Abstract
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1-5 ratings carried out over a period of 10 years (2012-2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.

Abstract
Purpose Eco-innovation has been identified as a source of gaining a competitive advantage on a global scale. To build upon that, this study aims to deepen the understanding of eco-innovation in the context of small- and medium-sized enterprises (SMEs) and investigates the impact of having a clear eco-innovation strategy on a company's sustainability and performance. Design/methodology/approach A sample of 249 SMEs located in Portugal and the UK participated and structural equation modelling (SEM) was applied to explore the relationship among the constructs. Findings The findings reveal that both internal and external factors influence the design of an eco-innovation strategy. However, the relevance of external factors seemed to be more significant for Portuguese SMEs. This study concludes that product/process eco-innovations and green innovation systems are determinants for sustainable performance in SMEs. In contrast, the environmental technologies and organisational eco-innovation dimensions are not determinants. This is observed both in Portuguese and UK SMEs. Originality/value Most studies in the field tend to explore the role of eco-innovation in large organisations. This study takes a different approach by exploring its impacts on the sustainable business performance of SMEs. Furthermore, it combines data from two countries, which constitutes a strength and gives the opportunity to explore this phenomenon empirically.

Abstract
This article investigates the influence of digital influencers on healthy food purchase intention within the context of Instagram. The research model is guided by the theory of source credibility and the elaboration likelihood model. A quantitative approach was employed, and data were collected through an online survey from Instagram users in Portugal (n = 221). A set of ten hypotheses was tested using structural equation modeling (SPSS-AMOS). The findings corroborated that purchase intention of healthy foods is positively influenced by digital influencer perceived credibility, involvement with healthy foods, and attitude toward advertising on Instagram. The findings also confirmed that involvement with healthy foods and with Instagram affect advertising avoidance behavior, and that these three constructs affect attitude toward advertising on Instagram. However, the expected relationship between attitude toward advertising and digital influencer credibility was not confirmed. The study contributes to the literature on influencer marketing, specifically in the context of healthy food, and it provides valuable insights for social media marketers and brand managers interested in adopting influencer marketing to leverage their communication effectiveness.

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Abstract
Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are weighted differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.