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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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Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression

Sangeeta mehrolia.

1 School of Business and Management, Christ University, Bangalore India

Subburaj Alagarsamy

Vijay mallikraj solaikutty.

2 The American College, Madurai India

This study aims to empirically measure the distinctive characteristics of customers who did and did not order food through Online Food Delivery services (OFDs) during the COVID‐19 outbreak in India. Data are collected from 462 OFDs customers. Binary logistic regression is used to examine the respondents’ characteristics, such as age, patronage frequency before the lockdown, affective and instrumental beliefs, product involvement and the perceived threat, to examine the significant differences between the two categories of OFDs customers. The binary logistic regression concludes that respondents exhibiting high‐perceived threat, less product involvement, less perceived benefit on OFDs and less frequency of online food orders are less likely to order food through OFDs. This study provides specific guidelines to create crisis management strategies.

1. INTRODUCTION

Post the outbreak of COVID‐19, restaurants and associated services were severely affected prompting the Indian government to categorize food and other related services under essential services. Hence, hotels, restaurants and food delivery services can now start their operations because at least 20% of the Indian population including students, paying guests and young professionals depend on them (Shrivastava,  2020 ). According to the industry reports, the COVID‐19 pandemic has ushered in a new threat to the business of food delivery, which could potentially affect the Online Food Delivery services (OFDs; Keelery,  2020 ). Restaurants and related services, mainly OFDs, are willing to supply food. However, the customers are hesitant to place orders during this pandemic even though many OFDs have mandated their delivery partners to use personal protective gear while encouraging the customers to pay digitally to ensure contactless delivery. The two critical issues for the drop in OFDs are the health of the individuals who deliver the food and the sanitary condition of the restaurants. These issues have forced existing customers to reconsider their future purchase decisions. The purpose of this research is to examine the differences between OFDs customers who did and did not order food through OFDs during the COVID‐19 outbreak period in India on the basis of their personal characteristics. The study examines the significant differences between these two groups of respondents on their characteristics, such as age, the number of online food orders before the nationwide lockdown, affective and instrumental beliefs, perceived benefit, product involvement and perceived threat.

This paper is organized as follows. The first part of the study discusses the literature review, specifically in the areas of self‐protective behaviour and customer intentions. The next part of the study explains the research method. The third part provides the detailed data analysis. The fourth part discusses the implications of the study. Finally, this study concludes with limitations and directions for future research.

2. LITERATURE REVIEW

2.1. theoretical underpinning.

An averting behaviour displayed by customers to condense the possibility of an odd outcome is known as self‐protective behaviour. It can also be a defensive action taken to decrease individual or group vulnerability to risk (Ehrlich & Becker,  1972 ). Chuo ( 2014 ) argues that the Health Belief Model (HBM) can explain the self‐protective behaviour in the field of customer food safety. The HBM is one of the most widely used models for understanding health behaviours while also explaining and predicting individual changes in health behaviours. The elements in the HBM focus on individual beliefs about health conditions to predict individual health‐related behaviours. The model defines the key factors that influence health behaviours. These include an individual's perceived threat to disease (perceived susceptibility), the belief of consequence (perceived severity), potential positive benefits of action (perceived benefits), perceived barriers to action and exposure to factors that prompt action (cues to action; Abraham & Sheeran,  2014 ; Becker et al.,  1977 ; Jeong & Ham,  2018 ). The HBM is a widely used theory in health education to describe health‐related behaviour preservation and as a guiding mechanism for behavioural health interventions. It is a behavioural model that tries to explain and predict health behaviours by focusing on individual beliefs, attitudes and behaviours influenced by their beliefs about a condition of disease and the approaches to decrease its prevalence. Hence, this model can be used to understand the purchase decisions of the customers during the pandemic.

2.2. The HBM constructs and relationships between the constructs

2.2.1. self‐protective behaviour.

Self‐protective behaviour can also be explained as a function of threat perceived by the customer (Jacoby & Kaplan,  1972 ; Taylor,  1974 ). Whenever people see risk somewhere, they develop self‐protective behaviour. In normal conditions, self‐protective behaviour is not observed by customers while they make a purchase decision. During disease outbreaks, such as SARs, Avian influenza, H1N1 Influenza, Bovine Spongiform Encephalopathy and COVID‐19, this self‐protective behaviour becomes significantly pronounced. The fear of getting infection spreads faster than the disease itself (Addo et al.,  2020 ; DeLisle,  2004 ; McKercher & Chon,  2004 ; Wen et al.,  2020 ). Thus, any increase in fear can lead to anxiety and a shift in the intention of behaviour (Addo et al.,  2020 ; Chuo,  2014 ; Ishida et al.,  2010 ; Schroeder et al.,  2007 ; Setbon et al.,  2005 ; Weitkunat et al.,  2003 ). This safety behaviour is usually cautionary behaviour, including the behaviour of collecting more information and taking additional care at the time of buying and preparing food. Such fear perception patterns were observed in various service industries such as travel (Lau et al.,  2004 ) and tourism (Chuo,  2007 ; Cooper,  2013 ; Pine & McKercher,  2004 ) and supply chain (Clark,  2012 ; Kumar,  2012 ; Kumar & Chandra,  2010 ). Customers, in particular, often avoid travel and ignore places or products to minimize the risk of illness during SARs and H1N1 Influenza outbreak and this disturbance of spending has a significant impact on the economy. Previous studies have linked fear appeal to the behaviour of respondents to pandemic diseases (such as Avian influenza and Bovine Spongiform Encephalopathy) in food or meat consumption environments (Brug et al.,  2009 ; Kuo et al.,  2011 ; Nam et al.,  2019 ; Shen et al.,  2020 ; Wise et al.,  2020 ; Yeung & Morris,  2001 ). From this discussion, it can be concluded that customer buying behaviour or purchase decision, considered in this study as self‐protective behaviour, is the outcome of the HBM (individual action). In this study, the self‐protective behaviour (purchase decision) is measured as dichotomous variables (did order and did not order food online during the COVID‐19 outbreak).

2.2.2. Perceived threat

Many academic reviews conclude that perceived threat is a core component and the most useful in understanding the practice of a variety of preventive health behaviours. According to the HBM, perceived threat refers to beliefs about the seriousness of a particular disease and how susceptibility they are to it (Berg & Lin,  2020 ; Bish & Michie,  2010 ; Carpenter,  2010 ; Cho et al.,  2020 ; Janz & Becker,  1984 ; Manika & Golden,  2011 ; Weitkunat et al.,  2003 ).

Many studies believe that it is possible to combine susceptibility and severity into one construct, namely perceived threat (Aucote et al.,  2010 ; Jeong & Ham,  2018 ; Manika & Golden,  2011 ). Studies have shown that perceived severity is hard to predict until it attains such high limits as to be dysfunctional (Jeong & Ham,  2018 ; Rosenstock,  1990 ). Perceived threat is a sequential function of perceived severity and susceptibility (Becker et al.,  1977 ; Strecher & Rosenstock,  1997 ; Von Ah et al.,  2004 ). Perceived threat is defined as a combination of perceived susceptibility and severity and is a construct that is more relevant to the resulting health‐related behaviours than an individual consideration of either of these factors (Jeong & Ham,  2018 ; Rosenstock,  1990 ).

In this research, perceived susceptibility refers to an individual's subjective perception of the risk of acquiring a particular disease. Perceived severity refers to an individual's feelings about the seriousness of contracting a particular disease. There is a vast difference in a person's feelings of severity and often a person considers the medical consequences and social consequences when evaluating the severity (Bish & Michie,  2010 ; Cao et al.,  2014 ; Tang & Wong,  2004 ). Based on the above discussions, the perceived threat of disease may have been increased by daily reports of particular disease infection figures, media news on a particular disease and documentation about patients infected with or who died of a particular disease (Berg & Lin,  2020 ; Bish & Michie,  2010 ; Tang & Wong,  2004 ; Wong & Tang,  2005 ). Centre for Disease Control and Prevention recommends various self‐protective measures to control COVID‐19 spread and one of the main recommendations on ‘Running Essential Errands’ is ‘Use online services when available’ (CDC,  2020 ). The chances of COVID‐19 spread are relatively high through online food delivery and this has been confirmed by national media news (The Times of India,  2020a ). With this note, it is clear that the perceived threat of COVID‐19 infection is high through OFDs, which may influence the respondent's purchase decision. Similar results were recorded by many researchers and are explained in the next section.

Circumstances such as technological disruption, natural disasters and animal‐spread pandemic influence an individual at the physical and psychological levels. Such situations bring much change in human behaviour and trigger a type of defensive and coping mechanism to fight against all odds. This protective mechanism is usually developed based on the level of perceived threat. Weber ( 2006 ) explains that fear acts as a motivator to reduce the feeling of risk and take specific action to tackle it. Perceived threat is always followed by a feeling of fear. So, if perceived threat is high, the feeling of fear appeal would also be high and, consequently, would result in withdrawal or escape (Addo et al.,  2020 ; Loewenstein & Lerner,  2003 ; Rhodes,  2017 ; Rountree & Land,  1996 ; Vermeir & Verbeke,  2006 ; Warr,  1987 ). Based on these discussions regarding perceived threat, the following hypothesis is proposed.

The perceived threat of catching COVID 19 through the use of OFDs negatively influences purchase decisions

2.2.3. Perceived benefits

Health‐related behaviours are also influenced by the perceived benefits and perceived risk of taking action (Carpenter,  2010 ; Glanz et al.,  1992 ; Janz & Becker,  1984 ; Tang & Wong,  2004 ). ‘Perceived benefits refer to an individual's assessment of the value or efficacy of engaging in a health‐promoting behaviour to decrease the risk of disease’ (Janz & Becker,  1984 ). When a person assumes that a specific activity can minimize the vulnerability to a health problem, then, they may participate in that behaviour irrespective of the objective facts about the activity's efficacy (Glanz et al.,  1992 ; Jeong & Ham,  2018 ). Due to the nationwide lockdown, many individuals were forced to stay inside their homes and they preferred to buy food items through OFDs. Local governments also encouraged individuals to buy products online in order to reduce the spread of the disease (Chang & Meyerhoefer,  2020 ; Richards & Rickard,  2020 ; The Times of India,  2020b ) and this discussion clears the positive effects of the perceived benefits of OFDs. OFDs are more convenient, safe and cost‐effective for individuals than going to hotels and restaurants. The perceived benefits of online grocery delivery have a positive impact on purchase decision during COVID‐19 situation and the researchers recorded it (Aldaco et al.,  2020 ; Hobbs,  2020 ). OFDs have perceived benefits like contact‐free delivery and e‐wallet payments, which can reduce the risk of COVID‐19 spread (Nguyen & Vu,  2020 ).

