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Article

Failure Analysis and SME Growth: The Role of Dynamic Capabilities and Environmental Dynamism

by
Xiaoshu Ma
,
Luqian Chen
and
Xiaoyu Yu
*
School of Management, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Systems 2025, 13(8), 690; https://doi.org/10.3390/systems13080690
Submission received: 10 July 2025 / Revised: 5 August 2025 / Accepted: 12 August 2025 / Published: 13 August 2025
(This article belongs to the Section Systems Practice in Social Science)
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Abstract

Although prior research acknowledges that small and medium-sized enterprises (SMEs) can turn failures into growth opportunities, the mechanisms through which failure analysis contributes to such growth remain underexplored. Grounded in organizational learning and dynamic capabilities theory, this study explores how failure analysis facilitates SME growth through the mediating role of dynamic capabilities and the moderating role of environmental dynamism. Drawing on survey data from 207 managers of China SMEs, the study employs linear regression and bootstrapping techniques to empirically test the proposed hypotheses. The results reveal that failure analysis significantly promotes SME growth, with dynamic capabilities—specifically, sensing, seizing, and reconfiguring—serving as key mediators. Furthermore, environmental dynamism positively moderates both the relationship between failure analysis and dynamic capabilities, and the indirect effect of failure analysis on growth via dynamic capabilities. Unlike previous research that focuses primarily on innovation or resilience, this study uniquely highlights the role of failure analysis in cultivating dynamic capabilities to drive SME growth.

1. Introduction

While growth is important for all kinds of businesses, it matters even more for small and medium-sized enterprises (SMEs), which often face limited resources and higher risks [1,2]. As Cressy (2006) put it, “most firms die young”, suggesting that for many SMEs, growth is not only about scaling up but also about surviving [3] (p. 3). Although SMEs play a key role in driving economic development, employment, and innovation, only a small number manage to achieve sustainable growth [4]. Due to constraints in funding, knowledge, and experience, SMEs are more prone to product failure, strategic missteps, and operational setbacks [5,6,7]. Unlike large firms, they operate with minimal tolerance for mistakes, and even a single unabsorbed failure can threaten their survival [8]. This raises a critical question: How can SMEs transform failures into learning opportunities that foster sustainable growth? Addressing this challenge is particularly urgent, as failure to do so may jeopardize their competitiveness and survival in an increasingly volatile business environment.
Recent studies suggest that failure, defined as actions whose outcomes fall short of expectations, is not necessarily the end [9]. Instead, failure analysis, the purposeful reflection on and learning from failed decisions, can help organizations identify root causes, reshape cognitive frames, and improve subsequent decision-making [10]. This drives organizational learning and ultimately contributes to firm growth [11]. For instance, the Finnish game company Supercell, known for its small-team structure and decentralized decision-making, especially during its early development, places strong emphasis not only on tolerating failure but also on analyzing it. When a game project fails to meet internal expectations, the company openly conducts post-mortem reviews to identify causes and lessons learned. This deliberate failure analysis fosters organizational learning and continuous improvement, which supports Supercell’s sustained growth. This example highlights the potential of failure analysis to serve as a driver of firm growth.
However, despite growing recognition of the importance of failure analysis in promoting firm growth, the theoretical understanding and empirical evidence on how failure analysis drives SME growth remain limited. This study identifies three critical gaps remaining in the existing literature: (1) Although prior research has explored multiple drivers of SME growth, including firm-level factors (e.g., organizational learning, social capital), individual attributes (e.g., human capital, digital capability), and environmental influences (e.g., crisis shocks), these studies rarely consider how SMEs specifically respond to failure during the growth process, which is a pivotal moment that can either hinder or accelerate their development [12,13,14,15,16]. Kim (2022) shows that the impact of innovation failure on firm growth varies depending on internal resources and resilience capabilities [17], while Koporcic et al. (2024) emphasize the lack of process-oriented research on how SMEs learn from failure [18]. Together, these findings highlight a significant theoretical gap in understanding the mechanisms through which SMEs interpret, analyze, and learn from failure—knowledge that is essential for advancing organizational learning theory and informing practical growth strategies. (2) While prior studies emphasize entrepreneurial outcomes [19,20], the link to strategic growth remains under-theorized. Specifically, there is a lack of comprehensive theoretical frameworks explaining how SMEs translate failure-based deliberate learning into strategic adaptation and capability development, a connection critical to explaining sustained long-term growth. (3) As organizational learning is context-dependent [21], the effectiveness of failure analysis is likely contingent upon environmental dynamism, which refers to the speed and unpredictability of changes in technologies, markets, and competitive conditions [22,23]. High levels of dynamism amplify the need and urgency for rapid learning and strategic adaptation [24,25]. However, existing research has yet to clarify how environmental dynamism influences the effectiveness of failure analysis in fostering dynamic capabilities and, ultimately, SME growth. Clarifying this relationship is essential for developing theories that accurately explain how SMEs learn and grow in volatile contexts.
To address these gaps, this study draws on organizational learning theory and dynamic capabilities theory to conceptualize the mechanism through which failure analysis enables SME growth. Organizational learning theory emphasizes how organizations adapt behavior through experience and reflection, highlighting the importance of failure analysis in complex environments [26]. Dynamic capabilities refer to a firm’s ability to sense opportunities, seize them through resource reconfiguration, and transform its strategic trajectory [27]. This study argues that failure analysis not only fosters organizational learning but also serves as a trigger for sensing threats, reconfiguring routines, and transforming resource bases—core components of dynamic capabilities. In this view, failure analysis becomes a deliberate capability-building activity that allows SMEs to overcome path dependence and evolve continuously [28].
Based on these insights, the study proposes a moderated mediation model linking failure analysis, dynamic capability, environmental dynamism, and SME growth. Specifically, this study addresses the following research questions: To what extent do dynamic capabilities mediate the effect of failure analysis on SME growth, and how does environmental dynamism influence this relationship?
Employing a survey design, this study collected data from 207 managers in Chinese SMEs to test the proposed hypotheses. The results show that failure analysis fosters SME growth, with dynamic capability, including sensing, seizing, and reconfiguring, playing a key mediating role. Moreover, environmental dynamism positively moderates the relationship between failure analysis and dynamic capabilities, and moderates the mediating effect of dynamic capabilities in the relationship between failure analysis and firm growth. By clarifying the role of failure analysis and unpacking the mechanisms through which it translates into growth, this study offers three theoretical contributions: (1) It enriches the literature on SME growth by identifying failure analysis as a critical yet underexplored antecedent [12,15,16]. Existing research has paid limited attention to how SMEs respond to failure during growth, even though SMEs, unlike larger firms, often lack the capacity to absorb mistakes, and a single unresolved failure may endanger their survival [1]. (2) By linking failure analysis with dynamic capabilities, the study reveals how SMEs transform failure into actionable capabilities that drive growth. This shifts the analytical focus beyond outcomes such as product innovation or resilience [10,21], and offers empirical support for the strategic value of failure analysis in capability building, thus advancing the application of dynamic capabilities theory in SME contexts [27,29]. (3) By revealing that the effect of failure analysis on growth is stronger in dynamic environments, the study shows that organizational learning from failure is context-dependent [17,21]. This deepens the understanding of how learning unfolds under uncertainty, enriching organizational learning theory with empirical insights from the SME context [21,24,25].

