Consumer Behavior Analysis Research Articles

Loss Aversion Distribution: The Science Behind Loss Aversion Exhibited by Sellers of Perishable Good

This research introduces the concept of the loss aversion distribution, a pioneering framework designed for the analysis of consumer behavior. Departing from the conventions of traditional exponential models, this innovative approach incorporates a non-memoryless characteristic, which modulates the consumer's response to loss aversion throughout the product's life cycle. This modulation is achieved by a variable exponent influenced by the parameter $b$, representing the psychological impact of loss aversion, and the constant $k$, which reflects the market value of the good at the time of manufacture. Together, these parameters adeptly encapsulate the dynamic nature of consumer loss aversion from the moment of manufacture to the point of expiry. The model elucidates an initial muted response from consumers at the onset of ownership, which then intensifies during the mid-life cycle of the product, before ultimately diminishing as the product approaches its expiry. Through a meticulous derivative analysis of the probability density function, the study delineates the distribution's key properties, including its monotonicity, boundedness within the interval [0, 1], and its adherence to non-negativity. This framework not only enhances our comprehension of consumer behavior in relation to perishable goods but also paves the way for further investigations into psychometrics and the intricacies of loss aversion modeling.

Utilizing machine learning algorithms for consumer behaviour analysis

Consumer behavior analysis is a cornerstone of modern marketing and business strategy. In today's data-rich environment, businesses have access to unprecedented data about their customers. This wealth of data presents both challenges and opportunities. Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for businesses to understand, predict, and optimize consumer behavior . This essay explores the application of machine learning algorithms in consumer behavior analysis, delving into the methods, benefits, challenges, and future directions in this dynamic field. By comprehensively examining relevant literature, case studies, and real-world examples, this research aims to provide a deep understanding of how machine learning is transforming the landscape of consumer behavior analysis.

Psychological Attribution and Policy Intervention Pathway Analysis of Low Carbon Consumption Behavior of Consumers

Currently, research in the field of shared consumption mainly focuses on analyzing the factors influencing consumer behavior. Although some studies involve individual psychological factors, most of these studies focus on some variables and lack completeness, and few scholars attempt to explain the gap between willingness and behavior. In previous studies on government regulation and governance of the sharing economy, scholars often overlooked the connection between policy intervention factors and individual behavior attribution, resulting in a lack of effectiveness and persistence in the policy intervention path and strategic effects of sharing consumption behavior. This article explores the psychological attribution of low-carbon consumer behavior and analyzes the path of policy intervention. Through literature review and empirical research, this article finds that consumer low-carbon consumption behavior is influenced by various psychological factors, such as environmental awareness, social responsibility, moral concepts, etc. Meanwhile, policy intervention plays an important role in promoting low-carbon consumer behavior. This article proposes specific paths for policy intervention, including raising public environmental awareness, guiding enterprises to assume social responsibility, and strengthening policy supervision.

Energy Consumption Behavior Analysis in the UAE Educational Buildings for Sustainable Economy: A Case Study of Ras Al Khaimah Schools

Energy-saving in educational building has a significant contribution and a vital role in the economic and social development of nations. In this paper, the energy consumption of some educational buildings in the United Arab Emirates (UAE) are collected and analyzed. Four schools within the region of Ras Al Khaimah were selected to study their buildings’ energy consumption behavior. Key energy, economic and environmental parameters were calculated and investigated such as the total annual energy consumption, energy consumption per person, energy consumption per area, energy consumption by category (AC, lighting, other devices, etc.), energy cost, and the amount of greenhouse gas (GHG) emissions. A detailed energy audit was applied to one of the selected schools in order to identify the major areas of energy usage within the school, and to suggest the appropriate measures for potential energy and cost savings. Results revealed that total annual energy consumption of the investigated schools is very high with a range of 3700 - 3900 MWh. Significantly, in these hot climatic regions, AC systems alone accounts for over 80% of the overall annual energy consumption, standing out as the predominant energy consumers particularly during the summer months. The annual energy consumption per unit area was also excessive with values ranging from 200 to 500 kWh per m2. Total annual cost of energy was relatively substantial for all schools with values in the order of 1.2 - 1.5 million AED ($330,000 - $400,000). The negative effects of the CO2 emissions were estimated to be around 500 - 600 ton of CO2/year per a single school.

