In This Issue
Summer Bridge on Issues at the Technology/Policy Interface
July 1, 2016 Volume 46 Issue 2

Thinking Big to Address Major Challenges: Design and Problem-Solving Patterns for High-Impact Innovation

Friday, July 1, 2016

Author: Joseph V. Sinfield and Freddy Solis

The world’s most pressing challenges are testing the limits of existing approaches to problem exploration, innovation, and design. Be it equitable provision of clean water (OECD 2012), creation of single-dose vaccines (Varmus et al. 2003), clean-energy agriculture (Ferguson 2014), or restored and improved urban infrastructure (NAE 2008), complex systems-level problems that broadly affect society are driven largely by the extraordinary growth in the human population and its demand for essential resources such as water, food, and energy, as well as the compounding implications that manifest as longer human lifespans increase encounters with formidable medical conditions.

Characterizing Major Challenges

While many examples of important local-scale success stories in these and similar problem areas exist and should be lauded, achieving success of the reach and significance required to comprehensively address major challenges has proven vexing (Cohen 2006; Hait 2010; Wulf 2000), exposing multiple failure modes such as lack of adoption, funding shortages, unanticipated system behaviors, and technical barriers. Major challenges are thus perceived to be daunting, complex (both figuratively and technically), and, by many, intractable.

Partly, the difficulty in addressing such challenges stems from the differing nature of needed solutions. Some require fundamental scientific breakthroughs, others await development of enabling innovations (Solis and Sinfield 2015), and still others call for efficient democratization of established capabilities in unique circumstances. These solutions span technical, economic, social, and cultural domains, and thus impede approaches derived from only one perspective. Definitive improvements that can be translated into long-lasting, significant impact thus remain infrequent in most domains.

However, major challenges also share many characteristics. Commonalities can be seen in the systems, complexity, design, and innovation domains, all of which are ambiguously bounded, involve multiple stakeholders and interdependencies, and display nonlinear emergent behavior, network effects, and hysteresis (e.g., Barabasi and Bonabeau 2003; Maroulis et al. 2010; Mostafavi et al. 2011; Norman and Stappers 2015). Effective solutions likely require prioritization and accommodation of stakeholder needs, development and diffusion of new technology, and conversion of habits to realize change—perspectives frequently considered in isolation in the design and innovation domains.

Together, these characteristics suggest that large-scale sociotechnical problems could be more systematically addressed through problem-solving methods that integrate contributions from all these fields. What is needed are frameworks and vocabulary that facilitate concept sharing across communities and link actionable innovation behaviors to society’s greatest needs.

Changing the Focus of Innovation from Novelty to Impact

This article puts forward a qualitatively different approach to design (problem solving) for major challenges, emphasizing a proactive focus on high-impact innovation. This is a different emphasis than is classically pursued in innovation efforts, which focus on the novelty and differentiation of an idea rather than its impact. Impact, if examined at all, is typically considered retrospectively.

In fact, until recently there was no definition of innovation impact. Recent research (Solis and Sinfield 2015) has taken a first step in this direction by breaking impact into four fundamental dimensions:

  • reach: the number of individuals, groups, or societal segments affected by an innovation;
  • significance: the magnitude of benefit across measures of economics, health, environment, and culture;
  • paradigm change: the degree to which an innovation alters implicit or explicit worldviews in a particular domain; and
  • longevity: the timespan over which an innovation has influence.

Thus, in addition to searching for the novel and different, the act of innovating should focus on driving new solutions toward achieving impact as characterized by these four criteria.

Research has shown that the way one approaches a problem changes the nature of the resulting solution (Chi and Hausmann 2003; Dorst 2015; Grant and Berry 2011; McCaffrey 2012). Some have framed the notion that design activities vary according to the nature of desired goals, using the phrase design for x, where x represents a goal. As such, recently uncovered patterns of high-impact innovation suggest that big ideas—such as anesthesia, vaccines, transistors, or microfinance—with the potential for meaningful and long-lasting impact require thinking big, considering “outcomes” early and often while innovating, and proactively designing for Big X.

