Bibliographic record
Abstract
Abstract Purpose – The purpose of this paper is to understand the origins and nature of unexpected events that affect complex projects, by relying on a view of projects as social systems. The authors argue that the project relation to its environment is mediated by a model of this environment that is embedded in the communications between project participants. The adequacy of this model to the causal texture of the environment inspired a first, epistemological, dimension for characterizing events: event predictability. The nature of the boundaries between system and environment inspired the second dimension: locus of generation. Design/methodology/approach – This study followed a multiple‐case study approach. The authors collected data in 17 complex projects, in three types of industries: construction, IT/IS, and pharmaceutical. Findings – In total, nine categories of unexpected events were identified from the intersection of two dimensions: event predictability and locus of generation. Research limitations/implications – The empirically validated two‐dimensional framework sheds new light on the way organizations react to unexpected events and on the reasons for the eventual project performance. Practical implications – The findings show that project managers tend to underestimate certain risks. This research will help managers better predict those types of risks. However, some risks are simply unpredictable, therefore the authors argue for the necessity to prepare projects for the unforeseen. Originality/value – Analyzing the previous literature in unexpected events, the authors identified two main, but opposing, theoretical perspectives: one rooted in decision theory and the other that sees projects as social systems. The value of this paper comes from the original mode in which the authors propose to reconcile these perspectives, by viewing projects as networks of communicative couplings between actors.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".