The Effect of IFRS Adoption on the Business Climate: A Country Perspective

Abstract

Based on the ten areas that are measured by the ease of doing business (EDB) and based on the getting credit (GC) indicator, this study seeks to analyze factors that lead to a more favorable business climate in different countries. The methodology of fuzzy-set qualitative comparative analysis (fsQCA) was used to determine the paths taken by configurations or conditions in which variables affect an outcome. The results showed that high EDB and GC scores may be obtained under specified levels of IFRS (International Financial Reporting Standards) adoption degree and user experience requirements. Therefore, the adoption of IFRS could result in a better business climate in a nation since it would increase the comparability of financial statements, which will lower costs for investors, draw in foreign investors, and boost trust. Finally, the findings indicated that, depending on the presence of specific levels of GDP per capita, entrepreneurship, income group, and foreign direct investment (FDI) inflows, low or high values of IFRS adoption and high experience in applying IFRS are necessary to achieve high GC scores.

1. Introduction

Numerous studies on the effects of adopting the International Financial Reporting Standards (IFRS), as well as its advantages and challenges, have been conducted over the past 20 years (Opare et al. 2019; Bui et al. 2020; Boolaky et al. 2020). Due to the conflicting results of the many studies, it is still difficult for the accounting literature to credit conclusive impacts as to the advantages of implementing the IFRS (Ball 2006; Brown 2011). Nevertheless, according to earlier research (Golubeva 2020; Mita et al. 2018), there may be a positive correlation between the adoption of IFRS and the business climate due to the potential advantages for investors, such as an improvement in accounting quality and comparability of financial data. Furthermore, previous research also demonstrates that after the implementation of IFRS, companies’ cost of capital was dramatically lowered (Leuz and Verrecchia 2000), mostly because of a reduction in information asymmetry and an increase in stock market liquidity. However, there have been limited studies aiming specifically at measuring the relationship between IFRS adoption and business climate, from both investors and creditor’s perspectives, despite the notion that different IFRS adoption levels may have varied business implications due to the conflicting effects of adoption that is both voluntary and mandated (Tiron-Tudor and Achim (Nasca) 2019).

Therefore, the goal of this study is to determine whether IFRS adoption has any impact on business climate, from an investor’s perspective, as measured by the ease of doing business (EDB) score, and from the standpoint of creditors, as measured by getting credit score (GC). Both EDB and GC rankings are calculated annually by the Doing Business (DB) initiative from the World Bank, and have recently been researched from numerous perspectives due to their importance in a firm’s decision-making (Contractor et al. 2020; Estevão et al. 2021).

For the current study, these are the two developed hypotheses: H1: There is an association between IFRS adoption and a friendly business climate measured by EDB; H2: There is an association between IFRS adoption and credit access.

The fuzzy-set qualitative comparative analysis (fsQCA) was applied, as it has not only been regularly used by researchers and academics in different business and management areas, such as entrepreneurship and innovation (Contractor et al. 2020; Estevão et al. 2020; Kraus et al. 2018), but this methodology also allows for multiple causality or numerous prerequisite factors to result in a particular outcome, which for this study, this benefit is crucial.

The findings of this research suggest that a country’s adoption of IFRS could generate a better business climate, as high EDB and GC scores can be achieved in the presence of certain conditions of IFRS adoption degree and using IFRS experience. IFRS adoption could improve a country’s business climate by increasing the comparability of financial statements, which will cut investment costs, attract foreign capital, and foster a sense of trust among investors. Results further indicate that low or high values of IFRS adoption degree and high IFRS experience are necessary to attain high values of GC, depending on levels of GDP per capita, entrepreneurship, income group, and FDI inflows.

The literature review that frames this investigation is presented first in this manuscript. The methods will then be described in depth. After that, the findings are discussed in terms of the conditions that must exist for the desired outcome to occur. Following a thorough analysis of the findings, we give our conclusions and contributions. The manuscript’s final part highlights the limitations of the study and calls for more investigation.

2. Literature Review and Hypothesis

2.1. Ease of Doing Business

Firms’ business decisions and performance are influenced by their environment, which includes both internal and external factors (Cherunilam 2021), such as economic, social/cultural, demographic, political/government, natural, technological, and/or global factors. Accordingly, the business climate has been the focus of several academic studies (Contractor et al. 2020; Djankov et al. 2006; Estevão et al. 2021).

Currently, there are several rankings that rate countries in business climate fields. The EDB ranking from the World Bank and the Global Competitiveness Report from the World Economic Forum are two of those rankings.

The EDB ranking was developed under the Doing Business project, initiated in 2002. In 2003, the World Bank published its first report, which included the effect of five sets of indicators for business regulation in 133 economies (Besley 2015). Over time, the World Bank has evolved regarding both the indicators and the countries under study, collecting 11 sets of indicators and including 189 countries in 2015 (Besley 2015). Currently, there are twelve business climate indicators, and ten are included in the EDB measure: starting a business; dealing with construction permits; getting electricity; registering property; getting credit; protecting minorities; investors paying taxes; trading across borders; enforcing contracts; and resolving insolvency and their subcomponents. Besides these ten indicators, employing workers and contracting with the government were also analyzed but are not included in the EDB score and ranking (Djankov 2016). By considering this set of indicators, the Doing Business project team published an annual report that includes the results of each economy’s performance in terms of the regulatory environment for the several indicators under study, as well as an aggregated country ranking that is called ‘ease of doing business’ (Djankov 2016). The report also identifies the most recent business climate reforms applied in each country, and the data used for reaching the published conclusions are collected based on relevant laws and regulations through communication with expert respondents, questionnaires, conference calls, written correspondence, and visits by the team (Djankov 2016).

Although EDB does not cover all the business climate factors, it is a complete ranking comprising a dozen different factors that affect domestic small- and medium-sized firms across 190 economies worldwide (Estevão et al. 2021). By providing timely data, EDB has been an important tool, because it encourages regulations to be efficient, transparent, and simple to implement and lists the recently implemented reforms that make it easier or more difficult for a country to do business (Williams and Kedir 2019). Investor perceptions of investment opportunities, domestic politics, and policy are also influenced by EDB, as proved by Doshi et al. (2019). Additionally, it has been proven and verified that the best regulatory environment is found where there is efficient, not less, regulation (Djankov et al. 2002; Shima and Gordon 2011).

EDB is also an important tool for academics because numerous studies related to entrepreneurship and regulations’ impacts use the annual ranking as the measure to achieve investigation conclusions (Estevão et al. 2020).

2.2. Consequences of Business Regulations

Literature around the regulatory business climate generally supports that business regulations have a considerable impact on the business life cycle. Thus, in terms of the impact of higher/lower level business regulation for a country, Contractor et al. (2020) focused on the impact of regulatory variables on foreign direct investment (FDI) inflows and, using the sample of 189 nations presented in World Bank’s database of 2016, established that regulatory and business climate factors are currently important to FDI inflows and that the factors of rule of law (contract enforceability) and ease of trade across borders are the most significant for FDI decisions. Similarly, Hossain et al. (2018) support that EDB has an impact on inward FDI, as well as a good institutional environment (Buchanan et al. 2012).

In contrast, when analyzing the effects of the country’s business regulatory environment and the inward stock of FDI, Ganic and Hrnjic (2019) did not find evidence that Central Eastern European nor Southeast European countries could benefit from the current business regulatory environment.

Consistent with prior literature (Dreher and Gassebner 2013) considering the greater number of procedures to start a business and minimal capital required as key factors that can deter a firm’s entry into markets, Dove (2020), through studying the impact of regulation on entrepreneurship activity in the US, verified that an increase in the regulatory burden imposed across states is associated with a reduction in entrepreneurship opportunity.

Research on SMEs in South Africa, focusing on identifying the effects of regulations (Nieuwenhuizen 2019), suggested that the regulatory environment and related legislation are the principal barriers to business start-up and growth and considered that excessive red tape is a key obstacle faced by SMEs. If, on one hand, the findings above suggest that a poor business climate can hamper a business, Haidar (2012) argued that a reform that makes it easier to do business is beneficial for an economy.

2.3. Research Hypotheses

2.3.1. Ease of Doing Business—Investors’ Perspective

A set of business regulations that may impact business is the IFRS, which are the accounting rules defined by an independent non-profit organization called the International Accounting Standards Board (IASB). The central goal of those rules is to bring transparency, accountability, and efficiency to financial markets worldwide (IFRS Foundation 2018), in order words, to publicly companies. Nevertheless, the board has also developed the IFRS for SMEs, which are used by small- and medium-sized companies without public accountability. This means that IFRS can apply to all businesses, not just large ones, and they can, therefore, have an impact on countries’ business climate.

Since the beginning, IFRS were adopted by numerous countries worldwide, which motivated several studies about the determinants of adoption (Alon and Dwyer 2014; Christensen et al. 2013; Judge et al. 2010; Ramanna and Sletten 2014; Stainbank 2014). Overall, studies indicate that the adoption of IFRS has a positive impact on economic growth in both developed and developing countries (Akisik and Mangaliso 2019; Lungu et al. 2017; Oppong and Aga 2019). A study of how IFRS adoption has affected EU economic growth from 2005 to 2014 found a positive and significant relationship between IFRS adoption and economic growth and that full adoption of IFRS has a significant impact on economic growth in both developed and developing countries, in contrast with partial adoption of IFRS that only impacts developing countries economic growth (Oppong and Aga 2019). Despite the motivations to adopt IFRS, there are also barriers, such as the country’s culture, religion, the accountants’ and auditors’ skills and experience, the local languages, implementation costs, and the discomfort of quitting local standards (Dowa et al. 2017; Hail et al. 2010).

Numerous studies have also focused on measuring the positive and negative effects of IFRSs adoption. However, it remains a challenge for the accounting literature to attribute conclusive effects as to the benefits of adopting the IFRS due to the varied conclusions in the different research (Ball 2006; Brown 2011). As suggested by Brown (2011), a country can benefit from IFRS application, but implementation is not sufficient to guarantee the expected gains. Research assessing the effects of adopting IFRS has used a sample of countries, such as EU countries, and developing or undeveloped countries, and some has only focused on the effect of a specific level of adoption (effect of voluntary/mandatory adoption) (Ahmed et al. 2013; Opare et al. 2019). Previous studies have shown that with the adoption of IFRS, countries and companies will have access to high-quality accounting standards that will be comparable with many other reports worldwide (Ball 2006). Thus, as much of the prior literature suggests, the financial reports increased in comparability (Ahmed et al. 2013; Brown 2011; Mita et al. 2018; Opare et al. 2019; Yip and Young 2012) and quality (Barth et al. 2008; Brown 2011; Daske and Gebhardt 2006), which are two strong potential benefits of the IFRS.

Despite all the studies that document an increase in the comparability of annual reports (DeFond et al. 2019; Neel 2017; Wang 2014; Yip and Young 2012), others have found opposite results, as in the study by Callao et al. (2007) investigating the effect of IFRS adoption on the comparability of financial reporting in 35 listed companies in Spain and concluded that it did not improve as a result of the application of both IFRS and local standards. Lin et al. (2019) examined the effects of IFRS adoption in Germany and concluded that IFRS adoption did not influence comparability improvement. The increase in the quality of financial information and comparability can contribute to both more efficient allocation and a reduction in the cost of capital (Brown 2011; Opare et al. 2019; Ruder et al. 2005), an increase in market liquidity and forecast accuracy (Drake et al. 2010; Neel 2017), an increase in market efficiency (Ball 2006; Brown 2011; Ruder et al. 2005), an enhancement of the foreign investors’ ownership (Mita et al. 2018) and attraction of foreign analysts (Brown 2011; Tan et al. 2011), an increase in firm value (Agyei-Boapeah et al. 2020), and have an impact on stock price informativeness (Kainth and Wahlstrøm 2021; Tiron-Tudor and Achim (Nasca) 2019) and economic growth (Oppong and Aga 2019).

Regarding financial report quality impact, Păşcan (2015) established a list of the different findings in previous literature in European countries, and the results indicated that the effects on quality are not always positive, suggesting an interpretation of the results related to country- and firm-specific factors. Consistent with studies supporting accounting quality, a particular investigation regarding Brazil (Eng et al. 2019) found that the mandatory adoption of IFRS in 2010 resulted in increased accounting information quality, while the results of an investigation by Firmansyah (2019) suggested that accounting information quality is not affected by IFRS adoption. The effect of IFRS on earnings management is closely related to the effect on accounting quality given that the smaller the earnings management found, the higher the accounting equality will be. Thus, some studies have used the metric of earnings management to evaluate improvements in accounting quality (Barth et al. 2008; Christensen et al. 2015), establishing that accounting quality was also improved with lower verified earnings management. Capkun et al. (2016) found that the first voluntary adopters had more incentives to improve the transparency and consequent decrease in earnings management in their financial reporting compared with local Generally Accepted Accounting Principles (GAAP), and that the countries that were forced to adopt IFRS, when it became mandatory in 2005, showed an increase in earnings management. In contrast to studies that support the hypothesis of a decline in earnings management post-IFRS, Jeanjean and Stolowy (2008) indicated that in three of the first IFRS adopters, Australia, France, and the UK, earnings management did not decline after the IFRS application. Morais et al. (2018) found that the adoption of a single set of accounting standards did not determine similar accounting practices or lead to similar levels of accounting quality across companies from different countries.

