Business information architecture for successful project implementation based on sentiment analysis in the tourist sector

In the today's market, there is a wide range of failed IT projects in specialized small and medium-sized companies because of poor control in the gap between the business and its vision. In other words, acquired goods are not being sold, a scenario which is very common in tourism retail companies. These companies buy a number of travel packages from big companies and due to lack of demand for these packages, they expire, becoming an expense, rather than an investment. To solve this problem, we propose to detect the problems that limit a company by re-engineering the processes, enabling the implementation of a business architecture based on sentimental analysis, allowing small and medium-sized tourism enterprises (SMEs) to make better decisions and analyze the information that most possess, without knowing how to exploit it. In addition, a case study was applied using a real company, comparing data before and after using the proposed model in order to validate feasibility of the applied model.This work has been partially funded by the following projects of the Spanish Ministry of Science, Innovation and Universities GROMA (MTM2015-63710-P), MODAS-IN (reference: RTI2018-094269-B-I00), PPI (RTC-2015-3580-7) and UNIKO (RTC-2015-3521-7), and the “methaodos.org” research group at URJC

A sentiment analysis software framework for the support of business information architecture in the tourist sector

In recent years, the increased use of digital tools within the Peruvian tourism industry has created a corresponding increase in revenues. However, both factors have caused increased competition in the sector that in turn puts pressure on small and medium enterprises' (SME) revenues and profitability. This study aims to apply neural network based sentiment analysis on social networks to generate a new information search channel that provides a global understanding of user trends and preferences in the tourism sector. A working data-analysis framework will be developed and integrated with tools from the cloud to allow a visual assessment of high probability outcomes based on historical data, to help SMEs estimate the number of tourists arriving and places they want to visit, so that they can generate desirable travel packages in advance, reduce logistics costs, increase sales, and ultimately improve both quality and precision of customer service

Deep learning in hospitality and tourism:a research framework agenda for future research

PurposeThis study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.Design/methodology/approachCovering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).FindingsFive application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.Research limitations/implicationsAlthough a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.Originality/valueTo the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area

Deep learning in hospitality and tourism:a research framework agenda for future research

PurposeThis study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.Design/methodology/approachCovering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).FindingsFive application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.Research limitations/implicationsAlthough a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.Originality/valueTo the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area

Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability: Proceedings of the TURITEC 2023 Conference, October 19–20, 2023, Málaga, Spain

Subido al repositorio por el editor (Real Decreto Legislativo 1/1996, art. 8)This open-access book presents the best research papers from the XIV International Congress on Tourism and Information and Communications Technologies (TURITEC2023), held in Málaga, Spain from 19 to 20 October 2023. The book explores the profound impact of COVID-19 on the tourism industry and the increasing importance of digitalization and Information and Communication Technologies (ICTs) as key drivers for the industry's recovery, alongside sustainability. This curated collection of research papers offers conceptualizations, methodologies, analyses, and empirical case studies that illuminate the path to a resilient and sustainable future for tourism.Instituto Andaluz de Investigación e Innovación en Turismo. Unviersidad de Málag

Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review

Purpose: Sentiment analysis is built from the information provided through text (reviews) to help understand the social sentiment toward their brand, product, or service. The main purpose of this paper is to draw an overview of the topics and the use of the sentiment analysis approach in tourism research. Methods: The study is a bibliometric analysis (VOSviewer), with a systematic and integrative review. The search occurred in March 2021 (Scopus) applying the search terms "sentiment analysis" and "tourism" in the title, abstract, or keywords, resulting in a final sample of 111 papers. Results: This analysis pointed out that China (35) and the United States (24) are the leading countries studying sentiment analysis with tourism. The first paper using sentiment analysis was published in 2012; there is a growing interest in this topic, presenting qualitative and quantitative approaches. The main results present four clusters to understand this subject. Cluster 1 discusses sentiment analysis and its application in tourism research, searching how online reviews can impact decision-making. Cluster 2 examines the resources used to make sentiment analysis, such as social media. Cluster 3 argues about methodological approaches in sentiment analysis and tourism, such as deep learning and sentiment classification, to understand the user-generated content. Cluster 4 highlights questions relating to the internet and tourism. Implications: The use of sentiment analysis in tourism research shows that government and entrepreneurship can draw and enhance communication strategies, reduce cost, and time, and mainly contribute to the decision-making process and understand consumer behavior

Nation branding strategy: a case study of brand Zimbabwe.

Doctoral Degree. University of KwaZulu-Natal, Durban.This study focused on nation branding strategy for Zimbabwe. The main objective of the study was to develop strategic insights and knowledge for the development of an effective and sustainable nation brand for Zimbabwe. This study deployed a combination of phenomenological and positivist approaches; hence benefited from methodological and concurrent triangulation. Qualitative and quantitative data were collected concurrently which allowed corroboration and validation of findings. The study adopted a non-probability sampling method, using both purposive and quota sampling techniques to allow for conscious selection of respondents based on competence, perspective, experience, and convenience. The sample comprised respondents involved with Brand Zimbabwe whose opinions and insights influence their behaviour towards Zimbabwe. Respondents came from Zimbabweans in the Diaspora, NGOs, academics, business, government and foreign visitors. Data were collected through questionnaires and interviews. Statistical Package for Social Science (SPSS) was used to analyse quantitative data while thematic analysis was applied on qualitative data. The study established that there are multiple key actors who should influence nation branding in Zimbabwe. Coordinated involvement among these key actors is crucial. The study also found that nation branding and management was not consciously practiced in Zimbabwe. It was also established that Zimbabwe ranked poorly on the Global Competitiveness Index (GCI), largely owing to policy instability, restrictive foreign currency regulations, and poor governance. The study proposed a seven-step process for branding Zimbabwe. Successful implementation of nation branding strategy requires wholesome participation and systematic coordination. Accordingly, a strategic framework for the implementation of Brand Zimbabwe strategy was also proposed. The study recommended the establishment of a formally recognised institution to steer the nation brand construction efforts. Nation brands cannot be strong and competitive if the underlying product is poor. No amount of astute marketing and brand communication can address what are perceived to be inherent and apparent ‘bad country brands. Positive nation brand equity does not just happen; there is need for a concerted effort to manage the development of an attractive nation brand and grow its equity. Key words: nation branding, brand strategy, nation brand equity, nation brand competitiveness, Brand Zimbabwe

How to monitor and generate intelligence for a DMO from online reviews

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceSocial media and customer review websites have changed the way the tourism sector is managed. Social media has become a new source of information, due to the large amount of UGC / e-Wom generated by consumers An information that is "available" but at the same time noisy and of great volume, which makes it difficult to access and analyze. This study investigates and verifies the possibility of using data present in content reviews of a Content Web Site Review - TripAdivsor - to generate actionable information for a Destination Management Organization. With a focus on negative reviews, tourist attractions of Lisbon and using the “R code” and its packages, the study shows that with the correct technique chosen and the action of an intelligence analyst, data can be extracted and provide substrate for actions, strategy and intelligence generation – which is Social Media Intelligence. The findings prove that the flood of web 2.0 data can serve as a source of intelligence for the Destination Management Organization (DMO). By monitoring sites like TripAdvisor, a DMO can hear what tourists talk about attractions and thereby generate insights for intelligence and strategy actions. A DMO can even, analyzing this data, make your attractions more desirable, and even act in adverse situations, reducing risky situations

Las redes sociales como instrumento de gestión de destinos turísticos

The main objective of this research focuses on determining the functions and application of social networks in the management of tourist destinations, with the aim of revealing the state of the art and degree of applicability. First, in order to fulfill the objectives and theoretical hypotheses, a bibliographic analysis is carried out, which leads to the elaboration of the state of the art regarding the topic on which this doctoral thesis revolves. In this sense, the state of the art of the research is elaborated from a systematic analysis of the scientific literature on smart destinations (concept, dimensions, components, management systems) and their integration with social networks. In order to respond to the second group of specific objectives and hypotheses, the methodology applied was quantitative, based on the analysis of a series of data from Spanish tourist destinations in terms of their presence and management of social networks. The quantification of these variables, for each of the 78 tourist destinations (among which were all the smart tourist destinations, hosted by the SEGITTUR project), allowed us to apply different quantitative statistical techniques, such as: a) Pearson's correlation analysis, to establish the type of interrelation between the independent variable (number of visitors) and the dependent variables, which referred to the presence and management of the destinations on the web and in social networks; and b) to determine the degree of use of social networks by the smart destinations with respect to the others, an ANOVA analysis was carried out between the variables of the most visited destinations with respect to those of the smart destinations, in order to detect possible statistically significant differences between the two groups of destinations with respect to their management of social networks. Finally, in order to fulfill the third group of objectives and specific hypotheses, and to demonstrate whether there is complementarity between the data provided by social networks and those offered by official statistics, in terms of tourism demand, a qualitative methodology is followed, since it is based on an exploratory case analysis. In this sense, the change experienced by the behaviors and feelings of tourists visiting Andalusia as a result of COVID-19 is analyzed, both with data from the Andalusia Tourism Situation Survey (ECTA, 2020) and by means of a sentiment analysis using Twitter data. For the exploratory sentiment analysis, using Twitter, the statistical program R and the library package (rtweet) were used to retrieve messages from the social network Twitter (tweets). Machine learning sentiment analysis algorithms were then applied to the resulting data. Therefore, based on the results obtained from this doctoral thesis, we believe that it is necessary for tourist destinations to have a professional specialized in the management of social networks (social media manager), as this will allow the destination to make the most of its presence in social networks. In short, it is considered that research focused on the applicability of social networks to the management processes of tourist destinations is still in its early stages of development, especially if we analyze the real applicability it is having in specific tourist destinations. This recommendation is important both when it comes to adapting to the progressive development of new technologies, as well as to the evolution of the behavior and profile of tourists, who are increasingly familiar with the use of new technologies, and demand flexible experiences adapted to their preferences, among other characteristics.El objetivo principal de esta investigación se centra en determinar las funciones y aplicación de las redes sociales en la gestión de los destinos turísticos, con objeto de poner de manifiesto el estado de la cuestión y grado de aplicabilidad. Primero para dar cumplimiento a los objetivos e hipótesis teóricas, se realiza un análisis bibliográfico, el cual da lugar a la elaboración del estado del arte respecto al tema sobre el que gira la presente tesis doctoral. En este sentido, el estado del arte de la investigación se elabora a partir de un análisis sistemático de la literatura científica acerca de los destinos turísticos inteligentes (concepto, dimensiones, componentes, sistemas de gestión) y su integración con las redes sociales. Para dar respuesta al segundo grupo de objetivos e hipótesis específicas, la metodología aplicada fue de tipo cuantitativa, basada en el análisis de una serie de datos que arrojan los destinos turísticos españoles en cuanto a su presencia y gestión de las redes sociales. La cuantificación de estas variables, para cada uno de los 78 destinos turísticos (entre los que se encontraban todos los destinos turísticos inteligentes, acogidos al proyecto de SEGITTUR), nos permitió aplicar diferentes técnicas estadísticas cuantitativas, tales como: a) el análisis de correlación de Pearson, para establecer el tipo de interrelación entre la variable independiente (número de visitantes) y las variables dependientes, que se referían a la presencia y gestión de los destinos la web y en las redes sociales; y b) para determinar el grado de utilización de las redes sociales por parte de los destinos turísticos inteligentes respecto a los restantes, se realizó un análisis ANOVA entre las variables de los destinos más visitados respecto a las de los destinos turísticos inteligentes, con objeto de detectar posibles diferencias estadísticamente significativas entre ambos grupos de destinos en lo que respecta a la gestión que hacen de las redes sociales. Por último, para dar cumplimiento al tercer grupo de objetivos e hipótesis específicas, y demostrar si existe complementariedad entre los datos que arrojan las redes sociales y los que ofrecen las estadísticas oficiales, en lo que respecta a la demanda turística, se sigue una metodología de corte cualitativa, ya que se fundamenta en un análisis de caso, de carácter exploratorio. En este sentido, se analiza el cambio experimentado por los comportamientos y sentimientos de los turistas que visitan Andalucía como consecuencia de la COVID-19, tanto con los datos de la Encuesta de Coyuntura Turística de Andalucía (ECTA, 2020) como mediante un análisis de sentimientos con datos de Twitter. Para el análisis exploratorio de sentimientos, mediante Twitter, se utilizó el programa estadístico R y el paquete de biblioteca (rtweet) para la recuperación de mensajes de la red social Twitter (tweets). A continuación, se aplicaron algoritmos de análisis de sentimientos mediante aprendizaje automático a los datos resultantes. Por todo ello, a partir de los resultados que se obtienen de esta tesis doctoral, consideramos que se hace necesario que los destinos turísticos cuenten con un profesional, especializado en la gestión de redes sociales (social media manager), pues ello permitirá al destino sacar el máximo provecho a su presencia en las redes sociales. No en vano, esta actuación posibilitará el máximo desempeño de las múltiples funciones que, a lo largo de la investigación, se han puesto de manifiesto, que pueden desempeñar esta herramienta, dentro de los procesos de gestión de los destinos turísticos En definitiva, se considera que la investigación centrada en la aplicabilidad de las redes sociales a los procesos de gestión de los destinos turísticos está aún en sus primeras etapas de desarrollo, sobre todo si analizamos la aplicabilidad real que está teniendo en destinos turísticos concretos. Esta recomendación es importante tanto a la hora de adaptarse al progresivo desarrollo de las nuevas tecnologías, como por la evolución que viene experimentando el comportamiento y perfil de los turistas, los cuales, cada vez, están más familiarizados con el uso de nuevas tecnologías, y demandan experiencias flexibles y adaptadas a sus preferencias, entre otras características