Knowledge Management drivers and Organizational Preformance: A PLS-SEM and FSQCA approach

Knowledge Management drivers and Organizational Preformance: A PLS-SEM and FSQCA approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Knowledge Management drivers and Organizational Preformance: A PLS-SEM and FSQCA approach Sanela Golubović Corcione, Milica Veličković, Aleksandra Fedajev, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9152161/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim Knowledge is a key strategic resource for modern organizations, crucial for achieving competitive advantage, resilience, and long-term performance. Although organizations increasingly invest in knowledge management (KM) initiatives and systems, the success of their implementation largely depends on the broader organizational context and the interaction of various organizational drivers. Hence, this study aims to examine how organizational drivers shape knowledge management practices and contribute to organizational performance (OP). Methodology: Empirical analysis was conducted on a sample of organizations in Serbia, using a combined methodological approach that integrates structural equation modeling based on partial least squares (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Results The results of PLS-SEM analysis confirmed theoretical model showing that studied organizational elements have significant positive impact on KM. The fsQCA results reveal that KM alone does not guarantee high performance, and it rather must be part of certain organizational configurations to be effective. In this sense, there are six configurations for successful KM implementation in Serbian companies, that results in high OP. Implications and recommendations: The findings indicate that high organizational performance can be achieved through different KM-driven configurations, meaning that firms should avoid one-size-fits-all solutions and instead align KM practices with their dominant organizational drivers, depending on their organizational context and resource constraints. Originality/value: By integrating two complementary methodologies, the study provides novel insights from a transition economy and highlights the value of this approach for exploring both direct and complex causal relationships, offering results that can serve as a benchmark for other transition economies. knowledge management organizational performance PLS-SEM analysis FSQCA approach Figures Figure 1 1. Introduction Today, knowledge is widely recognized as one of the most valuable economic assets, playing a critical role in organizational performance (OP) and long-term survival in the market. In an uncertain and dynamic market conditions, a strong commitment to the effective use of knowledge management (KM) systems is crucial for achieving and sustaining long-term competitive advantage. Even though many enterprises are investing in KM systems, there are numerous circumstances, and factors, leading to the accomplishment of the KM concept in an organization. Therefore, effective management of organizational elements is a prerequisite for efficient investment in KM initiatives (Rezaei et al., 2021 ; Cristache et al., 2025 ). Together with other organizational inputs, knowledge presents one of the principal factors in achieving organizational goals. According to Bolisani & Bratianu ( 2018 ), knowledge manifests in forms of tacit and explicit. Tacit knowledge refers to personal, experience-based know-how that is difficult to articulate, codify, or express in written form, as it resides in individuals’ minds and is acquired primarily through practical experience (Gascoigne & Thornton, 2014 ). Explicit knowledge can be written, stored and disseminated, such as official documents, databases, and manuals. Even though the definitions of KM may vary, KM is most often defined as the process which involves collecting, distributing, and using knowledge resources efficiently and is described as the main qualificator to add value for the organization (Kusa et al., 2024 ; Cristache et al., 2025 ). From a methodological perspective, recent KM performance and OP studies depend on symmetric, variance-based methods such as Partial Least Squares Structural Equation Modelling (PLS-SEM) (Payal et al., 2019 ; Cepeda-Carrion et al., 2019 ; Mohammadi et al., 2023 ). These studies provide insights into the effects of KM-related constructs on OP, but largely overlook causal complexity, equifinality, and asymmetric relationships. On the other hand, only a limited number of recent studies have applied fuzzy-set Qualitative Comparative Analysis (fsQCA) to explore how different combinations of organizational conditions lead to high OP (Olan et al., 2016 ; Olan et al., 2019 ; Vargas-Zeledon, 2023 ; Karadağ et al., 2024 ). Despite more researchers are calling for the combination of symmetric and configurational approaches in organizational and KM studies, few have integrated PLS-SEM and fsQCA in a single research framework (Kusa et al., 2024 ). This gap is especially noticeable in transition economies. Serbia, as a Western Balkan transition economy, has a unique setting with changing market systems, mixed ownership, and uneven development of knowledge infrastructure. Recent studies show that KM practices in Serbia are more developed in large, foreign-owned, and financially stable organizations, while smaller and locally owned firms tend to adopt systematic KM practices more slowly (Kavalić et al., 2021 ). Ongoing institutional changes and structural instability also limit innovation and organizational learning (Petrov et al., 2020 ). Despite growing recognition of KM for OP, research on the topic remains limited in transitional countries. Hence, this study aims to fill that gap by combining PLS-SEM and fsQCA to examine how organizational drivers shape KM practices and, through KM, contribute to OP in Serbian enterprises. By integrating these two complementary methodologies, the study provides novel insights from a transition economy and highlights the value of this approach for exploring both direct and complex causal relationships, offering results that can serve as a benchmark for other transition economies. 2. Literature Review In every organization, KM activities are not isolated from other activities. Various organizational components may affect the successful realization of KM actions (Alaarj et al., 2016 ). Bearing this in mind, several authors have examined key factors which can lead to the successful implementation of the KM concept in the company (Rezaei et al., 2021 ; Lam et al., 2021 ; Mohammadi et al., 2023 ). Based on the defined research aim, this study focus on a set of key organizational drivers, encompassing both structural and behavioral dimensions, which are expected to influence KM practices and, indirectly, OP. 2.1. Organizational culture A knowledge-committed culture is often cited as a crucial factor in ensuring efficient knowledge flow among organizational members. Culture is recognized as an essential driver of innovation when it is grounded in the values of flexibility, trust, creativity, diversity, and sustainable development (Lam et al., 2021 ). Organizational culture (OC) can be defined as a system of shared beliefs, values, norms, and practices that guide employee behavior and influence organizational processes (Ajmal & Koskinen, 2008 ). Rezaei et al. ( 2021 ) demonstrated that OC exerts a direct and significant influence on KM effectiveness. Similarly, Lam et al. ( 2021 ) reported a strong association between culture and KM practices, while Aldulaimi ( 2015 ) found that OC is positively related to both KM and organizational effectiveness. More recent studies extend these findings by showing that culture not only supports KM implementation, but also indirectly enhances OP through knowledge-based capabilities (Cristache et al., 2025 ). In light of the aforementioned studies, the following hypothesis is proposed: H1. Organizational culture positively impacts knowledge management. 2.2. Leadership commitment Leadership commitment (LC) and managerial support are increasingly recognized in the recent literature as vital for successful KM implementation in modern organizations. In particular, transformational and empowering leadership styles have been shown to foster mutual trust among employees, encourage proactive knowledge-related behaviors, and facilitate the integration of KM practices into daily organizational routines (Koohang et al., 2017 ; Gürlek & Çemberci, 2020 ). Donate & de Pablo ( 2015 ) suggests that LC directly influences KM processes by motivating employees to create, share, and apply knowledge, while simultaneously strengthening interpersonal trust and collective learning mechanisms within organizations. Leaders who actively support KM initiatives by allocating resources, promoting learning opportunities, and acting as role models significantly enhance the effectiveness of KM systems and practices (Paliszkiewicz et al., 2015 ; Shamim et al., 2019 ). Further, recent research indicates that LC not only facilitates KM implementation but also indirectly improves OP by reinforcing knowledge-based capabilities and cultivating a learning-oriented organizational environment (Donate & de Pablo, 2015 ; Naqshbandi & Jasimuddin, 2018 ). Hence, the next hypothesis is defined: H2. Leadership commitment positively impacts knowledge management. 2.3. Organizational strategy Organizational strategy (STG) defines long-term priorities, resource allocation and coordination mechanisms, which directly encourage the creation, acquisition, sharing, integration and exploitation of knowledge in the organization (Zack, 1999 ). A strong KM approach starts with a clear organizational strategy that treats knowledge as a key resource. Instead of treating KM as a separate activity, Huynh et al. ( 2024 ) highlight integrating KM into the organization’s overall strategy to keep efforts consistent and sustainable. Supporting this approach, research by Kılıç & Uludağ ( 2021 ) confirmed that a well-defined KM-oriented strategy positively influences innovation outcomes and overall OP by aligning knowledge processes with strategic priorities. In this context, KM acts as a strategic tool that turns organizational strategy into practical capabilities, enabling firms to respond better to environmental uncertainty and competition (Jami Pour & Asarian, 2019 ). Zheng et al. ( 2010 ) in their research analyzed the mediating role of KM between strategy and OP and confirmed that KM partially mediates this relationship. Therefore, the following hypothesis is proposed: H3. Organizational strategy positively impacts knowledge management. 