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This study contributes to research on service quality and value delivery in the context of online and mobile services, with emphasis on services delivered through mobile apps (software applications for smartphones and tablets). It explores the delivering of value through mobile apps.
Citation preview
Master thesis
An Exploration of Delivering Value
through Mobile Apps
by
Mark Hoskam
2013
2
Abstract
This study contributes to research on service quality and value delivery in the context of
online and mobile services, with emphasis on services delivered through mobile apps
(software applications for smartphones and tablets). It explores the delivering of value
through mobile apps. First, existing theories are reviewed regarding value-‐adding
strategies, perceived value, customer satisfaction and customer loyalty. Second, based
on Kano’s model for measuring perceived value, a questionnaire is conducted amongst
customers of a Dutch mobile telephone and internet network provider, asking about
their experiences with and attitudes towards mobile apps. Results reveal that different
customer segments have different attitudes towards mobile apps, with a significant
number of customers regarding a mobile app a must-‐have element of the overall value
proposition offered. Education, usage experience and user status are found to
significantly influence a customer’s perceived value regarding a mobile app. First, higher
educated customers regard an app more must-‐have compared to lower educated
customers. Second, the more smartphone and app experience a customer has, the
more must-‐have an app is perceived. Third, existing app users regard an app as must-‐
have, while non-‐users see an app as irrelevant. Fourth, on the app level, reliability was
found to be a must-‐have attribute amongst all customer segments, strongly
determining the success or failure of an app. Finally, also on the app’s attributes level
different customer segments are found to have different needs and expectations, with
age and education as significant influencing factors.
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TABLE OF CONTENTS
1. INTRODUCTION ................................................................................................................... 6
1.1 Background ........................................................................................................................................ 6
1.1.1 Customer service and competitive advantage .............................................................. 6
1.1.2 Online as a service channel ........................................................................................... 7
1.1.3 Mobile as a service channel .......................................................................................... 8
1.1.4 Smartphones, tablets, apps ......................................................................................... 10
1.1.5 Core vs. augmented product ....................................................................................... 12
1.1.6 Summary ..................................................................................................................... 13
1.2 Problem definition .......................................................................................................................... 14
1.3 Theoretical relevance ...................................................................................................................... 15
2. AIM AND OBJECTIVES ........................................................................................................ 16
2.1 Aim and objectives .......................................................................................................................... 16
2.2 Research questions ......................................................................................................................... 16
2.3 Report structure .............................................................................................................................. 16
3. THEORETICAL FRAMEWORK .............................................................................................. 18
3.1 Literature review ............................................................................................................................. 18
3.1.1 Value ........................................................................................................................... 18
3.1.2 Business vs. customer perspective ............................................................................... 20
3.1.3 Utilitarian vs. hedonic value ........................................................................................ 20
3.1.4 Dynamics of perceived value ....................................................................................... 24
3.1.5 Measuring perceived value ......................................................................................... 27
3.1.7 Conclusions on perceived value ................................................................................... 32
3.2 Definitions used for this study ........................................................................................................ 33
3.2.1 Perceived value ........................................................................................................... 33
3.2.2 Value proposition ........................................................................................................ 34
3.2.3 Mobile Service App ...................................................................................................... 34
3.3 Hypotheses ...................................................................................................................................... 34
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3.3.1 Demographics ............................................................................................................. 35
3.3.2 Behavior: Usage experience and user status ............................................................... 36
4. METHODOLOGY ................................................................................................................ 38
4.1 Objectives ........................................................................................................................................ 38
4.2 Research design ............................................................................................................................... 39
4.3 Sample strategy and sample size .................................................................................................... 40
4.4 Data collection ................................................................................................................................. 40
4.4.1 Measuring the dynamics of perceived value: Kano’s measurement model ................ 40
4.4.2 Questionnaire .............................................................................................................. 42
5. ANALYSIS AND RESULTS .................................................................................................... 47
5.1 Analysis of questionnaire data ........................................................................................................ 47
5.2 Characteristics of the sample .......................................................................................................... 47
5.3 Demographics and perceived value ................................................................................................ 49
5.4 Behavioural characteristics and perceived value ........................................................................... 57
5.5 Main findings ................................................................................................................................... 60
6. DISCUSSION AND CONCLUSIONS ....................................................................................... 62
6.1 Main conclusions ............................................................................................................................. 62
6.2 Reflection on Kano’s model for measuring perceived value .......................................................... 65
7. RECOMMENDATIONS ........................................................................................................ 67
7.1 Limitations and recommendations for future research ................................................................. 67
7.2 Managerial implications .................................................................................................................. 68
BIBLIOGRAPHY ...................................................................................................................... 71
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1. INTRODUCTION
In this chapter the background of the research is described, the problem definition is
stated and the research’s theoretical relevance is explained. First, customer service and
the importance of customer service as means for developing competitive advantage are
stressed. Second, the development of online and mobile as service channels is explained.
Third, the influence and evolution of smartphones, tablets and apps is described, with
emphasis on the usage of smartphones, tablets and apps as a service channel. Fourth,
the differences between core products and services and supporting services are given
and described in the perspective of mobile app services. Finally, the problem on which
this research focuses is defined and its theoretical relevance is explained.
1.1 Background
1.1.1 Customer service and competitive advantage
Over the last decades, customer service has become a key element of a company’s
marketing mix. Especially for companies in high competitive and matured markets,
were customer acquisition costs are often significantly higher than retention costs,
resulting in defensive marketing strategies (Fornell and Wernerfelt 1987; Mullins et al
2010). Contrary to offensive marketing strategies, focusing on acquisition of new
customers, defensive marketing is concerned with increasing retention of existing
customers. It focuses on the creation of customer loyalty, long-‐term relationships and
sustainable profit. Companies in high competitive markets have shifted from a
transactional focus towards a relational focus and have adopted relationship marketing
as new strategy with customer satisfaction and customer loyalty as key performance
indicators (Anderson et al, 1994; Wilson et al, 2012). As research has shown, ‘service
quality is almost always an important driver of customer satisfaction across all types of
industries’ (Wilson et al, 2012). Furthermore, several studies concluded that high
quality customer service has direct positive effect on customer satisfaction and leads to
customer loyalty and word-‐of-‐mouth in the long-‐run (Anderson et al, 1994; Kuo et al,
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2009). Thus, offering high quality service in order to create satisfied customers provides
businesses with both a retention incentive (loyalty) and an acquisition incentive (word-‐
of-‐mouth). Ultimately, creating highly satisfied customer through high quality customer
service results in increased financial performance for businesses (Reichfeld, 1990).
Moreover, by offering high quality and unique customer service, companies can add
value to their core product or service, enabling them to distinguish themselves from
competition in these highly competitive markets (Riel et al, 2004). As a result,
companies have rapidly adopted the online and mobile channel as a means to deliver
high quality and unique customer service, with mobile service applications (apps) as the
latest phenomenon.
1.1.2 Online as a service channel
‘The service industry is one of the most natural avenues for e-‐commerce because so
much of the value in services is based on collecting, storing and exchanging
information, something for which the Web is ideally suited’ (Laudon and Traver, 2012).
In this perspective, the Internet has been rapidly embraced by companies as a new
channel to sell to and service their customers. Traditional companies started developing
online extensions of their offline services like online shops and self-‐care portals or even
started creating new online services, adding value to the core product or service
offered. A famous example of such a new and successful service is Apple’s iTunes Store.
