Family firms, sustainable innovation and financing cost: Evidence from Chinese hi-tech small and medium-sized enterprises

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Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore

Family firms, sustainable innovation and financing cost: Evidence from Chinese hi-tech small and medium-sized enterprises Dong Xianga, Jiakui Chenb, David Tripec, Ning Zhangd,

⁎

a

China Institute for Micro, Small and Medium-sized Enterprises, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Rd, Changqing, Jinan, Shandong Province, 250000, China b School of Administration Management, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Rd, Changqing, Jinan, Shandong Province, 250000, China c School of Economics and Finance, Massey University, Palmerston North 4442, New Zealand d Department of Economics, Jinan University, No.601 Huangpu West Road, Guangzhou, Guangdong 510632, China

A R T I C LE I N FO

A B S T R A C T

JEL classification: C24 O32 G32

Using a large sample of hi-tech Chinese small and medium-size enterprises (SMEs), we examine whether familyowned businesses (FBs) can display more efficient use of innovation resources than non-FBs. We find that though family firms invest less in innovation input i.e. R&D expenditure, they outperform the non-FBs in terms of innovation output i.e. sales of new products or technology. We also find that the higher conversion rate of innovation input into output is closely related to financial constraints on innovation. The results suggest that the interaction between family ownership and financing cost has a significant negative effect on innovation, measured by R&D intensity and innovative sales. In addition, knowledge derived from competitors, universities and industry associations can effectively enhance innovative sales.

Keywords: Small and medium-sized enterprises (SMEs) Innovation input Innovation output Family firms Financing cost China

1. Introduction Reflecting the unique features of family businesses (FBs), the impact of family ownership and involvement in technological innovation has been growing, and this has become a promising research area (e.g. Sageder et al., 2016; Xi et al., 2015). Based on Schumpeter 's seminal work (1934), Freeman (1976) suggests that technological innovation can be defined as the set of activities through which a firm conceives, designs, manufactures, and introduces a new product, technology, system, or technique. For a family firm, the decision to undertake technological innovation can be quite complex, since family firms often have competing goals, such as economic efficiency and family social interests (Astrachan and Jaskiewicz, 2008; Chua et al., 2003; Kellermanns et al., 2012). Indeed, it has been argued that family firms are less innovative, and less involved in R&D activities than non-family firms (e.g. Block et al., 2013; Chen and Hsu, 2009). However, there are arguments that family ownership and involvement can be positively related to innovation, in terms of the conversion of input into output. Family ownership may discourage uncertain and resource-consuming R&D investments, but also lead to a higher conversion rate of innovation input into output (Duran et al., 2015). The ⁎

efficient allocation and utilization of R&D resources (Jensen, 2002) depends on organizational practices and routines (Henderson and Clark, 1990), adequate managerial incentive systems, and/or internal control systems (Hitt et al., 1991). Family governance can create incentives for efficiency and parsimony (Gedajlovic et al., 2004).Family owners and managers tend to prefer informal decision-making processes and organizational structures that allow them to avoid ceding power to non-family managers (Chrisman et al., 2014; Daily and Dollinger, 1992). Because of long-term family firm goals, such as transferring family control over the firm to the next generation, families may require higher-quality R&D activities (Chrisman and Patel, 2012). A large number of studies on FBs' innovation are based on large, publicly traded enterprises, and do not consider differences in small and medium-sized enterprises (SMEs) between family and non-family firms, regarding resources for investing in innovation. In financing innovation, financing constraints can restrict SMEs' R&D, to a much greater extent than other forms of investment (Brown et al., 2009). The unique characteristics of SMEs such as ownership structure can lead to outcomes very different from those for other firms, when applying for finance (Hamelin, 2011; Psillaki and Daskalakis, 2009). FBs are more creditworthy because of their lower likelihood of investing in risky

Corresponding author. E-mail addresses: [email protected] (D. Xiang), [email protected] (J. Chen), [email protected] (D. Tripe), [email protected] (N. Zhang).

https://doi.org/10.1016/j.techfore.2018.02.021 Received 5 November 2017; Received in revised form 23 February 2018; Accepted 28 February 2018 0040-1625/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Xiang, D., Technological Forecasting & Social Change (2018), https://doi.org/10.1016/j.techfore.2018.02.021

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Family ownership and control makes firms behave differently, particularly with regard to technological innovation. Straightforward economic goals such as shareholder value will be applied with a much longer time perspective – the family is likely to want the firm to survive for generations, rather than just until the next reporting cycle or until the business can be sold to some other operator (Habbershon and Williams, 1999).That makes them more risk averse, reflecting the overlapping nature of the family and business (Allio, 2004), and they may avoid new opportunities that might undermine the value of their assets when faced with risky opportunities (Corbett and Hmieleski, 2007). Moreover, family firms may be just reluctant to change (Beckhard and Dyer, 1983; Vago, 2004). Indeed, it has been argued that family firms are less innovative, and less involved in R&D activities than non-family firms (e.g. Block et al., 2013). Given that R&D expenditures are sunk costs with uncertain payoffs, family firms tend to pursue conservative innovation strategies associated with less intensive R&D investments (Miller et al., 2011). Anderson et al. (2012) find that family firms prefer investing in physical assets to riskier R&D projects. Schmid et al. (2014) and Matzler et al. (2015) underline the negative impact of family control on R&D intensity. Chrisman and Patel (2012), using a sample of 964 manufacturing public-held firms in the S&P 1500 index, find that family firms usually invest less in R&D than nonfamily firms and the variability of their investments is higher, especially when performance is below the levels they aspire to. Using a sample of firms listed in the Standard & Poor's 500, Block (2012) also points to a negative effect of family ownership on R&D intensity. Lower levels of R&D investment in family firms are also observed by Chen and Hsu (2009) among listed Taiwanese corporations, by Muñoz-Bullón and Sanchez-Bueno (2011) among publicly traded Canadian firms and by Munari et al. (2010) looking at firms in six European countries. Munari et al. (2010) find, in a sample of 1000 public traded firms from France, Norway, Sweden, Germany, Italy, Sweden, and the United Kingdom, that family ownership is negatively associated with R&D investments, due to the limited risk propensity of their controlling shareholders. However, there are increasing arguments family ownership of and involvement in firms can be positively related to innovation, especially for innovation outputs (e.g. Broekaert et al., 2016; Duran et al., 2015; Kellermanns et al., 2012). Moreover, the previously-cited findings that FBs invest less in innovation or R&D are all from studies of large, publicly traded enterprises, and do not consider differences in SMEs between family and non-family firms regarding resources for investing in innovation (Kellermanns et al., 2012). For SMEs, family ownership may discourage uncertain and resource-consuming R&D investments, but also have higher R&D productivity, causing firms to invest less in R &D (Duran et al., 2015).Firms need to possess dynamic capabilities (Eisenhardt and Martin, 2000; Teece et al., 1997; Zollo and Winter, 2002) to efficiently convert innovation input such as R&D expenditure into output such as patents. The efficient allocation and utilization of R &D resources (Hitt et al., 1991; Jensen, 2002) depends on organizational practices and routines (Henderson and Clark, 1990), adequate managerial incentive systems, and/or internal control systems (Hitt et al., 1991). For example, Gilbert (2005) illustrates how investments in new technologies do not necessarily result in superior products and services if, for instance, routines are not properly adapted. Family ownership may thus benefit innovation outputs or innovation productivity for the following reasons. First, family governance can create incentives for efficiency and parsimony (Gedajlovic et al., 2004). Family owners and managers tend to prefer informal decision-making processes and organization structures that allow them to avoid ceding power to nonfamily managers (Chrisman et al., 2014; Daily and Dollinger, 1992). Family management tends to effectively and efficiently exploit innovation and R&D activities, given that a large portion of the family's overall wealth is invested in the firm (Patel and Chrisman, 2014). Since controlling families are reluctant to reduce their

