Briscoe & Murphy (2012). Sleight of Hand? Practice Opacity, Third-party Responses, and the Interorganizational Diffusion of Controversial Practices

Author:             Forrest Briscoe (
                         Chad Murphy (
Interviewers:     Ivana Naumovska (
                         Johan Chu (
Article link:

Question 1. You investigate the diffusion of controversial innovations–reductions in retiree benefits in Fortune 500 companies–and find that the adoption of easy-to-understand transparent practices (i.e., benefit cuts) reduces subsequent diffusion, while opaque practices (i.e., spending caps) increase diffusion. This is an interesting result! What happens, though, as opaque innovations increase in prevalence, perhaps attracting more attention and becoming better understood (less opaque)? Are there negative consequences for the further diffusion of these “opaque” practices? Do you see any strategies used to keep diffusing practices opaque?

Thanks! You raise an important issue for thinking about opaque practices: if an increase in their prevalence leads to more public attention placed on them, then the resulting scrutiny and resistance should indeed put the brakes on diffusion. And yet, we think that under certain conditions, practices can increase in prevalence *without* attracting more attention (in other words, without becoming more transparent). How is that possible? Two features seem critical, both having to do with the role of professional experts:

a) Causal ambiguity. First and foremost, professional experts construct the innovation in a way that (intentionally or not) makes it hard to identify it as a cause of subsequent events. For the cap innovation we studied, it’s not that the innovation was “hidden,” but the cap’s design made it hard to claim that accelerating cost increases born by workers and retirees were caused by the cap. Because of the cap’s inherent design, if you try to explain this connection, you’d have to use statistical and/or probabilistic concepts, and accounting and/or actuarial logic — and you’re quite likely to lose your audience by the time you’re done!

b) Professional networks. The second key feature is the structure of the professional community. When the professional community that develops a controversial innovation is structured so that it crisscrosses the corporate world, this provides a conduit for the innovation to spread across corporate “interiors,” without gaining much visibility on corporate “exteriors.” For example, nearly all large U.S. corporations have relationships with the benefits consultants who promoted the cap innovation we studied—and many firms also employ internal staff who are similarly trained. These agents-of-diffusion can understand the implications and consequences of the innovation, enabling them to make the internal “business case” for adoption… but at the same time, that understanding of consequences need not extend beyond firm decision makers to members of the affected groups (be they employees, retirees, or other kinds of stakeholders).

How often does this situation arise for other types of controversial innovations in the corporate world, beyond the case we studied? We suspect these two features we identified are actually widespread, and can be applied to the wider ecology of professional services swirling around modern firms. Lawyers invent corporate takeover defenses (e.g. poison pills), accountants invent tax shelters (e.g. corporate inversions), PR specialists invent reputation-repair strategies (e.g. astroturfing), political consultants invent congressional-influence pathways, HR consultants invent employee monitoring systems… in all these domains, we think there are examples of innovations where the “interior” understanding from within the expert community includes consequences that are functionally opaque to the negatively affected parties. That sets the stage for the diffusion of a potentially controversial practice, minus the attention or contention..

Question 2. Your data show that adoption of benefit cuts increased the incidence of interest group protests and negative news reports. You suggest that this was because transparent practices, which are easily described, are susceptible to cogent negative perception by adversely-affected interest groups and the media. Also, you manage to convey two antecedents of opaqueness (p. 557-558): (1) complexity in design and implementation, and (2) complexity in language when announcing or explaining the practice (i.e., framing). The former being related to substance, whereas the latter mechanism would be more in line with established theories of symbolic management and decoupling, i.e., when legitimacy is attained through symbolic actions that disguise non-conformity (e.g., Meyer and Rowan, 1977). Could it be that controversial opaque practices enable adoption by being ambiguous enough to allow companies to deploy their own and distinct framing strategies? Do you observe that in your data? (You mention, for example, that a retiree interest group allowed their company’s Benefits Office to explain the new policy to them.)

Great point! Each firm’s customization can contribute to an innovation’s practice opacity on all four of the dimensions we identified in Table 1 (“What is being done?” “To what degree is it being done?” “When is it being done?” and “Who is doing it?”). Greater variation across firms on those dimensions would logically make it harder for audiences to see the big picture—that is, to perceive the innovation’s overall consequences at the field level. That, in turn, would make it less likely that the innovation would spark higher-level opposition from policymakers, journalists, NGOs or activists.

Importantly, though, we are struck by how much the “upstream” design of the innovation matters, relative to the “downstream” framing processes that have dominated a lot of our literature to date. When practices have opacity baked into their design, the consequences are difficult to register no matter how you frame, package, or otherwise explain them. This suggests that invented practices can have essential qualities that shape the extent to which one can communicate about their impacts effectively. In particular, practices that are invented within the abstract theoretical domain of a particular area of professional expertise can have qualities that are readily understood through the architecture of that expertise–but which are effectively opaque through the everyday cognitive structures of lay audiences. Think of the mortgage innovations that spread across banks in the lead-up to the financial crisis, or the user agreements adopted by Apple, Facebook and other web companies. No matter how they are “framed,” such practices may remain unclear for all but the most indoctrinated.

Question 3. In your empirical setting, members of a retiree interest group expressed frustration with the large and confusing premium increases coming from the opaque spending caps (p.559). However your overall results suggest that possible frustrations about opaqueness are less consequential compared to grievances about clear cause-effect relationships. Is there then a level of “optimal opaqueness” – just opaque enough to blur potential negative consequences, yet not too opaque as to lead to vagueness-related contestation? Or is more opaqueness (to possible antagonists of the innovation) always better?   

It is interesting to wonder if vagueness itself could serve as a basis for contestation–perhaps with oppositional sentiments and collective action roused under the rallying cry, “End the Opacity!”

On the one hand, vagueness-related contestation seems quite plausible, as when powerful actors have been caught deliberately obscuring the consequences of the innovation they are adopting or endorsing. The term “obfuscation” comes to mind — just the kind of thing that would generate lots of negative press for adopting companies and therefore cool diffusion. Indeed, some of the retiree interest-groups leaders we interviewed held the view, and some journalists covering retiree benefits cuts conveyed this message as well. Beyond our study, recall objections to financial instrument complexity during the Occupy Wall Street movement, and earlier during the Enron scandal.