Next to traditional players adopting online, new pure player brands established
themselves; companies which market, sell and service solely through the online
channel. Some famous examples are Ebay (online auction), Amazon (online warehouse),
Bol.com (online warehouse), Google (search engine), Facebook (social network), Spotify
(streaming music) and Netflix (streaming movies and series). Both types of companies,
the traditional and the pure players, are eager to harvest online customer servicing
opportunities and to conduct their sales and service activities online as much as
possible. Traditional banks like ABN Amro, employment agencies like Randstad and low-‐
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cost airlines like Ryanair have significantly reduced their physical service encounters
and moved some of their primary service activities to the online channels; activities like
checking your account balance, conducting financial transactions, applying for jobs and
booking flights. Main reasons for this movement are the reduction of transaction costs
and the improvement of margins, but also meeting up with changing customer
preferences. From a business perspective, the online channel provides them with the
opportunity to lower costs per customer interaction (Hughes, 2005; Laudon and Traver,
2012) and to extend market reach regionally, nationally, internationally or even
globally, again at relative low cost (Hughes, 2005). This in contrast to the pre-‐Internet
age, when companies had to sell and service customers through their expensive call
centre and retail channels. Moreover, the online landscape offers companies the
opportunity to meet the evolving customer behaviour and preferences. Customer’s
usage of the internet during the purchase decision making process is increasing every
year. Computers, tablets and smartphones are used to search for online information on
products and services, to evaluate alternatives and to purchase online. In the post-‐
purchase phase, social media platforms like Facebook and Twitter, comparison web
sites like Kieskeurig.nl and forums like TripAdvisor are used for post-‐purchase
evaluation and to share user experiences of the bought product or service (Laudon and
Traver, 2012). The website of the company itself is primarily used to find information on
post-‐purchase questions and issues, to up-‐ or downgrade subscriptions or to purchase
additional products or services. In all these phases of the customer lifecycle, companies
can add value by offering online services.
1.1.3 Mobile as a service channel
With emerging mobile enabling technologies like 4G (high speed mobile networks) and
NFC (near frequency communications), activities like high definition video calling and
instant payment through mobile phones and tablets become available at our fingertips.
In line with the rapid development of these mobile technologies, smartphone usage
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and sales rates show fast and ongoing growth. In a relatively short period of time,
mobile technology has penetrated significantly into society, capturing an entire age
spectrum of subscribers, from school children to senior citizens (Boulos et al, 2011). The
introduction of the touch screen by Apple offered customers an easy to use graphical
user interface and natural gesture control, which helped boosting smartphone
ownership. As a result, smartphone sales have exceeded PC sales in 2011 (Canalys,
2011) and in emerging markets like China and Africa, smartphone ownership already
exceeds PC ownership (TNS Mobile Life, 2013). In these emerging markets, mobile
internet is often the only internet access method available, resulting in high mobile
internet and smartphone penetration levels (ITU, 2012). In the Netherlands,
smartphone penetration passed the 50% point and increased from 42% to 58% from
2011 to 2012 (Telecompaper, 2012). The US is expected to pass the 50% smartphone
penetration point in 2013 and Western Europe in 2014 (eMarketer, 2013). In addition,
2.1 billion consumers worldwide were actively using mobile internet subscriptions in
2012. This is 29,5% of the global population, with adoption rates varying from 11% in
Africa to 67,5% in Europe. Mobile internet subscriptions have grown by 40% annually
over three years and already outnumber fixed internet connections by 3 to 1 (ITU,
2012). Emerging mobile enabling technologies combined with the ongoing growth of
smartphone and mobile internet penetration offer businesses new customer service
opportunities based on mobile technology’s unique characteristics. According to
Kleijnen et al (2007); ‘M-‐commerce is frequently regarded as an extension of e-‐
commerce, while m-‐commerce might also be regarded as a separate channel, because
it can deliver a unique value proposition to customers through the technological
differences it encompasses, including its communication mode and protocols and
access devices.’ First, activities have become more flexible in terms of time and space as
a result of mobile technology (Balasubramanian et al, 2002). Since consumers
experience utilitarian value from efficient and timely service delivery (Childers et al,
2001), exploitation of these unique factors is expected to contribute positively to a
customer’s service experience. Second, urgent and spontaneous customer needs can be
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serviced instantly as a result of these stretches in space and time (Anckar and D’Incau
2002). Third, the mobile channel offers unique mobile learning opportunities through
interfaces of voice, text, icons, pictures and videos (Aboelmaged 2010). Fourth, the
mobile technology significantly improves businesses’ contextual information on their
customers as a result of built-‐in Global Positioning System (GPS), enabling personalized
mobile customer services (Bouwman 2008).
On the other hand, mobile technologies can also negatively impact a customer’s service
experience due to ‘cost’ or ‘give’ factors. Two of these potential cost factors are
customer’s perceived risk of using mobile technologies and cognitive efforts demanded
from the customer (Kleijnen et al, 2007). Cognitive effort in this case can also be
translated as information search costs, the effort it asks from the customer to fulfill his
information needs (Suoranta et al, 2005).
Thus, from one perspective , these unique features of mobile technology enable
businesses to enhance their customer services. By offering their services through the
mobile channel, businesses aim at creating added value for the customer and as a
result, create competitive advantage. Although, the negative consumer beliefs
regarding mobile technologies based on perceived risk and cognitive effort must not be
overlooked.
1.1.4 Smartphones, tablets, apps
Smartphones, in contrast to feature phones, are phones which not only offer users
voice and texting services (SMS), but which also enable mobile internet access, e-‐
mailing, voice recording, music playing, photographing, GPS tracking (for navigation)
and measurement of movements (speed, distance, height) through built-‐in gyroscope
techniques. Due to their powerful on-‐board computing capability, capacious memories
and large screens enabling these unique mobile functions, the latest generation of
smartphones is increasingly seen as handheld computers instead of phones. They can
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easily process tasks which formerly could only be processed by PC’s and laptops. An
additional important characteristic distinguishing smartphones from feature phones is
the ability to download and install mobile applications, otherwise known as apps
(Dickinson et al, 2012). Apps are tailor made software packages for smartphones which
improve the delivery of mobile services by utilizing the unique features of a
smartphone. The numbers of apps available and used and the general popularity of
mobile apps has grown extensively over the past years, as a result of the rapid adoption
of smartphones and tablets. Portio Research (2013): ‘1.2 billion people worldwide were
using mobile apps at the end of 2012. This is forecast to grow at a 29.8 percent each
year, to reach 4.4 billion users by the end of 2017. Much of this growth will come from
Asia, which will account for almost half of app users in 2017.’ In addition, Portio
Research (2013) expects approximately 82 billion apps to be downloaded worldwide in
2013, exceeding the point of 200 billion annual downloads by 2017. Apple iTunes and
Google Play are the world’s biggest and most famous app stores and both offer over
800.000 apps in their stores (Canalys 2013). Some of the most popular apps to date are
Facebook (social network), WhatsApp (instant messaging), Gmail (email client) and
Google Maps (navigator). We can distinguish between B2B apps and B2C apps. B2B
apps are concerned with internal business processes like customer relationship
management (CRM), warehouse management and sales-‐force management. B2C apps
are aimed at consumers and can be categorized as content-‐, marketing-‐ or service
oriented (Cortimiglia et al, 2011). Content-‐oriented apps fulfill individual needs for
information, entertainment, communication, productivity, socialization and instant
messaging. Marketing-‐oriented apps are mostly used by companies for brand
advertising or promotion. Service-‐oriented apps offer users with self-‐service
functionalities like booking a flight, buying goods at an online shop or looking up
current mobile data usage of the user’s mobile internet subscription. Smartphone and
tablet users are using apps as a gateway to online services, as a fast and more
convenient alternative to accessing these services through their (mobile) web browser
(Xu et al, 2011).
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1.1.5 Core vs. augmented product
Like the Internet, smartphones and apps have changed the way in which customers
interact with companies and created opportunities for companies to deliver enhanced
or totally new services. Both companies offering products and companies offering
services have adopted apps as a new channel to service their customers, next to their
(mobile) website. Service companies like banks (f.e. ABN Amro, Rabobank) are offering
mobile payment apps, telecom operators (f.e. KPN, Vodafone) and utility providers (f.e.
Essent, Nuon) are offering self-‐service and usage management portals and insurance
companies (f.e. Interpolis) are offering self-‐service portals. More product oriented
companies like sports apparel manufacturer Nike and car manufacturer Volkswagen use
apps also in the post-‐purchase phase, offering self-‐service functionality (f.e.
Volkswagen’s Service app). Moreover, they use apps to deliver unique value added
services like the Nike+ Running app. This app offers the users of Nike+ running shoes
access to a unique social network for runners, enabling the user to track their own
performance, set targets, monitor progress and set-‐up running challenges with other
Nike+ users. Looking at these examples from service and product companies using apps
to deliver services to their customers, we can define these apps as peripheral services,
forming the augmented product. The main purpose of these peripheral services is to
increase the value of the total offer, they add value to the core product or service
offered (Riel et al, 2004). By adding more value to their core product, companies aim to
improve customer satisfaction and thereby customer loyalty (Ravald and Gronroos,
1996). Added value can be created by revolutionary or evolutionary means. An example
of revolutionary added value is the Nike+ example. More evolutionary examples are the
self-‐service apps of banks and telecom operators, which in the basis are similar to the
traditional services they offer, but digitalized. The purpose of peripheral services is to
enhance the complete product offering. Peripheral services are often used to
distinguish products and services in commodity or homogeneous markets, in which the
core products and services of different brands look very familiar to each other.
