Strategic Navigation in VUCA Environments: The Role of Relational Capital and New Product Development in Chinese Technology-Based SMEs

Abstract

In the volatile, uncertain, complex, and ambiguous (VUCA) global environment, technology-based small and medium-sized enterprises (SMEs) face acute resource constraints while striving for innovation-led growth. Drawing on social capital theory and resource-based view, this study investigates how political relational capital (PRC) and business relational capital (BRC) influence growth performance (GP) through the mediating role of new product development (NPD) and the moderating effect of environmental dynamism (ED). Using survey data from 253 Chinese technology-based SMEs, hierarchical regression and Bootstrap confidence interval methods were applied to test the proposed framework. The findings reveal that both PRC and BRC positively affect SME growth, but through distinct mechanisms. BRC exerts a stronger effect on GP via NPD, yet this pathway is significantly weakened under conditions of high environmental dynamism. In contrast, PRC provides a more stable and resilient growth trajectory, as its mediating effect through NPD is less influenced by environmental volatility. These results underscore the critical role of NPD in converting external relational resources into firm outcomes and highlight the conditional value of relational capital under turbulent environments. This research contributes to management theory by differentiating between PRC and BRC, advancing the resource-based view through the integration of NPD as a mediating capability, and contextualizing relational capital within VUCA conditions. Practically, the study offers guidance for SME managers to balance political and business ties and for policymakers to design supportive ecosystems that mitigate environmental uncertainties in emerging markets.

Plain Language Summary

How Business and Political Connections Help Small Tech Firms Grow through New Products in Uncertain Times

Small and medium-sized enterprises (SMEs) in technology industries are often short of money, knowledge, and skilled people. To survive and grow, they need to look outside their own firms and build relationships with others. These external relationships, which we call “relational capital,” can take two main forms: political ties with government agencies and business ties with suppliers, customers, and industry partners. Both types of ties can give firms valuable resources, but they may work in different ways. This study explores how these two types of connections affect the growth of technology-based SMEs in China. We also ask whether new product development (NPD)—that is, creating and introducing new products—explains how connections turn into growth. Finally, we examine whether changes and uncertainty in the business environment (environmental dynamism) make these effects stronger or weaker. We surveyed 253 small technology firms in China and analyzed the data using statistical methods. The results show that both political and business ties help firms grow, but business ties usually have a stronger impact by supporting new product development. However, when the business environment is very uncertain, the benefits of business ties become weaker. In contrast, political ties remain more stable, providing a safety net in turbulent times. This research highlights the importance of balancing political and business relationships. For managers, the study suggests investing in new product development to make the most of external connections. For policymakers, it underlines the need to create stable conditions and support networks that help SMEs turn their relationships into sustainable growth.

Keywords

relational capital new product development growth performance environmental dynamism technology-based SMEs

Introduction

In today’s business landscape, dominated by the ever-present forces of Volatility, Uncertainty, Complexity, and Ambiguity (VUCA; Etienne Fabian et al., 2024; Shirish et al., 2023), the quest for innovation has emerged as the linchpin of survival and growth, particularly for technology-based Small and Medium-sized Enterprises (SMEs). This shift toward embracing open innovation practices, especially in the realm of new product development, transcends strategic choice to become an indispensable necessity (Janka & Guenther, 2018; Ren et al., 2023). The critical significance of innovation in bolstering the resilience and prosperity of SMEs is, thus, a focal point of both academic scrutiny and managerial strategizing. However, the path to achieving successful innovation is fraught with challenges, chiefly among them being the scarcity of internal resources (Choy et al., 2023; Etienne Fabian et al., 2024). These enterprises often grapple with a critical challenge: balancing innovation aspirations with their inherent resource limitations (M. Cooper et al., 2022; Tsang et al., 2021). Addressing how to alleviate the innovation pressure on technology-based SMEs and assist in their growth is a critical issue of joint concern for scholars and practitioners (Akomea et al., 2023; Sarfraz et al., 2023). New product development, as a crucial innovation output for enterprises, serves as a key strategy for gaining competitive advantage. This strategic action poses significant challenges for technology-oriented small and medium enterprises (SMEs), requiring them to source various resources externally (Curchod et al., 2020; Inegbedion et al., 2024). Relational capital, embedded within external relationship networks, plays a vital role in procuring critical resources for technology-oriented SMEs from their external environments. It represents a collection of tangible and intangible resources within these external networks, essential for fostering enterprise innovation. However, effective management and utilization of these external resources are necessary to drive innovation effectively.

In this milieu, the unique context of China’s evolving market—marked by a transition from planned control toward market regulation—provides a fertile ground for exploring the strategic development of enterprise relationship networks (Zhang et al., 2022). Leveraging relational capital to integrate and acquire external resources has been identified as a key strategy for overcoming the “resource bottleneck” in new product development for technology-based SMEs (Balachandran & Eklund, 2024; Tsai & Ghoshal, 1998). Existing studies have divided it into political relational capital and business relational capital (Choy et al., 2023; Zhou et al., 2014). Although this classification is well-established, the empirical evidence regarding the impact of relational capital on firm performance remains inconclusive, ranging from enhancing performance to invoking the “resource curse” argument. We posit that these inconsistencies reflect a theoretical gap in prior research, where relational capital has often been treated as a direct determinant of performance, without specifying the mechanisms through which this transformation occurs, and with limited attention to the influence of a VUCA context. In such environments, the value of external relationships is inherently unstable, as information becomes increasingly perishable, and the coordination across partners becomes more tenuous (R. G. Cooper & McCausland, 2024; Dubey et al., 2021). In the era of emerging technologies, digital transformation, and data/AI-driven competition, the acceleration of information flows, the compression of product life cycles, and the increasing demand for information transparency, alliance coordination, and analytics-driven decision-making in new product development have all gained prominence (R. G. Cooper, 2024; R. G. Cooper & McCausland, 2024; Sun & Liu, 2021). Therefore, incorporating environmental dynamism into the research framework is not only theoretically justified but essential, as it captures the rapid pace and unpredictability of shifts in technological trajectories, customer preferences, and competitive dynamics (Etienne Fabian et al., 2024; Stringfellow et al., 2014).

This paper, based on social capital theory and resource-based view, explores the unique challenges faced by technology-oriented SMEs in China. It constructs a model of “Relational Capital-New Product Development-Growth Performance,” using data from 253 firms in China’s transitioning market economy. Hierarchical regression and Bootstrap confidence intervals are used to test the relationships within the model. The primary objective is to elucidate the delicate balance between leveraging relational capital to access external resources and maintaining strategic autonomy during the innovation processes of technology-oriented SMEs. The core research questions of this study are as follows: (1) What impact does relational capital have on growth performance? (2) What role does new product development play in mediating the relationship between relational capital and growth performance? (3) How does environmental dynamism moderate the relationship between new product development and growth performance? The structure of this paper is organized as follows: Chapter ‘Literature Review and Hypothesis Development’ offers the literature review and theoretical hypotheses. Chapter ‘Data and Methods’ details the research design. Chapter ‘Including But Not Limited to Statistic Analysis’ presents the data analysis result. Chapter ‘Conclusions and Implications’ concludes with the findings and contributions. Finally, the reference list is provided.

This study makes a significant contribution by investigating how new product development mediates the relationship between relational capital and growth performance in technology-based SMEs, specifically in the context of environmental dynamism. The study reveals that as environmental dynamism increases, the relationship between business relational capital, new product development, and growth performance becomes more unstable, underscoring the heightened sensitivity of business relationship capital to external uncertainties. In contrast, political relational capital provides a more stable environment for SMEs, enabling them to better navigate market fluctuations and sustain their innovation efforts. This finding highlights the practical importance of understanding the differential effects of BRC and PRC under varying levels of ED. For managers, it suggests that while BRC plays a crucial role in resource acquisition and innovation, its effectiveness is contingent on a stable or low-dynamism environment. Conversely, PRC offers a more resilient foundation for long-term growth, particularly in volatile and uncertain conditions. Overall, this study enriches our understanding of the strategic management of relational capital within the VUCA environment, offering valuable insights into how firms can strategically leverage relational resources to achieve business success amidst dynamic market conditions.

Literature Review and Hypothesis Development

Relational Capital and Growth Performance

Relational capital constitutes the investment enterprises make in cultivating, sustaining, and enhancing relationships with stakeholders to achieve their goals (Akomea et al., 2023; Hwang, 1987; Park & Luo, 2001). This form of capital is often bifurcated into political (PRC) and business (BRC) relational capital in scholarly discourse (Akomea et al., 2023; Carroll & Teo, 1996; Hwang, 1987) . PRC denotes the extent to which enterprises establish relationships with political entities, including government departments, key government officials, industry regulators, etc. (Choy et al., 2023; Liu & Wang, 2022; Vanacker et al., 2013). BRC reflects the extent of an enterprise’s relationships with business entities, such as suppliers, clients, competitors within the same industry, and research organizations (Balachandran & Eklund, 2024; Qi, 2013).

