Customer Behavior Research Articles

The dark side revealed: insights and implications from online brand communities

Purpose This study aims to present a comprehensive review of the literature on the dark side of online brand communities, identifying the dominant themes [determinants of the dark side in online brand communities (OBCs)] in the literature and proposing ways to advance the literature in this area. Design/methodology/approach This study reviewed 72 scholarly articles published between 2009 and 2024 in peer-reviewed journals. A descriptive and thematic analysis of dark-side literature is presented. Findings The contribution of this scoping review lies in identifying the dominant themes in the literature on the determinants of the dark side of online brand communities, proposing management strategies and identifying future research directions for advancing the literature. Six main themes of the dark side of online communities were extracted: information dynamics, group dynamics, unethical practices and brand transgression, provocation and schadenfreude, brand activism and hate and disengagement. Practical implications Firstly, this study highlights the need for brands to engage in the active moderation of OBCs to maintain information credibility and manage information overload. Secondly, to ensure an inclusive community environment for all members, brands need to foster a balanced forum culture and moderation that discourages heterogeneity. Originality/value This study is unique in conducting an in-depth analysis of the literature on the dark side of online brand communities, an area that has received little attention. This review offers new insights that would help brands effectively manage negative aspects of customer behaviours in online brand communities. For managers, this review enables brands to improve their image, reputation and customer value.

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design

Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.

Exploring the effect of government incentives on electric vehicle purchase intention in smart cities

The use of electric vehicles is gaining momentum throughout the world. Low-carbon mobility is a core area of focus for sustainable transportation in smart cities. Policy makers and governments in many countries are incentivizing the manufacture, sale, and purchase of low carbon-emitting vehicles such as electric vehicles. Not many studies have focused on the usage of electric vehicles in smart cities from the perspectives of customer behavior and government incentive. Therefore, the aim of this study is to understand how the moderator government incentives influence potential customers' intention to purchase electric vehicles for smart mobility in smart cities. With the help of the theory of planned behavior and various literature, a theoretical model has been developed which is later validated using CB-SEM technique considering responses from 415 potential customers of electric vehicles. This study has demonstrated that subjective norms and personal norms supported by users’ attitude and behavioral control to use EVs could impact on the purchase intention of electric vehicles with active support of the government incentives. This study has proposed a pragmatic framework helpful for the policymakers to motivate the potential purchasers of the smart cities to purchase EVs.

Breakage analysis for profitability management in high-value, low-frequency loyalty programs

Loyalty programs (LPs) have become crucial tools for businesses seeking to cultivate customer relationships while maintaining profitability. Among the important factors influencing the profitability of LPs is the expiration of points, especially for high-value, low-frequency (HVLF) programs with high financial liabilities. While expiration induces complex dynamics, its long-term effect has been underresearched in the literature. Point expiration can incentivize point redemption and purchases but may affect customer engagement. Thus, determining the overall long-term impact of expiration on profitability is challenging. To evaluate the role of expiration in the profitability of an LP, managers carefully monitor breakage, which corresponds to the fraction of accrued reward points that expire before being redeemed. Although breakage provides a succinct indicator of the impact of point expiration in the long term, its estimation presents several challenges associated with the projection of customer behavior over a long period of time. In this research, we propose a new methodology for estimating breakage, which can assist businesses in making informed decisions regarding the design of their LPs. Our proposal considers models of point redemption and purchases and a detailed description of point expiration according to the internal rules of the program. We apply our methodology to a large regional airline and find that (1) our approach provides better recovery of the breakage than other metrics used in the industry, (2) there are significant variations in the estimated breakage among customers and across markets, (3) customer heterogeneity and dynamics are important for calculating long-term metrics, (4) expiration can be quite inelastic to some strategies commonly used in the industry to equalize breakage, and (5) to decrease the breakage rate, it is more effective to promote more frequent redemptions than to favor rewards that require a greater number of points.

