Factors affecting online purchase intention on Facebook: The role of online customer experience
Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
International Conference on Marketing in the Connected Age (MICA-2018), October 6th, 2018
Danang City, Vietnam
Factors Affecting Online Purchase Intention on Facebook:
The Role of Online Customer Experience
Phuong Thao Nguyena*, Thi Khue Thu Ngoa
aFaculty of Marketing, University of Economics – The University of Danang, Da Nang City, Vietnam.
A B S T R A C T
The advancement of World Wide Web and particularly social networking sites such as Facebook has resulted
in the creation of a new form of retail transactions – electronic retailing or online shopping. Thus, customers’
involvements in online purchasing have become an important trend. As such, it is vital to identify the
determinants of the customer online purchase intention. The aim of this research is to evaluate the impacts of
perceived usefulness, perceived ease of use, past online shopping experience and trust on the customer online
purchase intention. A total of 210 female office workers in Da Nang participated in this research. The findings
revealed that past online purchase experience, trust and perceived ease of use were positively related to the
customer online purchase intention, of which customer experience exerts the strongest effect.
Keywords: TAM; Facebook; Trust; Customer experience; Purchase Intention
1. Introduction
The advent of the Internet, accompanied by the exponential growth of related technologies such as tablets,
smartphones, have encouraged the rapid emergence of social networking sites (SNSs) that Facebook is a prime
example. The Wall Street Journal reported that in the first quarter of 2015, Vietnam had 30 million Facebook
users, up from 8.5 million in 2012, becoming one of Facebook's fastest growing markets. According to the
Digital Economy and E-commerce Department - Ministry of Industry and Trade, shopping through online
websites or SNSs has significantly increased, from 53% in 2014 to 68% in 2015. In 2015, 28% of businesses
surveyed said they had advertised or sold merchandise through online social networks, up 4% over the previous
year. Shopping through SNSs was forecast to increase to 34% in 2016. More than 43% of surveyed respondents
said that Facebook ads had an impact on their purchase intentions. The tremendous impact of Facebook has not
only affected the way businesses operate, but also changed the way consumers behaved. As such, Facebook's
role is becoming so increasingly important in improving business efficiency that studies on Facebook will
provide useful insights to help businesses better understand customer behavior and thereby improve the
effectiveness of marketing programs on Facebook.
Women seem to prefer shopping through SNSs than men do. Previous research shows that women tend to
shop online for goods intended for enjoyment (Chang and Chen, 2008). Most online customers are really young,
from ages 25 to 39, working in a company, with a steady monthly salary, most of whom are women and have at
least one social networking site account. Before buying a product online, they usually rely on notifications from
forums, company websites, Facebook accounts or reviews. It is important to note that one of the reasons most
consumers prefer online transactions is its convenience and quick delivery. The most purchased products are
electronic appliances and clothing. People living in urban areas tend to shop online more than people living in
rural areas. In addition, studies have shown a gender difference in online shopping: the number of female
* Corresponding author. E-mail address: thaonp@due.edu.vn
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
shoppers is three times more than that of their male counterparts. The website 100ydesign.com (2014) describes
the behavior of female customers as follows: 78% of women shops online and over 58% buys more than twice a
month. The best-selling product is women's clothing, accounting for 62%. The explanation is that women like to
buy clothes at all times and change them every day even though they can not try on or touch them before buying.
Once a customer feels satisfied in the first buying experience, he or she will become regular shoppers. In
addition, women also intend to buy based on the clothing design and quality of the garment.
In summary, the benefits of using SNSs in marketing are enormous as they offer a huge opportunity for
marketers to create innovative activities that have not previously been viable. However, marketers need to
develop an insightful understanding of consumer behavior when purchasing products online. This information
will help marketing managers to plan their marketing mixes and offers to better meet customer’s requirements.
By doing so, companies will establish, maintain or increase customer satisfaction, build strong brand loyalty and
ultimately, provide consumers with a solid rationale for continuing to buy the same brand. This study is thus
significant as an attempt to identify factors and their relative strength in influencing customer online purchase
intention when shopping on Facebook. Particularly, the authors expect to find out the role of customer
experience during customer decision making process.
This research particularly focuses on female office workers in Da Nang (ages 25 to 40) who have a stable
source of income and easy access to new technologies such as Facebook. This group is considered potential for
businesses aimed to expand customer networks via Facebook.
The study begins with a literature review of previous studies on related subjects, in order to develop a
theoretical model for this study. A large quantitative survey was conducted in order to empirically test and
confirm the conceptualized model. This research also provides some recommendations for businesses or
individuals who intend to sell online via Facebook in Da Nang, in order to better satisfy customer demand and
achieve the highest business efficiency.
2. Literature review
2.1. Online shopping behavior
According to Kolter & Levy (1969), "consumer behavior is specific action of an individual who make the
purchasing decision and accept or dispose of products, services”. Consumer behavior is a process in which it
allows an individual or a group of people choose, buy, use or eliminate products or services, the accumulation of
experience with the purpose to satisfy their demands/needs (Solomon Micheal, 1992).
Online shopping is defined as the use of online stores by consumers up until the transactional stage of
purchasing and logistics (Monsuwe et al., 2004). Haubl and Trifts (2000) conceptualized shopping in online
environments as a shopping activity performed by a consumer via a computer-based interface, where the
consumer’s computer is connected to, and can interact with, a retailer’s digital storefront through a network (e.g.,
the WWW).
The decision-making process of an online consumer will be different from that of traditional consumer:
Internet shoppers are not able to gain the experience they usually get when shopping the traditional way, e.g.
interacting with a saleperson, feeling the atmosphere, and touching or trying the merchandise (Li et al., 1999).
According to previous studies, there are several fundamental differences. Firstly, according to Christopher &
Huarng (2003), unlike shopping at traditional retail stores where customers often rely on references from their
family and friends, consumer research group or word-of-mouth, customers shopping at an online stores will
experience different evaluation methods aided by new information technology, such as a link to product-related
websites, “help” on product section, product review by other customers and discussion groups. Second, while
customer experience very intensive personal contact in a face to face context, they have very little or even non-
existent online (Rose et al., 2011). Third, online customers are provided with a very rich source of information,
whereas in a face to face context, this may be more limited or may occur over a range of formats (e.g. brochures,
posters, customer sales representatives). Next, customers can purchase online anytime anywhere suited to
themselves, particularly now with Web access via mobile devices. Within the face-toface context, customer
interactions are defined and restricted by the opening hours of the organization. Finally, differences may exist in
terms of how the brand is presented. Online, the brand is presented in a predominantly audio-visual way,
whereas offline opportunities exist for the brand to be experienced via a range of artefacts such as staff and their
presentation, buildings and facilities, vehicles, livery and other tangible elements (Rose et al., 2011).
With all these abovementioned advantages, Monsuwe et al. (2004) indicated that online shopping fulfills
several consumer needs more effectively and efficiently than conventional shopping. From the consumer’s
viewpoint, online shopping allows the shopper to search and compare various product or service alternatives
from different online stores that are located in different parts of the world. The interactive nature of the Internet
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offers opportunities for consumers to use the web shopping facilities effectively by improving the availability of
product information, enabling direct multi attributes comparison, and reducing prospective buyers’ information
search costs (Alba et al., 1997). The Internet can also provide benefits to companies. As consumers are
increasingly using the Internet as a shopping approach in performing their purchasing activities, companies can
take this opportunity to use the Internet, especially SNSs, as a medium to attract and maintain current and
potential customers.
2.2. Theory of Technology Acceptance Model (TAM)
TAM is an adaptation of TRA, which hypothesized that behavioral intention is influenced by attitude and
subjective norms. However, TRA’s weak point was detected in the use of abstract concepts like ‘belief’ and
‘evaluation’ as constructs that affect attitude (Yu et al., 2005). The TAM, introduced by Davis (1986), has
received considerable attention in the information system (IS) field for predicting and explaining user behavior
and IT usage (Yu et al., 2005). For Davis et al. (1989, p. 985) the main goal of TAM was to ‘provide an
explanation of the determinants of computer acceptance that is general, capable of explaining user behavior
across a broad range of end-user computing technologies and user populations’.
Indeed, the TAM has gained considerable theoretical and empirical support in predicting technology
acceptance among potential users and decision makers (Ajzen, 1991; Wu and Lu, 2013). The TAM theorizes that
two key beliefs about a new technology, perceived usefulness (PU) and perceived ease of use (PEOU),
determine a person’s intention to adopt a new technology (Davis, 1989). According to Davis (1989), users’
acceptance of a new technology depends primarily on its function (PU) and secondarily on the ease or difficulty
with which its function can be performed (PEOU). The predictive power and parsimony of the TAM enables
researchers to analyze and understand different purchase behaviors.
Fig. 1. Technology Acceptance Model-TAM Model of Davis (1989)
Fig. 2. Extended Technology Acceptance Model
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
The choice for using TAM as a research model to explain consumers’ online shopping adoption is attributed
to its consistent capability to explain a substantial portion of variances between behavioural intention and actual
behaviours derived mainly from research into the purchase of technology related products (Bobbit & Dabholkar,
2001; Goldsmith, 2002; Grabner-Krauter & Kaluscha, 2003; Hanque, et al., 2006; King & He, 2006).
The technology acceptance model (Davis, 1993) has been applied to the topic of online shopping, with the
two main constructs ‘perceived usefulness’ and ‘perceived ease of use’ being applicable to online shopping
adoption. With the particular topic of online shopping, usefulness would therefore refer to the degree that a
consumer believes using the Internet enhances the outcome of their shopping experience, and ease of use would
refer to the consumer’s perception of the effort involved in the online shopping process. However, it is
important to note that the technology acceptance model has been criticised for ignoring other factors that can
influence technology acceptance (Chen et al., 2002). Some studies have specifically aimed at exploring other
factors that affect online shopping. Pentina et al. (2011) focus on website functions and how they affect
satisfaction and online retail performance. Pentina et al. (2011) suggest that future research in the area consider
the role that other factors apart from web site features and functions play in customer satisfaction, such as
customer service, price, and shipping policies.
In addition to PU and PEOU, an important factor involved in technology adoption is trust. Trust is defined as
the extent to which consumers expect that an e-retailer will meet their transaction expectations and will not
engage in opportunistic behavior (Morgan & Hunt, 1994; Pavlou, 2003; Pavlou et al., 2007). As such, the trust-
augmented TAM developed by Dahlberg et al. (2003) seems to be more useful in explaining customer
technology acceptance behavior than the basic TAM.
