Influences of the Different Organizational Performances on Application and Effects of Lean: Case of Serbian Food Companies

Abstract

This study examines the influences of various organizational performance factors on the application of Lean tools and the effects of Lean methodology implementation. Although Lean management has been widely studied, empirical evidence on the combined influence of internal organizational capabilities and external environmental pressures on Lean adoption and outcomes in transition economies remains limited. In particular, the relative importance of internal resources and competitive pressures in shaping Lean implementation results has not been sufficiently explored. Therefore, this study aims to analyze how different organizational and environmental factors influence both the application of Lean tools and the effects of Lean methodology implementation. The independent variables considered include: business performance, organizational culture, company size, technical infrastructure and resources, education and competence of employees, training for Lean methodology, management support, competitive pressure and motivation to reduce costs, degree of innovation in the company, the role of the Lean concept in strategic planning, years of company existence, and years of Lean tool implementation. The research was conducted among food industry companies in Serbia, and a total of 183 valid questionnaires were collected. The results indicate that the application of Lean tools is most strongly influenced by training for Lean methodology, followed by business performance and company size. In contrast, the effects of Lean methodology implementation are primarily affected by competitive pressure and motivation to reduce costs, as well as management support. Furthermore, the analysis shows that Lean application and Lean outcomes function as two distinct dimensions: companies may apply Lean tools without achieving significant effects if managerial support or competitive pressure is insufficient. Conversely, firms with strong competitive drivers and committed management achieve noticeably higher performance improvements even with moderate levels of Lean tool adoption. Overall, the findings suggest that the application of Lean tools largely depends on the company’s internal resources, such as employee knowledge and training, business strength, and scale of operations, while the success and outcomes of Lean implementation are more strongly driven by external competitive pressures and the degree of managerial understanding and support. By distinguishing between the determinants of Lean tool adoption and the determinants of Lean implementation outcomes, this study contributes to a clearer understanding of Lean effectiveness in the context of transition economies.

1. Introduction

Lean methodology, rooted in the Toyota Production System, has evolved into a widely adopted framework for achieving operational excellence through waste elimination, continuous improvement, and customer value focus. While its benefits are well-documented across manufacturing and services, the food industry poses distinctive constraints—perishability, stringent food safety regulation, seasonality, and fragmented supply chains—that shape how Lean tools are selected and how much impact they produce (e.g., recent reviews and sectoral studies). These studies consistently report gains in productivity, cycle time, and defect reduction when Lean is adapted to food-specific contexts [1].

Despite the breadth of Lean research, much of the empirical evidence still centers on advanced economies and traditional sectors (automotive, electronics). Emerging work targeting food processing in developing or transitional contexts shows promising results yet remains fragmented. For instance, a 2025 study on Tanzanian food and beverage SMEs finds that core Lean tools (5S, VSM, JIT) are strong predictors of output improvements when paired with structured assessment frameworks [2]. Similarly, case research in Indian food processing reports measurable savings and quality gains once Lean is tailored to packaging and hygiene-critical operations [3].

A second gap concerns the role of organizational performances as predictors of Lean application and outcomes. Prior studies highlight that Lean’s effects are not technology-driven alone. They depend on contextual conditions such as organizational culture, leadership support, employee competence, infrastructure, innovation, firm size, and strategic orientation. Culture, in particular, has been shown to moderate how Lean practices translate into operational performance—strong improvement-oriented cultures amplify Lean gains, whereas misaligned cultures dampen them [4,5]. Yet, the combined impact of these organizational dimensions is seldom modeled jointly for the food sector.

In Southeast Europe, evidence is growing but still limited and scattered. Serbian case studies document tangible Lean effects in confectionery and other manufacturing settings, while multi-case analyses examine success factors for sustainable Lean and changes in performance measurement after adoption [6,7,8]. Bosnia and Herzegovina case work shows how Value Stream Mapping exposes bottlenecks and guides improvement trajectories, and Croatian SME studies assess Lean knowledge and implementation levels—together they indicate the early-stage but rising Lean maturity in the region [5,9]. Recent research on food processing in Kosovo and North Macedonia further underscores Lean’s dual link to operational efficiency and sustainability goals, but also notes adoption challenges typical for smaller economies [10].

Against this backdrop, Serbia’s food industry remains under-represented in quantitative studies that jointly evaluate how internal organizational performances shape both the application of Lean tools and the effects of Lean implementation. Addressing this gap is practically relevant for managers operating under competitive pressure and resource constraints, and theoretically relevant for disentangling the pathways through which organizational conditions enable (or hinder) Lean success in transitional economies.

This paper examines the influence of distinct organizational performance dimensions—business performance, organizational culture, company size, technical infrastructure, employee competences, Lean training, management support, competitive pressure and cost motivation, innovation degree, strategic role of Lean, firm age, and Lean tenure—on (a) the Application of Lean Tools (ALT) and (b) the Effects of Lean Implementation (ELI) in Serbian food companies. Using survey data from 183 firms and correlation and multiple-regression techniques, the study contributes by providing integrated, multi-predictor evidence for a food-sector context in a transitional economy, and identifying the most influential organizational levers managers can strengthen to accelerate Lean application and outcomes. The remainder of the paper is structured as follows: Section 2 develops the theoretical background and hypotheses, Section 3 outlines the research method, Section 4 presents the results, Section 5 discusses implications and limitations, Section 6 concludes with key findings and avenues for future work.

2. Theory and Hypotheses

Lean implementation and its outcomes are influenced by a variety of organizational and contextual factors. Prior research has identified several internal performance dimensions that play a significant role in determining the extent to which Lean tools are adopted and how effectively they deliver results. These dimensions provide the theoretical foundation for the development of the hypotheses in this study.

2.1. Business Performance

Business Performance (BP) reflects the current market and financial position of a company. Firms with stronger performance are more likely to allocate resources to continuous improvement programs and sustain Lean initiatives over time. Higher business performance often provides both the strategic motivation and the financial means necessary for systematic Lean adoption and for achieving measurable operational improvements [3].

