Repurchase Behavior Research Articles

Predictive models of intent to repurchase based on customer data

As the business environment becomes ever more complex, predicting future buying patterns has become increasingly important. Moreover, there has been growth in the number of consumers focusing on health and self-care given rapid societal and economic changes. This shift is leading to heightened interest in athleisure wear and an increase in sales volume in this domain. It is accordingly necessary for companies in this field to establish marketing strategies based on predicting consumers’ future purchases to maintain continuous growth and competitiveness. This paper surveyed 400 consumers who purchased athleisure wear and established a predictive model for consumer buying behavior using Multivariate Discriminant Analysis. This paper found that a brand's authenticity and the purchaser’s involvement in sports were significant factors prediction customers’ repurchase behavior. Companies should accordingly engage in authentic business practices and consider clothing designs and marketing strategies that can connect consumers' daily lives with their sports activities.

Knowledge-reinforced explainable next basket recommendation

The next basket recommendation task aims to predict the items in the user’s next basket by modeling the user’s basket sequence. Existing next basket recommendations focus on improving recommendation performance, and most of these methods are black-box models, ignoring the importance of providing explanations to improve user satisfaction. Furthermore, most next basket recommendation methods are designed for consumer users, and few methods are proposed for business user characteristics. To address the above problems, we propose a Knowledge Reinforced Explainable Next Basket Recommendation (KRE-NBR). Specifically, we construct a basket-based knowledge graph and obtain pretrained embeddings of entities that contain rich information of the knowledge graph. To obtain high-quality user predictive vectors, we fuse user pretrained embeddings, user basket sequence level embeddings, and user repurchase embeddings. One highlight of the user repurchase embeddings is that they are able to model business user repurchase behavior. To make the results of next basket recommendations explainable, we use reinforcement learning for path reasoning to find the items recommended in the next basket and generate recommendation explanations at the same time. To the best of our knowledge, this is the first work to provide recommendation explanations for next basket recommendations. Extensive experiments on real datasets show that the recommendation performance of our proposed approach outperforms several state-of-the-art baselines.

Mobile Service Quality’s Impact on Customer Repurchase Intention in Food and Beverage Mobile Applications

This study aims to assess the impact of mobile service quality on customer repurchase intention in Indonesia’s food and beverage mobile applications. The research identifies key dimensions such as application design, ease of use, privacy, and customer support and evaluates their influence on customer e-satisfaction and repurchase behavior. A quantitative approach was employed, utilizing purposive sampling to gather data from 401 active users of these applications. The analysis, conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS), revealed that these dimensions significantly enhance overall mobile service quality, which in turn positively affects customer e-satisfaction and repurchase intention. The findings underscore the importance of a minimalist and user-friendly design, robust privacy measures, and responsive customer support—particularly for Gen-Z users in Indonesia, where privacy concerns are increasingly prominent. This study contributes to the existing literature by providing insights specific to the Indonesian market and offering practical recommendations for the food and beverage industry to improve mobile service quality, thereby fostering stronger customer loyalty and increasing repurchase rates. The novelty of this research lies in its focus on the rapidly growing mobile app market in Indonesia, addressing unique regional challenges and opportunities. Doi: 10.28991/HIJ-2024-05-03-011 Full Text: PDF

REVEALING THE MYSTERIES BEHIND REPURCHASE INTENTIONS AND PERSONALIZATION: THE MODERATING IMPACT OF AI ON ELEVATING CUSTOMER RELATIONSHIPS IN THE ERA OF PERSONALIZED ELECTRONICS

AI advancement in recent times has altered thoughts and life in many ways considering its importance in enhancing the technological world. The following research questions will be used to guide this study: What pitfalls of Indian customers affect the use of AI while repurchasing electronic products? This research employed a quantitative research approach and administered self-completed questionnaires on 450 customers of electronics shops in Vellore, Tamil Nadu, India to validate the proposed hypotheses. There are three procedures which were employed in development of the instrument for measuring the latent variables in the quantitative analysis. These were pre-testing of items, ensuring the objectives match the items, and translation followed by back-translation. The application of Amos software was employed in conducting the confirmatory factor analysis on the research model. Exact data analysis was performed by applying Structural Equation Modelling (SEM). Collectively, the findings of the research showed that the effectiveness of artificial intelligence (AI) mediated the relationship between RBI incentives; customer trust, knowledge sharing, and service value, and repurchase behavior.

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.

Exploring the Influence Mechanism of Brand Crisis on Consumer Repurchase Behavior

Exploring the Influence Mechanism of Brand Crisis on Consumer Repurchase Behavior

Research on the Motivation and Effect of "Deceptive" Share Repurchase—Take Orient Group Incorporationas an Example

With the continuous improvement of China's share repurchase system, the policy optimization actively promotes the application of share repurchase in the capital market, and makes it become a common way for listed companies to manage market value. However, the problems presented by listed companies in the implementation of share repurchase plans have gradually been exposed, especially the frequent occurrence of "deceptive" repurchase, which has become the focus of the market. In order to solve this problem, China has issued detailed rules for the implementation of share repurchase, clearly emphasizing the prevention of "deceptive" repurchase, aiming at guiding listed companies to operate in compliance. However, due to the low cost of violation, most listings still fail to comply with the standards and continue to appear "deceptive" buyback behavior. Therefore, by analyzing the motivation of listed companies to implement "deceptive" share repurchase behavior and the economic consequences, this paper provides a warning for investors, urges listed companies to self-examine the share repurchase plan, and puts forward regulatory suggestions for the regulatory authorities to be optimized.

The mediating effect of customer experience on word of mouth and repurchase behaviours in mobile payment services in Tanzania

The mediating effect of customer experience on word of mouth and repurchase behaviours in mobile payment services in Tanzania

The mediating role of brand trust in the effect of social media marketing on repurchase behaviour

This study investigates the impact of social media marketing activities on consumer repurchase behaviours and the mediating role of brand trust in this process. Analyses indicate that the use of social media platforms enhances consumers' trust in brands, and this trust, in turn, strengthens their intentions to repurchase, creating a positive effect. The findings underscore that social media marketing activities can generate significant value for businesses by boosting consumer interaction and brand trust. This research provides substantial evidence of brand trust's positive and meaningful impact in the relationship between social media marketing and repurchase behaviour and how social media marketing strategies can indirectly influence consumer behaviours. Hence, the study suggests that businesses should design their social media marketing strategies to establish solid and trust-based relationships with consumers, highlighting that this approach can positively affect consumers' repurchase behaviours. These findings demonstrate the strategic importance of social media marketing activities for businesses and how brand trust can be leveraged to maximize benefits from these activities.

Improving repurchase behaviour based on relationship marketing: evidence from the message framing of airline in China

ABSTRACT Short Message Service (SMS) marketing, as one of mobile marketing, has been a prominent marketing approach in the service business, particularly in airlines. However, for member groups with a social identity, how to send SMS to promote repurchase behaviour of members? This question is what current research tries to explore. A large field experiment involving 200,000 active members of an airlines was conducted to validate the message framing proposed in this study. Specifically, based on the message framing, we applied 2 promotional SMS (With identity vs. Without identity) x 2 Purchase intention (High vs. Low) and 2 promotional SMS (Feasibility vs. Desirability) x 2 Purchase intention (High vs. Low) between-subjects experimental design and a control group with general reminder messages in a ‘Member Day’ promotion. By analyzing 188,627 respondent’s actual repurchase data, the result shows that the framing effect between with-identity or feasibility messages and high purchase intention significantly positively impacts members repurchase behaviour. In contrast, the effect between without-identity or desirability messages and low purchase intention has no influence. Furthermore, the moderation effect of members’ features on the framing effect has also been discussed. This study has theoretical and practical guidance for the research on relationship marketing, membership management, and loyalty programmes.

Supporting organizational decisions on How to improve customer repurchase using multi-instance counterfactual explanations

Improving customer repurchase intention constitutes a key activity for maintaining sustainable business performance. Returning customers provide many economic and other benefits to businesses. In contrast, attracting new customers is a process that is associated with high costs. This work proposes a novel counterfactual explanations methodology that utilizes textual data from electronic word of mouth to recommend business changes that can improve customers' repurchase behavior. Counterfactual explanation methods gained considerable attention because their logic aligns with human reasoning and the fact that they can recommend low-cost actions on how to turn an unfavorable outcome into a favorable. Most counterfactual explanation methods however recommend actions that can change the outcome of individual instances (i.e. one customer) rather than a group of instances. Therefore, this work proposes a multi-instance counterfactual explanation method that recommends optimum changes to an organization's practices/policies that increase repurchase intention for many customers or customer segments.The proposed methodology utilizes topic modeling to extract customer opinions from online reviews' text and use topics as features to train a binary classifier that predicts customer revisit intention. Multi-instance counterfactual explanations are computed for all or different groups of non-revisiting customers, recommending optimum business changes that can increase revisit intention. The proposed methodology is empirically evaluated through a case study on the restaurant revisit problem and compared against a prominent alternative from the literature. The results show that the method has better performance to the alternative method and produces recommendations that are actionable and abide by the customer-repurchase literature.

Mengeksplorasi Minat Pembelian Ulang Pada Platform E-Commerce

This theoretical review aims to explore the potential influence of free shipping, customer experience, and perceived risk on repurchase intentions in the context of e-commerce. e-commerce has experienced significant growth globally, emphasizing the need to understand the factors that influence consumer repurchase behavior. Based on existing literature, this paper examines the theoretical basis and research results regarding the influence of free shipping interest, customer experience, and risk perception and their potential impact on consumers' willingness to repurchase on e-commerce platforms. This review highlights the importance of the factors free shipping, customer experience and risk perception in shaping consumer behavior and suggests avenues for future empirical research to validate the proposed relationships. Understanding these dynamics can help e-commerce businesses in formulating effective marketing strategies to increase customer retention and loyalty.

How Does Queueing Information in Pre-Sales Call Centers Affect Customer Repurchase Behavior

With the growth of mobile e-business, it has become common for customers to experience an explosion of instant desire to consume in a short period of time. Companies are harnessing this momentum, hoping to better capture, retain and convert these consumer desires into actual sales orders. Consequently, companies also need to use pre-sales call centers to provide the satisfied service and motivate more customers to repurchase, thereby increasing the revenue. The function of the call centers is updated from providing post-sales service to providing pre-sales consultation. In this regard, this paper examines the impact of delayed announcements consisting of queue information on customer repurchase behavior in pre-sales call centers. We classify the queuing information into no-information, part-information and full-information according to the level of information by constructing a simulation model. In the simulation experiments, some application scenarios are set up to describe the load of the call center. In all the given scenarios, we find out the full-information, which provides the delay time, is always the best. Moreover, the different methods for estimating the delay time to provide the full-information, namely LES, EA, EA2 and WA–LES, are compared. We find optimal announcements for the application scenarios set out in the time-varying scenario as well. Consequently, considering there exists certain cost for the company to make WA–LES announcements, while some call centers can only provide part-information instead, this paper investigates the impact of estimate bias on customer repurchase behavior and company revenue. Subsequently, the company revenue is further investigated in two extended experiments, where repurchase influence power and repurchase number are limited. The analysis ideas and simulation logic of this paper have good reference significance for e-commerce pre-sales call centers, and the suggestions for setting information levels in practice while considering customer repurchase behavior are also clearly given in the conclusion of the paper.

An Intelligent Network Method for Analyzing Corporate Consumer Repurchase Behavior Using Deep Learning Neural Networks

Earth system models (ESMs) are our key tools for analyzing the planet's existing state and predicting its evolution in the next continuing human-caused events. However, the use of artificial intelligence (AI) approaches to augment or even replace conventional ESM functions has expanded in recent years, raising hopes that AI will be able to overcome some of the major difficulties in climate research. We address the advantages and disadvantages of neural ESM neurons, as well as the unsolved question of whether AI will eventually replace ESMs. Dynamic geophysical events are the foundation of Earth and environmental studies. Given the widespread acceptance of AI and the growing amount of Earth data, the geoscientific community may wish to seriously explore using artificial intelligence (AI) approaches at a much deeper level. Although it is a tall ambition to integrate hybrid physics and AI approaches from a fresh perspective, geology has yet to figure out how to make such methods feasible. This research is an important step towards realising the concept of combining physics and artificial intelligence to address problems with the Earth's system.

The Effect of Delivery Time on Repurchase Behavior in Quick Commerce

“Quick commerce” refers to meeting consumers’ instant needs by delivering products ordered online within minutes. Although speed is thus inherently important, little is known about how deviations from communicated delivery times (whether late or early) might affect repurchase behavior. The authors study the effects of delivery time deviations on repurchase behavior using a large, customer-level, transaction data set from a Western European food delivery service and a controlled online experiment. The results show that late (early) deliveries increase (decrease) interpurchase times; these effects diminish with larger deviations. The results also show that late deliveries have a stronger effect on repurchase behavior than early deliveries of the same magnitude. The controlled online experiment establishes customer satisfaction as the underlying psychological mechanism that mediates the effect of delivery time deviations on repurchase behavior. These findings advance understanding of delivery time deviations and repurchase behavior by complementing prior research on disconfirmed waiting times and quick commerce. Practitioners can use the results to optimize their delivery algorithms, operations, and service recovery efforts.

메타버스에서의 정서적 경험이 고객반응에 미치는 효과

The Metaverse as a combination of physical and digital space, provides many consumers with a different process of search, evaluation, consumption, and disposal than before, based on hyper-connected and hyper-realistic services. Therefore, it is necessary to examine what kind of sensory experiences customers feel when performing various activities in the Metaverse and how these experiences affect customers' behavioral responses. This study measured the temporal and emotional experience of the Metaverse service environment felt during the Metaverse experience journey of customers experiencing medical tourism services. The results of the study showed that the emotional experience through Metaverse had a more positive impact on customer satisfaction and customer loyalty as the intensity of the Metaverse experience deepened. In particular, it was confirmed that the emotional experience at the end of the Metaverse service had a positive influence on repurchase behavior. These results show that, just as in the real world, customer experience in the Metaverse can provide important insight into understanding customers' post purchase behavior and how service providers can develop and implement effective customer experience strategies in the Metaverse environment.

