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Determinants of customers’
intention to use online food
delivery application through
smartphone in Malaysia
Anwar Allah Pitchay
School of Management, Universiti Sains Malaysia, Penang, Malaysia
Yuvaraj Ganesan and Nurul Syifa Zulkifli
Graduate School of Business, Universiti Sains Malaysia, Penang, Malaysia, and
Ahmad Khaliq
Kulliyah of Economic and Management Sciences,
International Islamic University Malaysia, Kuala Lumpur, Malaysia
Abstract
Purpose –The study aims to examine factors that influence customers’intention to use online food delivery
applications using a smartphone. The factors examined in this study are based on the existing theory of Unified
Theoryof Acceptanceand Use of Technology (UTAUT) namely performance expectancy,effort expectancy, social
influence, information quality, price-saving orientation and time-saving orientation towards intention to use the
applications. Moreover, this research model also has been expanded with an additional dimension, attitude
towards online food delivery services which lead to the intention to use online food delivery services through a
smartphone. Furthermore, the present study alsotested therole of age asmoderator constructs between attitude
towards online food delivery services and intention to use online food delivery services through a smartphone.
Design/methodology/approach –The study employed a quantitative method and 256 respondents
participated in this study. The questionnaires are distributed using a convenience sampling technique and the
data is analysed using partial least square approach.
Findings –The result shows that four (4) constructs, i.e. social influence, information quality, price-saving
orientation and time-saving orientation have a positive relationship and significant effect on attitude towards
online food delivery service where it enhances the intention to use the application. Attitude towardsonline food
delivery services also has a significant effect on the intention to use. Furthermore, age was not found significant
to moderate the relationship between attitude and intention to use.
Practical implications –The output of this study has several practical contributions such as enhances the
existing knowledge and skillset of the shared-economy industry, online food delivery service providers as well
as restaurant owners in improving the quality of life of the customers. It also provides contextual knowledge
and a deeper understanding of online food delivery applications for customers in Malaysia.
Originality/value –The findings provide a guiding principle for improving the present determinant factors,
attitude towards online food delivery service and intention to use online food delivery applications.
Keywords Intention to use, Food delivery application, Attitude, Age
Paper type Research paper
1. Introduction
In recent years, the pandemic COVID-19 has changed the business operation landscape,
which highly depends on online transaction (Alaimo et al., 2020;Galati et al., 2020;Chang and
Meyerhoefer, 2020;Troise et al., 2021), and Internet usage has increased rapidly. The trend of
using online transaction can be traced for the past several years, and the Internet World
Statistic reported that there are 4.574 million Internet subscribers worldwide reported in the
year 2019, representing 58.7% of the world’s total population and expected to grow year after
year (Internet World Statistic, 2019). Lim et al. (2016) coined this term in their research, in
which the growing number of Internet users worldwide has broadened new opportunities for
online business, especially in expanding market reach. Besides the new opportunities, the
Online food
delivery
applications
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0007-070X.htm
Received 21 January 2021
Revised 16 May 2021
25 June 2021
Accepted 27 June 2021
British Food Journal
© Emerald Publishing Limited
0007-070X
DOI 10.1108/BFJ-01-2021-0075
rapid growth of Internet adoption globally has allowed improvements in the consumer
environment and marketing environment (Troise et al., 2021;Oly Ndubisi et al., 2011). In other
words, growing Internet usage has resulted in the move towards the mobile business that
links suppliers and users through various mobile apps (Kim et al., 2019). Adding to this
scenario, the number of downloaded mobile apps also shows significant growth. In 2017, over
178.1 billion apps were downloaded, and $250 billion is forecast to be hit by the year 2022.
This statistic shows a growth of 45% compared to previous years (Statista.Com, 2019).
Although most of the company acknowledging this food industry as a growing field, the food
delivery market only represents a small portion of the market (Kandasivam, 2017). It is
expected that in the year 2022, this segment would have projected annual revenue of USD 956
million, among the fastest-growing food segment. The possible explanation could be an
increase in demand and potential growth in the food delivery industry (EC Insider, 2018).
With mobile applications growing prominence across new online platforms, the business of
delivering restaurant meals to the home has begun to capture markets and consumers around
the world (Hirschberg, 2016).
Based on the above discussion, the present study will scope the food delivery services
using a smartphone application in Malaysia. In Malaysia, the emergence of food delivery
services also grows with the increasing popularity of food delivery applications. Data showed
that by the year 2026, Malaysia’s food delivery segment is projected to expand continuously
and hit a market size of more than USD 319.1 million (Acumen Research and Consulting, 2019).
The key factors are possibly due to rising per capita income and people’s awareness.Although
the usage of a smartphone by Malaysian is less than in China, Malaysia generally remains
committed to mobile commerce. Concerning knowledge, convenience and effort among
individuals have evolved; thus, this may have led people to relax on the conventional purchase
of offline food to embrace new platforms of online food delivery applications as they can get a
variety of food choices (Hansen, 2005;Lau and Ng, 2019). Online food delivery systems are
recognised as a platform where only smartphone users can position orders. The online food
delivery apps will identify the number of orders, payment and track and monitor the system
but do not involve in the actual preparation of the food (Pigatto et al., 2017). Therefore, the use
of food delivery applications helps eliminate waiting for time and overcrowding of customers
at the restaurant, which can prevent harm to the environment (Berdychevsky and Gibson,
2012). There are numerous food delivery companies in Malaysia that offering services through
food delivery applications. Few examples are FoodPanda, Dahmakan, DeliverEat and
GrabFood (Similarweb, 2019). Data showed that the FoodPanda Malaysia application in
Google Play Store had been download nearly more than 10 million times (Rosli, 2018). This
indicates that when food delivery applications became available, people become more willing
to accept it. Most people turn to food delivery application in recent years becauseof the current
speed of life and the chance to discover more foods through the applications. A food delivery
application is a convenient choice formany people, especially during a busy day (Lau and Ng,
2019). Traffic congestion, afull-time schedule and active living are the reasons for developing
food delivery service and applications (Prabowo and Nugroho, 2019).