2. Theoretical Background

2.1. SME Growth

According to Penrose (1959), growth can manifest various dimensions: It may refer to quantitative increases, such as higher output, exports, or sales, or qualitative improvements, including enhanced capabilities or organizational sophistication [30]. The present study adopts commonly used growth indicators such as market share, sales, and employment, as suggested in prior research [12,15].
Prior studies on SME growth have examined a wide range of influencing factors, including firm-level characteristics (e.g., networks, strategies, and resources), individual-level attributes (e.g., owner–manager experience and digital capabilities), and environmental conditions (e.g., crises and institutional contexts) (see Appendix A Table A1) [12,13,14,15,16]. However, a critical yet underexplored aspect of SME growth lies in how firms experience and respond to failure as an integral part of their developmental trajectory. Given their inherent resource constraints, SMEs are especially vulnerable to setbacks and operate with limited margins for error [8]. As a result, failure occurs more frequently and can pose significant threats to survival. This makes it imperative for SMEs, especially in their formative years, to transform failure into a learning opportunity. While the value of purposefully leveraging failure for knowledge accumulation and growth has been acknowledged [18], empirical research on how SMEs actually learn from failure and turn that learning into sustained growth remains scarce.

2.2. Failure Analysis

Danneels and Vestal (2020) introduced the concept of failure analysis, defining it as a firm’s deliberate effort to convert failure experiences into organizational knowledge [10]. Using data from 106 U.S. manufacturing firms, they found that merely tolerating failure does not enhance product innovativeness. Only firms that actively analyze failures are more likely to develop innovative products, and this effect depends on an internal climate that supports constructive conflict.
Since then, only a few empirical studies have extended this line of research. Yao et al. (2021), based on survey data from 226 Chinese high-tech firms, found that failure analysis has a stronger positive impact on entrepreneurial resilience than simply normalizing failure [20]. They also revealed that knowledge breadth amplifies this effect, while knowledge depth enhances the role of failure normalization but weakens the impact of failure analysis. Li et al. (2023), using survey data from 316 Chinese high-tech ventures, showed that technological turbulence weakens the positive link between failure analysis and venture goal progress. However, deep market knowledge can buffer this effect, indicating a second-order moderation [19].
Despite these valuable insights, existing research has yet to fully clarify how failure-derived knowledge translates into firm-level growth, particularly for SMEs that face significant resource constraints and vulnerabilities [1,2]. Given the multifaceted nature of organizational failure, this study specifically focuses on failures in new product development, a critical and concrete area of setback frequently encountered by SMEs [7]. By concentrating on new product development failures, the study enables a deeper examination of how SMEs learn from these specific experiences to foster organizational learning and capability building, and ultimately achieve sustained growth.

2.3. Research Model

This study integrates organizational learning theory and dynamic capabilities theory to explain how failure analysis drives SME growth. Organizational learning theory explains how firms purposefully analyze failures to accumulate experiential knowledge and foster adaptive learning [26]. Furthermore, dynamic capabilities theory emphasizes the continuous sensing, seizing, and reconfiguring of resources and capabilities to maintain competitiveness and sustain growth [27].
This study proposes that failure analysis acts as an organizational learning catalyst that develops these dynamic capabilities. Double-loop learning—central to organizational learning theory—serves as a foundational process for building dynamic capabilities [26]. By prompting firms to critically examine and revise their underlying assumptions and routines, it enhances sensing (e.g., identifying weak signals and emerging threats), facilitates seizing (e.g., spotting novel opportunities and experimenting with new solutions), and supports reconfiguration (e.g., realigning internal resources or shifting strategies).
In this way, failure analysis not only generates knowledge but also enables transformative action, as it drives the organizational shift from learning to capability deployment. In this way, failure analysis not only generates knowledge but also enables capability transformation, helping SMEs evolve from learning to strategic adaptation. This aligns with Teece’s dynamic capabilities framework. As Corvello et al. (2024) noted, the learning process between failure and growth is dynamically complex [28], and a dynamic capabilities perspective is essential to unpack the underlying mechanisms.
For SMEs, failure analysis is particularly critical. Given their limited resources and higher vulnerability, failure represents a valuable opportunity for learning [31]. However, failure alone does not automatically generate learning [32]; deliberate analysis is key to transforming failure into knowledge. Failure analysis can disrupt existing innovation assumptions and lead to the development of new strategies [18], becoming a vital pivot through which SMEs adjust strategies and improve performance [33].
Dynamic capabilities theory, grounded in strategic processes and firm-specific knowledge and skills, was first defined by Teece et al. (1997) as a firm’s ability to develop new competitive advantages [34]. This framework highlights two overlooked aspects of the resource-based view: “dynamic” refers to the need to continuously renew competencies in changing environments, while “capabilities” emphasizes the firm’s capacity to integrate, reconfigure, and adapt internal and external resources and skills. Teece (2007, p. 1319) further defines three core dynamic capabilities: (1) sensing and shaping opportunities and threats, (2) seizing opportunities, and (3) transforming or reconfiguring assets to maintain competitiveness [27].
Recent studies have applied this perspective to SME growth, arguing that, unlike large firms, SMEs operate with limited resources and flexible structures. This gives them unique agility and makes failure a natural part of their growth journey [18]. Learning from failure through structured analysis is thus an essential component of their dynamic capabilities [29]. Moreover, failure reshapes SMEs’ interaction with their environment [33], altering how they sense, seize, and transform opportunities. Yet, the precise mechanisms through which failure fuels dynamic capability development remain underexplored.
A recent qualitative study by Corvello et al. (2024) investigated how startups translate learning from innovation failure into strategies for growth [28]. They called for more quantitative research to examine the link between failure and the development of dynamic capabilities. In response, this study integrates the perspectives of organizational learning and dynamic capabilities to explore how failure analysis contributes to SME growth, with particular emphasis on the mediating role of dynamic capabilities. Furthermore, the ability of SMEs to harness the knowledge derived from failure is shaped by external environmental conditions. Environmental dynamism, defined as the rate and unpredictability of change in a firm’s external environment, amplifies both challenges and opportunities [35]. For SMEs, which already face high failure rates, understanding how they transform setbacks into strategic advantage is not only theoretically meaningful but also practically relevant for entrepreneurs, investors, and policymakers.
Thus, this study develops the research model illustrated in Figure 1.

