentration variable range from 0.0258 to 0.2142. The positive sign for all quantiles indicates that as the industry concentration increases, the dependent variable Buying innovation increases.

Constant: The constant ranges from −69.0785 to −35.4677. It represents the intercept of the regression line, which is the value of the dependent variable when all variables equal to zero. R-squared: The R-squared values range from 0.8064 to 0.8143, which indicates that 80–81% of the variation in the dependent variable Buying innovation can be explained by the independent variables and the control variables.

The Machado–Santos Silva test for heteroscedasticity checks if the residuals, which are the differences between the observed and predicted values of the dependent variable, have constant variance. The test statistic is compared to the chi-square distribution with one degree of freedom. If the probability is greater than the chi-square, and the value is greater than 0.05, it indicates that the residuals have constant variance, and the assumption of homoscedasticity is not violated. In this case, the probability is greater than the chi-square values ranging from 0.463 to 0.996, with all values greater than 0.05, indicating that the residuals have constant variance, and the assumption of homoscedasticity is not violated.

The estimated coefficients of the independent variable “Foreign competition” at the five quantiles are −0.0292, 0.0143, 0.0714, 0.0879, and 0.1883. The estimated coefficients of the control variable “Industry size” at the five quantiles are 1.0050, 1.0112, 1.0739, 1.1262, and 1.1929. The estimated coefficients of the control variable “Industry concentration” at the five quantiles are 0.0258, 0.1561, 0.1836, 0.1487, and 0.2142. The standard error is a measure of the variability of the estimated coefficient. A smaller standard error indicates that the estimate is more precise and less likely to be far off from the true population value. The R-squared values in the table range from 0.7932 to 0.8143, which is relatively high and indicates that the model fits the data well.

6.3. Coefficients of Foreign Competition and the U-Shaped Relationship

At the 10th quantile (Q0.10), the coefficient for “Foreign competition” is −0.0292 *. This negative coefficient suggests that an increase in “Foreign competition” is associated with a decrease in the dependent variable at this quantile. At the 25th quantile (Q0.25), the coefficient for “Foreign competition” is 0.0143 *. This positive coefficient indicates that an increase in “Foreign competition” is associated with an increase in the dependent variable at this quantile. At the median (50th quantile or Q0.5), the coefficient for “Foreign competition” is 0.0714 *. Again, this positive coefficient suggests that an increase in “Foreign competition” is associated with an increase in the dependent variable at the median. At the 75th quantile (Q0.75), the coefficient for “Foreign competition” is 0.0879**. The double asterisks (**) indicate a higher level of statistical significance. This positive coefficient implies that an increase in “Foreign competition” is associated with an increase in the dependent variable at this quantile. At the 90th quantile (Q0.90), the coefficient for “Foreign competition” is 0.1883 **. This positive coefficient indicates that an increase in “Foreign competition” is associated with an increase in the dependent variable at the upper end of the distribution.

Taking these coefficients into account, we observe a pattern where the relationship between foreign competition and buying innovation changes across the quantiles. Initially, there is a negative relationship at the 10th quantile, followed by positive relationships at the 25th, median, 75th, and 90th quantiles. This pattern could be seen as a U-shaped relationship, where the effect of “Foreign competition” initially decreases, reaches a minimum, and then increases again. So, we reject the null hypothesis which states that there is no significant U-shaped relationship between foreign competition and buying innovation and accept the alternative hypothesis, which states that there is a significant U-shaped relationship between foreign competition and buying innovation.

6.4. Discussion

This research examined how international competition affects innovation activities in China’s high-tech industry using a panel data analysis of a representative sample of high-tech industries from 2010 to 2017. The study utilized econometric techniques to identify patterns and trends in the data and applied quantile regression to investigate the nuanced relationship between international competition and innovation endeavors. The statistical findings indicated that the effect of international competition on buying innovative actions differed through dependent quantiles of the innovation range, which would be neglected by standard linear regression models. This suggests that evaluating the quantile effect of the variable on innovative activities might be more informative than estimating the mean effect. Unlike previous research, this study addressed the influence of international competition on industry-level innovation accomplishments in a big rising country (China). Foreign competition was confidently linked through buying innovation, except at the quantile (Q = 0.01), where the correlation was negative. According to the authors’ analysis, there is a U-shaped correlation between foreign competition and innovation in China’s high-tech industry. The study further suggests that foreign competition is a vital determinant of the industry’s innovation strength. These findings underscore the significance of global competition in shaping the industry’s ability to innovate and have important policy implications.

