Entrepreneurial Decisions of the Location Choice for Mergers and Acquisitions in the EU-27

ABSTRACT

Locations that offer a positive business climate with strong environmental performance and sustainable practices are more likely to attract companies seeking to integrate carbon dioxide reduction strategies through mergers and acquisitions (M&A). The aim of this study is to analyze the extent to which the business climate in European countries influences the M&A market. We considered the conditions of institutional, economic or sustainability regulations to determine if the ease of doing business (EDB) index, the World Governance Indicators, and carbon dioxide emissions can influence the evolution of M&A. After applying the Ordinary Least Squares regression and panel analysis, the conclusion was that sustainability criteria are important factors for investors when choosing the companies to invest in.

KEYWORDS:

• Carbon dioxide emissions
• ease of doing business (EDB)
• entrepreneurial profile
• location choice
• mergers and acquisitions (M&A)

JEL CLASSIFICATION:

• G34
• L26
• Q56

Introduction

A country’s business climate embodies a government’s attitudes toward business activities. It includes economic regulations on tax rates, inflation, environmental sustainability regulations, as well as institutional regulations (Masron and Abdullah 2010; North 2021; Pires and Pereira 2020; Pramod and Vidyadhar 2008; Rahman, Dhakal, and Upadhyaya 2007).

The goals of this study are to determine why entrepreneurs choose to invest more in some European countries rather than others based on sustainability or business environment related criteria, and to help governments make more informed decisions about if and how to attract mergers and acquisitions (M&A).

Reorganization through mergers and acquisitions is a strategy of choice for upper management due to the advantages of such operations, namely: entering new markets, attracting trained employees, adding a new product line or increasing geographical distribution, risk and financial effort sharing, and promoting economic growth (Adams, Johnson, and Pilloff 2009; E. Alfaro and Rwegasira 2012; L. Alfaro et al. 2006; Basu and Guariglia 2007; Borenstztein, De Gregorio, and Lee 1998).

Mergers and acquisitions are the fastest way to grow a company’s business because it makes it easier to access different markets directly (Klein and Rosengren 1994; Lindblom 2001; Mahuni and Bonga 2017; Martynova and Renneborg 2008).

From a conceptual point of view, cross-border mergers take place for the same argument as domestic mergers: two companies merge when growth is obtained through their union (or their usefulness) from the perspective of the absorbing firms’ manager. In reality, borders can entail additional influences to these operations, which can either prevent or ease mergers (Basu and Guariglia 2007; Bayraktar 2015; Becker, Fuest, and Riedel 2012; Vlacic, Dabic, and Dabic 2021). For instance, cultural differences between geographical areas may hike the cost of fusing two companies. Differences in governance between nations may also stimulate the merger if the absorbing company better protects the target company shareholders due to higher standards of governance in the absorbing company’s country.

Some studies suggest that the quality of a country’s governance can encourage foreign direct investment (FDI) through M&A. Of the social, cultural, and political variables that measure government quality and can influence the entrepreneurs’ choice, the following stand out: regulatory quality, government effectiveness, political stability, corruption control, cost of labor, number of taxes to be paid, bureaucratic delays, market size, infrastructure quality, judicial independence, and the financial environment (Gillanders and Whelan 2014; Hayat 2019; Klapper and Love 2010; Knack and Keefer 1997; Kostevc, Redek, and Susjan 2007; Zhang and Wong 2008). Meanwhile, other studies do not find a relationship or find a very low dependency relationship between institutional variables and M&A volume (Barseghyan 2008; Ghosh 2007; Stein and Daude 2007; Walsh and Yu 2010).

In some countries, business measures have a major importance in estimating the investment potential and to this effect, the World Bank’s report on the ease of doing business (EDB) provides comparable measures for the indicators that make it up (Hossain et al. 2018). The report shows, for domestic and foreign investors alike, a clearer distribution of measures in different countries and domains that could serve the business cycle (Žylius and Basheka 2014). According to Khan et al. (2019), the economic approach that can favor investments must be complemented by a sustainable approach from both government and companies (public or private), with sustainability indicators such as carbon emissions and their evolution being significant for protecting the environment.

For this research, we selected the institutional indicator, i.e. the now-discontinued ease of doing business index, from the World Bank reports as an independent variable that classifies countries according to their economic environment. No studies have been conducted to date on the effects of these rankings on the evolution of mergers and acquisitions, with most studies focusing only on foreign direct investments (FDI).

Starting from the idea that the analyses carried out on types of nations in the European Union are very few and considering the fact that the authors reach different conclusions, as well as taking into account the current context in which more and more emphasis is placed on non-financial reporting and a sustainable business approach, we decided to include a sustainability independent variable in our study, namely carbon emissions (CO2), in addition to the institutional independent variable ease of doing business (EDB).

The question we are trying to answer through the study is whether entrepreneurs look for a host country that offers them economic, institutional, and technical support, or if they look for a cleaner country as well.

The model that formed the basis of our analysis is the one proposed by Chipalkatti, Le, and Rishi (2021). Therefore, in the study, we applied a panel analysis on a sample of 243 observations that were made between 2012 and 2020. For the cleaner and sustainable environment proxy in the study we used the per capita carbon dioxide emissions in tons (CO2PC). Sustainable behavior is extremely well known in developed countries, and emerging countries are also starting to follow the example, which provides major advantages for investors, despite the poor infrastructure, large public debt, bureaucracy or corruption (Bhatia and Tuli 2018; Khanna and Palepu 2010).

Considering the context presented above, the aim of the research is to discern and analyze the impacts of the business environment quality, economic growth, governance, and sustainability criteria (pertaining to “clean” host countries) on M&As. To fulfill this aim, we used a sample comprising the EU-27 countries, categorized into three development levels according to the FTSE Russell classification (FTSE 2021). The developed countries group in our analysis included Austria, Finland, Denmark, France, Belgium, Germany, Luxembourg, Italy, Netherlands, Ireland, Spain, Poland, Portugal, and Sweden. The frontier countries scrutinized were Romania, Croatia, Cyprus, Estonia, Lithuania, Latvia, Bulgaria, Slovakia, Slovenia, and Malta. The emerging group included the Czech Republic, Greece, and Hungary.

The data for the study was sourced from the Institute of Mergers and Acquisitions (IMAA 2021), World Bank Reports featuring Ease of Doing Business scores (2021), and the Worldwide Governance Indicators Report (2020).

In examining the inclination of nonresident companies to invest in environmentally sustainable nations, we utilized the carbon dioxide per capita emissions (CO2PC) as a variable of interest reflecting sustainable behavior. These variables were collected for a sample of EU countries over a nine-year period (2012–2020), resulting in 243 observations.

For measuring the business climate, the analysis incorporated indicators such as Starting a Business, Getting Credit, Trading Across Borders, Dealing with Construction Permits, Paying Taxes, Resolving Insolvency, and Enforcing Contracts (Ease of Doing Business Report 2021), as well as Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption (World Governance Indicators 2022).

We believe that countries with lower carbon emissions and greater ease of doing business will be perceived as more attractive destinations for the M&A deals. The research found that in European countries, the entrepreneurial focus was on location, quality of government regulation, effective regulation, and political stability, which are important for attracting mergers and acquisitions and for achieving the competitive advantage as host country.

The structure of the paper is as follows: the first part provides a theoretical background; the second part describes the sample, data, and the methodology applied; the third part describes and discusses the results obtained, and the last part presents the conclusions and future research directions.

Theoretical Background

Our research is related to the Ownership, Location, and Internationalization (O.L.I.) theory. The basic idea of this theory is that in external economies, entrepreneurs can take advantage of the horizontal or vertical diversification mechanism to develop and grow (Donaubauer, Meyer, and Nunnenkamp 2016; Dunning 1980; Dunning and Lundan 2008). Part of the diversification strategy includes M&A operations.

Some of the most important reasons why investors decide to resort to mergers include: synergies, diversification, and the benefits from imperfect capital markets across countries (Fubini, Price, and Zollo 2007; Ghinamo, Panteghini, and Revelli 2010). Broaddus (1998) mentioned that scientific studies on the macroeconomic determinants of mergers are relatively rare and that the economic, political, and social context may influence the evolution of the number of mergers and acquisitions.