3. Hypothesis Development

3.1. Failure Analysis and Firm Growth

Organizational learning theory posits that organizations improve performance by continuously acquiring, interpreting, and applying knowledge [26]. Among its core concepts is double-loop learning, where organizations not only detect and correct errors but also challenge and modify their underlying assumptions and routines [36]. For SMEs, deliberate failure analysis serves as a vital mechanism for this deeper level of learning [37]. As exemplified by a representative case from MedTechcorp, “When mutual understanding and interpretation of a failure is not present, it complicates and hinders the learning process and development of new ideas” [18] (p. 193).
However, failure does not automatically result in learning outcomes [38]. It is the intentional and structured analysis of failure that enables firms to uncover the causal relationships between past decisions and performance outcomes. Through this process, organizations can question their prior assumptions, reframe goals, and redesign decision-making processes in light of environmental feedback [33]. Such learning is particularly important for SMEs, which often operate with limited slack resources and face greater exposure to environmental uncertainty [31]. For these firms, each failure becomes an opportunity to reorient strategy, reduce future risk, and pursue more effective paths to growth.
More specifically, failure analysis directly contributes to SME growth by enabling firms to avoid repeating costly mistakes, thereby promoting more efficient allocation of scarce resources and reducing unnecessary expenditures. Moreover, by critically examining the root causes of failure, SMEs can identify when existing strategies are no longer effective and make timely strategic adjustments—such as refining product offerings, entering new markets, or reallocating resources to higher-potential initiatives. This perspective is illustrated through case evidence from Morais-Storz et al. (2020) [39], whose comparative study of innovation failures demonstrates how post-failure sensemaking processes shape organizational outcomes. After failing real-world tests despite passing industry compliance checks, Balato (maritime industry) conducted a structured failure analysis, recognized flaws in regulatory standards, and strategically terminated the project. By transparently communicating with stakeholders and reinforcing customer trust, Balato ultimately enhanced its reputation and strengthened long-term growth prospects. In contrast, Perula (sanitary systems industry) resisted deep failure analysis when its product faced persistent technical issues. By clinging to its original vision without reformulating the problem, the project stagnated for a decade without achieving its objectives.
Furthermore, failure analysis fosters collective sensemaking and the development of shared interpretations among organizational members. This not only enhances the firm’s knowledge base but also supports adaptive capabilities that enable firms to respond more effectively to future challenges [10]. For instance, by uncovering flawed assumptions or ineffective routines, SMEs may revise their innovation approaches, target new customer segments, or shift resource allocations to more promising areas [18].
In sum, failure analysis promotes organizational learning that is transformational in nature, thereby enabling SMEs to adapt, improve, and grow. Thus:
Hypothesis 1 (H1):
Failure analysis has a positive impact on SME growth.

3.2. Mediating Effect of Dynamic Capability

Dynamic capability refers to a firm’s ability to sense opportunities, seize them, and transform its resource base to maintain competitiveness in changing environments [27,34]. It builds on firm-specific knowledge and learning routines [40], particularly those developed through reflection and experience [41]. For SMEs operating under resource constraints, dynamic capabilities are essential for survival and growth [29].
Failure analysis, as a form of deliberate organizational learning, plays a critical role in fostering dynamic capabilities. First, it enhances sensing by disrupting existing cognitive frames and encouraging diverse interpretations of failure, enabling SMEs to better recognize emerging opportunities and threats [18]. Second, the seizing dimension involves mobilizing resources and aligning internal teams to exploit identified opportunities. This is exemplified in the Vacula case from Morais-Storz et al. (2020) [39], where the company faced product failures in new markets with prohibitively high recall costs. Through deliberate failure analysis and collective sensemaking, the team reframed their challenge as “how to integrate solutions within existing products”, ultimately developing innovative components that were subsequently extended to other product lines, thereby driving organizational growth. Third, by embedding new insights into routines and discarding ineffective practices, failure analysis strengthens transforming capabilities, allowing SMEs to adapt and reconfigure internal structures to meet changing demands [10,19].
In this way, failure analysis contributes to the development of dynamic capabilities through organizational learning processes. These capabilities, in turn, enhance SMEs’ adaptability, strategic responsiveness, and long-term growth potential [42,43]. Therefore, this study proposes the following hypothesis.
Hypothesis 2 (H2):
The dynamic capability mediates the relationship between failure analysis and SME growth.

3.3. Moderating Effect of Environmental Dynamism

Environmental dynamism refers to the degree of change and unpredictability in the external environment, particularly in terms of technology, markets, and competition [22,23]. For SMEs, such environments heighten uncertainty, disrupt strategic planning, and complicate decision-making due to greater causal ambiguity [25]. In this context, failure analysis becomes especially valuable, as it helps firms interpret complex environmental cues and respond adaptively [44].
From the threat perspective, high dynamism undermines the relevance of prior experience and renders existing knowledge and routines obsolete [45]. Under pressure to survive, firms must update their cognitive models and learning mechanisms to keep pace with change [46]. Failure analysis facilitates this process by identifying the root causes of misalignment with the environment, disrupting cognitive inertia, and encouraging organizational learning [18]. This allows firms to adapt more effectively by recalibrating routines and enhancing dynamic capabilities [10].
From the opportunity perspective, dynamic environments are fertile grounds for innovation, as they often generate new technologies, customer needs, and market niches [47]. SMEs, with their inherent flexibility and lower switching costs, are well-positioned to exploit such opportunities. Failure analysis enables them to make sense of failed attempts, refine strategic direction, and realign with evolving trends, thereby enhancing their ability to sense, seize, and transform in response to emerging opportunities [40].
In sum, environmental dynamism amplifies the need for and benefits of learning from failure. It increases both the urgency to adapt and the potential gains from doing so. Therefore, this study proposes the following hypothesis:
Hypothesis 3 (H3):
Environmental dynamism positively moderates the relationship between failure analysis and dynamic capability.

