Research Paper

Heterogeneous Effects of Business Collaboration on Innovation in Small Enterprises: China Compared to Brazil, Indonesia, Nigeria, and Thailand Junguang Gao1, Thomas Schøtt 2, Xuewei Sun1, and Ye Liu2,3,4

1Business School, Beijing Technology and Business University, Beijing, P.R. China; 2Department of Entrepreneurship and Relationship Management, University of Southern Denmark, Kolding, Denmark; 3Sino-Danish Center for Education and Research, Beijing, P.R. China; 4Department of Public Administration, Zhejiang Sci-Tech University, Hangzhou, P.R. China

ABSTRACT: The question is whether the benefits of collaboration for innovation for small enterprises in China are comparable to or different from other developing countries—Brazil, Indonesia, Thailand, and Nigeria—that have been surveyed by the Global Entrepreneurship Monitor (GEM). When it comes to innovation, the benefits of collaboration for small firms in China, as in Brazil and Indonesia, are found to be negligible, whereas the benefits are substantial in Thailand and Nigeria. The benefits are measured in terms of innovation. The findings contribute to understanding collaboration as a systemic property that benefits innovation in small firms that are embedded in institutions that moderate that benefit.

KEY WORDS: China, collaboration, innovation, small enterprise

The influence of business collaboration on innovation has been studied for decades (Ahuja 2000; Kline and Rosenberg 1986; Rosenberg 1982). It has become a very popular and important research topic, especially since the concept of open innovation, which was developed by Chesbrough (2003), gained widespread attention.

However, the extant literature seldom refers to the heterogeneous effects of business collaboration on innovation in terms of individual countries. Prior studies mainly focused on a single country, especially the developed countries (Akçomak and Weel 2009). Actually, the majority of existing research on open innovation is drawn from firms operating in North America and Europe (Chaston and Scott 2012). Some of the studies refer to the differences among developed European countries in terms of business collaboration benefiting innovation and find that the benefits differ among countries (Kaasa 2015). It is thus perhaps important to study whether the effects might be different outside these developed regions, especially in emerging and developing countries such as China.

China is a scholarly informative case for theoretical and empirical reasons. China has a tradition of networking (guanxi), characterized by strong, long-lasting personal bonds and solidarity. These personal networks are a foundation for business collaboration (Jensen, Liu, and Schøtt 2017), and while they may provide support and resources, they are unlikely to provide innovative ideas. In fact, an empirical comparison of China and Denmark has shown that Chinese firms’ collaboration brings much less innovation (Schøtt and Jensen 2016). This might be because of Denmark’s advanced development; it will therefore be informative to compare China to developing countries. In this article, China is compared to Brazil, Indonesia, Nigeria, and Thailand, where comparably large surveys have been undertaken.

Meanwhile, the ongoing globalization process highlights the importance of innovation in all small and medium-sized enterprises (SMEs) (O’Regan, Ghobadian, and Sims 2006). However, small firms’ innovative ability in China is still far from enough (Jiang, Sun, and Bai 2013). Some studies from

Address correspondence to Junguang Gao, Beijing Technology and Business University, 33 Fucheng Road, Zonghelou 412, Beijing 10048, P.R. China. E-mail: girl_lilac@126.com

Emerging Markets Finance & Trade, 55:795–808, 2019 Copyright © Taylor & Francis Group, LLC ISSN: 1540-496X print/1558-0938 online DOI: https://doi.org/10.1080/1540496X.2018.1510310

developing countries underline that cooperation or interaction involving innovation is an important way for SMEs in emerging economies and developing countries to promote their innovation abilities (Kaminski, De Oliveira, and Lopes 2008). Although the effects of collaboration on SMEs’ innovation have increasingly become a matter for discussion, few studies use SMEs in developing countries as samples for studying the effects of business collaboration on innovation (Mukhamad and Kiminami 2011). This article tries to clarify the effects of business collaboration on small enterprises’ innovation, as well as the heterogeneous effects in different countries including China. If similarities and differences can be identified in China in relation to other countries, targeted strategies and policies can be devised to promote China’s small enterprises and enhance their innovative capability.

The remainder of the article is organized as follows: the following second section comprises the theoretical background; the third section sets out the hypotheses; the fourth section presents the research design; the fifth section reports on the empirical results; and the sixth section consists of the conclusions, limitations, and future research.

1. Theoretical Background

1.1. External Sources of Innovation

During the 1950s, innovation was considered a discrete event resulting from knowledge developed by isolated inventors and isolated researchers (Landry, Amara, and Lamari 2002). Then innovation was considered to be an interactive process involving formal and informal relationships among actors in a firm’s environment (Kline and Rosenberg 1986), a diversified learning process including learning-by- sharing (Rosenberg 1982), and a problem-solving process (Felin and Zenger 2014; Nickerson and Todd 2004) integrating internal and external resources. The external resources are becoming more and more important in the innovation process. The literature in this field is elaborate and plentiful. One category includes the effect of various types of interfirm cooperation, ranging from R&D partnerships (Lichtenthaler, Ernst, and Hogel 2010) and equity joint ventures (Vanhaverbeke, Van De Vrande, and Chesbrough 2008) to collaborative manufacturing and complex co-marketing arrangements (Chesbrough 2007). Another category includes the various collaborators in the process of interfirm cooperation, including end users, the customer community, supplier competitors, universities, govern- ments, etc.; their effects on innovation have been well documented (Almirall and Casadesus-Masanell 2010; Nambisan 2002; Tsai 2009). Both categories illustrate that organizations themselves cover only part of their value chain and depend critically on their external environment (Pfeffer and Salancik 1978). Firms are truncated in their resource endowment; they outsource certain parts of the value chain and transact with other economic actors with complementary assets (Lee, Lee, and Penning 2001).

In addition, recent literature in economic sociology has considered the role of social networks in creativity (Rodan 2010), suggesting that a full understanding of innovation in complex organizations requires the analysis and appreciation of the roles played by both social and knowledge networks (Wang et al. 2014). Smaller firms have fewer resources, comparatively weak R&D capacity, and generally face more uncertainties and barriers to innovation; networks represent a complementary response to improve innovative capacity and reduce uncertainty in their process of innovation (Diez 2002).

1.2. Social Capital, National Institutions, and Innovation

Corporate social capital can be defined as “the set of resources, tangible or virtual, that accrue to a corporate player through the player’s social relationships facilitating the attainment of goals” (Gabbay and Leenders 1999). Studies have considered that social capital becomes an essential ingredient for successful innovation (MacKinnon, Chapman, and Cumbers 2004). The degree to which firms use their external networks to acquire and exploit knowledge is regulated by the amount of social capital they possess (Yli-Renko, Autio, and Sapienza 2001). At the enterprise level, many researchers have already studied social capital, which characterizes large corporations. It has been argued that social

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capital plays a significant role in large firms’ incremental and radical innovative capability (Subramaniam and Youndt 2005). The literature on the National System of Innovation considers that intangible capital should be part of the networks, and the studies focus on the national institutional environment (Edquist 1997). It has long been established that institutions set the rules that form a country’s incentive structures and economic specialization (North 1990). Institutions can have a profound effect on a country’s innovation system by determining the infrastructure, the quality of human capital, and the resources available for innovation (Bosker and Garretsen 2009).

On the other hand, research on neo-institutional theory argues that organizations deal with multiple sociocultural pressures that define appropriate ways of doing things (Greenwood, Hinings, and Whetten 2014). Most of this work has drawn on the concept of institutional logics (Thornton, Ocasio, and Lounsbury 2012), which are the “socially constructed historical patterns of material practices, assumptions, values, beliefs and rules” (Thornton and Ocasio 1999) that shape acceptable goals and organizing principles within a field, thus influencing organizations’ priorities (Pache and Santos 2013), strategies (Battilana and Dorado 2010), and practices (Battilana et al. 2015). Prior studies have emphasized that benefits of business collaboration for innovation are dependent on institutions (Akçomak and Weel 2009; Furman, Porter, and Stern 2002), both at the enterprise level (mainly for large enterprises) and at the national level (mainly for the developed countries). In accordance with these research studies, we underscore that the heterogeneous effects of collaboration on innovation vary by country because of the specific institutional logics in different societies. The latest research outcomes from Ramus, Vaccaro, and Brusoni (2017), which found a relationship between collaboration and institutional logics, also support our perspective.

