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TRV-2026-0280Version 1 · Certified

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TRUVACE RECORD VERSION
record: TRV-2026-0280
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-19T01:19:07.040123Z
status: published
lens: g_space
sector: entertainment
headline: Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States
dek: This article aims to investigate the factors that affect behavioural intention (BI) and user behaviour (UB) among Arabian users of generative artificial intelligence (GenAI) applications in the context of media content creation. The study’s theoretical framework is grounded in the unified theory of acceptance and use of technology (UTAUT2). A sample of 496 users was analysed using the partial least squares structural equation modelling technique (PLS-SEM). The results revealed that BI is significantly influenced…
gain_title: Among 496 Arabian users, intention to use GenAI for media content creation was most strongly driven by hedonic motivation alongside performance expectancy and other UTAUT2 factors, and actual use was positively driven by facilitating conditions, habit, trust and intention.
problem_title: (none)
trace_subject: (none)
gain_reading: Among 496 Arabian users, intention to use GenAI for media content creation was most strongly driven by hedonic motivation alongside performance expectancy and other UTAUT2 factors, and actual use was positively driven by facilitating conditions, habit, trust and intention.
gain_evidence: BI is significantly influenced by performance expectancy, effort expectancy, social influence, hedonic motivation, habit, and user trust | hedonic motivation having the greatest impact | facilitation conditions, habit, user trust, and BI were all found to have a positive and significant impact
problem_reading: (none)
problem_evidence: (none)
quick_read: A November 2024 peer-reviewed study investigated why Arabian users adopt generative AI for media content creation, using UTAUT2 to survey 496 users and test links to behavioural intention and user behaviour with PLS-SEM.

The findings matter for entertainment creators and platforms because enjoyment, not just usefulness, most strongly predicted intention, while trust, habit, facilitating conditions and intention predicted actual use, leaving open how these drivers vary across specific media formats and over time.
limitation: 
tag: Evidence-backed gain
key_points: Study grounded in UTAUT2 framework and analyzed 496 users with PLS-SEM. | Behavioural intention examined specifically for generative AI applications in the context of media content creation among Arabian users. | Hedonic motivation identified as having the greatest impact on behavioural intention compared to other predictors.
rundown: Researchers surveyed 496 Arabian users of GenAI for media creation and modeled responses with partial least squares structural equation modelling under UTAUT2.

Results separate drivers of intention from drivers of actual use, finding trust and habit influence both, while facilitating conditions matter for behaviour and hedonic motivation dominates intention.
sources:
- peer_reviewed | Journalism and Media | https://doi.org/10.3390/journalmedia5040101 | 2024-11-06
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