Drug and Alcohol ReviewEarly View COMMENTARYOpen Access The impact of tableside ordering technologies on alcohol sales to the intoxicated Michala Kowalski, Corresponding Author Michala Kowalski m.kowalski@unsw.edu.au orcid.org/0000-0002-3175-808X Drug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, Australia Correspondence Michala Kowalski, Social Policy Research Centre, UNSW Sydney, NSW 2052, Australia. Email: m.kowalski@unsw.edu.auSearch for more papers by this authorMichael Livingston, Michael Livingston orcid.org/0000-0002-8995-9386 National Drug Research Institute and enAble Institute, Faculty of Health Sciences, Curtin University, Perth, Australia Centre for Alcohol Policy Research, La Trobe University, Melbourne, AustraliaSearch for more papers by this authorClaire Wilkinson, Claire Wilkinson orcid.org/0000-0002-4815-5840 Drug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, Australia Centre for Alcohol Policy Research, La Trobe University, Melbourne, AustraliaSearch for more papers by this authorAlison Ritter, Alison Ritter orcid.org/0000-0001-9540-1920 Drug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, AustraliaSearch for more papers by this author Michala Kowalski, Corresponding Author Michala Kowalski m.kowalski@unsw.edu.au orcid.org/0000-0002-3175-808X Drug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, Australia Correspondence Michala Kowalski, Social Policy Research Centre, UNSW Sydney, NSW 2052, Australia. Email: m.kowalski@unsw.edu.auSearch for more papers by this authorMichael Livingston, Michael Livingston orcid.org/0000-0002-8995-9386 National Drug Research Institute and enAble Institute, Faculty of Health Sciences, Curtin University, Perth, Australia Centre for Alcohol Policy Research, La Trobe University, Melbourne, AustraliaSearch for more papers by this authorClaire Wilkinson, Claire Wilkinson orcid.org/0000-0002-4815-5840 Drug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, Australia Centre for Alcohol Policy Research, La Trobe University, Melbourne, AustraliaSearch for more papers by this authorAlison Ritter, Alison Ritter orcid.org/0000-0001-9540-1920 Drug Policy Modelling Program, Social Policy Research Centre, UNSW Sydney, Sydney, AustraliaSearch for more papers by this author First published: 08 March 2023 https://doi.org/10.1111/dar.13639AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Technological innovations in hospitality include measures such as social media platform advertising, online ordering and delivery services, and automation processes [1, 2]. While the alcohol industry's adoption of social media advertising [3-6] and online delivery services of alcohol [7] have garnered international research and regulatory interest [3-8], the introduction of table side ordering technologies [9] has been overlooked within alcohol policy circles to date. Tableside ordering technology entered the hospitality business in the United States as early as 2017 [10]. Global adoption of this technology was slow to start but was hastened by the COVID-19 pandemic [11]. Australian hospitality venues that implemented tableside ordering offer the following experience: patrons scan a QR code that is displayed on their table. The QR code directs the patron to the webpage portal of an ordering application. The menu opens with a pop-up that asks, ‘are you over 18?’ The patron can select ‘yes’ and proceed to scroll through the menu. Patrons add the food and drinks they would like to order to their cart. When patrons proceed to payment, they are asked to provide further demographic information for their registration. Patrons pay in-app and then wait for staff to deliver the orders to the table. Venue employees circulate to clear glassware and dishes. Patrons are free to go back to the online menu and continue ordering for as long as they like [12]. The venue and the app operator [13, 14] have access to all the ordering information. This information is retained in order to account for ordering trends and data optimisation. For instance, Goodfood published a list of the most ordered drinks in Melbourne in 2021: espresso martini, Carlton Draught, Aperol spritz, Coburg Lager and Balter XPA [11]. Retail and hospitality studies described business concerns regarding a loss of connection between the patron and venue staff that used to be facilitated during the ordering process [15]. This loss of connection between a venue's staff and their patrons also raises concerns from a public health perspective. Prohibition of service to intoxicated patrons is a standard element of liquor regulation in nearly all jurisdictions. Interaction with the patron at the point of sale is often how a staff member will determine whether a patron is intoxicated. Signs of intoxication are defined in policies as the behavioural cues staff are to look for in order to determine a patron's state of intoxication [16]. These include: speech, balance, coordination and if behaviour