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How Home-Country Political Connections Influence the Internationalization of Service Firms.

1 Introduction

Technological advancements and the liberalization of the service sector have propelled the internationalization of service firms; however, the internationalization of service firms remains under-researched (Goerzen and Makino 2007; Hitt et al. 2006; Kirca et al. 2016; Rammal and Rose 2014). Previous studies on the bases of internationalization of service firms mainly focus on how firm resources such as human capital (Hitt et al. 2006), firm size (Javalgi et al. 2003), technological and marketing assets (Kirca et al. 2016), tangible and intangible resources (Xue et al. 2013), etc. influence firms' internationalization. However, little attention has been paid to how service firms' relationships with their external stakeholders, such as political connections to government, influence their internationalization.

Previous studies have introduced the important role of the government in the home country in supporting and directing the internationalization activities of firms (Buckley et al. 2007; Luo et al. 2010; Yamakawa et al. 2008), and home-country connections have been argued to shape firms' foreign expansion (Guler and Guillen 2010). However, there are inconsistencies in the findings on the effect of political connections in the home country on firms' internationalization. On the one hand, political connections in the home country can facilitate firms' internationalization by allowing firms to gain access to extended resources (financial or non-financial) (Luo and Tung 2007; Luo et al. 2010); on the other hand, home-country political connections may discourage firms from expanding internationally and constrain firms' activities at home, because the advantage of political connections is context-specific and is normally locally contingent, which may increase the resource dependency of politically connected firms on the home country institutions (Cui and Jiang 2012; Du and Luo 2016). Furthermore, politically connected firms might be influenced by political factors instead of commercial interests, which may undermine the development of market-based capabilities needed for internationalization (Lu et al. 2014).

Our literature review indicated that previous empirical studies on political connections are mainly based on companies in all industries (e.g., Du and Luo 2016; Lu et al. 2014; Peng and Luo 2000) and have not examined differences between industries. We argue that it is necessary to contextualize the effects of political connections to resolve the conflicting results found in previous studies. For service firms, the distinct features of services, including intangibility, heterogeneity, inseparability (Boddewyn et al. 1986; Buckley et al. 1992; Song et al. 1999; Zeithaml et al. 1985) amplify the liabilities of foreignness in service firms, which demand greater localization in the host country (Javalgi et al. 2003; Zhang et al. 2015). In this case, even though home-country political connections can help firms to access external resources, the high importance of localization in the host country for service firms results in a constraining effect of political connections as they are home-context specific and normally contingent on local factors in the home country. Therefore, the opportunities to enjoy the benefits of political connections at home combined with the difficulty of transferring and utilizing home-country political connections in the host country make service firms less likely to go abroad. However, we go further and argue that there are differences within the service sector. Every service that is delivered requires specific content and a process, and the process is more easily standardized than the content of service (Kesavan et al. 2014). For content-oriented service sectors, therefore, it is even more important to customize service compared to process-oriented service sectors, which prioritize the local responsiveness for the focal firm. The negative impact of home-country political connections on internationalization would be stronger for content-oriented service sectors compared to the impact for process-oriented service sectors. We further argue that marketing capabilities can help firms transfer and utilize the advantages generated by home-country political connections to the host country, which reduces the negative effect of political connections on firms' internationalization.

We test our arguments on a sample of publicly listed service enterprises in China between the years 2012 and 2016. The significance of internationalization of service sectors and the importance and prevalence of business-political connections in China offers an ideal empirical context for our study. Our study explores the effects of home-country political connections on the internationalization of service firms, and the conditions when political connections at home have different levels of influence. By examining the moderating effect of service industry characteristics, we respond to the call to differentiate internationalization within the service industries (Contractor et al. 2003). By examining the moderating effect of marketing capabilities, we answer the question of how marketing capabilities and political connections interact and influence firms' internationalization, which has been rarely examined, especially in the service sector.

The rest of the paper is structured as follows. Firstly, we review the internationalization of service firms. Then we discuss the effect of home-country political connections on the internationalization of service firms. We present the arguments why this relationship varies among different service sectors and firms with different marketing capabilities. This is followed by a description of the methodology, results and discussion.

2 Theoretical Background and Hypotheses

2.1 Internationalization of Service MNEs

The internationalization of service firms can be related to developments in the general global business environment (Forsgren 2001) in which service firms have constituted the largest share of global foreign direct investment (FDI) in recent decades (Pla-Barber and Ghauri 2012), accounting for 64% of global FDI stock in 2014 (UNCTAD 2016). Service firm internationalization is particularly important in new developing countries (Kundu and Merchant 2008).