projects (Naldi et al., 2007), and may thus suffer less from the financial constraints due to innovation. The aim of our paper is to investigate whether FBs have a higher level of innovation output while investing less in innovation input, or a higher conversion rate of innovation input into output, and the factors influencing the differences in conversion rates between FBs and nonFBs. The paper contributes to the literature in three ways. Firstly, it contributes to the empirical research on comparing the conversion rate of innovation input into output between FBs and non-FBs. Until now, there have been few studies on whether family firms can convert innovation input into output at a higher rate than non-FBs. Employing a meta-analysis based on 108 studies from 42 countries, Duran et al. (2015) suggest that FBs invest less in innovation, but convert innovation input into output at an increased rate. However, there is still a relative lack of empirical studies on this topic in prior research. In this paper, looking at Chinese hi-tech SMEs, we find that despite investing less in innovation input, i.e. R&D expenditure, family firms produce a higher level of innovation outcomes or output such as sales based on the new products or technology. The R&D intensity of family firms is many (18–26) times greater than those for non-FBs and family ownership can significantly increase sales (up to 32.7%) on new products and technology, compared to non-family firms. Secondly, we also examine the moderate effect of financing cost on SMEs' innovation. Hottenrott and Peters (2012) suggest that financial constraints hold back innovation activities, and firms with high innovative capability but low financial resources are more likely to be constrained. In a similar vein, we examine whether and to what extent family ownership and financial constraints can affect SMEs' innovation. We find that the higher conversion rate of innovation input into output is closely related to financial constraints on innovation. Our results suggest that the interaction between family ownership and financing cost has a significant negative effect on innovation, measured by R&D intensity and innovative sales. We also examine the causality between financing cost and innovation and find that R&D intensity and financing cost have a two-way effect, whereas innovative sales and financing cost have a one-way effect. Third, we find that knowledge resources appear to have diverse effects on R&D intensity (Shao et al., 2016, 2017). Knowledge from universities, research institutions and advisory firms can significantly raise SMEs' R&D expenditure, whereas, knowledge derived from customers and/or clients can help SMEs save on R&D expenditure. Joint R &D proves to have better outcomes, with a strong increase in innovative sales, whereas independent R&D (Self-R&D) does not show a significant increase. The remainder of the paper is structured as follows. Section 2 provides a review of literature and develops the hypotheses used in the paper. Section 3 discusses the data, variables and estimation models, including how we adjust for sample selection and endogeneity. Section 4 presents the results and Section 5 the conclusion. 2. Literature review and hypothesis development 2.1. Family firms and innovation Despite substantial inquiry (Xi et al., 2015), there is still no agreed single description of a family firm (Harms, 2014). Three broad definitions are used in the literature (Sageder et al., 2016). The first consists of family-owned and managed firms, where family ownership is at least 50% and the family is actively involved in management or governance. The second is narrower and usually consists of large listed companies with a family ownership threshold of at least 5%, usually accompanied by family control or family members as board members. The final definition of a family firm is one in which the founder or a member of his/ her family, by either blood or marriage, is an executive, director, or blockholder (see Sageder et al., 2016). We adopt the first of these definitions. 2

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ownership stake in the firm (Gómez-Mejía et al., 2007; Zellweger et al., 2012), they rely heavily on family investments and internally generated cash flows to fund growth. Second, in family firms, family members are emotionally tied to the family and the firm (Miller and Le Breton-Miller, 2006). Thus, the goals of principals and stewards are aligned and both parties pursue the same vision for the business; stewards can decide on their own how to achieve objectives (Kloeckner, 2009). For a non-family firm, the goals of the principal may differ from those of management (Jensen and Meckling, 1976); the firm has to establish control mechanisms to align interests between the principal and management in innovation (e.g. Prym, 2011). Family ownership and management can thus mitigate agency costs (Jensen and Meckling, 1976). Third, family management are intrinsically motivated and act altruistically for the benefit of the business and its principals (owners) (Davis et al., 1997). Family governance often allows for the altruistic treatment of family members, which undermines accountability norms (Jaskiewicz et al., 2013; Pazzaglia et al., 2013; Schulze et al., 2001), with family members often not held fully accountable for their actions (Gedajlovic et al., 2004).Fourth, because of longterm family firm goals, such as transferring family control to the next generation, families may require higher-quality R&D activities (Chrisman and Patel, 2012). For example, industries with high potential for growth may prompt family firms to focus more on innovation activities than nonfamily firms to secure their long-term viability and control over the company (Choi et al., 2015). Based on the above literature, we present the following hypothesis:

even from potential investors (Brown et al., 2009). Several studies (e.g. Hall, 2002; Bond et al., 2003) find evidence suggesting that firms in the United States and other countries face financing constraints for R&D. For both external sources, including bank loans or other debt contracts, and internal sources, including retained profits or (new) equity, Brown et al. (2009) suggest that equity finance has advantages over debt finance for Hitech small firms, because: 1) there are no collateral requirements; 2) additional equity does not magnify problems associated with financial distress. 3) internal equity finance does not create adverse selection problems. However, given that most SMEs have very limited access to equity markets, especially in less developed financial markets such as in China, debt finance is still their major financing source. However, Brown et al. (2009) suggest that there are several negative factors affecting hi-tech SMEs' obtaining debt finance. First, the structure of a debt contract is not well suited for R&Dintensive firms with uncertain and volatile returns (Stiglitz, 1985). Second, adverse selection problems (Stiglitz and Weiss, 1981) are more likely in high-tech industries due to the inherent riskiness of investments. Third, debt financing can lead to ex post changes in behavior (moral hazard) that are likely more severe for high-tech firms because they can more easily substitute high-risk for low-risk projects. Fourth, the expected marginal cost of financial distress rises rapidly with leverage for young high-tech firms because their market value depends heavily on future growth options that rapidly deteriorate if they face financial distress (Cornell and Shapiro, 1988). Finally, the limited collateral value of intangible assets should greatly restrict use of debt: Risky firms typically must pledge collateral to obtain debt finance (Berger and Udell, 1990). However, the unique characteristics of SMEs such as ownership structure can lead to outcomes very different from those for other firms when applying for external finance (Hamelin, 2011; Psillaki and Daskalakis, 2009; Romano et al., 2001). Conventional pecking-order theory suggests that family firms prefer internal financing (either from equity contributions by family shareholders or retained earnings) to external financing (Lappalainen and Niskanen, 2013; López-Gracia and Sánchez-Andújar, 2007; Romano et al., 2001), comprising equity and debt in that order (Blanco-Mazagatos et al., 2007; Croci et al., 2011; Gottardo and Moisello, 2014). Moreover, because family firms are more reluctant to dilute the family's control (Mishra and McConaughy, 1999), they are usually less willing to raise capital through external equity offerings (Croci et al., 2011). For example, Keasey et al. (2015) argue that family SMEs, unlike other SMEs, may be able to access more debt financing because family members may be prepared to use their personal wealth as collateral. Drawing on these perspectives, we have the following hypothesis:

Hypothesis 1a. Family SMEs have lower innovation input than nonfamily SMEs. Hypothesis 1b. Family SMEs have higher innovation output than nonfamily SMEs.

2.2. Financing constraints and innovation Because of their inherent informational opaqueness and the limited finance sources available SMEs generally require special attention (e.g. Beck et al., 2005; Beck et al., 2008; Guiso and Minetti, 2010).SMEs are expected to suffer more from market failures such as a relative inability to capture the profits from innovation (Schumpeter, 1942; Nelson, 1959; Arrow, 1962) and uncertainty (Dixit and Pindyck, 1994; Pindyck, 1991), leading to information asymmetries between firms and external suppliers of finance. This results in underinvestment in R&D (Arrow, 1962; Stiglitz and Weiss, 1981). Himmelberg and Petersen (1994) and Carpenter and Petersen (2002), focusing on independent high-tech, small and young firms conclude that these firms are most financially constrained. Westhead and Storey (1997) compare the extent to which the most technologically sophisticated small firms are more financially constrained than less technologically sophisticated ones and find that the former are more impeded in growth due to financial constraints. Goodacre and Tonks (1995) state that innovation in high-tech industries is more likely to be of a new sort because it is more difficult for financiers to evaluate the investment. Storey and Tether (1998) focus specifically on so-called New Technology-Based Firms (NTBFs) and emphasize their higher need for external financing: others discuss their high innovative performance and growth (Storey and Tether, 1998; Colombo and Grilli, 2010; Licht and Nerlinger, 1998; Almus and Nerlinger, 1999). Based on the above literature, we have the following hypothesis:

Hypothesis 3a. Family SMEs have fewer financing constraints on innovation than non-family SMEs. Under the proposition that family firms might have higher innovation productivity, family SMEs can generate more innovation outputs such as patents for any level of inputs (R&D expenditure) when compared to non-family SMEs (Duran et al., 2015). We thus propose that family SMEs may be less financially constrained on innovation output than on input. The additional reasons why family SMEs may be less financially constrained on innovation output arise from the potential endogeneity between innovation and financing costs. The family owners represent a unique class of shareholders with undiversified portfolios (Anderson et al., 2003), which makes them less likely to invest in risky projects (Naldi et al., 2007). Thus, creditors will demand lower rents than from nonfamily firms with typically more diversified shareholders. The family's reputation (Anderson and Reed, 2003) and the ability to borrow (relational) social capital (Du et al., 2015) through family involvement can also lead to improved access to financing. On this basis, we have the following hypothesis:

Hypothesis 2. Hi-tech SMEs face higher financing costs for innovation than non-hi-tech SMEs. 2.3. Effect of family ownership on innovation financing

Hypothesis 3b. For family SMEs, the savings in innovation output (sales based on innovation) is greater than the savings in innovation input (R&D intensity) relative to non-family SMEs.

