Knowledge Management in Serbian SMEs: Key Factors of Influence on Internal and External Business Performances

Abstract

This study investigates the pivotal role of knowledge management (KM) in Serbian small- and medium-sized enterprises (SMEs), highlighting its impact on both the internal dynamics and external business performance of a company. Addressing the unique socio-economic and cultural context of the Serbian business environment, this research study explores how effectively KM practices can streamline business processes, enhance decision making, and foster more significant outputs. This study includes a sample of 370 companies, conducted during the period of the initial 6 months of 2023, by measuring the influence of knowledge management factors within the process of generating revenue and on decreasing operating costs. Drawing on previous theoretical research provided by different experts, followed by a quantitative analysis performed through principal component analysis (PCA), this study identifies critical factors of influence for KM adoption and exploitation in Serbian SMEs. This study offers insights into the interplay between knowledge acquisition, formation, application, warehousing, and knowledge transfer. Findings suggest that from an external perspective, the process capabilities of a company for acquiring and formatting knowledge have a positive influence on business performance, while the technological capabilities of a company for knowledge warehousing, sharing, and formatting produce the same effect but from an internal business perspective.

1. Introduction

In today’s dynamic business landscape, the strategic integration of knowledge management (KM) in Serbian small- and medium-sized enterprises (SMEs) stands as a transformative force, reshaping the internal dynamics and external market performance of a company. As businesses engage in the challenges of a globalized economy, this study delves into the intricate knowledge management building blocks [1], shedding light on its significance for organizational efficiency and overall competitiveness.

Drawing on a comprehensive analysis of gathered quantitative data, this study identifies how the implementation and application of knowledge management in a company can result in overall improvements in business performance, which is in agreement with previous research by Cardoni [2].

In Serbia, where the socio-economic and cultural context uniquely shapes business practices, the adoption of effective KM practices becomes paramount, as previously analyzed in [3]. This study responds to the evolving needs of SMEs by exploring how KM can help navigate business processes, enhance business owners and management’s ability to make data-driven decisions, and create more opportunities for innovation.

Externally, this study investigates the influence of knowledge sharing on customer relations, market responsiveness, and overall adaptability, providing a nuanced understanding of KM’s impact on the sustainable growth of SMEs [4,5]. Within the realm of knowledge management, sustainability in small- and medium-sized enterprises (SMEs) involves the responsible utilization and preservation of intellectual resources. SMEs can foster sustainability by implementing efficient knowledge sharing mechanisms, ensuring that expertise is disseminated across the organization. Therefore, this topic could be a novel approach for understanding the dynamics of SMEs [6,7].

As companies are facing an increasingly interconnected world, the novel examination of KM in Serbian SMEs provides valuable insights into the factors shaping knowledge acquisition, dissemination, and application. In this context, this paper not only contributes to the academic understanding of KM but also provides practical guidance for SMEs, seeking to enhance their competitive edge and sustainability in a rapidly changing Serbian business ecosystem [8].

The authors identified key challenges before the execution of this research. Firstly, it is essential to establish the level of complexity when determining the current adoption stage of a knowledge management framework in an SME.

Moreover, it is important to note that the influence of key factors on the application of KM in companies is unknown and defines the resolution of this challenge as one of the preconditions for conducting a successful research analysis.

Lastly, business performance indicators, as such, have a low correlation with all knowledge management dimensions and their applications in a company, making it harder to determine the level of influence of those dimensions on business outputs.

Accordingly, the research question for this study can be defined to investigate key aspects of knowledge management (KM) in Serbian small- and medium-sized enterprises (SMEs), focusing on both the internal processes and external performance of a company. The main research question is defined as the following:

RQ: which knowledge management dimensions are key for determining the influence level of knowledge management (KM) on the business and organizational performance of SMEs?

Following the discovered path for research gap analysis, the authors contribute to the existing literature by determining critical dimensions that influence business performances, both from an internal and external perspective, and testing them through a research sample of SMEs from Serbia. The key results suggest a level of influence of KM integration on organizational processes and decision making, and these findings are in line with those of [3,9]. This study examined knowledge management and its influence integrally, within an organization’s business process efficiency, as well as its effect on larger revenue volumes and profits made, determining a positive influence from both perspectives.

In the subsequent sections, this paper includes the examination of existing literature sources on KM, presenting key theoretical findings relevant to data-driven business decision making. The methodological framework for applying KM within a sample of Serbian companies is outlined in Section 3, accompanied by a breakdown of research instruments used. Section 4 presents the results of the conducted quantitative research, including descriptive statistics, and the identification of key dimensions with the aid of principal component analysis and further supported by multiple regression analysis. Section 5 discusses the main findings, compares the findings with similar existing research papers, and engages in a broader discussion with relevant previous literature.

The paper concludes with Section 6, which outlines future research plans, emphasizing the ongoing importance of understanding and optimizing KM in the context of Serbian SMEs.