Ray et al. (2019) indicate several concerns about food delivery applications that should be
given serious attention. This is in line with the study by Maimaiti et al. (2018), where the
issues concern food and cleanliness and the safety in food delivery services due to increased
road accidents, especially in China. Thus, companies should concentrate not only on
enhancing app performance but also on reducing internal issues. Ray et al. (2019) noted that
the essential predictors for an agent-based food ordering model should be food quality,
preparation time, time to deliver and the length of the online ordering system. This means
that operators and their food delivery apps suppliers must focus not only on the efficiency of
their service to be delivered successfully but also on optimising the ease of the system
operation. Pigatto et al. (2017) recommended that the site content, site functionality and site
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usability be the main concern when designing food delivery applications. Besides, the
cleanliness of the food package, condition of the product being ordered, conformity in the
ordering system, delivery time and reasonable delivery cost can help to prevent the problems
and contribute to a positive customer attitude towards the service and increased intention to
use the application (Elvandari et al., 2018).
Therefore, looking at the potential issue in this area of research, the present study uses the
extended UTAUT as a base theory. The present study is carried out to learn about the
conceptual framework that consists of six determinants factors (performance expectancy,
effort expectancy, social influence, information quality, price-saving orientation and time-
saving orientation), attitude towards online food delivery services and intention to use the
online food delivery application in Malaysia. It is also attempted to suggest a method to
enhance the efficiency of online food delivery apps for related businesses and researchers by
assessing factors affecting customers’intention to use online food delivery application.
2. Research model and hypotheses development
There are various existing theories, such as Theory of Reasoned Action (TRA) by Martin
Fishbein and Icek Ajzen in 1967; TRA has been expanded into Theory of Planned Behaviour
(TPB) by Icek Ajzen by introducing a new dimension in the year 1985. Besides, the expansion
in the technology system has contributed to another theory called the Technology
Acceptance Model (TAM) by Davis (1989). Similar to the expansion of TRA, studies related to
TAM have gained popular usage by scholars and Venkatesh and Davis (2000) have expanded
it and called as Unified Theory of Acceptance and Use of Technology (UTAUT) or TAM 2.
The UTAUT used as a primary theory in this analysis. Venkatesh et al. (2016) propose
UTAUT with the combination of eight influential models for individual acceptance of newly
developed IT technologies. Consists of Theory of Reasoned Action (TRA), Theory
Acceptance Model (TAM), Motivational Model, Theory Planned Behaviour (TPB),
Decomposed Theory of Planned Behaviour, Model of PC Utilisation, Innovation Diffusion
Theory and Social Cognitive Theory. This theory explains four primary constructs:
performance expectancy, effort expectancy, social influence and facilitating condition.
However, only three determinants constructs were chosen from this theory. First,
performance expectancy can be defined as the degree to which the user expects the system to
help him/her achieve job performance. Second, effort expectancy can be described as the ease
with which the system is used. Consequently, the third constructs are social impact refers to
the perception or belief that people should accept and use the new system (Venkatesh et al.,
2003). Besides, UTAUT is used as the theoretical model in this study. As there are
interdependence and close association between these models, this current study used
UTAUT as the based research model and the proposed three primary components from
UTAUT that consist of performance expectancy, effort expectancy and social influence as
primary constructs. In addition, this study also adopted two additional variables from
another two theories; attitude from TAM and price-saving orientation and time-saving
orientation from Attribution theory by (Fiske and Taylor, 1991), which provides the basic
theory to investigate how price and time factors affect consumer behaviour. This theory also
examines how people perceived the information to respond to an event. Besides, the present
study also introduces a variable, the role of information quality which has been tested
significantly by Suk Won et al. (2019), and this variable is important, especially in the context
of an online platform. Figure 1 demonstrates the proposed research model for this study.
2.1 Performance expectancy
Venkatesh et al. (2003) noted that performance expectancy is the expectation of user’s
expectation helps them enhance their job performance. Therefore, people tend to use new
Online food
delivery
applications
technologies when they consider it will benefit their job performance. Alalwan, Dwivedi,
Rana and Algharabat (2018) indicate that when people have a strong perception that
technology helps them perform their job, they will have more intention to use it. Oliveira et al.
(2014) indicate that performance expectancy is recognised as the main factor that contributed
to the success of customers’acceptance and its direct ties to both customer attitude and
intention to use in the context of mobile banking technology. This is consistent with prior
literature by (Shaikh et al., 2018), where the performance expectancy of mobile banking
anticipated a significant effect on attitudes and intention to use. However, there is a limited
finding of this relationship in the context of this study. Therefore, it is proposed that:
H1. Performance expectancy has a positive effect on attitude towards online food
delivery services.
2.2 Effort expectancy
Venkatesh et al. (2003) indicate effort expectancy as the ease of use when using a system.
UTAUT adopts a few concepts in this performance expectancy concept: complexity,
perceived ease of use and ease of use (Venkatesh et al., 2003). A prior study by (Okumus et al.,
2018), note that, in the context of mobile application, intention to use was influence by effort
expectancy. Meanwhile, Khechine et al. (2014) also found that customers can feel that some
obstacles related to the use of technology exist at the earliest stages of different behaviour,
but once they are used to the technology, the perceived ease of use will become more
significant. In a mobile banking context, effort expectancy plays a direct connection to
consumer attitude, indicating that effort expectancy (perceived ease of use associated with m-
banking) builds a positive consumer attitude towards m-banking services (Shaikh et al.,
Figure 1.