The authors also examined the relationship between foreign competition and buying innovation (and its squared term, which was negative), and the control variables of industry size, industry concentration, and time frame by two regression models: ordinary least squares (OLS) and Robust OLS. The results indicate that there is a positive relationship between “Foreign competition” and “Buying innovation.” This suggests that as foreign competition increases, “Buying innovation” is expected to increase. The analysis also reveals that the effect of foreign competition on buying innovation is non-linear, as indicated by the negative coefficient estimate for the squared term of foreign competition in both models. This implies that the impact of foreign competition on “Buying innovation” decreases at an increasing rate as foreign competition levels rise. Therefore, we rejected our null hypothesis and accepted the alternative hypothesis which states that there is a significant U-shaped relationship between foreign competition and buying innovation. Furthermore, the variables “Industry size” and “Industry concentration” also show a positive relationship with “Buying innovation” in both models, and their coefficients are significant. This suggests that as industry size and concentration increase, “Buying innovation” is predicted to rise.

The relationship between foreign competition and innovation is conceptualized as a U-shaped relationship, implying that there are certain levels of foreign competition that facilitate innovation, while too much or too little competition may hinder it. This relationship is often referred to as the “U-shaped curve of innovation”. At the early stages of foreign competition, when there is relatively low intensity, it can act as a stimulus for China’s high-technology industry subsectors to innovate. The presence of foreign competitors introduces new ideas, technologies, and business practices, which inspire China’s high-technology industry subsectors to improve their products, processes, and strategies. The competitive pressure encourages China’s high-technology industry subsectors to seek efficiency gains, explore new markets, and invest in research and development (R&D) to stay competitive. This initial phase of foreign competition generally leads to a decrease in innovation. As the intensity of foreign competition continues to rise, the U-shaped relationship suggests that there comes a point where innovation begins to increase. When competition becomes too fierce, China’s high-technology industry subsectors may focus more on short-term survival and cost-cutting measures rather than long-term investments in innovation. The emphasis on price competition and imitation can discourage China’s high-technology industry subsectors from taking risks and investing in R&D. Additionally, excessive competition may lead to a “winner-takes-all” scenario, where dominant Chinese high-technology industry subsectors can discourage potential innovators from entering the market due to the high risks associated with intense competition.

However, as the level of foreign competition surpasses a certain threshold, the U-shaped curve suggests that innovation starts to increase. At this stage, China’s high-technology industry subsectors face strong competitive pressures, and they are compelled to differentiate themselves through innovation. To remain competitive, China’s high-technology industry subsectors may invest in R&D, develop new technologies, or focus on product differentiation to capture market share. The intense competition can drive China’s high-technology industry subsectors to push the boundaries of innovation and seek new opportunities for growth. It is important to note that the U-shaped relationship between foreign competition and innovation is a theoretical concept and does not hold universally true in all industries or contexts. Additionally, the shape of the curve may vary for different firms within the same industry, as their responses to competition and innovation may differ based on their unique circumstances and strategies.

Previous empirical research on the impact of international competition on innovation in the high-tech industry has yielded mixed results. Some studies have found that foreign competition can have both beneficial and detrimental effects on innovation. For example, in the case of Chinese high-tech companies, foreign competition has been observed to have a positive impact on innovation efforts [98]. Meanwhile, some studies, such as Duan et al. in [99], have indicated that foreign competition negatively impacted innovation in Chinese manufacturing firms; other research has suggested that the connection between international competition and innovation may be more intricate and influenced by contextual factors. This relationship may vary across different industries and companies. Aldieri et al. [100] show that circular economy business models, sharing economy, and eco-innovation investments have a positive impact on firm performance.

Overall, the implications of these research findings are significant for policymakers and businesses striving to foster innovation and competitiveness in their industries. Policymakers must acknowledge the potential drawbacks of excessive foreign competition on innovation and introduce measures that encourage innovation within moderate levels of competition. Meanwhile, businesses must assess the extent of foreign competition they encounter and devise approaches that exploit the advantages of moderate competition while minimizing the risks associated with heightened competition.

