The development of social media has transformed many aspects of young people's lives, particularly in the way they consume information, adopt lifestyles, and interact in the digital world. This study aims to examine how social media use influences the formation of consumerist behavior among young people and its impact on social, psychological, and economic conditions. The research employs a qualitative approach using a literature review method. This method involves examining various journals, books, scientific articles, and documents related to the research topic. The findings indicate that social media is not only used for communication and information seeking but has also become a platform that shapes perceptions of needs, lifestyles, and social identity. Exposure to promotional content, digital trends, influencers, and the culture of following popular developments encourages young people to engage in consumption driven more by desires than by actual needs. In addition to affecting spending patterns, consumerist behavior also influences psychological well-being, social relationships, and individuals' ability to manage their finances. Therefore, strengthening digital literacy and financial literacy is essential to enable young people to use social media wisely and responsibly.

ABSTRACT As digital technology becomes increasingly prevalent, mobile payment is exerting a growing impact on socioeconomic activities. This article explores the impact of mobile payments on household carbon emission inequity (HCEI). Drawing on the China Household Finance Survey (CHFS) data, a cross-sectional difference-in-differences (DID) model is employed for empirical analysis. The article reveals that (1) mobile payments exacerbate HCEI by widening the gap between households’ actual carbon emissions and their fair carbon quotas; (2) key mechanisms include increased survival-oriented consumption and the promotion of small-scale, frequent payment behaviors in household spending patterns; and (3) heterogeneity tests reveal that the effect is most significant in urban households, eastern regions, and low-income groups. The research provides policy insights on aligning digital transformation and environmental sustainability, focusing on the role of micro households.

Does integrating pharmaceutical coverage into publicly financed health insurance reduce out-of-pocket spending? Difference-in-differences evidence from Kerala, India.

Introduction: Musculoskeletal conditions impose a substantial economic burden on the United States (U.S.) healthcare system, but contemporary national estimates of condition-specific spending for common orthopaedic conditions remain limited. This study utilized the 2023 Medical Expenditure Panel Survey (MEPS) to estimate the national prevalence, condition-specific expenditures, and payer distribution for treated knee injuries and shoulder disorders. Methods: Adults with treated knee injuries or shoulder disorders were identified using ICD-10-CM codes from the MEPS Medical Conditions File. Condition-specific expenditures were estimated by linking diagnoses to medical events and payments using the MEPS Condition–Event Link File. Expenditures were aggregated across inpatient, outpatient, office-based, emergency, home health, and prescribed medicine categories. Survey-weighted analyses were used to estimate national prevalence, mean expenditures, service-level spending patterns, and payer distributions. Survey-weighted Gamma generalized linear models with log link were used to examine patient characteristics associated with expenditures among the U.S. civilian noninstitutionalized population with positive condition-specific spending. Results: The analysis identified 2.55 million adults with treated knee injuries and 2.58 million adults with treated shoulder disorders. Mean annual condition-specific expenditures per person were higher for knee injuries ($10,552; 95% CI: $6128–$14,975) than for shoulder disorders ($4310; 95% CI: $3337–$5283). Knee injury expenditures were concentrated in inpatient and home health care, whereas shoulder disorder expenditures were concentrated in outpatient and office-based care. Private insurance, Medicare, out-of-pocket payments, and Worker’s Compensation each contributed to the financial burden, with payer distributions varying by condition. In adjusted models, fair/poor self-rated health and female sex were associated with higher knee injury expenditures, while no covariates were statistically significant for shoulder disorder expenditures. Conclusions: Treated knee injuries and shoulder disorders showed distinct condition-specific expenditure profiles across care settings and payer sources. These findings provide contemporary national benchmarks for orthopaedic spending and may support future research, utilization monitoring, and value-based reimbursement planning.

