The role of green logistics management practices, supply chain traceability and logistics ecocentricity in sustainability performance

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Abstract

Purpose

The priority giving to green practices in today's competitive market has made green logistics management practices (GLMPS) a significant driver of organizational performance. The purpose of this study is to explore the influence of GLMPS, logistics ecocentricity and supply chain traceability on sustainability performance.

Design/methodology/approach

The study uses structured questionnaires to gather data from 274 managers of manufacturing firms in Ghana. The partial least square structural equation modeling approach is used to analyze the data to test the proposed hypotheses.

Findings

The results obtained from the analysis indicate that GLMPS positively influence social sustainability and environmental sustainability. However, GLMPS negatively influence business performance. The results further reveal that logistics ecocentricity and supply chain traceability augment GLMPS to achieve significant improvement in both business performance and environmental sustainability through the mediation effect approach.

Originality/value

The study proposes a conceptual framework that tests the combined effect of GLMPs, logistics ecocentricity and supply chain traceability on environmental sustainability, social sustainability and business performance from the Ghanaian perspective.

Related collections

Most cited references 104

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Common method biases in behavioral research: A critical review of the literature and recommended remedies.

Philip M Podsakoff, Scott MacKenzie, Jeong-Yeon Lee … (2003)

Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.