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Citation: Lastauskaite, Aiste, and
Rytis Krusinskas. 2024. The Impact of
Production Digitalization Investments
on European Companies’ Financial
Performance. Economies 12: 138.
https://doi.org/10.3390/economies
12060138
Academic Editor: Tsutomu Harada
Received: 4 April 2024
Revised: 15 May 2024
Accepted: 21 May 2024
Published: 3 June 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
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4.0/).
economies
Article
The Impact of Production Digitalization Investments on
European Companies’ Financial Performance
Aiste Lastauskaite * and Rytis Krusinskas
School of Economics and Business, Kaunas University of Technology, Donelaicio St. 73, 44249 Kaunas, Lithuania
*Correspondence: aiste.lastauskaite@ktu.edu
Abstract: Businesses investing in production digitalization equipment are supposed to benefit from
increased productivity, enhanced efficiency, and revenue growth. Despite the increasing use of digital
technologies in business, many companies still struggle to measure and maximize their returns from
production digitalization investments. This research assesses the impact of production digitalization
investments on companies’ financial performance (operating revenue) for European businesses in
the period of 2013 to 2021. To achieve this target, we performed a Fixed Effects Panel Regression
analysis, using a sample size of 5706 records from the Orbis database for 30 countries, covering
634 business units. The production digitalization investment in this research is expressed by a
calculated variable value, measured as the annual change in a company’s Plant and Machinery value,
adjusted with corresponding an annual depreciation value for the assets. The regression output was
analyzed by considering the characteristics of the company size and business location. The results
suggest that companies in Eastern Europe benefit more from production digitalization than those in
Western Europe. The analysis highlights the tendency for the company costs of the employee and
intangible fixed asset value to increase as production digitalization investments grow. Additionally,
it shows that large companies tend to gain more from such investments than smaller ones. The
analysis provides support and guidance for businesses’ production digitalization investment strategic
decision-making processes.
Keywords: production digitalization; investment; Europe; company’s financial performance
1. Introduction
Digitalization is a performance tool for business development (Nasiri et al. 2020). It is
a business trend that affects single individuals and companies of all sizes and operational
sectors in all fields of activity (Hossnofsky and Junge 2019;Lichtenthaler 2021). The impacts
of digitalization have the potential to shape the future of business in a unique way, unlike
any other technological change (Hossnofsky and Junge 2019). In the research literature,
the concept of technological digitalization does not have a common definition, and terms
such as ‘digitalization’, ‘digital change’, and ‘digital transformation’ are commonly used
in the same context. Verhoef et al. (2021) discuss three stages of digital transformation:
digitization, digitalization, and digital transformation. Digitization refers to the automation
of daily tasks, digitalization refers to the integration of digital tools as robots into business
processes, and digital transformation refers to the introduction of new business models
which incorporate digital platforms. In this work, the concept of production digitalization
represents the integration of any tangible digital assets such as robots, smart production
systems, software, and others (Horvat et al. 2018,2019;Hsu and Spohrer 2009).
Following government encouragement and market conditions, numerous businesses
include digital transformation activities in their business strategy (Zeng et al. 2022). Un-
derstanding the potential return on investment in technological digitalization is crucial for
businesses’ strategic long-term planning (Zeng et al. 2022). Researchers highlight multiple
benefits for digital technology integration into business, such as resource savings, improved
Economies 2024,12, 138. https://doi.org/10.3390/economies12060138 https://www.mdpi.com/journal/economies
Economies 2024,12, 138 2 of 16
efficiency and lead time, business flexibility, developed new revenue streams, transparency
of operations, smoothened communication, and promoting innovation (Ladeira et al. 2019;
Cette et al. 2022;Bleicher and Stanley 2017;Heredia et al. 2022;Peng and Tao 2022). How-
ever, not all companies manage to reach the expected benefits of digitalization (Zeng et al.
2022) and there are doubts about digitalization’s direct impact on financial performance
(Viete and Erdsiek 2020;Li et al. 2021;Faff et al. 2013). The situation when firms invest in
digital transformation and do not obtain the expected benefit is called the digitalization
paradox (Gebauer et al. 2020;Guo et al. 2023).
The assessment of the financial impact on businesses has generated increasing interest
among researchers (Hess et al. 2016). By employing regression models, researchers uncover
different aspects of the impact area and present contradictory and inconsistent findings.
For example, research conducted by Guo et al. (2023) showed a negative result of digital
transformation on business, attributable to an increased operation cost rate and managerial
expenses, as well as a reduced total asset turnover. In contrast, Zeng et al. (2022)’s regression
model confirmed a positive increase in the value of return on assets (ROA). Therefore,
many businesses are facing the dilemma of whether and to what extent they should invest
in digitalization to achieve a valuable financial outcome (Zeng et al. 2022). To resolve
this dilemma, it is necessary for researchers to conduct more extensive examinations and
provide quantitative and reliable evidence of the impact of digitalization investments on
businesses’ financial results.
The year 2020 made a remarkable mark in the history of businesses’ digitalization. It
was the year when the COVID-19 pandemic hit firms around the globe with restrictions
on physical interactions and forced them to fundamentally change their way of working
(Amankwah-Amoah et al. 2021). A range of government-enforced measures, such as social
distancing, obliged many firms to accelerate digitalization actions at short notice (Sostero
et al. 2020). The pandemic forced many firms to shift to remote work and operate online. It
also accelerated the change from paper to digital document processing. These changes in
businesses had a direct impact on the large-scale and unavoidable adoption of digitalization
(Amankwah-Amoah et al. 2021;Pinzaru et al. 2020). However, with the sudden growth
in production digitalization, the question of financial benefit to firms has arisen. This is,
in other words, whether production digitalization investments that were supposed to be
quickly absorbed and the new tools that were supposed to be quickly implemented brought
valuable higher financial performance results.
