E-commerce Behavior Research Articles

The Impact of Psychological Factors and Marketing Strategies on Impulsive Customer Behavior in Indonesian E-Commerce

E-commerce in Indonesia has grown rapidly, becoming essential as more people prefer online shopping. A Populix (2023) survey indicates Indonesians tend to make unplanned purchases. Impulse buying is driven by external factors like sales promotions and internal factors like hedonic shopping motivation, with positive emotions mediating this effect. This study analyzes how sales promotions and hedonic shopping motivation influence impulse buying in e-commerce, with positive emotion serving as an intervening variable. The method used in this research is quantitative with a questionnaire carried out systematically following the research objectives. The population in this study was Indonesian people who had used e-commerce for online shopping, with 120 respondents. This study uses a 5-point Likert scale and PLS-based SEM for analysis. Results indicate that (1) sales promotion and (2) hedonic shopping motivation both positively affect impulse buying; (3) sales promotion and (4) hedonic shopping motivation positively influence positive emotions; (5) positive emotions, in turn, increase impulse buying; (6) positive emotion thoroughly explains the link between sales promotion and impulse buying; (7) it partially explains the effect of hedonic motivation on impulse buying. Recommendations suggest e-commerce should consider psychological factors and consumer motivation in marketing strategies, and future research could explore factors like celebrity endorsers, shopping lifestyle, and advertising.

THE ROLE OF ARTIFICIAL INTELLIGENCE (AI) AND PRODUCT POPULARITY IN SHAPING CONSUMER BUYING BEHAVIOR ON INDONESIAN E-COMMERCE PLATFORMS

This study aims to bridge the gap in the literature by examining how artificial intelligence (AI) and product popularity play a role in shaping consumer purchasing behavior on Indonesian e-commerce platforms. The sample is users who make purchases of cosmetic products with AI features on Indonesian E-Commerce. Using multiple linear regression methods and SPSS 25 as a calculation tool. It was found that artificial intelligence and product popularity have a significant influence on consumer purchasing behavior in Indonesian E-Commerce.

Predicting Consumer Behavior in E-Commerce Using Recommendation Systems

As e-commerce platforms continue to expand, understanding consumer behavior has become crucial for enhancing customer satisfaction and driving business success. Recommendation systems play a pivotal role in predicting consumer preferences and delivering personalized product suggestions. This paper presents an extensive literature review on recommendation techniques, including collaborative filtering, content-based approaches, and hybrid models. Notable advancements, such as the use of deep learning, trust-based filtering, and context-aware models, are highlighted. Building on these foundations, we propose a novel model that integrates advanced machine learning algorithms with consumer behavior analysis to predict preferences more accurately. The expected results suggest that this model will improve the precision of recommendations, effectively addressing challenges like data sparsity and evolving user preferences and enhancing overall customer engagement in e- commerce environments.

Predicting Consumer Behavior in E-Commerce Using Decision Tree: A Case Study in Malaysia

Understanding and predicting consumer behavior will help e-commerce businesses improve customer satisfaction and devise better marketing strategies. This study is intended to explore the use of decision tree algorithms in predictions of consumer purchase behavior in the e-commerce platform in Malaysia. Comparing the performances of J48, Random Tree, and REPTree decision tree models using an online shopper dataset collected by surveying 560 Malaysians, on various aspects like accuracy, precision, recall, and F1 score. Results indicate that the highest accuracy has been achieved with the Random Tree algorithm, outperforming J48 and REPTree. The results will, therefore, form the basis upon which e-commerce can restrategize its marketing programs for better customer engagement. This is an important study in that it shows the efficacy of applying a decision tree algorithm to understand customer behavior in the context of Malaysia and adds to the growing body of knowledge in predictive analytics in e-commerce.

Livestream sales prediction based on an interpretable deep-learning model

Although live streaming is indispensable, live-streaming e-business requires accurate and timely sales-volume prediction to ensure a healthy supply–demand balance for companies. Practically, because various factors can significantly impact sales results, the development of a powerful, interpretable model is crucial for accurate sales prediction. In this study, we propose SaleNet, a deep-learning model designed for sales-volume prediction. Our model achieved correct prediction results on our private, real operating data. The mean absolute percentage error (MAPE) of our model’s performance fell as low as 11.47% for a + 1.5-days forecast. Even for a 1-week forecast (+ 6 days), the MAPE was only 19.79%, meeting actual business needs and practical requirements. Notably, our model demonstrated robust interpretability, as evidenced by the feature contribution results which are consistent with prevailing research findings and industry expertise. Our findings provided a theoretical foundation for predicting shopping behavior in live-broadcast e-commerce and offered valuable insights for designing live-broadcast content and optimizing the user experience.

The impact of AR online shopping experience on customer purchase intention: An empirical study based on the TAM model.

Augmented Reality (AR) offers a rich business format, convenient applications, great industrial potential, and strong commercial benefits. The integration of AR technology with online shopping has brought tremendous changes to e-commerce. The Technology Acceptance Model (TAM) is a mature model for assessing consumer acceptance of new technologies, and applying it to evaluate the impact of AR online shopping experiences on consumer purchase intention is an urgently needed area of research. Firstly, the typical applications of AR in online shopping were reviewed, and the connotations and experiences of AR online shopping were summarized. Secondly, using the five types of AR online shopping experiences as antecedent variables, and perceived ease of use and perceived usefulness as intermediate variables, a theoretical model was constructed to explore the impact of AR online shopping experiences on customer purchase intentions, followed by an empirical study. Finally, suggestions were proposed for optimizing the online shopping experience to enhance purchase intentions. The article expands the application scenarios of the Technology Acceptance Model and enriches the theory of consumer behavior in Metaverse e-commerce.

Understanding Diverse E-commerce Behavior in Emerging Markets

Understanding Diverse E-commerce Behavior in Emerging Markets

Do Psychological Ownership and Communicative Presence Matter? Examining How User-Generated Content in E-Commerce Live Streaming Influences Consumers' Purchase Intention.

E-commerce live streaming has become a lucrative global industry. As the main carrier to convey information in live broadcasting, user-generated content (UGC)-and especially bullet screens-are crucial in influencing users' purchase intentions. However, the influence of bullet screens' multidimensional information characteristics on consumers' decision-making processes requires further exploration. Additionally, most existing studies start with the short-term effects of live product realization, and must address how to enhance customers' psychological ownership using new means of live streaming marketing to obtain long-term sustainable brand-building effects. This study introduces psychological ownership and the communicative presence as mediating variables based on the theory of elaboration likelihood modeling to explore the mechanism of the influence of UGC's multidimensional features on viewers' purchase intentions in live e-commerce broadcasting rooms. We collected 404 valid online questionnaires and tested our hypotheses using structural equation modeling. These findings indicate that UGC emotions, quality, and their interaction significantly and positively affect purchase intentions. Moreover, psychological ownership and the communicative presence mediate UGC's effect on purchase intentions. These results provide a new perspective for understanding consumer behavior in live e-commerce to improve marketing effectiveness of e-commerce live streaming platforms.

Impulse Buying Behavior in E-Commerce: Factors Influencing Spontaneous Purchases in Online Retail Environments.

Impulse Buying Behavior in E-Commerce: Factors Influencing Spontaneous Purchases in Online Retail Environments.

Understanding impulse buying in E-commerce: The Big Five traits perspective and moderating effect of time pressure and emotions

Impulsive buying behavior is crucial for understanding the psychological dynamics of consumer purchasing in the online marketplace. Identifying antecedents and moderating factors is vital to comprehensively understanding impulsive buying behavior. This research explores the influence of the Big Five personality traits on impulsive buying behavior in e-commerce settings and investigates the moderating effects of time pressure and emotions on these relationships. Data was collected through online questionnaires from 342 Indonesian participants with e-commerce purchasing experience. Structural Equation Modeling was utilized to validate the research hypotheses. The findings reveal that personality traits such as agreeableness, openness to experience, extroversion, and conscientiousness significantly drive impulsive buying behavior in e-commerce. Additionally, the study highlights that the interaction between emotions and neuroticism, as well as time pressure and agreeableness, significantly impacts impulsive buying behavior. The findings contribute valuable theoretical insights by demonstrating how specific Big Five personality traits influence impulsive buying behavior, while also providing practical implications for e-commerce platforms to effectively manage and leverage time pressure and emotional triggers to enhance consumer engagement and drive impulsive purchases.

