IT Capabilities and Strategic Improvisation: A Multi-Method Investigation
Bibliographic record
Abstract
Start-up organizations are disrupting previously stable industries with new business models. Existing organizations in these environments need to strategically improvise, that is develop the ability to rapidly seize opportunities, and without prior planning, creatively reconfigure their operational capabilities. As a dynamic capability, strategic improvisation (SI) requires three factors: real-time information, instantaneous communication and memory. We proposed that a well-developed information management capability (IMC), IT infrastructure flexibility (ITIF), and organizational memory (OM) facilitate SI. We analyzed results from a telephone survey of IT executives using two methods, variancebased PLS and set-theoretic qualitative comparative analysis (QCA). These methods provide a more complete understanding of the complex relationships among SI and IT capabilities such as IMC and ITIF. PLS findings confirm their enabling roles. QCA findings further indicate that these IT capabilities and OM play different, complementary roles in SI. Implications for research and practice are presented.
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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 | 0.077 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".