Extended dependency network diagrams: adding a strategic dimension
Bibliographic record
Abstract
Purpose Organizations increasingly form or join collaborations to gain access to resources paramount for achieving a sustained competitive advantage. This paper aims to propose an extension to the established dependency network diagram (DND) technique to better facilitate analysis, design and, ultimately, strategic management of such collaborations. Design/methodology/approach Based on the resource dependence theory, the constructs of power and secondary dependency are operationalized and integrated into the original DND technique. New rules and an updated algorithm for how to construct extended DNDs are provided. Findings The value of the proposed extension of the DND technique is illustrated by analysis of an application hosting collaboration case study from the Australian financial service industry. Research limitations/implications This study provides preliminary evidence for strategically managing resource collaborations. Future research could further test empirically the usefulness of the proposed extension of the DND technique and how much it contributes to better understanding resource collaborations. Practical implications The proposed extension of the DND technique enables managers to perform a broader analysis of dependencies among participants in a collaboration, helping them to more accurately comprehend the relationships between the entities in their collaborative environment and, thus, being in a better position of strategically managing resource dependencies. Originality/value The proposed extension of the DND technique makes a central contribution to the extant literature by adding a strategic dimension to a visualization technique used to represent collaborative environments.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".