The effect of food safety risk perception on customers' purchase 1ntention37 2.4.12.. However, it is vital to keep up with clients to keep utilizing the service differentiate, increase c
Research by Vincent Cheow Sern Yeo, See-Kwong Goh, Sajad Rezaei
Behavioral intention tc livery ds online food delivery services (BIOFDS)
Figure 2 The model of research by Vincent Cheow Sern Yeo, See-K wong Goh, Sajad Rezaei (2017)
The Extended Model of IT Continuance and the Contingency Framework are the foundations of the integrated theoretical research model suggested in this study, which was done in the USA To empirically validate the research model, 224 valid surveys were obtained using the partial least square (PLS) route modeling technique The principles of this theory are that loyal customers are far more valuable to businesses and that organizations must retain customers in order to be profitable The use of this is broadened to demonstrate that e-satisfaction influences e-loyalty, and it has been found that the link between beliefs and continuing intentions is impacted by post-usage usefulness
The research aims to analyze the structural relationships between the consumer's attitudes and intentions for actions toward OFD services, the consumer's prior online purchase experience, the post-usage usefulness, the hedonic motivation, the price- saving orientation, the time-saving orientation, and the convenience motivation,
The results of this study can help marketers and businesses can come up with tactics and alter a customer's online engagement to improve the encounter and perhaps raise a customer's desire to utilize OFD services
However, the study has the following limitations: First, Just 224 responses were collected for this study, which is a relatively small sample size A bigger sample would have given a more accurate picture of the population Also, the samples that were collected—mostly students of Chinese ethnicity between the ages of 18 and 22
—were not large enough to adequately reflect the community Second, Just Malaysia was used for this study and the Klang Valley in particular As a result, the context will only be relevant to this nation and region Due to cultural differences, the adoption of technology, and several other variables, it could differ in various nations
2.3.1.2 Ressearch by HakJun Song, Wenjia Jasmine Ruan , Yu Jung Jennifer Jeon (2021) Ệ 1 Ệ 1
Figure 3 The model of research by HakJun Song, Wenjia Jasmine Ruan , Yu Jung Jennifer Jeon (2021)
A group of writers examined how users adopted an on-demand food delivery app and assessed the influence of marketing communication on consumer behavior using
25 two theories: the attention-iterest-desire-action (AIDA) model and the technology acceptance model (TAM) This has broader applicability in that marketing communication now acts as an outside force in the process of accepting new innovations Also, it has been demonstrated that demand for the app is sparked by a person's attitude toward accepting new technologies The paper suggests a fresh approach for fusing models from the disciplines of marketing communication and information systems The research aims to use marketing communication and the TAM and AIDA models to determine how prospects react to newly created food delivery applications
The results of this study can support the role of marketing communication in new technology adoption, develop a technology acceptance model that corrects the TAM and AIDA models' limitations, and establish a useful framework for comprehending why people accept new technology It also can help firm can develop convemia and useful apps without neglecting their marketing efforts Furthermore, a marketer can create media information that is meaningful, attractive, believable, and distinctive
However, the study has the following limitations: First, because the study only included Korean populations, its findings could not be applicable to other foreign markets Second, It is likely that the sample won't logically reflect a larger population because more than 75% of the respondents in this survey are between the ages of 20 and 39 When compared to previous generations, younger generations are frequently more familiar with new technologies and have different perspectives on new business models As a result, it can have an impact on the results.