2.3. Online Customer Experience
Past studies have pointed out that there are several factors influencing customer online purchase intention,
such as perceived usefulness (PU), perceived ease of use (PEOU), trust, etc. as demonstrated in TAM model
below. However, intention to shop online is also influenced by consumers’ Internet shopping history (Shim et
al., 2001).
Meyer and Schwager (2007, p. 2) defined customer experience as ‘the internal and subjective response
customers have to any direct or indirect contact with a company’. The creation of the subjective response is via
the customer’s interaction with the various components of the organization’s offer, which includes the
performance of the product itself, packaging, pricing, advertising, retail environment and customer service
handling (Rose et al., 2011). Similarly, Carbone and Haeckel (1994) suggested that a customer experience takes
place whenever a customer interacts with an organization and its activities. It was defined as ‘the take-away
impression formed by people’s encounter with products, services and businesses’ (Carbone and Haeckel, 1994,
p. 9).
In the context of online environment, Rose et al (2012) defined customer experience to be a psychological
state manifested as a subjective response to the e-retailer’s website (Gentile et al., 2007; Meyer and Schwager,
2007). The customer engages in cognitive and affective processing of incoming sensory information from the
website, the result of which is the formation of an impression in memory. Previous research have pointed out
that prior online shopping experiences have a direct impact on Internet shopping intentions (Eastlick & Lotz,
1999; Weber & Roehl, 1999). Helson (1964) suggests that an individual’s response to a judgmental task is based
on three aspects: (1) sum of the individual’s past experiences, (2) the context or background, and (3) the
stimulus. To the extent that minimal context or system-specific information is given, the individual will make
system-specific evaluations based on prior experiences with the system. In the online shopping context,
consumers evaluate their Internet shopping experiences in terms of perceptions regarding product information,
form of payment, delivery terms, service offered, risk involved, privacy, security, personalization, visual appeal,
navigation, entertainment and enjoyment (Burke, 2002; Parasuraman & Zinkhan, 2002; Mathwick et al., 2001).
In case prior online shopping experiences resulted in satisfactory outcomes and were evaluated positively, this
leads consumers to continue to shop on the Internet in the future (Shim et al., 2001). Such past experiences
decrease consumers’ perceived risk levels associated with online shopping. However, if these past experiences
are judged negatively, consumers are reluctant to engage in online shopping in future occasions. This illustrates
the importance of turning existing Internet shoppers into repeat shoppers by providing them with satisfying
online shopping experiences (Weber & Roehl, 1999).
The main reason that past online purchase experience included in this study because Facebook is another
online shopping channel even though its original purpose is a social network. There are more risks and trust
involved in the social network compared to normal e-commerce website when the customers purchasing
products. Thus, it will be interesting to know whether users’ confidence and skills in online effect can overcome
those trust and risks.
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2.4.
Hypotheses development and research model
2.4.1. Online purchase intention:
Purchase intention measures have been used frequently to identify buying likelihoods for products within
defined time periods (Juster, 1966; Morrison, 1979; Whitlark et al., 1993). Earlier research has shown that
consumers who report intentions to purchase a product possess higher actual buying rates than consumers who
report that they have no intention of buying (Berkman and Gilson, 1978). While it is accepted that purchase
intention does not equate to actual purchase behaviour, it has been demonstrated that measures of purchase
intention do possess predictive usefulness (Jamieson and Bass, 1989; Stapel, 1971).
Customer online purchase intention was one of the intensive research areas in the extant literature. Customer
online purchase intention in the online shopping environment will determine the strength of a consumer’s
intention to carry out a specified purchasing behaviour via the Internet (Salisbury et al., 2001). Furthermore, the
theory of reasoned action suggested that consumer behaviour can be predicted from intentions that correspond
directly in terms of action, target and context to that consumer behaviour (Ajzen and Fishbein, 1980). According
to Day (1969), the intentional measures can be more effective than behavioural measures to capture customer’s
mind as customer may make purchases due to constraints instead of real preference when purchase is considered.
Purchase intention can be classified as one of the components of consumer cognitive behaviour on how an
individual intends to buy a specific brand. Laroche et al. (1996) assert that variables such as consideration in
buying a brand and expection to buy a brand can be used to measure consumer purchase intention. Based on the
argument of Pavlou (2003), online purchase intention is the situation when a customer is willing and intends to
become involved in online transaction. Online transactions can be considered as an activity in which the process
of information retrieval, information transfer, and product purchase are taken place (Pavlou, 2003). The
information retrieval and exchange steps are regarded as intentions to use a web site; however, product purchase
is more applicable to an intention to handle a web-site (Pavlou, 2003). Therefore, it is crucial to evaluate the
concept of online purchase intention in this study. In order to trigger customer online purchase intention, web
retailers have to explore the impact of shopping orientations on the customer online purchase intention.
