| Literature DB >> 32937820 |
Wengang Zhang1, Baiqing Sun1, Feng Xu2.
Abstract
By integrating internal green self-efficacy and external environmental regulation, this research investigates the relationship between green transformational leadership and green product development performance. Taking 23 new energy vehicle enterprises in China as samples, we collected 298 valid questionnaires and verified the hypotheses through structural equation modeling. The results show that both green transformational leadership and green self-efficacy can promote green product development performance; green self-efficacy mediates the positive relationship between green transformational leadership and green product development performance, while environmental regulation positively moderates the mediating effect of green self-efficacy. Furthermore, environmental regulation and green self-efficacy interact to promote green product development performance. Our research provides a new perspective to understand how green transformational leadership is related to green product development performance and how this relationship is molded by contextual antecedents. Enterprises need to comprehensively consider the green influence of transformational leadership, green driving of employees themselves, and green linkage among organizations (macro policy guidance, passive market incentives, and self-issued actions) to improve green product development performance. Limitations and future scope are discussed.Entities:
Keywords: environmental regulation; green product development performance; green self-efficacy; green transformational leadership
Year: 2020 PMID: 32937820 PMCID: PMC7558874 DOI: 10.3390/ijerph17186678
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1The hypothetical framework.
Descriptive statistics of the constructs.
| Constructs | No. of Items | Cronbach’s Alpha | Loadings Range | Average Variance Extracted | χ2/df | NNFI | CFI | RMSEA |
|---|---|---|---|---|---|---|---|---|
| Green transformational leadership | 6 | 0.94 | [0.85–0.96] | 0.759 | 1.43 | 0.943 | 0.956 | 0.02 |
| Green self-efficacy | 5 | 0.88 | [0.79–0.90] | 0.681 | 1.55 | 0.918 | 0.948 | 0.05 |
| Green product development performance | 5 | 0.91 | [0.82–0.95] | 0.724 | 1.79 | 0.925 | 0.953 | 0.04 |
| Environmental regulation | 9 | 0.83 | [0.75–0.89] | 0.667 | 2.32 | 0.902 | 0.950 | 0.03 |
Measurement model: discriminant validity.
| Construct | Green Transformational Leadership | Green Self-Efficacy | Green Product Development Performance | Average Variance Extracted |
|---|---|---|---|---|
| Green transformational leadership | 0.871 | 0.759 | ||
| Green self-efficacy | 0.696 | 0.825 | 0.681 | |
| Green product development performance | 0.721 | 0.684 | 0.851 | 0.724 |
| Environmental regulation | 0.623 | 0.421 | 0.703 | 0.667 |
Note: Square root of average variance extracted (AVE) for each construct was shown in the diagonal of the correlation matrix.
Results of path coefficient analysis.
| Hypothesis | Description of Path | Path Coefficient | Conclusion |
|---|---|---|---|
| H1 | green transformational leadership→green product development performance | 0.48 *** | H1(+): supported |
| H2 | green transformational leadership→green self-efficacy | 0.43 *** | H2(+): supported |
| H3 | green self-efficacy→green product development performance | 0.29 ** | H3(+): supported |
Notes: χ2/df = 1.81, NNFI = 0.96; TLI = 0.96; CFI = 0.96; RMSEA = 0.02. Tests of hypotheses are two-tailed tests; ** p < 0.01; *** p < 0.001.
Figure 2Modelestimationresults. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 3Interaction of green self-efficacy and environmental regulation on green product development performance.
Conditional indirect relationship between green transformational leadership and green product development performance through green self-efficacy at low and high values of environmental regulation.
| Environmental Regulation | Conditional Indirect Effect | SE | 95% Confidence Interval Lower Limit | 95% Confidence Interval Upper Limit |
|---|---|---|---|---|
| High(M + 1SD) | 0.08 * | 0.02 | 0.02 | 0.15 |
| Low(M − 1SD) | 0.02 | 0.02 | −0.02 | 0.06 |
Note. n = 298. Bootstrap sample size = 10,000; * p < 0.05.