Responding—or Not—to Information Technology Project Risks: An Integrative Model1
Bibliographic record
Abstract
This study proposes and tests a model that explains and predicts risk response decisions of information technology project managers (ITPMs), blending the domains of the theory of planned behavior (TPB) and behavioral decision theories, and leveraging information technology project risk management behavioral research. The model posits that a risk response decision is indirectly influenced by perceived risk exposure via overall risk response attitude. The model conceptualizes perceived risk exposure and overall risk response attitude as second-order constructs and examines the dimension-level relationships within each. The model hypothesizes that a risk response decision is also influenced by pressures ITPMs perceive for or against enacting a specific risk response, by a negative synergy effect between overall risk response attitude and perceived pressures, and by their perception of control—or lack thereof—over enacting the risk response. The model was instantiated for three specific risk responses: having user representatives as team members, appreciating team members’ work in a tangible way during the project, and dedicating much effort to planning. Each model instance was tested in a separate survey (N > 111 per survey, total N = 349). The results support the hypotheses, except for the influence of perceived control, which varied across instantiations of risk responses. Among other antecedents, overall risk response attitude is found to have the strongest effect on risk response decisions. The findings stress the effect of ITPMs’ salient beliefs about specific risk responses on their decision to enact a given response and thus pave the way for designing behavior change interventions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".