The discussion unpacked what it takes to scale AI responsibly, embed it into everyday government operations, and build the trust required for long-term impact:

Artificial Intelligence has dominated headlines this year, but for governments and public institutions, the question is no longer what AI can do, but how we make it work in practice and at scale.Ěý

At SAP, we recently released our Value of AI Report with Oxford Economics, providing a checkpoint on where organisations stand today. Across Australian government and business, AI now supports about one in four tasks, a figure expected to exceed 40 percent within two years. Encouragingly, three-quarters of organisations anticipate a positive return on AI investment within one to three years.Ěý

These findings highlight significant momentum, but also a challenge. Too many AI projects remain trapped in proof-of-concept stage. The real opportunity lies in moving from prototype to production, where innovation can scale, deliver measurable value and build public confidence.Ěý

Three priorities for progressĚý

Three priorities will enable Australian government agencies to move AI from experimentation to enterprise-wide impact:Ěý

1. Adopt embedded AI capabilities. By using applications that already contain AI, public sector organisations can deploy AI easily and scale rapidly.Ěý

1. Build capability and confidence. Success depends on equipping the public service and its technology partners with the skills to implement, monitor and govern AI responsibly.Ěý

1. Earn and maintain public trust. AI ethics policies are now widespread, but policy alone is not enough. Trust is earned when we demonstrate that those guidelines and guardrails are being put into practice.Ěý

Adopt embedded AI capabilitiesĚý

Globally, we’re already seeing transformative outcomes being delivered with custom AI.Ěý

In Germany, Hamburg’s Ministry of Finance uses machine learning to support processing of social benefit applications, saving 33,000 hours of manual review.Ěý

In France, the city of Antibes uses AI to align budgets with the UN Sustainable Development Goals, making over 138,000 decisions with AI assistance, work that would be impossible for humans alone.Ěý

But for enterprise AI deployments to become more widespread, organisations need to shift to adopting AI capabilities that are embedded in business applications. For governments, this means switching on the AI that already exists in enterprise applications for HR, finance, procurement and citizen services, rather than building custom AI solutions using technical platforms.Ěý

Build capability and confidenceĚý

As we move toward agentic AI – systems that operate autonomously – the need for transparency becomes even more important.Ěý

We must ensure that AI augments, rather than replaces, human judgment. This means giving users visibility into the reasoning behind AI decisions. At SAP, our applications include AI Analysis that shows which data sources were accessed, what steps the AI took, and how recommendations were generated, giving humans the insight they need to make informed decisions.Ěý

Building this kind of transparency into every AI scenario is critical for governments that must demonstrate accountability to citizens.Ěý

Earn and maintain public trustĚý

At SAP, we assess every new AI scenario against our global AI Ethics Policy, which is aligned to UNESCO’s Recommendation on the Ethics of Artificial Intelligence.Ěý

Several years ago, for example, we developed an “emotional AI” prototype capable of detecting human emotion through facial expression and tone of voice with 70 percent accuracy. Despite its potential, we chose not to productise it, determiningthat the risk of harmful or biased outcomes was too high.Ěý

This experience demonstrates how the right kind of regulation can accelerate innovation. Clear guidelines and guardrails focus experimentation on use cases that genuinely improve people’s lives.Ěý

Australian Research Alliance for Enterprise AIĚý

51·çÁ÷is an industry partner in a new Australian Research Alliance for Enterprise AI with the University of Queensland, QUT, UNSW, the University of Sydney, and the University of Melbourne. We’re exploring how AI agents can make enterprise systems more intuitive, responsive and productive across government and industry.Ěý

AI’s potential for the public sector is immense, but to realise it, we must move beyond experimentation. By adopting embedded AI, investing in skills, and building public trust, we can shift from isolated prototypes to scalable impact and unlock a new era of productivity and confidence in digital government.Ěý

For more information or to engage with the Australian Research Alliance for Enterprise AI: .

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Improving the ease and speed of accessing benefits is key to government efforts to extend social and economic safety nets to what the International Social Security Association (ISSA) refers to as the “”. Self-employed and gig workers, as well as rural, migrant, and domestic workers are typically time-poor, not already engaged in social protection systems, and are often not included in targeted outreach programs. This makes them vulnerable to economic shocks and cost-of-living increases that can tip them into poverty and homelessness.

As such, many government agencies and not-for-profit organisations are looking at ways to make social services more accessible and responsive by reducing the “hassle costs” associated with claiming benefits.

How AI can help

Governments around the world have been realising significant efficiency gains through applying artificial intelligence (AI) in the back-office to improve workforce productivity. Encouragingly, there are also recent examples of AI being leveraged in the front-office to improve the efficiency and effectiveness of citizen engagement.

