Assessing the Early Impact of Industry 4.0 Technologies on the Activity, Efficiency, and Profitability of Croatian Micro-, Small-, and Medium-Sized Enterprises

Abstract

This study examines the early impact of Industry 4.0 (I4.0) implementation on the financial performance of Croatian companies, focusing on indicators of profitability, efficiency, and activity. The research investigates whether firms adopting I4.0 technologies achieve superior results compared to traditional companies. A unique feature of this study is its integration of primary data—collected via an online survey of Croatian enterprises—with secondary data from publicly available financial reports. Statistical methods, including Analysis of Variance (ANOVA) and linear regression, were employed to test the hypotheses. The results show that I4.0 adopters perform significantly better in terms of net profit margin, return on assets, business efficiency, and supplier bonding days, while no significant difference was found in days sales outstanding. This paper contributes to the literature by offering one of the first empirical analyses of early-stage I4.0 adoption in the context of a transition economy, using firm-level financial data. The findings provide valuable insights for managers, policymakers, and investors aiming to understand the tangible business benefits of digital transformation. The results also highlight the importance of supporting I4.0 adoption strategies to enhance competitiveness and recovery in post-pandemic economic conditions.

1. Introduction

Industry 4.0 (I4.0), also known as the Fourth Industrial Revolution, refers to the integration of advanced digital technologies into manufacturing processes as well as other core business operations, such as supply chain management, logistics, marketing, and customer service. These technologies include the Internet of Things (IoT), artificial intelligence (AI), big data analytics, cloud computing, and robotics (Kagermann et al., 2013; Schwab, 2016). Among them, AI has become increasingly prominent across various business functions, including autonomous robotics, service automation, and everyday applications such as smartphones.

I4.0 fundamentally transforms business processes, organizational structures, and the ways in which companies operate (Lasi et al., 2014). While a growing body of global research explores these changes, the adoption and impact of I4.0 technologies remain underexplored in small and open economies such as Croatia.

Croatian enterprises operate in dynamic market conditions and face increasing pressure to adopt automation and AI solutions. The COVID-19 pandemic accelerated digital transformation, particularly in remote work and virtual team management, highlighting both challenges and opportunities for enterprises navigating technological change.

Small- and medium-sized enterprises (SMEs), in particular, have been significantly impacted by the COVID-19 crisis, which has accelerated digital transformation and underscored the critical need for technological readiness and agile business models (Bartik et al., 2020). SMEs have faced unprecedented challenges, including disruptions to supply chains, transitions to remote work, and shifts in consumer behavior. This crisis has reinforced the importance of adopting digital technologies to ensure business continuity and maintain competitiveness, especially in small and open economies.

I4.0 encompasses nine core technological trends (Schwab, 2016), including IoT, AI, additive manufacturing, and cyber-physical systems. The successful integration of these technologies often indicates an enterprise’s digital maturity and competitive advantage. Companies adopt I4.0 tools to improve operational efficiency, increase flexibility, enhance productivity, and strengthen their market position through innovation and digital transformation (Brettel et al., 2014).

SMEs, which constitute the majority of Croatian businesses, face unique challenges in adopting I4.0. Existing maturity models often assume a minimum baseline of digitalization that many SMEs have yet to achieve. Mittal et al. (2018) critically examine these models and propose the addition of a Level 0 to better represent the initial digital maturity stage of SMEs, emphasizing the significant investments and cultural shifts required for progress. This tailored approach can support SMEs in developing realistic I4.0 strategies that lower entry barriers and foster essential organizational transformation.

Given that Croatia is a relatively small and open economy, allocative efficiency may be considered a homogenous characteristic of Croatian enterprises. This is further reinforced by the rise of internet-based business models, especially in information, professional, scientific, and technical services, and increasingly in retail and manufacturing. This research focuses on enterprises operating in the following sectors:

• C—Manufacturing;
• G—Wholesale and Retail Trade;
• J—Information and Communication;
• M—Professional, Scientific and Technical Activities.

In these sectors, allocative efficiency plays a lesser role, and therefore sectoral differences are not considered in this analysis. Industry groups were used to identify companies engaged in I4.0 technologies.

The manufacturing sector includes physical or chemical transformations of materials, substances, or components into new products. The result may be either a finished or semi-finished product and includes the production of specialized components, assembly of processed product components—whether from self-produced or purchased parts. The boundary between manufacturing and other sectors, as noted by the Croatian Bureau of Statistics (National Classification of Activities, 2007a, 2007b), can sometimes be unclear.

The wholesale and retail trade sector encompasses the trade of all types of goods and the provision of services related to the sale of goods. The information and communication sector involves the production and distribution of information, data transmission, IT services, data processing, and other related activities. It includes software publishing, computer programming, IT consulting, and information service activities. The professional, scientific, and technical activities sector includes highly specialized services requiring expert knowledge and skills, such as legal, accounting, architectural, engineering, and consulting services.

Leadership plays a crucial role in this transformation. Strategic vision and organizational adaptation are essential for managing the complexity and uncertainty of I4.0 implementation (Kagermann et al., 2013). This paper investigates whether Croatian enterprises that adopt I4.0 technologies—defined as a set of advanced digital tools including IoT, AI, big data analytics, and robotics—achieve superior financial performance compared to traditional firms during the early stages of digital transformation.

This research provides insights relevant to managers, policymakers, and academics, particularly those interested in the intersection of digital maturity, economic development, and leadership in hybrid working environments.

1.1. Research Problem

The adoption of I4.0 technologies presents a significant transformation opportunity for enterprises worldwide. However, despite growing global interest and research on I4.0 (Kagermann et al., 2013; Lasi et al., 2014; Schwab, 2016), a notable research gap remains concerning the implementation and impact of these technologies in small and open economies, such as Croatia. Croatian enterprises face unique challenges, including limited resources, technological readiness, and market size, all of which affect their digital transformation journey (Mittal et al., 2018).

Although technological innovation is generally associated with performance improvement, its actual impact varies across industries and national contexts (Mittal et al., 2018; Lasi et al., 2014). While the benefits of I4.0 adoption—such as enhanced operational efficiency, increased productivity, and greater flexibility—have been documented in larger, more developed economies (Brettel et al., 2014), empirical evidence from Croatian businesses remains limited. This is particularly true in the early stages of I4.0 implementation, where the extent to which these advanced technologies translate into financial performance gains is unclear.

This study aims to bridge that gap by examining the financial outcomes of I4.0 adoption among Croatian enterprises and exploring how digital maturity affects business performance in the context of a small, open economy undergoing rapid digital and organizational change.

The core research question is whether the adoption of I4.0 technologies has a statistically significant impact on business performance—specifically on indicators of activity, efficiency, and profitability.

Despite the common assumption that I4.0 adoption leads to improved performance, empirical evidence—especially from Croatian companies—is scarce. The COVID-19 pandemic has further underscored the need for digital agility, making strategic leadership and technological readiness more critical than ever.

According to the Boston Consulting Group (BCG, 2020), I4.0 includes the following nine core technologies:

• Big data and analytics;
• Autonomous robots;
• Simulation;
• Horizontal and vertical system integration;
• The Internet of Things (IoT);
• Cybersecurity;
• Cloud computing;
• Additive manufacturing (3D printing);
• Augmented reality.

Motivations for adopting these technologies include operational efficiency, cost reduction, enhanced competitiveness, and long-term profitability. However, the academic literature still lacks focused studies on measurable financial outcomes—particularly in the context of small- and medium-sized enterprises (SMEs) in transitional economies.

Due to data constraints and a relatively small sample size, this study applies a targeted empirical approach. Companies are classified as I4.0 adopters or traditional firms based on BCG criteria, supported by a control survey conducted among informed company representatives.

By focusing on Croatian enterprises, this paper contributes to closing a relevant research gap and provides a foundation for future studies on the relationship between technological transformation and financial performance.

1.2. Objectives, Hypotheses, and Research Methodology

By defining the research problem and subject matter, and in connection with the hypotheses to be tested, the purpose and objectives of this study have been established. This section outlines the theoretical grounding and introduces the research methodology, which will be discussed in more detail in Chapters where the empirical results will also be presented.

