This study investigates the factors influencing employee satisfaction while working remotely during the COVID-19 pandemic in Ho Chi Minh City, Vietnam.Drawing on the Job Demands-Resources (JD-R) model and the Conservation of Resources (COR) theory, we examine the relationships between work-from-home productivity, work stress, health and safety measures, work-life balance, and job satisfaction.An online questionnaire was distributed to 230 employees aged between 25 and 40 in Ho Chi Minh City, and structural equation modeling was used to analyze the data.The findings indicate that work stress negatively affects job satisfaction and work-from-home productivity, while health and safety measures and work-life balance positively influence job satisfaction.Work-from-home productivity does not directly impact satisfaction but promotes work-life balance.The study highlights the importance of addressing work stress, ensuring a safe and healthy work environment, and promoting work-life balance to enhance job satisfaction among remote workers in Ho Chi Minh City during the pandemic.The findings provide valuable insights for organizations and managers to optimize employee well-being and satisfaction in remote work settings.

This study investigates the application of the Classification and Regression Tree (CART) method for evaluating housing loan eligibility in XYZ Cooperative, a financial institution in Jakarta.The research aims to address the challenges faced by the cooperative in conducting manual loan feasibility analyses, which have led to inefficiencies and prolonged application processes.Using historical loan data and relevant borrower variables, the study develops a credit scoring model based on the CART method to improve the accuracy and efficiency of predicting borrower feasibility.The results demonstrate that the CART-based model enhances the credit assessment process, reduces the risk of loan defaults, and accelerates decision-making.The study contributes to the literature by showcasing the effectiveness of the CART method in a cooperative lending context and provides practical recommendations for XYZ Cooperative to improve their credit risk management system.However, the generalizability of the findings beyond the specific case of XYZ Cooperative remains a limitation.Future research could extend this study by incorporating additional variables, comparing different credit scoring methods, and validating the model in other financial institutions.

This study applies text mining techniques, specifically the Nave Bayes algorithm, to classify complaint tickets at Bank XYZ.The aim is to demonstrate how leveraging business intelligence principles can enhance the bank's operational efficiency and service quality.The CRISP-DM framework is followed, and the dataset comprises 11,371 complaint tickets.The results show that the Nave Bayes algorithm achieves an accuracy of 100%, a Kappa score of 1.000, and a classification error of 0.00%.The findings highlight the potential of text mining in addressing challenges in complaint report handling and improving operational workflows in the banking industry.However, the study has limitations, and future research should explore the scalability and adaptability of text mining solutions in dynamic business environments.

Augmented reality (AR) mobile gaming saw massive popularity following Pokémon Go.However, many later entrant AR games have struggled with declining user retention over time.This study examines how certain user experience factors relate to immersion while playing AR games, and downstream impacts on intention to continue playing.An online survey was conducted with 214 experienced AR gamers.Results showed brand experience and user need fulfillment experience positively predicted immersion.Greater immersion subsequently showed positive effects on user confirmation, satisfaction, and ultimately intention to continue playing AR games.The findings provide developers useful guidance on elements of the user experience to focus on improving immersion and retention in this burgeoning category of mobile entertainment.

With the development of the times, technological innovation is inevitable.One of the changes that occurred was the development of the investment world.Investments can now be made digitally through the app.With the convenience provided, many new investors have emerged who use the Investment application.IPOT is one of the Investment application that quite popular in Indonesia but it seems to have problems with the rating and review that might affects the continuance intention to use of the application.This study investigates the factors influencing users' continuance intention to use digital investment applications, focusing on the IPOT application in Indonesia.Using a quantitative approach and PLS-SEM analysis of survey data from 101 IPOT users, the study finds that user interface and trust have significant positive effects on satisfaction, which in turn significantly influences continuance intention to use.However, ease of use, prompt response, and system quality do not significantly affect satisfaction.The study contributes empirical evidence on the key drivers of continuance intention in the context of digital investment applications and offers insights for application developers and managers to enhance user satisfaction and retention.Implications and future research directions are discussed.

