Improving ethical usage of corporate data in higher education
Enhanced Enterprise Data Ethics Framework
Bibliographic Data
| ID | 21595906 |
|---|---|
| Authors | Trinity McNicol (Queensland University of Technology), Bailey Carthouser (0000-0002-7902-988X, Queensland University of Technology), Ivano Bongiovanni (0000-0002-7191-8690, The University of Queensland), Sasenka Abeysooriya (0000-0003-2344-458X, Queensland University of Technology) |
| Year | 2024 |
| Volume | 37 |
| Issue | 6 |
| Pages | 2247-2278 |
| Publication date | 2024-09-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Information Technology and People (JOURNAL) |
| Journal identifiers | ISSN: 0959-3845 • E-ISSN: 1758-5813 |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/itp-12-2022-0971 |
| OpenAlex | W4386177083 |
| Language | EN |
| Citations received | 1 |
| References cited | 34 |
Purpose The purpose of this study is to address the generalised lack of guidance on ethical treatment of corporate (e.g. non-research) data in higher education institutions, by focusing on the case of the University of Queensland (Brisbane, Australia). No actionable framework is currently available in the country to govern the ethical usage of corporate data. As such, this research takes a stakeholder-centred approach to data ethics; the lived experience of the stakeholders involved coupled with a theory-based ethical framework allowed the authors build to build a framework to guide ethical data practice. Design/methodology/approach Adopting a revised canonical action research approach focused on intervention on the context, the authors conducted a review of the literature on ethical usage of data in higher education institutions; administered one survey to university students ( n = 168); and facilitated three workshops with professional staff (two) and students (one). Findings Collected data highlighted how, among other themes, the role and ethical importance of transparency was the dominant claim among all stakeholder groups. Findings helped the authors develop an Enhanced Enterprise Data Ethics Framework (EEDEF) emphasising transparency and stakeholder-centricity. Practical implications Legislation is the driver to regulate the use of corporate data in higher education; however, this can be problematic because legislation is retrospective, lacks normativity and offers scarce directions for cases that do not exactly follow within the legislative mandate. In light of these regulatory limitations, the authors’ EEDEF offers operators guidance on how to ethically manage corporate data in the higher education environment. Originality/value This study fills gaps in praxis and theory; that is the lack of literature and guiding ethical frameworks to inform data practice in higher education. This research fosters a more ethical data management by virtue of genuine and authentic engagement with stakeholders and emphasises the importance of strategic decision-making and maturity of data culture in the higher education sector
Business · Engineering ethics · Higher education · Knowledge management · Legislation · Mandate · Originality · Political science · Public relations · Qualitative research · Social science · Sociology · Stakeholder · Big Data and Business Intelligence · Computer Science · Engineering · Ethics and Social Impacts of AI · Privacy-Preserving Technologies in Data
Perspective
The current landscape of learning analytics in higher education
Learning analytics
Strategies for ensuring trustworthiness in qualitative research projects
Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector
Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics
Are We There Yet? Data Saturation in Qualitative Research
Higher Education’s Panopticon? Learning Analytics, Ethics and Student Engagement
Power-Dependence Relations
Design ethnography in information systems
Emerging models of data governance in the age of datafication
Australian public universities
Learning Analytics
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |