E-commerce Experience Research Articles

Hyper-Personalization with AI and Edge Computing: The Future of Customer Experience in E-commerce and Retail

Advancements in Artificial Intelligence (AI) and edge computing are reshaping what is possible in the world of hyper-personalization, currently powering customer experience in e-commerce and retail. In this study we explore when AI algorithms and edge computing frameworks enable real time, individualized customer interactions resulting in deeper engagement and higher loyalty. This study uses a quantitative research design to explore data driven personalization techniques with datasets from leading retail and ecommerce platforms. To evaluate the scalability and responsiveness of hyper-personalization technologies, a combination of machine learning models and terms edge computing infrastructures is methodologically analyzed. Through case studies of top tier e-commerce platforms, results show that integration of AI and edge computing can reduce latency in customer interactions by up to 70%, while upgrading conversion rates by roughly 30%. These findings emphasize the added value of creating real time customer experience driven sales and customer loyalty leveraging real time customer insights enabled by AI at the edge. This work presents a novel framework that enables scalable, secure hyper personalization in e-commerce to overcome existing limitations in data processing speed and privacy compliance. It offers actionable recommendations for retailers that look to adopt AI driven, edge enabled hyper personalization techniques for enhancing customer engagement and operational efficiency.

A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce

The primary goal of this study is to predict and analyze customer trust in e-commerce by leveraging neural computation within large language models (LLMs) alongside configurational approaches. We employ LLMs to predict trust levels based on customer reviews, applying artificial intelligence to analyze key aspects of the e-commerce experience, such as customer service, refund processes, item quality, and shipping. To extend beyond predictive performance, we integrate Qualitative Comparative Analysis (QCA) to identify the causal relationships between trust and various stages of the customer journey, including selection, delivery, and post-purchase support (recovery). This dual approach not only showcases the power of neural computation in predicting trust outcomes but also provides a deeper understanding of how specific configurations of customer experience elements contribute to either positive or negative trust. By combining machine learning techniques and QCA, this study contributes to the application of LLMs and configurational approaches, offering novel insights into the drivers of trust in e-commerce.

Assessing the Determinants of Metaverse Adoption for E-Commerce Retailing

ABSTRACT The advent of metaverse technology has impacted the retail sector, shaping e-commerce platforms into a new form of metaverse-based online shopping environments. The metaverse e-commerce experience is new to shoppers, making it crucial to comprehend consumer reactions to this technology in the context of retail. This study explores the shopping intention and potential use of the metaverse for shopping using the UTAUT2 model and metaverse-based context-specific antecedents. Using a structured questionnaire, data from 1340 consumers were collected and analyzed through PLS-SEM. The findings indicated that factors such as performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions influence shopping intention in the e-commerce metaverse. The metaverse-related antecedents, namely, a sense of immersion and imagination, have a positive influence, whereas technological anxiety and perceived security and privacy concerns have a negative impact on e-commerce shopping intention in the metaverse. It was also found that shopping intention influences the potential use of metaverse for shopping and that stickiness to traditional shopping negatively moderates this relationship. This unique research explores consumer buying behavior in the metaverse. It provides marketers, e-commerce managers, designers, and developers of metaverse platforms with the antecedents of the potential use of the metaverse for shopping insights. Consumer policymakers can also draw insights from this study.

AI and Machine Learning in Enhancing Scalability and Efficiency of Integrated E-commerce and ERP Systems

This article explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems. As E-commerce experiences explosive growth and ERP systems become increasingly complex, businesses face significant challenges in maintaining scalability and efficiency. We examine how AI and ML can optimize various aspects of these integrated systems, from intelligent automation and predictive analytics to anomaly detection and decision support. Through case studies and analysis of current trends, we demonstrate the tangible benefits of AI/ML implementation, including reduced costs, improved accuracy, and enhanced customer experiences. The article also addresses key challenges such as data quality, scalability, ethical considerations, and the skills gap. Finally, we explore future research directions in explainable AI, edge computing, blockchain integration, and natural language processing, highlighting their potential impacts on the E-commerce and ERP landscape.

