Evaluating inductive vs deductive research in management studies
Bibliographic record
Abstract
Purpose The purpose of this paper is to address the imbalance between inductive and deductive research in management and organizational studies and to suggest changes in the journal review and publishing process that would help correct the imbalance by encouraging more inductive research. Design/methodology/approach The authors briefly review the ongoing debate about the “developmental” vs “as-is/light-touch” journal review modes, trace the roots of the prevailing developmental review to the hypothetico-deductive research approach, and contrast publishing deductive and inductive research from the perspectives of authors, editors, and reviewers. Findings Application of the same developmental evaluation and review mode to both deductive and inductive research, despite their fundamental differences, discourages inductive research. The authors argue that a light-touch review is more appropriate for inductive research, given its different logic. Practical implications Specific criteria for the light-touch evaluation and review of and some concrete suggestions for facilitating inductive research. Social implications Advancing knowledge requires a better balance of inductive and deductive research, which can be facilitated by light-touch evaluation and review of inductive research. Originality/value Building on the debate on journal publishing, the authors differentiate the evaluation and review of inductive and deductive research based on their philosophical underpinnings and draw implications of pursuing inductive research for authors, editors, and reviewers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.762 | 0.889 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.038 | 0.028 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.007 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".