The upper echelons theory is utilized to establish how CEO’s attributes affect firm’s technological innovation decisions. The extant literature has largely ignored the impacts of CEO media exposure. An unbalanced panel data analysis is used to examine the effects of CEO media exposure on Chinese polluting firm’s green technological innovation. It is illustrated that CEO media exposure generally enhances Chinese polluting firms’ green technological innovation decisions. In addition, we find that firms with state ownership and environmental regulations all moderate positively the relationship between CEO media exposure and green technological innovation. The research suggests that CEO media exposure appears to be a stimulus to firm’s green technological innovation decisions.
From an upper echelons perspective, chief executive officers’ (CEOs) behaviors influence corporate policy and play an important role in driving firm’s behaviors [
By disclosing the relevant information and imposing public pressure on firm behaviors [
Besides, we also explore the previous researches by identifying the boundary conditions of institutional factors, such as the company’s ownership structure and legal environments. We are curious to see if the significance and strength of impact between CEO media exposure and green technological innovation for Chinese firms are universally valid or subject to certain institutional environmental factors. Additionally, as for their special status in the Chinese economic system, state-owned enterprises (SOEs) attract much more attention. In February 2016, Information Office of the State Council held a press conference with Chinese and foreign media which were aimed at promoting air pollution control and the implementation of the Environmental Protection Law of the People’s Republic of China. During the conference, some reporters expressed whether the special characteristic of SOEs hindered the enforcement of laws by the China Ministry of Environmental Protection, which led to the environmental control of SOEs lag behind that of non-SOEs. Thus, is it true that SOEs, which play a leading role in the national economic system, lag behind non-SOEs in fulfilling their environmental protection responsibilities because of government protection?
And considering the pressure of CEO media exposure on firm’s green technological innovation, the current situation of environmental regulation in the formal system should not be ignored. At present, China is improving various legal systems gradually, and the external environment for enterprises to fulfill their environmental protection responsibilities is being established step by step. Particularly, the exposure of Chinese media increases the possibility for administrative agencies to intervene in illegal enterprises. Media supervision should be an effective supplement to the legal supervision system. The effect of environmental regulation is full of uncertainty. Its solution to environmental pollution problem is not exogenous and is restricted by other rules, laws, regulations, customs, and other systems. Therefore, it is meaningful to explore the role of environmental governance by combining the interaction between environmental regulation and CEO media exposure.
Our finding contributes in the following aspects. First, a thorough empirical investigation has been conducted by using 2597 firm-year observations; we find that CEO media exposure indeed has promoted firm’s green technological innovation. Second, in order to identify the influence of specific institutional factors on the relationship of CEO media exposure and the firm’s green technological innovation, we divide the full sample into subsamples according to a firms’ ownership structure and the local legal environment of the firms. The subsample analysis allows us to examine the main research questions under different institutional conditions. Finally, our empirical findings provide more insight into the emerging Chinese polluting firms, which are closely related to economy and ecology. Such information is important for investors diversifying their portfolios and also for policy makers in China when regulating the balance between economic development and ecological protection. Findings from our empirical tests have the following implications. Considering long-term sustainable development of Chinese firms, well-known firms with more media exposure are expected to have more motivation and pressure for green technological innovation. And this impact is largely strengthened for firms which are SOEs and with a better legal environment.
The remainder of the paper is organized as follows. We introduce the background and develop hypotheses in Section
CEO media exposure has an important impact on business activities, environmental governance, and corporate value [
First, CEO media exposure corrects managers’ agency behavior and promotes green technological innovation by playing the role of external governor [
Second, CEO media exposure promotes enterprises’ green technological innovation by diminishing harmful information asymmetries between enterprises and the outside, reducing the financial constraints of investment in green technological innovation. The media exposure is the intermediary which disseminates enterprise information to the public, reduces the information asymmetries between enterprises and stakeholders [
Third, CEO media also plays a significant part in social constructions, which affects the public how to evaluate the enterprises concerned and how their behavior meets the public’s expectations [
Meanwhile, investors begin to focus on the environmental information of listed companies, especially when the enterprises carry out significant green technological innovation. It enhances not only the image and reputation of enterprises but also the capital market which will quickly respond positively to enterprises. Thus, with the enhancement of public awareness of environmental protection, CEO media exposure will force enterprises to consider the reputation effect of environmental protection innovation, which will further affect the profitability of enterprises. In this case, enterprises have the motivation to carry out green technological innovation.
