Investigating the Relationship between Technology Acceptance Factors and Behavioural Intention to Use of Online Food Delivery Applications in Sarawak

Vol. 13, No.


Introduction
Globally, the growth of internet providers and the rapid penetration of smartphones have fuelled the growth of various online food delivery applications such as MFood, Aomi, Meituan, and Eleme (Ray et al., 2019). Online food delivery applications enable customers to experience convenient online food ordering and offline delivery services, with two-way benefits for catering food enterprises and customers (Cho et al., 2019;Zhao & Bacao, 2020). In Malaysia, the increasing popularity of the smartphone has contributed to the expansion of online food delivery service applications. The vast majority of Malaysian customers are increasingly turning to their mobile devices to complete their online purchase transactions.
According to the Malaysian Communications & Multimedia Commission (MCMC, 2020), around 28.4 million Malaysians used their mobile smartphones to access the internet, with 64 percent of them using restaurants' online food ordering and delivery services to purchase food products. Recent data indicate that the trend of online food ordering and delivery services in Malaysia is on the rise, in line with the rapid growth of internet applications and technology that are making a positive impact on the country's business sector. For example, food delivery app and service have gradually influenced the food and beverage industry, mainly because of the growing potential of delivery service that can facilitate employee productivity, delivery order accuracy, and the creation of customer database (S. F. Yeo et al., 2021).
The increase in the number of food and beverage industry players has also contributed to the usage of online food delivery services in Malaysia (Chai et al., 2019). Online food delivery in the country is available from a wide variety of establishments, including restaurants and food delivery companies (Pitchay et al., 2021;Nayan & Hassan, 2020). Foodpanda is the first food delivery company established in Malaysia, reported that approximately 75 per cent of Malaysians were in favour of using the Foodpanda food delivery app with more than 45,000 restaurants worldwide, and it has never ceased to grow rapidly (Hassan, 2018). Data showed that the Foodpanda Malaysia application in Google Play Store had been download more than 10 million times (Rosli, 2018). This scenario indicates that people are becoming more willing to accept it because of the convenience it offers, and the chance to discover more food choices through the application (Pitchay et al., 2021). The greatest concentration of online food delivery services may be found in major urban areas such as Kuala Lumpur, the Klang Valley, and Johor Bahru (Nayan & Hassan, 2020).
Despite the significance of online food delivery applications in Malaysia and the shifting consumer behaviour towards these services, only a few studies have investigated consumers' behavioural intentions to utilise online food delivery services in Malaysia. Most of those studies were done in major urban areas such as Kuala Lumpur, the Klang Valley, and Johor Bahru (Allah Pitchay et al., 2021;Chai et al., 2019;Ramli et al., 2021). Although the online food delivery services industry seems very promising, the nature of this market is poorly understood (Agus et al., 2020;Yeo et al., 2017), especially in the context of Sarawak. This is because relatively few studies have investigated the usage of online food delivery applications in the aforesaid context. Therefore, it is important to examine the factors influencing the intention to use of online food delivery applications in the aforesaid context. Moreover, it is important to examine the factors affecting the behavioural intention to use online food delivery applications in the food segment (Alalwan, 2020a; Tandon et al., 2021). The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was adopted as a theoretical foundation that comprehensively captured the factors influencing the technology acceptance of online food delivery applications (Tamilmani et al., 2021). Additionally, consumers are also sceptical about using online food delivery services due to several risks and trust issues that are associated with online food delivery platforms (Cai & Leung, 2020;Hwang & Choe, 2019). However, the research done thus far on online food delivery has not looked at how consumers' reactions to online food delivery platforms are affected by the trade-off between the advantages and disadvantages of using these platforms (Hwang & Kim, 2019;Jebarajakirthy et al., 2021;Shankar et al., 2022). Therefore, this study included two additional variables, namely risk and trust, to the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model in order to examine the relationship of technology acceptance factors and behavioural intention to use of online food delivery applications. Accordingly, this study was designed to accomplish the following specific objectives: • To determine the relationships between technology acceptance factors and behavioural intention to use online food delivery applications. • To determine the relationship between behavioural intention to use and usage of online food delivery applications.

