E-commerce Customer Satisfaction Research Articles

Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data

Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data

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.

The influence of utilitarian and hedonic value on customer satisfaction and repurchase intention in e-commerce

This research aims to examine factors affecting consumers’ shopping value in the online environment, which, in turn, lead to customer satisfaction and repurchase intention based on an integrated model of the cognition-affect-behavior model and personal shopping value. An online questionnaire was administered to 202 consumers of e-commerce purchases in Vietnam. Partial least square structural equation modelling (PLS-SEM) was utilized for data analysis. Results show that system quality and perceived price fairness positively affect hedonic shopping value, while information quality and service quality positively affect utilitarian shopping value. These shopping values collectively nurture customer satisfaction, resulting in repurchase intention. This study contributes to the literature on online consumer behavior by examining both the antecedents and outcomes of consumer shopping values in e-commerce. The findings can help e-commerce enterprises optimize marketing strategies to improve consumer shopping experiences and repurchase intention.

Effect of Different Chatbot Features on Customer Satisfaction in E-commerce

Effect of Different Chatbot Features on Customer Satisfaction in E-commerce

Exploring The Relationship Between Responsiveness And Usability And Its Impact On Customer Satisfaction In E-Commerce

This research explores the use of chatbots in e-commerce from a customer perspective, with the aim of determining how responsiveness affects usability, as well as how this usability affects customer satisfaction. The data collection method used was a questionnaire distributed to 195 respondents. For model and hypothesis testing, SPSS and AMOS software were used. The results of the validity and reliability tests show that the respondent data is valid and reliable for use in hypothesis testing. The normality test shows that the questionnaire data is normally distributed. The multicollinearity test shows that multicollinearity has occurred. The results of the fit model test are that most of the criteria can be met or are fit, so the statistical interpretation results can not only be used on the sample but on the population as a whole. And the hypothesis test shows that there is a significant positive relationship between responsiveness (X) and usability (Y) of the chatbot. These findings indicate that the more responsive a chatbot is, the higher its level of usefulness in the eyes of customers. When a chatbot is able to provide relevant answers, customers tend to feel that the chatbot is effective and practical to use in meeting customer needs. Furthermore, this research also found that usability (Y) significantly influences customer satisfaction (Z). In other words, easy-to-use and effective chatbots can increase customer satisfaction in an e-commerce context. When a chatbot is designed with a simple interface, customers can easily ask questions and receive the answers or help they need. In addition, if the chatbot is able to provide accurate and relevant answers, customers feel that their needs can be met.

AN INVESTIGATION ON FACTORS INFLUENCING CUSTOMER SATISFACTION BY UTILISING ONLINE SHOPPING

Purpose: E-Commerce has unleashed yet another revolution, which is changing the way businesses buy and sell products and services. New methodologies have evolved. The role of geographic distances in forming business relationships is reduced. E-Commerce is the future of shopping. With the deployment of 3G ,4G& 5G wireless communication technologies, the internet economy will continue to grow robustly. Nowadays, online marketing is a new area of marketing which play a vital role in attracting customers. Theoretical Framework: Most organizations utilize online shopping in current technology age to please consumers and also to attract additional clients. Online purchasing has a major influence on the pleasure of customers. Online shopping is the key aspect of electronic commerce, and this electronic trade guides many clients in the manner they purchase things from the business.Convenience is one of the benefits that customer gets from the e-commerce and thus increasing customer satisfaction. This is due to customer can place a purchase an order from anywhere with internet connection. E-commerce business provider should give importance on every customer by giving smooth service and many options for payment and have more functions available online. Design/Methodology/Approach: The study investigated factors influencing customer satisfaction in e-commerce. Demographic variables were analyzed alongside perceived risks and influencing factors. Findings: Findings highlight diverse influences on online purchasing behavior and varying satisfaction factors. However, no significant differences were found in risk perceptions. Education on online purchasing steps is recommended based on the results.

Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches

The rapid growth of e-commerce has increased the need for retailers to understand and predict customer satisfaction to support data-driven managerial decisions. This study analyzes online consumer behavior through a comparative machine learning modeling approach to forecast future customer satisfaction based on review ratings. Using a large dataset of over 100 k online orders from a major retailer, traditional machine learning models including random forest and support vector machines are benchmarked against deep learning techniques like multi-layer perceptrons. The predictive models are assessed for their ability to accurately predict customer satisfaction scores for the next orders based on key e-commerce features including delivery time, order value, and location. The findings demonstrate that the random forest model can predict future satisfaction with 92% accuracy, outperforming deep learning. The analysis further identifies core drivers of satisfaction such as delivery time and order accuracy. These insights enable retail managers to make targeted improvements, like optimizing logistics, to increase customer loyalty and revenue. This study provides a framework for leveraging predictive analytics and machine learning to unlock data-driven insights into online consumer behavior and satisfaction for superior retail decision-making. The focus on generalizable insights across a major retailer enhances the practical applicability of the machine learning approach for the retail sector.

