INTRODUCTION
Research Background
The market for online products and services has been increasing rapidly as most daily activities nowadays involve the use of internet and mobile applications In Internet of Things era, this is not only potential but also essential for organization to work on online mode as it is gradually changing the traditional business model With more users worldwide, organizations will be able to engage and interact with consumers through online marketplace as promoting their products and services via website and mobile applications It has embarked many scholars studying on topic of online products and services for the past decade Notably, online shopping emerged as one of the most popular topic thanks to its practical implications on marketing and business strategy
According to Internet World Stats (2018), Vietnam has an Internet penetration rate roughly 65.7% which shows that Vietnamese people are actively involved in online activities Notably, Singh & Matsui, 2017 found that the increasing in Internet penetration rate positively attribute to the growth of online shopping because Internet users perceived many benefits through online shopping Even though Vietnam is relatively behind in Internet universalization in comparison with more developing countries in South East Asia like Malaysia (77.3%), Thailand (82.2%), Singapore (82.5%), Vietnam is emerging as very promising market for e-commerce and online services development
In today fast-paced lifestyle, many people pursuing productive and time saving orientation by reducing the time for personal need activities Instead of waiting for a meal in restaurant or going out to buy food and drink, a part of our citizen intends to enjoy online services’ advantages Online food ordering services are gradually showing their efficiency by matching with marketplace need In general, food industry is arguably a saturated market and customers tend to notoriously fickle, witnessed by frequently change in taste, fashion, and ease of access Technology supports this process and with the development of wireless communication technology and high penetration rate of the Internet, food service businesses are now relying on technology as a marketing tool and main information resources (E Y Lee, Lee, & Jeon, 2017) Taking to that chance, online food ordering services has been emerging as a vital mechanism for food retailers to keep remaining fresh and mobilized toward customer demand Development of online food ordering services means not only virtually flexible choice of taste that the consumers benefit but also cost saving, real time interactive communication and entertaining (Yeo, Goh, & Rezaei, 2017)
There are two main categories of retailer that offered online food ordering services First is the typical fast food restaurants that provides delivery to homes such as Pizza Hut, McDonalds, Kentucky Fried Chicken For instance, as a pioneer in fast food chain, Kentucky Fried Chicken (KFC) early entered Vietnam market in 1997 and started online delivery services in Vietnam in 2014 and positively enhanced their customer choice and satisfaction Gradually, smaller food retailers also quickly response for online services competition Thanks to the development of food delivery services, they will be able to provide customer the same online food hub experience at low operation cost This demand created second category which is comprised of multiple restaurant mediums which connecting the customer and a large range of restaurants by providing online delivery services and flexible online ordering mechanism Today, the local market witnessed fierce competition in this category between many international players such as Grab Food, Go-Food and local services providers like Foody Nows or Vietnammm
However, market researcher Euromonitor (2018) also gave a report that online food ordering in Vietnam will be projected to top US$33 million by 2020 and expected to show annual growth rate of 23.5%
For an emerging industry like online food ordering services, it is way more essential to understand customer insight and behavior intention It is well known that online food ordering services has become nature in many countries as well as the benefit it offered to users were highly convincing as convenience and flexibility which highly valued in today lifestyle On the other hand, food chains and service provider of online delivery services promptly seize this opportunity to increase revenue and expand their business
However, the problems of online food ordering services in Vietnam that exists was the adoption of online food ordering services remains very low despite the growth in appearance of online food service providers Thus, assessment of the key factors in usage intention to the adoption online food ordering services is critical
In short, reasons motivating consumers to use a specific service from an online medium are always important for service providers Because there are new opportunities and rapid increase of internet users, it is essential to identify the factors affecting consumer’s usage intention to online food ordering services
Since online ordering services is a technology, there have been numerous studies related to technology adoption that have revealed several key determinants as performance expectancy, price value, perceive of ease to use and so on which positively influence behavioral intention (Macedo, 2017; Yoo et al 2015; Wagner et al., 2016) For instance, Yeo et al., 2017 found that the motivations behind consumers’ preference to use online services are convenience, time-saving or post-use experiences Some scholars (Lee et al
2017, Alavi et al., 2016; Tsang and Tse, 2005) convinced that shopping motivations associated with price values and enjoyment that consumer derived from shopping A research conducted in Japan of Singh & Matsui, (2017) found that trust plays important role in shaping customer behavior and use intention of online shopping for both physical and service products Early research on adoption of internet shopping (Citrin et al., 2006) indicated that consumer innovativeness significantly moderated the effect of internet usage on internet shopping behavior However, most of previous researches had mainly examined the adoption of online shopping in general and there is lack of papers have explained consumer’s behavior in specific domain activity like online food ordering (E
Y Lee et al., 2017; Yeo et al., 2017).
Research objectives
The current research aims to contribute an early empirical research to identify driven factors for using online food ordering services – a domain specific part of online shopping in Vietnam market The study mainly adapts UTAUT2 framework to understand motivations behind usage intention of online food ordering services Besides that, there are two noticeable variables were added in original framework In particular, author will investigate the direct effect of trust on customer usage intention and the moderating effect of consumer innovativeness moderate the influence of research constructs on its outcome
The research questions investigated by this study are as follows:
1 What are the factors that significantly influence customers’ adoption of online food ordering services in Vietnam?
2 To what degree consumer innovativeness moderates the relationship between those factors and usage intention of online food ordering services in Vietnam?
Research Scope
In general, this paper is the study of online consumption behavior of consumer in Vietnam market and from existing online food ordering service providers in Vietnam
The participants of this research mainly included but not limited to Vietnamese consumer
Thanks to online questionnaires and data collections via social network, online channel this study was able to flexibly access participant’s feedback regardless of geographic distance, race, gender difference or time, cost constraints.
Research Structure
Research paper consists of five chapters The first chapter introduces the research background regarding the foundation of academic research and practical problem which motivated author to conduct this study The second chapter namely literature review and hypothesis development which literally concern about previous studies that closely associated to research topic as well as to summarize key determinants which incorporate in hypotheses formulation The third chapter explained about research design, research procedures, following by pilot test and survey adjustment as well as measurement of variables Results and findings of this study will be discussed in the chapter 4 The last chapter concluded hypotheses and their implications, so that author suggests some theoretical, practical contributions as well as acknowledgment of several limitations which recommend for improvement in future research.
LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
Review of conceptual framework and literature background
Since online ordering services is a technology-based service, the extended unified theory of acceptance and use of technology (UTAUT2), which on purpose of analyzing technology acceptance in consumer context is considered as the latest framework, will be adopted in this study UTAUT2, the theoretical framework derived from TAM and decades of development from internal organization framework UTAUT (Venkatesh et al., 2003) is granted as a strong prediction framework UTAUT2 effectively explains and analyzes the user technology adoption behavior not only information technology products but also online services
The UTAUT2 has been formed by seven constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit As shown in Figure 2.1 UTAUT2 model not only consists of seven predictors but also have several moderators (age, gender, experience) involved in research model to illustrate more aspect of influential factors to behavior and use intention
UTAUT2 or Extended Unified Theory of Acceptance and Use of Technology has been widely adopted in many recent researches and firmly to be a powerful tool for studying technology adoption such as mobile learning (Kang & Fortin, n.d.), internet banking (Alalwan, Dwivedi, & Rana, 2017), mobile money services (Narteh, Mahmoud, & Amoh,
2017) and online shopping (Singh & Matsui, 2017)
Regarding to past researches which applied UTAUT2 as the main framework for studying, the moderator variable varied (Aswani, Ilavarasan, Kar, & Vijayan, 2018;
Escobar-Rodríguez & Carvajal-Trujillo, 2014; Khalilzadeh, Ozturk, & Bilgihan, 2017;
Leicht, Chtourou, & Ben Youssef, 2018) Since age, gender, experience mainly belong to demographic description, these factors have been discussed in general of sample population rather than moderating effects on whole model Moreover, many researches tend to develop UTAUT2 model by adding more observations and independent variable
Those latent variables have been contributing intuitive knowledge of technology acceptance in specific aspect of technology-based adoption
Figure 2.1 The UTAUT2 Model (Venkatesh et al 2012)
Review of relevant theoretical models
Along the time of technology development and adoption in every aspect of human living, scholars including researchers, scientists, social psychologists have continuously studied and proposed many theories explaining and predicting consumer habitual and foreseen behavior in a variety of technology domains There are many profound theories and models subject to customer behavior such as: Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), the Diffusion of Innovation (DOI), Motivational Model (MM), Social Cognitive Theory (SCT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) Apparently, these theories and models have been widely accepting in many different research fields, its application is suitable for including but not limited to marketing research, strategic business developments, social psychology explanation and so on From standpoint of technology acceptance study, by rotating of attention from organization to individual, it dramatically shifts research orientation from internal firm and organization to their customer behavior It explains why UTAUT model have been focused on technology inside company itself with 4 core predictors only (PE, EE, SI, FC), UTAUT2 have been developed significantly in consumer context with price value, habit and hedonic motivation
The Table 2.1 summarized several relevant theoretical frameworks which are baseline for main research framework in this paper and also supporting for hypothesis developments
Table 2.1 Models and Constructs Summary Author & Year Models and Theories Model Constructs
Trialability Image Voluntariness Of Use
(TRA) to measure performance and behavioral intention
(SCT) to determine usage of information systems
Performance Outcome Expectations Personal Outcome Expectations Affect
Model of PC Utilization (MPCU) to determine behavior of PC usage
Behavior (TPB) to determine behavior and intention
Davis et al (1992) Motivational Model
(MM) to describe behavior of technology adoption and use
Perceived Usefulness Perceived Ease of Use Attitude
2 (TAM 2) which is an extended TAM
Perceived Usefulness Perceived Ease of Use Subjective Norm Experience
Job Relevance Output Quality Result
Hypotheses development and conceptual model
In summary, research model will conclude seven constructs of the UTAUT2 framework in context of research object – online food ordering services as shown in Table 2.2
Table 2.2 Definition of research constructs
Extent to which using online food ordering services provide benefits for users (Venkatesh et al., 2003)
2 Effort Expectancy Extent to which ease of use consumers interact with online food ordering services (Venkatesh et al., 2003)
3 Social Influence Extent to which user of online food ordering services consider their behavior associated with other people’s belief (Venkatesh et al., 2003)
User perceived of availability of manual, function and support from online food ordering services (Venkatesh et al., 2003)
5 Hedonic Motivation User perceived of enjoyment and entertainment derived from using online food ordering services (Venkatesh et al.,
6 Price Value User rational evaluation in term of cost and benefit of using food ordering services (Dodds, Monroe, & Grewal, 1991, Venkatesh et al., 2012)
7 Habit Extent to which user tend to use online food ordering services because of prior experience (Venkatesh et al.,
8 Trust The degree that user behavior intention changes due to the credibility from online food ordering service providers
The degree that user makes innovation decisions independently due to novelty-seeking behavior (Midgley &
Performance expectancy (PE) is an individual's belief that using information systems will help achieve high performance in work efficiency (Venkatesh el at., 2003) There are 5 structures employed meaningfulness of performance expectation construct in their models such as Technology Acceptance Model (TAM) model (Davis & ctg., 1989;
1993); TAM 2 (Venkatesh & Davis, 2000), external motivation in the Motivational Model (MM) (Davis & ctg., 1992), purposive work in Model of PC Utilization (MPCU) model (Thompson & ctg., 1991), relative advantages in Innovation Diffusion Theory (IDT) (Rogers, 1995) and expected outcome in Social Cognitive Theory (SCT) model (Compeau & Higgins, 1995) Performance expectancy in online food ordering services can be understood as to extent customer perceived usefulness of using online services, whether it help them focus on other working task instead of going out in limited time and location nor reducing stress of meal preparation
Many scholars found key significant impacts of performance expectancy on customer behavior intention Amoh (2016); Yu & Ting (2011) in their research on banking services affirmed that performance expectancy plays an important role in evoking user behavior of internet banking services Customer was primarily driven by perceive of usefulness when it comes to automated public transportation usage intention (Madigan el at., 2017)
Hypothesis 1: Performance expectancy positively affects the customers’ intention to use online food ordering services
Effort expectancy or well known as perceived ease of use is the degree to which user manage to use and accept technology without significant difficulty (Venkatesh el at.,
