We are pleased to welcome our Postdoctoral Fellow, Dr. Dan Sholler. Dan is an expert in qualitative research (yes, you read that correctly) and studies digital infrastructure creation, growth, and maintenance efforts. Through this research interest, he was drawn to the open science community and its ongoing development of tools and communities to support sustainable, reproducible, high-quality research. With rOpenSci, he intends to investigate what drives scientists to engage with or resist open science tools and communities.
Dan will be the first postdoc for the rOpenSci project, based at UC Berkeley and the Berkeley Institute for Data Science, supervised by Karthik Ram and co-supervised by Carl Boettiger and Daniel Katz.
We interviewed Dan to introduce you to him and his research (a fascinating conversation!). This short introduction can’t do him justice, but he'll share his research plan in another post in Fall 2017.
Q: Tell us a bit about your background
I'm a (mostly) qualitative, ethnographic researcher who studies user acceptance, resistance, and adaptation in digital infrastructure development programs. In other words, I like to study the factors that motivate people to engage with or resist new technologies, with the goal of helping to improve design and implementation strategies for eliciting engagement.
I completed my B.A. in Science, Technology and Society at University of Pennsylvania. During that time, I learned that technologies don’t just succeed or fail based on their merit; instead, a host of social and political factors play influential roles in determining technological outcomes. My Ph.D. research at the University of Texas School of Information involved looking at contemporary deployments of new information technologies in developing countries, in government agencies, and in healthcare clinics. Alongside my research, I took courses in organizational theory, organizational behaviour, and information systems, sparking my interest in studying how organizational IT persist and become robust, or fail in the face of resistance from users during the implementation and use phases.
Sometimes, the unanticipated actions users take actually help an IT implementation to succeed. In the first study I participated in at UT, we looked at a new branchless banking system intended to allow rural Brazilians to receive welfare benefits and pay utility bills without journeying to bank branches in major cities (think a network of simplified ATMs). The plan was to place point-of-service machines in places like grocery stores and post offices and allow clients to access self-service features. However, most of the clients were unable to use the machines themselves due to low technical literacy and they often encountered technical errors they couldn’t resolve. The owners and clerks in the shops took on new roles to fill the gap between the design of the technology and the reality of the situation: Elderly clients often handed over PINs; customers facing issues asked the shopkeepers to get in contact with the bank or utility companies; and some shop owners even borrowed money from their own registers to cover benefit checks when the system was down. All of these role-expanding actions ensured that the branchless system persisted.
In other cases, users’ reactions to a new implementation can lead to implementation failure. In my dissertation work, I found that doctors actively resisted and impeded a federally-mandated implementation of electronic medical records (EMR) in the U.S. healthcare industry. Clinics implemented “certified” EMR and had their costs partially subsidized by the federal government. In the study, I found that doctors were frustrated with the extra time they spent using EMR, particularly because it added little perceived benefit to patient treatment. Through interviews and observations, I learned that doctors could not affect any technological change within their local organizations because of strict federal policies. Through actions like lobbying Congress, holding town hall meetings, and voicing the medical community’s concerns in public outlets, the American Medical Association effectively stalled the progression of the federal program, which remains in jeopardy today.
I want to leverage my experience to explore how and why scientists engage with or resist open science communities and technologies, focusing on how particular communities like rOpenSci manage engagement and resistance to ensure positive outcomes. In turn, I hope to contribute to our understanding of best-practices for open science infrastructure development, including what community leaders, users, universities, government agencies, and other actors can do to develop robust infrastructures.
Q: What would you like to accomplish with your postdoc?
Just like any other academic postdoc, I plan to publish papers – in my case, examining the development of open science communities – and draw upon theories that might help to understand engagement and resistance. I intend to apply what I learn from the project and advise rOpenSci (and other open science communities) about strategies for anticipating, dealing with, and overcoming issues related to user engagement and/or resistance.
I’ll strive to gain a deeper understanding of the open science movement, focusing first on general questions like:
- “What drives community leaders to devote time and resources to building a robust infrastructure to support open science?
- What social and technical circumstances support or stand in the way of infrastructure development?
- What managerial strategies might be applied to get scientists to engage or to help them deal with the perceived detriments of participating in the open science community?”
To answer these questions, I’ll conduct a qualitative, comparative study of multiple open science communities, beginning with rOpenSci. In the study, I’ll interview and observe the leaders of these communities and the scientists who (a) actively engage with the open science tools produced by the communities and (b) might stand to benefit from the use of the tools, but instead resist use. I plan to evaluate the managerial strategies used across the communities. Focusing on multiple communities will assist the effort to generalize my findings to the broader open science movement. Throughout the study, I’ll consider the following prominent issues identified in the literature on digital infrastructure development, open science, open data, and related areas of study:
- Organizational motivations for building open science infrastructures and communities
- The need for balance between flexibility and standardization in managing users and their behaviors
- Best practices for creating infrastructures and managing their growth, including both technical and social elements
Q: How do you arrive at doing qualitative research in an environment full of quantitative researchers?
I first heard about rOpenSci through a member of my dissertation committee, James Howison, while at UT-Austin. James is a renowned researcher of scientific software issues, including topics like software citation and attribution in academic journals. He worked with members of the rOpenSci community and clued me into the ongoing development of open science infrastructures. Additionally, I had also heard from my peers about rOpenSci’s annual unconference and its novel approach to building technical and social capacity in the R community.
The open science community has no shortage of exciting, innovative software tools to support scientific research. However, time and again, authors recognize that perhaps the biggest impediment to the widespread use of these tools is eliciting engagement from users who aren’t software developers themselves or who are hesitant to open their methods and data to the broader scientific community. I think my research will support efforts to expand infrastructure participation to these scientists by uncovering what their concerns and hesitations may be and considering how we might begin to address them, both through technical design and social approaches.
The rOpenSci community, to me, is an ideal place to carry out a qualitative research project with quantitative researchers. Although many other communities exist and have their own merits, rOpenSci has quickly and effectively engaged an interdisciplinary community of researchers and produced tools with immediate impacts. Studying this community and comparing it to related communities will aid in understanding what makes rOpenSci’s approach so effective. I believe that drawing out lessons about managing engagement can be applied to other communities and benefit the open science community as a whole.