MARKETING COMMUNICATION STRATEGY PT. GO-JEK INDONESIA IN INCREASING THE NUMBER OF CONSUMERS

This study investigates the dynamic marketing and communication strategies used by PT. Gojek Indonesia to strengthen its consumer base. As one of Southeast Asia's leading technology-based platforms, Gojek has rapidly expanded its services beyond ride-hailing to include food delivery, digital payments, and more. This research uses a qualitative approach, combining interviews, case studies, and industry analysis to dissect the multifaceted strategy implemented by Gojek. This study explores Gojek's proficient use of digital channels and social media to drive consumer engagement. By analyzing consumer behavior and preferences, the study explains how Gojek tailors its marketing messages to resonate with diverse target audiences. In addition, this study examines the role of strategic partnerships and collaborations in strengthening Gojek's market presence and attracting new consumers. Furthermore, the study explores the impact of innovative features and services introduced by Gojek, such as loyalty programs and seamless user interfaces, on customer acquisition and retention. The study identifies the critical role of data analytics in shaping personalized marketing campaigns that appeal to specific consumer segments. The findings from this study contribute to a broader understanding of effective marketing and communication strategies in technology-based service industries. The insights derived from Gojek's approach provide valuable lessons for businesses looking to navigate a competitive market and expand their consumer base through strategic marketing initiatives.

Purchasing Vegetables in the Digital Era: A Systematic Literature Review on Online Buying Behavior in E-Commerce

This study aims to examine and analyze consumer behavior in purchasing vegetables online through E-Commerce in Indonesia. This can contribute to the creation of online food product marketing in Indonesia. A systematic literature review (SLR) was conducted based on studies of purchasing vegetables online through E-Commerce. The data collection method used the Population, Intervention, Comparison, and Outcome (PICO) framework. The selected articles were relevant to the research objectives (with keywords "Vegetable Purchase" and "Vegetable E-Commerce"). The selection process results showed that out of 171 articles, they met the specified criteria. Consequently, 40 articles were chosen based on the research statement for further review. Findings indicate that various factors, including technology adoption, trust, and social factors, influence consumer behavior in online vegetable purchases in Indonesia. By understanding and meeting consumer needs and expectations and leveraging digital technology, a satisfying shopping experience can be created, thus encouraging repeat purchases.

Cross-Sectional Analysis of Infant Diet, Outcomes, Consumer Behavior and Parental Perspectives to Optimize Infant Feeding in Response to the 2022 U.S. Infant Formula Shortage.

An anonymous, electronic, cross-sectional, retrospective survey was used. U.S. parents (n = 178) whose infants were ~10 weeks old during the shortage completed the survey. Of parents, 81% switched formulas during the shortage, 87% switched because they could not find the formula they typically used, 34% switched 3-5 times, 29% of parents visited ≥4 stores/24 h and 26% of parents traveled >20 miles/24 h to purchase formula. Use of infant formula increased (p < 0.01); in infants requiring specialty formula, use of intact cow's milk formula increased (p < 0.05) and use of premature infant formulas decreased (p < 0.05). Infants relying on specialty formulas experienced at least one undesirable outcome compared with non-specialty users. Parents used social media, relatives/friends and healthcare providers for support during the shortage, but their helpfulness scores were suboptimal. Parents reported the need for greater infant formula availability, free prenatal lactation education and postpartum lactation support. Government, regulatory and healthcare policy oversight are needed to protect the infant feeding system, including more commercially available products, access to banked donor milk and lactation support.

Parasocial Interaction on Indonesia’s Beauty Influencer: Antecedents and Consequences

Consumers' dependence on product reviews provided by other consumers makes consumers make social media influencers (SMI) a source of information and reference before purchasing products. The influence of SMI on their followers has led many beauty marketers to use social media influencers to communicate their products. However, the increasing use of influencer marketing by marketers has led to consumer suspicion of the authenticity of influencers. Therefore, based on the stimulus-organism-response (SOR) framework, this study analyzes consumer behavior through the content attributes and interaction strategies exposed by social media influencers to followers, leading to consumers' internal perceptions of parasocial interaction, hedonic value, and utilitarian, and ultimately influencing consumers' response of purchase intention. This study used PLS-SEM as a data analysis technique using a sample of 363 respondents. The PLS-SEM results revealed that content attributes (i.e., attractiveness and prestige) and interaction strategies (i.e., interactivity) positively influence parasocial interaction and, in turn, lead to purchase intention. The results also show that parasocial interaction has a significant effect on hedonic (perceived transparency and perceived enjoyment) and utilitarian (informativeness and credibility) responses. It was also found that hedonic (perceived enjoyment) and utilitarian (credibility) responses significantly influenced purchase intention. Meanwhile, the hedonic response (perceived transparency) and utilitarian response (informativeness) did not significantly affect followers' purchase intention. This research demonstrates the role of content attributes and interaction strategies in creating parasocial interactions and the effects of parasocial interactions on hedonic, utilitarian and purchase intention responses. Based on the results of this study, marketers and influencers are advised to build intimate relationships with the audience to increase purchase intention. This relationship-building can be created through high interactive power, attractiveness, and prestige content. Then, it is also recommended to pay attention to aspects of audience comfort and maintain the credibility of influencers to shape the buying interest of the audience of social media influencer content.

