In This Article Expand or collapse the "in this article" section Reporting Research Findings

  • Introduction
  • Books
  • Reference Resources
  • Journals
  • History and Trends

Management Reporting Research Findings
James T. Austin
  • LAST REVIEWED: 24 June 2020
  • LAST MODIFIED: 24 June 2020
  • DOI: 10.1093/obo/9780199846740-0032


Not all research culminates in publication. This updated article surveys themes in reporting research findings for scholars and students. As context, consider that investigations of organizational phenomena require a series of choices that are cast here as craft. Choices span primary, secondary, and synthesis designs across qualitative and quantitative traditions. Primary research is the traditional design, measurement, and analysis of collected data, while secondary research involves reanalysis of existing data sets (obtained from peers or repositories), and research synthesis involves narrative or quantitative aggregation of studies. This distinction also holds for the qualitative mode. Reporting research findings is important for dissemination and for synthesis and evidence-based management (EBM). Primarily, the importance lies in dissemination across conferences, journals, books, and increasingly digital media. Understanding and replication by outside scholars depend on complete and accurate reporting; this centrality to the research craft commands a learning-development focus. Within a communications paradigm, individuals or teams create or send a persuasive message and the reader or listener receives (or may choose not to receive) the message. Persuasion is targeted via rhetoric across writing and graphics. Although oral and written forms of dissemination dominate, data repositories are emerging. Two additional reasons for importance pertain to the accumulation of knowledge. One is research synthesis. Structuring knowledge through synthesis uses the results of individual studies as data, and the audience is scientists. Narrative and quantitative reviews depend on the completeness and accuracy of reported findings. A related source of importance pertains to evidence-based management at the interface of research and practice—translation of research findings into practices and bundles of practices that can be used by managers. Given that practicing managers appear to rely on obsolete knowledge (aka “fads, fashions, and folderol” as used by Dunnette), proponents of evidence-based management advocate that firms consider the adoption of evidence-based medicine (EBM). Communicating clearly and establishing a context of implementation to assist practitioners are essential for EBM (in parallel to research synthesis, for an audience of practitioners). This article organizes a range of resources on writing and reviewing articles across the taxonomy above. For completeness, this article includes citations for scientific graphics (tables, charts, figures, etc.) organized around conceptualizations of graphics and related guidance, research on perception of scientific graphics, and recent developments in computing technology. Especially relevant are software routines for interactive graphics based on “grammars.” While this article draws on work in management studies (organizational behavior and human resources), it necessarily searches beyond traditional boundaries for relevant insights.


There are sporadic specialized sources on reporting of research findings. On scholarly writing, Cummings and Frost 1995 is an influential analysis of the publishing system in the organizational sciences. Abelson 1995 defines rhetoric as styles of writing up results in psychology. Research synthesis writing is addressed comprehensively in Cooper, et al. 2009 (cited under Guidance on Reporting Quantitative Reports, Syntheses, and Meta-analyses). There are two major standards available for research synthesis: Meta-Analysis Reporting Standards (MARS) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).For graphics and quantitative studies, Tufte 2001 and Tukey 1977 are classics for guidance and perspective; others, including Cleveland 1985, Kosslyn 2006, Wainer 2000 (cited under History and Trends), and Wilkinson 2005, provide unique value. The work on maps in Börner 2015 is aptly named Atlas of Knowledge, while Grant 2019 provides a concise introduction to data visualization with a section on interactive graphics (a related instance is the class of data explorers used for large data sets as the Programme for International Student Assessment [PISA] and the National Assessment of Educational Progress [NAEP]—both large-scale testing programs). Sternberg and Sternberg 2010 is typical guidance offered to students and is not the only such resource. Many of these texts can be mined for dimensions to code the content and results of published organizational behavior and human resources research to facilitate critique A trio of books by Katy Börner (Börner 2010, Börner 2015) and colleagues (Börner and Polley 2014) represents the newest in knowledge mapping. In addition, a rapidly emerging topic across science is the reproducibility and replicability of results—the consensus review published in 2019 by a committee of the National Academies of Science, Medicine, and Engineering provides an excellent overview.

  • Abelson, Robert P. Statistics as Principled Argument. Mahwah, NJ: Lawrence Erlbaum, 1995.

    Describes magnitude-articulation-generality-interestingness-credibility (MAGIC) criteria to organize rhetoric in presenting research findings. Accepting statistics as an organizer of arguments using quantitative evidence allows identification of styles. Brash and stuffy are end points on a liberal-conservative style dimension. Management students and scholars could learn MAGIC for reporting quantitative findings; qualitative researchers might consider translation.

  • Börner, Katy. Atlas of Science: Visualizing What We Know. Cambridge, MA: Massachusetts Institute of Technology Press, 2010.

    Books by Katy Börner show the potential and the practice of science and knowledge mapping. Atlas of Science (2010) presents three themes: power of maps (switching from geographic cartography to research-collaboration mapping), reference systems, and forecasts, as well as numerous examples.

  • Börner, Katy. Atlas of Knowledge: Anyone Can Map. Cambridge, MA: Massachusetts Institute of Technology Press, 2015.