Business performance is one of the central dimensions examined in the context of Lean implementation, as improvements in operational efficiency, quality, and cost reduction are among the primary goals of Lean initiatives. Prior studies have emphasized that the adoption of Lean tools often leads to measurable improvements in performance indicators such as productivity, lead time reduction, defect rates, customer satisfaction, and financial outcomes [4,11,12]. These effects have been confirmed in various industries, including manufacturing and services, but research focusing on the food industry in transitional economies, such as Serbia, remains limited.

According to [6,7], Lean principles can improve production flow, hygiene control, and resource utilization in food processing, but their implementation is often partial and inconsistent. International studies (e.g., refs. [13,14]) underline that performance gains depend on both the extent and quality of Lean tool application as well as the organizational readiness to sustain improvements. This implies that business performance is not only a result variable but also a key driver influencing decisions regarding Lean implementation.

In transitional contexts, firms often face additional structural challenges such as limited financial resources, outdated equipment, and unstable market conditions [15]. These factors affect both baseline performance levels and the ability to fully implement Lean practices. Examining the relationship between organizational performance and Lean adoption in such settings provides insights into whether companies with stronger performance are more likely to invest in Lean, or whether Lean itself is the mechanism that drives performance improvement.

Based on the literature, we hypothesize that higher levels of business performance are associated with greater application of Lean tools and more pronounced effects of Lean implementation.

2.2. Organizational Culture

Organizational Culture (OC) is widely recognized as a critical enabler of Lean implementation. Empirical studies show that organizational culture can moderate the relationship between Lean practices and performance outcomes [4,5].

Organizational culture represents a set of shared values, norms, and behaviors that shape how employees perceive and engage with change processes inside an organization. In the context of Lean implementation, culture plays a critical role because Lean is not simply a collection of tools, but a philosophy that requires long-term behavioral alignment across all organizational levels [16,17]. A culture that supports continuous improvement, teamwork, openness to feedback, and disciplined execution provides the necessary foundation for Lean initiatives to take root and deliver sustainable results.

Prior research has shown that organizational culture influences both the extent of Lean tool adoption and the depth of Lean integration into daily routines [4,18]. Companies with cultures that promote learning, employee involvement, and data-driven decision making are more likely to achieve long-term Lean maturity. Conversely, hierarchical, rigid, or siloed cultures tend to hinder Lean transformations by resisting change and limiting communication flow [19,20].

In transitional economies such as Serbia, organizational culture often reflects a legacy of centralized decision-making, low employee autonomy, and limited trust in management processes [7]. Such environments may slow down Lean adoption or reduce its effectiveness. However, research in the food industry context indicates that even partial cultural shifts—such as introducing team meetings, visual management, or suggestion systems—can significantly improve Lean outcomes [3,6].

Therefore, organizational culture is not only a contextual variable but also a determinant of Lean implementation success, shaping the degree to which Lean practices are embedded and sustained throughout the organization.

2.3. Company Size

Company Size (CS) represents an important contextual factor influencing Lean implementation. Differences in resources, organizational structure, and managerial capacity between large firms and SMEs shape the scope and intensity of Lean adoption [9].

Company size is an important contextual factor that shapes how organizations approach Lean implementation. Larger firms typically have more financial and human resources, enabling them to invest in structured Lean programs, specialized teams, and training activities [14,18]. Their scale allows for more systematic integration of Lean tools across departments and greater potential to achieve economies of scale.

In contrast, small and medium-sized enterprises (SMEs) often face resource constraints, lower managerial capacity, and less formalized structures, which may limit the scope and depth of Lean adoption [15]. However, smaller firms can sometimes be more flexible and quicker to adapt changes, provided that Lean principles are adjusted to their operational realities [11].

In transitional economies such as Serbia, firm size is often a determinant of modernization level and investment capability. Larger companies, especially those with export orientation, tend to adopt Lean earlier and more systematically, while smaller food companies rely on incremental improvements and partial implementation [7]. Understanding the role of company size is therefore crucial for explaining variation in Lean tool application and performance outcomes across the sector.

2.4. Technical Infrastructure and Resources

Technical Infrastructure and Resources (TIR) determine whether organizations can effectively implement and sustain Lean practices. Adequate production technology, machinery, and process control systems provide the operational foundation necessary for stabilizing processes and maintaining improvements introduced through Lean initiatives [21].

Technical infrastructure and resources refer to the physical, technological, and digital assets that support production processes and organizational improvement efforts. In the context of Lean implementation, the availability and quality of technical infrastructure significantly influence the scope and effectiveness of Lean practices. Companies with modern equipment, stable process control systems, and adequate technological resources are better positioned to implement standardized work, real-time monitoring, and waste elimination techniques [14,22].

In recent years, the integration of Industry 4.0 technologies (such as IoT, cyber-physical systems, big data analytics, and digital twins) has opened new possibilities for enhancing Lean initiatives. Rather than replacing Lean, these technologies are increasingly viewed as complementary, enabling more precise data collection, real-time decision-making, and predictive maintenance, which amplify Lean’s impact on operational performance [23,24,25,26].

However, the level of technical infrastructure in transitional economies such as Serbia varies significantly across firms. Larger and export-oriented companies are more likely to have advanced production equipment and digital tools, whereas small and medium-sized enterprises often rely on outdated machinery and limited automation [7]. This technological gap affects their ability to fully implement Lean tools such as just-in-time, value stream mapping, and poka-yoke. Therefore, technical infrastructure acts as both an enabler and a boundary condition for Lean implementation success.

2.5. Employee Education and Competence and Training

Employee Education and Competence (ECE) together with Training for Lean Methodology (TLM) represent key enablers of Lean implementation. Lean requires employees to understand structured problem-solving techniques, improvement tools, and standardized work practices, which makes systematic training a crucial factor of successful Lean adoption [2].

