The Application of Artificial Intelligence Technology in Enhancing Customer Experience on The Shopee Platform for Viet Nhat Hoa Hong Trading and Import- Export Co.
Problem Statement
In the fast changing e-commerce environment of today, customer experience is increasingly recognized as a vital component of corporate success (Singh et al.,
In 2023, platforms such as Shopee, which thrive in fast-paced and customer-focused markets, require innovative strategies to satisfy the ever-increasing demands of consumers Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd., a regular vendor on Shopee, faces challenges in addressing these needs Key issues affecting customer satisfaction and loyalty include slow response times to inquiries, a lack of personalized product recommendations, and difficulties in maintaining consistent engagement with clients.
Traditional customer care methods often fail to effectively address consumer issues To provide the seamless, personalized, and efficient shopping experiences that modern consumers demand, leveraging advanced technologies is essential.
Incorporating AI-driven solutions into customer experience strategies can lead to an average revenue increase of 10–15% and a 20% boost in customer satisfaction (Thanyawatpornkul, 2024) Additionally, Gartner (2021) predicts that by 2025, 60% of companies will leverage AI to improve consumer experiences, highlighting the growing importance of artificial intelligence in e-commerce.
Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd is a representative small and medium-sized enterprise (SME) in Vietnam, facing common challenges in enhancing customer experience within the e-commerce sector, particularly on platforms like Shopee Customers demand quick and personalized shopping experiences, yet the company, like many SMEs, has limited resources and has not fully adopted artificial intelligence technologies This study seeks to address the specific challenges faced by Viet Nhat Hoa Hong and provide valuable analytical insights that can help similar businesses improve sales and customer satisfaction.
Implementing AI solutions specifically designed for Shopee operations will significantly enhance the customer experience at Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd AI-driven personal interactions can boost engagement, while real-time sentiment analysis enables proactive resolution of customer issues However, selecting the most suitable artificial intelligence technologies and aligning them with the company's operational objectives necessitates a systematic approach and strategic implementation (Al-Surmi, Bashiri & Koliousis, 2022).
This study explores how artificial intelligence can enhance the customer experience at Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd., while also addressing specific business challenges By focusing on AI adoption in e-commerce, particularly for SMEs in developing countries like Vietnam, we provide best practices and actionable recommendations This research offers valuable insights for similar companies, making it an essential case study for SMEs looking to leverage artificial intelligence to improve consumer experiences.
Research Objective(s)
This study investigates how artificial intelligence (AI) technology can improve customer experience on the Shopee platform for Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd.
This study aims to explore the psychological and perceptual factors influencing consumer experiences on the e-commerce platform Shopee It will first assess how perceived usefulness (PU) impacts user experience, followed by an examination of perceived ease of use (PEU) regarding product searches, payment processes, and other functionalities Additionally, the research will investigate perceived trust (PT) in Shopee's transaction security and its effect on purchasing decisions The study will also analyze attitudes (A) towards Shopee and their influence on customer experiences Finally, it will explore perceived control (PC), focusing on consumers' sense of authority over their Shopee interactions, including product selection and payment choices These objectives will provide valuable insights into the key factors affecting customer satisfaction and experiences on the platform, ultimately enhancing its operational effectiveness.
Research Question(s)
RQ1: Does Perceived Usefulness affect Customer Experience on Shopee ?
RQ2: Does Perceived Ease of Use affect Customer Experience on Shopee ? RQ3: Does Perceived Trust affect Customer Experience on Shopee ?
RQ4: Does Attitude affect Customer Experience on Shopee ?
RQ5: Does Perceived Control affect Customer Experience on Shopee ?
Scope of Study
This paper explores the application of artificial intelligence (AI) technologies at Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd and their impact on the consumer experience on the Shopee platform Utilizing the Technology Acceptance Model (TAM), the study focuses on key factors such as Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Perceived Trust to understand how these elements influence user engagement and satisfaction.
Attitude, and Perceived Control, the research is aimed on AI acceptability in e- commerce
This research focuses specifically on the AI implementations of Shopee as utilized by Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd., highlighting the impact on small and medium-sized enterprises (SMEs) within Shopee's network.
In 2024–2025, the focus will be on how artificial intelligence enhances the consumer experience This analysis will utilize customer surveys, feedback analysis, and performance indicators from Shopee's platform to assess the impact of AI on consumer satisfaction, engagement, and retention.
Methodology
This study employs a quantitative research methodology to assess the impact of artificial intelligence on Shopee customers' experiences Data will be collected through online questionnaires and surveys focusing on user satisfaction, engagement, and interactions with AI-driven features The survey results will be analyzed using PLS-SEM (Partial Least Squares Structural Equation Modeling) with SmartPLS 4 software, enabling the identification of key trends and the evaluation of AI's influence on customer experiences By utilizing a purely quantitative approach, the study aims to provide clear, data-driven insights into how AI technologies affect customer behavior and satisfaction.
Significance of Study
This research highlights the potential of artificial intelligence (AI) technology to enhance the online shopping experience on platforms like Shopee As the e-commerce sector grows and the demand for personalized and efficient customer service increases, businesses must adopt AI solutions The study explores how AI tools, including chatbots, recommendation systems, and predictive analytics, can optimize customer loyalty, user satisfaction, and engagement, ultimately improving consumer interactions.
Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd offers valuable insights on leveraging artificial intelligence to meet consumer expectations and gain a competitive advantage By understanding the impact of AI on consumer experiences, businesses can enhance client retention, adapt their products, and boost operational efficiency, ultimately fostering long-term growth and sustainable success.
This study presents innovative strategies for Shopee to leverage artificial intelligence in addressing consumer needs and fostering positive transformations on the platform By tailoring AI functionalities to align with user preferences, Shopee can enhance customer satisfaction and maintain long-term loyalty.
This study enhances the understanding of artificial intelligence (AI) in e-commerce and customer experience management By analyzing consumer behavior and attitudes towards AI, it paves the way for future research, contributing to broader discussions on the interplay between technology, customer experience, and e-commerce strategies.
Chapter 1: Introduction: Summarizes the research objectives, focusing on AI's impact on customer experience for Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd on Shopee
Chapter 2: Review of the Literature: Reviews key studies on AI and customer experience in e-commerce, offering a theoretical foundation and conceptual framework
Chapter 3: Research Methodology: Describes the research design, data collection methods, and ethical considerations
Chapter 4: Results and Discussion: Presents and analyzes data, discussing AI’s impact on business performance and customer satisfaction
Chapter 5: Conclusions: Summarizes findings, provides recommendations, and suggests areas for future research.
Background of Study
2.1.1 Overview of Artificial Intelligence (AI)
Artificial intelligence (AI) refers to the ability of computers to simulate human intelligence processes, including learning, reasoning, problem-solving, perception, and language comprehension AI is categorized into two types: general AI, which aims to perform any intellectual task that a human can do, and narrow AI, which is designed for specific tasks.
AI, which is made to accomplish certain tasks These days, machine learning, computer vision, robotics, and natural language processing are the most widely used
Artificial intelligence has transformed various sectors, including healthcare, banking, manufacturing, and entertainment By leveraging AI, businesses can make data-driven decisions, optimize processes, and improve customer experiences.
AI systems can process vast amounts of data at remarkable speeds Machine learning algorithms, a subset of artificial intelligence, allow computers to learn from data autonomously, eliminating the need for explicit programming This flexibility enables AI to evolve and improve over time (Sharma et al., 2024).
AI significantly enhances automation, enabling organizations to improve efficiency and reduce costs However, the integration of AI raises important ethical, privacy, and job displacement issues, highlighting the necessity for responsible and thoughtful development and implementation of AI technologies.
2.1.2 Artificial Intelligence Technology in E-commerce
Artificial intelligence (AI) is rapidly transforming the e-commerce sector by fundamentally altering how companies engage with consumers and manage operations As a necessity for businesses aiming to enhance operational efficiency and deliver personalized experiences, AI has become essential in navigating the increasingly competitive and technology-driven market.
Predictive analytics, natural language processing (NLP), and machine learning are transforming e-commerce by enabling companies to make quicker and more precise decisions By analyzing vast amounts of customer data, machine learning algorithms identify buying behavior trends, allowing platforms to generate personalized product recommendations tailored to individual consumers.
2020) Along with greatly raising conversion rates and client lifetime value, this enhances user experience
AI-driven chatbots have transformed customer support by utilizing natural language processing (NLP) to provide immediate and context-aware responses to client inquiries These virtual assistants operate around the clock, significantly reducing response times and efficiently handling a large volume of inquiries simultaneously This not only enhances customer satisfaction but also alleviates the workload for human support staff (Kumar et al., 2023).
Artificial intelligence plays a crucial role in supply chain and inventory management by analyzing real-time data to forecast demand, optimize stock levels, and suggest pricing adjustments, which helps prevent overstocking and stockouts, ultimately enhancing logistics and reducing costs (Omprakash, 2024) The integration of AI significantly impacts businesses, with studies showing that companies using AI-driven personalization techniques experience an average 25% increase in sales and improved customer retention (Kedi et al., 2024) Additionally, AI contributes long-term strategic value by enhancing data transparency, scalability, and fostering innovation throughout the organization.
Artificial intelligence is increasingly essential for automating business operations, enhancing customer engagement, and personalizing the shopping experience For e-commerce companies, embracing AI is no longer optional; it is a crucial element for achieving success in the evolving digital economy.
2.1.3 Customer Experience on the Shopee Platform
In e-commerce, the overall quality of customer experience (CX) significantly impacts client loyalty and satisfaction A seamless and engaging shopping experience, characterized by intuitive search tools, personalized product suggestions, and efficient customer service, directly influences purchase decisions and fosters long-term consumer trust Shopee, a leading e-commerce platform in Southeast Asia, continually integrates artificial intelligence (AI) technology to improve CX and strengthen its market presence.
AI-enabled solutions at Shopee provide customized product recommendations based on consumers' interests, past purchases, and browsing behaviors This level of personalization enhances user engagement and boosts conversion rates, as consumers are more likely to connect with products that align with their preferences Central to this strategy is Shopee's AI-powered recommendation engine, which maintains consumer interest throughout the buying process Additionally, AI-driven chatbots deliver quick, automated assistance for common inquiries, order tracking, complaint resolution, and order management These virtual agents can reduce service-related delays by up to 40%, allowing consumers to receive prompt responses without human intervention, thereby creating a seamless and more satisfying shopping experience.
Despite recent advancements, Shopee continues to face challenges in fraud detection, hyper-personalization, and logistics optimization, which are crucial for ensuring high customer satisfaction and retention Without effective solutions to address these issues, the long-term trust and loyalty of customers may be jeopardized.
Implementing AI-driven customer experience strategies can boost consumer satisfaction by 35%, showcasing the significant impact of intelligent systems on online purchasing behavior (Kushwah, 2024) In the rapidly evolving digital economy, Shopee can foster brand loyalty, improve service quality, and secure a sustainable competitive edge through the strategic adoption of artificial intelligence technologies.
2.1.4 Introduction to the Company and the Implementation of AI in Business Operations
Company Name: Viet Nhat Hoa Hong Trading And Import- Export Co., Ltd Ownership Structure: Limited Liability Company (LLC)
Business Sector: The company operates in the fields of trade, investment, and import-export, wholesale food sector, especially Japanese domestic products such as household appliances, cosmetics,
Office Address: No 1/35/261 Tran Nguyen Han, Nghia Xa Ward, Le Chan District,
• Involving in import-export, trading, and investment with a focus on food items at the wholesale and retail levels
• Centering on indigenous Japanese goods, such as cosmetics, appliances, and other consumer items
• Advising customers on product selection and providing post-purchase assistance
The company aims to become a leading supplier of local Japanese goods by focusing on both wholesale and retail markets To enhance operations, product recommendations, and customer support, it plans to boost its presence on e-commerce platforms and leverage AI technology By prioritizing quality and digital innovation, the company seeks to meet the growing demand for authentic Japanese products in Vietnam and ensure long-term success.
Figure 2.1: Viet Nhat Hoa Hong Trading And Import- Export Company’sbooth is on Shopee e-commerce platform Link
Implementation of AI in Business Operations
In the fast-paced Shopee ecosystem, Vietnam Nhat Hoa Hong Trading and Import-Export Co., Ltd prioritizes enhancing customer experience to sustain and boost its competitive advantage The integration of AI-driven solutions is crucial in today's digital commerce landscape, as they enhance consumer interactions, personalize product offerings, and streamline operational tasks These technologies contribute to a more intelligent, responsive, and efficient retail infrastructure.
Theoretical Framework
This study utilizes the Technology Acceptance Model 3 (TAM 3) to investigate the factors affecting customer experience on the Shopee platform Developed by Venkatesh and Bala in 2008, TAM 3 extends the original model by integrating additional elements that enhance the understanding of technology acceptance in digital environments like e-commerce Key constructs of TAM 3, including Perceived Usefulness (PU), Perceived Ease of Use (PEU), Perceived Trust (PT), Attitude (A), Perceived Control (PC), and Perceived Enjoyment, are essential in shaping the overall Customer Experience (CE).
Figure 2.2: Technology Acceptance Model (TAM 3) (Venkatesh and Bala, 2008)
Perceived usefulness (PU) refers to the extent to which users believe that a specific technology can improve their job performance or enhance their purchasing experience in e-commerce (Venkatesh and Bala, 2008) AI-powered tools, such as predictive search and personalized recommendations on platforms like Shopee, streamline the buying process and increase user satisfaction According to Inavolu (2024), users who find AI-driven tools beneficial are more inclined to engage with these features, ultimately improving their overall customer experience.
The second major concept in TAM 3 is perceived ease of use (PEU), which measures how free a user thinks utilizing a technology will be from effort (Venkatesh and Bala,
In the e-commerce sector, the effectiveness of AI tools hinges on their simplicity and speed of operation For instance, Shopee's AI chatbots and recommendation systems must be user-friendly to minimize effort for users Research by Kelly & Palaniappan (2023) indicates that consumers are more inclined to continue using technologies that are easy to interact with, highlighting that ease of use is a crucial factor in technology acceptance.
On e-commerce platforms such as Shopee, the importance of perceived trust (PT) in artificial intelligence algorithms is paramount, as users rely on these systems to handle their personal data Kaur et al (2022) highlight that trust in AI significantly influences the overall user experience, particularly regarding the accuracy of product recommendations and the security of financial transactions.
Attitude (A) reflects consumers' emotional responses to artificial intelligence tools, indicating that a positive attitude can enhance user engagement and satisfaction For instance, Shopee users are more inclined to interact with the platform when they perceive its AI features as useful and reliable, ultimately enriching their shopping experience.
Perceived Control (PC) refers to how users perceive their autonomy in interacting with AI-driven features Research indicates that user satisfaction with technology increases when individuals feel empowered to customize AI recommendations (Hashemi & Bosnjak, 2024).
(2024) research, allowing consumers to change AI-driven recommendations increases customer happiness since it lets them feel more in control of their purchase choices
The conceptual framework illustrated in Figure 2.2 analyzes the impact of Perceived Usefulness, Perceived Ease of Use, Perceived Trust, Attitude, and Perceived Control on Customer Experience within the Shopee platform This framework offers valuable insights for SMEs like Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd to enhance their customer experience strategies, providing a comprehensive approach to understanding how artificial intelligence can boost customer satisfaction and engagement.
Hypothesis Statement
Perceived Usefulness (PU) refers to the extent to which consumers believe that technology enhances their efficiency in achieving purchasing goals In the e-commerce sector, enhancing user experience relies on AI-driven services such as predictive analytics, automated customer support, and personalized recommendations When consumers recognize the utility of these technologies, they are more likely to engage with the platform, leading to increased customer satisfaction and loyalty Research indicates that AI-powered recommendation systems can boost conversion rates by 35% by enabling consumers to find relevant products more quickly, thereby reducing decision-making time This underscores the importance of improved platform interactions in driving consumer engagement.
PC to increase perceived utility, hence enhancing the whole customer experience (CX)
AI-powered chatbots and automated assistants significantly improve customer experience (CX) by minimizing wait times and increasing response accuracy Research indicates that these technologies can cut response times by up to 50%, leading to enhanced customer engagement and satisfaction (Sharma et al.).
AI-driven inventory and demand forecasting enhances the shopping experience by ensuring quicker order fulfillment and reducing stockout occurrences Companies that successfully implement artificial intelligence to improve product utilization are likely to see increased consumer confidence, engagement, and retention as e-commerce platforms continue to integrate AI into their offerings Based on these findings, the research suggests the following hypothesis:
H1: Perceived Usefulness (PU) positively influences Customer Experience on Shopee
Perceived Ease of Use (PEU) refers to a user's perception of how simple and effortless a technology is to use (Davis, 1989) In the realm of e-commerce, AI-driven technologies such as intelligent search engines, virtual assistants, and automated customer service systems significantly enhance user experience by minimizing friction in the shopping process These user-friendly AI solutions lead to increased customer satisfaction and streamline purchasing Research indicates that AI-powered search engines can enhance user navigation on major online platforms like Shopee by 40% (Amatus & Gisip, 2022), thereby improving product discovery By simplifying interactions, artificial intelligence fosters greater user engagement and encourages consumers to rely more on the platform for their shopping needs.
Customers may become frustrated and stop using AI-powered services if they find them too complex or difficult to navigate A poorly designed chatbot or recommendation system that provides irrelevant answers can significantly diminish user satisfaction Research indicates that 30% of online shoppers abandon AI-driven services when interactions feel impersonal or cumbersome, underscoring the critical need for usability-focused AI design (Aiolfi, 2023) To improve consumer experience, trust, and retention in the competitive e-commerce landscape, Shopee must ensure that its AI-driven interfaces are accessible and user-friendly Based on these findings, the research proposes the following hypothesis:
H2: Perceived Ease of Use (PEOU) positively influences Customer Experience on Shopee
Customers' trust in AI significantly impacts their experiences on Shopee and their willingness to use AI-powered products The level of trust in artificial intelligence hinges on the accuracy of product recommendations, the protection of user data, and the transparency of its processes When AI delivers valuable support and relevant suggestions, users are more inclined to trust and engage with the platform.