Designing for “Big X”

We present a conceptualization of a means to design for Big X developed from a scholarship of integration activity (Boyer 1990) that sought larger intellectual patterns by connecting insights from three lines of research (Solis 2015):

  1. meta-synthesis encompassing systems, complexity, innovation, entrepreneurship, and design literature;
  2. search for evidence of design behaviors across historical cases of high-impact technical and conceptual innovations, such as anesthesia, vaccines, transistors, the X-ray, and microfinance; and
  3. verbal protocol analysis of performance tasks completed by 20 innovators in industry and academia, who were asked to describe their approach to the representative major challenge of significantly increasing adoption of electric vehicles (EVs) in the United States.

Each method provided a perspective on innovation in complex contexts—research, history, and practice—which were then integrated. The result is a conceptual model that highlights shifts in problem-solving approaches for major challenges and is tied to an end-to-end conception of a design process, as illustrated in figure 1.

Figure 1

These shifts, described below, are significant in that they depart from hierarchies of generic design capability (Crismond and Adams 2012) and encompass activities to envision, shape, and pursue a new idea in which impact acts as a problem-solving guide.

Envisioning Big Ideas

From Design Briefs to Long-Term Visions Guided by Motifs

The first shift to design for Big X sets an aspirational goal for big ideas by defining a vision and strategic intent using innovation motifs. Because of the potential to get lost in the myriad issues that underlie complex challenges, it is critical to enter the design process with a long-term vision that encompasses the full scope of sought-after solutions. This is more than the typical focus on objectives, constraints, or performance characteristics stated in design briefs.

The vision should encompass a perspective on how the design outcome will affect its host ecosystem, the intent of the impact, and possible starting points for realization of the idea. To guide this aspiration, one can employ motifs, flexible design guides common in other design-based disciplines (e.g., in architecture, art deco or Prairie style). Motifs provide thematic considerations that help prioritize design choices. For example, when developing a technology for an emerging market, a disruptive motif would suggest that performance tradeoffs are acceptable in order to achieve affordability, accessibility, or ease of use (Anthony et al. 2008).

In efforts to create visions for high impact, innovation motifs can be used to relate a problem type to a solution form and its potential impact. The established motifs of enabling (Solis and Sinfield 2015) and competency-enhancing innovation (Abernathy and Clark 1985), as well as general purpose technology (Bresnahan and Trajtenberg 1995), can all describe not only the novel nature of a solution but also aspects of its impact on the problem at hand.

When combined, visions and motifs create aspirational yet actionable guides for success. The development of microfinance (Yunus 1999), for example, emerged from the aspiration of providing financial services for the poor. Grameen Bank, one of the first in this space, is an excellent example of an enabling innovation motif, as it was designed to drive a paradigm change in banking practices. Other innovations, such as insurance, transistors, and GPS, followed similarly aspirational visions.

Shaping Big Ideas

From Framing Isolated Problems to Framing Flaws in Paradigms

As mentioned, research suggests that the way a problem is framed inherently constrains the set of solutions one can develop. When designing for Big X, the stage is set for big ideas by framing flaws in paradigms. This approach identifies opportunities to change worldviews by uncovering important assumptions in problem and solution spaces.

Surfacing hidden assumptions—and attempting to proactively counter them—can help unearth new challenges and possibilities. Doing so entails going beyond framing isolated problems to question the validity of old assumptions and frameworks (Chi and Hausmann 2003; Sitkin et al. 2011), and unearthing not only what is known but also what is not known because it is inherent (hidden) in cultural and historical traditions or is at the outer limits of the body of knowledge.

Anesthesia, for instance, addressed the hidden assumption that pain in acute circumstances, such as surgery, was a normal part of life (Gawande 2012, 2013). Advancing the concept of the laser required reexamination of the assumptions of thermal equilibrium underlying the 2nd law of thermodynamics (Townes 1999). Microfinance challenged flawed assumptions in banking systems.

