Dual Perspectives on Financial Performance: Analyzing the Impact of Digital Transformation and COVID-19 on European Listed Companies

Abstract

This paper conducts an analysis of the impact of COVID-19 and digital transformation (DT) on the financial performance of European listed companies. Using a panel data regression model from 2015 to 2021, the study analyzed the financial performance of 2179 companies. The sample of companies was chosen based on the availability of financial statements and aimed to examine the effects of COVID-19 and DT on financial performance, as measured by return on assets (ROA). The study used a fixed-effect model and checked for robustness by introducing return on equity (ROE) as a dependent variable. The results indicated that COVID-19 had a negative significant impact on financial performance, while DT had a positive significant impact, consistent with previous research. This study provides valuable insights into the impacts of the COVID-19 pandemic and DT on the financial performance of listed companies.

1. Introduction

The outbreak of COVID-19 has not only affected the health of individuals worldwide but also brought about significant changes to the economic and social landscapes of countries (Bashir et al. 2020). Businesses have been forced to adapt to the new normal of remote work and online operations (Herath and Herath 2020). This shift has led to an acceleration of digitalization in various sectors (Döhring et al. 2021). The digitization of business and society will undoubtedly have a substantial effect on the future of all countries. Countries that are able to effectively implement digitalization strategies are likely to experience significant economic and societal benefits (Dwivedi et al. 2020). However, the rate of digitalization varies among nations and is closely tied to the existence of robust socioeconomic systems. Factors such as government policies and investments in technology are likely to perform a crucial function in determining the rate of digitalization. The ability to keep pace with the rapidly evolving digital economy is of paramount importance in the process of digitalization. This requires consistent investment in technology and infrastructure, as well as the development of a skilled workforce. Additionally, policies that support the growth of digital industries, such as e-commerce and fintech, will be critical to the success of digitalization efforts. The impact of digitalization on companies during the COVID-19 pandemic has been far-reaching (Quayson et al. 2020). As the pandemic spread and governments implemented measures such as lockdowns and social separation, many companies were forced to rapidly adopt digital technologies in order to continue operating and serving their customers (Kaushik and Guleria 2020).

The adoption of digital technologies is not merely a localized trend; it is a global movement with profound implications for social and industrial landscapes (Guo and Xu 2021). While research has focused on specific sectors such as the manufacturing industry, the universality of the digital transformation trend permeates businesses and countries across the globe. First, digital transformation is seen as a key driver in reducing regulatory burdens for businesses, enabling public sector organizations to become more efficient, agile, and responsive (Skare et al. 2023). By fostering a more accommodating regulatory environment, countries can empower businesses to thrive and adapt swiftly to ever-changing regulations. Furthermore, the potential of digital transformation to drive economic growth and development is well recognized (Fernández-Portillo et al. 2020). From enhancing productivity and creating new business models to improving citizens’ quality of life through better access to essential services, digital transformation promises a myriad of benefits. Yet, this transformation is not without its complexities. As Allen et al. (2021) elucidates, the transition to a digital society presents challenges that include cybersecurity threats, privacy concerns, and potential inequalities. A careful and comprehensive approach is required to ensure that digital transformation not only boosts economic growth and social welfare but also promotes sustainable development and inclusivity.

The concept of a digital economy, commonly known as the “Internet Economy”, has grown with technological breakthroughs. These advancements have led to the emergence of new industries that are directly influenced by the technological trends (Antonova 2015). In the current technological landscape, it is crucial for individuals and organizations to adapt to digital technologies and practices (Cetindamar Kozanoglu and Abedin 2020). This is particularly important as advances in technology and the proliferation of digitalization are resulting in changes to societies and organizations. The digitization process, which is a key component of the growth of the digital economy, can bring benefits. As the conventional economy and digital economy grow more linked, it may be challenging to distinguish between the two. The digital economy is built on the foundation of online connectivity between individuals, corporations, tools, and processes and relies on the robust integration of internet-enabled technologies, such as mobile devices and the Internet of Things (IoT) (Ertz and Boily 2019).

This study focuses on the exploration of the dual impacts of the COVID-19 pandemic and DT on the financial performance of European listed companies. Prior to this work, no study had examined these dual effects in this particular context. Leveraging the resource-based view (RBV) of the firm as its theoretical underpinning, the study provides new insights into how firms’ resources and capabilities were disrupted during the pandemic and how digitalization has served as a potential buffer against this disruption. The paper also presents the innovative use of the Digital Economy and Society Index (DESI) as a measure of digital transformation. To do so, we begin by discussing the theoretical background of the study and developing our research hypotheses. Next, we describe the sampling method and the variables included in our analysis. Then, we present the results of our study. Finally, we discuss our findings, acknowledge the limitations, and suggest areas for future study.

