Master Thesis:
Strategic Equity Based Entry Modes and Economic
Integration
University of Groningen Faculty of Economics and Business
Master Thesis: International Business & Management
Name Student: Stefan van Unen Student ID number: s3240088
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Abstract
In this paper the decision-making process between Joint Ventures (JVs) and Wholly-Owned Subsidiaries (WOSs) will be tested. The dependent variable will be a dummy variable that
will be either a WOS or JV. Institutional distance, Economic integration and Small and Medium sized Enterprises (SMEs) combined with control variables will be used to estimate the effects of economic integration agreements on this decision-making process. Numerous different political and economic treaties have been established aiming to increase economic integration in the last three decades (e.g. European Monetary Union, Mercosur). However,
the effects of this variable have not been tested for the decision between JVs and WOSs despite proven effects on conducting business. Economic integration affects investment behavior severely through restrictions on JVs and WOSs and protection of local businesses.
Additionally, economic risk is reduced between more economically integrated countries. Therefore, it is important to also test the effects of economic integration on this decision-making process. The relationship will be tested using data from Zephyr combined with the
economic integration levels on the country level gathered from NSF-Kellogg Institute database on Economic Integration Agreements. In this research the period from 2008 to 2012
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1. Introduction
The last two decades an increasing number of countries started participating in trade and political treaties (WTO, 2020). Through those treaties, countries developed themselves significantly both politically and economically (Baier et al., 2014). Currently there are 305 ratified trade agreements in the world (WTO, 2020). This has led to higher levels of economic integration between numerous countries through bilateral, multilateral and regional trade agreements (Bergstrand et al., 2015). These treaties increased foreign direct investment and the number of international businesses among their participants (Simionescu, 2018; Yew et al., 2010). Economic integration has also increased significantly through these treaties (Baier, et al., 2018). To date institutional distance has likely approximated the effects of economic integration. The aim of this study is to analyze the effects of economic integration on the choice between Joint Ventures (JVs) and Wholly-Owned Subsidiaries (WOSs)
In JVs two or more firms have shares in one company, thus the equity stake of one firm in the JV is relatively low (Kogut, 1988). In WOSs one firm acquires a majority stake in another firm or starts a new firm, thus equity stakes are higher in WOSs compared to JVs. Consequently, moving from JV to WOS increases the firm’s risk exposure, amount of control and commitment (Brouthers & Hennart, 2007). JVs are often used to retain local knowledge within the firm (Chang et al., 2020). Firms should first make the decision between equity and non-equity modes before looking into different specific modes which can be chosen within the two options (Pan & Tse, 2000). Therefore, it is still relevant to study the choices between JVs and WOSs as well.
Currently, the effects of formal institutional differences on the decision between JVs and WOSs are still unclear. Researchers have tried to distinguish separate effects within formal institutional differences (Fuentelsaz et al., 2020; Putzhammer et al., 2018). These distinctive effects did not seem to affect the choice between JVs and WOSs. However, this does not mean other distinctive effects can still be approximated by institutional distance. To date institutional distance has been an important method of approximating the effects of formal institutions. However, institutional distance is now proxying two (or even more) different factors that still relate to formal institutions, but in different aspects (Kostova et al., 2008). A first factor is the differences in laws and regulations within a country. A second factor is to predict the openness of the borders between two countries. Adding economic integration could potentially separate these two distinctive effects. In terms of Scott's (1995) pillars the regulatory pillar would be split into two dimensions. Firstly, the differences in laws and regulations will be estimated using frequently used approximations for institutional distance. Secondly, economic integration will approximate the openness of borders. Economically more integrated countries have less restrictions on their borders facilitating trade and foreign investments between those countries. However, this does not warrant similar laws and regulations. Currently, there are more active trade treaties than ever (WTO, 2020), which has led to more economic integration between countries (Baier et al., 2008). Therefore, the effects of economic integration on the decision between JVs and WOSs is a valuable addition to the literature.
4 A separate distinction will be made for Small and Medium-sized Enterprises (SMEs) in this research. The high risk involved in entering a new country leads to other decisions between JVs or WOSs for SMEs (Schwens et al., 2011). Characteristics unique to SMEs like closer relationships between employees and fewer financial resources are two of the main causes of this phenomenon (Del Bosco & Bettinelli, 2020). Therefore, SMEs are an important addition to the research which strongly affects the decision between JVs and WOSs (Musso & Francioni, 2012). When economies are more integrated the risk is reduced which also influences the decision between JVs and WOSs for SMEs (Elbanna et al., 2020). Therefore, distinguishing between SMEs and large corporations is worthwhile to better understand the effects of institutional distance and economic integration on the decision between JVs and WOSs for SMEs.
The goal of combining these two antecedents and a moderator is to create a better understanding of the effects posed by formal institutions on the decision between JVs and WOSs. Two different aspects of formal institutions are split in this research. Relations between countries and rules and laws within countries could have different or even opposing effects on the decision between JVs and WOSs. Lastly, moderating effects are added for SMEs due to their distinctive effects on the decision between JVs and WOSs. Therefore, institutional distance and economic integration combined with firm specific moderating effects could improve our understanding of the effects from formal institutions on the decision between JVs and WOSs. The following research question will be addressed:
How does the level of Economic Integration between two countries affect the decision between Joint Ventures and Wholly-Owned Subsidiaries?
