In the Indian context, financial planning for salaried individuals has gained increased importance due to economic fluctuations, rising living costs, and the need for robust retirement planning. Despite its importance, there is limited research on the specific factors that influence financial decision-making among salaried employees in India. Understanding these determinants is essential for developing effective strategies to enhance financial well-being among employees. This study explores the key factors influencing financial decision-making among employees, including financial goals, emergency savings, retirement planning, budgeting, financial confidence and literacy, financial stress, use of tax-saving instruments, income level, risk tolerance, and debt levels. A sample of 549 employees from diverse sectors in Uttar Pradesh participated in this research, highlighting the critical aspects of personal financial management that impact financial well-being. The study used a questionnaire-based survey to gather data on factors affecting financial decision-making. Descriptive statistics, correlation, and regression analyses were employed to identify significant predictors. The results reveal that financial literacy, access to resources, attitudes toward retirement planning, and cultural norms significantly influence financial decisions. Additionally, income level, job stability, and social support are crucial in shaping employees’ financial planning. The study recommends enhancing employees’ financial decision-making by offering financial education programs, budgeting tools, retirement planning assistance, debt management programs, tax planning workshops, financial counselling services, and employer match programs for retirement savings. These initiatives aim to boost financial literacy and confidence, enabling employees to make informed financial decisions and improve their financial well-being.

This paper analyzes the relevance of social accounting information for managing financial institutions, using Banca Transilvania Financial Group (BTFG) as a case study. It explores how social accounting data can enhance decision-making processes within these institutions. Social information from BTFG’s annual integrated reports was used to construct a social balance sheet, and financial data was collected to calculate economic value added (EVA) and social value added (SVA). Research question include: Does social accounting represent a lever for substantiating the managerial decision in financial institutions? Results show that SVA is a valuable indicator for financial institution managers, reflecting the institution’s contributions to social well-being, environmental impact, and community support. Policy implications suggest regulatory bodies should mandate the inclusion of social accounting metrics in financial reporting standards to encourage socially responsible practices, enhance transparency, and incentivize institutions achieving high SVA. This paper contributes to the literature by demonstrating the practical application of social accounting in financial institutions and highlighting the importance of SVA as a managerial tool. It aligns with existing research on integrating corporate social responsibility (CSR) metrics into financial decision-making, enhancing the understanding of combining social and economic indicators for comprehensive performance assessment The abstract covers motivation, methodology, results, policy implications, and contributions to the literature.

This study investigates the buying styles of young consumers, especially the millennials—Gen Y, and Gen Z whose idiosyncrasies and consumption peculiarities are quite different from the older generations. Besides Sproles and Kendall’s eight (8) consumer-style inventory dimensions, this study presents new dimensions and develops six constructs that define young consumers’ decision-style inventory in a developing market. The study population consisted of all younger consumers—Gen Y, and Gen Z in Lagos State, Nigeria. One hundred and twenty-five (125) respondents were selected randomly across all 20 Local Governments in Lagos State, Nigeria. Factor analyses through varimax rotation, latent root criterion (eigenvalue = 1), screen plot test and the percentage of variance were conducted to determine the significant factors to retain among the variables. The findings clearly showed that newly developed CSI constructs in this study (sexiness, trendiness, global branding, smartness, socialisation and entertainment) were strong and significant among young consumers’ decision-making styles. The six (6) constructs developed showed that the younger consumers’ consumption styles are evolving, becoming sophisticated and relatively dynamic, hence the reliance on Sproles and Kendall’s dimensions to measure the younger consumers’ consumption decision styles will be inadequate in business/behaviour strategy development. The dimensions of entertainment, sexy, social, trendy, smartness and global branding variables are mostly underpinned and dominate considerations in purchase decision styles and behaviours among young consumers.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This article aims to measure and identify the factors influencing the decision to use Chatbot in e-banking services for GenZ customers in Vietnam through 292 customers. Testing methods: Cronbach’s Alpha trust factor, EFA discovery factor analysis, and regression analysis have shown that 07 factors directly affect GenZ’s decision to use Chatbot. Those factors include (1) Customer attitude; (2) Useful perception; (3) Perception of ease of use; (4) Behavioral control perception; (5) Risk perception; (6) Subjective norms and (7) Trust. On that basis, the article has set out management implications for Vietnamese commercial banks to approach and increase the decision of customers aged 18–24 years in Vietnam.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.