From venture capital towards venture firm innovation performance: evidence from SMEs of China

Abstract

Based on information asymmetry, agency theory and resource-based view (RBV), this study investigates the impact of venture capital (VC) on venture firm innovation performance, ascertains the extent to which VC affects venture firm innovation performance and finds the mediating effect of management incentives. Constructing a sample of a novel panel dataset of firms listed on the SME Board of China, we examined a sample of 927 start-ups between 2008 and 2017, showing a notable negative relationship between VC and Patent, and a positive relationship between VC and total factor productivity (TFP), providing stable evidence that VC could not spur firm patent directly, but facilitate the commercialization of innovation. Moreover, it shows that management equity incentives (MEI) and management cash incentives (MCI) playing significant positive mediating role between VC and TFP, while there is no mediating effect between VC and Patent. Findings of this study strengthen the experience of VC and suggest how practitioners of SMEs to enhance the commercialization of innovation, considerably extends our understanding of the impact of VC on venture firm innovation performance.

Keywords

Venture capital (VC)management incentives (MI)innovation patent total factor productivity (TFP)

1. Introduction

Despite the seemingly modest volumes of venture capital (VC) investments compared with the rest of capital flows, the consequences of such financing are quite substantial for Small and Medium-sized Enterprises (SMEs) [1]. SMEs play a key role in modern service-based economies, as well as the source of new jobs, radical innovations, and productivity growth [2]. In terms of innovation, Kortum and Lerner [3] showed that VC-backed firms are characterized by significantly higher patenting rates compared with their non-VC-backed peers. Rin and Penas [1] also noted that VC-backed firms performed more innovation activities by studying over 10,000 Dutch companies.

First, related literature on VC and firm performance often focused on developed countries and regions [4]. Only few empirical studies have tested the role of VC in enterprise-level innovation in emerging markets such as China [5]. Despite the rapid growth in VC market, the Chinese VC industry has been long criticized for excessive government interventions [6], sophisticated and weak corporate governance structures [7], and great significant market uncertainty [8]. Hence, VC in China faces greater challenges, more in-depth research is called to find out VC’s influence and how they affect venture firm’s performance in China at a micro-level.

Secondly, in the present era, VCs and SMEs are facing uncertainty, complexity, competition, and rapid changes in such an emerging financial market [9]. Previous research conducted that a coherent VC strategy may possibly affect the venture firm management incentives, which is vital to ensure effective innovation in a rapidly changing commercial environment [10]. A large number of recent studies have testified the impact of VC on venture firm innovation performance [11]. Despite the existence of such an enormous empirical evidence, a major challenge posed to management researchers and practitioners is that only limited studies have tested the mediating role of management incentives toward innovation in Chinese firms particularly in the context of countries with emerging markets [12].

This research intends to make several theoretical and practical contributions by empirically investigating the nexus of VC, innovation, and management incentives of SMEs in China. More specifically, this study strives to address the following research questions:

RQ1: Does VC impacts innovation performance of SMEs in China?

RQ2: Does management incentives mediates the relationship between VC and innovation performance of SMEs in China?

Consistent with its objectives, this paper is structured as follows: first, a review of the literature is presented. Second, hypotheses are stated, Third, the research framework is developed. Fourth, a research methodology that covers sampling, data collection, measures and data analysis procedure is explained. Fifth, results and discussion, are provided. Finally, conclusion, practical implications and limitations of the study and directions for future research complete the paper.

2. Literature review

2.1. An overview of SMEs

SMEs are not only the source of innovation, but also the source of social economy development [13]. The most significant factors affecting the development of SMEs is the availability of financial resources [14]. SMEs often fail to get enough external financing to support their survival and development, they typically get trapped in the Valley of Death [15].