2003) Singh & Matsui (2017) when study about customer behavior of online shopping for book and air ticket by carrying out UTAUT2 model, found that effort expectancy significantly influences individual behavior intention in online purchasing Furthermore, ease of ordering is an important factor since it related to numerous benefits of using online food ordering services such as: easy in placing order, tracking order or cancelling order (Tandon, Kiran, & Sah, 2018) Besides, Yeo et al (2017) by adopting SEM technique found that perceived ease of use significantly affects to customer attitude, thus influences behavior of accepting online food delivery service Even though that study limited to university student population in Klang Valley, Malaysia, its recommendation of further testing motivates author to conduct a study in Vietnam context
Nonetheless, it is argued that effort expectancy insignificantly influences customer behavior intention of consumers to adopt mobile internet services in Taiwan Wu, Tao and Yang (2007) Or, America customers’ attitude toward e-commerce platforms was not depended on ease of use by study of Yang (2010)
Thus, it is necessary to examine the association between effort expectancy in technology adoption, or online food ordering services in particular
Hypothesis 2: Effort expectancy positively affects the customers’ intention to use online food ordering services
Social influence is an individual’s perception significant influenced by others people belief that individual should use an information technology application (Venkatesh el at.,
2012) Duy Thanh el at (2014) by using multi regression statistic found that social influence (mainly from working environment) is one of the most influential factors affect an individual intention to apply cloud computer on their work Social influence impact or subjective norms is further studied and proved in Taylor et al (2011) research on students’ adoption of mobile application while surrounding by friends and business adoption It is further supported by Tandon et al., 2018 when studying on online shopping that the first recognition and influence of online shopping derived from other people than itself marketing
However, other study of technology acceptance rejected the positive role of social influence by proving insignificant effect of social influence with behavior intention of using wireless device (Lu, Yao and Yu, 2005), mobile money services (Narteh et al.,
Hypothesis 3: Social influence positively affects the customers’ intention to use online food ordering services
Facilitating condition is defined by technical infrastructure and support when user interacts with information technology (Venkatesh et al 2003) It is critical for a technology acceptance to sufficiently equipped with good facilities and constant supports for its user Especially in services sector, Wu et al (2008) held a conclusion of significant impact of facilitating condition to usage intention of 3G mobile services
Alalwan et al., 2017 supports this hypothesis by study of customer behavior intention to use online banking in Jordan
On the other hand, m-learning adoption has no significant association with facilitating condition (Jambulingam, 2013), or using mobile application has become so popular and easy that user no need referring to manual and support (Tam, Santos, & Oliveira, 2018)
Hypothesis 4: Facilitating condition positively affects the customers’ intention to use online food ordering services
Hedonic motivation may refer to enjoyment or pleasure derived from using a technology (Brown & Venkatesh, 2005, Yeo et al., 2017) Hedonic is positively associated with convenient motivation and post-usage usefulness, thus it influences to behavior intention of using online food delivery services (Yeo et al., 2017) This is further illustrated in
Tandon et al (2018) study by affirming joyfulness of choosing, placing food on internet and mobile application Hedonic motivation is highly appreciated as a critical factor for turning customer from traditional shopping behavior to online shopping (Dellaert &
Ruyter, 2004; Limayem, Khalifa, & Frini, 2000; Singh & Matsui, 2017)
Hypothesis 5: Hedonic motivation positively affects the customers’ intention to use online food ordering services
Price value is defined as “the trade-off between the cost paid for using the technology and the perceived benefits received” (Dodds, Monroe & Grewal, 1991) Price value as a consumer orientation factor plays an important role in influencing behavior intention (Venkatesh et al 2003) By conducting a research on mobile application usage in Brazil, Prata, Moraes & Quaresma (2012) found that reason behind purchasing intention of mobile application is perceived of price value It is also supported by quantitative research of Munnukka (2004) for mobile service pricing in Finland Cost of product and online services also described by saving orientation which is proved as a significant driving factor of online food delivery (Yeo et al., 2017)
Hypothesis 6: Price value positively affects the customers’ intention to use online food ordering services
Summarizing from the past literatures, habit has been conceptualized in two meanings: according to Kim el at (2005) habit is formalized as prior behavior (Kim and Malhotra 2005); meanwhile, habit is defined as the extent individual perform automatically a certain activity by learning (Venkatesh et al., 2012, Limayem et al 2007) Empirical findings about the role of habits in technology use have identified different basic processes whereby habits affect the usage intention of technology
Notably, Venkatesh et al (2012) employed habits in forming UTAUT2 model and described a significant result of habit effects on behavior intention Habitual technology acceptance also illustrated by post-use experience (Yeo et al., 2017) and even strongly associated with customer satisfaction (Lin, 2014) On contradiction, Lin and Lekhawipat
(2014) challenged that habit is not a significant predictor of online repurchase intention
Since there exists a controversy whether habit positively support for customer intention of technology adoption or not, this study will provide an empirical testing habit effect in online food ordering context
Hypothesis 7: Habit positively affect the customers’ intention to use online food ordering services
Research conceptual model
METHODOLOGY
Research Design
Regarding to research paradigm, positivism approach will be adopted along with deductive hypotheses development Online questionnaire was selected due to various advantages such as cost reduction, greater geographical coverage In order to avoid bias feedback from respondents, author implemented a cross-sectional study because participant can enjoy answering the questions based on their interest (Yeo et al., 2017)
Also, cross-sectional survey approach can guarantee the information collected is appropriate in target timeframe and study objectives (Olsen et al 2004)
The quantity of the survey participants and target valid response is about 200 According to Hair et al., 2009, the reasonable sample size should be five times higher than number of observation constructs in research model Thus, a sample size of 200 represents a ratio 10:1 which is affordable to proceed data analysis The online questionnaire was designed by google form and divided into two sections: the demographic section (gender, age, education, marriage status, income) and research observations section The items in the only questionnaire were primarily adopted from Venkatesh et al (2012); Natarajan, Balasubramanian, & Kasilingam (2017) A research study should refer the research constructs from previous study to validate the scale of measurement (Luarn & Lin,
2005) 5-point scale will be adopted in second section range from opinion of “Strongly Disagree” to “Strongly Agree” because a 5 or 7 point scale is more appropriate in data processing as well as its output likely accurate in social research (Dawes, 2007)
Nonetheless, 5-point scale slightly produce better feedback from respondent in term of survey clarity By avoiding confusion causal, it seems to encourage survey participants giving as their genuine answer Thus, it will help data collected more reliable.
