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Article

How Knowledge Management Capability Drives Sustainable Business Model Innovation: A Combination of Symmetric and Asymmetric Approaches

by
Shuting Chen
1,
Liping Huang
2,* and
Aojie Zhou
3,*
1
School of Economics and Management, Jiangxi Agricultural University, Nanchang 330045, China
2
School of Humanities and Public Administration, Jiangxi Agricultural University, Nanchang 330045, China
3
School of Business, Central South University, Changsha 410083, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(15), 6714; https://doi.org/10.3390/su17156714
Submission received: 25 June 2025 / Revised: 16 July 2025 / Accepted: 19 July 2025 / Published: 23 July 2025
(This article belongs to the Special Issue The Role of Knowledge Management in Designing and Achieving Business Sustainability)
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Versions Notes

Abstract

In a business environment with rapidly growing digital technologies, knowledge management (KM) capability is an indispensable source for enterprise innovation activities. Nevertheless, there is limited understanding of the specific KM capability that leads to sustainable business model innovation (SBMI). This study therefore aimed to investigate the internal relationship between KM capability and SBMI by leveraging dynamic capability theory. A hierarchical regression analysis (HRA) and a fuzzy set qualitative comparative analysis (fsQCA) are used to analyze a sample of 115 Chinese innovative enterprises. The results indicate that organizational structure promotes information technology by improving human capital, and that information technology then stimulates collaboration depth by expanding collaboration breadth, thereby driving SBMI. Specifically, human capital, information technology, collaboration breadth, and collaboration depth play significant chain-mediating roles in the relationship between organizational structure and SBMI. This study contributes to the literature on KM and innovation management, extends the use of low-order and high-order dynamic capabilities in DCT, and assists managers in developing SBMI effectively.

1. Introduction

As digital technologies rapidly develop, firms are confronted with increasingly unexpected competition, enormous challenges, and new opportunities [1], transforming the commercial landscape and introducing brand-new modes in which enterprises operate and engage in business activities [2,3]. New rivals are not necessarily mature market participants but may even be newly established firms with various business models [4]. Many emerging business models have prominently altered the game rules within particular industries, and to survive and thrive in the long run, incumbent enterprises are compelled to periodically change their business models. A comprehensive CEO survey reported by IBM Global Business Services revealed that business model innovation (BMI) is a continuous source of value creation for global enterprises. Similarly, leading consulting firms in management and innovation have highlighted that BMI can provide core competitiveness in an age of constant change. Undoubtedly, BMI has become an indispensable innovation strategy for the sustainable growth of modern enterprises and even an essential means for late-developing firms to catch up with mature enterprises [5].
The concept of BMI has started to gain prominence in both management practice and the academic community. In management practice, many firms challenge traditional industries through BMI to satisfy changing consumer demands and generate new preferences in different areas such as transportation (e.g., Didi), consumption (e.g., Jingdong), communication (e.g., Facebook), and video (e.g., TikTok). In the academic community, BMI has been extensively explored via two key research methods: one mainly focuses on theoretical analysis and case studies, analyzing its specific typology, process, and logic [6,7,8]; the other focuses on quantitative research to determine its effects on performance outcomes [9,10,11]. Only recently have scholars gradually and increasingly started investigating the possible enablers of BMI. For example, Zhao et al. (2021) identified entrepreneurial alertness and entrepreneurial learning as enablers of BMI [12], while Zhang et al. (2023) found that digital transformation is a driving factor of BMI [13].
BMI is seen as an innovative behavior of a knowledge cluster, and its implementation requires the support of a collective body of knowledge [14]. More importantly, we are now in a knowledge-based economy era in which excellent KM capability is generally regarded as an essential driver of BMI. To date, some scholars have discussed the relationship between KM capability and BMI. Hock-Doepgen et al. (2021) investigated the effects of internal and external KM capabilities on BMI, as well as how these effects are moderated via risk-taking tolerance [2]. Liao et al. (2023) examined how KM capability influences BMI by analyzing the dual role of various types of legitimation motivations in stimulating KM capability and moderating the relationship between KM capabilities and BMI [15]. However, despite these studies having provided empirical evidence for the relationship, the following specific gaps need to be addressed: first, the existing literature treats KM capability as a simple construct, ignoring the possibility of interdependencies and interactions among the dimensions of KM capability; second, the previous literature mainly centered on traditional BMI, but paid less attention to sustainable business model innovation (SBMI); third, the extant literature has focused on the direct effect of KM capability on traditional BMI, while the complex influence mechanism of KM capability on SBMI has received insufficient attention; lastly, the research topic has not been completely explored in developed economies.
Motivated by these important research gaps, this study aimed to apply symmetric and asymmetric methods to investigate how KM capability affects SBMI based on a sample of Chinese innovative enterprises. We drew on DCT to develop our framework [16], in which internal KM capability, consisting of organizational structure, human capital, and information technology, is regarded as low-order DC; external KM capability, consisting of collaboration breadth and depth, is considered high-order DC; and SBMI is regarded as the result of DC. More specifically, organizational structure promotes information technology by virtue of strengthening human capital, and information technology stimulates collaboration depth by expanding collaboration breadth, ultimately achieving SBMI. Following this logic, we built a “low-order DC-high-order DC-result of DC” framework and constructed a chain mediation model to examine the influence mechanism of KM capability on SBMI.
In doing so, this study provides several key theoretical contributions. First, it clarifies the inner relationship between the elements of internal KM capability. To our knowledge, this study is the first to provide new insights into how these elements work together. Second, it reveals the impact of collaboration breadth on collaboration depth, thus contributing to the external collaboration literature. Third, by using low-order and high-order dynamic capabilities in DCT, it provides a more fine-grained understanding of the internal mechanisms through which KM capability influences SBMI, thereby enriching the literature linking KM with innovation management.
The rest of this manuscript is organized as follows. Section 2 presents a literature review of SBMI and KM capability. Section 3 describes our research hypotheses. Section 4 outlines the methodology. Section 5 presents the analysis results of HRA and fsQCA. Finally, Section 6 discusses the implications, limitations, and future directions.

