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Online Food Delivery Services: Making Food Delivery the New Normal

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Within the food and beverage industry in Malaysia, there is an emerging new wave, the online food delivery (OFD) service. Not just restricted to the take-away and eating out, online food ordering is the new eating out. The emergence of the online food delivery services could be attributed to the changing nature of urban consumers. Despite the importance and the changing consumer behavior towards OFD services in Malaysia, studies that address the contributing factors towards OFD services among urbanites still remain scant. Hence, the objective of this research is to establish an integrated model that investigate the relationship of several antecedents (perceived ease of use, time saving orientation, convenience motivation and privacy and security) with the behavioral intention towards OFD services among Malaysian urban dwellers. The results revealed positive effect of time saving orientation (TSO), convenience motivation (CM) and privacy and security (PS) towards behavioral intention (BI) of OFD services. The findings provide OFD service providers and scholars with significant insights into what compels urbanites to adopt OFD services.
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Volume 1, Issue 1, 2019
DOI: pending
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1Department of International Business, Universiti Tunku Abdul Rahman lautc@utar.edu.my
2Department of Accountancy, Universiti Tunku Abdul Rahman ngcy@utar.edu.my
Online Food Delivery Services: Making Food Delivery the
New Normal
Lau Teck Chai1 and David Ng Ching Yat2
INTRODUCTION
There is a huge food delivery market in Southeast Asia. While the food market is a trillion-
dollar business, the delivery market is only a small fraction of this market (Kandasivam,
2017). This presented a big opportunity for future growth. It is projected that by the year
2022, the food delivery business will grow to an annual revenue of USD 956 million, which
is one of the fastest growing sectors in the food market (EC Insider, 2018).
Within the food and beverage industry in Malaysia, there is an emerging new wave, the
online food delivery (OFD) service. Not just restricted to the take-away and eating out, online
food ordering is the new eating out. In Malaysia, there are numerous food delivery companies
with many offering online food delivery services. Among the companies are FoodPanda
which is the first delivery company that started aggressively in Malaysia. Others in the
market are companies such as DeliverEat, Uber Eats, Honestbee, Running Man Delivery,
FoodTime, Dahmakan, Mammam and Shogun2U. Most of these food delivery services are
concentrated in the urban cities such as Kuala Lumpur, Klang Valley, Penang and Johor
Bahru. This is understandable because unlike other e-commerce services which are easier to
ABSTRACT
Within the food and beverage industry in Malaysia, there is an emerging new wave, the
online food delivery (OFD) service. Not just restricted to the take-away and eating out,
online food ordering is the new eating out. The emergence of the online food delivery
services could be attributed to the changing nature of urban consumers. Despite the
importance and the changing consumer behavior towards OFD services in Malaysia,
studies that address the contributing factors towards OFD services among urbanites still
remain scant. Hence, the objective of this research is to establish an integrated model that
investigate the relationship of several antecedents (perceived ease of use, time saving
orientation, convenience motivation and privacy and security) with the behavioral intention
towards OFD services among Malaysian urban dwellers. The results revealed positive
effect of time saving orientation (TSO), convenience motivation (CM) and privacy and
security (PS) towards behavioral intention (BI) of OFD services. The findings provide
OFD service providers and scholars with significant insights into what compels urbanites
to adopt OFD services.
Keywords: Online food delivery, behavioral intention, perceived ease of use, time saving
orientation, convenience motivation, privacy and security
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scale with the reliance on 3PL delivery, food delivery services face the challenge of location
and coverage boundary, while at the same time maintaining high customer satisfaction with
on-demand delivery. Perhaps this is the reason that there are only few strong players in this
industry without anyone being entirely dominant.
