Market orientation and export performance: the moderation of channel and institutional distance

Xinming He (Ushaw College, Durham University Business School, Durham University, Durham, UK)
Keith D. Brouthers (King’s College London, University of London, London, UK)
Igor Filatotchev (Cass Business School, City University London, London, UK) (Vienna University of Economics and Business, Wien, Austria)

International Marketing Review

ISSN: 0265-1335

Publication date: 9 April 2018

Abstract

Purpose

Market orientation (MO) has been shown to provide a valuable resource-based advantage in domestic markets. How internationalizing firms from emerging markets can benefit from this capability is more complex while facing institutional distance. The purpose of this paper is to develop and test a theory to suggest that although MO capabilities can enhance export performance, the structure where they are deployed, namely the export channel a firm uses and the market in terms of institutional distance from home, can affect the benefits derived from MO.

Design/methodology/approach

With a sample of Chinese exporters and data collected via questionnaire survey, this research uses a multiple regression model to test the hypotheses.

Findings

It finds that firms with stronger MO capabilities can improve export performance by using hierarchical channels and by exporting to more institutionally distant markets where MO provides greater value.

Originality/value

This research claims to make several important contributions to the literature by providing a better understanding of how firms can successfully deploy MO capabilities when exporting.

Keywords

Citation

He, X., Brouthers, K. and Filatotchev, I. (2018), "Market orientation and export performance: the moderation of channel and institutional distance", International Marketing Review, Vol. 35 No. 2, pp. 258-279. https://doi.org/10.1108/IMR-09-2015-0194

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

There has been consensus that market orientation (MO) capabilities can lead to a firm’s superior business performance (Ellis, 2006; Kirca et al., 2005). Companies from emerging markets like China have used MO for competitive advantage and better performance (Murray et al., 2011; Song et al., 2015). MO focuses on learning from customers, competitors and the external environment, processing that information internally and utilizing it to achieve success (Day, 1994; Kohli and Jaworski, 1990; Sinkula, 1994). Recent scholarship also reveals that MO capabilities are particularly important within the context of exporting as MO helps firms learn about the foreign market and adjust strategies and products to better conform to market demand (Boso, Cadogan and Story, 2013; Brettel et al., 2008; Cadogan et al., 2002; Chung, 2012; Dong et al., 2013; Ellis, 2007; Julian et al., 2014; Madsen et al., 2015; Murray et al., 2011). How to maintain competitive advantage when rivals also turn market oriented becomes a challenge to businesses (Kumar et al., 2011), especially international firms that face complicated domestic and foreign environments. This research seeks to address this challenge by proposing and testing such a mechanism that deploying MO capabilities in suitable structures including export channel and market in terms of institutional distance from home can further boost export performance.

Exporting is an important strategy because it allows firms to expand market base, gain new customers and improve firm performance (Cavusgil and Zou, 1994; Sousa, 2004). It enables firms to leverage their existing capabilities across countries and create scale economies otherwise unavailable domestically (Leonidou et al., 2007). It provides new market opportunities in which the firm can sell product innovations, as well as facilitate the development of connections with important constituencies in diverse markets, allowing firms to obtain key resources economically (Cavusgil and Zou, 1994; Leonidou et al., 2007). Exporting is especially important for emerging market firms to engage in international markets as it requires fewer resources and involves fewer risks, and offers more flexibility compared with other foreign entry modes such as equity investment (Liu et al., 2011). Knowing how to use MO capabilities for firms’ export success is critically important (Cadogan et al., 2009).

Past research examining the relation between MO and export performance shows at least two significant shortcomings. First, these studies do not consider the export channel, an organizational structure that firms use to deploy MO capabilities when they enter foreign markets. The resource-based theory (RBT)[1] maintains that the way a firm structures its operations impacts its ability to fully benefit from firm-specific capabilities and create a competitive advantage; possession of capabilities does not lead to superior performance unless the firm is structured in a way that allows it to take advantage of these capabilities (Barney, 2014; Barney et al., 2011; Kozlenkova et al., 2014). Hence a firm may achieve better performance in a specific export market if it considers not only the strength of its MO capabilities but also the structures through which these capabilities are used. Export channel, in the form of an organizational structure, is one of the most important exporting strategies as it is the platform for export firms to deliver and realize the value in the target market, without which there will not be any export (He et al., 2013; Klein et al., 1990; Leonidou et al., 2002). Past research has largely ignored how MO and export channel work together to increase performance.

The second shortcoming is that these studies do not consider the institutional heterogeneity of foreign markets (Eden and Miller, 2004; Salomon and Wu, 2012; Schwens et al., 2011; Shenkar et al., 2008; Xu and Shenkar, 2002), assuming that MO capabilities are equally effective in different countries. MO may be related to performance subject to contexts (Dobni and Luffman, 2003). Every country, however, has a unique institutional environment that impacts how a firm does business, manages people, connects with customers and interacts with the government (Kostova and Zaheer, 1999). A country’s institutional environment includes the regulative, normative and cognitive factors that shape firm, consumer and employee behaviors (Scott, 1995). Like most RBT studies, past MO-exporting studies tend to assume that resource-performance inter-relationships are free of the institutional context (Boso, Story and Cadogan, 2013; Priem and Butler, 2001; Sirmon et al., 2007). However, when a firm internationalizes, it needs to be aware that differences in institutional setting may make the exploitation of MO capabilities more or less effective (Brouthers et al., 2008; Kostova and Zaheer, 1999).

This research seeks to contribute to the literature by addressing both these issues. Building on the resource-structure-performance perspective of RBT and institutional theory it theorizes that a firm’s ability to garner value and achieve superior export performance from its MO capabilities in foreign markets will depend not only on the strength of these capabilities, as suggested by previous research (e.g. Cadogan et al., 2002; Madsen et al., 2015; Murray et al., 2011), but also upon the export channel the firm uses in a specific market as well as the difference in institutional environments between the home and export market. Specifically, this research aims to make two important contributions. Drawing on the RBT, it adds to the literature by developing and testing theory that suggests the export channel a firm utilizes to deploy its MO capabilities in foreign markets will moderate the association between MO and market-specific export performance. The RBT suggests that the way a firm deploys its resources or capabilities has a significant impact on performance; firms that align their organizational structure with the capabilities they possess will achieve superior performance (Barney et al., 2001; Brouthers et al., 2008; Kozlenkova et al., 2014). Expanding this theoretical perspective, this research theorizes that firms with strong MO capabilities will achieve higher export performance when they use hierarchical export channels rather than if they use market-based or hybrid channels.