However, the question here is this: why do entrepreneurs prefer to invest more in one country rather than another?

In an attempt to answer this question, some empirical studies have investigated the effects of regulatory quality, government efficiency, and law enforcement to attract foreign direct investment flows to a nation. In most of these studies, bureaucratic delays, inefficient regulations, and corruption are presented as factors of rejection for FDI, and the volume of M&A activity is significantly larger in countries with better business environment standards (Godinez and Liu 2015; Herrera-Echeverri, Haar, and Estevez-Breton 2014; Sanchez-Martın, de Arce, and Escribano 2014; Zhang 2013).

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 assess the business regulatory environment (Ease of Doing Business Report), which measures governmental attempts to simplify business regulations and to create a good business environment. This report may be intended to reflect investor response to changes in regulation or government policies in a particular host country (World Bank 2021).

Different authors have studied the Doing Business indicators in attracting Foreign Direct Investment (FDI) and most of them studied the relationship between variables globally or in the Asia or Africa area, not in Europe, and not on types of economy, as we intend to do in this study. The studies that were conducted and their findings are presented below. Jayasuriya’s work (2011), which is the starting point for this paper, states that the higher Doing Business rankings attract more M&As. Djankov, Mc Liesh, and Ramalho (2006), after performing an analysis on 135 countries, found that the more effective the regulations are in a particular country, the better its growth in some respects, such as the growth of GDP and in terms of school enrollment.

Blonigen and Piger (2011) are probably the first researchers to examine the link between FDI and the Business regulatory environment indicators and to conclude that some component that measures the ease of doing business has low inclusion probability. Conversely, author Jayasuriya (2011) finds that when a country goes up in the business regulatory environment ranking, the number of M&As that take place in that country improves. A study by Morris and Aziz (2011) demonstrates that variables that characterize a friendly economic environment, namely trading across borders and registering property in Sub-Saharan African countries and in Asian countries, significantly influence FDI. The authors also found that there is no evidence that FDI from countries on other nearby continents carry any significant influence on FDI inflows. The previous results were rejected by the studies of Corcoran and Gillanders (2015), who conclude that for developing nations, the business regulatory environment has a noteworthy mark on FDI, while for low income countries, the relationship shows insignificant impacts between the analyzed variables.

The business regulatory environment can be measured using the Ease of Doing Business (EDB) report, but also the Worldwide Governance Indicators. The Worldwide Governance Indicators (WGIs) are widely used for institutional quality assessment in empirical studies in relation to M&A transactions (Rotberg 2014; Thomas 2010). These indicators are produced by the World Bank Institute, reporting on six dimensions of public governance for 215 countries since 1996: voice and accountability, political stability and absence of violence, governance effectiveness, regulatory quality, rule of law, and control of corruption (Kaufmann, Kraay, and Mastruzzi 2016). The value of each governance indicator ranges from about −2.5 to 2.5, where higher values correspond to better governance.

In 2020, the European Commission presented an action plan for the circular economy, with measures covering the entire product lifecycle (Sustainable Development Goals – SDGs). Some gases in the Earth’s atmosphere act like glass walls. Many of these gases occur naturally, but anthropic activity increases the concentrations of some of these in the atmosphere, such as carbon dioxide and nitrous oxide. This is one of the main causes of global warming, leading to extreme weather events such as floods, droughts, and heat waves, by creating a greenhouse-like environment that captures the sun’s heat and prevents it from leaking back into space.

Entrepreneurs are beginning to have an increasingly responsible behaviors toward the environment and their peers, and therefore invest in companies that try to fight against climate change and environmental destruction according to Chipalkatti, Le, and Rishi (2021). Municipal wastes, sulfur dioxide (SO2), nitrogen oxides (NOX), carbon monoxide (CO), traffic noise, and energy consumption are among the most significant municipal pollutants, and the greatest global pollutant is CO2 (Acharyya 2009; Ahmad and Ahmed 2014).

According to some authors (Abid, Schneider, and Scheffran 2016; Jinrong et al. 2022), CO2 emissions will increase in countries with poor legal systems and weak government measures which do not comply with the European Commission standards for a sustainable and clean environment. Unfortunately, only a few studies have been conducted on the link between FDI, the business environment, and CO2 emissions (Antweiler, Copeland, and Taylor 2001; Hasanov, Liddle, and Mikayilov 2018; Kurniawan and Managi 2018; Tamazian and Rao 2010); however, a sustainable environment has proven to be significant for local businesses in the case of countries within the European Union. A better business environment in the host country, considering both institutional and sustainability indicators, positively impact entrepreneurial behaviors (Baumohl and Kocenda 2022; Shah, Ahmad, and Ahmed 2016).

The literature on the topic of M&As clearly indicates that traditional location advantage factors (labor costs, degree of openness of the economy, inflation rate, unemployment rate, road infrastructure, GDP growth, natural resources, R&D investments, etc.) are useful (Doytch 2012; Doytch and Cakan 2011; Hyytinen and Pajarinen 2002; Kummer 2006; Xiaoxuan 2016), but are perhaps not the only conditions for investment decisions in the host country.

Research Methods. Data

Our research aims to analyze the determinant factors that can affect location choices for cross-borders mergers and acquisitions flows based on the business climate, from the investors’ perspective.

In accordance with this aim, the objectives of our research are to identify and analyze the effects of business environment quality, economic growth, governance, and sustainability criteria (“clean” host countries) on M&As.

Based on the empirical background, we formulate the following hypotheses:

H1:

More mergers and acquisitions take place in countries with good Ease of Doing Business rankings. In choosing the location for investments, entrepreneurs focus on countries that offer them economic, institutional, and technical support for business.

H2:

Better Ease of Doing Business rankings and lower degrees of CO2 emissions would be linked to a higher frequency of mergers and acquisitions.

H3:

Entrepreneurs will initiate more mergers and acquisitions in countries where environmental regulations are not strict and harsh.

Data. Variables Included in the Study

To achieve our research aim, we used as sample formed of the EU-27 countries, divided into three categories by level of development, according to FTSE Russell (FTSE 2021). Thus, the group of developed countries in our analysis included the following: Austria, Finland, Denmark, France, Belgium, Germany, Luxembourg, Italy, Netherlands, Ireland, Spain, Poland, Portugal, and Sweden; as frontier countries, we analyzed Romania, Croatia, Cyprus, Estonia, Lithuania, Latvia, Bulgaria, Slovakia, Slovenia, and Malta; and the emerging group of countries included the Czech Republic, Greece, and Hungary. The sources of the collected data are the Institute of Mergers and Acquisitions (IMAA 2021), World Bank Reports with Ease of Doing Business scores (2021), and Worldwide Governance Indicators Report (2020).

The model of authors Chipalkatti, Le, and Rishi (2021) was the starting point for our research. When establishing the relationship between the business climate and the choice of location for mergers, both CO2 per capita emissions and CO2/GDP can be useful statistical variables, but their relevance depends on the specific research question and the objectives of the analysis (Beser and Beser 2017; Xuehui and Boqiang 2013). If the primary focus is on assessing the environmental impact and sustainability of the location, CO2 per capita emissions may be more appropriate. It directly measures the average emissions generated by individuals, reflecting the overall carbon footprint of the population. This measure can provide insights into the environmental responsibility and ecological footprint of the location, which may be a significant factor for companies seeking environmentally conscious mergers.

Therefore, to investigate the intention of the nonresident companies to make an invest in clean and sustainable nations, we used the CO2 per capita emissions (CO2PC) as variable of interest for sustainable behavior. The variables were collected from a sample of EU countries, over a 9 year period (2012–2020), thus creating 243 observations.

Based on the theoretical background, in order to measure the business climate we used the following indicators in the analysis: Starting a business, Getting credit, Trading across borders, Dealing with construction permits, Paying taxes, Resolving insolvency, Enforcing contracts (Ease of doing business Report 2021), as well as Voice and accountability, Political stability and absence of violence/terrorism, Government effectiveness, Regulatory quality, Rule of law, and Control of corruption (World Governance Indicators 2022).

The statistical software R-studio was used to analyze our data.

Data Analysis Methods

A descriptive analysis was performed to study the distribution of variables included in the study. Thus, for each variable, summary indicators relating to distribution (mean, median, mode, variance, Skewness, Kurtosis, minimum, maximum, Jarque-Bera statistics) and dispersion (standard deviation) are identified and reported. Graphical methods (histogram, QQ-plot, Scatter plot) are used to check the normality of variable distribution. Also, the heterogeneity across countries and years is evaluated using graphics.

Because the behavior of countries in our dataset is observed across time, a panel regression model was used for testing the research hypothesis. According Baltagi (2008), Croissant and Millo (2019), and Hsiao (2022), the general form of a pooled panel data model can be written as:

(1) $Y_{\mathit{it}}={\beta }_1+{\beta }_2{x}_{2\mathit{it}}+\dots +{\beta }_k{x}_{\mathit{kit}}+e_{\mathit{it}}$(1)

where: i = 1, … , N denotes a cross sectional unit (in our case the units are the 27 European countries) and t = 1, … , T denotes the time period (in our case 2012–2020). The total number of observations is N × T. However, the model (1) does not allow the analysis of intercept differences among countries. The fixed effects model takes into consideration individual differences and assigns the subscript i to the constant (${\beta }_1$), as presented in equation (2)(2) $Y_{\mathit{it}}={\beta }_{1\mathit{i}}+{\beta }_{2\mathit{i}}{x}_{2\mathit{it}}+\dots +{\beta }_{\mathit{ki}}{x}_{\mathit{kit}}+e_{\mathit{it}}$(2) :

(2) $Y_{\mathit{it}}={\beta }_{1\mathit{i}}+{\beta }_{2\mathit{i}}{x}_{2\mathit{it}}+\dots +{\beta }_{\mathit{ki}}{x}_{\mathit{kit}}+e_{\mathit{it}}$(2)

Because the countries in the panel are randomly selected, their characteristics measured by the intercept (${\beta }_{1\mathit{i}}$) should be random. The random effect model can be elaborated on the fixed effects model and assumes the form of the intercept as given in equation (3)(3) ${\beta }_{1\mathit{i}}=\overline{{\beta }_1}+u_i$(3) (where $\overline{{\beta }_1}$ stands for the population average and u_i represents an individual-specific random term):

(3) ${\beta }_{1\mathit{i}}=\overline{{\beta }_1}+u_i$(3)

By replacing this form of the intercept in the equation (2)(2) $Y_{\mathit{it}}={\beta }_{1\mathit{i}}+{\beta }_{2\mathit{i}}{x}_{2\mathit{it}}+\dots +{\beta }_{\mathit{ki}}{x}_{\mathit{kit}}+e_{\mathit{it}}$(2) , the results is:

(4) $Y_{\mathit{it}}=\overline{{\beta }_1}+{\beta }_{2\mathit{i}}{x}_{2\mathit{it}}+{\vartheta }_{\mathit{it}}$(4)

In equation (4)(4) $Y_{\mathit{it}}=\overline{{\beta }_1}+{\beta }_{2\mathit{i}}{x}_{2\mathit{it}}+{\vartheta }_{\mathit{it}}$(4) , the error term ${\vartheta }_{\mathit{it}}$ incorporates both individual specifics and the initial regression error term:

(5) ${\vartheta }_{\mathit{it}}=u_i+e_{\mathit{it}}$(5)

Therefore, the error term of the random effects model has a special structure. Errors have 0 mean, a variance equal to ${\sigma }_u^2+{\sigma }_e^2$, uncorrelated across countries, and having timewise covariance equal to ${\sigma }_u^2$. The timewise correlation in the errors does not decline over time.

To decide between the fixed or random effects model, a Hausman test is applied. According to Greene (2008), the null hypothesis of the Hausman test is that the preferred model is random effects vs. fixed effects. The null hypothesis assumes that the unique errors (u_i) are not correlated with the regressors.

Also, other tests are performed for diagnosis purposes. For example, time-fixed effects testing includes an F test for individual effects. In the case of testing for random effects, the Breusch-Pagan Lagrange Multiplier Test (Breusch and Pagan 1980) is performed. This test is used to see if random effects are significant in our model. Stationarity is tested using the Dickey-Fuller test (Levin, Lin, and Chu 2002).

In our model, “Mergers and acquisitions” (M&A) represents the dependent variable, while the rest of the variables are independent.

Results and Discussions

Results of Descriptive Analysis

The results of the descriptive analysis are summarized in Table 2.

Table 2. Descriptive statistics for each variable.

Descriptive statistics		M&A	CO2	GDP	START	CONSTR	CREDIT	TAX	TRADE	CONTR	INSOLV	ACCOUNT	STAB	GOV	QUALITY	LAW	CORR
N	Valid	243	225	243	243	243	243	243	243	243	243	243	243	243	243	243	243
	Missing	0	18	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Mean		495.47	6.61	6.82	71.31	59.76	80.34	68.52	68.14	82.41	71.64	81.47	82.90	81.60	78.48	81.27	77.67
Median		57.70	5.97	2.10	73.35	60.00	81.00	68.37	69.83	82.30	72.60	83.20	82.70	83.10	79.30	83.10	77.90
Mode		42.90	3.70^a	2.00	21.66^a	70.00	70.00	100.0	56.38	99.50	76.40	81.30	93.30	80.80^a	98.10	97.10^a	98.10
Std. Deviation		1441.66	2.87	18.99	11.35	16.95	8.37	11.82	14.45	12.36	13.38	12.67	10.28	13.23	15.39	13.42	15.69
Variance		2078385	8.247	360.8	128.89	287.59	70.11	139.8	208.97	152.86	179.28	160.72	105.7	175.1	236.88	180.12	246.34
Skewness		4.421	1.812	3.107	−1.768	−.631	−.399	.192	−.203	−.313	−.143	−.741	−.104	−.531	−.341	−.467	−.215
Kurtosis		19.973	4.443	8.306	5.588	.537	−.023	1.150	−1.032	−1.030	−.369	0.18	−1.12	−.652	−1.010	−.797	−1.171
Minimum		5.62	2.96	−10.80	21.66	10.00	49.35	40.60	38.07	56.00	38.60	42.30	60.60	50.00	44.50	50.00	44.50
Maximum		9052.3	20.13	81.50	91.59	93.75	95.33	100.0	93.89	99.50	98.10	100.0	99.10	100.0	100.0	100.0	100.0
Jarque Bera Test		4664.2	283.62	536.23	18.645	269.63	24.321	13.62	22.346	11.83	14.309	13.389	3.541	18.40	12.029	15.299	15.738
p-value		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.002	0.000	0.001	0.170	0.000	0.002	0.000	0.000

Source: authors’ own calculations.

Table 2 shows that our database contains 243 observations for each variable considered in our study. The exception is variable “CO2” (Carbon dioxide emissions in metric tons per capita), for which we have 225 valid observations. For this variable we have no available data in the case of the Czech Republic and Slovakia. The descriptive statistics indicators describe asymmetric distributions, with deviation to the right or to the left. Also, the Jarque Bera Test statistics are high, and the p-values of the test are < 0.05. Those results confirm that the values of our variables are not normally distributed.

The heterogeneity across countries and years can be seen in Figure 1. and Figure 2.

Figure 1. Heterogeneity across countries.

A graph of numbers and lines. The vertical axis shows the average number of mergers and acquisitions, and the horizontal axis shows the countries. Luxembourg has the greatest average of M&As.

Source: authors’ own calculations.

Figure 2. Heterogeneity across countries and years.

A graph of numbers and lines. The vertical axis shows the average number of mergers and acquisitions, and the horizontal axis shows the years. The year 2015 has the highest average.

Source: authors’ own calculations.

The plots in Figure 1 show that some countries have different levels of mergers and acquisitions. The mean values of M&A were plotted for each country in graph (1) from Figure 1. Each point represents a country. Thus, we can see that Luxembourg has the greatest average of M&As compared to the rest of the countries. Cyprus, Hungary, Ireland, Malta, and Netherlands have a smaller average than Luxembourg, but they are slightly above other countries. There can be several explanations for these differences: size, different policy practices or business culture, among others.

Variation can also be observed across the years (graph (2) from Figure 1). For instance, years 2014, 2015, and 2016 show great variability compared to other years in the analyzed period. Variation across years can be explained by the implementation of national policy changes, international agreements or the economic situation. One must note that the studied variables vary across years, but not across countries.

