Abstract
Worldwide organic food consumption has registered a consistent rise in recent years. Despite the relevant body of literature on the topic, it is necessary to further understand the antecedents of purchase intention. This article aims to identify the factors that influence the consumer's intention to purchase organic food. It extends the theory of planned behavior model by including environmental concerns, health concerns, and perceived quality as determinants of attitude toward organic food products. Additionally, it considers the effect of product availability on consumers' perceived behavioral control. This article includes a quantitative study that was conducted in Portugal in 2020 (n = 206). Structural equation modeling was used to test the proposed set of research hypotheses. In line with extant literature, this study confirmed that attitude toward organic food is the main determinant of purchase intention. Additionally, it demonstrates that health concerns and perceived quality have a significant impact on attitude toward organic food. The impact of environmental concerns on attitude was not confirmed by this study. Based on these findings, it is recommended that managers stress health benefits and quality of organic food in order to foster positive attitudes and consequently leverage purchase intention.

Abstract
Social media are transforming relationships with customers for all sectors, including tourism. Since the search for information is a critical aspect of tourist purchase decision process, the importance of social media for tourism is evident. However, the presence of tourism brands in social media is not enough to have an impact on tourist purchase decisions: it is necessary to generate engagement. This chapter aims to conceptualize tourist engagement on social media and identify tourist engagement indicators. Tourist engagement was conceptualized through a literature review that identified four dimensions of engagement: popularity, commitment, virality, and post engagement. A set of indicators is proposed to measure tourist engagement in each of these dimensions. The proposed TSM engagement framework was validated through a mixed-method approach, using secondary data and interviews carried out with Brazilian tourist destinations.

Abstract
This chapter aims to contribute to a better understanding of the role of websites in business internationalization by exploring how website overall objectives and their coherence with website strategies support website internationalization effectiveness. It provides empirical evidence on the experiences of Portuguese companies shared by 20 managers of large companies and SMEs of various activity sectors. Results show the importance of a clear website strategy (e.g., clear objectives and coherent tactics) for an effective role in internationalization. Findings also confirm that, while many managers are skeptic about the effectiveness of websites as an internationalization touchpoint, namely due to sector characteristics (e.g., type of customers, type of products/services), the website is perceived as an essential tool for reaching, attracting, and involving international customers, supporting other communication instruments such as participating in international fairs and sales force.

Abstract
Hashtags are commonly used in social media communication not only to categorize conversations but particularly to raise attention and generate debate of certain topics. Hashtag activism is one of the areas that is gaining particular attention from academics and the overall society. The focus of this chapter is hashtag attributes. Particularly, it analyses and compares four hashtags related to violence against women that circulated on social networks during the COVID-19 pandemic: #16Days, #IsolatedNotAlone, #womensupportingwomen, and #NiUnaMenos. The chapter highlights important aspects to increase the effectiveness of communication with the use of hashtags.

Abstract
Online incremental models for recommendation are nowadays pervasive in both the industry and the academia. However, there is not yet a standard evaluation methodology for the algorithms that maintain such models. Moreover, online evaluation methodologies available in the literature generally fall short on the statistical validation of results, since this validation is not trivially applicable to stream-based algorithms. We propose a k-fold validation framework for the pairwise comparison of recommendation algorithms that learn from user feedback streams, using prequential evaluation. Our proposal enables continuous statistical testing on adaptive-size sliding windows over the outcome of the prequential process, allowing practitioners and researchers to make decisions in real time based on solid statistical evidence. We present a set of experiments to gain insights on the sensitivity and robustness of two statistical tests-McNemar's and Wilcoxon signed rank-in a streaming data environment. Our results show that besides allowing a real-time, fine-grained online assessment, the online versions of the statistical tests are at least as robust as the batch versions, and definitely more robust than a simple prequential single-fold approach.

Abstract
Although widely used, the majority of current music recommender systems still focus on recommendations' accuracy, user preferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations' quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address "similar concepts" rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items' discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs' similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.