Development and Validation of the Workplace Dignity Scale

[We’re pleased to welcome authors Dr. Benjamin Thomas of the University of Nebraska Omaha and Kristen Lucas of the University of Louisville. They recently published an article in Group and Organization Management entitled “Development and Validation of the Workplace Dignity Scale” which is currently free to read for a limited time. Below, they recount the story of how this research came about:]

Although our research and the result—a working measure of workplace dignity—merit discussion, Kristen (my coauthor) and I both tell the story of this project by describing how it started: As a graduate student studying employee motivation, I came across and grew fascinated by the concept of workplace dignity, only to find no real scale existed to measure it! On a bit of a whim and a wish, I sent an introductory email to Kristen—she has written pretty extensively on workplace dignity—asking if she would be interested in collaborating on a project to make that scale. After she agreed, we carried out our entire 4-study research project, data collection through manuscript writing and submission, remotely, exchanging insights completely through email and telephone. When we finally met in person at a conference, the paper had already been accepted!

Technology played a big part in how we collected our data too. Because we wanted to make a scale applicable to multiple work settings, we looked to Amazon Mechanical Turk, or MTurk, to gather responses for our scale validation. In each study using MTurk, we collected more than 450 responses in a number of hours, and were able to retain about 89% of responses after cleaning the data. Many times, I remarked how different this research process would look to an organizational scientist from a few decades ago. I think this kind of research study—authors with a strong, shared interest who meet and work remotely and use innovative data collection methods—will become more normal in coming years, and I would certainly encourage new scholars and researchers to explore digital connections and tools in their own research, especially if they connect with someone with a shared passion.
For us, the passion to advance our understanding of workplace dignity really sustained our research. Dignity is such an essential experience for humans, and work remains a major influence in people’s lives. A lot of previous work has looked at ways dignity can suffer as a result of work, because dignity is often only recognized in its absence, but we also know dignity can be enriched or affirmed by work. In developing a valid way to measure dignity, the good and the bad, we wanted to standardize, inform, and expand the conversations researchers are having on dignity, but also to give specific language to employees and leaders on how work impacts their dignity. The scale offers value to research and applied settings, by offering a standard for workplace dignity and a means of quantifying it, which will not only reveal what experiences harm dignity, but how work fosters and builds dignity for workers.

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How to Measure Shared Leadership?

[We’re pleased to welcome G. James Lemoine of the University at Buffalo–State University of New York, Gamze Koseoglu of the University of Melbourne, Hamed Ghahremani of the University at Buffalo–State University of New York, and Terry C. Blum of Georgia Institute of Technology. They recently published an article in Organizational Research Methods entitled, “Importance-Weighted Density: A Shared Leadership Illustration of the Case for Moving Beyond Density and Decentralization in Particularistic Resource Networks,” which is currently free to read for a limited time. Below, they reflect on the methodology and significance of this research:]


What motivated you to pursue this research?

This research started its life as a second-year PhD student seminar paper, with a completely different research question and design. I was very interested in shared leadership and how different patterns of leadership within a team might affect its outcomes. Over several iterations of that paper, though, I became increasingly dissatisfied with the way shared leadership was measured in the literature. Specifically, I wasn’t convinced that the ways shared leadership had been measured – as an aggregation, or as network density or decentralization – could fully capture it in a way consistent with its conceptual meaning. I shared these concerns with a few co-authors who are far smarter than I am, and we agreed that tackling this measurement issue was potentially more important and interesting than my original research question. Further, over the course of the manuscript’s development and with the help of co-authors and reviewers, I soon realized that these measurement issues aren’t limited to the study of shared leadership. In fact, we feel they’re widespread throughout the organizational literature on team properties with particularistic qualities. There are many other team constructs, like shared mental models and advice networks, where a ‘tie’ from one member to another becomes more valuable when the sender is better connected. For instance, someone receiving lots of advice from others should in turn offer better advice, and if someone views you as a team leader, that’s a more powerful link if that person is him or herself seen as a leader by others (and it’s more consistent with the core idea of ‘sharing’ leadership). When we realized how many streams of research might benefit from a network statistic that specifically accounted for these types of team configurations, we hoped that we might make an impact on the field by proposing a potential solution.

What has been the most challenging aspect of conducting your research? Were there any surprising findings?

