E-commerce Environment Research Articles

The Influential Mechanisms of Live-Streaming Hosts on Consumer Behavior

In recent years, with the rise of e-commerce live streaming, hosts promoting products have played a crucial role in connecting consumers with products and influencing consumer behavior intentions. This paper reviews the current state and trends in research on how these hosts affect consumer behavior intentions, exploring how their personal characteristics, interaction patterns, and discourse strategies impact consumer purchasing decisions. Through analysis from both quantitative and qualitative research perspectives, it reveals the influence mechanisms of these hosts in modern e-commerce environments. Additionally, it discusses the pivotal role of interactive information in e-commerce live streaming, including its ability to enhance purchase desire, enrich entertainment experiences, and improve consumer shopping experiences. While existing studies provide valuable insights, there are still research gaps, particularly in-depth studies on the influence of hosts in specific domains. The findings of this review are significant for understanding the central role of hosts in e-commerce live streaming and their impact on consumer behavior, providing a theoretical foundation and support for future research and practice.

Research on the Influence of Anchor Influence on Consumers' Purchase Intention in Live Broadcast Room under the Background of Interest E-commerce

In the emerging shopping mode of interest e-commerce live streaming, anchors are the key factors influencing consumers' purchase intentions, and their personal characteristics and behaviors have a significant impact on consumer decision-making. Based on the stimulus-organism-response (S-O-R) model, this study constructed a structural equation model under the framework of influencer marketing theory to explore the internal mechanism between the various dimensions of anchor influence, consumer trust and identity, and its impact on consumers' purchase decision-making behavior. The results show that in different confidence intervals, the five dimensions of anchors' professionalism, interactivity, authenticity, popularity and innovation can significantly enhance consumers' trust and identity, and then significantly improve consumers' purchase intention. In addition, consumers' trust and recognition play a mediating role between the influence of anchors and consumers' purchase intentions. This study can enrich the theoretical discussion of consumer behavior decision-making in the live streaming environment of interest e-commerce, and provide a strategic reference for e-commerce anchors and interest e-commerce platforms.

ANALYZING USER BEHAVIOR PATTERNS FOR PERSONALIZED RECOMMENDER SYSTEMS IN E-COMMERCE: A LITERATURE REVIEW

Abstract: E-commerce thrives on a user-centric strategy, and recommender systems are at the cutting edge of personalizing the purchasing experience. These systems may forecast preferences and recommend appropriate items by analyzing user behavior patterns, resulting in many benefits such as increased customer satisfaction, increased sales and conversions, and increased efficiency. To accomplish these benefits, recommender systems utilize complex algorithms that examine numerous elements of user behavior such as purchase history, browsing behavior, search queries, demographic data, and implicit feedback. Sophisticated algorithms can recognise complicated patterns in user data, resulting in more accurate and personalized suggestions. Analyzing user reviews, product descriptions, and social media interactions may help you better understand consumer preferences and product features. Systems can make real-time suggestions depending on a user’s current browsing session, resulting in a more dynamic purchasing experience. Personalized recommender systems will play an increasingly important role in molding the future of e-commerce as user behavior analysis techniques are constantly refined. The study intends to make important advances to the field of personalized recommender systems by undertaking a thorough research of user behavior patterns in the e-commerce domain. We strive to improve the performance of recommender systems by extracting insightful features from various data sources and exploring sophisticated machine learning techniques, resulting in a more engaging and tailored user experience that fosters customer satisfaction and drives business growth. A comprehensive review of user behavior patterns and their influence on personalized recommender systems in the e-commerce industry reveals the critical role of data analysis and machine learning algorithms in tailoring product suggestions to individual preferences, thereby enhancing customer satisfaction and driving sales growth. By implementing the tactics and approaches expressed in this study, e-commerce platforms may stay ahead of the curve, providing a smooth and tailored purchasing experience that surpasses customer expectations and contributes to their competitive advantage in the changing e-commerce environment.

The Effect of Observational Learning and Word of Mouth Learning on Consumer Behaviors

This article explores two learning methods of social interaction in social media and e-commerce environments: observational learning and word-of-mouth learning, and their impact on consumer behavior. This article summarize the influence of two learning methods on consumer behavior in social interaction, and their differences. The article found that both observational learning and word-of-mouth learning have a significant impact on consumer behavior, with positive observational learning having a clear effect on information., Negative word-of-mouth learning information has a stronger influence on consumer decision-making. Observational learning enhances external motivation, while word-of-mouth learning stimulates internal motivation. When highly involved in product decision-making, word-of-mouth details are more critical; Low involvement products allow for more effective observation and learning of information. The impact of word-of-mouth learning on long-term decision-making increases over time. Observing and learning information spreads quickly, while word-of-mouth information spreads slowly. Understanding these mechanisms is crucial for e-commerce platforms and sellers to develop marketing strategies.

Optimization Algorithm of Network Node Layout of Fresh Agricultural Products’ Online Consumption Behavior in E-Commerce Environment

In order to improve the efficiency of online consumption and supply chain efficiency of fresh agricultural products in the e-commerce environment, a network node layout optimization algorithm was designed. In the e-commerce environment, a network node deployment model has been constructed for the online consumption behavior of fresh agricultural products. A network transmission supply chain detection model was established through the rotation scheduling method. By combining distributed transformation protocols, the spatial layout characteristics of network nodes have been reorganized. Further introduction of a multi-coverage scheduling model, combined with sensor information fusion tracking technology, accurately detects the layout characteristics of network nodes. Based on these detection data, forwarding and adaptive control protocols were developed. Real-time control using the node traversal method was developed. Under the optimal node supply chain transmission mechanism, the scheduling of the network node layout has been achieved. Meanwhile, convolutional neural networks were utilized to dynamically adjust layout control to ensure convergence and stability. The simulation results show that the node forwarding ability of the network node layout design for online consumption behavior of fresh agricultural products in the e-commerce environment is good, which improves the output balance of the network supply chain transmission of online consumption behavior of fresh agricultural products in the e-commerce environment. In addition, the optimized delivery cost decreased by 20% compared to before optimization, delivery efficiency increased by 30%, and inventory turnover rate increased by 25%, confirming that the proposed method has good optimization performance.

Novel Particle Swarm Optimization Algorithm to Solve the Internet Shopping Optimization Problem with Shipping Costs

In this paper, we approach the Internet Shopping Optimization Problem with Shipping Costs (IShOP), an NP-hard-relevant problem in the current e-commerce environment. To our knowledge, several solution metaheuristic algorithms have been reported in the literature. In this paper, we propose a novel Particle Swarm Optimization algorithm (PSO) to solve the problem. PSO incorporates a neighborhood diversification technique (NDT), a local search (LS) technique, and an adaptive parameter tuning (APT) method. The proposed algorithm (NDTLSAA-PSO) includes two techniques at the end of each iteration to avoid premature convergence in the search process, balancing exploration and exploitation in selecting candidate solutions. A comparison of the performance of the proposed algorithm has been made against the performance of the best state-of-the-art memetic algorithm solution of the IShOP. A total of 90 instances classified into three groups of small, medium, and large sizes were resolved. The Wilcoxon and Holm non-parametric tests were applied to validate the significance of the differences observed between these two algorithms. The results show that the proposed NDTLSAA-PSO algorithm is superior to the memetic algorithm. In addition, the proposed algorithm obtains the best results in all the in-stances evaluated in terms of quality.

Society 5.0: Shaping the future of e-commerce

This paper explores the impact of Society 5.0, a Japanese initiative that integrates digital and physical spaces to address societal challenges and drive economic growth. It examines the role of decentralized technologies in enhancing e-commerce by adapting to consumer behavior changes and improving market dynamics. The study details the transition from Web 1.0’s basic functionalities to the more interactive and social Web 2.0, setting the stage for a more integrated and intelligent e-commerce environment under Society 5.0. This new paradigm emphasizes personalized consumer experiences facilitated by AI and big data, focusing on real-time data, predictive analytics, and tailored services. The paper also addresses how Society 5.0 prioritizes sustainability and ethical practices in AI, aligning e-commerce strategies with global sustainability goals through eco-friendly practices and green logistics. The methodology involves a detailed literature review using specified databases over a defined period, applying rigorous criteria for article selection and classification to ensure comprehensive topic coverage. This research highlights the potential of cutting-edge technologies to revolutionize e-commerce by making it more secure, efficient, and aligned with consumer expectations and environmental standards. The research employs a qualitative approach, synthesizing existing literature on Society 5.0 and its potential impact on e-commerce. We explore how blockchain and big data will personalize experiences, enhance operational efficiency, and introduce new products and services. While acknowledging the limitations of relying solely on secondary literature, the paper underscores the need for future research to include empirical studies and address challenges like data privacy, the digital divide, and evolving regulatory frameworks within the e-commerce landscape.