And yet, in our study, it did not really seem that vagueness or confusion was *itself* an important foundation for organizing resistance. Perhaps grievances based on vagueness or complexity have a hard time gaining traction with audiences because such grievances are themselves difficult to communicate succinctly. Or perhaps would-be activists are inhibited by their own embarrassment at not being able to understand the complex practices. Or perhaps it’s the barrier of proving that the opacity was deliberately concocted to deceive, and not just an incidental feature of the innovation. This would be an interesting topic for future research.

Of course, the point we raised earlier is critical here as well: a practice can be opaque on the “exterior” of the corporate landscape but transparent on the “interior” – implying that optimal opaqueness isn’t only a question of overall perception, but also a question of opaqueness *to whom*!

Question 4. Your data shed light on important questions of how the labor market changed in the United States, and what drove that change. Do you plan more studies with the data set? What other intriguing directions have the data suggested? 

How can we understanding the changing U.S. employment relationship and labor market overall? This is a major challenge, a “big hairy problem” that a growing number of scholars from management, economics, and sociology are all engaged in, in various ways.

For us, the Sleight of Hand study reinforced a growing suspicion that we need to approach these changes using a political lens. In particular, we need to apply a political lens to the firm’s stakeholders, defined as the constellation of actors that can affect or be affected by the firm’s decisions (investors, customers, suppliers, regulators, creditors, employees and unions, retirees, community groups and NGOs, and so on). For example, the corporate retirees who were negatively affected by the benefits reductions we studied are a weak stakeholder group, lacking in resources, power and organization vis-à-vis their former employers. In contrast, shareholders and executives, who had much to gain from the reduction in liabilities and increase in cash flow and operational flexibility, are well-resourced and powerful stakeholder groups.

All these major stakeholders are vying to influence the nature of changes in the employment relationship, through pressures they exert, directly or indirectly, on decision makers in the firm. So while it may be a truism that employment practices (like retiree benefits) will inevitably change, the nature of those changes is not pre-determined. The changes will create winners and losers among the stakeholders, depending on how they are designed. The process at its core thus involves stakeholders angling for influence, trying to expand (or at least preserve) their rewards as changes are implemented. We see this as another ripe area for future research.

Question 5. What question did we miss? Please ask yourselves a good question, and answer it.

Briscoe and Murphy (interviewers): Contentious diffusion is a cool topic, but there sure have been a lot of inter-organizational diffusion studies published already! After writing this paper, what future angles do you think are left for researchers to make a fresh contribution in this area?

Briscoe and Murphy (interviewees): Wow, good question. First off, we need studies that figure out how to incorporate a realistic political model of stakeholder pressure on firms (like the one we just described above shaping corporate employment practices) with a diffusion framework. The task is difficult, because stakeholders are a diverse group and they operate at multiple levels to influence firms, industries, and fields. On the other hand, data and analysis tools for this purpose are becoming ever easier to come by. And a toolkit of mechanisms may be emerging among scholars sitting at the intersection of social movements, stakeholder theory, and institutional analysis.

Second, we need work on how the *characteristics* of practices affect inter-organizational diffusion. Scholars need to dimensionalize and then measure these characteristics. Our findings concerning opacity may be useful by suggesting how a practice’s characteristics shape perceptions of its consequences, thereby affecting reactions to it. Future scholars could also integrate more insights from the psychology of diffusion (see “Switch” and “Stick”). But the key empirical need here is to find research settings where variations in practice characteristics can actually be observed and compared in their impacts on diffusion patterns.

Finally, we see great opportunities to understand the role of professional experts in constructing and spreading innovations across organizations. In particular, what’s the process by which professionals design and implement innovations that are externally opaque? In a sense, it’s the mirror image of occupational boundary spanning: instead of creating artifacts that help people communicate and understand one another across boundaries, these are innovations that erect barriers between professional and lay audiences. Take the financial statement footnotes we studied, which are often constructed in ways that disguise the causal agent of change (see example on page 568 in our paper). Researching this innovation-crafting process would probably require real-time access to the practitioners involved in it.

Pontikes (2012). Two Sides of the Same Coin: How Ambiguous Classification Affects Multiple Audiences’ Evaluations

Author:             Elizabeth G. Pontikes (
Interviewers:    Stefan Beljean (
                         Jensen Sass (
Article link:

Question 1. From your faculty profile on the University of Chicago website we learn that you used to be an insider of the software industry. You were in charge of the “sales strategies and competitive positioning” of the company you worked for. To what extent has your study been inspired (and guided) by your first-hand experience as a market-participant?

My work in the software industry has had a large influence on this study, and my work on categorization in general. In developing competitive positioning for both start-ups and mid-sized companies, issues of categorization were critical. We thought a lot about how to use market categories to convey what our organization did and why it would be useful. This is especially important in the sales process – just to get an initial meeting in order to be able to make a more detailed pitch.

Question 2. In your paper, you focus on four hypotheses regarding the relationship of classifications and audience evaluations. When you began your research, did you have other hypotheses in mind that are now not part of the paper?

I did not have other hypotheses when I started this paper. But, I did discover some surprising results. The effects of category spanning were not what I predicted. The overall effect of category spanning was positive for the customer audience (the opposite of what I predicted), and there was no effect for the venture capital audience. In thinking about this result, I realized that effects of category spanning depend on categories having clear boundaries. If we think about the theory behind what drives penalties to category spanning, it results from the fact that the categories spanned are well-bounded entities. Ruef and Patterson (2009) illustrate this point. When I separated organizations that span unambiguous categories from those that span ambiguous categories, the surprising effects resulted from those spanning ambiguous categories.

