Have you ever had a terrible online shopping experience?
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Have you ever had a terrible online shopping experience? Authors: Mengran Qu [email protected] Luqi Xu [email protected] Tutor: Michaela Sandell Examinator: Åsa Devine Subject: Relationship Marketing Group: A4 Course Code: 2FE21E 1
Transcript of Have you ever had a terrible online shopping experience?
Have you ever had a terrible online shopping
experience?
Authors:
Mengran Qu [email protected]
Luqi Xu [email protected]
Tutor: Michaela Sandell
Examinator: Åsa Devine
Subject: Relationship Marketing
Group: A4
Course Code: 2FE21E
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Acknowledgement
In this study, the authors hold a great thanks to the people who helped and guided and took
part in the research. Without these people’s help, the study is unlikely to be done smoothly.
Firstly, we, as the authors in this research, would like to give great appreciation and respect to
our two excellent teachers. One is our tutor Michaela Sandell, and the other one is our thesis
examiner Åsa Devine. During the whole research period, both of them provided us with
countless help, helping us correct inappropriate phrases and solve various hard problems in
our daily study life. Michaela Sandell and Åsa Devine are not only the teachers of marketing
but more like life mentors, assisting to plan the study and life for us. Moreover, we would
like to thank Magnus Willesson who taught us carefully on how to operate the SPSS table, so
that we can finish the methodology part and relevant SPSS tables.
It is sure that the improvement of contents in this study is inseparable from our lovely
opposition group’s help. Without their help, lots of good suggestions will not pass to us. They
are the people who make us know how to exactly make progress after each time of
rephrasing.
Last but not least, we would like to sincerely say thanks to every person who participated in
our online questionnaires. Filling over seventy questionnaires needed much time to take, but
with their help, we quickly finished the collection.
Stockholm, 2020-10-05
_______________ _________________
Mengran Qu Luqi Xu
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Abstract Background: Nowadays, the development of the internet makes online shopping more
popularized . The appearance of online shopping brings considerable benefits to customers,
however, there are still risks in the area. To maintain good relationship management between
customers and companies, it is necessary to understand the online customer experience and
its relevant important factors .
Purpose: The purpose of this study is to explain the impact of three factors (low-quality
delivery, low-quality online customer service, and low-quality website design) on online
customer experience.
Methodology: The paper used a quantitative approach in cross-sectional design and collected
totally 78 responses. The related data collection is conducted through a self-completion
questionnaire in the online form.
Findings: The negative impacts of low-quality online customer service and low-quality
website design on online customer experience are confirmed. There is an effective and strong
connection between low-quality website design and low-quality online customer service.
Conclusion: Based on the findings, H2 and H3 cannot be rejected but H1 needs to be rejected.
One can therefore say that even in the context of rapid technological development, modern
online shoppers have not changed their aversion to low-quality elements, that the view that
low-quality online customer service and low-quality website design negatively affect OCE
has not changed.
Keywords: Online customer experience, low-quality delivery, low-quality online customer
service, low-quality website design, negative impact..
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Table Of Contents
1. Introduction 6
1.1 Background 6
1.2 Problem Discussion 7
1.3 Purpose 9
2. Theoretical Framework 9
2.1 Online Customers Experience (OCE) 9
2.2 Low-Quality Delivery 10
2.3 Low-Quality Online Customer Service 12
2.4 Low-Quality Website Design 14
3. Conceptual Framework 16
3.1 Suggested Research Model 19
4. Method 24
4.1 Research Approach 24
4.1.2 Quantitative Research 22
4.2 Research Design 22
4.3 Data sources 24
4.4 Data Collection Method 25
4.5 Data Collection Instruments 25
4.5.1 Operationalization 29
4.5.2 Questionnaire Design 33
4.5.3 Pre-testing 35
4.6 Sampling 36
4.6.1 Sample Size 37
4.6.2 Sampling error 38
4.7 Data Analysis Method 38
4.7.1 Descriptive Analysis 39
4.7.2 Correlation Analysis 41
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4.7.3 Multiple Regression Analysis 42
4.8 Quality Criteria 43
4.8.1 Validity 43
4.8.2 Reliability 44
4.9 Ethical Issues 45
4.10 Societal issues 46
5. Results 47
5.1 Descriptive statistics 47
5.2 Correlation Statistics 49
5.2.1 Test of Validity 49
5.2.2 Test of Reliability 50
5.3 Multiple Regression Analysis (For The Hypothesis-Testing) 51
5.4 Hypothesis Results 54
6. Discussion 55
6.1 Delivery 55
6.2 Online Customer Service 57
6.3 Website design 58
7. Conclusion 59
7.1 Theoretical Implications 60
7.2 Managerial Implications 61
8. Limitations and Recommendation for the future research 62
8.1 Limitations 62
8.2 Recommendation For The Future Research 63
Reference List 63
Appendix 1 68
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1. Introduction
1.1 Background
Nowadays, the development of the internet makes online shopping more popularized. This
means more and more people join online shopping. As the statistics show, there are 1.8
billion people making online purchases in 2018 over the world (Statista, 2018) and which
create a total $2.8 trillion in sales (Mohsin, 2020). It also suggests that the world sale of
e-retail will increase to $4.8 trillion when it comes to 2021 (Mohsin, 2020). The appearance
of online shopping bring considerable benefits to customers, such as the advantage on
information acquisition and processing in interactive home shopping (Alba, Lynch, Weitz,
and Janiszewski, 1997), Another study states the positive effect through online shopping
when it cooperates with different goods types and in different situations, such as a good
intermediator to customers for communication and transaction when the product is
experienced and frequent, or a guide to customers for the product they want when product are
differentiated apparently ( Peterson, Balasubramanian, and Bronnenberg, 1997). However, it
does not mean online shopping is always good. For instance, there might be an uncertain
feeling for customers in the online context (Bhatnagar, Misra and Rao, 2000).
In this aspect, the relationship management between company and customers becomes
important, which is about value creations for both company and customers through acquiring,
maintaining and partnering with specific customers (Parvatiyar and Sheth, 2001). According
to Gartner (2011), the relationship between company and customers through the internet is
almost everywhere. Currently, it is declared that about 85% of relationships are managed
directly by customers and companies (Gartner, 2011). In order to maintain good relationship
management in e-commerce, online customer experience (OCE) is necessary. This is closely
related to what customers engage in the process of online shopping (Rose, Hair and Clark,
2011). The related studies state the relevant significance of delivery (Niu, Wang, Lee and
Chen, 2019), online customer service (Küster, Vila and Canales, 2016, Lim and Dubinsky,
2004) and website design (Hausman and Siekpe, 2009) to online customer experience, which
will be discussed in the following section.
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1.2 Problem Discussion
Nowadays, there are three elements that have been commonly and largely discussed by
marketing researchers in the context of e-commerce, they are delivery (Li, Lu and Talebian,
2014; Kim, Dekker and Heij, 2017; Coşar, Panyi and Varga, 2017; Niu, Wang, Lee and
Chen, 2019), online customer service (Küster, Vila and Canales, 2016; Lim and Dubinsky,
2004), and website design (Hausman and Siekpe, 2009; Hasan, 2016), respectively. In
general, they stand for different aspects of the phenomenon that online shoppers often touch
with. Delivery represents the transportation for shipping online purchased items (Li, Lu and
Talebian, 2014); Online customer service covers some main types of customer services which
would be involved and offered on websites (Lim and Dubinsky, 2004); As to website design,
as it name shows, which is what focuses on the design of shopping sites (Roghanizad and
Neufeld, 2015). All elements are closely related to the online shopping, however, when the
elements are in a low quality could result in a lot of negative impacts on customer minds
insofar they negatively affect online customer experiences (Kim, Dekker and Heij, 2017;
Coşar, Panyi and Varga, 2017; Ramaekers, Caris, Moons and van Gils, 2018; Küster, Vila
and Canales, 2016; Joines, Scherer and Scheufele, 2003; McLean and Wilson, 2016; Vos et
al., 2014; Hausman and Siekpe, 2009; Hasan, 2016). The first factor is low-quality delivery
which includes a lot of daily concerns. For example, when customers meet high shipping
costs, they are more likely to be impatient (Li, Lu and Talebian, 2014). Also, many tricky
incidents often occur in the shipping process, such as incomplete orders and product returns,
too long transportation time, ambiguous delivery time provided by poor systems, damaged
product packaging and so on (Kim, Dekker and Heij, 2017; Coşar, Panyi and Varga, 2017).
The second factor is low-quality online customer service which includes the issues such as
transactional privacy concerns (Joines, Scherer and Scheufele, 2003), the possibility of
service failure (McLean and Wilson, 2016). As to low-quality website design, the relevant
practical issues are such as bad layout, bad navigation design, and so on (Hasan, 2016;
Hausman and Siekpe, 2009).
Many researchers highlight the significance of learning customer’s online experiences as the
in-depth understanding of customers helps increase company competitiveness (Rose, Hair
and Clark, 2011; McLean and Wilson, 2016). This is due to the good e-commerce
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environment that can motivate shopper attitudes and positively influence their buying
intention (Martin, Mortimer and Andrews, 2015). Singh (2002) believed that a successful
company is inseparable from the interaction with customers. This idea is applicable for the
context of e-commerce as well. One can say that customers are the “heart” of e-business
(Singh, 2002). Hausman and Siekpe (2009) consistently considered customers and their
minds are the core value of being a successful company, meanwhile argued that if
e-commerce companies want shoppers to like them or even rely on them, they should first
understand and recognize their true favor (Hausman and Siekpe, 2009).
As research indicates, negative customer experiences have a greater impact on customer
buying decisions than pleasant experiences have, which pushes a research of understanding
customers through a “dark side” of e-commerce (Ou and Sia, 2010). Today, e-commerce has
become increasingly popular in the retail industry, facilitating numerous online stores
operating in many countries and regions (Kim, Dekker and Heij, 2017). The rapid
development of e-commerce makes customer expectations keep increasing (van Gils,
Ramaekers, Caris and de Koster, 2018). As such, researchers believe online customer
experience should be consistent with the development of technology (Rose, Hair and Clark,
2011). In other words, one is hard to confirm whether these significant elements in low
quality can still negatively affect online customer experiences or not (McLean and Wilson,
2016). It encourages to study the research with the subject of online consumer experience in a
new environment (Rose, Hair and Clark, 2011). Based on the significance of studying online
customer experience and its relevant three factors as mentioned above, the authors have an
interest in examining whether these factors still negatively affect online consumer experience
under the current context of rapid technological development. Therefore, this research is
designed as a quantitative study to test whether the factors (low-quality delivery, low-quality
online customer service, and low-quality website design) negatively affect online customer
experience.
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1.3 Purpose
The purpose of this study is to explain the impact of three factors (low-quality delivery,
low-quality online customer service, and low-quality website design) on online customer
experience.
2. Theoretical Framework
2.1 Online Customer Experience (OCE)
According to the literature, online customer experiences come from the things they have
touched during shopping (Rose, Hair and Clark, 2011). Online customer experience is
actually something that mimics the sensory experience in an offline environment (Bleier,
Harmeling and Palmatier, 2018). As findings showed, online customer experience is divided
in two branches, which are affective state and cognitive state (Rose, Clark, Samouel and Hair,
2012; Rose, Hair and Clark, 2011).
In regards to the affective side, it has a relation with people’s perceived control in online
shopping (Rose, Clark, Samouel and Hair, 2012). This type of control mainly comes from the
status of access and search as well as customer evaluation of the site using (Rose, Hair and
Clark, 2011). According to Rose, Clark, Samouel and Hair (2012), ease of use and
customization have impacts on affecting customer perceived control through empowerment.