2.4.2. Perceived Usefulness
PU is defined as the degree to which the user believes that the technology will enhance the performance of an
activity (Davis, 1989). Perceived usefulness is the most important factor influencing behavioral intention
especially when making an adoption decision (Venkatesh and Davis, 2000). Perceived usefulness is generally
associated with convenience and ease of use. In the context of e-commerce, Chau et al. (2000) declares that, in
general terms, the ‘purchase speed’ and the ‘convenience’ of the websites are determinant factors of their
usefulness. Additionally, Shih (2004) defined PU of e-shopping as the degree to which an individual believes
that trading on the Web would enhance the effectiveness of his or her shopping. Most of the prior studies on
perceived usefulness focused mainly on the usage or adoption of information technology and the World Wide
Web but not on the adoption to buy products online. This study was aimed at examining the impact of perceived
usefulness on the purchase intention to buy products via Facebook online. Perceived usefulness is hypothesized
to have a direct effect on purchase intention when buying online. Hence, it is expected that:
H1: Female office workers’ perceived usefulness of using the Facebook for buying goods and services has a
positive effect on their purchase intention via Facebook.
2.4.3.
Perceived Ease of Use
Perceived ease of use is defined as the degree to which a person believes that using a particular system would
be free of effort (Davis, 1989). It has a strong influence on behavioral intention to adopt information technology.
If a technology is perceived as too difficult to use, a person will choose an alternative option that is easier for
him or her to perform. According to Buton-Jones and Hubona (2005), the ease of learning and becoming skilful
at using pervasive technologies, including technologies and interfaces on online shopping sites, were concluded
as valid determinants as to what makes a technology easy to use. The work of Selamat et al. (2009) further added
that a technology which is perceived to be easier to use than another is more likely to be accepted by users
whereas the more complex a technology is perceived to be, the slower will be its rate of adoption. This is
supported by Teo (2001) as the study concluded that a system which is easy to use often requires less effort on
the part of users and thereby increases the likelihood of adoption and usage of a particular technology.
Accordingly, the study proposes that:
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H2: Female office workers’ perceived ease of using the Facebook for buying goods and services has a
positive effect on their purchase intention via Facebook.
2.4.4. Relationship between perceived ease of use and perceived usefulness
Perceived ease of use has been empirically verified by many studies as a predictor of perceived usefulness
(King & He, 2006). When all other factors are equal, users are likely to consider a technology useful when they
perceive it as easy to use (Bruner & Kumar, 2005). Nonetheless, the relationship remains contradictory
(Aladwani, 2002). The work of Gefen and Straub (1997) concluded that the relationship was not significant in
predicting e-mail acceptance as a technology, while more recent researches (e.g. Jantan, et al., 2001; Shyu &
Huang, 2011) proved otherwise. However, in existing studies around the area of electronic commerce, both are
expected to be closely linked as Ramayah and Ignatius (2005) argued that consumers who perceive that online
shopping is effortless should in turn develop a tendency to perceive it as useful. The rationale behind such a
phenomenon is due to the fact that consumers would inherently try to form his or her perception of online
shopping based on his or her own experiences in engaging in online shopping and the ease in which the shopping
activity was executed. This is in line with the work of Heijden (2000) which suggests that the easier it is for
consumers to use online shopping sites, the more useful online shopping will be perceived by consumers.
Therefore, this study anticipates that:
H3: Female office workers’ perceived ease of using the Facebook has a positive effect on female office
workers’ perceived usefulness.
2.4.5. Online customer experience
Based on the previous study (Monsuwe et al., 2004), intention to shop online is related to internet shopping
history and has a direct impact on internet shopping behavior. Customers with strong online purchase intention
in web shopping usually have prior purchase experiences that assist in reducing their uncertainties (Shim and
Drake, 1990). Also, Gefen and colleagues (2003)’s research showed two sets of unrelated usage antecedents by
customers: 1) customer trust in the e-vendor and 2) customer assessments of the IT itself, specifically the
perceived usefulness and perceived ease-of-use of the website as depicted in the technology acceptance model
(TAM). Research suggests, however, that the degree and impact of trust, perceived usefulness, and perceived
ease of use change with experience. Additionally, Ranganathan and Jha (2007) claimed that past online shopping
experience has the strongest association with purchase intention compared to other factors in their models. Thus
the hypothesis:
H4: Female office workers’ past online shopping experience has a positive effect on their purchase intention
via Facebook.
2.4.6. Trust
Kimery and McCard (2002) define trust as customers’ willingness to accept weakness in an online
transaction based on their positive expectations regarding future online store behaviour. According to Barber
(1983), trust is an expectation about individuals’ behaviour within the society where they are living or by which
they are ruled. Trust can be bestowed upon a person, an object (product), an organization (a business), an
institution (the government) or a role (a professional of some kind).
According to Blau (1964), trust can reduce uncertainty created by other people or artifacts and is therefore
essential for e-commerce. It has been shown to affect consumers’ fears of unreliability and risks of being
cheated. Trust in the online environment is particularly important because of the complexity and diversity of
online interactions and the resulting possibility of insincere and unpredictable behavior (Gefen and Straub,
2003). Kim and Bensabat (2003) claimed, in an online shopping context, consumers are vulnerable and likely to
expose themselves to loss if they: (i) provide the email address (with the vulnerability of spam email); (ii)
provide their shipping information (with the vulnerability of privacy invasion); (iii) provide their credit card
information (with the vulnerability of credit card fraud) or (iv) complete online purchase transaction (with the
vulnerability to quality and service inadequate).