The paradigm framing approach was also evident in the performance tasks observed in our research, which revealed significant differences in solution scope and richness between participants who simply listed problems with EVs and those that more deeply examined transportation paradigms to search for hidden assumptions. The former talked primarily of technical solutions, whereas those who challenged the transportation paradigm tended to integrate economic incentives, governmental policy, sociocultural issues, and broader infrastructure considerations in their proposed solutions.

From Focused Research to Systematic Multiscale, Multifaceted Exploration

Big ideas often face multiple types of resistance, making deep investigations of focused issues valuable but likely insufficient. When one thinks of a big idea and its broad adoption, many categories of issues emerge (e.g., technical, legal, social). “Thinking big” thus requires systematic exploration of technical, economic, systems, sociological, and psychological forces that may act on a promising concept.

The adoption of X-rays in the medical field, in the first half of the 20th century, for instance, encountered sociological barriers rooted in power struggles between X-ray technicians, physicians, and the nascent field of radiology (Kevles 1997). Technical issues also had to be overcome, like the resolution of X-ray machines, which was addressed through the development of Coolidge tubes and ray collimation techniques. Although these issues were ultimately solved, failure to anticipate and tackle them slowed adoption.

Similarly, in our study, a range of issues were raised in the performance tasks, from battery range and charging speed to economics, commuting patterns, and emotional attachment to cars. Stronger innovators attempted to systematically explore this set of issues to develop a comprehensive view.

Focusing on a single issue or scale may obscure others. Opportunities to accelerate adoption of big ideas become more apparent when a broader view of a challenge is considered early in the design process.

From Analogies to Thinking from First Principles

Typically, idea generation relies on techniques such as lateral thinking, heuristics, and analogies (Ahmed and Christensen 2009; de Bono 1975; Yilmaz and Seifert 2011). But when ideas are new to the world and represent a true paradigm change, the possibilities for thinking by analogy are somewhat limited. To think big, “idea spaces” themselves must broaden, and one way to do so is by connecting decontextualized first principles to new contexts.

Research on physics education suggests that thinking from first principles provides another alternative: getting to the fundamental core of ideas to derive new possibilities (Chi et al. 1981; Larkin et al. 1980; Stinner 1989). Connections of first principles to other ideas and/or application spaces create nonobvious opportunities to advance solution capabilities and impact. Jargon-free language that describes first principles without discipline-specific implications is critical to facilitating such links.

Consider the laser. When described as a coherent energy source that can precisely ablate material, many domains emerge in which this first principle has value—surgery, dentistry, manufacturing, cleaning. Thus, big thinkers might prioritize ideas with more first principle potential over others.

From Modelling to Assessing and Shaping Ecosystems

Thinking big also involves assessing and shaping ecosystems holistically because successful big ideas often proactively incorporate in their design elements that tackle ecosystem barriers. Embedding such elements implies thinking beyond a solution to consider how it interacts with a system.

This philosophy emphasizes “framing and solving” the ecosystems in which a solution will play a role, especially those that may host a solution in its path toward success. Insights from the systems literature shed light on this concept (Adner and Kapoor 2010; DeLaurentis and Ayyalasomayajula 2009; DeLaurentis and Callaway 2004; Maroulis et al. 2010).

Making early microfinance initiatives work required going beyond the creation of loan mechanisms for the poor. It entailed developing support groups in villages that encouraged repayment and proper use of funds, as well as policies and training adequate for areas with high illiteracy rates; hosting meetings in open spaces to inspire trust and reduce corruption; and identifying ways to overcome gender bias (Yunus 1999).

From Moonshots to Lily Pad Performance Development

Rethinking solution performance and connecting to early impact contexts may make it possible to accelerate and “de-risk” high-impact efforts. This philosophy focuses on agglomerating and disaggregating capabilities to create new notions of performance that can achieve early impact in contexts often different from the context of the overall goal. This goes beyond mapping and balancing solution tradeoffs (Kim and Mauborgne 2005) to include assessment of capability variations, performance trajectories, and context as key variables.

