Exploring Constraints and Sport Experiences: A Case Study of Adult Workers in Singapore.

Although rapid economic growth can produce various positive outcomes, the fast-paced society that inevitably accompanies it often results in longer working hours and higher stress levels, leading to reduced participation in sport activities among employees. To better understand this phenomenon, we aimed to explore the constraints and experiences of adult workers. We collected data from adult workers in Singapore who desired to participate in sport activities but were unable to do so due to various constraints. A total of 10 individuals were purposefully selected for interviews. This study identified four key themes: (a) factors causing the reduction of sport participation opportunities, (b) changes in sport activities and satisfaction, (c) nostalgic feelings associated with sport activities, and (d) the role of nostalgia in enhancing sport involvement. The findings of this study suggest the possibility of adapting the conceptual model of nostalgia to sport activities, as well as identifying four factors that hinder employees from engaging in sport activities.

"Extending the Technology Acceptance Model (TAM) to Predict University Students' Intentions to Use Metaverse-Based Learning Platforms".

Metaverse, which combines a number of information technologies, is the Internet of the future. A media for immersive learning, metaverse could set future educational trends and lead to significant reform in education. Although the metaverse has the potential to improve the effectiveness of online learning experiences, metaverse-based educational implementations are still in their infancy. Additionally, what factors impact higher education students' adoption of the educational metaverse remains unclear. Consequently, the aim of this study is to explore the main factors that affect higher education students' behavioral intentions to adopt metaverse technology for education. This study has proposed an extended Technology Acceptance Model (TAM) to achieve this aim. The novelty of this study resides in its conceptual model, which incorporates both technological, personal, and inhibiting/enabling factors. The empirical data were collected via online questionnaires from 574 students in both private and public universities in Jordan. Based on the PLS-SEM analysis, the study identifies perceived usefulness, personal innovativeness in IT, and perceived enjoyment as key enablers of students' behavioral intentions to adopt the metaverse. Additionally, perceived cyber risk is found as the main inhibitor of students' metaverse adoption intentions. Surprisingly, the effect of perceived ease of use on metaverse adoption intentions is found to be insignificant. Furthermore, it is found that self-efficacy, personal innovativeness, and perceived cyber risk are the main determinants of perceived usefulness and perceived ease of use. While the findings of this study contribute to the extension of the TAM model, the practical value of these findings is significant since they will help educational authorities understand each factor's role and enable them to plan their future strategies.

Impacts of the COVID-19 pandemic on household food waste behaviour: A systematic review.

Food waste has adverse economic, social, and environmental impacts and increases the prevalence of food insecurity. Panic buying at the beginning of the COVID-19 outbreak raised serious concerns about a potential rise in food waste levels and higher pressure on waste management systems. This article aims to investigate the impact of COVID-19 on food waste behaviour and the extent to which it occurs using the systematic review method. A total of 38 articles were identified and reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The findings showed that the COVID-19 pandemic led to reductions in household food waste in most countries. Several changes in shopping and cooking behaviours, food consumption, and managing inventory and leftovers have occurred due to COVID-19. Based on these insights, we predicted that some desirable food-management habits would be retained, and others would roll back in the post-COVID-19 world. The review contributes to the food waste literature by offering a comprehensive overview of behavioural changes during the COVID-19 pandemic and future research directions.

People's expectations and experiences of big data collection in the Saudi context.

Big data and machine learning technologies facilitate various business intelligence activities for businesses. However, personal data collection can generate adverse effects on consumers. Big data collection can compromise people's sense of autonomy, harming digital privacy, transparency and trust. This research investigates personal data collection, control, awareness, and privacy regulation on people's autonomy in Saudi. This study used a hybrid analytical model that incorporates symmetrical and asymmetrical analysis via fuzzy set qualitative comparative analysis (fsQCA) to analyze consumer sense of autonomy regarding big data collection. The symmetrical shows that 'Control' had the most significant influence on people's autonomy, followed by 'Big data collection' and 'Awareness'. The fsQCA shows 84% of the variation, explaining the people's autonomy.

The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis.

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.