2.4. Information technology infrastructure Incorporation of various technological platforms facilitates the sharing of existing knowledge (Islam et al., 2015 ). Technology infrastructure (hardware, software, networks, data storage) is an essential component of technology development and strengthens enterprises' performances (Akram et al., 2018 ). Companies which base their strategy on knowledge can significantly elevate the quality of their products by strategically harnessing technological resources, thereby improving performance by promoting innovation and market competitiveness (Rasool et al., 2023 ). Technology usage in supporting KM opens new capabilities (Valaei et al., 2017) in business processes. Information Technology tools help discovery of needed knowledge, while TI enables the conversion of tacit knowledge into explicit (Islam et al., 2015 ). It also helps to store the same explicit knowledge in official documents to facilitate access to it in the future, in which way the individual knowledge becomes organizational knowledge. Based on these arguments, the next hypothesis is proposed: H4. Information technology infrastructure positively impacts knowledge management. 2.5. Trust Open communication with leaders is essential to building trust (TR) among employees. TR is an essential part of productive and effective teamwork. When TR is present, employees are more willing to share knowledge, engage in cooperative problem-solving, and participate in knowledge-based initiatives, thereby enhancing the overall effectiveness of KM practices (Paliszkiewicz et al., 2016; Alaarj et al., 2016 ). Company members commit more to a knowledge-based strategy when there is trust among organizational members (Lam et al., 2021 ). Furthermore, recent research emphasizes that trust not only enhances knowledge sharing, but also indirectly contributes to improved organizational performance by reinforcing knowledge-based capabilities and reducing coordination and communication costs (Cristache et al., 2025 ). Hence, the next hypothesis is proposed: H5. Trust in the workplace positively impacts knowledge management. 2.6. Knowledge management and organizational performance The literature recognizes the significance of KM practices for an organization's overall success, so the relationship between KM and OP has been evaluated by many researchers (Iqbal et al., 2019 ; Payal et al., 2019 ; Mohammadi et al., 2023 ). An organization’s ability to achieve its goals efficiently and effectively defines its OP, which, when leveraged, can lead to a sustainable competitive advantage. Using knowledge through the concept of KM, organizations can significantly improve their performance (Zack et al., 2009 ; Paliszkiewicz et al., 2015 ; Yoshikuni & Albertin, 2020 ). Research by Kılıç & Uludağ ( 2021 ), Rezaei et al. ( 2021 ), Mohammadi et al. ( 2023 ) highlights the mediating effect of KM on OP and emphasizes that KM is significantly related to infrastructural factors. Also, numerous other researchers, including Hosseini et al. ( 2019 ), Rezaei et al. ( 2021 ), Lam et al. ( 2021 ) highlight the significant positive effect of KM on innovation and OP. Therefore, the next hypothesis is proposed: H6. Knowledge management significantly impacts organizational performance. Figure 1 presents the conceptual model of the proposed relationships between the examined organizational factors, KM, and OP (H1-H6). 3. Methodology 3.1. Sampling and data collection This study aims to explain how specific organizational elements, both hard and soft, influence KM practices and OP in organizations in the Republic of Serbia, based on research conducted in 2024–2025. The target population consisted of 637 employees working in various manufacturing and service organizations that had already adopted and implemented key KM practices. The first five questions collect demographic information (industry type, firm size, gender, education, and professional experience). The remaining questionnaire items were grouped into seven constructs reflecting key organizational dimensions. Specifically, OC was measured using 6 items adapted from (Gold et al., 2001 ; Wang et al., 2014 ; Islam et al., 2015 ; Lam et al., 2021 ); organizational STG was assessed through 3 items adopted from (Wang et al., 2014 ; Islam et al., 2015 ); TI was captured by 4 items based on (Gold et al., 2001 ; Islam et al., 2015 ); LC was measured using 5 items derived from (Carless et al., 2000 ; Shamim et al., 2019 ; Lam et al., 2021 ); employee TR was operationalized through 3 items adapted from (Carless et al., 2000 ; Park & Lee, 2014 ; Shamim et al., 2019 ); KM practices were measured using 8 items adopted from (Gold et al., 2001 ; Yang et al., 2012 ; Park & Lee, 2014 ); and OP was assessed through 6 items adapted from (Gold et al., 2001 ; Darroch, 2005). All items were measured using a five-point Likert scale. The sample mainly consist of male employees (60.7%), with a high school diploma (41.0%), less than five years of work experience (32.4%), indicating a relatively young workforce. 3.2. Data analysis techniques This study used both Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine the relationships among the variables. The Partial Least Squares (PLS) approach to Structural Equation Modeling (SEM) was employed to examine the hypothesized relationships among latent constructs. Following the recommendations of Hair et al. ( 2021 ), the SEM procedure was conducted in two sequential stages. The first stage focused on assessing the measurement model to confirm construct validity and evaluate the model fit. The second stage involved testing the proposed research hypotheses through structural model analysis. PLS-SEM is widely used in KM research and organizational studies, particularly when models involve complex relationships and multiple constructs measured through several indicators. To complement the outcomes obtained from PLS-SEM, fuzzy-set Qualitative Comparative Analysis (fsQCA) was applied. fsQCA is a set-theoretic method designed to identify combinations of conditions that are sufficient or necessary for a given outcome, allowing multiple causal paths to lead to similar results (Ragin, 2008). In organizational and management research, fsQCA has increasingly been used to uncover complex causal patterns that cannot be fully captured by symmetric methods alone (Pappas & Woodside, 2021 ; Radić et al., 2026 ). The analysis was performed using IBM SPSS Statistics v.24.0 and SmartPLS software (version 4) (Ringle et al., 2024 ). 4. Results Potential common method bias (CMB) was minimized by ensuring respondent anonymity and data confidentiality during the data collection process. For statistical checking, Harman single-factor test was used, and the results have shown that CMB does not pose any significant concern in this research, since a single factor explains 37.21% of the total variance, which is less than 50%, suggested by Fuller et al. ( 2016 ). In addition, the full collinearity test was performed, which is according to Kock ( 2015 ) the most suitable methods for identification of CMB in PLS-SEM. Full collinearity was assessed using the Variance Inflation Factor (VIF). The construct-level VIF values range from 1.466 to 2.899, remaining well below the recommended threshold of 5 (Hair et al., 2021 ), indicating that multicollinearity does not threaten the validity of the estimates. 4.1. Measurement model assessment The evaluation of the measurement model involved determining the construct reliability and validity of the measurement instrument. For that purpose, Cronbach's alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE) were used, as detailed in Table 1 . Table 1 Reliability and validity of the measurement model Variable Cronbach's alpha Composite reliability (CR) Average variance extracted (AVE) OC 0.886 0.914 0.638 LC 0.861 0.901 0.645 TR 0.896 0.898 0.832 STG 0.772 0.868 0.687 TI 0.823 0.883 0.654 KM 0.898 0.918 0.584 OP 0.859 0.895 0.587 The values of CA and CR for all variables significantly exceeded the recommended threshold of 0.7, indicating high internal consistency. In addition, to confirm convergent validity, the Average Variance Extracted (AVE) indicator was used. The AVE value for all constructs is higher than 0.5, suggested by Hair et al. ( 2021 ), meaning that every construct accounts for over half of the variance of its indicators. In addition, it is important to confirm the discriminant validity of the model, proving that the conceptual variables are not correlated with the measurement variables of other conceptual variables. According to Henseler et al. ( 2015 ) the heterotrait-monotrait ratio of correlations (HTMT) is the most suitable method to check discriminant validity. Franke and Sarstedt ( 2019 ) suggest that the values of the ratio should be 0.90 or lower. In current study, this criterion is met for each pair of constructs, verifying that each construct in the model is discriminant to other constructs (Table 2 ). Table 2 Discriminant validity of the measurement model Variables KM LC OC OP STG TI TR KM LC 0.831 OC 0.797 0.791 OP 0.829 0.697 0.617 STG 0.828 0.762 0.773 0.740 TI 0.893 0.764 0.726 0.739 0.898 TR 0.778 0.848 0.705 0.635 0.592 0.665 4.2. Structural model assessment The bootstrapping method was used for conceptual model testing and the findings are displayed in Table 3 . Table 3 Results of the conceptual model testing Hypothesis β SD t values p Results H1 : OC → KM 0.168 0.038 4.460 0.000 Confirmed H2 : LC → KM 0.139 0.043 3.244 0.001 Confirmed H3 : STG → KM 0.129 0.037 3.490 0.000 Confirmed H4 : TI → KM 0.364 0.036 10.167 0.000 Confirmed H5 : TR →KM 0.217 0.037 5.934 0.000 Confirmed H6 : KM →OP 0.731 0.022 32.736 0.000 Confirmed Notes: β – path coefficient, SD – standard deviation, p – level of significance The path coefficients ( β ) indicating the impact of the independent variables on KM, and further KM on OP, are all positive and statistically significant. Accordingly, the proposed conceptual model is confirmed. Finally, the evaluation of obtained R 2 (the coefficient of determination) and f 2 (the effect sizes of the paths) supplement the previous analysis (Table 4 ). Table 4 R 2 and f 2 values Predictor Outcome R 2 f 2 OC KM 0.743 0.046 LC 0.025 STG 0.027 TI 0.211 TR 0.076 KM OP 0.534 1.148 The findings in Table 4 indicate that the overall model explains 74.3% of the variance in KM. Also, the model explains 53.4% of the variance in variable OP, which means that some additional factors (not included in the model) impact KM and OP. 