Perceived barriers to taking action include perceived inconvenience, expense, danger and discomfort involved in engaging in the behaviour (Janz & Becker,  1984 ). In this research, the perceived barrier is not considered if customers perceive OFDs as inconvenient, expensive and, risky. In this case, they will not order food items online. However, in this study, only existing OFD customers are considered. It becomes clear that the customers who do not have perceived barrier towards OFDs find them convenient and inexpensive. Also, the customers’ fear appeal is measured through perceived threat. Therefore, with regard to the perceived benefits of OFDs, the following hypothesis is proposed.

Perceived benefits of OFDs positively influence customer's purchase decision

2.2.4. Affective and instrumental beliefs

Many studies have used theory of reasoned action/ planned behaviour to explain and predict behaviours. These social psychology models indicate that individual behaviour is defined by intentions that are in turn determined by perceptions, subjective norms and perceived behavioural control (Ajzen,  1985 ; French et al.,  2005 ; Hardeman et al.,  2002 ; Povey et al.,  2000 ). Underlying these three variables are assumptions that can form the foundation of behaviour change interventions. The above‐mentioned social psychological models have been used with varying degrees of success to develop approaches to improve health behaviours (French et al.,  2005 ; Hardeman et al.,  2002 ; Li et al.,  2019 ; Nam et al.,  2019 ; Povey et al.,  2000 ). In the cognitive tradition, these models are strongly grounded and concentrate on instrumental beliefs as the detriment of affective and other factors. The attitude component of a behavioural intention comprises both instrumental and affective beliefs (Ajzen,  2012 ; Keer et al.,  2013 ; Lawton et al.,  2007 ; Lowe et al.,  2002 ). Despite this, a growing body of correlational research shows affective and instrumental beliefs to be strong determinants of intentions and behaviour. Instrumental beliefs relate to the benefits and costs associated with behaviour (e.g., healthy or unhealthy). Affective beliefs are emotion‐laden judgements about the consequences of the behaviour (e.g., pleasant or unpleasant, enjoyable or unenjoyable). Thus, attitudes will be most favourable towards behaviours with outcomes that are believed to be both beneficial and pleasant (Lowe et al.,  2002 ). Many studies conclude that affective beliefs are strong predictors of intentions and action than cognitive beliefs (Conner et al.,  2011 ; Lawton et al.,  2007 , 2009 ). However, fewer studies have examined the relative importance of instrumental and affective beliefs in predicting observed health behaviour. From the above discussions, it is clear that instrumental and affective beliefs influence the purchase decision and hypothesis below is concluded from the discussions above.

Instrumental and affective beliefs towards OFDs positively influence the customer's purchase decision.

2.2.5. Cues to action & product involvement

Champion and Skinner ( 2008 ) define cues of any action as ‘anything that triggers or reminds individuals to take action’. Studies classify cues into two different types namely, internal (disease symptoms or physical changes in the body noticed by the individual) and external (media ads and publicity, posters, government interventions, public health awareness, family and peer advice; Cao et al.,  2014 ; Carpenter,  2010 ; Glanz et al.,  1992 ; Janz & Becker,  1984 ; Meshe et al.,  2020 ; Rabbi et al.,  2015 ). Studies find that cues of action can have a positive impact on health behaviour (Carpenter,  2010 ; Jeong & Ham,  2018 ; Rosenstock,  1990 ; Tang & Wong,  2004 ; Valeeva et al.,  2011 ). During the nationwide lockdown in India, the OFDs providers launched marketing campaigns to instil in viewers the belief that they were following all safety measures and prioritizing safety at each step of the delivery process (Economic & Times,  2020 ; The Times of India,  2020b ). These kinds of marketing campaigns and government interventions (external cues of actions) on online deliveries encouraged customers to buy food online.

Product involvement means the extent of a customer's interest in buying a particular type of product and how dedicated they are to buy a specific brand (N. M. Nguyen & Nguyen,  2019 ; Peng et al.,  2019 ; Zaichkowsky,  1994 ). Customer involvement in items appears to be greater for goods that have a higher cost and are purchased after extensive research and thought (Belanche et al.,  2017 ; Handriana & Wisandiko,  2017 ; Soliha & Widyasari,  2018 ). These above‐stated marketing campaigns and government interventions increase product involvement and help the customers to research OFDs. Hence, this study measures these external cues of actions by measuring the customer product involvement. Studies argue that higher product involvement positively influences the purchase decision (Hollebeek et al.,  2007 ; O’Cass,  2000 ; Prendergast et al.,  2010 ; Shirin & Kambiz,  2011 ). When external cues towards a particular product or service are high, they motivate individuals to try the product or service. It is, therefore, hypothesized that:

Product involvement about OFDs positively influences the customer's purchase decision

2.2.6. Other factors

In the HBM, individual characteristics such as age, gender, race and educational qualification, and so forth, can affect their perceptions and behavioural change (Abraham & Sheeran,  2014 ; Carpenter,  2010 ; Rosenstock,  1990 ; Strecher & Rosenstock,  1997 ). Based on the recent studies on COVID‐19, it can be concluded that a more significant number of deaths occurred among adults aged ≥65 years with the highest percentage of severe outcomes among persons aged ≥85 years. However, studies show that severe illness leading to hospitalization, including ICU admission and death with COVID‐19 can occur in adults of any age (Bialek et al.,  2020 ; Myers et al.,  2020 ). These kinds of external cues negatively influence the older customers' purchase decision on OFDs. In a marketing context, many researchers argue that the age of the respondent is the main factor that influences customer decision (Hervé & Mullet,  2009 ; Ketel et al.,  2019 ; Klein et al.,  2019 ; Lobb & Mazzocchi,  2006 ). Based on these discussions, the age of the respondent is considered as the main factor affecting the purchase decision in regard to OFDs. Grobe et al., ( 1999 ) show that demographical factors, such as purchase frequency and age of the customers are essential factors that motivate their self‐protective behaviour. A few studies conclude that frequency of purchase influences customer decision and loyalty (Grobe & Douthitt,  1995 ; Grobe et al.,  1999 ). In particular, Chuo ( 2007 , 2014 ) concludes that the self‐protective decision is affected by the purchase frequency. When a customer purchases a particular product more frequently, it implies that it has a high level of perceived benefit than perceived barrier and threat (Chuo,  2007 ; Grobe & Douthitt,  1995 ; Grobe et al.,  1999 ). Based on this discussion, we take age and purchase frequency as main demographical factors affecting the purchase decision. Based on this discussion regarding perceived threat, the following hypotheses are proposed (Figure  1 ).

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Object name is IJCS-45-396-g001.jpg

Conceptual model

Age of the respondents negatively influences the customer's purchase decision

Frequency of ordering food online before the nationwide lockdown positively influenced the customer's purchase decision

3.1. Data collection

The OFD customers are considered as the target population in this study. The snowball sampling method is used to collect data from 1st April 2020 to 30th April 2020. The nationwide lockdown started in India on 25th March 2020 to limit the movement of the population. However, the government allowed e‐commerce firms to remain operational during this period. An online‐based well‐structured questionnaire was developed using Google forms and shared with the respondents. Online‐based survey is the valid choice of data collection procedure during the lockdown to ensure the safety of the respondents and researchers. A screening question was used to filter eligible respondents for the research and only OFDs customers were considered for the study. The respondents were university students in Bangalore city, India (including a junior college student, undergraduates, postgraduate and doctoral students). We sent the questionnaire through WhatsApp and official e‐mail ids and invited university students from different regions of Bangalore to provide their response. Meanwhile, we also sent the questionnaire to the university teachers who had cooperated with us and used their contact network to spread the questionnaire. All the respondents have participated voluntarily in this study and no personal information was collected in this research. Samples were collected from Bangalore. During national‐wide lockdown, many Indian state governments did not allow operation of OFDs during the nationwide lockdown, many well‐established OFDs services like Zomato and Swiggy were fully operational in Bangalore, a city with people from diverse backgrounds. Bangalore city has an adequate representation of the robust Indian population and includes young paying guests and working professionals. The city is, therefore, ideal setting for the context of our study. In total, we received 600 samples during the data collection period in which 138 respondents were not OFDs customers and only 462 were found valid for further analysis, resulting in a response rate of 77%. Therefore, the final sample consisted of 462 respondents, all of whom indicated that they had previous experience with OFDs.

3.2. Instrument development

The well‐structured questionnaire consisted of three sections. The first section had questions on demographical details of the respondents, respondents’ patronage frequency before the lockdown and purchase decision during the lockdown. The second section questions were asked to measure the respondents’ opinions about the perceived benefit of OFDs and product involvement with OFDs. The perceived benefit scale developed by Forsythe et al. ( 2006 ) was modified and used to fit with the current context to measure the perceived benefits of OFDs. The product involvement scale was adopted from Chuo ( 2007 ) and initially used by McQuarrie and Munson ( 1992 ). Again, the product involvement scale was modified to the current research setting and the questions were administrated on a Likert 7‐point scale ranging from ‘1 = extremely strongly disagree’ to ‘7 = extremely strongly agree’. The last section of the research instrument was used to measure the perceived threat of the respondents towards OFDs. Turnšek et al. ( 2020 ) measured perceived risks with one item using seven‐point scale (0 = none; 7 = very high): ‘possibility of becoming sick while travelling or at destination’. Chuo ( 2007 ) study used three subjective scenarios to estimate the probability that a person will be infected with SARS. In their study, respondents were asked to rate the SARS‐infected possibility (perceived threat) in one of the scenarios in terms of percentage (from ‘0’ to ‘100’). Similarly, two scenarios were presented to the respondents and they were asked to select one suitable scenario, and subjectively estimate the probability (percentage from 0 to 100) that they will be infected with COVID‐19. The scenarios were: 1. If you have ordered food, please mention the percentage of chance of getting the infection from that online food delivery. 2. If you have not ordered food during this nation lockdown time yet but are thinking of placing the order then (if lockdown extended). Individual participants were asked to mention the percentage of chance that individual might get infected through the online food delivery based on any one scenario.

The respondents’ demographical distribution patterns are shown in Table  1 . The respondents’ age ranged from 18 to 56 years, with a mean of 27.81 years and standard deviation of 8.7 years. Similar findings were recorded by several researchers, particularly in e‐commerce‐based research (Ha,  2012 ; Ladhari et al.,  2019 ; Lissitsa & Kol,  2019 ). In India, online food ordering and delivery service was introduced in 2014. Several OFD start‐ups rose in 2015 with a focus on mobile apps. Over the last decade, the rate of internet access and online shopping increased continuously across all generations. Most of the customers of e‐commerce belonged to the age group of Gen Y and Gen Z. The market for Gen X is not too big and along with Baby Boomers, they are considered secondary targets. These age groups consist either of customers who are too old to recognize the new technology and e‐commerce, making them a low purchasing power customer group (Bresman & Rao,  2017 ).

