Customer Behavior Research Articles

The effects of new product quality choices and customer behavior on the in-house refurbishing model versus the outsourcing model

Background Refurbishing encompasses the process of collecting used products, refurbishing them into new ones, and offering them to consumers. Within this framework, manufacturers can opt for either in-house refurbishing, managed internally, or outsource these operations to third parties and retailers. Methods Our study also takes into account the limited supply of remanufacturable products and incorporates customer behavior distinctions, differentiating between high-end and low-end customers in our models. The primary aim of this research is to formulate and analyze models for in-house refurbishing, refurbishing outsourcing to third parties, and refurbishing outsourcing to retailers. We seek to identify optimal solutions, conduct a comprehensive analysis, and implement strategies to maximize benefits. Additionally, we compare the in-house refurbishing model with the outsourcing refurbishing model. Leveraging multivariable function optimization techniques alongside the Karush-Kuhn-Tucker method, we endeavor to ascertain the optimal strategy, thereby facilitating a nuanced and methodical examination of the intricate dynamics governing profitability and sustainability within the refurbishing sector. Results Our study has applied rigorous mathematical modeling and optimization methodologies to identify optimal configurations for each model, aiming to enhance profitability across diverse parameters, including product quantities, quality selections, and pricing strategies. Conclusions Our analysis underscores that the in-house refurbishing approach tends to yield superior profits for profit-oriented enterprises, whereas outsourcing refurbishing to retailers is conducive to meeting environmental sustainability goals. Furthermore, our investigation has considered pivotal factors such as the constrained availability of remanufacturable products and consumer behavior, with a particular emphasis on discerning preferences among high-end and low-end clientele. We also perform sensitivity analyses to assess the impact of parameter variations on optimal outcomes. Our findings yield three distinct refurbishing models: the in-house refurbishing model, suitable for profit-driven companies under specific production cost parameters, and the outsourcing refurbishing to retailers model, tailored for environmentally-conscious companies, contingent upon particular production cost parameters.

Customer Behavior Prediction using Machine Learning with Fuzzy Multiple Linear Regression

Customer Behavior Prediction using Machine Learning with Fuzzy Multiple Linear Regression

Review of Media Advertising's Effects

To sway customer behaviour, marketers make investments across a range of media channels(CB). Every media platform has a unique composition of advertisements that engage viewers in adistinctive way. The media habits of consumers have changed as a result of digitalization. Therefore, adeeper comprehension of media platform advertisements and their effects on CB must be formed. In thispaper discuss on media advertising effects.

Network Analysis of Two-Stage Customer Decisions with Preference-Guided Market Segmentation

Abstract Network-based analyses have shown their effectiveness in understanding customer preferences through interactions and relationships between customers and products, particularly for tailored product design. There is limited research on applying this analysis to diverse customers with varied preferences. This paper introduces a market-segmented network modeling approach, guided by customer preference, to explore heterogeneity in customers' two-stage decision-making process: consideration-then-choice. In heterogeneous markets, like household appliances, even customers with similar characteristics or purchasing similar products can exhibit different decision-making processes. Therefore, In this method, we segment customers into groups based on preferences rather than just characteristics, allowing for more accurate choice modeling. Using Joint Correspondence Analysis, we identify associations between customer attributes and preferred products, characterizing market segments through clustering. We then build individual bipartite customer-product networks and apply the Exponential Random Graph Model to compare the product features influencing customer considerations and choices in various market segments. Using a US household vacuum cleaner survey, our method detected different customer preferences for the same product attribute at different decision-making stages. The market-segmentation model outperforms the non-segmented benchmark in prediction, highlighting its accuracy in predicting varied customer behaviors. This study underscores the vital role of preference-guided segmentation in product design, illustrating how a deeper understanding of customer preferences at different decision stages can inform and refine design strategies, ensuring products are more closely aligned with the dynamic needs and tastes of diverse markets.

The impacts of coupon and discount on exposure and various stages of conversion funnel: An empirical investigation of the online food delivery service

AbstractWith the rapid development of delivery services, online‐to‐offline food delivery systems provide merchants the traffic support and marketing efforts to reach potential customers. Monitoring customer behavior allows us to measure conversion rates and analyze the relationship between marketing performance and the efforts of platforms or merchants. This study investigates the impact of incentives, including coupons from platforms and discounts from merchants, on exposure and conversion rates for different merchants. Besides, the dynamic analysis demonstrates significant adverse effects of lagged incentives on exposure and click rate. Platforms and merchants of online food delivery services stand to benefit from these insights.

Pengaruh Live Streaming dan Brand Ambassador Engagement terhadap Keputusan Pembelian Erigo: Studi Kasus Penggemar Grup Idola Jkt48 terhadap Produk Erigo di Karawang.

