The building evaluation system of innovation vitality on enterprises: Based on an exploratory factor analysis

Abstract

Being the major players in promoting innovation, enterprises are therefore central to innovation–based development. Establishing how vibrant they are in terms of innovation has become a heavily debated issue in both academic and industry circles. Through a sample involving Chinese listed companies in advanced material manufacturing, this study utilizes exploratory factor analysis to develop an evaluation system of enterprises’ innovation vitality on the basis of three dimensions: innovation persistence, volatility, and growth. The study establishes deeper interaction between innovation vitality and two major indicators-persistence and volatility formats input and cooperation, while also promoting the growth of innovative input and output. This study adds insights related to the objective assessment of enterprises’ innovation vitality and the promotion of subsequent innovation efforts.

Graphical abstract

Keywords

innovation vitality / innovation persistence / innovation volatility / innovation growth / exploratory factor analysis

1 Introduction

Innovation has become a central tool for increasing total assets and increasing the quality of life of people. Most of the developed nations have adopted technological innovation as one of their strategic development considerations (He et al., 2024). Specifically, the enterprises, including the implementers of strategic decision-making, research and development (R&D) investment, scientific research organization and transformation of achievements, have an important role in promoting technological innovation. Thus, it is necessary to unleash and upgrade enterprises’ innovation vitality as a precondition for the implementation of innovation-driven development strategy and formation of an innovative country. Therefore, the knowledge about how to foster the enterprises’ innovation vitality has become an essential precondition for innovation-driven development.

Innovation vitality refers to an enterprise’s innovation capacity, vitality and competence that can sustain and develop itself, derived from the meaning of vitality, which refers to the outside purposes of entities and functions as well as inside effects (Lavrusheva, 2020). Applying vitality in the context of enterprises is their ability to self-improvement and self-organization through relatively autonomous motives. This can be best described as an enterprise’s health or the energy left over within it. The activity of enterprises indicates their mobility and reflects the state and patterns of adaptive arrangements for enterprises in their management, indicating that enterprises should actively develop and must overcome barriers. Therefore, it shows the level of the enterprises’ liver function.

In this respect, enterprises’ innovation vitality is defined as the activeness of the enterprise innovation volatiles, which characterizes different aspects of innovation, excluding the static parameters defined by the legislation. It may also be considered today’s stage of development, processes of creating enterprises which pay attention to investments in R&D, to new technologies, products and processes and strive for excellence in commercialization. When improving their innovation vitality, enterprises facilitate the climate for innovation, stimulate employees’ potential, and result in the generation of new products classified by the application of fresh knowledge and innovative technology (Savolainen and Häkkinen, 2011). A high level of innovation vitality reflects the developmental potential of an enterprise, and it acts as a crucial driving force for the realization of sustainable growth and development.

To mobilize the leading position of enterprises in innovation activities and enhance their innovation vitality, the functions of evaluation innovation vitality must be fully utilized to guide and motivate. Nevertheless, the present research on enterprises’ innovation vitality is insufficient to meet policymakers’ requirement.

First, as innovation vitality is a key element in the innovation agenda of an enterprise, previous studies have not sufficiently identified innovation vitality and innovation capability (Conceição et al., 2006; Akinwale et al., 2018; Ganguly et al., 2019; Daronco et al., 2023). While innovation capability is defined considering mainly the inputs, the outputs and the outcomes of the innovation process, innovation vitality also includes the willingness, effort and initiative to innovate. As a result, it is still unsure about how to obtain a more accurate assessment of enterprises’ innovation vitality.

Second, the indicators selected in the current evaluation methods of innovation vitality fail to consider such volatile properties that are normally associated with changes in enterprises’ innovation activities. This is quite contrary to the dynamic characteristics of innovation vitality, which must be a system that can maintain itself, sustain itself and develop itself.

Therefore, theoretical and empirical studies on enterprises’ innovation vitality are still relatively scarce, let alone the detailed investigation on the related conception and influential mechanisms inside the enterprises. In this paper, we develop an evaluation index system for enterprises’ innovation vitality based on organizational vitality and innovation capabilities. First, we define the enterprises’ innovation vitality, in relation to an enterprise as the ability to achieve higher developmental status quo and remain innovatively active. Specifically, we construct our system around three key aspects: To sort out the relationships between those factors they are to be divided into innovation persistence, innovation volatility, and innovation growth. It is thus quantified under three subcategories; innovation input, innovation output, and collaboration. The findings indicate that the primary indicators influencing enterprises’ innovation vitality include innovation input persistence, cooperation persistence, innovation input volatility, cooperation volatility, innovation input growth, and innovation output growth.

This study is structured as follows. Section 2 provides a literature review of enterprises’ innovation vitality. Section 3 details the establishment of the evaluation system affecting enterprises’ innovation vitality. Section 4 outlines the data sources and applies the proposed method to specific industries, including non-metallic mineral manufacturing, chemical raw materials, and chemical products and construction. Section 5 presents the results. Finally, Section 6 concludes the study.

2 Literature review

The concept of enterprises’ innovation vitality is derived from the principles of organizational vitality and innovation capability. Innovation capability can be considered the potential and foundation for conducting innovative activities, while innovation vitality refers to the ability to convert this potential into tangible actions and results (Sun et al., 2022). Given the strong interrelationship between these two factors, we will undertake a literature review that includes both enterprise innovation capability and innovation vitality.

2.1 Enterprises’ innovation capability

Innovation is a fundamental driver of national economic growth, impacting and benefiting consumers, businesses, and the economy at large. Enterprise innovation is a crucial component for advancing the high-quality development of a nation’s economy. The long-term growth of enterprises relies heavily on innovation as a catalyst for business progress. Research on enterprises’ innovation capabilities is primarily focused on organizational competencies related to management and the creation of innovation throughout the developmental process (Smith et al., 2008; Saunila, 2020).

The concept of enterprises’ innovation capability includes a range of dimensions. Various studies classify these dimensions from multiple perspectives. First, based on prominent international frameworks such as the OECD Oslo Manual (OECD/Eurostat, 2005), innovation is categorized into four types: process innovation, product innovation, marketing innovation, and organizational innovation. The enterprises’ innovation capability can similarly be divided into the aforementioned four dimensions. Second, it can also be divided into innovation management capability, innovation incentive capability, and innovation realization capability. Innovation management capability refers to the ability to identify, develop, and assess innovation opportunities while organizing and managing related activities. Innovation incentive capability motivates enterprises to pursue and realize innovation endeavors. Innovation realization capability pertains to the actual innovative processes. Third, considering the innovation process itself, innovation realization capabilities may include R&D capabilities, production capabilities, and marketing capabilities (Dewangan and Godse, 2014; Battistella et al., 2023).