2.2. Venture capital

VC is considered as an effective way for SMEs to overcome moral hazard [16] and adverse selection [17] compared to other financial intermediaries. With the emergence of VC, SMEs have found a way to free themselves of such dilemmas since VC provides not only several rounds of long-term equity funding that relaxes investment constraints but also provides bundles of value-added activities [18]. VC performs a direct coaching function by spending much time participating in the personnel and strategic management of venture firms, serving as a board member, and working to raise additional funds [19]. Furthermore, VC has expertise and experience in a specific industry and a large network of relationships involving participants, such as customers, professional managers, skilled workers, alliance partners and financial intermediaries [20].

2.3. Firm innovation performance

At the micro level, firm innovation performance has been widely studied to promote firm value and competitiveness [21]. Many recent studies have studied influences of various factors on firm innovation performance such as relationships between institutional investor and firm innovation performance [22], network structure and firm innovation performance [23]. Firm innovation performance has been conceptualized and measured both from the perspectives of technology and commercialization. From the perspective of technology, indicators such as patent applications [24], and forward citation-weighted patent counts [25] are two of the most widely used indicators to measure firm innovation performance. From the perspective of commercialization, some other indicators such as total factor productivity (TFP) [26] and the product innovations [27] have also been used to measure firm innovation performance.

2.4. Management incentives

There are two basic type of management incentives (MI): management cash incentive (MCI) and management equity incentives (MEI) [28]. Numerous studies have found that VC-backed firms outperform their non-VC-backed counterparts on management incentives, to achieve good reputation and rapid exit strategy [29]. While existing literature has not yet reached consensus on the relationship between management incentives and firm innovation performance. Numerous studies have implied that management incentive is the key factor for improving firm innovation performance by mitigate principal-agent conflicts [30]. In contrast, the entrenchment effect holds that management incentives lead to managerial opportunism behavior and reduce firm performance [31].

3. Hypothesis development

3.1. Venture capital and firm innovation performance

VC plays a direct coaching function by spending significant time participating in the personnel and strategic management of venture firm, serving as board members, and working to raise additional funds [32]. Furthermore, VC has the expertise and experience of a specific industry and a large network of relationships involving participants such as customers, professional managers, skilled workers, alliance partners and financial intermediaries [33]. VC also provides venture firm numerous indirect benefits like the imprint effect to third parties [34]. Moreover, some studies have shown that VC change venture firm’s management strategy from innovating to the commercialization of innovation [11]. VC’s role is more to help the commercialization of innovation rather than to foster innovation [35]. Based on the above, the following hypotheses are proposed:

H1a: From the perspective of technology, VC has a positive impact on venture firm innovation performance.

H1b: From the perspective of commercialization, VC has a positive impact on venture firm innovation performance.

3.2. Mediating role of management incentives

Jensen and Meckling [36] proposed that managers are more likely to be concerned with private interests. When the objectives of VC and entrepreneurs are inconsistent or divergent, VC needs to more carefully monitor venture firms to achieve their goals [37]. Firstly, according to grandstanding hypothesis, VC attaches great importance to its own reputation, and intends improve management incentives to reduce agent costs, promote their work motivation. Secondly, compared with business founders, VC, as a short-term investor, aims for the maximization of short-term returns. VC expects to establish a valuable impression of the venture firm to other potential investors by improving management incentives, encouraging the Lake Wobegon Effect, and attracting more external talents to join in [29]. Hence, we infer that both management cash incentives and equity incentives play mediating roles in the relationship between VC and venture firm innovation performance. Based on the above, the following hypotheses are proposed:

H2a: Management cash incentives mediates the relationship between VC and venture firm innovation.

H2b: Management equity incentives mediates the relationship between VC and venture firm innovation.

4. Research methodology

4.1 Data and sample

To appropriately test our hypotheses, we started with a sample of enterprises listed on the SME board of China. Considerable manual work was done to extract complicated data from the widely used commercial databases WIND and CSMAR. We gathered a sample of enterprises listed on the SME board of China from the time period 2008 –2017, a total of 927 SMEs; altogether 3,895 data were obtained.