1. Quicker time to payment with AI-supported assessment processes

At , a combination of Machine Learning (ML) and Generative AI (GenAI) support staff to efficiently process applications for more than €3.5 billion in financial aid.

51·çÁ÷Machine Learning is used to link citizen application data to supporting documentary evidence, enabling case workers to expedite processing for the bulk of applications and to focus their attention on those most likely to be non-compliant. Across two programs, Hamburg reports that nearly 180,000 benefit applications have been processed, with more than 10 million pages of supporting documents automatically evaluated and classified by AI.

51·çÁ÷Generative AI Hub has also been introduced to summarise inbound applications and to generate draft outbound correspondence, further reducing time to payment for customers while minimising the burden of repetitive manual work for staff.

2. Improved customer service with AI-powered workflow automation

Similarly, uses 51·çÁ÷AI to automate workflows and to recommend potential benefits based on customer circumstance data. AI has contributed to a 20% increase in user productivity, which amounts to a substantial efficiency gain when applied to an agency of 9,000 public employees managing €1.8 billion in monthly payments. These efficiencies flow through to citizens, as described by the Deputy Director General of Benefits and Subsidies, who reports their AI-enabled system “…has empowered me to shift focus from administrative tasks to truly enhancing citizen service, allowing for quicker responses and more meaningful interactions.”

3. Faster query resolution with AI-enabled chatbots

At , an 51·çÁ÷AI chatbot responds to 50% of citizen inquiries with no human intervention, resulting in 77% being answered and closed within the same day. While social services inquiries would typically be more complex, there’s certainly potential for AI to categorise and prioritise inbound communications and to route them to the appropriate channel or group. This is the case for more than 83% of the inquiries being received by the Office every day, which embassy staff say “…means efficient communication, satisfied customers and a gain in personnel resources for other tasks.”

Reducing the barriers to adoption with embedded AI

To date, the types of use cases described above have been delivered as custom AI solutions, limiting uptake to agencies that are sufficiently resourced to assemble AI systems from Large Language Models (LLMs) and other necessary components. This is further exacerbated by the additional work needed to protect customer data, prevent bias, and ensure the reliability of AI recommendations.

Thankfully these barriers to adoption are being reduced as AI capabilities become embedded into enterprise software, enabling agencies to adopt out-of-the-box solutions. For example, something as simple as supporting staff to retrieve information using AI-enabled natural language search could reduce the time customers spend waiting on hold while their case worker struggles to locate their file.

Revolutionising social services with agentic AI

The advent of agentic AI could be a tipping point for social services AI use. By virtue of their ability to take multiple paths and iterative steps towards achieving an outcome, AI agents are particularly suited to the type of complex case processing inherent in social services. We can imagine a future where benefit applications are picked up and processed by a team of specialised AI agents that can autonomously validate compliance, determine eligibility and entitlement, identify potentially fraudulent claims, and present reasoned recommendations to human case workers for approval.

Such a future could be just around the corner. predicts that, “by 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously.” Similarly, it notes, “by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs”.

In summary, AI is already enabling early adopters like Hamburg’s Ministry of Finance to improve the efficiency of application processing for social benefits. AI adoption in social services is now set to scale as AI capabilities are embedded into enterprise software, and this could lay the groundwork for a big leap forward with agentic AI. AI will be increasingly capable of reducing the “hassle costs” associated with claiming benefits, helping to ensure that social and economic supports reach the people that need it, when they need it.

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The last article in our “Dealing with Disruption” series presented a conceptual architecture for Digital Nudges and demonstrated how it could be applied to improve crisis communications relating to a second-wave outbreak of the Coronavirus. In this companion piece, we seek to demonstrate that governments have ready access to the business applications and technologies required to deliver digital nudges today.

To achieve this, we’ll map our conceptual architecture to 51·çÁ÷products that are generally available and are already in use by governments around the world.

Conceptual Architecture

For reference, our conceptual architecture for digital nudges is depicted below.

Figure 1: A conceptual architecture for digital nudges.

Ěý51·çÁ÷Reference Architecture

Mapping our conceptual architecture to 51·çÁ÷products provides assurance that our conceptual architecture can be delivered in practice.

Figure 2: An example reference architecture for digital nudges.

Note that SAP’s will evolve over time, so this bill of materials should be considered representative rather than prescriptive.

• Predictive Analytics:
  • : enables organizations to analyze the behavior of customers and to generate risk scores and insights.
• Contextualization:
  • : enables organizations to use consent-based marketing and advanced data analytics to engage customers with pinpoint accuracy.
• Experience Management:
  • : enables organizations to gather experience data and combine it with operational data to close experience gaps.