Considering the literature presented below, it is reasonable to consider that either voluntary or mandatory IFRS adoption could affect business climate quality or at least in a selected set of indicators that comprise business climate quality. The first hypothesis will then seek, from an investor’s perspective, to find the relationship between the adoption of IFRS and the quality of the business climate, that is, the EDB. According to earlier research (Golubeva 2020; Mita et al. 2018), there may be a positive correlation between the adoption of IFRS and the environment for EDBs due to the potential advantages for investors, such as an improvement in accounting quality and comparability of financial data. Thus, as cited by Bui et al. (2020), when financial statements are reported using the same accounting principles, the results provided can guarantee comparability, helping investors’ analyses and reducing possible costs for information processing. All these arguments lead to the following hypothesis:

H1: 

There is an association between IFRS adoption and a friendly business climate measured by EDB.

2.3.2. Getting Credit—Creditors’ Perspective

From the standpoint of creditors, there are two main ways that the adoption of IFRS can benefit the GC. First, higher quality and comparable accounting information can contribute to reducing information asymmetry (Easley and O’Hara 2004; Lambert et al. 2007) between lenders and borrowers by providing lenders with information about companies’ specific, private, and forward-looking information, enabling the lenders to price the debt correctly. Previous studies have shown that companies’ cost of capital is significantly reduced after the IFRS adoption (Leuz and Verrecchia 2000), mainly due to a decrease in information asymmetry and an increase in stock market liquidity. Therefore, previous studies have indicated that IFRS adoption has positive consequences for companies’ debt financing (Agyei-Boapeah et al. 2020; Florou and Kosi 2015) by increasing the propensity to access the public rather than private debt market and by reducing bond yield spreads (Florou and Kosi 2015). The comparability of financial information is particularly important in providing trade credit in international trade. The adoption of different local accounting standards hinders the interpretation of accounting numbers (Florou et al. 2017) and, consequently, increases the information processing costs, and could negatively impact credit access. When studying the potential effect of the implementation of the IFRS on credit decisions for SMEs, Mamdouh (2015) suggested that IFRS for SMEs could help firms to solve their information opaqueness, leading to increased banks’ trust in firms’ financial position and their ability to repay the loan and also that financial information available for SMEs could decrease the possibility of financing and reduce the interest rate on credit and, as a consequence, its capital cost. de Moura et al. (2020) also found that IFRS adoption significantly reduced the cost of debt in Latin America, by enhancing comparability and disclosure and, consequently, reducing the information asymmetry problem.

Second, IFRS can also provide timely and relevant information about borrowers’ creditworthiness, reducing information asymmetry problems of debt providers when accessing firms’ ability to reimburse debt and when predicting future cash flows (Florou and Kosi 2015; Florou et al. 2017) and, consequently, contributing to more efficient contracts between lenders and borrowers (De George et al. 2016). However, previous studies provide mixed evidence. Some studies show that IFRS adoption decreased the likelihood of a company having restrictive covenants (Chen et al. 2015; Kim et al. 2011). Donelson et al. (2017) also provided evidence that commercial bank lenders tend to require more collateral and guarantees from borrowers that present lower financial reporting quality (in terms of conservatism and restatements).

However, the impact of IFRS adoption on the credit market can be affected by companies’ and countries’ specific factors. Daske et al. (2013) investigated the economic effect of adopting IFRS seriously or not by classifying firms as ‘label’ (firms that adopt IFRS only in name) or ‘serious’ adopters (firms that apply changes to improve their reporting practice) and concluded that serious adoption is a determinant for obtaining increases in liquidity and a decline in the cost of capital following the adoption. Country-specific factors, such as the legal enforcement environment and the level of credit protection can also play an important role in IFRS’s impact on the credit market. de Lima et al. (2018) show that the adoption of IFRS has a greater impact on the level of debt access in countries with weak legal enforcement and lower credit protection. Cameran and Campa (2020) found that IFRS adoption had a positive impact on financial reporting quality, and resulted in a decrease in the cost of debt, for unlisted companies voluntarily adopting IFRS. Thus, a positive relationship between IFRS adoption and the credit access score is expected.

Finally, IFRS adoption can also play a role in international aid. Lamoreaux et al. (2015) indicated that the World Bank tends to lend more to countries that require the adoption of IFRS due to higher accounting quality that allows for lower monitoring costs.

All these arguments lead to the following hypothesis:

H2: 

There is an association between IFRS adoption and getting access to credit.

Figure 1 provides an overview of the conceptual framework and investigation hypothesis.

3. Methodology

3.1. Sample and Variables Description

The Doing Business project’s participants served as the initial basis for the sample of economies to be used in this study, which was subsequently decreased in accordance with the missing values. A total of 117 nations are represented (see

Table 1. List of economies included in this sample.

Albania	Cambodia	Guatemala	Liberia	Nigeria	Spain
Algeria	Canada	Hong Kong SAR, China	Lithuania	North Macedonia	Sri Lanka
Antigua and Barbuda	Chile	Hungary	Luxembourg	Norway	Sweden
Argentina	Colombia	Iceland	Madagascar	Oman	Switzerland
Armenia	Costa Rica	Indonesia	Malawi	Pakistan	Tajikistan
Australia	Croatia	Iran, Islamic Rep.	Malaysia	Panama	Tanzania
Austria	Cyprus	Ireland	Maldives	Peru	Thailand
Azerbaijan	Czech Republic	Israel	Malta	Philippines	Tunisia
Bahrain	Denmark	Italy	Mauritania	Poland	Turkey
Bangladesh	Dominica	Jamaica	Mauritius	Portugal	Uganda
Belarus	Dominican Republic	Japan	Mexico	Qatar	Ukraine
Belgium	El Salvador	Jordan	Moldova	Romania	United Arab Emirates
Belize	Estonia	Kazakhstan	Mongolia	Russian Federation	United Kingdom
Bhutan	Finland	Kenya	Montenegro	Rwanda	Uruguay
Bolivia	France	Korea, Rep.	Morocco	Saudi Arabia	Uzbekistan
Bosnia and Herzegovina	Georgia	Kosovo	Myanmar	Serbia	Vietnam
Botswana	Germany	Kuwait	Namibia	Singapore	Zambia
Brazil	Ghana	Kyrgyz Republic	Nepal	Slovak Republic
Brunei Darussalam	Greece	Latvia	Netherlands	Slovenia
Bulgaria	Grenada	Lesotho	New Zealand	South Africa

Source: The authors.

). The time frame for the analysis was 2018–2020.

3.1.1. Ease of Doing Business Score (1) and Getting Credit Score (2)

As previously explained in the literature review, the EDB ranking, from the World Bank, measures the economy’s performance in terms of its regulatory practices across 41 different business climate sub-indicators for ten dimensions of the business climate. GC is one of those dimensions.

