Strategy and PowerPoint: An Inquiry into the Epistemic Culture and Machinery of Strategy Making
Bibliographic record
Abstract
PowerPoint has come to dominate organizational life in general and strategy making in particular. The technology is lauded by its proponents as a powerful tool for communication and excoriated by its critics as dangerously simplifying. This study takes a deeper look into how PowerPoint is mobilized in strategy making through an ethnographic study inside one organization. It treats PowerPoint as a technology embedded in the discursive practices of strategic knowledge production and suggests that these practices make up the epistemic or knowledge culture of the organization. Conceptualizing culture as composed of practices foregrounds the “machineries” of knowing. Results from a genre analysis of PowerPoint use suggest that it should not be characterized simply as effective or ineffective, as current PowerPoint controversies do. Instead, I show how the affordances of PowerPoint enabled the difficult task of collaborating to negotiate meaning in an uncertain environment, creating spaces for discussion, making recombinations possible, allowing for adjustments as ideas evolved, and providing access to a wide range of actors. These affordances also facilitated cartographic efforts to draw boundaries around the scope of a strategy by certifying certain ideas and allowing document owners to include or exclude certain slides or participants. These discursive practices—collaboration and cartography—are part of the “epistemic machinery” of strategy culture. This analysis demonstrates that strategy making is not only about analysis of industry structure, competitive positioning, or resources, as assumed in content-based strategy research, but it is also about how the production and use of PowerPoint documents that shape these ideas.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".