Before analyzing the relationship between the implementation of I4.0 technologies and business performance, it is essential to anchor the investigation in established economic theory. The neoclassical approach offers a suitable conceptual framework. According to neoclassical microeconomic theory, the firm is viewed as a production unit that transforms inputs into outputs, subject to technological and market constraints. This perspective emphasizes two key aspects of efficiency: technical efficiency, referring to the optimal use of inputs in the production process, and allocative efficiency, referring to the optimal distribution of resources in response to price signals (Kovačević, n.d.).

Within this framework, the cost function—largely shaped by the technological conditions of production—plays a central role in determining firm behavior, efficiency, and market outcomes. Technological advancements, such as the implementation of I4.0 tools, have the potential to shift the cost function downward, thereby improving productivity, reducing costs, and enhancing competitive positioning.

Building upon this theoretical foundation, the present study investigates whether the adoption of I4.0 technologies—defined here as advanced digital tools that support intelligent, automated, and interconnected operations—is associated with improved business performance. In particular, the study focuses on three key financial dimensions: activity, efficiency, and profitability, using empirical data from Croatian enterprises operating in a small and open economy.

The hypotheses to be tested in this study are as follows:

H1. 

I4.0 enterprises have, on average, at least one selected profitability indicator higher than traditional enterprises.

H1a. 

I4.0 enterprises have, on average, a higher net profit margin than traditional enterprises.

H1b. 

I4.0 enterprises have, on average, a higher return on assets (ROA) than traditional enterprises.

H2. 

I4.0 enterprises have, on average, at least one selected efficiency indicator higher than traditional enterprises.

H2a. 

I4.0 enterprises have, on average, higher overall business efficiency than traditional enterprises.

H2b. 

I4.0 enterprises have, on average, higher operational efficiency than traditional enterprises.

H3. 

I4.0 enterprises have, on average, at least one selected activity indicator better than traditional enterprises.

H3a. 

I4.0 enterprises have, on average, a lower days sales outstanding (DSO) than traditional enterprises.

H3b. 

I4.0 enterprises have, on average, a lower days payable outstanding (DPO) than traditional enterprises.

The initial idea for this study arose from the accelerated pace of technological advancement, particularly in the development of automated robotic systems, big data, the Internet of Things, additive technologies such as 3D printing, and augmented reality. These technologies are significantly transforming traditional business models and service delivery methods, introducing new paradigms in organizational and operational practices. The purpose of this research is to deepen and expand the current understanding of the impact of I4.0 technologies on the business performance and success of Croatian enterprises.

This study aims to investigate the relationship between the adoption of I4.0 technologies and business performance in Croatian enterprises, particularly during the early stages of digital transformation. The research objectives are structured as follows:

Theoretical Objectives:

• To develop a conceptual understanding of I4.0, its key components, and technological foundations.
• To analyze the core technological elements of I4.0—such as IoT, AI, big data analytics, and autonomous robotics—in the context of digital transformation.
• To establish a theoretical link between the implementation of I4.0 technologies and business performance, drawing from conventional microeconomic and neoclassical production theories (Kovačević, n.d.).

According to technological microeconomic theory, a firm is a production unit that transforms inputs into outputs under the constraints of available technology (technical efficiency) and market conditions (allocative efficiency). The neoclassical framework, in particular, views cost functions—shaped by technological and market factors—as key to understanding firm behavior and performance (Kovačević, n.d.).

Empirical Objectives:

• To identify and classify Croatian enterprises according to their level of I4.0 adoption, based on BCG criteria.
• To measure and compare the financial performance of I4.0 adopters and non-adopters across three key dimensions: activity, efficiency, and profitability.
• To test whether the adoption of I4.0 technologies has a statistically significant impact on selected financial indicators using ANOVA and linear regression models.
• To provide empirical insights into how digital maturity influences business performance in a small and open economy during early-stage I4.0 implementation.

Enterprise growth is primarily conditioned by business performance, as only successful firms have the potential to grow. The aim of this study is to test whether Croatian enterprises adopting I4.0 technologies are, on average, more successful than traditional firms, and therefore possess a greater potential for accelerated growth. This would also support the neoclassical theory linking firm size and technological efficiency, even in the early stages of advanced technology adoption. Furthermore, the study contributes to the discussion of I4.0 technology implementation in light of established theoretical models, particularly the technology acceptance model (Davis, 1980), with a specific focus on the perceived usefulness of new technology. Given the multiple dimensions of technological implementation, this study focuses primarily on the financial aspect. In particular, usefulness will be measured through selected financial performance indicators, identifying distinguishing elements of I4.0 compared to traditional models, with reference to Croatian enterprises and the broader economy.

The hypotheses are structured to test whether enterprises utilizing I4.0 technology elements in the early implementation phase achieve superior performance in at least one selected indicator of business success from the categories of profitability, efficiency, and activity, compared to traditional enterprises.

Specifically, to evaluate the impact of I4.0 technology implementation on selected financial performance indicators related to activity, efficiency, and profitability, and to test the main hypothesis, the following outcomes should be observed:

At least one selected profitability indicator of I4.0 enterprises is statistically significantly better than the same indicator for traditional enterprises.

At least one selected efficiency indicator of I4.0 enterprises is statistically significantly better than the same indicator for traditional enterprises.

At least one selected activity indicator of I4.0 enterprises is statistically significantly better than the same indicator for traditional enterprises.

The selected performance indicators belong to the categories of activity, efficiency, and profitability. Hypotheses H1, H2, and H3 will be tested using one-way analysis of variance (ANOVA) and simple linear regression analysis for each group of business performance indicators. Two indicators will be selected from each category. The hypotheses will not be rejected if the arithmetic means of indicators between groups (I4.0 and traditional) are statistically significantly higher for the I4.0 group. In the simple linear regression, an indicator variable I4a will take the value of 1 for I4.0 enterprises and 0 for traditional ones; the coefficient of the indicator variable will represent the difference in arithmetic means between the two groups, while the constant term will represent the mean value for traditional enterprises.

Each hypothesis will be further divided into two sub-hypotheses. All hypotheses will be tested using one-way ANOVA (Bahovec & Erjavec, 2009). To guide the analytical approach, this study draws conceptually on the Technology–Organization–Environment (TOE) framework, which provides a structured lens to examine factors influencing I4.0 adoption.

1.3. Scientific and Research Contribution

This study aims to fill a gap in the existing literature by empirically examining the impact of I4.0 technology adoption on business performance in Croatian enterprises.

Main Research Question:

Does the adoption of I4.0 technologies lead to significantly better financial performance—measured through indicators of activity, efficiency, and profitability—compared to traditional (non-I4.0) firms in Croatia?

Scientific Contribution:

The main academic contribution of this study is threefold:

1.

Empirical Evidence:

This research provides empirical validation—using firm-level financial data—that enterprises implementing I4.0 technologies exhibit statistically significant superior performance. Such evidence contributes to the still-developing academic discourse on the economic and operational outcomes of digital transformation at the firm level, especially in the under-researched context of Croatia.

2.

Methodological Originality:

By applying a classification methodology to distinguish between I4.0 and traditional enterprises, and by employing financial ratios as proxies for performance, this study introduces a novel approach in the regional context. This is particularly relevant due to the lack of domestic studies using this methodological framework to assess the financial impact of I4.0.

3.

Theoretical Contribution:

This study extends the Technology Acceptance Model (TAM; Davis, 1980) by empirically testing the perceived usefulness of I4.0 technologies through objective financial indicators. The research emphasizes the financial dimension of perceived usefulness as a driver for technology adoption, thus contributing to a broader understanding of TAM in the context of organizational-level digital transformation.

Practical Contribution:

The findings provide actionable insights for entrepreneurs, policymakers, and business strategists, supporting informed decisions to invest in advanced technologies. Demonstrating that such investments improve profitability, efficiency, and operational activity strengthens the economic rationale for accelerating digital transformation initiatives in Croatia.

