E-commerce Shopping Research Articles

Researching user experience with artificial intelligence application for customer care services on e-commerce platform

This study is conducted in the context of the booming development of e-commerce shopping in Vietnam. The rising demand for online shopping has resulted in increased pressure on workforce optimization and automation of customer service processes on e-commerce platforms. To meet the need for online customer care and response, significant resources in manpower, time, and cost are required. Therefore, to optimize costs, reduce response time, and enhance customer care, applying artificial intelligence to customer service has become a top choice. The purpose of this study is to explore the factors that directly affect the customer service experience using artificial intelligence on e-commerce platforms in Vietnam. This aims to help businesses understand customer feedback and psychology to develop strategies to improve and advance this artificial intelligence application model. This study provides an overview of the factors directly influencing the customer experience in AI-driven customer service on e-commerce platforms. Based on this, it offers managerial implications to enhance the quality of customer service for online businesses operating on e-commerce platforms in Vietnam.

Changes in People's Habits From Conventional Shopping to Shopee E-commerce In Padangpanjang City, West Sumatra Province

This research is entitled Changes in Community Habits from Conventional Shopping to Shopee E-commerce in Padangpanjang City, West Sumatra Province. The purpose of this study is to explain the factors that cause changes in shopping activities and how the form of change that occurs due to shopee E-commerce shopping habits. The theory used in this research is the modernization theory of Talcott Parsons. Talcott Parsons defines that the development process carried out by developing countries today can be achieved when following the process carried out by developed countries, distinguishing between "modern" and "traditional" where technology plays a key role in this theory. The method used is a qualitative method with data collection techniques of observation, interviews and documentation. The results of this study discuss the factors that cause changes in conventional shopping activities to shopee e commerce shopping, namely E-commerce as a shopping forum for the community, people's consumptive behavior, and people's hedonism style. The occurrence of factors that cause changes in E-commerce shopping has an impact on forms of change such as changes in shopping activities, changes in shopping places, and changes in payment procedures.

Investigation of consumer cross-border e-commerce shopping in China using the theory of EAM

Given that China's cross-border e-commerce (CBEC) industry has garnered significant attention from various overseas suppliers, this study aims to explore the CBEC shopping patterns of domestic Chinese consumers. The objective of the study is to integrate the theory of the e-commerce acceptance model (EAM) and multi-target trust to forecast the cross-border e-commerce shopping (CBECS) behavior of domestic Chinese consumers. PLS-based structural equation modeling analysis indicates that the CBECS intention is significantly influenced by perceived usefulness, perceived ease of use, and perceived risk. With regard to multi-target trust, seller trust impacts perceived risk and ease of use, whereas logistics trust influences perceived usefulness and perceived ease of use. Platform trust influences both seller trust and logistics trust. These results contribute to the advancement of CBEC literature and practices.

AI Chatbots: Elevating Customer Interactions Amidst Challenges

In the current era of rapid technological advancement, businesses must stay abreast of current trends to maintain competitive edge and ensure continued success. Prioritizing customer experience is paramount wherein showcasing a deep commitment to customer-oriented style is the differentiator. The modern marketing technologies give rise to intelligent conversational agents that has revolutionized customer engagement. As technology continues to shape the global business landscape, this study assessed the customer experience through their extent of interaction and challenges encountered in using artificial intelligence (AI) chatbots.Descriptive-quantitative research method was employed using validated survey questionnaire distributed to 258 locals from targeted respondents in the barangays of Las Pinas City who have experiences in interacting with AI-chatbots. The respondents were clustered to smaller groups who are knowledgeable about the topic, experienced using AI Chatbots by top e-commerce shops and are willing to participate. While AI chatbots have the potential to improve customer experience, their limitations and challenges must be addressed to ensure more effective and satisfying customer transactions.