A logistic panel regression model is used to assess the research questions. A total of 3004 cross-border deals are analyzed between 2008 and 2012 in four different regressions. The period is chosen due to the significant changes in the investment climate of equity entry modes and thus the decision between JVs and WOSs after the 2008 financial crisis (Claessens & Van Horen, 2015). This has also influenced the decision-making process for JVs and WOSs. Unavailability of data after 2012 for the economic integration database made it impossible for more recent data to be used. Scholars have previously investigated how the decision between JVs and WOSs is made (Schellenberg et al., 2018). However, none of these papers considered the effects of economic integration to the best of my knowledge, despite the distinctive differences between countries’ trade relations and domestic rules and laws. Governments have been trying to integrate their countries more and more economically (e.g. EU or ASEAN). Therefore, the effects of economic integration on the decision between JVs and WOSs should be more thoroughly investigated.
The paper will now continue with expanding on existing literature and developing the hypotheses. The methodology behind this research will be explained after that. It is followed by a description of the database. Then the results of the logistic panel regression will be reviewed. Lastly, the implications of these results will be discussed together with limitations of this research and recommendations for future research will be formed.
2. Literature review and Hypothesis development
5 there are many ambiguities like the different findings for institutional distance (Kostova et al., 2019; Shirodkar & Konara, 2017). Therefore, studying JVs and WOSs more in-depth is a valuable addition to the literature.
One of the fastest ways for businesses to expand abroad is to acquire local partners or collaborate with them, thus using JVs or WOSs (Puthusserry et al., 2018). It also shows commitment since vast financial resources are required to commit to JVs and WOSs relative to their non-equity counterparts (Wooster et al., 2016). Each mode has its own advantages when expanding abroad. In particular, JVs allow for risk sharing and more readily available local knowledge with the disadvantage of having less control due to cooperating with a partner firm (Gaur & Lu, 2007). In WOSs one firm has full control, however knowledge transferring is slowed down, a valuable aspect when expanding abroad (Berry et al., 2010). Additionally, any potential losses cannot be shared with a partner firm either (Demirbag et al., 2009). There are numerous factors which influence the decision-making process between both modes.
This paper will use transaction cost theory to study the effects of different antecedents on the decision between JVs and WOSs. It considers the costs of exchange which it tries to minimize (Roberts & Greenwood, 1997). These exchange costs can be broadly defined as costs incurred for controlling, managing and monitoring transactions within or between firms (Williamson, 1979). It explains how different factors provide advantages and disadvantages when monitoring, controlling and managing a business. These additional costs or benefits from variables derived with the transaction cost theory will form the theoretical foundation. When using JVs or WOSs there are numerous considerations regarding the control, management and monitoring of the firm (Wooster et al., 2016). Therefore, it is relevant to use this theory in assessing the decision between JVs and WOSs.
Institutional distance
Institutional distance has a significant effect on the decision between JVs and WOSs (Gaur & Lu, 2007; Xu, 2012). Currently a majority of the papers found that greater formal institutional differences increases WOSs over JVs (Kostova et al., 2019; Morschett et al., 2010). However, opposing effects have also been found (Mahnke & Venzin, 2003; Shirodkar & Konara, 2017). Institutional distance has cost and benefits for both organization forms, which influences decision-making between the two options. For example, when institutional distance is greater, local knowledge becomes more important, leading to an increase in JVs relative to WOSs (Yiu & Makino, 2002). Partnering with local partners helps bridge the gap of unfamiliarity with formal institutional rules and laws (Kostova et al., 2019). Firms might have to consider different regulations, which might require adaptations to the existing product or service (Musso & Francioni, 2012). Only after that do other institutional distance factors like trademark protection and consumer laws become more relevant, which might require a different entry mode (De Villa et al., 2015). These are all additional transaction costs that would not occur when doing business in one country. Therefore, it is still important to test institutional distance, especially because economic integration is now a separate factor which also approximates formal institutions.
6 These costs can be so high that firms want to take contingency measures. Without a partner the business can be fully controlled by using WOSs. Since they do not cooperate with another firm in WOSs, risk can be more easily controlled in WOSs. This increase in control is preferred over the more easily transferable local and tacit knowledge when institutional distance is higher. Therefore, these firms would prefer to use the WOS entry mode when institutional distance increases. Thus, I propose the following hypothesis:
H1: Wholly-Owned Subsidiaries are more likely to occur relative to Joint-Ventures when the institutional distance between two countries increases
Economic integration
Economic integration has strong effects on the FDI flows of a country (Azis, 2018; Motta & Norman, 1993). When economies are more integrated capital is more easily transferable between those countries. Baier et al. (2008) confirm these trends also showing that the number of economic integration agreements is increasing. Thus, when capital is more easily transferable this is profitable for both countries’ economies. Bilateral free trade agreements can almost double trade between two countries (Baier & Bergstrand, 2007). Economic integration thus has strong effects on trade between countries (Baier et al., 2018). Additionally, economic integration between countries can also dampen the effects of crises by maintaining higher transaction flows between countries (Berkmen et al., 2009). Moreover, when two countries are economically more integrated the risk between those countries is also reduced (Kalemli-Ozcan et al., 2001; Peritz et al., 2020). The reduced risk might in turn affect the decision-making between JVs and WOSs between these countries.
