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International Journal of Contemporary Hospitality Management

ISSN : 0959-6119

Article publication date: 26 May 2022

Issue publication date: 26 July 2022

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

Design/methodology/approach

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

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

Practical implications

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

Originality/value

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

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

Acknowledgements

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

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

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In the midst of the global 2020 COVID-19 outbreak, the advantages of online food delivery (FD) were obvious as it facilitated consumer access to prepared meals and enabled food providers to keep operating. However, online FD is not without its critics, with reports of consumer and restaurant boycotts. It is therefore time to take stock and consider the broader impacts of online FD and what they mean for the stakeholders involved. Using the three pillars of sustainability as a lens through which to consider the impacts, this review presents the most up-to-date research in this field revealing a raft of positive and negative impacts. From an economic standpoint, online FD while providing job and sale opportunities has been criticized for high commissions it charges restaurants and questionable working conditions for delivery people. From a social perspective, online FD is affecting the relationship between consumers and their food as well as influencing public health outcomes and traffic systems. Environmental impacts include the generation of worrying amounts of waste and its high carbon footprints. Stakeholders must consider how best to mitigate the negative and promote the positive impacts of online FD to ensure that it is sustainable, in every sense, moving forward.

1. Definition

Online to offline (O2O) is a form of e-commerce in which consumers are attracted to a product or service online and induced to complete a transaction in an offline setting. An area of O2O commerce that is expanding rapidly is the use of online food delivery (online FD) platforms. All around the world, the rise of online FD has changed the way that many consumers and food suppliers interact, and the sustainability impacts (defined by the three pillars of economic, social and environmental [ 1 ] ) of this change has yet to be comprehensively assessed.

2. Introduction

Economic growth and increasing broadband penetration are driving the global expansion of e-commerce. Consumers are increasingly using online services as their disposable income increases, electronic payments become more trustworthy, and the range of suppliers and the size of their delivery networks expand.

3. Overview of the Online Food Delivery Sector

3.1. e-commerce market size.

The e-commerce market has experienced strong growth over the past decade, as customers increasingly move online. This shift in how consumers shop has been driven by a wide range of diverse factors, some being market or country dependent, others occurring as a result of worldwide changes. These changes include: an increase in disposal income, particularly in developing nations; longer work and commuting times; increased broadband penetration and improved safety of electronic payments; a relaxing of trade barriers; an increase in the number of retailers having an online presence; and a greater awareness of e-commerce by customers [ 2 ] .

The strongest growth of e-commerce over the last few years has occurred in China, where, in 2019, sales were worth US$ 1.935 trillion—an amount which was more than three times higher than that spent in the United States (US$ 586.92 billion), the second largest market. On its own, China represents 54.7% of the global e-commerce market, a share nearly twice the market share of the next five highest countries (US, UK, Japan, South Korea, Germany) combined [ 3 ] . The rise of e-commerce in the Asia-Pacific region is demonstrated in Table 1, which highlights the massive increase in the amount spent during key online shopping days between 2015 and 2019. Of particular note is the US$ 38.4 billion spent on Singles Day (11.11) in the Asia-Pacific region in 2019, an amount which is more than double the total sum of the US$9.4 billion spent on Black Friday in North America and much of Europe and the US$ 7.4 billion spent on Cyber Monday in North America. The leading e-commerce platforms worldwide differ by region and include platforms which are now household names, such as Amazon (U.S.), Alibaba (China), and Flipkart (India).

Table 1. Regional sales value of featured online shopping days from 2015–2019 [ 4 ] .

3.2. Online to Offline Business and Online FD

The rapid growth of e-commerce has spawned many new forms of business, such as B2B (business to business), C2C (customer to customer), B2C (business to customer), and O2O (online to offline) [ 5 ] [ 6 ] . The business of O2O is a marketing method based on information and communications technology (ICT) whereby consumers place orders for goods or services online and receive the goods or services at an offline outlet [ 7 ] [ 8 ] .

One of the significant developments driving the O2O commerce explosion has been the proliferation of smartphones and tablets and the development of infrastructures to support payment and delivery. In 2019 there were 5.2 billion smartphone connections, and by the end of 2020, it has been predicted that half of the people in the world will have access to mobile internet services [ 9 ] .

O2O services have emerged in various fields, including the purchase of diverse product and service categories, such as food, hotel rooms, real estate, or car rentals [ 10 ] . Online FD refers to the process whereby food that was ordered online is prepared and delivered to the consumer. The development of online FD has been underpinned by the development of integrated online FD platforms, such as Uber eats, Deliveroo, Swiggy, and Meituan. Online FD platforms serve a variety of functions including providing consumers with a wide variety of food choices, the taking of orders and the relaying of these order to the food producer, the monitoring of payment, the organization of the delivery of the food and the provision of tracking facilities (Figure 1) [ 11 ] . Food delivery applications, or ‘apps’, (FDA) function within the broader context of online FD as they enable the ordering of food through mobile apps [ 12 ] .

Figure 1. The functions associated with online food delivery (FD) platforms. Arrows indicate movement of information or logistic; lines indicate necessary routes; dotted lines indicate optional routes.

3.3. Online FD Providers and their Delivery System

Food delivery providers can be categorized as being either Restaurant-to-Consumer Delivery or Platform-to-Consumer Delivery operations [ 13 ] . Restaurant-to-Consumer Delivery providers make the food and deliver it, as typified by providers, such as KFC, McDonald’s, and Domino’s. The order can be made directly through the restaurant’s online platform or via a third-party platform. These third-party platforms vary from country to country, and include examples, such as Uber eats in the U.S., Eleme in China, Just Eat in UK, and Swiggy in India. Third-party platforms also provide online delivery services from partner restaurants which do not necessarily offer delivery services themselves, a process which is defined as Platform-to-Consumer Delivery.

Online FD requires highly efficient and scalable real-time delivery services. Restaurants can use existing staff for self-delivery, such as the use of waiters in some small restaurants or they may use specialized delivery teams who are specifically employed and trained for this role, as is seen with some of the big restaurant brands, such as KFC, Domino’s, and Xibei. Alternatively, restaurants can employ crowdsourcing logistics, a network of delivery people (riders) who are independent contractors, a model that provides an efficient, low-cost approach to food delivery [ 14 ] . Online FD platforms can either be responsible for recruiting and training professional delivery people, or they may also resort to crowdsourcing logistics, using delivery people who are not necessarily employed by the online FD platform. Professional delivery people are usually trained, and at least part of their salary is guaranteed, while a portion is commission-based. In contrast, the independent delivery people who are frequently known as “riders” are paid on a commission (per order) basis (Figure 2).

Figure 2. Online FD delivery retailers (Eleme in China, for example).

3.4. Growth of Online FD Worldwide

The rise of online FD is a global trend with many countries around the world having at least one major platform for food delivery (Table 2). China leads the way in market share for online FD, closely followed by the US with the developing markets of India and Brazil, showing rapid (> 9% compound annual growth rate (CAGR)) growth.

Table 2. Revenue of the Online FD segment in major countries [ 13 ] .

The online FD industry has been very proactive in the way it develops new markets and cultivates consumers’ eating habits. For example, in 2018, a promotion campaign by the India-based online FD company Foodpanda offered consumers large discounts, which resulted in Foodpanda increasing the number of users by a factor of 10 [ 15 ] . Moreover, in 2018, Eleme in China, spent three billion yuan (US$443 million) over three months in a successful marketing strategy to increase its market share to more than 50 percent of the Chinese market [ 16 ] . Despite online FD being very strong in some regions, as a whole across the world online FD is in the early stages of market development, and it will require considerable investment to fund promotions and campaigns and to provide subsidies to participating restaurants [ 17 ] [ 18 ] [ 19 ] [ 20 ] [ 21 ] . For example, a restaurant may hold a campaign on an FD platform, in which a consumer obtains ¥8 as a discount if the total amount ordered reaches ¥20. In fact, this discount may only cost the restaurant ¥2, as it will receive a ¥6 subsidy from the FD platform (the actual rules may vary from one platform to another [ 22 ] ). Such an approach is beneficial for a restaurant because it will attract more consumers and orders. It is crucial for the future of online FD to cultivate consumers’ eating habits by introducing them to the choosing and purchasing of food online. By providing consumers with the option of having a meal at a cheaper price or by providing other services, such as free delivery, online FD platforms and providers are encouraging consumers to abandon cooking at home or going out to a restaurant to eat.