For SMEs, financing constraints can restrict R&D much more than other forms of investment. Reasons include the lack of collateral value for R&D“capital” and firms' need to protect proprietary information, 3

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3. Data and method

Our study uses data from the “China hi-Tech Small and Medium sized Enterprises Dynamic Growth Survey” (CTSMEDGS). The CTSMEDGS is a major project of the National Social Science Fund of China (NSSFC), which began in 2015 with the objective of building a database for the purpose of financial evaluation, credit rating, and policy-related research for hi-tech SMEs. Our interest in this sector is because Chinese hi-tech firms, both large and small, are growing rapidly and increasing their global reach. The CTSMEDGS survey comprises basic firm information, firm external growth environments, and firm internal growth factors. The first wave of the CTSMEDGS is for the five-year period from 2015 to 2019. To ensure the effectiveness and reliability of the survey, four Chinese universities administered 10,000 questionnaires for the initial survey in 2015 through onsite distribution and collection. The firms sampled by the China Statistics Bureau and the State Administration for Industry and Commerce were from registered nonfinancial hi-tech firms in China with fewer than 1000 employees. The 2015 CTSMEDGS starts with financial data for 2683 hi-tech firms. We then exclude 106 firms with negative or zero net assets, 352 firms with zero cash flow over the year, 409 firms in the financial industry because of their different financial structure, and 964 firms with significant missing observations. Our final sample comprises 958 hi-tech SMEs.

also include a standard set of industry dummy variables as controls. The innovation variables are; Joint_RD (largely relying on joint R&D), Self_RD (relying on self R&D), Tech_employee (to what extent firms can get competent employees for R&D), Training (to what extent the local government helps with training), Market_tech (to what extent the technology in the market is developing rapidly). In addition, we control for the sources of innovation knowledge, that is, whether knowledge is from suppliers (Knowledge_supplier), customers (Knowledge_customer), business rivals (Knowledge_rival), universities or similar institutions (Knowledge_university), associations (Knowledge_association), and/or advisory firms (Knowledge_advice). The innovation financing variables are External_debt and External_equity (reliance on external debt/equity), National_bank, Local_bank and Non-bank (relationship is with a national/local/nonbank bank). Table 1 provides full definitions and coding of the dummy variables. While investigating the impact of family ownership on innovation behavior, we need to address endogeneity arising from omitted variables, although our research design helps mitigate concerns about omitted heterogeneity by sampling a large number of firms and including many control variables. We address this endogeneity concern with two methodological approaches. First, we specify IV regressions, where following the approach of Lin et al. (2011) and Laeven and Levine (2008), we employ instrumental variables including the average family control rights (Control_right) by industry, Owner_age (age), Owner_gender (gender), and Owner_education (a university degree), along with the full set of control variables (see Eq.(2)).

3.2. Variables and models

Family = α 0 + α1Controlright + α 2Owner age + α3Owner gender

3.1. Data

+ α 4Owner education To examine the interaction between family ownership, financing cost, and innovation for SMEs, we employ the following multivariate methods: (1) instrumental variable (IV) models, (2) Heckman selection estimation procedures, and (3) simultaneous equation system (SEM) models. For Hypotheses 1a and 1b, we investigate whether family SMEs have a higher level of innovation productivity, i.e. have a lower level of innovation input but not a lower level of innovation output, using the following logit model:

+ α5Familymember + α 6Firmyear + α 7Growth + α 8Assets + α 9Export + α10Riskpreference + α11Externaldebt + α12Externalequity + α13Nationalbank + α14Localbank + α15Nonbank + α16Joint RD + α17Self RD + α18Govfund + α19Market tech + α 20Training + α 21Techemployee + α 22Knowledgeadvice + α 23Knowledgesupplier + α 24Knowledgecustomer + α 25Knowledgerival

RDintensity / Innovationsales = α 0 + α1Family + α 2Familymember + α3Firmyear + α 4Growth + α5Assets + α 6Export + α 7Riskpreference + α 8Externaldebt + α 9Externalequity + α10Nationalbank + α11Localbank + α12Non − bank + α13Joint RD + α14Self RD + α15Govfund + α16Market tech + α17Training + α18Techemployee + α19Knowledgeadvice + α 20Knowledgesupplier + α 21Knowledgecustomer + α 22Knowledgerival + α 23Knowledgeuniversity + α 24Knowledgeassociation + α 25Industry + ε

+ α 26Knowledgeuniversity + α 27Knowledgeassociation + α 28Industry + ε (2) Second, to deal with selection bias arising from the survey data, we employ a Heckman selection procedure. Following Ghoul et al. (2016) and Wooldridge (2010), the first stage of the procedure is a probit model of Eq.(2) including all the exogenous variables. In the second stage, we regress the dependent variables on the inverse Mills' ratio (λ) estimated from the first stage, along with the variable of interest and the control variables. For Hypotheses 2, 3a and 3b, we adopt the measurement of innovation financing from Eq.(1). Finance_cost based on interest rate ranges is an ordinal dummy variable for interest rates: < 10% (1), 10%–20% (2), 20–30% (3), 30–40% (4), and > 40% (5). Therefore, a positive effect of Finance_cost on innovation demonstrates an increase in financing cost for SME innovation, which may imply a constraint on SMEs' innovation financing. In addition, through an interaction between family ownership and Finance_cost, we examine the effect of family ownership on the innovation financial constraints of SMEs. While examining the effect of financing costs on innovation, we need to address additional endogeneity issues arising from the potential causality between innovation and financing costs. To do this, we use a simultaneous equation model (SEM) formed by Eqs.(3) and (4) as follows:

(1) 1

where RD_intensity is the ratio of R&D expenditure to total sales, which is the measurement of innovation input. Innovation_sales is sales based on innovation, that is, sales (in logarithm) from introduced products and/or technologyfor the year, which is the measure of innovation output. Family is a dummy variable indicating family ownership and control. We employ three sets of control variables: over the firm's current operational characteristics, over the firm's innovation, and for innovation financing characteristics. The operational variables include Family_member (two or more family members involved in the firm's management), State-owned (the firm is owned by the state), Risk-preference (the firm is not averse to high risk), Firm_year (the number of years since establishment), Growth (yearly sales have grown), Assets (total assets), and Export (if the firm exports). We

1 Note that, in the survey, R&D expenditure includes all expenses on R&D plus purchases of innovation and technology.

4

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Table 1 Definitions and descriptive statistics.

Dependent variables

Variable

Definition

Mean

Std. dev.

Min.

Max.