The emergence of the online food delivery services could be attributed to the changing nature
of urban consumers. These consumers use food delivery services for a variety of reasons but,
unsurprisingly, the most common reason seems to be the need for quick and convenient
meals during or after a busy work day. The various food delivery services that are readily
available take the hassle away from consumers to think about and plan meals, regardless of
whether the consumer is preparing the meal himself, going to the restaurant and dining in or
going to the restaurant and buying food to bring back to the office or home. Food delivery
services have changed consumer behaviour so much, especially urban consumers, that using
the OFD services have become normal and routine. More and more people are turning to food
delivery in recent years because of the current pace of life as well as the opportunity to
discover more restaurants that food delivery offers. For many busy urbanites, OFD services
are a convenient option during a busy work day in the city. Many prefer this option of food
delivery as this allow them to have fresh and healthy food at their offices or homes while they
have the freedom to continue to work. This is also an advantage as city dwellers can use OFD
services after a long day at work, preferring to go home and relax instead of spending a few
more hours out waiting for food or travelling to and fro just to get something to eat. It can be
seen that the OFD services provide convenience and time savings for customers as they can
purchase food without stepping out from their home or offices. The OFD services are slowly
but surely impacting the food and beverage industry because of its potential to grow the
business, ensuring higher employee productivity, delivering order accuracy and building
important customers database (Moriarty, 2016).
Perhaps another reason for the development of the OFD services is the growth in the usage of
smartphones in Malaysia. An increasing number of Malaysian consumers are using their
mobile devices to do their online shopping. In 2016, 17.9 million Malaysians accessed the
Internet via their mobile phones. By 2020, this figure is expected to reach 21.1 million mobile
phone Internet users (Zhang, 2017). The increasing penetration rate of the smartphone has
made it more convenient for consumers to shop anywhere and at any time. Retail sales via
mobile devices (including smartphones and tablets) accounted for 15% of all online sales in
2016. It is predicted that, by 2020, retail sales via mobile devices will account for 22% of the
total value of online sales (Zhang, 2017). The further convenience of accessing OFD services
through their smartphones could have motivated consumers to move from the traditional
offline food purchase to adopt OFD services as consumers can now get a wide selection of
food choices on a single click.
Despite the importance and the changing consumer behaviour towards OFD services in
Malaysia, studies that address the contributing factors towards OFD services among urbanites
still remain scant in the existing literature. Moreover, research studies pertaining to the online
food delivery services are also limited in the Malaysia context. Hence, the objective of this
research is to establish an integrated model that investigate the relationship of several
antecedents (perceived ease of use, time saving orientation, convenience motivation and
privacy and security) with the behavioural intention towards OFD services among Malaysian
urban dwellers. By addressing these gaps, this study can provide clearer understanding for
OFD service providers and future restaurant owners contemplating OFD services to
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comprehend the importance of consumer psychology especially in their behavioural intention
to use OFD services.
LITERATURE REVIEW
Behavioural Intention (BI)
Given the growing popularity of OFD services, customers tend to want to know more about
the electronic order delivery system and try to use it. This behaviour is called behavioural
intention. Behavioural intention refers to an individual’s likelihood to act or a customer
propensity to subscribe to the system in the future (Brown and Venkatesh, 2005; Dwivedi,
2005; Venkatesh and Brown, 2001). Behavioural intention can also be defined as a kind of
purchase intention which can be used to predict customer purchase behaviour. This will affect
an individual choice to adopt OFD or not to adopt OFD in the future. According to Yeo et al.
(2017), a person’s attitude can be highly predictable towards the person’s intention to
perform. The study pointed out that an individual’s action will depend on the criterion of the
behaviour which he or she will hold and a positive attitude will subsequently lead to the
behaviour to adopt the product or technology. Based on the past research from Olorunniwo et.
al. (2006) behavioural intention is related to customer experience. The more positive the
experience was, the more customers will be willing to adopt OFD. For example, with the
satisfaction of online takeaway system, customers who prefer to limit personal interaction
with others may have high intention to adopt the online system, especially those customers
who have had negative experience with frontline staff or sales personnel (Katawetawaraks &
Wang, 2011; Collier & Kimes, 2013).
Perceived Ease of Use (PEOU)
PEOU is the degree to which an innovation is perceived to be easy to understand, learn or
operate (Rogers, 1962). Similarly, Zeithaml et al. (2002) stated that PEOU is the degree to
which an innovation is not difficult to understand or use. Davis (1989) and Davis et al. (1989)
reaffirmed that the degree to which the respondents believe that they could use the particular
technology with minimum efforts could be considered as PEOU. PEOU according to Consult
(2002) is the ability of respondents to experiment with innovative technology and where they
could evaluate its benefits easily. It has been recognized as an important element to change
the attitude and behavioural intention of consumers and establish the acceptance of
technology usage among consumers (Cho & Sagynov, 2015).