Research on Collaborative Strategy of Dual-Channel Supply Chain System for Product and Service Selection Driven by Experiential Demand

With the intensification of dual-channel competition, manufacturers are increasingly inclined to invest in offline showroom services to improve consumers' shopping experience, making it an emerging trend. Based on this background, this paper makes an in-depth study of how manufacturers choose the business strategy of physical showrooms in the dual-channel e-commerce environment, discusses the advantages and disadvantages of different service models, and analyzes the impact of these choices on consumer behavior and market competition. Through this research, this paper aims to provide valuable strategic suggestions for manufacturers to optimize their dual-channel service models. Four Stackelberg game models are constructed: 1) no service model (N), 2) manufacturers invest in service online products (MSM), 3) retailers invest in services online products (RSM), and 4) retailers invest in services online products (RSR). Then the optimal decision under the above four strategies is compared and analyzed, and the optimal choice of the manufacturer and the optimal service model of the whole supply chain are obtained.

INFORMATION SECURITY AND THE CONTINUAL USAGE OF E-COMMERCE PLATFORMS: A CASE OF GHANA

Objective: This study investigates the impact of information security and trust on continued e-commerce usage in Ghana, where online commerce has become essential despite growing cyber threats. Theoretical Framework: Social exchange theory was used to explain how individuals form trust in social interactions. It helps to reveal how people weigh relationship rewards and costs, navigate power dynamics, and make choices. Method: Out of 117 collected responses, 100 were valid for analysis. Partial Least Squares-SEM (PLS-SEM) was used for structural equation modeling, following the "10-times rule" for minimum sample size. Results and Discussion: The research demonstrates that Information Security significantly enhances continual usage and directly impacts consumer trust. Also, we found that, trust serves as a mediator between information security and continual usage. Further, the study reveals that ICT skills play a key mediating role in the relationship between trust and continual usage. The findings highlight the importance of prioritizing information security, implementing user-friendly security measures, and offering training to improve users’ ICT skills. Research Implications: These insights have practical implications for e-commerce businesses, policymakers, and individuals. It emphasizes the necessity of robust information security measures to build trust and encourage continual usage among Ghanaians. Originality/Value: The study adds to existing literature by underscoring the essential role of Information Security in cultivating Ghanaian consumers trust and loyalty in the e-commerce environment.

Strategies for Online Market Entry: The Role of Network Anchors in China’s Live Streaming Sales Ecosystem

ABSTRACT Purpose This study explores the strategic implications of live-streaming sales and network anchors in China’s e-commerce landscape, particularly focusing on how different types of network anchors influence consumer trust and purchasing decisions. Methodology/approach A scenario-based experimental design was employed, utilizing a 2 x 2 factorial structure to analyze the effects of network anchor types (celebrity anchors vs. internet celebrity anchors) and product types (search goods vs. experience goods) on consumer behavior. Data were collected from 500 participants through an online survey, ensuring a robust statistical analysis of the main and interaction effects. Findings The results reveal that celebrity anchors generate significantly higher levels of consumer trust and purchase intention for search goods, while internet celebrity anchors are more effective for experience goods. Trust was found to play a partial mediating role in these relationships, particularly for experience goods. Research implications The study contributes to the existing literature by integrating traditional endorsement theories with the modern context of live streaming, offering new insights into the differential impacts of network anchor types across various product categories. It also highlights the need for a nuanced understanding of consumer trust in digital marketplaces. Practical implications Marketers and online enterprises can leverage these findings to optimize their live-streaming strategies by aligning the choice of network anchors with the specific characteristics of the products being promoted. This strategic alignment can enhance consumer engagement, trust, and ultimately, purchase intentions. Originality/value/contribution This research advances the theoretical discourse on digital marketing by elucidating the complex interplay between network anchor types, product characteristics, and consumer trust. It offers a comprehensive framework for understanding and optimizing live-streaming sales strategies in the context of China’s rapidly evolving e-commerce environment.

Customer satisfaction in e-commerce: The role of service quality, product quality, and e-servicescape influences via perceived value

Customer satisfaction was critical for optimizing profitability, especially in e-commerce environments. This study examined the impact of service quality, product quality, and e-servicescape on customer satisfaction in Samsung e-commerce, focusing on perceived value as a mediating variable. This quantitative study employed the Structural Equation Model (SEM). The population in this study consisted of Bekasi city residents who had made at least two purchases from Samsung e-commerce, with 115 respondents selected using a purposive sampling technique. Data was collected via a questionnaire, and KMO and MSA measurements were performed to assess validity. The data supported positive correlations between service quality, product quality, e-servicescape, and customer satisfaction. Furthermore, this study demonstrated that perceived value could mediate the relationship between service quality, product quality, and customer satisfaction.

