research topic on online food delivery

To read this content please select one of the options below:

Please note you do not have access to teaching notes, online food delivery research: a systematic literature review.

International Journal of Contemporary Hospitality Management

ISSN : 0959-6119

Article publication date: 26 May 2022

Issue publication date: 26 July 2022

Online food delivery (OFD) has witnessed momentous consumer adoption in the past few years, and COVID-19, if anything, is only accelerating its growth. This paper captures numerous intricate issues arising from the complex relationship among the stakeholders because of the enhanced scale of the OFD business. The purpose of this paper is to highlight publication trends in OFD and identify potential future research themes.

Design/methodology/approach

The authors conducted a tri-method study – systematic literature review, bibliometric and thematic content analysis – of 43 articles on OFD published in 24 journals from 2015 to 2021 (March). The authors used VOSviewer to perform citation analysis.

Systematic literature review of the existing OFD research resulted in six potential research themes. Further, thematic content analysis synthesized and categorized the literature into four knowledge clusters, namely, (i) digital mediation in OFD, (ii) dynamic OFD operations, (iii) OFD adoption by consumers and (iv) risk and trust issues in OFD. The authors also present the emerging trends in terms of the most influential articles, authors and journals.

Practical implications

This paper captures the different facets of interactions among various OFD stakeholders and highlights the intricate issues and challenges that require immediate attention from researchers and practitioners.

Originality/value

This is one of the few studies to synthesize OFD literature that sheds light on unexplored aspects of complex relationships among OFD stakeholders through four clusters and six research themes through a conceptual framework.

• Online food delivery
• Sharing economy
• Systematic literature review
• Bibliometric analysis
• Content analysis

Acknowledgements

The authors thank three anonymous reviewers, the guest editor, and the editor-in-chief for their critical and valuable comments in developing the manuscript in stages.

Shroff, A. , Shah, B.J. and Gajjar, H. (2022), "Online food delivery research: a systematic literature review", International Journal of Contemporary Hospitality Management , Vol. 34 No. 8, pp. 2852-2883. https://doi.org/10.1108/IJCHM-10-2021-1273

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

• Open access
• Published: 16 July 2022

Investigating experiences of frequent online food delivery service use: a qualitative study in UK adults

• Matthew Keeble 1 ,
• Jean Adams 1 &
• Thomas Burgoine 1  

BMC Public Health volume  22 , Article number:  1365 ( 2022 ) Cite this article

17 Citations

60 Altmetric

Metrics details

Food prepared out-of-home is typically energy-dense and nutrient-poor. This food can be purchased from multiple types of retailer, including restaurants and takeaway food outlets. Using online food delivery services to purchase food prepared out-of-home is increasing in popularity. This may lead to more frequent unhealthy food consumption, which is positively associated with poor diet and living with obesity. Understanding possible reasons for using online food delivery services might contribute to the development of future public health interventions, if deemed necessary. This knowledge would be best obtained by engaging with individuals who use online food delivery services as part of established routines. Therefore, we aimed to investigate customer experiences of using online food delivery services to understand their reasons for using them, including any advantages and drawbacks.

Methods and results

In 2020, we conducted telephone interviews with 22 adults living in the UK who had used online food delivery services on at least a monthly basis over the previous year. Through codebook thematic analysis, we generated five themes: ‘The importance of takeaway food’, ‘Less effort for more convenience’, ‘Saving money and reallocating time’, ‘Online food delivery service normalisation’ and ‘Maintained home food practices’. Two concepts were overarching throughout: ‘Place. Time. Situation.’ and ‘Perceived advantages outweigh recognised drawbacks’.

After considering each of the accessible food purchasing options within the context of their location and the time of day, participants typically selected online food delivery services. Participants reported that they did not use online food delivery services to purchase healthy food. Participants considered online food delivery service use to be a normal practice that involves little effort due to optimised purchasing processes. As a result, these services were seen to offer convenient access to food aligned with sociocultural expectations. Participants reported that this convenience was often an advantage but could be a drawback. Although participants were price-sensitive, they were willing to pay delivery fees for the opportunity to complete tasks whilst waiting for delivery. Furthermore, participants valued price-promotions and concluded that receiving them justified their online food delivery service use. Despite takeaway food consumption, participants considered home cooking to be irreplaceable.

Conclusions

Future public health interventions might seek to increase the healthiness of food available online whilst maintaining sociocultural values. Extending restrictions adopted in other food environments to online food delivery services could also be explored.

Peer Review reports

Purchasing food that is prepared out-of-home and served ready-to-consume is prevalent across the world [ 1 ]. The neighbourhood food environment includes all physically accessible food outlets where individuals can purchase and consume foods, including food prepared out-of-home (often referred to as ‘takeaway food’) [ 2 ]. An increased number of outlets selling this food in the neighbourhood food environment may have contributed to normalising its consumption [ 3 ]. Purchasing formats represent ways to buy takeaway food. Although the opportunity to purchase this food was once limited to visiting food outlets in person or placing orders directly with food outlets by phone, additional purchasing formats such as online food delivery services now exist [ 4 ]. Unlike physically accessing outlets in the neighbourhood food environment or contacting outlets by telephone before collection or delivery, online food delivery services exist within a digital food environment. On a single online platform, customers receive aggregated information about food outlets that will deliver to them based on their location. Customers then select a food outlet, and place and pay for their order. Orders are forwarded to food outlets where meals are prepared before being delivered to customers [ 5 ]. Online food delivery services have been available in the UK since around 2006. However, widespread internet and smartphone access has increased their use [ 6 ], with global online food delivery service revenue estimated at £2.9 billion in 2021 [ 7 ]. The COVID-19 pandemic may have accelerated and perpetuated market development [ 8 ].

Food sold by takeaway food outlets, and therefore available online, is typically nutrient-poor and served in portion sizes that exceed public health recommendations for energy content [ 9 , 10 ]. More frequent takeaway food consumption has been associated with poorer diet quality and elevated bodyweight over time [ 11 ]. Although it is currently unclear, using online food delivery services might lead to more frequent and higher overall takeaway food consumption. In turn, this could lead to increased risk of elevated bodyweight and associated comorbidities. Since an estimated 67% of men and 60% of women in the UK were already considered overweight or obese in 2019 [ 12 ], the possibility that using online food delivery services increases overall takeaway food consumption is a major public health concern, as recognised by the World Health Organization [ 4 , 13 , 14 ].

With respect to the neighbourhood food environment, food outlet accessibility (number) and proximity (distance to nearest), food availability (presence of variety), and attitudinal dimensions (acceptability) contribute to takeaway food purchasing practices [ 15 ]. Each of these domains apply to takeaway food access through online food delivery services. In 2019, the number of food outlets accessible through the leading online food delivery service in the UK ( Just Eat ) was 50% greater in the most deprived areas compared with the least deprived areas [ 16 ]. Furthermore, adults living in the UK with the highest number of food outlets accessible online had greater odds of any online delivery service use in the previous week compared to those with the lowest number [ 17 ]. To our knowledge, however, attitudinal dimensions of online food delivery service use have not been investigated in the public health literature. Given the complexity of takeaway food purchasing practices, there are likely to be unique and specific reasons for using online food delivery services. Understanding these reasons from the perspective of customers could contribute to more informed public health decision-making and intervention, which is important since public health interventions that include online food delivery services may be increasingly necessary as their growth in popularity continues worldwide [ 13 , 18 ].

In our study, we investigated experiences of using online food delivery services from the perspective of adults living in the UK who use them frequently. We aimed to understand their reasons for using these services, the possible advantages and drawbacks of doing so, and how they coexist with other food-related practices.

Between June and August 2020, we used semi-structured telephone interviews to study experiences of using online food delivery services from the perspective of adults living in the UK. We used the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist to guide the development and reporting of our study [ 19 ].

The University of Cambridge School of the Humanities and Social Sciences Research Ethics Committee provided ethical approval (Reference: 19/220).

Methodological orientation

We used a qualitative description methodological orientation to investigate our study aims. Qualitative description has been framed as less interpretative than other approaches [ 20 ]. However, it is theoretically and epistemologically flexible and can facilitate a rich description of perspectives [ 21 ], which matched our study aims.

Participants and recruitment

We used convenience sampling to recruit adults that used online food delivery services frequently. For the purpose of our study, we defined frequent customers as those who had used online food delivery services on at least a monthly basis over the previous year. We believed this level of use would make participants well-positioned to provide their experiences of using this purchasing format within established takeaway food purchasing practices. We also based participant recruitment on reported sociodemographic characteristics of online food delivery service customers [ 22 , 23 ]. As data collection progressed, we additionally considered level of education so that our sample included frequent customers who were less highly educated (see Table 1 ).

We used two social media platforms (Twitter and Reddit) to recruit participants. Participant recruitment through social media platforms can be fast and efficient [ 24 , 25 , 26 ]. If targeted advertising is not used (as in our study), participant recruitment in this way is also typically free. In our study, participant recruitment through social media was particularly appropriate, given that our aims were related to understanding experiences of using a digital purchasing format. Twitter users can publish and re-publish information, images, videos, and links to external sites. Reddit users can publish information, images and videos, and discuss topics within focused forums known as ‘Subreddits’. For Twitter, the primary researcher (MK) published recruitment materials using his personal account and relied on existing connections to re-publish them. For Reddit, MK created an alias account (he did not have a personal account at the time of our fieldwork) and published recruitment materials in Subreddits for cities in the UK with large populations according to the 2011 UK census, those related to online food delivery services, and those that discuss topics relevant to the UK [ 27 ]. See Additional file 1 (Box A1) for a complete list of Subreddits.

Recruitment materials asked interested individuals to contact MK by email. When contacted, MK responded by email with screening questions that asked about self-reported frequency of online food delivery service use over the past year, age, and level of education. When eligibility was confirmed, MK provided information about the study by email. This information included the study aims, details about researchers involved, the offer of a £20.00 electronic high street shopping voucher, and a formal invitation to participate. After five business days with no response to the invitation, MK sent a further email. After another five business days, we classified individuals that did not respond as ‘non-respondents’.

Data collection

Before data collection.

Before starting data collection, we planned to complete a maximum of 25 interviews. We did not target data saturation. Food purchasing and consumption are highly individual and influenced by previous experiences, cultural backgrounds, and preferences [ 28 ]. Therefore, we felt that it would be difficult to conclude data saturation was achieved based on the traditional conceptualisation of no new information being reported by participants [ 29 , 30 ]. Instead, we prioritised conceptual depth and information strength. This approach was aligned with the qualitative description methodological orientation of our study [ 30 ].

We wanted to investigate experiences of using online food delivery services from before the COVID-19 pandemic, when there were no restrictions on accessing multiple purchasing formats or consuming food on the premises. Therefore, we pre-specified that we would stop data collection if it became difficult for participants to refer to the time before March 2020, which is when pandemic related travel and food outlet access restrictions were first introduced in the UK. MK piloted an initial protocol with an eligible individual to confirm this would be possible, and made amendments based on their feedback.

Before starting data collection, MK reflected on his position as a population health researcher, and his previous training and experience in qualitative research [ 31 ]. MK also reflected on his own takeaway food consumption and previous use of online food delivery services. As of June 2020, MK consumed takeaway food infrequently and had previously placed one order with an online food delivery service. Although he was not a frequent customer according to our classification, MK was familiar with online food delivery services operating in the UK. MK concluded that despite having a broad understanding about why online food delivery services might be used, he could not use his own experiences to provide detailed reasons for favouring this purchasing format over alternative options.

Throughout data collection

MK completed one-off semi-structured telephone interviews with participants at a convenient time selected by them. At the start of the interview process, MK confirmed the rationale for the study, gave participants the opportunity to ask clarifying questions and asked them to provide verbal consent. MK used a topic guide that was developed based on a priori knowledge, pilot interview feedback and previous research related to takeaway food and online food delivery services [ 22 , 32 , 33 ]. MK amended the topic guide as data collection progressed so that points not initially considered could be discussed in future interviews. Interview questions focused on reasons for using online food delivery services, the perceived advantages and drawbacks of using these services, and how using them coexisted with other purchasing formats and food-related practices (see Box A2 in Additional file 1 for the final topic guide).

Although MK completed interviews during the COVID-19 pandemic, he did not ask questions related to this period of time, and prompted participants to think about the time before March 2020 so that pre-pandemic experiences could be discussed. MK digitally recorded interview audio and made field notes to track points for discussion within the interview.

After data collection

MK immediately reflected on topics discussed, data collection progress, possible links with existing theory, and the ability of participants to think about the time before the COVID-19 pandemic. We used these post-interview reflections to help inform our decision to stop data collection.

Data analysis

A professional company transcribed interview audio verbatim. Whilst listening to the corresponding audio, MK quality assured each transcript and anonymised it. Participants did not review their transcripts.

We used codebook thematic analysis. When using this analytic approach, researchers develop a codebook based on the final topic guide used during data collection and data familiarity that is achieved by reviewing collected data [ 34 , 35 ]. Codebook thematic analysis is aligned with qualitative description methodological orientations as it allows researchers to remain ‘close to the data’ and facilitates an understanding of a topic through the ‘spoken word’ of participants [ 36 ]. In practice, MK developed an initial codebook. MK, JA, and TB then reviewed three transcripts (a 10% sample). This number was manageable and allowed us to discuss a sample of collected data [ 37 ]. After discussion, MK refined the initial codebook to collapse codes that overlapped and to add new codes, which formed the final codebook. MK coded each transcript with the final codebook and reviewed reflections written after each interview. MK then studied the coded data to generate themes that were discussed and finalised with JA and TB. In the context of our study, themes summarise experiences of using online food delivery services from the perspective of participants. After discussion, we also identified that across the themes we generated, there were overarching concepts. For our study, concepts should be seen to offer an overall and consistent structure that capture the common and overlapping elements of each of the generated themes.

MK used NVivo (version 12) to manage the data and facilitate interpretation.

Participant and data overview

MK conducted interviews with 22 frequent online food delivery service customers between June and August 2020. Interviews lasted between 35 and 61 min. There were 12 male participants, 13 participants were aged between 20 and 29 years, and 15 had completed higher education. Since initial adoption, participants had typically used online food delivery services at least fortnightly but as often as daily, and during interviews they consistently referred to using the three most well-established online food delivery services operating in the UK ( Just Eat, Deliveroo, and Uber Eats ) (see Table 2 ).

During the 19 th interview, conducted in August 2020, it was difficult for the participant to think about the time before the onset of the COVID-19 pandemic in March 2020. MK completed three further interviews and then concluded that this difficulty was consistent so stopped data collection. We included data from all interviews in analyses. In addition to those who took part, three interviews were scheduled but cancelled by individuals without providing a reason, and there were nine non-respondents.

Summary and structure

We generated two concepts that were overarching throughout our data: ‘Place. Time. Situation.’ and ‘Perceived advantages outweigh recognised drawbacks’. Within these overarching concepts, we generated five themes: ‘The importance of takeaway food’, ‘Less effort for more convenience’, ‘Saving money and reallocating time’, ‘Online food delivery service normalisation’ and ‘Maintained home food practices’.

In the following sections, we present the findings for each of the overarching concepts, followed by each of the themes. Whilst we discuss each concept and theme in turn, all of their elements were present throughout the data and should be thought of as dynamic, overlapping, and non-hierarchical. For example, participants consistently reflected on features of online food delivery services within the context of their location at a specific time. The conclusion of this process dictated whether a feature was viewed as an advantage or a drawback, and in some cases whether an online food delivery service would be used. We provide examples of this comparison process at the end of our Results (Table 3 ).

Overarching concepts

Place. time. situation..

Participants described how their location and the time of day impacted their ability to access different types of food, including both ‘takeaway’ food and other types of food. When choosing one type of food over another, participants had a multi-factorial thought process that considered their food at home, immediate finances available for food, and the food already eaten that day.

Although data collection focused on takeaway food, participants were clear that this type of food was not always appropriate. As participant 10 (Female: 20–29 years) stated; “ I don’t always just go and get a takeaway; sometimes I’ll walk to the shop, get some food, and make something ”. This view was shared by participant 11 (Male 30–39 years); “ some days I’ll decide that it’s too expensive and I’ll either get something else direct from the restaurant or go to the supermarket and then make food ”.

Nonetheless, participants indicated that purchasing takeaway food was preferable in many situations. For example, when acting spontaneously, when meals had not been planned or if other types of food could not satisfy needs, then takeaway food was appropriate.

“ I think you’re more likely to get delivery and order online when it’s unplanned and you need a pick-me-up, or you need something quick, or you don’t have something and you’re really hungry .” Participant 15 (Male: 40-49 years)

When participants decided to purchase takeaway food, they recognised that their location and the time of day dictated the purchasing formats they could access and potentially use. Access to multiple purchasing formats created a second decision making process. Participants considered the cuisines they wanted, delivery times estimated by online food delivery services versus the time it would take to travel to a food outlet, the weather, their willingness to leave home, and previous experience with accessible food outlets. Alongside these influential factors, choosing one purchasing format over another was often based on what was most convenient.

“ If I’m out and about, on the way home and I’m passing via an outlet, then I’ll pick it up. If I’m at home and just kind of, don’t want to leave the house, then I’ll order via an app or online, just because it’s just convenient .” Participant 2 (Male: 20-29 years)

Despite having apparently decided how they would purchase takeaway food, participants stated that they could change their mind. In the case of online food delivery services, if estimated delivery times failed to meet expectations, this purchasing format would no longer be appropriate and another purchasing format or type of food would be selected. The need for food practices to align with other routines and schedules, and therefore meet expectations, was particularly clear when participant 8 (Female: 40–49 years) described that they used online food delivery services when they could “ relax on a Friday night with the whole evening free ”. However, if they do not have time to select a food outlet, place their order, and then wait for delivery they “ normally just have some spaghetti because that takes 10 min ”.

Participants also referred to online food delivery service marketing in their day-to-day environments, including branded food outlet signs and equipment used by delivery couriers. Participants stated that these things did not always trigger immediate use of online food delivery services, however, their omnipresence reminded them that these services were available.

“ I don’t know if I ever go onto Just Eat after seeing it advertised, I don’t think that’s ever directly led me to do it. But it certainly keeps it in your mind, it’s certainly at the forefront of your mind whenever you think of takeaway .” Participant 11 (Male: 30-39 years)

Perceived advantages outweigh recognised drawbacks

Throughout the data, participants recognised that a single online food delivery service feature could be an advantage or a drawback based on their location and the time of day. This was clearest when participant 2 (Male: 20–29 years) discussed the number of food outlets accessible online compared with those available through other purchasing formats. There was value in having access to “ 20, 30, 40 food outlets ” through online food delivery services as it meant more options, otherwise “ you’re more limited just by the virtue of where you are or what shops you’re passing ”. However, access to a greater number of food outlets was a drawback when it meant that making a selection was difficult. The constant comparison of advantages and drawbacks prompted MK to ask participants why they kept using online food delivery services. There was a consensus that features of these services were unique, mostly advantageous, and outweighed the instances where they were seen as drawbacks. Since participants continued to use online food delivery services to access unique features, this practice appears to be self-reinforcing, even if this means accepting that the same feature can sometimes be a drawback.

Participants favoured online food delivery services in many situations. Nevertheless, they acknowledged that if the overall balance between advantages and drawbacks changed then they would purchase takeaway food in other ways. This solution emphasises that takeaway food can often be accessed in multiple ways dependent on place and time. As it stands, participants anticipated that they would continue to use online food delivery services indefinitely.

“ I can’t see any reason why I would [stop using online food delivery services] , unless something went wrong with Just Eat, you know, the service had a massive problem, but at the moment I can’t see any reason why I would. ” Participant 16 (Male: 20-29 years)

Analytic themes

We now present each of the five themes generated from our analyses. As described, elements of each theme overlapped within the two overarching concepts presented above.

The importance of takeaway food

Participants emphasised that, ultimately, it was “ the food ” that they valued, and that directed them towards using online food delivery services.

“ It’s the food really, that leads me to use [online food delivery service] apps .” Participant 10 (Female: 20-29 years)

Participants reported that they did not use online food delivery services with the intent of purchasing healthy food. Participants told us that they expected takeaway food to be unhealthy and that online food delivery services facilitated access to this food. This perspective influenced the types of food that participants were willing to purchase through online food delivery services. For example, pizza (seen as unhealthy) was appropriate but a salad (seen as healthy) was not. Moreover, participants recognised that if they wanted to consume healthy food, they would most likely cook for themselves.

Participants stated that takeaway food had social, cultural, and behavioural value. For many, purchasing and consuming takeaway food at the end of the working week signified the start of the weekend, which was seen as a time for relaxation and celebration. This tradition was carried forward from childhood, with Friday night referred to as “ takeaway night ”. For participants, using an online food delivery service allowed them to maintain, yet digitalise, traditions.

“ It’s always a weekend thing, besides it being a convenient, really quick way of accessing food that is filling and tastes nice, for me, it marks the end of a work week .” Participant 4 (Female: 30-39 years)

Participants reported that in some situations consuming takeaway food as a group could be a way to socialise. This was especially the case during life transitions such as leaving home to start university.

“ When you move out you’re concentrating on making friends and getting a takeaway was quite an easy way for everyone to sit down around the table and socialise and to have drinks .” Participant 14 (Female: 20-29 years)

Participants did not value online food delivery services to the same extent that they did takeaway food. This perspective reinforced that online food delivery services were primarily used to satisfy takeaway food purchasing needs.

“ If Just Eat as an entity disappeared, or all online takeaways disappeared, I wouldn’t be upset […] it’s a luxury, it makes life easier .” Participant 9 (Male: 30-39 years)

Less effort for more convenience

Participants reported that it took little effort to use online food delivery services because they receive information about all food outlets that will deliver to them on a single platform. Additionally, participants valued the opportunity to save payment details, previous orders, and favourite food outlets for future use. Participants also informed us that they had a greater number of food outlets and a more diverse range of foods and cuisines to choose from compared with other purchasing formats. Due to the number of accessible food outlets, the selection process was not always fast. Nonetheless, participants indicated that online food delivery services make purchasing takeaway food easier and more convenient than other purchasing formats where information is less readily available.

“Y ou’ve got all of the different options laid out in front of you, it’s like one resource where everything is there and you can choose and make a decision, rather than having to pull out leaflets from a drawer or Google different takeaways in the area. It’s all there and it’s all uniform and it’s in one place .” Participant 3 (Female: 20-29 years) “ I can pick through a whole wide selection rather than being limited to the few takeaways down on my road or having to drive somewhere .” Participant 21 (Male: 20-29 years)

Participants emphasised that smartphone applications had been optimised to enhance this experience.

“ I guess it’s the convenience of just being able to open the app on my phone, and not have to go searching for menus or phone numbers and checking if places are open. So yeah, it’s the convenience .” Participant 15 (Male: 40-49 years) “ For me it’s just the ease of going on, clicking what you want, paying for it and it arriving. You don’t have to move, you don’t have to cook, you don’t have to think, it’s just there ready to go, someone’s doing the hard work for you .” Participant 1 (Female: 20-29 years)

However, greater convenience was not always advantageous. Some participants were concerned that convenient and easy access to takeaway food through online food delivery services might have negative consequences for health and other things.

“ It’s quite addictive in the way that it’s just so convenient to order. I’m not making stuff fresh at home, and I’m eating unhealthier .” Participant 21 (Male: 20-29 years) “ I think it adds to a general kind of laziness that is not good for people really. If you actually got up and went for a walk to go and get this food, at least there’s a slightly positive angle there .” Participant 17 (Male: 30-39 years) “ The convenience is not necessarily a positive thing, these apps can be abused because it’s so easy to access foods .” Participant 10 (Female: 20-29 years)

Saving money and reallocating time

Participants were price-sensitive and valued the opportunity to save money. When discussing financial aspects of online food delivery service use, participants referred to special offers they had received by email or through mobile device push notifications. Participants recognised that direct discounts (e.g. 10% off), free items (e.g. free appetizers on orders over £20.00), free delivery (e.g. on orders over £30.00), or time-limited price-promotions (e.g. 40% off all orders for the next three-hours) can justify takeaway food purchasing and online food delivery service use.

“ Getting a takeaway is always a treat, every time I do it I know I shouldn’t but then basically I’m convinced to treat myself, if there’s a discount I’m much more likely to do it because I don’t feel like it’s such a waste of money .” Participant 18 (Male: 20-29 years)

Participants recognised takeaway food as a distinct food category. Nevertheless, they appreciated that that they could use online food delivery services to purchase ‘restaurant food’. Since this food is usually accompanied by a complete dining experience that online food delivery services cannot replicate, participants expected to spend less on this food purchased online compared to when they dined inside a restaurant.

“ Some restaurants deliver through Deliveroo, [places] where you can sit down and have an experience, a dining experience, well that’s different […] you might go there for the dining experience .” Participant 4 (Female: 30-39 years) “ Sometimes I’m deterred from using Uber Eats because I noticed that the restaurants increase their prices if you buy it through them rather than directly […] I don’t want to pay over £10 for a takeaway dish, whereas I would pay that if I ate at a restaurant .” Participant 3 (Female: 20-29 years)

Although participants considered the price of food when deciding which outlet to order from, they traded money for time. Participants compared the time they would spend cooking or travelling to takeaway food outlets with the time taken to place orders through online food delivery services plus the tasks they could complete whilst waiting for meal delivery. Paying a delivery fee to have the opportunity to use time that would not have otherwise been available was acceptable.

