Panel: A call for action in tackling environmental sustainability through green information technologies and systems
Bibliographic record
Abstract
Managers continually invest in new information technology (IT) but the question of organizational value still seems vague. One explanation is poor evaluation. In practice the Business Case including Return on Investment (ROI) still dominate. Information System research has noted for a long time that the Economic Approach is not sufficient and instead the Interpretative IT Evaluation Approach has been put forward. However, the approach has reached limited acceptance in practice and it has been noted that what to evaluate is a far more complex process than might first appear. The aim of this study is to articulate factors and criteria that are important to consider when assessing the organizational value of IT investments. This study is part of a Collaborative Practice Research project that took place 2005-2008 at three public organizations. The findings indicate that it is time to take a step from a Business Case to a Value Case. The Value Case is a pluralistic, a formative and a formalized approach that includes factors and criteria that have its base in prior research and have been further discussed and analyzed by the respondents. The Value Case also put management’s attention to effectiveness and efficiency, the task of management.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.028 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".