Research by Chanmi Hong, Eun-Kyong( Cindy) Choi, Hyun-Woo( David)
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Figure 4 The model of research by Chanmi Hong, Eun-Kyong( Cindy) Choi, Hyun-Woo( David) Joung
According to the study's findings, performance expectations, trust, and social influence all have a beneficial impact on consumers’ intentions to use food delivery services online Positive correlations between determinants are also found, including those between social influence and performance expectation, effort expectancy and performance expectancy, and the considerable effect of trust on effort expectancy and risk perception related to food safety The correlations between the determinants and purchase intention are also noticeably moderated by usage frequency On the basis of the findings, restaurants and online meal delivery service providers are given theoretical contributions and management recommendations
It is important to recognize this study's limitations despite its theoretical and practical contributions Future research should, if at all feasible, measure customers’ actual purchases of OFDS since buy intention was used in this study as a stand-in
27 for real purchasing behavior Additionally, the data were gathered during the COVID-19 pandemic in 2020, which would have had an impact on the respondents’ psychological well-beimg and way of life, as well as their replies The results of this study in a "new normal” and post-pandemic environment need to be confirmed by other research Additionally, the present study's main focus was on the respondents’ perceptions of trust and the danger to food safety as factors impacting PI, SI, and technology-related qualities (i.e., EE and PE)
Third-party food delivery as a whole was taken into account in this analysis rather than just one particular OFDS platform Future research might examine how they differ in terms of the decision-making process because each person has unique preferences and attitudes toward various platforms
We choose to use the UTAUT model of Venkatesh et al (2003) in order to increase the accuracy of the forecasting of the factors influencing OFDS buying intention because of the fit of the aforementioned models This model includes the three primary UTAUT components of performance expectancy, effort expectancy, and social impact incorporate two extra constructs (1.¢., trust and food safety nsk perception) We included the variables "Safety food risk perception” and "Trust" to the model since we think that consumers' decisions to use OFDS and recent research in Vietnam both heavily depend on these two criteria Furthermore, the COVID-19 pandemic in 2020, which may have had an impact on respondents’ psychological well-being and lifestyles, and therefore, their reactions, was the time when preceding research's data were gathered In a "all-new normal" and post-pandemic period, our research is obligated to support the conclusions of this study The research model is
28 conceptualized in the manner that is indicated, based on the literature review and the hypothesis developed
Figure 5 Proposed model of this research illustrates the factors affecting to customers purchase intention 2.4, Hypothesis development
2.4.1, The effect of social influence on effort expectancy
People's perceptions about their peer groups, families, and friends have a direct impact on their behavior (Fishbein & Ajzen, 1977), as does the idea that these people expect a user to use a certain technology (Schepers & Wetzels, 2007) With this justification, the effect of social influence on expected effort has been empirically investigated across several fields For instance, Shen et al (2006) examined the influence of social influence on the online course delivery system and showed that peers’ perceptions of the usability of the online course were unaffected Contrarily, Choi and Chung (2012) demonstrated how social influence had a beneficial impact on effort expectation while utilizing social networking sites and suggested that social pressure makes effort expectancy easier to find
Hypothesis deveẽopmeft - 5: 2c 2211212211111 151 1111191111119 1 1121118111811 gvkg 29 1 The effect of social mmfluence on effort expeCcfancy - - co cecssssse2 29 2 The effect of social influence on performance expectancy
The effect of social influence on consumers' buying intentions
When placing an order, OFDS requires customers to use technology like mobile apps; as a result, it's critical to comprehend customers’ purchase intentions, which
30 reliably predict customers’ actual purchasing behavior (e.g., Ajzen et al., 2009; De Canni'ere et al., 2010; Fishbein & Ajzen, 2011) Social influence boosts purchase intention, according to several research on technology-related customer behavior, including online aircraft ticket purchases (Escobar-Rodrguez & Carvajal-Trujillo,
2014), mobile banking (Bhatiasevi, 2016), and diet applications (Okumus et al.,
2018) Beldad and Hegner (2018), for instance, looked at the variables influencing fitness application users’ desire to use the app continuously and discovered social influence to be a major predictor According to several research on OFDS, consumers’ decisions to use OFDS are favorably impacted by the opinions of their closely-knit refer- ence groups (Al Amin et al., 2021; Lee et al., 2019; Roh & Park, 2019; Troise et al., 2020)
H3 Social influence has a positive impact on consumers’ buying intentions toward OFDS
The effect of effort expectancy on performance expectancy
The degree of comfort associated with using the system is referred as the expectation of effort This component is derived from the perceived ease of use, as described by Venkatesh et al (2003) In Davis's (1989) technology acceptance model (TAM), perceived ease of use was shown to have a positive effect on usefulness (TAM, 1989), and both concepts were considered equivalent As a result, the expected positive effect of effort expectancy on performance expectancy has been documented in a variety of new imnovation adoption scenarios, including continuous behavior, such as the use of fitness apps, mobile learning apps, mobile shopping apps, and fashion image search apps, all of which have been demonstrated to have a positive effect on behavior (Beldad and Hegner, 2018; Hur et al., 2017; Al-
Emran et al., 2020) These studies demonstrate that clients who intend to exert more effort are more likely to believe that utilizing new technology would increase their productivity In reference to OFDS, Zhao and Bacao's (2020) study stated that ease of use no longer had a significant impact on the usefulness of the service in cases of repeated usage; however, most researchers who investigated OFDS-related topics discovered that once customers experienced ease in using OFDS, they tended to consider the service useful (Roh & Park, 2019; Troise et al., 2020)
H4 Effort expectancy positively affects performance expectancy.
