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The main objective of this research is to establish an integrated model of gastronomy tourism to help some of the main public and private stakeholders design strategies to improve tourists' gastronomic experience and satisfaction, taking gastronomic motivations as a starting point. Furthermore, the difference between destination satisfaction and gastronomic satisfaction has been established in order to determine the degree to which each one influences loyalty towards the destination.

After detailing the theoretical framework for the development of the hypotheses, the study was carried out using a quantitative methodology based on structural equation modelling (SEM). The final sample consisted of 710 tourists who visited Córdoba, Spain – a world heritage city of international renown.

Results indicate that gastronomic motivations, gastronomic experience and destination satisfaction have a direct influence on loyalty towards a destination. Also, destination satisfaction is found to play a mediating role in the relationship between gastronomic experience and loyalty towards the destination. Differences between destination and gastronomic satisfaction have been evidenced. For fostering a tourist's loyalty towards a destination, gastronomic satisfaction alone is not enough; other elements inherent to the destination itself are necessary for full loyalty, whether attitudinal or behavioural.

Correctly identifying tourist motivations can help managers of Destination Management Organizations (DMOs) to develop tailored marketing and communication campaigns that boost return visits to the destination or recommendations to family and friends. DMOs need to be aware that DMOs cannot overlook elements such as safety, hospitality or destination cleanliness at the expense of gastronomic satisfaction.

This paper aims to analyze the limitations of the mainstream definition of artificial intelligence (AI) as a rational agent, which currently drives the development of most AI systems. The authors advocate the need of a wider range of driving ethical principles for designing more socially responsible AI agents.

The authors follow an experience-based line of reasoning by argument to identify the limitations of the mainstream definition of AI, which is based on the concept of rational agents that select, among their designed actions, those which produce the maximum expected utility in the environment in which they operate. The problem of biases in the data used by AI is taken as example, and a small proof of concept with real datasets is provided.

The authors observe that biases measurements on the datasets are sufficient to demonstrate potential risks of discriminations when using those data in AI rational agents. Starting from this example, the authors discuss other open issues connected to AI rational agents and provide a few general ethical principles derived from the White Paper AI at the service of the citizen, recently published by Agid, the agency of the Italian Government which designs and monitors the evolution of the IT systems of the Public Administration.

The paper contributes to the scientific debate on the governance and the ethics of AI with a critical analysis of the mainstream definition of AI.

The purpose of this study is to gain new insight into the true nature of the relationship between product diversification and performance, as well as to explore the roles the home country environment and time can play on this relationship.

The study reviews a large part of the research that has addressed the relationship between product diversification and performance over the last four decades.

This study identifies the main views (models) that can help scholars to adequately understand, both theoretically and empirically, the potential effect of product diversification on performance: the premium diversification model; the discount diversification model; and the U‐inverted model. The study confirms a wide diversity of results. Drawing from the institutional‐based view, it is argued that a significant part of this heterogeneity stems from the effect of two factors that have often been ignored: the home country environment and time period. The review of recent empirical research seems to provide some support for the central argument that the value firms achieve through product diversification may be contingent both on the specific home country environment (environmental dependency) and time period (time dependency) under study.

This study yields an alternative explanation to the inconsistency in findings that goes beyond strictly theoretical and methodological reasons. It shows that the arguments related to different views (or models) need to be considered “environment‐dependent” and “time‐dependent”. It concludes by proposing a framework to guide future research.

This study aims to determine the level of satisfaction of secondary housing tourists in Vilcabamba.

The analysis of satisfaction of this tourism segment in a destination is essential for the different economic agents when evaluating various policies. The analysis is based on the answers provided to 16 items in a questionnaire administered to a representative sample (281 respondents). The authors have used the fuzzy logic to reduce ambiguity in the answers associated to subjective views of human beings who express themselves linguistically. A method based on the degree of similarity to ideal solutions has been used to obtain a classification of relevant satisfaction items.

The results indicate that the ideal solutions segments are represented by multicultural characteristics of second home tourists and the number of years they have been visiting Vilcabamba. The authors find that foreign tourists are more satisfied than Ecuadorians. Analyzing the most critical factors, it is seen that accessibility to the destination, socio-cultural environment and quality of water achieve a high priority.

Second home tourists’ satisfaction has been studied with a limited set of 16 attributes, and some attributes also have a multidimensional nature, so a further study analyzing the scale will be needed in the future.

The present study fills an existing gap in the literature of secondary housing tourism where the existing previous research has been mainly focused on retirees’ secondary housing tourism. The study provides interesting insights into local and national authorities, as well as other economic agents, to designing strategies and planning processes of the destinations for secondary housing tourists.

