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Analytics-enabled Green Supply Chain Management

Nebot Pérez, Lucas Noel LU and Cosijn, Olivier Julius Maria (2022) In 1 INFM10 20221
Department of Informatics
Abstract
The fashion industry with its complex supply chains deals with increasingly problematic environmental issues. IS and specifically analytics technologies are said to provide fashion companies with crucial insights to improve their environmental impact. However, these promises of this so-called analytics-enabled Green Supply Chain Management (GSCM) are not fully materializing in the European fashion industry. Guided by a conceptual model based on the TOE framework, this study critically explored the main organizational challenges fashion companies face in this context by conducting six qualitative interviews and contrasting the empirical findings with current academic literature. As a result, 55 challenges for analytics- enabled GSCM were... (More)
The fashion industry with its complex supply chains deals with increasingly problematic environmental issues. IS and specifically analytics technologies are said to provide fashion companies with crucial insights to improve their environmental impact. However, these promises of this so-called analytics-enabled Green Supply Chain Management (GSCM) are not fully materializing in the European fashion industry. Guided by a conceptual model based on the TOE framework, this study critically explored the main organizational challenges fashion companies face in this context by conducting six qualitative interviews and contrasting the empirical findings with current academic literature. As a result, 55 challenges for analytics- enabled GSCM were identified of which 15 were considered highly relevant. Overall, a low technological maturity, the lack of awareness regarding the benefits of Analytics as well as the deficient visibility into the complex supply chain were identified as main issues for analytics- enabled GSCM. Based on these findings an IS research agenda is proposed (Less)
Popular Abstract
The fashion industry with its complex supply chains deals with increasingly problematic environmental issues. IS and specifically analytics technologies are said to provide fashion companies with crucial insights to improve their environmental impact. However, these promises of this so-called analytics-enabled Green Supply Chain Management (GSCM) are not fully materializing in the European fashion industry. Guided by a conceptual model based on the TOE framework, this study critically explored the main organizational challenges fashion companies face in this context by conducting six qualitative interviews and contrasting the empirical findings with current academic literature. As a result, 55 challenges for analytics- enabled GSCM were... (More)
The fashion industry with its complex supply chains deals with increasingly problematic environmental issues. IS and specifically analytics technologies are said to provide fashion companies with crucial insights to improve their environmental impact. However, these promises of this so-called analytics-enabled Green Supply Chain Management (GSCM) are not fully materializing in the European fashion industry. Guided by a conceptual model based on the TOE framework, this study critically explored the main organizational challenges fashion companies face in this context by conducting six qualitative interviews and contrasting the empirical findings with current academic literature. As a result, 55 challenges for analytics- enabled GSCM were identified of which 15 were considered highly relevant. Overall, a low technological maturity, the lack of awareness regarding the benefits of Analytics as well as the deficient visibility into the complex supply chain were identified as main issues for analytics- enabled GSCM. Based on these findings an IS research agenda is proposed (Less)
Please use this url to cite or link to this publication:
author
Nebot Pérez, Lucas Noel LU and Cosijn, Olivier Julius Maria
supervisor
organization
alternative title
Exploring the organizational challenges in the European fashion industry.
course
INFM10 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
Analytics, Green Supply Chain Management, TOE, Challenges, Fashion Industry
publication/series
1
report number
INF22-16
language
English
additional info
AUTHORS: Olivier Cosijn and Lucas Nebot
PUBLISHER: Department of Informatics, Lund School of Economics and Management, Lund University
PRESENTED: May, 2022
DOCUMENT TYPE: Master Thesis
FORMAL EXAMINER: Osama Mansour, PhD
NUMBER OF PAGES: 141
KEY WORDS: Analytics, Green Supply Chain Management, TOE, Challenges, Fashion Industry
id
9091169
date added to LUP
2022-09-07 11:37:38
date last changed
2022-09-07 11:37:38
@misc{9091169,
  abstract     = {{The fashion industry with its complex supply chains deals with increasingly problematic environmental issues. IS and specifically analytics technologies are said to provide fashion companies with crucial insights to improve their environmental impact. However, these promises of this so-called analytics-enabled Green Supply Chain Management (GSCM) are not fully materializing in the European fashion industry. Guided by a conceptual model based on the TOE framework, this study critically explored the main organizational challenges fashion companies face in this context by conducting six qualitative interviews and contrasting the empirical findings with current academic literature. As a result, 55 challenges for analytics- enabled GSCM were identified of which 15 were considered highly relevant. Overall, a low technological maturity, the lack of awareness regarding the benefits of Analytics as well as the deficient visibility into the complex supply chain were identified as main issues for analytics- enabled GSCM. Based on these findings an IS research agenda is proposed}},
  author       = {{Nebot Pérez, Lucas Noel and Cosijn, Olivier Julius Maria}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{1}},
  title        = {{Analytics-enabled Green Supply Chain Management}},
  year         = {{2022}},
}