Consumer Behavior Analysis Research Articles

Intrinsic religiosity and purchase intention: The role of attitude and moral efficacy

The diversity of products on the market today raises various kinds of consumer considerations when making purchases. Consumers will consider the basics they have before making a purchase. Factors that influence consumers before making a purchase come from internal and external consumers. In this study, consumer behavior analysis is seen based on internal factors such as intrinsic religiosity, attitude, and moral efficacy. This study analyzes the effect of intrinsic religiosity on purchase intention by mediating attitude and moral efficacy. This study examines the behavior of consumers of cosmetic products in Yogyakarta, Indonesia and obtained a sample of 92 respondents. The research data were analyzed using the Smart PLS 4.0 software to test validity, reliability, and bootstrapping. The results of the study proved intrinsic religiosity does not negatively affect purchase intention, intrinsic religiosity has no positive effect on attitude, intrinsic religiosity has a positive effect on moral efficacy, attitude negatively affects purchase intention, moral efficacy has no negative effect on purchase intention, attitude does not mediate the influence of intrinsic religiosity on purchase intention, and moral efficacy did not mediate the effect of intrinsic religiosity on purchase intention.

Cross-generational Analysis of Consumer Behaviour towards Convenience Foods: A Study of Convenience Foods in Kerala

Aims: From customs dating back thousands of years to contemporary conveniences, food has always played a significant role in our societies. As we travel through history, we can see the tremendous change that has paved the way for the emergence of convenience foods in our lives. The behaviour of various generations is also on the go with time. Considering this, the study emphasises the intricate relationship between consumer behaviour towards convenience foods and age cohorts. Methodology: The data for the study was collected from a total of 180 respondents. Among these, 60 belonged to Gen X, Gen Y, and Gen Z respectively from the central region of Kerala state. Results: The results showed that all the respondents taken for the study irrespective of their generation showed a higher inclination towards home-cooked meals rather than convenience foods regardless of their popularity in today's market. For Gen X and Y, convenience food was a choice because of the little time and effort required for preparation while Gen Z preferred it as it fixes their hunger in a snap of a moment. The majority of the respondents purchased convenience food from the supermarkets. All the generations were moderately satisfied with the convenience foods available. Even though most showed modest satisfaction towards various parameters of satisfaction, the degree of satisfaction toward the availability of convenience food options differed among generations. Conclusion: The results showed the importance of understanding the behavioural differences across generations and how they can impact the acceptance of convenience food. With a comprehensive understanding of the needs and drives of each generation, marketers can adeptly navigate through the complexity of this fast-paced convenience food industry and can be a success.

Analysis Of Millennial Consumer Behavior In Coffeeshop At Riau Province

Analysis Of Millennial Consumer Behavior In Coffeeshop At Riau Province

AI-empowered consumer behavior analysis for trustworthy track recommendation over musical dance electronic products

—In the digital music era, accurate and trustworthy track recommendations for musical dance electronic products are becoming increasingly important to improve user experiences and attract more consumers. Consumer behavior modeling is critical in user interest learning and has been extensively used in recommender systems to improve recommendation accuracy. This paper proposes a novel AI-empowered consumer behavior analysis method for trustworthy track recommendations over musical dance electronic products. Specifically, we first model consumer behavior by integrating collaborative filtering and a hidden Markov model to capture the key interactive patterns between consumers and musical dance electronic products. Then, we develop a trustworthy track recommendation method based on multi-layer attention representation learning, which leverages scattering transform for audio preprocessing and attention-based independent recurrent neural networks for encoding user preferences and product features. Extensive experiments on real-world datasets demonstrate the superiority of our proposed method in terms of recommendation accuracy and trustworthiness.

Analysing Segmentation and Behavioural Dynamics in Customer Purchase Behavior

Consumer behavior is one of the most vital aspects of business, given the need to make an enterprise competitive in the market by improving marketing efforts and the product range. The project goes further into the actual segmentation and analysis of consumer behavior in relation to purchase through data analytics and machine learning for the identification of significant trends and patterns. The most important outcome of this phase is that a mere 1% of customers are able to fairly estimate delivery timings, hence showing the most room for further advancement. The implementation also takes place of an intelligent product recommendation system that personalizes product recommendations for a greatly enhanced customer experience. The paper provides strategic recommendation for optimal promotion, steering clear of purchase slumps, e.g. on Saturdays. The outcome is tangible insight for businesses by which they can fine- tune strategies adopted to elevate customer satisfaction. Keywords: Consumer Behaviour, Segmentation, Machine Learning, Data Analytics, Patterns of Purchase, Marketing strategy

Analysis of Consumer Behaviour in Purchasing Second-Hand Fashion Products: An Extended Theory of Planned Behaviour Model

Analysis of Consumer Behaviour in Purchasing Second-Hand Fashion Products: An Extended Theory of Planned Behaviour Model

Comparison of Studies Conducted in the Field of Neuromarketing and Artificial Intelligence Using Bibliometric Method

Abstract Neuromarketing research focuses on consumer purchase intention, decision-making processes, purchase behavior, brand awareness, brand loyalty, and repeat purchase behavior. In these studies, consumer behavior has been analyzed using neuroscientific methods and tools. The most commonly used tools include Functional Magnetic Resonance Imaging (fMRI), Eye Tracking, Electroencephalogram (EEG), Positron Emission Tomography (PET), Transcranial Magnetic Stimulation (TMS), Magnetoencephalogram (MEG), Steady State Topography (SST), Implicit Association Test (IAT), Facial Electromyography (fEMG), Automatic Face Coding (AFC), Skin Conductance Response (SCR), and other methods for measuring physiological responses. However, the use of these neuroscientific tools is not always possible due to economic constraints and lack of experimental design. Neuroscientific imaging and measurement methods are not preferred in every study due to their high costs and expertise requirements. However, when neuromarketing studies are examined, it is seen that methods such as Eye Tracking, EEG and fMRI are used more widely. These tools contribute to a deeper understanding of consumer behavior. In order to better analyze consumer behavior, it is of great importance to convey marketing stimuli and messages correctly. In the field of marketing, the effect of stimuli conveyed to consumers using the five senses is one of the focal points of neuromarketing. More than one neuroscientific method should be used together to understand consumer intentions, thoughts and purchasing behaviors. In this way, the obtained data can be analyzed more comprehensively and clearer insights can be provided about neuromarketing. The aim of this study is to present a comprehensive assessment of the use of neuroscientific tools by examining the publications in the field of neuromarketing in the Web of Science database between 2010-2024 with bibliometric analysis. The study will address the limitations of not using more than one neuroscientific tool together in neuromarketing research and the inadequacy of analyses supported by artificial intelligence. A more holistic approach will be proposed to address these shortcomings and a guiding resource for future research will be created.

Analysis of Consumer Behavior in Interest in Purchasing Rajapolah Handicraft MSME Products

Increased competition in the craft industry and reduced activity in the surrounding community have caused a significant decline in the number of MSMEs, which is indicated by internal and external factors. Rajapolah crafts are increasingly popular with consumers because they prefer modern products compared to crafts made in Rajapolah. Wicker is considered old-fashioned and out of date, especially by the millennial generation. This research aims to analyze how price and product quality influence consumer buying interest in Rajapolah craft products. This research can be expanded as quantitative research with a sample size of 100 respondents. Based on the results that have been carried out, price and product quality have a significant effect on buying interest in Rajapolah handicraft MSME products. It can be concluded that producers, in increasing purchasing interest, need to look at the price and quality aspects of their products.

Using Economic Mathematics to Enhance Consumer Insights: Pricing, Promotion, and Market Strategies

The increasingly competitive business environment demands a deep understanding of consumer behavior. Consumers influence the demand for products or services, making the analysis of their behavior essential for effective business strategies. This article discusses the use of economic mathematics to systematically and measurably analyze consumer behavior. Quantitative methods with descriptive and inferential analysis are used, with primary data from consumer surveys and secondary data from industry reports and e-commerce platforms. Data analysis involves linear and non-linear regression models as well as machine learning techniques such as logistic regression and decision trees. The results show that price, promotion, product quality, and consumer reviews are key factors influencing purchasing decisions. In the digital era, consumer behavior data is increasingly accessible and can be analyzed with machine learning algorithms and big data. These findings provide insights into how economic mathematics helps optimize marketing strategies and enhance competitiveness. Practical recommendations include optimizing pricing and promotion strategies as well as improving product quality. This research contributes to understanding and leveraging consumer behavior for business success