THE IMPACT OF E-COMMERCE LIVE STREAMERS' PROFESSIONALISM ON CONSUMERS' PURCHASING INTENTIONS

In the digital age, e-commerce livestreamers have notably impacted consumer purchasing behavior. This study investigates how their professionalism affects consumers' purchase intentions, focusing on the mediating role of perceived value and the moderating role of virtual touch. We collected the data from 559 consumers using a questionnaire survey and analyzed it using structural equation modeling and multiple linear regression. The findings reveal that e-commerce livestreamers’ professionalism significantly enhances consumers' purchase intentions and perceived value. Perceived value, in turn, positively impacts purchase intentions. Moreover, perceived value mediates the relationship between livestreamers' professionalism and purchase intentions, while virtual touch positively moderates the link between professionalism and perceived value. This study demonstrates that livestreamers’ professionalism directly boosts consumers' purchase intentions and indirectly does so by increasing perceived value. Additionally, virtual touch strengthens the positive effect of livestreamers’ professionalism on perceived value, further encouraging consumer purchasing behavior. This research enriches the theoretical foundation of consumer behavior in e-commerce and offers practical insights for e-commerce platforms and livestreamers.

End-to-End Modeling and Long Short-Term Memory Application in Time Series Modeling

With the vigorous development of e-commerce, accurately modeling and predicting user behavior has become a key factor in improving business efficiency. Precisely understanding user behavior not only enables companies to provide personalized services but also allows them to stand out in the intense market competition. This study aims to explore the effectiveness of applying end-to-end models, Long Short-Term Memory (LSTM), and attention mechanisms in time series modeling to enhance the performance of modeling and predicting user behavior in e-commerce. In the methodology section, we first introduce the basic principles of the end-to-end model, which extracts features directly from raw data for prediction, avoiding the need for intricate feature engineering. Simultaneously, we introduce Long Short-Term Memory (LSTM) to better capture long-term dependencies in time series data.

Investigating the impact of fear of missing out on inaction inertia in live streaming purchase intention

PurposeThe main purpose of the study is to investigate the influence of live streaming participation on purchase intention, specifically focusing on how fear of missing out (FOMO) impacts live streaming purchase intention and its subsequent effect on inaction inertia. Additionally, the study aims to ex-amine the indirect influence of FOMO on the relationship between live streaming participation and purchase intention.Design/methodology/approachThe research employs SEM to assess the properties of measures. The investigation consists of four main constructs: live streaming shopping participation, fear of missing out, live streaming purchase intention, and inaction inertia. Live streaming shopping participation is further divided into two dimensions: live streaming immersion and live streaming social presence, with the latter encompassing social presence and telepresence.FindingsThe study reveals the significant role of FOMO in livestream shopping. Viewers' fear of missing out on information and products presented in livestreams results in heightened attention to these streams and an elevated willingness to purchase. This finding emphasizes the impact of FOMO emotions in driving consumer action and purchasing intent, particularly in situations of product scarcity.Originality/valueThe study uniquely explores FOMO as a factor disrupting consumer inertia, influencing decisive purchasing. It shows that FOMO enhances perceived value of products, altering consumer behavior in live streaming and e-commerce, thus providing a novel perspective on FOMO’s extended impact.

Clustering Analysis of Online Shopping Behavior on the TikTok Platform: Revealing Different User Characteristics

With the integration of social media and e-commerce platforms, TikTok has become an important online shopping platform. This study aims to explore the online shopping behavior patterns of different user groups on the TikTok platform through cluster analysis methods. By designing and implementing a questionnaire survey, data on users' personal characteristics, usage habits, and purchasing behaviors were collected. Applying cluster analysis techniques, users were divided into different groups, and the characteristics of each group were deeply analyzed. This paper mainly focuses on identifying whether users characteristics are closely related to their shopping behaviors. This study finds that engagement with live streaming shopping, diversity of products purchased, and frequency of shopping on TikTok are key factors influencing their online shopping behaviors. This research not only provides targeted marketing strategy suggestions for the TikTok platform but also offers a new perspective for understanding e-commerce behavior on social media platforms.

