Purpose: This paper aims to establish a comprehensive structural framework for smart stadiums, examining their integration as key elements of smart tourism destinations and investigating their impact on visitors' intention to visit.Design/methodology/approach: The study reviews existing literature and corporate sources to identify emerging smart tools and determine key dimensions of smart stadiums. A research model was developed, and partial least square-structural equation modeling (PLS-SEM) was employed to analyze the relationships between these dimensions and their impact on visit intention.Findings: The proposed framework identifies key components such as sustainability, commercial opportunities, safety and security, customer service, entertainment and engagement and operational efficiency. The analysis indicates that sustainability has the highest impact on visit intention, followed by commercial opportunities and safety and security. Customer service and entertainment significantly enhance the visitor experience, while operational efficiency, though important, has a lower direct impact on visit intention.Practical implications: Implementing this framework can improve fan engagement, resource management and sustainability in stadium operations, thereby enhancing the overall tourism appeal of host cities.Originality/value: This study offers a structured approach to incorporating smart technologies in stadiums, emphasizing their role in advancing smart tourism destinations and providing empirical insights into the factors influencing visit intention.

Purpose: This research explores the intricate relationship between fashion, identity and religion in Muslim hijabi fashion. More specifically, it examines the impact of global brands’ inclusion of Muslim veiled females in their fashion lines. Design/methodology/approach: Employing a qualitative approach, the research involved 20 in-depth interviews with Muslim women who wear hijab and those who dress modestly without it. Participants were chosen through judgemental sampling, focusing on individuals interested in fashion and brand trends. The study investigated perceptions of brand strategies, the role of influencers and consumer responses within this context. Findings: The research suggests consumers have different viewpoints about these brands that are compliant with hijabi fashion. This incorporation benefits Muslim women by enhancing their status and recognition, hence fostering the growth of the Muslim fashion industry. In Western society, it promotes a sense of belonging and individuality. Certain individuals see the exploitation of their religious affiliation for financial gain, thereby raising doubts about these companies’ comprehension of Islamic modesty. This has engendered a sense of distrust and suspicion. Originality/value: This research illuminates the intricate connection between religion, fashion and market dynamics in the realm of Muslim hijabi fashion. This paper explores the multifaceted responses of Muslim women to the marketing methods used by global fashion designers, which include a delicate balance between cultural representation and commercial exploitation. The results shed light on the ways in which fashion both empowers and creates divisions among religious and cultural groups.

Solving global optimization problems is a significant challenge, particularly in high-dimensional spaces. This paper proposes a selective multistart optimization framework that employs a modified Latin Hypercube Sampling (LHS) technique to maintain a constant search space coverage rate, alongside Interval Arithmetic (IA) to prioritize sampling points. The proposed methodology addresses key limitations of conventional multistart methods, such as the exponential decline in space coverage with increasing dimensionality. It prioritizes sampling points by leveraging the hypercubes generated through LHS and their corresponding interval enclosures, guiding the optimization process toward regions more likely to contain the global minimum. Unlike conventional multistart methods, which assume uniform sampling without quantifying spatial coverage, the proposed approach constructs interval enclosures around each sample point, enabling explicit estimation and control of the explored search space. Numerical experiments on well-known benchmark functions demonstrate improvements in space coverage efficiency and enhanced local/global minimum identification. The proposed framework offers a promising approach for large-scale optimization problems frequently encountered in machine learning, artificial intelligence, and data-intensive domains.