2. Literature Review

2.1. KM Capability

Enterprises are the aggregates of explicit and tacit knowledge. Facing the large number and rich variety of knowledge elements, enterprises should aim to manage them scientifically and effectively to fully utilize the advantages of knowledge and avoid the pitfalls of knowledge explosion. In this regard, KM capability is crucial for the basic survival and sustainable development of firms, being generally conceptualized under two sub-capabilities, namely internal and external KM capabilities [17].
Internal KM capability is recognized as an enterprise’s capacity to retain, replicate, and apply knowledge from within its boundary [2]. This focuses on mastering, replicating, and utilizing existing knowledge to provide a solid foundation for knowledge storage, information availability, and social interaction [18]. Internal KM capability is based on the socio-technical theory, which describes the formation of an enterprise’s internal KM capability from the social and technical perspectives. The social perspective holds that knowledge exists among the employees and is embedded in the organizational structure [19], and the technical perspective emphasizes the information systems employed to share, store, and exploit knowledge [20]. In our study, internal KM capability comprises organizational structure, information technology, and human capital: organizational structure refers to the pattern of relationship and connection between individuals and departments, determining the direction of knowledge flow [21]; human capital refers to a unit-level ability generated from individuals based on the value of their experiences, skills, and intelligence, which affects the richness and diversity of knowledge storage [22]; and information technology refers to the sum of different technologies mainly applied to manage and process information and knowledge, influencing the speed of knowledge acquisition and transformation [23].
External KM capability is considered an enterprise’s capacity to obtain, transform, and utilize knowledge from outside its boundaries [17]. The emphasis is on acquiring external knowledge to analyze market conditions, grasp new opportunities and retain competitive advantages. Open innovation theory emphasizes that enterprises need to constantly develop external collaboration and alliances [24], based on which external KM capability components can be classified into two types, i.e., collaboration breadth and depth. Specifically, collaboration breadth refers to the number of partners firms involve in the innovation process, its value ranging from small to extensive as the quantity of partners grows; and collaboration depth refers to the level of reliance between a firm and its partners, ranging from shallow to profound as collaborative interaction intensifies [25,26]. From an information usage mode perspective, the former represents the diversified usage mode of external sources, whereas the latter represents the intensive usage mode of these sources [27].

2.2. SBMI

As the Internet economy has rapidly developed, the business model concept has received growing research attention [28]. A business model can be defined as a system of interconnected activities representing a template of how an enterprise conducts business [29]. To uncover the value logic of a business model, the literature identifies three critical elements: value proposition, creation, and capture. In detail, value proposition answers how an enterprise creates value for stakeholders by delivering solutions that satisfy their needs [30]; value creation concerns the methods and mechanisms through which an enterprise generates value by utilizing the resources and abilities of internal and external processes [31]; and value capture addresses how an enterprise gains revenues that offset costs and achieves profits that maintain sustainable performance [32]. These three elements interplay and interact across organizations.
As an important continuation of business model research, BMI has also been a research hotspot in the management field in recent years. BMI is described as designed, original, and nontrivial changes to the critical components of an enterprise’s business model connecting these components [33]. Effective BMI not only helps enterprises to generate new value growth, gain sustainable competitive advantages, and surpass their rivals, but also to shift the mode in which people live, work, and consume, as well as even reshape industries and society [34]. As a special type of BMI, SBMI puts more emphasis on sustainability. A consensus has not yet been reached on the precise connotation of SBMI, with scholars offering various perspectives and definitions. Regarding the triple bottom line, SBMI should comply with the requirements of economic, social, and environmental bottom lines, thereby going beyond the traditional view of economic value creation [35]. From a stakeholder perspective, SBMI signifies that the value proposition and value creation logic of enterprises integrate the value demands of multiple stakeholders [36]. It not only creates value for the targeted customers, as emphasized by traditional business models, but also needs to create value for employees, suppliers, government, society, and even the natural environment. From a strategy standpoint, SBMI is regarded as a new implementation framework that integrates or incorporates the strategic orientation of corporate social responsibility [37].

3. Hypotheses Development

3.1. Organizational Structure and Human Capital

Organizational structure has been advocated as one of these firm resources that can decisively contribute to the reinforcement of human capital. It is well known that different types of organizational structures have various effects on human capital. The mechanistic organizational structure, characterized by complexity, formalization, and centralization, easily hinders communication and collaboration among individuals, groups, and business units and even limits free exploration, open development and effective working practices of staff members [20], thus inhibiting human resource construction in enterprises. On the contrary, organic organizational structure reflects simplicity, flexibility, and decentralization, which encourages employees to exchange information and share knowledge, and provides employees with many opportunities to expand their relational network and self-display [38]. As such, this is helpful for the cultivation of enterprise human capital. Additionally, some extant studies have offered evidence for a positive relationship between organizational structure and human capital. For instance, Ramezan (2011) found that organic structure positively and significantly influences intellectual capital [39].
We thus put forward the following hypothesis:
H1. 
Organizational structure has a positive effect on human capital.