Political Relational Capital and Growth Performance

Enterprises can harness their PRC to swiftly and effectively access critical policy information and grasp the strategic intentions behind local government development strategies. This utilization of PRC enables enterprises to align their strategies with governmental policies and objectives, thereby fostering a conducive environment for their growth and sustainability (Kurtmollaiev, 2020; Xin & Pearce, 1994). Firstly, technology-based SMEs can use PRC to acquire resources to address internal resource shortages (Akomea et al., 2023; Peng & Luo, 2000). For instance, enterprises with higher PRC are more likely to obtain bank loans with lower capital costs, alleviating the financial strain on technology-based SMEs (Clauss et al., 2021). Good PRC facilitates access to fiscal subsidies, easing financial difficulties for businesses (Tao et al., 2017; Tzabbar & Vestal, 2015). Secondly, the government is often a primary customer for many emerging technology products (Jung et al., 2019). Well-established political relations are beneficial for securing government procurement contracts (Elfenbein & Zenger, 2014; J. Li et al., 2020). As early users, government procurement plays a significant role in leading user innovation and product development for technology-based SMEs, reducing the risk of innovation (Ceipek et al., 2019). Lastly, by quickly and timely understanding policy information and comprehending government strategic intentions, PRC can bring valuable information and knowledge to the enterprise (Y. Yi et al., 2016). This guides the direction of resource allocation and improves the efficiency of resource allocation, thereby providing a protective mechanism for technology-based SMEs to cope with complex and variable external environments, favoring business growth (Byun et al., 2018). Hence, this paper proposes the following hypothesis:

H1a: The political relational capital of technology-based SMEs has a positive relationship with growth performance.

Business Relational Capital and Growth Performance

BRC facilitates the sharing and exchange of knowledge between different commercial entities, promotes the diffusion of innovation, and aids in the creative combination of resource advantages among companies (Minguzzi & Passaro, 2001; Nones, 2023). Good business relationships also indicate a company’s recognition and acceptance, increasing its opportunities to access resources and exert greater influence in market competition (Cucculelli et al., 2019; Jiang et al., 2018). For instance, good relationships with suppliers help companies obtain high-quality raw materials and promote timely delivery; positive relationships with customers enhance customer loyalty, increase sales, and ensure timely settlement of payments, also aiding companies in accurately grasping changes in market demand (Ganson et al., 2022; Liu & Wang, 2022). Good relationships with industry competitors foster cooperation between companies, increasing mutual trust and reducing opportunistic behaviors (Adelino et al., 2017). Scientific research institutions are vital sources of external knowledge; good relationships with these institutions facilitate the transfer and flow of knowledge between companies and research structures, playing a significant role in grasping technological trends and promoting corporate innovation and growth (Al-Shammari et al., 2022; Cull et al., 2017). Therefore, this paper proposes the following hypothesis:

H1b: The business relational capital of technology-based SMEs has a positive relationship with growth performance.

Relational Capital and New Product Development

New Product Development (NPD) refers to the process of transforming market demands, technological advancements, or innovative ideas into marketable new products or services (Greven et al., 2023; Kiss & Barr, 2017). In the complex VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) environment, NPD stands as a pivotal strategy for enterprises to maintain their innovation autonomy and secure a competitive edge (Balachandran & Eklund, 2024). Compared to large enterprises, technology-based SMEs lack the benefits of economies of scale and scope, deriving their competitive advantages primarily from the capability to offer new products (or services; Cantó et al., 2019; Janka & Guenther, 2018). On one hand, through innovation in product functionality, they increase differentiation; on the other, they reduce product costs through technological improvements (Haeussler et al., 2012; Jung et al., 2019). As a key innovation activity for technology-based SMEs, NPD effectively transforms externally acquired resources, thereby further facilitating enterprise growth (Hånell et al., 2017). Therefore, the activity of NPD is the core mediating mechanism through which technology-based SMEs convert resources into GP.

Political Relational Capital and New Product Development

In China, the irregularities inherent in the political system, coupled with the high-risk nature of research and development, introduce substantial uncertainties for technology-based SMEs in their NPD endeavors (Greven et al., 2023; H. Li & Atuahene-Gima, 2001). Given the government’s significant authority in project approval and resource allocation, it can provide robust support to enterprises in areas such as financing and technology, and assist in the acquisition and cultivation of technical talent (Cucculelli et al., 2019; Jugend et al., 2018). PRC signifies an enterprise’s capability to secure specific scarce resources, functioning as a signaling mechanism for economic strength and social reputation under conditions of information asymmetry (Vanacker et al., 2013; Zhou et al., 2014). The acquisition of institutional information through PRC is paramount for enterprises to accurately understand and respond to technological and market dynamics(Tao et al., 2017). PRC ensures investment security for SMEs engaging in innovative activities and provides avenues for accessing industrial policy information, thereby enhancing their ability to seize innovation opportunities (Cantó et al., 2019; Vanacker et al., 2013). It underscores an enterprise’s proficiency in obtaining critical scarce resources, acting as a signaling mechanism for economic strength and social standing under information asymmetry, and facilitates the mobilization of external resources for technology-based SMEs in their NPD processes (Kiss & Barr, 2017; Tao et al., 2017). In resource-constrained environments, PRC offers a flexible resource allocation mechanism, providing significant advantages for technology-based SMEs in their NPD initiatives (D. J. Miller, 2006; Sheng et al., 2011). Hence, this paper proposes the following hypothesis:

H2a: The political relational capital of technology-based SMEs has a positive relationship with new product development.

Business Relational Capital and New Product Development

NPD, as a strategic initiative characterized by significant uncertainty, can substantially benefit from strong relationships with external business entities. Such relationships facilitate the acquisition of critical knowledge and information, thereby promoting the development of new products (Kiss & Barr, 2017; D. J. Miller, 2006). Specifically, BRC assists enterprises in securing essential resources and information required for NPD. These resources include technology, raw materials, and equipment, while the information pertains to market demands, technological trends, and competitor dynamics (Jayaram & Malhotra, 2010; J. Yi et al., 2021). By establishing close ties with suppliers, customers, research institutions, and other stakeholders, enterprises can access and utilize these resources and information more swiftly and accurately, thereby enhancing the efficiency and success rate of NPD (Cucculelli et al., 2019; Nahapiet & Ghoshal, 1998). Moreover, BRC bolsters innovative collaboration between enterprises and external partners. Through strategic alliances, joint R&D, and technical cooperation, enterprises can engage in NPD with other firms or institutions (Qi, 2013; Y. Yi et al., 2016). This collaboration not only enables the sharing of research and development costs and risks but also leverages the unique strengths and expertise of each party, generating synergies that enhance the enterprise’s innovative capacity (Murray & Chao, 2005). Additionally, BRC improves the market acceptance and promotion of new products (Sheng et al., 2011; Tao et al., 2017). By cultivating strong relationships with distributors, dealers, industry associations, and other market intermediaries, enterprises can more effectively promote and penetrate the market with their new products (Adelino et al., 2017; Tsai & Ghoshal, 1998). This also facilitates necessary adjustments and refinements during the product development process, thereby increasing the market adaptability and competitiveness of the new products (Aljanabi, 2022; Rochford & Rudelius, 1997). Hence, this paper proposes the following hypothesis:

H2b: The business relational capital of technology-based SMEs has a positive relationship with new product development.

New Product Development and Growth Performance

To undertake NPD, enterprises must accurately discern customer needs and possess the corresponding technological capabilities to act upon these insights. Consequently, NPD serves as a critical link between technology and customer demand. Research has demonstrated that NPD is associated with higher sales growth and enhanced profitability (Kiss & Barr, 2017; D. J. Miller, 2006; Schilke, 2014). Firstly, new product development is essential for market penetration and plays a significant role in a firm’s profitability (Cantó et al., 2019; Rochford & Rudelius, 1997). It can create new market opportunities, increase customer willingness to pay, and thereby boost customer loyalty and market share(Rainey, 2008; Zhang et al., 2022). Secondly, by developing new products, enterprises can reduce their dependence on a single product or market, which helps in risk diversification (Clark, 1989; Greven et al., 2023). This diversification enables firms to cater to different customer segments, enhancing customer loyalty and satisfaction, and generating stable and continuous revenue growth (Rochford & Rudelius, 1997). Lastly, new products typically have higher added value and profit margins, which can improve a firm’s profitability (Prange et al., 2015; Zhan et al., 2018). Successful NPD often results in profit margins above the industry average, strengthening the firm’s financial health (Cantó et al., 2019; Tao et al., 2017). Additionally, innovative products can enhance the firm’s brand image and market recognition, driving sales of other products and creating synergies that further improve the overall financial performance of the enterprise (Aljanabi, 2022; H. Li & Atuahene-Gima, 2001). In summary, NPD can effectively promote firm performance growth by meeting market demand, enhancing product competitiveness, and diversifying risk (Jung et al., 2019; Park & Luo, 2001). Hence, this paper proposes the following hypothesis:

H3: The new product development of technology-based SMEs has a positive relationship with growth performance.

The Mediating Role of New Product Development

The Mediating Role of New Product Development in the Relationship Between Political Relational Capital and Growth Performance

While NPD enables the transformation of external resources into a firm’s competitive advantage, the current transitional economic context in China poses significant uncertainties for technology-based SMEs due to the lack of standardization in formal institutions and the high risk associated with R&D activities (Griffin et al., 2018; Jung et al., 2019). Establishing connections with political entities can mitigate these risks and uncertainties for these enterprises (Reinecke & Donaghey, 2021; Tao et al., 2017). Firstly, acquiring institutional information through PRC is vital for technological SMEs to grasp technological and market dynamics (Greven et al., 2023). This understanding significantly reduces the risks faced in new product development, emanating from various aspects such as technology and market fluctuations (Aljanabi, 2022; Nahapiet & Ghoshal, 1998). Secondly, PRC provides investment security for SMEs’ innovative activities and offers channels to access industrial policy information. This access is beneficial for better seizing innovation opportunities (Haeussler et al., 2012). Lastly, PRC indicates a firm’s ability to acquire specific scarce resources (J. Yi et al., 2021). It serves as a signaling mechanism under conditions of information asymmetry, showcasing a firm’s economic strength and social reputation (Reinecke & Donaghey, 2021).

