Measuring the Innovation Return on S&T Investments

Janet E. Halliwell

Presentation overview The big picture - measuring the return on

S&T investments (ROI)Context, challenges and issues, variables,

methodologies, trendsInnovation and what our understanding

means for assessing ROIThe data challengeWrap-up

The ROI contextIncreasing pressure to measure impacts of public S&T investments

(nothing new here!)What do researchers tend think about (especially for P&T)?

Inputs (e.g., funding raised)Research productivityNumbers of HQP Perhaps commercialization

What are institutions most interested in?Competitiveness for students, prestige and fundsCosts - impacts on their bottom line

What is the larger public interest?Quality of the PSE systemLarger social and economic impacts from R&D and service

ROI measurement challengesVery diverse languages and expectations on what is meant by ROINo universal framework or universally applied methodologiesMeasurement of ROI needs to encompass diverse dimensions of

impact: Economic (e.g. jobs, new products and services, spin off companies,

business process change) Social and health (e.g. changes in policy and practice, improved

outcomes, costs avoided) Environmental (e.g. reduced footprint and environmental impact,

branding Canada green) In addition to practical stumbling blocks of measurement of these

, interpretation of any measures is non-trivialAnd all of the above does not necessarily measure the full impact

on innovation or the innovation system

Some issuesROI measurement requires us to think about what

happens down the value chain as a result of the research and research related activities - beyond the quality, volume and influence of research on other research – e.g. what difference did this S&T investment make in the real world

ROI measurement is NOT a classical economic I/O study (which measures the flow of monies resulting from an activity regardless of what that activity is)

A theoretically-sound ROI method is poor if key stakeholders are not consulted or don’t understand it

Variables to think aboutYour audience/targetScope and level of aggregationDistance down the value chain of outputs,

outcomes and impactsTime scale (how far back)MethodologiesDesired detail; how to communicate (e.g.

visualize)Balancing accuracy, longevity, comparability

and ease of collection of metrics

Downstream measurment …Categories relevant to innovation include (at

least):Direct – Using research findings for better ideas,

products, processes, policies, practice etc.Indirect – From participation in the research,

including HQP training, KT, tacit knowledge, better management, etc.

Spin-off – Using findings in unexpected ways and fields

Knock-on – Arising far after the research is doneAlso very important – outcomes that foster an

environment in which innovation flourishes

Example methodologiesQuantitative

SurveysBibliometrics, including publication counts, citation analysis, data

mining, international collaboration analysis, social network analysis Technometrics, including hotspot patent – publication linkagesEconomic rate of return – micro and macro levelsSociometricsBenchmarking

Qualitative Peer Review, Merit/Expert ReviewCase study method – exploratory, descriptive, explanatoryRetrospective documentary analysis Interviews

Mixed models (e.g. Payback, OMS, CAHS)

TrendsMixed methods Increasing attention to networks, linkages,

collaborationsGlobal frame of referenceInvolvement of stakeholders inside and

external to R&D unit External focus – e.g. short-term external

impacts for industry or government, rather than immediate outcomes for the R&D organization

What is innovation?Doing new things in new ways

Tom Jenkins

“Innovation” is (or should be) a very broad term, BUT …Many studies focus only on the easiest metrics to measure

– not innovation relevant issuesOr, they focus exclusively on industrial impacts such as

sales of new productsSo ….

Encourage other routes and types of impactsAttempt to measure them, including cost savingsEncourage “end-goal” thinking

And remember Innovation comes in many different guises, e.g.Incremental innovationIntegrated innovationOpen innovationInformal innovationSocial innovationDesign as innovation

Consider measurement in the innovation context

The macro level messagesMeasurement is important – but NOT just any

measurementNeed to ground measurement in a strong conceptual

framework connecting activities ultimate goals, intended uses, and both targeted users (logic models)

Then look at relationships of outcomes and impacts with innovation in your sector or sphere of activity

Measurement is BOTH qualitative and quantitativeProper measurement often takes deliberate effort and

time

Why quantitative and qualitativeQuantitative for understanding reach, scope,

& importance of impacts Qualitative for the how and why of impacts ,

barriers & solutions, incrementality and attribution, government, societal and environmental effects, etc.

There is no such thing as a purely quantitative system that measures full impacts of S&T

Reporting SE impacts

Impact Investigated and Described in:

Narrative fashion Quantitative terms Dollar terms

Qualitative

Quantitative

Economic

And …Consider “outcome mapping” a la IDRC –

where the focus is on people and organizations.

Go beyond simply assessing the products of a project/program to focus on changes in behaviours, relationships, actions, and/or activities of the people and organizations with whom a program works

This is a learning-based and use-driven approach

Recognizes the importance of active engagement of players in adaptation and innovation – “productive interactions”

The data challenge (1)Need a good MIS at levels of researcher and

project/activity - one that connects with your Network/Centre goals

Need to integrate in the MIS the needs of your reporting requirements, accountability plans and Centre/Network self monitoring/self-learning

Tie the MIS to performance measurement system by having automatic reports produced, red flags etc

Need a foundation of data standards

The data challenge (2)Standards can help:

Reduce burden on researchersFacilitate the interface with fundersAccess cost effective software solutionsComparisons with self over timeComparisons with other institutionsInternational benchmarking

CASRAI is a large part of the standards picture

Customized and flexible methodologiesThere are plenty of metrics and methods available

No need to invent any more (although you will likely need to intensify your data collection)

It’s how metrics and narrative are used and combined that make the differenceNo “one size fits all” methods or metrics work for all types

of S&T, for all types of organizations, or for all uses and users

All methods have substantial strengths and substantial weaknesses

Involve key stakeholders Remember that innovation requires many players, not

just the R&D team

Measurement can make a differenceAccountability and advocacy - Making the case on

the basis of outcomes and impacts:For overall program fundingFor the nature and dynamics of the staff

complementSelf awareness and understanding:

Internal - Strengths, weaknesses, gapsExternal – Threats, opportunities Forward directions/areas needing attention

Fine tuning the strategic visionFostering sustainable relationships

Finally …To achieve these objectives, you need:Good (and visionary) governanceGood management - capable staff complement Robust database with in-house expertiseActive engagement of players in using the

outcomes measures for adaptation and innovation

Measurement is “quantum”– It changes the system; you tend to get what you ask people

to measure

Thank you

jehalli@telus.net