In order to build a formula for a network statistic that would solve the issues we encountered, it was necessary to do a ‘deep dive’ into the literature on network methodology: You can’t suggest an additional path forward if you don’t understand where the literature has been. This was at times a difficult challenge for us, as none of us are focused methodologists. Speaking only for myself, many older methods papers can be difficult to decipher for a relative layperson like me (one of the reasons I like ORM is that so many papers are written relatively simply).

We tried to build on that research to generate a new measurement tool that would provide added value, and I can’t tell you how many weeks we spent going over and over our formulae to make sure they were accurate and appropriate. I have a stack of Pizza Hut napkins on my desk right now, covered with scribbled math and algebra (a habit which did not amuse my wife). And finally, after we were confident that we’d got it right, the next challenge was to distill it into a manuscript that everyone, not just network statisticians, could understand. Hopefully we did a good job of this.

What advice would you give to new scholars and incoming researchers in this particular field of study?

Don’t be satisfied with the way research is conducted, just because that’s the way research has always been conducted. Just because an assumption has never been seriously questioned does not mean it should not be questioned. Just because an idea or a theory or a method has been printed in an A-journal, doesn’t make it right. Always approach research from the perspective not of how it’s commonly done, but how it should best be done. Along those lines but more specific to our paper, this means carefully examining how a construct of interest is measured. We proposed the importance-weighted density (IWD) statistic for particularistic resource networks, but we acknowledge that what measure you use really depends on your research question. There are some hypotheses for which density or decentralization would be a better fit than our IWD. As always, conceptualization and theory should drive measurement.

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Business Perceptions of Biodiversity as Social Learning

[We’re pleased to welcome authors Dr. Thomas Smith, Dr. George Holmes, and Dr. Jouni Paavola of the University of Leeds. They recently published an article in Organization & Environment entitled “Social Underpinnings of Ecological Knowledge: Business Perceptions of Biodiversity as Social Learning,” which is currently free to read for a limited time. Below, they reflect on the methods, and contribution of their research:]


Despite mounting concerns regarding the degradation and loss of species, habitats and ecosystems occurring worldwide, biodiversity remains an underexplored issue in corporate sustainability. Increasingly, conservationists, policymakers and organisations such as the WBCSD are focussing on business contributions to tackling biodiversity loss. Yet we know little of how different institutional contexts influence efforts to reduce operational impacts on biodiversity, for instance. It is also unclear how different stakeholders can help – or hinder – reform.

This paper integrates social learning and institutional theory to understand business approaches to controlling impacts on biodiversity. Social learning is often used to examine processes of knowledge transfer and reform in natural resource management, but tends to focus on local communities and public bodies rather than businesses. Combined with institutional theory, social learning demonstrates how social systems shape responses to ecological contexts.

This paper adds to ONE research by demonstrating that to understand business responses to biodiversity, it is vital to focus on interactions between social and ecological systems, rather than each system in isolation. Biodiversity is complex, varying across contexts: successfully conserving it means integrating multiple forms of knowledge and values. Business responses to biodiversity need to be examined across multiple contexts, developed to developing country, tropical to temperate, terrestrial to marine, etc.

Although corporate sustainability scholars must be mindful of social and ecological factors specific to one or another context, this should not prevent us from seeking to identify universal principles underlying best practice. Work on stakeholder engagement and institutional views of the firm applied to other issues in corporate sustainability might be used to inform best practices. There is much left to consider and to research regarding business and biodiversity.

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Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale

[We’re pleased to welcome authors, Jesse Hoey of the University of Waterloo, Tobias Schröder of Potsdam University of Applied Sciences, Jonathan Morgan of Potsdam University of Applied Sciences, Kimberly B. Rogers of Dartmouth College, Deepak Rishi of the University of Waterloo, and Meiyappan Nagappan of the University of Waterloo. They recently published an article in Small Group Research entitled “Spotlight on Methods: Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale,” which is currently free to read for a limited time. Below, They discusses some of the findings of this research:]

SGR_72ppiRGB_powerpointTechnological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal, hierarchical organizations. In this article, we present a novel combination of data-driven and model-based approaches to explore the social and psychological mechanisms motivating these modern self-organized collaborations. We focus on the example of open, collaborative software development in online collaborative networks like GitHub ( The synthesized approach is based in affect control theory (ACT), and a recent framing in Artificial Intelligence known as Bayesian affect control theory (BayesACT). The general assumption of ACT is that humans are motivated in their social interactions by affective alignment: They strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general worldviews as constructed through culturally shared symbols. This alignment is used in BayesACT as a control mechanism to generate artificially intelligent agents that can learn to be functioning members of a social order (see for further information).