3.2. Human Capital and Information Technology

Human capital plays a crucial role in the cultivation of information technology, which is reflected in the two levels of staff and manager. On the one hand, staff with solid expertise and rich skills can reduce mistakes in the operation of information technology activities and grasp the direction of information technology more accurately, making it easy to maintain high efficiency and low risk in building information technology infrastructure. On the other hand, managers are usually the designers of information technology, and their professionalism, strategic vision, education level, personality characteristics, and behavior patterns profoundly influence the development of information technology. Information technology is ineffective if managers cannot leverage their intellectual skills to extract insights and make strategic decisions [40]. In addition, Danquah and Amankwah-Amoah (2017) found that human capital positively and significantly affects the adoption of technology [41], providing evidence to support this view.
Consequently, we propose the following hypothesis:
H2. 
Human capital has a positive effect on information technology.

3.3. Information Technology and Collaboration Breadth

An enterprise’s technology-based information capability lays the foundation for integration and a real-time link between itself and external organizations [42]. As enterprises invest in advanced information technology and exploit it to generate and communicate mission information, they can cultivate a sense of reliance between their partners. The commitment to enhancing their information technology conveys a powerful signal to partners and gives them high confidence that communication and information sharing will be secure, dependable, and timely, thereby increasing the likelihood of successful external collaboration [43]. Moreover, the development of advanced information technology cannot only rely on internal resources but necessarily needs more resources derived from external collaboration, thus enabling firms to identify, assimilate, and apply diverse new knowledge and obtain new technological and market opportunities. Attaching great importance to information technology is therefore more likely to inspire firms to cooperate with a larger number of partners. Some studies have proved that information technology exerts a positive effect on external collaboration; for example, Lin (2022) demonstrated that information technology resources in firms help facilitate external collaboration [44].
We therefore advanced the following hypothesis:
H3. 
Information technology has a positive effect on collaboration breadth.

3.4. Collaboration Breadth and Collaboration Depth

From an attention-based theory perspective, an enterprise is often recognized as a system that structurally allocates attention, wherein decision-makers are advocated to deeply focus their spirit, effort, and concentration on a limited set of problems and assignments [45,46]. We therefore argue that collaboration breadth is beneficial to collaboration depth. Specifically, with the increase in collaboration breadth, decision-makers are easily exposed to significant amounts of external information, such that they gain a clear understanding of the status quo involving what enterprises lack today and may require in the future [17]. It helps enterprises to choose the most valuable collaborators from their many partners according to their actual needs. Meanwhile, as external collaboration breadth expands, enterprises are likely to face complex and diverse information, which easily causes pressure of information overload [47]. However, it is impossible for decision-makers to digest and absorb all the information due to imperfections (e.g., inaccuracies, delays) and cognitive ability limitations. As a result, they must narrow their attention to a smaller number of partners who can bring opportunities and benefits to enterprises, thus obtaining valuable knowledge and information through deep collaboration.
Following the above analysis, we proposed the following hypothesis:
H4. 
Collaboration breadth has a positive effect on collaboration depth.

3.5. Collaboration Depth and SBMI

Scholars have previously proved that external collaboration is crucial in enhancing an enterprise’s internal innovation process [48]. In this study, we argue that deep collaboration with external partners can effectively promote SBMI. First, a deep collaborative relationship reflects an enterprise’s strong dependence on a few critical external knowledge sources. This is beneficial to prevent the enterprise from forming tunnel vision; improve the accurate understanding of newly obtained extrinsic information; and generate new thoughts, ideas, and strategies [27], which lays a solid foundation for the SBMI process. More importantly, a deep collaboration cultivates trust between the firm and its deeply related partner. Trust serves as an essential coordinated mechanism that facilitates timely information sharing and strong communication, thus beneficial to the efficient implementation of SBMI. Additionally, Chen and Yu (2024) demonstrated that firms with deeper external collaboration strategies tend to have better chance of achieving BMI [17], which provides preliminary evidence for the positive impact of collaboration depth on SBMI.
Consequently, we proposed the following hypothesis:
H5. 
Collaboration depth has a positive effect on SBMI.

