Paper Session #1: Defining the value proposition: Some ways that ethnographic praxis can move closer to the heart of business
The story of ethnographic praxis in industry is a story of practitioners and their methods drawing ever closer to the heart of business. The field's well-documented evolution has taken it from the status of an intriguing new perspective a few decades ago, to a must-have element in strategic decision-making today.
As part of this process, ethnographic practitioners found themselves moving beyond their accustomed research roles. Many pioneers of ethnography in business now occupy highly influential positions in their organizations; their ethnographic bona fides have become tightly bound to their managerial obligations. The ethnographic approach, meanwhile, continues to evolve in directions that further extend its reach and relevance.
This opening session tells a story about some of the field's early figures and institutions, and their evolving influence across a range of businesses. The papers also reveal some ways in which the ethnographic sensibility has been made more vital to business by asserting its intrinsic value proposition in new spheres.
Social-cultural tensions and instabilities are opportunities for businesses. Social values are frequently highlighted as the basis for design or the foundation for marketing and new product development. The traditional assumption is that values are a social-cultural constant and thus a reliable means to plan marketing communication and product design. Instead of focusing on the supposed stationary moments of values, it is useful for new business innovation and strategies to focus on values in flux. Flux is an approach that demonstrates one way to move the work from its traditional focus on design and making good products to the development of new business models. To start to shape directions for new business opportunities, ethnographic practice has an opportunity to look more broadly at shifts in social values taking place in the landscape, and at the specific practices that change in concert with those values.
As we reflect on the evolving nature of our practice, it is time to consider how these individual evolutions impact the broader field of ethnographic praxis in industry. First, we look at the career paths of senior members of the EPIC community to chart key transitions in their individual careers. We observe that their career paths have moved them away from fieldwork and into management where they shape projects, mentor staff and participate in decision-making. Thus, a key aspect of the evolution of the EPIC community lies in how senior members are influencing what industry expects from ethnographic praxis. In a second intersecting theme we review how these individual career evolutions influence the EPIC community of practice. We discuss how our field continues to evolve both on an individual level and within the community of practice to which we all belong.
Numbers indicate that the prevailing query-based research methods in market research are unreliable and that findings often do not accurately reflect customer values, which are critical for innovation decisions. We argue that both providers and buyers of research services are responsible for the popularity of query-based methods due to a lack of ROI transparency and a model of accountability. In addition to emphasizing why a formalization of accountability is crucial to the industry’s evolution, this paper proposes a minimum model to be expanded on a case-by-case basis that will hold researchers responsible for delivering insights that translate into results (savings or profits) and buyers for selecting methods that target the highest return on innovation investment. With such a model built into the buyer-provider relationship, the precision of research will increase such that buyers, in time, will naturally gravitate towards innovation research, which is driven by the insights of observational methods.
Technological innovation obsolesces not only earlier technologies, but also the knowledge, skills, and expertise of the users of those earlier technologies. This state of affairs receives little attention, generally being written off as the cost of participation in a vibrant economy and presumably offset by the benefits inherent in the innovation itself. However, not all changes are equal in the benefits they bestow, as indeed they are not equal in the costs they impose. This paper seeks to develop an understanding of what happens in the process of individual adaptation, looking both at the acquisition of new skills and especially the unlearning that lies behind it, based on findings from interviews and usability testing. Differentiating between types of unlearning, the paper offers a calculus of the individual costs imposed by upgrades, versions, redesigns, service packs, and other harbingers of change. It also seeks to inform a technology design practice that is cognizant of high-cost unlearning and minimizes unnecessary impacts.
‘For A Ruthless Criticism of Everything Existing’: Rebellion Against the Quantitative/Qualitative Divide
While research practitioners remain deadlocked in old debates about the incompatibility and validity of qualitative versus quantitative research, streams of real-time data are overwhelming leading companies with individual-level insights at a scale and velocity impossible to achieve with traditional methods. Remaining relevant in the age of analytics no longer depends on the perfection of either methodology, but on the evolution of a creative, inter-disciplinary combination of both qualitative and quantitative approaches. Nevertheless, until we are done with the past, the past is never truly done with us. This paper establishes a new inter-disciplinary epistemology by tracing the historical development of the current qualitative versus quantitative divide. In so doing, I aim to discredit the assumptions underpinning the current debate, and illustrate how the shared epistemological origins of both statistics and ethnography inform the empirical formulations behind new “hybrid” quantitative-qualitative methods, including social networks, crowd-sourcing, Bayesian models, and centering-resonance analysis.