This research study aims to examine the influence of live streaming and brand ambassador of idol group JKT48 on the purchasing decisions of Erigo’s product. On this era of digitalization marketing, brands image increasingly leverage interactive online methods and influential personalities to enchance their target market and lead customer behavior. As such live streaming providing a real time immersive and interactive shopping experience that creates a dynamic engaging platform for product presentation. Brand ambassador with their credibility and relatability that can reach the significantly impact customer perseptions and preferences, further leading purchace decisions. This research employs a quantitative employ with surveying questionnaire sample of JKT48’s fans that bought Erigo product to gather data impact from their engagement and live streaming with factors influence their purchasing decisions.

Optimalisasi Dashboard Pemesanan Makanan Online Menggunakan Looker dan JavaScript

Advances in information technology have enabled rapid development in internet-based services, including online food ordering applications. This growth demands efficient data management and analysis systems to improve user experience and operational performance. This research focuses on developing an optimal online food ordering dashboard using Looker and JavaScript. Research methods include needs identification, literature study, needs analysis, design, and implementation. The research results show that the dashboard developed is able to manage and analyze order data effectively, identify trends, predict customer needs, and increase operational efficiency. This dashboard visualizes important information such as number of orders based on age, gender, income, as well as customer behavior analysis. In doing so, service providers can gain better insight into consumer behavior and ordering trends, supporting more informed and strategic decision making. The results of this study contribute to the literature on the use of data visualization technologies in the online food service sector.

The Influence of TikTok Marketing on Consumer Purchase Intention among Students

TikTok marketing has an enormous and broad effect on the purchasing intentions of students. TikTok has become an extremely effective medium for marketers to connect with consumers due to its interesting and simple-to-read content structure. TikTok's algorithmic content distribution system is designed to draw in users and market a range of goods and services. This study looks into how TikTok marketing affects students' intentions to buy at selected Malaysian higher education institutions. The growing popularity of TikTok as a social media platform, especially among young people, emphasises the need to comprehend its influence on consumer behaviour. In this study, a quantitative methodology was applied, and analytical approaches employed, including reliability analysis and multiple regression analysis. The study concentrated on three important variables that affect consumers' intentions to buy: brand awareness, product quality, and price. A total of 115 respondents from the chosen universities took part in responding to the sent questionnaire. The research aims to offer insights into how businesses may effectively exploit the platform to engage with students as consumers by looking at these aspects in the context of TikTok marketing. The findings show that TikTok marketing has a major impact on student purchase behaviour, with product quality and price having a major influence on intentions to buy. All things considered, this study deepens our knowledge of how TikTok affects consumer behaviour, especially among students. It demonstrates the platform's potential as an ideal instrument for engaging students as customers and provides important insights for businesses trying to maximise their TikTok marketing efforts. Future studies on the evolving landscape of social media marketing and its effects on customer behaviour under various conditions can build on this research.

Factors Influencing Customers’ Restaurant Dining Behaviour During and After the COVID-19 Pandemic

The COVID-19 pandemic has drastically altered the dietary habits and preferences of the global population. Therefore, the aim of this study is to identify factors that influence customers’ restaurant dining behaviours and examine the most notable factors that influence the customers’ behaviours during and after the COVID-19 pandemic. This is because every patron would consider several matters before deciding to choose the type or location or restaurant they would like to eat at. Customers’ decisions are typically influenced by several variables and standards, including the quality of food, quality of service, physical environment, and facilities or amenities. This quantitative-research study has primarily targeted three states in the East Coast of Malaysia, namely Pahang, Terengganu, and Kelantan. The study has employed an online questionnaire to customers who have dined out at restaurants before and after the COVID-19 pandemic. A total of 405 participants have actively participated in the study and responded to all the questions in the online questionnaire. The findings of the study have indicated that food quality, service quality, restaurant environment, and facilities or amenities have a substantial impact on customer behaviour when dining out both during and after the COVID-19 pandemic.

Unlocking the key drivers of consumer purchase intentions for organic cosmetics: A study in Can Tho City, Vietnam

This study's objective to evaluate Can Tho City's customer behavior with respect to organic cosmetics. The research was carried out utilizing a cross-sectional descriptive design and a collection of inquiries according to the theory of planned behavior (TPB), which evolved from the 5-point Likert scale. The survey was undertaken to interview 559 Can Tho City residents who use organic cosmetics, applying various interview questions to measure their behavior when considering the characteristics of the research subjects. Research findings show that customers have a positive attitude towards skincare using organic cosmetics, and the trend toward safe, environmentally friendly products is increasing. Factors such as perceived behavioral control, advantages, difficulty, and subjective norms have a strong influence on customers' purchasing intentions. What is quite interesting is that promotion does not significantly affect consumer choices.In Viet Nam, organic cosmetics are not subject to any particular rules or regulations in this country, that lead many customers to see them as natural cosmetics. So that, businesses can develop policies, improve product quality specifically or create a discussion board where individuals may seek advice and impart additional organic cosmetics expertise. However, the number of survey samples is quite high (559), but this is still not statistically significant if you want to use it to analyze some anthropometric factors more closely and the factors surveyed are not all factors affecting the consumption behavior of organic cosmetics customers. The other researchs in the future can use different factors and theories to find or verify.

