Business Use of Information Technology methodology

1 This release presents detailed findings on the incidence of use of Information Technology (IT) in Australian business as collected by the 2015-16 Business Characteristics Survey (BCS).

2 The BCS is an annual survey and it is the vehicle for the ABS' Integrated Business Characteristics Strategy (IBCS). The strategy integrates the collection and quality assurance of data required for input into both longitudinal microdata product (including the ABS' Business Characteristic Longitudinal Statistics (BCLS)) and the production of point in time estimates for use of IT; innovation; and a broad range of other non-financial characteristics.

3 A key part of the IBCS is the production of annual use of Information Technology and innovation indicators, with a more detailed set of items for each of these topics collected every second year (i.e. in alternating years). The 2015-16 BCS collected detailed information relating to the use of Information Technology by Australian businesses.

4 The Economics Unit Model is used by the ABS to determine the structure of Australian businesses and other organisations. The model consists of:

The Enterprise Group (EG)
Legal Entities (LEs)
Type of Activity Units (TAUs)
Location Units

5 Businesses contributing to the estimates in this publication are sourced from the ABS Business Register (ABSBR), and are selected at either the Australian Business Number (ABN) unit or the Type of Activity Unit (TAU) level, as described below.

6 In the BCS the statistical unit used to represent the majority of businesses, and for which statistics are reported, is the ABN unit. The ABN unit is the business unit which has registered for an ABN, and thus appears on the ATO administered Australian Business Register (ABR). These units are suitable for ABS statistical needs when the business is simple in structure, and are generally referred to as the non-profiled population. In these instances, one ABN equates to one statistical unit.

7 For more significant and diverse businesses where the ABN unit is not suitable for ABS statistical needs, the ABS maintains its own unit structure through direct contact with the business, and the statistical unit used is the TAU. A TAU comprises one or more business entities, sub-entities or branches of a business entity within an Enterprise Group that can report production and employment activities. When a minimum set of data items is available, a TAU is created which covers all the operations within an industry subdivision (and the TAU is classified to the relevant subdivision of the Australian and New Zealand Standard Industrial Classification (ANZSIC)). These units are generally referred to as the profiled population.

8 ANZSIC is used to classify the industry in which the TAU or ABN has productive activity. Further information on this classification can be found in Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006 (Revision 2.0) (cat. no. 1292.0).

9 SISCA provides a framework for dividing the Australian economy into institutional sectors. Further information on this classification can be found in Standard Economic Sector Classifications of Australia (SESCA), 2008 (Version 1.1) (cat. no. 1218.0).

10 The scope of the estimates in this publication consists of all employing business entities in the Australian economy, except for:

SISCA 3000 General government
SISCA 6000 Rest of the world
ANZSIC06 Division O Public administration and safety
ANZSIC06 Division P Education and training
ANZSIC06 Groups 624 (Financial asset investing) and 633 (Superannuation funds)
ANZSIC06 Groups 954 (Religious services) and 955 (Civic, professional and other interest group services)
ANZSIC06 Subdivision 96 Private households employing staff

11 The frame for the BCS is a subset of the ABSBR and includes employing businesses only. These are defined as those businesses which register for the ATO's Pay As You Go Withholding (PAYGW) scheme. It is not unusual for some of these 'employing businesses' to have zero employment at various times during the reporting period. The frame is updated quarterly to take account of new businesses, businesses which have ceased employing, changes in employment levels, changes in industry and other general business changes. Businesses which have ceased employing are identified when the ATO cancels their ABN and/or PAYGW registration. In addition, businesses with less than 50 employees, which did not remit under the PAYGW scheme in each of the previous five quarters, are removed from the frame. The estimates in this publication include an allowance for the time it takes a newly registered business to be included in the survey frame.

12 The sample design for this survey is complex due to serving dual purposes: collection of characteristics data for the ABS' BCLS; and production of point in time estimates. While there are scope differences between the BCLS and point in time estimates, the intention is to maximise the number of businesses selected for which data collected can contribute to both purposes. More information about releases for the BCLS is provided in Explanatory Note 27.

13 Collection of data included in this release was undertaken based on a random sample of approximately 6,750 businesses via online forms or mail-out questionnaire. The sample was stratified by industry and an employment-based size indicator. All businesses on the ABSBR identified as having 300 or more employees were included in the sample. The 2015-16 BCS was dispatched in late October 2016.

14 As in previous years, the sample design of the 2015-16 BCS does not include state or territory as part of stratification design.

15 The reference period for most of the characteristics items included in the 2015-16 Business Characteristics Survey is during the year ended 30 June 2016 or as at 30 June 2016. Financial statistics relates to the most recent financial year ended on or before 30 September 2016.

16 In the BCS, the ABS uses the Organisation for Economic Co-operation and Development (OECD) narrow definition of e-commerce transactions when collecting data on internet orders and internet income. The definition states that "an internet transaction is the sale or purchase of goods or services, whether between businesses, households, individuals, governments, and other public or private organisations, conducted over the internet. The goods and services are ordered over the internet, but the payment and the ultimate delivery of the good or service may be conducted on or off-line" (i.e. the commitment to purchase is made over the internet). In Australia, orders placed or received via manually typed email are included.

17 Internet income is defined as income resulting from goods and services ordered over the internet where the commitment to purchase is via the internet. Excluded from these measures are orders, payments or transactions for which the commitment has been made using other arrangements. The ABS collects these data by asking businesses to estimate what percentage of their income from sales of goods or services can be attributed to orders received via the internet. The estimated value of internet income is derived by applying the percentage to business income from sales of goods or services. This method of collecting internet income has been put in place to address reporting errors previously observed when the actual dollar figure was requested.

18 The definition of internet income used in BCS, does not align with the ABS Australian System of National Accounts (SNA). The internet income estimate in BCS is generally higher as this value is derived from the proportion of business income due to orders received via the internet. This includes all business to business, and business to consumer orders.

19 Estimates of the number of businesses operating in Australia can be derived from a number of sources within the ABS. They may relate to a particular point in time or may be presented as an average annual figure. However, these estimates will not always show the same results. Variations will occur because of differing data sources, differing scope and coverage definitions between surveys, as well as variations due to sampling and non-sampling error. More information about business counts can be found in the Information Paper: A Statistical View of Counts of Businesses in Australia, Jun 2005 (cat. no. 8162.0).

20 The BCS is not designed to provide high quality estimates of numbers of businesses for any of the output classifications (for example, employment size or industry) and the number of businesses in this publication are only included to provide contextual information for the user. The estimate of the total number of businesses may not equal to the sum of each employment size range due to rounding of business counts to the nearest thousand. A more robust source of counts of Australian businesses is available from Counts of Australian Businesses, including Entries and Exits, (cat. no. 8165.0).

21 For output purposes, businesses are classified to employment size ranges based on actual data reported in the survey. For industry output, the classification is drawn from information held about the business on the ABSBR.

22 As indicated in Explanatory Note 14, the sample is designed to produce efficient estimates for industry and employment size, therefore it does not provide quality estimates for states/territories. As estimates may not reflect change over time for a selected state/territory or adequately enable comparison between states/territories, they are not available.

23 Upcoming ABS statistical releases from the 2015-16 BCS collection family include:

Selected Characteristics of Australian Business (cat. no. 8167.0). This release will include summary characteristics statistics for a selection of topics including business structure and arrangements, performance measures, barriers to innovation and general business activities or performance, government financial assistance, finance sought, markets and competition, innovation rates and IT usage. Online content will include tables and graphs with associated commentary. Detailed statistics (including some output cross-classified by business size, industry and innovator status) will be output as data cubes. This release is scheduled for 17 August 2017.

Management and Organisational Capabilities of Australian Business (cat. no. 8172.0). This release will include statistics on a selection of topics including: key performance indicators; use of data in decision making; strategic plans; skills; supply chain; environmental management; and demographic information related to the principal manager. Statistics are cross classified, where possible, by innovator status; business size (based on employment); and ANZSIC Subdivision. This release is scheduled for 25 August 2017.

24 The most recent issue of ABS releases related to demography of Australian business is:

Counts of Australian Businesses, including Entries and Exits (cat. no. 8165.0).

25 The most recent issues of other ABS releases related to innovation in business in Australia are:

Innovation in Australian Business (cat. no. 8158.0)
Research and Experimental Development, Businesses, Australia, (cat. no. 8104.0)
Research and Experimental Development, Higher Education Organisations, Australia, (cat. no. 8111.0)
Research and Experimental Development, Government and Private Non-Profit Organisations, Australia (cat. no. 8109.0)

26 The most recent issue of ABS releases related to IT Use and Innovation in Australian Business are:

Summary of IT Use and Innovation in Australian Business (cat. no. 8166.0)
Internet Activity, Australia, December (cat. no. 8153.0)

27 The primary outputs for the BCLS are Confidentialised Unit Record Files (CURFs). The BCLS design is comprised of panels (or waves) with each panel representing the entire population of in-scope small and medium businesses at the time of initialisation. Each panel is surveyed for five years. The most recent edition of the BCLS CURF was released 20 July 2016 and contained data for the five year period ended 30 June 2014. The CURF is available via the Remote Access Data Laboratory which can be accessed via the ABS website Microdata: Business Characteristics, Australia (cat. no. 8168.0.55.001).