4. Method

4.1. Sample

Research on learning from failure often relies on secondary data [48,49], suggesting that organizations accumulate learning through repeated failures. However, failure analysis emphasizes a conscious, cognitive process in which organizational members actively reflect on past decisions and outcomes [10]. To capture such internal processes, self-reported questionnaire surveys are more suitable [19,20].
This study focuses on SMEs in China for three interrelated reasons. First, China’s institutional, market, and technological environment is not only dynamic but also marked by regulatory complexity and regional heterogeneity. These features magnify both the occurrence of failure and the importance of firms’ adaptive learning. Compared with mature Western markets, Chinese SMEs often navigate a less stable institutional framework, rapid industrial upgrading, and evolving market rules, making failure analysis more critical for survival and growth [46]. Moreover, the institutional differences between China and other economies, such as the level of government intervention, legal frameworks, and market openness, shape unique challenges and opportunities for SMEs, influencing how they learn from failure and adapt.
Second, Chinese SMEs face acute resource constraints and intense market competition, which make them particularly sensitive to failure and more likely to engage in deliberate post-failure learning. Unlike large enterprises that can absorb the costs of failure, SMEs in China must frequently adjust their strategies, routines, and knowledge structures to survive in fast-changing markets [18]. These conditions create fertile ground to observe the mechanisms of failure analysis and the development of dynamic capabilities.
Third, existing literature on failure learning and SME adaptation is heavily concentrated in North America and Europe [14,50], while empirical research in emerging markets, especially in China, remains limited. Given China’s position as one of the largest emerging economies and the global relevance of its SME sector, studying Chinese SMEs not only fills a geographic gap but also enriches our understanding of how firms develop resilience and dynamic capabilities under institutional and environmental volatility.
In this survey, this study targets managers and entrepreneurs of SMEs. At the beginning of the study, these managers were asked whether their firms had experienced failure, defined as deviations from anticipated and expected outcomes [33], such as failures in new product development. If they had not experienced failure, they were excluded from the survey sample. Additionally, to minimize recall bias, the failure events must have occurred within the previous 12 months.
The survey was conducted in 2022, and a total of 500 questionnaires were distributed. A total of 248 responses were received, resulting in a response rate of 49.6% (248/500). To encourage participation, respondents were assured of anonymity and offered a signed bestselling book as an incentive, which likely contributed to the relatively high response rate. Following the removal of samples with missing values and those with clearly erroneous responses, the study obtained a total of 207 valid samples, resulting in an effective response rate of 41.4% (207/500). A total of 64.7% of the respondents were male (134/207). The characteristics of the sample are presented in Table 1.

4.2. Data Collection

The research team collaborated with a university-affiliated science park, which provided a list of SMEs as the sampling frame for this survey. Questionnaires were distributed to the sampled firms through both online channels (WeChat and email) and offline visits. To ensure translation accuracy, the survey items were translated from English to Chinese by two doctoral students using a back-translation procedure. Additionally, five SME managers with failure experience—three from technical departments and two from operations—participated in pre-survey interviews and provided feedback, which led to the refinement of several ambiguous items.

4.3. Variables and Measures

The measures of the variables were adapted from scales published in existing studies, and the items are displayed in Table 2. A 7-point Likert scale from 1, “disagree strongly”, to 7, “agree strongly”, was used to evaluate the multi-item constructs.

4.3.1. Failure Analysis (FA)

It is measured using a scale from Danneels and Vestal (2020) [10]. The items access the deliberate effort the organization devotes to learning from failures. Table 2 presents the results for five items, e.g., “We openly analyze past mistakes”.

4.3.2. Dynamic Capability (DC)

This study adopted Wilden et al. (2013), dividing dynamic capability into three dimensions: sensing, seizing capability, and reconfiguring capability [40]. Table 2 presents the results for eleven items, e.g., “People participate in professional association activities”.

4.3.3. Environmental Dynamism (ED)

Drawing on Pérez-Luño et al. (2011), five measurement items for environmental dynamism, such as “Our firm must change its marketing practices extremely frequently”, were used [35].

4.3.4. Firm Growth (FG)

This study adopted measurement scales developed by Eshima and Anderson (2017) to assess firm growth [51]. Each respondent was asked to compare their firm growth over the past three years with that of industry competitors in three aspects: sales growth rate, market share growth, and employee growth.

4.3.5. Control Variables

The study controlled for variables that might influence firm growth [52], including firm age (number of years since the firm was established), ownership (1 = state-owned, 2 = joint venture; 3 = private; 4 = foreign-funded; 5 = other), R&D investment (1 ≤ 1%, 2 = 1–2%, 3 = 2–3%, 4 = 3–5%, 5 ≥ 5%), and industry (1 = manufacturing, 0 = other). Firm age relates to resource accumulation, ownership affects strategic orientation and resource access, R&D investment drives innovation, and industry differences shape competitive environments. Controlling for these variables strengthened the robustness of this analysis.