Research Procedures
From standpoint of positivism approach, it makes sure that the data collected is independent with researcher's “feelings” (Yeo et al., 2017) Moreover, purposive sampling will be used on purpose of collecting sample which can represent for the population off specific research object Thus, it has been ensured that all participants selected acknowledge online food ordering service is and are user’s online food ordering services to give a better and more accurate population project to research objectives (Lee et al., 2017)
Kothari, 2004 suggested that pilot test is necessary in the questionnaires and survey constructions Thus, author initially conducted a pilot test in paper both English and Vietnamese versions with several Vietnamese respondents to ensure that the questions were correct translation and easy to understand The respondents were required to answer questionnaire in English and then in Vietnamese translation form, the order of questions on both versions were randomly mixed This step is necessary for manipulation checking and seems to be an effective way to avoid bias and inconsistent data Next, trial online survey had been distributed to seventeen persons who have been using online ordering services Thus, the procedure was duly followed purposive sampling technique On the other hand, (Ryu, Couper, & Marans, 2006) found that using incentives influence sample configuration by drawing more attention and responsibility from respondent because the pride of their valuable contribution Hence, author offered some small incentives like e- books and e-documents rewarding for sincere answer of respondents in the end of survey
In total, there were 27 selective respondents actively involved and contributed their valuable feedbacks on survey content and design Their comments were highly appreciated for this study accomplishment Since a minimum twenty participants of pilot test is sufficient to test the validity of the survey (Monette al at, 2002), the revised questionnaires after pilot test with 27 participants were reliable to proceed data collection in bigger sample size
In compared with original questionnaire items in English, the pilot test helps to eliminate some observations which cause misunderstanding The 5-point scale was also firmly accepted in data measurement
From standpoint of purposive sampling technique, the survey had been distributed widely via social network and weblog of some online food delivery mediums such as Foody, Lozi, Grab, in order to focus on the target respondents of the study The survey collection started from early April, 2019 and close data pool in April 23.
Sample Population
There are 224 questionnaires were duly collected from above duration Because of online survey limitation by self-administration response, there are 12 responses were invalid after screening by giving same answer for all questions In other words, 212 responses
(205 responses from Vietnamese survey and 7 collected from English version) are eligible to proceed in data analysis Interestingly, there are at least 3 respondents was from other nationalities who leaving their identity and comments for the survey and providing their perspective about Vietnam online food ordering services.
Variables and Measuring Instruments
As mentioned in previous chapter, the conceptual model was primarily adapted from UTAUT2 model (Venkatesh et al., 2012) It consists of 7 constructs namely performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value and habit The observation elements for variable were measured using a 5-point scale, ranging from “strongly disagree” to “strongly agree” Regarding pilot test on adoption of original questionnaire of UTAUT2, there were several questions removed because of participants’ feedback of meaning confusion Except facilitating conditions has 4 observation items and other remain 3 observation items only
Additional variable in this study – trust has been addressed in various research Trust was defined as individual perceived of secure and no privacy threats in product adoption (Zhao, Ni, & Zhou, 2018), or customer belief of integrity and risk-free when processing online service (Alalwan, Dwivedi, & Rana, 2017) In line with research assement on the role of trust, the measurement dimension for trust was fairly consistent Since online food ordering services mostly integrated with online payment services, some observation elements were adopted from banking and online payment context (Tandon, Kiran, &
Sah, 2018) Thus, author uses 3 observation items with 5-point scale ranging from
“strongly disagree” to “strongly agree” to evaluate trust effect on customer usage intention of online food ordering services
Goldsmith, 1991 described that measuring the consumer innovativeness is one of the most difficulty when researches conduct study on the diffusion of innovation By observing consumer tendency of purchase behavior on rock album, it supposed 21 measurement items (reversal included) for assessing consumer innovativeness with four point response format: Yes, Sometimes, Rarely, No This measuring instrument has been studied and developed for decades Recent scholars rationally adopted observation items for measuring consumer innovativeness with 5-point scale which mainly align with other variables’ dimension in their study It has been shown in adoption of internet shopping (Citrin et al., 2006), study on service loyalty in context of mobile service (Quoquab, Abdullah, & Mohammad, 2016), customer intention on installation of environmental friendly system (Chen, 2014) and high tech vehicle adoption (Leicht, Chtourou, & Ben Youssef, 2018) Therefore, this study also borrowed observation items from the recent literatures which using 5-point scale from “strongly disagree” to “strongly agree”.
Analysis Method
The current research will proceed data collected in descriptive analysis and inferential analysis using Statistic Package for Social Science software (SPSS) version 25.0, whose results and findings will be interpreted and presented in next chapter
The sample demographic (gender, age, education, income, post-experience) will be summarized in percentage tables and charts The tendencies of every latent variable will be also calculated and represented in the following chapter
Then, reliability test will be employed to examine whether data and scale measurement of observation construct is significant and reliable or not
There are 9 hypotheses will be evaluated by regression and hypothesis testing section
Subject to fitting regression models, it usually rises problem of multi-collinearity (Lin,
2006) In that case, several independent variables in research models are correlated, showed insignificant effect toward the dependent variable Thus, Pearson’s Correlation will be conducted The threshold for multi-collinearity is correlation at rate higher than 0.90 (Hair, Black & Babin, 2010)
Finally, multi linear regression will be used to find out linear relationship between the independent variables (PE, EE, SI, FC, HB, HM, PV, TR) and output - dependent variable (UI) of this study Significant testing, coefficient of determination, R 2 will be interpreted for final analysis results accordingly.
ANALYSIS RESULTS
Demographic Analysis
Closed-ended questions were used in the survey, so that the answers of respondents were limited Following demographic statistics describe general information of 212 participants of the empirical study
Table 4.1 Frequency of demographic information of participants
No Demographic characteristics Number of respondents Percentage (%)
Under 18 From 18 to 25 From 26 to 35 From 36 to 50 Over 50
High School Graduate Intermediate College Bachelor’s Degree Master’s Degree or Higher
As shown in Table 4.1, there are some representative criteria for each demographic characteristic The number of female respondents who also enjoyed online food ordering services is dominant with 76%, meanwhile male customer for food services stop by a modest 24% Thus, it reflects the different customer behavior depending on gender On the other hand, age of respondent mainly ranges from 18 to 35 (49%) which can be explained by the tendency of using technology service and their greater affordability in online shopping Next, among 212 participants, every 3 out of 4 have been completed bachelors’ degree, it implies education somehow related to technology literacy and recognition of using online food ordering services
Interestingly, the figure points out there is no relationship between marriage status and usage behavior of online food ordering services (52% and 48%) Nonetheless, majority of respondents are office staff who most likely spend their time on social media and mobile application which generally enjoying in online shopping activity and online food
Student Office Staff Manager Other
4% ordering services in particular Finally, monthly income statistic is relatively describing affordability of online food ordering service user since 79% respondents claim their monthly income from 5 to 20 million VND However, respondent from higher income class share a very modest proportion 4% of participant in this survey
Table 4.2 Respondents’ Frequency of Using Online Food Ordering Services
Source: Collected data analysis Since purposive sampling method employed in this research, most of survey participant actively use online food ordering service by almost 90% answer range from sometimes to very often Even though research sample size limited at 212 which is not enough for representation of population, but it is a positive signal of spreading online food ordering services in daily activities of customer nowadays
Table 4.3 Respondents’ post-experience of online food ordering services
Respondent’s experience Number of responses Percentage (%)
Direct from food restaurant only 10 5%
Through third party services only 28 13%
As stated in Table 4.3, most of participants (82%) in this study experienced both type of online food ordering services Thank to wide spread of online medium services, customer now can easily find food they want via third party services It means KFC or Lotteria are not only running their own online ordering and delivering services but also advertising their products via third party services like Foody, Grab, to maximize their appearance and recognition as well as increasing a new revenue stream The mutual benefit among customer, restaurant and medium service providers is great momentum accelerate online food ordering services’ development.