2. Hypotheses

The purpose of this article is to analyze the impact of business collaboration on small firms’ innovation at both the individual firm level and the national level. As internal resources are very limited for small firms, they focus on the external sources of open innovation. The degree to which firms use their external networks to acquire and exploit knowledge is regulated by the amount of social capital they possess, especially the varying institutional logics prevailing in different societies. This article also analyzes the heterogeneous benefits of business collaboration for small firms’ innovation across different countries.

2.1. Effects of Business Collaboration on Innovation for Small Firms

Studies of open innovation as defined by Chesbrough (2003, 2006) have shown that utilizing an external source of innovation can improve firm profitability by reducing costs, increasing prices, improving innovation outputs, gaining external knowledge, tracking changes in market demands, and improving firms’ learning capacity and thereby their innovation performance (Mukhamad and Kiminami 2011). West and Bogers (2014) further suggested a four-phase model for leveraging external sources of innovation. Case studies also provide evidence that firms rely on external sources of innovation to create value, such as externally sourced technology (West and Bogers 2014), including semiconductors (Chesbrough 2003), software (West and Gallagher 2006), and cellular phones (Dittrich and Duysters 2007), as well as low-technology industries like construction and textiles (Spithoven, Clarysse, and Knockaert 2010) and manufacturing (Rothaermel and Alexandre 2009). It is important to note that there are some articles in this field that present conflicting findings regarding the performance benefits of external sources (Belderbos et al. 2010); however, other articles provide methods to avoid the problems caused by external innovation sources (Christensen, Olesen, and Kjar 2005; West and Gallagher 2006). This conflict among findings may be due to the original open innovation studies’ focus on large, multinational enterprises expanding their R&D activities across borders within their global value chains (Bianchi et al. 2010). Most articles in this field focus on large firms in developed countries, especially in the high-tech industry.

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Small companies are characterized by scarce resources, so they need to innovate through coopera- tion to remain competitive (Bougrain and Haudeville 2002; Mukhamad and Kiminami 2011; Tsai 2009). According to Hewitt-Dundas (2006), the external collaboration resources and capabilities that small firms could access might provide them with innovative stimulus and capacity. Moreover, SMEs can benefit from opening up their internal boundaries by participating in networks and fostering partnership agreements with a plethora of external actors (Freel and Robson 2016). Cumbers, Mackinnon, and Chapman (2003) show that localized networks are important for small firms’ ability to obtain size-related advantages. Based on data from small firms in Japan, Fukugwa (2006) suggests that networking is a means of speeding up innovation and providing access to expertise and resources. Xie, Zeng, and Tam (2010) studied 137 SMEs in the Chinese manufacturing industry and also found a positive correlation between external sources and innovation performance. Based on data from 191 Italian winemakers, Presenza et al. (2017) concluded that SMEs with a higher propensity to access and use external knowledge sources show a greater ability to innovate and also concluded that their absorptive capacity impacts the use of external sources. Through external resources, SMEs can access complementary assets, which allows them to overcome the challenges faced due to scarce resources and capabilities. Eventually, they may enhance their business legitimacy and reputation and take advantage of a wider range of market opportunities (Presenza et al. 2017).

Based on the literature, although the cases mostly come from developed countries, we suggest that effective interfirm cooperation should be emphasized to improve innovation at small firms. We consider this issue in the following hypothesis.

Hypothesis 1: Effective business collaboration is beneficial to SMEs’ ability to innovate as well as their ability to operate efficiently. Business collaboration will positively benefit small firms’ innovation in developing countries.

2.2. Effects of Business Collaboration on Innovation for Small Firms in China

In social network theories, innovation results from a combination of tangible and intangible forms of capital characterized by disorderly and sustained interactions occurring between firms and diversified sets of actors. These interactions are holistic and are influenced by history, social values, institutions, and interdependence (Landry, Amara, and Lamari 2002). Akçomak and Weel (2009) argue that current levels of social capital are formed by historical institutions: institutions set the rules in legal infra- structure and incentive structures that govern economic transactions (North 1990). As firms are an integral part of the institutional environment (Hong, Wang, and Kafouros 2015), their innovation decisions, strategies, and performance are influenced by a multitude of institutional forces, which either promote or hinder the upgrading of existing capabilities.

