The Time-lag Effect of R&D Investment on the Value of Listed Companies in China: A Cross-industry Analysis

Abstract

Innovation plays an increasingly important role in economic growth, and R&D investment has become a key component of innovation initiatives. In this study, we performed analyses of 58 listed automobile manufacturers and 52 listed Internet companies in China. The empirical analysis is carried out by using STATA15.0 to preliminarily explore the mechanism of the time-lag effect of R&D investment on the value of these companies over the years of time-lag as well as conduct comparison analyses across industries. The results show that R&D investment has a positive effect on corporate value and this effect has a long-term time-lag effect. The mechanism of time-lag effect of R&D investment on the corporate value over the lag years has significant differences across industries. The time-lag effect of R&D investment on the value of the listed automobile manufacturer presents an inverted U shape, while the time-lag effect of R&D investment on the value of the listed Internet companies decreases gradually. The study helps local government better understand the different mechanisms of the time-lag effect of R&D investment on the value of the listed companies and also serves as a reference for local government to make decisions on subsidies and other supportive policies for innovation initiatives of the listed companies.

Keywords

R&D investment corporate value time-lag effect cross-industry

Introduction

Extensive research on R&D investment has been conducted by scholars. However, scholars have not drawn a unified conclusion on the impact of R&D investment on the performance and value of a listed company. There are mainly three kinds of views: promotion, inhibition and dual effect (Cheng, Zhang, Y. H., & Chang, 2006). For the research on the time-lag effect of R&D investment, the studies on the macro level mainly focus on the time-lag effect of national/regional R&D investment on local economic growth and technological innovation capacity, while the middle-level studies mainly focus on the time-lag effect of R&D investment on company performance in certain industries. These researches focus on whether there is a time-lag effect and the length of the lag. There is little research on the time-lag effect of corporate R&D investment on the corporate value. Prior studies have not investigated the pattern of the intensity of such effect with the extension of the lag year and the industry differences. In this article, we explored the time-lag effect of R&D investment on the value of companies by analysing the data of 58 listed automobile manufacturers and 52 listed Internet companies in China. This study aims to further explore the mechanisms of the effect of R&D investment on corporate value considering factors such as industries and lag years.

Automobile manufacturing is a traditional manufacturing industry, and the Internet industry represents the high-tech industry. We choose these two industries mainly for the following reasons: (a) these two industries represent traditional industry and emerging industry, respectively; (b) we can obtain long-term data of companies in these two industries, which is helpful for us to assess the time-lag effect of R&D investment on the corporate value and its pattern with the extension of lag years across industries.

Literature Review

R&D Investment and Corporate Value

In this article, we define corporate value as the sum of the expected market value of tangible assets and intangible assets of a company. R&D activities of a company have become an integral part of investment efforts to increase its value (Darrough and Ye, 2007). Based on the literature on a company’s R&D investment and its performance, there are three main research streams: R&D investment enhances performance, R&D investment inhibits performance and R&D investment has dual effect on performance.

Qiu and Wei (2016) conducted an empirical analysis using PSM based on the R&D data of Chinese manufacturing companies from 1998 to 2009. They found that R&D investment of private-owned companies and foreign companies leads to performance improvement. Sougiannis (1994) performed analyses on the R&D data of American companies from 1975 to 1985 and found that the R&D investment of the companies had a positive effect on both the profit and value. Morbey (1989) selected 800 American companies with R&D investment of more than US$1 million and analysed their R&D data. The results show that R&D investment greatly improves their profitability.

Ye and Chen (2015) performed analyses on the R&D investment data of science and technology companies listed on the main board and the growth enterprise board and found that there was a significant negative correlation between R&D investment and performance. Zhang and Deng’s (2016) study based on the listed companies of Big Data industry in China from 2011 to 2013 shows that there was no correlation between R&D investment and performance. Chen, Meng and Wang (2015) identified a dual effect of R&D intensity (RDI) on company performance based on the R&D investment of Chinese electronic equipment manufacturers in 2009–2013, which means RDI in a certain range can improve performance; however, when the RDI exceeds a certain level, performance will decrease instead.

