The Relation Between Strategic Orientations And Export Performance: A Meta-Analytic Review

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The Relation Between Strategic Orientations And Export

Performance: A Meta-Analytic Review

Name: Catharina Jantina Roos van der Laan Student number: S3590186

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Abstract

Purpose – It is the purpose of this study (1) to systematically review the existing literature on the relationship between strategic orientations and export performance, to meta-analytically evaluate the respective effect and strength of the strategic orientations on export performance, (2) to improve the precision of the estimate of efficacy, (3) to determine moderating effects on the relationship between strategic orientations and export performance, and (4) to empirically prove the common effects of strategic orientations. As the current literature in this field is too fragmented and too inconsistent, this study will help to generate more robust, reliable, and generalizable results. The tested strategic orientations are (1) market orientation, (2) entrepreneurial orientation, and (3) learning orientations, and a positive correlation between the strategic orientations and export performance is suggested in the hypotheses. A exploratory question is created to test the possible common effects of strategic orientations on export performance, using a commonality analysis.

Design/methodology/approach – Based on the effect sizes of 78 independent samples (n = 17,956), the study utilizes a bivariate meta-analysis, moderator analysis, and a commonality analysis. All to examine the nature of the relationships between strategic orientations and export performance, to test for potential moderators influencing these relationships, and to test for the common effects of strategic orientations on export performance.

Findings – The results show that all three strategic orientations (market orientation, entrepreneurial orientation, and learning orientation) (hypotheses 1-3) are positively related to export performance. Furthermore, the moderator analysis indicated that industry type, firm size, and region (divided by continents) do influence particular relationships between strategic orientations and export performance. Finally, the outcomes of the commonality analysis reveal that the simultaneously use of strategic orientations will lead to higher export performance, compared to the sole unique effects (exploratory question 1).

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List of figures

Figure 1: Conceptual model; relationship between strategic orientations and export

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II

List of tables

Table 1: Conceptualization and measurements of export performance ... 5

Table 2: Keywords ... 15

Table 3: Summary of codified studies ... 18

Table 4: Conceptualization of independent variables ... 25

Table 5: Bivariate meta-analysis results ... 28

Table 6: Moderating effect of industry type ... 31

Table 7: Moderating effect of firm size... 32

Table 8: Moderating effect of region ... 34

Table 9: Countries included for every strategic orientation and region ... 35

Table 10: Correlation matrix ... 38

Table 11: Regression results and relative importance ... 38

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1. Introduction

Exporting is an important economic activity, and used as a major mean for entering international markets. The internationalization of firms is a common strategy for expanding sales, profitability, and performance (Gnizy & Shoham, 2014; Morgan, Kaleka, & Katsikeas, 2004). In this respect, researchers have tried to find and understand the determinants of export performance (Balabanis & Katsikea, 2003; Katsikeas, Leonidou, & Morgan, 2000; Robertson & Chetty, 2000). Focusing on the determinants of export performance, many variables are studied in the past years, among these are market orientation, entrepreneurial orientations, and learning orientation (Abiodun & Kida, 2016; Cadogan, Kuivalainen, & Sundqvist, 2009; Jin, Jung, & Jeong, 2017; Lages, Silvia, & Styles, 2009; Murray, Gao, & Kotabe, 2011). With respect to these strategic orientations, the broader international business literature recognizes that the orientations positively influence firm’s export performance. A variety of researcher tested the direct relationship between strategic orientations and export performance, others created and tested more complex models by combining strategic orientations, and testing for moderating and mediating effects. As a consequence, the current literature in this field is fragmented and inconsistent, and conceptual confusion occurred (Beleska-Spasova, 2014; Sousa, Martínez-López, & Coelho, 2008; Zou & Stan, 1998). This makes it difficult to establish generalizable statements about the effect of strategic orientations and export performance, which in turn makes it harder for managers to take actions to actually improve firm’s performance on the basis of these orientations. Therefore, this study tries to integrate a major part of the literature to actually come up with robust results and generalizable statements to clarify this issue.

Drawing upon the resource-based view, firm’s sustained competitive advantage and organizational survival are based on its ability to hold capabilities and to generate resources (Autio, Sapienza, & Almeida, 2000; Barney, 1991; Jin & Cho, 2018). This study states that strategic orientations are part of firm’s resources and capabilities, and with their fulfilment of the VRIO-criteria (valuable, rare, imitable, and organisable), a source for sustainable competitive advantage and superior performance. Strategic orientation is clarified by Nasir et al. (2017: 2) who define the concept as ‘the strategic direction implemented by a firm to create

the proper behaviors for the continuous superior performance of the business’. Researchers

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2 & Jaworski, 1990; Narver & Slater, 1990) and (2) entrepreneurial orientation (EO), indicating the decision-makers actions for creating competitive advantages. Also learning orientation (LO) can be related to export performance. Learning orientation is the optimization of organizational processes and creatively use of information technology. The majority of the studies focus only on the sole relationship between a strategic orientation and export performance. It may be assumed that the strategic orientations have interrelated connections (Hakala, 2011). Hakala (2011), described three possible ways to structure strategic orientations. First, he explained orientations as sequences in development, meaning that there is only one best orientation, which is further improved by incorporating parts of other orientations. Which in turn, causes improved business performance. Second, he states that strategic orientations are alternatives that can be chosen from. He describes that one orientation is better than another depending on the circumstances of the organization, or the orientations are perceived as equally effective but have different routes and effects for accomplishing the same goal of the firm. The last and preferred structure by Hakala (2011) is orientations as complementary patterns, in this case, orientations correlate with or support one another. This structure is a combination of the first and second structures, investigating both the best and alternative patterns of strategic orientations combinations to explain business performance. This study is in line with and complementary to the last structure explained by Hakala (2011), showing that strategic orientations are not only complementary to one another but also mutually important in explaining export performance.

It is the purpose of this study (1) to systematically review the existing literature on the relationship between strategic orientations and export performance to meta-analytically evaluate the respective effect and strength of the strategic orientations on export performance, (2) to improve the precision of the estimate of efficacy, (3) to determine moderating effects on the relationship between strategic orientations and export performance, and (4) to empirically prove the common effects of strategic orientations.

