By Authors: Andrew Nelson (University of Oregon), Callen Anthony (NYU Stern), & Mary Tripsas (UC Santa Barbara)
Interviewers: Esther Yau Chau (University of Alberta) & Amirsalar Jafari Gorizi (University of Texas at Dallas)
Article link: https://doi.org/10.1177/00018392231163178
The paper talks about technology reemergence in a unique context—music synthesizers. Can you tell us more about how the collaboration came about? How did you all first notice this promising research context?
Andrew has been interested in synthesizers forever and had written a bit about music technology—in
particular, an article in Industrial and Corporate Change that Mary was a huge fan of. At the Academy of
Management meeting in 2012, Mary introduced herself to Andrew, and they had a long conversation
about potential collaboration opportunities. Callen was just beginning to work with Mary as she started
the PhD program at Boston College, and quickly became an indispensable part of the team. It’s worth
noting that we’re all deeply immersed in music: Andrew released a synth pop album as a PhD student,
Callen has performed with multiple bands, and Mary is an accomplished oboist—and even shared the
stage with the Indigo Girls!
It is awesome to have a team of music enthusiasts working on this project! Compared to other projects that you worked on, what was this experience like? Are there any pros to studying a context you love and care about?
Our overwhelming sentiment is that this project was especially fun—partly because we really enjoying
working with one another, and partly because of the context. We’d often share interview excerpt
“gems” from artists we admire and even, at times, coded data with synth music playing in the
background. It’s obviously hard work, but it’s also fun work. Two major pros to studying a context you
love and care about are that it’s hugely and inherently motivating, and that we have a very deep
understanding of important nuances and details.
On a related note, what are things we should be careful about when we study a context in which we
have some prior experience/knowledge?
One challenge is to maintain a diversity of perspectives and not to overly rely on one’s own idiosyncratic
engagement with the context to drive the analysis. Here, our team was ideal since Andrew had been
deeply immersed in synthesizers for years, while Callen and Mary were newer to the technology and
industry.
Incorporating occupational influence into the literature on technology trajectories is a fascinating
idea. Was it clear to you all from the beginning what this project is about? How did the theoretical idea unfold?
That idea emerged during the review process. As a bit of background, we’d gathered an enormous
amount of data on the synthesizer industry, some of which informed an earlier publication in Strategy
Science about the initial emergence of the industry (“Who are you?… I really wanna know”: Product
meaning and competitive positioning in the nascent synthesizer industry. Strategy Science, 1(3), 163-
183). We were always focused on this interplay between users (synth players) and technological change
(different synth models), as we had observed that the black-boxing of functionality by technological
advances frustrated users. But it was really the review process—and, specifically, some really insightful
comments from our editor, Christine Beckman, and the reviewers—that led us to examine our data through an occupational lens. By embracing an occupational lens, we were able to explore implications
of black-boxing that we believe generalize well beyond synthesizers or even technology reemergence.
For instance, our findings are relevant to the current discourse about occupations that are adopting AI-
based solutions that potentially black-box important features.
Indeed, the idea that occupations could shape technology development is fascinating and increasingly relevant to recent developments, like AI technologies. This study could be interesting to scholars and practitioners alike! Down the road, would this paper inspire more research on occupational dynamics and technology trajectories? Are you working on other projects to extend the conversation around this topic?
Definitely! We see the relationship between occupational and technological change as not only
interesting theoretically, but also essential to understand given what’s happening in the world right
now. There are several offshoot papers that we’ve discussed, starting with a piece that more explicitly
considers how our insights around black-boxed technologies and occupations apply to discussions
around AI. And, of course, much of Callen’s other work addresses these relationships, too.
We see that the project involves various kinds of data sources, and we can only imagine how time-
consuming it was to analyze them all. Can you share some key takeaways that you gained from this
experience of managing such a large amount of data?