However, compared to manufacturing firms, there is a shorter history of research on service firm internationalization (Buckley et al. 1992). Most early internationalization theories, for example, transaction cost theory (Anderson and Gatignon 1986; Williamson 1979), the Uppsala internationalization theory (Johanson and Vahlne 1977, 1990) and the eclectic paradigm (Dunning 1971, 1988) have been developed based on data on manufacturing firms (Laanti et al. 2009). It has been well accepted that service MNEs have different internationalization patterns compared to manufacturers because of the characteristics of services such as intangibility, inseparability and heterogeneity (Erramilli 1990; Javalgi et al. 2003; Knight 1999). These characteristics make the traditional theories based on manufacturing firms less applicable to the internationalization of service firms (Laanti et al. 2009). Previous studies have suggested the inherent differences in manufacturing and service firms necessitate a context-specific approach to understand a service firm's internationalization (Javalgi et al. 2003; Johanson and Vahlne 1990).

Due to the service characteristics, when compared to manufacturing companies, many service companies face additional challenges in their internationalization. Firstly, the intangibility of services makes it very difficult for them to transfer the same service across borders without significant transaction costs (Kirca et al. 2016), especially for some services such as soft-services (Erramilli 1990), location-bound services (Boddewyn et al. 1986), or asset-based services (Clark et al. 1996). Secondly, the heterogeneous nature of services entails independent exchanges, which involves a high degree of customization (Dunning 1989; Kundu and Merchant 2008; Merchant and Gaur 2008). In this case, it requires service firms to communicate and interact more with customers in the host market in order to maintain service quality and their brand reputation (Goerzen and Makino 2007). Thirdly, inseparability, one key attribute of service, causes the production and consumption to occur simultaneously, which requires very close customer contact (Goerzen and Makino 2007). The higher and closer interaction between service firms and local customers requires intensive local knowledge to deal with local customers. It has been argued that firms face the difficulty of getting market-related knowledge in the host market (Martinez and Dacin 1999). Applying professional knowledge in the host market requires learning and adaptation. The performance of service firms relies extraordinarily on developing a relationship between service firms and local customers (Dou et al. 2010). Therefore, there are good reasons to believe that the mechanisms that influence internationalization would be different between service and manufacturing firms. However, despite the above differences, the literature in international business has placed less emphasis on exploring the internationalization of service firms.

Studies on the internationalization of service firms mainly focus on how internal resources influence service firms' internationalization. For example, Kirca et al. (2016) find that service firms are more dependent on marketing assets to succeed in international markets, while manufacturing firms rely more on technological assets; Hitt et al. (2006) explore the resource bases for service firms' internationalization and find that human and relational capital has a positive effect on service firms' internationalization; Javalgi et al. (2003) find that firm size has a positive impact on service firms' internationalization; and Xue et al. (2013) find that tangible assets are less important for service firms' internationalization compared to manufacturing firms. However, these studies are mainly based on an internally focused perspective on resource needs to enter into the international market, and relatively little attention has been paid to firms' relationships with their external network of stakeholders (Capron and Chatain 2008). In order to respond to this research gap, our study explores the role of one important external network--political connections with the home government--on internationalization of service firms.

2.2 Political Connections and Service Firms' Internationalization

Previous studies have identified that capabilities and relationships play a key role in supporting firms' internationalization (Lindsay et al. 2003). Political connections or relationships with the government have also been considered as an important factor that influences firms' internationalization, especially for firms from emerging markets (Buckley et al. 2007; Luo et al. 2010; Yamakawa et al. 2008). A firm's connections to the government in the home country can be considered as a powerful resource (Pan et al. 2014; Shi et al. 2013), because firms can leverage government support (financial or non-financial) to overcome the liabilities of foreignness in the process of global competition (Luo et al. 2010). For example, political connections can provide various benefits, such as favorable financial support, taxation, exchange rates, and other favorable treatment, which support local firms to 'go abroad' (Lu et al. 2014; Luo and Tung 2007; Luo et al. 2010).

However, these benefits generated by connections with home government are normally contingent on local factors in the home country, which may make politically connected firms dependent on the home institutions (Cui and Jiang 2012; Du and Luo 2016) and reliant on political factors instead of commercial interests, thereby undermining the development of market-based capabilities needed for internationalization (Lu et al. 2014). Therefore, we argue that because of the higher demands of localization in the host country generated by the distinct features of services, home-country political connections may constrain service firms' internationalization.

More specifically, as we mentioned above, the intangibility of services makes it very difficult for service firms to transfer the same service across borders (Kirca et al. 2016), so the advantages generated by home government connections are more difficult for service firms to transfer to foreign markets; inseparability as one key attribute of service causes the production and consumption to occur simultaneously, which requires very close customer contact (Goerzen and Makino 2007). A high degree of inseparability requires a greater presence in the host market (Stevens et al. 2015), which may conflict with home-based political connections; the heterogeneous nature of services involves a high degree of customization (Dunning 1989; Kundu and Merchant 2008; Merchant and Gaur 2008), so that heterogeneous services tend to have less uniformity since each interaction is often a 'one-off operation' (Dunning 1989). In this case, the advantages generated by home-country political connections are very difficult to utilize in international markets.

In summary, we believe the distinct features of services, including intangibility, inseparability and heterogeneity, make the advantages generated by home political connections very difficult to transfer and utilize in international markets, which results in a constraining effect of political connections. This is especially the case for service firms in China. Because of the considerable control over the service sector in China (Xue et al. 2013), the advantages generated by political connections are more locally contingent, and more reliant on political rather than market-based factors, which reduces the motivations and capabilities of going abroad. Therefore, we hypothesize as follows.
Hypothesis 1: Home-country political connections have a negative
influence on the internationalization of service firms.


2.3 Differences Within Service Industry-Process Versus Content Service

Above we have argued that political connections in the home country have a negative influence on service firms' internationalization. However, as has been highlighted in some studies, the service sector is not homogeneous (Contractor et al. 2003), and there seem to be significant differences in how different service sectors have internationalized (Bouquet et al. 2004; Knight 1999; Lovelock and Yip 1996). It has been argued by many that research on the internationalization of services needs to be more sector specific (Bouquet et al. 2004; Knight 1999). This suggests that there is a need to dig deeper to investigate how political connections influence internationalization of firms in different service sectors.

There is evidence to show that industry characteristics of service firms influence the timing of foreign market entry, mode of entry, motivation for internationalization and the detailed complexities of foreign market behavior (O'Farrell et al. 1998). For instance, network industries that include airlines, telecommunications, utilities, and the banking sector (Stienstra et al. 2004) share some unique characteristics, such as network externalities and the role of government (Crystal 1999). Partly due to these factors, some researchers have argued that some traditional internationalization theories cannot adequately explain the internationalization process of such sectors (Laanti et al. 2009). For instance, Kundu and Merchant (2008) found that it is necessary to include industry characteristics as a moderator of the relationship between foreign market entries and performance.

Prior studies on service firms have proposed the extent to which the service can be standardized is a very important differentiating factor within service industries (e.g., Dunning 1989; Kesavan et al. 2014; Patterson and Cicic 1995). Following our argument above about the constraining effect of home-country political connections, we argue that when the service is more difficult to standardize, the constraining effect would be stronger as it is even more difficult to transfer and utilize the advantages generated by home government connections. The concept of process versus content has been considered as an important dimension to depict the extent of standardization of service (Light 1986). The process is often machine or equipment dependent, requires technical skills, is more capital-intensive, standardized and involves less involvement of consumers, while the content normally requires more personal attention, is more labor-intensive, and involves more consumer involvement (Kesavan et al. 2014). For example, modern biomedical equipment can read and record vital signs automatically, and the records can be transmitted to specialists located anywhere in the world, which transforms the health care service into a formal, routine, repetitive process of core services (Kesavan et al. 2014). Similarly, for industrial services related to banking, insurance, information technology and intranets, global standardization of services is more common, so these sectors can also be considered as process-oriented. In contrast, some service sectors, such as hotels, restaurants and leisure, are an advanced form of personalized service, which requires a great deal of interaction and customer involvement (Ball et al. 2006). These sectors, which require high customer involvement, rely more on the content of the service (Kesavan et al. 2014). Because of these contrasting characteristics of process versus content services, we argue that the influence of home country political connections on internationalization of firms in process-oriented versus content-oriented service sectors would be different.

The nature of content-oriented service sectors, which are less standardized, because of the high consumer involvement and personal attention needed, requires firms to be more locally-responsive. A high degree of consumer involvement normally requires physical presence in the host market and a highly customized service (Patterson and Cicic 1995), so firms have to adopt a high commitment entry mode and be more locally-responsive (Thompson et al. 1998). Because of more intimate and frequent contact with the customers in the host market, cultural sensitivities and awareness become more important (Dahringer 1991; Riddle 1986). In these firms, focus and adaptation to the host market are even more important, as home country political connections are context-specific and are normally home-country based, so the negative effect of political connections on internationalization of such service firms would be stronger.