For mergers and acquisitions, normality was also checked using graphs (Figures 3, 4).

Figure 3. Normality diagnosis for M&A data.

A graph and a diagram that show a data asymmetry to the right.

Source: authors’ own calculations.

Figure 4. Normality diagnosis for the logarithm of M&A data.

A graph and a diagram that show a normal slope.

Source: authors’ own calculations.

The histogram presented in Figure 2.1 shows a data asymmetry to the right, which is very different from a normal distribution. The Q-Q Plot also shows that the distribution of M&A cannot be deemed normal. This result involves a transformation of the data. The logarithmic transformation was selected and the diagnostic tests for normality shown in Figure 2.2 were rerun. Despite a slight left-handed skew, the distribution of the logarithmic transformation can be regarded as closer to normality. Also, in the Q-Q Plot, the quantiles (except for the first ones) are arranged on the straight line and, therefore, coincide with the theoretical quantiles. Therefore, we chose to operate with the data transformed into logarithms for the estimation of regression panel models.

Also, the independent variables measuring the business environment register significant variations.

The Resolving Insolvency indicator varies between a minimum of 42 in Malta, in 2020 and a maximum of 92 in Finland, in 2018. Trading across borders registers the maximum value in Croatia over the 2016–2020 period and the minimum of 72 in Cyprus, in 2012. Entrepreneurs were able to obtain loans the easiest in Romania, Estonia, and Slovakia over the 2016–2020 period. The Dealing with Construction Permits indicator varies considerably between a minimum of 22 in Croatia, in 2014 and a maximum of 92 in Denmark, in 2013. The Paying Taxes indicator recorded the lowest value in Romania, in 2013 and the largest value in 2013, in Ireland. The Starting a Business indicator had the minimum value in Malta, in 2015 (76), while in Hungary and Greece, in 2019, the indicator recorded the largest value (96).

The Easiest to Do Business in 2020 was in Denmark, and the hardest was Greece, in 2013.

The proxy for “clean” environment registered a minimum value in Slovenia and a maximum value in Denmark. The control variable taken into account in the analysis, i.e. the real GDP, also registered significant variations between countries, as we expected.

Results of the Estimated Models

Table 3 summarizes the results obtained for two regression models. The first model (Model 1) is an OLS regression, and was obtained by applying the Backward procedure.

Table 3. Details regarding final OLS regression models.

Model	Model 1: OLS regression (base model)				Model2: Fixed effects model
	Dependent variable: log (M&A)				Dependent variable: log (M&A)
Coefficients	Estimate	Std. Error	t value	Pr(>|t|)	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	3.557407	0.776921	4.579	7.64e-06 ***
CO2	0.059297	0.023699	2.502	0.013037 *	−0.000502	0.00879	−0.057	0.954488
GDP	0.034416	0.015671	2.196	0.029078 *	−0.001913	0.00209	−0.914	0.361409
CONSTR	−0.046317	0.006166	−7.512	1.27e-12 ***	0.004269	0.00152	2.790	0.005755 **
TAX	0.030047	0.007891	3.808	0.000180 ***	0.002251	0.00140	1.604	0.110178
INSOLV	−0.032902	0.004985	−6.600	2.79e-10 ***	−0.001258	0.00166	−0.757	0.449906
CREDIT	−0.031549	0.003863	−8.167	2.06e-14 ***	−0.002943	0.00089	−3.286	0.001192 **
TRADE	0.019501	0.005525	3.530	0.000502 ***	0.000818	0.00104	0.780	0.435879
GOV	−0.025823	0.014955	−1.727	0.085552.	0.004573	0.00268	1.703	0.090085.
QUALITY	0.076021	0.014237	5.340	2.22e-07 ***	−0.001096	0.00319	−0.342	0.732079
LAW	−0.028832	0.014847	−1.942	0.053352.	−0.007967	0.00353	−2.253	0.025287 *
CORR	0.030463	0.013455	2.264	0.024494 *	0.009271	0.00302	3.064	0.002476 **
Residual standard error		0.8588
Residual Sum of Squares					2.0261
R-squared		0.642			0.23517
Adjusted R-squared		0.6249			0.097124
F-statistic		37.66			5.73021
p-value		<2.2e-16			4.8029e-08
F Test for individual effects Model 2 vs. Model 1		F			683.83
		p-value			<2.2e-16

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1.

Source: authors’ own calculations.

The results obtained from Model 1 in Table 3 show that some factors have a positive effect and others a negative one on Mergers and Acquisitions (M&A). We provide further detail for each case.

The fact that Carbon Dioxide Emissions in Metric Tons Per Capita (CO2) and Real GDP (GDP) have a positive effect, shows that our results are consistent with those obtained by Wen et al. (2021), Canadell et al. (2007), and Holtz-Eakin and Selden (1995). The findings of these authors highlighted the role of different countries in terms of CO2 emissions. Considering this approach, we believe that the positive effect of CO2 emissions is also due to the sustained effort made by the countries analyzed in our study regarding the urgency of the ongoing climate change reforms and the investments in using cleaner technologies.

Our results showed that Paying taxes (TAX) positively affects the Mergers and Acquisitions (M&A). In the available literature (Faccio and Masulis 2005; Ohrn and Seegert 2019; Sankar and Leepsa 2018; Sedmihradsky and Klazar 2002), authors who debated the link between those two variables also pointed out the importance of M&A payment choices. We believe that the positive link can be due to the tax policy design implemented by each country in our sample. This is an important aspect considering the fact that investor taxation may negatively impact M&As when their capital is taxed at a preferable rate.

Trading across borders (TRADE) is a factor that also has a positive impact on M&As. The results reflect the importance of time to obtain VAT refunds, time to comply with VAT refunds, documentary compliance time, and cost to export-import a container for investors when they decide to invest in a market. Our results are consistent with those obtained by Carril-Caccia et al. (2023), and Qiu et al. (2022).

Regulatory quality (QUALITY) is a factor that plays an important role in increasing the number of M&As. As Kaufmann, Aart, and Mastruzzi (2009), Zámborský et al. (2021) or Cuervo-Cazurra et al. (2018) also argued, the capacity of country governments to formulate and implement policies and regulations to facilitate private sector development as motivators for engagement in M&As.

The level of control over corruption (CORR) is also one of the major determinants of M&As (Nguyen, Phan, and Simpson 2019). Our results revealed the positive and significant effect on M&As. This result in not accidental, for example in the case of bid premiums. It is known that corruption motivates investing companies to use excess liquidity to make payments. This practice shows that acquisitions help them turn cash into assets that are harder to justify and transfer them to other countries to avoid the vigilance of the authorities. That’s why a greater level of control over corruption is beneficial to attracting foreign investors.

Our results also revealed that Dealing with construction permits (CONSTR), Resolving insolvency (INSOLV), Getting credit (CREDIT), Government effectiveness (GOV) and Rule of law (LAW) have a negative effect on M&As. These results are not surprising if we consider the fact that, in recent years, many companies have had to go through severe global crises. These highly disruptive periods are correlated with periods of financial stress. Some companies went into insolvency, while others suffered losses due to financial stress or the economic decline in their countries. Such effects are owed to a weak governance system or the lack of effective management strategies.

In difficult times, companies are forced to reconsider their policies, to design their strategic formulations, or to use the maximum capacity value of the resources they have. How the executive reacts to financial problems determines the likelihood of bankruptcy and insolvency. Adequate management leads to the making and adoption of healthy and sustainable decisions that maximize value, while aberrant practices lead to value-increasing behavior that financially constrains firms, results in an acceleration of the financial crisis (Sewpersadh 2022). On the other hand, the effect of those variables can be regarded as an alternative way of credit rating and as a manifestation of political stability.

A model that includes interactions between independent variables is presented in the Appendices (Appendix 1: Model 1 - OLS regression with variables interaction). The best model in this case was also obtained by applying the Backward procedure. Our results show that adding the interactions between independent variables drastically changes the interpretation of all the coefficients. This model show that Carbon Dioxide Emissions in Metric Tons Per Capita (CO2) and Rule of law (LAW) have a negative effect on M&As. Another result shows that factors like Getting credit (CREDIT), Trading across borders (TRADE), Government effectiveness (GOV), Regulatory quality (QUALITY), and Control of corruption (CORR) have a positive and significant effect on dependent variables. Comparing the significance of coefficients from Model 1 in Table 3 above and Model 1 in Table 1 of the Appendices, we can see that the principal effects are modified by interactions between variables.

