Wednesday, September 27, 2006

Concurrent workshops

• Session C-1 Understanding the impacts of innovation
• Session C-2 New indicators for science and technology policies
• Session C-3 Global knowledge flows – the spatial dimension

Session C-1 Understanding the impacts of innovation

• New directions for understanding innovation
• Organisational forms and innovative performance
• What is missing in the analysis of input-output relationships of innovation processes?
• Where science, technology and innovation indicators hit the road and roadblocks

New Directions for Understanding Innovation

Biographical Note

Frances Anderson is a Special Advisor on Science and Technology Statistics and Standards at the Science, Innovation and Electronic Information Division at Statistics Canada.  She is chief of the Indicators Development Section and is responsible for carrying out and analysing surveys of innovation and advanced technologies. Prior to joining Statistics Canada, she was a strategic planner at the National Research Council of Canada.  She received her Ph.d. from the Institute for History and Socio-Politics of Sciences at the University of Montréal.

Susan Schaan is the head of the production unit in the Indicators Development section of the Science Innovation and Electronic Information Division at Statistics Canada. She is responsible for carrying out surveys of innovation and advanced technologies. She has been an employee of Statistics Canada since 1994. Prior to joining the Science, Innovation and Electronic Information Division in 1999 she gained experience working in Geography Division, Health Statistics Division and Prices Division. Susan has a Master of Science degree in geology from the University of Ottawa.

Ingrid Schenk is a Policy Analyst for the Innovation Policy Branch, Industry Canada.  She completed her Ph.D. at the Science and Technology Policy Research Institute (SPRU), University of Sussex, UK. Her areas of specialization include regional innovation policy, the commercialization of new technologies, and business R&D strategy.  Prior to joining Industry Canada, she was a consultant to the UK financial services industry designing strategies for the secure delivery of electronic commerce services.

Abstract

New Directions for Understanding Innovation

This paper will present survey work and indicators that have been developed by Statistics Canada's Science, Innovation and Electronic Information Division (SIEID) that provide new directions in understanding of innovation and will present some key findings from this work. The four following questions will provide the basis for a discussion of new directions to understanding innovation.

What are innovative products, processes, and practices?

Most questionnaires using the Oslo Manual guidelines ask firms to indicate if they have introduced a new or significantly improved product or process. These general questions enable a determination of whether the firm is innovative and provide a general description of the nature of the innovation. An alternative approach used by SIEID is to specify listings of innovative products, processes and practices when they are known and understood. Such listings allow, not only for precise indicators, but also for tracking the diffusion of advanced, emerging or enabling technologies, practices and products throughout the economy. It also allows for the analysis of public sector development and use of innovative products, processes and practices.

What are the determinants of innovation and the different modes of innovation?

Determinants of innovation and of different modes of innovation is a key issue for policy-makers and considerable work has been done by SIEID in collaboration with its policy partners to explore this issue. Work has been done examining the extent to which geographic location determines the innovative behaviour of a firm. The effect on innovation of firm size and a firm's position in a domestic or global supply chain has also been studied.

What is the relationship between innovation and a firm's decision-making processes and strategy?

The effect of decision-making and strategic intent on innovation is an area of study that has been examined by SIEID. Two areas that have been examined involve the firms in transition from one stage of development to another
1. when a firm is making the transition from venture capital firm to operational firm and 2. when a firm adopts a growth strategy to move from small to medium size. Indicators of management capability, human resources strategy, investment and capital procurement strategy, and the supply of business advice have been developed. The issue of entrepreneurship has be studied using a case study approach.

Innovation and firm performance: What is the relationship?

Firm success is most often measured by economic indicators such as productivity, growth, profitability, export performance or, at the limit, survival. SIEID has undertaken studies to examine the extent to which innovation contributes to firm success using these indicators. What emerges from these studies is the need to better understand what kinds of innovation lead to what kinds of success and what are the complementary assets and conditions that need to be in place in ensure firm success.