The effect of effort expectancy on customers’ purchase intention
Effort expectancy is a significant indicator of a user's intention to continue to embrace and use internet and mobile technologies (e.g., Beldad & Hegner, 2018; Bhatiasevi, 2016; Okumus et al., 2018) According to Consult (2002), effort expectancy refers to how ready individuals are to experiment with new technology and make rapid judgments about their outcomes People are more inclined to accept applications if they think they may be used nght away, according to Davis
(1989) Consumers trust in the system will grow if it is clear-cut, easy to use, and manipulable since they will believe the service provider is giving them convenience (Gefen, Karahanna, & Straub, 2003) According to Zhou (2012), making mobile systems simple to use is a feature that promotes trust When consumers think that technology is too sophisticated to be regulated, they view the risk as being higher and are less likely to trust it Customers have little trouble in recent research in the OFDS literature reveal that there is no substantial correlation between effort expectancy and intent when using new apps, including OFDS However, as the use of smartphones and apps has become proficient (Lee et al.,
2019; Zhao & Bacao, 2020), and the interface of smartphone apps has stabilized as a result of the development of information and communication technologies over time (Lee et al., 2019), customers have less difficulty using new apps (Lee et al., 2019; Zhao & Bacao, 2020) On the other hand, several studies on consumers’ willingness to buy OFDS have shown that the anticipation of effort is positively related to customers’ intent to buy (Ray et al., 2019; Roh & Park, 2019; Troise et al., 2020) The majority of the literature implies that customers are more likely to acquire technology if they think that it will be easy and uncomplicated to use, despite the fact that OFDS research offers contradictory data about the influence of effort anticipation on purchase intention
HS5 Effort expectancy positively affects customers’ purchase intention toward OFDS.
The effect of performance expectancy on customers’ purchase intention
1989) According to the idea of performance expectations, the customer expects usefulness, cost saving, fuel savings, and work efficiency Customers' perceptions of efficiency and effectiveness while utilizing physical delivery services online have a favorable influence on their trust, according to Kang and Namkung (2018) The performance expectation factor for the food delivery service app was investigated to see if it had an effect on users’ intentions to keep using the app (Alalwan, 2019; Lee et al., 2019)
Converging research has shown a connection between OFDS performance anticipation and purchase intention (Hong et al., 2021; Jun et al., 2021; Lee et al., 2019; Zhao & Bacao, 2020) Performance expectation, for instance, was shown by Hong et al (2021) to be the most important factor in deciding whether or not customers will purchase OFDs PE, in the opinion of Venkatesh et al., has a superb impact on a user's choice to embrace technology (2003) Zhao and Bacao (2020) revealed similar findings and showed that OFDS customers planned to keep using the service due to its usefulness On the basis of the findings of this study, the following hypothesis is put forth:
H6 Performance expectancy positively affects customers’ purchase intention toward OFDS.
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Client trust as stated in the definition, it is "a consumer's subjective expectation that the party selling the product will fulfill its contractual obligations in a timely manner.” (Kim et al., 2008, p 545) Customer trust may be defined as the customers’ faith in the OFDS to carry out its transaction responsibilities regarding orders in a trustworthy way when the idea is applied to the OFDS context In several research (Gefen, Karahanna, & Straub, 2003; Lai et al., 2013; Nguyen et al., 2019; Vatanasombut et al., 2008), the importance of trust in online/mobile services has been emphasised A crucial factor in recognizing service convenience is trust in technology and service providers (Lai et al., 2013; McCloskey, 2006) Particularly, after they trust e-commerce, online shoppers simply discover the convenience of online buying (McCloskey, 2006) In a similar manner, effort anticipation is positively influenced by confidence in an online bed and breakfast booking website (Lai et al., 2013) In contrast, customers have trouble identifying the service
34 provider's potential and instead place a greater emphasis on any potential hazards if they accept that the service provider is unreliable (Beldad & Hegner, 2018) H7 Trust positively affects effort expectancy
2.4.8 The effect of trust on perfomance expectancy
The benefits are perceived of the services, such as usability, is also made easier by trust in e-vendors (Beldad & Hegner, 2018; Gao & Bai, 2014; Lai et al., 2013; McCloskey, 2006) For instance, McCloskey (2006) showed that customers of online shopping become more aware of the advantages of doing so if they have confidence that their financial and personal data would be safely preserved Additionally, Gao and Bai (2014) found that determining the utility of Internet of Things technology depends critically on one's level of confidence in the service provider Beldad and Hegner (2018) found a substantial positive link between performance expectations and service provider trust in the context of a fitness application
As far as we are aware, none of the current OFDS research have looked at the correlations between performance expectancy and effort expectancy, despite several studies showing a major influence of confidence in online/mobile service providers on both variables in a variety of disciplines The study assumes that if customers think OFDS provides a trustworthy service and upholds its obligation, then customers are more likely to consider the service helpful This study builds on the converging evidence of past research on customers’ usage behavior of online and mobile services This is because OFDS and online/mobile service providers both conduct business through online/mobile channels