E-commerce consumer privacy protection and immersive business experience simulation based on intrusion detection algorithms

In the process of e-commerce shopping, immersive service experiences and entertainment shopping models are increasingly popular among consumers. This article analyzes the privacy protection and immersive business experience simulation of e-commerce consumers based on intrusion detection algorithms.After studying intrusion detection algorithms, this article delves into personal privacy protection models related to public network e-commerce consumption. This article first explores the significance and current status of intrusion detection algorithms, as well as the research status of deep learning in this field, and conducts comparative experiments on improved intrusion detection algorithms. Then, after analyzing the requirements of the evaluation support system for privacy protection algorithms, the overall framework of the personal privacy protection system was designed and studied. Intrusion detection algorithm identifies possible privacy threats by monitoring and analyzing user behavior data, and then proposes an immersive business experience simulation framework, which includes virtual reality technology and personalized recommendation system, enabling users to immerse themselves in the virtual environment of e-commerce platform and enjoy personalized shopping experience. Interactive experiences can provide e-commerce consumers with more shopping pleasure, and at the same time, businesses need to ensure that consumer privacy is effectively protected during the consumption process. In the end, a new personal privacy protection model was successfully constructed, which verified the great advantages of the algorithm in terms of running time, and concluded that using intrusion detection algorithms can greatly protect the personal privacy of e-commerce consumers using public networks.

Study on the Promotion Effect of “Live Broadcasting with Shouting Style” in Live Rooms Under the Elaboration Likelihood Model

A new phenomenon of “shouting-style sales” has emerged in live e-commerce, but the research on its effects is currently insufficient. Exploring the short-term and long-term effects of “shouting-style sales” is beneficial for exploring how live e-commerce can develop better and longer-lasting, providing experiences and insights for live e-commerce rooms, especially brand-owned ones. By conducting a questionnaire survey of consumers who have experience with live e-commerce shopping, we can understand how consumers perceive “shouting-style sales” and what factors influence consumers’ ordering and repurchasing. The survey shows that “shouting-style sales” has no short-term effect on promoting orders, and it cannot promote repurchase in the long term either. Consumers may use both central and peripheral routes when shopping in live e-commerce, but they tend to use central route thinking when repurchasing. The emotional appeal and the degree of discounts in live rooms are the main factors influencing consumer purchases, while product quality and the level of product description are the main factors influencing consumer repurchases.

Performance of aging consumers in an e-commerce product choice task: The role of working memory and decision strategies.

Over the past two decades, there has been a growing interest in research on aging and the decision-making behavior of older consumers. The subject of this article is multi-attribute decisions made using product comparison, a widely used functionality of many e-commerce stores. Studies on cognitive aging have established a negative relationship between age and accuracy in multi-attribute choice tasks; however, works in informatics (Human-Computer Interaction, UX research) have not accounted for how individual user differences affect the optimality of users' product comparison decisions. Our work attempts to bridge this gap between the disciplines of psychology of aging and informatics. We predicted and confirmed, in an online study simulating e-commerce shopping, the strong limitations of older adults in product comparison tasks. In subsequent modeling, other individual characteristics, such as visual working memory, were predicted and shown to be a reliable mediator of the relationship between age and decision accuracy. Popular product comparison tables are not sufficient for older consumers. Despite following UX guidelines for designing product comparison tables, overall correctness of consumer decisions in our study ranged from 90% to as little as 30%, depending on the difficulty of the task and the age of the consumer. These findings have important practical implications for UX design of e-commerce Websites.

An Analysis of the Influence of the Elderly Group on the Purchase Intention of E-Commerce Shopping

An Analysis of the Influence of the Elderly Group on the Purchase Intention of E-Commerce Shopping

Development of a web application for houseplants e-commerce shop based on technology stack Laravel + React