Similarly, I had not initially hypothesized about differences between Corporate Venture Capitalists (CVC) and Private Equity Venture Capitalists in terms of how they evaluated ambiguous categories. This idea came from a reviewer, who asked whether all Venture capitalists would have the same reaction. I dug into the literature on corporate venture capitalists, and realized that CVCs are very different in terms of how they approach their investments. Thinking about the theoretical mechanisms that underlie the hypotheses, I predicted that they would act more like consumers, and would not have a preference for ambiguity. – Both of these examples illustrate how sometimes it is the results that you don’t expect that lead to some of the most interesting parts of a study.

Question 3. Your key contribution to the literature on categories and valuation is that you introduce a much needed distinction between different types of audiences. In your analysis you then focus on two broad groups of actors: market-makers and market-takers. Two related questions on that: A) How did you decide to focus on these particular two groups and not on others (e.g. shareholders, market analysts etc.)? B) You acknowledge that within the category of market-makers, we have to distinguish between independent and corporate venture capitalists. What other within-category variations do you think might potentially matter, e.g. within the category of consumers?  

A) Venture capitalists and customers were good audiences to test the theoretical ideas presented in the paper. Venture capitalists are a key audience for software organizations, and they are traditional “market makers” who are interested in developing novel types of classification. Customers are at the other end of the spectrum, “market takers” who use classification to understand organizations and their products. In addition, looking at shareholders or stock market analysts would have excluded young, private firms that drive much of the innovation in this industry.

B) There are a number of distinctions within the customer audience that could affect preferences for ambiguity. Drawing on the theoretical reasoning, customers who are very engaged with the structure of the industry, and who enjoy trying out new types of things that cross market boundaries, will act like market makers and have positive reactions to ambiguity. I would expect that this would be the case for lead users or other early adopters of technology. Unfortunately, data on customer evaluations was too coarse to be able to test reactions of different customer segments.

Question 4. What (if anything) do you think might condition the scope of your argument? Could there be variation across different kinds of markets? For example, might venture capitalists evaluate software start-ups differently to, say, those in pharmaceuticals, bulk chemicals, or luxury goods? Further, could there be historical variation in their evaluations due to changes in the prevalence of different business models? Between the 1960s and 1980s, for example, we saw a profound shift from the model of the ‘diversified’ firm to that of the ‘focused’ firm. Perhaps now – as with Google and Microsoft – we are seeing the return of more highly diversified enterprises…

The argument is predicated on the orientation a person has to market classification. Audiences who are “market makers” seek novelty, and as a result are interested in engaging with, and changing, existing classification. Those that are “market takers” use classification to understand a complex environment. This means that if, in a different context, venture capitalists are not “market makers,” they will be less receptive to ambiguity. In industries that are less fast-paced, and where innovation is less important, venture capitalists may not be as interested in creating new markets and so would evaluate start-ups differently. It is also possible that venture capitalists of a different era were less focused on finding the next “new, new thing” and may have looked for safer investments.

It is possible that venture capitalists’ preference for ambiguity could lead to a greater shift in business thinking, where people see more value in broader, less focused firms. This is an interesting question for future research.

Question 5. To collect data, you made use of a cutting-edge method which involved “scraping” information from websites. What was the reaction of your reviewers, and how might this be combined with other research methods to build, extend, or refine your study?

Online data sources, whether scraped or from digital archives, are a critical source of data for social science research. When I first started this study it was an innovative way to collect data, but now is mainstream, and a critical skill to develop. The reviewers were not concerned about the method by which I gathered data. But they did have questions about using statements in press releases as a historical record of category affiliation, and asked me to validate these data. I did this by comparing claims made in press releases to other public documents using the archived Web and annual reports. In general, when using a new data source, it is important to compare it to familiar data sources as a validity check.

[Discussion] Davis (2014). Editorial Essay: Why Do We Still Have Journals?

Author:             Gerald F. Davis (
Article link:

Do you agree with Jerry Davis, editor of ASQ?

In the June 2014 issue of ASQ he argues that “the core technology of journals is not their distribution but their review process”, and that the system of journals is broken. (See abstract below, and full ungated article at link above.)

In a break from our normal fare of author interviews (we have many of these scheduled for posting over the summer!), ASQ Blog is hosting a discussion of Jerry’s essay. Jerry himself will be participating in the discussion, as will others on the ASQ editorial board.

Please add your voice below in the comments!

Abstract. The Web has greatly reduced the barriers to entry for new journals and other platforms for communicating scientific output, and the number of journals continues to multiply. This leaves readers and authors with the daunting cognitive challenge of navigating the literature and discerning contributions that are both relevant and significant. Meanwhile, measures of journal impact that might guide the use of the literature have become more visible and consequential, leading to “impact gamesmanship” that renders the measures increasingly suspect. The incentive system created by our journals is broken. In this essay, I argue that the core technology of journals is not their distribution but their review process. The organization of the review process reflects assumptions about what a contribution is and how it should be evaluated. Through their review processes, journals can certify contributions, convene scholarly communities, and curate works that are worth reading. Different review processes thereby create incentives for different kinds of work. It’s time for a broader dialogue about how we connect the aims of the social science enterprise to our system of journals.

Bernstein (2012). The Transparency Paradox: A Role for Privacy in Organizational Learning and Operational Control

Author:             Ethan S. Bernstein (
Interviewers:    Curtis Chan (
                         Stefan Beljean (
Article link:

Question 1. A striking aspect of your project is the “nearly unlimited access” that you had to your field site of a Chinese mobile phone factory for PrecisionMobile (p. 185). You describe the process by which you first gained access to PrecisionMobile in terms of network connections with board members and executives. But can you give us more detail about how you managed your relation with your field site contacts to gain such wide access—especially at the level where you could gain permission from management to conduct a field experiment? For example: how did you frame your research project to upper management, was there any initial resistance from managers, and how did you deal with any such resistance or other roadblocks? Also, what were the reactions of the upper-level management to the results of your study, and how did you speak to these reactions, if applicable?

As scholars, we can easily fall into the trap of becoming so focused on the phenomena of study that we lose track of the needs of those who are generously giving us access to their work environment and, often, themselves. That is only made worse by the fact that our pace, approach, and even language can be very different from the pace, approach, and language of the executives who open the door to us.

