Kaihan Yang, Ai Chin Thoo, Mohamed Syazwan Ab Talib, Hon Tat Huam
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This study develops a model and tests all hypothesised relationships using partial least square–structural equation modelling (PLS-SEM) with two-step analytical procedures.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The results indicate that SSCI and RL have a positive relationship with SP, and SSCI is positively related to RL. Moreover, the OLC moderates the relationship between RL and SP as well as the relationship between SSCI and SP.</p><!--/ Abstract__block -->\n<h3>Research limitations/implications</h3>\n<p>The findings of the study yield valuable managerial insights on how the effective implementation of green practices, coupled with the utilisation of learning capabilities, can contribute to improving the sustainability of manufacturing firms. The study has certain limitations that suggest potential avenues for future research, the most significant of which is our reliance on data from a single country, which may impede the generalisability of the findings.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>This study contributes to the existing literature on SP by considering RL and SSCI and offers a unique theoretical argument that describes the relationships by considering the moderating effect of OLC, which has not been empirically explored.</p><!--/ Abstract__block -->","PeriodicalId":16301,"journal":{"name":"Journal of Manufacturing Technology Management","volume":"199 2","pages":""},"PeriodicalIF":7.3000,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How reverse logistics and sustainable supply chain initiatives influence sustainability performance: the moderating role of organisational learning capability\",\"authors\":\"Kaihan Yang, Ai Chin Thoo, Mohamed Syazwan Ab Talib, Hon Tat Huam\",\"doi\":\"10.1108/jmtm-04-2023-0143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>This research attempts to explore how reverse logistics (RL) and sustainable supply chain initiatives (SSCI) affect sustainability performance (SP) in the manufacturing industry under the moderating effects of organisational learning capability (OLC). 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How reverse logistics and sustainable supply chain initiatives influence sustainability performance: the moderating role of organisational learning capability
Purpose
This research attempts to explore how reverse logistics (RL) and sustainable supply chain initiatives (SSCI) affect sustainability performance (SP) in the manufacturing industry under the moderating effects of organisational learning capability (OLC). At the same time, this study is expected to allow manufacturers to advance towards a high level of model generation in the green economy.
Design/methodology/approach
The data for this study was obtained from 451 manufacturing companies in the Hebei Province, China. This study develops a model and tests all hypothesised relationships using partial least square–structural equation modelling (PLS-SEM) with two-step analytical procedures.
Findings
The results indicate that SSCI and RL have a positive relationship with SP, and SSCI is positively related to RL. Moreover, the OLC moderates the relationship between RL and SP as well as the relationship between SSCI and SP.
Research limitations/implications
The findings of the study yield valuable managerial insights on how the effective implementation of green practices, coupled with the utilisation of learning capabilities, can contribute to improving the sustainability of manufacturing firms. The study has certain limitations that suggest potential avenues for future research, the most significant of which is our reliance on data from a single country, which may impede the generalisability of the findings.
Originality/value
This study contributes to the existing literature on SP by considering RL and SSCI and offers a unique theoretical argument that describes the relationships by considering the moderating effect of OLC, which has not been empirically explored.
期刊介绍:
The Journal of Manufacturing Technology Management (JMTM) aspires to be the premier destination for impactful manufacturing-related research. JMTM provides comprehensive international coverage of topics pertaining to the management of manufacturing technology, focusing on bridging theoretical advancements with practical applications to enhance manufacturing practices.
JMTM seeks articles grounded in empirical evidence, such as surveys, case studies, and action research, to ensure relevance and applicability. All submissions should include a thorough literature review to contextualize the study within the field and clearly demonstrate how the research contributes significantly and originally by comparing and contrasting its findings with existing knowledge. Articles should directly address management of manufacturing technology and offer insights with broad applicability.