Yet customization is less effective than ease of use since the idea of easy to use is the most
significant feature even if customization can empower customers by delivering advanced
technology (Rose, Clark, Samouel and Hair, 2012). Another study affirms the importance of
affective state and indicates that by gathering the effect of the ease of use and customization
customer sense of control to shopping sites can be strengthened (Martin, Mortimer and
Andrews, 2015). On the other hand, some studies mention telepresence and regard it as a
factor that strongly influences cognitive state (Martin, Mortimer and Andrews, 2015;
Bhattacharya, Srivastava and Verma, 2018). As researchers suggest, e-business should
particularly pay attention to telepresence as it is this factor that brings customers online
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experiences so that help them realize the goal of getting away from the real world. More
precisely, telepresence motivates a type of immersion called “cognitive immersion” for
customers that is similar to the pattern of offline shopping. In this context, the study indicates
a positive relationship between the length of time customers spend on shopping sites and
purchase decisions. In other words, it is important for e-commerce to focus on increasing
customer immersion time on the website. The proper way to facilitate customers to stay in
shopping sites is to make the shopping process more entertaining or engaging (Bhattacharya,
Srivastava and Verma, 2018). Also, telepresence can improve user engagement which insofar
increase customer interests to products or services. The visual element in regards to
telepresence including color vividness and graphic vividness can enhance user engagement
because the visual element is relatively more attractive to customers in an online environment
(Papagiannidis et al., 2017).
2.2 Low-Quality Delivery
Physical retailing satisfies needs that buyers take away their purchased products on the spot,
while the online stores are not the case with it. In e-commerce, goods need to be delivered
with some means of transportation within a period (Li, Lu and Talebian, 2014). Delivery
involves various transport options. Taking express delivery as an example, due to its fast
transportation speed, which is used as one of common approaches to deliver items. In
e-business, goods delivery is regarded as a critical problem. It is attributed that shoppers care
not only about the quality of products but that of delivery (Niu, Wang, Lee and Chen, 2019).
As research indicated, a mature international delivery can more effectively avoid issues in
terms of delivery, thereby saving time spent on the road (Kim, Dekker and Heij, 2017). On
the contrary, any problems related to the delivery work such as incomplete orders and
product returns, too long transportation time, the ambiguous delivery time provided by poor
systems. will cause relevant negative effects on online customers (Coşar, Panyi and Varga,
2017; Kim, Dekker and Heij, 2017). The fact is even if the issues have been roughly solved
afterward, it is hard to fairly remove customers’ unpleasant memories or their initial terrible
impressions on the delivery work (Kim, Dekker and Heij, 2017).
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In ecommerce, there are two types of shipping fee strategies. One is the PS strategy means
the shipping fee is not included in the product price, in other words companies usually charge
for product prices and delivery costs separately. The other one is the ZS strategy, which
refers to exempt shipping fees. In other words, shipping fees are already considered in
product fees (Gümüş, Li, Oh and Ray, 2013). According to research, online customers are
sensitive to shipping fees (Ramaekers, Caris, Moons and van Gils, 2018; Lewis, Singh and
Fay, 2006). In other words, online customers have a strong awareness of delivery fees (Kim,
Dekker and Heij, 2017). With this consideration, some companies operate free shipping
strategies to retain online customers (Kim, Dekker and Heij, 2017; Lewis, Singh and Fay,
2006; Guo et al., 2020; Gümüş, Li, Oh and Ray, 2013). Free shipping has the power to raise
the rate of online order incidence (online order occurrence) and suppress company revenues.
Nevertheless, although waiving shipping costs can please customers, it is not an option that
e-commerce companies only consider (Lewis, Singh and Fay, 2006). Unlike this, Gümüş, Li,
Oh and Ray (2013) stated that online customer attitudes towards the courier charge are
differently depending on company sizes. When shoppers choose to consume products from
large (i.e. popular) firms, they are willing to suffer the additional fee, i.e. the charge of
shipping fees. However, similar attitudes seldom exist when shoppers are facing small
enterprises (Gümüş, Li, Oh and Ray, 2013). In addition, it is difficult for consumers to accept
high shipping fees. As a result, the low acceptance from customers suppresses enterprise
sales. The interests of both buyers and sellers are closely related to shipping fees (Guo et al.,
2020).
Due to the time from dispatch to pickup is normally scheduled tightly, it is hard to make sure
all the items are arranged properly (van Gils, Ramaekers, Caris and de Koster, 2018). Some
practical issues occur during this period, such as inaccuracy of traveling time, uncertain
pickup demand (Chen, 2013). In specific, e-commerce companies consider delivery time is of
importance in delivery works. The findings in many studies have taken the delivery time into
consideration and introduced a conventional way to cope with relevant challenges: set up
delivery time windows (Ramaekers, Caris, Moons and van Gils, 2018; Moons, Ramaekers,
Caris and Arda, 2017; Chen, 2013). It is followed by a plentiful discussion about the problem
of vehicle routing and reaches the conclusion that offering rich delivery windows can provide
customers more options on delivery time. However, offering this service needs costs from
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companies. To make up for the loss, firms usually ask their customers to undertake this fee.
In other words, if consumers desire the delivery window service, they must pay for it
(Ramaekers, Caris, Moons and van Gils, 2018; Moons, Ramaekers, Caris and Arda, 2017).
Moreover, regarding the time issue, researchers indicated that when consumers preferred time
windows were not fulfilled, companies have the right to decide the time of delivering items
for the customers. As a result, home delivery failures often occur in daily shipping since it is
unrealistic for buyers to stay at home all day to wait for the goods (Ramaekers, Caris, Moons
and van Gils, 2018).
2.3 Low-Quality Online Customer Service
Online customer service is a big common concern for companies that are involved in the
domain of e-commerce (Küster, Vila and Canales, 2016). It is a bridge of communication
established between e-commerce companies and online customers, despite this type of
company-customer conversation normally conducted online. It differs from the traditional
way of face-to-face communication in the offline environment. Simply put, online customer
service is a certain system installed on shopping sites to provide customers with their needed
information during online shopping. At present, there are various forms of online customer
service presenting on online platforms, such as call centers, customer support, and choice
helpers (Lim and Dubinsky, 2004). Since the nature of online shopping is that the items are
listed on the websites and which are untouchable to shoppers (Lim and Dubinsky, 2004),
customers see the quality of e-service (electronic service) as an important element for
evaluating online shopping. Based on the study, the low-quality online customer service
reflects in some related practical issues, such as transaction privacy concerns, long searching
time on utilitarian product information, security/privacy concerns, and so on (Joines, Scherer
and Scheufele, 2003; McLean and Wilson, 2016; Rita, Oliveira and Farisa, 2019; Vos et al.,
2014; Pandey and Chawla, 2018; Coşar, Panyi and Varga, 2017). As stated by Küster, Vila
and Canales (2016), pre-purchase service and transaction related service are two different
service levels in the domain of e-commerce. The research highlights the significance of
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understanding customer thoughts with suitable service levels. This is due to all aspects of
e-service that can influence consumer experiences (Küster, Vila and Canales, 2016).
Pre-purchase service is one of service levels that involves product pricing, support of product
search and evaluation, and web appearance (Küster, Vila and Canales, 2016). E-search, as an
online search engine, which is used to encourage shoppers to search for product information,
payment information and customer opinions, allowing access to the information 24 hours
(Singh, 2002). One research mentioned two types of shopping sites that are based on
economic motivations and transactional privacy concerns respectively. When customers
search information for products or services, it shows a difference in time spendings of
customers between these two drivers. In specific, since the website based on economic
motivation aims to benefit people in price, people are more accepting of the passing of
surfing time. However, when the case comes to transactional privacy concerns, people hold
opposite attitudes. The main reason is that the website based on transactional privacy
concerns needs customers to provide the private information in order to get access to product
information databases, and this may result in a type of risk that disclosing customer privacy
(Joines, Scherer and Scheufele, 2003). In addition, the different search sources of information
that shoppers look for are depending on different product types. Firstly, utilitarian product
refers to a product that is goal orientated and more associated with functionality displayed.
There is literature discussing the time consciousness under utilitarian orientation. When the
time of searching is longer than what customers expected, a type of service failure related to
the searching will emerge and cause unpleasant customer experiences. In this context, an
online customer support which is run by a service representative becomes useful. According
to research, the quality of online customer support decides people’s final attitudes (McLean
and Wilson, 2016). In addition to utilitarian products, the hedonic product is another type of
product that focuses on how enjoyable customers are. Nevertheless, the abstract and blurred
features of hedonic products make it difficult for customers to accurately evaluate products in
the online searching process (Akalamkam and Mitra, 2017).
Another level of the service is transaction related service. As to this level of service, its scope
mainly involves security and privacy statements, and transaction mechanisms (Küster, Vila
and Canales, 2016). Based on the study, security/privacy is a dimension that triggers daily
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concerns of customers, meanwhile measures if online customer service is outstanding as well.
For example, customers worry about the security of the data in cards, which often occurs
when using the online service that offers credit card payment options (Rita, Oliveira and
Farisa, 2019). Security is also important when creating positive customer experience through
how it performed in the transaction process and product characteristics (Jaiswal and Singh,
2020). When customers emerge uncertain feelings towards products or services on shopping
sites, their evaluation of e-service quality will be correspondingly low (Vos et al., 2014). In
addition, security concerns would cause e-distrust (Pandey and Chawla, 2018; Coşar, Panyi
and Varga, 2017). According to a study, the existence of security vulnerabilities on websites
reveals a need to make up something in order to improve online consumer trust (Vos et al.,
2014).
2.4 Low-Quality Website Design
Website design is a vital task for companies operating e-commerce. By applying design
elements on shopping sites, e-commerce companies can effectively arouse customer’s online
experiences (Bleier, Harmeling and Palmatier, 2018; Bilgihan, Okumus, Nusair and Bujisic,
2013). It is perceived as a key factor to attract visitors to participate in online shopping
(Hausman and Siekpe, 2009; Bleier, Harmeling and Palmatier, 2018). Design work plays an
important role in promoting customer relationships (Hausman and Siekpe, 2009). A website
with an intuitively appealing look and clear layout is conducive to forming a favorable web
context. Once the designer frames a fine web interface, the goal of constructing a natural and
intuitive website context is likely to be further fulfilled (Roghanizad and Neufeld, 2015).
However, a large number of firms today have not yet reached this level of success since many
lack mature skills of designing web pages, meanwhile some improper ways of presenting
have aroused the dissatisfaction of some online customers. In fact, sometimes, just a tedious
interface design of a website is enough to enable customers to close it by clicking the mouse.
Some researchers revealed that such discomfort hides a series of intermediate negative
impacts, including the negative emotion and apathetic attitudes that emerged from the process
of shopping online (Hausman and Siekpe, 2009; Hasan, 2016).
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The computer factor with utilitarian characteristics influences customer perception of
websites in regards to the feature of usefulness and informativeness on shopping sites.
Importantly, the computer factor has a link with customer relevant irritation. The factor is
considered as significant as it helps consumers understand the site layout and the navigation
for searching products or services (Hausman and Siekpe, 2009). Another study sheds light on
the negative relationship between all three characteristics of shopping sites in low quality
(visual, navigation, and information design) and customer relevant perceived irritation, and
subsequently draws a conclusion that all improper presenting for each characteristic can
cause corresponding side effects. For instance, when customers are feeling uncomfortable
and annoyed while surfing a shopping site, they may exit the website. In terms of visuals, the
cases involve many, such as bad layouts, uncomfortable font design, terrible colors, that all
have the potential to cause negative effects and terrible consequences. Pop-up news and
banners are no exception as well. In short, a terrible visual design in a website can make
visitors disappointed and catalyze a feeling of irritation (Hasan, 2016). Moreover, some
researchers found that online customers link both website efficiency and the quality of
website contents with visual effects closely (Bauer, Falk and Hammerschmidt, 2006). In
these three characteristics, navigation has the most significant impact on customer perceived
irritation. The navigation path for shoppers to visit websites is diverse, including the order of
browsing web pages, pictures they see on websites, and the time they take (Hasan, 2016).
