Guidelines for Theory Selection: The IMPACT Framework
Bibliographic record
Abstract
ABSTRACT Though the role of adopting an appropriate theory in shaping the publishability and impact of research has long been recognized, psychology and marketing researchers lack specific guidance on how to choose a theory to frame their work. Addressing this gap in the literature, this article proposes a set of guidelines for the selection of relevant macro‐foundational theory to guide research projects addressing specific meso‐ or micro‐foundational theoretical entities (e.g., constructs or individual decision‐making) for research impact. Specifically, we develop a set of six guidelines to select an appropriate macro‐foundational theory for research impact, including Interestingness , Matching , Parsimony , Applicability , Conceptual rigor , and Testability (collectively abbreviated as the “IMPACT” guidelines). We next depict the proposed guidelines in an organizing framework that specifies Matching , the theoretical co ‐infusion of the studied micro‐ or meso‐foundational theoretical entity and the chosen macro‐foundational theory, as the central action item for researchers to shape the impact of their work. Researchers are then advised to use the five remaining guidelines to assess the quality of their proposed Matching for research impact. We conclude by discussing pertinent implications that arise from our analyses (e.g., to ensure Interestingness , we recommend researchers to select a macro‐foundational theory that refutes (vs. affirms) prior findings).
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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 | 0.432 | 0.692 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.038 | 0.026 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".