case study on online food delivery pdf

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

Innovation in online food delivery: Learnings from COVID-19

Diana gavilan.

a ESIC Business & Marketing School, Universidad Complutense de Madrid, Spain

Adela Balderas-Cejudo

b ESIC Business & Marketing School, Basque Culinary Center, Spain

Susana Fernández-Lores

Gema martinez-navarro.

c Department of Management & Marketing, Universidad Complutense de Madrid, Spain

The COVID-19 pandemic has forced some restaurants to shift their business models to innovative approaches in Online Food Delivery (OFD) services. This paper seeks to study the impact of innovations on OFD –new product/services– that aim to enhance the experiential value when ordering food online. Moreover, this paper analyses the willingness to order food delivery online during the COVID-19 pandemic. An experimental design survey was therefore used. The participants assessed one out of four OFD innovation options, its experiential value, and their own willingness to order it. Participants' fear of COVID-19 was measured and used as a moderator variable. A conditional process analysis was used to reveal that innovation in the OFD business can increase the experiential value for the consumer, but this effect should be examined in light of customers’ fear of COVID-19. Managerial implications and future research lines are suggested.

1. Introduction

Some restaurants are altering their business models by implementing rapid innovation in order to confront the changes and challenges of the coronavirus pandemic and to match the demands of this unprecedented scenario. Among the wide range of types of innovation ( Damanpour 1996 ), restaurants are changing their OFD offerings by improving product presentations and by providing new services and/or products.

The OFD business has emerged as a relevant channel to reach customers and provide them with higher quality services in these times ( Alalwan, 2020 ) and is playing a major role in sustaining business. The main advantage of this type of service is that, besides simplifying the ordering process for both the consumer and the restaurant ( Chavan et al., 2015 ) in times of social distancing, it offers restaurants alternative income, retains customers, and enhances customer experience by providing new delivery channels.

OFD has now become more than just a utilitarian alternative when it comes to food consumption. Restaurants are innovating to offer experiential OFD options. However, little is known about this challenge and the role that fear of COVID-19 contagion plays in the decision-process so as to properly understand consumer behaviour.

This paper aims to study the impact of OFD innovation on new products and services, which aim to enhance the experiential value of OFD and the consumers' willingness to order during the COVID-19 pandemic. Based on the level of meal preparation ( Costa et al., 2007 ) and according to the trends observed in the OFD business ( Keeble et al., 2020 ), three options of innovation in OFD were suggested by a panel of hospitality experts. Each meal option innovates in the product/service to address the challenges posed by the coronavirus pandemic by increasing consumers’ experiential value. This issue is addressed in a survey experiment with four experimental delivery conditions.

This paper is divided into four parts. First, a literature review of the current state of OFD, the experiential value of OFD and the influence of COVID-19 on consumer behaviour, are explored, leading to the formulation of the hypotheses. Second, the methodology to verify the hypotheses is described. The results obtained are then presented. The paper ends with a discussion of the results, the theoretical and professional implications, the limitations of the study, and future research prospects in the field are provided.

2. Literature review of online food delivery in the COVID-19 era

In the sector today, OFD practices include a wider range of products and services: from ready-to-eat meals to raw ingredients that the consumer receives along with the recipe to cook the meal at home ( Forbes, 2020 ).

Literature in OFD has focused on the study of attitudes and perceptions ( Jang et al., 2011 ; Kang and Namkung, 2019 ; Kimes, 2011 ), and the consumer experience, satisfaction, and loyalty generated when the offering exceeds the customer's expectation ( Suhartanto et al., 2019 ; Yeo et al., 2017 ).

OFD services offer advantages to both the business owners and their customers ( See-Kwong et al., 2017 ). OFD services grant consumers easy and effective access to food from a wide range of restaurants at different times and various locations ( Chai and Yat, 2019 ; Gupta and Paul, 2016 ; Yeo et al., 2017 ). Moreover, it provides customers with more comprehensive, up-to-date, and accurate information regarding restaurants, menu options, customers’ prior experiences through online reviews and online rating, or even monitors their orders and the progress of those orders ( Alalwan, 2020 ). The availability of online delivery service technology enables the restaurant industry to improve order accuracy, increase productivity, enhance customer relationship ( Kimes, 2011 ), and expand their market ( See-Kwong et al., 2017 ; Yeo et al., 2017 ). Thus, the ubiquitous use of the internet and mobile phones has triggered a flourishing OFD business, which is still on the rise ( Cho et al., 2019 ; Jang et al., 2011 ; Kang and Namkung, 2019 ). Nevertheless, some authors have warned about disadvantages of OFD such as increased expense, share sales revenue with delivery providers or packaging delivery defect ( Chai and Yat, 2019 ).

2.1. Experiential value in the context of OFD

Holbrook and Hirschman (1982) stressed that consumption has an experiential dimension. Consumers are looking for sensory stimulation; they want to have fun and a memorable experience. Experiential value refers to the aesthetic and experience-based enjoyment that results from the entire purchase decision-process, from need recognition, shopping, consumption, to post-purchase behaviour ( Mort and Rose, 2004 ). The experiential value does not replace the traditional utilitarian perspective. Rather, it works as an extension of the latter.

Prior research on OFD has shown that the enjoyment of the process of buying standard online food provides emotional arousal and experiential value ( Suhartanto et al., 2019 ; Yeo et al., 2017 ). Thus, experiential value results from the process and not from the act of consumption. Nevertheless, in light of the COVID-19 pandemic, consumers are considering OFD from a new perspective. Apart from the utilitarian criteria of comfort, ease, and convenience, which remain important, consumers are seeking the experience that they are afraid to enjoy in a restaurant. Innovation should be aimed at deepening the experiential value of OFD in order to fulfil these new restaurant consumers’ needs.

During the COVID-19 pandemic, OFD consumers who were already a fan of experiences are being seduced by premium offers. They expect sensory stimulation and are looking for emotional arousal ( Nielsen, 2020 ). Shifting restaurant consumption to OFD in the home environment needs, now more than ever before, to be a memorable experience.

Innovation to raise the experiential value in the context of OFD can focus on emotions, such as the surprise triggered by the sophisticated packaging of a quality meal and its unboxing; the pleasure elicited by the immersive experience of recreating a restaurant at the host's house, or the praise levied on the host by guests for a delicious meal, resulting in the feeling of pride over a meal that is effortlessly prepared with the delivery of and the recipe for cooking the ingredients.

Thus, when OFD provides a seamless culinary experience, customers are more willing to order online delivery food. Accordingly, the following mediation hypothesis is proposed:

The impact of innovation on the willingness to order online delivery food is mediated by the perceived experiential value.

2.2. Fear of COVID-19 and OFD

Lockdown measures, prompted by health crises and the associated economic damage, can provoke a sense of collective hysteria, fear, anxiety, and uncertainty and reduce social contact among consumers ( Ahmed et al., 2020 ).

While the COVID-19 pandemic ensues, the future is uncertain and concerns over what will happen next are plaguing everyone. New habits learnt during lockdown will remain afterwards. These include a preference for isolation, the choice to have a smaller, more intimate circle of friends, greater awareness of the presence of germs in public areas, and more home entertainment. However, consumers are also looking forward to regaining certain rewarding aspects of their lives. At this time, consumers miss socialization and contact with friends and family that usually takes place over food. For instance, consumers in Spain stand out for their intense contact with friends and family (76%) and for their desire to regain physical contact (51%) ( Mindshare, 2020 ).

Meeting peers around a table of food goes beyond the mere act of satiating one's appetite and acquires a socializing dimension. Eating is an experience that transcends food. We eat to enjoy the company, strengthen our social bonds and obtain gratification by sharing moments and experiences with others. Convivial is used to describe this ( Phull et al., 2015 ). Going to a restaurant makes for an exciting experience ( Dixit, 2020 ), filled with pleasure, and which provides a sense of personal well-being. This is especially visible in cultures such as the Mediterranean one ( Poole and Blades, 2013 ).

Despite the fact that restaurants have become the core of social life ( Gustafsson et al., 2006 ), customers have begun to experience mixed feelings in that regard as the COVID-19 pandemic continues. 67% of consumers feel that they would be more likely to meet at home or virtually rather than at a bar, and 65% say they are more likely to order food online from a restaurant than to dine there ( WWD Business News, 2020 ).

The fear of COVID-19 may influence the way consumers enjoy restaurants ( Zwanka and Buff, 2020 ), as they are more concerned about safety in terms of health and hygiene and awareness of which ingredients are used in dishes, where they come from, how they are prepared, and who prepares them. The OFD service is therefore used to shift consumption to safer, more controlled environments, such as the home ( Rabobank, 2020 ). Thus, based on previous research, we have formulated the following moderation hypothesis:

During the COVID-19 pandemic, the fear of COVID-19 moderates the willingness to order online delivery food.

Fig. 1 represents the research model proposed.

Research model.

3. Methodology

Based on the level of meal preparation ( Costa et al., 2007 ) and according to the trends observed in the OFD sector ( Keeble et al., 2020 ), a panel of experts in hospitality, restaurants and OFD — four restaurant owners, four academics, and three consultants – suggested three innovative OFD options that could increase consumers’ experiential value. First, a sophisticated box with a gourmet menu featuring high-quality ingredients based on the Spanish Coquettogo from Coquettobar (coquettobar.com/delivery.php) would be used. The delivery box would include a letter alongside the instructions to end-cook the recipe. Every item of food would be carefully packed and, thus, the client would enjoy the surprise of unboxing a luxury experience. We will refer to this option as unboxing. The second was catering for small dinners at home, staffed by a chef with the British Dineindulge ( www.dineindulge.co.uk/ ), a go-to destination concept. This would provide an immersive experience at home. We will refer to this option as home chef. The third innovative proposal was inspired by the Mexican restaurant Bello Puerto that delivers the ingredients sealed and refrigerated, along with instructions from the chef ( www.bellopuertoencasa.com/ ). This option comprised a meal box with fresh, high-quality ingredients, delivered with the recipe to prepare a dinner for the number of guests. The host would feel pride in providing them an excellent dinner in a controlled setting. We will refer to this option as a DIY meal kit. In addition, we selected a standard delivery option as the control condition consisting of an Asian meal with no innovation in either the food or the service. We conducted a between-subjects design survey to compare each of the three innovative delivery options (unboxing, home chef, and DIY meal kit) with a standard delivery option to test the influence of innovation in OFD on experiential value at the consumption stage and its further effect on the willingness to order online delivery food during the COVID-19 pandemic.

3.2. Questionnaire design and data collection

The questionnaire had three sections: contextualization, scales to rate the stimulus and the general data of participants.

Participants were initially informed that the study was to research OFD during the COVID-19 pandemic. The participants were asked to read a brief description to set the study in context: they were supposedly hosting a dinner party at home with friends after the COVID-19 lockdown. After thinking about what their friends would like to eat and searching the Internet, they would receive the food they had ordered (stimulus) at home at the appointed time. See Appendix I .

A professional photographer designed the layout of the pictures depicting the four stimuli to help the participants understand the delivery option they were supposed to enjoy. This helped us to provide very realistic pictures, presenting the food delivery option and with a brief description of the offering.

Participants were allowed to view the picture for as long as they wanted. Each participant had to then answer several questions about the innovativeness of the delivery option, the utilitarian and experiential value they perceived in the delivery option, and their willingness to order this online delivery food. Finally, participants answered questions related to their fear of COVID-19, psycho-sociodemographic data and past behaviour.

The questionnaire was examined by a panel of experts to ensure content validity. It was further tested in a group of 20 target participants to verify the clarity of the questions and gain feedback on the length of the questionnaire.

Data collection took place from May 25th to June 3rd. Filling in the questionnaire required between 5 and 8 min. The task was self-paced.

3.3. Measurement scales

The measurement scales were selected and adapted after a thorough review of the literature. The independent variable comprised the four online delivery service options described (standard delivery, unboxing, home chef, and DIY meal kit). The mediator variable was the experiential value of the delivery option, measured using 4-items adapted from Otto and Ritchie (1996) . Fear of COVID-19 was included in the model as a moderator variable, measured using 4-items adapted from Ahorsu et al. (2020) . The dependent variable was the willingness to order the specific online delivery food showed when inviting friends over for dinner, measured using 3-items adapted from Han et al. (2019) . Finally, two variables were included to check the manipulation of the independent variable. The first was the perceived innovation of the delivery food showed, measured using 2-items adapted from Zhao et al. (2009) , and the second was the utilitarian value of the delivery option, measured using 3-items adapted from Ryu et al. (2010) . All responses were measured using a 5-point Likert scale, ranging from “strongly disagree = 1” to “strongly agree = 5”. All scales displayed acceptable reliability levels ( Nunnally, 1978 ) (See Table 1 ).

Variables, items, and scales used.

3.4. Sample

The participants in the experiment were a convenience sample recruited from social media sites ( Mullinix et al., 2015 ) of Spanish subjects (N = 207), aged between 18 and 70 (mean age: 48 years old). Participants were randomly assigned to one of the four experimental conditions (standard delivery, unboxing, home chef, and DIY meal kit). Cell sizes ranged from 50 to 54. The study was conducted through a web survey ( Schonlau et al., 2002 ) (See Table 2 ).

Sample profile.

The participants were looking forward to meeting their friends and relatives, since lockdown had been a sad and lonely experience for everyone. Spanish customers love visiting restaurants (8.3 over 10) and prefer them over OFD (5.5 over 10). Moreover, their willingness to spend money when going to a restaurant is greater than when ordering online food.

4. Data analysis and results

The 16 items of the scales were subjected to a principal component analysis (PCA). The KMO value was 0.853, and Bartlett's Test of Sphericity was significant ( p  < .000). The number of extracted factors with eigenvalues equal to or greater than one was five. All the items were assessed on the appropriate factor, with factor loadings higher than 0.6. Reliability was measured with Cronbach's alpha = .851 ( Nunnally, 1978 ). The scales were summed and averaged to form several indices according to their dimensions (EXPERIENTIAL_V, W_ORDER, F_COVID, INNOVATION, and UTILITARIAN_V).

4.1. Manipulation check

Prior to the analysis, a manipulation check of the independent variable was conducted in order to prove that the alleged innovation of the delivery options was perceived in that way. Additionally, we tested that the innovation of the delivery options added experiential value while preserving the utilitarian value. Finally, we tested that there were no significant differences between groups based on their level of fear of COVID-19.

A multivariate general linear model (GLM) was run on INNOVATION, UTILITARIAN_V, and F_COVID in order to check the manipulations of the independent variable. The participants exposed to any of the innovative delivery options rated its perceived innovation significantly higher (M unboxing  = 3.74, SD = 0.85; M home chef  = 3.85, SD = 1.11; M DIY meal kit  = 3.57, SD = 1.04) compared to those exposed to the standard delivery option (M = 2.95, SD = 1.02) ( F (3,203) = 8.008, p  < .000). We used Sidak's test for pairwise comparison. The perceived innovation of standard delivery was significantly different from every innovative delivery option used in the experiment: unboxing ( p  < .00), home chef ( p  < .000), and DIY meal kit ( p  < .05). However, there were no significant differences among the innovative delivery options ( p  > .05 in all conditions).

The utilitarian value of each experimental condition (M unboxing  = 3.97, SD = 0.79; M home chef  = 3.73, SD = 0.94; M DIY meal kit  = 3.53, SD = 1.03; M standard  = 4.04, SD = 0.78, F (3,203) = 3.458, p  < 0.05) revealed significant main effect of the utilitarian value. However, Sidak's test for pairwise comparison showed that the difference to be only significant between the DIY meal kit and standard delivery ( p  < .05). This could be explained by the fact that the need for cooking requires an extra amount of effort.

Regarding fear of COVID-19, there was no significant difference among the groups ( F (3,203) = 1.392, p  > .05). Fig. 2 provides a summary of this initial result.

Means of the variables to check experimental conditions.

4.2. Conditional process analysis

We tested H1 and H2 using SPSS version 22.0 with Model 5 in the PROCESS v3 macro ( Hayes, 2018 ). The mediation analysis was based on 5000 bootstrap samples, with a 95% confidence interval (CI).

The independent variable was the online delivery option. This variable was multicategorical and coded whether the option in OFD was (1) the control condition (standard delivery), (2) the unboxing condition, (3) the home chef condition, or (4) the DIY meal kit condition. We used an indicator coding system to dummy code the independent variable; therefore, the control condition acts as the baseline group. Three dummy variables were constructed to code the experimental condition (X). D1 captured the effect of condition 2 vs condition 1. D2 captured the effect of condition 3 vs condition 1, and D3 captured the effect of condition 4 vs condition 1. Experiential value was used as a mediator variable; the willingness to order food online was the dependent variable, and fear of Covid-19 was the moderator variable.

Mediation analysis

H1 proposed a mediation effect of the customers’ perceived experiential value in the relationship between the different options of delivery and the willingness to order online delivery food when inviting friends over for dinner.

The results showed that the independent variable—the delivery options—had a significant relative positive effect on the perceived experiential values ( a 1 , a 2, and a 3; p  < .05). The individuals assigned to the more innovative delivery conditions showed higher perceived experiential values than those assigned to a standard delivery offer, with the DIY option (4) being the option that exerted a stronger influence. See Table 3 .

Coefficients for the mediation model.

The perceived experiential value, in turn, had a significant positive effect on the willingness to order online food when inviting friends over for dinner ( b  = 0.531, p  < .000). This result suggests that when perceived experiential value increases, consumers tend to show a higher willingness to order food to have dinner at home when inviting friends over.

The relative direct effect between the independent variable and the willingness to order online food, not attributable to the perceived experiential values, was also significant.

Finally, the relative indirect effects of the catering service option on the willingness to order food via the mediator were also significant. See Table 4 .

Coefficients of the relative indirect effects of the mediation model.

When taken together, the mediation model indicates that the effect of all different delivery services options on the willingness to order food is mediated by the perceived experiential value. Based on these results, H1 is supported.

Moderation analysis

In H2 , we predicted that the fear of COVID-19 moderates the influence of the different delivery options on the intention of ordering online food when inviting friends over for dinner.

Prior to conducting the moderation analysis, the moderator variable was mean-centred. Table 5 shows the coefficient of the relative conditional effects, standard errors, p -values, and other model summary information referring to the moderation effect.

Model coefficients for the relative moderation analysis.

D1, D2, D3, and W represent the relative conditional direct effects of the delivery options on the intention to order food.

The results depicted in Table 4 show that not all delivery options behave similarly in terms of intention to order food online, depending on the participants’ fear of COVID-19.

Unboxing vs. standard delivery option: Customers offered the unboxing option as compared to those who were offered the standard delivery option (D1) were more willing to order food online from a catering service. Yet, this effect was marginally significant ( p  = .050). However, as consumers reported more fear of COVID-19, the willingness to order the unboxing option increased significantly ( p  = .004) compared to the standard delivery option. This result suggests that the direct effect of being offered the unboxing delivery option vs. the standard one is dependent on the fear of COVID-19. See Fig. 3 .

Relative conditional effect of D1 (unboxing vs. control) × Fear of COVID-19 on the willingness to order.

Home chef vs. standard delivery option : When comparing the delivery options of home chef vs. the standard delivery option (D2), we found that there was a significant difference in the willingness to order food online when consumers’ fear of COVID-19 is low ( p  = .228).

However, when comparing the willingness to order between the home chef condition and the standard delivery condition among customers with high fear of COVID-19, the difference was not significant ( p  = .004). Therefore, the moderating effect of the fear of COVID-19 in this situation exerts the opposite influence to that of the unboxing option. See Fig. 4 .

Relative conditional effect of D2 (home chef vs. control) × Fear of COVID-19 on the willingness to order.

DIY home kit: Interestingly, customers were more willing to order a DIY meal kit than a standard delivery condition; however, the fear of COVID-19 did not exert a moderation effect in this condition.

Furthermore, when comparing the delivery options of DIY meal kit vs . the standard delivery option (D3), results showed that the fear of COVID-19 did not exert a moderating effect. See Fig. 5 .

Relative conditional effect of D3 (DIY meal kit vs. control) × Fear of COVID-19 on the willingness to order.

Based on the results, H2 was partially supported. Fear of COVID-19 exerted a moderation influence on consumers’ willingness to order innovative online delivery food.

5. Discussion, implications, and limitations

The COVID-19 pandemic has adversely impacted the restaurant industry and the industry is pivoting to stay afloat during this pandemic.

This research examined the effect of innovation in OFD aimed at increasing the perceived experiential value on the willingness to order innovative online delivery food, together with as the moderating role that fear of COVID-19 can play on the willingness to order these innovative options. The results show that OFD is a broad category that is not restricted to the convenience segment of ready-to-eat food. Innovation in OFD can increase the experiential value evolving toward a ready-to-enjoy concept, thereby influencing consumers' purchasing decisions. The impact of COVID-19 on society influences users’ willingness to order.

The findings of this study show that while innovation may increase the experiential value of a delivery option, the fear of COVID-19 will still influence consumers’ decision in various ways. Fear can be seen to favour choice in the case of the unboxing option. Although consumers may prefer to dine with friends in a restaurant rather than at home, such an attractive option becomes even more attractive when fear of COVID-19 increases.

In contrast, the influence of fear is reversed in the case of home chef, which has a high experiential value. The results suggest that the fear of contagion may extend to the fear of allowing strangers into one's home (despite following rigorous safety, cleaning, and sanitation protocols). Thus, in this situation, fear discourages the intention to order.