Digitalization process success in businesses is also impacted by countries’ historical,
cultural, and development differences (Jovanovi´c et al. 2018;Nelson and Ellis 2019;Hartl
and Hess 2017). It is important to consider that post-Soviet bloc countries in Europe ex-
perienced significant stagnation in technology adaptation and development during the
Soviet era, lacking access and the required infrastructure to use technology and to engage
in the earlier technological digitalization processes. Following the collapse of the Soviet
bloc, these countries undertook a very rapid development (jump) in digital technology
adaptation (Akatkin and Yasinovskaya 2019). In contrast, Western Europe countries experi-
enced steady development and growth in digital skills and infrastructure during the same
period. This historical aspect has led to a notable imbalance in digitalization maturity and
the digitalization adaptation capabilities of these countries.
Furthermore, business size itself, in the context of digitalization investments, influ-
ences company performance (Ribeiro-Navarrete et al. 2021;Buer et al. 2021). The research
literature presents conflicting opinions, highlighting digitalization benefits and challenges
for small, medium, and large companies. For example, larger firms face fewer resource
constraints compared to small companies (Becheikh et al. 2006;Horváth and Szerb 2018),
and they can financially support digitalization activities in unexpected events.
From the business financial management point of view, each strategic business deci-
sion as well as production digitalization investments should result in financial profit and
business growth. Therefore, the business’s main interest is to make such decisions that
would result in improved financial well-being, a better market position, and a competitive
Economies 2024,12, 138 3 of 16
advantage. While competition is very acute, digital investments and strategies related
to them are crucial. The assessment of the financial impact on businesses has generated
increasing interest among researchers (Hess et al. 2016). Through the employment of
regression analyses, scholars reveal various dimensions of the impact area, leading to
findings that are often contradictory and inconsistent. For instance, the study by Guo et al.
(2023) showed that digitalization negatively affected businesses due to a rise in operational
costs and managerial expenses, along with a decrease in total asset turnover. Conversely,
the regression analysis by Zeng et al. (2022) confirmed a positive impact on the return
on assets (ROA) value. Thus, numerous companies are confronted with the challenge
of deciding if, and to what degree, they should implement digitalization investments to
secure a significant financial benefit (Zeng et al. 2022). Addressing this dilemma requires
researchers to undertake more comprehensive studies and offer quantitative and reliable
data on how digitalization influences business financial performance, taking into account
factors like company size and the region of operation.
This paper addresses the impact of production digitalization on the companies’ fi-
nancial performance of European companies. Data from 634 European business units,
covering 30 countries are analyzed. The total sample size covers 5706 records. The analysis
was conducted over a 9-year period from 2013 to 2021 and includes the pandemic crisis
period. Furthermore, using a regression analysis, the output is compared, taking into
consideration the specifics of the company size and business location. This study aims
to analyze the impact of production digitalization investments on business, specifically
addressing conditions such as company size and European geographic region. Moreover,
it investigates which financial indicators most accurately reflect the impact of production
digitalization investments, and how we can measure the expected effect. Additionally,
it analyzes what patterns can be identified between the level of production digitaliza-
tion investment and financial performance, considering European geographic region and
company size differences. Furthermore, this research distinguishes itself by evaluating
production digitalization investments not through the lens of specific technology but by
considering technological shifts in industries. The paper extends the available literature on
production digitalization investment’s impact on business financial performance across
different company sizes, and Western and Eastern Europe regions, identifying key financial
indicators and patterns. The key benefit of the analysis is in providing quantitative evidence
for guidelines on where to focus strategic targets to gain maximum financial return from
production digitalization investments.
2. Literature Review and Hypothesis Development
Production Digitalization Investment’s Impact on Business Financial Performance: Evaluation of
Region and Company Size Differences
Digitalization is reshaping the fundamentals of business processes (Ghosh et al. 2022).
The integration of digital technologies into business processes, such as production, sales,
and management, creates competitive advantages for companies (Akhtar et al. 2019). There
is no general agreement in the research literature on the impact of digitalization on business
financial performance. For instance, research conducted by Huang et al. (2020) and Liu et al.
(2023) confirmed the positive impact of digitalization on business financial performance.
Huang et al. (2020)’s study showed a positive and significant impact on 8 financial metrics,
including labor productivity, return on assets (ROA), and profit margin. Meanwhile, Liu
et al. (2023) investigated Chinese manufacturing firms during the period of 2008–2017
and provided empirical evidence supporting a positive correlation between the adaptation
of digital technologies and company performance. Furthermore, the results of the study
by Peng and Tao (2022) showed that digitalization reduces costs, increases revenue, and
improves efficiency, with explanatory variables such as a return on assets, profit margin, and
innovation output. Based on the above findings, the following hypothesis was proposed:
H1. Production digitalization investment has a positive impact on business financial performance.
Economies 2024,12, 138 4 of 16
Controversially, a negative digitalization impact was confirmed by Jardak and Hamad
(2022), Guo and Xu (2021), and Viete and Erdsiek (2020). These controversial research
results and conflicting conclusions directly demonstrate the need for further and more
complex examinations of digitalization’s impact on business financial performance.
The European region consists of Western Europe and post-Soviet Eastern Europe
countries, which are different from each other considering their historical, cultural, and
technological developmental differences that have shaped their digital landscapes (Jo-
vanovi´c et al. 2018;Nelson and Ellis 2019;Hartl and Hess 2017). Notably, the legacy of
the Soviet era has left Eastern countries at a digital crossroads, facing a unique set of chal-
lenges for digital transformation. Post-Soviet Eastern European countries, characterized
by a period of significant technological stagnation, have been contrasted with the steady
growth and development observed in Western Europe. Following the Soviet era, Eastern
Europe focused on a rapid catch-up with digital technology, facing limited investment and
a shortage of a digitally skilled workforce (Akatkin and Yasinovskaya 2019). Dzarasov and
Gritsenko (2022) further underscore the low level of digitalization prevalent throughout
the post-Soviet period, which has resulted in uneven digital development across these
regions. The cultural differences between post-Soviet and Western European countries are
deeply ingrained in their way of living, influencing attitudes towards technology adoption,
innovation, and digitalization (Choi et al. 2014). Culture in Eastern Europe is influenced by
historical attitudes towards risk and individual initiatives (Schwartz and Bardi 1997).