The paper will conclude, based on SIEID experience, by proposing a number of projects that could, in the future, contribute to a broader understanding of innovation.

Organisational forms and innovative performance

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Biographical Note

Anthony Arundel is a Senior Researcher at the Maastricht Economic Research Institute on Innovation and Technology (UNU-MERIT) of the University of Maastricht. His research focuses on innovation surveys and the use of survey data to develop indicators for policy analysis.

Edward Lorenz received his BS from MIT, MA from UC Berkeley and PhD from the University of Cambridge. He is currently Professor of Economics at the University of Nice-Sophia Antipolis. His research interests include the development of empirical indicators of organisational innovation and the comparative analysis of national innovation systems.

Abstract

R&D and human capital are commonly measured innovation inputs. However, innovation could also depend on organisational environments that encourage problem solving and learning. This paper explores the link between organisational structures and innovation by developing national aggregate indicators for the EU member states of organisational forms and innovation modes (how firms innovate). The organisational indicators are constructed from the Third European Survey of Working Conditions results for 8,081 salaried employees in 2000. The innovation mode indicators are calculated using the results of the third Community Innovation Survey (CIS-3) for innovation activities between 1998 and 2000. Results are available from both surveys for 14 EU countries.

Multiple correspondence analysis (MCA) is used to identify four main organisational forms experienced by European employees. Two forms, Taylorism and traditional organisation, give employees little responsibility for problem solving and learning and are unlikely to support innovation, while two other forms give employees such responsibilities. One, termed 'discretionary learning', also places few constraints on employees and is similar to the 'operating adhocracy' in the organisational literature. The other, 'lean organisation', also emphasizes problem solving and learning but places substantial constraints on employees.

Logit regression, controlling for known factors that can influence organisation such as firm size, sector and occupation, shows that there are marked national differences in the distribution of organisational forms. This raises the possibility that the large differences in innovative performance across Europe could be linked to the distribution of organisational forms, particularly discretionary learning and lean organisation.

The CIS results are used to assign firms to one of five innovation modes: strategic innovators with intensive in-house innovative activities, second-stream innovators who develop innovations in-house when necessary, technology modifiers who adapt technology developed by other firms, technology adopters who only innovate through acquiring new products and processes "off the shelf", and non-innovators. These modes form a continuum in each firm's capacity to explore and develop new knowledge into innovations, with strategic innovators at one end and non-innovators at the other.

We correlated the national percentages of each innovation mode with the logit regression coefficients for each country for discretionary learning and lean organisation. Discretionary learning is positively correlated with all innovation modes that require some in-house capabilities to develop or adapt product and process innovations. We also expected lean organisation to be positively correlated with technology modifiers, since this type of organisation, based on the Japanese experience, should encourage incremental innovation. However, lean organisation is negatively correlated with strategic, second-stream and technology modifiers. Conversely, it is positively correlated with technology adopters and non-innovators.

These results suggests that European policy efforts to improve innovation performance as part of the revised Lisbon strategy would benefit from organisational indicators that could be directly linked to innovation performance. The bottleneck to improving the innovative capabilities of European firms might not be low levels of R&D, which are strongly determined by industry structures and difficult to change, but the widespread use of organisational forms that are unable to provide a fertile environment for innovation.

What is missing in the analysis of input-output relationships of innovation processes?

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Biographical Note

Johan Hauknes is senior researcher at NIFU STEP in Norway. His main research fields involve theoretical analysis of innovation activities and their structural impacts, and the rationales and basis this provides for the formation of economic and innovation policies.

Svein Olav Nås is senior researcher at NIFU STEP in Norway. Fields of work include indicator development and analysis within the broad field of innovation studies. He is also involved in preparation of the CIS surveys, revisions of the OM, and NESTI work.

Mark Knell joined NIFU STEP in January 2005, as a Senior Researcher.  In total, he has more than fifteen years of experience in teaching and research in the fields of technology and economic policy research.