PURCHASING BEHAVIOR AND SATISFACTION OF FAST-FOOD RESTAURANT CUSTOMERS IN CALBAYOG CITY

This paper examined the relationship between satisfaction and purchasing behavior of fast-food res­taurant customers in Calbayog City. In addition, this study also aimed to identify the respondents' profiles and the different factors influencing their satisfaction and purchasing behavior. The researchers used a descriptive-correlational research design to conduct this study. The researchers used a structured questionnaire adopted from a previous study. The questionnaires were provided to 384 participants in Calbayog City. The gathered data were analyzed using SPSS 29. The analyzed data by the researchers showed that family members, dine-in or online order options, and restaurant menus influence the purchasing behavior of fast-food restaurant cus­tomers. On the other hand, the satisfaction of fast-food restaurant customers in Calbayog City was influenced by menu options, payment methods, online application or website navigation, food placement technology, customers’ testimonials, friends and family recommendations, and services. The study revealed no significant relationship between satisfaction and purchasing behavior of restaurant customers in Calbayog City.

Personal Data Collection In Business As Seen By The Brazillian Data Protection Law (LGPD)

The collection and analysis of large volumes of data -Big Data- in the most diverse formats, from Internet browsing patterns to postings on social networks, allows stakeholders to make better-oriented decisions, solidifying the data-driven business standard. Nowadays, there is an explosion of information, whether through the internet and its social networks, or through corporate applications, as well as cell phones and smartphones, RFID reader tools and video control cameras for traffic, security, etc. culminate in a large mass of highly complex, structured and unstructured data. Such data are extremely relevant if it is taken into account that they can collaborate to indicate the type of purchase behavior of a certain customer until identifying a crisis in a sector of the economy, migration of consumers to the competition and outbreaks of infectious epidemics, such as the Zika Virus and the H1N1 flu. Turning attention to what is called Big Data is becoming more and more essential, for investing in new technologies, analysis methodologies and adequate software tools. Certainly, we currently have great strength in the information technology scenario, which mobilizes many individuals who research, talk about and seek to understand what it is, how it can be used and how this phenomenon called Big Data is formed. In this scenario, the debate on the right to privacy and intimacy becomes relevant in the face of the use of this information by companies and organizations, analyzing the vulnerability of the internet user in the face of Big Data and the national and international legislation on the subject, as well as aiming to explore and to describe how companies try to get to know their consumers better, through the analysis of data that are posted on virtual media.

Navigating the landscape of e-commerce: thematic clusters, intellectual turning points, and burst patterns in online reputation management

AbstractRecognizing the challenges identified in the vast literature exploring the intellectual landscape of Online Reputation Management (ORM) in the realm of e-commerce, this study performs a quantitative bibliometric analysis, specifically a co-citation analysis using CiteSpace software, to find thematic clusters in a sample of 1136 papers containing 48,385 cited references. This is the first co-citation analysis of ORM literature that cluster the intellectual structure and identifies both the intellectual turning points and burst papers. The results reveal 14 distinct co-citation clusters, each representing a unique thematic structure. An in-depth analysis further characterizes the clusters, ranging from the impact of online reputation on the hospitality industry to frameworks explaining trust formation in e-commerce. Additionally, the study identifies intellectual turning points by assessing betweenness centrality, highlighting four seminal papers that have strongly influenced the field. Furthermore, burst detection analysis uncovers the temporal dynamics of research trends, showcasing the enduring influence of certain clusters and the transient nature of burst patterns. The novelty and importance of the results from the detailed burst detection analysis lie in identifying a significant evolution in research focus over time. Initially, research was concentrated on foundational studies and understanding customer behavior. It then shifted towards practical applications in specific industries, particularly in hospitality and online reviews. In recent years, the emphasis has been on integrating ORM into broader business strategies, especially within e-commerce and the collaborative economy. This research not only contributes to a deeper understanding of ORM, but also serves as a valuable guide for researchers, practitioners, and policymakers in the evolving landscape of online reputation in e-commerce.