In conclusion, this study offers a comprehensive empirical, theoretical, and practical contribution to the fields of I4.0, digital transformation, and technology adoption, addressing a relevant knowledge gap in the regional context.

1.4. Structure of the Paper

This paper is organized into five chapters. The first chapter introduces the research, outlining the objectives, hypotheses, and methodology. It establishes the primary aim: to investigate the challenges related to the implementation of I4.0 technologies and their impact on business performance. The chapter defines the research problem and scope aligned with the hypotheses and explains the rationale inspired by rapid technological advances, such as robotics, big data, the Internet of Things, 3D printing, and augmented reality, all transforming business operations.

The second chapter provides the theoretical background, focusing on fundamental concepts of I4.0 through the lens of high technology and artificial intelligence (AI). It includes definitions, literature reviews, development trends, risks, and initiatives related to I4.0. The chapter also analyses structural characteristics of Croatian enterprises, sectoral distribution, and technological maturity, leveraging national statistical classifications (National Classification of Activities, 2007a, 2007b) to identify firms engaged in I4.0 technologies.

The third chapter presents the empirical research on the impact of I4.0 technologies on the business performance of Croatian enterprises during the early adoption phase. It details the research methodology, findings, limitations, and recommendations for future research.

The fourth chapter offers a discussion of the results. The fifth and final chapter presents concluding remarks.

2. Fundamental Concepts of Industry 4.0 Through the Lens of Advanced Technologies and Artificial Intelligence: Status and Challenges in Croatia

I4.0 represents the industrial revolution grounded in the digital transformation of manufacturing and business processes. Key features include the integration of physical and digital systems, automation, real-time data exchange, and the extensive application of smart technologies within industrial environments. In line with contemporary global trends, I4.0 increasingly involves the development and deployment of high technology and artificial intelligence (AI), which together enable significant improvements in efficiency, flexibility, and business process optimization. These technologies support interconnected and autonomous production units capable of responding to real-time changes and independently making decisions to optimize production.

Artificial intelligence holds a central role, offering advanced capabilities through machine learning algorithms, natural language processing, and predictive analytics.

Core concepts such as the digital factory, smart manufacturing, adaptive logistics, and cognitive automation are inherently linked to technological solutions derived from high technology and AI domains. A comprehensive understanding of these concepts is crucial for analyzing contemporary challenges in the industrial sector, especially in small open economies like Croatia, where the technological gap and institutional framework strongly influence the pace and quality of I4.0 implementation.

The following sections analyze selected technologies, their relationship with business performance indicators, and the capacity of Croatian enterprises to adapt to current industrial trends. Considering the multidimensional nature of I4.0 adoption, the TOE framework is employed to capture technological, organizational, and environmental aspects relevant to this study.

2.1. Theoretical Background: The Evolution of Industrial Revolutions and the Foundations of Industry 4.0

Understanding I4.0 requires considering the broader historical and technological context of previous industrial revolutions. The First Industrial Revolution (late 18th to early 19th century) introduced mechanization through water and steam power, shifting economies from agrarian to industrial. The Second Industrial Revolution (late 19th to early 20th century) brought mass production and electrification, significantly enhancing productivity. The Third Industrial Revolution, starting in the latter half of the 20th century, introduced automation through electronics, IT systems, and early digital technologies.

The current phase, known as I4.0, builds upon digital technologies and is characterized by integrating cyber-physical systems, smart automation, Internet of Things (IoT), artificial intelligence, big data analytics, robotics, and cloud computing into manufacturing and services. These enable real-time data exchange, autonomous decision-making, and increased flexibility in production and services.

I4.0 was first introduced in Germany in 2011 as a strategic initiative to boost industrial competitiveness via digital transformation. Initially focused on manufacturing, the concept has evolved into a broader economic and managerial paradigm influencing supply chains, customer engagement, human capital, and business models.

From a theoretical perspective, I4.0 is not only a technological shift but also an organizational transformation. It entails reconfiguring internal business processes, data-driven decision-making, and strategic agility. This is particularly relevant for emerging and transition economies like Croatia, where digital maturity varies by industry.

This study assumes that early I4.0 adoption enhances business performance through improved efficiency, responsiveness, and innovation capacity. Prior research indicates digital technologies positively affect profitability, asset utilization, and operational agility, although effects vary by adoption stage and sector.

By integrating this historical and conceptual overview, the paper establishes a solid theoretical foundation for analyzing how I4.0 technologies impact key financial indicators—activity, efficiency, and profitability—in Croatian companies.

2.2. Conceptual Definition of Industry 4.0

I4.0 denotes the comprehensive digitalization of production and integration of advanced technologies into industrial processes. Originating in Germany in 2011 as a strategic initiative to enhance manufacturing competitiveness, I4.0 has since become a global framework transforming industrial production, distribution, and management. It encompasses manufacturing processes, efficiency, data management, customer relations, and competitiveness.

The foundation of I4.0 lies in horizontal and vertical integration of manufacturing systems, driven by real-time data exchange and production flexibility through customized manufacturing.

The term I4.0 reflects its German origin, where the highest levels of implementation are observed, particularly within multinational tech corporations. Notably, 32% of scientific papers on I4.0 (Piccarozzi et al., 2018) are authored by German scholars. The concept has expanded beyond Germany and engineering into economics and management.

According to McKinsey Global Institute (Manyika et al., 2013, as cited in Piccarozzi et al., 2018), I4.0 marks the era of cyber-physical systems—integrating computing, networking, and physical processes through technologies such as mobile devices, IoT, AI, robotics, cybersecurity, and 3D printing. Due to growing global market complexity and demand for adaptable, sustainable manufacturing, I4.0 is seen as a socio-economic phenomenon transforming work, management, and business.

Oztemel and Gursev (2020) note manufacturing as a fundamental driver of economic and social progress, with I4.0 a focal point for research and business interest over the past decade. Although industrial digitalization concepts existed earlier, I4.0 has only recently gained broader recognition in academic and industrial circles.

In SMEs, research focuses on managing I4.0 and innovation adoption to leverage new technologies, and on whether I4.0 implementation may cause challenges or suboptimal outcomes due to SMEs’ specific constraints. Sustainability pillars—environmental and economic—are also discussed, identifying success factors for environmentally sustainable production within I4.0 (De Sousa Jabbour et al., 2018).

Despite many studies, exclusive research on I4.0’s business or management aspects is rare due to its multidisciplinary nature. Most combine business with technical, IT, or sustainability perspectives. This reflects the complexity and breadth of I4.0 topics and reveals research gaps in business impacts. I4.0 remains a nascent and not yet clearly defined research area.

Literature review shows a lack of comprehensive management and business operation studies. Often, national studies analyze small samples; broader, sectoral, and longitudinal research is needed to better understand factors affecting I4.0 implementation success.

High technology’s role in Croatian business is significant. Veža et al. (2018) highlight Croatia’s low industrial maturity, near the second industrial revolution stage, and weaknesses in employee training. Roland Berger classifies Croatia among countries with low I4.0 readiness alongside Bulgaria, Poland, and Portugal. Hrbić (2024) reports positive impacts of I4.0 adoption on Croatian business performance, while McKinsey (2018) identifies digitalization as a key growth driver in Central and Eastern Europe, including Croatia. The region’s mathematical literacy and digital infrastructure offer growth potential to close the gap with developed countries. Maravić et al. (2022) find Croatian economies use I4.0 technologies less than developed countries, with human resource limitations and financial constraints as barriers. Increased technology application is expected to yield positive results and support I4.0 strategy development.

Research shows I4.0 technologies transform firm-level performance. IoT, AI, and analytics enhance efficiency and productivity when integrated into strategic functions (Szalavetz, 2019; De Sousa Jabbour et al., 2018). Early adoption correlates with improved flexibility and resource optimization, though financial gains often lag (Dalenogare et al., 2018). Organizational readiness and digital maturity are key enablers (Frank et al., 2019). Most findings come from large economies; evidence from small, open economies like Croatia is scarce. This study aims to fill that gap by examining early I4.0 adoption effects on measurable financial indicators.