Indeed, high quality of navigation settings can positively affect customer perceptions of
websites and their trustworthiness, however, if navigation with a sloppy design or a
complicated and vague look can easily anger customers (Hasan, 2016), so that triggers the
crisis of trust (Roghanizad and Neufeld, 2015). Also, the findings provided support for the
argument that the factor of information design has a negative and significant impact on
irritation. One can say the factor is also a determinant of whether more customers will be
attracted to the website. In the counterexample, for instance, this characteristic presents in the
form of irrelevant information, incompatible information formats, or unwelcome information
transfer tools such as always running animation, that is likely to distract shoppers and
increase customer cognitive difficulty to online information, thereby offending and irritating
them (Hasan, 2016).
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3. Conceptual Framework
Delivery plays an increasingly important role in e-business as customers normally care about
the quality of goods delivery (Niu, Wang, Lee and Chen, 2019). A mature shipment is more
likely to help delivery work smoothly complete due to the high efficiency of transportation,
the competence of ingeniously avoiding relevant troubles, and so on (Kim, Dekker and Heij,
2017). Yet inferior delivery with even just a little problem can negatively affect customer’s
online shopping experiences (Coşar, Panyi and Varga, 2017; Kim, Dekker and Heij, 2017).
Even if the issues have been roughly fixed, it is difficult to remove customer unpleasant
memories or their initial terrible impressions of delivery works (Kim, Dekker and Heij,
2017). One of the topics that was largely discussed by research is the shipping fee (Gümüş,
Li, Oh and Ray, 2013; Ramaekers, Caris, Moons and van Gils, 2018; Lewis, Singh and Fay,
2006; Kim, Dekker and Heij, 2017; Guo et al., 2020). Based on studies, customers are
generally sensitive to it (Ramaekers, Caris, Moons and van Gils, 2018; Lewis, Singh and Fay,
2006). For example, customers are less acceptable to high shipping fees (Guo et al., 2020),
which encourages many firms to adopt free shipping fees to attract online shoppers (Kim,
Dekker and Heij, 2017; Lewis, Singh and Fay, 2006; Guo et al., 2020; Gümüş, Li, Oh and
Ray, 2013). Yet another study supplements when customers facing products from different
sizes of companies, they hold opposite attitudes in paying courier fees (Gümüş, Li, Oh and
Ray, 2013). Also, delivery time is a big concern and includes a lot of practical issues in daily
shipment (van Gils, Ramaekers, Caris and de Koster, 2018; Chen, 2013; Ramaekers, Caris,
Moons and van Gils, 2018; Moons, Ramaekers, Caris and Arda, 2017). Despite the advanced
service namely delivery time window benefits customers by bringing them more choices on
delivery time, it is not free for customers (Ramaekers, Caris, Moons and van Gils, 2018;
Moons, Ramaekers, Caris and Arda, 2017). Another serious issue related to delivery is home
delivery failure, meaning customer’s desired delivery time option has not been fulfilled by
companies, thus, bad experiences emerge (Ramaekers, Caris, Moons and van Gils, 2018).
According to previous studies, any problems that appear in delivery work can contribute
negative customer experiences to customers because they care about the quality of delivery,
the price of shipping fee, and so on (Coşar, Panyi and Varga, 2017; Kim, Dekker and Heij,
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2017; Gümüş, Li, Oh and Ray, 2013; Ramaekers, Caris, Moons and van Gils, 2018; Lewis,
Singh and Fay, 2006). However, since the development of technology is growing rapidly, the
volatile market environment has been difficult to ensure that consumer expectations remain
unchanged (Rose, Hair and Clark, 2011; van Gils, Ramaekers, Caris and de Koster, 2018).
Under this situation, it is hard to directly give a conclusion about if today’s low-quality
delivery work still negatively affects customers through these aspects and thereby bringing
them negative online experiences (McLean and Wilson, 2016). Based on the previous finding
in the mentioned studies and consumer’s psychological turbulence in this new context, a
hypothesis is proposed as:
H1: Low-quality delivery negatively affects online customer experiences.
Online customer service is a useful way for ecommerce companies to communicate with their
customers (Küster, Vila and Canales, 2016, Lim and Dubinsky, 2004), which also support the
online shopping process for customers (Lim and Dubinsky, 2004). According to the study, it
is important to understand what customers think through related service levels (Küster, Vila
and Canales, 2016). Based on the study, there are several practical issues regarding the
low-quality online customer service (Joines, Scherer and Scheufele, 2003; McLean and
Wilson, 2016; Rita, Oliveira and Farisa, 2019; Vos et al., 2014; Pandey and Chawla, 2018;
Coşar, Panyi and Varga, 2017) . In regards to pre-purchase service, customers have different
attitudes on product information searching in different situations (Joines, Scherer and
Scheufele, 2003; McLean and Wilson, 2016; Akalamkam and Mitra, 2017). The study shows
the website with transactional privacy concerns have higher negative impacts on customer
attitudes of searching time than websites with economic motivations (Joines, Scherer and
Scheufele, 2003). In terms of utilitarian products, if the time it takes to search for it exceeds
what customers expect to spend, it may cause service failure and customer’s negative
experiences, while online customer support is particularly important to what people think at
final (McLean and Wilson, 2016). Besides, the evaluation difficulty for hedonic products in
17
online shopping is pointed out as well (Akalamkam and Mitra, 2017). On the other hand,
transactional-related service touches upon a practical issue: security/privacy concerns
(Küster, Vila and Canales, 2016; Rita, Oliveira and Farisa, 2019). In this regard, the uncertain
feelings of online shoppers makes a negative impact on e-service quality evaluation (Vos et
al., 2014). According to the research, there is a problem of e-distrust when people have low
security (Pandey and Chawla, 2018; Coşar, Panyi and Varga, 2017). Security is also a
determinant of customer perception of online experiences (Jaiswal and Singh, 2020). As
mentioned above, many previous studies more or less discussed the practical issues in
low-quality online customer services and consistently considered those issues can affect
online customer experiences negatively (Joines, Scherer and Scheufele, 2003; McLean and
Wilson, 2016; Rita, Oliveira and Farisa, 2019; Vos et al., 2014; Pandey and Chawla, 2018;
Coşar, Panyi and Varga, 2017). However, with the development of technology, it is hard to
conclude that today’s customer attitudes towards low-quality online service under this next
context have not changed at all (Rose, Hair and Clark, 2011; McLean and Wilson, 2016; van
Gils, Ramaekers, Caris and de Koster, 2018). Considering this, there is a need to test if
low-quality online customer service still has a negative impact on online customer
experiences. As such, the hypothesis is shown as:
H2: Low-quality online customer service negatively affects online customer experiences.
Website design, as an important part of the construction of shopping sites, also plays an
important role in building customer relationships (Hausman and Siekpe, 2009). Design
elements on shopping sites can make e-commerce companies effectively evoke customer’s
online experiences (Bleier, Harmeling and Palmatier, 2018; Bilgihan, Okumus, Nusair and
Bujisic, 2013). The attractive look and clear layout can construct a friendly web context,
thereby bringing customers good and comfortable feelings (Roghanizad and Neufeld, 2015).
However, improper ways of presenting websites will lead to a series of side effects, including
customer dissatisfaction (Hausman and Siekpe, 2009; Hasan, 2016), crisis of trust
(Roghanizad and Neufeld, 2015), and customer irritation (Hasan, 2016). As the study shows,
sometimes even only one place in the web design disgusts consumers can force them to leave
18
the website (Hausman and Siekpe, 2009; Hasan, 2016). In specific, Hasan (2016) divided the
website features into three categories and deeply discussed the relationship between each of
the characteristics and customer perceived irritation. As previous findings show, all
characteristics with an inferior design can lead to negative customer experience. Among
them, navigation as one of the characteristics has the most significant impact on customer
irritation (Hasan, 2016), which can cause a crisis of trust as well (Roghanizad and Neufeld,
2015). While many previous studies believed improper ways of designing shopping sites can
lead to negative online customer experiences (Hausman and Siekpe, 2009; Hasan, 2016;
Roghanizad and Neufeld, 2015), customer perception towards websites would become
uncertain under the new context. That is due to the shopping sites customers have touched are
changed, customers today’s relevant perception of shopping sites becomes unknown (Rose,
Hair and Clark, 2011; McLean and Wilson, 2016; van Gils, Ramaekers, Caris and de Koster,
2018). Thus, it is unsure whether the statement that the low quality of website design
negatively affects online customer experiences in the previous research is consistent with
today’s situation or not. In this context, a hypothesis based on this conjecture is formulated as
below:
H3: Low-quality website design negatively affects online customer experiences.
3.1 Suggested Research Model
According to the section above, a new research model that considers all the hypotheses can
be built. It is visualized and showed as below:
19
Figure 1: Conceptual Model
4. Method
4.1 Research Approach
4.1.1 Deductive Research
20
According to the research, a deductive approach is the testing of theories that shows and
reflects the nature of the relationship between theory and the related study. When a deductive
approach is taken, the theoretical content in a specific area will be presented as the base of the
study and push the related hypothesis making, which means the hypothesis is what is deduced
from the theoretical base. The hypothesis then will be examined through the empirical study.
In detail, the hypothesis is constituted by the related concept which can be studied as the
researchable entities or the operational terms. In this respect, it guides a direction for
researchers on how to collect the data. Moreover, as the study showed, the usage of theory in
the deductive approach is explained as mainly for empirical study in sociology (Bryman &
Bell, 2015).
Compared to the induction approach, the deduction approach shows a study process from the
start of the theory content to a final observation or findings. When the study comes to the
final stage, there is one thing to note is that the approach here becomes the opposite, which
means an inductive process that researchers take out the implication from the related findings
that promote the study and moreover, the findings created by researchers includes the theory
revision and the knowledge contribution in the certain specific field. In other words, in the
world of deductive reasoning, it pursues to use universally acknowledged theories to
understand how things work and to prove them through the identified problem (Bryman &
Bell, 2015). Sierotowicz and Sierotowicz (2017) simply distinguish induction and deduction
with an example regarding white beans and a bag. That is, when facing the inductive basis of
“These beans are from this bag”, inductive reasoning can generate the statements that “These
beans are white” and then followed by a statement that “All the beans from this bag are
white”. Yet in a deductive world, when the basis is “All the beans from this bag are white”,
the following statement would be “These beans are from the bag; These beans are white”
(Sierotowicz and Sierotowicz, 2017).
The conduction of the deductive approach is considered as a step by step and linear process
which is clear and logical. However, the understanding of the theory or concept at the end
might be different compared to what the researchers present before. This is because of the
data collection process and its subsequent analysis. For example, the relevance between the
theory and the data collected is only shown after the data collection. There is also a situation
21
that the data collected contradicts the hypothesis. This means the hypothesis might be
rejected due to the contradicted fact (Bryman & Bell, 2015). Therefore, with the aim of
testing the proposed hypothesis considering the three independent variables (low-quality
delivery, low-quality online customer service, and low-quality website design), deductive
reasoning will be adopted by authors in the research. As such, the hypothesis deduced from
the relevant theoretical base will be rejected or accepted in the study by testing the theory,
and the concept of theories will be proved or not be proved with the deductive approach.