Worldwide online FD is becoming increasingly well accepted and embraced by young adults, and nowhere is this trend more evident than in China. A survey in 2019 of 1000 university students in Nanjing, revealed that at least 71.45% of them had used online FD for at least two years and that 85.1% of them used online FD more than once a week [ 23 ] . Online FD has been reported to be popular with Chinese university students because it saves time (50.35% of 141 students in Hebei, China), is convenient (44.35% of 124 students in Jiangxi, China), and is able to provide options that were tastier (39.52% of 124 students) or simply different from canteen meals (36.17% of 141 students) [ 24 ] [ 25 ] . Of course, different populations around the world have different opportunities to purchase food online owing to cultural, technological and economic reasons and these differences can be responsible for the differing rates of uptake of online FD seen around the world. By way of comparison to China, for example, a 2019 survey of 252 Greek university students aged 18–23, reported that most of them cook at home and rarely eat out or have food delivery (45.6%), while others mostly eat at the student restaurant or cook at home (23.4%), with only 21% of the students surveyed stating that they had food delivered [ 26 ] .

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Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic

With the emerging popularity of online food delivery (OFD) services, this research examined predictors affecting customer intention to use OFD services amid the Coronavirus disease (COVID-19) pandemic. Specifically, Study 1 examined the moderating effect of the pandemic on the relationship between six predictors (perceived usefulness, perceived ease of use, price saving benefit, time saving benefit, food safety risk perception, and trust) and OFD usage intention, and Study 2 extended the model by adding customer perceptions of COVID-19 (perceived severity and vulnerability) during the pandemic. Study 1 showed that all of the predictors except food safety risk perception significantly affected OFD usage intention, but no moderation effect of COVID-19 was found. In Study 2, while perceived severity and vulnerability had no significant impact on OFD usage intention, the altered effects of socio-demographic variables during the COVID-19 pandemic were found. Theoretical and managerial implications are provided.

1. Introduction

The World Health Organization (WHO) declared the Coronavirus disease (COVID-19) a pandemic due to the high risk of fatality and human-to-human transmission on March 11, 2020 ( World Health Organization, 2020 ). Accordingly, the majority of U.S. states and their local ordinances issued stay-at-home or shelter-in-place orders and forced foodservice operations to be closed or restricted ( Restaurant Law Center, 2020 ). The official orders have had harsh effects on the restaurant industry, such as job losses and worst sales than other sectors (National Restaurant Association [ NRA], 2020a ). For example, by April 2020, more than 8 million employees working in the restaurant industry were furloughed, and consumption at restaurants/bars in April 2020 plummeted to the lowest level after October 1984 ( NRA, 2020b ).

As restaurants struggle to find ways to survive, online food delivery (OFD) services have recently gained high demands by delivering food and drinks to customers’ doorstep ( NPD, 2020 ). OFD services refer to internet-based food ordering and delivery systems that connect customers with partner restaurants via their websites or mobile applications ( Ray, Dhir, Bala, & Kaur, 2019 ). Although the OFD market had significantly grown before the pandemic, more customers have utilized OFD services during the COVID-19 pandemic, as evidenced by a report by the NPD Group, which revealed that the number of the OFD orders surged 67% in March 2020 compared to March 2019 ( NPD, 2020 ).

To date, several researchers have provided a fundamental understanding of OFD customers' decision-making process and their behavioral intentions including motivations to use OFD services ( Yeo, Goh, & Rezaei, 2017 ) and factors affecting OFD usages ( Ray, Dhir, Bala, & Kaur, 2019 ). However, it remains unclear whether the pandemic influences customers' substantial OFD purchasing behavior and decision-making process regarding OFD services. As the COVID-19 pandemic has had the most impact on recent human behavior changes ( Laato, Islam, Farooq, & Dhir, 2020 ), it is salient to consider the COVID-19 pandemic as a contextual factor affecting customers' OFD usages ( Kim, Kim, & Hwang, 2021 ). Furthermore, with several findings demonstrating that people who perceived health risks altered their actions in preventive ways ( Ali, Harris, & Ryu, 2019 ; Cahyanto et al., 2016 ), more customers might utilize OFD services to avoid human contact with restaurant employees and other customers during and even post COVID-19 pandemic. However, no research has considered the impact of customers’ perceptions about the health risk on customer intention to use OFD during the COVID-19 pandemic.

In light of this, this study explores factors affecting customer intention to use OFD services across two-time frames (before and during the COVID-19 pandemic) and investigates how customer perceptions on the COVID-19 pandemic alter their relationships through two studies. Specifically, Study 1 investigates the prominent predictors affecting customer intention to use OFD before and during the COVID-19 pandemic and examines the moderating effect of the COVID-19 outbreak between the relationships. To better understand the high demand for OFD services during the pandemic, Study 2 incorporates customers’ perceptions about the COVID-19 pandemic—perceived severity and perceived vulnerability—into the relationship between the predictors and customer intention to use OFD.

2. Literature review

2.1. study 1, 2.1.1. online food delivery services.

Online food delivery (OFD) refers to “the process whereby food that was ordered online is prepared and delivered to the consumer” ( Li et al., 2020 , p. 3). The proliferation of OFD services was supported by the development of integrated OFD platforms, such as Uber Eats, DoorDash, and Grubhub. When a customer places an order from various restaurant options through an OFD service platform on its mobile application or website and pays for the order, the restaurant receives the order and prepares the food. Then, a delivery driver delivers the order to the customer. Customers can track the status of their orders and contact their drivers via the app. OFD services offer various benefits to their customers including no waiting in line, no traveling for pick-up, no misunderstanding of the order which happen frequently in restaurants or phone call orders, and discounts from daily offers ( The Other Stream , n.d.).

The customer demand of OFD services has increased tremendously over the last few years and is expected to grow steadily. The total revenue of the global OFD service market was estimated at approximately $107.4 billion in 2019 and is expected to exceed $182.3 billion by 2024 ( Statista, 2020 ). Moreover, since the COVID-19 outbreak, the OFD market has gained even more attention globally due to its contactless ordering and delivery system and is expected to continue attracting new customers ( Maida, 2020 ).

Researchers have explored various factors affecting customer intention to use OFD (CIU) ( Cho, Bonn, & Li, 2019 ; Gunden et al., 2020 ; Suhartanto, Helmi Ali, Tan, Sjahroeddin, & Kusdibyo, 2019 ; Yeo, Goh, & Rezaei, 2017 ). For example, Gunden et al. (2020) found that performance expectancy and congruity with a self-image significantly affect customers’ adoption intention of OFD. Additionally, Cho, Bonn, & Li, 2019 identified system trust, convenience, design, and various food choices as significant predictors of customer intention to continuously use food delivery apps. Roh and Park (2019) also revealed that compatibility, ease of use, and usefulness were significant predictors of CIU, but Ray & Bala, 2021 presented price benefits, trust, and app-interaction enhanced CIU. Considering the inconsistent findings, the significant predictors affecting CIU are not clearly outlined. Given the peculiarities of ordering food and beverage online rather than going to restaurants and based on existing literature related to technology acceptance (i.e., Technology Acceptance Model) and OFD-related literature ( Cho, Bonn, & Li, 2019 ; Gunden et al., 2020 ; Ray & Bala, 2021 ; Ray, Dhir, Bala, & Kaur, 2019 ; Roh & Park, 2019 ; Suhartanto, Helmi Ali, Tan, Sjahroeddin, & Kusdibyo, 2019 ; Won et al., 2017 ; Yeo, Goh, & Rezaei, 2017 ; Zhao & Bacao, 2020 ), Study 1 employs the six variables to predict customer intention to use OFD services. Moreover, two factors adopted from the Health Belief Model — perceived severity and perceived vulnerability—were included in Study 2 to reflect the COVID-19 pandemic context. In the following section, these factors are explained in detail.