RD_intensity

Ordinal dummy variables for the ratio of R&D expenditure to total sales: < 1% (1), 1%–3% (2), 3–5% (3), 5–10% (4), and > 10% (5). The sales on new products and/or new technology

2.361

1.168

1

5

27.835

37.449

0

Dummy variable with one if family ownership > 50% of control and zero otherwise Dummy variable with one if there are two or more family members involved in management and zero otherwise. Dummy variable with one if firm is owned by the state and zero otherwise Dummy variable with one if the firm is not averse to high risk and zero otherwise Number of years since firm establishment Ordinal dummy variables for the sales growth rates: < 10% (1), 10%–20% (2), 20–30% (3), 30–40% (4), and > 40% (5). Total assets in 10,000 RMB Dummy variable with one if firm has exports and zero otherwise Dummy variable with one if the firm is largely relying on joint R&D and zero otherwise Dummy variable with one if the firm is largely relying on self R&D and zero otherwise Dummy variable with one if the knowledge is from supplier and zero otherwise Dummy variable with one if the knowledge is from customers and zero otherwise Dummy variable with one if the knowledge is from business rivals and zero otherwise Dummy variable with one if the knowledge is from universities and zero otherwise Dummy variable with one if the knowledge is from association and zero otherwise Dummy variable with one if the knowledge is from advisory companies and zero otherwise to what extent firms can get competent employees for R&D to what extent the technology in the market is developing rapidly Governments or authorities help with training Ordinal dummy variables for the number of fund assistance from government: < 1 (1), 1–3 (2), and > 4 (3). Ordinal dummy variables for interest rates: < 10% (1), 10%–20% (2), 20–30% (3), 30–40% (4), and > 40% (5). Dummy variable with one if firm relies on external debt and zero otherwise Dummy variable with one if firm relies on external equity and zero otherwise Dummy variable with one if national bank is firm's relationship bank and zero otherwise Dummy variable with one if local bank is the firm's relationship bank and zero otherwise Dummy variable with one if nonbank financial firm is the firm's relationship bank and zero otherwise Length in years of current lender–borrower relationship Ordinal dummy variables for time from application to obtaining finance: < 3 days (1), 3–15 days (2), 15–30 days (3), 30–60 days (4), and > 60 days (5). Dummy variable with one if financing purpose is for working capital and zero otherwise Dummy variable with one if financing purpose is for innovation and zero otherwise Dummy variable with one if financing purpose is for firm expansion and zero otherwise Dummy variable with one if source of collateral is fixed assets and zero otherwise Dummy variable with one if source of collateral is variable assets and zero otherwise Ordinal dummy variables for loan term: < 1 years (1), 1–2 years (2), 3–5 years (3), and > 5 years (4). Average family control rights by industry

0.329 0.522

0.470 0.500

0 0

1132.472 1 1

0.029 0.058 8.207 1.970

0.167 0.234 6.864 0.842

0 0 0 1

1 1 67 5

5214 0.316 0.209 0.267 0.571 0.537 0.256 0.211 0.251 0.127 4.895 4.474 4.655 1.105

13,853 0.465 0.407 0.442 0.495 0.498 0.436 0.408 0.434 0.333 1.354 1.742 1.514 0.628

0.83 0 0 0 0 0 0 0 0 0 1 1 1 0

237,600 1 1 1 1 1 1 1 1 1 7 7 7 3

1.890

0.829

1

5

0.714 0.116 0.711 0.220 0.051

0.452 0.321 0.454 0.415 0.221

0 0 0 0 0

1 1 1 1 1

0.141 2.595

0.348 1.017

0 1

1 5

0.363 0.124 0.953 0.541 0.273 1.886

0.481 0.330 0.212 0.499 0.446 0.827

0 0 0 0 0 1

1 1 1 1 1 4

0.405

0.027

0.486

2.877

1.027

0.325 1

4

0.780 0.393

0.415 0.489

0 0

1 1

Innovative_sales(mil)

Operational characteristics

Family Family_member State-owned Risk-preference Firm_year Growth Assets Export Joint_RD Self_RD Knowledge_supplier Knowledge_customer Knowledge_rival Knowledge_university Knowledge_association Knowledge_advice Tech_employee Market_tech Training Gov_fund

Financial characteristics

Finance_cost External_debt External_equity National_bank Local_bank Nonbank Bank_year Application_time Working_cap Technology Expansion Collateral_FA Collateral_VA Loan_term

Instrumental variables

Control_right Owner_age Owner_sex Owner_education

Ordinal dummy variables for loan term: < 1 year (1), 1–2 years (2), 3–5 years (3), and > 5 years (4). Dummy variable with one if owner is male and zero otherwise Dummy variable with one if owner has a university degree and zero otherwise

where Finance_cost measures SME financing costs deriving from a number of interest rate ranges; Bank_year (length of borrower relationship), Application_time (time from application to financing), Working_cap, Technology and Expansion (financing for working capital/ innovation/expansion). Collateral indicates that most lending is supported by collateral, Loan_term (average length of loan term) is used to control for the term risk for the lending, while Collateral_FA and Collateral_VA (collateral is fixed/variable asset) in Eq.(5) are used to control for collateral characteristics: all the other variables have been defined above. The simultaneous equation model (SEM) is estimated through a three-stage-estimation procedure (see Pan and Tian, 2016).

RDintensity / Innovationsales = α 0 + α1Family + α 2Familymember + α3Firmyear + α 4Growth + α5Assets + α 6Export + α 7Riskpreference + α 8Externaldebt + α 9Externalequity + α10Nationalbank + α11Localbank + α12Non − bank + α13Joint RD + α14Self RD + α15Govfund + α16Market tech + α17Training + α18Techemployee + α19Knowledgeadvice + α 20Knowledgesupplier + α 21Knowledgecustomer + α 22Knowledgerival + α 23Knowledgeuniversity + α 24Knowledgeassociation + α 25Industry + α 26Financecost + ε

(3) Financecost = α 0 + α1Colateral + α 2Family + α3Familymember + α 4Firmyear + α5Growth + α 6Assets + α 7Export + α 8Riskpreference + α 9Externaldebt + α10Externalequity + α11Nationalbank + α12Localbank + α13Non − bank + α14Bankyear + α15Applicationtime + α16Workingcap + α17Technology + α18Expansion + α19Loanterm + α19RDintensity (Innovationsales ) + ε

4. Empirical results 4.1. Univariate analysis In Table 2, we report univariate tests of differences in means between family and non-family firms. The FBs show less R&D intensity

(4) 5

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than non-family firms, significant at the 5% level. At the same time, family firms have a shortfall of > 8 million in sales from the introduction of new products and/or new technology when compared with non-family firms, although the shortfall is not significant. FBs have significantly lower financing costs relative to non-FBs. With respect to innovative behaviors, 19.1% of FBs have joint R&D, slightly lower than that of non-FBs at 21.9%; in contrast, FBs appear to have a higher level of self R&D (29.7%) than non-FBs (25.1%). Regarding sources of knowledge, FBs and non-FBs do not differ significantly except that FBs have a higher likelihood (27.6%) of looking for knowledge from advisory institutions than non-FBs (23.8%). Unsurprisingly, FBs appear to face less pressure in terms of technological development in the markets. FBs also appear to find it easier to get competent staff, government assistance on training and funds; but none of these effects are significant. However, univariate analysis does not control for other variables that could affect firms' innovative behaviors, and we thus conduct multivariate analysis.

the government and related entities positively promotes SMEs' R&D intensity: for instance, government funds increase SMEs' R&D intensity by an odds ratio of 1.4, and help with training increases intensity by an odds ratio of 1.07. Knowledge resources appear to have diverse effects on R&D intensity. Knowledge from universities, research institutions and advisory firms can significantly raise the SMEs' R&D expenditure, whereas, knowledge deriving from the customers and/or clients can help SMEs save on R&D expenditure. This may imply that the SMEs get knowledge such as intellectual property rights from the universities, research and advisory institutions at a relatively higher cost when compared with knowledge from the parties such as customers and clients on the downstream business chain through, say, questionnaires, joint projects, or business models. 4.2.2. Family firms and innovation output Table 4 reports the results of the Heckman models for the relationship between family ownership, financing cost and innovation output. Columns 1 and 2 show the effects of family ownership on innovation output i.e. sales on new products or technology; Columns 3 and 4 present results from testing SMEs' innovation constraints for innovation output; Columns 5 and 6 show the moderating effect of family ownership on the relationship between innovation output and financing costs. In Columns 1 & 2, in contrast to the negative effects on R&D intensity, family ownership shows a significant positive effect on innovation output measured by sales of new products and new technology. Precisely, family ownership can increase the sales on new products and technology by 32.7% on its own. This provides support for Hypotheses 1a and 1b. That is, family firms can convert more innovation input into output, consuming less innovation input i.e. R&D expenditure given a certain level of innovation outcomes or output i.e. sales on the new products or technology. In Columns 3 & 4, the significant positive coefficient of Financial_cost demonstrates that SMEs are subject to financial constraints while engaging in innovation, measured by innovation outcomes. A one unit (i.e. 10%) increase in financing costs can generate 19.7% increase in sales based on innovation; alternatively, a rise in sales on innovation requires a rise in financing costs by 10%.2 In this sense, SMEs' innovation is still under increasing returns to scale, which means a shortfall of innovation financing. Therefore, Hypothesis 2 has been supported. The model in Columns 5 & 6 includes an interaction item between family ownership and financing costs, with this showing a significant negative coefficient. The negative relationship between the interaction item and the innovation outcomes implies that FBs may have lower financing costs for innovation compared to non-FBs. In the meantime, the coefficient for family ownership is much larger than in the previous models, indicating an increasingly positive effect for family ownership on innovation output, under higher financing costs. This suggests that FBs can generate an even higher level of innovation outcomes while consuming less finance. Together with the finding in Section 4.2.1, Hypothesis 3a is supported. That is, family SMEs have fewer financing constraints on innovation (measured by both input i.e. R&D intensity and output i.e. sales based on innovation) than non-family SMEs. With respect to control variables, larger firms and firms with export business can generate higher levels of innovative sales with marginal effects of approximately 27.9% and 85.2% (see Column 2 of Table 4). Thus, SMEs with export business can have an 85.2% increase in innovative sales compared to firms without; likewise, a 10% larger size in