To foster customer confidence, it is essential to ensure privacy and security, particularly regarding the use of artificial intelligence in handling personal data Shopee must prioritize data security to alleviate customer concerns Additionally, maintaining transparency in AI processes can mitigate fears of bias or inaccuracies in product recommendations Research indicates a strong correlation between customer satisfaction and loyalty with trust in AI systems, suggesting that customers who trust these technologies are more likely to remain loyal to the platform Based on these findings, the research proposes a hypothesis to further explore this relationship.
H3: Perceived Trust positively influences Customer Experience on Shopee
Customers' perceptions significantly influence their interaction with Shopee's AI-driven tools A favorable attitude towards artificial intelligence enhances customer engagement, while skepticism can hinder its adoption When consumers recognize the usefulness and reliability of AI, they are more inclined to utilize services such as automated assistance and personalized recommendations, ultimately leading to an improved shopping experience (Iyelolu et al., 2024).
User attitudes towards AI services are influenced by perceived usefulness, ease of use, and previous experiences Positive interactions, such as relevant AI recommendations and successful chatbot conversations, enhance customer perceptions In contrast, negative experiences, like ineffective AI support or irrelevant suggestions, can frustrate users and lead to reluctance in using AI-powered services (Castillo et al., 2021).
Research indicates that a positive perception of artificial intelligence significantly boosts consumer satisfaction and loyalty Shoppers who regard AI as a beneficial resource are more inclined to engage with AI-enhanced features on platforms like Shopee, thereby improving their overall shopping experience (Gunawan et al., 22) Based on these findings, the study puts forth the following hypothesis:
H4: Attitude positively influences Customer Experience on Shopee
Perceived control significantly enhances the quality of the buying experience on Shopee, as users tend to trust the platform more and remain engaged when they feel empowered to make autonomous decisions This includes the ability to modify AI-generated suggestions, manage personal information, and choose products that align with their preferences Such empowerment not only boosts user satisfaction but also fosters long-term loyalty.
Perceived control is significantly influenced by the openness and adaptability of artificial intelligence When Shopee provides users with options to manage their data, personalize recommendations, and filter content based on their purchasing behavior, they are more likely to feel that the platform meets their specific needs This user-driven personalization reinforces the idea that consumers are active participants in the buying process, rather than passive recipients of algorithm-generated outputs.
Excessive autonomy in artificial intelligence systems, coupled with a lack of customization options, can leave users feeling helpless and detached This emotional disconnection may reduce user engagement, leading to frustration and diminished trust (Sillanpọọ, 2022) In highly competitive e-commerce environments, such negative feelings can result in poor brand perceptions and loss of customers.
Balancing automation with user autonomy is essential for enhancing the customer experience By ensuring that its AI technologies are clear, flexible, and intelligent, Shopee can empower consumers, fostering greater engagement and a closer relationship with the platform This approach promotes a more fulfilling and personalized shopping experience Based on these insights, the research proposes the following hypothesis:
H5: Perceived Control positively influences Customer Experience on Shopee
In e-commerce, customer experience (CX) significantly impacts consumer behavior, influencing satisfaction, engagement, and loyalty It encompasses all interactions with a brand, from product browsing to after-sale support, and is shaped by factors such as responsiveness, personalization, and usability Enhancing CX through the optimization of the shopping experience relies on AI-powered technologies on platforms like Shopee, which utilize chatbots, recommendation engines, and predictive search algorithms.
Research Gap
Artificial intelligence (AI) is increasingly prevalent in e-commerce, particularly on platforms like Shopee, yet its impact on consumer experiences in small and medium-sized enterprises (SMEs) in developing countries such as Vietnam remains underexplored Most existing studies concentrate on large organizations or global platforms, creating a significant knowledge gap regarding AI's role in SMEs Vietnamese SMEs face unique challenges in technology adoption due to limited resources and expertise However, research indicates that AI can significantly improve customer satisfaction and operational efficiency, providing these businesses with a competitive edge.
Recent research often focuses on individual factors influencing customer experience, such as perceived ease of use (PEU), attitude (A), perceived control (PC), and perceived trust (PT), but lacks exploration of their interactions and overall impact on consumer experience (Kaushal & Yadav, 2023) While many studies emphasize characteristics like perceived utility and ease of use, the Technology Acceptance Model (TAM) has been a prevalent framework for analyzing technology acceptance (Davis, 1989; Venkatesh & Bala, 2008) The TAM 3 model offers a more comprehensive understanding by integrating constructs like perceived usefulness (PU), PEU, PT, A, and PC, highlighting how these elements collectively shape the customer experience.
The application of the TAM 3 model in Vietnamese SMEs highlights a significant research gap, as most studies have focused on international contexts, often overlooking the unique cultural, consumer behavior, and technology adoption factors specific to Vietnam (Hien & Tam, 2025) Current research fails to adequately explore the unique opportunities and challenges associated with AI adoption in the Vietnamese environment This study aims to bridge this knowledge gap and provide valuable insights for SMEs seeking to implement AI to improve customer experience by investigating these issues within the Vietnamese SME framework.
Despite the clear benefits of AI in enhancing customer experience, there is limited understanding of how SMEs in Vietnam can effectively implement AI to improve customer retention and satisfaction While larger organizations have thoroughly investigated AI-driven solutions such as chatbots and recommendation systems, the application of these technologies in SMEs remains largely uncharted (Schửnberger, 2023) This study aims to examine the potential of AI technologies in helping Vietnamese SMEs increase consumer loyalty and enhance overall business performance in the competitive e-commerce landscape (Phuong et al., 2024).
Research Paradigm
This study utilizes a descriptive research methodology to closely examine the qualities, behaviors, and perspectives within a specific group (Sirisilla, 2023) By systematically gathering data on attitudes and experiences, descriptive research allows researchers to identify trends and relationships between variables This thorough analysis enhances our understanding of events and provides valuable insights for future research and practical applications.
This study evaluates the impact of AI applications on consumer experiences within the Shopee platform of Vietnam Nhat Hoa Hong Trading and Import-Export Co., Ltd It specifically examines how perceived usefulness (PU), perceived ease of use (PEU), perceived trust (PT), attitude (A), and perceived control (PC) influence consumer engagement and satisfaction Utilizing a descriptive approach, the research captures customer perspectives on Shopee's AI-driven services and their effects on purchasing decisions The insights gained from this study contribute significantly to AI integration strategies in e-commerce, providing valuable knowledge for both academic researchers and business professionals to enhance AI applications and optimize user experience.
Research Data
Secondary data, which includes information previously gathered by external organizations or researchers, plays a crucial role in supporting new research Sources such as scholarly publications, corporate white papers, and government reports consistently offer this valuable material (Lochmiller, 2021) This type of data provides a robust empirical foundation for comprehending the current applications of artificial intelligence in e-commerce Additionally, secondary data is reliable, as it typically stems from thorough studies conducted by reputable institutions or scholars.
This study analyzed 84 online journal articles and reports to investigate the impact of AI-driven technologies on customer experience The sources encompass empirical research on AI in e-commerce, customer satisfaction metrics, and studies related to the Technology Acceptance Model (TAM) Notably, Davis, F is among the most frequently cited authors in this field.
D (1989); Sharma, A., Patel, N., & Gupta, R (2021); Zhang, Y (2024) These authors have made significant contributions to understanding AI's role in e- commerce, price sensitivity, customer satisfaction, and technology adoption
Primary data is information gathered straight from respondents for a given research goal, therefore guaranteeing accuracy and relevance to the study (Ganesha, & Aithal,
In 2022, primary data for this study was collected through an online survey conducted via Google Forms The survey targeted Shopee consumers who had previously engaged with AI-powered services, such as recommendation systems, chatbots, and automated search engines.
The decision to utilize online surveys stems from the need to efficiently collect large volumes of data in a cost-effective manner These surveys are particularly effective for analyzing digital consumer behavior, as they facilitate real-time responses and access to a broader demographic Additionally, online surveys encourage consumers to provide candid and unbiased feedback regarding their experiences with AI-driven Shopee features while maintaining anonymity Subsequent sections of this research will delve into the advantages of online survey flexibility, scalability, and real-time data processing.
Research Method
Researchers utilize online surveys to gain insights from a tech-savvy audience, providing a reliable foundation for assessing the role of artificial intelligence in e-commerce (Singh & Sagar, 2021) This method allows for rapid data collection and easy accessibility, making it a widely accepted tool in marketing, business, and social sciences To ensure a uniform approach in gathering responses, structured electronic questionnaires were distributed to participants.