At early times in the development of an innovative solution, the solution is unlikely to be ready for its ultimately envisioned application. However, this does not limit its potential to be applied and to gain faster adoption in contexts outside of traditional boundaries. Searching for performance-context opportunities that are right for the currently achievable level of performance can thus uncover new, counterintuitive paths to the overall vision for a big idea, avoiding reliance on “achieving the moonshot” to make progress. Collectively, a succession of these opportunities resembles a roadmap of stepping stones, or “lily pads,” that simultaneously advance performance, de-risk efforts, and refuel an innovation.

For X-rays, lily pads included short stints in department store entertainment, shoe fitting, customs inspection, and forensics before moving into dental and medical practices (Kevles 1997). X-rays thus made multiple lily pad “jumps” prior to broad adoption. For big ideas, these jumps across domains have historically occurred serendipitously, often over great lengths of time; but they can be pursued by design.

Pursuing Big Ideas
From Information Transfer to Persuasion

In the communication of big ideas, simply transferring information is not enough. Driving changes to worldviews and altering ecosystems requires artful persuasion to facilitate acceptance or use. This may involve stories, habit conversion techniques, and means to convey counterintuitive insights (Denning 2004; Graybiel 2008; Graybiel and Smith 2014; Kegan and Lahey 2009). These techniques tap emotion, empathy, and human nature, and are key to addressing the natural resistance to new ideas; stories, for example, help paint visions and trigger emotions that enhance idea adoption (Heath et al. 2001; Heath and Heath 2007).

In a preanesthetic world, for instance, surgeons were used to operating quickly to minimize patient suffering, and even after anesthesia’s invention some surgeons continued to proceed in their rushed ways—spectators even timed them with pocket watches (Gawande 2012). It took significant persuasion—through public surgical exhibitions, press coverage, and rigorous academic publications—to encourage the community of surgeons to adjust their habits—and to convince society that, despite resistance from some clergy and physicians who considered pain a natural part of life, anesthesia was a much needed paradigm change (Gawande 2013).

From Predicted and Deliberate to Emergent and Effectual Pursuit

Big ideas can take many implementation paths, so the design of implementation strategies is critical. In designing effectual and emergent paths to unfold the impact of a big idea,1 implementation strategies are defined by mapping and converting key assumptions necessary to achieve impact into actionable learning experiments. These experiments should aim to test and validate big idea assumptions—such as performance limitations, uncertainty in application spaces, and ecosystem-level barriers (Blank 2005; McGrath and MacMillan 1995; Mintzberg and Waters 1985). They also entail imagining new goals and means given existing means, resources, and relationships (Sarasvathy 2009). These implementation strategies are then pursued by deploying learning experiments to discover the path to impact, prioritizing opportunities to earn (for economic sustainability), learn (for solution improvement), and redirect efforts in light of learning.

In the early history of transistors, for example, interests shifted back and forth between germanium and silicon as candidate materials for semiconductors (Isaacson 2014). In our EV tasks, participants proposed experiments to learn what would drive adoption, focusing on sensitivity to gas prices, density of charging stations, urban community characteristics, and tax subsidies. The uncertainty around these issues makes predictive approaches less useful than emergent and effectual ones.

Implications

The unique shifts in behavior needed to design for Big X appear consistently in historical examples of high-impact innovation, are reinforced by connecting insights gleaned in multiple related fields, and are evident in the problem-solving strategies of contemporary innovators. Such an innovation framework is not positioned as better or more advanced than other approaches; it simply provides new entry points into the innovation process for problem solvers.

Awareness of these behaviors can help leaders in various types of organizations drive new kinds of solutions to a range of complex problems in the form of big ideas that can alter the way individuals, groups, and society live and act:

  • For governments and nonprofits, they could lead to answers to society’s major challenges.
  • For companies, they could lead to innovations that drive growth with longevity.
  • In academia, they could highlight new avenues for high-impact research and new ways to teach students to innovate.