In process-oriented service sectors, because the services are more machine or equipment-dependent and technical skills-oriented, they are more easily standardized (Kesavan et al. 2014). When the service can be standardized, the firm can more easily generate competitive advantages based on economies of scale instead of local responsiveness (Kesavan et al. 2014). Considering that process-oriented services require less consumer involvement, there is less need to be locally-responsive compared to content-oriented services. At the same time, since process-oriented service sectors are more machine and technical skills-related, they involve more fixed costs and are more capital intensive (Kesavan et al. 2014). In these firms, home country political connections can help meet these needs by providing financial support, taxation, and other favorable treatment (Luo et al. 2010). Considering the greater standardization and less local responsiveness needed, home-country based political connections can help service firms to get financial support to purchase the machines and technologies needed to provide the process service, so we argue that the negative influence of political connections on internationalization of service firms is weaker in process-oriented service sectors. Therefore, we hypothesize the following.
Hypothesis 2: The negative effect of home-country political connections
on service firms' internationalization is stronger in content-oriented
service sectors compared to process-oriented service sectors.


2.4 Differences Within Firm: Marketing Capability

Marketing capability refers to a firm's capability to utilize its knowledge, technology and resources to satisfy the needs of the market or its customers (Day 1992). Acquiring and developing marketing capability is likely to be associated with success in service firms (Jaklic et al. 2012; Leiponen 2012), since this capability enhances service innovation (Hertog et al. 2010), information and local market knowledge transfer (Zhang et al. 2015) and co-creation with customers (Zhang et al. 2015). As we discussed above, compared to manufacturing industries, which follow the traditional 'goods-dominant' logic of market exchange process (Lusch et al. 2008), 'service-dominant' logic in the service industries makes the capability to satisfy local customers more critical (Lusch et al. 2008). For a global service firm, achieving satisfaction of the customers in each host market is a key factor in internationalization performance (Dou et al. 2010).

Because of the importance of being local responsive in service firms, it is difficult for them to transfer and utilize the home context specific and contingent advantages generated by home government connections. However, strong marketing capabilities can help service firms to transfer and utilize the home-country political connections in the international markets. Firms with stronger marketing capabilities can better communicate and understand the needs of the local customers (Zhang et al. 2015), which can help firms alter their products and services accordingly to adapt more readily to the local market and differentiate their products (Kotabe et al. 2002). Prior studies have proposed a potential complementary relationship between firms' market capabilities and political connections, arguing that firms with greater market capabilities reap greater benefits from engaging in political strategies (e.g., Jia 2016; Jia and Mayer 2016). In the case of service firms, marketing capabilities can help firms utilize the home-based advantages generated from political connections in the host market by altering services according to the local customers' needs. Therefore, we hypothesize as follows.
Hypothesis 3: Marketing capabilities positively moderate the
relationship between home-country political connections and service
firms' internationalization.


3 Methods

3.1 Data and Sample

Our study examines the effects of political connections of Chinese service firms on their internationalization. China is an appropriate setting to test our hypotheses related to the effects of political connections on service firms' internationalization for several reasons. First, China has become an important source country of foreign investment since 2004 (Chen et al. 2015). Second, the service sector has become the largest industry sector, which contributes over half of the GDP in China (Statista 2016). Third, building political connections has been considered as a common and critical strategy in value creation and business success in China, since government influence has remained profound in the corporate sector (Shi et al. 2013; Sun et al. 2015). Finally, previous studies on Chinese firms' political connections and internationalization have mainly been based on samples of manufacturing firms (e.g., Sun et al. 2015; Xia et al. 2013). With a few notable exceptions (e.g., Xue et al. 2013), there have been few studies on the internationalization of service firms.

Our data consists of an unbalanced panel dataset of publicly listed Chinese service firms on the Shanghai or Shenzhen Stock Exchange from 2012 to 2016. Our dataset was drawn from several data sources including the China Stock Market and Accounting Research (CSMAR) database, company annual reports, and company websites. We obtained measures of firm marketing capabilities, the degree of internationalization, and financial variables from CSMAR, which has been widely used in previous studies (e.g., Sun et al. 2015; Wang and Qian 2011; Zhang et al. 2016). CSMAR is a leading data provider in China, developed by Shenzhen GTA Information Technology Company (Li et al. 2018). We manually coded the political connections based on firms' top executives' biographical profiles disclosed in the annual reports and company websites. All annual reports were obtained from the Shanghai and Shenzhen Stock Exchanges.

Table 1 shows the descriptive statistics of the sample.