At a national level, the studies about social capital benefiting innovation focus on institutional logics. An institutional approach is central to understanding the forces that shape firms’ innovation outcomes, because it helps us explain differences in innovation performance that do not result from variations in organizational factors (Sun et al. 2017). Institutions might facilitate or constrain colla- boration by influencing transaction costs and the set of rules, supportive structures, and resources (Wang et al. 2012). However, the question of whether—and how—different institutions lead to the differences in business collaboration that benefit innovation among different countries remains unan- swered (Dettmann, Proff, and Brenner 2015). Various other studies conducted by Chinese scholars also point out that it is plausible that the relationship between innovation and business collaboration in China differs from that in other countries because of different institutional environmental conditions and especially different institutional policies (Chen and Li 2005; Xie, Wu, and Zeng 2016). These research studies invite further investigation in the context of China, especially for small firms.

Cheng and Yiu (2016) selected four articles from a pool of over 50 submissions and identified areas where additional institutional changes will still be needed in order for China and Chinese firms to compete successfully in the new innovation-driven global economy. This means there are still many

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institutions in China that hinder firms’ innovation (Guo, Clougherty, and Duso 2016; Hitt and Xu 2016; Holmes et al. 2016; Tung 2016). Small enterprises in China experience institution-based barriers, especially in the area of innovation (Zhu, Wittmann, and Peng 2012). Zhu, Wittmann, and Peng (2012) identify five key institution-based barriers to innovation in China: (1) competition fairness, (2) access to financing, (3) laws and regulations, (4) tax burden, and (5) support systems. These findings enhance the institution-based view of entrepreneurship by shedding light on how institution-based barriers affect innovation in SMEs. These five barriers are consistent with those mentioned by Yang and Hu (2009). Furthermore, transparency and corruption issues have created an unreliable institutional environment for doing business in China (Akpolat and Chang 2008). Under these circumstances, the chances of cooperation will be considerably lower (Schiller, Fu, and Diez 2013). Another dimension that differentiates the Chinese and other countries’ cultures is individualism and collectivism (Hofstede 1991). Based on the collectivism theory in explaining the in-group/out-group distinction that people make in different cultures, Chen and Li (2005) argue that Chinese people make less cooperative decisions than people in developed countries, and they posit that the Chinese are likely to be less cooperative with foreigners than with fellow Chinese when they are on foreign territory.

Some literature considers the institutional factors that enhance the benefit of collaboration. Marzucchi and Antonioli (2013) argue that regional policy-makers can stimulate firms’ cooperation with research organizations. McEvily and Zaheer (1999) show that well-developed capital markets, intermediaries, and contract enforcement laws and intellectual property rights are facilitating R&D collaborations. In recent years, many formal institutions, such as laws, regulations, and organizations (work unions, research institutes, patent offices, etc.), have been established; China may be capable of compensating for comparatively poor institutional support for networking and innovation (Jensen and Schøtt 2014). Other articles also mention these areas, such as Yi et al. (2017).

There are articles, too, that mention the institutions that can promote the benefits of collabora- tion on innovation, such as informal guanxi networks (Xiao and Tsui 2007; Zhou et al. 2003) and agglomeration (Kafouros et al. 2015). The literature still suggests that despite improving environ- mental conditions for small enterprises in China, businesses still confront institution-based barriers that prevent them from unleashing their innovation potential, just as Jensen and Schøtt (2014) show. This article concludes that the benefits of networking for innovation in China are signifi- cantly less than in the rest of the world. In accordance with the above review of institutional conditions affecting the benefits of business collaboration for small firms’ innovation in China, we state our Hypothesis 2.

Hypothesis 2: The benefit to innovation arising from the collaboration of small firms in China will be small.

2.3. Development Level of a Country that Moderates the Benefits of Business Collaboration for Small Firms’ Innovation

Social relations, which are also often relative to geographical proximity, accelerate learning and constitute dynamic innovation synergies (Iammarino and Mccann 2006). In China, the majority of innovation is fundamentally driven by agglomeration forces, linked to population, industrial specia- lization, and infrastructure endowment. We know that subnational institutional variations have a profound impact on the relationship between collaboration and innovation performance (Kafouros et al. 2015). Richer regions with an intense agglomeration of activities, good infrastructure endow- ments, and a greater degree of industrial specialization not only have higher patenting rates but also absorb innovative potential from neighboring areas (Dahlman 2010). Although the analyses focus mainly on China, a number of the predictions of their framework could be adapted to other emerging economies that, like China, are characterized by polycentric institutions (Choi, Jia, and Lu 2015). Filatotchev et al. (2011) also suggest that in other countries, by contrast, innovation may be much more dependent on a combination of good local socioeconomic structures and investment in science and technology.