2.2. Predictors of online food delivery usage intention

2.2.1. service attributes.

The Technology Acceptance Model (TAM) originally proposed by Davis (1989) states that perceived usefulness and perceived ease of use of a new technology play significant roles in the adoption of the technology ( Davis, Bagozzi, & Warshaw, 1989 ). In the TAM model, perceived usefulness (PU) was defined as “the prospective user's subjective probability that using a specific application system will increase his or her job performance within an organizational context” ( Davis, Bagozzi, & Warshaw, 1989 , p. 985). When customers consider that new technology will improve their productivity, PU arises ( Gentry & Calantone, 2002 ). Previous studies revealed that PU positively affected technology adoption in a variety of fields, such as mobile phone adoption for shopping ( Hung et al., 2012 ), hotel self-service kiosks ( Kim & Qu, 2014 ), and healthcare wearable technology ( Zhang et al., 2017 ).

In this study, to apply PU to the OFD service setting, PU refers to the degree to which people believe that using an OFD service would be a useful way to order meals. Similar to other technology-related studies, OFD research has demonstrated a significant impact of PU on OFD usage intention. For example, Yeo, Goh, & Rezaei, 2017 demonstrated that PU positively influenced continuance intention toward OFD services. Similarly, Roh and Park (2019) revealed PU to be the strongest factor affecting OFD usage intention.

Perceived ease of use (PEOU) is defined as the degree to which a person expects mental or physical challenges in adopting new technology ( Pinho & Soares, 2011 ). Numerous studies have confirmed that PEOU has a significant effect on customers' usage intentions toward a wide variety of technologies. For instance, Ramayah and Ignatius (2005) proposed that if mobile devices and web interfaces are easy to access and require little effort, customers are willing to accept online shopping. They reported that PEOU is a critical factor affecting online shopping intention. The same positive association between PEOU and CIU has been reported in the OFD context ( Ray, Dhir, Bala, & Kaur, 2019 ; Roh & Park, 2019 ; Won et al., 2017 ). Roh and Park (2019) found that the higher the customer's PEOU, the greater the willingness to use OFD services, and ultimately the higher the chance of OFD service success. Ray, Dhir, Bala, & Kaur, 2019 also emphasized the importance of PEOU of OFD services by demonstrating the important roles of the order process, order tracking, and filtering options of the interface in determining CIU.

Besides PEOU and PU, this study employs trust (TR) as a technology-oriented service attribute because TR in the system has been validated as a key driver in adopting new technology in various disciplines, from self-service kiosks during check-in/out in hotels ( Kaushik, Agrawal, & Rahman, 2015 ) to electronic payments ( Mendoza-Tello, Mora, Pujol-López, & Lytras, 2018 ). TR refers to an index of a positive belief regarding the perceived reliability, dependence, and assurance in an individual, object, or procedure ( Fogg & Tseng, 1999 ). TR produces positive feelings toward the technology-based service ( Liu, 2012 ), and customers with low TR about the service tend to be skeptical and reluctant to adopt it ( Grabner-Kraeuter, 2002 ). In the OFD setting, while Jeon et al. (2016) revealed that TR does not affect intention to reuse OFD, several studies have agreed that TR is one of the most critical factors positively affecting CIU ( Cho, Bonn, & Li, 2019 ; Ray & Bala, 2021 ; Zhao & Bacao, 2020 ). Thus, this study generated the following hypotheses:

PU positively influences CIU.

PEOU positively influences CIU.

TR positively influences CIU.

2.2.2. Perceived benefits

Some OFD services charge customers extra fees, such as delivery charges and service fees ( Lichtenstein, 2020 ). However, as OFD companies compete to gain market shares, they frequently offer promotions that cover the fees or discount the total charges to attract new customers and accelerate orders from new and old customers. For example, Grubhub offers a $10-off promotion to new customers and a student discount ( Groupon, 2021 ). In OFD service setting, price saving promotions often serve as effective marketing tools as demonstrated by Kaur et al. (2021) as well as Ray & Bala, 2021 who revealed that free delivery, lower delivery fees, or promotional incentives enhance CIU. Kaur et al. (2021) further noted that customers using OFD services search for a price advantage. Thus, this study examines price saving benefits (PSB) as a critical predictor of CIU. PSB is defined as money-saving benefits (e.g., 10-off promotion, lower delivery/service fee) as well as not charging any additional costs for purchasing products/services (e.g., free delivery) ( Yeo, Goh, & Rezaei, 2017 ). Considering the significant role of PSB in customer OFD usage from existing literature, it is hypothesized that PSB would increase CIU.

Online shopping also saves time traveling to and from a retail store in a time-sensitive modern society ( Morganosky & Cude, 2000 ). Similarly, OFD services could save customers time by avoiding the time spent traveling to a restaurant and waiting in line. Moreover, many web browsers and OFD apps allow customers to store payment and previous order details for efficient checkout, enabling customers to save time ( Statista, 2020 ; Bansal, 2019 ). While Ray, Dhir, Bala, & Kaur, 2019 found no significant association between time saving benefits (TSB) and customer usage intention, much of the existing literature has indicated that TSB of OFD services positively influence CIU ( Correa et al., 2018 ; He, Han, Cheng, Fan, & Dong, 2019 ; Yeo, Goh, & Rezaei, 2017 ). In other words, when customers believe they can avoid traffic and save time by using OFD services, they are more likely to use OFD services. Hence, this study proposed the following hypotheses:

PSB positively influences CIU.

TSB positively influences CIU.

2.2.3. Perceived risk

When dining out, customers oftentimes do not possess tools or skills to measure actual food safety. Instead, customers evaluate the cleanliness and food safety of the restaurant based on various aspects of the restaurant, including restaurant hygiene and employees’ safety practices of wearing clean uniforms and sanitary gloves while touching food ( Liu & Lee, 2018 ). The perceived risk associated with food consumption is called food safety risk perception (FSRP) ( Nardi, Teixeira, Ladeira, & de Oliveira Santini, 2020 ).

FSRP plays a crucial role in the decision-making process of customers buying food ( Frewer et al., 2009 ). For example, customers who have higher FSRP have a higher willingness to buy and pay a premium for safer products or services ( Sharma et al., 2012 ). Customers might possess different FSRP depending on the selling site. A study by Kitsikoglou et al. (2014) demonstrated that consumers have higher FSRP when buying groceries or food online as compared to offline because they cannot see the freshness of products online.

OFD services are challenged to sustain food safety and hygiene because food delivered through OFD services can also be exposed to contamination due to the addition of delivery processes to the traditional restaurant business model. Specifically, controlling temperature, packaging, and using appropriate food containers during the delivery process are additional concerns with OFD services ( Maimaiti et al., 2018 ). Therefore, customers may have higher FSRP when using OFD because they cannot observe the restaurants and employees’ hygiene in person, which may play a negative role in CIU. Based on the previous research related to FSRP and characteristics of OFD services, the following hypothesis was formulated:

FSRP negatively influences CIU.

2.3. The moderating effect of COVID-19

The hospitality and tourism industry is subject to being immediately influenced by the external environment, such as natural disasters, pandemics, and terrorist incidents ( Jin, Qu, & Bao, 2019 ). One of the noticeable events that affected the hospitality and tourism industry was the September 11 attacks in 2001, which harmed travel demand dramatically with a 30% decline until two years after the attack ( Ito & Lee, 2005 ). A crisis event also can change human behavior positively or negatively.