Analysis of Green Consumer Behavior on Purchasing Waste Recycling Products Reviewed from Environmental Knowledges, Recycle Behavior, and Government Regulations

Indonesia is the second largest producer of waste after China with an estimated 0.48-1.29 million metric tons per year. Based on data from the Environment, Hygiene and Landscaping Agency (DLHKP), as of 2022 the Klotok Final Management Site (TPA) receives as much as 140 tons of waste per day, the waste is included in organic waste and inorganic waste. Meanwhile, inefficient waste handling can cause water quality in an area to be very poor and contain toxic waste and garbage that is allowed to accumulate can cause flooding and endanger public health. One of the community organizations that cares about waste processing is the Melati Waste Bank in Kediri City. The Melati Waste Bank is productive in carrying out activities to reduce waste in Kediri City, this is evidenced by the process of sorting waste, processing sorted waste, then distribute the product into the society. Unfortunately the efforts are under the expectations of the management and volunteers of the Melati Waste Bank are due to lack of interest in purchasing waste recycling products. This research uses qualitative methods using triangulation techniques where research is carried out by interviews. So as to obtain the results that consumer behavior in purchasing waste recycling products is based on environmental knowledges, recycle behavior and government regulations related to restrictions on the use of single-use plastics.

Analysis of Consumer Behavior in Purchasing Vegetable Products at Alfresh Store

This research is to determine consumer behavior in purchasing vegetable products at the Alfresh Store. This research was conducted using a qualitative descriptive method. The data sources used are primary data and secondary data which help complete the research data, with data collection techniques through interviews. The results of this research show that consumer behavior patterns are in accordance with the approach (Awareness, Appeal, Ask, Act, Advocate) Hermawan Kertajaya is Effective. Without realizing it, consumers have actually implemented an approach in accordance with Hermawan Kertajaya's 5A. Awareness consumers know Alfresh from their neighbors, Appeal is what makes consumers interested in Alfresh because it is attractive in terms of place and logo. Ask asks about the price and quality of the product. Act finally made a purchase at Alfresh because of the service and product quality at Alfresh. The consumer advocate recommended Alfresh to his neighbors

How Are Consumer Perspectives of PV Rooftops and New Business Initiatives in Indonesia’s Energy Transition?

In the evolving landscape of the energy sector, it is vital for energy companies to grasp consumer behaviors to foresee future business prospects and risks. This study delves into how consumers react to Indonesia’s Electricity Company’s innovative business propositions, particularly the PV rooftop product. It also seeks to understand market reactions to other novel business concepts beyond the PV rooftop product. This research employs a quantitative approach, utilizing surveys for data gathering and statistical methods for analysis. Key variables examined include Attitude, Environmental Concern, Subjective Norm, Perceived Behavior Control, Personal Norm, and Regulation, with the primary focus on the Purchase Intention for the PV rooftop product. The findings reveal that key variables, specifically Personal Norm, Subjective Norm, and Regulation, significantly influence consumer behavior toward rooftop PV. This study also uncovers a high market demand for yet-to-be-launched services like electrical repairs, smart home consultancy, and micro-loans, indicating a diverse market potential for these innovative ideas. This research highlights the importance of analyzing consumer behavior in predicting the potential for both opportunities and challenges in a company’s new business ventures, particularly in the renewable energy sector. The results underscore the significant impact of sociocultural factors and regulatory frameworks on consumer decision-making processes. These insights offer critical guidance for Indonesia’s energy stakeholders in developing countries, aiding in formulating new business strategies and identifying market opportunities amidst the global shift towards renewable energy. This study’s key results emphasize the need for energy companies to adapt and innovate in response to consumer preferences and regulatory environments in order to capitalize on emerging market trends.