Time-lag Effect of R&D Investment

Prior research on the time-lag effect of R&D investment is at two levels: macro level and middle level. At the macro level, scholars mainly focus on the relationship between R&D investment of a country/region and technological innovation capabilities and economic growth. Bai and Pan (2015) employed a VAR model in dynamic analysis using the data of R&D investment and technological innovation capabilities in China from 2000 to 2011 and found that R&D investment was positively related to the improvement of technological innovation capabilities with lag period of about 1 to 4 years. At the medium level, scholars mainly explored the lag effect of R&D investment on performance at the level of industries. Lee and Lee (2007) selected 63 pharmaceutical companies from 2001 to 2006 for empirical analysis and found that R&D investment with 1 year of lag had a positive impact on profit margin in the current period, while R&D investment in the other years had a negative impact on the profit. Jiang and Liu (2016) conducted an empirical analysis of the investment data of companies listed on the growth enterprise market from 2010 to 2014. The results indicate a negative relationship between R&D investment and current performance of the companies; however, when the lag time is 1–3 years, R&D investment can enhance their performance.

Theoretical Background and Hypotheses Development

Based on extensive literature review, scholars’ conclusion on the association between R&D investment and corporate value is not consistent. Endogenous growth theory points out that the improvement of endogenous technology such as R&D investment and knowledge spillover effect can result in the improvement of company performance. The R&D investment enhances the absorption capabilities, enriches the knowledge stock, exerts knowledge spillover effect and improves performance. Specifically, R&D investment facilitates technological innovation of companies and technological innovation is conducive to the improvement of performance and enhances the value of the companies. In their work, McMillan, Narin, and Deeds (2000) found that the knowledge stock within a company determines the R&D investment and R&D strategy. Fleming (2001) further pointed out that the increase in the internal knowledge stock of a company combined with the external-related knowledge of the company would increase the probability of generating more knowledge and technologies, and further increase the R&D investment of the company. Thus, we propose:

H₁: The R&D investment of automobile manufacturers and Internet companies has a positive impact on their corporate value.

The R&D investment of a company usually goes through the process of research and development, production and promotion, profit generation, and then performance improvement and value increase. Henderson and Clark (1990) pointed out that the advancements in the technology field are usually the results of integration of existing knowledge stock and technology in new product development, rather than from the radical change in the technology field, but this integration process takes a long time. From the R&D investment to the successful development of new products, and then to the generation of company profits and the resultant improvement of corporate value, there exists a time-lag effect and industry difference during the process. From the perspective of internal knowledge stock of enterprises, technology-intensive companies need to constantly acquire and integrate external knowledge into their internal knowledge to enrich their knowledge stock (Teece, 1997). Since it is a long process for companies to accumulate and update their current knowledge stock, the R&D investment of a company has a certain time-lag effect on the enhancement of its value. Thus, we propose that:

H₂: The R&D investment of automobile manufacturers and Internet companies has a time-lag effect on their corporate value.

Suzuki (1985) studied the time-lag effect of cross-industry R&D investment on corporate value and found that the time-lag effect of R&D investment on corporate value varies in different industries, that is, the industries of communication equipment, electrical machinery and metal manufacturing have 2-year time-lag, while pharmaceutical manufacturing has 5 years. He (2003) performed analyses on the R&D data of large-sized companies in China. He considered the total R&D expenditure occurred over 2 years as their R&D investment. It was found that the R&D investment of the companies had a time-lag effect on their performance, and the lag period was about 3 years. Our discussion above indicates that the time-lag effect of R&D investment varies across industries. Thus, we propose:

H₃: The lag period of the effect of R&D investment on corporate value varies in automobile manufacturers and Internet companies.

H₄: The time-lag effect of R&D investment on corporate value varies over years in automobile manufacturers and Internet companies.

H₅: The intensity of time-lag effect of R&D investment on corporate value varies in automobile manufacturers and Internet companies.