Predicting Consumer Shifts to Sustainable Products using Machine Learning Models

This study evaluates the extent to which consumers are willing to switch to sustainable products and identifies the strategic measures to establish sustainable brands. Consumer preferences and behaviours are analysed to identify the early adopters of sustainable products The study employs several machine learning algorithms including Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), to determine the likelihood of consumers switching towards sustainable choices. SMOTE (Synthetic Minority Over-sampling Technique) was applied to address the class imbalance in the data. The models were evaluated using metrics such as accuracy, precision, recall, and F1 score. The results indicated that the Random Forest and SVM outperformed the other models in predicting consumer willingness to adopt sustainable products. This study demonstrates the potential of machine learning techniques in understanding customer behaviour, thereby supporting marketers in promoting sustainable brands.

Predicting customer churn in telecommunication sector using Naïve Bayes algorithm

The telecom sector creates huge amounts of information every day as a result of its large customer base. Business professionals and decision-makers emphasized that maintaining existing clients is less expensive than recruiting new ones. Business analysts and customer relationship management (CRM)need to know the reasons why customers leave and the behavior patterns from earlier churn consumer’s data. Today, there is a problem with customer churn examination and prediction in the telecom industry since it is crucial for the sector to examine customer behavior to identify those who are going to stop their subscriptions. Customer retention could be increased by utilizing detection system to detect consumer behavior. Recent advancements in machine learning(ML)have made churn prediction more precise and practical. It is essential for identifying customers ready to leave using company’s products and services in the early stage. Hence in this work, predicting customers churn in telecommunication sector usingNaïveBayes(NB) model is presented. The performance of presentedNBalgorithmis evaluated using the parameters accuracy, precision, and sensitivity. The NB algorithm will have better performance than pervious approaches.

The Effect of Group Foodservice Servicescape on Store Image, Relationship Quality and Customer Behavior - Focusing on the Moderating Effect of Personality Type -

The Effect of Group Foodservice Servicescape on Store Image, Relationship Quality and Customer Behavior - Focusing on the Moderating Effect of Personality Type -

Structural Relationship Between Perceived Value, Customer Behavior, and Continuous Use Intention of Sports Business Platform Users

Structural Relationship Between Perceived Value, Customer Behavior, and Continuous Use Intention of Sports Business Platform Users

From Data to Decisions: Predictive Machine Learning Models for Customer Retention in Banking

This research proposes machine learning—a subfield of artificial intelligence-based approaches to forecast bank client attrition. The study promotes investigating the possibility of churn through the examination of consumer behaviour. In this paper, a method to predict customer churn in a bank using machine learning techniques, which is a branch of artificial intelligence, is proposed. The research encourages the exploration of the likelihood of churn by analysing customer behaviour. The number of service providers is increasing very rapidly in every business these days. As a result, customer churn and engagement have become one of the top issues for most of the banks. In this work, the Random Forest, SVC, XGB, LGBM, and Logistic Regression classifiers are employed. A few feature selection strategies have also been applied to validate system performance and determine which features are more relevant. The test was conducted using the churn modelling dataset from IEEE dataset. The results are compared to find an appropriate model with increased precision and predictability. As a result, when utilised after oversampling, the Random Forest model outperforms other models in terms of accuracy. The Random Forest model showed a significant improvement in accuracy compared to the other classifiers, making it the preferred choice for predicting customer churn in the banking sector. The feature selection strategies helped identify the most relevant features that contribute to accurate predictions. Overall, this study highlights the importance of implementing advanced machine learning techniques and feature selection methods to enhance customer retention strategies in the banking industry.

The Role of Leadership in Transforming Retail Technology Infrastructure with DevOps

In the fast changing retail technology market, DevOps principles are transforming how firms manage and improve their technological infrastructure. This study examines how leadership may use DevOps to improve retail technology and operations. As retail embraces digital transformation, technological strategies must fit with business goals. This change requires leaders to guide their firms through complicated infrastructure upgrade and operational efficiency. DevOps, which emphasizes communication between development and operations teams, continuous integration and delivery, and automation, has become essential for technological infrastructure agility and efficiency. DevOps benefits retail companies in a competitive world with quick customer behavior and market fluctuations. These include increased deployment frequency, time-to-market, dependability, and scalability. DevOps demands a fundamental change in corporate culture and leadership, not simply new technologies and procedures. This article discusses how successful leadership can promote collaboration, transformation, and DevOps alignment with strategic corporate objectives to achieve this cultural transition. Leadership helps overcome change resistance, foster cross-functional cooperation, and incorporate DevOps into the organization's technology and business operations. Leaders must also address talent shortages, resource limits, and outdated systems that might hinder DevOps rollout.

A effect of active senior customer experience value on subjective well-being, and customer behavior intention of hotel: moderating role of hotel booking method

A effect of active senior customer experience value on subjective well-being, and customer behavior intention of hotel: moderating role of hotel booking method