H8 Trust positively affects performance expectancy
2.4.9 The effect of trust on customers’ purchase intention
Trust has an undeniably favorable direct influence on usage intention, according to several research on consumer behavior connected to technology (Cho et al., 2019; Gefen et al., 2003; Lai et al., 2013; Nguyen et al., 2019; Vatanasombut et al., 2008; Zhao & Bacao, 2020) In specifically, Nguyen et al.'s (2019) research showed that users’ intentions to use an online grocery purchasing website are favorably influenced by their confidence in the service According to Vatanasombut et al
(2008), confidence in an online banking service is a major driver for continued usage of online banking services Customers are more likely to utilize OFDS when they think the service will go well, according to previous research on the subject (Cho et al., 2019; Hong et al., 2021; Jun et al., 2021; Muangmee et al., 2021; Zhao
H9 Trust positively affects customers’ purchase intention toward OFDS
2.4.10 The effect of trust on food safety risk perception
Consumers are not entirely free from food safety risks while consuming food; in fact, consumers’ perceptions of the risk have a greater impact on their behavior than the actual risk itself (Yost & Cheng, 2021) According to Nardi et al (2020),
"Individuals' perceptions of whether a quality (safety) is present in food and the likelihood and seriousness of any negative effects on their health that may result from consuming it " (p 2) is the definition of food safety risk perception Food safety is a delicate topic in the restaurant sector because customers’ perceptions of food safety risk have a significant impact on their decisions to purchase food (Dang
& Tran, 2020; Ha et al., 2020; Ling, 2018; Shim & You, 2015) Yeung and Morris
(2001) emphasize that consumers’ risk aversion increases when it comes to food
36 safety risk concerns, and most customers take the risks seriously since they are vulnerable in terms of their health Shim and You (2015) argued that customers were less inclined to buy food items connected to the 2008 melamine milk crisis and imported from the region of a nuclear plant disaster if they realized the products may pose a food danger
H10 Trust negatively affects food safety risk perception
2.4.11 The effect of food safety risk perception on customers’ purchase intention The concept of decision-making for consumers based on trust created by Kim et al (2008) states that a technology or service's adoption is hindered by perceived dangers; however, these risks may be reduced by growing client confidence in the product or service In particular, trust in service providers gives customers the assurance they need to voluntarily embrace a technology or service, despite the perceived dangers and uncertainties involved (Hsiao et al., 2010; Kim et al., 2008) According to various research, trust is crucial in addressing perceived risks that customers may experience while making purchasing decisions (Chang & Chen, 2008; Hsiao et al., 2010; Kim et al., 2008; Marriott & Williams, 2018) For instance, Dang and Tran (2020) found that customers who have confidence in food suppliers often view the danger of consuming meat from sick animals during an animal disease epidemic as low Ha et al (2020) hypothesized, in contrast, indicates consumers who have little trust in food suppliers are more likely to believe the products are dangerous This study makes the hypothesis that consumers who have greater faith in OFDS may be less likely to be aware of potential threats to food safety, such as contamination and food poisoning, which can happen throughout the delivery process
A restaurant foodborne illness outbreak, in particular, results in high expenses, a significant percentage of the restaurant's revenue was lost due to lawsuits, penalties, and medical costs (Arendt et al., 2013) Food safety-related issues can also result in foodborne illness, further endangering the health of customers Additionally, because cooked foods are delivered to customers' doorsteps rather than directly to consumers from the kitchen, food delivery firms cannot be free from food safety hazards A survey of OFDS consumers found that 36% had problems with food freshness and temperature (Freer, 2020) Customers also stated that worries regarding food safety and cleanliness prevented them from using OFDS (Opensurvey, 2020)
H11 Food safety risk perception negatively affects customers’ purchase intention toward OFDS
2.4.12 The moderate effect of usage frequency
Enhanced comprehension of the effects and advantages of the product or service is formed through more buying experience (Hernandez et al., 2010) Usage frequency has a significant impact on customer decision-making because frequent users change their attitudes and views about the service over time by accepting more knowledge about it (Li ebana-Cabanillas et al., 2016) (Boulding et al., 1993) As loyal consumers boost income for businesses by expecting value from excellent service experiences, retaining frequent customers 1s crucial for the success of any enterprise (Umashankar et al., 2017) 2019 (Alshurideh) For instance, Liang and Zhang (2011) showed that first-time diners! desire to return is primarily influenced by their overall satisfaction with their dining experience, whereas regular diners tend to be more inclined to return to a restaurant when the establishment effectively handles and engages with its patrons For the purpose of reviewing intentions toward a location, Tosun et al (2015) examined the distinct behaviors of regular and
