key: cord-0844662-52qq4vht authors: Mehrolia, Sangeeta; Alagarsamy, Subburaj; Solaikutty, Vijay Mallikraj title: Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression date: 2020-11-27 journal: Int J Consum Stud DOI: 10.1111/ijcs.12630 sha: 4f6ee794ea22336cc8fa6a0c6fb4f10553a53a8c doc_id: 844662 cord_uid: 52qq4vht 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. 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). 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 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. 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. 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 Lawton et al., , 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. tively influence the customer's purchase decision. 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 pur- 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: 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 Chuo ( , 2014 concludes that the selfprotective 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 ). Hypothesis 6 Frequency of ordering food online before the nationwide lockdown positively influenced the customer's purchase decision 3 | ME THODS 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. 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 or- 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 ecommerce-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) . 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 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) . Tables 2 and 3 , we can conclude that the constructs are free from construct validity issues. 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) To test the research objective, the binary logistic regression was done. 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 ra tio = (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 . 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 Chuo, , 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. (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. 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. Sangeeta Mehrolia https://orcid.org/0000-0003-3162-4361 Subburaj Alagarsamy https://orcid.org/0000-0003-1200-6381 The health belief model. Cambridge Handbook of Psychology COVID-19: Fear appeal favoring purchase behavior towards personal protective equipment From Intentions to Actions: A Theory of Planned Behavior The theory of planned behavior Food waste management during the COVID-19 outbreak: A holistic climate, economic and nutritional approach Rockfalls: Predicting highrisk behaviour from beliefs. Disaster Prevention and Management The health belief model and prediction of dietary compliance: A field experiment Understanding interactive online advertising: congruence and product involvement in highly and lowly arousing, skippable video ads Prevalence and predictors of early COVID-19 behavioral intentions in the United States Severe outcomes among patients with coronavirus disease 2019 (COVID-19) -United States Demographic and attitudinal determinants of protective behaviours during a pandemic: A review A survey of 19 countries shows how generations X, Y, and Z Are -and aren't -different Risk perceptions and behaviour: Towards pandemic control of emerging infectious diseases: Iional research on risk perception in the control of emerging infectious diseases Health belief model based evaluation of school health education programme for injury prevention among high school students in the community context Community pharmacists and communication in the time of COVID-19: Applying the health belief model A meta-analysis of the effectiveness of health belief model variables in predicting behavior Running Essential Errands. US Department of Health and Human Services The health belief model. Health Behavior and Health Education: Theory, Research, and Practice COVID-19 and the Demand for Online Food Shopping Services: Empirical Evidence from Taiwan Examining risk-reduction behavior toward water quality among restaurant guests Theme park visitors' responses to the SARS outbreak in Taiwan Restaurant diners' self-protective behavior in response to an epidemic crisis A paradigm for developing better measures of marketing constructs Understanding and reducing the risk of supply chain disruptions Changing exercise through targeting affective or cognitive attitudes Japanese tourism and the SARS epidemic of Logistic regression analysis of cruise vacation market potential: Demographic and trip attribute perception factors Atypical pneumonia and ambivalent law and politics: SARS and the response to SARS in China Culture and crisis communication: Nestle India's Maggi noodles case Market insurance, self-insurance, and self-protection The Coca-Cola Company: Allegations of Pesticides in Soft Drinks in India Evaluating Structural Equation Models with Unobservable Variables and Measurement Error Development of a scale to measure the perceived benefits and risks of online shopping The importance of affective beliefs and attitudes in the theory of planned behavior: Predicting intention to increase physical activity Health Belief Model Scale and Theory of Planned Behavior Scale to assess attitudes and perceptions of injury prevention program participation: An exploratory factor analysis Market justice, religious orientation, and entrepreneurial attitudes Health Behavior and Health Education: Theory, Research, and Practice Consumer acceptance of recombinant bovine growth hormone: interplay between beliefs and perceived risks Consumer risk perception profiles regarding recombinant bovine growth hormone (rbGH) Clinical characteristics of coronavirus disease 2019 in China The effects of online shopping attributes on satisfaction-purchase intention link: A longitudinal study Multivariate data analysis: a global perspective Multivariate data analysis: pearson new international edition Consumer attitudes toward advertisement and brand, based on the number of endorsers and product involvement: An experimental study Application of the theory of planned behaviour in behaviour change interventions: A systematic review Age and factors influencing consumer behaviour Food supply chains during the COVID-19 pandemic The influence of involvement on purchase intention for new world wine Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives Impact of BSE and bird flu on consumers' meat demand in Japan The Components of Perceived Risk The health belief model: a decade later Application of the Health Belief Model to customers' use of menu labels in restaurants Marketing research: An applied orientation COVID-19 impact on use of food ordering apps India 2020 The effects of integrating instrumental and affective arguments in rhetorical and testimonial health messages Age, gender, ethnicity and eating capability influence oral processing behaviour of liquid, semisolid and solid foods differently Influencing factors for the purchase intention of consumers choosing bioplastic products in Germany. Sustainable Production and Consumption Planning for avian flu disruptions on global operations: A DMAIC case study Supply chain disruption by avian flu pandemic for U.S. Companies: A case study Avian influenza risk perception and preventive behavior among traditional market workers and shoppers in Taiwan: Practical implications for prevention Generation Y and online fashion shopping: Orientations and profiles 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 Desire or reason: Predicting health behaviors from affective and cognitive attitudes Beyond cognition: Predicting health risk behaviors from instrumental and affective beliefs Socioeconomic status and the prediction of health promoting dietary behaviours: A systematic review and meta-analysis based on the theory of planned behaviour Four generational cohorts and hedonic m-shopping: Association between personality traits and purchase intention Risk perception and chicken consumption in the avian flu age: A consumer behaviour study on food safety information The role of affect in decision making. Handbook of Affective Science The influence of affective and instrumental beliefs on exercise intentions and behavior: A longitudinal analysis Self-efficacy, threat, knowledge, and information receptivity: Exploring pandemic prevention behaviors to enhance societal welfare Travel decision-making: From the vantage point of perceived risk and information preferences The over-reaction to SARS and the collapse of Asian tourism A revised product involvement inventory 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 Characteristics of hospitalized adults with COVID-19 in an integrated health care system in California The consumers' intention to purchase food: The role of perceived risk How do product involvement and prestige sensitivity affect price acceptance on the mobile phone market in Vietnam Food delivery service during social distancing: Proactively preventing or potentially spreading COVID-19? Psychometric theory-25 years ago and now An assessment of consumers product, purchase decision, advertising and consumption involvement in fashion clothing 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 The impact of SARS on Hong Kong's tourism industry Applied Multivariate Statistics for the Social Sciences The theory of planned behaviour and healthy eating: Examining additive and moderating effects of social influence variables The interactive influence of country of origin of brand and product involvement on purchase intention Automatic personalized health feedback from user behaviors and preferences using smartphones Fear-appeal messages: message processing and affective attitudes COVID-19 impact on fruit and vegetable markets The health belief model: explaining health behavior through expectancies Perceived risk versus fear of crime: Empirical evidence of conceptually distinct reactions in survey data Consumer food safety risk perceptions and attitudes: impacts on beef consumption across countries. The B.E The influence of terrorism risk perception on purchase involvement and safety concern of international travellers Risk perception of the "mad cow disease" in France: Determinants and consequences Product line design and quality differentiation for green and non-green products in a supply chain The Effect of the Country-of-Origin Image, Product Knowledge and Product Involvement on Consumer Purchase Decisions Zomato, Swiggy ordered to shut down in several states despite centre's intervention Message framing and source credibility in product advertisements with high consumer involvement The health belief model. Cambridge Handbook of Psychology Using multivariate statistics Upper Saddle River Factors influencing the wearing of facemasks to prevent the severe acute respiratory syndrome among adult Chinese in Hong Kong The Role of Risk in Consumer Behavior order ing-food-from-outsi de-this-is-what-docto rsfeel/artic lesho w/75180 601.cms The Times of India. (2020b). Government U-turn on home delivery of non-essential items leaves Amazon miffed, retailer body overjoyed Perceived threat of COVID-19 and future travel avoidance: Results from an early convenient sample in Slovenia Social marketing: Using the health belief model to understand breast cancer protective behaviours among women. International Journal of Nonprofit and Voluntary Sector Marketing Perceived risk and strategy efficacy as motivators of risk management strategy adoption to prevent animal diseases in pig farming Sustainable food consumption: Exploring the consumer "attitude -Behavioral intention" gap Predictors of health behaviours in college students Fear of victimization and sensitivity to risk Experience-based and description-based perceptions of long-term risk: Why global warming does not scare us (yet) Perceived risk of bovine spongiform encephalopathy and dietary behavior COVID-19: Potential effects on Chinese citizens' lifestyle and travel Changes in risk perception and protective behavior during the first week of the COVID-19 pandemic in the United States Practice of habitual and volitional health behaviors to prevent severe acute respiratory syndrome among Chinese adolescents in Hong Kong Food safety risk: Consumer perception and purchase behaviour Research notes: The personal involvement inventory: Reduction, revision, and application to advertising Binary logistic regression analysis in assessment and identifying factors that influence students ' academic achievement : the case of college of natural and computational