4.2. Dependent, independent and control variables

From the perspective of technology, the number of patent applications and the patent count is widely used to measure a firm innovation [35]. Consist of previous studies, we chose Patents, as the cumulative number, effectively represented the patents were still under protection. From the perspective of commercialization, TFP also reflects firm innovation performance [26], we also chose TFP as the other dependent variable by which firm innovation performance was measured.

In most research, a dummy variable VC dummy is widely used as an independent variable which switches from 0 to 1, taking the value 1 if a firm receives VC, and 0 otherwise. We also included VC as the independent variable standing for the shareholding ratio of VCs in the first 10 largest shareholders of each firm per year [38]. Following Yu and Lin et al. [39], the mediating variables reflecting management incentives were measured by MCI and MEI. MCI was measured as the logarithm of the average cash compensation of venture firm’s senior managers, directors and supervisors per year, including salary, bonus and other incomes. MEI was measured as the shareholding percentage of venture firm’s senior managers, directors, and supervisors per year.

Six control variables that may influence venture firm innovation performance were included in our analysis: LnAsset, Ratio, Age, Location, Industry and Year. Following the recommendation of Colombo and D’Adda et al. [40], we controlled for venture firm size (LnAsset), measured by the logarithm of a total asset to avoid the effects of multiple size-dependent measures. We also included Ratio [41], using the debt-to-asset ratio, to reflect the risk and management situation. Following Park and Steensma [42], Age was also included and calculated as the number of years since venture firm founded. Since the available resources might be different in different time and affect the venture firm innovation activity in prior research [11, 35], this paper also used Year, Industry and Location as control variables.

4.3. Econometric model

The causal steps introduced by Baron and Kenny [43]. First, we used the following models 1–2 to test our hypotheses to validate H1a and H1b, examining the relationship between VC and venture firm innovation.

$\begin{matrix} {Patent}_{i, t} = & β_{0} + β_{1} {VC}_{i, t} + β_{2} {LnAsset}_{i, t} \\ + β_{3} {Ratio}_{i, t} + β_{4} {Age}_{i, t} \\ + β_{5} {Industry}_{i, t} + β_{6} {Location}_{i, t} \\ + β_{7} {Year}_{i, t} + ɛ_{i, t} \end{matrix}$ (1)

$\begin{matrix} {TPF}_{i, t} = & β_{0} + β_{1} {VC}_{i, t} + β_{2} {LnAsset}_{i, t} + β_{3} {Ratio}_{i, t} \\ + β_{4} {Age}_{i, t} + β_{5} {Industry}_{i, t} \\ + β_{6} {Location}_{i, t} + β_{7} {Year}_{i, t} + ɛ_{i, t} \end{matrix}$ (2)

Further, we tested the significance of the regression coefficient between the VC and the meditation variables (MCI and MEI), as shown in Models 3–4. $\begin{matrix} {MCI}_{i, t} = & β_{0} + β_{1} {VC}_{i, t} + β_{2} {LnAsset}_{i, t} + β_{3} {Ratio}_{i, t} \\ + β_{4} {Age}_{i, t} + β_{5} {Industry}_{i, t} \\ + β_{6} {Location}_{i, t} + β_{7} {Year}_{i, t} + ɛ_{i, t} \end{matrix}$ (3) $\begin{matrix} {MEI}_{i, t} = & β_{0} + β_{1} {VC}_{i, t} + β_{2} {LnAsset}_{i, t} + β_{3} {Ratio}_{i, t} \\ + β_{4} {Age}_{i, t} + β_{5} {Industry}_{i, t} \\ + β_{6} {Location}_{i, t} + β_{7} {Year}_{i, t} + ɛ_{i, t} \end{matrix}$ (4)

Finally, we tested the regression coefficients of the VC, meditation variables (MCI and MEI) and the dependent variables (Patents and TFP) by adding the meditation variables to Models 1–2.