Research model
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2018). In connection with this analysis, however, there are minimal findings in the food
delivery application context, therefore:
H2. Effort expectancy has a positive effect on attitude towards online food delivery
services
2.3 Social influences
Social influence refers to the degree to which an individual perceives other people’s opinions
as necessary in influencing their behaviour to use the new system (Venkatesh et al., 2003).
Venkatesh et al. (2012) indicate that social impact also can be interpreted as the assumption
that using modern technology would improve one’s identity or social status as a human
being. Besides, the social influence reflects that peers’views may also influence people’s
perception of a program or technology. The higher the peer’s perceived value of using
emerging technologies, systems or facilities, the more likely they will adopt it (Lee et al., 2017).
According to previous literature, social influence positively influencing consumers’
behavioural intentions for using new technologies, products and services (Venkatesh et al.,
2012). People appear to perform a well-evaluated and common behaviour with others (Chen
et al., 2018). Chen et al. (2018) have also found a significant correlation in another context of a
study where social influence has positively affect attitude. Since there is limited finding in the
context of online food delivery services, therefore it is proposed that:
H3. Social influence has a positive effect on attitude towards online food delivery services
2.4 Information quality
Information quality refers to people’s understanding of the quality of information presented
on the system (Ghasemaghaei and Hassanein, 2019). Besides, it also applies to system
performance measurement, where the system’s quality is provided (Freeze et al., 2019).
Accuracy, currency, reliability, completeness, applicable, understandable and computerised
formats are among a few of the information quality desired characteristics (Swaid and
Wigand, 2009). Furthermore, in the e-commerce context, the study shows that information
quality had a positive impact on the intention to use by reinforcing the attitude of customers’
namely trust (Escobar-Rodriguez and Carvajal-Trujillo, 2014). On top of that, information
quality is a view as the primary concern (Rese et al., 2014). The quality of information is vital
in the context of companies selling clothes online, where the client is more likely to purchase a
product online if he/she is happy with the information provided in the system (Fanoberova
and Kuczkowska, 2016).
Since there is a lack of finding in the context of online food delivery services, therefore it is
proposed that:
H4. Information quality has a positive effect on attitude towards online food delivery
service.
2.5 Price-saving orientation (PSO)
Price-saving orientation refers to the financial benefits that people gain from technology, as it
helps them receive a good or service at a less expensive price. Attribution theory addresses
the use of information by a perceiver in response to events (Fiske and Taylor, 1991). Prior
literature showed that price savings would result in cheaper prices and time savings
associated with people who buy products and services on the website (Jung et al., 2014). In
other contexts, people will compare prices using various applications and sites businesses
that can give a lesser price would be seen as the more efficient platform (Yeo et al., 2017). Such
discounts or promotions will draw price-sensitive customers as they would prefer a channel
Online food
delivery
applications
that offered the best value-for-money. According to Ali et al. (2010), a customer’s price saving
will be assessed under service quality; both signed with a positive attitude. Therefore, it is
proposed that:
H5. Price-saving orientation has a positive effect on attitude towards online food delivery
services.
2.6 Time-saving orientation (TSO)
Time-saving orientation illustrates that when it involves online shopping, people always
have an intention to save time (Jensen, 2012). A prior study found that due to the changes in
customer lifestyle, people feel more difficult to shop at physical locations of stores (Wu, 2003).
People also prefer to assume that the shopping process would be convenient when a program
is easy to use (Chiu et al., 2014). This is consistent with (Yeo et al., 2017), where it gives
consumers a clear idea that the shopping process is more desirable when the program is
simple to use. Hence, time-saving orientation is found to have a relationship with customer
attitude and intention to use the system (Yeo et al., 2017). Since there is limited research in the
context of online food delivery application through the smartphone, therefore, it is
proposed that:
H6. Time-saving orientation has a positive effect on attitude towards online food delivery
services.
2.7 Attitude and intention to use
Attitude can be defined as predisposed behaviour consisting of ideas, feelings, thoughts and
emotions (Rosenberg and Hovland, 1960). According to Setiyawati and Haryanto (2016),
when attitude influences behaviour, it reflects that individual perceptions can either be
positive or negative towards certain conduct. While intentions, according to TAM, determine
the actions of people to use technology. People decide to use or not use such technology
dictated by personal attitudes (Davis, 1985). Attitude has consistently been established as a
significant predictor of intention to use when dealing with the acceptance of mobile services
(Chen et al., 2018). Customers are more likely to have a positive attitude towards using
technology if they benefit from using new technology (Hwang et al., 2019). In another context,
attitude towards mobile banking apps has been found to have a positive effect on the
intention to use, and similar findings have been found in the context of web-based retailing
(Chen et al., 2018). Since there are limited finding in the context of online food delivery
application, therefore, it is proposed that:
H7. Attitude towards online food delivery services has a positive effect on the intention to
use online food delivery application through the smartphone.
2.8 The moderating role of age
Age could moderate the attitude and intention to use of customers. Accordingly, previous
research indicates that there are age gaps between young people who embrace existing cell
phone apps and those who follow a new mobile phone feature (Zhou et al., 2014). It further
notes in mobile banking that young customers prefer more mobile banking, while old
customers want conventional physical banking (Hwang et al., 2019). Based on the above
study, the statement is that age is an important variable when discussing technology. In
contrast, Elias et al. (2012) suggest that attitudes of elderly workers towards technology
should not influence work outcome variables as younger employees usually have more
technical experience with technology. Compared to younger workers, older employees
generally have less technological skills; their attitudes towards technology will be more
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prominent in job performance. Elias et al. (2012) also indicated that, as older staff typically
have less technological experience than younger staff, older staff should be less positive
about using technology in their workplace. Besides, academic literature indicates that age
will adversely impact the use of technology by teachers (S
anchez-Mena et al., 2017) suggests
that, in the learning context, age negatively affects teacher’s intention to use the technology.
