A Systemic Mapping Study of Business Intelligence Maturity Models for Higher Education Institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Higher education institutions (HEIs) are investing in business intelligence (BI) to meet the increasing demand for information stemming from their operations. Information technology (IT) managers in higher education may turn to BI maturity models to evaluate the current state of HEIs’ BI operation capabilities and evaluate the readiness for future improvements. However, generic BI maturity models do not have domain-specific attributes that ensure a high degree of compatibility with HEIs. This study’s objective is to survey maturity models that could be used in HEIs and identify those used for BI to perform an analysis of their qualities and identify future avenues for research into HEI-specific BI maturity models. A systemic mapping was undertaken via both a keyword and snowball search of five indexing services, 6037 articles were processed using inclusion and exclusion criteria resulting in the identification of forty-one academic works regarding maturity model uses which were mapped to ten categories. The mapping reveals an increasing number of publications featuring maturity models for HEI, particularly since 2018, focused on e-learning and ICT. A single instance of a BI maturity model for HEI emerged in 2022 within the European HEI context. The HE-BIA MM has more dimensions than most other models identified, yet only a single co-occurrence of dimensions was identified in name only. We conclude that BI maturity models for HEI are emerging as a field of research with future directions for research including exploring co-occurrence of dimensions with existing maturity models, performing case studies, and validation of HE-BIA MM outside the European HEI context.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it