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This paper aims to replicate a model already proven in previous research in this field. This will make it possible to explain the possible relationships that may occur among the motivations, perceived value, satisfaction and loyalty of the tourist towards the Alhambra and Generalife inscribed as World Heritage Site (WHS) in 1984.

From a dataset containing 1,612 surveys, a model a model based on structural equations has been carried out through SmartPLS software, focus the analysis on the model dependent variables’ predictive power, as well as the size of the effect and the statistical inference of the structural relationships.

The main conclusions include the influence of perceived value on satisfaction as well as the influence of the latter on loyalty. it is remarkable the effect that the perceived value has on satisfaction, and satisfaction on loyalty. This implies that a positive assessment of world heritage destinations leads a subsequent loyalty to them.

The results obtained in this research can be used as a starting point for the establishment of new strategies for the promotion of the destination in terms of tourism and heritage.

The inclusion in the list of WHS is recognition in terms of material and historical quality, as well as a stimulus for tourism because it increases the number of visits to the destination. Several studies carried out in these types of destinations have shown the existence of a relationship between motivations, perceived value, satisfaction and loyalty. However, there are no previous studies carried out in the Alhambra and the Generalife that sustain this relationship. This work makes a contribution that completes the academic literature on the study of the emotional bonds between the historical and monumental heritage and the tourist who visits it and its behaviour.

The main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.

In order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).

The authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.

Extremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.

This study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.

Due to the global COVID-19 pandemic, UNESCO Landscapes and World Heritage sites have faced unstable situations. Both at the sites themselves and in the research centres, universities and even the homes of the people involved, they have acted and responded to the best of their ability. In this context, the aim of the comparative analysis of different cases carried out here is to understand the main effects of the pandemic in the short term. On the one hand, the purpose is to determine what the general response trends have been and, on the other, to measure the resilience capacity in each case.

Up to eight cases studies representing different and diverse kinds of Heritage and Protected Natural sites from Southern Europe and America are compared.

In a context of uncertainty, new responses, unique opportunities and hitherto unseen weaknesses have arisen in research and management of natural and cultural heritage. In general terms, the dialogue between officials, technicians and researchers that have put together this article underlines the need to work towards a governance model that engages everyone in dialogue. Discrepancies between overlapping strategies and plans, which is the main conflict detected, should be avoided while a decentralisation of policies could be more operational. In this sense, situated knowledge may be of help in configuring practical management tools.

This paper compares and contrasts for first time the effects of the pandemic in Europe and Latin America. This exercise has provided a valuable diagnostic for present and future heritage management.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

The purpose of this paper is to analyze how fashion consumers behave when they make purchases using social media platforms – specifically, Instagram. In particular, the work examines the role played by consumer–brand involvement and self–brand connection (SBC) as predictors of customer brand engagement (CBE).

In this paper, social commerce-adoption is modeled, using three variables: customer engagement, SBC and fashion-consumer brand-involvement. Using a personal online survey, data on social media users classified as millennials and Generation Z were collected. The model is analyzed using structural equation modeling (SEM).

Consumer involvement in fashion has a positive effect on cognitive processing and activation, but not on affection. SBC has a positive effect on all three analyzed dimensions. Finally, gender is found to exert a moderating effect on the relationship between the “CBE activation” dimension and brand loyalty.

The research was conducted during the lockdown imposed due to the COVID-19 pandemic, which may have influenced the responses. Other limitations and potential lines of research for the future are presented at the end of the paper.

Some of the results of this study can directly inform the social media strategies of fashion companies that use Instagram as a channel of communication with their customers. They can also contribute to incentivizing co-created content and increasing consumption among both men and women.

The present study contributes to addressing the scarcity of studies dealing with CBE and social commerce in the fashion sector and, in particular, fashion-consumer behavior on social media (specifically, Instagram).

This study aims to analyze the effect of the use of social media on the perception of brand personality and to identify its effect on customer brand engagement.

The study adopted an exploratory approach, adapting Aaker's brand personality scale (1997) to the context of cultural tourism before carrying out a quantitative study resorting to a structural equation modeling to obtain empirical evidence to identify these relationships.

The findings reveal that the use of social media has a positive effect on the perception of brand personality and that brand personality, likewise, has a positive effect on customer brand engagement.

This study indicates that transmission of an attractive brand personality according to the desires of the public, combined with dissemination through social media, is a valid strategy to improve customer brand engagement.

This study represents an advance in the specialized literature on the value that consumers place on information transmitted through social media. Specifically, it sheds light on how the transmission of brand personality through social media affects customer brand engagement.

本研究旨在分析使用社交媒体对品牌个性感知的影响, 并确定其对客户品牌参与度的影响。

该研究采用探索性方法, 将Aaker（1997）的品牌人格量表适应文化旅游的背景, 然后使用结构方程模型（SEM）进行定量研究, 以获取关于拟议关系的经验证据

研究结果表明, 社交媒体的使用对品牌个性的感知具有积极影响, 而品牌个性同样对客户品牌参与度也具有积极影响。

这项研究表明, 根据公众的需求来传递有吸引力的品牌个性, 并通过社交媒体进行传播, 是提高客户品牌参与度的有效策略。

这项研究代表了有关消费者对通过社交媒体传播的信息所具有的价值的专业文献的进步。 具体而言, 它揭示了通过社交媒体传播品牌个性如何影响客户品牌参与度。

este estudio tiene como objetivo analizar el efecto del uso de los social media en la percepción de la personalidad de marca e identificar su efecto en el customer brand engagement.

el estudio adoptó un enfoque exploratorio, adaptando la Brand Personality Scale de Aaker (1997) al contexto del turismo cultural antes de llevar a cabo un estudio cuantitativo mediante un modelo de ecuaciones estructurales (SEM) para obtener evidencia empírica sobre las relaciones propuestas.

los resultados revelan que el uso de los social media tiene un efecto positivo en la percepción de la personalidad de marca y que la personalidad de marca tiene un efecto positivo en el customer brand engagement.

este estudio demuestra que la transmisión de una personalidad de marca atractiva de acuerdo con los deseos del público, combinada con su difusión a través de los social media es una estrategia válida para mejorar el customer brand engagement.

este estudio representa un avance en la literatura especializada sobre el valor que los consumidores otorgan a la información transmitida a través de los social media. Específicamente, arroja luz sobre cómo la transmisión de la personalidad de la marca a través de los social media influye en el customer brand engagement.

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users.

The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models.

The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated.

The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place.

The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions.

This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体（或地点）和用户的感知（或方面）。

我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达（西班牙）的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。

测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。

我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。

该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。

本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT（深度学习模型）训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性（消极性）的原因。

This paper seeks to revisit the interrelationship among tourists’ perceived value of the destination, tourist satisfaction and destination loyalty in the heritage tourism site of Hoi An. In addition, the moderating role of tourists’ perceived crowding, which has become remarkably common at the site, in such a triangle relationship will also be explored. In other words, this study aims to validate an extended model of perceived value – tourist satisfaction – destination loyalty – perceived crowding.

The study collects data from 403 tourists who visited Hoi An during peak season through an online questionnaire. The data were later analysed using AMOS and Warp partial least squares.

The results validate the significant and positive correlation among perceived value, customer satisfaction and destination loyalty. Also, perceived crowding was confirmed to affect the relationship among these three variables negatively. In terms of academic contributions, this paper empirically proved that low levels of tourist satisfaction and destination loyalty among tourists who highly value their visiting experience at World Heritage Sites (WHS) can be caused by perceived crowding.

So far, current literature has investigated the direct (either positive or negative) relationship between perceived crowding and post-visit behaviours of tourists (Nie et al., 2022; Papadopoulou, Ribeiro, & Prayag, 2023; Stemmer, Gjerald, & Øgaard, 2022). Broadening this area of research, the authors of this paper used the social interference theory and the stimulus-overload theory to explain the low level of tourist satisfaction and destination loyalty among tourists who highly value their visiting experience at WHS.

Gastronomy can be a key destination choice factor. As tourists, people will be able to learn more about the culture of the place through its culinary assets. This paper aims to analyse the interest and the gastronomic motivations of tourists to the city of Granada (Spain), where two important UNESCO World Heritage Sites (WHS) are included.

To achieve the aim of this study, fieldwork was carried out on a representative sample of tourists in Granada (Spain). Specifically, a total of 1,612 valid surveys were filled out in culinary establishments and historical sites. In these surveys, the opinion of tourists regarding gastronomy and their motivations when travelling was assessed.

The results of this research allow to make a segmentation of tourists into three groups according to their position and their interest in gastronomy based on their destination choice, distinguishing among survivors, enjoyers and experiencers’ tourists. Additionally, it is confirmed that gastronomy is shaped as a motivation that influences the level of tourist satisfaction, performing as a differentiating element that can help increase the competitiveness of the destination.

This research contributes to the scarce academic literature on tourism experiences in a city with WHS recognitions. This study confirms the existence of a relationship between gastronomic motivations and the level of satisfaction achieved by tourists who visit the city of Granada, where no similar studies were found. In addition, this work confirms the connection between gastronomy and culture.

Natural and cultural tourism are important motivators for international tourism. Spain has impressive tourist attractions that are outstanding on the natural and cultural tourism dimensions. The purpose of this paper is to identify traits of the most attractive destinations in Spain and to understand the relative importance of natural, cultural, and dual (natural and cultural) attractions to target consumers.

The authors compare the level of tourism in the 17 major regions of Spain and identify the key natural, cultural, and dual attractions using a two-step cluster analysis to ascertain the relative importance of the three types of attractions.

The findings of the cluster analysis suggest that natural attractions had the highest importance, followed by dual attractions, with cultural attractions having the lowest importance in affecting the level of tourism in a region. The study identified four categories of regions resulting from “high vs low” total number of attractions by “high vs low” levels of tourism (operationalized via the number of tourist-nights). The regions with high levels of tourism were either located in the bodies of water (a group of islands) or on ocean/sea(s) surrounding Spain. The study suggests placing greater emphasis on promoting cultural attractions in Spain.

The findings suggest that there is a need to put more emphasis on promoting the cultural attractions in Spain. Spain is a diverse country with huge potential for tourism from people all over the world, due to its diverse geography and rich history.

To the best of the authors’ knowledge, this is the first study that analyzes 17 regions of Spain in relation to their tourism characteristics, identifying attractions that are not sufficiently leveraged, and suggesting strategies for identifying opportunities for the tourism industry in Spain.