E‐government maturity model using the capability maturity model integration
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
Purpose The purpose of this paper is to propose a framework for assessing the maturity level of electronic government (e‐government). Design/methodology/approach The conceptual framework is based on two models: the intellectual capital (IC) management and the capability maturity model integration (CMMI). Findings The framework is composed of four input areas (human capital, structural capital, relational capital, and IT investment) and five maturity stages (web presence, interaction, transaction, integration, and continuous improvement). These areas are assessed by using the IC management model and the CMMI model. Employing the IC management process enables not only practitioners to effectively manage resources, but also auditors to more objectively assess the input area. Using the CMMI model allows governments to conduct process‐based assessments. Originality/value The paper contributes to the literature and practice in the following ways. First, it outlines how to define and assess key attributes of e‐government activities. It can help governments to enhance the awareness and understanding of maturity levels of e‐government. Second, this research expands the scope of current studies on a maturity model by providing a balanced view between input factors (resources) and results (maturity stages). For practitioners, assessing the input factors enables them to realize how to prioritize strategies and resources. For academics, this attempt sheds light on the concepts of IC in e‐government studies. Third, considering the CMMI model will be helpful to conduct an objective and useful assessment. On the basis of a matrix for assessing maturity levels, governments can conduct self‐assessment and establish stable and mature implementation processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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