Kadiane Angaman Alphonse, Guitao Zhang, Bilal Aslam, Shujun Guo, Maowang Ji and Shoaib Maqsood
The purpose of this investigation is to examine how the adaption of digital supply chain management (DSCM) practices affects the efficiency of factories and sustainable…
Abstract
Purpose
The purpose of this investigation is to examine how the adaption of digital supply chain management (DSCM) practices affects the efficiency of factories and sustainable production. The research consists of eight constructs which, respectively, inspired eight hypotheses.
Design/methodology/approach
The emergence of DSCM practices has significant importance for sustainable production and enhances overall firm performance.
Findings
The smart PLS-SEM approach allowed us to examine the data from 450 factories in Côte d'Ivoire. The results indicated that research hypotheses are highly significant and exhibit a strong correlation with DSCM for firm performance and competitiveness. The outcomes underscore the significance of DSCM strategies in achieving competitive advantage, enhancing firm performance and promoting sustainable production within the manufacturing sector.
Originality/value
This study is useful for policymakers, industrialists and the government of Côte d’Ivoire.
Details
Keywords
Cong Li, Gongxu Lan, Guitao Zhang, Peiyue Cheng, Yangyan Shi and Yangfei Gao
This paper aims to focus on corporate social responsibility in relation to economic policy uncertainty in mergers and acquisitions (M&A). The following questions are addressed…
Abstract
Purpose
This paper aims to focus on corporate social responsibility in relation to economic policy uncertainty in mergers and acquisitions (M&A). The following questions are addressed: How does policy uncertainty impact corporate M&A? Does social responsibility play a mediating role in this process? How does policy uncertainty affect corporate M&A through social responsibility?
Design/methodology/approach
This paper selects the major M&A events in China as the research object, and uses the Probit model to analyze the impact of policy uncertainty on M&A behavior and the business performance after the event, and further analyzes the internal mechanisms that cause these phenomena.
Findings
This paper shows that the higher the policy uncertainty, the lower the probability of a successful M&A, and the worse the business performance of the business after the event.
Originality/value
This paper provides useful reference for the study of M&A and social responsibility in different policy environments. At the same time, it provides direct empirical evidence to enhance the success rate of M&A.
Details
Keywords
Yishan Liu, Wenming Cao and Guitao Cao
Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics…
Abstract
Purpose
Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.
Design/methodology/approach
This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.
Findings
We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.
Originality/value
First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.