Impact of Artificial Intelligence on E-Commerce Personalization for Customer Experience.
Das, Anindya (2024)
2024
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024120833696
https://urn.fi/URN:NBN:fi:amk-2024120833696
Tiivistelmä
This thesis explores the impact of AI-driven personalization on customer satisfaction within the online apparel retail industry. The study focuses on how personalization technologies shape customer trust, engagement, and perceived personalization, employing frameworks such as the Technology Acceptance Model (TAM), the Institutional-Based Trust Model, and Engagement Theory to establish theoretical foundations. The objectives of the study include assessing the relationship between AI-driven personalization and customer satisfaction and analyzing the roles of trust, engagement, and perceived personalization in influencing satisfaction levels.
A quantitative research approach was adopted, utilizing structured online surveys distributed to a sample of 384 respondents from the online apparel sector. The collected data were analyzed using descriptive statistics, regression analysis, and structural equation modeling (SEM) to test the study's hypotheses. Findings revealed that AI-driven personalization and perceived personalization significantly enhance customer satisfaction, while customer engagement also emerged as a critical factor influencing satisfaction. Interestingly, customer trust was found to have no significant impact on satisfaction, suggesting a potential shift in consumer priorities, particularly among younger demographics.
The study provides valuable insights for e-commerce platforms by highlighting the importance of optimizing AI technologies to enhance customer experiences. Practical recommendations include refining personalization algorithms, fostering customer engagement through interactive features, and maintaining transparency in data privacy practices to address varying levels of trust concerns. Future research is suggested to explore these relationships longitudinally and across diverse demographic groups to validate and expand the findings.
A quantitative research approach was adopted, utilizing structured online surveys distributed to a sample of 384 respondents from the online apparel sector. The collected data were analyzed using descriptive statistics, regression analysis, and structural equation modeling (SEM) to test the study's hypotheses. Findings revealed that AI-driven personalization and perceived personalization significantly enhance customer satisfaction, while customer engagement also emerged as a critical factor influencing satisfaction. Interestingly, customer trust was found to have no significant impact on satisfaction, suggesting a potential shift in consumer priorities, particularly among younger demographics.
The study provides valuable insights for e-commerce platforms by highlighting the importance of optimizing AI technologies to enhance customer experiences. Practical recommendations include refining personalization algorithms, fostering customer engagement through interactive features, and maintaining transparency in data privacy practices to address varying levels of trust concerns. Future research is suggested to explore these relationships longitudinally and across diverse demographic groups to validate and expand the findings.