• Analytics:
  • : enables organizations to provide a single source of truth to decision makers about the most important business metrics in real time.
    : enables organizations to combine BI, planning, predictive, and augmented analytics capabilities into one simple cloud environment.
• Intelligent Technologies:
  • : enables organizations to process distributed data and provide users with intelligent, relevant, and contextual insights with integration across the IT landscape.
    : enables organizations to define functions that can be called from within SQLScript procedures to perform analytic algorithms.
• Data Management:
  • : enables organizations to deliver a data warehouse in the cloud to unite multiple data sources in one solution.
    : enables organizations to accelerate data-driven, real-time decision-making and actions via a high-performance in-memory database.
• Application Development & Integration:
  • : enables organizations to model, implement, integrate, and monitor custom process applications and integration scenarios.
  • : enables organizations to accelerate integration, simplify development of application extensions, and expand business value with an open ecosystem.

In presenting this reference architecture, our intent has been to provide a worked example to demonstrate that governments have ready access to the business applications and technologies required to deliver digital nudges today, using business and technology components from SAP.

While other vendors might be able to offer some components of a digital nudge platform, we believe there is a benefit in sourcing the end-to-end solution from a single vendor.

To read more Public Sector content or find out more about SAP’s Public Sector customers and products, visit:

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The first two articles in our “Dealing with Disruption” series looked at how digital technologies might enable governments around the world to nudge citizens towards cooperation and coordinated action in containing COVID-19, and to address issues of hand washing, face touching, self-isolation, collective action, and crisis communication. In this article, the 51·çÁ÷Institute for Digital Government (SIDG) will present a conceptual architecture for Digital Nudges and demonstrate how it could be applied to improve crisis communications relating to a second-wave outbreak of the Coronavirus.

Using digital nudges to support government responses to coronavirus

To demonstrate how our conceptual architecture might be applied, we will consider the scenario of a second-wave outbreak of the Coronavirus, such as was .

Figure 1: The first- and second-wave outbreaks of COVID-19 in Australia.

was identified on 25 January 2020. The number of new cases rapidly increased and peaked nine weeks later, with reported on 28 March. The Australian government responded very successfully with a for flattening the curve, and by mid-April there were a relatively low number of new cases being reported daily. Although the virus had not been eliminated, it appeared to have been suppressed sufficiently for lockdown restrictions to be eased across Australia. Unfortunately, were identified in Melbourne on 20 June, foreshadowing a second-wave and prompting a reinforcement of restrictions to contain the outbreak. Even so, Australia’s second-wave proved more difficult to contain than the first, peaking at reported on 5 August.

Due to the localized nature of the second-wave outbreak, stay-at-home restrictions were reintroduced only in metropolitan . Most notably, in North Melbourne and Flemington were immediately locked-down, with residents of 33 Alfred Street subsequently required to isolate for two weeks. While it was generally agreed that this was a necessary measure, the immediacy of the action combined with various communication challenges resulted in widespread confusion and concern among the 3,000 public housing tenants. captured the sentiment at the time:

• “When I came back home I did see hundreds of cops everywhere, so it was really intimidating.”
• “It’s been getting more and more intense, people are really panicking.”
• “We weren’t told any information, they just shut us down, didn’t let us leave our houses.”
• “I just feel like we’re being treated like criminals.”
• “We do not need 500 officers guarding the nine towers. We need nurses, we need counsellors, we need interpreters.”

In what has been an unprecedented year, the hard lockdown of Melbourne’s public housing towers was an unprecedented action by the Australian government, law enforcement and public health services. To that point, Australian citizens had not experienced a lockdown under guard, except in cases of returned citizens undertaking hotel quarantine.

In special cases such as this, efficient and effective crisis communication is key – not only in ensuring compliance – but in promoting cooperation through credibility, empathy and respect. Behavioral Science can assist by influencing individual decisions towards the most positive outcome, and digital technologies can be used to scale and personalize traditional nudges to improve outcomes for mass cohorts.

Conceptual Architecture for digital nudgesĚý

Figure 2: A conceptual architecture for digital nudges.

Nudging is a delicate process, with significant preparation required to avoid unintended consequences – especially when the stakes are as high as they are in the case of COVID-19. These stakes are raised even higher when the nudges are to be delivered by governments, at scale, using digital technologies. The is to optimize utility and mitigate risk using an iterative process of randomized controlled trials with rapid cycle evaluation. Whether the nudge is to be delivered as part of a trial, or to the population at large, an iteration of the nudging process typically spans:

• Design and contextualize: The nudge is designed to achieve the outcome of interest, based on an exploration of the available data. A key consideration is the situational and social context of the environment in which the nudge is to be deployed. In the case of crisis communications, nudges need to for citizens’ circumstances.
• Simulate and deploy: Randomized controlled trials can be used to simulate the likely response to a given nudge. A variation of this approach would involve using , to enable simulations to be run faster and safer than with human subjects. In the case of crisis communications, these simulations could be aligned to the accepted thresholds of a national or local containment strategy.
• Monitor and measure: Having deployed the nudge, social listening and devices can be employed to monitor the actual response. Although it may be difficult to measure the effectiveness of nudges as a behavioral modifier, a control group who does not receive the nudge may be used. In the case of crisis communications, we might also consider performance against “fake news” as a measure of effectiveness.
• Analyze and improve: Here we distinguish between measurement and analysis, specifically within the context of diagnostics – analyzing why a particular action has been taken or a particular outcome achieved. Based on this analysis, improvements can be made to the design of the nudge, and thus the iteration continues. In the case of crisis communications, certain visualizations (e.g. ) might be published to encourage community cooperation and coordinated action.

Digital nudges: Core capabilities

As described in our first article, predictive analytics, contextualization, and experience management are the core capabilities required to deliver digital nudges. Breaking down these capabilities will enable us to illustrate how they can support policymakers and service agencies, working with behavioral scientists and technology partners, to improve the effectiveness of traditional nudges.

• Predictive Analytics:
  • Behavioral Insights: The ability to detect patterns in citizen behavior, based on transactional and experiential data. For example, based on their prior responses to government requests, we can expect Citizen X to comply with stay-at-home orders.
  • Journey Visualization: The ability to visualize the citizen’s journey over time, including major life events, changes in circumstance, and their interactions with government. For example, based on the healthcare, social services and financial supports they have recently accessed, Citizen X is likely a vulnerable person who will need additional supports.
  • Simulation: The ability to simulate the likely responses to a digital nudge, including the ability to compare alternative approaches. For example, Nudge A will increase compliance with stay-at-home orders by 5%, with 80% confidence.
  • Next Best Action: The ability to recommend the optimal course of action, based on (autonomous) machine learning. For example, Nudge A will be most effective for Citizen X, while Nudge B will be most effective for Citizen Y.

• Contextualization:
  • Profiling: The ability to assemble a digital profile of a citizen, by combining data from multiple sources (as permitted by government regulations). For example, we know that Citizen X is at high risk, since they are over 80 years of age and live in high-density public housing.
  • Segmentation: The ability to create target groups, comprising citizens with similar profiles and needs. For example, Segment A comprises citizens of working age, who are likely concerned about the impact of stay-at-home orders on jobs.
  • Campaigns: The ability to proactively outreach to target groups with nudges tailored to their circumstances. For example, Nudge A will be delivered to citizens of working age, while Nudge B will be delivered to citizens over the age of 65.
  • Preferences: The ability to communicate with citizens via their preferred channel, and at their preferred time and place. For example, Citizen X usually responds promptly to SMS sent around lunchtime.

• Experience Management:
  • Social Listening: The ability to monitor social media to track changes in citizen sentiment over time. For example, citizens under lockdown are complaining that police presence is making them feel like criminals.
  • Surveys: The ability to solicit direct feedback from citizens. For example, Citizen X responded that they couldn’t understand the specifics of the stay-at-home order because English is their second language and no translation service was provided.
  • Measurement: The ability to measure the response to a digital nudge, based on transactional and experiential data. For example, Nudge A increased compliance with stay-at-home orders by 3%, compared with the control group who did not receive the nudge.
  • Diagnostic Analytics: The ability to uncover why certain nudges are, or aren’t, working. For example, Nudge A was widely criticized as being disrespectful, resulting in a lower level of compliance than anticipated.

The underlying business platform supports the design, development, and management of our digital nudges.

• Analytics: The ability to analyze transactional and experiential data. Desirable features include the ability to:
  • surface actionable insights based on predictions;
  • dynamically drill-down into records of interest;
  • visualize citizen journeys over time; and
  • update data and visualizations in real-time.

• Intelligent Technologies: The ability to build, execute and manage machine learning applications. Desirable features include the ability to:
  • process big data holdings to build advanced machine learning models;
  • support profiling and segmentation of data in line with contextualization capabilities;
  • generate predictions and next best action recommendations; and
  • make improvements based on (autonomous) machine learning.

• Data Management: The ability to access and work with big data, in real-time. Desirable features include the ability to:
  • consolidate data from multiple sources;
  • work with transactional data in real-time, without impacting operational systems;
  • work with analytical data in-place, without the need for replication; and
  • ensure the security and privacy of citizen data.

• Application Development & Integration: The ability to develop and integrate business applications. Desirable features include the ability to:
  • accelerate the design and development of advanced machine learning applications;
  • run simulations in support of what-if analysis;
  • support an open ecosystem of development partners; and
  • integrate with external systems (e.g. geographic information systems).