4.4. Descriptive statistics

Tables 1, 2 shows the descriptive statistics and correlation matrix of the main variables. Table 2 demonstrates that all correlation coefficients between the independent variables and control variables were less than 0.5, excluding the effects of multicolli-nearity.

Table 1
Descriptive statistics

Variable Mean Std.Dev. Min Max

Patent 1.198 2.328 0.001 2.942

TFP 0.783 1.470 0.672 1.297

VC 10.50 16.28 0 88.731

LnAsset 5.41 1.940 2.322 9.865

Ratio 0.37 0.201 0.014 4.543

Age 15.47 5.862 3 63

Industry 0.69 0.463 0 1

Location 0.23 0.425 0 1

Variable	Mean	Std.Dev.	Min	Max
Patent	1.198	2.328	0.001	2.942
TFP	0.783	1.470	0.672	1.297
VC	10.50	16.28	0	88.731
LnAsset	5.41	1.940	2.322	9.865
Ratio	0.37	0.201	0.014	4.543
Age	15.47	5.862	3	63
Industry	0.69	0.463	0	1
Location	0.23	0.425	0	1

Table 2

Correlation matrix of main variables

	Patent	TFP	VC	LnAsset	Ratio	Age	Location	Industry
Patent	1
TFP	0.017	1
VC	–0.046^**	0.063^***	1
LnAsset	0.016^*	0.011^**	–0.037^***	1
Ratio	–0.012^***	0.052^***	–0.176^***	–0.123^***	1
Age	–0.028^**	–0.041^***	0.018^**	0.029	0.019^*	1
Location	0.023	0.016	0.012	0.037^**	0.024	0.061^***	1
Industry	0.035^***	0.024^***	0.040^***	0.036^***	0.067^***	0.215	0.165^*	1

Note: ^*, ^**, ^*** indicate significance at the 1%, 5%, and 10%levels, respectively.

5. Data analysis and results

5.1. VC’s effect on venture firm innovation performance

As shown in Table 3, Hausman test was conducted to rule out this issue, demonstrating that the fixed effects models were unbiased and consistent. Table 3 reports the effects of VC on firm innovation performance and the mediating effects of management incentives. Column (1) shows a significant negative effect of VC on patent (β=–0.0284, t=–2.15); Column (2) shows a significant positive effect of VC on TFP (β=0.0368, t = 2.97). Therefore, H1b was supported while H1a was rejected. Results show a significant negative effect of VC on the Patent and a significant positive effect of VC on the TFP of venture firm.

Table 3
Coefficients from regression analyses for innovation

(1) (2) (3) (4) (5) (6) (7) (8)

Variables Patent TFP MCI MEI Patent Patent FTP TFP

VC –0.0284^ 0.0368^* 0.1829^* 0.2172^* –0.0282^ –0.0283^ 0.0258^* 0.0172^

(–2.15) (2.97) (3.18) (3.16) (–2.13) (–2.25) (3.34) (2.56)

MCI –0.0534 0.0494^*

(–1.38) (3.26)

MEI –0.1295 0.0902^*

(–1.12) (3.14)

LnAsset 0.0192^* 0.0170^* 0.0475^* 0.0705^* 0.0143^* 0.0261^ 0.0118^ 0.0174^*

(4.48) (3.06) (3.28) (3.60) (3.31) (2.44) (2.57) (3.75)

Ratio –0.0944^* –0.0515^* –0.0552^* –0.0754 –0.0837^* –0.0972^* –0.0633^* –0.0526^*

(–5.65) (–3.78) (–3.16) (–0.91) (–4.70) (–4.65) (–3.93) (–3.29)

Age 0.0264 0.0327^ –0.1463^*** 0.0103 0.0392^* 0.0258 0.0403^ 0.0361^

(1.12) (2.47) (–3.71) (1.41) (1.72) (1.10) (2.57) (2.31)

