Organization development through<i>ad hoc</i>problem solving
Bibliographic record
Abstract
Purpose – The purpose of this research is to study thead hocproblem of developing capabilities for knowledge transfer between various constituencies of an enterprise resource planning (ERP) implementation project. The paper studies how an ERP project develops ability to network, link, and integrate its various knowledge resources over time. Design/methodology/approach – The paper conducted a case study of an ERP project, from its initiation in 2008 to its completion in 2011. Findings – The case demonstrates the dynamics of development of knowledge transfer capacities throughad hocproblem solving. The paper identifies five mechanisms used in this case for the development of knowledge transfer capacities. Practical implications –Ad hocproblem solving mechanisms demonstrated in this paper can be intentionally planned and utilized in similar projects to enable interaction, integration, and institutionalization. Originality/value – Even thoughad hocproblem solving as a model for change is prevalent in many organizations, studies ofad hocproblem solving capabilities as a mechanism for change are not extensive. This case describesad hocmechanisms that foster change and development of knowledge transfer capacities during large IT project implementations.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".