Scale Measurement of Constructs
For simplicity and ease of observation, author uses following table for encoded variables and notations used in SPSS calculation
Table 4.5 shows the testing result of reliability test which represented by Cronbach’s alpha coefficient value
Overall, all Cronbach’s alpha value of independent variables, dependent variables and testing moderator satisfy the condition that above threshold 0.6 It means the data collected and scale measurement for each variable are highly consistent and reliable
Table 4.5 Cronbach’s Alpha coefficient value of all variables
Variables Number of observation items Cronbach’s Alpha Value
Source: Data analysis by SPSS
After Cronbach’s Alpha test, it is necessary to examine validity of observations measure scale of independent variables Exploratory Factor Analysis (EFA) was conducted by SPSS Firstly, Kaiser – Meyer – Olkin (KMO) and Bartlett’s test is employed for testing the validity of items As shown in Table 4.6, all indicators confirm that all construct items were suitable for data analysis In particular, KMO measure of sampling adequacy greater than 0.5 and less than 1 (0.851) and sig value 0.000 less than 0.05 (Hoang Trong,
2008) In addition, rotated component matrix varimax rotation once again confirm convergence of selective observation items for the independent variables (Table 4.7)
Table 4.6 KMO and Bartlett’s Test results
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .851 Bartlett's Test of Sphericity Approx Chi-Square 5196.721 df 435
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 7 iterations
Source: Data analysis by SPSS
Inferential Analysis
Result of Pearson correlation test have shown in Table 4.8 All the sig value below 0.05 and no correlation value between dependent variable Usage Intention (UI) and 8 independent predictors in research model (PE, EE, SI, FC, PV, HB, HM, TR) greater than 0.90 (Hair el at., 2008) Hence, there is no multicollinearity problem in this model
Moreover, the correlation coefficient values are all range from 0.247 to 0.516 which represents a positive correlation Therefore, the relationships between the variables are statistically significant
Table 4.8 Pearson Correlation Coefficient Matrix
UI CI PE EE SI FC HM PV HB TR
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Source: Data analysis by SPSS
4.3.2 Multiple Linear Regression and Hypotheses testing
Multiple Linear Regression was adopted in order to examine the linear relationship between the eight independent variables and usage intention – the dependent variable
According to Table 4.9, adjusted R Square of 0.794 means that 79.40% of the outcome (UI) can be explained by all the predictors in research model (PE, EE, SI, FC, PV, HB,
HM, TR) Remaining 21.60% can be explained by other factors which was not included in this research Furthermore, Durbin-Watson value less than 2 enhance independence and explanation of variables in model
Std Error of the Estimate Durbin-Watson
1 846 a 797 794 48901 1.805 a Predictors: (Constant), TR, SI, HB, EE, PE, FC, PV, HM b Dependent Variable: UI
Squares df Mean Square F Sig
Total 83.268 211 a Dependent Variable: UI b Predictors: (Constant), TR, SI, HB, EE, PE, FC, PV, HM
Source: Data analysis by SPSS Based on the analysis result, p-value of independent variables are less than 0.05 (F.152 with df 211), indicating that the relationship has statically meaning with the data collected in this study or there exists a linear correlation between the predictors (TR,
SI, HB, EE, PE, FC, PV, HM) and the dependent variable: usage intention
As shown in Table 4.10, linear regression demonstrates estimated parameter of outcome variable usage intention (UI) Among 8 predictors, PE, EE, HM, PV, TR have p-value less than 0.05 Statistically, they have an influence on usage intention or hypothesis H1, H2, H5, H6, H8 are supported Price value (PV) and hedonic motivation (HM) represent for the strongest predictor in this study model as significant p-value of 0.000 and 0.002 respectively On the other hand, SI, FC, HB are failed to explain the influence to dependent variable, FC and HB even have negative effect toward customer usage intention of online food ordering services As the results, H3, H4, H7 are not supported
Table 4.10 Linear Regression Results and Extractions
Standardized Coefficients t Sig Collinearity Statistics
Model B Std Error Beta Tolerance VIF
Source: Extracted from SPSS Analysis
As shown in Table 4.11, the positive relationship between Performance Expectancy and Usage Intention (PE UI) was supported with path coefficient 0.168 and t-statistic of 2.339 The assumption of positive relationship between Effort Expectancy and Usage Intention (EE UI) was also approved according to positive coefficient 0.153 and t- statistic of 2.893 However, hypothesis H3 (SI UI) and H4 (FC UI) were not supported represented by analysis result of small coeficient and insignificant t-statistic of 1.264 and -1.284 respectively On the other hand, hedonic motivation and price value have positively related to customer usage intention with significant t-statistic of 3.182 (H5: HM UI) and 3.658 (H6: PV UI) In general, price value represented for the most significant predictor in this study Align with recent literatures, the relationship between habit and usage intention (H7: HB UI) was not positively supported with path coefficient -0.011 and t-statistic -0.129 Finally, the new predictor adopted in this research model – trust ((H8: TR UI) was supported regarding positive coefficient 0.184 and t-statistic 2.300
Table 4.11 Structural relationships and hypotheses testing
Hypotheses Path Path coefficient T-statistic Decision
Figure 4.1 Statistical Moderating Model (Baron and Kenny, 1986)
This study tried to explore the moderating effect of consumer innovativeness on the relationship between UTAUT2 predictors as well as trust and dependent variable: customers’ usage intention The moderator effect was analyzed by Process v3 – an add- on for SPSS (Andrew F Hayes, 2018) The interaction of moderator occurs when the moderator significantly affects the outcome variable, and the product of predictor and moderator variable also significantly affects the outcome variable In this study, there are 8 relationships between predictor mediator have to be verified The result is described in Table 4.12 as following:
Focal predictor: PE, Moderator variable: CI, Outcome: UI
Focal predictor: EE, Moderator variable: CI, Outcome: UI
Focal predictor: SI, Moderator variable: CI, Outcome: UI
Focal predictor: FC, Moderator variable: CI, Outcome: UI
Model Summary Coefficient Stand error t-statistic p-value Decision
Focal predictor: HM, Moderator variable: CI, Outcome: UI
Focal predictor: PV, Moderator variable: CI, Outcome: UI
Model Summary Coefficient Stand error t-statistic p-value Decision
Focal predictor: HB, Moderator variable: CI, Outcome: UI
Model Summary Coefficient Stand error t-statistic p-value Decision
Focal predictor: TR, Moderator variable: CI, Outcome: UI
According to moderator analysis result, there are 6 sub-hypotheses were not supported due to insignificant relationship between product of predictor (PE, EE, PV, HB, HM, TR) and consumer innovativeness, and outcome (UI) However, it shows that consumer innovativeness plays significant role in moderate the relationship between social influence and usage intention with p-value of 0.0002 and t-statistic of 2.6911 Same as social influence, despite of being insignificant direct effect on usage intention, facilitating condition is also positive related with usage intention by moderation effect of consumer innovativeness with p-value of 0.0077 and t-statistic of 2.6911.