4.3. Fuzzy-set qualitative comparative analysis To complement the variance-based findings and account for causal complexity, a fuzzy-set qualitative comparative analysis (fsQCA) was conducted. he analysis involved the calibration of variables into fuzzy sets, the examination of necessary conditions, and the identification of sufficient configurational paths leading to the outcome of interest. Table 5 presents the percentile-based thresholds used for direct calibration. The 5th percentile indicates complete non-membership, the 50th percentile marks the crossover point (highest ambiguity), and the 95th percentile indicates full membership. The calibration reveals heterogeneity across constructs. Namely, KM shows relatively low 5th percentile values (2.25), indicating substantial dispersion in KM practices, whereas OP exhibits higher threshold values (4.17), suggesting that very low performance levels are less prevalent in the sample. Table 5 Percentiles for data calibration Variable → Thresholds ↓ OC LC STG TI TR KM OP 5th percentile 3.33 3.4 3.67 3.25 3.00 2.25 4.17 50th percentile 6.00 6.00 6.00 6.00 6.00 4.00 6.33 95th percentile 6.67 6.80 7.00 6.50 7.00 5.00 6.83 Table 6 presents the necessity analysis for high and for low/medium (~ OP) performance. In fsQCA, a condition is typically considered necessary if its consistency exceeds the commonly accepted threshold of 0.90 (Ragin, 2008). The results in Table 6 indicate that KM is a necessary condition for high OP, with a consistency of 0.996, well above the threshold. None of the other organizational drivers (culture, leadership, strategy, technology, trust) meet the necessity criterion individually, highlighting that high performance cannot be attributed to any single organizational factor in isolation. For low/medium performance, the absence of KM (~ KM) shows high coverage, reinforcing the critical role of KM in differentiating performance outcomes. Table 6 Analysis of necessary conditions Conditions OP ~ OP Consistency Coverage Consistency Coverage fOC 0.761761 0.706160 0.620937 0.591467 ~fOC 0.559294 0.589476 0.691519 0.748908 fLC 0.777795 0.752468 0.596985 0.593451 ~fLC 0.579761 0.583332 0.750995 0.776431 fSTG 0.773142 0.753191 0.610574 0.611200 ~fSTG 0.600896 0.600266 0.753445 0.773383 fTI 0.831896 0.741756 0.606447 0.555627 ~fTI 0.501624 0.553659 0.718139 0.814463 TR 0.811875 0.742542 0.636356 0.598040 ~TR 0.560506 0.600006 0.726051 0.798622 KM 0.996270 0.527311 0.915396 0.497849 ~KM 0.051262 0.370934 0.130864 0.973010 Table 7 shows the configurational solutions associated with high and low/medium OP. Multiple sufficient configurations (S1-S5) lead to high OP, confirming the principle of equifinality. Across all high-performance solutions, KM consistently appears as a core condition, underscoring its central role. Other organizational drivers such as STG, TR, LC, OC, and TI function as either core or peripheral factors in various combinations, suggesting that different organizational approaches can lead to equally high performance results. Table 7 fsQCA findings Configuration Solutions (S) for high OP Solutions (S) for low/medium OP S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 OC ⊗ ● ● ● ● ⊗ ⊗ ⊗ LC ⊗ ● ● ● ● ⊗ ⊗ ⊗ STG ● ● ⊗ ⊗ ● ⊗ ⊗ ⊗ ⊗ TI ● ● ● ● ⊗ ⊗ ⊗ ⊗ ⊗ TR ● ⊗ ● ● ● ⊗ ⊗ ⊗ KM ● ● ● ● ● ● ● ● ● ● Consistency 0.940 0.932 0.934 0.908 0.900 0.937 0.888 0.912 0.917 0.903 0.867 Raw Coverage 0.360 0.428 0.448 0.444 0.601 0.608 0.449 0.436 0.436 0.412 0.325 Unique Coverage 0.007 0.004 0.006 0.009 0.007 0.004 0.029 0.048 0.018 0.024 0.052 Overall Solution Coverage 0.765 0.724 0.624 0.861 Notes: ● - causal condition present; ⊗ - causal condition absent; blank space - do not care. Large circle - core condition; small circle - peripheral condition. 5. Discussion and Conclusions For some traditional businesses, knowledge has been a reliable source of competitive advantage for hundreds of years. So, it cannot be said that this is a novel idea. However, in modern business, KM has become a key strategic resource that, when shared and applied, enhances organizational performance. Many organizations, especially in developing countries, have problems with the effectiveness of some organizational elements, such as OC and structure, lack of management participation in KM activities, low awareness of the benefits of KM and lack of employee motivation systems, which slows down the KM process, and it is a reason of their poor market position. Accordingly, this study examines the factors identified in prior literature as most relevant to KM success (STG, OC, LC, TI, and TR) and analyzed their direct effects on KM practices. The findings confirm all hypotheses (H1-H6). OC positively influences KM, supporting prior research (Rezaei et al., 2021 ; Lam et al., 2021 ) that shared values and supportive cultures enhance knowledge flow and reduce barriers to exchange. LC also strengthens KM, as empowering leaders promote trust, learning, and knowledge behaviors, indirectly improving OP (Donate & de Pablo, 2015 ). TI significantly enhances KM by facilitating knowledge sharing, storage, and innovation (Islam et al., 2015 ; Akram et al., 2018 ), thereby indirectly improving OP. STG positively affects KM by aligning knowledge practices with long-term organizational goals and strategic priorities (Kılıç & Uludağ, 2021 ). TR further supports KM by encouraging collaboration, commitment, and participation in knowledge activities (Alaarj et al., 2016 ; Paliszkiewicz et al., 2016). Finally, KM has a strong positive impact on OP, indicating that systematic knowledge creation, sharing, and application foster innovation, process optimization, and overall organizational effectiveness (Yoshikuni & Albertin, 2020 ; Rezaei et al., 2021 ; Mohammadi et al., 2023 ). The fsQCA results offer valuable insights that add to and enhance the PLS-SEM findings. Highlighting KM as a necessary condition for achieving high OP supports previous research that considers KM a fundamental organizational capability (Lam et al., 2021 ; Cristache et al., 2025 ). However, the fsQCA results reveal that KM alone does not guarantee high performance, and it rather must be part of certain organizational configurations to be effective. In this sense, there are six configurations for successful KM implementation in Serbian companies, that results in high OP. S1 is based on STG and TR, which jointly shape knowledge creation, sharing, and application. KM functions as the execution mechanism translating strategic intent into coordinated action, while TR enables open exchange without strong formalization, leading to high OP. S2 reflects a configuration where LC and STG strongly structure KM practices. Knowledge processes are formalized and technology-supported, relying on managerial authority rather than trust. KM operates as a controlled, strategy-aligned mechanism to achieve high OP. S3 is grounded in strong OC and TR. Knowledge is shared through informal routines and social interaction, with limited strategic formalization. KM functions as a socially embedded mechanism supporting performance, reinforced by TI. S4 highlights a strategically oriented but institutionalized KM system. LC, OC, and TR foster consistent knowledge sharing and application, even without explicit strategic formalization. S5 represents a highly integrated configuration where OC, LC, and STG jointly shape KM, supported by TI. KM serves as a central integrative mechanism enabling sustainable high OP in dynamic environments. S6 emphasizes social and leadership drivers. LC, OC, and TR create a relational context for knowledge exchange, while formal systems and TI play supportive roles. KM operates as a relational mechanism facilitating performance outcomes. This finding aligns with Kusa et al. ( 2024 ) combining PLS-SEM and fsQCA, which similarly identify multiple causal pathways to organizational success. Furthermore, the asymmetric nature of the results, where conditions leading to high performance differ from those associated with low or medium performance, highlights the limitations of symmetric, regression-based approaches when used in isolation. By integrating PLS-SEM and fsQCA, this study demonstrates the methodological value of combining symmetric and configurational analyses to capture both linear relationships and complex causal patterns, as recommended in research by Pappas & Woodside ( 2021 ). 5.1. Theoretical implications This study contributes to the theoretical understanding of KM in transition economies, since empirical research on this topic remains scarce. In particular, the study addresses a notable gap in the literature by combining PLS-SEM and the fsQCA approach that has not yet been systematically applied to examine the interplay between knowledge management drivers and organizational performance in transitional contexts. By integrating both PLS-SEM and fsQCA, it demonstrates the value of combining symmetrical and asymmetrical analytical approaches to capture the complex and multifaceted relationships between organizational factors, KM practices, and OP. The findings highlight that KM is not driven by single factor in isolation but emerges from the interplay of multiple organizational, strategic, and technological conditions. Consequently, this approach extends the findings not only by enhancing existing KM theories but also by establishing a methodological framework for future research examining complex organisational processes in transitional and emerging economies. 5.2. Practical implications The findings indicate that managers in Serbian organizations (but also in other transition economies) should treat KM as a strategic priority and integrate it with STG, OC, LC, TI, and TR to achieve high OP. Managers can enhance KM effectiveness by clearly communicating KM goals, fostering a supportive OC, promoting TR through team-building activities, implementing recognition and reward systems for knowledge-sharing behaviors, and leveraging technology The fsQCA findings suggest that managers in studied companies should view KM as a central mechanism for achieving high OP whose effectiveness depends on how well it is shaped by complementary organizational drivers rather than as a standalone practice. High OP can be achieved through different KM-driven configurations, meaning that firms should avoid one-size-fits-all solutions and instead align KM practices with their dominant organizational drivers, depending on their organizational context and resource constraints. Adopting a configuration-based approach to KM allows managers to translate existing organizational strengths into sustainable OP, especially in transition economies, like Serbia, where firms often face challenges related to institutional uncertainty and limited resources. Implementation of effective KM practices requires a shift from traditional bureaucratic structures toward more collaborative, knowledge-oriented organizational models. In environments dominated by hierarchical or autocratic leadership, employees may withhold knowledge, negatively impacting innovation development and OP. This study demonstrates that in studied firms, STG, OC, LC, TI, and TR align with KM practices, creating the conditions for competitive advantage. Importantly, each of these factors plays a critical role, and neglecting any factor can undermine KM effectiveness. 