The Institutional Roots of M&A Success: Evidence from European Business Environments

Abstract

This study investigates the relationship between the business environment and the financial performance of companies engaged in mergers and acquisitions (M&As), with a particular emphasis on how “ease of doing business” (EDB) indicators affect post-merger outcomes, as measured by return on assets (ROA), return on equity (ROE), and profit margin (PM). We consider a sample of 230 firms from fifteen European countries (Bulgaria, Cyprus, Denmark, France, Greece, Hungary, Italy, Germany, Lithuania, The Netherlands, Norway, Poland, Romania, Slovakia, and Spain) grouped according to COFACE criteria for five years (2015–2019). By applying panel data methods, the research highlights that, three years after the merger in low-risk countries, there is an increase in return on equity, better asset recovery, and economies of scale, largely due to effective government policies. The study highlights the differentiated effects of specific EDB sub-indicators, providing insight into how tailored regulatory frameworks can enhance M&A success across varying economic contexts. The business environment can stimulate the performance of firms after mergers and acquisitions if the regulations are friendly to the firms and are adapted to the state of the country’s economy.

1. Introduction

Mergers and acquisitions result as a logical and inevitable consequence of competition in a barrier-free and increasingly integrated world economy.

M&As involve two companies coming together—either through a merger, where two companies join forces, or an acquisition, where one company takes over another. The primary objective of mergers and acquisitions (M&As) is to enhance the financial, strategic, and operational performance of the combined entities. M&As have been widely recognized for their potential to drive growth, enhance competitiveness, and improve market positioning (Xu, 2025).

A vast number of studies are dedicated to analysing M&As. A part of the discussion focuses on identifying the advantages of these operations. Thus, among the benefits of these operations can be mentioned: entering new markets; adding a new product line or increasing geographical distribution; attracting trained employees to the company who would otherwise be difficult to attract; some transactions are motivated by the need to change the company’s identity; risk and financial effort sharing; etc. A large body of literature argues that M&As, as a form of foreign direct investments (FDI), promote economic growth (Hasudungan & Pulungan, 2021; Riddiough & Zhang, 2022; Jafari Fesharaki et al., 2022).

Foreign Direct Investment (FDI) refers to an investment made by a company or individual in one country in assets or business interests in another country. M&As are often considered a key form of FDI, as they involve the direct acquisition of a controlling interest in a foreign firm. However, FDI also encompasses other forms of investment, including greenfield investments and joint ventures. This research aims to analyze how M&As—as a subset of FDI—are influenced by regulatory factors, focusing on the ease of doing business indicators across countries (Dua & Verma, 2024).

Many successful companies face, at some point in their history, a decline or stagnation in performance. Reorganisation strategies can be defined as strategies to change and reconfigure the enterprise and its business activity to overcome a difficult situation and improve performance, which ultimately leads to the survival of the company. A study of Indian companies has demonstrated that the primary reason why managers resort to carrying out reorganisation operations through mergers and acquisitions is to improve economic performance and financial position (Pramod & Vidyadhar, 2008). As an option, the entrepreneurs can also resort to strategic alliances, which might help increase their performance (Oliveira et al., 2016).

Hitt et al. (2006) pointed out that around 70% of the mergers and acquisitions procedures did not improve the economic performance of companies. In a study published in 2024, Baruch Lev and Feng Gu analyzed 40,000 merger and acquisition transactions over 40 years, concluding that between 70% and 75% of them fail to achieve their stated objectives (Lev & Gu, 2024).

M&A can negatively influence economic performance when the following causes are met: the impossibility of realising synergies between the absorbing company and the absorbed company, and the existence of cultural differences at the management level or the employee level.

So that the risk of failure is as low as possible, managers should pay close attention to the manufacturing process, marketing activities, quality characteristics of products, the number of customers, the profile of employees, and institutional indicators (private property rights, legal rights). The paper of Cernat-Gruici et al. (2010) considers that cultural and environmental differences can create significant challenges in the success of M&As.

The problem that we observe starts with identifying whether the ease of doing business in certain economies can influence the performance of M&As.

Thus, this study aims to explore the relationship between the business environment (measured through EDB) and the financial performance of companies involved in mergers and acquisitions (M&As) (assessed through ROE, ROA, and PM).

We also intend to emphasize whether the actions of the government in a country influence M&A performance.

In this research, we will use the Doing Business ranking as an institutional variable as it monitors countries starting from their ease of doing business.

So far, there are no studies that analyze the impact of these standings on mergers and acquisitions; most studies have considered only FDI.

This gap in the literature presents the novelty of our study. Our research realises a direct analysis of the performance indicators of companies involved in M&As for three years post-transaction. We use key performance indicators such as Return on Equity (ROE), Return on Assets (ROA), and Profit Margin (PM), which are the most used measures to assess post-merger performance.

The results of this study have important implications for decision-makers, as they suggest potential measures to improve the business environment with a stimulating effect on M&A performance. Furthermore, the research underscores the significant role played by external factors, such as the business and economic environment, in determining the performance of entrepreneurs. It is particularly valuable to examine whether this general index can predict the outcome of a merger or acquisition success.

We consider that the results of this study are relevant for both theoretical and practical research, and also for entrepreneurs.

The paper is structured in four parts. The initial part consists of a short literature review, followed by an analyzis of the sample, the methodology, and the data used. In the third part, the obtained results are described and discussed. The final part describes conclusions and future research directions.

2. Theoretical Framework

This theory has been the subject of extensive research and is known in economic literature as the Ownership, Location, and Internalization (O.L.I.) model. Previous studies have demonstrated that institutions and good governance are essential for economic and social well-being, as well as for the business climate (Dunning & Lundan, 2008; Donaubauer et al., 2016; North, 2021).

In the context of M&A, the regulatory environment plays a crucial mediating role, as it influences both the market’s attractiveness for investment and the success of mergers. A stable institutional framework, characterized by clear rules, property rights protection, and an efficient legal system, can reduce uncertainty, facilitate contract enforcement, and create a favorable environment for post-merger integration.

Researchers have used various data analysis methods and diverse variables to identify the factors determining the success of M&A restructuring processes, with regulations remaining a central element in this dynamic.

2.1. Relevance of the Study in the Context of the Existing Literature

The economic fundamentals of target countries significantly shape M&A decisions (Blonigen, 2011; Cortés et al., 2017; Bhasin et al., 2021). High economic growth suggests efficiency opportunities (Walsh & Yu, 2010), while currency appreciation or depreciation alters acquisition costs (Blonigen, 2019). Inflation and tax regimes also influence profitability and risk (Desai et al., 2004). The impacts of imports and exports on M&A vary, being positive in some studies and negative in others (Masron & Abdullah, 2010).

The literature reveals nuanced findings regarding governance. While some studies suggest that high governance indicators do not always have a positive impact on the number of mergers and acquisitions, as economic and market factors can decisively influence these transactions, limiting the effects of effective governance (Teti & Spiga, 2023; Chen et al., 2023; Stein & Daude, 2007; Ghosh, 2007; Walsh & Yu, 2010; Herrera-Echeverri et al., 2014; Godinez & Liu, 2015), other papers (Kostevc et al., 2007; Masron & Abdullah, 2010) consider that the failure or the success of an M&A procedure is determined by institutional indicators (private property rights, legal rights).

Effective governance, characterized by transparency, political stability, the rule of law, and reduced corruption, can create a favorable investment climate, thereby stimulating M&A activity (Y. Cai et al., 2025; Li & Lee, 2025; Vissa & Thenmozhi, 2022).