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Substantial differences are also observed between countries. Jensen and Schøtt (2014) conclude that the benefit of networking for innovation in China is significantly less than in the rest of the world. Kafouros et al. (2015) demonstrate that the effectiveness of academic collaborations in enhancing the innovation performance of emerging-market enterprises differs between China and Western countries. One key explanation for such variation is the quality of institutions (North 1990): usually the region with a higher level of development will experience a greater impact of collaboration on innovation because of the higher quality of its institutions. Even though some studies focus on the effect of the development level on the relationship between business colla- boration and innovation, most studies were carried out in developed countries, using large enterprises as the object of study (Akçomak and Weel 2009). For example, Calapez (2011) has argued that Nordic countries exhibit high levels of both cooperation and innovation, while some eastern and southern European countries perform much less well. However, a theoretical model established in a developed country is probably not suitable for developing countries. Thus, it is important to clarify whether the impact of business collaboration upon innovation in developing countries depends, as expected, on the quality of institutions, which is associated with wealth in the country. We state this expectation as Hypothesis 3.

Hypothesis 3: The benefit of business collaboration for innovation in a society depends on the quality of the institutions in the society. That is, development level moderates the benefit of business collaboration with innovation, in that the wealth of a society enhances this benefit.

3. Research Design and Data

The benefit of collaboration for innovation concerns the businesses in China and other countries. The Global Entrepreneurship Monitor (GEM) conducts an annual survey around the world asking about innovation in businesses, and in 2012 and 2013 it also asked about business collaboration (Jensen, Liu, and Schøtt 2017).

3.1. Sampling

A country is included in GEM when a national team is formed that conducts the survey. Mainland China has been included most years, led by a team at Tsinghua University. The survey randomly samples adults for an interview, which identifies entrepreneurs as those who own and manage a starting or operating business. The sample in China comprises 1,963 entrepreneurs.

China will be compared to several other developing countries, namely, those with at least as many surveyed entrepreneurs as in China. They are Indonesia (2,037 surveyed in 2012), Thailand (2,462 surveyed in 2012–2013), Nigeria (2,878 surveyed in 2012–2013), and Brazil (3,183 surveyed in 2012). Smaller samples were surveyed in many other countries, but too few for qualitative compar- isons. The high degree of representativeness in sampling implies that findings can be generalized to the population of businesses in the five societies.

3.2. Measurements

The innovation in a firm comprises process innovation, product innovation, and uniqueness. These kinds of innovation are indicated when the survey asks the entrepreneur,

- How long have the technologies or procedures used for this product or service been available? Less than a year; between one and five years; or longer than five years?

- Do all, some, or none of your potential customers consider this product or service new and unfamiliar?

- Right now, are there many, few, or no other businesses offering the same products or services to your potential customers?

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Each question is answered on a three-point scale, coded 1, 2, and 3 to indicate the degree of innovation. The three measures are positively intercorrelated and averaged into an index of innovation going from 1 to 3. This measure of innovation has been used extensively in earlier studies (Jensen and Schøtt 2014; Schøtt and Cheraghi 2015; Schøtt and Jensen 2016; Schøtt and Sedaghat 2014).

The business collaboration of a firm here denotes its collaborative relations. Relations are distinguished by their content or substance of the collaboration, namely as collaboration in terms of seven endeavors: production, supplies, marketing, search for new markets for current pro- ducts, creation of new products for current markets, development of new products for new markets, and improving effectiveness of the business. These kinds of relations were measured by asking,

- Is your business working together with other enterprises or organizations to produce goods or services?

