Digital

This paper examines the status of the development of national digital health information systems (HIS) in Commonwealth of Independent States (CIS) member states. Data for research were collected using a questionnaire adapted from the questionnaire of the WHO’s Third Global Survey on eHealth. The results showed that the digital transformation of HIS was occurring in all seven CIS member states (participating in the study), which were financed by different resources. Laws and regulations on electronic medical records (EMR) use were present in almost all participating CIS member states. Various international standards and classifications were used to support development and the interoperability of digital health information system (d-HIS), including International Classification of Diseases (ICD), Digital Imaging and Communications in Medicine (DICOM), ISO 18308, Logical Observation Identifiers, Names, and Codes (LOINC), Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and ISO TC 215. Several CIS member states had adopted a national information security strategy for the safe processing of both personal data and medical confidential information. The digital transformation of healthcare and the Empowerment through Digital Health initiative are taking place in all CIS member states, which are at different stages of introducing electronic medical and health records. Full article

Convolutional Neural Networks (CNNs) are well-studied and commonly used for the problem of object detection thanks to their increased accuracy. However, high accuracy on its own says little about the effective performance of CNN-based models, especially when real-time detection tasks are involved. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of their speed/accuracy trade-off. This work performs a review and hands-on evaluation of the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory footprint, and computational and storage costs. In addition, we review available datasets for medical mask detection and train YOLOv5 on the Properly Wearing Masked Faces Dataset (PWMFD). Next, we test and evaluate a set of specific optimization techniques, transfer learning, data augmentations, and attention mechanisms, and we report on their effect for real-time mask detection. Based on our findings, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD while maintaining the same level of mean Average Precision (67%). Full article

Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship, new skills, and jobs, especially in small communities and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being, and protect digital rights, we propose data cooperatives as a vehicle for secure, trusted, and sovereign data exchange. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted application programming interfaces (“APIs”) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This review article analyses an array of transformative use cases that underline the potential of cooperative data governance. These case studies exemplify how data and platform cooperatives, through their innovative value creation mechanisms, can elevate digital commons and value chains to a new dimension of collaboration, thereby addressing pressing societal issues. Guided by our research aim, we propose a policy framework that supports the practical implementation of digital federation platforms and data cooperatives. This policy blueprint intends to facilitate sustainable development in both the Global South and North, fostering equitable and inclusive data governance strategies. Full article

AI-empowered sweat metabolite analysis is an emerging and open research area with great potential to add a third category to biometrics: chemical. Current biometrics use two types of information to identify humans: physical (e.g., face, eyes) and behavioral (i.e., gait, typing). Sweat offers a promising solution for enriching human identity with more discerning characteristics to overcome the limitations of current technologies (e.g., demographic differential and vulnerability to spoof attacks). The analysis of a biometric trait’s chemical properties holds potential for providing a meticulous perspective on an individual. This not only changes the taxonomy for biometrics, but also lays a foundation for more accurate and secure next-generation biometric systems. This paper discusses existing evidence about the potential held by sweat components in representing the identity of a person. We also highlight emerging methodologies and applications pertaining to sweat analysis and guide the scientific community towards transformative future research directions to design AI-empowered systems of the next generation. Full article

This article aims to present the authors’ perspective regarding the challenges and opportunities of mouse-tracking methodology while performing experimental research, particularly related to the map-reading process. We briefly describe existing metrics, visualization techniques and software tools utilized for the qualitative and quantitative analysis of experimental mouse-movement data towards the examination of both perceptual and cognitive issues. Moreover, we concisely report indicative examples of mouse-tracking studies in the field of cartography. The article concludes with summarizing mouse-tracking strengths/potential and limitations, compared to eye tracking. In a nutshell, mouse tracking is a straightforward method, particularly suitable for tracking real-life behaviors in interactive maps, providing the valuable opportunity for remote experimentation; even though it is not suitable for tracking the actual free-viewing behavior, it can be concurrently utilized with other state-of-the-art experimental methods. Full article

Digital transformation (DT) has attracted the attention of management and organizational scholars in the past decade. In addition, firms are increasingly interested in using DT to obtain a competitive advantage. Nevertheless, studies on DT outcomes remain scarce. Therefore, this study empirically investigated the effect of digital transformation on firm performance by classifying the capabilities required to realize digital transformation, customer experience, and IT innovation. A structured questionnaire was used to collect data from 164 representatives of service sector firms in Saudi Arabia, namely chief information officers, chief transformation officers, and IT managers. Based on the findings of this study, it is evident that digital transformation, customer experience, and IT innovation positively impact a firm’s performance, with customer experience exhibiting the strongest effect. Full article