An Analysis of Tamil Nadu Income-Based Consumption Patterns and Consumer Behaviour for Jaggery and Khandsari Sugar: A Review

Purpose and Objectives: Markets such as Coimbatore, Tiruppur and Erode district Cooperative Jaggery Marketing Federation (CTEDCJMF), Selur Farmers Sugarcane Products Market (Farmers jaggery market), Pilikalpalayam and Chitthode jaggery market exclusively for jaggery and khandsari sugar, coconut and palm sugar. Methodology: Snowball sampling method was used for the data collection and also producers, visiting the market also enrolled in the study. Totally 30 producers of each sugar, 60 middlemens and 120 consumers who purchased sugar through the identified marketing channels were interviewed for the study. The data collected were analyzed to determine the cost and returns for three types of sugar. The price spread at various marketing levels, trader concentration ratios, and consumer buying behavior towards alternate forms of sugar were also examined. Principal component analysis was used to identify factors influencing consumer purchases of alternate forms of sugar. Key Findings: The results of the sample respondents showed that sample respondents of jaggery and khandsari sugar producers, majority of the sample respondents were aged between 31-40 years (33.33%) followed by 26.67 percent in the category of 20-30 years, about 23.33 per cent of the sample respondents in the producer category were aged above 50 years, followed by 16.67 per cent aged between 41-50 years. Implications: Hence, the demand for coconut sugar is very low, so promotional efforts should be made to popularize its health benefits among consumers. In the case of jaggery and khandsari sugar, product modification into cube form (similar to white sugar cubes) will increase market preference and acceptability.

An Analysis of Consumer Behaviour in Multichannel Retailing and Omnichannel Retailing

This paper investigates consumer behavior in multichannel and omnichannel retailing environments. With the evolution of technology and the increasing importance of the internet, consumer expectations and shopping behaviors have significantly transformed. Multichannel retailing, which involves using multiple channels for selling products, and omnichannel retailing, which ensures a seamless consumer experience across all channels, have emerged as critical strategies for businesses. This study aims to explore the differences in consumer behavior in these two retailing models, examining factors such as customer satisfaction, purchase frequency, and loyalty.

Exploring the Sustainability of Upcycled Foods: An Analysis of Consumer Behavior in Taiwan.

Given the urgent climate change and food security challenges, upcycled food products are crucial for sustainable food production and waste management. This study investigates Taiwanese consumer behavior towards upcycled foods using the value-attitude-behavior (VAB) theory, focusing on "product knowledge", "green perceived quality", and "price sensitivity". Of the 335 distributed surveys, 320 valid responses (95.5% effectiveness) were analyzed. The results indicated that eco-conscious values strongly influenced consumer attitudes and anticipated guilt (β = 0.647, p < 0.001; β = 0.691, p < 0.001), shaping behavioral intentions (β = 0.290, p < 0.001). Attitudes significantly correlated with intentions, validating the VAB framework. However, anticipated guilt showed a minimal impact (β = 0.029, p = 0.629), revealing complex consumer emotions. Green perceived quality and product knowledge were the key decision-making factors (β = 0.193, p < 0.001; β = 0.146, p < 0.001). Surprisingly, price sensitivity positively influences intentions (β = 0.764, p < 0.001), suggesting the consumer prioritization of quality and environmental values over price. These insights inform strategies for businesses to enhance consumer engagement and sustainability alignment, advancing progress towards Sustainable Development Goals (SDGs).

Bibliometric Exploration of Consumer Behavior towards Sustainable Products

This research provides a comprehensive bibliometric analysis of consumer behavior towards sustainable products, examining key themes, research trends, and author collaboration networks from 1971 to 2024. The study identifies central themes such as consumer behavior, food and organic products, planned behavior, consumer perception, and strategic approaches, with a notable emphasis on the role of social media. Research trends indicate a significant focus on the food sector, particularly organic products, and a consistent interest in understanding the psychological and social factors driving sustainable consumption. The analysis also highlights potential future research areas, including deepening the understanding of consumer behavior, enhancing consumer perceptions, leveraging social media for sustainability promotion, and developing effective strategic interventions. The author collaboration network reveals strong co-authorship ties within distinct clusters of researchers, suggesting the need for increased cross-collaboration to enhance the field's breadth and impact. This study offers valuable insights for researchers, practitioners, and policymakers aiming to promote sustainable consumer behaviors and develop effective strategies for sustainability.

Insignificant but Essential: The Critical Role of Non-Significant Variables through Necessity Logic in Sport Event Tourism Context

This study explores the application of Single Necessary Condition Analysis (NCA) alongside traditional regression-based techniques, such as Structural Equation Modeling (SEM), to provide a comprehensive understanding of consumer behavior in sport and tourism settings. Traditional methods, including SEM, often rely on additive logic, which predicts outcomes using multiple variables that can compensate for each other. While useful, this approach may offer an incomplete picture of complex consumer phenomena. In contrast, necessity logic, exemplified by NCA, identifies conditions that must be present for an outcome to occur. This study applies NCA to evaluate the essential factors influencing intentions to visit sport events in Bangkok, Thailand, specifically focusing on attitudes, subjective norms, and perceived behavioral control as necessary conditions. The findings indicate that if these conditions are not met, the intention to visit a sport event typically will not occur. By integrating both necessity and additive logics, this study reveals that all variables in the Theory of Planned Behavior (TPB) are indispensable for forming an intention to visit a sport event. The analysis shows that attitudes, subjective norms, and perceived behavioral control are necessary conditions, even though subjective norms and perceived behavioral control may not appear significant in traditional SEM tests. The study underscores the importance of considering multiple causal perspectives—deterministic, probabilistic, and typicality—to achieve a holistic understanding of consumer intentions. This dual approach ensures that significant variables are not overlooked, providing robust support for the TPB in the context of sport event marketing. Future research should continue to incorporate complementary perspectives to enhance the accuracy and depth of consumer behavior analysis in various settings.

Enhancing Community E-Commerce Repurchase Prediction Through Information Entropy Analysis

In this study, we delve into the prediction and recommendation algorithm for user repurchase behavior within the community e-commerce landscape, leveraging the framework of information entropy. By analyzing consumer behavior data from a product-centered perspective, we observe that the majority of consumers engage in approximately 0-8 information search clicks before making a purchase, primarily focusing on product details, with an average of about 4 browsing interactions. Subsequently, a minimal portion of consumers exhibit 0-5 instances of preference behavior after browsing activities. Similarly, consumers who add items to their shopping carts typically engage in this behavior from 0 to 4 times, showcasing limited participation. Leveraging attribute probability, information entropy serves as a driving force for predicting community e-commerce users' repurchase behavior, contrasting with traditional outlier detection methods and underscoring the advantages of information entropy.

The Role of Artificial Intelligence in Targeted Advertising and Analysis of Consumer Behavior

The Role of Artificial Intelligence in Targeted Advertising and Analysis of Consumer Behavior

From E-Commerce to the Metaverse: A Neuroscientific Analysis of Digital Consumer Behavior.

The growing interest in consumer behavior in the digital environment is leading scholars and companies to focus on consumer behavior and choices on digital platforms, such as the metaverse. On this immersive digital shopping platform, consumer neuroscience provides an optimal opportunity to explore consumers' emotions and cognitions. In this study, neuroscience techniques (EEG, SC, BVP) were used to compare emotional and cognitive aspects of shopping between metaverse and traditional e-commerce platforms. Participants were asked to purchase the same product once on a metaverse platform (Second Life, SL) and once via an e-commerce website (EC). After each task, questionnaires were administered to measure perceived enjoyment, informativeness, ease of use, cognitive effort, and flow. Statistical analyses were conducted to examine differences between SL and EC at the neurophysiological and self-report levels, as well as between different stages of the purchase process. The results show that SL elicits greater cognitive engagement than EC, but it is also more mentally demanding, with a higher workload and more memorization, and fails to elicit a strong positive emotional response, leading to a poorer shopping experience. These findings provide insights not only for digital-related consumer research but also for companies to improve their metaverse shopping experience. Before investing in the platform or creating a digital retail space, companies should thoroughly analyze it, focusing on how to enhance users' cognition and emotions, ultimately promoting a better consumer experience. Despite its limitations, this pilot study sheds light on the emotional and cognitive aspects of metaverse shopping and suggests potential for further research with a consumer neuroscience approach in the metaverse field.

