Authors: A Renuka ., Dr. Ajit Kumar Singh, Dr. Neetu Jindal

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Abstract

The rapid expansion of e-commerce has transformed the manner in which consumers search for products, compare alternatives and make purchasing decisions. Among the technologies contributing to this transformation, Artificial Intelligence (AI)-based personalisation and product recommendation systems have become increasingly important. These systems analyse consumer information and behavioural patterns to provide relevant products, content and shopping suggestions. The present study examines the impact of AI-based personalisation and product recommendation systems on customer experience and customer satisfaction in e-commerce. The study adopts a quantitative, consumer-oriented research design and proposes a structured questionnaire using a five-point Likert scale. The analytical framework considers AI-based personalisation and product recommendation systems as major explanatory variables, customer experience as an important experiential construct and customer satisfaction as the principal outcome. A proposed sample of 384 e-commerce consumers is used for the illustrative statistical framework. The study examines the relationships among the constructs through reliability analysis, descriptive statistics, correlation and regression techniques. The conceptual argument is that relevant personalisation and useful recommendations can reduce search effort, improve product discovery and increase the perceived convenience of online shopping. At the same time, excessive personalisation may generate concerns regarding privacy, manipulation and loss of consumer control. The study therefore treats AI as a consumer-value mechanism rather than merely a technological innovation. The proposed research provides implications for e-commerce firms, marketing managers and researchers seeking to develop more relevant, convenient and customer-centric digital shopping environments.

Introduction

1.1 Background of the Study

E-commerce has changed the traditional relationship between consumers and sellers by enabling customers to search, evaluate and purchase products through digital platforms. However, the expansion of online product assortments has also created a new challenge: consumers may have difficulty identifying the most suitable product from a very large number of alternatives. Artificial Intelligence provides e-commerce firms with tools for addressing this challenge through personalisation and recommendation systems.

AI-based personalisation involves adapting digital content, product displays, offers and communications to the characteristics or previous behaviour of individual consumers. Product recommendation systems, in contrast, identify products that may be relevant to a consumer based on browsing patterns, purchase history, preferences, similarities with other users or product characteristics. Research has demonstrated that recommendation systems can influence consumer product choices, while web personalisation can affect information processing and decision outcomes.

 

Figure 1: AI in Retail and E-Commerce

From a Commerce perspective, the significance of these technologies lies in their ability to connect consumer information with marketing decisions. AI enables firms to move from a relatively standardised online shopping experience towards a more individualised environment. Kumar et al. argue that AI can support personalised engagement by helping firms curate the large volume of information available to consumers.

Customer experience is particularly important because consumers interact with e-commerce platforms through multiple touchpoints before, during and after purchase. Lemon and Verhoef conceptualise customer experience as developing across the customer journey rather than being restricted to an individual transaction. Thus, AI-based personalisation should be evaluated according to its effect on the overall shopping journey.

1.2 Statement of the Problem

E-commerce platforms increasingly use AI to personalise shopping experiences and recommend products. Nevertheless, the mere presence of AI does not establish that consumers experience greater satisfaction. Recommendations may sometimes be irrelevant, repetitive or perceived as commercially motivated. Similarly, consumers may appreciate convenience while simultaneously expressing concerns regarding data collection.

Therefore, the central problem addressed by this study is:

To what extent do AI-based personalisation and product recommendation systems influence customer experience and customer satisfaction in e-commerce?

1.3 Aim of the Study

The primary aim is to examine the impact of AI-based personalisation and product recommendation systems on customer experience and customer satisfaction in e-commerce.

Objectives

  1. To examine the influence of AI-based personalisation on customer experience.
  2. To determine the influence of product recommendation systems on customer experience.
  3. To examine the relationship between customer experience and customer satisfaction.
  4. To analyse the influence of AI-based personalisation on customer satisfaction.
  5. To examine the influence of product recommendation systems on customer satisfaction.
  6. To assess whether customer experience acts as an explanatory mechanism between AI-enabled shopping features and customer satisfaction.
  7. To develop managerial recommendations for improving AI-enabled e-commerce experiences.

1.4 Hypotheses

The following hypotheses are proposed:

H1: AI-based personalisation has a significant positive effect on customer experience.

H2: Product recommendation systems have a significant positive effect on customer experience.

H3: Customer experience has a significant positive effect on customer satisfaction.

H4: AI-based personalisation has a significant positive effect on customer satisfaction.

H5: Product recommendation systems have a significant positive effect on customer satisfaction.

H6: Customer experience mediates the relationship between AI-based personalisation and customer satisfaction.

H7: Customer experience mediates the relationship between product recommendation systems and customer satisfaction.

Conclusion

The study examined the proposed influence of AI-based personalisation and product recommendation systems on customer experience and customer satisfaction in e-commerce. The conceptual and illustrative statistical analysis indicates a coherent positive relationship among the principal constructs. AI-based personalisation can improve the relevance of online shopping interactions by presenting information and products that correspond more closely to consumer preferences. Product recommendation systems can similarly assist consumers in discovering products and reducing search effort. However, the value of these technologies depends on their ability to produce a meaningful improvement in the customer experience. The proposed results particularly emphasise the importance of customer experience as a central mechanism connecting AI-enabled shopping features with satisfaction. This means that technological implementation alone does not guarantee superior consumer outcomes. AI creates value when consumers perceive the resulting experience as convenient, relevant, efficient and useful. At the same time, the study recognises important limitations associated with AI-based personalisation. Privacy concerns, algorithmic bias, excessive targeting, repetitive recommendations and reduced consumer autonomy may weaken the benefits of AI. Consequently, e-commerce firms should pursue a balanced strategy that combines technological capability with transparency, consumer choice and responsible data management. The central conclusion is therefore that AI-based personalisation and product recommendation systems should be treated as instruments for creating consumer value rather than as ends in themselves. Their success should ultimately be evaluated through the quality of customer experience and the satisfaction generated by that experience.

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