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The purpose of the current study is to help remove the obstacles to sustainable production and consumption by revealing the determinants of green consumption behaviour, which is one of the precursors of sustainable economic growth. This study aims to expand the theory of planned behaviour (TPB) model and contribute to the relevant literature by investigating the factors of social media usage, social media marketing and digital marketing interactions that have not been investigated before in relation to green product purchasing behaviour.

This study examines the effect of the extended TPB model on consumers’ intention to buy green products in Turkey, which has a Middle Eastern culture and is a developing economy. In the study, data collected from 409 participants with the questionnaire method were analysed using SmartPLS 4.0 and IBM SPSS 26 statistical programs.

The study findings revealed that all the structural elements of TPB (attitude, subjective norms, personal behaviour controls) and social media marketing and digital marketing interactions contribute to consumers’ green product purchasing behaviour. The study findings also demonstrated that the use of social media is not effective in the purchasing of green products.

Understanding consumers’ perspective on purchasing green products is crucial for policymakers, businesses and marketers, as it helps formulate appropriate strategies to support sustainable economic growth. In this respect, this study has important implications for sustainable consumption and production. In addition, to the best of the authors’ knowledge, the study is the first to examine consumers’ green product purchasing behaviour in the context of sustainable economy.

Artificial intelligence (AI) is shaping the future of the marketing world. This study is the first to examine the effect of AI marketing efforts, brand experience (BE) and brand preference (BP) in light of the stimulus-organism-response (SOR) model.

The data collected from 398 participants by the questionnaire method were analyzed by SEM (structural equation modeling) using Smart PLS 4.0 and IBM SPSS 26 programs.

We find that four SOR elements of AI marketing efforts (information, interactivity, accessibility and personalization) positively impact bank customer BE, BP and repurchase intention (RPI). Further, we find that BE plays a mediator role in the relationship between AI marketing efforts, RPI and BP.

The findings of the study have significant implications for the bank marketing literature and the banking industry, given the limited evidence to date regarding AI marketing efforts and bank–customer relationships. Moreover, the study makes important contributions to the AI marketing and brand literature and helps banks increase customer experience with artificial intelligence activities and create long-term relationships with customers.

The purpose of this study is to investigate and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.

This research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.

The study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.

The study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.

The research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.

The research holds significant social implications by contributing insights that extend beyond the realm of sports.

The originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.

The purpose of this paper is to introduce a novel cross (+) type yoke with hybrid electromagnets and new reluctance modeling to precisely calculate attraction force is given.

The comparison of attraction force and torque analyses between the proposed formulation and the existing formulation in the literature is comparatively presented. For the correctness of the force and torque values calculated in the model created, the system was created in ANSYS Maxwell and its accuracy was proved by making analyses. The maglev carrier system is inherently unstable from the point of view of control engineering. For that, it needs an active controller to eliminate this instability. For the levitation of the carrier system, it is necessary to design a controller in three axes (z, α and β). I-PD controller was designed for the air gap control of the carrier system in three axes and the controller parameters were determined by the canonical method.

While the new formulation proposed in the modeling of the carrier system has a maximum error of 1.03%, the existing formula in the literature has an error of 16.83% in the levitation distance point.

A novel cross-type hybrid carrier system has been proposed in the literature. With the double integral used in modeling the system, it takes a long time to solve symbolically, and it is difficult to simulate dynamic behavior in control validation. To solve this problem, attraction force and inclination torque values are easily characterized by new formulation and besides the simulations are conducted easily. The experimental setup was manufactured and assembled, and the carrier system was successfully levitated, and reference tracking was performed without overshoot.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

This study aims to investigate the challenges associated with the integration and implementation of blockchain technology in the education sector. The primary objective is to identify and categorize these challenges using the Technology-Organization-Environment (TOE) framework, shedding light on the technological, organizational and environmental factors that influence the adoption of blockchain solutions in educational settings.

A comprehensive literature review was conducted across multiple databases including Science Direct, Web of Science, Springer, IEEE Xplore and MDPI. The selected articles were analyzed based on the TOE framework to categorize challenges from the technological, organizational and environmental perspectives. The methodology involves a systematic search, analysis and synthesis of relevant articles to provide an in-depth understanding of the challenges hindering blockchain adoption in education.

This review revealed a predominant focus on technological challenges, emphasizing scalability issues, integration complexities, security and privacy concerns and data immutability. However, there is a notable research gap in the exploration of organizational and environmental challenges. The scarcity of studies addressing these perspectives may impact acceptance and resistance to blockchain adoption in educational settings.

This study contributes to the literature by systematically categorizing and analyzing the challenges associated with blockchain implementation in education using the TOE framework. It identifies the need for further research on organizational and environmental aspects, addressing a significant gap in the current scholarship on blockchain adoption in educational institutions.

To encourage entrepreneurship, which accelerates economic growth by increasing employment opportunities and competitiveness, stakeholders must conduct studies and develop policies that consider both the current situation and future expectations. This study aims to examine the environmental and personal factors that influence students’ entrepreneurial intentions (EIs), using a model based on the theory of planned behaviour (TPB) and social cognitive theories (SCT).

This study proposed an institutional framework demonstrating contextual features to achieve this objective. This theoretical framework is evaluated using a sample of 375 university students in Türkiye.

The empirical findings can help policymakers develop effective policies to encourage entrepreneurship.

The study focuses on EIs; it is possible that even if a participant indicated a high EI in the survey, they will ultimately pursue a completely different career path.

The study also contributes to entrepreneurship literature studies investigating the relationships between the TPB and SCT.

By testing specific hypotheses for Türkiye, this study contributes to the demand for entrepreneurship research in countries that are major global players but have vastly different sociocultural contexts than Western countries.

The study draws a theoretical model that explains the factors affecting the EIs of university students and attempts to explain the EIs of university students with and without business education within this model.

This study examines the effect of some motivations of domestic tourists visiting Mugla related to the consumption of local Mugla food on behavioural intentions. The mediating impact of destination image on the behavioural intention effect of tourists' local food consumption motivations is also investigated.

A quantitative approach was adopted for the study’s objectives. The population of the study consisted of domestic tourists visiting Mugla, Turkiye. The sample was determined by the convenience sampling method, which is one of the non-probability-based sampling techniques, because of the characteristics of the research population. Around 390 questionnaires collected from tourists visiting Mugla were analysed. The structural equation model (SEM) was applied for testing the research hypotheses, and Bootstrap technique was used for mediation effect analysis.

Sensory motivation, which is one of the local food consumption motivations, was found to have a statistically significant effect on behavioural intention. No significant effect was found between other motivation factors (excitement, cultural, interaction and health) and behavioural intention. Also, there is a partial mediation effect of destination image in the effect of sensory motivation on behavioural intention. Whilst sensory value alone explains 28% of the effect on behavioural intention, it explains 31% of the effect on behavioural intention together with destination image.

The development of food culture in a destination and the increase in awareness of the food culture of the destination through different consumer components are of great importance for regional tourism. This research contributes to the gastronomy tourism literature in terms of examining the local food consumption motivations that are effective in visitors' experiences of Mugla food, the effect of these motivations on revisits and the role of Mugla destination image.

Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes.

This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF).

Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis.

The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.