Green Promotion Service Allocation and Information Sharing Strategy in a Dual-Channel Circumstance

Credit purchase enables the manufacturers in the e-commerce environment to provide pre-sales service that consumers can experience first and pay later. This paper considers demand associated with price and green promotion service level and builds four decentralized game models to study two green promotion service allocation strategies and demand forecasting information sharing strategies in a dual-channel environment. The effects of the degree of dual-channel competition and free-riding on the perfect Bayesian Nash equilibrium are studied. The results show that the retailer should actively cooperate with the manufacturer and share private forecasting information if the coefficient of channel substitution is relatively high. Sharing information will aggravate double marginalization and hurt the retailer. In addition, the retailer’s profit is positively influenced by the forecasting accuracy in four models. When the manufacturer invests in the green promotion service, the prediction accuracy hurts the manufacturer’s profit without information sharing and there is a positive impact with information sharing. In particular, when a retailer provides service, we take the consumer’s free-riding behavior into account, and we find that free-riding hurts both parties and the whole supply chain. In addition, the manufacturer’s profit is irrelevant to the prediction accuracy without information sharing and positively influenced by the accuracy with information sharing.

Design and Implementation of a Mobile E-Commerce Platform Based-on Machine Learning

In this paper, the “STORY”, which is an e-commerce mobile platform, is developed based-on machine learning techniques in order to consolidate selling methods into a unified, efficient, and personalized environment. Also, to elevate user experiences, and cultivate a thriving e-commerce environment in Libya. STORY is designed to serve the needs of both customers and merchants, aiming to bridge the gap between consumer demands and efficient business interactions. This platform strives to provide customers with a wide range of services and functions that match their preferences, while empowering merchants with a user-friendly mobile interface for effective business management. Among its notable features, STORY boasts an image-based search function, which simplifies the process of searching for desired or similar products. Additionally, the platform integrates an intelligent recommendation system capable of tracking and analyzing customer behaviors within the platform. This system collects pertinent data to offer personalized product recommendations and tailored offers, elevating the shopping experience. The system underwent an evaluation via a questionnaire, which had overwhelmingly positive results. User feedback and satisfaction regarding the system's functionalities and performance were notably favorable.

Routing Optimization of Two-Echelon Two-Way Logistics in Rural Areas Based on E-Commerce Environment

Routing Optimization of Two-Echelon Two-Way Logistics in Rural Areas Based on E-Commerce Environment

An analysis of consumer’s trusting beliefs towards the use of e-commerce platforms

This study evaluates the consumers’ views on various security measures in e-commerce platforms and determines their impact on the trusting beliefs of consumers, which may further lead to an intention to use e-commerce platforms. The study also investigates the moderating effects of gender, age, and the frequency of e-commerce platform use on the proposed model. A total of 780 respondents were used for the study. We used structural equation modeling (SEM) to analyze the proposed trust-based model. The findings indicate a strong positive impact of information integrity and information confidentiality on the consumers’ trusting beliefs. In addition, the study indicates the strong mediating effect of trusting beliefs with the association between information integrity, information confidentiality, and behavioral intention to use e-commerce platforms. A moderating effect of frequency of use on the relationship between perceived information confidentiality and preventing unauthorized secondary data usage on trusting beliefs was found to be significant. This research underwent an in-depth examination of different security aspects that e-commerce firms should consider in order to develop the trusting beliefs of consumers in e-commerce environments.

Dominant Color Detection For Online Fashion Retrievals

This paper introduces a novel approach aimed at efficiently extracting dominant colors from online fashion images. The method addresses challenges related to detecting overlapping objects and computationally expensive methods by combining K-means clustering and graph-cut techniques into a framework. This framework incorporates an adaptive weighting strategy to enhance color extraction accuracy. Additionally, it introduces a two-phase fashion apparel detection method called YOLOv4, which utilizes U-Net architecture for clothing segmentation to precisely separate clothing items from the background or other elements. Experimental results show that K-means with YOLOv4 outperforms K-means with the U-Net model. These findings suggest that the U-Net architecture and YOLOv4 models can be effective methods for complex image segmentation tasks in online fashion retrieval and image processing, particularly in the rapidly evolving e-commerce environment.

