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The choices that international students make regarding abroad study destination selection or leave the host country after graduation are influenced by a variety of factors that are both related to positive and negative aspects of the host country.

This study builds on the push-pull factor theory and examines the factors that influence international students' decision to choose abroad study destination (Finland) or leave the country after their graduations. The data were collected through an online survey of 195 international students currently studying in Finland and were analysed using partial least squares structural equation modelling (PLS-SEM) technique. This method offers a flexible and robust approach to test relationships, particularly in situations where sample size and the conceptual model are small and complex.

The results show that international students' choice of study destination (Finland) is influenced by the host country's quality of life, academic excellence and economic factors such as salary and benefits. Unfamiliarity with the culture and language barriers have a negative impact on their decisions to stay in the host country after graduation.

By utilising a comprehensive analysis of both push and pull factors in relation to the host country, this study unveils a novel perspective in the field of international student mobility. The results provide insights to the institutional leaders and policymakers into how to attract and retain international students by focusing on the factors that matter most to international students. To attract more international students, higher education institutions (HEIs) should include career development activities, e.g. job fairs, language training, scholarships and internships in their curriculum. Moreover, it provides recommendations on how to create a welcoming and supportive environment that promotes academic excellence and career development.

This article aims to develop a measurement scale for assessing agripreneurial competencies relevant to emerging economies in alignment with the SDG2 of the UN Sustainable Development Goals 2030.

The scale development procedure includes item development and refinement, data collection, reliability and validity tests and scale purification with exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

The validated scale carries eight dimensions of competencies: Agreeableness (AG), Technological Competency (TC), Competitive Spirit (CS), Innovativeness (IN), Self-Confidence (SC), Social Responsibility (SR), Conscientiousness (CO) and Leadership (LS). The analysis puts forth a good and fit model, and the new scale reports sufficient convergent and discriminant validity.

This study is focused on the agripreneurial competencies of individual agripreneurs; institutional agripreneurs are excluded from the study.

Identifying prominent agripreneurs using the scale developed from this study will aid in allocating various government and non-governmental organisations' assistance to agripreneurs. Since developing economies rely heavily on agriculture, any positive contribution can help alleviate poor economic growth, end hunger, and promote sustainable agriculture (SDG 2 of 2030).

Though several scales for measuring entrepreneurial competencies are available, there is no standard scale to measure agripreneurial competencies. This article presents the development and validation of a measurement scale to assess the major competencies of agripreneurs that influence agripreneurship performance.

This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.

This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.

With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.

This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.

This study proposes a new, innovative framework summarising the balance between AI and academic integrity.