Due to the fewer findings in this context of the study, which is online food delivery services
and application, the hypotheses related to the moderating effects of age were the following:
H8. Age will moderate the relationship between attitude towards online food delivery
services and intention to use online food delivery application through smartphones.
3. Research methodology
3.1 Research design and data collection
The study conducted uses a quantitative method where a cross-sectional survey research
design is applied to collect the data through a self-administered questionnaire. All the data
collected then has been used to test the cause–effect relationship among the study variables.
Therefore, this study considered an explanatory study which examines the effect of
determinants of customers’intention to use online food delivery application in subject
population in this study is Malaysian, and the unit of analysis is the individual aged 18 and
above. The age comprises a reasonable age to use the applications and have access to use the
payment system.
This study used a convenience sampling method requested by the respondent to answer
the online survey which developed in the Google forms platform for data collection. Few
procedural remedies adopted to minimise the common method bias (CMB) as this study is a
single data source. The items for the variables were intermixed to minimise the CMB.
Additionally, before the respondent beginning answer the survey, they need to read the cover
letter that provided information about the motive of the research and to ensure the
participants understood that the survey was for academic purposes, without regard to correct
or wrong responses, the definition of the variables and participants were fully aware that
their participation was voluntary and entirely anonymous. Besides that, the survey was
validated by the expert in the research field then has pretested with five respondents before
the final survey created in the Google form. Apart from that, Harman’s single-factor test was
utilised as the statistical remedies, and the result showed that CMB is not the issue for this
study as the first factor derived is less than 50% (Ooi et al., 2020;Podsakoff et al., 2003).
Thus, 256 respondents have answered the online questionnaire, and the responses were
used in the data analysis. This indicates that this number surpasses the minimum sample size
of 146, which calculated based on G-power software with a statistical power of 95%. Thus, the
256 respondents for this research are considered sufficient as the minimum power needed in
social science management study is 0.80 (Ooi et al., 2020;Yong et al., 2019).
3.2 Measures
In this study, a total of 33 items to measure the variables with a five-point Likert scale was
used to measure the items in the questionnaire where (1) reflects strongly disagree and (5)
reflects strongly agree. The questionnaire items were adapted from previous existing scales
where four items to measure effort expectancy were adapted from Palau-Saumell et al. (2019),
meanwhile the items for information quality (four items) and intention to use (four items) were
adapted from Suk et al. (2019). Then, three items were adapted from Escobar-Rodriguez and
Carvajal-Trujillo (2014) to measure price-saving orientation and four items adapted from
Childers et al. (2001) to assess the attitude towards online food delivery services. Apart from
Online food
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applications
that, to determine time-saving orientation is composed of four items adapted from Alreck and
Settle (2002). Besides that, the construct of performance expectancy and social influence items
are combined from two studies to reflect the environment of the current study. The
performance expectancy and social influence have five indicators for each construct, which
adapted three indicators from Palau-Saumell et al. (2019) and two indicators from Suk et al.
(2019). All the selected items and adapted sources are depicted in Table 8.
3.3 Testing the moderating effect of age
The moderating effect of age towards attitude and intention to use was tested in this study,
and the data of age were separated into two groups. According to Herrando et al. (2019),
Generation Y is referred to as millennials ages between 25 and 34, whereas Generation Z
applies to people between the ages of 15 and 24. It can be concluded that Generation Z (the
youngest) and Generation Y (medium age). Thus, the moderator of age was studied by
considering two groups of respondents (young and older) to determine whether it is
moderated the relationship between attitude towards online food delivery service and
intention to use the application (see Table 1).
4. Results
4.1 Demographic profile
In demographic profile statistics (Table 2), data showed that, out of the 256 responses, in
terms of respondent age, 54.7% were within the age group below 25, whereas 45.3% falls in
the age above 25 years old respectively. In terms of gender, 41.4% were the male respondent,
and 58.6% were female respondents. 68.4% are single for marital status, 29.7% represent
married and 2% reflect divorced. In terms of ethnicity, the highest percentage which is 70.7%
were from Malay respondents, followed by 16.0% from Chinese respondents, 10.2% from
Indian respondents and 1.6% from Sabah and Sarawak respondents, respectively. For
employment status, it showed that 16.4% of the respondent worked in the government sector,
44.1% worked in the private sector, 32.0% are students and only 3.5% and 3.9% were from
unemployed and others sector. For monthly income, 60.9% of respondents received income
between RM 1000 to RM 2999, and the least is between RM 4000 to RM 4999,
representing 1.6%.
4.2 Descriptive results
Descriptive statistics interpreted standard deviation and mean derived from 256 responses,
as shown in Table 3. SPSS Statistic Software was used in conducting this analysis. This
indicates that the composition for all constructs in this study which encompasses dimension
of performance expectancy (PE), effort expectancy (EE), social influence (SI), information
quality (IQ), price-saving orientation (PSO), time-saving orientation (TSO), attitude towards
online food delivery services (ATT) and intention to use online food delivery application
through smartphone (ITU). The previous study found that if the mean value is lower than
three, it is defined as low, between 3 and 5 is contemplated moderated and more than five is
Demographic characteristics (n5256) Category Frequency Percentage
Age <25: Young respondent 140 54.7
≥25: Old respondent 116 45.3
Note(s): From the median of age distribution, the sample was divided into two groups: respondent aged 25 or
younger and respondent older than 25
Table 1.
