Updated @April 24, 2024
User archetypes are useful tools to segment and succinctly describe the different drivers, behaviours and needs observed throughout research. CDR behavioural archetypes are representations of actions and general attitudes toward data sharing.
About CDR behavioural archetypes
Sceptic Low propensity to consent
Sceptics are wary of the current ways of data sharing and need to be shown how CDR is different. They need assurance that they will retain control of their data once it is shared. Understanding what is happening and how data will be kept secure can help them feel more in control. They tend to distrust institutions and prefer recommendations from trusted independent parties.
"The consequences [of data sharing] are that you can lose control over who has access to your more personal data."—R6P09 (2020)
Assurance seeker Medium Low propensity to consent
Assurance seekers are averse to trying new things. Familiarity and recognisable authorities (plain language, accreditation) help them feel safe. They need value propositions and data sharing information explained in a clear and easy to understand manner. They feel assured when they can seek additional information from other sources or obtain support from outside the data sharing process.
"I feel I would need to be eased into this [new way of data sharing] more and have more knowledge."—R4P02 (2020)
Sensemaker Medium High propensity to consent
Sensemakers want to understand what is happening and be across all the details. This allows them to make sense of the process themself based on the information they've been given. Detailed information upfront helps them understand exactly what they are agreeing to. They'll usually proceed if the information meets their expectations and the process is logical.
"Since I’m quite privacy conscious, I’m grateful that it was so detailed, it gave me trust."—R7P10 (2020)
Enthusiast High propensity to consent
Enthusiasts will completely trust the data sharing process if they see that a recognisable authority or company is involved. They are more accepting of sharing their data to get the benefit they desire. They may not always dig into the details so it is crucial that important information is easily scannable.
"If I trust the company I usually go with it and just don't read everything."—R5P01 (2020)
CX research spanning Q1 2020 to Q2 2023 has informed and tested the mapping and attributes of the CDR behavioural archetypes. A pool of 383 participants have been assigned to a CDR behavioural archetype. Archetype descriptions, behaviours and details may be iterated as CX research continues.
Design rationale
The following illustrates an analysis and mapping that proposes how certain design patterns tend to the needs and expectations of the behavioural archetypes. For each item, the main (or primary) archetype targeted by the design pattern has been called out. However the additional archetypes that would benefit from these patterns are also listed.
Annotation reference | Archetype | Statement |
01 | Sceptic | Additional information is provided to explain data sharing options. This allows users to more easily compare suitability and preference. |
02 | Assurance seeker
Also: Sceptic, Sensemaker, Enthusiast | Upfront explanation of unfamiliar terms, such as CDR, provided context and facilitated understanding and comprehension.
WCAG 2.1, success criteria 3.1.3 |
03 | Sceptic | Upfront and contextual information about CDR educates users about this method of data sharing.
For sceptics highlighting actors and data handling builds confidence and trust in the process. |
04 | Assurance seeker | Upfront and contextual information about CDR educates users about this method of data sharing.
For assurance seekers explaining the government’s role and data handling builds confidence and trust in the process. |
05 | Sensemaker | Upfront and contextual information about CDR educates users about this method of data sharing.
For sensemakers providing a concise explainer about CDR data access, control and handling aids understanding of the process and system. |
06 | Enthusiast | Upfront and contextual information about CDR educates users about this method of data sharing.
For enthusiasts explaining the government’s role builds trust in the process. |
07 | Sceptic
Also: Sensemaker, Enthusiast | Upfront information about the process encouraged users to explore the CDR data sharing option.
Seeing this information before entering the consent flow helped users feel that the process was less of a ‘marketing trick’ and more of a legitimate process. |
08 | Assurance seeker
Also: Sceptic, Sensemaker, Enthusiast | Content is linearised and sequenced to facilitate comprehension, even in the absence of styling and formatting.
WCAG 2.1, success criteria 1.3.2 |
09 | Enthusiast | Section headings are used, where appropriate, to structure content and facilitate comprehension. It allow users to scan and move through content more easily.
WCAG 2.1, success criteria 2.4.10 |
10 | Assurance seeker
Also: Sceptic, Sensemaker, Enthusiast | Plain language is used to help all users read and understand text.
WCAG 2.1, success criteria 3.1.5 |
11 | Assurance seeker
Also: Sensemaker | Consistent, or repeated, elements ensures users know where to look for controls and actions.
