Research in Information Technology Service Management (ITSM) (2000 – 2010)
Bibliographic record
Abstract
In information systems (IS) research, Information Technology Service Management (ITSM) has become a popular research area among IS and management researchers as a result of industry push and the development and advancement of research in service sciences. From academic perspective, a growing number of papers have been published addressing many aspects of ITSM issues. This paper presents the results based on a study of comprehensive review of publications in ITSM from 2000 to 2010. A total of 152 research papers from leading information systems (IS) journals and conference proceedings were identified, categorized and analyzed from the perspectives of reference discipline, theoretical foundation, research method, level of analysis, and research topic. The findings suggest five primary conclusions: 1) there is generally a lack of theoretically driven researches; 2) the field is still improving, with a growing number of published papers dealing with the development of concepts, constructs, models, methods and implementations for theory formalization; 3) ITSM performance issues, justifications, and IT Infrastructure Library (ITIL) topics are among the most popular topics of research; 4) ITSM researchers do not seem to consider research at an individual level; 5) the most popular research method was the conceptual orientation. Recommendations for future research in ITSM are presented and articulated.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.018 | 0.045 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".