Evidence map›Paper›PMID 42827834›Full record

ArticleFrontiers in psychology2026

A process model of psychological acceptance in technology-based service innovation: a symbolic interactionism perspective.

Wen-Hong Chiu, Chi-Quyen Luong, Hui-Ru Chi

Abstract read
In one paragraph

Article in Frontiers in psychology, 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
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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

3 authors.

Wen-Hong ChiuSchool of Law and Public Affairs, Jiaying University, Meizhou, China.
Chi-Quyen LuongSchool of Economics and Management, Hunan University of Science and Engineering, Yongzhou, China.
Hui-Ru ChiSchool of Law and Public Affairs, Jiaying University, Meizhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As digital technologies become increasingly embedded in service systems, the diffusion of technology-based service innovations is often gradual and contested rather than automatic. Prior research has provided robust explanations for early-stage technology evaluation by treating adoption as a discrete decision or momentary experience. However, it provides limited insight into the longer-term process through which user acceptance gradually develops through ongoing use. Drawing on symbolic interactionism, this study reconceptualizes diffusion of technology-based service innovation as a process of symbolic meaning construction unfolding through ongoing interaction and use. Based on a qualitative study of an evidence-based medicine (EBM) system implemented in hospitals in Taiwan region, the study develops an integrative process model that explains how users' relationships with a technology evolve from initial unfamiliarity to routine integration in professional practice. The findings show that diffusion advances not only through positive functional evaluation but also through interpretive alignment and the gradual internalization of shared meanings through everyday service interactions. Diffusion begins with users moving from initial evaluative meanings, which are progressively shaped into collectively negotiated and socially validated interpretations, ultimately leading to the internalization of shared meanings and their circulation in use. By conceptualizing diffusion as a process of symbolic meaning construction in use, the study reveals the psychological and interpretive dynamics through which user acceptance develops beyond initial evaluation.

Indexed as

diffusion of innovationevidence-based medicineprocess modelsymbolic interactionismtechnology-based service innovation

Identifiers

PMID42827834
PMCPMC13630923

What OpenQuestion holds

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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.