Considering historical, cultural, and digital development challenges shaping the
digital landscapes of Western and Eastern European countries, a hypothesis was raised
to test the differences in the impact of technological digitalization on business financial
performance across these regions. The following hypothesis was constructed:
H1.2. The impact of production digitalization investment on business financial performance differs
significantly between Western and Eastern European countries.
Furthermore, company size also has an influential role in the digitalization impact
on company performance (Buer et al. 2021). For example, small companies more often
face difficulties in technology adaptation than bigger ones (Nguyen 2009). Small and
medium-sized companies often have problems such as lower productivity, difficult access
to additional financial resources, and prolonged technology integration time (Telukdarie
et al. 2023;Thrassou et al. 2020). Müller et al. (2018)’s research confirmed that Industry 4.0
technologies’ implementation results are affected by company size, where the favor goes
to larger and medium-size companies. Company size is directly associated with business
financial well-being, available additional investments, and in general, technological capabil-
ity (Reichert and Zawislak 2014). Small companies associate digitalization as an expensive
activity (Bokša et al. 2020). Furthermore, small businesses lack bargaining power with
suppliers to acquire the best packages and support for technology integration agreements
(Malekifar et al. 2014). Based on this context, the following hypothesis was formed:
H1.3. The size of the business investing in production digitalization impacts its financial performance.
Additionally, there are several important research limitations to discuss. First, most of
the available existing academic research work has been narrowed in its technology scope,
focusing on measuring and evaluating the impact of digitalization on company perfor-
mance, considering specific technology from individual countries’ datasets (Dalenogare
et al. 2018;Duman and Akdemir 2021;Scott et al. 2017). For example, Dalenogare et al.
(2018) focused on Industry 4 technologies in Brazilian industries, whereas Cette et al. (2022)
relied on an analysis of 1065 French firms with a focus on cloud and big data technologies.
However, digitalization today is more about the joint effect of multiple digital technologies
on the business level than about the impact of a single technology (Bharadwaj et al. 2013;
Kim et al. 2021). Second, to the best of our knowledge, the research literature does not
address the differential impacts of technological digitalization across geographic regions
Economies 2024,12, 138 5 of 16
and company sizes. This research extends the literature and offers a more comprehensive
analysis by evaluating the combined impact of digital technologies across businesses, in-
vestigating the specifics of regional differences and business size, and utilizing data from
30 European countries.
3. Data and Methodology
For the analysis, company-level data from the Orbis database were used, with a
sample size of 5706. An analysis was performed utilizing the STATA statistical software.
The geographic area covered Eastern and Western Europe. According to the NACE code,
companies included in the analysis belonged to the following sectors: manufacturing,
wholesale and retail, transport and storage, postal and courier services, hotels and tourism,
professional and technical services, other services, and education. The dataset was aggre-
gated and winsorized at 1 to 99 percent in order to eliminate outlier effects. A financial
performance indicator was used as the dependent variable, and records with missing
profitability data were dropped. The final sample consisted of 634 business units.
In this study, data from 634 business units spread across 30 European countries were
analyzed. Data was segmented into Eastern and Western European regions. The dataset
consisted of 81 unique business names from Eastern Europe and 553 unique business
names from Western Europe. Furthermore, the whole data sample of 634 companies
were categorized based on their size, considering the number of employees per company.
Specifically, the dataset included 20 unique company names of very small businesses,
70 unique
company names of small enterprises, 137 unique company names of a medium
size, and 511 unique company names of large businesses. This detailed breakdown allowed
for insights into how investment in technological digitalization impacts business financial
performance across Eastern and Western European regions. Results should be interpreted
with caution due to the limited number of very small companies available in the dataset.
Data represented a period of 9 years (2013–2021), which allowed us to distinguish
comparatively long-term trends. The period also included the COVID-19 pandemic, which
had a significant impact on digitization trends (Amankwah-Amoah et al. 2021;Pinzaru
et al. 2020).
Variables have been selected with reference to the available literature on the subject of
measuring the digitalization impact on business financial performance (Zeng et al. 2022;
Sanchez-Riofrio et al. 2022;Yu et al. 2019;Jacobs et al. 2016). Variable definitions including
calculation components are presented in Table A1. In the research work, digital technologies
affected firm performance through variables like changes in sales, operating revenue, return
on assets, return on equity, and others (Heredia et al. 2022;Guo et al. 2023;Martín-Peña
et al. 2019). In this study, operating revenue represents business financial performance,
which is often used in the literature as a business financial performance indicator.
Production digitalization investment (furthermore—digital investment (di)) is the
main independent variable in the analysis, expressed as follows:
dii,t=pmi,t−pmi,t−1−pmdi,t(1)
where pm is the Plant and Machinery value in thousands of Euros for an individual
company, represented as i, the time period is t, and pmd represents the Plant and Machinery
depreciation value in thousands of Euros.
Due to the absolute numbers’ correlation with company size, in the regression model,
all variables were divided by the number of employees per company. For example, large
companies naturally have higher operating revenues and higher expenses for employees.
Relative size measures allowed us to control for the size effect.
Economies 2024,12, 138 6 of 16
This study examined the impact of production digitalization investments on business
financial performance. To accomplish this, a fixed effects ordinary least squares (OLS)
regression model of the following form was constructed:
ori,t=a+b1costei,t+b2i nt f assi,t+b3rdei,t+b4net pi,t+b5dii,t+b6t an f assi,t+ε(2)
where or is the operating revenue of the company iin year t; following this, coste is the cost
of employees, intfass is intangible fixed assets, rde is research and development expenses,
netp is the net profit, di is digital investment, and tanfass is tangible fixed assets. The
company name is used as the panel identifier and year as the time variable. The fixed effect
implies that the model controls for fixed effects at the company level.