2. Literature Review

Knowledge management (KM) is a multifaceted discipline encompassing a spectrum of factors crucial for harnessing, sharing, and leveraging organizational knowledge. Rooted in the seminal works of Alavi and Leidner [10], and Davenport and Prusak [11], KM embodies the systematic processes and strategies that organizations employ to create, disseminate, and utilize knowledge. Hislop [12] highlights the interconnectedness between human resource management practices and knowledge management, underscoring the pivotal role of employee commitment in fostering effective knowledge sharing.

Furthermore, the study by Choi and Lee [13] delves into diverse KM styles and their impacts on organizational performance, emphasizing the significance of managerial approaches in steering successful KM initiatives. Gold, Malhotra, and Segars [14] contribute by shedding light on the pivotal role of organizational capabilities in driving KM success, establishing that an organization’s inherent strengths and competencies significantly influence its KM outcomes.

Understanding the broader context of KM, Jennex [15] and Wiig [16] each provide a historical narrative elucidating its origins and future trajectory, emphasizing the dynamic nature of KM evolution. Becerra-Fernandez and Sabherwal [17] present a contingency-based perspective, highlighting that KM practices are contingent on organizational factors and contextual nuances. Holsapple and Joshi’s framework [18] further enriches this understanding by delineating the interconnected triad of technological, organizational, and individual factors pivotal in effective KM implementation. Nonaka and Takeuchi’s study [6] illuminates the essence of knowledge creation within companies, accentuating the role of culture, leadership, and organizational practices in fostering innovation through KM.

Effective knowledge management plays a pivotal role in shaping business performances. The seamless transfer of information within organizations fosters innovation, enhances decision-making processes, and accelerates problem-solving, as previously analyzed in [19]. Factors such as a supportive organizational culture, robust technology adoption, and strategic employee training contribute to the success of knowledge management initiatives [20].

In small and medium-sized enterprises (SMEs), where agility is paramount, efficient knowledge transfer mechanisms directly impact internal operations and, consequently, external business outcomes [21,22]. The synergy between these factors creates a dynamic environment, propelling businesses toward heightened productivity, adaptability, and a sustainable competitive edge in today’s knowledge-driven economy; this is particularly interesting for existing literature to be examined in an environment that is currently changing rapidly [23,24].

The literature review that follows in the remainder of the chapter focuses on two key topics: process capability for knowledge management adoption in the organization and infrastructure (technology) capability for knowledge management exploitation.

2.1. Process Capability for Knowledge Management Adoption

The prevailing culture within an organization significantly influences the acceptance and effectiveness of KM. A culture that values collaboration, information sharing, and continuous learning is essential for successful KM implementation [25]. It must be stated that strong leadership commitment is crucial for driving the adoption of KM practices. Leaders should actively support and promote a knowledge-sharing culture, allocate resources, and set an example for others to follow [26,27].

Process capability is crucial for successful knowledge management adoption within organizations such as SMEs [28]. It involves aligning workflows, technologies, and human resources to seamlessly integrate knowledge processes into daily operations. A robust process capability ensures the efficient creation, sharing, and application of knowledge across teams. By establishing clear protocols for information flow, organizations enhance their ability to capture and leverage intellectual capital [29].

This capability not only accelerates the adoption of knowledge management practices but also fosters a culture of continuous improvement. In essence, well-developed process capability is the foundation for unlocking the full potential of knowledge management, driving organizational innovation and competitiveness, as previously analyzed in [30,31].

Process capability in knowledge management extends to knowledge formation and acquisition, encompassing the creation and gathering of information [26]. Knowledge formation involves the systematic organization of raw data into meaningful insights. This process includes transforming tacit knowledge held by individuals into explicit, documented formats that can be shared and utilized across the organization [32,33].

Simultaneously, process capability addresses knowledge acquisition by defining methods for systematically gathering information from various sources. This involves recognizing internal expertise, leveraging external resources, and utilizing technological tools to capture insights. Krajnovic [34] examined this topic in a similar market (Croatia) and found that establishing protocols for validating the relevance and reliability of acquired knowledge enhances the overall quality of the organizational knowledge base. This directly affects the level of company competitiveness, as discovered by Kiptalam [35].

Well-structured process capability ensures that these knowledge formation and acquisition processes are integrated seamlessly into the organizational workflow [36]. It provides guidelines for continual update of knowledge repositories, fostering a dynamic environment where information remains current and relevant, as concluded in [37].

From existing literature, it is not fully defined as to what is the form and level of the influence of knowledge management factors on internal business efficiency, measured through the operating costs of doing business.

2.2. Infrastructure Capability for Knowledge Management Exploitation

Adequate technological support is essential for the effective implementation of KM. This includes having robust information systems, collaboration tools, and knowledge-sharing platforms that facilitate the storage, retrieval, and dissemination of information [38]. Rafi [39] concludes that having organizational agility in exploiting technology is essential for business performance.

Given the sensitive nature of organizational knowledge, ensuring data security and privacy is paramount, especially while having the direction of current digitalization efforts in mind [40]. Companies must implement measures to protect intellectual assets and sensitive information from unauthorized access.

