THE EFFECT OF INDUSTRY STRUCTURE ON FIRM PROFITABILITY AND
THE MODERATING EFFECT OF HOST COUNTRY POLITICAL STABILITY
By F.L. Steenbergen
S1833243
leonsteenbergen@hotmail.com
University of Groningen Faculty of Economics and Business
10 August 2017 [9875 words]
Abstract
Despite voluminous prior research on industry structure as a driver of firm profitability, prior quantitative research is solely based on US data. This firm level study examines the effect of industry structure on firm profitability based on non-US data for the first time. Through studying 75.495 very large firms from all industries and countries from all over the world, this study investigates if industry structure drives firm profitability. The 75.495 firms are divided into ten groups based on the US SIC (Standard Identification Classification) system. On top of this, the study investigates if “host country political stability” has a positive effect on industry structure and firm profitability. This study significantly proves that industry structure drives firm profitability and that “host country political stability” positively influences industry structure and firm profitability.
Keywords: industry structure, firm profitability, political stability, host country, international business, international business management
Table of Contents
Abstract 1 Table of Contents 2 1. Introduction 3 2. Literature Review 6 3. Methodology 13 3.1. Research Design 13 3.2. Data Collection 16 3.3. Measurement of variables 15 4. Analysis 18 4.1. Descriptive statistics 18 4.2. Correlation 26 4.3. Testing assumptions 27 5. Conclusions 29 5.1. Discussion 29 5.2. Implications 315.3. Limitations and further research 31
6. References 33
1. Introduction
In the international business (IB) and strategic management literature, researchers often discuss how geography and industry structure relate to firm profitability. The basis for the answer to this question lies in the industrial organization economics (IOE) or the so-called “industry-based view” (IBV). The SCP of Bain (1951, 1954) validates this IBV. The IBV emphasizes the importance of market structure in shaping firm profitability. Firms are, according to the IBV, integral parts of an industry. Bain (1951) stated that an industry with a distinct market- structure, conduct and performance tends to differ significantly and that the structure of an industry is exogenous and influenced by internal competitive forces (As mentioned in Bamiatzi, Bozos, Cavusgil, Tomas and Hult, 2015). A lot of researchers used the five forces framework of Porter (1980) in their international business (IB) studies. The five forces framework of Porter draws on the structure-conduct-performance paradigm (SCP) of Bain (1951, 1954). The five forces of this framework are: threat of new entrants, threat of substitutes, bargaining power of customers, bargaining power of suppliers, and industry rivalry.
The massive internationalization of firms characterized the world economy for the last twenty years. Although there are a lot of success stories, there are also a lot of failures, because firms face numerous challenges when they expand internationally. Therefore even the MNC’s, which have successfully expanded internationally, have conquered several challenges along the way. Despite these challenges, some firms still maintain reliable profits. But why do some firms remain profitable while others don’t? In the current research literature regarding strategic management, one the most profound goals is to identify the determinants and sources of profitability differences between firms.
Prior research has focused on industry effects such as market concentration, growth, and entry barriers. The results of this research largely support the notion that industry is an important determinant of firm profitability. Industry structure determines profitability, which means that we can find high-profit firms in high-profit industries with favorable competitive structure (As mentioned in Spanos & Zaralis, 2004).
industrial economics), firm profitability depends on matters like R&D, pricing- and investment policy (As mentioned in Spanos & Zaralis, 2004). Scherer and Ross (1990) state that firm conduct depends on industry structure, which includes matters like entry barriers, concentration level, and product differentiation. According to Bain (1959), the structural characteristics of industries affect firms’ strategies and their performance.
In addition to Bain’s research, other researchers conducted a lot of empirical studies to the SCP relationships using the U.S. Census Bureaus of Manufactures and the PIMS database. According to Scherer and Ross (1990), there are limitations regarding the specification of data quality and relevant measures. However, other researchers showed important relationships between industry structure and profitability (as mentioned in Spanos & Zaralis, 2004).
stability influences the performance of firms can help further the literature.
In the present study, I bridge the gap in the literature by examining if industry structure drives firm profitability, and by testing the moderating effect of host country political stability. I expect that the political stability (i.e. the stability of the political institutional environment) of the host country location will have a significant influence on firm profitability.
Based on the resource-based view (Barney, 1991) and transaction cost economics (Williamson, 1971), I think that host country political institutions have a substantial influence on how firms extract value and exploit their assets in the host country. Therefore I expect that host country political stability will have a positive moderating effect on industry structure and firm profitability. This leads to the following research question:
“Does Political Stability of the Host Country affect Industry Structure and Firm Profitability?” The aim of this study is to supplement existing international business literature by shedding further light on the influence of host country political stability on industry structure and firm profitability. MNC’s are constantly seeking more FDI possibilities in distant markets and, given the inconsistent results in prior research (Wan and Hoskisson, 2003, Chakrabarti et al, 2007), this paper is of great importance for successful firm profitability in international business.
When we know how the political stability of the firm’s host country affects firm profitability, firms will be able to safeguard better against negative influences or potential biases of their host countries’ institutional environment. With the examination of the moderating role of the firm’s host country political stability, the contribution of my study to existing literature is twofold. First, this study adds to the clarification of existing conflicting results. Second, it shows how a specific exogenous non-controllable factor, namely the political stability of the host country, influences industry structure and firm profitability.
2. Literature Review
2.1. Industry structure
The central empirical question for strategic management is regarding industry- and firm effects on firm profitability. Schmalensee (1985) studied the industry- and firm effects on firm profitability using Federal Trade Commission (FTC) Line of Business data and return on assets (ROA) as the measure of firm profitability. Schmalensee (1985) concluded that although firm factors were insignificant, industry effects play a very important role in determining firm profitability. Because Schmalensee (1985) used a data set from just one year and therefore left 80% of the variance in business unit returns unexplained, Rumelt's (1991) tried to create a clarification for this large error degree. He stated that industry membership explained around 9% of the variance in business unit returns and that business units effects explained more than 44% of business unit variations in profits (as mentioned in Hawawini, Subramanian and Verdin (2003)).