2. Theoretical Background

In the field of information systems research, the resource-based approach has received a lot of attention as a way to explain how businesses might improve their performance and gain a competitive advantage. According to the core principle of the “resource-based view” (RBV), superior business performance is linked to resources and talents that are unique to the company, rare, and difficult to imitate by other organizations that are in direct competition with the firm (Barney 2001; Bharadwaj 2000). In addition, the idea holds that companies have varying levels of talent, competence, and other resources and that these varying levels of resources are the primary factors that determine a company’s level of success. Therefore, businesses that are able to identify the aspects of their resources or abilities that cannot be replicated by their rivals will have a sustainable competitive edge (Barney 2001). RBV places its emphasis on the identification and selection of resources, while the dynamic capability viewpoint places its emphasis on the deployment of resources and the construction of capabilities in order to accommodate shifting trends in technology and consumers (Helfat and Peteraf 2003). Therefore, the characteristics that make up RBV are on equal footing with the dynamic capability perspective. The RBV characteristics are therefore on par with the dynamic capability perspective. Indeed, academics have recognized the importance of digitalization, and in line with the RBV perspective, they have found that an IT capability that possesses the traits of rarity, non-substitutability, and non-replicability can lead to improved business results (Wade and Hulland 2004).

3. Literature Review

The term “digital transformation” refers to a strategic shift in a company’s business model, which, at its most fundamental level, places the customer front and center and, in addition to the incorporation of digital technologies, necessitates a change in the company’s organizational structure and culture (Singh and Hess 2020). Because of the changes that have taken place within the organization, people and digital technologies are now connected. This has resulted in the formation of a certain “socio-technical system”, whereby the integration of digital technology into social institutions constitutes a further evolutionary stride for society and, by extension, for the ways in which we conduct business (Baxter and Sommerville 2011). During the various stages of DT, the significance of key performance indicators (KPIs) and how they are utilized may shift. Changes brought forth within an organization as a consequence of DT may result in enhanced performance. A trend toward more agile and intelligent business operations, facilitated by the use of digital technology such as powerful application analytical tools and artificial intelligence, is what is meant by “cultural change”. These practices are supported by the adoption of these digital technologies. On the other hand, people engaged in work that involves digital technologies are gradually increasing (Broo et al. 2021). This shift needs to be observed in particular in reference to the rising external pressure that society places on digital capabilities. A great number of workers are concerned about the impact that DT will have on their jobs, particularly if they do not possess the appropriate digital skills (Kane et al. 2015). The proliferation of digital technology has resulted in an increase in the amount of information, computing power, communication, and overall connectedness; as a result, new types of collaboration are now possible (Vial 2021).

Listed companies are undergoing a transformation that is affecting their ecosystems as well as their value chains. This revolution is altering the manner in which listed firms engage across company boundaries, whether upstream or downstream. It is also clarifying interactions between suppliers and customers and improving data acquisition, warehousing, big data analytics, and implementation (Porter and Heppelmann 2015). The adoption of digital technologies not only opens the door to potentially profitable new company prospects but also boosts operational effectiveness. Investments in information technology should increase data utilization at the front end and ultimately at the back end, which will boost value chain activities overall (Porter and Heppelmann 2015). The automation of data collection, warehousing, and diagnosis is made possible by manufacturing businesses’ investments in digitalization, which in turn enables these organizations to reduce the costs of data processing (Alcácer and Cruz-Machado 2019). We conclude that there are three primary prerequisites that must be met before the digitalization paradox may take place. First, in order to obtain the required returns from massive investments in digitalization, large improvements in both value creation and value appropriation are required. Additionally, investments in digitalization may produce direct financial impacts if the systems involved are easy to implement and integrate. This is due to the fact that these investments concentrate on crucial aspects of enhanced financial performance and solution delivery, which boost up the efficacy of customer co-creation and the effectiveness with which solutions are delivered, thereby reducing transaction costs (Thomson et al. 2022). Second, the introduction of digitalization necessitates the commitment of resources to the development of additional skills. The development of new organizational structures and operational processes necessitates the completion of development work as well as the acquisition of new capabilities and activities. This results in an increase in project payback time as well as implementation costs (Wamba et al. 2017). Furthermore, the adoption of new digital systems necessitates the development of human competencies at the microlevel. Employees must to be trained on how to use the new systems, which necessitates new IT skills as well as training and coaching (Wamba et al. 2017).