In presenting this conceptual architecture, our intent has been to provide a framework that governments can use to deliver digital nudges. We believe this framework to be general-purpose, while acknowledging that certain scenarios will require additional capabilities. Our chosen use case of crisis communications serves as an illustrative example. Please note that, since this conceptual architecture is vendor-agnostic, the described capabilities could be sourced from any technology provider.

To read more about how digital technology can be used to improve public sector services, visit .

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The online event attracted over 60 delegates from the Australian and New Zealand public services, representing all levels of government.

Measuring citizen engagement

Included in the agenda was an online poll, focussing on how agencies measure the citizen experience and how they respond to citizen feedback. While the sample size is small and not necessarily representative of citizen engagement across the public sector, the responses were intriguing and prompted valuable discussion.

As shown, Customer Satisfaction (CSAT) is the most popular approach for measuring the citizen experience among our respondents.

A characteristic of this approach is that it’s a transactional measurement – CSAT reflects satisfaction with a specific interaction or service.

By comparison, relational measurements like Net Promoter Score (NPS) are better approaches for longitudinal analysis. Admittedly, it can be difficult to apply standard NPS questions about customer loyalty within a public sector context, but it’s possible to adapt the questions to focus rather on citizen trust in government.

Another measurement worth considering isĚý, which reflects the ease (or difficulty) of doing business with the organisation. In the commercial world, CES is an excellent predictor of customer churn, and while this typically isn’t an issue for government, agencies are motivated to make their online services accessible and easy to use.

Since this was a multiple-choice question, it was possible for the survey participants to select more than one response, and possibly that’s the optimal approach… A sensible combination of these measurement tools can provide excellent insight into citizen satisfaction with service delivery, and the impact that experience has on citizen trust in government.

Using feedback

Encouragingly, all our respondents ask the citizen about their service delivery experiences.

Yet the responses to this question seem to align with the transactional measurement approach of CSAT.

Adopting a more relational approach, by embedding feedback throughout the process, can enable agencies to take proactive action and mitigate risks before they turn into problems.

We could argue the merits of all these responses – it’s important that agencies respond in a variety of ways to close-the-loop with citizens.

We’ve observed that citizen satisfaction is increasingly being included in agency service commitments, and it’s encouraging to see that this feedback is also being actively used to inform service design.

Untapped opportunity

There appears to be an untapped opportunity for data-driven policy development among our respondents, to truly close-the-loop on citizen feedback.

It’s interesting that more than half of respondents cited issues with motivating and engaging a representative sample of citizens as their biggest challenge in measuring citizen experience.

SIDG research intoĚý, suggests that a bi-directional view could help to increase participation in government surveys.

Two-way conversation

The rationale being that, if the citizen can see how the data the government is collecting will be used to serve them better, they will be more willing to engage and contribute.

Improving the efficiency and effectiveness of service delivery has always been a motivating factor for collecting citizen feedback, so the leading response here is not all that surprising.

It’s encouraging to see a relatively high percentage of our respondents wanting to focus on keeping citizens informed throughout the service delivery process.

Public sector best practice

Experience from leading government agencies suggests that providing transparency and traceability into government processes can improve the citizen’s perception of the timeliness of service delivery.

This might be because the citizen can see their case progressing through the system in real-time, giving them confidence that their feedback has been heard and is being actioned.

The SIDG would like to thank all respondents to our online poll, as well as our partners from the PSN and QUT. We found the participants’ responses to be very insightful and through-provoking, and we hope that sharing these reflections will further progress the conversation.

If you’d like to find out more about becoming a Responsive Government,Ěý.

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The ESRI researchers are a team of applied Behavioural Scientists who specialise in generating evidence for policy in Ireland and international organisations, while the SIDG is an 51·çÁ÷think-tank working with government, academic and commercial partners to accelerate technology innovation within the Public Sector. It’s therefore unsurprising that the ESRI researchers view solutions to the COVID-19 pandemic through the lens of Behavioural Science, while the SIDG view those same solutions through the lens of digital technology. But these are complimentary views, as detailed in our prior collaboration with the Australian National University, “”. Specifically, this research describes howĚýinformation technology can be applied to Behavioural Science theory to improve social outcomes through nudging via digital channelsĚý(Gregor & Lee-Archer, 2016).

In summary, the ESRI team’s rapid narrative review of more than 100 papers yielded evidence that Behavioural Science interventions could be effective to increase hand washing, butĚýnotĚýto reduce face touching. The review also suggested various Behavioural Science approaches to reduce the disincentive to self-isolate, to promote public-spirited behaviour, and to prompt coordinated action in response to crisis communications. In part, the researchers observe that the effectiveness of these interventions depends not only on the content of the nudge, but also on the speed with which the nudge can be disseminated, and the degree to which it can “grab attention” (Lunn et al, 2020). These are precisely the areas in whichĚýpredictive analytics,Ěýexperience managementĚýandĚýcontextualisationĚýcapabilities can help to improve the efficiency and effectiveness of traditional nudges (van Leent & Ryan, 2020).