Characteristics of the respondents

Source: The authors.

This age‐wise classification clears that mostly young generations prefer to buy food through OFDs. About 44.2% of the total respondents were female, whereas the remaining 55.8% were male. The frequency of ordering food through OFDs before nationwide lockdown (last month before the lockdown) ranged from 0 to 18 times with a mean of 4.49 and a standard deviation of 3.75. The perceived threat of the respondents ranged from 0% to 100% with a mean of 45.5% and a standard deviation of 28.95%. Most of the respondents (64.5%) had master's degree and 31.4% of the respondents had bachelor's degree, only 4.1% respondents had basic school level educational qualification and 56.9% of the respondent's monthly income was less than Rs.20000.

Exploratory factor analysis was used to check the factor structure of the research items. The sample adequacy was tested using Kaiser–Meyer–Olkin (KMO) and Bartlett‘s test of sphericity. The KMO (0.948) value was large and Bartlett's test of sphericity (χ2 = 7,307.56; df  = 190; p  < .001) was significant, implying that the present research has an adequate sample size and correlations among at least some of the items. The rotated component matrix was used from these 20 items; three components were extracted and they were able to capture 70.9% of the variability in the data. The first component, perceived benefits of OFDs consisted of seven items and explained 27.75% of variance and the second component, affective and instrumental beliefs towards OFDs, consisted of four items and explained 26.23% of the variance. The last component, named as OFDs product involvement, consisted of nine items and accounted for 16.96% of the variance. In the final analysis, only items with a factor load above 0.6 were retained.

The confirmatory factor analysis was used to test the reliability and validity of the constructs by developing a measurement model. The construct validity of the instrument was explained by convergent validity and discriminant validity. The convergent validity was assessed using Cronbach's alpha (α), Composite reliability (CR), Average Variance Extracted (AVE) and statistical significance of the item factor loadings (β; Hair et al.,  2010 ). Results provided in Table  2 show that item factor loadings (β) were higher than 0.5 and that no items were deleted in this study. Cronbach alpha coefficients obtained from all the dimensions range from 0.883 to 0.939. The Average Variance Extracted for all dimensions varied from 0.567 to 0.693. The composite reliability ranged from 0.883 to 0.940. All these measures were above the recommended levels (i.e., 0.7 for Cronbach's alpha, 0.7 for composite reliability and 0.5 for Average Variance Extracted), indicating acceptable levels for the reliability of constructs (Hair et al.,  2014 ; Kahle & Malhotra,  1994 ; Nunnally,  1975 ) and supporting the convergent validity. Discriminant validity is inferred when measures of each construct converge on their respective true scores, which are unique from the scores of other constructs (Churchill,  1979 ). AVE and the square root of AVE were higher than inter‐construct correlations and AVE values were larger than Maximum Shared Variance (MSV), which support the discriminant validity of the constructs and show that each construct in this research is unique (Fornell & Larcker,  1981 ; Hair et al.,  2014 ). Based on results in Tables  2 and ​ and3, 3 , we can conclude that the constructs are free from construct validity issues.

Result of the exploratory and confirmatory factor analysis

Reliability and validity results

The measurement models show an adequate fit because χ2/ df  = 3.193 [χ2 = 482.11; df  = 151] is between the cut of range 1–5. Also, studies by Hair et al. ( 2014 ) and Hu and Bentler ( 1999 ) conclude that for the model fit, the Comparative Fit Index (CFI), Goodness Fit Index (GFI) and Adjusted Good Fit Index (AGFI) should be closer to one; and RMSEA and Root Mean Square Residual (RMR) values should be near to zero; GFI = 0.903; AGFI = 0.865; CFI = 0.954. In this study, SRMR = 0.059 and RMSEA = 0.069 and all these values show a reasonable model fit.

To test the research objective, the binary logistic regression was done. Table  4 summarizes the binary logistic regression results. In the present study, whether or not the respondents ordered food through online food delivery services (OFDs) during the COVID‐19 outbreak was taken as the dependent variable (0‐ do not order; 1‐ordered); Age of the respondents, frequency of purchase of OFDs, before the nationwide lockdown (last month), respondents affective and instrumental beliefs to buy food from OFDs, respondents’ perceived threat about COVID‐19 through OFDs, perceived benefits of OFDs and respondents’ level of product involvement about OFDs were taken to be the predictor variables.

Results of logistic regression analysis

The values of the regression coefficients and their statistical significance obtained by ‘Enter logistical regression method’ were included in Table  4 . Likelihood ratio (LR) chi‐square test is one way of evaluating the overall model fit. Significant likelihood ratio chi‐square test indicates the model containing the predictors is a significant improvement in the fit over the intercept‐only model (De La Viña & Ford,  2001 ; Galbraith et al.,  2007 ; Pituch,  2015 ; Zewude & Ashine,  2016 ). Based on the LR chi‐square test, we infer that the full model represents a significant improvement in fit relative to the null model, LR χ 2 (6) = 248.855, p =.001.

The logistic regression could use two indicators, such as Cox and Snell R 2 ( R 2  = 0.416) and Nagelkerke R 2 ( R 2  = 0.585), the same as for coefficient R 2 from linear regression that estimates the contribution of predictor variable to the variability of the dependent variable. We used the Nagelkerke R 2 indicator to analyse the contribution of all the six predictor variables to the variability of the dependent variable. It has been unanimously recognized that Cox and Snell R 2 indicator underestimates the real value (De La Viña & Ford,  2001 ; Galbraith et al.,  2007 ; Pituch,  2015 ; Zewude & Ashine,  2016 ). The test results based on the six predictor variables (age, purchase frequency, affective and instrumental beliefs, perceived benefits, perceived threat and product involvement) could explain 58.5% of the variance in respondents purchase decision on OFDs selection. The Hosmer and Lemeshow test is another way of testing for the overall model fit (Tab achnick & Fidell,  2013 ). A nonsignificant test result indicates a good fitting model. Here, we see that the test is nonsignificant, χ 2 (8) = 13.513, p  = .095––suggesting a good fitting model.

Table  4 also provides information on the impact of the independent variables considered in determining the purchasing decision through OFDs (see odds ratio [OR]). The regression slope for purchase frequency (b = 0.477, p  < .01), perceived benefits (b = 0.275, p  < .05) and product involvement (b = 0.297, p  < .05) are positive and statistically significant indicating that the probability of a respondent who likes to order food through online food delivery services was higher for those who have higher purchase frequency, perceived benefits and product involvement. The odds ratio for the predictor indicates that the odds of a respondent who likes to order food through OFDs change by a factor of 1.564 with each raw score increment on purchase frequency, 1.317 with raw score increment on perceived benefit and 1.345 on product involvement.

The regression slope for the perceived threat was negative (b = −0.03, p  < .01) and statistically significant indicating that a respondent with a high perceived threat on OFDs was less likely to order food from OFDs. The odds ratio for the predictor indicates that the odds of a respondent who likes to order food through OFDs change by a factor of 0.97 with each raw score decrease on the perceived threat of OFDs.

Increasing purchase frequency (56%), perceived benefits (32%) and product involvement (35%) were associated with an increased likelihood of respondents who purchase food through online food delivery services, but increasing perceived threat (−3%) was associated with a reduction in the likelihood of respondents who purchase food through online food delivery services. However, age, affective and instrumental beliefs did not significantly influence the respondents’ purchase decision. Thus, H 1 , H 2 , H 4 and H 6 are supported. Respondents’ age (H 5 ) and perceived benefit (H 2 ) were not significant predictors of respondents’ decision towards ordering food through OFDs during the pandemic and national‐wide lockdown; thus, H 3 and H 5 are not supported.

The classification table summarizes that 100 cases were correctly predicted to be in the group where respondents ordered food on OFDs and 45 were wrongly predicted. Out of the 317 respondents who did not order food through OFDs during the pandemic, 299 cases were correctly predicted and 18 cases were incorrectly predicted. From these values, it can be observed that 86.4% (Hit ratio = (299 + 100)/462 = 86.36%) of data were correctly classified and this hit ratio indicates a good predictive capacity, as is shown in Table  5 .

Classification results

5. DISCUSSION AND IMPLICATIONS

In this study, we developed a successful regression function to differentiate the personal characteristics of OFDs customers who did and did not order food through OFDs during the COVID‐19 outbreak period in India. This study concludes that among the five personal characteristics, frequency of purchase, perceived threat, perceived benefit and product involvement were the contributing factors of the inter‐group differences. In other words, the customers who purchased food online through OFDs during the COVID‐19 outbreak were linked with less perceived threat and customers who purchased food online through OFDs during the COVID‐19 outbreak were associated with a high level of purchase pattern, high perceived benefits and high product involvement. Since the above binary logistic regression has around 58.5% of the variance in the dependent variable, we can explore some substantial marketing implications from the results.

Studies conducted by Aucote et al. ( 2010 ), Seabra et al. ( 2014 ) and Jeong and Ham ( 2018 ) show that perceived threat positively influences the buying decision. However, the present study is negatively consistent with the study in OFDs, where high product involvement leads to positive purchase intentions and high‐perceived threat on COVI‐19 leads to negative purchase intentions towards OFDs. In disease‐based outbreak, perception of threat is very high in OFDs, since the chances of disease spreading are higher through delivery partners, which suggests that respondents think about the uncertainty involved in their purchase (Addo et al.,  2020 ; Chuo,  2007 , 2014 ; Guan et al.,  2020 ). Even though the possibility of COVID‐19 spread was very less through OFDs, but lack of awareness resulted in high‐perceived threat, creating uncertainty around the purchase, thus, affecting the purchase decision. Mäser and Weiermair ( 1998 ) conclude that higher the perceived risk felt by the customers, the less they buy and become more irrational in their decision‐making process. Also, current results are consistent with Forsythe et al. ( 2006 ), who show that more frequent purchasers are highly motivated towards particular products than the less frequent purchasers. Frequency of purchases will determine customer decision making. Perceived benefit is the sum of benefits an individual expects to attain on following a behaviour (Gabriel et al.,  2019 ; Tweneboah‐Koduah,  2018 ). The present study result is consistent with previous studies (Carico et al.,  2020 ; Gabriel et al.,  2019 ; Janz & Becker,  1984 ). For example, a person who stays at home during COVID‐19 pandemic and orders food through OFDs, not only safeguard themselves from the disease, but also save in terms of expenditure on travelling. The level of product involvement and the risk perceived by the customer throughout the purchasing process is demonstrated to assess the depth, complexity and degree of cognitive and behavioural processes during the customer decision process and our analysis also concludes the same.