Customer Behaviour towards Online Travel Agency (OTA) Services in the Hotel Industry: A Case Study in Doha, Qatar

This research investigates customer behaviour towards online travel agency (OTA) services in the hotel industry using a quantitative design and correlation research method with 139 tourists in Doha, Qatar. 95% response rate is collected and analysed using SPSS software. The results show that service quality, perceived value, and brand loyalty significantly affect customer satisfaction, while customer expectation does not. However, the study finds that customer satisfaction significantly influences customer behaviour toward OTA services in the hotel industry. The study provides practical insights for hotel managers and OTA services to enhance customer experiences by identifying the key factors that drive customer behaviour. Moreover, it contributes to the existing literature on customer behaviour towards OTA services in the hotel industry by identifying the crucial factors that influence customer behaviour.

Boosting repurchase rates in B2C e-commerce: the role of customer satisfaction and relationship factors

This study examines the impact of critical factors on customer behavior in the B2C e-commerce sector, particularly focusing on e-customer satisfaction, electronic word-of-mouth (e-WOM), e-service quality, electronic trust (e-trust), perceived value, and the intention to repurchase. Using a structured questionnaire, data were collected from 418 respondents in Jakarta Province, Indonesia, and analyzed via Partial Least Squares-Structural Equation Modeling (PLS-SEM) 4.0. The research confirms the significant influence of all 13 hypothesized relationships on the tested variables, both directly and through mediating effects. Key findings reveal that e-customer satisfaction, e-WOM, e-service quality, e-trust, and perceived value serve as critical mediating factors that significantly shape repurchase intentions. Specifically, e-customer satisfaction has been identified as a crucial moderating variable, enhancing the direct effects of other factors on repurchase intentions. Furthermore, the study underscores the paramount importance of e-service quality and e-trust in fostering a positive perceived value among customers, which in turn enhances e-customer satisfaction and encourages repeat purchases. These insights provide valuable strategic perspectives for online retailers aiming to enhance customer loyalty and facilitate repeated purchases. The study advances the understanding of the interplay between these key factors and their collective impact on customer loyalty in e-commerce. It underscores the necessity for e-commerce platforms to maintain high levels of customer satisfaction and trust, ensuring consistent service quality and leveraging positive e-WOM to build enduring customer relationships. Additionally, the implications of these findings are relevant for cross-cultural studies and can be applied across diverse product categories, suggesting broad applicability for future research and practical implementations in e-commerce. This comprehensive analysis not only contributes to the academic discourse on e-commerce but also offers practical recommendations for online retailers to optimize their strategies in a competitive market.

Customer Segmentation by using Annual E-Invoice Data

As businesses navigate the digital landscape, the proliferation of electronic transactions has led to an abundance of valuable data that can be harnessed for strategic decision-making. This study explores the application of CRM and RFM analysis for customer profiling and segmentation, utilizing e-invoice data as a rich source of information. By leveraging these advanced statistical techniques, the research aims to uncover hidden patterns within electronic transaction records, allowing for the identification of distinct customer segments based on their purchasing behavior. The methodology involved collecting and pre-processing one year of e-invoice data from Fit IT Company, followed by applying statistical models to uncover underlying structures and relationships. Furthermore, the research examines the implications of customer segmentation on marketing strategies, customer relationship management, and personalized service offerings. CRM and RFM analyses were performed on the annual sales data obtained as a result of e-invoice usage service to customers. When the results of the analysis were analyzed, the number of transactions belonging to the sender, recipient, and parties in the top 10 every month were extracted. It has been demonstrated that customer segmentation can be conducted more comprehensively by using CRM and RFM analyses together. While CRM analysis focuses on transaction volume and customer relationships, RFM analysis provides a more detailed perspective on customer behavior by evaluating purchase frequency, recency, and monetary value. In the study, by analyzing e-invoice data through these two methods, the most valuable customer groups were identified, and how strategic marketing approaches can be developed for these groups was illustrated. The combined use of CRM and RFM analyses allows for more accurate customer segmentation based on both transaction volume and spending habits. This approach concludes that strategies can be developed to increase customer loyalty, optimize marketing strategies, and improve business performance.

Top-up Frequency Forecasting for Lao Telecommunication Company Using Time-Series Analysis

The study aimed to forecast the top-up frequency for Lao Telecommunication Company (LTC) customers using time-series analysis. Accurate forecasts help the company optimize resource allocation, improve customer service, and boost revenue by anticipating customer demand. The time-series analysis techniques are employed, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average + exogenous variables (SARIMAX) and exponential smoothing (ETS) models, to analyze historical top-up data. The data is pre-processed, identified patterns, and trained the model to forecast future top-up frequencies. The model's predictions closely aligned with the actual data, indicating its effectiveness. The ARIMA model produced reliable forecasts for short-term top-up frequencies, showing the best performance overall with the lowest RMSE and MAE, and a positive R-squared indicating some ability to explain the data. SARIMAX and EST both perform poorly, with very high RMSE and MAE, and negative R-squared values. The results demonstrated that time-series analysis could enhance decision-making in the telecommunications industry by enabling better forecasting of customer behavior. This helps in optimizing marketing strategies, inventory management, and service availability.

Approaches to Modelling Marketing Strategies in E-Commerce

Introduction. Increasing the efficiency of business activities requires developing a marketing strategy using e-commerce to attract a wide range of customers and maximise the company's income. The key marketing metric for most companies is the customer lifetime value indicator, which is why studying approaches to forecasting this indicator in e-commerce requires special attention, considering the changing business operation environment. Aim and tasks. The study aims to develop approaches to modelling the marketing strategy of e-commerce enterprises. The tasks are a comparative characterisation of modern marketing planning tools and an empirical analysis of the possibilities of using the BG/NBD model for marketing planning in e-commerce companies, considering the need to implement scenario marketing planning in unstable conditions. Results. A comparison of the characteristics of the main marketing planning tools was conducted according to features such as content and focus, functional purpose, and planning period, which allows more purposeful use of these tools to solve specific tasks. In order to automatically obtain the required data format for modelling, a Visual Basic program was developed, a forecast of customer behaviour was built, and an indicator of customer transactions was determined for further calculation of customer lifetime value (CLV). During the simulation, four parameters of the BG/NBD model were evaluated, and the maximum value of the logarithmic likelihood function equal to 14460.54 was obtained. The expected number of transactions (forecast time = 26 weeks) was determined for each client from the database. After calculations the information was received, the selected client will make 2.35 transactions during the forecast period of 55-80 weeks. The recommended algorithm for using the BG/NBD model as part of a scenario approach for forecasting the cost of the customer's life cycle allows for more flexible forecasting of digital business revenues and building an optimal marketing strategy. Conclusions. An empirical study of the possibilities of using the BG/NBD model for marketing planning in e-commerce companies showed that, for the simulation of indicators that affect a company's marketing strategy, it is crucial at the initial stage to convert the database of customer purchases into the input format required for modelling. Using a scenario approach allows e-commerce enterprises to carry out variable marketing planning and reduce risks under growing uncertainty. A well-designed marketing strategy that can be adapted to different business development scenarios enables an e-commerce company to increase its sales.