3.2 Variables

Dependent variables Degree of internationalization. We follow previous studies in measuring the degree of internationalization as the ratio of a firm's foreign sales to its total sales (Cavusgil 1984; Javalgi et al. 2003; Ramaswamy et al. 1996).

Independent variables In China, firms normally build up their political connections through hiring former government officials or getting their staff elected as delegates to the People's Congress or the Chinese People's Political Consultative Conference (Shi et al. 2013; Zhang et al. 2016). Therefore, we measure political connections as the number of TMT/board members who are former government officials and current delegates to the People's Congress or the Chinese People's Political Consultative Conference.

Moderators Type of service. Services are far from homogeneous, and several differences among service firms have been identified. Our study focuses on a comparison between process-oriented and content-oriented service sectors. The concept of process versus content is a very important element, and this concept has been examined in relation to distribution in the service industry (Light 1986). We follow Kesavan et al. (2014) and distinguish between process-service sectors such as media, financials, and insurance, telecommunication service, banks, health care service, versus content-services such as transportation, hotels, restaurants, leisure, retailing, and software.

To measure marketing capability, we follow previous studies in using the ratio of marketing expenses to total sales, which reflects a firm's capabilities in differentiating itself from competitors (Seifert et al. 2004; Wang and Qian 2011).

Control variables We also included some control variables that have been demonstrated to have an influence on firms' internationalization. We include firm size, measured as the logarithm of the number of employees, and firm age, measured as the number of years since the firm's initial public offering, as proxies for resources and experience since older and well-established firms normally have more resources and experience than younger ones (Delmar and Shane 2004). Firm profitability, measured by return on assets (ROA), calculated as the net income divided by total assets, is also included as it is expected that better performing firms are more likely to go abroad (Lu et al. 2014). To control for brand strength and R&D capability, we included the intangible asset ratio, measured by the value of intangible assets divided by total assets (McWilliams and Siegel 2001). Debt ratio, measured by total long-term debt to total asset, is included as a control variable to reflect the firm's leverage and access to outside finance, which can financially constrain a firm (Ellul et al. 2016) and thus will impact its internationalization. As state-owned firms may benefit from certain resource and political advantages in internationalization, we control for state ownership, measured as a dummy variable that equals '1' if the actual ultimate controller of the firm is the state or governmental authorities, and '0' otherwise (Inoue et al. 2013). To control for industry-specific and time-specific effects, we included industry and year dummies. Finally, to control for possible autocorrelation, we included a one-year lagged dependent variable. Table 2 summarizes the variable descriptions and data sources. Table 3 provides the descriptive statistics.

3.3 Analysis

We used a dynamic panel model as initial simple regression models indicated that our dependent variable showed significant autocorrelation. A dynamic panel model corrects for the endogeneity by including a lagged dependent variable. Furthermore, as our data was panel data consisting of 1576 firm-year observations from a relatively large number of firms (379 firms) with a short time period (five years), a dynamic panel estimator using quasi-maximum likelihood (Bhargava and Sargan 1983) was considered the most appropriate. This was tested using the Stata routine xtdpdqml (Kripfganz 2016).

The dynamic panel model estimates the effects using quasi-maximum likelihood as follows.

[FSTS.sub.i,t] = [[beta].sub.i][FSTS.sub.it-1] + [[beta].sub.2][X.sup.'.sub.it] + [[beta].sub.3][industry.sub.i] + [[beta].sub.4][year.sub.t] + [E>.sub.it],

where X is a vector of the independent variables described above:

X = (polconn, r&d, roa, age, fixedass, size, stateown, marketing, process).

A Hausman test confirmed that a random effects model was appropriate. To take account of non-independence of observations over time, the Huber--White sandwich estimator was used to provide robust standard errors (Kripfganz 2016).

4 Results

Table 4 shows the correlation between the variables. The maximum correlation was 0.471 (between state ownership and size), confirming no significant multicollinearity issues with the variables in our models.

Table 5 shows the results of the dynamic panel regression models. Model 1 shows the effects of just the control variables. The industry and year dummies have been omitted to save space. The results show a significant effect of the lagged dependent variable, confirming the need to include this in the model. The other variables (intangible assets, debt, size, ROA, state ownership, age and marketing) are all not significant. Model 2 adds the effect of political connections on internationalization, which is shown to be negative and significant at the 0.05 level, supporting our hypothesis 1. This finding is in line with previous research which has highlighted the difficulties for service firms to transfer the same service across borders (Kirca et al. 2016), owing to inseparability of production and consumption, which requires very close customer contact (Goerzen and Makino 2007) and a high degree of customization (Dunning 1989; Kundu and Merchant 2008; Merchant and Gaur 2008). State ownership now shows a moderately significant positive effect at the 0.10 level, suggesting that state-owned firms have an advantage over non state-owned firms in internationalization. Model 3 adds the interaction term between political connections and marketing, which is not significant.