Brand, Price, and Word of Mouth Impact Global Consumer Choices

This study examines the influence of brand image, price, and Electronic Word of Mouth (E-WOM) on purchasing decisions in the e-commerce platform, Shopee. Using a quantitative approach, data from 96 respondents in Sidoarjo Regency were analyzed through Smart-PLS Version 3.0. Results reveal significant effects of brand image, price, and E-WOM on purchasing decisions, with brand image reflecting reputation, effectiveness, and trustworthiness, while price considerations include affordability, quality alignment, competitiveness, and benefit congruence. E-WOM's impact is seen through intensity, content, and valence of opinion. These findings highlight the importance of these factors in influencing consumer behavior on Shopee. However, the study's focus on these three variables suggests a need for future research to explore additional factors impacting e-commerce purchasing decisions, contributing to a deeper understanding of consumer behavior in online retail settings. Highlights: Consumer Behavior: Brand image, price, and E-WOM influence Shopee purchasing decisions. Data Analysis: Smart-PLS analyzes 96 respondents' data on e-commerce behavior. Research Implications: Further exploration needed for comprehensive understanding of online consumer behavior. Keywords: e-commerce, Shopee, purchasing decisions, brand image, Electronic Word of Mouth (E-WOM)

Exploring Product Return Behavior in E-Commerce : Insights from an Emerging Market Context

Exploring Product Return Behavior in E-Commerce : Insights from an Emerging Market Context

Impact of Human-Like Empathy of AI Chatbots and Privacy Concerns on Consumer Complaint Behavior in E-Commerce

Impact of Human-Like Empathy of AI Chatbots and Privacy Concerns on Consumer Complaint Behavior in E-Commerce

Trust and Online Transactions: An In-Depth Case Study of Consumer Perceptions and Behavior in E-commerce

In the world of online shopping, gaining and keeping consumer trust is crucial for successful transactions. It explores how consumers perceive and act when it comes to trusting e-commerce websites. Using a detailed case study method, this study looks closely at what influences consumer trust online, including website security, privacy concerns, brand reputation, and the impact of customer reviews. Ten (10) samples of respondents selected by the researchers. Those who have utilized online transactions and made purchases from online retailers in Noveleta, Cavite, such as Shopee and Lazada, are the participants of this study. Moreover, the increasing use of online shopping and the challenges of building trust in virtual spaces highlight the importance of understanding what influences how consumers behave when shopping online. This study aims to thoroughly explore the factors that shape consumer behavior in e-commerce and analyze the role of consumer trust regarding the role of multiple vendors in electronic commerce. Starting from the functional perspective of vendors and trust, the researchers used a qualitative method through initial surveys, in-depth interviews, and focus group discussions in Noveleta. The findings show that trust is a complex concept that affects how consumers make decisions. Additionally, the study investigates how e-commerce platforms work to build trust and address consumer worries. This research provides practical insights for online businesses to improve trust-building efforts and create better user experiences. By exploring the relationship between trust, consumer behavior, and online shopping, this study contributes to the researchers understanding of how e-commerce can engage consumers effectively in digital markets.

Spending points during crises: Adaptive behavior on E-loyalty programs

This study investigates the impact of macro-level dynamics, such as the COVID-19 pandemic, on customer point-usage behavior in e-commerce, focusing on how global crises alter consumer spending and loyalty program engagement. Using big data from SPK, an F&B holding company, and employing binary logit and panel regression models, this study highlights significant shifts in post-pandemic customer behavior. The findings reveal that both customers with low and high usage levels decreased their spending, transaction frequency, and point accumulation. However, the former showed an increased propensity to use points for purchases, with heightened redemption rates and average points used per transaction, whereas the latter exhibited a decline in these areas. These results underscore the importance of understanding customer-specific responses to economic crises for designing effective loyalty program strategies in e-commerce, and offer valuable insights for the development of promotional strategies and theories in this domain.

Why do dissatisfied consumers remain loyal? The role of switching barriers in online shopping

The existence of e-commerce has two sides of the coin for marketers. E-commerce can be very effective in marketing products or services. On the other hand, it also makes it easier for consumers to switch their choice to competing brands due to the ease of features in comparing prices and quality. Therefore, maintaining consumer satisfaction and dissatisfaction in the online shopping experience is very important for companies to retain their consumers. This study explores the relationship between dissatisfaction and switching behavior in e-commerce. The research was conducted qualitatively using the Critical Incident Technique (CIT) to achieve this goal. Informants in this study were consumers who experienced dissatisfaction when making online purchases in the last six months. The results show that even though consumers experience dissatisfaction, they persist in not switching behavior. The existence of switching barriers in online shopping in the form of ease of use of technology, time efficiency, convenience, and variety seeking are why consumers continue using e-commerce platforms even though they have experienced disappointing services.