Promotion of Sustainable Products: Can Sustainability Labels Promote User Selection of Environmentally Friendly Products?

Sustainable development is growing in importance in today’s climate crisis. With the percentage of sales via digital channels increasing annually and consumers becoming aware of the environmental impact of their choices, a huge opportunity presents itself for promoting sustainable goods online if designers can find an effective way to raise awareness in consumers. Using a simulated e-commerce site (webshop), we investigated whether the presence or absence of sustainability labels displayed next to product images influenced users’ product selections. There was a significant association between the presentation of sustainability labels and the number of selected sustainable products. Overall, participants were familiar with sustainability labels and indicated willingness to pay ‘extra’ for sustainable products, while there was more variation in the way they felt that sustainability labels influenced their product choices. The findings highlight the complexities of factors influencing purchasing decisions and the need for more design-inspired research in this area. Whilst user interface design may be an effective means to influence sustainable product choices, design should also enable consumers to make informed product choices, while still providing a ‘fair’ e-commerce environment.

The Recent Trend for Marketing Mix in the Context of E-Commerce: A Literature Review

The influence of internet technology on all aspects of human life, and the convenience offered to customers by the e-commerce firms, in terms of flexibility in time and price, have raised the popularity of e-commerce in the world of business. This article aims to review the relevance of the traditional 4P marketing mix model in the era of digitalization, and discuss how it has been and can be modified to apply perfectly to the e-commerce industry. The pandemic also has caused a dramatic rise in online consumption, and the resulting changing trends in customers’ buying behaviour has reiterated the need for reviewing and reshaping the traditional marketing mix concept to adapt to the new business environment. The study elicits the conflicts that arise when the traditional marketing concepts are applied in the e-commerce environment. Starting from the traditional 4 P’s marketing mix, introduced by Jerome McCarthy in the 1960s, the article considers recent studies that outlined improved models for marketing, specifically in context with e-commerce, and identifies a plethora of factors and concepts in relation to e-commerce marketing. The involvement of science and technology makes the e-commerce industry very dynamic in nature. In addition, a crisis like the COVID-19 pandemic has led to drastic changes in the operation of businesses as well as the customers’ purchase behaviour in the last few years. Therefore, it is important to keep constructing new concepts and models that are compatible with the e-commerce industry.

Evaluating Arabic Emotion Recognition Task Using ChatGPT Models: A Comparative Analysis between Emotional Stimuli Prompt, Fine-Tuning, and In-Context Learning

Textual emotion recognition (TER) has significant commercial potential since it can be used as an excellent tool to monitor a brand/business reputation, understand customer satisfaction, and personalize recommendations. It is considered a natural language processing task that can be used to understand and classify emotions such as anger, happiness, and surprise being conveyed in a piece of text (product reviews, tweets, and comments). Despite the advanced development of deep learning and particularly transformer architectures, Arabic-focused models for emotion classification have not achieved satisfactory accuracy. This is mainly due to the morphological richness, agglutination, dialectal variation, and low-resource datasets of the Arabic language, as well as the unique features of user-generated text such as noisiness, shortness, and informal language. This study aims to illustrate the effectiveness of large language models on Arabic multi-label emotion classification. We evaluated GPT-3.5 Turbo and GPT-4 using three different settings: in-context learning, emotional stimuli prompt, and fine-tuning. The ultimate objective of this research paper is to determine if these LLMs, which have multilingual capabilities, could contribute to enhancing the aforementioned task and encourage its use within the context of an e-commerce environment for example. The experimental results indicated that the fine-tuned GPT-3.5 Turbo model achieved an accuracy of 62.03%, a micro-averaged F1-score of 73%, and a macro-averaged F1-score of 62%, establishing a new state-of-the-art benchmark for the task of Arabic multi-label emotion recognition.

The impact of changes in sales promotion depth on consumers’ purchase intentions in an e-commerce environment

ABSTRACT Variations in sales promotion in e-commerce impact online purchase decisions. Managers of e-commerce marketplaces must comprehend the effects of sales deviations on purchase decisions. We categorize sales promotion changes as either recognisable or unrecognisable to the consumer. We used the stimulus-organization-response model to assess the influence of diverse sales promotions influence consumer purchase decisions via the perceived attractiveness of the product. Using data from 587 online consumers we discovered distinct impacts between recognisable and unrecognisable changes on perceived attractiveness and purchase intention. Additionally, product value moderates these effects. Current research provides valuable insights with interesting theoretical and managerial implications.