38 infrequent visitors According to the study, whereas first-time visitors gave little weight to the destination emotive image, frequent travelers gave it substantial consideration when deciding whether or not to return Customers with regular interactions may be better able to comprehend the outcomes and advantages of OFDS; in addition, their reference groups’ influence, level of service trust, and perception of the danger to food safety may be different from non-frequent consumers
H12a-e Usage frequency moderates the relationships between the determinants and customers’ purchase intention toward OFDS
RESEARCH METHODOLOGY Q LH He 39 SN u3lv::ểểâaaậặỤV
Questionnaire des1ứm - 1 c1 201121112211 152125 1111115115111 1111811511110 1 1k ket 40 3.3 Measurement sœaẽeĐ - - c1 1 201211121121 1121 11111111111 11 1111111111111 11 111 1x HH 4]
A questionnaire includes two parts: Demographics and the Main question used to study people who used online food delivery services Each part of the questionnaire has the purpose of helping the study to have sufficient data The factors are ranked through the 5-point- Likert scale where | = strongly disagree and increases to 5 = strongly agree The data was collected in March 2023 through social media platforms such as Facebook, and Instagram using Google Form The survey was sent to the participants who have experience OFDS in crowded cities such as Hanoi, Ho Chi Minh City, Da Nang City,
The survey begins with an Introduction that provides general information for the respondents to understand about the research: Who are the authors, what is the research about, what is the purpose of this survey, instructions for answering this questionnaire, information about the concept of usage frequency of online food delivery services and all the information about the participants conducting the survey is kept confidential
The next part is the Demographic question The survey collects information about gender, income, education, age, employee status, marital status, and usage frequency of online food delivery services The questionnaire has screening questions developed to ensure that the participants have used online food delivery services To make sure all
41 respondents have already experienced and attitudes toward OFDS so they could provide accurate information for variables in the research
In the main questions part participants answer questions about the factors that affect their usage frequency OFDS There are stx factors the measure which is how the society affects their intention (SI), the ease of use of the system (EE), performance expectancy (PE), the trust for food delivery service (TR), perception of risks in the process of ordering food online (FSRP), and last is the participants’ purchase intention (PI) and are ranked by the 5 point scale
The questionnaire ended with a thank you letter to all the participants who took the time to complete the questionnaire so that the study had enough data for analysis
The question model is based on construction scale theory and prior studies on how the factors affect the purchase intention of customers in crowded cities in Vietnam, Hanoi Capital, Ho Chi Minh City, and Da Nang City This consists of 6 factors and 17 items:
(1) Social influence has 2 items; (2) Effort expectancy has 3 items; (3) Performance expectancy has 3 items; (4) Trust has 3 items; (5) Food safety risk perception has 3 items; (9) Purchase intention has 3 items The scale used in this study is the five-point Likert scale, where | = strongly disagrees, 2 = disagrees, 3 — uncertain, 4 = agree, and
5 = strongly agreed to measure the degree of agreement of the surveyors with the factors
SH Family, friends, and Xie et al (2017) colleagues think I should use online food delivery services for meals
S22 People I know think that using online food delivery services is a good idea
EFFORT EXPECTANCY (EE) EE1 My interactions with Castaneda et al
OFDS apps/websites are (2007); Xie et al clear and easy to (2017) understand EE2 I easily mastered the use of vision and navigation systems through the use of food delivery EE3 Overall, OFDS apps are easy to use PERFORMANCE EXPECTANCY (PE) PE1 I use online fast food Castaneda et al delivery services to order (2007) meals because it is a very convenient way
PE2 Using online fast food
43 delivery services has made my life easier
PE3 Using OFDS is a useful way to order food
Tl I trust an OFDS Hung et al (2006)
T3 I trust an OFDS to do the job right
FOOD SAFETY RISK PERCEPTION (SF)
SRI When I order food through Lando et al (2006) food delivery services, I can get food poisoning due to the food delivery
SR2 Unhygienic food still being delivered to customers by online food delivery services is a notable problem
Food delivered by online food delivery services may be contaminated with bacteria, making customers sick
PH I am planning to use a food Yeo at al (2017) delivery service in the future
PI2 If possible, I will try to use the food delivery service
PI3 I will use food delivery services if necessary
The quantity of samples selected is significant since it determines the study's dependability and the accuracy of its findings To determine the size of the sample in this study, two formulas of researchers were applied
The first formula drew from Hair, Anderson, and Black (1998) According to them, they found that the mmimum sample size should be five times the total observed variable The formula is as follows:
N: the minimum sample size m: the number of questions
Based on this formula, my team has the result: N = 5*17 = 85
Research by Tabachnick and Fidell (1996) indicates that the necessary minimum sample size is:
M: the number of independent variables
Based on this formula, my team has the result: N = 50 + 8*2 = 66
Thus, the minimum required sample is 85, however, in order to maximize the validity and persuasiveness of the research, my team projected that it would gather roughly 330 samples.