Therefore, PRC can facilitate stable NPD activities in technology-based SMEs. Stable NPD implies that firms can continuously provide the market with innovative and differentiated products (Kiss & Barr, 2017). Differentiation enhances consumer utility and helps enterprises to capture a larger market share (Ganson et al., 2022). Based on the above discussion, this paper posits that PRC promotes stable NPD activities in technology-based SMEs. Furthermore, stable NPD is a crucial means of achieving a differentiated competitive advantage (Jugend et al., 2018; Zheng & Xia, 2018). This form of competitive advantage enables firms to attain higher sales, growth rates, and profit margins. Consequently, this paper proposes the following hypothesis:

H4a: New product development mediates the relationship between the political relational capital of technology-based SMEs and their growth performance.

The Mediating Role of New Product Development in the Relationship Between Business Relational Capital and Growth Performance

NPD is characterized by high resource consumption. Establishing good relationships with business entities can reduce the cost and enhance the efficiency of NPD (Wu, 2011; J. Yi et al., 2021). Firstly, forming strong relationships with customers provides multiple perspectives, capabilities, and experiences for enterprises engaged in NPD (Cantó et al., 2019). This aids in accurately grasping market demand fluctuations, thereby enhancing the effectiveness of the enterprise’s NPD initiatives (Griffin et al., 2018; Y.-H. Li et al., 2009). Secondly, suppliers possess a wealth of skills and information pertinent to NPD and determine the timeliness and pricing of raw material supply (Cantó et al., 2019; Moreno-Moya & Munuera-Aleman, 2016). Closer ties with suppliers enable enterprises to acquire the necessary resources for NPD more swiftly and cost-effectively (Cantó et al., 2019; Hanelt et al., 2021). Furthermore, good relationships with competitors can enhance trust and mutual understanding, thus facilitating knowledge sharing and collaboration (Vanacker et al., 2013). Lastly, strong relationships with research and development institutions allow for greater familiarity and the formation of interactive norms (Akomea et al., 2023; Jugend et al., 2018). This mutual understanding enables both parties to discuss technical issues in a common language, which benefits the efficiency of new product development.

By reducing costs and improving efficiency through BRC, NPD enables technology-based SMEs to launch new products faster and better than their competitors (Moreno-Moya & Munuera-Aleman, 2016). This establishes advantages in several aspects, including time, technology, and industry standards (Huang et al., 2019). Given that customers are willing to pay a premium for faster access to products, enhanced NPD can lead to higher profit margins and larger market shares for the enterprises (M. Cooper et al., 2022; Kiss & Barr, 2017). More efficient development of new products allows technology-based SMEs to significantly widen the gap with their competitors (Jugend et al., 2018; Kiss & Barr, 2017). Therefore, this paper posits that BRC can promote the efficiency and reduce the costs of NPD in enterprises, thereby significantly enhancing GP. Based on the above discussion, this paper proposes the following hypothesis:

H4b: New product development mediates the relationship between the business relational capital of technology-based SMEs and their growth performance.

The Moderating Role of Environmental Dynamism

Environmental dynamism (ED) captures the rate and unpredictability of changes in markets and technologies (Dess & Beard, 1984; Soluk et al., 2021). ED is particularly relevant for innovation research because it shapes the timeliness and reliability of external information, the stability of inter-firm relationships, and the length of opportunity windows—conditions that are central to new product development (NPD; Etienne Fabian et al., 2024; Stringfellow et al., 2014).This relevance has become more salient in the contemporary technology landscape. Digital transformation and data competition accelerate information flows and compress product life cycles, making NPD increasingly dependent on rapid sensing of market signals, analytics-driven decision-making, and effective cross-organizational collaboration (Mokhtarzadeh et al., 2022; Schommer et al., 2019; Teece, 2018). As a result, the same relational resources may generate substantially different innovation outcomes depending on the level of ED. For technology-based SMEs, this contingency is especially pronounced because they typically face limited slack resources and heavier reliance on external ties to obtain knowledge and complementary assets for NPD (Elfenbein & Zenger, 2014; Shirish et al., 2023).

When ED is relatively low, changes in customer preferences, technologies, and competitive actions occur more gradually. In such settings, firms can better evaluate external partners, maintain stable collaboration routines, and plan the sequencing of NPD activities, thereby translating relationship-based resources into development progress more reliably (Kurtmollaiev, 2020; Teece, 2018). While turbulent environments may create opportunities for innovation, for technology-based SMEs, high environmental dynamism (ED) often translates into instability and uncertainty that can constrain resource acquisition and thus impede NPD (Adelino et al., 2017; Simerly & Li, 2000; Stringfellow et al., 2014). Under low ED, environmental change is relatively slow and relationships with external actors are more stable, enabling firms to identify reliable partners through embedded networks and obtain the resources required for NPD more consistently (Saka-Helmhout et al., 2022; Schilke, 2014). As ED increases, environmental relationships become more fluid and less transparent, making it harder for firms to evaluate external networks and sustain collaboration (Hussain et al., 2021; Meuleman et al., 2010; Xu & Hou, 2024). High ED also increases information gaps and coordination volatility, raising the difficulty of mobilizing and utilizing external resources for NPD (Tzabbar & Vestal, 2015; Y. Yi et al., 2016). Based on the above analysis, this paper proposes the following research hypotheses:

H5a: Environmental dynamism negatively moderates the relationship between the political relational capital of technology-based SMEs and new product development.

H5b: Environmental dynamism negatively moderates the relationship between the business relational capital of technology-based SMEs and new product development.

Moderated Mediation Effects

From the aforementioned hypotheses, it is evident that relational capital promotes business growth through the mediating mechanism of new product development, while ED weakens the relationship between relational capital and new product development (Hussain et al., 2021; Mokhtarzadeh et al., 2022; Teece, 2018). Integrating these discussions, ED plays a moderating role in the transmission process of “Relational Capital - new product development - Growth Performance,” implying the presence of a further moderated mediation mechanism (Elfenbein & Zenger, 2014; Jung et al., 2019; Qi, 2013). Consequently, this paper proposes the following research hypotheses.

H6a: Environmental dynamism negatively moderates the mediating role of new product development between the political relational capital of technology-based SMEs and their growth performance. In essence, the stronger the environmental dynamism, the weaker the mediating effect of new product development.

H6b: Environmental dynamism negatively moderates the mediating role of new product development between the business relational capital of technology-based SMEs and their growth performance. In essence, the stronger the environmental dynamism, the weaker the mediating effect of new product development.

The theoretical framework and hypothesized relationships of the study in this paper are shown in Figure 1.

Figure 1.

Theoretical framework.

Data and Methods

Samples and Data

The research sample of this article is derived from technology-based SMEs. The industry types specifically include industries such as information technology, high-end equipment manufacturing, new materials, biotechnology, new energy, energy conservation and environmental protection, digital creativity, etc. located in Chengdu, Beijing, Shanghai, Shenzhen, Hangzhou, Mianyang, and other regions. These cities are characterized by a high density of technology-oriented enterprises and robust economic infrastructures, which strengthens the overall representativeness and external validity of the sample. The data collection methods included both on-site interviews and online submissions. A one-way ANOVA was conducted to compare these two sources of data, which showed no significant differences (p > .05), allowing for the integration of both offline and online questionnaire data for analysis. All measurement scales employed in this study were adapted from well-established instruments in prior research. To ensure their suitability for technology-based SMEs in the Chinese context, minor wording adjustments were made to reflect firm size, technological orientation, and institutional environment. Specifically, ambiguous expressions referring to large firms or diversified business groups were revised to better capture the operational reality of small and medium-sized technology enterprises. In addition, all items were reviewed by two academic experts and three practitioners from technology-based SMEs, and a pilot test was conducted to confirm clarity and contextual appropriateness.

In addition, prior to the formal distribution of the questionnaires, to ensure the statistical validity of the research data, we used G*Power 3.1 statistical software to scientifically calculate the minimum required sample size. Following common practices in social science research, we selected t-test as the base analysis method and paired it with a linear multiple regression model for sample size estimation. Additionally, based on the effect size standards from Curchod et al. (2020), we defined the effect size as medium (f² = 0.15) and set the significance level (alpha) at the widely accepted .05 in academia. The calculations showed that to achieve a statistical power of 0.95, at least 89 participants were required. Therefore, the minimum required sample size was N = 89. Accordingly, 89 respondents were determined to be the minimum required sample size, and the subsequent data collection was conducted in line with this threshold.

Following the principle of simple random sampling, from April 2023 to October 2023, a total of 1,000 questionnaires were distributed, and ultimately 341 samples were collected. After excluding invalid samples, 253 valid samples were obtained, resulting in an effective response rate of 25.30%. In light of the foregoing justification of the minimum required sample size, the results derived from the current sample can be considered to possess adequate statistical validity.

Among the 253 enterprises surveyed, a considerable proportion have been established for more than 8 years. In terms of ownership structure, 183 firms are privately owned or privately controlled. Regarding industry classification, 146 firms (57.7%) operate in strategic emerging industries, indicating strong representation of innovation-driven sectors. With respect to firm size, enterprises employing between 100 and 300 employees constitute 40.7% of the sample. In terms of total operating revenue, 92 firms report annual revenues ranging between 10 and 50 million. Table 1 presents the descriptive statistical analysis results of the background characteristics of the 253 enterprises.

Table 1.

Sample Descriptive Analysis.