Table 1. Firm industry, and other effects on performance identified in past research. Percentage of variance explained of the dependent variable (ROA) (As showed in Hawawini, Subramanian and Verdin (2003)). Schmalensee (1985) Rumeltb (1991) Sample A Rumeltb (1991) Sample B McGahan and Porter (1997) Industry effects 19,6% 8,3% 4,0% 18,7% Firm effects of which 0,6% 47,2% 45,8% 36,0% Business-level effects 0,6% 46,6% 44,2% 31,7%
Corporate effects N/A 0,8% 1,6% 4,3%
Year effects N/A N/A N/A 2,4%
Industry/year effects
N/A 7,8% 5,4% N/A
Error 80,4% 36,9% 44,8% 48,4%
a In both Schmalensee and Rumelt the business-level effects are business unit effects as they use FTC data sets. In the other studies, the business-level effects are business segment effects, as they are based on Computstat data set.
b Rumelt uses two samples, naming them Sample A and Sample B. Sample A is similar to Schmalensee and Sample B covers a larger set of firms than Sample A.
non-substitutable) in order to create a sustainable competitive advantage.
In the IB and strategic management field, a lot of research has focused on how geography and industry structure relate to firm profitability. Only recently the agenda of the scholars of firm and industry effects (McGahan & Porter, 1997, 2002; Rumelt, 1991; Schmalensee, 1985) merged with the agenda of scholars of the influence of home- and host-country effects on firm profitability (As mentioned in Teece, Pisano and Shuen (1997)). Makino, Isobe, and Chan (2004) have for example studied the relative importance of firm, industry, and host-country effects on the performance of multinational enterprises (MNEs) headquartered in Japan. The findings of Makino, Isobe, and Chan’s research (2004) have raised questions regarding the effects of firm strategy, industry structure, and host country characteristics on firm profitability. Prior research has focused on identifying the dimensions of firm specific capabilities, and combinations of resources and competences that might be sources of advantage. This is called the 'dynamic capabilities' approach. The 'dynamic capabilities' approach is focused on how existing internal and external firm- specific competences can be exploited in order to address rapidly changing environments. Parts of the origin of the 'dynamic capabilities' approach can be found in Schumpeter (1942), Penrose (1959), Nelson and Winter (1982), Prahalad and Hamel (1990), Teece (1976, 1986a, 1986b, 1988) and in Hayes, Wheelwright, and Clark (1988) (As mentioned in Teece, Pisano and Shuen (1997)).
Considering the inconsistent findings in the literature, it is not logical to assume that industry structure drives ROA. With my research I want to test the relationship based on non-US data for the first time. In order to confirm to existence of this relationship, the hypothesis is formulated as:
Hypothesis 1 (H1): Industry structure drives ROA.
2.2. Institutions and organizations
mechanisms according to DiMaggio and Powell (1983) and has instrumentality logicality. This pillar has a legally sanctioned basis of legitimacy. Scott (1995) stated: “Force and fear and expedience are central ingredients of the regulative pillar, but they are tempered by the existence of rules, whether in the guise of informal mores or formal rules and laws”. Weber (1968) showed that no rulers or maybe very few be pleased with basing their regime on force alone. All rulers try to cultivate a belief in the legitimacy of their lordship. The enforcement of rules will be carried out by interacting parties or by outside enforcing parties, for example the state or the state agent. The rules can be carried out by force, but most of the time they involve inducements or rewards for compliance. Scott (1995) says: “A stable system of rules backed by surveillance and sanctioning power is one prevailing view of institutions”, namely the regulative view.
Scott (1995) states that: “the emphasis within the normative pillar is placed on the normative rules that introduce a prescriptive, evaluative, and obligatory dimension into social life. Normative systems include both values and norms. These values are conceptions of the preferred or the desirable together with the contraction of standards to which existing structures or behavior can be compared and assessed. Norms specify how things should be done; they define legitimate means to pursue valued ends. Normative systems define goals or objectives (e.g., winning the game or making a profit) but also designate the appropriate ways to pursue them (e.g., conceptions of fair business practices).”
According to Scott (1995) this pillar is about: “the rules that constitute the nature of reality and the frames through which meaning is made.” “In the cognitive paradigm, what a creature does is, in large part, a function of the creature’s internal representation of its environment” (D’Andrade, 1984). Scott (1995) says that: “Symbols -words, signs, and gestures- have their effect by shaping the meanings we attribute to objects and activities. Meanings arise in interaction and are maintained -and transformed- as they are employed to make sense of the ongoing stream of happenings.” Therefore the meanings of the different actions are socially created through interaction and communication.
“Weber regarded social action, in his central premise, as action to which subjective meaning is attached. To understand or explain any action, the analyst must take into account not only the objective conditions but the actor’s subjective interpretation of them” as mentioned in Scott (1995). Berger and Kellner (1981) summarized: “Every human institution is, as it were, a sedimentation of meanings or, to vary the image, a crystallization of meanings in objective form.” When people have shared understandings about the world and have shared experiences in which particular social actions are taking place, than this will result in a taken for granted way of operating and this will be the basis of compliance of the cognitive pillar. It includes mimetic mechanisms and has orthodoxy logicality. Some examples of cognitive processes are: prevalence and isomorphism.
In the cognitive pillar there are culturally supported and conceptually correct bases of legitimacy. As mentioned in Scott (1995): “Unlike the regulative view, cognitive theorists insist that games involve more than rules and enforcement mechanisms: They consist of socially constructed players endowed with differing capacities for action and parts to play. In short, as constitutive rules are recognized, individual behavior is seen to often reflect external definitions rather than (or as a source of) internal intentions. A cognitive conception of institutions stresses the central role played by the socially mediated construction of a common framework of meaning.”