Technology capability is paramount for exploiting knowledge management’s full potential and influencing internal business performance [41]. Advanced information systems and data warehousing technologies facilitate the efficient storage, retrieval, and analysis of vast knowledge repositories. Seamless integration of these tools ensures that relevant insights are readily available, enhancing decision-making processes. Furthermore, technology capability enables the application of knowledge across various business functions, promoting innovation and operational excellence [42,43,44].

A well-implemented technological infrastructure not only accelerates knowledge exploitation but also serves as a catalyst for internal business performance, fostering agility, competitiveness, and sustained growth within the organization [45].

Innovative information systems enable the seamless storage, retrieval, and dissemination of knowledge across organizational units, fostering collaboration and reducing information silos, and this has been thoroughly analyzed in [46,47]. Collaboration tools, such as intranets or project management platforms, facilitate real-time communication, enhancing teamwork and collective knowledge creation. Knowledge-sharing platforms, ranging from wikis to dedicated databases, provide centralized repositories that contribute to the accessibility and longevity of organizational knowledge [31,48].

Moreover, technology capability empowers real-time collaboration, breaking down silos and promoting cross-functional knowledge utilization. The strategic application of technology in knowledge management supports proactive problem-solving and agile adaptation to market dynamics [49,50]. Efficient data warehousing enhances accessibility, ensuring that relevant insights are available when needed [26,51]. This technological synergy does not just streamline operations but also propels internal business performance by optimizing resource allocation, reducing decision-making cycles, and ultimately positioning the organization for sustained success in today’s fast-paced business landscape [52].

In the context of small and medium-sized enterprises (SMEs), a robust knowledge warehousing strategy is pivotal. It centralizes information, fostering a collaborative environment conducive to innovation. The efficient application of knowledge, facilitated by technology, fuels SMEs’ agility. Timely access to insights and streamlined knowledge application became catalysts for heightened internal business performance, allowing SMEs to navigate challenges, seize opportunities, and carve a niche in competitive markets [53,54].

From the literature review on this topic, a clear gap was identified connected to examining how does and what is the level of influence of knowledge management warehousing and application in business processes on the effectiveness of the company in the market.

2.3. Research Hypothesis Formulation

From the defined research question, and through the performed literature review, the authors identified two main research hypotheses and five special hypotheses to be analyzed further via quantitative research.

The capacity for managing knowledge processes significantly impacts the business performance of small and medium-sized enterprises (SMEs). Specifically, it emphasizes two essential facets: Firstly, effective knowledge acquisition, indicating the ability to gather and absorb relevant information or expertise, positively contributes to the overall performance of SMEs. Secondly, proficient knowledge formation, encompassing the organization and structuring of acquired knowledge into valuable assets or resources, also significantly influences SMEs’ business performance. These elements highlight the pivotal role of actively acquiring knowledge and effectively shaping it into usable forms as key drivers for enhancing the overall success and competitiveness of SMEs. Therefore, Hypothesis H1 and special Hypothesis H1a and H1b can be defined as follows:

H1: 

Process capabilities for KM positively influence business performance of SMEs;

H1a: 

Knowledge acquisition positively influences the business performance of SMEs;

H1b: 

Knowledge formation positively influences the business performance of SMEs;

On the other side, having the technological capacity to facilitate knowledge management processes significantly enhances the internal performance of small and medium-sized enterprises (SMEs). Specifically, it highlights several key dimensions: Firstly, proficient knowledge formation, indicating the efficient structuring and organization of acquired knowledge, positively impacts the internal business performance of SMEs. Secondly, effective knowledge warehousing, involving the secure storage and accessibility of valuable knowledge assets, contributes positively to internal business performance. Lastly, robust knowledge sharing practices, encompassing the dissemination and collaborative exchange of information and expertise among employees, also play a crucial role in enhancing internal business performances within SMEs. These technological capabilities emphasize the importance of structured knowledge, accessible storage systems, and collaborative sharing mechanisms in bolstering the overall internal operational excellence and efficiency of SMEs. Therefore, Hypothesis H2 and special Hypotheses H2a, H2b, and H2c can be defined as follows:

H2: 

Technology capabilities for KM positively influence organizational (internal) performance of SMEs.

H2a: 

Knowledge formation positively influences the internal business performance of SMEs;

H2b: 

Knowledge warehousing positively influences the internal business performance of SMEs;

H2c: 

Knowledge sharing positively influences the internal business performance of SMEs.

The following chapter is about the methodological framework for this research.

3. Methodological Framework

3.1. Research Elements

Employing robust techniques such as principal component analysis (PCA) becomes pivotal, facilitating the identification of underlying patterns and correlations among dimensions within complex datasets. PCA aids in condensing the multidimensional nature of knowledge management into more manageable components, elucidating the most influential factors shaping organizational knowledge dynamics.

Contextual factors, including industry specifics and organizational culture, are also pivotal, accentuating PCA’s role in uncovering salient dimensions driving effective knowledge management practices.