Methods

Sampling

This study chose listed companies in the industries from automobile manufacturing and Internet. This study used Stata 15.0 for regression analysis. The main data of this research come from WIND database, and we also refer to the annual reports of some companies and other online resources for more financial data. In the process of data collection and sorting, the respective meaning and statistical calibre of data were clarified, and the data standard was ensured consistent. The final sample includes 58 automobile manufacturers and 52 Internet companies, a total of 110 companies.

Variables

Explanatory Variables and Explained Variables

James Tobin (1969) proposed Tobin’s Q theory, which is mainly used to measure whether the target companies are worth potential investment. In this study, Tobin’s Q is selected as the proxy variable of corporate value, which is the explained variable (or dependent variable) and shortened as TQ. In line with Chung and Pruitt (1994), Cummins, Lewis, and Wei (2006), TQ was defined as a company’s market value divided by its total assets

RDI was selected as the independent variable and expressed by RDI. RDI is generally defined as the expenditure on R&D divided by the total amount of sales. In this study, the proxy variable of RDI is R&D expenditure divided by total revenue, so as to eliminate the errors caused by the relative difference in the size of each company.

Control Variables

In terms of control variables, all aspects of companies should be comprehensively considered, including their growth rate, liquidity, leverage, major shareholders, foreign ownership, size and age, as shown in Table 1.

Table 1.

Definition and Calculation Methods of Variables

Variables	Abbreviation	Formula
Explained variables
Tobin Q	TQ	TQ = MV/TA (MV: market value, TA: total asset)
Explanatory variables
R&D intensity	RDI	RDI = R&D expense/total revenue × 100%
Control variables
Growth	Growth	Year-over-year growth rate of turnover = (turnover of current year – turnover of last year)/turnover of last year × 100%
Liquidity	Cr	Current ratio = current assets/current liabilities
Leverage ratio	Dr	Debt ratio = (total debts/total assets) × 100%
Major shareholders	Owner1	The shareholding ratio of the major shareholders
Foreign ownership	Fo	The shareholding ratio of foreign ownership
Firm size	Size	Size = ln(total assets)
Firm age	Age	Age = 2018 − (the year the company went public)

Source: Authors’ own.

Descriptive Statistics of Variables

Descriptive Statistics of the Automobile Manufacturing Industry

Table 2 is the statistical data of variables related to 58 listed automobile manufacturers in China from 2008 to 2017. The mean of TQ is 1.40 and the standard deviation is 1.22, indicating that the valuation of listed automobile manufacturers is reasonable.

Table 2.

Descriptive Statistics of Variables of the Automobile Manufacturing Industry

Variables	Sample Size	Mean	Median	Std	Min	Max
TQ	556	1.40	1.03	1.22	0.26	8.06
RDI_t (%)	530	3.20	3.24	1.84	0.1	9.34
RDI_t−1 (%)	472	3.10	3.12	1.80	0.05	9.34
RDI_t−2 (%)	414	2.97	3.01	1.71	0.05	7.46
RDI_t−3 (%)	356	2.81	2.86	1.64	0.04	6.44
RDI_t−4 (%)	298	2.61	2.56	1.57	0	6
RDI_t−5 (%)	240	2.41	2.30	1.54	0	5.86
Growth1 (%)	580	13.21	11.45	25.30	−42.54	101.2
Cr	579	1.54	1.30	0.97	0.46	7.13
Dr (%)	579	51.33	52.11	18.09	10.56	94.5
Owner1 (%)	559	36.67	34.72	16.03	7.31	74.3
Fo (%)	580	0.12	0	0.51	0	3.45
Size	579	13.21	13.06	1.33	10.66	17.28
Age	580	16.66	18.50	5.92	8	26

Source: Authors’ own.

Table 3.