5.3. Limitations and future research directions This research has several limitations that open avenues for future investigation. First, the data were collected from a single country, which may limit the generalizability of the findings. Second, the model explains 74.3% of the variance in KM and 53.4% in OP, indicating that other relevant factors, such as organizational structure, employee motivation, or incentive systems, were not captured in the model and may also impact KM and OP. Third, organizational performance was assessed by respondents based on their subjective perceptions, which can cause certain errors. Future research could address these limitations by employing larger and more diverse samples across multiple countries, including a broader set of organizational factors, and combining perceptual measures with objective performance data to enhance the robustness and generalizability of the results. Declarations Ethical approval and Consent to Participate : The Ethics Committee approved, mentioning that no personal data were collected, the survey being anonymous. Consent for Publication: Publication consent was obtained from all authors and participants. Funding: The research presented in this paper was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, within the funding of scientific and research work at the University of Belgrade, Technical Faculty in Bor, according to contract with registration number 451-03-137/2026-03/ 200131. References Ajmal, M. M., & Koskinen, K. U. (2008). Knowledge transfer in project-based organizations: an organizational culture perspective. Project management journal, 39(1), 7–15. Akram, M. S., Goraya, M. A. S., Malik, A., & Aljarallah, A. M. (2018). Organizational performance and sustainability: exploring the roles of IT capabilities and knowledge management capabilities. Sustainability, 10(10), 3816. Alaarj, S., Abidin-Mohamed, Z., & Bustamam, U. S. B. A. (2016). 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Fuzzy-set qualitative comparative analysis (fsQCA): guidelines for research practice in information systems and marketing. International journal of information management, 58, 102310. Park, J. G., & Lee, J. (2014). Knowledge sharing in information systems development projects: Explicating the role of dependence and trust. International Journal of Project Management, 32(1), 153–165. Payal, R., Ahmed, S., & Debnath, R. M. (2019). Impact of knowledge management on organizational performance: An application of structural equation modeling. VINE Journal of Information and Knowledge Management Systems, 49(4), 510–530. Petrov, V., Ćelić, Đ., Uzelac, Z., & Drašković, Z. (2020). Three pillars of knowledge management in SMEs: evidence from Serbia. International Entrepreneurship and Management Journal, 16, 417–438. Radić, A., Arsić, S., Nikolić, Đ., Bobek, S., & Sternad Zabukovšek, S. (2026). Factors influencing ERP system usage in manufacturing and service industries: A comparative study using PLS-SEM and fsQCA. Serbian Journal of Management, 21(1), 15–36. Ragin, C. C. (2009). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago press. Rasool, S. F., Samma, M., Mohelska, H., & Rehman, F. U. (2023). Investigating the nexus between information technology capabilities, knowledge management, and green product innovation: evidence from SME industry. Environmental Science and Pollution Research, 30(19), 56174–56187. Rezaei, F., Khalilzadeh, M., & Soleimani, P. (2021). Factors affecting knowledge management and its effect on organizational performance: Mediating the role of human capital. Advances in Human-Computer Interaction, 2021(1), 8857572. Ringle, C. M., Wende, S., and Becker, J.-M. (2024). SmartPLS 4. Bönningstedt: SmartPLS, https://www.smartpls.com Shamim, S., Cang, S., & Yu, H. (2019). Impact of knowledge oriented leadership on knowledge management behaviour through employee work attitudes. The International Journal of Human Resource Management, 30(16), 2387–2417. Valaei, N., & Rezaei, S. (2017). Does Web 2.0 utilisation lead to knowledge quality, improvisational creativity, compositional creativity, and innovation in small and medium-sized enterprises? A sense-making perspective. Technology Analysis & Strategic Management, 29(4), 381–394. Vargas-Zeledon, A. A. (2023). Knowledge management requirements for information systems in small ventures: A fuzzy-set qualitative comparative analysis (fsQCA). Small Business International Review, 7(1), e570. https://doi.org/10.26784/sbir.v7i1.570 Wang, Z., Wang, N., & Liang, H. (2014). Knowledge sharing, intellectual capital and firm performance. Management Decision, 52(2), 230–258. Yang, L. R., Chen, J. H., & Wang, H. W. (2012). Assessing impacts of information technology on project success through knowledge management practice. Automation in construction, 22, 182–191. Yoshikuni, A. C., & Albertin, A. L. (2020). Leveraging firm performance through information technology strategic alignment and knowledge management strategy: an empirical study of IT-Business Value. International Journal of Research-GRANTHAALAYAH, 8(10), 304–318. Zack, M. H. (1999). Developing a knowledge strategy. California management review, 41(3), 125–145. Zack, M., McKeen, J., & Singh, S. (2009). Knowledge management and organizational performance: an exploratory analysis. Journal of knowledge management, 13(6), 392–409. Zheng, W., Yang, B., & McLean, G. N. (2010). Linking organizational culture, structure, strategy, and organizational effectiveness: Mediating role of knowledge management. Journal of Business research, 63(7), 763–771. Additional Declarations No competing interests reported. Supplementary Files PripremljenipodaciOFKMOP.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9152161","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615422679,"identity":"874d4898-f169-43b8-b319-99ce8a939a8e","order_by":0,"name":"Sanela Golubović Corcione","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie2OMUvDQBSAXwhclsOsrxTJX7gjcC1U2r9yR4YuwdUMRQKBcxGztn+ks1I4l7hnMy5OHeIiTsWELApni5vgfXAPHvc++AAcjr9K048ASDcRQv++X/3jihxuBmWk5e8UAGZOKNHN00MjVzAvfSqaNz2NYhO8ImQzlY8fG5vCqsuESQPJpqATvtHIt4YKhGqp8rOUWRVIBUoCCdtRMaYVSvF8S9DTO5VTag8r95MPefiixLoL8w69EljDoE4FKA3zQclQMgICvbxXwB5W72NUdyhHBbni6wz52tB4Ks0y1pRalahMedu+XyzCoNg2LbuOQh281O1qdl7+FDaAKv+2y+6RI/c9ixP/DofD8Z/5BIhqVEaknJApAAAAAElFTkSuQmCC","orcid":"","institution":"University Business Academy in Novi Sad","correspondingAuthor":true,"prefix":"","firstName":"Sanela","middleName":"Golubović","lastName":"Corcione","suffix":""},{"id":615422681,"identity":"94f01427-c589-4f50-83be-4a817b543eac","order_by":1,"name":"Milica Veličković","email":"","orcid":"","institution":"University of Belgrade, Technical Faculty in Bor","correspondingAuthor":false,"prefix":"","firstName":"Milica","middleName":"","lastName":"Veličković","suffix":""},{"id":615422683,"identity":"b4568c41-aea0-40bc-894b-59b0ff9af08e","order_by":2,"name":"Aleksandra Fedajev","email":"","orcid":"","institution":"University of Belgrade, Technical Faculty in Bor","correspondingAuthor":false,"prefix":"","firstName":"Aleksandra","middleName":"","lastName":"Fedajev","suffix":""},{"id":615422684,"identity":"85f1bcee-264b-425f-9190-41a4ac782dda","order_by":3,"name":"Sanela Arsić","email":"","orcid":"","institution":"University of Belgrade, Technical Faculty in Bor","correspondingAuthor":false,"prefix":"","firstName":"Sanela","middleName":"","lastName":"Arsić","suffix":""}],"badges":[],"createdAt":"2026-03-17 19:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9152161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9152161/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105959590,"identity":"03d905f5-e872-4e52-9c54-e99b6217e7db","added_by":"auto","created_at":"2026-04-01 22:18:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170483,"visible":true,"origin":"","legend":"\u003cp\u003eThe conceptual model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9152161/v1/93cd307844c0a86d9dd11e30.png"},{"id":106095585,"identity":"f33689da-2f11-4499-a880-153d3a602e98","added_by":"auto","created_at":"2026-04-03 11:49:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1278081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9152161/v1/4c74fe64-5203-44c1-ba35-cb2cb93c659d.pdf"},{"id":106093360,"identity":"c6ec3e23-6b5e-460b-80dd-ea67bf575e47","added_by":"auto","created_at":"2026-04-03 11:36:58","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":101902,"visible":true,"origin":"","legend":"","description":"","filename":"PripremljenipodaciOFKMOP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9152161/v1/e293f68587f8880329fe1e3e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eKnowledge Management drivers and Organizational Preformance: A PLS-SEM and FSQCA approach \u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eToday, knowledge is widely recognized as one of the most valuable economic assets, playing a critical role in organizational performance (OP) and long-term survival in the market. In an uncertain and dynamic market conditions, a strong commitment to the effective use of knowledge management (KM) systems is crucial for achieving and sustaining long-term competitive advantage. Even though many enterprises are investing in KM systems, there are numerous circumstances, and factors, leading to the accomplishment of the KM concept in an organization. Therefore, effective management of organizational elements is a prerequisite for efficient investment in KM initiatives (Rezaei et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cristache et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Together with other organizational inputs, knowledge presents one of the principal factors in achieving organizational goals. According to Bolisani \u0026amp; Bratianu (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), knowledge manifests in forms of tacit and explicit. Tacit