First impressions were important. On November 15, 2007, I met for the first time with a board member at Precision.  I already had a draft research proposal in hand. We discussed it, revised it together, and by December 4, we had a short, three-page document that he felt comfortable sharing with senior executives at the company and could stand on its own as it circulated. The first words in the title were “research proposal,” but the summary, project overview, hypotheses, intended results, and benefits from participation were written with management in mind—asking questions, framing ideas, and stating hypotheses from their perspective. I was clear about our objectives (advancement of social science through scholarly publication) but also clear in recognizing their interests (learning and productivity). The project was designed for rigorous scholarship but framed in the language, norms, and interests of PrecisionMobile executives, both in the short proposal and in the Powerpoint which ultimately accompanied it. This was, by no means, a core initiative at PrecisionMobile (indeed, it was highly peripheral), but at least senior executives knew enough about it not to be concerned. They also had clarity on what we intended to do: a Phase 0 (initial visit), Phase 1 (qualitative research), and Phase 2 (field experiment) approach, which not only set expectations on timing but also envisioned check-ins with management that were frequent enough to satisfy their organization’s norms of fast-paced work. My years in management consulting at the Boston Consulting Group (BCG) were excellent training for this aspect of academic field research, and I drew on that training heavily.

On December 19, the board member spoke with one of the most senior executives at the company, who was highly supportive and said that the company is “full of research projects” for this kind of work. In perhaps the greatest moment of luck in this project, he put us in touch with a rising-star VP within PrecisionMobile who was everything a researcher could hope for in a partner-sponsor. The hardest part of confidentiality in this project is not being able to publicly thank him by name. Years after the work (and several promotions later), he remains a thought partner.

All of that preparation at the senior executive level was enough to lay the groundwork for the participant-observation phase of the research (especially given how, by design, only three people at the site were supposed to know anything about the research at that stage). As you point out in your question, however, the field experiment required a new level of signoff. To make matters worse, our rising-star VP had risen into a new position that was no longer in the PrecisionMobile business, thus putting all of our access at risk.

The next step was a stroke of genius by Professor Willy Shih at HBS. Together, we wrote an HBS case on PrecisionMobile to be taught in the Building and Sustaining Successful Enterprises second-year MBA course at HBS. In relatively few pages, it captured one core aspect of what we observed via the embedded participant-observers. It also required CEO signoff. With signoff came engagement—and a desire to address the issues raised by the case. The challenge was that, given the phenomenon (hiding behavior that was so well hidden that they didn’t know about it before our research), it was not clear what should be done about the situation. We proposed a field experiment. After coming to HBS to see the case taught for the first time, company executives agreed. It was one of the most intricate (and time-intensive) processes I have witnessed for gaining permission to run a field experiment, but it worked.

That is not the full story. In re-reading the last five paragraphs, I must offer a caveat: that clean-cut retrospective masks what felt, on a day-to-day basis, much more like climbing a brittle rock face where you’re never sure what is going to happen next . If it weren’t for unconditional support from mentors and peers, deepening relationships with thoughtful individuals at PrecisionMobile, and (quite frankly) a good dose of luck, this would have been impossible. No day in the field is “normal,” and I believe being fast-on-your-feet (while remaining true to the fundamentals of rigorous research standards) is one of the most important virtues of any field researcher trying to do this kind of work.

I still remember when, with about two hours’ notice, I was asked to provide a five-minute briefing to the CEO (of this multi-billion dollar enterprise) on why I had put up curtains in one of his most important facilities. I thought that would be the end of the experiment. But he was extremely thoughtful and interested in the science behind it. Indeed, my view is that most managers are quite interested in this kind of scholarly work if they know we aren’t just going to muck about but rather actually help their organizations get work done better.

Question 2. Your paper demonstrates and documents a fascinating causal effect of privacy on higher performance. And yet, a question that burns with both managerial and theoretical implications is under what boundary conditions this relationship between privacy and higher performance might hold, and conversely under which conditions it may not. Would you like to share any thoughts about what these boundary conditions might be?

It is very interesting to speculate on the boundary conditions for these findings. Based on the conversations I have had over the past 18 months, much of the speculation around limits to generalizability involves either industry (manufacturing) or geography (China). However, one of the tremendous benefits of having this article published in ASQ has been the large number of individuals who have reached out to me with examples of how this theory applies in their own contexts—health care, financial services, professional services, government, technology startups—across a wide array of geographies. The evidence is, of course, purely anecdotal at this point. But the emerging view is that neither industry nor geography is likely to impose barriers to generalizability.

Far more likely to be fruitful, I believe, is an evolving set of boundary conditions around the presence of enablers required for privacy from observability to be productive (and, conversely, the absence of eroders which undermine that relationship). Recall, for example, that performance remained closely tracked at PrecisionMobile all the time, even when work activity became less observable with the curtains. Were that not the case, I could imagine increased privacy yielding a very different result. Of course, track performance too precisely and one ends up back in the transparency paradox—in a recently published article on “Analytics 3.0,” Thomas Davenport writes: “Just as analytics that are intensely revealing of customer behavior have a certain ‘creepiness’ factor, overly detailed reports of employee activity can cause discomfort. In the world of Analytics 3.0, there are times we need to look away.” So an interesting set of boundary condition questions exists around how adjusting various levers of transparency, not just observability, may enhance our understanding of the paradox.

A second important boundary condition is emerging around time. Rather than asking under what conditions the relationship between privacy and higher performance holds, a better question to ask may be how long that relationship will hold in different context.

I am continuing to investigate these questions of generalizability, and I would genuinely welcome both input and collaboration from others who are equally interested in such research.

Question 3. From your qualitative data, you considered the nature of the mechanisms for how privacy may increase performance. You focused on mechanisms largely concerned with learning (e.g., productive deviance, localized experimentation, distraction avoidance, continuous improvement). Given the extensive and detailed observations conducted by your embeds, we would imagine that there was a considerable amount of rich material that you left on the figurative “cutting room floor” of the paper-writing process. Was there evidence for other potential mechanisms in your qualitative data that you found interesting, but ultimately were not the focus of this paper for particular reasons? More generally, what else did you find intriguing in the data that might be a focus of future work?