4.4. Reliability and Validity

First, Cronbach’s α (CA) is an important criterion for measuring the internal consistency of the scale. When the coefficient is greater than 0.7, it indicates that the scale meets a certain reliability standard [53]. Table 3 shows the reliability and validity indicators of the variables. The CA values of failure analysis, dynamic capability, environmental dynamism, and firm growth are 0.881, 0.903, 0.850, and 0.890, respectively, which are all greater than 0.7, indicating that the scale has good reliability. In addition, the composite reliability (CR) values for all constructs range from 0.852 to 0.904, which are all above the recommended threshold of 0.80, further demonstrating strong internal consistency.
Second, this study conducted confirmatory factor analysis (CFA) to evaluate the validity of the core constructs, focusing on both convergent and discriminant validity. According to established criteria, convergent validity is supported when factor loadings exceed 0.50 and the average variance extracted (AVE) is greater than 0.50 [54]. As shown in Table 2 and Table 3, all item loadings surpassed the 0.50 threshold, and most constructs exhibited AVE values above 0.50, confirming adequate convergent validity. One exception is the construct of dynamic capability, which had an AVE of 0.463—slightly below the recommended threshold. Nevertheless, this marginal shortfall is offset by other evidence of validity. Specifically, as shown in Table 3 and Table 4, the square root of the AVE for dynamic capability (0.733) exceeds its correlations with other constructs, indicating satisfactory discriminant validity. Taken together, despite the slightly low AVE for dynamic capability, the overall measurement model demonstrates acceptable construct validity for the purposes of this study.
Finally, the results of the confirmatory factor analysis in Table 5 indicate that the single-factor model had poor fit (χ2/df = 5.596, TLI = 0.506, CFI = 0.549, RMSEA = 0.149), while the four-factor model demonstrated the best fit and met the criteria for good fit (χ2/df = 1.881, TLI = 0.905, CFI = 0.916, RMSEA = 0.065). This suggests good discriminant validity [55].

4.5. Common Method Variance

First, at the procedural control level, the following measures were taken: emphasizing the academic purpose of the survey, protecting the respondents’ privacy, reducing item ambiguity, and randomly ranking scale items [56]. Second, for the statistical test for common method variance (CMV), as shown in Table 5, the single-factor model had poor fit, while the four-factor model demonstrated the best fit and met the criteria for good fit. Third, through Harman’ s single-factor test on the survey data, the variance explained by the largest factor was 34.199%, and no single dominant factor was found. Therefore, based on both procedural remedies and statistical evidence, the concern of CMV is not severe in this study [57].

5. Results

5.1. Correlation Matrix

Table 4 presents the means, standard deviations, and correlation matrices for each variable. Failure analysis is significantly positively correlated with firm growth (β = 0.208, p < 0.01). Failure analysis is significantly positively correlated with dynamic capability (β = 0.489, p < 0.001). Dynamic capability is significantly correlated with firm growth (β = 0.476, p < 0.001). These findings provide preliminary support for the hypotheses of this study.

5.2. Results of Hypothesis Tests

Consistent with prior studies [10,19,20], this study adopted hierarchical regression analysis to test our hypotheses. The results are presented in Table 6. The maximum variance inflation factor (VIF) values for all models are below 5, indicating that multicollinearity is not a problem.

5.2.1. The Main Effect

In Table 6, Model 1 establishes firm growth as the dependent variable, incorporating control variables into the regression equation. Building upon this, Model 2 adds failure analysis as an independent variable, revealing a positive correlation between failure analysis and firm growth (β = 0.187, p < 0.01), suggesting failure analysis is a meaningful predictor of SME growth. The adjusted R2 increased from 0.043 to 0.073, indicating a notable improvement in the model’s explanatory power. These results suggest that failure analysis promotes SME growth, thus supporting Hypothesis 1 (H1). This finding underscores the critical role of failure analysis as a learning mechanism that enables SMEs to better understand the causes of setbacks, adjust their strategies, and improve decision-making. By systematically dissecting failures, SMEs can avoid repeated mistakes and more effectively allocate resources, which collectively contribute to enhanced firm growth.

5.2.2. The Mediating Effect

To examine the mediating role of dynamic capability, this study followed the standard three-step procedure. First, Model 6 in Table 6 demonstrates a positive correlation between failure analysis and dynamic capability (β = 0.466, p < 0.001), suggesting that failure analysis exerts a positive influence on dynamic capability. Second, Model 3 shows a positive correlation between dynamic capability and firm growth (β = 0.450, p < 0.001), suggesting that dynamic capability positively influences firm growth in SMEs. Third, Model 4 builds upon Model 2 by adding dynamic capability as an independent variable into the regression equation. The results indicate that dynamic capability significantly affects firm growth (β = 0.464, p < 0.001), while the influence of failure analysis on firm growth is not significant (β = −0.030, p > 0.1). Combining Models 2 (excluding the mediator) and 4 (including the mediator), it can be inferred that the effect of failure analysis on firm growth changes from significant (β = 0.187, p < 0.01) to non-significant (β = −0.030, p > 0.1), suggesting that the impact of failure analysis on firm growth is weakened due to the mediating effect of dynamic capability, thus preliminarily verifying the full mediating effect of dynamic capability on the relationship between failure analysis and firm growth. Therefore, Hypothesis 2 (H2) is supported, which indicates that dynamic capabilities fully account for the relationship between failure analysis and firm growth, implying that the transformation from knowledge to capability is essential.
The analysis further examined the mediating effect of dynamic capability using the bootstrap method [58,59]. Specifically, 5000 repetitions of random sampling were set, with a confidence interval level of 95%. The Process plugin was used to test whether dynamic capability mediated the effect. The results indicate that the confidence interval (CI) for the indirect effect ranges from 0.122 to 0.420, which notably does not include zero. This confirms a significant mediating effect of dynamic capability, thereby validating Hypothesis 2 (H2). This mediating role of dynamic capability can be understood as the mechanism through which failure analysis translates into tangible growth outcomes. By analyzing failures, SMEs develop the ability to sense changes, seize emerging opportunities, and reconfigure their resources effectively—core aspects of dynamic capability. This transformation empowers firms to overcome the limitations of their initial resources and knowledge, continuously renew themselves, and sustain competitive advantage. This insight is particularly critical: Dynamic capability explains not only whether failure analysis leads to growth but also how and why this process unfolds within SMEs.

5.2.3. The Moderating Effect

As shown in Table 6, Model 8, showing the interaction term between failure analysis and environmental dynamism, has a significant positive effect on dynamic capability (β = 0.148, p < 0.05), indicating that environmental dynamism positively moderates the relationship between organizational failure analysis and dynamic capability. Thus, Hypothesis 3 (H3) is supported.
This study conducted a simple slope analysis following Aiken and West (1991) to gain a deeper understanding of this interaction [60]. As illustrated in Figure 2, when environmental dynamism is high, the slope representing the relationship between failure analysis and dynamic capability is steeper compared to when environmental dynamism is low. This suggests that in volatile environments, the relationship between failure analysis and capability development becomes significantly stronger. In contrast, under conditions of low environmental dynamism, the relationship between failure analysis and capability development is weaker, possibly because stable environments reduce the urgency or necessity for firms to act on failure-derived insights. These findings underscore the context-dependent value of failure analysis and emphasize the importance of environmental scanning and adaptability in capability building.