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According to research on value enhancing online services (Riel et al, 2004), these
peripheral services are only of value if customers find them useful and unique
compared to alternatives. They state: ‘Companies need to investigate what type of
value they are currently creating, but also what type of value is lacking. Customers
could be segmented according to the type of experienced value and services could be
designed to increase the preference value of each segment.’ But, according to Ravald
and Gronroos (1996): ‘Far too many companies alienate themselves from the customers
and the value added has consequently nothing to do with the actual needs of the
customers’. In other words, companies seeking to increase customer satisfaction and
customer loyalty by developing value added service often forget to put the customer’s
needs central to the development of these services. If companies are planning to
develop apps to create sustainable added value for their customers, they must first find
out how their different customers are actually evaluating app services and how apps
can add value to the core product or service offered from different customer
perspectives. Additionally, when companies finally have decided to start developing an
app service, they must understand the needs and preferences of the specific customer
segment(s) regarding an app service in order to develop an app which is really adds
value for the customer.
1.1.6 Summary
As a result of the rapid adoption of smartphones and tablets amongst customers and
based on the unique based opportunities offered by mobile technology and apps,
mobile commerce (m-‐commerce) and mobile service (m-‐service) have become the
latest areas of business interest. The mobile channel is seen as a serious opportunity for
businesses to create extra value for the customer and to reduce operational costs.
Businesses have started adopting the mobile channel as an alternative or additional
service channel next to their call centers, retail stores and websites. Examples are banks
like Rabobank and ABN Amro and telecom providers like KPN. During the past decade,
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banks started offering mobile banking services on its customers’ smartphones and
tablet computers. This, as a more convenient alternative to their website based banking
services. Telecom providers started self-‐service environments offering mobile text-‐
based chat solutions to their customers enabling instant contact with their service
agents through a customer’s smartphone or tablet computer. Primary reasons for this
movement are lower operational costs and to provide customers with a more
convenient alternative to call center services. But, according to research by Riel et al
(2004) on value enhancing online services, apps offering peripheral services are only
valuable if customers find them useful and unique compared to alternatives. They state:
‘Companies need to investigate what type of value they are currently creating, but also
what type of value is lacking. Customers could be segmented according to the type of
experienced value and services could be designed to increase the preferred value of
each segment.’
1.2 Problem definition
Customers are adopting smartphones and mobile apps rapidly (Boulos et al, 2011;
Canalys, 2011) and companies are significantly increasing investments in mobile
strategies (Forrester, 2011) and are starting to develop apps to create added value.
Costs of developing apps are significant, ranging between $ 25.000 and $ 100.000 per
app for relative simple mobile functionalities increasing to $ 100.000 and more for
complex mobile functionalities (BusinessNewsDaily, 2013). Since customers are
becoming more experienced with smartphones and apps and businesses are allocating
significant amounts of resources to app development and maintenance, it has become
important to increase the understanding of the value of an app within the total product
or service package offered to the customer. Companies need to ask themselves; do our
customers demand mobile app services, and if so, which problems do these app
services need to solve and how important are these services compared to other
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customer demands? Asking these questions helps a company to allocate its resources
to the development of services which have high impact on customer satisfaction and
loyalty. In addition, when companies are actually starting to developing an app, it is
important to understand customer needs and preferences of the different user
segments regarding apps and to develop apps accordingly. This, to ensure that the app
really adds value for its users by fulfilling a need or solving a problem. Only when
developed accordingly, a value added service app could help companies improve their
customer satisfaction and loyalty levels.
1.3 Theoretical relevance
While service delivery in e-‐commerce and e-‐service context has been researched
extensively, little scientific research seems to be conducted yet on service delivery in
mobile commerce (m-‐commerce) and mobile service (m-‐service) environments.
Especially, few studies have investigated the delivery of services through mobile
applications (apps) on smartphones and tablets. As Riel et al (2004) already stated in
their study on online support services: ‘Next generation mobile phones are already
opening up many new opportunities as a channel for online support and the value and
enjoyment of receiving various supporting services through that channel should be
investigated’. Therefore, this research project aims to increase the understanding of
creating added value by developing mobile applications (apps) from the theoretical
perspective of perceived value, customer satisfaction and customer loyalty and from
the perspective of different customer segments having different needs, preferences
and expectations.
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2. AIM AND OBJECTIVES
In this chapter the aim and objectives of this study are explained, the research questions are stated and the report’s structure is briefly explained.
2.1 Aim and objectives
This research aims to increase the understanding of value creation through the
development of mobile apps (applications on smartphones and tablets) from the
theoretical perspective of perceived value, customer satisfaction and customer loyalty
and from the perspective of different customer segments having different needs,
preferences and expectations. First, its objective is to determine the perceived value of
a mobile service app within the overall value proposition offered to the customer.
Second, it aims to determine the key attributes of the app influencing its perceived
value.
2.2 Research questions
• What is the value of a mobile service app within the overall value proposition
offered to the customer, and how does the value differ between customer
segments?
• What are the key attributes to the app's perceived value, and how do these
attributes differ between customer segments?
2.3 Report structure
The paper is structured as follows. First, existing theories are reviewed regarding value-‐
adding strategies, perceived value, customer satisfaction and customer loyalty.
Additionally, the definitions of a value proposition, a mobile service app and perceived
value are explained and a mobile app’s value attributes are described. Hypotheses are
formulated and tested based on a customer survey amongst users of a smartphone
service application (app). To conclude, results and findings will be discussed,
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recommendations and managerial implications will be given and directions for future
research are proposed.
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3. THEORETICAL FRAMEWORK
In this chapter, we summarize existing theories and literature on value creation from a
business perspective and perceived value from a customer perspective. Utilitarian and
hedonistic value and their underlying drivers are described and the dynamics of
perceived value are explained. In addition, we establish the definitions for perceived
value, a value proposition and a mobile service app and describe the different
measurement models available for measuring perceived value. Finally, the hypotheses
are developed which must help in answering the main research questions.
3.1 Literature review
3.1.1 Value
Value is an important driver of relationship marketing and delivering superior customer
value to customers is regarded an important competitive strategy. Companies are
adding value to their products by improving product quality, developing and improving
supporting services and by developing additional services enhancing the core product
(Ravald and Gronroos, 1996). In other words, they are trying to improve the total value
proposition offered to the customer. This, in order to improve customer satisfaction
with their products and to strengthen customer loyalty (Ravald and Gronroos, 1996).
Research by Cronin Jr. et al (2000) underline these cause and effect links between
value, satisfaction and customers’ behavioral intentions ‘loyalty’ and ‘word-‐of-‐mouth’,
as shown in figure 1. They state that ‘numerous studies have specified relationships
between quality, value, satisfaction and consequences as customer loyalty, positive
word-‐of-‐mouth and repurchase intentions’. Their research amongst six service
industries has proven direct links between value, satisfaction and behavioral intentions.
Value was found to be directly related to satisfaction and satisfaction directly related to
the behavioral intentions of re-‐purchasing and spreading positive word-‐of-‐mouth
(Cronin Jr. et al, 2000). In other words, by increasing value, companies can increase
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satisfaction and increased value and satisfaction result in increased customer loyalty
(Ravald and Gronroos, 1996; Woodruff, 1997; Cronin Jr. et al, 2000).
Figure 1: The relationship between quality, value, satisfaction and loyalty (Cronin Jr. et al, 2000)
However, added value is only realized when customers perceive the improved or added
product or service as important to solve their problem(s) or fulfill their need(s) and
when it meets or exceeds their expectations (Riel et al, 2004; Witell and Fundin, 2005).
