Evidence map›Paper›PMID 41728272›Full record

ArticleFrontiers in artificial intelligence2026

Intention to use artificial intelligence among SME account executives.

Gehad Mohammed Ahmed Naji, Muhammad Shoaib Saleem, Lim Cheah, Khairul Shafee Kalid, Yulita Hanum P Iskandar

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Gehad Mohammed Ahmed NajiGraduate School of Business, Universiti Sains Malaysia, USM, George Town, Malaysia.
Muhammad Shoaib SaleemINTI International University, Nilai, Malaysia.
Lim CheahGraduate School of Business, Universiti Sains Malaysia, USM, George Town, Malaysia.
Khairul Shafee KalidPositive Computing Research Group, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
Yulita Hanum P IskandarGraduate School of Business, Universiti Sains Malaysia, USM, George Town, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purposes: The study investigated the intention of account executives from Small and Medium Enterprises (SMEs) to employ artificial intelligence at their workplace. This study will examine the Unified Theory of Acceptance and Use of Technology (UTAUT), as well as technological and personal characteristics, and the role of SME account executives in adopting artificial intelligence. This study addresses the knowledge gaps in SME account executives' understanding of artificial intelligence. Methodology: employed an online questionnaire distributed in collaboration with SMEs in Malaysia to gather responses from 273 account executives who work in SMEs. The data were analyzed using PLS-SEM and Artificial Neural Networks (ANN) to investigate SME account executives' intentions to employ artificial intelligence. The demographic information of the individuals was analyzed using SPSS software. Results: The study's findings revealed positive and significant relationships between performance expectancy, effort expectancy, social influence, facilitating conditions, system quality, employee awareness, and personal innovativeness toward artificial intelligence. Insignificant relationships were found between time-saving features and technological self-efficacy, and a negative, significant relationship existed with internet technology (IT) features toward artificial intelligence. Limitation: The cross-sectional approach focuses on SMEs in Malaysia, where the study's applicability to other industries and countries is limited due to changes in the cultural, economic, and regulatory environment. Because participants may give socially acceptable answers rather than honest ones, using self-reported data raises the possibility of bias. Because inquiry assumes a certain level of knowledge with AI technology, respondents' varying levels of digital competency may influence the findings. Practical implication: The findings of this study can help SMEs adopt artificial intelligence for their operations, particularly in accounting departments. Collaboration among organizations can help improve employee motivation to increase intention to use artificial intelligence. Originality/value: This study uses the Unified Theory of Acceptance and Use of Technology (UTAUT), technical qualities, and individual traits.

Indexed as

account executiveartificial intelligence (AI)health system accessindustrial growthsmall and medium-sized enterprises (SMEs)technology adoption and acceptance

Identifiers

PMID41728272
PMCPMC12920514

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.