TY - JOUR
T1 - Identification of stakeholder related barriers in sustainable manufacturing using Social Network Analysis
AU - Yip, W. S.
AU - To, S.
N1 - Funding Information:
The work described in this paper was mainly supported by the funding support to the State Key Laboratories in Hong Kong from the Innovation and Technology Commission (ITC) of the Government of the Hong Kong Special Administrative Region (HKSAR), China. The authors would also like to express their sincerely thanks to the financial support from the Research Office of The Hong Kong Polytechnic University (Project code: BBXM and BBX), and State Key Laboratory in Ultra-precision Machining Technology of The Hong Kong Polytechnic University,
Publisher Copyright:
© 2021
PY - 2021/7
Y1 - 2021/7
N2 - Sustainable manufacturing (SM) is crucial for our future manufacturing and it starts to have an evolutionary development. However, manufacturing industries are difficult to adopt sustainability measures because of considerable barriers under the triple bottom line (TBL): economy, environment and people. Previous research works mainly focused on the originality and the definitions of the barriers in SM, they seldomly consider the stakeholder-related affairs on SM and the causal relationships between the barriers in SM. Therefore, this study aims to propose a comprehensive analysis of barriers in SM using social network analysis (SNA), with detailed consideration of stakeholder concerns in terms of TBL. This study applies SNA to recognize and investigate the underlying stakeholder-related barriers and their causal relationships in SM, and the suggestions were provided upon the findings of SNA to help for mitigating the negative influences from critical barriers to SM. The validation is provided by showing the effectiveness of suggestions in which decreases in the network density and average geodesic distance simultaneously after the modification. This study contributes to not only displaying the efficient way to probe the stakeholder-related barriers in SM, but also offering the guideline to support the measures proposing in the future by using the same analytic technique in SNA which is demonstrated in this study.
AB - Sustainable manufacturing (SM) is crucial for our future manufacturing and it starts to have an evolutionary development. However, manufacturing industries are difficult to adopt sustainability measures because of considerable barriers under the triple bottom line (TBL): economy, environment and people. Previous research works mainly focused on the originality and the definitions of the barriers in SM, they seldomly consider the stakeholder-related affairs on SM and the causal relationships between the barriers in SM. Therefore, this study aims to propose a comprehensive analysis of barriers in SM using social network analysis (SNA), with detailed consideration of stakeholder concerns in terms of TBL. This study applies SNA to recognize and investigate the underlying stakeholder-related barriers and their causal relationships in SM, and the suggestions were provided upon the findings of SNA to help for mitigating the negative influences from critical barriers to SM. The validation is provided by showing the effectiveness of suggestions in which decreases in the network density and average geodesic distance simultaneously after the modification. This study contributes to not only displaying the efficient way to probe the stakeholder-related barriers in SM, but also offering the guideline to support the measures proposing in the future by using the same analytic technique in SNA which is demonstrated in this study.
KW - Barriers to sustainability
KW - Social network analysis
KW - Stakeholder
KW - Sustainable manufacturing
KW - Triple bottom line
UR - http://www.scopus.com/inward/record.url?scp=85105736765&partnerID=8YFLogxK
U2 - 10.1016/j.spc.2021.04.018
DO - 10.1016/j.spc.2021.04.018
M3 - Journal article
AN - SCOPUS:85105736765
SN - 2352-5509
VL - 27
SP - 1903
EP - 1917
JO - Sustainable Production and Consumption
JF - Sustainable Production and Consumption
ER -