Due to the intangible nature of innovation capability, its measurement largely relies on indirect measurement, which may be either objective or subjective. In terms of objective measurements, researchers typically assess innovation capacity from both input and output perspectives. Key indicators, such as R&D intensity and the number of R&D personnel, are commonly used to evaluate inputs (Sher and Yang, 2005; Wang and Dass, 2017). Conversely, the output measures often utilized include the number of patents and licenses granted, as well as applications are the most commonly utilized measures of outputs (Nagaoka et al., 2010).

2.2 Enterprises’ innovation vitality

The research on enterprises’ innovation vitality is relatively limited. Early studies, such as that by Aspden (1983), introduced the concept of technological vitality to define innovation dynamism, suggesting it could be quantified using patent-related metrics, particularly the number of patents filed. Building on this foundation, Zhou et al. (2008) advocated for a comprehensive assessment of enterprises’ innovation vitality that considers multiple dimensions, evaluating it from the viewpoints of innovation awareness, resources, management practices, and skills. Additionally, Bishwas (2015) identified 15 indicators to measure organizational innovation vitality, focusing on three primary aspects: innovation continuity or survival, growth or change, and overall innovation performance.

While the aforementioned studies focused on measuring innovation vitality from an internal perspective through relevant indicators, it is crucial to recognize that external factors also significantly influence enterprises’ innovation vitality. These external factors include the developmental status of the industry, as well as the economic and political environments in which the enterprises operate, which include government policies and regulations, market conditions, access to financing, and the broader socio-technological landscape. To address this holistic view, Hu (2012) developed a comprehensive evaluation system for enterprise innovation vitality that integrates both internal and external aspects, including innovation assets, human resources, organizational management, innovation performance, and the innovation environment. Similarly, Rydvalova et al. (2020) elucidated the rationale and methodologies for evaluating the vitality of family businesses, assessing vitality levels within various specific domains, including legal, management, and economic sectors, based on data gathered from questionnaire surveys.

Despite recognizing the importance of measuring enterprises’ innovation vitality, existing research has fallen short of distinctly differentiating between the concepts of innovation vitality and innovation capability.

2.3 Differences between enterprises’ innovation capability and innovation vitality

Although innovation vitality and innovation capability are closely related, they are different concepts. Innovation capability refers to the various resources, abilities, and mechanisms that enterprises possess and demonstrate throughout the innovation process, serving as the internal conditions essential for innovation (Forsman, 2011). In contrast, innovation vitality highlights the volatile aspect of an enterprise’s active development and its willingness to pursue upward breakthroughs. It can be viewed as the fundamental driving force behind the cultivation of innovation systems and the enhancement of an enterprise’s technological innovation capabilities. In summary, while both concepts relate to innovation, they differ primarily in the following areas.

First, regarding definitions and concepts, innovation capability emphasizes performance and results in enterprise technology innovation (Dewangan and Godse, 2014; Battistella et al., 2023). It relies on the diverse abilities and resources that an enterprise possesses throughout the innovation process. Conversely, innovation vitality includes the activity and enthusiasm of enterprises in their innovative endeavors. It represents a state that reflects an enterprise’s willingness and motivation to innovate (Forsman, 2011). Thus, innovation vitality is contingent upon an enterprise’s willingness to invest in the innovation process, rather than simply the accumulated results of its capabilities and resources.

Second, in terms of measurement, the indicators used to assess an enterprise’s innovation capability primarily include innovation input and output metrics, such as R&D intensity, the number of R&D personnel, and the total quantity of patent applications and granted patents (Sher and Yang, 2005; Wang and Dass, 2017; Nagaoka et al., 2010). These indicators focus on aggregate performance and intensity. Innovation vitality, on the other hand, reflects the dynamic characteristics of changes in an enterprise’s innovation activities. The metrics used in this context are geared toward measuring the frequency and extent of these activities. Typically, the rate of change in innovation input and output indicators is employed to represent an enterprise’s innovation vitality.

Innovation vitality is a crucial component for enhancing an enterprise’s innovation capabilities. The development of these capabilities and the strengthening of core competitiveness are inherently linked to the enterprises’ innovation vitality. In today’s marketplace, companies must prioritize continuous innovation to satisfy customer demands and sustain a competitive edge. Innovation vitality fosters an enterprise’s awareness and motivation for innovation, driving efforts to improve products, explore new markets, and deliver differentiated solutions that address competitive challenges. Consequently, it is essential to explore the fundamental meaning of innovation vitality and assess its levels within enterprises. Additionally, it is important to establish a robust evaluation system rooted in this understanding, selecting a series of relevant indicators and exploring the intrinsic relationships among them.

3 Theoretical framework of enterprises’ innovation vitality

3.1 The framework of the evaluation system

A comprehensive evaluation system consists of three layers: the goal layer, the criterion layer, and the indicator layer (Zhou et al., 2016; Song, 2018). Specifically, the goal layer signifies the purpose of establishing the evaluation system, the criterion layer includes the main attributes of this system (Song, 2018), and the indicator layer comprises the specific indicators utilized within the evaluation system (Zhou et al., 2016).

As previously discussed, the enterprises’ innovation vitality evaluation system represents the goal layer, while several subsystems form the criterion layer (as shown in A, B, and C). The indicator layer consists of more granular indicators (depicted as A1/B1/C1, A2/B2/C2,…, An/Bn/Cn). Therefore, we have established the framework for the enterprises’ innovation vitality evaluation system, which is illustrated in Fig.1.

3.2 The criterion layer of the evaluation system

The enterprises’ innovation vitality evaluation system is grounded in the concepts of organizational vitality and innovation capabilities. From the perspective of organizational vitality, enterprises share similarities with living organisms: they require appropriate activities to maintain good health and sustain a vibrant state (Bishwas and Sushil, 2016). Vicenzi and Adkins (2000), Bishwas and Sushil (2016) have proposed that organizational vitality includes three dimensions: persistence, volatility, and growth. The enterprises’ innovation vitality is a specific manifestation of organizational vitality in the context of innovation activities. Therefore, we evaluate enterprises’ innovation vitality based on these three dimensions of organizational vitality. Specifically, innovation persistence reflects the continuity and consistency of innovative activities over time (Bishwas, 2015). Sustained innovation persistence indicates a robust and well-established innovation system capable of generating a continuous stream of new ideas and outputs. Innovation volatility captures fluctuations and turbulence in innovation outputs, signaling dynamism, flexibility, and adaptability within the innovation system (Zhang et al., 2023). Innovation growth reflects the trajectory and momentum of innovation development, demonstrating the expanding scale and scope of innovative activities (Hai et al., 2020).