Descriptive Statistics of Internet Industry Variables

Variables	Sample size	Mean	Median	Std	Min	Max
TQ	520	3.34	2.44	3.11	0.21	19.59
RDI_t (%)	449	7.76	5.30	8.17	0.08	41.53
RDI_t−1 (%)	397	7.47	5.28	7.87	0.08	41.42
RDI_t−2 (%)	345	7.17	4.99	7.53	0.08	38.76
RDI_t−3 (%)	293	6.82	4.81	7.31	0.07	41.33
RDI_t−4 (%)	241	6.30	4.46	6.53	0.08	30.83
RDI_t−5 (%)	190	5.86	4.34	6.15	0.07	30.83
Growth1 (%)	520	15.96	12.51	32.33	−42.54	169.78
Cr	520	2.82	1.93	2.62	0.46	17.15
Dr (%)	520	36.61	37.23	18.05	10.56	83.07
Owner1 (%)	520	29.72	27.11	15.08	7.31	63
Fo (%)	520	0.09	0	0.52	0	4.15
Size	520	12.46	12.41	1.24	10.66	17.76
Age	520	16.92	16	5.79	8	28

Source: Authors’ own.

Descriptive Statistics of the Internet Industry

Table 3 is the statistical data of related variables of 52 listed Internet companies in China from 2008 to 2017. The average value and standard deviation of TQ are 3.34 and 3.31, indicating that the valuation of listed Internet companies is relatively high.

The Regression Model and Results

The Regression Model

The quantile of less than 1 per cent and more than 99 per cent of all variables are first reduced to avoid the influence of outliers on the research results. The data in this study are not randomly extracted from listed companies, so the fixed effect model can be used for estimation. Since the samples include listed automobile manufacturers and Internet companies, the characteristics of their entities must be considered. Therefore, F test is used to test the goodness of fit of the fixed effect model. The results show that the p value of the whole model is less than 0.01, that is, the fixed effect model is more suitable than the OLS model. Finally, fixed effect model is selected. Based on the comparison of the time-lag effect of R&D investment of the listed automobile manufacturers and Internet companies in China, we extend the lag time to the year t − 5, and construct the model as follows:

\begin{array}{l} {TQ}_{i t} = \partial_{1} + β_{1} {RDI}_{i, t} + β_{2} {RDI}_{i, t - 1} + β_{3} {RDI}_{i, t - 2} + β_{4} {RDI}_{i, t - 3} + β_{5} {RDI}_{i, t - 4} + β_{6} {RDI}_{i, t - 5} \\ + β_{7} {Growth}_{i, t} + β_{8} {Cr}_{i, t} + β_{9} {Dr}_{i, t} + β_{10} Owner 1_{i, t} + β_{11} {Fo}_{i, t} + β_{12} {Size}_{i, t} + \\ β_{13} {Age}_{i t} + ε_{i, t} \end{array}

(1)

\begin{array}{l} {TQ}_{i t} = \partial_{1} + β_{1} {RDI}_{i, t} + β_{2} {Growth}_{i, t} + β_{3} {Cr}_{i, t} + β_{4} {Dr}_{i, t} + β_{5} Owner 1_{i, t} + β_{6} {Fo}_{i, t} + \\ β_{7} {Size}_{i, t} + β_{8} {Age}_{i, t} + ε_{i, t} \end{array}

(2)

\begin{array}{l} {TQ}_{i t} = \partial_{1} + β_{1} {RDI}_{i, t - 1} + β_{2} {Growth}_{i, t} + β_{3} {Cr}_{i, t} + β_{4} {Dr}_{i, t} + β_{5} Owner 1_{i, t} + β_{6} {Fo}_{i, t} + \\ β_{7} {Size}_{i, t} + β_{8} {Age}_{i, t} + ε_{i, t} \end{array}

(3)

\begin{array}{l} {TQ}_{i t} = \partial_{1} + β_{1} {RDI}_{i, t - 2} + β_{2} {Growth}_{i, t} + β_{3} {Cr}_{i, t} + β_{4} {Dr}_{i, t} + β_{5} Owner 1_{i, t} + β_{6} {Fo}_{i, t} + \\ β_{7} {Size}_{i, t} + β_{8} {Age}_{i, t} + ε_{i, t} \end{array}

(4)

\begin{array}{l} {TQ}_{i t} = \partial_{1} + β_{1} {RDI}_{i, t - 3} + β_{2} {Growth}_{i, t} + β_{3} {Cr}_{i, t} + β_{4} {Dr}_{i, t} + β_{5} Owner 1_{i, t} + β_{6} {Fo}_{i, t} + \\ β_{7} {Size}_{i, t} + β_{8} {Age}_{i, t} + ε_{i, t} \end{array}