Age groups
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considered high (Sekaran and Bougie, 2013). In this study, the highest mean is time-saving
orientation with 4.337. Meanwhile, the lowest mean is price-saving orientation, where the
value is 4.214. The result obtained shows the highest standard deviation is price-saving
orientation (0.693); meanwhile, the lowest standard deviation is social influence (0.575).
Demographic characteristics Category Frequency Percentage
Age <25 140 54.7
≥25 116 45.3
Gender Male 106 41.4
Female 150 58.6
Marriage status Single 175 68.4
Married 76 29.7
Divorce 5 2.0
Ethnic Malay 181 70.7
Chinese 41 16.0
Indian 26 10.2
Sabah 4 1.6
Sarawak 4 1.6
State of residence Johor 12 4.7
Melaka 10 3.9
Negeri Sembilan 2 0.8
Selangor 36 14.1
W. Persekutuan 14 5.5
Pahang 3 1.2
Sarawak 3 1.2
Terengganu 13 5.1
Kelantan 18 7.0
Kedah 36 14.1
Perak 13 5.1
Pulau Pinang 93 36.3
Perlis 2 0.8
Sabah 1 0.4
Occupation Government sector 42 16.4
Private sector 113 44.1
Student 82 32.0
Unemployed 9 3.5
Others 10 3.9
Monthly income Less than RM999 83 32.4
RM1000-RM2999 156 60.9
RM3000-RM3999 13 5.1
RM4000-RM4999 4 1.6
Nationality Malaysian 256 100.0
Constructs Mean SD
Performance expectancy 4.267 0.668
Effort expectancy 4.311 0.633
Social influence 4.264 0.575
Information quality 4.230 0.648
Price-saving orientation 4.214 0.693
Time-saving orientation 4.337 0.582
Attitude 4.249 0.607
Intention to use 4.334 0.625
Table 2.
Frequency table
(profile of respondents)
Table 3.
Descriptive statistics
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4.3 Measurement model
The present study includes internal reliability, convergent validity and discriminating
criteria for validity to test the reflective construct measurement model. Hair Jr et al. (2016)
reported that the model consists of the average variance extracted (AVE), factor loading (FL)
as well as composite reliability (CR) for this study.
Constructs Items Factor loadings CR AVE
Performance expectancy PE1 0.877 0.932 0.734
PE2 0.883
PE3 0.890
PE4 0.818
PE5 0.811
Effort expectancy EE1 0.838 0.911 0.720
EE2 0.878
EE3 0.891
EE4 0.784
Social influence SI1 0.742 0.900 0.642
SI2 0.819
SI3 0.841
SI4 0.775
SI5 0.826
Information quality IQ1 0.878 0.920 0.743
IQ2 0.894
IQ3 0.851
IQ4 0.824
Price-saving orientation PSO1 0.849 0.891 0.731
PSO2 0.834
PSO3 0.881
Time-saving orientation TSO1 0.831 0.901 0.696
TSO2 0.881
TSO3 0.824
TSO4 0.799
Attitude ATT1 0.839 0.916 0.731
ATT2 0.866
ATT3 0.867
ATT4 0.850
Intention to use ITU1 0.815 0.923 0.749
ITU2 0.888
ITU3 0.878
ITU4 0.878
Note(s): CR: Composite Reliability; AVE: Average Variance Extracted
Dependent variable Independent variable VIF
Attitude Performance expectancy 1.613
Effort expectancy 1.886
Social influence 2.151
Information quality 2.461
Price-saving orientation 1.661
Time-saving orientation 2.425
Intention Attitude 1.000
Table 4.
Measurement model
Table 5.
Result of collinearity
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Based on Table 4, the result indicates that the range of composite reliability varies between
0.891 and 0.932, and it is above 0.70 minimum score as suggested by Hair et al. (2016). The
result reflects robust internal consistency reliability. Next, AVE was used to test the
convergent validity, where the average values of each construct would be above 0.5,
respectively. The data obtained also show that this study has good convergent validity where
the AVE ranged from 0.642 to 0.749 and pass the minimum requirement of 0.5. In internal
reliability, all standardised loadings in FL’s were range from 0.742 to 0.894, and it is higher
than the minimum cut-off score of 0.70.
According to Henseler et al. (2015) stated that, in discriminant validity, correlations
between factors are tested using HTMT correlations. A strong relationship between the
constructs is crucial as it reflects a good correlations value. The results thus showed that the
constructs have a positive relationship, and the value is below 0.90, as suggested by (Gold
et al., 2001). The discrimination validity results that meet the recommended standard level are
depicted in Table 5. It can be concluded that the measurement model has shown adequate
reliability and validity; therefore, the model is appropriate to test the hypotheses.
ATT EE IQ ITU PE PSO SI TSO
ATT
EE 0.615
IQ 0.752 0.659
ITU 0.772 0.591 0.707
PE 0.543 0.615 0.570 0.508
PSO 0.749 0.532 0.625 0.647 0.459
SI 0.735 0.615 0.762 0.652 0.557 0.589
TSO 0.759 0.666 0.781 0.807 0.507 0.708 0.738
Note(s): ATT, Attitude; EE, Effort Expectancy; IQ, Information Quality; ITU, Intention to Use; PE,
Performance Expectancy; PSO, Price-Saving Orientation; SI, Social Influence; TSO, Time-Saving Orientation
Hypotheses Relationship
Std.