Based on the above analysis, we propose the following hypothesis: H1: CEO media exposure can enhance firm green technological innovation decision.
Ownership structure affects significantly corporate cognitive logic, leading to firms’ heterogeneous responses via green technological innovation [
Moreover, reputation can stimulate and restrain the behavior of management. Media research has emphasized that positive press coverage can act as a valuable firm resource, in large part because it reduces the inherent uncertainty about firm and leader quality [
Some research has shown that enterprises affiliated with higher levels of the government have initiated more innovation programs and received more public funding and policy support [
Hence, we propose the following hypothesis: H2: state ownership positively moderates the relationship between CEO media exposure and green technological innovation.
Legitimacy theory holds that an organization cannot succeed or even survive unless it believes in the goals, methods, and results recognized by society [
“Potter Hypothesis” holds that proper government management of the environment will stimulate enterprises to break the inherent mode of production and operation and product structure. Government’s environmental supervision is the biggest source of pressure faced by enterprises when considering environmental problems. Enterprises will be forced to take certain measures to avoid punishment caused by noncompliance with environmental laws and regulations. On the one hand, enterprises should choose energy saving, emission reduction, and cleaner production; on the other hand, enterprises should accumulate business experiences through product innovation and process change and seek new and unique core competitiveness from them [
Thus, we propose the following hypothesis: H3: environmental regulations positively moderate the relationship between CEO media exposure and green technological innovation.
We use an unbalanced panel data regression analysis to analyze the effects of CEO media exposure on the green technological innovation decision. Our model is expressed as follows:
In previous studies, green technological innovation has been measured by indicators, such as green R&D [
The independent variable CEO mediait measures the extent of CEO media exposure. Following prior studies, CEO media is computed by using the natural log of one plus the number of CEOs news reports (i.e., [
Ten yearly dummies (I = 10) and 16 industry dummies (
State ownership may moderate the relationship, which is important to see how effective they are in promoting innovations. And environmental regulations are targets set by the government with which firms must comply. Thus, we attempt to examine the impacts of state ownership and environmental regulations on innovations at the most highly polluting firms. Following Javorcik and Wei [
Variable definitions.
Green invention | The number of applications for environmental inventions |
Green utility | The number of applications for environmental utility patents |
CEO media | The natural log of one plus the number of CEOs news reports |
State | If the firm is an SOE, which is controlled by the government or its various entities, it equals 1 and 0 otherwise |
Rule | The number of environmental regulations issued by the national government |
Control variables | |
Firm size | The natural logarithm of total assets at the end of the year |
Leverage | Total liabilities over total assets |
ROA | Net income over total assets |
Cash holding | The ending balance of cash and cash equivalents over sales |
R&D | The natural logarithm of enterprise investment in research and development, which affects patents |
Subsidy | The natural logarithm of government subsidies |
TMT | The number of top management team members |
Duality | Equals 1 if the CEO also serves as chairman and 0 otherwise |
Our initial dataset includes all Chinese firms listed on Shanghai and Shenzhen Stock Exchanges over the period of 2010–2016. Zhang et al. [
Our final sample consists of 2,597 firm-year observations, covering 16 highly polluting industries at the two-digit SIC level: nonmetallic mineral products; power, thermal production and supply; metal products; petroleum processing, coking, and nuclear fuel processing; nonferrous metal smelting and calendaring processing; pharmaceutical manufacturing; chemical raw materials and chemical products manufacturing; rubber and plastic products; ferrous metal mining; chemical fiber manufacturing; gas production and supply; paper and paper products; coal mining and washing; ferrous metal smelting and rolling; nonferrous metals mine mining; ecological protection and environmental governance; and petroleum and natural gas mining.
