| Literature DB >> 30939767 |
Yuan Ma1, Qiang Zhang2, Qiyue Yin3, Bingcheng Wang4.
Abstract
Abundant extant literature emphasizes the impact of board members attributes' influence on environmental information disclosure. Considering the voluntary nature of environmental information disclosure, executives have strong managerial discretion when they make such decisions, so this article focuses on top managers' influence on environmental information disclosure. We hypothesize that top managers' educational background and age will affect companies' environmental decision. The hypotheses are verified with the data from Chinese listed manufacturing companies. As the results show, a Master of Business Administration (MBA) educational background and average age of top managers positively affect environmental information disclosure, while the impact of legal educational background is negative. The company's environmental performance plays a U-shaped moderating effect on the relationship between MBA educational background and environmental information disclosure.Entities:
Keywords: educational background; environmental information disclosure; environmental performance; moderating effect; top manager
Mesh:
Year: 2019 PMID: 30939767 PMCID: PMC6479823 DOI: 10.3390/ijerph16071167
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Research framework.
The descriptive statistics and their correlation coefficients.
| Loc | Own | Dua | Csize | Prop | Bsize | EP | MBA | Legal | Age | EID | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Loc | 1 | ||||||||||
| Own | 0.096 ** | 1 | |||||||||
| Dua | −0.069 * | 0.039 | 1 | ||||||||
| Csize | 0.251 ** | 0.161 ** | 0.032 | 1 | |||||||
| Prop | 0.071 * | 0.582 ** | 0.049 | 0.173 ** | 1 | ||||||
| Bsize | 0.026 * | 0.014 * | 0.037 | 0.161 * | 0.046 | 1 | |||||
| EP | −0.010 | 0.022 | −0.010 | −0.016 | 0.038 | 0.043 | 1 | ||||
| MBA | −0.249 ** | −0.006 | 0.068 | −0.052 | 0.005 | 0.031 | 0.009 | 1 | |||
| Legal | 0.049 | −0.038 | −0.037 | 0.020 | 0.040 | 0.017 | −0.025 | 0.057 | 1 | ||
| Age | 0.051 | 0.052 | 0.084 * | 0.032 | 0.025 | 0.022 | 0.077 * | −0.008 | −0.040 | 1 | |
| EID | 0.242 ** | 0.067 | 0.012 | 0.351 ** | 0.117 ** | 0.112 ** | 0.017 | 0.032 ** | −0.059 ** | 0.080 * | 1 |
| Mean | 0.350 | 0.596 | 0.890 | 3.720 | 0.312 | 9.012 | 1.412 | 0.263 | 0.057 | 3.873 | 0.280 |
| S.D. | 0.478 | 0.321 | 0.311 | 0.527 | 0.141 | 2.374 | 1.412 | 0.200 | 0.516 | 0.141 | 0.449 |
** p < 0.05, * p < 0.1. S.D. = standard deviation; Loc = listing location; Own = state ownership; Dua = chief executive officer duality; Csize = company size; Prop = proportion of shares held by the largest shareholder; Bsize = board size; EP = environmental performance; MBA = the proportion of MBA; Legal = the proportion of legal background; Age = the average age of top managers; EID = Environmental information disclosure.
Regression results.