Beta
Std.
error
t-
value
p-
value Decision R
2
f
2
Q
2
H1 PE →attitude 0.069 0.047 1.466 0.072 NS 0.610 0.008 0.433
H2 EE →attitude 0.058 0.055 1.058 0.145 NS 0.005
H3 SI →attitude 0.197 0.074 2.682 0.004 S 0.046
H4 IQ →attitude 0.203 0.079 2.565 0.005 S 0.043
H5 PSO →attitude 0.275 0.053 5.202 0.000 S 0.117
H6 TSO →attitude 0.167 0.067 2.502 0.006 S 0.030
H7 Attitude →intention 0.682 0.052 13.234 0.000 S 0.468 0.874 0.342
Note(s): significant level at 1-tail *p< 0.05, **p< 0.01, ***p< 0.001; PE, Performance Expectancy; EE, Effort
Expectancy; SI, Social Influence; IQ, Information Quality; PSO, Price-Saving Orientation; TSO, Time-Saving
Orientation; S 5Supported; NS 5Not Supported
Hypotheses Relationship Std. Beta Std. error t-value p-value Decision
H8 Att*Age →intention 0.058 0.047 1.242 0.215 NS
Note(s): Significant level at 2-tail *p< 0.05, **p< 0.01, ***p< 0.001; Att, Attitude; S 5Supported; NS 5Not
Supported; Std. Beta 5Standard Beta
Table 6.
Discriminant
validity (HTMT)
Table 7.
Path coefficients and
hypothesis testing
Table 8.
Results of the
moderator analysis
Online food
delivery
applications
4.4 Structural model
4.4.1 Lateral collinearity assessment. The purpose of the lateral collinearity assessment is to
detect if there are any lateral collinearity issues. For VIF to be acceptable, the amount must be
less than or equal to 5 (Hair et al., 2016). If it’s over five, the possible explanation could be that
it shows that the independent variable in the model is correlated and creates problems to
match the model and interpret the results. Table 6 represents the result of lateral collinearity,
where the internal VIF for all independent variables is below 5, which means that lateral
multicollinearity in this study does not require concern.
4.4.2 Path analysis. The standard regression coefficient (or path coefficient β) is used to
analyse the direct outcome of one construct (independent variable) on the other construct
(dependent variable). Furthermore, the weight of the coefficient reflects the outcome it will
carry. The stronger the relationship, the higher the outcome. At the same time, path
coefficient tools were used to test the relationship and direction of hypotheses proposed in the
study. According to Anderson and Gerbing (1988), a complete collinear was acknowledged
when the correlation coefficient value is 1. Once the assessment validity of the model
completed, the hypotheses tested were confirmed. For the determination of coefficient and the
significance level for a beta, it will be represented in the R
2
value (Hair et al., 2011).
The value of R
2
for intention to use is 0.468, meaning that 46.8% of the variance of
intention to use can be described by attitude. Meanwhile, the value of R
2
for attitude was
0.610, meaning that 61.0% of the variance in the attitude can be explained by performance
expectancy, effort expectancy, social influence, information quality, price-saving orientation
and time-saving orientation. According to Cohen (1988), the effect size (f
2
) has its based
principle where the value of 0.02 has a small effect, 0.15 has a medium effect and 0.35 has a
large effect, respectively. The result presented that attitude has a large effect on producing R
2
for intention to use. Then, the results indicate performance expectancy (0.008), effort
expectancy (0.005) does not have any effect on attitude meanwhile, social influence (0.046),
information quality (0.043), price-saving orientation (0.117) and time-saving orientation
(0.030) have a small effect on attitude. Therefore, all results are shown in Table 5.Henseler
et al. (2015) indicate that the relevance value of Q
2
is used to evaluate the predicting capability
of the model. Based on the blindfolding method in SmartPLS software, the value of Q
2
should
be more than zero after the predictive validity of a model was tested. The result obtained
showed the value of Q
2
is relevant (above zero) and reflects satisfactory predictive as the
(Q
2
50.433) for attitude and intention to use (Q
2
50.342) (see Figure 2).
For the current study, seven direct hypotheses between the variables are recognised. To
identify the t-values significant level, it can be calculated using a bootstrapping function in
SmartPLS software. Based on the samples, one-tailed and a significance level of 0.05 was
discovered, referred to assessment of the path coefficients as revealed in Table 7. Thus, found
that if t-values have ≥1.645, it is considered significant at 0.05 level. Precisely, the analyst for
social influence at β50.197, p< 0.05, information quality at β50.203, p< 0.05, price-saving
orientation at β50.275, p< 0.05 and time-saving orientation at β50.167, p< 0.05 are
positively connected on attitude which stated that 61.0% of variance of attitude. While
attitude towards online food delivery services also directly and positively influences intention
to use online food delivery services application at β50.682, p< 0.05. Therefore, H3,H4,H5,
H6 and H7 are supported. In contradicting, the result shows that performance expectancy and
effort expectancy constructs are not significant, therefore not supported the relationship on
attitude. Hence, it is considered that two hypotheses (H1 and H2) were not supported (see
Figure 3).
4.4.3 Moderating effect. t-Values effect was determined using SmartPLS software. The
purpose is to evaluate the effect of moderating variable using bootstrapping function, with
500 samples, run at two-tailed and at 0.05 significant level. The results confirmed that the
relationship between attitude and intention to use did not moderated by age at β50.058,
BFJ
p> 0.05, thus hypotheses (H8) were rejected as described in Table 8. This is inconsistent with
the hypothesis proposed in this study. The result showed age is negatively related to attitude
towards using the technology in the short term or long term (Morris and Venkatesh, 2000). In
another context of the study, age does not moderate the attitude and behavioural intention of
the teacher’s(S
anchez-Mena et al., 2017). It suggests that the age gap between the two groups
does not affect attitude and intention to use.