WCAG 2.1, success criteria 3.2.3 |
12 | Enthusiast
Also: Assurance seeker, Sensemaker | Visual cues, such as text styling and icons, help break up content and assist in making content scannable, digestible and memorable. |
13 | Assurance seeker
Also: Sceptic, Sensemaker | Upfront and contextual information about data handling aids informed consent and builds confidence in the CDR process.
Users valued assurances that their data would be handled and stored responsibly and in accordance with regulations. |
14 | Enthusiast | Information about time provides the user with an indication of the effort needed. |
15 | Sensemaker
Also: Sceptic, Assurance seeker, Enthusiast | Upfront and contextual information about purpose aids informed consent and builds confidence in the CDR process and its actors. |
16 | Sceptic
Also: Assurance seeker, Sensemaker | Clarifying what will and will not be shared with the non-accredited person aids informed consent and builds trust with the process.
Users want assurances that:
1. raw data won’t be shared to non-accredited persons (as part of the insight disclosure consent process);
2. only data that is absolutely necessary is accessed and shared (as per Data Minimisation Principle and as part of the insight disclosure consent process). |
17 | Assurance seeker
Also: Sceptic, Sensemaker | A direct statement that the user’s most sensitive information will not be shared outside of the CDR system gives consumers peace of mind and builds trust. |
18 | Sceptic | Providing a clear option to decline the process at any time means users don’t feel trapped or locked into providing consent. |
19 | Assurance seeker
Also: Sensemaker, Enthusiast | Social proof, such as government accreditation or involvement of known/trusted parties, increases trust and legitimacy in the process. It also provided instant reassurance and played an invaluable part in influencing users’ decision to continue with the process. |
20 | Assurance seeker
Also: Sensemaker, Sceptic | Providing links to external sources was valued by users. This allowed them to verify/confirm information. |
21 | Assurance seeker
Also: Sensemaker, Enthusiast | Rather than just stating the raw arrays, access periods are expressed with easy to understand descriptions, such as “6 months,” to avoid overwhelming and intimidating users. |
22 | Sceptic
Also: Sensemaker | Providing genuine choice around what information could be shared was valuable and empowering. |
23 | Enthusiast | Interactions that slow down the process aid informed consent. |
24 | Sensemaker
Also: Assurance seeker, Enthusiast | Progressive disclosure, such as through the use of expandable/collapsable content, makes a screen easier to scan. This allows users to reveal more detailed information only if they need it. |
25 | Sensemaker
Also: Sceptic, Assurance seeker | Understanding how and why data is used to generate insights builds confidence in data handling and CDR process. |
26 | Sensemaker
Also: Assurance seeker, Enthusiast | Inclusion of example/actual insights facilitated scannable comprehension about what an insight might reveal and include. |
27 | Sceptic
Also: Assurance seeker | Displaying data management information, specifically about how to stop data sharing, helps users understand that they are in control of their data and builds trust. |
28 | Assurance seeker
Also: Sceptic, Sensemaker | Informing the user that a record and details of their sharing arrangement can be later accessed builds confidence in the process. |
29 | Assurance seeker | External links to '.gov.au' websites increased comfort and trust in the CDR. |
30 | Assurance seeker
Also: Sceptic | Displaying complaint and dispute resolution information contextually throughout the process builds trust. |
31 | Assurance seeker
Also: Sceptic, Sensemaker | Assurances that data would be handled and stored responsibly builds trust in the CDR process and its actors. |
32 | Enthusiast
Also: Assurance seeker | If the user missed or forgot vital information during the consent flow, having a CDR receipt sent to them allows them to return to view the details of their sharing arrangement at a later time. |
Mapping participants to archetypes
Participants complete survey questions to self-assess their attitudes towards data sharing, privacy, general trust in Government and Industry, as well as digital adoption habits. Participant responses are used to assign them to one of the 4 CDR behavioural archetypes.
Participants are surveyed twice to allow for a comparison of any changes in behaviour due to the CDR use case.
- Before the research session to understand their existing attitude. This set of questions establish their baseline archetype, and gives an indication of general behaviour.
- At the end of the research session to understand their attitude in the context of the CDR process and proposed use case.
The step-by-step process is outlined below.
Step 1: Collecting baseline data (before research session)
Participants provided scores and responses to open-ended questions about their privacy, general trust in Government and Industry, attitudes toward data sharing, and digital adoption habits. To more accurately reflect actions, each topic consisted of questions relating to attitudinal and historical behaviour.
Participants were asked:
How much trust they place in digital Government resources?
How much trust they place in digital Industry/Commercial technology?
What digital apps or services they use/have used?
Participants were asked:
How much benefit they see in being able to share data online in general?
Approximately how many times in the last year they’ve shared data online?
Participants were asked:
How much risk they feel exists sharing their data to streamline processes?
How they last verified their identity, either in person or online?
Participants were asked:
How willing they are to adopt new technologies that require data sharing?
What they’ve focused on, the advantages or disadvantages, when faced with new technologies that require data sharing?
Participants were asked:
How important is the privacy of their data when using a digital app or service?
What privacy related actions they’ve taken on their digital devices?
Participants were asked:
When new digital apps or services come out, how and why do they adopt them?
What technologies that require data sharing have they adopted?
Participants responded to these questions by:
- Marking a Likert scale with a score from 1 to 5. ‘1’ being a negative indicator, ‘3’ being a neutral indicator, and ‘5’ being a positive indicator.
- Providing open-ended responses for more qualitative questions.
Very negative = 1
Negative = 2
Neutral = 3
Postive = 4
Very positive = 5Step 2: Using and modifying baseline data
To infer each participant's existing propensity to share (PtsE), we average their scores for Trust, Benefit, Risk, Willingness, Privacy and Digital Adoption.
Propensity to share Existing = average(Trust, Benefit, Risk, Willingness, Privacy, Digital adoption)Notes:
- Trust is calculated using scores relating to overall trust in Government as well as overall trust in Industry.
- Benefit, Risk, Willingness, Privacy, Digital adoption are calculated using scores reflecting attitudinal and historical behaviour.
Step 3: Mapping baseline scores to archetypes
Initial mapping and segmentation was based on Propensity to share averages. Coupling averages with responses to open-ended questions suggested commonalities in behaviour, attitude and factors that may influence people to increase or decrease their propensity to share.
Currently mapping and attributes reflect CX research spanning 2020 to 2022. Testing and iteration of archetypes may occur as research continues, as well as the thresholds that differentiate them. The existing thresholds have been defined by the scores of the participants that exemplify the behaviours of each particular archetype. These thresholds will continue to be reviewed as research continues.
Participants fall into one of the following 4 segments on the propensity to share scale:
Low (1-2.5) = Sceptic
Medium low (2.6-3.4) = Assurance seeker
Medium high (3.5-4.4) = Sensemaker
High (4.5-5) = EnthusiastStep 4: Collecting contextual data (at the end of research session)
After completing the research tasks related to a specific CDR scenario and value prop, such as going through a prototype for a particular use case, participants provided scores and responses to open-ended questions. This included questions related to privacy, trust, benefit, risk, attitudes toward data sharing, and likelihood to adopt the CDR when it’s presented in the proposed context.
Participants were asked:
How much trust they place in the process they’ve just been through?
Participants were asked:
How much benefit they see in sharing their data for this use case?
Participants were asked:
How much risk they feel exists sharing their data in this use case?
Participants were asked:
How willing they would be to adopt sharing data with the CDR in this use case if it were the new way of doing things?
Participants were asked:
How important is the privacy of their data in this use case?
Participants were asked:
If this new CDR option were available for this use case today, what would they do?
Participants responded to these questions by:
- Marking a Likert scale with a score from 1 to 5. ‘1’ being a negative indicator, ‘3’ being a neutral indicator, and ‘5’ being a positive indicator.
- Providing open-ended responses for more qualitative questions.
Step 5: Using and modifying contextual data
To infer each participant's propensity to share in a specific context (PtsC), we average their Trustworthiness, Benefit, Risk, Willingness, Privacy and Digital Adoption.
Propensity to share Contextual = average(Trust, Benefit, Risk, Willingness, Privacy, Digital adoption)Step 6: Mapping contextual scores to archetypes
This uses the same segmentation and scales as Step 3.
CX research has focused on recruiting consumers with differing attitudes, literacies, and access requirements, as well as those with varied backgrounds, needs and experiences. For figures and percentages, see Participant demographics - CDR behavioural archetypes
Quick links to CX Guidelines:
