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This article aims to know the direction of current research based on the previous research in the last ten years (2012–2021).

Text mining was integrated with a network and content analysis as part of the mix methodological approach. The scientific articles, on the other hand, were assembled on Litmaps through web scraping. This process selected 86 articles about medical tourism published between 2012 and 2021. This study employed AntConc, RStudio and Gephi tools for data analysis and visualization.

A total of 138 articles were identified through Litmaps using web scraping and 86 studies met the criteria. The trend of medical tourism research is a positive sign for tourism and health industries; this is the beginning to recognize the importance of elaborating on these two topics. Several researchers have frequently studied issues of destination, hospital, development, quality, stakeholders, surgery, service, economics and policy. Policymakers must establish a medical tourism ecosystem to accommodate all stakeholders in this industry. This study also recommends focusing on supply and institution for medical tourism future research.

This literature review presents research trends on medical tourism in 2012–2021 based solely on articles available on the Litmaps search engine. If the time span is extended and the sources of articles are expanded there will be more literature available for analysis. The articles obtained are also only articles published in English due to the language limitations of the author.

Policymakers must establish a medical tourism ecosystem to accommodate all stakeholders in this industry. Stakeholders must work together to provide medical tourism package therefore people can get their health services while visiting available tourist areas.

The literary study of medical tourism over 10 years is considered the most recent systematic literature review.

The objective of this paper is to further the understanding of netnography as a research method for supply chain academics. Netnography is a method for gathering and gaining insight from industry-specific online communities. We prescribe that viewing netnography through the lens of the supply chain will permit researchers to explore, discover, understand, describe or report concepts or phenomena that have previously been studied via survey research or quantitative modeling.

To introduce netnography to supply chain research, we propose a framework to guide how netnography can be adopted and used. Definitions and directions are provided, highlighting some of the practices within netnographic research.

Netnography provides the researcher with another avenue to pursue answers to research questions, either alone or in conjunction with the dominant methods of survey research and quantitative modeling. It provides another tool in the researchers’ toolbox to engage practitioners in the field.

The development of netnography as a research method is associated with Robert Kozinets. He developed the method to study online communities in consumer behavior. We justify why this method can be applied to supply chain research, how to collect data and provide research examples of its use. This technique has room to grow as a supply chain research method.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

This study aimed to examine the similarities and differences between the ability to analyze the environment and exploit new knowledge (absorptive capacity) and the skills to generate value from innovation (appropriation). These fields have similar origins and are sometimes confused by practitioners and academics.

A review was conducted based on a full-text analysis of 681 and 431 papers on appropriation and absorptive capacity, respectively, from Scopus, Science Direct and Lens, using methodologies such as text mining, backward citation analysis, modularity clustering and latent Dirichlet allocation analysis.

In business disciplines, the fields are considered different; however, in other disciplines, it was found that some authors defined them quite similarly. The citation analysis results showed that appropriation was more relevant to absorptive capacity, or vice versa. From the dimension perspective, it was found that although appropriation was considered a relevant element for absorptive capacity, the last models did not include it. Finally, it was found that studies on both topics identified the importance of appropriation and absorptive capacity for innovation performance, knowledge management and technology transfer.

This is one of the first studies to examine in-depth the relationship between appropriation and absorptive capacity, bridging a gap in both fields.

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.

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.

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 paper aims to explore the consumer insights and ethical concerns surrounding the online payday loan services available in the Google Play Store. This research was conducted to compare whether the presence or absence of debt collection protection acts in a country creates differences in consumer experiences regarding the ethics of payday loan collection. Specifically, the study compares customers’ experiences in both the Indonesian and US markets.

Indonesia and the USA were chosen because they have very different regulatory structures for the payday loan industry. The data was scraped using Python from 27 payday loan apps on the Indonesian Play Store, resulting in a total of 244,697 reviews extracted from the Indonesian market. For the US market, 446,010 reviews were extracted from 14 payday loan apps. The data was further analyzed using NVIVO.

The results suggest that consumers of payday loans in Indonesia and the USA hold positive views about the benefits of payday loan apps, as revealed by the word frequency and word cloud analysis. Notably, customers in both countries did not express any negative sentiments regarding the unethical interest rate charged by the payday loan, contradicting what is commonly reported in academic literature. However, a distinct pattern of unethical conduct was observed in both countries concerning marketing communication and debt collection practices. In the Indonesian market, payday loan companies were found to engage in unethical debt collection activities. In the US market, payday lenders exhibited unethical behavior in their marketing communication, particularly through deceptive advertising that makes promises to consumers that are not delivered.

The study aims to provide evidence on the various experiences of customers in the presence and absence of debt collection regulations using a novel methodology and a large sample, which strengthens the results and conclusions of the study. The study also intends to inform policymakers, particularly the Indonesian government, about the need for specific laws to regulate the debt collection process and prevent unethical practices. Ultimately, the study is expected to protect the rights of consumers from a deceptive marketing communication or unethical debt collection practices in both the Indonesian and US markets.

This research aims to contribute to the literature on sustainable hospitality and tourism by applying social network analysis to identify sustainable tourism business networks and untangle the role of cognitive and geographical proximity in their formation.

Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant information from the internet.

The results support the existence of geographical clusters of tourist accommodation providers that are linked by strong cognitive proximity based on sustainability principles that are well communicated via their websites. This does not appear to be greenwashing because companies that have agreed on sustainability principles have also implemented concrete actions and tend to signal these actions through a variety of sustainability certifications.

The results may guide tourism managers and policymakers in developing tourism initiatives directed at the creation of fruitful collaborations between similarly oriented organizations and methods to support clusters of sustainable tourism accommodation. Identifying sustainable tourism networks may assist in the identification of potential actors of change, fueling a widespread transition toward sustainability.

In this study, the authors adopted an innovative methodology to detect sustainability-oriented tourism business networks. Additionally, to the best of the authors’ knowledge, this study is one of the first to simultaneously explore the cognitive and geographical connections between tourism businesses.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.