Evaluating innovation has always been a difficult job for innovators, investors, facilitators, and managers. With increased pace of developing ideas, it becomes critical to evaluate innovations effectively and quickly. Before I begin developing an innovation evaluation framework, I will define what I think is an innovation and draw some characteristics first. Innovations are
- purposeful action and aligns with some personal or organizational vision
- developing ideas that are perceived as new and valuable
- impactful at a scale, may include financial, social, environmental, life impact
- investments that may lead to disproportionate returns
Innovations are evaluated for various purposes like
- Qualifying for investment/grant/other resources
- Quantifying impact of the innovation
- Modifying the development process for a set of ideas
“While we recognize that the American economy is changing in fundamental ways- and that most of this change related directly to innovation-our understanding remains incomplete…centrality of the need to advance innovation measurement cannot be understated” – Carl Schramm in the committee report to Secretary of Commerce 2008
At level 0, I believe the following facets have to be considered
Evaluation includes the following phases/activities around data and reporting
1. Data Collection, depending on the kind of evaluation it may include quantitative and qualitative information. Typically if data is collected from primary sources aka the field through surveys, direct interview, or secondary sources like agencies. Every collection effort should include independent variables, and dependent variables. It is useful to segregate between input variables, and outcome variables. Units of measure for all variables have to be standardized or they should be convertible. In case of comparison between different variables, you might want to consider some normalization process. Data quality standards are to be set prior to beginning the data collection and for any further analysis data has to be of some agreed minimum quality.
2. Analysis and Data representation, depending on the kind of data collected analysis methods will vary. For example representations for financials will be in spread sheets and charts, social data will be on maps, stories will be as fitness landscapes. Typically here is where any hypothesis is provided, and tested, future state predictions like forecasts based on models are put forth. Comparison with history or benchmarks will happen at this stage as well.
3. Results of evaluation, should be an action or recommendation. In most cases evaluation leads to decisions by parties other than the evaluator. If this party is not identified prior to evaluation process, the effort is most likely to go waste.
“What are we really going to do with this? Why are we doing it? What purpose is it going to serve? How are we going to use this information?” This typically gets answered casually: “We are going to use the evaluation to improve the program” — without asking the more detailed questions: “What do we mean by improve the program? What aspects of the program are we trying to improve?” So a focus develops, driven by use.” – Michael Quinn Patton
Once you have decided which facet of innovation you are trying to evaluate, we can now adopt from many of available methods for doing the actual evaluation. I will try and list some of them below, with links to external resources that I have found useful.
Impact: EPSIS provides a detailed framework and clearly distinguishes between output, impact and performance and provides a set of indicators that can be used for direct measurements or indirect impact measurements. Social Impact evaluation on philanthropy from Stanford is a good place to start.
Investment: Investments related evaluation includes both input costs and outcome returns to compare innovations. For example we use something called as the t-shirt sizing for ideas at first level, that will give a scale estimate of cost. Return on Investment as a ratio is a good measure but the underlying assumptions for predicting returns has to clear, and the other common error is around data quality when predicting returns.
I personally use value investing check for fundamentals when getting into stocks, and the factors that are checked are around stability, margin of safety, and historical dividends. Investment evaluation should be reduce the impact of any failure and enhance experiment design. In many cases ‘closed world’ resources (freely available locally, and has potential use) play a significant role in reducing investment.
Diffusion: Interdisciplinary classic work in this field Diffusion of Innovations by E Rogers lists different ways and covers a broad range of research that has already happened in diffusion. I like the stages around innovation diffusion as awareness, persuasion, decision and implementation. Data collected should focus on units of adoption (individual, community, user groups, etc), rates of adoption over time, and other social aspects of the adoption.
Model: In this facet of evaluation we only focus on what model of development was used for generating and developing the innovation, and should cover business model elements and how each of the elements are being looked at. Data collection would typically include metrics (see size, time, interface and costs worksheet below from NUS below) on needs, stages of development, partner structure, productivity, etc. For example Villgro, kickstarter, and Google ventures all operate in distinct models for developing innovations.
Development: Entire field of Developmental Evaluation is dedicated to evaluating during innovation and applicable for complex, highly social, non-linear situations. McConnel foundation’s practitioner guide is probably the best you can get for free.
I will cover a few methods for selecting innovation like PUGH matrix, decision trees, possibly in another post. This will be my last post for the year 2012, and I hope to build on the momentum covering deeper and meaningful innovation topics in 2013. Happy new year…