Employee competence and education play a decisive role in the successful implementation and sustainability of Lean initiatives. Lean transformation requires not only technical changes but also the development of skills, knowledge, and attitudes that support continuous improvement and problem-solving at all organizational levels [27,28]. Competent employees are better able to understand Lean principles, identify waste, and contribute actively to kaizen activities, which directly influences both the depth and speed of Lean adoption [29].

Training programs are a critical mechanism for building these competencies. Structured on-the-job training approaches such as Training Within Industry (TWI) have been shown to enhance employees’ ability to standardize work, solve problems, and sustain improvements over time [28,30]. Organizations that invest in regular and systematic Lean training typically achieve higher implementation success, especially when training is aligned with the company’s strategic goals and supported by management [31,32].

In the context of transitional economies such as Serbia, companies often face challenges related to outdated training systems, limited budgets, and a shortage of Lean expertise. Nevertheless, research indicates that even incremental training initiatives can significantly improve Lean outcomes when combined with employee involvement and leadership support [7]. Therefore, employee competence and education act as a key enabler of Lean, influencing both the effectiveness of tool application and the sustainability of achieved improvements.

2.6. Training and Lean Management Support

In addition to employee competence, successful Lean implementation requires strong managerial support and structured training initiatives that reinforce continuous improvement practices [5].

Effective Lean transformation hinges on visible and sustained management support and on structured training that builds problem-solving capabilities at all organizational levels. Recent evidence shows that Lean leadership behaviors (e.g., clarifying roles, coaching, enabling participation) are directly associated with higher work-unit performance and indirectly with continuous-improvement maturity [33,34]. When leaders consistently model Lean routines (gemba walks, A3 thinking, visual management), they lower barriers to change and institutionalize improvement cycles across functions.

Training acts as the operational engine of this leadership intent. Newer empirical work and meta-syntheses report that well-designed Lean training programs (with practice-oriented modules and follow-up coaching) strengthen employees’ standard work discipline, speed up problem detection, and translate into measurable performance gains [35,36]. Importantly, training is most effective when embedded in a management system that sets clear objectives, allocates time for learning, and recognizes improvement efforts; otherwise, training effects fade and implementation stays superficial [5].

In transitional economies (including Serbia), firms often face resource constraints and fragmented improvement efforts. Regional studies underline that leadership continuity and systematic employee development distinguish organizations that sustain Lean beyond pilot projects [8,37]. In food-industry settings, even incremental training routines combined with management sponsorship have been linked to better adoption depth and more stable performance effects. Overall, management support and training are mutually reinforcing: leadership provides strategic direction and removes obstacles, while training equips people to apply tools reliably and sustain improvements over time.

2.7. Innovation

Innovation (IN) is increasingly recognized as a strategic complement to Lean rather than its opposite. While Lean traditionally emphasizes efficiency, waste elimination, and incremental improvement, contemporary research highlights that Lean practices can stimulate innovation capabilities by creating structured problem-solving routines, freeing up resources, and building organizational discipline [38,39]. This “Lean-driven innovation” perspective sees Lean as a foundation for experimentation and cross-functional learning, rather than merely a cost-reduction program.

Recent studies emphasize that firms that successfully combine Lean with digital transformation and Industry 4.0 technologies (e.g., IoT, advanced analytics, automation) achieve higher innovation ambidexterity—the ability to exploit existing processes while exploring new solutions [40,41]. By stabilizing core processes through Lean and simultaneously introducing enabling digital tools, organizations can accelerate product development, shorten innovation cycles, and improve customization capabilities. This integration is especially relevant for the food industry, where product innovation is crucial but process variability is traditionally high.

However, the relationship between Lean and innovation is not automatic. Without supportive organizational culture and leadership, Lean can reinforce rigid routines and reduce exploratory behaviors. Ref. [5] note that cultural openness, team autonomy, and management encouragement are critical mediators between Lean practices and innovative outcomes. In transitional economies such as Serbia, where firms often operate with limited resources, combining Lean and innovation requires deliberate strategic alignment and capacity building.

Overall, Lean and innovation should not be treated as competing approaches, but as complementary strategic capabilities that, when integrated, enable both operational excellence and adaptive resilience.

2.8. Competitive Pressure and Motivation

Competitive Pressure and Motivation to Reduce Costs (CPM) reflect external drivers for Lean adoption. In highly competitive environments, companies often adopt Lean to reduce costs, increase flexibility, and maintain market position [10].

Competitive pressure has long been identified as a critical external driver of Lean adoption. Firms often initiate Lean programs not solely for internal efficiency, but as a strategic response to competitive market dynamics, customer expectations, and regulatory changes. Recent research shows that increasing global competition and rapidly changing consumer demands have intensified the need for simultaneous cost reduction and innovation, positioning Lean as a strategic necessity rather than a choice [42,43].

Empirical studies from the post-pandemic period highlight that organizations facing higher competitive intensity adopt Lean tools earlier and more comprehensively, often motivated by survival and market differentiation imperatives [44]. In particular, firms in the food industry—characterized by low margins, strict quality requirements, and strong buyer power—experience intense pressure to streamline operations and enhance responsiveness [35]. Lean provides these firms with a structured way to reduce variability, increase throughput, and sustain competitiveness.

At the same time, internal motivation—such as leadership vision, employee engagement, and organizational learning culture—amplifies the impact of external pressures. Firms that view Lean as a long-term strategic capability, rather than a short-term reaction to competition, achieve more sustainable performance improvements [45]. Motivation to adopt Lean can be both reactive (responding to competitors) and proactive (seeking to lead the market through operational excellence and innovation).

In Serbia and the wider Western Balkans, firms often adopt Lean under growing pressure from foreign buyers, EU market requirements, and supply-chain integration with larger international players. Recent domestic research indicates that competitive pressure is one of the primary triggers for initial Lean adoption, but that sustained motivation depends on leadership commitment and employee participation [7,8]. This combination of external pressure and internal motivation determines the depth and longevity of Lean implementation efforts.

