A Reading List for Innovation Teams

Last month I presented on Innovation Teams at UC Berkeley’s Center for Science, Technology, Medicine & Society (CSTMS). Someone asked if I had thought of assembling a reading list for people on Innovation Teams or curious about them. I said no, but it sounded like a fantastic idea! Over lunch one day Holly (who is on the Long Beach iTeam) and I (who has worked with the Los Angeles iTeam) got to talking about what makes these teams tick. Luckily Holly was game for thinking through helpful concepts to keep Innovation Teams effective and reflexive. We pulled together a few important readings from our different perspectives. In the process we interviewed each other about why we selected the readings we did. We hope you find this post as interesting as it was for us to write!

— Andrew (and Holly)

Dr. Holly Okonkwo is a cultural anthropologist with an emphasis in Gender and Science & Tech Studies from UC Riverside. She is currently the researcher for the Long Beach Innovation Team.

Dr. Andrew Schrock has a Ph.D. in Communication from USC and was a Data & Design Research Fellow with the Los Angeles Innovation Team from 2015–2016. He currently is a director at the Long Beach Community Database and faculty at Woodbury University.

Holly in reply to Andrew…

Andrew: How would you describe Innovation Teams to someone who has never heard of them before?

Holly: Innovation teams are essentially in-house consultant groups focused on developing create solutions for challenges that cities have not been able to solve. These challenges may include improving the accessibility and utilization of government services or city licensing and permitting processes. The central idea is that if you put together a multidisciplinary team of people from outside of government, they will be able to better understand the nuances of these problems and rapidly develop solutions to fix them.

A: What else mattered in how you selected these readings?

H: I purposefully selected readings that were tools for thinking about the methods and ideas that underlie the innovation team concept. First, I believe ethnographic methods to be the most important method for investigating the challenges innovation teams seek to solve. Clarity in how people experience these challenges are key to the development of meaningful solutions. In many ways, innovation teams are trying to solve challenges academics have long theorized about and researched but may not have translated these findings into solutions. Next, I selected readings that highlighted the impact of ethnography in system redesign and business innovation. Two areas that are not commonly associated with anthropology. One of my personal commits is to advocate for the relevance of the discipline in solving contemporary challenges. Finally, I often hear the terms culture and cultural shift misused in the context of system and service design and innovation. Culture can be defined in many ways; from a complex shared system of values and expectations to constructs that limit human behavior. It is far more intangible and tends to not be the things people say are “culture”. So when teams discuss the work of innovation as shifting culture, they are reducing culture to a one-dimensional variable and underestimating the agency people have in either adapting or rejecting the initiatives they develop.

A: How has your identity as a cultural anthropologist shaped the texts you selected? Why is ethnography a particularly important methodology for Innovation Teams to use?

H: Gaining an in-depth understanding of human experiences in the world is the crux of anthropology and ethnography is the primary method that we go about doing this work. The aim of ethnography is to observe human activities as social actions embedded within a socially and spatially organized domain. Human-centered and user-experience design methods are clearly rooted in sociocultural anthropology. Ethnography often gets reduced to participant-observation (which is our hallmark method) however it also includes interviewing, conversations with key informants, free-listing (asking people to key terms to describe an idea/concept) and survey research to just name a few. These are common methods I see adapted in the user-centered design space just under different terminology. This method of inquiry was popularized by Anthropologists such as Franz Boas and Margaret Mead, in the late 19th century. Although ethnography may be trendy now, we as a discipline have been practicing these methods for some time. Further, ethnography is inherently collaborative and reflexive. Cultural relativism (understanding the values and behaviors based on their own standards) is the foundation of ethnographic work. How well we understand the lived experiences of the challenges we are trying to solve and the accuracy of our solutions we develop, is the distinguishing factor between being truly innovative or simply reconfiguring the problem.

Andrew in response to Holly:

Holly: How would you describe Innovation Teams to someone who has never heard of them before?

Andrew: Innovation teams are in-house innovation consultancies with diverse skill sets that work in teams on improving government processes, policies, and technologies. “Innovation Teams” historically have existed for decades, only recently being applied to the government context. So although I am primarily thinking of Bloomberg “iTeams” in Long Beach and Los Angeles, I’d also include close cousins such as 18F at the federal level and New Urban Mechanics in Boston. Each has a slightly different set of specialties — 18F does a lot with agile development, while New Urban Mechanics embraces an experimental urbanism. What these groups have in common is a cyclical model of research, design and implementation. They are also often “outsiders” to government, which can be an asset to developing ideas.

H: What are the ways innovation teams should think about collaboration between government and the public in solving civic challenges?

A: The key question to me is how Innovation Teams use communication to work towards the public good. I see them as balancing a model of “expert teams” with a model of “publics.” They interact with the public in various ways. iTeams go into the field and bring resident voices and needs into the conversation. These are often participatory design and user-centered design practices, echoing what civic technologists often refer to as “building with, not for.” At other times iTeams act as expert teams. They work in small groups and discuss how technologies might best match with government stakeholders’ needs. They get to know a lot about things invisible to the general public: the behind-the-scenes work of local government, what stakeholders’ interests are, what kinds of strategies have worked elsewhere, and so on. Communication scholars believe more in public participation, but good arguments can be made for both! Teams of problem-solvers “inside” government can accomplish what the public cannot because they just don’t have the time, motivation, or mechanisms to participate. So my readings were, in part, selected to help members of these teams balance problem-solving in small groups with communication with a wider public.

H: What else mattered in how you selected these readings?

