Consumer Spending Patterns Research Articles

Consumer behavior and spending patterns on cultural goods and services

This study investigates the factors influencing consumer behavior and spending patterns on cultural goods and services. Using a quantitative research approach with Partial Least Squares Structural Equation Modeling (PLS-SEM), data were gathered through a structured survey of 350 respondents from diverse demographic backgrounds. The results reveal that cultural interest, social influence, and perceived value have a significant and positive impact on consumer spending behavior, with cultural interest showing the strongest influence (β = 0.45, p < 0.01). Additionally, the study highlights the mediating role of perceived value in the relationship between social influence and spending behavior. These findings provide practical insights for marketers and cultural institutions aiming to better understand consumer motivations in the cultural sector. Future research should consider expanding the sample size and exploring other potential mediating variables.

Systematic Review of Consumer Price Index Methods and Software: Evidence from ASEAN Countries and Scholarly Research

This comprehensive systematic review provides an in-depth analysis of consumer price index (CPI) methods and software use in Asian countries. The study looks at the practises used by countries to calculate CPI, with an overwhelming preference for monthly calculations, reflecting the dynamic economic growth of the region. The use of household expenditure surveys and weighting sources ensures that CPI accurately captures changes in consumer spending patterns and the relative importance of different goods and services. Applications of CPI in Asian countries cover a wide range, including indexation of wages, pensions and social security contributions, as well as its central role as a main indicator for monetary policy decisions, macroeconomic modelling and various analytical purposes. The systematic review highlights various calculation methods for CPI, including the Carli index, the Laspeyres index, the chained Jevons index and the Dutot index, each of which has different strengths and weaknesses that affect the accuracy and reliability of CPI's measurements. In terms of software, the study encounters a range of applications, including Microsoft Excel, SAS, STATA, R, Python, EViews and SPSS, each offering versatile capacities for statistical analysis and econometric modelling. In addition, the study explores potential software alternatives such as Gretl, Julia, EpiData, GNU Octave, Tableau, Matplotlib, Seaborn and QGIS, whose suitability for CPI calculations should be considered. The research findings highlight the importance of transparent and accurate CPI calculations in shaping effective policy decisions. Policy makers can use these findings to refine CPI measurements to enable well-informed strategies to steer the economy. The study suggests avenues for future research that include assessing specific impacts of CPI calculations, further developing software tools, and incorporating consumer perceptions into CPI development.

The Psychology of Consumer Behavior during Economic Recessions and its Impact on Political Attitudes

This study examines the intricate relationship between consumer behavior during economic recessions and subsequent shifts in political attitudes. Using quantitative and qualitative study, we analyzed data from 1,000 participants across diverse demographic groups during the 2008 and 2020 economic downturns. Our findings reveal significant correlations between changes in consumer spending patterns, psychological factors such as risk aversion and economic anxiety, and evolving political ideologies. The results suggest that economic hardships lead to increased conservative fiscal attitudes and a preference for protectionist policies. This research contributes to our understanding of the complex interplay between economic conditions, consumer psychology, and political landscape, offering insights for policymakers, marketers, and social scientists.

BEHAVIORAL ECONOMICS & SOCIAL MEDIA: TO WHAT EXTENT DOES CONTENT CONSUMPTION ON INSTAGRAM REELS AFFECT CONSUMER BEHAVIOR AND SPENDING PATTERNS FOR GEN-ZS?

This research explores the impact of Instagram Reels on consumer behavior and spending patterns, particularly within the Gen-Z demographic. The study hypothesizes that Gen-Z, being more active on Instagram, would spend more on products advertised through the platform compared to older generations. A survey was conducted, targeting two groups: those who primarily use Instagram and allocate more of their budget to purchases rather than savings, and those who do not. Contrary to the initial hypothesis, the survey results indicated that older demographics, such as Millennials and beyond, were more likely to purchase items advertised on Instagram, particularly clothes, accessories, and shoes. The study utilizes theories from behavioral economics, including Maslow’s Hierarchy of Needs, Nudge Theory, and the theory of Bounded Rationality, to explain consumer choices. However, these theories fall short in accounting for the diverse and unique behaviors of individual consumers. The findings suggest that while Gen-Z engages heavily with Instagram content, it is the older users who are more susceptible to the platform's advertisements. The study highlights the complexity of consumer behavior and the limitations of existing economic theories in predicting spending patterns influenced by social media.

Challenges in access, representativeness, and bias in smart financial data relating to income volatility and economic insecurity.

Introduction & BackgroundFinancial transaction data are highly valuable sources of digital footprints data for behavioural and economic research, but to properly create impact we must closely consider their limitations. Financial institutions hold a wealth of consumer data with untapped potential for community intelligence. These datasets combine excellent coverage with extremely granular information on consumer finances, income and spending, yet these institutions face great challenges in leveraging this data for social good. Smart Data Foundry is a university-owned, non-profit organisation that facilitates safe access to these datasets for researchers and provides insights to enable government bodies to tackle today's major challenges including the cost-of-living crisis and climate change. Objectives & ApproachWe will explore the opportunities afforded by these datasets for social and economic research. For example, using pseudonymised individual consumer banking data from NatWest Group, we have developed metrics for understanding income volatility and economic insecurity in collaboration with the Joseph Rowntree Foundation. We can also use these data to study consumer spending patterns and responses to economic changes such as interest rate rises and the net zero transition. We will assess the limitations of the data including issues of representativeness, bias, and missing data, and describe methods and mitigations to account for these challenges. We also discuss the barriers to accessing this type of data, in both relationship development with data partners, and privacy and governance concerns. Relevance to Digital FootprintsIndividual level customer transaction data provides a rich and novel form of digital footprint for behavioural and economic analyses. Every point of income or expenditure is recorded in a uniquely valuable digital footprint by financial institutions. These can provide a variety of insights, such as responses to macroeconomic shocks across demographic sets, emerging areas of financial distress, and help us better understand the drivers and risks of financial vulnerability. In both its aggregated and individual form, the data can provide an additional layer of understanding for trends we may see in other data, such as health or administrative data. Conclusions & ImplicationsHaving addressed the challenges of data access and data quality, we demonstrate that consumer banking data is an incredibly valuable form of digital footprints data, capturing key information on consumer behaviour. We conclude with a call for further research to develop use cases of this data for social good.

THE EFFECT OF BUY NOW, PAY LATER FINTECH ON TRADITIONAL FINANCIAL SERVICES AND CONSUMER BEHAVIOR IN SAUDI ARABIA

This research investigates the impacts of financial technology (Fintech) on traditional financial services and the emergence of Buy Now Pay Later (BNPL) companies within the Kingdom of Saudi Arabia. Fintech signifies a paradigmatic shift in the financial landscape, utilizing technology to transform banking, payment systems, investment strategies, and lending practices. The BNPL model, recognized for its disruptive nature, enables consumers to divide purchases into interest-free installments, thereby reshaping global consumer spending patterns. This study concentrates specifically on the Saudi Arabian context, where Fintech adoption is experiencing rapid growth. It scrutinizes the factors that influence BNPL adoption behavior among Saudi consumers, addressing a research void in localized studies. A sample of 100 consumer participants responded to an online survey. The statistical analysis underscores the strength, significance, and correlation between financial elements and the factors crucial to the decision-making process for BNPL adoption. The descriptive analysis of Independent Variables (IVs) encompassing Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) has provided significant insights into the perceptions of consumers regarding the adoption of Buy Now Pay Later (BNPL) services. The analysis has revealed noteworthy patterns in the data. Performance Expectancy (PE) demonstrates moderate perceptions (Mean = 2.98), indicating a measured belief in the usefulness of BNPL. Furthermore, Facilitating Conditions (FC) registered moderate scores (Mean = 3.04), underscoring the significance of favorable external factors. Collectively, these findings depict a moderate inclination and a generally positive outlook among respondents towards BNPL adoption, providing insights into the factors influencing their adoption behaviors. The research findings contribute to academic comprehension, provide insights for industry stakeholders, and inform the development of policies and regulations within Saudi Arabia's dynamic Fintech ecosystem. Future studies may utilize longitudinal studies, conduct cross-cultural comparisons, and explore technological integration in exploring the BNPL consumer behavior studies.

Application of the FP-Growth Algorithm in Consumer Purchasing Pattern Analysis

Technology is currently used in various ways, one of which is businesses engaged in selling daily products. The right marketing strategy makes knowledge of consumer shopping patterns important to study because consumers are the main actors in carrying out transactions. The more diverse the types of goods sold in a company, the more diverse the resulting consumer spending patterns will be. Data mining is an analysis process that is carried out automatically on complex and large amounts of data to obtain patterns or trends that are generally not realized. The FP-Growth algorithm is an alternative algorithm that can be used to determine the data set that appears most frequently (frequent itemset) in a data set. The method used in this research is the FP-Growth method which is implemented in the PHP programming language and MySQL as the database. Designing a data mining program using the FP-Growth method can analyze and manage consumer purchasing patterns based on goods purchased simultaneously. The data processed in this research is transaction data that has been processed into information so as to gain knowledge in calculating stock of goods sourced from the owner of Toko Asra. From testing this method, results were obtained from the 10 transactions in December 2021, by limiting the minimum support value to 0.2 and minimum confidence to 0.75, 33 patterns of consumer shopping habits were obtained, meaning that 33 products were most frequently purchased by consumers. Designing a data mining program using the FP-Growth method can help analyze consumer purchasing patterns based on items purchased simultaneously. The results of frequent itemset calculations can help find a sequence of combinations that can be used as product recommendations in business decision

Money Talks: A Holistic and Longitudinal View of the Budget Basket in the Face of Climate Change and Sustainable Finance Matters

This study presents a holistic and longitudinal view of the household budget basket concerning climate change and sustainable finance matters. It aims to understand their impact on the budget basket by examining the relationship between money, climate change and sustainable finance in a global economy for transition countries. Comprehensive CPI data were collected in Kosovo from 2002 to 2022, and data analysis was performed using statistical methods such as t-tests and proximity matrixes in SPSS. The results show significant differences between the average and desired values within the budget basket, indicating changes in consumer behavior, particularly in food expenditures, budget allocations, and climate change impacts. Interesting patterns emerge, such as correlations between bread, cereals, and meat, and the absence of fish in some purchases. Spending on clothing and other goods also deviates from desired values. These findings highlight the complex relationship between money, climate change, sustainable finance, and consumer spending patterns, and underscore the need to address the gap between expected and desired spending values for the global economy in transition economies. Future research should focus on analyzing household spending and its interaction with other factors to improve personal financial management and promote sustainable financial behavior in a larger number of global economies.

Balancing Act: Exploring the Relationship between Inflation and Consumer Behaviour

This study investigates the intricate relationship between inflation and consumer behaviour in the contemporary economic landscape. Through a comprehensive analysis, we explore the impact of inflation on consumer spending patterns, considering demographic influences and adaptive strategies employed by individuals. The research utilizes regression analysis, ANOVA, and qualitative survey data to unravel the complexities of this relationship. Findings reveal a significant positive correlation between inflation rates and specific consumer spending categories. Demographic factors collectively exhibit a noteworthy influence on how diverse groups respond to inflation, emphasizing the need for targeted strategies. Additionally, the prevalence of adaptive strategies, with a focus on savings adjustments, highlights practical measures consumers adopt to navigate economic challenges. The study contributes valuable insights for businesses, policymakers, and researchers seeking to understand and navigate the dynamics of consumer behaviour in the face of inflationary pressures.

Key Factors that Influence Post Pandemic Online Purchase Intention of Generation Y and Z in Malaysia

As the Movement Control Order (MCO) was imposed in Malaysia in March 2020, many small and medium enterprises (SMEs) have engaged in online platforms to sustain their business. However, there have been significant changes in consumer trends and spending patterns accompanied by rapid digital adoption since MCO. Consumers’ shopping habits have changed as a result of MCO, and they maintained this habit even after MCO. Therefore, this research examined the key factors that influence post-pandemic online purchase intention in Malaysia, which include influencer marketing, live streaming commerce, loyalty programs, and online shopping experience. The theory of Planned Behaviour (TPB) model was used to explain consumer behaviour. 30 sets of surveys were used to carry out the pilot test. Purposive sampling was used to collect 384 sets of data from 5 states with the most e-commerce users in Malaysia which included Selangor, Kuala Lumpur, Johor, Pulau Pinang, and Perak. Cronbach’s Alpha was used to determine the reliability of the variables. Pearson Correlation Analysis was used to measure how 2 sets of data were related. Multiple Regression Analysis was used to describe relationships between variables. The result showed that loyalty programs and online shopping experience have a significant relationship with online purchase intention while influencer marketing and live-streaming commerce have no significant relationship with online purchase intention. The online shopping experience has the strongest influence on online purchase intention. In this regard, this study will provide profound insights to online purchase intention of generation Y and Z in Malaysia.

Sales in Commercial Alleys and Their Association with Air Pollution: Case Study in South Korea

We investigate the dynamic interplay between air pollution (PM10) and income and their joint association with quarterly sales in commercial alleys, focusing on the pre-COVID-19 (2018–2019) and COVID-19 (2020–2021) periods in Seoul, South Korea. The objective of this study is to identify how air pollution and income collectively influence consumer spending patterns by looking at the increase and decrease in sales in commercial alleys, with a focus on contrasting these effects before and during the COVID-19 pandemic, utilizing advanced machine learning techniques for deeper insights. Using machine learning techniques, including random forest, extreme gradient boosting, catboost, and lightGBM, and employing explainable artificial intelligence (XAI), this study identifies shifts in the significance of predictor variables, particularly PM10, before and during the pandemic. The results show that before the pandemic, PM10 played a notable role in shaping sales predictions, highlighting the sensitivity of sales to air quality. However, during the pandemic, the importance of PM10 decreased significantly, highlighting the transformative indirect impact of external events on consumer behavior. This study also examines the joint association of PM10 and income with sales, revealing distinctive patterns in consumer responses to air quality changes during the pandemic. These findings highlight the need for dynamic modeling to capture evolving consumer behavior and provide valuable insights for businesses and policymakers navigating changing economic and environmental conditions. While this study’s focus is on a specific region and time frame, the findings emphasize the importance of adaptability in predictive models and contribute to understanding the complex interplay between environmental and economic factors in shaping consumer spending behavior.

MARKET BASKET ANALYSIS MENGGUNAKAN ALGORITMA HASH BASED PADA DATA PENJUALAN DI O2 SWALAYAN

O2 Supermarkets have stock of goods available in the warehouse. During this time there is a buildup of goods in the warehouse because there is no system to analyze transaction data so that losses occur. Therefore, a system is needed to manage these goods in order to reduce stocks of goods that are not selling well and make stocks of goods that sell well in warehouses through consumer spending patterns. To analyze data on consumer spending patterns, the Market Basket Analysis Method can be used. This method can be used to analyze consumer spending patterns. The algorithm used is the Hash Based and Association Rule Algorithm because it can overcome the determination of frequent itemsets from k-itemset candidates with a large number and speed up information on consumer spending patterns. With existing data and using this algorithm it can produce a lot of information. The results of this study get 6 Rules with a minimum Confidence of 50% and minimum Support>= 4. From consumer spending patterns that can be used as a strategy to support sales, knowing which items are selling best, placing goods so that they are more efficient in picking up the goods desired by consumers, not only shopping patterns but also more controlled stock of goods.

Analisis Tingginya Penjualan Album KPop Dalam Pertumbuhan Ekonomi di Indonesia

This study aims to determine the high sales of K-pop albums on economic growth, identify factors that contribute to high sales of K-pop albums, understand how the popularity of K-pop in Indonesia affects consumer spending patterns, explain the role of social media and online platforms in promoting K-pop album sales in Indonesia. The study uses a mixed method by combining quantitative and qualitative approaches. Quantitative data were obtained from official sources such as the Central Bureau of Statistics and reports on K-pop album sales in Indonesia. While qualitative data were collected through in-depth interviews with stakeholders in the music industry, such as distributors, music stores, and K-pop fans. The results of the study found several interesting things including The Courtship, The Relationship, The Marriage, Kpop Album Marketing Strategy Through Online Media and the Influence of Album Quality on the Interest in Buying Physical Kpop Music Albums Quality.

Research on Wotobuy and Platform Capitalism

The Web 3.0 era has made the use of digital e-platforms extremely popular, and people can access various digital platforms for online communication through mobile phones, computers and other devices. Nowadays, when material life is booming, the emergence of digital e-platforms has also led to great changes in people's consumption patterns. The platform capitalism arising from digital e-platforms has gradually taken a dominant position in the global economic market, and e-commerce-based platforms combined with social media platforms have created a new type of s-commerce. This article will use the research method of qualitative analysis, taking Wotobuy as an example, to introduce Wotobuy's platform characteristics and operation modes, and to explore and explain how platform capitalism affects consumer spending patterns, sectors of the economy, the labor market, the value creation process and social dynamics. The research finds that platform capitalism has changed the way consumers shop, but it has also raised significant concerns about power, privacy and the treatment of employees. In order to address these concerns, a balance must be found between innovation and regulation, and the development and intervention of relevant laws is necessary due to the growing influence of platforms.

Sentiment Analysis Of E-Commerce Product Reviews For Content Interaction Using Machine Learning

With the growth of various e-commerce applications, it has become increasingly important to understand consumer spending patterns and provide products that best respond to their needs. One way of doing this is by utilizing the data available in consumer product reviews to improve the level of content interaction. I
 n order to tap into this data, companies can utilize a process called sentiment analysis to identify consumers’ sentiments or reactions towards certain products. This study proposes to compare different methods of sentiment analysis focused on one specific product, utilizing a machine learning approach with Naïve Bayes and Support Vector Machine classifiers with the aim of finding which method produces the best results in terms of accuracy, precision, recall and F-measure. A specific focus has been made to analyze the consumer reviews of wireless earphone products from the Indonesian e-commerce company Tokopedia. The results of this study show that utilizing the Naïve Bayes classifier, enhanced with hyperparameter tuning produced the best results in terms of accuracy, recall, and F-measure with a value of 77.03%, 73.03%, and 74.41% respectively. Whereas the best precision was obtained by utilizing an SVM classifier enhanced with hyperparameter tuning, producing a value of 76.05%.

