Leveraging paradigms to foster theoretical contributions in information systems research
Bibliographic record
Abstract
Despite all our theorizing efforts and the importance that we and our major information systems (IS) journals ascribe to theory development, making theoretical contributions to our field remains challenging. Recognizing that we cannot develop better theories without improving how we theorize, our field is now engaged in an in-depth discussion of the theorizing process. This manuscript contributes to this discussion by exposing why and how leveraging paradigms when theorizing can foster theoretical contributions within our field. Its premise is that we need to stop working within the confines of a limited set of well-entrenched paradigms and move beyond what is known as true and correct to come up with improvements that significantly alter the way we come to rationalize, explain, and master our world. Anchored on this premise, this manuscript begins by discussing the origin, role, and features of paradigms as well as explaining that they are of three different but interrelated forms (i.e., metaphysical, sociological, and artefactual). The manuscript then adds to this understanding of paradigms by detailing the unique relationships that tie paradigms of each form to theory and explaining why taking advantage of these unique bonds when theorizing may help us make theoretical contributions. Lastly, to foster theoretical contributions within our field, this manuscript proposes a set of guidelines to help us leverage paradigms of the different forms when theorizing.
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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 | 0.148 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.026 | 0.062 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".