Comply or resist? The use of service robots for law enforcement

Qingxuan Zhang and Liliana L. Bove
The University of Melbourne, Australia

The global pandemic increased the need for monitoring and enforcement of COVID-related policies such as mandatory vaccinations, face mask-wearing, travel restrictions, and quarantine which were largely carried out by law enforcement officers (Kugler et al. 2021). This increase in demand for law enforcement officers, in addition to their greater risk of exposure brought about by their close contact with the public (Jennings and Perez, 2020) resulted in many countries, such as Australia (Opie, 2022), the US (Lee, 2021), and Canada (Piapot et al., 2022) experiencing severe staff shortages. At times officer shortage was so acute that police stations were forced to close (The Age, 2021). A potential solution to this chronic shortage is the use of robots to monitor and enforce COVID compliance.

The use of robots in the law enforcement sector was estimated to be US$ 1.6 billion in 2020 and is expected to increase to US$ 4.2 billion by 2027 (GIA, 2021). With recent advancements in technology, robots can carry out tasks that were previously only reserved for human law enforcement officers. For instance, robots, equipped with state-of-the-art cameras, sensors, tracking technology, and connectivity capabilities, can now conduct autonomous surveillance without the presence of human officers (Market Trends, 2021).

Despite the large number of studies that explore how service robots can enhance customer comfort (Lin et al., 2022), bring enjoyment (Van Pinxteren et al., 2019), and elicit compensatory consumption (Mende et al., 2019) in multiple settings, scant attention has been paid to contexts in which the primary role of the service robot is to gain customer compliance. The overall service experience is largely unexplored, and the unintended consequence of such robot uses on consumers are unclear.

In response to these gaps, this study aims to explore citizen attitudes and emotional reactions towards service robots in law enforcement settings. Drawing on a Norwegian study where fishers had a lower acceptance rate of modern control activities (e.g., remote monitoring with drones) compared to traditional means of control (e.g., physical inspection by coastguards) (Diekert et al., 2021), we anticipate citizens will feel and act similarly towards service robots in law enforcement settings.

Of further interest is understanding citizen behavior in law enforcement settings where the rules and regulations are deemed to be unfair and not legitimate. Underpinned by reactance theory, we predict that when an individual’s freedom is threatened, the threatened behavior becomes more attractive (Brehm, 1966) and reactance, manifested in negative emotions such as offense (Heide et al., 2007), hostility, aggression, anger and frustration (Clee and Wicklund, 1980; Miron and Brehm, 2006) is aroused. In these situations, the affected individual may be motivated to restore freedom by seeking opportunism to misbehave towards the source of the threat (Dillard and Shen, 2005) i.e., the robot.

Using in-depth interviews of Chinese citizens who have experienced interactions with service robots charged with COVID-related control activities, we will explore how robot characteristics such as level of anthropomorphism and/or biomimetricity impact customer emotional attitudes, and compliance or reactance behaviors.

Robots as law enforcers
Singapore appears to have led the way in the use of robots for law enforcement possibly due to the high level of trust their citizens place on the government (Rieger and Wang, 2022). It first trialed the use of a robot dog called Spot, which carried a loudspeaker to broadcast coronavirus-related messages to enforce social distancing in parks (BBC, 2020). It also deployed a Boston Dynamics robot dog with sensors and speakers at a reservoir to warn people about the laws against loitering and gathering (Su, 2020). More recently, Singapore introduced a pair of patrol robots, dubbed Xavier, to augment the work of public officers by helping to enforce COVID-19 protocols and deter other undesirable civic habits (e.g., smoking in banned areas) in a busy shopping district (Barrett, 2021). When Xavier detects any undesirable behaviors, it sends real-time alerts to the command-and-control center, and officers can choose to respond in person or remotely by displaying an appropriate message on the robot’s interactive dashboard (Adams, 2021; HTX, 2021).

Similarly, humanoid anti-pandemic robots, dubbed Jasiri, are put to work at an international airport in Nairobi, Kenya (Reuters, 2021). Jasiri, checks passengers’ temperatures, tells those not wearing masks to put them on, and enforces social distancing rules with a camera mounted on an extendable neck (Reuters, 2021).

Given the scarcity of research as to how consumers react to robots used in law enforcement settings exploratory research in the form of in-depth interviews will be used. A phenomenological approach will be taken to obtain a first-person description of personal experience with a law-enforcement robot.

The first context investigates several eastern Chinese cities where robot dogs, referred to as Preserved Egg, a famous Chinese dish, patrol the streets three or four times a day and help the authorities spread Covid-related messages, such as calling for mandatory COVID-19 tests or encouraging people to vaccinate (McMorrow, 2022). The second context is in several major airports in China where service robots like Xavier are used to check travelers’ body temperature and QR codes related to past traces.

Participants will be recruited through snowball sampling beginning with personal contacts and then following up with informant referrals (Noy, 2008). They will also be encouraged to use their native language to allow freer, flowing conversation. The messaging app WhatsApp will be used as the platform for communication because of its free access and assured anonymity to participants with its end-to-end encryption (WhatsApp, 2022). While videoconferencing allows the interview to be recorded, due to the perceived sensitivity of the topic, informants will be asked if interviews can be restricted to audio recording to allow verbatim transcription. Participants will also be assured of anonymity, referred only to by their pseudonym during the interview. If audio recording approval is not given, extensive notes will be taken during the interview.

To ensure a generalizable sample across various age groups, educational backgrounds, and risk preferences, individuals such as elderly people who do not use WhatsApp will be interviewed in person via an intermediary family member who is a WhatsApp user.

Adams, E. (2021). Singapore is testing robots to patrol the streets for “undesirable” behavior like smoking. USA TODAY. Retrieved August 5, 2022, from
Barrett, C. (2021). “Exterminate bad habits”: Meet Xavier, Singapore’s COVID-compliance robot. The Sydney Morning Herald. Retrieved August 6, 2022, from
BBC (2020). Robot dog enforces social distancing in city park. BBC. Retrieved August 5, 2022, from
Brehm, J. W. (1966). A theory of psychological reactance. New York: Academic Press.
Clee, M. A. & Wicklund R. A. (1980). Consumer behavior and psychological reactance. Journal of Consumer Research, 6 (4), 389-405.
Diekert, F., Nøstbakken, L., & Richter, A. (2021). Control activities and compliance behavior—Survey evidence from Norway. Marine Policy, 125.
Fang, S., Han, X., & Chen, S. (2022). The Impact of Tourist–Robot Interaction on Tourist Engagement in the Hospitality Industry: A Mixed-Method Study. Cornell Hospitality Quarterly.
GIA. (2021). Security and Law Enforcement Robots. (n.d.). Retrieved August 9, 2022, from
Heide, J. B., Wathne, K. H. & Rokkan, A. I. (2007). Interfirm monitoring, social contracts, and relationship outcomes. Journal of Marketing Research, (August), 425-433.
Jennings, W. G., & Perez, N. M. (2020). The Immediate Impact of COVID-19 on Law Enforcement in the United States. American journal of criminal justice: AJCJ, 45(4), 690–701.
Kugler, M. B., Oliver, M., Chu, J., & Lee, N. R. (2020). American law enforcement responses to COVID-19. J. Crim. L. & Crimin. Online, 111, 19.
Lee, R. (2021). Officials say there’s a nationwide police shortage and it’s affecting Polk County as well. The Ledger. Retrieved August 6, 2022, from
Market Trends. (2021). How are robotics applications facilitating law enforcement. Retrieved August 15, 2022, from
McMorrow, R. (2022). The robot dogs policing Shanghai’s strict lockdown. Financial Times. Retrieved August 8, 2022, from
Noy, Chaim. (2008), “Sampling Knowledge: The Hermeneutics of Snowball Sampling in Qualitative Research,” International Journal of Social Research Methodology, 11 (4), 327–44.
Opie, R. (2022) “It’s failing the community of South Australia”: Union calls for policing model overhaul. ABC News. Retrieved August 7, 2022, from
Piapot, N., Freeze, C., & Woo, A. (2022). COVID-stricken police forces across Canada struggle with shortage of officers. The Globe and Mail. Retrieved August 7, 2022, from
Reuters. (2021). Airport robots give hi-tech boost to Kenya’s Covid-19 fight. Deccan Herald. Retrieved August 8, 2022, from
Richter, A., & Grasman, J. (2013). The transmission of sustainable harvesting norms when agents are conditionally cooperative. Ecological Economics, 93, 202–209.
Rieger, M.O., Wang, M. Trust in Government Actions During the COVID-19 Crisis. Soc Indic Res 159, 967–989 (2022).
Su, E. (2020). Roaming “robodog” tells Singapore park goers to keep their distance. The Sydney Morning Herald. Retrieved August 5, 2022, from
The Age. (2021). Calls for police to return to Melbourne as COVID response triggers station closures. Retrieved August 7, 2022, from
WhatsApp. (2022). Retrieved August 7, 2022, from