5.3. Robustness Check and Additional Analysis

First, using Stata software 15.0 for Structural Equation Modeling (SEM) path analysis, as shown in Table 7, we found that failure analysis positively impacts dynamic capability (β = 0.542, p < 0.001), dynamic capability positively impacts SME growth (β = 0.507, p < 0.001), and environmental dynamism positively moderates the positive relationship between failure analysis and dynamic capability (β = 0.167, p < 0.01).
Second, the Process plugin was used to test the moderated mediator effect. As shown in Table 8, under conditions of low environmental dynamism, the impact of failure analysis on firm growth through dynamic capabilities is significant (β = 0.289, p < 0.001, with a 95% confidence interval of [0.196, 0.382], which does not include zero). Furthermore, under conditions of high environmental dynamism, this impact is significantly enhanced (β = 0.442, p < 0.001, with a 95% confidence interval of [0.301, 0.582], which does not include zero). This indicates that environmental dynamism moderates the mediating effect of dynamic capabilities in the relationship between failure analysis and firm growth.

6. Discussion

6.1. Theoretical Contributions

First, this study enriches the literature on SME growth by identifying failure analysis as a critical yet underexplored antecedence. While prior research has emphasized factors such as firm characteristics [15], networks [31], and external environments [14], limited attention has been paid to how SMEs actively learn from failure during their growth journey. This oversight is particularly striking given that SMEs—unlike larger firms—often operate with limited buffers, making the ability to reflect on and respond to failure essential for their survival and development [1]. By positioning failure analysis as a deliberate learning behavior, this study contributes to a more nuanced understanding of how SMEs accumulate growth-relevant knowledge under constraint and uncertainty [12,16]. Importantly, this model provides empirical validation for this relationship, offering robust evidence that links failure analysis to SME growth in dynamic business environments.
Second, the study advances the application of dynamic capabilities theory in the SME context by explaining how the knowledge derived from failure is not inherently transformative but must be restructured, interpreted, and integrated into firm routines to generate growth. While previous studies often associate failure analysis with innovation outcomes [10] or entrepreneurial resilience [20], this study highlights the capability-building process through which failure analysis enhances firms’ sensing, seizing, and reconfiguring capacities [27,29]. In doing so, it demonstrates that failure analysis serves not only as a trigger for experiential learning but also as a dynamic mechanism for fostering adaptive change—especially when guided by intentional interpretation and internal integration efforts.
Third, by examining environmental dynamism as a boundary condition, the study sheds light on when failure analysis is more likely to translate into growth-enhancing capabilities. Whereas much of the literature focuses on internal contingencies, such as organizational climate [10] or the knowledge base [20], this research emphasizes that the value of failure analysis is contingent on external conditions, particularly the rate and unpredictability of market and technological change. In dynamic environments, where rapid feedback and adaptation are crucial, the capacity to learn from failure becomes especially valuable. This finding contributes to organizational learning theory by reinforcing the idea that learning is context-dependent [17,21]. It also extends this insight with empirical evidence from the SME context, where adaptive learning is often a matter of survival [20,61].

6.2. Practical Implications

This study provides valuable insights not only for SME managers but also for policymakers and society at large, offering guidance to better support SME development. For managers, the findings emphasize the importance of integrating failure analysis into daily organizational learning practices. Failure analysis should be recognized as a vital process for learning and knowledge renewal. SMEs are encouraged to establish formal post-failure review routines and cross-functional learning forums where teams from different departments can regularly discuss unsuccessful projects, share insights, and identify improvement opportunities. This kind of structured reflection promotes knowledge diffusion, builds organizational resilience, and enhances adaptability to changing market and technological conditions. Managers should also allocate resources—such as dedicated time, personnel, and digital tools—to support systematic documentation and dissemination of failure-related lessons. These efforts help SMEs strategically update capabilities and foster long-term flexibility and competitive advantage.
For policymakers, these findings suggest the need to develop targeted support mechanisms that encourage SMEs to engage in effective failure analysis and learning. Governments could implement initiatives such as “failure grant audits”—financial support for SMEs that demonstrate structured failure learning practices—and “organizational learning support programs” that fund the development of internal knowledge systems, post-failure review tools, or employee training in reflective learning. Public programs such as training workshops, mentoring, and online knowledge platforms can further strengthen SMEs’ capacity to turn failure into learning opportunities. In addition, policies aimed at reducing the stigma of failure—by promoting it as a natural part of the innovation process—can empower more entrepreneurs and SME leaders to take calculated risks and pursue growth.
At the societal level, this study highlights the importance of reshaping public attitudes towards failure. Cultural barriers and negative perceptions around business failure often discourage experimentation and learning. Media campaigns, entrepreneurship education, and public success stories that focus on lessons learned rather than just outcomes can help normalize failure as a step toward growth. Such cultural shifts not only benefit individual firms but also enhance overall economic dynamism and resilience.

6.3. Limitations and Future Research Directions

Although this study offers valuable insights, several questions remain for future research. First, the use of cross-sectional data limits the ability to draw robust causal inferences regarding the relationship between failure analysis and firm growth. Future studies could adopt longitudinal designs to better capture how failure analysis shapes growth trajectories over time. While anonymity was assured, self-reported measures may still lead to inflated accounts of learning behaviors; more advanced methods—such as applying machine learning to textual or audio data—could help more accurately capture failure analysis processes and reduce bias. In addition, although the sample size of 207 SMEs is comparable to prior studies (e.g., 169 SMEs in Oman [29]), it may constrain the generalizability of the findings. Future research could employ larger samples and conduct cross-country comparisons, especially in other emerging markets with dynamic policy environments, to assess the broader applicability of failure analysis and learning mechanisms.
Second, while this study primarily examines the impact of failure analysis on quantitative growth indicators, such as sales growth, market share expansion, and employee numbers, it does not address qualitative dimensions of growth, including brand equity, innovation capabilities, and customer satisfaction [62,63]. Future studies could explore how failure analysis contributes to these qualitative aspects, enriching our understanding of firm development beyond measurable financial metrics.
Third, this research treats failure analysis as a relatively uniform process without distinguishing between different types and complexities of failures. For example, marketing failures—highly influenced by dynamic external factors like customer preferences and competitive actions—may affect firm outcomes differently than more tangible technical failures, which are often easier to measure with specific performance indicators [10,64]. Moreover, following Appio et al. (2024) [65], failures can be categorized into conventional failures (resulting from flawed processes or routines) and fallacious failures (occurring without identifiable process errors but still leading to negative consequences). The latter poses greater challenges due to complex causal mechanisms and potential organizational trauma. Future research should differentiate these failure types and investigate whether distinct failure analyses have unique effects on firm outcomes.

7. Conclusions

This study examined how failure analysis influences firm growth in SMEs, with a particular focus on the mediating role of dynamic capabilities and the moderating role of environmental dynamism. Based on survey data from 207 Chinese SMEs, the findings reveal that failure analysis contributes to firm growth by enhancing dynamic capabilities—namely, sensing, seizing, and reconfiguring capabilities—that enable firms to better identify opportunities, mobilize resources, and adapt to change. Furthermore, environmental dynamism strengthens the positive relationship between failure analysis and dynamic capability, suggesting that SMEs operating in more volatile and uncertain environments benefit more from learning through failure. Additionally, environmental dynamism moderates the mediating effect of dynamic capabilities in the relationship between failure analysis and firm growth. These results deepen the understanding of how SMEs can transform failure into a strategic learning process that fosters growth. The study offers both theoretical insights and practical implications for leveraging failure analysis as a strategic tool for SME growth.

Author Contributions

Conceptualization, X.Y. and X.M.; methodology, X.M. and L.C.; software, X.M. and L.C.; data curation, X.M. and L.C.; writing—original draft preparation, X.M. and L.C.; writing—review and editing, X.Y. and X.M.; supervision, X.Y.; project administration, X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Relative data have been included in the paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Representative studies on SME growth.
Table A1. Representative studies on SME growth.
Author (Year)Factors Influencing SME GrowthTheorySampleMethodKey Findings
Havnes and Senneseth (2001) [66]1. Firm’s network/1700 firms SMEs in 8 European countriesSecondary dataNetworking is associated with high growth in the geographic extension of markets, which suggests that networking sustains long-term objectives of the firms.
Hossain et al. (2016) [13]1. Owner–manager characteristics
2. Characteristics of the firm
3. Financial factors
4. External environment		/34 papers during 2006–2014ReviewThe four broad areas of factors have been focused on—namely, owner–manager characteristics, characteristics of the firm, financial factors, and external environment.
EI Shoubaki et al. (2020) [12]1. Human capitalHuman
capital theory		46,412 French small businessesSecondary dataReasons to start a business mediate the relation between firm growth and SME owner–managers’ human capital (discriminating between specific and general human capital).
Lim et al. (2020) [14]1. Global crisisResource system perspectiveCanadian high-growth SMEsQualitativeFirm growth is the expansion of the system of resource components, including strategic, physical, financial, human, and organizational resources.
Ikram et al. (2020) [67]1. Corporate social responsibility (CSR) activitiesStakeholder theory340 SMEs in PakistanSurveyResults reveal significant relationships between CSR and two determinants of firm performance, namely, employee commitment and corporate reputation.
Rafiki (2020) [15]1. Human capital (manager’s experience, education, training)
2. Social capital (firm’s networks)
3. Firm’s strategy (financing)
4. Firm characteristics (size; age)		Resource-based view 119 managers from SMEs in the Kingdom of Saudi ArabiaSurveyFirm size, manager’s experience, education, training, financing, and the firm’s networks have a significant relationship with the firm’s growth. However, manager’s education and firm age do not have a significant relationship with the firm’s growth.
Audretsch and Belitski (2021) [50]1. Knowledge complexity/102 European SMEsSurveyCompared to other acumens of knowledge complexity, managerial and operational acumens contribute the most to a firm’s performance (sales and productivity).
Scuotto et al. (2021) [16]1. Individual digital capabilities (information skills, communication skills, software skills)Micro-foundations lens2,156,360 European SMEsSurvey Individual digital capabilities have assumed an equally crucial role for growth and innovation in our increasingly digital competitive reality.
Rafiki et al. (2023) [68]1. Entrepreneurial orientation (EO)
2. Personal value
3. Organizational learning (OL)		Resource-based view128 respondents (owner-managers) of SMEsSurveyInnovativeness of EO and personal value both have a significant relationship with firm growth. OL, proactiveness and risk-taking of EO are insignificantly related to firm growth, while risk-taking of EO also insignificantly mediates the relationship of OL and firm growth.