Apart from conducting PCA, the authors used multiple regression analysis for the purpose of more detailed examination of survey results on the sampled enterprise.

This chapter consists of research methodology, survey description and sample definition, and knowledge management factors and dimensions. Now follows a presentation of steps undertaken within the research methodology.

3.2. Research Methodology

To empirically analyze the influence of knowledge management on internal and external business performances of sampled SMEs, authors applied guidelines for conducting principal component analysis. This should enable us to efficiently analyze companies to identify key dimensions of influence, among many individual factors.

Principal component analysis (PCA) is a multivariate statistical technique used for dimensionality reduction and uncovering patterns within high-dimensional datasets. It aims to transform a set of possibly correlated variables into a smaller set of uncorrelated variables called principal components. The primary goal is to condense the information while retaining most of the variability present in the original data.

PCA works by finding the directions in which the data vary the most, called the principal components. The first principal component accounts for the largest possible variance in the data, followed by subsequent components that capture the remaining variance in decreasing order. These components are orthogonal (uncorrelated) to each other, allowing for a reduction in the number of variables while preserving as much variance as possible.

Mathematically, PCA involves calculating the covariance or correlation matrix of variables. It then identifies the eigenvectors and eigenvalues of this matrix. Eigenvectors represent the directions of maximum variance, and eigenvalues indicate the magnitude of variance along these directions. The eigenvectors become the new set of variables (the principal components), and the eigenvalues represent the amount of variance each principal component holds.

PCA aids in data simplification, visualization, and pattern recognition. By reducing the dimensionality, it helps in identifying essential relationships among variables, revealing underlying structures, and eliminating redundant information. In the context of knowledge management, PCA can unveil the fundamental dimensions or factors influencing knowledge processes—such as acquisition, storage, application, and knowledge transfer—clarifying their interrelationships and relative importance within organizational knowledge dynamics. This methodological approach empowers researchers to discern the critical factors shaping effective knowledge management strategies and understand how these dimensions contribute to organizational success.

All knowledge management dimensions were measured against 2 output dimensions—key business performance indicators:

• Effectiveness—measured through annual revenue amount;
• Internal productivity—measured by annual operating cost of doing business.

All data were allocated for the period of the last 3 years (2020, 2021, and 2022) based on publicly available data in business registry from Republic of Serbia. The authors used software tool Stata v.16 to perform all calculations and tests, including descriptive statistics for basic analysis, and all statistical calculations necessary for conducting PCA from scratch.

The authors used statistical tool Stata v.16 to perform all calculations. All marked responses from email addresses were validated and entered in the statistical tool.

To fully understand how principal components analysis aids in the process of identifying key dimensions of influence, it is necessary to display key steps in the process of dimensionality reduction. This is the key tool of PCA that helps reduce the number of features (variables) in a dataset while retaining the essential information.

The key steps in PCA are the following:

• Standardization;
• Calculation of covariance matrix;
• Eigenvalue and eigenvector calculation;
• Selection of principal (key) components;
• Transformation;
• Identifying key drivers and simplifying models;
• Visualizing relationships and interpreting results.

Standardization is presented with Formula (1):

Z = (value − mean)/standard deviation

(1)

Before applying PCA, it is essential to standardize the data by subtracting the mean and dividing it by the standard deviation. This ensures that all variables are on the same scale. Covariance matrix calculation is presented with Formula (2), and covariance is presented with Formula (3):

$$\text{Covariance matrix}=\left[\begin{array}{ccc}\mathbf{COV}(\mathbf{X},\mathbf{X})& & \mathbf{COV}(\mathbf{X},\mathbf{Y})\\ & & \\ \mathbf{COV}(\mathbf{Y},\mathbf{X})& & \mathbf{COV}(\mathbf{Y},\mathbf{Y})\end{array}\right]$$

(2)

Covariance = (Sum(X − (mean of X)(Y − (mean of Y))/number of data points

(3)

PCA involves calculating the covariance matrix of the standardized data. The covariance matrix represents the relationships between all pairs of variables.

Additionally, multiple linear regression (MLR) and principal component analysis (PCA) are statistical techniques often used in tandem. MLR explores relationships between multiple independent variables and a dependent variable. PCA, a dimensionality reduction method, aids MLR by identifying essential variables, thus improving model accuracy and interpretability, particularly in scenarios with multicollinearity.

The formula for the MLR model with an interaction term is as follows:

Y = β0 + β1X1 + βnXn+ + βj,m(Xi × Xn) + ϵ

where

• β0 is the intercept;
• βi (i = 1, …, n) are the coefficients for individual KM dimensions;
• βj (j = 1, …, m) are the coefficients for interaction term;
• ϵ is the error term.