In order to further test if there are differences between industries, the tests were repeated with subsamples consisting of firms from the content- and process-oriented sectors respectively. Table 6 shows the results of the regression with a subsample of firms from the content-oriented service sectors (i.e. transportation, hotels, retailing and software). Model 4 shows the effects of just the control variables (excluding industry and year dummies). As in model 1, the results show a significant effect of the lagged dependent variable, confirming the need to include this in the model. Model 5 adds the effect of political connections on internationalization, which is shown to be negative and strongly significant at the 0.005 level. Model 6 adds the interaction term between political connections and marketing, which now shows a moderately significant positive effect at the 0.10 level. The results with the subsample of firms from the process-service sectors (not shown here to save space) showed no significant effects of political connections in any of the corresponding models. Taken together, the results support our hypothesis 2 that the negative effect of home-government political connection is stronger for content-oriented industries; however, our hypothesis 3 is only partially supported as we only find a positive moderating effect of marketing capabilities in content-oriented service sectors.

Our findings highlight two important contingency factors. First, our finding that the negative effect of political connections is more significant in content-oriented service sectors compared with process-oriented sectors highlights that there are differences even within the services sector and the need to distinguish between service sectors on the basis of the need for localization versus standardization of services. The need to be locally-responsive in content-oriented sectors increases the negative effect of home-country based political connections.

Secondly, our finding of a positive moderating effect of marketing capabilities on the relationship between political connections and internationalization in content-oriented service sectors, but not in process-oriented service sectors supports the theoretical argument that there might be complementarity among different types of resources and capabilities, that is to say, marketing capabilities can help firms better utilize the benefits of political connections (Jia 2016; Jia and Mayer 2016). This can be seen more clearly in Fig. 1 which shows a graphical representation of the moderation effects of marketing capabilities on political connections. Although both lines are negative, at high levels of marketing capabilities the line is less steep, showing that marketing capabilities alleviate to some degree the negative effects of home political connections on internationalization.

5 Discussion and Conclusion

Our study responds to the criticism that the phenomenal growth of service MNEs has not been reflected in the relatively few academic studies on the internationalization of service firms (Contractor et al. 2003). The few papers on the internationalization of service firms mainly focus on exploring the patterns of service firms' internationalization (e.g., Goerzen and Makino 2007; Thompson et al. 1998), or on investigating how firms' own characteristics, such as firm size and management methods, influence service firms' internationalization (e.g., Javalgi et al. 2003; Zhang et al. 2015). To date, there has not been much attention paid to the impact of service firms' external connections on internationalization. Our study fills in this gap by looking at one specific type of external connection i.e. the firm's connection with the government.

Firstly, we find that home-country political connections have a negative effect on service firms' internationalization. However, our study also shows heterogeneity within the service sector. We show that the conflicting findings in previous studies on the effect of political connections on firms' internationalization based on a sample of manufacturing firms or both service and manufacturing firms (e.g., Buckley et al. 2007; Luo et al. 2010; Yamakawa et al. 2008) may be due to a failure to consider differences between manufacturing and service firms and heterogeneity within the service sector, which may affect their internationalization strategies (Bouquet et al. 2004; Knight 1999).

Secondly, our study empirically tests a typology of service industries that distinguishes between content-oriented versus process-oriented services. Although this typology has previously been proposed to have important implications for service firms' internationalization (Kesavan et al. 2014), as far as we are aware, our study is the first empirical study which confirms significant differences in the internationalization of process-oriented versus content-oriented services.

Thirdly, our study also contributes to political strategy research in demonstrating that the transfer of benefits from home-country based political connections is conditioned in international markets. Within the service industry context, as we discussed above, since service firms face additional liabilities of foreignness, it is very critical to pay attention to conditions in the host market. In this situation, even though political connections can bring financial resources and support for service firms' internationalization, this more home-country focused advantage may not be able to be effectively utilized in the host market. However, we find that for content-oriented service sectors, marketing capabilities can help firms transfer and utilize the home-based advantages generated from political connections to the host country.