In conclusion, significant research potential exists globally and in Croatia to understand challenges and opportunities in I4.0 implementation.

2.3. Fundamental Components of Industry 4.0

I4.0 comprises technologies transforming business processes. This study focuses on big data, autonomous robots, simulations, system integration, IoT, cybersecurity, cloud computing, 3D printing, and augmented reality. Each enables optimization and modernization of operations.

• Big data involves large datasets analyzed for decision-making insights, requiring new storage and processing methods (Kocijan, 2014).
• Autonomous robots operate in industries such as agriculture and healthcare, enabling human–machine collaboration and increasing adaptability (Schwab, 2016).
• Simulations optimize decision-making, risk assessment, and planning of complex manufacturing systems (De Paula Ferreira et al., 2020).
• Horizontal and vertical system integration provides a holistic business approach, improving process efficiency and sustainability.
• IoT connects devices into networks that communicate, linking physical and digital worlds (Piccarozzi et al., 2018).
• Cybersecurity protects data and systems in digital environments.
• Cloud computing allows remote data storage and processing, used by 39% of Croatian businesses (Croatian Bureau of Statistics, 2021).
• 3D printing produces objects layer-by-layer from digital models, enhancing manufacturing flexibility (Schwab, 2016).

2.4. Structure and Key Characteristics of the Business Sector in Croatia and Technological Transformation of Enterprises

An analytical approach was applied using available data, focusing on specific activity groups per the National Classification of Activities, a key statistical standard in Croatia.

Research by Arroyabe et al. (2024) reveals a wide range of digitization levels among Croatian companies, from those already using advanced technologies (robotics, cloud computing, smart devices) to those just starting I4.0 implementation. Hence, this study focuses on the early implementation phase.

Hrbić and Grebenar (2021) identified 141 companies (1.97% of analyzed subjects) with I4.0 potential, representing about 27% of the active sample and 26% of business revenues.

Between 2018 and 2022, Croatia’s ICT market grew steadily but remained underdeveloped. The International Trade Administration in 2022 forecasts continued growth in IT adoption, including cloud computing and IoT, elements of I4.0. Demand for ICT also increased in municipalities, manufacturing, transport, tourism, public sector, and SMEs.

Examples of Croatian high-tech industries include the fastest electric hypercar and multifunctional robotic systems for demining. Advanced technologies gain popularity: 29% of companies use cloud solutions and 21% use AI solutions.

Croatia’s National Development Strategy 2030 prioritizes four digital areas: economy’s digital transition, public administration/judiciary digitalization, broadband networks development, and digital skills/jobs development.

Croatian Bureau of Statistics (2021) reports 8% of companies using AI-based technologies, with the highest use (22%) among large firms. Common AI applications include software robots for process automation and decision-making, text processing, and machine learning for data analysis. This period marks the early diffusion of AI in Croatian business.

3. Empirical Research on the Impact of Industry 4.0 Implementation on Business Performance in the Early Stages of Adoption

3.1. Research Methodology, Sample Definition, and Measurement of Business Performance

This section outlines the research methodology, the definition and scope of the sample, and the approach used to define and measure business performance. It also describes the variables employed in the analysis. The empirical approach is conceptually guided by the Technology–Organization–Environment (TOE) framework, which structures the selection of variables across three dimensions: technological readiness, organizational capacity, and external pressures. Although the model is not tested explicitly in this study, the TOE framework provides a valuable lens for interpreting the observed patterns in adoption and performance outcomes.

3.1.1. Sample Definition and Scope of the Study

The analysis is based on publicly available company data from 2016 to 2020, including only companies with at least one employee. This period was selected to capture the early phase of I4.0 technology implementation. The study population was determined using the official Court Registry of companies operating in Croatia, which provides a comprehensive and up-to-date list of active enterprises. This registry served as the primary sampling frame for identifying companies, which were then further screened using publicly available information and web-based identification methods.

For the purposes of this analysis, companies were divided into two groups: I4.0 adopters and traditional companies. The classification was based on the model of nine core I4.0 technologies proposed by the Boston Consulting Group (BCG, 2020). A company was classified as an I4.0 adopter if it had implemented at least three of the nine technologies.

Data on technology implementation were collected through a survey sent to relevant company representatives (e.g., production managers, IT managers, board members), who indicated which of the listed technologies were actively used in their business processes. Companies that had not implemented any of the listed technologies were classified as traditional.

This classification method is consistent with the approach used by several authors in previous studies (Dalenogare et al., 2018; Müller et al., 2018; BCG, 2020).

Craft businesses and non-profit associations were excluded from the analysis. To eliminate potential bias due to industry sector differences (e.g., I4.0 companies performing better simply because they operate in more successful sectors), the sample was limited to companies from sectors identified as relevant to I4.0: C, G, J, and M. Sector C includes manufacturing; sector G includes wholesale and retail trade; sector J includes information and communication; and sector M includes professional, scientific, and technical activities.

Table 1. Structure of the population by characteristics.

Categorization of Companies	Number of Companies According to the Annual Financial Reports of Entrepreneurs in the Early Stages of Implementation
Assumed/surveyed/confirmed by the survey	2016	2017	2018	2019	2020	Total
Assumed I4.0 companies	75	83	92	95	95	440
I4.0 company	20	23	26	27	27	123
Traditional	55	60	66	68	68	317
Not an I4.0 company	4	5	6	7	7	29
Did not respond to the survey	51	55	60	61	61	288
Traditional	15.237	15.974	16.823	17.421	17.615	83.070
Total number of companies	15.312	16.057	16.915	17.516	17.710	83.510

Source: author’s processing, 2025.

presents the number of initial units per year, distinguishing between I4.0 and traditional companies, based on annual financial statements (GFI-POD).

Survey data were collected online between October 2021 and January 2022—a period deliberately selected to capture responses during the early phase of high-tech adoption in Croatian enterprises. This timing allowed the study to reflect the initial stage of I4.0 implementation and to provide relevant insights into the digital transformation process.

The selection of 132 companies invited to participate in the survey was based on a systematic screening process. Initially, enterprises were identified through a detailed examination of their official websites and publicly available sources, focusing on those explicitly indicating the use or implementation of I4.0 technologies, either as producers or service providers. In addition to web-based identification, supplementary criteria were applied to ensure a representative sample, including company size, sector, and geographic distribution, covering both manufacturing and service industries relevant to I4.0.

The selection process prioritized companies with a clear digital presence and accessible contact information for key decision-makers (e.g., IT managers, production managers, executives), which facilitated effective survey distribution. This approach ensured a balance between targeting likely I4.0 adopters and overcoming data accessibility constraints, ultimately yielding a reliable dataset for empirical analysis.

The survey was distributed via email, accompanied by a clear explanation of the research purpose and instructions for completion. A total of 41 valid responses were received: 28 from companies identified as I4.0 adopters and 7 from traditional companies. Duplicate responses and those lacking a valid company identification number (OIB) were excluded.

The survey targeted Croatian enterprises employing at least one worker (based on working hours) and involved in the production or provision of services linked to I4.0 technologies (as defined by BCG criteria). The overall response rate was 31.1%.

Contact persons were identified through company websites. Where necessary, the survey was sent to general email addresses and internally redirected to relevant individuals. Identified respondents were primarily decision-makers involved in technology adoption processes, ensuring informed responses. Data validity was enhanced through the use of a structured online questionnaire focused on the active use of specific technologies. Responses were cross-checked with publicly available information where possible, and statistical rules were applied to filter out financial outliers, enhancing internal validity.

Companies with atypical business performance (outliers)—defined as values exceeding ±3 standard deviations from the mean—were excluded, with the exception of confirmed I4.0 companies, due to their limited number. This threshold was empirically determined in line with Chebyshev’s theorem (Bahovec et al., 2015) and was also applied to total assets and revenue/expense indicators.