Research on Prediction of User Repurchase Behavior and Marketing Strategies in Internet Marketing

Abstract While users consume and shop on e-commerce platforms, they will generate a huge amount of data information, and tapping the potential value of these data can optimize online marketing and bring users a better consumption experience. This study aims to predict users’ repurchase behavior and formulate personalized marketing strategies by analyzing their repurchase behavior on e-commerce platforms. First, the improved RFM model and K-means++ algorithm are utilized for user value classification. Then, a model for predicting user repurchase behavior was constructed based on Logistic regression, XGBoost, and SVM, respectively, and the prediction effects were compared. Then, the prediction models UI and U-C are built based on the XGBoost algorithm from the perspective of user and product category, respectively, and fused using the Soft-Voting method. The prediction effect of the fused models is verified at the end. The F1 values for all three models in the test set are approximately 0.2, and the XGBoost model has a significantly superior prediction effect than the other two models. The precision, recall, and F1 values of the fused model are about 0.31, 0.26, and 0.28, respectively. These values have been improved by about 4%-19% compared to the pre-fusion. The fusion model’s ROC curve is located at the upper left corner and has an AUC of 0.82, indicating high accuracy and stable results. This study provides feasible suggestions for the development of online marketing strategies to promote user repurchase behavior.

Impact of customer intelligence on customer repurchase behaviour in hotels in South-West Nigeria

This study examined the effect of customer intelligence on customer repurchase behaviour in hotels in South-West Nigeria. The model for the research was developed on the consequent variables of the study to test the research hypotheses. A total of 1972 copies of usable questionnaires were retrieved from customers of 160 conveniently sampled midscale and luxury hotels, and used for analysis. Descriptive statistics were used to analyze the demographic elements, while multivariate analyses were conducted to reveal the statistical relationship between customer repurchase behaviour and the customer intelligence constructs. The Pearson's correlation was employed to test the research hypotheses to determine significant associations and relationships between the consequent variables of the study. Results showed the effectiveness of the regression model in identifying relationships between the consequent variables, in which the four null hypotheses were rejected. The study concludes that a consideration of the factors that have a significant effect on the various dimensions of customer repurchase behaviour, and the emergence of customer attrition and retention, can help hotel organizations make better product marketing decisions. Recommendations include that hotels should ensure that their customer analysis is directly targeted, to ensure the continuous retrieval of actual information that will directly influence business decisions.

The Effect of Product Diversity, Price, Service Quality, and Location on Repurchase Intention Mediated by Pharmacy Customer Satisfaction

This research aims to analyze the Effect of Product Diversity, Price, Service Quality, and Location on Repurchase Intention mediated by Pharmacy Customer Satisfaction in Banda Aceh City. The study involved 208 respondents who shopped at six pharmacies in the city. Data collection was conducted using a Likert scale questionnaire, and the analysis utilized Structural Equation Modeling (SEM) with SmartPLS Version 3.0. The results of the analysis show that although Product Diversity has a positive effect on repurchase intention, this effect is not significant, suggesting that pharmacy managers should prioritize other factors that significantly influence repurchase behavior. Price positively and significantly affects repurchase intention but does not significantly impact customer satisfaction, indicating that customers may value other aspects more. Service Quality shows a positive and significant effect on both repurchase intention and customer satisfaction, emphasizing the importance of providing high-quality service. Location emerges as the most significant factor influencing customer satisfaction, with an O value of 0.366, T Statistics of 4.685, and a P-value of 0.000. Additionally, Customer Satisfaction has the strongest impact on repurchase intention, with an O value of 0.419, T Statistics of 4.759, and a P-value of 0.000, highlighting that efforts to improve customer satisfaction should be a top priority. To enhance customer satisfaction and drive repurchase intention, pharmacy managers should focus on strategic location choices, high-quality service, and initiatives such as loyalty programs, responsive customer service, and relevant product offerings. By prioritizing these elements, pharmacies can better meet customer needs and foster long-term loyalty.

Controlling shareholders' share pledge and share repurchase notices of listed companies.

States encourage listed companies to use stock repurchase to elevate the market value of listed firms. After China's promulgation of the new Company Law in 2018, the number of listed companies that issued stock repurchase notices has increased, and the frequency is also increasing. But whether market value management is the real incentive for the action remains debatable. To reduce the risk of pledges, controlling shareholders may use stock repurchases to maintain the security of control rights, and stock repurchase notice may become a tool for controlling shareholders to manage pledge risks. From the perspective of pledge risk management, this paper selects the listed companies from 2012 to 2019 and finds that the share pledge of the controlling shareholder affects the stock repurchase behavior of listed companies by affecting the current pledge risk and the quality of information disclosure plays the interactive role between the two.