Innovation persistence (A)

Innovation persistence refers to an enterprise’s ability to uninterruptedly develop novel technologies over a specified period. This capability guides the enterprise to consistently maintain its innovation vitality, thereby achieving a sustainable competitive advantage (Bishwas, 2015). Innovation persistence is a critical component of an enterprise’s innovation vitality, providing insights into areas and assets with innovation potential. It indicates the innovation process over a defined timeframe, relying on continuous learning from innovation activities. An enterprise must consistently integrate its key resources to achieve uninterrupted growth and sustainable development. In a dynamic and unstable environment, enterprises view sustainable innovation as a fundamental crucial rather than a transient or occasional endeavor (Bakker et al., 2020). Scholars have noted that the focus of innovation persistence increasingly lies in the continuous enhancement of an enterprise’s innovation skills and the acquisition of advanced techniques, all aimed at boosting innovation vitality. Therefore, we adopt innovation persistence as a vital aspect of enterprises’ innovation vitality.

Innovation volatility (B)

Innovation volatility refers to an enterprise’s ability to engage in stable innovation activities over a specific period, reflecting the dynamic characteristics of changes in innovation vitality. Significant fluctuations in innovation activities serve as observable markers of an enterprise’s innovation behaviors and indicate the extent of enterprises’ dynamic characteristics (Hai et al., 2020). Prior research has emphasized that innovation volatility is crucial for enterprises to effectively adapt to evolving dynamics and sustain their innovation vitality (Bos-Nehles and Veenendaal, 2019; Zhang et al., 2023). Additionally, innovation volatility illustrates the flexibility of resource allocation for innovation activities and is a key manifestation of an enterprise’s innovation vitality (Zhang et al., 2018). Consequently, this study incorporates innovation volatility as a vital component in evaluating an enterprise’s innovation vitality.

Innovation growth (C)

Innovation growth refers to an enterprise’s capacity to sustain the enhancement of its technological capabilities over a designated period (Smith, 2009). Organizational vitality has been defined as synonymous with organizational growth (Vicenzi and Adkins, 2000). In today’s landscape, characterized by abrupt and rapid changes, growth is a primary objective for any innovative organization (Bishwas, 2015). Innovation growth provides enterprises with opportunities for development, enhances their knowledge, skills, and capabilities, and increases their potential for heightened innovation vitality (Huang and Chen, 2021). Furthermore, innovation growth reflects an organization’s developmental aspirations; every enterprise seeks to achieve growth in innovation and attain greater innovation vitality. The health or vitality of an organization can be assessed by its capacity for creative growth (Vicenzi and Adkins, 2000). Many scholars have empirically supported the notion that innovation growth is integral to driving and maintaining sustainable long-term innovation benefits, affirming its role as part of innovation vitality (Smith, 2009). Thus, this study recognizes innovation growth as a critical dimension in evaluating an enterprise’s innovation vitality.

In summary, innovation persistence, innovation viability, and innovation growth represent three essential indicators in the criterion layer for assessing an enterprise’s innovation vitality. The higher the levels of innovation persistence, innovation volatility, and innovation growth, the more active the technology development and the greater the innovation vitality. To provide a more detailed description of the enterprise’s innovation vitality evaluation system from these three perspectives, a more concrete indicator layer will be presented in Section 3.3.

3.3 The indicator layer of the evaluation system

Based on the analysis above, enterprises’ innovation vitality is intrinsically linked to their innovation capability. This capability is primarily evaluated through two key dimensions: innovation input and output. Furthermore, sustaining a robust level of innovation vitality necessitates that enterprises engage in close collaboration with partners and maintain frequent interactions to acquire external resources. Therefore, we incorporate a cooperation dimension alongside innovation input and output to provide a more comprehensive measurement of enterprises’ innovation vitality. Each subsystem within the criterion layer of the enterprises’ innovation vitality evaluation system comprises six indices, resulting in a total of 18 specific indicators (Tab.1), detailed as follows:

Innovation input persistence (A1–A2)

The continuous influx of innovation inputs is vital for progressively enhancing an enterprise’s innovation capabilities, which in turn reflects its innovation vitality. From the perspective of physical and human capital, innovation input is categorized into R&D intensity and R&D staffing (Coad and Rao, 2008). Enterprises that consistently maintain higher levels of R&D intensity and a larger number of R&D personnel typically demonstrate greater innovation vitality. R&D intensity signifies the extent of an enterprise’s commitment to technological development, while the input of R&D personnel directly indicates the organization’s motivation to pursue R&D activities at the individual level. Building on previous research, we propose that the persistence of innovation input in enterprises is characterized by two criteria: whether R&D intensity exceeds the industry average for three consecutive years (A1) and whether the number of R&D personnel exceeds the industry average for three consecutive years (A2). To assess R&D intensity, we evaluate the ratio of R&D expenditures to total sales, while R&D staff input is quantified by the number of R&D personnel reported by the enterprise (Zhang et al., 2018).

Innovation output persistence (A3–A4)

Continuous innovation output is the key driving force behind the successful adoption of innovative practices by enterprises (Henard and Szymanski, 2001). Patent applications serve as an indication of enterprises’ commitment to technological development, with an enterprise’s technological innovation output being represented through its patents. Additionally, an enterprise’s patent assignments reflect its ability to translate technological innovation output into commercialization, thereby enhancing the effective utilization of patents. Building on previous studies, we assert that the enterprise innovation output persistence includes whether there have been patent applications for three consecutive years (A3) and whether there have been patent transfers for three consecutive years (A4). Patent applications are measured by the number of patents filed by enterprises with the China National Intellectual Property Administration (CNIPA), while patent transfer is measured by the number of patents transferred by enterprises.

Cooperation persistence (A5–A6)

Cooperation significantly enhances enterprise innovation by providing opportunities to leverage alternative marketing channels and boost innovation vitality. It represents an enterprise’s capacity to acquire external technical knowledge resources, with sustained cooperation leading to ongoing growth in innovation benefits. In a stable and continuing partnership, each partner contributes unique enhancements and complementary resources to bolster innovation vitality, such as access to reliable information and the chance to engage with higher-level management (Davis and Eisenhardt, 2011). The number of jointly applied patents reflects an enterprise’s capability to co-develop technologies with its partners; an enterprise collaborates with more partners secures a more advantageous position within the innovation network and demonstrates greater innovation vitality. Therefore, we assess the partnerships of the focal enterprise with other enterprises based on their co-applied patents. Specifically, enterprise cooperation persistence includes whether there has been a fixed partner over the past three years (A5), and the proportion of cooperative patents during the past three years (A6).

Innovation input volatility (B1–B2)

Innovation input volatility reflects the intrinsic and intellectual values of entrepreneurs at the micro level, fostering the development of an enterprise’s innovation activities. R&D investment volatility is recognized as an indicator of innovative vitality (Zhang et al., 2023). Flexibility in ideas and critical thinking are often prevalent in relation to R&D intensity and R&D personnel input. Many researchers have utilized variations in R&D intensity and personnel input to assess innovation volatility. Additionally, some scholars have suggested that R&D personnel input encourages the generation of creative ideas, knowledge, and skills, thereby enhancing an enterprise’s innovative vitality (Huang and Chen, 2021). The standard deviation statistically reflects the fluctuation and dispersion of data. A larger standard deviation in innovation input activities indicates higher levels of input volatility within an enterprise. Consequently, this study employs the standard deviation of various indicators to represent innovation input volatility. Key indicators include R&D intensity (B1) and R&D personnel input (B2) (Zhang et al., 2018).

