Survey on the Commercialisation of Innovation
Detailed information for 2007
Science, Innovation and Electronic Information Division is engaged in a joint project with Industry Canada to investigate the commercialization of innovation process in Canadian firms of small and medium size.
Data release - March 27, 2009
This survey is a joint project of Statistics Canada and Industry Canada attempting to better understand the process of commercializing innovations.
From a business firm view, commercialization is part of the innovative process and could be described as the set of conditions that should be met and the set of activities to perform for a firm to generate revenues from an innovation introduced in the market.
This survey was conducted to explore activities performed by firms and factors contributing to commercial success of innovation. We were able to explore factors such as:
Commercialization activities carried out
Objectives of commercialization
Time to reach the objectives
Importance assigned to commercialization strategies
Financial activities related to commercialization
Cooperative agreements on commercialization
The compiled data obtained from this survey will be used by enterprises to analyze markets and by trade associations to study industry's performance. They will also be used by academics for research purposes and by government departments and agencies to support and develop strategies for economic development.
Science and technology (S&T) and the information society are changing the way we live, learn and work. The concepts are closely intertwined: science generates new understanding of the way the world works, technology applies it to develop innovative products and services and the information society is one of the results of the innovations.
People are looking to Statistics Canada to measure and explain the social and economic impacts of these changes.
The purpose of this Program is to develop useful indicators of S&T activity in Canada based on a framework that ties them together in a coherent picture.
- Research and development
- Science and technology
Data sources and methodology
The survey population consisted of small and medium firms from the manufacturing sector that introduced one or several innovations to the market in the last three years. A secondary population of firms active in biotechnology was included in the sample.
For the purpose of this survey, small and medium enterprises are defined as enterprises having between 20 and 500 employees and 250 000$ or more in annual revenue. Industries having a NAICS code with three digits from 311 to 339 are cover.
The questionnaire was developed by internal experts and reviewed by a steering committee of representatives from Statistics Canada, Industry Canada and other departments.
The questionnaire has been tested with close to twenty respondents in Montreal, Toronto and Ottawa. The whole questionnaire has been elaborated in consultation with Industry Canada.
This is a sample survey with a cross-sectional design.
Only simple enterprises having between 20 and 500 employees and more than 250 000$ in annual revenue have been considered in the sample. This is the criterion that allows defining a small and medium enterprise (SME) of the target population.
The sample has been drawn from units having introduced an innovation on the market or having performed research and development activities in 2005.
The business register and the survey of Innovation 2005 have been used to the frame database.
The sample size is 2300 units and covers all NAICS at 3 digits of the manufacturing sector and activities on biotechnology.
The stratum is based on provinces and the size of employment.
Data collection for this reference period: 2007-10-29 to 2008-01-25
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Data were collected using a paper mail-out, mail-back questionnaire.
View the Questionnaire(s) and reporting guide(s) .
Firms were selected to provide a representative sample based on size, industry and province. The finalized response rate was 70%. The results were weighted to reflect the entire count of firms in the selected industries. Estimates were vetted for compliance with confidentiality rules. Data quality was assessed in consultation with the methodology team, and unreliable data are not published.
Because of the qualitative nature of most of the questions, the "hot deck" imputation method was used for the majority of the questions. The imputation groups were formed based on the province, the sector of activity and the size of the provincial enterprise.
Firms were selected to provide a representative sample based on size, industry and province. In order to account for non-response, a weighting adjustment factor was applied to the homogeneous response groups created from the sector of activity and the size of the statistical units. This adjustment factor is used as a final weight to produce estimates. To calculate the variance, a stratified random sample formula was used. The strata were formed by the respondent homogeneous groups mentioned previously.
A set of tables was carefully examined to ensure internal coherence of data. Quality indicators such as coefficient of variation and standard error were computed for all estimates produced. Coefficient of variation and standard error formulas take into account the sample design and imputation rates.
Since this survey is unique, it is difficult to compare results with other surveys. However, the percentage of innovators found in this survey were compared and are consistent with what was found in the Statistics Canada Survey of Innovation 2005.
Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.
Revisions and seasonal adjustment
This methodology type does not apply to this statistical program.
Quality assurance measures were implemented at each step of the data collection and processing cycle to monitor the quality of the data.
These measures included the use of error detection programs and systematic edits and verification. Skip pattern verification was performed to detect inconsistencies in responses.
Coefficient of variation and standard errors were computed for all estimations.
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