Some other studies present optimism as a factor that can stimulate the M&As waves (da Fonseca & de Souza e Almeida, 2022), while others (Gugler et al., 2012) show that merger waves are registered only for listed firms.

Excessive taxation complexity can serve as a barrier to M&A activity (Ozekhome, 2022). Regulatory efficiency fosters economic integration and market confidence, leading to increased cross-border transactions. Stringent regulations may deter mergers by imposing excessive compliance costs (Challapalli, 2023; Teti & Spiga, 2023).

Corcoran and Gillanders (2015) use the ease of doing business indicator in their research on a sample of data from 2004 to 2009. The authors demonstrate that the ranking of countries according to this indicator influences the attraction of foreign direct investments. Researching in more detail, they find that the “trading across borders” indicator is important for reasonable-income countries, while for regions with very low incomes, there is no impact.

Starting from those stated above, in this study, we intend to see if the characteristics of the business environment of a country, expressed by the “ease of doing business” indicators, influence the performance of entrepreneurs involved in M&As.

Most studies analyzing the success of mergers using ROE, ROA, and profit margin indicators employ macroeconomic variables (such as GDP, inflation, and interest rates) as independent variables. On the other hand, research examining foreign direct investments (FDI), which include mergers and acquisitions (M&A), correlates to the number of foreign investments with business factors, such as the “ease of doing business” index developed by the World Bank.

Business factors are broader and include elements such as infrastructure, access to financing, market competitiveness, labor costs, and indicators like “ease of doing business”, which assesses how easily firms can start a business, obtain credit, pay taxes, etc.

As a novelty, our study aims to combine these two approaches by analyzing whether and to what extent “ease of doing business” indicators influence the performance of mergers and acquisitions. Merger success will be evaluated using three financial variables: return on equity (ROE), return on assets (ROA), and profit margin, measured both before the merger operation and three years afterward.

Our contribution lies in combining these approaches—testing whether EDB factors correlate with improved post-merger performance using financial metrics. Given that around 70% of M&As fail to generate long-term performance improvements, this research seeks to explore whether aspects of the business environment might help explain such outcomes. This perspective provides a more comprehensive understanding of how a country’s institutional context influences M&A performance, thereby offering a novel contribution to specialized literature.

2.2. The Relationship Between the Business Environment (Ease of Doing Business) and Post-M&A Performance

The best-known method to measure the financial performance of M&As is through the ROE indicator because it can be seen as a critical performance variable both by the company’s management and by potential investors (Khan & Bin Tariq, 2023; Lindblom, 2001). The most significant measure of profit, ROE, calculated by dividing net income by average total equity, points out the outcomes of entrepreneurship and shows shareholders the efficiency of their investment level (Gupta et al., 2023).

Another important indicator for measuring economic performance is the return on assets (ROA). This indicator is assessed by dividing the net income by the average total assets. The profitability of capital employed in exploitation activities is reflected by this indicator (B. J. Kim et al., 2022; Mironiuc, 2006). A higher value of the ROA indicator shows that the companies have acquired a perfect asset portfolio that ensures higher financial results. This indicator is the least sensitive to the changes that take place in leverage or bargaining power after a merger or acquisition, argue Jandika and Makhija (2005).

Profit margin (PM) is a measurement tool used to see the stability of the entity to obtain a specific sales level (Lindiyani et al., 2023).

The Ease of Doing Business project, designed by the World Bank, aims to create a regulatory framework that can stimulate foreign investments and, implicitly, mergers and acquisitions. To measure this process, the World Bank introduced a report with 10 indicators that evaluate the Ease of Doing Business (EDB), which measures the government’s attempts to simplify business regulations and to create a good business environment. This report may be intended to reflect investor responses to changes in regulations or government policies in a particular host country (World Bank, 2024). The report from the World Bank regarding the Ease of Doing Business appreciates the business regulatory environment and includes the following components: ease of starting a business, ease of getting electricity, ease of dealing with construction permits, ease of getting credit, ease of registering property, ease of paying taxes, protecting investors, ease of trading across borders, enforcing contracts and ease of resolving insolvency.

Many studies have analysed the link between foreign direct investment (FDI) and the ease of doing business (EDB) as a proxy of the business environment (Sondermann & Vansteenkiste, 2019; Pietrucha & Zelazny, 2019; Nketiah-Amponsah & Sarpong, 2020; Pawan & Kumar, 2022), only a few of them have estimated the effects of EDB on M&A success (number/value) and also on the greenfield Foreign Direct Investments (FDI), which are the two components of FDI.

According to Femina et al. (2022), indicators of the Ease of Doing Business report are an important way to attract FDI in a country and, therefore, governments should improve the regulations and laws.

Saez-Fernandez et al. (2021) demonstrate that the global banking industry has improved substantially since 2008 because of several regulatory reforms directed by the Basel Committee for Banking Supervision—reforms created to solve the problem of moral hazard and implicitly improve banking performance.

Some research has found significant results for both positive (Kostevc et al., 2007; A. Kim, 2016; Vogiatzoglou, 2016; Hossain et al., 2018) and negative links (Hermes & Lensink, 2003; Bellak & Leibrecht, 2009; X. Cai et al., 2016; Ge et al., 2020; Chung, 2014) between business environment and merger and acquisition success.

Among the numerous macroeconomic variables, the real interest rate, gross domestic product (GDP), inflation, and real exchange rate are consistently analyzed in M&A research (Kumar et al., 2023; Ulian, 2022; Darayseh & Alsharari, 2023). Macroeconomic scenarios determine the volume and location of production, motivating firms to expand through M&As (Ibrahimi & Liassini, 2022).

The literature reveals both positive and negative correlations between EDB components and M&A performance, often depending on the country context.

2.3. The Impact of EDB Subcomponents on Post-M&A by Country Specifics

Studies at the European level on types of country risk are very restrictive and have contradictory conclusions.

COFACE, the international organization that measures the country’s potential influence on businesses’ financial commitments, describes the country’s risk as below.

• Low Country Risk refers to a nation with stable policies, strong infrastructure, a skilled workforce, and diverse exports, but that faces vulnerabilities like high household debt, dependency on trading and financial services, and the impact of economic conditions in the euro area;
• Reasonable Country Risk refers to a nation with low public debt, strong tourism potential, and a skilled workforce but facing challenges like high youth unemployment, corruption, and significant regional disparities;
• Extreme Country Risk refers to a nation with abundant natural resources and a strategic location but plagued by poor administration, high seismic risk, and heavy dependence on imports and external economic factors.

The Ease of Doing Business (EDB) sub-indicators can influence post-M&A financial performance. For instance, indicators such as Getting Credit and Paying Taxes are expected to support the efficient use of equity capital, reducing costs and maximising shareholder profitability (ROE—Return on Equity). Similarly, Resolving Insolvency and Starting a Business can contribute to more effective asset management, enhancing operational performance (ROA—Return on Assets). Additionally, indicators like Trading Across Borders and Paying Taxes can lower operational and administrative costs, increasing net revenue relative to sales (PM—Profit Margin).

In the following, we explore the specific influence of each EDB sub-indicator on M&A activities, as highlighted in the specialised literature, with a particular focus on variations across different levels of country risk.

Ease of starting a business has a positive influence on the number of M&As in most studies, but in opposition, other researchers conclude that at the level of low-risk countries, there is no effect on these operations (Sedmihradsky & Klazar, 2002; Dinuk, 2011). Other authors, at the same time, have asserted a positive influence at the level of reasonable risk countries (Jain et al., 2025; Piwonski, 2010; Haliti et al., 2019). The study by Fernandez et al. (2025) showed that most mergers occur in regions with the most business-friendly environment.

Getting electricity has a positive influence according to Haliti et al. (2019), and a negative influence according to Vučković et al. (2020) and Stannard and Barry (2024).

There is no link between registering property and M&As number realised in low-risk countries (Dinuk, 2011), but there is a positive and significant effect on the operations performed in reasonable-risk countries according to other studies (Olival, 2012; Haliti et al., 2019).

Contradictory results are also related to the indicator Protecting minority investors. The indicator reveals that in high and reasonable-risk countries, the business environment does not affect M&As (Cao, 2024; Azam et al., 2011; Haliti et al., 2019) but has a negative impact in other countries (Hermes & Lensink, 2003; Azman-Saini et al., 2010).

Studies that have analysed Dealing with construction permits indicators also have different conclusions. Thus, in high-risk countries and reasonable-risk countries, some studies conclude there is a negative relation between M&As and the business environment (Mahuni & Bonga, 2017; Haliti et al., 2019), and according to the opinion of other authors, the ease of doing business did not have any effect on attracting these diversification operations (Naushahi & Ur Rehman, 2024; Bayraktar, 2015; Hossain et al., 2018).

For mergers and acquisitions carried out in high-risk regions, the authors Sedmihradsky and Klazar (2002) say that paying taxes is not impactful, while Dinuk (2011) detected good and effective outcomes among the analysed indicators. For mergers and acquisitions carried out in low-risk groups of countries, the existence of a negative result is established in compliance with the results obtained by Sedmihradsky and Klazar (2002), but according to Shahadan et al. (2014), there is no effect. The link between M&As and paying taxes in reasonable-risk regions is important and direct in some studies (Cheung et al., 2023; Sethi et al., 2003; J. Anderson & Gonzales, 2013) and negative in other studies (Bellak & Leibrecht, 2009; Ghinamo et al., 2010; Becker et al., 2012; Haliti et al., 2019; Vučković et al., 2020), and any impact according to Klapper and Love (2010).

Trading across Borders in reasonable-risk countries has not shown any impact on M&As (Hu et al., 2023; Haliti et al., 2019). But a survey of J. Anderson and Gonzales (2013) shows that the scores that measure the distance to the frontier were related to higher M&A performance.

The contradictory effects of EDB components on M&As across countries with different risk levels can be explained in different ways (Morano et al., 2023). Thus, the impact of EDB components can vary across different industries. For example, regulatory changes in the telecommunications sector might have a distinct different impact on M&As compared to changes in the manufacturing sector. The same EDB component might facilitate M&As in one industry while hindering them in another. Investors and companies might perceive and react to the same EDB components differently based on their risk tolerance, strategic objectives, and previous experiences. But it is important to note that the changes in the Ease of Doing Business environment may not have immediate effects on M&As. Companies might take time to adapt to new regulations or economic conditions, and the initial effects observed might differ from the long-term impacts. Also, there might be inconsistencies in how the EDB components are measured and reported across different countries. Variations in data quality, reporting standards, and methodological differences can contribute to contradictory findings in the literature.

Bortoluzzo et al. (2014) showed that performance is positive when the cultural distance between countries is low to medium and when the institutional context in which the acquired company operates is developed.

These inconsistencies in previous findings may be due to several contextual factors, such as differences in national economic and institutional frameworks, variations in investor perceptions and strategic objectives, or even delays in the impact of regulatory reforms on corporate behavior. Additionally, discrepancies in how EDB indicators are measured and reported across countries may contribute to the lack of consensus in the literature. These considerations highlight that the influence of the business environment on M&A activity is complex and not easily generalizable. Therefore, a more integrated and comparative approach is needed to explore whether certain aspects of the business environment (reflected in EDB subcomponents) can systematically influence post-merger firm performance.

At the European level, regulations are crucial in shaping investment outcomes, with a more pronounced impact on greenfield FDI compared to M&As. Nonetheless, improvements in Doing Business rankings can drive better performance in M&A procedures. This sets the foundation for the hypothesis to be analyzed in the following section:

H1. 

Improving the business environment has a positive and significant influence on the performance of entrepreneurs involved in M&A procedures.

3. Materials and Methods

The main objective of our research is to establish the link between several characteristics of the business environment and the performance of entrepreneurs involved in mergers and acquisitions. To achieve this purpose, we applied the analysis to an example consisting of European countries. Figure 1 shows the grouping of countries according to the degree of risk. Each country has a color and is classified into a category, from A1—green, representing very low risk, to E brown, representing extreme risk.

The division of European countries was made by the COFACE Institute of Economic Studies, focusing on the strengths of each country’s economy but in close connection with the weaknesses (see

Table 1. Strengths and weakness of countries with low, reasonable and high-risk.

Strengths	Weakness
Risk assessment: Country risk is low
Tax stability. Quality infrastructure. Multilingual skilled labor force. Favorable norms affairs. Important international financial center. High standard of living. Good capitalization of the banking system. The existence of a port. Stable policy. Natural gas and oil deposits. Diversified exports. High quality technology. European crossroads with great communication network.	The economy is vulnerable to economic conditions in the euro area. Reducing the tax base due to international pressure on tax reform. Very addicted on trading and financial services. The debt of private households is very high. Banks dependent on real estate financing. The population is aging and the pension system is under pressure. Very high labor costs. High housing prices with rising vacancy rates. The aging population is compensated by migration.
Risk assessment: Country risk is reasonable
Low public debt. Tourism potential. Low production costs and good price competitiveness. Tourist potential and long coastline. Good location between Europe, Asia, and Africa. Offshore finance hub. Rich and unexploited offshore natural gas deposits. Skilled and English-speaking workforce. Developed industries. Comparative advantage in deluxe food. Developed tourist industry with still unexploited potential. High agricultural potential: wheat, barley, and colza. Production of a large amount of renewable electricity. Low energy dependence due to oil, coal, uranium and gas deposits. Cheap labor generating diversified and competitive industry	Corruption and organized crime. Unstable government and fragmented political landscape. Poor population. Public debt is high. Ineffective insolvency treatment. Small industrial diversification. High unemployment among young people. Oversized banking sector. A large quota of small, low-productivity companies (more than 90% of companies have 10 employees or fewer). Significant differences in education between regions, vocational training, healthcare, and transport; rural regions lag behind. A large and slow bureaucracy. Reduced participation rate in the economy for the minorities, women, and also for young people
Risk assessment: Country risk extreme
Mineral potential in oil, chromium, copper, iron-nickel, silicates, and coal. Abundant and cheap labor. Flexible exchange rate. Tourism. Low public deficit to GDP ratio Large industrial production in tissue and a developed tourism industry. Competitive export base. Good and strategic location.	Aging population and immigration. Poor administration and an inefficient political system. High seismic risk. Arid regions with dependence on rain. High energy and financial dependence on Russia. Sensibility to the level of petroleum product prices (purchase price negotiated with Russia). Reduction in labor force. Small allocations from GDP in health, education, and transport. Dependence on imports. Businesses that do not offer trust. Public administration with delays, cronyism, and corruption. Exposure to lira volatility. Lack of skilled workers. Public debt is very high.