Innovation output volatility (B3–B4)

Innovation output volatility serves as a catalyst for enterprise transformation and the enhancement of innovation capacity, playing a critical role in facilitating innovation development. Technological innovation output volatility and transformation are not optional but essential for successful innovation. To achieve effective innovation, enterprises must enhance their vitality by efficiently utilizing available resources and consistently converting knowledge and skills into products and processes, ultimately benefiting both the enterprise and its stakeholders (Rajapathirana and Hui, 2018). In statistical terms, the standard deviation illustrates data fluctuation. Thus, this study employs the standard deviation of technological activities to represent innovation output volatility. The specific indicators include patents applied for by the enterprise (B3) and patents transferred by the enterprise (B4).

Cooperation volatility (B5–B6)

Cooperation, a vital driver of innovation and the continual enhancement of creative volatiles, empowers enterprises to improve existing products by establishing interactive channels within collaborative relationships (Rosenbusch et al., 2011). Vicenzi and Adkins (2000) regarded the degree of cooperation volatility among organizations as a key component of enterprises’ innovation vitality. The inherently collaborative nature of technology has opened up additional avenues for learning and knowledge sharing, as well as for flexible resource utilization and the development of innovative products (Bishwas and Sushil, 2016). This study employs the standard deviation of cooperative activities to quantify cooperation volatility, with specific indicators including the number of patents resulting from collaboration with other organizations (B5) and the number of partners an enterprise engages with (B6) (Sun et al., 2020).

Innovation input growth (C1–C2)

Innovation input growth reflects the advancement of existing innovation resources and the development of new technologies, closely linked to the vitality of enterprises’ innovation efforts. The importance of innovation input growth has been recognized as a significant concern throughout the organizational life cycle (Bishwas, 2015). Indicators of innovation input growth are utilized to monitor and evaluate development, with the core measurement scale determining the scale of this growth. The level of an enterprise’s R&D efforts can be assessed through resource allocation to its R&D department, including both human and financial resources (Coad and Rao, 2008). From the perspective of innovation input growth, the rate of change in these indices may serve as a vitality index. Specifically, indicators of innovation input growth include the growth rate of R&D investment (C1) and the growth rate of R&D personnel input (C2).

Innovation output growth (C3–C4)

The vitality of enterprises is manifested through innovation output growth, enabling them to surmount technical challenges, accelerate innovation processes, and ultimately enhance organizational vitality (Zhang et al., 2023). Changes in innovation vitality can be observed through the output growth of technological innovation activities, with the rate of variation in these indices serving as indicators of innovation vitality. Indicators of innovation output growth reflect the dynamism and flourishing nature of innovative organizations (Smith, 2009). Additionally, Coad and Rao (2008) identified the number of patent applications as a crucial metric for assessing innovation output growth. Therefore, this study assesses patent applications (C3) and the growth rate of patent transfers (C4).

Cooperation growth (C5–C6)

Cooperative innovation presents a viable solution for enterprises facing resource limitations, allowing them to surmount constraints and boost their innovation vitality, thus securing a competitive advantage (Coluzzi et al., 2015). The concept of cooperation growth suggests that enterprises can forge broader or deeper collaborative relationships with other organizations, leading to enhanced opportunities for acquiring external technological knowledge. Mastery of cooperation and co-creation skills is potentially crucial for sustaining innovation vitality and competitiveness (Savolainen and Lopez-Fresno, 2013). Furthermore, cooperation growth increases the likelihood of enhancing an enterprise’s innovation vitality through the recombination of both internal and external knowledge. Consequently, this study proposes that the growth of cooperation in enterprises includes the growth rate of cooperative patents (C5) and the number of new partners (C6).

To present the structure of the enterprises’ innovation vitality evaluation system, this study builds upon prior research and integrates the goal layer, criterion layer, and indicator layer, culminating in the construction of an index system (Zhou et al., 2016; Song, 2018). The details of the evaluation system for enterprise innovation vitality are presented in Tab.1.

4 Methodology

4.1 Reasons for choosing EFA

This study employs exploratory factor analysis (EFA) to assess enterprises’ innovation vitality for three key reasons. First, EFA is recognized as an effective method for evaluating innovation vitality within enterprises. Current evaluation methods primarily include the DEA method (Yang et al., 2022), the Delphi method (expert survey method), fuzzy evaluation methods, principal component analysis, and EFA. The DEA method determines the relationship between multiple indicators of inputs and outputs but imposes high requirements on data quality, limiting its application. Conversely, the Delphi and fuzzy evaluation methods rely on expert scoring to construct judgment matrices, introducing a degree of subjectivity. In contrast, EFA employs a relatively objective approach to evaluating outcomes. Presently, EFA is the predominant method utilized for assessing performance-related outcomes and has gained widespread acceptance in management research (e.g., Reio and Ghosh, 2009; Reio and Shuck, 2015).

Second, EFA not only considers the collinearity among indicators but also provides a more rational evaluation result. When multiple indicators are highly correlated, interpreting the meaning between them can be challenging. The use of the roots criterion and the scree test in EFA offers a reliable and consistent indication of the number of factors to extract (Reio and Ghosh, 2009). The results obtained from EFA yield not only interpretable factors but also a simple structure, making the procedure particularly suited for determining variable weights and ensuring the reliability of results.

Third, EFA facilitates dimensionality reduction while preserving the integrity of the original information, resulting in more concise and analyzable outcomes (Reio and Shuck, 2015). At the indicator level, the evaluation system of enterprises’ innovation vitality includes 18 indices derived from innovation input, innovation output, and cooperation. There may be collinearity among these indices, making it difficult to determine which indicators are more critical for evaluating a firm’s innovation vitality. In instances where the underlying dimensions of a data set are unknown, EFA is an appropriate method. To streamline the analysis and reduce variable complexity, it is essential to extract key variables that effectively represent the enterprises’ innovation vitality, i.e., EFA is well-suited for this analysis.

4.2 The principles and steps for applying EFA

The fundamental principle of EFA is as follows: while preserving the original information, multiple related evaluation indicators are transformed through linear transformation into a few uncorrelated common factors. This process reduces and classifies the evaluation indicators, revealing the correlations among them. To eliminate the impact of different dimensions among the indicators and enhance their comparability, this study employs the mean value transformation method for data standardization. Equation (1) for mean normalization is expressed as follows:

(1)X_{st}=\left(X_i-X_{\mathrm{mean}}\right)/\left(X_{max}+X_{min}\right),

where X_{st} represents the standardized index, X_i represents the original index, X_{\text{mean}} represents the mean index, X_{\text{max}} and X_{\text{min}} represent the maximum and minimum indicators, respectively.