28 Estimates of proportions have been calculated using unrounded figures, but are shown in the tables rounded to one tenth of a percentage point. Where figures have been rounded, discrepancies may occur between the sum of the component items and the total. Figures presented in the commentary have been rounded to the whole percentage.

29 The ABS acknowledges the ongoing contribution made by the Department of Industry, Innovation and Science towards the conduct of the BCS and the collection of information technology statistics.

1 When interpreting the results of a survey, it is important to take into account factors that may affect the reliability of the estimates. Estimates in this publication are subject to both non-sampling and sampling errors.

2 Non-sampling errors may arise as a result of errors in the reporting, recording or processing of the data and can occur even if there is a complete enumeration of the population. These errors can be introduced through inadequacies in the questionnaire, treatment of non-response, inaccurate reporting by respondents, errors in the application of survey procedures, incorrect recording of answers and errors in data capture and processing.

3 The extent to which non-sampling error affects the results of the survey is difficult to measure. Every effort is made to reduce non-sampling error by careful design and testing of the questionnaire, efficient operating procedures and systems, and the use of appropriate methodology.

4 Some of the items collected in the BCS are dynamic in nature and the concepts measured are subject to evolution and refinement over time; it is not possible to measure the impact of these changes on data quality.

5 The approach to quality assurance for the BCS aims to make the best use of Australian Bureau of Statistics (ABS) resources to meet user prioritised requirements - both in terms of data quality and timing of release. The approach specifies the level and degree to which each data item is quality assured, noting that only some of the total output from the BCS is able to be quality assured to the highest standards. Different priorities are assigned to groups of data items, with highest priority being assigned to key point in time data on business use of IT and innovation.

6 The 2015-16 BCS had a response rate of 93%.

7 The difference between estimates obtained from a sample of businesses, and the estimates that would have been produced if the information had been obtained from all businesses, is called sampling error. The expected magnitude of the sampling error associated with any estimate can be estimated from the sample results. One measure of sampling error is given by the standard error (SE), which indicates the degree to which an estimate may vary from the value that would have been obtained from a full enumeration (the 'true' figure). There are about two chances in three that a sample estimate differs from the true value by less than one standard error, and about nineteen chances in twenty that the difference will be less than two standard errors.

8 The following is an example of the use of standard error on the total proportion of businesses with internet access. As presented in this release, the estimated proportion of businesses with internet access was 95.3%. The standard error of this estimate was 0.45%. There would be approximately two chances in three that a full enumeration would have given a figure in the range 94.85% to 95.75%, and 19 chances in 20 that it would be in the range of 94.4% to 96.2%.

9 In this publication, indications of sampling variability are measured by relative standard errors (RSEs). The relative standard error is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer to the size of the estimate. Relative standard errors are shown in the Relative Standard Error table in this section. RSEs for all statistics included in this release (including data cube content) are available upon request.

10 To annotate proportion estimates, a value of 50% has been used in the calculation of RSE rather than the estimated proportion from the survey data. This avoids inconsistencies between the way very low and very high proportions are annotated. Relative standard errors for estimates in this publication have been calculated using the actual standard error and the survey estimate (referred to as x) in the following manner: RSE%(x) = (SE(x)*100)/50.

11 Using the previous example, the standard error for the estimated proportion of businesses with internet access was 0.45%. Multiplied by 100 and then divided by 50 gives an RSE calculated on this basis of 0.9%. It is these figures that appear in the table appended to this chapter.

12 For the tables in this publication, estimates with RSEs between 10% and 25% are annotated with the symbol '^'. These estimates should be used with caution as they are subject to sampling variability too high for some purposes. Estimates with RSEs between 25% and 50% are annotated with the symbol '*', indicating that the estimates should be used with caution as they are subject to sampling variability too high for most practical purposes. Estimates with an RSE greater than 50% are annotated with the symbol '**', indicating that the sampling variability causes the estimates to be considered too unreliable for general use.

13 For estimates of proportion the symbol '^' means that the estimate from full enumeration could lie more than a decile away so the estimate should be used with caution. For example a proportion estimate of 30% annotated with '^' means the full enumeration value could lie beyond the range 20% to 40%. The symbol '*' means the estimate from full enumeration could lie more than a quartile away and is subject to sampling variability too high for most practical purposes. A proportion estimate of 30% annotated with '*' means the full enumeration value could lie beyond the range 5% to 55%. Proportion estimates annotated with the symbol '**' have a sampling error that causes the estimates to be considered too unreliable for general use.

14 Readers of this release should note that most of the statistics have an RSE of less than 10%.