独创性
系统回顾
工作(物理)
价值(数学)
实证研究
社会学
公共关系
政治学
社会科学
工程类
定性研究
计算机科学
梅德林
哲学
机器学习
认识论
法学
机械工程
作者
Khalid Farooq,Mohd Yusoff Yusliza
出处
期刊:Benchmarking: An International Journal
[Emerald (MCB UP)]
日期:2023-02-07
卷期号:30 (10): 4681-4716
被引量:7
标识
DOI:10.1108/bij-02-2022-0079
摘要
Purpose This research offered a systematic and comprehensive literature review in analysing current studies on employee ecological behaviour (EEB) strategies and settings to determine various emphasised workplace ecological behaviour areas and contribute a precise mapping for future research. Design/methodology/approach This systematic literature review method involved 106 peer-reviewed articles published in reputable academic journals (between 2000 and the first quarter of 2021). This study was confined to a review of empirical papers derived from digital databases encompassing the terms ‘Employee green behaviour’, ‘Green behaviour at workplace’, ‘Employee ecological behaviour’, ‘Employee Pro-environmental behaviour’ and ‘Pro-environmental behaviour at workplace’ in the titles. Findings This study identified relevant journal articles (classified as EEB at work) from the current body of knowledge. Notably, much emphasis was identified on EEB over the past two decades. Overall, most studies employing quantitative approaches in both developed and emerging nations. Notably, ecological behaviour application garnered the most significant attention from scholars among the four focus areas in the literature review: (i) EEB concepts, models, or reviews, (ii) EEB application, (iii) EEB determinants and (iv) EEB outcomes. Practical implications Significant literature gaps indicate this field to be a relatively novel phenomenon. Thus, rigorous research on the topic proves necessary to develop a holistic understanding of the subject area. Originality/value This study expands the current body of knowledge by providing the first comprehensive systematic review on EEB themes, methods, applications, determinants, contextual focus, outcomes and recommending future research agenda.
科研通智能强力驱动
Strongly Powered by AbleSci AI