Table 1. Description of variables used in the analysis.

Variable name	Abbreviation	Definition	Variable type	Data source
Year	Year	The years included in period between 2012 and 2020	Interval	-
Country	COUNTR	EU-27 countries	Nominal	-
Level of development	DEV	FTSE Russell Classification (2021) (1 = Developed countries, 2 = Emerging countries, 3= Frontier countries)	Nominal	FTSE
M.A
Mergers and acquisitions	M.A	Is a union of two companies that are located in different countries, a union that results in a third company.	Numerical continuous	IMAA
		Number of cross-borders mergers and acquisitions.
Business regulatory environment – EDB
Starting a business	START	Represents the average of the scores for each of the component indicators: the procedures, time, and cost for an entrepreneur to start a business, and also the paid-in minimum capital requirement.	Numerical continuous	World Bank
Getting credit	CREDIT	Takes scores on a scale from 0 to 100, where 0 is the worst regulatory performance and 100 the best regulatory performance.	Numerical continuous	World Bank
Trading across borders	TRADE	Reflects the average of the scores obtained for the time and cost required to complete the import and export customs formalities.	Numerical continuous	World Bank
Dealing with construction permits	CONSTR	Reflects the score average for each of the following components: the number and cost of procedures, the time required to obtain construction permits, the building quality control index, and the safety mechanisms score for insurance liability.	Numerical continuous	World Bank
Paying taxes	TAX	Represents the calculated average of the scores for the following subcomponents: the number of taxes, the amount of taxes and contributions to be paid by a company, the time and bureaucracy required to process a VAT refund request.	Numerical continuous	World Bank
Resolving insolvency	INSOLV	this is the average from 0 to 100 for the recovery rate of insolvency proceedings and the strength of the legal framework regarding liquidation and judicial reorganization procedures.	Numerical continuous	World Bank
Enforcing contracts	CONTR	This is the simple average of the scores for each of the component indicators: the time and cost for resolving a commercial dispute through a local court of first-instance, the quality of judicial processes that promotes quality and efficiency in the court system.	Numerical continuous	World Bank
Sustainable environment
Carbon dioxide emissions in metric tons per capita	CO2	Reflects the emissions from the burning of fossil fuels and manufacture of cement.	Numerical continuous	Eurostat
Macroeconomic variable
Real GDP (%)	GDP	Measures the GDP from one period to another, adjusted for inflation or deflation.	Numerical continuous	Eurostat
Business regulatory environment – WGI
Voice and accountability	ACCOUNT	Captures perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.	Numerical continuous	World Bank
Political stability and absence of violence/terrorism	STAB	Measures perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism.	Numerical continuous	World Bank
Government effectiveness	GOV	Captures perceptions of the quality of public services, the quality of the civil service and its degree of independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies.	Numerical continuous	World Bank
Regulatory quality	QUALITY	Captures perceptions of the government’s ability to formulate and implement sound policies and regulations that allow and promote private sector development.	Numerical continuous	World Bank
Rule of law	LAW	Captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.	Numerical continuous	World Bank
Control of corruption	CORR	Captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ‘capture’ of the state by elites and private interests.	Numerical continuous	World Bank

Source: Eurostat database and World Bank report, FTSE, IMAA.

Because Model 1 in Table 3 does not consider heterogeneity across countries or time, a fixed effect model was estimated (Model 2). The results obtained for Model 2 showed that Dealing with construction permits (CONSTR) and Control of corruption (CORR) have a positive and significant effect on M&As, while Getting credit (CREDIT) and Rule of law (LAW) have a negative effect, with the other variables not having a significant effect.

An F test for individual effects was applied in order to choose between the fixed effects model (Model 2) and the OLS model (Model 1). Because the p-value of this test is < 0.05, we can conclude that the fixed effects model is better. The summary of the results obtained for the estimation of the fixed effects model using dummy variables for countries and time is presented in Appendix 2 of the Appendices.

The random effects model has two components: a random intercept term – which varies across countries, but is constant over time, and a traditional random error – which varies across countries. The results obtained for this model are summarized in Table 4.

Table 4. Estimates regarding the random effects model and partial pooling model.

Model	Model 3: Random effects model				Model 4: OLS Partial pooling model
	Dependent variable: log (M&A)				Dependent variable: log (M&A)
Coefficients	Estimate	Std. Error	t value	Pr(>|t|)	Estimate	Std. Error	t value	Pr(>|t|)
Intercept	3.9224938	0.437520	8.9653	<2.2e-16 ***	4.4567057	0.9221164	4.8331	2.451e-06 ***
CO2	0.0028836	0.0093995	0.3068	0.7590057	0.0687307	0.0240289	2.8603	0.00462 **
GDP	−0.0020017	0.0022526	−0.8886	0.3742185	0.0403121	0.0158969	2.5358	0.01188 *
CONSTR	0.0044133	0.0016322	2.7038	0.0068552 **	−0.0440314	0.0067411	−6.5318	4.095e-10 ***
TAX	0.0024519	0.0015040	1.6303	0.1030458	0.0350152	0.0083591	4.1889	3.991e-05 ***
INSOLV	−0.0019194	0.0017713	−1.0836	0.2785276	−0.0331018	0.0052590	−6.2944	1.536e-09 ***
CREDIT	−0.0033143	0.0009613	−3.4475	0.0005658 **	−0.0336535	0.0040496	−8.3103	8.165e-15 ***
TRADE	0.0006739	0.0011294	0.5967	0.5506997	−0.0122864	0.0080421	−1.5278	0.12794
GOV	0.0047717	0.0028850	1.6540	0.0981346.	−0.0208717	0.0152490	−1.3687	0.17241
QUALITY	−0.0001265	0.0034359	−0.0368	0.9706235	0.0817715	0.0144786	5.6478	4.753e-08 ***
LAW	−0.0074100	0.0037606	−1.9704	0.0487903 *	−0.0167521	0.0148044	−1.1316	0.25899
CORR	0.0095091	0.0032268	2.9469	0.0032100 **	0.0210997	0.0134859	1.5646	0.11905
R-squared		0.2038			0.62646
Adjusted R-squared		0.1659			0.60867
Residual Sum of Squares		2.6571			177.75
F statistic					35.2187
Chisq		59.1501			<2.22e-16
p-value		1.3331e-08

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1.

Source: authors’ own calculations

A Hausman Test was applied in order to choose between the fixed and random effects model. The results showed that the p-value is > 0.05 (0.659) and the random effects model is preferred.

The Lagrange Multiplier Test was used to test the time fixed effects. The p-value of this test is > 0.05 (0.812) and shows that there is no need to use time-fixed effects model.

The Breusch-Pagan Lagrange Multiplier for testing random effects was also applied. The p-value of this test is < 0.05 (<2.2e-16), providing evidence that the random effects model is appropriate and that there are significant differences across countries.

The Dickey-Fuller test was applied to check for stochastic trends. The resulting p-value is < 0.05 (0.01), which shows that no unit roots are present.

The profile of the entrepreneurs is deduced according to the average of the country-type group in Table 5. Across Developed EU countries, the business environment has been stimulated by 246 government measures compared to the very low number of just 85 measures registered across Emerging EU countries. The governments of developed countries have paid close attention to making paying taxes easier. The governments of Cyprus, Romania, Croatia, Latvia, Estonia, Lithuania, Slovenia, Bulgaria, Malta, and Slovakia tried to create a sustainable business environment by means of 242 measures, but many of them had a negative effect on investors. For example, in 2019, Romania made the launch of a business more complicated by introducing tax risk assessment criteria for value added tax (VAT) applications, thus increasing the time required to register as a VAT payer. Cyprus in 2017 and Croatia in 2020 made it difficult to access credit information by stopping the distribution of historical individual credit data.

Table 5. The profile of entrepreneurs involved in M&As.