“ Yeah, it costs money but at the same time we’re getting more time with the kids, and more time to do other stuff, so it’s absolutely fine as far as I’m concerned .” Participant 9 (Male: 30-39 years)

However, some participants were unsure about the appropriateness of paying to have food delivered as it might be unfair to delivery couriers.

“ I don’t feel like it’s necessarily right to make a delivery driver drive two minutes up the road just because I can’t be bothered to go and collect something that’s not very far away .” Participant 10 (Female: 20-29 years)

Online food delivery service normalisation

Participants had positive previous experiences of using online food delivery services. These experiences influenced future custom and contributed to an overall sense that using this purchasing format was now a normal part of living in a digital society. Some participants referred to watching television online to exemplify this point.

The normalisation of using online food delivery services was particularly evident when MK prompted participants to think about the term ‘takeaway food’. Participants often referred to online food delivery services in the first instance and saw them as synonymous with takeaway food.

“ If you were to say ‘takeaway food’ I’d pull out my phone and I’d open one of the apps and say ‘okay, what should we order’, I wouldn’t say ‘oh let’s go to this road’, or ‘let’s go to that road’, I’d say ‘yeah, let’s look on the app’ .” Participant 21 (Male: 20-29 years)

For participants in our study, using online food delivery services replaced purchasing takeaway food in other ways. This perspective was linked to habitual takeaway food purchasing and sociocultural values. Participants rarely purchased takeaway food outside of set routines (for example only doing so at the weekend) because they did not think it was appropriate. As a result, participants reported that they had a limited number of opportunities to use multiple purchasing formats and thus increase their existing levels of consumption.

Maintained home food practices

Most participants were responsible for cooking at home, enjoyed doing so, and said they were competent at it. Nonetheless, cooking at home required personal effort and being “ lazy ” or “ tired ” or “ having nothing in the cupboards ” was used as a justification for using online food delivery services.

“ I cook, when I’m not using these apps I cook and prepare food for myself , it’s just on the odd occasion I might be feeling tired or want something different […] the rest of the time, I’m quite happy to cook .” Participant 10 (Female: 20-29 years)

Despite the apparent normalisation of using online food delivery services, participants did not feel that they would ever completely eliminate cooking at home. Most participants consumed home cooked food daily, whereas they consumed takeaway food less frequently. This contributed to the view that these two types of food were different. As a result, participants used online food delivery services to purchase food they could not or would not cook at home; for a break from normality, and as a “ cheat ” or “ treat ”.

Summary of findings

To our knowledge, this is the first published study in the public health literature to investigate experiences of using online food delivery services from the perspective of frequent customers.

Participants recognised that their location and the time of day meant that they could often access different types of food through multiple purchasing formats, at the same time. Participants stated that purchasing takeaway food was appropriate in many situations and typically favoured using online food delivery services. For many participants, using these services was now part of routines in their increasingly digital lives. As such, using online food delivery services appeared to be synonymous with takeaway food purchasing. This meant that participants expected food sold online to be unhealthy and that it was inappropriate to purchase healthy food in this manner. Participants consistently thought about how features of online food delivery services were an advantage or a drawback within the context of their location at any given point in time. This was a complex and dynamic thought process. Participants described how the advantages of these services were a strong enough reason to continue use, overcoming drawbacks such as the acknowledged unhealthfulness of takeaway food. Participants reported that using online food delivery services involved little effort as they were provided with food outlet information, menus, and payment facilities on one platform that had been optimised for use. Moreover, although the cost of food was an important consideration for participants, they were willing to pay a fee in exchange for the opportunity to complete tasks whilst waiting for meal preparation and delivery. Finally, using online food delivery services substituted purchasing takeaway food in other ways. Nevertheless, participants reported that cooking at home was a distinct food practice that occurred more frequently and was irreplaceable.

Interpretations

Participants described sociocultural values assigned to takeaway food. These values are proposed to develop from previous experiences [ 38 , 39 ]. For our participants, purchasing takeaway food at the weekend was a traditional routine that celebrated the end of the working week. In the past, this tradition might have meant visiting food outlets in the neighbourhood food environment. However, online food delivery services are now used and favoured. Since participants reported that it was takeaway food in and of itself that was a fundamental reason for seeking out online food delivery services, it is reasonable to conclude that sociocultural values linked to this food exist, and transfer, across purchasing formats.

Food purchasing has been recognised as situational and made in the context of place and time [ 40 , 41 ], with convenience reported as a consistent consideration [ 42 ]. Participants in our study reported that takeaway food was appropriate in many situations and acknowledged that it could often be accessed through multiple purchasing formats. Using one purchasing format over another came after considering multiple factors, including the level of effort required to find a suitable food outlet and place orders. As using online food delivery services took little effort, this purchasing format was often most convenient. However, participants were clear that although their decision had seemingly been made, it could be changed, especially if an online food delivery service feature that was supposedly an advantage became a drawback. For example, if estimated delivery times were too long or delivery fees were too high an alternative option would be considered. Our findings support that the decision about if and how to purchase takeaway food is dynamic and influenced by place and time [ 32 ].

Food access has previously been summarised within the domains of availability, accessibility, affordability, accommodation, and acceptability [ 15 ]. Although Caspi and colleagues described these domains in the context of physical food access, they are applicable to digital food environments. Broadly speaking, our research investigated the ‘acceptability’ of using online food delivery services, and participants made explicit reference to the domains of food ‘accessibility’ and ‘affordability’.

For example, participants told us that one particularly valuable aspect of using online food delivery services was the ability to access a greater number of food outlets compared with other purchasing formats. This finding speaks to our previous research that found a positive association between having the highest number of food outlets accessible online and any use of online food delivery services in the previous week amongst adults living in the UK [ 17 ]. The experiences of using online food delivery services reported in the current study support the possibility that having more food outlet choice contributes to the decision to adopt, and maintain, use of these services rather than necessarily increasing the frequency in which they are used. Other features of online food delivery services, such as having information about each of the accessible food outlets on one platform, likely amplify the perceived benefit of greater food outlet access. Notably, however, access to an increased number of food outlets was not always advantageous. This finding recognises a general awareness about the negative aspects of takeaway food consumption, previously captured from the perspectives of young adults in Australia and Canada [ 38 , 43 ].

Participants also discussed how the price of food influenced their use of online food delivery services. This reflects that food affordability is a fundamental purchasing consideration [ 32 ]. Beyond this, our findings provide insight into actions that food outlets registered to accept orders online might take to attract customers. Given that online food delivery service customers can often select from multiple food outlets at the same time, food outlets might aim to compete with one another by lowering the price of food sold or by introducing price-promotions in an attempt to capitalise on customer demand. Particularly in the case of the latter, participants acknowledged the importance of price-promotions. Previous evidence shows that price-promotions contribute to unhealthy food purchasing practices [ 44 , 45 ]. Access to price-promotions through online food delivery services has not been systematically documented. However, it is possible that their availability is positively associated with the number of food outlets accessible online. Since both price-promotions and the number of food outlets accessible online appear to influence online food delivery service use, the possibility of interaction between them is concerning for overall consumption of food prepared out-of-home, and subsequently, diet quality and health.

In some cases, participants reported that they used online food delivery services because they did not have time to cook at home. A number of tasks, including household chores, work, travel, and childcare, can limit the time available for, and take priority over, home cooking [ 46 ]. Using online food delivery services (and paying associated delivery fees) instead of cooking at home allowed participants in our study to complete non-food related tasks whilst waiting for meal preparation and delivery. Due to sociocultural values and perceived ‘rules’ about how frequently takeaway food 'should' be purchased, participants did not see online food delivery services as a complete replacement for cooking at home. Nevertheless, even partial replacement has implications for diet quality and health, especially since the food available and purchased online was acknowledged as unhealthy by participants in the current study.

Possible implications for public health and future research

Participants reported that using online food delivery services had mostly substituted, not supplemented, their use of other purchasing formats. Given the perspectives of participants in our study, an increasing number of food outlets could be registering to accept orders online to supply an apparent customer demand. Further research is required to understand the extent to which customer demand is driven by food outlet accessibility, and vice versa.

Participants in our study reported that despite using online food delivery services frequently, their overall takeaway food consumption had remained the same. We do not yet know if this perception would be reflected in objective assessment of takeaway food consumption. Further research that quantifies the use of multiple purchasing formats and takeaway food consumption over time is required to understand the potential public health implications as a result of using online food delivery services. Although evidence from Australia suggests that food sold through online food delivery services tends to be energy-dense and nutrient-poor [ 47 ], this has not been established in the UK, to our knowledge. Nor does it necessarily reflect the balance of what food is purchased. Objective assessment of the nutritional quality of foods available, and purchased, through online food delivery services in the UK could be the focus of future research. This evidence will help to better understand the extent to which public health concern is warranted.

With a few exceptions, food sold through online food delivery services is prepared in food outlets that are also physically accessible in the neighbourhood food environment [ 13 ]. From a public health perspective, this reinforces the intrinsic link between neighbourhood and digital food environments [ 48 ]. Therefore, public health interventions adopted in the neighbourhood food environment may also influence the digital food environment. For example, urban planning policies have been adopted to prevent new takeaway food outlets from opening in neighbourhoods [ 49 ]. By extension, this stops new food outlets from becoming accessible online. Other public health interventions that operate synergistically between physical and digital food environments might be increasingly required in the future. It will also be vital for any future interventions to consider how the geographical coverage of online food delivery services expands neighbourhood food outlet access [ 50 ], potentially undermining the effectiveness of interventions adopted in the neighbourhood food environment. Doing so would help address concerns that these services increase access to food prepared out-of-home [ 4 , 13 ]. Interventions of this nature could be particularly important in more deprived areas that have the highest number of accessible food outlets across multiple purchasing formats [ 16 , 51 ].

Participants recognised that online food delivery services provide access to takeaway food that was associated with being unhealthy. Participants were aware that they could purchase healthy food through online food delivery services, but this did not mean that they would . From a public health perspective, this finding indicates that the success of interventions intended to promote healthier takeaway food purchasing through online food delivery services might be limited by existing sociocultural values if they are not taken into consideration. A possible way to navigate this would be to improve the nutritional quality of food available online without necessarily making any changes salient. Interventions of this nature include healthier frying practices and reduced food packaging size [ 52 , 53 ]. Although these interventions were acceptable and feasible when implemented inside takeaway food outlets [ 54 ], further investigation is required to understand the extent to which they are appropriate in the context of online food delivery services. Changing the types of food available to purchase through online food delivery services could also lead to improved food access for those with limited kitchen facilities at home or limited mobility.

Public health interventions intended specifically for online food delivery services could also be developed. Potential approaches include preferential placement of healthy menu items, introducing calorie labelling and offering healthier food swaps. Embedding these approaches within existing online food delivery service infrastructures would allow implementation to be uniform [ 55 ], and their implementation could be optimised to enhance customer awareness and interaction. The potential success of approaches of this nature requires exploration. Nevertheless, in February 2022, the UK Behavioural Insights Team (formerly of the UK Government) published a protocol to investigate approaches to promoting the purchase of lower energy density foods through a simulated online food delivery service platform [ 56 ].

Price-promotions influenced and justified the use of online food delivery services. Legislation to restrict the use of volume-based price-promotions (e.g. buy-one-get-one-free, 50% extra free) on less healthy pre-packaged food sold both in-store and online were due to be introduced in England in October 2022 [ 57 ]. However, the introduction of this legislation has now been delayed. Although hot food served ready-to-consume was due to be excluded, given what is known about the impact of price-promotions on purchasing other food [ 58 ], and our participants’ description of the importance of price-promotions on their purchasing practices, extension of these restrictions to hot food served ready-to-consume might be warranted. Understanding how price-promotions influence food purchased from online food delivery services represents a first step to understand the need for future regulation.

Limitations

We recruited participants through two social media platforms, which means that our study sample was formed from a subset of all social media users. However, online recruitment was appropriate since we wanted to understand experiences of using a digital purchasing format. Moreover, the participants we recruited were mostly highly educated, potentially reflecting reported online food delivery service use amongst this socioeconomic group [ 22 , 23 ]. After 12 telephone interviews we acknowledged this and adjusted our recruitment strategy to ensure a more balanced sample with respect to level of education. Nevertheless, future research should explore the perspectives of frequent online food delivery service customers with lower levels of education, since it is possible that they have different reasons for using these services. Although we did not recruit infrequent online food delivery service customers or non-customers, they would not have been well-positioned to help us investigate our study aims. However, since we have described experiences of using online food delivery services from the perspective of frequent customers, future work should seek to understand perspectives of non-customers, customers who use them less frequently, and customers who use them for specific reasons.

As the first study in the public health literature to investigate frequent customer experiences of using online food delivery services, we chose a descriptive methodological orientation. Our descriptive approach meant that we did not investigate the underlying meaning of the language used by participants, however, this was not aligned with our aims. Furthermore, our descriptive methodological orientation allowed us to use codebook thematic analysis and include multiple researchers in analysis. Coding a 10% sample of interviews transcripts and discussing analytic themes would have been less appropriate with reflexive approaches to thematic analysis [ 34 , 35 , 59 ], but assisted with our interpretations.

We conducted fieldwork during the early stages of the COVID-19 pandemic, which might have altered the recent experiences of online food delivery service use and participant perspectives. However, MK asked participants to think about the time before the COVID-19 pandemic and reflected on their ability to do so. This reflexivity is in line with established practices regarding qualitative rigour [ 20 , 60 ], and allowed us to determine when it would be most appropriate to stop fieldwork. Nonetheless, we acknowledge the possibility that food-related practices have changed during the COVID-19 pandemic. As a result, it is possible that online food delivery services are now used for different reasons, both initially and over time, and by individuals with different sociodemographic characteristics than those in our study.

We used telephone interviews with frequent online food delivery service customers to investigate experiences of using this purchasing format. We found that the context of place and time influenced if and how takeaway food would be purchased. Online food delivery services were often seen as most appropriate. In part, this was due to the opportunity to access advantages not available through other purchasing formats, such as efficient and convenient ordering processes that had been optimised for customers. Fundamentally, however, online food delivery services provide access to takeaway food, which despite being acknowledged as unhealthy, has strong sociocultural value. There was a consistent awareness that some advantages of online food delivery services may also be drawbacks. Despite this, the drawbacks were not sufficiently negative to stop current or future online food delivery service use. Finally, price-promotions justified online food delivery service use and made this practice appealing. Public health interventions that seek to promote healthier food purchasing through online food delivery services may be increasingly warranted in the future. Approaches might include increasing the healthiness of the food available whilst maintaining sociocultural values and expectations, and extending restrictions on price-promotions to hot food prepared out-of-home.

Availability of data and materials

Processed and anonymised qualitative data from this study is available from the corresponding author upon reasonable request. Additional raw data related to this publication cannot be openly released; the raw data contains interview audio containing identifiable information.

Wellard-Cole L, Davies A, Allman-Farinelli M. Contribution of foods prepared away from home to intakes of energy and nutrients of public health concern in adults: a systematic review. Crit Rev Food Sci Nutr. 2021. https://doi.org/10.1080/10408398.2021.1887075 .

Lake A. Neighbourhood food environments: food choice, foodscapes and planning for health. Proc Nutr Soc. 2018;77:1–8.

Article   Google Scholar  

Burningham K, Venn S. “Two quid, chicken and chips, done”: understanding what makes for young people’s sense of living well in the city through the lens of fast food consumption. Local Environ. 2021;27:1–17.

Google Scholar  

World Health Organization: Regional Office for Europe. Digital food environments: factsheet. 2021. https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/publications/2021/digital-food-environments-factsheet-2021 . Accessed 07 Mar 2022

Granheim SI, Løvhaug AL, Terragni L, Torheim LE, Thurston M. Mapping the digital food environment: a systematic scoping review. Obes Rev. 2022;23:e13356.

Article   PubMed   Google Scholar  

Maimaiti M, Ma X, Zhao X, Jia M, Li J, Yang M, Ru Y, Yang F, Wang N, Zhu S. Multiplicity and complexity of food environment in China: full-scale field census of food outlets in a typical district. Eur J Clin Nutr. 2020;74:397–408.

Mak G. Online food ordering and delivery platforms in the UK. 2021. https://my.ibisworld.com/uk/en/industry-specialized/sp0.040/about . Accessed 08 Jan 2022.

Chang M, Green L, Cummins S. All change. Has COVID-19 transformed the way we need to plan for a healthier and more equitable food environment? URBAN DES Int. 2020;26:291–5.

Jaworowska A, Toni MB, Rachel L, Catherine T, Matthew A, Leonard S, Ian GD. Nutritional composition of takeaway food in the UK. Nutr Food Sci. 2014;44:414–30.

Robinson E, Jones A, Whitelock V, Mead BR, Haynes A. (Over)eating out at major UK restaurant chains: observational study of energy content of main meals. BMJ. 2018;363:1–8.

Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR Jr, Ludwig DS. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet. 2005;365:36–42.

NHS Digital. Health Survey for England, 2019: data tables. 2020. https://digital.nhs.uk/data-and-information/publications/statistical/health-survey-for-england/2019/health-survey-for-england-2019-data-tables . Accessed 07 Jan 2022.

World Health Organization: Regional Office for Europe. Slide to order: a food systems approach to meals delivery apps: WHO European Office for the prevention and control of noncommunicable diseases. 2021. https://apps.who.int/iris/handle/10665/350121 . Accessed 08 Mar 2022.

World Health Organization. WHO European Regional Obesity Report. 2022. https://apps.who.int/iris/bitstream/handle/10665/353747/9789289057738-eng.pdf . Accessed 28 May 2022.

Caspi CE, Sorensen G, Subramanian SV, Kawachi I. The local food environment and diet: a systematic review. Health Place. 2012;18:1172–87.

Article   PubMed   PubMed Central   Google Scholar  

Keeble M, Adams J, Bishop TRP, Burgoine T. Socioeconomic inequalities in food outlet access through an online food delivery service in England: a cross-sectional descriptive analysis. Appl Geogr. 2021;133:102498.

Keeble M, Adams J, Vanderlee L, Hammond D, Burgoine T. Associations between online food outlet access and online food delivery service use amongst adults in the UK: a cross-sectional analysis of linked data. BMC Public Health. 2021;21:1968.

Stephens J, Miller H, Militello L. Food delivery apps and the negative health impacts for Americans. Front Nutr. 2020;7:1–2.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19:349–57.

Milne J, Oberle K. Enhancing rigor in qualitative description: a case study. J Wound Ostomy Continence Nurs. 2005;32:413–20.

Bradshaw C, Atkinson S, Doody O. Employing a qualitative description approach in health care research. Global Qual Nurs Res. 2017;4:1–8.

Keeble M, Adams J, Sacks G, Vanderlee L, White CM, Hammond D, Burgoine T. Use of online food delivery services to order food prepared away-from-home and associated sociodemographic characteristics: a cross-sectional, multi-country analysis. Int J Environ Res Public Health. 2020;17:5190.

Article   PubMed Central   Google Scholar  

Dana LM, Hart E, McAleese A, Bastable A, Pettigrew S. Factors associated with ordering food via online meal ordering services. Public Health Nutr. 2021;24:5704–9.

O’Connor A, Jackson L, Goldsmith L, Skirton H. Can I get a retweet please? Health research recruitment and the twittersphere. J Adv Nurs. 2014;70:599–609.

Hokke S, Hackworth NJ, Bennetts SK, Nicholson JM, Keyzer P, Lucke J, Zion L, Crawford SB. Ethical considerations in using social media to engage research participants: perspectives of Australian researchers and ethics committee members. J Empir Res Hum Res Ethics. 2020;15:12-27. https://doi.org/10.1177/1556264619854629 .

Gelinas L, Pierce R, Winkler S, Cohen IG, Lynch HF, Bierer BE. Using social media as a research recruitment tool: ethical issues and recommendations. Am J Bioeth. 2017;17:3–14.

Office for National Statistics. 2011 Census: Key statistics for England and Wales: March 2011. 2011. https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/2011censuskeystatisticsforenglandandwales/2012-12-11#qualifications . Accessed 20 Jan 2021.

Okumus B. A qualitative investigation of Millennials’ healthy eating behavior, food choices, and restaurant selection. Food Cult Soc. 2021;24:509–24.

Braun V, Clarke V. To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qual Res Sport Exerc Health. 2019;13:1–16.

Saunders B, Sim J, Kingstone T, Baker S, Waterfield J, Bartlam B, Burroughs H, Jinks C. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52:1893–907.

Keeble M, Burgoine T, White M, Summerbell C, Cummins S, Adams J. Planning and public health professionals’ experiences of using the planning system to regulate hot food takeaway outlets in England: a qualitative study. Health Place. 2021;67:102305.

Lachat C, Nago E, Verstraeten R, Roberfroid D, Van Camp J, Kolsteren P. Eating out of home and its association with dietary intake: a systematic review of the evidence. Obes Rev. 2012;13:329–46.

Article   PubMed   CAS   Google Scholar  

Janssen HG, Davies IG, Richardson LD, Stevenson L. Determinants of takeaway and fast food consumption: a narrative review. Nutr Res Rev. 2018;31:16–34.

Braun V, Clarke V. Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Couns Psychother Res. 2020;21:1–11.

Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qual Res Sport Exerc Health. 2019;11:589–97.

Willis DG, Sullivan-Bolyai S, Knafl K, Cohen MZ. Distinguishing features and similarities between descriptive phenomenological and qualitative description research. West J Nurs Res. 2016;38:1185–204.

Barbour RS. Checklists for improving rigour in qualitative research: a case of the tail wagging the dog? BMJ. 2001;322:1115–7.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Grunseit AC, Cook AS, Conti J, Gwizd M, Allman-Farinelli M. “Doing a good thing for myself”: a qualitative study of young adults’ strategies for reducing takeaway food consumption. BMC Public Health. 2019;19:525–37.

Neve KL, Isaacs A. How does the food environment influence people engaged in weight management? A systematic review and thematic synthesis of the qualitative literature. Obes Rev. 2021;23:1–14.

Sobal J, Bisogni CA. Constructing food choice decisions. Ann Behav Med. 2009;38:s37–46.

Devine CM. A life course perspective: understanding food choices in time, social location, and history. J Nutr Educ Behav. 2005;37:121–8.

Verain MCD, van den Puttelaar J, Zandstra EH, Lion R, de Vogel-van den Bosch J, Hoonhout HCM, Onwezen MC. Variability of food choice motives: two Dutch studies showing variation across meal moment, location and social context. Food Qual Prefer. 2021;98:1–12.

McPhail D, Chapman GE, Beagan BL. “Too much of that stuff can’t be good”: Canadian teens, morality, and fast food consumption. Soc Sci Med. 2011;73:301–7.

Riesenberg D, Backholer K, Zorbas C, Sacks G, Paix A, Marshall J, Blake MR, Bennett R, Peeters A, Cameron AJ. Price promotions by food category and product healthiness in an Australian Supermarket Chain, 2017–2018. Am J Public Health. 2019;109:1434–9.

Hawkes C. Sales promotions and food consumption. Nutr Rev. 2009;67:333–42.

Widener MJ, Ren L, Astbury CC, Smith LG, Penney TL. An exploration of how meal preparation activities relate to self-rated time pressure, stress, and health in Canada: a time use approach. SSM Popul Health. 2021;15:100818.

Partridge SR, Gibson AA, Roy R, Malloy JA, Raeside R, Jia SS, Singleton AC, Mandoh M, Todd AR, Wang T, Halim NK, Hyun K, Redfern J. Junk food on demand: a cross-sectional analysis of the nutritional quality of popular online food delivery outlets in Australia and New Zealand. Nutrients. 2020;12:3107.

Wyse R, Jackson JK, Delaney T, Grady A, Stacey F, Wolfenden L, Barnes C, McLaughlin M, Yoong SL. The effectiveness of interventions delivered using digital food environments to encourage healthy food choices: a systematic review and meta-analysis. Nutrients. 2021;13:2255.

Keeble M, Burgoine T, White M, Summerbell C, Cummins S, Adams J. How does local government use the planning system to regulate hot food takeaway outlets? A census of current practice in England using document review. Health Place. 2019;57:171–8.

Brar K, Minaker LM. Geographic reach and nutritional quality of foods available from mobile online food delivery service applications: novel opportunities for retail food environment surveillance. BMC Public Health. 2021;21:458.

Maguire ER, Burgoine T, Penney TL, Forouhi NG, Monsivais P. Does exposure to the food environment differ by socioeconomic position? Comparing area-based and person-centred metrics in the Fenland Study, UK. Int J Health Geogr. 2017;16:1–14.

Hillier-Brown F, Lloyd S, Muhammad L, Summerbell C, Goffe L, Hildred N, Adams J. Feasibility and acceptability of a Takeaway Masterclass aimed at encouraging healthier cooking practices and menu options in takeaway food outlets. Public Health Nutr. 2019;22:2268–78.

Hillier-Brown FC, Summerbell CD, Moore HJ, Wrieden WL, Adams J, Abraham C, Adamson A, Araújo-Soares V, White M, Lake AA. A description of interventions promoting healthier ready-to-eat meals (to eat in, to take away, or to be delivered) sold by specific food outlets in England: a systematic mapping and evidence synthesis. BMC Public Health. 2017;17:93–110.

Bagwell S. Healthier catering initiatives in London, UK: an effective tool for encouraging healthier consumption behaviour? Crit Public Health. 2014;24:35–46.

Goffe L, Chivukula SS, Bowyer A, Bowen S, Toombs AL, Gray CM. Appetite for disruption: designing human-centred augmentations to an online food ordering platform. 34th British HCI Conference 34. 2021. p. 155–67.

