Department of
Sociology

Center for
Innovation

Innovation
Problem Solving at Four Analytical Levels

The central guiding assumption that activates the basic thematic equation for this section is that increasing diversity leads to more creativity, innovation, and adaptiveness or problem solving. Applied to the following thematic equation:

Knowledge + Learning = Innovation = New Knowledge = Adaptation

means diversity in knowledge specialties or ways of thinking, diversity in the channels of communications or methods for learning will lead to diversity in knowledge, which can be translated into radical innovation or knowledge advances or major adaptive steps.

Given this framework The Center for Innovation has been attempting to solve three important issues: (1) how to increase innovation at each analytical level; (2) how to measure innovation, again at four levels; and (3) how to increase the amount of learning or knowledge sharing. The action theory solutions at each of four levels are:

Action Steps to Increase Innovation

The Individual Level: How to be more Creative and Fulfil One’s Sense of Promise

How can this be done by an individual?

  • Choose to live in a diverse, racially integrated innovative region in your country
  • Acquire a solid understanding of two languages and their cultures
  • Travel to other countries and live with their citizens for an extended period
  • Take two majors in college, one for work and one for pleasure
  • Develop friendships with people who have different nationalities, ethnicities, color, economic backgrounds, political preferences, sexual orientations, and religions/moral principles
  • Participate in social action programs in high school and college that are designed to help the disadvantaged defined in some way

The Organizational Level: How to Develop Radical Innovations and Be Adaptive in a Changing Environment

What are the levers that managers can use?

  • Increase the Level of Knowledge by recruiting diverse individuals with different backgrounds, training, and gender/race to create a complex division of labor
  • Increase the Amount of Learning by involving these individuals in decision-making so that the power structure is decentralized
  • Increase the Amount of Learning by utilizing a diverse set of mechanisms to encourage both internal and external learning networks
  • Increase the Amount of Learning by pursuing a visionary strategy for changing the world

The Regional Level: How to Create Innovative Districts in a Global World

What strategies should policy makers employ?

  • Increase the Level of Knowledge in the region by diversifying research fields, occupations, research on hardware and software, and the presence of national and foreign firms
  • Increase the Amount of Learning by adding brokers, informal associations, diversity of migrants, and density of knowledge exchanges in communities of practice and across supply and idea innovation networks
  • Increase the Level of Economic Resources by encouraging angels, venture capital, and government funding
  • Increase the Level of Political Support at all three levels: local, regional, and central government policies to obtain policies designed to facilitate the level of knowledge, amount of learning, and availability of economic resources
  • Develop a Distinctive Sub-Culture the combines cooperation with competition, trust, and risk-taking

The Macro Level: How to Increase Societal Innovation for Faster Economic Growth, Higher Productivity, More Secure Employment with Rising Incomes

  • Increase knowledge by forming a systematic coordinated inter-organizational network between basic scientific research, product development research, manufacturing research, quality research, and commercialization research or the idea innovation network (Hage and Hollingsworth, 2000).
  • Increase the amount of learning by regular meetings of project leaders from the different sections and the choice of individuals who have had experience in more than one research arena.
  • Economic resources come from the pooling of fundings from government agencies, business firms and when relevant NGOs.
  • Gain political support for identifying how this will solve the current crisis of advanced capitalist societies.

How to Measure Innovation

An important issue is how does one define radical product/service innovation. Essentially and at minimum there are eight ways of measuring the degree of radicaliness:

Measuring Radical Product/Service Innovations

  • New sectors or niches
  • Increases in performances (added and weighted by importance relative to price)
  • Reduced externalities (harm to the user or the environment)
  • Adding multiple functions

For examples of how to measure radical innovation in science, see the papers by Hage and Mote (2008, 2010) on the Institute Pasteur.

Radical Product Process or Service Provision Innovations

  • New technology
  • Higher productivity or lower cost
  • High quality (fewer errors, less recidivism, longer lasting, etc.)
  • Flexibility in product or service mix

The problem of the measurement of innovation and especially from a perspective of its costs and benefits is particularly difficult in the area of public research and yet is vitally important. To demonstrate how this problem can be solved, the Center for Innovation did a socio-economic cost-benefit study of the outputs of the Center for Satellite Applications and Research (STAR) in the National Oceanographic and Atmospheric Agency (NOAA), which provides satellite reports and other vital meteorological information. STAR has developed products for measuring the thickness of arctic sea ice, predicting harmful algal blooms in the Chesapeake Bay, measuring the size of the ozone hole in Antarctica, and detecting wild forest fires in the Amazon, among many others (Powell et al., 2012: 148, 98, and 120, respectively). Our report (Hage, Mote, Ngulie, 2007) demonstrated much greater value than had been previously realized.

