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Emotional Dynamics and Strategizing Processes: A Study of Strategic Conversations in Top Team Meetings

2012· article· en· W2140211181 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

VenueJournal of Management Studies · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDynamics (music)Process (computing)Mechanism (biology)Key (lock)PsychologyKnowledge managementBusinessPublic relationsSocial psychologyComputer sciencePolitical scienceEpistemologyPedagogy

Abstract

fetched live from OpenAlex

Abstract An important but largely unexplored issue in the study of strategy‐as‐discourse is how emotion affects the discursive processes through which strategy is constructed. To address this question, this paper investigates displayed emotions in strategic conversations and explores how the emotional dynamics generated through these displays shape a top management team's strategizing. Using microethnography, we analyse conversations about ten strategic issues raised across seven top management team meetings and identify five different kinds of emotional dynamic, each associated with a different type of strategizing process. The emotional dynamics vary in the sorts of emotions displayed, their sequencing and overall form. The strategizing processes vary in how issues are proposed, discussed, and evaluated, and whether decisions are taken or postponed. We identify team relationship dynamics as a key mechanism linking emotional dynamics and strategizing processes, and issue urgency as another important influence.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.411
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.041
GPT teacher head0.267
Teacher spread0.226 · 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