The EDB scores and EDB individual indicators scores, like the GC scores, are published on the World Bank website annually. The most current data, as well as the historical data, were extracted directly from this website. This study will apply this secondary data to test the developed hypothesis.

3.1.2. IFRS Adoption Degree (3) and Country Experience in Using IFRS (4)

For this research, it was necessary to develop two variables to measure the IFRS Adoption in the countries, as there is no secondary data available. The first variable aims to measure how IFRS was implemented in each country (mandatory, permitted, not required, among others), which constitutes the variable IFRS Adoption Degree (3).

Three data sources provided the information needed to determine each country’s level of IFRS adoption: the Deloitte IASplus.com website; IFRS jurisdictional profiles presented on the ifrs.org website; and the adoption status by country publication provided by PWC on the pwc.ru website. Based on the data sources, a database was constructed, with IFRS adoption information presented in each data source compiled to determine the adoption degree.

On the constructed database, each country was scored according to the level of obligation of reporting in IFRS and according to the adoption type (original or modified) for both domestic listed and unlisted companies. As the literature suggested that different implementations IFRS may deteriorate the assumption that accounting information remains comparable between countries adopting IFRS, the classifications for all countries were defined not only as IFRS are permitted, IFRS are not permitted, or IFRS are required. Countries that had not implemented the original IFRS but had adapted their accounting principles to IFRS were classified with a lower adoption degree. While the classifications of permitted, not permitted, and required have been used previously by several authors (Boolaky et al. 2020; Golubeva 2020; Ramanna and Sletten 2014), the adoption type, modified or not modified, has not frequently been used. Despite this, it has been noted that countries have adopted a modified version of IFRS (Nnadi and Soobaroyen 2015; Trimble 2018).

These scores follow the indirect method of calibration suggested by Schneider and Wagemann (2012), in which the researcher classifies the cases using the values of 1 (for full membership), 0.8, 0.6, 0.4, 0.2 and 0 (for full non-membership) (6 groups). Due to the different possibilities of patterns within each country in terms of IFRS adoption, for each value, a set of cases was considered.

Table 2. IFRS adoption degree.

Adoption Degree	Domestic Listed Companies		Domestic Unlisted Companies
1 (Full Membership)	IFRSs required for all	+	IFRSs required for all
0.8	IFRSs required for all	+	IFRSs permitted
0.8	IFRSs required for all	+	IFRSs required for some
0.8	local GAAP (IFRS-based) is required	+	local GAAP (IFRS-based) is required
0.8	local GAAP or IFRSs is required	+	local GAAP or IFRSs is required
0.6	IFRSs required for all	+	IFRSs permitted
0.6	IFRSs required for some	+	IFRSs permitted
0.6	IFRSs required for some	+	IFRSs required for some
0.6	local GAAP (IFRS-based) is required	+	local GAAP (based on IFRS) is required for some
0.6	local GAAP (IFRS-based) is required	+	local GAAP (based on IFRS) is required for some
0.6	local GAAP (based on IFRS or convergence) is required	+	IFRSs or modified IFRSs/local GAAP permitted
0.6	local GAAP or IFRSs is required	+	IFRSs required for some
0.6	local GAAP or IFRSs is required	+	local GAAP or IFRSs is required for some
0.6	local GAAP or IFRSs is required	+	local GAAP (based on IFRS) is required for some
0.6	local GAAP or IFRSs is required	+	IFRSs permitted
0.6	N/A	+	IFRSs required for all
0.4	IFRSs required for all	+	IFRSs not permitted
	IFRSs required for some	+	IFRSs not permitted
0.4	IFRSs permitted	+	IFRSs required for some
0.4	IFRSs permitted	+	IFRSs permitted
0.4	IFRSs not permitted	+	IFRSs required for all
0.4	IFRSs or modified IFRSs/local GAAP permitted	+	IFRSs or modified IFRSs/local GAAP permitted
0.4	IFRSs or modified IFRSs/local GAAP permitted	+	IFRSs permitted
0.4	N/A	+	IFRSs required for some
0.2	IFRSs permitted	+	IFRSs not permitted
0.2	IFRSs not permitted	+	IFRSs permitted
0.2	N/A	+	IFRSs permitted
0.2	local GAAP (IFRS-based) is required	+	IFRSs not permitted
0.2	local GAAP (based on IFRS) is required for some	+	IFRSs not permitted
0 (FULL NON-MEMBERSHIP)	N/A	+	IFRSs not permitted
	IFRSs not permitted	+	IFRSs not permitted

Note: GAAP stands for Generally Accepted Accounting Principles, the United States accounting standards (alternative to IFRS); N/A indicates that there is no stock exchange in the country. Source: The authors.

summarizes the output for the IFRS adoption degree variable.

The second variable developed to measure the IFRS Adoption in the countries is based on the user experience using the accounting standards—Country experience in using IFRS (4). To gather information about each country’s experience using the IFRS variable, the adoption year by country had to be collected. Trimble (2018), when studying the current and historical status of IFRS adoption worldwide, collected the adoption year by country, so most of the data could be verified on the basis of that study. In cases where the adoption year was indicated as not applicable, a double check was conducted to see if the country has adopted IFRS between 2018 and 2020 on its jurisdiction profile published on the ifrs.org website, and the variable value was updated if so.

3.1.3. GDP per Capita (5), FDI Inflows (6) Income Group (7), and Entrepreneurship Level (8)

Prior literature found that there was an association between certain economic indicators and the EDB score, such as the GDP per capita (Estevão et al. 2020), FDI inflows (Contractor et al. 2020), and income group (Bajra et al. 2020), which led to the inclusion of these control variables in the tested models. Thus, if different degrees of GDP, FDI inflows, and income groups impact the EDB score, then the adoption degree and experience using IFRS experience combined with those variables may be relevant for achieving higher or lower EDB scores. All the data for these variables were collected from the World Bank website.

One more economic variable was also added to the models—the entrepreneurship level. This variable was added since a proven association was verified between greater EDB to higher levels of entrepreneurship in the latest report published by the World Bank (World Bank 2017). The data for this variable were likewise gathered from the World Bank website, and were based on the number of newly registered enterprises in each nation.

For the variables GDP per capita, FDI inflows, income group, and entrepreneurship level, the data included in the models were based on the average value since the country adopted IFRS or since 2001, that being the year marking the foundation of the IASB (the organization that issued the IFRS), if IFRS were adopted before, or if the country had not adopted IFRS at all.

Table 3. Variables overview.