knowledge refers to personal, experience-based know-how that is difficult to articulate, codify, or express in written form, as it resides in individuals\u0026rsquo; minds and is acquired primarily through practical experience (Gascoigne \u0026amp; Thornton, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Explicit knowledge can be written, stored and disseminated, such as official documents, databases, and manuals. Even though the definitions of KM may vary, KM is most often defined as the process which involves collecting, distributing, and using knowledge resources efficiently and is described as the main qualificator to add value for the organization (Kusa et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cristache et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a methodological perspective, recent KM performance and OP studies depend on symmetric, variance-based methods such as Partial Least Squares Structural Equation Modelling (PLS-SEM) (Payal et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cepeda-Carrion et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies provide insights into the effects of KM-related constructs on OP, but largely overlook causal complexity, equifinality, and asymmetric relationships. On the other hand, only a limited number of recent studies have applied fuzzy-set Qualitative Comparative Analysis (fsQCA) to explore how different combinations of organizational conditions lead to high OP (Olan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Olan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vargas-Zeledon, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Karadağ et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite more researchers are calling for the combination of symmetric and configurational approaches in organizational and KM studies, few have integrated PLS-SEM and fsQCA in a single research framework (Kusa et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This gap is especially noticeable in transition economies. Serbia, as a Western Balkan transition economy, has a unique setting with changing market systems, mixed ownership, and uneven development of knowledge infrastructure. Recent studies show that KM practices in Serbia are more developed in large, foreign-owned, and financially stable organizations, while smaller and locally owned firms tend to adopt systematic KM practices more slowly (Kavalić et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ongoing institutional changes and structural instability also limit innovation and organizational learning (Petrov et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite growing recognition of KM for OP, research on the topic remains limited in transitional countries. Hence, this study aims to fill that gap by combining PLS-SEM and fsQCA to examine how organizational drivers shape KM practices and, through KM, contribute to OP in Serbian enterprises. By integrating these two complementary methodologies, the study provides novel insights from a transition economy and highlights the value of this approach for exploring both direct and complex causal relationships, offering results that can serve as a benchmark for other transition economies.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eIn every organization, KM activities are not isolated from other activities. Various organizational components may affect the successful realization of KM actions (Alaarj et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Bearing this in mind, several authors have examined key factors which can lead to the successful implementation of the KM concept in the company (Rezaei et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lam et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on the defined research aim, this study focus on a set of key organizational drivers, encompassing both structural and behavioral dimensions, which are expected to influence KM practices and, indirectly, OP.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Organizational culture\u003c/h2\u003e \u003cp\u003eA knowledge-committed culture is often cited as a crucial factor in ensuring efficient knowledge flow among organizational members. Culture is recognized as an essential driver of innovation when it is grounded in the values of flexibility, trust, creativity, diversity, and sustainable development (Lam et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Organizational culture (OC) can be defined as a system of shared beliefs, values, norms, and practices that guide employee behavior and influence organizational processes (Ajmal \u0026amp; Koskinen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Rezaei et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that OC exerts a direct and significant influence on KM effectiveness. Similarly, Lam et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported a strong association between culture and KM practices, while Aldulaimi (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that OC is positively related to both KM and organizational effectiveness. More recent studies extend these findings by showing that culture not only supports KM implementation, but also indirectly enhances OP through knowledge-based capabilities (Cristache et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In light of the aforementioned studies, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1. Organizational culture positively impacts knowledge management.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Leadership commitment\u003c/h2\u003e \u003cp\u003eLeadership commitment (LC) and managerial support are increasingly recognized in the recent literature as vital for successful KM implementation in modern organizations. In particular, transformational and empowering leadership styles have been shown to foster mutual trust among employees, encourage proactive knowledge-related behaviors, and facilitate the integration of KM practices into daily organizational routines (Koohang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; G\u0026uuml;rlek \u0026amp; \u0026Ccedil;emberci, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Donate \u0026amp; de Pablo (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) suggests that LC directly influences KM processes by motivating employees to create, share, and apply knowledge, while simultaneously strengthening interpersonal trust and collective learning mechanisms within organizations. Leaders who actively support KM initiatives by allocating resources, promoting learning opportunities, and acting as role models significantly enhance the effectiveness of KM systems and practices (Paliszkiewicz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shamim et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further, recent research indicates that LC not only facilitates KM implementation but also indirectly improves OP by reinforcing knowledge-based capabilities and cultivating a learning-oriented organizational environment (Donate \u0026amp; de Pablo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Naqshbandi \u0026amp; Jasimuddin, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hence, the next hypothesis is defined:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2. Leadership commitment positively impacts knowledge management.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Organizational strategy\u003c/h2\u003e \u003cp\u003eOrganizational strategy (STG) defines long-term priorities, resource allocation and coordination mechanisms, which directly encourage the creation, acquisition, sharing, integration and exploitation of knowledge in the organization (Zack, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). A strong KM approach starts with a clear organizational strategy that treats knowledge as a key resource. Instead of treating KM as a separate activity, Huynh et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight integrating KM into the organization\u0026rsquo;s overall strategy to keep efforts consistent and sustainable. Supporting this approach, research by Kılı\u0026ccedil; \u0026amp; Uludağ (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) confirmed that a well-defined KM-oriented strategy positively influences innovation outcomes and overall OP by aligning knowledge processes with strategic priorities. In this context, KM acts as a strategic tool that turns organizational strategy into practical capabilities, enabling firms to respond better to environmental uncertainty and competition (Jami Pour \u0026amp; Asarian, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Zheng et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) in their research analyzed the mediating role of KM between strategy and OP and confirmed that KM partially mediates this relationship. Therefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH3. Organizational strategy positively impacts knowledge management.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Information technology infrastructure\u003c/h2\u003e \u003cp\u003eIncorporation of various technological platforms facilitates the sharing of existing knowledge (Islam et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Technology infrastructure (hardware, software, networks, data storage) is an essential component of technology development and strengthens enterprises' performances (Akram et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Companies which base their strategy on knowledge can significantly elevate the quality of their products by strategically harnessing technological resources, thereby improving performance by promoting innovation and market competitiveness (Rasool et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Technology usage in supporting KM opens new capabilities (Valaei et al., 2017) in business processes. Information Technology tools help discovery of needed knowledge, while TI enables the conversion of tacit knowledge into explicit (Islam et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It also helps to store the same explicit knowledge in official documents to facilitate access to it in the future, in which way the individual knowledge becomes organizational knowledge. Based on these arguments, the next hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH4. Information technology infrastructure positively impacts knowledge management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Trust\u003c/h2\u003e \u003cp\u003eOpen communication with leaders is essential to building trust (TR) among employees. TR is an essential part of productive and effective teamwork. When TR is present, employees are more willing to share knowledge, engage in cooperative problem-solving, and participate in knowledge-based initiatives, thereby enhancing the overall effectiveness of KM practices (Paliszkiewicz et al., 2016; Alaarj et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Company members commit more to a knowledge-based strategy when there is trust among organizational members (Lam et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, recent research emphasizes that trust not only enhances knowledge sharing, but also indirectly contributes to improved organizational performance by reinforcing knowledge-based capabilities and reducing coordination and communication costs (Cristache et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Hence, the next hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH5. Trust in the workplace positively impacts knowledge management.