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3 this topic is conducted yet. Finally, this research proves that strategic orientations have common effects. The outcomes of the commonality analysis reveal that the simultaneously use of strategic orientations will lead to higher export performance, compared to the sole unique effects.

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2. Theoretical background

As exemplified in a large number of export performance studies in the business and marketing literature, a wide variety of strategies, practises and orientations influence the magnitude of export performance. In this study, the focus is on the influence of strategic orientations on export performance. This section bundles the large amount of literature that exists on these subjects. First, the concept of export performance is explained, followed by the three strategic orientations; market orientation, entrepreneurial orientation, and learning orientation. The section ends with the formulation of three hypotheses and one exploring question.

2.1 Export performance

Export performance is part of the overall firm performance, which includes three dimensions; (1) effectiveness (how well firms are performing in relation to its competitors), (2) efficiency (how well firms are using and implementing resources to create favourable outcomes), and (3) adaptiveness, (how well firms are able to adapt to their changing environment and response to opportunities) (Walker & Ruekert, 1987). Even if the three dimensions of firm performance are related to export performance, academics have continuously worked on the improvement of a more specific definition of export performance. According to Zou, Taylor, and Osland (1998), the concept can be conceptualized and measured in three different ways, each is associated with a different conceptualization of export performance. The first and most commonly used conceptualisation and measure method is based on the financial outcomes of exporting, as most firms are gauged in financial terms. Another conceptualization is based on the strategic outcomes of exporting. Those measures are more in line with the firm’s strategic goals and improvement of their position in the export market. The last dimension is a subjective dimension, where the perception and attitude toward export activities are measured. The positive perception and being satisfied with export activities in export markets can be a strong indicator of success in exporting (Zou et al., 1998). Table 1 shows the three conceptualization and measure dimensions, with each some illustrative studies as examples.

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Table 1: Conceptualization and measurements of export performance Dimension Conceptualization

and measures

Illustrative studies

Financial Export sales Cavusgil (1984); Cooper & Kleinschmidt (1985); Madsen (1989); Rose & Shoham (2002)

Export sales growth Cooper & Kleinschmidt (1985); Madsen (1989)

Export profits Johnson & Arunthanes (1995); Mac & Evangelista (2016); Rose & Shoham (2002)

Export intensity Argouslidis & Indounas (2010); Ellis (2007); Fernández-Mesa & Alegre (2015)

Strategic Market share Çavuşgil & Kirpalani (1993); Johnson &

Arunthanes (1995); Murray, Gao, & Kotabe (2011)

Competitive position Lin, Huang, & Peng (2014); Murray, Gao, & Kotabe (2011)

Subjective Satisfaction Boso (2010); Cadogan, Diamantopoulos, & Siguaw (2002); He, Brouthers, & Filatotchev (2012)

Propensity to export Denis & Depelteau (1985); Navarro-García, Arenas-Gaitán, & Rondán-Cataluña (2014) Attitude toward

exporting

Brady & Bearden (1979); Calabrò, Campopiano, Basco, & Pukall (2017); Johnston & Czinkota (1982)

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6 hypothesis is formulated for the common effect of strategic orientations as the literature is too fragmented, weak, and inconsistent to create such statements. The hypotheses and the exploratory question are stated in the last section of this chapter.

2.2 Strategic orientations

Market orientation

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7 The other decision criterium is profitability, profitability is included as it is the main objective of market orientation (Narver & Slater, 1990).

To explain market orientation, most researchers mention either one or both conceptualizations. No matter which conceptualization was used, several papers have proven a positive effect of MO on firm performance or export performance (Cadogan, Diamantopoulos, et al., 2002; He et al., 2012; Kohli & Jaworski, 1990; Murray, Gao, Kotabe, & Zhou, 2007; Narver & Slater, 1990; Rose & Shoham, 2002). The main reason for the positive results is that firms use market orientation to respond and adapt to the needs and preferences of customers to deal with competitors (Kohli & Jaworski, 1990; Narver & Slater, 1990). Cadogan, Diamantopoulos, & De Mortanges (1999), conceptualized the term ‘export market orientation’, and based their conceptualization on Kohli & Jaworski (1990). The authors described ‘export market orientation’ as the (1) generation of export intelligence and (2) dissimilation, and (3) the responsiveness to the export market intelligence, towards export customers, export competitors, and external export market influences.

Entrepreneurial orientation

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8 (Covin & Slevin, 1991; Lumpkin & Dess, 1996). Again, both dimensions are used in export context (Boso, Cadogan, & Story, 2012).

Learning orientation

Learning is a continuous process that involves all departments of an organization (Day, 1994a), and is described as critical for organizational success (Calantone, Cavusgil, & Zhao, 2002; Sinkula, 2002). Day (1994b) conceptualizes that learning orientation is an inside-out activity that optimizes organizational processes (e.g. product development and service delivery) that influence the firm’s performance directly. Organizations that anticipate future needs, redesign underlying processes, and creatively use information technology are better in responding to market requirements and external environmental factors. Therefore, they are able to create superior profitability and long-run competitive advantage (Day, 1994b). Sinkula, Baker, and Noordewier (1997) talk about three components to explain LO. The first component is commitment to learning, which represents the organization’s degree of value given, and the promotion of, learning. The second one is described as the organization’s shared vision, where the focus on learning is organizational-wide. The third is open-mindedness, where the organization critically evaluate its operational routines and come up and accept new ideas. Employees are encouraged to think outside the box (Baker & Sinkula, 1999). Calantone, Cavusgil, and Zhao (2002) introduced a fourth component: intra-organizational knowledge sharing. The latter one is based on the study by Miner & Moorman (1998), who mention that learning is not possible if an organization is not able to share its information through an effective and efficient system. This system allows the organization to evaluate past decisions and activities. The conceptualization of LO by Sinkula et al. (1997) does not change in the context of export and is therefore also applicable in this situation as performed by several researchers (Abiodun & Kida, 2016; Lages et al., 2009; Nguyen, Barrett, & Nguyen, 2007).

