Preparing for Environmental Change — Strategies and Determinants of External and Internal Fit
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.
Bibliographic record
Abstract
Preparing for the future seems to be an intuitive ingredient for a firm’s long-term success, particularly as there is widespread agreement that the level of industry dynamics and uncertainty has been increasing over the last years (D’Aveni 1994; Hamel/Prahalad 1994; Sanchez 1997; Grant 2003). Firms that are not prepared for change run into the risk of not being able to adapt to changes quickly enough to survive, as Thornhill/Amit 2003 show in their analysis of 339 Canadian corporate bankruptcies.1 Yet preparing for future change appears almost paradoxical as it implies preparing for something that cannot be fully understood or measured today. This might be the reason why firms today often prepare for the future simply by improving the efficiency of their current processes, yet don’t spend much time imagining what processes will be required to address future change.2 Besides the difficulties in picturing an industry’s future, firms face another problem: to date, it remains unclear what resources and capabilities a firm needs to be well prepared for change, and in what type of external environment they are most promising.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it