The purpose of this investigation is to develop a hybrid fuzzy TOPSIS methodology in order to understand in a practical and integrated way, the consuming and buying behavior of EU residents towards Fishery and Aquaculture Products (FAPs), with an emphasis in the consumption and buying frequency.

Data were obtained from the Special Eurobarometer Survey (European Union, 2018b), which is a survey of 27,732 EU residents with different socio-demographic characteristics that represent the 28 EU countries. A hybrid fuzzy TOPSIS methodology that synthesizes the consuming and buying behavior of the EU residents toward FAPs was developed.

The results show that among the countries, Spain has the highest consumption and buying patterns of FAPs, while among the generations it corresponds to the residents born between 1928 and 1945. In addition, there are important differences that depend on the country of residence as well as the generation of the residents. The elasticity analysis evidenced that marketing strategies would have the biggest impact in the countries located in the Central-Eastern zone of the EU and on the generation formed by the people born after 1980.

Although in the literature there are many studies that aimed to understand the behavior of consumers for FAPs, few investigations have focused on analyzing and integrating both the consumption and buying behavior, and to our best knowledge, there are no studies providing a methodology that allow making comparisons between different countries regarding the consumption and buying behavior of FAPs.

The purpose of this paper is to eview the papers presented at the international conference “Berlin5: from practice to impact. Consequences of knowledge dissemination”, 19‐21 September 2007, Padua, Italy.

Discusses the content of papers presented at the conference, all dedicated to aspects of Open Access. Findings The papers debate the challenges of Open Access and the possibilities of achieving a winning solution suitable for all stakeholders participating in the scholarly communication process.

A valuable review of the conference papers and current debate on Open Access.

The purpose of the paper is to present a service design (SD)-based methodology developed to help small and medium enterprises (SMEs) undertake organisational change.

This research used the design science research methodology, which enabled the creation of the Service Design for Organisational Change (SD4OCh) methodology. A real case study of a small service company specialised in neuropsychological disorders was used for the definition and validation of SD4OCh.

The main outcome of this study is the SD4OCh methodology, which is based on three key stages: diagnosis (knowing where to begin by detecting the organisation's strengths and weaknesses), innovation (improving the structure/processes and designing/redesigning services by employing a customer-centric approach), and implementation (enabling the definition of the route towards organisational change). There is also a transversal evaluation stage, which quantifies the organisational changes.

This study adds valuable knowledge to the service science research field and contributes to the awareness of the usefulness of SD theory within companies, especially those which are small and medium-sized, since those companies lack the tools and methods required to tackle organisational change, signifying that the challenges the companies confront are different to those of larger companies.

Although this is a SD-based research, the SD4OCh methodology was developed in order to enable companies to make holistic changes, namely, to innovate their services, structure, and processes, thus supporting and guiding organisational change.

Foreign markets possess different characteristics to domestic ones; this means that dynamic marketing capabilities (DMCs) should be adjusted. This paper aims to understand how these DMCs enable firms to achieve greater legitimacy in international markets.

This research uses a qualitative approach based on in-depth interviews in collaboration with an association of exporting firms.

Findings suggest five areas of DMCs to improve organizational legitimacy overseas: flexibility, relationship management, local market sensitivity, anticipation and exemplariness. Those capabilities should be combined and will play a different role depending on the implementation phase of the external company. Resource allocation and capability development should follow an integrative approach emphasizing proximity, adaptability, alliances, engagement and credibility to reach differentiation in foreign markets.

The originality is mainly focused on the cohabitation and strong synergies between DMCs and legitimacy. This aspect is particularly relevant because legitimized companies have higher levels of survival, which is fundamental in the international venture.

This exploratory study aims to identify the main risk reduction strategies when individuals suffer from coeliac disease (CD) or non-coeliac gluten sensitivity (NCGS) travel. Based on Yeung and Yee's (2013; 2019) model, the paper offers a new framework for analysing the main travel risk reduction strategies for people with specific food needs. The empirical work focuses on the perspectives of different stakeholders (CD and NCGS community, restaurant managers/service providers and nutritionists).

A qualitative approach based on thematic analysis is adopted. The results of 32 semi-structured in-depth interviews reveal different stakeholders' perspectives in order to understand the various strategies.

The findings confirm the high level of consensus that the main food risk reducers are: travel information, staff training in safety assurance, legislation and risk prevention protocols. The findings also show significant limitations in the information offered by restaurants, organisations and tourist destinations and the negative repercussions on the tourist experience and the reputation on a particular destination.

The results will help hospitality business managers and destination management organisations develop food risk reduction strategies to solve some of the most important food-related problems when people in this market segment travel.

This work contributes to the literature by providing a new framework on travel risk reduction strategies for people with specific food needs. The novelty of this research is mainly found in the study of risk reduction strategies related to the travel decision-making process for those with CD and NCGS from different perspectives.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.