In addition, it enriches the literature by theorizing and testing the notion that institutional differences between the export market and a firm’s home country also moderate the MO-export performance relation by impacting the effectiveness of deploying MO capabilities in foreign markets. Institutional theory suggests that differences in regulative, normative and cognitive environments can impact the value of capabilities like MO (Priem and Butler, 2001; Sirmon et al., 2007). Building on this perspective this research develops theory to explain how institutional differences can impact the value of MO capabilities when firms export.

This research provides empirical analysis of our theoretical inferences by using a hand-collected sample of Chinese exporting firms. Examining the export performance of each firm in one specific market, our analysis indicates that MO leads to better export performance when firms use hierarchical export channels and when firms expand into institutionally distant markets, compared with institutional close markets. Thus building on the RBT and institutional theory, we develop a unique perspective to explain how a firm can harvest greater value from its MO capabilities when exporting to foreign markets. As theorized, our study indicates that the choice of export channel can significantly influence the firm’s ability to garner value from its MO capabilities. In addition, our study provides evidence that MO appears to provide greater value in institutionally distant markets, helping firms with strong MO capabilities out compete rivals in those markets.

2. Theory and hypotheses

MO becomes an important source for competitive advantage for organizations in emerging markets (Li and Zhou, 2010; Song et al., 2015). Research on MO has focused largely on domestic activities (Kirca et al., 2005) examining the antecedents and measurement of MO (Kohli and Jaworski, 1990; Narver and Slater, 1990; Slater and Narver, 1994), the links between MO and performance (Dong et al., 2013; Ellis, 2006; Kirca et al., 2005; Narver and Slater, 1990), as well as exploring how MO can be implanted and defused within an organization (Day, 1994; Gebhardt et al., 2006; Kumar et al., 2011; Lam et al., 2010). These studies indicate that the MO provides firms with a specific advantage that can lead to superior firm performance.

Studies looking at MO in the international context make a similar assumption, suggesting that MO will lead to superior performance in foreign market, as it does domestically. This international research primarily investigates the association between MO and export performance (Boso, Cadogan and Story, 2013; Cadogan et al., 2002; Morgan et al., 2009; Murray et al., 2007, 2011; Zhou et al., 2007) and the measurement and antecedents of international or export MO (Brettel et al., 2008; Cadogan et al., 2002; Ellis, 2007). These studies indicate that firms possessing stronger MO capabilities have significantly higher export performance (Boso, Cadogan and Story, 2013; Cadogan et al., 2002; Morgan et al., 2009; Murray et al., 2007, 2011; Zhou et al., 2007).

Insightful as these studies are, the deployment of MO capabilities for performance improvement and the institutional impact in an international setting remain missing in the literature. First, the recent RBT development maintains that to generate greater value, organizational resources/capabilities need to be structured properly (Brouthers et al., 2008; Kozlenkova et al., 2014). The organization of these organizational resources/capabilities provides a platform for them to be effectively employed for advantage and performance. The strategic fit paradigm supports this notion that the congruence of the different factors serves as the performance driver (Venkatraman and Camillus, 1984; Vorhies and Morgan, 2003). Export channel is a critical strategic arrangement in exporting operations (He et al., 2013; Klein et al., 1990; Leonidou and Katsikeas, 1996; Leonidou et al., 2002). Following this logic, we suggest that MO may exert greater influence on performance when organized in a particular export channel structure, i.e. hierarchical mode (Klein et al., 1990).

Second, international firms confront different institutions across borders (Eden and Miller, 2004; Salomon and Wu, 2012; Schwens et al., 2011; Shenkar et al., 2008; Xu and Shenkar, 2002), which more or less influence the effectiveness of the resources/capabilities (Peng et al., 2008). The institutions constrain the way how businesses engage in competition, and interact with the customers and governments (North, 1990). Exporting firms see differences in institutions when operating internationally, e.g., between the home country and export markets. Their MO capabilities are also subject to the influence of these differences underpinning the context of international competition and operations (Peng et al., 2008). Therefore, this translates into another mechanism for boosting the association between MO capabilities and export performance, which is arranging MO capabilities in compatible markets in terms of institutional distance from the home country.

2.1 Moderating effects on MO-performance link

This research means to examine such a model; the relationship between MO capabilities (predictor variables) and firm performance (criterion variable) is moderated by the organizational structure where MO is deployed and the institutional distance between home and export markets (moderator variables). This is the “fit as moderation” perspective by Venkatraman (1989, p. 424). Strategic fit among many activities is fundamental to both competitive advantage and its sustainability, because it is harder for a competitor to match an array of interlocked activities (Porter, 1996). So based on the logic of the resource-structure-performance perspective of RBT (Barney et al., 2001; Brouthers et al., 2008; Kozlenkova et al., 2014), this research looks at how two strategic fits between a firm’s MO capabilities and where they are positioned and the institutional contexts may help to improve business performance.

Prior MO study also investigates the conditions where effects of MO on performance will be influenced (Jaworski and Kohli, 1993; Kirca et al., 2005; Kumar et al., 2011; Slater and Narver, 1994). In domestic/general settings, researchers have investigated moderators including business strategy (Matsuno and Mentzer, 2000), environment uncertainty and competition intensity (Jaworski and Kohli, 1993; Slater and Narver, 1994). In export MO research, scholars have explored the moderating role of the complexity of external environment in the international setting, e.g., competitive intensity (Boso et al., 2012) and market dynamics (Cadogan et al., 2009).

In general, past MO research largely emphasizes the moderating effects of external environment (Kirca et al., 2005), particularly the classic factors (namely market turbulence, technological turbulence and competitive intensity) identified by Jaworski and Kohli (1993). These environmental factors certainly have importance, but what have not been studied are the moderating effects of the organizational structure (i.e. export channel) and the institutional differences which are of paramount influence in international markets (Eden and Miller, 2004; Salomon and Wu, 2012; Schwens et al., 2011; Shenkar et al., 2008; Xu and Shenkar, 2002). The coalignment profiles between the environment, culture and strategy exerts critical implications for performance, while the conceptualization and empirical tests of such an alignment remain a significant weakness in both marketing and strategy research (Dobni and Luffman, 2003). The interaction between MO and strategy/structure is far less than well understood (Jaworski and Kohli, 1993). In addition, MO may be related to performance subject to context (Dobni and Luffman, 2003). In an exporting setting, institutional difference between the export market and the firm’s home may substantially constrain a firm’s capabilities to create value in a market that is institutionally different from where it grew.

Therefore, despite the valuable research, past MO-exporting studies ignore both the export structures through which MO capabilities are deployed and the impact of institutional differences firms encounter when expanding abroad.