The “Digital Economy and Society Index” (DESI) is a proper measurement tool designed within the framework of the European Union (EU) to evaluate the level of readiness for DT and the progress made in this field. The index is based on four main dimensions: “connectivity”, “human capital”, “integration of digital technology”, and “digital public services” (European Commission 2022). These dimensions encompass a wide range of indicators that provide a comprehensive view of the digital landscape of EU countries. The “connectivity” dimension measures the availability and quality of fixed and mobile broadband internet, as well as the coverage of next-generation networks. “Human capital” measures the extent of digital competence and education of the population. The “integration of digital technology” dimension evaluates the rate at which companies and governmental agencies employ digital technology. The “digital public services” dimension assesses the digitalization of public services and e-participation. DESI gathers and integrates statistics data from the 27 EU member states, highlighting the intricacies of the digital socioeconomic transition. The relationship between the DESI and financial performance is complex and multifaceted. It has been shown that the digitalization of economies and societies can lead to increased productivity, efficiency, and innovation (Pouri and Hilty 2018). The use of digitalization can improve business operations and increase competitiveness, leading to improved financial performance (Nagy et al. 2018). Additionally, the increased connectivity and access to information provided by digital technologies can enable individuals to access new economic opportunities, leading to increased income and economic growth (Berman 2012). Based on the discussed literature, we formulated our first hypothesis as follows:

Hypothesis 1 (H1).

Digital transformation can be a tool of increase in financial performance of listed companies.

Overall, the COVID-19 pandemic has enhanced the trend toward digitalization in many industries, with companies that were already investing in digital technologies prior to the pandemic finding themselves well positioned to weather the crisis (Belitski et al. 2022). As the world continues to grapple with the impacts of the pandemic, it is likely that the role of digital technologies in business will continue to evolve and expand, shaping the way companies operate and interact with their customers for years to come (Dwivedi et al. 2020). The pandemic has led to a rapid intensification in the use of digital technologies as companies seek to adapt to new market conditions and continue operations in the face of lockdowns and other restrictions. The impact of digitalization on corporations during the COVID-19 pandemic has been significant, with many companies reporting increased efficiency, productivity, and customer satisfaction as a result of their DT efforts (Stalmachova et al. 2022). Digital technologies have enabled companies to continue serving customers and operating remotely and have also facilitated the creation of new business models and revenue streams (Lech 2022). However, digitalization has also presented some challenges for businesses, including the need for investment in new technologies and the need to upskill employees to work with these technologies. Overall, the impact of digitalization on businesses during the COVID-19 pandemic has been significant, with the adoption of digital technologies proving to be an essential strategy for companies seeking to adapt to the changing market conditions and continue operations in the face of the crisis. The COVID-19 pandemic has had a significant impact on businesses around the world, including in Europe. Many European companies have had to adapt to new market conditions and continue operations in the face of lockdowns and other restrictions, leading to an acceleration of digitalization efforts.

The following hypotheses may be inferred from the findings of this review:

Hypothesis 2 (H2).

The outbreak of COVID-19 has led to a decline in the financial performance of listed companies.

4. Materials and Methods

The DESI values were collected from digital-strategy.ec.europa.eu. Using stock exchange market platforms across Europe, we gathered financial information from the financial statements of listed European corporations. We extracted data of 2179 listed firms in Europe from the ORBIS database for a period from 2015 to 2021. VanVoorhis and Morgan (2007) suggest that when using regression equations with six or more predictor variables, a minimum sample size of 10 participants per predictor variable is required. However, a larger sample size of 30 participants per variable can provide more power to detect small effect sizes. With a sample size that is sufficient for statistical testing and regression, the results of the study can be generalized to the broader population.

Table 1. Sample distribution.