Indicator name	Classification	Frequency	Percentage
Years of establishment	Less than 1	14	5.5
	1–3	48	19.0
	3–5	66	26.1
	5–8	52	20.6
	More than 8	73	28.9
Ownership	State-owned/state-controlled	41	16.2
	Privately-owned/privately-controlled	183	72.3
	Foreign-owned/foreign-controlled	21	8.3
	others	8	3.2
Industry (whether it is a strategic emerging industry)	Yes	146	57.7
Industry (whether it is a strategic emerging industry)	No	107	42.3
Total number of employees	Less than 100 people	67	26.5
	100–300 people	103	40.7
	300–500 people	83	32.8
Operating revenue (million)	Less than 1	38	15.0
	1–10	51	20.2
	10–50	92	36.4
	50–200	72	28.5

Measurement of Variables

Political Relational Capital (PRC)

Drawing upon the measurements of corporate political relational capital as proposed by Peng and Luo (2000) and Sheng et al. (2011), this study scores the following six items: (1) The company has relatively close connections with government departments (such as tax, industrial and commercial administrations); (2) The company maintains relatively close contacts with officials at various levels of government; (3) The company has good relationships with state-owned banks and other financial institutions; (4) The company has good relationships with local industrial parks and management committees; (5) The company can quickly and timely access major policy information; (6) The company is able to well understand the strategic development intentions of the local government.

Business Relational Capital (BRC)

Referencing the measurements of corporate business relationships by Sheng et al. (2011), this study assesses the following four items: (1) The company has good relationships with its customers; (2) The company maintains good relationships with its suppliers; (3) The company has good relationships with its industry competitors; (4) The company has good relationships with scientific research institutions.

New Product Development (NPD)

In line with the research on new product development by H. Li and Atuahene-Gima (2001) and Herrmann et al. (2022), the scale for this study includes the following four items: (1) The company places a high value on the development of new products and invests a significant amount of financial resources into it; (2) The company develops a greater variety of new products simultaneously; (3) The company introduces new products to the market at a faster rate; (4) The company has increased its overall commitment to the development and promotion of new products.

Environmental Dynamism (ED)

Based on the measurement approach by D. Miller (1987), Mokhtarzadeh et al. (2022), Sheng et al. (2011), this concept includes three items: (1) The industry in which the company operates undergoes significant technological changes; (2) The preferences of the company’s customers change greatly over time; (3) The company faces intense market competition.

Growth Performance (GP)

Corporate growth encompasses both qualitative and quantitative aspects. In accordance with the research of Homburg et al. (2010) and Wamba (2022), this study evaluates growth performance from the following four dimensions: (1) The company experiences rapid growth in sales; (2) The company’s assets grow rapidly; (3) The company has a higher relative market share; (4) The company has stronger production capabilities.

Control Variables

This paper selects control variables at two levels. At the individual level, the control variables specifically include aspects of the company’s top management, encompassing four variables: gender, academic qualifications, age, and length of professional experience. At the organizational level, the control variables specifically include years of establishment, ownership, industrial attributes (i.e., whether it is a strategic emerging industry as defined by the National Bureau of Statistics in the “Strategic Emerging Industries (2018)” document), total number of employees, and operating revenue (Fort et al., 2013).

Including But Not Limited to Statistic Analysis and Results

Reliability and Validity Test

Table 2 presents the results of the reliability and validity tests. The internal consistency coefficients (Cronbach’s Alpha) for the measurement scales of the five latent variables all exceed the threshold value of .7, indicating that the measurement scales used in this study demonstrate good reliability. The standardized factor loadings for each item exceed the threshold value of 0.5. Moreover, the Composite Reliability (CR) for each latent variable is greater than 0.7, and the Average Variance Extracted (AVE) exceeds 0.5. These results suggest that the measurements of the latent variables in this study exhibit strong convergent validity.

Table 2.

Results of Reliability and Validity Analyses for Each Latent Variable.

Variables	Items	Factor loading	t-Stat	Cronbach’s alpha	CR	AVE
PRC	PRC1	0.713	-	.937	0.940	0.723
	PRC2	0.796	12.518(***)
	PRC3	0.920	14.329(***)
	PRC4	0.928	14.514(***)
	PRC5	0.849	13.245(***)
	PRC6	0.876	13.659(***)
BRC	BRC1	0.789	-	.861	0.867	0.621
	BRC2	0.869	15.093(***)
	BRC3	0.740	11.917(***)
	BRC4	0.748	12.046(***)
NPD	NPD1	0.779	-	.900	0.902	0.696
	NPD2	0.810	13.915(***)
	NPD3	0.873	15.122(***)
	NPD4	0.872	15.081(***)
GP	GP1	0.787	-	.908	0.910	0.718
	GP2	0.817	14.247(***)
	GP3	0.911	16.289(***)
	GP4	0.869	15.508(***)
ED	ED1	0.680	-	.722	0.747	0.502
	ED2	0.841	7.325(***)
	ED3	0.579	7.532(***)

Note. ***p < .001.

To further test for discriminant validity, confirmatory factor analysis was performed on a sample of 253 data points. The results, as presented in Table 3, indicate that the baseline model meets several key criteria for good model fit: the chi-square to degrees of freedom ratio (χ²/df) is less than 3, the Root Mean Square Error of Approximation (RMSEA) is less than 0.07, and the Standardized Root Mean Square Residual (SRMR) is below 0.05. In addition, the Comparative Fit Index (CFI), Incremental Fit Index (IFI), Normed Fit Index (NFI), and Non-Normed Fit Index (NNFI) all exceed 0.9. These results collectively suggest that the model demonstrates a good fit across various metrics, supporting the discriminant validity of the constructs within the study. The four-factor models 1 and 2, the three-factor model, the two-factor model, and the single-factor model all showed fit indices that were inferior to those of the baseline model. This indicates that compared to the alternative models, the baseline model of this study exhibits superior fit quality. Such a finding suggests that there is effective discriminant validity among the variables, as the baseline model more accurately represents the data compared to the simpler or alternative factor structures.

Table 3.

Validation Factor Analysis Results.

Indicator name	χ²	Df	χ2/df	RMSEA	SRMR	CFI	IFI	NFI	NNLI
Baseline Model	376.53	179	2.104	0.066	0.041	0.949	0.950	0.908	0.941
Four-Factor Model 1	697.06	183	3.809	0.106	0.094	0.868	0.869	0.830	0.849
Four-Factor Model 2	549.61	183	3.003	0.089	0.050	0.906	0.907	0.866	0.892
Three-Factor Model	865.149	186	4.651	0.120	0.098	0.826	0.827	0.789	0.803
Two-Factor Model	991.928	188	5.276	0.130	0.106	0.794	0.795	0.758	0.770
Single-Factor Model	1897.061	189	10.037	0.189	0.132	0.562	0.564	0.538	0.513

Note. N = 253; The baseline model is a five-factor model; Four-Factor Model 1: PRC and BRC are combined into a single latent factor; Four-Factor Model 2: NPD and GP are merged into one latent factor; Three-Factor Model: PRC + BRC, ED, NPD + GP; Two-Factor Model: PRC + BRC, ED + NPD + GP; Single-Factor Model: All five constructs represent a single underlying latent factor.

Given the cross-sectional design and the reliance on a single key informant, common method bias (CMB) may potentially influence the observed relationships and, in turn, the accuracy of the study’s results. To assess the extent to which CMB may be a concern, we employed a multi-step approach. Firstly, this paper employed the Harman single-factor test (Podsakoff & Organ, 1986). An unrotated factor analysis was conducted on all items of the variables involved. The variance explained by the first factor was 29.76%, and the second factor accounted for 10.19% of the variance. The variance contributions of the remaining factors were all less than 10%, indicating no significant common method bias. Secondly, we performed a confirmatory factor analysis (CFA), and estimated a single-factor measurement model. As reported in Table 3, Compared to the five-factor model proposed in this study, the fit of the single-factor model is poor, with the following fit indices: χ² = 1897.061, χ²/df = 10.037, RMSEA = 0.189, SRMR = 0.132, CFI = 0.562, IFI = 0.564, NFI = 0.538, and NNLI = 0.513. Therefore, the potential impact of common method bias in this study is negligible. The results from the tests above suggest that common method bias has little to no effect on the findings, thus enabling further analysis.

Correlation Analysis

Table 4 reports the Pearson’s correlation coefficient matrix. As shown in Table 4, the correlations among the focal constructs are significantly positive. To address potential multicollinearity concerns arising from these associations, we calculated variance inflation factors (VIFs) in the regression analyses. The VIF values for political relational capital, business relational capital, new product development, and growth performance are all well below the commonly used cutoff of 10, suggesting that multicollinearity is not a serious concern in the present study.

Table 4.

Correlation Analysis Results.

Variable	Mean	Std	1	2	3	4	5
1. PRC	3.64	0.89	(0.850)
2. BRC	3.83	0.73	0.575**	(0.788)
3. NPD	3.49	0.86	0.408**	0.490**	(0.834)
4. ED	3.69	0.78	0.198**	0.304**	0.421**	(0.709)
5. GP	3.32	0.84	0.431**	0.572**	0.729**	0.319**	(0.847)

Note. N = 253; The diagonal value is the square root of AVE; **p < .01 (two-tail test).

Hypothesis Testing

Utilizing hierarchical regression analysis and methods such as Bootstrap, this study conducts hypothesis testing for several key assumptions: the primary effect hypothesis, the mediating effect hypothesis, the moderating effect hypothesis, and the moderated mediation effect hypothesis.

Main and Mediating Effects

Table 5 presents the results of the hypotheses concerning main and mediating effects. In model M2, both PRC and BRC have a positive relationship with GP (β1 = .132, p < .05; β2 = .511, p < .01), supporting hypotheses H1a and H1b. In model M3b, both PRC and BRC have a positive relationship with NPD (β1 = .187, p < .01; β2 = .376, p < .01), supporting hypotheses H2a and H2b. Model M3a shows a positive relationship between new product development and growth performance (β = .683, p < .01), and hypothesis H3 was supported.