As the coronavirus has dramatically spread, administrative governments or local ordinances have mandated staying-at-home or shelter-in-place orders in March 2020 onward to help prevent person-to-person transmission and shut down businesses ( Sibley et al., 2020 ). The lockdown has promoted sweeping changes to people's lifestyles and psychological aspects ( Laato, Islam, Farooq, & Dhir, 2020 ). Notably, customers showed unusual buying behavior after the COVID-19 outbreak, such as panic buying, which caused a shortage of toilet paper, hand sanitizer, and canned food products in every store ( Laato, Islam, Farooq, & Dhir, 2020 ). Consequently, this study anticipated that the coronavirus alters customer behavior to use OFD amid the pandemic and devises the following hypothesis:

The COVID-19 outbreak moderates the relationships between the predictors and CIU.

2.4. Study 2

Regardless of the actual risk or contagion of the disease, consumers' perception of the COVID-19 pandemic plays a critical role in their purchase decision-making ( Ali, Harris, & Ryu, 2019 ). Among various measurements used to determine people's perceptions of a disease, researchers have widely used perceived severity (PS) and perceived vulnerability (PV), which have their roots in the Health Belief Model (HBM) proposed by Hochbaum (1958) . PS is defined as a personal concern with the seriousness of a situation, and PV refers to personal belief(s) regarding the risk of getting a disease ( Cahyanto et al., 2016 ). The HBM explains that when people have higher PS and PV to an adverse health condition and such outcomes, individuals are more likely to take actions that reduce the threat ( Carpenter, 2010 ).

In the hospitality literature, researchers have widely utilized PS and PV to predict customer behaviors that might be affected by an event or disease such as foodborne illness ( Ali, Harris, & Ryu, 2019 ), Ebola ( Cahyanto et al., 2016 ), norovirus ( Fisher, Almanza, Behnke, Nelson, & Neal, 2018 ), or H1N1 (swine flu) pandemic ( Scherr, Jensen, & Christy, 2017 ). According to Ali, Harris, & Ryu, 2019 , PS and PV negatively affect customer intention to patronize restaurants, as diners hesitated to revisit restaurants after an outbreak of foodborne illness, mostly when they recognized their high vulnerability and the severity of foodborne illness. In a similar vein, travelers who reported higher PS and PV were more likely to avoid domestic travel after the outbreak of Ebola than those who showed low PS and PV ( Cahyanto et al., 2016 ). Accordingly, this study assumes that customers who have high PS and PV may utilize OFD services to minimize the possibility of exposure to the COVID-19 from dining out at restaurants. Thus, the following two hypotheses were developed:

PS positively influences CIU.

PV positively influences CIU.

Fig. 1 depicts the proposed hypotheses in this study.

The proposed conceptual framework.

Note. PU = Perceived Usefulness, PEOU = Perceived Ease of Use, TR = Trust, PSB = Price Saving Benefits, TSB = Time Saving Benefits, FSRP = Food Safety Risk Perception, PS = Perceived Severity, PV = Perceived Vulnerability, CIU = Customer Intention to Use OFD.

3. Methodology

3.1. sampling and data collection.

The target population of this study was U.S. consumers over 18 years old. The data were collected through Amazon's Mechanical Turk (MTurk) over two time periods: the third week of June 2019 and the fifth week of July 2020, representing before and during the COVID-19 pandemic, respectively.

A total of 1045 responses (571 for the before-COVID-19 group and 474 for the during-COVID-19 group) were collected. In the data screening process, 90 incomplete questionnaires and 46 respondents who incorrectly answered attention check questions were omitted. Additionally, three participants who provided straight-lining answers were dropped. Also, 150 responses that took less than 150 s of response time were removed following the cutoff norms of response time suggested by DeSimone & Harms, 2018 and Huang, Curran, Keeney, Poposki, & DeShon, 2012 . Lastly, 56 responses with the same internet protocol and location were removed to prevent duplicate participants. After scrutinizing the data, a total of 700 responses were retained with 333 respondents in the before-COVID-19 group and 367 respondents in the during-COVID-19 group.

The chi-square ( χ 2 ) test of homogeneity was conducted to determine whether frequency counts in the socio-demographic variables were distributed identically between the before-COVID-19 and during-COVID-19 group. The results showed that the majority of demographic variables had no significant differences between before-and during-COVID-19 respondents ( p  > .05), except for education level ( p  < .001) (see Table 1 ).

Profiles of respondents ( N  = 700).

Note . *** p  < .001.

3.2. Measurements

A self-administered questionnaire was developed based on a comprehensive review of previous literature ( Castañeda, Muñoz-Leiva, & Luque, 2007 ; Hung et al., 2006 ; Lando et al., 2016 ; Xie et al., 2017 ; Yeo, Goh, & Rezaei, 2017 ). At the beginning of the questionnaire, a definition of OFD was presented. The first section of the questionnaire was comprised of items measuring study constructs, including PU, PEOU, TR, PSB, TSB, FSRP, PS ( Study 2 only ), PV ( Study 2 only ), and CIU using a 7-point Likert scale (1 being “strongly disagree”; 7 being “strongly agree”). The second section included questions asking the socio-demographic information of the respondents. The measurement items and their references are listed in Appendix A .

3.3. Data analysis

The collected data were analyzed using IBM SPSS v26 and AMOS v25. In Study 1, descriptive statistics including frequencies, means, and standard deviations were conducted to summarize the data, and a hierarchical multiple regression analysis was conducted to test the proposed hypotheses ( H1 −7). Before the hierarchical multiple regression analysis, confirmatory factor analysis was performed to check the validity and reliability of the measurement items. Additionally, in Study 2, multiple regression analysis was conducted to test hypotheses 8 and 9, and an independent samples t- test was used to examine the differences between frequencies to use OFD services both before and during the COVID-19 pandemic.

As previous studies discovered the significant effect of demographic factors on consumers' online shopping behavior ( Chiang & Dholakia, 2003 ; Hernández, Jiménez, & Martín, 2011 ), four demographic factors—age, gender, household income, and residency—were controlled to determine the pure relationships between the predictors and CIU. Before conducting the hierarchical multiple regression analysis, respondents' age and income were regrouped. Based on the studies by Dhanapal et al. (2015) and Priporas, Stylos, & Fotiadis, 2017 , age was categorized into two groups comprising of Generation Y/Z and Generation X/Baby Boomers. Furthermore, respondents’ household income was grouped into low (less than $69,999) and high (above $70,000) income categories based on the median household income ($68,703) in the United States ( Ahn & Back, 2018 ; U.S. Census Bureau, 2020 ). All categorical control variables were dummy coded, and all continuous predictor variables were mean-centered to clarify regression coefficients and reduced multicollinearity.

4.1. Study 1

4.1.1. profile of the sample.

Table 1 presents a breakdown of the socio-demographics of both samples. In terms of gender, 365 respondents (52.1%) were male, and 335 respondents (47.9%) were female. The respondents' average age was 39.92 years. The majority of the respondents were Caucasian (73%), and about half of the respondents were married (48.1%). More than half of the respondents (60%) worked full-time, and the largest respondent group reported an annual household income between $30,000 and $49,999 (24.3%). Regarding respondents’ residency, over half of the respondents (52.7%) reported living in suburban.

4.1.2. Validity and reliability of constructs

Confirmatory factor analysis was conducted to evaluate the reliability and convergent and discriminant validity of the measurement model which was comprised of seven factors: PU, PEOU, TR, PSB, TSB, FSRP, and CIU. Each of the overall goodness-of-fit indices suggested that the seven-factor model fit the data well, χ 2 (168)  = 403.90, p  < .001, χ 2 /df = 2.404, CFI = 0.978, TLI = 0.972, RMSEA = 0.045 (90% CI: 0.039-0.050), SRMR = 0.035. The constructs' internal consistency was acceptable with composite reliability coefficients ranging from 0.758 to 0.931 ( Fornell & Larcker, 1981 ). Construct validity was examined by assessing convergent and discriminant validity. For convergent validity, both factor loadings and average variance extracted (AVE) were satisfied with the acceptable ranges ( Anderson & Gerbing, 1988 ). Discriminant validity was determined by comparing AVEs with the squared multiple correlations between constructs, but the results indicated that distinctions between PU and PEOU and between PU and TSB were not established. Therefore, chi-square difference tests were conducted and found that six-factor models—PU-PEOU combined and PU-TSB combined—statistically degraded the original measurement model, PU-PEOU combined: Δχ 2 (6)  = 174.47, p  < .001 and PU-TSB combined: Δχ 2 (6)  = 175.99, p  < .001, suggesting that the seven-factor model showed a significant improvement in chi-squares over both six-factor models. Thus, discriminant validity was ensured.