(5)

\begin{array}{l} {TQ}_{i t} = \partial_{1} + β_{1} {RDI}_{i, t - 4} + β_{2} {Growth}_{i, t} + β_{3} {Cr}_{i, t} + β_{4} {Dr}_{i, t} + β_{5} Owner 1_{i, t} + β_{6} {Fo}_{i, t} + \\ β_{7} {Size}_{i, t} + β_{8} {Age}_{i, t} + ε_{i, t} \end{array}

(6)

\begin{matrix} {TQ}_{i t} = \partial_{1} + β_{1} {RDI}_{i, t - 5} + β_{2} {Growth}_{i, t} + β_{3} {Cr}_{i, t} + β_{4} {Dr}_{i, t} + β_{5} Owner 1_{i, t} + β_{6} {Fo}_{i, t} + \\ β_{7} {Size}_{i, t} + β_{8} {Age}_{i, t} + ε_{i, t} \end{matrix}

(7)

TQ is used as the dependent variable through model (1) to model (7), and the control variables remain consistent. In model (1), the RDI of the last 6 years is used as the explanatory variable, and the RDI of year t, t − 1, t − 2, t − 3, t − 4 and t − 5 is used as the explanatory variable, respectively, in model (2) to model (7), where TQ_it represents TQ of the ith enterprise in year t; RDI_i,t, RDI_i,t−1, RDI_i,t−2, RDI_i,t−3, RDI_i,t−4 and RDI_i,t−5 represent the RDI of the ith enterprise in year t, t − 1, t − 2, t − 3, t − 4 and t − 5, respectively. Growth_i,t refers to the year-over-year growth rate of turnover of the ith enterprise in year t; Cr_i,t represent the current ratio of the ith enterprise in year t; Dr_i,t represent the debt ratio of the ith enterprise in year t; Owner1_i,t represents the shareholding ratio of the majors shareholders of the ith enterprise in year t; Fo_i,t is the shareholding ratio of foreign ownership of the ith enterprise in year t; Size_i,t is enterprise size of the ith enterprise in year t; Age_i,t is enterprise ages of the ith enterprise in year t. In addition, ∂₁ represents the constant and ε_i,t represents the general error.

Regression Results and Analyses

The Time-lag Effect of R&D Investment on Corporate Value for Automobile Manufacturers

The regression analysis results of the total samples of China’s listed automobile manufacturers from 2008 to 2017 are shown in Table 4. Although the correlation coefficient between RDI of year t − 2 and TQ in regression 1 is negative, it is not significant and the explanatory power is not enough. The correlation coefficients between RDI are all greater than 0.5, that is, there is no significant difference between the regression coefficients of RDI in different models, indicating that the time-lag effect of R&D investment on corporate value first increases and then decreases and presents an inverted U shape.

The year-over-year growth rate of turnover is an important indicator to measure the growth of an enterprise, which is both positively and negatively correlated with TQ, so the exact relationship cannot be determined. Similarly, there is no significant correlation between TQ and the current ratio representing the internal capital liquidity of the companies. As a proxy variable of corporate leverage ratio, debt ratio has a significant negative correlation with TQ. There is a significant positive correlation between the shareholding ratio of the major shareholders of the automobile manufacturers and its corporate value, while there is no significant correlation between the shareholding ratio of foreign investors and corporate value. However, there is a significant negative correlation between firm size and TQ. In addition, there is a significant positive correlation between firm age and TQ.

Table 4.