The steps of factor analysis are as follows: Step 1: Standardize the original data; Step 2: Calculate the correlation coefficient between the standardized sample indicators; Step 3: Perform the factor analysis applicability test; Step 4: Determine the common factor according to the assumption that the eigenvalue is greater than 1 or the cumulative variance contribution rate is greater than 80%; Step 5: Calculate the comprehensive evaluation value based on factor analysis method according to the variance contribution rate and score coefficient matrix.

4.3 Sample and data

Manufacturing plays a vital role in a country’s economic development and is central to the competition among industrial powers and in the global industrial landscape (Baily and Bosworth, 2014; Yuan et al., 2016). With the continual advancement of science and technology, advanced material manufacturing has emerged as the frontrunner in the future of the manufacturing industry. The technologies within advanced material manufacturing not only drive innovation across existing industries but also pave the way for new opportunities in sectors such as energy, healthcare, and electronics, thus contributing to a more advanced, efficient, and sustainable future for humanity (Ryan et al., 2021). Advanced materials manufacturing is a critical sector that shapes the developmental trajectory of future knowledge-intensive industries characterized by innovation. This area also possesses significant growth potential and high added value, making manufacturing an essential engine for high-quality economic development and a fundamental pillar of national security in recent years. Analyzing and enhancing the innovative vitality of the advanced manufacturing industry is crucial for executing the innovation-driven development strategy and expediting the construction of an innovative nation. Consequently, we selected enterprises operating in the field of advanced material manufacturing as our sample. In accordance with the industry classification standards set by the China Securities Regulatory Commission and considering the availability of data, we focused on three industries linked to advanced material manufacturing: chemical raw materials and chemical products, non-metallic mineral manufacturing, and construction. Following this, we excluded enterprises with significant missing information and irregular financial data, resulting in a final sample of 471 listed enterprises. Ultimately, we aggregated multiple data sources into a panel data format, covering the period from 2015 to 2021, yielding a total of 3,297 observations.

The data on enterprise R&D intensity and R&D personnel were obtained from the Wind database, a resource frequently utilized by researchers (Boeing et al., 2016; Chen et al., 2018). Additionally, data regarding patent applications, patent transfers, and partner-related information were sourced from the CNIPA database, which has been extensively employed in previous studies (Rong et al., 2017; Chen et al., 2018).

5 Results

5.1 Suitability test

A prerequisite for factor analysis is the presence of correlations among variables. Two main statistics, the KMO (Kaiser-Meyer-Olkin) value and Bartlett’s test of sphericity, are used to assess this correlation. The KMO value typically ranges from 0 to 1, with a value exceeding 0.6 suggesting it is suitable for factor analysis (Reio and Shuck, 2015). The null hypothesis of Bartlett’s test states that “the correlation coefficients between variables form an identity matrix.” Therefore, rejecting the null hypothesis indicates appropriateness for factor analysis. As displayed in Tab.2, the KMO value is 0.70 (greater than 0.6), and Bartlett’s test rejects the null hypothesis, confirming that the sample data used in this study are suitable for factor analysis.

5.2 Total variance explains

The total variance indicates the extent to which the extracted factors, following dimensionality reduction, account for the original variables. As shown in Tab.3, six factors with eigenvalues greater than 1 have been extracted, and the cumulative variance contribution rate of these six factors after dimensionality reduction is approximately 70%, demonstrating a high level of information integrity.

The scree plot for the factor analysis is illustrated in Fig.2. Six factors exhibit eigenvalues greater than 1, while the eigenvalues of all other factors are below 1, aligning with the variance explanation results. During the factor selection process, 12 indices were excluded after completing the PCA, leaving six indices. Ultimately, the goal of dimensionality reduction was achieved.

5.3 Rotated component matrix

Tab.4 presents the orthogonal factor loading matrix, derived from the initial factor loading matrix through orthogonal rotation using variance maximization. This matrix displays the loadings of the six common factors on each variable. The values of the rotated factors are clearly differentiated, with each factor showing only a few indicators with relatively high factor loads, highlighting practical significance.

In the first component (Comp 1), the indicators with the highest absolute coefficient values are A1, A2, A3, and A4. In the second component (Comp 2), the indicators with larger absolute values include A5, A6, and B1. The third component (Comp 3) features indicators B2, B3, and B4 with significant absolute coefficient values. In the fourth component (Comp 4), the indicators B5 and B6 are highlighted for their considerable absolute coefficient values. The fifth component (Comp 5) includes C1 and C2 as significant indicators, while in the sixth component (Comp 6), indicators C4, C5, and C6 are noted for their larger absolute coefficient values.

The analysis demonstrates that the 18 indicators from the indicator layer can be distilled into 6 core factors that effectively encapsulate the multifaceted nature of enterprises’ innovation vitality. Specifically, Comp 1 is associated with innovation persistence, Comp 2 relates to both innovation persistence and innovation volatility, while Components 3 and 4 pertain to innovation volatility. Finally, Components 5 and 6 are linked to innovation growth.

Indicators A5 and A6, which are related to cooperation, represent aspects of innovation persistence, whereas B1, representing R&D investment intensity, is an indicator of innovation vitality. However, the findings from the EFA reveal that Component 2 includes A5, A6, and B1. Two explanations may account for this observation. First, theoretically, A5, A6, and B1 are not entirely independent; cooperation tends to enhance the intensity of R&D investment within enterprises. Specifically, increased cooperation facilitates access to external resources, thereby stimulating innovative activities and prompting more intensive R&D investments (Dawid et al., 2013). Cooperative enterprises often exhibit higher levels of R&D investment intensity because they can effectively utilize the external resources acquired through collaboration to complement their internal capabilities (Becker and Dietz, 2004). Second, empirically, cooperation has been shown to have a strong correlation with R&D investment intensity. Previous research indicates a statistically significant relationship between the two factors (Runge et al., 2022), with A5, A6, and B1 exhibiting significant correlations, with coefficients of 0.697 and 0.694, respectively, aligning with existing findings.

5.4 The score of enterprises’ innovation vitality

5.4.1 The goal layer score of enterprises’ innovation vitality

This study aims to estimate the total score of enterprises’ innovation vitality, specifically the overall score of the goal layer. The component score coefficient reflects the weight of each indicator within a particular common factor. The component score matrix for enterprises’ innovation vitality is presented in Tab.5.