4.2. Multivariate analysis 4.2.1. Family firms and innovation input Table 3 shows the relationship between family ownership, financing cost and innovation input. Column 1 is a probit model with instrument variables; Columns 2–7 show the estimation results from the Heckman models discussed in section 3. Columns 2 and 3 show the effects of family ownership on innovation input i.e. R&D intensity; Columns 4 and 5 show results for testing SMEs' innovation constraints in terms of innovation input; while Columns 6 and 7 show the moderating effect of family ownership on the relationship between innovation input and financing costs. Column 1 reports the results for the probit model of family ownership which serve as the first stage for the Heckman procedures. We see that, all other things being equal, family firms are more likely to have two or more family members associated with management. Moreover, older or male owners tend to run family firms. As expected, family ownership shows a significant negative effect on R&D intensity, consistent across all the models (see Columns 2–7). In general, with other variables held constant, the odds of low levels of R&D intensity versus the combined middle and high levels of R&D are between 0.038 and 0.057 times lower for FBs compared to non-FBs. Put differently, for FBs, the chances of low level of R&D intensity are from 18 (1/0.057) to 26 (1/0.038) times greater than those for non-FBs. This is consistent with the results of the univariate analysis, with a difference in R&D intensity of −1.74%.The results in Columns 4 & 5 show that financing costs have significantly increased with R&D; the odds ratio of 1.433 demonstrates that the increase in financing costs is 1.433 times greater, provided that the other variables are held constant in the model. This implies that SMEs generally face financial constraints while engaging in R&D activities. Next, in Columns 6 & 7, the interaction item between family ownership and financing cost shows a significant negative effect on R&D intensity with a coefficient of −0.11 and an odds ratio of 0.896. This provides evidence that family firms have lower levels of R& D intensity compared to non-FBs, also in line with the univariate analysis reported in Table 2. Results for the control variables suggest that larger and growing firms are more likely to invest in R&D, with odds ratios of approximately 1.06 and 1.6 respectively. Interestingly, the length of the banking relationship has a significant negative effect on R&D expenditure. This is in line with the proposition that, when compared with equity investors, creditors prefer firms not to invest in risky projects such as R&D (see Brown et al., 2009). Both joint and independent R&D can promote R&D expenditure, and the values of the odds ratios suggest that independent R&D proves more costly than joint R&D with odds ratios of approximately 3.5 and 2.7 respectively. Assistance from

2 Since the variable Financial_cost is an ordinal dummy variable corresponding to the interest rates: < 10% (1), 10%–20% (2), 20–30% (3), 30–40% (4), and > 40% (5), a one unit increase in Financial_cost indicates an approximate 10% increase in interest rates.

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Table 2 Univariate tests: family (n = 340) versus nonfamily (n = 618) firms. Variables

RD_intensity Innovation_sales(mil) Finance_cost Collateral Family_member Firm_year Growth Assets Risk-preference Export Joint_RD Self_RD Knowledge_supplier Knowledge_customer Knowledge_rival Knowledge_university Knowledge_association Knowledge_advice Tech_employee Market_tech Training Gov_fund External_debt External_equity National_bank Local_bank Non-bank Bank_year Application_time Working_cap Technology Expansion Colateral_FA Colateral_VA Bank_year Loan_term Collateral_FA Collateral_VA

Family firms

Nonfamily firms

Difference in means

Mean

Std. dev.

Mean

Std. dev.

t-Test

2.247 21.996 1.805 0.549 0.687 8.008 1.954 5364.457 0.072 0.282 0.191 0.297 0.571 0.547 0.256 0.126 0.229 0.276 4.221 4.394 4.074 1.076 0.708 0.108 0.728 0.226 0.049 0.149 2.574 1.810 0.372 0.118 0.944 0.538 0.264 2.574 0.538 0.264

0.064 6.810 0.841 0.498 0.464 5.633 0.928 13,526.340 0.258 0.451 0.213 0.024 0.026 0.027 0.024 0.018 0.022 0.024 0.081 0.080 0.083 0.032 0.455 0.310 0.445 0.419 0.216 0.356 1.110 0.817 0.484 0.323 0.231 0.499 0.441 0.056 0.025 0.022

2.421 30.852 1.932 0.548 0.442 8.304 1.977 5140.820 0.052 0.332 0.219 0.251 0.571 0.532 0.257 0.127 0.202 0.238 4.284 4.516 4.171 1.120 0.717 0.121 0.702 0.218 0.053 0.137 2.605 1.923 0.358 0.127 0.957 0.542 0.278 2.605 0.542 0.278

0.045 17.636 0.820 0.498 0.497 7.395 0.796 14,019.510 0.221 0.471 0.016 0.017 0.019 0.019 0.017 0.013 0.015 0.016 0.056 0.055 0.058 0.025 0.451 0.326 0.458 0.413 0.224 0.344 0.969 0.829 0.480 0.333 0.202 0.499 0.448 0.034 0.017 0.016

−0.0174⁎⁎ −8.856 −0.127⁎⁎⁎ 0.000 0.246⁎⁎⁎ −0.297 −0.024 223.637 0.020⁎ −0.050⁎⁎ −0.028 0.046⁎ 0.000 0.015 −0.001 −0.001 0.027 0.038⁎ −0.063 −0.122⁎ −0.097 −0.043 −0.009 −0.013 0.026 0.008 −0.004 0.012 −0.031 −0.113 0.013 −0.009 −0.014 −0.004 −0.014 −0.030 −0.004 −0.014

Notes: Asterisks denote significance at the ⁎0.10,

⁎⁎

0.05 and

⁎⁎⁎

0.01 level.

financing costs by using a simultaneous equation model (SEM) as per Eqs.(3) and (4). The estimation results for the three-stage procedures for the SEM models are reported in Table 5. Models 1 & 2 (columns 1 to 4) examine the causality between R&D intensity and financing costs, whereas Models 3 & 4 (columns 5 to 8) explore the causality between innovative sales and financing cost. For SMEs' innovative input, we can see that R&D intensity and financing cost may have a two-way relationship: one way, financing cost has a positive effect on R&D intensity with a coefficient of 0.795; the other way, SMEs' R&D expenditure also can positively affect firms' financing costs with a coefficient of 0.235. In other words, SMEs effectively engaging in R&D increase their financing costs. This two-way positive effect between R& D expenditure and financing costs provides evidence that SMEs face severe financial constraints while conducting R&D activities, supporting Hypothesis 2. In models 1 & 2, family ownership can significantly reduce financing costs; while financing costs have a positive effect on R&D intensity, the interaction item between Family ownership and financing cost has an insignificant effect on R & D intensity in Model 2. Similarly, we cannot find any two-way effect for innovation output and financing cost in Models 3 & 4. Instead, innovative sales have a consistent positive effect on financing costs. Family ownership has a significant negative effect on financing costs, whereas it does not show a significant effect on innovative sales. With the solid negative effect of family ownership on R&D intensity, Hypotheses 1a and 1b still hold. That is, family firms are able to consume less innovation input, given a level of innovation output, compared with non-FBs. Although

assets leads to 24.6% more innovative sales.3 However, younger firms appear to have a higher level of innovative sales than older ones: 10% younger firms can produce 0.16% more innovative sales. The other financial-set control variables do not have significant effects on innovation outcomes, except that relationship bank years in the first model appear to have a positive effect at the 10% level. As for innovative-set control variables, joint R&D proves to have better outcomes, which indicates a 44.1% increase in innovative sales, whereas, self- R&D does not show a significant increase. Together with the positive effects of self-R&D on R&D intensity, this implies that joint R&D is more costeffective and efficient than self-R&D. Again, the government funding assistance leads to a positive innovative outcome, with an improvement up to 66.9%. Firms in a market with a rapid development of technology have higher level innovative sales of 14%. Interestingly, knowledge deriving from competitors, universities and industry associations can effectively raise innovative sales with marginal effects of 44.5%, 169.7% and 42.2%. In particular, the knowledge provided by universities can substantially promote firms' innovative sales by 169.7%.