Regression Analysis of Automobile Manufacturing Enterprise Samples from 2008 to 2017

Variable	Regr 1	Regr 2	Regr 3	Regr 4	Regr 5	Regr 6	Regr 7
RDI_t	0 .049 (0.065)	0.113*** (0.025)
RDI_t−1	0.080(0.078)		0.118***(0.026)
RDI_t−2	−0.075(0.091)			0.131***(0.030)
RDI_t−3	0.022(0.089)				0.141***(0.033)
RDI_t−4	0.036(0.086)					0.124***(0.041)
RDI_t−5	0.066(0.069)						0.112**(0.049)
Growth1	−0.005*(0.003)	0.003(0.002)	0.002(0.002)	0.0002***(0.002)	−0.003(0.002)	−0.004(0.002)	−0.006**(0.003)
Cr	0.085(0.108)	0.074(0.060)	0.062(0.062)	0.051(0.066)	−0.001(0.074)	0.027(0.088)	0.107(0.107)
Dr	−0.016***(0.006)	−0.012***(0.003)	−0.013***(0.004)	−0.013***(0.004)	−0.014***(0.004)	−0.016***(0.005)	−0.017***(0.005)
Owner1	0.008*(0.005)	0.004(0.003)	0.005*(0.003)	0.005(0.003)	0.003(0.003)	−0.004(0.005)	0.007(0.005)
Fo	−0.035(0.133)	−0.009(0.087)	−0.038(0.093)	−0.006(0.099)	0.004(0.102)	−0.038(0.117)	0.020(0.133)
Size	−0.498***(0.067)	−0.417***(0.038)	−0.445***(0.041)	−0.444***(0.046)	−0.406***(0.050)	−0.458***(0.057)	−0.520***(0.067)
Age	0.024(0.015)	0.022***(0.009)	0.027***(0.009)	0.028***(0.010)	0.030***(0.010)	0.035***(0.012)	0.031**(0.015)
Constant	7.707***(0.922)	6.542 ***(0.519)	6.917***(0.556)	6.880***(0.607)	6.549***(0.658)	7.335***(0.763)	8.137***(0.889)
R ²	0.4227	0.3456	0.3612	0.3459	0.3224	0.3462	0.4082
Adj. R²	0.3895	0.3351	0.3499	0.3330	0.3068	0.3281	0.3877
Obs.	240	508	462	414	356	298	240

Source: Authors’ own.

Note: The first line of each independent variable is estimated coefficient, and the standard deviation is in parentheses; ***, ** and * are significant at confidence intervals of 1%, 5% and 10%, respectively

The Time-lag Effect of R&D Investment on Corporate Value for Internet Enterprises

The regression analysis results of the samples of listed Internet companies in China from 2008 to 2017 are shown in Table 5. Although the correlation coefficients between RDI in year t − 4 and year t − 5 and TQ in regression 1 are negative, they are not significant. The correlation coefficients between RDIs are all greater than 0.5, that is, there is no significant difference between the regression coefficients of RDI in different models, indicating that the time-lag effect of R&D investment on enterprise value decreases gradually.

The year-over-year growth rate of turnover is an important indicator to measure the growth of enterprises, which has a significant positive correlation with TQ. Similarly, the current ratio, which represents the internal capital liquidity of the companies, also has a significant positive impact on TQ. However, the debt ratio, which acts as the proxy variable of enterprise leverage ratio, has no significant correlation with TQ. There is a positive correlation between the shareholding ratio of the major shareholder in Internet companies, so is that of shareholding ratio of foreign investors and corporate value. However, there is a significant negative correlation between firm size and TQ. In addition, there is a positive correlation between firm age and TQ.

Table 5.