2.3 Hypotheses and exploratory question development

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Figure 1: Conceptual model; relationship between strategic orientations and export performance

This study draws upon the resource-based view (RBV)(Barney, 1991). RBV, within the field of strategic management, says that firm’s sustained competitive advantage and organizational survival are based on its ability to hold capabilities and to generate resources (Autio et al., 2000; Barney, 1991; Jin & Cho, 2018). Analysing different resources and capabilities a firm has, creates the possibility to identify and evaluate firm’s internal strengths and its internal weaknesses. The internal analysis can be accomplished with the help of the VRIO framework (Barney, 1991). The VRIO framework consists of four questions asking about the value, rarity, imitability, and organization of firm’s resources and capabilities, to determine its competitive potential (Barney, 1991; Barney & Hesterly, 2015). Organizational capabilities can be defined as the ability of a firm to effectively deploy, leverage, and reconfigure its resources (Barney, 1991; Knight & Cavusgil, 2004). Capabilities are therefore considered as more important than resources, as they have the ability to consolidate the firm’s assets to maintain the firm’s competitive position (Teece, Pisano, & Shuen, 1997). Prior research has created several definitions of organizational capabilities, which can be classified into three categories (Collis, 1994). This first can be described as operational capabilities, that reflect a firm’s ability to do basic functional activities (e.g. distribution logistics)(Collis, 1994). The second category is described as dynamic capabilities (Collis, 1994; Jin & Cho, 2018), which is ‘the ability of an

organization to learn, adapt, change and renew over time’ (Teece et al., 1997: 20) to develop

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10 and improve their organizational capabilities achieve more economic value compared to their competitors (Jin & Cho, 2018). The possession of organisational capabilities and the way firms structure these capabilities makes it possible for firms to take advantage of these organizational capabilities and to create superior performance (Barney, 2014; Barney, Ketchen Jr., & Wright, 2011; Kozlenkova, Samaha, & Palmatier, 2013). This study states that strategic orientations are part of firm’s resources and capabilities, and with their fulfilment of the VRIO-criteria, a source for sustainable competitive advantage and superior performance. Therefore, the first three hypotheses (H1-3) described the positive relation between the strategic orientations (MO, EO, LO) and export performance.

The positive relationship between MO and firm performance stems from the creation of value for customers and strategic responses to competitors. Firms’ routines and processes enable firms to gather information about customers, competitors and the external environment, to disseminate this information and respond in an adequate way (Kohli & Jaworski, 1990; Narver & Slater, 1990). Diamantopoulos & Cadogan (1996) have shown that MO is also applicable in the context of export. MO helps firms to gain knowledge about foreign markets, to identify foreign demand, to gain legitimacy with foreign consumers, and to compete effectively against competitors in foreign markets (He et al., 2012). Aaby and Slater (1989) identified in their study market orientated capabilities, such as export market knowledge, export planning, and utilization of export opportunities, that led to export performance. More recent studies found full or partial support for the positive relation between MO and export performance (Cadogan, Diamantopoulos, et al., 2002; Murray et al., 2007; Rose & Shoham, 2002). Hence, this study assumes the following hypothesis:

H1: Is market orientation positively associated with export performance?

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11 & Katsikea, 2003). Based on several studies, the expected relationship between EO and export performance is positive (Boso et al., 2012; Celec, Globocnik, & Kruse, 2014; Lisboa, Skarmeas, & Lages, 2011a; Martin & Javalgi, 2015). Consequently, the following hypothesis is:

H2: Is entrepreneurial orientation positively associated with export performance?

Learning orientation is positively related to firm performance (Baker & Sinkula, 1999; Calantone et al., 2002). Firm’s ability to learn and apply the learned knowledge to risk full, turbulent, challenging export markets is the key to creating, achieving, and sustaining competitive advantage in those markets (Abiodun & Kida, 2016; Day, 1992). Based on existing theory, it can be assumed that the positive relationship between LO and export performance exists, but little empirical research is conducted to actually prove this relation. International learning orientations can be explained as a firm’s tendency to actively acquire, disseminate and use knowledge on foreign markets to reduce uncertainty and to penetrate foreign markets (Dimitratos & Plakoyiannaki, 2003; Gnizy & Shoham, 2014). An important determinant of a firm’s performance is its ability to learn from its experiences (Farrell, 2000; Nevis, DiBella, & Gould, 1995; Slater & Narver, 1995). To improve performance, firms should understand and satisfy customers’ needs (Day, 1994a; Narver & Slater, 1990). Learning orientation enables firms to target and penetrate new markets, and to enhance firm performance (Kropp, Lindsay, & Shoham, 2006; Mccann, 1991; Zahra, Ireland, & Hitt, 2000). Ismail (2011) found a direct positive relationship between LO and export performance. Hence, this study proposes the following hypothesis:

H3: Is learning orientation positively associated with export performance?

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12 drove performance. In other words, strategic orientations are related, and the combination of them might create higher export performance than if they are considered in isolation. Therefore, a firm’s adoption of more than one strategic orientation at the same time can increase firm’s export performance and competitive position.

More specifically, the question is whether strategic orientations only show unique effects when implemented in a firm’s activities or whether the combination of strategic orientations create additional value (common or joint effects). This means, in statistical terms, that the explained variance is decomposed into 2k – 1 components of variance explained, k describes the number of strategic orientations. This means that not only the three unique effects are decomposed but also the 4 common effects (23 – 1 = 7). The 4 common effects are created by the different possible combinations of pairs of strategic orientations (MO, EO; MO, LO; EO, LO) and one common effect of the combination of all three strategic orientations (MO, EO, LO). This concept is illustrated in a Venn diagram figure 2.