Firms listed for less than one year are excluded. To minimize the impact of outliers, we follow common practice in the literature and winsorize all variables at the 1st and 99th percentiles (i.e., [
The CEO media exposure information is extracted from Baidu, which is the leading online search engine in China. Following the method in Nguyen [
Data on R&D, government subsidies, age, the size of the TMT, duality, firm size, leverage, ownership, ROA, cash holdings, and others in highly polluting industries come from the China Stock Market and Accounting Research (CSMAR) database for the Chinese firms listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2016. Prior studies have defined highly polluting industries as industries that discharge large-scale industrial waste [
We collected manually personal profiles of chairs and CEOs for each company. The data come primarily from the relevant securities financial websites (e.g.,
We excluded “special treatment” companies (i.e., firms classified as failing by the stock exchanges) and companies listed on the exchange for less than two years.
Table
Characteristics of CEO media and green technological innovation.
Year | CEO media exposure | Green invention | Green utility | |||||
---|---|---|---|---|---|---|---|---|
Mean | Std. dev. | Mean | Std. dev. | % | Mean | Std. dev. | % | |
2010 | 2.995 | 1.471 | 0.765 | 3.133 | 22.41 | 0.615 | 2.968 | 15.91 |
2011 | 2.981 | 1.442 | 0.776 | 2.174 | 28.83 | 0.625 | 1.922 | 23.71 |
2012 | 2.894 | 1.466 | 0.908 | 2.596 | 29.92 | 0.693 | 2.557 | 20.84 |
2013 | 3.035 | 1.544 | 0.960 | 2.436 | 32.86 | 0.679 | 2.037 | 21.33 |
2014 | 3.029 | 1.569 | 1.197 | 3.922 | 32.14 | 0.825 | 2.980 | 22.92 |
2015 | 2.919 | 1.475 | 1.043 | 3.204 | 32.33 | 0.803 | 2.852 | 20.51 |
2016 | 3.206 | 1.590 | 1.140 | 3.280 | 32.64 | 1.089 | 4.576 | 22.44 |
2010–2016 | 3.008 | 1.510 | 0.970 | 3.015 | 30.15 | 0.761 | 2.956 | 21.06 |
Table
Descriptive statistics per firm.
Variable | Mean | Std. dev. | Min | P10 | p25 | p50 | p75 | P90 | Max |
---|---|---|---|---|---|---|---|---|---|
Green invention | 0.97 | 3.02 | 0 | 0 | 0 | 0 | 1 | 3 | 59 |
Green utility | 0.76 | 2.96 | 0 | 0 | 0 | 0 | 0 | 2 | 55 |
Media exposure | 3.01 | 1.51 | 0 | 1.10 | 1.95 | 2.77 | 4.29 | 4.98 | 7.21 |
State | 0.37 | 0.48 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
Rule | 1.40 | 0.70 | 0 | 0 | 0.69 | 1.79 | 1.79 | 2.08 | 2.20 |
Firm size | 22.17 | 1.22 | 19.06 | 20.82 | 21.31 | 21.95 | 22.88 | 23.9 | 26.43 |
Leverage | 0.44 | 0.22 | 0.04 | 0.14 | 0.25 | 0.43 | 0.61 | 0.73 | 1.21 |
ROA | 0.04 | 0.06 | −0.26 | 0 | 0.01 | 0.04 | 0.07 | 0.1 | 0.23 |
Cash holding | 0.31 | 0.40 | 0.01 | 0.05 | 0.09 | 0.17 | 0.36 | 0.73 | 2.67 |
R&D | 17.48 | 1.54 | 0 | 15.66 | 16.65 | 17.58 | 18.37 | 19.3 | 22.08 |
Subsidy | 16.27 | 1.85 | 0 | 14.4 | 15.4 | 16.3 | 17.31 | 18.25 | 21.79 |
Top hold | 0.36 | 0.15 | 0.04 | 0.18 | 0.24 | 0.34 | 0.47 | 0.56 | 0.89 |
TMT size | 7.37 | 2.40 | 3 | 5 | 6 | 7 | 9 | 10 | 15 |
CEO duality | 0.23 | 0.42 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
Table
Descriptive correlations.
