Abstract
The telecommunications industry is highly competitive, as operators engage in fierce attacks, especially in bundled services, to acquire new customers originating high churn rate. The objective of this paper is to gain a comprehensive understanding of the factors influencing the switching of operators for bundled services among telecom operators. The paper includes a quantitative study with 3,004 customers utilizing bundled services from a Portuguese telecom operator. Employing covariance-based structural equation modeling and logit regression, the research shows that internet service, television service, and the service provided by the contact center exert the greatest impact on loyalty to the operator. In contrast, landline service has an insignificant effect, while loyalty has a negative influence on customer churn. This study offers telecommunications managers insights for identifying the main factors to retain customers and curbing customer defection. Additionally, it provides a framework for assessing customer experience within bundled telecom services, which is useful for researchers, managers and marketing practitioners alike.

Abstract
In global commerce, marked by the relentless advance of digital technology, businesses find themselves constantly challenged to devise innovative and disruptive marketing strategies. Adapting to these changes is no longer a choice but a necessity. To thrive, companies must remain vigilant, updating their resources and adopting emerging trends with unwavering agility. Contemporary Trends in Innovative Marketing Strategies explores the demands and dynamics of modern marketing. This book is tailored to meet the needs of students, educators, and managers seeking a profound understanding of today's marketing trends. Firstly, the book delves deep into the current trends steering marketing innovation. It dissects the latest developments that are reshaping the marketing landscape, identifies pivotal trends, and elucidates their ramifications for businesses. Secondly, the book embarks on a journey to explore innovative marketing strategies engineered to confront contemporary business challenges and seize emerging opportunities. It unlocks novel approaches that adeptly cater to the market, providing insights into strategic frameworks, methodologies, and practices. Lastly, the book illustrates these concepts with real-world case studies, offering proof of innovative marketing's successful applications across diverse business sectors. These cases serve to inspire and demonstrate how innovative marketing strategies can be put into action, resulting in tangible outcomes. This book is designed for a diverse audience, including academics and students keen on exploring the latest trends in innovative marketing, educators searching for compelling case studies to enhance their teaching materials, and practitioners eager to bridge the gap between research and practical application in innovative marketing. Its contents span a wide array of topics at the forefront of marketing innovation. Key themes encompass digital marketing strategies, sustainability-driven marketing, the marriage of data analytics and marketing intelligence, and the impact of emerging technologies on marketing strategies. Additionally, it delves into the challenges and best practices of driving marketing innovation within organizations, touching on subjects such as entrepreneurship, supply chain management, and internal marketing. © 2024 by IGI Global. All rights reserved.

Abstract
Keeping high service levels of a fast-growing number of servers is crucial and challenging for IT operations teams. Online monitoring systems trigger many occurrences that experts find hard to keep up with. In addition, most of the triggered warnings do not correspond to real, critical problems, making it difficult for technicians to know which to focus on and address in a timely manner. Outlier and concept drift detection techniques can be applied to multiple streams of readings related to server monitoring metrics, but they also generate many False Positives. Ranking algorithms can already prioritize relevant results in information retrieval and recommender systems. However, these approaches are supervised, making them inapplicable in event detection on data streams. We propose a framework that combines event aggregations and uses a customized clustering algorithm to score and rank alarms in the context of IT operations. To the best of our knowledge, this is the first unsupervised, online, high-dimensional approach to rank IT ops events and contributes to advancing knowledge about associated key concepts and challenges of this problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series' target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % +/- 1.2 and an F1-score of 95.4 % +/- 1.3.

Abstract

Abstract
Transthyretin-associated Familial Amyloid Polyneuropathy (TTR-FAP) is a chronic fatal disease with a high incidence in Portugal. It is therefore relevant to provide professionals and citizens with a tool that enables a detailed geographical and territorial study. For this reason, we have developed an web based application that brings together techniques applied to spatial data that allow the study of the historical progression and growth of cases in patients' residential areas and areas of origin as well as an epidemic forecast. The tool enables the exploration of geographical longitudinal data at national, district and county levels. High density regions and periods can be visually identified according to parameters selected by the user. The visual evaluation of the data and its comparison across different time spans of the disease era can have an impact on more informed decision making by those working with patients to improve their quality of life, treatment or follow-up. The tool is available online for data exploration and its code is available on GitHub for adaptation to other geospatial scenarios.