Collection method c1 2012111211211 113111 11111111111 11 1111111111111 111111 na 45 9c nh a4
We have created a survey using the Google Form tool and this Survey for this study will take about 2-3 minutes for participants to think and act After completing the questionnaire, in order to attract as many survey participants as possible, we sent the survey to community groups, and groups/clubs in the university, and sent it to fnends and relatives via Messenger and Zalo
The survey took place at the end of March, from March 21 to March 27, and after 1 week the group received more than 330 responses However, after removing the faulty data at the end of the survey, the results showed 300 valid responses to use for further data analysis
The quantitative method was chosen for this research and the data were collected through a questionnaire After collecting valid data, the data will be analyzed by Smart PLS4 software
Choosing using Smart PLS4 because it has a number of advantages over regression for structure-activity correlation, including the capacity to robustly handle more descriptor variables than compounds, nonorthogonal descriptors, and multiple biological results, while offering more predictive accuracy and a significantly lower risk of chance
46 correlation In addition, to avoid mistakes in analyzing the data variables, the team needs to take note of the recommendations as follow:
Discriminant validity Sqrt (AVE) > Latent variable correlations
Table 3 2 Criteria for measuring SEM in Smart PLS
Ajzen, I, Czasch, C and Flood, M.G (2009) “From intentions to behavior: Implementation intention, commitment, and conscientiousness,” Journal of Applied Social Psychology, 39(6), pp 1356-1372 Available at: https://doi.org/10.1111/j.1559-1816.2009.00485.x
Al Amin, M ef al (2021) “Using mobile food delivery applications during COVID-19 pandemic: An extended model of planned behavior,” Journal of Food Products Marketing, 27(2), pp 105-126 Available at: https://doi.org/10.1080/10454446.2021.19068 L7
Al-Emran, M., Arpaci, I and Salloum, S.A (2020) “An empirical examination of continuous intention to use M-learning: An integrated model,” Education and Information — Technologies, 25(4), pp 2899-2918 — Available — at: https://doi.org/10.1007/s10639-0 19- 10094-2,
Alshurideh, D.M (2019) “Do electronic loyalty programs still drive customer choice and repeat purchase behaviour,” Jnternational Journal of Electronic Customer Relationship Management, 12(1), p 40 Available at: https://doi.org/10.1504/1jecrm.2019.098980
Anderson, J.C and Gerbing, D.W (1988) “Structural equation modeling in practice: A review and recommended two-step approach.,” Psychological Bulletin, 103(3), pp 411-423 Available at: https://doi.org/10.1037/0033-2909.103.3.411 Arendt, S.W., Paez, P and Strohbehn, C (2013) “Food safety practices and managers’ perceptions: A qualitative study in Hospitality,” /nternational Journal of Contemporary Hospitality Management, 25(1), pp 124-139 Available at: https://doi.org/10.1108/09596111311290255,
Aslam, W et al (2019) “Underlying factors influencing consumers’ trust and loyalty in e-commerce,” Business Perspectives and Research, 8(2), pp 186-204 Available at: https://dot.org/10.1177/227853371988745 L
Assaker, G., O’Connor, P and El-Haddad, R (2020) “Examining an integrated model of green image, perceived quality, satisfaction, trust, and loyalty in Upscale Hotels,” Journal of Hospitality Marketing & Management, 29(8), pp 934-955 Available at: https://dotorg/10.1080/19368623.2020.1751371
Bagozzi, R.P and Yi, Y (1988) “On the evaluation of structural equation models,” Journal of the Academy of Marketing Science, 16(1), pp 74-94 Available at: https://do1.org/10.1007/bf02723327
10 Baumgartner, H and Steenkamp, J.-B.E.M (2001) “Response styles in marketing research: A cross-national investigation,” Journal of Marketing Research, 38(2), pp 143-156 Available at: https://doi.org/10.1509/jmkr.38.2.143.18840