2.3. Host country political stability and risk
made is regarding the complex strategy of geographic diversification. Firms have to discover how they can exploit rent from growth opportunities abroad. The have to reconfigure their internal resources and capabilities in the best way (Hitt et al., 1997).
The second assumption is regarding the guaranteed increase in complexity for firms because of the geographic diversification, regardless of their host country environment. Firms have to deal with a lot of extra external factors as they start doing business abroad (Larsen et al., 2013). According to Vermeulen and Barkema (2002), the firms’ managerial information processing demands increase significantly because of the complexity of geographic diversification. This can lead to a decrease in their performance. When doing business in countries with low political stability firms need to spent more resources on their host countries’ unstable environment. Firms for example need to pay bribes to corrupt government officials or have other costs to influence the political system (Simon, 1984; Holburn and Zelner, 2010). But when the political stability increases in the host country, this will lead to a more stable business environment.
As mentioned before, Cuervo-Cazurra (2011) focuses on if and how the country of origin (COO) affects a firm’s advantage abroad. Cuervo-Cazurra (2011) states that this depends on the host countries individuals’ valuation of the foreign home country. Peng et al (2008) and Cuervo-Cazurra (2011) state that both host and home country institutions affect a firm’s profitability directly. Dunning (2009) and McGahan and Victer (2010) stated that host and home country both matter, but more research should be conducted on how host country political stability influences the profitability of firms.
In order to show the effect of host country political stability on industry structure and firm profitability, the hypothesis is formulated as:
Figure 1: Conceptual Model
Industry structure: Different industry groups
based on US SIC
Firm profitability: Return on Assets (ROA)
Host Country Political Stability
H1
+
3. Methodology
In this section, I describe the methods that I propose to answer my research question. I start with introducing my research design. Thereafter, I present my data collection and the method that I propose to analyze the data.
3.1. Research Design
The underlying question of this paper is: “Does industry structure affect firm profitability? What is the moderating effect of the firms’ host country political stability?” My research is designed based on a positivism assumption on the phenomena. Ryan, Scapens, & Theobald (2002) state that positivism holds: “What can be perceived as true, is based on the reality.” In the previous part, I developed my hypotheses and they are based on existing theory. The positive assumption permits the formulation of hypotheses and the statistical testing of expected results to an acceptable level of probability. Because of the positivism assumption, the closed nature of the main research question (Does industry structure affect firm profitability?), and the quantitative nature of the research, I follow a deductive approach.
3.1.1. Research Strategy and Time Horizon
In order to have a better understanding on the effect of host country political stability on industry structure and firm profitability, by testing hypotheses using numerical data, a quantitative research will be employed. The application of quantitative research implies a necessity to collect a quantity of data to establish a statistically significant result. Therefore, my research will apply an archival research strategy. This strategy is conducted based on existing material. Because I apply quantitative research, my research can be characterized as mono-method research.
scale to use worldwide data. My sample of countries covers countries with different levels of political stability (Javidan et al., 2006). My study contributes to the literature by examining if host country political stability influences industry structure and firm profitability. I measure firm profitability using the accounting profitability measure of a firm’s return on assets (ROA). By doing the study in this specific way, I hope to answer the call for theoretical development of more detailed measures to capture the richness and complexity of the factors affecting the industry structure-performance relationship (Aoki, 2001; Oetzel et al., 2001; Wiersema and Bowen, 2011). In prior research on firm profitability, ROA is often used as a measure (Schmalensee, 1985; Rumelt's, 1991; Wan and Hoskisson, 2003; Bausch and Krist, 2007; Chakrabarti et al., 2007; Chacar et al., 2010).
Table 2: Sample distribution based on SIC Codes
Industry Range of SIC Codes N
Agriculture, Forestry and Fishing 0100-0999 745
Mining 1000-1499 1170
Construction 1500-1799 3160
Manufacturing 2000-3999 20425
Transportation, Communications,
Electric, Gas and Sanitary service 4000-4999 6575
Wholesale Trade 5000-5199 9606
Retail Trade 5200-5999 3566
Finance, Insurance and Real Estate 6000-6799 19369
Services 7000-8999 10784
Public Administration 9100-9729 94
3.2. Data Collection 3.2.1. Data sources
For my analysis, I propose to use the Orbis Database to collect data. This database aims to provide detailed information about the ROA of very large international active firms across the world and provides information about the industry group of the firm (based on US SIC). I derived the data to measure the host country political stability from the World Governance Indicators. The World Bank provided this data.
3.2.2. Sample selection
I propose to use secondary data, based on the above-presented information. Because of the time efficiency in the collection and analyzing of secondary data, compared to primary data, I choose for secondary data.
Because I want to eliminate alternative influences, I employ a number of criteria to determine an appropriate sample. I proposed the time span of 2013, 2014, and 2015, because these are the most recent years in which a lot of firms reported on their financial data. Because I need one group of firms to do the analyses, I propose to select the firms that reported on their financial data in all the years (2013, 2014, and 2015). I also propose this time span because political stability is a phenomenon that should be measured over a period of time. The most recent period for my moderating variable (host country political stability) is the time span of 2013, 2014, and 2015.
3.3. Measurement of variables 3.3.1. Dependent variable (DV)
suitable measure for firm profitability.