4.1.2 Quantitative Research
There are two research strategies which are named quantitative research and qualitative
research, while quantitative research is what is associated with the deductive approach. One
difference between quantitative research and qualitative research is that quantitative research
adopts measurement in the study while the qualitative one does not. With regard to
measurement, concept is necessary and which can split into dependent and independent
variables. In quantitative research, the quantification of related information is shown through
the process of data collection which is numerical and its following analysis. Through the
process, the study result can be more precise. Moreover, the strategy is conducted in a
deductive approach and reflects positivism with the natural science norms and the objectivity
of the social reality. The latter two are related to the thinking of epistemology and ontology
(Bryman & Bell, 2015). In this study, the authors are going to figure out the impact of
low-quality delivery, low-quality online customer service and low-quality website design on
online customer experience. As the theoretical base already exists and which creates the
hypothesis, it is accessible to do the quantitative study to improve the understanding of the
study through quantified information.
4.2 Research Design
One of the critical parts when researchers conducting quantitative research is the research
design. Generally, a good selection of research design connotes the final collected data is
22
effective as if it is tailored to the study. Bryman and Bell (2015) defined research design as a
significant tool that helps offer a framework for both data collection and data analysis. The
reasons why research design is considered significant are many, such as its role of explaining
causal relationships between variables, learning behaviors and finding out the meanings of
the behaviors under a particular social background, enhancing the breadth of the study that
more people related to the former individuals who participated in the investigation will be
generalized through the research. Importantly, it escaped the shackles of outdated knowledge,
pursued the current learning of social phenomena and their interrelationships and admitted
the temporary effects of such operations (Bryman and Bell, 2015).
In the book by Bryman and Bell (2015), five common research designs are mentioned in
total. They are experimental designs; cross-sectional design; longitudinal design; case study
design; comparative design. Different categories of research designs decide different
constructions of the framework. Among them, the cross-sectional design is the most
commonly used design in social survey research, which explains why it has another name
called “social survey design”. It is normally applicable for testing multiple cases and for
many data on variables of interest that are collected at the same time. In this regard,
cross-sectional design and experimental design are distinguished accordingly. In other words,
in conducting the work of data collection, individuals can use questionnaires to help
researchers collect and analyze variables under the cross-sectional design simultaneously,
while the collection of data needs to be fulfilled through some time phases under the
experimental design, and which needs researchers to divide individuals into several groups to
conduct the collection work. Importantly, cross-sectional design can effectively test the
relationships between variables. Also, the design is suitable for both quantitative and
qualitative study (Bryman and Bell, 2015). Since this study is intended to be conducted
quantitatively with the task of testing three relationships between independent variables and
dependent variables respectively, the authors decide to choose cross-sectional design as the
research design in order to effectively complete the data collection.
23
4.3 Data Sources
What exactly is data? It is a hard question to answer. Because there are tremendous types of
data existing in today’s world. Yet it is also an easy question to answer since regardless of
what types of data all express one meaning: facts. More precisely, data can be categorized
with two basic dimensions: quantitative or qualitative, root or derived. The former dimension
is the main focus in this study. Quantitative data records facts in numerical form while
qualitative data records facts in non-numeric form. In the domain of business, quantitative
data and qualitative data have essentially the differences in collecting facts. For quantitative
data types, which usually highlights the role of respondents in the process of data collection,
and the numeric, objectively format for analysis is followed to adopt. In contrast, qualitative
data types pursue producing facts in a non-numerical way of going deep into the individual
insights (Banasiewicz, 2013).
With regard to quantitative data types, primary data and secondary data are considered as two
common types of data sources in marketing research. Literally, primary data refers to
collecting primary data whereas secondary data works for secondary captures. One of the
most obvious distinctions between two data types is the perception of the outcomes and
sources. That is, primary data believes that the outcomes of the study are property of sources,
while secondary data hold totally opposite ideas. In general, primary data is a type of
collecting quantitative industry data that mainly involves surveys and is conducted for a
specific purpose based on different studies. Also, the spotlight put on respondent
representativeness in primary data reflects individual discrepancy including demographics,
specific behavior, lifestyles. As a result, the nature of quantitative data in the statistic is
normally forecastable and analyzable insofar as it holds out the wider generalizations
(Banasiewicz, 2013). Due to the research having a specific focus on testing three
relationships between variables, one intends to adopt the primary data to access and collect
data sources to fulfill the particular purpose in this study.
24
4.4 Data Collection Method
In a deductive approach, data collection is the third step after presenting theory and
hypothesis. At this stage, a researcher needs to consider a technique to collect data, which
refers to the research method. The research method can be a self-completion questionnaire,
structured interview or participant observation. Among them, the self-completion
questionnaire is one of the common tools for data collection with a social survey design. As
the name shows, a self-completed questionnaire is what is completed by respondents
themselves. For example, postal-questionnaire is one of the forms of self-completion
questionnaires. In this form, the questionnaire will be posted to the respondent for answering
and then returned back to the sender when the questionnaire is completed. The research
mentioned that the self-completion questionnaire is almost the same type of structural
interview. One significant difference here is that the self-completion questionnaire does not
need a host to participate in the process. In this context, the respondent is who takes part in
the whole process. Therefore, it is important to have an instrument which easily guides the
respondent. A question that is hard to answer is inappropriate here. Compared to structural
interviews, the self-completion questionnaire is mostly with closed questions and with the
shorter length. These characteristics make it easy to operate and prevent the possibility of
being tired in answering questions and give up on it by respondents. A well-prepared
self-completion questionnaire is useful to collect massive data without high time-consuming.
It also reduces the cost of the researcher in data collection, especially when the sample for the
study has a wide geographical distribution (Bryman and Bell, 2015). As mentioned above, the
nature of self-completion questionnaires match the area of this study and meanwhile this
research method benefits both authors and respondents. Therefore, the authors decide to use
the self-completion questionnaire to collect relevant data for the study.
4.5 Data Collection Instruments
Compared to doing interviews, the questionnaire has a limitation that it might have a lower
response rate. This especially happens in postal-questionnaire. As it stated, a lower response
25
rate will cause bias in the study since the difference between respondents and people who are
no-answering is considered. Accordingly, there is an alternative which is named online
questionnaire. In this method, the data will be collected through the web. The research shows
that there is a higher degree of completion of questions in an online form compared to a
postal form. The advantages of conducting online questionnaires are many, such as lower
cost, faster response, unrestricted compass, etc (Bryman and Bell, 2015). As Gallhofer and
Saris (2014) states, the advanced web technology makes it possible to get access to a huge
amount of data with cost advantage. Therefore, the study is going to conduct the
self-completion questionnaire online. Even though there are still some limitations the method
has, such as the difficulty to get a representative sample out (Bryman and Bell, 2015;
Gallhofer and Saris, 2014), since the topic of the study is about online customer experience,
this can be ignored to some extent.
4.5.1 Operationalization
The process for the authors to create measures on related concepts that are going to study is
named as operationalization (Bryman and Bell, 2015). According to Gallhofer and Saris
(2014), the term operationalization is also considered as a process to convert the concept into
questions (Gallhofer and Saris, 2014). To make up measures on concepts, the authors need to
develop indicators to represent the concept. It can be associated with questions that are going
to be asked through a questionnaire, the relevant structural observation schedule and so forth
(Bryman and Bell, 2015). The operationalization below is divided into six parts, which are
concept, concept definition, sub concepts, items, questions and reference. The related
concepts formed 21 questions in total for the following measurement.
26
4.5.1.1 Operationalization Table
Table 1: Operationalization Table
Concept Concept Definition Sub concepts
Items Questions Reference
Online
Customer
Experience
(OCE)
Online customer
experiences come from the
things they have touched
during shopping (Rose,
Hair and Clark, 2011). It is
actually something that
mimics the sensory
experience in an offline
environment (Bleier,
Harmeling and Palmatier,
2018). Online customer
experience is divided in
two branches, which are
affective state and
cognitive state (Rose,
Clark, Samouel and Hair,
2012; Rose, Hair and
Clark, 2011)
Affective
State
Ease of use OCE1: The function of easy to use on shopping sites empowers me with the feeling of control.
Rose, Clark,
Samouel and Hair
(2012)
Martin, Mortimer
and Andrews
(2015)
Customization OCE2: The function of customization (the functions with advanced technology) on shopping sites empowers me
Rose, Clark,
Samouel and Hair
(2012)
Martin, Mortimer
and Andrews
(2015)
27
with the feeling of control.
Cognitive
State
Entertaining
shopping
process
OCE3: An entertaining shopping process makes me want to stay in the shopping site longer.
Bhattacharya,
Srivastava and
Verma (2018)
Engaging
shopping
process
OCE4: An engaging shopping process makes me want to stay in the shopping site longer.
Bhattacharya,
Srivastava and
Verma (2018)
Color vividness
OCE5: The color vividness of a shopping site is something that improves my online engagement.
Papagiannidis et
al. (2017)
Graphic
vividness
OCE6: The graphic vividness of a shopping site is something that improves my online engagement.
Papagiannidis et
al. (2017)
Low-Quality Delivery
In e-commerce, goods need
to be delivered with some
means of transportation
within a period (Li, Lu and
Talebian, 2014). The
low-quality delivery
Shipping
Fees
High shipping fees
LQD1: I am not willing to accept high shipping fees.
Guo et al. (2020)
28
includes issues such as
incomplete orders and
product returns, too long
transportation time, the
ambiguous delivery time
provided by poor systems,
and so on (Coşar, Panyi
and Varga, 2017; Kim,
Dekker and Heij, 2017).
Customer attitudes towards shipping fee in small companies
LQD2: I am not willing to accept shipping charges when shopping for products from small companies.
Gümüş, Li, Oh and Ray (2013)
Delivery
Time
Charges for multiple delivery time windows
LQD3: I am not willing to pay for delivery time windows (A service that provides multiple shipping time options).
Ramaekers, Caris, Moons and van Gils (2018)
Moons, Ramaekers, Caris and Arda (2017)
Home delivery failure
LQD4: I am not willing to accept home delivery failures.
Ramaekers, Caris, Moons and van Gils (2018)
29
Low-Quality
Online
Customer
Service
Online customer service is
a certain system installed
on shopping sites to
provide customers with
their needed information
during online shopping. At
present, there are various
forms of online customer
service presenting on
online platforms, such as
call centers, customer
support, and choice helpers
(Lim and Dubinsky, 2004).
The low-quality online
customer service reflects in
some related practical
issues, such as transaction
privacy concerns, long
searching time on
utilitarian product
information,
security/privacy concerns,
and so on (Joines, Scherer
and Scheufele, 2003;
McLean and Wilson, 2016;
Rita, Oliveira and Farisa,
2019; Vos et al., 2014;
Pandey and Chawla, 2018;
Coşar, Panyi and Varga,
2017).
Pre-Purchase
Service
Transactional privacy concerns
LQOCS1: When a shopping site asks me to provide private information to get product information, my privacy is at risk of being disclosed.
Joines, Scherer and Scheufele (2003)
30
Time attitudes under transactional privacy concerns
LQOCS2: I am not willing to stay in the shopping site when it asks me to provide private information to get product information.
Joines, Scherer and
Scheufele (2003)
Time consciousness under utilitarian orientation
LQOCS3: I am not willing to spend more time searching for utilitarian products (a goal-oriented product with functionality) when the time exceeds what I expected.
McLean and Wilson (2016)
Weak online customer support
LQOCS4: I am not willing to stay in the shopping site if its online customer support is weak.
McLean and Wilson (2016)
Attribute defects of hedonic products
LQOCS5: The abstract and blurred features of hedonic (enjoyable) products make it difficult for me to evaluate the product.
Akalamkam and
Mitra (2017)
Transaction related service
E-distrust based on security/privacy concerns
LQOCS6: I am not willing to trust the shopping site when it arouses me security/privacy concerns.
Pandey and Chawla (2018) Coşar, Panyi and Varga (2017).
