AI and business management: Tracking future research agenda through bibliometric network analysis

Patra, Ashok Kumar; Praharaj, Ashyashree; Sudarshan, Desul; Chhatoi, Biswajit Prasad

doi:10.1016/j.heliyon.2023.e23902

Cited by 5 publications

(2 citation statements)

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“…The Web of Science database is selected for its enormous collection of peer-reviewed scientific papers with excellent indexing for research exploration [83]. The Web of Science database has also been widely used for bibliometric analyses [84][85][86][87][88]. A bibliometric analysis on the application of genetic algorithms in the logistics and supply chain sector has also not been conducted using the Web of Science database.…”

Section: Methodsmentioning

confidence: 99%

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

Lam,

Lee

2024

Mathematics

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As a famous population-based metaheuristic algorithm, a genetic algorithm can be used to overcome optimization complexities. A genetic algorithm adopts probabilistic transition rules and is suitable for parallelism, which makes this algorithm attractive in many areas, including the logistics and supply chain sector. To obtain a comprehensive understanding of the development in this area, this paper presents a bibliometric analysis on the application of a genetic algorithm in logistics and supply chains using data from 1991 to 2024 from the Web of Science database. The authors found a growing trend in the number of publications and citations over the years. This paper serves as an important reference to researchers by highlighting important research areas, such as multi-objective optimization, metaheuristics, sustainability issues in logistics, and machine learning integration. This bibliometric analysis also underlines the importance of Non-Dominated Sorting Genetic Algorithm II (NSGA-II), sustainability, machine learning, and variable neighborhood search in the application of a genetic algorithm in logistics and supply chains in the near future. The integration of a genetic algorithm with machine learning is also a potential research gap to be filled to overcome the limitations of genetic algorithms, such as the long computational time, difficulties in obtaining optimal solutions, and convergence issues for application in logistics and supply chains.

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Section: Methodsmentioning

confidence: 99%

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

Lam,

Lee

2024

Mathematics

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“…Though most utilization emanates from structured decisions, it cannot undermine the other two decision types as they are also used in organizations in shaping their management decision dispositions. Many studies have been undertaken in the field of AI which started to gain attention in 2018 till date but few studies focused on integrating AI into decision-making (Patra et al, 2024). Studies were conducted on business intelligence and data analytics but most of the research that emerged in 2018 was considered relatively new topics (Liang & Liu, 2018).…”

Section: Introduction 11 Introduction and The Problemmentioning

confidence: 99%

Artificial Intelligence and Management Decision Making: a Bibliometric Analysis

Jalagat, Jr.

2024

IJEBMR

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The main thrust of this paper is to explore global trends in the topic of AI integration in management decision-making. Bibliometric data and information were carried mainly from 2000-2023 to determine the extent of studies performed during these years. Data was collected from the Lens database and 1,770 publications from different platforms were analyzed using VOSviewer. Findings revealed that; the highest number of publications were found in 2023 and mostly from journals. The most active author was Bingzhen Sun with 6 documents and the most topics of investigation are artificial intelligence and knowledge management respectively. In the aspect of top journals in the field, the Journal of Business Ethics topped the list and most of the Top 10 journals are found in notable publishers such as Springer, Talor and Francis, and Science Direct. The top three productive countries in the field include the United States, China, and the United Kingdom. Most publications were seen at the height of the COVID-19 pandemic, and the distribution of countries contributing to the field includes developed and emerging countries. While there is growing interest by scholars in publishing papers in 2019-2023 the researcher has found that there are still scarcities of diversity in co-authorship and bibliometric coupling involving different nations and regions. 2016-2019 accounted for more cases of bibliometric coupling than any other years from 1988-2023. Therefore, it is recommended that more studies be undertaken to address the scarcity of co-authorship and bibliometric coupling involving a diversity of countries like the Arabian and African regions. More studies can also be conducted in emerging countries to keep up with the trends in developed countries and more opportunities for collaboration.

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