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Business Analytics and Applications
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Business Analytics and Applications

A special issue of Analytics (ISSN 2813-2203).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 10963

Special Issue Editors

Prof. Dr. Tatiana Ermakova

E-Mail Website
Guest Editor
School of Computing, Communication and Business, Hochschule für Technik und Wirtschaft, University of Applied Sciences for Engineering and Economics, 10318 Berlin, Germany
Interests: data science; statistics; machine learning; NLP; artificial intelligence; analytics; algorithms; programming; security; privacy; ethics; cloud computing; data infrastructures; psychology; behavioral science
Prof. Dr. Benjamin Fabian

E-Mail Website
Guest Editor
Technische Hochschule Wildau (TH Wildau), Hochschulring 1, 15745 Wildau, Germany
Interests: artificial intelligence; data science; information security; privacy; ethics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue focuses on business analytics, analytical approaches, and applications. In particular, we aim to publish comprehensive surveys of related existing and emerging trends, opportunities and challenges of scientific and practical significance, their evaluations, and innovative solutions.

Of special interest are contributions that reflect current technological advances (e.g., artificial intelligence, cloud computing, open data, and models), including the consideration of ethics, in particular data security and data privacy, explainability, fairness, etc.

Other suggested topics include:

  • Concepts, definitions, theories, models, methods, frameworks, applications, and influencing factors.
  • Performance, maturity, capabilities, limitations, etc.
  • Readiness, provision, use, and collaboration/cooperation in organizations.
  • Impact on business performance and mechanisms for increasing business growth.
  • Education, expertise, specializations, and the specialist market.
  • Unintentional, (un)responsible, and/or (un)ethical use.

Prof. Dr. Tatiana Ermakova
Prof. Dr. Benjamin Fabian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Analytics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • business analytics
  • business application
  • analytics
  • analytical approach
  • artificial intelligence
  • business intelligence
  • ethics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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17 pages, 327 KiB  
Article
Directed Topic Extraction with Side Information for Sustainability Analysis
by Maria Osipenko
Analytics 2024, 3(3), 389-405; https://doi.org/10.3390/analytics3030021 - 11 Sep 2024
Viewed by 895
Abstract
Topic analysis represents each document in a text corpus in a low-dimensional latent topic space. In some cases, the desired topic representation is subject to specific requirements or guidelines constituting side information. For instance, sustainability-aware investors might be interested in automatically assessing aspects [...] Read more.
Topic analysis represents each document in a text corpus in a low-dimensional latent topic space. In some cases, the desired topic representation is subject to specific requirements or guidelines constituting side information. For instance, sustainability-aware investors might be interested in automatically assessing aspects of firm sustainability based on the textual content of its corporate reports, focusing on the established 17 UN sustainability goals. The main corpus consists of the corporate report texts, while the texts containing the definitions of the 17 UN sustainability goals represent the side information. Under the assumption that both text corpora share a common low-dimensional subspace, we propose representing them in such a space via directed topic extraction using matrix co-factorization. Both the main and the side text corpora are first represented as term–context matrices, which are then jointly decomposed into word–topic and topic–context matrices. The word–topic matrix is common to both text corpora, whereas the topic–context matrices contain specific representations in the shared topic space. A nuisance parameter, which allows us to shift the focus between the error minimization of individual factorization terms, controls the extent to which the side information is taken into account. With our approach, documents from the main and the side corpora can be related to each other in the resulting latent topic space. That is, the corporate reports are represented in the same latent topic space as the descriptions of the 17 UN sustainability goals, enabling a structured automatic sustainability assessment of the textual report’s content. We provide an algorithm for such directed topic extraction and propose techniques for visualizing and interpreting the results. Full article
(This article belongs to the Special Issue Business Analytics and Applications)
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26 pages, 808 KiB  
Article
A Longitudinal Tree-Based Framework for Lapse Management in Life Insurance
by Mathias Valla
Analytics 2024, 3(3), 318-343; https://doi.org/10.3390/analytics3030018 - 5 Aug 2024
Cited by 1 | Viewed by 714
Abstract
Developing an informed lapse management strategy (LMS) is critical for life insurers to improve profitability and gain insight into the risk of their global portfolio. Prior research in actuarial science has shown that targeting policyholders by maximising their individual customer lifetime value is [...] Read more.
Developing an informed lapse management strategy (LMS) is critical for life insurers to improve profitability and gain insight into the risk of their global portfolio. Prior research in actuarial science has shown that targeting policyholders by maximising their individual customer lifetime value is more advantageous than targeting all those likely to lapse. However, most existing lapse analyses do not leverage the variability of features and targets over time. We propose a longitudinal LMS framework, utilising tree-based models for longitudinal data, such as left-truncated and right-censored (LTRC) trees and forests, as well as mixed-effect tree-based models. Our methodology provides time-informed insights, leading to increased precision in targeting. Our findings indicate that the use of longitudinally structured data significantly enhances the precision of models in predicting lapse behaviour, estimating customer lifetime value, and evaluating individual retention gains. The implementation of mixed-effect random forests enables the production of time-varying predictions that are highly relevant for decision-making. This paper contributes to the field of lapse analysis for life insurers by demonstrating the importance of exploiting the complete past trajectory of policyholders, which is often available in insurers’ information systems but has yet to be fully utilised. Full article
(This article belongs to the Special Issue Business Analytics and Applications)
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27 pages, 1190 KiB  
Article
Interconnected Markets: Unveiling Volatility Spillovers in Commodities and Energy Markets through BEKK-GARCH Modelling
by Tetiana Paientko and Stanley Amakude
Analytics 2024, 3(2), 194-220; https://doi.org/10.3390/analytics3020011 - 16 Apr 2024
Viewed by 1589
Abstract
Food commodities and energy bills have experienced rapid undulating movements and hikes globally in recent times. This spurred this study to examine the possibility that the shocks that arise from fluctuations of one market spill over to the other and to determine how [...] Read more.
Food commodities and energy bills have experienced rapid undulating movements and hikes globally in recent times. This spurred this study to examine the possibility that the shocks that arise from fluctuations of one market spill over to the other and to determine how time-varying the spillovers were across a time. Data were daily frequency (prices of grains and energy products) from 1 July 2019 to 31 December 2022, as quoted in markets. The choice of the period was to capture the COVID pandemic and the Russian–Ukrainian war as events that could impact volatility. The returns were duly calculated using spreadsheets and subjected to ADF stationarity, co-integration, and the full BEKK-GARCH estimation. The results revealed a prolonged association between returns in the energy markets and food commodity market returns. Both markets were found to have volatility persistence individually, and time-varying bidirectional transmission of volatility across the markets was found. No lagged-effects spillover was found from one market to the other. The findings confirm that shocks that emanate from fluctuations in energy markets are impactful on the volatility of prices in food commodity markets and vice versa, but this impact occurs immediately after the shocks arise or on the same day such variation occurs. Full article
(This article belongs to the Special Issue Business Analytics and Applications)
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Review

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25 pages, 1197 KiB  
Review
Artificial Intelligence and Sustainability—A Review
by Rachit Dhiman, Sofia Miteff, Yuancheng Wang, Shih-Chi Ma, Ramila Amirikas and Benjamin Fabian
Analytics 2024, 3(1), 140-164; https://doi.org/10.3390/analytics3010008 - 1 Mar 2024
Cited by 2 | Viewed by 6794
Abstract
In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper [...] Read more.
In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into “Sustainability of AI” and “AI for Sustainability”, revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field’s maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations’ Sustainable Development Goals (SDGs), and addressing stakeholders’ influence. Full article
(This article belongs to the Special Issue Business Analytics and Applications)
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