In this unprecedented time, and there is undoubtedly a need for urgent and decisive action to contain the spread of COVID-19, but we at the SIDG believe that such action should notĚý. Additionally, Thaler and Sunstein’s (2008) definition of aĚýNudge:Ěý“Any aspect of the choice architecture that alters people’s behaviour, in a predictable way, without forbidding any options, or significantly changing their economic consequences” is equally applicable today and in the case ofĚýDigital Nudges. Hence, as we explore the potential for digital technologies to improve the efficiency and effectiveness of traditional nudges, we will endorse implementation scenarios that incorporate transparency and consent, and respect individual rights to privacy and autonomy.

Hand washing

The ESRI team found strong evidence that Behavioural Science interventions can increase hand washing. Traditional methods focus on attracting attention, making compliance easy, and invoking feelings of disgust. The researchers therefore suggest placing hand sanitiser centrally in prominent public spaces with colourful signs to increase use (Lunn et al, 2020).

But hand sanitiser can’t be provided in every public space, so it might also be helpful to encourage people to carry their own. This is where digital technologies could be used to predict or detect when someone is likely to be travelling and remind them to pack a personal hand sanitiser.Ěý, for example to access a personal calendar, or to track location changes using a mobile device, and to receive SMS notifications from a trusted agent. Additional notifications could be sent to remind people to use their hand sanitiser at intervals, or when they’re scheduled to travel on to the next location.

Face touching

In contrast to hand washing, the ESRI team’s rapid narrative review failed to uncover any evidence that Behavioural Science interventions can reduce the frequency with which people touch their face. Adding to the challenge, the researchers observed that making people conscious of face touching actually increases the incidence of this particular habit. The ESRI team therefore submits ideas, generated using prominent Behavioural Science frameworks, for example to encourage scratching itches with the sleeve (Lunn et al, 2020), but it seems there are few easy solutions to this difficult problem.

Digital interventions will similarly struggle to influence such a personal habit as face touching. But perhaps, rather than trying to prevent or intercept the action in real-time, digital technologies could be used to gradually change the behaviour over time. For example, a webcam could be used to record the number of times a person touches their face while using a computer, tablet or mobile phone. At the end of each day, an app (similar to Apple’sĚýĚýmonitoring app) could report the tally and provide a comparison with previous days. Since this is an otherwise innocuous activity, there might even be the potential for gamification, in which social media groups compete to reduce face touching.

Self-isolation

In their paper, the ESRI team explored the potential for Behavioural Science interventions to reduce the negative effects of isolation. They conclude that if these interventions can reduce negative consequences for individuals, they have the potential to increase voluntary compliance with government rules and regulations. In addition to traditional approaches, the researchers acknowledge the important roles that ICT and social media are already playing in activating social networks for people in self-isolation (Lunn et al, 2020).

Here we suggest that more advanced digital technologies can be applied to further improve conditions for people in isolation. AI and IoT technologies are already being used to support people who might not otherwise have active social networks, for example in aĚýDigital Aged CareĚýsetting. Adding experience management capabilities (including digital surveys) could help us to understand the emotional drivers that influence non-compliance with self-isolation rules and regulations. Once these factors are understood, contextualisation capabilities (including personalised outreach) could be used to proactively engage with people on a more personal level, and to encourage and sustain voluntary compliance.

Collective action

The ESRI team cited Behavioural Science literature that suggests that most people are “conditional cooperators, who are willing to make sacrifices for the public good provided that others are too, but cease cooperation if too many other people don’t bother” (Lunn et al, 2020). Evidence is provided that three factors strongly influence people’s willingness to participate in collective action: communication, group identity and punishment. Importantly in our context, the ESRI team’s rapid narrative review failed to identify any scientific literature addressing the drivers of panic buying, or efforts to prevent it, using traditional nudges.

Although not a scientific paper, the SIDG article “” specifically addresses how Digital Nudges could be applied to reduce panic buying – illustrated with an example of stockpiling toilet paper. In some jurisdictions, supplies of essential goods are slowly returning to normal as a result of additional stocking and controlled purchasing arrangements. However, these measures have not so much changed this undesirable behaviour as suppressed it, so there’s a chance that panic buying could return as soon as the controls are removed. The SIDG therefore continues to promote the use of Digital Nudges in relation to this use case in particular.

Crisis communication

The ESRI team dedicates a large portion of their paper to Behavioural Science approaches to crisis communication. This is for good reason, given the importance of effective communication and the notable use of traditional nudges in this area, for example in response to theĚý. The researchers note that “making communication sensitive to the demographics of the intended recipient helps people to feel that society is more prepared” (Lunn et al, 2020). While social media has been hugely successful at delivering targeted campaigns, the researchers point out that “social media can also contribute to the spreading of inaccurate information, whether malign or merely misinformed” (Lunn et al, 2020).