31
Low-QualityWebsite Design
Website design refers to
the design of the web
interface. A website with
an intuitively appealing
look and clear layout can
form a favorable web
context (Roghanizad and
Neufeld, 2015). The
counterexample (improper
ways) of the website
design includes many, such
as bad layouts,
uncomfortable font design,
terrible colors, pop-up
news, banners, irrelevant
information, incompatible
information formats, and
so on (Hasan, 2016).
Dissatisfaction with website design
Improper ways of presenting
LQWD1: The improper presenting of the website design makes it difficult for me to feel satisfied.
Hausman and Siekpe (2009)
Hasan (2016)
Customer Perceived Irritation
Inferior visual design
LQWD2: I am not willing to accept low-quality visual design (e.g. bad layouts, uncomfortable font design, terrible colors, pop-up news, banners, etc.) of a shopping site.
Hasan (2016)
Inferior navigation
LQWD3: I am not willing to accept low-quality navigation settings (e.g. sloppy design, a complicated look, vague look, etc.)
Hasan (2016)
32
of a shopping site.
Inferior information design
LQWD4: I am not willing to accept low-quality information design (e.g. irrelevant information, incompatible information formats, etc.) of a shopping site.
Hasan (2016)
Trustworthiness
Crisis of trust to inferior navigation
LQWD5: I am not willing to trust a shopping site with low-quality navigation settings (e.g. sloppy design or a complicated or vague look, etc.).
Roghanizad and
Neufeld (2015)
4.5.2 Questionnaire Design
Respondents can effectively respond to the questionnaire when it is easy and suitable for
them (Gillham, 2015). Many authors mentioned a lot of pros and cons of questionnaires in
books despite they also acknowledge that designing a good questionnaire is a complicated
task and requires enterprises to make efforts for it (Gillham, 2015; Gallhofer and Saris, 2014;
Bryman and Bell, 2015). This phenomenon is mainly attributed to the untouched nature of
questionnaires, which may cause some misunderstandings uncontrollably between
questionnaire designers and respondents (Gillham, 2015). Gallhofer and Saris (2014)
believed that “In designing a question, many decisions are made”. In other words, if people
who design the questionnaire can foresee the influence of the designed questionnaire in the
following responses would be good (Gallhofer and Saris, 2014). Gillham (2015) consistently
33
expressed that the wording of questions have a great effect on the answers. As such, one
suggests conducting careful consideration and asking researchers themselves if respondents
can effectively understand the designed questions before attempting to design optimal
questions (Gillham, 2015).
Knowing how to design a superior questionnaire is an important part of research methods for
researchers to consider. In this study, the authors pay attention to not only keeping the entire
layout of the self-completion questionnaire as concise as possible but also following the
designing guidelines suggested by Gillham (2015) that simplify the survey questions and
make sure the information regarding all respondents are anonymous (Gillham, 2015). The
entire questionnaire is conceived and designed around the subject of online customer
experience in the online environment and is presented to respondents via an online channel
namely Google Form. It is noted that the authors consciously divided all questions into three
zones in order to guide respondents to gradually adapt the answering environment. The zones
are general questions, general information, questions regarding the subject area, respectively.
By adding the statements under the second and third title of three main sections may help
respondents easily understand the entire design of the questionnaire. Moreover, the sentences
that tell respondents they will enter the subject area when they arrived in the third question
zone can to some extent remind them to fill out the survey carefully.
One should note here is the authors designed each survey question regarding independent
variables into the “double negative” style. Take LQOCS4 as an example, this survey question
“I am not willing to stay in the shopping site if its online customer support is weak.” includes
two pieces of information, one is “I am not willing to”, the other one is “ the weak online
customer support”. The first piece means the customer expresses negative emotion, the
second piece means an online customer support with a negative status. Combining these two
pieces of information presents a negative attitude towards negative items. Therefore, if
participants see the question and hold the same attitudes they can choose “agree”, otherwise
they should choose “disagree” to express they will not hold negative attitudes towards
negative items.
34
4.5.3 Pre-testing
Regardless of a structural interview or a self-completion questionnaire, pre-testing is a
necessary step for a preparatory work of the study. The term pre-testing is also named as pilot
study. For self-completion questionnaires, considering the setting which does not include an
interviewer to participate, the status of pre-testing is especially important. Specifically
speaking, pre-testing moderates the situation of no interviewer which makes it unable to
make an explanation to respondents about the questions. Moreover, pre-testing is useful to
reduce the possible large amount of wastage assuming that there are still problems in the
questionnaire when it is sent out (Bryman and Bell, 2015).
Generally, pre-testing is mainly functioned on questions control. For instance, if there is any
question that is difficult for the respondent to understand or the question makes the
respondent unwilling to answer, a pre-testing can help the researcher to find it out in advance.
For the latter problem, the research stated several possible reasons, such as confusing or
threatening paraphrasing, poor worded instructions or confusing positioning of the questions.
Pre-testing can also detect the situation for questions that get almost the same answer and
therefore researchers can remove or adjust the questions afterwards. Through the process, the
flow for whole questions and the quality of the instruction in the questionnaire can be
examined (Bryman and Bell, 2015).
In this study, the authors considered 10 respondents in the pre-testing. These respondents are
ensured not included in the formal study by authors since it might influence the result of the
study due to the problem of sample representativeness can be arised (Bryman and Bell,
2015). Through the pre-testing, the unclear described place in a question that is considered
with a tendency to confuse respondents are found and then adjusted by authors. Besides, the
most important problem the authors found after the pre-test is that the answers of the survey
questions related to the independent variables look a little bit scattered. After carefully
thinking, the authors finally find the reason behind this phenomenon, which is that the likert
order listed under the questions of independent variables is wrong. Since the paper is to test
35
the relationship between three elements in low quality and online customer experience, to
keep the correct measures and the related relationship between that is going to test, it is
necessary to keep consistent in the measurement of the dependent variable and independent
variables. This means the same order of likert scale under each of the statements. More
precisely, the order related to independent variables needs to be corrected into the same order
as dependent variables’, that is the order from “strongly disagree” to “strongly agree” (the
previous order under independent questions is incorrectly from “strongly agree” to “strongly
disagree”). The details about likert scale setting for the questions will be discussed in the data
analysis method.
4.6 Sampling
Sampling is defined as what links with the selection of individuals for interviews or
questionnaires. One can say that in quantitative research, the sample is always an essential
need. It is a subset of the population because the sample is segmented by the population for
investigations. More precisely, the sample is selected from the rich and universal units in the
entire broader scales (population) through the sample frame. The way of selecting the sample
often includes two sampling approaches, one is probability sampling and the other is
non-probability sampling. The biggest distinction between these two approaches is whether
or not to adopt a random way to select samples. As their name shows, the former approach
focuses on randomly selecting samples while the latter focuses on the opposite way. In other
words, non-probability sampling is more likely to choose some units as samples than others
(Bryman and Bell, 2015). Come back to the actual sampling work in this study, since the time
and efforts for choosing samples to collect data are limited, one intends to use the social
platform namely Facebook to conduct the relevant surveys. As such, probability sampling is
not applicable in this case. Following the meanings of Bryman and Bell (2015), convenience
sampling is the most appropriate non-probability sampling method in this study because it
guides managers easily to find a group of people that is available to them. However, this kind
36
of sampling approach has a defect that it may fail to achieve the effect of generalizing the
findings in the end as managers have no idea what the population the samples exactly stand
for. Despite this, the approach can deliver a springboard to the research and help establish
some links with existing findings in the specific area. Therefore, convenience sampling is still
a very common and useful sampling approach in the field of business and management
(Bryman and Bell, 2015).
4.6.1 Sample Size
Before collecting data, researchers usually consider the question about how many n needs to
be collected to achieve excellent detection of N. However, this is a challenge for most
managers. Indeed, the determination of the sample size in the quantitative method connotes
multiple aspects of consideration, such as data accuracy requirements, time and cost
considerations. In addition, adopting a good sample size is not saying a larger or smaller
sample size means the best but is more depending on the population. When collecting a
sample of a country or a city, it means that the sample is very heterogeneous and diverse, so
the large-scale sample collection is required. In specific, when the sample size increases, the
sampling error is followed to decrease insofar the accuracy of the sample will increase.
That’s also why it is stated that the small sample size may produce many uncertain results.
Nevertheless, one is noted that if the accuracy of the sample is too pursued, it may take more
time and cost for it, thereby causing a likely high degree of uneconomical collecting
processes. Also, conducting a too large sample size has the potential to waste scarce
resources (Bryman and Bell, 2015).
Green (1991) cited in VanVoorhis and Morgan (2007) believed that when measuring the
number of individuals being sampled in a study, there is a need to calculate the sample size
with a formula, which is: N > 50 + 8m, where the N represents the minimum number of the
participants that a study should have and m refers to the number of independent variables
37
(Green, 1991 cited in VanVoorhis and Morgan, 2007). Since this study includes three
independent variables in total, thus, the sample size could be formulated into 74 respondents
(N > 50 + 8 x 3 = 74) based on the suggested formula (Green, 1991 cited in VanVoorhis and
Morgan, 2007).
4.6.2 Sampling Error
It is impossible to ensure that no sampling errors appear in a study. The sampling deficiency
such as bad sampling frame or non-response or poorly designed survey questions makes
samples have discrepancy with the population more or less. As mentioned above, the times of
the frequency of errors will decrease as the sample size increases insofar the relevant
precision gets high. Since convenience sampling is not likely to realize generalizability and
support the existing research in specific subject areas, the phenomenon of “non-response”
may occur. That is, if some individuals of the sample do not truly appear to answer the survey
or lose contacts, the so-called “non-response” will thus appear in this way. It certainly exists
some people who are selected in the sample fail to effectively participate in the survey due to
some personal reasons such as some psychological or physical barriers (Bryman and Bell,
2015; Gallhofer and Saris, 2014).
4.7 Data Analysis Method
How to make data analysis is an important thing to consider and keep in mind for researchers
even in very early stages of the study. The choice of which technique to use will be related to
questionnaire design, observation schedule, coding frame, etc. As the research shows, the
decision on technique is mainly affected by the type of variables and the nature and the
sample size (Bryman and Bell, 2015).
A tool that is most widely used for quantitative data analysis is SPSS (Bryman and Bell,
2015). According to Bryman and Cramer (2012), SPSS is effective for researchers to analyze
the data with the benefit of time saving, calculation support and inevitable mistakes avoided
38
(Bryman and Cramer, 2012). Through SPSS, the study is supported with the result through
data analysis. As the research shows, it is necessary to do coding of the data before the data
collection is finished (Bryman and Bell, 2015). In this study, the five-point likert scale is used
for the questions (exclude the control questions), which indicates the attitude of people to an
object, which usually represents the degree from agreement to disagreement (Bryman and
Bell, 2015). In the questionnaire, 1 stands for “strongly disagree” and 5 stands for “ strongly
agree”. The closed questions makes the data pre-coded, which assists the computer conduct
the data analysis in an easier way (Bryman and Bell, 2015; Gillham, 2015). In this study, all
the survey questions related to the dependent and independent variables are followed with a
likert order from strongly disagree to strongly agree, which matches the code 1 “strongly
disagree” and 5 is “ strongly agree”. Besides, there is a situation where missing data occurs
after data is collected. The reason for this might be unwilling to answer or a wrong operation
by respondents. One way to deal with this is to code the answer with a number that can be
identified for computer analysis (Bryman and Bell, 2015).