A: The readings admittedly reflect two fascinations. The first is an interest in the American philosophical tradition of pragmatism. A pragmatic tradition is quite different from how the term is usually used — more as a synonym for “practical.” John Dewey famously suggested you can’t really know without doing, and promoted cycles of learning rather than relying on strict rules. Another way to think about pragmatism vs. pure empiricism is something like this… The question empiricists ask is “is it true?” The question pragmatists ask is “does it work?” Dewey also pushed us to think about empathy and foresight as human capacities. So to Dewey, communication also had an inherently communal element — it brought people together and helped them collaborate on shared problems. I see Innovation Teams as a similar form of radical pragmatism. They use a data-driven model where government policies, technologies, and processes are iteratively improved. They often “know” by “doing.”

The second interest that is reflected is about technological myths. I probably got this interest from my mentors from some time back danah boyd and Anne Balsamo. We often believe technology can do more than it really does. Innovation teams are an interesting alternative to the “big data” paradigm and they buck many of the stereotypes about big data. But, as many within government can tell you, iTeams are just the latest in a long lineage of models of improving civic life through technologies. So we have to be aware to not “drink the kool aid.”

H: How should innovation teams think about quantitative/qualitative data both in their approach and solutioning?

A: I really appreciate both how thoughtful Innovation Teams are when collecting and interpreting data. I think there is a certain stereotype that goes something like this: qualitative data provides depth of insight, while quantitative data tells you about how often you find a particular feature. But when you’re interested in variables like processes, individual opinions, and behavior it is difficult to quickly do large-scale data collection. I have experience with analyzing large sets of socio-demographic data, so I came in anticipating doing a survey. The director of the LA Innovation Team suggested that wasn’t the best way to go, and she was right. Qualitative data was easier to rapidly collect and interpret, and added value to existing socio-demographic data sets. In retrospect, it was a good move to do more focus groups and interviews. The resulting data helped bolster the team’s case for initiatives. Qualitative data, in my opinion, also lets researchers be more “reflexive” — a word you used too, Holly, that essentially means “I am thinking about how I think I know what I know.” You can quickly gain insights from small-scale data collection that would be difficult with large-scale quantitative research. By comparison, “big data” can be difficult to find inside government and full of spurious correlations even when you do— variables that look related but really aren’t.

The Reading List

Balsamo, A. (1996). Myths of information: the cultural impact of new information technologies. Technology Analysis & Strategic Management, 8(3), 341–348.

Belman, L. (1977). John Dewey’s Concept of Communication. Journal of Communication, 27(1), 29–37.

boyd, d., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), 662–679. doi:10.1080/1369118x.2012.678878

Brayne, S. (2014). Surveillance and System Avoidance: Criminal Justice Contact and Institutional Attachment. American Sociological Review, 79(3), 367–391. doi:10.1177/0003122414530398

Brereton, M., Roe, P., Schroeter, R., & Lee Hong, A. (2014, April). Beyond ethnography: engagement and reciprocity as foundations for design research out here. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1183–1186). ACM.

Denis, J., & Göeta, S. (2014). Exploration, Extraction and ‘Rawification’: The Shaping of Transparency in the Back Rooms of Open Data. Paper presented at the Neil Postman Graduate Conference,, New York.

Frascara, J. (2002). People-centered Design. In J. Frascara (Ed.), Design and the social sciences making connections. New York: Taylor & Francis.

Goodspeed, R. (2014). Smart cities: moving beyond urban cybernetics to tackle wicked problems. Cambridge Journal of Regions, Economy and Society. doi:10.1093/cjres/rsu013

Henrich, J. (2001), Cultural Transmission and the Diffusion of Innovations: Adoption Dynamics Indicate That Biased Cultural Transmission Is the Predominate Force in Behavioral Change. American Anthropologist, 103: 992–1013. doi:10.1525/aa.2001.103.4.992

Herdman, R. C., & Jensen, J. E. (1997). The OTA Story: The Agency Perspective Technological Forecasting and Social Change, 54, 131–143.

Hollands, R. G. (2008). Will the real smart city please stand up? City, 12(3), 303–320. doi:10.1080/13604810802479126

Hughes, J., King, V., Rodden, T., & Andersen, H. (1994, October). Moving out from the control room: ethnography in system design. In Proceedings of the 1994 ACM conference on Computer supported cooperative work (pp. 429–439). ACM.

Joerges, B. (1999). Do Politics Have Artefects? . Social Studies of Science, 29(3), 411–431.

Kolko, B., Hope, A., Sattler, B., MacCorkle, K., & Sirjani, B. (2012). Hackademia. Paper presented at the 12th Participatory Design Conference.

Plouffe, L., & Kalache, A. (2010). Towards global age-friendly cities: determining urban features that promote active aging. Journal of urban health, 87(5), 733–739.

Ramakrishnan, S. K., & Bloemraad, I. (Eds.). (2008). Civic Hopes and Political Realities: Immigrants, Community Organizations, and Political Engagement: Immigrants, Community Organizations, and Political Engagement. Russell Sage Foundation.

Sheller, M. (2015), Racialized Mobility Transitions in Philadelphia: Connecting Urban Sustainability and Transport Justice. City and Society, 27: 70–91. doi:10.1111/ciso.12049

Spinuzzi, C. (2005). The Methodology of Participatory Design. Technical Communication, 52(2), 163–174.

Townsend, A. M. (2013). A New Civics for a Smart Century Smart Cities: Big data, civic hackers, and the quest for a new utopia (pp. 282–320). London: W. W. Norton & Company Ltd.

Wilf, E. (2015), Routinized Business Innovation: An Undertheorized Engine of Cultural Evolution. American Anthropologist, 117: 679–692. doi:10.1111/aman.12336

Zuckerman, E. (2013). The “good citizen” and the effective citizen. Retrieved from http://www.ethanzuckerman.com/blog/2013/08/19/the-good-citizen-and-the-effective-citizen/

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