    Börner deftly gives readers principles for visualizing knowledge with more than forty large-scale and over a hundred small-scale color maps. Drives home the point that data literacy is as important as language literacy. She introduces a theoretical framework meant to guide readers through user and task analysis; data preparation, analysis, and visualization; visualization deployment; and the interpretation of science maps. Together with Börner 2010 and Börner and Polley 2014, this trio provides levels of analysis from frameworks to workflow that support improved visualizations of science, knowledge, and interdisciplinary collaboration.

  • Börner, Katy, and David E. Polley. Visual Insights: A Practical Guide to Making Sense of Data. Cambridge, MA: Massachusetts Institute of Technology Press, 2014.

    Along with Börner 2010 and Börner 2015, a practical book by Börner and Polley based on the Information Visualization MOOC includes seven chapters—from a visualization framework through “when, where, what, and with whom” and dynamic visualizations—and concludes with chapters on case studies and discussion/outlook.

  • Cleveland, William S. The Elements of Graphing Data. Monterey, CA: Wadsworth Advanced Books and Software, 1985.

    Cognitive science and statistical principles help dissect and improve graphics (a predecessor book from 1983 and articles that searched prestigious journals for common graphic errors are also useful). Based on extensive experience with AT&T data, the author distills and emphasizes procedural knowledge for constructing graphic displays.

  • Cummings, Larry L., and Peter J. Frost, eds. Publishing in the Organizational Sciences. 2d ed. Foundations of Organizational Science. Thousand Oaks, CA: SAGE, 1995.

    This classic covers most aspects of publishing in organizational behavior and human resources (absent are emergent digital-technological issues). Organized into sections on perspectives on and realities of publishing, which are insightful for scholar and student alike. Benjamin Schneider’s ten propositions on “getting research published” end with practicing the skill of writing. This edition inaugurated the Foundations of Organizational Science series, and the 1985 edition is also useful.

  • Few, Stephen. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics, 2009.

    Suggests that in a data-dense world, the human brain—and hence, visualization—is key to avoiding overload. Three sections, namely “Building Core Skills for Visual Analysis” and “Honing Skills,” each with six chapters plus a “Further Thoughts and Hopes” with eight promising trends, cover much ground. Based on quantitative preferences, the most substantive portion is contained in Part 2. The book ends with an excerpt from the poetry of T. S. Eliot.

  • Grant, Robert. Data Visualization: Charts, Maps and Interactive Graphics. Boca Raton, FL: CRC Press, 2019.

    This author provides a vast range of examples of data visualization, mostly open source and with code available on a website. It provides a good mix of detail with sharing of tacit knowledge.

  • Kosslyn, Stephen M. Graph Design for the Eye and Mind. New York: Oxford University Press, 2006.

    DOI: 10.1093/acprof:oso/9780195311846.001.0001

    Based on sound cognitive science and ample research by the author, presents and elaborates eight principles of effective graph construction (summarized in pp. 5–20). Analyzes prominent guidance on graphics, Edward R. Tufte for example, and suggests flaws. that could lead to productive research.

  • Sternberg, Robert J., and Karin Sternberg The Psychologist’s Companion: A Guide to Writing Scientific Papers for Students and Researchers. 5th ed. Cambridge, UK: Cambridge University Press, 2010.

    DOI: 10.1017/CBO9780511762024

    Aligned to American Psychological Association (APA) style as a prototype of good practice in publishing; the author is a productive researcher and APA journal editor; thus tacit knowledge in this edition is well grounded and expressed. Represents a class of books on research communication. Some translation required to organizational behavior and human resources context. Comparable to Cooper 2010 (cited under Writing Review Articles). Next edition will need to conform to the seventh edition of the Publication Manual of the American Psychological Association.

  • Tufte, Edward R. The Visual Display of Quantitative Information. 2d ed. Cheshire, CT: Graphics Press, 2001.

    Revises a classic 1983 text in analytic design (Tufte’s preferred term); presents and expands on five core principles and coins numerous terms (“chartjunk” as well as “sparkline” and “data-ink ratios” are personal favorites). Critiqued for its advice, however, by other researchers on graphics (Kosslyn 2006).

  • Tukey, John W. Exploratory Data Analysis. Reading, MA: Addison-Wesley, 1977.

    A classic presenting Tukey’s data detective work rooted in his 1962 “The Future of Data Analysis” exposition (Annals of Mathematical Statistics 33.1: 1–67). Premise is that exploratory data analysis (EDA) deserves status with confirmatory. Loaded with philosophy of EDA and tools—the stem leaf, box plot, and “five-number summary.” Graphic display and analysis are covered in the service of learning about data. A part of research craft to be honed post-schooling.

  • Wilkinson, Leland L. The Grammar of Graphics. 2d ed. New York: Springer-Verlag, 2005.

    Cited by many, this conceptualization rooted in the work of Jacques Bertin extends work done with the Task Force on Statistical Reporting in 1999. Within an object-oriented design approach, the grammar consists of the rules and elements of graphics, for example, geoms, scales, and coordinates. Framework has been useful for deriving tools, such as Wilkinson’s GPL, Wickham’s ggplot2, and others.

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