Bibliometric Analysis on the Application of Fuzzy Logic into Marketing Strategy

Rosário, Albérico Travassos; Dias, Joana Carmo; Ferreira, Helder

doi:10.3390/businesses3030025

Cited by 2 publications

(1 citation statement)

References 91 publications

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“…Bibliometric analysis has gained momentum and is increasingly used in all fields because its main benefit is the complex scientific mapping [21], over a specific time period, highlighting new trends that practitioners or decision-makers in various domains can apply [22,23]. Lately, there has been a growing number of bibliometric studies where authors emphasize the importance of utilizing fuzzy logic in various domains such as marketing [24], big data [25], decision management [26], financial modeling [27], and more [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study

Nica,

Delcea,

Chiriță

2024

Mathematics

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In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing the bibliometrix package in RStudio and data from the Web of Science, we focused on identifying mathematical models and the evolving role of fuzzy information granulation in this domain. The research addresses the urgent need to understand the development and impact of fuzzy logic and AI within the broader scope of evolving technological and analytical methodologies, particularly concentrating on their application in financial and banking contexts. The bibliometric analysis involved an extensive review of the literature published during this period. We examined key metrics such as the annual growth rate, international collaboration, and average citations per document, which highlighted the field’s expansion and collaborative nature. The results revealed a significant annual growth rate of 19.54%, international collaboration of 21.16%, and an average citation per document of 25.52. Major journals such as IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, the Journal of Intelligent & Fuzzy Systems, and Information Sciences emerged as significant contributors, aligning with Bradford’s Law’s Zone 1. Notably, post-2020, IEEE Transactions on Fuzzy Systems showed a substantial increase in publications. A significant finding was the high citation rate of seminal research on fuzzy information granulation, emphasizing its mathematical importance and practical relevance in financial analysis. Keywords like “design”, “model”, “algorithm”, “optimization”, “stabilization”, and terms such as “fuzzy logic controller”, “adaptive fuzzy controller”, and “fuzzy logic approach” were prevalent. The Countries’ Collaboration World Map indicated a strong pattern of global interconnections, suggesting a robust framework of international collaboration. Our study highlights the escalating influence of fuzzy logic and AI in financial analysis, marked by a growth in research outputs and global collaborations. It underscores the crucial role of fuzzy information granulation as a mathematical model and sets the stage for further investigation into how fuzzy logic and AI-driven models are transforming financial and banking analysis practices worldwide.

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Section: Literature Reviewmentioning

confidence: 99%

Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study

Nica,

Delcea,

Chiriță

2024

Mathematics

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Information technology for the analysis of mobile operator sales outlets based on clustering methods

Narushynska,

Motrunych,

Arzubov

et al. 2023

UJIT

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This research presents the development and implementation of information technology for monitoring and analyzing segments of a mobile operator's stores using clustering methods. The study addresses a pertinent issue in marketing and business optimization, namely the enhancement of strategies for the network of mobile communication stores. The research paper presents a novel approach to segmenting mobile operator stores using clustering algorithms. A software product was developed that includes machine learning algorithms for clustering stores according to critical parameters. A comprehensive analysis of the mobile operator's database was conducted to identify critical characteristics of the stores, such as profitability, patterns of mobile operator service usage, the number of new and lost customers, geographical location, and other vital indicators. Particular attention was paid to developing tools for preparing and processing input data, ensuring the accuracy of subsequent clustering. With the created product, the mobile operator can identify the most profitable stores, uncover growth opportunities, and develop targeted strategies for each segment. By applying the developed technology, the mobile operator gains the ability not only to identify crucial and profitable sales points but also to develop focused strategies for different groups of stores, taking into account their unique characteristics. This approach strengthens the company's market position, increasing customer satisfaction and profitability. Additionally, when examining the possibilities of analyzing store dynamics over time, it is necessary to consider the ever-evolving business environment. Such a tool can assist the operator in swiftly adapting strategies and responding to new trends and challenges while preserving stability and profitability. Similar innovative approaches not only facilitate the management of a mobile operator's store network but also enable the establishment of more open and flexible customer relationships. By providing personalized services and responding to their needs, businesses can enhance customer loyalty and increase their profits. In conclusion, this research endeavour carries significant practical implications for the realms of marketing and mobile operator development. Its findings can be harnessed to enhance the efficiency of operations and profitability within this industry.

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Bibliometric Analysis on the Application of Fuzzy Logic into Marketing Strategy

Cited by 2 publications

References 91 publications

Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study

Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study

Information technology for the analysis of mobile operator sales outlets based on clustering methods

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