Chief Scientist Office N. A multi-armed randomised controlled trial comparing the efficacy of four behavioural interventions promoting lower calorie options in a simulated online food delivery platform through product positioning. 2022. https://osf.io/bxjpt .

UK Government. Restricting promotions of products high in fat, sugar and salt by location and by price: government response to public consultation. 2020. https://www.gov.uk/government/consultations/restricting-promotions-of-food-and-drink-that-is-high-in-fat-sugar-and-salt/outcome/restricting-promotions-of-products-high-in-fat-sugar-and-salt-by-location-and-by-price-government-response-to-public-consultation#references . Accessed 04 Mar 2022.

Bennett R, Zorbas C, Huse O, Peeters A, Cameron AJ, Sacks G, Backholer K. Prevalence of healthy and unhealthy food and beverage price promotions and their potential influence on shopper purchasing behaviour: a systematic review of the literature. Obes Rev. 2020;21:e12948.

Nowell LS, Norris JM, White DE, Moules NJ. Thematic analysis: striving to meet the trustworthiness criteria. Int J Qual Methods. 2017;16:1609406917733847.

Krefting L. Rigor in qualitative research: the assessment of trustworthiness. Am J Occup Ther. 1991;45:214–22.

Download references

Acknowledgements

Not applicable

Matthew Keeble was funded by the National Institute for Health and Care Research (NIHR) School for Public Health Research (SPHR) [grant number PD_SPH_2015]. This work was supported by the Medical Research Council [grant number MC_UU_00006/7]. The views expressed are those of the authors and not necessarily those of any of the above named funders. The funders had no role in the design of the study, or collection, analysis and interpretation of the data, or in writing the manuscript. For the purpose of open access. the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

Author information

Authors and affiliations.

MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK

Matthew Keeble, Jean Adams & Thomas Burgoine

You can also search for this author in PubMed   Google Scholar

Contributions

Matthew Keeble: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. Jean Adams: Conceptualization, Methodology, Supervision, Writing – review & editing. Thomas Burgoine: Conceptualization, Methodology, Supervision, Writing – review & editing. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Matthew Keeble .

Ethics declarations

Ethics approval and consent to participate.

Verbal informed consent was obtained from all respondents prior to data collection.

Data for this study were collected through telephone interviews and not in person, therefore, we opted for verbal informed consent, rather than written informed consent.

The University of Cambridge School of the Humanities and Social Sciences Research Ethics Committee provided ethical approval for this research, including the verbal consent process (Reference: 19/220). All research methods were carried out in accordance with relevant guidelines and regulations laid out by The University of Cambridge.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:.

Box A1. Names of Subreddits used during participant recruitment. Box A2. Final telephone interview topic guide.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Keeble, M., Adams, J. & Burgoine, T. Investigating experiences of frequent online food delivery service use: a qualitative study in UK adults. BMC Public Health 22 , 1365 (2022). https://doi.org/10.1186/s12889-022-13721-9

Download citation

Received : 11 April 2022

Accepted : 27 June 2022

Published : 16 July 2022

DOI : https://doi.org/10.1186/s12889-022-13721-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Meal delivery
• Online food delivery services
• Qualitative methods
• Thematic analysis

BMC Public Health

ISSN: 1471-2458

• General enquiries: [email protected]

• Reference Manager
• Simple TEXT file

People also looked at

Perspective article, perspective: are online food delivery services emerging as another obstacle to achieving the 2030 united nations sustainable development goals.

• 1 Engagement and Co-design Hub, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
• 2 Menzies Centre for Health Policy and Economics, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
• 3 Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
• 4 Prevention Research Collaboration, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
• 5 Nutrition and Dietetics Group, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
• 6 The George Institute for Global Health, The University of New South Wales, Sydney, NSW, Australia

Online food delivery usage has soared during the 2019 novel coronavirus (COVID-19) pandemic which has seen increased demand for home-delivery during government mandated stay-at-home periods. Resulting implications from COVID-19 may threaten decades of development gains. It is becoming increasingly more important for the global community to progress toward sustainable development and improve the wellbeing of people, economies, societies, and the planet. In this perspective article, we discuss how the rising use of these platform-to-consumer delivery operations may impede advances toward the United Nations 2030 Sustainable Development Goals (SDGs). Specifically, online food delivery services may disrupt SDGs that address good health and wellbeing, responsible consumption and production, climate action and decent work and economic growth. To mitigate potential negative impacts of these meal delivery apps, we have proposed a research and policy agenda that is aligned with entry points within a systems approach identified by the World Health Organization. Food industry reforms, synergised public health messaging and continuous monitoring of the growing impact of online food delivery should be considered for further investigation by researchers, food industry, governments, and policy makers.

Introduction

Unhealthy diets, non-communicable diseases, urbanization, and climate change are recognized as significant challenges to global health ( 1 ). The United Nations have urged countries to act on 17 Sustainable Development Goals (SDGs) across economic, social and environmental dimensions to promote healthy lives and wellbeing and make cities inclusive, safe, and sustainable by 2030 ( 2 ). Online food delivery services (OFDS) are potentially impeding our progress toward the SDGs—impacting the way we eat, work, and care for the environment. Defined as “platform-to-consumer delivery operations” of ready-to-consume meals, OFDS offer delivery of a wide variety of takeout foods and beverages from kitchens to doorsteps ( 3 ).

The OFD industry is now widespread across the globe and big multinational corporations are dominating the market. Billion-dollar companies such as UberEats, DoorDash, and Just Eat operate in thousands of cities and show no sign of slowing down. Globally, OFDS market revenue increased by 27% in 2020, reaching $136.4 billion USD ( 4 ). These services are likely to proliferate further, as UberEats estimates that despite the return to dine-in restaurants, consumers are now spending three times more on OFDS compared to pre-pandemic levels ( 5 ). Furthermore, Just Eat or otherwise known as Menulog, reported a 79% increase in total orders between 2020 and 2021, across its 17 operating countries including UK, Germany, Canada, and Netherlands ( 6 ).

Considering the growing prevalence and market influence of OFDS, it is important to track the impact of OFDS on key public health challenges, such as increasing accessibility of unhealthy foods, promotion of excessive consumption, poor working conditions of delivery couriers in a gig economy and the environmental implications of takeout food packaging. This perspective piece will discuss how OFDS may disrupt progress toward SDGs that address good health and wellbeing, responsible consumption and production, climate action and decent work and economic growth.

Junk Food on Demand: Impact on Nutrition, Health, and Wellbeing

OFDS may pose a considerable risk to the aim of SDG 3 to “Ensure healthy lives and promote wellbeing for all at all ages”. Research has shown that these OFDS have an abundant offering of menu items that are of poor nutritional quality. From an investigation of 680 popular food outlets on the market leading OFDS in Sydney, Australia, 37.6% (256/680) of popular outlets were classified as a “fast-food franchise” store, and out of the 2,463 most popular menu items identified, 2,358 (95.7%) were identified as “discretionary foods” ( 7 ), characterized as high in saturated fat, sodium and sugar, and not essential for health.

Moreover, the two leading OFDS (UberEats, Menulog) in Australia are partnered with the top 10 fast food franchise stores (Subway, McDonalds, Dominos, KFC, Hungry Jacks, Red Rooster, Nando's, Pizza Hut, Zambero, Oporto) ( 8 ). This signifies the dominance of fast-food franchise outlets on these platforms which are now another additional avenue for consumers to access their menu items. It is well-known that offerings from fast food franchise outlets are “energy-dense and nutrient poor” ( 9 ) and evidence has highlighted the strong association between high fast-food consumption and obesity ( 10 ). Furthermore, research has shown that diets high in inflammatory foods such as refined grains, sugary drinks, processed meats and other “junk foods”, have been associated with increased inflammation in the body and can elevate subsequent risk of heart disease by 46% and stroke by 28% ( 11 ). These highly inflammatory foods are widely available on these online food delivery platforms as shown in findings from a recent cross-sectional study ( 12 ). From over 196 independent takeaway food outlets available on UberEats in Sydney, Australia, discretionary cereal-based mixed meals was the largest category found within complete menus (42.3%, 5849/13,841). These include foods such as pizzas, burgers, pides, pasta, wraps, and sandwiches ( 12 ).

A Canadian study analyzed the full menus of retailers partnering with a large OFDS and similarly found low Healthy Eating Index-2015 scores—ranging from 19.95 to 50.78 out of 100 (with a score of 100 being the healthiest) ( 13 ). This study also found a mean delivery distance of 3.7 km or 2.3 miles measuring from postal codes in Ontario to online food retailers ( 13 ). Australian research has likewise, demonstrated that the mean delivery distance from food outlet to suburbs was 3 km, and around 90% of delivery distances were greater than 1 km ( 7 )—a distance that typically defines the neighborhood food environment. The neighborhood food environment reflects the spatial extent of an individual's typical shopping behavior which could be reasonably walked by an adult in 15–20 min ( 14 ). As such, these platform-to-consumer services may be expanding local neighborhood food environments.

Altogether, findings suggest OFDS are expanding the traditional definition of the neighborhood food environment, increasing the accessibility of food outlets which mostly offer items with poor nutritional quality.

Over-Consumption and Excess Promotion

In addition to increasing accessibility of food outlets, OFDS further encourage excessive consumption with aggressive marketing and promotion tactics. Macromarketing researchers are wary of the current marketing systems which promote an era of excess as business models choose to “create” rather than “address” consumer needs, without consideration of the waste generated from overall consumption ( 15 ). OFDS may add further burden to unsustainable practices of mass consumerism which threaten progress toward SDG 12: “Ensure sustainable consumption and production patterns”.

UberEats and Menulog frequently distribute promotional vouchers that offer free meals, discounts, and free delivery ( 16 , 17 ). These are often disseminated through emails to past customers signed up to the OFD platform ( 18 ) or handed-out in person at high-traffic locations such as train stations ( 19 ). Moreover, there is evidence that OFD companies “COVID-wash” their social media promotions—a practice where companies align themselves with social or health issues of COVID-19 to enhance their own image ( 20 ). In a content analysis of Instagram posts from leading OFDS in 2020 during the pandemic, the most used COVID-19 marketing strategy was related to “combatting the pandemic” (76/123, 62%) ( 21 ). This theme helped brands position themselves to be “in this together” and encourage consumers to “support their local businesses”. These findings were echoed in another content analysis study conducted in New Zealand. The most used theme in 36% of all COVID-19 related social media posts intended to generate feelings of community support during the challenging time. Fast-food brands were also found to be the largest proponents of COVID-washing, accounting for 46% of all COVID-19 posts ( 20 ).

Furthermore, during the pandemic, an increase in social media posts promoting “junk foods” from leading OFD brands was observed. In a recent study, we found junk foods accounted for 69.1% of all food and beverage items featured, compared to 58.3% in 2019 ( 21 ). Similarly, a study from Brazil indicated widespread presence of unhealthy food advertising as ultra-processed beverages such as soft drinks were among the most shown in advertisements for OFDS throughout COVID-19. Free delivery also prevailed in advertisements of junk food items such as ice cream, candy, high sodium snacks, and pizza ( 22 ). More research found that menus offering unhealthy meals had more photos and discounts compared to meals offering unprocessed and/or minimally processed foods ( 23 ). Taken together, OFDS continue to facilitate fast-food delivery at heavily discounted prices and excess promotions, perpetuating the culture of excessive consumption.

Unsustainable Plastic Waste and CO2 Emissions

Plastic waste is a key global environmental concern with annual plastic consumption currently at over 300 million tons, which is expected to double in the next 20 years ( 24 ). High volumes of online food delivery consumption exacerbates plastic waste and adds to the increasing contamination of natural environments such as the ocean, freshwater systems, and terrestrial areas ( 25 ). Subsequently, OFDS may have a huge climate cost and are another impediment to SDG 13: “Take urgent action to combat climate change and its impacts”.

Takeout meals ordered from OFDS can come with extensive quantities of plastic material, namely food containers, cutlery, napkins, and plastic bags among others ( 26 ). These materials are often single-use, requiring large quantities of energy and raw materials to produce, transport, and be disposed ( 27 ). A study on the environmental impacts of takeout food containers revealed that single-use polypropylene containers are the worst packaging material for takeout food, with many negative impacts on the environment ( 26 ). In China, researchers found that the total amount of packaging waste from food delivery surged from 0.2 million metric tons in 2015 to 1.5 million metric tons in 2017 ( 28 ). Plastic containers made from polypropylene and polystyrene foam accounted for approximately 75% of the total food delivery packaging waste in weight. COVID-19 lockdowns further aggravated China's plastic waste dilemma: during the lockdown in Wuhan, an average of 130,000 takeout orders were made per day, which totaled to more than 279,500 m of lunchboxes over a 6-week period ( 29 ).

Excessive energy consumption and carbon emissions are associated with the waste produced from food delivery. Based on annual online food delivery data of 179.2 million active users, a 2016 Chinese study found an average ordering frequency of 2 times/week and average delivery distances of 25 km ( 30 ), which resulted in an estimated Green House Gas (GHG) emission of 73.89 Gigatonnes (Gt) carbon dioxide-equivalent emissions (CO 2eq ) ( 30 ). In Australia, COVID-19 lockdowns led to a 20% increase in household solid wastes, partly due to a surge in food deliveries which contributed sizeable amounts of paper, plastic packaging waste and single-use waste ( 31 ). Another study found in 2018, the disposal of single use packaging from online food orders in Australia led to 5,600 tons of CO 2eq ( 32 ). With online food orders expected to increase to 65 million in 2024, researchers project a 132% rise in carbon emissions to 13,200 tons of CO 2eq ( 32 ). As such, the environmental threat of OFDS is progressively evident and needs to be considered as governments globally move toward carbon emission reduction targets.

Fuelling the Gig Economy

Instead of steering toward the SDG 8: “Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all”, OFDS stimulate the gig economy and may veer away from sustainable economic growth that will create quality jobs. While OFDS have facilitated new job opportunities and increased flexibility of work, the quality of these jobs is questionable with little-to-no employment rights and poor work health and safety conditions ( 33 ).

Advances in online technology have fuelled the rise of the “gig economy”—a free market system in which mobile apps or websites connect consumers with individual workers providing services. Gigs are denoted by short-term, one-off employment contracts mediated by online platforms which include online food delivery. Although spending on gig economy in Australia declined severely during the period of early COVID-19 lockdown restrictions in March 2020, it increased to 40% above pre-lockdown levels. This growth was almost entirely driven by the online food delivery sector, which itself increased by more than 100% between August and October 2020 ( 34 ). Indeed, UberEats Australia, a leading OFDS, has reported providing 59,000 work opportunities during 2020 which is an eight-fold increase since 2016 ( 5 ).

In a report on digital platform work in Australia, it has been revealed that food delivery workers choose to work with OFDS for flexibility and to supplement existing income streams ( 35 ). However, food delivery workers are more dependent on income generated from meal delivery compared to gig workers on other digital platforms ( 35 ). This report also suggests food delivery workers were more likely to work longer hours in a week and were more likely to say the income was essential for meeting basic needs ( 35 ). Moreover, food delivery platforms may vary in their contractual agreements where workers may be independent contractors rather than employees. This places workers at risk of insecure income, no insurance, personal or paid leave, no workers compensation, superannuation or certain taxes. Over a long term, gig work as a food delivery worker may be financially untenable. A gig worker who spends 5–10 years in the gig economy full time, could potentially be $40,000–$100,000 AUD worse off in accumulated superannuation at retirement compared to a minimum wage earner ( 34 ). Major reforms of OFD work in the gig economy are required to increase the quality of these jobs, improve the livelihood of workers and be sustainable in the long term.

Existing reports shows achieving the 2030 UN SDGs will require tremendous efforts ahead by governments and industries globally given the considerable setback induced by the COVID-19 pandemic ( 36 ). A systems approach to the complexities of public health issues has been proposed by notable researchers—as outlined in the 2011 and 2015 Lancet Series on Obesity ( 37 , 38 ) and has been a developing research area to inform the National Institute for Health and Care Excellence guidelines on obesity prevention ( 39 ). The synergising of goals and targets within and between systems affecting health including manufacturing, financial, transportation, and food, may be essential to meaningful progress. The EAT-Lancet commission on Food, Planet, and Health is an example which shows the power of goal alignment ( 40 ). This report has outlined the role of diet with human health and environmental sustainability—addressing both the rise in unhealthy diets, the targets of the UN Sustainable Development Goals and the Paris Agreement. As research on the impacts of OFDS is still in its infancy, robust solutions to resolving the issues outlined in this perspective have yet to be developed. However, using a similar systems-based approach, the following calls to action identify areas for existing systems to merge.

Proposed Calls to Action

The World Health Organization (WHO) ( 41 ) has now acknowledged the growing impact of online food environments on people's diet choices. In a recently published report, WHO Europe has proposed the use of a systems approach to make informed decisions on potential interventions and/or regulation of these OFDS or otherwise known as “Meal Delivery Apps” (MDA). Taking a systems approach, several entry points to change were identified. These include Nutrition, Physical Activity, Alcohol consumption, Labor, Road Safety, Food Safety which are key elements that use existing mechanisms to solve complex issues ( 41 ). The entry points align with the SDGs discussed in this perspective and may benefit from collective action by food industry, governments, policy makers and researchers.

In Table 1 , we propose a research and policy agenda with action points that address SDGs and entry points from the WHO report. We have also illustrated examples of current and emerging research across these action points. Figure 1 was designed to demonstrate how these action points can work together to strive toward SDG 3: “Good Health and Wellbeing” for the benefit of public health. The proposed action points may also later converge with recommendations outlined from WHO Europe's commentary piece ( 58 ).

Table 1 . Proposed action points to mitigate negative impacts of online food delivery services and address the 2030 UN Sustainable Development Goals and entry points identified in the WHO Meal Delivery Apps Report.

Figure 1 . Conceptual diagram identifying areas for entry points and Sustainable Development Goals relating to online food delivery services to merge, forming action points to ultimately address SDG 3 Good Health and Wellbeing. Actions are defined and described in detail in Table 1 .

As research on the impact of OFDS continues to grow, ongoing monitoring and evaluation is critical to the development of policy options for regulating the digital food environment. Dashboards are considered as useful tools to help users visualize and understand complex information in a snapshot. They have been developed to monitor global food systems ( 59 ) and food environments ( 60 ) and may also be essential to tracking progress of online food delivery services. We therefore also propose the inclusion of online food settings within existing monitoring and evaluation frameworks of food systems and food environments ( 60 ).

In a world now grappling with ongoing repercussions of the COVID-19 pandemic, resulting societal and environmental changes exacerbated by COVID may further derail the trajectory toward meeting the SDGs set by the United Nations. OFDS are likely to proliferate, providing valued convenience in an increasingly fast-paced modern society. However, the potential disruption to our health and the environment is substantial, interfering with overarching SDGs. Food industry reforms synergised public health messaging and continuous monitoring of the growing impact of OFDS may be part of the solution to collectively address the issues of sustainability, environmental health, decent work and economic growth and nutrition. Multidisciplinary action and research are urgently needed to further investigate such solutions.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

SJ and SP contributed to the conceptualization of the manuscript. SJ wrote the first draft of the manuscript and designed all tables and figures included in submission. SP provided primary supervision of SJ, reviewing the first draft of the manuscript. AAG, DD, PP, MA-F, and JR edited and reviewed the manuscript drafts. All authors contributed to manuscript revision, read, and approved the submitted version.

SJ was supported by the Australian Government's Research Training Program Stipend Scholarship, AAG receives funding from the National Health and Medical Research Council, NSW Health and Diabetes Australia, JR receives fellowship and research grants from the National Health and Medical Research Council, NSW Health, and Medical Research Future Fund, DD receives funding from the National Health and Medical Research Council and NSW Health, MA-F receives funding from the National Health and Medical Research Council, Australian Research Council, NSW Health and Cancer Council NSW, PP receives funding from NSW Health, SP receives funding from the National Health and Medical Research Council, National Heart Foundation, NSW Health and the Medical Research Future Fund.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Giles-Corti B, Vernez-Moudon A, Reis R, Turrell G, Dannenberg AL, Badland H, et al. City planning and population health: a global challenge. Lancet. (2016) 388:2912–24. doi: 10.1016/S0140-6736(16)30066-6

PubMed Abstract | CrossRef Full Text | Google Scholar

2. United Nations. Transforming our World: The 2030 Agenda for Sustainable Development: United Nations. (2015). Available online at: https://sdgs.un.org/2030agenda (accessed January 10, 2022).

Google Scholar

3. Statista. Online Food Delivery: Statista. (2021). Available online at https://www.statista.com/outlook/dmo/eservices/online-food-delivery/australia (accessed January 10, 2022).

4. AJOT. Online Food Delivery Market to Hit $151.5B in Revenue and 1.6B Users in 2021 A 10% Jump in a Year. (2021). Available online at: https://ajot.com/news/online-food-delivery-market-to-hit-151.5b-in-revenue-and-1.6b-users-in-2021-a-10-jump-in-a-year (accessed January 10, 2022).

5. Accenture Strategy. Making Delivery Work for Everyone (2021).

6. Just Eat Takeaway.com. Trading Update . Amsterdam (2021).

7. Partridge SR, Gibson AA, Roy R, Malloy JA, Raeside R, Jia SS, et al. Junk food on demand: a cross-sectional analysis of the nutritional quality of popular online food delivery outlets in Australia and New Zealand. Nutrients. (2020) 12:3107. doi: 10.3390/nu12103107

8. Franchise Buyer. The 10 Biggest Fast Food Franchises in the Australian Market. (2020). Available online at: https://www.franchisebuyer.com.au/articles/the-10-biggest-fast-food-franchises-in-the-australian-market (accessed October 18, 2021).

9. Kirkpatrick SI, Reedy J, Kahle LL, Harris JL, Ohri-Vachaspati P, Krebs-Smith SM. Fast-food menu offerings vary in dietary quality, but are consistently poor. Public Health Nutr. (2014) 17:924–31. doi: 10.1017/S1368980012005563

10. Rosenheck R. Fast food consumption and increased caloric intake: a systematic review of a trajectory towards weight gain and obesity risk. Obes Rev. (2008) 9:535–47. doi: 10.1111/j.1467-789X.2008.00477.x

11. Li J, Lee DH, Hu J, Tabung FK, Li Y, Bhupathiraju SN, et al. Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S. J Am Coll Cardiol. (2020) 76:2181–93. doi: 10.1016/j.jacc.2020.09.535

12. Wang C, Korai A, Jia SS, Allman-Farinelli M, Chan V, Roy R, et al. Hunger for home delivery: cross-sectional analysis of the nutritional quality of complete menus on an online food delivery platform in Australia. Nutrients. (2021) 13:905. doi: 10.3390/nu13030905

13. Brar K, Minaker LM. Geographic reach and nutritional quality of foods available from mobile online food delivery service applications: novel opportunities for retail food environment surveillance. BMC Public Health. (2021) 21:458. doi: 10.1186/s12889-021-10489-2

14. Smith G, Gidlow C, Davey R, Foster C. What is my walking neighbourhood? A pilot study of English adults' definitions of their local walking neighbourhoods. Int J Behav Nutr Phys Act. (2010) 7:34. doi: 10.1186/1479-5868-7-34

15. Wooliscroft B, Ganglmair-Wooliscroft A. Growth, excess and opportunities:marketing systems' contributions to society. J Macromarket. (2018) 38:355–63. doi: 10.1177/0276146718805804

CrossRef Full Text | Google Scholar

16. Mott B. 10 takeaway and delivery food tips. In: Berry L, editor. Money Saving Expert (2021). Available online at: https://www.moneysavingexpert.com/deals/deals-hunter/2021/01/takeaway-and-delivery-food-tips/ (accessed June 21, 2021).

17. Business Insider. Londoners Are Gaming UberEats to Get Free Food. (2016). Available online at: https://www.businessinsider.com.au/how-to-get-free-ubereats-vouchers-promotions-food-hundreds-pounds-london-2016-9?r=US&IR=T (accessed September 20, 2021).

18. Sun T. Uber Annoying Angry Uber Eats Customers Left Hungry After Discount Code Doesn't Work 'Everything Is Unavailable'. (2019). Available online at: https://www.thesun.co.uk/money/10627147/uber-eats-discount-code-doesnt-work-everything-unavailable/ (accessed September 20, 2021).

19. Wongm Rail Gallery. Handing Out Uber Eats Vouchers on the Main Concourse at Southern Cross Station. Melbourne, VIC (2018). Available online at: https://railgallery.wongm.com/commercialising-commuters/F129_5537.jpg.html (accessed September 14, 2021).