Machine Learning in Online Car Retail: Enhancing User Experience and Conversion Rates

This article examines the transformative impact of machine learning (ML) applications on the online car buying experience. We explore three key areas where ML significantly enhances user engagement and drives conversion rates: image processing, personalized recommendations, and data-driven insights. Advanced ML models are shown to improve image quality, standardize vehicle presentations, and facilitate easier comparisons. Personalization algorithms, leveraging vector embeddings and reinforced feedback loops, tailor the browsing experience to individual preferences. Additionally, ML-driven insights provide users with valuable information on pricing trends and deal rankings. Our analysis reveals that these applications not only streamline the car buying process but also address critical challenges in the digital automotive retail space. The article highlights the potential for increased customer satisfaction, improved inventory management, and competitive advantages for early adopters. While acknowledging implementation challenges, including data privacy concerns and integration complexities, we conclude that ML technologies are poised to revolutionize the online car buying landscape, setting new standards for user experience and operational efficiency in automotive e-commerce

Cross-cultural exploration of determinants of e-commerce adoption: Perspectives from logistics service quality and payment driver

This study explores the overarching factors that influence e-commerce adoption across countries to understand how culture interweaves the determinants. Specifically, it examines the crucial online experiences related to logistics service quality (LSQ) and payment driver (PD). Employing a grounded theory approach, interviews were conducted with 41 respondents from Morocco. Secondary data from the US, China, and South Korea were utilised for comparative analysis. The results indicate greater diversity in PD determinants compared to those in LSQ across these countries. This empirical study provides insight into designing effective e-commerce experiences for emerging markets with diverse cultural backgrounds.

Personalization and Customer Experience in E-Commerce

Innovation is crucial for maintaining a competitive edge and delivering exceptional shopping experiences in the ever-evolving realm of e-commerce. Businesses continuously seek creative approaches to enhance the e-commerce landscape, incorporating technologies like immersive experiences, seamless payment solutions, Artificial Intelligence (AI), and personalized services. This study investigates the emerging technologies and innovations within e- commerce, particularly focusing on the application and impact of AI. Utilizing conceptual research methodology and secondary data from various scholarly articles, journals, and online sources, the study concludes that recent innovations such as AI-driven personalization, Augmented Reality (AR) and Virtual Reality (VR), chatbots, blockchain for transaction security and supply chain management, social commerce, drone delivery, and e-wallet solutions have significantly advanced the e- commerce sector. Noteworthy AI applications include platforms like Google AI Cloud Platform, IBM Watson, BigML, and Infosys Nia, which are also leveraged for product pricing and distribution strategies. Additionally, the adoption of diverse payment methods such as e- wallets and UPI contributes to enhancing user satisfaction in e-commerce transactions.

A Conceptual Approach to Understanding the Customer Experience in E-Commerce: An Empirical Study

A Conceptual Approach to Understanding the Customer Experience in E-Commerce: An Empirical Study

ASTROMARG

ABSTRACT— The ASTROMARG research organization website outlines the detailed specifications and requirements for the development of a dynamic and feature- rich web platform. It defines the functional and non-functional requirements, user interfaces, and system behaviour necessary to create a user-friendly, informative, and secure online presence for the ASTROMARG research organization. This platform aims to serve as a comprehensive online resource for client and visitors, offering valuable information about the ASTROMARG’s services, facilitating virtual consultations, and providing a seamless e-commerce experience. INDEX TEARMS –Astrology, Numerology, Vastu Shastra .

AR E-Commerce Experience: Case Study Brand Maybelline at Tokopedia

AR E-Commerce Experience: Case Study Brand Maybelline at Tokopedia

ShopAssistAI

Abstract: E-commerce sites are constantly searching for fresh and inventive approaches to boost customer satisfaction and engagement. Including chatbots with the ability to haggle with clients at no cost is a viable strategy. A detailed review of earlier research on chatbots used in e-commerce pricing negotiations is given in this study. We look at a variety of aspects, including the look, feel, effectiveness, and user experience of these chatbots. We also discuss the underlying technologies machine learning and natural language processing (NLP) that were utilized in the creation of these systems. We also highlight areas of possible interest and challenge for future research and development in this developing field. Our synthesis aims to provide practical advice for scholars, business experts, and organizations wishing to enhance the e-commerce experience through the usage of price negotiation chatbots.

Disentangling e-commerce gamification affordances on recommendation acceptances from a perceived value perspective

PurposeGamification is a strategic approach employed by practitioners to foster meaningful engagement and enhance the acceptance of recommendations. Gamification affordances (e.g. achievement, self-expression, interaction, and cooperation) catalyze significant psychological processes in consumers, leading to behavioral changes. Despite its application, a gap remains in understanding how these gamification affordances in e-commerce contexts impact customers' perceived values and drive recommendation acceptances.Design/methodology/approachEmploying affordance theory and perceived value theory as our foundation, we have crafted a comprehensive framework that addresses the multifaceted nature of e-commerce gamification, thereby unifying the fragmented knowledge in this area. We implemented a quantitative research design to empirically test the proposed model.FindingsThe research reveals that the four principal affordances of gamification – achievement, self-expression, interaction, and cooperation – significantly enrich consumer values across hedonic, utilitarian, and social dimensions. This enrichment facilitates an increased propensity for accepting recommendations.Originality/valueThis study provides a novel lens through which to view the influence of gamification affordances on recommendation acceptance within gamified e-commerce settings. It delineates the effects of each affordance on consumers' perceived value and highlights the pivotal affordances that shape gamified e-commerce experiences. These insights yield actionable strategies for practitioners aiming to refine e-commerce gamification designs and cultivate more engaging consumer interactions.