In this article a current state of art of the web commerce applications is analyzed, especially the applications which can be used to build a web shop to sell flowers and plants were reviewed. The international and Ukrainian experience of building e-commerce projects in current area was analyzed, some specific features of e-commerce traiding of plants goods were highlighted, as well as specific ways to assortment segmenting and sales organization. The web commerce field focused mostly on selling plants and gardening accessories is not too busy in Ukraine. The existing programmatic web solutions are too complex and sometimes are excess for small or middle size project. That’s why current project could be efficient. In current paper the solutions for organization of programming stack for the project are analyzed, also the project’s structure and some features were reviewed. The params based on which the decision about database management system’s choice were made as well as programming language and additional features are separately covered. The main criteria for the development method choice and development approaches were effectiveness because the project belongs to a specific field and its features are to be rationally limited with ability to scale and improve it through time, because of this an important task was to find the balance between rapid development and quality and supportability of the project. Based on the analysis results a stack of popular framework Laravel with supporting technologies was selected one of the key solutions in web store development or any other e-commerce platform is filtering as well as categorizing items. Selling plants is a rather specific field because the params are very different from the usual goods params, for example features of electronics or size and quality for clothes sold in Internet. Filtering requires deep approach to it, starting from corresponding and effective database management system’s choice, constructing entities system and code adaptation on backend and frontend, when the routine tasks as order placement, payment and delivery could have standard approaches. The categories and filters often require and individual solution

Analysis of Shopping Activities in E-Commerce among Students and College Students

This research aims to analyze e-commerce shopping activities among students and college students, as well as to find out what factors influence e-commerce shopping activities. This research shows what platforms are most chosen by students and college students. This research also shows that e-commerce has benefits for students and college students to find a variety of goods at affordable prices, easier transactions, more energy saving, and ease of comparing prices with other online shops (olshop) businesses.

The Impact Analysis of E-Commerce Usage on Behavior Changes of Shopee Customers in Indonesia

The purpose of this study was to determine the impact of e-commerce on changes in habits and consumptive behavior among online buyers. The research method used is qualitative research, with data collection techniques through in-depth interviews with ten informants consisting of students and employees who have shopped online at least once a month. The results of the study show that the presence of various e-commerce platforms in Indonesia influences the changing patterns of people's shopping in this digital era. Consumer trust in e-commerce exists because the hopes and expectations that exist in the community as online buyers can be realized. The existence of various e-commerce sites in Indonesia has an influence on people's shopping habits. In addition, the influence of product advertisements, flash sales, free shipping, and discounts makes people, as consumers, continuously want to shop online, resulting in a consumptive lifestyle. In line with Baudrillard's consumptive theory, consumers in e-commerce shop no longer because of use value or exchange value but because there is a sign value attached to the product. So that people no longer buy goods to fulfill their daily needs but to fulfill their desires and to improve their image, lifestyle, and social status in society.

PENGARUH KUALITAS PRODUK DAN PERSEPSI RESIKO TERHADAP KEPUTUSAN PEMBELIAN E-COMMERCE TOKOPEDIA PADA MASYARAKAT KOTA BATAM

This study aims to observe and analyze the associations experienced with variables, product quality and risk perception in Tokopedia E-commerce shopping decisions for residents of Batam City. This study uses a quantitative descriptive method. The data collection was carried out by distributing questionnaires to a total of respondents in which the respondents were residents of Taman Anugrah Housing Rt/Rw 001/015 Tembesi Village, Sagulung District, Batam City, a total of 156 respondents. Using the SPSS tool version 26. set on randome sampling technique. Through statistical tests it was observed if the variable product quality and risk perception were normally distributed. Through the acquisition of the t test shows if the product quality variable has a significant impact on the purchase decision as well as the risk perception variable has a significant impact on the purchase decision. but the results of the f test with others say that the variable product quality and risk perception have a positive and significant impact on Tokopedia E-commerce shopping decisions for residents of Batam City.
 
 Keywords: Product Quality; Perceived Risk; Purchase Decision.

The Influence of Perceived Price and Customer Satisfaction on Customer Loyalty for Shopee E-Commerce Online Shopping

This study aims to determine the effect of Price Perception and Customer Satisfaction variables on Customer Loyalty for Shopee E-Commerce Shopping partially. This type of research is quantitative. The population in this study were Shopee consumers in Surabaya, aged 17-25 years who had made at least two purchases at Shopee e-commerce. The sample of this research is 78 respondents. The sampling technique used in this study was no change sample. Collecting data using a questionnaire as a source of primary research data. While the data processing technique used is Partial Least Square (PLS). Based on the results of the study it can be concluded that Price Perception has a significant positive effect on Customer Loyalty, Customer satisfaction has a positive effect on Customer Loyalty.

