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TMT Temporal Practices – Short on Time, Balancing Exploiting vs Exploring Strategic Conversations.

2025· article· en· W4416005364 on OpenAlexaff
Stefan Cousquer, Matthew Gitsham, Ioan Fazey, Nadine Page

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsYork University
Fundersnot available
KeywordsStrategic planningTime horizonExecutive summaryStrategic managementKey (lock)Time management

Abstract

fetched live from OpenAlex

Ambidextrous organizations need ambidextrous managers, particularly when addressing grand challenges and intertemporal tensions in business sustainability, yet we still know relatively little about the top management team (TMT) temporal practices. The purpose of the research was to understand what is enabling and constraining TMTs balance exploiting and exploring strategic conversations, as a key practice in navigating sustainable pathway enactment. Through in-depth interviews with 11 TMT executives in different organisations, 6 common enablers and constrainers emerged. Executives interviewed wanted to shift on average 29% of their team meetings from ‘exploiting’ to ‘exploring’ strategic conversations and that their exploring conversations were less than effective. We validated these high-level quantitative findings with 150 executives on executive programmes and then returned to the interviewees to both validate initial findings and explore the practical implications for TMTs going forward. Changing TMT temporal practices was perceived as a whole system challenge, requiring a systemic approach to address. A time horizon lens was powerful in illuminating the TMT differences in trust, power, and purpose across the horizons. We then elaborate how our findings contribute to the TMT literatures, and in particular the TMT practice of working with time horizons and intertemporal tensions.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score1.000

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 communication0.0000.003
Open science0.0010.001
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 head0.065
GPT teacher head0.273
Teacher spread0.208 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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