Determinants of e-commerce customer satisfaction: mediating role of IT innovation acceptance

Determinants of e-commerce customer satisfaction: mediating role of IT innovation acceptance

Impact of Contemporary Logistics Factors on Customer Satisfaction in E-commerce

Impact of Contemporary Logistics Factors on Customer Satisfaction in E-commerce

Factors Affecting E-Commerce Customer Satisfaction in Hanoi, Vietnam

Factors Affecting E-Commerce Customer Satisfaction in Hanoi, Vietnam

Impact of E–Supply Chain Management on Customer Satisfaction Through Service Quality in Agricultural E-Commerce

The research aimed to assess the impact of applying e-supply chain management (E-SCM) on service quality (SQ) and customer satisfaction (CS) in agricultural e-commerce. It was motivated by challenges in Indonesia that led to foodwaste due to lengthy supply chain, particularly evident in the horticulture category, such as vegetables. The inefficiencies along the chain resulted in substantial annual losses, ranging from IDR 213 to 551 trillion, equivalent to 4% - 5% of Indonesian GDP. Using associative investigation with three variables and 446 samples selected through simple random sampling, the research aimed to evaluate and enhance E-SCM performance in supply chain. The effective applicationof E-SCM offered benefits, including time and cost efficiency, work flexibility, and other conveniences. Not only this did benefit the company but also enhanced service quality and customer satisfaction. SEM-PLS was used with a reflective method, and data processing was facilitated by SMART-PLS 4 software. The results show a significant influence ofE-SCM application on service quality and customer satisfaction. Furthermore, service quality has a significant and direct impact on customer satisfaction. E-SCM influences customer satisfaction through both direct and indirect pathways, which are mediated by service quality variables.

Analyzing the Influence of COVID-19 on the E-Commerce Customer’s Retail Experience in the Supermarket Industry: Insights from Brazil

Background: E-commerce’s convenience, speed, and ability to overcome geographical barriers have made it increasingly popular across industries. This study analyzed the impact of the COVID-19 pandemic on customer experience and satisfaction in supermarket retail e-commerce in the Brazilian market. The objective was to understand how the pandemic influenced satisfaction in this sector. Methods: A survey research method was employed, utilizing the Critical Incident Technique to identify key quality dimensions important to customers. A total of 133 valid responses underwent exploratory factor analysis. Results: Data analysis identified three dimensions significantly affecting overall customer satisfaction. “Presentation of Products in the Application (app)” had the highest correlation, while “Delivery Logistics” had the lowest. However, when considering the COVID-19 factor, “Delivery Logistics” showed the highest correlation. This indicates that the dimension most affected by the pandemic has the least impact on overall satisfaction in supermarket e-commerce. Conclusions: The study emphasizes the significance of understanding customer satisfaction in supermarket e-commerce, not just during the COVID-19 pandemic but also its post-pandemic consequences. Retailers should prioritize improving product presentation in apps, as it greatly influences overall satisfaction. Addressing the pandemic’s impact on delivery logistics is also crucial to ensure competitiveness in the e-commerce market.

Improving E-commerce User Satisfaction in Logistics System during the Big Data: A Case Study of JD.com

Finding the unimaginably great value beneath the surface of big data, more and more industries have considered big data as a kind of strategic resource that can bring huge benefits, including logistics industry. JD.com is taken as an example in this paper to analyze the approaches to enhance logistics and improve the satisfaction of e-commerce users in the logistics system with the power of big data technology. This essay starts with the introduction and literature review of related topics, followed by an analysis of how JD.com leverages big data to improve logistics in areas like warehousing, transportation, and information systems. Then this paper discusses how JD.com uses big data to enhance e-commerce customer satisfaction, such as improving logistics service quality and speed. Hopefully, the advanced methods that JD.com combines big data technology with logistics systems can bring some aspirations to other logistics enterprises and promote the overall development of the logistics industry.

Determinants of Customer Satisfaction in E-Commerce: A Case Study in Higher Learning Institutions in Johor

Customer satisfaction is an essential experience in e-commerce. The objective of this study was to identify determinants of customer satisfaction in e-commerce. This case study adopted its theoretical framework from the Theory of Reasoned Action (TRA). It also applied a quantitative research methodology by using self-administered questionnaires as a measurement tool in gathering sample data. The sample were student’s students from various course backgrounds in higher learning institutions in Johor. A total of 224 students answered the questionnaires. IBM SPSS version 26 was used to analyse the sample data. A test of reliability was carried out using the Cronbach's alpha method. Later, using Pearson correlation and multiple regression analysis to assess the interrelationship between the independent and dependent variables, predictors and criteria values were identified. The findings revealed that user interface quality, product information quality, security perception, and privacy perception had a positive relationship with customer satisfaction. However, e-service quality had a negative relationship and had no significant impact on customer satisfaction. This study provides insight into the influencing aspects and their functions. This would allow e-commerce firms to improve their websites based on the findings. Keywords: customer satisfaction, e-commerce, B2C, TRA, influencing factors