Literature Review Performance Expectancy
According to Venkatesh et al (2003Venkatesh et al ( , 2012, performance expectancy is the anticipation of a user, in which a person believes that by using a system, they will be able to improve their task or professional performance. Therefore, when people think new technology will improve their performance at work, they are more likely to employ that technology. Alalwan (2020b) found that when people strongly believed that technology could help them do their jobs, their intentions to use it grew. According to Ramos (2022), the findings that the continuation intention towards food delivery apps could be predicted by the elements uncovered, including performance expectancy, confirmed that customers perceived an increased benefit, such as saving the time and effort needed to prepare meals. These findings pertaining to the significant influence of performance expectancy are comparable to the results of Palau-Saumell et al (2019), for example in restaurant searches and/or reservations (MARSR) context and performance expectations were found to have a positive influence on future usage intentions. Hence, this study hypothesised that: H1: Performance expectancy has a significant influence on the behavioural intention to use online food delivery applications

Effort Expectancy
According to Venkatesh et al (2003), the ease of use of a system can be defined as having a certain effort expectancy in relation to the degree of simplicity linked to the operation of the system. Customers may feel that some obstacles related to the use of the technology exist at the beginning of different activities. The layout of an online food delivery application and its capacity to rapidly and effectively process the online food orders placed by customers are, therefore, the most crucial factors in determining whether or not a consumer will make a purchase (Gunden et al., 2020a(Gunden et al., , 2020bSaad, 2021). The study by Ramos (2022) discovered that customers' continuance use intention towards online food delivery applications was affected by their previous experiences about effort performance. In addition, clients reported that online food delivery applications were beneficial in satisfying their growing need to purchase food and keep a social distance while they were being quarantined for COVID-19. Hence, this study hypothesised that H2: Effort expectancy has a significant influence on the behavioural intention to use online food delivery applications.

Social Influences
Social influence refers to the extent to which an individual considers the perspectives of other people to be important in order to modify their behaviour towards using a new system (Venkatesh et al., 2003). According to Venkatesh et al. (2012), social influence can also be defined as the assumption that using modern technology would improve one's identity or social status as a human being. Roh and Park (2019) found that their study participants held the belief that social influence had a significant impact on the participants' intention to use. The perception of an application's compatibility with an individual's lifestyle and the individual's expectations regarding the results of using the application were both impacted by the social pressure exerted by other people. Jasim et al (2022) investigated the impact of social influence on customers' behavioural intention and found the impact to be significant. The above discussion indicates that people may have a greater capacity for motivation if they are surrounded by a certain level of encouragement provided by co-workers, family members, and friends. Hence, this study hypothesised that H3: Social influence has a significant influence on the behavioural intention to use online food delivery applications

Facilitating Conditions
The extent of use of modern applications and the degree to which customers are satisfied with their experience in using those applications are largely determined by the amount of technical infrastructure and human support that is readily available whenever it is required by customers (Venkatesh et al., 2003). Human support plays an essential role in ensuring that a high quality of service is provided to customers in terms of customer service, call centres, and delivery. Researchers in the fields of information technology and digital marketing have reached the conclusion that facilitating conditions have a significant influence on customers' intention to use a product and their actual behaviour when using that product (Khalilzadeh et al., 2017;Verkijika, 2018). Palau-Saumell et al (2019) found that facilitating conditions were the final driver in the order of relevance that affected usage. For example, their findings showed that facilitating conditions had a greater influence on restaurant searches and/or reservations (MARSR) usage than restaurant searches and/or reservations (MARSR) users' intentions to use the resource in the future. Besides, the degree to which people believed they would receive assistance and access to relevant resources when using restaurant searches and/or reservations (MARSR) context was a critical component in determining how widely this technology would be adopted. Based on the foregoing, is was hypothesised that H4: Facilitating condition has a significant influence on the behavioural intention to use online food delivery applications

Price Value
According to the definition provided by Venkatesh et al (2012), price value is the cognitive exchange that takes place between the perceived benefits of an application and the monetary cost for utilising it. This is referred to as the price value equation. When customers believe that the value they receive from using an application is greater than the cost of doing so, price value has a positive in fluence on their intention to use the application. Concerning the price value, Alalwan (2020) argued that online food delivery applications do not incur any monetary cost. There is no additional price to be paid by adding a free app (Shaw & Sergueeva, 2019). In fact, customers can obtain significant monetary savings through loyalty programmes or discounts perceived as benefits (Koiri et al., 2019;Tomacruz & Flor, 2018). For example by imposing discount offers, price comparisons, and ease of choice are among the factors that online food delivery applications are responsible for processing in order to account for demand, signals, and price fluctuations (Jain et al., 2020;Saad, 2021). Furthermore, many retailers who use online food delivery applications may be able to offer food at reasonable prices as a result of the online food delivery applications' financial incentives, increased sales, and reduced operating costs including on rent, taxes, and employee salaries. Therefore, price value has a positive impact on continuance intention when the customer recognises that the benefits outweigh the price of the product (Tandon et al., 2021;Venkatesh et al., 2012). Hence, this study hypothesised that: H5: Price value has a significant influence on the behavioural intention to use online food delivery applications