This study aims to identify the key drivers of Insurtech adoption intentions among consumers in Indonesia, a developing country where Insurtech usage remains low despite its potential benefits.Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the study examines the influence of performance expectancy, effort expectancy, facilitating conditions, price value, perceived risk, social need, self-efficacy, and perceived regulatory support on behavioral intentions to use Insurtech.A survey of 400 Insurtech users in the Greater Jakarta area was conducted, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM).The results show that performance expectancy, price value, social need, self-efficacy, and perceived regulatory support have significant positive effects on Insurtech adoption intentions, while effort expectancy, facilitating conditions, and perceived risk do not.Age was found to moderate the relationship between performance expectancy and adoption intentions for Generation X and Y.The study contributes to the literature by extending the UTAUT model with additional variables relevant to the Indonesian context and providing insights for Insurtech providers and policymakers to promote wider adoption of digital insurance services.

This study examines the moderating effect of internship volunteer turnover on the relationship between work motivation and employee performance in Amil Zakat Institutions (AZI) using a mixed-methods approach.Data were collected through questionnaires from 60 respondents and interviews with informants.The quantitative results showed that work motivation positively and significantly influenced employee performance, while internship volunteer turnover negatively moderated this relationship.The qualitative findings revealed that frequent turnover of internship volunteers could hinder employee adaptation, foster materialistic attitudes, and increase training costs for AZI.The study highlights the importance of maintaining a stable internship program and providing attractive job opportunities for volunteers to enhance employee motivation and performance in AZI.The findings contribute to the understanding of human resource management in non-profit organisations and offer practical recommendations for AZI managers.

This study investigates the relationship between compulsory citizenship behavior (CCB) and emotional exhaustion in a collectivist, uncertainty avoidant, power-distant, and traditionalist culture.The authors propose a serial mediation model, in which work stress and work-to-family conflict mediate the relationship between CCB and emotional exhaustion.Data were collected from employees working in service organizations in Pakistan at two time points, resulting in a final sample of 441 respondents.The results support the serial mediation model, indicating that CCB indirectly influences emotional exhaustion through the mechanisms of work stress and work-to-family conflict.The study contributes to the literature by examining the collective effects of work and family domains on employee well-being and provides empirical evidence from a developing, collectivist, power-distant, and uncertainty avoidant society.The findings highlight the importance of considering the negative consequences of CCB and the need for organizations to create a supportive work environment that balances work and family demands.

Small and medium companies (SMEs) play a vital part in Nepal's economy; nevertheless, there is insufficient study on boosting staff productivity within this sector.This study investigates how essential aspects of work-life quality impact employee performance in service organizations in Nepal.The research surveyed 387 employees and analyzed five predictor variables: attitude, nature of job, individual growth and development, stress level, and challenges.The study found that attitude explains 51.0% of the variance in work performance (WP) in hierarchical regression analysis, increasing to 63.5% when all factors are included.The findings suggest that service-oriented firms can improve productivity by focusing on job design, developmental opportunities, and supportive settings to reduce workplace stress.This research offers evidence-based insights to help managers create specific initiatives to enhance employee well-being and overall organizational effectiveness.

This study analyzed different data mining classification techniques for predicting assistant acceptance decisions in a computer lab's recruitment process.Historical data was utilized on 128 accepted and 150 rejected candidates over 4 years.After pre-processing, classifiers like decision trees, Naïve Bayes, and K-Nearest Neighbor were implemented using Python for comparative evaluation.Accuracy metrics revealed KNN as optimal with 92.86% correct prediction over alternatives.However, precision and false positive rates require additional scrutiny from a recruitment perspective.The findings imply that customized algorithm tuning and balanced error analysis allow better leveraging of predictive recruiting to minimize selection inaccuracies despite growing applicant volumes.Though specific to an organizational context, results initiate valuable trajectories for effective data-driven human resources practice.