Sustainable leadership plays a pivotal role in fostering long-term economic, social, and environmental development. As businesses increasingly integrate sustainability into their core strategies, leaders must adopt approaches that align profitability with ethical responsibility. This paper explores how sustainable leadership contributes to sustainable development by examining key leadership principles, decision-making frameworks, and corporate strategies. The discussion highlights the shift from short-term financial goals to long-term value creation, emphasising stakeholder engagement, ethical governance, and innovation. By integrating environmental, social, and governance (ESG) principles, leaders can enhance resilience, drive corporate sustainability initiatives, and positively impact communities. The study also underscores the importance of knowledge sharing, organisational culture, and adaptability in embedding sustainability into business practices. Through a systematic bibliometric literature review, this research provides insights for executives, policymakers, and academics seeking to navigate the evolving landscape of sustainability-driven leadership. The findings underscore the necessity of aligning leadership strategies with global sustainability imperatives.

Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, but there seems to be a lack of clarity and a holistic view regarding all its advantages, especially concerning the benefits of AutoML for companies. In order to deeply understand how AutoML can improve businesses, a systematic review examined the bibliometric literature of this field, analyzing 74 academic and scientific documents from the Scopus database. Results showed that AutoML (i) reduces the time and resources needed to develop and deploy machine learning models, (ii) accelerates decision-making and enables quicker responses to market changes, (iii) empowers businesses to build accurate predictive models using sophisticated algorithms, (iv) optimizing model performance for reliable insights and better outcomes, and (v) enhances accessibility by reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. In sum, AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success.

Sustainable leadership plays a pivotal role in fostering long-term economic, social, and environmental development. As businesses increasingly integrate sustainability into their core strategies, leaders must adopt approaches that align profitability with ethical responsibility. This paper explores how sustainable leadership contributes to sustainable development by examining key leadership principles, decision-making frameworks, and corporate strategies. The discussion highlights the shift from short-term financial goals to long-term value creation, emphasising stakeholder engagement, ethical governance, and innovation. By integrating environmental, social, and governance (ESG) principles, leaders can enhance resilience, drive corporate sustainability initiatives, and positively impact communities. The study also underscores the importance of knowledge-sharing, organisational culture, and adaptability in embedding sustainability into business practices. Through a systematic bibliometric literature review, this research provides insights for executives, policymakers, and academics seeking to navigate the evolving landscape of sustainability-driven leadership. The findings underscore the necessity of aligning leadership strategies with global sustainability imperatives.

Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, making advanced analytics accessible to a broader range of organizations. By automating complex tasks like data preprocessing, model selection, and hyperparameter tuning, AutoML reduces the time and resources needed to develop and deploy machine learning models. This accelerates decision-making and enables quicker responses to market changes. AutoML empowers businesses to build accurate predictive models using sophisticated algorithms, optimizing model performance for reliable insights and better outcomes. A key advantage of AutoML is its accessibility; even organizations without a dedicated data science team can leverage machine learning, reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success. The systematic review will examine the bibliometric literature on how AutoML can boost business, analyzing 74 academic and scientific documents from the Scopus database.

The importance of ethics practiced by influencers must be considered. Influencers significantly impact their audience and can shape public opinion, attitudes, and behaviors. Ethical behavior helps influencers establish and maintain trust with their audience. Influencers are transparent, honest, and authentic in their content, which fosters trust and credibility. Ethical influencers are transparent about their partnerships, collaborations, and sponsored content. Ethical influencers have the potential to inspire and empower their audience to make a difference. They can influence their audience to adopt positive behaviors and attitudes, promoting social norms conducive to a more ethical and compassionate society. Influencers prioritizing ethical behavior contribute to a healthier online community and set a positive example for others in their field. Based on the above, it is intended to systematically review the bibliometric literature on ethics practiced by influencers using the Scopus database with the analysis of 134 academic and/or scientific documents.

The concept of Smart Cities has gained global attention as a strategy to address the challenges of rapid urbanization, resource depletion, and environmental sustainability. This paper explores the integration of smart technologies and data-driven systems in urban environments to enhance sustainable urban development. Through a systematic bibliometric review, we examine how smart city initiatives leverage digital infrastructure, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to optimise resource use, reduce carbon emissions, improve mobility, and enhance the quality of life for urban residents. We also investigate the role of policy frameworks, governance, and stakeholder engagement in fostering sustainable and inclusive urban ecosystems. Based on the above, it is intended to systematically review the bibliometric literature on Smart Cities and Sustainable Urban Development in the Scopus database with the analysis of 94 academic and/or scientific documents. Key challenges, including data privacy concerns, digital inequality, and the financial and ethical implications of smart city projects, are discussed. The findings suggest that while smart technologies can significantly contribute to sustainability goals, a holistic approach incorporating social, environmental, and economic dimensions is essential for long-term success. This paper provides strategic insights for urban planners, policymakers, and researchers on balancing technological innovation with sustainability in the context of future urban development.