Increasing the Sale of Long Tail Items on E-Commerce Websites

The advent of e-commerce has transformed the retail industry, offering a platform for retailers to reach a vast audience, enabling consumers to shop from anywhere in the world at any time. However, most e-commerce websites are dominated by sales of popular or mainstream items, referred to as the head of the distribution. The long-tail items, which make up the tail of the distribution, are often overlooked, leading to reduced sales and low revenue for online retailers. This research paper aims to explore strategies that online retailers can use to increase the sale of long-tail items on their websites. It also includes a review of existing literature, a qualitative analysis of consumer behavior, and a quantitative analysis of sales data. The findings indicate that online retailers can increase the sale of long-tail items by optimizing their website design, improving search functionality, using data-driven pricing strategies, and implementing targeted marketing campaigns. These strategies have the potential to improve the visibility of long-tail items, increase consumer engagement, and boost revenue for online retailers. A study has also been conducted on the Retail Rocket e-commerce dataset from Kaggle which involved a meticulous examination of various aspects of user interactions within the online retail platform. The dataset provided a rich source of information, including events such as page views, add-to-cart actions, and completed purchases. The analysis aimed to uncover specific patterns and trends related to these events, shedding light on how users engage with both popular and long-tail items.

Piecewise linear convolutional deep belief classifier for consumer Behavior Analysis in Social Network in Gaussian-embedded clustering

ABSTRACT A novel technique Gaussian-Embedded Clustering-based Piecewise Linear Convolutional Deep Belief Classifier (GEC-PLCDBC) is introduced for enhancing the accuracy of consumer behaviors pattern prediction with minimum time consumption. The proposed GEC-PLCDBC technique performs three major processes, namely, preprocessing, clustering, and behavior pattern analysis. First, preprocessing is carried out using Gaussian distributive neighbor embedding technique to remove the repeated, retweets, and duplicate tweets from the dataset. After preprocessing, the clustering process is said to be performed using Tversky qualitative Hartigan–Wong clustering for grouping similar tweets to minimize the time complexity of the behavior pattern analysis. Finally, the Piecewise Linear Regressive Convolutional Deep Belief Neural Classifier (PLRCDBNC) is used for behavioral pattern prediction such as positive, negative, or neutral based on the extracted words from the tweets. In this way, the GEC-PLCDBC technique improves behavioral pattern prediction while maintaining a low error rate. GEC-PLCDBC technique’s experimental assessment is carried out with respect to clustering accuracy, behavior pattern prediction accuracy, behavior pattern prediction time, and error rate, with respect to different number of tweets. The quantitative results show that GEC-PLCDBC outperforms existing methods by 8% in clustering accuracy and 8% in prediction accuracy of behavior pattern prediction with 15% less time and 26% mean absolute error than the conventional methods.

Analysis of Current Drink Consumption Behavior on Blood Sugar Levels in Adolescents

Adolescents are becoming increasingly familiar with drinks that are currently popular. Things that are “current” at a certain time are things that are trending or popular. Teenagers are increasingly consuming modern drinks for various reasons. Drinking modern beverages has raised some concerns, primarily due to their potentially excessive levels of sugar, caffeine, and calories. Numerous studies have revealed that excessive consumption of sugary drinks is associated with several health problems, including type 2 diabetes, obesity, and other problems. This research contributes to understanding current drink consumption behavior among teenagers and its impact on blood sugar levels. The research method used was quantitative with a cross-sectional approach. The sample size was 182 respondents. The sampling technique is simple random sampling. Research instrument with a questionnaire. Bivariate analysis results: age with current drinking frequency 0.85 (&gt;0.05), amount of pocket money with current drinking frequency 0.01 (&lt;0.05), central obesity with current drinking frequency 0.78 (&gt;0.05), transportation with current drinking frequency 0.03 (&lt;0.05), taste and brand with current drinking frequency 0.00 (&lt;0.05), Blood Sugar Levels with drinking frequency current drink p Value 0.47 (&gt;0.05). Conclusions that influence current drink consumption by students: the amount of pocket money, type of transportation, interest in the taste and brand of current drinks and there is no relationship between current blood sugar levels and the frequency of drinking current drinks.

Analyzing consumer perceptions about food safety by applying the food-related lifestyle approach.

Food safety standards remain a major concern for consumers, especially in developing and emerging economies, due to gaps in the capacities of food safety enforcement institutions and limited investments from private sector actors. This study analyzes consumer behavior and food safety-related lifestyles in Kosovo, a post-communist emerging economy. Consumer groups were identified according to their food-related lifestyle using the classical 2-stage segmentation approach of factor analysis as well as cluster analysis. The study findings indicate that almost two-thirds of the surveyed consumers are fairly or very concerned about food safety. The results reveal that highly educated female consumers are more concerned about personal health risks, food safety-related content, and related diseases. This study provides interesting insights for food safety policymakers and the agroindustry on how to promote food safety consumer awareness effectively with targeted communication strategies.

