Abstract
To maintain the performance of the latest generation of onshore and offshore wind turbine systems, a new methodology must be proposed to enhance the maintenance policy. In this context, this paper introduces an approach to designing a decision support tool that combines predictive capabilities with anomaly explanations for effective IoT predictive maintenance tasks. Essentially, the paper proposes an approach that integrates a predictive maintenance model with an explicative decision-making system. The key challenge is to detect anomalies and provide plausible explanations, enabling human operators to determine the necessary actions swiftly. To achieve this, the proposed approach identifies a minimal set of relevant features required to generate rules that explain the root causes of issues in the physical system. It estimates that certain features, such as the active power generator, blade pitch angle, and the average water temperature of the voltage circuit protection in the generator's sub-components, are particularly critical to monitor. Additionally, the approach simplifies the computation of an efficient predictive maintenance model. Compared to other deep learning models, the identified model provides up to 80% accuracy in anomaly detection and up to 96% for predicting the remaining useful life of the system under study. These performance metrics and indicators values are essential for enhancing the decision-making process. Moreover, the proposed decision support tool elucidates the onset of degradation and its dynamic evolution based on expert knowledge and data gathered through Internet of Things (IoT) technology and inspection reports. Thus, the developed approach should aid maintenance managers in making accurate decisions regarding inspection, replacement, and repair tasks. The methodology is demonstrated using a wind farm dataset provided by Energias De Portugal.

Abstract

Abstract
Early identification of failures is a critical task in predictive maintenance, preventing potential problems before they manifest and resulting in substantial time and cost savings for industries. We propose an approach that predicts failures in the near future. First, a deep learning model combining long short-term memory and convolutional neural network architectures predicts signals for a future time horizon using real-time data. In the second step, an autoencoder based on convolutional neural networks detects anomalies in these predicted signals. Finally, a verification step ensures that a fault is considered reliable only if it is corroborated by anomalies in multiple signals simultaneously. We validate our approach using publicly available Air Production Unit (APU) data from Porto metro trains. Two significant conclusions emerge from our study. Firstly, experimental results confirm the effectiveness of our approach, demonstrating a high fault detection rate and a reduced number of false positives. Secondly, the adaptability of this proposal allows for the customization of configuration of different time horizons and relationship between the signals to meet specific detection requirements.

Abstract
Purpose Citizen engagement plays a crucial role in transitioning to sustainable service ecosystems. While customer engagement has been extensively studied in service research, citizen engagement has received significantly less attention. By synthesizing customer and citizen engagement literatures, this study develops an integrated framework to conceptually clarify the dual role of customer-citizen engagement for sustainability. Design/methodology/approach This study builds on a systematic literature review of customer engagement literature in service research and citizen engagement literature. Following a theory synthesis approach, we qualitatively analyzed 126 articles to develop an integrated conceptual framework of customer-citizen engagement for sustainability through a process of abductive reasoning. Findings The analysis showed that customer engagement and citizen engagement literatures have developed mostly separately but provide complementary views. While the customer engagement literature has traditionally focused on business-related facets, such as engagement with brands, the citizen perspective broadens the engagement scope to other citizens, communities and society in general. The integrated framework highlights the interplay between citizen and customer roles and the impact of their relationships with multiple objects on sustainability. Originality/value This integrated framework contributes to advancing our understanding of customer-citizen engagement, broadening the scope of subject-object engagement by examining the interplay between these roles in how they engage for sustainability and moving beyond the traditional dyadic perspective to a multi-level perspective of service ecosystems. This framework also enables the development of a set of research directions to advance the understanding of engagement in sustainable service ecosystems.

Abstract
[No abstract available]

Abstract
The growing use of technology and social media has resulted in the emergence of digital influencers, a new profession capable of changing the mentalities and behaviours of those who follow them. This study arises to better understand the potential impact digital influencers might have on the Portuguese population's purchase behaviour and patterns, and for this purpose, seven hypotheses were formulated. An online questionnaire was conducted to respond to these theoretical assumptions and collected data from 175 respondents. A total of 129 valid answers were considered. It was possible to conclude that purchase intention does not necessarily translate into a purchase action. It was also concluded that the relationship between social network use and the purchase of products/services recommended by influencers is only statistically significant for Instagram. Furthermore, the individuals' generation is not statistically significant / linked with purchasing a product/service recommended by influencers. Yet further, a small percentage of respondents have also identified themselves as impulsive shoppers and perceived Instagram as their favourite social network. With the results of this study, it is also possible to state that the influencer's opinion was classified as the last factor considered in the purchase decision process. Additionally, there is a weak negative association between purchasing a product/service recommended by influencers with sponsorship disclosure and remunerated partnership, which decreases credibility and discourages purchasing, in Portugal, a feminine culture which dislikes materialism.