In the context of online social networks such as Facebook, there are several reasons why trust is an important
factor in the online purchase intention. First, Facebook users must provide personal information when they
register to Facebook. Such personal information is subject to potential abuse as the data might be used for
marketing purposes or shared with third parties. Thus, users may have concerns about the misuse of their
personal information done by Facebook. Second, there are unclear security settings (such as https, SSL, or third
party certificate) in Facebook. Therefore, consumers must trust either Facebook or online social network vendor
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not to violate their privacy and security. Finally, anybody can open a shop in Facebook as long as they are
registered member. Therefore, buyers may have concerns about whether the sellers are reliable.
Kim and Benbasat (2003) stated that trust helps the complexity and vulnerability a consumer feels while
engaging in e-commerce by allowing the consumer to subjectively rule out undesirable yet possible behaviors of
the online vendor. According to Van der Heijden et al. (2003), trust hence helps consumers reduce their risk
perceptions when dealing with online vendors makes them more comfortable sharing their personal information
which is necessary in ecommerce transactions. Consumer trust in a company’s website has been shown to
directly and positively affect the attitude toward the company and purchase intention from that company. Based
on the arguments above, we propose that:
H5: Female office workers’ trust of using the Facebook for buying goods and services has a positive effect on
their purchase intention via Facebook.
2.4.7. Research model
The Technology Acceptance Model has been used in different contexts with numerous supporting empirical
studies. Therefore, our study is constructed based on the theoretical framework of TAM with the elimination of
attitudes. There were three main reasons why we decided to elimnate attitudes from the Technology Acceptance
Model. Firstly, prior empirical studies showed a nonsignificant effect on behavioral intention (Davis et al.,
1989). Perceived usefulness was found to be the major determinant of behavioral intention while attitudes
illustrated a non-significant impact toward behavioral intention. Although perceived usefulness has an important
influence on attitude formation, it is possible that attitudes might not play a strong role in predicting behavioral
intention after an individual is exposed long enough to the technology. Secondly, why some researchers have
chosen to take attitudes out of the Technology Acceptance Model might be in the interest of parsimony because
the revised model has fewer indicators, which do not significantly lower its predictive capability (Mathieson,
1991; Davis, 1985). Thirdly, the Technology Acceptance Model relies on the premise that attitude factors are
comprehensively included within the construct of perceived usefulness. People may use a technology even if
they do not have positive attitudinal affect towards it as long as it is useful or provides productivity enhancement
(Davis et al., 1989). Therefore, attitudes are eliminated from the structural model proposed for this research.
In the Technology Acceptance Model, perceived usefulness is the major determinant of behavioral intention
and the effect of perceived ease of use on behavioral intention is largely indirect through the construct of
perceived usefulness (Davis et al., 1989). Based on the literature review, two more constructs namely trust (TR)
and online customer experience (OCE), have also been added into the TAM Model to better explain the
phenomenon of female office workers’ online shopping via Facebook in Da Nang (Goldsmith, 2001; Shim et al.,
2001; Phau & Poon, 2000; Haubl & Trifts, 2000; Novak et al., 2000; Tan, 1999).
Fig. 3. below presents a proposed model for this study which features the hypothesized relationships.
Online Customer
Perceived
Experience (OCE)
Usefulness (PU)
H1+
H3+
H4+
Perceived Ease of
H2+
Use (PEOU)
Purchase
Intention (PI)
H5+
Trust (TR)
on Facebook
Fig. 3. Proposed research model
3. Research methodology
3.1. Measurement items
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Table 1. Constructs and measurement items for all major study variables
Construct
PU (Perceived
Usefulness)
Measurement Items
Author
The variety of products and services when shopping on Facebook help - Shu-Hung Hsu,
buyers have more options.
Shopping on Facebook is more time-saving than shopping at
traditional stores.
Bat-Erdene
Bayarsaikhan
(2012)
- Claudia (2012)
Shopping on Facebook is more cost-effective than shopping at
traditional stores.
When shopping on Facebook, it is easier to compare prices and quality
than shopping at traditional stores.
When shopping on Facebook, consumers can place orders at any
location and at any time.
When shopping on Facebook, consumers are quick to grasp the trends
and new product information through its various features.
It is easy to search for product information on Facebook.
It is easy to make purchases on Facebook.
PEOU
(Perceived Ease
of Use)
- Leelayouthayotin
(2004)
- Burke (2002)
- Claudia (2012)
It is easy to make payments on Facebook.
It is easy to find sellers’ information on Facebook.
It is easy to interact with sellers about product information.
Consumers can quickly get feedback from sellers when there is a
problem with the product.
OSE (Online
Customer
Experience)
Previous Internet purchases help me master the use of information
technology when buying goods and services.
I have a positive view on the prices and quality of the products on
previous Facebook purchases.
I have a positive view on the seller's service attitude and delivery time
on previous Facebook purchases.
- Gefen et al., 2003
- Burke, 2002
- Shim et al., 2001
- Leeraphong and
Mardjo, 2013
I have learned about the risks of shopping online on previous
Facebook purchases.
TR (Trust)
Products or services purchased by using Facebook will be trustworthy
The seller is reliable
- Leeraphong and
Mardjo (2013)
Seller is committed to send the product after payment
PI (Purchase
Intention)
Consumers will use Facebook to search for the product types they
intend to buy.
Consumers will use Facebook to search for product information of
which they intend to buy.
- Pavlou, 2003
- Laroche et al.,
1996
Consumers will use Facebook as a channel to make purchases when
needed.
Consumers will use Facebook to find information about sellers before
making a purchase.