Exploring Customer Segmentation in E-Commerce using RFM Analysis with Clustering Techniques

The proliferation of big data and the growth of e-commerce have intensified the challenges associated with extracting actionable data for personalised recommendations and decision-making. With data-driven marketing strategies, understanding and predicting customer behaviour has become paramount for maintaining competitive advantage. This study leverages business analytics tools, focusing on Recency, Frequency, and Monetary (RFM) Analysis, alongside K-Means and Hierarchical (Agglomerative) Clustering algorithms, to segment customer transactional data. Data normalisation, a critical step for accurate clustering, was performed using log transformation and the Power Transformer technique with the Yeo-Johnson parameter, the latter proving more effective for handling both positively and negatively skewed data, enhancing data normalisation and suitability for analysis. This study reveals that RFM Analysis with Hierarchical Clustering outperforms K-Means Clustering, achieving a Silhouette Score of 0.47 and a Calinski–Harabasz Index of 3787.1, indicating a more accurate identification of customer segments. RFM Analysis alone generated eight clusters, while integrating RFM Analysis with both Hierarchical Clustering and K-Means generated three similar-sized clusters with interchanged labels. These metrics highlight the proficiency of Hierarchical Clustering in identifying unique customer segments and customising marketing strategies. The findings indicate that the RFM-Hierarchical Clustering approach enhances segmentation precision and facilitates more refined and effective marketing strategies.

Do People Really Care if it's Phygital Retail? Exploring the Relationship Between Customer Experiences and Customer Behaviors

Do People Really Care if it's Phygital Retail? Exploring the Relationship Between Customer Experiences and Customer Behaviors

Understanding the dynamics of customer satisfaction and repurchase intentions in the era of platform service supply chain – a case study of Foodpanda in Pakistan

ABSTRACT This study investigates the evolving restaurant sector in Pakistan, highlighting the significant role of Platform Service Supply Chains (PSSC) facilitated by food ordering apps like Foodpanda. Understanding PSSC is essential for businesses to thrive in the digital age and effectively manage the dynamics of digital service delivery. Foodpanda exemplifies a complex multi-stakeholder ecosystem that involves intricate technology integration, logistics challenges, robust data management, and a continuous need for innovation in a competitive market. This research aims to identify the key factors driving customer satisfaction and repurchase intention in this dynamic environment, acknowledging the transformative impact on consumer behavior. It also examines how health consciousness and food safety concerns influence these relationships in Pakistan. The study employs an integrated model based on the Theory of Planned Behaviour (TPB) and the Unified Theory of Acceptance and Use of Technology (UTAUT), collecting data from a convenient sample of Foodpanda app users. Using structural equation modeling, the findings highlight critical determinants such as effort expectancy, perceived usefulness, information quality, perceived risk, social influence, and trust in shaping customer satisfaction. This satisfaction, in turn, positively affects repurchase intention. Importantly, the study reveals that health consciousness and food safety concerns negatively impact the positive relationship between customer satisfaction and repurchase intention. The paper contributes theoretically by exploring customer behavior intricacies within the PSSC framework and offers practical insights for researchers and professionals in the food app industry. However, it acknowledges limitations, such as the use of a convenient sample, and suggests avenues for further research in this growing field.

Can reusable packaging revolutionise e-commerce? Unveiling the environmental impact through a comparative carbon footprint analysis

E-commerce is a rapidly growing, evolving sector. Its environmental impact has also increased, with shipping packaging being a key contributor. The sector is, however, struggling to tackle this environmental impact, as well as to follow new packaging regulations. Previous studies on reusable packaging have predominantly been qualitative or concentrated on material selection, overlooking essential elements of supply chain design and consumer behaviour. Industry reports attempting to quantify the sustainability of reusable packaging have produced varied results that lack generalisability or transferability to other contexts. Consequently, it is difficult to determine the actual environmental sustainability of reusable packaging in e-commerce. In this research, we assess the carbon footprint of reusable packaging in e-commerce, through a comparison of eight case studies. A multiple case study approach is followed, employing an embedded design where more than one unit of analysis is explored in each case. We evaluate the CO2 emissions of all processes related to the circular supply chain of reusable packaging employing a method that can evaluate different solutions and situations and a sensitivity analysis. Findings highlight three specific factors influencing the carbon footprint of reusable packaging: (1) reusable packaging material, (2) return and reuse rate, and (3) supply chain design (i.e., centralised versus decentralised design, travel distance, transport mode). For the same type of reusable packaging, we found that polyester generates 215% more CO2 emissions than cardboard in production and waste management. However, by analysing the same reusable packaging and supply chain, these same emissions can drastically increase if the return and reuse rate decreases. Changes in the return and reuse rate mainly linked, among others, to customer behaviour and involvement. Furthermore, for the same reusable packaging and return and reuse rate, a decentralised supply chain can reduce the CO2 emissions compared to a centralised structure. Interestingly, reusable packaging is environmentally sustainable long before it reaches its maximum life cycle. Most of the analysed solutions were more environmentally friendly than a cardboard box when they reached 10% of their estimated life cycle.