The DIY meal kit condition is the only option for which the experiential value increases with respect to the standard delivery, but the utility value decreases. The increase in the experiential value favours the decision to buy, but the fear of the COVID-19 does not exert a significant influence with respect to this option. It seems that other factors, which are perhaps related to the perception of the utilitarian value, would provide a deeper explanation of the intention to opt for this option.

This research has several academic implications. First, this study helps to demonstrate how innovation brings experiential value to OFD. Furthermore, it sheds some light and broadens knowledge on COVID-19 when it comes to consumer decisions. Thus, it shows that the level of fear experienced by consumers significantly affects their decisions. Consumer behaviour, which is always dynamic, can be even more so, depending on the severity perceived at different times of the pandemic. COVID-19, in a way, forces us to learn from the consumer, as something that could be very attractive under normal conditions (outside the COVID-19 pandemic) ceases to be so in times of COVID-19 due to fear. In the medium and long term, these results could help understand consumer behaviour in the face of an upsurge in the disease, which would consequently raise the level of fear of COVID-19.

Moreover, there are several practical implications from this research. Perceived innovativeness has a profound effect on the profitability of a company ( Hwang et al., 2019 ). This experiment shows that participants’ perceived innovation in every option of OFD tested. This could help restaurants to understand how consumers perceive innovativeness from their products/services.

Innovation has so far been directed at the moment of purchase and was essentially technological. However, the findings of this study have shown that innovation in consumption experience is worth paying attention to as well.

It is important to be aware that today's consumer is a fan of an experience demanding enjoyment as well as emotional stimulation ( Nielsen, 2020 ). However, for the experience to be a real factor influencing decision-making, it must also ensure the complete safety of the customer, who is very sensitive to the fear of contagion. Many of the changes we have incorporated in our personal interactions to avoid contagion (preference for isolation, choice to maintain a smaller, more intimate circle of friends, greater awareness of the presence of germs in public areas, more home entertainment) are likely to remain long after the pandemic ends ( Veeck and Xie, 2020 ).

Regarding limitations, data collection is based on a convenience sample. There is a common concern that the features of a given convenience sample may diverge from a representative population sample. However, Mullinix et al. (2015) provided evidences that convenience samples can produce treatment effect estimates similar to population-based samples in terms of direction and significance.

Future researchers could examine the effect of these types of innovations in different countries and also in the post-COVID-19 era. COVID-19 is pushing the food and the restaurant industry to anticipate customer concerns, needs and fears, to pivot, to redesign, and to innovate. Once the pandemic has ended, the new habit of working from home may create new opportunities, such as OFD breakfasts, to be investigated.

Innovation will have to respond to the growing demand for OFD. Research is needed to create products that travel well and to develop packaging that preserve food quality.

Cooking at home has increased as a result of the pandemic and consumers are likely to continue doing so post-pandemic. Therefore, DIY meal kits could be a future meal trend to be researched.

6. Conclusion

This paper highlights the importance of exploiting the possibilities offered by rapid innovation that seeks to make the consumption of OFD more experiential. For restaurants, and restaurateurs, modifying, updating, pivoting, and innovating their operations in an attempt to meet the current needs of a changing customer and adapting customer experience in the time of COVID-19 and the future will be crucial.

Implications for gastronomy

First, this study helps to demonstrate how innovation brings experiential value to OFD. While OFD has traditionally been a utilitarian alternative to solve the need for effortless eating, evidence is provided that experiential value is possible by innovating in both product and service. The experiment conducted shows that participants perceive innovation in all OFD options tested. This could help restaurants to understand how consumers perceive innovation in their products/services. Secondly, increasing the experiential value of OFD means broadening and extending the offer beyond popular Italian, American and Chinese dishes. This, in turn, implies the need to develop packaging that travels well and preserves the quality of the food. Covid-19 has a strong impact on consumption habits. Here we show the moderating role of fear in consumer choice. However, in the aftermath of the pandemic, consumption of food at home has increased and consumers are likely to continue to do so after the pandemic. Therefore, DIY meal kits could be a future food trend to investigate.

Author statement

Diana Gavilan, Ph.D. : Conceptualization, Methodology, Formal Analysis, Writing - Original Draft, Supervision. Adela Balderas-Cejudo, Ph.D. : Conceptualization, Investigation, Writing - Review & Editing, Data Curation. Susana Fernández-Lores, Ph.D. : Resources, Investigation, Writing - Original Draft. Gema Martinez-Navarro, Ph.D. : Project administration, Resources, Writing - Original Draft.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix I. Visual stimulus used in the questionnaire

• Ahmed M.Z., Ahmed O., Aibao Z., Hanbin S., Siyu L., Ahmad A. Epidemic of COVID-19 in China and associated psychological problems. Asian J. Psych. 2020:102092. doi: 10.1016/j.ajp.2020.102092. [ PMC free article ] [ PubMed ] [ CrossRef ] [ 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 doi: 10.1007/s11469-020-00270-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Alalwan A.A. Mobile food ordering apps: an empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. Int. J. Inf. Manag. 2020; 50 :28–44. doi: 10.1016/j.ijinfomgt.2019.04.008. [ CrossRef ] [ 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):63–78. [ Google Scholar ]
• Chavan V., Jadhav P., Korade S., Teli P. Implementing customizable online food ordering system using web based application. Int. J. Innov. Sci. Eng. Technol. 2015; 2 (4):722–727. http://ijiset.com/vol2/v2s4/IJISET_V2_I4_112.pdf Available from: (accessed 02.06.20.) [ Google Scholar ]
• Cho M., Bonn M.A., Li J.J. Differences in perceptions about food delivery apps between single-person and multi-person households. Int. J. Hospit. Manag. 2019; 77 :108–116. doi: 10.1016/j.ijhm.2018.06.019. [ CrossRef ] [ Google Scholar ]
• Costa A., Schoolmeester D., Dekker M., Jongen W.M. To cook or not to cook: a means-end study of motives for choice of meal solutions. Food Qual. Prefer. 2007; 18 (1):77–88. doi: 10.1016/j.foodqual.2005.08.003. [ CrossRef ] [ Google Scholar ]
• Damanpour F. Organizational complexity and innovation: developing and testing multiple contingency models. Manag. Sci. 1996; 42 (5):693–716. [ Google Scholar ]
• Dixit S.K. The Routledge Handbook of Tourism Experience Management and Marketing. Routledge; 2020. [ Google Scholar ]
• Forbes Reinventarse para sobrevivir: la estrategia de los restaurantes en tiempos de coronavirus. 2020. https://www.forbes.com.mx/negocios-reinventarse-para-sobrevivir-la-estrategia-de-los-restaurantes-en-tiempos-de-coronavirus/ Available from: (accessed 15.06.20.)
• Gupta T., Paul K. Consumer attitude towards quick service restaurants: a study across select quick service restaurants in Gurgaon. Indian J. Appl. Res. 2016; 6 (4):639–641. [ Google Scholar ]
• Gustafsson I.B., Öström Å., Johansson J., Mossberg L. The five aspects meal model: a tool for developing meal services in restaurants. J. Foodserv. 2006; 17 (2):84–93. doi: 10.1111/j.1745-4506.2006.00023.x. [ CrossRef ] [ Google Scholar ]
• Han H., Lho L.H., Al-Ansi A., Ryu H.B., Park J., Kim W. Factors triggering customer willingness to travel on environmentally responsible electric airplanes. Sustainability. 2019; 11 (7):2035. doi: 10.3390/su11072035. [ CrossRef ] [ Google Scholar ]
• Hayes A.F. Partial, conditional, and moderated moderated mediation: quantification, inference, and interpretation. Commun. Monogr. 2018; 85 (1):4–40. doi: 10.1080/03637751.2017.1352100. [ CrossRef ] [ Google Scholar ]
• Holbrook M.B., Hirschman E.C. The experiential aspects of consumption: consumer fantasies, feelings, and fun. J. Consum. Res. 1982; 9 (2):132–140. doi: 10.1086/208906. [ CrossRef ] [ Google Scholar ]
• Hwang J., Lee J.S., Kim H. Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: the moderating role of gender and age. Int. J. Hospit. Manag. 2019; 81 :94–103. [ Google Scholar ]
• Jang Y.J., Kim W.G., Yang I.S. Mature consumers' patronage motives and the importance of attributes regarding HMR based on the food-related lifestyles of the upper middle class. Int. J. Hospit. Manag. 2011; 30 (1):55–63. doi: 10.1016/j.ijhm.2010.06.001. [ CrossRef ] [ Google Scholar ]
• Kang J.W., Namkung Y. The information quality and source credibility matter in customers' evaluation toward food O2O commerce. Int. J. Hospit. Manag. 2019; 78 :189–198. doi: 10.1016/j.ijhm.2018.10.011. [ CrossRef ] [ Google Scholar ]
• Kimes S.E. The current state of online food ordering in the US restaurant industry. Cornell Hosp. Rep. 2011; 11 (17):6–18. [ Google Scholar ]
• Keeble M., Adams J., Sacks G., Vanderlee L., White C.M., 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. Publ. Health. 2020; 17 (14):5190. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mindshare Covid-19 new normal tracker. 2020. https://www.mindshareworld.com/news/mindshare-new-normal-tracker-reveals-global-differences-as-more-countries-come-out-of-lockdown Available from: (accessed 04.06.20.)
• Mort G.S., Rose T. The effect of product type on value linkages in the means‐end chain: implications for theory and method. J. Consum. Behav. 2004; 3 (3):221–234. doi: 10.1002/cb.136. [ CrossRef ] [ Google Scholar ]
• Mullinix K.J., Leeper T.J., Druckman J.N., Freese J. The generalizability of survey experiments. Journal of Experimental Political Science. 2015; 2 (2):109–138. [ Google Scholar ]
• Nielsen Six consumer behavior thresholds of COVID-19 concern. 2020. https://www.nielsen.com/ssa/en/insights/article/2020/key-consumer-behavior-thresholds-identified-as-the-coronavirus-outbreak-evolves/ Available from: (accessed 04.06.20.)
• Nunnally J.C. McGraw-Hill; New York: 1978. Psychometric Theory. [ Google Scholar ]
• Otto J.E., Ritchie J.B. The service experience in tourism. Tourism Manag. 1996; 17 (3):165–174. doi: 10.1016/0261-5177(96)00003-9. [ CrossRef ] [ Google Scholar ]
• Poole S., Blades M. The Mediterranean diet – a review of evidence relevant to the food and drink industry. Nutr. Food Sci. 2013; 43 (1):7–16. doi: 10.1108/00346651311295851. [ CrossRef ] [ Google Scholar ]
• Phull S., Wills W., Dickinson A. Is it a pleasure to eat together? Theoretical reflections on conviviality and the Mediterranean diet. Soc. Compass. 2015; 9 (11):977–986. doi: 10.1111/soc4.12307. [ CrossRef ] [ Google Scholar ]
• Rabobank . Hormel Foods Corporation; 2020. Personal Review from Jack Shao. March 18. [ Google Scholar ]
• Ryu K., Han H., Jang S. Relationships among hedonic and utilitarian values, satisfaction and behavioral intentions in the fast‐casual restaurant industry. Int. J. Contemp. Hospit. Manag. 2010; 22 (3):416–432. doi: 10.1108/09596111011035981. [ CrossRef ] [ Google Scholar ]
• Schonlau M., Fricker R.D., Elliott M.N. RAND Corporation; Santa Monica, CA: 2002. Conducting Research Surveys via E-Mail and the Web. https://www.rand.org/pubs/monograph_reports/MR1480.html 2002. Retrieved from. Also available in print form. [ Google Scholar ]
• See-Kwong G., Soo-Ryue N., Shiun-Yi W., Lily C. Outsourcing to online food delivery services: perspective of F&B business owners. J. Internet Bank. Commer. 2017; 22 (2):1–18. http://www.icommercecentral.com/open-access/outsourcing-to-online-food-delivery-services-perspective-of-fb-business-owners.php?aid=86136 Available from: (accessed 06.06.20.) [ Google Scholar ]
• Suhartanto D., Helmi Ali M., Tan K.H., Sjahroeddin F., Kusdibyo L. Loyalty toward online food delivery service: the role of e-service quality and food quality. J. Foodserv. Bus. Res. 2019; 22 (1):81–97. doi: 10.1080/15378020.2018.1546076. [ CrossRef ] [ Google Scholar ]
• Veeck A., Xie H. WMU News; 2020. WMU Researchers Study Food Consumption Behavior during COVID-19 Pandemic. https://wmich.edu/news/2020/03/58581 Available from: (accessed 03.06.20.) [ Google Scholar ]
• WWD Business News Reports predict consumer behavior in a post-COVID-19 world. 2020. https://wwd.com/business-news/business-features/consumer-behavior-reports-predict-a-post-covid-world-1203562275/ Available from: (accessed 07.06. 20.)
• Yeo V.C.S., Goh S.K., Rezaei S. Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. J. Retailing Consum. Serv. 2017; 35 :150–162. doi: 10.1016/j.jretconser.2016.12.013. [ CrossRef ] [ Google Scholar ]
• Zhao M., Hoeffler S., Dahl D.W. The role of imagination-focused visualization on new product evaluation. J. Market. Res. 2009; 46 (1):46. doi: 10.1509/jmkr.46.1.46. [ CrossRef ] [ Google Scholar ]
• Zwanka R.J., Buff C. COVID-19 generation: a conceptual framework of the consumer behavioral shifts to Be caused by the COVID-19 pandemic. J. Int. Consum. Market. 2020:1–10. doi: 10.1080/08961530.2020.1771646. [ CrossRef ] [ Google Scholar ]

The rise of online food delivery culture during the COVID-19 pandemic: an analysis of intention and its associated risk

European Journal of Management and Business Economics

ISSN : 2444-8494

Article publication date: 21 March 2022

Issue publication date: 18 March 2024

• Supplementary Material

This study aims to understand consumer behaviour in the context of online food delivery (OFD), especially given the mandatory lockdown imposed in some countries that have modified the behaviour of consumers. Using model goal-directed behaviour (MGB), this study was conducted to investigate consumer perceived risk on the use of OFD services.

Design/methodology/approach

Responses of food delivery services users were collected online throughout April 2020 to understand their risk profile and behaviour. A total of 339 responses were collected and subsequently analysed using partial least square (PLS). Both measurement and structural model were evaluated to ensure that the structural equation modelling (SEM) is valid.

The results revealed that attitude (ATT), subjective norm (SN), positive anticipated emotion (PAE) and negative anticipated emotion (NAE) and perceived behavioural control (PBC) significantly influenced users' desire. It was also found that PBC significantly influenced users' intention. The empirical result suggests that performance, privacy, financial, physical and the risk of contracting COVID-19 negatively influenced users' desire. In contrast, only physical and the risk of contracting COVID-19 negatively influenced users' intention to use OFD services.

Practical implications

These findings provide OFD service providers and scholars with significant insights into what compels urbanites to adopt OFD services amid a health pandemic. It also allows OFD companies to realign their operation in addressing these concerns and changes in consumer behaviour.

Originality/value

Against the backdrop of the pandemic, this study provides insights for OFD providers in developing new strategies and approaches for business development and consumer retention in a post-pandemic world.

本研究擬瞭解與網上訂餐相關的消費行為；尤其當有些國家推行了改變消費者行為的強制性封鎖政策的情況下，這類研究更具意義。透過應用目標導向行為模型，本研究擬探討消費者在使用網上訂餐服務時所意識到的風險。

研究人員於 2020年4月網上收集使用訂餐服務人士的意見，以瞭解其風險狀況和行為。研究共收集了339位人士的意見，並以偏最小二乘法進行分析。測量和結構模型均加以評估，以確保結構方程模型是站得住腳的。

研究結果顯示，態度、主觀規範、預期的正面和負面情緒、以及感知的行為控制，顯著地影響了用戶的慾望。研究結果亦發現，感知的行為控制顯著地影響了用戶的意圖。研究的經驗性結果暗示了表現、私隱、金錢上的爭議、可能會導致的身體損傷和感染2019冠狀病毒病的風險，負面地影響用戶的慾望。相比之下，影響著用戶使用網上訂餐服務的意圖的因素就只有可能會導致的身體損傷和感染2019冠狀病毒病的風險。

研究的結果，為提供網上訂餐服務的營運者和研究學者提供了重要的啟示，使他們更瞭解是什麼因素會迫使都市人在與健康息息相關的大流行病期間使用網上訂餐服務。而且，提供網上訂餐服務的公司亦可藉此重新調整其營運，以能處理客戶的憂慮和應付消費行為的變化。

在大流行病肆虐的背景下，本研究為網上訂餐服務提供者給予了啟示，以便他們能在後疫情時代制定新的策略和營運方法，以拓展其業務和留住消費者。

• Food delivery culture
• Perceived risk
• Consumer behaviour
• COVID-19 pandemic

Poon, W.C. and Tung, S.E.H. (2024), "The rise of online food delivery culture during the COVID-19 pandemic: an analysis of intention and its associated risk", European Journal of Management and Business Economics , Vol. 33 No. 1, pp. 54-73. https://doi.org/10.1108/EJMBE-04-2021-0128

Emerald Publishing Limited

Copyright © 2022, Wai Chuen Poon and Serene En Hui Tung

Published in European Journal of Management and Business Economics . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The rapid growth of the Internet and wireless technologies has substantially impacted online shopping. Cheaper smart devices, rapid improvement in telecommunication infrastructure, coupled with the increase in purchasing power, lack of time and convenience has forced the food and beverage (F&B) industry to adapt and provide new offerings to cater to the growing demands of consumers ( Bezerra et al. , 2013 ). Consumers are attracted to shopping online since it is much more convenient, comfortable and at their leisure ( Jiang et al. , 2013 ). Online shopping has enabled consumers to reduce their decision-making efforts by offering more comprehensive options to choose from, screen information and compare products ( Alba et al. , 1997 ). Das and Ghose (2019) observed these changes in consumer behaviour, noting that the working population has less time due to the work-life culture in big cities. This busy lifestyle contributes to the rise of online shopping as consumers are too busy to enter shopping malls physically. Similarly, on-demand food and grocery delivery services quickly flourish among the urban working community.

Over the past year, the popularity of online food delivery (OFD) has been on the rise worldwide. Cho et al . (2019) argues that OFD is an innovative way that allows consumers to purchase a wide range of food selection via platform(s). OFD platforms collect orders from consumer and pass on the information to restaurants and delivery personnel ( Troise et al. , 2021 ). This opens up new opportunity for restaurants to reach new market while increasing their revenues and consumers the convenience of having food delivered to their home. In the past, researchers have mainly focused on traditional retail, e-commerce behaviours, characteristics of mobile application ( Cho et al. , 2019 ), not much discussion around OFD consumers ( Yeo et al. , 2017 ; Suhartanto et al. , 2019 ) and even lesser on the use of mobile application to order food from a restaurant ( Rodríguez-López et al. , 2020 ).

In 2020 and 2021, due to strict lockdown order, OFD had cemented itself as the most significant trend around the world ( Durai, 2020 ), representing a significant shift from frequenting restaurant to ordering food online. According to Statista (2021) , the estimated market size for OFD worldwide is around 107.44 billion U.S. dollars for 2019 and projected to be 154.34 billion U.S. dollars by 2023. Before the COVID-19 pandemic, majority of urban consumers are warming up to the concept of OFD. However, this trend had a major shift with multiple regions reporting a surge in OFD services, such as an increase 65% for Asia Pacific region, 21% for North America, 23% for Europe and 150% for Latin America region ( Statista, 2021 ; Hussey, 2021 ). The new behaviour imposed upon due to the pandemic will most likely remain as long-term behaviours, altering consumer's behaviours permanently. The present study coin this new phenomenon as food delivery culture. Food delivery culture refers to the consumer shift of practices and attitude (ATT) from the traditional model (i.e. dine-in or take-away) to delivery services enabled by the rise of technology. Nevertheless, with the rise of food delivery culture, little is known regarding this new behaviour and decision-making process. These changes were more evident as COVID-19 causes significant economic disruptions often up-ending years of traditional practices among consumer and companies worldwide. Faced with major disruption, it is relevant to focus on behavioural change among existing and new OFD consumer in response to the uncertainty.

A significant increase in OFD services resulted in a huge number of first-time users, but they also have concerns about the adoption of a new technology because they perceive unforeseen negative outcome before, during or after use, which is also known as “perceived risk” ( Hwang and Choe, 2019 ). The change in consumer behaviours shifted some risks traditionally that are not associated to dine-in to the consumers such as financial risk, privacy risk and performance risk. Business and consumers are expected to incur an additional cost when engaging in commission-based OFD services (e.g. signup fees, commission, packaging fees and delivery charges). In contrast, a patron is not subjected to additional fees while dining in the restaurant and businesses are not required to invest in new technology to cater for OFD. In this light, while pivoting to OFD may be the logical choice for the business's survival, the adoption of OFD is entirely dependent on the consumers due to the cost and risk posed by the adoption. More importantly, such risks are negatively associated with the adoption of a new technology. Even though studies related to perceived risk in e-commerce and hospitality industry is not uncommon ( Lutz et al. , 2018 ; Yi et al. , 2020 ), studies related to perceived risk in the domain of mobile application for food deliveries is extremely limited.

OFD represents a significant innovation in food delivery that is changing consumers' habits ( Troise et al. , 2021 ). Hence, the present study examines the consumer's intention to use OFD and its associated risks to address the research gap. More specifically, this study proposes evaluating consumer's desire and intention in engaging with OFD using the model goal-directed behaviour (MGB), which is an extension of the theory of planned behaviour (TPB). The application of MGB could elicit meaningful insight in examining consumers' behavioural intention and decision-making as MGB focuses on areas overlooked by TPB, which are desire, affect and habit in providing more accurate OFD user prediction of behaviour and decisions ( Perugini and Bagozzi, 2001 , 2004 ). Furthermore, this study incorporates the influence of perceived risk on desire and behavioural intention to use OFD. The investigation of perceived risk's role would reveal the hurdles preventing users from engaging with OFD and assisting OFD service provider in formulating relevant strategies to target their market and mitigate consumer risk profiles.

2. Theoretical framework and hypothesis development

According to the first lacuna, this study developed a conceptual model that revisited the intention theory – theory of planned behaviour (TPB) (see Figure 1 ). TPB was first proposed by Ajzen (1991) , who claims that when behaviour is rational, the best predictor of that behaviour is intention. This theory postulates that intentions are a fundamental antecedent of actual behaviour. TPB posits the link between subjective norm (SN), ATT and perceived behavioural control (PBC) influencing intention and subsequent behaviour. Likewise, Fishbein and Ajzen (2010) also suggest that once intentions have been formed, individuals will be highly inclined to act on such intentions once the opportunity arises. TPB has been widely applied in numerous research such as health-related studies ( Cooke et al. , 2016 ), marketing ( Rehman et al. , 2019 ) and e-commerce ( Dakduk et al. , 2017 ).

TPB has been criticised recently due to concerns over its validity and utility, with some arguing that the theory should be retired ( Sniehotta et al. , 2014 ; Erasmus et al. , 2010 ). Esposito et al. (2016) argues that TPB fails to capture people really want to do something and the emotions after they have done it. Due to these shortcomings, researchers have sought to extend and improve upon TPB by establishing new constructs and new relationships ( Sutton, 1998 ; Perugini and Bagozzi, 2001 ; Tommasetti et al. , 2018 ), such as MGB (see Figure 2 ). MGB accounts for significantly more variance, parsimonious and prediction in studies regarding intention and behaviour in comparison to the TPB ( Perugini and Bagozzi, 2001 ).

In the MGB, the intention to perform a behaviour is primarily motivated by the desire to perform the behaviour, and this desire is assumed to reflect the effects of ATT, SN, PBC, anticipated emotions (AEs) and frequency of past behaviour and to mediate their influence on intention ( Perugini and Bagozzi, 2001 ). The concept of desire is defined as “a state of mind whereby an agent has a personal motivation to perform an action or achieve a goal” ( Perugini and Bagozzi, 2004 , p. 71). This desire represents a motivational state of mind where the reasons to act are translated into motivation. Desire is a state in which an individual is eager to take a particular action through internal stimulation (e.g. achievement, curiosity and shortage) ( Perugini and Bagozzi, 2004 ). One of the crucial causes of desire formation is individuals' previous experiences ( Leone et al. , 2004 ). Therefore, the construct of desire is to capture whether people want to do something, out of joy or a feeling of satisfaction, instead of out of obligation ( Esposito et al. , 2016 ). Meanwhile, AE evaluates one's emotional state in the decision-making process ( Perugini and Bagozzi, 2001 ). Desire is presumed to mediate the influence of ATT, SN and PBC on intention, while AE appear in the model as a factor influencing desire. Thus, the MGB explained significantly greater amount of variance in intention in comparison to the TPB ( Esposito et al. , 2016 ).

Ample of evidence in the literature that supports the significant relationship rooted in TPB. TPB argues that an individual's beliefs about their ability to perform the behaviour in question influence whether or not they engage in the behaviours. ATT represents the degree to which a person has a favourable or unfavourable evaluation of the behaviour. At the same time, SN refers to the belief that whether the majority of people approve or disapprove of the behaviour while, PBC reflects a persons' perception of the ease or difficulty of performing a given behaviour ( Ajzen, 1991 ). An individual with a positive evaluation of the behaviour will increase the likelihood that the behaviour will be performed. Consumer's ATTs toward buying food online have a significant positive effect on their behavioural intentions ( Chen et al. , 2020 ). Mosunmola et al. (2018) argued that ATT has a significant positive effect on engaging in online purchases. Behavioural intentions are also influenced by important people in the consumers' lives, such as their family and friends ( Bhattacherjee, 2000 ). Bui and Kemp (2013) argued that consumers' ATTs, emotion regulation, SN and PBC influence repeat purchase intentions. In the field of marketing, a recent study has applied MGB to the purchase of sporting goods online ( Chiu et al. , 2018 ), and Yi et al. (2020) applied MGB in the field of tourism.

ATT positively influences the desire to use OFD services

SN positively influences the desire to use OFD services

PBC positively influences the desire to use OFD services

PBC positively influences behavioural intention to use OFD services

PAE positively influences the desire to use OFD services

NAE positively influences the desire to use OFD services

Desire positively influences behavioural intention to use OFD services

2.1 Perceived risk

Consumers are notably more anxious when using new technology-based services (e.g. OFD applications), especially in providing personal information, such as their full name, identification number and credit card details. The uncertainty of unpleasant consequences can result in perceived risk ( Yang et al. , 2015 ). Suppose the intention and desire to make a certain purchase goal are not met. Consumer will experience negative consequences such as negative view toward the service, financial losses, violation of privacy, product or service delivery failure, anxiety, discomfort or wasted time. The higher the perception of negative consequences among consumers, the more it would deter consumers' purchasing intention. Hence, building consumer confidence and the mitigation of such risk influences consumers' purchasing intentions.

Since the 1960s, perceived risk theory has been used to explain consumers' behaviour. Perceived risk is a kind of expected loss ( Schierz et al. , 2010 ) to pursue the desired outcome, which plays a significant role in dictating consumer purchase intention. Consumers’ perceived risk is higher in online purchases compared to traditional in-store purchases. As such, perceived higher risk behaviours would hinder the individual's motivation to act a certain way. Zhao et al. (2017) and Kim and Lennon (2013) posit that if a consumer perceived that it is risky to purchase from online retailers, it is less likely for the consumer to purchase from the online retailer. The majority of scholars agreed that consumers' perceived risk is a multi-dimensional construct and may vary according to its product, service, industry or situation. Five constructs or component of perceived risks have been identified that could influence online purchasing habits: performance risk, financial risk, time risk, physical risk and privacy risk ( Featherman and Pavlou, 2003 ; Han and Kim, 2017 ). Furthermore, the pandemic's abrupt behavioural change causes' anxiety and stress among the OFD users. Hence, the present research investigated five types of risk: performance risk, privacy risk, financial risk, physical risk and COVID-19 risk and its influence on the desire and intention to use OFD services.

The first concern of OFD is performance risk. Performance risk refers to the potential loss incurred when the service does not perform as expected ( Kushwaha and Shankar, 2013 ). Compared to an established traditional in-dining service restaurant or take-away option, OFD services are more likely to be managed by an amateur delivery person resulting in sub-par or failed delivery service. On top of that, purchasing a product or service online without touching, smelling, seeing or feeling the product may increase the level of perceived performance risk ( Forsythe and Shi, 2003 ). Previous studies have commonly suggested that OFD consumers are more likely to have a negative experience. Unable to make correct decision, order prepared wrongly by the restaurant, delayed in delivery or stolen food would significantly impact the performance of OFD services resulting in higher performance risk for the consumer. Privacy risks are concerned with the possibility of a consumer's personal information, such as name, email address, phone number, credit card information, leaked or misused by an unscrupulous individual ( Forsythe and Shi, 2003 ). OFD companies might store sensitive information through their mobile applications, making them likely target of hackers. Fortes and Rita (2016) found that the privacy concern negatively impacts trust, behaviours and online purchase intention. As all OFD transactions are conducted online, OFD users are more likely to be concerned about any unauthorised access of information that could cause harm to the owner. Thus, privacy risk could hinder consumer's intention and desire to engage in OFD services.

Performance risk negatively influences the desire to use OFD services.

Performance risk negatively influences behavioural intention to use OFD services.

Privacy risk negatively influences the desire to use OFD services.

Privacy risk negatively influences behavioural intention to use OFD services.

Financial risk negatively influences the desire to use OFD services.

Financial risk negatively influences behavioural intention to use OFD services.

Physical risk negatively influences the desire to use OFD services.

Physical risk negatively influences behavioural intention to use OFD services.

COVID-19 risk negatively influences the desire to use OFD services.

COVID-19 risk negatively influences behavioural intention to use OFD services.

3. Research method

3.1 data collection and sample.

The study's respondents are customers with OFD experience. The non-probability convenience sampling method was applied to examine the proposed framework ( Figure 1 ) due to the unknown total population and the absence of a sampling frame for the customer of OFD. This study adopted Kline's (2015) recommendation to estimate the minimum samples size using the G*Power 3.1 program ( Faul et al. , 2009 ). This program is designed to analyse the statistical power commonly used in social behavioural studies. It provides power analysis options for frequently used analysis, including correlation and regression. In terms of sampling, this study adopted the sample size calculation by Poon et al. (2018) . The minimum estimated sample size is 208 respondents with the power at 95%, with an alpha set at 0.05 and a medium effect size of 0.15. The convenience sampling method was used in this study. Potential respondents were contacted online (messaging application) enclosed with an online survey link. In a bid to encourage respondents to complete the survey, important information such as the study's introduction and purpose guarantee data confidentiality, option to decline and progress bar. A total of 355 respondents recruited for the study; 349 responses were gathered from OFD customer residing in Selangor and Kuala Lumpur over two weeks in April 2020. After data cleaning, ten responses were discarded due to poor data quality and only 339 useable responses were included in the data analysis.

3.2 Scale measurement

The proposed research model (see Figure 3 ) consists of 12 variables-ATT, SN, PAE, NAE, PBC, desire, intention and five constructs of perceived risk (performance risk, financial risk, privacy risk, physical risk and COVID-19 risk). The items for MGB were adapted and contextualised from Perugini and Bagozzi (2001) and Bagozzi and Dholakia (2002) to represent OFD behaviours. The constructs of perceived risks were adapted from Murray and Schlacter (1990) and Yi et al. (2020) . All items were assessed on a five-point Likert scale from strongly disagree (1) to strongly agree (5). The English language questionnaire was pre-tested on 30 OFD customers to ensure that the questions and instructions are well comprehended. Minor amendments were made to the questionnaire based on the feedback from the pre-testing.

3.3 Analysis and results

To better understand the characteristics of respondents, the frequency distribution method was presented. In all, 62.2% of the respondents are female, 49.9% of them are aged between 20 and 29, 71.6% of the respondents are college or university graduates, and a majority earn a monthly income between RM 4,000 and RM 7,999 (34.5%). The summary of the demographic data is presented in Table 1 .

As suggested by Cain et al. (2016) , this study assessed the multivariate skewness and kurtosis. The results indicated that the data was not multivariate normal, Mardia's multivariate skewness ( β  = 12.982, p  < 0.01) and Mardia's multivariate kurtosis ( β  = 191.807, p  < 0.01). Thus, to analyse the data, the partial least squares structural equation modelling (PLS-SEM) technique was adopted using SmartPLS 3.3.2 ( Ringle et al. , 2015 ). The present study employed PLS-SEM due to the prediction-oriented variance-based approach compared with covariance-based structural equation modelling (CB-SEM), which is more confirmed-orientated ( Hair et al. , 2017a ). PLS-SEM was chosen to examine the predictability of exogenous variable (ATT, SN, PAE, NAE, PBC and perceived risk) on the endogenous variable (desire and behavioural intention). Furthermore, the two-stage analytical procedures by Gerbing and Anderson (1988) validity and goodness of the measurement model were first tested to evaluate the proposed research model ( Poon and Mohamad, 2020a ). For reflective constructs, item factor loading, construct reliability, composite reliability (CR) and average variance extracted (AVE) are evaluated ( Hair et al. , 2017a ). The minimum cut-off value for item factor loadings are above 0.70, AVE in each construct exceeds 0.50 and CR in each construct exceeds 0.708 ( Bagozzi and Yi, 1988 ; Hair et al. , 2013 ). As can be observed, the MacDonald's Omega reliability indices exceed 0.70 which indicates satisfactory reliability ( Hayes and Coutts, 2020 ). The model satisfies all of these criteria, as depicted in Table 2 .

Henseler et al. (2009) suggested the use of heterotrait-monotrait (HTMT) ratio, which is the average of the heterotrait-heteromethod correlations (i.e. the correlations of indicators across constructs measuring different phenomena) relative to the average of the monotrait-heteromethod correlations (i.e. the correlations of indicators within the same construct). Thus, this study used the most conservative criterion HTMT to examine discriminant validity at the cut-off value of 0.85. A value is greater than 0.85 signifies an issue with discriminant validity ( Henseler et al. , 2009 ; Voorhees et al. , 2016 ). As depicted in Table 3 , the measurement model attains discriminant validity based on HTMT analysis. In assessing the model fit, the present study adopts standardised root mean square residual (SRMR). As suggested by Hu and Bentler (1999) , the cut-off value of less than 0.08 for SRMR indicates a good fit. In this light, the present study's SRMR value is 0.07, indicating a good model fit.

Following the recommendation from Hair et al. (2017a , b) , the bootstrapping method of 5,000 resampling procedures was applied to determine the level of significance of each indicator weight. Bootstrapping is a resampling technique that draws a large number of subsamples from the original data (with replacement) and estimates models for each subsample. Table 4 summarises the results from the PLS path analysis for structural model evaluation. ATT ( β  = 0.374, p  < 0.001), SN ( β  = 0.088, p  < 0.10), PBC ( β  = 0.096, p  < 0.10), PAE ( β  = 0.192, p  < 0.05), and NAE ( β  = 0.169, p  < 0.05) have positive effect on desire. The result indicates that individual ATT, SN, PBC, PAE and NAE influences desire. PBC ( β  = 0.154, p  < 0.001), and desire ( β  = 0.738, p  < 0.001) have a positive effect on intention.

Performance risk ( β  = −0.119, p  < 0.05), privacy risk ( β  = −0.133, p  < 0.05), financial risk ( β  = −0.088, p  < 0.05), physical risk ( β  = −0.150, p  < 0.05) and COVID-19 risk ( β  = −0.105, p  < 0.10) have a negative effect on desire. The findings indicate that performance risk, privacy risk, financial risk, physical risk and the risk of contracting COVID-19 negatively influence the desire of OFD users. Physical risk ( β  = −0.147, p  < 0.05) and COVID-19 risk ( β  = −0.106, p  < 0.05) have a negative effect on intention. The result implies that users' intention is influenced by their physical risk and the risk of contracting COVID-19. However, the results indicate the insignificant relationship between performance risk ( β  = −0.012, p  > 0.10), privacy risk ( β  = −0.020, p  > 0.10), financial risk ( β  = −0.013, p  > 0.10) and intention. Table 4 presents the value of R 2 for endogenous variables. The R 2 values are 0.626 for desire and 0.491 for intention. The R 2 value indicates that 62.6% of the variance explained for desire and 49.1% of variance explained for intention (see Table 4 ).

PLS predict was used to examine the model's predictive relevance. Shmueli et al. (2019) described the method comprises training and holdout sample-based procedure generating case-level predictions on the item or construct level using the PLS-predict with a 10-fold procedure to identify predictive relevance. According to Shmueli et al. (2019) , the PLS model offers predictive performance if the Q 2 prediction value is positive. There is a strong predictive power and vice versa if all the item differences linear regression model (LM) are lower than the PLS model. There is moderate predictive power when the majority item differences in LM are lower than the PLS model, while there is low predictive power if the minority item differences in LM are lower than the PLS model, then. As shown in Table 5 , using ten folds and ten repetitions, all PLS models' items were lower than the PLS LM model; thus, the result concludes that the model has strong predictive power for behavioural intention and desire.

Following the guideline by Ringle and Sarstedt (2016), all outer weights of the measurement model are positive. The importance performance matrix (IPMA) revealed that PBC appears as the most important influencing factor for behavioural intention to use OFD with a score of (0.225; 75.336). The second most influential factor for behavioural intention with a score of (0.276; 68.128) was the ATT towards OFD. Interestingly, the result revealed that financial risk as the third most influential factor for behavioural intention with a score of (0.276; 67.259). Desire with a score of (0.738; 62.366) appears to be the fourth most influential factor for behavioural intention (see Table 6 ).

4. Discussion and implications

Little attention was paid to OFD consumer's behaviour and the decision-making process of OFD users. This study's objective is to use MGB to investigate the relationship between consumer's intentions towards OFD services to address the gap in the literature. Perugini and Bagozzi (2001) hypothesised that desire reflects the ATT, SN, PBC and AEs. Consistent with expectations, ATT, SN, PBC and AEs positively influence desire and intention. This study's results have further demonstrated the validity of the theoretical foundation used. The addition of desire and PAE and NAE guided by the literature suggests that what people want to do is an essential variable in explaining intention. On top of that, the study contributes to our understanding of various types of perceived risk that have not been explored in-depth in previous literature and their effects on the desire and intention to use OFD. OFD users would be more open towards continuance adoption of food delivery services when they are sure that it is safe to do so. Addressing specific concerns among OFD users would lead to an increase in desire. Subsequently, the desire will influence the intention to use OFD services.

Consumer's favourable ATT towards OFD suggests that consumers are comfortable with the concept of purchasing products or services online, given that the e-commerce industry had more than a decade to educate and build trust among OFD user. Such favourable ATTs could be attributed back to policies by e-commerce players, such as a 100% authenticity or money back warranty. The entry of branded and luxury items into the online platform is a signal of consumers' willingness to spend money on big-ticket items. These trends indicate the increasing level of trust among consumers towards online purchases, positively impacting OFD services. Therefore, capitalising on this trend, restaurant operators should introduce high-ticket items that could generate a higher margin. Currently, offerings available on OFD for consumers are largely cheaper options that are design to entice first-time users to try out the delivery service. Current restaurant operators could offer family meal menu or monthly subscription meal plan that could provide a better financial stability long-term. As ATT towards OFD changes, Michelin-starred chef and high-end restaurants could venture into the food delivery space and offer higher ticket items. Conversely, OFD operators would need to revamp its deliver processes taking into account the need of these high-end restaurants such as timeliness of delivery or the use of temperature-controlled storage boxes.

Apart from having a favourable ATT, PAE and NAE forms the desire to use OFD services. AE influences the decision-making process ( Bagozzi et al. , 2016 ); hence OFD users are more motivated to engage with OFD services in anticipation of a positive outcome from the delivery service. To leverage on these emotions, platforms could also employ promotional strategies such as “Refer-a-Friend” campaign after each successful transaction. Having succeeded in achieving the desired goal-directed behaviour, consumers are more likely to become platform ambassadors offering vouchers or cash rebates to entice new users. It is worthy to note that PBC had significant influences on consumer's desire and intention to use OFD services. In the performance of behavioural intention to engage with OFD services, PBC emerged as the most critical factor. OFD users who are capable of using mobile application to make purchases and past experiences, will likely contributed to an increase in the usage of OFD services. Therefore, OFD operators ought to focus on removing obstacles that would inhibit consumer's intention to use OFD services. These improvements will undoubtedly improve consumer's overall experience. On top of that, due to a number of reasons, the delivery service could be delayed. OFD operators should consider appropriate action in ensuring that the consumer's experience and emotion remains positive as these favourable perspective influences consumer's desire and subsequently their intention. These findings provide a more complex view in understanding consumers” intention to use OFD services that both OFD operators and restaurant managers must consider in their decision-making.

The present study had also explored the influences of perceived risks on desire and intention to use OFD services, and the results indicated interesting results. Performance, privacy, financial physical and COVID-19 risk negatively affect consumer's desire, while only physical and COVID-19 risk negatively affect consumer intention to engage in OFD services. These findings are consistent with previous studies on perceived risk and consumer behaviour research ( Han and Kim, 2017 ; Kamalul Ariffin et al. , 2018 ; Bashir et al. , 2018 ; Lăzăroiu et al. , 2020 ). An intricate mechanism influences one's motivation to act a certain way. The results suggest that OFD users consider different critical risk factors (i.e. performance risk, privacy risk, financial risk, physical risk and COVID-19 risk) in determining one's motivation.

Issues ranging from poor delivery service, food being stolen, wrong order, leak of personal information, financial dispute, potential physical harm and the risk of contracting COVD-19 from the delivery person could reduce users' desire to engage with OFD services. In a physical restaurant, issues such as delivery service, stolen food, wrong order or financial dispute could have been quickly addressed. However, with OFD, consumers need to contact the OFD service provider online to ask for a refund, and this could take up to a week to resolve or longer. Therefore, managerial actions should specifically consider improving consumer complaint process and establish hiring guidelines along with appropriate key performance index for delivery person. Clear procedures should be developed to manage simple issues faced by users. However, for more complicated issues, an independent third party should be appointed to manage it effectively and efficiently. Apart from performance issues, financial concerns could be mitigated through a money-backed guarantee scheme introduced by OFD companies. OFD operators that introduces a guaranteed scheme gain a competitive edge, develop loyal customers over time, and this information be used to provide useful feedback to the restaurant if ordered were wrongly completed, eventually improving both OFD and F&B operators' performance.