The effect of PEOU ultimately will affect consumers’ behavioural intention in online
environment and has significant positive effect on purchase intentions (Cho & Sagynov,
2015). Chen & Barnes (2007) also discovered that PEOU significantly affect the adaptation
intentions of customer. To encourage more people to use a new technology, it is suggested
that companies develop systems that are easy to use (Jahangir & Begum, 2008). Study by
Chiu & Wang (2008) discovered that PEOU positively affect the continuance intention of
customers in the context of Web-based learning. The behavioural intention to use any online
services is dependent upon the perception of the potential adopters, which could be
favourable or unfavourable. Ramayah & Ignatius (2005) found that customers are unwilling
to shop online if the PEOU is hampered by certain barriers such as the long download times
of the Internet retailer websites and the poorly designed websites. Thus, it is imperative that
the design of OFD websites to be clear and understandable so that it will smoothen customers
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experience to make an order easy. Besides, Venkatesh & Davis (2000) reaffirmed that the
extent of customers PEOU of the technology will lead to behavioural intention. Based on the
discussion above, it can be hypothesized that:
H1: Perceived ease of use (PEOU) positively influence behavioural intention of online food
delivery services.
Time Saving Orientation (TSO)
Time saving orientation is the most critical factor to influence customers’ motivation to use
the technology-based self-service (Meuter et al., 2003). When an individual find himself lack
of time due to daily activities, such as work and leisure activities, this will lead the person to
look for instances where they could save time (Bashir et al 2015, Settle & Alreck, 1991). In
recent years due to the hectic lifestyle, many people dislike the effort to look for food and
waiting for the food at restaurants. They would prefer that food comes to them without much
effort and to be delivered as fast as possible (Yeo et al., 2017). Time saving is one of the
major contributory factors that influence behavioural intention of people to purchase online
(Khalil, 2014). Shopping online is considered time saving because shoppers do not need to
physically leave the current place to purchase something. Based on the research from Sultan
& Uddin (2011), time saving has a positive effect on behaviour intention toward online
shopping. The researchers found that many people perceived that online shopping takes lesser
time as it does not require them to waste time to travel out as compared to traditional offline
shopping (where they need to be physically present at the store). Alreck & Settle (2002)
reaffirmed that traditional modes of offline shopping is more time consuming than online
shopping as customers do not need to travel out to face traffic jam, search for parking and
also to queue in line to do payment. In another study, Alreck et al (2009), found that many
consumers wish that they could save more time. Consumers tend to want to save time so that
they could complete other urgent matters as soon as possible. Research from Ganapathi
(2015), and Zendehdel et al (2015) have also shown a significantly positive effect of time
saving towards behavioural intention to adopt online shopping. Based on the above
supporting evidence, it can be postulated that:
H2: Time saving orientation positively influence behavioural intention of online food delivery
services.
Convenience Motivation (CM)
The rapid urbanization has created a situation where urban dwellers find limited time
especially during the weekdays for them to prepare their own meals or even to have their
meals in the restaurants. Hence, they tend to consume more fast foods or just skip the meals
entirely (Botchway et al, 2015). In order to satisfy the needs of the customers and to increase
the business sales, many restaurants started to create new business models by offering OFD
services to consumers. In OFD services context, convenience is defined as the perceived time,
value and effort required to facilitate the use of OFD system. Research has shown that
convenience was seen as an ongoing barrier that affect the future intention (Seiders et al,
2005). This means that the system needs to achieve a certain desired level of convenience
before it could encourage future intention. Motivation is also important as it will affect the
attitude and willingness of customers. Once the convenience level meets the expectation of
customers, they would be motivated to use that system continuously.
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Jiang et al. (2011) stated that convenience is one of the principal motivations for users to
adopt electronic technology because customers must be convinced of their value before they
are willing to use this technology. As companies introduce new electronic ordering and
delivery system to the public, the convenience in using it spur customers to use it. Kimes
(2011) mentioned that users have the capability to utilize the new, easy and safe electronic
technology. Allowing consumers to place order and receive the foods anytime and anywhere,
customers would prefer to do online food purchase rather than store purchase. Making online
takeaway have many advantages such as the avoidance of poor customer service (Chen &
Hung, 2015) and the prevention of in-store traffic (Katawetawaraks & Wang, 2011).