Location 0.0929^* 0.0494^* 0.0968^* –0.0167^* 0.0850^* 0.0831^* 0.0473^* 0.0464^*

(4.66) (3.47) (3.03) (–5.68) (4.24) (4.70) (3.11) (3.64)

Industry 0.1043 0.0873 0.0559^* 0.0921^* 0.0985^* 0.1017 0.0760 0.0929

(0.46) (0.57) (7.81) (4.40) (1.65) (0.45) (0.43) (0.08)

Constant 1.1777^ 2.3044^* 2.1497^* 1.4696^* 2.8039 2.6425^ 2.2898^* 1.5042^*

(2.27) (7.76) (4.76) (5.65) (1.38) (2.34) (4.39) (6.97)

Year effect yes yes yes yes yes yes yes yes

Observations 3,160 3,759 3,895 3,895 3,160 3,160 3,759 3,759

Hausman test 170.18^* 252.63^* 89.37^* 104.92^* 230.15^* 169.31^* 247.26^* 195.38^***

R-squared 0.1741 0.2119 0.1318 0.1976 0.1926 0.1742 0.2251 0.2297

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
VC	–0.0284^**	0.0368^***	0.1829^***	0.2172^***	–0.0282^**	–0.0283^**	0.0258^***	0.0172^**
	(–2.15)	(2.97)	(3.18)	(3.16)	(–2.13)	(–2.25)	(3.34)	(2.56)
MCI					–0.0534		0.0494^***
					(–1.38)		(3.26)
MEI						–0.1295		0.0902^***
						(–1.12)		(3.14)
LnAsset	0.0192^***	0.0170^***	0.0475^***	0.0705^***	0.0143^***	0.0261^**	0.0118^**	0.0174^***
	(4.48)	(3.06)	(3.28)	(3.60)	(3.31)	(2.44)	(2.57)	(3.75)
Ratio	–0.0944^***	–0.0515^***	–0.0552^***	–0.0754	–0.0837^***	–0.0972^***	–0.0633^***	–0.0526^***
	(–5.65)	(–3.78)	(–3.16)	(–0.91)	(–4.70)	(–4.65)	(–3.93)	(–3.29)
Age	0.0264	0.0327^**	–0.1463^***	0.0103	0.0392^*	0.0258	0.0403^**	0.0361^**
	(1.12)	(2.47)	(–3.71)	(1.41)	(1.72)	(1.10)	(2.57)	(2.31)
Location	0.0929^***	0.0494^***	0.0968^***	–0.0167^***	0.0850^***	0.0831^***	0.0473^***	0.0464^***
	(4.66)	(3.47)	(3.03)	(–5.68)	(4.24)	(4.70)	(3.11)	(3.64)
Industry	0.1043	0.0873	0.0559^***	0.0921^***	0.0985^*	0.1017	0.0760	0.0929
	(0.46)	(0.57)	(7.81)	(4.40)	(1.65)	(0.45)	(0.43)	(0.08)
Constant	1.1777^**	2.3044^***	2.1497^***	1.4696^***	2.8039	2.6425^**	2.2898^***	1.5042^***
	(2.27)	(7.76)	(4.76)	(5.65)	(1.38)	(2.34)	(4.39)	(6.97)
Year effect	yes	yes	yes	yes	yes	yes	yes	yes
Observations	3,160	3,759	3,895	3,895	3,160	3,160	3,759	3,759
Hausman test	170.18^***	252.63^***	89.37^***	104.92^***	230.15^***	169.31^***	247.26^***	195.38^***
R-squared	0.1741	0.2119	0.1318	0.1976	0.1926	0.1742	0.2251	0.2297

Note: ^*, ^**, ^*** indicate significance at the 1%, 5%, and 10%levels, respectively. Values of t-statistics are provided in parenthesis below the coefficient estimates.

