Finding the Emic in Systemic Design

A paper presented at RSD7 in Turin, Italy.

 

Abstract

I argue that an under-emphasized but crucial variable of success in systemic design is the perspective through which systemic design processes are implemented and executed. While rooted in design (a consciously empathetic discipline, especially in recent years; cf. Kimbell, 2011), it is easy for systemic designers to conduct the research required for their projects in externalized ways. These approaches risk misrepresenting the stakeholders who contribute to projects and, in turn, they are a danger to the potential impact of these misresearched problem systems. I propose to advance a theoretical argument for this danger, the development of an assessment framework to check whether an internalized perspective has been effectively achieved, and provide a proof of concept of this framework through hermeneutic case study analysis.

As I will show, systemic design processes that are not executed with the direct and explicit engagement of stakeholders – to the extent of achieving an emic (or from within) understanding of the system – may be flawed at their foundation. By fostering recognition of the importance of an emic perspective, and by providing a framework of principles, practices, and process to accomplish systemic design with this perspective, I hope to ensure that systemic design processes are as accurate and valid as possible with respect to the stakeholders of the system.

This is not to suggest that systemic design practice is "too etic". In fact, with roots in design, systemic design is often deliberately emic. Systemic designers make use of designerly tools that help the researcher to build empathy with system stakeholders (e.g., soft systems methodology, critical systems heuristics, appreciative inquiry; Jones, 2014). They often seek to engage stakeholders in the systemic design process and include reflective analysis of what has been learned in order to assess where deeper engagement with the system is required (Ryan, 2014). That said, with the advent of crowdsourcing (the facilitated involvement of the general public in problem solving, usually using online tools; Lukyanenko & Parsons, 2012) and data science (the use of computational tools to analyze and understand large quantities of data; cf. Scepanovic, 2018), it is likely that data-driven methods will increasingly influence systemic design practice. One recent example sought input from hundreds of people to identify opportunities for change in Canadian post-secondary systems through an iterative online survey (cf. Second Muse, Intel, & Vibrant Data, 2016). This data-driven direction is a powerful opportunity, of course, but it underscores the need to develop principles and best practices for assessing and supporting emic understanding as we gain more data from these tools.

This proposal consists of two steps. First, I will look to the principles and theorists of ethnography to develop a framework for assessing the emic/etic perspective of a given research project. Namely, Geertz’ "Thick Description: Toward an Interpretive Theory of Culture" (found in The Interpretation of Cultures, 1973, chapter 1) provides a foundation for the process of emic research, while Creswell and Miller (2000) provide a set of procedural principles for emic validity. Taken together, we generate a critical research framework with which we may assess a given research project’s emic perspective. Second, I will provide a proof-of-concept of this framework (and its theoretical underpinnings) via a casebased assessment of three systemic design projects. Case studies provide an effective venue for learning about the context-dependent manifestations of the phenomena being studied (Flyvbjerg, 2006). One of these case studies is one I have developed through my experience in participating and contributing to the development of the Canadian National Youth Leadership and Innovation Strategy framework, which convened hundreds of youth and youth-serving organizations in order to understand the youth leadership and innovation system in Canada (cf. MaRS Studio Y, 2017). The second and third case studies are those profiled by Ryan and Leung (2014).

In order to interpret and analyze the chosen case studies, I turn to the methodology of phenomenological hermeneutics (Eberle, 2014, p. 196; cf. Wernet, 2014). Phenomenological hermeneutics are appropriate as I have access to the described phenomena of the systemic design projects captured by the chosen cases, but these phenomena are not explicitly captured with reference to emic or etic perspectives – thus some construction of the inherent emic or etic data is necessary in order to make judgments about the perspectives found in the projects.

In each case, I will use identify phenomena representing the practice of emic (or etic) understanding in the research orientation of the work, as acknowledged by the above framework. In each case, I will examine the step-by-step procedure and any associated notes about the experience of the researchers and participants involved. In each step or experience, I will look for evidence of the four steps of emic understanding or the six techniques of emic validation reported above.

References

Creswell, J. W., & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry. Theory Into Practice, 39(3), 124–130. https://doi.org/10.1207/s15430421tip3903_2

Eberle, T. S. (2014). Phenomenology as a Research Method. In U. Flick (Ed.), The SAGE Handbook of Qualitative Data Analysis (pp. 184–202). Los Angeles, Calif. [u.a.]: Sage. Retrieved from https://www.alexandria.unisg.ch/228374/

Flyvbjerg, B. (2006). Five Misunderstandings About Case-Study Research. Qualitative Inquiry, 12(2), 219245. https://doi.org/10.1177/1077800405284363

Geertz, C. (1973). The interpretation of cultures: Selected essays (Vol. 5019). Basic books.

Jones, P. (2015). Design Research Methods for Systemic Design: Perspectives from Design Education and Practice. Proceedings of the 58th Annual Meeting of the ISSS - 2014 United States, 1(1). Retrieved from http://journals.isss.org/index.php/proceedings58th/article/view/2353

Kimbell, L. (2011). Rethinking Design Thinking: Part I. Design and Culture, 3(3), 285–306. https://doi.org/10.2752/175470811X13071166525216

Lukyanenko, R., & Parsons, J. (2012). Conceptual modeling principles for crowdsourcing (pp. 3–6). ACM. https://doi.org/10.1145/2390034.2390038

MaRS Studio Y. (2017). A strategic framework for youth leadership & innovation in Canada: Insights from the 2016 National Youth Leadership and Innovation Strategy Summit. Toronto, ON. Retrieved from http://www.studioy.marsdd.com/wp-content/uploads/2016/12/MaRS_NYLISstrategic_framework_Final.pdf

Ryan, A. (2014). A Framework for Systemic Design. FORMakademisk–research Journal for Design and Design Education, 7(4). Retrieved from https://journals.hioa.no/index.php/formakademisk/article/view/787

Ryan, A., & Leung, M. (2014). Systemic Design: Two Canadian Case Studies. FormAkademisk - Research Journal of Design and Design Education, 7(3). Retrieved from https://journals.hioa.no/index.php/formakademisk/article/view/794

Scepanovic, S. (2018). Data science for sociotechnical systems - from computational sociolinguistics to the smart grid. Aalto University. Retrieved from https://aaltodoc.aalto.fi:443/handle/123456789/30187

Second Muse, Intel, & Vibrant Data. (2016, May 11). What Your Data Says: Post-Secondary Education Mapping Survey Highlights. RECODE. Retrieved from http://re-code.ca/whats_happening/watch-recodewebinar-what-your-data-says/

Wernet, A. (2014). Hermeneutics and Objective Hermeneutics. In U. Flick, The SAGE Handbook of Qualitative Data Analysis (pp. 234–246). SAGE Publications, Inc. https://doi.org/10.4135/9781446282243.n16

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