Convenience in time and effort are important attributes for consumers to adopt the OFD
services (Collier & Kimes, 2013). Convenience-oriented shopper would always take time and
effort into consideration (Zhou et al., 2007). They would prefer to shop at home to minimize
the time, avoid crowded market and initiate the transaction at anytime and anywhere. Thus,
by using the online purchase system, the location is irrelevant to them during purchasing
(Chen & Hung, 2015). Thus it can be hypothesized that:
H3: Convenience motivation positively influence behavioural intention of online food
delivery services.
Privacy and Security (PS)
Belanger et al (2002) defined privacy as the probability to access, copy, use, and destroy
personal information of oneself. Example of personal information are name, phone number,
mailing address, bank account, email address, password and so on. Due to the many highly
publicized news on the breach of personal data by well-known companies, consumers are
increasingly feeling insecure on how and where their personal information are used during
online transaction (Flavian and Guinaliu, 2006). Security according to Kalakota and Winston
(1997) is threat which created potential incidents related to security of payments and storing
of information through online transactions. Many customers avoid online purchase due to
privacy factors, non-delivery service, credit card fraud, post purchase service and more.
Zulkarnain et al. (2015) found that the degree of trust will affect customer’s intention to
purchase products online. They discovered that privacy and security has become the main
concern for online shoppers. To ease people’s minds about the issues of privacy and security,
many websites have implemented policies to enable customers to verify, audit and certify
privacy policies for online transactions (Ranganathan and Ganapathy, 2002).
Generally, privacy and security are positively interrelated (Lichtenstein & Williamson, 2006).
The more the privacy and security are assured to the customers in online shopping, the more
the level of confidence of customers to shop online (Bashir et al 2015). Privacy and security
is also positively related to online purchase behaviour (Miyazaki & Fernandez, 2000). Based
on the research from Sultan & Uddin (2011), there is a positive effect between privacy and
security and behavioural intention to adopt online shopping. The authors also found that most
of the respondents think that trustworthiness is important while shopping online. The lack of
trust in companies handling personal information and security prompted many consumers in
European Union to avoid making online purchases (Flavián & Guinalíu, 2016). Belanger et al
(2002) found that over seventy percent of consumers refused to provide information online or
to make purchase online due to privacy and security problems. The reason given is that they
are worried about the lack of protection of their personal information. Companies that
provide verification system in their website will made consumers feel more secure (Belanger
et al 2002). Thus it can be hypothesize that:
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H4: Privacy and security positively influence behavioural intention of online food delivery
services.
RESEARCH METHOD
This study adopted a quantitative approach and used cross-sectional survey for data collection.
The unit of analysis is current and potential adopters of OFD services located in the Klang
Valley, Malaysia. The data were collected using a self-administered questionnaire survey via
Google Docs and direct distribution to respondents located in the Klang Valley. The inclusion
of the latter approach was to complement the online survey approach, knowing that the
response rate via online survey in Malaysia would be low. The questionnaire was designed
using simple and unbiased wording so that the respondents could understand the questions
easily. Questions items were adapted from earlier studies with minor modifications. Items
that measured behavioural intention (BI) and time saving orientation (TSO) were adopted
from Yeo et al (2017), perceived ease of use (PEOU) from Roca et al (2009), convenience
motivation (CM) from Kimes (2011) and privacy and security (PS) from Sultan and Uddin
(2011) and Bashir et al (2015). All the constructs were measured using a 5-point Likert scale
of 1- strongly disagree to 5 - strongly agree.
SmartPLS 3.0 software was used to assess the relationship among the research constructs by
performing partial least square (PLS) analysis (Hair et al., 2017) which is a structural
equation modeling (SEM) technique that permits concurrent analysis within latent constructs
and between measurement items. PLS-SEM was believed to be an appropriate data analysis
technique as (i) this study intends to investigate the predictive association between
independent and dependent variables, and (ii) new measures and structural paths were added
into the conceptual model based on previous literature.
RESULTS
Respondents’ Profile
A total sample of 302 respondents were collected. As shown on Table 1, most of the
respondents were female with 57.62% while male respondents were 42.38%. Most of the
respondents were between the age of 18 to 25 which recorded at 59.27%. In terms of
ethnicity, the majority were Malaysian Chinese with 81.45.4%, followed by Malays, 9.27.1%
and Indian, 7.62%. There were 44.70% who used the Internet between 6 to 12 hours per day,
followed by 40.07% (less than 5 hours per day), 12.25% (between 13 to 18 hours per day)
and 2.98% (19 hours and above).
Table 1: Respondent’s Profile
Profile
Sample (N = 302)
Percentage
Gender
Male
128
42.38
Female
174
57.62
Age Group
17 and below
26
8.61
18 25
179
59.27
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26 33
38
12.58
34 41
14
4.64
42 49
13
4.30
50 and above
32
10.60
Ethnicity
Malays
28
9.27
Chinese
246
81.45
Indian
23
7.62
Others
5
1.66
Internet usage per day
Less than 5 hours
121
40.07
6 12 hours
135
44.70
13 18 hours
37
12.25
19 hours and above
9
2.98
Measurement Model Analysis
The measurement model analysis consists of two types of validity, namely convergent
validity, and discriminant validity. The assessment of convergent analysis is ascertained by
examining the loadings, average variance extracted (AVE) and also the composite reliability
(Gholami et al, 2013). The loadings were all higher than 0.50, the composite reliabilities
were all above 0.70 and the AVE of all constructs were also higher than 0.50 as suggested in
the literature and exhibited in Table 2. Indicator item PS3 were removed in the scale
refinement process.
Table 2: Measurement Model
Loadingsa
AVEb
CRc
Rho_Ad
Perceived
0.916
0.838
0.940
0.905
Ease of Use (PEOU)
0.927
0.903
Time Saving
0.924
0.809
0.927
0.883
Orientation (TSO)
0.904
0.870
Convenience
0.865
0.679
0.892
0.836
Motivation (CM)
0.905
0.888
0.601
Privacy &
0.871
0.727
0.889
0.824
Security (PS)
0.880
0.806
Behavioural
0.906
0.816
0.930
0.895
Intention (BI)
0.924
0.880
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Items removed: indicator items are below 0.5: - PS3
a. All Item Loadings > 0.5 indicates indicator Reliability (Hulland, 1999, p. 198)
b. All Average Variance Extracted (AVE) > 0.5 as indicates Convergent reliability (Bagozzi and Yi
(1988), Fornell and Larker (1981))
c. All Composite reliability (CR) > 0.7 indicates internal consistency (Gefen et al, 2000)
d. All Cronbach’s alpha > 0.7 indicates indicator Reliability (Nunnally, 1978)
Discriminant validity identifies the degree to which items differentiate among constructs or
measure distinct concepts. This was ascertained by examining the indicators item cross
loading (Table 3), the Fornell and Larker (1981) criterion (Table 4) and the HTMT method
(Table 5). Table 3 showed that all loadings were higher than the total cross-loadings, which
indicated the discriminant validity. Subsequent analysis was conducted following the Fornell
and Larcker (1981) criterion by comparing the correlations between constructs and the square
root of the average variance extracted for that construct. The results of discriminant validity
based on Fornell and Larcker (1981) criterion was shown in Table 4. The results indicated
that all the values on the diagonals were greater than the corresponding row and column
values indicating the measures were discriminant. Subsequently, Henseler, Ringle, and
Sarstedt (2015) uncovered that the Fornell and Larcker (1981) criterion do not reliably detect
the lack of discriminant validity in common research situations. They proposed using the
multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio
of correlations (HTMT). As such, HTMT method was adopted to test the discriminant
validity and the results were shown in Table 5. If the HTMT value is greater than HTMT0.85
value of 0.85 (Kline, 2011), or HTMT0.90 value of 0.90 (Gold et al, 2001) it will indicate a
problem of discriminant validity. Table 4 showed that all the values passed the HTMT0.90
and also the HTMT0.85, hence discriminant validity was ascertained in the measurement
model.