Great question! The answer is an unqualified yes—there is certainly evidence in my qualitative for elaboration on the mechanisms. Indeed, in addition to the embed data, I have a factory-floor employee survey that was completed before and after the curtain intervention (both for control and treatment lines) which is the basis, along with the transcripts, for a new paper which is currently a work in progress.

Question 4. A key empirical strength of your paper is your field experiment at PrecisionMobile. In it, you were conscientious to control for potential Hawthorne effects, and you argue that workers on the night shift were convinced that the experimental manipulation of the privacy curtain was for the day shift, suggesting that Hawthorne effects were not the driving factor behind the results (p. 195-196). What might you have done, though, if it had not been the case that the night shift workers were oblivious to the manipulation? Did you consider other general strategies for controlling for possible Hawthorne effects either by a priori design or by post hoc arguments?

Every context offers idiosyncratic assets and liabilities for research. I chose PrecisionMobile in part because of its scale for a field experiment like this one, so any intervention would have been designed a priori to control for Hawthorne effects. Because the night shift and the day shift share the same space, it was the easiest control to accomplish, but any control would have had the same feature: similar space used by different groups with different perceptions on whether they were being studied. Given the nature of the field context, one could imagine other experimental designs that would have accomplished the same goal (although none as clean as the night shift / day shift design).

In my own view, post hoc arguments are best avoided, especially when one goes to such significant efforts to design and implement a field experiment. Perhaps another good question to ask is what I would have done if I had found a Hawthorne effect. On the one hand, that would have made the effect of privacy very hard to tease out. On the other hand, in words borrowed from my phenomenal Editor in the ASQ review process, the similarities between my study and the old Hawthorne studies could be viewed as “a feature, not a bug”—and I might have had a very different paper as a result.

Question 5. Given that a large amount of work in the contemporary knowledge economy involves IT and computers, what might be the equivalent of the “privacy curtain” in the digital realm?

I do think there are interesting digital analogues. Let me start with an example. I just finished teaching an extraordinary first-year section of MBAs at HBS. About halfway through the semester, I ran into one of my students on-campus as I was carrying my 15-month-old son to daycare. Shortly thereafter, I ran into another student who mentioned my son. Apparently, the first student had sent a “GroupMe” to the section about the rendezvous—something she would “never do on Twitter” because it would be too public, but something she would do within the boundary of the 92 other individuals in her section.

The moral of the story: in the digital realm, we actually have even more ways to design (and enforce) boundaries that keep selected information “within these four walls” (at least metaphorically). When we do anything digitally, we consider how widely it will spread (and if we don’t consider it, as demonstrated by a number of notable situations, we often learn our lesson a little too late). How many times do you open an email and, even before reading the body of the note, see who is on the “To:” and “cc:” lines (not to mention wondering about the “bcc:” line) before deciding what to write in a “reply all” message—or deciding to pick up the phone and call someone to chat “offline” about something? A promising, new Silicon Valley startup is based on that premise and may provide a research opportunity to learn more about this phenomenon in the digital space over the next few years. In the meantime, one thing seems clear: just like in the physical realm, the presence and location of boundaries in digital spaces may indeed partially dictate how we perform.

Dahl, Dezső, & Ross (2012). Fatherhood and Managerial Style: How a Male CEO’s Children Affect the Wages of His Employees

Authors:           Michael S. Dahl (
                         Cristian L. Dezső (
                         David Gaddis Ross (
Interviewers:    Johan Chu (
                         Ivana Naumovska (
Article link:

Question 1. This is a fascinating paper, both in its subject matter and its rhetorical strategy (it was refreshing to see a quantitative paper that eschewed the rigid hypothesis-test framework). How did you end up with this paper? What path led you to this setting, finding, and framing?

All three of the authors are very interested in the internal dynamics of firms and in particular whether and how top managers impact firm outcomes. We are also all three fathers and can identify, at least in a general way, with the idea that fatherhood is life changing and thereby has the potential to change the way top managers manage their firms. In addition, focusing on the birth of a child rather than just the number of children of a top manager allowed us to exploit the random nature of the gender of a newborn to provide evidence of a causal relationship between top managers and firm outcomes. We eschewed formal hypotheses because we wanted to accurately convey the spirit of the study: it is using what we would like to believe is an elegant empirical design and an interesting research question to develop new theoretical possibilities rather than an attempt to test existing theory using formal hypotheses.

Question 2. You make a strong case that fatherhood and other “external”, “personal” factors matter for organizations because such factors affect top managers’ values. Can you speculate a bit on how much the actual findings of the study (daughters have a less negative influence on wages and female employees are in general less adversely affected) would be similar or different across countries and cultures? For example, countries like India where having daughters creates future costs (in dowries), or other non-Danish Western countries (e.g., the U.S. where CEOs are paid more compared to average workers)?

First, let us clarify that “CEO” in our study refers to the “boss” of all but the smallest firms, which we exclude. We do not necessarily mean the CEO of a large, public firm. One is always hesitant to argue forcefully that the results of a given study will obtain in a different context but we would not be surprised to see similar results in other countries. Virtually all of the laboratory studies that we cite in our theoretical motivation, for example, were conducted outside Scandinavia. Other results related to gender, moreover, are remarkably stable across countries, for example, the degree to which women are particularly underrepresented in senior business positions. Having said that, we agree that in cultures whereby women face a discriminatory environment from childhood or even from birth, our results could be different. This would be an interesting question to study.

Question 3. You draw upon literature on the effect of children on their parents to frame your findings. An especially intriguing finding from your paper was that there was a strong relationship between the birth of a first son and an increase in salaries (generosity) towards female employees and decrease in salaries (stinginess) towards male employees (p. 682). This is a striking result! You argue that this result obtains because CEOs become more selfish with each child, but a first child positively affects male CEOs’ attitudes towards women. Could you explain further how you see the two changes (more selfish yet more pro-female) interacting? Are there other predictions (perhaps in other settings) we can make using these two effects? How would a similar situation affect female top managers?