The regression method was chosen with reference to the studies of Zeng et al. (2022),
Guo et al. (2023), and Sanchez-Riofrio et al. (2022). A regression specification error test
(RESET) was included to enhance the reliability and account for potential nonlinearities.
In order to account for possible differences in geographical region (H1.2) and company
size (H1.3), the data were segmented, and a regression analysis was performed separately
for each group. Two geographical regions, Eastern and Western Europe, were used in
the analysis, where Eastern Europe includes countries that were once part of the Soviet
bloc. Company size, considered in terms of the number of employees per company, was
categorized based on the European Commission definition (2003) of very small size (up to
10 employees), small size (11–50 employees), medium size (51–250), and large companies
(251 and more employees). This segregation allowed us to investigate how the relationship
between dependent and independent variables varies with regards to company size and
geographical region.
For a robustness check of the model, a quartile-based comparative analysis was
included. Following this method, the dataset was sorted by the digital investment (di)
values in an ascending order and divided into 4 quartiles. The first and third quartiles
of digital investment (Q1 and Q3) were compared, as the fourth quartile (Q4) contained
insufficient observations per company over time to support a robust fixed effects analysis.
This methodological approach enhanced the credibility of the results.
Before running the regression analysis, the Augmented Dickey–Fuller (ADF) test was
run for all time-series variables. The results of the test indicated that all variables used in
the analysis became stationary after the first differencing, as p-values were less than 0.05.
Variables in use did not have a unit root. Furthermore, using a Hausman specification
test, we found that the chi-squared statistic was 16.63 with a p-value close to zero (refer
to Table A2). Finally, we calculated variance inflation factors (VIFs) for the variables of
interest, excluding the fixed effects, finding that the VIFs for the explanatory variables were
all below 3 (refer to Table A3).
4. Empirical Results
Table 1presents the variables, which were used in the analysis together with their
main descriptive statistics. Table 1also provides a detailed segmentation of the financial
variables across Eastern and Western Europe regions, highlighting differences in indepen-
dent variables. All variables in Table 1are relative size measures, where values are divided
by the number of employees per company. All numerical data in the analysis are annual
data. The dataset covers 5706 observations, which represent a wide variety of companies.
From Table 1, it is important to note that the mean of operating revenue per employee in
Europe is around EUR 285,000, with a wide range ranging from EUR 1000 to EUR 13 million.
The skewness (Skew) value of 13.191 suggests that the distribution is heavily right-skewed,
meaning there are a small number of companies with revenues significantly higher than
the majority of firms in dataset. The production digitalization investment has an average of
EUR 77,000 per employee, representing a commitment to digitalization within the range of
the dataset. The kurtosis (Kurt) value of 486.138 is extraordinarily high, which suggests
a very peaked distribution with thick tails. Furthermore, the average of the research and
development expenditures variable is relatively low compared to other variables. It also
Economies 2024,12, 138 7 of 16
has a Skew value of 15.841 in Europe, which suggests a significant right-skewness. This
pattern indicates that most companies spend relatively low amounts on research and devel-
opment expenditures, with a few companies spending substantially more. From Table 1,
key regional differences can be noted between Eastern and Western Europe, notably in
operating revenue and tangible fixed assets, with Western European companies generally
reporting higher figures. Eastern European companies show lower average costs of em-
ployees (coste) and a smaller scale in intangible assets (intfass), indicating a divergence in
resource allocation and investment priorities. Additionally, the scale of digital investment
(di) and R&D expenses (rde) is more modest in Eastern Europe, reflecting variations in
innovation focus and digital transformation strategies across the regions.
Table 1. Variables definition and descriptive statistics for European regions.
Reg.
Variable Abbr. Obs Mean
Median Std. Dev.
Min Max Skew Kurt
Europe
Operating Revenue th Eur or 5706 285 206 457 1 13,520 13.191 267.638
Cost of employees th Eur coste 5706 54 52 48 4.1 ×10−22766 32.026
1708.202
Intangible Fixed Assets th Eur intfass 5706 64 21 209 0 9783 25.744
1010.584
Tangible Fixed Assets th Eur tanfass 5706 104 43 334 7.2 ×10−29098 14.464 277.361
R&D expenses th Eur rde 5706 9 0 54 0 1760 15.841 351.778
Net profit th Eur netp 5706 −4 6 111 −1983 614 −7.538 92.328
Digital Investment th Eur di 5706 77 35 178 0.006 6942 16.694 486.138
Eastern Europe
Operating Revenue th Eur or 729 159 93 241 1 3127 5.621 52.191
Cost of employees th Eur coste 729 23 17 30 4.1 ×10−2346 6.996 63.414
Intangible Fixed Assets th Eur intfass 729 11 1 32 0 447 6.391 62.022
Tangible Fixed Assets th Eur tanfass 729 162 38 609 1 7740 8.211 82.96
R&D expenses th Eur rde 729 4.94 ×10−10 5 0 97 13.079 187.372
Net profit th Eur netp 729 −8.81 ×10−12 111 −1802 614 −8.806 135.945
Digital Investment th Eur di 729 39 17 62 4.23 ×10−1481 3.398 16.447
Western Europe
Operating Revenue th Eur or 4977 303 219 478 2 13,520 13.044 254.399
Cost of employees th Eur coste 4977 59 55 49 2.81 ×10−12766 35.18
1862.314
Intangible Fixed Assets th Eur intfass 4977 71 26 222 0 9783 24.393 900.239
Tangible Fixed Assets th Eur tanfass 4977 96 44 270 7.2 ×10−29098 17.17 419.003
R&D expenses th Eur rde 4977 10 43 58 0 1760 14.848 308.862
Net profit th Eur netp 4977 -4 7 111 −1983 551 −7.357 86.121
Digital Investment th Eur di 4977 82 39 188 6E-3 6942 16.032 441.683
Source: The authors’ calculation using data from the ORBIS database. Note: Variables presented in this table are
adjusted to provide relative size measures. For example, ‘Operating Revenue’ represents ‘Operating Revenue
per Employee.’ All figures are normalized by dividing them by the total number of employees per company.
