TMT Temporal Practices – Short on Time, Balancing Exploiting vs Exploring Strategic Conversations.
Bibliographic record
Abstract
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 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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".