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This study aims to identify the determining factors of perceived altruism and attitude toward an inclusive sponsorship activation, as well as the impact of these variables on the attitude toward the sponsor.

Online survey data were obtained from 1,228 respondents from France, the UK and South Africa. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).

The results show that the cause-brand fit has a strong positive impact on the perceived altruism toward the motivations underlying inclusive activation, while skepticism toward advertising has a very weak negative impact. In return, perceived altruism positively influences the attitude toward inclusive activation and sponsor attitude. Furthermore, this attitude toward inclusive activation is positively influenced by involvement in women’s soccer and France men’s national football team identification. The attitude toward inclusive activation also positively influences the attitude toward sponsor attitude. However, contrary to what had been advanced, identification with the France women’s national football team and the nationality of the respondents (French, British or South African) had no impact on the attitude toward inclusive activation, while the perceived importance of the cause had very weak impact on attitudes toward inclusive activation.

This study highlights the potential benefits of investing in inclusive sponsorship activations, particularly with respect to their positive impact on consumer attitude toward sponsor attitude. It also highlights the importance of establishing, in advance, a strong association between the brand image and the cause supported, so that the motivations underlying the inclusive activations are perceived as more altruistic.

The purpose of this study was to examine how participants experienced and perceived an M.Ed. program that had a school-based design. In particular, the authors sought to understand: (1) how participants experienced being in a school-based cohort and (2) whether and how participants experienced the three designated tenets of the M.Ed. program: teacher inquiry, social justice and student engagement and motivation.

This qualitative study used semi-structured focus group interviews (n = 7) to examine teachers’ perceptions, using a constant comparative method (Corbin & Strauss, 2008) of open coding to analyze the data and determine emergent themes.

The findings indicate the design of this school-based M.Ed. program provided both social and academic benefits including strengthening teachers’ working relationships and their understanding of students outside their own classroom and a transfer from individual learning to organizational benefit. Teachers positively perceived the three tenets that guided the first year of the program, especially the ability to study social justice and student motivation in depth.

This study has implications for teacher education and retention as well as how boundary spanning roles in PDS schools can impact graduate students’ experiences in schools. Given the current teacher shortage concerns, it is important to understand how M.Ed. programs can be designed with teacher needs at the forefront so learning is relevant and rewarding, both to the individual and the school.

While there are many studies that examine the use of cohorts in education, particularly in doctoral programs, few, if any, studies examine a school-based cohort M.Ed. program for practicing teachers. This study also puts a unique spotlight on how boundary-spanning roles can benefit not only teacher candidates but also practicing teachers in their M.Ed. programs.

This study aims to examine the literature from the Web of Science database published on board committees between 2002 and 2023 and outline the quantitative summary, journey of board committees’ research and suggest future research directions.

This study examines bibliometric-content analysis combined with a systematic literature review of articles on board committees to document the summary of the field. The authors used co-citation, co-occurrence and cluster analysis under bibliometric-content analysis to present the field summary.

Board committee composition, such as their gender, independence and expertise, as well as factors affecting corporate governance, such as reporting quality, earnings management and board monitoring, all have a significant impact on board committee literature. The field is getting growing attention from authors, journals and countries. Nevertheless, there is a need for further exploration in areas like expertise, member age and tenure, the economic crisis and the nomination and remuneration committee, which have not yet received sufficient attention.

This paper has both theoretical and practical contributions. From a theoretical perspective, this study substantiates the prevalence of agency theory within board committee literature, reinforcing the foundational role of agency theory in shaping discussions about board committees. On practical ground, the comprehensive overview of board committee literature offers scholars a road map for navigating this field and directing their future research journey. The identification of research gaps in certain areas serves as a catalyst for scholars to explore untapped dimensions, enabling them to strengthen the essence of the committees’ performance.

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.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.