Performance indicators were standardized by year and company size (micro, small, medium, and large) to neutralize the effects of time and scale. Standardized indicators, expressed in standard deviation units, ensured comparability across company sizes and years. According to Bahovec et al. (2015), values exceeding ±2 standard deviations are considered atypical (covering 95.45% of a normal distribution), while ±3 standard deviations cover 99.73%. When the distribution shape was unknown or asymmetric, the ±3 standard deviation rule was applied, in line with Chebyshev’s theorem. Most indicators exhibited positive skewness and leptokurtic distributions.

The sample includes micro, small, and medium-sized enterprises. One large company responded, but was excluded from the final analysis due to significant differences in business models and reporting practices. Companies that did not respond to the survey were excluded, as their I4.0 status could not be verified. Also excluded were firms with zero performance indicators, zero operating revenues, or incomplete annual reports (e.g., covering less than 12 months), as these were considered inactive or irregular.

A final exclusion rule was applied: if any performance indicator exceeded ±3 standard deviations in any year, the company was removed from the sample entirely for all years. This was particularly important for ensuring homogeneity within the traditional company group, which had a larger and more heterogeneous composition.

The resulting dataset contains 18,440 company-year observations (2016–2020), based on publicly available financial reports from the GFI-POD database. Each observation corresponds to a company’s financial and performance data for a single year.

To assess performance differences between I4.0 and traditional enterprises, the analysis was conducted using five-year average values per company. The final sample provides a robust and balanced representation of Croatian enterprises actively engaged in or transitioning toward I4.0 during its early implementation phase.

By focusing on key sectors—manufacturing (C), wholesale and retail trade (G), information and communication (J), and professional, scientific, and technical activities (M)—the study controls for sector-specific performance variations. This sectoral focus allows for a more accurate comparison and minimizes potential biases arising from differing industry dynamics.

The exclusion of large enterprises and inactive firms strengthens the reliability and homogeneity of the sample, while strict statistical controls and performance standardization further enhance internal validity. The relatively high survey response rate and rigorous informant identification process support the credibility of the dataset.

Overall, the sampling strategy employed purposive sampling—a non-probability approach aimed at targeting enterprises likely to adopt I4.0 technologies. The classification of companies followed the BCG (2020) model, with I4.0 adopters defined as those implementing at least three of the nine core technologies, and traditional companies as those implementing none. Stratification by company size, sector, and geography ensured broad and balanced coverage of the Croatian business landscape. Though non-probabilistic, this stratified, purposive approach ensured practical feasibility, representativeness, and internal consistency, making the findings relevant for researchers, policymakers, and business leaders interested in digital transformation in emerging economies.

3.1.2. Defining and Measuring Business Performance Indicators

Business performance was assessed using financial ratio indicators, adapted from Žager et al. (2020) to align with the specific objectives of this study. The analysis was based on publicly available financial data. A financial ratio is defined as the quotient of two economic variables, expressing the relationship between them.

These indicators provide essential information for managing company operations and strategic development, and they serve as a basis for business decision-making. The primary data sources were the balance sheet and the income statement. While the balance sheet offers a snapshot of the company’s financial position at a specific point in time, the income statement reflects its operational performance over a defined period, typically one year.

Liquidity ratios are commonly used for short-term analysis, whereas profitability and efficiency ratios are more relevant for long-term performance assessment. From a managerial perspective, all categories of indicators hold significance.

For the purposes of this research, indicators were selected from the categories of activity, efficiency, and profitability. Efficiency, profitability, and investment ratios were classified as performance indicators, while activity indicators were considered both performance and stability indicators.

According to Žager et al. (2020), performance and stability are typically opposing objectives in the short term; however, in the long term, they are interdependent.

Performance indicators for companies identified as I4.0 adopters were compared to those of traditional firms. The analysis focused on selected indicators from the performance category.

Table 2. Variables Used in the Research.

Variable Name	Variable Definition	Variable Measurement Method
COMPANY CHARACTERISTICS (I4.0 or not I4.0)	Based on the survey questionnaire sent to companies and the respondents’ assessment, it is determined whether the company belongs to I4.0.	Survey questionnaire—binary
FINANCIAL INDICATORS	Indicators from the categories of profitability, efficiency, and activity, showing the impact of I4.0 elements on the company’s financial indicators.	Use of secondary data sources (annual financial statements).
Net Profit Margin	The net profit margin shows the profit after taxation divided by total revenue.	Annual financial statements of the entrepreneur: Income Statement (P&L), balance sheet. Formula: P_P_3 = AOP184/AOP177 (higher is better)
Return on Assets (ROA)	Return on assets (ROA) shows the profit after taxation divided by total assets.	Annual financial statements of the entrepreneur: Income Statement (P&L), balance sheet. Formula: P_P_4 = AOP184/AOP065 (higher is better)
Overall Business Efficiency	Business efficiency is the ratio of total revenue to total expenses.	Annual financial statements of the entrepreneur: Income Statement (P&L), balance sheet. Formula: P_E_1 = AOP177/AOP178 (higher is better)
Sales Efficiency	Sales efficiency is the ratio of sales revenue to sales expenses.	Annual financial statements of the entrepreneur: Income Statement (P&L), balance sheet. Formula: P_E_2 = (AOP126 + AOP127)/(AOP133 + AOP137) (higher is better)
Days of Customer Receivables	Days of customer receivables refer to the number of days in the year multiplied by receivables from customers, divided by regular business income.	Annual financial statements of the entrepreneur: Income Statement (P&L), balance sheet. Formula: P_A_2 = (365 * AOP049)/AOP125 (lower is better)

Source: Žager et al. (2020), author’s adaptation.

presents the variables, grouped into company characteristics and financial indicators, along with their definitions and methods of measurement.

The key company characteristic is the classification as an I4.0 enterprise, confirmed through the survey.

The financial indicators include net profit margin, return on assets (ROA), total operational efficiency, sales efficiency, and accounts receivable turnover (measured in days).

3.2. Analysis of the Research Results on the Impact of Industry 4.0 Technology Implementation on Selected Financial Indicators

The main hypothesis posits that, on average, companies have at least one superior business performance indicator compared to traditional companies across the three selected groups of indicators.

Table 3. Business Performance Indicators used in the Research.

Indicator Group	Indicator Name	Numerator	Denominator
Activity	Days of Customer Receivables	Number of days in the year (365) × Short-term receivables from customers	Revenue from regular operations
Activity	Days of Supplier Payables	365 × Short-term payables to suppliers	Revenue from regular operations
Efficiency	Overall Business Efficiency	Total revenue	Total expenses
Efficiency	Sales Efficiency	Sales revenue	Sales expenses
Profitability	Net Profit Margin	Profit after taxation	Total revenue
Profitability	Net Asset Return (ROA)	Profit after taxation	Total assets

Source: Žager et al. (2020), author’s adaptation.

presents the activity, efficiency, and profitability indicators used in the research, along with their respective calculation methods.

The main hypothesis is tested using auxiliary hypotheses corresponding to a selected set of business performance indicators. Two indicators were selected from each of the three main categories: profitability, efficiency, and activity. The main hypothesis is not rejected if at least one tested indicator from each category shows superior performance for I4.0 companies, at a 5% significance level.

For activity indicators, a lower value indicates better performance, while for efficiency and profitability indicators, higher values are considered better.

Due to the observed differences in mean values, simple linear regression is employed to estimate the extent to which performance indicators are better for I4.0 companies compared to traditional ones. The interpretation of whether a particular indicator is better depends on its direction—that is, whether a higher or lower value is desirable—based on its underlying economic rationale. This direction is specified in the methodology section for each indicator.

For indicators where higher values indicate better performance, a positive regression coefficient is expected; for those where lower values are preferred, a negative coefficient is expected. This implies that the arithmetic mean of the indicator for I4.0 companies is either higher (positive sign) or lower (negative sign) than that of traditional companies.

Results of Analysis of Variance (ANOVA) and Simple Linear Regression

Hypotheses H1, H2, and H3 were tested using one-way analysis of variance (ANOVA), while their economic significance was further assessed through simple linear regression analysis for each selected group of business performance indicators. Two indicators were selected from each of the three main categories: activity, efficiency, and profitability. A hypothesis is not rejected if the arithmetic means of the indicators are significantly better for the I4.0 group of companies compared to traditional companies.

