The Application of IT for Competitive Advantage at Keane, Inc.
Bibliographic record
Abstract
The Keane Company, founded in 1965 by John F. Keane, has grown from a local software service company into a national firm which has three operating divisions and over 45 branches throughout the United States, Canada and the United Kingdom. Within these operating divisions are multitudes of consulting opportunities, ranging from supplemental staffing, project management and application outsourcing. This case will focus on Keanes approach to Project Management and how they provide this service to their clients. This includes not only how Keane is hired for Project Management but how they train their clients on how they too can implement the Keane philosophy of Productivity Management. Instead of focusing on any one client of Keane, their overall technology strategy will be highlighted, from their early days through the present to illustrate how Keane has successfully incorporated information technology and Project Management to become a major player in the software service and consulting field. The goal of this case is to provide the student with an example of business-technology strategy in action and allow them to explore future paths that Keane may take based on how they use technology today and in the decade to come.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".