Abstract
Sustainable development policies trigger a shift in the global development paradigm by aligning economic, social, and ecological goals. Concurrently, the rapid surge in digitalization is transforming business processes and communications across all sectors and levels. As a result, the integration of e-business and e-governance becomes a critical component in achieving Sustainable Development Goals (SDGs). In this context, the aim of this article is to analyze the effects of digitalization, specifically e-governance and e-business, on the attainment of SDGs in European Union (EU) countries. The method used is a panel of corrected standard errors and feasible generalized least squares models to identify the impact and significance of e-governance and e-business on SDG achievement. The e-governance indicators considered by this study were found to significantly impact SDG achievement. Moreover, e-business indicators were also found to positively impact the attainment of SDGs, with some exceptions. The findings suggest that EU countries should continue to intensify digitalization across all sectors as it enhances the transparency accountability of all business processes and communications and increases trust in government services, which are the core drivers of achieving SDGs.

Abstract
The telecommunications sector faces a major challenge of high customer churn. Despite this, there is still a lack of research that explores the switching intention for telecommunication services, particularly with bundle services that currently dominate the market. This study aims to provide insight into consumer behaviour regarding bundle telecommunication services by examining the factors that impact satisfaction and switching intention, both directly and indirectly. Eighteen hypotheses were defined based on the literature, and were tested through a quantitative study with 910 bundle service customers using structural equation modelling with Smart-PLS. The results show that internet and television services have the strongest indirect impact on switching intention, mediated by overall satisfaction and loyalty. Additionally, the results indicate that switching costs and barriers do not significantly affect switching intention, and surprisingly, perceived contractual lock-in positively influences switching intention. This study provides a comprehensive understanding of the customer experience with bundled telecommunications services and offers relevant insights for telecommunication managers to prevent customer loss to competitors.

Abstract

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
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.

Abstract