In addition, each of the philosophies described here can help leaders understand whether stakeholders are asking the right questions about promising concepts to drive breakthroughs toward impact. They can also help assess whether the right people (with the right mindset) are involved in big idea projects—whether innovation teams are balanced in terms of insights and perspectives as well as expertise, because no individual is likely to excel in all areas.

Perhaps more importantly, these behaviors can help inform pedagogy for innovators of the future, encouraging them to proactively and systematically outline conceptual problem-solving shifts when needed. Awareness and practice of these competencies will be valuable to all organizations and individuals pursuing significant impact in the world.

Acknowledgments

This research was conducted with generous support from the Purdue Engineer of 2020 Initiative, the Consejo Nacional de Ciencia y Tecnología (CONACYT) of Mexico, and Purdue’s Bilsland Strategic Initiatives Fellowship Program.

References

Abernathy WJ, Clark KB. 1985. Innovation: Mapping the winds of creative destruction. Research Policy 14:3–22.

Adner R, Kapoor R. 2010. Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations. Strategic Management Journal 31(3):306–333.

Ahmed S, Christensen BT. 2009. An in situ study of analogical reasoning in novice and experienced design engineers. Journal of Mechanical Design 131(11):111004.

Anthony S, Johnson M, Sinfield J, Altman E. 2008. The Innovator’s Guide to Growth: Putting Disruptive Innovation to Work. Boston: Harvard Business School Press.

Barabasi AL, Bonabeau E. 2003. Scale-free networks. Scientific American 288(5):50–59.

Blank S. 2005. The Four Steps to Epiphany: Successful Strategies for Products That Win, 2nd ed. Pescadero, CA: K&S Ranch Press.

Boyer EL. 1990. Scholarship Reconsidered: Priorities of the Professoriate. Princeton, NJ: Carnegie Foundation for the Advancement of Teaching.

Bresnahan TF, Trajtenberg M. 1995. General purpose technologies “engines of growth”? Journal of Econometrics 65(1):83–108.

Chi M, Feltovich PJ, Glaser R. 1981. Categorization and representation of physics problems by experts and novices. Cognitive Science 5:121–152.

Chi M, Hausmann R. 2003. Do radical discoveries require ontological shifts? In: International Handbook on Innovation, ed. Shavinina L, Stenberg R. New York: Elsevier.

Cohen J. 2006. The new world of global health. Science 311(5758):162–167.

Crismond DP, Adams RS. 2012. The informed design teaching and learning matrix. Journal of Engineering Education 101(4):738–797.

de Bono E. 1975. The Uses of Lateral Thinking. New York: Harper and Row.

DeLaurentis DA, Ayyalasomayajula S. 2009. Exploring the synergy between industrial ecology and system of systems to understand complexity. Journal of Industrial Ecology 13(2):247–263.

DeLaurentis D, Callaway R. 2004. A system-of-systems perspective for public policy decisions. Review of Policy Research 21(6):829–837.

Denning S. 2004. Telling tales. Harvard Business Review 82(5):122–129.

Dorst K. 2011. The core of “design thinking” and its application. Design Studies 32(6):521–532.

Dorst K. 2015. Frame creation and design in the expanded field. She Ji: The Journal of Design, Economics, and Innovation 1(1):22–33.

Ferguson D. 2014. Introduction: A grand challenge for next generation solutions. USAID Frontlines: Grand Challenges for Development, July/August. Available at www.usaid.gov/news-information/frontlines/grand-challenges/ introduction-grand-challenge-next-generation-solutions.

Gawande A. 2012. Two hundred years of surgery. New England Journal of Medicine 366(18):1716–1723.

Gawande A. 2013. Slow ideas. The New Yorker, July 29.

Grant AM, Berry JW. 2011. The necessity of others is the mother of invention: Intrinsic and prosocial motivations, perspective taking, and creativity. Academy of Management Journal 54(1):73–96.

Graybiel AM. 2008. Habits, rituals, and the evaluative brain. Annual Review of Neuroscience 31:359–387.

Graybiel AM, Smith KS. 2014. Good habits, bad habits. Scientific American 310(6):38–43.