Data-Driven decision making in agriculture and business: The role of advanced analytics

Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, and drive innovation. This paper explores the transformative role of advanced analytics in these sectors, highlighting key benefits, challenges, and future directions. In agriculture, advanced analytics enables precision farming by integrating AI, IoT sensors, and satellite imagery. Predictive models forecast crop yields, optimize irrigation, and enhance soil management practices, improving productivity and sustainability. Similarly, advanced analytics supports strategic decision-making in business by analyzing consumer behavior, predicting market trends, and optimizing supply chain operations. However, adopting advanced analytics faces challenges such as data quality, technical expertise, cost constraints, and ethical considerations. Addressing these challenges requires investments in data infrastructure, talent development, and regulatory compliance to ensure secure and ethical data usage. Emerging trends include AI-driven automation, blockchain for supply chain transparency, and augmented analytics for democratizing data access. Recommendations for stakeholders include investing in data capabilities, fostering collaborative partnerships, and promoting a culture of data-driven decision making. In conclusion, advanced analytics offers profound opportunities to enhance efficiency, inform decision making, and drive sustainable growth in agriculture and business. Embracing these technologies is essential for organizations seeking to thrive in a data-driven economy. Keywords: Advanced Analytics, Precision Farming, Predictive Analytics, Data-driven Decision Making, Business Intelligence.

Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies

Purpose: The aim of the study was to examine the consumer behavior analysis in the age of big data for effective marketing strategies. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that consumer behavior analysis in the age of big data presents a transformative opportunity for businesses to enhance their marketing strategies and better understand their target audiences. The studies discussed demonstrate the effectiveness of leveraging Big Data analytics in various domains such as social media sentiment analysis, personalized marketing, predictive analytics, customer segmentation, location-based marketing, and real-time personalization. These strategies enable businesses to tailor their marketing efforts more precisely, leading to increased consumer interest, improved engagement, and higher conversion rates. Unique Contribution to Theory, Practice and Policy: Social Identity Theory, Information Processing Theory &amp; Technology Acceptance Model (TAM) may be used to anchor future studies on Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies. Promote the adoption of advanced data analytics tools and techniques by businesses to enhance their marketing strategies. Companies should invest in robust data infrastructure, analytics platforms, and talent to effectively collect, analyze, and interpret consumer data. By leveraging predictive analytics, sentiment analysis, customer segmentation, and real-time personalization, businesses can tailor their marketing efforts to individual consumer preferences and behaviors, leading to improved engagement, customer satisfaction, and ultimately, increased sales and profitability. Advocate for the development of regulatory frameworks that balance consumer privacy rights with the benefits of data-driven marketing. Policymakers should collaborate with industry stakeholders to establish guidelines and standards for ethical data collection, usage, and storage.

The Role of Religiosity, Consumer Animosity, and Ethnocentrism in Explaining the Boycott Motivation

The protracted conflict between Israel and the Palestinian Hamas group, culminating with an Israeli offensive in October 2023, has caused significant suffering to the Palestinian people. The response to the attack, including international support and a fatwa from the Indonesian Ulema Council (MUI), has prompted Indonesians to boycott products linked to Israel. This study aims to investigate the effect of religiosity, animosity and ethnocentrism on the motivation for boycotting Unilever Indonesia products related to the company's commitment to Israel, focusing on these factors. This type of research is quantitative descriptive with the SEM-PLS approach. The application used is SmartPLS version 4.0.0 with sample data used by 162 respondents through the distribution of questionnaires. The results show that the variables religiosity, consumer animosity and ethnocentrism have a positive and significant influence on the motivation to boycott Unilever Indonesia products. These findings highlight the importance of paying attention to psychological and social factors in the analysis of consumer behavior and their impact on companies and society at large. It also suggests that companies must take into account the social and political implications of their policies, as well as understand the values and beliefs underlying consumers' motivations for boycotting.