This study aims to test whether foreign capital reputation can moderate the influence of auditee and auditor factors on audit report lag during the Covid-19 Pandemic in the Industrial, Consumer Cyclicals, Financials, Properties & Real Estate, and Transportation & Logistics sectors. The population in this study was all companies in the Industrial, Consumer Cyclicals, Financials, Properties & Real Estate, and Transportation & Logistics sectors. The number of samples in this study was 266 companies in the Industrial, Consumer Cyclicals, Financials, Properties & Real Estate, and Transportation & Logistics sectors. The sampling technique used was the purposive sampling method. The research method used is quantitative, with data sources in the form of secondary data. The data analysis method uses multiple linear regression analysis and MRA with SPSS. The results of this study provide empirical evidence that partially the leverage and audit switching variables have a positive effect on audit report lag, then company size, TCWG, and audit opinion harm audit report lag, while profitability and audit tenure do not affect audit report lag. Furthermore, foreign capital reputation is able to moderate the influence between profitability, company size, TCWG, audit tenure, and audit switching on audit report lag, while foreign capital reputation cannot moderate the influence between leverage and audit opinion on audit report lag.
The study aims to examine the impact of artificial intelligence (AI) on the quality of financial decision-making of investors in Maharashtra, India. Specifically, it examines the impact of AI predictive analytics (AIPA), data processing speed (DPS), bias reduction (BR), and cost savings (CS) on financial decision-making quality (FDMQ) and the moderating effect of investor experience (IE). Data were gathered from 228 investors in Maharashtra through a closed-ended questionnaire administered through Google Forms and WhatsApp. Structural Equation Modeling (SEM) was employed using SmartPLS 4 to examine the hypothesized relations. The research finds that AIPA, DPS, BR, and CS considerably enhance FDMQ. Moreover, IE positively moderates these connections and strengthens the role of AI in decision quality. This research confirms the utility of incorporating AI into investment decisions aimed at enhanced precision, speed, and justice of decisions. Moreover, experience-led training and tactful usage of AI aids become necessary to benefit from various profiles of investors. This study contributes to the current body of research on AI in finance and provides actionable recommendations for investors, financial institutions, and policymakers with empirical evidence from one of India’s leading financial hubs.
This conceptual paper builds on a sustainability-based perspective and research into sustainable innovation to develop a conceptual framework, which underscores the relevance of a positive sustainable purpose at the organizational level to match the growing sustainability awareness at individual and interorganizational levels. Illustrative examples indicate that a positive sustainable purpose does not generally question a firm’s “for profit” strategy. It is defined as a higher corporate purpose, which describes an organization’s enduring reason to exist in the future, considering a positive impact on the planet and people in addition to profits, not instead of profits. In a circular economy, the present focus of most firms’ ESG (environment, social, and governance) strategies will be insufficient, and sustainable innovations to achieve real transformation toward circular business models deserve more attention. The drivers of sustainable development call for adapting a firm’s strategic goals, innovation projects, and reason to exist toward including environmental and social aspects.
The abstract in an era where healthcare demands precision and personalized solutions, “OpenHealth” emerges as a groundbreaking accessibility initiative at the intersection of technology and medicine. This comprehensive project focuses on Multi-disease Detection, employing a diverse set of algorithms, encompassing deep learning, standard machine learning, transfer learning, and hybrid models such as VGG-19, ResNet-50, Random Forest (RF), and Gradient Boosting. Diseases across specific organs, such as the brain, kidney, heart, liver, and lungs, are accurately predicted, and model performance is rigorously assessed through metrics like accuracy and recall. Multi-disease detection enables simultaneous identification of multiple conditions, reducing diagnostic time and supporting early detection of comorbidities in clinical decision-making scenarios. Adding a layer, “OpenHealth” integrates with large language models from open-source libraries like Hugging Face, providing personalized information based on individual health profiles. Likewise, the design extends its impact by incorporating an AI dietitian and food recommender, acclimatizing salutary recommendations to individual health conditions. Scrupulous association is assured through devoted directory structures, fostering a modular and justifiable framework. Leveraging Machine Learning Operations (MLOps) like Dockers, DVC, Evidently, and MLflow enhances the overall efficiency and reliability of healthcare systems.
This study evaluates the effect of human resource management (HRM) practices on the job satisfaction levels of employees working for public sector undertakings (PSUs) located in Telangana, India. A structured questionnaire survey method has been used to gather data on the human resource policies of organizations in relation to employee involvement and job satisfaction levels, among other variables. The data obtained were then subjected to statistical analysis, using correlations, structural models and trends, among others. Structural equation modeling was further applied in testing the relationships between the HRM practices, employee engagement and job satisfaction. In addition to that, confirmatory factor analysis was also conducted to validate the measurement model and assess its reliability and validity. According to the findings of this study, there exists a significant relationship between HRM practices and job satisfaction, with employee engagement having a powerful mediating role. The structural equation model showed good model fit, with acceptable indices (RMSEA [Formula: see text] 0.04, CFI [Formula: see text] 0.90, and TLI [Formula: see text] 0.88). The use of these fit indices in the study shows that the analytical approach used was valid and reliable. Therefore, the findings indicate that the application of proper HRM practices such as development plans, fair treatment and good performance appraisals increases job satisfaction among employees.