According to the component score matrix and the variance contribution and cumulative variance of the six factors, we calculated scores for the score of enterprises’ innovation vitality. The specific formula is expressed as follows:

(2)\begin{array}{rl}Factor1=& -0.134\ast A1-0.042\ast A2-0.151\ast A3+\\ & 0.048\ast A4-0.010\ast A5+\cdots -0.033\ast C6\end{array}

(3)\begin{array}{rl}Factor2=& -0.017\ast A1+0.368\ast A2-0.024\ast A3+\\ & 0.052\ast A4-0.018\ast A5+\cdots +0.018\ast C6\end{array}

(4)\begin{array}{rl}Factor3=& -0.287\ast A1+0.066\ast A2+0.499\ast A3+\\ & 0.145\ast A4+0.408\ast A5+\cdots -0.015\ast C6\end{array}

(5)\begin{array}{rl}Factor4=& -0.045\ast A1+0.009\ast A2-0.083\ast A3-\\ & 0.040\ast A4+0.044\ast A5+\cdots +0.553\ast C6\end{array}

(6)\begin{array}{rl}Factor5=& 0.027\ast A1-0.017\ast A2+0.152\ast AJ+\\ & 0.290\ast A4-0.010\ast A5+\cdots -0.048\ast C6\end{array}

(7)\begin{array}{rl}Factor6=& 0.451\ast A1-0.089\ast A2-0.017\ast A3+\\ & 0.004\ast A4-0.089\ast A5+\cdots +0.021\ast C6\end{array}

(8)\begin{array}{rl}Score=& 23.94/66.525\ast Factor1+9.915/66.525\ast \\ & Factor2+9.947/66.525\ast Factor3+\\ & 9.147/66.525\ast Factor4+7.016/66.525\ast \\ & Factor5+6.761/66.525\ast Factor6\end{array}

An analysis of the scores for the top 10 enterprises reveals that their innovation vitality has shown continuous improvement from 2015 to 2019. However, it is important to note that there was a decline in innovation vitality from 2020 to 2021. This decline may be attributed to the impact of COVID-19, which led to increased environmental uncertainty and heightened risks associated with enterprise innovation. As innovation activities faced restrictions, obtaining sufficient resources to support innovation became challenging.

From a temporal perspective, innovation vitality among all sampled enterprises demonstrated an upward trend from 2015 to 2019, followed by a downturn post-2019, which aligns with prior analyses. The heightened uncertainty in the external environment, particularly following the outbreak of COVID-19, has resulted in a diminished willingness among enterprises to innovate, thereby reducing their innovation vitality. The trend in enterprises’ innovation vitality scores from 2015 to 2021 is illustrated in Fig.3.

5.4.2 The criterion layer score of enterprises’ innovation vitality

Concerning the criteria for enterprises’ innovation vitality, innovation growth received the highest score, followed by innovation persistence, while innovation volatility recorded the lowest score. Notably, the difference between the maximum and minimum values of innovation volatility was the largest among all criterion-level indicators, reaching 12.46. These findings suggest that enterprises’ innovation activities are unstable, potentially due to rapid changes in the innovation environment, coupled with insufficient stable financial support and a lack of long-term innovation planning. The main statistics related to the criterion layer of enterprises’ innovation vitality are summarized in Tab.7.

From 2015 to 2021, the innovation persistence scores of enterprises ranged between 89.05 and 90.02. The scores exhibited a gradual increase from 2015 to 2019, reaching a peak of 90.02 in 2019. However, following this period, a marked decline was observed, with the score dropping to its lowest point of 89.05 in 2021. This decrease may be attributed to the onset of the epidemic, a highly volatile external environment, and diminished expectations for innovation among enterprises. The trend in the enterprises’ innovation persistence scores from 2015 to 2021 is illustrated in Fig.4.

In contrast to the innovation persistence scores, the enterprises’ innovation volatility score ranges from 79.94 to 80.11 from 2015 to 2021; during the same period. These scores demonstrated a more pronounced upward trend after 2019, likely due to increased environmental uncertainty. As a result, enterprises found it challenging to sustain high expectations for future innovation returns. The trend in the enterprises’ innovation volatility scores from 2015 to 2021 is illustrated in Fig.5.

Between 2015 and 2021, the enterprises’ innovation growth scores varied from 89.8 to 90.05. Specifically, from 2015 to 2019, the innovation growth scores exhibited an overall upward trajectory, peaking in 2019. Subsequently, a noticeable decline occurred, with scores falling to 89.97 in 2021. The trend in the enterprises’ innovation growth scores from 2015 to 2021 is depicted in Fig.6.

6 Conclusions and policy implications

6.1 Conclusions

Based on the definition of enterprises’ innovation vitality, this paper constructs the evaluation system for enterprises’ innovation vitality from the EFA method. The proposed evaluation criterion includes three dimensions: innovation persistence, innovation volatility, and innovation growth. The key indicators in the evaluation system for enterprises’ innovation vitality include innovation input persistence, cooperation persistence, innovation input volatility, cooperation volatility, innovation input growth, and innovation output growth. This framework offers insights into how other enterprises and industries can engage in objective evaluations of the innovation capacity, which in turn can provide ideas for increasing vitality to encourage the steady development of industry and enterprise-level performance.

6.2 Theoretical contributions

First, we aim to advance the research on enterprise innovation. Existing studies primarily focus on innovation capabilities and innovation performance (Zheng et al., 2010; Akinwale et al., 2018; Ganguly et al., 2019; Daronco et al., 2023), while only mentioning the concept of innovation vitality (Lavrusheva, 2020). However, as improving innovation vitality is a crucial policy objective (Vicenzi and Adkins, 2000), the absence of quantitative measures has resulted in a limited understanding of the current state and progress of enterprises’ innovation vitality. We propose that innovation vitality emphasizes the willingness, efforts, and initiatives of enterprises to actively pursue innovation. To facilitate this understanding, we develop an evaluation system for enterprises’ innovation vitality that integrates organizational vitality and innovation capabilities, employing the EFA method to assess this vitality, which holds significant theoretical value for analyzing enterprise innovation.

Second, we evaluate the innovation vitality of enterprises through three dynamic dimensions: innovation persistence, innovation volatility, and innovation growth, thereby enriching the research on the evaluation of enterprises’ innovation vitality. Current academic work often considers aspects such as enterprise innovation assets, human resources, organizational management innovation, innovation performance, and innovation environment (Hu, 2012), while neglecting the dynamic changes in enterprise inputs and outputs related to innovation activities. In line with the idea of innovation vitality, our focus is on capturing the dynamic characteristics of self-sustaining, self-supporting, and self-developing systems. Accordingly, we propose detailed indicators, including innovation input, innovation output, and collaboration to measure enterprises’ innovation vitality across the three dynamic dimensions of persistence, volatility, and growth, thereby enriching the literature on enterprises’ innovation vitality and offering a framework for evaluating the innovation vitality of other entities.

6.3 Policy recommendations

On the basis of the findings, we identified factors that were independent and internal as well as external indicators that strongly and mostly influenced enterprise innovation vitality. The technological aspect pushing the internal part determines the continuity of innovation input; the basic reason behind this is that a high level of R&D intensity strongly boosts the innovation vitality of enterprises (A1). Externally, collaboration through technical partnerships substantially increases enterprises’ innovation vitality (A5, B5, B6). In view of these conclusions, recommendations are given, first to the enterprise and secondly to policymakers from these two perspectives.