2.2 Export channels

Although exporters manufacture their products at home, they need to understand the foreign market to know how to position these products in that market. Knowledge of the export market also helps the firm identify changes in products that will lead to greater acceptance and sales. The export channel a firm uses can influence its ability to access foreign market information (Wu et al., 2007). Firms with strong MO capabilities have the ability to obtain information in the market (customer, competitor and external environmental information), process that information internally and use that information to respond effectively (Boso, Story and Cadogan, 2013; Kirca et al., 2011; Kohli and Jaworski, 1990; Morgan et al., 2009; Murray et al., 2011). We theorize that hierarchal export channels provide such firms with greater benefits resulting in improved export performance.

Using a hierarchical export channel (e.g. e representative office or a trading subsidiary) instead of teaming up with a partner or relying on a foreign agent allows firms with strong MO capabilities to improve export performance for several reasons. First, because firms with strong MO capabilities are proficient at information generation, partner firms may simply offer information that replicates the information already captured by the focal firm providing little if any additional benefit. In this case, information proffered by the partner organization provides little new knowledge for the focal firm because the firm has the ability to obtain this information on its own. Thus, overall export sales may be the same whether a firm uses hierarchical or hybrid channels, but because hybrid channels require firms to share rents (Anderson and Gatignon, 1986) under conditions of information redundancy the focal firm will exhibit lower export performance. The outcome may be worse if the firm uses a market-based export channel, because in these channels firms have no control over the export operation and sell products at a discount to only one or two foreign market-based agents, significantly reducing potential sales (He et al., 2013; Klein et al., 1990).

Second, once information is generated, it needs to be disseminated through the organization to be useful (Boso, Story and Cadogan, 2013; Kirca et al., 2011; Maltz and Kohli, 1996; Morgan et al., 2009; Murray et al., 2011). Firms with strong MO capabilities have the ability to disseminate information internally (Kohli and Jaworski, 1990). Information generated by a partner or agent needs to be transferred to the focal firm and transfer of knowledge across firm boundaries is not as efficient as internal transfers (Barkema and Vermeulen, 1998). Thus, a firm with strong MO capabilities using an internalized hierarchical export channel will have better export market performance because this structure provides a more effective method to deploy MO capabilities allowing a firm to gain greater value from the information generated because it possesses more efficient information dissemination capabilities.

Finally, a firm with strong MO capabilities wants to maintain control of its MO capabilities to be sure it is obtaining reliable information about potential customers, competitors and other external parties and that the response to this information is properly implemented so it can gain the greatest value from these capabilities. The RBT suggests that control of capabilities, like MO, is important since losing control may lead to poor value creation and/or rapid imitation by competitors (Knott, 2003). Sharing control of the venture or giving control to an agent may result in miscommunications or weak strategy implementation by a partner firm (Wu et al., 2007). In addition, if the focal firm sets up a joint operation with another firm, it has to share MO capabilities and facilitate training of local staff in how to use them. Ultimately, training partner organizations to take advantage of the firm’s MO may result in the creation of a competitor and reduce returns to the focal firm.

In addition to the information-related benefits that the match of MO capabilities and a hierarchical structure can generate, we also need to consider the legitimacy challenge a foreign firm faces in international markets and the benefits a local partner can contribute. Foreign firms face pressure to gain local legitimacy in a host country. Having a local partner may be helpful to lift this pressure. However, as we argue above, partnership can also significantly increase transaction costs, especially in handling valuable market intelligence and potential friction (Shenkar et al., 2008), for a market-oriented firm. Researchers introduce measures such as customized contract and relational governance to tackle the difficulty in obtaining local legitimacy (Yang et al., 2012); however, devising, enforcing and monitoring well-specified and articulated contractual clauses (Brouthers and Hennart, 2007) and developing and maintaining relationships with external parties (Parkhe et al., 2006) can significantly increase transaction costs (Williamson and Ghani, 2012).

In sum, this research theorizes that the export channel a firm uses in a particular country will moderate the association between MO and market-specific export performance. For firms with strong MO capabilities, the use of hierarchical channels leads to improved export performance because this structure allows the firm to retain all the rents generated from these capabilities, maintain control of these capabilities and provide a more efficient mechanism for transferring and using tacit export market knowledge within the organization; use of hybrid or market-based export channels does not provide strong MO firms with these same benefits. Although running hierarchical channel services creates costs, it helps to strengthen the positive effect of MO on export performance, especially by means of facilitating market intelligence generation, dissemination and responsiveness, and to outweigh the drawbacks (He et al., 2013). Therefore our first hypothesis states:

H1.

The use of hierarchical export channels positively moderates the association between MO and market-specific export performance.

2.3 Institutional differences

When expanding to international markets, MO capabilities can be of value helping firms learn about the export market (Cadogan et al., 2002). Because every country has a unique institutional environment, when a firm expands abroad, it may enter countries with similar or very different institutional arrangements (Eden and Miller, 2004; Salomon and Wu, 2012; Schwens et al., 2011; Shenkar et al., 2008; Xu and Shenkar, 2002).

According to Scott (1995), institutions include three pillars: regulatory, normative and cultural-cognitive. The regulative pillar rests on a nation’s legal system and regulations, and specifies what can or cannot be done enforced by legal sanctions. The normative pillar pertains to beliefs, values and norms which prescribe desirable goals and expected ways to achieve them. The legitimacy of normative institutions is rooted in societal beliefs and norms that define what should or should not be done. The cognitive pillar emphasizes internal representation of the environment by social players; the legitimacy of this pillar is anchored in cultural orthodoxy which defines what will be typically done. The difference between the formal (regulative) and informal (normative and cognitive) institutional environments of home and foreign countries is called institutional distance (Eden and Miller, 2004; Kostova and Zaheer, 1999; Madsen, 2009; Xu and Shenkar, 2002; Yang et al., 2012).

Previous research has noted that institutional distance has a negative impact on export performance (e.g. Sousa and Bradley, 2008). These studies suggest that institutional differences make it more difficult to understand and correctly interpret local customers, competitors and government agencies, consequently influencing the degree of adjustment needed (Kostova and Zaheer, 1999). Below we theorize that the institutional distance between a firm’s home and export market will moderate the relation between MO and export performance, making MO capabilities more valuable as institutional distance increases because these capabilities allow the firm to learn about institutional differences, adjust products and strategies to fit institutional demands, resulting in improved export performance.