4.1.3. Hypotheses testing

A three-step hierarchical multiple regression was conducted to test the hypotheses. First, the control variables of gender, age, income, and residency were entered. Second, predictor variables (PU, PEOU, TR, PSB, TSB, and FSRP) and a moderator variable (COVID-19) were entered. In the third step, interaction terms were entered into the model.

Table 2 presents the results of the hierarchical multiple regression analysis. As for the control variables, the results indicated that female ( β  = −0.08, p  < .05), Gen Y/Z ( β  = 0.11, p  < .01; comparing to Gen X/Baby Boomer), urban ( β  = 0.17, p  < .01; comparing to rural resident), and suburban residents ( β  = 0.15, p  < .05; comparing to rural resident) showed significantly higher CIU. However, there was an insignificant difference in CIU between high-income and low-income groups ( β  = −0.03, n.s. ). Hypotheses 1–6 predicted that six predictors regarding OFD services influence CIU. As proposed, PU (H 1 : β  = 0.45, p  < .001), PEOU (H 2 : β  = 0.08, p  < .05), TR (H 6 : β  = 0.19, p  < .001), PSB (H 3 : β  = 0.11, p  < .001), and TSB (H 4 : β  = 0.11, p  < .01) were positively associated with CIU, supporting H1 , H2 , H3 , H4 , and H5 , respectively, controlling for participants’ gender, age, income, and residency (see Model 2). However, FSRP showed an insignificant, negative relationship with CIU ( β  = −.02, n.s. ), failing to support H6 . Additionally, COVID-19—as an independent variable—showed a positive, significant impact on CIU, controlling for other variables. This finding implies that customers tend to show more positive CIU during the COVID-19 pandemic than the before-COVID-19 pandemic.

Results of hierarchical regression analysis predicting customer intention to use OFD.

Note . Ref : Reference group; Durbin-Watson statistic = 2.07.

* p  < .05.

** p  < .01.

*** p  < .001.

Lastly, H7a-f proposed that the COVID-19 pandemic moderates the relationships between the predictors and OFD usage intention. However, as Model 3 shows, none of the interaction terms were statistically significant, and the addition of interactions to the model did not improve the model's predictability (Δ R 2 = 0.00, n.s. ), meaning that H7a-f were not supported. The findings indicate that the pandemic event was significantly associated with CIU but did not affect the relationships between the OFD predictors and CIU.

4.2. Study 2

In response to the impact of the COVID-19 pandemic on the restaurant industry, Study 2 further incorporated PS and PV to the COVID-19 into the OFD usage intention prediction model by conducting a multiple regression.

The results indicated that there were no significant impacts of PS ( β  = 0.03, n.s. ) and PV ( β  = 0.03, n.s. ) on CIU, failing to support H8 and H9 (see Table 3 ). Although PS and PV were not significantly associated with CIU, the degrees of the effects of the other independent variables—including socio-demographic variables—have changed significantly. Considering these variables in the model, more situation-appropriate findings were proposed, i.e., during the COVID-19 pandemic situation. That is, female customers ( β  = −0.05, n.s. ) and urban residents ( β  = 0.08, n.s. ) are no longer more favorable to CIU compared to their counterparts. On the other hand, the results indicated that Gen Y/Z customers are more willing to use OFD compared to older generations ( β  = 0.07, p  < .05). Besides, PU ( β  = 0.43, p  < .001), TR ( β  = 0.17, p  < .001), PSB ( β  = 0.14, p  < .01), and TSB ( β  = 0.13, p  < .01) were still significantly associated with CIU; however, PEOU was no longer significant ( β  = 0.06, n.s. ).

The results of multiple regression analysis predicting customer intention to use OFD during the COVID-19 pandemic.

Note . Ref : Reference group.

R 2 ( adj. R 2 ) = 0.59 (0.57), F (13, 353)  = 38.64***, Durbin-Watson statistic = 2.07.

Lastly, the respondents’ actual usage (frequency) of OFD was compared before and during the COVID-19 pandemic. An independent samples t- test was conducted to compare the frequencies of OFD usage. The results indicated that respondents tended to use OFD more frequently during the COVID-19 pandemic than before the COVID-19 pandemic ( t  = 5.14, p  < .001). Specifically, the number of respondents who used OFD services 2–3 times a month, 1–2 times, or 3–5 times a week has increased during the COVID-19 pandemic, while those who used OFD services once a month or less has decreased. Fig. 2 shows more detailed frequencies between the two conditions.

Comparison of the OFD usage between before and during the COVID-19 pandemic.

5. Discussion and conclusion

5.1. discussion.

The results of both Study 1 and Study 2 showed that PU was the most influential factor in increasing CIU. Similar to previous studies ( Lee, Lee, & Jeon, 2017 ; Yeo, Goh, & Rezaei, 2017 ), this study confirmed that customers are more likely to adopt OFD if they perceive it as useful. The second most significant factor was TR. This finding is paralleled with Flavián et al. (2006) and Wang, Lin, & Luarn, 2006 who found that TR has a significant effect on customer technology adoption intention in the online shopping context. Considering the nature of OFD services that customers place an order via OFD platforms, customers might doubt whether the restaurant accurately receives orders or the quality of food delivered is as good as the quality of food served at the restaurant which explains the importance of TR in the OFD setting.

Surprisingly, Study 1 found that the COVID-19 pandemic did not moderate the relationships between the predictors and CIU. This finding differs from earlier studies, which claimed that a crisis event brings significant behavioral changes to people ( Jin, Qu, & Bao, 2019 ; Laato, Islam, Farooq, & Dhir, 2020 ). The insignificant moderating effect can be interpreted as the factors that significantly influenced CIU before the pandemic still play decisive roles to customers.

Study 2 revealed that PS and PV did not significantly affect OFD usage intention during the pandemic, contradicting the findings of Ali, Harris, & Ryu, 2019 and Cahyanto et al. (2016) . The insignificant effects of PS and PV might be attributable to OFD usage itself not being considered health-related behavior because the Health Belief Model indicated that PS and PV affect consumer's health-promoting behavior. Additionally, Study 2 uncovered situation-appropriate results under the COVID-19 pandemic situation precisely, showing that younger customers (Generation Y/Z) are more willing to use OFD than older customers (Generation X/Baby Boomers). This finding is consistent with other research that revealed Generation Y/Z's online purchasing frequency was higher than Generation X/Baby Boomers, possibly because Generation Y/Z use the internet more frequently than older generations ( Dhanapal et al., 2015 ; Priporas, Stylos, & Fotiadis, 2017 ).

Another notable finding of Study 2 is that FSRP did not significantly affect CIU during the pandemic even though customers are generally more concerned about their safety and health during the pandemic ( Shin & Kang, 2020 ). This could be because customers are aware of the low risk of getting sick with COVID-19 from food as the Center for Disease Control and Prevention (CDC) and other media have reported ( Centers for Disease Control and Prevention, 2020 ). Moreover, considering that both PSB and TSB significantly increased CIU, customers might also perceive benefits received from food products/services obtained through OFD as outweighing the risks associated with using OFD which is in line with the findings of Nardi, Teixeira, Ladeira, & de Oliveira Santini, 2020 .

5.2. Theoretical implications

This study contributes to the current literature with various theoretical implications. Most importantly, as the OFD market share has grown, researchers have devoted increased attention to OFD customers and their decision-making process. The present research extended the existing literature related to OFD by incorporating various predictors and perceptions of OFD driven from the TAM with additional constructs of TR, PSB, TSB, and FSRP (Study 1). Additionally, under the pandemic situation, Study 2 integrated customers’ PS and PV adopted from the Health Belief Model to the COVID-19 pandemic to better predict CIU. Even though PS and PV were not significant predictors of OFD usage intention, the findings showed the altered effects of different socio-demographic variables and OFD perceptions by controlling severity and vulnerability factors. In this respect, this study fills a significant gap in the extant literature on OFD attributes and CIU.