6.3.1 Policy recommendations for enterprises

Enterprises can enhance their innovation capabilities through two main strategies, which include steps such as the focus on the growth of the R&D and partnership with the external actors.

However, the first one is that enterprises need to invest adequate capital and resources to R&D activities and mobilize necessary funds from internal and external resources for technological innovation. On the internal level, this means that the certain part of the overall company’s revenues has to be assigned for the technological innovation funds. On the outside, organizations must leverage on national technological innovation policies and utilize various forms to enhance the investment technological innovation. This includes recklessly seeking government R&D funds, acquiring for financial subsidies, and using tax credits and preferential policies.

Second, enterprises should pay attention to constructing the cooperation relations and building the sharing platform. The first one is building an industry-university research model where enterprises form research cooperation with one or more universities, research institutions or other enterprises, to conduct research on innovation needs. Another approach relates to the creation of units for technological innovation in cooperation with universities as a direct access to R&D staff and groups. Through such partnerships, enterprises can transfer core R&D human resources to universities to boost their knowledge and do research.

6.3.2 Policy recommendations for policymakers

Policymakers can invigorate enterprise innovation by establishing innovation funds and promoting industry-university-research collaboration.

First, policymakers can help the further development of the sources of financing for enterprises with the formation of an effective system of policy loan aids, support from the financial structure and credit guarantee systems. Such a complex approach will offer enterprises diverse opportunities to avail themselves of the needed financing. Furthermore, it is also possible for policymakers to foster venture capital in innovative enterprises. In this manner, it shall be supporting these firms through venture funds necessary in financing technological advances. Moreover, for the policymaker, the capital market can be an operational means to organize social capital and develop new forms of financing as well as new financial instruments. Specifically, the creation of a double-layered capital market, including SSE (Shanghai Stock Exchange) STAR (Sci-Tech innovAction boaRd) Market and Growth Enterprise Market (GEM), can greatly promote technological innovation activities.

Apart from direct financial backing, policymakers can further initiate and encourage cooperation and more effective integration between enterprises and academic institutions through providing platforms for communication and innovative dissemination of information. Via these platforms, enterprises would be able to access important resources which include professional knowledge, research findings and information on patents from research organizations. Thus, by providing information exchange, and connection opportunities, these platforms help enterprises to find appropriate partners and develop interaction and cooperation. Moreover, policymakers may introduce the following push factors like providing subsidies like R&D funding subsidies or tax credits or enhancing R&D investment incentives to entice the enterprises for the formation of partnership with academic institutions. It can encourage enterprises (and other stakeholders) to participate in collaborative R&D projects and create synergy between enterprises, universities and research institutions.

6.4 Limitations and future research

This study has some limitations. The first limitation pertains to the generalization of the research findings. The patent data in this work have been extracted from the CNIPA database. Although the CNIPA database is very popular and provides comprehensive patent information, it is quite possible that some useful data can also be present in another patent database like the USPTO that may have slipped under our radar. Therefore, findings will be of greater value if information from varied sources is included.

Furthermore, our focus has been on evaluating the enterprises’ innovation vitality. As said, there exists a relationship between enterprises’ innovation vitality and innovative capability. Future research will further focus on the inspection of the correlation between the enterprises’ innovation vitality and innovation ability. In future research, we will further explore the relationship between the enterprises’ innovation vitality and innovation capability, which would enrich the understanding of innovative quest for enterprise innovation.

References

[1] Akinwale Y O, Akinbami J F K, Akarakiri J B, (2018). Factors influencing technology and innovation capability in the Nigerian indigenous oil firms. International Journal of Business Innovation and Research, 15( 2): 247–268

[2] Aspden H, (1983). Patent statistics as a measure of technological vitality. World Patent Information, 5( 3): 170–173

[3] Baily M N, Bosworth B P, (2014). US manufacturing: Understanding its past and its potential future. Journal of Economic Perspectives, 28( 1): 3–26

[4] Bakker A B, Petrou P, Op den Kamp E M, Tims M, (2020). Proactive vitality management, work engagement, and creativity: The role of goal orientation. Applied Psychology, 69( 2): 351–378

[5] Battistella C, Ferraro G, Pessot E, (2023). Technology transfer services impacts on open innovation capabilities of SMEs. Technological Forecasting and Social Change, 196: 122875

[6] Becker W, Dietz J, (2004). R&D cooperation and innovation activities of firms—Evidence for the German manufacturing industry. Research Policy, 33( 2): 209–223

[7] Bishwas S K, (2015). Achieving organization vitality through innovation and flexibility: An empirical study. Global Journal of Flexible Systems Managment, 16( 2): 145–156

[8] Bishwas S K, (2016). LIFE: an integrated view of meta organizational process for vitality. Journal of Management Development, 35( 6): 747–764

[9] Boeing P, Mueller E, Sandner P, (2016). China’s R&D explosion—Analyzing productivity effects across ownership types and over time. Research Policy, 45( 1): 159–176

[10] Bos-Nehles A C, Veenendaal A A, (2019). Perceptions of HR practices and innovative work behavior: the moderating effect of an innovative climate. International Journal of Human Resource Management, 30( 18): 2661–2683

[11] Chen J, Heng C S, Tan B C Y, Lin Z, (2018). The distinct signaling effects of R&D subsidy and non-R&D subsidy on IPO performance of IT entrepreneurial firms in China. Research Policy, 47( 1): 108–120

[12] Coad A, Rao R, (2008). Innovation and firm growth in high-tech sectors: A quantile regression approach. Research Policy, 37( 4): 633–648

[13] Coluzzi C, Ferrando A, Martinez-Carrascal C, (2015). Financing obstacles and growth: An analysis for euro area non-financial firms. European Journal of Finance, 21( 10–11): 773–790

[14] Conceição P, Heitor M V, Vieira P S, (2006). Are environmental concerns drivers of innovation? Interpreting Portuguese innovation data to foster environmental foresight. Technological Forecasting and Social Change, 73( 3): 266–276

[15] Daronco E L, Silva D S, Seibel M K, Cortimiglia M N, (2023). A new framework of firm-level innovation capability: A propensity–ability perspective. European Management Journal, 41( 2): 236–250

[16] Davis J P, Eisenhardt K M, (2011). Rotating leadership and collaborative innovation: Recombination processes in symbiotic relationships. Administrative Science Quarterly, 56( 2): 159–201

[17] Dawid H, Kopel M, Kort P M, (2013). R&D competition versus R&D cooperation in oligopolistic markets with evolving structure. International Journal of Industrial Organization, 31( 5): 527–537

[18] Dewangan V, Godse M, (2014). Towards a holistic enterprise innovation performance measurement system. Technovation, 34( 9): 536–545

[19] Forsman H, (2011). Innovation capacity and innovation development in small enterprises. A comparison between the manufacturing and service sectors. Research Policy, 40( 5): 739–750

[20] Ganguly A, Talukdar A, Chatterjee D, (2019). Evaluating the role of social capital, tacit knowledge sharing, knowledge quality and reciprocity in determining innovation capability of an organization. Journal of Knowledge Management, 23( 6): 1105–1135

[21] Hai B, Gao Q, Yin X, Chen J, (2020). R&D volatility and market value: the role of executive overconfidence. Chinese Management Studies, 14( 2): 411–431

[22] He Q, Wu Z, Chen X, (2024). An integrated framework for automatic green building evaluation: A case study of China. Frontiers of Engineering Management, 11( 2): 269–287

[23] Henard D H, Szymanski D M, (2001). Why some new products are more successful than others. Journal of Marketing Research, 38( 3): 362–375

[24] HuB (2012). Evaluation model for enterprise’s innovation vigor on fuzzy mathematics. In: 2012 Second International Conference on Business Computing and Global Informatization, IEEE. 545–548

[25] Huang Y, Chen X, (2021). A moderated mediation model of idiosyncratic deals and innovative performance of R&D employees: Roles of vitality. Chinese Management Studies, 15( 4): 785–800

[26] Kafouros M C, Wang P, Piperopoulos Zhang M, (2015). Academic collaborations and firm innovation performance in China: The role of region-specific institutions. Research Policy, 44( 3): 803–817

[27] Lavrusheva O, (2020). The concept of vitality. Review of the vitality-related research domain. New Ideas in Psychology, 56: 100752

[28] Nagaoka S, Motohashi K, Goto A, (2010). Patent statistics as an innovation indicator. In: Handbook of the Economics of Innovation. North-Holland, 2: 1083–1127

[29] OECD/Eurostat (2005), Oslo manual-guidelines for collecting and interpreting innovation data, OECD Publishing, Paris

[30] Rajapathirana R J, Hui Y, (2018). Relationship between innovation capability, innovation type, and firm performance. Journal of Innovation & Knowledge, 3( 1): 44–55

[31] Reio Jr, T G, & Ghosh R, (2009). Antecedents and outcomes of workplace incivility: Implications for human resource development research and practice. Human Resource Development Quarterly, 20( 3): 237–264

[32] Reio Jr T G, & Shuck B, (2015). Exploratory factor analysis: implications for theory, research, and practice. Advances in Developing Human Resources, 17( 1): 12–25

[33] Rosenbusch N, Brinckmann J, Bausch A, (2011). Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs. Journal of Business Venturing, 26( 4): 441–457

[34] Rong Z, Wu X, Boeing P, (2017). The Effect of Institutional Ownership on Firm Innovation: Evidence from Chinese Listed Firms. Research Policy, 46( 9): 1533–1552

[35] Runge S, Schwens C, Schulz M, (2022). The invention performance implications of coopetition: How technological, geographical, and product market overlaps shape learning and competitive tension in R&D alliances. Strategic Management Journal, 43( 2): 266–294

[36] Ryan K R, Down M P, Banks C E, (2021). Future of additive manufacturing: Overview of 4D and 3D printed smart and advanced materials and their applications. Chemical Engineering Journal, 403: 126162

[37] Rydvalova P, Antlova K, (2020). Vitality and continuity of family businesses. Journal of Business, Economics and Management, 21( 5): 1432–1450

[38] Saunila M, (2020). Innovation capability in SMEs: A systematic review of the literature. Journal of Innovation & Knowledge, 5( 4): 260–265

[39] SavolainenTHäkkinen S (2011). Trusted to lead: Trustworthiness and its impact on leadership. Open Source Business Resource, 2011

[40] Savolainen T, Lopez-Fresno P, (2013). Trust as intangible asset enabling intellectual capital development by leadership for vitality and innovativeness. Electronic Journal of Knowledge Management, 11( 3): 244–255

[41] Sher P J, Yang P Y, (2005). The effects of innovative capabilities and R&D clustering on firm performance: The evidence of Taiwan’s semiconductor industry. Technovation, 25( 1): 33–43

[42] Smith M, Busi M, Ball P, Van Der Meer R, (2008). Factors influencing an organisation’s ability to manage innovation: A structured literature review and conceptual model. International Journal of Innovation Management, 12( 4): 655–676

[43] Smith W S, (2009). Vitality in business: executing a new strategy at Unilever. Journal of Business Strategy, 30( 4): 31–41

[44] Song Y, (2018). Improving primary students’ collaborative problem solving competency in project-based science learning with productive failure instructional design in a seamless learning environment. Educational Technology Research and Development, 66( 4): 1–30

[45] Sun X, Zhao D, Zhang D, Tian F, (2022). Entrepreneurship and sustainable innovation capabilities in platform enterprises: The mediating role of knowledge integration. Chinese Management Studies, 16( 3): 627–652

[46] Sun Y, Zhang C, Kok R A W, (2020). The role of research outcome quality in the relationship between university research collaboration and technology transfer: Empirical results from China. Scientometrics, 122( 2): 1003–1026

[47] Vicenzi R, Adkins G, (2000). A tool for assessing organizational vitality in an era of complexity. Technological Forecasting and Social Change, 64( 1): 101–113

[48] Wang X, Dass M, (2017). Building innovation capability: The role of top management innovativeness and relative-exploration orientation. Journal of Business Research, 76: 127–135

[49] Wang C, Kafouros M, Yi J, Hong J, & Ganotakis P, (2020). The role of government affiliation in explaining firm innovativeness and profitability in emerging countries: Evidence from China. Journal of World Business, 55( 3): 101047

[50] Yang J, Wu J, Li X, Zhu Q, (2022). Sustainability performance analysis of environment innovation systems using a two-stage network DEA model with shared resources. Frontiers of Engineering Management, 9( 3): 425–438

[51] Yuan B L, Ren S G, Hu X, Yang X Y, (2016). The difference effect of environmental regulation on two stages of technology innovation in China’s manufacturing industry. Frontiers of Engineering Management, 3( 1): 24–29

[52] ZhangHWang ZChangYTianX (2018). Research on theory, connotation and evaluation system of state owned enterprises’ innovative vitality. In: 2018 4th International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2018). Atlantis Press, 342–346

[53] Zhang Y, Li R, Xie Q, (2023). Does digital transformation promote the volatility of firms’ innovation investment? MDE. Managerial and Decision Economics, 44( 8): 4350–4362

[54] Zheng Y, Liu J, George G, (2010). The dynamic impact of innovative capability and inter-firm network on firm valuation: A longitudinal study of biotechnology start-ups. Journal of Business Venturing, 25( 6): 593–609

[55] ZhouQChen CWuX (2008). An empirical research on the relationship between enterprises’ technology innovation vitality and performance. In: 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings. Long Beach, CA, USA, 1614–1619

[56] Zhou Z, Ou J, Wang S, Chen X, (2016). The building of papermaking enterprise’s recycling economy evaluation index system based on value flow analysis. Frontiers of Engineering Management, 3( 1): 9–17

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