Evidence map›Paper›PMID 41628118›Full record

ArticlePloS one2026

The effects of human training data (HTD) explanation on purchase intention for artificial intelligence (AI) technologies.

Stephanie Kwari Dharmaputri, Greg Nyilasy, Anish Nagpal, Jing Lei

Abstract read
In one paragraph

Article in PloS one, 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

4 authors.

Stephanie Kwari DharmaputriDepartment of Management and Marketing, University of Melbourne, Melbourne, Victoria, Australia.ORCID https://orcid.org/0009-0004-8648-1796
Greg NyilasyDepartment of Management and Marketing, University of Melbourne, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0002-5222-1636
Anish NagpalDepartment of Management and Marketing, University of Melbourne, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0003-3950-4364
Jing LeiDepartment of Management and Marketing, University of Melbourne, Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Consumers exhibit resistance to Artificial Intelligence (AI) technologies despite their widespread deployment. One common thread linking various reasons for this is the perceptions of AI technologies' lack of inherent human qualities. This paper explores the effect of human training data (HTD) explanation on purchase intention for AI technologies as a potential source-based explainable AI (XAI) solution to this problem. We define HTD explanation as a statement that describes the role that humans play within a given AI system's training data in lay terms that is easy to understand for end-users. This includes descriptions of what human data is used and for what purpose (e.g., "Our ready-made meals are developed by an AI system trained on millions of people's food and taste preferences"). We predict HTD explanation to have a positive effect on consumer purchase intention that is mediated by perceived transfer of human essence. We tested our hypotheses in four between-subjects experiments. Study 1 supports the proposed main effect. Studies 2 and 3 show evidence for the mediation mechanism. Study 4 provides marginal evidence that HTD explanation may reduce adoption intention among consumers high pre-existing trust in AI technologies. Our findings suggest that the psychological processes linking HTD explanation to consumer outcomes may be more complex than originally theorized. This study introduces HTD as a form of source-based XAI solution and offers preliminary insight into PET and deep humanization effects, where consumers may perceive AI technologies to possess human qualities by virtue of HTD use.

Indexed as

Artificial IntelligenceConsumer BehaviorIntentionAdultFemaleHumansIntelligent SystemsMaleYoung Adult

Identifiers

PMID41628118
PMCPMC12863500

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.