DISCUSSION AND CONCLUSION
Discussion
According to statistical analysis, majority of hypotheses are supported but several assumptions are insignificant in context of this study As UTAUT2 is one of the most the comprehensive and acceptable model of technology adoption, many constructs of original UTAUT2 model were confirmed fairly consistent
Firstly, in line with the past literatures, performance expectancy was found as a significant driven of usage intention of online food ordering services (Alalwan et al., 2017; Chopdar, Korfiatis, Sivakumar, & Lytras, 2018; Shaw & Sergueeva, 2019; Singh
& Matsui, 2017; Venkatesh, 2012) It seems to be obvious since people rationally ultilize a new technology service’s benefits to optimize their outcome Nowadays, people tend to enjoy their little time afterwork with outdoor relaxation activities, thus, they certainly shorten time for dinning preparation or saving wasted time for going and waiting to buy foods (Yeo, Goh, & Rezaei, 2017a) Notably, most of paper using UTAUT2 as the core model indicated that performance expectancy is one of the most influential factor on customer behavior (Aswani, Ilavarasan, Kar, & Vijayan, 2018; Singh & Matsui, 2017;
Tam, Santos, & Oliveira, 2018) However, the significant index of performance expectancy in this study is relatively low Since online food ordering services is an incremental innovation rather than a radical innovation, most of users shall acknowledge the main benefit of technology before use, thus, its lower impact on overall customer intention
The second hypothesis was also supported In the other word, usage intention of online food ordering services positively depend on how ease people feel about placing and ordering food online (Lee, Lee, & Jeon, 2017; Zulkarnain Kedah, Ismail, Ahasanul Haque, & Ahmed, 2015) The wide spread of mobile applications and services were yet reasoned by its flexibility and convenient, however, low effort requirement also plays significant role to motivate user changing their behavior to online and mobile applications People augured that they are not going to be lazy but rather being efficient, the simplicity of modern technology is a great momentum to encourage people to use new product and services In this study, many respondents confirm that it is fairly easy to be good command of online food ordering services In a recent research, Singh &
Matsui, 2017 believed that the impact of effort expectancy on Japanese customer intention to use online shopping is not significant because of wide adoption of a developed country when ease of use have become a norm Vietnamese customer, in other hand, highly appreciated the simplicity of product and service in the beginning stage of adoption Hence, effort expectancy is an appropriate driven factor of customer usage intention of online food ordering services in Vietnam
The fact is Vietnam is an emerging market in Asia with high economic growth rate, it enables Vietnamese people enjoy financial well-off, and various activities for entertainment (Euromonitor, 2018) In the big city, citizen tend to shopping on purpose of seeking joyfulness rather than doing a task of necessary demand only This shopping trend happened in the world thanks to rapidly increasing of human prosperity Hedonic motivation yet has been found in consumer context researches for long time, but it has gradually shown significant effect in many products and services adoption in recent (Escobar-Rodríguez & Carvajal-Trujillo, 2014; Khalilzadeh, Ozturk, & Bilgihan, 2017;
Yeo, Goh, & Rezaei, 2017) Furthermore, multi regression result of this study pointed out a higher significant effect of hedonic motivation than effort expectancy or performance expectancy That reflects the tendency of millennial generations toward products and services usage intention In short, hedonic motivation positively related to customer usage intention of online food ordering services
Online food ordering services in Vietnam commenced on about 10 years ago and recently booming in remarked due to the participation of some big region players like Grab, Go-Jek In general, local service providers and new players have been competing by huge marketing and promotion campaigns One of the primary mechanisms is price value By offering attractive promotion packages for user, those services providers are initially gained success in term of customer awareness and usage attention of online food ordering Price value is also perceived by restaurant owner or many fast food chain providers because it ultimately increases their revenue stream by reaching to more and more potential customer Online service model is not only saving operation cost for food provider but also offering food demander well-off by discount and promotion In this study, price value was the most significant factor affecting Vietnamese usage intention of online food ordering services As mentioned, it is reasonable by not only typical price sensitive purchasing behavior of Vietnamese but also the beginning stage of online food ordering services in Vietnam
According to Nielsen survey, Vietnamese customers’ purchase intentions prioritize on brand name and wellness of products or services Apparently, the service providers who can provide the guarantee the service quality will enjoy competitive advantages in long- run Online food ordering services provide user a whole virtual environment interface but at the same time it will recode user personal data and likelihood Moreover, most of online food ordering services in Vietnam offer online payment or e-wallet to offer in- app purchase So that, trust will play an important role in relationship with customer usage intention (Narteh, Mahmoud, & Amoh, 2017) In addition, the trustworthy online vendor not only provide better protection for customer online transaction but also provide more credibility for those food suppliers using their application Thus, Vietnamese customers concern trust as one of driven factor to their usage intention of online food ordering services
Social influence, facilitating conditions and habit are the insignificant factors influencing customer adoption of online food ordering services in Vietnam Those three factors have been assessed as inconsistent predictors of customer behavior intention in some UTAUT2 adoption studies (Tandon & Kiran, 2018; Singh & Matsui, 2017) It can be explained that online food ordering service is relatively new industry in Vietnam, thus, habit certainly has low impact on people behavioral intention Traditionally, majority of Vietnamese people enjoyed their own food preparation Nonetheless, American or Western people preferred fast food and restaurant meals outside This different in behavior likelihood leads to insignificant adoption of habit toward online food ordering services in context of this study On the other hand, social influence, facilitating conditions slightly failed to influence usage intention of Vietnamese customer Even though it is in line with many previous researches, this study found the customer with high novelty-seeking tendency will associate social influence and facilitating condition with their usage intention of online food ordering services
Finally, consumer innovativeness is an additional contribution to this research Its moderating effect has been studied in many pieces of researches on domain specific of online shopping and technology adoption Several researchers found positive enhancement of consumer innovativeness to moderate the effect of performance expectancy, price value or hedonic motivation to behavioral intention (Alagoz &
Hekimoglu, 2012; Escobar-Rodríguez & Carvajal-Trujillo, 2014; Fowler & Bridges, 2010; Leicht et al., 2018; Natarajan, Balasubramanian, & Kasilingam, 2017) However, this study challenges those results by insignificant statistic result in online food ordering context It could be reconsidered in large sample size study but also describe a different perspective for consumer innovativeness on food and edible product and service-related study.