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Knowledge management and organizational performance\u003c/h2\u003e \u003cp\u003eThe literature recognizes the significance of KM practices for an organization's overall success, so the relationship between KM and OP has been evaluated by many researchers (Iqbal et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Payal et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An organization\u0026rsquo;s ability to achieve its goals efficiently and effectively defines its OP, which, when leveraged, can lead to a sustainable competitive advantage. Using knowledge through the concept of KM, organizations can significantly improve their performance (Zack et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Paliszkiewicz et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yoshikuni \u0026amp; Albertin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research by Kılı\u0026ccedil; \u0026amp; Uludağ (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Rezaei et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Mohammadi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlights the mediating effect of KM on OP and emphasizes that KM is significantly related to infrastructural factors. Also, numerous other researchers, including Hosseini et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Rezaei et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Lam et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlight the significant positive effect of KM on innovation and OP. Therefore, the next hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH6. Knowledge management significantly impacts organizational performance.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the conceptual model of the proposed relationships between the examined organizational factors, KM, and OP (H1-H6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sampling and data collection\u003c/h2\u003e \u003cp\u003eThis study aims to explain how specific organizational elements, both hard and soft, influence KM practices and OP in organizations in the Republic of Serbia, based on research conducted in 2024\u0026ndash;2025. The target population consisted of 637 employees working in various manufacturing and service organizations that had already adopted and implemented key KM practices.\u003c/p\u003e \u003cp\u003eThe first five questions collect demographic information (industry type, firm size, gender, education, and professional experience). The remaining questionnaire items were grouped into seven constructs reflecting key organizational dimensions. Specifically, OC was measured using 6 items adapted from (Gold et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lam et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); organizational STG was assessed through 3 items adopted from (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); TI was captured by 4 items based on (Gold et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); LC was measured using 5 items derived from (Carless et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Shamim et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lam et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); employee TR was operationalized through 3 items adapted from (Carless et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Park \u0026amp; Lee, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shamim et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); KM practices were measured using 8 items adopted from (Gold et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Park \u0026amp; Lee, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); and OP was assessed through 6 items adapted from (Gold et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Darroch, 2005). All items were measured using a five-point Likert scale.\u003c/p\u003e \u003cp\u003eThe sample mainly consist of male employees (60.7%), with a high school diploma (41.0%), less than five years of work experience (32.4%), indicating a relatively young workforce.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data analysis techniques\u003c/h2\u003e \u003cp\u003eThis study used both Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine the relationships among the variables.\u003c/p\u003e \u003cp\u003eThe Partial Least Squares (PLS) approach to Structural Equation Modeling (SEM) was employed to examine the hypothesized relationships among latent constructs. Following the recommendations of Hair et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the SEM procedure was conducted in two sequential stages. The first stage focused on assessing the measurement model to confirm construct validity and evaluate the model fit. The second stage involved testing the proposed research hypotheses through structural model analysis. PLS-SEM is widely used in KM research and organizational studies, particularly when models involve complex relationships and multiple constructs measured through several indicators.\u003c/p\u003e \u003cp\u003eTo complement the outcomes obtained from PLS-SEM, fuzzy-set Qualitative Comparative Analysis (fsQCA) was applied. fsQCA is a set-theoretic method designed to identify combinations of conditions that are sufficient or necessary for a given outcome, allowing multiple causal paths to lead to similar results (Ragin, 2008). In organizational and management research, fsQCA has increasingly been used to uncover complex causal patterns that cannot be fully captured by symmetric methods alone (Pappas \u0026amp; Woodside, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Radić et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The analysis was performed using IBM SPSS Statistics v.24.0 and SmartPLS software (version 4) (Ringle et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003ePotential common method bias (CMB) was minimized by ensuring respondent anonymity and data confidentiality during the data collection process. For statistical checking, Harman single-factor test was used, and the results have shown that CMB does not pose any significant concern in this research, since a single factor explains 37.21% of the total variance, which is less than 50%, suggested by Fuller et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, the full collinearity test was performed, which is according to Kock (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) the most suitable methods for identification of CMB in PLS-SEM. Full collinearity was assessed using the Variance Inflation Factor (VIF). The construct-level VIF values range from 1.466 to 2.899, remaining well below the recommended threshold of 5 (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), indicating that multicollinearity does not threaten the validity of the estimates.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Measurement model assessment\u003c/h2\u003e \u003cp\u003eThe evaluation of the measurement model involved determining the construct reliability and validity of the measurement instrument. For that purpose, Cronbach's alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE) were used, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and validity of the measurement model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach's alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComposite reliability (CR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe values of CA and CR for all variables significantly exceeded the recommended threshold of 0.7, indicating high internal consistency. In addition, to confirm convergent validity, the Average Variance Extracted (AVE) indicator was used. The AVE value for all constructs is higher than 0.5, suggested by Hair et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), meaning that every construct accounts for over half of the variance of its indicators.\u003c/p\u003e \u003cp\u003eIn addition, it is important to confirm the discriminant validity of the model, proving that the conceptual variables are not correlated with the measurement variables of other conceptual variables. According to Henseler et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) the heterotrait-monotrait ratio of correlations (HTMT) is the most suitable method to check discriminant validity. Franke and Sarstedt (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggest that the values of the ratio should be 0.90 or lower. In current study, this criterion is met for each pair of constructs, verifying that each construct in the model is discriminant to other constructs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity of the measurement model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSTG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Structural model assessment\u003c/h2\u003e \u003cp\u003eThe bootstrapping method was used for conceptual model testing and the findings are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the conceptual model testing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH1\u003c/b\u003e: OC \u0026rarr; KM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConfirmed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH2\u003c/b\u003e: LC \u0026rarr; KM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConfirmed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH3\u003c/b\u003e: STG \u0026rarr; KM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConfirmed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH4\u003c/b\u003e: TI \u0026rarr; KM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConfirmed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH5\u003c/b\u003e: TR \u0026rarr;KM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConfirmed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH6\u003c/b\u003e: KM \u0026rarr;OP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConfirmed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes: β\u003c/em\u003e \u0026ndash; path coefficient, SD \u0026ndash; standard deviation, \u003cem\u003ep \u0026ndash;\u003c/em\u003e level of significance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe path coefficients (\u003cem\u003eβ\u003c/em\u003e) indicating the impact of the independent variables on KM, and further KM on OP, are all positive and statistically significant. Accordingly, the proposed conceptual model is confirmed.