Research on E-commerce Customer Satisfaction Evaluation Method Based on PSO-LSTM and Text Mining

With the increase of social technology, e-commerce platforms have entered a period of rapid development. Improving customer satisfaction and purchase rate is the key to the survival of e-commerce platforms. Text mining and analysis of customer evaluation data will help to grasp the focus of customers and optimize the e- commerce platform. To this end, through text mining technology, the text comment data of five e-commerce platforms such as Amazon, eBay, Alibaba, Jingdong, and Taobao are collected, and the cleaned text is analyzed by particle swarm algorithm (PSO)-long short-term memory (LSTM) model. The data is subject to time scale extraction, and the extraction results are visualized and interpreted. The research shows that the logistics, price, freshness, quality and packaging of e-commerce platform merchants are important factors that affect the evaluation of e-commerce customer satisfaction.

E-Commerce Customer Satisfaction Analysis On Microblogs

Technology influences various things in people's lives, ranging from socialization to shopping behaviour, and hence marketplace inevitably appears as a part of e-commerce concepts. Opinions related to shopping activities are thus interesting for further research. This paper investigates the appropriate methods for analyzing e-commerce customer satisfaction. The research was conducted by compiling aspects of customer satisfaction and then by determining the appropriate lexicon method for sentiment analysis. A dataset containing 88,816 tweets was drawn from Twitter microblogs with a predefined set of keywords related to 5 e-commerce organizers. To analyze the data in a fine-grained manner, we also propose six aspects of customer satisfaction obtained from literature studies. Then we use the WordCloud visualization technique and topic modelling to get 73 keywords to categorize tweets into aspects. Our experiments carried out two scenarios: Scenario 1 compares several opinionated dictionaries, while Scenario 2 compares different approaches for computing sentiment scores. The application produces an appropriate method using Scenario 1 with an accuracy of 0.54. Scenario 2 produces the highest accuracy is 0.46. The application of the lexicon-based (or dictionary-based) method to sentiment analysis results that throughout e-commerce in every aspect, it has dominant positive sentiment, and the most dominant aspect throughout and in every e-commerce is the "product" aspect.

E-Commerce Customer Satisfaction Analysis On Microblogs

Technology influences various things in people's lives, ranging from socialization to shopping behaviour, and hence marketplace inevitably appears as a part of e-commerce concepts. Opinions related to shopping activities are thus interesting for further research. This paper investigates the appropriate methods for analyzing e-commerce customer satisfaction. The research was conducted by compiling aspects of customer satisfaction and then by determining the appropriate lexicon method for sentiment analysis. A dataset containing 88,816 tweets was drawn from Twitter microblogs with a predefined set of keywords related to 5 e-commerce organizers. To analyze the data in a fine-grained manner, we also propose six aspects of customer satisfaction obtained from literature studies. Then we use the WordCloud visualization technique and topic modelling to get 73 keywords to categorize tweets into aspects. Our experiments carried out two scenarios: Scenario 1 compares several opinionated dictionaries, while Scenario 2 compares different approaches for computing sentiment scores. The application produces an appropriate method using Scenario 1 with an accuracy of 0.54. Scenario 2 produces the highest accuracy is 0.46. The application of the lexicon-based (or dictionary-based) method to sentiment analysis results that throughout e-commerce in every aspect, it has dominant positive sentiment, and the most dominant aspect throughout and in every e-commerce is the "product" aspect.

Analysis of The Effect of Impulsive Purchase and Service Quality on Customer Satisfaction and Loyalty in Beauty E-Commerce

Analysis of The Effect of Impulsive Purchase and Service Quality on Customer Satisfaction and Loyalty in Beauty E-Commerce

Service Factors Influence on Customer Satisfaction in Innovative Ecommerce

Service Factors Influence on Customer Satisfaction in Innovative Ecommerce

Determinants of Customer Satisfaction in E-Commerce: A Case Study in Higher Learning Institutions in Johor

Customer satisfaction is an essential experience in e-commerce. The objective of this study was to identify determinants of customer satisfaction in e-commerce. This case study adopted its theoretical framework from the Theory of Reasoned Action (TRA). It also applied a quantitative research methodology by using self-administered questionnaires as a measurement tool in gathering sample data. The sample were student’s students from various course backgrounds in higher learning institutions in Johor. A total of 224 students answered the questionnaires. IBM SPSS version 26 was used to analyse the sample data. A test of reliability was carried out using the Cronbach's alpha method. Later, using Pearson correlation and multiple regression analysis to assess the interrelationship between the independent and dependent variables, predictors and criteria values were identified. The findings revealed that user interface quality, product information quality, security perception, and privacy perception had a positive relationship with customer satisfaction. However, e-service quality had a negative relationship and had no significant impact on customer satisfaction. This study provides insight into the influencing aspects and their functions. This would allow e-commerce firms to improve their websites based on the findings. Keywords: customer satisfaction, e-commerce, B2C, TRA, influencing factors