Digital Nudges can assist with combating “fake news” by empowering governments to apply the CDC’s six principles of :

1. Be first: The ability to communicate information quickly depends not only on having efficient communication channels (instant messaging, email, telephone, etc), but also on knowingĚý. Advanced analytics tools can provide insights into individual preferences and circumstances, allowing officials to select the most effective channel to deliver any given message.
2. Be right: Although there is currently no “best practice” for containing COVID-19, the Internet provides a rich medium for evidence-based communications.ĚýĚýand interactiveĚýĚýare already being used to assist citizens understand the rationale for public health recommendations and government policy.
3. Be credible: Digital channels obviously have the potential to enable experts, such as from the World Health Organisation and the U.S. Centre for Disease Control, to reach large numbers of people – . Appealing to individual interests and preferences through contextualisation (including personalised messages) can help official communications be heard through the noise of big data.
4. Express empathy: Empathy is a characteristically human trait, so one might think that digital technologies don’t have a role to play here. But advanced analytics and machine learning can help surface data-driven insights to enable government officials toĚýĚýand thereby to engage with greater empathy.
5. Promote action: In the main, containment of COVID-19 relies on everyone doing pretty much the same thing:Ěý. But right now in the USA, people are being advised toĚýĚýover maintaining social distancing. As previously discussed, digital technologies can assist to deliver targeted outreach and to elicit appropriate responses from individuals in particular circumstances.
6. Show respect: The CDC observes that respectful communication is key to building rapport and encouraging cooperation. But respectful communication is not just about knowing how to address someone politely – rapport is built through active listening and two-way dialogue. Experience management capabilities (including embedded feedback loops) can help governments toĚýĚýon citizen sentiment.

“We’re all in this together” is perhaps one of the most effective nudges utilised during the COVID-19 crisis. What’s becoming more and more clear is thatĚýwe’re all in this for the long-haul. Behavioural Science approaches have already been successfully applied by governments and public health officials to nudge people to act in the collective interest. But additional measures and supports will be required to sustain this public-spirited behaviour in the months to come. Digital technologies, including predictive analytics, experience management and contextualisation, are part of the overall toolkit available to governments as they engage with the world’s conditional cooperators.

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Right now, in our communities, we are witnessing the consequences ofĚýlimited rationality,Ěýsocial preferencesĚýandĚýlack of self-control. In their seminal work “Nudge: Improving Decisions on Health, Wealth, and Happiness,” Professors Richard Thaler () and Cass Sunstein postulated that these human traits systematically affect individual decisions and market outcomes. It’s instructive to explore how these factors might be influencing individual decisions, for example, to stockpile toilet paper:

• Limited rationality: People focus on the narrow impact of individual decisions rather than the overall effect. For example, I’ll buy some extra toilet paper now because I’ve heard that it might be in short supply later. I make this individual decision without realising that I’m inadvertently contributing to the overall effect of supplies running short, which will ultimately impact me – along with everyone else – in the long run.

• Social preferences: People have a social preference for equitable outcomes. For example, I’ll be less accepting of my local supermarket increasing the price of toilet paper in response to a growth in demand than in response to a rise in their cost of supply. Even if the price rise is the same in both cases, my willingness to pay a premium is influenced by my perception of fairness.

• Lack of self-control: People tend to give in to short-term temptation rather than stick to a long-term plan. For example, even though I have more than enough toilet paper at home, I’ll still buy more if I find it somewhere on sale. I know that I don’t have anywhere to store additional rolls of toilet paper, but when presented with the opportunity to purchase such a sought-after item at a discounted price, I won’t be able to resist.

As has been demonstrated across the globe, government assurances, pleas, and directives have failed to prevent emotional shoppers from emptying shelves in anticipation of future shortages. Now similar assurances, pleas, and directives are being made in relation to the much more serious issues of self-isolation, social distancing, and personal hygiene. Will citizens heed government rules and regulations now when they haven’t in the past? Certainly, the Chinese governmentĚýĚýin curbing the spread of COVID-19, but most Democratic governments don’t have the same controls available to them as in Communist China. What then is to be done?

In our aforementioned research, the SIDG and the ANU described howĚýdigital nudging might be used by governments to drive behavioural change for social good. Empirical evidence told us that certain human actions result in better social outcomes, and digital technology is enabling us to reliably predict those outcomes based on observed behaviours. This caused us to ask: how might we leverage default human nature to positively influence social outcomes, and could we apply technology to influence individual decisions at scale?

Where Thaler and Sunstein (2008) defined aĚýnudge as: “Any aspect of the choice architecture that alters people’s behaviour, in a predictable way, without forbidding any options, or significantly changing their economic consequences.” We defined aĚýdigital nudgeĚýas: “Individually targeted processes, facilitated by information technology, to achieve social policy outcomes” (Gregor & Lee-Archer, 2016).

Figure 1: At the intersection of agile policy, information technology and behaviouralĚýscience is the digital nudge.

Moreover, we proposed thatĚýpredictive analyticsĚýand contextualisationĚýcapabilities can improve the effectiveness of traditional nudging by enabling the shift from reactive to proactive interventions and by making nudges more targeted to individual circumstances.

• Predictive analytics is a specific field of data mining in which large stores of data are analysed to detect patterns and to predict future outcomes and trends. While predictive algorithms have been used for many years, they have typically been restricted to operating on pre-existing data. Real-time computing platforms have changed this by allowing data to be analysed as it’s created. This means that analytical discoveries can be applied to adjust government action dynamically, thereby influencing trends as they emerge.

• ContextualisationĚýis the next evolution of personalisation: blending together information about past interactions and anticipated behaviours with present motivations and intent. Where personalisation attempts to anticipate future behaviours based on past activities, it lacks the in-the-moment context of the citizen’s current circumstance. This is important because it’s precisely that current context that’s most relevant and useful for predicting future behaviour.

Figure 2:ĚýOur framework for the design and application of digital nudges.

Of course, our thinking has evolved since 2016, and so we would now addĚýexperience managementĚýinto the mix.

• Experience managementĚýbrings together operational data (O-data) aboutĚýwhatĚýis happening, with experience data (X-data) that tells usĚýwhyĚýit’s happening. This fusing of X+O data can enable governments to better understand citizen sentiments and motivations, and thereby take effective action. Importantly, since sentiments and motivations are constantly changing, governments need to embed feedback and analysis throughout their business processes and at every point of citizen interaction.

With this in mind, let’s return to our example of stockpiling toilet paper and see how governments might apply digital nudging to curb this behaviour…

An onlineĚýĚýsuggests that to last 14 days in isolation, each person requires only four rolls of toilet paper. So, the average American household (2.6 people) should be able to get by with just a single pack (10 rolls). Most likely, very few consumers did this calculation prior to purchasing, so a simple SMS informing citizens about how much toilet paper they actually need could be quite effective. It might even be possible to target the digital nudge by advising the required number of rolls for a given household.

Another approach would be to leverage the behavioural science influencer of .ĚýĚýof over 6,000 Australians indicated that only 9% had purchased more than 20 rolls of toilet paper due to COVID-19. This sort of statistic could be promoted via digital channels, especially in geographic areas where a small percentage of people have been observed to be buying in bulk. To further improve effectiveness, the poll could be extended to understand what’s motivating consumer purchasing decisions (e.g.,ĚýWhyĚýdid you decide to purchase X rolls of toilet paper?).

Figure 3:ĚýA conceptual architecture for digital nudges.

These same capabilities could be applied by governments to nudge citizens towards cooperation with rules and regulations relating to self-isolation, social distancing, and personal hygiene. The Behavioural Insights Team’s provides nine of the most robust (non-coercive) influences on human behaviour, including:

• Messenger:ĚýWe are heavily influenced by who communicates information.Ěý suggests that “Scientists and physicians are the most trusted authorities [on COVID-19], along with officials from the World Health Organisation and the U.S. Centre for Disease Control.”

• Norms:ĚýWe are strongly influenced by what others do. Governments, researchers, public health authorities, and the general public areĚýĚýsuccessful responses to COVID-19 and to avoid repeating the missteps of others.

• Affect:ĚýOur emotional associations can powerfully shape our actions. The CDC has dedicatedĚýĚýto managing anxiety and stress related to COVID-19.

Finally, it’s important to be mindful of the iterative nature of our digital nudge framework. Under normal circumstances, nudges are tested with focus groups inĚý. While there’s a need to change certain behaviours relating to COVID-19 immediately, the potential for unintended consequences is heightened as a result of panic, so it’s important not to skip this important step. Ěýapproaches can assist in expediting the test-and-improve cycle, both prior to disseminating the initial nudge and to inform adaptation of the nudge as circumstances change.

While digital nudging is not a silver bullet for containing COVID-19, it is part of the overall toolkit available to governments today. As we’ve shown by way of examples, digital technologies can be used to both scale and personalise traditional nudges to improve outcomes for mass cohorts. Specifically, the combination of predictive analytics, experience management, and contextualisation capabilities can enable governments to predict social outcomes, understand what’s motivating those outcomes, and take effective action to avoid today’s emerging trends from becoming tomorrow’s next crisis.

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