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All Journal EMITTER International Journal of Engineering Technology International Research Journal of Business Studies
N, Sivakumar
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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How Financial Literacy Influences Budgeting, Investment, and Savings Behaviors S, Dheepiga; N, Sivakumar
International Research Journal of Business Studies Vol. 18 No. 1 (2025): April - July 2025
Publisher : Universitas Prasetiya Mulya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21632/irjbs.18.1.101-114

Abstract

This research plans to analyze the interrelationships among financial literacy, budgeting behavior, investment behavior, savings behavior, and financial confidence. It seeks to understand how financial literacy influences individuals' financial behaviors and to assess the potential impacts on their overall financial well-being. A quantitative research methodology was implied, using a cross-sectional study method to collect data from 100 respondents. The connection between the constructs was evaluated through data analysis using Structural Equation Modeling (SEM) with Smart PLS. The results indicated a direct and positive connection between financial literacy and both budgeting behavior and investment behavior and, a negative and a connection that is statistically insignificant between budgeting behavior and savings behavior. This paper highlights the value of financial literacy in enhancing budgeting and investment behaviors. They suggest that financial education programs should address both the practical and emotional aspects of financial management, particularly focusing on fostering financial confidence.
An Exploring the Power of Feature Representations: An Empirical Study on Product Reviews for Sentiment Analysis Lian Ben, Thian; R N, Ravikumar; Kumar Singh, Sushil; Bharatbhai Chauhan, Pratikkumar; N, Sivakumar; V, Manoj Praveen
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.821

Abstract

With the rise of e-commerce and online shopping, customer reviews have become a crucial factor in determining the quality and reputation of a product. Online shoppers rely heavily on customer reviews to make informed purchasing decisions, as they don't have the opportunity to physically examine the product before buying. As a result, companies are also investing in sentiment analysis to understand and respond to customer feedback, as well as to enhance the quality of their products and services. Using natural language processing (NLP) and machine learning techniques, sentiment analysis classifies the tone of a customer review as positive, negative, or neutral. It involves analysing text data to determine the overall tone, emotion, and opinion expressed in a review. In this work, we study sentiment analysis of client reviews using machine learning algorithms with different vectorization techniques. The strategy outlined here consists of three distinct phases. The initial step involves some pre-processing to get rid of irrelevant information and find the useful terms. Then, feature extraction was accomplished utilizing numerous vectorization strategies as Bag-Of-Words (BoW), Term Frequency Inverse Document Frequency (TF-IDF), and N-grams. After extracting the features from text data, the final stage is classification and predictions based on machine learning approaches. We evaluated the proposed models on Yelp reviews dataset. The experimental results are evaluated using metrics such as precision, recall, and f1-score, and K-fold cross-validation.
Co-Authors Bharatbhai Chauhan, Pratikkumar Kumar Singh, Sushil Lian Ben, Thian R N, Ravikumar S, Dheepiga V, Manoj Praveen