Back-translation methods were employed in the design and validation of the questionnaire to ensure clarity for Vietnamese respondents (Rahmatkhah et al., 2024) A pilot test involving thirty participants was conducted to enhance reliability and allow the research team to assess question clarity, response consistency, and potential biases (Alordiah).
& Ossai, 2023) Pilot study comments resulted in minor improvements that guaranteed the best interpretability and readability
From February 22 to March 2, 2025, an official survey was conducted via Google Forms, distributed across various social media platforms such as Facebook, Instagram, Messenger, Zalo, and Telegram to enhance response rates This multi-platform approach enabled the research to engage a diverse group of Shopee users, ensuring a comprehensive dataset that accurately represents the AI-driven consumer experience.
Sampling
A sample population refers to a specific group selected from a larger target population, ensuring that the research gathers relevant and representative data (Andrade, 2021) In this study, the sample population comprises customers who have made purchases on Shopee's platform at Viet Nhat.
Hoa Hong Trading and Import-Export Co., Ltd aims to enhance the shopping experience for Vietnamese Shopee customers by leveraging AI-powered tools such as chatbots, automated customer support systems, and personalized product recommendations This study focuses on understanding the impact of these technologies on customer interactions and satisfaction.
The poll targets individuals aged 18 to 65 who regularly purchase products from Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd.'s Shopee platform, encompassing young adults, working professionals, and senior citizens While younger consumers tend to be more tech-savvy and open to AI-driven customization, older users may exhibit varying levels of receptiveness to artificial intelligence.
To capture diverse perspectives on AI adoption, a specific age range was selected (Bergene & Rứd, 2023) By including various demographics in the context of Shopee's offerings, the findings ensure a broad representation of consumer experiences, satisfaction levels, and trust in AI-driven features.
Vietnam's rapid e-commerce growth and increasing use of artificial intelligence make it an ideal setting for research Shopee, one of the leading e-commerce platforms in the country, has effectively integrated AI to enhance user experience, providing valuable data on how AI influences online shopping behaviors This study will assess the impact of AI-driven applications on consumer satisfaction, engagement, and loyalty through survey-based data collection focused on Shopee.
A sample frame guarantees that the study appropriately represents the target group by outlining the standards for choosing participants from a broader population
This study focuses on Vietnamese Shopee users aged 18 to 65 who have utilized AI-powered features, such as tailored recommendations, AI chatbots, and automated customer support, exclusively on the Shopee platform of Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd This approach ensures a relevant analysis of how artificial intelligence enhances the shopping experience for customers of this specific company.
The study targets individuals aged 18 to 65 to include both younger, tech-savvy consumers and older users with varying levels of familiarity with artificial intelligence This diverse age range allows for an assessment of differences in AI acceptance, satisfaction, and confidence across different groups Participants are selected based on their online shopping frequency, ensuring the research focuses on active Shopee users who significantly interact with AI technologies (Loh, 2021).
This study intends to capture a wide spectrum of consumer experiences by constructing a structured sample frame, so offering insights on the part artificial intelligence shapes e-commerce activities in Vietnam
This study employs convenience sampling due to its cost-effectiveness, time efficiency, and ease of access to participants, making it a popular choice in consumer behavior research (Stratton, 2021) This method is particularly appropriate for examining consumer experiences with artificial intelligence on the Shopee platform, as it facilitates the rapid collection of relevant data from readily available individuals in the fast-paced digital commerce environment.
Participants were selected based on specific criteria to enhance the relevance and depth of the data collected These criteria included a high frequency of online purchasing behavior and previous interactions with AI-driven Shopee services, such as automated support systems, personalized product recommendations, and AI chatbots for customer engagement By focusing on users with direct experience using these AI tools, the research ensures the inclusion of informed perspectives, thereby strengthening the validity of insights regarding the impact of artificial intelligence technologies on the overall consumer experience (Zaid & Patwayati, 2021).
Despite its limitations in generalizing results to larger populations, convenience sampling is valuable in exploratory research This study supports its application to gain timely, contextual insights from consumers engaging with artificial intelligence on Shopee The rapidly evolving and adaptable nature of Vietnam's e-commerce landscape allows researchers to quickly reach a pool of tech-savvy consumers, reinforcing the effectiveness of this approach.
Convenience sampling has provided valuable insights into the acceptance and impact of artificial intelligence in online shopping Despite its limitations, this method enhances our understanding of user interactions with AI in e-commerce, laying the groundwork for future research that employs more rigorous or randomized sampling techniques to validate the findings.
In research, the sample size refers to the total number of participants or observations selected from a larger population This representative subset allows researchers to generalize their findings to the entire population (Sim et al., 2022).
A sample size calculator determined that a sample of 196 respondents was necessary for a population of 6,000,000 Shopee consumers in Hanoi, Vietnam, in 2024, with a 95% confidence level and a 7% confidence interval To enhance accuracy and minimize errors, the study ultimately collected data from 210 respondents This sample size allows for a statistically reliable representation of consumer experiences with AI-powered features, chatbots, personalized recommendations, and automated customer assistance on Shopee, specifically for Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd.
Questionnaires Design
A questionnaire is an essential research tool designed to systematically gather data from participants through a structured set of questions (Robinson & Leonard, 2024) The choice of method—whether phone surveys, in-person interviews, online platforms, or mail—depends on the specific research objectives.
The study utilized a structured questionnaire as its primary data collection method, featuring 27 carefully designed questions on a 5-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree) This format ensures a quantifiable and consistent dataset, allowing respondents to express their level of agreement on various aspects of AI-driven customer experience on Shopee The use of the Likert scale is particularly suitable for this research, as it facilitates the identification of trends in consumer behavior through statistical analysis (Davis, 1989).
Table 3.1 The 5-point Likert scale
Strongly Disagree Disagree Neutral Agree Strongly Agree
The questionnaire was carefully crafted to gauge consumer perceptions of AI applications, including automated customer service, AI-driven recommendations, and personalized marketing Its design is based on established frameworks from prior academic research, notably the studies conducted by Ruiz-Herrera et al (2023) and Azlyna & Nugraha.
In 2023, researchers including Tulcanaza-Prieto et al., Adawiyah et al., Karim et al., and Zaato et al focused on ensuring the reliability and validity of their findings To improve clarity and minimize bias, a pre-test of the survey was conducted with a small group of respondents, enabling essential refinements prior to the official distribution.
PU1 AI on Shopee takes a short time to select the products I want
PU2 It is easy for me to order the products I want to buy
PU3 I believe that the use of AI on Shopee in personalized recommendations can help me find more affordable products
PU4 I believe that the use of AI on Shope can help me find products that better suit my needs
PU5 I feel that the use of AI on Shope is beneficial Adawiyah et al
PEU1 Buying products recommended by AI on Shopee is easy and efficient in many ways
PEU2 I believe that AI on Shopee in personalized recommendations is easy to understand and use
PEU3 Communicating with Shopee chatbot can be understood clearly
PEU4 The Shopee chatbot is easy to use
PEU5 The Shopee chatbot provides information quickly
PT1 I believe that AI on Shopee can protect my personal information and privacy
PT2 I am confident that AI on Shopee can provide trustworthy product suggestions
PT3 I am confident that AI on Shopee only collect the user’s personal data necessary for its activities
PT4 The staff of Shopee help me to solve problems with confidence
Tulcanaza-Prieto et al (2023) PT5 I am pleased with the electronic security of Shopee
Attitude A1 I shop at Shopee because the reviews of the goods are appropriate
A2 I shop at Shopee by looking at other customer reviews
A3 I use Shopee when shopping, and it is enjoyable
A4 I like the idea of AI recommendations on Shopee Ruiz-Herrera et al
A5 I am excited about Shopee platforms powered by AI recommendations
PC1 Using AI on Shopee recommendations in buying goods saves time
PC2 I intend to use AI to make online purchases/ transactions on Shopee
PC3 I feel more in control of my online shopping when using AI on Shopee
PC4 I have the resources, knowledge, and skills to use AI to buy a product on Shopee
CE1 I feel optimistic using Shopee application Karim et al (2022)
CE2 I am satisfied to purchase using Shopee application the most, compared with store shopping
CE3 I am happy with the current experience of shopping with Shopee app.
Data Analysis Technique
This study employs structured data analysis to ensure the reliability and validity of its findings Initially, descriptive statistics will be utilized to effectively summarize key characteristics of the dataset, such as response distribution and demographic information of respondents Measures like mean, standard deviation, and frequency distribution will provide an overview of the central tendencies and variations within the data (Sarstedt et al., 2021).
This article explores the use of Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate associations between variables PLS-SEM is particularly suitable for studies involving latent variables and ongoing theoretical development, as noted by Alim et al (2022) Unlike conventional SEM, PLS-SEM is more adaptable to non-normal data distributions (Kim, 2024) and allows for the simultaneous assessment of multiple relationships This method is ideal for analyzing interactions among key factors such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Perceived Trust (PT), Attitude (A), Perceived Control (PC), and Customer Experience (CE), especially in the context of complex AI-driven customer experience frameworks.
