Digital Interorganizational Collaboration
Bibliographic record
Abstract
How can digital technology enable flexible interorganizational collaborations (IOCs)? This study investigates a challenge facing firms seeking to build highly flexible interfirm relationships to remain competitive in the digital age. It explores how flexible IOCs characterized by changing goals, organizations and organizational actors can leverage digital technology to rapidly generate interorganizational dynamic capabilities (IDCs) in the absence of pre-existing routines. Using multiple case studies of COVID-19 task forces in the US and Canada, we observe how digital generativity derives from a diverse and changing set of digital tools used together to respond to a rapidly changing environment. In doing so, this study extends digital generativity beyond digital platforms into more flexible applications of digital technology. This approach addresses a central problem in the IOC literature: how organizations competing in the digital age can shift their strategic focus from competition to collaboration (Gkeredakis & Constantinides 2019).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".