Figure 2: Venn diagram of the unique and common effects of strategic orientations explaining the variance in export performance

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3. Methodology

The available literature on strategic orientations in relation with export performance faces several challenges, such as the variability of results and lack of generalizability. With a meta-analysis, a systematic review of available studies can be created. This makes it possible to combine individual effect sizes to get more robust results, increase generalizability, reduce limitations that come along with single research studies and single data(bases) (e.g. no comparability), and to identify moderating effects. To face the fragmented and inconsistent findings of studies and to increase generalizability, this study uses a bivariate meta-analysis. With the meta-analysis steps described by Ellis (2010) in his book ‘The essential guide to effect

sizes: Statistical power, meta-analysis, and the interpretation of research results’ the following

sections are created. The bivariate meta-analysis is complemented with a moderator analysis, that explains the moderating effect on the relationship between strategic orientations and export performance. At the end, a commonality analysis is explained, which shows the unique and common effects of independent variables.

3.1 Collected studies and sample

The identification of relevant published and unpublished studies is the first step when conducting a meta-analysis (Ellis, 2010). Only published papers are used, to increase the overall reliability and validity of this study. Included journals, among others, are International Journal

of Management Science and Business Administration, Journal of Small Business Management, International Marketing Review, International Business Review, Journal of Business Research, and Journal of International Marketing. The time frame of the studies had no particular starting

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Table 2: Keywords

Variable Keywords used in literature search

Export performance Export performance; Export intensity; Export-market performance; International exporting (focus on export); Strategic export performance; Exporting

Market orientation Market orientation; Export market orientation; International market orientation; Export market-orientated activities; market(/export) intelligence generation; market(/export) intelligence dissemination; market(/export) intelligence responsiveness; Customer orientation; Competitor orientation; inter-functional coordination

Entrepreneurial orientation

Entrepreneurial orientation; International entrepreneurial orientation; Entrepreneurship; Proactiveness; Risk-taking; Innovativeness;

Learning orientation Learning orientation; International learning orientation; Commitment to learning; Shared vision; Open-mindedness; Inter-organizational knowledge sharing

Overall, 220 studies were identified by the keyword search. From those 220 papers, 170 are screened. The screened papers are sorted out on the basis of inclusion criteria, which are described in the next section.

3.2 Inclusion criteria and coding

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16 (Grégoire & LeLorier, 1995). This means that the results presented in this paper can change if papers are written in other languages then English are included in the analysis. The third inclusion criterium is about the dependent variable. To secure validity of this study, the dependent variable should be described as export performance or as another related conceptualization (see table 2), which is operationalized as commonly described in prior research. Fourth, the strategic orientations (market orientation, entrepreneurial orientation, and learning orientation) must be operationalized as described in the literature review. As the strategic orientations consist of several sub-concepts, at least two of the sub-concepts must be used in the paper screened to make it suitable for this study (e.g. in case of LO, at least two variables from (1) commitment to learning, (2) shared vision, (3) open-mindedness should be included). Inclusion criteria three and four are created to ensure the variability of the meta-analysis, as specific concepts are measured within a pre-determined range of possibilities. Finally, to actually conduct the meta-analysis, it is important that the papers are quantitative and present correlation coefficients such as the Pearson correlation coefficient and the standardized regression coefficients. The latter can be recalculated to make it suitable for the meta-analysis. From the 170 studies screened, 78 studies (78 independent samples, n = (17,956) remain for analysis after applying the inclusion criteria. The reasons why most of the studies did not pass the inclusion criteria was because they were (1) qualitative or (2) did not report a Pearson correlation coefficient or a standardized regression coefficient.

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Table 3: Summary of codified studies

Reference Yp Yc n Country Industry

Firm size

Variables included

Abdghani, Nikmat, & Sulaiman 2019 2015 101 Malaysia Single SML MO

Abdolvand, Heidarzadeh, & Kuzegar 2012 2008 91 Iran Single SML MO, EO

Abiodun & Kida 2016 2012 201 Nigeria Multiple SME EO, LO

Ahimbisibwe & Abaho 2013 2009 195 Uganda Multiple SME EO

Ahimbisibwe, Ntayi, & Ngoma 2013 2009 56 Uganda Multiple SML MO

Alotaibi & Zhang 2017 2013 175 Saudi Arabia Multiple SML MO

Argouslidis & Indounas 2010 2005 243 UK Multiple SML MO

Armario, Ruiz, & Armario 2008 2004 112 Spain Multiple SME MO

Balabanis & Katsikea 2003 1999 82 UK Multiple SML EO

Birru, Runhaar, Zaalberg, et al. 2019 2013 159 Ethiopia Multiple SML MO

Boso 2010 2009 212 UK Multiple SML MO, EO

Boso, Cadogan, & Story 2012 2008 212 UK Multiple SML MO, EO

Boso, Story, Cadogan, et al. 2013 2009 164 Ghana Multiple SME MO, EO

Cadogan, Boso, Story, et al. 2016 2012 212 UK Multiple SML MO, EO

Cadogan, Cui, & Li 2003 1999 137 Hong Kong Multiple SML MO

Cadogan, Diamantopoulos, & De Mortanges 1999 1995 198 UK Multiple SML MO

Cadogan, Diamantopoulos, & De Mortanges 1999 1995 103 Netherlands Multiple SML MO