The current study is arguably among the first to identify relationships between various predictors and CIU across different time frames (before and during the COVID-19 pandemic) to evaluate the effect of a crisis on OFD usage intention. While the results do not indicate that COVID-19 served as a moderator between the predictors and CIU, this study still enriches the literature on consumer behavior toward OFD and OFD usage intention.

5.3. Practical implications

This research provides several unique practical implications for OFD stakeholders. First, considering that PU was the most significant predictor of CIU in both studies, OFD marketers should focus on increasing current and/or potential customers’ awareness of the business and advertising service efficiency to their customers. Specifically, marketing materials should highlight the usefulness of OFD services by emphasizing that customers can stay where they are, enjoy their food anywhere they want and avoid ordering by phone, traveling to pick up meals, and waiting for pick-up. Moreover, OFD services can be useful during a pandemic like COVID-19 because the service minimizes contact between customers and restaurant employees and allows customers to enjoy their favorite restaurant food at home. For example, Uber Eats and Deliveroo, among others, launched contactless “leave at your door service” to help drivers and customers adhere to social distancing guidelines. This gives the restaurant industry, which has been severely damaged, another opportunity to thrive and evolve by meeting the changing demand in the foodservice market.

Second, the results demonstrate that TR is the second most significant factor of CIU, which means that the more customers trust OFD services, the more willing they are to use them. From the business's perspective, gaining trust from their customers is building relationships with their customers. Therefore, OFD businesses should invest in customer relationship management (CRM) through various communication channels such as social media and newsletters by being transparent, authentic, and willing to listen to their customers. Furthermore, like major online retailers, OFD providers could present tangible evidence to reduce customer uncertainty on the quality of OFD service by showing 100% customer satisfaction guaranteed and statistics on customer satisfaction scores or number of users. Additionally, customers who have not used OFD might consider it a new technology, which might cause them to doubt how OFD operates or how personal information will be protected. Thus, OFD companies need to explain how they work and how personal and payment information collected through the company will be restored and protected.

Third, because this study confirmed that PSB and TSB positively affect CIU, companies should understand that customers expect benefits from using OFD services. Therefore, using promotional materials such as ads and social networking site (SNS) postings, OFD companies should accentuate potential benefits—time and cost— that the customers can receive as compared to cooking over a hot stove all day or waiting in a long line at a restaurant. Additionally, OFD marketers can provide regular discount promotions, such as free delivery, to attract new customers and launch reward programs. For instance, Uber Eats regularly offers a “$0 delivery fee” promotion and advertises this on their website. Furthermore, they recently teamed up with American Express to provide a Free Eats Pass membership, which provides free delivery and 5% off restaurant orders. By providing information on the estimated delivery time on the OFD platform, customers can visualize the time saving benefits they would gain from using the service.

Fourth, the change in frequency of customer usage of OFD between time periods before and during the pandemic indicates that social distancing measures associated with the pandemic led customers to use OFD services more frequently. Thus, as restaurant business models are shifting in keeping with changing consumer preferences, restaurants can benefit from the popularity of OFD services by partnering with them. Many restaurants transformed their service methods during the pandemic, offering curbside pickup and OFD service, to adapt to the new normal and survive in the competitive market. As an extreme case, DoorDash recently launched a “Reopen for Delivery” program, which gives bankrupted restaurants a fighting chance by matching them with ghost kitchen facilities. Thus, for restaurants, a new business model or re-shaping operation could be a plausible strategy to survive in this era.

Lastly, the findings of Study 2 highlight that during the COVID-19 pandemic, generations Y and Z were more willing to use OFD compared to older generations. OFD businesses should target younger generations to maximize business growth. For instance, OFD service marketers can use SNSs to hold competitions and/or distribute discount codes because the younger generations actively use SNSs to communicate with others ( Williams & Page, 2011 ). Utilizing social media influencers to promote OFD would also appeal to the younger generations.

5.4. Limitations and future studies

As with any research, this study is not free from limitations. This study focused on the general perception of OFD rather than focusing on a specific OFD platform. As customers might perceive each OFD service platform differently, future studies can examine whether significant predictors affecting CIU differ depending on the different OFD services. In addition, this study only considered the platform-to-consumer delivery type of OFD services (e.g., DoorDash, Uber Eats) and did not assess restaurant-to-consumer OFD (e.g., Domino's Pizza, Pizza Hut) ( Poluliakh, 2020 ). Factors affecting CIU might change depending on the type of OFD which is worth investigating for future research. Also, this study focused on CIU to use OFD regardless of their previous experience with OFD. Future studies may consider adding more attitudinal and behavioral intention constructs—customer satisfaction, positive word-of-mouth, willingness to pay a premium, and revisit intention—to provide more fruitful explanations of the linkages between them. Lastly, this study collected the during-pandemic data in July 2020, but CIU may change in the early or late stage of the COVID-19 pandemic. Future research can analyze what factors have a significant impact on the CIU in the later period of COVID-19.

Appendix A. Measurement items

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The Evolution of the Online Food Delivery Industry

• The Evolution of the Online Food Delivery Industry Replay

Heath Terry, Goldman Sachs Research’s business unit leader for the Technology, Media and Telecom Group, discusses the surge in demand for food delivery during the pandemic and the outlook for the industry beyond it. 

This video was recorded on January 15, 2021  

This video should not be copied, distributed, published or reproduced, in whole or in part. the information contained in this recording was obtained from publicly available sources, has not been independently verified by goldman sachs, may not be current, and goldman sachs has no obligation to provide any updates or changes. all price references and market forecasts are as of the date of recording. this video is not a product of goldman sachs global investment research and the information contained in this video is not financial research. the views and opinions expressed in this video are not necessarily those of goldman sachs and may differ from the views and opinions of other departments or divisions of goldman sachs and its affiliates. goldman sachs is not providing any financial, economic, legal, accounting, or tax advice or recommendations in this podcast. the information contained in this video does not constitute investment advice or an offer to buy or sell securities from any goldman sachs entity to the listener and should not be relied upon to evaluate any potential transaction. in addition, the receipt of this video by any listener is not to be taken to constitute such person a client of any goldman sachs entity. neither goldman sachs nor any of its affiliates makes any representation or warranty, express or implied, as to the accuracy or completeness of the statements or any information contained in this video and any liability therefore (including in respect of direct, indirect or consequential loss or damage) is expressly disclaimed.    , explore more insights, sign up for briefings, a newsletter from goldman sachs about trends shaping markets, industries and the global economy..

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Food Delivery Services in Malaysia Essay Sample

The food delivery industry is a booming business in Malaysia. It’s not hard to see why – people are too busy nowadays to cook their meals, and they want the food delivered right to their doorstep. But what does it take for your company to be successful?

What is a food delivery service?

A food delivery service is a company that sends lunch to hungry employees, usually by running the orders through restaurants nearby.

Food delivery services are up-and-coming in the world of business. We  provide an alternative to brown bag lunches or selecting unhealthy fast food nearby for meeting deadlines. It takes about 10 minutes on average from ordering online to having hot and tasty lunch delivered to your doorstep; there’s no need to spend time walking down the street searching for a momentary distraction while you’re ravenous and ready for something satisfying!

For Malaysian companies that depend heavily on people’s ingenuity and ability to work sharp despite long strenuous hours, this alternative does not only save money but also fulfils workers’ hunger during their working hours.

Importance of food delivery service in pandemic

From a Malaysian governmental standpoint, alternative food delivery services are helpful during pandemics because they can provide people with the resources needed to survive. When access to groceries is severely limited, alternative suppliers can assist by providing critical resources.

Alternative supply chains that deliver goods on-demand or on as-needed basis offer benefits for increasing public health and safety during pandemic events. These services have the potential to improve both preparedness for future events and how individuals manage through these difficult times.

