Evidence map›Paper›PMID 42099704›Full record

ArticleFrontiers in artificial intelligence2026

Innovative technologies and workplace collaborations in the energy sector based in the United Arab Emirates.

Jack Charles Boath, Sayed Abdul Majid Gilani, Tamaralaiyefa Harold Tiemo, Ansarullah Tantry

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jack Charles BoathKings Business School UAE, Dubai, United Arab Emirates.
Sayed Abdul Majid GilaniSchool of Management, Birmingham City University, Birmingham, United Kingdom.
Tamaralaiyefa Harold TiemoGlobal Banking School, London, United Kingdom.
Ansarullah TantryFatima College of Health Sciences, Abu Dhabi, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The UAE energy sector is navigating digital transformation mandates such as the UAE AI Strategy 2031 and Net Zero commitments, with technologies like AI, IoT and cloud computing creating new avenues for real-time coordination, data-driven decision-making and cross-functional collaboration. These oppor tunities are tempered by challenges of organisational readiness, cultural iner tia and technological integration. Yet, research on innovative practices in the UAE energy context remains limited. Therefore, this study investigates the role of AI, IoT and cloud computing in shaping workplace collaboration in the UAE energy sector. Methods: An explanatory sequential mixed-methods design was adopted which involved Phase 1 (15 October, 2024-31 January, 2025) interviews with 15 professionals in operations, IT and leadership roles from major energy companies, analysed via thematic analysis. Phase 2 (15 February, 2025-15 May, 2025) distributed a survey to a broader sample, yielding 115 valid responses, which were analysed quan titatively. The study is primarily grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), with the Technology Acceptance Model (TAM), Resource-Based View (RBV) and Actor-Network Theory (ANT) serving as supporting interpretive lenses. Results: Findings show that AI, IoT, and cloud platforms enhance collaboration, especially in remote coordination and predictive decision sup port, but adoption is hindered by resistance to change, fragmented systems and uneven digital literacy. Discussion: Practical implications include modular rollouts, digital maturity audits and AI onboarding programs. Policy recommendations include national collaboration standards, KPI integration and incentives for joint innova tion projects.

Indexed as

energy sectorinnovative technologiessustainabilityUAEworkplace collaborations

Identifiers

PMID42099704
PMCPMC13144080

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.