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Firm size

Cadogan, Kuivalainen, & Sundqvist 2009 2005 783 Finland Multiple SML MO

Cadogan, Sundqvist, Salminen, et al. 2002 1998 783 Finland Multiple SML MO

Calabrò, Campopiano, Basco, et al. 2017 2012 113 Germany Multiple ML EO

Celec, Globocnik, & Kruse 2014 2010 102 Slovenia Multiple SME MO, EO

Chang & Fang 2015 2011 235 Taiwan Multiple SML MO

Chung 2012 2008 100 New Zealand Multiple SML MO

Diamantopoulos & Kakkos 2007 2003 171 UK Multiple SML MO

Dodd 2005 2001 115 Australia Multiple SML MO

Ellis 2007 2003 345 Taiwan Multiple SML MO

Emőke–Szidónia 2015 2013 122 Romenia Multiple SME EO

Etchebarne, Geldres, & García-Cruz 2010 2002-2005 88 Chile Multiple SML EO

Fernández-Mesa & Alegre 2015 2004/2006 150 Italy, Spain Single SME EO

Fuchs 2011 2009 146 Germany Multiple SME MO, EO, LO

Gnizy & Shoham 2014 2010 103 Israel Multiple SML MO

He, Brouthers, & Filatochev 2012 2008 195 China Multiple SML MO

He, Brouthers, & Filatotchev 2018 2008 214 China Multiple SML MO

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Firm size

Variables included Hernández-Perlines, Moreno-García, &

Yañez-Araque 2016 2014-2015 174 Spain Multiple SML EO

Hoang 2015 2011 142 Taiwan Multiple SME MO

Imran, Aziz, & Hamid 2019 2015 45 Pakistan Multiple SME MO, EO

Imran, Aziz, Hamid, et al. 2018 2014 364 Pakistan Multiple SME EO

Ismail 2011 2007 228 Malaysia Multiple SME MO, LO

Jin & Cho 2018 2014 470 Korea Multiple SME EO

Jin, Jung, & Jeong 2017 2013 401 Korea Multiple SME EO

Johansen & Knight 2010 2006 369 USA Multiple SME MO, EO

Kayabasi & Mtetwa 2016 2012 443 Turkey Multiple SML MO

Kazem & van der Heijden 2006 2005 23 Egypt Single SME EO

Knight 2001 1997 268 USA Multiple SME EO

Knight & Cavusgil 2004 2000 203 USA Multiple SML MO, EO

Kropp, Lindsay, & Shoham 2006 2002 396 South Africa Multiple SML MO, EO, LO

Lages, Silva, & Styles 2009 2005 112 Portugal Multiple SML LO

Lengler, Sousa, & Marques 2013 2009 197 Brazil Multiple SML MO

Lin, Huang, & Peng 2014 2009 244 Taiwan Multiple SML MO

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Firm size

Lisboa, Skarmeas, & Lages, 2011b 2011 2007 262 Portugal Multiple SML MO

Mac & Evangelista 2016 2012 128 China Multiple SML MO

Makri, Theodosiou, & Katsikea 2016 2012 168 Greece Multiple SME MO

Martin & Javalgi 2015 2011 260 Mexico Multiple SME EO

Mavrogiannis, Bourlakis, Dawson, et al. 2008 1999-2001 155 Greece Single SML EO

Miocevic & Crnjak-Karanovic 2011 2008-2009 125 Croatia Multiple SME MO

Monteiro, Soares, & Rua 2013 2011-2012 265 Portugal Multiple SML EO

Murray, Gao, & Kotabe 2011 2002 491 China Multiple SML MO

Murray, Gao, Kotabe, et al. 2007 2002 240 China Multiple SML MO

Murray, Gao, Kotabe, et al. 2007 2002 251

Asia, EU,

USA Multiple SML MO

Mutlu & Aksoy 2014 2010 33 Turkey Single SML EO

Nakos, Brouthers, & Dimitratos 2014 2005 162 USA and UK Multiple SME EO

Navarro-García Arenas-Gaitan, & Rondan-Cataluna 2014 2010 212 Spain Multiple SML MO

Nguyen, Barrett, & Nguyen 2007 2003 283 Vietnam Multiple SML MO, LO

Olabode, Adeola, & Assadinia 2018 2014 249 Nigeria Multiple SME MO

Patel & D’Souza 2009 2005 270 USA Multiple SME EO

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Firm size

Roxas & Chadee 2011 2007 175 Phillippines Multiple SME EO

Sørensen & Madsen 2012 2008 249 Denmark Multiple SME MO

Sundqvist, Kyläheiko, Kuivalainen, et al. 2012 2008 783 Finland Multiple SML EO

Sundqvist, Puumalainen, Salminen, et al. 2000 1996 783 Finland Multiple SML MO

Tantong, Karande, Nair, et al. 2010 2006 252 Thailand Multiple SML MO

Thirkell & Dau 1998 1994 213 New Zealand Multiple SML MO

Yan, He, & Cheng 2016 2012 230 China Multiple SML MO

Zehir, Köle, & Yıldız 2015 2011 186 Turkey Multiple SME MO

Zhang & Zhu 2016 2012 220 China Multiple SML MO

Notes: Yp = Year of publication, Yc = year of publication, n = total sample size per study, UK = United Kingdom, USA = United States of America, SME = small and medium-sized enterprises,

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23 3.3 Statistical procedures

3.3.1 Meta-analytic procedure

A bivariate meta-analysis creates a systematic review of coded studies, in which the magnitude, direction, and significance of the relations between independent variables (MO, EO, LO) and the dependent variable (export performance) are tested. This analysis makes it possible to combine individual effect sizes to get more robust results, increase generalizability, reduce limitations that come along with single research studies and single data(bases), and identify moderating effects. Therefore, the next step, when conducting the meta-analysis, is to calculate the mean effect size (Ellis, 2010). This information helps to evaluate the three hypothesis. Pearson’s correlation coefficient and the standardized regression coefficients are determined as effect sizes from the studies selected. This means that either a Pearson’s r or a regression coefficient (β = beta) should be reported. The values extracted from studies that reported a regression coefficient, had to be converted into a correlation coefficient, using the procedure introduced by (Peterson & Brown, 2005). They explain that if the standardized regression coefficient (β = beta) is negative, it equals the Pearson’s correlation coefficient. In case the value is positive, the value should be added with a number of 0.05 (r = β + 0.05). The dependent variable (export performance or as conceptualized in table 1) can be measured by several measures and the independent variables (MO, EO, LO, see table 4) can be measure with several sub-concepts.