Table 5.

Hierarchical Regression Results.

Variables	GP					NPD
Variables	M1	M2	M3a	M4a	M4b	M3	M3b
Control variables
Gender	−0.187*	−0.031	−0.178**	−0.147*	−0.102	−0.012	0.122
Academic qualifications	−0.055	0.055	0.035	0.081	0.057	−0.131*	−0.017
Age	−0.044	0.022	−0.088	−0.056	−0.055	0.064	0.130
Length of professional experience	0.048	0.083	0.045	0.046	0.067	0.004	0.030
Years of establishment	0.019	0.026	0.002	−0.007	0.015	0.025	0.023
Ownership	0.144	0.120	0.006	0.036	0.005	0.210**	0.196**
Industrial attributes	−0.099	−0.066	−0.181**	−0.155*	−0.158**	0.121	0.156
Total number of employees	0.060	0.060	0.039	0.038	0.044	0.030	0.029
Operating revenue	0.305***	0.178***	0.109***	0.094**	0.082**	0.286***	0.174***
Independent variables
PRC		0.132**		0.148***			0.187***
BRC		0.511***			0.315***		0.376***
Mediating variable
NPD			0.683***	0.628***	0.565***
R ²	.176	.412	.570	.586	.622	.179	.351
Adjusted R²	.146	.385	.552	.576	.604	.149	.321
Change in R²	.176	.235	.394	.410	.445	.179	.171
F-value	5.781***	15.325***	32.102***	31.021***	35.984***	5.905***	11.836***

Note. The non-standardized regression coefficients are shown in the table; N = 253.

p < .05, **p < .01, ***p < .001.

Models M4a and M4b respectively display the outcomes of the mediation regression analysis. Model M4a shows that, upon incorporating the mediating variable of NPD, the positive relationship of PRC with GP decreased from 0.379 (p < .01) to 0.148 (p < .01), preliminarily supporting hypothesis H4a. Model M4b indicates that, after the inclusion of NPD as a mediating variable, the positive relationship of BRC with GP decreased from 0.596 (p < .01) to 0.315 (p < .01), preliminarily supporting hypothesis H4b. To further test the robustness of the mediating effect, a Bootstrap analysis was conducted (with 5,000 samples, CI = 95%). The results, as shown in Table 6, reveal that the total effect of PRC on GP is 0.379, with a direct effect of 0.148, accounting for 38.92%, and an indirect effect of 0.232, accounting for 61.08%. The 95% confidence interval for the indirect effect ranges from [0.143, 0.325], not including 0, indicating that NPD partially mediates the relationship between PRC and GP. The total effect of BRC on GP is 0.596, with a direct effect of 0.315, accounting for 52.91%, and an indirect effect of 0.281, accounting for 47.09%. The 95% confidence interval for the indirect effect ranges from [0.189, 0.379], not including 0, indicating that NPD partially mediates the relationship between BRC and GP. Hence, hypotheses H4a and H4b of this study are supported.

Table 6.

Results of Bootstrap Mediation Effect Test.

Independent variable	Dependent variable: GP
	Type of effect	Value of effect	Boot SE	Bootstrap 95% confidence interval		Proportion of effect value (%)
	Type of effect	Value of effect	Boot SE	LLCI	ULCI	Proportion of effect value (%)
PRC	Total effects	0.3790	0.0586	0.2636	0.4944	100.00
	Main effects	0.1475	0.0485	0.0520	0.2431	38.92
	Mediating effects	0.2315	0.0465	0.1430	0.3252	61.08
BRC	Total effects	0.5957	0.0625	0.4727	0.7187	100.00
	Main effects	0.3152	0.0551	0.2067	0.4238	52.91
	Mediating effects	0.2805	0.0480	0.1888	0.3786	47.09

Moderating Effect

As indicated in Table 7, the regression coefficient for the interaction term between PRC and ED on NPD is negative and significant (β = −.118, p < .05). In addition, to further examine the moderating effect, we plotted the interaction effect (see Figure 2a) and conducted simple slope analyses. The results indicate that when ED is low (one standard deviation below the mean), PRC is positively and significantly related to NPD (simple slope = 0.415, SE = 0.068, p < .001). When ED is high (one standard deviation above the mean), the positive relationship between PRC and NPD remains significant but is attenuated (simple slope = 0.230, SE = 0.068, p < .001). Therefore, Hypothesis H5a is supported. Likewise, the regression coefficient for the interaction term between BRC and ED on NPD is negative and significant (β = −.170, p < .01). The corresponding interaction plot is presented in Figure 2b. The simple slope analysis further shows that when ED is low (one standard deviation below the mean), BRC is positively and significantly associated with NPD (simple slope = 0.539, SE = 0.086, p < .001). However, when ED is high (one standard deviation above the mean), the positive relationship between BRC and NPD is substantially weakened, although it remains significant (simple slope = 0.273, SE = 0.083, p < .01). Accordingly, Hypothesis H5b is supported. This indicate that ED serves as a boundary condition: as external uncertainty increases, the effectiveness of relational capital in promoting NPD diminishes. This constraining effect is more pronounced for BRC, indicating that BRC are more sensitive to environmental turbulence than PRC.

Table 7.

Moderating Effect of ED on Relational Capital and NPD.

Variables	NPD
Variables	M4	M5	M6	M7
Control variables
Gender	0.012	0.018	0.049	0.064
Academic qualifications	0.047	0.048	−0.032	−0.018
Age	0.190**	0.195**	0.150*	0.163*
Length of professional experience	−0.001	0.008	0.026	0.030
Years of establishment	−0.005	−0.002	0.031	0.038
Ownership	0.237***	0.217***	0.164**	0.139*
Industrial attributes	0.136	0.158	0.109	0.118
Total number of employees	0.049	0.027	0.053	0.030
Operating revenue	0.182***	0.163***	0.177***	0.157***
Independent variable
PRC	0.322***	0.322***
BRC			0.401***	0.405***
Moderating variable
ED	0.370***	0.391***	0.315***	0.357***
Interaction term
PRC × ED		−0.118**
BRC × ED				−0.170***
R ²	.378	.388	.401	.416
Adjusted R²	.350	.357	.374	.387
Charge in R²	.087	.010	.070	.015
F-value	13.318***	12.683***	14.670***	14.256***

Note. The non-standardized regression coefficients are shown in the table; N = 253.

p < .05, **p < .01, ***p < .001.

Figure 2.

(a) Diagram illustrating the moderating effect of ED on the relationship between PRC and NPD and (b) diagram illustrating the moderating effect of ED on the relationship between BRC and NPD.

Moderated Mediation Effect

As the indirect effects are significant under different conditions of ED, relying solely on conditional indirect effects is insufficient to determine the presence of moderated mediation effects. The next step involves analyzing the moderated mediation effect of ED within the “RC-NPD-GP” mediation path. Utilizing the Product of Coefficients approach, this study examines the magnitude of the mediating role played by NPD between corporate relational capital and GP across varying levels of ED. This serves to test the moderated mediation effect of ED. The analysis is conducted using the Process macro plugin in SPSS, specifically Model 8 within Process. A Bootstrap sampling of 5,000 iterations is employed, with a confidence interval set at a 95% confidence level.

Table 8 reports the moderated mediation effect determination metrics outputted by the Process plugin, in which the moderating effect of ED on the indirect relationship between PRC and GP is assessed. The moderating metric is −0.071, indicating a negative moderation. The standard deviation is 0.041, with a confidence interval of [−0.153, 0.010]. This interval includes zero, suggesting that the hypothesis of a moderated mediation effect is not supported. Therefore, hypothesis H6a is not supported. The feasible explanations are as follows: first, PRC involves long-term, trust-based relationships with political entities, making it less susceptible to short-term environmental changes. Second, the Chinese government’s strong support for SMEs in science and technology makes political relationship capital provide more stable resources, and less vulnerable to fluctuations in the external environment. The moderating effect of ED on the indirect relationship between BRC and GP is indicated by a moderation metric of −0.096, representing a negative moderation. The standard error is 0.042, with a confidence interval of [−0.173, −0.007]. This confidence interval does not include zero, thereby signifying a significant moderated mediation effect. Consequently, hypothesis H6b is validated. Table 9 summarizes the hypothesis testing results of this study.

Table 8.

Moderated Mediation Test.

IV	MV	RC → NPD → GP
		Conditional mediating effects				Index of moderated mediation
		Effect	SE	LLCI	ULCI	Index	SE	LLCI	ULCI
PRC	Low ED	0.2417	0.0557	0.1313	0.3505	−0.0709	0.0411	−0.1526	0.0099
PRC	High ED	0.1471	0.0421	0.0598	0.2238	−0.0709	0.0411	−0.1526	0.0099
BRC	Low ED	0.2952	0.0591	0.1681	0.4055	−0.0962	0.0417	−0.1729	−0.0072
BRC	High ED	0.1669	0.0453	0.0755	0.2543	−0.0962	0.0417	−0.1729	−0.0072

Note. High and low ED is defined as one standard deviation above and below the mean, respectively; LLCI and ULCI are the lowest and highest confidence intervals respectively.

Table 9.

Summary of Hypothesis Testing Results.