In this perspective, Ravald and Gronroos (1996) define customer perceived value as the
ratio between perceived benefits and perceived sacrifice. Benefits are perceived when a
product or service fulfils the customer’s needs. Sacrifices are perceived when a
customer has to pay in order to use the product or service (monetary sacrifices), when a
customer has to invest time or effort in order the be able to use a product or service or
when a customer perceives inconvenience or risk in using a product or service (non-‐
monetary sacrifices) (Ravald and Gronroos, 1996; Boksberger and Melsen, 2011). When
the ratio between perceived benefits and sacrifices is positive, this will result in
customer satisfaction with the product or service (Ravald and Gronroos, 1996). As such,
perceived value is found to directly influence customer satisfaction (Woodruff, 1997;
Cronin Jr. et al, 2000). In addition, previous research found that not only the perceived
20
value of the total value proposition offered influences the customer’s satisfaction with a
product or service, but also at attribute-‐level customer satisfaction is influenced (Oliver,
1993). Thus, when analyzing the customer’s perceived value of a product or service, it is
important to focus on both the overall value proposition as well as on the specific
attributes included in the value proposition.
3.1.2 Business vs. customer perspective
Since there is little consensus with regards to the definition and the concept of
perceived value (Boksberger and Melsen, 2011), it is important to establish a definition
of perceived value which fits this study. Value literature (Zeithaml, 1988; Ravald and
Gronroos, 1996; Kleijnen et al, 2007, Boksberger and Melsen, 2011) describes value
from both a business and a customer perspective. The business’ value perspective is
often concerned with a customer’s lifecycle value; the net worth of a customer from its
acquisition phase, through its development and retention phases and finally, to its exit
phase. The customer’s value perspective is often described by the utilitarian
perspective of perceived value where perceived value is considered a trade-‐off between
a customer’s perceived benefits of using a product or service and the perceived
sacrifices made to use the service (Boksberger and Melsen, 2011). In line with the
customer’s value perspective, Zeithaml (1988) states: ‘Value is the customer’s overall
assessment of the utility of a product based on perceptions of what is received and
what is given.’ Sacrifices include monetary costs (purchase price, acquisition costs) and
non-‐monetary costs of a service, in which the factors ‘time, effort, search costs,
convenience and perceived risk are considered as non-‐monetary sacrifices in value
literature (Ravald and Gronroos, 1996; Boksberger and Melsen, 2011).
3.1.3 Utilitarian vs. hedonic value
In literature on value we find two types of customer perceived value; utilitarian value
and hedonic value (Kleijnen et al, 2007). First, according to Kleijnen et al (2007),
21
utilitarian value is concerned with the goal of a customer of completing a task. When
looking at literature on service quality, perceived value, customer satisfaction and
customer loyalty in the perspective of online and mobile services, divisions of utilitarian
value into different dimensions are made. Based on these existing studies, we propose
a division of utilitarian value into the following five different utilitarian value
dimensions; usefulness, ease of use, availability and speed, reliability, and support. An
overview of these different utilitarian dimensions and its measurements is given in
figure 2. As figure 2 shows, usefulness is regarded to as the functionality of the service
as perceived by the user and is measured by the relevance of the service’s information
and features, the service’s completeness, the frequency of updates, the actuality of
information and the relative advantage of the service compared to alternatives (Choi et
al, 2008; Pura, 2005; Sahadev and Purani, 2008; Wu and Wang, 2005; Yang et al, 2003;
Zhang and Von Dran, 2002). Ease of use is concerned with the ease if using the service
as perceived by the user and depend on the intuitiveness of operations (Aladwani and
Palvia, 2002; Bauer et al, 2006; Choi et al, 2008; Cyr et al, 2006; Lee and Lin, 2005; Pura,
2005; Santos, 2003; Wu and Wang, 2005; Zhang and Von Dran, 2002). Availability and
speed describe the ease of access to the service as perceived by the user and depend
on the availability and the response time of the service (Aladwani and Palvia, 2002;
Ancar and D'Incau, 2003; Bauer et al, 2006; Choi et al, 2008; King and Liuo, 2004; Lee
and Lin, 2005; Loiacono et al, 2002; Parasuraman et al, 2005; Pura, 2005; Sahadev and
Purani, 2008; Yen and Lu, 2008; Zhang and Von Dran 2002). Reliability is about the
trustworthiness of the service as perceived by the user and depends on the level of
security included in the service (Aladwani and Palvia, 2002; Bauer et al, 2006; Choi et al,
2008; Lee and Lin, 2005; Lin and Wang, 2006; Parasuraman et al, 2005; Sahadev and
Purani, 2008; Santos, 2003; San Martin Gutierrez et al, 2012; Wu and Wang, 2005; Yang
et al, 2003; Yen and Lu, 2008; Zhang and Von Dran, 2002). Support is concerned with
the help provided by the system to the user, in order to complete the user’s task and
depends on the availability of a friendly user manual and service supporting personnel
(Bauer et al, 2006; Parasuraman et al, 2005; Santos, 2003; Yen and Lu, 2008).
22
Second, according to Kleijnen et al (2007), hedonic value is concerned with emotional
involvement, the extent to which the user becomes emotionally involved through
interacting with a product or service. When looking at literature on service quality,
perceived value, customer satisfaction and customer loyalty in the perspective of online
and mobile services, divisions of hedonistic value into different measures are made. An
overview of these measurements can be found in figure 2. As figure 2 shows, we
propose hedonistic value to depend on a user’s emotional involvement which depends
on the measurements fun of using a service, visual appeal of a service and
innovativeness of a service (Bauer et al, 2006; Cyr et al, 2006; King and Liuo, 2004; Lee
and Lin, 2005; Loiacono et al, 2002; Pura, 2005; Zhang and Von Dran 2002).
23
24
Figure 2: Value attributes of a service mobile app
3.1.4 Dynamics of perceived value
Existing theories on perceived value (Zeithaml, 1988; Webster, 1989; Ravald and
Gronroos, 1996; Mittal and Katrichis, 2000; Kano, 2001; Witell and Fundin, 2005; Zhao
and Dholakia, 2009; Kim and Hwang, 2012) state that perceived value has a dynamic
nature and is influenced by demographic variables (f.e. age and education),
psychographic variables (f.e. social class and lifestyle) and behavioral variables (f.e. user
experience and user status). The following paragraph covers existing literature on this
dynamic nature of perceived value.
Demographics
Zeithaml (1988) studied consumer’ perceptions of value and found that perceived value
is subjective and individual and therefore varies amongst customers and customer
segments. Ravald and Gronroos (1996) suggest these differences are a result of
different personal values, needs and preferences. This, in line with previous studies on
service quality which have shown demographics are influencing service quality
expectations (Webster, 1998; Kim and Hwang, 2012). Webster (1989) studied whether
customers could be segmented based on their service quality expectations. Based on
this study, it can be concluded that customers can be segmented into different
categories of service needs, based on demographic variables like age and education. In
a mobile specific context, Kim and Hwang (2012) studied the effect of mobile
consumers’ value tendency on their perception of mobile internet service quality.
Consumers can have a more hedonistic value tendency (pleasure-‐oriented) or a more
utilitarian value tendency (productivity-‐oriented). They studied the relationship
between maturity and value tendency. Maturity was measured through the
demographic variables age and education. The older of age and/or the higher educated,
the more mature a customer was defined. Their study found a direct relationship; the
more mature a consumer, the higher the level of tendency towards utilitarian value. In
25
other words, mature customers have a more productivity-‐oriented tendency in contrast
to less mature customers, which have a more pleasure-‐oriented value tendency (Kim
and Hwang, 2012). Moreover, research on the use of mobile information systems in the
insurance industry found higher educated insurance agents to perceive mobile
information systems more valuable than lower educated customers (Lee et al, 2005).
Psychographics
Several studies distinct between functional value (utilitarian) and social and emotional
value (Day and Crask, 2000; Pura, 2005; Boksberger and Melsen, 2009). Social and
emotional value are concerned with psychographics (f.e. social class and lifestyle). First,
social value is defined as the perceived utility of using a product or service, through the
association of that product or service with specific demographic, socioeconomic and
cultural-‐ethnic groups. In this perspective, research found that mobile phones are
linked to Maslow’s hierarchy of needs, creating a sense of belonging, especially
amongst the younger generations which see ‘being mobile’ in sense of ‘being’ cool
(Kolsaker and Drakatos, 2009). Second, emotional value is concerned with the impact of
using a product or service on the consumer’s emotional state. Kolsaker and Drakatos
(2009) revealed that users of mobile devices are emotionally attached to their devices.