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24 effect sizes (step 5 of Ellis (2010)), the Q-statistic and I2 are used. The two measuresindicate the amount of heterogeneity of the effect size estimates, and determine if a moderator analysis (see subsection 3.3.3) should be conducted. To determine if the distribution is homogenous (i.e. a single population mean), the Q-statistic is calculated to determine the amount of difference between observed and expected effect sizes (Ellis, 2010). If the Q-statistic has a significant result, the Q-value show a significant amount of heterogeneity. I2 explains the proportion of the

total variation in the observed effect sizes that is due to heterogeneity, and is and is expressed as a percentage with a range from 0 to 100 percent (Higgins & Thompson, 2002). How higher the percentage, how higher the amount of heterogeneity, a percentage above 75 percent represents considerable heterogeneity (Higgins & Green, 2011). Furthermore, the so-called trim-and-fill procedure is used to test for potential publication bias. Publication bias exists if researchers or reviewers prefer to report statistically significant results over methodologically sound nonsignificant results (Ellis, 2010). Articles that present statistically significant results have a higher change to get published. As this study only use published studies, the chance of publication bias is present. The trim-and-fill procedure estimates how many studies it would take for the funnel plot to be symmetrical when important small samples and effect sizes are missing from the left side of the funnel plot. The #TF indicates the number of imputed studies that is needed to make the funnel plot symmetrical. Whereas, ρTF report the adjustment of the

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Table 4: Conceptualization of independent variables

Variable Conceptualization Reference

Market Orientation

Market intelligence generation, market

intelligence dissemination, market intelligence responsiveness

Customer orientation, competitor orientation, inter-functional coordination

Export intelligence generation, export

intelligence dissemination, export intelligence responsiveness

Kohli & Jaworski, (1990)

Narver & Slater (1990)

Cadogan,

Diamantopoulos, & De Mortanges (1999) Entrepreneurial

Orientation

Innovativeness, risk-taking, proactiveness

added with competitive aggressiveness and autonomy

Export product innovativeness, export risk-taking, export proactiveness, export competitive aggressiveness and export autonomous

behaviors

Miller (1983)

Covin & Slevin (1991)

(Boso, Cadogan, & Story (2012)

Learning Orientation

Commitment to learning, shared vision, open-mindedness

added with intra-organizational knowledge sharing

Sinkula, Baker, & Noordewier (1997) Calantone, Cavusgil, & Zhao (2002)

3.3.2. Moderator analysis

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26 (Allen, 2017). This study only has categorical variables, as no continuous variables are coded. The first step when conducting a moderator analysis is to determine the subgroups. Values can be calculated once the subgroups of the tested variable are conceived. Linear multiple regression analysis is used to determine if moderating effects exist, which is also known as the moderated multiple regression (Aguinis, 2004). The moderator analysis can be compared to a multiple regression equation, which includes an interaction term. When the moderator analysis is finished, the reliability adjusted average correlation (ρ), standard deviation (SD(ρ)), 95% confidence interval (95% CI), the Q-statistic, and the I2 are given. These values are explained in the previous section.

3.3.3. Commonality analysis

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4. Results

This chapter presents the results from, and interpretation of, the meta-analysis, moderator analysis, and the commonality analysis. Which is described as the last and final step by Ellis (2010). The results of the meta-analysis displayed in table 5 and figure 3. The results of the moderator analysis are explained in section 4.2. Industry type, firm size, and region are tested as potential moderators. In the last section of this chapter, the results of the commonality analysis are presented in tables 11 and 12.

4.1 Bivariate meta-analysis results

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Table 5: Bivariate meta-analysis results

Notes: k = number of studies, n = total sample size, r = average correlation, ρ = reliability adjusted average correlation, SD(ρ)

= standard deviation, 95% CI = 95% confidence interval, Q = Q-statistic, I2 _{= proportion of the total variation in the observed}

effect sizes, #TF = amount of imputed studies, ρ TF = publication bias adjusted reliability adjusted average correlation.

Based on the three hypotheses, a positive and statistically significant relationship between the strategic orientations (MO, EO, LO) and export performance is expected. To start with the relationship between MO and export performance; 55 studies have been selected (k = 55), which created a total sample size of 12,836 independent samples (n = 12,836). A high validity of the overall results is created by the magnitude of the gathered samples. In line with the hypotheses 1, a positive relation between MO and export performance is found. Both the average correlation coefficient (r = 0.27) and the reliability adjusted average correlation coefficient (ρ = 0.3) show this result. The effect size can be interpreted as weak as the correlation coefficients are around 0.3 (Akoglu, 2018). The standard deviation explains the overall amount of variation in the selected studies (k= 55) and is reported to be 0.17. The test is statistically significant at a 5% level, as the 95% confidence interval (95% CI = .24/.35) does not include zero. The two tests for heterogeneity, show that the groups are dissimilar. First, the Q-statistic (Q = 427.07), which is influenced by the number of studies included, is significant at a 1% level, indicating heterogeneity. The more accurate measure, I2, states that 87% of the variance results from heterogeneity across studies. A percentage above 75 percent represents considerable heterogeneity (Higgins & Green, 2011), and it is, therefore, likely that the relation between MO and export performance is influenced by other factors, such as moderators. As #TF does not show any imputed studies, the reliability adjusted average correlation is not corrected for publication bias (ρ = ρTF). To continue, the analysis of the relationship between EO and export

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29 samples (n = 7,117). Again, a moderation effect is very likely as the Q-statistic (Q = 430.75) is statistically significant and the I2 shows a percentage of considerable heterogeneity (92% > 75%). The amount of imputed studies (#TF) is 3, which means that the reliability adjusted average correlation is adjusted for publication bias with the difference between the observed and adjusted ‘correlation (z)’ (= 0.07) (ρTF = 0.35). Finally, in line with H3, the relationship

between LO and export performance show a statistically significant positive effect of moderate strength (r = 0.3; ρ = 0.33; 95% CI = .17/.47, does not include zero). Only 6 studies (k = 6) were selected, with a total amount of 1,323 independent samples (n = 1.323). The low number of studies could endanger external validity, therefore, the results should be interpreted with caution. The analysis indicates considerable heterogeneity, as the Q-statistic (Q = 26.66) is statistical significance and I2 (I2 = 81) indicates that 81% of the variance results from heterogeneity across studies. The amount of imputed studies is 1, consequently, the reliability adjusted average correlation is adjusted for publication bias with the difference between the observed and adjusted ‘correlation (z)’ (= 0.05) (ρTF = 0.28). Overall, it can be said that the

strategic orientations (MO, EO, LO) and export performance have a positive statistically significant relationship with a small (MO) to moderate (EO, LO) strength (see figure 3).