Abstract

It is frequently argued that innovation is the core economic determinant of growth and structural change in modern capitalist economies (Nelson and Winter, TEP, OM). On this background one would expect to observe strong correlations between innovation inputs and economic results. Part of the rationale of the Oslo Manual is to facilitate such analysis by operationalising a series of input and intermediate innovation indicators. However, econometric estimations of the effects on economic performance of the OM types of innovation inputs only explain small fractions of the observed variance. Why is this? Can we do something about it? Those are the core questions raised in the paper, with the aim of contributing to improved indicators and analysis.

The problems are addressed by investigating the three aspects of the relationship in turn: To what degree are the relevant input factors identified and introduced in the estimations? What are the expected and identifiable results and effects of innovation from the perspectives of business firms and society? What are the main characteristics to take into account when modeling the relationships between inputs and outputs?

Three basic points are raised on the input side. Firstly, that knowledge is complex and many facetted, arriving in different forms of which embedded forms and well established truths make up the major parts – a complexity that is not fully covered within CIS type data and related analysis. Secondly, that innovation analysis tend to focus the parts of knowledge management that deals with extending or changing knowledge, usually the minor parts of the knowledge that is actually utilized to generate economic outputs and profitability for the firms. To a large extent the accumulated knowledge, know-how, where, when etc. are overlooked, routines that – it can be argued – is the core of what makes up a firm. Thirdly, firms belong to different kinds of networks and relationships that allow them to utilize knowledge created or controlled outside their formal boundaries. Such linkages are still poorly understood and analyzed.

On the output side it is pointed out that the relevant indicators already exist in the form of such basic measures of economic performance as profitability, productivity, growth, national product and employment. These are, however, the results of the total activities of the firms and national economies, and not solely of innovation over a limited period (understood as recent incremental or radical changes in how the business is carried out). What is also missing in this is a robust understanding of the outputs in terms of measures of economy-wide economic growth.

Several topics are brought into the discussion of how the relationship can be modeled. This includes problems of simultaneity, time lags, spill-overs, uncertainty and the ability to capture benefits. The difference between business performance and benefits to society is brought up, being highly relevant to motivate policy intervention. Linking micro-level innovation activities and endogenous changes in the structure of economies and ultimately their aggregate economic performance is essential for improving the quality of innovation – and wider economic policies.

Where Science, Technology and Innovation Indicators hit the Road and Roadblocks

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Biographical Note

Susan McDaniel is currently Professor of Sociology, University of Windsor. She was for 15 years, Professor of Sociology at the University of Alberta, and was awarded the honorific title, University Professor there. She has written on social dimensions of innovation and served as Chair of the Statistics Canada Advisory Committee on Science and Technology that helped develop the framework and agenda for the cohesive collection of science, technology and innovation data in Canada during the past decade. She has recently been appointed to the Scientific Advisory Committee of the new Canadian Academies of Science. She is a Fellow of the Royal Society of Canada.

Abstract

The impacts of technologies on societies and on organizations/ institutions/societies is a large and growing focus of indicators work. The impacts of society and of organizational/institutional arrangements and cultures on technological changes and on innovation have received far less attention. In this paper, evidence is amassed on what is known about the social, cultural and organizational factors that create and foster climates for technological innovation. Research from Statistics Canada and other sources such as the Innovation Systems Research Network tells us that human capital and efficient and effective organizations matter greatly to innovation. Yet, organizations with the same functions in societies and similar organizational arrangements, such as universities, differ greatly in their capacities to innovate and to bring innovations to societal benefit. Findings from a qualitative comparative case study of several universities in Canada reveal that elusive factors such as receptivity to organizational innovation, the degree to which networking is structurally encouraged or not, and the connections university researchers build with society, with firms and with government agencies matter greatly to how and whether innovations occur. These non-STI indicators could be usefully incorporated as emerging indicators for the purpose of Science, Technology and Innovation policy illumination and policy development.