Theorising forward: positioning deductive elaboration in the Information Systems research repertoire
Bibliographic record
Abstract
Theorising plays a foundational role in Information Systems (IS) research. While the field has made important advances through theory borrowing, via adaptation and instantiation, as well as through contextualisation of established frameworks and models, comparatively little attention has been devoted to the elaboration of existing theories through structured, logic-driven approaches. This commentary problematises that imbalance and advances the concept of deductive theory elaboration as a valuable, yet underutilised, form of theorising in behavioural IS research. We define deductive theory elaboration as a process that extends existing theories by introducing conceptual modifications to their constructs, relationships, or boundary conditions prior to empirical testing. We distinguish this approach from related forms of theorising and propose a four-step framework supported by a repertoire of elaboration patterns for both variance and process theories. We also offer practical reporting guidelines to promote transparency and rigour in elaboration-based contributions. Our aim is to encourage more systematic elaboration efforts to enhance the precision, generalisability, and cumulative potential of IS theories, an optimistic vision of how behavioural IS research can evolve to meet the conceptual challenges of a rapidly transforming digital landscape.
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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 | 0.101 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.014 |
| 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".