2.9. Digitalization and Industry 4.0 (ILI4.0)

The digital transformation of manufacturing, often referred to as Industry 4.0, has become a key enabler of Lean implementation and performance improvement. Digital technologies such as IoT, advanced data analytics, cyber-physical systems, AI, and automation strengthen Lean capabilities by providing real-time data, increasing process transparency, and enabling faster decision-making [41,46]. Rather than replacing Lean, Industry 4.0 acts as a complementary infrastructure, enhancing Lean’s potential for variability reduction, predictive maintenance, and dynamic resource allocation.

Recent studies show that organizations integrating Lean with digital tools achieve higher operational resilience and flexibility, particularly in volatile environments [40,47]. This synergy is especially important in the food industry, where production processes are sensitive to variability and quality requirements are strict. Digitalization allows firms to better monitor process parameters, trace product flows, and react quickly to market changes, amplifying the effects of Lean practices.

However, this integration requires more than technological investment. Firms must develop digital competencies, leadership commitment, and strategic alignment to avoid superficial adoption [46]. Without these supporting elements, digital tools may remain isolated pilot projects with limited impact on Lean performance. In transitional economies, including Serbia, the level of digital maturity is often low, but there is growing awareness that digitalization can support Lean in overcoming structural inefficiencies.

Overall, digitalization and Lean should be approached as mutually reinforcing strategies, where Lean provides the process discipline and cultural foundation, while digital technologies supply real-time intelligence and adaptive capacity.

Although digitalization and Industry 4.0 are conceptually linked to Lean implementation, these aspects were not operationalized as separate constructs in the present survey and are instead considered as directions for future research.

Overall, the reviewed literature indicates that successful Lean implementation depends on several interrelated organizational factors, including management support, employee training, organizational culture, and the availability of adequate infrastructure. Previous studies emphasize that these factors collectively shape the conditions under which Lean tools can be effectively adopted and sustained within organizations. However, the importance and interaction of these factors may vary depending on the economic and industrial context in which companies operate.

In the context of transition economies, companies often face additional challenges in implementing modern management practices such as Lean. These challenges may include limited resources, organizational restructuring, and evolving managerial practices. Therefore, examining the determinants and effects of Lean implementation in food companies operating in such environments can provide valuable insights into how organizational factors influence the success of Lean initiatives.

3. Research Method

Based on the theoretical insights presented above, this section presents the methodological framework used to empirically test the proposed relationships between organizational performance dimensions, the application of Lean tools, and the effects of Lean methodology implementation.

3.1. Experience-Based Factors (Years of Existence and Years of Lean Implementation)

Years of Existence (YC) and Years of Lean Implementation (YL) represent experience-based factors that can influence a firm’s ability to institutionalize Lean routines through accumulated knowledge, organizational learning, and the stabilization of core processes. Companies with a longer market presence and more extensive Lean experience often possess stronger internal capabilities, enabling them to apply Lean tools more consistently and achieve more sustainable performance improvements. This is particularly relevant in transitional economies, where organizational maturity can partially offset structural and resource limitations.

In line with previous sectoral evidence, YC and YL are incorporated into the empirical model as control variables, capturing experience-driven heterogeneity in the adoption and effectiveness of Lean practices [6].

3.2. Conceptual Framework and Research Question

Synthesizing the theoretical arguments presented in Section 2.1, Section 2.2, Section 2.3, Section 2.4, Section 2.5, Section 2.6, Section 2.7, Section 2.8 and Section 2.9, this study conceptualizes Lean implementation as a multidimensional phenomenon shaped by both internal organizational capabilities (e.g., business performance, culture, employee competence, training intensity, and management support) and external contextual factors (e.g., company size, competitive pressure, innovation, digitalization, and experience-based factors). These dimensions jointly influence the Application of Lean Tools (ALT) and the Effects of Lean Implementation (ELI) within the food industry context.

Based on this conceptual foundation, the following research question is formulated:

RQ: Which organizational performance dimensions exert the strongest influence on the Application of Lean Tools (ALT) and the Effects of Lean Implementation (ELI) in the context of the Serbian food industry?

3.3. Research Hypotheses

Based on the above theoretical considerations, the following hypotheses are proposed:

• H1: There is a statistically significant correlation between organizational performance dimensions and both the Application of Lean Tools (ALT) and the Effects of Lean Methodology Implementation (ELI) in Serbian food companies.
• H1a–H1l: Each observed organizational performance dimension (BP, OC, CS, TIR, ECE, TLM, MS, CPM, IN, SP, YC, YL) is positively associated with ALT and ELI.
• H2: There is a statistically significant predictive effect of the organizational performance dimensions on ALT and ELI.

3.4. Survey Instrument

The study employed a structured online questionnaire specifically crafted for this research. Its design drew upon existing literature on Lean implementation and was tailored to the Serbian food industry context. The survey was divided into six main sections, each focusing on organizational characteristics, performance metrics, leadership factors, and Lean-related practices. Questions within each section were organized thematically, enabling both individual dimension analysis and a comprehensive assessment of factors affecting Lean adoption.

Prior to the main survey rollout, a pilot test was conducted with a small sample of industry professionals and academic experts experienced in Lean methodology. This pilot involved 12 participants from food manufacturing firms and two university researchers. The goal was to assess question clarity, relevance, and phrasing. Feedback from this phase led to minor revisions to enhance understanding and ensure precise responses.

The questionnaire addressed twelve critical dimensions aligned with the previously presented theoretical framework. These dimensions included Business Performance, Organizational Culture, Company Size, Technical Infrastructure and Resources, Employee Competence and Education, Lean Training, Management Support, Competitive Pressure and Motivation, Innovation, Strategic Role of Lean, Years of Existence, and Years of Lean Implementation. For a clearer overview of how the sections, dimensions, and individual items are structured, see

Table 1. Structure of the survey instrument and measurement dimensions.

