Abstract
Cities are complex and dynamic systems in which a network of actors interact, creating value through different activities. Cities can, therefore, be viewed as service ecosystems. Municipalities take advantage of digitalization to implement a service-dominant logic in urban and mobility planning and management, developing strategies with which citizens, local authorities, and other actors can create value together. While citizens are offered a better service experience, local authorities use citizens' input to improve decision-making processes. This research considers that designing an integrated service supported by an integrated information system can respond to current challenges in decision-making and information access for transport and mobility. Through a multidisciplinary methodological approach, this work proposes some guidelines to design an integrated information system to improve citizens' participation in urban planning and mobility services.

Abstract
Is there an initialization for deep networks that requires no learning? ExplainFix adopts two design principles: the fixed filters principle that all spatial filter weights of convolutional neural networks can be fixed at initialization and never learned, and the nimbleness principle that only few network parameters suffice. We contribute (a) visual model-based explanations, (b) speed and accuracy gains, and (c) novel tools for deep convolutional neural networks. ExplainFix gives key insights that spatially fixed networks should have a steered initialization, that spatial convolution layers tend to prioritize low frequencies, and that most network parameters are not necessary in spatially fixed models. ExplainFix models have up to x100 fewer spatial filter kernels than fully learned models and matching or improved accuracy. Our extensive empirical analysis confirms that ExplainFix guarantees nimbler models (train up to 17% faster with channel pruning), matching or improved predictive performance (spanning 13 distinct baseline models, four architectures and two medical image datasets), improved robustness to larger learning rate, and robustness to varying model size. We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.This article is categorized under:Technologies > Machine LearningFundamental Concepts of Data and Knowledge > Explainable AIFundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

Abstract
This article examines the impact of digital influencers ' recommendations, especially Instagrammers, on the pur-chase intention of healthy food. In addition to the direct influence of source credibility on behavioral intention, the study also examines the influence of self-brand congruence and consumers' involvement with healthy food on purchase intention. To test research hypotheses, a quantitative study was conducted with 221 Portuguese con-sumers. High and low involvement with healthy food groups were classified by K-Means Clustering, and the analysis of the structure and the measurement models was performed by using Smart-PLS software. The results confirmed that Instagrammers' credibility drives self-brand congruence and purchase intention for healthy food. It was also confirmed that the involvement with healthy food moderates the influence of self-brand congruity and Instagrammers' credibility on consumers' intention to purchase healthy food, and that brand self-congruence partially mediates the influence of Instagrammers' credibility on purchase intention. Overall, this work offers rel-evant insights for both marketing managers and researchers, as it demonstrates the importance of considering the indirect effects of source credibility on purchase intention of healthy food and of comparing consumers with high and low product involvement to effectively evaluate the impact of digital influencers' in healthy food endorsement.

Abstract
Social Engineering (SE) is a significant problem for enterprises. Cybercriminals continue developing new and sophisticated methods to trick individuals into disclosing confidential information or granting unauthorized access to infrastructure systems. These attacks remain a significant threat to enterprise systems despite significant investments in technical architecture and security measures. User awareness training and other behavioral interventions are critical for improving SE resilience. However, their effectiveness still needs to be determined, as personality traits may turn some individuals more susceptible to SE attacks. This paper aims to provide a comprehensive assessment of the state of knowledge in this field, identifying best practices for improving SE resilience in organizations and supporting the development of new research studies to address this issue. Its goal is to help enterprises of any size develop a framework to reduce the risk of successful SE attacks and create a culture of security awareness.

Abstract
In the chapter, we can find the recommendation for entrepreneurs. The authors are trying to answer the question: How should employers treat Generation Z employees? A complex analysis of the research carried out by the authors as well as other examples from Europe and other continents have been pointed out. A recommendation for enterprises has been included. © 2023 selection and editorial matter, Joanna Niezurawska, Radoslaw Antoni Kycia and Agnieszka Niemczynowicz; individual chapters, the contributors.

Abstract
This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.