Another concern among OFD users is that their credentials (i.e. banking details, credit card information, house address, hand phone number, email address and password) are not sufficiently secured, and their online behaviour data could be leaked or sold to third-party advertisers. Practitioners, such as OFD companies, should continuously seek to reassure consumers that their data and private information are managed and stored securely according to the government's countries' regulation and guideline. Apart from that, government too plays an active role in regulating all businesses, including OFD operators, by enacting relevant data protection regulation to protect its citizen from exploitation, such as General Data Protection Regulation (GDPR) in the European Union. While such moves could incur additional cost to some industries, consumers' privacy concern would be alleviated, prompting higher consumption in data-driven industries such as e-commerce, healthcare, insurance services and financial services.

Furthermore, the physical risk and risk of contracting COVID-19 negatively influence consumer's desire and intention to use OFD services. Undoubtedly, all consumers would not subject themselves to any physical harm or potential exposure to diseases regardless of physically patronising a restaurant or purchasing food online. With COVID-19 risk looming in the back of mind, the introduction of contactless delivery, pick-up, or unmanned last-mile delivery would make it more comfortable among OFD customer and F&B operators significantly reduce the risk of transmission. F&B operators could designate an area in the restaurant as a contactless pick-up area for both walk-in customers and delivery personnel. Apart from that, OFD platform could implement “Just-In-Time” concept where delivery personnel would only enter the restaurant once the food is ready for collection. These steps are deemed necessary to protect essential workers, such as delivery personnel, by preventing overcrowding, enforcing strict standard operating procedures (SOPs) and upholding high safety standards throughout the entire F&B industry. It is worth noting that while this study was conducted during the height of the COVID-19 pandemic, consumers might be more biased towards health-related issues. However, contracting any transmittable diseases or hygiene issues would naturally be the top concern of many consumers, primarily when it comes to food. Traditional take-away option offered by restaurants, consumers that do so are more likely to be in contact with other people if they were to leave their houses. If health advisories such as physical distancing, wearing of face mask or washing of hands are not adhered to religiously by the community at large, consumers that leaves their home are more likely to contract COVID-19 ( World Health Organization, 2020 ) or other types of transmittable diseases. Conversely, if consumers were to use OFD services, it would pose a lower risk if compared with take-away option. With that said, necessary precaution would need to be practised by the OFD users and workers to reduce the risk of transmission. OFD operators raise awareness for OFD users and workers on the latest health advisors by health experts and secondly, to introduce a feedback system for the public to raise concerns if food delivery riders are seen flaunting these SOPs.

Continuous real-time tracking via Global Position System (GPS) or geolocation allows for unscrupulous actors the ability to pinpoint the exact location of consumer and delivery personnel, hence exposing them to unnecessary physical risk. As OFD requires a location-based tracking system, the application developer should introduce a method to quickly disable location tracking in food delivery applications if the user felt that their life is threatened. On top of that, OFD operator should introduce a panic button alert would allow users to report any issues quickly. OFD and F&B operators could not afford to overlook these risks as concerns on physical safety would ultimately steer the customer away from the OFD provider. As OFD and F&B operators are in the service sector, poor performance would create a bad image of the service resulting in lower purchases ( Hwang and Choe, 2019 ). As such, both physical risk and risk of contracting COVID-19 would influence consumer's decision-making process.

All in all, any of these concerns show that consumers' desire and intention to use OFD services are impaired. Consumers tend to be more motivated to engage with OFD services if they perceived it would bring minimal adverse effects. Hence, it falls on the OFD operators and restaurants to be ambidextrous in managing and mitigating these risks through new policies and strategies. This study postulates that the food delivery culture worldwide is set to grow in the coming years. With the rise of ghost kitchen coupled with efficient operation and lowering cost, the food delivery culture is set to transform the F&B industry. All stakeholders need to come together to create a new sustainable framework in addressing these dynamic changes.

4.1 Limitation and future research

While this study offers important implication for practitioners, it has several limitations. The first concern is related to external validity. As the data were only collected from Malaysian consumers, the data might offer a narrow perspective and could differ from other countries. Future studies are recommended to collect data in other parts of Malaysia and other countries to provide a broader view of perceived risk among OFD consumers. Another limitation is that the study was conducted during the pandemic, and the responses might only be relevant to the current scenario. Thus, future studies should consider conducting the research post-pandemic and compare the findings with the current results. The convenience sampling method was used for data collected in the current study, which can cause selection biases ( Wright, 2005 ). It is recommended to use another type of sampling method to reduce biases. In this study, the respondent may be biased towards the looming threat of a pandemic, which may skew their overall risk perception. Thus, future studies could use longitudinal data to assess the perceived risk profile more accurately as the relationship may change over time.

The theory of planned behaviour

The model of goal-directed behaviour

Proposed research model

Demographic profile of respondent and SMEs

Loadings, McDonald's Omega, composite reliability and average variance extracted

The supplementary material for this article can be found online.

Ajzen , I. ( 1991 ), “ The theory of planned behavior ”, Organizational Behavior and Human Decision Processes , Vol.  50 No.  2 , pp.  179 - 211 .

Alba , J. , Lynch , J. , Weitz , B. , Janiszewski , C. , Lutz , R. , Sawyer , A. and Wood , S. ( 1997 ), “ Interactive home shopping: consumer, retailer and manufacturer incentives to participate in electronic marketplaces ”, Journal of Marketing , Vol.  61 , pp.  38 - 53 .

Bagozzi , R.P. and Dholakia , U.M. ( 2002 ), “ Intentional social action in virtual communities ”, Journal of Interactive Marketing , Vol.  16 No.  2 , pp.  2 - 21 , doi: 10.1002/dir.10006 .

Bagozzi , R.P. and Yi , T. ( 1988 ), “ On the evaluation of structural equation models ”, Journal of the Academy of Marketing Science , Vol.  16 No.  1 , pp.  74 - 94 .

Bagozzi , R.P. , Belanche , D. , Casaló , L.V. and Flavián , C. ( 2016 ), “ The role of anticipated emotions in purchase intentions ”, Psychology and Marketing , Vol.  33 , pp.  629 - 645 , doi: 10.1002/mar.20905 .

Bashir , S. , Anwar , S. , Awan , Z. , Qureshi , T.W. and Memon , A.B. ( 2018 ), “ A holistic understanding of the prospects of financial loss to enhance shopper’s trust to search, recommend, speak positive and frequently visit an online shop ”, Journal of Retailing and Consumer Services , Vol. 42 , pp. 169 - 174 , doi: 10.1016/j.jretconser.2018.02.004 .

Bezerra , I.N. , De Moura Souza , A. , Pereira , R.A. and Sichieri , R. ( 2013 ), “ Consumo de alimentos fora do domicılio no Brasil ”, Revista de Saude Publica , Vol.  47 No.  1 , pp.  200 - 211 .

Bhattacherjee , A. ( 2000 ), “ Acceptance of e-commerce services: the case of electronic brokerages ”, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans , Vol.  30 No.  4 , pp.  411 - 420 .

Bui , M. and Kemp , E. ( 2013 ), “ E tail emotion regulation: examining online hedonic product purchases ”, International Journal of Retail and Distribution Management , Vol.  41 No.  2 , pp.  155 - 170 .

Cain , M.K. , Zhang , Z. and Yuan , K.H. ( 2016 ), “ Univariate and multivariate skewness and kurtosis for measuring nonnormality: prevalence, influence and estimation ”, Behavior Research Methods , Vol.  49 , pp.  1716 - 1735 .

Chen , H.S. , Liang , C.H. , Liao , S.Y. and Kuo , H.Y. ( 2020 ), “ Consumer attitudes and purchase intentions toward food delivery platform services ”, Sustainability , Vol.  12 No.  23 , pp.  1 - 18 .

Chiu , W. , Kim , T. and Won , D. ( 2018 ), “ Predicting consumers' intention to purchase sporting goods online: an application of the model of goal-directed behavior ”, Asia Pacific Journal of Marketing and Logistics , Vol.  30 No.  2 , pp.  333 - 351 .

Cho , M. , Boon , M.A. and Li , J.J. ( 2019 ), “ Differences in perceptions about food delivery apps between single-person and multi-person households ”, International Journal of Hospitality Management , Vol. 77 , pp. 108 - 116 , doi: 10.1016/j.ijhm.2018.06.019 .

Cinar , D. ( 2020 ), “ The effect of consumer emotions on online purchasing behavior ”, Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage January , pp.  221 - 241 , doi: 10.4018/978-1-7998-0035-4.ch011 .

Cooke , R. , Dahdah , M. , Norman , P. and French , D.P. ( 2016 ), “ How well does the theory of planned behaviour predict alcohol consumption? A systematic review and meta-analysis ”, Health Psychology Review , Vol.  10 No.  2 , pp.  148 - 167 , doi: 10.1080/17437199.2014.947547 .

Dakduk , S. , ter Horst , E. , Santalla , Z. , Molina , G. and Malavé , J. ( 2017 ), “ Customer behavior in electronic commerce: a bayesian approach ”, Journal of Theoretical and Applied Electronic Commerce Research , Vol.  12 No.  2 , pp.  1 - 20 , doi: 10.4067/S0718-18762017000200002 .

Das , S. and Ghose , D. ( 2019 ), “ Influence of online food delivery apps on the operations of the restaurant business ”, International Journal of Scientific and Technology Research , Vol. 8 No. 12 , pp. 1372 - 1377 .

Durai , A. ( 2020 ), Food Delivery Services Will Thrive in 2020 , available at: https://www.thestar.com.my/food/food-news/2020/01/04/food-delivery-will-continue-to-be-a-big-trend-in-2020 ( accessed 25 January 2021 ).

Erasmus , A. , Boshoff , E. and Rousseau , G. ( 2010 ), “ Consumer decision-making models within the discipline of consumer science: a critical approach ”, Journal of Family Ecology and Consumer Sciences , Vol. 29 No. 1 , pp. 82 - 90 .

Esposito , G. , van Bavel , R. , Baranowski , T. and Duch-Brown , N. ( 2016 ), “ Applying the model of goal-directed behavior, including descriptive norms, to physical activity intentions: a contribution to improving the theory of planned behavior ”, Psychological Reports , Vol.  119 No.  1 , pp.  5 - 26 .

Faul , F. , Erdfelder , E. , Buchner , A. and Lang , A.G. ( 2009 ), “ Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses ”, Behavior Research Methods , Vol.  41 No.  4 , pp.  1149 - 1160 .

Featherman , M.S. and Pavlou , P.A. ( 2003 ), “ Predicting e-services adoption: a perceived risk facets perspective ”, International Journal of Human-Computer Studies , Vol.  59 No.  4 , pp.  451 - 474 .

Fishbein , M. and Ajzen , I. ( 2010 ), Predicting and Changing Behavior: The Reasoned Action Approach , 1st ed. , Psychology Press , doi: 10.4324/9780203838020 .

Forsythe , S.M. and Shi , B. ( 2003 ), “ Customer patronage and risk perceptions in internet shopping ”, Journal of Business Research , Vol.  56 No.  11 , pp.  867 - 875 .

Fortes , N. and Rita , P. ( 2016 ), “ Privacy concerns and online purchasing behaviour: towards an integrated model ”, European Research on Management and Business Economics , Vol.  22 No.  3 , pp.  167 - 176 .

Gerbing , D.W. and Anderson , J.C. ( 1988 ), “ An updated paradigm for scale development incorporating unidimensionality and its assessment ”, Journal of Marketing Research , Vol.  25 No.  2 , pp.  186 - 192 .

Hair , J.F. , Babin , B.J. and Krey , N. ( 2017a ), “ Covariance-based structural equation modeling in the journal of advertising: review and recommendations ”, Journal of Advertising , Vol.  46 No.  1 , pp.  163 - 177 .

Hair , J.F. , Hult , G.T.M. , Ringle , C.M. and Sarstedt , M. ( 2017b ), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) , 2nd ed. , Sage , Thousand Oaks, CA .

Hair , J.F. , Ringle , C.M. and Sarstedt , M. ( 2013 ), “ Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance ”, Long Range Planning , Vol.  46 Nos  1-2 , pp.  1 - 12 .

Han , M.C. and Kim , Y. ( 2017 ), “ Why consumers hesitate to shop online: perceived risk and product involvement on taobao.com ”, Journal of Promotion Management , Vol.  23 No.  1 , pp.  24 - 44 .

Han , H. , Lee , M.J. and Kim , W. ( 2018 ), “ Antecedents of green loyalty in the cruise industry: sustainable development and environmental management ”, Business Strategy and the Environment , Vol.  27 No.  3 , pp.  323 - 335 .

Hayes , A.F. and Coutts , J. ( 2020 ), “ Use omega rather than cronbach's alpha for estimating reliability. But… ”, Communication Methods and Measures , Vol.  14 No. 1 , pp.  1 - 24 .

Henseler , J. , Ringle , C.M. and Sinkovics , R.R. ( 2009 ), “ The use of partial least squares path modeling in international marketing ”, Advances in International Marketing , Vol.  20 , pp.  277 - 319 .

Hu , L.T. and Bentler , P.M. ( 1999 ), “ Cut-off criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives ”, Structural Equation Modeling , Vol.  6 No.  1 , pp.  1 - 55 .

Hussey , A. ( 2021 ), The Global State of Foodservice Delivery , available at: https://kerry.com/insights/kerrydigest/2020/global-foodservice-delivery ( accessed 13 Augest 2021 ).

Hwang , J. and Choe , J.Y. ( 2019 ), “ Exploring perceived risk in building successful drone food delivery services ”, International Journal of Contemporary Hospitality Management , Vol.  31 No.  8 , pp.  3249 - 3269 .

Jiang , L.A. , Yang , Z. and Jun , M. ( 2013 ), “ Measuring consumer perceptions of online shopping convenience ”, Journal of Service Management , Vol.  24 No.  2 , pp.  191 - 214 .

Kamalul Ariffin , S. , Mohan , T. and Goh , Y.N. ( 2018 ), “ Influence of consumers' perceived risk on consumers' online purchase intention ”, Journal of Research in Interactive Marketing , Vol.  12 No.  3 , pp.  309 - 327 .

Kim , J. and Lennon , S.J. ( 2013 ), “ Effects of reputation and website quality on online consumers' emotion, perceived risk and purchase intention: based on the stimulus-organism-response model ”, Journal of Research in Interactive Marketing , Vol.  7 No.  1 , pp.  33 - 56 .

Kim , L.H. , Kim , D.J. and Leong , J.K. ( 2005 ), “ The effect of perceived risk on purchase intention in purchasing airline tickets online ”, Journal of Hospitality and Leisure Marketing , Vol.  13 No.  2 , pp.  33 - 53 .

Kim , H.W. , Chan , H.C. and Chan , Y.P. ( 2007 ), “ A balanced thinking–feelings model of information systems continuance ”, International Journal of Human-Computer Studies , Vol.  65 No.  6 , pp.  511 - 525 .

Kline , R.B. ( 2015 ), Principles and Practices of Structural Equation Modeling , 4th ed. , Guilford Press , New York, NY .

Kushwaha , T. and Shankar , V. ( 2013 ), “ Are multichannel customers really more valuable? The moderating role of product category characteristics ”, Journal of Marketing , Vol.  77 No.  4 , pp.  67 - 85 .

Lăzăroiu , G. , Neguriţă , O. , Grecu , I. , Grecu , G. and Mitran , P.C. ( 2020 ), “ Consumers' decision-making process on social commerce platforms: online trust, perceived risk, and purchase intentions ”, Front. Psychol. , Vol.  11 , p. 890 , doi: 10.3389/fpsyg.2020.00890 .

Leone , L. , Perugini , M. and Ercolani , A.P. ( 2004 ), “ Studying, practicing, and mastering: a test of the model of goal- directed behavior (MGB) in the software learning domain ”, Journal of Applied Social Psychology , Vol.  34 No.  9 , p. 1945 .

Liang , A.R.D. and Lim , W.M. ( 2011 ), “ Exploring the online buying behavior of specialty food shoppers ”, International Journal of Hospitality Management , Vol.  30 No.  4 , pp.  855 - 865 .

Lutz , C. , Hoffmann , C.P. , Bucher , E. and Fieseler , C. ( 2018 ), “ The role of privacy concerns in the sharing economy ”, Information, Communication and Society , Vol.  21 No.  10 , pp.  1472 - 1492 .

Menon , S. and Kahn , B. ( 2002 ), “ Cross-category effects of induced arousal and pleasure on the internet shopping experience ”, Journal of Retailing , Vol.  78 No.  1 , pp.  31 - 40 .

Mosunmola , A. , Omotayo , A. and Mayowa , A. ( 2018 ), “ Assessing the influence of consumer perceived value, trust and attitude on purchase intention of online shopping ”, Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning , San Diego, CA, USA , 11 January 2018 , pp.  40 - 47 .

Murray , K.B. and Schlacter , J.L. ( 1990 ), “ The impact of services versus goods on consumers' assessment of perceived risk and variability ”, Journal of the Academy of Marketing Science , Vol.  18 No.  1 , pp.  51 - 65 .

Nguyen , T. and Vu , D.C. ( 2020 ), “ Food delivery service during social distancing: proactively preventing or potentially spreading coronavirus disease-2019? ”, Disaster Medicine and Public Health Preparedness , Vol.  14 No.  3 , pp.  e9 - e10 , doi: 10.1017/dmp.2020.135 .

Perugini , M. and Bagozzi , R.P. ( 2001 ), “ The role of desires and anticipated emotions in goal-directed behaviours: broadening and deepening the theory of planned behaviour ”, British Journal of Social Psychology , Vol.  40 No.  1 , pp.  79 - 98 , doi: 10.1348/014466601164704 .

Perugini , M. and Bagozzi , R.P. ( 2004 ), “ The distinction between desires and intentions ”, European Journal of Social Psychology , Vol.  34 No.  1 , pp.  69 - 84 .

Poon , W.C. and Mohamad , O. ( 2020a ), “ Organizational context and behavioural complexity affecting ambidextrous behaviours among SMEs ”, International Journal of Organization Theory and Behavior , Vol.  23 No.  3 , pp.  225 - 244 , doi: 10.1108/IJOTB-03-2019-0037 .

Poon , W.C. and Mohamad , O. ( 2020b ), “ The impact of emotional intelligence on ambidextrous behaviours in small and medium enterprises in Malaysia ”, International Journal of Society Systems Science , Vol.  12 No.  1 , pp.  36 - 50 .

Poon , W.C. , Mohamad , O. and Yusoff , W.F.W. ( 2018 ), “ Examining the antecedents of ambidextrous behaviours in promoting creativity among SMEs in Malaysia ”, Global Business Review , Vol.  21 No.  3 , pp.  645 - 662 , doi: 10.1177/0972150918779267 .

Rehman , S.U. , Bhatti , A. , Mohamed , R. and Ayoup , H. ( 2019 ), “ The moderating role of trust and commitment between consumer purchase intention and online shopping behavior in the context of Pakistan ”, Journal of Global Entrepreneurship Research , Vol.  9 No.  1 , doi: 10.1186/s40497-019-0166-2 .

Ringle , C.M. , Wende , S. and Becker , J.M. ( 2015 ), “ SmartPLS 3. Bönningstedt: SmartPLS ”, available at: http://www.smartpls.com .

Rodríguez-López , M.E. , Alcántara-Pilar , J.M. , Del Barrio-García , S. and Muñoz-Leiva , F. ( 2020 ), “ A review of restaurant research in the last two decades: a bibliometric analysis ”, International Journal of Hospitality Management , Vol.  87 April , p. 102387 , doi: 10.1016/j.ijhm.2019.102387 .

Schierz , P.G. , Schilke , O. and Wirtz , B.W. ( 2010 ), “ Understanding consumer acceptance of mobile payment services: an empirical analysis ”, Electronic Commerce Research and Applications , Vol.  9 No.  3 , pp.  209 - 216 .

Shmueli , G. , Sarstedt , M. , Hair , J.F. , Cheah , J.-H. , Ting , H. , Vaithilingam , S. and Ringle , C.M. ( 2019 ), “ Predictive model assessment in PLS-SEM: guidelines for using PLSpredict ”, European Journal of Marketing , Vol.  53 No.  11 , pp.  2322 - 2347 .

Sniehotta , F.F. , Presseau , J. and Arau´ jo-Soares , V. ( 2014 ), “ Time to retire the theory of planned behaviour ”, Health Psychology Review , Vol.  8 No.  1 , pp.  1 - 7 .

Statista ( 2021 ), “ Global online food delivery market size 2023 ”, available at: https://www.statista.com/statistics/1170631/online-food-delivery-market-size-worldwide/ ( accessed 13 Augest 2021 ).

Suhartanto , D. , Mohd Helmi Ali , Tan , K.H. , Sjahroeddin , F. and Kusdibyo , L. ( 2019 ), “ Loyalty toward online food delivery service: the role of e-service quality and food quality ”, Journal of Foodservice Business Research , Vol.  22 No.  1 , pp.  81 - 97 , doi: 10.1080/15378020.2018.1546076 .

Sutton , S. ( 1998 ), “ Predicting and explaining intentions and behavior: how well are we doing? ”, Journal of Applied Social Psychology , Vol.  28 No.  15 , pp.  1317 - 1338 .

Szymkowiak , A. , Gaczek , P. , Jeganathan , K. and Kulawik , P. ( 2020 ), “ The impact of emotions on shopping behavior during epidemic. What a business can due to protect customers ”, Journal of Consumer Behaviour, June , pp.  48 - 60 , doi: 10.1002/cb.1853 .

Tommasetti , A. , Singer , P. , Troisi , O. and Maione , G. ( 2018 ), “ Extended Theory of Planned Behavior (ETPB): investigating customers' perception of restaurants' sustainability by testing a structural equation model ”, Sustainability (Switzerland) , Vol.  10 No.  7 , pp.  1 - 21 .

Troise , C. , O'Driscoll , A. , Tani , M. and Prisco , A. ( 2021 ), “ Online food delivery services and behavioural intention – a test of an integrated TAM and TPB framework ”, British Food Journal , Vol.  123 No.  2 , pp.  664 - 683 , doi: 10.1108/BFJ-05-2020-0418 .

Voorhees , C.M. , Brady , M.K. , Calantone , R. and Ramirez , E. ( 2016 ), “ Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies ”, Journal of the Academy of Marketing Science , Vol.  44 No.  1 , pp.  119 - 134 .

Wang , Y.J. , Minor , M.S. and Wei , J. ( 2011 ), “ Aesthetics and the online shopping environment: understanding consumer responses ”, Journal of Retailing , Vol.  87 No.  1 , pp.  46 - 58 .

World Health Organization ( 2020 ), A Guide to WHO's Guidance on COVID-19 , WHO , available at: https://www.who.int/news-room/feature-stories/detail/a-guide-to-who-s-guidance .

Wright , K.B. ( 2005 ), “ Researching internet-based populations: advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services ”, Journal of Computer-Mediated Communication , Vol. 10 No. 3 , doi: 10.1111/j.1083-6101.2005.tb00259.x .

Yang , S. , Li , L. and Zhang , J. ( 2018 ), “ Understanding consumers’ sustainable consumption intention at China’s Double-11 online shopping festival: an extended theory of planned behavior model ”, Sustainability , Vol. 10 No. 6 , pp. 1 - 19 .

Yang , Y. , Liu , Y. , Li , H. and Yu , B. ( 2015 ), “ Understanding perceived risks in mobile payment acceptance ”, Industrial Management and Data Systems , Vol. 115 No. 2 , pp. 253 - 269 , doi: 10.1108/IMDS-08-2014-0243 .

Yeo , V.C.S. , Goh , S.-K. and Rezaei , S. ( 2017 ), “ Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services ”, Journal of Retailing and Consumer Services , Vol. 35 , pp. 150 - 162 , doi: 10.1016/j.jretconser.2016.12.013 .

Yi , J. , Yuan , G. and Yoo , C. ( 2020 ), “ The effect of the perceived risk on the adoption of the sharing economy in the tourism industry: the case of Airbnb ”, Information Processing and Management , Vol. 57 No. 1 , pp. 102 - 108 .

Zhao , X. , Deng , S. and Zhou , Y. ( 2017 ), “ The impact of reference effects on online purchase intention of agricultural products: the moderating role of consumers' food safety consciousness ”, Internet Research , Vol.  27 No.  2 , pp.  233 - 255 .

Corresponding author

Supplementary materials.

EJMBE-04-2021-0128_suppl1.docx (28 KB)

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

16k Accesses

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

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Sustainable successes in third-party food delivery operations in the digital platform era

• Original Research
• Open access
• Published: 12 April 2023

Cite this article

You have full access to this open access article

• Hau-Ling Chan 1 ,
• Ting-Ting Cheung 2 ,
• Tsan-Ming Choi   ORCID: orcid.org/0000-0003-3865-7043 3 , 4 &
• Jiuh-Biing Sheu 5  

4453 Accesses

2 Citations

Explore all metrics

In the digital era, third-party food delivery operations are very popular all around the world. However, to achieve a sustainable operation for food delivery businesses is a challenging issue. Motivated by the fact that there is a lack of consolidated view towards the topic in the literature, we conduct a systematic literature review to identify how to achieve a sustainable operation for third-party food delivery and highlight the recent advances in this important area with the discussion of real-world practices. In this study, first, we review the relevant literature and apply the triple bottom line (TBL) framework to classify prior studies into economic sustainability, social sustainability, environmental sustainability, and multi-dimensional sustainability. We then identify three major research gaps, including inadequate investigation on the restaurant’s preferences and decisions, superficial understanding on the environmental performance, and limited examination on the multi-dimensional sustainability in the third-party food delivery operations. Finally, based on the reviewed literature and observed industrial practices, we propose five future areas that deserve an in-depth further investigation. They are namely applications of digital technologies, behaviors and decisions of the restaurants, risk management, TBL, and post-coronavirus pandemic.

Similar content being viewed by others

Supply chain disruptions and resilience: a major review and future research agenda

K. Katsaliaki, P. Galetsi & S. Kumar

Sustainability trends and gaps in the textile, apparel and fashion industries

Stefano Abbate, Piera Centobelli, … Emanuela Riccio

A systematic literature review on greenwashing and its relationship to stakeholders: state of art and future research agenda

Célia Santos, Arnaldo Coelho & Alzira Marques

Avoid common mistakes on your manuscript.

1 Introduction

1.1 background.

Due to the coronavirus pandemic, governments all around the world have encouraged the provision of food delivery services or implemented different dine-in restrictions. For example, the UK government advised the restaurants to sell food online and provide delivery service. Footnote 1 In Hong Kong, the dine-in service was prohibited after 6 pm to avoid the spread of virus (Kathleen & Lo, 2022 ). With a growing necessity for stay-at-home during the coronavirus pandemic, restaurants must adopt new distribution channels for delivering their foods to customers. Hence, third-party food delivery platforms such as Foodpanda and Deliveroo consolidate a trend of significant growth (Yang et al., 2020 ).

According to Pandey et al. ( 2022 ), food delivery platforms can be categorized into two types: (i) Restaurants operated, and (ii) third-party food delivery platform operated. For the latter one, restaurants form a partnership with third-party food delivery platforms who provide the food delivery services to customers (Preetha and Iswarya, 2019 ). Under such operations, a customer first places an order through a third-party food delivery platform (e.g., website or application (app)), and the ordering details will then be passed to the corresponding restaurants. Later on, the third-party food delivery platform will arrange personnel (i.e., rider) to pick up the food from the restaurants and provide delivery services to the customer. Footnote 2 The revenue of the third-party food delivery platforms comes from both restaurants and customers with five major sources (Ahuja et al., 2021 ). To be specific, restaurants have to pay the commission fees. It was reported that the third-party food delivery platform charges around 20 to 25 percent of each order from those exclusive restaurants, 30 to 35 percent of each order from non-exclusive restaurants, and 3 to 8 percent from each customer self-pickup order as restaurant commission fees (Leung, 2022 ). In case the restaurants desire to promote their brands through the third-party food delivery platform (e.g., website or app), they will be charged for the advertising services. On the other hand, customers are required to pay the delivery fees and service fees. For the delivery fees, it depends on traveling distance and time between the restaurants and customers. Apart from delivery, the third-party food delivery platform also provides various services, such as 24/7 ordering, pre-ordering, customer rating on the restaurants (Lichtenstein, 2020 ), which are regarded as the service fees. Finally, customers can tip the riders which may lower the operating cost of the food delivery platform for staff retention. It is found that different food delivery platforms (e.g., Doordash, Uber Eats) have different settings on the fees charged to both restaurants and customers (Lichtenstein, 2020 ). Overall, in a typical third-party food delivery system, it involves the participation of the restaurants, third-party food delivery platforms, riders, and end-customers, and they are connected by the digital technologies, and the operations are driven by big data analytics (Bozkaya et al., 2022 ).

Overall, with the support of information communication technology and the governments’ restrictions on the dine-in services, there is a significant business growth of the third-party food delivery industry during the coronavirus pandemic (Chokshi, 2020 ). It is estimated that the market of food delivery industry has grown four to seven times in 2020 and 2021, compared with 2018 (Ahuja et al., 2021 ), and the market revenue of the third-party food delivery services will hit the level of US$208 billion in 2022 and reach US$302.5 billion by 2027. Footnote 3

In addition to the economic performance, social and environmental sustainability aspects are also crucial in constructing a sustainable economy (Song et al., 2022 ). It is reported that about 45% of the unwanted materials sending to the U.S. landfill is in fact food waste and food packaging, Footnote 4 and the carbon emissions generated from the food delivery service will increase about 32% by 2022 (Joselow, 2020 ). Furthermore, more than half of the delivery riders suffered from work injuries and 60% of them claimed that they were not paid after injuries (Young, 2021 ). To sustain a long-term success, food delivery companies should contribute to the economic, social and environmental sustainability to improve both customers’ short-term values and all stakeholders’ long-term well-being (Barthel & Ivanaj, 2007 ; Crane & Desmond, 2002 ). Theoretically, the triple bottom line (TBL) framework addresses that business firms should commit to the economic, social and environmental responsibilities. By applying the TBL framework, businesses can improve their financial performance, satisfy their stakeholders and develop competitive advantages to differentiate them from the rivals in a dynamic market (Schulz & Flanigan, 2016 ). Therefore, in this study, the TBL framework is adopted to let us understand the prior studies in each sustainability aspect which will provide a guideline for developing the future research agenda for the food delivery operations.

1.2 Contribution and organization

Third-party food delivery operation is a timely topic especially during the coronavirus pandemic. However, there is a lack of consolidated and up-to-date view of this topic, which will be crucial for the post-pandemic era. This study hence aims to fill this gap by conducting a systematic literature review to identify how to achieve a sustainable operation for third-party food delivery. As a remark, in the existing literature, Seghezzi et al. ( 2021 ) also conducted a comprehensive literature review on the food delivery services and identified the role of each player in the food delivery operations. Different from Seghezzi et al. ( 2021 ), this study not only provides an up-to-date view towards food delivery operations with the application of the triple bottom line (TBL) framework, but also highlights the recent advances in this important area with the discussion of real-world practices. Besides, this study also proposes future research directions to enriching the knowledge in this type of businesses during and after the coronavirus pandemic.

The rest of this paper is organized as follows. In Sect.  2 , we present the methodology in selecting the relevant articles on the sustainable third-party food delivery operations and illustrate the distribution statistics results. In Sect.  3 , we review the selected articles based on economic sustainability, social sustainability, environmental sustainability, and multi-dimensional sustainability. In Sect.  4 , we discuss the findings and propose future research agenda on five different areas, namely, digital technologies in third-party food delivery operations, behaviors and decisions of the restaurants, risk management, TBL, and post-coronavirus pandemic. Finally, we present the concluding remarks in Sect.  5 .

2 Methodology and distribution statistics

This study presents a review of third-party food delivery operations and its sustainable successes in the digital platforms era by addressing the economic, social and environmental sustainability under the triple bottom line (TBL) framework.

To collect the relevant articles for review, a systematic search procedure was adopted. First, Web of Science database was used for articles identification. Next, we searched the articles by using food-delivery platform as the topic and obtained 190 articles. As we focused on those articles published in well-established journals, we performed several rounds of articles filtering. To be specific, we excluded open access journals in the quick filters function, proceeding papers in the document types of classification, non-English articles in the languages, but selected both Social Sciences Citation Index (SSCI) and Science Citation Index Expanded (SCI-Expanded) journals. In this stage, we had 68 articles. Besides, we further refined the articles by considering the research areas in business economics, engineering, computer science, operations research and management science, public environment, telecommunication, and transportation which resulted in 44 articles. We then read the abstract and introduction section of each article and determined its relevancy to address the sustainability issue. We found that three articles were not relevant and should be excluded. Finally, we sought for article recommendation from scholars studying the third-party food delivery operations. Footnote 5 Eventually, we reviewed 54 articles. The article selection process was conducted in August 2022. Figure  1 illustrates the articles selection process adopted in this study.

Articles selection process adopted in this study

After collecting the articles, descriptive analyses were conducted. Figure  2 summarizes the journal publications of the reviewed articles. In this study, the relevant articles were published in 34 journals, including Annals of Operations Research, International Journal of Production Economics, Management Science, Manufacturing & Service Operations Management, etc. Figure  3 shows the number of publications in each year. It is obvious that the publication number has increased exponentially from 2018. As a remark, the number of publications in 2022 was only counted from January 2022 to August 2022, and hence the whole year figure should be much higher. Finally, Fig.  4 presents the distribution of the articles based on analytical and empirical studies. In this paper, “empirical” studies refer to those which employ empirical data in the analyses and cover the ones with computational experiments, qualitative case studies, and statistical based quantitative studies. On the other hand, “analytical” studies mainly represent those which are based on mathematical models with theoretical results derived in closed-form. The majority of the articles belongs to the empirical studies in which one of them is a review type of research article.

Specific journals and the publication numbers of the reviewed articles

The total number of publications in each year

Distribution of the reviewed articles by research types

3 Literature review

In this section, we review the relevant articles based on the triple bottle line (TBL) framework, i.e., economic, social and environmental sustainability. Høgevold et al. ( 2014 ) indicated that an organization needs to address economic development continuously, while it is also important to address both social and environmental impacts. The prevalence of food delivery business is caused by the coronavirus pandemic in recent times, and its sustainable development should be carefully examined to ensure that the third-party food delivery businesses achieve sustainability by maximizing the positive impacts and minimizing the negative impacts (Li et al., 2020 ). In the following, we classify the relevant articles into economic sustainability, social sustainability, environmental sustainability, and multi-dimensional sustainability for review.

3.1 Economic sustainability

Economic sustainability addresses the financial performance of an organization for survival (Jawahar et al., 2017 ). Therefore, in this study, economic sustainability refers to the financial performance of food delivery platforms and restaurants. We find that the existing literature related to economic sustainability in food delivery operations has considered consumer behaviors and various operational problems. We review the relevant studies as follows.

3.1.1 Consumer behaviors and preferences

One important issue that will significantly affect the financial performance of both food delivery platforms and restaurants is the customer relationship management as it can build a higher customer loyalty and generate a repeated purchase intention in the future. Jha ( 2022 ) proposed techniques (Generalized Savitzky-Golay Filter (GS-GF) and Hybrid Self Constructing Neural Fuzzy based African Buffalo Optimization (HSCFN-ABO)) to extract features of customer comments and classified them into food quality, delivery and payment categories to improve customer relationship management. In addition, consumer behavior of using the food delivery services is also one prominent research area. To be specific, Gunden et al. ( 2020 ) statistically investigated the factors affecting customer intention to adopt online food delivery services. They found that having a higher level of performance expectancy, congruity with self-image, habit and mindfulness can boost the customer’s intentions to adopt the online food delivery services. However, customer’s impulse purchase tendency will generate a negative effect on it. Similar to Gunden et al. ( 2020 ), Francioni et al. ( 2022 ) also examined the customer’s intention to use food delivery platforms. Differently, Francioni et al. ( 2022 ) considered another set of factors that may affect the customer’s intention and investigated the moderating role of gender. Their statistical results showed that perceived healthiness, perceived hygiene, quarantine procedures, perceived ease of app use, and attitude toward the benefits of using food delivery services are the determinants positively associated with the customer’s intention to utilize the online food delivery services continuously. By considering the moderating effect of gender, the perceived healthiness is a crucial factor for both male and female customers to use the online food delivery service. Wang and He ( 2021 ) explored the effects of ease of food access and built environment on the on-demand food delivery (ODFD) services by using published data. They revealed that both issues will affect the ODFD services consumption. The proportion of using the land for green spaces and the degree of urbanization have negative and positive effects on using the ODFD services, respectively. Yen ( 2022 ) statistically explored the relationship between channel integration and food delivery platform (FDP) services consumption by considering different perceived values and perceived risks, and then examined how the customer’s innovativeness and experience moderate such relationships. It was found that channel integration through perceived usefulness, enjoyment and price will affect FDP services consumption. In addition, customer’s innovativeness and experience demonstrate a moderating effect in such relationship.

Food delivery operations are supported by apps and there are relevant articles studying consumer behavior of using the food delivery apps. Kapoor and Vij ( 2018 ) statistically explored how the attributes of the mobile apps affect customer intention to use the online food ordering services. They concluded that the mobile apps attributes of collaboration designs, such as promotion and discount offerings, have the greatest effect on the online food ordering services utilization, followed by information design, navigational design, and visual design. Kaur et al. ( 2021 ) identified the values of stimulating the food-delivery app usage. They showed that visibility has the greatest impacts on food delivery apps usage intention, followed by affordances, values for money and prestige social benefits. However, food safety and health concerns are not significantly associated with the apps’ usage intention. Yeo et al. ( 2021 ) statistically evaluated the driving forces of customer’s repurchase intention of the food delivery apps. They found that perceived usefulness, social influence and trust are the drivers of repurchase intention of the food delivery apps. However, effort expectancy, information quality and perceived risks are not significantly associated with the repurchase intention. Raza et al. ( 2022 ) statistically explored the trust transfer from the online food delivery apps to the restaurant and its relationship with the reuse intention of the apps. They presented that trust disposition and online ratings will affect customer’s trust in the food delivery apps which will eventually generate a positive effect on the customer’s trust in restaurant and apps reuse intension. Besides, the perceived effectiveness of dispute resolution acts as a moderator between the trust in an app and the trust in a restaurant.

Some other studies have examined brand equity of the food delivery platforms and evaluated customer selection preference. For instance, Ahn and Kwon ( 2021 ) discovered that perceived economic exchange, social exchange and mutual interests with the food delivery apps have positive relationships with the equity of the food delivery brand which will subsequently generate a positive behavior toward the platform’s brand. Tsai et al. ( 2022 ) revealed that performance expectancy, effort expectancy, and security are the factors affecting the food delivery apps selection decision. Table 1 summarizes the articles addressing economic sustainability with the consideration of customer behaviors and preferences.

3.1.2 Operational strategies and performances

In addition to customer behaviors, the existing literature has also examined different operational problems (e.g., routing planning, operational modes and optimal strategies) of food delivery services. For example, Lang and Zhao ( 2021 ) proposed a cloud computing resource scheduling approach for forecasting the take-out order quantity. Besides, by studying the route planning problem of the food delivery services, Du et al. ( 2019 ) developed a spatial crowdsourcing-based system, namely, crowd delivery network (CrowDNet) with the consideration of delivery cost and time trade-off. The proposed system consists of a hitchhiking ride service served by taxis and a ranking module for the delivery route planning. Kohar and Jakhar ( 2021 ) proposed an augmented 2-index formulation to determine the optimal food delivery routing by considering the constraints of time windows of both the restaurants and customer locations, and the capacity constraints of the fleets of vehicles. In addition, Wang et al. ( 2021 ) proposed an Extreme Gradient Boosting-enhanced (XGBoost-enhanced) algorithm to generate the routing solution within a limited computational time and minimal total cost. Zheng et al. ( 2022a , 2022b ) studied a stochastic online food delivery problem (SOFDP) by developing the route planning and order assignment solutions. The solutions are characterized by large search space, strong coupling, capturing the uncertain food preparation time, and fulfilling the speedy evaluation requirements. Interesting, Ulmer et al. ( 2021 ) proposed an anticipatory customer assignment (ACA) policy by addressing delivery delay avoidance and random restaurant ready times. The proposed policy is featured by having postponement on customer’s assignment, including time buffer to avoid delay delivery, and bundling operations.

Another well-examined problem is related to the operational strategies of food delivery services. Taylor ( 2018 ) investigated the effect of delay sensitivity and agent independence on the platform’s optimal service price and wage in both static and uncertain customer’s valuation or agent’s opportunity cost. The analytical results demonstrated that the uncertainties of customer valuation and agent cost affect the optimal service price differently when considering the customer delay sensitivity and agent independence. Agent independence reduces the service price if the agent opportunity cost uncertainty is high, or the customer valuation uncertainty is low. Besides, delay sensitivity increases the optimal service price if the customer valuation uncertainty is moderate. Dai and Liu ( 2020 ) analyzed the optimal workforce capacity and order allocations among three different O2O delivery modes, namely in-house delivery, full-time crowdsourced riders, and part-time crowdsourced riders. Du et al. ( 2021 ) examined four different delivery strategies in either restaurant-operated or third party-operated platforms, and then derived the pricing decisions of each strategy. They found that the advertising effect from the restaurant delivery and the consumer benefit from third-party platform’s promotion will affect the optimal decisions of the restaurant to select the delivery strategy. Zhu et al. ( 2022 ) investigated the impacts of cooperating with the social media platform from the food delivery platform’s perspective. Their analytical results demonstrated that such cooperation would benefit the food delivery platform, but the users may have to bear higher registration fees. In addition, the authors applied the Nash negotiation framework to design a profit-sharing scheme such that both the food delivery platform and social media platform can be better-off. Jia et al. ( 2022 ) determined whether or not a restaurant should form partnership with the third-party delivery platform and statistically analyzed the optimal number of riders during the coronavirus pandemic based on the public data. Lin et al. ( 2022 ) explored the food delivery system under regular and physical internet driven self-delivery, outsourcing-delivery and volunteer-delivery business models. They developed a multi-objective mixed-integer linear programming (MOMILP) which aim to minimize the total cost but maximize the customer service level. The experiment results illustrated that the adoption of physical internet can enhance the performance of all three business models, but it generates greatest value to the volunteer-delivery business model. He et al. ( 2019 ) designed an agent based O2O food ordering model to investigate how the customer’s and platform’s behaviors affect the location and food quality decisions of the restaurant. The numerical analysis revealed that the customer’s food quality preference and the platform’s routing planning have a positive effect on the restaurant’s food quality decision and location decision, respectively. In addition, when the online platform is cost-saving conscious, it will affect the waiting time of the customers and the location decision of the restaurants.