Our results also provide some practical suggestions for managers of service firms. We suggest managers need to pay extra attention to the need to adapt services in the host market, since they face additional liabilities of foreignness in the international market. Managers of service firms also need to be aware that although home country political connections can create competitive advantages at home, the effects may be limited or even negative in international markets, especially for firms in the content-oriented service sector. Our study also suggests that companies need to look at their resources and capabilities pool as a whole, since not only do different types of resources and capabilities influence the internationalization of firms independently, there could also be potential synergies or conflicts among them. Our result shows that marketing capabilities can help firms better utilize their political connections in international markets. In addition, it is also critical to investigate the conditions when political resources and capabilities will create advantages. Our study demonstrates the importance of identifying the conditions under which political resources may create advantages for the focal firm. Our study also provides implications for policy makers by showing that simply promoting service firms' internationalization through government financial support and other favorable terms is not enough. It is important that the government assists and encourages service firms to develop their marketing capabilities in order to better adapt to the requirements of different host countries.

A limitation of our study is that it is based solely on data on Chinese service firms. Since the government still plays a dominant role in the Chinese economy, political connections make firms highly dependent on resources in the home-country institutions (Cui and Jiang 2012). Future research can test whether political connections also make firms more home-country focused in economies where the government plays a less significant role. Secondly, even though our study investigates differences between several service industries, we only focus on comparing content-oriented services with process-oriented services. Future studies can continue to examine the heterogeneity of service industries based on other important attributes. Thirdly, owing to lack of data, our study does not examine the effects of different entry modes or individual host country characteristics, which may affect internationalization. Future studies where such data is available should examine this.

Acknowledgements We acknowledge the financial support of National Natural Science Foundation of China (Grant No. 71672146), and Xi'an Jiaotong-Liverpool University RDF 14-03-03.

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Tao Bai (1) * Stephen Chen (2) * Xiao He (3)

[??] Tao Bai

tao.bai@xjtlu.edu.cn

(1) International Business School Suzhou, Xi'an Jiaotong-Liverpool University, Jiangsu, China

(2) Newcastle Business School, University of Newcastle, Newcastle, NSW, Australia

(3) Business School, University of Otago, Dunedin, New Zealand

Received: 1 May 2017 / Revised: 10 July 2018 / Accepted: 28 July 2018 / Published online: 4 June 2019

https://doi.org/10.1007/sl1575-019-00386-7
Table 1 Sample statistics

Industry          Industry name                   Frequency   Percent

2030              Transportation                  81          21.37
2530              Hotel, restaurant and leisure   31           8.18
2540              Media                           45          11.87
2550              Retail                          66          17.41
3510              Health care                     24           6.33
4020              Finance                         12           3.17
4030              Insurance                        1           0.26
4510              Software                       115          30.34
5010              Telecommunication services       4           1.06
Total                                            379         100
Ownership         Frequency                                   Percent
Non-state owned   217                                         54.51
State owned       162                                         45.49
Total             379                                         100

Table 2 Variable descriptions and data sources

Variable     Description                            Source

fsts         Percentage of foreign                  CSMAR dataset
             sales to total sales
Ll.fsts      Foreign sales/total                    CSMAR dataset
             sales lagged by 1 year
Polconn      Number of political                    Annual reports
             connected TMT/board                    and company
             members                                websites
Intangible   The value of intangible                CSMAR dataset
             assets divided by total
             assets, winsorized (a)
ROA          Net income divided                     CSMAR dataset
             by total assets, winsorized (a)
Age          The number of years since the          CSMAR dataset
             firm's initial public offering
Debt         Debt ratio as total long-term          CSMAR dataset
             debt to total assets
Size         The logarithm of the                   CSMAR dataset
             number of employees, winsorized (a)
Marketing    The ratio of marketing expenses        CSMAR dataset
             to total sales, winsorized (a)
Stateown     State ownership dummy,                 CSMAR dataset
             equals 1 of the actual ultimate
             controller of the firm is
             the state or governmental
             authorities, 0 otherwise
Process      Dummy, value 1 if process-oriented,    CSMAR dataset
             0 if content-oriented service sector

(a) Winsorization minimizes the effect of extreme values by shrinking
outlying observations to be closer to the rest of the observations

Table 3 Descriptive statistics

Variable     No. of observations   Mean     Minimum   Maximum

fsts         1576                   0.078    0.000     0.845
Intangible   1576                   0.052    0.000     0.218
Debt         1576                   0.049    0.000     0.579
Size         1576                  21.713   19.719    24.939
ROA          1576                   0.051   -0.086     0.158
Age          1576                   9.681    0.000    24.000
Marketing    1576                   0.072    0.000     0.314
Statedummy   1576                   0.455    0.000     1.000
ind_l        1576                   0.225    0.000     1.000
ind_2        1576                   0.091    0.000     1.000
ind_3        1576                   0.117    0.000     1.000
ind_4        1576                   0.184    0.000     1.000
ind_5        1576                   0.058    0.000     1.000
ind_6        1576                   0.030    0.000     1.000
ind_7        1576                   0.003    0.000     1.000
ind_8        1576                   0.280    0.000     1.000
ind_9        1576                   0.012    0.000     1.000
Year2012     1576                   0.148    0.000     1.000
Year2013     1576                   0.191    0.000     1.000
Year2014     1576                   0.229    0.000     1.000
Year2015     1576                   0.230    0.000     1.000
Year2016     1576                   0.202    0.000     1.000