\u003c/p\u003e \u003cp\u003eFinally, the evaluation of obtained R\u003csup\u003e2\u003c/sup\u003e (the coefficient of determination) and f\u003csup\u003e2\u003c/sup\u003e (the effect sizes of the paths) supplement the previous analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e and f\u003csup\u003e2\u003c/sup\u003e values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that the overall model explains 74.3% of the variance in KM. Also, the model explains 53.4% of the variance in variable OP, which means that some additional factors (not included in the model) impact KM and OP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Fuzzy-set qualitative comparative analysis\u003c/h2\u003e \u003cp\u003eTo complement the variance-based findings and account for causal complexity, a fuzzy-set qualitative comparative analysis (fsQCA) was conducted. he analysis involved the calibration of variables into fuzzy sets, the examination of necessary conditions, and the identification of sufficient configurational paths leading to the outcome of interest.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the percentile-based thresholds used for direct calibration. The 5th percentile indicates complete non-membership, the 50th percentile marks the crossover point (highest ambiguity), and the 95th percentile indicates full membership. The calibration reveals heterogeneity across constructs. Namely, KM shows relatively low 5th percentile values (2.25), indicating substantial dispersion in KM practices, whereas OP exhibits higher threshold values (4.17), suggesting that very low performance levels are less prevalent in the sample.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentiles for data calibration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable \u0026rarr;\u003c/p\u003e \u003cp\u003eThresholds \u0026darr;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50th percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95th percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the necessity analysis for high and for low/medium (~\u0026thinsp;OP) performance. In fsQCA, a condition is typically considered necessary if its consistency exceeds the commonly accepted threshold of 0.90 (Ragin, 2008). The results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicate that KM is a necessary condition for high OP, with a consistency of 0.996, well above the threshold. None of the other organizational drivers (culture, leadership, strategy, technology, trust) meet the necessity criterion individually, highlighting that high performance cannot be attributed to any single organizational factor in isolation. For low/medium performance, the absence of KM (~\u0026thinsp;KM) shows high coverage, reinforcing the critical role of KM in differentiating performance outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of necessary conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e~ OP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.761761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.706160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.620937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.591467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~fOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.559294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.589476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.691519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.748908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.777795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.752468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.593451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~fLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.579761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.583332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.750995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efSTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.773142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.753191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.610574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~fSTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.600896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.753445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.773383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.831896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.741756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.606447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.555627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~fTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.501624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.553659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.718139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.814463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.811875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.742542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.636356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.598040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~TR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.560506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.726051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.798622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.996270\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.527311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.915396\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.497849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~KM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.051262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.370934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.130864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the configurational solutions associated with high and low/medium OP. Multiple sufficient configurations (S1-S5) lead to high OP, confirming the principle of equifinality. Across all high-performance solutions, KM consistently appears as a core condition, underscoring its central role. Other organizational drivers such as STG, TR, LC, OC, and TI function as either core or peripheral factors in various combinations, suggesting that different organizational approaches can lead to equally high performance results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003efsQCA findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConfiguration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSolutions (S) for high\u003c/p\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eSolutions (S) for low/medium\u003c/p\u003e \u003cp\u003eOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eS11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026otimes;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026otimes;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026otimes;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026otimes;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026otimes;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOverall Solution Coverage\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNotes: ● - causal condition present;\u003c/em\u003e \u003cb\u003e\u0026otimes;\u003c/b\u003e \u003cem\u003e- causal condition absent; blank space - do not care. Large circle - core condition; small circle - peripheral condition.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion and Conclusions","content":"\u003cp\u003eFor some traditional businesses, knowledge has been a reliable source of competitive advantage for hundreds of years. So, it cannot be said that this is a novel idea. However, in modern business, KM has become a key strategic resource that, when shared and applied, enhances organizational performance. Many organizations, especially in developing countries, have problems with the effectiveness of some organizational elements, such as OC and structure, lack of management participation in KM activities, low awareness of the benefits of KM and lack of employee motivation systems, which slows down the KM process, and it is a reason of their poor market position. Accordingly, this study examines the factors identified in prior literature as most relevant to KM success (STG, OC, LC, TI, and TR) and analyzed their direct effects on KM practices.\u003c/p\u003e \u003cp\u003eThe findings confirm all hypotheses (H1-H6). OC positively influences KM, supporting prior research (Rezaei et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lam et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that shared values and supportive cultures enhance knowledge flow and reduce barriers to exchange. LC also strengthens KM, as empowering leaders promote trust, learning, and knowledge behaviors, indirectly improving OP (Donate \u0026amp; de Pablo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). TI significantly enhances KM by facilitating knowledge sharing, storage, and innovation (Islam et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Akram et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), thereby indirectly improving OP. STG positively affects KM by aligning knowledge practices with long-term organizational goals and strategic priorities (Kılı\u0026ccedil; \u0026amp; Uludağ, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). TR further supports KM by encouraging collaboration, commitment, and participation in knowledge activities (Alaarj et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paliszkiewicz et al., 2016). Finally, KM has a strong positive impact on OP, indicating that systematic knowledge creation, sharing, and application foster innovation, process optimization, and overall organizational effectiveness (Yoshikuni \u0026amp; Albertin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rezaei et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fsQCA results offer valuable insights that add to and enhance the PLS-SEM findings. Highlighting KM as a necessary condition for achieving high OP supports previous research that considers KM a fundamental organizational capability (Lam et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cristache et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the fsQCA results reveal that KM alone does not guarantee high performance, and it rather must be part of certain organizational configurations to be effective. In this sense, there are six configurations for successful KM implementation in Serbian companies, that results in high OP.