The digital presence of the leading brands in the category of Food for Special Medical Purposes

Abstract ObjectiveFood for Special Medical Purposes (FSMP) is a niche area in the food and healthcare industries that produces foods to meet the dietary requirements of people with specific medical conditions. These products provide specific nutrient amounts and ratios, which can be used as a supplement to a conventional diet, part of an entire medical treatment plan, or as a sole source of Nutrition. The market is expected to generate revenues of $19.67 billion in 2028. Most FSMPs products are purchased individually by patients or caregivers. Although these products are not advertised directly to the public might be bought online from pharmacies and other online shops. This research aims to evaluate the online presence of the most important brands within the FSMPs category and how the patients get to the online shops without direct-to-consumer advertisement. Methods:This exploratory qualitative research study examines the online presence of FSMP products and key brands using SEO as the main digital technique. Data sources included Romanian websites, Google SERPs, Brandmentions.com, Semrush, and Ubersuggest. Results: The findings showed that these websites received only a small amount of direct traffic, with only a few hundred monthly visits. To increase organic visibility, businesses must follow regulatory requirements, collaborate with medical experts, engage with potential clients, and exhibit their products at medical events and fairs. Content marketing is crucial for FSMP brands to engage with their target audience and deliver valuable information. Conclusions: FSMP brands cannot advertise directly to consumers. Instead, they should focus on SEO, reputable online pharmacies, e-commerce shops, and content marketing. This research applies to FSMP brands, digital marketers, and online pharmacies to effectively connect with target demographics, promote products, navigate regulatory constraints, and enhance online presence, improving accessibility and awareness.

How household food shopping behaviors changed during COVID-19 lockdown period: Evidence from Beijing, China

The COVID-19 pandemic has had a great impact on food shopping behavior. However, changes in food shopping behavior and related decision-making mechanisms remain unexplored. This study conducted a face-to-face questionnaire survey with 900 residents in three communities in Haidian, Beijing, China, to analyze food shopping behavior during the periods of pre-lockdown, lockdown (January–March 2020), and post-lockdown. Our results indicated that the preference for fresh food e-commerce shopping was accelerated by the lockdown with the usage rate increasing by 48%; the wet markets were seriously hit by the lockdown policy and had not fully recovered by the post-lockdown period (with a 75% reduction in the usage rate during lockdown). The psychological mechanisms of changes in food shopping behavior revealed that the context (e.g., community facilities) and the perceived risk of COVID-19 were two significant factors impacting the four shopping choices investigated. Specifically, the context factors affected behavior directly for supermarkets and convenience stores and indirectly for e-commerce and wet markets. The findings are expected to help in guiding emergency responses during a future pandemic as well as the long-term construction of food supply facilities.

A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media

Machine learning frameworks categorizing customer reviews on online products have significantly improved sales and product quality for major manufacturers. Manually scrutinizing extensive customer reviews is imprecise and time-consuming. Current product research techniques rely on text mining, neglecting audio, and image components, resulting in less productive outcomes for researchers and developers. AI-based machine learning frameworks that consider social media and online buyer reviews are essential for accurate recommendations in online e-commerce shops. This research paper proposes a novel machine-learning-based framework for categorizing customer reviews that uses a bag-of-features approach for feature extraction and a hybrid DNN framework for robust classification. We assess the performance of our machine learning framework using AliExpress and Amazon e-commerce product review data provided by customers, and we have achieved a classification accuracy of 91.5% with only 8.46% fallout. Moreover, when compared with state-of-the-art models, our proposed model shows superior performance in terms of sensitivity, specificity, precision, fallout, and accuracy.

