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Proponents of micro-credentials envision them as vehicles for upskilling or re-skilling individuals. The study examines how integrating micro-credentials in the higher education ecosystem enhances employability. It aims to offer insights from the perspective of stakeholders who may benefit from these credentials at an institutional or individual level.

Online in-depth interviews are conducted with 65 participants from India, Nigeria, the United Arab Emirates and the United Kingdom to explore how micro-credentials can be a valuable addition to the higher education ecosystem. A multi-stakeholder approach is adopted to collect data.

The analysis highlights two possible methods of integrating micro-credentials into the higher education ecosystem. First, micro-credentials-driven courses can be offered using a blended approach that provides a flexible learning path. Second, there is also the possibility of wide-scale integration of micro-credentials as an outcome of standalone online programs. However, the effectiveness of such programs is driven by enablers like student profiles, standardization and the dynamics of the labor market. Finally, the study stipulates that micro-credentials can enhance employability.

The study's findings suggest that, for successful integration of micro-credentials, an operational understanding of micro-credentials, their enablers and strategic deliberation are critical in higher education. Institutions must identify the determinants, address technological limitations and select a suitable delivery mode to accelerate integration. However, micro-credentials can augment employability, considering the increasing emphasis on lifelong learning. An overview of the findings is presented through a comprehensive framework.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

In this paper, the philosophy of John Rawls, known as “Justice as Fairness,” is discussed. This philosophy examines the responsibilities social actors hold toward their societal organizations. From an Islamic perspective, justice is pivotal in safeguarding collective interests, aligning with Rawls' conviction that just societies nurture happiness and foster well-being across various life aspects. To achieve customer welfare, our viewpoint underscores the importance of justice in reflecting on consumer well-being from both Rawls’ theory and Islamic perspectives.

We adopt a conceptual approach where secular views of Rawls’ “Theory of Justice” are merged with the Islamic view, resulting in novel insights regarding the hermeneutics involved in the notion of justice and the preservation of consumer well-being.

Our analysis reveals that John Rawls' “A Theory of Justice” aligns with the Islamic perspective in several respects yet diverges in others, notably in the concepts of pre-creation consciousness and divine guidance. These distinctions are emphasized in our paper.

Our paper presents a perspective on justice founded on the concepts of the “Original Position” and the “Veil of Ignorance.” The commentary explores consumer well-being by integrating Rawls' principles with an analysis that elucidates the role justice plays in enhancing societal welfare.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

This study aims to synthesize and conceptualize the highly fragmented yet important literature on racial discrimination in entrepreneurship.

A bibliometric analysis and literature review were performed that involved 523 articles containing 26,926 references.

The bibliometric analysis identified three dominant research themes that comprehensively illustrate the state of research in this domain: strategic, sociocultural and individual-level perspectives. The synthesis of extant literature helped in formulating a holistic conceptual model that portrays the genuineness of racial discrimination in entrepreneurship. The sources, factors and impact of racial discrimination faced by entrepreneurs were identified. Based on the review and analysis of keywords, certain fruitful future research directions were formulated that will take the field forward.

This work is the first attempt to review the literature that narrows down the focus to racial discrimination in entrepreneurship (from other discriminations such as gender, cultural and religious discrimination) as one form of discrimination due to its unique origins and consequences.

Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.