M-COMMERCE:

Effects of mobile perceived convenience and

enjoyment on the mobile purchase intention

Student: Ruben van Eekeren

2578402

R.j.l.van.eekeren.student.vu.nl

Thesis supervisor: Emile Lancée

Faculty: School of Business and Economics

Programme: Bedrijfskunde

Specialization: Marketing

June 30th , 2018

June 30th, 2018

 1. Abstract

In the present day, more and more consumers shift to shopping on their mobile phones. Although mobile
shopping is considered advantageous in terms of convenience as consumers can shop anywhere at any time,
there is still a lack of understanding about the consumer’s perceptions of the multiple convenience
dimensions of mobile shopping, and how they impact mobile purchase behavior. Additionally, enjoyment is
seen as an important determinant for the intention to shop on mobile, but it is not clear how this hedonic
motivation relates to the utilitarian motivation of convenience and how they together predict the mobile
purchase intention. This study was conducted to examine the effects of mobile perceived access, search,
evaluation and transaction convenience, together with enjoyment, on the mobile purchase intention. In order
to do this examination, data was collected and quantitively analyzed from a sample of Dutch consumers. The
results show that access, search, evaluation and transaction convenience only indirectly affect the mobile
purchase intention, when they are mediated by enjoyment. The findings have practical implications for
marketers by providing a better understanding of the importance of the several forms of convenience and
enjoyment as influencers of the consumer’s mobile purchase intention.
 2. Introduction

In 2002, when m-commerce was just getting off the ground, Balasubraman, Peterson & Jarvenpaa (2002)
already predicted that: “it is only a matter of time until m-commerce becomes a ubiquitous element in
marketing strategies, tactics, and operations.” This prediction now seems like reality, as most businesses of
today incorporate m-commerce in their strategy. Just having a mobile website as a business is already a
commitment to the potential value that m-commerce can deliver.

Opportunities in the context of m-commerce only seem to get bigger. Worldwide there has been a great
increase in the amount of smartphone users over the last decade. Today 2,53 billion people make use of a
smartphone and when this is compared to the 1,57 billion people of smartphone users in 2014 (Statista, 2018),
there can clearly be identified a strong upward trend. One important reason of this trend is the increasing
popularity of smartphones over other mobile phones. In 2014, smartphone users contributed for
approximately 38% to the total amount of mobile phone users, while in 2018 this share is expected to grow
above 50% (Statista, 2018). Related to this trend, is the growth in share of sales generated through m-
commerce. According to research firm eMarketer: “Mcommerce sales worldwide reached an estimated
$1.357 trillion in 2017, or 58.9% of ecommerce spending overall. By 2021, mcommerce will account for
72.9% of the ecommerce market” (eMarketer, 2018). The smartphone market and with that, m-commerce, are
thus growing continuously.

Based on these numbers and trends, there are many opportunities for managers in this mobile domain of e-
commerce. Previous research has suggested however, that mobile devices have certain characteristics which
make them better fitted for searching rather than purchasing (de Haan et al., 2015; Lemon & Verhoef, 2016).
Evidence that is given for the claim that mobile is used more in the earlier stages of the path to purchase, is
that mobile generates lower conversion rates in comparison to fixed devices (de Haan et al., 2015). Recent
statistics show that this lower conversion rate is still the case, with for example a total global 1,25%
conversion rate on mobile and a 3,63% conversion rate on desktop in Q1 of 2017 (Chaffey, 2018). But with
the increasing dominance of m-commerce sales in mind, one of the purposes of this research is to show that
mobile is not a device which should be regarded as more suited for searching rather than for purchasing.

A factor that differentiates the mobile device from other devices, is convenience. Okazaki and Mendez
illustrated this by (2012) describing a theoretical model wherein they identify the mobile intrinsic attributions
of portability and interface design and the mobile extrinsic attributions of speed, searchability and
simultaneity. They find that there is a positive causal relationship between the intrinsic attributions on ease of
use, which in turn positively influences the extrinsic attributions. Finally, these extrinsic attributions have a
positive causal effect on the dependent variable; perceived convenience. The former research makes thus
clear, that these specific attributions which are embedded in mobile devices, lead to higher amounts of
perceived convenience.
Another factor that sets mobile apart from other devices, is fun or enjoyment. Davis (2009) claims that
consumers use the services of m-commerce to hedonically experience fun and joy. Literature has also made
clear that multichannel-shoppers experience, or expect to experience fun when they utilize multiple channels
(Lancée, 2015). Fun therefore is not bound just to mobile, but to the total multichannel experience.

From previous research it has thus become quite clear that mobile users are likely to experience convenience
and enjoyment when utilizing the services of m-commerce. It is however not evident, in which way the
perceived convenience and how the perceived amount of enjoyment, are influencing the actual purchase
intention of mobile consumers. This is summarized in the following problem statement:

“It is not clear how perceived convenience and the perceived experience of enjoyment on mobile devices are
influencing the consumer’s purchase intention regarding m-commerce”.

Phrased as the main research question, it becomes:

“What is the influence of mobile perceived convenience and the mobile perceived amount of enjoyment, on
the purchase intention in the context of m-commerce”.

The concept of convenience was only made applicable to measure in e-commerce environments since 2012,
when Jiang, Yang & Jun (2012) identified the key dimensions of convenience in the online shopping
environment. The authors find that these core dimensions are: access, search, evaluation, transaction and
possession/post-purchase convenience. The first four of these dimensions will be included in this paper to
measure perceived convenience on mobile devices.

Utilizing this recent and valid convenience measures in the context of m-commerce, is a very valuable
contribution to both the existing theory of m-commerce as of the literature of convenience. Firstly, because it
is the first time that the online shopping convenience dimensions are integrated in the m-commerce context.
Then, there is also another study that measured the impact of perceived convenience, based on Brown’s
(1997) definition, on the mobile purchase intention (Jih, 2007). At the end of the study, the author suggested
that another framework of convenience in the future should be used to find if there are any significant
differences that would result from using different definitional frameworks of convenience. So far, no study
has done this and with the usage of the definition of online convenience from Jiang et al., (2012), this study
will fill in this gap and will therefore also be a theoretical contribution that follows up the work of Jih (2007).
Besides, these dimensions of online shopping convenience are also quite related to the pre-purchase and the
purchase stage of the customer journey, as described by Lemon & Verhoef (2016). Therefore this study will
also be a relevant theoretical contribution to the literature of customer experience. Especially the perceived
transaction convenience is a very important variable. Those who argue that mobile is more suited for
searching than for purchasing, hereby imply that in the transaction phase, consumers rather opt for
desktops/laptops than for mobile. It will be evident if this is really the case, by measuring what the perceived
transaction convenience on mobile is and in which way it influences the purchase intention on mobile.
Furthermore, the examination of convenience in this research will provide many practical relevancies for
managers, as it will provide a better understanding about the perceptions of the several forms of convenience
on mobile and on which form they can best focus to generate the most amount of m-commerce purchases.
Each of the four dimensions of convenience will be regarded in the following, respective sub questions:
“What is the perceived access convenience on mobile and how is it influencing the mobile purchase
intention?(1)”; “What is the perceived search convenience on mobile and how is it influencing the mobile
purchase intention?(2)”; “What is the perceived evaluation convenience on mobile and how is it influencing
the mobile purchase intention?(3)” “What is the perceived transaction convenience on mobile and how is it
influencing the mobile purchase intention?(4)”

The other independent variable of the research question, enjoyment, is often described as a hedonic
motivation to shop. As the effect of enjoyment on the mobile shopping intention has been found once before
in earlier research (Kwek, Tan & Lau, 2013), this study will investigate if this finding is also theoretically
supported with the current results. Also, as convenience is more seen as an utilitarian shopping motivation, it
is interesting to see how these two kinds of shopping values interact and how they differ in predicting the
mobile purchase intention. This differences can also yield practical implications, as managers can then assess
whether it is more effective to focus more on utilitarian or hedonic aspects when constructing a mobile
website. The study will also help managers in providing understanding about the importance of experiencing
enjoyment during the mobile shopping process. The subquestion that follows from the independent variable
of enjoyment is as follows: “What is the perceived enjoyment on mobile and how is it influencing the mobile
purchase intention?(5)”.

In addition, the available literature suggests that the constructs of convenience and enjoyment themselves
have some sort of causal relationship with each other (Asunmaa, 2015; Childers, Carr, Peck & Carson 2001).
This paper will therefore examine the influence of convenience on enjoyment as well: “How is perceived
enjoyment on mobile influencing the perceived mobile convenience?(6)” As there is believed that enjoyment
is influenced by the convenience perceptions and that it is influencing the mobile purchase intentions, there
will also be explored if enjoyment can be seen as a mediator between those variables. The subquestion that
arises from this investigation, will be: “How are the dimensions of convenience, via enjoyment, indirectly
affecting the mobile purchase intention?(7)”

To answer this research question and subquestions, there will be made use of a survey-based research design.
Data is collected from 102 mobile users that are participating in the survey and this data is thereafter
statistically analyzed with the statistics program SPSS. This research is therefore an empirical study which is
analyzed quantitatively. Also, in the study enjoyment is the measured construct of choice, as there are more
appropriate measurement scales available for enjoyment than for fun. However, the two concepts are
regarded as having the same meaning, as Davis (2009) defines fun as experiencing a state of enjoyment.

To give a quick overview about the content of the paper: The third chapter will provide a theoretical
background, which will sketch this article’s theoretical stance and in which way relevant literature justifies
this stance. This is followed by the methodology in chapter four, which will include a detailed elaboration of
the research design and the methods concerning data collection and processing. Chapter five will present the
results of the study and chapter five contains a conclusion and a discussion based on those results. Lastly,
chapter six will provide a general discussion of the paper, which considers the conclusion, implications,
limitations and suggestions for future research. Mobile, in this paper, will be the used term for every small,
portable device capable of using internet services, with the exception of smartwatches.

3. Theoretical Background

M-commerce

The research question that is answered in this paper, is a contribution to the literature about m-commerce. The
concept of m-commerce is defined by the Merriam-Webster online dictionary as: “business transactions
conducted by using a mobile electronic device (such as a cell phone)”. Researchers that defined the term m-
commerce also had definitions that were similar to the latter (Tarasewich, Nickerson & Warkentin, 2002;
Siau & Shen, 2002). For instance, Siau & Shen (2002) argue that m-commerce can be defined as the
transactions which take place through mobile devices using wireless telecommunication networks. Here m-
commerce is seen as something that is self-contained, while O’Dia (2000) defines it more as an extension of
e-commerce. Still, the definitions are similar in the sense that they all include some sort of “transaction”
which is conducted by a “mobile device”. This elements of the definition are thus supported by different
authors and will therefore be the fundament for how m-commerce should be understood in this paper.