Factors Analysis of Consumers' Purchasing Intention Under the Background of Live E-commerce Shopping

<p>With the development of 5G network, artificial intelligence, cloud computing, big data and other digital technologies, we have witnessed the E-commerce live broadcasting industry has also jumped on this fast train, injecting fresh blood into People’s Daily shopping. The main contribution of this paper is to combine theory with practice to build a model from three aspects: people, goods and market, set up assumptions, and analyze the purchasing factors that affect people’s daily shopping. Using SmartPLS software to conduct descriptive statistics, reliability analysis and validity test on the collected questionnaires, the following conclusions and research objectives are drawn: the interactivity of live-streaming, entertainment of live-streaming, promotion price of live-streaming and opinion leaders will have a significant impact on consumers’ cognition and emotion, and meanwhile, cognition and emotion will have a significant impact on consumers’ purchase intention. Opinion leaders have the greatest impact on consumers’ willingness to purchase.</p> <p> </p>

Study on the Distribution of Fresh Food Support System—An Example of Shanghai during the Epidemic Closure of 2022

One of the key indicators to measure the sustainability and resilience of a city during a public health crisis is how well it can meet the daily needs of its residents. During the COVID-19 lockdown in Shanghai in 2022, e-commerce shopping and delivery became the most important method for ensuring the city’s material supplies. This article uses the distribution data of a fresh e-commerce platform’s pre-warehouse and static population distribution data to establish a basic material supply system evaluation model for the city and explore its resilience potential. Focusing on the central urban area of Shanghai, this study uses a population heat map with geographic coordinates to reflect the static distribution of residents and obtains the distribution data of the e-commerce pre-warehouses. Using kernel density analysis, the relationship between the pre-warehouses and the residents’ needs is established. Through analysis, it was found that the supply capacity of fresh food in different areas of Shanghai during the lockdown could be categorized as insufficient, adequate, or excessive. Based on these three categories, improvement strategies were proposed. Finally, this article suggests establishing a scientific supply security system to promote urban sustainability and prepare for future challenges.

Analysis of Buyer Trust in Web E-Commerce Shop and Travel

The development of information technology is increasing rapidly until now the latest technologies are being developed so that the role of technology can also improve the regional economy. Economic growth and technological developments in the business world have developed very rapidly, marked by the emergence of companies that are trying to create products and services to meet consumer needs. Business development illustrates that people's welfare is increasing, thus causing an increase in people's purchasing power. This study aims to develop a research model that uses Kim's approach model by representing based on Trust, Benefit, Risk, Intention and Purchasing with this model so that factors can be identified. anything that influences public trust to conduct online transactions (E-commerce), especially on web shops and travel.

Strategic Choice of Commercial Real Estate in China's First-Tier Cities in The Oost-Epidemic Era

In recent years, affected by the Covid-19 and the current policy regulation of China's residential real estate market, the commercial real estate market has shown repeated fluctuations. During a historical period (2005-2015), thanks to the government's vigorous development of real estate and the influx of many people into large cities, commercial real estate in first-tier cities have experienced long-term prosperity. Some leading companies have earned high profits and occupied a large market share through the cycle of "financing - investment and construction - selling or operating - refinancing...". However, this phenomenon began to change in 2015. As the country adjusted its macro policies, financing costs continue to increase, the total area of new commercial real estate projects is decreasing year by year, and the market size of traditional commercial real estate is also shrinking, making people soberly aware that the prosperity and success of commercial real estate are deeply dependent on national development strategies, while depends on social and economic development and people's consumption habits. The study's results found that since 2019, the expansion scale and profitability of China's commercial real estate have both shrunk. On the one hand, residents' consumption ability and willingness have weakened due to the impact of Covid-19 and the business cycle. On the other hand, young consumer groups are no longer satisfied with a single business model; the upgrade of the consumption content and the rise of e-commerce shopping have had an impact on traditional commercial real estate, which forces companies to make corresponding adjustments to changes in the external environment when deciding long-term strategies. Using the PEST analysis tool to create a comprehensive and objective analysis of the current political, economic, social, and technological environment will help enterprises better judge external factors. Therefore, when formulating development strategies, they can better align their development plans with the country's new development strategy, earning strategic initiative and maintaining competitive vitality in the recent historical period.