One reason MO leads to superior performance is because firms with strong MO capabilities can generate market-specific information more efficiently (Kohli and Jaworski, 1990). Yet previous studies like Kohli and Jaworski (1990) and Kumar et al. (2011) suggest that environmental factors can make MO capabilities more or less valuable to firms (Cadogan et al., 2002; Slater and Narver, 1994). Building on this research, it is suggested that when firms expand to export markets, the difference in regulatory, normative and cognitive institutional environments between home and export market will moderate the MO-export performance relation making MO capabilities more valuable because there is a greater need to generate market-specific knowledge as differences in regulatory, normative and cognitive institutional escalate.

A higher level of MO provides exporting firms with an ability to produce important, accurate knowledge and information about their target market, including those regulative, normative and cognitive factors that may influence customers, competitors and other parts of the external environment (Jaworski and Kohli, 1993). Firms with strong MO capabilities gain additional benefits when entering institutionally distant markets, being able to obtain new knowledge that can help them understand how to do business in the foreign market and become more successful in this market.

Another reason that MO capabilities are of higher value when institutional distance increases is that firms with strong MO capabilities provide an ability to internalize and use the information generated to adjust strategies and product offerings (Kohli and Jaworski, 1990). Firms with strong MO capabilities can internalize and share internally generated information about customers, competitors and other external factors such as regulatory, normative and cognitive factors that can exert influence on the effectiveness of marketing strategies. This capability is particularly useful as institutional distance increases because it allows foreign export market knowledge to be shared throughout the firm, increasing the firm’s understanding of export market regulative, normative and cognitive behaviors. Because of this, firms with strong MO capabilities will be in a better position to respond to and adjust products for the institutional demands of the export market resulting in improved export performance. For example, Chinese toy manufacturers exporting to the USA need to understand and make adjustments for health and safety standards in the USA; they need to be able to make changes to their products to be locally legitimate or face very high penalties.

In sum, having strong MO capabilities is particularly important for exporters because it provides these firms with the ability to obtain and use export market information allowing the firm to align its strategies better with the foreign market’s institutional environment, particularly when regulative, normative and cognitive distance is great (Madsen, 2009). MO capabilities also help firms develop or position products, to provide an advantage over foreign rivals and to more closely align with export market buyer wants and needs. Thus, we suggest that the institutional distance between home and export market moderates the relation between MO capabilities and export performance increasing the importance of these capabilities as institutional distance increases. Hence our second hypothesis suggests:

H2.

The institutional distance between home and export country will positively moderate the association between MO capabilities and market-specific export performance.

3. Methods

To test our hypotheses we collected data from Chinese firms. Chinese firms provide an appropriate sample for this study for several reasons. First, exporting is the dominant mode of international market participation for Chinese firms (Zhao and Zou, 2002). Second, like other emerging market firms, Chinese firms typically do not have well-recognized global brands or other resource-based advantages and lack the financial prowess, international market experience, resources and managerial expertise needed to customize products for export markets (Steensma et al., 2005). Finally, recent research suggests that Chinese firms tend to rely on MO as a means to compete in foreign markets (Wei and Lau, 2008). Thus, Chinese exporters provide an excellent context in which to test our theory.

Our sample consists of firms from Fujian Province because this province is one of the fastest-growing regions and is one of the main exporting provinces/municipalities in China, which had an export volume of $49.94 billion (NBSC, 2009). Data were collected in 2008 through a postal survey using a sample of manufacturing firms drawn from the Exporting Firms Directory of Fujian Province, provided by the customs authorities. A random sample of 600 firms was selected from a total population of about 7,300 firms listed in the directory. One author contacted each firm by telephone and explained the purpose of the research and asked for management’s cooperation in this study. After numerous calls and e-mails to the 600 sample firms, 501 firms agreed to participate. For the excluded firms, the database contained incorrect contact details on 21 firms, 49 firms were export intermediaries, 22 firms refused to take part and 7 had ceased exporting.

The CEOs/MDs of these 501 firms were sent a three-page questionnaire with cover letter and prepaid postage envelope. The questionnaire was developed originally in English, and the back-translation method was used to guarantee the accuracy of the Chinese version. A bilingual person who was a native speaker of Chinese translated the questionnaire into Chinese. Then it was back-translated into English and checked for consistency with the original. The questionnaire asked respondents to provide information about the firm’s most important (with largest sales) export market. An initial mailing and two following waves of requests resulted in 285 responses. However, 71 of these responses were not usable either because they failed to complete large portions of the questionnaire in particular those related to performance, MO and/or export channel, or because there were no values available for our institutional measures. Thus, our usable sample comprised 214 exporting firms (42.7 percent). On average, respondent firms had about 1,200 employees, over 9 years export experience and exported to over 12 different countries. These firms are from four major sectors, 28 percent in domestic industries, 10 percent in electronics industries, 23 percent in clothing industries and 29 percent in food industries. The identified most important export markets include Australia, Canada, France, Germany, The Netherlands, India, Indonesia, Italia, Japan, Malaysia, Russia, Korea, Singapore, Spain, Sweden, Switzerland, Thailand, Taiwan, the UK and the USA.

3.1 Variables

For all multi-item constructs, we used confirmatory factor analysis (CFA) to explore their reliabilities. Our dependent variable export performance is often measured at the firm level as overall export performance (Sousa, 2004). Yet our theory suggests that the benefits of MO and the moderating influence of export channel and institutional distance are market specific. Therefore, we measure export performance at the country level, for the most important export market for each firm. While the use of objective measures may be preferred, it is not normally possible in export studies because firms do not disclose market-specific export performance figures (Brouthers and Xu, 2002). In addition, Chinese managers are extremely concerned about leakage of business secrets and are therefore unwilling to offer objective data (Brouthers and Xu, 2002). Because of this, as in previous export studies (Morgan et al., 2004; Sousa, 2004), we used subjective indicators to assess export performance. Respondents were asked to indicate (on a seven-point Likert-type scale) the level of satisfaction over the past three years in order to balance short-term export performance fluctuations (Katsikeas et al., 2000) with the following items in their most important export market: overall export performance, export sales growth and export profitability. The values of these three items were then summed and averaged to create our export performance construct (Cronbach’s α=0.93).

We considered various constructs for measuring our independent variable market orientation. In this study, we adopt an 11-item (seven-point) scale by Cadogan et al. (2002) rooted in the mainstream MO literature (Kohli et al., 1993; Narver and Slater, 1990) and shown to be reliable and valid in the Chinese context (Murray et al., 2007). This 11-item scale included four measures of information generation, four measures of information dissemination and three measures of information responsiveness; the three component parts of MO (Cadogan et al., 2002; Kohli et al., 1993). As in previous studies, the values for these 11 items were summed and averaged to create our MO construct (Cronbach’s α=0.87).