Type of hypothesis	Hypothesis number	Hypothesis content	Supported
Main effect	H1a	The political relational capital of technology-based SMEs has a positive relationship with growth performance.	Yes
	H1b	The business relational capital of technology-based SMEs has a positive relationship with growth performance.	Yes
	H2a	The political relational capital of technology-based SMEs has a positive relationship with new product development.	Yes
	H2b	The business relational capital of technology-based SMEs has a positive relationship with new product development.	Yes
	H3	The new product development of technology-based SMEs has a positive relationship with growth performance.	Yes
Mediation effect	H4a	New product development mediates the relationship between the political relational capital of technology-based SMEs and their growth performance.	Yes
Mediation effect	H4b	New product development mediates the relationship between the business relational capital of technology-based SMEs and their growth performance.	Yes
Moderation effect	H5a	Environmental dynamism negatively moderates the relationship between the political relational capital of technology-based SMEs and new product development.	Yes
Moderation effect	H5b	Environmental dynamism negatively moderates the relationship between the business relational capital of technology-based SMEs and new product development.	Yes
Moderated mediation effect	H6a	Environmental dynamism negatively moderates the mediating role of new product development between the political relational capital of technology-based SMEs and their growth performance. In essence, the stronger the environmental dynamism, the weaker the mediating effect of new product development.	No
Moderated mediation effect	H6b	Environmental dynamism negatively moderates the mediating role of new product development between the business relational capital of technology-based SMEs and their growth performance. In essence, the stronger the environmental dynamism, the weaker the mediating effect of new product development.	Yes

Conclusions and Implications

Conclusions

This study investigates how relational capital contributes to the growth performance (GP) of technology-based SMEs by unpacking new product development (NPD) as a conversion mechanism and environmental dynamism (ED) as a contextual boundary condition.

First, the research underscores the significant positive impact of both political relational capital (PRC) and business relational capital (BRC) on growth performance, with business relational capital demonstrating a more pronounced effect on facilitating business growth. These findings challenge the notion of a “Resource Curse” within the context of technology-based SMEs, suggesting instead that the acute challenges of scale, resource scarcity, and technological accumulation these enterprises face render an “the more, the better” approach to external resources not only beneficial but necessary for their growth trajectory.

Second, the study illuminates the pivotal role of new product development as a mediator between relational capital and GP. This mediation reveals a nuanced pathway through which the relational capital of technology-based SMEs can be transformed into GP highlighting the strategic importance of new product development in leveraging relational capital for business growth. This insight enriches our understanding of the mechanisms at play in the conversion of relational resources into tangible outcomes.

Third, the moderating analysis reveals that BRC is significantly more sensitive to ED than PRC. As turbulence increases, the NPD-mediated impact of BRC on GP declines substantially, underscoring their vulnerability to rapid market and technological shifts. By contrast, the pathway associated with PRC remains comparatively stable. This finding highlights that BRC, while potentially high return, require stronger adaptive coordination and faster innovation cycles under dynamic conditions, whereas institutionally anchored ties serve as a more stable growth base.

In conclusion, the principal contribution of this study lies in advancing a more refined understanding of how relational capital is translated into firm growth and under which environmental conditions this translation becomes stronger or weaker. By identifying new product development as the central mediating conduit, this research clarifies the mechanism through which relational resources are transformed into performance outcomes, thereby unpacking the previously underexplored linkage between relational capital and growth performance. Furthermore, by demonstrating that environmental dynamism systematically conditions this mechanism and particularly heightens the vulnerability of business relational capital under turbulent conditions, the study offers a more nuanced and context-sensitive perspective on the performance implications of relational capital.

Theoretical Contributions

This paper makes several theoretical contributions to the field of management, particularly in the domains of innovation management, SME growth, and the strategic use of relational capital within the VUCA global business environment. These contributions engage with and extend existing management theories, offering new insights into the mechanisms and conditions under which relational capital influences the GP of technology-based SMEs.

Firstly, the paper enriches the theory of relational capital by distinguishing between PRC and BRC and demonstrating their differential impacts on GP (Al-Shammari et al., 2022; Park & Luo, 2001). This finding adds complexity to the understanding of relational capital within management literature, challenging the traditional view that treats relational capital as a homogeneous resource. It aligns with and extends the work of Akomea et al. (2023) who underscored the multifaceted nature of social capital, by providing empirical evidence on how its different types—PRC and BRC—uniquely contribute to firm outcomes in the context of technology-based SMEs.

Secondly, by identifying NPD as a mediating mechanism in the relationship between relational capital and GP, this research contributes to the innovation management literature (Aljanabi, 2022; Cantó et al., 2019). It provides a nuanced understanding of how external resources, when channeled through innovation activities such as NPD, can translate into enhanced business growth. This finding underscores the importance of strategic action in the resource-based view (RBV) theory, specifically how the strategic utilization of resources—conceptualized as NPD in this case—can mediate the path from resource acquisition to competitive advantage (Barney, 1991; Greven et al., 2023) .This mediation perspective enriches the RBV by integrating it with the dynamic capabilities framework (Akomea et al., 2023; Nahapiet & Ghoshal, 1998), emphasizing the role of firm capabilities in transforming resources into outcomes.

Thirdly, the paper advances our understanding of the contingent nature of strategic management theories by elucidating the moderating role of ED on the mediating path from BRC to GP via NPD (Nones, 2023; Shirish et al., 2023). This contribution is significant as it highlights the conditional effects of external environmental factors on the utility of relational capital for firm growth, aligning with the contingency theory in management (Akomea et al., 2023; Choy et al., 2023). The finding that ED negatively moderates the impact of BRC, but not PRC, on GP through NPD, suggests that the value derived from relational capital is contingent upon external market conditions, adding a layer of complexity to strategic decision-making in SMEs. Moreover, the emphasize on strategic flexibility and adaptive strategy for SMEs in a VUCA environment (Ren et al., 2023), offering a framework for SMEs to balance external resource acquisition with internal capabilities development in the face of environmental uncertainties.

Empirical Implications

This study offers practical guidance for technology-based SMEs seeking innovation-led growth in volatile environments by clarifying how relational capital can be converted into performance through NPD and how this conversion depends on ED.

Firstly, the differential impact of PRC and BRC underscores the need for strategic portfolio management of external relationships. While BRC exerts a stronger overall influence on GP, managers should recognize that its benefits are closely tied to the stability of market conditions. Strong ties with suppliers, customers, and research institutions can accelerate innovation and market responsiveness, but they are also more exposed to competitive volatility and technological shifts.

Secondly, the mediating role of NPD highlights that relational resources do not automatically translate into growth. SMEs must actively convert relational inputs into innovation outputs. Investing in structured NPD processes, cross-organizational coordination, and knowledge integration mechanisms ensures that relational capital becomes a productive capability rather than a passive asset.

Most importantly, the finding that BRC is significantly more sensitive to ED carries substantial implications for managerial decision-making. In highly dynamic environment characterized by rapid technological change and shifting customer preferences, market-based ties may become unstable, information may quickly lose relevance, and collaborative arrangements may be disrupted. Overreliance on BRC under such conditions can therefore expose firms to innovation discontinuity and performance volatility. Managers should not assume that stronger commercial networks always guarantee superior outcomes. Instead, they should diversify partnership portfolios, build flexible collaboration routines, and complement business ties with more institutionally anchored relationships to hedge against turbulence.

Finally, the relative resilience of PRC under high ED suggests that institutionally embedded relationships can serve as a stabilizing mechanism. PRC may provide policy visibility, resource continuity, and strategic predictability when market signals fluctuate. For technology-based SMEs operating in turbulent sectors, cultivating a balanced relational strategy that integrates both market-oriented and institutionally anchored ties may enhance long-term growth stability. Policymakers, in turn, can facilitate this balance by designing supportive institutional frameworks that reduce uncertainty and strengthen innovation ecosystems.

Limitations

While this study provides important theoretical and empirical insights into the role of relational capital in driving innovation and growth among Chinese technology-based SMEs, several avenues remain open for further exploration. Firstly, the sample was carefully drawn from 253 technology-based SMEs located in technologically advanced regions of China, ensuring data reliability and contextual relevance. Nevertheless, extending the framework to less developed regions or to SMEs in other industries may enrich the understanding of how contextual heterogeneity shapes the relational capital–NPD–performance nexus. Second, the study employed a cross-sectional design, which is well-suited for establishing associations and testing the proposed model. However, future longitudinal analyses could further illuminate how PRC and BRC evolve over time and how their effects on NPD and growth performance unfold dynamically. Finally, this paper has identified NPD as a key mediator linking relational capital to growth performance, offering a focused and parsimonious model. Future studies could build upon this foundation by incorporating additional mediators such as absorptive capacity, innovation culture, or market orientation, as well as by employing more objective performance measures.

Footnotes

ORCID iD

Yunqing Liu

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Natural Science Foundation of China (No. 72202001).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

Adelino

Robinson

D. T.

(2017). Firm age, investment opportunities, and job creation. Journal of Finance, 72(3), 999. https://doi.org/10.2139/ssrn.2375173

Akomea

S. Y.

Agyapong

Aidoo

S. O.

Kyei

S. M.

(2023). The roles of social capital, entrepreneurial orientation and competitive intensity in managerial capability and performance relationship: Evidence from an emerging market economy. Journal of Strategy and Management, 16(2), 341–361. https://doi.org/10.1108/jsma-04-2022-0073

Aljanabi

A. R. A.

(2022). The role of innovation capability in the relationship between marketing capability and new product development: Evidence from the telecommunication sector. European Journal of Innovation Management, 25(1), 73–94. https://doi.org/10.1108/ejim-04-2020-0146

Al-Shammari

M. A.

Banerjee

S. N.

Rasheed

A. A.

(2022). Corporate social responsibility and firm performance: A theory of dual responsibility. Management Decision, 60(6), 1513–1540. https://doi.org/10.1108/md-12-2020-1584

Balachandran

Eklund

(2024). The impact of partner organizational structure on Innovation. Administrative Science Quarterly, 69, 00018392231212680. https://doi.org/10.1177/00018392231212680

Barney

(1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Byun

Frake

Agarwal

(2018). Leveraging who you know by what you know: Specialization and returns to relational capital. Strategic Management Journal, 39(7), 1803–1833. https://doi.org/10.1002/smj.2790

Cantó

Frasquet

Irene

G.-S.