This, since their mobile devices enable an always and everywhere online mode, making
it possible to keep in touch with family, friends and colleagues regardless of their
proximity. Next to that, emotional attachment is developed by using the mobile devices
as a personal management device for both personal and work life. In other words,
consumers derive emotional value from using their mobile devices, as it as in integral
part of their lifestyle. Unfortunately, psychographic research has been criticised for its
problems associated with measurement and validity and its practical limitations as a
result of lacking psychographic segmentation opportunities available for marketers
(Gilbert et al, 1995).
Behavior
26
Woodruff (1997) found differences in value perceptions between existing and potential
customers and found usage experience to be influencing the customer’s value
perceptions. Additionally, Mittal and Katrichis (2000) argue that the service or product
attributes important to existing customers are not necessarily the same as for non-‐
customers. In line with these findings, Zeithaml (1988) states that a person might
evaluate the same product differently on different occasions. Thus, the attributes which
make the customer purchase are not similar to the attributes perceived important
during the usage of a product or service, or even after usage of the product or service. A
customer’s actual usage experience with a product or service is considered to be
influencing these perceptions, resulting in different value perceptions between existing
customers and potential customers (Zhao and Dholakia, 2009). Kano (2001) states that
customer’s perception of service attributes vary over time, depending on the
customer’s position in the product lifecycle. His study found customer’s perception of
remote TV controls to change over time. In 1983 a remote control was considered a
nice to have attribute, while in 1998 it has become a must-‐have attribute, resulting in
dissatisfaction when absent. Based on these findings, Kano (2001) identified a specific
pattern of change over time: indifferent attribute ! attractive attribute ! must-‐have
attribute (Zhao and Dholakia, 2009). In other words, service attributes are dynamic and
will change over time from being unimportant to customer satisfaction to ultimately
become a must have requirement in order to satisfy customers. In other words,
customers who do not have usage experience with a certain service value the service as
unimportant. But, customer’s which frequently have used the service value it as a must-‐
have requirement (Witell and Fundin, 2005). Extending on Kano’s findings, Witell and
Fundin (2005) found the same pattern to be true for online ordering of cinema tickets.
Although, in this context, the customer’s perception of the service transforms from nice
to have to must have after only five times of usage. In other words, customers’
adoption speed of services highly influences their value perceptions of services. Based
on these studies, we conclude that it is important for companies to understand the
needs and expectations of their different customer segments (existing and potential
27
customers), with different levels of usage experience, at different moments in the
product lifecycle, in order to provide the right value to the right customer segment.
Additionally, companies must be aware of impact of adoption speed on customer value
perceptions of services, in order to provide value to the customer at the right time.
Then only, a company will be able to deliver sustainable added value to its customers
and enhance customer satisfaction and loyalty levels (Ravald and Gronroos; 1996).
3.1.5 Measuring perceived value
Different models are developed which can be applied to assess the perceived value of
products and services. Most models measure perceived value at the overall level of a
value proposition (Zeithaml, 1988; Cronin, 2000; Parasuraman and Grewal, 2000;
Petrick, 2002). Zeithaml (1988) and Cronin Jr. et al (2000) measure perceived value as
perceived quality minus perceived monetary and non-‐monetary sacrifices. In addition,
Parasuraman and Grewal (2000) propose a distinction of perceived quality in two sub-‐
drivers of quality; product quality and service quality. They state: ‘In instances where
the core of what the seller offers to the buyer is a service, there is no tangible product
and, as such, product quality and service quality overlap.’ Also, their distinction enables
the inclusion of value added services (f.e. after sales support) when determining
perceived value. Sweeney and Soutar (2001) developed the PERVAL model, which
measures customer’s perceived value of a product at brand level. Their model suggests
perceived value is driven by four sub-‐dimensions of value; emotional value, social value,
functional value in terms of value for money and functional value in terms of perceived
versus expected performance. Petrick’s SERV-‐PERVAL (2002) measures perceived value
as the sum of the emotional outcome of using a service, the quality experienced during
usage of the service, the reputation of the service or service provider and the
(monetary) sacrifices involved with using the service.
For measuring perceived value at the attribute level, the Kano model (Matzler and
Hinterhuber, 1998) can be applied. Kano’s theory of attractive quality helps companies
28
in analyzing the role of different product or service attributes in relation to the
customer’s perceived value and satisfaction regarding the product or service. Although
the model was developed by the Japanese professor Kano back in 1984, over 20 years
ago, it still is relevant and widely used. From 1998 to 2012, the number of academic
articles covering Kano’s model actually increased (Luor et al, 2012). It has been used
extensively in quality management, product and service development, strategic
thinking, employee management, business planning and service management (Witell
and Lofgren, 2007). In addition, it has successfully been applied to assess the
classification of website attributes (Zhang and Von Dran, 2002), web community
attributes (Kuo, 2004) and e-‐learning services’ attributes (Chen and Kuo, 2011).
According to Matzler and Hinterhuber (1998), the strength of the Kano methodology, in
relation to other methods, is that it can provide guidance in trade-‐off situations and it
can point out opportunities for service differentiation. Moreover, Kano’s model is able
to capture the dynamic nature of customer perceptions and expectations regarding
products and services. Thus, Kano’s model is able to identify changes in customer’s
perception and expectations over time, based on variables like usage experience and a
user’s status in the product lifecycle (Zhang and Von Dran, 2002). The model is based on
Herzberg’s Motivator-‐Hygiene Theory in behavioral science, which states that the
factors causing satisfaction are different from the factors causing dissatisfaction (Witell
and Fundin, 2004). As figure 3 shows, Kano distinguishes five categories of product /
service attributes which influence customer satisfaction, which may differ between
customer segments and differ over time, due to the dynamics of perceived value;
29
Figure 3: The Kano model
1. Must-‐be attributes; an attribute which absence will result in customer
dissatisfaction, but whose presence does not significantly contribute to
customer satisfaction.
2. Attractive attributes; an attribute that gives satisfaction when present, but that
produce no dissatisfaction when absent.
3. One-‐dimensional attributes; an attribute that is positively and linearly related to
customer satisfaction.
4. Indifferent attributes an attribute which presence or absence does not cause any
satisfaction or dissatisfaction to customers.
30
5. Reverse attributes; an attribute which presence causes customer dissatisfaction,
and whose absence results in customer satisfaction.
Must-‐be attributes
Must-‐be attributes are the basic requirements for a product and very important in the
customer’s evaluation of alternatives. If these requirements are not fulfilled, the
customer will not purchase and/or use the product at all. Or, when the customer
acquires and/or uses the product, he or she will become extremely dissatisfied. On the
other hand, the customer takes ‘must-‐be’ attributes for granted and do not explicitly
demand them, therefore fulfilling of these requirements will not increase customer
satisfaction (Matzler and Hinterhuber, 1998; Witell and Fundin, 2005). An example of a
must-‐be attribute is the network coverage of a mobile telephony and internet service
provider. A customer takes network coverage for granted and expects to be able to
have connection everywhere, anytime. Customers do not explicitly demand this. When
the network coverage is good, this does not result in increased satisfaction. But, when
the coverage is bad, this will result in dissatisfaction.
Attractive attributes
Attractive attributes are the product requirements which have the greatest impact on
customer satisfaction (Matzler and Hinterhuber, 1998; Witell and Fundin, 2005). These
attributes are not explicitly demanded or expected by the customer, fulfill unconscious
customer needs and can be regarded as surprise and delight attributes. Fulfillment of
attractive requirements positively influences customer satisfaction. On the contrary,
when an attractive attribute is missing this will not result in dissatisfaction. This, since
the customer did not expect or demand the requirement. By delivering attractive
attributes, companies can increase the perceived value of their offering and increase
customer satisfaction (Matzler and Hinterhuber, 1998; Witell and Fundin, 2005).
Attractive attributes can become must-‐have attributes over time (Matzler and
Hinterhuber, 1998). For example, an attractive attribute could be the offering of free
31
wireless internet on airports. When these facilities were not offered, customers were
not expected to be dissatisfied. This, since these facilities were not fulfilling the
travelers primary need; travelling. But, since free wireless internet has become globally
available at almost every airport, customers are probably starting to expect this service
to be delivered. As a result, free wireless internet on airports is expected to shift from
an attractive requirement into a must-‐have requirement. Other attributes such as
‘airbags in automobiles have experienced similar shifts (Zhao and Dholakia, 2009).
‘One-‐dimensional’ attributes
One-‐dimensional attributes are often explicitly demanded by the customer. These
attributes have a direct linear relationship with customer satisfaction. When one-‐
dimensional requirements are fulfilled, this positively influences customer satisfaction.
But, when unfulfilled, customer satisfaction is negatively influenced. In case of a
negative relationship between an attribute and satisfaction, the attribute is regarded as
reverse attribute. (Matzler and Hinterhuber, 1998; Witell and Fundin, 2005). An
example of a one-‐dimensional attribute is the size of the mobile data bundle offered by
a mobile internet provider. The bigger the data bundle, the higher the satisfaction of
the customer with the service offered. An example of a reverse attribute is the cost of a
mobile telephony and internet subscription. The higher the monthly costs of the
subscription, the higher the dissatisfaction of the customer with the service offered.
‘Indifferent’ attributes
Indifferent attributes are attributes which do not influence customer satisfaction at all.
These attributes can become attractive attributes over time. Therefore, companies
should always take the development of indifferent attributes into consideration, since
these can provide strategic means for customer acquisition and customer retention in
the future (Yang, 2005).
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Yang (2005) states that ‘for any quality attribute, its influence on customer satisfaction
is closely related to the degree of importance attached to it by customers. For example,
in a car, an automatic gearbox and a luggage carrier are both attractive quality
requirements. However, most customers consider an automatic gearbox to be more
important than a luggage carrier. Therefore, adding an automatic gearbox will create
greater customer value than adding a luggage carrier’ (Yang, 2005). In other words, it is
important to not only measure the Kano category of an attribute, but also its relative
importance compared to other attributes. As figure 4 shows, Yang’s refined Kano model
takes importance into account and splits attractive attributes into highly and less
attractive, one-‐dimensional attributes into high value-‐added and low-‐value added, must
be attributes into critical and necessary and indifferent attributes into potential and
care free, based on customer’s self-‐stated importance.
Figure 4: The refined Kano model, including importance (Yang, 2005)
3.1.7 Conclusions on perceived value
Based on this literature study on perceived value we find Zeithaml’s customer
perspective (1988) on value to best fit the research: ‘Value is the customer’s overall
assessment of the utility of a product based on perceptions of what is received and
what is given.’ This assessment of value is based on two factors; utilitarian value and
hedonistic value (Kleijnen et al, 2007). Utilitarian value is concerned with the goal the
customer wants to accomplish when using a service or product and the convenience in
33
achieving this goal through the service or product. Hedonistic value concerning
emotional involvement when using a service or product. As figure 2 shows, utilitarian
value and emotional value can be split into sub-‐dimensions, which enable us to
measure perceived value on the attribute level of a service. The overall perceived value
of a customer is found to be dynamic and is influenced by demographic variables like
age and education, psychographic variables like social class and lifestyle and behavioral
variables like usage experience user status (Zeithaml, 1988; Webster, 1989; Ravald and
Gronroos, 1996; Mittal and Katrichis, 2000; Kano, 2001; Witell and Fundin, 2005; Zhao
and Dholakia, 2009; Kim and Hwang, 2012). Therefore, Kano’s methodology for
measuring customer value will used as analysis tool; it helps us determine if specific
service attributes are must-‐have, attractive or irrelevant from a customer’s point of
view and it captures the dynamic nature of perceived value (Matzler and Hinterhuber,
1998; Zhang and Von Dran, 2002). This, in order to find answer’s to our main research
questions:
• What is the value of a mobile service app within the overall value proposition
offered to the customer, and how does the value differ between customer
segments?
• What are the key attributes to the app's perceived value, and how do these
attributes differ between customer segments?
3.2 Definitions used for this study
3.2.1 Perceived value
As the aim of this study is to determine the perceived value of an mobile service app
and its attributes from a customer’s perspective, the value definition of Zeithaml (1988)
will be used for the rest of this research: ‘Value is the customer’s overall assessment of
the utility of a product based on perceptions of what is received and what is given.’
34
3.2.2 Value proposition
A value proposition is the complete product or service a company offers to its
customer. It consists of a core product or service and is often extended with additional
services, creating the augmented product (Riel et al, 2004). The core product represents
the customer’s minimal purchase conditions (Witell and Fundin, 2005). An example of a
core service is a banking offering customers the opportunity to save and lend money
and to conduct financial transactions. The augmented product exceeds the customer’s
basic needs or expectations (Witell and Fundin, 2005). A bank’s augmented product
consists of value adding services like online payment portals and mobile payment (m-‐
payment) portals, services enhancing the customer’s banking experience and reducing
the bank’s costs. The core and augmented product together are considered as the
bank’s value proposition. For this research, we will focus on the perceived value of a
mobile service app as part of the total value proposition offered.
3.2.3 Mobile Service App
A mobile app is defined as a software application on a smartphone or tablet, enabling
anywhere, anytime interaction between a company and its customers. It offers
customers a mobile gateway to online services (Xu et al, 2011). In light of this study, the
app investigated can be categorized as a service-‐oriented app and must be seen as a
peripheral service, part of the augmented product, aimed at adding value to the core
offering of the company to the customer. This, contrary to stand-‐alone apps which are
the core product by themselves (f.e. instant messaging apps, game apps). The research
will focus on the perceived value of the mobile service app as part of the total value
proposition offered. In addition, it will analyze the perceived value of the different
attributes of a mobile service app.
3.3 Hypotheses
35
Zeithaml (1988) states that ‘perceived value is the customer’s overall assessment of the
utility of a product based on perceptions of what is received and what is given’ and that
it varies amongst customers as a result of the individual and subjective nature of
perceived value. In addition, perceived value is found to be transforming over time
(Kano, 2001; Witell and Fundin, 2005). Based on these definitions of perceived value,
we will adopt a segmentation approach to determine the perceived value of a mobile
service app and its attributes within different customer segments. Segmentation will be
based on demographic and behavioral characteristics. The effect of the demographics
age and education will be analyzed. In addition, the effect of the behavioural
characteristics user status and usage experience will be investigated, in line with the
concept of perceived value transforming over time.
Because of the theoretical and practical problems associated with psychographic
segmentation (Gilbert et al, 1995), we will not include psychographic variables in this
study.
3.3.1 Demographics
Based on previous studies on perceived value and service quality (Zeithaml, 1988;
Webster, 1989; Ravald and Gronroos, 1996; Lee et al, 2005; Kim and Hwang, 2012),
different segments of users are expected to have different value perceptions of a
mobile service app. Age and education are expected to be of significant influence on
customers’ perceived value of the app. Therefore, we hypothesize;
H1a: Customers classify the mobile service app into different Kano categories
H1b: The mobile service app’s Kano classification differs between customer
segments based on age
H1c: The mobile service app’s Kano classification differs between customer
segments based on education
36
In addition, the mobile service app consists of different attributes which can add value
for the customer. These elements are; usefulness of the service, ease of use of the
service, ease of accessing the service, reliability of the service, supporting services and
emotional involvement of the customer when using the service (see literature review,
figure 2). Based on the expectation of different segments of users having different value
perceptions, we expect these attributes within the mobile service app to be perceived
differently by different customer segments;
H2a: Customers classify the mobile service app’s attributes into different Kano
categories
H2b: The mobile service app attributes’ Kano classification differs between
customer segments based on age
H2c: The mobile service app attributes’ Kano classification differs between
customer segments based on education
3.3.2 Behavior: Usage experience and user status
Previous studies (Zeithaml, 1988; Kano, 2001; Witell and Fundin, 2005; Zhao and
Dholakia, 2009) found differences in a customer’s perceived value of a service or
technology based his or her level of usage experience with the service of technology.
Therefore, we expect smartphone usage to directly influence the app’s Kano
classification category. We hypothesize;
H3a: Smartphone usage experience directly influences the mobile service app’s
Kano classification.
H3b: Frequency of app usage (in general) directly influences the mobile service
app’s Kano classification
37
H3c: The number of apps in use directly influences the mobile service app’s Kano
classification.