	Acronym in the Article	What It Measures	Data Source
(1) Ease of Doing Business Score	EDB	Ease of doing business score measures business regulations in terms of 10 indicators: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts, and resolving insolvency.	Collected from World Bank
(2) Getting Credit Score	GC	Getting credit score measures the legal rights of borrowers and lenders regarding secured transactions through one set of indicators and the reporting of credit information through another.	Collected from World Bank
(3) IFRS Adoption Degree	Adoption_Degr	Degree of adoption by country. This value was classified between 0 and 1, in which 0 indicated that the country had not adopted IFRS for listed or unlisted companies, and 1 that IFRS was mandatory for all companies.	Variable created by authors based on three data sources: Deloitte IASplus.com website; IFRS jurisdictional profiles presented on ifrs.org website, and adoption status by country publication provided by PWC on the pwc.ru website
(4) IFRS Experience	Exp_IFRS	The number of years that each country has applied IFRS as an accounting principle was obtained by calculating the difference between the current year and the IFRS adoption year per country	Variable created by authors based on Trimble (2018), and IFRS jurisdictional profiles presented on ifrs.org website
(5) GDP Per Capita		GDP per capita is gross domestic product divided by midyear population.	Collected from World Bank
(6) FDI Inflows		FDI net inflows are the value of inward direct investment made by non-resident investors in the reporting economy.	Collected from World Bank
(7) Income Group		The World Bank assigns the world’s economies to four income groups—low, lower-middle, upper-middle, and high-income countries. The classifications are based on GNI per capita in the current USD and are updated every year.	Collected from World Bank
(8) Entrepreunship Level		Number of new businesses registered in each country	Collected from World Bank

Source: The authors.

summarizes the information for each variable.

For illustration purposes, Figure 2 presents the conceptual framework from the literature review including the tested variables.

3.2. Data Analysis Technique

Fuzzy-set qualitative comparative analysis (fsQCA) was chosen to determine whether IFRS adoption has any impact on the business climate from the standpoint of investors, as measured by the EDB score (Hypothesis 1), and from that of creditors, as measured by the GC score—(Hypothesis 2). The fsQCA identifies the paths taken by combinations or conditions of variables that result in an outcome. In other words, rather than defining a single independent variable, it discovers causal “recipes” (Park et al. 2017). Ragin (2008b) asserts that such conditions might be both necessary and sufficient. Conditions that are always present in configurations are considered necessary. Conditions that are incorporated in various configurations are sufficient conditions. As a result, this methodology permits multiple causality or many necessary conditions to produce a certain result. This benefit is essential for the goal of this study. The same strategy has been used in recent research to uncover combinations or configurations of GDP determinants using the conceptual framework of DB indicators proposed by Estevão et al. (2020). This method is also particularly pertinent for the examination of small samples (Greckhamer et al. 2013), which is what is being done in the current study.

This is a growing methodology in terms of application in the accounting and economic context; however, it has produced some relevant manuscripts, e.g., Foli et al. (2022), Khuong and Anh (2022), Boratyńska (2021), and Bedford et al. (2016). The fsQCA methodology has also been used in several prior studies on finance and entrepreneurship (Bedford et al. 2016; Cervelló-Royo et al. 2020; Devece et al. 2016; Estevão et al. 2020) to analyze the several multivariable configurations leading to some event/value of a certain economic indicator (Park et al. 2017).

In terms of the procedures for applying this technique, according to Fiss (2011), calibrating each variable is the first step in achieving the desired output on fsQCA. As a result, the collected data are transformed into fuzzy scores, or into a continuous scale between 0 and 1, where 0 signifies the lowest score and 1 the highest score (Fiss 2011). The variables’ names are then prefixed with ‘fs’, denoting that the values have already been calibrated. Following data calibration, the fsQCA tool is ready to provide the intended research results: the necessary conditions essential to obtain high or low values of certain outcome variables, and the sufficient solutions (causal configurations), i.e., the combined conditions that result in greater values of the same outcome variable (Rihoux and Ragin 2009).

Therefore, in the current study, the necessary conditions, and causal configurations that could lead to the occurrence of high values for EDB, and GC were studied, using the aforementioned six calibrated variables. As noted, the analysis comprised two study hypotheses (output variables), so it was necessary to design and obtain results from two models. Models 1a and 2a looked into what contributed to high EDB and GC scores, respectively. In addition to this main analysis, a supplementary analysis was performed that included the necessary conditions and the causal configurations that drive the absence of high EDB and GC scores (Models 1b and 2b), in order words, that lead to low values of EDB and GC. All the variables used in the empirical study are presented per model in

Table 4. Research Models overview.

Variable Description	Variable Type	Acronym in the Article	H1: Model 1a	H1: Model 1b	H2: Model 2a	H2: Model 2b
Ease of Doing Business Score	Dependent variable	EDB	Presence (high values)	Absence (low values)	-	-
Getting Credit Score	Dependent variable	GC	-	-	Presence (high values)	Absence (low values)
IFRS Adoption Degree	Independent variable	Adoption_Degr	Causal condition
IFRS Experience	Independent variable	Exp_ifrs	Causal condition
PIB per Capita	Independent variable	GDP_pc	Causal condition
FDI Inflows	Independent variable	FDI_inf	Causal condition
Income Group	Independent variable	Inc_Group	Causal condition
Entrepreneurship Level	Independent variable	Entrep_Level	Causal condition

Source: The authors.

. The frame also shows the variable type and its acronym from now on.

4. Results

4.1. Descriptive Analysis

The EDB score, IFRS adoption degree (which takes into account the adoption degree and IFRS experience), and four additional economic factors—the GDP per capita, FDI inflows, income group, and entrepreneurship level—were the variables used in the sample, as stated in the methodology. The dataset included information from 117 nations and covered the years from each nation’s adoption of IFRS through 2019. The average economic variable value was used to include data for all years.

Table 5. Sample descriptive statistics.

	EDB	GC	Adoption_Degr	Exp_IFRS	GDP_pc	FDI_Inf	Inc_Group	Entrep_Level
Mean	65.46	57.51	0.7	13	18,933	12,259,815,172	3.1	35,294
Standard Error	0.94	1.72	0.02	0	2015	2,511,378,047	0.09	6665
Median	64.19	59.84	0.8	15	9731	2,489,439,211	3	11,166
Standard Deviation	10.15	18.56	0.24	5	21791	27,164,706,960	0.92	72,094
Minimum	42.14	11.88	0	0	412	8,776,004.47	1	35
Maximum	87.12	96.73	1	19	114,705	2.12788 × 1011	4	508,513
Count	117	117	117	117	117	117	117	117
Calibration	(82; 65; 47)	(84; 61; 23)	(1; 0.7; 0.1)	(19; 15; 4)	(65,000; 10,000; 1000)	(60 × 109; 2.5 × 109; 100 × 106)	(4; 2.5; 1)	(150,000; 12,000; 700)

displays the descriptive data, including the mean, minimum, maximum, and standard deviation (std. dev). The study covered 117 countries, with EDB scores ranging from 42.14 to 87.12 and GC scores ranging from 11.88 to 96.73. Additionally, the sample comprised both high- and low-income economies with a range of FDI inflows, entrepreneurship, and GDP per capita levels.

4.2. Identification of Necessary Conditions

The existing literature supports that if a value of set membership for a condition is to be considered ‘necessary’, its consistency value will exceed 0.9, and to be considered ‘almost always necessary’, the same value must reach 0.8 (Ragin 2008b; Schneider et al. 2010).

According to

Table 6. Analysis of necessary conditions (Model 1a and Model 2a).

	Model 1a		Model 2a
	Outcome Variable: fsEDB Conditions Tested:		Outcome Variable: fsGC Conditions Tested:
	Consistency	Coverage	Consistency	Coverage
fsAdoption_Degr	0.726956	0.641082	0.717023	0.612719
~fsAdoption_Degr	0.566479	0.703086	0.573824	0.690123
fsExp_IFRS	0.628394	0.649457	0.635454	0.636393
~fsExp_IFRS	0.649683	0.668339	0.649117	0.647055
FsGDP_pc	0.744363	0.874732	0.601540	0.684980
~fsGDP_pc	0.560511	0.514847	0.674594	0.600426
fsFDI_Inf	0.583720	0.749627	0.555175	0.690867
~fsFDI_Inf	0.712865	0.614023	0.737553	0.615593
FsInc_Group	0.948919	0.711105	0.817399	0.593556
~fsInc_Group	0.307406	0.507919	0.406423	0.650705
FsEntrep_Level	0.654343	0.831297	0.545081	0.671019
~fsEntrep_Level	0.641412	0.556531	0.745423	0.626726

Note: The symbol (~) indicates the absence of the outcome or condition. The variables’ names are prefixed with ‘fs,’ denoting that the values have already been calibrated. The numbers in bold refer to nearly/always necessary conditions. Source: The authors, based on fsQCA tool output.

, the income group appeared to be the only variable with a consistency value of more than 0.9 after examining the outcomes of the fsQCA’s necessary criteria for the first model. Considering this, it was acceptable to assume that for the sample under consideration, higher income level values were present when countries had higher EDB scores. The outcomes for the second model similarly showed that only the income group variable was important. High GC scores were “nearly always necessary” for the variable, with consistency greater than 0.8.

4.3. Identification of Sufficient Solutions

The findings of the causal conditions were acquired after the calibration and after obtaining the results of the necessary conditions. The fsQCA results of the analysis of the sufficient conditions for both main models (Model 1a and Model 2a) in this empirical study are shown in

Table 7. Solutions from fsQCA: configurations for achieving high EDB scores—Model 1a.

	fsEDB
	1	2	3	4	5
fsAdoption_Degr	⚫	⚫
fsExp_IFRS		◯	◯	●
fsGDP_pc				⚫	⚫
fsFDI_Inf					●
fsInc_Group	⚫	⚫	⚫	⚫	⚫
fsEntrep_Level	⚫		⚫
Consistency	0.915963	0.900749	0.935580	0.896250	0.952510
Raw coverage	0. 506521	0.503918	0.471540	0.533225	0.452212
Unique coverage	0.029555	0.045724	0.049809	0.043501	0.025317
Overall solution consistency	0.852178
Overall solution coverage	0.805635

Note: The variables’ names are prefixed with ‘fs,’ denoting that the values have already been calibrated. Source: The authors, based on fsQCA tool output. Large black circles (⚫) indicate that is a core condition and it is present, while small black circles (●) indicate that the condition is present but peripheral. Large white circles (◯) indicate that the condition is core and absent. The empty areas indicate that the condition is not part of the configuration.

and

Table 8. Solution from fsQCA: configurations for achieving high GC scores—Model 2a.

	fsGC
	1	2
fsAdoption_Degr	⚫	◯
fsExp_IFRS	⚫	⚫
fsGDP_pc	◯	●
fsFDI_Inf	⚫	◯
fsInc_Group	◯	●
fsEntrep_Level	◯	⚫
Consistency	0.901037	0.904852
Raw coverage	0.193213	0.253190
Unique coverage	0.116579	0.176556
Overall solution consistency	0.894317
Overall solution coverage	0.369769

Note: The variables’ names are prefixed with ‘fs,’ denoting that the values have already been calibrated. Source: The authors, based on fsQCA tool output. Large black circles (⚫) indicate that is a core condition and it is present, while small black circles (●) indicate that the condition is present but peripheral. Large white circles (◯) indicate that the condition is core and absent.

.

The models presented in this article could be regarded as suitable for the analysis, given that the overall solution consistency is greater than 0.8, as described by Feurer et al. (2016), because the criterion for the consistency of adequate conditions is 0.8. The term “solution coverage” refers to the coefficient indicating the extent of the outcomes that are covered, or explained, by a specific solution (Mikalef and Krogstie 2020), indicating the proportion of the outcome that is described by a particular solution, determined on the basis of both its raw coverage and the proportion of the outcome that is uniquely explained by it according to its unique coverage (Schneider et al. 2010).

The results show all configurations or paths that result in high values of EDB, indicating for each configuration which conditions are present (represented by a black circle (⚫)), which conditions are absent (represented by a white circle (◯)), and which conditions are indifferent to the configuration (represented by a space). Additionally, it should be highlighted that larger circles (⚫ and ◯) reflect the core conditions, while smaller circles (● and ○) represent the periphery (Estevão et al. 2020; Park et al. 2017).

Regarding Model 1a—Table 7—five possible configurations that may achieve high EDB scores were identified by the fsQCA, and each of them included the variable income group.

As the overall solution consistency (>0.80) and coverage (>0.25) levels exceeded those set by Ragin (2008a), and each configuration had a consistency level that was within acceptable limits (>0.80), a high level of IFRS adoption was identified as a pertinent variable for achieving high EDB scores in two of the five combinations. Additionally, since FDI inflow was absent from four of the five configurations, it was possible to confirm that it was a non-significant variable in the presence of other circumstances.

The first and second configurations indicate that upper-middle-income or high-income countries with a high adoption degree and little prior experience with IFRS (applicable for the second configuration) or many newly registered businesses (applicable for the first configuration) are two groups of conditions that are able to achieve high EDB scores. The third configuration shows that high EDB scores happened when nations had just implemented IFRS, were high- or upper-middle-income countries, and reported a high level of entrepreneurship, displaying a consistency of 0.935580 and a raw coverage of 0.471540. The degree of IFRS adoption, GDP per capita, and FDI inflows were irrelevant to achieving the presence of the study’s outcome variable for this scenario. The fourth and fifth configurations indicate two approaches that prioritized having a higher income level and a high GDP per capita as necessary elements to achieve high EDB scores.

With regard to Model 2a—Table 8—it should be noted that all of the conditions were relevant for both paths when analyzing the relevance of this model’s variables (i.e., no blank space was found), although not all were recognized as fundamental prerequisites. For instance, GDP per capita was seen as a non-core requirement in both arrangements. Furthermore, scheme interpretation reveals that high IFRS experience resulted in high GC values in both configurations.

An in-depth analysis of the results showed that the first configuration demonstrated that high GC scores could be attained even in the presence of lower levels of development, as determined by GNI, and a lower level of entrepreneurship when countries demonstrated high levels of adoption degree and had adopted IFRS from the start.

Considering the second configuration, a high GC score might be attained by nations that had long since implemented the IFRS and demonstrated high levels of entrepreneurship but low levels of IFRS adoption degree and FDI inflows.

Comparatively speaking, it is vital to note that getting high GC scores required a higher value of adoption degree when the country had high values of FDI inflows and low levels of entrepreneurship. On the other hand, a lower level of adoption was necessary to achieve the same result when the country had a smaller value of FDI inflows and a high level of entrepreneurship.

The results of both models also suggest that belonging to a high-income group is a requirement for obtaining high EDB scores in all paths, and that this is also true when evaluating what motivates the achievement of high GC scores, but even if a country’s economy is weaker, it is still possible for it to have high GC ratings by implementing IFRS broadly and having a particular level of entrepreneurship, GDP per capita, and IFRS experience (solution 1 from Table 8).

4.4. Supplementary Analysis

As was already noted, this study will also examine the configurations that lead to low EDB (Model 1b) and low GC (Model 2b) scores. Thus, using the same variables, it was possible to examine the conditions and potential causal relationships that would explain why high DB and GC scores are absent (low). The fsQCA results of the analysis of the necessary conditions for both supplementary models (1b and 2b) in this empirical study are shown in

Table 9. Analysis of necessary conditions (Model 1b and Model 2b).

	Model 1b		Model 2b
	Outcome Variable: ∼fsDB Conditions Tested:		Outcome Variable: ∼fsGC Conditions Tested:
	Consistency	Coverage	Consistency	Coverage
fsAdoption_Degr	0.745413	0.617690	0.742784	0.635819
~fsAdoption_Degr	0.566867	0.661111	0.547566	0.659671
fsExp_IFRS	0.656892	0.637941	0.646535	0.648598
~fsExp_IFRS	0.639044	0.617723	0.637550	0.636612
FsGDP_pc	0.437897	0.483538	0.551836	0.629456
~fsGDP_pc	0.886556	0.765190	0.723826	0.645348
fsFDI_Inf	0.523112	0.631254	0.540222	0.673409
~fsFDI_Inf	0.792519	0.641439	0.752007	0.628731
FsInc_Group	0.683054	0.480981	0.782207	0.568973
~fsInc_Group	0.589732	0.915600	0.441233	0.707646
FsEntrep_Level	0.456069	0.544440	0.556789	0.686605
~fsEntrep_Level	0.858680	0.700086	0.733219	0.617520

Note: The symbol (~) indicates the absence of the outcome or condition. The variables’ names are prefixed with ‘fs’, denoting that the values have already been calibrated. The numbers in bold refer to nearly/always necessary conditions. Source: The authors, based on fsQCA tool output.

.

Since neither variable had any consistency values larger than 0.9, the necessary conditions collected from the fsQCA confirmed that neither model had any necessary conditions. However, since the consistency values of GDP per capita and entrepreneurship level are more than 0.8 in Model 1b, there are “nearly always essential” requirements (Ragin 2008b; Schneider et al. 2010). Model 2b does not indicate the existence of conditions that are almost always or always necessary.

The causal configurations that lead to low levels of EDB and GC are both presented in

Table 10. Model 1b and 2b: configurations for achieving the absence of high EDB and GC scores.

	Model 1b				Model 2b
	∼fsEDB				∼fsGC
	1	2	3	4	1	2
fsAdoption_Degr				◯	⚫	⚫
fsExp_IFRS	◯		⚫	◯	⚫	⚫
fsGDP_pc	◯	◯	◯	◯	◯	◯
fsFDI_Inf		⚫	◯	◯	●	◯
fsInc_Group	◯	◯			◯	●
fsEntrep_Level			◯	◯	⚫	⚫
Consistency	0.907862	0.963830	0.921620	0.931521	0.917906	0.919917
Raw coverage	0.424245	0.331929	0.443297	0.163390	0.251506	0.162263
Unique coverage	0.120921	0.017160	0.163390	0.014918	0.162263	0.036366
Overall solution consistency	0.899961				0.912204
Overall solution coverage	0.710825				0.287872

Note: The symbol (~) indicates the absence of the outcome or condition. The variables’ names are prefixed with ‘fs’, denoting that the values have already been calibrated. Source: The authors, based on fsQCA tool output. Large black circles (⚫) indicate that is a core condition and it is present, while small black circles (●) indicate that the condition is present but peripheral. Large white circles (◯) indicate that the condition is core and absent. The empty areas indicate that the condition is not part of the configuration.

. Likewise, both models are reliable for usage, as the consistency is greater than 0.8 (Feurer et al. 2016). The results of the fsQCA allow us to conclude that the countries’ ability to implement IFRS is irrelevant for low levels of EDB. However, the fourth configuration also suggests that low adoption values paired with other low levels of other indicators can result in poor EDB values.

The paths in Model 2b that resulted in low GC scores demonstrated that low levels of GC may be attained in nations with high levels of IFRS adoption and a long history of adoption, suggesting consistency in the solutions.

5. Discussion

The conclusions regarding necessary conditions and sufficient conditions corroborated what Bajra et al. (2020) discovered, which is that high-income economies are more advanced in terms of EDB. The premises included that being a member of higher income groups is required to achieve high EDB scores, and that all paths to high EDB scores included countries that belonged to high-income groups.

The findings also indicate that there are combinations with a high IFRS adoption degree that result in a larger EDB. When this factor is in line with other economic indicators, such as entrepreneurship levels and the income group the country is classified in, it can be significant in achieving a most favorable business climate, according to that premise. Countries can adopt IFRS in one of three ways: optionally, modifiedly, or mandatedly. These findings are consistent with the idea that the adoption of IFRS in a nation affects its effects (Ball 2006; Brown 2011), as different adoption levels have varying effects on DB scores. When high levels of IFRS adoption, specific conditions in IFRS experience, and levels of entrepreneurship are present in high-income group countries, Solutions 1 and 2 of Model 1a predict that measures like GDP per capita and FDI inflows become irrelevant to high-level EDB performance.

The literature also implies that mandated IFRS adoption, which corresponds with higher levels of IFRS adoption, is a potentially alluring strategy for US investors in the midst of a robust regulatory environment (Shima and Gordon 2011).

High EDB values are related to increased levels of entrepreneurship, according to a 2017 World Bank analysis. Two Model 1a configurations, specifically the first and third, supported this claim.

According to research by Haidar (2012), every implemented change in business regulations is linked to an increase in GDP of 0.15 percent. It is reasonable to assume that GDP is positively associated with the business climate as business regulatory reforms are implemented to improve the business climate (Djankov et al. 2002); this assumption is consistent with two configurations that result in high values of EDB by having high-value GDP per capita as a primary condition.

Given that adopting IFRS helps countries finance their debt more efficiently (Florou and Kosi 2015; Mamdouh 2015), it makes sense to investigate a correlation between adopting IFRS and the receiving credit indicator, which was supported by our inquiry models.

Estevão et al. (2020) found in their study that high GDP per capita values are associated with low levels of loan availability. Since in both configurations, low values of GDP per capita result in the absence of high values of GC, the conclusions of the current analysis do not quite match those of their study.

Finally, it should be highlighted that, contrary to some authors’ claims, no relevance was discovered for FDI inflows (only in configuration 5) in the presence of other requirements for achieving high DB scores and, consequently, a more business-friendly environment (Buchanan et al. 2012; Contractor et al. 2020). According to Hossain et al. (2018), although certain indicators have a significant positive impact on FDI inward, the GC indicator shows a significant negative impact on inward FDI, which is consistent with the results for the second configuration that results in high levels of GC.

6. Conclusions

In this section the conclusions found when measuring the association between IFRS adoption and the business climate will be collated. This analysis was motivated by the potential benefits that the literature suggests for IFRS adopters. Succinctly, by adopting IFRS, a country can improve financial statements’ comparability, which results in easier and less costly analysis for investors (Bui et al. 2020) and increased attractiveness for foreign investors due to confidence in their investment (Lungu et al. 2017). Thus, when adopting IFRS, a country can improve its business climate, which is an indication that IFRS adoption and a country’s business climate could be associated.

The analysis was performed using two models: one to find the association between IFRS adoption with the DB score, and one to verify whether IFRS adoption is associated with the GC score.

Having established the objective of the study, a qualitative analysis, using the fsQCA tool, was performed to access the association between IFRS adoption degree and IFRS experience to a high EDB score, which represents the presence of a more favorable business climate. The tool provided multiple paths that can lead to the desired outcome. In this case, a high EDB and GC score were set as outcomes.

By including the adoption degree in the study, with five different levels, instead of only characteristics of adopted/not adopted, this analysis was able to distinguish countries that adopted mandatory IFRS for all firms from countries where IFRS was not mandatory. Additionally, the IFRS experience indicator was able to differentiate countries where IFRS was used from the outset from countries where IFRS was recently adopted.

The results show two possible groups of conditions where the presence of adoption degree is relevant in leading to a better business climate, as measured by EDB. Those groups indicate that in the presence of certain conditions of entrepreneurship levels and IFRS experience, being in the presence of a high adoption degrees is relevant to reach higher values of EDB. However, when the country faces high values of FDI inflows and GDP per capita, the adoption degree is revealed to be irrelevant in achieving high values of EDB.

These findings prove that countries that adopt IFRS on a larger scale (countries with a high adoption degree) and that adopted IFRS from the outset, when in the presence of other conditions, are associated with more favorable business climates.

The second model, which accessed the favorable association between IFRS adoption and GC, found that two groups of conditions achieved high GC scores. The first group considered the presence of high levels of adoption degree and FDI inflows as relevant, even if the country presented a low entrepreneurship level and belonged to a lower income group. The second group considered that, in the absence of high adoption degree and FDI inflows, belonging to a higher income group and having high levels of entrepreneurship were two relevant conditions.

Therefore, low or high values of IFRS adoption degree and high IFRS experience are necessary to reach high values of GC when in the presence of certain levels of GDP per capita, entrepreneurship levels, income group, and FDI inflows.

However, where the IFRS adoption condition is shown to be irrelevant, it can be verified that in the presence of other economic conditions, adopting IFRS can be indifferent to a friendly business climate.

7. Main Contributions

The following are the academic contributions made by this article: (1) It adds to the body of knowledge already available on the implications of IFRS implementation. A new study on the effects of IFRS adoption on the EDB ranking, which consists of 10 business environment indicators including 41 other business climate sub-indicators, is added to the academic literature on IFRS implications. (2) A contemporary method for determining the adoption degree level is proposed. The majority of past studies have employed more straightforward methods to determine each nation’s adoption level. Additional adoption of IFRS characteristics, such as whether the original or updated IFRS have been adopted, whether the obligation applies to all companies or just a select few, and whether the country adopted them for listed or unlisted firms, are collated in this study. (3) The IFRS adoption level and IFRS experience are two new measures that were linked to the EDB and GC scores. The EDB score was already connected to other economic metrics like FDI inflows and GDP per capita. This study shows that the two key variables, the extent of IFRS adoption and experience, might influence the EDB score depending on the values of other variables. (4) Arguments in favor of the idea that the adoption of IFRS would result in a more welcoming and alluring business environment for outside investment were presented. This study provides theoretical evidence that IFRS adoption has the capacity to help a country enhance its business climate.

8. Limitations and Suggestions for Future Research

This study has several drawbacks. These include the potential for skewed data with respect to several factors, such as the text interpretation method used to classify the type of IFRS adoption for unlisted enterprises. Additionally, the data used for the other variables may not have included all of the planned years because data were not available for all years for all nations, which may have caused the data to be skewed in comparison to the actual data.

There was a reduction in the sample to 117 nations, since for some variables there values were not available for all 156 countries in the initial sample. Undoubtedly, more reliable and consistent results could have been obtained if all data had been accessible and if we had examined all 156 nations.

Despite these drawbacks, the study can be used as a starting point for further research because it adds to the body of knowledge in the disciplines mentioned in the main contributions section.

To understand the impact that IFRS adoption can have on nations with lower levels of development, it may be worthwhile measuring the association between IFRS adoption and the business climate by income group level, or specifically for lower-middle-income or low-income countries.

Due to the predicted positive effects that IFRS adoption can provide to countries on this topic, such as the decrease in barriers to international investment, the attraction of foreign analysts, and the forecasting accuracy of foreign analysts, this study could be expanded to examine the association between other aspects of the business climate and IFRS adoption, such as the trading across borders indicator. Additionally, a qualitative approach was employed to determine the relationship between IFRS adoption and the business environment. Thus, a quantitative analysis of the same association with nations that fall into the same income group level or even an extension of this research to include the remaining EDB project indicators might be performed.