# Transforming data to delight: AI-led campaign personalization shapes digital natives’ intention to accept over a TAM-VAM approach

## TL;DR

This study explores how digital-savvy consumers decide to accept AI-driven personalized marketing campaigns by combining technology and value-based models.

## Contribution

The study integrates TAM and VAM to explain AI-led campaign acceptance in cause-related marketing, revealing the role of perceived value.

## Key findings

- Technology readiness strongly influences perceived ease of use and enjoyment.
- Perceived value is the strongest predictor of acceptance of AI-led campaigns.
- Both functional and emotional factors drive consumer acceptance of AI personalization.

## Abstract

To explain consumers’ intention to accept artificial intelligence (AI)-led campaign personalization in cause-related marketing, this study integrates the technology acceptance model (TAM) and value-based adoption model (VAM). While TAM explains acceptance through functional and usability-driven evaluations of technology, it is insufficient to capture the emotional and value-driven judgments that are central to cause-related marketing contexts. VAM complements TAM, which is critical when AI-led campaign personalization is used to promote socially and ethically oriented initiatives. Focusing on digitally savvy consumers with high expectations for personalized initiatives, the study empirically examines these relationships using a purposive sample of 270 university students from Chennai, India. The data were collected through an online survey assessing key TAM factors, such as technology readiness, perceived usefulness, and perceived ease of use (PEOU), and VAM factors, such as perceived enjoyment and perceived value. Structural equation modeling using AMOS revealed that technology readiness strongly influences perceived ease of use (β = 0.48) and perceived enjoyment (β = 0.52), while perceived usefulness (β = 0.46) and perceived enjoyment (β = 0.49) significantly enhance perceived value. Perceived value emerged as the most substantial predictor of intention to accept AI-led campaign personalization (β = 0.60). These findings indicate that both functional and emotional benefits drive consumers’ intention to accept AI-led marketing. By integrating TAM and VAM, this study provides an empirical insight into how digital natives perceive and engage with AI-led strategies. The research offers theoretical contributions of AI intention to acceptance models and practical insights for businesses aiming to design personalized, value-driven, and emotionally engaging AI-led campaigns.