Section of Questionnaire	Dimension	Abbreviation	Number of Items	Measurement Scale
Organizational characteristics	Company size	CS	3	Likert (1–7)
Organizational performance	Business performance	BP	4	Likert (1–7)
Organizational culture	Organizational culture	OC	4	Likert (1–7)
Technical capabilities	Technical infrastructure and resources	TIR	4	Likert (1–7)
Human resources	Education and competence of employees	ECE	4	Likert (1–7)
Lean implementation factors	Training for Lean methodology	TLM	4	Likert (1–7)
Leadership factors	Management support	MS	4	Likert (1–7)
External environment	Competitive pressure and motivation	CPM	4	Likert (1–7)
Innovation orientation	Degree of innovation	IN	1	ordinal
Strategic orientation	Role of Lean in strategic planning	SP	1	ordinal
Experience factors	Years of company existence	YC	1	categorical
Experience factors	Years of Lean implementation	YL	1	categorical
Lean application	Application of Lean tools	ALT	10	Likert (1–7)
Lean outcomes	Effects of Lean implementation	ELI	8	Likert (1–7)

.

Responses were captured using a seven-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree). The study focused on two dependent variables—Application of Lean Tools (ALT) and Effects of Lean Implementation (ELI)—measured through 10 and 8 items, respectively.

The questionnaire was distributed electronically via Google Forms, using a combination of outreach channels to maximize participation. The link was shared through direct e-mail invitations, LinkedIn professional networking, and communication with professional associations and industry clusters in the food sector. Additionally, it was circulated via university mailing lists and alumni networks related to engineering and food technology faculties, as well as through internal communication channels of partner companies that agreed to support the study.

In total, 183 valid responses were collected from approximately 450 distributed invitations, representing a response rate of about 40%. Respondents primarily included production managers, engineers, supervisors, and quality assurance officers familiar with Lean-related activities within their organizations. Considering the distribution approach, the study employed a convenience sampling method, complemented by elements of snowball sampling, allowing for broader dissemination within professional networks and improved coverage of the Serbian food industry.

Data collection was conducted during the first half of 2025, ensuring that all responses reflected the most recent state of Lean implementation and organizational practices within the Serbian food sector.

3.5. Sample and Data Collection

The target population consisted of companies from the food industry in Serbia, including processing, production, and packaging firms of different sizes. The survey was distributed electronically via email and professional networks to company managers, production supervisors, engineers, and quality assurance personnel involved in operational and improvement activities.

Data collection was carried out during the first half of 2025. Participation was voluntary and anonymous, with respondents providing answers on behalf of their companies. In total, 183 valid responses were collected and included in the analysis. The sample covers a broad range of food industry sub-sectors (e.g., meat and dairy processing, bakery, confectionery, beverage production), as well as different geographical regions of Serbia. This diversity improves the generalizability of the findings.

3.6. Data Analysis

The collected data were analyzed using SPSS 26.0 statistical software. First, descriptive statistics (means, standard deviations) were computed to summarize the main characteristics of the sample and the variables. Pearson correlation analysis was then used to test relationships between organizational performance dimensions and the two dependent variables (ALT and ELI).

To examine the predictive effects of organizational performance dimensions on ALT and ELI, multiple regression analyses were conducted. Separate models were estimated for ALT and ELI to identify which factors had the strongest influence on each outcome. Statistical significance was assessed at the 0.05 and 0.01 levels. All constructs used in the analysis correspond directly to the items measured in the survey instrument, ensuring consistency between theoretical concepts and empirical testing.

4. Results

4.1. Descriptive Statistics

To examine the predictive effects of organizational performance dimensions on ALT and Descriptive statistics for the observed dimensions and items are presented in

Table 2. Descriptive statistics.

Names of Dimensions	Abbr.	N	Min	Max	Mean	Std. Deviation	Cronbach’s Alpha
Business Performance	BP	183	1.364	6.545	4.17039	1.011695	0.902
Organizational culture	OC	183	1.850	6.300	4.11749	0.987502	0.941
Company Size	CS	183	1.000	6.429	4.16237	1.017319	0.858
Technical Infrastructure and Resources	TIR	183	1.000	6.250	4.07268	1.046799	0.946
Education and Competence of Employees	ECE	183	1.100	6.400	4.13361	1.008292	0.945
Training for Lean Methodology	TLM	183	1.000	6.200	4.16831	0.987188	0.945
Management Support	MS	183	1.000	6.450	3.93770	1.023769	0.948
Competitive Pressure and Motivation to Reduce Costs	CPM	183	1.000	6.211	3.95715	1.006340	0.936
Application of Lean Tools	ALT	183	1.000	6.200	4.13843	0.967442	0.927
Effects of Lean Methodology Implementation	ELI	183	1.000	6.350	3.93962	1.058911	0.951
Degree of Innovation in the Company	IN	183	1	4	2.69	0.970
Role of the Lean concept in Strategic Planning	SP	183	1	3	2.06	0.705
Years of Existence of the Company	YC	183	1	4	2.53	1.047
Years of Implementation of Lean Tools in the Company	YL	183	1	4	2.30	1.100

. In addition, abbreviations and mean values are provided for each dimension and item, as well as Cronbach’s alpha values for the dimensions (ranging from α = 0.858 to α = 0.951).

To evaluate the reliability of the measurement scales, Cronbach’s alpha coefficients were calculated for all multi-item constructs. The obtained values ranged from 0.858 to 0.951, which exceeds the recommended threshold of 0.70 and indicates a high level of internal consistency among the items within each dimension. This confirms the reliability of the measurement instrument used in the study.

4.2. Correlation Analysis

The correlation coefficients between the dimensions and items of the observed organizational performances and the dimensions ALT (Application of Lean Tools) and ELI (Effects of Lean Methodology Implementation) are presented in

Table 3. Correlation analysis.