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Green invention | 1 | ||||||||||||
Green utility | 0.590 | 1 | |||||||||||
CEO exposure | 0.092 | 0.087 | 1 | ||||||||||
State | 0.071 | 0.138 | −0.225 | 1 | |||||||||
Rule | 0.020 | 0.029 | 0.036 | 0 | 1 | ||||||||
Firm size | 0.271 | 0.258 | 0.030 | 0.213 | 0.126 | 1 | |||||||
Leverage | 0.102 | 0.101 | −0.152 | 0.262 | 0.007 | 0.477 | 1 | ||||||
ROA | −0.048 | −0.042 | 0.164 | −0.180 | −0.046 | −0.128 | −0.499 | 1 | |||||
Cash holding | −0.087 | −0.077 | 0.142 | −0.206 | −0.078 | −0.231 | −0.478 | 0.177 | 1 | ||||
R&D | 0.224 | 0.198 | 0.147 | 0.022 | 0.131 | 0.511 | 0.082 | 0.060 | −0.156 | 1 | |||
Subsidy | 0.185 | 0.141 | 0.037 | 0.081 | 0.098 | 0.542 | 0.279 | −0.018 | −0.176 | 0.320 | 1 | ||
Top hold | 0.058 | 0.097 | −0.115 | 0.101 | −0.050 | 0.257 | 0.062 | −0.0030 | −0.0080 | 0.124 | 0.081 | 1 | |
TMT size | 0.093 | 0.061 | 0.067 | 0.093 | 0.057 | 0.251 | 0.142 | −0.056 | −0.038 | 0.125 | 0.184 | −0.046 | 1 |
CEO duality | −0.081 | −0.073 | 0.345 | −0.214 | −0.018 | −0.186 | −0.138 | 0.042 | 0.169 | −0.027 | −0.093 | −0.105 | −0.041 |
Table
The univariate analysis of green invention and green utility.
Type | Level | Number of observations | Green invention | Green utility | ||
---|---|---|---|---|---|---|
CEO media exposure | High | 1554 | 0.431 | 0.308 | 0.081 | 0.068 |
Low | 1421 | 0.35 | 0.239 | |||
State | With | 1067 | 0.587 | 0.391 | 0.304 | 0.181 |
Without | 1908 | 0.283 | 0.21 | |||
Rule | High | 490 | 0.666 | 0.418 | 0.328 | 0.171 |
Low | 2485 | 0.338 | 0.247 |
We have examined the effect of CEO media exposure on green technological innovation (green invention and green utility). As we know, the dependent variable is counted data with zero entries, and the typical approach is to use the negative binomial (NB) model. The distribution of green invention (mean value = 0.97 and sd = 3.02) and green utility counts (mean value = 0.76 and sd = 2.96) shows a large dispersion in Table
Table
Zero-inflated negative binomial regression of CEO media exposure on green invention and green utility.