3.2. Data collection
This study uses the convenience sampling method. The study use Likert-scale to measure the relationship
between customer online purchase intention and customer experience, trust, perceived usefulness and perceived
ease of use.
According to Hair et al (1998), in order to select appropriate research sample size for Exploratory Factor
Analysis (EFA), the minimum sample size is N≥5*x (x: the total number of observed variables). In this research,
we selected the sample size large enough to satisfy the conditions of the EFA test N ≥ max. We conducted data
collection with our questionnaire being sent face-to-face directly to 250 postgraduate students at Danang
University of Economics as it was more convenient and easier to control for the researchers to do so. They come
from different cities and work at numerous companies and organizations. We received 230 responses, out of
which 210 was valid. The authors then processed these surveys using the software SPSS 20.0.
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The participants in the study are female office workers aged 25-40 years old, having a Facebook account and
previously shopping on Facebook.
4. Results
4.1. Descriptive results
Table 2. Profile of respondents in this study
Percentage
Variables
Frequency
%
Below 1 year
3
1.4%
From 1 year to below 3 years
Form 3 years to below 5 years
Above 5 years
30
78
99
5
14.3%
37.1%
47.1%
2.4%
Facebook usage experience
Below 0.5 hour
From 0.5 to below 1.5 hours
From 1.5 hours to below 3 hours
From 3 hours to below 5 hours
Above 5 hours
38
81
55
31
7
18.1%
38.6%
26.2%
14.8%
3.5%
Average time spent on
Facebook a day
Never
From 1 to below 10 times
From 10 times to below 20 times
Above 20 times
108
63
32
3
51.4%
30%
Number of purchases on
Facebook
15.2%
1.4%
Never
From 1 to below 10 times
From 10 times to below 20 times
From 20 times to below 30 times
Above 30 times
62
50
33
62
73
98
29.5%
23.8%
15.7%
29.5%
34.8%
46.7%
Average times of visits
From 20 to below 26
Ages
From 26 to below 30
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
From 30 to below 40
Above 40
34
5
16.7%
2.4%
Below 5 million VND
40
77
64
29
19.0%
36.7%
30.5%
13.8%
From 5 million VND to below 7 million VND
Monthly income
From 7 million VND to below 10 million
VND
Above 10 million VND
Source: SPSS analysis results
Out of 210 respondents, more than 80% are aged between 20 to 30 years and only 18.6% are over 30 years
old, which is generally quite young young and thus higher adaptability to new technologies such as Facebook.
Most of the respondents in the survey have an income from 5 to 10 million VND (accounting for 67.2%). A
smaller number of participants have a monthly income below 4 million VND or over 10 million VND.
Regarding Facebook usage experience, more than 80% of respondents have over 3 years of usage experience.
In particular, the average time spent on Facebook everyday ranges from 1.5 to 5 hours (accounting for over two
thirds of people surveyed). Nearly 50% of participants have purchased on Facebook over 10 times. About 40%
of respondents frequently visit Facebook shopping pages over 10 times a month.
4.2. Cronbach’s Alpha reliability test
The scale was evaluated through Cronbach Alpha coefficients in order to eliminate unreliable variables
before, the variables which have a Corrected Item- Total Correlation less than 0.3 will be excluded and will
select the scale which its credibility Alpha is more than 0.6, especially for the case that the research concept is
new to the respondents in the context of research (Nunnally, 1978; Peterson, 1994; Slater, 1995). The results of
Alpha Cronbach reliability are following:
Table 3. Cronbach’s Alpha Reliability
Corrected Item- Cronbach Alpha if
Var.
Items
Total Correlation
Item Deleted
(PEOU) Perceived Ease of Use. Cronbach’s Alpha= .752
It is easy to search for product information on Facebook.
PEOU1
PEOU2
PEOU3
PEOU4
PEOU5
.539
.577
.588
.466
.701
It is easy to make purchases on Facebook.
It is easy to make payments on Facebook.
It is easy to find sellers’ information on Facebook.
.685
.681
.726
.742
It is easy to interact with sellers about product information. .421
(OCE) Online Customer Experience. Cronbach’s Alpha= .727
Previous Internet purchases help me master the use of
OCE1
.529
.658
information technology when buying goods and services.
I have a positive view on the prices and quality of the
products on previous Facebook purchases.
I have a positive view on the seller's service attitude and
OCE2
.577
.497
.631
.676
OCE3
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
delivery time on previous Facebook purchases.
I have learned about the risks of shopping online on
previous Facebook purchases.
OCE4
.466
.697
(TR) TRUST. Cronbach’s Alpha=.698
The seller is committed to deliver the goods after the buyer
makes payment.
The actual quality of the goods received is the same as the
information advertised on Facebook.
My credit card account number and personal information
are strictly confidential.
TR1
TR2
TR3
.494
.560
.492
.632
.551
.637
(PU) Perceived Usefulness. Cronbach’s Alpha=.849
The variety of products and services when shopping on
PU1
.611
.616
.668
.640
.579
.827
.826
.817
.822
.834
Facebook help buyers have more options.
Shopping on Facebook is more time-saving than shopping
at traditional stores.
PU2
Shopping on Facebook is more cost-effective than
shopping at traditional stores.
PU3
When shopping on Facebook, it is easier to compare prices
and quality than shopping at traditional stores.