Hedonic Motivation
Intrinsic motivation, in addition to extrinsic motivation such as performance expectancy, has been repeatedly considered an important driver of customers' intention and willingness to use new systems and applications (Alalwan, 2018;Brown & Venkatesh, 2005;Davis, Bagozzi, & Warshaw, 1992;Van der Heijden, 2004;Venkatesh et al., 2012). Palau-Saumell et al. (2019) found that hedonic motivation was a strong predictor of intentions to use, with the relationship being stronger than in the previous applications of Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model in the context of online purchase. Users were more likely to give positive ratings to mobile applications that were fun, enjoyable, and entertaining. On the other hand, the study by Lee et al. (2019a) found that hedonic motivation was not a key determinant of a person's intention to use delivery apps continuously. It was because the development of smartphones and other forms of ICT that were largely responsible for the advent of online food delivery, along with the implementation of apps, had been stabilised to the point where customers had very few problems when using the apps. It was likely that the impact of hedonic motivation was attributed to the short process from search to order only, giving customers very little time for enjoyment. Hence, this study is hypothesised that: H6: Hedonic motivation has a significant influence on the behavioural intention to use online food delivery applications

Habit
Limayem et al (2007) defined a customer's habit as their propensity to act without a conscious thought as a result of their accumulated learning experience. People are becoming more dependent on their smartphones and have developed a pattern of behaviour regarding the use of mobile applications that are associated with them. In addition, the influence of habit on continuation intention is dependent on familiarity with the technology. This means that the continuity intention grows as individuals become more familiar with a specific technology (Nascimento et al., 2018). In the context of MARSR, the findings showed that habit was the most powerful predictor of both intentions to use and actual usage (Palau-Saumell et al., 2019). It could be that the intention to use a restaurant app became less critical when the habit was stronger, given that the probability of using it continuously was much higher. Similarly, Zanetta et al (2021) found that habit was the second most important determinant of continuation intention. The strong influence of routine on people's intentions to continue doing something might be the result of new routines that developed as a result of the pandemic (Zhao & Bacao, 2020). Hence, this study hypothesised that H7: Habit has a significant influence on the behavioural intention to use online food delivery applications

Trust
Many scholars have studied the influence of trust on behavioural intention (Ashrafet al., 2019; Alagoz & Hekimoglu, 2012). Jasim et al (2022) proposed in their study that trust has a significant positive effect on the behavioural intention to use drone food delivery (DFD) services. The respondents agreed with the statement related to trust. A high level of trust could increase customers' desire to depend on the drone to get their food daily. The result is compatible with other literature studies that included trust as a predictor of new technology acceptance. For example, Hamid et al. (2022) found that in countries like India, trust directly influenced the behavioural intention of consumers. This phenomenon could be due to the inherited and engraved values, customs, and traditions in the Indian subcontinent. Thus, trust emerged as a strong predictor of behavioural intention in ordering food and beverage items using e-commerce during the recent COVID-19 pandemic. Hence, this study hypothesised that H8: Trust has a significant influence on the behavioural intention to use online food delivery applications

Risk
In the case of online purchases made for food delivery, consumers' anticipation of what will happen as a direct result of online transactions, which may be something unfavourable, is part of the perceived risk associated with making such purchases. Additionally, consumers have fewer cues about the service or product, which increases the uncertainty regarding whether or not they will accomplish their purchase objective (Forsythe & Shi, 2003). Hwang Choe (2019) investigated the importance of managing perceived risk in the context of drone food delivery (DFD) services. The study found that perceived risk had a positive effect on customers' intention to use and willingness to pay more. The study provides important theoretical and practical implications for the successful development of drone food delivery (DFD) services. In particular, the most important contribution of the study is in suggesting to foodservice companies offering the DFD services on how to reduce the perceived risk of the services. Poon, Tung and Hui En (2022) investigated the influence of perceived risks on consumers' desire and intention to use online food delivery services. Consumers' perception of risk was found to have a negative impact on their intention to use online food delivery services. Hence, this study hypothesised that H9: Risk has a significant influence on the behavioural intention to use online food delivery applications