Simple linear regression was performed using a binary indicator variable (I4a), which takes the value 1 for I4.0 companies and 0 for traditional companies. The estimated coefficient for the I4a variable represents the difference in the arithmetic means of the indicators between the two groups, while the intercept represents the arithmetic mean of the indicators for the traditional companies. Accordingly, each hypothesis was broken down into two sub-hypotheses.

The hypotheses were tested using two separate one-way ANOVAs, as suggested by Bahovec and Erjavec (2009). If at least one of the ANOVAs shows a statistically significant difference in favor of the I4.0 group, and the simple linear regression coefficient for the I4a variable is positive and statistically significant, then the hypothesis is confirmed—namely, that the selected average profitability indicators of I4.0 companies are superior to those of traditional companies. For profitability indicators, a higher value signifies better performance. The results of the variance analysis and simple regression, shown in

Table 4. Results of Variance Analysis and Simple Regression—Net Profit Margin.

Dependent Variable/ Significance Level	Source of Variation	Simple Linear Regression					One-Way ANOVA
		Mean Indicator Value	Estimate β	Std. Error	t-Value	Pr(>|t|)	Degrees of Freedom	Sum of Squares	Mean Square	F-Ratio	p-Value
Net Profit Margin ***	Between Groups	0.0944	0.0422	0.0083	5.0971	0.0000	1	0.0407	0.0407	25.9804	0.0000
Net Profit Margin	Within Groups	0.0522	0.0522	0.0006	82.0056	0.0000	3899	6.1153	0.0016
Net Profit Margin	Total						3900	6.1561

Note: Empirical significance level: *** p < 0.001 Source: author’s processing, 2025.

, indicate that the Net Profit Margin is significantly higher for I4.0 companies compared to traditional companies.

The ANOVA result at the 5% significance level rejects the null hypothesis H0 that there is no significant difference between the groups. The alternative hypothesis H1 is accepted, indicating that the average profitability indicator, Net Profit Margin, is higher for I4.0 companies compared to traditional companies.

The results of the ANOVA in the simple regression model for the indicator Net Profit Margin (profit) show that the average for I4.0 is 0.0944 (9.44% expressed as a percentage, as profitability margin is typically represented), while for traditional is 0.0522 (5.22%).

Based on one-way ANOVA, it is tested whether the average indicator of net asset profitability is statistically significantly better. If the net asset profitability indicator proves to be statistically significantly better, this means that I4.0 companies, on average, have a significantly better net asset profitability indicator compared to traditional companies. In the case of the net asset profitability indicator, this means that I4.0 companies have, on average, higher net asset profitability than traditional companies. A simple linear regression will be used to determine the coefficient β^₁, which represents the difference in the averages of the indicators. The results of the variance analysis and simple regression, shown in

Table 5. Results of the analysis of variance and simple regression—Net Asset Return (ROA).

Dependent Variable/ Significance Level	Source of Variation	Simple Linear Regression					One-Way ANOVA
		Mean Indicator Value	Estimate β	Std. Error	t-Value	Pr(>|t|)	Degrees of Freedom	Sum of Squares	Mean Square	F-Ratio	p-Value
Net Asset Return (ROA) ***	Between Groups	0.1888	0.1017	0.0143	7.1133	0.0000	1	0.2367	0.2367	50.5989	0.0000
Net Asset Return (ROA)	Within Groups	0.0870	0.0870	0.0011	79.1498	0.0000	3889	18.1914	0.0047
Net Asset Return (ROA)	Total						3890	18.4280

Note: Empirical significance level: *** p < 0.001 Source: author’s processing, 2025.

, indicate that Net Asset Return (ROA) is significantly higher for I4.0 companies compared to traditional companies.

The result of the ANOVA at the 5% significance level rejects the null hypothesis (H0) that there is no significant difference between the groups. The alternative hypothesis (H1) is accepted, meaning that the average profitability indicator, Net Asset Profitability (ROA), is better (higher) for I4.0 companies compared to traditional companies.

The results of the ANOVA in the simple regression model for the indicator Net Asset Profitability (ROA) show that the average for I4.0 is 0.1888 (18.88% expressed as a percentage, as profitability is typically represented), while for traditional companies it is 0.0870 (8.70%).

Based on one-way ANOVA, it is tested whether the average indicator of overall business efficiency is statistically significantly better. If the overall business efficiency indicator proves to be statistically significantly better, this means that I4.0 companies, on average, have a significantly better overall business efficiency indicator compared to traditional companies. In the case of the overall business efficiency indicator, this means that I4.0 companies have, on average, higher overall business efficiency than traditional companies. A simple linear regression will be used to determine the coefficient β^₁, which represents the difference in the averages of the indicators. The results of the variance analysis and simple regression, shown in

Table 6. Results of the analysis of variance and simple regression—overall business efficiency.

Dependent Variable/ Significance Level	Source of Variation	Simple Linear Regression					One-Way ANOVA
		Mean Indicator Value	Estimate β	Std. Error	t-Value	Pr(>|t|)	Degrees of Freedom	Sum of Squares	Mean Square	F-Ratio	p-Value
Overall business efficiency ***	Between Groups	1.1398	0.0698	0.0120	5.8335	0.0000	1	0.1114	0.1114	34.0294	0.0000
Overall business efficiency	Within Groups	1.0700	1.0700	0.0009	1162.8925	0.0000	3887	12.7223	0.0033
Overall business efficiency	Total						3888	12.8337

Note: Empirical significance level: *** p < 0.001 Source: author’s processing, 2025.

, indicate that overall business efficiency is significantly higher for I4.0 companies compared to traditional companies.

The result of the ANOVA at the 5% significance level rejects the null hypothesis (H0) that there is no significant difference between the groups. The alternative hypothesis (H1) is accepted, meaning that the average efficiency indicator, Overall Business Efficiency, is better (higher) for I4.0 companies compared to traditional companies.

The results of the ANOVA in the simple regression model for the indicator Overall Business Efficiency show that the average for I4.0 is 1.1398 (113.98% expressed as a percentage), while for traditional companies it is 1.0700 (107%).

Based on one-way ANOVA, it is tested whether the average indicator of business (sales) efficiency is statistically significantly better. If the business (sales) efficiency indicator proves to be statistically significantly better, this means that I4.0 companies, on average, have a significantly better business (sales) efficiency indicator compared to traditional companies. In the case of the business (sales) efficiency indicator, this means that I4.0 companies have, on average, higher business (sales) efficiency than traditional companies. A simple linear regression will be used to determine the coefficient β^₁, which represents the difference in the averages of the indicators. The results of the variance analysis and simple regression, shown in

Table 7. Results of the analysis of variance and simple regression—business (sales) efficiency.

Dependent Variable/ Significance Level	Source of Variation	Simple Linear Regression					One-Way ANOVA
		Mean Indicator Value	Estimate β	Std. Error	t-Value	Pr(>|t|)	Degrees of Freedom	Sum of Squares	Mean Square	F-Ratio	p-Value
Business (sales) efficiency ***	Between Groups	1.3753	0.1879	0.0319	5.90000	0.0000	1	0.8074	0.8074	34.8095	0.0000
Business (sales) efficiency	Within Groups	1.1874	1.1874	0.0024	484.9448	0.0000	3890	90.2290	0.0232
Business (sales) efficiency	Total						3891	91.0364

Note: Empirical significance level: *** p < 0.001 Source: author’s processing, 2025.

, indicate that business (sales) efficiency is significantly higher for I4.0 companies compared to traditional companies.

The result of the ANOVA at the 5% significance level rejects the null hypothesis (H0) that there is no significant difference between the groups. The alternative hypothesis (H1) is accepted, meaning that the average efficiency indicator, Business (Sales) Efficiency, is better (higher) for I4.0 companies compared to traditional companies.

The results of the ANOVA in the simple regression model for the indicator Business (Sales) Efficiency show that the average for I4.0 is 1.3753 (137.53% expressed as a percentage), while for traditional companies it is 1.1874 (118.74%).