Figure 3: Bivariate meta-analysis results; relationship between strategic orientations and export performance

Notes: the numerical values represent ρ (reliability adjusted average correlation coefficient) or ρTF (reliability adjusted average correlation coefficient adjusted for publication bias). The ** represent the 95% CI (95% confidence interval), which states that the values of ρ are statistically significant at a 5% level.

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30 4.2 Moderator analysis results

Based on the outcomes of the Q-statistic and especially I2_{, considerable heterogeneity is} detected. The results of all relationships (MO-EP; EO-EP; LO-EP) indicate statistically significant Q-statistics and I2_{’s above the considerable heterogeneity threshold of 75% (Higgins} & Green, 2011). To determine potential moderators three factors were tested; industry type (single or multiple), firm size (SME or SML), and region. The factor region is divided based on continents excluding Antarctica, as no studies selected in this meta-analysis were conducted with a sample form this continent. In total there are 7 groups within the region factor; Europe, Asia, Africa, North America, South America, Oceania, and others. ‘Others’ includes samples that have more than one country included.

The results of the first moderator analysis are presented in table 6. The factor industry type is tested. There is only one moderation which is statistically significant for both industry types (95% CI does not include zero). This relation is between EO and export performance, whereby industry type moderates the relation between EO and export performance in such a way that the relation is stronger for samples from multiple industries (ρ = 0.43), compared to those of single industries (ρ = 0.38). The opposite is true for the relation between MO and export performance. Industry type moderates this relation in such a way that the relationship is weaker for samples with multiple industries (ρ = 0.28), compared to those samples which focus on a single industry (ρ = 0.64). This result is not significant (-.96/1), as the 95% CI does include zero for the single industry type, therefore more primary research is needed to get more robust results and smaller

95% CI. Finally, the relation between LO and export performance could not be tested on the

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Table 6: Moderating effect of industry type

Notes: k = number of studies, n = total sample size, ρ = reliability adjusted average correlation, SD(ρ) = standard deviation, 95% CI = 95% confidence interval, Q = Q-statistic, I2 _{= proportion of the total variation in the observed effect sizes, ΔQ =}

difference between the observed and adjusted ‘correlation (z)’.

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Table 7: Moderating effect of firm size

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Table 8: Moderating effect of region

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Table 9: Countries included for every strategic orientation and region Market

Orientation

Europe Asia Africa North

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Table 9: Countries included for every strategic orientation and region Entrepreneurial

Orientation

America South America Oceania Others Finland (1) Germany (1) Greece (1) Italy, Spain (1) Portugal (2) Romenia (1) Slovenia (1) Spain (1) United Kingdom (4) Iran (1) Israel (1) Korea (2) Pakistan (2) Phillippines (1) Turkey (1) Egypt (1) Ghana (1) Nigeria (1) South- Africa (1) Uganda (1) Mexico (1) United States (4)

Chile (1) United States and

United Kingdom (1)

Learning Orientation

America South America Oceania Others Portugal (1) Ireal (1) Malaysia(1) Vietnam (1) Nigeria (1) South- Africa (1)

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Table 10: Correlation matrix

Notes: The diagonal indicates the mean reliability coefficients (Cronbach’s alphas). The off-diagonal numbers in the lower

left-hand corners present the reliability adjusted average correlation. The off-diagonal numbers in the upper right-hand corner show the total sample sizes (n). Harmonic mean = 4467.94.

Table 11: Regression results and relative importance

Notes: b/Beta = beta weights, r = reliability adjusted average correlation, rs = structure coefficient, rs2_{= squared structure}

coefficient, Unique = unique effect, Common = absolute common effect, GenDom = general dominance, Pratt = Pratt’s measure, RLW = relative weights.

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39 accompanied by other methods (e.g. commonality analysis), to prevent misinterpretation of the results (Nimon & Reio, 2011). This is relevant for the results reported in table 11 too. Based on the regression analysis, MO (.049) does not seem very relevant for EP, but the commonality analysis says something different. MO combined with EO (5.8%) and LO (4.8%) does contribute to the explained variance in export performance, even as it does through the common effect of all strategic orientations (34.5%). The results show that EO is the most import unique effect, but the unique effect separately (cumulative 43.8%) does not beat the strength of the simultaneously use of the three strategic orientations (cumulative 56.2%) in explaining the variance in export performance.

Table 12: Commonality analysis for export performance

Notes: MO = market orientation, EO = entrepreneurial orientation, LO = learning orientation, Commonality = absolute common

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5. Discussion

In this research, the author (1) systematically review existing literature on the relationship between strategic orientations and export performance, (2) meta-analytically cumulated the corresponding correlations, (3) tested factors moderating the relationship between strategic orientations and firm performance, and (4) found proof for the common effects of strategic orientations. The analysis of 78 independent samples (n = 17,956) revealed a weak to moderate positive overall relationship between strategic orientations and export performance (MO-EP ρ = 0.3; EO-EP ρ = 0.42; LO-EP ρ = 0.33) and the existent of moderators. These findings and more implies implications for theory and practice, as elaborated in the next sections. At the end of this chapter, the limitations and future research directions are discussed.