In addition, based on previous studies on differences in perceived value between
existing users and non-‐users of services (Woodruff, 1997; Mittal and Katrichis, 2000),
we expect that the perceived value of a mobile service app is more valuable to existing
app users than to non app-‐users. Therefore, we hypothesize:
H3d: Existing app users classify a mobile service app into a different Kano category
compared to non app-‐users
38
4. METHODOLOGY
This chapter covers the operationalization of the research. It explains the research
design, sample strategy and sample size and the data collection method. Kano’s model
for measuring perceived value is used to operationalize the research on the perceived
value of a mobile service app and the operationalization process is described. Finally,
the development and execution of the questionnaire used to collect the data is
described.
4.1 Objectives
This research aims to determine the perceived value of a mobile service app within the
overall value proposition offered to the customer. Second, it aims to determine the key
attributes influencing the app’s perceived value. In our research, we suggest a mobile
service app to be part of the augmented product, aimed at adding value to a core
product or service in order to distinguish the product or service from competition.
Kano’s model of customer satisfaction (Matzler and Hinterhuber, 1998; Yang, 2005) will
be used to find answers to the research questions;
• What is the value of a mobile service app within the overall value proposition
offered to the customer, and how does the value differ between customer
segments?
• What are the key attributes to the app's perceived value, and how do these
attributes differ between customer segments?
Kano’s methodology makes it possible to classify service attributes based on customer
perceptions. Using this model will enable us to determine the perceived value of a
mobile service app within the overall value proposition offered. This, in order to find if a
mobile app is really adding value for the customer and if the app’s perceived value
varies between customer segments. Second, it enables us to determine the perceived
39
value of the different attributes of a mobile service app, for different customer
segments. This, in order to find the app’s attributes which are of key influence on the
app’s value for its users.
4.2 Research design
We tend to execute the research in such a way that conclusions could be generally
applied, to different kinds of businesses and situations. Therefore, this study adopts a
deductive approach, based on a survey, with a descriptive and explanatory aim. The
questionnaire approach enables us to generalizing outcomes and to find relationships
between variables (Saunders et al, 2009), which fits with the research questions and the
wish for the outcomes to be generally applicable. This, contrary to inductive research
methods like focus groups and interviews, which aim to build new theories and which
are less concerned with a need for generally applying theory to practical situations
(Saunders et al, 2009).
We develop and test hypotheses based on existing theories and aim to extend existing
theories on perceived value with specific findings on perceived value regarding mobile
apps. Through the hypotheses, different perspectives of customer’ perceived value of a
mobile app are examined and described and relations between variables are analyzed
in order to explain these different perspectives.
First, literature review has been conducted to establish a good overview on existing
theories of perceived value and to determine the attributes of a mobile service app
which customers’ perceive valuable. Second, the measurement method for measuring
perceived value was determined and hypotheses have been developed, based on
existing value theories. Third, in order to test these hypotheses, a questionnaire has
been set-‐up. Development and execution of this questionnaire are explained in the
following paragraphs.
40
4.3 Sample strategy and sample size
To determine the perceived value of a mobile service app and the value of the mobile
app’s attributes, we will sample amongst customers of a Dutch mobile network
provider offering mobile telephony and internet network services. The company offers
its mobile customers a mobile service app for smartphones, which provides customers
information on monthly bills and actual usage of their bundle (call minutes, text
messages, data bundle), and offers the possibility to instantly upgrade or downgrade
the subscription and to purchase value added services. In that sense, the mobile service
app should be considered part of the augmented product, aimed at adding value to the
core product; a mobile telephony and internet network service. This fits with our
research context. We have contacted a random set of customers the company’s
subscriber base by email and through an online questionnaire we asked them if they
have usage experience with smartphones and with mobile apps in general. In addition,
we asked if they have usage experience with the company’s mobile My service app.
The company’s customer base includes more than 1 million subscribers. For a 95%
confidence level of the data collected and 5% margin of error, we need at least 384
complete responses (Saunders et al, 2009). This, to ensure that the characteristics of
the sample data collected will represent the characteristics of the total population. A
simple random sampling approach (probability sampling) has been adopted to select
the sample.
4.4 Data collection
4.4.1 Measuring the dynamics of perceived value: Kano’s measurement model
Kano’s measurement model for measuring product attribute classifications is used to
collect and analyze the data. The model suggests a specific method to collect data
which involves a functional-‐dysfunctional form of asking the customer’s perceived value
of the different attributes of a product or service (Sauerwein et al, 1996). This,
41
reflecting Herzberg’s Motivator-‐Hygiene Theory, which states that the factors causing
satisfaction are different from the factors causing dissatisfaction (Witell and Fundin,
2004). The functional question analyzes the customer’s perception if the product or
service offers a specific attribute. For example, it asks; ‘How do you feel if attribute X is
present in a mobile service app?’ The dysfunctional question analyzes the customer’s
perception if the product or service lacks a specific attribute. For example, it asks; ‘How
do you feel if attribute X is not present in a mobile service app?’ As figure 5 shows,
respondents can give five different answers to the functional/dysfunctional questions:
1. I like it, 2. I require it (must-‐be), 3. neutral, 4. I don’t mind (live with), 5. I don’t like it
(dislike). Kano’s attribute classification table (Matzler and Hinterhuber, 1998) in figure 5
combines the answers to the functional and dysfunctional question and classifies an
attribute to one of the Kano attribute categories; must have, attractive, one-‐
dimensional, reverse, indifferent or questionable (see literature review for explanation).
A questionable classification shows us that the question concerning an attribute has
been phrased incorrect or has been misunderstood by the respondent (Matzler and
Hinterhuber, 1998).
Figure 5: Kano’s attribute classification table
Other methodologies for classification of product and service attributes are the direct
classification method and Kano’s 3-‐level questionnaire (Witell and Lofgren, 2007). First,
the direct classification method directly asks customers to classify attributes into Kano’s
42
various categories themselves. Main advantage of this method is that fewer questions
need to be asked, which shortens the questionnaire length and stimulates response
(Mikulic and Prebežac, 2011). But, according to Mikulic and Prebežac (2011) this
method is only preferred in situations where the respondents’ understanding of Kano’s
different categories is guaranteed. In addition, the direct classification method is found
to overestimate the role of must be attributes and underestimate the role of attractive
attributes (Witell and Lofgren, 2007). Second, Kano’s 3-‐level questionnaire is similar to
Kano’s 5-‐level questionnaire, but only measures on a 3-‐point scale (satisfied, neutral,
dissatisfied). This increases the ease of completing the questionnaire for the
respondent, stimulating response. But, like the direct classification method, the Kano 3-‐
level questionnaire is also is found to overestimate the role of must be attributes and
underestimate the role of attractive attributes (Witell and Lofgren, 2007). Since our
sample is not expected to be known with the Kano methodology, we chose to apply the
most commonly used Kano 5-‐level classification method, asking both functional and
dysfunctional questions, accepting that the extra questions result in a lengthier
questionnaire.
4.4.2 Questionnaire
75.000 mobile subscribers have been contacted by email and asked if they would like to
participate in a questionnaire on smartphone and app usage. An incentive was used to
stimulate responses. Data has been collected through an online questionnaire based on
SurveyMonkey.com’s online questionnaire tool. In total, 1.016 customers responded
and shared their smartphone and apps experience with us.
The questionnaire was constructed according to the Kano model (Sauerwein et al,
1996). First, in order to determine the overall proposition’s attributes to be investigated
through the questionnaire, we conducted exploratory desk research on the value
proposition attributes of a mobile telephony and internet subscription. These attributes
43
have been based on research within the company’s mobile customers base. See figure 6
for an overview of these attributes.
Second, in order to determine the value attributes of the mobile service app to be
investigated through the questionnaire, we conducted exploratory desk research on
value attributes in online and mobile service contexts (see chapter 3, theoretical
framework). Based on the value attributes found, a set of value attributes for a mobile
service app has been constructed. An overview of this set is given in figure 7.
Third, to increase the respondents’ understanding of the questions asked and increase
the value of the data to be collected, a couple of pilot questionnaires were distributed
amongst the target group. Feedback on these pilot questionnaires has been collected
and used to develop the final questionnaire.