Values smaller than 1 are presented in the scientific notation. In this notation, ‘E’ represents ‘times ten raised to
the power of.’
The dataset covers a sample from very small and large companies where the number
of employees varies from 6 to 189 thousand employees. Descriptive statistics of variables
based on company size are presented in Table 2. To achieve comparable numbers, all
variables in the analysis are divided by the number of employees in the business unit.
Based on the detailed statistics presented in Table 2, several key differences across company
sizes can be highlighted. First, large companies demonstrate a higher median in operating
revenue (op) per employee, suggesting a more efficient utilization of workforce in gener-
ating revenue compared to smaller companies. The skewness (Skew) value of 6.052 for
large companies indicates a right-skewed distribution, meaning that while the majority of
large companies have revenues around the median, there is a long tail of companies that
earn significantly more. Furthermore, despite small companies having a higher mean in
tangible fixed assets (tanfass), it is the very small companies that exhibit a significantly
higher mean in intangible fixed assets (intfass), indicating a potential focus on intellectual
property and non-physical assets within smaller businesses. Moreover, the R&D expenses
(rde) of medium-sized companies show moderate mean values, but it is the small com-
panies that exhibit higher R&D expenses (rde) on average, possibly reflecting a strategic
investment in innovation to enhance competitiveness. In addition, net profit (netp) trends
demonstrate that large companies tend to be more profitable on average, with a positive
net profit (netp) mean, contrasting with the negative mean net profit (netp) observed in
Economies 2024,12, 138 8 of 16
very small companies, highlighting the financial challenges smaller companies may face.
Lastly, digital investment (di) as a relative measure shows that large companies invest
more in digital technologies per employee, possibly due to greater financial resources and
a strategic emphasis on technological digitalization.
Table 2. Variables definition and descriptive statistics by company size.
Co.
Size
Variable Abbr. Obs Mean Median Std. Dev. Min Max Skew Kurt
Very small
Operating Revenue th Eur or 104 540.726 200.683 1471.996 19.178
10,603.875
5.743 37.9
Cost of employees th Eur coste 104 87.786 74.88 46.921 34.225 288.1 2.137 8.294
Intangible Fixed Assets th Eur intfass 104 187.541 8.952 723.612 0
6526.046
7.096 59.275
Tangible Fixed Assets th Eur tanfass 104 50.78 4.818 203.704 1.746 1578.5 6.348 44.35
R&D expenses th Eur rde 104 19.181 0 113.441 0 915.2 6.566 47.152
Net profit th Eur netp 104 −223.487 −
112.654
361.832 −
1983.432
136.571 −2.313 9.021
Digital Investment th Eur di 104 31.929 14.021 48.542 2.8 227.167 2.818 10.572
Small
Operating Revenue th Eur or 348 314.722 127.898 922.057 3.995
13,520.756
10.765 138.122
Cost of employees th Eur coste 348 87.45 68.434 158.149 7.356
2766.082
14.309 238.993
Intangible Fixed Assets th Eur intfass 348 76.631 11.581 243.399 0
3652.747
10.08 137.953
Tangible Fixed Assets th Eur tanfass 348 289.242 17.322 987.749 0.541
9098.005
5.79 41.514
R&D expenses th Eur rde 348 46.309 0 178.767 0
1760.364
5.674 41.27
Net profit th Eur netp 348 −133.481 −
27.041
299.459 −
1802.269
545.385 −2.644 12.245
Digital Investment th Eur di 348 96.312 28.683 389.373 0.7
6942.003
15.755 276.221
Medium
Operating Revenue th Eur or 841 247.058 153.24 413.349 1.789
4222.104
6.052 46.749
Cost of employees th Eur coste 841 54.117 54.58 33.46 4.408 198.817 0.741 3.722
Intangible Fixed Assets th Eur intfass 841 44.882 8.174 117.329 0
1306.818
5.649 45.138
Tangible Fixed Assets th Eur tanfass 841 62.001 38.274 71.493 0.171 654.233 2.587 13.991
R&D expenses th Eur rde 841 16.469 0 61.349 0 523.569 4.73 27.481
Net profit th Eur netp 841 −21.187 0.711 101.873 −778.623 551.303 −1.962 16.495
Digital Investment th Eur di 841 58.449 31.528 66.11 0.206 487.805 1.802 7.267
Large
Operating Revenue th Eur or 4412 284.109 216.773 344.929 8.326
8388.141
6.052 46.749
Cost of employees th Eur coste 4412 51.144 50.847 26.621 0.041 751.503 0.741 3.722
Intangible Fixed Assets th Eur intfass 4412 63.983 25.387 191.42 0 9783.27 5.649 45.138
Tangible Fixed Assets th Eur tanfass 4412 99.475 46.426 250.623 0.072
5492.634
2.587 13.991
R&D expenses th Eur rde 4412 5.162 0.603 12.362 0 164.93 4.73 27.481
Net profit th Eur netp 4412 14.261 8.332 34.369 −385.943 614.25 −1.962 16.495
Digital Investment th Eur di 4412 80.6 36.884 167.499 0.006
3272.066
1.802 7.267
Source: The authors’ calculation using data from the ORBIS database. Note: Variables presented in this table are
adjusted to provide relative size measures. For example, ‘Operating Revenue’ represents ‘Operating Revenue per
Employee.’ All figures are normalized by dividing them by the total number of employees per company.
