Search results

Employability skills training programmes are an effective means for reducing unemployment rates. Such programmes also have the potential to improve the general well-being (e.g. self-efficacy) of disadvantaged individuals, however, reliable longitudinal evaluations of the psychological benefits of such programmes are limited. The present study evaluated the impact of an employability programme offered to disadvantaged adults in North-West England on self-efficacy. Additionally, the study aimed to identify risk factors for programme disengagement to identify at-risk groups that require further support.

Secondary longitudinal data pertaining to the background characteristics, programme engagement and self-efficacy scores (repeatedly measured on a monthly basis) of 308 programme users were analysed.

Results demonstrated that employability programme engagement significantly increased self-efficacy scores. Additionally, the findings suggested that individuals with mental health and learning difficulties were more likely to disengage from the programme. The findings demonstrate that employability programmes can have a positive impact on the well-being of individuals from disadvantaged backgrounds, however, prolonged engagement is needed for which some individuals require further support with.

The present study analysed longitudinal data from a diverse sample of disadvantaged individuals to reliably evaluate psychological outcomes from employability training programmes.

The teenager community is the most affected community by cybercrime in the COVID-19 era. Increasing social networks and facilitating teenager access to the Internet have increased the probability of cybercrimes. On the other hand, entertainment such as mobile and computer games is top-rated among teenagers. Teenagers' tendency to cybercrime may be influenced by individual, parent, social, economic and political factors. Studying the impact of social networks, mobile games and parents' religious attitudes on teenagers' tendency to cybercrimes in the COVID-19 era is the primary goal of this paper.

The outbreak of COVID-19 caused a considerable change in the world and the lifestyle of all people. Information and Communication Technology (ICT) was also affected by the special conditions of this virus. Changes in ICT and rapid access to it have empowered individuals and organizations, and people have increased civic participation and interaction through ICT. However, the outbreak of COVID-19 has created new challenges for the government and citizens and may cause new crimes. Cybercrime is a type of crime that occurs in a cyber environment. These crimes range from invasions of privacy to crimes in which the offender vaguely paralyzes the macroeconomic. In this research, 265 students of high schools and universities are used for collecting data by utilizing a survey. Measuring actions have been done in all surveys employing a Likert scale. The causal pattern is assessed through a constructional equation modeling procedure to study the scheme's validity and reliability.

The outcomes have indicated that social networks have no significant relationship with teenagers' tendency to cybercrimes in the COVID-19 era. Mobile games have a mild effect on teenagers' tendency to cybercrimes in the COVID-19 era, and parents' religious attitudes significantly impact teenagers' tendency to cybercrimes in the COVID-19 era.

Current research also has some restrictions that must be noticed in assessing the outcomes. First, sample research was selected from high schools and universities in one city. So, the size of the model is small, and the generalization of results is limited. Second, this research may have ignored other variables that affect the tendency of teenagers' to cybercrime. Future researchers intend to investigate the parents' upbringing system's impact on teenager's trend to cybercrime in the COVID-19 era. Future research can also examine practical factors such as parental upbringing, attitudes toward technology development and virtual addiction in the COVID-19 era.

In this study, teenagers' tendency to cybercrimes in the COVID-19 era is investigated, and a procedure is applied depending on a practical occasion. This article's offered sample provides a perfect framework for influencing parents' social networks, mobile games and religious attitudes on teenagers' tendency to cybercrimes in the COVID-19 era.

Restrictions imposed on freedom of movement and interaction with others due to the COVID-19 pandemic have had the effect of causing many people, especially students, to become addicted to social media. This study aims to investigate the effect of social media addiction on the academic performance of Sri Lankan government university students during the COVID-19 pandemic.

A convenience sampling technique was used to conduct a quantitative cross-sectional survey. The survey involved 570 respondents from nine state universities in Sri Lanka. The raw data from the completed questionnaires were coded and processed using SPSS for descriptive and inferential statistical analysis.

The findings of this study indicated that the overall time spent on social networking increased dramatically during COVID-19. Based on the results, this study found that there was no association between the time spent on social media and the academic performance of students before COVID-19 came on the scene. However, a significant association was found between the time spent on social media and students’ performance during the pandemic. The authors concluded that overblown social media use, leading to addiction, significantly negatively affects academic performance.

This study helps to understand the impact of social media use on the academic performance of students during COVID-19. Restrictions imposed by COVID-19 have changed the typical lifestyle of the students. Therefore, social media usage should be reassessed during the COVID-19 pandemic. The findings of the study will comprise these new insights, and they may well show how to adapt social media to contribute to academic work in meaningful ways.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study aims to develop a conceptual model for GET products that participate in brands’ online communities, based on social cognitive theory (SCT), with environmental factors, personal factors and behavioral factors being used to explore whether users of GET products participate in brand online communities as well as to determine whether participation in a community forum causes users to stick with GET products. In addition, expectancy confirmation is also considered in the research model.

This research examines whether environmental and personal factors have a positive effect on the behavioral factors of Gogoro users, and then further effects on green energy technology (GET) product stickiness for users in online communities. A website was used to distribute links to two Facebook club sites: Gogoro Series 2 Fan Club and the Gogoro Fan Club. The respondents’ qualification criteria were restricted to people who had used Gogoro products and participated in a Gogoro online community. A total of 581 valid responses were collected for structural equation modeling (SEM) analysis, and expectancy confirmation was found to be moderate from a hierarchical regression.

The results of SEM show that virtual interactivity has a positive effect on product-related content, and social norms were found to have significant effects on creating product-related content. Brand community identification, perceived relative advantage and brand knowledge self-efficacy are found to be related to both creating and contributing product-related content. Also, creating product-related content and contributing user participation behaviors influence ET product stickiness.

Online community managers can boost user participation by increasing interaction, and community identification by enhancing users’ perceptions of benefiting from participating in their communities. Companies can also encourage users to create product-related content to increase users’ stickiness to GET products. Further, GET companies can try to enhance users’ intrinsic connection with other community users to increase their brand community identification if they want to increase users’ willingness to participate.

This study adopted SCT to measure the GET product stickiness formation process in an attempt to determine what factors boost user participation based on triadic reciprocality. Also, expectancy confirmation plays an important role in the relationship between community users’ participation behaviors and GET product stickiness. The results indicated that it was appropriate to add virtual interactivity to environmental factors and perceived relative advantage to personal factors to measure users’ participation in an online social community. Actual product users’ online community participation behavior could be a very influential indicator of actual product stickiness formation.

Bridge deterioration is a critical risk to public safety, which mandates regular inspection and maintenance to ensure sustainable transport services. Many models have been developed to aid in understanding deterioration patterns and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks.

Three long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely vanilla LSTM (vLSTM), stacked LSTM (sLSTM), and convolutional neural networks combined with LSTM (CNN-LSTM). The models are developed by utilising the National Bridge Inventory (NBI) datasets spanning from 2001 to 2019 to predict the deck condition ratings in 2021.

Results reveal that all three models have accuracies of 90% and above, with mean squared errors (MSE) between 0.81 and 0.103. Moreover, CNN-LSTM has the best performance, achieving an accuracy of 93%, coefficient of correlation of 0.91, R2 value of 0.83, and MSE of 0.081.

The study used the NBI bridge inventory databases to develop the bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

This study provides a detailed and extensive data cleansing process to address the shortcomings in the NBI database. This research presents a framework for implementing artificial intelligence-based models to enhance maintenance planning and a guideline for utilising the NBI or other bridge inventory databases to develop accurate bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.