This study examines the performance of the Unified Payments Interface (UPI) and its impact on individual spending behavior in India. Since its launch in 2016, UPI has emerged as a dominant digital payment system, offering fast, secure, and convenient cashless transactions. The research aims to evaluate UPI’s efficiency in terms of ease of use, speed, reliability, and security, while also analyzing its influence on consumer spending patterns. The study is based on a descriptive research design, using primary data collected from 154 respondents through a structured questionnaire, along with secondary data from reports and existing literature. Statistical tools such as percentage analysis, Chi-square test, Spearman’s correlation, and regression analysis were applied to understand the relationship between UPI usage and spending behavior. The findings reveal that UPI is widely accepted and highly preferred due to its convenience and efficiency. However, it has also significantly influenced spending habits, with a majority of users reporting increased expenditure and a rise in impulse purchases. The ease and immediacy of digital payments reduce the psychological barrier associated with cash spending, leading to more frequent and unplanned transactions. At the same time, UPI provides digital records that can support expense tracking and financial management. The study concludes that while UPI has successfully enhanced financial convenience and accelerated India’s transition towards a digital economy, it also acts as a behavioral driver that encourages higher spending. Therefore, promoting financial awareness and responsible usage is essential to ensure sustainable digital financial practices.

Background: India's digital payments landscape has undergone a dramatic shift following the large-scale rollout of the Unified Payments Interface (UPI) and mobile wallet platforms. As cashless transactions become embedded in everyday consumer routines, understanding their behavioural and financial consequences has become central to research in consumer behaviour and fintech policy. Objective: This study explores how adoption of digital wallets and UPI influences spending frequency, impulsive buying, financial management practices, and user satisfaction levels among Indian consumers. Methodology: A quantitative, survey-based design was used. Structured questionnaires were administered via Google Forms to 120 respondents drawn from varied demographic backgrounds across India. Descriptive statistics, Chi-Square tests, and One-Way ANOVA were applied for data analysis. Key Findings: More than half (52.5%) of respondents noted higher spending frequency after adopting digital payments. Around 57.5% felt these platforms make impulsive purchases easier. Google Pay emerged as the dominant platform (24.2%). A sizeable 61.7% had faced at least one security or technical concern, and nearly half (49.1%) expressed overall dissatisfaction with existing platforms. Conclusion: Digital payment platforms have materially altered consumer spending patterns by increasing transaction convenience and lowering the psychological cost of spending. However, their effect on financial discipline is largely contingent on individual literacy and self-regulatory capacity. Practical implications are drawn for fintech companies, regulatory bodies, and end-users.

Background: Cigar/cigarillo smoking and cannabis use frequently co-occur, reflecting overlapping patterns of substance use and shared risk factors. However, little is known about how co-use of cigars/cigarillos and cannabis is associated with sociodemographic factors, as well as preferences for cannabis forms and spending patterns among co-users. Methods: We analyzed data from the second wave of a national longitudinal survey of U.S. adults who reported recreational cannabis use in January 2025 (N = 1,524). The survey included detailed information on past-30-day cigar/cigarillo use, enabling comparisons between cannabis-cigar/cigarillo co-users and cannabis-only users. We compare these two groups by applying logistic regression to examine sociodemographic correlates and product-specific cannabis preferences, and OLS regression to evaluate differences in total cannabis spending. Results: Overall, 8.26% of recreational cannabis adult users also smoke cigars/cigarillos. Compared with White, non-Hispanic cannabis users, Black, non-Hispanic users are five times (p < 0.001) more likely to smoke cigars/cigarillos. Compared with cannabis users with less than high school education, those with some college or a bachelor’s degree are less likely to smoke cigars/cigarillos. Cigar/cigarillo smoking among cannabis users is associated with higher use of flowers (OR = 2.16, p < 0.05) and pre-rolls (OR = 2.05, p < 0.01). Cannabis-cigar/cigarillo co-users report significantly $72 higher monthly cannabis expenditures than cannabis-only users. Conclusions: Adult cannabis users who are Black, non-Hispanic and have less than an associate degree are more likely to smoke cigars/cigarillos. Cannabis-cigar/cigarillo co-users are more likely to consume smoking forms of cannabis such as flowers and to spend more on cannabis, suggesting possible increased risks of harm and addiction associated with co-use.