Effects of customer inoculation on artificial intelligence service failure

Purpose This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience. Design/methodology/approach Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey. Findings The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages. Practical implications This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers. Originality/value This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions.

Optimizing Shopping Experience by Integrating Augmented Reality and Payment Gateway in SME Storefronts

In the rapidly evolving digital era, leveraging advanced technologies to enhance the online shopping experience has become crucial, especially for small and medium-sized enterprises (SMEs) aiming to compete in the global market. This research focuses on optimizing the shopping experience by integrating Augmented Reality (AR) and Payment Gateway technologies into a storefront platform specifically designed for women's clothing SMEs. The study begins with a comprehensive analysis of market trends, customer behavior, and the challenges faced by SMEs in adopting these technologies. A detailed literature review provides the foundation for developing a robust conceptual framework. The prototype development phase involves designing 3D models using CLO | 3D Fashion Design Software, converting these models into AR experiences with Meta Spark Studio, and integrating the Midtrans Payment Gateway using Laravel. The AR feature enables users to virtually try on clothes via Instagram, while the Payment Gateway ensures quick and secure transactions. The final phase involves rigorous functional and substantive testing, focusing on user experience, transaction efficiency, and overall system performance. Results demonstrate that the integrated prototype significantly enhances the shopping experience, providing seamless AR interactions and efficient payment processing. Users reported high satisfaction levels with the system's usability and security. The study concludes that integrating AR and Payment Gateway technologies into SME storefronts can substantially improve their competitiveness in the digital market, offering practical insights and guidelines for future implementations in the e-commerce sector.

Digital Marketing and Consumer Nutritional Behavior

Digital marketing involves promoting and selling products and services online and it is becoming extremely critical for retailers to gain new customers. Digital marketing uses online platforms, e-mail communications and messaging services, advertisements, and multi-modal online techniques. Digital marketing techniques can be used in parallel with machine learning and artificial intelligence to provide more effective results in analyzing customer behavior. Artificial intelligence (AI) has become an important and popular tool in the 21st century, affecting almost every sector, including engineering, science, medicine, business, finance, and marketing. However, AI’s dominance in every field of life, especially in digital marketing, may cause some ethical concerns related to consumers. In addition to these moral concerns, digital marketing creates doubts about its negative impact on human health. For this reason, understanding consumer behavior and providing products according to consumer needs has become indispensable for companies today. Digital marketing is everywhere in our daily lives and is also frequently used in the food industry. Through digital marketing, food industries promote the consumption of foods that are particularly high in energy and poor in nutrients. Consequently, individuals exposed to digital marketing in the food industry may be prone to obesity-related problems. This study analyzes the relationship between digital marketing, AI, and consumer behavior specifically on eating habits along with references from the literature.

