REVISITING IT PROJECT UNCERTAINTY: OPERATIONALIZING THE SAMBAMURTHY–ZMUD MODEL FOR DE-RISKING DIGITAL TRANSFORMATION PROJECTS
Bibliographic record
Abstract
Uncertainty in project deliverables remains a pervasive source of IT project failure, yet its structural origins are rarely operationalized. Sambamurthy and Zmud (2017) proposed a conceptual model linking IT project uncertainty to two fundamental dimensions: (1) the clarity or ambiguity of project deliverable specifications and (2) the number, diversity, and power of stakeholders involved. Despite its strong resonance with practice, this framework has not been empirically developed or tested. This paper extends and operationalizes the Sambamurthy–Zmud model by defining measurable constructs for project deliverable specification clarity and stakeholder structure complexity and by theorizing their joint effect on IT project risk. Drawing on information processing theory, stakeholder theory, and sociotechnical systems perspectives, the paper argues that IT project uncertainty is not merely a descriptive condition, but a primary driver of project risk. A conceptual model and testable hypotheses are proposed to guide future empirical research and managerial practice in digital transformation projects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".