Indicators/Countries Type		EM	D	F
Starting a business	Number of procedures	9	6	5
	Number of days	24	18	15
	Cost (% of income per capita)	12	6	5
	Paid-in Minimum capital (% of income per capita)	36	24	12
Number of government measures to make it easier to start a business		17	41	44
Dealing with construction	Number of procedures	19	13	15
	Number of days	211	154	226
	Index of professional certifications (0–4)	3	4	2
Number of government measures to make it easier to obtain construction permits		5	16	16
Paying taxes	Payments (number per year)	10	11	15
	Time (hours per year)	240	157	196
	Total tax and contribution rate (% of profit)	48	44	37
	Profit tax (% of profit)	12	14	10
	Labour tax and contributions (% of profit)	34	28	25
	Other taxes (% of profit)	2	2	2
Number of government measures to make it easier to pay taxes		20	44	38
Resolving insolvency	Time (month)	31	18	33
	Cost (% of estate)	14	9	11
	Recovery rate (cents on the dollar)	67	76	47
Number of government measures to make it easier to resolve insolvency		6	33	29
Trading across borders	Time to obtain VAT refund (days)	148	107	112
	Time to comply with VAT refund (hours)	13	7	6
	Time to export: Documentary compliance (minutes)	45	42	208
	Cost to export a container ($)	1071	1065	1072
	Cost to import a container ($)	1100	1102	1125
Number of government measures to make it easier to trade		4	14	11
Enforcing contracts	Trial and judgment (month)	23	14	15
	Cost (% of claim)	24	20	21
	Court fees (% of claim)	6	4	5
Number of government measures to make it easier to resolve a commercial dispute		7	30	24

Source: authors’ own calculations based on the WORLD BANK database.

In an economy, limited access to credit can act as a significant constraint on business activities, investment, and consumption. When firms or individuals face difficulties in obtaining credit, they may have to cut back on expansion plans, research and development, and capital investments, leading to slower economic growth. As a result, a negative sign for “access to credit” might indicate that a lack of credit availability hampers the economic activity and restricts M&A transactions. Access to credit can influence investor confidence and risk appetite. When credit is easily accessible, investors may be more willing to finance M&A transactions, leading to an increase in deals. Conversely, when credit becomes scarce, investors might become more cautious, leading to a decline in M&A transactions.

The developed countries in the EU provide us with examples of reforms that improve the business environment and make it sustainable. Thus, the governments of countries that want to attract more M&As should follow their example.

Taking into consideration the profile of entrepreneurs in Table 5, the governments of Greece, Hungary, and the Czech Republic should adopt the following business reforms: to reduce company incorporation procedures by 3; to reduce time to set up a firm by 6 days; to reduce the cost of opening a company by 6%; to reduce bureaucracy by 6% by reducing the number of building permits, licenses, and certificates; the reduction of the time required to complete the steps for obtaining the construction permit by 57 days; capping taxes and salary taxes to a maximum of 28% of the profit; the time to obtain a VAT refund should not exceed 107 days and the time to comply with a VAT refund should not exceed 7 days, and the export cost should be lowered by $6 per container.

The governments of Estonia, Romania, Croatia, Cyprus, Bulgaria, Latvia, Slovenia, Slovenia, Lithuania, Malta, and Slovakia should adopt the following sustainable business reforms: to reduce the time required to obtain construction permits by 72 days; the number of consumption taxes should be reduced by 4; the time required to calculate the tax payable and to complete the tax returns to the state needs to be improved by 44 hours; the time required to complete customs documents should not exceed 42 minutes; the cost to import should be around $ 1.102 per container, and the time required to recover debt through insolvency procedure must improve by 15 months.

The link between business climate, CO2 emissions, and location choices for mergers and acquisitions (M&A) can be understood through several interconnected factors:

1. Environmental Regulations and Compliance: The business climate of a location, including its environmental regulations and policies, can impact the level of CO2 emissions produced by businesses. Stricter environmental regulations and a favorable business climate that promotes sustainability and carbon reduction initiatives can attract companies seeking M&A opportunities. Companies may prefer locations with robust environmental regulations to ensure compliance and mitigate future risks associated with changing environmental standards.

2. Sustainability Goals and Commitments: Businesses increasingly prioritize sustainability and carbon reduction as part of their corporate strategies. When considering M&As, companies may seek locations that align with their sustainability goals and offer opportunities for reducing CO2 emissions. Locations with a strong sustainability focus and established renewable energy infrastructure may be attractive to companies aiming to integrate environmentally friendly practices through M&A transactions.

3. Access to Clean Technologies and Innovation: The business climate and location can influence the availability and accessibility of clean technologies and innovation hubs. Companies engaged in M&As may prioritize locations that offer access to advanced clean technologies, research institutions, and innovation ecosystems focused on CO2 reduction. These factors can facilitate the integration of sustainable practices and accelerate the transition to low-carbon operations.

4. Market Opportunities and Demand for Sustainable Solutions: Location choices for M&As can be influenced by market opportunities and consumer demand for sustainable solutions. Companies may consider locations where there is a growing market for environmentally friendly products and services. Access to environmentally conscious consumer bases can drive business growth and justify investments in CO2 reduction strategies. A favorable business climate that supports sustainability initiatives can provide a conducive market environment for M&A transactions.

5. Reputation and Stakeholder Expectations: The business climate and CO2 emissions performance of a location can impact a company’s reputation and stakeholder perceptions. Businesses are increasingly aware of the importance of environmental responsibility and may prioritize locations that have a positive reputation for sustainability. Stakeholder expectations, including customers, investors, and employees, may influence location choices for M&As, as companies seek to align with their stakeholders’ values and expectations.

6. Risk Management and Long-Term Viability: Environmental considerations, including CO2 emissions, are increasingly recognized as factors affecting long-term business viability and risk management. Locations with high CO2 emissions, inadequate environmental regulations or vulnerability to climate-related risks may present higher long-term risks for M&A transactions. Companies may opt for locations with a favorable business climate and lower emission profiles to mitigate potential environmental risks and ensure long-term sustainability.

Conclusions

Mergers and acquisitions are very important factors in the development of the economy, therefore there is a clear anticipation both among the home countries and the host countries that private capital will be the main driver for the development of markets and for future economic growth. Entrepreneurs will always look for different ways to run a successful business, which means they need to find a suitable and sustainable business environment in which to invest. Thus, they use a lot of time and resources in choosing the location for merging with or buying a company.

This study emphasizes the location choices for cross-borders mergers and acquisitions in search of a “clean(er)” business environment. The findings of this study are consistent with those of Olival (2012) and Chipalkatti, Le, and Rishi (2021), namely that a more friendly business environment is more likely to attract entrepreneurs to merge with or acquire a company, particularly in developed countries. Like us, they also found that countries with lower carbon emissions will be more favorable locations for investing and for reorganization operations through mergers or acquisitions.

The conclusions of our study are different from those obtained by Dinuk Jayasuriya (2011), who found that when he focused on developed countries, the link between a sustainable economic environment and M&As was insignificant. As a general conclusion, the research reveals the factors that are responsible for the decision to invest in developed European countries, rather than in frontier and emerging countries.

This indicates that the main advantages considered by foreign investors in developed EU countries was the environmental sustainability (CO2 emissions) and the good environment for doing business. Governments can take direct action to influence the volume of mergers and acquisitions into their countries. Emerging and frontier countries need to follow the example of developed countries in order to become sustainable.

Some of the most important reasons for deciding on the location for M&As include the presence of good coefficients regarding the time, cost, and number of formalities to complete for setting up companies, the insurance mechanisms, the value of the minimum subscribed capital, the quality of the judicial process, the taxes on salaries, income, goods, sales, and services, the time and cost to export and import, the rate of recovery of insolvencies, etc.

Choosing M&A operations as an alternative to Greenfield Investments (GI) as a vehicle for international direct investment (FDI) can be justified by several key reasons:

1. Reduced Time and Entry Barriers: M&A operations often provide a faster entry into foreign markets compared to GI. Instead of starting from scratch with GI, which involves building new facilities and establishing operations, M&A operations involve acquiring an existing local company or partnering with a local entity. This allows companies to bypass lengthy setup processes, secure market share more quickly, and accelerate their market entry.