We love data, which is both good and bad—good in that it’s fun to get lost in the data and to continually
gather more; bad in that it definitely introduces challenges in terms of how to manage and analyze a
really large and diverse data corpus. We tried to be our own biggest critics by taking a skeptic’s lens to
our data and analysis, and by being very systematic in how we gathered and analyzed the different data
sources. Tactically, that meant lots of version control; notes and memos; and a very well-organized
Dropbox project folder. We also balanced regular Zoom meetings and email updates against in-depth
data analysis “residencies,” as when Andrew visited Callen and Mary in Boston, when Callen visited
Andrew in Eugene, and when the three of us rented a house together in Seattle to work on the paper.
We could tell a lot of work was involved in organizing and analyzing the data corpus. Your team must have developed many ways to manage the data and communicate with one another effectively. Do you have any practical tips for junior scholars using inductive historical methods (a) as a single author and/or (b) as a team?
Having a clear and consistent plan for organizing data and tracing the evolution of analysis is essential.
Part of our approach actually drew from scientific lab culture, where people keep a notebook that
details every aspect of a project. We obviously didn’t keep a physical notebook, but we were careful to
annotate everything—every data source, the state of the analysis, emergent questions tied to the data
and analysis, and so on, and we tried to ensure that everything was dated for the sake of version
control. Tactically, we worked a lot with different spreadsheets, even for our qualitative coding, and we
had Dropbox subfolders (and sub-subfolders!) for each component of the project. As we proceeded, we
moved a lot of things into an “archive” folder. But we tried to organize things so that we could recreate
the then-current state of the project at any point in time.
Regarding historical methods, it is crucial to be sensitive to the historical context of source
materials—for instance, the specific cultural and temporal contexts in which people interviewed
musicians and firms developed technologies was key to our analysis.
How did you all decide which journal to submit to? Do you have any advice for Ph.D. students and
junior scholars considering submitting to ASQ?
ASQ has a well-deserved reputation as the premier outlet for edgy projects based on really extensive
data. From the outset, we knew we wanted to publish this paper there. Two key bits of advice we’d give
to anyone interested in submitting their research to ASQ are to ensure that the rich data really shine
and to get a lot of feedback prior to submission. Collectively, we’ve presented this paper at more than
two dozen university seminars and conferences. That may be too many, but the terrific feedback from
diverse scholars certainly helped us hone this paper into what it became. This may give junior scholars
looking to submit to ASQ an advantage: dissertation projects are often more empirically and
theoretically ambitious, and often receive lots of feedback throughout the dissertation process. Perhaps
this is why ASQ is known as a great place to publish papers based on dissertations.
Relatedly, did you face any resistance from reviewers and peers during the review process? Was the
review process challenging?
Review processes are always challenging in that they tend to reveal inconsistencies, gaps, questions and
concerns that the authors may have overlooked. But we were blessed to have a wonderful and
supportive review team. For the shift to an occupational lens, specifically, it helped that two of us
(Andrew and Callen) had already done lots of work on occupations and technology.
Overall, what did you love most about this study?
Callen: I love that our findings are faithful to the experiences of musicians and industry-insiders. I also
love our research design. And I love the intense and thorough process we engaged in across every
aspect of what we did – up to and including the analysis and selection of the title.
Mary: I love analyzing puzzles – in this case, why would professional musicians return to an older
technology that they had pretty much abandoned? I also loved immersing myself in the data – what
could be more fun than 20+ years of Keyboard Magazine?!
Andrew: I love all those things, too. And more generally, I love that our profession lets us study most any
topic we find interesting (music synthesizers?!) and to pursue the work with our friends. I couldn’t ask
for anything more!
Interviewer Bios:
Esther Chau (esther.chau@ualberta.ca) is a PhD student in the Strategy, Entrepreneurship, and Management Department at the University of Alberta. She is a qualitative researcher interested in understanding the creation, maintenance, and evolution of rituals, as well as the socialization and interactions of occupational members across various cultural contexts.
Amirsalar Jafari Gorizi (Amirsalar.jafarigorizi@utdallas.edu) is a PhD student in Organizational
Behavior, Strategy, and International Management Studies at the University of Texas at Dallas. His
research straddles between Technology and Sustainability. He examines the effectiveness of
sustainability transition and digital transformation strategies across different organizational and
institutional settings.