- Is your business working together with other enterprises or organizations to procure supplies? - Is your business working together with others to sell your products or services to your current

customers? - Is your business working together with others to sell your products or services to new customers? - Is your business working together with others to create new products or services for your current

customers? - Is your business working together with others to create new products or services for new

customers? - Is your business working together with others on how to make your business more effective? Furthermore, when a relationship was reported to exist, a follow-up question asked whether the

collaboration was intense or not so intense. The relationship is thus measured on a scale going from no relationship, through a weak relationship, to an intense relationship, coded 0, 0.5, and 1. The seven measures of relationships are positively intercorrelated and averaged into an index of business collaboration going from 0 to 1. This measure of business collaboration has been used in earlier studies (e.g. Schøtt and Jensen 2016), as well as in studies of China (Jensen and Schøtt 2014). The validity of this measure of business collaboration is indicated by its positive correlation with advice network (e.g. Jensen and Schøtt 2015).

Several other characteristics of the firm and the entrepreneurs are used as control variables: • Owners are number of owners, transformed logarithmically to reduce skewness. • Firm age is the number of years since starting, transformed logarithmically. • Firm size is the number of people working for the firm, transformed logarithmically. • Motive behind the business is a five-category variable: opportunity, necessity, a combination thereof, having as a job but pursuing a better opportunity, and other reasons. (The opportunity motive is used as a reference against which the other four are compared, using four dummy variables.)

• Education of owner-manager is years of education. • Gender of owner-manager is a dummy, coded 0 for men and 1 for women. • Age of the owner-manager is measured in years. The validity and reliability of these measures from GEM are well established (Bosma et al. 2012).

3.3. Analytical Strategy and Modeling

The effect of collaboration upon innovation in businesses in each country is analyzed by multiple linear regression, as is common practice (Table 1). For cross-country comparisons of effects, the regression is modified to become a hierarchical linear model that is similar to regression but takes into account that the data are hierarchical, with businesses nested within countries (Table 2) (Snijders and Bosker 2012). Hypotheses are tested with coefficients like in linear regression.

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4. Results

Hypothesis 1 is that business collaboration benefits innovation. For each country, the benefit of collaboration for innovation can be ascertained in a linear regression, controlling for several other conditions, including characteristics of the business and the entrepreneur, as listed in Table 1.

In China, the effect of collaboration on innovation for small firms is estimated to be near 0, indicated by the standardized coefficient, and is insignificant. In Brazil, the effect is small but positive. In Indonesia, the effect seems negative. In Nigeria and Thailand, the effect is positive and the benefit of collaboration for innovation is substantial, as indicated by the standardized coefficients.

To test whether China differs from the other countries in the effect of collaboration upon innova- tion, we analyzed the total sample in a linear model, where we explicitly included the effects of being in a particular country, as listed in Table 2.

The model of the main effects shows that, for the entrepreneurs in these countries, collaboration tends to benefit innovation. The benefit is of notable magnitude, as indicated by the standardized coefficient. This supports Hypothesis 1.

The model of the main effects, compared to China, shows that innovation is the lowest in Brazil and the highest in Indonesia, but this is of no interest for our purpose.

Hypothesis 2 is that the benefit of business collaboration for innovation differs across countries. The difference between the benefit in China and the benefit in each other country is ascertained by including an interaction term, the product of the collaboration variable and the dummy for the other country, the second model in Table 2.

For China, the effect of collaboration is the coefficient for collaboration in the model with interactions, .002, which is negligible. For Brazil, the interaction term −.014 is not significant, so the effect in Brazil is not discernibly different from the effect in China. For Brazil, the estimated effect of collaboration upon innovation for small firms is .002 minus .014, or −.012, which is negligible. For Indonesia, the interaction term is significant and negative, so the effect in Indonesia is significantly lower than the effect in China (consistent with Table 1). For Indonesia, the effect of collaboration upon innovation for small firms is .002 minus .104, or −.102, which is notably negative (consistent with Table 1). For Nigeria and Thailand, the interaction terms are significant and positive, so the effects in these two countries are higher than in China. In these countries, the effects of collaboration upon

Table 1. Innovation affected by collaboration, for each country.