Variables | Green inventiont | Green utilityt | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
CEO media | 0.222 | 0.165 | 0.094 | 0.309 | 0.203 | 0.159 |
(0.033) | (0.044) | (0.070) | (0.043) | (0.056) | (0.096) | |
State | −0.271 | 0.139 | ||||
(0.209) | (0.269) | |||||
CEO media × state | 0.128 | 0.267 | ||||
(0.063) | (0.078) | |||||
Rule | −1.406 | −0.433 | ||||
(0.386) | (0.279) | |||||
CEO media × rule | 0.085 | 0.098 | ||||
(0.042) | (0.057) | |||||
Firm size | −0.263 | −0.263 | −0.222 | −0.260 | −0.282 | −0.238 |
(0.042) | (0.043) | (0.044) | (0.047) | (0.048) | (0.048) | |
Leverage | 0.328 | 0.379 | 0.264 | 0.955 | 0.792 | 0.871 |
(0.309) | (0.308) | (0.308) | (0.421) | (0.410) | (0.424) | |
ROA | −0.513 | −0.237 | −0.538 | 0.575 | 0.468 | 0.379 |
(1.099) | (1.108) | (1.091) | (1.482) | (1.481) | (1.487) | |
Cash holding | −0.423 | −0.362 | −0.348 | −0.371 | −0.049 | −0.342 |
(0.162) | (0.164) | (0.162) | (0.189) | (0.189) | (0.190) | |
R&D | 0.227 | 0.230 | 0.232 | 0.203 | 0.196 | 0.205 |
(0.039) | (0.039) | (0.039) | (0.047) | (0.046) | (0.047) | |
Subsidy | 0.121 | 0.121 | 0.124 | 0.065 | 0.084 | 0.065 |
(0.031) | (0.031) | (0.031) | (0.031) | (0.032) | (0.031) | |
Top hold | −0.227 | −0.273 | −0.167 | −0.120 | 0.008 | −0.133 |
(0.335) | (0.337) | (0.335) | (0.439) | (0.430) | (0.441) | |
TMT size | 0.073 | 0.070 | 0.074 | 0.073 | 0.067 | 0.075 |
(0.020) | (0.020) | (0.020) | (0.025) | (0.024) | (0.025) | |
Duality | −0.601 | −0.547 | −0.578 | −0.542 | −0.403 | −0.519 |
(0.121) | (0.123) | (0.121) | (0.161) | (0.158) | (0.161) | |
Constant | 1.095 | 1.135 | 1.095 | 1.729 | 1.632 | 1.723 |
(0.239) | (0.250) | (0.247) | (0.066) | (0.068) | (0.066) | |
Year | Yes | Yes | Yes | Yes | Yes | Yes |
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
N | 2597 | 2597 | 2597 | 2597 | 2597 | 2597 |
Chi2 | 366.274 | 366.062 | 379.864 | 332.946 | 391.544 | 337.586 |
Note that standard errors are in parentheses:
Model 2 and model 5 show, respectively, the interaction effect of CEO media exposure and state on green invention and green utility, with the inclusion of the dummy variables industry and year as control variables. CEO media exposure has a positive and significant coefficient (
Model 3 tests the moderating effect of environmental regulations on the relationship between CEO media exposure and green invention. The effect of CEO media exposure on green invention is insignificant (
First, the positive relation between CEO media exposure and green technological innovation may be driven by the current patents of the firm. Consequently, the direction of causality may run from green technological innovation to CEO media exposure. To address this potential endogeneity, we modify our CEO media exposure by using dependent variable forward two periods. This has the effect of ensuring that the measure of CEO media exposure is unrelated to the period in which green invention or green utility is released or measured.
Table
Dependent variable forward two periods.