The Pearson correlation results are presented in Table 3. It was also used to test
the multicollinearity between variables. The results show that the correlation coefficient
for digital investment (di) is significantly positive, suggesting that digital investment
(di) is positively correlated with business financial performance. The Pearson correlation
presented in Table 3shows strong interdependencies among variables used in the regression
analysis. Notably, the operating revenue (or) shows a very high correlation with the cost of
employees (coste) of 0.941, suggesting that as companies increase their employee-based
expenses, there is a corresponding significant increase in operating revenue. It highlights
the importance of human capital for the financial profitability of business. Similarly, tangible
fixed assets (tanfass) exhibit a substantial correlation with digital investment (di) at 0.844,
indicating that firms investing heavily in physical assets also tend to invest significantly in
digital technologies. Furthermore, R&D expenses (rde) are positively correlated with nearly
all variables but show a slightly lower correlation coefficient with tangible fixed assets
(tanfass). The net profit (netp) also shows strong correlations with operating revenue (op),
cost of employees (coste), and intangible fixed assets (intfass), highlighting the relationship
between a firm’s financial profitability and its business strategy.
Economies 2024,12, 138 9 of 16
Table 3. The Pearson correlation matrix for variables used in regression analysis.
No. Variables 1 2 3 4 5 6 7
1 or 1
2 coste 0.941 *** 1
3 intfass 0.782 *** 0.776 *** 1
4 tanfass 0.852 *** 0.804 *** 0.689 *** 1
5 rde 0.745 *** 0.780 * 0.643 *** 0.607 *** 1
6 netp 0.814 *** 0.783 *** 0.736 *** 0.736 *** 0.730 *** 1
7 di 0.789 *** 0.768 *** 0.630 *** 0.844 *** 0.671 *** 0.677 *** 1
Source: The authors’ calculation using data from the ORBIS database. Note: *** p< 0.01, * p< 0.1.
Table 4presents a detailed view of the fixed-effect regression analysis (based on
Equation (2)) and the RESET test results, exploring technological digitalization’s impact on
business financial performance across different regions and company sizes. The positive
and significant coefficient for digital investment (di) in the main model underscores the
beneficial role of technological digitalization in enhancing business financial performance,
supporting hypothesis H1.1. Technological digitalization has a positive impact on business
financial performance. This observation is particularly noteworthy in the Eastern European
context, where digital investments yield a higher coefficient (0.572) compared to the Western
region (0.297), suggesting a potentially greater impact of technological digitalization on
financial performance in the Eastern Europe region. This observation provides partial
support for hypothesis H1.2.
Table 4. Fixed-effect regression analysis indicating the impact on business financial performance.
Variables
Main Model
(Fixed-Effect)
(Coeff./Std.
Error)
Eastern
Europe
(Coeff./Std.
Error)
Western
Region
(Coeff./Std.
Error)
Very Small
Companies
(Coeff./Std.
Error)
Small
Companies
(Coeff./Std.
Error)
Medium size
Companies
(Coeff./Std.
Error)
Large
Companies
(Coeff./Std.
Error)
coste 3.481/0.124
(***) 8.751/0.419 3.440/0.134 1.616/5.498 0.181/0.172 2.719/0.334 2.068/0.127
intfass 0.119/0.018
(***) −0.502/0.234 0.129/0.019 0.025/0.254 0.176/0.083 0.130/0.067 0.259/0.013
tanfass
0.058/0.027 (**)
−0.173/0.017 0.195/0.059 0.520/6.021 0.065/0.018 0.225/0.103 0.116/0.029
rde 0.732/0.113
(***) −0.190/0.864 0.773/0.119 −0.218/2.785 0.918/0.089 0.720/0.152 1.247/0.471
netp 0.583/0.045
(***) 0.187/0.033 0.758/0.057 0.103/1.086 0.295/0.035 1.035/0.058 0.852/0.062
di 0.441/0.052
(***) 0.572/0.173 0.297/0.079 2.902/12.290 −0.054/0.204 0.417/0.171 0.054/0.038
Constant 43.218/5.109
(***) −34.083/9.765 43.030/6.033
302.354/581.036
268.607/24.670 65.792/19.943 127.258/6.210
Model Summary
Observations 5706 729 4977 104 348 841 4412
R-squared 0.464 0.463 0.472 0.003 0.451 0.386 0.382
RESET Test
SS 288,316,237 12,450,849 267,895,723 9,116,545.9 181,144,486 29,459,103 102,152,830
df7777777
MS 41,188,033 1,778,692 38,270,817 1,302,363 25,877,783 420,8443. 14,593,261
F 258.88 42.69 218.26 0.58 77.27 30.73 152.06
Prob>F 0.00 0.00 0.00 0.7674 0.00 0.00 0.00
R-squared 0.241 0.2930 0.2352 0.0408 0.6140 0.2053 0.1947
Adj R-squared 0.240 0.2862 0.2341 −0.0291 0.6061 0.1986 0.1934
Root MSE 398.87 418.75 1493.3 578.72 370.04 309.79
Note: *** p< 0.01, ** p< 0.05. Source: The authors’ calculation based on data from the ORBIS database.
The differences in digital investment’s (di) impact across groups of company sizes
shows that while medium-sized and large companies benefit significantly, the impact
on very small companies, though positive, is based on a limited sample and can not
Economies 2024,12, 138 10 of 16
be conclusive. Furthermore, large companies demonstrate the most substantial positive
impact, aligning with hypothesis H1.3. The size of business investing into technological
digitalization impacts its financial performance.
Further investigating the coefficients across all variables, can be noted that the cost
of employees (coste) is higher in Eastern Europe, indicating regional differences in labor-
related expenses and their influence on business financial performance. The fixed-effect
regression analysis reveals a layered relationship between both intangible (intfass) and
tangible fixed assets (tanfass) and business financial performance. The tangible fixed
assets (tanfass) variable shows a stronger association in the Western region and for larger
companies, possibly due to their extensive physical operations base. In addition, intangible
fixed assets (intfass) show a negative coefficient in Eastern Europe, suggesting a differential
utilization of such assets in this specific region.
The R&D expenses (rde), while positive in all models, are significantly higher in larger
companies, highlighting the scalability of technological digitalization benefits and its direct
correlation with enhanced business financial performance. The net profit (netp) positive
impact, noted especially in large companies, highlights the cumulative advantage of scale,
digital investment (di), and R&D expenses (rde) in impacting business profitability. This
analysis underscores the importance of a balanced investment strategy for both physical
and intellectual assets.