In my study I use return on assets (ROA) as the measure for firm profitability. I choose ROA because a lot of prior research used ROA as a measure for firm profitability (Balkin, Markman & Gomez-Mejia, 2000; Wan and Hoskisson, 2003; Bausch and Krist, 2007; Chakrabarti et al., 2007; Chacar et al., 2010; Fredrickson, Davis-Blake & Sanders, 2010; Connely et al., 2013). ROA is a better measure for firm profitability than, return on equity (ROE) or return on sales (ROS) according to Hagel, Brown & Davison (2010). Hagel, Brown & Davison (2010) state that ROA “may foster a better view of fundamentals of the business” when compared to ROE, and it “determines whether the company is able to generate an adequate return on these assets rather than simply showing robust return on sales”, when compared to ROS. Another very important reason is that there is a lot of data available on ROA. I found a lot of data on ROA in the Orbis database of the University of Groningen.
3.3.2. Independent variable (IV)
Based on my conceptual model, I can identify industry structure as the independent variable (IV) in my study. To analyze the level of competition within an industry and to measure the attractiveness of that industry, the five forces framework of Porter (1980) is often used in the IB literature. The attractiveness refers to the overall industry profitability in this context. The five forces framework of Porter draws on the SCP of Bain (1951, 1954). I take a sample of 75.495 very large firms from all industries and countries from all over the world. I bundle firms with the same industry SIC-code into groups in order to show that industry structure drives firm profitability. In my research, I specifically focus on firms from all industries and countries from all over the world, because prior research on this topic is solely focused on the US.
3.3.3. Moderating variable (MV)
I choose 75.495 very large firms from all industries and countries from all over the world that reported on their financial data in the years 2013, 2014, and 2015. I propose this time span because political stability is a phenomenon that should be measured over a period of time. The most recent period for my moderating variable (host country political stability) is the time span of 2013, 2014, and 2015. All these countries from all over the world have very different scores on the political stability index. Therefore, I hope to measure a significant influence of host country political stability on industry structure and firm profitability.
In an effort to test whether the political stability of the host country will impact the relationship between industry structure and firm profitability, I collect a political stability index from the World Bank. In this index the Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism. This measure of political stability of the host country is based on Worldwide Governance Indicators (WGI) data from the World Bank.
The Worldwide Governance Indicators (WGI) is a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. These data are gathered from a number of survey institutes, think tanks, non-governmental organizations, international organizations, and private sector firms.
3.4. Statistical Model of Proposed Analysis 3.4.1. Statistical model
To test the hypotheses I will use the following model with one dependent variable (Y), one independent variable (X), and one moderating variable (Z):
Y = α + β1 (X) + β2 (Z) + β3 (XZ) + ε
4. Analysis
In this section I cover the empirical results of my research paper. I initiate this chapter with the descriptive statistics. Then I will examine the ANOVA analysis and thereafter the correlation analysis. After the correlation analysis I will investigate the underlying assumptions of the ANOVA tests.
4.1. Descriptive Statistics
As can be observed in Table 3, my final sample consisted of 75.495 firms. The firms contain the necessary values for all required variables. The total of 75.495 firms are distributed over 10 industry groups, according to the US Standard Industrial Classification. As presented earlier in Table 2, the Agriculture, Forestry and Fishing industry contains of 745 firms, the Mining industry contains 1170 firms, the Construction industry contains 3160 firms, the Manufacturing industry contains 20425 firms, the Transportation, Communications, Electric, Gas and Sanitary service industry contains 6576 firms, the Wholesale Trade industry contains 9606 firms, the Retail Trade industry contains 3566 firms, the Finance, Insurance and Real Estate industry contains 19369 firms, the Services industry contains 10784 firms, and the Public Administration industry contains 94 firms.
Table 3: Descriptive statistics 2015
N Minimum Maximum Mean Std. Deviation
ROA 75.495 -99,838000 100,000000 4,267 12,092
Host country Political Stability 75.495 -2,539 1,450 0,393 0,687
Industry 75.495 1 10 4
4.1.1. Hypothesis 1 (H1): Industry structure drives ROA. Table 4: Anova results 2015
Industry M (SD)
Agriculture, Forestry and Fishing 4,823 (13,887)
Mining -0,786 (20,139)
Construction 4,095 (10,259)
Manufacturing 4,935 (12,787)
Transportation, Communications,
Electric, Gas and Sanitary service 3,726 (12,424)
Wholesale Trade 5,653 (10,078)
Retail Trade 5,533 (12,630)
Finance, Insurance and Real Estate 2,780 (9,804)
Services 4,926 (14,293)
Public Administration 2,412 (11,398)
Note. N =75.495
***p<0,01; **p<0,05; *p<0,1
There was a significant effect of industry structure on ROA, F (9,75494) = 86,95, p < .001. The Games-Howell post hoc test showed that overall there was no significant difference between the ROA for firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), firms in the Mining industry (M = -0,786, SD = 20,139), firms in the Construction industry (M = 4,095, SD = 10,259), firms in the Manufacturing industry (M = 4,935, SD = 12,787), firms in the Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), firms in the Wholesale Trade industry (M = 5,653, SD = 10,078), firms in the Retail Trade industry (M = 5,533, SD = 12,630), firms in the Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804), firms in the Services industry (M = 4,926, SD = 14,293), and firms in the Public Administration industry (M = 2,412, SD = 11,398).
industry (M = 4,823, SD = 13,887), there was a significant difference between the ROA of firms in the Agriculture, Forestry and Fishing industry and the ROA of firms in the Mining industry (M = -0,786, SD = 20,139) and the Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804). On top of this there was an insignificant difference between the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887) and the ROA of firms in the Construction industry (M = 4,095, SD = 10,259), Manufacturing industry (M = 4,935, SD = 12,787), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), Wholesale Trade industry (M = 5,653, SD = 10,078), Retail Trade industry (M = 5,533, SD = 12,630), Services industry (M = 4,926, SD = 14,293), and the Public Administration industry (M = 2,412, SD = 11,398).