Regression Analysis of Internet Enterprise Samples from 2008 to 2017

Variable	Regr 1	Regr 2	Regr 3	Regr 4	Regr 5	Regr 6	Regr 7
RDI_t	0.163**(0.071)	0.119***(0.017)
RDI_t−1	0.003(0.082)		0.117***(0.019)
RDI_t−2	0.012(0.073)			0.101***(0.023)
RDI_t−3	0.033(0.065)				0.071***(0.026)
RDI_t−4	−0.123*(0.073)					0.023(0.033)
RDI_t−5	−0.075(0.064)						−0.020(0.039)
Growth1	0.013*(0.007)	0.009**(0.004)	0.008*(0.004)	0.007(0.005)	0.009(0.005)	0.011*(0.006)	0.007(0.007)
Cr	0.523***(0.121)	0.197***(0.068)	0.248***(0.075)	0.274***(0.085)	0.340***(0.096)	0.433***(0.107)	0.674***(0.123)
Dr	0.328*(0.018)	−0.001(0.011)	0.004(0.011)	0.009(0.013)	0.017(0.015)	0.014(0.017)	0.027(0.019)
Owner1	0.016(0.014)	0.019**(0.009)	0.019**(0.010)	0.017(0.011)	0.015(0.012)	0.015(0.014)	0.012(0.015)
Fo	2.083*(1.170)	−0.062(0.300)	0.357(0.385)	0.715(0.490)	0.260(0.505)	0.251(0.530)	0.462(0.648)
Size	−1.599***(0.253)	−0.844***(0.124)	−1.034***(0.138)	−1.154***(0.161)	−1.258***(0.190)	−1.300***(0.225)	−1.521***(0.255)
Age	0.021(0.041)	0.011(0.024)	0.026(0.026)	0.046(0.030)	0.070**(0.034)	0.068*(0.038)	0.063(0.042)
constant	20.000***(3.212)	11.635***(1.582)	13.736***(1.745)	15.024***(2.048)	15.836***(2.397)	16.695***(2.831)	19.208***(3.245)
R ²	0.4874	0.2720	0.2956	0.2673	0.2420	0.2661	0.3740
Adj. R²	0.4475	0.2588	0.2811	0.2499	0.2207	0.2408	0.3463
Obs.	181	449	397	345	293	241	190

Source: Authors’ own.

Discussion

Research Conclusions

The results show that the higher the overall RDI is, the higher the TQ becomes. It indicates that there is a positive correlation between R&D investment and corporate value, that is, R&D investment increases the enterprise value. Therefore, H1 is supported.

The results of regression analysis show that there is a significant positive relationship between the RDI of the automobile manufacturers and TQ in each year. The RDI of Internet companies in most years shows a positive connection with TQ, but RDI_t−5 shows a negative association with TQ. In conclusion, it was found that R&D investment has a relatively obvious time-lag effect on corporate value. So H2 is supported.

The correlation coefficients between R&D intensities and TQ in all years in the auto manufacturing industry are all positive. But the correlation coefficient between RDI and TQ of the Internet industry in year t − 5 is negative. This indicates that the lag periods of R&D investment on enterprise value in the automobile manufacturing are greater than or equal to 5 years, while it is 4 years in the Internet industry. Second, the correlation coefficient indicates that the time-lag effect of RDI on enterprise value is high in automobile manufacturing industry and is low in Internet industry. Based on the correlation coefficients, the time-lag effect of R&D investment on enterprise value of listed automobile manufacturing companies reaches the maximum value in the t − 3 year. The time-lag effect of R&D investment of listed Internet companies on corporate value is gradually declining. There are differences in lag period and delay effect across the industries, as shown in Figure 1. So H3, H4 and H5 are all supported.

The research creates insightful value for other companies and related government departments. The research conclusions objectively and truly reflect the objective law of the time-lag effect intensity change between R&D input and enterprise value in China’s listed automobile manufacturing industry and Internet industry. It is helpful for enterprises in these two industries to formulate appropriate R&D investment strategies according to their own R&D investment level, so as to maximize their corporate value. Secondly, this study is helpful for relevant departments to regulate the R&D level of the industry by adjusting R&D policies, so as to maximize the value of the industry.

Recommendations for Policymakers

Based on the research conclusions, the following recommendations are proposed:

The government, companies and society should fully understand the link between R&D investment and value of companies, and actively take corresponding measures to promote the R&D investment of companies. In order to develop an innovation-oriented economy in China, the government should also introduce relevant policies, such as policy subsidies and tax incentives, and build a good financing environment to increase the R&D investment of companies.

The R&D investment has a long time-lag effect on corporate value, so companies need to invest in R&D for a long time. The innovation capabilities of a company are the long-term accumulation of its R&D investment, which ultimately leads to qualitative changes and forms the core competitiveness that can continuously improve performance. R&D investment in China has favourable outcomes. In the future, we should enhance the R&D investment to facilitate innovation and increase value of companies.

