Evidence map›Paper›PMID 41788610›Full record

ArticleFrontiers in artificial intelligence2026

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

L Durgha Devi, Thangaraja Arumugam

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

L Durgha DeviVIT Business School, Vellore Institute of Technology - Chennai Campus, Chennai, India.
Thangaraja ArumugamVIT Business School, Vellore Institute of Technology - Chennai Campus, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

Indexed as

AI acceptanceAI-led campaign personalizationdigital nativesTAMtechnology advantageVAM

Identifiers

PMID41788610
PMCPMC12957231

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.