| Variables | Model 1 | Model 2 | ||||
|---|---|---|---|---|---|---|
| B | Wald | Exp(B) | B | Wald | Exp(B) | |
| Intercept | −7.401 | 91.962 | 0.001 | −11.778 | 20.531 | 0.000 |
| Loc | 0.823 *** | 61.188 | 2.277 | 0.860 ** | 60.669 | 2.364 |
| Dua | −0.073 | 0.070 | 0.930 | −0.033 | 0.014 | 0.968 |
| Csize | 1.415 *** | 68.257 | 4.117 | 1.412 *** | 64.787 | 4.106 |
| Bsize | 0.612 *** | 23.175 | 1.844 | 0.609 *** | 22.522 | 1.838 |
| Prop | 1.860 ** | 14.466 | 6.425 | 1.923 *** | 15.061 | 6.839 |
| MBA | 1.592 ** | 23.668 | 4.912 | |||
| Legal | −0.205 ** | 16.204 | 0.815 | |||
| Age | 1.102 ** | 8.526 | 3.012 | |||
| Chi-square | 354.085 *** | 371.079 *** | ||||
| −2 log likelihood | 2370.495 *** | 2338.016 *** | ||||
| Cox & Snell R2 | 0.142 | 0.153 | ||||
| Nagelkerke R2 | 0.205 | 0.221 | ||||
*** p < 0.01, ** p < 0.05. Loc = listing location; Dua = chief executive officer duality; Csize = company size; Bsize = board size; Prop = proportion of shares held by the largest shareholder; MBA = the proportion of MBA; Legal = the proportion of legal bacakground; Age = the average age of top managers; the dependent variable is EID.
The results of moderating effect.
| Variables | Model 3 | Model 4 | Model 5 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| B | Wald | Exp(B) | B | Wald | Exp(B) | B | Wald | Exp(B) | |
| Intercept | −11.583 | 56.775 | 0.000 | −11.661 | 56.824 | 0.000 | −11.691 | 57.547 | 0.000 |
| Loc | 0.860 *** | 60.365 | 2.362 | 0.862 *** | 60.787 | 2.367 | 0.852 *** | 58.984 | 2.343 |
| Dua | −0.035 | 0.016 | 0.966 | −0.038 | 0.025 | 0.963 | −0.042 | 0.023 | 0.959 |
| Csize | 1.414 *** | 64.398 | 4.114 | 1.412 *** | 63.926 | 4.105 | 1.418 *** | 65.298 | 4.131 |
| Bsize | 0.610 *** | 22.471 | 1.840 | 0.610 *** | 22.368 | 1.840 | 0.613 *** | 23.013 | 1.846 |
| Prop | 1.926 *** | 15.094 | 6.859 | 1.925 *** | 15.075 | 6.853 | 1.926 *** | 15.079 | 6.860 |
| MBA | 1.180 *** | 18.108 | 3.254 | 1.591 *** | 23.186 | 4.908 | 1.665 *** | 25.694 | 5.285 |
| Legal | −0.209 *** | 19.241 | 0.811 | −0.201 *** | 16.762 | 0.818 | −0.203 *** | 18.562 | 0.816 |
| Age | 1.110 ** | 8.550 | 3.304 | 1.108 | 7.966 | 3.028 | 1.136 ** | 8.918 | 3.114 |
| EP | 0.045 | 1.016 | 1.046 | 0.033 | 1.539 | 1.034 | 0.024 | 1.260 | 1.024 |
| EP2 | −0.157 | 2.163 | 0.855 | −0.146 | 2.251 | 0.864 | −0.114 | 2.259 | 0.892 |
| MBA × EP2 | −0.128 ** | 4.385 | 0.880 | ||||||
| Legal × EP2 | 0.119 | 0.171 | 0.887 | ||||||
| Age × EP2 | 0.059 | 0.433 | 1.061 | ||||||
| Chi-square | 371.196 *** | 371.332 *** | 372.223 *** | ||||||
| −2 log likelihood | 2337.889 | 2337.762 | 2336.871 | ||||||
| Cox & Snell R2 | 0.157 | 0.155 | 0.154 | ||||||
| Nagelkerke R2 | 0.226 | 0.222 | 0.222 | ||||||
*** p < 0.01, ** p < 0.05. Loc = listing location; Dua = chief executive officer duality; Csize = company size; Bsize = board size; Prop = proportion of shares held by the largest shareholder; MBA = the proportion of MBA; Legal = the proportion of law; Age = the average age of top managers; EP = environmental performance; EP2 = the square of environmental performance; MBA × EP2 = MBA × the square of environmental performance; Legal × EP2 = Legal × the square of environmental performance; Age × EP2 = Age × the square of environmental governance; the dependent variable is EID.