Table 3: Indicator Item Cross Loading
CM
PEU
PS
TSO
BI1
0.508
0.438
0.353
0.538
BI2
0.481
0.390
0.294
0.476
BI3
0.442
0.305
0.308
0.438
CM1
0.865
0.483
0.347
0.452
CM2
0.905
0.49
0.318
0.447
CM3
0.888
0.474
0.297
0.401
CM4
0.601
0.155
0.236
0.421
PEU1
0.433
0.916
0.348
0.452
PEU2
0.428
0.927
0.364
0.404
PEU3
0.492
0.903
0.368
0.417
PS1
0.289
0.335
0.871
0.184
PS2
0.262
0.338
0.88
0.203
PS4
0.366
0.328
0.806
0.303
TSO1
0.444
0.413
0.229
0.924
TSO2
0.475
0.413
0.219
0.904
TSO3
0.502
0.428
0.306
0.870
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Table 4: Discriminant Validity (Fornell and Larker Criterion)
BI
CM
PEU
PS
TSO
BI
0.904
CM
0.530
0.824
PEU
0.422
0.492
0.915
PS
0.354
0.366
0.393
0.853
TSO
0.539
0.526
0.465
0.279
0.899
Note: The diagonal are the square root of the AVE of the latent variables and indicates the
highest in any column or row
Table 5: Discriminant Validity (HTMT)
BI
CM
PEU
PS
TSO
BI
CM
0.614
PEU
0.465
0.567
PS
0.403
0.436
0.455
TSO
0.606
0.617
0.52
0.318
Structural Model Analysis
In view of the measurement model assessment provides satisfactory quality, we moved on to
the second step of PLS-SEM analysis which was the structural model assessment. Prior to
structural model analysis, the constructs were checked for potential collinearity issues.
Sarstedt, Ringle and Hair (2017) proposed that Variance Inflated Factor (VIF) values 5 are
indicative of collinearity among the predictor constructs. The VIF values of constructs were
shown in Table 5, with all values below 2.0. This showed that all the constructs did not suffer
from collinearity issues.
Table 5: Checking of Collinearity Issues
Construct
VIF
Perceived Ease of Use (PEOU)
1.520
Time Saving Orientation (TSO)
1.500
Convenience Motivation (CM)
1.605
Privacy & Security (PS)
1.242
Upon checking the potential collinearity issues, we begin to focus on the predictive
capabilities of the model through structural model analysis. Figure 1 showed the
bootstrapping direct effect result. According to Sang, Lee and Lee (2010), structural model
denotes the causal relationships among the constructs in the model that includes the estimates
of the path coefficients and the R2 value, which determine the predictive power of the model
tested. Hair et al. (2017) advised looking at the R2, beta (β) and the corresponding t-values via
a bootstrapping procedure with a resample of 5,000. Additionally, they also recommended
researchers to report the predictive relevance (Q2) as well as the effect sizes (f2). Table 6
reported all the direct relationship for hypotheses testing. TSO = 0.316, t value >1.96, p
value < 0.05), CM (β = 0.274, t value >1.96, p value < 0.05) and PS (β = 0.130, t value >1.96,
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p value < 0.05) affected BI positively. Thus, H2, H3 and H4 were supported. However, there
was no effect of PEOU on BI with t value less than 1.96 and p value higher than 0.05.
Therefore, H1 was not supported.
Figure 1: Hypothesis testing: Bootstrapping Direct Effect Result
Table 6: Direct Relationship for Hypothesis Testing
Relationship
Std
Beta
Std
Error
t-value
Decision
R2
Q2
f2
H1
PEU -> BI
0.091
0.081
1.104
Not supported
0.447
0.000000
0.008319
H2
TSO -> BI
0.316
0.071
4.499
Supported
0.073864
0.113145
H3
CM -> BI
0.274
0.084
3.231
Supported
0.044034
0.073211
H4
PS -> BI
0.130
0.053
2.474
Supported
0.012784
0.021631
Importance Performance Matrix Analysis (IPMA)
This study also conducted a post-hoc importanceperformance matrix analysis (IPMA) using
behavioral intention as the target construct. The IPMA builds on the PLS estimates of the
structural model relationships (importance of each latent variable) and includes an additional
dimension to the analysis that considers the latent variables’ average values (performance)
(Hair et al 2017). The importance scores were derived from the total effects of the estimated
relationships in the structural model for explaining the variance of the endogenous target
construct. On the other hand, the computation of the performance scores or index values were
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carried out by rescaling the latent variables scores to range from 0 (lowest performance) to
100 (highest performance). The ndings, as noted in Figure 2 and Table 7 revealed that CM,
PEU and TSO were very important BI elements. Besides, the three constructs also showed
good performance influencing the BI. In the context of importance, TSO and CM had the
greatest influence.