The Role of AR And VR Integration in Elevating the E-Commerce Experience

Virtual reality (VR) and augmented reality (AR) technology integration with e-commerce platforms has become a disruptive force that is changing how customers engage with businesses and items online. In order to clarify the critical role these immersive technologies play in improving the digital purchasing experience, this article examines the dynamic interaction between AR/VR and e-commerce. This study highlights the numerous advantages of integrating AR and VR in e-commerce environments through a thorough examination of current trends, case studies, and technology developments. Augmented reality (AR) and virtual reality (VR) empower individuals to make better educated and confident purchasing decisions by providing virtual product try-ons, interactive product demos, personalized shopping trips, and a reduction in purchase uncertainty. Additionally, this study looks at the difficulties and roadblocks preventing wider adoption, such as the complexity of the technology, financial concerns, and user experience issues. By examining these intricacies, we are able to provide businesses looking to use AR/VR capabilities to increase customer engagement, improve conversion rates, and set themselves apart in the crowded e-commerce market with useful insights and strategic recommendations. Anticipating further advancements and development in AR/VR e-commerce technologies, we believe that these developments will usher in a new era of immersive, engaging online purchasing. Keywords – augmented reality, virtual reality, e-commerce, customer engagement, online shopping experiencefd

CUSTOMER SATISFACTION AND E-COMMERCE EXPERIENCE OF THE SENIOR HIGH SCHOOL STUDENTS

E-commerce is popular among teenagers throughout the country. Shopping online is advantageous for consumers but also carries higher risks than shopping personally. When online stores provide products that do not exceed the customers expectations, that will lead to customer dissatisfaction. Thus, this study was conducted to determine the significant relationship between the two variables, the customer satisfaction and the e-commerce experience of the senior high school students. The researchers used a descriptive correlation non-experimental quantitative research method. A stratified random sampling of 108 responses collected across the Rizal Memorial Colleges senior high school department, and the entire data collection and analysis process took about two weeks. Furthermore, a modified survey questionnaire was used to collect data from the respondents, and all of the data was analyzed appropriately with the assistance of a competent statistician. According to the findings, Information and Website Design have the highest value of all indicators of the two variables, implying that these two are the most affected factors when it comes to online shopping. The findings also revealed that the level of customer satisfaction and the e-commerce experience was high and often evident. The correlation between the two variables was moderately high positive (r = 0.728), which ended up rejecting the studys null hypothesis. The analysis of the findings confirmed that students who are pleased with their online shopping experience have a high level of e-commerce experience. As a result, the researchers suggested that people who like to shop online must be aware of all the risks that may arise and cause them to be dissatisfied with the product. KEYWORDS: Customer satisfaction, E-commerce experience, Information, Website design, Senior high students, Philippines

“Amazon Clone”

Amazon, the world's largest online retailer, has continually evolved to meet the changing needs and expectations of consumers. In the wake of the digital age, where convenience and personalization reign supreme, Amazon is poised to embark on its next transformative journey - Amazon 2.0. At the core of Amazon 2.0 lies a commitment to innovation and personalization. Leveraging advanced machine learning algorithms and big data analytics, Amazon aims to create a hyper-personalized shopping experience for each user. By analyzing past purchase behavior, browsing history, and demographic information, Amazon can deliver tailored product recommendations that resonate with individual preferences, leading to increased customer satisfaction and loyalty. Furthermore, Amazon 2.0 seeks to redefine the boundaries of convenience through seamless integration of emerging technologies. From voice-activated shopping assistants to drone delivery services, Amazon is at the forefront of revolutionizing the e-commerce landscape. By embracing cutting-edge technology, Amazon aims to streamline the shopping experience, making it faster, easier, and more intuitive for users worldwide. In addition to enhancing the consumer experience, Amazon 2.0 places a strong emphasis on sustainability and social responsibility. Through initiatives such as Amazon's Climate Pledge and commitment to renewable energy, Amazon is working towards a more sustainable future while also giving back to the communities it serves. In summary, Amazon 2.0 represents a bold new chapter in the company's storied history. By embracing innovation, personalization, and sustainability, Amazon is poised to redefine the e-commerce experience and set new standards of excellence for the industry as a whole. Keyword: Amazon Shopping, Retailer, Sales, Purchase