Table 4 Correlation table (excluding industry and year dummies)

Variable     fsts     Intangible   Debt     Size     ROA      Stateown

I'sts         1.000
Intangible   -0.084    1.000
Debt         -0.022    0.121        1.000
Size         -0.095    0.083        0.417    1.000
ROA          -0.106    0.019       -0.087    0.058    1.000
Stateown     -0.152    0.137        0.199    0.471    0.061    1.000
Age          -0.168    0.064        0.183    0.200   -0.079    0.328
Marketing    -0.028   -0.088       -0.189   -0.215   -0.038   -0.246
Political    -0.111    0.071        0.196    0.390    0.045    0.335

Variable     Age      Marketing   Political

I'sts
Intangible
Debt
Size
ROA
Stateown
Age           1.000
Marketing    -0.194    1.000
Political     0.003   -0.138      1.000

Table 5 Regression results

                    Model 1                       Model 2
                    Coef.     Robust S.E.   Sig   Coef.     Robust S.E.

Constant             0.056         0.060           0.044         0.060
L1.fsts              0.894         0.028   (***)   0.892         0.029
Intangible          -0.015         0.025          -0.016         0.026
Debt                 0.010         0.023           0.011         0.023
Size                -0.001         0.002           0.000         0.002
ROA                 -0.029         0.074          -0.030         0.074
Stateowned           0.005         0.004           0.007         0.004
Age                  0.000         0.000           0.000         0.000
Marketing            0.041         0.027           0.042         0.027
Polconn                                           -0.013         0.006
Polconn
x marketing
+ industry
dummies
+ year dummies
No. of            1576                          1576
observations
Groups             379                           379

                         Model 3
                  Sig.   Coef.     Robust S.E.   Sig.

Constant                  0.043         0.060
L1.fsts          (***)    0.892         0.029   (***)
Intangible               -0.017         0.026
Debt                      0.012         0.023
Size                      0.000         0.002
ROA                      -0.027         0.075
Stateowned       (+)      0.007         0.004   (+)
Age                      -0.001         0.000
Marketing                 0.023         0.036
Polconn          (*)     -0.017         0.008   (*)
Polconn                   0.057         0.054
x marketing
+ industry
dummies
+ year dummies
No. of                 1576
observations
Groups                  379

(***) P<0.0005, (*) P<0.05, (+) P<0.1

Table 6 Regression results with content-oriented service sector
subsample (transportation, hotels, retailing and software)

                Model 4                        Model 5
                Coef.     Robust S.E.  Sig.    Coef.    Robust S.E.

Constant          -0.028        0.046          -0.046        0.046
L1.fsts            0.874        0.035  (***)    0.870        0.036
Intangible        -0.043        0.026          -0.041        0.027
Debt               0.010        0.025           0.013        0.025
Size               0.002        0.002           0.003        0.002
ROA               -0.004        0.085          -0.008        0.084
Stateown           0.008        0.005           0.010        0.005
Age                0.000        0.000           0.000        0.000
Marketing          0.047        0.030           0.045        0.031
Polconn                                        -0.018        0.006
Polconn x
marketing
+ industry
dummies
+ year dummies
No. of          1229                         1229
observations
Groups           293                          293

                       Model 6
                Sig.   Coef.     Robust S.E.   Sig.

Constant                -0.050         0.046
L1.fsts        (***)     0.870         0.036
Intangible              -0.045         0.028  (+)
Debt                     0.015         0.026
Size                     0.003         0.002
ROA                      0.001         0.087
Stateown       (*)       0.011         0.005  (*)
Age                      0.000         0.000
Marketing                0.010         0.036
Polconn        (**)     -0.025         0.009  (**)
Polconn x                0.116         0.062  (+)
marketing
+ industry
dummies
+ year dummies
No. of                1229
observations
Groups                 293

(***) P<0.0005, (**) P<0.005, (*) P<0.05, (+) P<0.1
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Author:Bai, Tao; Chen, Stephen; He, Xiao
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Geographic Code:9CHIN
Date:Aug 1, 2019
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