China Brazil Indonesia Nigeria Thailand

Collaboration .01 .04 * −.08 * .09 *** .08 *** Owners of the business −.07 ** −.01 .04 .06 ** −.03 Size of the business .04 −.01 .02 .04 .05 * Age of the business −.09 ** −.06 * −.31 ** −.12 *** −.27 *** Age of the owner .01 .01 −.10 ** .01 −.03 Education of the owner .06 * .03 * −.05 .03 −.01 Gender of the owner, male −.02 −.04 * −.02 .01 .02 Motive of owner: opportunity −.02 .01 −.03 .03 .01 Phase of business: operating .03 −.08 ** .05 −.02 −.03 Sector: extracting −.14 *** .01 −.03 −.01 −.04 Sector: manufacturing .02 .02 .01 −.01 .00 Sector: business services .03 .01 .03 −.02 .00 N businesses 1695 3104 1047 2615 2196 R-square .05 .02 .12 .03 .12

Linear regression with standardized coefficients. Sector has consumer services as the reference to which each other sector is compared. * p < .05; ** p < .01; *** p < .01

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innovation for small firms are between .081 and .106, i.e. notable. The evidence thus supports Hypothesis 2, that the effect of business collaboration upon innovation in China differs from effects in other countries.

5. Conclusions, Managerial Implications, Limitations, and Future Research

The research question has been whether the business collaboration benefiting innovation in small firms in China differs from that in the rest of the world, specifically whether the benefit in China is less than the benefit in other countries. Business collaboration benefiting innovation was found to be as expected. However, the benefit of business collaboration for innovation in small firms is not as large in China as in some other countries. The benefit is estimated to be tiny in China and to be quite large in Thailand and Nigeria. In other words, business collaboration is less efficient in China than in some other developing countries. This might be due to elements of traditional Chinese culture such as collectivism, as well as some organizational problems such as government policies, administrative systems, control mechanisms, and the implementation of solutions to the above-mentioned problems. On the other hand, although guanxi is helpful for developing long-term confidence and supports the company’s long-term development, it is unlikely to promote innovation. This contrasts with relationships in secular-rational societies, which are typically

Table 2. Innovation affected by collaboration and by country.

Model with main effects Model including interaction effects

Collaboration .05 *** .01 § Brazil −.87 *** −.87 § Indonesia .41 *** .40 § Nigeria −.09 *** −.09 § Thailand −.13 *** −.13 § Collaboration * Brazil −.01 Collaboration * Indonesia −.11 ** Collaboration * Nigeria .10 *** Collaboration * Thailand .08 *** Phase of business: operating −.02 −.03 Age of the business −.15 *** −.15 *** Owners of the business −.01 −.01 Size of the business .03 ** .03 ** Motive of the owner .03 .03 Gender of the owner: male .00 .00 Age of the owner .00 .00 Education of the owner .01 .01 Sector: extraction −.16 *** −.16 *** Sector: manufacturing .01 .01 Sector: Business services .00 .01 Constant .21 *** .22 *** N countries 5 5 N businesses 10,643 10,643

Hierarchical linear modeling. Country has China as the reference to which each other country is compared. Sector has consumer services as the reference to which each other sector is compared. The dependent variable is standardized. Each numerical independent variable is standardized and centered in each country. Each dichotomous variable is a 0–1 dummy. * p < .05; ** p < .01; *** p < .001 § Significance not tested (main effect is tested in the first model in the table).

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pursued for their cost-effectiveness, i.e. for their expected benefit in terms of competitive advantages in the competition among firms for innovation (Ghazinoory, Bitaab, and Lohrasbi 2014). As for countries’ level of development, we compared the results with the GDP of each country. As hypothesized, we find that the benefit is correlated with the GDP—i.e. the greater the GDP, the less the benefit—except in Indonesia. The GDP of Indonesia is in the middle of these five countries, but the benefit is negative.

These results provide many managerial implications for improving the innovative capabilities of small Chinese firms. First, small firms should actively search for external innovation sources because of the business collaboration benefiting small firms’ innovation. Second, Chinese policy-makers should be concerned about institutional quality, considering the poor performance of business colla- boration contributing to small firms’ innovation. Third, the quality of institutions will improve dynamically with the development of the Chinese economy. Thus, the effects of business collaboration on small firms’ innovation will change dynamically too. This article also presents some policy implications on the necessity to reform rules and regulations so as to improve the effectiveness of business collaboration to benefit innovation in small firms: for instance, policies for encouraging business collaboration should be different among areas in accordance with their development levels. However, the article introduces some interesting questions with respect to understanding innovation in small firms in China, as well as with regard to developing suitable polices for improving the innovative capabilities of small Chinese firms. It is also helpful to predict the possible roles of innovation on the development of China’s economy in the future, e.g. our results suggest that business collaboration with foreign enterprises will provide greater benefits for Chinese enterprises’ innovation with time.

