Business organizations use job advertisements to find and attract the high-quality workforce they need. Skillfully crafted job advertisements not only provide job-related information to job seekers but also help develop a strong employer brand in the employee market. Based on signaling theory and person-environment fit theory, we propose that the content and specificity of information provided in job advertisements influence job advertisement effectiveness through various mechanisms. In a scenario-based experiment on 310 young job seekers, we probed the direct and indirect effects of job advertisement informativeness on job pursuit intentions. Using structural equations modelling and multi-group path analysis, the mediating roles of perceived job appropriateness and ad truthfulness, along with the moderating role of previous employment experience, were examined. By manipulating the information content of a hypothetical job advertisement, we demonstrated that: a) both advertisement informativeness and perceived job appropriateness had positive direct effects on application intentions, while the latter had a greater effect; b) perceived job appropriateness mediated the relationship between advertisement informativeness and job pursuit intentions; c) the indirect (mediated) effect of advertisement informativeness on application intentions was moderated by previous employment experience; d) perceived ad truthfulness did not exert any significant effect on application intentions. These findings imply that HR practitioners should provide specific information in job postings to help candidates, especially those with less work experience, evaluate how well the job suits them and increase their motivation to apply.

The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.

This paper aims to develop a holistic framework for the Maqasid al-Shariah in Responsible Investment (MSRI) index for selected publicly listed companies in the Malaysian capital market. To test the validity of the MSRI framework, a sample of 30 publicly listed companies from 2021 was selected using purposive sampling. The framework consists of eight themes with forty-five elements to evaluate companies based on their annual reports, sustainability reports, and public disclosures. The scores are classified into three categories: Shariah compliant, Shariah non-compliant, and Hajiyyat. Out of the 30 selected companies, the summary of MSRI scores concludes that twenty (20) companies were identified as Shariah compliant, while the remaining four (4) were classified as Shariah non-compliant, and six (6) as Hajiyyat. Overall, the results of the analyses show that the sustainability of the company and society has a higher percentage than the wealth preservation of companies. This research differs substantially from prior work by offering a novel approach that develops a holistic framework integrating Maqasid al-Shariah with elements of responsible investment. This study believes it can provide valuable guidance for formulating Islamic investment public policy for selected investment portfolios.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.