2. Access to Existing Infrastructure and Market Presence: M&A operations provide companies with immediate access to established infrastructure, distribution networks, and market presence in the host country. Acquiring or partnering with a local subsidiary allows companies to leverage existing resources, including physical assets, customer relationships, and local market knowledge. This reduces the need for extensive upfront investments and allows for a more efficient market entry.

3. Mitigation of Political and Regulatory Risks: M&A operations can help mitigate the political and regulatory risks associated with entering a new market. By acquiring or partnering with a local company, companies establish relationships with local stakeholders and gain insights into the political and regulatory environment. This can provide better protection against unfavorable policy changes and navigate complex regulatory landscapes, reducing potential risks and uncertainties.

4. Enhanced Market Access and Customer Base: M&A operations provide companies with immediate market access and an existing customer base. Acquiring a local subsidiary allows companies to tap into an established customer network, distribution channels, and market relationships. This accelerates the process of capturing market share and reaching customers, enabling companies to generate revenue more quickly compared to GI, which requires building customer relationships from scratch.

5. Leveraging Local Expertise and Knowledge: M&A operations enable companies to leverage the expertise and knowledge of local employees and management teams. Acquiring a local subsidiary or partnering with a local entity provides access to local talent, industry insights, and understanding of the local market dynamics. This can facilitate adaptation to the local business environment, enhance market responsiveness, and improve the chances of success in the new market.

6. Cost and Resource Efficiency: M&A operations can offer cost and resource efficiencies compared to GI. Acquiring an existing local company may be more cost-effective than building new facilities, purchasing land, and setting up operations from scratch. It can also allow companies to leverage shared resources, economies of scale, and cost synergies, leading to improved operational efficiency and profitability.

7. Risk Diversification and Business Synergies: M&A operations provide opportunities for risk diversification and synergistic business integration. By acquiring a local subsidiary or partnering with a local entity, companies can diversify their business risks across different markets and sectors. Additionally, M&A operations allow for the integration of complementary capabilities, technologies, and market insights, enabling the parent company and subsidiary to achieve synergies and create value.

Overall, choosing M&A operations as an alternative to GI (Greenfield Investment) as a vehicle for FDI (international direct investment) can offer advantages such as faster market entry, access to existing infrastructure and market presence, risk mitigation, enhanced market access, leveraging local expertise, cost efficiency, risk diversification, and business synergies. These factors make M&A operations an attractive option for companies seeking international expansion and growth, while minimizing risks and maximizing market opportunities.

A limitation of this research is that we took into account a single indicator that measures the sustainable behavior of entrepreneurs. Further research could be extended to the link between the Ease of Doing Business, more sustainable development goals (SDG) indicators, and a number of cross-borders M&As; it would also be interesting to look at other indicators for environmental regulations, such as « Env. Performance Index ».

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Irina Chiriac

Irina Chiriac is Teaching Assistant PhD in the Department of Accounting, Economic Informatics and Statistics, Alexandru Ioan Cuza University of Iași, Romania. The areas of interest are: Financial Accounting, Public Accounting, Accounting in Tourism. After studying Economics and a master’s degree in Accounting, Expertise and Audit, obtains the title of doctor in Accounting with the thesis Analysis of the impact of the merger on the financial position and performance of absorbing companies and becomes project director of postdoctoral research Study on the evolution of mergers and acquisitions in the current economic context, grant POSDRU. Signs studies and articles in international and national scientific journals in the field of Mergers and acquisitions, Entrepreneurship, Sustainable environment, audit. The research results were disseminated through participation in international conferences in Lisbon, Seville, Istanbul, Barcelona, Prague, Rome. Received award for research results - Web of Science articles red and yellow area - 2023 competition - Ministry of Education/UEFISCDI. She completed documentary internship in Italy and Greece.

Ionel Bostan

Ionel Bostan is Professor, PhD, at the “Ștefan cel Mare” University of Suceava; he is a PhD supervisor on Economics. His main areas of interest are Financial Law, and International Business. He is author and co-author of more than 110 articles published in international scientific journals indexed in Web of Science; he is also the author or co-author of about 9 books as well as of teaching and learning materials for students. Awarding the results of scientific research (12 awards for articles/ Red Area and Yellow Area ISI/WoS/Clarivate Analytics) - Ministry of Education/UEFISCDI, PNCDI 2013-2020. Motivation: “To increase the quality, impact and international visibility of research Romanians by recognizing and rewarding the significant results published in the prestige of the main international scientific flow”. He is Corresponding Member of the American Romanian Academy and Doctor Honoris Causa of the Academy of Economic Studies

Iuliana Eugenia Georgescu

Iuliana Eugenia Georgescu is full Professor in the Department of Accounting, Economic Informatics and Statistics, Alexandru Ioan Cuza University of Iași (Romania), with over 30 years of experience in higher education, author and co-author of numerous books and specialized studies in accounting field. The areas of interest are General Accounting, Public Accounting, Managerial Accounting, Accounting for the reorganization of commercial companies, Accounting in tourism, etc. She participated in numerous national and international scientific conferences in the country and abroad and carried out documentation internships in France, Austria and Spain.

Mihai Bogdan Afrasinei

Mihai Bogdan Afrasinei is Phd University Lecturer in the Department of Accounting, Economic Informatics and Statistics, Alexandru Ioan Cuza University of Iași (Romania) author and co-author of some accounting studies and papers. His areas of interest are Accounting Basics, Financial Accounting, Public Accounting, Management Accounting, Tourism Accounting, Tax havens, Offshore companies, Value relevance. He completed a documentary internship in France.

Alina Morosanu

Alina Morosanu is a research assistant at the Department of Social Sciences and Humanities of the Institute of Interdisciplinary Research (ICI). Since 2011, Alina has had a Ph.D. in the fundamental field of Economics, specializing in Cybernetics and Economic Statistics. She carried out a research internship at the University of Perugia, Italy (November 2010 – April 2011), where she participated in courses and seminars on data analysis in R software. As author or co-author, she has published several works, both in Romania and abroad. The published papers cover areas such as surveys, bibliometrics, human resources management, healthcare management, environmental pollution, and transport. After completing her Ph.D., she worked in Romanian or foreign-owned companies as a statistician expert, analyst, and manager. During these professional experiences, She participated in numerous projects, which involved conducting surveys, data processing and analysis, writing research reports, and developing local/regional development strategies.