The failure to invest in new technologies is also aptly demonstrated in this case. Congress in the first decade of the new century refused to invest in the proposed hyper-spectral suite of instruments to be launched in a new satellite. This would have reduced the number of deaths due to tornados, allowed better prediction of the tracks of major storms, and more frequent updates of weather predictions. Europe did invest in this new technology and now American weather forecasters use what is called the European model, which tends to be more accurate.

Evaluating the National Innovation System at Three Levels

Besides specific recommendations at each of three levels, Restoring the Innovative Edge: Driving the Evolution of Science and Technology (Hage, 2011) carefully outlines why innovation has become a crisis in the United States in particular. Each of the six chapters contains a series of action solutions for improving the innovation rate of the society.

What are some action theory strategies for increasing scientific learning within public research organizations? Answers to this question can be gleaned from those factors that appear to increase learning. To get these answers, the Center for Innovation engaged in two major studies of publicly-funded research organizations. The first focuses on a six-year period of STAR. Internal learning was measured with four indicators: (1) critical thought; (2) cross-fertilization of technical ideas, (3) communication among project members; and (4) communication between project managers and senior management. Other variables that were measured are fundamental understanding, knowledge exchanges, decentralization, risk-taking, and manager quality. Given STAR’s special mission, many of its scientists work with NASA, the Department of the Navy, and several other external organizations. The amount of STAR’s participation in external networks was therefore also measured. The analysis concluded that the most important variables for explaining the amount of knowledge exchange are collaboration, management quality and a risk-taking culture.

The second case study of internal knowledge exchanges among scientists conducted by the Center of Innovation was much more ambitious because it focused on 60 research projects including alternative energies, biology, chemistry, geosciences, inter-disciplinary work, and material sciences in six federally-funded research organizations. Five of these were attached to the science division of the Department of Energy, and one was attached to NOAA (but not STAR). To improve the measurement of diversity of knowledge exchanges, especially as it relates to research in broad research programmatic areas, new indexes were developed distinguishing between exchanges within the same discipline and those that occur across disciplines, as well as exchanges within the same national laboratory and those that occur with other organizations in the national innovation system or other external networks. Relationships in this study are weaker than in the first study because of the greater diversity in disciplines, which do vary in how much knowledge exchange they engage in. In particular, geosciences (the basic discipline for STAR) has a much higher rate, presumably given the diversity of different fields (air, earth, and water) within it. Nonetheless, the results are similar. The key variables for explaining knowledge exchanges are risk-taking culture and management quality.

In both of these studies the strength of risk-taking culture and the quality of management were perceived by the scientists to be higher in the smaller research organizations. Therefore, From this study, emerged an action theory implication, namely that the large public research laboratories need to be deconstructed into networks of smaller organizations (See Mote et al., 2015).

References

Hage, J. 2011. Restoring the Innovative Edge: Driving the Evolution of Science and Technology. Stanford, CA: Stanford University Press.

Hage, J, and JR Hollingsworth. 2000. A Strategy for the Analysis of Idea Innovation Networks and Institutions. Organization Studies 21 (5):971–1004. doi: 10.1177/0170840600215006 [pdf].

Hage, J, GB Jordan, and JE Mote. 2007. A Theory-Based Innovation Systems Framework for Evaluating Diverse Portfolios of Research, Part Two: Macro Indicators and Policy Interventions. Science and Public Policy 34 (10):731-41. doi: 10.3152/030234207X265385.

Hage, J, and JE Mote. 2008. Transformational Organizations and Institutional Change: The Case of the Institut Pasteur and French Science. Socioeconomic Review 6 (2):313–36. doi: 10.1093/ser/mwm022.

———. 2010. Transformational Organizations and a Burst of Scientific Breakthroughs: The Institut Pasteur and Biomedicine, 1889–1919. Social Science History 34 (1):13–46. doi: 10.1017/S0145553200014061.

Hage, J, JE Mote, and GB Jordan. 2013. Ideas, Innovations, and Networks: A New Policy Model Based on the Evolution of Knowledge. Policy Sciences 46 (2):199-216. doi: 10.1007/s11077-012-9172-8.

Hage, Jerald, JE Mote, and Ngulie. 2007. The Economic Benefits of Satellite, Remotely-Sensed Data: A Report to NOAA’s Center for Satellite Applications and Research (STAR). Center for Innovation, University of Maryland.

Jordan, GB, J Hage, and JE Mote. 2008. A Theories-Based Systemic Framework for Evaluating Diverse Portfolios of Scientific Work Part One: Micro and Meso Indicators. New Directions for Evaluation 118:7-24. doi: 10.1002/ev.257.

Mote, JE, G Jordan, J Hage, WC Hadden, and A Clark. 2016. Too Big to Innovate: Exploring Organizational Size and Innovation Processes in Scientific Research. Science and Public Policy 43 (3):332–7. doi: 10.1093/scipol/scv045.

Updated 18 April 2023