is noticeably affected, and it is reasonable in the circumstances that these are affected as the result of the consumption of alcohol [16]. None of these behavioural cues are likely to be detectable by staff when drinks are ordered from a patron's own phone. Reports of an increase in online shopping while intoxicated [17] suggest that intoxication is not a barrier to operating online shopping apps and webpages such as those used in tableside ordering. Tableside ordering challenges the service dynamics on which the Responsible Service of Alcohol policy [16] is predicated. Traditionally, staff would refuse service before pouring a drink and accepting payment for the patron's order. However, an order placed using tableside ordering has been poured and paid for by the time a staff member could even begin contemplating enacting a refusal of service. This new ordering format also shifts the power dynamics involved in the service interaction. In a traditional interaction an individual patron would stand at a bar which is staffed by multiple staff members (in which staff may have felt supported in carrying out a refusal of service). Tableside ordering shifts this dynamic to an interaction involving a lone server offloading drinks amongst tables of patrons (in which staff may feel a lack of institutional support in carrying out a refusal of service). Shifting the ordering interaction from the bar to the table may also be cutting into biological feedback mechanisms which are engaged when we stand up. One of the ways patrons can gauge how intoxicated they are is by standing up and seeing how they feel. There are some early indications that tableside ordering may increase the consumption of alcohol. According to tableside ordering companies marketing information, customers spend more per person when their technology is implemented [11, 18]. This increased spend may be translated into increased consumption of alcohol. When considered together with the ordering technology sidestepping biological feedback mechanisms used to gauge sobriety, customers may not only be consuming more alcohol than previously, they may be consuming more alcohol than they would like. We have very little evidence to suggest that restricting the service of alcohol to intoxicated people is working particularly well [19, 20]. Although this measure follows a seemingly intuitive logic, it is difficult to implement and unpopular with the alcohol industry and licensees [21] (the very people who are responsible for implementing it). Responsible service of alcohol pilots in controlled environments [22, 23] showed promising results but despite widespread implementation in community settings, these programs have not delivered their intended results [19]. The lack of observed efficacy is often attributed to poor implementation [19]. The technological innovation of tableside ordering introduces new barriers to the implementation of responsible service of alcohol. It also presents a timely opportunity to rethink policies targeting service in licensed premises. There may be four potential opportunities for public health benefits associated with the introduction of tableside ordering technologies. Firstly, it may restrict access to alcohol for minors. Alcohol policies which enforce widespread identity checks of all patrons are associated with reductions of service to minors [24]. If tableside ordering technologies were to integrate stringent identity checks, their usage could reduce the burden of in-person identity checks, while reducing instances of serving alcohol to minors. Second, it may provide an opportunity to present alcohol content information and health warnings [25] for non-packaged alcohol. Tableside ordering technologies could present this information next to the drink item in app [26]. Alcohol labelling has not been particularly effective at moderating drinking behaviours [27], however, providing the information allows consumers to make informed choices and may lead to gradual changes over time [28]. Third, it may decrease the ordering of rounds, by encouraging individualised consumption instead [29]. Drinking in rounds is considered a risky drinking practice, as it prolongs the duration of the drinking session and sets a (fast) rate of consumption in said drinking session [30]. Tableside ordering could potentially encourage each patron to order individually, especially if the menus remove multiple-serve sizes such as jugs or bottles. Finally, individuals could set an upper limit on the number of drinks they can order for their own consumption. Pre-commitment systems, reliant on nudge theory [31] and technological capabilities [32], have been trialled for behaviours such as gambling [33] and saving commitments [34], although there is limited evidence of their effectiveness [32, 33]. These opportunities may be marred by technological adaptations to local drinking culture norms, such as group tables [35], setting up multiple accounts