5.1 Research implications

This study advances research on the resource-based view (RBV), strategic orientations, and export performance. First, with the bivariate meta-analysis, a systematic review of available studies of strategic orientations and export performance is conducted to combine individual effect sizes. This is done to face fragmented and inconsistent findings in the empirical literature, to create more robust results, increase reliability and generalizability, and to reduce limitations that come along with single research studies and single data(bases) (e.g. no comparability). As strategic orientations are part of the firm’s organizational capabilities, the results of this study drawn upon the RBV, and have influences on the firm’s export performance. The study reveals that the three strategic orientations investigated have a positive overall effect on export performance. The relation between EO and export performance appears to have the highest correlation (ρ = 0.42). Followed by the relation between LO and export performance (ρ = 0.33) and MO and export performance (ρ = 0.27). The analysis indicated that publication bias is present for the strategic orientations EO and LO. After the publication bias adjustment, EO still appears to have the highest correlation with export performance (ρTF = 0.35), but is now

followed by the relation between MO and export performance, as the correlation between LO and export performance is adjusted to ρTF = 0.28. During the conduction of the meta-analysis,

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42 Africa (ρ = 0.34) and North America (ρ = 0.28), compared to Europe (ρ = 0.45). This implies that not only region influence the effect between MO/EO and export performance, but that this effect difference between the two strategic orientations as well. This finding may be of great interest for future research. For example, the investments in a particular strategic orientation (e.g. MO) can be more valuable in some regions (e.g. North Amerika and Afrika) compared to other combinations of strategic orientations and regions.

Third, this study went beyond the total effects of strategic orientations and investigated the decomposed unique and common effects of the independent variables (MO, EO, LO) on export performance using a commonality analysis. As commonality analysis is not often conducted in the research field of the influence of strategic orientations on export performance, this study presents novel results. The study studied the importance of the common effects of strategic orientations. As it turns out, using strategic orientations simultaneously leads to higher export performance. For example, the commonality analysis indicates that LO’s unique effect (1.0%) explains 4.9% of the total variance in export performance, but in combination with EO, it explains 11.1% of the total variance in export performance. All three strategic orientations explain an even higher total variance in export performance (34.5%). Furthermore, the analysis indicates that MO has barely any unique effect, and is only able to influence export performance when EO and LO are present. These outcomes show that researchers should pay special attention to the combination of strategic orientations and export performance, as it can benefit firms. Firm’s adaptation of several strategic orientations at the same time may increase firm’s export performance and competitive position.

5.2 Managerial implications

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43 firm size, and region of conducted business. For example, the relationship between EO and export performance higher for firms that conduct business in multiple industries, implying that investments in EO are more interesting and beneficial for firms conducting business in multiple industries compared to firms in a single industry. Furthermore, firm size moderates the MO-export performance and EO-MO-export performance relationships in such a way that these relationships are weaker for SMLs compared to SMEs. Therefore, the structure of MO and EO becomes increasingly important for managers of SMEs as their performance of MO and EO increase their export performance more. This does not mean that managers of SMLs should not invest in MO and EO, as it will provide them with higher export performance, but the investment would not be as fruitful as the investments done by SMEs. Finally, the third tested moderator region, discovered that region does influence the relationships between MO-export performance and EO-export performance. The outcomes for the relationship between MO-export performance is the opposite of the outcomes for the relationship between EO-MO-export performance. So does region moderates the relationship between MO and export performance (EO and export performance) in such a way that the relationship is stronger for North America and Afrika (Asia and South America), and weaker for Asia and South America (North America and Afrika) compared to Europe. The finding could be of great managerial relevance as region influences the effect between MO/EO and export performance, but this effect also differs between the two strategic orientations as well. Managers should keep in mind that investments in particular strategic orientations could be more fruitful than others in specific regions.

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44 guaranteed with solid export performance, as they are more innovative, proactive, and take more risks (Miller, 1983) to increase competitive advantages (Rauch et al., 2009). Combining EO with MO, the firm has the opportunity to learn about market demand, market needs, and competitors, to react in an innovative, proactive and, somethings risky way to serve (future) market needs and to secure firm’s competitive position, profitability and future survival. By also adding LO firms can improve underlying processes, use newly generated knowledge, and create new ideas, to anticipate on market requirements, and future market needs. This, in turn, creates more profitability and long-run competitive advantage, as in line with EO, and helps MO to improve performance. Overall, managers should keep in mind the simultaneously use of strategic orientation will lead to higher export performance.

5.3 Limitations

This study must be viewed in light of certain limitations. First, the number of identified studies for the relationship between LO and export performance is limited. Limiting the advantages of the meta-analysis, and the moderator analysis. Second, the meta-analysis of this study is not coded by two authors, which can endanger coding reliability and internal validity. Third, a lot of studies with a sample from the United Kingdom are conducted before the Brexit, meaning that the results and effect of those studies can differ nowadays and in the future, influencing the results of the meta-analysis. As most of them are published before the Brexit, they are considered to be part of the continent Europe within the moderator analysis, but this result should be taken with caution as new literature from after the Brexit can come up with different results. Fourth, there were two countries lying on two continents within the moderator analysis. The author chooses to place the country within the region that was most represented, for example, Turkey lies for 97% on Asia and is therefore assigned to the continent Asia. Another limitation within the moderator analysis is that most of the samples included in the region North America are represented by the United States, which makes the outcomes less generalizable for this continent. Finally, the study may also include potential language bias (a.k.a. Tower of Babel bias) as only English literature is used.

5.4 Avenues for future research

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6. Conclusion

The importance of strategic orientations on export performance is firmly established in business literature. However, the literature in this field is fragmented and general statements were hard to make. Therefore, this study was interested in the association between strategic orientations and export performance, the moderating effects, and the common effects of strategic orientations. Based on the bivariate meta-analysis with 78 independent samples, this study finds a positive relationship between strategic orientations and export performance. Moderators as industry type, firm size, and region (divided by continent) influence specific relationships. The commonality analysis indicated that the simultaneous use of strategic orientations results in higher export performance, compared to their unique effect. Strategic orientations can be a powerful tool for the creation of export performance, especially in simultaneously use, and is worthy of future research.

Acknowledgements

I would like to thank my advisor Dr. Christopher Schlägel for the guidance in the past few months. I highly appreciate your dedication to my success and your enthusiastic posture. You always took your time to explain and answer my questions, and to reassure me when needed.

I would like to thank the University of Groningen for the excellent education they offered. The university did not only improve my education-related knowledge but also created a save environment for my career-related and personal development.

I would like to thank two special friends, Mireille Smit and Dave van Dam, for listening to me, for encouraging me, and for sparring with me at moments I needed it the most.

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The Relation Between Strategic Orientations And Export Performance: A Meta-Analytic Review