Looking into the explanatory power of the models, the R-squared values reported in
Table 4show differences across segments. With an R-squared of 0.464 in the main model,
a considerable portion of the variance in business financial performance is accounted
for, underscoring the significant role of the included variables. Moreover, the regional
analysis reveals a slightly higher R-squared value for the Western Region (0.472) compared
to Eastern Europe (0.463). It may suggest that the model is more effective at explaining
financial performance variations in Western European companies. However, the difference
is not highly significant. In addition, among company sizes, the R-squared values decrease
from small (0.451) to medium (0.386) and large (0.382) companies, indicating a diminishing
model fit as company size increases. This trend may reflect the increasing complexity of
factors impacting larger businesses’ financial performance. The very small companies’
model set up stands out with an R-squared value of only 0.003, significantly lower than the
others, highlighting the model’s limited explanatory power due to limited sample size.
The results of the RESET tests for all models (except very small companies) indicate
possible specification errors, suggesting that the equation model may be missing key
variables or interactions. This underscores the need for further enhancement of the model
in future studies. There is a need to explore additional variables, such as sector-specific
variables or macroeconomic variables, that could improve the model’s robustness and
explanatory power. Table 5details the outcomes of the robustness test, which compares
the coefficients across the first (Q1) and third (Q3) quartiles of digital investment (di). This
test is used to check the stability of the main regression model across different levels of
digital investment.
Table 5. Robustness test results comparing quartile Q1 and quartile Q3.
Variables
Main Model
(Fixed-Effect)
(Coeff./Std. Error) Q1
t Q1 P > |t| Q1
Main Model
(Fixed-Effect)
(Coeff./Std. Error) Q3
t Q3 P > |t| Q3
coste 2.797/0.244 11.44 0.000 4.543/0.161 28.05 0.000
intfass 0.020/0.0565 0.36 0.720 0.073/0.096 0.77 0.443
tanfass −0.087/0.101 −0.87 0.385 0.069/0.059 1.16 0.245
rde 0.543/0.142 3.80 0.000 0.171/0.515 0.33 0.740
netp 0.934/0.064 14.59 0.000 0.588/0.114 5.14 0.000
di 0.752/0.516 1.46 0.145 0.098/0.074 1.31 0.190
Constant 69.295/16.808 4.12 0.000 81.514/10.504 7.76 0.000
Source: The authors’ calculation based on data from the ORBIS database.
Economies 2024,12, 138 11 of 16
The robustness test results show major differences in the coefficients between the first
(Q1) and third (Q3) quartile for several variables. The coefficient for cost of employees
(coste) almost doubles from the first to third quartile, suggesting that the impact of em-
ployee costs on financial performance intensifies with higher levels of digital investment
(di). Furthermore, the coefficients for the net profit (netp) and the constant values show
significant variations between the first and third quartile, suggesting that financial per-
formance can vary across different investment levels. The findings from the robustness
test suggest that the model requires refinement to consistently capture the effects across
varying intensities of digital investment (di).
5. Conclusions
Technological advancement encourages businesses to invest in digital technologies
(Nasiri et al. 2020;Ghosh et al. 2022). It has become a success factor for businesses across
various aspects such as innovation, competitiveness, and efficiency (Ladeira et al. 2019;
Cette et al. 2022;Bleicher and Stanley 2017;Heredia et al. 2022;Peng and Tao 2022). Existing
research work on the relationship between investment in production digitalization and
business financial performance presents mixed results, pointing to impact differences
considering region and company size aspects. As companies increasingly focus on digital
technologies to enhance business performance, understanding the connection between
digital investment and financial performance becomes vital. Moreover, in the context
of global events, such as the COVID-19 pandemic, which accelerated the technological
digitalization process, digital investment has become a survival strategy for businesses.
This study addresses the research gap on production digitalization investment’s impact
on business financial performance in the European region and explores the difference of
impact on company size and regional factors. The research’s insights support business
decision makers to achieve maximum benefit in digitalization investment strategies.
The results of the analysis of 5706 companies across 30 European countries demon-
strate that digitalization investment plays a significant role in balancing business financial
performance, and this impact differs across company sizes and regions within Europe. By
utilizing a fixed-effect regression model, the research evaluates the impact on business
operating revenue. The empirical results presented in the analysis align with the existing re-
search literature in several ways. First, the finding that production digitalization positively
impacts business financial performance supports the observations made by Huang et al.
(2020) and Liu et al. (2023), who confirmed the significant positive impact of production
digitalization on financial metrics and overall company performance. Additionally, it
supports the proposed hypothesis H1, highlighting the beneficial role of digitalization in
enhancing business financial performance.
The differences in the impact of technological digitalization between Eastern and
Western European countries, as well as across company sizes, offer empirical evidence for
discussions by Jovanovi´c et al. (2018) and Buer et al. (2021). The findings suggest that
the impact of digitalization investment increases operating revenue. This trend varies by
business location, with companies in Eastern European countries benefiting more than
those in Western Europe. The observed impact reflects the rapid catch-up with production
digitalization in Eastern European countries after the post-Soviet period, confirming the
discussion by Akatkin and Yasinovskaya (2019). This aligns with hypothesis H1.2 and
suggests that businesses operating in the Eastern European region with historically lower
levels of production digitalization demonstrate more pronounced benefits from such in-
vestments. This finding underscores the importance of considering regional differences
in maturity of digitalization, as discussed by Hartl and Hess (2017) and Nelson and Ellis
(2019). Furthermore, the impact is more pronounced among medium to large-sized busi-
nesses. The variation in production digitalization impact across company sizes contributes
to discussions by Ribeiro-Navarrete et al. (2021) and Müller et al. (2018). The variation
in magnitude of production digitalization impact across company sizes reflects business
differences in resource, technology capability, and strategic priorities (H1.3). The analysis
Economies 2024,12, 138 12 of 16
also highlights the tendency for the costs of employees per company and the intangible
fixed asset value to increase with the growth of digital investment.