11 Beaton, K (2021) Which consumers are driving food delivery growth? The Food Institute https://foodinstitute.com/focus/which-consumers-are-driving-food- delivery-growth/
12 Beldad, A.D and Hegner, 5.M (2017) “Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach,” /nternational Journal of Human— Computer Interaction, 34(9), pp 882-893 Available at: https://doi.org/10.1080/10447318.2017.1403220
13 Bhatiasevi, V (2016) “An extended UTAUT model to explain the adoption of mobile banking,” /nformation Development, 32(4), pp 799-814 Available at: https://do1.org/10.1177/0266666915570764
14 Bonn, M.A ef al (2015) “Purchasing wine online: The effects of social influence, perceived usefulness, perceived ease of use, and wine involvement,” Journal of Hospitality Marketing & Management, 25(7), pp 841-869 Available at: https://doi.org/10.1080/19368623.2016.1115382
15 Boulding, W ef al (1993) “A dynamic process model of service quality: From expectations to behavioral intentions,” Journal of Marketing Research, 30(1), p 7 Available at: https://doi.org/10.2307/31725 10
16 Bozionelos, N and Simmering, M.J (2021) “Methodological threat or myth? evaluating the current state of evidence on common method variance in Human Resource Management Research,” Human Resource Management Journal, 32(1), pp 194-215 Available at: https://doi.org/10.1111/1748-8583.12398
17.Cai, R and Leung, X.Y (2020) “Mindset matters in purchasing online food deliveries during the pandemic: The application of Construal level and regulatory focus theories,” /nternational Journal of Hospitality Management, 91, p 102677 Available at: https://doi.org/10.1016/).14jhm.2020.102677
18 Castafieda, J.A., Mufioz-Leiva, F and Luque, T (2007) “Web acceptance model (WAM): Moderating effects of user experience,” /nformation & Management, 44(4), pp 384-396 Available at: https://doi.org/10.1016/}.im.2007.02.003
19 Hsin Chang, H and Wen Chen, S (2008) “The impact of online store environment cues on purchase intention,” Online Information Review, 32(6), pp 818-841 Available at: https://dot.org/10.1108/14684520810923953
20.Cho, M., Bonn, M.A and Li, J.J (2019) “Differences in perceptions about food delivery apps between single-person and multi-person households,” /nternational Journal of Hospitality Management, 77, pp 108-116
Available at: https://doi.org/10 10 16/1.1]hm.2018.06.019,
21 Choi, G and Chung, H (2012) “Elaborating the technology acceptance model with social pressure and social benefits for social networking _ sites (SNSS),” Proceedings of the American Society for Information Science and Technology, 49(1), pp 1-3 Available at: https://doi.org/10.1002/meet 14504901376
22 Ciftei, O., Choi, E.-K.(C and Berezina, K (2021) “Let’s face it: Are customers ready for facial recognition technology at quick-service restaurants?,” /nternational Journal of Hospitality Management, 95, p 102941 Available at: https://doi.org/10.1016/).1jhm.202 1.102941,
23 Dabholkar, P.A and Bagozzi, R.P (2002) “An attitudinal model of technology- based self-service: Moderating effects of consumer traits and situational factors,” Journal of the Academy of Marketing Science, 30(3), pp 184-201 Available at: https://doi.org/10.1177/00970302030003001
24 Dang, H.D and Tran, G.T (2020) “Explaining consumers’ intention for traceable pork regarding animal disease: The role of Food Safety Concem, risk perception, trust, and Habit,” /nternational Journal of Food Science, 2020, pp 1-13 Available at: https://doi.org/10.1155/2020/8831356
25 Davis, F.D (1989) “Perceived usefulness, perceived ease of use, and user acceptance of Information Technology,” MM/S Quarterly, 13(3), p 319 Available at: https://do1 org/10.2307/249008
26.De Canniére, M.H., De Pelsmacker, P and Geuens, M (2009) “Relationship quality and purchase intention and behavior: The moderating impact of relationship strength,” Journal of Business and Psychology, 25(1), pp 87-98 Available at: https://doi.org/10 1007/s 10869-009-9127-z,