4.2.3. Endogeneity between innovation and financing cost As discussed in Section 3.2, we also address additional potential endogeneity from the possible causality between innovation and 3 Since the dependent variable, asset, is also in logarithm, the percent increase is equal to the coefficient value instead of marginal effect.

7

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Table 3 Family firms and innovation inputs.

Dep V

Family Coef.

RD_intensity Coef.

Odds

Coef.

Odds

ratio (1)

(2) −3.281***

Family

Coef.

(3)

(4) −2.993**

0.038

(1.235)

(5) 0.050

(1.237)

(6) −2.861**

0.500***

0.186

1.205

0.178

member

(0.093)

(0.230)

(

(0.231)

State-

−0.551*

0.388

1.474

0.373

1.453

0.386

owned

(−0.308)

(0.393)

(0.394)

(

(0.394)

Risk-

0.161

0.235

1.265

0.274

1.315

0.264

preference

(−0.18)

(0.264)

(

(0.265)

Firm_year

0.020**

0.016

1.016

0.009

(−0.008)

(0.012)

(

(0.012)

1.665

0.067

0.510***

(−0.053)

(0.074)

−0.016

0.072**

(−0.023)

(0.031)

−0.051

−0.012

(−0.103)

(0.145)

Internal_

0.432***

0.058

finance

(−0.105)

(0.218)

External_

−0.214**

0.088

debt

(−0.107)

(0.159)

External_

0.149

0.194

debt

(−0.14)

(0.186)

National_

0.218

0.184

bank

(−0.176)

(0.244)

Local_bank

0.258

0.153

(−0.175)

(0.250)

0.171

0.163

(−0.231)

(0.329)

Asset Export

Nonbank

0.165

−0.591***

(−0.127)

(0.191)

−0.044

1.091***

(−0.117)

(0.158)

0.057

1.266***

(−0.108)

(0.157)

−0.066

0.386***

(−0.076)

(0.101)

−0.034

0.037

(−0.033)

(0.047)

0.01

0.063*

(−0.032)

(0.044)

Tech_

0.029

−0.057

employee

(−0.034)

(0.048)

Knowledge

−0.034

−0.006

_supplier

(−0.096)

(0.133)

Knowledge

−0.018

−0.402***

_customer

(−0.097)

(0.134)

Knowledge

−0.008

−0.020

_rival

(−0.108)

(0.152)

Knowledge

0.024

0.332*

_advice

(−0.136)

(0.187)

Knowledge

−0.002

0.357**

_university

(−0.115)

(0.158)

Knowledge

0.117

−0.023

_association

(−0.104)

(0.145)

Bank_year Joint_RD Self_RD Gov_fund Market_tech Training

0.456***

1.195

0.082*** −0.015

1.009

0.120

1.577

0.088

1.086

0.054

0.985

0.255

1.127

0.186

1.092

0.203

1.056

−0.570***

1.291

0.992***

1.204

1.241***

1.225

0.331***

0.566

0.030

2.696

0.073*

3.461

−0.059

1.393

0.004

1.030

−0.342**

1.076

−0.045

0.943

0.319*

1.004

0.400**

0.711

−0.023 (0.146)

0.191

1.211

0.203

1.225

−0.569***

0.566

0.982***

2.669

1.256***

3.511

0.328***

1.389

0.028

1.028

0.073*

1.076

−0.056

0.945

0.005

1.005

−0.340**

0.711

(0.135) 0.956

−0.040

0.961

(0.152) 1.376

0.332*

1.394

(0.188) 1.491

(0.160) 0.977

1.302

(0.134)

(0.188) 1.429

0.264

(0.048)

(0.152) 1.394

1.054

(0.044)

(0.135) 0.980

0.053

(0.047)

(0.134) 0.669

1.073

(0.103)

(0.048) (0.133)

0.070

(0.157)

(0.044) 0.945

1.131

(0.160)

(0.047) 1.065

0.123

(0.193)

(0.103) 1.038

0.987

(0.329)

(0.157) 1.470

−0.013

(0.251)

(0.160) 3.547

1.086

(0.246)

(0.193) 2.978

0.083***

(0.189)

(0.331) 0.554

1.591

(0.159)

(0.252) 1.178

0.464***

(0.219)

(0.247) 1.165

1.009

(0.145)

(0.189) 1.202

0.009

(0.031)

(0.159) 1.214

1.303

(0.075)

(0.218) 1.092

1.471

(0.012)

(0.145) 1.060

1.154

(0.265)

(0.031) 0.988

0.143 (0.232)

(0.075) 1.075

(7) 0.057

(1.240)

Family_

Growth

Odds ratio

ratio

0.406**

1.501

(0.160) 0.977

−0.018

0.982

(0.145) (continued on next page)

8

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Table 3 (continued)

Owner_age

0.158***

Owner_sex

0.342***

(0.045) (0.113) invmills

−0.611

0.543

−0.545

(0.579)

0.580

(0.578) 0.359***

Finance_cost

−0.621

1.433

(0.083)

0.394***

1.483

(0.085)

Family*

−0.110*

Finance_cost

(0.066)

cons

0.537

(0.580)

0.896

−4.43 −6.773

Cut1

−0.054

0.696

0.647

(1.055)

(1.069)

(1.068)

1.543

2.317

2.273

(1.057)

(1.072)

(1.071)

Cut3

3.025

3.825

3.783

(1.061)

(1.077)

(1.076)

Cut4

4.861

5.673

5.630

Cut2

(1.071)

(1.088)

(1.087)

Industry FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Obs.

958

958

958

958

958

958

958

LR chi2

109.40***

367.10***

367.10***

386.29***

386.29***

389.05***

389.05***

Pseudo R2

0.089

0.130

0.130

0.137

0.137

0.138

0.138

Notes: Asterisks denote significance at the ⁎0.10,

⁎⁎

0.05 and

⁎⁎⁎

0.01 level.

find that family firms can produce a higher conversion rate of innovation input into output, consuming less innovation input i.e. R&D expenditure generating higher innovation output (i.e. sales on the new products or technology). For Family firms, the chances of low level R & D intensity are from 18 (1/0.057) to 26 (1/0.038) times greater than those for non-FBs; in the meantime, family ownership can effectively increase the sales of new products and technology by 32.7% relative to non-family firms. We also examine the moderating effect of financing cost on SMEs' innovation suggested by Hottenrott and Peters (2012). We find that the interaction between family ownership and financing cost has a significant negative effect on innovation measured by R&D intensity and innovative sales. The results in our models show that the interaction between family ownership and financing cost has a significant negative effect on R&D intensity with a coefficient of −0.11 and an odds ratio of 0.896; in the meantime, the negative relationship between the interaction item and innovative sales implies that FBs may have a reduction in financing costs for innovation output as compared with non-FBs. We further find that, for SMEs' innovative input, R&D intensity and financing cost may have a two-way relationship: in one way, financing cost has a positive effect on R&D intensity with a confident value of 0.795; the other way, SMEs' R&D expenditure can positively affect firms' financing costs with a coefficient of 0.235. This two-way positive effect between R&D expenditure and financing costs suggest that SMEs face severe financial constraints while conducting R& D activities. Furthermore, while comparing the negative effects of family ownership on financing cost, the reduction in effects on R&D intensity appears to be less than the effects on innovative sales. That is, family ownership can reduce financing costs for the models of R&D input by marginal effects of 36.1%–37.1% at a 10% significance level, and reduce financing costs for the models of the R&D output by marginal effects of 62.2%–63.8% at a 5% significance level. As for the control variables, knowledge resources appear to have the diverse effects on R&D intensity. Knowledge from universities, research institutions and advisory firms can significantly raise SMEs' R&D expenditure, whereas, knowledge deriving from customers and/or clients