We show in this article how such a model solves two basic problems in the social scientific study of groups and teams. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). The ACT-based models we present allow for sophisticated machine learning techniques to be combined in a parsimonious way with validated social-psychological models of group behaviour such that both of these problems are solved in a single computational model.

The purpose of the present article is to discuss the promises of this cross-disciplinary, computational approach to the study of small group dynamics. We review computational methods for using large amounts of social media data, and connect these methods to theoretically informed models of human behaviour in groups. To use a metaphor, we are digging into digital group dynamics data with a sophisticated, artificially intelligent shovel, and showing how computational social science can be taken to a new level with this unique and novel combination of data-driven and model-based approaches. The work is an international collaboration called THEMIS.COG ( between researchers in Canada (University of Waterloo), the USA (Dartmouth College), and Germany (Potsdam University of Applied Sciences).

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ORM Best Paper Awards

orma_21_3_coverWe are excited to congratulate the following authors for winning the Organizational Research Methods 2017 Best Paper Award. This year two papers tied for Best Paper! Below are the abstracts of each article. Please note that the full articles will be free to read for a limited time.

Congratulations Jose M. Cortina of George Mason University, Jennifer Green of George Mason University, Kathleen Keeler of George Mason University, and Robert J. Vandenberg of the University of Georgia.

Below is the abstract from the award winning article, Degrees of Freedom in SEM: Are We Testing the Models That We Claim to Test? in which their research processes and findings are briefly introduced.

800px-6dof_en.jpgStructural equation modeling (SEM) has been a staple of the organizational sciences for decades. It is common to report degrees of freedom (df) for tested models, and it should be possible for a reader to recreate df for any model in a published paper. We reviewed 784 models from 75 papers published in top journals in order to understand df-related reporting practices and discover how often reported df matched those that we computed based on the information given in the papers. Among other things, we found that both df and the information necessary to compute them were available about three-quarters of the time. We also found that computed df matched reported df only 62% of the time. Discrepancies were particularly common in structural (as opposed to measurement) models and were often large in magnitude. This means that the models for which fit indices are offered are often different from those described in published papers. Finally, we offer an online tool for computing df and recommendations, the Degrees of Freedom Reporting Standards (DFRS), for authors, reviewers, and editors.

Congratulations Thomas Roulet of King’s College London, Michael Gill of the University of Bath, Sebsatien Stenger of the Institut Superieur de Gestion, Paris, and David Gill of the University of Nottingham.

You can find the abstract from their outstanding article, Reconsidering the Value of Covert Research: The Role of Ambiguous Consent in Participant Observation below, in which the authors briefly explain their research methods and introduce their interesting results.people-295145_960_720

In this article, we provide a nuanced perspective on the benefits and costs of covert research. In particular, we illustrate the value of such an approach by focusing on covert participant observation. We posit that all observational studies sit along a continuum of consent, with few research projects being either fully overt or fully covert due to practical constraints and the ambiguous nature of consent itself. With reference to illustrative examples, we demonstrate that the study of deviant behaviors, secretive organizations and socially important topics is often only possible through substantially covert participant observation. To support further consideration of this method, we discuss different ethical perspectives and explore techniques to address the practical challenges of covert participant observation, including; gaining access, collecting data surreptitiously, reducing harm to participants, leaving the site of study and addressing ethical issues.

Thank you for your hard work and dedication!

Meet the ORM editorial team! Click here to view their bios.

Degrees of Freedom Photo attributed to Free Photos.