5. Discussion
The study examines factors that influence customers’intention to use online food delivery
applications using a smartphone. The factors examined in this study are based on the
existing theory of UTAUT, namely performance expectancy, effort expectancy, social
influence, information quality, price-saving orientation and time-saving orientation towards
intention to use the applications. Moreover, this research model also has been expanded with
an additional dimension, attitude towards online food delivery services, which lead to the
intention to use online food delivery services through a smartphone. Furthermore, the present
study also tested the role of age as moderator constructs between attitude towards online
food delivery services and intention to use online food delivery services through a
smartphone. Data analysis showed that social influence, information quality, price-saving
orientation and time-saving orientation positively affect attitude towards online food delivery
services. Besides, attitude towards online food delivery services also had a significant result
in using an online food delivery application. In this regard, age was not found to moderate the
relationship between attitude and intention to use. On the other hand, this study found that
performance expectancy and effort expectancy were not key determinants of using online
Figure 2.
PLS structure model
Online food
delivery
applications
food delivery applications. This analysis necessarily implies that among the eight hypotheses
proposed, only five hypotheses are accepted.
First, the study investigates whether performance expectancy has a significant effect on
attitude. The findings suggest no positive effect, and the result is inconsistent with earlier
literature (Oliveira et al., 2014;Shaikh et al., 2018). Thus, this indicates that the customer’s
attitude towards the services does not force by performance expectancy. Possible reasons for
this disconnect could be that the customers might have a negative attitude towards the
services as they did not perceive online food delivery application to be beneficial for them in
ordering food. Another reason could be that using food delivery apps prevents them from
accomplishing the purchasing process more quickly. Second, the study investigates whether
effort expectancy positively affects attitude. No significant effect has been found, similar to
the above finding. This result contradicts previous literature by Shaikh et al. (2018). It may be
that customer’s attitude, where they believe that their interaction with online food delivery
apps is not clear or will be hard to understand as well as learning to use the apps require time.
Result in decreasing intention to use the applications. Third, the study examines whether
social influence positively affects attitude towards online food delivery services. The current
study showed that the result is positive, which is in line with previous studies (Chen et al.,
2018). This suggests that if peers influenced customers, they would be positive about service.
Besides, they may think that feedback on food purchasing from other people using apps is
important and therefore strengthened their attitude to the service and their intention to use
online food delivery applications.
Fourth, the study investigates whether information quality positively affects attitude
towards online food delivery service. The result is significant and in line with the previous
study (Escobar-Rodriguez and Carvajal-Trujillo, 2014). The study results show that if
Figure 3.
PLS structured model
for path coefficients
with moderating
effects
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consumers assume they obtain exact and accurate information from food delivery apps, it
makes delivery apps viewed as useful; this can motivate their attitude and increase their
intention to use online food delivery applications. Fifth, the study finds a positive effect of
price savings on the attitude towards online food delivery services, and the result is in line
with previous research (Ali et al., 2010). A possible explanation might be that the customer
will value online food delivery apps if the apps offer better deals for their money. It plays a
key role in approaching a positive customer attitude towards the service. It also strengthens
their intention to use online food delivery applications. Sixth, the study finds that time-saving
orientation positively affects the attitude towards online food delivery services. This result
corresponds to the previous literature (Yeo et al., 2017). This shows that the positive attitude
of consumers to online food delivery service is motivated by the desire to reduce traffic and
time-consuming at the restaurant and save time by using the apps and thereby increase their
intention to use online food delivery applications. Seventh, the study finds that attitude
towards online food delivery service positively affects intention to use online food delivery
applications. This result is consistent with the study conducted by Chen et al. (2018) and
Hwang et al. (2019). This could be due to the customers’attitude towards the service and
driven by the value that online food delivery applications help them in food choices. This
motivates customers to use application for online food delivery.
Finally, the study examines the role of age to moderate the relationship between attitude
towards online food delivery services and the customer’s intention to use online food delivery
application through smartphones. The findings are insignificant, which contrasts with the
past literature (Elias et al., 2012). Therefore, it shows that age does not play a moderating role
in the relationship between attitude and intention of the customers to use online food delivery
application through a smartphone. It could be that intention to use and attitudes towards the
service are not affected by the age gaps between customers regarding online food delivery
application usage.
6. Theoretical and practical contributions
In this study, there are two main theoretical contributions. The current study examined
various contexts of online food delivery application, with UTAUT as a base theory. The
results of the research therefore significantly contribute to the emerging literature on online
food delivery applications and food delivery services by examining the effect of performance
expectancy, effort expectancy, social influence, information quality, price-saving orientation,
time-saving orientation on attitude towards food delivery service and the intention to use the
app. Besides, the study also investigated what factors influence the customer in the
diversified countries, namely Malaysia, to use online food delivery applications. Various
researchers in various contexts such as online shopping, tourism and hospitality, catering
services and ridesharing companies may use the variables used in this analysis to conduct
their research in the future. At present, Malaysia is experiencing a massive digitalisation
phase, and this country’s online demand for food delivery is expected to increase as well.
Therefore, the current study is one of the initiation contributions in exploring the Malaysian
online food delivery application segment. Hence, the body of knowledge generated in this
study provides a new platform for future researcher to examine the factor that affects attitude
and online food delivery application.
There are three practical contributions from this current study. First, the present study
contributes to the knowledge and skillset in the shared-economy industry, online food delivery
service providers and restaurant owners to enhance the quality of customer life as there are
various reasons behind the use of the applications. The results from this study also help those
parties geta deeper understanding of the conceptand different purposesof using food delivery
service and food delivery applications. With the finding in this research, food delivery service
providers and restaurant owners should understand that the key success of online food
Online food
delivery
applications
Constructs Items Description Source (adapted)
Performance expectancy PE1 I find that online food delivery service
application is useful in my daily life when
searching for food
Palau-Saumell et al.