Country	Number of Companies	Percentage
France	357	16.4%
Germany	325	14.9%
Sweden	273	12.5%
Poland	250	11.5%
Italy	113	5.2%
Spain	102	4.7%
Finland	84	3.9%
Greece	79	3.6%
Belgium	73	3.4%
Bulgaria	65	3.0%
Netherlands	58	2.7%
Denmark	55	2.5%
Romania	50	2.3%
Croatia	46	2.1%
Austria	41	1.9%
Ireland	38	1.7%
Cyprus	32	1.5%
Luxembourg	30	1.4%
Portugal	25	1.1%
Hungary	16	0.7%
Lithuania	16	0.7%
Estonia	13	0.6%
Malta	13	0.6%
Slovenia	12	0.6%
Czech Republic	5	0.2%
Slovakia	5	0.2%
Latvia	3	0.1%
Total	2179	100%

represents the distribution of 2179 listed companies across various European countries. It appears that France and Germany had the largest number of companies in the sample, with 357 and 325 companies, respectively (30% of the total sample). Sweden, Poland, and Italy also had a significant number of companies in the sample, with 273, 250, and 113 companies, respectively. On the other hand, some countries had a relatively small number of companies in the sample, such as Latvia, with only three companies, and the Czech Republic and Slovakia, with only five companies.

In this study, a panel data model was employed to assess the impact of DT and COVID-19 on the financial performances of listed companies in Europe. The model included several variables (

Table 2. Study variables.

Variable	Symbol	Measurement
Dependent variables
Return on asset	ROA	Net profit/total assets
Return on equity	ROE	Net income/equity
Independent Variables
Human capital	HumCap	The index of the country j in the year t
Connectivity	Connect	The index of the country j in the year t
Integration of digital technology	IntegDT	The index of the country j in the year t
Digital public services	DPS	The index of the country j in the year t
COVID-19	COVID	Dummy variable (1 if there is COVID-19)
Firm specific variables
Firm size	Size	Number of employees
Leverage	Lev	Long term debt/Total assets
Liquidity	Liquid	Current assets/current liabilities
Age	Age	The age of the company i in the year t
Price to book value	Pbv	Market price per share/book value per share
Firm size	Size	Number of employees

) in order to capture the different factors that may affect the financial performance of the firms. The level of DT was measured using a composite index, which was based on the four dimensions of the DESI (connectivity, human capital, integration of digital technology, and digital public services) developed by the European Commission. Connectivity measures the availability and quality of internet infrastructure in a country, including fixed and mobile broadband coverage, as well as the deployment of 5G technology. Human capital measures the digital skills and competencies of the population, including education and training in digital technologies. The integration of digital technologies measures the extent to which individuals and businesses in a country use the internet and digital technologies for various purposes, such as online shopping and banking. Digital public services measure the extent to which digital technologies are used in the delivery of public services, such as e-government and e-health (European Commission 2022). In order to estimate the impact of the DESI, principal component analysis (PCA) was employed, which is a technique used to simplify a large set of correlated variables by reducing them to a smaller set of uncorrelated variables called principal components (Jolliffe 2005). After predicting the DESI using PCA, we can now formulate our econometric models as follows:

$$\begin{array}{c}Financial Performanc{e}_{i,t}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+{\beta }_{1}Desinde{x}_{i,j}+{\beta }_{2}Siz{e}_{i,t}+{\beta }_{3}Le{v}_{i,t}+{\beta }_{4}Liqui{d}_{i,t}+{\beta }_{5}Ag{e}_{i,t}+{\beta }_{6}Pb{v}_{i,t}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{ϵ}_{i,t}\hfill \end{array}$$

(1)

$$\begin{array}{c}Financial Performanc{e}_{i,t}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+ {\beta }_{1}Covi{d}_i+{\beta }_{2}Siz{e}_{i,t}+{\beta }_{3}Le{v}_{i,t}+{\beta }_{4}Liqui{d}_{i,t}+{\beta }_{5}Ag{e}_{i,t}+{\beta }_{6}Pb{v}_{i,t}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{ϵ}_{i,t}\hfill \end{array}$$

(2)

$$\begin{array}{c}Financial Performanc{e}_{i,t}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+{\beta }_{1}Desinde{x}_{i,j}+{\beta }_{2}Covi{d}_i+{\beta }_{3}Siz{e}_{i,t}+{\beta }_{4}Le{v}_{i,t}+{\beta }_{5}Liqui{d}_{i,t}+{\beta }_{6}Ag{e}_{i,t}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{7}Pb{v}_{i,t}+{ϵ}_{i,t}\hfill \end{array}$$

(3)

Table 3. Descriptive statistics.

