Algorithmic Marketing Intensity and Consumer Resistance in AI-Driven Advertising: A PLS-SEM Analysis of Social Media Users

Introduction

The increased application of Artificial Intelligence (AI) and algorithmic processes has changed the nature of digital advertising by creating very personalized marketing messages. Although algorithmic personalization could enhance advertising relevance and interactions, overly algorithmic targeting can also lead to adverse reaction among consumers.

Objectives

This paper investigates the role of the intensity of algorithmic marketing in resisting consumers in the context of AI-driven advertising.

Theoretical Framework

Based on psychological reactance theory and technology overload theory, our conceptual model is that the intensity of algorithmic marketing augments the feeling of personalization and cognitive overburden which, in turn, results in consumer resistance and avoidance behavior.

Methodology

The data was gathered with 400 social media users who had been subjected to personalized digital advertising, and they were examined with the help of Partial Least Squares Structural Equation Modeling (PLS-SEM).

Moderating Variable

The model also examines how artificial intelligence moderates the negative consumer responses through the moderating role of trust.

Key Findings

The findings suggest that perceived personalization and cognitive strain caused by the strength of the algorithmic marketing have great influence, leading to consumer resistance and avoidance of ads. In addition, consumer resistance decreases with trust in AI and undermines the connection between cognitive strain and resistance.

Contribution/Significance

The results add to the literature on algorithmic marketing and consumer behavior by identifying the psychological processes underlying resistance to advertising by AI.