These results contribute to a larger body of literature examining the impact of digi-
talization investment on business financial performance. These results also offer valuable
guidance for business strategy creators and shareholders in order to maximize expected
profits. Strategically, the observed positive impact of production digitalization on business
financial performance highlights the strategic value of digital investments for improving
competitiveness, business innovation, and financial business efficiency. Digitalization
strategies should account for the initial digital maturity of the country, as demonstrated
by businesses in the Eastern European region finding a pronounced benefit of increased
growth and catch-up with companies in the Western European region. Strategy creators in
segments of medium to large-sized companies should prioritize production digitalization
investments, as the results indicate a stronger impact on operating revenue.
Additionally, the research findings suggest that digital investment is not just about
adopting new technologies in business, it requires a comprehensive approach. This includes
developing the digital skills of employees and focusing on intangible assets, in addition to
production digitalization and concentration on fixed assets. By understanding complex
relationship between variables, financial outcome, and influencing segments, strategy
creators can enhance their digitalization strategies.
The potential limitations of this research are regional and industry specific variations.
While the sample covers 30 European countries and a wide range of industries, there are
no controls for differences related to the cultural approach to digitalization, regulatory
frameworks, or industry specific conditions. The sample covers a limited number of
small business units, where the majority of companies belong to medium to large size
businesses. Furthermore, the RESET test results for the main model underscore the need
for further possible enhancement of the model. There is a need to explore additional
variables, such as sector-specific variables or macroeconomic variables, that could improve
the model’s robustness and explanatory power. Additionally, the robustness test comparing
the first (Q1) and third (Q3) quartile results reveals variations in the influence of production
digitalization investments (di); therefore, future research could focus on the varied impacts
of production digitalization investments across different investment quartiles.
Author Contributions: Conceptualization, A.L. and R.K.; methodology, A.L. and R.K.; software A.L.;
validation, A.L. and R.K.; formal analysis, A.L.; investigation, A.L. and R.K.; resources, A.L. and R.K.;
data curation, A.L.; writing—original draft preparation, A.L. and R.K.; writing—review and editing,
A.L. and R.K.; visualization, A.L.; supervision, R.K.; project administration, A.L. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data can be available based on request at aiste.lastauskaite@ktu.edu.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Variable definitions including calculation components.
Variables Symbol Definition Measurement
Operating
Revenue or
The total revenue generated from a
business primary operation. In thousands of Euros
Production
digitalization
investment
di
Investment in production digital
assets, assessed through changes
in Plant and Machinery values
adjusted for depreciation.
Calculated as
dii,t=pmi,t−
pmi,t−1−pmdi,t
In thousands of Euros
Economies 2024,12, 138 13 of 16
Table A1. Cont.
Variables Symbol Definition Measurement
Number of
Employees noe The total number of full-time
employees at a company. Count of employees
Intangible Fixed
Assets intfass
Long-term, non-physical assets
such as patents, trademarks, and
copyrights held by the business.
In thousands of Euros,
adjusted for the effects of
amortization
R&D
Expenditures rde
Funds spent on research and
development activities to innovate
and introduce new products or
services.
In thousands of Euros
Plant and
Machinery Value pm
The value of the physical assets
used in the production of goods
and services.
In thousands of Euros for
current financial year
Plant and
Machinery
Depreciation
pmd
The annual depreciation expense
for Plant and Machinery,
representing the cost of the asset
consumed during the year.
In thousands of Euros for
current financial year
Cost of
Employees coste
The total expenses incurred by the
company for its employees,
including salaries, benefits, and
related taxes.
In thousands of Euros
Net Profit netp
The total profit of the company
after deducting all expenses, taxes,
and costs from its total revenue.
In thousands of Euros
Tangible Fixed
Assets tanfass
Physical, long-term assets such as
buildings, machinery, and
equipment owned by the business.
In thousands of Euros
Source: The authors’ calculation using data from the ORBIS database. Note: All variables in regression models are
divided by the number of employees (noe).
Table A2. Regression analysis and VIF results.
Variable
Coefficient
Std. Error t-Value P > |t| 95% Conf. Interval VIF
coste 3.377658 0.137498 24.57 0 3.108111 to 3.647205 1.6
intfass 0.134007 0.026352 5.09 0 0.0823463 to 0.1856668 1.08
tanfass 0.087002 0.021427 4.06 0 0.0449969 to 0.1290075 1.83
rde 0.107757 0.108679 0.99 0.321 −
0.1052944 to 0.3208084
1.24
netp 0.815786 0.055757 14.63 0 0.7064813 to 0.925091 1.38
di 0.35469 0.042531 8.34 0 0.2713133 to 0.4380662 2.05
_cons 58.53096 8.196971 7.14 0 42.46178 to 74.60014
N/A
Number of obs
5706
F(6, 5699) 294.60
Prob > F 0.0000
R-squared 0.2367
Adj R-squared 0.2359
Root MSE 400.03
Mean VIF 1.53
Source: The authors’ calculation using data from the ORBIS database. Note: VIF values assess multicollinearity,
with values > 4 indicating high multicollinearity.
Economies 2024,12, 138 14 of 16
Table A3. Hausman test results.
Variable Fixed Random Difference Std. Err. Chi-
Square
Prob >
Chi-Square
coste 3.481176 3.45774 0.0234357 0.0448005
intfass 0.1186349 0.120141 −
0.0015061
0.0032903
tanfass 0.0579912 0.0650502 −0.007059 0.0112416
rde 0.7324102 0.6062212 0.1261891 0.0393317
netp 0.582682 0.6138948 −
0.0312128
0.0110532
di 0.4412479 0.436537 0.0047109 0.0204835
Hausman Test 16.63 0.0107
Source: The authors’ calculation using data from the ORBIS database. Note: the chi-square value of 16.63 and its
associated probability leads to the preference for the fixed effect model.
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