There are still several limitations in this article requiring further study. First, we did not consider the differences between industries. Future research could therefore continue collecting data from different industries and compare the industries in different countries. Second, although we did find a correlative relationship between business collaboration and innovation, as well as the moderating effects of development level on that relationship, we did not explore the mechanism of the main and moderating effects. Future research could study the mechanism through case analysis or comparative analysis. This could entail investigating sources of business collaboration, specifically how business collaboration effects are contingent on trust and institu- tional support in society and how business collaboration is affected by development level, comparing societies around the world. Further study could also investigate the consequences of business collaboration for outcomes other than innovation, especially for exporting (Schøtt and Sedaghat 2014) and for companies’ growth expectations (Ashourizadeh and Schøtt 2015), also comparing China to the world.

Funding

Data were collected by the Global Entrepreneurship Monitor. Responsibility for analysis and inter- pretation rests with the authors. This work was supported by the Beijing Municipal Natural Science Foundation [9162003], the Beijing Social Science Foundation [17GLB011], and the BTBU Project for Nurturing Social and Natural Science Foundation [LKJJ2016-05]. The work of Ye Liu is supported by an award from the National Natural Science Foundation of China [Grant Number: 71603241] for her research project, “On the collaborative ability and improvement of hybrid organization in entrepre- neurial universities.”] and Fostering Project for Two Science Funds of Beijing Technology and Business University [LKJJ2016-05].

ORCID

Thomas Schøtt http://orcid.org/0000-0002-8604-6709

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Appendix

Table A. Mean and standard deviation of each variable on original scale; for each country.

China Brazil Indonesia Nigeria Thailand

Mean Std Mean Std Mean Std Mean Std Mean Std

Innovation 1.48 .41 1.13 .21 1.65 .37 1.46 .45 1.42 .40 Collaboration .26 .34 .07 .18 .11 .26 .21 .31 .26 .31 Owners of business 1.5 1.5 1.4 .8 1.2 .6 1.3 1.0 1.5 1.2 Size of business 9.3 97.7 1.5 7.5 1.6 2.1 1.4 3.4 1.5 5.7 Age of business 5.1 y 6.2 y 7.1 y 8.7 y 5.1 y 6.1 y 3.7 y 6.2 y 9.0 y 9.9 y Age of entrepreneur 37.9 y 10.5 y 39.4 y 11.8 y 38.9 y 10.5 y 36.7 y 11.6 y 41.5 y 11.2 y Education 10.7 y 3.4 y 8.1 y 5.8 y 10.1 y 3.8 y 11.2 y 4.1 y 10.6 y 4.8 y Gender: male .55 .50 .53 .50 .52 .50 .48 .50 .49 .50 Motive: opportunity .43 .50 .61 .49 .50 .50 .61 .49 .62 .49 Phase: operating .82 .39 .80 .40 .91 .29 .62 .48 .81 .39 Sector: extractive .06 .24 .03 .18 .05 .21 .05 .23 .20 .40 Sector: manufacturing .15 .36 .31 .46 .11 .32 .17 .37 .14 .35 Sector: business services .05 .22 .13 .34 .04 .20 .06 .23 .06 .24 Sector: consumer services .73 .44 .52 .50 .80 .40 .72 .45 .60 .49

Table B. Correlations of variables of interest (variables centered within each country).

Innovation Collaboration

Innovation Collaboration .07 *** Owners of business .02 ** .14 *** Size of business .00 .19 *** Age of business −.18 *** −.05 *** Age of entrepreneur −.06 *** −.06 *** Education .06 *** .14 *** Gender: male .01 .05 *** Motive: opportunity .05 *** .14 *** Phase: operating −.11 *** −.03 ** Sector: extractive −.07 *** −.03 *** Sector: manufacturing .01 ** .01 Sector: business services .01 .08 *** Sector: consumer services .03 ** −.04 ***

*p < .05; **p < .01; ***p < .001

808 J. GAO ET AL.

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