Modeling the Knowledge Perspective of IT Projects
Bibliographic record
Abstract
Information technology (IT) projects are often viewed as arenas in which action is paramount, and tasks, budgets, people, and schedules need to be managed and controlled to achieve expected results. This perspective is useful because it encourages the project manager to scope work, manage time and budget, and monitor progress. Another perspective views a project as a place where learning and knowledge is paramount. In this view, projects are seen as a conduit for knowledge, which enters through people, methodologies, and prior learning. During the project, knowledge must be transferred, integrated, created, and exploited to create new organizational value. Knowledge is created, and knowledge can be lost. Within an IT project, this focus on knowledge yields new insights, because IT projects are primarily knowledge work. From this perspective, the project manager's primary task is to combine multiple sources of knowledge about technologies and business processes to create organizational value. These and other views of the IT project are complementary. However, this article focuses only on the knowledge perspective, leaving aside other views. This article is designed to bring together the empirical literature, which has investigated the impact of knowledge perspectives on IT project performance, and to suggest a temporal model of this perspective. In the first part of this article, we consider the knowledge-based view of an IT project and suggest definitions and a typology of knowledge. Then the knowledge risks model (Reich, 200?) is used as a framework within which to collect and examine the empirical data that support the knowledge-based view of an IT project. In the third part of this article, the problem of modeling knowledge and learning within IT projects is addressed. The study begins with the Temporal Model of IT Project Performance (Gemino, Reich, & Sauer, 2008) and discusses evidence that its knowledge-based constructs and subconstructs are influential with respect to project performance. The article ends by proposing a temporal model of the knowledge perspective of an IT project. There are five constructs in this model: knowledge resources, knowledge creation, knowledge loss, project performance, and learning. The content of these constructs and their expected interaction is discussed. Although this stream of work is at its early stages, hopefully it will convince researchers that further investigation into knowledge and learning within projects is warranted because it has the potential to impact both the theory and performance of IT projects.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".