The Ripple Effect: Unraveling the Intricate Relationship Between Tech Sector Layoffs and Consumer Spending Patterns in the San Francisco Bay Area

The Ripple Effect: Unraveling the Intricate Relationship Between Tech Sector Layoffs and Consumer Spending Patterns in the San Francisco Bay Area

COVID‐19 and consumer behavior: Food stockpiling in the U.S. market

AbstractThe emergence of the COVID‐19 pandemic has brought radical changes in consumer spending patterns. One aspect of this change is food stockpiling detected in several countries. Using a univariate probit model, this paper relies on the COVID‐19 Impact Survey (2020) for American households to assess the likelihood of consumers stockpiling food in response to the stringent lockdown measures imposed by the government's pandemic regulations. Our findings reveal a set of significant correlations between marital status, age, race, occupation, household structure, and the propensity of stockpiling food during the pandemic. Furthermore, the results show that residents in urban areas are more likely to engage in food stockpiling compared with residents in rural and suburban areas. The paper also examines the nexus between residence areas, lockdown measures, and the probability of stockpiling food. This research reveals a significant association between psychological factors and the likelihood of stockpiling food in response to the COVID‐19 pandemic [EconLit Citations: D12, H12, I18, P25].

The Role and Importance of Online Advertising in Shaping Malaysian Consumer Behaviour During the COVID-19 Pandemic

Online advertisements influence consumer decisions to purchase and use the knowledge and attitudes toward marketing. Internet advertising has become a key determinant in influencing consumer behaviour. This study examines the paradigm shift on Malaysian consumers’ behaviours in relation to online purchases during the Covid-19 pandemic. Very limited research has been conducted on online advertising on consumer behaviour during a pandemic crisis because it can vary and have a complex trend. Prior studies in this area often centered on online advertisements' effect on consumer spending patterns. The aim of this study, based on literature reviews carried out on impact of internet advertising on consumer spending is also to introduce a new conceptual framework to the existing theory of consumer behavioural changes and the impact of COVID-19 pandemic that has become the stimulus to change the consumers’ behaviour. This study employed Theory of Planned Behaviour as an underpinning theory to explain consumer behaviour during this pandemic. The study showed mass and social media inputs were influential in assessing the severity of the crisis and thereby impacting the shopping experience. Keywords: Online advertising, internet, consumer behaviour, pandemic, Theory of Planned Behavior. DOI: 10.7176/JMCR/87-05 Publication date: November 30 th 2022

IMPACT OF SOCIAL MEDIA ENGAGEMENT AND ARTIFICIAL INTELLIGENCE ON ONLINE BUYING BEHAVIOUR

It has been observed that the Artificial intelligence (AI) has significantly transformed the business strategies as well as consumer behaviour very rapidly. The present study proposes a framework for understanding the impact of AI involving intelligence and deliberate social media engagements on consumers' online purchase behaviour. The analysed primary data have been collected randomly from 577 respondents through a self-administered questionnaire. Both online and interview methods have been adopted for the collection of these data. The findings of the study provide an empirical evidence on how the use of artificial intelligence helps to improve the consumer purchase frequency and spending patterns. More surprisingly, the study has revealed that consumers who spend more time on shopping apps are less likely to spend more money as their purchase frequency is low. On the other hand, social media engagements and the average time spent on social media apps are observed to have a significant and positive impact on online purchase behaviour.

Implementasi Algoritma Frequent Growth (FP-Growth) Menentukan Asosiasi Antar Produk

Data accumulation is caused by the amount of transaction data stored. By utilizing the sales transaction data in the database, the data can be further processed into useful information for managers to make decisions. With the existence of data mining, it is hoped that it can help the Leaning Shop to find the information contained in the transaction data into new knowledge. Association Rule, which is a procedure in Market Basket Analysis to find relationships between items in a data set or it can be said that this association rule aims to find a collection of items that often appear at the same time and display them in the form of consumer habits in shopping. The FP-Growth algorithm is an algorithm that can be used to determine the data set that appears most often (frequent itemset) in a data, in the search for frequent itemset in a data set by generating a prefix-tree structure or often called the FP-Tree. From the test results it can be concluded that the application of data mining using the FP-Growth Algorithm can be used to analyze consumer spending patterns.