A Knowledge Development Perspective on Literature Reviews: Validation of a new Typology in the IS Field
Bibliographic record
Abstract
Literature reviews (LRs) play an important role in developing domain knowledge in all fields. Yet, we observe insufficient insights into the activities with which LRs actually develop knowledge. To address this important gap, we 1) derive knowledge-building activities from the extant literature on LRs, 2) suggest a knowledge-based LR typology that complements existing typologies, and 3) apply the typology in an empirical study that explores how LRs with different goals and methodologies have contributed to knowledge development. In analyzing 240 LRs published in 40 renowned information systems (IS) journals between 2000 and 2014, we draw a detailed picture of knowledge development that one of the most important genres in the IS field has achieved. With this work, we help to unify extant LR conceptualizations by clarifying and illustrating how they apply different methodologies in a range of knowledge-building activities to achieve their goals with respect to theory.
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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 | 0.140 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.064 | 0.052 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.025 | 0.043 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.002 |
| 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".