Bibliographic record
Abstract
Information system (IS) project alignment has been defined as the degree to which an IS's project deliverables are consistent with the project's objectives, which are shaped by the organization's IS strategy. These objectives may change as the project unfolds, reflecting emergent opportunities, limitations, and strategy (Jenkin and Chan, 2005). The IS project alignment process refers to the sequence and flow of events that lead to project alignment and, thus, strategy execution. To develop an understanding of project alignment processes – how IS projects become aligned or misaligned – this study examines nine projects across two organizational settings to analyze the key events and processes of project alignment. By analyzing three different project patterns using different organizational metaphors, this study explains evidence contradicting widely held beliefs about project management. Rather than planning and process maturity, the results of this study highlight the importance of executing processes, change, learning, knowing in practice, and heedful interrelating between project team members to align final project deliverables. This research, based on an empirical study, contributes to both the IS alignment and project management literatures by further developing the emerging concept of project alignment to include a process view of how to achieve alignment of final project deliverables with IS project objectives and the IS strategy.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".