The Influence of Marketing Mix on Patient Decision-Making in Choosing Nur'aini General Hospital South Labuhan Batu

Analysis of consumer behavior in health services is crucial in securing a company's sustainability. Amid intense competition, consumer perceptions about the quality of service at hospitals greatly influence their visiting decisions. This research focuses on the case study of Nur'aini General Hospital, which experienced a decline in patient visits from 2013 to 2016. The research method used a survey with 79 respondents from inpatients. The results show that the majority of respondents are adults, have secondary education, and most are women. Data is described in various variables such as service products, rates, service places, promotions, people involved, service processes, and physical facilities. The results of this research can help RSU Nur'aini improve marketing and service strategies to overcome the decline in the number of patient visits.

Prediction Of Vegetable Pricing and Replenishment Based on ARIMA Model

Vegetables are indispensable consumer goods in the daily diet of the majority of consumers. Reasonable pricing and replenishment strategies of vegetables are conducive to maintaining the interests of agricultural producers and promoting the stable development of the market. The main contents of this paper are two points: one is to visualize the pre-processed data and establish the distribution map of vegetable sales in recent years; Secondly, the ARIMA time series prediction model was established by linear fitting of the data, and the feasibility test of the prediction results was carried out to obtain the total daily replenishment quantity and price of each vegetable category in a period of time. Under the background of the new era with low-carbon green as the mainstream, this research has broad development space and prospect. The pricing and replenishment strategies formulated in this study can reduce the waste of vegetables in the whole consumption supply chain, maintain the purchasing power and living standard of consumers, and meet the market demand. Marketing techniques that promote healthy foods in supermarkets are important to encourage healthy eating at the population level. The content of this study can provide ideas for the optimization of vegetable supply chain, further use of new technology, reduce waste, improve efficiency, and ensure the quality and supply stability of vegetables. This study is also helpful to analyze consumer behavior and understand consumers' reactions to vegetable price fluctuations and changes in purchasing behavior in order to better predict market trends and adjust production and supply strategies.

A Consumer Behavior Analysis Framework toward Improving Market Performance Indicators: Saudi’s Retail Sector as a Case Study

Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.

Revealing the Influence of Green Product Prices on the Green Purchasing Behavior of Young Chinese Consumers A TPB Perspective

Using the Theory of Planned Behavior (TPB) as the theoretical framework, this article aims to reveal the impact of green product prices on their purchase decisions by in-depth research on the motivations and influencing factors of green purchasing behavior of young Chinese consumers. First, through a comprehensive definition and characteristic analysis of green consumption behavior, the paper comprehensively understands the attitudes and behaviors of the younger generation in the environmental protection trend. A review of domestic and foreign research progress provides a theoretical basis for the research. In terms of green purchasing behavior among young Chinese consumers, research highlights the influence of their attitudes, social responsibility and social media. Through in-depth analysis of the motivations for green purchases, researchers revealed the importance of factors such as environmental awareness, social responsibility, and individual health awareness. In terms of research methods, a comprehensive quantitative and qualitative data collection method was adopted to construct a scientific and reliable research design. In the sample description and analysis of main research variables, samples with different characteristics are described in detail, and factors such as attitudes, subjective norms, and perceived behavioral control are analyzed in depth. Finally, through a specific analysis of the impact of each component of TPB on green purchasing behavior, the interrelationship between these factors is revealed, providing profound insights into understanding young consumers' shopping decisions.

Would OTT-contents satisfy people’s demand? The Korean OTT-services demand analysis

ABSTRACT With the influence of the COVID-19 pandemic, Over-the-Top (OTT) services have gained considerable prominence in the traditional media market, generating increased interest in this domain. However, limited research has been conducted thus far on analysing consumer behaviour within the context of OTT service company business models. Then, this study aims to analyse the Korean over-the-top (OTT) services demand market with the G-K model, considering cost and time constraints. This study differentiate OTT services into two categories: advertisement video on demand (AVOD) and subscription video on demand (SVOD). Subsequently, an analysis of consumer demand is conducted based on each model. The results of the analysis reveal distinct behavioural patterns among users who engage with SVOD and AVOD services. Additionally, it was observed that SVOD consumers exhibit a greater inclination towards substituting their consumption patterns when opportunities for other leisure activities increase, as compared to AVOD consumers.