PU4
When shopping on Facebook, consumers can place orders
at any location and at any time.
PU5
When shopping on Facebook, consumers are quick to grasp
the trends and new product information through its various
features.
PU6
.675
.815
(PI) Purchase Intention. Cronbach’s Alpha=.821
Consumers will use Facebook to search for the product
PI1
.557
.731
.630
.664
.814
.736
.781
.765
types they intend to buy.
Consumers will use Facebook to search for product
information of which they intend to buy.
PI2
Consumers will use Facebook as a channel to make
purchases when needed.
PI3
Consumers will use Facebook to find information about
sellers before making a purchase.
PI4
4.3. Exploratory Factor Analysis (EFA)
4.3.1. EFA for independent variables
Exploratory analysis was undertaken next in order to test the measurement items used in this research.
Eighteen items were proposed to contribute to 5 constructs in this survey.
In the first analysis, KMO = 0.883 with Sig. = 0.000 which confirmed the relationships among variables
were statistically significant and these variables were suitable for applying exploratory factor analysis to provide
a more parsimonious set of factors. Chi-Square = 1594.07 with Sig. = 0.000 << 0.05.
However, at the 1st EFA, the difference between the two loading factors is not be greater than 0.3; so the
item PU5, PU2, PU6, PEOU4, OCE1, PU1 and TR3 were disqualified. We continued to analyze the 2nd EFA
and 3rd EFA. The items PU3, PU4 and PEOU5 were eliminated. Finally, the result of EFA for independent
variable is below:
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.760
Approx. Chi-Square
365.252
28
Bartlett's Test of Sphericity
df
Sig.
.000
Rotated Component Matrixa
Component
1
2
3
PEOU2
PEOU1
PEOU3
OCE4
.839
.756
.703
.318
.779
.742
.708
OCE3
OCE2
.340
.823
.817
TRUST1
TRUST2
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
The results of above table show that the variables of PEOU1, PEOU2 and PEOU3 correlated with component
1; the variables of OCE2, OCE3, OCE4 correlated with component 2; the variables of TRUST1 and TRUST2
correlated with component 3.
4.3.2. EFA for dependent variable:
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Approx. Chi-Square
.780
301.483
Bartlett's Test of Sphericity
Df
6
Sig.
.000
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
Component Matrixa
Component
1
PI2
PI4
PI3
PI1
.866
.824
.798
.740
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
For the table above, we can see the variables of PI1, PI2, PI3 and PI4 correlated with component PI (purchase
intention). These items are retained in the subsequent analysis.
Adjusting the research model
Based on the Cronbach's Alpha Factor Analysis and Factor Analysis and Exploratoire Factor Analysis (EFA),
the authors provide a modified research model as below:
Online Customer
Experience (OCE)
H3+
Perceived Ease of
Use (PEOU)
H1+
H2+
Purchase
Intention (PI)
on Facebook
Trust (TR)
Fig. 4. Proposed research model
With the above test results, the authors adjusted the initial proposed research model. The adjusted model now
consists of 3 independent variables with 8 measurement items, and PI as dependent variable including 4
measurement items.
The hypotheses were adjusted according to the new research model:
H1: Female office workers’ perceived ease of using the Facebook for buying goods and services has a
positive effect on their purchase intention via Facebook.
H2: Female office workers’ trust of using the Facebook for buying goods and services has a positive effect on
their purchase intention via Facebook.
H3: Female office workers’ past online shopping experience has a positive effect on their purchase intention
via Facebook.
4.4. Hypothesis testing
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
4.4.1. Examining the correlation between variables
The first step in conducting a linear regression analysis is to examine the linear correlation between the
dependent variable and each independent variable as well as between the independent variables together. The
assumption is that the independent variables are not perfectly correlated with each other (the correlation
coefficient isn’t equal 1).
Correlations
PEOUnew
OCEnew
.000
TRUSTnew
.000
PInew
.418**
Pearson Correlation
Sig. (2-tailed)
1
PEOUnew
1.000
1.000
.000
N
210
.000
210
1
210
.000
1.000
210
1
210
.442**
.000
210
Pearson Correlation
Sig. (2-tailed)
N
OCEnew
1.000
210
210
.000
Pearson Correlation
Sig. (2-tailed)
N
.000
.422**
.000
210
TRUSTnew
1.000
1.000
210
210
210
Pearson Correlation
.418**
.442**
.422**
1
PInew
Sig. (2-tailed)
N
.000
210
.000
210
.000
210
210
**. Correlation is significant at the 0.01 level (2-tailed).
Next, all variables are taken into the linear regression analysis in order to examine the influence of the
independent variables on the dependent variable.
Model Summary
Model
1
R
R Square
.548
Adjusted R Square
.541
Std. Error of the Estimate
.67715642
.740a
a. Predictors: (Constant), TRUSTnew, OCEnew, PEOUnew
ANOVAa
Model
Sum of Squares
114.541
df
3
Mean Square
38.180
F
Sig.
Regression
83.265
.000b
1
Residual
Total
94.459
206
209
.459
209.000
a. Dependent Variable: PInew
b. Predictors: (Constant), TRUSTnew, OCEnew, PEOUnew
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t
Sig.