Variables | Green inventiont + 2 | Green utilityt + 2 | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
CEO media | 0.223 | 0.185 | 0.133 | 0.327 | 0.151 | 0.180 |
(0.036) | (0.049) | (0.064) | (0.053) | (0.068) | (0.089) | |
State | −0.105 | −0.215 | ||||
(0.233) | (0.317) | |||||
CEO media × state | 0.084 | 0.376 | ||||
(0.070) | (0.093) | |||||
Rule | −1.136 | −0.931 | ||||
(0.404) | (0.396) | |||||
CEO media × rule | 0.071 | 0.117 | ||||
(0.044) | (0.059) | |||||
Firm size | −0.198 | −0.205 | −0.163 | −0.224 | −0.223 | −0.200 |
(0.045) | (0.046) | (0.047) | (0.056) | (0.056) | (0.057) | |
Leverage | 0.499 | 0.529 | 0.461 | 0.887 | 0.658 | 0.788 |
(0.367) | (0.366) | (0.366) | (0.512) | (0.495) | (0.515) | |
ROA | −1.201 | −0.980 | −0.986 | 1.448 | 0.622 | 1.526 |
(1.269) | (1.278) | (1.266) | (1.693) | (1.712) | (1.689) | |
Cash holding | −0.117 | −0.046 | −0.092 | −0.282 | 0.103 | −0.238 |
(0.164) | (0.169) | (0.163) | (0.235) | (0.231) | (0.236) | |
R&D | 0.193 | 0.199 | 0.190 | 0.172 | 0.171 | 0.181 |
(0.045) | (0.045) | (0.045) | (0.055) | (0.054) | (0.055) | |
Subsidy | 0.086 | 0.088 | 0.086 | 0.049 | 0.064 | 0.049 |
(0.031) | (0.031) | (0.031) | (0.035) | (0.036) | (0.036) | |
Top hold | −0.537 | −0.541 | −0.476 | −0.352 | −0.244 | −0.370 |
(0.387) | (0.387) | (0.385) | (0.503) | (0.491) | (0.505) | |
TMT size | 0.032 | 0.031 | 0.035 | 0.114 | 0.073 | 0.120 |
(0.024) | (0.024) | (0.024) | (0.031) | (0.030) | (0.031) | |
Duality | −0.637 | −0.592 | −0.612 | −0.781 | −0.639 | −0.789 |
(0.138) | (0.141) | (0.138) | (0.192) | (0.186) | (0.192) | |
Constant | 1.135 | 1.161 | 1.143 | 1.767 | 1.650 | 1.758 |
(0.280) | (0.301) | (0.292) | (0.074) | (0.082) | (0.073) | |
Year | Yes | Yes | Yes | Yes | Yes | Yes |
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
N | 1855 | 1855 | 1855 | 1855 | 1855 | 1855 |
Chi2 | 243.554 | 243.661 | 252.839 | 212.109 | 257.527 | 227.563 |
Note that standard errors are in parentheses:
Second, in an attempt to strictly control for observed selection bias, a propensity score matching (PSM) identification strategy is conducted. In the first stage of this estimation strategy, if firms with high level CEO media exposure are fundamentally different from those with low level, then the control variables employed in the main specification that capture linear relations may be inadequate. To alleviate concerns over such functional form misspecification biases, we use the methodology of
Comparison of differences before and after variable matching.
Variable | Unmatched | Mean | |||
---|---|---|---|---|---|
Matched | Treated | Control | T | ||
Firm size | U | 22.153 | 22.166 | −0.280 | 0.782 |
M | 22.153 | 22.102 | 1.040 | 0.300 | |
Leverage | U | 0.402 | 0.466 | −7.480 | 0.000 |
M | 0.402 | 0.402 | −0.010 | 0.991 | |
ROA | U | 0.049 | 0.032 | 7.720 | 0.000 |
M | 0.049 | 0.049 | 0.120 | 0.901 | |
Cash holding | U | 0.371 | 0.256 | 7.330 | 0.000 |
M | 0.371 | 0.353 | 1.010 | 0.311 | |
R&D | U | 17.670 | 17.325 | 5.720 | 0.000 |
M | 17.670 | 17.662 | 0.150 | 0.885 | |
Subsidy | U | 16.279 | 16.254 | 0.340 | 0.736 |
M | 16.279 | 16.091 | 2.320 | 0.020 | |
Top hold | U | 0.349 | 0.375 | −4.400 | 0.000 |
M | 0.349 | 0.338 | 1.830 | 0.068 | |
TMT size | U | 7.453 | 7.267 | 1.980 | 0.048 |
M | 7.453 | 7.387 | 0.670 | 0.500 | |
Duality | U | 0.361 | 0.115 | 15.450 | 0.000 |
M | 0.361 | 0.362 | −0.040 | 0.967 |
Robust check for the effects of CEO media on green invention and green utility.