3.6. The Chain-Mediating Effect

Dynamic capability theory (DCT) emphasizes that DC allows enterprises to adjust, integrate, and recombine skills, resources, and capacity to meet the demands of a dynamic circumstance [16]. In a past system of characteristics, DC is often divided into lower-order and higher-order ones [49]. Among them, lower-order DC aims to ensure an enterprise’s basic operation and survival in a dynamic environment, whereas higher-order DC is devoted to improving an enterprise’s environmental adaptability and development potential, both indispensable in an enterprise’s innovation process. A progressive relationship exists between them in logic; i.e., lower-order DC is the prerequisite of higher-order DC [49]. In this study, the relationship between internal KM capability, external KM capability, and SBMI can be explained by the paradigm of “low-order DC-high-order DC-result of DC”; that is, internal KM capability is considered a type of low-order DC, external KM is considered a type of high-order DC, and SBMI is regarded as the result of DC. In other words, internal KM capability can affect external KM capability, which in turn determines the SBMI.
According to the above research hypotheses (H1-H5) and the paradigm of “low-order DC-high-order DC-result of DC”, the influence mechanism of KM capability on SBMI is as follows. First, it is plausible to assume that firms possessing a good organizational structure naturally tend to develop human capital in accordance with duties, responsibilities, and rights. Second, high-quality human capital comprising managers and employees not only implements but also guides the cultivation of information technology development. Third, enterprises that invest in information technology are likely to grasp new technological frontiers, seize market opportunities, and identify their own deficiencies, thus stimulating cooperation with diverse external organizations. Fourth, as enterprises cooperate with numerous external organizations, they will gradually focus on partners that can bring opportunities and benefits. Finally, firms with deep external collaboration tend to seek innovation opportunities beyond the ways and thought patterns in which the organization normally operates, being more willing to adopt significant changes in their sustainable business model. In summary, organizational structure promotes information technology by enhancing human capital, and then information technology stimulates collaboration depth via an increasing collaboration breadth, ultimately leading to SBMI.
We therefore posited the following hypothesis:
H6. 
Human capital, information technology, collaboration breadth, and collaboration depth play a significant chain-mediating role in the relationship between organizational structure and SBMI; that is, organizational structure promotes information technology by improving human capital, and then information technology stimulates collaboration depth via expanding collaboration breadth, thereby driving SBMI.

4. Methodology

4.1. Sample Selection

Strategy& (PwC’s consulting group) published their “2018 Global Innovation 1000 Study”, which identifies the top 1000 global firms based on their R&D spending. The list includes a significant number of Chinese firms. At the same time, Renmin University of China announced the “2018 Chinese Innovation 100 Study”, highlighting the 100 leading Chinese firms known for their innovative capability. The enterprises in those two studies can all be categorized as innovative, and in this study, Chinese enterprises in the above studies are selected as the research objects.
We selected and analyzed Chinese innovative enterprises for the following three reasons. First, these enterprises adhere to China’s innovation-driven development strategy. Next, a research gap exists wherein most studies in the literature emphasize technological innovation in innovative enterprises but overlook SBMI. Third, innovative enterprises typically allocate considerable resources to conduct KM activities and improve KM capability.
The process for selecting our sample is as follows. First, to guarantee the adequacy of sample size, we chose all Chinese enterprises from the “2018 Global Innovation 1000 Study” list and all companies from the “2018 Chinese Innovation 100 Study” list. To assure data availability, enterprises listed in Hong Kong, Taiwan, and the United States and unlisted enterprises were then deleted. To guarantee the integrity of data, we then eliminated enterprises with relevant information incomplete or missing. Finally, to guarantee sample independence, duplicate samples were removed. Following the above steps, this study retained 115 Chinese innovative enterprises as the target sample, whose characteristics are shown in Table 1.

4.2. Measures

4.2.1. Explanatory Variable

Internal KM capability is constituted by organizational structure, human capital, and information technology, organizational structure is assessed though the ratio of administrative expense to operating income [17], human capital is measured as the proportion of highly skilled R&D personnel [50], and information technology is evaluated via the ratio of R&D expenditure to operating income [51].
External KM capability comprises collaboration breadth and depth. In line with previous studies [47], collaboration breadth is defined as the aggregate of nine sources of external knowledge for innovation. These sources include suppliers, customers, competitors, governments, universities and educational institutions, and consultancy companies. We coded each source as a binary variable, where 1 indicates that a source is exploited, and 0 indicates that the source is not exploited. The total score for the nine sources ranges from 0 to 9. Similarly, the collaboration depth is considered the intensity of collaboration with external partners. We employed a three-point scale for this measurement: 1 for no or a low degree, 2 for a medium degree, and 3 for a high degree of use. Each source is rated individually with a binary variable of 1 indicating a high degree of use, and 0 representing no, a low degree, or a medium degree of use, resulting in a total score ranging from 0 to 9 for each firm.

4.2.2. Explained Variable

We set SBMI as a dependent variable and measured it from a comprehensive perspective, dividing it into the two dimensions of efficiency and novelty. Efficiency refers to the strategies adopted by enterprises to improve transaction efficiency via their sustainable business models, aiming to reduce transaction costs for participants. In contrast, novelty refers to the emerging sustainable modes that perform economic exchanges between different participants, such as connecting past unlinked parties, linking partners in new forms, and devising new transaction methods [52].
Specifically, SBMI efficiency comprises three dimensions: value creation, proposition, and capture [53]: value creation is assessed using the current ratio, equity-to-debt ratio, and debt coverage ratio; value proposition is calculated from inventory turnover, accounts receivable turnover, and total assets turnover; and value capture is determined using the net profit growth rate, operating income growth rate, and operating profit ratio. To estimate efficiency, we adopted the TOPSIS-Entropy model, a multi-criteria decision-making analysis approach that combines the entropy weight method and the TOPSIS method, which optimizes scheme ranking through objective weighting and comprehensive distance evaluation. Its core advantage lies in determining the indicator weights using the entropy weight method, and then calculating the degree of closeness of each scheme to the ideal solution through the TOPSIS method, ultimately obtaining a scientific and objective evaluation result.
Additionally, we developed a separate scale to assess the novelty dimension of SBMI. Raters were required to use a five-point Likert scale to reflect the four aspects of novelty: (1) the establishment of a global R&D center and platform; (2) the implementation of an advanced intelligent manufacturing system; (3) the development of a diversified, sophisticated, and intelligent sales system; (4) the creation of a modern, flexible, and intelligent service system.
Based on the above, we performed data preprocessing to normalize the values of efficiency and novelty into a [0, 1] interval and then calculated SBMI though the average value of their scores.