B
-3.063E-017
.418
Std. Error
.047
Beta
(Constant)
.000
8.927
9.431
9.009
1.000
.000
.000
.000
PEOUnew
OCEnew
.047
.418
.442
.422
1
.442
.047
TRUSTnew
.422
.047
a. Dependent Variable: PInew
Results of linear regression analysis showed that the model had R2 = 0.548 and adjusted R2 = 0.541. This
means that 54.1% of the variance of the dependent variable: Purchase intention on Facebook is explained by
three factors PEOUnew, OCEnew, TRUSTnew.
The analysis shows sig = 0.000 confirming that the regression model is consistent with the data collected and
all the variables are statistically significant with a 5% significance level.
Regression equation of the model representing factors that affect the purchase intention on Facebook is as
follows:
PInew = 0,418*X1 + 0,422*X2 + 0,442*X3 + ui
The regression equation shows linear relationship between purchase intention on Facebook and PEOUnew,
TRUSTnew, OCEnew. All these three factors have positive effects in purchase intention via Facebook. In
particular, customer past online purchase experience exerts the most powerful impact on purchase intention
through Facebook (ß = 0.442 and p = 0.000 < 0.05)
Table 4. Multiple regression analysis results
Statistical
Sig.
Value
0.000
No.
H1
Hypothesis
ß
Results
Testing Method
Female office workers’ perceived ease of using Regression
0.418
Accepted
the Facebook for buying goods and services has
a positive effect on their purchase intention via
Analysis
Facebook.
H2
H3
Female office workers’ trust of using the Regression
0.422
0.442
0.000
0.000
Accepted
Accepted
Facebook for buying goods and services has a
positive effect on their purchase intention via
Analysis
Facebook.
Female office workers’ past online shopping Regression
experience has a positive effect on their
purchase intention via Facebook.
Analysis
Thus, it can be concluded that, in order to increase the intention to make a purchase on Facebook of female
office workers in Da Nang, businesses need to raise the perception about the ease of use when shopping on
Facebook as well as enhance consumer trust in the sellers and past customer purchase experience also has strong
influence on their buying intentions.
5. Discussion and conclusion
This study has stipulated that there are 3 determinants affecting purchase intention on Facebook, namely
perceived ease of use when looking for information and making payment when shopping online; Trust in the
sellers and products, and Customer past shopping experiences on Facebook.
With Sig = 0.000 and the regression coefficient is 0.418, H1 is accepted: The higher the perceived ease of
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Phuong Thao Nguyen, Thi Khue Thu Ngo/ MICA 2018 Proceedings
using Facebook, the higher intention to purchase on Facebook and vice versa. The more easily customers find
information about goods and make payments, the more likely they intend to make a purchase.
With sig = 0.000 and regression coefficient is 0.422; H2 is accepted: The stronger the belief is in the seller,
the product and the ability to interact with the seller, the higher intention to purchase on Facebook and vice
versa. When buyers trust the sellers as well as the products that are sold on Facebook and interact regularly to
address questions and feedbacks of the buyers, they are more likely to intend to use Facebook to shop.
With sig = 0.000 and regression coefficient is 0.442; H3 is accepted: The more positive the past purchase
experience on Facebook is, the higher intention to purchase on Facebook and vice versa. If prior online shopping
experiences are good and customers realize many benefits when buying online, then they are more likely to
make a purchase.
Additionally, the regression coefficients represent the relative strength that these factors affect purchase
intention: the greatest impact is past customer experience (H3); next is trust in the seller (H2) and lastly
perceived ease of use (H1) when searching for information and making payments via facebook.
The elimination of perceived usefulness, albeit surprising, provides credence for Davis et al.’s (1989)
argument that perceived usefulness at the beginning of a development project provides little influence on
behavioral intention. They argued that usefulness is a performance measure that takes time and actual use to
assess. The strength of these effects should be stronger with repeat customers because potential customers base
their usefulness perceptions on relatively superficial acquaintance with its features. Consequently, Davis et al.
were able to find that the influence of usefulness on behavioral intention to use the system increased over time
and use. This finding is not surprising since both Davis et al. (1989), Adams et al. (1992) and Jackson et al.
(1997) found this relationship to be significant only after prolonged use. Even though the majority of
respondents in this study have used the Facebook for over 5 years, they only purchased online less than 10 times.
Furthermore, it is possible that respondents found that buying product from the Internet was not as time- and
cost-saving. Having a large assortment of products was not necessarily the best strategy to sell online. Cheap
prices were also not the ultimate goal for those who shopped online.
From these findings, we offer some recommendations for businesses or individuals who intend to sell online
via Facebook in Da Nang, in order to better satisfy customer demand and achieve the highest business
efficiency: (1) Improving the ease of use when buying on Facebook by the ways that seller should properly and
logically arrange and display their products on the Facebook pages so that customers do not spend too much time
searching and that sellers should make it easier for customers to order and pay online; (2) Enhancing trust in the
sellers and products by showing commitment to quick delivery and good quality as well as frequently responding
to customer inquiries; (3) Creating good impressions and positive experiences with first-time customers by
ensuring quick delivery, excellent services to make customers feel comfortable when shopping via Facebook.
This research has several limitations: (1) the research scope is in Da Nang, (2) the sample size is small (210
respondents), (3) the number of study variables is low. Future studies can extend the sample to different
geographies and participants as well as incorporate other theoretical frameworks besides TAM with new
variables to better explain the customer purchase intentions on Facebook.
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