Dynamic Capabilities in Information Systems Research: A Critical Review, Synthesis of Current Knowledge, and Recommendations for Future Research
Bibliographic record
Abstract
Over the past twenty years, the dynamic capabilities view (DCV) has gained prominence in the IS field as a theoretical perspective from which to explain competitive advantage in turbulent environments. While there are quite a few review studies of dynamic capabilities (DCs) in the strategic management domain, research on DCs in the IS area has not been synthesized nor critically analyzed. The result is that the role that IT plays in the DCV remains largely ambiguous, and the way we think and conduct IS research on DCs is unquestioned. Addressing this, we conducted a critical review of DCs in IS research based on 136 papers. Our review provides a synthesis of contemporary knowledge on DCs that emphasizes the role of IT in this research, and a critical analysis of the assumptions underlying this literature. In addition, we develop a minimum DC definition for future research as a solution to the conceptual issues that we uncovered via the critical analysis. We further leverage the remaining findings of our critical review by providing a detailed research agenda for future investigations on DCs by IS scholars.
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 | 0.033 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".