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Systems 13 00690 g001
Figure 1. Theoretical model.
Figure 1. Theoretical model.
Systems 13 00690 g001
Systems 13 00690 g002
Figure 2. Interaction term of failure analysis (FA) and environmental dynamism (ED) on dynamic capability.
Figure 2. Interaction term of failure analysis (FA) and environmental dynamism (ED) on dynamic capability.
Systems 13 00690 g002
Table 1. Sample profile.
Table 1. Sample profile.
CharacteristicTypeNumberPercentage/%
Firm size1–102813.53
11–304521.74
31–1003014.49
101–3003114.98
301–500199.18
501–10005426.09
Firm age≤10 years9746.86
11–20 years6330.43
21–30 years2914.01
>31 years188.70
Ownership State-owned2110.14
Joint venture115.31
Private15976.81
Foreign-funded115.31
Other52.42
R&D investment<1%3315.94
1–2%2411.59
2–3%3516.91
3–5%3315.94
>5%8239.61
IndustryManufacturing6028.99
Other14771.01
Table 2. Measurement of survey variables.
Table 2. Measurement of survey variables.
VariablesItemsLoadingSource
Failure analysis 1. We openly analyze past mistakes.0.751Danneels & Vestal, 2020 [10]
2. We go to great lengths to learn from failures.0.826
3. We review past decisions, especially if they did not lead to success.0.768
4. We conduct post-mortems.0.840
5. We examine failures for “lessons learned”.0.705
Dynamic capability *1. People participate in professional association activities.0.602Wilden et al., 2013 [40]
2. We use established processes to identify target market segments, changing customer needs, and customer innovation.0.714
3. We observe best practices in our sector.0.746
4. We gather economic information on our operations and operational environment.0.652
5. We invest in finding solutions for our customers.0.675
6. We adopt the best practices in our sector.0.712
7. We respond to defects pointed out by employees.0.684
8. We change our practices when customer feedback gives us a reason to change.0.602
9. In the past three years, we have implemented new kinds of management methods.0.701
10. Substantial renewal of business processes0.709
11. New or substantially changed ways of achieving our targets and objectives0.674
Environmental dynamism1. Our firm must change its marketing practices extremely frequently.0.716Pérez-Luño et al., 2011 [35]
2. The rate of obsolescence is very high.0.860
3. Actions of competitors are unpredictable.0.607
4. Demand and tastes are almost unpredictable.0.752
5. The modes of production/service change often and in major ways.0.709
Firm growth1. Sales growth0.839Eshima & Anderson, 2017 [51]
2. Market share growth0.858
3. Employee growth0.867
* Dynamic capability includes three dimensions: items 1–4 represent sensing capability, items 5–8 represent seizing capability, and items 9–11 represent reconfiguring capability. In the analysis, the factor loading for item “12. New or substantially changed marketing method or strategy” fell below 0.4, resulting in its exclusion.
Table 3. Reliability and validity test results of variables.
Table 3. Reliability and validity test results of variables.
VariablesCronbach’s αAVECR
Failure analysis 0.8810.6080.885
Dynamic capability0.9030.4630.904
Environmental dynamism0.8500.5380.852
Firm growth0.8900.7310.891
Table 4. Correlation matrix.
Table 4. Correlation matrix.
12345678
1. Ownership1.000
2. Firm age−0.0891.000
3. R&D−0.1250.159 *1.000
4. Industry−0.0940.150 *0.214 **1.000
5. FA−0.0310.0460.1260.148 *1.000
6. ED−0.0980.051−0.0260.0350.275 ***1.000
7. DC−0.0910.1190.224 **0.1180.489 ***0.313 ***1.000
8. FG−0.164 *0.044−0.204 **0.0500.208 **0.179 **0.476 ***1.000
Mean2.8514.683.520.295.834.995.685.27
SD0.7717.781.500.450.931.150.741.00
N = 207, * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 5. Confirmatory factor analysis results.
Table 5. Confirmatory factor analysis results.
ModelFactorsχ2dfχ2/dfTLICFIRMSEA
Four-factor modelFA, DC, ED, FG462.8392461.8810.905 0.916 0.065
Three-factor modelFA + DC, ED, FG799.7042493.2120.7620.7850.103
Three-factor modelFA + ED, DC, FG826.9202493.3020.7750.7500.106
Three-factor modelFA, ED + DC, FG811.1112493.2570.7570.7810.104
Two-factor modelFA + DC, ED + FG1202.5572514.7910.592 0.6290.135
Two-factor modelFA + DC + ED, FG1142.2952514.5510.6180.6530.131
One-factor modelFA + DC + ED + FG1410.2762525.5960.5060.5490.149
N = 207, “+” indicates the combination of two factors into one factor.
Table 6. Hierarchical regression showing direct and mediated effects.
Table 6. Hierarchical regression showing direct and mediated effects.
Firm Growth Dynamic Capability
Model 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8
Control variables
Ownership−0.141 +
(0.090)		−0.140 *
(0.089)		−0.114 +
(0.081)		−0.113 +
(0.081)		−0.060
(0.065)		−0.057
(0.058)		−0.027
(0.054)		−0.035
(0.054)
Firm age0.002
(0.004)		0.000
(0.004)		−0.031
(0.004)		−0.032
(0.004)		0.075
(0.003)		0.069
(0.003)		0.056
(0.002)		0.055
(0.002)
R&D0.187 **
(0.047)		0.169 *
(0.047)		0.107 +
(0.043)		0.107 +
(0.043)		0.180 *
(0.034)		0.135 *
(0.030)		0.160 **
(0.029)		0.157 **
(0.028)
Industry−0.004
(0.155)		−0.027
(0.154)		−0.033
(0.139)		−0.030
(0.140)		0.064
(0.112)		0.006
(0.100)		0.008
(0.094)		0.005
(0.093)
Independent variable
FA (H1) 0.187 **
(0.073)		 −0.030
(0.076)		 0.466 ***
(0.048)		0.377 ***
(0.046)		0.445 ***
(0.051)
Mediating variable
DC (H2) 0.450 ***
(0.087)		0.464 ***
(0.099)
Moderating variable
ED 0.316 ***
(0.037)		0.304 ***
(0.037)
Interaction
FA * ED (H3) 0.148 *
(0.028)
R20.0610.0950.2510.2520.0600.2710.3620.379
△R20.0430.0730.2330.2300.0420.2530.3430.357
F3.2994.22413.5011.233.24414.9318.8917.37
Max VIF1.081.091.111.371.081.091.121.38
N = 207, + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Standard errors appear in parentheses.
Table 7. SEM results.
Table 7. SEM results.
PathwayStandardized Path CoefficientsStandard ErrorC.R.p
DC <---FA0.5420.0469.440***
FG <---DC0.5070.0787.022***
FG <---FA−0.0320.097−0.4360.663
FG <---FA * DC0.1670.0272.9120.004
*** p < 0.001.
Table 8. Bootstrap test for mediated effects with moderation.
Table 8. Bootstrap test for mediated effects with moderation.
Indirect Effects PathwayVariableEfficiency ValueStandard Errortp95% CI
Lower LimitUpper Limit
FA→DC→FGLow ED (−1 SD)0.2890.0476.1310.0000.1960.382
Average value (M)0.3650.5097.1690.0000.2650.466
High ED (+1 SD)0.4420.7146.1820.0000.3010.582
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Ma, X.; Chen, L.; Yu, X. Failure Analysis and SME Growth: The Role of Dynamic Capabilities and Environmental Dynamism. Systems 2025, 13, 690. https://doi.org/10.3390/systems13080690

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Ma X, Chen L, Yu X. Failure Analysis and SME Growth: The Role of Dynamic Capabilities and Environmental Dynamism. Systems. 2025; 13(8):690. https://doi.org/10.3390/systems13080690

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Ma, Xiaoshu, Luqian Chen, and Xiaoyu Yu. 2025. "Failure Analysis and SME Growth: The Role of Dynamic Capabilities and Environmental Dynamism" Systems 13, no. 8: 690. https://doi.org/10.3390/systems13080690

APA Style

Ma, X., Chen, L., & Yu, X. (2025). Failure Analysis and SME Growth: The Role of Dynamic Capabilities and Environmental Dynamism. Systems, 13(8), 690. https://doi.org/10.3390/systems13080690

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