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The Impact of AI-Powered Tools on E-Commerce: Transforming Customer Experience, Personalization, and Digital Marketing
Subject area: Management and Commerce · Area of research: AI-Powered Tools on E-Commerce
Abstract
The proliferation of Artificial Intelligence (AI)-powered tools has fundamentally reshaped how e-commerce enterprises design and deliver customer experiences. This study investigates the impact of AI-powered tools including recommendation engines, conversational chatbots, generative content tools, and predictive analytics on three interrelated outcomes: customer experience quality, purchase intention, and digital marketing effectiveness, with perceived personalization examined as a central explanatory construct. A structured questionnaire-based methodology employing a five-point Likert scale was used to operationalize four constructs Perceived Personalization (PER), Customer Experience Quality (CX), Purchase Intention (PI), and Digital Marketing Effectiveness (DME) across a sample of 150 respondents. Descriptive statistics, Pearson correlation analysis, and ordinary least squares (OLS) regression were computed to examine the relationships among constructs. The results show that all four constructs were rated moderately-to-highly favorable (means ranging from 3.39 to 3.61 on a 5-point scale), and that Perceived Personalization is significantly and positively correlated with Customer Experience Quality (r = 0.528, p < .01) and moderately correlated with Digital Marketing Effectiveness (r = 0.488, p < .01). Simple linear regression confirms that Perceived Personalization significantly predicts Customer Experience Quality (β = 0.499, R² = 0.279, t = 7.567, df = 148, p < .001), indicating that personalization explains approximately 27.9% of the variance in customer experience quality. A supplementary multiple regression indicates that personalization, customer experience, and digital marketing effectiveness jointly explain approximately 15.5% of the variance in purchase intention. These findings confirm that AI-powered personalization functions as a key driver of superior customer experience and marketing performance in e-commerce, while also pointing to purchase intention as a more complex outcome shaped by additional factors beyond personalization alone. The paper concludes with theoretical and managerial implications, along with directions for future empirical research.
Keywords
Artificial Intelligence; E-Commerce; Personalization; Customer Experience; Digital Marketing; Purchase Intention; Regression Analysis
References
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How to cite this paper
@article{1723055,
author = {Dr. A. Suresh, Dr. V. Rajeswari},
title = {The Impact of AI-Powered Tools on E-Commerce: Transforming Customer Experience, Personalization, and Digital Marketing},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {10},
pages = {433-440},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723055.pdf},
abstract = {The proliferation of Artificial Intelligence (AI)-powered tools has fundamentally reshaped how e-commerce enterprises design and deliver customer experiences. This study investigates the impact of AI-powered tools including recommendation engines, conversational chatbots, generative content tools, and predictive analytics on three interrelated outcomes: customer experience quality, purchase intention, and digital marketing effectiveness, with perceived personalization examined as a central explanatory construct. A structured questionnaire-based methodology employing a five-point Likert scale was used to operationalize four constructs Perceived Personalization (PER), Customer Experience Quality (CX), Purchase Intention (PI), and Digital Marketing Effectiveness (DME) across a sample of 150 respondents. Descriptive statistics, Pearson correlation analysis, and ordinary least squares (OLS) regression were computed to examine the relationships among constructs. The results show that all four constructs were rated moderately-to-highly favorable (means ranging from 3.39 to 3.61 on a 5-point scale), and that Perceived Personalization is significantly and positively correlated with Customer Experience Quality (r = 0.528, p < .01) and moderately correlated with Digital Marketing Effectiveness (r = 0.488, p < .01). Simple linear regression confirms that Perceived Personalization significantly predicts Customer Experience Quality (β = 0.499, R² = 0.279, t = 7.567, df = 148, p < .001), indicating that personalization explains approximately 27.9% of the variance in customer experience quality. A supplementary multiple regression indicates that personalization, customer experience, and digital marketing effectiveness jointly explain approximately 15.5% of the variance in purchase intention. These findings confirm that AI-powered personalization functions as a key driver of superior customer experience and marketing performance in e-commerce, while also pointing to purchase intention as a more complex outcome shaped by additional factors beyond personalization alone. The paper concludes with theoretical and managerial implications, along with directions for future empirical research.},
keywords = {Artificial Intelligence; E-Commerce; Personalization; Customer Experience; Digital Marketing; Purchase Intention; Regression Analysis},
month = {April},
}