(2019). Design orientation in new product development and its measurement. European Journal of Innovation Management, 24(1), 131–149. https://doi.org/10.1108/ejim-07-2019-0187

Carroll

G. R.

Teo

A. C.

(1996). On the social networks of managers. Academy of Management Journal, 39(2), 421–440. https://doi.org/10.2307/256786

10.

Ceipek

Hautz

Mayer

M. C. J.

Matzler

(2019). Technological diversification: A systematic review of antecedents, outcomes and moderating effects. International Journal of Management Reviews, 21(4), 466–497. https://doi.org/10.1111/ijmr.12205

11.

Choy

S. K.

Koci

Wang

(2023). Does social capital foster sustainable investment? European Financial Management, 29(3), 799–840. https://doi.org/10.1111/eufm.12376

12.

Clark

K. B.

(1989). Project scope and project performance: The effect of parts strategy and supplier involvement on product development. Management Science, 35(10), 1247–1263. https://doi.org/10.1287/mnsc.35.10.1247

13.

Clauss

Abebe

Tangpong

Hock

(2021). Strategic agility, business model innovation, and firm performance: an empirical investigation. IEEE Transactions on Engineering Management, 68(3), 767–784. https://doi.org/10.1109/tem.2019.2910381

14.

Cooper

Knott

A. M.

Yang

(2022). RQ innovative efficiency and firm value. Journal of Financial and Quantitative Analysis, 57(5), 1649–1694. https://doi.org/10.1017/s0022109021000417

15.

Cooper

R. G.

(2024). The artificial intelligence revolution in new-product development. IEEE Engineering Management Review, 52(1), 195–211. https://doi.org/10.1109/emr.2023.3336834

16.

Cooper

R. G.

McCausland

(2024). AI and new product development (Vol. 67, pp. 70–75). Taylor & Francis.

17.

Cucculelli

Peruzzi

Zazzaro

(2019). Relational capital in lending relationships: evidence from European family firms. Small Business Economics, 52, 277–301. https://doi.org/10.1007/s11187-018-0019-3

18.

Cull

L. C.

Yang

Zhou

L. A.

Zhu

(2017). Market facilitation by local government and firm efficiency: Evidence from China. Journal of Corporate Finance, 42, 460–480. https://doi.org/10.1016/j.jcorpfin.2015.06.002

19.

Curchod

Patriotta

Cohen

Neysen

(2020). Working for an algorithm: Power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676. https://doi.org/10.1177/0001839219867024

20.

Dess

G. G.

Beard

D. W.

(1984). Dimensions of organizational task environments. Administrative Science Quarterly, 29, 52–73. https://doi.org/10.2307/2393080

21.

Dubey

Bryde

D. J.

Graham

Foropon

Kumari

Gupta

(2021). The role of alliance management, big data analytics and information visibility on new-product development capability. Annals of Operations Research, 333(2), 1–25. https://doi.org/10.1007/s10479-021-04390-9

22.

Elfenbein

D. W.

Zenger

T. R.

(2014). What is a relationship worth? Repeated exchange and the development and deployment of relational capital. Organization Science, 25(1), 222–244. https://doi.org/10.1287/orsc.2013.0824

23.

Etienne Fabian

Dong

J. Q.

Broekhuizen

Verhoef

P. C.

(2024). Business value of SME digitalisation: when does it pay off more? European Journal of Information Systems, 33, 383–402. https://doi.org/10.1080/0960085x.2023.2167671

24.

Fort

T. C.

Haltiwanger

Jarmin

R. S.

Miranda

(2013). How firms respond to business cycles: The role of firm age and firm size. IMF Economic Review, 61(3), 520–559. https://doi.org/10.1057/imfer.2013.15

25.

Ganson

T. L.

Henisz

W. J.

(2022). Business and peace: The impact of firm–stakeholder relational strategies on conflict risk. Academy of Management Review, 47(2), 259–281. https://doi.org/10.5465/amr.2019.0411

26.

Greven

Kruse

Vos

Strese

Brettel

(2023). Achieving product ambidexterity in new product development: The role of middle managers’ dynamic managerial capabilities. Journal of Management Studies, 60(7), 1786–1818. https://doi.org/10.1111/joms.12886

27.

Griffin

P. A.

Hong

H. A.

Ryou

J. W.

(2018). Corporate innovative efficiency: Evidence of effects on credit ratings. Journal of Corporate Finance, 51, 352–373. https://doi.org/10.1016/j.jcorpfin.2018.06.007

28.

Haeussler

Patzelt

Zahra

S. A.

(2012). Strategic alliances and product development in high technology new firms: The moderating effect of technological capabilities. Journal of Business Venturing, 27(2), 217–233. https://doi.org/10.1016/j.jbusvent.2010.10.002

29.

Hånell

S. M.

Nordman

E. R.

Tolstoy

(2017). New product development in foreign customer relationships: A study of international SMEs. Entrepreneurship and Regional Development, 29(7–8), 715–734. https://doi.org/10.1080/08985626.2017.1336257

30.

Hanelt

Firk

Hildebrandt

Kolbe

L. M.

(2021). Digital M&A, digital innovation, and firm performance: an empirical investigation. European Journal of Information Systems, 30(1), 3–26. https://doi.org/10.1080/0960085x.2020.1747365

31.

Herrmann

A. M.

Storz

Held

(2022). Whom do nascent ventures search for? Resource scarcity and linkage formation activities during new product development processes. Small Business Economics, 58(1), 475–496. https://doi.org/10.1007/s11187-020-00426-9

32.

Homburg

Klarmann

Schmitt

(2010). Brand awareness in business markets: When is it related to firm performance? International Journal of Research in Marketing, 27(3), 201–212. https://doi.org/10.1016/j.ijresmar.2010.03.004

33.

Huang

Liao

(2019). Loaning scale and government subsidy for promoting green innovation. Technological Forecasting and Social Change, 144, 148–156. https://doi.org/10.1016/j.techfore.2019.04.023

34.

Hussain

Nguyen

V. C.

Nguyen

Q. M.

Nguyen

H. T.

Nguyen

T. T.

(2021). Macroeconomic factors, working capital management, and firm performance—A static and dynamic panel analysis. Humanities and Social Sciences Communications, 8(1), 1–14. https://doi.org/10.1057/s41599-021-00778-x

35.

Hwang

K.-K.

(1987). Face and favor: The Chinese power game. American Journal of Sociology, 92(4), 944–974. https://doi.org/10.1086/228588

36.

Inegbedion

H. E.

Thikan

P. R.

David

J. O.

Ajani

J. O.

Peter

F. O.

(2024). Small and medium enterprise (SME) competitiveness and employment creation: The mediating role of SME growth. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-023-02434-y

37.

Janka

Guenther

T. W.

(2018). Management control of new product development and perceived environmental uncertainty: Exploring heterogeneity using a finite mixture approach. Journal of Management Accounting Research, 30(2), 131–161. https://doi.org/10.2308/jmar-52019

38.

Jayaram

Malhotra

M. K.

(2010). The differential and contingent impact of concurrency on new product development project performance: A holistic examination. Decision Sciences, 41(1), 147–196. https://doi.org/10.1111/j.1540-5915.2009.00262.x

39.

Jiang

Liu

Fey

Jiang

(2018). Entrepreneurial orientation, network resource acquisition, and firm performance: A network approach. Journal of Business Research, 87, 46–57. https://doi.org/10.1016/j.jbusres.2018.02.021

40.

Jugend

Jabbour

C. J. C.

Alves Scaliza

J. A.

Rocha

R. S.

Junior

J. A. G.

Latan

Salgado

M. H.

(2018). Relationships among open innovation, innovative performance, government support and firm size: Comparing Brazilian firms embracing different levels of radicalism in innovation. Technovation, 74–75, 54–65. https://doi.org/10.1016/j.technovation.2018.02.004

41.

Jung

Kim

B. C.

Park

Straub

D. W.

(2019). Innovation and policy support for two-sided market platforms: Can government policy makers and executives optimize both societal value and profits? Information Systems Research, 30(3), 1037–1050. https://doi.org/10.1287/isre.2019.0851

42.

Kiss

A. N.

Barr

P. S.

(2017). New product development strategy implementation duration and new venture performance: A contingency-based perspective. Journal of Management, 43(4), 1185–1210. https://doi.org/10.1177/0149206314549251

43.

Kurtmollaiev

(2020). Dynamic capabilities and where to find them. Journal of Management Inquiry, 29(1), 3–16. https://doi.org/10.1177/1056492617730126

44.

Atuahene-Gima

(2001). Product innovation strategy and the performance of new technology ventures in China. Academy of Management Journal, 44(6), 1123–1134. https://doi.org/10.2307/3069392

45.

Shan

Tian

Hao

(2020). Labor cost, government intervention, and corporate innovation: Evidence from China. Journal of Corporate Finance, 64, 28. https://doi.org/10.1016/j.jcorpfin.2020.101668

46.

Liu

Wang

(2022). Entrepreneurial orientation, new product development and firm performance: The moderating role of legitimacy in Chinese high-tech SMEs. European Journal of Innovation Management, 25(1), 130–149. https://doi.org/10.1108/ejim-05-2020-0204

47.

Y.-H.

Huang

J.-W.

Tsai

M.-T.

(2009). Entrepreneurial orientation and firm performance: The role of knowledge creation process. Industrial Marketing Management, 38(4), 440–449. https://doi.org/10.1016/j.indmarman.2008.02.004

48.