Japan faces an unprecedented demographic shift, characterized by the world's most rapidly aging population and a projected surge in annual deaths, leading to a "frequent death society." This trend places substantial fiscal pressure on national healthcare and long-term care (LTC) systems, with expenditures already representing a significant share of gross domestic product (GDP) and continuing to rise. To support sustainability, accurate and proactive cost-prediction models are needed for resource allocation and policy planning. Japan's Long-Term Care Insurance (LTCI) system, established in 2000, provides services based on a seven-level care needs certification, which directly determines monthly benefit limits and strongly influences overall LTC expenditures. Ongoing revisions to the certification system underscore the need to understand how changes in care levels relate to future costs. Traditional cost-prediction models often rely on static, short-term aggregates and may miss dynamic spending patterns. In contrast, data-driven approaches (e.g., trajectory-based methods and machine learning) can identify evolving patterns over longer periods and leverage routinely collected data to enable earlier risk stratification and targeted interventions. This preliminary study addresses a specific research gap by uniquely focusing on estimating lifetime LTC costs based on "changes in care levels," utilizing only initial (first three months) service utilization data and associated costs, without requiring extensive patient background information. Although we refer to "lifetime cost estimation," the present analysis is based on observed service utilization and expenditures over a 12-36-month observation window; therefore, findings should be interpreted as an estimation of longer-term cost trajectories rather than directly observed lifetime costs. We analyzed data from 5,925 LTC users who initiated services at one of 91 home care service centers operated nationwide by a single company in Japan in June 2015 or later, continued service use for 12-36months, and were certified at Care Levels 1-4. The provider is a privately held (non-listed) corporation; therefore, publicly available audited financial statements and dividend policies are limited. As supplementary context, we referenced publicly available Official Gazette (Kanpo)-derived corporate information (Kanpo-derived database; CATR) [1]. The outcome was monthly average LTC service cost. Predictors included initial care level, first-month costs, binary indicators for seven LTC service types used in the first month, binary indicators for changes in costs for each service type during the first three months, and interruption of LTC service use during the first three months. We constructed prediction models using random forest and multivariable linear regression, with an 80/20 split for training/validation. For cost comparisons, users were categorized into a Maintenance/Improvement Group (final care level unchanged or improved from baseline) and a Deterioration Group (final care level worsened from baseline). The Deterioration Group showed significantly higher costs from the first month, particularly among users with higher independence (Care Level 1 or 2), which may reflect early anticipation of deterioration by care providers. Predictive performance was high for both random forest (R2 = 0.677 in the preliminary study) and linear regression models. The linear regression model performed best primarily in the stable Care Level 1 Maintenance Group, whereas the random forest model performed better across most other cohorts, particularly at higher Care Levels (3 and 4). High predictive accuracy was achieved without requiring basic patient attributes (e.g., age, sex) or underlying disease information. In contrast, predictive performance was relatively low in the Care Level 1 Deterioration Group, suggesting greater heterogeneity in cost trajectories among users who are mild at baseline but subsequently deteriorate. This preliminary study demonstrates the feasibility of estimating longer-term LTC cost trajectories based on early service utilization patterns, highlighting the potential role of care managers in shaping future cost trajectories. These findings may inform efforts to enhance the fiscal sustainability and quality of Japan's LTCI system.

The development of digital technology and social media has driven changes in Generation Z's consumption behavior, particularly through self-reward lifestyle, shopaholic behavior, and Fear of Missing Out (FOMO). These three factors have the potential to increase consumptive spending patterns and decrease the quality of financial management. This study aims to analyze the influence of self-reward lifestyle, shopaholic behavior, and FOMO on consumptive spending patterns and financial management of Generation Z. This study uses a quantitative method with a causality approach. The research sample consisted of 152 Generation Z students majoring in Islamic Banking at UIN North Sumatra who were selected using the Slovin formula. Data were collected through a Likert scale questionnaire and analyzed using multiple linear regression with the help of IBM SPSS 26. The results showed that self-reward lifestyle, shopaholic behavior, and FOMO have a positive and significant effect on consumptive spending patterns. In addition, the third variable also has a negative and significant effect on Generation Z's financial management. These findings emphasize the importance of controlling consumptive behavior and increasing financial literacy to create more rational and sustainable financial management.

Urban households in developing cities are required to manage rising living costs, digital payments, and increasingly diverse financial products. This paper examines how financial literacy influences saving habits among the general urban population of Lucknow. It argues that saving behavior is shaped not only by income but also by the ability to budget, understand interest, compare products, and prepare for financial risk. Recent Lucknow-based studies suggest that financial literacy remains moderate for many residents, while only a smaller proportion show strong understanding of compound interest, inflation, and diversification. At the same time, household spending patterns show that a major share of income is absorbed by essentials, leaving limited room for disciplined saving. The paper develops a conceptual framework that connects financial literacy with saving frequency, saving amount, formal financial participation, and emergency fund creation. A Lucknow-specific research plan is also proposed using survey data, literacy scoring, and regression analysis. The paper concludes that financial literacy can improve household resilience and support more stable saving behavior in urban Lucknow. Keywords: Financial Literacy, Saving Habits, Urban Households, Lucknow, Household Finance, financial behavior

Effective personal money management is increasingly challenged by expense leakage, which refers to the gradual and often unnoticed loss of money through unused subscriptions, repeated small transactions, and irregular spending behavior. Most existing budgeting tools focus on summarizing expenses after they occur and provide limited support for identifying hidden or inefficient spending patterns. The primary aim of this project is to assist individuals in managing their finances more effectively by detecting and reducing unnecessary expenses. This project presents a Personal Expense Leakage Detection and Budget Optimization system developed using Python and machine learning techniques, employing a dual-layer unsupervised learning framework in which the Isolation Forest algorithm is used to identify abnormal transactions such as unexpected charges and billing inconsistencies, while K-Means clustering groups frequent low-value transactions that may be overlooked individually but have a significant cumulative impact. The system further incorporates features such as identification of unused recurring subscriptions, prediction of end-of-month balance based on current spending trends, analysis of behavioral spending patterns to reduce impulsive purchases, and visualization of the long-term financial impact of small recurring expenses. Experimental evaluation using synthetic transaction data demonstrates that the proposed system is more effective than traditional rule-based budgeting methods in detecting hidden spending patterns, indicating that the integration of machine learning and behavioral analysis into personal finance tools can significantly improve money management, reduce financial waste, and support longterm financial stability.

The rapid development of online financial operations in India has significantly changed the spending patterns of younger customers. One of the most accepted digital payment platforms has emerged in the form of Unified Payments Interface (UPI), which is known to be fast, convenient, and user-friendly. Nevertheless, cash is still relevant especially in small or casual transactions. This paper compares and contrasts spending habits of students at Lovely Professional University (LPU) who pay using UPI and cash and examines the impacts of each mode of payment on the financial behaviors, the frequency of transactions, and the general spending habits of students. The structured questionnaire was used to collect primary data of 124 students of the various programs and demographic backgrounds. Statistical analysis - including frequency analysis, descriptive statistics, Cronbachs Alpha, Chi-square tests, Kruskal-Wallis tests, Mann-Whitney U tests, and Spearman rank correlations- was performed using SPSS. Findings reveal that UPI has become the payment system of choice to most students. Nevertheless, UPI encourages more micro-transactions and impulsive expenditure whereas cash is more likely to promote more prudent financial practices. The paper points to the need of better financial literacy in students to enable them to spend money responsibly in an ever more digital economy.