	ALT	ELI
BP	0.907 **	0.675 **
OC	0.897 **	0.695 **
CS	0.897 **	0.671 **
TIR	0.844 **	0.664 **
ECE	0.902 **	0.687 **
TLM	0.924 **	0.702 **
MS	0.672 **	0.949 **
CPM	0.683 **	0.952 **
IN	0.470 **	0.487 **
SP	0.496 **	0.424 **
YC	0.241 **	0.253 **
YL	0.429 **	0.414 **

Note: ** p < 0.01; * p < 0.05. In this study, all reported coefficients are significant at the 0.01 level.

. Pearson’s correlation was used: * p < 0.05; ** p < 0.01.

4.3. Regression Analysis

The predictive effects of the dimensions and items of the observed organizational performances (independent variables) on the dimensions ALT (Application of Lean Tools) and ELI (Effects of Lean Methodology Implementation) (dependent variables) were examined using multiple regression analysis. The regression results are presented in

Table 4. Regression analysis (dependent variable: ALT—Application of Lean Tools).

	Model 1			Model 2			Model 3
	Unstd. B	Std. β	Sig.	Unstd. B	Std. β	Sig.	Unstd. B	Std. β	Sig.
Const.	0.240		0.035	1.699		0.000	0.112		0.335
BP	0.286	0.299	0.000				0.289	0.302	0.000
OC	0.151	0.154	0.148				0.109	0.111	0.265
CS	0.189	0.199	0.007				0.194	0.204	0.004
TIR	−0.087	−0.107	0.272				−0.048	−0.065	0.423
ECE	0.120	0.125	0.225				0.142	0.148	0.125
TLM	0.444	0.453	0.000				0.459	0.469	0.000
MS	0.054	0.058	0.562				0.053	0.056	0.546
CPM	−0.066	−0.068	0.503				−0.098	−0.102	0.293
IN				0.312	0.313	0.000	−0.057	−0.057	0.071
SP				0.501	0.365	0.000	−0.023	−0.017	0.592
YC				0.180	0.195	0.003	0.085	0.092	0.001
YL				0.049	0.056	0.467	0.066	0.075	0.019
R	0.941			0.632			0.951
R2	0.886			0.399			0.904
Sig. F change	0.000			0.000			0.000

B—unstandardized coefficient; β—standardized coefficient; Sig.—p-value.

(dependent variable: ALT—Application of Lean Tools) and

Table 5. Regression analysis (dependent variable: ELI—Effects of Lean Methodology Implementation).

	Model 1			Model 2			Model 3
	Unstd. B	Std. β	Sig.	Unstd. B	Std. β	Sig.	Unstd. B	Std. β	Sig.
Const.	−0.062		0.555	1.374		0.000	−0.107		0.353
BP	0.004	0.004	0.951				0.016	0.015	0.827
OC	0.051	0.047	0.595				0.040	0.037	0.681
CS	−0.048	−0.046	0.460				−0.052	−0.050	0.434
TIR	−0.006	−0.006	0.937				0.001	0.001	0.989
ECE	−0.048	−0.046	0.598				−0.046	−0.044	0.613
TLM	0.056	0.052	0.530				0.053	0.049	0.559
MS	0.450	0.435	0.000				0.445	0.430	0.000
CPM	0.553	0.526	0.000				0.548	0.521	0.000
IN				0.393	0.360	0.000	0.006	0.006	0.838
SP				0.417	0.278	0.000	−0.007	−0.005	0.863
YC				0.211	0.208	0.002	0.028	0.027	0.276
YL				0.050	0.052	0.507	0.004	0.004	0.888
R	0.959			0.606			0.959
R2	0.920			0.368			0.920
Sig. F change	0.000			0.000			0.000

B—unstandardized coefficient; β—standardized coefficient; Sig.—p-value.

(dependent variable: ELI—Effects of Lean Methodology Implementation). In these tables, statistically significant predictive effects are marked in bold and shaded cells. The analysis was performed separately for two groups of influencing variables (Model 1 and Model 2), and then for all variables together (Model 3). Thus, for each dependent variable, three regression models were evaluated.

According to Table 5, regression equations can be formulated for the dependent variable ALT (Application of Lean Tools). These equations are based on the unstandardized coefficients B for each group of independent variables. Accordingly, each model yields one regression equation:

Ŷ(ALT, Mod1) = 0.240 + 0.286 · BP + 0.151 · OC + 0.189 · CS − 0.087 · TIR + 0.120 · ECE + 0.444 · TLM + 0.054 · MS − 0.066 · CPM

(1)

Ŷ(ALT, Mod2) = 1.699 + 0.312 · IN + 0.501 · SP + 0.180 · YC + 0.049 · YL

(2)

Ŷ(ALT, Mod3) = 0.112 + 0.289 · BP + 0.109 · OC + 0.194 · CS − 0.048 · TIR + 0.142 · ECE + 0.459 · TLM + 0.053 · MS − 0.098 · CPM − 0.057 · IN − 0.023 · SP + 0.085 · YC + 0.066 · YL

(3)

The regression analysis results for the dependent variable, ALT—Application of Lean Tools (Table 4), indicate that all three examined models are statistically significant, as confirmed by the F-tests. The coefficient of determination (R²) shows that the models explain a meaningful proportion of variance in the dependent variable. Although the R² values are moderate (0.399 for Model 3), this level of explanatory power is considered acceptable in studies dealing with complex organizational and managerial phenomena. The results therefore provide meaningful insights into the influence of organizational factors on Lean tool application.

Similarly, the regression models presented in Table 5 are statistically significant. The R² values (e.g., 0.368 for Model 2) indicate moderate explanatory power, reflecting the complexity of factors affecting employee engagement in Lean practices and confirming that the included predictors contribute to explaining the observed relationships.