Our moderating variable export channel was measured using the instrument developed by Klein et al. (1990). Respondents were asked to indicate which statement best represented the export channel they used in their most important export market. As in Klein and Roth (1990), hierarchical channels include two types: “We have a wholly owned sales subsidiary in the foreign market” and “We serve the market directly from China, using company personnel.” As in Klein et al. (1990), we combined two items for hybrid channels: “We are involved in a joint venture with another company to handle sales of this product in this market” and “We use commission agents.” Finally, the use of market-based export channels was captured as: “We sell to a merchant distributor who takes title to our product and contacts buyers himself” taken from Klein et al. (1990). The export channel variable was coded as 3=hierarchical, 2=hybrid and 1=market.

As in previous research, we include three dimensions of the institutional environment: regulative, normative and cognitive (Scott, 1995). We calculated each of the components separately for China and the target market (most important export market) and created three institutional distance measures by subtracting the target market value from the Chinese value.

To measure the regulative institutional environment for exporters, we took 12 items from the World Economic Forum’s (WEF, 2007) global competitiveness report. First as in Luo and Zhao (2013), we included the protection of intellectual property measure. Exports are concerned with protecting intellectual property such as brands and trademarks. Second, we included three items from the government inefficiency measure: burden of regulation, efficiency of legal framework and transparency of policy making. Such governmental policies/actions can make it difficult for exports to understand the regulative environment and as a result increase the risk and costs of doing business in a particular market. Third, we included eight items from the Goods Market Efficiency portion of the database. These items included effectiveness of anti-monopoly policy, extent and effect of taxation, total tax rate, prevalence of trade barriers, tariff rates, prevalence of foreign ownership, burden of customs procedures and imports as a percentage of GDP. These factors highlight governmental attitudes toward exporters and the barriers such firms face in a particular market. All 12 items loaded on one factor that we called regulative distance (Cronbach’s α=0.73).

As in Yiu and Makino (2002), our normative institutional variable examines the ethnocentrism of the market. Exporters will have greater difficulty selling their products in markets marked by higher ethnocentrism since in these markets, buyers tend to prefer domestic goods and services (Shimp and Sharma, 1987). This factor was computed by taking the difference between China and the target market in four items taken from the IMD World Competitiveness Yearbook (IMD, 2007) which looks at the openness of the market. These four items examine protectionism: “protectionism does not impair the conduct of your business,” international transactions: “international transactions can be freely negotiated with foreign partners,” foreign investors: “foreign investors are free to acquire control in domestic companies” and capital markets: “capital markets (foreign and domestic) are easily accessible.” All four items loaded onto one factor that we called normative distance (Cronbach’s α=0.94).

Finally, as in Gaur et al. (2007), cognitive institutional distance was captured by examining the cultural distance between countries, measured using Hofstede’s (1980) four constructs. Data for this variable were obtained from Taras et al. (2012). They provide updated measures of these cultural dimensions and we used those determined in the 2000s.

Measurement of MO and institutions:

  1. MO (Cadogan et al., 2002) (on seven-point Likert scale):

    • In this company, we generate a lot of information concerning trends (e.g. regulations, technological developments, political, economic) in our export markets.

    • We constantly monitor our level of commitment and orientation to serving export customer needs.

    • We periodically review the likely effect of changes in our export environment (e.g. regulation, technology).

    • We generate a lot of information in order to understand the forces which influence our overseas customers’ needs and preferences.

    • Too much information concerning our export competitors is discarded before it reaches decision makers.

    • Information which can influence the way we serve our export customers takes forever to reach export personnel.

    • Information about our export competitors’ activities often reaches relevant personnel too late to be of any use.

    • Important information concerning export market trends (regulation, technology) is often discarded before it reaches decision makers.

    • If a major competitor were to launch an intensive campaign targeted at our foreign customers, we would implement a response immediately.

    • We are quick to respond to significant changes in our competitors’ price structures in foreign markets.

    • We rapidly respond to competitive actions that threaten us in our export markets.

  2. Institutions (secondary data):

    • Regulative institutions (WEF, 2007):

      1. protection of intellectual property;

      2. burden of regulation;

      3. efficiency of legal framework;

      4. transparency of policy making;

      5. effectiveness of anti-monopoly policy;

      6. extent and effect of taxation;

      7. total tax rate;

      8. prevalence of trade barriers;

      9. tariff rates;

      10. prevalence of foreign ownership;

      11. burden of customs procedures; and

      12. imports as a percentage of GDP.

    • Normative institutions (IMD, 2007):

      1. we constantly monitor our level of commitment and orientation to serving export customer needs;

      2. protectionism: “protectionism does not impair the conduct of your business”;

      3. international transactions: “international transactions can be freely negotiated with foreign partners”;

      4. foreign investors: “foreign investors are free to acquire control in domestic companies”; and

      5. capital markets: “capital markets (foreign and domestic) are easily accessible”.

    • Cognitive institutions (Taras et al., 2012):

      1. power distance;

      2. individualism;

      3. masculinity; and

      4. uncertainty avoidance.

We created three moderating variables by first centering the values of the MO, regulative, normative and cognitive distance measures and then multiplying the centered MO value by the centered regulative distance measure, the centered normative distance measure and the centered cognitive distance measure (Aiken and West, 1991). Our fourth moderating variable was developed by multiplying the centered value of MO by the trichotomous export channel measure.

Our analysis included a number of control variables, including firm size, R&D intensity, international experience (i.e. exporting experience, scope of export), external uncertainty, sales growth, ownership and industry that previous studies found to relate to export performance (Brouthers and Xu, 2002; Cadogan et al., 2002; Morgan et al., 2004). We controlled for firm size, operationalized as the number of employees in the firm (Brouthers and Xu, 2002), R&D intensity, calculated as R&D spending divided by total sales (Morgan et al., 2004), exporting experience, measured as the number of years the firm had been exporting (Brouthers and Xu, 2002) and number of export markets (Cadogan et al., 2002). Control variables were also included for external uncertainty, a four-item semantic differential scale (Cronbach’s α=0.78) adapted from Shervani et al. (2007) that focuses on the uncertainty in the export market and sales growth, a measure of firm-level growth (as a percentage) in total export sales over the previous year.

In addition, we controlled for ownership and industry differences that may impact export performance (Brouthers and Xu, 2002). To control for ownership differences, we created three ownership dummy variables for state-owned enterprises (SOEs), foreign firms and private firms. Each dummy variable takes the value of 1 if the firm’s ownership structure matches the variable and takes the value of 0 if they have another ownership structure. We also created four dummy variables for firms representing the primary industries in our sample: domestic articles industry (for products for domestic use like umbrellas, bags, toys, locks and keys, etc.), the electrical and electronic industry, clothing industry and food industry, based on Standard Industrial Classification of Chinese Export Commodities (MOFCOM, 2008). For each of these industry dummy variables, a value of 1 means that the firm is in the industry while a value of 0 indicates that the firm is not in the specific industry.

