CiteNScope — Scholarly Index & Analytics
Discover / IJRBSPUBLICATION RECORD

Why do organizational support and social influence drive the use of artificial intelligence?

Chang-Hong Wu · Ruey-Der Twu · Kuo-Chung Huang

International Journal of Research in Business and Social Science · 2026 · Vol. 14 / Issue 9 · 93-108

Research reach

Publication metrics

External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.

Dimensions

Live provider badge. If unavailable, use the Dimensions link above.

CROSSREF CITATIONS0

Observed 21 Oct 2026, 07:22 UTC

OpenAlex · Citations0Cached provider observation

Observed 21 Oct 2026 07:23 UTC

Mendeley · Readers8Cached provider observation

Observed 21 Oct 2026 22:57 UTC

OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.

OpenAlex / Citation year

Annual citations to this article

View provider totals ↑

Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.

2017–2026 Annual history last updated:

Citations received

OpenAlex · year of the citing work

OpenAlex

Hover over a year or focus the chart and use arrow keys to inspect values.

2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.

View data & coverage

2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.

Overview

Abstract

Artificial intelligence has gradually become an important tool for organizational strategy and research and development activities, with applications involving human–machine collaboration, process transformation, and the dynamic process of technology adoption. However, AI usage behavior of research and development personnel in professional contexts has not yet been thoroughly investigated. Grounded in social cognitive theory, this study examines the mechanisms through which organizational support, social influence, performance expectancy, and personal innovativeness affect AI usage. Using purposive sampling, data were collected from 238 valid responses of technology R&D personnel in Kaohsiung, and structural equation modeling was employed for analysis. The results indicate that the direct effects of organizational support and social influence on AI usage are not significant, but both indirectly influence AI usage through performance expectancy, highlighting performance expectancy as the core driver of AI adoption. In addition, personal innovativeness demonstrates a significant moderating effect, as individuals with higher innovativeness are better able to translate performance beliefs into actual behavior. These findings suggest that in knowledge-intensive environments, external support and social norms must be integrated with employees’ perceptions of AI benefits to facilitate actual adoption. Theoretically, this study extends the application of social cognitive theory to AI usage, addressing the limitations of prior research that focused solely on external factors; practically, it reminds managers to strengthen employees’ performance expectancy and design promotion strategies tailored to individual differences in order to foster organizational digital transformation.

Keywords: {'name': 'Organizational Support'}, {'name': 'Social Influence'}, {'name': 'Performance Expectancy'}, {'name': 'AI Usage'}, {'name': 'Personal Innovativeness'}

Authors & affiliations

Chang-Hong Wu
Nanhua University

Ruey-Der Twu
Nanhua University

Kuo-Chung Huang
Nanhua University

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1108/ITP-01-2020-0002

DOI: 10.1108/JHASS-12-2023-0164

DOI: 10.1287/isre.9.2.204

DOI: 10.1037//0033-2909.103.3.411

Aprianto, R., Haris, R., Williams, A., Agustian, H., & Aptwell, N. (2025). Social influence on AI-driven air quality monitoring adoption: SmartPLS analysis. Sundara Advanced Research on Artificial Intelligence, 1(1), 28–36. https://doi.org/10.34306/sundara.v1i1.17

DOI: 10.1007/s11747-011-0278-x

DOI: 10.1146/annurev.psych.52.1.1

DOI: 10.3386/w31161

DOI: 10.3390/app15063363

DOI: 10.1016/j.techfore.2024.123247

DOI: 10.2196/55399

DOI: 10.1007/s10639-024-12835-4

DOI: 10.1007/s10551-022-05166-2

DOI: 10.1145/3637326

DOI: 10.1108/BIJ-09-2022-0585

DOI: 10.1080/2331186X.2024.2403287

Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social influence and facilitating conditions on acceptance of e-banking services in Iran: The moderating role of age and gender. Middle-East Journal of Scientific Research, 12(6), 801–807. https://doi.org/10.5829/idosi.mejsr.2012.12.6.2536

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2017). Multivariate data analysis (7th ed.). Bookman.

DOI: 10.3390/bs15081046

DOI: 10.1080/03637750903310360

Hidayah, N., & Iriani, L. D. (2024). Technology-based factors of university students' intentions to use AI-enabled mobile banking: The moderating role of personal innovativeness. In IECON: International Economics and Business Conference (Vol. 2, No. 1, pp. 13–32).

DOI: 10.1016/j.chb.2023.107760

DOI: 10.5465/amj.2022.0426

DOI: 10.1108/JEBDE-07-2022-0016

DOI: 10.1038/s41598-023-49710-0

Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Publications.

DOI: 10.1016/j.chb.2022.107527

DOI: 10.1080/14703297.2025.2496942

DOI: 10.1016/j.jbusres.2004.07.008

DOI: 10.1016/j.im.2018.03.006

DOI: 10.1016/j.im.2008.06.004

DOI: 10.3390/systems13080639

DOI: 10.1016/j.ijhm.2024.103875

DOI: 10.1016/j.apmrv.2021.01.002

DOI: 10.1016/j.jsis.2005.07.003

DOI: 10.5465/amj.2020.1516

DOI: 10.1016/j.jbusres.2024.114542

DOI: 10.5604/01.3001.0054.7264

DOI: 10.1016/j.chb.2011.11.021

DOI: 10.1177/2158244015607585

DOI: 10.1016/j.chb.2009.12.002

DOI: 10.1016/j.compedu.2017.02.005

DOI: 10.1126/science.adh2586

DOI: 10.28945/4085

DOI: 10.1177/002224379503200308

DOI: 10.1016/j.actpsy.2022.103681

DOI: 10.54259/satesi.v5i1.4117

DOI: 10.64382/mjii.v4i3.112

DOI: 10.36655/jeb.v1i2.169

Singh, S., Bansal, S., Saddik, A. E., & Saini, M. (2025). From ChatGPT to DeepSeek AI: A comprehensive analysis of evolution, deviation, and future implications in AI-language models. arXiv Preprint, arXiv:2504.03219. https://doi.org/10.48550/arXiv.2504.03219

DOI: 10.1108/IJQSS-03-2013-0013

DOI: 10.1111/bjet.13425

DOI: 10.1016/j.sbspro.2015.11.327

DOI: 10.47747/ijcse.v2i4.529

DOI: 10.2307/30036540

DOI: 10.1016/j.ijpe.2023.109015

DOI: 10.1108/EJIM-02-2024-0213

Wang, W. T., & Lin, Y. L. (2021). The relationships among students' personal innovativeness, compatibility, and learning performance. Educational Technology & Society, 24(2), 14–27. https://www.jstor.org/stable/27004928

West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). Guilford Press.

DOI: 10.1080/10447318.2024.2375686

DOI: 10.3390/ijerph20065147

DOI: 10.1007/s10639-025-13441-8

DOI: 10.19168/jyasar.631019

DOI: 10.1016/j.landusepol.2015.04.022

DOI: 10.1080/13602381.2024.2367526

DOI: 10.3390/bs15040491