In general, one could hypothesize that, following the birth his first child, a male CEO could change a number of firm policies to be more advantageous to women. Examples include maternity leave and other accommodations for motherhood, more opportunities for promotion for women, etc. Yet, at the same time, the CEO would be less generous in general towards his male employees, with respect to these same dimensions and others. It is also possible that the birth and gender of a child could more generally impact gender dynamics in the workplace, whereby male employees would evaluate more highly the work of their female colleagues or superiors, or would work more effectively in mixed-gender teams. With regard to the impact of the birth of a child to a female top manager on firm outcomes, we view this as an interesting question for future research.

Question 4. You have a very nice data source! The Scandinavian countries seem to have available many rich and comprehensive datasets for organizational research. Could you give us some tips for accessing these databases? Are there any sources you think particularly intriguing for organizational scholars?

The Danish data has been used by many researchers at this point. Usually, a Dane needs to be part of the research team to access the data, but there is a strong trend towards more openness. Statistics Denmark is doing more and more to open up access to non-Danish researchers with the use of secure online systems.

Question 5. What question did we miss? Please ask yourselves a good question, and answer it.

We hope that our study is received in the spirit in which it was conducted, namely as one small step into a new and largely understudied area: how parenthood and other large life events change the way people do business – for instance, could the birth and gender of a child impact firm performance more generally? In that regard, while we quite like our results, we think they may be less important in the end than the encouragement we give to others to pursue their own questions in this domain. We also think that the research design may be a model for other researchers to follow.

Tilcsik & Marquis (2013). Punctuated Generosity: How Mega-events and Natural Disasters Affect Corporate Philanthropy in U.S. Communities

Authors:           András Tilcsik (
                         Christopher Marquis (
Interviewers:    Johan Chu (
                         Michael Mauskapf (
Article link:

Question 1. You make a strong argument that large events (planned or not) create changes in the behavior of organizations within a geographic community. What got you thinking about emplaced events? How did you decide on the setting of corporate philanthropy?

We became interested in local events for a couple of different reasons. One is that even though there has been a revival of interest in geographic communities, most research tended to look at how the persistent features of communities matter for organizations. A common assumption has been that, for example, being headquartered in, say, Atlanta rather than New York matters because these places have longstanding differences in their local norms, networks, traditions, and so on. While that’s very important, we felt that a key mechanism was missing. Atlanta, for example, hosted the Summer Olympics in 1996, and being headquartered in that community at that time had some really powerful effects on Atlanta-based firms. Mary Ann Glynn has a wonderful paper on this specific case in JMS in 2008, where she shows how the Olympics reshaped the networks of local organizations and changed their behaviors, at least for some time. This is an example of an important, geographically delimited, community-based influence on organizations, but if we focused solely on stable differences between communities, we would completely miss this. That’s why it’s so important to have what we call an “emplaced and eventful” perspective.

Another reason why we were drawn to this is that these large local events—from hurricanes to the Super Bowl to major political conventions—are interesting in and of themselves. Even if they only last for a day or a week, their ramifications can shake up communities. And policymakers, urban leaders, economists, and other scholars hotly debate the consequences. But typically the focus has been on regional economic consequences, such as job creation and tourism due to mega-events. We wanted to understand the social and organizational consequences.

The focus on corporate philanthropy as an outcome made sense because prior work suggested several interesting mechanisms that could link these events to charitable giving by locally based firms even beyond the event itself. And because the majority of corporate donations tend to stay in the headquarters community, this outcome is important because it has major consequences for the vibrancy of the local nonprofit sector and civic life.

Question 2. You looked at Fortune 1000 companies in core-based statistical areas (CBSAs). Can you speculate on how your research applies to smaller organizations or to organizations that are “networked”? Would the same dynamics apply in virtual online communities (e.g., Anonymous and its Project Chanology protests against Scientology)?

It is interesting to speculate how these findings may generalize. In regard to smaller organizations, there are reasons to think that such organizations may be even more susceptible to effects of local events than larger, more global firms. And much prior work has shown that larger organizations serve as role models for smaller ones, and so as larger organizations become more involved in local philanthropy and nonprofits as a result of events, smaller organizations may follow. We think that the dynamics of how these effects may vary by type of organization is very interesting to study in the future.

But for online communities, we are not as sure if the findings generalize to that context. As we note in the paper, a focus on events provides an important rationale for studying geographic communities because events “take place.” That is, geographic communities are sites of shared experience, and the identity-based and mobilization mechanisms we theorize in the paper are particularly likely to be triggered by direct, in-person involvement. While research has shown that many of the social, cultural and regulative processes of geographic communities do generalize to online communities, the event effects we study in the paper may mainly apply to geographic communities.

Question 3. You touch on the endogeneity of mega-events and philanthropy, and argue that such “institutional recursivity” can act as a mechanism for imprinting. Can you see in your data whether certain communities are institutionally different—i.e., have higher levels of corporate philanthropy and propensity to host mega-events? Also, it seems that natural disasters are not always unexpected (e.g. hurricanes in New Orleans). Are communities with a propensity for natural disasters different from other communities?

Certainly, there seem to be some important and persistent institutional differences across communities. For example, even after controlling for a bunch of relevant factors, the typical level of corporate giving in some communities is much higher than in others. This has been well documented in the literature. For example, Joe Galaskiewicz has a number of important studies about how the organization of corporations in the Twin Cities maintained a high level of giving in that community. And we see it in our data as well. Of course, some communities are also more prone to hosting mega-events or experiencing disasters. In this paper we took pains to control away such differences and to isolate within-community changes over time while holding constant stable institutional and other differences across communities. But you do bring up a very interesting question about repeated events and their connections to the institutional characteristics of a community. This may be a nice way to connect punctuating local events to stable community characteristics.