According to Table 5, regression equations can be formulated for the dependent variable ELI (Effects of Lean Methodology Implementation). These equations are based on the unstandardized coefficients B for each group of independent variables. Accordingly, each model produces one regression equation:

Ŷ(ELI, Mod1) = −0.062 + 0.004 · BP + 0.051 · OC − 0.048 · CS − 0.006 · TIR − 0.048 · ECE + 0.056 · TLM + 0.450 · MS + 0.553 · CPM

(4)

Ŷ(ELI, Mod2) = 1.374 + 0.393 · IN + 0.417 · SP + 0.211 · YC + 0.050 · YL

(5)

Ŷ(ELI, Mod3) = − 0.107 + 0.016 · BP + 0.040 · OC − 0.052 · CS + 0.001 · TIR − 0.046 · ECE + 0.053 · TLM + 0.445 · MS + 0.548 · CPM + 0.006 · IN − 0.007 · SP + 0.028 · YC + 0.004 · YL

(6)

4.4. Model of the Influences on the Application and Effects of Lean Methodology

Based on the results of the correlation and regression analyses (across all three models), an integrated model of the influences of the observed organizational performance factors on ALT (Application of Lean Tools) and ELI (Effects of Lean Methodology Implementation) can be formulated. This model is presented in Figure 1. All examined organizational performance dimensions exhibit statistically significant effects in at least one of the analyses, and the graphical representation aims to highlight those with the strongest influence. Accordingly, in Figure 1, statistically significant relationships that appear in both the correlation analysis and in two of the three regression models are marked with thick lines; statistically significant relationships that appear in the correlation analysis and in only one regression model are marked with solid lines; and statistically significant relationships identified solely through correlation analysis are marked with dashed lines.

Figure 1 illustrates the conceptual model used in this study, showing the relationships between organizational factors and Lean outcomes. The model distinguishes between the application of Lean tools and the resulting performance effects, providing a structured framework for empirical analysis.

5. Discussion

5.1. Discussion of the Correlation Analysis Results

The findings of this study are broadly consistent with prior research emphasizing the importance of organizational context in Lean implementation. For example, the strong influence of training for Lean methodology (TLM) confirms earlier studies [2,28,30], which highlight that employee knowledge and structured training programs are critical enablers of Lean adoption. Similarly, the significant role of business performance and company size aligns with findings from [14,18], suggesting that resource availability and organizational capacity strongly determine the extent of Lean tool application.

In contrast, the results related to the Effects of Lean Implementation (ELI) emphasize the dominant role of external and strategic factors, particularly competitive pressure (CPM) and management support (MS). This supports previous research [42,43,44], which identifies competition as a key driver of Lean adoption and performance improvement, especially in industries with high cost pressure such as food processing. Furthermore, the strong influence of management support is consistent with studies [33,34], which underline leadership commitment as a critical determinant of successful Lean transformation.

The results of the correlation analysis (Table 3) show that all correlations are statistically significant, positive, and strong. In this way, Hypothesis H1 is confirmed. Although all correlations are notably high, it is useful to identify which independent variables exert the strongest influence. Among the examined organizational performance dimensions, the most influential are CPM—Competitive Pressure and Motivation to Reduce Costs, followed by TLM—Training for Lean Methodology and MS—Management Support.

The findings clearly indicate that competitive pressure plays a highly significant role in both the application and the effects of Lean. Competition is a driving force that motivates and directs companies toward continuous development and improvement. Falling behind competitors can seriously affect a company’s market position and long-term outlook. Therefore, maintaining competitiveness is essential; if competitors adopt Lean practices, the observed company inevitably must do the same.

Furthermore, the implementation and effective use of Lean tools require advanced knowledge and continuous development of human resources. Without this, a company lacks personnel capable of applying modern Lean tools. This highlights why training for Lean has such a strong impact on both the application and the outcomes of Lean methodology.

Management support also plays a major role in shaping Lean application and its effects. This is fully expected, considering that management must be the first to recognize and understand the importance of Lean, as well as the need to keep pace with competitors, ensure adequate training of employees, and allocate the necessary time and financial resources. Finally, management must find ways to motivate employees to make additional and continuous efforts aimed at improving processes and business performance. For all these reasons, management support has a substantial influence on strengthening both dependent dimensions examined in this study.

It should be noted that the influences of the observed organizational performance factors are somewhat stronger on ALT—Application of Lean Tools than on ELI—Effects of Lean Methodology Implementation. This phenomenon can be explained by the fact that organizational characteristics are predominantly internal in nature and therefore have a more direct impact on the extent to which Lean tools are applied. In contrast, the effects of Lean implementation are more susceptible to external influences, such as changes in the business environment, competitive actions, consumer behavior, and market dynamics.

It is important to note that the correlation coefficients reported in this study are relatively high. While this indicates strong relationships between the observed variables, it may also suggest the presence of multicollinearity or common method bias, which is a known limitation of survey-based research. Future studies could address this issue by applying additional statistical controls, such as variance inflation factor (VIF) analysis or using multiple data sources.

These findings are particularly relevant in the context of transition economies, where organizational transformation and resource constraints may significantly influence the adoption and effectiveness of Lean practices.

5.2. Discussion of the Regression Analysis Results

The regression analysis results for the dependent variable, ALT—Application of Lean Tools (Table 4), show that all three examined models have statistically significant corrected determination indices (R²). Likewise, the regression analysis results for the dependent variable, ELI—Effects of Lean Methodology Implementation (Table 5), also indicate statistically significant corrected R² values across all three models. In this way, Hypothesis H2 is confirmed.

According to Table 4, the strongest statistically significant predictive effect on ALT in Model 1 is exerted by TLM—Training for Lean Methodology, followed by BP—Business Performance and CS—Company Size. In Model 2, the greatest influence is associated with SP—Role of the Lean Concept in Strategic Planning, followed by IN—Degree of Innovation in the Company and YC—Years of Existence of the Company. Model 3 largely confirms the findings from the first two models, again showing the strongest effects for TLM, followed by BP, CS, YC, and YL—Years of Lean Tools Implementation in the Company.

The magnitude of the standardized coefficients β clearly highlights the first three independent variables as the most influential across all models, especially in Model 3. Training and continuous development of employee knowledge and skills for Lean represent the dominant determinant of whether a company will apply Lean tools. Business performance is also an important factor, as it reflects the current strength and position of the company. In addition, larger companies have a greater likelihood of adopting Lean due to their increased availability of resources (spatial, technological, financial), higher levels of qualified personnel, and greater operational need for Lean implementation.

According to Table 5, the strongest statistically significant predictive effect on ELI—Effects of Lean Methodology Implementation in Model 1 is found for CPM—Competitive Pressure and Motivation to Reduce Costs, followed by MS—Management Support. In Model 2, the greatest influence is exerted by IN—Degree of Innovation in the Company, followed by SP—Role of the Lean Concept in Strategic Planning and YC—Years of Existence of the Company. Model 3 shows the dominance of variables from Model 1, as only CPM and MS retain statistically significant effects.

For the dependent variable ELI, the predictive roles of CPM and MS stand out most clearly. The necessity of maintaining competitiveness pushes and motivates companies to achieve the required results and performance outcomes. Competitive pressure acts as a driving force across numerous business domains, including the effects of Lean implementation. Management understanding and support greatly shape the success of Lean tool implementation, as Lean-related tasks are complex and require new knowledge, continuous learning, and frequent training—none of which can be achieved without strong commitment from organizational leaders and their close associates. It should also be noted that in Model 2, IN—Degree of Innovation in the Company emerges as the dominant factor: higher innovation levels within the company increase the likelihood of achieving stronger Lean outcomes.

The regression equations derived from the models provide a practical tool for predicting the level of Lean application and its effects under different organizational conditions. Although not used for optimization in this study, they offer a basis for future research and managerial decision-making.

Compared to existing literature, this study contributes by simultaneously examining a broad set of organizational performance dimensions and distinguishing between two dependent constructs—Application of Lean Tools (ALT) and Effects of Lean Implementation (ELI). This distinction provides a more nuanced understanding of Lean, showing that implementation intensity and achieved outcomes are not necessarily aligned, particularly in transitional economies such as Serbia. This represents an extension of prior research, which typically treats Lean implementation as a single-dimensional construct.

The regression models developed in this study provide an analytical basis for examining how organizational characteristics shape both the implementation of Lean tools and their practical effects within companies. Taken together, these findings extend existing knowledge on Lean implementation by highlighting the importance of organizational context in determining both the application of Lean tools and their resulting effects.

In this way, the study offers a useful foundation for further research on Lean implementation and its performance implications across different industries and organizational environments.

To further position the findings within the existing body of literature, it is important to emphasize both their consistency with prior studies and their distinct contributions. The obtained results largely confirm earlier research highlighting the importance of training for Lean methodology (TLM), management support (MS), and competitive pressure (CPM) as key determinants of Lean implementation success [2,28,30,42,43,44]. In line with these studies, the present research demonstrates that both internal organizational capabilities and external environmental pressures significantly shape Lean adoption and outcomes.

However, the results also reveal notable differences compared to previous findings. While many earlier studies emphasize internal organizational factors as the primary drivers of Lean outcomes, this study indicates that external factors, particularly competitive pressure, may exert a stronger influence on the effects of Lean implementation (ELI). This finding is partially consistent with [42,43,44] but extends their conclusions by emphasizing the dominant role of external factors in transition economies. This suggests that, in the context of transition economies such as Serbia, external market dynamics play a more dominant role than previously assumed.

Building on this distinction, the findings of this study indicate that the mere adoption of Lean tools does not necessarily lead to significant performance improvements, especially in the absence of strong managerial support or sufficient competitive pressure. This extends the existing literature by demonstrating that Lean effectiveness depends not only on the extent of tool implementation but also on the broader organizational and environmental context.

From a methodological perspective, the regression models presented in this study provide a quantitative framework for understanding the relationships between organizational factors and Lean outcomes. Their purpose is not to identify mathematical extrema, but to enable interpretation and prediction of ALT and ELI based on different configurations of organizational variables. From a practical perspective, these models can assist managers in identifying priority areas for improvement, such as strengthening training programs or enhancing leadership commitment, thereby contributing to both theoretical and practical understanding of Lean implementation.

6. Conclusions

6.1. Theoretical Implications

This study contributes to the existing literature by providing an integrated analysis of multiple organizational performance dimensions influencing Lean implementation in the food industry within a transitional economy. Unlike prior studies that often examine individual factors in isolation, this research simultaneously analyzes a comprehensive set of internal and external variables and distinguishes between the application of Lean tools (ALT) and the effects of Lean implementation (ELI).

The findings confirm that Lean should not be treated as a single-dimensional construct. Instead, the results demonstrate that different groups of factors influence implementation and outcomes in distinct ways, thereby extending existing theoretical models of Lean adoption.

6.2. Practical Implications

The results of this study provide clear guidance for managers in the food industry. The strong influence of training for Lean methodology indicates that investments in employee development and structured training programs are essential for increasing the application of Lean tools. At the same time, the dominant role of competitive pressure and management support suggests that organizational leadership and external market conditions are crucial for achieving tangible performance improvements.

Managers should therefore adopt a dual approach: strengthening internal capabilities (knowledge, training, resources) while simultaneously aligning Lean initiatives with strategic goals and competitive positioning.

6.3. Limitations and Future Research

This study has several limitations. First, the analysis is based on data collected from food industry companies in Serbia, which may limit the generalizability of the findings. Second, the use of self-reported survey data introduces the possibility of common method bias.

Future research could extend this study by including longitudinal data, cross-country comparisons, and additional statistical techniques to address multicollinearity. Furthermore, future studies could explore the role of digitalization and Industry 4.0 in greater depth, as well as analyze the individual impact of specific Lean tools.

In addition, the cross-sectional nature of the study limits the ability to draw strong causal conclusions regarding the observed relationships. This limitation can be addressed in future research by employing longitudinal designs or mixed-method approaches in order to capture the dynamic nature of Lean implementation over time.