the interaction item between family ownership and financing cost does not show a significant effect in Model 4, the steadily negative effect of family ownership on financing cost in Models 3 & 4 and Models 1 & 2 still supports Hypothesis 3a. That is, family ownership can help reducing financial constraints for SMEs. Furthermore, while comparing the negative effects of family ownership on financing costs, the reduction in R&D intensity appears to be less than the effects on innovative sales, by the values of coefficients and marginal effects. That is, family ownership can reduce financing costs for the models of the R&D input by the marginal effects of 36.1%–37.1% at a p value of 10%, and reduce financing costs for the models of the R&D output by the marginal effects of 62.2%–63.8% at a p value of 5%. This supports Hypothesis 3b, “For Family SMEs, reduction in the level of financing constraints on innovation output (sales based on innovation) is greater than the level of financing constraints on innovation input (R&D intensity)”. As for financing costs, older and/or growing firms may have increasing financing costs; while larger firms have lower financing costs. Unsurprisingly, firms which rely on internal finance have lower financing costs whereas firms relying on external equity are subject to higher financing costs. Also, SMEs whose relationship banks are larger have lower financing costs. However, application time has a positive effect on financing cost, which may imply a credit rationing for SMEs (e,g. Stiglitz and Weiss,1981). Loans for working capital may have lower financing costs. In addition, while conducting R&D activities, SMEs which have higher risk-taking, or loan applications for business expansion, may have higher financing costs. By contrast, for a certain level of innovative sales, having more family members involved in the business may raise financing cost. 5. Concluding remarks Motivated by the arguments about whether and to what extent family firms invest less in innovation projects than do non-family firms, this paper aimed to investigate financial aspects of SMEs' innovation.Specifically, using a sample of Chinese hi-tech SMEs, we 9

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Table 4 Family firms and innovation out-puts. Dep V

Innovation_sales(ln) EXPMarginal

Coef. (1) Family Family_member State-owned Risk-preference Firm_year Growth Asset Export Internal_finance External_debt External_debt National_bank Local_bank Nonbank Bank_year Joint_RD Self_RD Gov_fund Market_tech Training Tech_employee Knowledge_supplier Knowledge_customer Knowledge_rival Knowledge_advice Knowledge_university Knowledge_association invmills

Eff

−1

(2) ⁎

(3)

(4) ⁎

0.317

0.275 (0.150) 0.153 (0.233) −0.521 (0.469) 0.304 (0.286) −0.016⁎ (0.009) 0.126 (0.079) 0.246⁎⁎⁎ (0.041) 0.616⁎⁎⁎ (0.170) −0.207 (0.158) 0.242 (0.155) −0.086 (0.227) 0.075 (0.274) −0.266 (0.281) 0.300 (0.369) −0.292⁎ (0.176) 0.365⁎⁎ (0.185) 0.156 (0.177) 0.512⁎⁎⁎ (0.119) 0.131⁎⁎⁎ (0.049) −0.053 (0.047) −0.076 (0.051) 0.011 (0.142) −0.072 (0.144) 0.368⁎⁎ (0.159) −0.291 (0.226) 0.992⁎⁎⁎ (0.198) 0.352⁎⁎ (0.172) 0.204 (0.427)

EXPMarginal

Coef.

−0.016

0.279 0.852

−0.253 0.441

0.669 0.140

0.445

1.697 0.422

Finance_cost

0.282 (0.150) 0.168 (0.232) −0.531 (0.469) 0.331 (0.286) −0.018⁎⁎ (0.009) 0.096 (0.080) 0.252⁎⁎⁎ (0.042) 0.613⁎⁎⁎ (0.169) −0.154 (0.159) 0.239 (0.155) −0.152 (0.228) 0.112 (0.278) −0.240 (0.285) 0.312 (0.376) −0.261 (0.177) 0.317⁎ (0.186) 0.138 (0.178) 0.482⁎⁎⁎ (0.120) 0.128⁎⁎⁎ (0.049) −0.049 (0.047) −0.078 (0.051) 0.018 (0.142) −0.045 (0.144) 0.357⁎⁎ (0.159) −0.301 (0.225) 1.010⁎⁎⁎ (0.199) 0.358⁎⁎ (0.171) 0.217 (0.427) 0.180⁎⁎ (0.087)

Family ∗ Finance_cost cons Industry FE Obs. LR chi2 Pseudo R2

−0.573 (0.809) Yes 958 9.92⁎⁎⁎ 0.324

Notes: Asterisks denote significance at the ⁎0.10,

−0.926 (0.830) Yes 958 10.11⁎⁎⁎ 0.327 ⁎⁎

0.05 and

⁎⁎⁎

0.01 level.

10

0.325

−0.018

0.287 0.846

0.373

0.619 0.137

0.429

1.746 0.430

0.197

Eff

−1

EXPMarginal

Coef. (5)

(6) ⁎⁎⁎

1.003 (0.343) 0.151 (0.232) −0.551 (0.467) 0.335 (0.287) −0.019⁎⁎ (0.009) 0.112 (0.081) 0.252⁎⁎⁎ (0.041) 0.605⁎⁎⁎ (0.169) −0.166 (0.159) 0.228 (0.154) −0.172 (0.229) 0.112 (0.278) −0.255 (0.285) 0.302 (0.372) −0.261 (0.176) 0.283⁎ (0.184) 0.160 (0.178) 0.478⁎⁎⁎ (0.120) 0.129⁎⁎⁎ (0.049) −0.050 (0.047) −0.078 (0.051) 0.014 (0.141) −0.047 (0.143) 0.381⁎⁎ (0.159) −0.277 (0.225) 1.009⁎⁎⁎ (0.197) 0.363⁎⁎ (0.171) 0.220 (0.426) 0.322⁎⁎⁎ (0.108) −0.388⁎⁎ (0.166) −1.204 (0.831) Yes 958 10.00⁎⁎⁎ 0.331

1.726

−0.019

0.287 0.831

0.327

0.613 0.138

0.464

1.743 0.438

0.380 −0.322

Eff

−1

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Table 5 Endogeneity between innovation and financing cost. Dep V

Independent variables Family Finance_cost

RD_intensity

Finance_cost

RD_intensity

Finance_cost

Innovation_sales(ln)

Finance_cost

Innovation_sales(ln)

Finance_cost

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Coef.

Coef.

Coef.

Coef.

−0.559 (0.305) 0.103 (0.442)

−0.638⁎⁎ (0.294)

Coef.

Coef. ⁎⁎

−0.944 (0.408) 0.795⁎⁎⁎ (0.222)

−0.361 (0.248)

Coef. ⁎

Coef. ⁎⁎

−0.996 (0.436) 0.853⁎⁎⁎ (0.241)

0.235⁎⁎⁎ (0.062)

RD_intensity

−0.371 (0.253)

⁎

−0.501 (0.305) −0.029 (0.415)

0.234⁎⁎⁎ (0.062)

Innovation_sales Family_member State-owned Risk-preference Firm_year Growth Asset Export Internal_finance External_debt External_debt National_bank Local_bank Nonbank Bank_year

0.146 (0.110) 0.181 (0.232) 0.307⁎⁎ (0.152) 0.000 (0.007) 0.125⁎⁎ (0.057) 0.069⁎⁎⁎ (0.019) −0.034 (0.084) 0.189 (0.115) 0.040 (0.087) −0.235⁎ (0.140) 0.222 (0.147) 0.132 (0.145) 0.120 (0.186) −0.048 (0.110)

Application_time Working_cap Technology Expansion Joint_RD Self_RD Gov_fund Market_tech Training Tech_employee Knowledge_supplier Knowledge_customer Knowledge_rival Knowledge_advice Knowledge_university Knowledge_association Family ∗ Finance_cost

0.466⁎⁎⁎ (0.093) 0.584⁎⁎⁎ (0.081) 0.180⁎⁎⁎ (0.056) 0.020 (0.019) 0.015 (0.018) −0.026 (0.019) −0.012 (0.054) −0.194⁎⁎⁎ (0.065) 0.029 (0.062) 0.142⁎ (0.078) 0.128⁎ (0.069) −0.035 (0.059)