Meuleman

Lockett

Manigart

Wright

(2010). Partner selection decisions in interfirm collaborations: The Paradox of relational embeddedness. Journal of Management Studies, 47(6), 995–1019. https://doi.org/10.1111/j.1467-6486.2009.00897.x

49.

Miller

(1987). The structural and environmental correlates of business strategy. Strategic Management Journal, 8(1), 55–76. https://doi.org/10.1002/smj.4250080106

50.

Miller

D. J.

(2006). Technological diversity, related diversification, and firm performance. Strategic Management Journal, 27(7), 601–619. https://doi.org/10.1002/smj.533

51.

Minguzzi

Passaro

(2001). The network of relationships between the economic environment and the entrepreneurial culture in small firms. Journal of Business Venturing, 16(2), 181–207. https://doi.org/10.1016/s0883-9026(99)00045-2

52.

Mokhtarzadeh

N. G.

Mahdiraji

H. A.

Jafarpanah

Cao

(2022). Examining the influence of environmental turbulence on firm innovation performance in emerging markets: using an environment-strategy-performance framework. International Journal of Innovation Management, 26(04), 2250028. https://doi.org/10.1142/s1363919622500281

53.

Moreno-Moya

Munuera-Aleman

J. L.

(2016). The differential effect of development speed and launching speed on new product performance: An analysis in SMEs. Journal of Small Business Management, 54(2), 750–770. https://doi.org/10.1111/jsbm.12170

54.

Murray

J. Y.

Chao

M. C. H.

(2005). A cross-team framework of international knowledge acquisition on new product development capabilities and new product market performance. Journal of International Marketing, 13(3), 54–78. https://doi.org/10.1509/jimk.13.3.54

55.

Nahapiet

Ghoshal

(1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242–266. https://doi.org/10.2307/259373

56.

Nones

(2023). Business politicians and fiscal consolidation. Business and Politics, 25(4), 495–516. https://doi.org/10.1017/bap.2023.24

57.

Park

S. H.

Luo

(2001). Guanxi and organizational dynamics: Organizational networking in Chinese firms. Strategic Management Journal, 22(5), 455–477. https://doi.org/10.1002/smj.167

58.

Peng

M. W.

Luo

(2000). Managerial ties and firm performance in a transition economy: The nature of a micro-macro link. Academy of Management Journal, 43(3), 486–501. https://doi.org/10.2307/1556406

59.

Podsakoff

P. M.

Organ

D. W.

(1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544. https://doi.org/10.1177/014920638601200408

60.

Prange

Eng

(2015). Collaborative new product alliances: A review of the literature and research perspectives. Strategic Change, 24(4), 351–371. https://doi.org/10.1002/jsc.2015

61.

(2013). Guanxi, social capital theory and beyond: Toward a globalized social science. British Journal of Sociology, 64(2), 308–324. https://doi.org/10.1111/1468-4446.12019

62.

Rainey

D. L.

(2008). Product innovation: leading change through integrated product development. Cambridge University Press.

63.

Reinecke

Donaghey

(2021). Political CSR at the Coalface – The roles and contradictions of multinational corporations in developing workplace dialogue. Journal of Management Studies, 58(2), 457–486. https://doi.org/10.1111/joms.12585

64.

Ren

Ting

I. W. K.

Kweh

Q. L.

Zhang

(2023). Innovation and firm performance: The moderating role of intellectual capital among Chinese companies. Asian Academy of Management Journal of Accounting and Finance, 19(1), 155–180. https://doi.org/10.21315/aamjaf2023.19.1.6

65.

Rochford

Rudelius

(1997). New product development process: Stages and successes in the medical products industry. Industrial Marketing Management, 26(1), 67–84. https://doi.org/10.1016/s0019-8501(96)00115-0

66.

Saka-Helmhout

Chappin

M. M. H.

Rodrigues

S. B.

(2022). Corporate social innovation in developing countries. Journal of Business Ethics, 181(3), 589–605. https://doi.org/10.1007/s10551-021-04933-x

67.

Sarfraz

Ozturk

Yoo

Raza

M. A.

Han

(2023). Toward a new understanding of environmental and financial performance through corporate social responsibility, green innovation, and sustainable development. Humanities and Social Sciences Communications, 10(1), 1–17. https://doi.org/10.1057/s41599-023-01799-4

68.

Schilke

(2014). On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism. Strategic Management Journal, 35(2), 179–203. https://doi.org/10.1002/smj.2099

69.

Schommer

Richter

Karna

(2019). Does the diversification–firm performance relationship change over time? A meta-analytical review. Journal of Management Studies, 56(1), 270–298. https://doi.org/10.1111/joms.12393

70.

Sheng

Zhou

K. Z.

J. J.

(2011). The effects of business and political ties on firm performance: Evidence from China. Journal of Marketing, 75(1), 1–15. https://doi.org/10.1509/jm.75.1.1

71.

Shirish

Srivastava

S. C.

Panteli

(2023). Management and sustenance of digital transformations in the Irish microbusiness sector: Examining the key role of microbusiness owner-manager. European Journal of Information Systems, 32(3), 409–433. https://doi.org/10.1080/0960085x.2023.2166431

72.

Simerly

R. L.

(2000). Environmental dynamism, capital structure and performance: A theoretical integration and an empirical test. Strategic Management Journal, 21(1), 31–49. https://doi.org/10.1002/(sici)1097-0266(200001)21:13.0.co;2-t

73.

Soluk

Miroshnychenko

Kammerlander

De Massis

(2021). Family influence and digital business model innovation: The enabling role of dynamic capabilities. Entrepreneurship Theory and Practice, 45(4), 867–905. https://doi.org/10.1177/1042258721998946

74.

Stringfellow

Shaw

Maclean

(2014). Apostasy versus legitimacy: Relational dynamics and routes to resource acquisition in entrepreneurial ventures. International Small Business Journal, 32(5), 571–592. https://doi.org/10.1177/0266242612471693

75.

Sun

Liu

(2021). Business model designs, big data analytics capabilities and new product development performance: Evidence from China. European Journal of Innovation Management, 24(4), 1162–1183. https://doi.org/10.1108/ejim-01-2020-0004

76.

Tao

Sun

Zhu

Yang

(2017). Political connections and government subsidies: Evidence from financially distressed firms in China. Emerging Markets Finance and Trade, 53(8), 1854–1868. https://doi.org/10.1080/1540496x.2017.1332592

77.

Teece

D. J.

(2018). Dynamic capabilities as (workable) management systems theory. Journal of Management & Organization, 24(3), 359–368. https://doi.org/10.1017/jmo.2017.75

78.

Tsai

Ghoshal

(1998). Social Capital and value creation: The role of Intrafirm Networks. Academy of Management Journal, 41(4), 464–476. https://doi.org/10.2307/257085

79.

Tsang

Wang

K. T.

Liu

(2021). Integrating corporate social responsibility criteria into executive compensation and firm innovation: International evidence. Journal of Corporate Finance, 70, 27. https://doi.org/10.1016/j.jcorpfin.2021.102070

80.

Tzabbar

Vestal

(2015). Bridging the social chasm in geographically distributed R&D teams: The moderating effects of relational strength and status asymmetry on the novelty of team innovation. Organization Science, 26(3), 811–829. https://doi.org/10.1287/orsc.2015.0969

81.

Vanacker

Collewaert

Paeleman

(2013). The relationship between slack resources and the performance of entrepreneurial firms: The role of venture capital and angel investors. Journal of Management Studies, 50(6), 1070–1096. https://doi.org/10.1111/joms.12026

82.

Wamba

S. F.

(2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 102544. https://doi.org/10.1016/j.ijinfomgt.2022.102544

83.

(2011). Asymmetric roles of business ties and political ties in product innovation. Journal of Business Research, 64(11), 1151–1156. https://doi.org/10.1016/j.jbusres.2011.06.014

84.

Xin

Pearce

J. L.

(1994). Guanxi: Connections as substitutes for formal institutional support. Academy of Management Journal, 39(1), 163–167. https://doi.org/10.5465/ambpp.1994.10341845

85.

Hou

(2024). CEO overseas experience, dynamic capabilities and corporate digital transformation: An imprinting theory perspective. Humanities and Social Sciences Communications, 11(1), 1–14. https://doi.org/10.1057/s41599-024-03227-7

86.

Murphree

Meng

(2021). The more the merrier? Chinese government R&D subsidies, dependence, and firm innovation performance. Journal of Product Innovation Management, 38(2), 289–310. https://doi.org/10.1111/jpim.12564

87.

Hitt

M. A.

Liu

Wei

(2016). The influence of resource bundling on the speed of strategic change: Moderating effects of relational capital. Asia Pacific Journal of Management, 33, 435–467. https://doi.org/10.1007/s10490-016-9458-z

88.

Zhang

Wang

Zhao

Meadows

M. E.

(2022). Finding pathways to synergistic development of Sustainable Development Goals in China. Humanities and Social Sciences Communications, 9(1), 1–10. https://doi.org/10.1057/s41599-022-01036-4

89.

Zhan

Tan

K. H.

Tse

Y. K.

(2018). Unlocking the power of big data in new product development. Annals of Operations Research, 270(1–2), 577–595. https://doi.org/10.1007/s10479-016-2379-x

90.

Zheng

Xia

(2018). Resource dependence and network relations: A test of venture capital investment termination in China. Journal of Management Studies, 55(2), 295–319. https://doi.org/10.1111/joms.12255

91.

Zhou

K. Z.

Zhang

Sheng

Xie

Bao

(2014). Are relational ties always good for knowledge acquisition? Buyer–supplier exchanges in China. Journal of Operations Management, 32(3), 88–98. https://doi.org/10.1016/j.jom.2014.01.001