Common methods variance and response bias

We assessed the CMV in several ways. First, we utilized both methods suggested by Podsakoff et al. (2003) to control for common method biases: through the design of the study’s procedures and through statistical controls. For example, in the questionnaire we used different response formats for the measurement of variables, e.g. Likert scales for MO and performance, semantic differential for behavioral uncertainty, direct selection for variables like channel selection and ownership and open-ended questions for items like export experience and firm age. Second, some independent variable items were reverse-scaled to avoid the occurrence of response patterns affecting the accuracy of data. Third, our institutional distance variables were not obtained through the questionnaire but were collected from secondary sources. Fourth, we used clearly defined multiple scales to capture cognitively independent constructs.

Second, besides these procedural remedies, we also employed two statistical remedies to verify whether we have method biases: Harmon’s one-factor test and CFA (Podsakoff et al., 2003). While there are some questions about the usefulness of Harmon’s one-factor test, at present it is the most widely used statistical method (Podsakoff et al., 2003). The results of Harmon’s one-factor test showed a 14-factor solution in which the largest factor explained only about 15.5 percent of the variance. The fit indexes for CFA (TLI =0.16; CFI =0.26; IFI =0.27; RMSEA =0.16) suggest a poor model fit. Both tests demonstrate that common method bias is not likely to explain any observed relation between model variables in our study.

Third, we used a marker variable (MV) method. We selected an MV to proxy CMV (Lindell and Whitney, 2001). A six-item variable measuring foreign networks (Peng and Luo, 2000) was used as the MV (Cronbach’s α=0.775), as it is theoretically unrelated to at least one of our variables. We selected the lowest positive correlation (r=0.001) between the MV and other variables to adjust the variable correlations and statistical significance. All significant correlations remained significant after adjustment. Thus, the MV analysis suggests that CMV is not a major threat to our tests.

To explore response bias we performed two tests. First we compared characteristics of usable and non-usable respondents. We had 71 non-usable responses and 214 usable responses. The t-tests showed no significant differences between usable and non-usable questionnaires on any of the items tested (MO – t=1.60, p=0.11; export performance – t=−0.13, p=0.90; R&D percentage – t=−1.16, p=0.23; number of export markets – t=0.20, p=0.84; export experience – t=1.46, p=0.11; number of employees – t=1.16, p=0.23). Our second test involved comparing characteristics of our population of exporting firms to the respondent firms. We noted no significant differences (export experience – t=1.46, p=0.15; export sales volume – t=0.61, p=0.56; number of export markets – t=−1.43, p=0.21; number of employees – t=0.31, p=0.76). Thus, our analyses tend to indicate that our respondent firms are representative of firms exporting from China.

Construct validity

Following Anderson and Gerbing (1988), we assessed the construct validity of the latent constructs with a six-factor CFA measurement model that includes all the theoretical measures (Arbuckle, 2006). For our sample, the standardized factor loadings for each individual indicator on its respective constructs are statistically significant (p<0.001) and sufficiently larger than an arbitrary 0.50 (Hair et al., 2010; Zhou et al., 2007). The model fits that data satisfactorily: χ2(650)=457.37, p<0.00; IFI=0.99; TLI=0.94; CFI=0.98; and RMSEA=0.08, in support of the dimensionality of the constructs. Thus, these measures demonstrate adequate convergent validity.

We employed two methods to assess the discriminant validity of the measures, following Anderson and Gerbing (1988). First, we ran 15 pairwise tests for all the scales to examine the χ2 difference. This is to determine whether the freely estimated model (in which the correlation is estimated without restriction) fits the data significantly better than the restricted model in which the correlation is fixed at 1.00 (Anderson and Gerbing, 1988). All χ2 differences were highly significant. Second, we calculated shared variance between all possible pairs of constructs to determine whether they were lower than the average variance extracted (AVE) for the individual constructs. The results indicated that for each construct, the AVE was much higher than its highest shared variance with other constructs, providing additional support of discriminant validity (Fornell and Larcker, 1981). Overall, these results show that measures in the study possess satisfactory reliability and validity.

4. Results

Prior to testing our hypotheses we examined correlations between variables. Table I shows the means, standard deviations and correlations for all our constructs. Although we note high variability, there is no indication of multicollinearity. We examined the variance inflation factors (VIF) scores for the regression models and noted no VIF score larger than 3.49, suggesting that multicollinearity is not an issue with our data.

To tests our two hypotheses we used hierarchical regression analysis. We developed seven models (see Table II). The first contains only the control variables. In Model 2, we add the MO variable. Model 3 contains the direct effects of export channel, regulatory distance, normative distance and cultural distance. In Models 4-7, we test the four interactions, MO export channel, MO regulatory distance, MO normative distance and MO cultural distance, respectively.

Model 1 contains the control variables and was significant (p<0.01). Models 2 and 3 include linear effects of MO, export channel and institutional distance and were also significant (p<0.01). As Models 2 and 3 indicate, stronger MO is associated with better export performance, in line with previous studies (Cadogan et al., 2002; Morgan et al., 2004; Murray et al., 2011). Model 3 also shows that using a hierarchical export channel is associated with higher export performance. However, Model 3 indicates that greater regulatory, normative and cultural distances are associated with reduced export performance (although only the regulative measure is significant). Again, this is in line with studies which suggest that although venturing into institutionally distant countries may provide new market opportunities for exporters, this strategy may be associated with inferior performance outcomes (Sousa and Bradley, 2008).

Model 4 examines the interaction between MO and export channel. This model was significant (p<0.01) and the interaction term MO×export channel was also significant (p<0.05). Our results indicate that firms with stronger MO capabilities improve export performance when using hierarchical export channels. Thus, this model provides support for H1.

Model 5 looks at the interaction between MO and regulatory distance between China and the export markets. This model was significant (p<0.01) and the interaction term MO×regulatory distance was also significant (p<0.05). We found that stronger MO capabilities help firms improve export performance when regulatory distance increases, providing support for H2.

Model 6 explores the interaction between MO and normative distance between China and the export markets. This model was significant (p<0.01) and the interaction term MO×normative distance was also significant (p<0.05). We found that stronger MO capabilities help firms improve export performance when normative distance increases. Thus, Model 6 also provides support for H2.