\u003c/p\u003e \u003cp\u003eS1 is based on STG and TR, which jointly shape knowledge creation, sharing, and application. KM functions as the execution mechanism translating strategic intent into coordinated action, while TR enables open exchange without strong formalization, leading to high OP. S2 reflects a configuration where LC and STG strongly structure KM practices. Knowledge processes are formalized and technology-supported, relying on managerial authority rather than trust. KM operates as a controlled, strategy-aligned mechanism to achieve high OP. S3 is grounded in strong OC and TR. Knowledge is shared through informal routines and social interaction, with limited strategic formalization. KM functions as a socially embedded mechanism supporting performance, reinforced by TI. S4 highlights a strategically oriented but institutionalized KM system. LC, OC, and TR foster consistent knowledge sharing and application, even without explicit strategic formalization. S5 represents a highly integrated configuration where OC, LC, and STG jointly shape KM, supported by TI. KM serves as a central integrative mechanism enabling sustainable high OP in dynamic environments. S6 emphasizes social and leadership drivers. LC, OC, and TR create a relational context for knowledge exchange, while formal systems and TI play supportive roles. KM operates as a relational mechanism facilitating performance outcomes.\u003c/p\u003e \u003cp\u003eThis finding aligns with Kusa et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) combining PLS-SEM and fsQCA, which similarly identify multiple causal pathways to organizational success. Furthermore, the asymmetric nature of the results, where conditions leading to high performance differ from those associated with low or medium performance, highlights the limitations of symmetric, regression-based approaches when used in isolation. By integrating PLS-SEM and fsQCA, this study demonstrates the methodological value of combining symmetric and configurational analyses to capture both linear relationships and complex causal patterns, as recommended in research by Pappas \u0026amp; Woodside (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Theoretical implications\u003c/h2\u003e \u003cp\u003eThis study contributes to the theoretical understanding of KM in transition economies, since empirical research on this topic remains scarce. In particular, the study addresses a notable gap in the literature by combining PLS-SEM and the fsQCA approach that has not yet been systematically applied to examine the interplay between knowledge management drivers and organizational performance in transitional contexts. By integrating both PLS-SEM and fsQCA, it demonstrates the value of combining symmetrical and asymmetrical analytical approaches to capture the complex and multifaceted relationships between organizational factors, KM practices, and OP. The findings highlight that KM is not driven by single factor in isolation but emerges from the interplay of multiple organizational, strategic, and technological conditions. Consequently, this approach extends the findings not only by enhancing existing KM theories but also by establishing a methodological framework for future research examining complex organisational processes in transitional and emerging economies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Practical implications\u003c/h2\u003e \u003cp\u003eThe findings indicate that managers in Serbian organizations (but also in other transition economies) should treat KM as a strategic priority and integrate it with STG, OC, LC, TI, and TR to achieve high OP. Managers can enhance KM effectiveness by clearly communicating KM goals, fostering a supportive OC, promoting TR through team-building activities, implementing recognition and reward systems for knowledge-sharing behaviors, and leveraging technology The fsQCA findings suggest that managers in studied companies should view KM as a central mechanism for achieving high OP whose effectiveness depends on how well it is shaped by complementary organizational drivers rather than as a standalone practice. High OP can be achieved through different KM-driven configurations, meaning that firms should avoid one-size-fits-all solutions and instead align KM practices with their dominant organizational drivers, depending on their organizational context and resource constraints. Adopting a configuration-based approach to KM allows managers to translate existing organizational strengths into sustainable OP, especially in transition economies, like Serbia, where firms often face challenges related to institutional uncertainty and limited resources.\u003c/p\u003e \u003cp\u003eImplementation of effective KM practices requires a shift from traditional bureaucratic structures toward more collaborative, knowledge-oriented organizational models. In environments dominated by hierarchical or autocratic leadership, employees may withhold knowledge, negatively impacting innovation development and OP. This study demonstrates that in studied firms, STG, OC, LC, TI, and TR align with KM practices, creating the conditions for competitive advantage. Importantly, each of these factors plays a critical role, and neglecting any factor can undermine KM effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Limitations and future research directions\u003c/h2\u003e \u003cp\u003eThis research has several limitations that open avenues for future investigation. First, the data were collected from a single country, which may limit the generalizability of the findings. Second, the model explains 74.3% of the variance in KM and 53.4% in OP, indicating that other relevant factors, such as organizational structure, employee motivation, or incentive systems, were not captured in the model and may also impact KM and OP. Third, organizational performance was assessed by respondents based on their subjective perceptions, which can cause certain errors. Future research could address these limitations by employing larger and more diverse samples across multiple countries, including a broader set of organizational factors, and combining perceptual measures with objective performance data to enhance the robustness and generalizability of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The Ethics Committee approved, mentioning that no personal data were collected, the survey being anonymous.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003ePublication consent was obtained from all authors and participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe research presented in this paper was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, within the funding of scientific and research work at the University of Belgrade, Technical Faculty in Bor, according to contract with registration number 451-03-137/2026-03/ 200131.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjmal, M. 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Journal of Business research, 63(7), 763\u0026ndash;771.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"knowledge management, organizational performance, PLS-SEM analysis, FSQCA approach","lastPublishedDoi":"10.21203/rs.3.rs-9152161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9152161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eKnowledge is a key strategic resource for modern organizations, crucial for achieving competitive advantage, resilience, and long-term performance. Although organizations increasingly invest in knowledge management (KM) initiatives and systems, the success of their implementation largely depends on the broader organizational context and the interaction of various organizational drivers. Hence, this study aims to examine how organizational drivers shape knowledge management practices and contribute to organizational performance (OP).\u003c/p\u003e\u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003eEmpirical analysis was conducted on a sample of organizations in Serbia, using a combined methodological approach that integrates structural equation modeling based on partial least squares (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results of PLS-SEM analysis confirmed theoretical model showing that studied organizational elements have significant positive impact on KM. The fsQCA results reveal that KM alone does not guarantee high performance, and it rather must be part of certain organizational configurations to be effective. In this sense, there are six configurations for successful KM implementation in Serbian companies, that results in high OP.\u003c/p\u003e\u003ch2\u003eImplications and recommendations:\u003c/h2\u003e \u003cp\u003eThe findings indicate that high organizational performance can be achieved through different KM-driven configurations, meaning that firms should avoid one-size-fits-all solutions and instead align KM practices with their dominant organizational drivers, depending on their organizational context and resource constraints.\u003c/p\u003e\u003ch2\u003eOriginality/value:\u003c/h2\u003e \u003cp\u003eBy integrating two complementary methodologies, the study provides novel insights from a transition economy and highlights the value of this approach for exploring both direct and complex causal relationships, offering results that can serve as a benchmark for other transition economies.\u003c/p\u003e","manuscriptTitle":"Knowledge Management drivers and Organizational Preformance: A PLS-SEM and FSQCA approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 22:18:30","doi":"10.21203/rs.3.rs-9152161/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6507f0ff-b2b5-46ce-bb11-248730ecd270","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T16:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 22:18:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9152161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9152161","identity":"rs-9152161","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}