Observation Photo attributed to Free Photos.

fMRI and Organizational Psychologists

[We’re pleased to welcome Ewa A. Miendlarzewska of the University of Geneva, Michael Kometer of ZZ Vermogensberatung and Kerstin Preuschoff of the University of Geneva. They recently published an article in Organizational Research Methods entitled, “Neurofinance,” which is currently free to read for a limited time. Below, they reflect on the methodology and significance of this research:]


Decision-making is a ubiquitous feature of human behavior and a topic of study found in many different disciplines. In the comparatively new field of neuroeconomics, theoretical models from economics and psychology are combined with measures of brain activity to explore decision-making. For instance, neuroscience has established the role of the neurotransmitter dopamine in reward processing. Within neuroeconomics, behavioral models of utility have been applied to neural data from dopaminergic regions, so as to examine said models’ validity and violations. The result is that, in neuroeconomics, neuroscientific tools bring us a step closer to observing hidden, internal states.

Neuroeconomics has embraced the use of non-invasive neuroimaging methods such as (functional) magnetic resonance imaging ((f)MRI). MRI was originally used in medicine where – to this day – it serves as a powerful, non-invasive diagnostic means of looking into the body, including the brain. It has since evolved into an attractive tool for non-clinical behavioral scientists. Changes in neural signals observed can reflect differences in, for instance, emotional states, and can also provide a neural basis for individual behavioral differences. These insights may supply the organizational psychologist with an understanding of human behavior grounded in physiology, which in turn may better inform an organizational setting.

In this paper, we provide a primer on the use of fMRI in organizational research. First, we consider that the method presents a significant barrier to entry; and second, we drafted this paper in a spirit of inclusion, to encourage researchers unfamiliar with fMRI to overcome a first hurdle in understanding the method. Guided by those two premises, we endeavored to write a cogent introduction aimed at organizational psychologists.

While we strove to write a concise paper on fMRI methods, we also address the caveats fMRI presents. In recent years, the method’s statistical analyses have come under fire, which has induced skepticism towards fMRI in lay-people and researchers alike. In our view, public scrutiny of fMRI has been a welcome development in the field as it has led to a refinement of data analyses methods. In our paper, we explicitly address these potential pitfalls in study design and analysis and offer solutions to mitigate them.

Challenges notwithstanding, fMRI has increased in popularity with good reason. The method provides a non-invasive means by which to glimpse the inner workings of the brain in response to its environment. For a behavioral researcher, whose putative prime mover is the brain, fMRI can edify, or indeed edit, theoretical models of decision-making and social cognition. And with that, our hope is that this paper can be a useful first contact with the necessary knowledge to envisage an fMRI study.

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How Should Paradox Be Studied?

[We’re pleased to welcome Dr. Gail T. Fairhurst of  the University of Cincinnati and Linda L. Putnam of the University of California, Santa Barbara. They recently published an article in Organizational Research Methods entitled, “An Integrative Methodology for Organizational Oppositions: Aligning Grounded Theory and Discourse Analysis,” which is currently free to read for a limited time. Below, Dr. Fairhurst reflects on the methodology and significance of this research:]


Does the study of organizational paradox require its own unique methods?

As scholarship on paradox weaves itself ever more strongly into the fabric of the organizational sciences, we take the unusual position in our article that the answer is “yes.” Grounded theory methods have certainly done yeoman’s work in explaining this concept but, like all methods, it has its limitations. There is also a complexity to paradox due to its embeddedness in the daily actions and interactions of organizational life that are hard to capture. This complexity may explain the rampant definitional confusion in the literature over such related concepts as tensions, contradictions, and dialectics. It may explain the relative lack of attention to power dynamics in paradox research and the underutilized data from exhaustive interview or mixed method studies that could tell us something more about the origins of paradox and how it organizes life in organizations.

Our article offers paradox researchers a more refined method in the hopes of addressing some of these concerns. We propose an integrative methodology for studying paradox (and related oppositional phenomena) by aligning grounded theory techniques with the little “d” and big “D” orientations of organizational discourse analysis. This integrative methodology not only aids in identifying and determining various types of organizational oppositions and responses to them, but also fosters assessment of their potential power effects and micro organizing dynamics.

We should hasten to add that we provide an extended example explaining our methodology for the adventurous paradox researcher wishing to give it a try. We also conclude with a discussion of some possible new directions for using this approach, including the study of disorder and disequilibrium in organizations—and moving beyond just the study of paradox. We believe that grounded theory and organizational discourse analysis have some natural compatibilities that could serve other research areas as well. We very much hope to inspire paradox researchers to give this new methodology a try!

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