The third hypothesis will be tested using two linear regressions, and if in at least one regression the estimators with the indicator variable I4 are significant and negative, the hypothesis will be proven that the activity indicators of I4 companies are better than the same indicators for traditional companies. In the case of the activity indicators group, a better indicator means a lower value.

The hypothesis will also be tested using two one-way ANOVAs, and if in at least one ANOVA the difference in arithmetic means is significantly lower for the I4 group of companies, along with the estimator from the simple linear regression with the I4 indicator variable being negative, the hypothesis will be proven that the selected average activity indicators of I4 companies are better than the average of the same indicator for traditional companies. In the selected group of activity indicators, a better indicator means a lower value.

Based on one-way ANOVA, it is tested whether the average indicator of customer retention is statistically significantly better. If the customer retention indicator proves to be statistically significantly better, this means that I4.0 companies, on average, have a significantly better customer retention indicator compared to traditional companies. In the case of the customer retention indicator, this means that I4.0 companies have, on average, lower customer retention rates compared to traditional companies. A simple linear regression will be used to determine the coefficient β^₁, which represents the difference in the averages of the indicators. The results of the variance analysis and simple regression, presented in

Table 8. Results of the analysis of variance and simple regression—customer retention rates.

Dependent Variable/ Significance Level	Source of Variation	Simple Linear Regression					One-Way ANOVA
		Mean Indicator Value	Estimate β	Std. Error	t-Value	Pr(>|t|)	Degrees of Freedom	Sum of Squares	Mean Square	F-Ratio	p-Value
Customer retention rates ***	Between Groups	66.6235	11.9561	7.7994	1.5329	0.1254	1	3268.4437	3268.4337	2.3499	0.1254
Customer retention rates	Within Groups	54.6674	54.6674	0.5985	91.3418	0.0000	3904	5,429,933.3730	1390.8641
Customer retention rates	Total						3905	5,433,201.8067

Note: Empirical significance level: *** p < 0.001. Source: author’s processing, 2025.

, show that the difference in customer retention rates between I4.0 and traditional companies is not statistically significant.

The result of the ANOVA shows that at the 5% significance level, we cannot reject the null hypothesis (H0) that there is no significant difference between the groups. The p-value is 12.54%, which is higher than the 5% significance level, so the null hypothesis (H0) cannot be rejected.

The customer retention indicator is not significantly different between I4.0 companies and traditional companies, meaning their customer retention rates are, on average, equal. Therefore, no conclusions can be drawn about the sign of the coefficient β^₁ in the simple linear regression.

Based on one-way ANOVA, it is tested whether the average indicator of supplier retention is statistically significantly better. If the supplier retention indicator proves to be statistically significantly better, this means that I4.0 companies, on average, have a significantly better supplier retention indicator compared to traditional companies. In the case of the supplier retention indicator, this means that I4.0 companies have, on average, lower supplier retention rates compared to traditional companies. A simple linear regression will be used to determine the coefficient β^₁, which represents the difference in the averages of the indicators. The results of the analysis of variance and the simple regression for the supplier retention indicator (days payable outstanding) are presented in

Table 9. Results of the analysis of variance and simple regression—days payable outstanding.

Dependent Variable/ Significance Level	Source of Variation	Simple Linear Regression					One-Way ANOVA
		Mean Indicator Value	Estimate β	Std. Error	t-Value	Pr(>|t|)	Degrees of Freedom	Sum of Squares	Mean Square	F-Ratio	p-Value
Days payable outstanding ***	Between Groups	0.0764	−0.0371	0.0182	−2.0351	0.0419	1	0.0315	0.0315	4.1414	0.0419
Days payable outstanding	Within Groups	0.1135	0.1135	0.0014	80.8808	0.0000	3886	29.5828	0.0076
Days payable outstanding	Total						3887	29.6143

Note: Empirical significance level: *** p < 0.001 Source: author’s processing, 2025.

.

The ANOVA result at the 5% significance level rejects the null hypothesis H₀, indicating that there is a statistically significant difference between the groups. The alternative hypothesis H₁ is accepted, suggesting that the average profitability indicator, Days Payable Outstanding, is more favorable (lower) for I4.0 companies compared to traditional ones.

The ANOVA results within the simple regression model for the indicator Days Payable Outstanding show that the average value of the indicator for I4.0 companies is 0.0764 (7.64% expressed as a percentage), while for traditional companies it is 0.1135 (11.35%).

The main hypothesis is tested using auxiliary hypotheses for a selected set of business performance indicators. From each main category of performance indicators—profitability, cost-efficiency, and activity—two indicators were selected. The main hypothesis is not rejected if at least one tested indicator from each group shows more favorable results for I4.0 companies at the 5% significance level.

As shown in Figure 1, the main hypothesis has been proven: I4.0 companies in the early stages of implementing I4.0 technology have better indicators in the Supplier Bonding Days from the activity indicators group, as well as the Overall Business Efficiency and Business Efficiency (Sales) from the cost-effectiveness indicators group, and the Net Profit Margin and Net Return on Assets (ROA) from the profitability indicators group. The only indicator, Days sales outstanding, is not significantly different in I4.0 companies compared to traditional ones, and therefore no conclusions can be drawn about its sign. Based on the above, it can be said that companies using I4.0 technologies have better business indicators than traditional companies.

Regarding the reasons for this phenomenon, there is clear alignment with the theory elaborated in earlier chapters of the paper. Although it is still an early phase in the development and application of I4.0 technologies globally, and thus in the Croatian market, it can be said that companies using I4.0 technologies have better business indicators than traditional companies. According to the previously mentioned conventional (technological) microeconomic theory, a company is a production unit that transforms input into output, constrained by available technology (technological efficiency) and the market environment in which it operates (allocative efficiency). Neoclassical theory (Kovačević, n.d.) focuses on the role of companies as production units, transforming inputs into outputs, and examining the behavior of companies under the constraints of given technology and the market environment. The neoclassical approach is fundamentally technological because the main determinant of an efficient, equilibrium industrial structure and the distribution of company size within it is a cost function (which is largely technologically determined). Croatia is a relatively small country, and allocative efficiency can be considered a homogeneous characteristic of Croatian companies, significantly contributed to by the development of the internet-based business model, particularly in IT, professional, scientific, and technical sectors, as well as increasingly in trade and manufacturing. The research is specifically focused on companies from the following groups, where allocative efficiency is less pronounced, and these differences are ignored in this study. The growth of companies is primarily conditioned by business success, as only successful companies can grow. The goal of this research was to test the hypothesis of whether, on average, companies using I4.0 technologies in Croatia are more successful than traditional companies and, therefore, have a higher potential for faster growth, which would support the neoclassical theory of the determination of company size and its technological efficiency, even in the early stages of development and application of the most advanced technologies. By proving the hypothesis, the contribution is the result of the introduction and use of I4.0 technology in light of the current theoretical assumptions, which fundamentally address the reasons for implementing high technology. This is related to the acceptance theory (Davis, 1980), specifically the segment of the utility of introducing new technology. The aspect of technology introduction observed in this paper is financial. Specifically, the utility of the introduction was measured through financial indicators, and the distinguishing elements of I4.0 in relation to traditional ones were identified, with respect to Croatian companies and the economy.

4. Discussion

The results of this study confirm a significant relationship between the implementation of I4.0 technologies and improved business performance indicators of Croatian companies, especially in the areas of profitability and efficiency. These findings are consistent with previous research highlighting how digital transformation and automation contribute to optimizing business processes and increasing competitiveness (Kagermann et al., 2013; Lu, 2017). It is particularly important to note that indicators such as net profit margin and return on assets (ROA) are significantly better in companies adopting I4.0 technologies, confirming that investments in digital technologies are associated with higher financial outcomes.

On the other hand, the negligible difference in the days sales outstanding indicator suggests that technological transformation alone may not be sufficient to improve all aspects of business, particularly those related to customer relationships and market dynamics. This opens room for further analysis of organizational and managerial practices accompanying digitalization. Previous studies emphasize that success in digital transformation largely depends on the synergy of technology, human factors, and leadership (Hess et al., 2016; Fitzgerald et al., 2014).