Figure 2: Importance-performance matrix analysis (IPMA) for behavioral intention
Table 7: Total Effect and Performance for Behavioral Intention
Construct
Importance
(Total Effect)
Performance
Convenience Motivation (CM)
0.262
68.448
Perceived Ease of Use (PEU)
0.076
67.825
Privacy and Security (PS)
0.144
58.542
Time Saving Orientation (TSO)
0.270
66.556
DISCUSSIONS
The result on Table 6 showed that PEOU was not a significant contributor towards BI. Most
of the online users would most probably be very familiar with surfing and had much
browsing experience. The would be more likely be able to apply the website without
encountering much difficulty. TSO was found to be significant. According to Sultan and
Uddin (2011), they mentioned that OFD will save consumer time to find a place for food and
the time to wait in restaurant, which mean consumer will prefer to use OFD service because it
will save them time. Yeo et al 2017) also state that customers can search for information on
different types of food and to compare the food prices at anytime and anywhere by the OFD
services. Thus, time saving factor would be an important element in motivating customers to
use the OFD services (Sultan & Uddin, 2011). OFD service providers need to ensure a
reasonable lead time of the food reaching their customer, and the lead time should be lesser
than when consumers take the alternative ways.
Journal of Marketing Advances and Practices
73
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The finding also found significant positive effect of convenience motivation. This seems to
be aligned with past studies (Cho and Sagynov, 2015; Jiang et al., 2011). This showed that
users were somehow convinced that convenience was one of the motivating factors to adopt
OFD services. OFD website that could provide clear guideline on what and how the users
should do and adopt the electronic ordering and delivery system may convince the consumers
to conduct the OFD services because it could achieve a desired level of convenience. Once
the convenience level of using the OFD services meet the expectation of customers, they
would be motivated to use the service continuously. Urban dwellers in the Klang Valley
would probably place convenience associated with the OFD services as one of the top
priorities as many might find it inconvenience for them to prepare food on their own and
limitations in cooking space due to them not allowed to cook in their place of stay. Rather
than step out and having a meal in restaurants in which they might face certain
inconveniences such as finding parking, walking distance, restaurant full house etc,
consumers might prefer to prefer place order through OFD and then wait for the food come to
the door step.
Privacy and security was also found to affect BI positively. Bashir et al. (2015) pointed out
that privacy and security had become the main concern for customers in online purchase. The
higher the level of confidence customers placed in the particular OFD website, the higher will
be the behavioural intention to adopt these services. Zulkarnain et al. (2015) also stated that
the high degree of trust may increase customers’ intention to purchasing online. To ease
consumers’ minds about the issues of privacy and security, OFD websites may need to
implement policies to enable customers to verify, audit and certify their information to
enhance the degree of trust. In the era where high profile cases of data security breach were
reported in the news daily, it will be crucial for OFD service providers to strengthen their
customers data security to ensure the high level of confidence and trust placed on them.
FUTURE RESEARCH DIRECTIONS
In terms of limitations, the study could not include all possible factors that might affect the
OFD services behavioural intention. In addition, it also lacks diversity in terms of the sample
used. The survey concentrates on the urban area (focusing only in the Klang valley), and this
does not represent the whole of Malaysia. Future researcher could perhaps include other
urban areas where OFD services are available. Moreover, the study also relied on structured
interviews only as the single survey administration procedure. Hence, future researchers
could use other data collection strategies such as using focus groups discussion or combining
both the quantitative as well as the qualitative (personal interview) to solicit richer response.
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