Matt Ege, Texas A&M University

Kim Westermann, California Polytechnic University

Mike Wilkins, University of Kansas

In recent years, many researchers have begun broadening their methodological expertise and/or collaborating with researchers with different methodological expertise. In response, AJPT is issuing a Call for Mixed Methods Papers. The purpose of the Call is to provide a forum for research that blends and draws inferences from different methodological approaches.

Studies should use at least two different research methods to gain a more comprehensive answer to the research question(s) being investigated than can be achieved by a single method alone. Studies should give relatively equal weighting to a discussion of the research design choices for each method, and analyses from each method should make significant contributions to the paper. Papers that only present descriptive statistics from a survey or descriptive quotes from a few interviews that are then used to motivate archival analyses would not be considered mixed methods papers under this Call.

Studies using method(s) incorporating human participants must obtain Institutional Review Board (IRB) approval.

Papers will be published in a Special Section as they are accepted, coordinating the timing of acceptances and space availability in individual journal issues.

Potential topics

• Studies using at least two different research methods (e.g., archival, analytical, experimental, interviews, surveys, other) to gain comprehensive answers to research questions
• Research that blends and draws inferences from different methodological approaches
• Studies giving relatively equal weighting to discussion of research design choices for each method
• Analyses from each method making significant contributions to the paper

Timeline

• September 30, 2026: Deadline for submissions

Christopher Koch, Johannes Gutenberg University Mainz

Quinn Swanquist, University of Alabama

Mike Wilkins, University of Kansas

Recognizing the critical role of strong research methods in producing high-quality scholarship, AJPT is issuing a Call for Methodological Papers. The purpose of the Call is to provide a forum for studies that introduce new methods or present recommendations regarding existing methods that are applicable to auditing research.

This Call is research-focused and does not extend to papers investigating new methods or recommendations related to audit procedures or audit methodologies. Such studies are welcome at AJPT but are outside the scope of this call.

Papers will be published in a Special Section as they are accepted while coordinating the timing of acceptances and space availability in individual journal issues. Submissions will be screened and assigned to handling editors by the editorial team. Handling editors will then assign submissions to two reviewers, consistent with AJPT's standard review process.

Potential topics

• New or improved analytical or statistical techniques applied to archival, experimental, qualitative, or other data
• Evaluation of existing statistical techniques and recommendations for best practices
• Evaluation of research design, modeling, or variable selection and recommendations for best practices
• New or improved techniques related to data collection or variable construction
• Established but underutilized experimental or qualitative research designs relevant for auditing research
• Guidance on effective manipulation of independent variables and measurement of dependent variables in experimental studies
• Guidance on participant selection and recruitment for experimental and qualitative papers

Timeline

• September 30, 2026: Deadline for submissions

Lori Bhaskar, Indiana University

Tamara Lambert, The University of Manchester

There is a growing concern among academics that publication bias negatively affects our understanding of the phenomena studied. In auditing research, there is also concern that scarce experimental participant resources may be consumed inefficiently by continuing to test theories or research questions that have failed to be supported by data because other researchers are unaware of the respective designs and outcomes.

The Call seeks to counteract distorted inferences that might occur as a result of publication bias. Submissions could demonstrate that a potentially important result in the experimental literature is not reliably replicated or document and explore conditions under which tests that are well-supported by theory deliver results that are unexpected or not statistically significant. Publishing these results will inform the research community about the conditions under which these theories have been tested so that future research can take these designs and findings into account.

The Call aims to help auditing researchers develop a toolbox for designing, analyzing, and reporting experiments in which unexpected or non-significant results nonetheless have inferential value. Submissions could highlight methods for ensuring that a test is adequately powered, constructs are valid, and the design has sufficient internal validity so that a failure to reject the null hypothesis provides reliable information. Submissions should also develop guidelines for inference when results are not statistically significant.

Given the Call's focus on unexpected or non-significant results, submissions must address relevant topics and demonstrate a meaningful contribution to the literature. Editors will pay particular attention to issues such as the theoretical basis for the hypotheses, power and construct validity, and internal validity of the design.

Papers will be published in a Special Section of the journal as they are accepted, coordinating the timing of acceptances and space availability in individual journal issues. Submissions will be screened and assigned to handling editors, who will then assign submissions to two reviewers, consistent with AJPT's standard review process.

Potential topics

• Publication bias and experimental design in auditing research
• Replication of experimental findings and documentation of non-significant results
• Conditions under which theoretically-supported hypotheses yield unexpected or non-statistically significant outcomes
• Methods for ensuring adequate statistical power, construct validity, and internal validity in experiments
• Guidelines for inference and interpretation when results are not statistically significant
• Efficient use of experimental participant resources and research design
• Development of best practices for designing, analyzing, and reporting experiments with non-significant results

Timeline

• August 31, 2027: Deadline for submissions

Clotilde Coron, Université Paris-Saclay

Data is increasingly recognized as a crucial resource for organizational innovation, with extensive and organized data exploitation considered a key driver of the ongoing fourth industrial revolution. HRM is not exempt from this trend, and the methodological challenges of leveraging increasingly diverse and massive datasets present significant stakes for both practitioners and academic researchers.

Recent reflections on applicable methods for extracting value from data often emphasize the disruptive potential of big data, presenting it as the foundation for a renewal of empiricism based on intensive exploration of data masses through exploratory methods, aimed at generating new knowledge through purely inductive logic. However, inductive, data-driven approaches remain rare in quantitative HRM studies, despite their capacity to transcend the classical dichotomy between qualitative/inductive and quantitative/deductive approaches in management sciences. Data-driven methodologies also help address criticisms leveled at hypothetico-deductive approaches, such as their strong standardization and difficulty in generating truly innovative theories.

This special issue aims to provide an updated overview of the challenges and opportunities of data-driven HRM, as well as inductive quantitative methods applicable in empirical research. Expected contributions may address these themes from methodological perspectives (such as presenting an innovative method with illustration in HRM), empirical perspectives (conducting a quantitative study on a subject by adopting an inductive approach), or conceptual perspectives (analyzing the stakes for HRM of data-driven management). Proposals presenting emerging or underutilized methods in francophone quantitative HRM studies will be particularly appreciated, including supervised learning, data mining, textual statistics, and longitudinal approaches.

Potential topics

• Data-driven approaches in HRM
• Inductive quantitative research methods
• Big data exploitation and analysis
• Supervised learning applications in HRM
• Data mining techniques
• Textual statistics
• Longitudinal approaches
• Innovative quantitative methods in HRM research
• Theory building from data
• Emerging methodologies in francophone HRM studies

Timeline

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• March 25, 2023: Submission deadline for articles

This special issue seeks to explore how Large Language Models (LLMs) are transforming methodological approaches in technology management research. We invite submissions that demonstrate innovative applications of LLMs as research tools, methodological enhancers, and theoretical instruments for advancing our understanding of technology management, innovation, and organizational change.

Contributions should address both the opportunities and challenges of incorporating LLMs into research practice, including questions of validity, reliability, ethical considerations, and the development of new theoretical frameworks that account for LLM capabilities and limitations in research contexts.

Potential topics

• Applications of LLMs in technology management research
• LLMs for data analysis and pattern recognition in technology studies
• Novel methodological approaches enabled by LLMs
• Theoretical frameworks for understanding LLM capabilities and limitations in research contexts
• LLMs for literature review and synthesis
• LLM-assisted qualitative and quantitative research methods
• Validation and reliability of LLM-based research findings
• Ethical considerations in using LLMs for academic research
• LLMs for forecasting technological trends
• Integration of LLMs with traditional research methodologies

Timeline

• January 1, 2027: 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