4.2.3. Control Variables

We selected the following four control variables: For firm ownership, state-owned enterprises are coded as 1, and private-owned, foreign-owned, and others are coded as 0. Firm age is the number of years since the enterprise’s establishment. Firm size is measured using the natural logarithm of employee scale. Industry is a dummy variable that takes a value of 1 if an enterprise belongs to the materials, consumer discretionary, healthcare, or energy industry, and 0 otherwise.

4.3. Data

The process of data collection mainly includes two distinct phases. For the initial phase, composite scales are developed to measure collaboration breadth, collaboration depth, and the novelty dimension of SBMI. These scales are constructed through a content analysis of firms’ disclosed information. For the subsequent phase, the data for additional variables are sourced from the CSMAR Database.
Given the growing reliance on panelists during the study period, we built a panel comprising one professor and two doctoral students. First, the professor meticulously chose candidates (i.e., two doctoral students) from his research team, ensuring that they possessed a strong grasp of external collaboration and novelty dimension. After selecting two suitable doctoral students, they were requested to thoroughly review the announcements, information, and files of the sample enterprises, and acquaint themselves with the specifics of external collaboration and novelty dimension, as well as jointly develop measurement scales. Subsequently, the professor provided advanced training to the two doctoral students, equipping them with expertise in data analysis. Additionally, the two raters received detailed instructions on how to effectively respond to the items of measurement scales. The basic documents of data collection comprise financial statements, investment analysts’ reports, enterprise news, enterprise websites, and other relevant materials for the 2016–2020 period. Each rater spent approximately six months gathering data on external collaboration and novelty dimension. Finally, the correlation coefficient among the scores of the two raters was 0.910 (p < 0.01), meaning their scores were similar overall. In cases of score discrepancies, they engaged in discussions under the professor’s supervision and eventually reached an agreement.
The data for additional variable is sourced from the CSMAR Database, an authoritative financial and economic database mainly holding financial data such as stocks, funds, and bonds. The data collection period is specified as the year 2018. To mitigate the effect of outliers on these findings, we winsorized the data obtained from CSMAR.

4.4. Statistical Technique

The joint use of HRA and fsQCA enables the integration of both symmetric and asymmetric viewpoints in model assessment. HRA utilizes the principles of linearity, unifinality, and additive impacts to analyze the effects of antecedent variables on outcomes [54]. In addition, fsQCA is a set-theoretic method built upon Boolean logic that examines how the combinations of antecedent variables lead to a specific outcome [55]. This method is supplemented to detect potential non-linear effects among constructs, which provides a more nuanced understanding of the relationships within the research model.

5. Results

5.1. HRA Results

5.1.1. Descriptive Statistics and Correlations

Table 2 presents the correlation coefficient for these main variables. It shows positive and significant relationships between (a) organizational structure and human capital (r = 0.613, p <0.01), (b) human capital and information technology (r = 0.684, p < 0.01), (c) information technology and collaboration breadth (r = 0.462, p < 0.01), (d) collaboration breadth and collaboration depth (r = 0.562, p < 0.01), and (e) collaboration depth and SBMI (r = 0.560, p < 0.01). These results provide initial support for the hypotheses advanced in our study.

5.1.2. Direct Effect

The results of HRA are shown in Table 3. The variance inflation factor (VIF) values across all models are lower than the threshold of 10, demonstrating that there is not a serious problem of multicollinearity.
Specifically, M1 shows that organizational structure significantly and positively influences human capital (β = 0.470, p < 0.01), thus confirming H1. M2 indicates that human capital significantly and positively affects information technology (β = 0.272, p < 0.01); H2 is therefore supported. In M3, there is a significant and positive relationship between information technology and collaboration breadth (β = 0.541, p < 0.01), which confirms H3. Furthermore, M4 shows a significant and positive effect of collaboration breadth on collaboration depth (β = 0.393, p < 0.01), thereby supporting H4. M5 indicates that collaboration depth significantly and positively influences SBMI (β = 0.253, p < 0.01), which affirms H5. More details about path coefficients are shown in Figure 1.

5.1.3. Chain-Mediating Effects

To assess the chain-mediating effect, we employed a bias-corrected bootstrapping procedure. The test is conducted at a 95% confidence interval using 5000 bootstrap samples. If zero is not involved within the confidence interval, the mediating effect is deemed significant; otherwise, its effect is deemed insignificant. The chain-mediating effect test results are presented in Table 4. In detail, the chain-mediating effect of organizational structure on SBMI via human capital, information technology, collaboration breadth, and collaboration depth in sequence is significant (estimate = 0.007, 95% CI = [0.003, 0.023]). H6 is therefore supported.

5.2. fsQCA Results

5.2.1. Calibration

The initial step in the fsQCA process is the calibration of conditions and outcomes. Following existing studies [56], the original values of conditions and outcomes are calibrated into fuzzy sets though adopting these thresholds, i.e., the 5th percentile for full non-membership, the 50th percentile for the cross-over point, and the 95th percentile for full membership.

5.2.2. Necessary Condition Analysis

We examined whether any of the five antecedents are always present or absent in every case where the outcome is present. An analysis of the necessary conditions is presented in Table 5, showing that the consistency coefficient of every condition is below 0.9. It suggests that none of our conditions constitute a necessary condition for achieving greater SBMI.

5.2.3. Sufficient Condition Analysis

After the fuzzy set calibration and the analyses of the necessary conditions, we performed a sufficiency analysis to generate the truth table. To exclude the less significant configurations, we set the number-of-cases threshold to 1. To determine sufficient configurations for realizing desired outcomes, we specified the thresholds of raw consistency and PRI consistency as 0.80. Though applying these thresholds, three solutions are reached: complex, parsimonious, and intermediate solutions.
As shown in Table 6, the overall solution consistency is 0.935, which exceeds the critical threshold of 0.740 [57], meaning that this configuration can lead to high SBMI. The overall solution coverage is 0.554, which surpasses the critical threshold of 0.450 [57], suggesting that the configuration identifies about 55.4% of the cases with great SBMI. For this configuration, organizational structure, human capital, collaboration breadth, and collaboration depth are core conditions, and information technology is a peripheral condition. The configuration shows that the combination of high levels of organizational structure, human capital, information technology, collaboration breadth, and collaboration depth can achieve greater SBMI, thereby again supporting the existence of the chain-mediating effect (H6).

5.3. Robustness Test

We conducted a robustness test via the changing sample size method. Considering the various sample sources and to ensure the sample’s robustness, we eliminated 14 enterprises that appeared only in the 2018 China Innovation 100 Study, and retained 101 enterprises from the 2018 Global Innovation 1000 Study. Following repeated analyses with the same procedures, the robustness test results of HRA and fsQCA are presented in Table 7 and Table 8, respectively. We observed that these test results are highly consistent with initial results, indicating our findings are relatively robust overall.

6. Discussion

6.1. Theoretical Implications

There are several theoretical implications of this study.
First, this study reveals the specific relationship among the elements of internal KM capability. Most current research focuses on the impacts of internal KM capability components on organizational performance. For example, Mills and Smith (2011) found that the effects of some internal knowledge resources (e.g., organizational structure, organizational culture, and technology infrastructure) are directly related to organizational performance [58]. However, the conducted literature review demonstrates that neither the intrinsic relationship nor the influence mechanism among these components has been previously discussed. In this study, three factors closely related to internal KM capability, namely organizational structure, human capital, and information technology, were selected to explore their intrinsic influence mechanism. We concluded that organizational structure positively affects human capital, and the latter could enhance information technology. Our findings unlock the black box of the inner relationship between the components of internal KM capability, and provide the initial empirical evidence of how these components work together.
Second, this study investigates the concrete relationships among the elements of external KM capability. Based on an analysis of prior studies, collaboration breadth and collaboration depth are typically explored as a pair of parallel constructs. Shi et al. (2019) examined their direct roles in the collaboration network and the moderating effect of network embeddedness in the knowledge network [59]. Zhang et al. (2022) investigated the effects of the two constructs on the growth of new technology-based enterprises along with the moderating effects of government subsidies [60]. However, we insist that the two constructs are neither orthogonal nor mutually exclusive. There may exist a relationship in which the two constructs influence and complement each other. This study specifically addresses the positive impact of collaboration breadth on collaboration depth according to the attention-based theory. This study therefore lays the groundwork for a greater understanding of external KM capability and offers significant advancements in the field of open innovation.
Third, this study elaborates the influence mechanism of internal KM capability on SBMI though external KM capability. KM capability is a complex, multi-dimensional and hierarchical construct, and the impact of KM capability on SBMI is more likely to be described as a complex phenomenon. To date, the potential influence mechanism between them has received little scholarly attention. Following the “low-order DC - high-order DC - result of DC” link, our study is dedicated to investigating how internal KM capability affects SBMI via external KM capability against a background of Chinese emerging economies. Both HRA and fsQCA results demonstrate that human capital, information technology, collaboration breadth, and collaboration depth play significant chain-mediating roles in the relationship between organizational structure and SBMI. These results can enhance scholars’ comprehension of the complex mechanism by which KM capability drives SBMI, both from symmetric and asymmetric perspectives. Furthermore, we offer a fresh perspective to interpret how SBMI is driven in today’s knowledge-based economy. This study thus enriches the literature linking KM and innovation management, and extends the use of low-order and high-order dynamic capabilities in DCT.

6.2. Practical Implications

This study further offers three managerial implications:
First, it is suggested that firms should attach great importance to enhancing KM capability. To improve internal KM capability, we encouraged managers to focus on the construction of KM infrastructure, such as optimizing the organizational structure, recruiting knowledgeable staff, and introducing advanced KM systems or software. At the same time, to boost external KM capability, we encourage managers to make efforts to strengthen external cooperation, e.g., establishing online platforms, building cooperative alliances, and jointly developing new products.
Second, firms should pay attention to SBMI. The overall goal of SBMI is to create value for companies and other stakeholders by capitalizing on business opportunities emerging from social, technological, environmental, and economic change. Managers should aim to innovate their business model to cope with complicated and volatile circumstances. In other words, enterprises can continuously improve their production and transaction efficiency to maintain their competitive advantage, and conduct economic exchanges between various participants in new ways to retain core competitiveness.
Lastly and foremost, according to the discussed chain-mediating role, firms are recommended to better leverage internal and external KM capabilities to induce SBMI. Managers should devote particular attention to clarifying the inner logic between the components of KM capability and SBMI. Specifically, firms need to build a sound organizational structure which will form a solid foundation for cultivating human capital, with the latter positively stimulating rapid information technology development. Consequently, when firms are devoted to developing advanced information technology, the scope for external collaboration gradually increases, which further fosters deep collaborations with VIRN characteristics. Based on the above pathway, SBMI can be effectively developed.

6.3. Limitations and Future Research

There are some limitations that offer directions for future research. First, this study uses a cross-sectional design, which identifies correlations among variables but does not establish causal relationships. Future research should consider using time-lagged or longitudinal data. Second, the model is tested exclusively on a sample of Chinese innovative enterprises. We recommend researchers analyze the representativeness of these firms across industries or ownership types to enhance the general applicability of these research results. Third, the measurement for variables (e.g., organizational structure and SBMI) may be deficient, so more improvements are still needed. Future research might use questionnaires or conduct interviews with corporate executives to capture relevant facets of variables. Additionally, future research should focus on potential endogeneity issues among the model variables. Fourth, while the sample size meets the requirements for parameter estimation in HRA and fsQCA, it is relatively insufficient given the large number of enterprises in China. Future research should aim to expand the sample size to achieve more comprehensive results. Lastly, the relationship between KM capability and SBMI may be affected by various potential moderators. Future research can therefore conduct studies that introduce potential moderators such as environmental turbulence, organizational culture, and top management style.

Author Contributions

Conceptualization, S.C.; methodology, S.C. and A.Z.; software, S.C.; validation, S.C. and A.Z.; formal analysis, A.Z. and L.H.; investigation, S.C.; resources, S.C.; data curation, S.C.; writing—original draft preparation, S.C.; writing—review and editing, S.C. and A.Z.; visualization, L.H. and A.Z.; supervision, L.H. and A.Z.; project administration, L.H. and A.Z.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Social Science Foundation of Jiangxi Province (Grant No. 24GL35), the Humanities and Social Science Research Project of Universities in Jiangxi Province (Grant No. GL23204), and the Degree and Graduate Education Teaching and Research Reform Project of Jiangxi Province (Grant No. JXYJG-2024-031).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data can be found according to the corresponding data source. Scholars requesting more specific data may email the corresponding author or the first author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 06714 g001
Figure 1. The fitted model of our study.
Figure 1. The fitted model of our study.
Sustainability 17 06714 g001
Table 1. Sample characteristics.
Table 1. Sample characteristics.
ItemCategoryNumberPercentage (%)
Firm age<10 years119.6%
11–20 years6153.0%
>20 years4337.4%
Firm size (employee number)<10,0002320.0%
10,001–30,0005043.5%
>30,0004236.5%
OwnershipState-owned7060.9%
Non-state-owned4539.1%
IndustryInformation technology3227.8%
Advanced materials1714.8%
Consumer discretionary2622.6%
Others4034.8%
Table 2. Descriptive statistics and correlation matrix.
Table 2. Descriptive statistics and correlation matrix.
VariablesMeanS.D.12345678910
1 Firm Ownership0.6100.4901
2 Firm Age18.5205.319−0.0721
3 Firm Size (Ln)10.0531.0950.145−0.1151
4 Industry0.5500.500−0.084−0.339 ***−0.0901
5 Organizational Structure0.0960.078−0.207 **0.001−0.427 ***0.263 ***1
6 Human Capital0.1910.158−0.110−0.011−0.434 ***0.355 ***0.613 ***1
7 Information Technology0.0610.051−0.266 ***0.046−0.428 ***0.254 ***0.840 ***0.684 ***1
8 Collaboration Breadth5.9001.533−0.0390.142−0.0160.1150.338 ***0.390 ***0.462 ***1
9 Collaboration Depth2.5101.624−0.1310.167 *−0.222 **0.0070.402 ***0.524 ***0.500 ***0.562 ***1
10 SBMI0.4130.128−0.367 ***0.262 ***−0.309 ***−0.1050.426 ***0.503 ***0.519 ***0.357 ***0.560 ***1
Notes: * p < 0.10, ** p < 0.05, *** p < 0.01 (two-tailed).
Table 3. Hierarchical regression analysis results.
Table 3. Hierarchical regression analysis results.
VariablesHuman
Capital		Information
Technology		Collaboration BreadthCollaboration
Depth		SBMI
M1M2M3M4M5
βT ValueβT ValueβT ValueβT ValueβT Value
Constant−0.303 **−2.0720.1321.338−0.118−0.6890.272 *1.8070.604 ***4.225
Control Variables
Firm Ownership0.0820.555−0.199 **−2.0250.1600.929−0.123−0.815−0.551 ***−3.874
Firm Age0.0450.5850.0360.7110.162 *1.8580.0420.5430.1071.468
Firm Size (Ln)−0.214 ***−2.714−0.016−0.3020.266 ***2.867−0.026−0.307−0.028−0.357
Industry0.461 ***2.946−0.020−0.1870.0380.203−0.359 **−2.207−0.490 ***−3.146
Main Variables
Organizational Structure0.470 ***5.7640.648 ***10.490−0.123−0.819−0.008−0.0570.0090.072