Variables	Mean	SD	Min	Max
Dependent variables (firm financial performance)
ROA	2.20	10.30	−98.72	93.52
ROE	2.21	41.84	−990.68	269.64
Independent variables
Desindex	0.10	0.02	0.05	0.16
COVID-19	0.60	0.49	0.00	1.00
Firm-specific variables
Size	6.69	2.46	0.00	13.42
Lev	0.19	0.15	0.00	0.84
Liquid	1.49	2.55	0.00	95.31
Age	49.66	44.66	2.00	356.00
Pbv	2.35	4.15	0.00	146.84

displays descriptive statistics of study variables for the selected companies. To capture the effect of COVID-19 on the overall financial performance of sample companies, a dummy variable was included in the model. This variable takes the value of 0 in years prior to the crisis and 1 in years of the crisis. Additionally, several firm-specific variables were included in the model, such as the size, leverage, liquidity, age, and price-to-book value. The natural logarithm of the size variable was also included, as it displayed large numbers. To measure the financial performance, the study used ROA as the primary measure of performance. To check for robustness, ROE was also used as a secondary dependent variable.

5. Results

This section addresses limitations that may be encountered when using the panel data analysis and presents the results of the regression analysis conducted. Our analysis was based on the assumption of a linear relationship between independent and dependent variables.

High collinearity can be a problem because it can make it difficult to determine the unique effect of each independent variable on the dependent variable (Belsley et al. 2005; Kleinbaum et al. 2013). One way to detect collinearity is by examining the correlation matrix of the independent variables. A correlation coefficient of greater than 0.8 is often considered to be high and a sign of collinearity (although the exact threshold can vary depending on the context) (Menard 2002).

Table 4. Correlation matrix.

	ROA	ROE	Desi	COVID	Size	Liquid	Age	Lev	Pbr
ROA	1.00
ROE	0.64 ***	1.00
Desi	−0.02 *	−0.01	1.00
COVID	−0.02	−0.03 **	0.39 ***	1.00
Size	0.19 ***	0.12 ***	0.07 ***	0.00	1.00
Liquid	0.02 *	0.02 *	0.03 **	0.01	−0.17 ***	1.00
Age	0.08 ***	0.04 ***	0.02	0.02 *	0.27 ***	−0.08 ***	1.00
Lev	−0.03 **	−0.04 ***	0.11 ***	0.08 ***	0.04 ***	−0.08 ***	−0.05 ***	1.00
Pbr	0.00	−0.12 ***	0.15 ***	0.04 ***	0.01	0.00	−0.05 ***	0.01	1.00
Vif	-	-	1.23	1.18	1.11	1.04	1.09	1.03	1.03

***: Significant at 1% level. **: Significant at 5% level. *: Significant at 10% level. Source: authors, using Stata/MP version 14.0 for Mac.

displays the correlation matrix of all the study variables. The correlation matrix does not display high coefficients that can cause multicollinearity issues. There are also several methods to measure multicollinearity such as the variance inflation factor (VIF). A VIF < 3 indicates no multicollinearity among the independent variables, whereas a VIF > 3 indicates that multicollinearity is present (Hair et al. 2014; Kleinbaum et al. 2013). In Table 4, all the VIF values are less than 1.23; this means that there is no multicollinearity among the predictor variables.

We started by looking at correlation and multicollinearity concerns before evaluating how well the regression model fit the panel data. We initially ran pooled OLS and FEM to achieve this. According to the F-test results, FEM fit better in both models. As a result, we used the Hausman test to compare FEM with REM, and we discovered that FEM performed better in the study model.

Table 5. Regression results.

