Quantitative studies are increasingly relying on inference to the best explanation (IBE) or modern abduction. I discuss how historical methods—hermeneutics, contextualization, and source criticism—can improve IBE by helping scholars arrive at “best” explanations that are lovely, in the sense that they are useful, general, and provide meaning, and likely, in the sense that they are close to the truth. Further, I discuss how such scholarly work can be presented within the constraints of a typical management journal article. I propose an abductive testimonial structure, termed PEEBI, which consists of five sections in which the authors take prior knowledge and theories, establish the context and observations that are worthy of scholarly interest, identify candidate explanations that may explain the observed patterns, evaluate the candidate explanations, determine the best explanation and their reasoning for accepting it, and abstract the best explanation to a more generalizable theoretical contribution. This structure foregrounds transparency and the author’s judgment, elevating the reader’s role by providing them with the information to make their own informed judgments.

As behavioral scientists studying organizations and their members increasing integrate technology into their research, they are also increasingly conducting interdisciplinary research without realizing it and with limited expertise in the technology domain they are borrowing from. This has created an epidemic of poorly designed, poorly developed, and poorly understood technologies in organizational research studies. The resulting shortcomings, rather than minor methodological concerns, often threaten the fundamental validity and generalizability of those studies. In this talk, we’ll tackle this problem by exploring how and why the assumptions of behavioral organizational science and technology domains differ. Next, given this foundation, we’ll discuss how organizational researchers can build better technologies through modern engineering practices, select better technologies for inclusion in their research, and better work with technology teams in support of their research goals.

Qualitative meta-studies (QMS) are increasingly recognized as a fruitful qualitative methodology in management research. QMS serves as an umbrella term for scientific inquiries that reanalyze and synthesize rich, contextualized qualitative case studies or case material to generate novel theoretical insights and enhance the transferability of qualitative findings. In this lecture, we will explore different approaches to QMS and their epistemological foundations examine the kinds of theoretical and practical insights they can generate, and challenge some of the common myths surrounding this methodology. The session provides a hands-on introduction to QMS and illustrates, through empirical examples, the core methodological choices in QMS as well as the reflective, yet often implicit, meta-practices essential for deriving meaningful results from QMS.

An effective strategy for conducting high-quality qualitative research under academic publication pressures begins with deliberate choices about what data to collect and how to collect it. In this talk, we’ll explore strategic approaches to designing qualitative data collection that enhance analytical potential and methodological rigor. I’ll present practical strategies for context selection, sampling, and design choices that leverage variance and process. We’ll discuss how to design studies for meaningful contrasts and comparisons, and develop research protocols that generate rich, comprehensive data. This session emphasizes the critical front-end decisions that determine what data you have available and how they enable the development of compelling theoretical insights. Participants will gain practical tools for establishing a foundation for logical, persuasive methods sections that demonstrate scholarly rigor.