From these findings, we can propose managerial implications to OFDs. Many OFDs are using their mobile apps to create COVID‐19 awareness; however, this is not enough. The customers are curious and give attention to news and reports related to COVID‐19. OFDs can, therefore, use mass media advertisements to create more reliable communication channels. Coca‐Cola (Erdman et al.,  2017 ) and Nestle (Dhanesh & Sriramesh,  2018 ) followed a similar strategy of mass communications to maintain their brand image during the allegation crisis. This approach would advise customers to reduce any spill‐over effects and correct any perceptions that may be misleading about perceived disease threats, which would again positively influence the external cues (product involvement). This would further increase the perceived benefits in terms of convenience, enjoyment and also increase the value associated with the services. Online retailing is emerging in India and the prevalence of OFD services is proliferating. To face potential uncertainty in the future, this problem needs to be expertly examined and effective crisis management tools based on collaborative frameworks by industry respondents and government bodies have to be developed. OFDs companies are taking all reasonable efforts and best practice measures to comply with the safety & health standards/guidelines issued by the Government of India amid COVID‐19 to eliminate all risks in their services.

Restaurants and hotels can include hygiene ratings on their OFD apps. The OFD service provider can make such ratings mandatory for all restaurants along with the presence of a food supervisor to monitor compliance of food regulation and ensure the safety of food served. This practice will reduce the level of a perceived threat of OFDs and influence more respondents to opt for OFDs. Many OFD service agents are following contact‐free delivery options. In some developing countries, OFDs have implemented the contactless grab transaction for which delivery workers leave the meals at the designated position, standing 2 meters away to await customers (Nguyen & Vu,  2020 ). Indian OFD service agents can follow a similar delivery model, instead of ‘leave at my door delivery’, which will increase trust among the customers and increase product involvement. Delivery agents should wear new face masks and gloves and frequently apply hand sanitizers to minimize contamination with diseases (Nguyen & Vu,  2020 ). OFDs should encourage their customers not to take the delivery if the delivery agent is not using self‐protective measures.

The use of e‐Wallet and digital payments saw an increase during the pandemic. In developing countries, digital payment or credit card payment is encouraged to limit contact with delivery partners (Nguyen & Vu,  2020 ). OFDs can provide attractive cashback offers or reward points, for digital payments, which motivates customers to use e‐Wallet and digital payments and increase the perceived benefits of OFDs usage. There is currently no evidence of COVID‐19 transmission from food. COVID‐19 is particularly troubling because it can live on surfaces for extended periods of time, including the two most commonly used in food delivery: paper bags and cardboard boxes. The risk of transmission from food packaging is extremely low (Food & Drug Administration,  2020 ). The best practice is to transfer the food out of the packaging, dispose of the packaging and thoroughly wash hands. Finally, clean the area where the bag or packaging was resting and this awareness needs to be created by ODFs (Nguyen & Vu,  2020 ). The most competent practices followed by the restaurant staff and delivery agents should be monitored regularly and proper training should also be given to them on how to maintain hygiene standards at restaurants and during the delivery process.

Moreover, governments should encourage citizens to follow social distancing and not go out for unnecessary activities. OFDs can use this advice to promote their services by delivering essential products along with their food items. This activity can encourage individuals to follow social distancing. More customers are likely to opt for OFDs shortly, so to gain repeat customers, good value‐for‐money offers should be used by the OFDs to expand their reach. The OFDs can invest a significant amount of their profit to improve their safety and hygiene standards and the government should insist that OFDs do not trade‐off safety with low‐cost services (Chuo,  2014 ). These practical implications can help build customer confidence.

From an academic perspective, no research has been done previously to study differentiating characteristics between OFDs customers who did and did order food through OFDs during the COVID‐19 outbreak period in India. This study is intended to bridge the gap by developing a significant binary logistic regression function to predict customer decisions towards purchasing OFDs. The measurement used in the study was adopted, modified and validated to the OFDs context. Subsequent researchers can adopt these scales to measure the product involvement, perceived benefits and perceived threats in the OFD context. The outcome variables (self‐protective behaviour) were adopted from HBM. The results are consistent with HBM, which provides better insight into theory. The research can assist academicians to look further into the other constructs that could influence customers' purchase decisions during the pandemic.

6. LIMITATIONS AND FUTURE SCOPE OF THE STUDY

This study has a few limitations that can be addressed by future researchers. Here, we have used OFDs customers as a target population, but by including other online retailers, we can better understand customer decision towards online retailers. We have used two scenarios to measure customers’ perceived threat, as recommended by Chuo ( 2007 ); however, future studies should use a specific scale to measure the perceived threat towards this disease and other biological crisis. This model predicts the customers’ decision towards OFDs and only 22% is explained by personal characteristics. It is recommended to use other personal characteristics like customer risk attitude, gender, educational qualification and monthly income to develop a more significant function.

Mehrolia S, Alagarsamy S, Solaikutty VM. Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression . Int J Consum Stud .2021; 45 :396–408. 10.1111/ijcs.12630 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

  • Abraham, C. , & Sheeran, P. (2014). The health belief model . Cambridge Handbook of Psychology, Health and Medicine, 2 nd ed., 4, 97–102.
  • Addo, P. C. , Jiaming, F. , Kulbo, N. B. , & Liangqiang, L. (2020). COVID‐19: Fear appeal favoring purchase behavior towards personal protective equipment . Service Industries Journal , 40 ( 7–8 ), 471–490. 10.1080/02642069.2020.1751823 [ CrossRef ] [ Google Scholar ]
  • Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior . In Action Control (pp. 11–39). Springer. 10.1007/978-3-642-69746-3_2 [ CrossRef ] [ Google Scholar ]
  • Ajzen, I. (2012). The theory of planned behavior . Handbook of Theories of Social Psychology: Volume 1, 50(2), 438–459. 10.4135/9781446249215.n22 [ CrossRef ]
  • Aldaco, R. , Hoehn, D. , Laso, J. , Margallo, M. , Ruiz‐Salmón, J. , Cristobal, J. , Kahhat, R. , Villanueva‐Rey, P. , Bala, A. , Batlle‐Bayer, L. , Fullana‐i‐Palmer, P. , Irabien, A. , & Vazquez‐Rowe, I. (2020). Food waste management during the COVID‐19 outbreak: A holistic climate, economic and nutritional approach . Science of the Total Environment , 742 , 140524. 10.1016/j.scitotenv.2020.140524 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Aucote, H. M. , Miner, A. , & Dahlhaus, P. (2010). Rockfalls: Predicting high‐risk behaviour from beliefs . Disaster Prevention and Management: An International Journal , 19 ( 1 ), 20–31. 10.1108/09653561011022117 [ CrossRef ] [ Google Scholar ]
  • Becker, M. H. , Maiman, L. A. , Kirscht, J. P. , Haefner, D. P. , & Drachman, R. H. (1977). The health belief model and prediction of dietary compliance: A field experiment . Journal of Health and Social Behavior , 18 ( 4 ), 348–366. 10.2307/2955344 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Belanche, D. , Flavián, C. , & Pérez‐Rueda, A. (2017). Understanding interactive online advertising: congruence and product involvement in highly and lowly arousing, skippable video ads . Journal of Interactive Marketing , 37 , 75–88. 10.1016/j.intmar.2016.06.004 [ CrossRef ] [ Google Scholar ]
  • Berg, M. B. , & Lin, L. (2020). Prevalence and predictors of early COVID‐19 behavioral intentions in the United States . Translational Behavioral Medicine , 10 ( 4 ), 843–849. 10.1093/tbm/ibaa085 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bialek, S. , Boundy, E. , Bowen, V. , Chow, N. , Cohn, A. , Dowling, N. , Ellington, S. , Gierke, R. , Hall, A. , MacNeil, J. , Patel, P. , Peacock, G. , Pilishvili, T. , Razzaghi, H. , Reed, N. , Ritchey, M. , & Sauber‐Schatz, E. (2020). Severe outcomes among patients with coronavirus disease 2019 (COVID‐19) — United States, February 12–March 16, 2020 . MMWR. Morbidity and Mortality Weekly Report , 69 ( 12 ), 343–346. 10.15585/mmwr.mm6912e2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bish, A. , & Michie, S. (2010). Demographic and attitudinal determinants of protective behaviours during a pandemic: A review . British Journal of Health Psychology , 15 ( 4 ), 797–824. 10.1348/135910710X485826 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bresman, H. , & Rao, V. D. (2017). A survey of 19 countries shows how generations X, Y, and Z Are — and aren’t — different . Harvard Business Review , 25 , 1–8. [ Google Scholar ]
  • Brug, J. , Aro, A. R. , & Richardus, J. H. (2009). Risk perceptions and behaviour: Towards pandemic control of emerging infectious diseases: Iional research on risk perception in the control of emerging infectious diseases . International Journal of Behavioral Medicin , 16 ( 1 ), 3–6. 10.1007/s12529-008-9000-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cao, Z. J. , Chen, Y. , & Wang, S. M. (2014). Health belief model based evaluation of school health education programme for injury prevention among high school students in the community context . BMC Public Health , 14 ( 1 ), 26. 10.1186/1471-2458-14-26 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Carico, R. , Sheppard, J. , & Thomas, C. B. (2020). Community pharmacists and communication in the time of COVID‐19: Applying the health belief model . Research in Social and Administrative Pharmacy . 10.1016/j.sapharm.2020.03.017 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Carpenter, C. J. (2010). A meta‐analysis of the effectiveness of health belief model variables in predicting behavior . Health Communication , 25 ( 8 ), 661–669. 10.1080/10410236.2010.521906 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • CDC (2020). Running Essential Errands . US Department of Health and Human Services, CDC. https://www.cdc.gov/coronavirus/2019‐ncov/daily‐life‐coping/essential‐goods‐services.html [ Google Scholar ]
  • Champion, V. L. , & Skinner, C. S. (2008). The health belief model . Health Behavior and Health Education: Theory, Research, and Practice , 4 , 45–65. [ Google Scholar ]
  • Chang, H.‐H. , & Meyerhoefer, C. (2020). COVID‐19 and the Demand for Online Food Shopping Services: Empirical Evidence from Taiwan . In NBER Working Paper No. 27427. National Bureau of Economic Research. 10.3386/w27427 [ CrossRef ]
  • Cho, M. , Bonn, M. A. , & Li, J. (2020). Examining risk‐reduction behavior toward water quality among restaurant guests . Cornell Hospitality Quarterly , 61 ( 3 ), 255–270. 10.1177/1938965520919106 [ CrossRef ] [ Google Scholar ]
  • Chuo, H. Y. (2007). Theme park visitors’ responses to the SARS outbreak in Taiwan . Advances in Hospitality and Leisure , 3 , 87–104. 10.1016/S1745-3542(06)03006-2 [ CrossRef ] [ Google Scholar ]
  • Chuo, H. Y. (2014). Restaurant diners’ self‐protective behavior in response to an epidemic crisis . International Journal of Hospitality Management , 38 , 74–83. 10.1016/j.ijhm.2014.01.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs . Journal of Marketing Research , 16 ( 1 ), 64. 10.2307/3150876 [ CrossRef ] [ Google Scholar ]
  • Clark, G. (2012). Understanding and reducing the risk of supply chain disruptions . Journal of Business Continuity & Emergency Planning , 6 ( 1 ), 6–12. [ PubMed ] [ Google Scholar ]
  • Conner, M. , Rhodes, R. E. , Morris, B. , McEachan, R. , & Lawton, R. (2011). Changing exercise through targeting affective or cognitive attitudes . Psychology and Health , 26 ( 2 ), 133–149. 10.1080/08870446.2011.531570 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cooper, M. (2013). Japanese tourism and the SARS epidemic of 2003 . Tourism Crises: Management Responses and Theoretical Insight , 19 ( 2–3 ), 117–132. 10.1300/J073vl9n02_10 [ CrossRef ] [ Google Scholar ]
  • De La Viña, L. , & Ford, J. (2001). Logistic regression analysis of cruise vacation market potential: Demographic and trip attribute perception factors . Journal of Travel Research , 39 ( 4 ), 406–410. 10.1177/004728750103900407 [ CrossRef ] [ Google Scholar ]
  • DeLisle, J. (2004). Atypical pneumonia and ambivalent law and politics: SARS and the response to SARS in China . Temple Law Review , 77 ( 2 ), 193–245. [ Google Scholar ]
  • Dhanesh, G. S. , & Sriramesh, K. (2018). Culture and crisis communication: Nestle India’s Maggi noodles case . Journal of International Management , 24 ( 3 ), 204–214. 10.1016/j.intman.2017.12.004 [ CrossRef ] [ Google Scholar ]
  • Ehrlich, I. , & Becker, G. S. (1972). Market insurance, self‐insurance, and self‐protection . Journal of Political Economy , 80 ( 4 ), 623–648. 10.1086/259916 [ CrossRef ] [ Google Scholar ]
  • Erdman, M. , Kelly, S. , Lerum, E. , & O’Rourke, J. S. (2017). The Coca‐Cola Company: Allegations of Pesticides in Soft Drinks in India . In The Coca‐Cola Company: Allegations of Pesticides in Soft Drinks in India. The Eugene D. Fanning Center for Business Communication, Mendoza College of Business, University of Notre Dame. 10.4135/9781526405951. [ CrossRef ]
  • Food and Drug Administration . (2020). COVID‐19 Frequently Asked Questions . COVID‐19 Frequently Asked Questions.
  • Fornell, C. , & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error . Journal of Marketing Research , 18 ( 1 ), 39. 10.2307/3151312 [ CrossRef ] [ Google Scholar ]
  • Forsythe, S. , Liu, C. , Shannon, D. , & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping . Journal of Interactive Marketing , 20 ( 2 ), 55–75. 10.1002/dir.20061 [ CrossRef ] [ Google Scholar ]
  • French, D. P. , Sutton, S. , Hennings, S. J. , Mitchell, J. , Wareham, N. J. , Griffin, S. , Hardeman, W. , & Kinmonth, A. L. (2005). The importance of affective beliefs and attitudes in the theory of planned behavior: Predicting intention to increase physical activity . Journal of Applied Social Psychology , 35 ( 9 ), 1824–1848. 10.1111/j.1559-1816.2005.tb02197.x [ CrossRef ] [ Google Scholar ]
  • Gabriel, E. H. , Hoch, M. C. , & Cramer, R. J. (2019). Health Belief Model Scale and Theory of Planned Behavior Scale to assess attitudes and perceptions of injury prevention program participation: An exploratory factor analysis . Journal of Science and Medicine in Sport , 22 ( 5 ), 544–549. 10.1016/j.jsams.2018.11.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Galbraith, C. S. , De Noble, A. , Singh, G. , & Stiles, C. H. (2007). Market justice, religious orientation, and entrepreneurial attitudes . Journal of Enterprising Communities: People and Places in the Global Economy , 1 ( 2 ), 121–134. 10.1108/17506200710752548 [ CrossRef ] [ Google Scholar ]
  • Glanz, K. , Rimer, B. K. , & Viswanath, K. (1992). Health Behavior and Health Education: Theory, Research, and Practice . San Francisco, CA: Annals of Internal Medicine, Vol. 116, Issue 4. John Wiley & Sons. 10.7326/0003-4819-116-4-350_1 [ CrossRef ] [ Google Scholar ]
  • Grobe, D. , & Douthitt, R. (1995). Consumer acceptance of recombinant bovine growth hormone: interplay between beliefs and perceived risks . Journal of Consumer Affairs , 29 ( 1 ), 128–143. 10.1111/j.1745-6606.1995.tb00042.x [ CrossRef ] [ Google Scholar ]
  • Grobe, D. , Douthitt, R. , & Zepeda, L. (1999). Consumer risk perception profiles regarding recombinant bovine growth hormone (rbGH) . Journal of Consumer Affairs , 33 ( 2 ), 254–275. 10.1111/j.1745-6606.1999.tb00070.x [ CrossRef ] [ Google Scholar ]
  • Guan, W.‐J. , Ni, Z.‐Y. , Hu, Y. U. , Liang, W.‐H. , Ou, C.‐Q. , He, J.‐X. , Liu, L. , Shan, H. , Lei, C.‐L. , Hui, D. S. C. , Du, B. , Li, L.‐J. , Zeng, G. , Yuen, K.‐Y. , Chen, R.‐C. , Tang, C.‐L. , Wang, T. , Chen, P.‐Y. , Xiang, J. , … Zhong, N.‐S. (2020). Clinical characteristics of coronavirus disease 2019 in China . New England Journal of Medicine , 382 ( 18 ), 1708–1720. 10.1056/nejmoa2002032 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ha, H. Y. (2012). The effects of online shopping attributes on satisfaction‐purchase intention link: A longitudinal study . International Journal of Consumer Studies , 36 ( 3 ), 327–334. 10.1111/j.1470-6431.2011.01035.x [ CrossRef ] [ Google Scholar ]
  • Hair, J. , Black, W. , Babin, B. , & Anderson, R. (2010). Multivariate data analysis: a global perspective . In Multivariate Data Analysis: A Global Perspective (Vol. 7th). Upper Saddle River, NJ: Pearson.
  • Hair, J. , Black, W. , Babin, B. , & Anderson, R. (2014). Multivariate data analysis: pearson new international edition , 7th ed. Pearson Education Limited. [ Google Scholar ]
  • Handriana, T. , & Wisandiko, W. R. (2017). Consumer attitudes toward advertisement and brand, based on the number of endorsers and product involvement: An experimental study . Gadjah Mada International Journal of Business , 19 ( 3 ), 289–307. 10.22146/gamaijb.18338 [ CrossRef ] [ Google Scholar ]
  • Hardeman, W. , Johnston, M. , Johnston, D. , Bonetti, D. , Wareham, N. , & Kinmonth, A. L. (2002). Application of the theory of planned behaviour in behaviour change interventions: A systematic review . Psychology and Health , 17 ( 2 ), 123–158. 10.1080/08870440290013644a [ CrossRef ] [ Google Scholar ]
  • Hervé, C. , & Mullet, E. (2009). Age and factors influencing consumer behaviour . International Journal of Consumer Studies , 33 ( 3 ), 302–308. 10.1111/j.1470-6431.2009.00743.x [ CrossRef ] [ Google Scholar ]
  • Hobbs, J. E. (2020). Food supply chains during the COVID‐19 pandemic . Canadian Journal of Agricultural Economics , 68 ( 2 ), 171–176. 10.1111/cjag.12237 [ CrossRef ] [ Google Scholar ]
  • Hollebeek, L. D. , Jaeger, S. R. , Brodie, R. J. , & Balemi, A. (2007). The influence of involvement on purchase intention for new world wine . Food Quality and Preference , 18 ( 8 ), 1033–1049. 10.1016/j.foodqual.2007.04.007 [ CrossRef ] [ Google Scholar ]
  • Hu, L. T. , & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives . Structural Equation Modeling , 6 ( 1 ), 1–55. 10.1080/10705519909540118 [ CrossRef ] [ Google Scholar ]
  • Ishida, T. , Ishikawa, N. , & Fukushige, M. (2010). Impact of BSE and bird flu on consumers’ meat demand in Japan . Applied Economics , 42 ( 1 ), 49–56. 10.1080/00036840701564392 [ CrossRef ] [ Google Scholar ]
  • Jacoby, J. , & Kaplan, L. B. (1972). The Components of Perceived Risk . 382–393.
  • Janz, N. K. , & Becker, M. H. (1984). The health belief model: a decade later . Health Education & Behavior , 11 ( 1 ), 1–47. 10.1177/109019818401100101 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jeong, J.‐Y. , & Ham, S. (2018). Application of the Health Belief Model to customers’ use of menu labels in restaurants . Appetite , 123 , 208–215. 10.1016/j.appet.2017.12.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kahle, L. R. , & Malhotra, N. K. (1994). Marketing research: An applied orientation . Journal of Marketing Research , 31 ( 1 ), 137– 10.2307/3151953 [ CrossRef ] [ Google Scholar ]
  • Keelery, S. (2020). COVID‐19 impact on use of food ordering apps India 2020 . https://www.statista.com/topics/6304/covid‐19‐economic‐impact‐on‐india/
  • Keer, M. , Van Den Putte, B. , De Wit, J. , & Neijens, P. (2013). The effects of integrating instrumental and affective arguments in rhetorical and testimonial health messages . Journal of Health Communication , 18 ( 9 ), 1148–1161. 10.1080/10810730.2013.768730 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ketel, E. C. , Aguayo‐Mendoza, M. G. , de Wijk, R. A. , de Graaf, C. , Piqueras‐Fiszman, B. , & Stieger, M. (2019). Age, gender, ethnicity and eating capability influence oral processing behaviour of liquid, semi‐solid and solid foods differently . Food Research International , 119 , 143–151. 10.1016/j.foodres.2019.01.048 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Klein, F. , Emberger‐Klein, A. , Menrad, K. , Möhring, W. , & Blesin, J. M. (2019). Influencing factors for the purchase intention of consumers choosing bioplastic products in Germany . Sustainable Production and Consumption , 19 , 33–43. 10.1016/j.spc.2019.01.004 [ CrossRef ] [ Google Scholar ]
  • Kumar, S. (2012). Planning for avian flu disruptions on global operations: A DMAIC case study . International Journal of Health Care Quality Assurance , 25 ( 3 ), 197–215. 10.1108/09526861211210420 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kumar, S. , & Chandra, C. (2010). Supply chain disruption by avian flu pandemic for U.S. Companies: A case study . Transportation Journal , 49 ( 4 ), 61–73. 10.1109/emr.2016.7448786 [ CrossRef ] [ Google Scholar ]
  • Kuo, P. C. , Huang, J. H. , & Liu, M. D. (2011). Avian influenza risk perception and preventive behavior among traditional market workers and shoppers in Taiwan: Practical implications for prevention . PLoS One , 6 ( 9 ), e2415710. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Ladhari, R. , Gonthier, J. , & Lajante, M. (2019). Generation Y and online fashion shopping: Orientations and profiles . Journal of Retailing and Consumer Services , 48 , 113–121. 10.1016/j.jretconser.2019.02.003 [ CrossRef ] [ Google Scholar ]
  • Lau, J. T. F. , Yang, X. , Tsui, H. Y. , & Pang, E. (2004). SARS related preventive and risk behaviours practised by Hong Kong‐mainland China cross border travellers during the outbreak of the SARS epidemic in Hong Kong . Journal of Epidemiology and Community Health , 58 ( 12 ), 988–996. 10.1136/jech.2003.017483 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lawton, R. , Conner, M. , & McEachan, R. (2009). Desire or reason: Predicting health behaviors from affective and cognitive attitudes . Health Psychology , 28 ( 1 ), 56–65. 10.1037/a0013424 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lawton, R. , Conner, M. , & Parker, D. (2007). Beyond cognition: Predicting health risk behaviors from instrumental and affective beliefs . Health Psychology , 26 ( 3 ), 259–267. 10.1037/0278-6133.26.3.259 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Li, A. S. W. , Figg, G. , & Schüz, B. (2019). Socioeconomic status and the prediction of health promoting dietary behaviours: A systematic review and meta‐analysis based on the theory of planned behaviour . Applied Psychology: Health and Well‐Being , 11 ( 3 ), 382–406. 10.1111/aphw.12154 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lissitsa, S. , & Kol, O. (2019). Four generational cohorts and hedonic m‐shopping: Association between personality traits and purchase intention . Electronic Commerce Research , 1–26 , 10.1007/s10660-019-09381-4 [ CrossRef ] [ Google Scholar ]
  • Lobb, A. , & Mazzocchi, M. (2006). Risk perception and chicken consumption in the avian flu age: A consumer behaviour study on food safety information. Annual Meeting of. http://ageconsearch.umn.edu/bitstream/21464/1/sp06lo05.pdf
  • Loewenstein, G. , & Lerner, J. S. (2003). The role of affect in decision making . Handbook of Affective Science , 619 ( 642 ), 619–642. [ Google Scholar ]
  • Lowe, R. , Eves, F. , & Carroll, D. (2002). The influence of affective and instrumental beliefs on exercise intentions and behavior: A longitudinal analysis . Journal of Applied Social Psychology , 32 ( 6 ), 1241–1252. 10.1111/j.1559-1816.2002.tb01434.x [ CrossRef ] [ Google Scholar ]
  • Manika, D. , & Golden, L. L. (2011). Self‐efficacy, threat, knowledge, and information receptivity: Exploring pandemic prevention behaviors to enhance societal welfare . Academy of Health Care Management Journal , 7 ( 1 ), 31–45. [ Google Scholar ]
  • Mäser, B. , & Weiermair, K. (1998). Travel decision‐making: From the vantage point of perceived risk and information preferences . Journal of Travel and Tourism Marketing , 7 ( 4 ), 107–121. 10.1300/J073v07n04_06 [ CrossRef ] [ Google Scholar ]
  • McKercher, B. , & Chon, K. (2004). The over‐reaction to SARS and the collapse of Asian tourism . Annals of Tourism Research , 31 ( 3 ), 716–719. 10.1016/j.annals.2003.11.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • McQuarrie, E. F. , & Munson, J. M. (1992). A revised product involvement inventory . Advances in Consumer Research , 19 ( 1 ), 108–115. [ Google Scholar ]
  • Meshe, O. F. , Bungay, H. , & Claydon, L. S. (2020). Participants’ experiences of the benefits, barriers and facilitators of attending a community‐based exercise programme for people with chronic obstructive pulmonary disease . Health and Social Care in the Community , 28 ( 3 ), 969–978. 10.1111/hsc.12929 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Myers, L. C. , Parodi, S. M. , Escobar, G. J. , & Liu, V. X. (2020). Characteristics of hospitalized adults with COVID‐19 in an integrated health care system in California . JAMA , 323 ( 21 ), 2195–2198. 10.1001/jama.2020.7202 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nam, N. K. , Hang Nga, N. T. , & Huan, N. Q. (2019). The consumers’ intention to purchase food: The role of perceived risk . Academy of Strategic Management Journal , 18 ( 1 ), 1–12. [ Google Scholar ]
  • Nguyen, N. M. , & Nguyen, H. T. (2019). How do product involvement and prestige sensitivity affect price acceptance on the mobile phone market in Vietnam? Journal of Asia Business Studies , 14 ( 3 ), 379–398. 10.1108/JABS-07-2017-0096 [ CrossRef ] [ Google Scholar ]
  • Nguyen, T. H. D. , & Vu, D. C. (2020). Food delivery service during social distancing: Proactively preventing or potentially spreading COVID‐19? Disaster Medicine and Public Health Preparedness , 1–2 , 10.1017/dmp.2020.135 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nunnally, J. C. (1975). Psychometric theory—25 years ago and now . Educational Researcher , 4 ( 10 ), 7–21. [ Google Scholar ]
  • O’Cass, A. (2000). An assessment of consumers product, purchase decision, advertising and consumption involvement in fashion clothing . Journal of Economic Psychology , 21 ( 5 ), 545–576. 10.1016/S0167-4870(00)00018-0 [ CrossRef ] [ Google Scholar ]
  • Peng, L. , Zhang, W. , Wang, X. , & Liang, S. (2019). Moderating effects of time pressure on the relationship between perceived value and purchase intention in social E‐commerce sales promotion: Considering the impact of product involvement . Information and Management , 56 ( 2 ), 317–328. 10.1016/j.im.2018.11.007 [ CrossRef ] [ Google Scholar ]
  • Pine, R. , & McKercher, B. (2004). The impact of SARS on Hong Kong’s tourism industry . International Journal of Contemporary Hospitality Management , 16 ( 2 ), 139–143. 10.1108/09596110410520034 [ CrossRef ] [ Google Scholar ]
  • Pituch, K. A. (2015). Applied Multivariate Statistics for the Social Sciences . In Applied Multivariate Statistics for the Social Sciences. Routledge. 10.4324/9781315814919 [ CrossRef ]
  • Povey, R. , Conner, M. , Sparks, P. , James, R. , & Shepherd, R. (2000). The theory of planned behaviour and healthy eating: Examining additive and moderating effects of social influence variables . Psychology and Health , 14 ( 6 ), 991–1006. 10.1080/08870440008407363 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Prendergast, G. P. , Tsang, A. S. L. , & Chan, C. N. W. (2010). The interactive influence of country of origin of brand and product involvement on purchase intention . Journal of Consumer Marketing , 27 ( 2 ), 180–188. 10.1108/07363761011027277 [ CrossRef ] [ Google Scholar ]
  • Rabbi, M. , Aung, M. H. , Zhang, M. , & Choudhury, T. (2015. MyBehavior: Automatic personalized health feedback from user behaviors and preferences using smartphones . UbiComp 2015 ‐ Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 707–718. 10.1145/2750858.2805840 [ CrossRef ]
  • Rhodes, N. (2017). Fear‐appeal messages: message processing and affective attitudes . Communication Research , 44 ( 7 ), 952–975. 10.1177/0093650214565916 [ CrossRef ] [ Google Scholar ]
  • Richards, T. J. , & Rickard, B. (2020). COVID‐19 impact on fruit and vegetable markets . Canadian Journal of Agricultural Economics , 68 ( 2 ), 189–194. 10.1111/cjag.12231 [ CrossRef ] [ Google Scholar ]
  • Rosenstock, I. M. (1990). The health belief model: explaining health behavior through expectancies. In: Glanz K. Lewis F. M. & Rimer B. K. (Eds.), Health Behavior and Health Education: Theory, Research, And practice (pp. 39–62). San Francisco, CA: Jossey‐Bass/Wiley. [ Google Scholar ]
  • Rountree, P. W. , & Land, K. C. (1996). Perceived risk versus fear of crime: Empirical evidence of conceptually distinct reactions in survey data . Social Forces , 74 ( 4 ), 1353–1376. 10.1093/sf/74.4.1353 [ CrossRef ] [ Google Scholar ]
  • Schroeder, T. C. , Tonsort, G. T. , Pennings, J. M. E. , & Minter, J. (2007). Consumer food safety risk perceptions and attitudes: impacts on beef consumption across countries . The B.E. Journal of Economic Analysis & Policy , 7 ( 1 ), 10.2202/1935-1682.1848 [ CrossRef ] [ Google Scholar ]
  • Seabra, C. , Abrantes, J. L. , & Kastenholz, E. (2014). The influence of terrorism risk perception on purchase involvement and safety concern of international travellers . Journal of Marketing Management , 30 ( 9–10 ), 874–903. 10.1080/0267257X.2014.934904 [ CrossRef ] [ Google Scholar ]
  • Setbon, M. , Raude, J. , Fischler, C. , & Flahault, A. (2005). Risk perception of the “mad cow disease” in France: Determinants and consequences . Risk Analysis , 25 ( 4 ), 813–826. 10.1111/j.1539-6924.2005.00634.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Shen, B. , Cao, Y. , & Xu, X. (2020). Product line design and quality differentiation for green and non‐green products in a supply chain . International Journal of Production Research , 58 ( 1 ), 148–164. 10.1080/00207543.2019.1656843 [ CrossRef ] [ Google Scholar ]
  • Shirin, K. , & Kambiz, H. H. (2011). The Effect of the Country‐of‐Origin Image, Product Knowledge and Product Involvement on Consumer Purchase Decisions . Chinese Business Review , 10 ( 8 ), 601–615. [ Google Scholar ]
  • Shrivastava, A. (2020). Zomato, Swiggy ordered to shut down in several states despite centre’s intervention . https://economictimes.indiatimes.com/small‐biz/startups/newsbuzz/zomato‐swiggy‐ordered‐to‐shut‐down‐in‐several‐states‐despite‐centres‐intervention/articleshow/74836083.cms
  • Soliha, E. , & Widyasari, S. (2018). Message framing and source credibility in product advertisements with high consumer involvement . European Research Studies Journal , 21 ( Special Issue 3 ), 413–422. 10.35808/ersj/1392 [ CrossRef ] [ Google Scholar ]
  • Strecher, V. J. , & Rosenstock, I. M. (1997). The health belief model . Cambridge Handbook of Psychology, Health and Medicine , 113 , 117. [ Google Scholar ]
  • Tabachnick, B. , & Fidell, L. (2013). Using multivariate statistics Upper Saddle River . Pearson. [ Google Scholar ]
  • Tang, C. S. K. , & Wong, C. Y. (2004). Factors influencing the wearing of facemasks to prevent the severe acute respiratory syndrome among adult Chinese in Hong Kong . Preventive Medicine , 39 ( 6 ), 1187–1193. 10.1016/j.ypmed.2004.04.032 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Taylor, J. W. (1974). The Role of Risk in Consumer Behavior . Journal of Marketing , 38 ( 2 ), 54. 10.2307/1250198 [ CrossRef ] [ Google Scholar ]
  • The Economic Times . (2020). Covid‐19 pandemic: Safety measures food delivery apps are taking to win back customers’ trust . https://economictimes.indiatimes.com/tech/software/aarogyasetu‐an‐app‐that‐alerts‐you‐about‐covid‐positive‐people‐in‐your‐vicinity/risk‐assessment/slideshow/74961245.cms
  • The Times of India . (2020a). Delhi food delivery boy tests positive for COVID‐19: Should you be ordering food from outside? This is what doctors feel. https://timesofindia.indiatimes.com/life‐style/health‐fitness/diet/delhi‐food‐delivery‐boy‐tests‐positive‐for‐covid‐19‐should‐you‐be‐ordering‐food‐from‐outside‐this‐is‐what‐doctors‐feel/articleshow/75180601.cms
  • The Times of India . (2020b). Government U‐turn on home delivery of non‐essential items leaves Amazon miffed, retailer body overjoyed . https://timesofindia.indiatimes.com/business/india‐business/government‐u‐turn‐on‐home‐delivery‐of‐non‐essential‐items‐leaves‐amazon‐miffed‐retailer‐body‐overjoyed/articleshow/75236391.cms
  • Turnšek, M. , Brumen, B. , Rangus, M. , Gorenak, M. , Mekinc, J. , & Štuhec, T. L. (2020). Perceived threat of COVID‐19 and future travel avoidance: Results from an early convenient sample in Slovenia . Academica Turistica , 13 ( 1 ), 3–19. 10.26493/2335-4194.13.3-19 [ CrossRef ] [ Google Scholar ]
  • Tweneboah‐Koduah, E. Y. (2018). Social marketing: Using the health belief model to understand breast cancer protective behaviours among women . International Journal of Nonprofit and Voluntary Sector Marketing , 23 ( 2 ), e1613. 10.1002/nvsm.1613 [ CrossRef ] [ Google Scholar ]
  • Valeeva, N. I. , van Asseldonk, M. A. P. M. , & Backus, G. B. C. (2011). Perceived risk and strategy efficacy as motivators of risk management strategy adoption to prevent animal diseases in pig farming . Preventive Veterinary Medicine , 102 ( 4 ), 284–295. 10.1016/j.prevetmed.2011.08.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Vermeir, I. , & Verbeke, W. (2006). Sustainable food consumption: Exploring the consumer “attitude ‐ Behavioral intention” gap . Journal of Agricultural and Environmental Ethics , 19 ( 2 ), 169–194. 10.1007/s10806-005-5485-3 [ CrossRef ] [ Google Scholar ]
  • Von Ah, D. , Ebert, S. , Ngamvitroj, A. , Park, N. , & Kang, D. H. (2004). Predictors of health behaviours in college students . Journal of Advanced Nursing , 48 ( 5 ), 463–474. 10.1111/j.1365-2648.2004.03229.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Warr, M. (1987). Fear of victimization and sensitivity to risk . Journal of Quantitative Criminology , 3 ( 1 ), 29–46. 10.1007/BF01065199 [ CrossRef ] [ Google Scholar ]
  • Weber, E. U. (2006). Experience‐based and description‐based perceptions of long‐term risk: Why global warming does not scare us (yet) . Climatic Change , 77 ( 1–2 ), 103–120. 10.1007/s10584-006-9060-3 [ CrossRef ] [ Google Scholar ]
  • Weitkunat, R. , Pottgießer, C. , Meyer, N. , Crispin, A. , Fischer, R. , Schotten, K. , Kerr, J. , & Überla, K. (2003). Perceived risk of bovine spongiform encephalopathy and dietary behavior . Journal of Health Psychology , 8 ( 3 ), 373–381. 10.1177/13591053030083007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Wen, J. , Kozak, M. , Yang, S. , & Liu, F. (2020). COVID‐19: Potential effects on Chinese citizens’ lifestyle and travel . Tourism Review , 10.1108/TR-03-2020-0110 [ CrossRef ] [ Google Scholar ]
  • Wise, T. , Zbozinek, T. D. , Michelini, G. , Hagan, C. C. , & Mobbs, D. (2020). Changes in risk perception and protective behavior during the first week of the COVID‐19 pandemic in the United States . PsyArXiv [Working Paper], 4, 1–13. 10.31234/OSF.IO/DZ42 [ PMC free article ] [ PubMed ] [ CrossRef ]
  • Wong, C. Y. , & Tang, C. S. K. (2005). Practice of habitual and volitional health behaviors to prevent severe acute respiratory syndrome among Chinese adolescents in Hong Kong . Journal of Adolescent Health , 36 ( 3 ), 193–200. 10.1016/j.jadohealth.2004.02.024 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yeung, R. M. W. , & Morris, J. (2001). Food safety risk: Consumer perception and purchase behaviour . British Food Journal , 103 ( 3 ), 170–187. 10.1108/00070700110386728 [ CrossRef ] [ Google Scholar ]
  • Zaichkowsky, J. L. (1994). Research notes: The personal involvement inventory: Reduction, revision, and application to advertising . Journal of Advertising , 23 ( 4 ), 59–70. 10.1080/00913367.1943.10673459 [ CrossRef ] [ Google Scholar ]
  • Zewude, B. T. , & Ashine, K. M. (2016). Binary logistic regression analysis in assessment and identifying factors that influence students ’ academic achievement : the case of college of natural and computational . Journal of Education and Practice , 7 ( 25 ), 1–6. [ Google Scholar ]