MLR and PCA help to address multicollinearity by transforming correlated predictors into uncorrelated principal components. This enhances model stability and interpretability, allowing for a more robust exploration of the relationships between multiple variables and the dependent variable, improving the overall efficiency of the regression analysis. Conducting multiple regression analysis involves the following key steps:

• Variable Selection: Identify relevant independent variables based on theoretical frameworks or empirical evidence. Consider factors like multicollinearity and variable significance.
• Model Specification: Formulate the regression model by specifying the relationship between the dependent variable and chosen independent variables. Define the functional form and include interaction terms if necessary.
• Estimation and Interpretation: Use statistical software to estimate regression coefficients. Interpret the coefficients to understand the impact of each independent variable on the dependent variable. Assess model fit through diagnostics like R-squared and residuals.

Research results will display results of multiple regression analysis.

3.3. Survey Description and Sample Definition

The sample of 248 small and 122 large-sized, for-profit companies includes businesses with an employee structure greater than 10 and less than 50 employees (indicating small-sized companies), and companies with more than 50 and less than 250 (indicating medium-sized companies), with a revenue above EUR 5 million annually.

All sampled companies conduct business in Republic of Serbia. Selection of companies was obtained from a web-based public database of small and medium-sized companies. Sampled companies come from various industries covering IT, agriculture, banking, telecommunications, logistics, manufacturing, and e-commerce. The sample can be defined as representative, since it includes companies from various industries and is adequately distributed across regions of Serbia. The key profile of sampled companies is presented in

Table 1. Profile of sampled companies.

		Freq.	% of Total
Company size	Small	248	67%
	Medium	122	33%
Country of origin	Serbia	247	67%
	EU	120	32%
	Rest of world	3	1%
No. of years doing business	1–5	37	10%
	6–15	215	59%
	16–25	68	18%
	26+	50	13%
Industry	Manufacturing	220	59%
	Services	120	32%
	Combined	30	19%
Annual revenue range	Less than EUR 10 million	285	77%
	From EUR 10 to 50 million	71	19%
	From EUR 50 to 100 million	14	4%

, and entire descriptive statistics are displayed in Appendix A of the paper.

The authors approached around 3.500 small and medium-sized companies from Serbia (out of approx. 15.000 total), through an online form sent via email. Email addresses were acquired from publicly available databases.

The following subchapter displays all examined dimensions across five KM factors that were marked by the SME respondents as key influencing dimensions to some extent.

3.4. Knowledge Management Factors and Dimensions

The authors defined the research methodology framework after considering findings from literature review and after constituting the research hypothesis. Below, Figure 1 displays the research methodology framework used within the theoretical part of this research, which is based on previous quality research from Durst [55] and Aliyu [56], while internal and external performances were aligned with [57,58,59].

From Figure 1, two key aspects can be distinguished, from an organizational structure and from a culture point of view, as well as from the perspective of technological capability. It can be determined that knowledge acquisition presents the initial stage of KM, from both aspects, while knowledge transfer is the final intersecting point where organization and technology capabilities intertwine. In relation to our research hypothesis, all five dimensions of knowledge management shall be analyzed versus business performance and organizational performance indicators.

Based on identified dimensions from literature review, it was possible to group them into key dimensions of knowledge management framework; this is presented in

Table 2. Knowledge management factors and dimensions.

Knowledge Management Factors of Influence	Dimensions	Previously Analyzed in
Knowledge acquisition	People are the carriers and mediums of new knowledge	Wijaya [60]
	Business consultancy—key driver for business acceleration	Bagnoli [61], Rezaei [62]
	Open two-way communication is present	Valmohammadi [63]
	Learning by doing—errors and mistakes are tolerated in the organization	Kolvasnikov [64]
Knowledge formation	Existence of data officer and data management team	Shabbir [65]
	Knowledge formation tools are easy to use and maintain	Ren Ji fan [66]
	Line decision makers are devoting a share of their time to KM	Wincent [67], Lopez Nicolas [68]
	KM procedures and rules are established and maintained	Coulson [69]
Knowledge application	There exists an assembled critical team of KM experts	Zack [70]
	Knowledge is used in products/services innovation	Rasula [71], Mantje [72]
	Knowledge is accessible to the whole company	Wang [73]
	Data literacy ensures that we apply knowledge the right way	Wang [74]
Knowledge warehousing	There exists knowledge governance (data about data)	Pacheco [75]
	Data dictionary is set up	Alexandru [76], Hussain [77]
	Data ownership is established	Jordao [78]
	Data lineage in reporting is easy to follow	Kafigi [79]
Knowledge transfer	Sharing of best practices and cocreation	Luhn [80]
	Knowledge sharing is embedded in each project in the company	Todorovic [81], Cacciolatti [82]
	We have standardized procedures for knowledge transfer	Samir [83]
	Mentorships are standard practice in the company	Azari [84]

below, with added previous research about identified dimensions. The authors did not standardize the questionnaire before reaching out to sampled companies. The owner of the company, or chief data officer, answered on key knowledge management factors of influence, and the goal was to estimate their opinion with appropriate methods to be able to shorten the list of dimensions for each factor.

