Evidence map›Paper›PMID 38470889›Full record

ArticlePloS one2024

From theory to practice: An integrated TTF-UTAUT study on electric vehicle adoption behavior.

Ayed Alwadain, Suliman Mohamed Fati, Kashif Ali, Rao Faizan Ali

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Ayed AlwadainComputer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia.
Suliman Mohamed FatiInformation Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
Kashif AliFaculty of Management Science (FOMS), UCP Business School, University of Central Punjab, Lahore, Pakistan.ORCID 0000-0003-3985-0937
Rao Faizan AliDepartment of Software Engineering, University of Management and Technology, Lahore, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In Industry 4.0, the adoption of new technology has played a major role in the transportation sector, especially in the electric vehicles (EVs) domain. Nevertheless, consumer attitudes towards EVs have been difficult to gauge but researchers have tried to solve this puzzle. The prior literature indicates that individual attitudes and technology factors are vital to understanding users' adoption of EVs. Thus, the main aim is to meticulously investigate the unexplored realm of EV adoption within nations traditionally reliant on oil, exemplified by Saudia Arabia. By integrating the "task technology fit" (TTF) model and the "unified theory of acceptance and usage of technology" (UTAUT), this research develops and empirically validates the framework. A cross-section survey approach is adopted to collect 273 valid questionnaires from customers through convincing sampling. The empirical findings confirm that the integration of TTF and UTAUT positively promotes users' adoption of EVs. Surprisingly, the direct effect of TTF on behavioral intentions is insignificant, but UTAUT constructs play a significant role in establishing a significant relationship. Moreover, the UTAUT social influence factor has no impact on the EVs adoption. This groundbreaking research offers a comprehensive and holistic methodology for unravelling the complexities of EV adoption, achieved through the harmonious integration of two well-regarded theoretical frameworks. The nascent of this research lies in the skilful blending of technological and behavioral factors in the transportation sector.

Indexed as

AttitudeIntentionArabiaSurveys and QuestionnairesTechnology

Identifiers

PMID38470889
PMCPMC10931523

What OpenQuestion holds

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LicenceCC BY
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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.