27.DeSimone, J.A and Harms, P.D (2017) “Dirty data: The effects of screening respondents who provide low-quality data in survey research,” Journal of Business and Psychology, 33(5), pp 559-577 Available at: https://doi.org/10.1007/s10869-017-9514-9
28 Escobar-Rodríguez, T and Carvajal-Trujilo, E (2014) “Online purchasing tickets for low-cost carriers: An application of the unified theory of acceptance and use of technology (utaut) model,” Tourism Management, 43, pp 70-88 Available at: https://doI.org/10 1016/1.tourman.20 14.01.017
29 Hill, R.J., Fishbein, M and Ajzen, I (1977) “Belief, attitude, mtention and behavior: An introduction to theory and research.,” Contemporary Sociology, 6(2), p 244 Available at: https://do1.org/10.2307/2065853
30 Fishbein, M and Ajzen, I (2011) “Predicting and changing behavior.” Available at: https://do1 org/10.4324/9780203838020
31 Fornell, C and Larcker, D.F (1981) “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, 18(1), p 39 Available at: https://doi.org/10.2307/3151312
32 Li, X and Namkung, Y (2021) “The effect of service convenience on customer satisfaction and behavioral intention in food delivery apps,” foodservice
Management Society of Korea, 24(4), pp 73-98 Available at: https://do1 org/10.47584/jfm.2021.24.4.73
33 Fuller, C.M ef al (2016) “Common methods variance detection in Business Research,” Journal of Business Research, 69(8), pp 3192-3198 Available at: https://doiorg/10.1016/) jbusres.2015.12.008
34 Gao, L and Bai, X (2014) “A unified perspective on the factors influencing consumer acceptance of internet of things technology,” Asia Pacific Journal of
Marketing and Logistics, 26(2), pp 211-231 Available at: https://do1org/10.1108/apjml-06-2013-0061
35 Gefen, Karahanna and Straub (2003) “Trust and tam in online shopping: An integrated model,” MIS Quarterly, 27(1), p 51 Available at: https://do1.org/10.2307/300365 19
36 Gunden, N., Morosan, C and DeFranco, A (2020) “Consumers’ intentions to use online food delivery systems in the USA,” /nternational Journal of Contemporary Hospitality Management, 32(3), pp 1325-1345 Available at: https://do1.org/10.1108/14jchm-06-2019-0595
37 Black, W and Babin, B.J (2019) “Multivariate Data Analysis: Its approach, evolution, and impact,” The Great Facilitator, pp 121-130 Available at: https://doi.org/10.1007/978-3-030-06031-2 16
38 Han, H and Hyun, 8.8 (2017) “Impact of hotel-restaurant image and quality of physical-environment, service, and food on satisfaction and intention,”
International Journal of Hospitality Management, 63, pp 82-92 Available at: https://do1.org/10.1016/).1jhm.2017.03.006
39 Ha, T.M., Shakur, S and Pham Do, K.H (2020) “Linkages among food safety risk perception, trust and information: Evidence from Hanoi consumers,” Food Control,
110, p 106965 Available at: https://doi.org/10.1016/).foodcont.2019.106965
40 Henseler, J., Ringle, C.M and Sarstedt, M (2014) “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, 43(1), pp 115-135 Available at: https://do1 org/10.1007/s11747-014-0403-8
41.Hemandez, B., Jiménez, J and Martin, M.J (2010) “Customer behavior in electronic commerce: The moderating effect of e-purchasing experience,” Journal
53 of Business Research, 63(9-10), pp 964-971 Available — at: https://doiorg/10.1016/).jbusres.2009.01.019
42 Hong, C ef al (2021) “Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic,” Journal of Hospitality and Tourism Management, 48, pp 509-518 Available at: https://do.org/10.1016/.jhtm.2021.08.012
43 Hsiao, K.L ef al (2010) “Antecedents and consequences of trust in online product recommendations,” Online Information Review, 34(6), pp 935-953 Available at: https://doi.org/10.1108/14684521011099414
44 Huang, J.L et al (2011) “Detecting and deterring insufficient effort responding to surveys,” Journal of Business and Psychology, 27(1), pp 99-114 Available at: https://doI.org/10 1007/s 10869-0 11-923 1-8