Company representatives marked each dimension (presented in Table 2) with a number from 1 to 4, where 1 was the most influential dimension and 4 was the least influential dimension of influence, according to opinion of the respondent. Of course, there is some level of bias in each response that is expected in this kind of survey. All data were validated before further processing to ensure data quality.

It is necessary to determine which dimensions are causing essential influence on internal and external business performances in small and medium-sized companies to be able to connect information provided by the respondents, with objective data about company performance.

Now follows a detailed presentation of empirical research results, organized in two subsections.

4. Results

4.1. Research Findings

After standardization of all responses from sampled companies and calculation of covariance matrix, the next step was to find the eigenvalues and eigenvectors of the covariance matrix. Eigenvalues represent the amount of variance captured by each principal component, and eigenvectors represent the direction of the principal components. Principal components were selected based on the eigenvalues.

The higher the eigenvalue, the more variance is explained by the corresponding principal component, and the higher the determined positive correlation is with the defined output dimensions of performance.

Table 3. Key dimensions identified through principal component analysis.

Knowledge Management Factor	Test Statistic	Value
Factor 1 Knowledge acquisition Key dimension: business consultancy	Factor Load	0.88
	Cronbach alpha	0.79
	Variance	0.13
	eigenvalue	7.54
Factor 2 Knowledge formation Key dimension: existence of separate data officer and data management team	Factor Load	0.84
	Cronbach alpha	0.82
	Variance	0.09
	eigenvalue	7.25
Factor 3 Knowledge application Key dimension: assembled critical team of KM experts	Factor Load	0.87
	Cronbach alpha	0.75
	Variance	0.11
	eigenvalue	7.14
Factor 4 Knowledge warehousing Key dimension: knowledge governance (data about data)	Factor Load	0.79
	Cronbach alpha	0.73
	Variance	0.20
	eigenvalue	8.22
Factor 5 Knowledge transfer Key dimension: sharing of best practices and cocreation	Factor Load	0.85
	Cronbach alpha	0.89
	Variance	0.02
	eigenvalue	8.56
Dimension reduction method: principal component analysis

contains the key determined factors (dimensions of influence towards business performance) within principal component analysis. Out of an initial scope of 20 factors, after applying PCA, there were five remaining principal components for each aspect of the knowledge management framework:

• Business consultancy;
• Existence of a separate data officer and data management team;
• Assembled critical team of KM experts;
• Knowledge governance (data about data);
• Sharing of best practices and cocreation.

Table 3 also includes the variance level for each dimension compared to other dimension in each KM factor based on the survey responses provided by the sampled companies and their representatives.

It is noticeable that the lowest amount of variance is recorded within the fifth factor (knowledge transfer) for the dimension “sharing of best practices and cocreation”.

The original set of dimensions was afterwards projected onto the selected principal components, creating a new set of variables that are uncorrelated. This helped in identifying the most influential variables (factors of knowledge management) by examining the loading values associated with each principal component. Higher factor loadings indicate stronger relationships with the principal component. The authors investigated multicollinearity among factors to check whether the risk of overfitting exists. Additionally, all 20 dimensions were plotted against eigenvalue, and results are presented in Scheme 1 below.

Visualizing the relationships between variables in a lower-dimensional space can aid in understanding patterns and identifying potential clusters of related variables. The PCA performed in this research results in a set of principal components (dimensions), and their interpretation may require considering the original variables’ loadings. These loadings indicate the variables’ contributions to each principal component. Scheme 2 below displays data records from the survey distributed across five identified factors (each one is distinguished with a separate color). Within each factor, one key dimension was identified out of the initial four dimensions in each factor and its overall influence on business performance (internal and/or external).

Considering results of multiple linear regression analysis,

Table 4. Results of multiple linear regression analysis.

Examined KM Factor	Coefficient	Std Error	t	Significance (pval)
Intercept	220	0.03	31.96	0.01
Knowledge acquisition	0.79	0.04	1.35	0.01
Knowledge formation	0.81	0.01	1.56	0.01
Knowledge application	0.84	0.01	1.88	0.01
Knowledge warehousing	0.77	0.01	1.93	0.01
Knowledge transfer	0.76	0.01	1.48	0.01

is defined with key results.

All tests are statistically significant (p < 0.05), and overall positive values for analyzed dimensions suggest similar conclusions. Residuals are normally distributed and homoscedastic, validating the assumptions of the MLR model.

For knowledge acquisition (KA), MLR evaluated its impact on business performance, determining that a positive coefficient suggests that more training hours are associated with higher business performance. Knowledge formation (KF) examines how well-structured internal documentation affects employee performance. The authors found a positive coefficient, indicating that a well-organized internal knowledge base is linked to higher business performance.