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International Conference on Information Systems and Management Science

ISMS 2020: Information Systems and Management Science pp 33–45 Cite as

Assessing the Performance of Online Food Delivery (OFD) in India

  • Amogh Bhaskara 18 ,
  • Siddharth Menon 18 ,
  • U. Dinesh Acharya 18 &
  • H. C. Shiva Prasad   ORCID: orcid.org/0000-0002-1296-8970 19  
  • Conference paper
  • First Online: 05 September 2021

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 303)

The purpose of this paper is to analyze the application quality of the online food delivery companies by using a qualitative and exploratory approach through the collection of primary data through a consumer survey and secondary data through a sample of 25 companies operating in the online food delivery sector in India. The analysis has been conducted through the parameters involving the aspects of Content, Usability, Functionality, Trust, Satisfaction and Brand Loyalty. The Data gathered has been analyzed using the SPSS followed by the Structural equation modelling (SEM). The conceptual model for the project has been achieved using the SmartPLS (v.2.3.8). The result shows better usability to the customer has a link to enhance application quality for business cycle and loyal customer trust the OFD system to allow efficiency building and look for profiteering space.

  • Online food delivery
  • Brand loyalty
  • Application quality

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Gupta, A.: India hyperlocal market outlook to 2020 - Driven by Surge in Number of Startups and Series of Funding, kenresearch.com, para, 3 March 14 (2016). https://www.kenresearch.com/technology-and-telecom/it-and-ites/india-hyperlocal-market-research-report/7209-105.html . Accessed 28 Jan 2019

India Brand Equity Foundation, Growth of Ecommerce Industry in India-Infographic, October 2018. https://www.ibef.org/industry/ecommerce/infographic . Accessed 28 Jan 2019

AIMS Institute Pvt. Ltd., Online Food Service in India: An Analysis. AIMS, July 2017. https://theaims.ac.in/resources/online-food-service-in-india-an-analysis.html . Accessed 28 Jan 2019

RedSeer Consulting Agency, Food-Tech Market Updates, March 2019. https://redseer.com/newsletters/food-tech-market-updates-june18/ . Accessed 19 Mar 2019

Meenakshi, N., Sinha, A.: Food delivery apps in India: wherein lies the success strategy? Strateg. Dir. 35 (7), 12–15 (2019). https://doi.org/10.1108/SD-10-2018-0197

Article   Google Scholar  

Anumolu, K., Shiva Prasad, H.C., Gopalkrishna, B., Adarsh, P.K.: A digital marketing: a strategic outreaching process. Intl. J. Manage. IT Eng. 5 (7), 254–260 (2015)

Google Scholar  

Vilella, R.M.: Conteúdo, usabilidade e funcionalidade: três dimensões para an avaliação de portais estaduais de governo eletrônico na web, [Dissertação de Mestrado] Escola de Ciência da Informação, Universidade Federal de Minas Gerais, Belo Horizonte, 263f (2003). http://bogliolo.eci.ufmg.br/downloads/VILELLA%20Conteudo%20Usabilidade%20e%20Funcionalidade.pdf . Accessed 14 Feb 2019

Daim, T.U., Basoglu, A.N., Gunay, D., Yildiz, C., Gomez, F.:, Exploring technology acceptance for online food services. Int. J. Buss. Info. Syst. 12 (4), 383–403 (2013). https://www.inderscienceonline.com/doi/pdf/10.1504/IJBIS.2013.053214

Pigatto, G., Guilherme, J., Negreti, A., Machado, M.L.: Have you chosen your request? Analysis of online food delivery companies in Brazil. British Food J. 119 (3), 639–657 (2017)

Thongpapanl, N., Ashraf, A.R.: Enhancing online performance through website content and personalization. J. Comp. Info. Syst. 52 (1), 3–13 (2011)

Statista, Platform-to-Consumer Delivery Statistics, March 2019, Online statistics portal. https://www.statista.com/outlook/376/119/platform-to-consumer-delivery/india . Accessed 25 Mar 2019

Das, J.: Consumer perception towards online food ordering and delivery services: an empirical study. J. Mange. 5 (5), 155–163 (2018). http://www.iaeme.com/JOM/issues.asp?JType=JOM&VType=5&IType=5 . Accessed 31 Jan 2019

Building Brands Online: Interactive Branding: Best Practices in a Direct Response-Driven Media, AdAge Insights (2010)

Kedah, Z., Ismail, Y., Ahasanul, K.M., Ahmed, A., Ahmed, S.: Key success factors of online food ordering services: an empirical study. Malays. Manage. Rev. 50 (2), 19–25 (2015). https://www.researchgate.net/publication/291074636_Key_Success_Factors_of_Online_Food_Ordering_Services_An_Empirical_Study . Accessed 31 Jan 2019

Pallant, J.: SPSS Survival Manual - A Step by Step Guide to Data Analysis Using SPSS for Windows, 3rd edn. Open University Press, Maidenhead (2007)

Srinivasam, S.: Swiggy pilots B2B offering under Swiggy Café at corporate cafeterias, ET Bureau, 3 October (2018). https://www.economictimes.indiatimes.com/articleshow/66048384.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst . Accessed 21 Apr 2019

Wong, K.K.: Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Market. Bull. 24 , 1-32 (2013). Technical Note 1. https://www.academia.edu/9210442/Partial_Least_Squares_Structural_Equation_Modeling_PLS-SEM_Techniques_Using_SmartPLS

Hair Jr., J.F., Hult, G.T.M., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications, USA (2016)

Bruin, J.: newtest: command to compute new test. UCLA: Statistical Consulting Group (2006). https://stats.idre.ucla.edu/spss

Zaiontz, C.: Real Statistics using excel. http://www.real-statistics.com/reliability/cronbachs-alpha/

Fornell, C., Larcker, D.F.: Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Market. Res. 18 , 382–388 (1981)

Tabachnick, B.G., Fidell, L.S.: Using Multivariate Statistics. HarperCollins, New York (1996)

Rossiter, J.R.: The C-OAR-SE procedure for scale development in marketing. Int. J. Res. Mark. 19 (4), 305–335 (2002)

Vos, K.: Ecommerce delivery trends: what contributes to a positive experience? (2016). https://www.mycustomer.com/selling/ecommerce/ecommerce-delivery-trends-what-contributes-to-a-positiveexperience

Alves, H., Fernandes, C.I., Raposo, M.: Social media marketing: a literature review and implications: implications of social media marketing. Psychol. Mark. 33 (1), 1029–1038 (2016). https://doi.org/10.1002/mar.20936

Krishnan, A.: Accessing the construct and content validity of uncertainty business using Sem approach - an exploratory study of manufacturing firm (2011). https://www.globaljournals.org/GJMBRVolume11/1-Accessing-the-Construct-and-Content-Validity-of-Uncertainty-Business.pdf

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Amogh Bhaskara, Siddharth Menon & U. Dinesh Acharya

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Bhaskara, A., Menon, S., Dinesh Acharya, U., Shiva Prasad, H.C. (2022). Assessing the Performance of Online Food Delivery (OFD) in India. In: Garg, L., et al. Information Systems and Management Science. ISMS 2020. Lecture Notes in Networks and Systems, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-030-86223-7_4

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  1. Online Food Delivery System in India: Profile of Restaurants and

    Online Food Delivery System in India: Profile of Restaurants and Nutritional Value of Food Items - Rizwan Suliankatchi Abdulkader, Kathiresan Jeyashree, Vivek Kumar, K. Senthamarai Kannan, Deneshkumar Venugopal, 2022 Browse by discipline Vision: The Journal of Business Perspective Impact Factor: 2.8 5-Year Impact Factor: 2.0 JOURNAL HOMEPAGE

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    The present research reports the results of an empirical study covering 300 respondents across India,based on exploratory,confirmatory factor analysis and Structural Equation Modelling (SEM) to...

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    According to the "Online Food Delivery (OFD) Services Global Market Report 2020-2030," the OFD market is projected to grow from $107.44 billion in 2019 to $154.34 billion in 2023...

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    Fig. 2 summarizes the research methodology adopted in this paper. Primarily, a four steps method was utilized for: (i) data collection, (ii) topic extraction, (iii) sentiment analysis, and (iv) moderation variable analysis. ... Boston Consulting Group India's online food delivery industry to touch $8-bn mark by 2022: Report. Business Standard.

  5. Emerging Trends Towards Online Food Delivery Apps in India

    Emerging Trends Towards Online Food Delivery Apps in India 10 Pages Posted: 6 May 2021 S. C. B Samuel Anbu Selvan The American College, Madurai Susan Anita Andrew The American College Date Written: April 30, 2021 Abstract In recent times, a growing trend has been noticed in the usage of online food delivery services.

  6. Online food delivery research: a systematic literature review

    3748 Abstract Purpose 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.

  7. Online food delivery: A systematic synthesis of literature and a

    Volume 104, July 2022, 103240 Online food delivery: A systematic synthesis of literature and a framework development Author links open overlay panel Amit Shankar, Jebarajakirthy, Preeti Nayal, Haroon Iqbal Maseeh, , Achchuthan Sivapalan Add to Mendeley Cite https://doi.org/10.1016/j.ijhm.2022.103240 Get rights and content Highlights •

  8. Online food delivery: A systematic synthesis of literature and a

    Online food delivery has emerged as a popular trend in e-commerce space, and serves as a tool to reach a larger number of consumers in a cost effective manner (Ray et al., 2019). Online food delivery (OFD) refers to online channel that consumers use to order food from restaurants and fast-food retailers (Elvandari et al., 2018).

  9. Customers response to online food delivery services during COVID‐19

    In India, online food ordering and delivery service was introduced in 2014. Several OFD start‐ups rose in 2015 with a focus on mobile apps. ... including the two most commonly used in food delivery: paper bags and cardboard boxes. ... Food Research International, 119, 143-151. 10.1016/j.foodres.2019.01.048 ...

  10. Assessing the Performance of Online Food Delivery (OFD) in India

    Abstract. The purpose of this paper is to analyze the application quality of the online food delivery companies by using a qualitative and exploratory approach through the collection of primary data through a consumer survey and secondary data through a sample of 25 companies operating in the online food delivery sector in India.

  11. (PDF) An Analysis of Online Food Ordering Applications in India: Zomato

    Currently Indian Online food market is$350billion.Food technology in broad area, online food delivery apps are just part of it. This conceptual study will give more insight about...

  12. Customer Satisfaction and Loyalty for Online Food Services Provider in

    Anand Prasad Sinha ([email protected]) is currently working as an assistant professor in Department of Management, BIT, Mesra, Ranchi.He joined as a Senior Research Associate in 2002 in Department of Management, BIT, Mesra, Ranchi, assisted Govt Sponsored Project in Department of Scientific and Industrial Research, Ministry of HRD, Govt. of India.

  13. Assessment of Competitiveness of Food-tech Start-ups in India

    Online food delivery with a market volume of USD7,120 million in 2018. Expected revenue (annual growth rate) (CAGR 2018-2022) of 11.8%, resulting in a market volume of USD11,137 million by 2022. b. Restaurant-to-consumer (R2C) delivery, USD6,629 million in 2018 ( Statistica, 2018) Global revenue ranking—India.

  14. Review of Online Food Delivery Platforms and their Impacts on ...

    1 Department of Food Science, University of Otago; PO Box 56, Dunedin 9054, New Zealand 2 New Zealand Food Safety Science Research Centre * Author to whom correspondence should be addressed. Sustainability 2020, 12 (14), 5528; https://doi.org/10.3390/su12145528

  15. Innovation in online food delivery: Learnings from COVID-19

    Abstract. 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.

  16. PDF An Overview of Rapid Evolution of Online Food Delivery

    EBET Knowledge Park, Nathakadaiyur, Kangeyam, Tiruppur, Tamilnadu. India Abstract Online Food Delivery (OFD) refers to online channel that consumers use to order food from restaurants and fast-food retailers. The rapid growth of online food delivery services has disrupted the traditionally offline restaurant industry. Consumer are influenced by

  17. An exploratory study on perception of job (in)dignity of delivery

    Abstract. This study examines challenges faced by food and grocery delivery agents in India's expanding online delivery sector. Key focus areas include issues of reward and recognition, low wages, work-life balance, job satisfaction, and workplace dignity.

  18. Review of Online Food Delivery Platforms and their Impacts on

    Miranda Mirosa University of Otago Phil Bremer University of Otago Abstract and Figures During the global 2020 COVID-19 outbreak, the advantages of online food delivery (FD) were obvious, as...

  19. Review on Customer Perception Towards Online Food Delivery Services

    The paper explores different dimensions involved in consumer perception towards online food delivery services which is an emerging industry. Design/methodology/approach - This research investigates a wide range of empirical and conceptual studies on consumer perception towards online food delivery services.

  20. A Study on Growth of Online Food Service Industry in India

    The internet has provided new chances for the online food service industry by offering them new ways to promote, communicate and distribute products and information to their target consumers. Similarly, innovative technology likes door delivery, online ordering of food, and online delivery apps (Swiggy, Fassos, Zomata, and Food Panda etc.) have resulted in a major change in the food and fast ...

  21. PDF Indian Multinational Restaurant Aggregator and Food Delivery

    technology adoption in food processing or manufacturing industry, now it refers to its application in food ordering and delivery market. The pace (double-digit CAGR) with which India's online food delivery market is growing, food supply market in India is projected to reach $8 billion market by the year 2022 (Google-BCG report, 2020).

  22. EMERGING TRENDS TOWARDS ONLINE FOOD DELIVERY APPS IN INDIA

    Samuel Anbu Selvan S.C.B Susan Anita Andrew In recent times, a growing trend has been noticed in the usage of online food delivery services. With the increased usage of technology, every...

  23. Online Food Delivery Research Papers

    The case traces the origin of online food delivery services in India and highlights the challenges to be faced by restaurateurs because of latest technological developments in food delivery ecosystem. It further discusses the possibility of predatory pricing in online food delivery market keeping in view the two-sided structure of this market.

  24. A Comparative Study of Online Food Delivery Start-ups in The Food Industry

    A COMPARATIVE STUDY OF ONLINE FOOD DELIVERY START-UPS IN THE FOOD INDUSTRY International Journal of Current Research 13 (5):17540-17549 DOI: 10.24941/ijcr.41407.05.2021 Authors:...