##Impact of food delivery services

Online food delivery is currently booming, with its increasing adoption by restaurants and customers. According to research group IBISWorld, revenue for this Malaysian market will increase at an annualized rate of 13% between now and 2020. The potential drivers behind this growth are the convergence of technologies (e.g., car or bike-sharing services) that allow consumers to satisfy their food needs more easily; lifestyles that prioritize convenience; and significant advancements in mobile payment systems.

Since 2011, investment in online food ordering has grown exponentially globally. Analysts predict that it will continue to grow as people order regularly from home or work rather than waiting for their takeaway meal until they arrive home after a long day of work/school play.

##How does food delivery service work?

Now a business in Malaysia can offer online food ordering and delivery, managing customer orders from start to finish. A restaurant administers the menu, deals with customer orders, prints menus and other materials does marketing for the business by offering coupons via third-party search engines, and provides reports on how well he or she is doing at attracting customers all from behind a computer screen.

In addition to the time saved by the Malaysian company’s employees not having to take each order individually over the phone or in person, there are other benefits as well. The company saves money on payroll hours spent fielding phone calls and waiting on customers who walk in person – which isn’t a problem that CafeMom has right now!

##Food delivery business model

The business model of food delivery services depends on the type of food service. For over-the-counter services, different models are possible.

One example is franchising, where a franchiser sets up an individual restaurant company in all major metropolitan areas for exclusive rights to sell its franchised restaurant locations within that franchiser’s territory.

Restaurants may be either self-owned locations or franchised locations depending on the agreement with the franchiser. The majority of restaurants in western countries are franchisee operations, motivated by economic benefits offered by establishing a centralized management system that minimizes commercial risks involved in opening their restaurant location compared to starting an independent establishment).

##Demand for food delivery service

Malaysia is experiencing a huge boom in the demand for food delivery services, with one industry report projecting that 300,000 employees could be employed to meet this demand.

One reason for this surge in demand is the increase in city population due to globalization. The other reason for this trend is that Kuala Lumpur has gone through rapid modernization over the past few years, which has resulted in employment opportunities shrinking even as the cost of living grows higher and higher (for instance, it’s now possible to buy smartphones delivered directly to your home).

Food delivery services in Malaysia grew from a market size of 73.5 million in 2015 to 115 million in 2016, an increase of 41%. In 2016, catering is the most popular category, doubling its share from 8.4% to 16.9%. Spurred by the hyped-about restaurant scene in Kuala Lumpur and KL has been touted as hitting a tipping point with consumers demanding more cuisines with a range of prices and flavours at their fingertips, according to industry experts.

Among the factors driving this growth is that many people have migrated into urban centres with less space for food preparation or storage amenities available on-site including cooking equipment and refrigerators even microwaves.

##Pros and cons of food delivery service

The pros and cons of food delivery service are too many to list.

• Little to no prep work. All ingredients are automatically included in the package, you just have to open and eat! Some websites even offer to grab lunch for you when they deliver.
• No need to figure out what your coworkers want.
• Time saved: because of the convenience and simplicity of food delivery services, the frequency with which dinner is eaten outside has risen dramatically in recent years.
• Pricey compared to cooking at home, especially for those with less disposable income and smaller budgets. Food delivery service companies may be able to offer low prices because of their volume purchasing power but even then the price is often higher than what you would pay if you shopped around or bought in bulk from an online grocer like BigBasket.
• Less customer control over ingredients depending on how picky your dietary requirements are could be a pro!
• Relying on someone else’s schedule can become frustrating when it doesn’t work well with yours (especially true if it’s not pre-negotiated). This isn’t quite as much of an issue if you are ordering lunch or breakfast where typically there is less urgency about when the food needs to be delivered.
• Food delivery services require a high level of trust on both sides for customers who have no idea how long it will take before their order arrives and whether what they receive is exactly as they ordered, and vice versa with staff at restaurants that may not know anything about them or even see the final details of their order until moments before it’s being prepared/delivered.
• Today many busy professionals rely on food delivery services to save time and money, with the average Malaysian spending $151 per month on them.

##Food delivery industry analysis

The food delivery industry is in the growth stage. It is predicted that by 2022 it will be worth $200 billion, which is a 41% increase from 2016.

Food delivery companies, specifically startups like UberEats are taking advantage of America’s notorious work-from-home culture and getting people to order their next meal straight from their couch. Why not save money with Taco Bell or get fancy with Yelp/Eat24? You don’t have to leave the house! And you can make someone else do your grocery shopping for you which Google searches for “grocery store” corroborate Americans’ low self-care standards when buying groceries (have kids who hate vegetables?).

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David Moscow travels the world to discover deliciousness From Scratch

By cristine struble | feb 29, 2020.

Have you wondered why some meals just taste better? David Moscow goes on a food adventure in the new show From Scratch and uncovers the secret to delicious food.

Sometimes the most memorable meals have a story behind them. David Moscow discovers the stories behind the food can and do create the most delicious dish. In the new television series, From Scratch , this culinary adventure can encourage foodies to explore the beauty of making food from scratch.

While the home kitchen holds a bounty of deliciousness, sometimes convenience overtakes cooking from scratch. When a cook takes the time to use quality ingredients, appreciate the craftsmanship and uncover the passion behind the ingredients, that meal can become more than just sustenance. It can become a gift.

In the new FYI series , From Scratch , David Moscow goes on a culinary journey. As he travels the world, David seeks to recreate a chef’s recipe. But, this show is more than just cooking. David explores the stories behind each of those ingredients. Through this culinary adventure, people see that food is intertwined with a country’s history and culture.

Many people might know David Moscow from his feature film debut in Big . Over the years he has numerous film, television and stage credits. Additionally he co-developed and co-produced the first stage production of In the Heights and has produced several films. In this new FYI show, he puts himself into the food world.

In the first ten episodes of From Scratch , David dives into a culinary world that many foodies would dream to discover. Even though more people are appreciating farm to table cuisine, David takes that approach to the source. From milking a cow to make butter to foraging for the perfect ingredient for a dish, the show proves that ingredients bring the story of food to the table.

Recently, David Moscow graciously answered some questions about his new show, From Scratch . While some people may not have the opportunity to go on this extraordinary culinary adventure, the lessons learned from his experience can be brought to any home cook’s table.

Cristine Struble: Many Americans are focused on convenient food (or delivery, grab & go), how can your show get people to discover the deeper connection that food can bring a person?

David Moscow: While sourcing ingredients is definitely hard and hard to find time for in our demanding days/schedules, it also can be quite fun and sometimes even exciting. These thrills are present all across the season. But they also sit right up alongside the simple pleasures of wandering in the woods looking for mushrooms or fishing on a river under a midnight sun. The hope is that our show will shake that love of nature and the joy that come with work particularly when it ends in a pizza pie.

CS: There is a growing movement to know your farmer or know where food comes from – do you think that people are understanding that where food comes from impacts the how food tastes?

DM: There are a couple oppositional things happening at once. At the same time that a few people are able to take the time and money to know where our food is coming from, the majority are becoming even more removed through delivery apps and the growth of fast food. Thoughtless eating has never been such a problem. BUT we are only a generation or two away from a healthy interaction with the food we eat. And I do think that all people still pine for making fresh food and eating it with friends around – something that is innately part of being human.

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CS: As you traveled the world, did you find that food traditions are stronger in some countries?

DM: I found that there was a direct relationship with free time, a social safety net and strong food traditions. Places like Iceland, Sardinia and Finland have little fast food and place great importance in the people who harvest and source the meals they eat. Some of the other places are fighting to keep their traditions alive in the face of the cheap fast food everywhere in modern life.

CS: While many people think that global cuisines are very different, there are often some underlying similarities. What similarities surprised you the most?

DM: When building the episodes for the show, we found that there aren’t that many major ingredients across the planet. A huge chunk of what we eat are grass, seeds, fish and few other animals. And we eat these things with the help of fire or fermentation. Cooking meat on a grill and making alcohol out of fermenting food is everywhere.