The social media application TikTok allows users to view and upload short-form videos. Recent evidence suggests it has significant potential for both industry and health promoters to influence public health behaviours. This protocol describes a standardised, replicable process for investigations that can be tailored to various areas of research interest, allowing comparison of content and features across public health topics. The first 50 appearing videos in each of five relevant hashtags are sampled for analysis. Utilising a codebook with detailed definitions, engagement metadata and content variables applicable to any content area is captured, including an assessment of the video’s overall sentiment (positive, negative, neutral). Additional specific coding variables can be developed to provide targeted information about videos posted within selected hashtags. A descriptive, cross-sectional content analysis is applied to the generic and specific data collected for a research topic area. This flexible protocol can be replicated for any health-related topic and may have a wider application on other platforms or to assess changes in content and sentiment over time. This protocol was developed by a collaborative team of child health and development researchers for application to a series of topics. Findings will be used to inform health promotion messaging and counter-advertising. Full article

This study offers a comprehensive examination of the literature surrounding technology and tools in the hospitality industry. A bibliometric analysis was performed on 709 Scopus-indexed publications from 2000 to January 2023, with a focus on identifying key players, institutions, research trends, and the co-occurrence of keywords. The results shed light on the scientific landscape of technology and tools in the hospitality sector, emphasizing the significance of big data and the customer experience in the sharing economy. The study also presents the architecture of new software that offers guests the ability to customize their hotel stay, classified as part of the first cluster in the co-occurrence of keywords analysis. This approach highlights the growing importance of big data and customer experience and makes a valuable contribution to the field by offering a tool for hotel booking customization. Furthermore, the study underscores the importance of collaboration between academic institutions and private companies in providing a mutually beneficial platform that exceeds the expectations of both hotels and guests. Full article

Heralded by promises for the long-awaited economic empowerment of digital art and the paradigmatic shift of creative production, the art market’s fusion with blockchain technologies and the crypto economy has polarized opinions among artists, cultural workers, and economists. Its capricious dynamics and exuberance largely shroud the continuation of the art market’s ideology and the reinforcement of the disturbing political vectors of the crypto/blockchain complex. In this paper, I address several interrelated aspects of art tokenization in a compact and comprehensive critical framework that may be useful for a constructive discourse of contemporary digital art. By focusing on the core poetic principles of artmaking—which concern the historically informed autonomy of expression and socially responsible freedom of creative thinking—I identify some of the prospects for advancing digital art towards an ethically coherent and epistemologically relevant expressive stratum. The opening sections Introduction, Markets, and Contrivances outline the art market, its adoption of crypto technologies, and its influences on the production and expressive modes of digital art. Sections Ideologies and Myths describe the ideological and technical issues of the crypto economy, while Shams and Fallouts delve into the conceptual shortcomings and ethical, political, and creative consequences of the standard art tokenization rhetoric. The closing sections Options and Conclusion present the considerations for a productive assessment of blockchain technologies in digital art and summarize some of the alternative approaches for navigating and interfacing with the crypto art world. Full article

Suboptimal performance of cross-lingual word embeddings for distant and low-resource languages calls into question the isomorphic assumption integral to the mapping-based methods of obtaining such embeddings. This paper investigates the comparative impact of typological relationship and corpus size on the isomorphism between monolingual embedding spaces. To that end, two clustering algorithms were applied to three sets of pairwise degrees of isomorphisms. It is also the goal of the paper to determine the combination of the isomorphism measure and clustering algorithm that best captures the typological relationship among the chosen set of languages. Of the three measures investigated, Relational Similarity seemed to capture best the typological information of the languages encoded in their respective embedding spaces. These language clusters can help us identify, without any pre-existing knowledge about the real-world linguistic relationships shared among a group of languages, the related higher-resource languages of low-resource languages. The presence of such languages in the cross-lingual embedding space can help improve the performance of low-resource languages in a cross-lingual embedding space. Full article

Today, augmented reality (AR) applications are being used in many fields, such as advertising, entertainment, tourism, and education. Location-based augmented reality (AR) is a technology where interactive digital content is associated with real-world locations and their geo-based markers. The merging of the real-world environment with the digital content occurs when the user reaches these locations. Location-based AR applications are typically experienced using mobile devices with the ability to report the user’s location via GPS. These applications are increasingly used in education, since it has been shown that they positively affect student satisfaction and engagement. Furthermore, it is known in the literature that learner satisfaction and engagement increase when gamification and storytelling techniques are incorporated into the educational process. The aim of the study is to present two location-based, educational augmented-reality applications that utilize gamification and storytelling to provide cultural heritage knowledge about a prehistoric lake settlement. The study also aims to provide ideas and guidance to educators who wish to create applications that transform educational visits to archeological sites and museums into engaging augmented-reality experiences. Both applications underwent a preliminary evaluation using a sample of 71 higher-education students and a sample of 58 school students. The findings showed that the applications scored well in aspects such as ease of use, student satisfaction, and perceived educational usefulness. Full article

The rapid proliferation of social media has been redefining every facet of the old marketing and customer engagement tactics, not only for low-end and mass-market products but also for luxury brands. In this context, brands are dealing with the challenge of maintaining a balance between using mass marketing strategies concurrent with accentuating the exclusivity of their offerings. Social media can be considered beneficial if brands employ it to reach the right audience and use the right platform and incorporating the right content. In this work, we propose a sector-specific, integrated, and holistic investigation of the social media strategies of luxury brands together with the impact they generate in terms of the engagement level of the users as an indicator of their success. We provide empirical validation of the methods used in the Italian market of the luxury fashion sector, providing a qualitative and quantitative analysis of the content shared on social media, considering the type, timing, and modality of the sharing. We evaluate consumer-brand engagement in different contexts, including important live events in the field. Full article

In recent years, Natural Language Processing (NLP) has become increasingly important for extracting new insights from unstructured text data, and pre-trained language models now have the ability to perform state-of-the-art tasks like topic modeling, text classification, or sentiment analysis. Currently, BERT is the most widespread and widely used model, but it has been shown that a potential to optimize BERT can be applied to domain-specific contexts. While a number of BERT models that improve downstream tasks’ performance for other domains already exist, an optimized BERT model for tourism has yet to be revealed. This study thus aimed to develop and evaluate TourBERT, a pre-trained BERT model for the tourism industry. It was trained from scratch and outperforms BERT-Base in all tourism-specific evaluations. Therefore, this study makes an essential contribution to the growing importance of NLP in tourism by providing an open-source BERT model adapted to tourism requirements and particularities. Full article

In the context of Industry 4.0, IoRT-aware BPs represent an attractive paradigm that aims to automate the classic business process (BP) using the internet of robotics things (IoRT). Nonetheless, the execution of these processes within the enterprises may be costly due to the consumed resources, recruitment cost, etc. To bridge these gaps, the business process outsourcing (BPO) strategy can be applied to outsource partially or totally a process to external service suppliers. Despite the various advantages of BPO, it is not a trivial task for enterprises to determine which part of the process should be outsourced and which environment would be selected to deploy it. This paper deals with the decision-making outsourcing of an IoRT-aware BP to the fog and/or cloud environments. The fog environment includes devices at the edge of the network which will ensure the latency requirements of some latency-sensitive applications. However, relying on cloud, the availability and computational requirements of applications can be met. Toward these objectives, we realized an in-depth analysis of the enterprise requirements, where we identified a set of relevant criteria that may impact the outsourcing decision. Then, we applied the method based on the removal effects of criteria (MEREC) to automatically generate the weights of the identified criteria. Using these weights, we performed the selection of the suitable execution environment by using the ELECTRE IS method. As an approach evaluation, we sought help from an expert to estimate the precision, recall, and F-score of our approach. The obtained results show that our approach is the most similar to the expert result, and it has acceptable values. Full article

It is hard to trust any data entry on online websites as some websites may be malicious, and gather data for illegal or unintended use. For example, bank login and credit card information can be misused for financial theft. To make users aware of the digital safety of websites, we have tried to identify and learn the pattern on a dataset consisting of features of malicious and benign websites. We treated the problem of differentiation between malicious and benign websites as a classification problem and applied several machine learning techniques, for example, random forest, decision tree, logistic regression, and support vector machines to this data. Several evaluation metrics such as accuracy, precision, recall, F1 score, and false positive rate, were used to evaluate the performance of each classification technique. Since the dataset was imbalanced, the machine learning models developed a bias during training toward a specific class of websites. Multiple data balancing techniques, for example, undersampling, oversampling, and SMOTE, were applied for balancing the dataset and removing the bias. Our experiments showed that after balancing the data, the random forest algorithm using the oversampling technique showed the best results in all evaluation metrics for the benign and malicious website feature dataset. Full article

The need to evaluate museum websites is an issue that has been highlighted by several researchers. In this paper, we focus on museums’ website evaluation and use as a case study the evaluation of natural history museums’ websites. For this evaluation experiment, MCDM methods are combined and compared. The focus of this paper is twofold: (1) checking the consistency of AHP for calculating the weights of criteria and (2) comparing Fuzzy TOPSIS and Fuzzy VIKOR with each other and with a usability evaluation questionnaire. Full article

The digital transformation is a complex and multi-faceted phenomenon, which companies hamper to manage effectively. One particular facet of this phenomenon is the role of managers, which is still underrepresented in research. This study aims to identify and explain why and what managerial practices and competencies are particularly needed to effectively govern through this transformation. We choose the case study methodology as the research design with eight manufacturing companies in Western Europe, where we applied within- and cross-case analyses. Specific barriers for digital transformation and four aggregated managerial practices, such as strategy/organization, collaboration, cross-functionality and data-driven use cases, were identified. These were supported by 13 competencies to facilitate digitalization. We explicate these practices based on the change management theory and provide a model describing the impact of these practices on profitability. This study contributes to the emergent change theory by analyzing practices and competencies that managers should be equipped with to foster digitalization. Full article