4.7.1 Descriptive Analysis
According to Bryman and Cramer (2012), descriptive analysis is considered as the first stage
of the analysis which provides a general understanding about respondents through their
answers. The use of it is wide and makes impact to further analysis. It also emphasizes the
fitness between closed questions and the method (Bryman and Cramer, 2012). Around
descriptive analysis, there are several important figures that need to be noticed. As the
research shows, central tendency is what can be seen through distribution of values. In this
area, there are three averages for researchers to access. The first one is Arithmetic mean,
which is known as the average by people in a general view. It is calculated by dividing the
sum of values to the numbers of values in the distribution. The number is easier to be
influenced by extreme values or outliers in other words. The second one is median, which is
positioned in the middle of the distribution that obtains the lists of all of the values from the
small to large order. If the total number of the values is even then the researcher needs to find
the two middle numbers and calculate the mean of them. Compared to arithmetic mean, this
number is more stable. The third one is mode, which refers to the number with highest
39
occurrences in the distribution. Besides, the variation of the sample can be seen through
dispersion. The value of dispersion is affected by outliers and which can be measured by the
range that is calculated by the maximum value minus the minimum value from the
distribution of values. There is also another way of measurement of dispersion which is by
standard deviation. This term is defined as the average amount of variation of the mean and
which is calculated by the sum of the difference between each value and mean, and then use
this number to divide the number of the values. It argued that standard deviation is also
affected by outliers, however, the calculation step which divides the number of the values
makes the effect eliminated (Bryman and Bell, 2015).
Moreover, the measurement of skewness is what needs to be considered in descriptive
analysis as well. The research states that when a histogram is not symmetric, it can be said
that the histogram is skewed. There are two possible results as left-skewed (negatively
skewed) and right-skewed (positively skewed). Left-skewed means the left-hand tail is over
the right-hand tail and the right-skewed is the opposite meaning. The measurement of
skewness can be done through where arithmetic mean, median and mode are located in the
distribution. There are totally three situations in the measurement. The first situation is that
there are no differences among three values since the histogram is perfectly symmetric. In
this situation, the level of skewness is 0. The second situation is that the mean is smaller than
the median and then smaller than the mode as the histogram is left-skewed. The last situation
is the reverse of the second one as the histogram is right-skewed. Such result is closely
related to the sensitive impact of arithmetic mean to the extreme values. The skewness
coefficient can be calculated by a specific skewness value over the sample standard deviation.
As there is a factor 3 in the formula, which delimit an interval between -3 to 3 for skewness
values lies (Goos and Maintrup, 2015) . As stated by Martin and Bridgmon (2012) , skewness
is a measurement on normality of the data, which means it aims to present the information
from the normal population. Therefore, if the result is left-skewed or right-skewed, it means
there are extreme scores or outliers on the left (with negative skewness value) or right side
(with positive skewness value) while the others on the opposite site on the curve. In other
words, if the result is more close to 0, then it means there is more normality on the data
40
(Martin and Bridgmon, 2012). As mentioned above, the measurement of skewness shows the
distribution of the data and elaborate the thinkings of the samples in the study.
Lastly, kurtosis is the value that appears when a histogram with a sharp peak. It measures the
steepness of the data. In a normal distribution, the value of kurtosis should be 0. When the
peak of the histogram is sharper, the value is larger than 0, otherwise the situation is the
opposite (Goos and Maintrup, 2015). According to Martin and Bridgmon (2012), kurtosis is
also a measurement which aims at the normality of the data. When there is a sharper peak
(leptokurtic), it means the data are more concentrated in the middle of the curve. Relatively,
if there is a flatter peak (platykurtic), it means the data are more dispersed. The range of the
kurtosis value can not exceed positive 3.29 or negative 3.29, or it will cause problems on the
data since there is a significant difference from the data to normality (Martin and Bridgmon,
2012).
4.7.2 Correlation Analysis
Correlation analysis is what can measure the degree of the relationship between variables
more precisely. Bryman and Bell (2015) perceives correlation as a tool to measure the
strength of a relationship between two variables. The book introduces a classic method
associated with correlation analysis called “Pearson’s r” which is used to test relationships
between interval/ratio variables. Some features of this measurement are followed to be
mentioned. For example, from the perspective of Pearson’s r, the coefficient is considered as
an effective way to judge how strong the relationship is. More precisely, coefficient is a
measure in the scale between 1 and 0. When coefficient is 0, it means the relationship
between the two variables is nonexistent. When coefficient reached 1, which means a perfect
relationship appeared (Bryman and Bell, 2015). In other words, close to 0 means that the
relationship becomes weaker (Bryman and Bell, 2015; Gallhofer and Saris, 2014), and close
to 1 means that the relationship becomes stronger (Byrman and Bell, 2015). A negative or
positive correlation can reflect the direction of a relationship. It is certainly that if none of
correlation exists between variables, it is unlikely to form an obvious pattern on the diagram
(Bryman and Bell, 2015).
41
4.7.3 Multiple Regression Analysis
Montgomery, Peck and Vining (2012) defined the term regression analysis as a statistical
technique which is used to investigate and mold the relationship between dependent and
independent variables. The scale of adopting regression methods is huge and various, but the
field of statistical study is the place where used regression models mostly. Firstly, regression
can be simply perceived as a tool to determine the relationship between x and y. The simple
linear regression predicts how Y is related to X in a linear form, where X represents
independent variables and Y represents the dependent variable. When a regression model
includes more than one variable, the so-called “multiple regression model” will be adopted
under this situation. In other words, the multiple regression analysis focuses on one single
dependent variable and multiple independent variables. It aims to see how the dependent
variable is determined by the independent variables (Montgomery, Peck and Vining, 2012).
When square the value r of Pearson’s r, “R2” is produced in this way. It has a statistical name
: coefficient of determination, which measures the strength of the relationship between the
dependent variable and at least two independent variables. Similar with r, R2 also lies
between 0 and 1. The feature of the coefficient of determination is that it explains variance in
percentage. For example, a model with R2 = 0.8 explained 80% of the variance of the
dependent variable (Byrman and Bell, 2015; Montgomery, Peck and Vining, 2012). F-test is
a test model for linear regression that involves a null hypothesis and explains a zero variance
in the dependent variable. In other words, R2 = 0. The variance will increase if the sample
means are more different. One can therefore say that the value of F is high when the
variability is high. Importantly, if the computed F value is greater than the tabled F-value, one
can reject the null hypothesis (Schumacker and Tomek, 2013). Moreover, the beta coefficient
(β) and p-value are two key elements to test the significance of the relationship between
variables. For β-value which is used to see the slope or direction of the relationship. Simply, a
β-value less than zero means that the relationship is negative, and a β-value over zero means
that the relationship is positive. As to P-value, which is considered as important as β-value.
42
The concept of p-value is that if it is lower than the significance level of 0.05, the relationship
between the variables is significant (Schumacker and Tomek, 2013).
4.8 Quality Criteria
4.8.1 Validity
Validity is one of the measures to the quality of the study. It states that validity is what
reflects the completeness through the related conclusions. One of the important types of
validity in quantitative study is measurement validity, which is also named as construct
validity. It is suitable for social scientific concepts and which is presented by the degree of
connection between concept and its measures. In other words, it means if the measure can
represent the concept. When a result shows that there is no relationship between two
variables, the reason behind might be the wrong setting of theory itself and which influences
the related hypothesis, or the measure is invalid. A problem here will cause the findings of
the study negatively affected and lower the acceptance of the study to some extent.
Reliability is one point to examine validity, since it shows whether the measure is effective or
not from a standard of stableness (reliability) (Bryman and Bell, 2015). In this paper, the
authors searched and collected theoretical information that they thought are relevant to the
study. To make sure the validity of the study, the authors discussed their choices of theories
(low-quality delivery, low-quality online customer service and low-quality website design)
and the related hypothesis that they are going to use to test on the concept (online customer
experience) with the professionals in the area. According to the feedback, the authors make
some changes to the necessary parts and therefore ensure the construct validity of the study.
Internal validity is an important criterion for researchers to ensure in quantitative study.
According to Bryman and Bell (2015), internal validity shows the causality of the study. This
stays in line with the nature of social science study, which means to find the reason for a
specific phenomenon that the researchers are interested in the study. In detail, internal
validity aims to measure if the statement on the relationship between the variables
(independent variables and dependent variables) is real and if the related independent variable
43
is the only incentive (this means, without other interferences) to the change of the dependent
variable (Bryman and Bell, 2015). As the survey of the study will be conducted through a
cross-sectional design, there will be a problem that the direction of the causal influence is not
so clear since the theoretical information about independent variables and the dependent
variable are both collected and presented (Bryman and Bell, 2015). To ensure a clear
direction shown from the independent variables to the dependent variable, the authors
generalize the theoretical information for related hypothesis deduction in the part of
conceptual framework and follow it to present the suggested research model. Meanwhile, to
ensure the degree of relationship between variables, the correlation analysis is considered and
used by the authors.
4.8.2 Reliability
Reliability is mainly responsible for concerning if the content of the new research is similar
or repeatable to the previous studies. In fact, reliability is especially applicable for
quantitative study. Simply, it is a tool to see if the measures of concepts are consistent. In
other words, if someone can evaluate the reliability of the measure of a concept, the process
that makes up that measure must be replicable by other people. Accordingly, reliability is
naturally connected with validity since the evaluation of the validity of measurement regards
a reliable measure as the premise. Importantly, both the reliability and the validity of
measurement are fundamentally associated with the adequacy of measures, which is most
obvious in the quantitative study (Bryman and Bell, 2015).
However, the lack of coherence may occur during measuring each respondents’ questions in a
multiple-item way, that the indicators may be irrelevant to the same things. Thus, in order to
make all indicators associated with each other, one needs to use a common method namely
Cronbach’s alpha to test the internal reliability. In general, the value equals or over 0.7 is
expected since which is seen as an acceptable level of the internal reliability (Bryman and
Bell, 2015).
44
4.9 Ethical Issues
Ethical issues exist in a lot of business research, including the operation of the research. It is
considered important as it covers the integrity of a study and relevant subjects. In the
consideration of ethical issues, four ethical principles are essential for managers to consider
and understand, which are related to harm on participants, absence of informed consent,
invasion of privacy, and deception. Literally, the harm to participants is about harming
participants in various ways, such as physical harm, put pressure on participants, harm
participants esteem, mental damage and so on. Absence of informed consent connotes they
are put in an observation which is disguised or covert but they know nothing about it. The
principle indicates that even if the participants of the survey have known everything, they
have the right to be told the research process. As to invasion of privacy means and deception,
as their names shows, both reveal a risk of deceiving participants in aspects such as exposing
participants personal information, lying to them with fake information. Nevertheless, these
four ethical principles have more or less links with each other. For example, “it is difficult to
imagine how the principle of informed consent could be built into an investigation in which
research participants were deceived.” But one cannot deny their common importances in
business research (Bryman and Bell, 2015).
In this study, when authors conduct the questionnaire, they have had the ethical responsibility
throughout the whole process. Not harming the participants, whether psychologically or
physically, has always been the social moral theme guarded by the authors. They disclose the
information of the entire questionnaire to all participants without hiding or deceiving. When
participants open the homepage of the questionnaire, they can clearly see one instruction
telling them all their personal information is anonymous. Also, participants can click the
button to leave this webpage at will which means they are not enforced or threaten to answer
the survey. The authors consciously make sure not to bring pressure to participants.
