A Strategic Roadmap for Navigating Academic-Industry Collaborations in Information Systems Research
Bibliographic record
Abstract
Research collaboration between industry and academia remains challenging despite progress being made on a number of fronts. We are not implying that all information systems researchers should be engaging in industry collaborations, nor should the value of critique and work exclusively addressing academic and other audiences be viewed as less valuable than those engaging industry. The intent is instead to encourage more industry collaboration, and for those considering such initiatives to roadmap the activities strategically giving consideration to the full range of activities required so reasonable tradeoffs can be made in the context of building longer-term sustainable relationships. We develop a roadmap from literature that organizes the building blocks (component activities) needed to plan and implement strategic academic-industry collaborations over longer time horizons. Specifically, we identify the key themes or layers of (1) Strategic Business Problem(s), (2) Governance, (3) Funding Criteria, (4) Privacy, Security and Ethics, (5) Intellectual Property, (6) Research Design and (7) Recognized Outputs, which need to be in place if you are to succeed with academic-industry research. The roadmap framework highlights the need to consider the implications of the components across each of the layers at a particular point in time (vertical slice), the relationship between layers (dependencies) and the need for aligning the various activities in a coordinated manner; otherwise success at one layer is often undermined by a lack of awareness or failure at another layer. Our hope is that these themes (layers) facilitate organizing this collaboration in a systematic and coordinated manner to produce academic-industry collaborations that progressively improve over time.
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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 | 0.169 | 0.116 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.029 | 0.041 |
| Scholarly communication | 0.069 | 0.072 |
| Open science | 0.010 | 0.052 |
| Research integrity | 0.028 | 0.032 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".