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Professor Jeffrey Soar

Professor Jeffrey Soar
Name Jeffrey Soar
Position Professor and Chair in Human-Centred Technology
Section School of Business
Office A521-B
Location Springfield Campus
Phone +61 7 3470 4831
Extension 4831
Qualifications BA(Hons) Monash , GDipEd HawthorneInstEd , GDipCommDataProc RMIT , MEd La Trobe , PhD NSWUT
Languages French (non accredited translator)

Fields of Research (FoR)

  • Public Health and Health Services not elsewhere classified ( 111799 )
  • Information Systems not elsewhere classified ( 080699 )
  • Public Health not elsewhere classified ( 420699 )
  • Information Systems not elsewhere classified ( 460999 )

Research interests
Professor Soar has over 150 peer-reviewed publications as well as other papers, posters, abstracts and commissioned reports to governments; he has given over 40 invited key-note addresses at national and international events; and has attracted grants and commissions to support over $10M in research.
His research interests include:
- smart homes and intelligent assistive technologies
- smart ageing & independent living
- e-health, aged & community care informatics,
- technology and economic development
- aged care leadership and innovation

Professional memberships
Fellow of the Australian Computer Society
Fellow of the Australasian College of Health Informatics

USQ Research affiliations

  • Institute for Resilient Regions (IRR)
  • Centre for Health Sciences Research (CHSR)
  • Australian Centre for Sustainable Business and Development (ACSBD)
  • Australian Digital Futures Institute (ADFI)

Currently teaching courses/programs
Post-graduate research supervision, current student research topics include:
- e-government
- e-banking
- e-health
- e-learning
- technology and economic development
- eprocurement and economic development
- home telehealth for chronic illness support
- hospital avoidance and connected communities of care
- multi-media to support Aboriginal and Torres Strait Islander people's health
- ehealth development

Teaching experience(Tertiary)
20 Years

Teaching experience(Other)
5 Years

Administrative responsibilities
Personal Chair Human-Centred Technology

Research most recent
recent papers can be found at:

Research most notable
Professor Soar's most notable research outcomes include:
- establishing the Queensland Smart Home Initiative in 2006, and
- building the SAIL (Smart Ageing & Independent Living) research consortium.
SAIL will develop, through research, pathways to adoption of ehealth in home and community settings - home telecare, telehealth and intelligent assistive technologies.
Its role is in attracting funds for its collaborative research program through grant writing, philanthropy, awareness-raising and networking.

Publications in ePrints

Ramakrishnan, Muralidharan and Shrestha, AnupORCID: and Soar, JeffreyORCID: (2021) Innovation Centric Knowledge Commons—A Systematic Literature Review and Conceptual Model. Journal of Open Innovation: Technology, Market, and Complexity, 7 (1):35. pp. 1-19. ISSN 2199-8531

Yusif, Salifu and Hafeez-Baig, Abdul and Soar, JeffreyORCID: (2020) A model for evaluating eHealth preparedness – a case study approach. Transforming Government: People, Process and Policy, 14 (3). pp. 561-587. ISSN 1750-6166

Yusif, Salifu and Hafeez-Baig, Abdul and Soar, JeffreyORCID: (2020) An Exploratory Study of the Readiness of Public Healthcare Facilities in Developing Countries to Adopt Health Information Technology (HIT)/e-Health: the Case of Ghana. Journal of Healthcare Informatics Research, 4 (2). pp. 189-214. ISSN 2509-4971

Li, Yuan and Li, Yutong and Wang, Yan and Ma, Guodong and Liu, Xinsheng and Li, Yonghong and Soar, JeffreyORCID: (2020) Application of zeolitic imidazolate frameworks (ZIF-8)/ionic liquid composites modified nano-carbon paste electrode as sensor for electroanalytical sensing of 1-hydroxypyrene. Microchemical Journal, 159:105433. pp. 1-7. ISSN 0026-265X

Bargshady, Ghazal ORCID: and Zhou, Xujuan and Deo, Ravinesh C.ORCID: and Soar, JefferyORCID: and Whittaker, Frank and Wang, Hua (2020) Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Systems with Applications, 149:113305. pp. 1-10. ISSN 0957-4174

View full listing in ePrints