−0.058 (0.051) −0.075 (0.160) −0.132 (0.105) 0.012⁎⁎⁎ (0.004) 0.100⁎⁎⁎ (0.034) −0.052⁎⁎⁎ (0.013) 0.002 (0.058) −0.270⁎⁎⁎ (0.057) −0.022 (0.060) 0.340⁎⁎⁎ (0.078) −0.218⁎⁎ (0.098) −0.143 (0.098) −0.050 (0.129) −0.078 (0.073) 0.076⁎⁎⁎ (0.021) −0.082⁎⁎ (0.041) 0.053 (0.070) −0.198⁎ (0.108)

−0.622 (0.309)

⁎⁎

0.151 (0.112) 0.184 (0.237) 0.315⁎⁎ (0.155) −0.001 (0.007) 0.109⁎⁎ (0.055) 0.072⁎⁎⁎ (0.019) −0.035 (0.086) 0.209⁎ (0.117) 0.043 (0.089) −0.263⁎ (0.142) 0.232 (0.152) 0.139 (0.149) 0.123 (0.191) −0.080 (0.073)

−0.058 (0.051) −0.074 (0.160) −0.130 (0.105) 0.012⁎⁎⁎ (0.004) 0.100⁎⁎⁎ (0.034) −0.052⁎⁎⁎ (0.013) 0.002 (0.058) −0.271⁎⁎⁎ (0.057) −0.021 (0.060) 0.312⁎⁎⁎ (0.078) −0.217⁎⁎ (0.098) −0.143 (0.098) −0.051 (0.129) −0.080 (0.073) 0.072⁎⁎⁎ (0.020) −0.077⁎ (0.041) 0.054 (0.069) −0.187⁎ (0.106)

0.457⁎⁎⁎ (0.099) 0.571⁎⁎⁎ (0.081) 0.174⁎⁎⁎ (0.059) 0.020 (0.020) 0.015 (0.019) −0.027 (0.020) −0.009 (0.054) −0.185⁎⁎⁎ (0.065) 0.024 (0.062) 0.134⁎ (0.079) 0.130⁎ (0.070) −0.037 (0.061) 0.042 (0.057)

0.092 (0.230) −0.387 (0.436) 0.263 (0.284) −0.019 (0.013) 0.172 (0.106) 0.256⁎⁎⁎ (0.036) 0.689⁎⁎⁎ (0.157) −0.234 (0.226) 0.232 (0.162) −0.039 (0.261) 0.106 (0.276) −0.251 (0.271) 0.344 (0.348) −0.097 (0.074)

0.545⁎⁎⁎ (0.206) 0.267 (0.166) 0.616⁎⁎⁎ (0.133) 0.155⁎⁎⁎ (0.050) −0.048 (0.048) −0.055 (0.051) −0.002 (0.142) −0.125 (0.155) 0.394⁎⁎ (0.162) −0.180 (0.204) 0.911⁎⁎⁎ (0.177) 0.338⁎⁎ (0.155)

0.099⁎⁎⁎ (0.037) −0.104⁎⁎ (0.052) −0.008 (0.161) −0.102 (0.107) 0.015⁎⁎⁎ (0.004) 0.145⁎⁎⁎ (0.031) −0.073⁎⁎⁎ (0.016) −0.042 (0.065) −0.303⁎⁎⁎ (0.057) −0.041 (0.061) 0.317⁎⁎⁎ (0.079) −0.202⁎⁎ (0.099) −0.114 (0.098) −0.061 (0.131) −0.097 (0.074) 0.113⁎⁎⁎ (0.025) −0.124⁎⁎ (0.056) 0.021 (0.094) −0.206 (0.147)

0.139 (0.227) −0.369 (0.434) 0.288 (0.282) −0.022 (0.013) 0.121 (0.101) 0.269⁎⁎⁎ (0.035) 0.682⁎⁎⁎ (0.156) −0.152 (0.221) 0.259 (0.162) −0.115 (0.259) 0.138 (0.277) −0.227 (0.271) 0.355 (0.346) −0.082 (0.076)

0.136⁎⁎⁎ (0.037) −0.109⁎⁎ (0.053) −0.007 (0.165) −0.114 (0.109) 0.015⁎⁎⁎ (0.005) 0.136⁎⁎⁎ (0.031) −0.083⁎⁎⁎ (0.017) −0.074 (0.067) −0.298⁎⁎⁎ (0.058) −0.048 (0.063) 0.309⁎⁎⁎ (0.081) −0.214⁎⁎ (0.101) −0.114 (0.100) −0.084 (0.134) −0.082 (0.076) 0.105⁎⁎⁎ (0.024) −0.113⁎⁎ (0.055) 0.015 (0.092) −0.204 (0.145)

0.573⁎⁎⁎ (0.205) 0.257 (0.157) 0.606⁎⁎⁎ (0.131) 0.152⁎⁎⁎ (0.048) −0.051 (0.046) −0.054 (0.048) 0.006 (0.135) −0.145 (0.148) 0.361⁎⁎ (0.154) −0.169 (0.194) 0.824⁎⁎⁎ (0.171) 0.283⁎ (0.150) 0.270 (0.623) (continued on next page)

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Table 5 (continued) Dep V

Independent variables cons Industry FE Obs. LR chi2 R2

RD_intensity

Finance_cost

RD_intensity

Finance_cost

Innovation_sales(ln)

Finance_cost

Innovation_sales(ln)

Finance_cost

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Coef.

Coef.

Coef.

Coef.

−0.145 (0.148) Yes 958 433.22⁎⁎⁎ 0.301

2.009⁎⁎⁎ (0.265) Yes 958 295.54⁎⁎⁎ 0.184

Coef.

Coef. ⁎⁎⁎

−0.194 (0.065) Yes 958 387.01⁎⁎⁎ 0.162

Coef. ⁎⁎⁎

−0.185 (0.065) Yes 958 448.01⁎⁎⁎ 0.1196

1.641 (0.235) Yes 958 327.16⁎⁎⁎ 0.2333

Notes: Asterisks denote significance at the ⁎0.10,

⁎⁎

0.05 and

Coef. ⁎⁎⁎

⁎⁎⁎

1.653 (0.235) Yes 958 322.72⁎⁎⁎ 0.2335

−0.125 (0.155) Yes 958 421.37⁎⁎⁎ 0.303

⁎⁎⁎

1.983 (0.266) Yes 958 302.34⁎⁎⁎ 0.221

⁎⁎⁎

0.01 level.

can help SMEs save in R&D expenditure. Knowledge deriving from competitors, universities and industry associations can effectively raise innovative sales with marginal effects of 44.5%, 169.7% and 42.2%. In particular, knowledge provided by universities can substantially promote firms' innovative sales by 169.7%. Joint R&D proves to have better outcomes, which indicates a 44.1% increase in innovative sales, whereas, self- R&D does not show a significant increase. Together with the positive effects of self-R&D on R&D intensity, it implies that joint R &D is more cost-effective and efficient than self-R&D. Again, government funding assistance leads to a positive innovative outcome, with an improvement up to 66.9%. Firms in a market with rapid technology development have a 14% higher level of innovative sales. However, due to the limitations of the database, two issues need to be addressed in future research. First, the study uses data from the “China Hi-Tech Small and Medium-sized Enterprises Dynamic Growth Survey” (CTSMEDGS). So far, the available dataset of the survey is cross-sectional for 2015. Without panel data, we are unable to further examine the time-series effects of family ownership on innovation, and thus the causality between financing cost and innovation. Second, some dummy variables arising from the survey questions are less informative than continuous variables.

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Dong Xiang is a Professor of China Institute for Micro, Small and Medium-sized Enterprises, Qilu University of Technology, 3501 Daxue Rd., Changqing, Jinan, Shandong Province. His research area is Chinese Economy, corporate finance. Jiakui Chen is a Professor of School of Administration Management, Qilu University of Technology, 3501 Daxue Rd., Changqing, Jinan, Shandong Province, 250000, China. His research area is Chinese Economy and corporate finance. David Tripe is a Professor of School of Economics and Finance, Massey University, Palmerston North 4442, New Zealand. His major is Financial Economics. Ning Zhang is a Professor of Department of Economics, Jinan University, No.601 Huangpu West Road, Guangzhou, Guangdong, 510632, China. His Research area is sustainability measurement.

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