| Literature DB >> 35967646 |
Wenhan Wu1, Wenzhuo Wu2, Kouhua Wu3, Chen Ding4, Chenya Fan3.
Abstract
Objective: The main purpose of this study is to investigate the impact of green product and process innovation on the competitive advantages of the Chinese automobile industry during coronavirus disease 2019 (COVID-19). This study also examined the mediating role of corporate environmental ethics (CEE) and the moderating role of corporate environmental management in the relationship between the green product and process innovation on the competitive advantages of the Chinese automobile industry during COVID-19.Entities:
Keywords: COVID-19; competitive advantages; corporate environmental ethics; corporate environmental management; green innovation
Year: 2022 PMID: 35967646 PMCID: PMC9364043 DOI: 10.3389/fpsyg.2022.832895
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
FIGURE 1A two-step process of partial least squares (PLS) path model assessment (Henseler et al., 2009).
FIGURE 2Measurement model assessment.
Internal consistency, convergent validity, composite reliability, and average variance extracted (AVE).
| Construct | Indicators | Loadings | Cronbach’s alpha | Composite reliability | AVE |
| Competitive advantages | CA1 | 0.786 | 0.842 | 0.888 | 0.613 |
| CA2 | 0.789 | ||||
| CA3 | 0.775 | ||||
| CA4 | 0.776 | ||||
| CA5 | 0.789 | ||||
| CEE | CEE1 | 0.811 | 0.837 | 0.885 | 0.607 |
| CEE2 | 0.765 | ||||
| CEE3 | 0.837 | ||||
| CEE4 | 0.778 | ||||
| CEE5 | 0.697 | ||||
| CEM | CEM1 | 0.771 | 0.818 | 0.869 | 0.570 |
| CEM2 | 0.800 | ||||
| CEM3 | 0.725 | ||||
| CEM4 | 0.742 | ||||
| CEM5 | 0.735 | ||||
| GPrdI | GPrdI1 | 0.872 | 0.843 | 0.887 | 0.611 |
| GPrdI2 | 0.741 | ||||
| GPrdI3 | 0.718 | ||||
| GPrdI4 | 0.754 | ||||
| GPrdI5 | 0.814 | ||||
| GPrsI | GPrsI1 | 0.645 | 0.842 | 0.884 | 0.562 |
| GPrsI2 | 0.689 | ||||
| GPrsI3 | 0.733 | ||||
| GPrsI4 | 0.824 | ||||
| GPrsI5 | 0.827 | ||||
| GPrsI6 | 0.764 |
Heterotrait-monotrait (HTMT) ratio.
| CA | CEE | CEM | GPrdI | GPrsI | |
| CA | |||||
| CEE | 0.715 | ||||
| CEM | 0.753 | 0.793 | |||
| GPrdI | 0.783 | 0.620 | 0.717 | ||
| GPrsI | 0.763 | 0.655 | 0.821 | 0.825 |
Fornell–Larcker criterion.
| CA | CEE | CEM | GPrdI | GPrsI | |
| CA | 0.783 | ||||
| CEE | 0.604 | 0.779 | |||
| CEM | 0.740 | 0.726 | 0.755 | ||
| GPrdI | 0.685 | 0.541 | 0.619 | 0.782 | |
| GPrsI | 0.642 | 0.557 | 0.670 | 0.705 | 0.750 |
FIGURE 3Structural model assessment.
Structural model assessment (direct effect results and decision).
| Hypotheses | Relationship | Beta | STD | ||
| H1 | GPrdI - > CEE | 0.294 | 0.102 | 2.868 | 0.004 |
| H2 | GPrsI - > CEE | 0.350 | 0.107 | 3.276 | 0.001 |
| H3 | GPrdI - > CA | 0.334 | 0.078 | 4.258 | 0.000 |
| H4 | GPrsI - > CA | 0.269 | 0.084 | 3.202 | 0.002 |
Structural model assessment (moderation effects).
| Hypotheses | Relationship | Beta | STD | ||
| H7 | GPrdI*CEM - > CA | 0.342 | 0.082 | 4.348 | 0.000 |
| H8 | GPrsI*CEM - > CA | 0.313 | 0.069 | 4.568 | 0.003 |
Structural model assessment indirect effect (mediation effects).
| Hypotheses | Relationship | Beta | STD | ||
| H5 | GPrdI- > CEE - > CA | 0.350 | 0.107 | 3.276 | 0.000 |
| H6 | GPrsI- > CEE - > CA | 0.298 | 0.101 | 2.950 | 0.007 |