As organizational scientists, we should be striving to produce useful and replicable research. Recently, how we conduct our research has drawn critique. From our relative disuse of inductive research methods, to unethical practices during the review process, to publication bias, to HARKING, to P-hacking, and so on, we must take meaningful steps so that we and the consumers of our research have confidence that our findings are meaningful, replicable, and honest.

Journal of Business and Psychology will be launching a special initiative — results-masked review submission option. In this alternative path, authors submit the intro, methods, measurement info, and analysis plan of a completed study (no results or discussion in the first round). This abbreviated paper then undergoes peer review and is evaluated on the merits, rigor, and quality of the project rather than what was actually found.

The goal is to encourage authors to propose conceptually sound, interesting, and methodologically rigorous research without concern for whether the results will be statistically significant. Instead, we want the focus to be on the importance of the research question and the rigor of the research design. We should welcome the results from sound research no matter if they support proposed hypotheses, yield null results, or replicate (or fail to replicate) previous work. Simply speaking, well conceived, designed, and conducted research should form the corpus of knowledge.

The results-masked review approach is appropriate for inductive, deductive, mixed methods, and papers involving multiple studies. In the case of the latter type, the author is encouraged to contact the editor to decide on the best approach for submission. There are multiple options as the results-masked review approach is highly flexible. At times, it may make sense to include the first study with results, and then the follow-up studies without results. Other times it is useful to include all studies without results. Overall, an initial conversation with the editor can clarify an approach that makes sense for the project in question. We also welcome traditional registered reports submissions following the same process.

Potential topics

• Inductive and deductive research methods
• Replicable and rigorous research design
• Publication bias and research integrity
• Null results and hypothesis-disconfirming findings
• Mixed methods research
• Multi-study research designs
• Research question importance and methodological rigor
• Registered reports

Timeline

• Invalid DateTime: Submission deadline

Claudine Gartenberg, .pop

Sharique Hasan, .pop

Lamar Pierce, .pop

Christopher Bail, .pop

Hengchen Dai, .pop

Oliver Hauser, .pop

Hatim Rahman, .pop

Dennis Zhang, .pop

This special issue asks a fundamental question about artificial intelligence and social science: do we want it to produce faster, cheaper versions of what we already do, or do we want fundamentally new science? Returning to Organization Science's founding mission—Daft and Lewin's 1990 call to break out of the "normal science straitjacket" and March's "exploration of new possibilities"—we want to shift our focus to how AI is changing the production of science and how it can expand our knowledge, rather than merely increasing the number of papers through efficiency and reduced labor.

In this call for science, we seek contributions that reimagine what a social science research contribution is in an AI-enabled world, encouraging wild ideas and radical innovation over obvious incremental improvement. We are not looking for conventional full-length papers with AI-related content, nor "AI slop"—we want the innovative applications themselves.

The issue follows a three-stage process—a research proposal and prototype, a collaborative development phase with an in-person workshop, and finalization—culminating in short Science/Nature-style articles and shorter "letters," all treated as true peer-reviewed contributions. We welcome submissions from scholars across the social sciences and adjacent fields, so long as they address organizational or managerial implications, broadly interpreted.

Potential topics

• AI-enabled research loops under human direction
• Reusable research infrastructure
• New forms of measurement
• AI-enabled qualitative and theory-building work
• Synthetic social systems
• New approaches to established research designs
• Critical or boundary-setting work on the limits of AI-enabled science
• Reimagining social science research contributions in an AI-enabled world

Timeline

• October 1, 2026: Submissions Open
• November 1, 2026: Submissions Close