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Record W1997898646 · doi:10.1108/17538371211214932

A typology of unexpected events in complex projects

2012· article· en· W1997898646 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Managing Projects in Business · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsTypologyPredictabilityEvent (particle physics)Unexpected eventsAffect (linguistics)EpistemologyDimension (graph theory)SociologyCognitive sciencePsychologyEngineeringCommunicationSystems engineeringPhilosophy

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.158
GPT teacher head0.408
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it