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<title>Entrepreneurship and Sustainability Issues</title>
<link>http://jssidoi.org/jesi/</link>
<description>Entrepreneurship and Sustainability Issues is a peer-reviewed international issue which publishes original research articles.</description>
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<rdf:li rdf:resource="http://jssidoi.org/articles/articles/view/6665" />
<rdf:li rdf:resource="http://jssidoi.org/articles/articles/view/6664" />
<rdf:li rdf:resource="http://jssidoi.org/articles/articles/view/6663" />
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<dc:date>2026-07-18T00:05:44+00:00</dc:date>
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<item rdf:about="http://jssidoi.org/articles/articles/view/6666">
<title>AI-driven student profiling: a cross-industry review and future research directions on machine learning for admissions and retention</title>
<link>http://jssidoi.org/articles/articles/view/6666</link>
<description>Higher education institutions are increasingly facing challenges in student recruitment and retention, necessitating the development of data-driven segmentation and profiling models. Traditional segmentation approaches rely heavily on historical data and often fail to capture shifts in applicant behaviour. In contrast, AI-based techniques enable real-time analysis of interactions, supporting more adaptive and personalised recruitment strategies. Across fields like marketing, finance, and e‑commerce, researchers have shown that clustering, supervised learning, and hybrid ML models can meaningfully improve segmentation tasks. This article explores the application of such methodologies in the context of higher education. A systematic literature review (SLR) and a Weighted Sum Model (WSM) were applied to evaluate and rank the relevance and adaptability of segmentation techniques used in the analysed studies. Results indicate that K-Means and Random Forest are widely applied in student segmentation and performance prediction. At the same time, hybrid models, such as K-Means combined with decision trees, offer improved accuracy and interpretability. However, real-time behavioural analytics (e.g., Google Analytics) and natural language processing (NLP) methods remain underexplored. Overall, the findings suggest that AI‑driven segmentation models can enhance student engagement, academic success prediction, and strategic enrolment management. Future work will focus on integrating real-time behavioural data, refining segmentation models, and improving predictive analytics for university admissions and retention strategies.</description>
<dc:date>2026-03-19T00:00:00+00:00</dc:date>
</item>
<item rdf:about="http://jssidoi.org/articles/articles/view/6665">
<title>Enhancing employee engagement through transformational leadership and AI use: evidence from the healthcare sector in Saudi Arabia</title>
<link>http://jssidoi.org/articles/articles/view/6665</link>
<description>The research examines how transformational leadership and AI usage affects healthcare employee engagement in Saudi Arabia. Additionally, it investigates how employee voice moderates the relationship between transformative leadership and employee engagement. The data was collected from 408 healthcare employees who worked at hospitals and healthcare facilities throughout the Kingdom of Saudi Arabia. The researcher utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the collected data. The findings showed that the four dimensions of transformational leadership—inspirational motivation, idealized influence, intellectual stimulation, and individualized consideration—significantly influence employee engagement. The findings further show that the usage of AI technologies can lead to more employee engagement. Furthermore, the findings confirm the moderating role of employee voice in enhancing the relationship between transformational leadership and employee engagement. It concludes that employee voice enables transformational leaders to achieve higher employee engagement through settings which support open dialogue and staff involvement. This article contributes to knowledge by combining leadership theory with employee voice concepts and digital transformation elements into a single model which provides healthcare leaders and policymakers with practical methods to boost employee engagement through leadership development and AI-powered workplace technology. Specifically, it underscores the importance of transformational leadership and AI technologies in enhancing employee engagement.</description>
<dc:date>2026-05-15T00:00:00+00:00</dc:date>
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<item rdf:about="http://jssidoi.org/articles/articles/view/6664">
<title>Sustaining luxury brand presence across different generations: strategies to enduring distinction and growth</title>
<link>http://jssidoi.org/articles/articles/view/6664</link>