20. Gerritsen S, Sing F, Lin K, Martino F, Backholer K, Culpin A, et al. The timing, nature and extent of social media marketing by unhealthy food and drinks brands during the COVID-19 pandemic in New Zealand. Front Nutr. (2021) 8:65. doi: 10.3389/fnut.2021.645349

21. Jia SS, Raeside R, Redfern J, Gibson AA, Singleton A, Partridge SR. #SupportLocal: how online food delivery services leveraged the COVID-19 pandemic to promote food and beverages on Instagram. Public Health Nutr. (2021) 24:4812–22. doi: 10.1017/S1368980021002731

PubMed Abstract | CrossRef Full Text

22. Horta PM, Matos JdP, Mendes LL. Digital food environment during the coronavirus disease 2019 (COVID-19) pandemic in Brazil: an analysis of food advertising in an online food delivery platform. Brit J Nutr. (2020) 126:767–72. doi: 10.1017/S0007114520004560

23. Horta PM, Souza JPM, Rocha LL, Mendes LL. Digital food environment of a Brazilian metropolis: food availability and marketing strategies used by delivery apps. Public Health Nutr. (2021) 24:544–8. doi: 10.1017/S1368980020003171

24. Closing the plastics loop. Nat Sustain. (2018). 1:205. doi: 10.1038/s41893-018-0075-3

CrossRef Full Text

25. Barnes DK, Galgani F, Thompson RC, Barlaz M. Accumulation and fragmentation of plastic debris in global environments. Philos Trans R Soc Lond B Biol Sci. (2009) 364:1985–98. doi: 10.1098/rstb.2008.0205

26. Gallego-Schmid A, Mendoza JMF, Azapagic A. Environmental impacts of takeaway food containers. J Clean Product. (2019) 211:417–27. doi: 10.1016/j.jclepro.2018.11.220

27. Yi Y, Wang Z, Wennersten R, Sun Q. Life cycle assessment of delivery packages in China. Energy Proc. (2017) 105:3711–9. doi: 10.1016/j.egypro.2017.03.860

28. Song G, Zhang H, Duan H, Xu M. Packaging waste from food delivery in China's mega cities. Resour Conserv Recycl. (2018) 130:226–7. doi: 10.1016/j.resconrec.2017.12.007

29. Liu J, Vethaak AD, An L, Liu Q, Yang Y, Ding J. An environmental dilemma for china during the COVID-19 pandemic: the explosion of disposable plastic wastes. Bull Environ Contam Toxicol. (2021) 106:237–40. doi: 10.1007/s00128-021-03121-x

30. Jia X-x, Klemeš J, Varbanov P, Wan Alwi SR. Energy-emission-waste nexus of food deliveries in China. Chem Eng Trans. (2018) 70:661–6. doi: 10.3303/CET1870111

31. Infrastructure Australia. Infrastructure Beyond COVID-19: A National Study on the Impacts of the Pandemic on Australia. Australian Government (2020).

32. Arunan I, Crawford RH. Greenhouse gas emissions associated with food packaging for online food delivery services in Australia. Resour Conserv Recycl. (2021) 168:105299. doi: 10.1016/j.resconrec.2020.105299

33. Sprint Law. The Gig Economy Australian Law: What's Next? (2021). Available online at: https://sprintlaw.com.au/gig-economy-in-australia/ (accessed September 14, 2021).

34. Actuaries Institute. The Rise of the Gig Economy its Impact on the Australian Workforce. (2020). Available online at: https://www.actuaries.digital/2020/12/16/the-rise-of-the-gig-economy-and-its-impact-on-the-australian-workforce/

35. McDonald P, Williams P, Stewart A, Oliver D, Mayes R. Digital Platform Work in Australia: Preliminary Findings from a National Survey. Victorian Department of Premier and Cabinet (2019).

36. Hughes BB, Hanna T, McNeil K, Bohl DK, Moyer J.D. Pursuing the Sustainable Development Goals in a World Reshaped by COVID-19. Denver, CO; New York, NY: Frederick S. Pardee Center for International Futures and United Nations Development Programme (2021).

37. Rutter H. Where next for obesity? Lancet. (2011) 378:746–7. doi: 10.1016/S0140-6736(11)61272-5

38. Kleinert S, Horton R. Rethinking and reframing obesity. Lancet. (2015) 385:2326–8. doi: 10.1016/S0140-6736(15)60163-5

39. Garside R, Pearson M, Hunt H, Moxham T, Anderson R. Identifying the Key Elements and Interactions of a Whole System Approach to Obesity Prevention. Exeter: Peninsula Technology Assessment Group (PenTAG) (2010).

40. Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet. (2019) 393:447–92. doi: 10.1016/S0140-6736(18)31788-4

41. World Health Organisation Europe. Digital Food Environments: Factsheet 2021. (2021). Available online at: https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/publications/2021/digital-food-environments-factsheet-2021

42. Bonyhardy NW, Shane E. The Right Thing To Do: Menulog's Move to Employ Couriers Ups Pressure on Gig Economy. Sydney Morning Herald (2021).

43. Corlett E. New Zealand Uber Drivers Launch Class Action Against Ride Share Company. Wellington: The Guardian (2021).

44. Moreno C, Allam Z, Chabaud D, Gall C, Pratlong F. Introducing the “15-minute city”: sustainability, resilience and place identity in future post-pandemic cities. Smart Cities. (2021) 4:93–111. doi: 10.3390/smartcities4010006

45. Willsher K. Paris Mayor Unveils '15-Minute City' Plan in Re-election Campaign. Paris: The Guardian. (2020).

46. Grodach C, Kamruzzaman L, Harper L. 20-Minute Neighbourhood - Living Locally Research. Melbourne, VIC: Monash University (2019).

47. Weng M, Ding N, Li J, Jin X, Xiao H, Zhi-ming H, et al. The 15-minute walkable neighborhoods: measurement, social inequalities and implications for building healthy communities in urban China. J Transport Health. (2019) 13:259–73. doi: 10.1016/j.jth.2019.05.005

48. Land Transport Authority Singapore. Land Transport Master Plan 2040. Singapore: Land Transport Authority (2020).

49. City of Portland. 20-Minute Neighbourhoods Portland, United States of America. (2021). Available online at: https://www.portlandonline.com/portlandplan/index.cfm?a=288098&c=52256

50. Guzman LA, Arellana J, Oviedo D, Moncada Aristizábal CA. COVID-19, activity and mobility patterns in Bogotá. Are we ready for a '15-minute city'? Travel Behav Soc. (2021) 24:245–56. doi: 10.1016/j.tbs.2021.04.008

51. World Health Organisation. Monitoring Food and Beverage Marketing to Children via Television and the Internet. A Proposed Tool for the WHO European Region. Copenhagen: WHO Regional Office for Europe (2016).

52. Sweney M. UK to Ban Junk Food Advertising Online and Before 9pm on TV from 2023. London: The Guardian (2021).

53. Wyse R, Delaney T, Stacey F, Zoetemeyer R, Lecathelinais C, Lamont H, et al. Effectiveness of a multistrategy behavioral intervention to increase the nutritional quality of primary school students' web-based canteen lunch orders (click & crunch): cluster randomized controlled trial. J Med Internet Res. (2021) 23:e26054. doi: 10.2196/26054

54. Coles J. Calorie-Labelled Menus Set for Cafes, Restaurants and Takeaways in England. Available online at: https://www.mirror.co.uk/news/uk-news/calorie-labelled-menus-set-cafs-24095206 (accessed May 12, 2021).

55. Global Ecolabelling Network. What is Ecolabelling? (2021). Available online at: https://globalecolabelling.net/what-is-eco-labelling/#:~:text=Ecolabelling%20is%20a%20voluntary%20method ,preferable%20within%20a%20specific%20category

56. Potter C, Bastounis A, Hartmann-Boyce J, Stewart C, Frie K, Tudor K, et al. The effects of environmental sustainability labels on selection, purchase, and consumption of food and drink products: a systematic review. Environ Behav. (2021) 53:891–925. doi: 10.1177/0013916521995473

57. Coelho PM, Corona B, ten Klooster R, Worrell E. Sustainability of reusable packaging-Current situation and trends. Resour Conserv Recycl X. (2020) 6:100037. doi: 10.1016/j.rcrx.2020.100037

58. Halloran A, Faiz M, Chatterjee S, Clough I, Rippin H, Farrand C, et al. The cost of convenience: potential linkages between noncommunicable diseases and meal delivery apps. Lancet Region Health Eur. (2022) 12:100293. doi: 10.1016/j.lanepe.2021.100293

59. Fanzo J, Haddad L, McLaren R, Marshall Q, Davis C, Herforth A, et al. The Food Systems Dashboard is a new tool to inform better food policy. Nature Food. (2020) 1:243–6. doi: 10.1038/s43016-020-0077-y

60. INFORMAS G, Institute for Health Transformation, Deakin University. The The University of Queensland, University of Wollongong. The The University of Newcastle, The The George Institute for Global Health, Obesity Policy Coalition, VicHealth. Australia's Food Environment Dashboard. (2021). Available online at: https://foodenvironmentdashboard.com.au/

Keywords: online food delivery, sustainable development goals, global health, public health, systems approach

Citation: Jia SS, Gibson AA, Ding D, Allman-Farinelli M, Phongsavan P, Redfern J and Partridge SR (2022) Perspective: Are Online Food Delivery Services Emerging as Another Obstacle to Achieving the 2030 United Nations Sustainable Development Goals? Front. Nutr. 9:858475. doi: 10.3389/fnut.2022.858475

Received: 20 January 2022; Accepted: 07 February 2022; Published: 03 March 2022.

Reviewed by:

Copyright © 2022 Jia, Gibson, Ding, Allman-Farinelli, Phongsavan, Redfern and Partridge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Si Si Jia, sisi.jia@sydney.edu.au

This article is part of the Research Topic

Innovation and Trends in the Global Food Systems, Dietary Patterns and Healthy Sustainable Lifestyle in the Digital Age

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier - PMC COVID-19 Collection

Online food delivery companies' performance and consumers expectations during Covid-19: An investigation using machine learning approach

Purushottam meena.

a Department of Supply Chain & Information Management, School of Business, College of Charleston, Charleston, SC, 29424, USA

Gopal Kumar

b Department of Operations Management, Indian Institute of Management, Raipur, Chhattisgarh, 492015, India

Online food delivery (OFD) businesses flourished during COVID-19; however, OFD companies experienced different challenges and customers' expectations. This paper uses social media data to explore OFD companies' performance and customers' expectations during the COVID-19 pandemic. The most important topics in developed and developing countries are identified using machine learning. Results show that customers in India are more concerned about social responsibility, while financial aspects are more important in the US. Overall, customers in India are more satisfied with OFD companies during the COVID-19 pandemic than the US customers. We further find that factors such as OFD companies' brand, market size, country, and COVID-19 waves play a crucial role in moderating customer sentiment. The results of the study offer several managerial insights.

1. Introduction

The COVID-19 pandemic has caused a profound and severe impact on the global economy. As the COVID-19 pandemic started, many restaurants worldwide lost billions of dollars, and many even faced business closures ( National Restaurant Association, 2020 ). According to the National Restaurant Association (2020) report, the restaurant industry has already lost approximately $240 billion by 2020. Restaurants that remained in business found it essential to adapt to recent changes in the industry and offer online food delivery (OFD) services for their survival ( Brewer and Sebby, 2021 ). Many restaurants started using third-party food delivery providers' services during the pandemic. It resulted in a significant increase in online OFD businesses worldwide. For example, Uber Eats observed a substantial increase in OFD orders following the mandate of no dine-in service ( Raj et al., 2021 ).

The platform economy in the food sector is not more prominent in terms of labor participation in India ( Sundararajan, 2016 ; Pant and Shende, 2020 ). However, with entries of global OFD leading companies such as Uber Eats along with some Indian companies—Zomato and Swiggy—the OFD industry is proliferating with a 25–30 percent annually ( Boston Consulting Group, 2020 ). The overall platform economy in India is expected to be $30 billion by 2025 ( NASSCOM, 2018 ). In 2019, more than 48 million people used OFD platforms in the US, which is likely to be approximately 70 million in 2024. The OFD revenue in the US is expected to grow steadily at 7.26 percent annually and is estimated to be $41,504 million by 2025 ( Statista, 2021 ). As per the 2022 Statista report, the global OFD service market is expected to reach $223.7 billion in 2025 from $115.07 billion in 2020 (Statista, 2022).

In recent years, the number of laborers who work in the platform economy has increased significantly. According to the NITI Aayog (a policy think tank of the India government), the platform economy employs around 15 million workers, and approximately 0.44 million of them work in the food sector ( Tiwari et al., 2019 ). Food delivery workers are not hired as full-time employees like any other sharing economy sector. These workers are primarily considered interchangeable or “gig workers” by food delivery companies ( Sundararajan, 2016 ).

During the first wave of COVID-19, most restaurants in the US were forced to suspend dine-in services and were only allowed to operate via takeout, drive-through, or contactless deliveries. As the COVID-19 outbreak started, restaurants demand started plumbing globally and affected the financial performance of the restaurant industry. In US restaurants, customer demand decreased drastically with increasing COVID-19 cases ( Yang et al., 2020 ). However, more and more people started ordering foods online via food delivery platforms like DoorDash, Grubhub, Uber Eats (in the US), Zomato, and Swiggy (in India) ( Jain, 2020 ; Kim et al., 2021 ). Restaurants that provided OFD and curbside pickup services for contactless delivery experienced a lesser effect on financial performance. Most of these OFD services businesses were in operation during the COVID-19 outbreak as they are considered among the essential services. The OFD service platforms were instrumental for restaurants to remain in business during challenging times ( Puram et al., 2021 ). Compared to March 2019, customer spending on food delivery increased significantly (i.e., 70 percent)—during the first COVID-19 wave in March 2020 ( Chen McCain et al., 2021 ).

On the flip side, OFD service providers (i.e., riders or drivers) and consumers faced several challenges during the ongoing pandemic. For example, Indian OFD workers have faced personal and social risks, ranging from loss of income to exposure to COVID-19 and risking their lives ( Lalvani and Seetharaman, 2020 ). The OFD became crucial and popular as millions of people were quarantined and unquarantined required foods ( Chen McCain et al., 2021 ; Kim et al., 2021 ). While delivering food, OFD companies needed to follow strict COVID-19 appropriate behaviors; otherwise, riders and consumers would be exposed to life-threatening health risks.

Overall, during the COVID-19 pandemic, consumers prioritized expectations from OFD companies, and the challenges faced by OFD companies were different from those of the pre-COVID-19 pandemic ( Gavilan et al., 2021 ). The adaptability of food delivery companies to a situation of a global healthcare crisis, where customers' expectations are entirely different ( Gavilan et al., 2021 ; Nguyen and Vu, 2020 ). It may depend on several factors, such as fear ( Balakrishnan, 2020 ; Mehrolia et al., 2021 ; Ahorsu et al., 2020 ; Lo Coco et al., 2021 ), marketing strategies during COVID-19 waves ( Jia et al., 2021 ), brand perception ( Dsouza and Sharma, 2021 ; Prasetyo et al., 2021 ), market size, region (developing or developed countries) of their operations ( Keeble et al., 2020 ; Zanetta et al., 2021 ), public listing ( Bao and Zhu, 2022 ), and COVID-19 waves ( Kohút et al., 2021 ; Mohan et al., 2022 ), etc. For example, OFD companies with better brand perception, bigger market size, and listed in the stock market should do better in a crisis. They may also improve their performance in the second wave better than in the first wave of the COVID-19 pandemic. Therefore, to have a deeper understanding of the delivery operations of OFD companies and consumers' behaviors/expectations during a global healthcare crisis, it is crucial to understand how OFD companies performed, to what extent consumers' expectations were met, and factors that can explain OFD companies' performance or customers' satisfaction during COVID-19 pandemic.

Most of the erstwhile work in the literature on OFD is conducted predominantly using primary data with a limited sample size. To the best of our knowledge, no study exists in the literature that considers social media-based big data relating to OFD companies during the pandemic and uses qualitative (machine learning algorithms) as well as quantitative approaches to investigate issues faced by OFD companies, service providers, and customers during the first two waves of the COVID-19 in the US and Indian market. Specifically, this study investigates how do consumers' sentiments vary on OFD companies' services during COVID-19 across different dimensions of consumer expectations, market characteristics, and companies' characteristics?

The following research objectives (RO) are considered to address the research question:

RO1: To identify the broader issues/topics prominently discussed about OFD companies and service providers during the pandemic.

RO2: To investigate consumers' net sentiment and negative sentiment across identified dimensions, OFD companies, and different countries.

RO3: To test the impact of different market characteristics and OFD companies' characteristics on consumers' sentiments.

To address these research objectives, first, we collected data for four OFD companies (i.e., Uber Eats, Grubhub, Zomato, and Swiggy) from Twitter during the first two waves of the COVID-19 pandemic. Second, the text mining approach is used to identify different topics/issues discussed on Twitter about these OFD companies. Third, for each identified topic, positive and negative sentiments of people are computed. Fourth, different regression models are developed to investigate the relationship between the moderating variables and people's sentiments. Finally, the results of both countries' companies are compared to examine the differences among the topics and customers' sentiments. The results offer several important practical insights which are helpful for OFD companies.

The remainder of the paper is organized as follows. Section 2 discusses the analyses of relevant literature. Section 3 presents the research hypotheses. Data collection, research methodology, and results are provided in Section 4 . Section 5 presents discussion and managerial insights. Finally, section 6 concludes the research and offers the scope for future studies.

2. Literature review

There was not much research conducted on the OFD area before the COVID-19 pandemic. Previous studies have mainly focused on issues such as the OFD platform's performance and customer satisfaction ( Seghezzi and Mangiaracina, 2020 ; Gilitwala and Nag, 2019 ), consumers behavior and attitude ( Pigatto et al., 2017 ; Hwang and Choe, 2019 ; Yeo et al., 2021 ), vehicle routing problems ( Liu et al., 2020 ; Correa et al., 2019 ), adoption and acceptance of technology in OFD apps ( Pigatto et al., 2017 ; Gunden et al., 2020 ; Roh and Park, 2019 ), and riders issues related to wages and health ( Bates et al., 2020 ; Correa et al., 2019 ; Kougiannou and Mendonça, 2021 ). For a detailed discussion on these issues, interested readers can refer to Li et al. (2020) and Seghezzi et al. (2021) , who have provided an excellent review of OFD literature published before the COVID-19 period.

Research on the OFD has witnessed significant growth during the last two years. One of the reasons for this rapid growth was the rapid increase in OFD services demand during the COVID-19 pandemic. For safety reasons, more people started ordering their food from home via OFD apps ( Mehrolia et al., 2021 ; Zanetta et al., 2021 ; Hong et al., 2021 , Hong et al., 2021 ), such as Uber Eats, DoorDash, Zomato, and Swiggy. Several researchers ( Hong et al., 2021 , Hong et al., 2021 ; Gani et al., 2021 ) have studied how these new behaviors of people affect the food delivery business during the COVID-19 outbreak.

As per the world health organization (WHO), more than 6.28 million people have already died because of the COVID-19 pandemic. It has affected people's life emotionally, psychologically, and physically ( Ahorsu et al., 2020 ; Conte et al., 2021; Watson and Popescu, 2021 ). People have used several ways to deal with the stress, anxiety, and loneliness caused by the COVID-19 (Kumarand Shah, 2020; Ahorsu et al., 2020 ; Lo Coco et al., 2021 ). Consumers have extensively utilized different social media platforms to share their opinion and remain updated with the public policies and regulations during the COVID-19 ( Rydell and Kucera, 2021 ; Trivedi and Singh, 2021 ). Several researchers studied how the COVID-19 has changed consumers' behavior, habits, and tastes when buying food online ( Watson and Popescu, 2021 ; Birtus and Lăzăroiu, 2021 ; Rydell and Suler, 2021 ; Smith and Machova, 2021 ). Michalikova et al. (2022) have reviewed the literature on customers' judgment, behavior intentions, and purchase decision dynamics during the COVID-19 pandemic in the food delivery sector.

COVID-19 has considerably impacted the financial strength of restaurant firms; however, restaurant firms that used OFD and other contactless delivery services experienced relatively lesser financial glitches ( Kim et al., 2021 ). Before COVID-19, the primary marketing strategies were based on product imagery, links, and sponsorships. While combatting the COVID-19 pandemic, selling social distancing, appropriating frontline workers, and accelerating digitalization were the selling points ( Jia et al., 2021 ). We classify the research conducted on OFD during the COVID-19 outbreak into three categories—customer satisfaction/experience, intention to use OFD apps, and workers/drivers/riders' conditions during operations of OFD.

The first set of studies ( Kumar and Shah, 2021 ; Mehrolia et al., 2021 ; Prasetyo et al., 2021 : Sharma et al., 2021 ) focused on customer experience and OFD companies' performance. Mehrolia et al. (2020) and Sharma et al. (2021) investigated the characteristics of customers who used and did not use online food delivery services (OFDS) during the first wave of COVID-19 in India. Customers who purchased food via OFD platforms have experienced lesser perceived threats and a high purchase pattern, perceived benefits, and product involvement ( Al Amin et al., 2021 ; Mehrolia et al., 2021 ; Sharma et al., 2021 ; Uzir et al., 2021 ). Studies found that during the COVID-19 pandemic, hedonic motivation ( Prasetyo et al., 2021 ; Sharma et al., 2021 ; Shah et al., 2021 ), food quality, variety, and safety ( Dsouza and Sharma, 2021 ; Shah et al., 2021 ), and mobile app information design/features ( Pal et al., 2021 ) significantly affect customers satisfaction and loyalty. Hedonic motivation is also important for price, information quality, and promotion ( Prasetyo et al., 2021 ; Bao and Zhu, 2022 ; Shah et al., 2021 ; Sharma et al., 2021 ; Ramos, 2021 ; Pal et al., 2021 ). Unlike other studies, Zenetta et al. (2021) found that hedonic motivation was not the most crucial factor for the continuance intention.

India is one of the most populated countries and was severely affected by the COVID-19. Several studies ( Dsouza and Sharma, 2021 ; Mehrolia et al., 2021 ; Pal et al., 2021 ) have been conducted to understand OFD companies and customers' purchase behaviors in the Indian market. Trivedi and Singh (2021) found that Zomato received the most positive and less negative customer sentiments than other competitors, such as Swiggy in India. Dsouza and Sharma (2021) found that the food quality and safety measure of OFDAs positively affects customer satisfaction and loyalty in India. Pal et al. (2021) investigate student satisfaction and loyalty to OFDAs. Their findings suggest that satisfaction is the main predictor of loyalty, whereas mobile app information design has the highest impact on satisfaction and loyalty. Chen McCain et al. (2021) assessed customers' satisfaction with Uber Eats on different dimensions—OFD apps performance, food quality, and service quality during the first wave of COVID-19 in the US. They found that service quality was the most important dimension, followed by OFD app performance and food quality.

The second set of studies focused on the continuous usage intention of OFD apps during the COVID-19 outbreak period in different countries, such as India ( Mehrolia et al., 2021 ), the USA ( Hong et al., 2021 , Hong et al., 2021 ), China ( Zhao and Bacao, 2020 ), Brazil ( Zanetta et al., 2021 ), Mexico ( Ramos, 2021 ), Vietnam ( Tran, 2021 ). These and several other studies have found that performance expectancy ( Mehrolia et al., 2021 ; Zanetta et al., 2021 ; Pal et al., 2021 ; Ramos, 2021 ), habit ( Zanetta et al., 2021 ; Rydell and Kucera, 2021 ), effort expectancy ( Kumar and Shah, 2021 ; Ramos, 2021 ; Pal et al., 2021 ), price saving orientation ( van Doorn, 2020 ; Ramos, 2021 ; Pal et al., 2021 ), perceived usefulness, and employee trust ( Gavilan et al., 2021 ; Chakraborty et al., 2022 ; Trivedi and Singh, 2021 ; Uzir et al., 2021 ) affect consumers intention to use OFD services during the COVID-19 period.

Similarly, delivery, hygiene, subjective norms, attitudes, behavioral control, and social isolation ( Al Amin et al., 2021 ; Gani et al., 2021 ; Tran, 2021 ; Sharma et al., 2021 ; Yeo et al., 2021 ; Hopkins and Potcovaru, 2021 ) positively affect the consumers' continuance intention to use Mobile food delivery apps. Further, studies have also found that OFD apps features, ease of use, convenience, price saving, and food variety ( Pigatto et al., 2017 ; van Doorn, 2020 ; Pal et al., 2021 ; Dirsehan and Cankat, 2021 ; Shah et al., 2021 ; Kumar et al., 2021 ; Bao and Zhu, 2022 ) also affect the continued intention to use the FDA. Gavilan et al. (2021) found that customers preferred innovative solutions by OFD companies during COVID-19. Kumar and Shah (2021) observed that the app aesthetics were responsible for customers' pleasure, significantly affecting the customers' continued usage intent.

The third set of studies ( Huang, 2021 ; Parwez & Ranjan, 2021 ; Puram et al., 2021 ) attempted to understand food delivery drivers' conditions during COVID-19 in China and India. The precarity of work among the food delivery workers had aggravated during COVID-19—and it impacted workers' job loss, health risks, and occupational distress ( Huang, 2021 ; Parwez & Ranjan, 2021 ). Among drivers in China, work insecurity, financial distress, health risks, livelihood crisis, and inflamed racism were also observed ( Huang, 2021 ). Puram et al. (2021) analyzed the challenges faced by last-mile food delivery riders working for different OFD platforms in India during the COVID-19 pandemic. They categorize the riders' challenges under operational, customer-related, organizational, and technological categories.

Apart from the variables discussed above, several other variables moderate or affect customers' satisfaction ( Hu et al., 2009 ), sentiments about the food delivery service ( Oliver, 1977 ; Gavilan et al., 2021 ), and intent to use and reuse OFD services ( Mittal et al., 2001 ; Kim et al., 2021 ; Gani et al., 2021 ). A large number of studies show that a firm's brand image ( Fornell, 1992 ; O'Sullivan and McCallig, 2012 ; Peng et al., 2015 ; Chai and Yat, 2019 ; Hwang and Kim, 2020 ; Prasetyo et al., 2021 ) and market value/size (Daniel et al., 2015; Dai et al., 2021 ) positively affect customers satisfaction in different service industries. The customer's expectations about the OFD service performance may vary between different waves of the COVID-19 ( Sv et al., 2021 ; Mohan et al., 2022 ). The geographic location of customers may affect their expectations and purchase intention about the product and service ( Steyn et al., 2010 ; Ng, 2013 ; Leng et al., 2019 ; Punel et al., 2019 ). Rizou et al. (2020) found similar findings in the food delivery sector as well. Studies have explored the difference in people's sentiments during the first and second waves of the COVID-19 ( Lo Coco et al., 2021 ).

None of the above studies utilize secondary data from social media platforms like Twitter, where customers regularly express their concerns, experience, and advice to OFD companies and their businesses. This data can help companies comprehensively understand customers' expectations and measure their performance during the pandemic. This paper addresses this issue by collecting consumer data on the top two Indian and US OFD companies from Twitter and exploring the issues OFD companies face by employing the text mining approach and regression analyses.

3. Research hypotheses development

In addition to the traditional OFD business market factors, several other variables may affect or moderate customers' sentiments about OFD companies' performance and service during the COVID-19. The variables and related hypotheses are discussed in the following sub-sections.

3.1. The COVID-19 waves and people's sentiments

The COVID-19 pandemic brought havoc to everyone's life and affected people in different ways—financially, emotionally, psychologically, and physically ( Trivedi and Singh, 2021 ; Conte e t al., 2021). At the beginning of the COVID-19 outbreak (i.e., in the first wave), it was difficult for OFD companies and restaurants to run their business and serve customers efficiently and safely. In the first COVID-19 wave, most OFD companies were clueless about the customers' expectations of their delivery services and how to meet them. Due to safety reasons ( Zhao and Bacao, 2020 ), customers with self-protective behavior were hesitant to order food online during the first COVID-19 wave ( Ahorsu et al., 2020 ). The primary reasons for their safety concern were lack of information about hygiene, how and who is preparing food, ingredients used in foods, source of ingredients, and safety measures adopted at restaurants ( Gavilan et al., 2021 ).

However, to address these concerns, soon, OFD companies started adopting government regulations, offered contactless delivery, and implemented several measures for customers' safety ( Nguyen and Vu, 2020 ). During the first COVID-19 wave, OFD companies have learned to meet consumers' expectations in the best way possible. As COVID-19 time passed, OFD service providers started offering more information about safety measures adopted by them to consumers (Keeble et al., 2021). Therefore, in the second wave of COVID-19, companies' performance and consumers' sentiment about their performance should improve. Literature suggests a difference in people's sentiments between the first and second wave of the COVID-19 ( Kohút et al., 2021 ; Sv et al., 2021 ; Mohan et al., 2022 ), which also suggests people's psychological adoption of the new normal situation ( Koppehele-Gossel et al., 2022 ) as well satisfaction with improved services of OFD firms. We believe customers' sentiments on the operations of OFD companies ( Kim et al., 2021 ) should vary from COVID-19 first to the second wave. Therefore, we hypothesized:

COVID-19 waves moderate people's sentiments.

3.2. Market size and public listing impact on people's sentiments

From the expectancy theory perspective ( Oliver, 1977 ), customers will have specific expectations from a firm based on their previous experiences, influencing their behaviors to achieve specific goals. Before the COVID-19 outbreak, OFD companies with a bigger market size and listed publicly may have provided better customer experiences. Customers would be expecting similar services during the COVID-19 period as well. Some OFD companies (e.g., Zomato and Groubhub) are publicly listed and have a better brand value than others ( Savitri et al., 2020 ; Yao et al., 2021 ). OFD companies with more resources can invest liberally to bring innovation to delivery operations to handle the COVID-19 healthcare crisis and market them to the consumers. Different innovative OFD options are discussed in the literature ( Keeble et al., 2020 ; Richardson, 2020 ; Shah et al., 2021 ; Sharma et al., 2021 ; Bao and Zhu, 2022 ), which helps in reducing customers' fear of safety concerns and increases their experiential value ( Gavilan et al., 2021 ) during COVID-19. Such companies can be more successful in conveying their safety-related measures to consumers.

Numerous studies ( O'Sullivan and McCallig, 2012 ; Peng et al., 2015 ) have investigated how a firm's value affects customer satisfaction. Bolton et al. (2004) and Luo et al. (2010) found that customer satisfaction significantly affects the firm's value in the stock market and vice-versa. It is established in the literature that high firm value not only helps increase customer satisfaction ( Hu et al., 2009 ) but also enhances its brand image ( Prasetyo et al., 2021 ), purchase and repurchase intention ( Bao and Zhu, 2022 ), customer retention ( Mittal et al., 2001 ), and reduces complaints ( Fornell, 1992 ). Dai et al. (2021) studied the impact of the COVID-19 outbreak on small and medium-sized enterprises (SMEs) across different industries during the first and second waves. They found that during the second wave, companies were more prepared based on their learning from the first wave. Daniel et al. (2005) find a higher correlation between a firm's market values and customers' confidence in the firm. Based on the above studies, one can conclude that if a firm has a higher market size or value and is listed publicly, it leads to high customer confidence or sentiments in the firm.

The market size of OFD companies moderates people's sentiments .

Public listing of OFD companies moderates people's sentiments.

3.3. The brand image and people's sentiments

The brand image of a food company is crucial for customers to use their OFD service ( Prasetyo et al., 2021 ). In the restaurant industry, brand image is defined as “emotions, ideas or attitudes that customers associate with full-service dining restaurants” ( Jin et al., 2012 ). It is established in the literature that customers prefer to purchase products or services from well-known brands as they offer high-quality food ( Aaker and Equity, 1991 ). During the COVID-19 outbreak, several restaurants have increased their presence on OFD platforms to increase brand awareness ( Chai and Yat, 2019 ; Hwang and Kim, 2020 ). In general, a better brand image helps increasing consumer trust, purchase intention ( Erdem and Swait, 2004 ), and customer satisfaction with a better service and quality ( Baek et al., 2010 ). Aureliano-Silva et al. (2022) argued that brand love and service recovery are important for purchase intention and brand trust in food delivery platforms. Studies show that brand love is instrumental to customer satisfaction and intentions for future purchases ( Han et al., 2011 ; Erkmen and Hancer, 2019 ; Bao and Zhu, 2022 ).

Ibrahim et al. (2017) found a positive relationship between consumer sentiments on Twitter and the company's brand image. A positive brand image creates a good reputation for the company in customers' minds for a long time, and it increases customer loyalty toward a high brand image company ( Pitta and Katsanis, 1995 ). Customers heavily rely on a company's brand image when they are concerned about uncertain product or service quality ( Berry, 2000 ; Erdem and Swait, 2004 ), food safety, and quality under uncertain times like the COVID-19 outbreak ( Kim et al., 2021 ). A company's brand image positively affects customer loyalty and is a significant predictor of customer satisfaction in the restaurant ( Hwang and Kim, 2020 ) and other industries ( Jin et al., 2012 ; Ryu et al., 2012 ; Faullant et al., 2008 ). The high brand image of a food delivery firm positively affects the customer's behavioral intention to use OFD service ( Hwang and Choe, 2019 ; Gani et al., 2021 ). Therefore, we hypothesized:s.

The brand image of OFD companies moderates people's sentiments.

3.4. Operating regions and people's sentiments

The customers' expectations about the same products and services may vary between different regions based on different factors such as behaviors, loyalty, attitudes, and cultural influence ( Steyn et al., 2010 ). Punel et al. (2019) found that customers' experiences and expectations about the same airline service vary based on customers' geographical region. The authors find a difference in the Noth American and Asian passengers' expectations of the ticket price and in-flight service. Some studies show that cultural regions moderate online purchase intention ( Ng, 2013 ). Leng et al. (2019) observed differences in the US and Japanese people's choices and expectations about food products and services.

Twitter is a popular social media platform where people freely express their feelings. People's reactions on Twitter to the COVID-19 varied in different regions. For example, the US people's sentiment score was more negative than the UK people toward the COVD-19 ( Zou et al., 2020 ). Similarly, the fear and awareness of the COVID-19 safety measures vary from country to country Rizou et al. (2020) ; Lo Coco et al. (2021) . Consumers' expectations and sentiments about the OFD service providers may also vary according to their country ( Keeble et al., 2020 ), as consumers in developed countries would be more educated and aware and, hence, have more expectations. In India, the OFD services business is growing exponentially. However, it is still new compared to the US market. Thus, OFD companies may focus more on consumer-centric service delivery in developed countries than in developing countries. Variation in the above factors should change customer sentiment. Therefore, we hypothesize that:

Operating regions (i.e., countries) of OFD companies moderate people's sentiments .

4. Research methodology and results

We have collected consumer data for four popular OFD companies from India (e.g., Zomato and Swiggy) and from the US (e.g., Uber Eats and Grubhub). The consumer-level data were extracted from Twitter —a social networking site—on which billions of users express their ideas and experiences. Each post by a user is known as a “tweet”. The customer-level data were collected between February 01, 2020 to November 30, 2021 covering the first two COVID-19 waves.

As shown in Fig. 1 , in India, the first COVID-19 wave is considered from March 01, 2020 to February 28, 2021 and the second wave is considered from March 01, 2021 to November 30, 2021. For the US, the first wave is considered from February 01, 2020 to June 30, 2021 and the second wave is considered from July 01, 2021 to November 30, 2021. The second wave in India brought more negative impacts as the number of new daily cases and fatality rate were significantly higher than the first wave. The second wave inflicted more fear on people's minds as the number of people hospitalized, and the load on medical infrastructure were significantly higher than the first wave of COVID-19.

COVID-19 cases and deaths in India and US.

Fig. 2 summarizes the research methodology adopted in this paper. Primarily, a four steps method was utilized for: (i) data collection, (ii) topic extraction, (iii) sentiment analysis, and (iv) moderation variable analysis. We used the company name, their Twitter handle, and related hashtags as keywords to extract the data. We extracted a total sample of 11,134 for Zomato, 14,355 for Swiggy, 15,583 for Uber Eats, and 2030 for Grubhub. The data were preprocessed by employing language identification, cleaning, tokenization, lemmatization, and removing stopwords. We kept data in English for further analysis, leading to a useful sample of 9447 for Zomato, 13,160 for Swiggy, 12,536 for Uber Eats, and 1951 for Grubhub. The remaining three steps are described in subsections 4.1, 4.2, and 4.3.

The proposed research methodology framework.

4.1. Text analytics and topic extraction

We employed the latent Dirichlet allocation (LDA) tool ( Blei et al., 2003 ) to identify hidden topics discussed in the large volume of unstructured data, as it does not assume any structure of grammar properties. The model was trained to identify the number of topics in unstructured documents. The extant literature ( Nikolenko et al., 2017 ; Lee, 2022) suggests a coherence score as a more reliable measure to discover the most prominent number of topics in a large volume of documents. Fig. 3 shows the variation of coherence score over topics 2 to 40 for each OFD company.

Coherence score of each OFD company.

We found the highest coherence score for each OFD company when the number of topics extracted was 13 for Zomato, 6 for Swiggy, 2 for Uber Eats, and 11 for Grubhub. We believe that there must be more than two topics related to Uber Eats discussed by customers, so we preferred to extract topics that correspond to the next highest coherence score. It results in 7 topics for Uber Eats. Topics and their associated 10 most important keywords of Zomato, Swiggy, Uber Eats, and Grubhub are presented in Table 1 , Table 2 , Table 3 , Table 4 , respectively.

Keywords, topics, and dimensions of Zomato and their statistics.

Keywords, topics, and dimensions of Swiggy and their statistics.

Keywords, topics, and dimensions of Ubereats and their statistics.

Keywords, topics, and dimensions of Grubhub and their statistics.

Topics extraction results are analyzed in three steps: (i) an expert team reviewed keywords of each topic and gave an appropriate name, as shown in Table 1 , Table 2 , Table 3 , Table 4 , (ii) each topic was then reviewed and higher dimensions which were a collection of topics were formed (see Table 1 , Table 2 , Table 3 , Table 4 ), keeping literature in mind, and (iii) based on percent tweets related to the dimensions (see Table 5 ), most prominent dimensions of each food delivery company and regions were identified and discussed in Section 5 . To label topics, the keywords of each topic were discussed and reviewed carefully by experts from academics. Further, we observed that a set of topics represents a higher-level dimension. Thus, we identified sets of topics representing higher-level dimensions. These dimensions are also shown in Table 1 , Table 2 , Table 3 , Table 4 .

Performance of delivery companies across different dimensions.

Further, we also extracted topics based on the COVID-19 waves to check if there is any difference in topics. The results show that in India, people discussed more regarding help and support for drivers and restaurants, vaccination requests, customized orders, and food quality in the second COVID-19 wave. In the first wave, we observed more discussion about consumers' perceived responsibility, perceived solutions, and companies' social responsibility. On the other hand, US people discussed more about coupons and promocode during the first COVID-19 wave. Whereas in the second wave, people emphasize more about vaccination, money spending, and delivery operations in the US market.

4.2. Sentiment analysis

Sentiment analysis is a procedure to quantify customers' experiences or emotions based on the subjective text data expressed by the customers (Lee, 2022). The sentiment was labeled based on the python package, Gensim/VADER. We preferred the VADER-based model as they provide a parsimonious rule -or lexicon-based model specially designed for social media text sentiment analysis. Social media text, especially tweets data, is characterized as short sentences/descriptions with emoji, copious use of question marks and exclamation marks, and repetitive words. This technique of unsupervised sentiment computation does not require features and is widely used in the literature ( Ibrahim and Wang, 2019 ; Lee, 2022; Mehta et al., 2021 ).

Customer sentiment may include positive, negative, or neutral (Lee, 2022; Liu et al., 2021), depending upon VADER based compound score that varies from −1 to +1. If the compound score was ≤ −0.05, between −0.05 and +0.05, and ≥ +0.05, the sentiment was labeled as negative, neutral, and positive (Lee, 2022). Lexicon-based sentiment computation approach matches words with a dictionary of words. First, we assessed the sentiment score of each tweet. Subsequently, we assessed topic-level and dimension-level sentiments using dictionaries or a pre-defined list of words approach. The topic- and dimension-level sentiment was determined by averaging the tweet- and topic-level sentiments, respectively. Dimension-level positive and negative sentiments of delivery companies are shown in Table 5 . Based on the mean score of positive and negative sentiment, the results are further discussed in section 5 .

4.3. Moderating variables

To test hypotheses, we considered two types of sentiment: net sentiment and negative sentiment, as response variables. Five important variables—COVID-19 wave, brand, market size, country, and listed, may influence customers' both types of sentiment/satisfaction. We employ the ordinal least square (OLS) regressions to estimate response variables, net sentiment, and negative sentiment. The relationship between the response variable and explanatory variables takes the form of equations (1) , (2) .

The COVID-19 wave, country, and “listed” are binary variables. The COVID-19 wave has two labels —the first and the second wave. The country has two labels—India and US. Similarly, listed has labels yes and no, where yes implies a company is listed in the stock market, and no for otherwise. Zomato and Grubhub are publicly listed OFD companies, while Uber Eats and Swiggy are not. The brand and market size are categorical variables, where the brand has four labels—Zomato, Swiggy, Uber Eats, and Grubhub. The market size has three labels: small, medium, and large. With a nearly $80.53 billion market cap, Uber Eats is considered a large-market size company. Zomato, Swiggy, and Grubhub, with a market cap of nearly $13.82 billion, $5 billion, and $10.55 billion, are considered medium, small, and medium market sizes, respectively.

The ordinary least squares (OLS) regression results are presented in Table 6 . The results show that there is a significant relationship between net sentiment and explanatory variable, the COVID waves (p < 0.05), brand (p < 0.05), market size (p < 0.05), country (p < 0.05), and listed (p < 0.10). Thus, the hypotheses H1 , H2 , H3 , H4 , and H5 are supported for the net sentiment. For the negative sentiment, we found that all the explanatory variables are significant (p < 0.05) except the country (p > 0.10). Therefore, hypotheses H1 , H2 , H3 , and H4 are supported for the negative sentiment; hypothesis H5 is rejected.

OLS regression results for hypotheses.

To identify specific pairs of groups that differ from each other, we conducted the Post-Hoc test of multiple comparisons of means—Tukey Honestly Significant Difference (Tukey HSD). The results are shown in Table 7 and discussed in the Discussion section.

Multiple comparison test results.

5. Discussions and managerial implications

The topic modeling results (from Table 1 , Table 2 , Table 3 , Table 4 ) convey the prominence of 13 topics for Zomato, 6 topics for Swiggy, 7 for Uber Eats, and 11 for Grubhub. These topics are further used to form higher-level dimensions that resulted—6 dimensions for Zomato, 4 dimensions for Swiggy, 5 dimensions for Uber Eats, and 4 dimensions for Grubhub. We find a wide range of topics (and dimensions) relating to OFD companies were expressed and discussed during the COVID-19 pandemic. Delivery operations, social responsibility, perceived consumers responsibility, the financial impact of COVID-19 on consumers, consumers perceived behaviors, perceived solutions, perceived negative impact of OFD, promocode and food delivery, free delivery, adapting to pickup/takeout service, and appreciation for drivers/restaurants are dimensions that emerged from our analysis.

As count or percent contribution is shown in Table 5 , overall, perceived consumer responsibility, delivery operations, social responsibility, perceived negative impact, free delivery, etc., are the most prominent dimensions related to OFD companies across emerging and developed countries. These findings differ from the previous literature ( Chen McCain et al., 2021 ; Dsouza and Sharma, 2021 ; Shah et al., 2021 ; Trivedi and Singh, 2021 ) that observed food quality and safety measures, socialization, and marketing are the main themes for OFD companies during COVID-19. Our topics seem more enlightening and COVID-19 pandemic-oriented rather than topics related to the usual expectations from OFD companies. Reasons for the difference may be due to the fact that this study is more comprehensive in terms of the number of companies included, the sample size considered, and the time period during which the sample was collected.

Table 5 further conveys that the mean score of the positive sentiment of most dimensions is more than the mean score of a negative sentiment of most dimensions of all OFD companies. Exceptions to this are financial impact and consumers' perceived behaviors of Zomato; delivery operations of Swiggy; promocode and food delivery of Uber Eats. These findings are different from the findings of Chen McCain et al. (2021) in the sense that they found that negative sentiment is more than positive sentiment.

Most of the identified topics for Indian firms Zomato and Swiggy belong to delivery operations, social responsibility, and perceived consumer responsibility dimensions (see Table 1 ). However, most of the topics of Uber Eats operating in the US belong to promocode/coupon on food delivery orders and free delivery. In contrast, for Grubhub, most topics belong to the appreciation of drivers/restaurants for delivery service and food delivery operations. Interestingly, a closer look at the topics and dimensions of Zomato and Swiggy conveys that people in India are more concerned about society, such as the social responsibility of OFD companies and the responsibility of consumers to society in such a hard time. People are more inclined toward spreading awareness and highlighting the responsibility of both consumers and delivery companies. As expected for OFD companies, we also found people discussing delivery operations dimension concerning to responsiveness of OFD companies and customized orders by following COVID-19 appropriate behaviors. Results in Table 5 show that perceived customer responsibility, delivery operations, and social responsibility are the three most prominent dimensions in the Indian market. On the other hand, delivery operations, fee delivery, and perceived customer responsibility are the three most prominent dimensions in the US.

Table 8 shows our identified dimensions and compares them with dimensions discussed in the literature. We observe that most of the existing studies ( Kim et al., 2021 ; Pal et al., 2021 ) deal with the operational aspect of delivery, while a few studies ( Li et al., 2020 ; Yeo et al., 2021 ) highlight the sustainability aspect of OFD companies, discount and promotion ( Kim et al., 2021 ), and concerned for drivers ( Huang, 2021 ; Ramos, 2021 ). However, we find perceived customer responsibility (contributing 28.27 percent) is the most important dimension, followed by delivery operations (contributing 25.9 percent) during the COVID-19 (see Table 5 ). Interestingly, compared to the existing literature, this study finds perceived consumers' responsibility, COVID-19 impacts on delivery business, benefits of competition, free food delivery service, and pickup/takeout services as unique concerns expressed and discussed by customers during the COVID-19 pandemic. During the COVID-19 pandemic, customers in India want OFD companies to be more socially sensitive, while US customers are concerned about discounts, coupons, free food delivery, etc.

Identified dimensions in this study and comparison with dimensions in literature.

We further observed that the net sentiment (i.e., positive-negative ) differs across COVID-19 waves, brands, market sizes, countries, and listed (see Table 6 , Table 7 ). Net sentiment during the second COVID-19 wave is significantly higher than that of the first COVID-19 wave. However, we also found that negative sentiment in the second wave (0.0761) was higher than in the first wave (0.0696) and significantly different (p < 0.05). It conveys that even though negative sentiment increased in the second wave, OFD companies have used their learning and experiences from the first wave and therefore could improve net sentiment during the second wave of COVID-19. Our findings are different from the literature ( Hong et al., 2021 , Hong et al., 2021 ) that finds no moderation effect of COVID-19 on the relationship between explanatory variables and intention to use OFD apps. We find that COVID-19 waves significantly influence customer sentiment, which aligns with the literature ( Birtus and Lăzăroiu, 2021 ; Mehrolia et al., 2021 ; Watson and Popescu, 2021 ), that observed that COVID-19 reshaped customers behaviors, attitude, and expectations.

Analyzing net sentiment across brands, we find that except for Grubhub and Swiggy (p > 0.05), all other pairs of brands are significantly different based on the net sentiment. Grubhub's mean net sentiment (0.0398) is significantly higher (p < 0.05) than Uber Eats (0.0175) and Zomato (0.0274). It shows that Grubhub performed better than other OFD companies. Swiggy received the second-highest mean net sentiment score (0.035), which is significantly higher (p < 0.05) than the net sentiment of Uber Eats and Zomato (see Table 7 ). Finally, Zomato's net sentiment is significantly higher (p < 0.05) than Uber Eats. Overall, Grubhub received the highest net sentiment, which is significantly higher than all other OFD companies. In India, Swiggy performed better than Zomato, whereas Grubhub did better than Uber Eats in the US. Grubhub also received the highest negative sentiment (0.07489), followed by Zomato (0.0729), Uber Eats (0.0710), and Swiggy (0.0683).

Table 7 further conveys that Grubhub's and Zomato's negative sentiments are significantly (p < 0.05) higher than Swiggy's negative sentiments. The negative sentiment of the remaining pair of companies does not differ significantly. Our findings indicate that brands play an important role in driving customer sentiment. This finding is aligned with previous literature ( Gani et al., 2021 ) that observed that restaurant reputation influences OFD apps' usefulness during the COVID-19 pandemic. These findings are different from the literature ( Prasetyo et al., 2021 ) that discovers restaurant credibility does not impact the intention to use OFD apps. It also differs from Trivedi and Singh (2021) , who observed more positive and less negative sentiment for Zomato than Swiggy.

Observing the importance of market size, we found that OFD companies with low, medium, and large market sizes received a mean net sentiment score of 0.0350, 0.0295, and 0.0175, respectively. Further, Table 7 clearly shows that low and medium market size companies have significantly higher (p < 0.05) mean net sentiment scores than large market size companies. This interesting finding conveys that low or medium market size companies can do better and receive better customer appreciation if they perform better during a crisis or on special customer demands.

We further observed that the medium market size company received the highest negative sentiment (0.0732), followed by large (0.071) and small market share (0.068). The negative sentiment of medium and large companies is significantly (p < 0.05) higher than those with small market sizes. It conveys that smaller companies may satisfy customers more in odd times than bigger companies. It may be because smaller companies might be delivering more customer-friendly services as they may be determined to increase their market share by satisfying customers. Challenging times like the COVID-19 pandemic can be used as an opportunity to create more customer-based value. Our findings are different from the literature ( De Mendonca and Zhou, 2019 ) that says larger companies can generate higher profit and customer satisfaction. Our results are more contextual and complex than the literature that says companies with higher market value may positively influence customers' perception ( Hu et al., 2009 ) and repurchase intention ( Bao and Zhu, 2022 ).

Analyzing results at the country level, we noticed that the mean net customer sentiment scores in India and the US are 0.0318 and 0.0205, respectively. Table 7 further confirms that India's net sentiment score is significantly higher (p < 0.05) than the US. As the US is a developed country with excellent infrastructure and customer-based operations are more prevalent—one may expect better customer satisfaction and customer experience from OFD companies in the US. However, interestingly, we find customers are more satisfied with OFD companies in India than in the US. These findings are in line with previous studies ( Charm et al., 2020 ; Steyn et al., 2010 ) that found customer sentiments vary greatly across different countries. Consumers in India, China, and Indonesia display more optimism than in the US (and the rest of the world) ( Charm et al., 2020 ). As far as negative sentiment is concerned, we find no significant difference (p > 0.10). OFD companies in India and US attract similar negative sentiments. It may be because the challenges faced by the companies are almost the same, and the reasons for customers to get discontent are identical, especially in terms of sanitization and COVID-19 appropriate behaviors.