(2019)
PE2 I believe that using an online food delivery
service application helps me to search for food
more quickly
PE3 I believe that using an online food delivery
service application increases my productivity
when searching for food
PE4 Using an online food delivery service
application increases my chances of
purchasing food according to my taste/
appetite
Suk Won et al. (2019)
PE5 Using online food delivery services application
enables me to accomplish the purchasing of
food more quickly
Effort expectancy EE1 I believe that learning how to use online food
delivery service application is easy
Palau-Saumell et al.
(2019)
EE2 I believe that my interaction with online food
delivery service application is clear and
understandable
EE3 I find that online food delivery service
application easy to use
EE4 I believe it is easy for me to become skilful at
using online food delivery service application
Social influence SI1 I believe that many people in my country use
an online food delivery service application
Palau-Saumell et al.
(2019)
SI2 I believe that many people in my country
express their desire to use online food delivery
service application
SI3 I believe that many people in my country
search for food using online food delivery
service application
SI4 People who influence my behaviour think that
I should use an online food delivery service
application for purchasing food
Suk Won et al. (2019)
SI5 People whose opinions I value prefer that I use
an online food delivery service application for
purchasing food
Information quality IQ1 Using an online food delivery service
application provides accurate information on
food, charges and approximate delivery time
Suk Won et al. (2019)
IQ2 Using an online food delivery service
application provides believable information on
food, charges and approximate delivery time
IQ3 Using an online food delivery service
application provides information at the right
level of detail on food
IQ4 Using an online food delivery service
application presents the information in an
appropriate format
(continued )
Table 9.
Questionnaire adapted
items and source of the
studies
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delivery services and applications for the online food delivery segment is driven by social
influence,information quality, price-saving orientation and time-saving orientation. Therefore,
to achieve greater acceptance and adoption among theirtarget population, they can concentrate
on these four elements. The study results could also be useful for start-ups business, politicians,
government authorities and private food service providers. The possible explanation could be,
food delivery applications are commonly popular nowadays and thus can provide a new idea
for those in this industry to survive gradually. In conclusion, the study results could also be
important both for marketers and advertisers to create a more substantial customer base.
Finally, this current study will offer competitive guidance to foreign companies, especially
those interested in broadening their online food delivery business in developing countries.
7. Limitations and future research
There are three key limitations found in this study. First, current research is focused
primarily on customers in Malaysia regarding online food delivery services and the intention
Constructs Items Description Source (adapted)
Price-saving orientation PSO1 I can save money by using prices from
different online food delivery service
application
Escobar-Rodriguez
and Carvajal-Trujillo
(2014)
PSO2 I like to search for cheap food deals in online
food delivery service application
PSO3 Online food delivery service application offers
better value for my money
Time-saving orientation TSO1 I believe that using an online food delivery
service application is very useful in the
purchasing process of food
Alreck and Settle
(2002)
TSO2 I believe that using an online food delivery
service application helps me accomplish
things more quickly in the purchasing process
of food
TSO3 I believe that I can save time by using an online
food delivery service application in the
purchasing process
TSO4 It is important for me that the purchase of food
is done as quickly as possible using an online
food delivery service application
Attitude towards online
food delivery services
ATT1 Purchasing food using online food delivery
services is wise
Childers et al. (2001)
ATT2 Purchasing food using online food delivery
services is good
ATT3 Purchasing food using online food delivery
services is sensible
ATT4 Purchasing food using online food delivery
services is rewarding
Intention to use online
food delivery application
ITU1 I Intend to continue using online food delivery
service application in the future
Suk Won et al. (2019)
ITU2 I will always try to use an online food delivery
service application in my daily life
ITU3 I plan to continue to use an online food delivery
service application frequently
ITU4 I have decided to use an online food delivery
service application for purchasing foods the
next time Table 9.
Online food
delivery
applications
to use the applications. And the results of the study may not be generalised in another region.
Furthermore, this research also focuses primarily on the factors that drive customer intention
to use the apps. Therefore, the findings cannot be applied in other food services contexts.
Scholar in their future study should address such limitations. To overcome this limitation,
this study suggests a few ideas for future research purposes. First, future studies should
consider customers from many countries including international customers who have
remained in Malaysia to expand the study results. Second, future research can focus on issues
concerning this food industry segment to add relevant constructs. Thirdly, the present thesis
focussed solely on exploratory research, to add value to the results, qualitative research can
be carried out as well.
8. Conclusion
In conclusion, the present study contributes significantly to the literature on online food
delivery applications. The factors examined in this study are based on the existing theory of
UTAUT. Moreover, this research model also has been expanded with an additional
dimension, attitude towards online food delivery services which lead to the intention to use
online food delivery services through a smartphone. Six determinant variables, namely
performance expectancy, effort expectancy, social influence, information quality, price-
saving orientation and time-saving orientation, were used in the driven framework.
The findings concluded that social influence, information quality, price-saving
orientation and time-saving orientation result significantly affects attitude towards online
food delivery services and intention to use the applications. The outcome of performance
expectancy and effort expectancy showed it does not positively affect and is inconsistent
with previous literature. On the other hand, age as a moderator was found not to influence the
relationship between attitude and intention. In a nutshell, this study offered various
theoretical and practical contributions to the researcher and parties involved in the food
industry segment.
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Corresponding author
Yuvaraj Ganesan can be contacted at: yuvaraj@usm.my
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