We provided empirical evidence of the negative impact of VC on venture firm innovation and the negative impact of VC on venture firm innovation. These results demonstrate that VCs in China do not focus on nurturing innovation but devoted much to commercialization of new technology, illustrating some specific characteristics like making fast money [44]. Our finding is in line with the view of short-termism, it is impossible for ventures to share all information with VCs, resulting in VCs mainly pursue short-term return and lack of their long-term cooperation.

5.2. Mediation analysis

In Table 3, column (3) shows a positive relationship between VC and MCI (β=0.1829, t = 3.18), indicating that the MCI of VC-backed firms are higher than non-VC-backed firms; columns (4) shows a positive relationship between VC and MEI (β=0.2172, t = 3.16), indicating that the MEI of VC-backed firms are higher than non-VC-backed firms. In columns (5) and (6), MCI (β=–0.0534, t=–1.38) and MEI (β=–0.1295, t=–1.12) do not show a significant relationship with the Patent of investee firm, demonstrating that both MCI and MEI do not exhibit a mediating effect between VC and patent. In columns (7) and (8), MCI (β=0.0494, t = 3.26) and MEI (β=0.0902, t = 3.14) both show a significant positive relationship with the TFP of investee firm, implying that both MCI and MEI mediate the relationship between VC and the TFP of investee firm. H2a and H2b were rejected, while H2a and H2b were confirmed.

From the perspective of commercialization, we showed that both MCI and MEI had a partial mediating effect on both VC and firm innovation performance. Our finding indicates that VC promotes firm innovation performance directly or indirectly (through MCI and MEI), suggesting that VC affect MCI and MEI, in turn, influence venture firm innovation performance. These results implying that higher MCI and MEI can reduce agent cost, improve the enthusiasm of management and firm innovation performance [45]. While, from the perspective of technology, higher MCI and MEI could not promote innovation. MCI and MEI do not have a mediation effect between VC and Patent. This can be attributed to China’s immature corporate governance mechanisms, which emphasizes much more on short-term benefits, not ignores long-term benefits and competitiveness [46].

6. Conclusion and implications

In conclusion, our empirical results worth highlighting in several aspects. This study found a significant positive effect of VCs on the commercialization of venture firm innovation, and a negative effect of VC on the venture firm innovation, based on patent. This study also emphasizes the mediating effect of venture firm management incentives.

Several aspects of this paper are worth highlighting. First, this study suggests that VC can improve the commercialization of innovation in SMEs of China and emphasizes the need to fit the MI (including MCI and MEI) of venture firms and reveals that VC’s participation could not improve venture firm innovation from the perspective of technology. Second, this study contributes to the stream of literature on VC and innovation, highlighting the positive relationship between VC and venture firm innovation performance. Third, this study highlights the role of MI as a critical process that can help venture firms to attain higher commercialization of innovation, revealing that VC improves the commercialization of innovation of venture firm through direct and indirect (MI) ways. For practitioners, they should focus on the management incentives of venture firms to improve firm innovation performance. It is important for VCs and entrepreneurs to understand that innovation is the foundation of long-term development not only for the national economy but also for enterprises at the micro-level. Our research indicates that VCs and entrepreneurs can take advantage of MI’s mediating effect to promote innovation from the perspective of commercialization.

Conflicts of interest/competing interests

The authors declare that they have no competing financial interests.

Footnotes

Acknowledgments

This research was supported by the Social Science Foundation of Shaanxi Province (No. 2016ZDA15) and Central University Basic Research Project–Research on the Mechanism of Ecological Compensation Poverty Alleviation under the Background of Targeted Poverty Alleviation (No. 300102239665).