or other evasive tactics, and the potential for increased surveillance [36]. Previous research on the alcohol industry's interest in automated advertising [2] and social media's continued data mining for the purposes of advertising [37], suggest that social media companies may be tracking patron's tableside ordering purchases. In theory, a patron's ordering behaviour could create a pernicious feedback cycle in which ordering alcohol triggers their exposure to alcohol-related content on social media, which in turn is associated with increased consumption of alcohol [3]. Tableside ordering technologies also present a research opportunity. The companies administering this technology are collecting a treasure trove of individual drinking session consumption data alongside patrons' demographic data [12-14]. In Australia, alcohol sales data are collected in the Australian Capital Territory, Western Australia, Victoria, Queensland and the Northern Territory [38]. Sales data represents wholesale beer, wine and spirits sales, and are used to measure per capita consumption [38]. Tableside ordering apps on the other hand, are collecting the ‘big data’ equivalent of consumption from each individual's alcohol purchases per drinking session. While regulation is needed to ensure that patrons' data is safeguarded from automated encouragement of excessive consumption, it could also ensure access for public health researchers to de-identified consumption data. If public health researchers were to access this data, it could be used to better understand drinking patterns and practices in licensed venues, with the potential to promote public health via appropriate interventions. In summary, the introduction of tableside ordering technologies represents a regulatory challenge, alongside a research opportunity. Tableside ordering technologies as currently implemented in licensed premises may be circumventing policies that ban the service of alcohol to intoxicated people. Close ethnographic research is needed to study how policy and technologies can be adapted to better meet public health goals. In short, tableside ordering technologies pose both new threats and new opportunities to public health interventions. A new program of research, alongside regulatory attention, will be needed to begin to address them. AUTHOR CONTRIBUTIONS Each author certifies that their contribution to this work meets the standards of the International Committee of Medical Journal Editors. ACKNOWLEDGEMENTS Michala Kowalski is supported by a UNSW Scientia PhD scholarship. Michael Livingston is funded via an Australian Research Council Future Fellowship (FT210100656). Claire Wilkinson is supported by a National Health and Medical Research Council Early Career Fellowship (11402942). Alison Ritter is funded via a National Health and Medical Research Council Senior Research Fellowship (APP1136944). Open access publishing facilitated by University of New South Wales, as part of the Wiley - University of New South Wales agreement via the Council of Australian University Librarians. CONFLICT OF INTEREST STATEMENT None to declare. REFERENCES 1Dixon M, Kimes SE, Verma R. Customer preferences for restaurant technology innovations. Cornell Hospitality Report. 2009; 9: 1– 20. 2Goodwin I. Programmatic alcohol advertising, social media and public health: algorithms, automated challenges to regulation, and the failure of public oversight. Int J Drug Policy. 2022; 109:103826. 3Russell AM, Bergman BG, Colditz JB, Massey PM. Algorithmic accountability on social media platforms in the context of alcohol-related health behavior change. Addiction. 2023; 118: 189– 90. 4Jernigan D, Ross CS. The alcohol marketing landscape: alcohol industry size, structure, strategies, and public health responses. J Stud Alcohol Drugs Suppl. 2020; Sup 19: 13– 25. 5O'Brien P, Room R, Anderson-Luxford D. Commercial advertising of alcohol: using law to challenge public health regulation. J Law Med Ethics. 2022; 50: 240– 9. 6Colbert S, Wilkinson C, Thornton L, Richmond R. COVID-19 and alcohol in Australia: industry changes and public health impacts. Drug Alcohol Rev. 2020; 39: 435– 40. 7Colbert S, Wilkinson C, Thornton L, Feng X, Richmond R. Online alcohol sales and home delivery: an international policy review and systematic literature review. Health Policy. 2021; 125: 1222– 37. 8 Liquor Amendment (24-hour Economy) Bill 2020, (2020). 9Berezina K, Ciftci O, Cobanoglu C. Robots, artificial intelligence, and service automation in restaurants. Robots, artificial intelligence, and service automation in travel, tourism and hospitality. Bingley: Emerald Publishing Limited; 2019. 10Larivière B, Bowen D, Andreassen TW, Kunz W, Sirianni NJ, Voss C, et al. “Service encounter 2.0”: an investigation into the roles of technology, employees and customers. J Bus Res. 2017; 79: 238– 46. 11Breheny E. Steaks, cocktails and chicken parmas the most-ordered by Melbourne diners. North Sydney: Nine Entertainment Co.; 2021. 