Guidelines for Theory Selection: The IMPACT Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it