The operational decisions of the food delivery platforms will affect their profitability, and it is important to quantify it. For example, Seghezzi and Mangiaracina ( 2020 ) examined the financial performance of the last-miles deliveries in the on-demand food delivery industry. They constructed a model to evaluate the profitability of the food delivery platform. The sensitively analyses showed that there is a fixed delivery price threshold that can make the food delivery platform business profitable regardless of the daily demand. Besides, having a higher demand does not necessarily help improving the profit of the food delivery platform as it may increase the delivery cost when the number of destinations increases. Sun et al. ( 2022 ) and Bai and Tang ( 2022 ) individually built analytical models to explore the impacts of having price and lead-time competition in food delivery platform operations on profitability. Sun et al. ( 2022 ) found that single dimensional competition with either price or lead-time will harm the profitability of the food delivery platform. However, under the joint price and lead-time competition situation, the platform will be better off if the intensities of pricing and lead time competitions are different. Apart from competing on price and lead-time, Bai and Tang ( 2022 ) also considered the platforms can offer higher wages to attract more riders to join. They showed that only one platform can result in a “payoff dominant stable equilibrium” who will take all the benefit. Feldman et al. ( 2022 ) explored a congested service system of the restaurant and analytically analyzed the supply chain performance under the commonly seen simple revenue-sharing contract in a supply chain with one restaurant and one third-party delivery platform. They concluded that a simple revenue-sharing contract is inefficient to coordinate the supply chain and harms the profitability of the restaurant. However, a properly set generalized revenue-sharing contract which consists of shared revenue and fixed fee sharing can help achieve supply chain coordination. Table 2 summarizes the articles addressing economic sustainability with the consideration of different operational problems and performances.

3.2 Social sustainability

Social sustainability relates to the benefits and welfares of the community (Hess et al., 2022). In the food delivery operations, it involves customers to engage in the food ordering process and requires riders to provide prompt delivery services. Therefore, the welfares of both the customers and riders are vital in the social sustainability.

3.2.1 Labor control

There are numerous articles studying various issues related to the welfares of workers and riders such as autonomy, and fairness. For instance, Galière ( 2020 ), Veen et al. ( 2020 ), Gregory and Sadowski ( 2021 ), Franke and Pulignano ( 2021 ), Heiland ( 2021 ), and Shanahan and Smith ( 2021 ) examined the labor control mechanism in the food delivery industry. To be specific, Galière ( 2020 ) revealed that food delivery platform executes the governmentality dispositive subjectification techniques to create a hyper-meritocratic ideal of justice such that the riders may give consent to the algorithmic management. Veen et al. ( 2020 ) showed that riders are monitored via the apps, provided with limited choices (as information is asymmetric), and work in an unclear performance management system. This finding is similar to Franke and Pulignano ( 2021 )’s study in which the food delivery platform withholds the information and executes rules and regulations to increase its power for controlling the stakeholders and creating its value. Gregory and Sadowski ( 2021 ) found that riders have to do self-investment to “fit for work” and trade the autonomy for algorithmic dispatch. Both algorithmic management and temporal control are the regimes executed by the food delivery platform on the riders (Heiland, 2021 ). Shanahan and Smith ( 2021 ) also highlighted that the food delivery platform uses the unilateral modification of exchange terms, communication and technology designs, and neoliberalism and tribalism to force the riders to accept the job obligations. Table 3 summarizes the articles addressing social sustainability with the consideration of labor control.

3.2.2 Work conditions

To better understand the job quality, work conditions and work safety of riders, Goods et al. ( 2019 ) suggested that workers have to comprehend individual circumstances, job opportunity, and socio-political. Furunes and Mkono ( 2019 ) found that riders have both positive and negative work experiences. The negative work experiences of the riders mainly come from their salary and the challenges from communicating with all the restaurants, employer, and customers. Le Breton and Galiere ( 2022 ) observed that riders rely on the online peer discussion groups for knowledge sharing, symphonizing and shaping in social learning process. In practice, Sun et al. ( 2021 ) identified that riders are required to perform duty at fixed schedule and such de-flexibilization is caused by the labor management tactics, technological-driven operations, and cultural normalization of platform dependency. Piasna and Drahokoupil ( 2021 ) reported that riders are willing to work in a regular schedule basis and prefer to be self-employed. Besides, riders will base on the autonomy level, job market vulnerability and economic attachment of the food delivery platform to select the employment status and work schedule. Behl et al. ( 2022 ) concluded that the major obstacles of being a rider include high competition as well as long login hours and late-night delivery services provision, followed by poor remuneration and unfavorable conditions for getting incentives. Moreover, Puram et al. (2021) found that rider faces different work pressures and difficulties especially during the COVID-19 pandemic, including operational, customer-related, organizational, and technological issues. By comparing the working conditions between regional and international platforms, Muszynski et al. ( 2022 ) illustrated that the regional food delivery platforms evade price competition with the international ones, but they provide better working conditions (e.g., higher safety precautions) than that of the international ones due to the country’s specific work regulations. In addition to the work conditions, work safety of the riders should be considered. Zheng et al. ( 2022a , 2022b ) found that the amount of delivery orders and the rider’s occupational injuries have an inverted U-sharped form, and the work pressure plays as a mediator between such relationship. To improve the welfares of the riders (such as reducing the occupational injuries or traffic accidents), government can play a role in the food delivery system. For instance, Fan et al. ( 2022 ) analytically showed that spot check and information publicity policies are effective measures to reduce traffic violation of the riders. However, the spot check policy would induce more fines on overdue delivery of the food delivery platform. Table 4 summarizes the articles addressing social sustainability with the consideration of work conditions.

3.2.3 Worker’s commitment

Finally, Lin et al. ( 2020 ) and Lee et al. ( 2022 ) evaluated different factors affecting the work commitment and work performance of the riders. Specifically, Lin et al. ( 2020 ) statistically presented that work centrality, entitlement norms, obligation norms, and intrinsic orientation are positively associated with work engagement, which will subsequently generate a positive impact on the work commitment of the riders in the food delivery industry. Recently, Lee et al. ( 2022 ) showed that low levels of work commitment of the crowdsourced riders and perceived risks of the crowdsourcing technical systems are positively associated, and they eventually lead to a lower level of work performance and intention to work continuously in the food delivery system. However, the food delivery platform can provide support, develop a trust relationship and facilitate information sharing to mitigate the negative impacts of the perceived risks of crowdsourcing technical systems. Table 5 summarizes the articles addressing social sustainability with the consideration of worker’s commitment.

3.3 Environmental sustainability

Environmental sustainability has a focus on mitigating the negative impacts to the environment, such as reducing carbon emission, recycling wastes, and using renewable energy. Despite it is an important area to be studied, we observed that there is limited article addressing the environmental sustainability in food delivery operations. To be specific, Liu et al. ( 2020 ) examined the packaging waste problem that is generated from the food delivery operations and evaluated its environmental impacts. By applying the big data mining, their findings revealed that plastic bags contribute most to the food packaging waste in the food delivery services and the use of paper boxes harm the environment most in terms of the quantity of carbon dioxide emission during the production process. Besides, the distribution of the pollution and distribution of food delivery service providers are positively associated. Table 6 summarizes the reviewed articles addressing environmental sustainability.

3.4 Muti-dimensional sustainability

In real-world practices, business firms would address more than one sustainability dimension depending on the levels of stakeholder involvement and stakeholder expectations (Fischer et al., 2020 ), as well as their goals. In the following, we review the articles addressing multi-dimensional sustainability in the food delivery operations and summarize them in Table 7 .

3.4.1 Economic and social sustainability: Operational strategies and social welfare

Wang ( 2022 ) and Chen et al. ( 2022 ) studied both economic and social sustainability performances in the food delivery operations. To be specific, Wang ( 2022 ) examined the best operational strategy of the food delivery platform by considering the bounded rationality and the expected benefits of the supply chain members including restaurants, food delivery platform and customers. The author showed that food delivery platform should penalize the restaurants heavily if they violate the regulations. Besides, the food delivery platform should also use the supervision strategy if the total supervision cost is lower than that of the negative social evaluations under the non-supervision strategy. Chen et al. ( 2022 ) analytically analyzed the value of collaborating with the online food delivery platform from the restaurant’s perspective under the revenue-sharing contract and investigated the way to achieve supply chain coordination. They showed that collaborating with the online food delivery platform is not necessarily beneficial to the restaurant in increasing the demand. Both the platform and the restaurant will be better off if a revenue-sharing contract with a price ceiling or a two-way revenue-sharing contract are adopted. When the supply chain is not coordinated, the platform can increase its profit and social welfare through better controlling its number of riders.

3.4.2 Economic and environmental sustainability: operational strategies and environmental pollution

Regarding the economic and environmental considerations in the food delivery operations, Niu et al. ( 2021 ) first evaluated the optimal pricing mechanism under platform’s delivery strategy and restaurant’s self-delivery strategy, and then examined their impacts on restaurant’s financial performance and supply chain’s environmental sustainability. The authors found that restaurant prefers adopting platform’s delivery strategy when the market demand is low. Besides, the platform’s delivery strategy is more environmentally sustainable than that of the restaurant’s self-delivery strategy when the market demand is high. In addition, Chen and Lee ( 2022 ) identified how the environmental performance of a food delivery platform shapes the customer behavior on it. They statistically found that green brand legitimacy and perceived biosphere value orientation have a significant positive impact on the customer’s trust in the food delivery platform which will eventually lead to a positive consumer behavior in using the environmentally friendly platform.

3.4.3 Social and environmental sustainability: social welfare and environmental pollution

Last but not least, Moncef and Dupuy ( 2021 ) and Sinha and Pandit ( 2021 ) considered both social and environmental sustainability performances in the food delivery operations. Moncef and Dupuy ( 2021 ) investigated the paradoxical tensions faced by different sharing economy in logistics management. They concluded that the food delivery platforms should put more resources in lowering the carbon dioxide emission in the delivery process. Besides, they should also improve the work conditions, and provide better policies and support to the riders. Interestingly, Sinha and Pandit ( 2021 ) quantified the amount of environmental pollution generated from the food delivery and the workload of the riders. By simulating 2100 customer food orders, it was found that about 163 gm carbon dioxide is emitted for each order delivery, and 15 orders are handled by each rider with idle time of about 59.2%.

3.4.4 Economic, environmental and social sustainability: inter-relationship and values of food delivery

There is limited literature addressing all the economic, environmental and social sustainability challenges together in food delivery operations. Seghezzi et al. ( 2021 ) conducted a literature review on the on-demand food delivery to identify the roles of each player and explore the value-adding activities in the food delivery operations. Moreover, the authors conducted interviews with practitioners to uncover the underexplored research areas. Overall, they showed that operations activities (such as food preparation) and restaurant’s benefits require an in-depth analysis as the current discussion on these areas in the literature is insufficient.

4 Discussions and future research agenda

After reviewing the relevant literature, we have identified three research areas that are underexplored in the field of third-party food delivery operations. See Fig.  5 and Table 8 for the details. First, there is inadequate investigation on the restaurant’s preferences and decisions in the third-party food delivery system. Referring to the economic sustainability literature, Jia et al. ( 2022 ) is the only study which examined the restaurant’s decision on forming partnership with the third-party food delivery platform. The number of partner restaurants in a food delivery platform may affect the customer’s incentive to use delivery services, and hence the profitability of the food delivery platform. It is crucial to understand the restaurants’ preferences, concerns and decisions as it will affect the success of a third-party food delivery platform. Second, the understanding on environmental performance in the third-party food delivery operations literature is insufficient. It is found that the impacts of food packaging and waste in the food delivery service was addressed only in Liu et al. ( 2020 ). In practice, the food delivery platforms have developed various environmentally sustainable programs to reduce carbon emission and pollution. However, the studies related to food delivery platform operational green practices, and the customer’s attitude and behavior towards such green practices have not been well explored. Finally, the examination on the multi-dimensional sustainability in the third-party food delivery operations is very limited. Each dimension in TBL is inter-related. For example, the delivery strategy of the food delivery operations will affect the environmental sustainability performance (Niu et al., 2021 ) while a proper design of a supply chain contract between the restaurant and food delivery platform will affect the food delivery platform’s profitability and social welfare (Chen et al., 2022 ). It is essential to consider the benefits of supply chain members, impacts on the environment, as well as the food delivery platform’s financial performance for its sustainable growth and development.

Distribution of the reviewed literature in the TBL framework. (Remarks: The number indicated in the bracket represents the number of articles)

In the following, we propose a future research agenda on five different areas, namely, applications of digital technologies, behaviors and decisions of the restaurants, risk management, TBL, and post-coronavirus pandemic. Table 9 summarizes the future research agenda proposed in this study.

4.1 Applications of digital technologies

Information sharing is a crucial element in achieving sustainable operations (Zhang et al., 2018 ). With the emergence of disruptive technologies in Industry 4.0 such as blockchain (Luo & Choi, 2022 ), Internet-of-Thing (IoT), and robot (Sheu & Choi, 2022 ), it will affect the existing food delivery operations, enable an efficient information sharing among all stakeholders and facilitate novel business models (Akter et al., 2022 ). For example, Uber Eats has used robots to support driverless autonomous delivery and order tracking services to the end-customers in the US. It is believed that such services are time and cost efficient as the end-customers can get their meal in a quicker manner and save tip costs paid to the riders. From the perspective of Uber Eats, the autonomous delivery reduces the number of riders requirement, and is more eco-friendly than that of the traditional car delivery. Footnote 6 However, it will also require labours to monitor the operations and a higher level of customer involvement in the operations as the robot is not able to enter an apartment. Footnote 7 Since this is a new service, in the future, it is promising to examine the perceived value, experience and preference of the end-customers on the driverless autonomous delivery. Besides, it deserves our efforts to analytically investigate the values of using the autonomous delivery in the third-party food delivery operations by comparing with the traditional operations in terms of profitability, consumer utility and environmental performance.

Another disruptive technology that has been applied in the food delivery operations is the blockchain technology (Choi & Shi, 2022 ). Blockchain technology is regarded as a distributed digital ledger that improves information transparency, ensures food safety and hygiene, and supports faster and secure payment (Choi et al., 2022a , 2022b ). In real-world example, Bistroo has adopted the blockchain technology in its food delivery ecosystem to facilitate token payments and smart contracts. As the token payments do not involve intermediates, Bistroo is able to lower the commission fee charged to the restaurant, which will result in a more competitive pricing setting. Besides, by using the blockchain technology, customers can acquire trusted and reliable information about the food quality and restaurant rating, and are beneficial from the customer rewards programs which are enabled by the blockchain-supported smart contracts. Footnote 8 However, the success of applying the blockchain technology in the food delivery operations requires the support from both the end-customers and restaurants, as well as the investment from the food delivery platform. Thus, it is important to identify the determinants of using the blockchain technology in the food delivery operations from both the end-customers’ and restaurants’ perspectives and then understand what kind of support that should be provided by the food delivery platform. On the other hand, one may consider illustrating such business model, and analyzing the impacts of using smart contracts to enable the consumer reviews valuation and rewards programs.

4.2 Behaviors and decisions of the restaurants

The use of third-party food delivery app is a new trend during the coronavirus pandemic, and the corresponding perceived benefits and risks should be identified (Gupta & Duggal, 2020 ). In this field of study, the majority of prior studies consider the perspective of the individual end-customers only and there are very few studies emphasizing the driving forces of forming a partnership with different third-party food delivery platforms from the perspective of restaurants (Sin et al., 2021 ). In the food delivery operations, restaurants are also the business customers of the third-party food delivery app and play an important role in the food delivery business. By forming a partnership with the third-party food delivery platform, it can increase the customer base of the restaurants. In reality, we have observed that some restaurants form partnership with one food delivery platform only. See Table 10 for details. Therefore, one possible future research topic is to explore the behaviors and decisions of the restaurants. For example, it is interesting to examine the determinants in selecting the food delivery platform and then evaluate the benefits of such selection.

Moreover, after forming a partnership with the food delivery platforms, restaurants not only need to manage the operations of their own restaurants but also the food delivery apps. Restaurants, such as Presidio Pizza Company and Proposition Chicken in San Francisco, intended to form partnership with more than one food delivery platforms to increase revenues. However, they withdrew the use of the third-party food delivery apps as each platform’s app has its own hardware and software. This caused employees to shuffle multiple orders from iPads, platform device, phone and in-store customer lines (Houck, 2017 ). Therefore, it is also crucial to explore the effects of forming business partnership with more than one food delivery platforms and the values and risks of such practices. In addition, the decisions about the optimal operational strategy of the restaurant, including the optimal number of business partnerships with the food delivery platforms and the optimal contract setting with the consideration of the competition between platforms, deserve an in-depth investigation as well.

4.3 Risk management

Risk management is undoubtedly an important issue in the service industry (Choi et al., 2016 ), however, it is underexplored in food delivery operations. Niu et al. ( 2021 ) stated that food and logistics are connected to food delivery. Food cannot be delivered from the restaurants to the end-customers without the logistics support. There are different types of risk in food delivery operations. The first one is logistics risk, which consists of “loss of food” and “late delivery”. For the loss of food, it can be either an entire order is missing, or part of an order is missing. Common reasons for missing items include restaurant’s false, rider’s false (Butler 2022 ) or customers’ false claim (Mok, 2021 ; Helling, 2022 ). Helling ( 2022 ) presented that restaurants may often make mistakes on order items when they have to handle multiple orders at once and rider may deliver the order to a wrong address. Therefore, “loss of food” will decrease the reliability of a food delivery platform and lower the consumer’s utility. Besides, Mok ( 2021 ) mentioned that certain customers make a false claim on “missing” food to take advantage of fraud. For instance, Uber Eats provides refund to the customers if they report the “missing” food items. However, it does not investigate the root cause of having “missing” food items but simply deduct such cost directly from the restaurant payouts (Mok, 2021 ). This will increase the profit risk of the restaurants. On the other hand, “late delivery” is another logistics risk. Uber Eats in Melbourne always faces the late delivery problem (Dexter, 2021 ). This may be due to the fact that the order assignments are scheduled by the algorithms in which factors such as rider’s driving experience are not well considered. Besides, customer may deliberately provide an incorrect delivery address to avoid high delivery fee (Spence, 2022 ) which will increase the deliver time as the riders need more time to search for the right location. Therefore, new algorithms should be developed to address the logistics risks in the route planning. Besides, alternative operational strategy that can enhance the capacity flexibility of the food delivery platform should be explored and its effects should be examined. Zhang et al. ( 2022 ) provide an analytical discussion on this area.

Furthermore, food quality risk is also an obstacle to the restaurants’ operations. It is difficult for the restaurants to control their food quality once their food is out of the restaurants (Marks, 2020 ). According to Currington ( 2022 ), 82% of customers complained about the restaurants instead of the riders or the food delivery platforms for the food quality problems. This illustrated that the uncontrollable food quality problem in the last-mile delivery may affect the restaurant’s reputation. In the future, it is interesting to investigate the impacts of food quality problem arose from the last-mile delivery on the restaurant’s brand equity. It is also important to apply machine learning and data analytics to estimate the food delivery time and food temperature which can help the restaurants to develop measurements to maintain the food quality.

Finally, profit risk should also be examined. Restaurants, including both giant chain restaurants or small restaurants such as teahouses and hawker stalls, may prefer to form partnership with food delivery platform who offers a lower commission fee. Deepak Kaul, an owner of an Indian restaurant in Portland, indicated that he has to pay about 30% commission fee to the food delivery platform which will result in a large variation in the restaurant’s profitability. Therefore, many restaurants encourage customers to order food delivery by calling the restaurants directly (Shah, 2020 ). It is promising to compare different business partnership models in the real-world situation, and then analytically determine the best operational strategy in the food delivery operations. To study risk management and quantify risk in the supply chain context, Choi et al. ( 2018 ) and Li et al. ( 2013 ) provide good discussion on building the related analytical models.

4.4 Triple bottom line (TBL)

Economic sustainability is a crucial dimension in triple bottom line (TBL) framework as the business firms have to be profitable for survival in the competitive marketplace. In the existing literature, two articles analytically examined the supply chain performance under the revenue-sharing contract and proposed the mechanism to achieve supply chain coordination (Chen et al., 2022 ; Feldman et al., 2022 ). In real-world observation, we have found that third-party food delivery platform offers new subsidy scheme to restaurants to lessen their financial burden on the commission fee. For example, the platform, SkipTheDishes, provided 15% of rebate on the total commission fees paid by the restaurants during the coronavirus pandemic to increase the customer demand as the food price will be set lower (McLean, 2020 ). On the other hand, during the coronavirus pandemic, the business environment is highly dynamic which will significantly affect the profitability and performance of the supply chain. It is recommended to first conduct the risk sensitivity estimation of the business firm to enhance the operational efficiency of the on-demand platform (Choi & Shi, 2022 ). Apart from using revenue-sharing contract, other sophisticated supply contracting (e.g., a supply chain contract with rebate component) can be explored to determine the win–win situation of the food delivery operations. Chiu et al. ( 2011 ) provide a good reference in this area.

Regarding the environmental sustainability, it is found that limited literature addressed this dimension when studying the food delivery businesses. As the operations of the online food delivery platforms are driven and supported by the technology and big data, their environmental performance (e.g., carbon dioxide emission) can be evaluated based on new methods (Song et al., 2017 ; Yao et al., 2020 ). Besides, it is reported that the motorcycles last-mile delivery contributes to noise and air pollution if they are not using eco-friendly electric ones (García, 2022). In reality, Swiggy has planned to execute electric vehicles for food delivery Footnote 9 to tackle this problem. Moreover, the well-established food delivery platform Foodpanda has implemented different measures to be environmentally sustainable including launching “reusable packaging” and “plastic containers recycling” programs. Footnote 10 However, the food delivery platform needs to take initiative to invest in environmental sustainability measurement. In the future, it is suggested to conduct an in-depth investigation to understand the customers behaviors in using the “green” food delivery platform which will provide a good reference on the future development (such as the use of electric vehicles for food delivery, launching environmentally sustainable programs). Based on the findings, it can help to construct analytical models to evaluate the performance of the green practices under different environmental schemes imposed by the government (e.g., carbon tax, and cap-and-trade policy) (Homayouni et al., 2021 ; Sheu & Chen, 2012 ). The food delivery platform should strike a balance between the economic sustainability and environmental sustainability, where existing literature addressing multi-dimensions of sustainability is very few and deserve our further exploration.

Finally, in terms of the social sustainability, it is found that customers may experience service inconsistency between the restaurant and the food delivery platform (Furunes & Mkono, 2019 ) which will affect the consumer utility. In this case, both the restaurant and food delivery platform should seek for a mechanism to improve the situation and evaluate the benefits. Besides, there were more than a thousand cases of the delivery accident reported over the past years. Footnote 11 In the future, one potential area for investigation is whether the government should provide subsidy or issue penalty scheme to the food delivery platforms to ensure the safety of the riders when providing the last-mile delivery, and explore its impacts on the supply chain performance.

4.5 Post-coronavirus pandemic

Previous studies have discussed some findings on food delivery services and operations during the coronavirus (COVID-19) pandemic situation. However, to be successful in an industry, a long-term sustainable growth is much more important than focusing on the short-term development during the COVID-19 pandemic only (Li et al., 2020 ; Ivanov, 2022 ). It is observed that customers have changed their eating habit from dining out to dining at home since the time when governments have imposed social distancing restrictions during the pandemic. Therefore, there is a boom in the “ghost kitchen” business model that the restaurants prepare their meals from a “virtual” facility operated by the third-party food delivery companies (rather than in their own physical kitchens) and offer the delivery-only service to the customers. For example, Uber Eats has run more than 1500 ghost kitchens in the US and Canada, where customers can place an order from the restaurants’ physical kitchens or ghost kitchen of Uber Eats (Sherred, 2019 ). Under this virtual business model, the restaurants can save both the fixed cost (e.g., rental) and variable cost (e.g., waitress’s salary) in their operations. It is estimated that the market size of “ghost kitchen” will hit the level of level of USD139.37 billion by 2028 (AFP, 2021 ). However, if all the social distancing restrictions are cancelled after the coronavirus pandemic, customer’s preference may be different, and they are more likely to dine out of their home again. Therefore, the customer preference of ordering from the third-party food delivery platform as well as from the ghost kitchen during and after the coronavirus pandemic should be studied in the future (Li et al., 2018 ). For example, it is important to identify the key drivers of ordering from third-party food delivery platforms, conduct a customer demand analysis and analyze the operational decisions (e.g., optimal pricing decisions and optimal number of riders in the ghost kitchen model) in the post-coronavirus pandemic era.

5 Concluding remarks

To conclude, third-party food delivery is in a rapid evolution especially during the coronavirus pandemic. To maintain a sustainable development of the food delivery businesses, it is important to satisfy both the customers’ short-term benefits and all stakeholders’ long-term values. This study first conducts a systematic literature review to identify how to achieve a sustainable operation for third-party food delivery. Then, it highlights the recent advances in this important area with the discussion of real-world practices.

In particular, in this study, we first examine the relevant literature and apply the triple bottom line (TBL) framework to classify prior studies into economic sustainability, environmental sustainability, social sustainability, and multi-dimensional sustainability. We then identify three major research gaps, including inadequate investigation on the restaurant’s preferences and decisions, insufficient understanding on the environmental performance, and limited examination on the multi-dimensional sustainability in third-party food delivery operations. Finally, based on the reviewed literature and observed industrial practices, we propose five future research areas that deserve an in-depth further investigation. These areas include the applications of digital technologies, behaviors and decisions of the restaurants, risk management, TBL, and post-coronavirus pandemic.

Overall, to achieve a sustainable operation for third-party food delivery, the food delivery businesses should commit to the economic, social and environmental responsibilities. Specifically, the food delivery platform should understand the customer’s behaviors and preferences in using the food delivery service. Besides, it has to carefully decide the optimal operational strategies (including the routing, pricing, and delivery mode), analyze the effect of market competition, and study the operational impacts of food delivery on the environment. Moreover, it needs to consider the worker’s (i.e., riders) benefits, engagement, and safety issue as well as cooperation scheme with the restaurants such that the food delivery businesses can sustain a long-term success. Last but not least, government’s policies will affect the operations of the food delivery service and the economic sustainability of entire food delivery system. For instance, the government of New South Wales has imposed laws to improve the safety for riders who should be provided with training and personal protective equipment (PPE). Footnote 12 In China, government requires food delivery platforms to lower the commission fee charged to the restaurants (Qu, 2022 ). Therefore, the food delivery businesses should keep reviewing the government policies when making operational decisions.

We do admit some limitations of this study. First, the literature review is systematic and follows a well-defined logic. However, as we all know, some related papers may still be missing. Second, for the related practices, we have included many commonly seen and well-established operations as a support to our arguments. However, there are always exceptions and the practices of food delivery platforms in different cities/countries may differ. So, we do not claim that our discussions are universally true. Readers should understand these limitations when they interpret our results and proposals.

https://www.woking.gov.uk/environmental-services/food-hygiene/coronavirus-takeaway-food-and-deliveries-guidance (accessed 8 August 2022).

https://yalantis.com/blog/three-types-of-on-demand-delivery-platforms-pros-and-cons/ (accessed 8 August 2022).

https://www.statista.com/outlook/dmo/eservices/online-food-delivery/platform-to-consumer-delivery/worldwide#:~:text=Revenue%20in%20the%20Platform%2Dto,US%24302.40bn%20by%202026 (accessed 8 August 2022).

https://www.epa.gov/sites/production/files/2015-08/documents/reducing_wasted_food_pkg_tool.pdf (accessed 2 December 2022).

The experts recommended 13 articles related to the food delivery operations. To ensure consistency, the author (who filtered the papers collected from Web of Science) read the abstract and introduction section of each article to determine their relevancy for review. All the recommended articles are relevant, and they are not duplicated in the initial searching.

https://www.makeuseof.com/how-uber-eats-new-autonomous-deliveries-will-work/ (accessed 8 August 2022).

https://www.inc.com/ben-sherry/ai-delivery-robots-autonomous-vehicles.html (accessed 8 August 2022).

https://bistroo.io/whitepaper (accessed 8 August 2022).

https://www.zeebiz.com/india/news-food-deliveries-on-electric-vehicles-how-swiggy-is-using-evs-to-deliver-15-million-orders-every-month-96963 (accessed 8 August 2022).

https://www.foodpanda.hk/zh/contents/sustainability-reduce-plastic-waste?r=1 (accessed 2 December 2022).

https://www.thestar.com.my/news/nation/2022/07/21/1242-food-delivery-rider-accidents-from-2018-to-may-2021 (accessed 8 August 2022).

https://www.nsw.gov.au/customer-service/media-releases/new-safety-laws-food-delivery-riders-need-to-know (accessed 18 January 2023).

AFP (2021). “Ghost kitchens’ boom in Asia as pandemic sparks huge demand . Hong Kong Free Press. Retrieved November 30, 2022, https://hongkongfp.com/2021/09/19/ghost-kitchens-boom-in-asia-as-pandemic-sparks-huge-demand/

Ahn, J., & Kwon, J. (2021). Examining the relative influence of multidimensional customer service relationships in the food delivery application context. International Journal of Contemporary Hospitality Management, 33 (3), 912–928.

Article   Google Scholar  

Ahuja, K., Chandra, V., Lord, V., & Peens, C. (2021). Ordering in: The rapid evolution of food delivery . McKinsey & Company. Retrieved August 30, 2022, https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ordering-in-the-rapid-evolution-of-food-delivery .

Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308 , 7–39.

Bai, J., & Tang, C. S. (2022). Can two competing on-demand service platforms be profitable? International Journal of Production Economics, 250 , 108672.

Barthel, P., & Ivanaj, V. (2007). Is sustainable development in multinational enterprises a marketing issue? Multinational Business Review, 15 (1), 67–88.

Behl, A., Rajagopal, K., Sheorey, P., & Mahendra, A. (2022). Barriers to entry of gig workers in the gig platforms: Exploring the dark side of the gig economy. Aslib Journal of Information Management . https://doi.org/10.1108/AJIM-08-2021-0235

Bozkaya, E., Eriskin, L., & Karatas, M. (2022). Data analytics during pandemics: A transportation and location planning perspective. Annals of Operations Research . https://doi.org/10.1007/s10479-022-04884-0

Butler, S. (2022). Delivery driver drops food off at wrong house then tells customer to fetch it himself . Indy100. Retrieved August 30, 2022, https://www.indy100.com/viral/delivery-driver-food-wrong-house .

Chau, C. (2021). Hong Kong police warn striking Foodpanda workers to disperse or face possible force . Hong Kong Free Press. Retrieved August 30, 2022, https://hongkongfp.com/2021/11/16/hong-kong-police-warn-striking-foodpanda-workers-to-disperse-or-face-possible-force/ .

Chen, M., Hu, M., & Wang, J. (2022). Food delivery service and restaurant: Friend or foe? Management Science, Published Online, . https://doi.org/10.1287/mnsc.2021.4245

Chen, X., & Lee, T. J. (2022). Potential effects of green brand legitimacy and the biospheric value of eco-friendly behavior on online food delivery: A mediation approach. International Journal of Contemporary Hospitality Management . https://doi.org/10.1108/IJCHM-07-2021-0892

Chiu, C. H., Choi, T. M., & Tang, C. S. (2011). Price, rebate, and returns supply contracts for coordinating supply chains with price-dependent demands. Production and Operations Management, 20 (1), 81–91.

Choi, T. M. (2022a). Achieving economic sustainability: Operations research for risk analysis and optimization problems in the blockchain era. Annals of Operations Research . https://doi.org/10.1007/s10479-021-04394-5

Choi, T. M. (2022b). Financing product development projects in the blockchain era: Initial coin offerings versus traditional bank loans. IEEE Transactions on Engineering Management, 69 (6), 3184–3196.

Choi, T. M., Kumar, S., Yue, X., & Chan, H. L. (2022). Disruptive technologies and operations management in the Industry 4.0 era and beyond. Production and Operations Management, 31 (1), 9–31.

Choi, T. M., & Shi, X. (2022). On-demand-ride-hailing-service platforms with hired drivers during coronavirus (COVID-19) outbreak: Can blockchain help? IEEE Transactions on Engineering Management . https://doi.org/10.1109/TEM.2021.3131044

Choi, T. M., Wallace, S. W., & Wang, Y. (2016). Risk management and coordination in service supply chains: Information, logistics and outsourcing. Journal of the Operational Research Society, 67 (2), 159–164.

Choi, T. M., Zhang, J., & Cheng, T. C. E. (2018). Quick response in supply chains with stochastically risk sensitive retailers. Decision Sciences, 49 (5), 932–957.

Chokshi, M. (2020). Online food delivery apps: Why are they so much in demand? CustomerThink. Retrieved August 1, 2022, https://customerthink.com/online-food-delivery-apps-why-are-they-so-much-in-demand/ .

Cini, L., & Goldmann, B. (2021). The worker capabilities approach: Insights from worker mobilizations in Italian logistics and food delivery. Work, Employment and Society, 35 (5), 948–967.

Crane, A., & Desmond, J. (2002). Societal marketing and morality. European Journal of Marketing, 36 (5/6), 548–569.

Currington, E. (2022). Why 3rd-party delivery platforms are problematic for restaurants . The Digital Restaurant. Retrieved August 16, 2022, https://thedigitalrestaurant.com/food-delivery-service-apps-problem-for-restaurants/ .

Dai, H., & Liu, P. (2020). Workforce planning for O2O delivery systems with crowdsourced drivers. Annals of Operations Research, 291 (1), 219–245.

Dexter, R. (2021). Why is your Uber Eats order taking so long to arrive? The Age. Retrieved August 16, 2022, https://www.theage.com.au/national/victoria/why-is-your-uber-eats-order-taking-so-long-to-arrive-20211111-p59824.html .

Du, J., Guo, B., Liu, Y., Wang, L., Han, Q., Chen, C., & Yu, Z. (2019). CrowDNet: Enabling a crowdsourced object delivery network based on modern portfolio theory. IEEE Internet of Things Journal, 6 (5), 9030–9041.

Du, Z., Fan, Z. P., & Gao, G. X. (2021). Choice of O2O food delivery mode: Self-built platform or third-party platform? Self-delivery or third-party delivery? IEEE Transactions on Engineering Management . https://doi.org/10.1109/TEM.2021.3069457

Fan, B., Lv, L., & Han, G. (2022). Online platform’s corporate social responsibility for mitigating traffic risk: Dynamic games and governmental regulations in O2O food delivery industry. Computers & Industrial Engineering, 169 , 108188.

Feldman, P., Frazelle, A. E., & Swinney, R. (2022). Managing relationships between restaurants and food delivery platforms: Conflict, contracts, and coordination. Management Science . https://doi.org/10.1287/mnsc.2022.4390

Fischer, D., Brettel, M., & Mauer, R. (2020). The three dimensions of sustainability: A delicate balancing act for entrepreneurs made more complex by stakeholder expectations. Journal of Business Ethics, 163 (1), 87–106.

Francioni, B., Curina, I., Hegner, S. M., & Cioppi, M. (2022). Predictors of continuance intention of online food delivery services: Gender as moderator. International Journal of Retail & Distribution Management . https://doi.org/10.1108/IJRDM-11-2021-0537

Franke, M., & Pulignano, V. (2021). Connecting at the edge: Cycles of commodification and labour control within food delivery platform work in Belgium. New Technology, Work and Employment . https://doi.org/10.1111/ntwe.12218

Furunes, T., & Mkono, M. (2019). Service-delivery success and failure under the sharing economy. International Journal of Contemporary Hospitality Management, 31 (8), 3352–3370.

Galière, S. (2020). When food-delivery platform workers consent to algorithmic management: A Foucauldian perspective. New Technology, Work and Employment, 35 (3), 357–370.

García, S. (2022). The negative effects of food delivery: From pollution to malnutrition . El Pais. Retrieved August 16, 2022, https://english.elpais.com/society/2022-06-29/the-negative-effects-of-food-delivery-from-pollution-from-malnutrition.html .

Goods, C., Veen, A., & Barratt, T. (2019). “Is your gig any good?” Analyzing job quality in the Australian platform-based food-delivery sector. Journal of Industrial Relations, 61 (4), 502–527.

Gregory, K., & Sadowski, J. (2021). Biopolitical platforms: The perverse virtues of digital labour. Journal of Cultural Economy, 14 (6), 662–674.

Gunden, N., Morosan, C., & DeFranco, A. (2020). Consumers’ intentions to use online food delivery systems in the USA. International Journal of Contemporary Hospitality Management, 32 (3), 1325–1345.

Gupta, V., & Duggal, S. (2020). How the consumer’s attitude and behavioral intentions are influenced: A case of online food delivery applications in India. International Journal of Culture, Tourism and Hospitality Research, 15 (1), 77–93.

Gupta, V., & Sajnani, M. (2019). Risk and benefit perceptions related to wine consumption and how it influences consumers’ attitude and behavioral intentions in India. British Food Journal, 122 (8), 2569–2585.

He, Z., Han, G., Cheng, T. C. E., Fan, B., & Dong, J. (2019). Evolutionary food quality and location strategies for restaurants in competitive online-to-offline food ordering and delivery markets: An agent-based approach. International Journal of Production Economics, 215 , 61–72.

Heiland, H. (2021). Neither timeless, nor placeless: Control of food delivery gig work via place-based working time regimes. Human Relations, 75 (9), 1824–1848.

Helling, B. (2022). DoorDash missing item? Here’s what to do . Ridester.com. Retrieved August 16, 2022, https://www.ridester.com/doordash-missing-item/ .

Hess, D., Rogovsky, N., & Dunfee, T. W. (2002). The next wave of corporate community involvement: Corporate social initiatives. California Management Review, 44 (2), 110–125.

Høgevold, N. M., Svensson, G., Wagner, B., J. Petzer, D., Klopper, H., Carlos Sosa Varela, J., Padin, C., & Ferro, C. (2014). Sustainable business models. Baltic Journal of Management , 9 (3), 357–380.

Homayouni, Z., Pishvaee, M. S., Jahani, H., & Ivanov, D. (2021). A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty. Annals of Operations Research . https://doi.org/10.1007/s10479-021-03985-6

Houck, B. (2017). Why some restaurants are cutting ties with mobile ordering apps. Eater. Retrieved August 16, 2022l, https://www.eater.com/2017/8/29/16214442/restaurant-order-pay-apps-seamless-postmates-uber-eats .

Ivanov, D. (2022). Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research , 319 (1), 1411–1431.

Jawahar, N., Satish Pandian, G., Gunasekaran, A., & Subramanian, N. (2017). An optimization model for sustainability program. Annals of Operations Research, 250 (2), 389–425.

Jha, R. (2022). A novel hybrid intelligent technique to enhance customer relationship management in online food delivery system. Multimedia Tools and Applications, 81 , 28583–28606.

Jia, H., Shen, S., RamírezGarcía, J. A., & Shi, C. (2022). Partner with a third-party delivery service or not? A prediction-and-decision tool for restaurants facing takeout demand surges during a pandemic. Service Science, 14 (2), 139–155.

Joselow, M. (2020). Delivery vehicles increasingly choke cities with pollution . Scientific American. Retrieved November 30, 2022, https://www.scientificamerican.com/article/delivery-vehicles-increasingly-choke-cities-with-pollution/

Kapoor, A. P., & Vij, M. (2018). Technology at the dinner table: Ordering food online through mobile apps. Journal of Retailing and Consumer Services, 43 , 342–351.

Kathleen, M., & Lo, H.Y. (2022). Hong Kong social distancing: Malls, delivery platforms seek to turn restaurants’ loss into their gain. South China Morning Post. Retrieved August 1, 2022, https://www.scmp.com/news/hong-kong/hong-kong-economy/article/3162425/hong-kong-social-distancing-malls-delivery .

Kaur, P., Dhir, A., Talwar, S., & Ghuman, K. (2021). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, 33 (4), 1129–1159.

Kohar, A., & Jakhar, S. K. (2021). A capacitated multi pickup online food delivery problem with time windows: A branch-and-cut algorithm. Annals of Operations Research . https://doi.org/10.1007/s10479-021-04145-6

Lang, K., & Zhao, Y. (2021). Cloud computing resource scheduling based on improved ANN model takeaway order volume forecast. Journal of Intelligent & Fuzzy Systems, 40 (4), 5905–5915.

Le Breton, C., & Galiere, S. (2022). The role of organizational settings in social learning: An ethnographic focus on food-delivery platform work. Human Relations, . https://doi.org/10.1177/00187267221081295

Lee, S., Chang, H. S., & Cho, M. (2022). Applying the sociotechnical systems theory to crowdsourcing food delivery platforms: The perspective of crowdsourced workers. International Journal of Contemporary Hospitality Management, 34 (7), 2450–2471.

Leung, H. (2022). ‘The price of participation’: For Hong Kong eateries, delivery giants Foodpanda and Deliveroo are a double-edged sword . Hong Kong Free Press. Retrieved August 30, 2022, https://hongkongfp.com/2022/01/23/the-price-of-participation-for-hong-kong-restaurants-delivery-giants-are-a-double-edged-sword/ .

Li, C., Mirosa, M., & Bremer, P. (2020). Review of online food delivery platforms and their impacts on sustainability. Sustainability, 12 (14), 5528.

Li, J., Choi, T. M., & Cheng, T. C. E. (2013). Mean variance analysis of fast fashion supply chains with returns policy. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44 (4), 422–434.

Li, L., Chi, T., Hao, T., & Yu, T. (2018). Customer demand analysis of the electronic commerce supply chain using Big Data. Annals of Operations Research, 268 (1), 113–128.

Lichtenstein, N. (2020). The hidden cost of food delivery. TechCrunch. https://techcrunch.com/2020/03/16/the-hidden-cost-of-food-delivery/ .

Lin, M., Lin, S., Ma, L., & Zhang, L. (2022). The value of the Physical Internet on the meals-on-wheels delivery system. International Journal of Production Economics, 248 , 108459.

Lin, P. M., Au, W. C., Leung, V. T., & Peng, K. L. (2020). Exploring the meaning of work within the sharing economy: A case of food-delivery workers. International Journal of Hospitality Management, 91 , 102686.

Liu, G. Y., Agostinho, F., Duan, H. B., Song, G. H., Wang, X. Q., Giannetti, B. F., Santagata, R., Casazza, M., & Lega, M. (2020). Environmental impacts characterization of packaging waste generated by urban food delivery services. A big-data analysis in Jing-Jin-Ji region (China). Waste Management, 117 , 157–169.

Luo, S., & Choi, T. M. (2022). E-commerce supply chains with considerations of cyber-security: Should governments play a role? Production and Operations Management, 31 (5), 2107–2126.