The post hoc test also showed that when I focus on the Mining industry (M = -0,786, SD = 20,139), there was a significant difference between the ROA of firms in the Mining industry (M = -0,786, SD = 20,139) and the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), Construction industry (M = 4,095, SD = 10,259), Manufacturing industry (M = 4,935, SD = 12,787), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), Wholesale Trade industry (M = 5,653, SD = 10,078, Retail Trade industry (M = 5,533, SD = 12,630), Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804), and the Services industry (M = 4,926, SD = 14,293). On top of this there was an insignificant difference between the ROA of firms in the Mining industry (M = -0,786, SD = 20,139) and the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398).
Forestry and Fishing industry (M = 4,823, SD = 13,887), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), and firms in the Public Administration industry (M = 2,412, SD = 11,398).
The post hoc test also showed that when I focus on the Manufacturing industry (M = 4,935, SD = 12,787), there was a significant difference between the ROA of firms in the Manufacturing industry (M = 4,935, SD = 12,787) and the ROA of firms in the Mining industry (M = -0,786, SD = 20,139), Construction industry (M = 4,095, SD = 10,259), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), Wholesale Trade industry (M = 5,653, SD = 10,078), and the Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804). On top of this there was an insignificant difference between the ROA of firms in the Manufacturing industry (M = 4,935, SD = 12,787) and the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), Retail Trade industry (M = 5,533, SD = 12,630), Services industry (M = 4,926, SD = 14,293), and the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398).
The post hoc test also showed that when I focus on the Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), there was a significant difference between the ROA of firms in the Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424) and the ROA of firms in the Mining industry (M = -0,786, SD = 20,139), Manufacturing industry (M = 4,935, SD = 12,787), Wholesale Trade industry (M = 5,653, SD = 10,078), Retail Trade industry (M = 5,533, SD = 12,630), Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804), and the Services industry (M = 4,926, SD = 14,293). On top of this there was an insignificant difference between the ROA of firms in the Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424) and the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), Construction industry (M = 4,095, SD = 10,259), and firms in the Public Administration industry (M = 2,412, SD = 11,398).
Trade industry (M = 5,653, SD = 10,078) and the ROA of firms in the Mining industry (M = -0,786, SD = 20,139), Construction industry (M = 4,095, SD = 10,259), Manufacturing industry (M = 4,935, SD = 12,787), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804), and the Services industry (M = 4,926, SD = 14,293). On top of this there was an insignificant difference between the ROA of firms in the Wholesale Trade industry (M = 5,653, SD = 10,078) and the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), Retail Trade industry (M = 5,533, SD = 12,630), and the Public Administration industry (M = 2,412, SD = 11,398).
The post hoc test also showed that when I focus on the Retail Trade industry (M = 5,533, SD = 12,630), there was a significant difference between the ROA of firms in the Retail Trade industry (M = 5,533, SD = 12,630) and the ROA of firms in the Mining industry (M = -0,786, SD = 20,139), Construction industry (M = 4,095, SD = 10,259), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), and the Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804). On top of this there was an insignificant difference between the ROA of firms in the Retail Trade industry (M = 5,533, SD = 12,630) and the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), Manufacturing industry (M = 4,935, SD = 12,787), Wholesale Trade industry (M = 5,653, SD = 10,078), Services industry (M = 4,926, SD = 14,293), and the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398).
of firms in the Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804) and the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398).
The post hoc test also showed that when I focus on the Services industry (M = 4,926, SD = 14,293), there was a significant difference between the ROA of firms in the Services industry (M = 4,926, SD = 14,293) and the ROA of firms in the, Mining industry (M = -0,786, SD = 20,139), Construction industry (M = 4,095, SD = 10,259), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), Wholesale Trade industry (M = 5,653, SD = 10,078), and the Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804). On top of this there was an insignificant difference between the ROA of firms in the Services industry (M = 4,926, SD = 14,293) and the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), the Manufacturing industry (M = 4,935, SD = 12,787), Retail Trade industry (M = 5,533, SD = 12,630), and the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398).
The post hoc test also showed that when I focus on the Public Administration industry (M = 2,412, SD = 11,398), there was no significant difference between the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398) and the ROA of firms in the other industries. But there was an insignificant difference between the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398) and the ROA of firms in the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887), Mining industry (M = -0,786, SD = 20,139), Construction industry (M = 4,095, SD = 10,259), Manufacturing industry (M = 4,935, SD = 12,787), Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424), Wholesale Trade industry (M = 5,653, SD = 10,078), Retail Trade industry (M = 5,533, SD = 12,630), Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804), Services industry (M = 4,926, SD = 14,293), and the ROA of firms in the Public Administration industry (M = 2,412, SD = 11,398).
industry, firms in the Mining industry, firms in the Construction, firms in the Manufacturing industry, firms in the Transportation, Communications, Electric, Gas and Sanitary service industry, firms in the Wholesale Trade industry, firms in the Retail Trade industry, firms in the Finance, Insurance and Real Estate industry, firms in the Services industry, and firms in the Public Administration industry. But, similar as in 2015, the subsequent post hoc test showed that when I focus on a singular industry, there are significant differences as opposed to other industries. For the descriptive statistics and the results of the ANOVA of 2013 and 2014, please check the appendix table A2 and A3 for 2014, and table A4 and A5 for 2013.
4.1.2. Hypothesis 2 (H2): Industry structure results in higher ROA if the political stability of the host country is high.
Table 5: Descriptive statistics
Industry M (SD) N
Agriculture, Forestry and Fishing 4,823 (13,887) 745
Mining -0,786 (20,139) 1170
Construction 4,095 (10,259) 3160
Manufacturing 4,935 (12,787) 20425
Transportation, Communications,
Electric, Gas and Sanitary service 3,726 (12,424) 6576
Wholesale Trade 5,653 (10,078) 9606
Retail Trade 5,533 (12,630) 3566
Finance, Insurance and Real Estate 2,780 (9,804) 19369
Services 4,926 (14,293) 10784
Public Administration 2,412 (11,398) 94
Note. N =75.495
***p<0,01; **p<0,05; *p<0,1
appendix (Table A1 in the Appendix).
To illustrate the significant interaction effect between the political stability of the host country and industry structure on ROA, I will show the effect per industry for three countries that are different in geographical location, score on political stability, have a lot of observations (firms that are included in the sample, based on the selection requirements), and are present in most of the industries. The countries that I chose are China (mean political stability = -0,547054251; N = 6040), the USA (mean political stability = 0,636458894; N = 4579), and the Netherlands (mean political stability = 1,025108675; N = 2466).
In the Agriculture, Forestry and Fishing industry (M = 4,823, SD = 13,887) there is a significant result for the USA (M = 0,817, SD = 4,265795471), and the Netherlands (M = 8,463428571, SD = 7,912544769).
In the Mining industry (M = 0,786, SD = 20,139) there is a significant result for the USA (M = -13,4247, SD = 22,9055346), and the Netherlands (M = 2,433029412, SD = 17,14797582).
In the Construction industry (M = 4,095, SD = 10,259) there is a significant result for the USA (M = 3,293076923, SD = 7,430101878), and the Netherlands (M = 6,326971429, SD = 8,272042884).
In the Manufacturing industry (M = 4,935, SD = 12,787) there is a significant result for China (M = 5,727996198, SD = 9,628877872), the USA (M = -0,807293769, SD = 19,88654953), and the Netherlands (M = 7,231352025, SD = 12,64679929).
In the Transportation, Communications, Electric, Gas and Sanitary service industry (M = 3,726, SD = 12,424) there is a significant result for China (M = 5,284612121, SD = 8,124432145), the USA (M = 1,5430625, SD = 12,14508969), and the Netherlands (M = 5,031834532, SD = 9,387616284).
In the Retail Trade industry (M = 5,533, SD = 12,630) there is a significant result for China (M = 3,607783333, SD = 10,94107402), the USA (M = 9,064534483, SD = 12,04319118), and the Netherlands (M = 8,512170213, SD = 16,52557838).
In the Finance, Insurance and Real Estate industry (M = 2,780, SD = 9,804) there is a significant result for China (M = 4,080687764, SD = 6,86230659), the USA (M = 1,137803139, SD = 2,83639959), and the Netherlands (M = 4,265629848, SD = 12,21057034).
In the Services industry (M = 4,926, SD = 14,293) there is a significant result for China (M = 6,127207424, SD = 13,17052526), the USA (M = -3,181196721, SD = 19,25253374), and the Netherlands (M = 6,965779468, SD = 15,46936336).
In the Public Administration industry (M = 2,412, SD = 11,398) there is a significant result for China (M = 3,959142857, SD = 7,628488741).
4.2. Correlation
I have performed a Pearson correlation test, in order to obtain more information about the preliminary indicators of data patterns. Table 6 reports the main correlations. The significant correlations are marked with one (P<0.01) asterisk. As can be observed from the table, all the correlations are significant. More specifically, based on the two ANOVA tests, the descriptive statistics, and the correlation test, I conclude that for all industries ROA will increase when the political stability of the host country is high as opposed to low, because as can be observed from the table (r=0,018, P<0,01).
Table 6: Correlations
Variables 1. 2. 3.
1. ROA 1
2. Industry structure -0,017** 1
3. Political stability of the host country 0,018** 0,201** 1
4.3. Testing Assumptions
In statistical analysis, different statistical models assume some certain characteristics about the data. These characteristics are also known as assumptions. In order for the model to give a proper reflection of reality, the assumptions that are made need to be true. When these assumptions are violated, the conclusions of the research and the interpretation of the results change (Field, 2009). In the next section I will briefly deal with the assumptions of the ANOVA analysis. There are six critical assumptions when using a one way or a two way ANOVA. To provide the best-unbiased estimates, these six assumptions have to be satisfied. In my statistical analysis I will clearly illustrate that my sample data will satisfy the first five assumptions and how I will handle the violation of the sixth assumption of the ANOVA analysis.
4.3.1. Continuous data for the dependent variable (DV)
The first assumption is that the dependent variable should be measured at the interval or ratio level (i.e., the data for the dependent variable should be continuous). The data that I used for my independent variable is continuous, because I used the ROA of firms stated in USD.
4.3.2. Two or more categorical, independent groups for the independent variable (IV)
The second assumption is that the independent variable should consist of two or more categorical, independent groups. My independent variable consists of 10 categorical, independent groups, namely the different industry groups according to the SIC (standard industrial classification).
4.3.3. Independence of observations
The third assumption is that there should be independence of observations. This means that there is no relationship between the observations in each group or between the groups themselves. Every firm in my research sample is only present in one industry group and therefore this assumption is not violated.
4.3.4. No significant outliers
firms, outliers do not influence the results of the test. The problem of outliers is more substantial when there is a small sample. But my sample does not violate this assumption even though it is not an issue because of my sample size.
4.3.5. Approximately normally distributed dependent variable
The fifth assumption is that the dependent variable should be approximately normally distributed. The dependent variable should also be approximately normally distributed for each category of the independent variable. This is the case in my sample and therefore this assumption is not violated.
4.3.6. Homogeneity of variances
The sixth assumption is that there needs to be homogeneity of variances. I tested this assumption in SPSS Statistics using Levene's test for homogeneity of variances. The Levene statistic was significant at the 0,001 level. Therefore I rejected the null hypothesis that the groups have equal
variances and concluded that my data violated the assumption of homogeneity of variances. I
used the Brown-Forsythe test and the Welch test to assess the equality of means when groups are unequal in size. These tests do not assume homogeneity of variance. This statistic was also significant at the 0,001 level and therefore I rejected the null hypothesis that the groups have
equal means. Because my data violated the fifth assumption of the ANOVA, the LSD post-hoc
test (least significant difference) method that applies standard t-tests to all possible pairs of group means could not be used in my situation. I needed a post hoc test that adjusted for unequal
variances and sample sizes in the groups. Therefore I used the Games-Howell test. The
5. Conclusions
The following section presents a discussion on the outcomes of the empirical analysis. Subsequently I will address the theoretical and managerial implications of my research. Finally I will address the limitations of my study and highlight possibilities for further research.
5.1. Discussion
I have observed in prior research that internal resources of the firm (as derived from the resource based view) and external factors (as derived from the market based view) are both drivers of firm profitability (Porter, 1980; Barney, 1991). Although there are plenty pieces of empirical evidence that confirm the relationship between industry structure and firm profitability, theory prevailing in existing literature is solely based on U.S. data.
Therefore I proposed a research sample that consists of countries from all over the world, because this will increase the credibility of industry structure as a driver of firm profitability. More specifically, my research aimed to address the research question whether industry structure drives firm profitability.
Through an empirical analysis using a sample of 75.495 firms from all over the world, that reported their financial data in the years 2013,2014, and 2015, this research sought a linkage between industry structure and firm profitability. In line with institutional theory I investigated whether the political stability of the host country, measured by taking the mean of a period of three years (2013,2014, and 2015), has a moderating effect on industry structure and firm profitability.
In an effort to test whether the political stability of the host country influenced industry structure and firm profitability, I collected a political stability index from the World Bank. In this index the Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism. This measure of political stability of the host country is based on Worldwide Governance Indicators (WGI) data from the World Bank.
the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. These data are gathered from a number of survey institutes, think tanks, non-governmental organizations, international organizations, and private sector firms.
I aimed my first hypothesis at confirming industry structure as a driver of firm profitability and I aimed my second hypothesis at establishing the moderating effect of the political stability of the host country on industry structure and firm profitability.
In line with the above, Hypothesis 1 predicted that industry structure drives firm profitability and Hypothesis 2 predicted that host country political stability would have a positive effect on industry structure and firm profitability.
My significant findings entail that industry structure in fact drives firm profitability and therefore Hypothesis 1 is confirmed. More specifically, there is a significant relationship between the industry in which a firm is active and the profitability of a firm. I earlier stated that the Games-Howell post hoc test showed that overall there was no significant differences between the ROA for firms in different industries. But when I focused on the different industries and checked whether they are significantly different than other industries, I actually found multiple significant differences as explained in my ANOVA results.
My significant findings also entail that host country political stability has a positive effect on industry structure and firm profitability. Therefore, Hypothesis 2 is also confirmed. When the host country political stability increases, the relationship between the industry in which a firm is active and the profitability of a firm will strengthen. More specifically, industry structure results in higher ROA if the political stability of the host country is high.
5.2. Implications
From a methodological point of view, two methodological contributions can be highlighted based on the findings of my paper. Firstly, my findings contribute to the structure-conduct-performance paradigm [SCP], by showing that there is a significant relationship between the industry in which a firm is active and the profitability of a firm, based on a very large sample (N = 75.495) of non-U.S. data for the first time. Secondly, my findings contribute to the international business literature on institutional theory, by illustrating the significant positive effect of the host country political stability on industry structure and firm profitability.
From a managerial point of view, my research makes two important contributions for managers of international firms, who want to have a successful internationalization strategy, by significantly identifying a main driver of firm profitability and a positively moderating influencer on the main driver and firm profitability. I conclude that when managers of firms know that host country political stability has a significant positive effect on industry structure and firm profitability, they must choose a host country with a high political stability when this is an option, in order to increase the profitability of their firm.
Finally, from a theoretical point of view my research makes a great contribution to the literature. Because of the very large sample (N = 75.495) of firms from all over the world, the theoretical implication that industry structure is an important driver of firm profitability can be made. Therefore I contribute to improved clarity in the structure-conduct-performance paradigm [SCP], the ongoing discussion between internal resources of the firm (as derived from the resource based view) and external factors (as derived from the market based view) as drivers of firm profitability (Porter, 1980; Barney, 1991).
5.3. Limitations and further research
quality of the data obtained from the previously mentioned databases.
The second limitation of my research is related to the first limitation. Because my research was limited by the use of secondary data from existing databases, I’ve created my moderating variable by taking the mean of the host country political stability from the World Governance Indicators of 2013, 2014, and 2015. Real phenomena like the political stability of a country might be too complex to capture in such straightforward measures. Therefore, I call for a verification of the World Governance Indicators as the most accurate measure for host country political stability.
The third limitation of my research is that I only focused on the ‘very large’ firms in the Orbis database that reported their financial figures in 2013, 2014, and 2015.
Considering these limitations, I would like to note that my research might not be a complete representation of the studied phenomena and of the world. Therefore, I encourage future research to review my research question.
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7. Appendix
Table A 1:
Univariate analysis of variances 2015: Descriptive Statistics
Dependent Variable: ROA
2015 Industry
groups Country Mean
Std.
Deviation N
1 PAKISTAN 3,618428571 4,832649406 7
IRAQ 2,873 . 1
UKRAINE 13,09016279 21,83007766 86
EGYPT, ARAB REP. 10,474 . 1
BANGLADESH 4,636 . 1
TURKEY 8,124 . 1
ISRAEL 0,833 0,797616449 2
INDIA 2,420666667 2,206534916 3
IRAN, ISLAMIC REP. 11,985 11,72246422 4
CÔTE D'IVOIRE 2,839 . 1 RUSSIAN FEDERATION 10,65574194 14,70964816 93 ZIMBABWE -‐1,603 10,00453227 4 CHINA 3,858452381 10,11677846 84 INDONESIA 2,802 8,755456119 14 SRI LANKA 4,695454545 10,56467085 11 BOSNIA AND HERZEGOVINA
MALAYSIA 2,035 0,717006276 2 SPAIN 5,775285714 9,120881556 28 MONTENEGRO -‐2,055 . 1 FRANCE 0,051857143 4,051813274 7 ITALY 3,290931034 5,495106069 29 UNITED KINGDOM 4,8843125 12,84683697 80 LATVIA 3,310428571 9,442682473 7 CYPRUS -‐19,6415 8,309211786 2 CROATIA 2,021 4,581374848 5 OMAN 5,6925 6,614983938 2 PUERTO RICO 0,817 4,265795471 3 HUNGARY 7,207 2,46125212 4 BELGIUM 2,508166667 27,52959837 6 LITHUANIA 4,85125 4,431536932 4 PORTUGAL 1,45 . 1 TAIWAN, CHINA 1,84 . 1 GERMANY 3,08425 4,688625775 12 POLAND 2,755 2,529430766 4 DENMARK 7,04525 6,85827985 8 URUGUAY 5,188 . 1 IRELAND 4,285 . 1 JAPAN 8,368357143 5,119554302 14 BERMUDA -‐13,2085 14,13718588 2
IRAN, ISLAMIC REP. 8,107157895 12,13143579 19 RUSSIAN FEDERATION 11,41665289 16,87086789 242 PHILIPPINES 1,303333333 7,683543079 12 MEXICO 3,222 . 1 JORDAN 2,879333333 2,253567246 3 CHINA -‐ 0,151853659 7,783776598 82 INDONESIA -‐ 5,087083333 19,55363941 36
BOSNIA AND HERZEGOVINA 2,9264 8,037909211 10
BRAZIL -‐4,58 . 1 GREECE 1,3924 10,23403099 5 KAZAKHSTAN 5,7978 27,22985895 30 SOUTH AFRICA -‐1,1596 11,00050458 10 MACEDONIA, FYR 3,83425 6,201424588 4 KUWAIT -‐4,342 7,980407132 2 VIETNAM 6,935866667 9,164305935 15 BULGARIA 7,011 7,280925422 9 SERBIA 15,98677778 15,54386956 -‐ 9 ROMANIA -‐ 4,810333333 11,02983646 6 KOREA, REP. 16,7005 2,250720885 2 MALAYSIA 6,197 . 1 GABON -‐0,88 . 1 SPAIN -‐ 2,937421053 12,28912353 19 FRANCE 1,973222222 22,10655025 -‐ 27 ITALY 2,116470588 7,681046398 17 UNITED KINGDOM -‐ 9,854909836 22,32056643 244 CYPRUS -‐ 14,25966667 51,73314843 3 CROATIA -‐5,775 19,17249327 2 OMAN 13,06 . 1 PUERTO RICO -‐13,4247 22,9055346 30 ESTONIA 16,044 . 1 HUNGARY -‐2,682 4,489482041 3 BELGIUM 2,943857143 1,846279721 7 PORTUGAL 3,826666667 2,36684396 3
UNITED ARAB EMIRATES 27,735 . 1
TAIWAN, CHINA -‐0,189 . 1
GERMANY 3,605 15,79441604 29
SLOVENIA -‐24,1555 41,36786802 2 DENMARK -‐15,1145 26,30933222 4 URUGUAY 3,801 . 1 IRELAND -‐ 8,941368421 16,7282639 19 JAPAN 2,493857143 7,11258382 14 BERMUDA -‐4,238 14,65035711 18 CZECH REPUBLIC 5,1422 8,965441244 5 HONG KONG SAR, CHINA -‐9,782625 9,489111067 8 SLOVAK REPUBLIC 0,8095 0,63710321 2 NETHERLANDS 2,433029412 17,14797582 34 SWEDEN -‐5,23332 28,89546984 25 CAYMAN ISLANDS -‐ 11,02616667 17,66163821 24 CANADA 4,146 . 1 NORWAY 12,14357895 19,84651481 -‐ 19 FINLAND 12,42066667 13,72770557 3 AUSTRIA 8,791625 11,67544043 8 SWITZERLAND 3,912 . 1 3 PAKISTAN LUXEMBOURG -‐13,466 31,85780011 4,281 4,016366517 3 2 UKRAINE -‐ 4,503695652 10,17828288 46 TURKEY 4,9662 6,371568033 5 ISRAEL -‐ 2,348642857 11,88424869 14 INDIA 6,749 . 1
IRAN, ISLAMIC REP. 6,729555556 6,934776008 9 RUSSIAN FEDERATION 1,784874608 13,79641315 319 PHILIPPINES 2,1 . 1 MEXICO 5,212 . 1 JORDAN -‐10,972 . 1 CHINA 3,684227848 5,197038501 158 INDONESIA 1,454454545 20,05294636 22 SRI LANKA 9,98325 12,47505897 4
BOSNIA AND HERZEGOVINA 0,344222222 7,374554601 27
BULGARIA -‐0,953 23,43255268 7 SERBIA -‐ 2,120173913 16,08478869 69 ROMANIA -‐ 0,879269231 10,31715601 26 KOREA, REP. 4,687094017 13,12632843 117 MALAYSIA 9,599 7,065494533 3 SPAIN 1,308606383 8,96891503 188 MONTENEGRO -‐11,39 . 1 FRANCE 3,380223301 7,527917544 103 CHILE 3,656 0,168291414 2 ITALY -‐ 0,227578313 7,506418638 166 UNITED KINGDOM 5,792725047 11,25538076 531 LATVIA 6,046 0,127279221 2 CROATIA 0,147333333 6,727393143 9 PUERTO RICO 3,293076923 7,430101878 13 ESTONIA 3,009 3,577960313 2 HUNGARY 11,10394444 13,37901347 18 BELGIUM 3,112434783 8,025969699 69 LITHUANIA 6,714 3,602001943 2 PORTUGAL 0,053925926 7,271224836 27
