Survey of Innovation and Business Strategy

Detailed information for 2012





Record number:


Statistics Canada has undertaken this survey to provide statistical information on the strategic decisions, innovation activities and operational tactics used by Canadian enterprises. The survey also collects information on the involvement of enterprises in global value chains.

Data release - February 14, 2014 (First in a series of releases for this reference period.)


Statistics Canada has undertaken this survey to provide statistical information on the strategic decisions, innovation activities and operational tactics used by Canadian enterprises. The survey also collected information on the involvement of enterprises in global value chains. The survey's questions address the following themes: business strategies and monitoring, enterprise structure, operational activities, relocation of business activities, sales activities, business practices and relationships with suppliers, advanced technology use, product/process/marketing/organizational innovation, production performance management, human resource management, main product and market structure, government support programs, and obstacles to innovation.

To increase the analytical potential of this survey, Statistics Canada plans to combine the data obtained from this survey with data from other Statistics Canada surveys or administrative data. Statistics Canada may combine the information collected through this survey with information collected from publicly available sources, including websites. The information compiled from this survey will be used by the Canadian government to better understand the impact of strategy and innovation decisions and operational adaptations on the Canadian economy, as well as to develop policies to help businesses improve their productivity and competitiveness.


  • Business performance and ownership
  • Innovation
  • Science and technology

Data sources and methodology

Target population

The target population for the Survey of Innovation and Business Strategy was defined to meet information needs at different levels of industry detail for the core survey content (business strategies), for a module on global value chains and for a module on innovation. The population was limited to enterprises within the following 14 sectors defined according to the North American Industry Classification System (NAICS, Statistics Canada, 2007):

- Agriculture, Forestry, Fishing and Hunting
- Mining, Quarrying, and Oil and Gas Extraction
- Utilities
- Construction
- Manufacturing
- Wholesale Trade
- Retail Trade
- Transportation and Warehousing
- Information and Cultural Industries
- Finance and Insurance
- Real Estate and Rental and Leasing
- Professional, Scientific and Technical Services
- Management of Companies and Enterprises
- Administrative and Support, Waste Management and Remediation Services

To reduce response burden on small businesses, only enterprises with at least 20 employees and revenues of at least $250,000 were considered for sample selection.

Instrument design

The Survey of Innovation and Business Strategy used both electronic and paper versions of the survey questionnaire to collect information from respondents.

With minor modifications, the 2012 questionnaire used the same questions as the 2009 iteration to collect information on business strategy, global value chains and innovation. The 2009 questionnaire was developed in partnership with Industry Canada and with Foreign Affairs and International Trade Canada.

Both the paper and electronic versions of the 2012 questionnaire were field-tested with potential respondents and their comments were incorporated into the final versions.


This is a sample survey with a cross-sectional design.

A stratified simple random sample of 7,818 enterprises was selected from a target population of 67,807 enterprises on the October 2012 version of Statistics Canada's Business Register. The target population was stratified by industrial grouping, region and three size classes based on number of employees per enterprise:

- Small enterprises (20 to 99 employees)
- Medium-sized enterprises (100 to 249 employees)
- Large enterprises (more than 249 employees)

Detailed population and sample counts can be found in Tables 5 and 6, by sector and region respectively at the link below.

The sample was selected to meet two sets of objectives:

1) Produce estimates of proportions for defined characteristics for Canada and for selected provinces or regions with a target standard error (quality measure) to satisfy the requirements for estimates by module and geography.

Each of the three modules (core module on business strategies, global value chains, and innovation) required national estimates for different industrial groupings and at different levels of precision:

- For the core module (business strategies), 64 industrial groupings were identified. A census of large enterprises was targeted in order to support data quality targets and microdata analysis. For small and medium-sized enterprises, a standard error of 10% was target for the proportions to be produced.

- For the global value chains module, 34 industrial groupings were identified. For industrial groupings within the manufacturing sector, a standard error of 8% was targeted for the proportions to be produced for each enterprise size category. For other industrial groupings, a standard error of 10% was targeted for all enterprise size categories combined.

- For the innovation module, 43 industrial groupings were identified. For industrial groupings within the manufacturing sector, a standard error of 8% was targeted for the proportions to be produced. For other industrial groupings, a standard error of 10% was targeted. Enterprise size was not considered in the precision requirements for this module.

The precision requirements for regional domains were based on partnership agreements with Atlantic Canada Opportunities Agency; Institut de la statistique du Québec; Ontario Ministry of Economic Development and Trade and Employment and Ontario Ministry of Research and Innovation; and Alberta Innovation and Advanced Education. For Atlantic Canada, Quebec, Ontario and Alberta, a standard error of 12% was targeted for the proportions to be produced within 42 industrial groupings at the sector (NAICS 2-digit) and subsector (NAICS 3-digit) levels, for all enterprise size categories combined. These groupings were constructed to maintain consistency with the industry stratification at the national level. It should be noted that, although not specifically targeted, the remaining provinces and territories were also sampled to ensure precision requirements at the national level were met.

NAICS codes and descriptions for the modules and regional domains can be found in Tables 1 through 4 at the link below.

2) Permit microdata analysis within a linked file environment.

Data sources

Data collection for this reference period: 2013-03-06 to 2013-08-02

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Electronic or E-questionnaires and mail out/mail back paper questionnaires were used to collect data from respondents. Before questionnaires were sent out, all sampled enterprises were contacted to collect the name, email and mailing address for the respondent with enough knowledge of the enterprise and its strategic vision to complete the survey (e.g. entrepreneur, CEO or senior manager). Invitations to complete E-questionnaires were sent to respondents with email addresses. Paper questionnaires were mailed to respondents who requested this mode during pre-contact and to sampled units with no email address. In addition, paper questionnaires were also mailed out to all sampled units which had not responded to their E-questionnaire invitations one month before the end of collection in order to maximise response rates. Intensive non-response follow-up was conducted by email, telephone and fax as appropriate. Upon receipt, paper questionnaires were imaged and data from these questionnaires were captured.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Error detection is an integral part of both collection and data processing activities.

During collection, edit rules were applied to the survey data as they were entered into the electronic questionnaires by respondents in order to identify capture and reporting errors. Edits were also applied to data collected using paper questionnaires during capture.

Respondents were contacted by telephone to validate collected data which failed collection edits.

Prior to imputation, subject matter specialists used a variety of tools and approaches to detect and resolve inconsistencies, invalid responses and outliers in the collected data. They reviewed questionnaires missing key variables for imputation and populated these variables where possible. Responses written in by respondents for "other please specify" questions were coded to the list of options supplied to respondents for the question whenever possible. In addition, errors introduced during the capture of paper questionnaires were reviewed and resolved. Other edit rules were applied the collected data to identify and correct records with inconsistent, incomplete or invalid responses in an automated way. Finally, rules were applied to the data to detect inconsistencies in the patterns of variable responses and outlier values requiring review and resolution.

Following imputation, subject matter specialists compared patterns in the 2012 estimates by industry, business size and region (where possible) with those observed in the data from the 2009 iteration of the survey. The largest differences in the patterns were investigated by reviewing the source of the change in the micro data and resolving any issues in the quality or consistency of the micro data or in imputation processing.


Imputation was used to determine plausible values for missing or inconsistent variables in the collected data which could not be resolved through editing.

Donor imputation was performed using the generalised system BANFF. This involved identifying respondent records (donor) that were as similar as possible to the records requiring imputation (recipient) based on information that was available for both enterprises. The default matching variable for imputation was employment size. The data available for the donor record was then used to derive the data for the record requiring imputation.

Instead of imputing each variable independently, variables were grouped into blocks. These blocks were defined based on relationships amongst variables. For a given recipient, all missing variables within the block were imputed from the same donor, thereby maintaining these relationships.

Donor records were required to pass imputation edits and were selected from the same imputation class as the recipient. Imputation was only performed if there were at least 5 donors in the imputation class and at least 30% of all records in the imputation class were donors.

Imputation classes were defined based on a combination of employment size, industry classification and geography in stages. Recipient and donor matches were attempted at the most detailed level of imputation class first to ensure the highest possible degree of similarity. Recipients that failed to find a donor were matched to records in successively less detailed imputation classes.

Imputation was not performed on out-of-scope units, out-of-business units, partial units who failed to answer the mandatory questions, or non-responding units.


A complete file of weighted micro data was created for all sampled enterprises in the survey population for which data were reported or imputed. Weights were adjusted by a factor to account for total non-response so that the final estimates would be representative of the entire survey population. Weighted estimates were produced using the Generalised System of Estimation.

Quality evaluation

The survey estimates for 2012 were compared with those from the 2009 iteration by industry, business size and region (where possible). The largest differences were investigated and explained or resolved. In addition, subject matter experts from outside Statistics Canada were given an opportunity to review the estimates and provide feedback on their quality prior to their official release.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which 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. Direct disclosure or primary confidentiality occurs when the value in a tabulation cell is composed or dominated by few enterprises. Residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology does not apply to this survey.

Data accuracy

Data quality is assessed based on measures of non-sampling errors and sampling errors. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Under or over-coverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with delinquent respondents to maximize response rates.

The response rate for the survey was calculated as the total number of completed questionnaires as a percentage of the total active, in-scope survey sample. The overall response rate for the survey was 59.9% for a total of 4,483 completed questionnaires.

Sampling errors occur as a result of taking a sample of the population. The sample drawn for this survey was only one of many possible samples that could have been drawn. The standard error is a commonly used statistical measure indicating the error of an estimate associated with sampling and was calculated for use in assessing the reliability of estimates are expressed as a percentage. Coefficient of variations are the standard errors expressed as a percentage of the estimate which have been calculated for use in interpreting the reliability of estimates based on an average of responses.


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