Model 7 explores the interaction between MO and cultural distance between China and the export markets. This model was significant (p<0.01) but the interaction term MO×cultural distance was not significant (p>0.10). Hence, we find no support for the notion that stronger MO capabilities help firms improve export performance when cultural distance increases. Thus, Model 7 provides no support for H2.

5. Discussion

Previous scholarship has identified a link between MO and performance (Ellis, 2006; Kirca et al., 2005; Narver and Slater, 1990). Yet when firms export to foreign markets, they need to consider the structure through which these MO capabilities are deployed and how differences/similarities in the institutional environment, between home and export market, may impact the value of these capabilities. Building on the RBT’s resource-structure-performance perspective and institutional theory, we develop a unique perspective investigating the moderating impact of export channel and institutional distance on the MO-export performance relation. Our analysis indicates that firms possessing strong MO capabilities will perform better in the export market if they deploy these capabilities through hierarchical export channels. Furthermore, we show that MO provides even greater value in institutionally distant markets, allowing firms to learn and adjust strategies to align better with export market demands. Thus, our study presents further evidence that MO provides a firm with a source of competitive advantage; one that is fungible and can be exploited in foreign export markets, but also contingent on important contextual factors associated with resource deployment and institutional distance to a foreign market.

We make several important contributions to the literature by providing a better understanding of how firms can successfully deploy MO capabilities when exporting. First, we seek to contribute to the RBT by theorizing and testing how two important moderators can impact the value of resource-based advantages when expanding internationally. While scholars like Barney et al. (2001) suggest that deploying resources through the proper structure can influence the value firms derive from these resources, few scholars have actually examined this issue. We extend this thinking to export markets and theorize and test the idea that the export channel a firm uses can significantly influence the value exporters generate from firm-specific resources and, as a consequence, the export performance they achieve.

More specifically, despite the growth in research focusing on MO as a valuable firm resource, these studies ignore the structure through which this potentially valuable resource is deployed (Cadogan et al., 2002; Ellis, 2006; Kirca et al., 2005). The results of our investigation suggest that the organizational structure used in foreign export markets significantly influences a firm’s ability to benefit from the MO capabilities it possesses. We found that firms possessing strong MO capabilities, using hierarchical export channels had higher performance in the export market than did firms using other export channels.

We make a second contribution by examining the heterogeneity of export markets and how these differences in intuitional settings influence the value firms can generate from resource-based advantages. Although Priem and Butler (2001) noted this potential impact, only a few recent studies have started to explore this issue of resource-based advantages having different values in different institutional setting (Brouthers et al., 2008; Meyer et al., 2009). We add to this stream of research by extending these concepts to export markets. We suggest and find that institutional differences can have a significant impact on the value firms derive from the resource-based advantages they possess.

Although previous studies have generally not found the environment to play an important role in the MO-performance relation (Kirca et al., 2005), studies like Kumar et al. (2011) note that the environment does matter. Because not all countries and people are the same, it is important to recognize these differences when considering how MO can create value as firms expand abroad. We found that differences in both the regulative and normative institutional environments significantly moderated the relation between MO and export performance. MO capabilities appear to help firms overcome regulative and normative differences and achieve superior performance in foreign export markets.

Practitioners can also benefit from our study. Managers have control over the export channel and our study indicates that the choice managers make can significantly influence the firm’s ability to garner value from its MO capabilities. Managers also have a choice over which countries to export. Our study provides evidence that MO can provide value in institutionally distant markets, helping firms with strong MO capabilities out compete rivals in that market.

This study is subject to several theoretical and methodological limitations, which may offer additional research opportunities. First, we examined only Chinese exporting firms therefore our findings may not be generalizable to firms from other countries, or to non-manufacturing firms. Future research can extend this work by examining service exporters and firms from other countries. Second, we obtained responses from only one person in each firm. Although we took precautions to avoid common methods bias in the data, the use of multiple informants or collecting data at two different times might improve our ability to detect any biases that do exist. Given the difficulties of collecting data in emerging markets, the single informant method seemed best, but future research can adopt a multi-informant approach to verify the accuracy of our results.

Third, we measure export performance at the country-market level, and export MO at the firm level. Although MO is suggested to be organization-wide culture and behaviors (Narver and Slater, 1990; Kohli and Jaworski, 1990), it would be more appropriate to investigate the relationship between MO capabilities for the most important export marketing and the performance in that market.

Finally, we employed cross-sectional data rather than longitudinal data. Cross-sectional data are appropriate for exploring what is happening at a certain point in time. However, we cannot explain the dynamic processes such as changes in MO capabilities. Kumar et al.’s (2011) study of MO identified important longitudinal trends which have implications for exporting firms. Future research may wish to use longitudinal data and explore how firms can gain value from MO capabilities when exporting to foreign markets.

In conclusion, our study provides important extension to past research that explores the impact of MO on firm performance (Kirca et al., 2005). It appears at least in the international exporting context that institutional environmental differences and export channel are important and significantly moderate the MO-performance relation. Our theory and findings indicate that the institutional environment does moderate the MO-performance relation, in contrast to most past research that provides mixed results (Kirca et al., 2005). We also theorize and find that export channel selection is critically important, a fact that has been ignored in previous MO studies (Cadogan et al., 2002; Morgan et al., 2004; Murray et al., 2011). These results help extend our knowledge of how firms can improve export performance by leveraging firm-specific capabilities in foreign markets. As a result of integrating MO research with insights from international business and institutional frameworks, our research provides a richer theoretical perspective on export performance.