By using a refined K-means algorithm that includes optimized initial centroid selection and lessened distance computations, this research clusters students based on their campus behavior patterns utilizing a sample of 324 students who are enrolled at the XX Vocational College. Associations between these behavior patterns and academic results are then computed through an improved version of the Apriori algorithm. To optimize SVM parameters, a fruit fly optimization algorithm (FOA) is presented to allow early detection of students at academic risk. Main observations indicate that most students spend between 600 and 900 yuan per month, with the average being 789.37 yuan. Internet fees on the campus are mostly 37.64 yuan per month (43.52 percent), but it has been seen that the cost ranges between 9 and 48 yuan. Frequency of bathing is 9-17 per month in 48.77 percent of the sample and the lowest book borrowing group is 73.15 percent of the students who borrow an average of only 6.67 books each. Daily living habits and academic engagement were found as the main determinants of academic performance among the behavioral dimensions evaluated, with spending patterns having relatively low predictive power. It is worth noting that irregular routines seem to result in increased expenditure, implying that lifestyle discipline affects financial behavior too. The suggested model shows high fitting precision and low prediction error, providing a consistent model to be used by vocational college administrators to establish a constant loop of monitoring, early warning, specific intervention, and systematic improvement when it comes to student development.

Abstract The research here studies the financial literacy level of students in Greater Noida region and its effects on their spending patterns and long-term financial well-being. As money decisions become more complicated from handling electronic payments to comprehending student loans young adults are making groundbreaking money decisions without having the basic knowledge they need. The motive of this study is to find out to what extent students know frequent money matters like budgeting, saving, investing, and credit handling and how own the same affect their daily life decisions and long-term planning. The major data were gathered using a questionnaire from a undergraduate students of various institutes of Greater Noida. Descriptive statistics and regression analysis approaches will be applied for responses to determine trends, relationship, and cause-and-effect relationships. This study also relies on a extensive review of the existing literature to place its outcomes in wider national and international contexts. The aim of this research is to disclose the important monetary problems faced by students, where they acquire knowledge related to money and how they think of personal finance. It is expected that the outcome will offer productive suggestions to universities so that they can execute proper financial education schemes in favor of students. The end goal is not only to prepare the students for their professional and academic journey but also to prepare them with the instruments that will give them financial independence and stability. Key words: Financial literacy, electronic payments, budgeting, saving, credit handling, investing, financial independence and stability.

The rapid growth of digital financial services has resulted in an overwhelming increase in the volume, variety, and complexity of personal financial data. Users frequently struggle to monitor spending patterns, manage budgets, forecast savings, and make informed financial decisions due to a lack of time, financial literacy, or analytical tools. To address these challenges, Artificial Intelligence (AI) provides an effective solution by enabling automated, personalized, and data-driven financial management. This paper presents the design, development, and evaluation of an AI-Based Personal Finance Manager capable of automating expense classification, budget planning, financial forecasting, savings optimization, bill reminders, and anomaly detection for unusual transactions. The proposed system integrates supervised machine learning, deep learning models, and rule-based intelligence to deliver real-time financial insights tailored to individual user profiles. The system architecture consists of four primary modules: data acquisition, AIdriven analytics, prediction and recommendation engine, and user interface layer. Users upload or link financial transaction data, which is then cleaned, tokenized, and classified using machine learning models such as Random Forest, Gradient Boosting Machines, or LSTMbased sequence models for long-term financial pattern recognition. The recommendation engine generates suggestions related to saving opportunities, budget adherence, expense reduction, investment options, and goal-based financial planning. The system also incorporates reinforcement learning to refine recommendations based on user behavior and historical response patterns, ensuring continuous improvement. Experimental evaluation demonstrates that the model achieves high accuracy in expense categorization (93–96%), outperforming conventional rule-based systems. Financial forecasting using LSTM models also shows promising accuracy in predicting monthly expenditures, recurring payments, and potential savings. Usability studies indicate that users strongly benefit from automated financial planning features, especially real-time alerts and personalized budget recommendations. The integration of explainable AI techniques enhances transparency by providing justifications for financial suggestions, thereby improving user trust. Overall, the proposed AI-powered personal finance management system reduces manual effort, enhances financial literacy, and supports informed decisionmaking. It demonstrates that AI can transform traditional financial tracking into a personalized, intelligent, and proactive financial assistant

Remittance income accounts for a significant share of household income in Thailand. This study examines its impact on household expenditure patterns using data from the Household Socio-Economic Survey (2009–2023) and applies an instrumental variable approach to address endogeneity. The findings confirm that remittances increase spending on education and healthcare. However, they also lead to higher unproductive consumption, particularly lottery and gambling expenditures among recipient households. Spending patterns vary by gender and living area. Female-headed households allocate more to education and less to alcohol and tobacco than male-headed households. Meanwhile, municipal households receiving remittances spend more on alcohol, tobacco, lottery, and gambling than non-municipal households. These findings suggest that the Thai government should promote the productive use of remittances by supporting financial literacy programs, strengthening the regulation of gambling activities, promoting remittance-linked savings and education-focused financial products, and empowering women as financial decision-makers to enhance long-term economic development.

Good Food at Home Indianapolis: A Fruit and Vegetable Incentive Model Comparing Brick-and-Mortar to Online Ordering.

In 2021, there was a historic, although temporary, expansion to the Child Tax Credit (CTC), shifting it to a near-universal monthly child allowance. Building on previous literature that suggests a growing heterogeneity in family structure intersects with access to safety net programs, we examine how household structure and race/ethnicity independently shaped the patterns of receiving and spending the monthly CTC, and how they jointly shaped the receipt and spending patterns during the second half of 2021. Using the Census Household Pulse Survey and multivariate regression models, we find population-level evidence among CTC-eligible households that unmarried women and unmarried men were both less likely than their married counterparts to report receiving the monthly CTC, with significantly larger gaps observed for unmarried men than for unmarried women. The interaction effect analysis reveals a more nuanced finding that such disparities between married couples and unmarried women are more pronounced among White populations. Analyses on spending show that the monthly CTC played a particularly important role in helping unmarried women of color meet their basic needs and child-related expenses. The present study demonstrates the potential benefit of having a child allowance policy, while highlighting ongoing inequities in access to the CTC despite its near-universal expansion. Findings underscore the need for more targeted outreach efforts, accessible tax filing support services, and a more simplified tax filing process.

The study examines the link between the “Budol” phenomenon and the impulsive consumption behavior exhibited by young consumers for the purpose of understanding its influence on purchasing decisions. The “Budol” phenomenon, often driven by social media trends, influencer marketing, and promotional content, may encourage unplanned and emotional purchases. This study is conducted to assess the level of exposure of young consumption to the “Budol” phenomenon and to describe its effects on their impulse buying behavior and overall spending patterns. The respondents of this study are young consumers 14-29 years old residing in Marikina City. The selected participants provide a localized context in examining how the “Budol” phenomenon influences the purchasing decisions and spending behavior of youth within the area. Based on the results, exposure to “budol” content influences young Filipino consumers living in Marikina City by increasing their awareness of products, guiding their purchasing decisions, and shaping their online shopping habits, while occasionally causing post-purchase regret.

ABSTRACT - This study examines the Corporate Social Responsibility (CSR) practices of selected manufacturing companies in Coimbatore over the period 2015 to 2025, mainly focusing on the allocation and impact of CSR activities across various sectors. The research integrates primary data collected from stakeholders with secondary data obtained from company reports to analyze CSR spending patterns and stakeholder perceptions. The findings indicate that CSR initiatives are largely concentrated in areas such as education and poverty alleviation, while comparatively less emphasis is placed on other sectors. The study also reveals varying levels of stakeholder awareness and a generally moderate perception of CSR impact. Overall, the research highlights that, although CSR practices show structured implementation, there is a need for more balanced allocation of resources and stronger stakeholder engagement to enhance their effectiveness and long-term sustainability. Key words: CSR Practices, Stakeholder Awareness, Environmental Sustainability, Social Impact, CSR Spending.