Since the emergence of internet-enabled mobile phones, a lot of attention in the (marketing) literature was
drawn to the possibilities of m-commerce and its’ implications. Balasubraman, Peterson & Jarvenpaa (2002)
were some of the first authors that elaborated and conceptualized m-commerce. They identified the
opportunities for businesses that arose from the greater flexibility in time and space that consumers
experienced when they adopted mobile. Since then, many more literature has focused on the unique
advantages of m-commerce (Clarke, 2001; Ng-Kruelle et al., 2002; Frolick & Chen, 2004; Zhang, Yuan &
Archer, 2002). Clarke (2001) for example, argues that m-commerce differentiates from traditional e-
commerce in getting value via the dimensions of ubiquity, convenience, localization and personalization. Ng-
Kruelle et al. (2002) also identified those specific advantages of m-commerce as ubiquity, convenience,
localization and personalization, but added reachability and security to them as well. The construct of
convenience is for these authors thus seen as an advantage specific to the mobile context. This construct will
be furtherly elaborated and examined in this paper, as it plays a big role in the conceptual model.

Also, when more and more consumers adopted the services of m-commerce, research papers have
investigated the factors that influence the trust and loyalty concerning m-commerce. For example, there is
found that the design aesthetics of a mobile website, significantly influence this trust (Li & Yeh, 2010) and
loyalty (Cyr, Head & Ivanov, 2007). Furthermore, findings of a study suggested that customer loyalty in the
context of m-commerce, was positively affected by trust (Lin &Wang, 2006). So, there is also a relationship
between the variables of trust and loyalty. Loyalty in this study was defined and measured as the intention of
consumers to re-purchase, so this touches on the purchase intention variable in this paper.

Mobile Purchasing

The dependent variable of this study is the purchase intention . This is a form of behavioral intention, which,
as suggested by the famous model of Ajzen & Fishbein (1975), will affect the overt behavior. Thus, this
behavioral intention can be a good predictor of real behavior, or, in this case, the mobile purchase behavior.
Evidence for this is furtherly constructed by Shephard, Hartwick & Warshaw, (1988) who conclude in a
meta-analysis of 87 studies about the intention and behavior relationship, that there is strong support for the
predictive utility of the model. In the case of the purchase intention, this means that it can be a good predictor
of sales. Warshaw (1980) argues however, that a context-specific measurement of behavioral intention is
more predictive of the real subsequent behavior, than a general measurement.

The purchase intention in the online context has been explored several times. For example, there is been
found that website design positively influences the purchase intention of consumers (Kim & Lennon, 2013).
Although this finding was not specified for the m-commerce setting, there can be assumed that it applies for
mobile websites as well. This is because results from a comparative study between e-commerce and m-
commerce, indicated that m-commerce should be seen as a limited mobile version of traditional e-commerce
(Ozok & Wei, 2010). Furthermore, in this study there is argued that m-commerce should be regarded as an
complementary shopping medium instead of a direct alternative e-commerce and that these two channels
should thus not be treated completely differently. Another research that has relevant findings for this study,
but has not specified on the m-commerce setting, is the paper of Kwek, Tan & Lau (2010). They find
significant positive effects of quality orientation, brand orientation and convenience orientation on the
customer online purchase intention. They also tried to identify which of the orientations - which reflect the
perceptions of consumers - has the most overall impact on the purchase intention. From their findings they
conclude that the convenience orientation is the most important contributor and therefore there is suggested
that e-tailers should minimize the process of placing an order.

Little is written about the purchase intention in the m-commerce context. Jih (2007) investigated the effects of
the perceived mobile operational and transactional convenience on the mobile shopping intention, which will
be furtherly elaborated in the next part of the Theoretical Background. Furthermore, Lu & Yu-Jen Su (2009)
tried in their research to identify the drivers of the purchase intention on mobile shopping websites and
therefore their research has some similarities to the study of this paper. Unfortunately, convenience was not a
measured factor in this study. Enjoyment on the other hand was a measured factor and as well as usefulness
and compatibility, it positively affected the purchase intention on mobile shopping websites.
Although literature concerning purchase intentions on mobile is thus limited, there is some interesting
research that involves the process of purchasing on or with mobile. One of the factor that makes purchasing
with mobile unique, is the ability to get and use mobile promotions/coupons. Also, from a managerial
perspective, these mobile promotions are effective. Hui, Inman, Huang & Suher (2013) assessed the effect of
mobile promotions on unplanned spending. They find that granting mobile coupons which required store
customers to deviate from their planned path, leads to a significantly higher amount of unplanned spending.
Another factor that adds to the uniqueness of purchasing with mobile, is the touchscreen, which most of the
smartphones have incorporated. There are findings that suggest that this touchscreen affects the customer
decision making. Brasel and Gips, (2014) find that when a consumer is touching an item via a touchscreen,
he/she experiences a greater deal of ownership than when the item is clicked on. Concluded is that because of
this higher perceived ownership, the touchscreen device magnifies the endowment effect. In a later follow up
research, they find that the touchscreen interface also lead to the search of more alternatives before eventually
purchasing (Brasel and Gips, 2015). Furthermore, in the study was found that tangible attributes were
perceived as more important than intangible attributes during product choice making, when the interface was
a touchscreen. The touchscreen is therefore also an important part of the mobile phone that really affects the
searching and purchasing of products.

Lastly, a study was conducted that examined the effect that adopting mobile shopping had on purchase
behavior in general (Wang, Malthouse & Krishnamurthi, 2015). The results indicated that when mobile
shopping was adopted, the total amount of orders placed overall was increasing. Interestingly, they also found
that mobile shoppers mostly shop on their mobile phones for habitual products, which they already have a
history of purchasing with and thus are familiar with. Their explanation for this is that the mobile devices
allows the shoppers for convenient access, which then translates into incorporating mobile shopping in their
habitual routines. They also suggest that there needs to be caution for managers, as this finding also means
that the mobile channel is less suited for new, more risky products

Convenience

The concept of convenience has throughout the marketing literature been explored multiply. One of the first
business papers that concerned convenience, was from Copeland (1923). Here Copeland introduces the
concept of “convenience goods”, which are easily accessible and do not require much physical and mental
effort to purchase. From this paper onwards, much literature has had similar definitions of convenience,
mainly considering the amount of time and effort needed to do a certain action. This is for example the case
in the paper of Brown, (1989) in which convenience is also explained in terms of certain resources that are
required in shopping for particulair products.
From around 1990, much of the literature regarding convenience shifted from convenience based on the
attributes of products (Copeland, 1923; Brown, 1989), to convenience based on the attributes of service
(Berry et al., 2002; Brown, 1990; Colwell et al., 2008; Seiders et al., 2007). The authors that investigated
this service convenience, all implicitly or explicitly stated that convenience is a multidimensional concept.
Those dimensions vary a little bit depending on the author, but they roughly come down to the same.

The most well known and most validated dimensions are those of Seiders et al., (2007). They developed the
multidimensional “SERVCON” instrument , which consists of decision, access, benefit, transaction and post
benefit convenience. These dimensions of the SERVCON instrument however, are based on the traditional
brick-and-mortar retailing. So they don’t take into account the context of online retailing, which brings all
kinds of new aspects that are relevant for the concept of convenience. Some of the research in this new
(online) context has been done on online service quality, which identified some service convenience factors
which are unique to the online context, like for example ease of use (Parasuraman et al., 2005; Wolfinbarger
& Gilly 2003). Also, a study was conducted that examined the differences in perceptions of convenience in
both the online and the offline commercial environment (Beauchamp & Ponder, 2010). Interestingly, their
findings suggest that consumers that are shopping online, experience a higher level of access and search
convenience, but a lower level of transaction convenience in comparison to when they are shopping offline.
This research does however not take into account the differences between convenience in mobile online
shopping and online shopping via a desktop or laptop, so the perception of online convenience here is
generalized.

So while there might be some research available concerning the topic, Jiang, Yang & Jun (2012) argued that
research has not yet made the online dimensions of convenience salient. Knowing that convenience is a
multidimensional concept, these authors therefore wanted to identify the key dimensions of online shopping
convenience. To do this, they drew on the work of other authors, like the dimensions out of Seiders et al.,
(2007). In this study Jiang et al. (2012) identify access, evaluation, search, transaction and possession/post-
purchase convenience. This are the first order constructs that explain the second order construct of customer’s
perceived overall online shopping convenience. In addition to constructing the dimensions of online
perceived convenience, Jiang et al., (2012) also measured the effects of this dimensions on behavioral
intentions. They found that transaction, search and possession/post purchase convenience significantly and
positively influence these behavioral intentions.

As earlier mentioned, this study will zoom in and make use of the first four of the online shopping
dimensions of Jiang et al., (2012), but then in the m-commerce context. Firstly, the concept of access
convenience will be elaborated. Access convenience is concerned with the accessibility of websites and the
flexibility of both time and space when shopping online (Jiang, et al., 2012). Access convenience has also
been identified as a key dimension in offline shopping (Seiders et al., 2007). But in this brick-and-mortar
retailing context, access convenience is about proximity, park availability and the opening hours, whereas in
the online context, it is about an accessible website and the unlimited access to shopping. Beauchamp &
Ponder (2010) state that: “access convenience is an extremely important dimension of retail convenience,
because if the consume cannot reach the retailer then the consumer would never be given the opportunity (on
that particular shopping attempt) to make a decision, to complete a transaction, or to possess the desired
product.”

An related concept to access convenience, is efficiency. Kim (2006) and Yang & Kim (2012) suggest that
efficiency is an important motivational determinant for consumers to go (online) shopping. Kim (2006)
defines this efficiency as the need to save time and other resources during the shopping process. Therefore,
this efficiency construct is in line with access convenience, which is also about the flexibility and efficiency
in the time and space dimension during the shopping process. This efficiency construct is found to be a
significant determinant in explaining why mobile shoppers choose the mobile channel (Yang & Kim, 2012).
This together with the fact that Wang, Malthouse & Krishnamurthi (2015), as earlier stated, found that the
convenient access to shopping on mobile devices leads to the incorporation of mobile shopping in their
habitual routines, forms the fundament for the following hypothesis:

𝑯𝟏: 𝑇ℎ𝑒 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑠 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒 𝑎𝑐𝑐𝑒𝑠𝑠 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑚𝑜𝑏𝑖𝑙𝑒 , 𝑡ℎ𝑒 ℎ𝑖𝑔ℎ𝑒𝑟 𝑡ℎ𝑒 𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒
𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛.