It is also interesting to consider whether there is something special about communities that experience mega-events or major disasters relatively frequently. As you note, in certain communities, such as New Orleans, there is the expectation that disasters may occur, which likely affects the community and resident corporations in an enduring way. Statistically controlling for these enduring features of communities is why including community fixed-effects in our models was crucial. But we think that further study on what these characteristics mean for organizations headquartered in the area is worthy of future study. We agree that the notion that some communities experience repeated punctuations that are, at least to some degree, anticipated (even if their timing often isn’t predictable) is quite intriguing.

Question 4. Do mega-events durably change the identities of the recipients of corporate philanthropy? It’s not clear where increased giving goes: Do nonprofits associated with a relevant cause benefit disproportionately? The mechanisms you suggest in the paper seem to privilege large, high-visibility, and prestigious non-profits: the build-up to mega-events creates enduring network ties while disasters have no such build-up. (And the data sources in your triangulation analysis only cover larger non-profits). Could mega-events create solidarity among elite non-profits and contributors, and so shift contributions to elite “want”-based organizations from “need”-based organizations serving the disadvantaged?

Great questions! We started exploring some of these issues, but these analyses didn’t fit in the paper. One thing we looked at, although only cursorily, was whether the boost to corporate giving due to a mega-event tended to benefit certain types of nonprofits, and we compared effects across the standard nonprofit categories like culture, recreation, sports, housing, health care, youth development, and so on. No major differences jumped out—it didn’t seem like any such categories benefitted disproportionately more or less than the others. But we haven’t looked at the broader categories of elite-oriented versus welfare-oriented nonprofits, and that’s a great question to explore. For the reasons you mention, it’s certainly possible that mega-events (or some mega-events, or mega-events in some communities) will shift contributions to elite-oriented nonprofits, so these events might have some interesting distributional consequences. Recent research by Marquis, Davis, and Glynn (2013 in Organization Science) on how corporations in communities with more cohesive local elites are more likely to support elite nonprofits also supports this line of thinking. And this would also be very much in line with the complex image that emerges from the economics and sociology literatures on mega-events: Despite the claims of event promoters and sports boosters, these events bring a really mixed bag of blessings and curses, can have all sorts of distributional effects, and may have important implications for inequality. And you’re suggesting that they might matter for inequality not just between, say, urban developers and the urban poor who are often displaced by events like the Olympics but also for inequality between different organizations, such as nonprofits with different orientations—which in turn can have further effects on the community itself. It’s definitely worth looking at this, and perhaps that could be Paper #2, so thank you!

Question 5. What question did we miss? Please ask yourselves a good question, and answer it.

[András & Chris's question:] How did the paper evolve and change over time?

We initially started with the general idea that major public spectacles like the Olympics or the Super Bowl may have a powerful effect on corporate social action in the host community. It was only somewhat later that we also became interested in natural disasters, which are a very different kind of event—an exogenous destructive shock.

When we initially submitted it to ASQ, the paper focused on documenting the main-effect relationships, some of which were surprising and quite dramatic, but the paper was largely silent on the mechanisms and contingencies. The reviewers were very helpful in making us think through the mechanisms more clearly, and the paper evolved quite a bit as a result. But each reviewer had a fairly different take on this. One reviewer helped us think about firm- and community-level moderators that would get at the mechanisms indirectly. Another reviewer suggested the use of qualitative and anecdotal illustrations, which helped us talk about potential mechanisms in a more concrete and compelling way. And another reviewer was really helpful in pushing us to explicate the logic of the underlying theory and to identify boundary conditions more clearly. Our editor, Martin Ruef, was great at synthesizing all these fairly different comments and helping us make the paper better without ballooning it. So, the final paper was much more explicit about both the potential mechanisms and the cases when these effects are less likely to occur. The process really was a constructive conversation with the reviewers—the way it should be.

McPherson & Sauder (2013). Logics in Action: Managing Institutional Complexity in a Drug Court

Authors:           Chad McPherson (
                         Michael Sauder (
Interviewers:    Johan Chu (
                         Curtis Chan (
Article link:

Question 1. You build a compelling argument for discretionary use of institutional logics by individuals in your study of drug court deliberations . How did you end up with your final framing? Did you strongly consider other framings? Why did you choose your final framing over these alternatives?

Well, we definitely did not enter into the research with institutional logics in mind. We were drawn to the case because it was a site at which people of various professional and organizational backgrounds were required to discuss cases, negotiate interpretations, and reach an agreement about how to proceed. We knew we were interested in this as a nexus of several institutional backgrounds and the micro-processes by which these backgrounds were negotiated.

About halfway through the data collection, however, it became clear in our discussions that institutional logics played an important role in drug court activities. We noticed that there were clearly different logics that were being employed in interactions and we became interested in how these were being used, by whom, and to what effect. It is important to admit, though, that other theoretical frames were floating around at the same time—to us this case seemed to be a clear example of inhabited institutionalism, hybrid organizations, institutional complexity, and micro-institutionalism as well. We stuck with the logics frame because it fit the data so well (this was truly an exercise in grounded theory) and because we thought our ethnographic data could make a real theoretical contribution to the logics literature.

Of course, this type of retrospective description hides a lot of the messiness and doubt that accompanied the framing process. For example, because we saw connections with so many theoretical approaches, early versions of the paper tried to speak to as many of these as possible. So the paper attempted to “make contributions to” institutional logics AND inhabited institutionalism AND hybrid organizations AND . . . you get the picture. Two rounds of very incisive reviews at ASQ helped us (forced us) to address this issue. The framing of the paper was much more focused and the contributions to the logics literature were made much more clear in the final version of the paper than they were in any of the previous iterations.

Question 2. You use some innovative techniques not often seen in qualitative papers: using count data to support your arguments and classifying outcomes by comparing “actual decisions of the judge” to “estimates of the ‘default decision’ that would typically be handed down, given the circumstances” (p. 171). What compelled you to take these novel approaches, especially the latter approach, which is more uncommon than the former? More generally, when might such approaches be appropriate and desirable in qualitative work? Also, could you provide a concrete example of how you estimated a “default decision”?