Human Capital 0.272 ***4.2810.215 *1.8230.354 ***3.3620.306 ***2.956
Information Technology 0.541 ***3.2790.0990.6530.1340.942
Collaboration Breadth 0.393 ***4.6290.0320.372
Collaboration Depth 0.253 ***2.789
Goodness-of-fit
R20.458 0.763 0.310 0.472 0.543
Adj R20.433 0.750 0.265 0.433 0.503
F18.388 *** 57.916 *** 6.861 *** 11.861 *** 13.842 ***
Maximum VIF1.334 1.843 4.218 4.641 4.660
Durbin–Watson1.736 2.146 2.113 1.835 2.103
Notes: * p < 0.10, ** p < 0.05, *** p < 0.01 (two-tailed).
Table 4. The results of the chain-mediating effect test.
Table 4. The results of the chain-mediating effect test.
PathsEstimateBootSE95%CI
BootLLCIBootULCI
Direct Effect (dir): OS→ SBMI0.0090.101−0.2020.204
Indirect Effect
(ind1) OS→HC→SBMI0.1440.0670.0310.293
(ind2) OS→IT→SBMI0.0870.096−0.0970.294
(ind3) OS→CB→SBMI−0.0040.023−0.0690.029
(ind4) OS→CD→SBMI−0.0020.038−0.0970.067
(ind5) OS→HC→IT→SBMI0.0170.019−0.0210.057
(ind6) OS→HC→CB→SBMI0.0030.010−0.0150.027
(ind7) OS→HC→CD→SBMI0.0420.0250.0050.104
(ind8) OS→IT→CB→SBMI0.0110.038−0.0520.097
(ind9) OS→IT→CD→SBMI0.0160.038−0.0460.112
(ind10) OS→CB→CD→SBMI−0.0120.022−0.0720.011
(ind11) OS→HC→IT→CB→SBMI0.0020.007−0.0100.021
(ind12) OS→HC→IT→CD→SBMI0.0030.008−0.0080.025
(ind13) OS→HC→CB→CD→SBMI0.0100.008−0.0010.029
(ind14) OS→IT→CB→CD→SBMI0.0350.0260.0060.106
(ind15) OS→HC→IT→CB→CD→SBMI0.0070.0060.0030.023
Total Indirect Effect: ind1+ind2+……+ind150.3600.1060.1780.602
Total Effect: dir+ind1+ind2+……+ind150.3690.0760.2260.527
Notes: OS = Organizational Structure; HC = Human Capital; IT = Information Technology; CB = Collaboration Breadth; CD = Collaboration Depth; SBMI = Sustainable Business Model Innovation.
Table 5. Results of the fsQCA on necessary conditions.
Table 5. Results of the fsQCA on necessary conditions.
ConditionsHigh Level of SBMI
ConsistencyCoverage
Organizational Structure0.7400.769
~Organizational Structure0.6600.603
Human Capital0.7560.802
~Human Capital0.6360.571
Information Technology0.7770.811
~Information Technology0.6280.572
Collaboration Breadth0.7750.717
~Collaboration Breadth0.5600.574
Collaboration Depth0.8330.771
~Collaboration Depth0.5470.560
Table 6. Results of the fsQCA on sufficient conditions.
Table 6. Results of the fsQCA on sufficient conditions.
Antecedent ConditionsHigh Level of SBMI
Organizational Structure⬤
Human Capital⬤
Information Technology●
Collaboration Breadth⬤
Collaboration Depth⬤
Consistency0.935
Raw coverage0.554
Unique coverage0.554
Overall solution consistency0.935
Overall solution coverage0.554
Notes: ⬤ represents the presence of core condition, and ● represents the presence of peripheral condition.
Table 7. Robustness test of HRA.
Table 7. Robustness test of HRA.
HypothesesPathsSubsample
βT Value
H1OS→HC0.541 ***6.423
H2HC→IT0.224 ***3.309
H3IT→CB0.505 ***2.663
H4CB→CD0.413 ***4.651
H5CD→SBMI0.214 **2.235
EstimateBootSE95%CI
BootLLCIBootULCI
H6OS→HC→IT→CB→CD→SBMI0.0050.0060.0020.022
Notes: ** p < 0.05, *** p < 0.01 (two-tailed).
Table 8. Robustness test of fsQCA.
Table 8. Robustness test of fsQCA.
Antecedent ConditionsHigh Level of SBMI
Organizational Structure⬤
Human Capital⬤
Information Technology●
Collaboration Breadth⬤
Collaboration Depth⬤
Consistency0.938
Raw coverage0.553
Unique coverage0.553
Overall solution consistency0.938
Overall solution coverage0.553
Notes: ⬤ represents the presence of core condition, and ● represents the presence of peripheral condition.
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Chen, S.; Huang, L.; Zhou, A. How Knowledge Management Capability Drives Sustainable Business Model Innovation: A Combination of Symmetric and Asymmetric Approaches. Sustainability 2025, 17, 6714. https://doi.org/10.3390/su17156714

AMA Style

Chen S, Huang L, Zhou A. How Knowledge Management Capability Drives Sustainable Business Model Innovation: A Combination of Symmetric and Asymmetric Approaches. Sustainability. 2025; 17(15):6714. https://doi.org/10.3390/su17156714

Chicago/Turabian Style

Chen, Shuting, Liping Huang, and Aojie Zhou. 2025. "How Knowledge Management Capability Drives Sustainable Business Model Innovation: A Combination of Symmetric and Asymmetric Approaches" Sustainability 17, no. 15: 6714. https://doi.org/10.3390/su17156714

APA Style

Chen, S., Huang, L., & Zhou, A. (2025). How Knowledge Management Capability Drives Sustainable Business Model Innovation: A Combination of Symmetric and Asymmetric Approaches. Sustainability, 17(15), 6714. https://doi.org/10.3390/su17156714

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