Source: realized by the authors based on the COFACE Romania reports.

). Table 1 describes the strengths and weaknesses of three groups of countries: with very low risk, reasonable risk and extreme risk, facilitating the understanding of the specifics of the economies of each group.

The data included in the analysis were obtained from the Eurostat European Statistics Institute (2024), Amadeus Database (Amadeus, 2024), the Ease of Doing Business reports (World Bank, 2024), and the Institute of Economic Studies COFACE (COFACE, 2024) over five years (2015–2019). Company-level data comes from the Amadeus database.

Thus, to construct the sample, we initially selected active companies from the three country-specific categories: low-risk countries, moderate-risk countries, and high-risk countries.

Then, we filtered the data, and we only kept the companies that had a merger or acquisition process in the sample. The mergers and acquisitions carried out by the companies in the sample were: horizontal and vertical mergers and acquisitions. Another condition for the companies in the sample was to have the financial statements available for the entire period considered for analysis (2015–2019).

As a result, fifteen countries remained in the analysis. Financial data were then manually collected from merger projects and the financial statements for the three years following the merger.

Thus, from the low-risk group of countries, we analyzed The Netherlands, Norway, France, Denmark, Germany, and Hungary, from the reasonable-risk group, Poland, Lithuania, Italy, Spain, Greece, and Slovakia, and from the high-risk group of countries, Romania, Cyprus, and Bulgaria. The sample of firms meeting these criteria is a representative sample of 230 companies in total of all sizes with 101 companies from low-risk countries, 69 companies from medium-risk countries, and 60 companies from high-risk countries. The analysis conducted in this study covers the period up to the year 2019. The choice of this timeframe was driven by the desire to exclude the influences of the 2020 crisis, ensuring that the results reflect a situation unaffected by its impact.

Starting from the model proposed by A. Kim (2016), as an econometric method, we use the panel data estimation techniques. We consider this method to be the most appropriate for our data because we have an extensive database composed of 230 companies, focusing on thirty-three variables for a period of years.

For testing the hypothesis and running the panel data regression, we use the EViews 10 program.

The equation of the regression model used for empirical investigation is:

Perf_it = β₁busenvir_it + β₂ Z_it + μ_it

(1)

where i symbolize the country; t expresses the time included in the analysis (2015–2019); Perf_it symbolise the performance of firms and is used as a dependent variable; busenvir_it symbolizes the set of indicators considered in the analysis for assessing different characteristics of the business environment; Z_it: symbolize the control variables; β₁ and β₂: are the coefficients; and μ_it symbolizes the error term.

In addition to the internal factors that can influence the results of mergers and acquisitions, they can also be influenced by the economic environment in which the companies operate. Therefore, in our model, besides the independent variables, we have included two control variables expressed by two indicators that measure the state of an economy: the level of economic growth and the inflation rate.

For running the regression analysis, we tested the models with fixed effects, random effects, and OLS adapted to panel data. The results of the tests that we ran (the Hausman test and the Redundant fixed effects test) showed us which is the best model for our data: the fixed effects model.

The variables considered for empirical analysis are defined in

Table 2. Presentation of the variables included in the econometric model.

Indicators (Abbreviation)	Definition
M&As performance
Return on equity (ROE)	it is obtained by the ratio between net income and equity (the company’s assets minus debt).
Return on assets (ROA)	it is obtained by the ratio between net income by total assets.
Profit margin (PM)	it is obtained by the ratio between net income by net sales.
Characteristics of the business environment
Ease of doing business (EDB)	represents the score’s average for each of the Doing Business topics.
Starting a business (START)	is calculated as the average of each component indicator: the procedures (number), time (days), and cost for an entrepreneur to start a business, and also the paid-in minimum capital requirement.
Dealing with construction permits (CONSTR)	is calculated as the average of each component indicator: the number and cost of procedures, and the time needed to obtain construction permits (days), the building quality control index, and the safety mechanisms, score for insurance liability.
Getting electricity (ELECTR)	reflects the scores’ average for the next indicators: the number, time (days), and cost of the procedures that are necessary in order to obtain electricity connection, the transparency of tariffs index, and reliability of supply.
Registering property (PROPERT)	is the average of the scores obtained for the number, time (days), and cost for procedure necessary to transfer the property to another buyer, also for the quality of land administration, transparency of information and land dispute resolution, and equal access to property rights.
Getting credit (CREDIT)	represents a value from 0 to 100, going from the worst credit regulatory performance (with the value 0) to the best credit regulatory performance (maximum value of 100).
Protecting minority investors (INVEST)	represents a value from 0 to 100, thus being able to take values that express the worst-protecting minority investors regulatory performance (value 0) to values that measure the best regulatory performance (100).
Paying taxes (TAX)	represents the calculated average of the scores of the following subcomponents: the number of taxes, the amount of taxes and contributions that must be paid by a company, the time (days), and the bureaucracy required to process a VAT refund request.
Trading across borders (TRADE)	reflects the average values obtained for the time (days) and cost required to complete the import and export customs formalities.
Enforcing contracts (CONTR)	is the average score for the time (days) and cost of resolving a commercial dispute at court, also measuring the efficiency of the judicial system and its quality.
Resolving insolvency (INSOLV)	is the average value, from 0 to 100, which measures the recovery rate of insolvency procedures and also the strength of the legal framework regarding liquidation and judicial reorganization procedures.
Macroeconomic indicators (as control variables)
Real GDP growth rate (GDP)	measures the GDP from one period to another, adjusted for inflation or deflation.
Inflation rate (INFL)	symbolizes the change in the prices of consumer goods and services and is calculated using the Harmonized Index of Consumer Prices.

Source: according to Eurostat and World Bank definitions.

.

4. Results

The indicators that measure performance vary significantly between firms. Therefore, we have firms with increased and sustained performance and firms with negative results (see

Table 3. Descriptive analysis of the indicators included in the econometric model.

Variable	Mean	Mean Risk Low	Mean Reasonable Risk	Mean High Risk	Maximum	Minimum	Standard Deviation	No. of Observations
ROE	5.547	25.26	28.89	7.8	473.034	−924.476	111.612	1150
ROA	2.051	6.73	10.49	3.69	85.272	−98.757	23.220	1150
PM	6.438	10.57	10.73	11.24	97.568	−81.771	26.090	1150
EDB	75.282	74.8	77.89	72.348	82.900	65.500	2.511	1150
START	89.027	94.28	87.83	89.558	94.300	82.600	5.143	1150
CONSTR	70.410	69.32	52.92	61.094	84.900	57.400	6.227	1150
ELECTR	77.312	82.24	81.46	65.194	88.400	53.000	9.691	1150
PROPERT	76.883	79.02	82.22	70.07	94.100	58.900	5.821	1150
CREDIT	61.576	42.00	77.73	70.5	80.000	40.000	16.177	1150
INVEST	62.956	58.00	68.6	67.48	76.000	58.000	4.677	1.150
TAX	82.317	87.46	75.31	81.32	88.700	72.300	4.866	1150
TRADE	98.892	100.0	98.86	94.2	100.000	88.400	3.274	1150
CONTR	63.616	62.94	70.85	59.962	78.800	44.200	7.388	1150
INSOLV	74.131	84.02	60.29	65.126	84.300	45.700	10.756	1150
GDP	3.632	2.30	3.92	4.984	7.300	2.000	1.362	1150
INFL	1.003	1.24	1.242	1.006	4.400	−1.500	1.332	1150

Source: authors’ own calculations.

