Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers

A Master of Business Administration (MBA) by Sara Almaazmi entitled, “Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers”, submitted in November 2025. Thesis advisor is Dr. Aaron Gazley. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and A...

Full description

Saved in:
Bibliographic Details
Main Author: Almaazmi, Sara (author)
Format: doctoralThesis
Published: 2025
Subjects:
Online Access:https://hdl.handle.net/11073/33257
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1864513431745331200
author Almaazmi, Sara
author_facet Almaazmi, Sara
author_role author
dc.contributor.none.fl_str_mv Gazley, Aaron
dc.creator.none.fl_str_mv Almaazmi, Sara
dc.date.none.fl_str_mv 2025-11
2026-03-24T06:41:00Z
2026-03-24T06:41:00Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv 33.232-2025.24
https://hdl.handle.net/11073/33257
dc.language.none.fl_str_mv en_US
dc.relation.none.fl_str_mv Master of Business Administration (MBA)
dc.subject.none.fl_str_mv Artificial Intelligence
Symptom Checkers
Consumer Behavior
Digital Health
Technology Acceptance Model (TAM)
UAE
Healthcare
dc.title.none.fl_str_mv Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/doctoralThesis
description A Master of Business Administration (MBA) by Sara Almaazmi entitled, “Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers”, submitted in November 2025. Thesis advisor is Dr. Aaron Gazley. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).
format doctoralThesis
id aus_d8c22c30e461f2af00c3649dd350df00
identifier_str_mv 33.232-2025.24
language_invalid_str_mv en_US
network_acronym_str aus
network_name_str aus
oai_identifier_str oai:repository.aus.edu:11073/33257
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
spelling Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom CheckersAlmaazmi, SaraArtificial IntelligenceSymptom CheckersConsumer BehaviorDigital HealthTechnology Acceptance Model (TAM)UAEHealthcareA Master of Business Administration (MBA) by Sara Almaazmi entitled, “Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers”, submitted in November 2025. Thesis advisor is Dr. Aaron Gazley. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).As artificial intelligence reshapes healthcare delivery, symptom checker applications powered by artificial intelligence are increasingly used for early self-assessment and triage. However, limited research has examined how consumers perceive and adopt these technologies, particularly within the United Arab Emirates. This study addresses two key gaps in the literature: the lack of empirical research on consumer attitudes toward artificial intelligence–based symptom checkers in the United Arab Emirates, and the limited application of established theoretical frameworks, such as the Technology Acceptance Model, in analysing their adoption. Using a quantitative cross-sectional survey of residents in the United Arab Emirates (one hundred and forty respondents), this study examined behavioural intention, perceived risk, trust, social influence, privacy concerns, and core constructs of the Technology Acceptance Model. The findings indicate that generative artificial intelligence tools, including ChatGPT, Gemini, and Microsoft Copilot, were the most frequently used platforms for symptom checking, substantially surpassing specialized medical tools. Social influence emerged as the strongest positive predictor of future adoption among non-users, while perceived risk was associated with a reduction in intention among both users and non-users. Constructs related to perceived usefulness, perceived ease of use, and trust did not significantly influence the persistence of intention among current users. Age was also not a significant predictor of symptom checker usage. These results provide new insights into consumer acceptance of artificial intelligence–based symptom checkers in the United Arab Emirates and highlight the importance of social influence, familiarity with generative artificial intelligence, and perceived risk relative to traditional usability factors. The findings offer practical implications for healthcare practitioners and developers seeking to enhance trust, safety, and cultural appropriateness in artificial intelligence–enabled health technologies within the United Arab Emirates.School of Business AdministrationDepartment of Management, Strategy and EntrepreneurshipMaster of Business Administration (MBA)Gazley, Aaron2026-03-24T06:41:00Z2026-03-24T06:41:00Z2025-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf33.232-2025.24https://hdl.handle.net/11073/33257en_USMaster of Business Administration (MBA)oai:repository.aus.edu:11073/332572026-03-25T05:16:47Z
spellingShingle Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
Almaazmi, Sara
Artificial Intelligence
Symptom Checkers
Consumer Behavior
Digital Health
Technology Acceptance Model (TAM)
UAE
Healthcare
status_str publishedVersion
title Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
title_full Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
title_fullStr Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
title_full_unstemmed Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
title_short Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
title_sort Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
topic Artificial Intelligence
Symptom Checkers
Consumer Behavior
Digital Health
Technology Acceptance Model (TAM)
UAE
Healthcare
url https://hdl.handle.net/11073/33257