Shaping the future of industrial-organizational psychology: The transformative potential of research collaborations
Bibliographic record
Abstract
Abstract It is important for the research produced by industrial-organizational (I-O) psychologists to be rigorous, relevant, and useful to organizations. However, I-O psychology research is often not used in practice. In this paper, we (both practitioners and academics) argue that engaged scholarship—a particular method of inclusive, collaborative research that incorporates multiple stakeholder perspectives throughout the research process—can help reduce this academic–practice gap and advance the impact of I-O psychology. To examine the current state of the field, we reviewed empirical evidence of the current prevalence of collaborative research by examining the number of articles that contain nonacademic authors across 14 key I-O psychology journals from 2018 to 2023. We then build on these findings by describing how engaged scholarship can be integrated throughout the research process and conclude with a call to action for I-O psychologists to conduct more collaborative research. Overall, our goal is to facilitate a fruitful conversation about the value of collaborative research that incorporates multiple stakeholder perspectives throughout the research process in hopes of reducing the academic–practice gap. We also aim to inspire action in the field to maintain and enhance the impact of I-O psychology on the future world of work.
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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 | 0.161 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.077 |
| Scholarly communication | 0.051 | 0.052 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.008 | 0.011 |
| 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".