<description>This study aims to analyse how luxury companies can remain timeless and compatible with the ideals of new generations. Additionally, it aims to identify what are the strategies that luxury brands are adopting to attract the new generation (Gen Z). A qualitative study was conducted with brand activation, public relations and advisory professionals who work with the luxury market, who would be able to tell us what strategies are used and what the weight of the distinction between generational cohorts is. The findings reveal that younger generations are more technologically and old approaches such as advertising through magazines or paper press are already not the focus of the marketing budget available. Digital presence through social media profiles, ads and partnerships with influencers has become the focus for keeping brands in touch with their audience. In addition, it is important to provide experiences for the public. For Gen Z, it is important to take a stand on social and environmental issues and to show concern for larger themes. This generation cares about what brands have to offer other than their name, they are looking for authenticity and humanism. The originality of this study lies on analysis of how luxury brands evolve to meet the expectations of Gen Z, a demographic with unique values and purchasing behaviors compared to past generations. While existing research often focuses on digital marketing or generational consumer trends, this study distinguishes itself by analysing the interplay between tradition and innovation. It offers industry perspectives, highlights the shift toward experiential and values-driven marketing, and explores changes in marketing budget allocation. By integrating real-world industry insights with academic analysis, this research adds to the conversation on luxury brand adaptation and provides practical guidance for brand managers navigating the evolving luxury landscape.</description>
<dc:date>2026-05-08T00:00:00+00:00</dc:date>
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<item rdf:about="http://jssidoi.org/articles/articles/view/6663">
<title>Holistic neuromarketing as a conceptual framework for stakeholder behaviour and communication in environmental and resource-based projects</title>
<link>http://jssidoi.org/articles/articles/view/6663</link>
<description>This study addresses the need to understand better stakeholder behaviour and communication processes in environmental and resource-based projects, where social acceptance and securing a social licence to operate are critical to project success. Traditional marketing approaches, although effective in coordinating organisational activities, often fail to capture the cognitive and emotional mechanisms that influence stakeholder decision-making under conditions of uncertainty and perceived risk. This paper aims to develop a conceptual framework of holistic neuromarketing that integrates holistic marketing principles with insights from neuroscience. The study is based on a conceptual research design that uses a systematic literature review, theoretical abstraction, and conceptual modelling. The proposed framework captures the relationships between communication inputs, cognitive-emotional processing, perceived risk, trust formation, and stakeholder behavioural responses. The findings indicate that integrating neuromarketing into holistic marketing enhances the analytical capacity of traditional frameworks by incorporating subconscious and affective dimensions of behaviour. The framework provides a structured basis for improving communication effectiveness, reducing stakeholder resistance, and supporting stakeholder acceptance, while also highlighting ethical considerations. The study contributes to interdisciplinary research and offers a foundation for future empirical investigation.</description>
<dc:date>2026-05-06T00:00:00+00:00</dc:date>
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<item rdf:about="http://jssidoi.org/articles/articles/view/6662">
<title>Athletes’ dropout from Slovak football – social impact of the sport determined via its geographical perspective</title>
<link>http://jssidoi.org/articles/articles/view/6662</link>
<description>The article focuses on athletes’ decision to leave organized football in Slovakia. It reveals the impact of this sport on society from a sports geography perspective, understood as the spatial distribution of football participation, accessibility of clubs and infrastructure, and regional disparities affecting athletes’ retention, which is still underdeveloped in research. We aim to explore selected aspects to understand the current decrease in the young athletes’ participation in the Slovak Football Association’s (SFA) competitions. The quantitative methods applied to the data include logistic regression and correlation analysis, with the models being assessed by their accuracy, sensitivity, and specificity. We used an exclusive data set provided directly by the SFA, as well as data extracted from the Sportnet API. This created a unique primary material for our research needs, complemented by official Sport in Figures reports (2015–2021) as secondary data sources. We identified variables significantly influencing the likelihood of athletes staying in organized football (including the age of starting with football, basic line-up, and yellow cards). Based on the results, we defined recommendations for effective athletes’ retention strategies so that the sport’s social impact, quantified by its geographical dispersion, can be maximized.</description>
<dc:date>2026-04-29T00:00:00+00:00</dc:date>
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