Like the framing of the paper, these techniques emerged from the study itself rather than being pre-planned. One of the reasons we started to play with “counts” was to try to manage the huge pile of data that had been collected. Early on in the data analysis (which, for qualitative researchers, is just a fancy way of saying that we were re-reading notes), we realized that we could accurately identify the invocations of logics in drug court proceedings. Thanks to McPherson’s meticulous notes, we could examine how each case unfolded in terms of the employment of logics as well as draw a more general picture of logic use in the courtroom.

In terms of the estimates, we made an early decision to track each case proceeding as a discrete event. We noticed that people who invoked logics seemed to be able to influence the decisions adopted by the drug court team and the judge, and our quantitative tracking and counts showed that case outcomes very frequently matched the proposed solution of persons who had invoked logics. This was exciting because it offered us the chance to show that logics—at least here—were consequential, and not just vacuous frames used in conversation. Because most drug court actions are very formulaic, it quickly became apparent to us (and our data supported the idea) that the use of logics often created variation from these normal outcomes.

We definitely do not believe that this type of quantification of qualitative data is necessary or even preferable for most studies. The nature of our site and our data collection methods allowed us to do it, and in this case counting and quantifying was an effective way of summarizing the processes at work and demonstrating our findings in a convincing way. It is important to note as well, however, what was lost in this process: much of the depth, fluidity, and character of the interactions as they are represented in the qualitative data. We are working on a second paper that focuses on these aspects of decision making in the drug court.

Question 3. You focus on deliberations in the “team-members-only meeting” (p. 169) rather than in the official court proceedings. A cynic might say that this is “backroom justice” which subverts due process, and that the “team members” were colluding to build legitimacy for this process, their decisions, and their group by practicing a discourse for later public consumption. This suggests another motive for bringing in different logics: making sure the team would be perceived as legitimate by others. Did you see evidence of anything like this either in the team meetings or in differences from the team meeting and subsequent official court proceedings?

In a sense, drug courts are designed to promote “backroom justice.” Drug courts attempt to eliminate a “one size fits all” system for addressing substance abusing criminal offenders by incorporating strategies and input from medicine, psychology, and the social sciences. Offenders (clients) agree to waive their rights to typical legal processes so that their case will be considered with all of these perspectives in mind, their sanctions will be tailored to their specific circumstances, and their jail time will be significantly reduced. In this way, drug courts—and Stone City exemplifies this—offer a productive alternative to traditional courtroom proceedings (an alternative, we might add, that also offers an ideal setting for data collection on legal issues since discussions are less formal, various institutional perspectives must be considered, and outcomes are based on individual circumstances).

Given the atypical features of the drug court model, however, legitimacy was indeed a persistent concern among drug court members. They consistently expressed concern that their decisions and processes would be seen as legitimate to others in the legal community. This concern often led to arguments for harsher sanctions for fear that the drug court would otherwise be seen as too “soft” or too removed from normal legal responses. In addition, drug court members worried about their legitimacy in the community more generally. We frequently observed the team reflecting on and making subjective assessments of the drug court’s worth and value to the community and how they could enhance the perceived and actual contributions being made.

Question 4. You mention three types of structural constraints on the use of logics—procedural, definitional, and positional (p. 183-185). You also suggest that “a varied use of the available tools perpetually reinforces the validity and relevance of these multiple logics and, more generally, the drug court itself” (p. 183). Do you think procedural, definitional, and positional constraints generally evolve to strengthen the functioning and legitimacy of the team? If so, how so? If not, what actors or factors can influence these constraints? It seems that changing structure should change outcome. In your setting, making the clinicians initiate conversations and manage case information should increase their flexibility in using different institutional logics and lead to more “rehabilitative” outcomes, for example. Who decides procedure, logics, and positions?

Yes, on some levels, these constraints likely do strengthen the functioning of the team. These constraints seem to provide various levels of informal and formal limitation on actors’ appeals to institutional logics and on the relevance of these external systems of rules and practices. They also set limits on the amount of interpretation and negotiation that is acceptable as well as on how flexibly given logics can be used to interpret situations. Of course, the structural constraints discussed in our paper also create inequalities in access and influence among drug court professionals. In particular, the positional constraint seems to subvert a level playing field at times in that it provides some actors with more exposure to, knowledge of, practice using, and liberty drawing upon external logics—all resources that they can then use to shape and influence case outcomes.

As such, our findings do imply that changes in the structure would likely affect the outcomes of some of the deliberations. In most circumstances—the cases in which discussion does not take place and logics are not invoked—outcomes would likely be very similar. However, if different professionals acted as the gatekeepers of information or if the flow of communication and interaction were structured differently, other professionals—say, clinicians—might be in a better position to enhance the relative influence of different logics. We believe that the structure of relationships within these multi-institutional environments matter a great deal and that future work using formal network analyses or other methods could specify more clearly how these structures matter and under what circumstances.

Question 5. What question did we miss? Please ask yourselves a good question, and answer it.

[Chad & Michael's question:] Do you intend to continue work in this area? If so, what theoretical questions might you explore? Would this work use the data you’ve collected in a different way?

We are currently working on a follow-up paper in which we focus more directly on the ethnographic data, a paper that will look like a more traditional qualitative article. Drawing on insights from social psychology, we drill deeper into the micro-level negotiations among professionals in drug court cases. In particular, we dissect the elements of the institutional appeals discussed in our ASQ paper, examine why drug court actors use elements of extra-local culture to shape interactions, and how these cultural resources define the identity of the drug court.

To do this, we use our data to look closely at the decision-making in the court as it unfolds in real time, and the identity conflicts that arise when a team composed of professionals from diverse institutional backgrounds negotiates solutions without shared bases of understanding, interpretation, and worldviews. This approach allows us to highlight the professional creativity used by these actors in collaborations, show how they draw upon cultural resources to reach a sometimes difficult consensus, and provide insight into how actors—both individually and as a group—reconcile their various professional identities with the organizational identity of the drug court.