Environmental passion and AI literacy shape the impact of green rewards on pro-environmental behaviors

环保热情和人工智能素养决定了绿色奖励对环保行为的影响。

阅读:1

Abstract

PURPOSE: Considering the importance of pro-environmental behaviors in achieving Sustainable Development Goals, this paper examines and strengthens the understanding of how green rewards shape employees' sustainable actions by integrating Stimulus-Organism-Response theory with Social Cognitive Theory. This paper explores the interplay between green rewards and pro-environmental behaviors. Specifically, this paper highlights the role of environmental passion as an internal mediating mechanism, and also analyzing the moderating influence of AI literacy as a critical capability in the digital era. METHODS: This paper employed the Partial Least Squares Structural Equation Modeling combined with Artificial Neural Network analysis to capture both linear and nonlinear patterns in employee behaviors. The questionnaire respondents were from employees across various manufacturing and service sectors in China, with a total of 445 valid responses. RESULTS: The results revealed that green rewards not only motivate pro-environmental behaviors but also foster positive environmental passion, which further enhance sustainable actions. Moreover, AI literacy significantly strengthens the positive influence of green rewards, amplifying their impacts on both environmental passion and pro-environmental behaviors. In addition, Artificial Neural Network analysis consistently identifies green rewards as the most influential predictor for both pro-environmental behaviors and environmental passion. CONCLUSION: These findings provided empirical support for the Stimulus-Organism-Response theory and the Social Cognitive theory. Results demonstrates that external incentives and internal psychological states jointly shape sustainable employee behaviors. To effectively promote pro-environmental behaviors, policymakers and managers should design targeted, sector-specific incentives and boost AI literacy.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。