Further, we found mean net sentiment score of the publicly listed companies (0.0295) is significantly higher (p < 0.10) than those of not listed companies (0.0264). Listed companies may have better perceptions and brand names that may positively influence customers' net sentiment. It is also possible that listed companies may adopt a more stakeholder-oriented approach ( Hickman, 2019 ), bringing better customer experiences. These findings align with the literature ( De Mendonca and Zhou, 2019 ; Hickman, 2019 ), highlighting that customers may be more loyal to publicly traded companies as they are more likely to be responsible to the environment and society. The negative sentiment of listed OFD companies (0.07325) also differs significantly (p < 0.05) from unlisted companies (0.06964). Noticeably, the negative sentiment of listed companies is higher than that of unlisted companies. It is possibly because customers have more expectations from listed companies ( Hickman, 2019 ) during the COVID-19 pandemic. Service delivery below the expectation may generate more negative sentiment. Our observations in terms of negative sentiment are different from the literature ( Hickman, 2019 ).

6. Conclusions and future scopes

This study brings a deeper understanding of customers' experience of OFD companies operating in the COVID-19 pandemic, a worse health crisis globally. During the pandemic, thousands of people were quarantined, and the remaining people could not go out to have food in restaurants because of strict lockdowns. During this time, OFD companies delivered food to people who had different expectations from the companies than expectations during the normal time. Especially, sanitization, COVID-19 appropriate behaviors, and social responsibility were among the most important expectations of customers. The central theme of this paper is to explore how OFD companies performed during the COVID-19 pandemic and whether they performed up to the expectations. It also analyzed whether OFD companies' performance differs across emerging and developed countries and different COVID-19 waves. We collected consumer data from Twitter, conducted a qualitative and quantitative analysis to understand several intricacies of OFD companies' operations during the COVID-19 pandemic, and compared our findings with existing literature. This study throws several exciting findings.

The most talked-about topics were social responsibility, delivery operations, perceived consumer responsibility, financial impacts of COVID-19, etc. People in India and the US think differently regarding OFD service during such a hard time. People in India are more concerned about the responsibility of OFD and consumers towards society, but in the US, people discuss more about discounts, coupons, free food delivery, etc. Therefore, managers of OFD companies in India and the US should prioritize their services accordingly. Our findings convey to managers of OFD companies that customer expectations during a crisis are not usual expectations (such as prompt service, good taste, etc.); they expect something beyond. Therefore, managers must understand expectations first and deliver experience accordingly. A few previous studies demonstrated more generic expectations. Theoretically, we observe that dimensions of customer expectations during the COVID-19 crisis differ from normal times.

We further offer important theoretical contributions that the net sentiment of people differs across different COVID-19 waves, brands, market size, countries, and publicly listed (or unlisted) companies. OFD companies improved net sentiment in the second wave of COVID-19, perhaps by using their learnings from the first wave. Overall, Grubhub received the highest net sentiment as well as the negative sentiment. Hence, to minimize negative sentiment, managers of Grubhub should align their service operations as per customer expectations. In India, Swiggy did better than Zomato. Managers of OFD companies should see the events like COVID-19 as opportunities where OFD companies with smaller market sizes also can do better than medium and large-market size companies. Customers are more satisfied in India than in the US; however, negative sentiment is almost equal in the two countries. The publicly listed OFD companies generate higher net sentiment as well as negative sentiment than those of unlisted companies. Hence, managers of listed OFD companies should control negative sentiment as per the performance on different topics identified. Interactions of COVID-19 waves, brands, market size, countries, and publicly listed (or unlisted) companies with net and negative sentiment of customers of OFD companies and its intricacies are interesting theoretical implications of this research.

This paper compares and contrasts OFD companies' consumer service and operations across emerging and developed countries and during the first and second waves of the COVID-19. By studying OFD during the COVID-19 pandemic, this study significantly and uniquely contributes to the existing literature ( Huang, 2021 ; Kim et al., 2021 ; Yeo et al., 2021 ) on retailers and consumers. We identified new topics of importance during a hard time of the COVID-19 pandemic, which are entirely different from topics discussed before the COVID-19 outbreak. We further identified different variables which are significantly influencing customers' sentiment.

Like any other study, this paper also has some limitations. We mainly studied OFD companies operating in India and US by collecting customers' data from Twitter. Future studies can extend the scope of this study further. In addition to the US and Indian markets, it would be interesting to study people's sentiments and identify concerning dimensions about OFD companies' performance in the European and South American markets. Further, the inclusion of financial variables, such as revenue and profit for OFD companies' delivery performance, would be another exciting study. Nonetheless, this study documents the service and operations performance of OFD companies during the COVID-19 health crisis on a large scale which can be used for a future contingency plan. Finally, another promising problem for future studies is investigating how people's sentiment affects the OFD companies' performance in the stock market during the COVID-19 period.

• Aaker D.A., Equity M.B. vol. 28. 1991. pp. 35–37. (Capitalizing on the Value of a Brand Name). New York 1. [ Google Scholar ]
• Ahorsu D.K., Lin C.Y., Imani V., Saffari M., Griffiths M.D., Pakpour A.H. The fear of COVID-19 scale: development and initial validation. Int. J. Ment. Health Addiction. 2020:1–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Al Amin M.A., Arefin M.S., Alam M.R., Ahammad T., Hoque M.R. Using mobile food delivery applications during COVID-19 pandemic: an extended model of planned behavior. J. Food Prod. Market. 2021; 27 (2):105–126. [ Google Scholar ]
• Aureliano-Silva L., Spers E.E., Lodhi R.N., Pattanayak M. Who loves to forgive? The mediator mechanism of service recovery between brand love, brand trust and purchase intention in the context of food-delivery apps. Br. Food J. 2022 doi: 10.1108/BFJ-07-2021-0819. [ CrossRef ] [ Google Scholar ]
• Baek T.H., Kim J., Yu J.H. The differential roles of brand credibility and brand prestige in consumer brand choice. Psychol. Market. 2010; 27 (7):662–678. [ Google Scholar ]
• Balakrishnan V. Delhi food delivery boy tests positive for COVID-19: should you be ordering food from outside? This is what doctors feel. Indiana. 2020 https://timesofindia.indiatimes.com/life-style/health-fitness/diet/delhi-food-delivery-boy-tests-positive-for-COVID-19-should-you-be-ordering-food-from-outside-this-is-what-doctors-feel/articleshow/75180601.cms from. [ Google Scholar ]
• Bao Z., Zhu Y. Why customers have the intention to reuse food delivery apps: evidence from China." British Food Journal (2021) Br. Food J. 2022; 124 (1):179–196. [ Google Scholar ]
• Bates S., Reeve B., Trevena H. A narrative review of online food delivery in Australia: challenges and opportunities for public health nutrition policy. Publ. Health Nutr. 2020 doi: 10.1017/S1368980020000701. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berry L.L. Cultivating service brand equity. J. Acad. Market. Sci. 2000; 28 (1):128–137. [ Google Scholar ]
• Birtus M., Lăzăroiu G. The neurobehavioral economics of the COVID-19 pandemic: consumer cognition, perception, sentiment, choice, and decision-making. Anal. Metaphys. 2021; 20 :89–101. [ Google Scholar ]
• Blei D.M., Ng A.Y., Jordan M.I. Latent dirichlet allocation. J. Mach. Learn. Res. 2003; 3 :993–1022. [ Google Scholar ]
• Bolton R.N., Lemon K.N., Verhoef P.C. The theoretical underpinnings of customer asset management: a framework and propositions for future research. J. Acad. Market. Sci. 2004; 32 (3):271–292. [ Google Scholar ]
• Boston Consulting Group India's online food delivery industry to touch $8-bn mark by 2022: Report. Business Standard. 2020 https://www.business-standard.com/article/current-affairs/india-s-online-food-delivery-industry-to-touch-8-bn-mark-by-2022-report-120012800822_1.html Accessed from. [ Google Scholar ]
• Brewer P., Sebby A.G. The effect of online restaurant menus on consumers' purchase intentions during the COVID-19 pandemic. Int. J. Hospit. Manag. 2021; 94 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Chai L.T., Yat D.N.C. Online food delivery services: making food delivery the new normal. Journal of Marketing advances and Practices. 2019; 1 (1):62–77. [ Google Scholar ]
• Chakraborty D., Kayal G., Mehta P., Nunkoo R., Rana N.P. Consumers' usage of food delivery app: a theory of consumption values. J. Hospit. Market. Manag. 2022:1–19. [ Google Scholar ]
• Charm T., Grimmelt A., Kim H., Robinson K., Lu N., Ortega M. Consumer sentiment is diverging across countries. McKinsey. 2020 https://www.mckinsey.com/business-functions/growth-marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19 available at. [ Google Scholar ]
• Chen McCain S.L., Lolli J., Liu E., Lin L.C. An analysis of a third-party food delivery app during the COVID-19 pandemic. Br. Food J. 2021 doi: 10.1108/BFJ-03-2021-0332. [ CrossRef ] [ Google Scholar ]
• Cheng C.C., Chang Y.Y., Chen C.T. Construction of a service quality scale for the online food delivery industry. Int. J. Hospit. Manag. 2021; 95 [ Google Scholar ]
• Correa J.C., Garzon W., Brooker P., Sakarkar G., Carranzaa S.A., Yunado L., Rincon A. Evaluation of collaborative consumption of food delivery services through web mining techniques. J. Retailing Consum. Serv. 2019; 46 :45–50. [ Google Scholar ]
• Dai R., Feng H., Hu J., Jin Q., Li H., Wang R., Wang R., Xu L., Zhang X. The impact of COVID-19 on small and medium-sized enterprises (SMEs): evidence from two-wave phone surveys in China. China Econ. Rev. 2021; 67 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Daniel K.D., Hirshleifer D.A., Subrahmanyam A. 2005. Investor psychology and tests of factor pricing models. Available at SSRN 854024. [ Google Scholar ]
• De Mendonca T.R., Zhou Y. Environmental performance, customer satisfaction, and profitability: a study among large U.S. Companies, Sustain. Multidiscip. Digit. Publish. Inst. 2019; 11 (19):5418. [ Google Scholar ]
• Dirsehan T., Cankat E. Role of mobile food-ordering applications in developing restaurants' brand satisfaction and loyalty in the pandemic period. J. Retailing Consum. Serv. 2021; 62 [ Google Scholar ]
• Dsouza D., Sharma D. Online food delivery portals during COVID-19 times: an analysis of changing consumer behavior and expectations. Int. J. Innovat. Sci. 2021; 13 (2):218–232. [ Google Scholar ]
• Erdem T., Swait J. Brand credibility, brand consideration, and choice. J. Consum. Res. 2004; 31 (1):191–198. [ Google Scholar ]
• Erkmen E., Hancer M. Building brand relationship for restaurants: an examination of other customers, brand image, trust, and restaurant attributes. Int. J. Contemp. Hospit. Manag. 2019; 31 (3):1469–1487. [ Google Scholar ]
• Faullant R., Matzler K., Füller J. The impact of satisfaction and image on loyalty: the case of Alpine ski resorts. Manag. Serv. Qual. 2008; 18 (2):163–178. [ Google Scholar ]
• Fornell C. A national customer satisfaction barometer: the Swedish experience, J. Market. 1992; 56 :6–22. [ Google Scholar ]
• Gani M.O., Faroque A.R., Muzareba A.M., Amin S., Rahman M. An integrated model to decipher online food delivery app adoption behavior in the COVID-19 pandemic. J. Foodserv. Bus. Res. 2021:1–41. [ Google Scholar ]
• Gavilan D., Balderas-Cejudo A., Fernández-Lores S., Martinez-Navarro G. Innovation in online food delivery: learnings from COVID-19. Int. J. Gastronomy Food Sci. 2021 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gilitwala B., Nag A.K. A study of factors influencing repurchase intention of online food delivery in Bangkok, Thailand. Int. J. Emerg. Technol. 2019; 10 (3):193–201. [ Google Scholar ]
• Gunden N., Morosan C., DeFranco A. Consumers' intentions to use online food delivery systems in the USA. Int. J. Contemp. Hospit. Manag. 2020; 32 (3):1325–1345. [ Google Scholar ]
• Han H., Kim W., Hyun S.S. Switching intention model development: role of service performances, customer satisfaction, and switching barriers in the hotel industry. Int. J. Hospit. Manag. 2011; 30 (3):619–629. [ Google Scholar ]
• Hickman L.E. Information asymmetry in CSR reporting: publicly-traded versus privately-held firms, Sustainability Accounting. Manag. Policy J. 2019; 11 (1):207–232. [ Google Scholar ]
• Hong C., Choi H., Choi E.K., Joung H.W. Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. J. Hospit. Tourism Manag. 2021; 48 :509–518. [ Google Scholar ]
• Hong C., Choi H.H., Choi E.-K.C., Joung H.-W.D. Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. J. Hospit. Tourism Manag. 2021; 48 :509–518. [ Google Scholar ]
• Hopkins E., Potcovaru A.M. Consumer attitudes, values, needs. Anal. Metaphys. 2021; 20 :202–215. [ Google Scholar ]
• Hu H.H., Kandampully J., Juwaheer T.D. Relationships and impacts of service quality, perceived value, customer satisfaction, and image: an empirical study. Serv. Ind. J. 2009; 29 (2):111–125. [ Google Scholar ]
• Huang H. Riders on the storm: amplified platform precarity and the impact of COVID-19 on online food-delivery drivers in China. J. Contemp. China. 2021:1–15. [ Google Scholar ]
• Hwang J., Choe J.Y. Exploring perceived risk in building successful drone food delivery services. Int. J. Contemp. Hospit. Manag. 2019; 31 (8):3249–3269. [ Google Scholar ]
• Hwang J., Kim H. The effects of expected benefits on image, desire, and behavioral intentions in the field of drone food delivery services after the outbreak of COVID-19. Sustainability. 2020; 13 (1):117. [ Google Scholar ]
• Ibrahim N.F., Wang X. A text analytics approach for online retailing service improvement: evidence from Twitter, Decis. Support Syst. 2019; 121 :37–50. [ Google Scholar ]
• Ibrahim N.F., Wang X., Bourne H. Exploring the effect of user engagement in online brand communities: evidence from Twitter. Comput. Hum. Behav. 2017; 72 :321–338. [ Google Scholar ]
• Jain D. Effect of COVID-19 on restaurant industry–how to cope with changing demand. Eff. COVID-19 Restaur. Ind. How Cope Chang. Demand. 2020 doi: 10.2139/ssrn.3577764. April 16, 2020. [ CrossRef ] [ Google Scholar ]
• Jia S.S., Raeside R., Redfern J., Gibson A.A., Singleton A., Partridge S.R. #SupportLocal: how online food delivery services leveraged the COVID-19 pandemic to promote food and beverages on Instagram. Publ. Health Nutr. 2021; 24 (15):4812–4822. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jin N., Lee S., Huffman L. Impact of restaurant experience on brand image and customer loyalty: moderating role of dining motivation. J. Trav. Tourism Market. 2012; 29 :532–551. [ Google Scholar ]
• Keeble M., Adams J., Sacks G., Vanderlee L., White C.M., Hammond D. Use of online food delivery services to order food prepared away-from-home and associated sociodemographic characteristics: a cross-sectional, multi-country analysis. Int. J. Environ. Res. Publ. Health. 2020; 17 (14):5190. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kim J., Kim J., Wang Y. Uncertainty risks and strategic reaction of restaurant firms amid COVID-19: evidence from China. Int. J. Hospit. Manag. 2021; 92 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kohút M., Kohútová V., Halama P. Big Five predictors of pandemic-related behavior and emotions in the first and second COVID-19 pandemic wave in Slovakia. Pers. Indiv. Differ. 2021; 180 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Koppehele-Gossel J., Weinmann L.M., Klimke A., Windmann S., Voss U. Adapting to a major crisis: sleep and mental health during two lockdowns. J. Sleep Res. 2022:e13565. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kougiannou N.K., Mendonça P. Breaking the managerial silencing of worker voice in platform capitalism: the rise of a food courier network. Br. J. Manag. 2021; 32 (3):744–759. [ Google Scholar ]
• Kumar S., Shah A. Revisiting food delivery apps during COVID-19 pandemic? Investigating the role of emotions. J. Retailing Consum. Serv. 2021; 62 [ Google Scholar ]
• Kumar S., Jain A., Hsieh J.K. Impact of apps aesthetics on revisit intentions of food delivery apps: the mediating role of pleasure and arousal. J. Retailing Consum. Serv. 2021; 63 [ Google Scholar ]
• Lalvani S., Seetharaman B. The personal and social risks that India's food delivery workers are taking during COVID-19. Wire. 2020 https://thewire.in/business/COVID-19-food-delivery-workers from. [ Google Scholar ]
• Leng X., Ochi M., Sakaki T., Mori J., Sakata I. A cross-lingual analysis on culinary perceptions to understand the cross-cultural difference. AAAI Spring Symp. Interpretable AI Well-being. 2019 [ Google Scholar ]
• Li C., Mirosa M., Bremer P. Review of online food delivery platforms and their impacts on sustainability. Sustainability. 2020; 12 (14):5528. [ Google Scholar ]
• Liu S., Jiang H., Chen S., Ye J., He R., Sun Z. Integrating Dijkstra's algorithm into deep inverse reinforcement learning for food delivery route planning. Transport. Res. Part E. 2020; 142 [ Google Scholar ]
• Lo Coco G., Gentile A., Bosnar K., Milovanović I., Bianco A., Drid P., Pišot S. A cross-country examination on the fear of COVID-19 and the sense of loneliness during the first wave of COVID-19 outbreak. International. J. Environ. Res. Publ. Health. 2021; 18 (5):2586. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Luo X., Homburg C., Wieseke J. Customer satisfaction, analyst stock recommendations, and firm value. J. Market. Res. 2010; 47 (6):1041–1058. [ Google Scholar ]
• Mehrolia S., Alagarsamy S., Solaikutty V.M. Customers response to online food delivery services during COVID-19 outbreak using binary logistic regression. Int. J. Consum. Stud. 2021; 45 :396–408. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mehta M.P., Kumar G., Ramkumar M. Customer expectations in the hotel industry during the COVID-19 pandemic: a global perspective using sentiment analysis. Tour. Recreat. Res. 2021:1–18. [ Google Scholar ]
• Michalikova K.F., Blazek R., Rydell L. Delivery apps use during the COVID-19 pandemic: consumer satisfaction judgments, behavioral intentions, and purchase decisions. Econ. Manag. Financ. Mark. 2022; 17 (1):70–82. [ Google Scholar ]
• Mittal V., Anderson E., Sayrak A., Kamakura W. Satisfaction, repurchase intent, and repurchase behavior: investigating the moderating effect of customer characteristics. J. Market. Res. 2001; 38 :131–142. [ Google Scholar ]
• Mohan S., Solanki A.K., Taluja H.K., Singh A. Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: a time series forecasting and sentiment analysis approach. Comput. Biol. Med. 2022; 144 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• NASSCOM Catalyzing the ecosystem for a trillion-dollar digital economy. Hyderabad: Natl. Assoc. Software Serv. Co. 2018 https://nasscom.in/sites/default/files/NASSCOM-annual-guidance-fy-2018.pdf from. [ Google Scholar ]
• National Restaurant Association Coronavirus information and resource. 2020. https://restaurant.org/Covid19 Accessed from.
• Ng C.S.P. Intention to purchase on social commerce websites across cultures: a cross-regional study. Inf. Manag. 2013; 50 (8):609–620. [ Google Scholar ]
• Nguyen T.H., Vu D.C. Food delivery service during social distancing: proactively preventing or potentially spreading COVID-19? Disaster Med. Public Health Prep. 2020; 14 (3):e9–e10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Nikolenko S.I., Koltcov S., Koltsova O. Topic modelling for qualitative studies. J. Inf. Sci. 2017; 43 (1):88–102. [ Google Scholar ]
• Oliver R.L. Effect of expectation and disconfirmation on postexposure product evaluations: an alternative interpretation. J. Appl. Psychol. 1977; 62 (4):480. [ Google Scholar ]
• O'Sullivan D., McCallig J. Customer satisfaction, earnings and firm value. Eur. J. Market. 2012; 46 (6):827–843. [ Google Scholar ]
• Pal D., Funilkul S., Eamsinvattana W., Siyal S. Using online food delivery applications during the COVID-19 lockdown period: what drives University Students' satisfaction and loyalty. J. Foodserv. Bus. Res. 2021:1–45. [ Google Scholar ]
• Pant B., Shende U. The Impact of COVID-19 on the sharing economy in India. 2020. [ CrossRef ]
• Parwez S., Ranjan R. The platform economy and the precarisation of food delivery work in the COVID-19 pandemic: evidence from India. Work Organisa. Lab. Glob. 2021; 15 (1):11–30. [ Google Scholar ]
• Peng C.L., Lai K.L., Chen M.L., Wei A.P. Investor sentiment, customer satisfaction and stock returns. Eur. J. Market. 2015; 49 (5/6):827–850. [ Google Scholar ]
• Pigatto G., Machado J.G.d.C.F., Negreti A.d.S., Machado L.M. Have you chosen your request? Analysis of online food delivery companies in Brazil. Br. Food J. 2017; 119 (3):639–657. [ Google Scholar ]
• Pitta D.A., Katsanis L.P. Understanding brand equity for successful brand extension. J. Consum. Market. 1995; 12 (4):51–64. [ Google Scholar ]
• Prasetyo Y.T., Tanto H., Mariyanto M., Hanjaya C., Young M.N., Persada S.F., Miraja B.A., Redi A.A.N.P. Factors affecting customer satisfaction and loyalty in online food delivery service during the COVID-19 pandemic: its relation with open innovation. J. Open Innovat.: Technol. Market Complex. 2021; 7 (1):76. [ Google Scholar ]
• Punel A., Hassan L.A.H., Ermagun A. Variations in airline passenger expectation of service quality across the globe. Tourism Manag. 2019; 75 :491–508. [ Google Scholar ]
• Puram P., Gurumurthy A., Narmetta M., Mor R.S. Last-mile challenges in on-demand food delivery during COVID-19: understanding the riders' perspective using a grounded theory approach. Int. J. Logist. Manag. 2021 doi: 10.1108/IJLM-01-2021-0024. [ CrossRef ] [ Google Scholar ]
• Raj M., Sundararajan A., You C. Evidence from Uber Eats NYU Stern School of Business, 2021; 2021. COVID-19 and Digital Resilience. [ CrossRef ] [ Google Scholar ]
• Ramos K. Factors influencing customers' continuance usage intention of food delivery apps during COVID-19 quarantine in Mexico. Br. Food J. 2021; 124 (3):833–852. [ Google Scholar ]
• Research and Markets Global online food delivery services market report 2021: COVID-19 growth. Impacts Change 2030. 2021 https://www.marketresearch.com/Business-Research-Company-v4006/Online-Food-Delivery-Services-Global-14484234/ Accessed from. [ Google Scholar ]
• Richardson L. Platforms, markets, and contingent calculation: the flexible arrangement of the delivered meal. Antipode. 2020; 52 (3):619–636. [ Google Scholar ]
• Rizou M., Galanakis I.M., Aldawoud T.M.S., Galanakis C.M. Safety of foods, food supply chain and environment within the COVID-19 pandemic. Trends Food Sci. Technol. 2020; 102 :293–299. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Roh M., Park K. Adoption of O2O food delivery services in South Korea: the moderating role of moral obligation in meal preparation. Int. J. Inf. Manag. 2019; 47 :262–273. [ Google Scholar ]
• Rydell L., Kucera J. Cognitive attitudes, behavioral choices, and purchasing habits during the COVID-19 pandemic. J. Self Govern. Manag. Econ. 2021; 9 (4):35–47. [ Google Scholar ]
• Rydell L., Suler P. Underlying values that motivate behavioral intentions and purchase decisions: lessons from the COVID-19 pandemic. Anal. Metaphys. 2021; 20 :116–129. [ Google Scholar ]
• Ryu K., Lee H.R., Kim W.G. The influence of the quality of the physical environment, food, and service on restaurant image, customer perceived value, customer satisfaction, and behavioral intentions. Int. J. Contemp. Hospit. Manag. 2012; 24 (2):200–223. [ Google Scholar ]
• Savitri P.D., Krisnatuti D., Hannan S. The effect of innovation and marketing mix toward brand image and usage decision in online food delivery services industry. Asia Pac. Manag. Bus. Appl. 2020; 9 (2):99–110. [ Google Scholar ]
• Seghezzi A., Mangiaracina R. On-demand food delivery: investigating the economic performances. Int. J. Retail Distrib. Manag. 2020; 49 (4):531–549. [ Google Scholar ]
• Seghezzi A., Winkenbach M., Mangiaracina R. On-demand food delivery: a systematic literature review. Int. J. Logist. Manag. 2021; 32 (4):1334–1355. [ Google Scholar ]
• Shah A.M., Yan X., Qayyum A. Adoption of mobile food ordering apps for O2O food delivery services during the COVID-19 outbreak. Br. Food J. 2021 doi: 10.1108/BFJ-09-2020-0781. [ CrossRef ] [ Google Scholar ]
• Sharma R., Dhir A., Talwar S., Kaur P. Over-ordering and food waste: the use of food delivery apps during a pandemic. Int. J. Hospit. Manag. 2021; 96 [ Google Scholar ]
• Smith A., Machova V. Consumer tastes, sentiments, attitudes, and behaviors related to COVID-19. Anal. Metaphys. 2021; 20 :145–158. [ Google Scholar ]
• Statista Online food delivery: highlights. 2021. https://www.statista.com/outlook/dmo/eservices/online-food-delivery/united-states from.
• Steyn P., Pitt L., Strasheim A., Boshoff C., Abratt R. A cross-cultural study of the perceived benefits of a retailer loyalty scheme in Asia. J. Retailing Consum. Serv. 2010; 17 (5):355–373. [ Google Scholar ]
• Sundararajan A. MIT Press; Cambridge: 2016. The Sharing Economy: the End of Employment and the Rise of Crowd-Based Capitalism. [ Google Scholar ]
• Sv P., Ittamalla R., Balakrishnan J. Analyzing general public's perception on posttraumatic stress disorder and COVID-19: a machine learning study. J. Loss Trauma. 2021:1–3. [ Google Scholar ]
• Tiwari S., Ram S.G., Roy S. 'What is it like to work in a gig economy job'. Indiana. 2019 https://timesofindia.indiatimes.com/india/what-it-is-like-to-work-in-agig-economy-job/articleshow/69371217.cms Available from. [ Google Scholar ]
• Tran V.D. Using mobile food delivery applications during the COVID-19 pandemic: applying the theory of planned behavior to examine continuance behavior. Sustainability. 2021; 13 :1206. [ Google Scholar ]
• Trivedi S.K., Singh A. Twitter sentiment analysis of app based online food delivery companies, Global Knowledge. Memory Commun. 2021; 70 (8/9):891–910. [ Google Scholar ]
• Uzir M.U.H., Al Halbusi H., Thurasamy R., Hock R.L.T., Aljaberi M.A., Hasan N., Hamid M. The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: evidence from a developing country. J. Retailing Consum. Serv. 2021; 63 [ Google Scholar ]
• van Doorn N. At what price? Labour politics and calculative power struggles in on-demand food delivery, Work Organisation. Lab. Glob. 2020; 14 (1):136–149. [ Google Scholar ]
• Watson R., Popescu G.H. Will the COVID-19 pandemic lead to long-term consumer perceptions, behavioral intentions, and acquisition decisions? Econ. Manag. Financ. Mark. 2021; 16 (4):70–83. [ Google Scholar ]
• Yang Y., Liu H., Chen X. COVID-19 and restaurant demand: early effects of the pandemic and stay-at-home orders. Int. J. Contemp. Hospit. Manag. 2020; 32 (12):3809–3834. [ Google Scholar ]
• Yao Q., Zeng S., Sheng S., Gong S. Green innovation and brand equity: moderating effects of industrial institutions. Asia Pac. J. Manag. 2021; 38 (2):573–602. [ Google Scholar ]
• Yeo S.F., Tan C.L., Teo S.L., Tan K.H. The role of food apps servitisation on repurchase intention: a study of FoodPanda. Int. J. Prod. Econ. 2021; 234 [ Google Scholar ]
• Zanetta L.D., Hakim M.P., Gastaldi G.B., Seabra L.M.J., Rolim P.M., Nascimento L.G.P., Medeiros C.O., da Cunha D.T. The use of food delivery apps during the COVID-19 pandemic in Brazil: the role of solidarity, perceived risk, and regional aspects. Food Res. Int. 2021; 149 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zhao Y., Bacao F. What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? Int. J. Hospit. Manag. 2020; 91 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zou C., Wang X., Xie Z., Li D. 2020. Public reactions towards the covid-19 pandemic on Twitter in the United Kingdom and the United States. [ CrossRef ] [ Google Scholar ]

Next on the menu for food delivery

In this episode of the McKinsey on Start-ups podcast, McKinsey executive editor Daniel Eisenberg speaks with McKinsey partners Vishwa Chandra and Victoria Lord about the recent rapid growth of the food delivery sector and what lies ahead for this complex ecosystem. An edited transcript of their conversation follows.

