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

Wai Chuen Poon (Sunway University Business School, Sunway University, Subang Jaya, Malaysia)
Serene En Hui Tung (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia)

European Journal of Management and Business Economics

ISSN: 2444-8494

Article publication date: 21 March 2022

Issue publication date: 18 March 2024




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


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


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

Practical implications

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


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




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









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



Emerald Publishing Limited

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


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

1. Introduction

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

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

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

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

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

2. Theoretical framework and hypothesis development

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

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

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

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

In this light, one's intention to perform the behaviour is strongly weighted by available resources and opportunity (Ajzen, 1991). An individual will be subjectively affected by external factors, such as past experiences or expectations which may hinder or encourage them, individual acknowledgement of self-competence (ability), awareness of critical needs (resources) and the awareness of convenience (opportunity) (Chen et al., 2020). Consumers' past experiences will significantly influence their behavioural intention toward online food services (Liang and Lim, 2011). Hence, PBCs are essential factors influencing online purchases (Yang et al., 2018). MGB assumes that PBC bolsters an individual's desire and intention of a specific behaviour (Perugini and Bagozzi, 2001). Drawing similarities from consumer online purchase intention, ATT, SN and PBC would, therefore, have similar influence on OFD users. When consumers have favourable views towards OFD services, they substantially improve a person's motivation that would result in lesser resistance in ordering food online, triggering a higher level of desire to use OFD services. Therefore, the present study posited that individuals' behavioural, normative and control belief are key factors influencing their desire and intention towards OFD services. Based on these discussions, the following hypotheses have been developed.


ATT positively influences the desire to use OFD services


SN positively influences the desire to use OFD services


PBC positively influences the desire to use OFD services


PBC positively influences behavioural intention to use OFD services

A long-held belief is that individuals tend to avoid discomfort or pain while seeking pleasure or happiness. Many, if not all, of our decisions are bias towards pursuing happiness or avoiding unhappiness. Emotions play a significant role in persuading consumer's intention. External stimuli such as restaurant ambience, customer-employee relationship, virtual reality simulation, aromas and service failures influence their satisfaction and intention (Rodríguez-López et al., 2020). Additionally, emotions were found to be a significant predictor of online shopping behaviours in numerous studies (Szymkowiak et al., 2020; Poon and Mohamad, 2020b; Cinar, 2020). Emotions are dynamic and heavily influenced by our surroundings, current happenings and other people who affect our decision-making, resulting in impulsive buying. Affirming the importance of emotions in affecting an individual's online shopping behaviours, the underlying assumption of AE is that individuals anticipate that the emotional consequences of their action or inaction post decision-making (Bagozzi et al., 2016). Positive anticipated emotions (PAEs) are related to emotions felt when the individual succeeds in accomplishing the behaviour. These emotions, such as pleasure or arousal post–purchase, are important as they positively affect users' behaviour to use a particular online service (Kim et al., 2007; Menon and Khan, 2002). Meanwhile, negative anticipated emotions (NAEs) constitute regret, angry or guilt related to action or inaction after the purchase (Bagozzi and Dholakia, 2002). These emotions relate to emotions one fell when failing to achieve the desired behaviour. Wang et al. (2011) found both PAE and NAE affect intention depending on customers' reasons for engaging in the process. A recent study by Chiu et al. (2018) found that both PAE and NAE significantly influence consumers' purchase intention. Bagozzi and Dholakia (2002) argued that the effect of AEs is based upon an individual's argument when deciding to act or not in goal-directed situations, taking into account emotional consequences or both action and inaction. As such, AEs play a role in shaping an individual's behaviour towards the use of OFD services. With the rise of technology enabled services such as OFD, external stimuli such as ambience, aroma or meaningful relationship with customer that are traditionally used to stimulate consumer emotions are no longer effective. Consumer instead draws upon experience of past purchases to shape their emotions. In summary, consumers will intend to order food from mobile application if the emotions they anticipate from doing so are positive, which in turn will drive their desire. Hence, based on the discussion, the following hypotheses have been developed.


PAE positively influences the desire to use OFD services


NAE positively influences the desire to use OFD services

Desire functions as a motivator. Desires are thought to be very important in the first step of human actions and argued to lead to intentions to perform a behaviour (Perugini and Bagozzi, 2001, 2004). Perugini and Bagozzi (2004) argue that desire is the motivational state of mind in which internal assessment to carry out a particular behaviour are transformed into a motivation to do so. Desires act as a stimulus of decisions when individual consider their desires and endorse them as motivators to act (Bagozzi and Dholakia, 2002). Since humans naturally want to satisfy their desires for a gratifying self-image, they tend to believe, intuit and act in certain ways to achieve their goals (Perugini and Bagozzi, 2001). Han et al. (2018) investigate the effect of desire to engage in pro-environmental action on green loyalty. The results suggest that desire to engage in such action was an integral factor affecting green loyalty. Therefore, the desire to engage in OFD services will be a vital determining factor in an individual's intention to engage in OFD services. Based on previous studies on desire as a strong motivator for intention, the following hypothesis has been developed.


Desire positively influences behavioural intention to use OFD services

2.1 Perceived risk

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

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

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

The financial aspect also poses a risk to the consumer. Financial risk refers to the possibility of consumer's loss of money due to inappropriate purchases of product and services online (Forsythe and Shi, 2003). If consumer's perception of value towards the online product or service is low, they are more likely to perceive that it is financially risky for them to engage in OFDs (Kim et al., 2005). Such risk is more significant for first-time OFD users, where consumer expectations are not met in reality (Hwang and Choe, 2019). In short, OFD users could suffer from potential financial losses when using OFD services. Lastly, the physical risk is directly related to physical safety, while COVID-19 risk is concerned with an individual's perception of contracting COVID-19. Consumer engaging in OFD services is exposed to some form of physical risk. Consumer's physical location must be shared in real-time via a mobile application; with this, they are more likely to be concerned about their safety. Additionally, contacts with delivery persons might increase the likelihood of contracting COVID-19 if the recommended safety measures are not being adhered to. It was found that more than sixty percent of food delivery workers in Hanoi, Vietnam display mildly ill or presymptomatic while working (Nguyen and Vu, 2020). This raises the concern that food delivery workers may infect other riders and their customers as more and more people are adhering to shelter-in-place order. Altogether, consumers that decides to use OFD services are directly or indirectly exposing themselves to potential physical harm and risk of contracting unwanted illnesses from the delivery person. Hence, the following hypotheses have been developed based on the literature.


Performance risk negatively influences the desire to use OFD services.


Performance risk negatively influences behavioural intention to use OFD services.


Privacy risk negatively influences the desire to use OFD services.


Privacy risk negatively influences behavioural intention to use OFD services.


Financial risk negatively influences the desire to use OFD services.


Financial risk negatively influences behavioural intention to use OFD services.


Physical risk negatively influences the desire to use OFD services.


Physical risk negatively influences behavioural intention to use OFD services.


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


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

3. Research method

3.1 Data collection and sample

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

3.2 Scale measurement

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

3.3 Analysis and results

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

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

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

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

Performance risk (β = −0.119, p < 0.05), privacy risk (β = −0.133, p < 0.05), financial risk (β = −0.088, p < 0.05), physical risk (β = −0.150, p < 0.05) and COVID-19 risk (β = −0.105, p < 0.10) have a negative effect on desire. The findings indicate that performance risk, privacy risk, financial risk, physical risk and the risk of contracting COVID-19 negatively influence the desire of OFD users. Physical risk (β = −0.147, p < 0.05) and COVID-19 risk (β = −0.106, p < 0.05) have a negative effect on intention. The result implies that users' intention is influenced by their physical risk and the risk of contracting COVID-19. However, the results indicate the insignificant relationship between performance risk (β = −0.012, p > 0.10), privacy risk (β = −0.020, p > 0.10), financial risk (β = −0.013, p > 0.10) and intention. Table 4 presents the value of R2 for endogenous variables. The R2 values are 0.626 for desire and 0.491 for intention. The R2 value indicates that 62.6% of the variance explained for desire and 49.1% of variance explained for intention (see Table 4).

PLS predict was used to examine the model's predictive relevance. Shmueli et al. (2019) described the method comprises training and holdout sample-based procedure generating case-level predictions on the item or construct level using the PLS-predict with a 10-fold procedure to identify predictive relevance. According to Shmueli et al. (2019), the PLS model offers predictive performance if the Q2 prediction value is positive. There is a strong predictive power and vice versa if all the item differences linear regression model (LM) are lower than the PLS model. There is moderate predictive power when the majority item differences in LM are lower than the PLS model, while there is low predictive power if the minority item differences in LM are lower than the PLS model, then. As shown in Table 5, using ten folds and ten repetitions, all PLS models' items were lower than the PLS LM model; thus, the result concludes that the model has strong predictive power for behavioural intention and desire.

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

4. Discussion and implications

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

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

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

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

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

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

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

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

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

4.1 Limitation and future research

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


The theory of planned behaviour

Figure 1

The theory of planned behaviour

The model of goal-directed behaviour

Figure 2

The model of goal-directed behaviour

Proposed research model

Figure 3

Proposed research model

Demographic profile of respondent and SMEs

AgeBelow 19 years old92.7
20–29 years old16949.9
30–39 years old12035.4
40–49 years old3410.0
50–59 years old72.1
Highest education levelHigh/secondary school102.9
Monthly incomeNo fixed monthly income6719.8
Less than RM 4,0009728.6
RM 4,000 to RM 7,99911734.5
RM 8,000 and above5817.1

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


Note(s): a Composite reliability (CR) = (square of the summation of the factor loadings)/ [(square of the summation of the factor loadings) + (square of the summation of the error variance)

b Average variance extracted (AVE) = (summation of squared factor loadings)/(summation of squared factor loadings) (summation of error variances)

ω = Omega, CR = Composite reliability, AVE = Average variance extracted, ATT = attitude, BI = behavioural intention, DE = desire, FR = financial risk, NAE = negative anticipated emotions, PAE = positive anticipated emotions, PBC = perceived behavioural control, PER = performance risk, PPR = privacy risk, PR = physical Risk, COR = COVID-19 risk, SN = subjective norms

Heterotrait-monotrait (HTMT) ratio analysis

1. Attitude
2. Behavioural intention0.733
3. COVID-19 risk0.2200.157
4. Desire0.7180.8480.173
5. Financial risk0.2180.1750.4240.219
6. NAE0.3460.2870.0620.4610.092
7. PBC0.3880.3750.2630.2430.1230.092
8. Performance risk0.2040.1290.5220.1100.6070.0580.114
9. Physical risk0.2180.1670.6750.0710.3530.0640.2560.475
10. PAE0.6230.5370.1440.6180.2470.4850.1450.1980.090
11. Privacy risk0.0890.1640.3620.1460.3550.1170.0930.5950.4470.111
12. Subjective norms0.6310.4770.1480.5320.1900.3600.1430.1240.0750.5880.077

Note(s): NAE = Negative anticipated emotions, PAE = Positive anticipated emotions, PBC = Perceived behavioural control

Standard beta, standard error, t-Value and variance explained

βStd. Errort-valueHypothesis testingR2
H1 ATT → desire0.3740.0596.376***Supported0.626
H2 SN → desire0.0880.0521.694*Supported
H3 PBC → desire0.0960.0491.939*Supported
H4 PBC → BI0.1540.0384.026***Supported0.491
H5 PAE → desire0.1920.0633.051**Supported
H6 NAE → desire0.1690.0503.353**Supported
H7 desire → BI0.7380.02826.578***Supported
H8 PER → desire−0.1190.0562.138**Supported
H9 PER → BI−0.0120.0450.262Rejected
H10 PPR → desire−0.1330.0433.093**Supported
H11 PPR → BI−0.0200.0400.505Rejected
H12 FR → desire−0.0880.0402.178**Supported
H13 FR → BI−0.0130.0340.379Rejected
H14 PR → desire−0.1500.0592.524**Supported
H15 PR → BI−0.1470.0483.094**Supported
H16 COR → desire−0.1050.0581.825*Supported
H17 COR → BI−0.1060.0462.308**Supported

Note(s): *p < 0.10, **p < 0.05, ***p < 0.001, ATT = attitude, SN = subjective norms, BI = behavioural intention, FR = financial risk, NAE = negative anticipated emotions, PAE = positive anticipated emotions, PBC = perceived behavioural control, PER = performance risk, PPR = privacy risk, PR = physical risk, COR = COVID-19 risk

PLS predict

PLSLMPLS - LMQ2_predict

Note(s): BI = behavioural intention, DE = desire, LM = Linear Regression Model, RMSE = root mean squared error

Performance and total effects

DesireBehavioural intention
Total effectTotal effectPerformances
COVID-19 risk−0.1050.02849.101
Financial risk−0.088−0.07767.259
Negative anticipated emotions0.1690.12533.029
Perceived behavioural control0.0960.22575.366
Performance risk0.1190.07654.642
Physical risk0.150−0.03727.136
Positive anticipated emotions0.1920.14254.970
Privacy risk−0.133−0.11855.268
Subjective norms0.0880.06558.568

The supplementary material for this article can be found online.


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Durai, A. (2020), Food Delivery Services Will Thrive in 2020, available at: (accessed 25 January 2021).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hussey, A. (2021), The Global State of Foodservice Delivery, available at: (accessed 13 Augest 2021).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Statista (2021), “Global online food delivery market size 2023”, available at: (accessed 13 Augest 2021).

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

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

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

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

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

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

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

World Health Organization (2020), A Guide to WHO's Guidance on COVID-19, WHO, available at:

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

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

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

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

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

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

Corresponding author

Wai Chuen Poon can be contacted at:

Related articles