The transferability of capital, trade barriers and political relationships all influence potential cost and benefits of operating a business in a country (Kim et al., 2006). These are all factors which economic integration takes into consideration. The ease of transferring a product or service is higher when countries are economically more integrated, thus reducing transaction costs. For example, trade tariffs can increase transaction costs significantly, by increasing export costs for foreign businesses (Shen & Puig, 2018). Lastly, when two countries have a higher level of economic integration the risks for doing business are lower (Baier et al., 2018). Despite the stable relationship between the countries differences in laws and regulations can still affect the difficulty of doing trade. Therefore, it is important to split the formal institutional pillar to test if economic integration can explain the ambiguous findings for institutional distance between various previous researches.
Despite all these findings the effects of economic integration on the decision between JVs and WOSs has never been tested to the best of my knowledge. Using economic integration as a variable might explain some of the different results that have been found in previous papers (Kali & Reyes, 2007). The effects of institutional distance will be split, since border-specific investment issues and differences in rules and laws are two distinctive features when moving abroad as a firm. Therefore, studying the effects of economic integration can be a valuable addition to the literature, which can assist in better understanding the effects of formal institutions on the decision between JVs and WOSs.
7 from WOSs is not necessary when economic integration is higher, thus I suspect JVs to occur more often. This has led to the following hypothesis:
H2: Joint Ventures are more likely to occur relative to Wholly-Owned Subsidiaries when the level of economic integration between two countries increases
Small and Medium sized Enterprises
The effects of economic integration might differ for SMEs. Schwens et al. (2011) recommended to consider SMEs separately in business and entry mode related research. SMEs often react differently to external variables compared to large corporations (Bruneel & De Cock, 2016; Hennart, 2009). For example, SMEs are more likely to use JVs or WOSs due to the reduced risk and higher form of control (Rasheed, 2005). The effects of institutional distance seem to have stronger effects on SMEs compared to corporations (Laufs & Schwens, 2014). Since these businesses have less resources, investments are riskier for those firms. Therefore, additional moderating variables for SMEs are added to this research. To estimate the effects two moderating variables will be added. These variables influence both the effects of institutional distance and economic integration due to definitive characteristics SMEs possess (Laufs & Schwens, 2014).
The initiator will indicate the acquiring or initiating firm in a WOS or JV deal for the continuation of this report. Knowledge between a SME and another firm (either initiator or target) is more smoothly transferred when JVs are used due to the nature of the organization (Kirby & Kaiser, 2003; Schwens et al., 2018). SMEs can create a feeling of family or are family firms, this aspect is often lost when WOSs are used (Andreu et al., 2020; Riege, 2005). This can be prevented by using the JV entry mode due to increased autonomy (Sestu & Majocchi, 2018). WOSs are more likely to destroy this particular facet of SMEs because the employees would become part of a larger organization. Transferring tacit knowledge will be more difficult too (Dhanaraj et al., 2004; Tamer et al., 2003). These examples would all increase transaction costs. JVs would decrease these transaction costs again through the increased autonomy for the targeted firm (Bruneel & De Cock, 2016). Additionally, risk can be shared with another firm when using the JV mode, thus requiring fewer financial resources (Gaur & Lu, 2007). Which indirectly decreases transaction costs since it is now easier to start this new business entity when financial burdens are shared. These are all different facets of SMEs which will affect the decision between JVs and WOSs. Therefore, adding a moderating variable for SMEs will assist in better understanding the effect of both institutional distance and economic integration, thus creating a better understanding of the effects of formal institutions.
When the institutional distance between two countries is greater the increased risk may be too high for SMEs to invest into WOSs. The shared risk could enable these SMEs to still expand into these countries, thus preferring JVs. When the level of economic integration increases parts of these risks are removed, which in turn reduces the need to share risks with other firms. However, the reduced amount of financial resources combined with other SME specific characteristics outweigh the reduced risk (Schwens et al., 2018). JVs may thus still be preferred over WOSs due to these aspects. Therefore, I propose the following hypotheses: H3a: Joint Ventures are more likely to occur relative to Wholly-Owned Subsidiaries for SMEs (either initiator, target or both) when institutional distance increases between the main operating countries of both businesses
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3. Methodology
This research will use a logistic panel regression to study the effects of different levels of economic integration with data from 2008 to 2012. The investment climate changed significantly after the financial crisis (Claessens & Van Horen, 2015). Both the number of deals and the relationship between JVs and WOSs was affected by it (Xie, Reddy, & Liang, 2017). Using 2008 as the starting year will more accurately describe trends after the crisis. Observations before 2008 might have different antecedents or different importance of antecedents due to the shift in 2008. Hence, the analysis is started with observations from 2008 onwards. The latest available data for economic integration agreements database was in 2012, thus limiting the research to this time window. The regression formula below will be used to assess the values of the different variables. The model will look as follows:
𝑦(𝐸𝑛𝑡𝑟𝑦𝑚𝑜𝑑𝑒)𝑐𝑦𝑖 = 𝛽1(𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑐𝑦+ 𝛽2(𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛)𝑐+ 𝛽3(𝑆𝑀𝐸 ∗ 𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑓+ 𝛽4(𝑆𝑀𝐸 ∗ 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛)𝑓+ 𝛽5(𝐶𝑢𝑙𝑡𝑢𝑟𝑎𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑐 + 𝛽6(𝐿𝑜𝑔[𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑖𝑛𝑖𝑡𝑖𝑎𝑡𝑜𝑟])𝑓𝑦+ 𝛽7(𝐿𝑜𝑔[𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑡𝑎𝑟𝑔𝑒𝑡])𝑓𝑦+ 𝛽8(𝐶𝑜𝑚𝑚𝑜𝑛 𝐿𝑎𝑛𝑔𝑢𝑎𝑔𝑒)𝑐 + 𝛽9(𝐶𝑜𝑚𝑚𝑜𝑛 𝐵𝑜𝑟𝑑𝑒𝑟)𝑐 + 𝛽10(𝐹𝑜𝑟𝑚𝑒𝑟 𝐶𝑜𝑙𝑜𝑛𝑦)𝑐+ 𝛽11(𝐿𝑎𝑛𝑑𝑙𝑜𝑐𝑘𝑒𝑑 ℎ𝑜𝑚𝑒)𝑐 + 𝛽12(𝐿𝑎𝑛𝑑𝑙𝑜𝑐𝑘𝑒𝑑 ℎ𝑜𝑠𝑡)𝑐 + 𝛽13(𝐼𝑠𝑙𝑎𝑛𝑑 ℎ𝑜𝑚𝑒)𝑐 + 𝛽14(𝐼𝑠𝑙𝑎𝑛𝑑 ℎ𝑜𝑠𝑡)𝑐 + 𝛽15(𝐿𝑜𝑔[𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 ℎ𝑜𝑚𝑒])𝑐𝑦+ 𝛽16(𝐿𝑜𝑔[𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 ℎ𝑜𝑠𝑡])𝑐𝑦+ 𝛽17(𝐺𝑒𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑐 + 𝜀
The denominators can be interpreted as follows: The c implies that a variable depends on the country the firm is located. The y means that the variable depends on time, in this case the year in which the deal took place. The i implies that the variable depends on the industry of the company. Lastly, f is a firm level variable. Random effects are implemented in this regression.
Dependent variable
The dependent variable, the entry mode, will be an indicator variable which describes if the entry mode is either a WOS or a JV. This variable will take the value of 0 when the entry mode is a Wholly-Owned Subsidiary and 1 if it is Joint Venture based. Data is gathered from Zephyr, a database with deals from 46 different countries that consist of 75% of all deals globally. For this research WOS and JV deals from 2008 to 2012 are collected only when they cross borders. A total of 23,468 deals from 43 countries targeting a total of 55 countries were collected from Zephyr. A list of the countries can be found in appendix 1.
Independent variables
9 𝐼𝐷𝑐 = √∑[𝐼(ℎ𝑜𝑚𝑒)𝑐𝑗 − 𝐼(ℎ𝑜𝑠𝑡)𝑐𝑗]2
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𝑗=1
Economic integration checks if businesses in economically more integrated countries are more likely to choose for one option over the other (i.e. JVs over WOS). Data for this variable is gathered from NSF-Kellogg Institute database on Economic Integration Agreements. This database is compiled using data on a national level (Baier et al., 2018). It compares the integration of two national economies. This prevents biases where some countries in the same trade area might still have different levels of economic integration. It has seven different levels of economic integration where 0 is the lowest level of economic integration and 6 the highest level of economic integration. Thus, the higher the value the more integrated those countries are. The definitions of these values can be found in table 1. The number of observations was not influenced by this variable.
Table 1: Definitions of different levels of economic integration
Two moderating variables are added which consider the potentially different effects of both institutional distance and economic integration on the decision between JVs and WOSs for SMEs. Firstly, a dummy variable was created which checked if a SME was targeting or initiating the deal (or both). A SME is considered such when annual revenues are below 50 million USD. The dummy takes the value of 1 when a SME is involved in a deal and 0 otherwise. This newly created dummy variable is then multiplied with the institutional distance variable to create the moderating variable. Similarly, economic integration was also multiplied with the SME dummy variable to create the moderating SME variable for economic integration. When institutional distance increases, I suspect the JV entry mode to become more likely for SMEs. Firstly, risk is shared between two companies when the JV entry mode is used. Secondly, knowledge transfers between a SME and another firm often occur smoother with the JV mode (Lu & Beamish, 2006). When the level of economic integration increases JVs will become more likely for SMEs. Market risks are decreased when the level of economic integration is higher, which would indicate that WOSs would become more likely. However, SME specific characteristics like less available financial resources outweigh this decreased market risk. Therefore, JVs are more likely for SMEs when the level of economic integration rises compared to large corporations. Data for this variable is also found in Zephyr.
Control variables
Cultural distance is a control variable for this regression. Data is gathered from the Hofstede database on cultural values for countries. It consists of six dimensions that describe a country’s culture, which are: power distance, individualism, masculinity, uncertainty avoidance, long term orientation and indulgence. The formula in a paper by Kogut & Singh
0 No Existing Economic Integration Agreement 1 One-Way Preferential Trade Agreement 2 Two-Way Preferential Trade Agreement 3 Free Trade Agreement
10 (1988) is an approximation to estimate cultural distance and is also used in this paper using the data from the Hofstede database. The formula used to define cultural distance can be found below. Here V describes the variance. Denominator j denotes one of the six cultural dimensions from Hofstede’s database and c the country. The observations in the Hofstede database are relatively old, but not less relevant. Despite cultures changing over time, there is also evidence that cultures are moving in the same direction, parallel to each other (Beugelsdijk et al., 2015). Not all countries from the Zephyr database were represented in this database reducing the number of observations to 8410. Local knowledge is valuable when dealing with a large cultural distance (Beugelsdijk et al., 2018). Therefore, I suspect that JVs are more likely to occur relative to WOSs when cultural distance is higher.