To hear more episodes of McKinsey on Start-ups , subscribe on Apple , Audible , Google Podcasts , Spotify , or Stitcher .

Daniel Eisenberg: Hello and welcome to McKinsey on Start-ups , I’m Daniel Eisenberg.

Many companies and industries have been upended by the pandemic over the past two years, but few consumer sectors have been more radically reshaped by the changes in our daily lives than food delivery. At the outset of COVID-19, amid lockdowns and physical-distancing requirements, delivery became a lifeline for the ailing restaurant industry. Two years later, food delivery has gone from a steadily growing but still small piece of the restaurant (and grocery) business to a veritable dynamo. The industry has doubled in the US, and is worth more than $150 billion globally.

Whereas restaurants largely used to handle the limited delivery options that existed, these days a complex ecosystem of players is involved, with an economic structure that is still evolving. The fact that the sector has mostly remained unprofitable hasn’t dimmed the appetites of investors who continue to plough funds into it; at the same time, global quick-delivery or q-commerce players are raising the stakes, promising the arrival of groceries, restaurant food or virtually anything else in only 10 or 15 minutes.

Today, we are excited to explore this dynamic sector with co-authors of a recent McKinsey article on its ongoing, rapid evolution, McKinsey partners Vishwa Chandra and Victoria Lord. 1 Kabir Ahuja, Vishwa Chandra, Victoria Lord, and Curtis Peens, “ Ordering in: The rapid evolution of food delivery ,” McKinsey, Sept 22, 2021. In addition to their experienced consultants’ view of the food delivery business, both Vishwa and Victoria bring an operational, even personal, perspective to their work with the industry. Vishwa, who is based in the firm’s San Francisco office, was previously a member of the executive team at Instacart, one of the earliest grocery delivery platforms. Victoria, who is based in Miami, has served in commercial leadership roles for fast-growth startups in the food industry; she has a natural interest in the sector, as her family has owned a franchise restaurant for more than 50 years.

Vishwa, Victoria, it’s great to have you with us on the podcast today. Thanks so much for joining us.

Vishwa, what did the evolution of the food delivery ecosystem look like in the two to three years before the COVID-19 pandemic?

Vishwa Chandra: It’s a fascinating time to be in food. We are genuinely living through a once-in-a-lifetime evolution in how people eat and how people engage with food. And this has been playing out for a number of years.

Prior to the COVID-19 pandemic, the food sector was experiencing a good amount of growth in delivery. However, it seemed to be incremental progress. There had been some consolidation in delivery platforms, with new offerings being introduced, but it was steady growth in both restaurant delivery and grocery delivery.

There had also been some stabilization in the competition. The big four in the US—DoorDash, Grubhub, Postmates, and Uber Eats—were the primary delivery platforms. There was clear delineation between channels: restaurant delivery was restaurant delivery, grocery delivery was grocery delivery. We had seen some convenience delivery pop up. And while growth was continuing, in absolute terms, delivery wasn’t necessarily a major sales area.

Right before the pandemic, restaurant delivery probably accounted for $40 billion of a $600 billion industry. A large majority of that $40 billion was pizza delivery, which had been there for ages.

Victoria Lord: We were also starting to see experimentation with new business models around food and food delivery. For instance, dark kitchens were starting to emerge in the ecosystem, but they hadn’t fully landed. While there was some investor backing, restaurants were still hesitant to experiment in those ways. Those sorts of changes in business models, and that desire for experimentation, has been much more in the news in the past two years.

Daniel Eisenberg: So, you had a foundation, but you didn’t really have a fundamental shift until the pandemic forced lockdowns and restaurant closures. How have players in this delivery ecosystem responded to the pandemic? And how has it impacted the growth trajectory of the sector?

Vishwa Chandra: First and foremost, I think everyone in the sector was very conscious of ensuring safety for their employees, colleagues, and customers—and recognizing what everyone was going through.

After that, from a delivery perspective, there was almost five years of growth within five weeks. There was some volatility early on when people were a little apprehensive about ordering delivery. Once that quickly stabilized, there was a significant uptick. Suddenly the only means of reaching customers was through delivery. Restaurants had to adapt very quickly and evolve their offering, and make changes in how they thought about staffing, production, and the packaging their product—and their own economics. It required a fundamental rethink of their business and how they engage with their customers.

Daniel Eisenberg: As your article noted, the US market roughly doubled in size in the last 18 to 24 months.

Vishwa Chandra: We didn’t expect that to happen for at least another five years. The two things that were fascinating were the absolute amount of growth, and the speed at which that happened.

We talked a little bit about restaurants having to adapt their operations. But delivery platforms had to grow their delivery base as well. They had to recruit, onboard, and train a number of delivery drivers to meet demand. They had to make changes from a product perspective. Contactless delivery was never anticipated before. Suddenly, it was incredibly important.

To be honest, consumers had to adapt too. That meant changing their understanding of this new kind of customer experience, and almost redeveloping their relationship with their favorite brands.

There was also experimentation, and exposure to new brands and new cuisines, along with a sense of nostalgia. We saw a growth in demand for traditional cuisines that had always been part of delivery, but also for things that had resonance from people’s childhoods, from people’s experiences through life.

Daniel Eisenberg: In your recent article, you talk about three factors that will play a key role in the success of the various players— geographic competition, commission rates for restaurants, and driver delivery fees. Of those three, which do you expect to play the biggest role for the industry in the next two to three years?

Vishwa Chandra: It’s an interesting question. It depends who you ask.

From a delivery platform perspective, geographic competition is one of the primary drivers of their business. Just because three or four platforms are serving a market, it doesn’t mean that consumers are necessarily going to eat more food. They are sharing the same demand within an area. As consumers have started cross shopping, and shopping between platforms, the competition for their share of stomach and their share of wallet continues to grow.

The degree of competition and loyalty from customers in a particular market became incredibly important—and delivery platforms made deliberate efforts to attract that kind of following. You saw the growth of loyalty programs, and subscription-based models trying to lock consumers in, to ensure that a larger portion of their spend was on their platform.

For restaurants, it was all about commission rates. At the end of the day, those rates are what drive their economics. Whether someone eats in a restaurant or orders delivery, the food, labor, and packaging costs show only slight variations. But in a world where commission rates are the highest single expense item for a restaurant, that is the largest determinant for how sustainable this can be for them. Once you factor in everything from a delivery cost perspective, a tip perspective, and a surcharge perspective, that has an implication for the amount restaurants have to spend, and how much are they getting for what they are spending.

Daniel Eisenberg: I know sometimes I’ll order with one of the apps, but I’ll do pickup instead of delivery.

Vishwa Chandra: It’s interesting that you mention that. We talked about the necessity of evolving the product. There was a first wave of product evolution during the start of the pandemic and that was for things like contactless pickup.

As volume stabilized, and demand grew, the next wave of evolution involved product features. Pickup is one area where people are investing heavily because it is pushed from the consumers, and pushed from restaurants. As costs and fees are different, the experiences are different. Restaurants can maintain that connectivity with their customers—they can get people in and have a personal touch.

Daniel Eisenberg: There is also this growing trend of convergence in the types of products that delivery players are delivering, or want to deliver, whether it’s ready-to-eat or groceries. What is driving this trend? And what will it mean for the competitive landscape overall among these platforms?

Vishwa Chandra: We are definitely seeing a convergence. Traditional restaurant delivery customers and platforms are going into grocery, and grocery delivery platforms are getting into ready-to-eat and meal delivery, both directly and with their partners.

In the end it’s a battle for share of stomach. Prior to the last two years, consumers had thought about these channels very distinctly. Going out to eat, getting something delivered, and having ingredients to cook your own meal were very distinct occasions. As folks have spent more time at home in the last years, they have redefined their relationship with food as an experience. For many, it was the only experience that breaks up the day.

Some of this convergence is also driven by the question, is this a winner-takes-all market or not? We believe there will still be multi-marketplaces and multi-platforms. But there will be continued consolidation as fewer platforms gain consumer loyalty and are able to control a greater share of stomach.

Victoria Lord: Vishwa mentioned share of stomach, but as the delivery platforms start to expand into different categories, it’s no longer just share of stomach. It starts to become more share of wallet for a given consumer because now a consumer can log into their app and buy anything.

For instance, I can place an order for dinner tonight, for alcohol, groceries, and for sunscreen and pet food all through the same ecosystem. So, convergence means that these apps are making it easier for us to have more of our occasions and more of our shopping needs met through one ecosystem.

Think about what Amazon did in the retail space. It started with books and expanded into basically everything. Or what some of the super apps are starting to do in Latin America and in Asia. Those are some very interesting examples for how broad some of these app ecosystems can become over time.

Daniel Eisenberg: I know Getir is moving into California with 10-minute delivery and DoorDash is doing 15 minutes or less for grocery delivery in New York City. Is this going to be a major focus going forward? And is there a race to the bottom in terms of time that they’re going to promise?

Vishwa Chandra: The question becomes: In terms of absolute market size, how big is that? And that comes down to occasions. There is a convenience occasion. There is an emergency fill-in occasion where speed becomes important. But as you think about consumption patterns, and demand patterns, it is a smaller portion of the market. That’s why you see some platforms approaching it differently by asking, how do we actually serve up the right offering that meets the customers’ needs at that moment?

Whether you’re ordering restaurant delivery and want to add on a pint of ice cream or a six-pack of beer, or you’re suddenly out of baby products and diapers in the middle of the night, your ability to get that product at that moment of need with the right offering is what is going to be important.

Victoria Lord: The other thing I would add is consumer’s willingness to pay. You hit a limit at some point. I’m willing to pay a premium if I realize halfway through making a meal that I need something to finish it off. I’m less willing to pay a premium if I’m doing a bulk grocery order that I don’t necessarily need tonight, or even tomorrow, or the next day.

I think we’ll start hitting the boundaries on consumer’s willingness to pay across different categories and occasions, like Vishwa mentioned. That will be part of the limit as to how much is possible through these app ecosystems over time.

Subscribe to the McKinsey on Start-ups podcast

Daniel Eisenberg: Victoria, let’s shift our focus to the economic forces on some of the stakeholders. And in particular, let’s start with restaurants. How should restaurants think about balancing growth in delivery versus their core in-restaurant dining?

Victoria Lord: This is a tough question, and there isn’t one single answer. Restaurants really need to be deliberate. We’ve heard before, in other contexts, that not all revenue is good revenue—and that’s true in food delivery too.

If I were a restaurant owner, I’d be taking a close look at my economics for my restaurant, or my chain of restaurants. I’d ask, how much do I actually make on an average delivery order after all of my costs and the third-party fees? And that includes refunds, by the way. How does that compare to my in-store profit margin? And can I handle that delivery volume within my existing overhead structure and staffing level? Or am I starting to adversely affect my in-store business or incur additional operational cost to meet that delivery need? I think, especially now, many restaurants feel that they must participate in delivery. But the decision to do so is much more nuanced.

I can think of several restaurants in my local market that have chosen to use their own delivery fleet rather than participate with a third-party platform. They might require customers to order through their own website, even if they have a third party running last-mile logistics, or they offer pickup only. Those are all ways that restaurants can regain some control of the margin.

For the larger chains, I would be testing new business models. I would consider if, operationally, it makes sense to use a dark kitchen, or a commissary, that pulls the delivery out of the existing restaurants. Do smaller-format restaurants that are more streamlined for delivery and pickup make more sense? And I’d be working really hard on getting to good partnership terms with the third-party platforms, which have a real incentive to be able to feature great restaurants.

Daniel Eisenberg: You had an exhibit in the article that showed how the economics of a restaurant generating more and more of its revenues through delivery could be a double-edged sword. So, what is the impact you expect to see on restaurants’ profitability if more revenue is generated that way?

Victoria Lord: Restaurants traditionally make margins of seven to 22 percent. So, on a $35 order, you’re looking at $2.50 to $7.70 in margin. Delivery platform commissions are roughly 15 to 30 percent. I don’t even need to do the calculations for us to know that the math just simply doesn’t work.

While the revenue from delivery grows, profitability for the average restaurant declines. Of course, the more of the restaurant’s business that goes to delivery, the worse and more unsustainable that business model becomes. I have family members who are in the restaurant business, and they’ve decided against offering delivery from their restaurants for exactly this reason.

Daniel Eisenberg: Do you expect, at a certain point, that consumers will bear more of the cost of commissions? That they will pay more for delivery items? Or that restaurants will have different menu prices for delivery versus in-store dining?

Victoria Lord: There are two parts to that question. Restaurants can, and will, increase their prices online. Your average consumer is probably not pulling up the menu in the delivery app and comparing it with the one on the restaurant website—but I do because I’m curious. And I see anywhere from a few cents to a dollar difference in menu pricing depending on the menu item and the underlying menu cost. So, there is flexibility for restaurants to pass on some cost to consumers.

I think we’ve seen that in other spaces where you’re paying a premium for delivery, for convenience. It’s hard to say that consumers will bear all the cost. They’re going to bear some of it, but restaurants will continue to have to bear some of those costs because they cannot possibly pass on a 15 to 30 percent delivery platform commission on top of the 10 to 15 percent service charge, the delivery fee, and the tip that consumers are already paying.

We talk about the economics in the article, and we broke it down by each of the players, and the ecosystem. Consumers are already paying quite a bit. The restaurants are too, but I don’t think you’ll see a complete shift to the restaurants’ portion of those costs being passed on to the consumer.

Daniel Eisenberg: What are two or three key areas in which the platforms can drive down costs to achieve profitability going forward?

Victoria Lord: I hate to even talk about batching because, as a consumer, I don’t like thinking about my order getting picked up, with three stops on the way, and my food getting cold. But the math works for the platforms. The more orders that can be picked up at the same time and delivered in the same delivery run, the lower cost of delivery per order.

The economics there make sense. That is something that the platforms will continue to experiment with. And as their technology and the routing software get more sophisticated, they will be able to do this better and better over time.

And getting very tight on operational timing matters a ton for the platforms. All of them have quantified exactly how much money every minute costs. The easier they can make it for drivers to pick up orders without waiting, to drive tightly optimized routes, and to drop off orders, the better it is.

Vishwa mentioned the contactless delivery feature. It’s great for reducing the time it takes a driver to drop the order at the door. So, the delivery platforms benefit from that feature too.

Some of this is achievable through process enhancements, for instance through system and underlying technology enhancements on the delivery platform side, like the routing software I mentioned. And then some relies on restaurants doing operational and process improvements, like having dedicated pickup areas, or better signage.

Earlier we mentioned the cost of getting a customer, and the importance of loyalty and subscription programs. I think we will continue to see the platforms push on that, because when you lock me into your ecosystem as a consumer, and I prefer your app over others, it means I’m more likely to spend across those categories we talked about. It also means you don’t have to offer me additional promotions to get me into the app.

Daniel Eisenberg: How big a part of the market could subscription programs become? They not only drive loyalty, but also recurring revenue, which investors prize. Will that remain niche?

Vishwa Chandra: If you look at many delivery platforms, whether it’s on the restaurant delivery side or the grocery delivery side, subscriptions are already a large part of their offering. Not necessarily meal-kit subscriptions, but subscriptions where customers get preferences such as a reduced service charge, free delivery, or better access to promotions—these benefits have a lot of appeal for customers.

It’s a playbook that has been followed by many in the past, from a payment provider’s perspective, and from hospitality and airlines that have long-lasting loyalty programs. But I think it also becomes very interesting as consumers start saying: Who’s going to start paying for that?

At the end of the day, yes, the platform is rewarded with customer loyalty, but it does come at a cost. If your service fees are five percent versus 15 percent, that’s very meaningful. What we’re seeing is that it becomes a point of discussion between retailers, platforms and restaurants regarding the cost of that loyalty—where the benefit flows, and where the cost flows.

Daniel Eisenberg: Speaking of the consumer’s perspective, what shifts in price and experience can consumers expect from delivery services in the next few years?

Vishwa Chandra: I think you’ll see a couple of things. One is the continued channel blurring. Currently a restaurant platform looks like a restaurant platform, and a grocery platform looks like a grocery platform, with some add-ons. I think you’ll see that line blurring between each of them, with convergence into adjacent categories like alcohol, pharmacy, or office products, where you’re trying to capture a greater and greater share of a consumer’s daily consumption needs, whatever that consumption may be.

You’re also going to see a much more personalized, emotional connection that these platforms are going to try to make as they work to move away from just being a transactional platform. Whether that’s increased use of social, increased use of video, or a greater degree of engagement with each of the consumers. You already see that in other geographies where they’re ahead of what we see here in the US.

The last thing is a generational shift. Young families with kids drive the food industry, whether you’re a grocer or a restaurant chain. As more millennials and Gen Z’s start to have kids, and continue to progress in their professional lives, it’s going to be interesting to see how it plays out. We’ve already seen certain platforms making a bet on being more relevant to the next generation of customers.

Daniel Eisenberg: In the article you talk about untapped revenue pools. And you mention quite a few: Dark kitchens, virtual brands, and brand spin-offs. Can you talk briefly about which of these have shown the most promise to date?

Vishwa Chandra: Many of these are now at scale, whereas 12 or 18 months ago they were experiments. We are seeing quite a bit of growth in dark kitchens. This is a very operationally intensive business. Dark kitchens allow you to get some of the batching benefits, a greater chance of grouping orders together, than if you were a single kitchen or a single brand.

In addition to that, we’re also seeing more virtual brands. Retailers and restaurants are suddenly realizing that their brands have a resonance, and they can use them to expand. You have grocers launching ready-to-eat restaurant brands or full-service kitchens. Or, you’ve got existing restaurant brands realizing they can launch a third, fourth or fifth brand, leveraging much of the same equipment, ingredients and expertise that they have.

Daniel Eisenberg: Victoria, one of the other potentially promising opportunities you talk about is “menu engineering.” Can you expand on what that might look like, and what it will take to get there?

Victoria Lord: Menu engineering is fascinating. It’s very similar to what we see on a traditional restaurant’s menu board, where every single item has a role to play in terms of the level of its popularity, its profitability, whether it’s bringing folks into the restaurant, and whether its driving volume or driving margin.

But it’s much more dynamic because you’re able to use data to make well-informed decisions on a much more frequent basis. You can collect and use data on ordering patterns to revise your online menu regularly and promote those higher margin items up at the top, or promote items that you need to move quickly because you have significant supply of burgers sitting in the cooler. You can change your bundling strategy to drive volume, and you can change prices easily. You can also use that data to find the right assortment, the balance of number of items, that people are expecting to see.

You’re seeing this in the brick-and-mortar world too. Burger King, for example, just announced they’re moving to a streamlined, simplified menu to speed up some of their drive-thru operations. And they’re not the only chain that’s done this recently.

What this level of menu engineering will take, though, is deliberate data collection, deliberate data analysis, as well as the ability to make and implement quick decisions.

Some restaurants simply won’t have the scale and the capability to be able to do this. But some can. And the platforms could make this a service offering for restaurants because they have the data and they can compare across multiple restaurant banners, multiple markets, and multiple menus, and are able to provide rich insights.

Daniel Eisenberg: And they have the data scientists who can do it, right? Whereas the restaurants may not, except for maybe the big chains.

Victoria Lord: Exactly. I think the other part of this is personalization. We see a little bit of user-specific personalization in digital menus already. If you open the menu for your favorite restaurant, odds are good that you will see a section at the top that says, “items for you.” Those are chosen based on your past preferences and what the app has learned about the menu items that you like.

You could imagine a scenario where you would see an altogether different menu for the same restaurant if you pull up your app compared to what I would see.

You could potentially also see different pricing there at some point. This is much harder to implement operationally. I think the possibility of consumer blowback is much higher for something like this. So, who knows if that level of personalization and menu engineering would happen?

Daniel Eisenberg: We’ve talked already about the profitability challenges in this sector. But despite all of that, money continues to pour into the sector from investors. What is the outcome that investors are betting on, given that they’re conscious of all these economic challenges?

Vishwa Chandra: I think it comes down to the fact that food is still one of the largest sectors in the world. If we take the US, for example, there is a $2 trillion annual spend in aggregate between consumables, groceries, and restaurants, and not just on the delivery side. It is a very, very significant market that is going through a lot of change. Ultimately the platform that is able to influence the consumer is the one that will be able to drive significant value.

Daniel Eisenberg: With dark kitchens, the operations are happening behind the scenes, not in front of the customer. Is it safe to assume that a lot of the innovation in this sector is going to be on the back end?

Victoria Lord: I’ve been hearing much more about restaurants starting to trial their own dark kitchens where they are doing small, delivery-only storefronts, in some cases offering out part of that capacity as a third-party service. I think it will be very interesting to see, as that type of business model evolves, how much of it will continue to be with the third-party players who have existed to date versus the larger restaurant chains making their own investments in this space. The business model evolution behind this will also continue to be quite interesting not only on the platform side, but on the restaurant side as well.

Over the past couple of years, we have seen a tremendous amount of innovation at a much more accelerated pace than we expected. As a consumer, as someone who is passionate about this space, and who has family in the restaurant business, I am very excited to see what comes next.

Daniel Eisenberg: When you think about it, we’re all both participants and observers in this space, and it’s going to be fascinating to continue to watch and experience the evolution. This discussion has been great, and it’s made me hungry, as well. Vishwa and Victoria, thank you for joining us on the podcast today.

Vishwa Chandra: Our pleasure.

Victoria Lord: Our pleasure. Thanks, Daniel.

Daniel Eisenberg: Well, that’s it for today’s episode. Thanks again to our guests, McKinsey partners Vishwa Chandra and Victoria Lord.

As always, I also want to thank our McKinsey on Start-ups production team: Molly Karlan, Polly Noah, Sid Ramtri, Myron Shurgan, and Katie Znameroski.

And of course, thank you for listening. We hope you’ll join us again for McKinsey on Start-ups .

Vishwa Chandra is a partner in McKinsey’s Bay Area office, and Victoria Lord is a partner in the Miami office. Daniel Eisenberg is an executive editor in the firm’s New York office.

Explore a career with us

Related articles.

Ordering in: The rapid evolution of food delivery

Winning formula: How Europe’s top tech start-ups get it right

Eating out(side): Restaurant dining in the next normal

Encouraging online consumers into making better food choices: The power of nature exposure on healthy food choices

Affiliations.