Secondly, the concept of search convenience will be examined. Search convenience is associated with the
degree to which a consumer can research and find desired products (Jiang et al., 2012). E-commerce websites
try to facilitate search convenience to let consumers quickly and easily find what they are looking for
(Beauchamp & Ponder, 2010). The website design plays a very important role in the convenience of
searching for products online (Jiang et al., 2012; Beauchamp & Ponder, 2010). Furthermore, Manes (1997)
argues that a good website design is well organized and that it is easy to search on it. Quick presentations,
simple searching paths and minimal cluttering are all part of this. Szymanski & Hise (2000) stated that the
website design, which involves the ease of search and navigation, impacts the consumer’s online satisfaction
and they found evidence for this in their study. Also, in the mobile context, de Haan et al. (2015) argue that
the mobile device is primarily fitted for earlier stages in the path-to-purchase, when the consumer is still
searching. Mobile search convenience should thus be perceived well. Contradictory research of Sweeney &
Crestani (2006) has findings however, that suggest that consumers rather opt for PC’s in exploratory search
tasks, as the search costs on mobile are higher because of the small screen which makes it harder to retrieve
information on it.

From the interviewees out of the exploratory study of Jiang et al., (2012) became clear that search
inconvenience is seen as a major obstacle in convenient online shopping. For example, when the search
function is not conveniently working, this can become a big problem for the perceived search convenience in
the online shopping process. They found evidence for this in the finding of the significant, positive
relationship of search convenience on behavioral intention. Therefore, the following is hypothesized:

𝑯𝟐: 𝑇ℎ𝑒 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑠 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒 𝑠𝑒𝑎𝑟𝑐ℎ 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑚𝑜𝑏𝑖𝑙𝑒, 𝑡ℎ𝑒 ℎ𝑖𝑔ℎ𝑒𝑟 𝑡ℎ𝑒 𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒
𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛.

Thirdly, the construct of evaluation convenience will be clarified, which is about the evaluation of products
by the consumers. Product information and product descriptions play a big part in this. Taken into account is
if this product descriptions are sufficient and easy to understand, which involves the use of text, graphics and
videos that together create such a product description (Jiang et al., 2012). The importance of this product
information was emphasized by Lynch & Ariely, (2000), who found that when information on product quality
was easier to find and navigate, price sensitivity decreased and more expensive products were purchased.
Bronnenberg, Kim & Mela, (2016) also stress the importance of “informational” elements on retailer
websites, such as reviews that help the consumers along their search journey. Furthermore, they find that only
20% of the website visits in the online customer journey is on non-retailer websites, like for example price
comparison sites. Hereby they indicate that far of the most time is invested in evaluating products on e-
commerce websites. In this process of evaluating, online shoppers often have a very broad range of products
to choose from and just little time to decide. Because of this, many online retailers offer decision aids, like
recommendation agents or shopping bots, that make the process of searching and evaluating in the
consideration set more convenient for the consumers (Punj & Moore, 2009).

There is a clear trend in recent years with the increasing amount of product information in text, graphics
and/or videos which are present in the product description on shopping websites, which makes consumers
more sensitive to this evaluation convenience (Jiang et al., 2012). Especially because consumers can in the e-
commerce context not feel and touch products, clear product information and descriptions are extra important
to sell the products (Sharma, 2017). Therefore, the fourth hypothesis is the following:

𝑯𝟑: 𝑇ℎ𝑒 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑠 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒 𝑒𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑚𝑜𝑏𝑖𝑙𝑒, 𝑡ℎ𝑒 ℎ𝑖𝑔ℎ𝑒𝑟 𝑡ℎ𝑒 𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒
𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛

Lastly, transaction convenience is considered. This is the convenience that one experiences in the complete
online check-out process (Jiang et al., 2012). With online transaction convenience, there is the benefit that
people don’t have the possibility that they physically have to wait in line, as is the case with conducting
transactions in traditional retailing (Wolfinbarger & Gilly, 2001). Herzberg (2003) in 2003 already argued
that convenience is one of the two reasons why consumers would, especially in the future, prefer mobile
devices to conduct transactions. This is because transactions can be conducted anywhere on the go, but also
because the mobile device can support secure banking/paying applications. In the Netherlands, where this
study is conducted, 60% of the online payments are conducted via the platform iDeal (Adyen, 2017), which
facilitates payments by bank between a consumer and a merchant/retailer. Direct payments by bank are thus
mostly used to pay online in the Netherlands. This has implications for paying by bank with mobile, as
Laukkanen (2017) argues that mobile banking offers benefits in transaction efficiency in comparison to
computer-based banking. Furthermore, Ozok & Wei (2010) argue that active mobile applications, like
financial/banking apps offer a big potential for m-commerce in the transaction phase of the shopping process.
But they also added that passive applications, like having billing and address information to appear
automatically on the mobile device screen when conducting a transaction, enhances this m-commerce
potential as well.

The effect of transactional convenience on the mobile purchase intention has earlier already been explored,
by Jih (2007). When measuring the effect of both operational and transactional convenience, he found that
only the latter had a significant and positive impact on the mobile shopping intention. The study of Jih (2007)
was conducted in 2007 and since then, m-commerce and mobile phones have evaluated a great deal. Still, as
he found a significant positive effect of the transactional convenience on the mobile shopping intention, the
following hypothesis will be in line with the significant finding of Jih (2007). Likewise, it is in line with the
significant effect found in Jiang et al., (2012), which is that transactional convenience positively influences
the behavioral intention;

𝑯𝟒: 𝑇ℎ𝑒 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑠 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑚𝑜𝑏𝑖𝑙𝑒, 𝑡ℎ𝑒 ℎ𝑖𝑔ℎ𝑒𝑟 𝑡ℎ𝑒 𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒
𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛.

Notably, there has also been research done that investigated the effect of overall convenience on the (mobile)
purchase intention. Kwek, Tan & Lau (2010) and Delafrooz, Paim & Khatibi (2011) both found that the
higher the perceived convenience overall, the higher the online purchase intention.

Fun and Enjoyment

Enjoyment can be seen as the extent to which the activity is perceived to provide reinforcement in its’ own
right, apart from the consequences that may result from doing this activity (Childers, Carr, Peck & Carson,
2001). Fun and enjoyment in a shopping context has often been described as hedonic value or hedonic
motivation. This hedonic value is the counterpart of utilitarian value, which is often associated with words as
ergic, task-related and rational (Batra & Ahtola, 1991). Hedonic values or motivations result more from fun
and playfulness which is experienced during the task, than from task completion (Babin, Darden & Griffin,
1994). Arnold & Reynolds (2003) describe adventure, gratification, value, role, social and idea as hedonic
shopping motivations. In all this different shopping motivations, the consumers experience some sort of fun
or enjoyment. For example, the concept of social shopping, “which refers to the enjoyment of shopping with
friends and family, socializing while shopping, and bonding with others while shopping” (Arnold &
Reynolds, 2003).

In the online context, there is found that enjoyment is a very important factor, which significantly influences
both search and purchase attractiveness on the internet (Verhoef, Neslin, and Vroomen, 2007). Also, there has
been investigated how the hedonic motivations should be translated to the online context. Childers et al.,
(2001) did exactly this, by identifying the hedonic and utilitarian motivations for online retail shopping
behavior. They find evidence for the constructs of ease of use, usefulness and enjoyment as hedonic
motivations for online shopping. Emphasized is the role of enjoyment, which has a very strong significant
effect on the consumer’s attitude towards online shopping. Furthermore, they found support for another
relevant hypothesis, which states that the increasement of (the utilitarian motivation) perceived convenience,
will lead to an increasement in perceived enjoyment. They attribute this relation to the fact that higher
convenience leads to less frustration and therefore less physiological costs during the shopping process,
which ultimately leads to more perceived enjoyment. The positive effect of convenience on enjoyment in the
context of online shopping, is also supported in the paper of Asunmaa, (2015). Here the relationship is
explained by the data analysis of the interviews, wherein became clear that enjoyment was mainly caused by
the easiness of use and usefulness of the site. She concluded that this thus meant that convenience made them
enjoy the online shopping process more. Theoretical evidence is thus found for the effect of convenience on
enjoyment. The following hypothesis is proposed:

𝑯𝟓: 𝑇ℎ𝑒 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑠 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑚𝑜𝑏𝑖𝑙𝑒, 𝑡ℎ𝑒 ℎ𝑖𝑔ℎ𝑒𝑟 𝑡ℎ𝑒 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑒𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 𝑜𝑛
𝑚𝑜𝑏𝑖𝑙𝑒.

There have been several studies that investigated enjoyment and fun in the m-commerce environment as well.
Davis (2009) for example, conceptualizes fun and identifies the core areas that can enhance a consumer’s
perception of fun in the domain of m-commerce. He argues that managers should focus on the content, social
and self-orientations to let consumers experience more fun in mobile shopping. Enhancing the consumer’s
enjoyment perceptions on mobile can be a very effective strategy for managers. This is proven in other
studies. For example, Chong (2013) conducted a study which investigated the determinants of mobile
shopping adoption and found that a higher perceived enjoyment increases the chance of adopting m-
commerce services. Then, another study identified the determinants of the usage of the mobile activities:
content delivery, transactions, location-based services and entertainment (Chan & Yee-Loong Chong, 2013).
Especially the activity of transactions is relevant here, as it involves the transactions between businesses and
consumers via mobile. The results indicated that enjoyment was one of the determinants of the use of m-
commerce for the transaction activity.
Both the dependent variables of m-commerce adoption and the use of m-commerce for transactions, are not
entirely the same, but in line with the dependent variable of purchase intention on mobile. As the adoption of
m-commerce basically means that a consumer starts using the services of m-commerce, and the definition of
m-commerce is about business transactions that are conducted by using a mobile device, it is similar to the
dependent variable of purchasing or purchase intention on mobile. The same applies for the use of mobile for
transactions, which is also about transactions on mobile and therefore in line with purchasing on mobile. This,
together with the work of Lu & Yu-Jen Su, (2009) who in their research explicitly find that a higher
perceived enjoyment leads to a higher purchase intention on mobile websites, brings forwards the following
hypothesis:

𝑯𝟔: 𝑇ℎ𝑒 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑠 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒 𝑒𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 𝑜𝑛 𝑚𝑜𝑏𝑖𝑙𝑒, 𝑡ℎ𝑒 ℎ𝑖𝑔ℎ𝑒𝑟 𝑡ℎ𝑒 𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒
𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛.

Enjoyment is thus both hypothesized as being significantly influenced by convenience and as a significant
influencer of the mobile purchase intention. Therefore, there is assumed that enjoyment is a mediator between
those variables. Access, search, evaluation and transaction convenience together form the online shopping
convenience construct (Jiang et al., 2012), which in earlier literature is thus found to be affecting enjoyment
(Childers, Carr, Peck & Carson, 2001; Asunmaa, 2015). Therefore, there is hypothesized that the effects of
the first order constructs of online shopping convenience on the mobile purchase intention are all significantly
mediated by enjoyment:

𝑯𝟕𝒂: 𝐸𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝑠 𝑡ℎ𝑒 𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑎𝑐𝑐𝑒𝑠𝑠 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑡ℎ𝑒
𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛
𝑯𝟕𝒃: 𝐸𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝑠 𝑡ℎ𝑒 𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑠𝑒𝑎𝑟𝑐ℎ 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑡ℎ𝑒
𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛
𝑯𝟕𝒄: 𝐸𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝑠 𝑡ℎ𝑒 𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑒𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑡ℎ𝑒
𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛
𝑯𝟕𝒅: 𝐸𝑛𝑗𝑜𝑦𝑚𝑒𝑛𝑡 𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝑠 𝑡ℎ𝑒 𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑛𝑣𝑒𝑛𝑖𝑒𝑛𝑐𝑒 𝑜𝑛 𝑡ℎ𝑒
𝑚𝑜𝑏𝑖𝑙𝑒 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝑖𝑛𝑡𝑒𝑛𝑡𝑖𝑜𝑛
Conceptual Model

The variables and the hypotheses are graphically presented in the Conceptual Model, which is presented in
Figure 1.

Figure 1:
 4. Methods

Research Design and Sample

In order to answer the research question of this study, a quantitative research method was used. In particular,
an online questionnaire survey was conducted. Such a survey can be used to suggest possible reasons for
particular relationships between variables and to create models of these relationships (Saunders, 2009). The
online survey was also chosen because it is is fast, versatile, accurate and has low associated costs (Grover &
Friens, 2006). Besides, a survey makes it possible to obtain findings which are representative of the whole
population, but at much a lower cost than collecting the data from the whole population (Saunders, 2009).
This is necessary, as it is not feasible to collect data from the whole population (Bryman & Bell, 2015).
Furthermore, because it is not feasible to let everyone in the Dutch population to get an even chance to
participate in this study, non-probability sampling was used. Specifically, the self-selection sampling
technique was used, as the relative costs of obtaining this sample are low (Saunders, 2009). The self-selection
sample was obtained by spreading the survey via the medium of Facebook and hereby asking individuals to
take part. When they voluntarily wanted to respond, they could click on the link that leaded them to the
survey. Convenience sampling was used as well, by asking friends and family to fill in the survey.

The survey was made using the online tool Qualtrics. At the beginning of the questionnaire the respondents
were introduced with clear instructions, as well as some brief information about the content of the study.
Then, there was also mentioned that the response was gathered completely anonymous, to reduce the chance
of only getting answers that respondents find more socially desirable instead of what they really think
(Grover & Friens, 2006). In order to obtain informed consent, information was also given about the rights of
the respondent and the way the data was going to be used (Saunders, 2009) After the respondents on base of
thisis information freely indicated that they accepted to take part in the study, they were directed to the real
survey, which contained four sets of questions. The first question set only consisted of control questions,
which were about age, gender and mobile usage. The next three question sets involved the scales that really
measured the variables and above those questions there were also some very short introductions given. All the
questions in the questionnaire were structured, as these types of questions offer the possibility to use scales
(Grover & Friens, 2006).

Overall, data from 102 respondents was drawn, which is just exceeding the minimum amount of 100, which
is needed to perform statistical analysis (Hair, Black, Babin, Anderson & Tatham, 2006). Descriptive
statistics of the sample are presented in Table 1. In contrary to the ratio between male and female, the ratio
between the diverse age groups was quite unbalanced. Almost 60 percent of the respondents were aged 18-24,
likely because this is the age of most of the researcher’s Facebook connections. The ratio of average mobile
usage per day was quite balanced. However, the two middle options were most frequently chosen, which
might be a result of “midpoint responding”, which is the tendency of respondents to choose the middle
options regardless of the content, because of for example indecision or indifference (Grover & Friens, 2006).

Table 1: Descriptive statistics of the sample

Demographics Frequency Percentage

Gender 102 100

Male 50 49,0
Female 52 51,0
Age 102 100
12-17 1 1,0
18-24 61 59,8
25-34 7 6,9
35-44 9 8,8
45-54 17 16,7
55+ 7 6,9

Average mobile usage per day 102 100

0-2 hours 13 12,7
2-4 hours 35 34,3
4-6 hours 33 32,4
6+ hours 21 20,6

Measures

The creation of the survey and the measurement items, is a crucial factor in terms of the reliability and
validity. Therefore, it is better to use an already existing measurement instrument, which is already proven to
be valid and reliable (Bryman & Bell, 2015). Thus, for the measurement of the variables of convenience,
enjoyment and purchase intention, already validated measuring scales were used.

The independent variables of access, search, evaluation and transaction convenience will be measured using
the scales provided by Jiang et al., (2012). There already existed many instruments that measured
convenience in traditional retailing, for example the SERVCON instrument (Seiders et al., 2007). However,
Jiang et al., (2012) wanted to create a convenience measurement instrument for the online shopping context
and therefore they modified existing dimensions and measurement items of convenience (e.g. SERVCON),
based on empirically processed data, to make them externally valid to the online context of e-commerce. The
fifth dimension Jiang et al., (2012) propose, possession/post-purchase convenience, has to be measured after
the purchase is already been done and therefore it is not a relevant variable as influencer of the purchase
intention. The possession/post-purchase convenience scale is thus not included in the survey.
To test the reliability and thus if the measurement items are internally consistent, the Cronbach’s alpha is
most frequently used (Saunders, 2009). The Cronbach alpha’s of access, search, evaluation and transaction
convenience are respectively: 0,725; 0,832; 0,764; 0,784 (Jiang et al., 2012). This thus indicates that the
constructs can be regarded as reliable. All the convenience constructs were measured by three items, except
for search convenience, where 4 items were used. These items were then measured by using a 5-point Likert
scale, ranging from 1 = “strongly disagree” to 5 = “totally agree”, where the respondents had to fill in what
their perceptions were. Likert scales have the benefit that it is easy for the researcher to construct and
administer this scale, and it is easy for the respondent to understand (Grover & Friens, 2006). Then, this
whole set of questions was also completely randomized in choice options, as this is a powerful tool to reduce
order bias (Grover & Friens, 2006).

The questions out of Jiang et al., (2012) were all modified slightly, to make them fit in the m-commerce
context. The scale only applies on websites and thus for this study these items are transformed to be about m-
websites. Especially the items from the scale of evaluation convenience were adjusted, as the original items
are concerning one specific website, while in this study there is attempted to measure the perceptions on
mobile websites in general. So for example the item: “The web site uses both text and graphics to provide in-
depth product information”, is adjusted and translated to: “Op mobiele websites wordt op een overzichtelijke
manier gebruik gemaakt van tekst en foto's/plaatjes die diepgaande informatie geven over een product.”
Emphasis is shifted from whether or not the information is there in the first place (which can differ very much
from (m-)website to (m-)website), to if the informational sources of text and images are actually clear on
mobile phones. Notably, as the questions are thus constructed around mobile websites, mobile apps as a
means of utilizing m-commerce services are disregarded in this study. This exclusion was made because
otherwise the original scale had to be altered even more, but also because it would be less feasible to gather
enough respondents that had experience with mobile shopping apps within time constraints.

The independent variable and mediator enjoyment was measured by a scale previously used in the paper of
Lu & Yu-Jen Su (2009). The reason for this is that their study also considered enjoyment in a m-commerce
environment and thus this scale can be easily applied to this study. Also, this scale is highly reliable, as the
Cronbach’s alpha of enjoyment is 0,95. There were three items measuring the construct of enjoyment,
whereof one item was adjusted to set it in the present time. All the other questions of the survey are also in
the present time so the modification was made to keep the consistency. This consistency is also kept by the
usage of the same 5-point Likert scale for the measurement of enjoyment, as for the convenience variables.
Besides, modification of the Likert scale was not even needed, as in Lu & Yu-Jen Su (2009) the scale was
also a 5-point.

The dependent variable of the mobile purchase intention is also measured by a 5-point Likert scale out of the
paper from Lu & Yu-Jen Su (2009). Just as for enjoyment, the same context of the study makes this scale
very applicable to this study. With an Cronbach’s alpha of 0,89, this measure is also very reliable. The scale
consists of just two items, which aren’t adjusted for this study at all.

Lastly, it is important to note that apart from the small adjustments that were made at some questions, all the
questions were translated from English to Dutch. This was done because it made the survey more accessible
for the Dutch Facebook-users among who the survey was distributed. The direct translating technique was
used to translate the questions, as this technique is easy to implement and relatively inexpensive (Usinier,
1998). Saunders (2009) emphasizes that it is important to include both the “source” as the “target”
questionnaire in the paper. Therefore, in the following table (table 2), the items as from the source are
displayed, while in the appendix the adjusted and translated version is included.

Table 2: Measurement items

Perceived mobile Jiang et al., (2012)

convenience
Access I could shop anytime I wanted
The web site is always accessible
I could order products wherever I am
Search The web site is user-friendly for making purchases
The web site is easy to understand and navigate
I am able to find desired products quickly
The product classification is intuitive and easy to follow
Evaluation The web site provides product specifics, such as volume, weight,
and size
The web site provides sufficient information so that I can identify
different products within the same category
The web site uses both text and graphics to provide in-depth
product information
Transaction Online payment is simple and convenient
Payment methods are flexible
I am able to complete my purchases without difficulty.
Perceived mobile Lu & Yu-Jen Su, (2009)
enjoyment
The process of surfing m-shopping web sites is enjoyable
While accessing m-shopping web site, I have experienced pleasure
Overall, I believe that visiting m-shopping web sites is fun
Mobile purchase Lu & Yu-Jen Su, (2009)
intention
Assuming that I had access to a m-shopping web site, I intend to
make purchasing transactions on it.
 Given that I had access to a m-shopping web site, I predict that I
would make purchasing transactions on it.

Data Analysis

In order to test if the proposed hypotheses can be accepted, the obtained data was statistically analyzed on
several ways. Firstly, the data was checked to be clean. The respondents needed to respond to every question
in order to finish their response, so there were no missing data points. Also, the frequency tables were
checked to see if the obtained data was alright.

After inspection of the data, there was checked if the measures could be regarded as valid and reliable. If the
items from the four dimensions of convenience were providing valid results and measured their underlying
constructs as intended in the m-commerce context, was tested by applying a confirmatory factor analysis. A
way to determine how many factors are practically significant, is to look how many factors have an
Eigenvalue higher than 1, as the factor should explain more than the variance of any one of the input
variables (Grover & Friens, 2006). In addition to this factor analysis, also the Kaiser-Meyer Olin (KMO)
values were obtained for all variables, which makes clear what the proportion of variance among variables is
that might be common variance. It ranges from 0 to 1 and how closer to 1, how more acceptable the measure
is, where a value of 0,5 represents the absolute borderline of acceptability (Kaiser, 1974). Also, the reliability
of the items was controlled for by conducting a reliability analysis in order to find the Cronbach’s alpha. “In
general, reliabilities less than 0.60 are considered to be poor, those in the 0.70 range, acceptable, and those
over 0.80 good” (Sekaran & Bougie, 2016).

To test if all the independent variables are truly independent from each other, they were checked for
multicollinearity. Multicollinearity is the statistical phenomenon which appears when two or more
explanatory variables are strongly correlated to each other. When this is the case, the estimates for the
regression coefficients following from the regression analysis can become unreliable (Grover & Friens,
2006). To check whether or not multicollinearity is the case in the current study, the values of tolerance and
the VIF (Valuable Inflation Factor) were inspected. With an tolerance value that is lower than .20 and a VIF
which is higher than 10, there is a real chance of multicollinearity, which is undesirable (Grover & Friens,
2006).

Subsequently, the multiple regression analysis was applied. The determinantal impact of multiple
independent variables on a criterion variable is tested by this type of analysis (Grover & Friens, 2006;
Saunders, 2009). This multiple regression analysis was thus used to test what the impact of the perceived
convenience variables and the enjoyment variable was, on the mobile purchase intention. Also, one simple
linear regression analysis was performed. This was done, in order to test the effect of overall perceived
convenience on enjoyment. As the dimensions of online convenience together measure the overall construct
of online convenience (Jiang et al., 2012), the mean of those dimensions was calculated in order to create the
overall convenience variable. Furthermore, for both the regression analyses the R2 and adjusted R2 are
checked, as this statistics reveal the explanatory power of the model (Saunders, 2009).

Then, there was also tested if enjoyment can be indeed regarded as a mediator between perceived
convenience and mobile purchase intention. This was statistically tested by using model 4 in the PROCESS
tool for SPSS (Hayes, 2012). As explained in earlier literature, there are four steps that correspond with four
paths which together confirm a mediation effect (Baron & Kenny, 1984; Frazier, Barron & Tix, 2004).
Following from Preacher & Hayes, (2004): “The [SPSS] macros also provide all the output that one needs in
order to assess mediation using the Baron and Kenny (1986) criteria”. Thus, via a combination of model 4
(Hayes, 2012) and the steps provided by Baron & Kenny (1986), there was checked if there were mediation
effects. Kenny (2016) emphasizes that full mediation is only the case when in step 4, or c’, the β = 0. If with
path c’ the β ≠ 0, then there is only a partial mediation effect. This is a reaction to his earlier work (Baron and
Kenny, 1986), where he argued that in step 4 the effect only had to be insignificant in order for it to be an full
mediation. Furthermore, mediation was also tested by Sobel test’s (Sobel, 1984). A Sobel test is a measure of
the ratio of the indirect effect to the direct effects and this ratio is then treated as a z-score where an value
larger than 1.96 is significant at the .05 level (Kenny, 2016). Indirect effects have the same meaning as
mediation effects and these terms are thus used interchangeably (Frazier, Barron & Tix, 2004).

5. Results

In order to see whether the proposed hypotheses have to be accepted or rejected, statistical analyses have
been made, which will be discussed furtherly in this chapter. First of all, a descriptive table of the
questionnaire (Table 3) will be presented which includes the mean scores of all the variables. Indications of
the mean scores will be furtherly investigated in the discussion.

Table 3: Mean and variation scores

Variables Mean Std. Deviation

Access convenience 3,88 ,74
Search convenience 3,45 ,69
Evaluation convenience 3,34 ,67
Transaction convenience 3,86 ,63
Enjoyment 3,36 ,77
Mobile purchase intention 3,61 ,89
Also, to get a clear image of all the data and the connections between the variables, the Pearson correlations
are considered (Table 4). All the correlations were significant, except for evaluation convenience and the
mobile purchase intention.

Table 4: Pearson correlations

Variables 1 2 3 4 5 6
Access convenience 1
Search convenience ,566** 1
Evaluation convenience ,461** ,570** 1
Transaction convenience ,553** ,535** ,360** 1
Enjoyment ,366** ,531** ,383** ,358** 1
Mobile purchase intention ,248* ,252* ,192 ,210* ,371** 1
** = significance on the 0,01 level. * = significance on the 0,05 level.

To test for validity, a factor analysis was applied on the items measuring the perceived convenience
dimensions of Jiang et al., (2012). This analysis revealed only two factors with an Eigenvalue higher than
one. The two other factors that were needed to statistically maintain the four dimensional construct of
convenience, showed an Eigenvalue just below one. The rotated component matrix with Varimax rotation
also revealed that the items just loaded on two factors. However, the choice was made to continue with the
four original constructs of perceived mobile convenience. Not only because the research questions and
hypotheses are constructed around these dimensions, but also because these factors, when individually looked
at, all loaded on one factor with an Eigenvalue higher than one. Moreover, they all individually showed KMO
values of higher than 0,6 and they can thus be seen as sufficient in terms of validity. Furthermore, they
showed decent Cronbach’s alpha’s which confirm their internal consistency, except for transaction
convenience, which had an Cronbach’s alpha of 0,617. This might be because the items measuring
transaction convenience varied from the flexibility of payment options to simple and convenient payment.
These questions can thus be answered very differently and therefore their reliability can be negatively
affected. However, in Jiang et al., (2012) the out of three items consisting construct of transaction
convenience was tested with an Cronbach’s alpha of 0,784, which was a reason to maintain these items.
Besides, in Jiang et al., (2012) a sample of 550 was used and with the high Cronbach alpha’s and the
execution of both exploratory and confirmatory analyses in their research, they proved the reliability and
validity of all the scales. The current study only considered 102 respondents, which makes it less
representative as the study of Jiang et al., (2012). This was therefore also a reason to maintain the four
dimensions with their measure scales as intended.

For the variables of enjoyment and mobile purchase intention, which were measured by the scales of Lu &
Yu-Jen Su, (2009), also analyses where performed in order to find out their validity and reliability. As
expected, the factor analysis revealed that both the variables significantly loaded on one factor with an
Eigenvalue larger than 1. Furthermore, the enjoyment construct proves to be acceptable in terms of validity,
with a KMO value of 0,69. The mobile purchase intention however, has a KMO value of 0,50. This is the
case due to the fact that it is an only two item construct. Because the KMO statistic for this construct was thus
not that useful, there was looked at the Barlett’s test of sphericity, which presented the significant value of
0,00, which indicated that the construct can be regarded as suitable for a factor analysis (IBM, w.d.). Lastly,
both enjoyment and the mobile purchase intention show to be highly reliable variables, as their Cronbach’s
alphas are both higher than 0,8.

To check for multicollinearity, all the variables where analyzed through regression, in order to obtain their
tolerance and VIF values. The low VIF values suggest that a problem in terms of multicollinearity is not the
case, so no variables have to be removed in order to correctly execute the regression analyses. All the
variables with their corresponding KMO, Cronbach’s alpha and VIF values are presented in table 4.

Table 5: KMO, Cronbach’s alpha and VIF values

Variables KMO Cronbach’s alpha VIF

Access convenience (3 items) 0,65 0,71 1,75
Search convenience (4 items) 0,76 0,75 2,19
Evaluation convenience (3 items) 0,65 0,69 1,56
Transaction convenience (3 items) 0,63 0,62 1,62
Enjoyment (3 items) 0,69 0,81 1,43
Mobile purchase intention (2 items) 0,50 0,92

In order to test the hypotheses, a multiple regression analysis was conducted with the four dimensions of
convenience and enjoyment as independent variables and the mobile purchase intention as dependent
variable. The regression model appears to be significant (F = 3,48, df = 5, p = .006). However, the R2 of
0,153 suggests that only 15,3 % of the model is explained by the predictor variables (adj. R2 = 0,109). This
model is therefore statistically not that strong in predicting the mobile purchase intention.

When in this regression model there is looked at the effect of access convenience on the mobile purchase
intention, there can be recognized that the effect is not significant (β = .111, t = 0,892, p = .374). Hypothesis
1 will thus be rejected. The same goes for search convenience, which also does not have a significant effect
on the mobile purchase intention (β = .000, t = -.002, p = .999). Based on this, hypothesis 2 will also be
rejected. Furtherly, evaluation convenience also does not have a direct significant effect on the mobile
purchase intention (β = .008, t = .068, p = .946. This thus also means that hypothesis 3 will be rejected. Then,
for transaction convenience also no significant direct effect on the mobile purchase intention was found (β =
.033, t = .272, p = .786). Based on these results, hypothesis 4 is rejected. For enjoyment however, it appears
that there is a significant positive effect on the mobile purchase intention (β = .316, t = 2.818, p = .006). As
hypothesized, a higher perceived enjoyment will lead to a higher mobile purchase intention and thus,
hypothesis 6 is confirmed.

When the control variables of age, gender and mobile usage were incorporated into the regression model, not
much changed. As expected, the control variables didn’t had significant effects on the mobile purchase
intention. Only mobile usage can be regarded as having a marginal significant effect on the mobile purchase
intention, with an p-value of .098 (β = .177, t = 1.670, p = .098). Also, the fact that there was almost no
difference between the two adjusted R2’s of the models (Δadj. R2 = -.001), suggests that the control variables
were not explaining the dependent variable, as they did not give the model more explanatory power. The
direct effects of the independent variables on the dependent variable are graphically presented in Table 5,
where model 1 is the regression analysis without the control variables and model 2 is the regression model
with the control variables.

Table 6: Direct effects on the mobile purchase intention

MODEL 1 MODEL 2
BETA SIG. BETA SIG.
ACCESS CONVENIENCE .111 .374 .134 .293
SEARCH CONVENIENCE .000 .999 -.001 .993
EVALUATION CONVENIENCE .008 .946 -.025 .832
TRANSACTION CONVENIENCE .033 .786 .032 .791
ENJOYMENT .316** .006 .313** .007
GENDER (M = 1, W = 2) -.011 .914
AGE .088 .406
MOBILE USAGE .177 .098 †
R2 Adj. R2 R2 Adj. R2
.153 .109 .178 .108
** = significance on the 0,01 level. * = significance on the 0,05 level. † = Marginal significance (on the 0,1 level)

Besides the multiple regression model, there was also a simple linear regression performed in order to find
out the direct effect of overall convenience on enjoyment. The analysis revealed that, as hypothesized, overall
convenience is significantly influencing enjoyment (β = .516, t = 6.020, p =.000). Therefore, hypothesis 5
was confirmed. Furthermore, the model explains more variance than the multiple regression model did
(adj.R2 = .259, Δ Adj. R2 = .150). Table 6 shows the results of the simple linear regression model. The
 Table 7: Direct effect on enjoyment

BETA SIG.
CONSTANT - .000
OVERALL CONVENIENCE .516** .000
R2 Adj. R2 Δ Adj. R2
.266 .259 .150

** = significance on the 0,01 level.

After the direct effects of access, search, evaluation and transaction convenience on the mobile purchase
intention had been found, the indirect effects (Frazier, Barron & Tix, 2004) of these variables were also
tested. This was done by setting enjoyment as the mediator in PROCESS model 4 (Hayes, 2014) for SPSS
and applying the four steps of mediation (Baron & Kenny, 1986) on the PROCESS output. First the indirect
effect of access convenience on the mobile purchase intention was considered. Following from step 1 of the
mediation model, the regression of access convenience with the mobile purchase intention, without the
mediator, was significant (β = .300, t = 2.563, p = .012). In step 2 it became clear that the regression of access
convenience on the mediator of enjoyment was also significant (β = .382, t = 3.936, p = .000). Step 3 of the
mediation model revealed that enjoyment, with access convenience included in the model, was a significant
predictor of the mobile purchase intention (β = .374, t = 3.258, p = .002). In step 4 of the analysis, there was
found that the regression coefficient of access convenience on the mobile purchase intention, when enjoyment
was also incorporated into the model, was not equal to zero (β = .143, t = 1.304, p = .195). Furthermore, a
Sobel test was conducted which confirmed that there was mediation of enjoyment in the model (Z = 2.463, p
= .014). However as in step 4 the effect was not equal to zero, this mediation effect was only partial (Kenny,
2016). Based on these results, hypothesis 7a was partially accepted.

The same steps were applied on the search convenience variable in order to find out if enjoyment is a
mediator for this construct as well. For search convenience, the total effect on the mobile purchase intention
was significant (β = .325, t = 2.608, p = .011). Also, the effect of search convenience on enjoyment was
significant (β = .591, t = 6.267, p = .000). Then, there was found significance in the regression between
enjoyment and the purchase intention when search convenience was included in the model (β = .382, t =
3.010, p = .003). Then, the direct effect of search convenience on the mobile purchase intention was found to
be not equal to zero (β = .099, t = .699, p = .486). This, together with the significant outcome of the Sobel test
(Z = 2.685, p = .007) indicates that the effect of search convenience on the mobile purchase intention is
partially mediated by enjoyment. Therefore, hypothesis 7b was partially confirmed.
For evaluation convenience the same analysis was done. There was found a marginal significant total effect
of evaluation convenience on the mobile purchase intention (β = .253, t = 1.955, p = .053). Then, the
regression between evaluation convenience and enjoyment was found to be significant (β = .438, t = 4.150, p
= .000). Also, enjoyment is found to be a significant predictor of the mobile purchase intention when
evaluation convenience is also included in the model (β = .403, t = 3.461, p = .001). Finally, there was found
that the direct effect of evaluation convenience on the mobile purchase intention was not equal to zero (β =
.077, t = .575, p = .567). Additionally, there was found a significant ratio of the indirect effect to the direct
effects, using the Sobel test (Z = 2.614, p = .009), which together with the outcome of the 4 steps suggests
that the effect of evaluation convenience on the mobile purchase intention is partially mediated by enjoyment.
These results provides evidence for hypothesis 7c, which is thus partially accepted.

Lastly, the PROCESS analysis was executed on the transaction convenience as independent variable. The
regression of the total effect of transaction convenience on the mobile purchase intention was significant (β =
.294, t = 2.147, p = .034). Subsequently, there was found that transaction convenience significantly
influenced enjoyment (β = .434, t = 3.837, p = .000). Then, there was found that enjoyment significantly
affected the mobile purchase intention when transaction convenience was also incorporated in the model (β =
.393, t = 3.413, p = .001). The output also indicated that the regression of transaction convenience on the
mobile purchase intention when enjoyment was also included in the model, was not equal to zero (β = .124, t
= .886, p = .379). The Sobel test was significant as well ( Z = 2.503, p = .123). All of this together provides
evidence for the partial mediation effect of enjoyment between transaction convenience and the mobile
purchase intention. Hypothesis 7d was thus partially confirmed. All the paths of the mediation analyses and
their corresponding Beta’s and significance scores are visually presented in Figure 2. In table 7 there is
presented an overview that summarizes the results of the hypotheses.

Figure 2: Indirect effects with enjoyment as mediator

 ** = significance on the 0,01 level. * = significance on the 0,05 level. † = Marginal significance (on the 0,1 level)

Table 8: Summary of hypotheses.

Hypothesis Independent variable Dependent variable Mediator Result

𝑯𝟏 Access convenience Mobile purchase intention - Rejected
𝑯𝟐 Search convenience Mobile purchase intention - Rejected
𝑯𝟑 Evaluation convenience Mobile purchase intention - Rejected
𝑯𝟒 Transaction convenience Mobile purchase intention - Rejected
𝑯𝟓 Overall convenience Enjoyment - Accepted
𝑯𝟔 Enjoyment Mobile purchase intention - Accepted
𝑯𝟕𝒂 Access convenience Mobile purchase intention Enjoyment Partially accepted
𝑯𝟕𝒃 Search convenience Mobile purchase intention Enjoyment Partially accepted
𝑯𝟕𝒄 Evaluation convenience Mobile purchase intention Enjoyment Partially accepted
𝑯𝟕𝒅 Transaction convenience Mobile purchase intention Enjoyment Partially accepted

6. Discussion

Discussion and Conclusion

This study was concerned with convenience and enjoyment in the m-commerce context and it addressed the
research question: “What is the influence of mobile perceived convenience and the mobile perceived amount
of enjoyment, on the purchase intention in the context of m-commerce”. In order to attempt to answer this
question, data was collected and quantitively analyzed from a sample of Dutch consumers.

When there was only looked at the mean scores of all the variables that followed from the 5-point Likert scale
items, the highest mean score of access convenience was notable, which is to be expected as mobile provides
convenient access to shopping anywhere and anytime (Wang, Malthouse & Krishnamurthi, 2015).
Interestingly, transaction convenience was overall perceived better than search and evaluation convenience,
suggesting that in terms of convenience, consumers generally rather make transactions than search and
evaluate on mobile. This thus contradicts the literature of authors who state that mobile devices have certain
characteristics which make them better fitted for searching than for purchasing (de Haan et al. 2015; Lemon
& Verhoef, 2016).
The results of the regression analysis indicated that, against expectations, there were no direct effects of the
four forms of mobile perceived convenience on the mobile purchase intention. The perceptions of these
dimensions of convenience and the mobile purchase intention as variables, were thus probably a bit too
distant from each other to have a direct link. This can be partly explained by the Fishbein model of Ajzen &
Fishbein, (1975), where is argued that the behavioral intention is mainly influenced by the attitude towards
that behavior. The perceptions of convenience did not really tell anything about the attitude towards
purchasing on mobile. Perhaps if this attitude was also included in the conceptual model, the convenience
variables would have had an influence on this and that in turn could have influenced the mobile purchase
intention.

Furthermore, the low R2 and adjusted R2 of the regression model, where enjoyment was also included,
indicates that just looking at the utilitarian value of convenience and the hedonic value of enjoyment alone
might not be enough to correctly predict the mobile purchase intention. This is theoretically supported by the
research of Lai et al., (2012), who found that even though consumers value m-shopping for utilitarian and
hedonic reasons, they will not switch to purchasing on the mobile channel when they have concerns about
security, trust and privacy. There are thus other factors like trust, security and privacy that are more important
determinants of the intention to purchase on mobile. However, the results also indicated that enjoyment
definitely was a determinant of this mobile purchase intention, suggesting that hedonic motivations are
crucial for this intention and might be even more important than utilitarian ones (e.g. convenience). This latter
idea is also supported by the paper of Yang, (2010) who finds that the hedonic or entertainment aspect of m-
commerce services is the most critical driver of the intention to use mobile shopping services in the US. The
direct effect of enjoyment on the mobile purchase intention is probably explained by when a consumer has
fun in mobile shopping for its’ own sake, it stimulates this consumer to also purchase on mobile as it is just
fun to do so.

The results also showed that the perception of overall mobile shopping convenience influenced the perception
of enjoyment. This is probably due to the fact that a higher amount of perceived convenience reduces the
physiological costs of shopping, which in turn makes the shopper enjoy the shopping process more, as also
explained by Childers et al., (2001). In other words, convenience just facilitates the way into experiencing
more enjoyment when shopping on mobile, as it is lowering the particular frustrations which can occur when
the process is inconvenient.

The hypothesis which stated that enjoyment was causing a mediation effect (7a; 7b; 7c; 7d), were all partially
accepted based on the results. Only the indirect effect of perceived evaluation convenience on the mobile
purchase intention has to be taken with a grain of salt, as the relationship between those two variables without
enjoyment as a mediator, was only marginally significant. As earlier stated, perhaps if attitude towards
purchasing on mobile was also considered in the conceptual model, the convenience variables would have
had significant effects. In this case, enjoyment fulfills this role and facilitates significant indirect effects of
access, search, evaluation and transaction convenience on the mobile purchase intention. These results
provide indications for the key finding that although a consumer might find mobile shopping convenient in
terms of accessibility, searching, evaluating and conducting transactions, he/she is not likely to actually
purchase on mobile if he/she is not enjoying the mobile shopping process. This again forms proof for the idea
that hedonic motivations are more important than utilitarian ones when using the services of m-commerce.
Support for this is also found in another paper of Yang, (2012) who found that the hedonic motivation of idea
shopping, which is the enjoyment that one experiences when browsing for information, is the biggest
motivation to go mobile shopping. He therefore states that the mobile shopping channel is a place to enjoy
obtaining more information about new trends, fashion, and products. When consumers thus don’t experience
this enjoyment in browsing on mobile, then the general biggest motivation to go and m-shop is absent.
Therefore, the chance will be very small that a consumer would purchase on mobile if he/she is not enjoying
the process, even if he/she finds it convenient. Another explanation for the importance of enjoyment, is given
in the paper of Davis, (2009). He argues that fun helps consumers to learn about new technologies and
services (e.g. m-commerce), which are not easy to use and time consuming to master. Just having fun is
important to the learning process without them having the idea that mistakes will lead to problematic
consequences. In this case, enjoyment will thus help consumers to learn and pursue the capabilities of m-
commerce which includes purchasing on mobile.

Implications

The examination of the research question and the sub questions offer relevant implications for both the theory
as the practices. First of all, although there are several studies conducted that investigated convenience in the
online context, only a few studies considered convenience in the m-commerce context. Furthermore, no
research yet had examined convenience in the m-commerce context with a validated online convenience
measurement scale, like the instrument of Jiang et al., (2012). Besides, research on the purchase funnel in the
m-commerce domain is still very limited (Lemon & Verhoef, 2016). With the investigation of the mobile
perceptions of access, search, evaluation and transaction convenience, together with enjoyment, this research
contributes to a better understanding of what is driving the mobile purchase intention. The findings suggest
that none of the four forms of convenience are direct antecedents of the mobile purchase intention. Hereby
this study is also helping to understand about differences between convenience effects in the e-commerce
setting and the m-commerce setting, as for the e-commerce setting Jiang et al., (2012) did find direct effects
of search and transaction convenience on behavioral intentions. These effects are not present in the mobile
domain of e-commerce, as examined in this study.

In addition, the finding of the direct and positive effect of enjoyment on the mobile purchase intention is a
supportive contribution to the literature that had similar findings (Kwek, Tan & Lau, 2013; Chong, 2013). It
also gives support to the idea that consumers shop on mobile phones to hedonically experience enjoyment
and fun (Davis, 2009). This finding, and the fact that enjoyment was a significant mediator between the
convenience variables and the mobile purchase intention, leads to further managerial implications as well.
First, as the importance of enjoyment in generating mobile sales became evident in this study, it is a
straightforward conclusion that managers should focus on making their mobile websites more enjoyable.
Davis (2009) provides three ways to do this. First of all, the content on the m-website should be novel,
entertaining and challenging. Secondly, there should be an emphasis on the social aspect of the m-website,
allowing communication between humans on this m-website (e.g. reviews, chat). Thirdly, as also supported in
this study, there should be focused on the consumers self-orientation by providing convenience which the
consumers can then pursue in order for them to experience fun. A second implication is that managers should
not just focus on convenience alone when creating a mobile website. Although it definitely helps to achieve
higher perceptions of enjoyment, convenience alone is just not enough to obtain higher purchase intentions.
Therefore, managers could best only focus on improving their mobile websites in terms of accessing it,
searching for products on it, evaluating between products on it and making transactions on it, when they want
to increase the enjoyment aspect of the mobile website or are certain already that the m-website is fun. An
last managerial implication to boost mobile sales, would be to focus on those consumers who find mobile
shopping enjoyable, however this might be hard to track down. For example, marketers could use clickstream
data in order to determine which mobile shoppers spent a lot of time browsing for information and thus
probably experience enjoyment from idea shopping (Yang, 2012). These mobile shoppers could subsequently
be targeted with promotions/advertisements as they are likely to have high mobile purchase intentions.

Limitations & Further Research

The current study has several limitations which will be discussed here. First of all, the scales were all
translated to Dutch, which may have threatened the reliability and validity of these scales. Especially the
convenience variables showed much lower Cronbach alpha’s when compared to the original scale (Jiang et
al., 2012). Besides, the factor analysis only revealed two underlying factors for measuring convenience,
instead of four. This and the low reliability of some scales could be a consequence of the adjustment of the
questions to fit them in the m-commerce setting, but also because of the translation. It could be valuable for
future research to replicate this study and then maintain the measures in English, in order to see if this would
change the reliability and validity of the constructs. If this change would not be the case, then the scale is
probably not a good fit in the context of m-commerce. Additionally, other factors that drive m-commerce
purchase intentions could be also integrated in future studies to improve the predictive power of the direct
effects model. Trust, privacy and security are examples of these potential drivers. Also, this research
emphasized access, search, evaluation and transaction convenience as utilitarian shopping values and
enjoyment as a hedonic shopping value, and drew conclusions about whether utilitarian or hedonic aspects
were more important in the m-commerce domain. However, there are many more utilitarian shopping values
than convenience and also more hedonic shopping values than just enjoyment. When those other utilitarian
and hedonic shopping values are thus added to the analyses, there might be drawn very different conclusions
in terms of which value is a bigger motivation for mobile purchasing. Furthermore, the current study
considered enjoyment as a mediator between access, search, evaluation and transaction convenience and the
mobile purchase intention. However, as also the clear direct link between enjoyment and the mobile purchase
intention was found, it might also be interesting for future studies to discover whether and how the
convenience variables might moderate this effect.

Then, this study is further limited because it only included Dutch people as respondents. Future similar
research could attempt to create samples with people of other nationalities to see if the results would be the
same. Furthermore, most of the sample of the current study was young of age (18-24), so future replicative
studies could also try to create a more diverse sample in terms of age.

Lastly, this study only considered m-websites as a means of shopping in the m-commerce domain. However,
m-commerce of course also involves the shopping via mobile (m-commerce) apps. Therefore, it would be
interesting to see if in future research also this part of m-commerce could be included, to see whether there
are any differences in perceptions and outcomes of the analyses between apps and websites. Lastly,
experiencing convenience and enjoyment in the m-commerce domain might also be a result of receiving m-
coupons and location-based advertisements. Though, this was just as m-commerce apps not considered in the
methodology of the current study. In conclusion, there can be stated that there is still plenty of room for future
research in this subject area.
Appendix

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Epilogue:

In this epilogue I will reflect on the complete process of writing my thesis. Looking back on the start, I realize
that I have spent quite some time on finding a good research question. I knew I wanted to do something
with mobile, as this is were I am interested in. However, I was thinking to broad and came up with many
research questions like: “What is the effect of the mobile channel on the consumers’ customer journey?”
When I finally came up with using convenience and enjoyment as variables related to mobile, things became
easier. However, I had put much emphasis on the multiple dimensions of convenience and probably saw
them too much as separate independent variables, while actually they together form one variable. This
wasn’t a problem persé, but I expected to find differences in the outcomes of the analyses between for
example transaction convenience and search convenience. However, no such difference was found and it
was either all the four convenience variables had significant effects or they all did not have this. I also had
problems with finding literature concerning these convenience variables, as most of the literature just
considers convenience as one construct and often thus not considers for example only “evaluation
convenience”. Finding literature for enjoyment was much easier, as this construct is used a lot in research.
Another problem was when I found literature which didn’t specify for the m-commerce setting. So when I
found a connection, I couldn’t be sure if it would also apply to the m-commerce setting, as it was only tested
in the e-commerce context.

The collection of the data went very well and quick. Within a week I had enough results to begin with the
analysis. Actually analyzing the data was more difficult though, as I was not that experienced with SPSS.
Especially performing factor analyses and mediation analyses was hard for me, as I had never done that
before. However, watching several Youtube tutorials about these analyses helped me a lot. When I firstly
did the multiple regression analysis with the four convenience variables and enjoyment, I was shocked a bit.
The R2 and adjusted R2 were both very low, and I knew that this was a very important statistic for the
quality of the model. Additionally, I discovered that there were no direct effects of the convenience
variables on the mobile purchase intention, which also was against my hopes. However, luckily I still found
significant effects which all had to do with the enjoyment variable. Writing the discussion I found most fun
to do, as this really could be regarded as writing about academical findings which were based on my own
study.

There were two aspects about what I learned the most during this thesis. Firstly, I learned a lot about
reading academical papers. You have to read a lot of academical papers, but when it is for exam you read
them very differently than for your own study. This is because for your own study, you just have to collect
the pieces of information that are usable for your research. Secondly, I learned a lot more about SPSS than
when I had the Statistics course. Although it was definitely hard to understand everything, it still sometimes
was fun when I figured something out by myself. All in all, there are definitely some things which could have
went better or I would have changed when I could start over. However, I am still satisfactory with the
overall paper as it was really the first time I have ever wrote one.

SPSS Output:

Regression Models:
 Model 4, Mediation Testing Models

Run MATRIX procedure:

************* PROCESS Procedure for SPSS Release 2.16.3 ******************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com

**************************************************************************
Model = 4
Y = Purchase
X = Access
M = Enjoymen

Sample size
102

**************************************************************************
Outcome: Enjoymen

Model Summary
R R-sq MSE F df1 df2 p
,3662 ,1341 ,5151 15,4898 1,0000 100,0000 ,0002

Model
 coeff se t p LLCI ULCI
constant 1,8752 ,3838 4,8865 ,0000 1,1139 2,6366
Access ,3823 ,0971 3,9357 ,0002 ,1896 ,5750

**************************************************************************
Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,3905 ,1525 ,6802 8,9068 2,0000 99,0000 ,0003

Model
coeff se t p LLCI ULCI
constant 1,7478 ,4908 3,5608 ,0006 ,7738 2,7217
Enjoymen ,3743 ,1149 3,2575 ,0015 ,1463 ,6023
Access ,1565 ,1200 1,3043 ,1952 -,0816 ,3945

************************** TOTAL EFFECT MODEL ****************************

Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,2483 ,0617 ,7455 6,5708 1,0000 100,0000 ,0119

Model
coeff se t p LLCI ULCI
constant 2,4497 ,4617 5,3059 ,0000 1,5337 3,3657
Access ,2996 ,1169 2,5634 ,0119 ,0677 ,5314

***************** TOTAL, DIRECT, AND INDIRECT EFFECTS ********************

Total effect of X on Y
Effect SE t p LLCI ULCI
,2996 ,1169 2,5634 ,0119 ,0677 ,5314

Direct effect of X on Y
Effect SE t p LLCI ULCI
,1565 ,1200 1,3043 ,1952 -,0816 ,3945

Indirect effect of X on Y
Effect Boot SE BootLLCI BootULCI
Enjoymen ,1431 ,0693 ,0400 ,3172

Normal theory tests for indirect effect

Effect se Z p
,1431 ,0581 2,4627 ,0138

******************** ANALYSIS NOTES AND WARNINGS *************************

Number of bootstrap samples for bias corrected bootstrap confidence intervals:

5000

Level of confidence for all confidence intervals in output:

95,00

------ END MATRIX -----

Run MATRIX procedure:

************* PROCESS Procedure for SPSS Release 2.16.3 ******************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com

**************************************************************************
Model = 4
Y = Purchase
X = Search
M = Enjoymen

Sample size
102

**************************************************************************
Outcome: Enjoymen

Model Summary
R R-sq MSE F df1 df2 p
,5310 ,2820 ,4271 39,2708 1,0000 100,0000 ,0000

Model
coeff se t p LLCI ULCI
constant 1,3218 ,3315 3,9869 ,0001 ,6640 1,9796
Search ,5909 ,0943 6,2666 ,0000 ,4038 ,7779

**************************************************************************
Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,3771 ,1422 ,6885 8,2037 2,0000 99,0000 ,0005

Model
coeff se t p LLCI ULCI
constant 1,9885 ,4531 4,3883 ,0000 1,0894 2,8876
Enjoymen ,3821 ,1270 3,0095 ,0033 ,1302 ,6340
Search ,0988 ,1413 ,6992 ,4861 -,1815 ,3791

************************** TOTAL EFFECT MODEL ****************************

Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,2524 ,0637 ,7439 6,8021 1,0000 100,0000 ,0105