The observed better results in efficiency and activity indicators (e.g., shorter supplier bonding days) point to greater operational agility and supply chain optimization capabilities in I4.0 companies, which aligns with the concepts of smart factories and industrial automation (Lasi et al., 2014). This aspect further confirms that the integration of advanced technologies impacts not only financial results but also operational excellence.

Although the research sample is relatively small and focused mainly on SMEs, limiting the generalizability of the findings, the results are valuable as indicators of initial trends and challenges in I4.0 implementation in the Croatian context. Furthermore, the application of the TOE framework provides a useful theoretical basis for understanding the impact of technological, organizational, and environmental factors on the adoption of I4.0 technologies, which should be further investigated through longitudinal and comparative studies.

It is important to highlight the need for deeper research incorporating qualitative methods to cover organizational changes, leadership, and employee impact, which are key elements of successful digital transformation (Westerman et al., 2014). Additionally, future research should address social sustainability, as existing studies often neglect the social impact of I4.0, such as employee quality of life, health protection, and working conditions.

In summary, this study contributes to understanding how the early phases of I4.0 technology adoption affect business outcomes in Croatia and emphasizes the importance of a comprehensive approach that includes technological, organizational, and social dimensions of digital transformation. Given the rapid pace of technological change, continuous monitoring and evaluation of the impact of I4.0 technologies remain imperative for both the scientific community and practitioners.

5. Conclusions

This research analyzed data collected between 2016 and 2020, which covers the key phase of the initial implementation of I4.0 technologies in the Republic of Croatia. Although the research was published in 2025, the selected period of analysis provided a deeper understanding of the challenges and obstacles that Croatian companies faced during the early stages of digital transformation. The time gap between the end of the observed period and the publication of the results allowed for a more detailed analysis of the subsequent impacts, including the effect of the pandemic, which marked a turning point in business models and company strategies. The data from this period provides a methodologically consistent and reliable foundation for understanding key trends and behavior patterns, which are relevant not only for Croatia but also for other transitioning countries facing the challenges of implementing I4.0. This analysis offers important guidelines for future research and practice, as it provides a deeper insight into how the adoption of new technologies can be integrated into business processes and how companies can shape their long-term digital transformation strategy.

The conclusion of the research on the impact of I4.0 technology on the business performance of Croatian companies, based on primary and secondary data, surveys, and publicly available financial statements and profit and loss accounts, is that there is a statistically significant relationship between companies that use I4.0 elements and the selected performance indicators.

The main hypothesis is that, on average, companies that use I4.0 technologies have better at least one business indicator compared to traditional companies from the selected three groups of indicators. The main hypothesis was tested using auxiliary hypotheses for the selected set of business indicators. From each main group of performance indicators—profitability, efficiency, and activity—two indicators were selected, and the main hypothesis was not rejected if at least one tested indicator from each group was better for I4.0 companies at a 5% significance level. Hypotheses H1, H2, and H3 were tested using one-way analysis of variance (ANOVA), and their economic significance was explained through simple regression analysis for each selected set of business indicators. The conclusion based on publicly available financial data, regarding the hypothesis testing, is that:

• The average profitability indicators (net profit margin and net return on assets (ROA)) are better, with higher indicators for I4.0 companies compared to traditional ones.
• The average efficiency indicators (overall business efficiency and business efficiency (sales)) are better (higher) for I4.0 companies compared to traditional ones.
• The average days sales outstanding indicator is not significantly different for I4.0 companies compared to traditional companies, meaning their days sales outstanding are on average the same.
• The average activity indicator, supplier bonding days, is better (lower) for I4.0 companies compared to traditional companies.

The main hypothesis has been proven: I4.0 companies have better indicators in Supplier Bonding Days from the activity indicators group, Overall Business Efficiency and Business Efficiency (Sales) from the efficiency indicators group, as well as Net Profit Margin and Net Return on Assets (ROA) from the profitability indicators group. Only the Days sales outstanding indicator is not significantly different in I4.0 companies compared to traditional ones, and therefore no conclusions can be drawn about its sign. Based on the above, it can be said that companies using I4.0 technologies have better business indicators than traditional companies.

Based on this research, it can be concluded that I4.0 technology does impact the business success of Croatian companies. It is important to note the environment in which the analyzed companies operate, as this is a new technology that has not been present for long in the world, nor in Croatia. Positive examples of companies that are part of the Croatian economy and operate globally should be highlighted, and it can be concluded that this is an area that will show its full potential in the future.

This area is relatively new, and the companies are young, as well as recommendations for future research, which will be necessary since the industry is mostly in the early stages and expanding. Regarding limitations, the sample size of the research is not large, but given the number of companies deeply involved in I4.0, it is acceptable.

Additional limitations should be noted. First, the classification of companies into I4.0 adopters and traditional firms relies on self-reported data collected via questionnaires, which may be subject to response bias or inaccuracies regarding the extent of technology adoption. Second, the sample mainly includes small- and medium-sized enterprises (SMEs), which might limit the generalizability of the findings to larger companies or different sectors. Third, the study focuses primarily on quantitative financial and operational indicators and does not include qualitative factors such as organizational culture, leadership approaches, or employee skills that might affect I4.0 adoption success. Fourth, due to the lack of established benchmark values specific to I4.0 performance in Croatia, the analysis is limited to internal comparisons within the sample. Lastly, the rapidly evolving nature of I4.0 technologies means that longer-term impacts on business performance may not yet be fully captured.

These limitations highlight the need for future research with larger and more diverse samples, incorporating mixed-method approaches and longitudinal data to gain a more comprehensive understanding of the effects of I4.0 technologies in Croatia. A review of existing literature reveals a notable gap in research on social sustainability—one of the key dimensions of sustainability alongside economic and ecological aspects. The social dimension includes factors such as quality of life, health protection, and environmental care, which remain underexplored in the context of I4.0.

As the concept of I4.0 is still in its early stages of development, there is a pressing need for research employing various paradigms, especially within the field of management. The market has yet to fully develop products and companies that are emblematic of the I4.0 era, particularly in Croatia. Establishing and studying such I4.0 companies will be crucial, making this a valuable area for near-future research. Additionally, comparative analyses of I4.0 companies in similar EU economies and globally would provide important insights.

The application of the TOE (Technology–Organization–Environment) framework in this study offered a useful interpretive lens, and future research could expand upon this theoretical foundation through comparative or longitudinal analyses. Building on the findings and limitations identified here, future studies should aim to increase sample sizes and include a broader range of industries to enhance generalizability. Longitudinal research is recommended to assess the long-term impact of I4.0 technologies on business performance, as current findings primarily reflect early adoption stages.

Moreover, qualitative research methods could offer deeper insights into organizational changes, leadership challenges, and employee adaptation during digital transformation. Investigating the specific roles and combined effects of individual I4.0 technologies on efficiency and profitability would further enrich understanding of their impact. Finally, comparative studies across different countries or economic contexts would help identify contextual factors influencing successful technology adoption and digital maturity.

The practical implications of this study are particularly relevant for managers, policymakers, and investors. The findings demonstrate that companies in the early stages of Industry 4.0 (I4.0) implementation achieve superior results in terms of profitability, efficiency, and operational activity. This provides clear evidence that digital transformation yields measurable business benefits even in its initial phases. For business leaders, this underscores the strategic importance of investing in advanced digital technologies—especially within micro, small, and medium-sized enterprises (MSMEs), where resource constraints often pose challenges but the potential for improvement is significant.

Moreover, the results offer a solid foundation for shaping targeted public policies and support programs aimed at accelerating digital maturity in the business sector. Institutions responsible for designing incentive schemes may use these insights to justify more focused financial and advisory support. Financial institutions and investors may also consider incorporating digital readiness as a key factor in risk assessment and investment decision-making. Finally, the study highlights the growing importance of digital competencies and e-leadership in managing organizational change and fostering agility—critical elements for successfully navigating the complexities of I4.0 transformation.