CS: This show seems to encourage people to better understand the food and culture connection. What’s one easy way to start that type of food conversation on the typical family home?

DM: I think apple picking (or any kind of fruit picking) as a family outing is an amazing starting point. It gets you out in the fresh air and gets your blood flowing – and I’ve never met anyone who doesn’t like a crisp apple pulled from a tree. During the shoot I sat my son down in a strawberry patch to graze before he could walk. The act of picking and eating with his own hands has had a lasting effect. Strawberry was one of his first words and still to today is his favorite fruit to eat.

CS: You travel the world in this series. Which location was your favorite? Which meal was your favorite?

DM: Each one of the places I went was a spot I had dreamed of going and each has a special place in my heart. How can I compare going on a safari in South Africa vs taking a boat off the Amalfi coast. I would say the same with the food. I had the best pizza on the planet and the best tacos. I had incredible Icelandic seafood and Finnish lake fish. They each stand alone.

If you would like to follow along with David Moscow’s culinary adventure, From Scratch airs on FYI and can be streamed online. New episodes air on Sundays at 6 p.m. ET/ 5 p.m. CT.

6 Gordon Ramsay recipes inspired by Gordon Ramsay Uncharted. light. Related Story

Do you know the story behind the ingredients on your plate? Take the time to appreciate the flavor, the story and the journey when cooking is made from scratch.

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Netizens slam food delivery platform as girl dies after eating b’day cake ordered online

The police suspect that the bakery is a cloud kitchen. Additionally, another receipt invoice of Zomato shows billing done from Amritsar, not Patiala.

New Delhi: After a 10-year-old girl reportedly died post eating her birthday cake ordered online in Patiala, netizens slammed online food delivery platforms for poor regulation over Cloud kitchens listed as food-delivering apps.

The police said on Saturday that a case under Sections 273 and 304-A of the Indian Penal Code (IPC) has been registered following a complaint lodged by the girl’s family members, alleging that the girl died after consuming the birthday cake. Other family members also fell ill after consuming the cake.

As per the bill copy of the cake, which was ordered by the deceased girl’s mother Kajal, there is no shop named ‘Cake Kanha’ at the registered address in Patiala.

Despite reaching multiple times, Zomato did not comment.

Dr Nandita Iyer, a seasoned food and nutrition columnist, posted on X that Swiggy and Zomato should clearly highlight on each listing whether it is a cloud kitchen so people are aware of it before ordering.

“Such incidents are a harsh reminder that we have no idea what goes into the food we order from these completely unregulated places,” Iyer wrote on the social media platform.

Fitness professional Chirag Barjatya said that food safety is a joke.

“You will be surprised to know that people are running 20 different ‘restaurants’ in 1RK (room kitchen) as cloud kitchens listed over food-delivering apps. You have no idea how many mice and cockroaches were around the food you just ordered. And you have absolutely no idea if the cooked dal or rice you ordered has expired,” Barjatya posted on X.

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Herald, Foster's, News-Letter, Union and York Weekly mail delivery starts April 1

Starting April 1, the U.S. Postal Service will begin delivering the Portsmouth Herald, Foster's Daily Democrat, Hampton Union, Exeter News-Letter and York Weekly newspapers.

"The move from driver delivery to postal service delivery is another step in Seacoast Media Group's evolution from a print-first to a digital-first news and marketing source," said Executive Editor Howard Altschiller. "Our unwavering commitment to the Seacoast communities and readers we serve remains rock solid. Readers looking for the latest Seacoast news, sports and advertising can count on Seacoastonline , Fosters.com and our printed newspapers to deliver."

Like many other newspaper publishers, Gannett Co. Inc., which operates more than 200 daily newspapers, including Seacoast Media Group's papers in New Hampshire and Maine, has already successfully introduced the switch from driver delivery to mail in dozens of markets across the country, and is expanding the initiative.

The markets which have switched to mail delivery report high customer satisfaction rates. While the paper will arrive later in the day with the mail, it will arrive consistently in the same place at the same time. In recent years it has been a challenge to adequately staff the delivery force needed to hand deliver papers across the Seacoast region of New Hampshire and Maine.

Papers will be delivered to local post offices early enough each morning to allow same-day delivery. Because there is no mail delivery on Sunday, Seacoast Sunday will arrive in mailboxes on Saturday. The digital replica of the Sunday paper, the eNewspaper , will post to Seacoastonline and Fosters.com on Sunday morning.

Delivery of non-Seacoast newspapers like the Wall Street Journal and New York Times, previously handled by Seacoast Media Group drivers, has been taken over by a new company. Questions regarding delivery of non-Seacoast papers should be directed to those other newspapers.

The change to postal delivery will not impact subscription rates.

“For many years now, the printed newspaper has served as a culmination of the stories that will become our collective history, while our websites and mobile apps deliver the news of the day,” said Michael A. Anastasi, vice president of local news for Gannett. “We know that by the time our informed readers pick up the paper, they know what happened yesterday — the print newspaper should provide additional context, to help readers better understand their community and the world around them.”

Readers can also visit Seacoastonline.com and Fosters.com to access the eNewspaper at seacoastonline.com/enewspaper and fosters.com/enewspaper .

Subscribers with questions or concerns can visit help.seacoastonline.com/contact-us and help.fosters.com/contact-us.

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MOSCOW WIDENS NEW POLICY LINE; Essay, Scored During Rule of Khrushchev, Praised for View of Hard Rural Life

MOSCOW, Dec. 26—A growing reversal of the policies of former Premier Nikita S. Khrushchev, especially in agriculture, was‐extended today to the field of literary criticism.

The literary‐union newspaper Literaturnaya Gazeta published a laudatory review of Yefim Dorosh's essay “Half Rain, Half Sunshine,” which gives what is widely regarded as a realistic depiction of the countryside of central European Russia.

The essay, published last summer in the liberal literary monthly Novy Mir, was violently attacked in the Soviet press just before Mr. Khrushchev's overthrow in October as misrepresenting life in rural areas.

One critique, by L. Lebedev, a collective farm chairman from the Galich area northeast of Moscow, appeared in Selskaya Zhizn (Rural Life), the farm newspaper of the Communist party's Central Committee over whose content Mr. Khrushchev had direct control.

Mr. Lebedev charged Mr. Dorosh with conveying a picture of “prerevolutionary dreariness, despondency, stagnation, and complete hopelessness drifting from every page.”

The farm chairman accused the author of concentrating attention “on an old monastery, an ancient lake, an abandoned grave of some count instead of writing, say, about the new widescreen moviehouse.”

Mr. Lebedev said Mr. Dorosh had misrepresented the cultural level of farm youth and the rural intelligentsia by depicting them as “primitive, uneducated people without interest in literature or the arts.”

Mr. Dorosh had written that the residents of his fictitious country town of Raigorod “read little, went, to be sure, to the movies, but had not been in the regional museum, in the picture gallery, in the theater or at the philharmonic concert.”

Today's review in Literaturnaya Gazeta by Vladimir Voronov, a critic, contended that Mr. Dorosh had performed a useful service by drawing attention to problems that continued to bedevil Soviet agriculture and life in the countryside.

The essay, published while Mr. Khrushchev was still in power, questioned the effectiveness of some reforms inspired by the former Premier and criticized the continuing close supervision of farm production and the imposition of output plans from above.

In an evident allusion to Mr. Khrushchev's style of running Soviet agriculture, Mr. Voronov wrote:

“Dorosh regards the struggle for a growth of the rural economy not as a short‐lived, noisy campaign but as a long, complicated haul.”

Mr. Voronov assailed the farm chairman for having judged the essay simply on the basis that his own area was more prosperous than the one pictured in “Half Rain, Half Sunshine.”

The reviewer said it was not literary criticism to say:

“We live better” and to tell “about a milkmaid who had obtained 800 quarts of milk more from a cow than in the previous year.”

The controversial essay is part of a series of “rural diaries” that Mr. Dorosh, a resident of Moscow, has been writing since 1956 on the basis of periodic visits to an unidentified small town and the surrounding countryside in central Russia.

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