Theoretical implications
As online food ordering services is emerging industry in Vietnam, this study successfully proposes an early research on new section of online services and e-commerce in local area By using one of the latest models of technology adoption, this research provides an theoretical basement for assessing driven factors affecting customer behavior intention to use online food ordering services in Vietnam
On the other hand, by adding trust as an additional construct to original UTAUT2 model, this study contributes to enrich knowledge and utilization of UTAUT2 model in every aspect related to technology adoption The analysis results also challenge several previous conclusions on impact of UTAUT2 constructs to behavioral intention and use intention Thus, it embarked future researchers to find a concrete result
It also marked author effort to examine the moderator effect of consumer innovativeness in research model This paper is an early research on assessing the effects of consumer innovativeness reflected on overall UATUT2 constructs Even though the effects are not widely supported for majority of research variables It is considerable as a theoretical contribution and reference for future research.
Practical implications
From the standpoint of practical implications, understanding influential factors to online food ordering service will return in benefit for marketing activity of services provider
Indeed, since performance expectancy and effort expectancy show significant impact on customer behavior intention, raising awareness of being useful and ease of use will match online food ordering services with potential customers’ attention Besides that, customer perceived of joyfulness, fun and excitement when using service product is precious notation for service provider Thus, providing quality services but also entertain procedures will accelerate the customer usage time and quick adoption The typical price sensitive of Vietnam consumer also give a competitive advantage for a service supplier who can offer good promotion and better price value campaign Thus, this study is a valuable reference for manager and marketer prior to conducting market penetration research and releasing their products and services that footprints their brand awareness and effectiveness in customer mind.
Limitations and future research directions
Firstly, the sample size of the study is small and limited of 224 respondents In addition, the sampling method is purposive sampling Therefore, it might not duly represent the population of this research Purposive sampling also gives lack of population characteristics since many potential customers opinion might not appeared in the research Moreover, because of time and cost constrains, the questionnaire was mainly distributed online, so that time and interaction is uncontrollable criteria, that may lead to respondent bias and incorrect data in subsequence For more accuracy in data analysis, a more appropriate data collection method and a larger sample size must be considered
Secondly, in the data analysis section, the author used two approaches toward evaluate the effect of independent variables on the research outcome The regression model brings overview of customer behaviors when all research elements were tested simultaneously
Thus, its result reflected different direction in compared with moderating model which applied for isolate consideration the influence of individual predictor on the usage intention Nonetheless, as mentioned discussion, those hypotheses are reasonable in context of beginning stage of online food ordering service in Vietnam In conclusion, the future research is in needed to both clarify the consistency of hypothesis testing and find out the consensus of research model analysis approach
Since one of the profound contributions of this paper is testing on an additional independent variable and a moderator, future research could investigate and establish research model based on their own viewpoint of study objectives Because even if UTAUT2 widely accepted in technology adoption or online shopping, there are many domain specific objectives require a further development of research constructs Thus, follow-up studies should incorporate more dimensions and variables on existing model to provide more comprehensive and accurate results for study of technology adoption
Finally, this research explains the time frame of technology adoption relatively related to effect of driven factors toward research outcome At beginning stage of online food ordering services in Vietnam, price value might be the most significant, but future studies can observe the adoption intention in the middle and post adoption stages
Alagoz, S M., & Hekimoglu, H (2012) A Study on Tam: Analysis of Customer Attitudes in Online Food Ordering System Procedia - Social and Behavioral Sciences, 62, 1138– 1143 https://doi.org/10.1016/j.sbspro.2012.09.195
Alalwan, A A., Dwivedi, Y K., & Rana, N P (2017) Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust
International Journal of Information Management, 37(3), 99–110 https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Amoh, S (2016) Mobile Money Services Adoption and Customer Behavioural Intentions in Ghana., 37, 426–447
Aswani, R., Ilavarasan, P V., Kar, A K., & Vijayan, S (2018) Adoption of public WiFi using UTAUT2: An exploration in an emerging economy Procedia Computer Science, 132, 297–306 https://doi.org/10.1016/j.procs.2018.05.180
Dellaert, B G C., & Ruyter, K De (2004) What drives consumers to shop online? A literature reviews https://doi.org/10.1108/09564230410523358
Dodds, W B., Monroe, K B., & Grewal, D (1991) Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations Journal of Marketing Research, 28(3),
Escobar-Rodríguez, T., & Carvajal-Trujillo, 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, 70–88 https://doi.org/10.1016/j.tourman.2014.01.017 Harris, M A., Brookshire, R., & Chin, A G (2016) Identifying factors influencing consumers’ intent to install mobile applications International Journal of
Information Management, 36(3), 441–450 https://doi.org/10.1016/j.ijinfomgt.2016.02.004
Hayes, A F (2017) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach Guilford Publications
Kang, H., & Fortin, D R (n.d.) Effects of Perceived Behavioral Control on the Consumer Usage Intention of E-coupons, 23(October 2006), 841–864 http://doi.wiley.com/10.1002/mar.20136
Khalilzadeh, J., Ozturk, A B., & Bilgihan, A (2017) Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry
Computers in Human Behavior, 70, 460–474 https://doi.org/10.1016/j.chb.2017.01.001
Lee, E Y., Lee, S B., & Jeon, Y J (2017) Factors Influencing the Behavioral Intention
To Use Food Delivery Apps Social Behavior & Personality: An International Journal, 45(9), 1461–1473 https://doi.org/10.2224/sbp.6185