Behavioural Intention to Use
Behavioural intention evaluates a user's likelihood to perform a future action (Venkatesh et al., 2003(Venkatesh et al., , 2016. The extent to which perceived performance meets initial expectations determines user behaviour. Ali et al (2020) found that behavioural intention had a beneficial effect on adoption behaviour. The findings are consistent with those of Jasim et al (2022), which showed a substantial association between users' behavioural intention and their actual behaviour when using drone food delivery (DFD). Similarly, in the study conducted by Puriwat and Tripopsakul (2021) in Thailand, users' behavioural intention to use was observed to positively affect their use behaviour towards online food delivery. The findings showed that concepts such as performance expectancy, effort expectancy, social influence, and facilitating condition had strong impacts on the behavioural intention to use food delivery mobile application technology during the COVID-19 pandemic. Hence, this study hypothesised that H10: Behavioural intention to use has a significant influence on online food delivery applications usage

Methodology
The sample of this study consisted of online food delivery application users aged 18 years and above in Sarawak. Since the sampling frame was not available, this study used G*Power to perform the power analysis (Faul et al., 2007;Kang, 2021). Power analysis determines the minimum sample size by taking into account the part of the model with the largest number of predictors (Hair et al., 2019;Uttley, 2019). For this study, the parameters' values for the minimum sample size determination were set at f2 = 0.15, power = 0.95, Alpha = 0.05, and predictors = 10. The results from G*Power 3.1.9.7 software indicated that this study required a minimum sample size of 96. The researcher planned to limit the sample to 400 respondents, as a sample larger than 400 respondents would cause Structural Equation Model (SEM) to become sensitive, causing any difference to be detected and the goodnessof-fit measures to exhibit poor fit (Awang et al., 2015). Therefore, the sample size for this study should be in the range between the minimum of 96 and the maximum of 400 responses.
Due to the non-availability of a sampling frame, the convenient sampling technique was used for this study. The technique is useful when the target population is defined in terms of a very broad category. With this sampling technique, any member of the target population who is available at the moment is approached and asked to participate in the research; if the person shows consent, the investigation is done (Alvi, 2016). For the present study, the researcher included those participants who were easy or convenient to approach. The convenient sampling technique was selected because it requires less effort, cost, and time as the sample is quick and easy to approach.
Questionnaires are considered to be one of the most suitable data collection tools for collecting data from large samples (Saunders et al., 2019). The online questionnaire may provide a viable alternative method for carrying out a research plan (Siva et al., n.d.). Besides, it offers a higher response rate (Wu et al., 2022). In this study, the selection of respondents was based on the coverage areas of online food delivery services by Foodpanda and GrabFood. This was because Foodpanda (70.36%) and GrabFood (63.19%) were the popular online food delivery applications among Malaysian online food delivery users due to the userfriendliness of their systems . Besides, GrabFood was available in Kuching, Miri, Bintulu, and Sibu, while Foodpanda was available in Kuching, Petrajaya, Miri, Sibu, Bintulu, dan Samarahan. Data were collected over a period of 2 months from Sarawak's urban and sub-urban population. The urban and sub-urban population was selected since they had better internet connection and the online food delivery applications were available only in urban and sub-urban areas in Sarawak. During the data collection period, a Google Form link was sent to potential respondents after they were contacted online via messaging applications such as Email, WhatsApp, and Facebook. In total, 400 responses were gathered via this method of sampling for further analysis using IBM-SPSS-AMOS 24.0.

Results and Findings
The outcomes of the tests on the direct effect hypotheses were determined based on the probability values (p-values). A p-value of less than .05 indicated that the hypothesis was significant or supported. The value of the regression coefficient showed the effect of an exogenous construct on its corresponding endogenous construct. In this regard, a one-sided arrow represented the causal effect of an exogenous construct on its corresponding endogenous construct (Awang, 2015;Awang et al., 2018).

Performance Expectancy
The result showed that performance expectancy was not significantly related to behavioural intention to use (b = 0.067, p > .05). Hence, this research hypothesis was not supported, as shown in Table 1. The result from SEM showed that the relationship between performance expectancy and behavioural intention to use was not statistically significant (p>.05). Thus, H1 was not supported. This finding implies that performance expectancy has no significant effect on consumers' behavioural intention to use in Sarawak. It is in line with the suggestion of past research that it is imperative to ensure that online food delivery applications are designed to provide users with gratification from using the apps. This will create a need that transforms into a way of life and results in profits for the owners of food delivery businesses with the sustenance of the venture over time. When it comes to app application and deployment, as much information as possible should be provided to satisfy consumer curiosity and inquisitiveness in order to activate a recommendation status for the business based on previous users' experience (Muangmee et al., 2021).