Critical development of Islamic financial technology ecosystem: lesson learned from financial technology literature

Purpose This study aims to draw lessons from the financial technology (FinTech) ecosystem literature through a systematic literature review. Design/methodology/approach This study systematically studied a sample of 134 articles from the Scopus database to assess the pattern of research development within the scope of the FinTech ecosystem over the last 15 years (2008–2023). Findings The results obtained indicated that the current research focus leads to several aspects: digital technology and financial inclusion, FinTech and customer behavior, FinTech ecosystem, business model, as well as aspects of governance and regulation. In the effort to develop Islamic FinTech, some aspects that might be targeted include aspects of business development and the Islamic FinTech ecosystem in general, extending financial inclusion to governance and managerial implementation of Islamic FinTech itself. Research limitations/implications This research has limitations because it did not focus on the study of more specialized sectors, such as insurance or microfinance institutions, in adopting FinTech, requiring the use of other specifications of institutions in addition to Islamic banking. Practical implications This research has substantial theoretical implications in mapping the intellectual structure of Islamic FinTech research, which has been underexplored by previous researchers, as well as providing essential information about which sectors should be prioritized to encourage inclusiveness and overall performance of financial institutions. Originality/value This research explores more deeply with a comprehensive approach so that it becomes a pioneer in the study of FinTech ecosystem literature for the development of Islamic FinTech.

Banking-as-a-service? American and European G-SIBs performance

PurposeThe purpose of this study is to investigate how the implementation of digital banking services (mobile applications) by globally systemically important banks (G-SIBs) affects banks’ performance in the USA and Europe from 2005 to 2022.Design/methodology/approachThe study employs advanced econometric methods to analyze the link between deposits and banking performance, utilizing linear regressions and multivariate Bayesian regressions.FindingsOur results indicate that customer deposits positively impact a bank’s performance after the introduction of the mobile application feature of check deposits, whereas social risk negatively impacts banking financial performance. These findings support the hypothesis that technology implementation improves the profitability and growth of traditional banks.Research limitations/implicationsWhile findings are robust econometrically in linear and Bayesian regressions, variables reflecting the digitalization of banks remain limited. For instance, the number of mobile users or the volume of digital transactions per bank since the implementation of the mobile app is not available.Practical implicationsIn a rapidly growing technology and constantly changing customers behaviors, this research has practical implications from bankers’ perspective to continue the technological innovation efforts and from regulators’ perspective to strengthen requirements for the digital banking services.Social implicationsWe provide empirical evidence that including a banking app for smartphones’ users for remote banking services benefit the financial performance of banks. However, the social risk remains significant for banks in terms of customers' satisfaction, data privacy and cybersecurity.Originality/valueThis paper employs an innovative approach to create a mobile app “discriminatory” factor and examine the relationship between deposits and banks’ performance before and after the introduction of a mobile app for too-big-to-fail banks in Europe and the USA. Additionally, we consider the social risk component of the ESG score, as a bank’s decision to implement mobile applications and technology for its customers potentially affects social risks associated with customer satisfaction and technology usability.

Exploring online advocacy mechanisms through self-presentation: a comparative analysis between e-commerce website customers

PurposeUnderpinning social identity theory (SIT) and service-dominant logic (SDL), the current paper seeks to explore the effect of self-presentation on online brand advocacy (OBA). Furthermore, this paper investigates the mediating role of hedonic value and the moderating role of customer interaction with e-commerce websites (i.e. Amazon, Walmart and eBay).Design/methodology/approachData were collected from customers of three e-commerce platforms (i.e. Walmart, Amazon and eBay) using a structured questionnaire – multi-group analysis applied on SmartPLS 4.4.FindingsSelf-presentation has a positive role in increasing hedonic value and its impact on OBA. The moderating effect of customer interaction on these relationships is also investigated and found to be significant.Social implicationsOur findings underscore the significance of fostering inclusive online communities and favorable online settings. Existing findings are consistent with overarching objectives of digital empowerment and enhanced online interaction quality. This paper contributes to harmonious and collaborative digital societies by encouraging personalized experiences that foster a sense of belonging among diverse customers.Originality/valueThis paper adds to the existing body of knowledge by comparing customer behavior on three major e-commerce platforms, going beyond the traditional focus on a single platform. Drawing on SIT and SDL, this paper provides a distinct nomological framework for OBA that unifies disparate constructs, filling theoretical gaps in our understanding of online customer behavior.