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Showing 3 results for Systematic Literature Review
Corporate Social Responsibility- Sustainability Nexus: A Systematic Literature Review and Bibliometric Analysis
Ag Kaifah Riyard Kiflee, Nornajihah Nadia Hasbullah, Faerozh Madli,
Volume 35, Issue 2 (6-2024)
Abstract
Over the years, the attention given to corporate social responsibility (CSR) and sustainability topics has received a lot of attention significantly and various new terms have been introduced. This result has sparked a wide-ranging and unspecified discussion, particularly in the fields of economics and business management. The presents of functional CSR and sustainability enable management to make better decisions for the benefit of the entire society.  As a result, understanding the topic of interest and broadening research collaboration are critical for advancing research development.  The purpose of this study is to identify global research trends in CSR and sustainability based on publication numbers, co-authorship, affiliated countries, and keyword co-occurrences. This study used RTools and Prisma for its analysis. The findings indicate a significant rise in the number of articles published in the field of corporate social responsibility and sustainability since 2015. The USA contributed more than half of the publications, with Italy and Spain following closely behind.

The Relationship between Green Human Resource Management and Green Supply Chain Management: A systematic literature review
Daniel Atnafu, Shimelis Zewdie Werke,
Volume 35, Issue 2 (6-2024)
Abstract
The incorporation of sustainable practices becomes crucial as firms transition from Industry 4.0 to Industry 5.0. Therefore, this systematic review explores the relationship between the two sustainability approaches; Green Human Resource Management (GHRM) and Green Supply Chain Management (GSCM) using peer-reviewed studies from 2016-2023, retrieved from Scopus and Web of Science databases. 2016 marks the starting point as the first relevant paper emerged in the literature in that year. The PRISMA approach was used to identify relevant studies, resulting in the inclusion of 30 studies for analysis purposes. The study reveals a growing interest in understanding the relationship between GHRM and GSCM practices and their impact on sustainable performance. The majority of reviewed studies utilized quantitative survey methods, suggesting the need for future research utilizing qualitative and mixed methods for gaining deeper insights. The review indicates that most studies are conducted in emerging countries, and there is a significant gap in research on the relationship between GHRM and GSCM practices in other context. Finally, the study provides valuable insights for practitioners and researchers, emphasising the importance of integrating GHRM and GSCM practices for a sustainable competitive advantage.

AI-based demand forecasting models: A systematic literature review
Mariam Atwani, Mustapha Hlyal , Jamila El Alami ,
Volume 35, Issue 2 (6-2024)
Abstract
In today's dynamic and competitive manufacturing landscape, accurate demand forecasting is paramount for optimizing production processes, reducing inventory costs, and meeting customer demands efficiently. With the advent of Artificial Intelligence (AI), there has been a significant evolution in demand forecasting methods, enabling manufacturers to enhance the accuracy of the forecasts.
This systematic literature review aims to provide a comprehensive overview of the state-of-the-art on demand forecasting models in the manufacturing sector, whether AI-based models or hybrid methods merging both the AI technology and classical demand forecasting methods. The review begins by establishing an overview on demand forecasting methods, it then outlines the systematic methodology used for the literature search.
The review encompasses a wide range of scholarly articles published up to September 2023. A rigorous screening process is applied to select relevant studies. Accordingly, a thorough analysis in the basis of the forecasting methods adopted and data used have been carried out. By synthesizing the existing knowledge, this review contributes to the ongoing advancement of demand forecasting practices in the manufacturing sector providing researchers and practitioners an overview on the advancements on the use of AI models to improve the accuracy of demand forecasting models.

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