7. Conclusions

In this research article, we have used quantile regression to explore the nexus between international competitions and buying innovation at the industry level. Panel data analysis was used from 2010 to 2017 for a sample of China’s high-technology industries. We investigated the applicability of quantile regression analysis for China’s high-technology industries empirically. Using the quantile regression method, our study examined the impact of foreign competition on the innovation capabilities of China’s high-technology industry. Our results are consistent with previous research that has established a U-shaped, nonlinear relationship between innovation and competition [101]. Nonetheless, some studies suggest an inverted U-shaped link [102], which may be due to the diverse features of the industries analyzed. Understanding the influence of international competition on emerging economies’ innovation activities is critical. We found that the major issue impacting China’s high-tech industry is foreign competitiveness [101]. This relationship has been observed in various industries and contexts and is explained by several factors. At low levels of competition, firms may not have enough incentive to innovate because they face little threat of losing customers to competitors. This can result in a lack of innovation and a reluctance to invest in research and development (R&D) [103]. At high levels of competition, firms may focus more on short-term gains and competing on price rather than investing in long-term innovation. This can lead to a focus on incremental improvements rather than breakthrough innovations [104]. In contrast, at moderate levels of competition, firms may face enough pressure to innovate and differentiate themselves from competitors, but not so much that they become overly focused on short-term gains at the expense of innovation. Consequently, this may lead to an increased level of innovation [105]. Numerous studies support the U-shaped correlation between innovation and competition and indicate that it could fluctuate depending on the sector, the innovation type, and the productivity level of the firms or industries examined [61]. Arabeche et al. [106] found that entrepreneurial orientation and organizational culture positively affect business performance in Algerian SMEs. Nevertheless, these studies generally endorse the notion that innovation can benefit from a reasonable level of competition.

Our investigation determined that foreign competition has a generally positive impact on innovation; however, at the 10th conditional quantile (Q = 0.10), a negative impact becomes predominant. Our research diverges from previous studies that employed mean regression analysis to explore the correlation between foreign competition and innovation. In contrast, we utilized a quantile regression model, which produced a range of outcomes at various conditional quantiles [101]; our research investigated the influence of foreign competition on the success of buying innovation at different stages of innovation in China’s high-technology industries, providing us with a comprehensive understanding of this relationship. To the best of our knowledge, no prior research has utilized the most recent data on this topic. Furthermore, our study went beyond previous research by examining how foreign competition affects both buying and making innovation activities in China’s high-technology industries. In developing economies, buying and making innovation activities are critical to climbing the technology ladder and reducing technology dependence [101]. Innovation is regarded as critical for creating notable competitive advantages and long-term success [101]. This research adds to the discussion on the impact of foreign competition on innovation in emerging economies and highlights the critical role of foreign competitiveness as a catalyst for top businesses to invest in research to enhance their goods and processes. The study also provides insights for policymakers and managers in China’s high-tech industries on the importance of international competition in driving innovation and the need for continuous investment in innovation to achieve long-term success.

7.1. Theoretical Implications and Contributions

On a theoretical level, this research adds to the discussion on the impact of foreign competition on innovation in emerging economies. It highlights the critical role of foreign competitiveness as a catalyst for top businesses to invest in research to enhance their goods and processes. On a practical level, the research provides insights for policymakers and managers in China’s high-tech industries on the importance of international competition in driving innovation and the need for continuous investment in innovation to achieve long-term success.

The principal contribution of the research article lies in the utilization of quantile regression analysis to investigate the link between international competition and innovation in China’s high-technology industries at an industry level. The study furnishes empirical evidence that foreign competition substantially influences the capacity of China’s high-tech sector to innovate and further illuminates the non-linear U-shaped correlation between competition and innovation. Furthermore, the research exhibits the utility of quantile regression analysis in scrutinizing the relationship between foreign competition and innovation at diverse stages of innovation.