Enhancing User Experience in E-commerce through Augmented Reality and Virtual Reality

Enhancing User Experience in E-commerce through Augmented Reality and Virtual Reality

Trustworthiness of Review Opinions on the Internet for 3C Commodities

The rapid development of the internet has resulted in rapid e-business growth, with online malls attracting many shoppers due to the privacy and convenience they offer. Like traditional malls, online malls can provide photos, specifications, prices, etc. However, consumers cannot touch the products in reality, which creates risks for the purchase. To date, there has been no research focusing on topic-specific search engines for 3C product reviews based on the trustworthiness of the reviews. This study is the first to sort the reviews of electronic products according to the degree of trust, by analyzing the characteristics of the reviews and the reviewers. This study proposes the criteria for features of the reviews and reviewers to consider to evaluate the trustworthiness of the reviews; builds a search engine to collect the product reviews scattered in opinion websites; and sorts the results by trustworthiness to provide a reliable e-commerce experience. To demonstrate the effectiveness of the proposed method, we conducted a set of experiments, and we adopted the Spearman’s rank correlation coefficient to evaluate the similarity between our method and experts’ opinions. The experimental results showed a high correlation coefficient with the opinions of experts, demonstrating that our method is effective at finding trustworthy reviews on the internet.

Modeling the Consumers’ Flow Experience in E-commerce: The Integration of ECM and TAM with the Antecedents of Flow Experience

To achieve sustainable development of e-commerce and promote customers’ online shopping conduct, companies develop online shopping platforms to enhance customers’ online shopping behavior. The growing significance of technology in advertising has sparked intense interest in the worlds of education and business to create enjoyable experiences for online clients. Analyzing flow-related states is crucial for generating these experiences. Based on a combined theoretical framework comprising the antecedents of flow, the expectation confirmation model, and the technology acceptance model, this research examines how flow experiences influence the willingness to participate in online purchasing through online shopping platforms, with a focus on Chinese internet users of online shopping platforms. Three hundred internet clients were selected using the convenience sampling technique, and a survey methodology was employed to collect information from internet consumers. Findings from this study suggest that flow had a significant effect on continuous intention, perceived usefulness, and satisfaction. Furthermore, concerning flow’s antecedents, flow was significantly influenced by feedback, perceived enjoyment, and perceived vividness. Moreover, perceived usefulness and perceived ease of use were found to have substantial relationships with satisfaction and continuous intention. This research provides significant implications for research scholars and practitioners.

Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN

In the domain of e-commerce, customer reviews wield significant influence over business strategies. Despite the existence of various recommendation methodologies like collaborative filtering and deep learning, they often encounter difficulties in accurately analyzing sentiment and semantics within customer feedback. Addressing these challenges head-on, this paper introduces BERTFusionDNN, a novel framework merging BERT for extracting textual features and a Deep Neural Network for integrating numerical features. We assess the efficacy of our approach using a Women Clothing E-Commerce dataset, benchmarking it against established techniques. Our method adeptly extracts valuable insights from customer reviews, fortifying e-commerce recommendation systems by surmounting barriers associated with deciphering both textual nuances and numerical intricacies. Through this endeavor, we pave the way for more robust and effective strategies in leveraging customer feedback to optimize e-commerce experiences and drive business success.

Enhancing User Experience in E-commerce through Personalization Algorithms A Study on Information System Design

This research aims to improve user experience in e-commerce through the use of personalization algorithms in information system design. The research methods used involve literature analysis, prototype development, and user testing. Literature analysis was conducted to understand the concept of personalization, relevant algorithms, and factors influencing user experience in e-commerce. Based on this understanding, a prototype e-commerce information system with personalization features was implemented. User testing is carried out to collect data about user experiences before and after implementing a personalization algorithm. The research results show that the use of personalization algorithms significantly improves user experience in e-commerce. Users report feeling more engaged, increased relevance of content, and ease in finding products that match their preferences. Apart from that, this research also identifies several important factors that need to be considered in the design of e-commerce information systems that use personalization algorithms, such as data privacy, transparency, and user control. In conclusion, the use of personalization algorithms can effectively improve user experience in e-commerce, with the important note of considering relevant factors.