Variable | Sample | Treated | Controls | Difference | SE | |
---|---|---|---|---|---|---|
Green invention | Unmatched | 1.176 | 0.789 | 0.387 | 0.119 | 3.26 |
ATT | 1.176 | 0.827 | 0.350 | 0.166 | 2.11 | |
Green utility | Unmatched | 0.919 | 0.623 | 0.296 | 0.117 | 2.54 |
ATT | 0.919 | 0.616 | 0.303 | 0.165 | 1.83 | |
Variables | Green inventiont | Green utilityt | ||||
(1) | (2) | (3) | (4) | (5) | (6) | |
CEO media | 0.220 | 0.080 | 0.092 | 0.369 | 0.195 | 0.147 |
(0.046) | (0.060) | (0.104) | (0.065) | (0.078) | (0.151) | |
State | −1.201 | −0.463 | ||||
(0.355) | (0.451) | |||||
CEO media × state | 0.324 | 0.387 | ||||
(0.091) | (0.113) | |||||
Rule | −1.501 | −0.177 | ||||
(0.529) | (0.407) | |||||
CEO media × rule | 0.082 | 0.147 | ||||
(0.061) | (0.088) | |||||
Firm size | −0.271 | −0.250 | −0.224 | −0.314 | −0.318 | −0.300 |
(0.055) | (0.055) | (0.057) | (0.061) | (0.061) | (0.065) | |
Leverage | 0.525 | 0.655 | 0.455 | 0.945 | 0.861 | 0.951 |
(0.401) | (0.396) | (0.399) | (0.560) | (0.540) | (0.571) | |
ROA | 0.864 | 1.372 | 1.011 | 1.622 | 1.597 | 1.532 |
(1.413) | (1.423) | (1.402) | (1.913) | (1.930) | (1.913) | |
Cash holding | −0.476 | −0.385 | −0.403 | −0.406 | −0.066 | −0.361 |
(0.191) | (0.194) | (0.191) | (0.220) | (0.219) | (0.222) | |
R&D | 0.222 | 0.221 | 0.228 | 0.260 | 0.250 | 0.264 |
(0.052) | (0.052) | (0.052) | (0.063) | (0.061) | (0.063) | |
Subsidy | 0.111 | 0.104 | 0.109 | 0.053 | 0.067 | 0.054 |
(0.037) | (0.037) | (0.037) | (0.033) | (0.034) | (0.033) | |
Top hold | 0.557 | 0.337 | 0.528 | 0.163 | −0.141 | 0.252 |
(0.430) | (0.435) | (0.429) | (0.575) | (0.569) | (0.584) | |
TMT size | 0.066 | 0.062 | 0.064 | 0.077 | 0.065 | 0.083 |
(0.025) | (0.024) | (0.024) | (0.031) | (0.030) | (0.031) | |
Duality | −0.720 | −0.644 | −0.690 | −1.014 | −0.819 | −0.983 |
(0.138) | (0.142) | (0.138) | (0.191) | (0.188) | (0.192) | |
Constant | 1.048 | 1.038 | 1.074 | 1.624 | 1.509 | 1.615 |
(0.284) | (0.286) | (0.236) | (0.080) | (0.086) | (0.081) | |
Year | Yes | Yes | Yes | Yes | Yes | Yes |
Industry | Yes | Yes | Yes | Yes | Yes | Yes |
N | 1568 | 1568 | 1568 | 1568 | 1568 | 1568 |
Chi2 | 252.846 | 262.193 | 261.407 | 226.921 | 262.613 | 230.509 |
Note that standard errors are in parentheses:
In this study, we examine with yearly data the impact of firm CEO’s media exposure on the firm green technological innovation. Our sample consists of 2,597 firm-year observations for Chinese companies listed on the Shanghai and Shenzhen Stock Exchanges over the period of 2010–2016. We apply ZINB models to test whether the CEO’s media coverage influences the patent application data, which reflects the level of firm green technological innovation. Given the specific market environment in the Chinese economy, we consider the effect under different institutional situations. In particular, we investigate the moderating effect of the firm’s ownership structure (i.e., state-owned vs. non-state-owned) and the environmental regulations (i.e., stronger vs. weaker). We also conduct a thorough robustness check for additional validity of our methodology and main findings.