Hait WN. 2010. Anticancer drug development: The grand challenges. Nature Reviews Drug Discovery 9(4):253–254.

Heath C, Heath D. 2007. Made to Stick: Why Some Ideas Survive and Others Die. New York: Random House Publishing Group.

Heath C, Bell C, Sternberg E. 2001. Emotional selection in memes: The case of urban legends. Journal of Personality and Social Psychology 81(6):1028–1041.

Isaacson W. 2014. The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution. New York: Simon & Schuster.

Kegan R, Lahey LL. 2009. Immunity to Change: How to Overcome It and Unlock Potential in Yourself and Your Organization. Boston: Harvard Business Press.

Kevles BH. 1997. Naked to the Bone: Medical Imaging in the Twentieth Century. New Brunswick, NJ: Rutgers University Press.

Kim WC, Mauborgne R. 2005. Blue Ocean Strategy. Boston: Harvard Business School.

Larkin JH, McDermott CM, Simon DP, Simon HA. 1980. Models of competence in solving physics problems. Cognitive Science 4(4):317–345.

Maroulis S, Guimera R, Petry H, Stringer MJ, Gomez LM, Amaral LA, Wilensky U. 2010. Complex systems view of educational policy research. Science 330(6000):38–39.

McCaffrey T. 2012. Innovation relies on the obscure: A key to overcoming the classic problem of functional fixedness. Psychological Science 23(3):215–218.

McGrath RG, MacMillan IC. 1995. Discovery-driven planning. Harvard Business Review 73(4):44–54.

Mintzberg H, Waters JA. 1985. Of strategies, deliberate and emergent. Strategic Management Journal 6(3):257–272.

Mostafavi M, Abraham DM, DeLaurentis D, Sinfield JV. 2011. Exploring the dimensions of systems of innovation analysis: A system of systems framework. IEEE Systems Journal 5(2):256–265.

NAE [National Academy of Engineering]. 2008. Grand Challenges for Engineering. Washington: National Academies Press.

Norman DA, Stappers PJ. 2015. DesignX: Complex sociotechnical systems. She Ji: The Journal of Design, Economics, and Innovation 1(2):83–106.

OECD [Organization for Economic Cooperation and Development]. 2012. OECD Environmental Outlook to 2050. Paris.

Sarasvathy SD. 2009. Effectuation: Elements of Entrepreneurial Expertise. Northampton, MA: Edward Elgar Publishing.

Sitkin SB, See KE, Miller CC, Lawless MW, Carton AM. 2011. The paradox of stretch goals: Organizations in the pursuit of the seemingly impossible. Academy of Management Review 36(3):544–566.

Solis F. 2015. Characterizing enabling innovations and enabling thinking. PhD, Civil Engineering, Purdue University.

Solis F, Sinfield JV. 2015. Rethinking innovation: Characterizing dimensions of impact. Journal of Engineering Entrepreneurship 6(2):83–96.

Stinner A. 1989. The teaching of physics and the contexts of inquiry: From Aristotle to Einstein. Science Education 73(5):591–605.

Townes CH. 1999. How the Laser Happened: Adventures of a Scientist. New York: OUP USA.

Varmus H, Klausner R, Zerhouni E, Acharya T. 2003. Grand challenges in global health. Science 302(5644):398–399.

Wulf WA. 2000. Great achievements and grand challenges. The Bridge 30(3&4):5–10.

Yilmaz S, Seifert CM. 2011. Creativity through design heuristics: A case study of expert product design. Design Studies 32(4):384–415.

Yunus M. 1999. Banker to the Poor. New York: PublicAffairs.

FOOTNOTES

1  “Emergent” here is the opposite of “deliberate.” Essentially, it refers to an unpredictable and unanticipated path that unfolds as progress is made. This term is broadly used in strategic management literature in relation to a seminal article by Mintzberg and Waters (1985).

About the Author:Joseph V. Sinfield is associate professor of civil engineering and Freddy Solis is postdoctoral research associate, both at Purdue University.