).

The indicator expressing the ease of doing business takes values between a minimum of 65.5 (in Cyprus, 2015) and a maximum of 82.9 (in Norway, 2019). We also notice differences for the other indicators characterizing the business environment, for some with a fairly high standard deviation. The largest standard deviations are recorded by indicators measuring the ease of getting credit, resolving insolvency, and getting electricity. These emphasize the significant differences between the business environments of the countries considered in the analysis, especially between the countries included in different risk groups.

Thus, the countries included in the low-risk group have a more conducive business environment (tax stability; multilingual skilled labor force; quality infrastructure; favorable norms affairs; high standard of life; good capitalization of the banking system; stable policy; the existence of rich natural deposits; diversified exports; high technology) compared to those included in the high-risk group (poor administration and inefficient political system; ageing population; high immigration; high energy and financial dependence on Russia; small allocations from GDP in health, education and transport; reduction in the labor force; dependence on imports; corruption, lack of skilled workers).

The econometric investigation has two parts. The first part focuses on identifying the relationship between the ease of doing business indicator and the performance of entrepreneurs after M&As. The second part tests the effects of each area of business regulations (which compound the EDB index) on performance.

The correlation analysis pointed out the existence of high correlation coefficients between the variables that measure the characteristics of the business environment, showing that some of the ease of doing business indicators are correlated with each other (see

Table A1. Correlation matrix of the variables used in the empirical analysis.

	ROE	ROA	PM	EDB	START	CONSTR	ELECTR	PROPERT	CREDIT	INVEST	TAX	TRADE	CONTR	INSOLV	GDP	INFL
ROE	1.000000
	-----
ROA	0.496400	1.000000
	0.0000	-----	1.000000
PM	0.356111	0.600444	-----
	0.0000	0.0000	−0.096072
EDB	0.040644	−0.049015	0.0726	1.000000
	0.4485	0.3606	0.0000	-----
START	−0.214722	−0.123010	−0.171222	−0.129635	1.000000
	0.0001	0.0213	0.0013	0.0152	-----
CONSTR	−0.037529	−0.029307	−0.104282	0.785038	−0.355213	1.000000
	0.4840	0.5848	0.0513	0.0000	0.0000	-----
ELECTR	−0.096460	0.052534	0.063294	0.708751	0.157759	0.176709	1.000000
	0.0715	0.3271	0.2376	0.0000	0.0031	0.0000	-----
PROPERT	−0.044667	−0.007687	−0.136995	0.444059	0.249365	0.403051	0.242425	1.000000
	0.4048	0.8861	0.0103	0.0000	0.0000	0.0000	0.0000	-----
CREDIT	−0.194121	−0.120874	−0.136835	−0.125558	−0.827150	0.021598	−0.558741	−0.185161	1.000000
	0.0003	0.0237	0.0104	0.0188	0.0000	0.6872	0.0000	0.0005	-----
INVEST	−0.119009	−0.034764	0.098306	−0.099427	0.330915	0.179665	−0.079978	−0.498376	0.661065	1.000000
	0.0260	0.5168	0.0662	0.0632	0.0000	0.0007	0.1354	0.0000	0.0000	-----
TAX	0.225320	0.102970	−0.157924	0.165924	0.516768	−0.162537	0.344586	0.261500	−0.662746	−0.534025	1.000000
	0.0000	0.0543	0.0031	0.0018	0.0000	0.0023	0.0000	0.0000	0.0000	0.0000	-----
TRADE	−0.046837	−0.087940	−0.315750	0.372508	−0.077778	0.213195	−0.020454	0.578362	−0.019339	−0.522105	0.003742	1.000000
	0.3823	0.1005	0.0000	0.0000	0.1465	0.0001	0.7030	0.0000	0.7185	0.0000	0.9444	-----
CONTR	0.083763	−0.092368	0.237720	0.115197	−0.086318	0.011234	−0.336790	0.368028	0.266904	−0.288805	−0.115002	0.580638	1.000000
	0.1178	0.0844	0.0000	0.0312	0.1069	0.8341	0.0000	0.0000	0.0000	0.0000	0.0315	0.0000	-----
INSOLV	0.123494	0.045242	0.031606	0.352978	0.360981	0.206570	0.067100	−0.025369	−0.763362	−0.480568	0.500002	0.187028	−0.395016	1.000000
	0.0208	0.3988	0.5556	0.0000	0.0000	0.0001	0.0000	0.6362	0.0000	0.0000	0.0000	0.0004	0.0000	-----
GDP	−0.126411	0.069564	0.016340	−0.092884	−0.620205	−0.083164	−0.416383	−0.423720	0.754762	0.654682	−0.622159	−0.254897	0.035727	−0.515114	1.000000
	0.0180	0.1942	0.7607	0.0827	0.0000	0.1204	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.5053	0.0000	-----
INFL	0.037173	0.014832	−0.013637	0.316102	0.100145	−0.085289	−0.021008	0.109639	−0.048116	−0.217988	0.258024	0.178340	0.102545	0.089602	0.013977	1.000000
	0.4882	0.7822	0.7993	0.0000	0.0613	0.1112	0.6953	0.0404	0.3695	0.000	0.000	0.0008	0.0553	0.0942	0.7944	-----

Source: authors’ calculations in Eviews 10; Note: In bold are marked the highly correlated variables.

from Appendix B). We consider a high correlation coefficient the one that has values above 0.7 (related to the studies of D. R. Anderson & Williams, 1990; Bryman & Cramer, 2001). Therefore, from the correlation matrix, we find that starting a business indicator is highly correlated with getting credit, while getting credit is highly correlated with starting a business and with paying taxes, resolving insolvency, and protecting minority investors.

To cover this problem, we first ran one panel data regression model for each dependent variable, including all the EBD sub-indices. In the following, we eliminate from the model, one by one, the strongly correlated independent variables, running for each step a panel data regression followed by the correlation matrix. Thus, the resulting models do not pose multicollinearity problems and show the main indicators that influence the performance of companies after M&As. Also, to test the robustness of our results, we analyze regression models for each dependent variable proposed: ROA, ROE, and PM.

The results obtained for the first part of the analysis are described in

Table 4. The relationship between the business environment and the entrepreneur’s performance.

Dependent Variable	ROE	ROA	PM
EDB	12.114 *** (0.009)	2.002 ** (0.031)	3.004 * (0.022)
GDP	−3.204 (0.017)	0.388 * (0.021)	0.355 (0.393)
INFL	3.255 (0.015)	0.057 (0.877)	−0.573 (1.463)
Obs.	1150	1150	1150
R-squared	0.480	0.530	0.279
R-squared adjusted	0.410	0.490	0.271
F-statistic	2.431 ***	3.792 ***	5.114 ***
S.D. dependent var	111.6126	111.6126	23.22060
Akaike info criterion	12.25828	12.15444	9.106258
Schwarz criterion	12.35105	13.15433	9.192219
Hannan-Quinn criter.	12.29508	12.55108	9.140221
Durbin-Watson stat	1.833416	2.173667	2.291180

Note: *, ** and *** represent significant values at 1%, 5%, and 10%, respectively. Note: Standard error in parenthesis. Source: author’s elaboration.

, and the results for the second part are presented in

Table 5. The relationship between the business environment and entrepreneurs’ performance after M&As, by components.

