Abstract
Interactive Voice Assistants (IVAs) are intelligent conversational agents capable of communicating with users using natural language. Although IVAs are more frequent in our lives, customer experience research with these agents is still in its infancy. This article aims to identify the factors that form the customer experience (CX) with Alexa and assesses its impact on traditional marketing outcomes: satisfaction and recommendation. This research presents a conceptual model of CX with IVAs and an empirical validation of the model using Structural Equation Modelling based on a sample of 580 IVA users. The results confirm that CX is a multidimensional higher-order construct composed of six factors (usefulness, ease of use, trust, privacy concerns, communication skills, and enjoyment). We also highlight the positive impact of experience on satisfaction and recommendation. Finally, we test the enthusiasm moderating role, showing its negative influence on the investigated relationships. Theoretical and practical implications are discussed.

Abstract
This paper aims to thoroughly discuss the use of Digital Twin technology in complex operations environments, highlighting its potential applications and the research challenges that need to be addressed. This is necessitated by the fact that currently there is no comprehensive literature review and framework for implementing Digital Twin technology in complex operations environments. Furthermore, existing interpretations of DT implementation are inadequately detailed and not very informative in this area. This may be a consequence of the difficulties of collecting and extracting useful information from data in real-time. Another drawback worth mentioning is that Digital twins at the moment center on an individual or isolated part instead of integrating the whole system and no current work talks about this holistic approach. This paper will focus on Digital Twins in complex operations environments and their applications. A review of scientific literature on the use of Digital Twins in complex operations environments is performed and the articles are categorized by the problems and challenges that they address requiring DT as a solution. A selection of papers that focus on this topic and represent the current situation of research will be emphasized. In conclusion, this work will be utilized as a baseline study to propose a Digital Twin reference framework, which eventually leads to implementing and evaluating a comprehensive Digital Twin methodology in complex systems. © 2023 IEEE.

Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsibleAI. Themain goal of thiswork is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.

Abstract
The technological development of recent years has impacted the way companies, workers, and customers organize and interact with each other. Food retail stands out amongst the most affected sectors. New technologies, such as Artificial Intelligence (AI), lead to the emergence of a new retail concept, Smart Retailing, bringing benefits, not only for the retailer, but also for the consumer. In addition, they impact the jobs, in particular, store assistants' job. Despite the growing academic interest in these topics, the acceptance and impact of AI-based solutions on store assistants is scarcely studied. This work aims, therefore, to study the acceptance and perception of AI-based solutions by store assistants in food retail. Qualitative research was performed, having carried out 20 interviews with food retail store assistants that already work with AI-based solutions. Results show that store assistants are aware of what AI is and in which solutions it is used. They perceive these solutions as being beneficial for the performance of their duties, complementing their work instead of replacing them. They are willing to use these solutions and perceive them as being easy and intuitive to use. This study contributes with a starting point for future research on the topic. © 2023 IEEE.

Abstract
The full realization of the potential of a technology requires good understanding of its imple-mentation. During implementations, lack of compatibility between technology and its adopters require dynamic sequences of alignment. This process is understood to be central to the success in technology assimilation. This paper proposes a configurational model to explain and predict the alignment process during technology implementations, derived from a multiple case research of the implementation of a retinopathy screening program in networks of healthcare providers. It builds on and expands previous research capturing in a holistic way the alignment process and its nature of adaptation over time.

Abstract
This article addresses the Production Routing Problem (PRP), which consists of determining, in an integrated way, production and inventory planning, and vehicle routing to minimize the costs involved. In the problem, a plant is responsible for producing several types of products to meet the known demand of a set of customers using a homogeneous fleet of vehicles over the planning horizon. In the literature, evolutionary approaches have not been explored in depth for the PRP, specifically for the problem with multiple products. Thus, this work mitigates this gap, presenting a novel Memetic Algorithm and testing its effectiveness on randomly generated sets of instances, comparing the results obtained with a commercial optimization solver. In our solution approach, several classic operators from the literature were implemented. Furthermore, we propose four novel genetic operators. In addition, we evaluated the proposed method's performance in classical instances of literature considering a single item. The computational experiments were carried out to assess the impact of the numerous parameter combinations involving the metaheuristic, and, from statistical analyses, we evidence the proposed technique's robustness. Computational experiments showed that our proposed method outperforms the commercial solver Gurobi in determining feasibly high-quality solutions, mainly on large instances for the PRP with multiple items.