Marks, G. (2020). Restaurant owners may not like delivery services–but can they do without them? The Guardian. Retrieved August 1, 2022, https://www.theguardian.com/business/2020/dec/02/restaurant-owners-delivery-services-grubhub-doordash-fees-pandemic .

McLean, H. (2020). SkipTheDishes to help restaurant partners with 30-day support package . Dished. Retrieved August 16, 2022, https://dailyhive.com/vancouver/skipthedishes-restaurant-support-package-canada .

Mok, D. (2020). Deliveroo couriers in Hong Kong take protest over new pay policy into third day . South China Morning Post. Retrieved August 16, 2022, https://www.scmp.com/news/hong-kong/society/article/3086032/deliveroo-couriers-hong-kong-take-protest-over-new-pay .

Mok, T. (2021). Toronto restaurant owner calls out customers scamming delivery apps for free food . blogTO. Retrieved August 16, 2022, https://www.blogto.com/eat_drink/2021/03/toronto-restaurant-owner-calls-out-customers-scamming-delivery-apps-free-food/ .

Moncef, B., & Dupuy, M. M. (2021). Last-mile logistics in the sharing economy: Sustainability paradoxes. International Journal of Physical Distribution & Logistics Management, 51 (5), 508–527.

Muszyński, K., Pulignano, V., & Marà, C. (2022). Product markets and working conditions on international and regional food delivery platforms: A study in Poland and Italy. European Journal of Industrial Relations, 28 (3), 295–316.

Niu, B., Li, Q., Mu, Z., Chen, L., & Ji, P. (2021). Platform logistics or self-logistics? Restaurants’ cooperation with online food-delivery platform considering profitability and sustainability. International Journal of Production Economics, 234 , 108064.

Pandey, S., Chawla, D., & Puri, S. (2022). Food delivery apps (FDAs) in Asia: An exploratory study across India and the Philippines. British Food Journal, 124 (3), 657–678.

Piasna, A., & Drahokoupil, J. (2021). Flexibility unbound: Understanding the heterogeneity of preferences among food delivery platform workers. Socio-Economic Review, 19 (4), 1397–1419.

Preetha, S., & Iswarya, S. (2019). An analysis of user convenience towards food online order and delivery application (Food app via platforms).  International Journal of Management. Technology and Eng ineering, pp. 429–433.

Puram, P., Gurumurthy, A., Narmetta, M., & Mor, R. S. (2022). Last-mile challenges in on-demand food delivery during COVID-19: Understanding the riders’ perspective using a grounded theory approach. The International Journal of Logistics Management, 33 (3), 901–925.

Qu, T. (2022). Chinese directive asking on-demand platforms to lower merchant fees triggers sell off in Meituan shares in Hong Kong . South China Morning Post. Retrieved January 18, 2023, https://www.scmp.com/tech/policy/article/3167567/chinese-directive-asking-demand-platforms-lower-merchant-fees-triggers?module=inline&pgtype=article

Raza, A., Asif, M., & Akram, M. (2022). Give your hunger a new option: Understanding consumers’ continuous intention to use online food delivery apps (OFDAs) using trust transfer theory. International Journal of Consumer Studies, Published Online, . https://doi.org/10.1111/ijcs.12845

Schulz, S. A., & Flanigan, R. L. (2016). Developing competitive advantage using the triple bottom line: A conceptual framework. Journal of Business & Industrial Marketing, 31 (4), 449–458.

Seghezzi, A., & Mangiaracina, R. (2020). On-demand food delivery: Investigating the economic performances. International Journal of Retail & Distribution Management, 49 (4), 531–549.

Seghezzi, A., Winkenbach, M., & Mangiaracina, R. (2021). On-demand food delivery: A systematic literature review. The International Journal of Logistics Management, 32 (4), 1344–1355.

Shah, K. (2020). Delivery platforms need to give restaurants a break . Food & Wine. Retrieved August 1, 2022, https://www.foodandwine.com/fwpro/delivery-apps-restaurants-coronavirus-commission .

Shanahan, G., & Smith, M. (2021). Fair’s fair: Psychological contracts and power in platform work. The International Journal of Human Resource Management, 32 (19), 4078–4109.

Sherred, K. (2019). Why “ghost” restaurants are changing the delivery game . Restaurant Dive. Retrieved November 30, 2022, https://www.restaurantdive.com/news/why-ghost-restaurants-are-changing-the-delivery-game/546624/

Sheu, J. B., & Chen, Y. J. (2012). Impact of government financial intervention on competition among green supply chains. International Journal of Production Economics, 138 (1), 201–213.

Sheu, J. B., & Choi, T. M. (2022). Can we work more safely and healthily with robot partners? A human-friendly robot-human coordinated order fulfillment scheme. Production and Operations Management . https://doi.org/10.1111/poms.13899

Sin, K. Y., Lo, M. C., & Mohamad, A. A. (2021). The determinants and barriers of outsourcing third-party online delivery: Perspectives of F&B entrepreneurs in Malaysia. Journal of Asian Finance, Economics and Business, 8 (5), 979–986.

Google Scholar  

Sinha, D., & Pandit, D. (2021). A simulation-based study to determine the negative externalities of hyper-local food delivery. Transportation Research Part D: Transport and Environment, 100 , 103071.

Song, M., Du, Q., & Zhu, Q. (2017). A theoretical method of environmental performance evaluation in the context of big data. Production Planning & Control, 28 (11–12), 976–984.

Song, M., Fisher, R., de Sousa Jabbour, A. B. L., & Santibañez Gonzalez, E. D. (2022). Green and sustainable supply chain management in the platform economy. International Journal of Logistics Research and Applications, 25 (4–5), 349–363.

Spence, A. (2022). Driver refused to deliver order 12 miles away after customer changed address . Newsweek. Retrieved August 1, 2022, https://www.newsweek.com/driver-refused-deliver-order-12-miles-away-after-customer-changed-address-1673986 .

Sun, P., Chen, J. Y., & Rani, U. (2021). From flexible labour to ‘sticky labour’: A tracking study of workers in the food-delivery platform economy of China. Work, Employment and Society . https://doi.org/10.1177/09500170211021570

Sun, Y., Wu, Z., & Zhu, W. (2022). When do firms benefit from joint price and lead-time competition? European Journal of Operational Research, 302 (2), 497–517.

Taylor, T. A. (2018). On-demand service platforms. Manufacturing & Service Operations Management, 20 (4), 704–720.

Tsai, P. H., Hsiao, W. H., & Chen, C. J. (2022). Which food delivery platforms are winning the restaurant food delivery wars? Analysis from a consumer perspective. International Journal of Consumer Studies . https://doi.org/10.1111/ijcs.12816

Ulmer, M. W., Thomas, B. W., Campbell, A. M., & Woyak, N. (2021). The restaurant meal delivery problem: Dynamic pickup and delivery with deadlines and random ready times. Transportation Science, 55 (1), 75–100.

Veen, A., Barratt, T., & Goods, C. (2020). Platform-capital’s ‘app-etite’ for control: A labour process analysis of food-delivery work in Australia. Work, Employment and Society, 34 (3), 388–406.

Wang, H. T. (2022). Analysis of a tripartite evolutionary game model of food delivery platform supervision and strategy selection. Technology Analysis & Strategic Management . https://doi.org/10.1080/09537325.2022.2090329

Wang, X., Wang, L., Wang, S., Chen, J. F., & Wu, C. (2021). An XGBoost-enhanced fast constructive algorithm for food delivery route planning problem. Computers & Industrial Engineering, 152 , 107029.

Wang, Z., & He, S. Y. (2021). Impacts of food accessibility and built environment on on-demand food delivery usage. Transportation Research Part D: Transport and Environment, 100 , 103017.

Yang, Y., Liu, H., & Chen, X. (2020). COVID-19 and restaurant demand: Early effects of the pandemic and stay-at-home orders. International Journal of Contemporary Hospitality Management, 32 (12), 3809–3834.

Yao, X., Cheng, Y., Zhou, L., & Song, M. (2020). Green efficiency performance analysis of the logistics industry in China: Based on a kind of machine learning methods. Annals of Operations Research . https://doi.org/10.1007/s10479-020-03763-w

Yen, Y. S. (2022). Channel integration affects usage intention in food delivery platform services: The mediating effect of perceived value. Asia Pacific Journal of Marketing and Logistics . https://doi.org/10.1108/APJML-05-2021-0372

Yeo, S. F., Tan, C. L., Teo, S. L., & Tan, K. H. (2021). The role of food apps servitization on repurchase intention: A study of FoodPanda. International Journal of Production Economics, 234 , 108063.

Young, A. (2021). Most gig economy workers have been injured at work–and tool unpaid time off . Mirror. Retrieved November 30, 2022, https://www.mirror.co.uk/money/jobs/gig-economy-workers-injured-work-25587129 .

Zhang, T., Choi, T. M., & Zhu, X. (2018). Optimal green product’s pricing and level of sustainability in supply chains: Effects of information and coordination. Annals of Operations Research . https://doi.org/10.1007/s10479-018-3084-8

Zhang, W., Dai, Y., & Tian, L. (2022). Impact of capacity flexibility on service product line design. Annals of Operations Research, 312 , 1095–1118.

Zheng, J., Wang, L., Wang, L., Wang, S., Chen, J. F., & Wang, X. (2022a). Solving stochastic online food delivery problem via iterated greedy algorithm with decomposition-based strategy. IEEE Transactions on Systems, Man, and Cybernetics: Systems . https://doi.org/10.1109/TSMC.2022.3189771

Zheng, Q., Zhan, J., & Feng, X. (2022b). Working safety and workloads of Chinese delivery riders: The role of work pressure. International Journal of Occupational Safety and Ergonomics . https://doi.org/10.1080/10803548.2022.2085915

Zhu, X., Yang, C., Liu, K., Zhang, R., & Jiang, Q. (2022). Cooperation and decision making in a two-sided market motivated by the externality of a third-party social media platform. Annals of Operations Research, 316 (1), 117–142.

Download references

Acknowledgements

Hau-Ling Chan’s research is partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC Project No. UGC/FDS24/B01/21). Research of Tsan-Ming Choi and Jiuh-Biing Sheu is supported in part by M.O.S.T. project (code: MOST 111-2410-H-002-081-MY3).

Author information

Authors and affiliations.

Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Hau-Ling Chan

Division of Business and Hospitality Management, College of Professional and Continuing Education, The Hong Kong Polytechnic University, 9 Hoi Ting Road, Yau Ma Tei, Kowloon, Hong Kong

Ting-Ting Cheung

Centre for Supply Chain Research, University of Liverpool Management School, Chatham Building, Liverpool, L69 7ZH, UK

Tsan-Ming Choi

Department and Graduate Institute of Business Administration, College of Management, National Taiwan University, Roosevelt Road, Taipei, 10617, Taiwan, Republic of China

Department of Business Administration, National Taiwan University, Taipei, Taiwan, Republic of China

Jiuh-Biing Sheu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jiuh-Biing Sheu .

Ethics declarations

Conflict of interest.

All the authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors. No ethical approval is needed.

Additional information

Publisher's note.

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

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/ .

Reprints and permissions

About this article

Chan, HL., Cheung, TT., Choi, TM. et al. Sustainable successes in third-party food delivery operations in the digital platform era. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05266-w

Download citation

Accepted : 28 February 2023

Published : 12 April 2023

DOI : https://doi.org/10.1007/s10479-023-05266-w

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

• Third-party food delivery
• Digital platform
• Future research agenda
• Sustainability

Advertisement

• Find a journal
• Publish with us
• Track your research

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

A case study on Zomato – The online Foodking of India

2020, IP innovative publication pvt ltd

Zomato started in 2008 underneath the name, ‘Foodiebay’ to begin with. Later in 2010, it had been renamed to ‘Zomato’. Constantly 2011, Zomato extended to increasingly urban regions the country over in Mumbai, Delhi NCR, Chennai, Bangalore, Kolkata and Pune. After that in the year 2012, the corporate extended working all around in various countries like the UAE, Qatar, Sri Lanka, UK, South Africa and Philippines. In the year 2013, Zomato had moved their organizations in Brazil, New Zealand, Turkey and Indonesia, with its applications and site open in various lingos isolated from English. After that in April 2014, Zomato impelled its organizations in Portugal Republic, trailed by Canada, Lebanon and Ireland around a similar time. The acquiring of Settled - based sustenance zone ‘Urban spoon’ signified the organization’s passageway into the United States, Canada and Australia, and conveyed it into direct test with ‘Wail’, ‘Zagat' and ‘Open Table’. With the introduction of .xxx zones in 2011, Zomato also impelled ‘zomato.xxx’, a site dedicated to finding spot to eat near to your territory. It later moved a print adjustment of the site substance named, ‘Citibank Zomato Restaurant Guide’, got together with Citibank in May 2012, at any rate later it was halted.

Related Papers

TVISHA BHANOT

With digitization, the food industry has very efficiently utilized the e-commerce platform in the online food review and food ordering business. Through a vertical system of food delivery apps, the entire system has brought almost every restaurant in India under a single roof in the hands of the consumer. Now, finding a great place to eat, ordering food, reserving tables, and giving positive or negative feedback for a restaurant has become very easy and accessible for the consumer. This vertical has shown tremendous growth over the last 10 years signifying the untapped potential. The pioneer and leader of this segment in India has been Zomato. Zomato is a brand, which every consumer would relate to when s/he thinks of an e-food platform. This paper aims at critically examining the marketing and positioning aspect of the company by drawing on the works published in the form of surveys, journals, articles and reports by independent authors as well. This paper covers certain figures me...

IAEME Publication

Our Heritage

Dr. Suresh Kumar S

The country's GDP grew at a modest 4.5 per cent in the September quarter 2019, and the official data released showed a sixth straight fall in quarterly GDP growth and also the first time fall below the psychologically important 5 per cent mark in almost seven years. It is in this context that the festive sales hosted by the e-commerce sector ended first week of October 2019 where the e-tailers in India, mainly Amazon and Flipkart, achieved a record $3 billion (about Rs 19,000 crore) of Gross M erchandise Value (GM V) during the period as per a report by consulting firm RedSeer has to be evaluated. The success of business models, whether it be in e-tailing (amazon, flip-kart etc.), transportation (Uber, Ola Cabs etc..) or online ordering from eatery apps (Ubereats, Swiggy, Zomato etc.) despite the reverse trend in GDP growth and sustained recession, needs to be evaluated in the context of innovation applied and technology adoption. It is in the backdrop of above said upsurge of business model innovations that can combat the challenges in downfalls of an economy and/ or ever-increasing competition on a global platform, the effectiveness of business models assumes significance. A laggard manager clinging on to his age-old business model is now forced to look forward to articulate their existing business model, since the core enabler of a firm's performance is an effective business model. Understanding the possibilities for innovating through theoretical insight and practical guidelines needs identification of types and the development of a typology of business model innovations. The online eatery business of restaurants, with key partners such as payment processors, mapping data providers and delivery bike drivers through channels such as mobile apps and telephone ensures customer relations by providing convenience in the form of wide choice of sourcing and menu as well as easy payments has found its own way into urban and semi-urban centres of almost all the

IAEME PUBLICATION

Technology innovation has changed all the everyday exercises we do. Online food ordering service is among them. Online food ordering service was first begun by Zomato in the year 2018 pursued by Swiggy, Food Panda and Uber eats. Even though the physical sustenance administrations given by different Restaurants and lodging is as yet ruling the time at present, the online structure which has risen as of late is going to command the physical form. This pattern is very unmistakable in created developed cities in India. Subsequently, this article endeavors to discover the pattern in creating city like Bhubaneswar and different elements influencing that. This article talks about the inclinations of online food preferences that measurably and confirms the outcomes with statistics tools and techniques. Besides, it talks about the fate of offline food services in near future.

Rupkatha Journal on Interdisciplinary Studies in Humanities

Gitanjali Roy

Food habit articulates the local culture of a region. Tripura, a land-locked state of varied communities (the tribes and Bengalis of the soil) negotiates the countercultural exchange of cuisines. Traditional ethnic foods are markers of shared cultural values and identity. Preparation of traditional food involves the role of memory which involves passing down culinary skills, techniques, and ingredients from one generation to the next. The marketing industry and the restaurant culture have changed the taste of the consumers but again the ‘losses’ and the ‘need’ to preserve the traditional cuisines are archived in digital platforms. With the rise in YouTube food channels, Facebook pages, food delivery companies like Swiggy and Zomato; the local food met with the global consumer culture. On one hand, lost ethnic food habits are preserved by documenting the procedures of cooking traditional dishes. On the other, restaurants and bloggers are experimenting to prepare local food using glob...

Publisher ijmra.us UGC Approved

Online food delivery portals and applications have changed the way people select and order their food. The advancements in technology and easy availability of the internet and smartphones paved the way for online food ordering services. It was a concept unheard of in India, when the founders of Zomato decided to create a platform where people could conveniently browse through food menus of different restaurants, order online and get the orders delivered at their doorsteps. It was on a random afternoon when Mr Pankaj Chaddha and Mr Deepender Goyal noticed a long queue of people to glance through the menu card and order their lunch. This got them thinking and eventually resulted in the inception of Zomatothat time called Foodiebay. Since its inception in the year 2008, Zomato has grown exponentially. It started as a food directory which instantly gained popularity in Delhi NCR. And within 9 months the website gained popularity in other cities like Mumbai, Kolkata, Chennai, Bengaluru. In 2012, Zomato went international with its presence in many countries like UAE, UK, Sri Lanka, Philippines etc. Zomato steadily kept increasing its customer database and also ventured into online table reservation, finding nearby restaurants. However, Zomato has also undergone its share of crises. But be it because of its unconventional communication or other practices, Zomato does not shy away from communicating with different stakeholders. Zomato"s communication has never held back from responding and reaching out to their customers and community. Today Zomato has over 119 thousand restaurants against 43 thousand restaurants in 2019. Zomato has all the necessary elements that makes it a successful case to be explored. Apart from crisis management, it also has been able to strike the right chord with the sentiments of Indian customers by connecting with them at an emotional level. This case study delineates the journey of brand Zomatoan Indian entrepreneurial ventureand analyses the communication strategy adopted by the brand especially while managing any crisis situation. The paper also studies the measures adopted by Zomato to emerge as a stronger brand as the country grappled with lockdowns and other restrictions during the Pandemic. Based on the broad framework provided by the Situational Crisis Communication theory, the researchers have undertaken the evaluation using SWOT analysis to cover all aspects of the journey of Zomato so far.

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

Online food delivering system is the process where various restaurants and food outlets deliver their foods and drinks according to the demand of the customer and give the maximum customer satisfaction and quality service. Although, there are various factors that depends on demand and supply and what goes in the customer's mind. What motivates the customers, what benefits they will be having by ordering food online and what are the issues they face while adoption online food and ordering it. This paper will provide information of such factors. It is discussed what are the biggest reasons for same, how people achieve the benefits. How and where people face the issues and how can we solve it. In times of outbreak of Covid-19, technology helped in different ways. One of them was to deliver food to people in those tough times when people were sick, could not go out of their houses and protecting themselves and others as well. Hassle-free online payment made the things easier and still doing the same.

The Cornell Hotel and Restaurant Administration Quarterly

Edward Forrest

International Journal of English Language, Literature and Translation Studies

fahima faigee

In recent years, social media usage and growth in India have been equally significant. It has developed into a potent instrument for social networking, information exchange, and communication among India's vast population. Digital food culture has recently gained popularity and significantly altered India's culinary culture. Therefore, the purpose of this paper is to investigate the pervasive phenomena of digital food culture in Indian society today. The study also emphasizes on the transformation of capitalism into media capitalism. Furthermore, this paper also looks at how media capitalism makes customers into spectators and turns them into sheeple for monetary gain. Netizens, however, act in a sycophantic manner while denying the influence of media capitalism. Thus, the study intends to investigate how media capitalism affects customers and viewers in India through the lens of digital food culture and food bloggers. Therefore, the theory of media capitalism and spectacle is used to analyze these issues.

European Proceedings of Social and Behavioural Sciences

Syahlan Syahlan

RELATED PAPERS

Zerbitzuan Gizarte Zerbitzuetarako Aldizkaria Revista De Servicios Sociales

Enrique Sacanell Berrueco

Caio Macieira de Almeida Aguilar

DESALINATION AND WATER TREATMENT

Mehdi Hassanshahian

Communication Management in Sports Media

journal of Communication Management in Sports Media

Budi heri pirngadie

normawaty mohammad noor

In K. Bicknell & J. Sutton (Eds.). Collaborative Embodied Performance: Ecologies of Skill (pp. 187–206). London: Methuen Drama.

Michael Kimmel

Simona Osredkar

Dennis Pettibone

Jean Van Schaftingen

Biochemical and Biophysical Research Communications

Brazilian Archives of Biology and Technology

William César Pollonio Machado

Revue internationale de droit pénal

Maryse Alié

Transfusion Clinique et Biologique

PASCAL STACCINI

IOSR Journal Of Humanities And Social Science

Peter Kimemia

Arthur McKee

BMC Medical Education

Nicolas Hoertel

International Journal of Hydrogen Energy

Susan SCHOENUNG

Electrochimica Acta

Yu Morimoto

Cancer Gene Therapy

Patrizia Lavia

Jackline Alinda Ndiiri

Encouraging creativity as a mediating variable between learning organizations and organizational performance (a case study applied to the Magdy Yacoub Foundation for Titles)

Ontology for the Intelligence …

Richard Mayer

Daniel Girardi

Revista Educação em Questão

Rosas Rivera

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024