	OLS						Fixed Effect
	ROA	ROA	ROA	ROE	ROE	ROE	ROA	ROA	ROA	ROE	ROE	ROE
	(1.1)	(2.1)	(3.1)	(1.2)	(2.2)	(3.2)	(1.1)	(2.1)	(3.1)	(1.2)	(2.2)	(3.2)
Constant	−5.723 *	−4.383	−10.448 ***	−11.737	−13.083	−22.523 *	7.849	−95.887 ***	−69.227 ***	88.005	−314.723 ***	−174.786 *
	(3.267)	(3.184)	(3.372)	(13.229)	(12.889)	(13.670)	(10.576)	(10.668)	(18.451)	(54.209)	(54.682)	(94.568)
Size	0.837 ***	0.837 ***	0.836 ***	2.233 ***	2.232 ***	2.230 ***	−0.395	−0.368	−0.395	0.692	0.836	0.692
	(0.045)	(0.045)	(0.045)	(0.182)	(0.182)	(0.182)	(0.245)	(0.245)	(0.246)	(1.259)	(1.257)	(1.259)
Liquid	0.256 ***	0.260 ***	0.256 ***	0.708 ***	0.714 ***	0.708 ***	0.401 ***	0.399 ***	0.401 ***	0.928 ***	0.921 ***	0.928 ***
	(0.041)	(0.041)	(0.041)	(0.167)	(0.167)	(0.167)	(0.040)	(0.040)	(0.040)	(0.207)	(0.207)	(0.207)
Age	0.008 ***	0.008 ***	0.008 ***	0.002	0.003	0.002	−0.121	2.112 ***	1.462 ***	−2.068	6.739 ***	3.330
	(0.002)	(0.002)	(0.002)	(0.009)	(0.009)	(0.009)	(0.271)	(0.218)	(0.426)	(1.388)	(1.116)	(2.186)
Lev	−2.041 ***	−1.858 **	−1.842 **	−11.671 ***	−11.243 ***	−11.217 ***	−15.083 ***	−15.102 ***	−15.083 ***	−50.135 ***	−50.236 ***	−50.135 ***
	(0.725)	(0.726)	(0.725)	(2.938)	(2.941)	(2.940)	(1.133)	(1.133)	(1.133)	(5.808)	(5.808)	(5.808)
Pbr	0.023	0.027	0.022	−1.317 ***	−1.311 ***	−1.320 ***	−0.056 *	−0.054 *	−0.056 *	−2.997 ***	−2.988 ***	−2.997 ***
	(0.026)	(0.025)	(0.026)	(0.105)	(0.105)	(0.105)	(0.029)	(0.029)	(0.029)	(0.148)	(0.148)	(0.148)
Desi	13.474		80.879 ***	−27.998		125.873 **	58.699 *		58.699 *	308.104 *		308.104 *
	(8.745)		(14.996)	(35.406)		(60.778)	(33.146)		(33.146)	(169.889)		(169.889)
COVID		−0.405 **	−1.956 ***		−2.051 **	−4.466 ***		−7.053 ***	−6.334 ***		−25.372 ***	−21.596 ***
		(0.206)	(0.354)		(0.835)	(1.434)		(0.787)	(0.886)		(4.037)	(4.542)
R-squared	0.064	0.064	0.067	0.043	0.043	0.044	0.053	0.053	0.053	0.071	0.07	0.071
F test	21.27 ***	21.32 ***	21.61 ***	13.79 ***	13.97 ***	13.68 ***	48.67 ***	54.35 ***	48.67 ***	66.07 ***	73.89 ***	66.07 ***
Hausman test	-	-	-	-	-	-	137.80 ***	133.42 ***	145.04 ***	202.37 ***	204.48 ***	205.42 ***
Standard errors in brackets

***: Significant at 1% level. **: Significant at 5% level. *: Significant at 10% level. Source: authors, using Stata/MP version 14.0 for Mac.

displays the findings of the regression analysis.

The results of our multiple fixed effect regression analysis are presented in Table 5. Model 1.1 confirmed that digital transformation was positively related to the financial performance (β = 58.699, p < 0.1), supporting Hypothesis 1. This implies that investing in digital transformation has a favorable impact on the success of the organization: a 1% increase in digital capacities improves the financial performance by about 58%. In model 2.1, the results indicated that COVID-19 was negatively related to the financial performance (β = −25.372, p < 0.01), supporting Hypothesis 2. Model 3 included the two estimates (digital transformation and COVID-19). The results showed that the results remained the same as the previous models, reinforcing our results. It is a good practice to check the robustness of the estimation by using different dependent variables as it helps to ensure that the results are not specific to one particular dependent variable. In this case, we used ROA as the dependent variable in our first analysis, and then we used ROE as the dependent variable in a second analysis to check the robustness of the results. Specifically, we were checking the robustness of the estimates of the effect of digital transformation and COVID-19 on the financial performance. This would give us an indication of whether the results were robust to changes in the dependent variable. The estimates of the effect of digital transformation (model 1.2) and COVID (model 2.2) on ROE were similar to the effects on ROA, and they were both statistically significant (p < 0.1). This suggests that the effect of digital transformation and COVID-19 on financial performance is robust to changes in the dependent variable.

In our examination of the control variables, we identified three variables that had a substantial and robust impact on the financial performances of the listed companies. First, our findings emphasized the major role that the liquidity of a business can have in enhancing financial performance. This suggests that higher liquidity may provide companies with greater flexibility in managing financial obligations and capitalizing on investment opportunities. Second, we observed that the leverage was inversely related to the financial performance; a higher degree of leverage corresponded to diminished financial success. This may reflect the increased risk or financial burden associated with borrowing. Finally, our analysis revealed a negative relationship between the price-to-book value ratio and financial performance. This relationship could be indicative of market perceptions and valuation intricacies.

6. Discussion

While there is an extensive literature exploring the impact of COVID-19 on financial performance, the relationship between digital transformation and financial performance has received less attention. This study aimed to fill this gap by emphasizing the significant role that digital transformation can play not only in society at large but also in the business realm. By incorporating the DESI as a measure of digital transformation, this study analyzed its impact on financial performance. Our results reveal compelling and robust findings that underscore how the digital transformation of a society can have a profound effect on businesses. This influence is largely attributed to the competitiveness of the market in international business. A country that has made significant strides in digital transformation can offer more valuable resources and competencies, allowing companies to be more competitive in the global market and realize higher profits. Similar results have been observed in the work of Guo and Xu (2021), who found an inverted U-shaped relationship between digital transformation and a company’s financial performance. Initially, digital transformation may entail high investment levels leading to elevated leverage and cash outflows. However, in the long run, the costs associated with digital transformation begin to be amortized, and the company starts reaping the benefits from the prior investments in digitalization.

In line with our research, the study conducted by Skare et al. (2023), which utilized the DESI, scrutinized the effect of digital transformation on the performance of European SMEs. They discovered that digital transformation directly impacts business competitiveness, with input costs, such as labor, closely intertwined with digital transformation and vital to the overall performance. Digital transformation can notably enhance competitiveness through innovations in various aspects like value creation, proposition, delivery, and capture (Teoh et al. 2022). However, realizing competitive advantages from digital technology is not without challenges. Constraints include the demand for highly skilled labor, the need for awareness among the stakeholders and managers of digital technology’s significance, and the alignment of digital skills and tools with broader digitalization aims for SMEs (Proksch et al. 2021). Additionally, digital transformation offers comparative benefits for SMEs, such as increased adaptability and problem-solving capabilities, but brings risks like a shortage of skilled personnel, the potential loss of inherent competitiveness, and managerial challenges. Moreover, studies by Borowiecki et al. (2021) and Zhai et al. (2022) affirm that digitalization positively correlates with growth and productivity, and companies that prioritize digital investment typically experience higher revenue growth, profitability, and shareholder returns and lower costs. Our analysis can also be viewed through the resource-based view (RBV) of the firm, which emphasizes that a company’s resources, capabilities, and competencies are key to its competitive edge and superior performance. Digital technologies, employed wisely, can augment these aspects and become a pivotal source of competitive advantage, enhancing areas like supply chain management and customer engagement and facilitating remote work.

From a macroeconomic viewpoint, the digital transformation of societies extends beyond merely enhancing financial performance; it actively contributes to the overall development of countries. Research conducted by Fernández-Portillo et al. (2020) highlights how the development of information and communication technology (ICT) can significantly impact economic growth. This impact stems from productivity improvements explicitly linked to ICT applications, as well as the realization of externalities derived from the application and progression of ICT. The mechanisms through which ICT can fuel economic growth encompass the dissemination of knowledge and innovation, enhancements in resource allocation effectiveness and efficiency, the reduction of production costs, stimulation of demand, and an increase in investments spurred by ICT penetration. Our results reinforce this notion.

In the context of the COVID-19 pandemic, the negative impact on financial performance can be seen as a result of the disruption of a firm’s resources, capabilities, and competencies. For instance, firms that are more exposed to the pandemic may have experienced disruptions in their supply chains, difficulties in maintaining their customer base, and challenges in operating under new health and safety protocols. These disruptions can lead to a decline in the firm’s revenue, profitability, and overall financial performance. In a recent study by Mahssouni et al. (2022), the authors evaluated the effect of COVID-19 on the financial performance of Belgian pharmaceutical companies and discovered that the crisis had a negative impact on the overall financial performance of these companies. Similarly, Shen et al. (2020) conducted a study using data from Chinese firms to examine the impact of the pandemic on firm performance and found the pandemic has a great negative impact on corporate performance, leading to a significant decline in the performance of high-impact industries. The negative impact of COVID-19 on firm performance is more pronounced when a firm’s investment scale or sales revenue is smaller

Our study suggests that digitalization can act as a buffer against the negative effects of the pandemic on financial performance. These findings provide additional evidence that digitalization can be a key driver of financial performance, particularly during times of economic disruption.

7. Conclusions

In conclusion, the topic of COVID-19 and DT is of paramount importance in today’s rapidly changing business environment. This study conducted an extensive analysis of the impact of COVID-19 and DT on the financial performance of European listed companies. Using a panel data regression model from 2017 to 2021, the study analyzed the financial performance of 2179 companies. The results of the study indicated that COVID-19 had a negative significant impact on financial performance, while DT had a positive significant impact. On the other hand, the results of this study also suggested that companies that have embraced DT have been better able to adapt to the challenges posed by the pandemic and have experienced a positive impact on their financial performance.

This study contributes to the RBV theory by adding to the understanding of how external shocks, like the COVID-19 pandemic, can disrupt a firm’s resources, capabilities, and competencies, impacting financial performance. The research also extends the RBV theory by demonstrating how digital resources can serve as a buffer during these shocks. The results reinforce existing research indicating the positive impact of digital transformation on a firm’s financial performance. This suggests that digitalization can enhance a firm’s competitiveness, profitability, and growth. The study highlights the role of digitalization in crisis management, a relatively unexplored area in business theory. It suggests that during times of economic disruption, digitalization can act as a protective factor for business performance. The study shows the effectiveness of the DESI as a measure of a country’s digital transformation, which can provide a useful theoretical tool for future research.

The results of this study have important implications for companies and policymakers. Given the evident positive relationship between DT and financial performance, managers should prioritize and strategize investments in digital technologies. This could include cloud computing (Gangwar 2017), data analytics (Zhu and Yang 2021), automation (Uchida et al. 2011), and other related technologies to maintain and improve performance even during turbulent times. As the digital transformation process may encounter resistance from employees especially during times of change (Touijer and Elabjani 2022), effective change management strategies should be in place. Managers should emphasize the benefits of digital transformation and provide necessary training for employees to adapt to the new digital environment. Digital technologies can enhance customer engagement, improving the quality of customer service and experience. Managers should consider implementing technologies such as AI chatbots, CRM systems, or personalization algorithms to boost customer satisfaction and loyalty. Managers should consider digital solutions to improve supply chain management, particularly during times of crisis when traditional supply chains may face disruptions. Additionally, digital technologies such as fintech can help businesses to better understand and manage their financial data, through the use of advanced analytics and reporting tools (Allen et al. 2021).

In our sample, we observed that companies in countries boasting the highest values on the DESI demonstrated a substantial average ROA, whereas those with the lowest DESI values exhibited the opposite. For example, Belgium, Denmark, Finland, Ireland, and Luxembourg, all of which had high DESI values, showed companies with an average ROA above 3%. In contrast, countries like Bulgaria and Romania, characterized by relatively smaller DESI indices, had companies whose average ROA in our sample fell below 3%. Companies that have high DESI values have worked on their strategies to attract competencies from all across the world. As we can notice, Belgium, for example, follows a very well-designed strategy to attract competencies from across the world so it can provide the resources for a digitized society, which could enhance the financial performance of companies. Countries with low DESI values should work more on attracting competencies by providing a better work environment and supplying them with the resources needed to continue performing in those countries.

One of the limitations of this study is that it addressed the components of the DESI as a single indicator by estimating it using PCA, while future research should focus more on the impact that each component of the DESI has on financial performance. Additionally, this study used a sample from various countries across the European Union. However, there are several differences in each country that can be related to economic growth, culture, governance, regulatory systems, and other variables. Future research should concentrate more on the countries that share cultural or economic similarities. Furthermore, while our analysis focused on active companies and potentially bypassed survivorship bias, we acknowledge that a deeper examination of these aspects could provide valuable insights into the broader impact of the COVID-19 pandemic on the business landscape. Future research could undertake a targeted exploration of the factors leading to delisting or acquisition of firms, employing methodologies such as logistic regression, random forest, or decision trees to determine the probability of survival in relation to various independent variables.

Finally, the effect of the crisis shed light on how COVID-19 affected the financial performance of businesses. Future research could focus more on the recovery strategies that must be applied to help companies recover. There should also be an effort to identify which specific digital technologies have the most influence on financial performance. An in-depth study of how different sectors have been affected by the pandemic and their success rate in digital transformation would be beneficial. Conducting a longitudinal study to observe the impact of the pandemic and digital transformation over time can provide insights into their evolving interaction and how firms adapt to sustained external shocks.