In summary, the empirical test results are generally consistent with our hypotheses. First, CEO media coverage has a positive influence over firm green technological innovation, implying that news reports facilitate the investment in green innovation. Second, the interaction term of CEO media coverage and ownership has a positive impact on firm green innovation for the full sample data, which proves that if the firm is a SOE, the positive influence of CEO media coverage on the firm green innovation will be increased. And the interaction term of CEO media coverage and environmental regulations also has the same influence on firm green innovation, implying that firms with stronger environmental rules will invest more on green innovation. It is apparent that the firm green innovation is exposed to a stronger influence of CEO media coverage when firms are state-owned and operate in stronger environmental regulations.
Our research also provides some important practical implications. Firstly, more attention should be paid to informal systems such as media, which play an important governance role in firm’s green technological innovation. As an important aspect of external governance, the external regulatory pressure of media attention has a positive effect on firm’s environmental protection innovation behavior. Therefore, we should promote the role of media supervision and reputation mechanism, make it an important external mechanism of corporate environmental governance, promote companies to fulfill actively their social responsibilities, and enhance the level of environmental innovation of enterprises.
Secondly, we ought to strengthen the level of environmental regulation and promote information disclosure and public participation. The effect of CEO media exposure on firm green innovation under the different intensity of environmental regulation not only reflects the quality of regulation and the effect of implementation, but also shows that the strengthening of moderate environmental regulation plays its supervisory part and promotes firm green innovation decision. Media participation in environmental governance under environmental regulation has played a greater role, indicating that public opinion has restrictive and supervisory function. Therefore, the future institutional arrangement of environmental regulation should also consider social regulation and improve the information disclosure system. Join Ministry of Industry and Information Technology and the various regulatory bodies of the Securities Regulatory Commission to give full play to the mechanism of public participation in the supervision of firm green innovation behavior. We should promote enterprises to fulfill their environmental responsibility through stakeholder environmental pressure and form market supervision beyond government supervision.
Thirdly, the support for non-SOE’s green innovation should be increased. The result of our study shows that media attention has a more significant impact on firm green innovation of SOE. The essential reason is that the investment cost of environmental protection innovation of state-owned enterprises is guaranteed by the government’s “umbrella,” while the investment cost of environmental innovation of non-SOEs is mostly borne by themselves. Therefore, we should formulate corresponding environmental regulations and encourage non-SOEs to invest in environment. At the same time, enterprises investing in environmental protection should be given certain tax subsidies or preferential treatment to improve the motivation of non-SOEs investing in environmental protection.
Our study still has some limitations on which further research will be explored. First, there is no distinction between positive or negative media coverage of CEOs. We will further explore the effect of the media exposure distinction on green innovation in the future. Second, there is no specific distinction between media types, such as traditional media and new media and official media and nonofficial media. Future research can further distinguish different types of media and the differences of their attention effects. Third, we did not consider the impact of public perception and mentality (including stock investors) and the intrinsic effect of media ecology (such as media corruption) on green innovation of enterprises from social culture level. Future research may benefit from these works in these directions.
The data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.
The co-authors confirm that this work was original research that has not been published previously and was not under consideration for publication elsewhere, in whole or in part.
The authors declare that there are no conflicts of interest regarding the submission of this article.
All the authors have read and approved the final manuscript for publication.
The authors acknowledge the financial support from the National Natural Science Foundation of China (Grant nos. 71702084, 71762020, and 72002029), the Humanity and Social Science Foundation of the Ministry of Education of China (17YJC630112 and 20YJA840022), the Heilongjiang Province Postdoctoral Science Foundation (LBH-Q19086), the Humanity and Social Science Foundation of the Ministry of NEPU (WKJD2020001), and the Northeast Petroleum University Innovation Foundation for Postgraduate (YJSCX2017-033NEPU).