Leicht, T., Chtourou, A., & Ben Youssef, K (2018) Consumer innovativeness and intentioned autonomous car adoption Journal of High Technology Management Research, 29(1), 1– 11 https://doi.org/10.1016/j.hitech.2018.04.001
Limayem, M., Khalifa, M., & Frini, A (2000) What makes consumers buy from Internet? A longitudinal study of online shopping IEEE Transactions on Systems,
Man, and Cybernetics Part A: Systems and Humans., 30(4), 421–432 https://doi.org/10.1109/3468.852436
Lin, C (2014) Factors affecting online repurchase intention, 114(4), 597–611 https://doi.org/10.1108/IMDS-10-2013-0432 Madigan, R., Louw, T., Wilbrink, M., Schieben, A., & Merat, N (2017) What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems Transportation Research Part F:
Traffic Psychology and Behaviour, 50, 55–64 https://doi.org/10.1016/j.trf.2017.07.007
Narteh, B., Mahmoud, M A., & Amoh, S (2017) Customer behavioral intentions towards mobile money services adoption in Ghana Service Industries Journal, 37(7–8), 426–447 https://doi.org/10.1080/02642069.2017.1331435
Natarajan, T., Balasubramanian, S A., & Kasilingam, D L (2017) Understanding the intention to use mobile shopping applications and its influence on price sensitivity
Journal of Retailing and Consumer Services, 37(January), 8–22 https://doi.org/10.1016/j.jretconser.2017.02.010
Ryu, E., Couper, M P., & Marans, R W (2006) Survey incentives: Cash vs in-kind;
Face- to-face vs mail; Response rate vs nonresponse error International Journal of Public Opinion Research, 18(1), 89–106 https://doi.org/10.1093/ijpor/edh089
Singh, M., & Matsui, Y (2017) How Long Tail and Trust Affect Online Shopping Behavior: An Extension to UTAUT2 Framework Pacific Asia Journal of the
Tam, C., Santos, D., & Oliveira, T (2018) Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model Information Systems Frontiers https://doi.org/10.1007/s10796-018-9864-5
Tandon, U., Kiran, R., & Sah, A N (2018) The influence of website functionality, drivers and perceived risk on customer satisfaction in online shopping: an emerging economy case Information Systems and E-Business Management, 16(1), 57–91 https://doi.org/10.1007/s10257-017-0341-3
Venkatesh, V (2012) Consumer acceptance and use of information technology:
Yeo, V C S., Goh, S K., & Rezaei, S (2017) Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services Journal of Retailing and Consumer Services, 35(July 2016), 150–162 https://doi.org/10.1016/j.jretconser.2016.12.013
Yu, V F., & Ting, H I (2011) Identifying key factors affecting consumers’ choice of wealth management services: An AHP approach Service Industries Journal, 31(6), 929–939 https://doi.org/10.1080/02642060903078750
I am Dinh Tien Dung - a graduate student from Vietnam Japan University (VJU), Master Business Administration (MBA)
I am conducting marketing research on "Factors affecting customers' usage intention of online food ordering services in Vietnam" Online food ordering services are hereby understood as directly online ordering food via website or mobile application of a restaurant (eg Pizza Hut, KFC, Lotteria and so on), or you can simply search for your meals via a third-party mobile applications whose service act as mediator between customer and restaurant (such as Grab Foods, Go Food, Now (Foody), Vietnammm and so on)
Online food ordering, have you ever tried?
If YES, congratulations! I hope you have been enjoying it
If NO, this survey is more than a curiosity waiting for you to explore the momentum of using this special online service
Your response is always highly appreciated There is no right, no wrong and I hereby assure you that your opinions are for research purposes only and are strictly confidential
P/s: Gift4U - If you are looking for a business/personal development/foreign language learning e-book or research paper, I'm more than pleased to find it for you Don't forget to leave your request at the end of this survey
Section 2: What kind of online food ordering services which you have already used?
Direct online service from the restaurants, food stores (KFC/Lotteria/Pizza Hut/ )
Indirect via a third-party application (Grab Foods/GO-Food/NOW (Foody)/ )
Your frequency of using online food ordering services?
Seldom (1-2 times/month) Sometimes (3-4 times/month)
Often (1-2 times/week) Very often (3-4 times/week)
Always (more than 4 times/week)
Section 3: Please choose to what extent you agree with followings statement
(Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), Strongly Agree (5))
1 I find online food ordering service is useful
2 Using online food ordering service enhance my effectiveness on the food ordering
3 I find using online food ordering service advantageous for me
4 Learning how to use online food ordering service is easy for me Amoh
5 Online food ordering service’s interface is concise and understandable
6 It is easy for me to become skillful at using online food ordering service
7 People who influence my behavior think that I should use online food ordering service Venkatesh et al (2003)
8 People who are important to me think that I should use online food ordering service
9 People whose opinions that I value suggest me to use online food ordering service
10 I can control over whole online food ordering service process Aswani et al (2018)
11 I have the knowledge necessary to use online food ordering service
12 Online food ordering service is compatible with other technologies
13 I can get assistant when I have difficulties using online food ordering service
14 Using online food ordering service is fun Yeo et al
15 Using online food ordering service is enjoyable
16 Using online food ordering service is entertaining
17 Online food ordering service is reasonably priced
18 Online food ordering service is value for money
19 Online food ordering service often offers discounts and promotions which are highly tractive, value for money
20 I find myself familiar with online food ordering service Yeo et al
21 The use of online food ordering service has become natural to me
22 I am addicted to use online food ordering service
23 I find that online food ordering service from online vendor or/and application is trustworthy Narteh et al
24 I feel secured to install online food ordering service application (if any)
25 I feel secured to proceed payment for online food ordering service
26 If there is an opportunity, I would like to utilize online food ordering service
27 I have an intention to recommend online food ordering service to my friends
28 In general, I am among the first in my circle of friends to use online food ordering service when it appears
29 If I heard that a new online food ordering service was available, I would be interested to use it
30 Compared to my friends I use a lot of online food ordering service
31 In general, I am the first in my circle of friends to know the latest online food ordering service
32 I like to use online food ordering service before other people do.