Appendix 1

OLS regression with variables interaction

Table

Model 1: OLS regression with variables interaction Dependent variable: log (M&A)
Variable	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	−35.11	8.061	−4.356	2.24e-05 ***
CO2	−1.621	0.3638	−4.455	1.48e-05 ***
GDP	−0.1994	0.155	−1.287	0.199807
CONSTR	0.01942	0.07964	0.244	0.807633
TAX	0.01539	0.06673	0.231	0.817865
INSOLV	0.01175	0.07365	0.159	0.873465
CREDIT	0.1919	0.04714	4.070	7.08e-05 ***
TRADE	0.5588	0.07978	7.004	4.98e-11 ***
GOV	0.5946	0.2323	2.560	0.011296 *
QUALITY	0.417	0.1272	3.279	0.001254 **
LAW	−1.183	0.25	−4.731	4.55e-06 ***
CORR	0.4372	0.1344	3.252	0.001371 **
CO2: GDP	0.00058	0.005549	0.105	0.916300
CO2: START	0.02159	0.005122	4.215	3.98e-05 ***
CO2:CREDIT	−0.00398	0.00125	−3.187	0.001697 **
CO2: CONTR	0.00893	0.002875	3.107	0.002201 **
CO2:ACCOUNT	−0.01072	0.005833	−1.838	0.067713.
CO2:GOV	−0.01746	0.005367	−3.252	0.001371 **
CO2: QUALITY	0.01979	0.004563	4.338	2.41e-05 ***
GDP:TAX	0.00447	0.001438	3.109	0.002185 **
GDP: TRADE	0.00230	0.001525	1.514	0.131878
GDP: CONTR	−0.00709	0.001182	−6.001	1.08e-08 ***
GDP:GOV	−0.00551	0.002295	−2.403	0.017286 *
GDP: QUALITY	−0.00448	0.002126	−2.108	0.036433 *
GDP: LAW	0.01119	0.002464	4.540	1.04e-05 ***
START: ACCOUNT	−0.00574	0.001489	−3.860	0.000159 ***
GOV: START	−0.00490	0.002184	−2.245	0.026034 *
LAW: START	0.00996	0.002413	4.131	5.56e-05 ***
CONSTR: INSOLV	0.00121	0.000668	1.824	0.069877.
CONSTR: CREDIT	0.00117	0.000446	2.634	0.009184 **
CONSTR: CONTR	0.00537	0.000908	5.923	1.61e-08 ***
CONSTR: ACCOUNT	−0.00311	0.001851	−1.685	0.093769.
CONSTR:GOV	−0.00306	0.001374	−2.231	0.026957 *
CONSTR: QUALITY	−0.00471	0.001022	−4.614	7.55e-06 ***
CONSTR: CORR	0.00366	0.001364	2.688	0.007883 **
TAX: TRADE	−0.00368	0.000805	−4.580	8.75e-06 ***
TAX: ACCOUNT	0.00240	0.000891	2.699	0.007638 **
TAX: QUALITY	0.00175	0.001064	1.651	0.100530
INSOLV: CREDIT	0.00099	0.000282	3.509	0.000571 ***
INSOLV: TRADE	−0.00371	0.000522	−7.105	2.83e-11 ***
INSOLV: CONTR	0.00430	0.000707	6.078	7.30e-09 ***
INSOLV: ACCOUNT	0.00256	0.000988	2.598	0.010172 *
INSOLV: STAB	−0.00279	0.000471	−5.924	1.60e-08 ***
INSOLV:GOV	−0.00181	0.000742	−2.443	0.015549 *
CREDIT: TRADE	−0.00139	0.000473	−2.940	0.003726 **
CREDIT: CONTR	−0.00297	0.000411	−7.244	1.29e-11 ***
CREDIT:GOV	−0.00111	0.000856	−1.302	0.194512
CREDIT: LAW	0.00115	0.000825	1.401	0.163038
TRADE: CONTR	−0.00333	0.000640	−5.205	5.33e-07 ***
TRADE: STAB	−0.00047	0.000369	−1.283	0.201207
TRADE: LAW	0.00405	0.000682	5.937	1.50e-08 ***
CONTR: STAB	0.00308	0.000528	5.830	2.58e-08 ***
GOV: CONTR	−0.002854	0.001370	−2.083	0.038679 *
QUALITY: CONTR	−0.001470	0.001023	−1.437	0.152556
LAW: CONTR	0.002870	0.001474	1.947	0.053109.
CORR: CONTR	−0.004538	0.001118	−4.057	7.43e-05 ***
ACCOUNT: STAB	−0.002080	0.000959	−2.168	0.031455 *
GOV: ACCOUNT	−0.003492	0.002775	−1.258	0.209878
LAW: ACCOUNT	0.01088	0.002292	4.747	4.24e-06 ***
QUALITY: STAB	−0.002076	0.000725	−2.864	0.004693 **
LAW: STAB	0.004758	0.000815	5.837	2.49e-08 ***
GOV: QUALITY	0.01220	0.002759	4.421	1.71e-05 ***
GOV: LAW	−0.007987	0.002599	−3.073	0.002453 **
GOV: CORR	0.006459	0.002510	2.573	0.010904 *
QUALITY: LAW	−0.007007	0.002644	−2.650	0.008780 **
QUALITY: CORR	−0.006422	0.002310	−2.780	0.006018 **
LAW: CORR	−0.004808	0.001799	−2.672	0.008238 **
Residual standard error			0.4123
Multiple R-squared			0.9368
Adjusted R-squared			0.9135
F-statistic			40.34
p-value			<2.2e-16
F test	Model 1 vs. Model 2		F	Sig.
			7.171	<2.2e-16 ***

Source: authors’ own calculations

Appendix 2

Fixed effects models with dummy variables

Table

Model	Model 2a: Fixed country effects				Model 2b: Fixed time effects
	Dependent variable: log (M&A)				Dependent variable: log (M&A)
Coefficients	Estimate	Std. Error	t value	Pr(>|t|)	Estimate	Std. Error	t value	Pr(>|t|)
CO2	−0.0005024	0.0087926	−0.057	0.95449	0.0119834	0.009262	1.2938	0.197252
GDP	−0.0019137	0.0020921	−0.915	0.36141	0.0009264	0.003377	0.2743	0.784139
CONSTR	0.0042692	0.0015298	2.791	0.00575**	0.0030899	0.001538	2.0083	0.045982*
TAX	0.0022518	0.0014035	1.604	0.11018	0.0014040	0.001418	0.9905	0.323160
INSOLV	−0.0012586	0.0016626	−0.757	0.44991	−0.001648	0.001637	−1.006	0.315251
CREDIT	−0.0029433	0.0008955	−3.287	0.00119**	−0.001990	0.000950	−2.094	0.037476*
TRADE	0.0008181	0.0010479	0.781	0.43588	−0.001261	0.001727	−0.729	0.466302
GOV	0.0045735	0.0026856	1.703	0.09008.	0.0036747	0.002663	1.3795	0.169297
QUALITY	−0.0010961	0.0031972	−0.343	0.73208	0.0015953	0.003249	0.4910	0.623962
LAW	−0.0079677	0.0035357	−2.253	0.02529*	−0.009252	0.003674	−2.518	0.012595*
CORR	0.0092715	0.0030258	3.064	0.00248 **	0.009141	0.003003	3.0439	0.002654**
Austria	3.5380111	0.4476246	7.904	1.63e-13 ***
Belgium	4.5927314	0.4337952	10.587	<2e-16 ***
Bulgaria	4.0080943	0.2988490	13.412	<2e-16 ***
Croatia	3.5128540	0.3040933	11.552	<2e-16 ***
Cyprus	6.9630516	0.3694944	18.845	<2e-16 ***
Czech Republic	3.9183148	0.3897883	10.052	<2e-16 ***
Denmark	3.1265201	0.4730736	6.609	3.28e-10 ***
Estonia	3.8718271	0.4307671	8.988	<2e-16 ***
Finland	3.1847994	0.4778082	6.665	2.39e-10 ***
France	3.0512786	0.4183782	7.293	6.49e-12 ***
Germany	3.0268066	0.4487205	6.745	1.53e-10 ***
Greece	2.2139241	0.3078590	7.191	1.18e-11 ***
Hungary	4.8599536	0.3323430	14.623	<2e-16 ***
Ireland	5.3990856	0.4366000	12.366	<2e-16 ***
Italy	2.7076927	0.3413423	7.932	1.37e-13 ***
Latvia	3.4997435	0.3739760	9.358	<2e-16 ***
Lithuania	3.1670446	0.3791437	8.353	9.93e-15 ***
Luxembourg	7.9278685	0.4517220	17.550	<2e-16 ***
Malta	6.7026631	0.3703757	18.097	<2e-16 ***
Netherlands	5.5905856	0.4564137	12.249	<2e-16 ***
Poland	3.3138702	0.3749456	8.838	4.45e-16 ***
Portugal	3.6953598	0.3962837	9.325	<2e-16 ***
Romania	3.5231539	0.3045457	11.569	<2e-16 ***
Slovakia	3.7419750	0.3483520	10.742	<2e-16 ***
Slovenia	2.9697085	0.3846321	7.721	5.01e-13 ***
Spain	3.5526748	0.3878131	9.161	<2e-16 ***
Sweden	3.7043463	0.4638121	7.987	9.81e-14 ***
2013					0.0133940	0.026762	0.5005	0.617297
2014					0.0683907	0.028954	2.3620	0.019153*
2015					0.0657857	0.032556	2.0206	0.044670*
2016					0.0967929	0.038137	2.5380	0.011922*
2017					0.0888064	0.038808	2.2883	0.023180*
2018					0.0517837	0.038412	1.3481	0.179180
2019					0.0784510	0.037850	2.0727	0.039504*
2020					0.1364849	0.044629	3.0582	0.002537**
R-squared		0.9997			0.29925
Adjusted R-squared		0.9996			0.13918
F-statistic		15470			4.42769
p-value		<2.2e-16			2.9416e-08

Source: authors’ own calculations.