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Article
Publication date: 18 January 2023

Linqi Xu, Fu Jia, Xiao Lin and Lujie Chen

This study aims to systematically review the current academic literature on the role of technologies in low-carbon supply chain management (SCM), identify and analyse critical…

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Abstract

Purpose

This study aims to systematically review the current academic literature on the role of technologies in low-carbon supply chain management (SCM), identify and analyse critical themes and propose an integrated conceptual model.

Design/methodology/approach

A systematic literature review of 48 papers published between 2010 and 2022 was conducted. A conceptual model was advanced.

Findings

Based on the analysis and synthesis of the reviewed papers, this review provides an initial attempt to integrate technology adoption and low-carbon SCM by developing a diffusion of innovation model of technology-enabled low-carbon SCM within the technology–organisation–environment (TOE) framework, in which drivers, enablers and barriers to technology adoption practices are identified. The environmental, economic and social outcomes of adoption practices are also identified.

Originality/value

This study provides a novel and comprehensive roadmap for future research on technology-enabled low-carbon SCM. Furthermore, policy, as well as managerial implications, is presented for policymakers and managers.

Details

Supply Chain Management: An International Journal, vol. 28 no. 4
Type: Research Article
ISSN: 1359-8546

Keywords

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Article
Publication date: 20 September 2022

Jinzhu Zhang, Yue Liu, Linqi Jiang and Jialu Shi

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic…

487

Abstract

Purpose

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Design/methodology/approach

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

Findings

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

Originality/value

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

Details

Aslib Journal of Information Management, vol. 75 no. 3
Type: Research Article
ISSN: 2050-3806

Keywords

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