• 1 Deakin University, CASS Food Research Centre, Australia. Electronic address: [email protected].
• 2 Deakin University, CASS Food Research Centre, Australia. Electronic address: [email protected].
• PMID: 38723667
• DOI: 10.1016/j.appet.2024.107382

Background: online environments can influence food desire and choices. We tested if online calming nature and stressful street environments can affect desire for healthy and unhealthy foods.

Method: we asked 238 participants (40 ± 14 yrs) to rate their desire (100 mm VAS) for 7 low calorie nutrient rich foods (Healthy) and 7 high calorie nutrient poor foods (Unhealthy), and perceived stress (state anxiety in STAI), before and after imagining themselves in a control, nature park, or busy street condition.

Results: participants who imagined themselves being in a nature park had a significant higher desire for Healthy foods, than participants in the busy street condition (p < 0.05). Participants in the busy street condition decreased their desire for Healthy foods after they imagined themselves in a busy street (p < 0.05)). However, perceived stress did not impact the association between condition and desire for low calorie foods nor high calorie foods.

Conclusion: this study suggests that online environments can have an impact on healthy food desires, which could be of importance for the increased number of food choices which are made in online environments.

Keywords: Attention restoration theory; Desire; Food choice; Health; Nature; Online; Urban.

Copyright © 2024. Published by Elsevier Ltd.

How online shoppers really judge your delivery experience: new research revealed

In e-commerce, the delivery experience matters. A lot. According to new Inside Retail research, shoppers really do judge brands by the quality of their deliveries. The question is, what do they want at each stage of the delivery journey?

From the moment a shopper hits your checkout to the moment your package lands safely in their hands, they are judging you. 

First, they assess the security and simplicity of your shipping page. Then, they weigh up whether you offer the best options for delivery. They evaluate how easy it is to track their parcel. And, ultimately, they give you a mental ‘pass’ or ‘fail’ depending on whether their parcel arrives on time and undamaged or not. 

All these observations add up to one thing: the delivery experience. Get it right and you’re on your way to securing long-term loyalty and a competitive advantage. Get it wrong and you could get left behind.

To find out what ‘right’ looks like in today’s competitive e-commerce landscape, we asked over 750 online shoppers to tell us what they really want from the delivery experience. The results of the survey – which we conducted together with Inside Retail – are now available in a new research report .

Nailing each stage of the delivery journey

The research identifies a close link between the delivery experience and customer retention. And, to help retailers prioritise areas for improvement, it breaks down the delivery experience into five key stages: completing the purchase, organising the delivery, tracking delivery progress, receiving the package, and the post-delivery experience.

Shoppers have identified 26 areas as ‘very’ or ‘critically important’ across these five stages.

For instance, when they’re at the checkout, shoppers want easy-to-understand delivery options and the reassurance that your e-commerce site will keep their personal details safe. 

And when it comes to receiving a package, they want to get their purchase safely and in one piece. While that isn’t unexpected, it’s also business critical: a staggering 91 per cent of shoppers will abandon you entirely if their package is damaged.

Your delivery partner should be able to provide proof of delivery and offer secure and reliable delivery options that both you and your customers trust.

Across every stage, transparency and choice are key

Delayed deliveries are a major pain point for online shoppers. In fact, one-third of them will abandon your brand if their parcel doesn’t arrive on time. Building transparency into the delivery experience can help alleviate this risk.

An easy way to do this is by partnering with a delivery provider that has robust tracking capabilities . For example, using Team Global Express e-Care’s MyTeamGE portal, shoppers can easily track their parcels and control notifications around its imminent arrival.

To win over more shoppers, the research suggests you should also offer a choice of delivery options. This could include convenient drop-off locations, instructions for leaving parcels behind, more delivery speeds, or even support for handling bulky items. It all adds up. 

And it responds to what your customers want. The research reveals that more than half (57 per cent) of shoppers want to be able to specify a safe place for unattended deliveries, and 46 per cent think it’s important that retailers offer options for the speed of delivery.

Delivering progress for e-commerce retailers

The shoppers have spoken, and it’s clear that e-commerce retailers should think carefully about what they need from a delivery partner.

Team Global Express e-Care delivers the experience that more shoppers and shippers are looking for by bringing greater confidence, choice and control to the entire journey .  

This direct-to-customer shipping solution responds to the pain points and concerns raised in the research, helping e-commerce brands realise progress in a competitive retail landscape. 

• Visit Team Global Express e-Care to download the full report , including breakdowns of what each generation wants from their delivery experience, plus tips and insights about how to enhance every stage of the journey.

Recommended By IR

“Failed to evolve”: Why customers are pushing back on City Chic

“Change in behaviour”: Why Ikea’s Swedish meatballs are selling like hotcakes

Why brands are going all out to reach basketball’s biggest fans

Online liquor retailer Hairydog Group acquires Boozebud

How Indian brands could benefit from Walmart’s ambitious export goals

This is for ir pro members only..

Log in below or subcribe.

By continuing, you agree to Octomedia Terms And Conditions and Privacy Policy .

Naval Research Program

Research proposal guide - naval research program, research proposal guide.

Once an Initial Research Estimate Form (IREF)  is validated and selected for funding, the next step is to complete a Research Proposal. Proposals must be completed and approved before funding can be authorized and released.

Faculty that have an IREF validated and selected for funding are required to complete the following steps before funding will be authorized and established. More details for each step are provided below; this list can be used as a checklist if desired.

Please notify NRP at [email protected]  if you have any reservations about accepting your funded project (e.g. impending retirement, emergent obligations, etc.).

Jump to section

Eligibility, annual pi training, acknowledgment of terms.

• Technical Proposal/Narrative

Proposal tab

Nps personnel tab, proposal questions tab, proposal data tab, abstracts and attachments tab, proposal budget tab, review process, proposal amendments, troubleshooting.

More events

Events & Deadlines

Quick links, principal investigators.

The Principal Investigator (PI) is the researcher who has primary responsibility for the design, execution, and management of a sponsored research project and is named on the proposal to the sponsoring agency. The PI has the primary responsibility for the fulfillment of the Statement of Work. Even when collaborating with one or more Co-PIs, the PI has the ultimate responsibility for the project and remains the sole individual responsible for managing expenditures in support of the project. 

Only eligible Naval Postgraduate School faculty participating in the mission of NPS may submit proposals and act as a PI/PD/Co-I/Co-PD for sponsored projects. Individuals in a faculty or staff category other than those listed in the SPPGM-22 require a waiver to be eligible. Sponsored Program Policy/Guidance Memo 22 (SPPGM-22): Who can be a PI/PD/Co-I/Co-PD?  (PDF, 4 January 2023) PI/PD/Co-I/Co-PD Justification Memorandum (Waiver Form)  (PDF)

^ Back to top

Training is required for anyone who functions as a PI/PD or has direct access to funds on sponsored projects. Annual PI Training is completed within Sakai, and includes four modules:

• Accountability/Fiscal Law: 13/14 required to pass
• Protection of Human Subjects: 7/8 required to pass
• OPSEC for the PI/PD: pass/fail
• Stewardship: 18/20 required to pass

PIs must pass all modules for training to be complete. Please visit Sponsored Programs Related Annual Online Trainings  to begin.

Please send a screenshot or export of your passing scores to NRP upon successful completion of your Annual PI Training.

All PIs and Co-PIs must digitally sign the Acknowledgment of Terms form . This form must then be uploaded as an attachment to your Research Proposal.

To digitally sign the form, you may need to download and save it to your computer, and open in Adobe Reader. Opening it in your web browser doesn't reliably activate the signature fields.

When uploading as an attachment, go to the Abstracts and Attachments tab in Coeus, and upload the form using the "Supplementary Documentation" category.

Please do not email any documents directly to NRP unless requested to do so.

PDF, 142KB. Last updated November 2023

All PIs must complete a Technical Proposal/Narrative, using the official NRP template.  These documents must be submitted as attachments to your Research Proposal.

• Read all template guidance prior to starting your proposal
• Complete all required template elements
• Supporting TASKS section must include direct correlation/justification for all budget expenses
• Coeus is approved for CUI, but please mark all documents accordingly if applicable
• Remove the first page containing template guidance before submitting to Coeus

Topic Advocate signatures are no longer required. Research Proposals will be routed through the chain for approval by NPS personnel and teams using Coeus. For efficiency purposes and to prevent delays, please  do not bypass  this process.

Word, 57KB. Last updated October 2023

Coeus Proposal Package

Koali Coeus is the system NRP uses to manage and review Research Proposals. To submit a Research Proposal, you will use a Proposal Development Document (PDD).

NRP creates empty PDD shells within Coeus, to pre-populate required information and simplify setup. Each PI will receive a Coeus-generated, automated email prompting you to begin your proposal.

Please email the NRP office at [email protected] with any questions, or if you don't receive your Coeus access email.

• Period of performance and milestone dates, in all proposal documents, must match.
• Proposals are only accepted through Coeus. Incomplete proposal packages will be rejected.
• Spend plans must be realistic as you will be required to execute as scheduled (burn rate).
• Expenses must include direct correlation/justification to the TASKS in your technical proposal.
• If someone is assisting you in preparing your proposal package, give them a link to this page.

This tab will be completed by NRP. Please do not make any changes to the fields present on this tab.

At minimum, you must include the PI and any Co-PIs. You can be a PI on no more than two concurrent NRP projects across all Cycle Years, and no more than one NRP project per Cycle Year.

• Enter the last name, email address, etc. of the person you wish to add. You only need to fill out one field, and the search is not case-sensitive. Click search .
• Click return value  on the far left of the row containing the correct personnel record.
• The top section of the page will now have the person's name, as well as a  Proposal Role  section. Select the appropriate role, then click  add person  below that.

If additional staff/faculty are expected to participate but have yet to be identified, input their positions in your project using To be named  instead of Employee Search .

Please answer all Proposal Questions, including any sub-questions that may appear. If you have any questions about this tab, please contact NRP .

Please fill out all Proposal Data. If you have any questions about this tab, please contact NRP .

To select multiple items in a "select all that apply" list, use  Ctrl  on Windows, or  Cmd  on Mac.

Upload your required attachments including your  Acknowledgment of Terms form  and your Technical Proposal/Narrative  under the categories below:

• Acknowledgment of Terms:  "Supplementary Documentation"
• Technical Proposal/Narrative:  "Technical Proposal/Narrative"
• Budget Justification:  "Budget Justification"
• Equipment Justification:  "Equipment"
• You must upload a waiver for  each  person requiring one

Copy-paste the abstract from your Technical Proposal/Narrative into the  Publicly Releasable Abstract  section, under Enter Abstract . Make sure to click  add  or it will not save to your PDD correctly.

When entering keywords, please enter one keyword per line,  not  a comma-separated list. Keywords are used by other integrated systems and comma-separating them can cause errors in the data integrations.

The quarterly spend plan you enter into Coeus generates your  required  burn rate schedule. This data is reported to the Budget Submission Office (BSO) and the burn rate must be executed as input into Coeus. PIs are expected to plan for salary adjustments within their budget. The authorized project amount is not increased due to promotions or Cost of Living Allowance increases.

Ensure your quarterly spend plan is realistic by accounting for:

• Cost-of-living adjustments
• Raises and promotions
• Reasonable flexibility

Official research proposal budgets must be submitted using the Coeus budget proposal tool. The  FY24 NRP Budget Spreadsheet  is used for budget/spend plan updates during the PoP. The spreadsheet can be used for general offline proposal budget planning purposes but it is highly recommended that you use the  Coeus training instance  to draft your proposal budget.

Your proposal budget is an embedded document within your PDD, and has tabs of its own. NRP has created a quarterly budget shell for you to fill out; open this budget by using the  open  button on the far right of the page.

Using your IREF budget allocations, enter quarterly expenses in the  NPS Labor  and Other Direct Costs  (Non-Labor) tabs. Your PDD's budget must be equal to or less than the budget proposed in your IREF.

If financial expenditure questions arise during budget development or execution, PIs should consult with the NRP SPFA .

How can the money be spent?

• NRP funds are RDT&E BA 6.6 and are appropriated solely for specific selected NRP projects. There must be a direct relationship between funds spent and the selected NRP research project.
• This money cannot be used for academic/curriculum support.
• This money cannot be used for office supplies, printers or cell phones.
• This money cannot be used to hire administrative personnel.

Financial Terms

• Indirect Costs:  The NRP uses NR&DE funds, and indirect cost are not collected. The indirect rate is 0%.
• Fringe  (aka Acceleration or Fully Burdened Rate): Fringe is always included in the cost of payroll regardless of how your payroll is being charged.  Fringe addresses the actual cost of benefits paid by the government for each employee (TSP, FERS, Medicare, FEGLI, TSP Matching, Annual Leave, Sick Leave, Vacation).  NPS recommends using 52.5% for projection purposes.
• Overhead:  The NRP takes a small percentage off the top of the annual budget to run the program. Therefore, no overhead cost needs to be factored into each individual project budget.

Spend Plan Justification

The Description & Purpose field(s) in Coeus for all expenses (Other Direct Costs/Non-Labor) must include a direct correlation/justification to the TASKS in your Technical Proposal/Narrative. Do not use blanket terms.

If your budget includes costs that exceed the NRP allowances, attach a Budget Justification document. Alternatively, you can include line-item Budget Justification notes in your PDD's budget.

• Ensure that quarters with proposed travel include appropriate corresponding labor hours
• For projects with budgets exceeding $175,000, total travel will be capped at $26,250. This cap is for the entire project team: PI, Co-PI, researchers and students who are listed in the approved project proposal.
• People who are not listed in the specific NRP project are not allowed to utilize these funds. Travel percentage and total cap may change in FY25.
• Unique business such as project kick-off meetings, mid-year progress review meetings, final projects delivery meetings, data collection, and/or one conference attendance that are directly tied to the project can be conducted via travel, but all “regular business” should be conducted on Teams. Virtual/online conferences are preferred and encouraged. Labor must be charged to the project at the same time you are on travel.
• Ensure travel and labor expenses align with research completion timelines, to include any travel for the purpose of final debriefing/delivering the final product(s)
• Students:  Student participation is allowed; however, travel requests must state how the travel applies to the associated NRP research.
• DTS form justification box must include: NRP project number, PI Name, detailed purpose of the travel i.e., how the travel is directly related to the NRP project, travel is/is not included in the original proposal.
• All travelers are required to submit a  Travel Report  after each trip.
• Academic purposes
• Thesis development
• Student graduations
• Other research projects
• List all known project personnel. List additional planned but unidentified individuals under "To be named." Notify the NRP SPFA  of project personnel changes immediately to avoid a payroll approval delay.
• Labor is charged using actual benefits and varies per individual. Rates are updated/calculated in Coeus routinely. Consult your SPFA for individual rates. NRP is exempt from other Indirect Costs.
• NRP funds are not appropriate for employee cash awards. All awards using NRP funds will be reversed upon detection.
• The fully burdened amount is listed in Coeus. PIs are expected to plan for salary adjustments within their budget. The authorized project amount is not increased due to promotions or Cost of Living Allowance increases .

External Support

If you are intending/planning to outsource labor/skills that cannot be performed by NPS personnel you must obtain approval from your department chair.

NRP funds cannot be used to hire administrative personnel.

Acquisitions

All purchases must align with the tasks and deliverables cited in your proposal and require a detailed justification to be submitted in ERP. As per the Annual PI Training, purchasing for "the greater good" is not allowed.

Provide an explanation, in plain language, detailing how each purchase contributes to the tasks and deliverables of the project. Blanket terms such as "Mission Essential / Critical" are not a valid justification.

Total purchasing exceeding 25% of your project's total budget will require additional justification.

• Orders should be submitted early in order to contribute to the project deliverable(s), and must be acquired within the project's Period of Performance.
• Acquisitions for computers, equipment, contracts, and MIPRS are  only  approved for the benefit of the selected NRP project. Therefore, each item must be ordered soon enough that it arrives early enough to contribute to the project deliverables.
• PIs may be allowed to purchase equipment with justification (e.g., computers) once every three years, however purchasing peripheral equipment that is considered office equipment is not allowed.
• All equipment must be shipped directly to the warehouse and registered in the NPS property accounting system prior to receipt under the PI’s name. The PI is accountable to produce records during an audit.
• Cell phones & cell phone services
• Printers, ink, and toner
• Office supplies
• Publications (Please contact NRP for NRP-related publication expenses)
• Items considered to be for general purposes
• Checklist of OSHE Related Hazards to Consider
• Include labor, time, and costs for safety controls.
• Include safety and environmental planning and training hours.
• Include funds for safety assessments if needed, protective equipment and physical controls.
• Critical to plan ahead for off-campus activities, UUVs, lithium batteries, RF emitter, hazmat, lasers, etc.

Review  Safety Information Needed from PIs for Project Descriptions

For safety questions or concerns email  [email protected]  or visit the  Safety Review and Planning page on the  Safety website.

Finalizing your budget

Once your budget is complete, save it with the button at the bottom, and use the blue  Return to Proposal  button at the top right. Check the  Final  box for your budget in the  Proposal Budget  tab.

• Coeus User Guide
• Contact NRP
• Contact Coeus Ombudsman
• Contact NRP SPFA for financial expenditure questions
• Budget Spreadsheet

Submit Your Proposal

Once your PDD is completed, run Data Validation .

• Go to the Proposal Actions tab
• On the  Data Validation  section, click the  show  button to expand it
• Click  turn on validation
• Go through each of the  Validation Errors  and  Warnings , and fix them
• Reach out to NRP at  [email protected]  if you need any help

Once your PDD passes validation, please use the  Submit  button at the bottom of the  Proposal Actions  tab to submit it for approval. NRP will receive an automated email from Coeus letting us know when your PDD is ready to review.

See the Troubleshooting guide below for help resolving errors. NRP is also happy to assist; contact us at [email protected] .

Once your PDD is successfully submitted, it goes through several stages of review:

• Your department chair reviews and approves your PDD. If you have Co-PIs listed, their department chairs must review and approve your PDD as well.
• NRP reviews your PDD for completeness, accuracy, and adherence to requirements and guidance.
• NRP's Financial Manager reviews your budget and spending allocations.
• NRP's Program Manager reviews and approves your PDD.
• The Vice Provost for Research and Innovation reviews and approves your PDD.

Once the VPR has approved your PDD, NRP double-checks a few more requirements:

• Completion of your annual PI training
• Completion and submission of all previous Cycle Years' required research deliverables
• Successful approval from the  Human Research Protection Program Office & Institutional Review Board (IRB) , if required

Once these are complete, NRP then issues funding for you to begin your research.

To check your proposal's current status in the review process:

• Log in to Coeus
• In the  Proposals  section of the Coeus homepage, click "Search Proposals"
• In the  Document ID  field, enter the five-digit Document ID of your proposal. If you don't know the Document ID, please reach out to NRP and we can provide that to you.
• Click the  Search  button
• Click "view" on the left of the row your proposal shows up in, in the search results
• Go to the  Proposal Actions  tab
• Open the  Route Log  section by clicking the "show" button on it
• In Action List to Complete:  Approval is required
• In Action List to FYI:  Approval is not required; the person was notified for their situational awareness
• If multiple people are listed within the same approval line, only one of them is required to approve

To make amendments to an approved and finalized PDD, email the Coeus Ombudsman  with your amendment request.

The types of changes that require an amendment through Coeus are:

• Changes to the Period of Performance
• Budget increases
• Changes to the PI or Co-PI(s)
• Topic Advocate changes
• Statement of Work changes

Here are solutions to common problems encountered through the Research Proposal process.

If you're still having trouble, please reach out to NRP . We're happy to help!

PDDs are "checked out" when someone opens them to edit them, and locked until the person editing clicks the  close  button to "check in" the proposal.  Closing the browser tab will not check in the PDD.

If your proposal was locked by someone else, Coeus will display their name at the top of the PDD. Simply email them and request that they open the proposal and close it using the  close  button at the bottom of the page rather than just closing the browser tab.

If you're not able to get a hold of the person your PDD is checked out to, email the Coeus Ombudsman to request an administrator to unlock your PDD.

Reach out to NRP and let us know; we can add them as an authorized user to your PDD.

Opening the PDF in your browser doesn't always activate the signature fields. Download it to your computer and open it in Adobe Reader or Adobe Acrobat. The fields should show up.

Internet Explorer Alert

It appears you are using Internet Explorer as your web browser. Please note, Internet Explorer is no longer up-to-date and can cause problems in how this website functions This site functions best using the latest versions of any of the following browsers: Edge, Firefox, Chrome, Opera, or Safari . You can find the latest versions of these browsers at https://browsehappy.com

• Publications
• HealthyChildren.org

Shopping cart

Order Subtotal

Your cart is empty.

Looks like you haven't added anything to your cart.

Career Resources

• Philanthropy
• About the AAP
• Infant Feeding for Persons Living With and at Risk for HIV in the United States: Clinical Report
• Research on HIV transmission through breastfeeding spurs update to AAP guidance
• Breastfeeding for People with HIV: AAP Policy Explained
• American Academy of Pediatrics Shifts Position on Breastfeeding for People with Human Immunodeficiency Virus
• News Releases
• Policy Collections
• The State of Children in 2020
• Healthy Children
• Secure Families
• Strong Communities
• A Leading Nation for Youth
• Transition Plan: Advancing Child Health in the Biden-Harris Administration
• Health Care Access & Coverage
• Immigrant Child Health
• Gun Violence Prevention
• Tobacco & E-Cigarettes
• Child Nutrition
• Assault Weapons Bans
• Childhood Immunizations
• E-Cigarette and Tobacco Products
• Children’s Health Care Coverage Fact Sheets
• Opioid Fact Sheets
• Advocacy Training Modules
• Subspecialty Advocacy Report
• AAP Washington Office Internship

Online Courses

• Live and Virtual Activities
• National Conference and Exhibition
• Prep®- Pediatric Review and Education Programs
• Journals and Publications
• NRP LMS Login
• Patient Care
• Practice Management
• AAP Committees
• AAP Councils
• AAP Sections
• Volunteer Network
• Join a Chapter
• Chapter Websites
• Chapter Executive Directors
• District Map
• Create Account
• News from the AAP
• Latest Studies in Pediatrics
• Pediatrics OnCall Podcast
• AAP Voices Blog
• Campaigns and Toolkits
• Spokesperson Resources
• Join the AAP
• Exclusive for Members
• Membership FAQs
• AAP Membership Directory
• Member Advantage Programs
• Red Book Member Benefit
• My Membership
• Join a Council
• Join a Section
• National Election Center
• Medical Students
• Pediatric Residents
• Fellowship Trainees
• Planning Your Career
• Conducting Your Job Search

Making Career Transitions

• COVID-19 State-Level Data Reports
• Children and COVID-19 Vaccination Trends
• Practice Research in the Office Setting (PROS)
• Pediatrician Life and Career Experience Study (PLACES)
• Periodic Survey
• Annual Survey of Graduating Residents
• Child Population Characteristics Trends
• Child Health Trends
• Child Health Care Trends
• Friends of Children Fund
• Tomorrow’s Children Endowment
• Disaster Recovery Fund
• Monthly Giving Plans
• Honor a Person You Care About
• Donor-Advised Funds
• AAP in Your Will
• Become a Corporate Partner
• Employment Opportunities
• Equity and Inclusion Efforts
• RFP Opportunities
• Board of Directors
• Senior Leadership Team
• Constitution & By-Laws
• Strategic Plan

Log In to Your Account

Access your account, the AAP Member Directory, transcripts and more. Already logged in? Find your Account here.

Collaborate

Connect with other AAP members and explore the AAP communities you belong to.

Become a Member

Not a member yet? Learn more about the benefits of AAP membership.

What's New at the AAP

Research on hiv transmission through breastfeeding spurs update to aap guidance, 33rd edition of the red book is now available. order today, register for the 2024 national conference and exhibition, aap announces candidates for president-elect, learning opportunities.

Continuing Medical Education from AAP helps you stay current in practice and provides tools and resources for every stage of your career.

Live & Virtual Events

Neonatal resuscitation program®.

From your time as a medical student to becoming a pediatric resident and beyond, there are resources and opportunities only available through AAP.

Physician Health & Wellness

Equity, Diversity, and Inclusion

From the beginning, the American Academy of Pediatrics has been guided by its mission to ensure the health and well-being of all children. This includes promoting nurturing, inclusive environments and actively opposing intolerance, bigotry, bias, and discrimination. 

Our Equity Agenda  

Global Health

We work for every child's future. The AAP's global mission is to attain optimal physical, mental, and social health and well-being for all children around the world. 

Our Global Initiatives

You have the power to change a life. The AAP is dedicated to the health of all children and the pediatric professionals who care for them. Your gift today makes that possible. 

More AAP Resources

Journals & publications.

The American Academy of Pediatrics is the leading publisher, globally, in the field and practice of Pediatrics.

shopAAP is the official store of the American Academy of Pediatrics. All purchases directly benefit and support the health and well-being of all infants, children, adolescents, and young adults. Find a new look and feel now on shopAAP!

• Our Solutions
• Why Virgin Pulse?
• News & Press
• Science Advisory Board
• Resource Center
• Case Studies
• Events & Webinars

Goodbyes are never easy.

Our free, public text programs are closing..

The end of our public text programs

After much consideration, we have made the difficult decision to no longer offer our text programs. Support for these programs will end on May 24, 2024.  

To prepare for this change, we encourage you to speak to your healthcare provider to find a replacement program that’s right for you.

© 2024 Virgin Pulse