12 Australian Venue Co. Order to your table. Melbourne: Australian Venue Co.; 2022. 13 Tableside Ordering Hospitality Technology. Available from: https://hospitalitytech.com/tableside-ordering 14 Table Ordering Orderup!. Available from: https://www.orderup.com.au/table-ordering/?utm_campaign=OrderUp%20-%20S2%202022&utm_source=ppc&utm_medium=table_ordering&utm_term=tableside%20ordering%20system&utm_campaign=Generic+%7C+Table+Ordering&utm_source=adwords&utm_medium=ppc&hsa_acc=5309964268&hsa_cam=17874623918&hsa_grp=134616644850&hsa_ad=612889867155&hsa_src=g&hsa_tgt=kwd-179397793518&hsa_kw=tableside%20ordering%20system&hsa_mt=p&hsa_net=adwords&hsa_ver=3&gclid=EAIaIQobChMIv9W_o-OV-gIVw38rCh1NXQrKEAAYASAAEgJsBPD_BwE 15Christ-Brendemühl S. Bridging the gap: an interview study on frontline employee responses to restaurant technology. Int J Hosp Manag. 2022; 102:103183. 16 Liquor and Gaming NSW. NSW responsible Service of Alcohol. Course handbook. Sydney, New South Wales: Liquor and Gaming NSW; 2022. 17Berethiaume D. Survey: intoxicated online shopping is soberingly big business. Chain Store Age: The Business of Retail. 2021. 18 meandu. $15.1m generated with me&u's data driven features: meandu.com. Available from: https://www.meandu.com/blog-post/15-1m-generated-with-me-us-data-driven-features. 2022 19Stockwell T. Responsible alcohol service: lessons from evaluations of server training and policing initiatives. Drug Alcohol Rev. 2001; 20: 257– 65. 20Donnelly N. Young adults' experience of responsible service of alcohol in NSW: 2011 update. Crime and Justice Bulletin No 162. 2012. Available from: https://www.bocsar.nsw.gov.au/Pages/bocsar_publication/Pub_Summary/CJB/cjb162-Young-adults-experience-of-responsible-service-of-alcohol-in-NSW-2011-update.aspx 21Hawkins N, Sanson-Fisher R, Shakeshaft A, Webb G. Differences in licensee, police and public opinions regarding interventions to reduce alcohol-related harm associated with licensed premises. Aust N Z J Public Health. 2009; 33: 160– 6. 22Saltz RF. The roles of bars and restaurants in preventing alcohol-impaired driving: an evaluation of server intervention. Eval Health Prof. 1987; 10: 5– 27. 23Gliksman L, McKenzie D, Single E, Douglas R, Brunet S, Moffatt K. The role of alcohol providers in prevention: an evaluation of a server intervention programme. Addiction. 1993; 88: 1195– 203. 24Smith J, Adamson E. Process evaluation of the banned drinker register in the Northern Territory. Darwin: Menzies School of Health Research; 2018. 25 Food Standards Australia New Zealand. P1050 – Pregnancy warning labels on alcoholic beverages. 2020. Available from: https://www.foodstandards.gov.au/code/proposals/Pages/P1050Pregnancywarninglabelsonalcoholicbeverages.aspx 26Martin-Moreno JM, Harris ME, Breda J, Møller L, Alfonso-Sanchez JL, Gorgojo L. Enhanced labelling on alcoholic drinks: reviewing the evidence to guide alcohol policy. Eur J Pub Health. 2013; 23: 1082– 7. 27Wilkinson C, Room R. Warnings on alcohol containers and advertisements: international experience and evidence on effects. Drug Alcohol Rev. 2009; 28: 426– 35. 28 World Health Organisation. Alcohol labelling: a discussion document on policy options. Copenhagen: Regional Office for Europe: World Health Organisation; 2017 Contract No.: 2017-4124-43883-61793. 29Riazi S, MacLean S. Young adults' accounts of buying rounds of alcoholic drinks for friends: implications for harm reduction. Int J Alcohol Drug Res. 2016; 5: 125– 9. 30Roberts S, Ralph B, Elliott K, Robards B, Savic M, Lindsay J, et al. Exploring men's risky drinking cultures. Melbourne: Victorian Health Promotion Foundation; 2019. 31Beshears J, Dai H, Milkman KL, Benartzi S. Framing the future: the risks of pre-commitment nudges and potential of fresh start messaging. Working Paper. 2017. 32Thomas A, Rintoul A, Deblaquiere J, Armstrong A, Moore S, Carson R, et al. Review of electronic gaming machine pre-commitment features: transaction history statements. Melbourne: Australian Institute of Family Studies (AIFS); 2016 Report No.: 1760160768. 33Ladouceur R, Blaszczynski A, Lalande DR. Pre-commitment in gambling: a review of the empirical evidence. Int Gambl Stud. 2012; 12: 215– 30. 34Roll S, Grinstein-Weiss M, Gallagher E, Cryder C. Can pre-commitment increase savings deposits? Evidence from a tax-time field experiment. J Econ Behav Organ. 2020; 180: 357– 80. 35 meandu. New Feature: Introducing Group Tabs: me&u. Available from: https://www.meandu.com/blog-post/new-feature-introducing-group-tabs 36Zuboff S. The age of surveillance capitalism: the fight for a human future at the new frontier of power. London: Profile Books; 2019. 37Binder M. Lawsuits against meta claim its apps track users despite Apple's rules. Mashable. 2022. 38Rankin G, Livingston M. Understanding alcohol sales data in Australia. Canberra: Foundation for Alcohol Research and Education; 2016. Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation