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This paper aims to give meaning to the smart tourism destination (STD) certification, highlighting its main advantages and limitations. The case of Medellín (Colombia) presents characteristics worth studying. The city has suffered from stigmatization and has recently started the steps to become an STD with the Spanish company Segittur (December 2020). Thus, this study aims to focus on the implications of the STD process, especially in an area that has been impacted by tourist activity.

This paper is a qualitative and exploratory case study about Medellín in Colombia and its tourism development in an STD framework. After a theoretical exploration of the STD from an urban perspective, the paper will present a multiple data corpus to analyze the city’s certification process to turn into an STD. This methodology explores stakeholders on the destination, including an observation, and allows us to obtain an overview of the implications of the STD certification for Medellín.

During the past decade, Medellín passed from a dangerous destination to a trendy destination. Findings reveal interesting results, considering the need to consider all aspects of territory as a central issue for the STD settlement and look beyond a technological approach.

This paper lets to understand better the STD process established by the institution. Moreover, it highlights the gap between the coveted certification to become the first STD in Colombia and the realities of a Latin American territory.

Este articulo pretende dar un significado a la certificación de destino turístico inteligente (DTI), destacando sus principales ventajas y limitaciones. El caso de Medellín (Colombia) presenta características relevantes de estudio. La ciudad ha sido ampliamente estigmatizada y recientemente ha iniciado el proceso de certificación para convertirse en destino turístico inteligente con la entidad española Segittur (dic. 2020). Dicho esto, queremos enfocarnos en las implicaciones de este proceso, especialmente en un área que ha sido impactada por la actividad turística.

Este artículo comprende un estudio cualitativo y exploratorio sobre Medellín en Colombia y su desarrollo turístico en un marco de DTI. Luego de una exploración teórica del destino turístico inteligente desde una perspectiva urbana, el artículo presentará un corpus de múltiples datos para analizar el proceso de certificación de la ciudad en su conversión en una DTI. Nuestra metodología explora a los stakeholders sobre el destino, incluyendo una observación, y nos permite obtener una visión general de las implicaciones de la certificación DTI para Medellín.

Durante la última década, Medellín pasó de ser un destino denominado de alto riesgo a un destino altamente recomendado. Los hallazgos revelan resultados interesantes, mostrando la necesidad de considerar todos los aspectos del territorio como un tema central para el asentamiento del DTI y mirar más allá de un enfoque tecnológico.

Este trabajo permite tener una mejor comprensión del proceso de DTI establecido por la entidad Segittur. Además, resalta la brecha existente entre la deseada certificación y las realidades de un territorio latinoamericano.

The purpose of this study paper is to focus on developing novel ways to monitor an economy in real time during the COVID-19 pandemic. A fully automated framework is proposed for collecting and analyzing online food prices in Poland. This is important, as the COVID-19 outbreak in Europe in 2020 has led many governments to impose lockdowns that have prevented manual price data collection from food outlets. The study primarily addresses whether food price inflation can be accurately measured during the pandemic using only a laptop and Internet connection, without needing to rely on official statistics.

The big data approach was adopted to track food price inflation in Poland. Using the web-scraping technique, daily price information about individual food and non-alcoholic beverage products sold in online stores was gathered.

Based on raw online data, reliable estimates of monthly and annual food inflation were provided about 30 days before final official indexes were published.

This is the first paper to focus on measuring inflation in real time during the COVID-19 pandemic. Monthly and annual food price inflation are estimated in real time and updated daily, thereby improving previous forecasting solutions with weekly or monthly indicators. Using daily frequency price data deepens understanding of price developments and enables more timely detection of inflation trends, both of which are useful for policymakers and market participants. This study also provides a review of crucial issues regarding inflation that emerged during the COVID-19 pandemic.

The main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.

The design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.

The modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.

Business limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.

The modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.

The adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

Empirical studies on occupational choice have typically concentrated on a specific sector. The purpose of this study is to compare two sectors wherein there are grounds to hypothesise that lifestyle reasons play a key role for occupational choice.

Arguing that the potential for qualitative web scraping is still underused, the hypothesis is tested through qualitative web scraping for occupational choices.

It is shown that incomes for farmers are both documented in a better way and higher than in arts. The central roles played by farmers in the provision of basic needs and in powerful value chains are possible reasons for this difference. As a common factor between the sectors, two-thirds of both farmers and artists choose their profession for reasons of self-realisation or societal motives.

This study is the first to show both common grounds and differences in occupational choices of two different sectors.

This paper aims to examine the primary supply data sources that have been used for research into the sharing economy, and the advantages and limitations of these sources in the literature.

To address the research aims, this study conducted a systematic literature review and content analysis of all relevant articles. Following the review, the methodological sections of the selected papers were examined to identify the characteristics and limitations of all data sources used in the papers.

This study revealed several limitations of the use of three major data sources, namely, web scraping with self-made bots, inside Airbnb and AirDNA, for sharing economy research. The review shows that the majority of the selected papers did not acknowledge any limitations, nor did they discuss the quality of the data sources.

The findings of this paper can serve as guidelines for selecting appropriate data sources for research into the sharing economy and cautions researchers to address the limitations of the data sources used.

To the best of the authors’ knowledge, this is the first study that explores the advantages and limitations of data sources used in short-term rental market research.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.

The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.

The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).

The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.

The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.

There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.

Explore the use of Gender-Fair Language (GFL) by influencers on Instagram.

The clustering methodology. A digital Bag-of-Words (BoW) Method called GFL Clustering BoW Methodology to identify whether an inclusive marketing (IM) strategy can be used. Thus, this research has a methodological and practical contribution to increasing the number of marketing technology tools.

This study is original as it proposes an inclusive digital marketing strategy and contributes with methods associated with digital transfers in order to improve marketing strategies, tactics and operations for inclusive content with a data integrity approach.

Due to the limitations of the application programming interface (API) of the social network Instagram, a limited number of text data were used, which allowed for retrieving the last 12 publications of each studied profile. In addition, it should be considered that this study only includes the Spanish language and is applied to a sample of influencers from Chile.

The practical contribution of this study will lead to a key finding for the definition of communication strategies in both public and private organizations.

The originality of this work lies in its attractive implications for nonprofit and for-profit organizations, government bodies and private enterprises in the measurement of the success of campaigns with an IM communicational strategy and to incorporate inclusive and non-sexist content for their consumers so as to contribute to society.

本研究擬探究有影響力的人士在使用即時電報 (Instagram) 時、如何使用性別中立語言。

研究使用了聚類分析法;具體來說, 研究人員採用一個叫 GFL聚類詞袋法的數位詞袋分析法, 去確定研究可否使用信息管理策略。因此, 本研究在行銷科技方面、添加了一個工具, 就此而言, 本研究在學術的研究法和實務方面、均作出貢獻。

本研究建議了一個包括一切的數位行銷策略;研究亦構建了若干與數位傳輸有關的方法, 以能利用數據完整性的理念, 為行銷策略、行銷戰術和市場營銷, 在內容的全面包含度方面取得改善。

因為社交網站即時電報的應用程式介面有其局限, 故使用了少量的文本數據, 這可使每個被探討的傳略的最後12個發佈能被撿回。另外需注意的是、本研究只涵蓋西班牙語, 而且, 研究使用的樣本只是來自智利有影響力的人士。

本研究在實務方面的貢獻是、它為探討在公共機構和私營機構內使用的溝通策略的定義上、帶來重要的啟發和發現。

本研究的原創性在於它給營利和非營利組織、政府機關和私人企業帶來頗具吸引力的啟示。而這些啟示是與測量以包括一切的行銷溝通策略進行的專門活動是否成功有關的。另外, 涵蓋一切和無性別歧視的內容被納入供消費者使用, 以此為社會帶來裨益。

This study aims to highlight the challenges and opportunities of using GitHub as a data source in both research and programming education.

This study provides general overview of the challenges and opportunities faced while conducting empirical research using GitHub as a data source. The challenges and opportunities are framed using the input–process–output model of open-source software.

GitHub data accessed from the application programming interface (API) can have several limitations, which can be overcome by Web scraping and using external data repositories such as GHArchive and GHTorrent. There are also several idiosyncrasies about GitHub that researchers need to be aware of to be able to use the data effectively, which can represent an opportunity for research. The challenges and opportunities are summarized for the licenses, community, development process and product of free/libra and open-source software communities hosted on GitHub.

This study provides a summary of GitHub-related challenges and opportunities that researchers can leverage to improve their empirical research. Furthermore, this summary can be a valuable resource for instructors that plan to use GitHub as a data source in their data-focused programming courses.

This study aims to propose a data-driven approach, based on open-source tools, that makes it possible to understand customer satisfaction of the accommodation offer of a whole country.

The method starts by extracting information from all hotels of Portugal available at TripAdvisor through Web scraping. Then, a support vector machine is adopted for modeling the TripAdvisor score, which is considered a proxy of customer satisfaction. Finally, knowledge extraction from the model is achieved using sensitivity analysis to unveil the influence of features on the score.

The model of the TripAdvisor score achieved a mean absolute percentage error of around 5 per cent, proving the value of modeling the extracted data. The number of rooms of the unit and the minimum price are the two most relevant features, showing that customers appreciate smaller and more expensive units, whereas the location of the hotel does not hold significant relevance.

National tourist offices can use the proposed approach to understand what drives tourists’ satisfaction, helping to shape a country’s strategy. For example, licensing new hotels may take into account the unit size and other characteristics that make it more attractive to tourists. Furthermore, the procedure can be replicated at any time and in any country, making it a valuable tool for data-driven decision support on a national scale.