𝐶𝐷𝑐 = ∑([𝐼(ℎ𝑜𝑚𝑒)𝑐𝑗− 𝐼(ℎ𝑜𝑠𝑡)𝑐𝑗]2 6
𝑗=1
/𝑉𝑗)/6
One firm-level variable is added. This variable is based on the resource-based view that looks at firm specific factors that can affect the decision between JVs and WOSs (Francis et al., 2009). Revenue of the firm is the first firm-level control variable added. It again has important implications on the decision between JVs and WOSs (Brouthers, 2002). Firms with higher revenue levels are also more likely to have more available resources. Therefore, the number of WOSs is expected to increase when firms have higher revenues (Brouthers, 2002). I suspect the variable to have the similar effects in this regression using the same line of reasoning. The logarithm of this variable was taken to reduce heteroscedasticity. Data for this variable can again be found in the Zephyr database. Many firms do not make this data available reducing the number of observations to 3004.
The gravity equation database between countries is a determining factor in international investment. It consists of several factors which affect the amount of investment between economies. It takes into consideration: Common Borders, Common Language, Former Colonies, Landlocked Nations, Island Nations, Home country’s population, Host country’s population, and Geographical distance (Anderson, 2011). The data is assessed from the International Trade Commission of the US. These will all be separate control variables used in the different regressions. Adding these variables did not remove any observations from the dataset.
Common borders and common language both facilitate doing business in a country. Both variables are added as dummy variables where 1 indicates the businesses are located in countries with common borders or common languages and 0 indicates this is not the case. Communication is improved when both firms speak a common language, thus JVs might be less necessary when both firms speak the same language (López-Duarte & Vidal-Suárez, 2010). WOSs are thus more likely for businesses that operate in countries speaking the same language. Common borders make it easier for businesses to transfer parts of an existing business to a new country, WOSs might occur more often in these countries (Tang & Buckley, 2020). This reasoning goes both ways, expectations are thus the same for host and home country. When businesses are headquartered in a country that has formerly colonized the country the target is located in takes the value of 1, otherwise it is 0. Countries often have different relationships with their former colonizer or subject. This also affects the decision between JVs and WOSs. The improved relationships and established connections from their inhabitants make WOSs more likely to occur in these countries (Mingo et al., 2018).
11 harder to access for countries not bordering it. JVs are more likely in these countries since knowledge about trade complications is often not directly accessible in the initiating firm (Kashiha et al., 2016). Island nations are harder to access for the opposite reasons. JVs here might again improve accessibility of the nation (Kahouli & Maktouf, 2015). Again, the reasoning goes both ways and the expectations are valid for both home and host countries.
Population of both host and home country are added as indicators for the size of their respective economies. When economies are larger this can affect the decision between JVs and WOSs. More profits might be reaped from operations when countries are larger due to economies of scale. Therefore, WOSs might be more attractive there compared to JVs (Filatotchev et al., 2007). Geographical distance is used since controlling and executing daily business activities is more difficult when countries are far apart (Boeh & Beamish, 2012). The increased transaction costs cause the WOSs to be more likely relative to JVs (Brouthers, 2013).
Regression
In Table 2 the results for four different regressions can be found. It is important to note that heteroscedasticity was high for all regressions. The results of a likelihood-ratio test and chi-square test, which both test for heteroskedasticity, can be found in appendix 2. The results of this research should be considered carefully due to the heteroscedasticity.
A correlation table for all the variables can be found in appendix 3. Correlation was found between some of the variables. Firstly, correlation was found between the moderating SME variables (0.343) due to the reduced number of deals of which SMEs are part of, which caused the correlation. Correlation was also found between the moderating SME variables on institutional distance (0.411) and economic integration (0.701), which is again logical since they are based upon the same variable. Another notable strong negative correlation is between geographic distance (-0.813) and common border (0.499) on economic integration. Economic integration agreements are more effective with nearby countries, which explains the correlation. Lastly, population home (-0.538) and host (-0.467) correlate with the economic integration variable. Smaller countries benefit more from economic integration agreements compared to larger countries which explains this correlation (Baier et al., 2018). Variance Inflation Factor (VIF) tests are presented in appendix 4 which also checks for multicollinearity. All variables are below the VIF value of 10, which indicates that the model is not severely affected by multicollinearity (Allison, 2012; O’Brien, 2007). Lastly, results might have been affected by the correlation between the different variables. However, the correlations can be explained logically, thus limiting these effects.
Following the results of the Hausman test, found in appendix 5, random effects were used for all regressions. A cutoff value of 0.05 was used. All regressions were above this cutoff point indicating that random effects should be used. This means that no fixed effects were found for specific years or countries in the regressions. Therefore, random effects will be used for all of the regressions. Finally, a description of all the variables can be found in appendix 6, which includes means, standard deviations, minimum and maximum values for all variables used in the regressions.