Our effort pays attention to the empirical literature via different techniques. First, we give new findings using a different data set. We approach innovation as a dynamic process while doing panel data analysis. As a result, our research stands out from other studies that use a cross-sectional research approach. Furthermore, a firm-level panel data study leaves out critical information on the entry and exit of new industries. This absence might lead to skewed results, even though this issue is not mentioned in industry studies. As a result, the findings of an industry study strengthen our understanding of how industry-relevant characteristics affect buying innovation behavior, with significant rule insinuations for developing nations, especially for China.

Second, we moved beyond linear regression, which gives a single value and has no details about the contingent situation; we used quantile regression analysis, which gives us detailed analysis about foreign competition and buying innovation across China’s high-technology industry. With more up-to-date quantile regression analysis, our study examined the impact of foreign competition on buying innovation at the industry level. We measured foreign competition using the quantified logarithm of the ratio of imported items to total production value, as used in previous studies. The findings of our study provide insights into the interplay between foreign competition and purchasing innovation in industries.

Third, our findings contribute to the study fresh insights into buying innovation and a better knowledge of how to buy innovation, which are influenced by the industrial environment. We look at how the strength of international competition impacts buying innovation efforts, in particular in China’s high-technology industry. This leads to a better understanding of the impact of foreign-invested companies in emerging economies.

Fourth, we look at how international competition affects the strength of buying innovation in Chinese high-tech sectors, in comparison to other current research that primarily concentrates on the link between innovation and performance. Since these industries in emerging nations are the most active, technical leaders, therefore, it is crucial to assess their innovation efforts in the circumstance of foreign competition.

7.2. Limitations and Future Research

One of the main limitations of this study is its focus on high-tech industries in China, which limits the generalizability of the findings to other sectors and countries. Therefore, future research should expand to include additional sectors and examine how different industrial sectors approach innovation in other growing economies. Moreover, future research should investigate the influence of industry and other contextual variables on the pattern of innovative activities in developing nations. Finally, the study’s reliance on industry-level data may obscure individual enterprises’ conduct within an industry, which should be considered when evaluating the analysis’ results. Therefore, future research should use firm-level data to supplement the current results. This research has certain drawbacks. The research is restricted to high-tech industries in China. Future research should be expanded to include additional sectors and examine how different industrial sectors approach innovation. Future research should look into the influence of industry, in addition to the Chinese setting and the circumstances of other growing economies, such as Bangladesh, India, etc., on the pattern of innovative activities. Researchers would be able to compare whether the impact of this prolongation was positive or negative. Local institutions restrict industrial characteristics. Finally, while industry-level data are useful, analysis aids in the generation of new insights into many forms of innovation initiatives. It is vital to note that an industry-level analysis can only provide a broad pattern or trend in innovation activity because it is based on the aggregate of individual firms in the sector. Individual enterprises’ conduct within an industry may be obscured by industry aggregation. This restriction should be considered when evaluating the analysis results. Given how little research has been performed on the influence of foreign competition and industrial context variables on the pattern of innovative activities at the industry level in developing nations, despite the suggested empirical study utilizing industry data, we feel that this restriction should be illuminating. Firm-level data should be used in future research to supplement the current results.

7.3. Policy Implications

The findings of this study have significant policy implications for governments and policymakers in developing nations seeking to foster innovation in their economies. Therefore, they should adopt a sector-specific approach when designing innovation policies. Policymakers should consider the influence of industry and other contextual variables on the pattern of innovative activities in developing nations. They should tailor their policies to suit the unique circumstances of their economies, taking into account factors such as the level of foreign competition, institutional factors, and industry-specific characteristics. Policymakers should focus on creating a conducive environment for firms to innovate. This includes providing incentives such as tax breaks, grants, and subsidies to firms engaged in innovative activities. Additionally, they should invest in education and training programs that build the human capital needed to drive innovation in their economies. Finally, policymakers should recognize the limitations of industry-level data and supplement it with firm-level data when evaluating the effectiveness of their innovation policies. This will help them better understand the conduct of individual enterprises within an industry and adjust their policies accordingly. In summary, policymakers in developing nations should adopt a sector-specific approach to innovation policies, consider industry and contextual variables, focus on creating a conducive environment for innovation, and supplement industry-level data with firm-level data. These steps will help foster innovation in their economies and support long-term economic growth.