Model
coeff se t p LLCI ULCI
constant 2,4935 ,4375 5,6989 ,0000 1,6255 3,3616
Search ,3245 ,1244 2,6081 ,0105 ,0777 ,5714

***************** TOTAL, DIRECT, AND INDIRECT EFFECTS ********************

Total effect of X on Y
Effect SE t p LLCI ULCI
,3245 ,1244 2,6081 ,0105 ,0777 ,5714

Direct effect of X on Y
Effect SE t p LLCI ULCI
,0988 ,1413 ,6992 ,4861 -,1815 ,3791

Indirect effect of X on Y
Effect Boot SE BootLLCI BootULCI
Enjoymen ,2258 ,0991 ,0585 ,4481

Normal theory tests for indirect effect

Effect se Z p
,2258 ,0841 2,6852 ,0072

******************** ANALYSIS NOTES AND WARNINGS *************************

Number of bootstrap samples for bias corrected bootstrap confidence intervals:

5000

Level of confidence for all confidence intervals in output:

95,00

------ END MATRIX -----

Run MATRIX procedure:

************* PROCESS Procedure for SPSS Release 2.16.3 ******************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com

**************************************************************************
Model = 4
Y = Purchase
X = Evaluati
M = Enjoymen

Sample size
102

**************************************************************************
Outcome: Enjoymen

Model Summary
R R-sq MSE F df1 df2 p
,3833 ,1469 ,5075 17,2187 1,0000 100,0000 ,0001

Model
coeff se t p LLCI ULCI
constant 1,8969 ,3594 5,2773 ,0000 1,1838 2,6101
Evaluati ,4375 ,1054 4,1495 ,0001 ,2283 ,6466

**************************************************************************
Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,3752 ,1408 ,6896 8,1120 2,0000 99,0000 ,0005

Model
coeff se t p LLCI ULCI
constant 2,0013 ,4738 4,2242 ,0001 1,0612 2,9413
Enjoymen ,4035 ,1166 3,4618 ,0008 ,1722 ,6348
Evaluati ,0765 ,1331 ,5751 ,5665 -,1875 ,3405
************************** TOTAL EFFECT MODEL ****************************
Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,1918 ,0368 ,7653 3,8205 1,0000 100,0000 ,0534

Model
coeff se t p LLCI ULCI
constant 2,7667 ,4414 6,2679 ,0000 1,8910 3,6425
Evaluati ,2531 ,1295 1,9546 ,0534 -,0038 ,5099

***************** TOTAL, DIRECT, AND INDIRECT EFFECTS ********************

Total effect of X on Y
Effect SE t p LLCI ULCI
,2531 ,1295 1,9546 ,0534 -,0038 ,5099

Direct effect of X on Y
Effect SE t p LLCI ULCI
,0765 ,1331 ,5751 ,5665 -,1875 ,3405

Indirect effect of X on Y
Effect Boot SE BootLLCI BootULCI
Enjoymen ,1765 ,0819 ,0493 ,3737

Normal theory tests for indirect effect

Effect se Z p
,1765 ,0675 2,6138 ,0090

******************** ANALYSIS NOTES AND WARNINGS *************************

Number of bootstrap samples for bias corrected bootstrap confidence intervals:

5000

Level of confidence for all confidence intervals in output:

95,00

------ END MATRIX -----

Run MATRIX procedure:

************* PROCESS Procedure for SPSS Release 2.16.3 ******************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com

**************************************************************************
Model = 4
Y = Purchase
X = Transact
M = Enjoymen

Sample size
102

**************************************************************************
Outcome: Enjoymen
Model Summary
R R-sq MSE F df1 df2 p
,3582 ,1283 ,5185 14,7224 1,0000 100,0000 ,0002

Model
coeff se t p LLCI ULCI
constant 1,6829 ,4427 3,8013 ,0002 ,8046 2,5613
Transact ,4340 ,1131 3,8370 ,0002 ,2096 ,6585

**************************************************************************
Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,3804 ,1447 ,6864 8,3754 2,0000 99,0000 ,0004

Model
coeff se t p LLCI ULCI
constant 1,8164 ,5449 3,3332 ,0012 ,7351 2,8977
Enjoymen ,3927 ,1151 3,4132 ,0009 ,1644 ,6210
Transact ,1235 ,1394 ,8860 ,3778 -,1531 ,4001

************************** TOTAL EFFECT MODEL ****************************

Outcome: Purchase

Model Summary
R R-sq MSE F df1 df2 p
,2099 ,0441 ,7595 4,6101 1,0000 100,0000 ,0342

Model
coeff se t p LLCI ULCI
constant 2,4773 ,5358 4,6233 ,0000 1,4142 3,5403
Transact ,2940 ,1369 2,1471 ,0342 ,0223 ,5656

***************** TOTAL, DIRECT, AND INDIRECT EFFECTS ********************

Total effect of X on Y
Effect SE t p LLCI ULCI
,2940 ,1369 2,1471 ,0342 ,0223 ,5656

Direct effect of X on Y
Effect SE t p LLCI ULCI
,1235 ,1394 ,8860 ,3778 -,1531 ,4001

Indirect effect of X on Y
Effect Boot SE BootLLCI BootULCI
Enjoymen ,1704 ,0834 ,0507 ,3841

Normal theory tests for indirect effect

Effect se Z p
,1704 ,0681 2,5032 ,0123

******************** ANALYSIS NOTES AND WARNINGS *************************

Number of bootstrap samples for bias corrected bootstrap confidence intervals:

5000

Level of confidence for all confidence intervals in output:

95,00

------ END MATRIX -----

Survey:

Q1 Beste respondent,

Hierbij wil ik u graag uitnodigen om deel te nemen aan deze survey. Deze vragenlijst wordt uitgevoerd
onder de verantwoordelijkheid van de Vrije Universiteit Amsterdam.
De survey is onderdeel van het onderzoek dat ik uitvoer voor mijn bachelorscriptie Bedrijfskunde. Het
onderzoek en de hieraan gerelateerde vragen zullen gaan over percepties van consumenten op het gebied
van shoppen op een mobiele telefoon.
De enquête duurt ongeveer 4 minuten.
Voordat u begint met de vragenlijst is het belangrijk om u zich volgende dingen te realiseren:

- Als u deelneemt aan deze enquête blijft u volledig anoniem.

- U kunt op elk moment tijdens de vragenlijst besluiten om te stoppen. Uw gegevens zullen dan niet
gebruikt worden voor het onderzoek.

- De data die u verstrekt zal enkel en alleen gebruikt worden voor het doeleinde van dit onderzoek.

Mocht u na het lezen van het bovenstaande nog vragen hebben, dan kunt u altijd contact met me opnemen.
Ik hoop u hiermee voldoende geïnformeerd te hebben en dank u bij voorbaat voor uw deelname.

Ruben van Eekeren

Student Bedrijfskunde

Vrije Universiteit Amsterdam

r.j.l.van.eekeren.@student.vu.nl

Ik heb bovenstaande informatie gelezen en wil deelnemen aan het onderzoek:

o Ja (1)

Q2 Wat is uw geslacht?

o Man (1)
o Vrouw (2)
Q3 Wat is uw leeftijd?

o 12-17 (1)
o 18-24 (2)
o 25-34 (3)
o 35-44 (4)
o 45-54 (5)
o 55+ (6)

Q4 Hoeveel gebruikt u uw mobiele telefoon gemiddeld genomen per dag?

o 0-2 uur (1)

o 2-4 uur (2)
o 4-6 uur (3)
o 6+ uur (4)

Q5 Voor het beantwoorden van de volgende vragen, denkt u alstublieft aan momenten waarop u aan het
shoppen was via een mobiele website op uw mobiele telefoon. Met shoppen wordt niet enkel bedoeld dat u
daadwerkelijk iets besteld en afgerekend moet hebben. Het gaat hier om het volledige proces van shoppen.

Geeft alstublieft aan in welke mate u het eens bent met de volgende stellingen;

Bij shoppen op mijn mobiele telefoon, heb ik persoonlijk het gevoel dat:
 Helemaal mee Helemaal mee
Mee oneens (2) Neutraal (3) Mee eens (4)
oneens (1) eens (5)

Ik kan shoppen op
ieder moment dat ik
wil (1) o o o o o
Websites altijd
toegankelijk zijn (2) o o o o o
Ik producten kan
bestellen waar ik
ook ben (3) o o o o o
Websites
gebruikersvriendelijk
zijn om aankopen te
doen (4)
o o o o o
De websites
gemakkelijk te
begrijpen en
gemakkelijk te o o o o o
navigeren zijn (5)

Ik snel gewenste
producten kan
vinden (6) o o o o o
Product categorieën
intuïtief en
overzichtelijk zijn
weergegeven (7)
o o o o o
Product details
aanwezig zijn en op
een duidelijke
manier worden o o o o o
aangegeven (8)

Er voldoende
informatie gegeven
wordt om
verschillen tussen
producten
gemakkelijk te
o o o o o
kunnen identificeren
(9)

Er op een
overzichtelijke
manier gebruik
gemaakt wordt van
tekst en
foto's/plaatjes die
diepgaande
o o o o o
informatie geven
over een product
(10)
 Het simpel en
gemakkelijk is om
online te betalen
(11)
o o o o o
Er flexibiliteit is in de
manieren om te
betalen (12) o o o o o
Het afronden van
mijn aankoop
zonder
moeilijkheden o o o o o
verloopt (13)

Q6 De volgende stellingen zullen gaan over in welke mate u plezier ervaart, wanneer u shopt op uw mobiele
telefoon.

Geef alstublieft aan in hoeverre u het eens bent met de volgende stellingen:

Helemaal mee Helemaal mee

Mee oneens (2) Neutraal (3) Mee eens (4)
oneens (1) eens (5)

Het proces van

surfen en
shoppen op
mobiele-
shopping o o o o o
websites is
aangenaam (1)

Terwijl ik
gebruik maak
van mobiele-
shopping
websites, ervaar
o o o o o
ik plezier (2)

Algeheel vind ik
het bezoeken
van mobiele
shopping
websites leuk
o o o o o
(3)

Q7
De volgende twee stellingen gaan over uw intenties wat betreft aankopen doen op uw mobiele telefoon.

Geef alstublieft aan in hoeverre u het eens bent met de volgende stellingen;

Aangenomen dat ik een werkende telefoon heb en dat mobiele shopping-websites dus voor mij toegankelijk
zijn:
 Helemaal mee Helemaal mee
Mee oneens (2) Neutraal (3) Mee eens (4)
oneens (1) eens (5)

Ben ik van plan

om er aankopen
op te doen (1) o o o o o
Verwacht ik dat
ik er aankopen
op zal doen (2) o o o o o