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	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Variables	Patent	TFP	MCI	MEI	Patent	Patent	FTP	TFP
VC	–0.0284^**	0.0368^***	0.1829^***	0.2172^***	–0.0282^**	–0.0283^**	0.0258^***	0.0172^**
	(–2.15)	(2.97)	(3.18)	(3.16)	(–2.13)	(–2.25)	(3.34)	(2.56)
MCI					–0.0534		0.0494^***
					(–1.38)		(3.26)
MEI						–0.1295		0.0902^***
						(–1.12)		(3.14)
LnAsset	0.0192^***	0.0170^***	0.0475^***	0.0705^***	0.0143^***	0.0261^**	0.0118^**	0.0174^***
	(4.48)	(3.06)	(3.28)	(3.60)	(3.31)	(2.44)	(2.57)	(3.75)
Ratio	–0.0944^***	–0.0515^***	–0.0552^***	–0.0754	–0.0837^***	–0.0972^***	–0.0633^***	–0.0526^***
	(–5.65)	(–3.78)	(–3.16)	(–0.91)	(–4.70)	(–4.65)	(–3.93)	(–3.29)
Age	0.0264	0.0327^**	–0.1463^***	0.0103	0.0392^*	0.0258	0.0403^**	0.0361^**
	(1.12)	(2.47)	(–3.71)	(1.41)	(1.72)	(1.10)	(2.57)	(2.31)
Location	0.0929^***	0.0494^***	0.0968^***	–0.0167^***	0.0850^***	0.0831^***	0.0473^***	0.0464^***
	(4.66)	(3.47)	(3.03)	(–5.68)	(4.24)	(4.70)	(3.11)	(3.64)
Industry	0.1043	0.0873	0.0559^***	0.0921^***	0.0985^*	0.1017	0.0760	0.0929
	(0.46)	(0.57)	(7.81)	(4.40)	(1.65)	(0.45)	(0.43)	(0.08)
Constant	1.1777^**	2.3044^***	2.1497^***	1.4696^***	2.8039	2.6425^**	2.2898^***	1.5042^***
	(2.27)	(7.76)	(4.76)	(5.65)	(1.38)	(2.34)	(4.39)	(6.97)
Year effect	yes	yes	yes	yes	yes	yes	yes	yes
Observations	3,160	3,759	3,895	3,895	3,160	3,160	3,759	3,759
Hausman test	170.18^***	252.63^***	89.37^***	104.92^***	230.15^***	169.31^***	247.26^***	195.38^***
R-squared	0.1741	0.2119	0.1318	0.1976	0.1926	0.1742	0.2251	0.2297

From venture capital towards venture firm innovation performance: evidence from SMEs of China

Abstract

Keywords

1. Introduction

2. Literature review

2.1. An overview of SMEs

2.2. Venture capital

2.3. Firm innovation performance

2.4. Management incentives

3. Hypothesis development

3.1. Venture capital and firm innovation performance

3.2. Mediating role of management incentives

4. Research methodology

4.1 Data and sample

4.2. Dependent, independent and control variables

4.3. Econometric model

Table 1 Descriptive statistics Variable Mean Std.Dev. Min Max Patent 1.198 2.328 0.001 2.942 TFP 0.783 1.470 0.672 1.297 VC 10.50 16.28 0 88.731 LnAsset 5.41 1.940 2.322 9.865 Ratio 0.37 0.201 0.014 4.543 Age 15.47 5.862 3 63 Industry 0.69 0.463 0 1 Location 0.23 0.425 0 1

5.1. VC’s effect on venture firm innovation performance

6. Conclusion and implications

Conflicts of interest/competing interests

Footnotes

Acknowledgments

References

Table 1
Descriptive statistics

Variable Mean Std.Dev. Min Max

Patent 1.198 2.328 0.001 2.942

TFP 0.783 1.470 0.672 1.297

VC 10.50 16.28 0 88.731

LnAsset 5.41 1.940 2.322 9.865

Ratio 0.37 0.201 0.014 4.543

Age 15.47 5.862 3 63

Industry 0.69 0.463 0 1

Location 0.23 0.425 0 1