Mean, standard deviations and correlations

Construct M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
 1. SOE 0.03 0.18 1.00
 2. Private firms 0.44 0.50 −0.16* 1.00
 3. Foreign firms 0.42 0.49 −0.16* −0.75** 1.00
 4. Domestic article industry 0.28 0.45 0.00 −0.03 0.07 1.00
 5. Electrics industry 0.10 0.30 0.03 −0.20** 0.14* −0.20** 1.00
 6. Clothing industry 0.23 0.42 −0.10 0.06 0.01 −0.34** −0.18** 1.00
 7. Food industry 0.29 0.46 −0.00 0.21** −0.19** −0.40** −0.21** −0.36** 1.00
 8. Size 1,178.15 2,624.72 0.02 −0.26** 0.10 −0.08 0.31** −0.06 −0.18** 1.00
 9. Experience 9.77 6.71 0.34** −0.22** 0.08 0.01 0.04 −0.02 −0.06 0.27** 1.00
10. No. of export markets 12.08 14.67 0.16* −0.09 −0.13 −0.06 0.32** 0.00 −0.14* 0.31** 0.36** 1.00
11. Sales growth 0.19 .32 −0.09 0.11 −0.12 −0.00 0.02 −0.03 0.00 −0.05 −0.19** 0.03 1.00
12. Cultural distance 2.07 0.71 0.02 0.04 −0.04 0.01 −0.20** −0.01 0.12 −0.21** −0.08 −0.09 −0.04 1.00
13. R&D 0.08 0.08 −0.08 0.17* −0.08 −0.02 0.04 0.07 −0.04 −0.09 −0.06 −0.09 0.04 −0.01 1.00
14. External uncertainty 4.30 1.57 −0.17* 0.16* −0.06 −0.08 −0.09 −0.15* 0.34** −0.14* 0.02 −0.20** −0.07 0.08 −0.01 1.00
15. Regulative institutions distance 1.07 .26 0.16* −0.05 0.01 0.17* −0.05 −0.16* −0.05 −0.06 0.06 0.02 −0.02 0.31** −0.05 −0.29** 1.00
16. Normative institutions distance 1.61 0.57 0.07 −0.04 0.02 0.18** 0.03 −0.03 −0.20** 0.03 0.03 0.06 −0.00 0.23** 0.08 −0.36** 0.70** 1.00
17. MO 4.89 1.15 −0.07 −0.04 0.14* 0.14* 0.03 0.14* −0.37** 0.12 −0.04 0.10 0.05 −0.08 0.00 −0.45** 0.16* 0.26** 1.00
18. Export channel dummy 2.35 0.73 −0.12 −0.09 0.19** 0.06 0.08 −0.01 −0.14* −0.09 0.03 −0.07 0.01 −0.02 0.11 0.09 0.01 0.08 0.37** 1.00
19. Export performance 4.21 1.32 −0.06 −0.01 −0.00 −0.03 0.09 −0.08 −0.10 0.07 −0.08 0.07 0.29** −0.12 0.11 −0.35** −0.34** −0.27** 0.37** 0.15*

Notes: n=214. *p<0.05; **p<0.01

Multiple regression analysis of export performance

Regression models
1 2 3 4 5 6 7
Control variables
Ownership SOEs −0.10 (−1.46) −0.09 (−1.29) −0.09 (−1.21) −0.09 (−1.23) −0.09 (−1.1733) −0.09 (−1.243) −0.09 (−1.21)
Private firms 0.12 (1.13) 0.04 (0.42) −0.01 (−0.11) −0.01 (−0.08) −0.00 (−0.02) −0.02 (−0.15) −0.01 (−0.05)
Foreign firms 0.08 (0.72) −0.01 (−0.05) −0.05 (−0.42) −0.05 (−0.49) −0.04 (−0.35) −0.05 (−0.49) −0.04 (−0.38)
Industry domestic articles −0.33*** (−3.43) −0.32*** (−3.38) −0.29*** (−2.80) −0.29*** (−2.85) −0.29*** (−2.80) −0.30*** (−2.83) −0.28*** (−2.75)
Electrical and electronic −0.14* (−1.81) −0.11 (−1.40) −0.11 (−1.30) −0.12 (−1.44) −0.10 (−1.13) −0.10 (−1.28) −0.10 (−1.15)
Clothing −0.42*** (−4.39) −0.39*** (−4.17) −0.33*** (−3.26) −0.33*** (−3.28) −0.34*** (−3.33) −0.33*** (−3.27) −0.33*** (−3.27)
Food −0.29*** (−2.89) −0.21** (−2.15) −0.16 (−1.46) −0.17 (−1.60) −0.16 (−1.45) −0.17 (−1.52) −0.16 (−1.49)
Firm size −0.02 (−0.33) −0.04 (−0.59) −0.02 (−0.32) −0.01 (−0.10) −0.02 (−0.22) −0.02 (−0.33) −0.01 (−0.20)
Export experience 0.02 (0.30) 0.03 (0.42) 0.01 (0.07) 0.01 (0.07) 0.02 (0.28) 0.01 (0.12) 0.01 (0.19)
No. of export markets 0.03 (0.37) 0.00 (0.03) 0.01 (0.07) −0.03 (−0.34) 0.00 (0.01) 0.00 (0.04) 0.00 (0.01)
Sales growth 0.26*** (4.36) 0.25*** (4.35) 0.23*** (3.79) 0.21*** (3.47) 0.25*** (3.97) 0.24*** (3.82) 0.23*** (3.84)
R&D 0.05 (0.83) 0.06 (0.98) 0.08 (1.36) 0.11** (1.83) 0.08 (1.28) 0.08 (1.33) 0.08 (1.35)
External uncertainty −0.34*** (−5.22) −0.24*** (−3.51) −0.29*** (−3.35) −0.25*** (−2.82) −0.33*** (−3.61) −0.31*** (−3.39) −0.32*** (−3.47)
Independent variables
MO 0.26*** (3.87) 0.22*** (2.79) 0.21** (2.58) 0.22*** (2.69) 0.22*** (2.71) 0.23*** (2.84)
Export channel 0.16** (2.58) 0.18** (2.74) 0.15** (2.48) 0.14** (2.32) 0.16** (2.61)
Regulative distance −0.15** (−2.59) −0.17** (−2.75) −0.16** (−2.65) −0.16** (−2.67) −0.17** (−2.84)
Normative distance −0.18 (−1.92) −0.16 (−1.70) −0.18 (−1.89) −0.19 (−1.98) −0.17 (−1.81)
Cultural distance −0.08 (−1.19) −0.05 (−0.67) −0.07 (−0.97) −0.08 (−1.24) −0.07 (−0.94)
MO×export channel 0.18** (2.18)
MO×regulative distance 0.13** (2.97)
MO×normative distance 0.14** (2.61)
MO×cultural distance 0.03 (0.36)
F-value 6.21*** 7.21*** 5.27*** 5.34*** 5.23*** 4.99*** 5.15***
R2 0.27 0.32 0.35 0.39 0.38 0.38 0.36
R2 change from Model 1 0.05*** 0.08*** 0.12*** 0.11*** 0.11*** 0.09***
R2 change from Model 3 0.04*** 0.03** 0.03** 0.01

Notes: n=214. *p<0.10; **p<0.05; ***p<0.01 (t-values)

Note

1.

We use RBT instead of RBV following Barney et al. (2011, p. 1303) that “there are strong indications that RBT has reached maturity as a theory […] scholars are increasingly using the term resource-based theory instead of resource-based view. This reflects the fact that resource-based research has reached a level of precision and sophistication such that it more closely resembles a theory than a view.”

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Acknowledgements

The authors would like to thank participants in Academy of Management Annual Meeting, and the research seminar at Cass Business School, City University London for helpful suggestions.

Corresponding author

Xinming He can be contacted at: xinming.he@durham.ac.uk