Dependent Variable	ROE	ROA	PM
START	−0.025 *** (0.006)	−0.005 *** (0.009)	−0.013 ** (0.011)
CONSTR	−0.007 ** (0.003)	−0.004 ** (0.008)	-
ELECTR	−0.011 ** (0.004)	0.001 * (0.004)	-
PROPERT	−0.005 ** (0.001)	-	-
INVEST	-	−0.416 *** (0.082)	0.007 *** (0.002)
TAX	-	-	−0.002 * (0.001)
TRADE	-	−0.002* (0.001)	−0.046 *** (0.007)
CONTR	0.004 * (0.002)	-	0.001 ** (0.001)
INSOLV	0.002 * (0.001)	0.001 * (0.002)	0.001 * (0.001)
GDP	−4.889 *** (0.046)	0.979 * (0.501)	1.793 *** (0.412)
INFL	2.047 * (0.019)	0.812 *** (0.022)	−1.224 * (0.046)
Obs.	1150	1150	1150
R-squared	0.480	0.510	0.115
R-squared adjusted	0.400	0.490	0.101
F-statistic	2.398 ***	2.651 ***	5.432 ***
S.D. dependent var	111.6126	111.6126	26.09096
Akaike info criterion	12.26534	12.25256	9.317284
Schwarz criterion	12.33750	12.32472	9.388064
Hannan-Quinn criterion.	12.29397	12.28118	9.345333
Durbin-Watson stat	2.124046	1.922055	1.974806

Note: *, ** and *** represent significant values at 1%, 5%, and 10%, respectively. Note: Standard error in parentheses. Source: author’s elaboration.

.

Table 4 summarizes the results obtained for the panel data regressions when considering the ease of doing business indicator as an independent variable. The ease of doing business positively influences firms’ performance after M&As. The high value of the coefficient shows this indicator’s high power on the performance of companies measured by ROE. For ROA and PM, the values of the coefficients are smaller. Therefore, when it is easier for enterprises to carry out their activity, it is also easier to obtain an increased performance after an M&A procedure.

As with the control variables, real GDP growth resulted in being negatively correlated with ROE and positively correlated with ROA. Similar results were also obtained in the literature by Kanwal and Nadeem (2013). The inflation rate is positively and statistically significant, related only to ROE. These differences in the effect of macroeconomic variables on performance are largely related to how each indicator chosen to measure performance is calculated and the effects on its components. However, we must also consider the fact that, as has been proven in the literature, the differences in signs also appear depending on the sample of countries chosen, the types of companies, the industry where they operate, and the considered time frame (Mohd & Siddiqui, 2020).

Table 5 summarizes the results of the panel data regressions when considering as independent variables some of the ten areas of business regulations. The steps taken to identify the final models that best fit our data, and the purpose of the paper are presented in

Table A2. Multiple panel data regression models for testing the relation between EDB sub-indices and ROE.

Dependent Variable	Independent Variables	Regression Coefficient	Std. Error	t-Statistic	Prob.
Model 1
ROE	START	−0.02812	0.009814	−2.96508	0.0063
	CONSTR	−0.00834	0.004567	−2.14852	0.0312
	ELECTR	−0.00073	0.003891	−2.65287	0.0116
	PROPERT	−0.005231	0.003752	1.756658	0.0901
	CREDIT	−1.09245	0.663612	−1.69327	0.8934
	INVEST	−0.696314	0.422751	−1.44639	0.0711
	TAX	−0.005141	0.006792	−0.63987	0.5714
	TRADE	−0.002727	0.008010	0.255671	0.6761
	CONTR	0.003667	0.004115	1.256812	0.2226
	INSOLV	0.003654	0.003234	0.866789	0.4491
	GDP	−3.67312	1.138327	−3.56039	0.0004
	INFL	2.279735	1.413610	1.556781	0.0876
Model 2
ROE	START	−0.02705	0.009641	−2.80569	0.0053
	CONSTR	−0.00715	0.003366	−2.12458	0.0343
	ELECTR	−0.01079	0.004305	−2.50618	0.0126
	PROPERT	−0.005104	0.003082	1.656176	0.0985
	INVEST	−0.869304	0.496922	−1.74938	0.0811
	TAX	−0.00491	0.007296	−0.67242	0.5017
	TRADE	−0.001927	0.006804	0.283254	0.7771
	CONTR	0.003892	0.003118	1.248513	0.2126
	INSOLV	0.003176	0.003323	0.955487	0.3400
	GDP	−3.86227	1.063783	−3.63069	0.0003
	INFL	2.175915	1.314016	1.655927	0.0986
Model 3
ROE	START	−0.02572	0.006402	−4.01748	0.0001
	CONSTR	−0.00733	0.003554	−2.06152	0.0399
	ELECTR	−0.01200	0.004994	−2.40295	0.0168
	PROPERT	−0.006012	0.001938	3.101431	0.0021
	INVEST	−0.38841	0.423179	−0.91785	0.3593
	TRADE	−0.001927	0.006339	0.249730	0.8029
	CONTR	0.004565	0.002679	1.704211	0.0892
	INSOLV	0.002885	0.001992	1.448244	0.1484
	GDP	−4.15886	0.747952	−5.56032	0.0000
	INFL	1.715744	0.920345	1.864239	0.0631
Model 4
ROE	START	−0.02572	0.006402	−4.01748	0.0001
	CONSTR	−0.00733	0.003554	−2.06152	0.0399
	ELECTR	−0.012	0.004994	−2.40295	0.0168
	PROPERT	−0.006012	0.001938	3.101431	0.0021
	INVEST	−0.38841	0.423179	−0.91785	0.3593
	CONTR	0.004565	0.002679	1.704211	0.0892
	INSOLV	0.002885	0.001992	1.448244	0.1484
	GDP	−4.15886	0.747952	−5.56032	0.0000
	INFL	1.715744	0.920345	1.864239	0.0631
Model 5
ROE	START	−0.02567	0.006175	−4.15812	0.0000
	CONSTR	−0.00749	0.003489	−2.1468	0.0325
	ELECTR	−0.01195	0.004908	−2.4354	0.0153
	PROPERT	−0.005716	0.001858	3.075702	0.0023
	CONTR	0.00451	0.002581	1.747812	0.0813
	INSOLV	0.002847	0.001785	1.594886	0.0616
	GDP	−4.89932	1.46061	−3.3543	0.0009
	INFL	2.047329	1.097566	1.865335	0.0629

Source: author’s elaboration.

,

Table A3. Multiple panel data regression models for testing the relation between EDB sub-indices and ROA.

Dependent Variable	Independent Variables	Regression Coefficient	Std. Error	t-Statistic	Prob.
	Model 1
ROA	START	−0.00595	0.001620	−3.9864	0.0001
	CONSTR	−0.00170	0.000547	−3.1401	0.0019
	ELECTR	0.00120	0.000999	1.18204	0.2224
	PROPERT	−0.00010	0.000692	0.12750	0.8810
	CREDIT	−0.29192	0.000133	−2.02365	0.9975
	INVEST	−0.51534	0.087462	−5.96264	0.0000
	TAX	−0.00167	0.002366	−1.09245	0.2877
	TRADE	−0.005773	0.001931	1.647527	0.1193
	CONTR	0.000515	0.00162	0.573295	0.8456
	INSOLV	0.001754	0.000745	1.083567	0.2678
	GDP	1.080445	0.492754	2.006581	0.0333
	INFL	0.845946	0.538714	2.200335	0.0278
	Model 2
ROA	START	−0.00593	0.001501	−3.94665	0.0001
	CONSTR	−0.00168	0.000534	−3.13924	0.0018
	ELECTR	0.001162	0.000983	1.182041	0.2379
	PROPERT	−8.66 × 10−5	0.000686	0.126143	0.8997
	INVEST	−0.51519	0.086403	−5.96266	0.0000
	TAX	−0.00142	0.001316	−1.07659	0.2823
	TRADE	−0.002823	0.001803	1.565373	0.1183
	CONTR	0.000205	0.00061	0.336476	0.7367
	INSOLV	0.000587	0.000537	1.092999	0.2750
	GDP	1.090844	0.518209	2.105025	0.0359
	INFL	0.946948	0.423971	2.233518	0.0261
	Model 3
ROA	START	−0.00590	0.001577	−3.7432	0.0002
	CONSTR	−0.00163	0.000833	−1.95187	0.0516
	ELECTR	0.001133	0.000813	1.393148	0.1643
	INVEST	−0.51299	0.081519	−6.29283	0.0000
	TAX	−0.00144	0.001259	−1.14456	0.2531
	TRADE	−0.002824	0.001827	1.545591	0.1230
	CONTR	0.000194	0.000664	0.292236	0.7703
	INSOLV	0.000591	0.000507	1.166533	0.2441
	GDP	1.108568	0.493938	2.244347	0.0253
	INFL	0.954355	0.436944	2.184157	0.0295
	Model 4
ROA	START	−0.00522	0.00094	−5.5514	0.0000
	CONSTR	−0.00158	0.000965	−1.64051	0.0117
	ELECTR	0.000715	0.00065	1.099658	0.2721
	INVEST	−0.40454	0.070306	−5.754	0.0000
	TRADE	−0.002729	0.001581	1.726361	0.0850
	CONTR	0.000341	0.000525	0.650115	0.5160
	INSOLV	0.000471	0.000442	1.065393	0.2873
	GDP	1.045929	0.502133	2.082971	0.0379
	INFL	0.771332	0.237157	3.252409	0.0012
	Model 5
ROA	START	−0.00522	0.009044	−5.52397	0.0000
	CONSTR	−0.00417	0.008097	−1.64024	0.0117
	ELECTR	0.001912	0.001470	1.940017	0.0531
	INVEST	−0.41626	0.082312	−5.05705	0.0000
	TRADE	−0.002864	0.001561	1.834041	0.0674
	INSOLV	0.001307	0.002056	1.196307	0.0723
	GDP	0.979434	0.501864	1.951594	0.0517
	INFL	0.821972	0.227091	3.619577	0.0003

Source: author’s elaboration.

and

Table A4. Multiple panel data regression models for testing the relation between EDB sub-indices and PM.

Dependent Variable	Independent Variables	Regression Coefficient	Std. Error	t-Statistic	Prob.
Model 1
PM	START	−0.00901	0.001086	−8.29961	0.0000
	CONSTR	−0.00296	0.001936	−1.52803	0.1273
	ELECTR	−0.00121	0.001179	−1.02237	0.3072
	PROPERT	−0.00061	0.001518	−0.39902	0.6901
	CREDIT	−1.09245	0.663612	−1.69327	0.8934
	INVEST	0.008674	0.052894	1.63992	0.1018
	TAX	−0.00092	0.002782	−0.32981	0.7417
	TRADE	−0.00253	0.002106	1.201294	0.2304
	CONTR	0.001897	0.000687	2.763085	0.0060
	INSOLV	0.000974	0.000995	0.978821	0.3283
	GDP	0.867421	0.528941	1.63992	0.1018
	INFL	−0.00092	0.002782	−0.32981	0.7417
Model 2
PM	START	−0.00966	0.00178	−5.42829	0.0000
	CONSTR	−0.00149	0.0019	−0.78401	0.4335
	ELECTR	−0.00042	0.001208	−0.34768	0.7283
	PROPERT	−0.00091	0.00155	−0.58822	0.5567
	INVEST	0.008548	0.003444	2.481781	0.0135
	TAX	−0.00282	0.001961	−1.44	0.1507
	TRADE	−0.00478	0.001367	3.497247	0.0005
	CONTR	0.001208	0.000553	2.183625	0.0296
	INSOLV	0.000798	0.000916	0.87146	0.3841
	GDP	1.870035	0.51467	3.633467	0.0003
	INFL	1.025654	0.70689	1.450938	0.1476
Model 3
PM	START	−0.00997	0.001622	−6.14424	0.0000
	CONSTR	−0.00197	0.002559	−0.76896	0.4424
	ELECTR	−0.00011	0.001463	−0.07721	0.9385
	INVEST	0.00793	0.001859	4.266007	0.0000
	TAX	−0.00257	0.001595	−1.60974	0.1083
	TRADE	−0.004659	0.001309	3.558235	0.0004
	CONTR	0.001306	0.000594	2.198054	0.0285
	INSOLV	0.000747	0.000997	0.749687	0.4539
	GDP	1.665134	0.889989	1.87096	0.0621
	INFL	0.967847	0.840475	1.151548	0.2502
Model 4
PM	START	−0.00996	0.001616	−6.16552	0.0000
	CONSTR	−0.00197	0.002555	−0.77004	0.4418
	INVEST	0.007887	0.002198	3.588171	0.0004
	TAX	−0.00259	0.001377	−1.88141	0.0607
	TRADE	−0.00468	0.00107	4.374721	0.0000
	CONTR	0.001272	0.000459	2.772388	0.0058
	INSOLV	0.000706	0.000582	1.21132	0.2265
	GDP	1.656475	0.790152	2.096399	0.0367
	INFL	0.977117	0.900318	1.085302	0.2785
Model 5
PM	START	−0.01039	0.00114	−9.12037	0.0000
	INVEST	0.007421	0.002071	3.583066	0.0004
	TAX	−0.00265	0.001461	−1.81514	0.0703
	TRADE	−0.046182	0.007198	3.492244	0.0005
	CONTR	0.001989	0.001424	2.335451	0.0200
	INSOLV	0.001479	0.001664	0.720351	0.0471
	GDP	1.793653	0.612644	2.927726	0.0036
	INFL	1.224708	0.663755	1.845121	0.0658

Source: author’s elaboration.

from Appendix B. Our findings show that the characteristics of the business environment have a significant relationship with the performance of entrepreneurs after M&As. When it is easier to start a new business, the performance of the firms after M&As will increase. If the number of procedures, the time, the costs, and the paid-in minimum capital required for starting up a new business are lower, the performance of the companies will be enhanced. In our analysis, the relationship is negative. This counter-intuitive result may suggest that in countries where it is easier to start a business (i.e., high “Starting a Business” scores), the increased number of new market entrants may lead to greater competition, thereby reducing profit margins and ultimately affecting the return on equity of existing firms.

The indicator dealing with construction permits is negatively related to ROE and ROA. This shows that if the number of procedures, the time, and the cost to build a warehouse are reduced to a minimum, the performance of the companies after M&A procedures will increase.

Getting electricity is negatively related to ROE because as the time and cost of obtaining electricity approvals increase, the transparency in the price display decreases, or the tariff index increases, lowering the post-merger performance. This indicator is also positively related to ROA. Similar signs were obtained by other studies in the literature (Farooq et al., 2022), although they did not result in being statistically significant.

Registering property indicator is negatively related to ROE, pointing out that if the number of procedures, the time and costs of registering a real estate are reduced, then the performance of companies could increase.

Protecting minority investors indicators is negatively affecting the performance after M&As measured by ROA because, in the period analysed, there were no investor protection policies for the countries in the sample. Except for Cyprus (in 2019), where the rights and roles of shareholders in making major corporate decisions were strengthened and the disclosure of related party transactions increased. The relation between protecting minority investors and the profit margin of the companies after M&As turned out to be positive because when conflict of interest regulation and the extent of shareholder governance indexes are clearly established it will stimulate the companies in obtaining profits.

Paying taxes index is negatively and statistically significant related only to the profit margin. This shows that a decrease in the number of taxes and the share of the tax payable from the gross profit, together with tax incentives and fiscal deductions, determines the growth of the post-merger performances. Therefore, to ensure the increased performance of the companies after M&As, an easing of the fiscal pressure is necessary.

Easing the trading across borders procedures will enhance performance measured by ROA and PM. While entering international markets is essential in the era of globalisation for all economic markets, countries with low and reasonable risks are influenced by trade policy and government measures taken in this regard because ensuring their growth is related to their capability to import capital-intensive products. Some capital-intensive industries include oil production and refining, automobile manufacturing, steel production, transportation sectors, and telecommunications.

Countries with low and reasonable risk when they cannot expand on international markets are forced to produce goods with higher costs and thus lose their competitive advantage. Moreover, the low values of GDP per capita in these countries reduce the opportunities to obtain economies of scale. Therefore, the performance of businesses can be encouraged and stimulated by adopting a trade regime focused on permitting low-cost producers to extend their activity and not be stuck in local markets. To stimulate post-merger performance, trading across borders must be clearly inseparable from government development policy.

The indicator measuring the number of procedures, the time and the costs for enforcing a debt contract is statistically significant and positively related to ROE and PM. Easing access to credit does not stimulate the performance of companies after mergers and/or acquisitions because firms that use too much credit increase their risks of non-payment by negatively affecting their performance.

The indicator expressing resolving insolvency proved to be positively related to ROE, ROA and PM. The explanation is related to the fact that ROE increases when the time to recover the receivables and to solve the lack of liquidity is shorter. This is noticeable in countries with a high and reasonable risk, such as Lithuania, Romania and Cyprus, where the indicator score is low: 45.7. Resolving insolvency indicator is also positively related to ROA because the shorter the time to resolve the insolvency process, the faster the capitalisation of assets, which is noticeable in low-risk countries such as The Netherlands and Norway where the maximum of 84.3 is reached.

As regards the control variables, real GDP growth is negatively correlated with ROE and positively correlated with ROA and PM. High economic growth, in principle, positively determines firms’ profitability. Still, the differences in the signs are due to the calculation formula for each of the indicators that measure performance. A negative relation between GDP and ROE was also obtained in the literature by Haider et al. (2018). The inflation rate is positively related to ROE and ROA and negatively related to PM.

The Adjusted R-squared values show the overall effect of the variables included in the model on the performance of entrepreneurs after a restructuring procedure. Thus, only between 4% and 11% of the variation in the performance of the firms considered in the analysis can be explained by the changes in the business environment. These low values point out the fact that although the characteristics of the business environment influence the performance of firms after M&As, this influence is reduced. Other factors can also determine the performance of companies after M&As, such as book-to-market ratio, method of payment (cash or stock), cross-border versus domestic M&A, firm size, mergers versus tender offers, macro-economic conditions, time of the transaction, cultural differences, type of merger or acquisition transaction (related or unrelated), etc. Therefore, given the specifics of social sciences and the analyzed phenomena’s complexity, these relatively low values for Adjusted R-squared are usually and are considered to validate the analyzed models.

The conceptual model (see Figure 2) illustrates the relationships between significant Ease of Doing Business (EDB) sub-indicators and post-M&A financial performance (ROE, ROA, PM). GDP and Inflation are included as control variables. Although EDB sub-indicators are expected to positively influence performance, our empirical results reveal both positive and negative effects depending on the specific indicator and country context.

5. Conclusions

Managers will always seek modalities to improve their companies, which translates into increased profitability and minimized costs. In this very inclusive world, companies are now looking around the globe for these opportunities, consuming a lot of resources and spending a lot of time making more profit or to achieve economies of scale (to save more money on costs). However, the task is challenging, as each country has its own set of rules on trade operations. Usually, countries are open to foreign investment, and foreign investors can enter other markets more easily, or, on the contrary, it is very difficult, depending on the nature of the unique regulations of each country.

Companies should use funds to determine what type of business environment the government has created through the measures adopted in their market, so that they are never caught off-guard. Errors in assumptions or incorrect data can be costly for companies.

The findings of our study point out the significant relationship between the characteristics of the business environment and the profitability of firms after mergers and acquisitions. Although this relationship is weak, it shows us that a favorable business environment can enhance firms’ performance. Our results are valid for the sample of companies in the countries analyzed. The relationships obtained may be different when analyzing different regions or industries. This opens a future direction of research that will consist of conducting comparative analyses between groups of countries, industries, and regions within a country to see if differences appear in the way regulations influence the performance of companies after M&As.

Three years after the merger in low-risk countries, there is an increase in return on equity, better asset recovery, and economies of scale, due to good government policies (Norway and The Netherlands).

Our results could be of interest to the decision-makers because they emphasize that improving the business environment could influence the results of companies after M&As. Thus, to help companies survive and boost their performance after M&A, which would generate positive effects on the economy, decision-makers should take steps to ease the business environment. Moreover, our results, broken down by indicators that quantify the business environment, show that it is essential that decision-maker intervention be performed at the level of those indicators that stimulate performance.

The decision-makers should take direct action to influence the performance of mergers and acquisitions in their country. As highlighted by our findings, the external environment (business environment, economic environment) is significant for ensuring the achievement of performance by entrepreneurs. Thus, a business-friendly economic and business environment should be created to provide them with opportunities for growth and development.

For a certain country, high performance registered by new companies after M&A is easier to achieve if the government creates policies when countries face weaknesses such as: the debt of private households is very high; banks dependent on real estate financing; the population is ageing and the pension system is under pressure; very high labor costs; very dependent on financial services and trading; high housing prices with rising vacancy rates; corruption and organized crime; the ageing population compensated by migration; unstable government and fragmented political landscape; poor population; public debt is high; ineffective insolvency treatment; small industrial diversification; high unemployment among young people; healthcare and transport, rural regions lag; slow bureaucratic and legal processes; poor allocation from GDP in transportation, health and education; big corruption and clientelism; industrial dependence on imported inputs and administrative delays.

Our results should be interpreted with caution; they apply to the case of the countries in the sample, and their generalization should be made with caution. Thus, the limits of our study are related to the number of countries and firms analyzed. These come in relation to the availability of data. In further analysis, we could try to expand the sample if we manage to obtain access to data at the firm level for a larger number of firms. We also consider that it could be of interest to incorporate industry characteristics in the analysis, using specific indicators such as environmental and technological regulations.

For future directions, we aim to conduct a comparison between the pre-crisis and post-crisis periods to identify potential changes or trends. However, such an analysis requires data up to the year 2024, which is not yet available. Once we have access to this information, we will be able to expand the study to include this comparison. Also, we will aim to broaden the analysis by accounting for additional differentiating factors, including industry sector and company size.