Effort Expectancy
The result showed that effort expectancy was significantly related to behavioural intention to use (b = 0.128, p < .05). Hence, this research hypothesis was supported, as shown in Table 2. The result from SEM showed that the relationship between effort expectancy and behavioural intention to use was statistically significant (p<.05). Thus, H2 was supported. This finding implies that effort expectancy has a significant relationship with the behavioural intention to use online food delivery applications among users in Sarawak. Many studies (see Gunden et al., 2020a;Ramos, 2022;Alalwan, 2020a;Allah Pitchay et al., 2021;Palau-Saumell et al., 2019;Lee et al., 2019;Muangmee et al., 2021;Jasim et al., 2022;Zanetta et al., 2021) had developed and tested the effort expectancy construct via the expanded and extended UTAUT2 model in the online food delivery context. The empirical findings of these previous research works demonstrated the role of effort expectancy as one of the antecedents of intention to use online food delivery. Their results also showed that effort expectancy had a significant effect on users' intention to use online food delivery for as long as the users perceived that online food delivery applications were effective and user-friendly for searching and placing orders for food and beverages.

Social Influence
The result showed that social influence was not significantly related to behavioural intention to use (b = −0.078, p > .05). Hence, this research hypothesis was not supported, as shown in Table 3. The result from SEM showed that the relationship between social influence and behavioural intention to use was statistically not significant (p>.05). Thus, H3 was not supported. This finding implies that social influence has no significant relationship with the behavioural intention to use online food delivery applications among users in Sarawak. The result indicates that social influence is not one of the technology acceptance factors that help software providers understand how to develop better online food delivery applications. In other words, the social influence factor does not contribute to the intention to use online food delivery applications among users in Sarawak. The finding of this study is similar with the result of Palau-Saumell et al (2019), which found that social influence was one of the least powerful determinants of intention to use. In contrast, the findings surrounding more specialised applications indicated that social influence was the most powerful antecedent of the intention to use the applications. Consequently, it was suggested that the widespread adoption of mobile services might have had the effect of diminishing the significance of the role of social influence. This might have occurred as a consequence of the normative pressure exerted by reference groups, and if routine was the most powerful motivator of the intention to use, then social influence would necessarily become less relevant.

Facilitating Condition
The result showed that facilitating condition was significantly related to behavioural intention to use (b = 0.489, p < .05). Hence, this research hypothesis was supported, as shown in Table  4. The result from SEM showed that the relationship between facilitating condition and behavioural intention to use was statistically significant (p<.05). Thus, H4 was supported. This finding implies that facilitating condition has a significant relationship with the behavioural intention to use among users in Sarawak. This result supports the results of previous research, which found that customers heavily emphasised the quality of the applications and their capacity to operate reliably without experiencing any downtime or technical issues. In addition, the role of human support was found to be essential in ensuring the provision of high-quality service to customers in terms of customer service, call centres, and delivery. Thus, researchers in the fields of information technology and digital marketing have concluded that facilitating conditions have a significant influence on customers' intention to use a product and their actual behaviour when using that product (Khalilzadeh et al., 2017;Verkijika, 2018).

Price Value
The result showed that price value was not significantly related to behavioural intention to use (b = −0.007, p > .05). Hence, this research hypothesis was not supported, as shown in Table 5. The result from SEM showed that the relationship between price value and behavioural intention to use was statistically not significant (p>.05). Thus, H5 was not supported. This finding implies that price value does not have a significant relationship with the behavioural intention to use among users in Sarawak. The finding is supported by the study of Lee et al. (2019), which found that price value was not a key factor in determining the continuous use intention towards delivery apps. This was because of the developments in smartphones and ICT that helped to standardise app implementation to the point where users experienced very few difficulties when using these apps. Customers did not believe there was a price-value benefit available to them because there was no visible difference in the material benefits arising from placing an order through a phone or smart phone as opposed to using an app that facilitated delivery.

Hedonic Motivation
The result showed that hedonic motivation was significantly related to behavioural intention to use (b = 0.261, p < .05). Hence, this research hypothesis was supported, as shown in Table  6. The result from SEM showed that the relationship between hedonic motivation and behavioural intention to use was statistically significant (p<.05). Thus, H6 was supported. This finding implies that hedonic motivation significantly affects the behavioural intention to use among users in Sarawak. This result supports the result of previous research by Jasim et al (2022), which found that hedonic motivation influenced the behavioural intention to use drone food delivery services. This was because users were more encouraged to use delivery services once they had the perception that interacting with the new delivery services would result in a pleasant experience. Additionally, Kumar and Shah (2021) also demonstrated the significance of enjoyment in the online food delivery intention to continue using a product despite the pandemic. On the other hand, this aspect seemed to have more to do with the aesthetic aspect of the app, according to the evaluation of the study by Zanetta et al. (2021). The differences in results may be due to factors such as the type of app tested, the amount of experience one had, or the gender of the participants. Apps that facilitate routine activities, in this scenario, customers place a high value on useful applications.

Habit
The result showed that habit was not significantly related to behavioural intention to use (b = 0.014, p > .05). Hence, this research hypothesis was not supported, as shown in Table 7. The result from SEM showed that the relationship between habit and behavioural intention to use was statistically not significant (p>.05). Thus, H7 was not supported. This finding implies that habit does not have a significant relationship with the behavioural intention to use among users in Sarawak. The finding of this study is supported by the finding of Lee et al. (2019), demonstrating that the typical value of the habit factor implies that there is, a negative relationship. Therefore, respondents' use of delivery apps was not the result of compulsion or habit.

Trust
As hypothesised, trust was found significant related to intention to use (b=0.125, p<0.05). Hence, this research hypothesis was supported, as shown in Table 8. The result from SEM showed that the relationship between trust and behavioural intention to use was statistically significant (p<.05). Thus, H8 was supported. This finding implies that trust has a significant relationship with the behavioural intention to use among users in Sarawak. This result supports the finding of previous research by Muangmee et al (2021); Wen et al (2022) that perceived trust was among the factors influencing users' intention to use online food delivery. Their finding provides the users and owners of online food delivery applications with the aspects that they should focusing on pertaining to the overall technology. Insofar as the enhancement of online food delivery applications and their performance are concerned, it would appear that the appropriateness of technology in terms of its fit and perceived trust, as well as its appropriateness in terms of its operational period, should be considered as relevant considerations.

Risk
The result showed that risk was significantly related to behavioural intention to use (b = 0.345, p < .05). Hence, this research hypothesis was supported, as shown in Table 9. The result from SEM showed that the relationship between risk and behavioural intention to use was statistically significant (p<.05). Thus, H9 was supported. This finding implies that risk has a significant relationship with the behavioural intention to use among users in Sarawak. This result supports the result of previous research by Hwang and Choe (2019), which investigated the importance of managing perceived risk in the context of drone food delivery services. Their study found that perceived risk had a positive effect on the intention to use and willingness to pay more. The study provides important theoretical and practical implications for the successful development of drone food delivery services. In particular, the most important contribution of the study is in suggesting how foodservice companies providing drone food delivery services can reduce the perceived risk. In the study by Poon and Tung (2022), some interesting findings were found from the investigation of the influences of perceived risk on consumers' desire and intention to use online food delivery services. Consumers' perception of risk was found to have a negative impact on their intention to use online food delivery services because of a complicated mechanism influencing one's motivation to act in a particular manner. The findings imply that people who use online food delivery services consider various critical risk factors when deciding what motivates them.

Conclusion
It is essential to investigate how technology acceptance factors influences customers' reactions to online food delivery services. Moreover, an unintended consequence of online food delivery platforms is the information asymmetry between platforms and restaurants that reduces service efficiency, as the orders are received by the platforms and fulfilled by the restaurants without any integration (Dai et al., 2020). Thus, online food delivery application services are an outcome of the coordination between platforms, food delivery drivers, and restaurants (Richardson, 2020). Therefore, in order to analyse users' behavioural intention to use of online food delivery applications, this study integrated the trust and risk variables with technology acceptance factors. Hence, with regard to this investigation, the potential for assessing the actual usage is an important development for subsequent research. In order to examine users' intention to utilise and the use of online food delivery applications as proposed by prior studies, this research expanded on by proposing an extended UTAUT2 model with the additional constructs of trust and risk, which were not considered in previous studies with regard to their capability to help improve the knowledge regarding customers of online food delivery services applications.