This work will analyze internal consistency of the measurement model using Cronbach's
Alpha and Composite Reliability (CR) are essential for confirming construct validity, with CR values exceeding 0.7 indicating strong validity Additionally, a Cronbach's Alpha value above 0.7 suggests adequate dependability (Okoro et al., 2022) To assess convergent validity, Average Variance Extracted (AVE) will be analyzed, where values greater than 0.5 confirm that indicators accurately represent their constructs (Duan, 2020).
Bootstrapping resampling enables the calculation of path coefficients and T-statistics for the structural model A T-statistic exceeding 1.96 confirms statistical significance at the 95% confidence level, validating the anticipated correlations (Mukherjee et al., 2021).
Analyzed will also be R-squared (R²) values to ascertain the independent variables' explanatory ability in forecasting customer experience results
The study employs a second-order reflective-formative model within the hierarchical component model (HCM) framework to address collinearity issues and enhance construct interactions (Alim et al., 2022) By utilizing PLS-SEM, the research aims to maximize the explained variation in dependent constructs and provide accurate estimations of cause-effect relationships This approach ensures a comprehensive understanding of how AI-driven customization, chatbot support, and recommendation systems influence consumer experience on Shopee.
Descriptive Analysis
Level of education High school 8 3.8%
Frequency of online Shopping at
Table 4.1 lists the demographic traits of the 210 survey participants With regard to gender distribution, men account for 35.2%; women constitute the majority at 64.8%
A higher participation of women in the survey, which might reflect the general user demographic of the platform
The survey reveals that a significant majority, 83.8%, of respondents are young adults aged 18 to 30 The second largest demographic comprises individuals aged 31 to 40, accounting for 13.3% of the total Notably, there are no respondents over the age of 50, with only a small fraction, 2.9%, falling within the 41 to 50 age range.
A significant majority of respondents, 72.4%, hold a Bachelor's degree, while 22.4% possess postgraduate degrees Only 3.8% have completed high school, and the remaining 1.4% represent various educational backgrounds This indicates that the platform appeals to a highly educated demographic, likely contributing to a higher disposable income among its users.
The respondents come from diverse occupational backgrounds, with the business and marketing sectors representing the largest group at 38.1% Following this, 18.1% are in information technology, 15.2% in education, and 13.8% in finance and accounting, while 14.8% are employed in other professions Shopee successfully attracts users from various industries, particularly those connected to commerce and technology.
About monthly income, most replies fall between 10 to 20 million VND (31.4%) and
The data indicates that the platform primarily caters to the middle-income demographic, with 34.8% of users earning between 5 to 10 million VND Additionally, 11.9% of users have incomes exceeding 20 million VND, while a smaller segment, 13.8%, earns less than 5 million VND Furthermore, 8.1% of respondents reported varying income levels.
Regarding frequency of online buying, the biggest group (37.1%) follows closely daily consumers (31.4%), then weekly shoppers While only 1.9% shop less than once a month, monthly consumers figure at 29.5%
A recent survey reveals that 54.8% of Shopee users regularly utilize AI features, while 34.3% use them occasionally, and only 11% have never engaged with these technologies This data indicates a growing acceptance and integration of artificial intelligence into the shopping experiences of consumers on the Shopee platform.
This demographic study provides valuable insights into Shopee's customer base, enabling Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd to develop effective strategies that enhance consumer experiences through the use of artificial intelligence technologies.
Minimum Maximum Mean Std Deviation
Table 4.2 "Descriptive Statistics" presents the mean values for Perceived Usefulness (PU), Perceived Ease of Use (PEU), Perceived Trust (PT), Attitude (A), Perceived Control (PC), and Customer Experience (CE) related to Shopee's customer experience, ranging from 3.87 to 4.26 This indicates a general consensus among participants regarding the utility, ease of use, trustworthiness, attitude, control, and overall customer experience on the Shopee platform The standard deviations, which range from 0.79 to 0.91, suggest limited data dispersion, reflecting consistent opinions among users Overall, these findings indicate that Shopee customers typically find the AI experience to be satisfactory.
Evaluation of Measurement Model
The evaluation of the measurement model, illustrated in the accompanying image, reveals that high factor loadings (exceeding 0.7) indicate good reliability Additionally, strong positive path coefficients between Customer Experience (CE) and the constructs of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) suggest robust relationships.
PT, A, PC, etc.) Each construct has a significant impact on the entire client experience, as confirmed by the pathways and factor loadings
4.2.1 Measurement Model: First-order constructs level
Table 4.3: Item Loadings of Constructs
Item PEU1 PEU2 PEU3 PEU4 PEU5
Item PT1 PT2 PT3 PT4 PT5
Item PU1 PU2 PU3 PU4 PU5
Table 4.3 presents the outer loadings of the study model's measuring items, with values exceeding 0.7 indicating a strong correlation between the components and the constructs they represent According to Hair et al (2017), outer loadings above this threshold signify a reliable measuring model that accurately reflects the constructs The data reveals that all outer loadings surpass the 0.7 criterion, indicating high correlations between the measurement objects and their respective constructs Notably, the "Attitude" category (A1 to A5) shows a significant influence of user attitude, with outer loadings ranging from 0.822 to 0.901 Additionally, the "Perceived Ease of Use" (PEU1 to PEU5) category demonstrates the ease of using AI technologies on Shopee, with outer loadings between 0.818 and 0.903.
Particularly the items connected to customer experience (CE1 to CE3), which range from 0.937 to 0.948, so confirming the great impact of other constructions such
"Perceived Trust" (PT1 to PT5) and "Perceived Usefulness" (PU1 to PU5) As
Item PC1 PC2 PC3 PC4
Purwanto and Sudargini (2021) emphasized that the validity and reliability of the research model are confirmed when the outer loadings exceed the 0.7 threshold, indicating a significant relationship between the items and the variables they assess.
4.2.2 Assessing reliability of the constructs
By means of Cronbach's Alpha and Composite Reliability coefficients,
Table 4.4 evaluates the constructural reliability Cronbach's Alpha and Composite
Reliability scores exceeding 0.7 indicate strong validity of the measuring instruments in the model The "CE" (Customer Experience) construct demonstrates the highest Composite Reliability at 0.956, reflecting a robust connection with its indicators and significant stability Although the "PT" (Perceived Trust) construct has the lowest Composite Reliability at 0.896, it remains within an acceptable range, ensuring the model's dependability Constructs such as "A" (Attitude), "PC" (Perceived Control), "PEU" (Perceived Ease of Use), and "PU" (Perceived Usefulness) exhibit excellent dependability, with Cronbach's Alpha and Composite Reliability values surpassing 0.9 According to Izah et al (2023), a standard for validating the measurement model's dependability is having Cronbach's Alpha and Composite Reliability values above 0.7.
4.2.3 Assessing validity of the constructs
Construct validity refers to how accurately a test or measurement represents the construct it aims to evaluate In Table 4.5, the average variance extracted (AVE) values for all constructs exceed 0.7, indicating that they significantly account for the variance in their measurement items Notably, the AVE values for Attitude (A) and Customer Experience (CE) are 0.732 and 0.879, respectively, demonstrating that these constructs are robust and well-defined According to Fornell & Larcker (1981), AVE values above 0.5 indicate good construct validity, confirming that the constructs effectively account for the variance in the measurement items.
Table 4.6: Discriminant Validity Results based on HTMT
A CE PC PEU PT PU
Discriminant validity refers to the extent to which a construct in a model differs from other constructs As illustrated in Table 4.6, the Heterotrait-Monotrait ratio (HTMT) is used to measure the degree of association among constructs According to Ringle et al (2023), the model demonstrates strong discriminant validity, indicated by HTMT values being less than 0.9, suggesting minimal overlap among the constructs.
Given this, all HTMT values are below the threshold, implying obvious variation between the constructs For instance, the HTMT value between Attitude (A) and
Customer Experience (CE) is 0.775, which is quite within the permitted range, thereby assuring that these constructions are not confused with one another
If the HTMT value exceeds the square root of AVE, it indicates excessive overlap among constructs, which can undermine discriminant validity and affect the accuracy of the model Table 4.6 presents all HTMT values that meet this criterion, confirming the distinctiveness of the model's constructs.
According to Hair et al (2017), the study model demonstrates strong construct validity and discriminant validity, as confirmed by both sets of results in structural equation modeling.
Structural Model Assessment For Hypothesis Testing
Table 4.7: VIFs of Exogenous Variables
The results of multi-collinearity detection using the variance inflation factor (VIF) are summarized in Table 4.7 VIF values indicate the extent to which model variables are explained by other variables, with values exceeding 5 or 10 suggesting strong multi-collinearity that could affect model accuracy (Hair et al., 2017) Most VIF values in the table are below 5, with PT4 showing a notably low value of 1.510, indicating minimal multi-collinearity issues However, variables such as PEU2 (4.056) and PT3 (4.320) have VIF values approaching 5, suggesting a need for monitoring due to potential correlations.