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4. Results
(1) (2) (3)
VARIABLES Entry Mode Entry Mode Entry Mode
Institutional Distance 0.0932*** 0.121*** 0.111*** (0.0163) (0.0178) (0.0193) Economic Integration -0.00446 -0.00817** -0.00834**
(0.00275) (0.00325) (0.00327) SME * Institutional Distance -0.0811*** -0.0743***
(0.0213) (0.0231) SME * Economic Integration 0.00573*** 0.00616***
(0.00213) (0.00238) Cultural Distance 0.00667 0.00320 0.00431 (0.00472) (0.00483) (0.00484) Initiator’s Revenue -0.000493 (0.000391) Target’s Revenue 0.000796** (0.000396) Common Language -0.0149* -0.0187** -0.0166** (0.00793) (0.00800) (0.00803) Common Border 0.0186* 0.0150 0.0162 (0.00986) (0.00992) (0.00995) Former Colony -0.000237 -0.00264 -0.000614 (0.00823) (0.00832) (0.00838) Landlocked Home -0.0144 -0.0122 -0.0127 (0.0176) (0.0177) (0.0178) Landlocked Host -0.0158 -0.0154 -0.0197 (0.0137) (0.0138) (0.0139) Island Home 0.0319*** 0.0277*** 0.0329*** (0.00833) (0.00844) (0.00902) Island Host 0.0114 0.00871 -0.000638 (0.00911) (0.00923) (0.0103) Population Home 0.00203 0.00152 0.00193 (0.00259) (0.00259) (0.00260) Population Host 0.00576** 0.00588** 0.00580** (0.00252) (0.00252) (0.00254) Geographic Distance -0.00230 -0.00262* -0.00266* (0.00152) (0.00152) (0.00152) Constant -0.0753* -0.0602 -0.0718 (0.0455) (0.0458) (0.0505) Observations 3,004 3,004 3,004
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table 2: Regression results (Random effects are used in all regressions) Independent variables
13 1% level in the robustness test which can be found in appendix 7. The results for institutional distance are thus consistent for all regressions. The positive effects would indicate JVs are more likely when the institutional distance between two countries increases. This is the opposite of the expected results. Hypothesis 1 is thus not supported by the data.
The level of economic integration between countries has no significant result in the first regression. However, in the second and third regression economic integration is negative and significant to the 5% level (-0.00853; -0.00870). This would indicate that a higher level of economic integration would lead to more WOSs compared to JVs. However, when testing for robustness, the significance is lost in the first regression and reduced to the 5% significance level in the second and third regression. The negative impact does not align with hypothesis 2 which is thus not supported by the data.
The moderating variable for SMEs on institutional distance has significant negative effects at the 1% level (-0.0811; -0.0743). The significance is reduced to the 5% level in the second regression and to the 10% level in the third regression. This would indicate that SMEs are more likely to choose WOSs over JVs when institutional distance is greater compared to large firms. This is the opposite of my expectations. Hypothesis 3a is thus not supported by the data. The moderating variable for SMEs on the level of economic integration has a significant positive effect at the 1% level in the second and third regressions (0.00573; 0.00616). When testing for robustness the results remain significant at the 1% level. This would indicate that SMEs are more likely to choose JVs over WOSs compared to large firms. This aligns with my expectations, thus hypothesis 3b is supported by the data.
Control variables
Cultural distance has no effect on the decision between JVs and WOSs in any of the regressions. The insignificant results do not align with my expectations. The initiator’s revenue has no significant result in the third regression. The target’s revenue has a significant positive effect at the 5% level in the third regression (0.000796). When testing for robustness the results are significant on the 5% level too. JVs are thus more likely to occur than WOSs when the targeted firm’s revenue is higher.
Significant negative effects were found for common language at the 10% level in the first regression (0.0149) and on the 5% level in the second and third regression (0.0151; -0.0172). It implies that WOSs are more likely to occur than JVs between countries that speak the same language. However, robustness for these results are low. A positive effect was found for common border on the 10% level in the first regression (0.0203). Results became insignificant in the second and third regression. Businesses operating in countries bordering each other might thus be more likely to engage in JVs relative to WOSs following these results. When checking robustness for the results the significance increases to the 1% level for the first and third regression and to the 5% level for the second regression. Former colonies do not seem to affect the decision between JVs and WOSs in any of the regressions. Insignificant results were found for this variable in all regressions.
Businesses in landlocked countries are not affected by this geographic aspect when considering the decision between JVs and WOSs. Insignificant results were found in all regressions for initiating and targeted firms. The decision between JVs and WOSs is influenced when firms operating from the island are looking to expand abroad. Significant positive effects at the 1% level were found in all regressions (0.0319; 0.0277; 0.0329). This would imply that firms operating from island countries are more likely to choose JV entry mode over the WOS entry mode. Insignificant results were found for targeted firms operating on island nations.
14 is located). In all regressions these results are significant at the 5% level (0.00576; 0.00588; 0.00580). However, when checking for robustness the results all become insignificant. This indicates that firms operating in countries with large populations are more likely to enter a JV deal compared to WOS. Lastly, the geographic distance between the countries in which the firms operate has a significant negative impact in the second and third regressions at the 10% level (-0.00262; -0.00266) and insignificant results for the first regression. Geographically more distant firms are more likely to use the WOS entry mode compared to JVs according to these results. However, the robustness is low for this variable.
5. Discussion
The main purpose of this paper was to split the formal institutional pillar into two parts. To analyze if this was successful the results for both institutional distance and economic integration should be further discussed. The results found for institutional distance imply that JVs are more likely compared to WOSs when institutional distance is greater. When businesses are looking to expand into a country which has similar institutions WOSs are thus more likely to occur. These findings were against my expectations, I suspected that the risks associated with institutionally more distant countries would make WOSs more likely to occur. Firms would have more control over the amount of risk they are exposed to which would reduce transaction costs (Beugelsdijk et al., 2018). However, transaction cost theory can also explain why firms would prefer WOSs over JVs in this case. Local knowledge might be more important in dealing with institutional issues as initially thought. Institutionally more similar countries have rules and laws that are more similar, consequently businesses will have less problems implementing an existing format by using a WOS (Gaur & Lu, 2007). When institutional distance increases, products or services might have to be adapted more to comply with regulations (Brouthers & Hennart, 2007). JVs with local partners can help develop this product or service to be legally marketed and fit to customer expectations (Shirodkar & Konara, 2017). Therefore, using the JV entry mode can be more suitable in institutionally distant countries.
The findings for economic integration indicate that the effects of the variable are significant in this time period. This implies that the effects of economic integration on the decision between JVs and WOSs is influential. I suspected JVs to occur more often compared to WOSs between countries with higher levels of economic integration due to the reduced market risk which reduces transaction costs (Baier et al., 2018). However, the results found in the regressions suggest the opposite, WOSs are more likely to occur compared to JVs. This result can be explained using the following two arguments. Firstly, economic integration creates more competition in this business climate due to the larger available market through reduced transaction cost of operating between those countries (Baier et al., 2008). Economies of scale are required to stay competitive in the new enlarged market, with more businesses from both countries competing with each other (Hennart, 1988). However, this argument is only valid in the first period after the rise to a higher level of economic integration. Once the new market has settled down these effects will be reduced. Secondly, higher levels of economic integration reduce the power of the state to interfere in the market (Yu et al., 2015). For example, a government might intervene in deals to protect national and local businesses from foreign investors to protect the local or national economy. Once the level of economic integration is higher, interventions by governments become more difficult as long as firms act within the regulations of the economic integration agreement (Kim et al., 2006). Therefore, WOSs become more likely compared to JVs when the level of economic integration increases.
15 effects the choice between JVs and WOSs can be better understood. The effects of economic integration have often been approximated using institutional distance. However, effects are better described using a separate variable which considers the level of economic integration.
Lastly, SMEs are affected differently by both institutional distance and economic integration. This was expected since SMEs often have different resources and characteristics compared to large corporations (Laufs & Schwens, 2014). Firstly, SMEs are more likely to use WOS when the institutional distance increases compared to large corporations. This was unexpected since I argued that SMEs had less available resources to use WOSs. However, the control provided in WOSs appear to outweigh the additional costs and risks imposed. When the level of economic integration increases JVs become more likely compared to WOSs. This aligned with my reasoning, I hypothesized that the reduced market risk would not outweigh the effects of limited resources and other SME specific characteristics. This makes JVs more likely to occur when the level of economic integration increases. Additionally, the market risk decrease also reduces the need for control over a business. Therefore, transferring tacit knowledge and integrating the new business become more important when this risk is reduced. Which explains the increased likeliness of JVs to occur over WOSs when the level of economic integration increases for SMEs compared to large corporations.
Theoretical implications
The results found for institutional distance add to the already existing debate where significant results are found for an increase in the number of WOSs in institutionally more distant countries (Morschett et al., 2010; Shirodkar & Konara, 2017). The paper thus contributes to existing literature by adding new data points for the influences of institutional distance on the decision between JVs and WOSs. Moreover, the findings in this paper contradict some of the latest papers that find the exact opposite results in which WOSs are more likely than JVs (Del Bosco & Bettinelli, 2020; Kostova et al., 2019; Xie, 2017). However, by splitting the formal institutional pillar successfully adds a new credible argument to the existing debate on the effects of formal institutions.
This research tried to split the formal institutional pillar into two dimensions (Scott, 1995). The first dimension approximated rules and regulations within the country; the second dimension approximated trade relations through economic integration. Splitting the formal dimension into two parts explains some of the existing ambiguity regarding the effects of formal institutions on the decision between JVs and WOSs. Distinct and differentiating effects were found for both institutional distance and economic integration, indicating that both have distinctive effects on the decision between JVs and WOSs. Future research assessing the decision between JVs and WOSs might thus want to include economic integration as either control variable or independent variable. It can assist in more accurately describing the effects of formal institutions. Adding institutional distance and economic integration to other research which considers formal institutions might also be valuable. However, this should first be further tested in order to check if the effects of economic integration are consistent across different fields of study.
16
Managerial implications
Managers should still be careful in deciding upon the decision between JVs and WOSs when considering formal institutions. Effects for institutional distance have not been consistent even when considering more recent papers (Devigne et al., 2018; Kostova et al., 2019). Effects for both JVs over WOSs and vice versa have been found when studying the effects of institutional distance on the decision between JVs and WOSs. Secondly, the level of economic integration between countries affects the decision between JVs and WOSs when considering 3004 deals. Therefore, future decisions between JVs and WOSs might want to consider institutional distance (i.e. differences in laws and regulations) and economic integration (the political and economic relationship between two countries) separately. It can assist in more accurately assessing the advantages and disadvantages of both JVs and WOSs.
Limitations
There are several limitations to this research. In this research not all countries were considered due to database restrictions. Zephyr has data from only 46 countries. This number was further reduced due to the cultural distance variable. Influential countries like China and Germany were missing from this research due to the lack of available data. Additionally, the resource-based view is underrepresented in this research. Factors like the firm’s age and number of employees are known to strongly affect the decision between JVs and WOSs (Brouthers et al., 2008; Brouthers & Hennart, 2007). Other company specific variables like CEO’s behavior (Herrmann & Datta, 2002) or previous experience in using JVs or WOSs (Lavie & Miller, 2008) are still missing from this regression. The results found in this research can be affected due to these missing variables.
Additionally, high heteroscedasticity and limited time period might have influenced the results found in this paper. High heteroscedasticity means that data points are not equally spread among one linear line. This increases standard deviations and decreases the accuracy of the regressions. Despite several attempts to reduce the heteroscedasticity by using logarithms it was not fully removed. Lastly, the short time period might not represent earlier or later periods. After 2012 the investment climate has continually fluctuated (Shen et al., 2017). Therefore, results found in this paper might have changed after this period.
Besides that, there has been debate on the approximations of cultural distance. A relatively old method with the Hofstede database has been used to estimate cultural distance (Fang, 2003). However, the methods used to approximate cultural dimensions and potential skewness of the database might influence the results. The goal of this paper was not to find an appropriate means of estimating cultural distance, thus the old commonly used method is also applied in this research.
Future research
Several attempts at splitting different effects from institutional distance have not been successful in explaining the ambiguous results for institutional distance in past papers (Fuentelsaz et al., 2020; Putzhammer et al., 2018). Studying the effects of specific cases like one trade area or one country could increase the accuracy of estimating the decision between WOSs and JVs. Every country has its unique characteristics which also affect approximators for formal institutions. These unique characteristics might be hard to generalize into one or few variables approximating institutional distance. Therefore, research on a country-level might help improve our understanding of the decision-making process between JVs and WOSs.
17 economic union (from level five to six) might have different implications compared to moving from no existing economic integration agreement to one-way preferential trade agreement (from level zero to one). This research only aimed to study any existing effects of economic integration. Future research could assist in further explaining the effects on different levels of economic integration on the decision between JVs and WOSs. Moreover, the effects of moving from economic integration level x to economic integration level y are entirely unclear for the decision between JVs and WOSs. Lastly, the effects of economic integration have only been tested for the decision between JVs and WOSs. Therefore, the effects of economic integration might also be relevant for other research topics that use the effects of formal institutions.
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22
Appendix 1
List of all countries that were added in this research. All countries denoted in italics are only used when they receive foreign investments in the form of WOSs or JVs. These countries did not have data available on investments towards other countries.
Albania Argentina Armenia Austria Azerbaijan Belarus Belgium Benin Brazil
23
Appendix 2
Likelihood-ratio test for all regression. These tests were done by first assuming the data was heteroscedastic. These results were then compared with the data from the regression assuming data was homoscedastic. For all regression Chi-square was high. This would suggest that the data used in this research is very heteroscedastic.
1. Likelihood-ratio test results for the first regression
Likelihood-ratio test LR chi2(4) = 122.93 (Assumption: . nested in hetero) Prob > chi2 = 0.0000 2. Likelihood-ratio test results for the second regression
Likelihood-ratio test LR chi2(4) = 120.23 (Assumption: . nested in hetero) Prob > chi2 = 0.0000 3. Likelihood-ratio test results for the third regression
Likelihood-ratio test LR chi2(4) = 117.90 (Assumption: . nested in hetero) Prob > chi2 = 0.0000
A Breusch-Pagan test was also conducted for all regressions with similar results. Chi-square values are very high in this test too. The results can be found below.
1. Breusch-Pagan test results for the first regression
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: fitted values of Entry Mode chi2(1) = 1780.72
Prob > chi2 = 0.0000
2. Breusch-Pagan test results for the second regression Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: fitted values of Entry Mode chi2(1) = 2017.97
Prob > chi2 = 0.0000
3. Breusch-Pagan test results for the third regression
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: fitted values of Entry Mode chi2(1) = 2139.29
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Appendix 3
25
Appendix 4
The tables below describe multicollinearity between different variables. For this research VIF values greater than 10 would be considered problematic. This is not the case for any of the regression. Therefore, it is assumed that multicollinearity does not affect the results of this research.
1. VIF table for the first regression
Variable VIF 1/VIF
Economic Integration 5.16 0.193778 Geographic Distance 4.26 0.234651 Common Border 2.21 0.452275 Cultural Distance 1.92 0.521240 Population Home 1.83 0.547733 Institutional Distance 1.69 0.591467 Common Language 1.69 0.591982 Population Host 1.69 0.593329 Former Colony 1.67 0.597867 Island Host 1.52 0.656247 Island Home 1.24 0.808952 Landlocked Host 1.19 0.840017 Landlocked Home 1.18 0.850673 Mean VIF 2.10
2. VIF table for the second regression
Variable VIF 1/VIF
26 3. VIF table for the third regression
Variable VIF 1/VIF
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Appendix 5
Hausman tests for all different regressions. The chi-square values are all greater than 0.05, therefore random effects were used in all regressions.
1. Hausman test for the first regression
Test: Ho: difference in coefficients not systematic; chi2(15) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 17.26
Prob>chi2 = 0.3038
(V_b-V_B is not positive definite) 2. Hausman test for the second regression
Test: Ho: difference in coefficients not systematic; chi2(15) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 17.26
Prob>chi2 = 0.3038
(V_b-V_B is not positive definite) 3. Hausman test for the third regression
Test: Ho: difference in coefficients not systematic; chi2(17) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 10.29
Prob>chi2 = 0.8912
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Appendix 6
Statistical summary for all variables used in the regressions. In this table SD stands for Standard Deviation.
Variable Mean SD Minimum Maximum
Entry Mode .0248804 .1557854 0 1
Institutional Distance .2735177 .2347387 .0480606 1.168504
Economic Integration 2.271451 2.312668 0 6