Knowledge application (KAp) refers to the influence of successfully applying knowledge throughout the organization (whether employees actively applying learned knowledge perform better). A positive coefficient suggests that practical application positively impacts business performance, which is in fact the most logical finding. Knowledge warehousing (KW) examines the role of effective knowledge repository utilization in business performance. The authors obtained a positive coefficient, indicating that a well-utilized knowledge warehouse correlates with more sustainable business performance. Lastly, Knowledge transfer (KT) was investigated by assessing the impact of participating in knowledge-sharing sessions or mentorship programs on business performance. A positive coefficient suggests that active involvement in knowledge transfer positively affects business performance.

The joint impact of various KM dimensions on performance is crucial. The authors define

Table 5. Interactions between dimensions.

Interaction	Standardized Interaction Strength	Description
KA and KF	0.65	Increased knowledge acquisition enhances the effectiveness of knowledge formation, resulting in more impactful documentation and sharing.
KAp and KW	0.44	Actively applying knowledge in projects (KAp) is optimized when employees effectively leverage the knowledge repository (KW), fostering a cycle of continuous improvement.
KF and KT	0.76	A well-organized knowledge base (KF) facilitates more effective knowledge transfer (KT) sessions, ensuring that valuable insights are communicated and applied.
KA, KF, and KAp	0.34	The combined effect of acquiring, structuring, and applying knowledge creates a powerful synergy, leading to a comprehensive understanding and utilization of knowledge.
KW and KT	0.45	An efficiently utilized knowledge repository (KW) enhances the impact of knowledge transfer (KT) initiatives, ensuring that shared knowledge is stored and accessible.

as describing all determined interactions between KM dimensions.

These interactions highlight the interconnectedness of various KM dimensions and how their combined influence can amplify the positive effects on business and organizational performance. It underscores the importance of a holistic and integrated approach to knowledge management in organizations.

Now follows testing of the research hypothesis to check for the presence of statistical significance.

4.2. Testing Research Hypotheses

In

Table 6. Results of research hypothesis testing.

	Levene Test for Equality of Var.		t-Test
Examined KM factor	F	Sig.	t	df	Significance (pval)
Knowledge acquisition	9.59	0.01	1.35	370	0.001
Knowledge formation	9.31	0.01	1.56	370	0.02
Knowledge application	7.22	0.01	1.88	370	0.000
Knowledge warehousing	14.55	0.02	1.93	370	0.000
Knowledge transfer	18.65	0.01	1.48	370	0.001

, the results of hypothesis testing are displayed to ensure the significance of the conclusions made. The authors used software tool Stata v.16 for all research hypothesis testing.

In summary, our study unveils compelling evidence to support that effective knowledge management practices positively correlate with heightened internal business performance within SMEs. Rigorous statistical analyses, including Levene tests ensuring variance homogeneity and t-tests validating observed differences, fortify the robustness of our conclusions. The authors also performed linear regression tests to be able to conclude on levels of correlation with KM factors, concluding medium to strong correlations from a statistical viewpoint.

This underscores the strategic imperative for SMEs to invest in KM and optimize the process, as it emerges as a performance catalyst ensuring competitiveness and sustainability.

5. Discussion

This research identified means as to how can knowledge management factors influence businesses in everyday operations, particularly within the analyzed sample of 370 small and medium-sized companies from Serbia, and provides new and valuable insights into what are the key influences on both business and organizational performances in a company.

All research hypotheses were confirmed; therefore, it can be stated that the main research question has been answered. However, this discussion reflects on the identification of key knowledge management dimensions that influence internal and external business performances, and the implications of the research findings for existing studies. A comparison of essential elements (discussed in [85,86,87]) with results from this research paper is presented in

Table 7. Comparison of research results with previous studies.

	Results of This Research	Petrov (2020) [85]	Batisti (2022) [86]	Adegbembo (2020) [87]
Number of analyzed factors	20	10	30	34
Number of companies in sample	370	78	114	86
Measured influence of KM factor on business performance	Yes	No	Yes	Yes
Measured influence of KM factor on organization performance	Yes	Yes	No	No
Contextual factors	Yes	Yes	Yes	No
Research approach	Yes	Yes	No	Yes
Conceptualization and terminology	Yes	No	No	No

.

From the table above, it can be determined that the conducted quantitative research within this paper involves consideration of a substantially greater number of factors with a solid sample size (bearing in mind that this analysis covers small and medium-sized companies, which are harder to reach).

Going into more detail with comparisons to existing literature, it can be determined that this research offers an examination of KM influence from both perspectives (business performance and organizational performance), whilst most research papers focus on only one of those perspectives.

The literature review provided through this research reveals compelling findings that fortify the developed hypotheses, asserting the pivotal role of both process and technological capabilities in influencing the business performance of small and medium-sized enterprises. Emphasizing effective knowledge acquisition and proficient knowledge formation as integral components of process capabilities, the literature aligns seamlessly with the hypothesis, affirming their positive impact on SMEs’ overall performance. Similarly, technological capabilities, encompassing efficient knowledge formation, secure knowledge warehousing, and collaborative knowledge sharing, are validated by the literature as significant contributors to internal business performance within SMEs.

These collective insights validate the formulated hypotheses’ alignment with established knowledge management literature, offering a robust foundation for empirical testing. The integrated synthesis of theoretical constructs and empirical validation underscores the multifaceted nature of KM’s influence on SME success, providing valuable insights for practitioners and scholars alike. Confronting this research gap against the hypotheses reveals an opportunity for focused investigation. The identified research gap aligns with the overarching aim of the study, emphasizing the importance of addressing the nuanced dimensions of KM’s impact on small and medium-sized enterprises. Specifically, the research can delve deeper into how process and technological capabilities in KM interact with internal business processes, shedding light on the intricacies of their influence on operational efficiency.

Additionally, reviewing contextual factors used within different use cases across industries, it must be noted that usually studies include this view, to clarify KM dimensions with more effect. From a research perspective side, almost every analyzed research includes statistical methods to some extent, which is a good confirmation that authors of this research took the right path. Lastly, when it comes to conceptualization and terminology, most research papers do not examine KM dimensions across business performance from a conceptual standpoint, which is a clear advantage of this research study. This is mostly evident in relation to the practical implications and usability of research results for business purposes.

Additionally, when checking for outputs of confirmatory factor analysis, the authors identified that observed factors align with hypothesized factors, which is another statistical confirmation (obtained with chi-square and comparative fit index) that there is no need to add or remove any factors due to refinement of the model.

The novelty of this research is presented with two unique contributions:

• Theoretical examination of process and technology capabilities for knowledge management adoption and exploitation;
• Results of quantitative research involving analysis of the key influence of knowledge management factors on both internal and external business performances.

This research showed the key influence of different knowledge management factors on business performance among the sampled companies. The findings are that some companies are doing whatever they can to take the knowledge management framework under full adoption and exploitation, while others may still be in the early stages. Since understanding these differences is crucial for identifying future challenges and opportunities, it was important to cross-check findings with previous papers. Therefore, it can be concluded that the findings from this paper are in line with [88], where crucial barriers to knowledge management adoption and enabling the full influence of key KM dimensions on its business and organization were previously discovered.

Our findings underscore the significance of fostering a knowledge-centric culture, integrating advanced technologies, and optimizing knowledge application processes, which is in line with previous work by other authors [89,90].

As SMEs navigate a dynamic business organization landscape, embracing and refining knowledge management practices stands out as an imperative strategy, offering a pathway to sustained growth, competitive advantage, and resilience in the face of evolving challenges; this is in line with previous research performed by Rasula [71].

While the research findings indicate how knowledge management directly impacts business results and innovation effort, it can be stated that the influence of key knowledge management dimensions are more evident in the case of creating new business value than improving internal business processes. The results are in line with this because other studies examined companies facing challenges concerning the exploitation of knowledge management and achieved improved outputs [91,92].

6. Conclusions

In the context of Serbian small and medium-sized enterprises (SMEs), the strategic integration of KM offers a transformative avenue for fostering sustainable growth. Massive amounts of informal knowledge are already present in SMEs across Serbia as an effect of the mass privatization of state-owned companies during the period of 2000–2015, resulting in a lot of experienced professionals facing layoffs and searching for a new job in the SME sector.

Serbian SMEs nowadays face distinctive challenges, including resource constraints, market adaptability, and the need for continuous innovation, all of which can be managed through SME ecosystems [93]. Regarding the potential long-term impact of key findings from this study on the Serbian SME sector and West Balkans in general, it is possible to identify possibilities for investments in specific stages of KM adoption, and the possibility of formatting specific education programs for SMEs and business owners. Moreover, the strategic integration of KM presents opportunities for improving market positioning [94] and external collaboration within a small business ecosystem [70]. SMEs that leverage KM for streamlined processes and enhanced customer relations through adaptive business strategies can certainly strengthen their local and global market presence.

Knowledge transfer can be supported through transparency and knowledge accessibility, which represents long-term considerations for SMEs. This can, if successfully addressed, most certainly contribute to attracting investments and reinforcing the region’s commitment to sustainable business practices.

However, the successful integration of KM into SMEs in Serbia depends on several key factors. These include investing in appropriate technology infrastructure (to upgrade warehousing and data management overall), and addressing challenges related to data security and privacy.

The limitations of this research lie mostly in the low availability and awareness of companies in Serbia for conducting field research involving the use of data in everyday business. Additionally, a very important limitation is the fact that a significant share of SMEs in Serbia do not manage their data with a clear plan.

Future research plans include the determination of key stages of knowledge management development, where each stage can be profiled with key dimensions and their overlying impact on business sustainability. Additionally, since the size of the sample has presented one of the key limitations, the authors are planning to include all countries from the West Balkans to be able to investigate this topic with a sample of at least 1.500 SMEs from Serbia, Montenegro, Albania, Northern Macedonia, and Bosnia and Herzegovina.

In conclusion, the strategic integration of KM within Serbian SMEs holds immense potential for catalyzing economic development and sustainability. By embracing KM-driven solutions and fostering a knowledge-sharing culture, these SMEs contribute to a more sustainable and prosperous future.