Technology acceptance research: Meta-analysis

Marikyan, Davit; Papagiannidis, Savvas; Stewart, Gavin

doi:10.1177/01655515231191177

Cited by 140Technology Adoption and User BehaviourGender and Technology in EducationInnovative Human-Technology Interaction

Technology acceptance research: Meta-analysis

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Marikyan, Davit, Papagiannidis, Savvas, Stewart, Gavin·2026·Journal of Information Science

DOI 10.1177/01655515231191177

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Abstract

Rapid digitalisation has resulted in a literature about technology acceptance that is ever increasing in size, naturally creating debates about the developments in the field and their implications. Given the size of the literature and the range of factors, theories and applications considered, this article reviewed the relevant literature using a meta-analytical approach. The objective of this review was twofold: (a) to provide a comprehensive analysis of the factors contributing to technology acceptance and investigate their effects, depending on theoretical underpinnings, and (b) to explore the conditions explaining the variance in the effects of predictors time-, application- and journal-wise. This review analysed data from 693 papers. A total of 21 independent predictors having differential effects on attitude, intention and use behaviour were found. The effects of the predictors were different depending on the theoretical frameworks they were related to. The analysis of the consistency of the role of the predictors suggested that there was no longitudinal change in their effect sizes. However, a significant variance was found when comparing predictors across research applications and the journals in which the papers were published. The analysis of publication bias demonstrated a tendency to publish studies with significant results, although no evidence was found of p -value manipulation.

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A meta-analysis of technology adoption studies identified trust as strongly associated with perceived ease of use and perceived usefulness.
“A meta-analysis of technology adoption studies identified trust as strongly associated with PEU and PU (Marikyan et al., 2023).”
From Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI (2025) · cites this paper as Marikyan et al., 2023
A recent meta-analysis found that the social influence association with behavior was non-significant but had an overall significant impact on attitudes.
“While a recent meta-analysis concluded that the social influence association with behavior was non-significant, it did find an overall significant impact on attitudes (Marikyan et al., 2023).”
From Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI (2025) · cites this paper as Marikyan et al., 2023

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Authors: Marikyan, D., Papagiannidis, S., & Stewart, G.
Affiliations: University of Bristol (GB)
Newcastle University (GB)
Corresponding author: Marikyan, Davit
ORCID: Marikyan, Davit
Papagiannidis, Savvas
Stewart, Gavin
Venue: Journal of Information Science
Publisher: SAGE Publications
Published: 2023-08-10
Volume: 52
Issue: 2
Pages: 429-450
ISSN: 0165-5515 (ISSN-L)
1741-6485
DOI: 10.1177/01655515231191177
OpenAlex: W4385724764
Cited by: 140
References: 154
Citations by year: 2026: 54 · 2025: 63 · 2024: 22 · 2023: 1
FWCI: 36.01
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Information Science (publishedVersion, cc-by-nc)
Bristol Research (University of Bristol) (publishedVersion, cc-by-nc)
Explore Bristol Research (submittedVersion, cc-by-nc)
Newcastle University ePrints (Newcastle Univesity) (cc-by-nc)
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Social Sciences › Gender Studies › Gender and Technology in Education
Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords: technology acceptance, technology acceptance model, user attitudes, user intention, effect size heterogeneity, publication bias, digitalization

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