Evidence map›Paper›PMID 35270716›Full record

ArticleInternational journal of environmental research and public health2022

How Does Environmentally Specific Servant Leadership Fuel Employees' Low-Carbon Behavior? The Role of Environmental Self-Accountability and Power Distance Orientation.

Yuhuan Xia, Yubo Liu, Changlin Han, Yang Gao, Yuanyuan Lan

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuhuan XiaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.ORCID 0000-0002-7339-9206
Yubo LiuSchool of Business, Qingdao University, Qingdao 266071, China.
Changlin HanSchool of Business, Qingdao University, Qingdao 266071, China.ORCID 0000-0001-7019-2544
Yang GaoSchool of Business, Qingdao University, Qingdao 266071, China.
Yuanyuan LanSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.ORCID 0000-0002-7761-8015

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental problems caused by excessive carbon emissions are becoming increasingly prominent and have received heightened attention in recent years. Encouraging people to adopt low-carbon behavior to reduce carbon emissions is desirable. Based on social learning theory, we developed and tested a moderated mediation model to investigate when and how environmentally specific servant (ESS) leadership impacts employees' low-carbon behavior (i.e., private low-carbon behavior and public low-carbon behavior). We tested our theoretical framework with a sample of 483 subordinates and their direct supervisors working in northern China. The results indicate that ESS leadership is positively related to employees' low-carbon behavior, and that environmental self-accountability plays a mediating role in this relationship. In addition, power distance orientation strengthens the direct effects of ESS leadership on employees' environmental self-accountability and low-carbon behavior, as well as the indirect effect of ESS leadership on private low-carbon behavior via environmental self-accountability. Our findings contribute to the literature surrounding ESS leadership and low-carbon behavior, and help to promote green development and thus achieve the goals of carbon neutrality and decreasing carbon dioxide emissions.

Indexed as

LeadershipSocial ResponsibilityChinaHumansNegotiatingcarbon neutralityenvironmentally specific servant leadershipenvironmental self-accountabilitylow-carbon behaviorpower distance orientationsocial learning theory

Identifiers

PMID35270716
PMCPMC8910728

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.