Research Limitations and Future Direction

First, this article only selects two major industries in China to analyse the time-lag effect of R&D investment on corporate value. Future comparative studies can be conducted on the lag effect of R&D investment and corporate value in more industries and more countries/regions.

Second, in this article, the important indicators to measure RDI are RDI_t, RDI_t−1, RDI_t−2, RDI_t−3, RDI_t−4 and RDI_t−5, that is, the longest lag time is 5 years. The time-lag effect of R&D investment on corporate value in some industries may be more than 5 years, and the lag year can be extended appropriately in the future research.

Third, this article analysed the time-lag effect of R&D investment on corporate value by choosing two major industries and found that the intensity of time-lag effect of R&D investment on corporate value varies with the extension of the lag year. The reasons for this difference have not been deeply explored in this study, which need to be further studied in the future.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

This research is supported by National Natural Science Foundation of China (71772163 and 71673240), Zhejiang Provincial Natural Science Foundation of China (LY17G020024, LY16G020009).

ORCID iD

Wei Yu

References

Bai

Y. F.

Pan

Z. Z.

(2015). A study on the time lag effect of China’s R&D input on science and technology innovation. Science and Technology Management Research , 4, 1–5.

Chen

J. L.

Meng

L. J.

Wang

(2015). Nonlinear link between R&D investment and performance of listed companies. Forum on Science and Technology in China , 5, 67–73.

Cheng

H. W.

Zhang

Y. H.

Chang

(2006). Empirical study on the correlation between R&D input and performance. Journal of Scientific Management Research , 24(3), 110–113.

Chung

K. H.

Pruitt

S. W.

(1994). A simple approximation of Tobin’s Q. Finance Management , 23(3), 70–74.

Cummins

J. D.

Lewis

C. M.

Wei

(2006). The market value impact of operational loss events for US banks and insurers. Bank Finance , 30(10), 2605–2634.

Darrough

(2007). Valuation of loss firms in a knowledge-based economy. Review of Accounting Studies , 12(1), 61–93.

Fleming

(2001). Recombinant uncertainty in technological search. Management Science , 47(1), 117–132.

(2003). The influence of R&D expenditure on output of large and medium-sized industrial enterprises in China. Economics Science , 25(3), 5–11.

Henderson

R. M.

Clark

K. B.

(1990). Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly , 35(1), 9–30.

10.

Jiang

W. P.

Liu

D. D.

(2016). R&D intensity, slack resources and firm performance. The Theory and Practice of Finance and Economics , 37(5), 57–62.

11.

Lee

K. M.

Lee

G. C.

(2007). The effect of R&D investment for the business performance of the firms in Korean pharmaceutical industry. Professional Management , 10(2), 81–101.

12.

McMillan

G. S.

Narin

Deeds

D. L.

(2000). Analysis of the critical role of public science innovation: The case of biotechnology. Research Policy , 29(1), 1–8.

13.

Morbey

G. K.

(1989). R&D expenditures and profit growth. Research-Technology Management , 32(3), 20–23.

14.

Qiu

Y. J.

Wei

(2016). The influence of R&D investment on enterprise performance. Contemporary Finance & Economics , 3, 96–106.

15.

Sougiannis

(1994). The accounting based valuation of corporate R&D. The Accounting Review , 69(1), 44–68.

16.

Suzuki

(1985). Knowledge capital and the private rate of return to R&D in Japanese manufacturing industries. International Journal of Industrial Organization , 3(3), 0–305.

17.

Teece

G. D. J.

(1997). Managing intellectual capital: Licensing and cross-licensing in semiconductors and electronics. California Management Review , 39(2), 8–41.

18.

Tobin

(1969). A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking , 1(1), 15–29.

19.

J. M.

Chen

(2015). Financing constraints, R&D investment and corporate performance: A comparative analysis of high-tech listed enterprises on the main board and the growth enterprise board. Finance and Accounting Monthly , 12, 24–28.

20.

Zhang

T. S.

Deng

X. C.

(2016). Research on the relationship between operating performance and R&D investment based on the listed Big Data companies. Journal of Industrial Technological Economics , 35(9), 77–84.