Figure 4.2: The structural model for testing hypotheses
4.3.2 Analysis of R-square of constructs
Table 4.8 presents the R-square analysis of the research model, indicating that the model accounts for 67.1% of the variance in Customer Experience (CE) with an R-square value of 0.671 The adjusted R-square value is 0.663, reflecting the number of independent variables in the model This suggests that the model effectively explains a significant portion of the variation in Customer Experience, demonstrating a strong fit According to Hair et al (2017), an R-square value exceeding 0.5 is considered to have good explanatory power.
Table 4.9 displays the results of the hypothesis testing for the model, including the hypotheses, standardized path coefficients (β), T statistics, P values, and conclusions based on the significance of the findings.
Hypothesis H4 (A -> CE) demonstrates a positive correlation between Attitude (A) and Customer Experience (CE), with a β value of 0.276 The T statistics value is 3.900, and the P value is 0.000, which is below the 0.05 threshold, leading to the acceptance of the hypothesis This indicates that customer experience is significantly influenced by attitude, supporting previous research that highlights the critical role of attitude in shaping consumer experiences (Kuppelwieser et al., 2021).
The β value for Hypothesis H5 (PC -> CE) is 0.528, demonstrating a significant positive relationship between Customer Experience (CE) and Perceived Control (PC) With a T statistic of 8.567 and a P value of 0.000, this hypothesis is accepted This finding aligns with research suggesting that perceived control enhances customer experience by empowering consumers and providing them with a sense of autonomy in their decision-making process (Hu, 2023).
The hypotheses H2 (PEU -> CE), H3 (PT -> CE), and H1 (PU -> CE) have been refuted, with H2 showing a β value of -0.053 and a P value of 0.391, which exceeds the 0.05 significance level This indicates that Customer Experience is not significantly influenced by Perceived Ease of Use (PEU) Supporting this conclusion, Davis (1989) emphasized that perceived usefulness is a more significant predictor of user behavior, suggesting that ease of use alone is insufficient to shape consumer experience.
The β values for Hypothesis H3 and H1 are 0.078 and 0.082 respectively; both have
P values exceeding 0.05 indicate that Perceived Trust (PT) and Perceived Usefulness (PU) do not significantly impact Customer Experience in this model This may be attributed to other factors affecting the relationship between these variables or the research framework itself According to Wiadi et al (2023), these factors may not directly influence user experience in specific e-commerce settings, especially when other variables are more prominent.
The hypothesis testing indicates that Attitude and Perceived Control are significant factors influencing Customer Experience, while Perceived Ease of Use, Perceived Trust, and Perceived Usefulness have minimal impact in this context.
Discussion
The hypothesis testing results indicate that while some elements have minimal impact, others significantly influence Customer Experience (CE) Specifically, attitude (A) and perceived control (PC) were approved as influential factors, whereas perceived ease of use (PEU), perceived trust (PT), and perceived usefulness (PU) were not This highlights the potential to explore how various components can enhance customer experience across different contexts.
Research indicates that customer experience is significantly enhanced by both attitude and perceived control A consumer's perspective and engagement with a brand often mirror their overall attitude towards the product or service As noted by Kenny et al (2023), attitude plays a crucial role in shaping consumer perceptions and behaviors.
Customers with a positive attitude are more inclined to rate their experiences highly and remain loyal to a business This finding aligns with the research of Cachero-Martínez et al (2024), which emphasizes that favorable attitudes significantly influence consumer satisfaction and overall experience, particularly in the realm of e-commerce.
Perceived control significantly enhances the consumer experience, as customers who feel empowered in their decision-making and purchasing processes tend to enjoy a more satisfying experience According to Chai & Li (2024), this sense of control fosters independence in customers, directly impacting their brand loyalty and overall satisfaction.
Recent data indicates that factors such as Perceived Ease of Use, Perceived Trust, and Perceived Usefulness have minimal impact on Customer Experience Notably, Perceived Ease of Use (PEU), often emphasized in previous research, appears to be largely irrelevant in this context Hossain & Biswas (2024) propose that this may vary depending on specific circumstances.
Perceived Usefulness (PU) is often a more significant predictor of user behavior than ease of use, especially when consumers assess a product or service based on its tangible benefits rather than its simplicity The absence of emphasis on complex technology or ease of use in the experimental setting accounts for the minimal impact of Perceived Ease of Use (PEU) in this study.
In this research model, customer experience was minimally affected by perceived trust (PT) and perceived usefulness (PU), likely due to the specific context of e-commerce When consumers are familiar with a platform or product, these factors may not significantly impact their experience According to Yum & Kim (2024), consumers may prioritize clear value and utility over complete trust in a product or platform This insight supports the decision to overlook perceived usefulness and trust in this study.
Modern e-commerce systems highlight a significant trend where users become so familiar with products or services that traditional factors like Trust and Usefulness no longer influence their decisions According to Zhang (2024), the perception of trust diminishes as consumers grow comfortable and accustomed to the technology or service.
In summary, while Perceived Ease of Use, Perceived Trust, and Perceived Usefulness have minimal impact, the study highlights that Attitude and Perceived Control are crucial for enhancing Customer Experience This suggests a shift in consumer preferences, emphasizing the importance of autonomy and positive attitudes in creating valuable user experiences These insights not only clarify the factors affecting Customer Experience but also open avenues for further research into the internal dynamics of contemporary e-commerce platforms.
Theoretical Implications
This paper explores the factors affecting Customer Experience (CE) within the Shopee e-commerce platform through the lens of the Technology Acceptance Model (TAM) The TAM highlights that Perceived Usefulness (PU) and Perceived Ease of Use (PEU) are critical in determining technology acceptance However, in the context of e-commerce, the study finds that PU and PEU do not significantly influence Customer Experience, as consumers are already well-acquainted with the purchasing process and products.
Attitude (A) and Perceived Control (PC) significantly influence Customer Experience, highlighting that psychological and emotional factors, such as a positive attitude and a sense of control during shopping, may outweigh functional elements like ease of use or perceived usefulness This finding enhances the relevance of the Technology Acceptance Model (TAM) by demonstrating that, in e-commerce, the psychological and emotional dimensions of technology usage play a crucial role alongside functional aspects.
This study highlights that the Technology Acceptance Model (TAM) reveals varying influences of certain elements based on the research context and client characteristics It also helps identify the key factors that significantly enhance customer experience.
Practical Implications
E-commerce platforms like Shopee are focusing on enhancing Customer Experience (CE) and boosting consumer loyalty, relying on the practical implications of recent research While Perceived Ease of Use (PEU), Perceived Trust (PT), and Perceived Usefulness (PU) may not always hold the same level of importance in certain contexts, findings indicate that factors such as Attitude (A) and Perceived Control (PC) play a significant role in shaping the overall customer experience.
This report emphasizes the importance of fostering a positive attitude towards e-commerce platforms Businesses should prioritize customer-centric marketing strategies that build trust, happiness, and favorable impressions By offering personalized experiences, encouraging positive feedback, and providing proactive customer support, companies can significantly enhance consumer perceptions and overall satisfaction.
Customer Experience is significantly impacted by Perceived Control (PC), indicating that e-commerce companies should enhance consumer autonomy through customization options, diverse payment methods, and real-time order tracking By empowering customers with greater control over their purchasing decisions, businesses can foster increased loyalty and satisfaction.
The findings indicate that when customers are already familiar with a platform, businesses may prioritize psychological and emotional factors over Perceived Ease of Use and Perceived Usefulness By focusing on these aspects, companies can enhance client connections and retention This research offers essential insights for businesses to tailor their designs and policies to align with consumer preferences, ultimately fostering long-term loyalty and sustainable growth.
Limitations of Research
Although this study provides interesting examination of the factors influencing Customer Experience (CE) on e-commerce platforms such as Shopee, numerous restrictions should be taken under consideration
The study's sample is limited to Vietnamese Shopee customers, which may not accurately represent the behaviors and experiences of consumers from other regions or on different e-commerce platforms A larger and more diverse sample would yield more generalizable results and a broader perspective.
The cross-sectional nature of the survey limits our understanding of how consumer experiences and impressions develop over time To gain deeper insights into the evolution of consumer experiences as e-commerce platforms implement new technologies or modify their offerings, longitudinal research would be more effective.
This study is limited to the context of Shopee and does not account for the dynamics of other e-commerce platforms Factors such as cultural differences in online buying behavior, regional preferences, and platform-specific characteristics may influence different platforms Consequently, the applicability of the findings is primarily relevant to Shopee and similar platforms in Southeast Asia.
Conclusion
This study, grounded in the Technology Acceptance Model (TAM), explores the psychological and emotional factors that shape Consumer Experience (CE) on the Shopee e-commerce platform By examining perceived utility, ease of use, trust, and enjoyment, the research sheds light on the formation of customer behavior in digital shopping environments It highlights how consumer impressions evolve into happiness and loyalty, which are essential for sustainable e-commerce growth Key elements influencing a positive consumer experience include the emotional impact of interface design, the trustworthiness of platform operations, and the personalization of user interactions.
The findings of this study significantly impact Viet Nhat Hoa Hong Trading and Import-Export Co., Ltd., allowing the company to identify and enhance key elements of its online services By understanding the factors that drive consumer satisfaction and their interactions with digital platforms like Shopee, the business can tailor its strategies to boost engagement, streamline consumer journeys, and implement customer-centric innovations This involves improving mobile responsiveness, ensuring seamless navigation, and developing targeted marketing campaigns based on user preferences and behavior patterns.
Despite limitations in sample size and a primary focus on the Shopee platform, this study offers valuable insights for future research Other e-commerce platforms can adapt the Technology Acceptance Model (TAM) framework to enhance their user experience By doing so, they can improve their service ecosystems, boost customer satisfaction, and ultimately increase client retention Additionally, the study encourages a multidisciplinary approach that integrates technology, psychology, and business to gain a deeper understanding of online customer behavior.
The research enhances theoretical understanding of consumer experience in e-commerce while providing valuable insights for practitioners As competition in the digital market intensifies, companies must prioritize user experience as a strategic advantage By effectively applying the Technology Acceptance Model (TAM) and continuously evaluating user feedback, e-commerce businesses can foster stronger customer relationships, create enjoyable and user-friendly platforms, and ensure sustained success in the rapidly evolving online retail landscape.
Adawiyah, S R., Purwandari, B., Eitiveni, I., & Purwaningsih, E H (2024) The influence of AI and AR technology in personalized recommendations on customer usage intention: a case study of cosmetic products on Shopee
Applied Sciences, 14(13), 5786 https://doi.org/10.3390/app14135786
Aiolfi, S (2023) How shopping habits change with artificial intelligence: smart speakers’ usage intention International Journal of Retail & Distribution
Management, 51(9/10), 1288–1312 https://doi.org/10.1108/ijrdm-11- 2022-0441
Alim, M A., Tan, K L., Jee, T W., Voon, B H., Hossain, M J., & Mia, M U
(2023) To explain and to predict: analysis of opportunity recognition on the relationship between personal factors, environmental factors and entrepreneurs' performance Asia-Pacific Journal of Business Administration, 15(5), 772-794
Alordiah, C O., & Ossai, J N (2023) Enhancing Questionnaire Design:
Theoretical Perspectives on Capturing Attitudes and Beliefs in Social Studies Research International Journal of Innovative Science and
Al-Surmi, A., Bashiri, M., & Koliousis, I (2022) AI based decision making: combining strategies to improve operational performance International
Amatus, A., & Gisip, I A (2022) Effects of website appearance, security and electronic word-of-mouth (EWOM) on online customer loyalty: Trust as mediating factor International Journal of Academic Research in
Andrade, C (2021) The inconvenient truth about convenience and purposive samples Indian journal of psychological medicine, 43(1), 86-88
Aung, N N (2024) Online Customer Experience, Customer Satisfaction and
Repurchase Intention In E-Retailing (Nyi Nyi Aung, 2024) (Doctoral dissertation, MERAL Portal)
Azlyna, V N., & Nugraha, J (2023) Influence of Perceived Usefulness on Using the Shopee Application: Study on College Students Journal of Office Administration: Education and Practice, 3(2), 74-83
Barakhanov, M., & Kaya, M (2024) Consumer decision-making process in E- commerce
Bitkina, O V., Jeong, H., Lee, B C., Park, J., Park, J., & Kim, H K (2020) Perceived trust in artificial intelligence technologies: A preliminary study Human
Factors and Ergonomics in Manufacturing & Service Industries, 30(4),
Cachero-Martínez, S., García-Rodríguez, N., & Salido-Andrés, N (2024) Because
I'm happy: exploring the happiness of shopping in social enterprises and its effect on customer satisfaction and loyalty Management
Castillo, D., Canhoto, A I., & Said, E (2021) The dark side of AI-powered service interactions: exploring the process of co-destruction from the customer perspective The Service Industries Journal, 41(13-14), 900-925
Chai, J., & Li, H (2024) Consumer empowerment in the ethical spectrum:
Rethinking retention in live-streaming markets Journal of Retailing and
Davis, F D (1989) Perceived Usefulness, Perceived Ease of Use, and User
Acceptance of Information Technology MIS Quarterly, 13(3), 319-340 Davis, Fred D "Perceived usefulness, perceived ease of use, and user acceptance of information technology." MIS quarterly (1989): 319-340
Edwards, J (2024, April 25) How AI is Reshaping Retail https://www.informationweek.com/machine-learning-ai/how-ai-is- reshaping-retail
Fornell, C., & Larcker, D F (1981) Evaluating Structural Equation Models with
Unobservable Variables and Measurement Error, 18(1), 39–50
Ganesha, H R., & Aithal, P S (2022) How to choose an appropriate research data collection method and method choice among various research data collection methods and method choices during Ph D program in
India International Journal of Management, Technology, and Social
Gartner (2021) "Future Trends in AI for Customer Experience."
Gkikas, D C., & Theodoridis, P K (2021) AI in consumer behavior In Advances in Artificial Intelligence-based Technologies: Selected Papers in Honour of Professor Nikolaos G Bourbakis—Vol 1 (pp 147-176) Cham:
Gunawan, F., Santoso, A S., Yustina, A I., & Rahmiati, F (2022) Examining the effect of radical innovation and incremental innovation on leading e- commerce startups by using expectation confirmation model Procedia
Habil, S G M., El-Deeb, S., & El-Bassiouny, N (2024) The metaverse era: leveraging augmented reality in the creation of novel customer experience Management & Sustainability: An Arab Review, 3(1), 1-15 Hair, J F., Hult, G T M., Ringle, C M., Sarstedt, M., & Thiele, K O (2017)
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods Journal of the academy of marketing science, 45, 616-632
Hashemi, M., & Bosnjak, D (2024) AI and consumer satisfaction: A descriptive study of how AI can strengthen consumer satisfaction
Hien, L T., & Tam, P T (2025) Applied Structure Equation Model for Policy
Suggestions to Develop the Digital Economy in Vietnam Journal of
Hossain, M E., & Biswas, S (2024) Technology acceptance model for understanding consumer’s behavioral intention to use artificial intelligence based online shopping platforms in Bangladesh SN
Hsu, Y S., Chen, Y P., & Shaffer, M A (2021) Reducing work and home cognitive failures: The roles of workplace flextime use and perceived control Journal of Business and Psychology, 36(1), 155-172
Hu, X (2023) Empowering consumers in interactive marketing: examining the role of perceived control In The Palgrave Handbook of Interactive
Marketing (pp 117-147) Cham: Springer International Publishing
Huang, M., & Rust, R T (2021) Artificial Intelligence in Service Journal of
Inavolu, S M (2024) Exploring AI-driven customer service: Evolution, architectures, opportunities, challenges and future directions International Journal of Engineering and Advanced
Iyelolu, T V., Agu, E E., Idemudia, C., & Ijomah, T I (2024) Driving SME innovation with AI solutions: overcoming adoption barriers and future growth opportunities International Journal of Science and Technology Research Archive, 7(1), 036-054
Iyer, D., Sharma, D., Singh, N., & Patel, S (2022) Enhancing Digital Advertising through AI-Powered Personalization: Leveraging Reinforcement Learning and Collaborative Filtering Algorithms International Journal of AI ML Innovations, 11(8)
Izah, S C., Sylva, L., & Hait, M (2023) Cronbach's alpha: A cornerstone in ensuring reliability and validity in environmental health assessment ES
Kakolu, S., & Faheem, M A (2023) Digitization and automation in mobile applications: A catalyst for operational efficiency and user engagement Kaledio, P., & Doris, L (2024) EXPLORING AI-POWERED CHATBOTS FOR
CUSTOMER SERVICE ENHANCEMENT Available at SSRN
Karim, R A., Sobhani, F A., Rabiul, M K., Lepee, N J., Kabir, M R., &
Chowdhury (2022) explores the connection between fintech payment services and customer loyalty intentions within the hospitality sector, emphasizing the significant roles of customer experience and attitude as mediators Meanwhile, Kaur et al (2022) provide a comprehensive review of trustworthy artificial intelligence, highlighting its importance in enhancing user trust and engagement in various applications.
Kaushal, V., & Yadav, R (2023) Learning successful implementation of Chatbots in businesses from B2B customer experience perspective Concurrency and
Kedi, W E., Ejimuda, C., Idemudia, C., & Ijomah, T I (2024) AI software for personalized marketing automation in SMEs: Enhancing customer experience and sales World Journal of Advanced Research and
Kelly, A E., & Palaniappan, S (2023) Using a technology acceptance model to determine factors influencing continued usage of mobile money service transactions in Ghana Journal of Innovation and Entrepreneurship, 12(1), 34
Kenny, T A., Woodside, J V., Perry, I J., & Harrington, J M (2023) Consumer attitudes and behaviors toward more sustainable diets: a scoping review Nutrition reviews, 81(12), 1665-1679
Kim, J (2024) Factored regression approach for structural equation models with non-normal continuous data (Doctoral dissertation)
Kumar, M P., Chandel, A., Dua, A., & Giri, A (2023) Revolutionizing Customer
Service: The Power of Web ChatBot IRSD2024, 129
Kushwah, U (2024) THE ROLE OF ARTIFICIAL INTELLIGENCE IN
PERSONALIZING CONSUMER EXPERIENCES: A STUDY ON
PREDICTIVE ANALYTICS IN THE E-COMMERCE
SECTOR Vidhyayana-An International Multidisciplinary Peer- Reviewed E-Journal-ISSN 2454-8596, 10(si1), 298-320
Lochmiller, C R (2021) Conducting thematic analysis with qualitative data The qualitative report, 26(6), 2029-2044
Loh, Y X (2021) Using collaborative model to examine online persuasion through e-commerce website design: Comparative study between Gen X and Gen Y online shoppers in Malaysia (Doctoral dissertation, Universiti
Majeed, S., Kim, W G., & Nimri, R (2024) Conceptualizing the role of virtual service agents in service failure recovery: Guiding insights International Journal of Hospitality Management, 123,
Misra, R R., Kapoor, S., & Sanjeev, M A (2024) The impact of personalisation algorithms on consumer engagement and purchase behaviour in AI- enhanced virtual shopping assistants
Mohsin, S (2024) The Influence of AI-Driven Personalization on Consumer
Decision-Making in E-Commerce Platforms Al-Rafidain Journal of
Mukherjee, D., Lim, W M., Kumar, S., & Donthu, N (2022) Guidelines for advancing theory and practice through bibliometric research Journal of business research, 148, 101-115
Okoro, C., Owojori, O M., & Umeokafor, N (2022) The developmental trajectory of a decade of research on mental health and well-being amongst graduate students: A bibliometric analysis International journal of environmental research and public health, 19(9), 4929
Omprakash, M K (2024) Optimizing demand forecasting and inventory management with AI in automotive industry
Patel, N., & Trivedi, S (2020) Leveraging predictive modeling, machine learning personalization, NLP customer support, and AI chatbots to increase customer loyalty Empirical Quests for Management Essences, 3(3), 1-
Phuong, G N T., Dong, T T., Phuong, D N B., Le Huu, H., & Hong, N L T
(2024) Factors Affecting the Intention to Continue Using Online Payment Applications of SMEs at Viet Nam Theoretical and Practical Research in
Purwanto, A., & Sudargini, Y (2021) Partial least squares structural squation modeling (PLS-SEM) analysis for social and management research: a literature review Journal of Industrial Engineering & Management
Rafique, S., & Mujawinkindi, F (2023) How can Artificial Intelligence (AI) help
SMEs development in emerging economies
Rahmatkhah, T., Dashti-Kalantar, R., Vosoghi, N., Mirzaei, A., & Mehri, S (2024)
Psychometric evaluation of Persian version of the oral presentation evaluation scale in nursing students BMC nursing, 23(1), 932 Bergene,
T E., & Rứd, E M (2023) Consumer Adoption of AI-Powered
Chatbots: Developing a Customized Adoption Model (Master's thesis)
Rane, N (2023) Enhancing customer loyalty through Artificial Intelligence (AI),
Internet of Things (IoT), and Big Data technologies: improving customer satisfaction, engagement, relationship, and experience Internet of
Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience (October 13,
Raphela, L J M (2023) Investigating the influence of chatbots on customer experience and frustrations in self-help functions in South Africa
Ringle, C M., Sarstedt, M., Sinkovics, N., & Sinkovics, R R (2023) A perspective on using partial least squares structural equation modelling in data articles Data in Brief, 48, 109074
Robinson, S B., & Leonard, K F (2024) Designing quality survey questions Sage publications
Ruiz-Herrera, L G., Valencia-Arias, A., Gallegos, A., Benjumea-Arias, M., &
Flores-Siapo, E (2023) Technology acceptance factors of e-commerce among young people: An integration of the technology acceptance model and theory of planned behavior Heliyon, 9(6)
Saputra, F H., & Sutarso, Y (2024) Factors influencing satisfaction and intention to use Chatbot on purchase intention on E-Commerce Shopee At-
Sarstedt, M., Ringle, C M., & Hair, J F (2021) Partial least squares structural equation modeling In Handbook of market research (pp 587-632) Cham: Springer International Publishing
Schửnberger, M (2023) Artificial intelligence for small and medium-sized enterprises: Identifying key applications and challenges Journal of
Sharma, A., Patel, N., & Gupta, R (2021) Enhancing Customer Experience with
AI-Powered Sales Assistants: Leveraging Natural Language Processing and Reinforcement Learning Algorithms European Advanced AI
Sharma, R., Bose, P., Sharma, R., & Chopra, A (2021) Enhancing Customer
Engagement through AI-Powered Marketing Personalization Engines: A Comparative Study of Collaborative Filtering and Natural Language Processing Techniques International Journal of AI Advancements, 10(1)
Sharma, S K., Dixit, R J., Rai, D., & Mall, S (2024) Artificial intelligence and machine learning in smart education In Infrastructure Possibilities and
Human-Centered Approaches With Industry 5.0 (pp 86-106) IGI Global
Sheikh, H., Prins, C., & Schrijvers, E (2023) Artificial intelligence: definition and background In Mission AI: The new system technology (pp 15-41) Cham: Springer International Publishing
Sikkandher, M M., Gopi, V., Kumar, R., & Rajalakshmi, M (2024) AI
STRATEGIES Asian And Pacific Economic Review, 17(2), 1145-159 Sillanpọọ, T (2022) Design against passivity
Sim, M., Kim, S Y., & Suh, Y (2022) Sample size requirements for simple and complex mediation models Educational and Psychological Measurement, 82(1), 76-106
Singh, S., & Jasial, S S (2021) Moderating effect of perceived trust on service quality–student satisfaction relationship: evidence from Indian higher management education institutions Journal of Marketing for Higher
Singh, S., & Sagar, R (2021) A critical look at online survey or questionnaire- based research studies during COVID-19 Asian Journal of
Singh, V., Sharma, M P., Jayapriya, K., Kumar, B K., Chander, M A R N., &
Kumar, B R (2023) Service quality, customer satisfaction and customer loyalty: A comprehensive literature review Journal of Survey in Fisheries
Sirisilla, S., & Sirisilla, S (2023) Bridging the Gap: Overcome these 7 flaws in descriptive research design Enago Academy
Stratton, S J (2021) Population research: convenience sampling strategies Prehospital and disaster Medicine, 36(4), 373-374
Tan, Y S (2020) Beauty and skincare e-commerce mobile application with advanced searching module using image processing (Doctoral dissertation, UTAR)
Thanyawatpornkul, R (2024) Implementing AI-driven Customer Relationship
Management (CRM) systems: Enhancing customer experience in the retail industry of Thailand World J Adv Res Rev., 24(1), 1691-1699 Tran, H (2023) Exploring last-mile delivery opportunities and challenges in the e- commerce market Vietnam
The study by Tulcanaza-Prieto et al (2023) explores how customer perception factors influence AI-enabled customer experiences within the Ecuadorian banking sector, highlighting the importance of understanding these perceptions for enhancing service delivery Additionally, Venkatesh and Bala (2008) present the Technology Acceptance Model 3, which outlines a research agenda focused on interventions that can improve technology acceptance in various contexts Together, these works emphasize the critical role of customer perceptions and technology acceptance in shaping effective banking experiences.
Virvou, M (2023) Artificial Intelligence and User Experience in reciprocity:
Contributions and state of the art Intelligent Decision Technologies, 17(1), 73-125
Wiadi, I., Mudrika, S., Suharjo, D., Azmy, A., & Deni, D (2023) The Effect of
Factors of E-marketing on Purchase Decision in MSME's snack product: a case study in PT Saikho Indo Kreatif Management, 27(1)
Yum, K., & Kim, J (2024) The Influence of perceived value, customer satisfaction, and trust on loyalty in entertainment platforms Applied
Zaato, S G., Zainol, N R., Khan, S., Rehman, A U., Faridi, M R., & Khan, A A
(2023) The Mediating Role of Customer Satisfaction between Antecedent Factors and Brand Loyalty for the Shopee Application Behavioral Sciences, 13(7), 563 https://doi.org/10.3390/bs13070563
Zaid, S., & Patwayati, P (2021) Impact of customer experience and customer engagement on satisfaction and loyalty: A case study in Indonesia The
Journal of Asian Finance, Economics and Business, 8(4), 983-992
Zhang, Y (2024) Impact of perceived privacy and security in the TAM model: the perceived trust as the mediated factors International Journal of