45
4.10 Societal Issues
Considering the social aspect, the research shows that it is not influenced by a study’s
scientific quality so much. In fact, it is kind of difficult to measure social impacts which come
from research. However, as stated by Bornmann (2012), there are still several indicators for
social considerations that are accessible for researchers in a study. One of the indicators is
called economic benefit, which is about the effectiveness to improve the productivity of the
society. There is also a wide concern of economic benefit in a specific research area
(Bornmann, 2012). In this study, it aims to give online customers better experience in the
related context, however, this result can not be transported by the authors themselves. The
authors are the mediators of the study contribution while the E-commerce companies are the
direct influencers of the research. Therefore, this would raise a social concern, which is about
how they will use the study result. When they see the chance of economic benefit that they
can get through the result, whether they will work hard to solve the issues that online
customers already have and therefore make a win-win situation or they will focus on their
own benefit obtaining and deceive customers by solutions that are not truly effective to the
issues. It is hard to ensure this on the side of authors. Besides, there is also a probability that
when online customer experience is improved by the study, people might give more time to
online shopping. This would make the productivity of the society negatively influenced to
some extent and therefore suppress the anticipated economic benefit, which related to another
societal issue. Nevertheless, Since the topic in this study is about online customer experience,
therefore, the study can be still considered to contribute to the economy as it is anticipated to
give some insight to business to improve online customer service and therefore improve their
own performance as the contribution to the economy. The fact is that the study is considered
not so big so as to influence society on a large scale. In addition, most of the E-commerce
companies in the world are committed to business operation legally and the related gains. In
this aspect, one can argue that the study could make a positive effect on the development of
the economy in the society and which is useful to improve the online customer service
through different aspects. Meanwhile, people are more than an individual, they have family
members, friends etc. Therefore, one can say that it is not so easy and general that a huge
group of people are negatively influenced by the study and make bad impacts on society.
46
5. Results
5.1 Descriptive Statistics
There are totally 78 respondents answering the questionnaire. Through control questions, it
shows that all of the respondents replied “Yes” to the question of “Do you shop online”. As
the topic of the study is about online customer experience, this means all of the respondents
can be included in the study. This question is important as it ensures the respondents are all
relevant to the study. However, only 77 answers will be used for the study, since there is a
respondent who does not complete the whole questionnaire which makes the answer invalid.
In this questionnaire, most of the respondents are aged between 18-25 which occupied 73.1%
and the following are between 26-35 which occupied 20.5%. The lowest number of
respondents are those aged between 36-45 as 2.6% while the rest of them are aged under 18
as 3.8% of the whole. The distribution of respondents in gender are almost at an average
level, with females slightly ahead with 52.6% while male occupied 46.2% of the whole
respondents. Through the questionnaire, the main force for the answers are respondents who
live in Sweden, which is 81.8% of the whole. In the education background, half of the
respondents are undergraduates. Besides, monthly income less than 10000 SEK are the
majority of the respondents in this questionnaire.
47
In table 2 it shows the descriptive statistics through the questionnaire. The descriptive statistic
indicates the central tendency and which can be measured by three values as arithmetic mean,
mode and median. From table 3, the central tendency is shown as from 1 to 5, since the
questionnaire uses the five-point likert scale as the measurement of the questions, which 1
represents for strongly disagree and 5 represents for strongly agree. First of all, look at the
mean value, the results are all around 3 and 4, which indicates positive results through an
average level (closer to strongly agree). The relatively high average results shows a relatively
high consistency between the respondents and the statements. On the dependent variable,
the highest value of mean is positioned at OCE1 as 4.38, which indicates the importance of
the item in OCE and its impacts on OCE. Meanwhile, on independent variables, while the
highest value positioned at LQWD1 as 4.42, which proves the importance of the statement
among all independent variables averagely and proves the importance of the independent
variable (LQWD) to dependent variable (OCE) to some extent. Meanwhile, as a more stable
measure, median values are whether 4 or 5, which enhance the acceptance of the result. In the
table, the mode values are also high as 4 or 5 for all of the items while the measurement of
dispersion can be seen from the figure of standard deviation. The item with highest standard
deviation is LQD2, which accounts for 1.110 as a high variability. The item with lowest
standard deviation is OCE1, which accounts for 0.812 as a low variability. Skewness means a
status of asymmetry. All of the amount lies between -3 to 3 which can be accepted. Most of
the values are closer to -1, which indicates a left-skewed distribution. In other words, a
negative skewness shown. This means, there are extreme values on the left-hand side of the
curve. Lastly, the value of kurtosis indicates the sharpness of the distribution. In table 3, The
data all lie between positive 3.29 to negative 3.29, which are acceptable. In detail, the value
that larger than 0 is more than the value that is smaller than 0. This means the distribution of
the data is sharper and more concentrated. There is also data that is closer to 0, such as 0.376
that is positioned at OCE1, which shows data that is close to the normal distribution.
48
Table 2: Descriptive Statistics
5.2 Correlation Statistics
5.2.1 Test of Validity
Table 3: Test of Validity
(Correlation Analysis)
49
Variable
Low-Quality
Delivery
Low-Quality Online
Customer Service
Low-Quality
Website Design
Low-Quality Delivery
1 ,679** ,569**
Low-Quality Online Customer
Service
,679** 1 ,623**
Low-Quality Website Design
,569** ,623** 1
** Correlation is significant at the 0.01 level (2-tailed).
In table 4, a correlation analysis among total 3 items are shown. The figure of the Pearson
coefficient presents the degree of relationship between the items as weak or strong. Through
the table, all of the values of the pearson coefficient are closer to 1, which means there is a
strong relationship between two variables. Meanwhile, the positive correlation in the table
presents a positive relationship between each variable. The highest correlation is between
low-quality delivery and low-quality online customer service, while the lowest correlation is
between low-quality delivery and low-quality website design. All of the factors are
significant at 0.01 level. Therefore, as the degree of the relationship between variables are
reflected, the validity of the study is proved to some extent.
5.2.2 Test of Reliability
Table 4: Test of Reliability
(Cronbach’s Alpha)
Variable
Number of items Cronbach’s alpha
50
Online Customer Experience 6 0.863
Low-Quality Delivery 4 0.746
Low-Quality Online Customer Service
6 0.835
Low-Quality Website Design
5 0.866
As mentioned before, Cronbach’s Alpha is a commonly used method for testing the
reliability of research (Bryman and Bell, 2015). In other words, testing the internal reliability
through Cronbach’s alpha has the potential to enhance the links between all the indicators
shown in the table 4. According to the suggested criteria, the value = or >0.7 is expected to
see an acceptable level of the internal reliability (Bryman and Bell, 2015). In table 4, it is
clear to see the value of Cronbach’s alpha for each variable is over 0.7 insofar four variables
are involved with acceptable levels of the internal reliability. In specific, the independent
variable of low-quality website design reached the highest value 0.866 despite the other two
variables meaning low-quality online customer service and online customer experience also
reached the value over 0.8 respectively. The table of cronbach’s alpha shows only
low-quality delivery interpretes a relatively low value, however, its value has reached over
0.7. Throughout the column of number of items, the test of reliability considers all of the
items related to the four variables.
5.3 Multiple Regression Analysis (For The Hypothesis-Testing)
51
Table 5: Multiple Regression Analysis
B Sig.
Model 1
Model 2
Model 3
Model 4
(All)
Independent
Variable
Hypothesis
Low-quality
delivery
H1: Low-quality
delivery negatively
affects online
customer
experiences.
0.495 (.000)
0.045 (.706)
Low-quality
online customer
service
H2: Low-quality
online customer
service negatively
affects online
customer
experiences.
0.647 (.000)
0.453 (.001)
Low-quality
website design
H3: Low-quality
website design
negatively affects
0.571 (.000)
0.263 (.022)
52
online customer
experiences.
R Square 0.245
0.419 0.326 0.467
Adjusted R
Square
0.235
0.411 0.317 0.444
Std. Error of the
Estimate
0.606 0.528 0.568 0.516
F-value 23.984 54.030 36.324 20.989
Degrees of
Freedom
1 1 1 3
Dependent Variable: Online Customer Experience
Significant at level = 95% (p < .05)
In table 5, the multiple regression analysis table involves four important models in total, they
are model 1, 2, 3, 4. The former three models aim to test the individual significance of each
of independent variables to the dependent variable. It is easily observed that all of three
independent variables (low-quality delivery, low-quality online customer service, low-quality
website design) can individually significantly affect online customer experience as they are
all with the significant P-value of .000 which is smaller than 0.05. As to the fourth model,
which is used for examining if the hypotheses are accepted by computing all the independent
variables together with one dependent variable i.e. online customer experience. In this table,
F-value was significant at the given level of significance P < 0.05 for all models. It is
consistent with the R square that all models (0.235; 0.411; 0.317; 0.444) are lying in the
proper zone from 0 to 1 (Byrman and Bell, 2015; Montgomery, Peck and Vining, 2012).
However, when looking at the fourth model, it shows not all hypotheses are with significant P
value (<0.05), that H2 (0.001) and H3 (0.022) are with the significant levels since both values
are less than 0.05 but H1 is with its p-value of 0.706 which is more than 0.05. The β value
53
regarding the three hypotheses in the forth model is 0.045; 0.453; 0.263, which means these
three relationships of the independent variables and the dependent variable are shown
positive insofar as the negative direction between three factors (low-quality delivery,
low-quality online customer service, low-quality website design) and online customer
experience. (The detailed explanation is shown in the questionnaire design and discussion.)
5.4 Hypothesis Results
Based on the results in table 5, one could infer that H1 is rejected but both of H2 and H3 are
failed to reject. See table 6 below:
Table 6: Hypothesis Results
The Significance Level The Result Of Hypothesis
H1: Low-quality delivery
negatively affects online
customer experiences.
0.706
Reject
H2: Low-quality online
customer service negatively
affects online customer
experiences.
0.001
Fail to reject
H3: Low-quality website
design negatively affects
online customer
experiences.
0.022
Fail to reject
54
6. Discussion
As questionnaire design introduced, all the questions in regards to independent variables are
designed with a “double negative” style. Simply put, the sentence is like a double negative
sentence because it consists of two parts: the customer’s negative attitude and terrible things.
These sentences can directly express customer negative attitudes when facing low-quality
items or events. In other words, when a participant clicks "agree", the strength of confirming
the negative attitude is enhanced. Coming back to the multiple regression analysis table 5, it
shows the β value for model 1 (0.495), 2 (0.647), 3 (0.571), and 4 (0.045; 0.453; 0.263) are
all positive. This is because the sentences of independent variables are made of two negative
parts, the attribute of the sentence itself is positive, so the order of the Likert scale is the same
as that of OCE’s questions. In other words, it means the answers for both dependent variables
and independent variables tend to the same direction "agree". Yet the contents of all
independent questions mean negative attitudes just as mentioned above, so the relationship
between three independent variables and the dependent variable is still negative. Plus the
p-value for all models is significant (less than 0.05) except for H1, one can therefore confirm
each of the relationships between the factors and OCE is significant and negative. To the
three hypotheses, one can say H2 and H3 cannot be rejected but reject H1.
6.1 Low-Quality Delivery The quality of delivery is one part where online customers are concerned with (Niu, Wang,
Lee and Chen, 2019). In the study, there are several areas under low-quality delivery that the
authors are interested in, which are shipping fees and delivery time, while shipping fees is
measured by the items of free shipping fees (Lewis, Singh and Fay, 2006) and company sizes
(Gümüş, Li, Oh and Ray, 2013), delivery time is measured by the items of its windows
55
charges (Ramaekers, Caris, Moons and van Gils ,2018; Moons, Ramaekers, Caris and Arda,
2017) and home delivery failure (Ramaekers, Caris, Moons and van Gils, 2018). In the study
of low-quality delivery, the authors want to figure out the relationship between low-quality
delivery and online customer experience. Generally, from the related SPSS table 5 for
multiple regression, though the variable itself has a significance p-value to prove its ability to
affect OCE, the data shows that H1 for low-quality delivery can not be accepted since its
p-value is more than 0.05 as a no significance meaning. Meanwhile, the value of cronbach
alpha of the measure of LQD is 0.746, though the value lies in the range of acceptance for
internal reliability (over 0.7) (Bryman and Bell, 2015), however, it needs to notice that the
value is the lowest among all of the variables. The result also shows that low-quality delivery
has the highest correlation with low-quality online customer service as 0.679** and the
lowest correlation with low-quality website design as 0.569**, which shows an unstable
relationship with other variables as well. In detail, the descriptive statistics in table 2 indicates
a relatively high dispersion between the data and its average value as low-quality delivery is
the only variable with values of standard deviation around 1 in the study, which means it has
a relatively high variability between its data and mean value. This means, people’s attitude
regarding low-quality delivery in online customer experience is scattered. Besides, as the
mean value represents the average level of the respondents in the case, it reflects the
relatively high instability of two items, which are LQD2 and LQD3. The result of the
questionnaire also shows that there are quite a group of people who choose the middle option
in the likert scale for both questions, which is no less than the people who choose 4 or 5 in
the likert scale. As the items testing in the case are both related to shipping fees (Ramaekers,
Caris, Moons and van Gils, 2018; Moons, Ramaekers, Caris and Arda, 2017; Chen, 2013;
Gümüş, Li, Oh and Ray, 2013) one can argue that shipping fees might not be so important to
their experience in online shopping. In other words, they are more ambiguous on those items.
In contrast, the questions for LQD1 and LQD4 gain relatively stable answers from people
through both SPSS results and the questionnaire. The standard deviations are relatively low
and it is obvious that more answers are on the high level of likert scale (Strongly agree).
LQD1 relates to the item of high shipping fees (Guo et al. , 2020) while LQD4 relates to the
item of home delivery failure (Ramaekers, Caris, Moons and van Gils, 2018). One can say
that the result implies a tendency to get commodities smoothly instead of getting
commodities cheaply.
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6.2 Low-Quality Online Customer Service
Online customer service is an important tool in e-commerce since it connects customers and the
business through the related offering (Lim and Dubinsky, 2004). Through the result in table5
which run by multiple regression, it shows that the effect of low-quality online customer on
online customer experience is confirmed through the significance shown from its p-value
.000 (less than 0.05) and it is certain that low-quality online customer service has a negative
impact on online customer experience with the highest p-value 0.001 among all independent
variables (less than 0.05), which means, H2 is failed to be rejected. Through the data,
low-quality online customer experience has relative high correlation with other two
independent variables, which are 0.679** with low-quality delivery and 0.623** with
low-quality website design, which indicates the validity of the concept with a clear
relationship directed. Meanwhile, the cronbach's alpha for low-quality is the second highest
among both dependent and independent variables as 0.835 which shows the concept is with
acceptable internal validity. According to the results on descriptive statistics, the highest
mean value of LQOCS positioned at LQOCS6 as 4.35. This means, in average, online
customers are confirmed as very sensitive to security/privacy concerns in online shopping
since it destroy the trust of them to the shopping site (Pandey and Chawla 2018, Coşar, Panyi
and Varga, 2017). The next important item in LQOCS should be on LQOCS4 as 4.21, which
indicates that the function of the support department in online shopping cannot be ignored
(McLean and Wilson, 2016). Such a result is considered reasonable since it is consistent with
the fact that online shopping operates on a visual platform and the main function of online
customer support is to build the bridge between customers and E-commerce companies(Lim
and Dubinsky, 2004). In contrast, the lowest value of mean is in LQOCS3 as 3.99. So one can
say that a long product information searching time is not so serious an issue to online
customers, at least for utilitarian products (McLean and Wilson, 2016).
57
6.3 Low-Quality Website Design
By reviewing all responses to the survey questions related to low-quality website design
through the former tables, there is a fact which is easy to discover, that most participants hold
similar strong and positive attitudes when being asked if surrounding terrible website design
on shopping sites could bring them terrible emotions. From table 2 one can see the high and
stable value of mean and mode from LQWD1 to LQWD5. Among all of LQWD statements,
LQWD1 has the highest mean value 4.42 despite the rest of LQWD have similarly high
values of the mean no lower than 4.22. Based on the given theory of descriptive statistics,
when the value of the mean is lying between 3 and 4 means its involved answers are inclined
to the "strongly agree" (Bryman and Bell, 2015). In other words, most respondents agree bad
website design can more or less cause bad online experiences. Also, since the median value is
a stable measure that is presented as 5 in all LQWDs, one can further confirm that the
statements are agreed by most participants. In other words, one can also infer that online
customers are less likely to trust a terrible navigation system. This does not rule out the
possibility of a crisis of trust of the navigation that would truly happen in online shopping
(Roghanizad and Neufeld, 2015). In the Cronbach’s alpha table, it is obvious to find LQWD
has the highest value with 0.866 among all variables, which implies it has high and strong
reliability in regards to confirming its similarity and consistency with the previous study.
Accordingly, the strong significance value in model 3 (.000) and model 4 (.022) shown in the
final multiple regression analysis further indicates that LQWD has a significant impact on
online customer experience, which in turn improves the fact of that online customers are
highly likely holding negative attitudes when facing low-quality website design. Simply put,
hypothesis three is failed to reject, and which proves that customers still consider an inferior
website design that can negatively influence their online experiences (Hausman and Siekpe,
2009; Hasan, 2016; Roghanizad and Neufeld, 2015). Moreover, the second-highest value
,623** the correlation analysis table tells the truth that in this study, there is an effective and
strong connection between low-quality website design and low-quality online customer
service. The important finding by reviewing the tables is the low-quality website design may
have the strongest negative impacts on customer online experiences, and indeed, all the
settings in low quality mentioned in the survey can take negative effects on customers, which
is also something that is highly consistent with the previous study, this includes such as the
58
inferior navigation setting, inferior information design, and inferior visual design (Hausman
and Siekpe, 2009; Hasan, 2016).
7. Conclusion
Based on the findings, H2 and H3 cannot be rejected but H1 needs to be rejected. In other
words, it is hard to reject that both low-quality online customer service and low-quality
website design have negative impacts on online customer experience. One can therefore say
that even in the context of rapid technological development, modern online shoppers have not
changed their aversion to low-quality elements, that the view that low-quality online
customer service and low-quality website design negatively affect OCE has not changed. In
short, it is clear to see low-quality website design plays an important role in influencing
customer emotions through the inferior design elements such as inferior navigation, and for
online customer service in low quality would bring customer negative experiences through
inadequate service equipment such as a low security paying system. Besides, one discovers
that there is a strong and effective connection between low-quality website design and
low-quality online customer service as two independent variables. The new model confirms
what the results tell.
59
Figure 2. Research Model
7.1 Theoretical Implications
Theoretically, the study confirms the negative effect on the dependent variable online
customer experience from the independent variable low-quality online customer service (H2)
(Joines, Scherer and Scheufele, 2003; McLean and Wilson, 2016; Rita, Oliveira and Farisa,
2019; Vos et al., 2014; Pandey and Chawla, 2018; Coşar, Panyi and Varga, 2017) ) and
low-quality website design (H3) (Hausman and Siekpe, 2009; Hasan, 2016; Roghanizad and
Neufeld, 2015), while it failed to accept H1 which related to the negative impact on online
customer experience from low-quality delivery (Coşar, Panyi and Varga, 2017; Kim, Dekker
and Heij, 2017; Gümüş, Li, Oh and Ray, 2013; Ramaekers, Caris, Moons and van Gils, 2018;
Lewis, Singh and Fay, 2006) . The study also shows that all of the independent variables are
closely correlated with each other by correlation analysis. For low-quality delivery, though
the hypothesis is rejected, the study still confirms the result from previous study about the
negative impacts on online customer experience from the practical issues of high shipping
fees and home delivery failure since the answers of respondents support the idea. On the
other hand, the aspect of delivery time window and the different attitudes on shipping charges
in relation to company size is not so important to influence the online customer experience
negatively. Regarding low-quality online customer service, the hypothesis is with the highest
60
significance shown with P-value 0.001 and the clear direction of relationship with β-value
0.453, which is the most effective independent variable to the dependent variable that is
connected by the negative relationship between. Importantly, the result confirms the
seriousness of security/privacy concerns to negative impacts on online customer experience
and the importance of online customer support to online customer experience, which is
consistent with previous study (Pandey and Chawla 2018, Coşar, Panyi and Varga, 2017,
McLean and Wilson, 2016). As stated by Vos et al., (2014), the result about security/privacy
concerns raise a thinking to study more on trust building in online shopping (Vos et al.,
2014). Lasety, from the study of low-quality website design, it significantly confirmed the
generation of negative attitude on the shopping site in a low-quality website design setting,
especially for LQWD1 (improper presenting of WD with dissatisfaction) and LQWD5 (trust
and low-quality navigation settings). The related hypothesis is accepted with the p-value
0.022 (less than 0.05) which states there is a negative relationship between low-quality
website design and online customer experience (Hausman and Siekpe, 2009; Hasan, 2016;
Roghanizad and Neufeld, 2015).
7.2 Managerial Implications
The findings of the study connotes that regardless of developing the technology in the
modern world, one has still not changed yet is online customer minds. In other words,
E-commerce companies should keep studying and understanding their customers. Even if the
increasing number of online items may emerge in the future, the e-business cannot be sloppy
in maintaining consumer relations. With this consideration, the authors collected some
approaches in aspects of delivery, online customer service and website design from current
marketing research, and thought they would be more or like applicable for e-business to
operate online customer relationships in some cases.
Firstly is about the pricing in delivery. van Gils, Ramaekers, Caris and de Koster (2018)
believed that a fast and cheap delivery can easily please customers (van Gils, Ramaekers,
Caris and de Koster, 2018). Although the pricing work is not simple, one suggests some
companies put their eyes on executing free shipping fees as customers are normally sensitive
to it. They further discussed the delivery options, and considered that “Awareness of the
61
influence of individual order picking planning problems on the overall performance is
required to manage operations, resulting in enhanced customer service” (van Gils,
Ramaekers, Caris and de Koster, 2018). Importantly, in designing the online customer service
and website, the firms should note that there are many individuals hold different attitudes or
perceptions towards the type of services or products, in turn, customer experience under the
online context could be shaped in this way. Nevertheless, owning a high level of online
environment is always good for e-business. The former sections in this study have discussed
the side effects customers will have when experiencing low-quality website design. Good
communication with customers and suitable website design is conducive to leave customers a
good impression on the website. For example, companies can design the website which is
specialized in presenting suitable information on web pages based on customers’ different
favors as well as their daily browse history (Kim, Dekker and Heij, 2017).
8. Limitations and Recommendation for the future research
8.1 Limitations There are some limitations in the study that would be discussed here. First of all, due to the
limited time and effort it can take to study, the sample size that the authors collect is not quite
large, which might lead the result of study biased to some extent. Meanwhile, the research
design cross-sectional in this paper is not so effective on generalization of the sample which
also needs to be considered. The second limitation of this paper is the authors did not
consider the control variables in conducting the multiple regression analysis. It is mainly
attributed to the fact that the authors intend to mainly focus on examining the response of
participants towards those questions in the domain of the subject although it is hard to
acknowledge adding this part could strengthen the arguments in the final discussion.
62
8.2 Recommendation For The Future Research
Since e-commerce is a research area that researchers are highly studying in recent years, one
suggests to continue the study of online customer experience in the future research. Perhaps
researchers can be inspired by the relationship between two independent variables to dig out
more relevant items to learn online consumer experience in a new way. More precisely, we
learned there is a strong relationship between low-quality website design and low-quality
online customer service in conducting this study. And the authors interestingly found when
talking about online customer negative attitudes, both factors dug out the findings related to
the trust from different aspects, and the dissatisfaction in regards to low-quality online
customer service. Thus, future research can try to use a similar way to explore more items
and gather them to sum up more new relationships in the domain of online customer
experience.
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