Variable
Promotions / discounts
Mobile Service App
Internet speed
Size of internet bundle (MB's / GB's)
Size of calling & texting bundle (minutes & SMS-‐es)
Free WiFi Hotspots
Brand (image / trustworthiness)
Price / monthly subscription costs
Network coverage
Service quality (website, helpdesk, store)
Figure 6: Attributes of a mobile telephony and internet subscription
44
45
46
Figure 7: Value attributes of a service mobile app
47
5. ANALYSIS AND RESULTS
In this chapter, we describe the data analysis process, the characteristics of the sample
analyzed and the main results of our analysis regarding perceived value and differences
in perceptions based on demographic factors age and education and behavioural factors
user status and usage experience.
5.1 Analysis of questionnaire data
The data has been collected through SurveyMonkey.com’s online questionnaire tool. In
Excel, Kano categories have been determined based on the acquired data. Afterwards,
the enriched data was exported to SPSS 20.0 for analysis. Where applicable, a 0,05
criterion of statistical significance has been used to determine if hypotheses were
significantly supported or not.
5.2 Characteristics of the sample
Figure 8 gives an overview of the characteristics of the study’s respondents. The sample
consists of 63% male and 37% female respondents. 8% of the respondents was between
15 and 24 years of age, 15% between 25 and 34 years of age, 19% between 35 and 44
years of age, 20% between 45 and 54 years of age and 38% was 55 years or older. This
relative high age can be explained by the relative old customers of the population from
which the sample was selected; the company’s subscriber base. The sample’s education
level shows 2% of the respondents did not have any higher education at all, 17%
finished high school, 34% finished MBO, 35% finished HBO and 13% finished university.
The sample’s general level of smartphone experience can be considered high, with only
13% of the sample lacking smartphone experience. 5% had less than half a year of
smartphone experience, 9% half a year to a year, 24% one to two years and 50% had
over 2 years of smartphone experience. This can also be explained by the population
from which the sample has been selected; company’s mobile subscriber base. Of these
smartphone experience respondents, only 3% did not use mobile apps at all. The other
48
97% uses apps at least once a month or more with 22% of the respondents using apps
over 100 times a month. Amongst the app users, 10% only uses 1 to 2 apps, 39% uses 3
to 5 apps, 36% uses 6 to 10 apps and 16% uses more than 10 apps a month. In addition,
59% of the respondents use the My service app at least once a month, while the other
41% does not use this mobile service app.
Figure 8: Sample characteristics
Count %Male 642 63%Female 374 37%15 years or younger 3 0%15 -‐ 24 years 77 8%25 -‐ 34 years 148 15%35 -‐ 44 years 190 19%45 -‐ 54 years 207 20%55 years or older 391 38%No education 16 2%High school 168 17%MBO 343 34%HBO 358 35%WO / University 131 13%No experience 134 13%< 1/2 year 50 5%1/2 -‐ 1 year 90 9%1 -‐ 2 years 239 24%2 years + 503 50%Never 28 3%< 1x per month 38 4%1 -‐ 10x per month 113 11%11 -‐ 50x per month 264 26%51 -‐ 100x per month 210 21%100x per month or 226 22%N/A 137 13%1 -‐ 2 apps 82 10%3 -‐ 5 apps 331 39%6 -‐ 10 apps 301 36%10 apps + 132 16%Never 351 41%< 1x per month 143 17%1 -‐ 2x per month 137 16%3 -‐ 5x per month 112 13%6 -‐ 10x per month 61 7%10x per month + 42 5%
Gender
Age
Education
Smartphone experience
Frequency of app usage
MyKPN usage
Number of apps in use
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5.3 Demographics and perceived value
The first set of hypotheses concern the effects of demographics on the perceived value
of a mobile service app within the overall value proposition and the perceived value of
the app’s different attributes.
First, we analyze the perceived value of the mobile service app on the level of the
overall value proposition offered. As figure 9 shows, generally, respondents consider
the mobile service app a one-‐dimensional attribute within the Kano classification and a
low-‐value added attribute within Yang’s classification. Yang’s classification distinct
between high and lower importance items, in which all items rated above the
importance mean (7,74) are ranked as high important and all items below the
importance mean are ranked less important. But, as figure 10 shows, classification of
the mobile service app is highly dispersed amongst customers. In total, 49% of the
respondents consider the app as an interesting attribute, with 13% classifying the app
as must-‐have attribute, 23% as one-‐dimensional attribute and 13% as attractive
attribute. Based on these results, hypothesis 1a is supported; customers classify the
mobile service app into different Kano categories.
Figure 9: Value proposition attribute classification
50
Figure 10: Mobile app attribute classification
In order to answer hypothesis 1b, a bivariate correlation analysis has been conducted in
SPSS, in order to determine if Kano classification of the mobile service app is correlated
with a respondent’s age. A two-‐tailed test is applied, since we cannot predict if age has
a positive or negative effect on the app’s classification. Kendall’s tau is used to find if
there is the correlation between app classification and age exists, which is suggested a
better estimate of the correlation in a population than the more popular Spearman’s
correlation coefficient (Field, 2009). Figure 11 shows the dispersion in the app’s
classification between different age segments and the outcome of the analysis.
Kendall’s tau shows there is no significant correlation with 0,735 significance. Thus,
hypothesis 1b is not supported; age does not significantly influence the app’s Kano
classification.
51
Figure 11: Age vs. app classification
In order to answer hypothesis 1c, a bivariate correlation analysis has been conducted in
SPSS, in order to determine if Kano classification of the mobile service app is correlated
52
with a respondent’s level of education. A two-‐tailed test is applied, since we cannot
predict if education has a positive or negative effect on the app’s classification. Again,
Kendall’s tau is used to find if there is the correlation between app classification and
education exists. Figure 12 shows the dispersion in the app’s classification between
different education segments and the outcome of the analysis. Kendall’s tau shows
there is a significant correlation with 0,047 significance. Thus, hypothesis 1c is
supported; education does significantly influence the app’s Kano classification.
53
Figure 12: Education vs. app classification
Second, we analyze the perceived value of the app’s attributes. As figure 13 shows,
generally, respondents consider security of the app’s information exchanged as a must-‐
be attribute based on Kano’s classification and a critical attribute based on Yang’s
classification. Yang’s classification distinct between high and lower importance items, in
which all items rated above the importance mean (7,38) are ranked as high important
and all items below the importance mean are ranked less important. Availability,
54
actuality, relevance and speed are considered one-‐dimensional attributes in Kano’s
classification and high value-‐added in Yang’s classification. Ease of use is considered an
attractive attribute in Kano’s classification and highly important according to Yang’s
model. The other features of the app are less important when considering the general
perspective of the respondents. But, as figure 14 shows, also the classification of the
mobile service app’s attributes is highly dispersed amongst customers. Based on these
results, hypothesis 2a is supported; customers classify the mobile service app’s
attributes into different Kano categories.
Figure 13: App attribute classification
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Figure 14: App attribute classification
In order to answer hypothesis 2b, a bivariate correlation analysis has been conducted in
SPSS, in order to determine if Kano classification of the mobile service app’s attributes
is correlated with a respondent’s age. Again, a two-‐tailed Kendall’s tau test is used to
find if there is the correlation. Figure 15 shows the correlation coefficients for the app’s
attributes classification and age. Kendall’s tau shows there is no significant correlation
between age and one or more of the app’s attributes classifications. Thus, hypothesis
2b is not supported; age does not significantly influence the app attributes’ Kano
classification.
Figure 15: Age vs. app attribute classification
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Although, when analyzing importance ratings on a 1-‐10 scale with Spearman’s
correlation coefficient for such parametric scales (Field, 2009) shows us correlations
between age and importance rating. As figure 16 shows, the importance of availability
(significance of 0,021) and a user manual (significance of 0,001) are significant
correlated with age. For the attribute availability, the correlation is negative, meaning
the younger the respondent the more important availability is rated. For the attribute
user manual, the correlation is positive, meaning the older the respondent the more
important the user manual becomes.
Figure 16: Age vs. importance of availability and user manual
In order to answer hypothesis 2c, a bivariate correlation analysis has been conducted in
SPSS, in order to determine if Kano classification of the mobile service app’s attributes
is correlated with a respondent’s education. Again, a two-‐tailed Kendall’s tau test is
used to find if there is the correlation. Figure 17 shows the correlation coefficients for
the app’s attributes classification and education. Kendall’s tau shows there is a
significant correlation between education and the classifications of a number of the
app’s attributes; relevance (0,000 significance), completeness of features (0,007
significance), actuality (0,018 significance), relative benefit (0,009 significance),
availability (0,003 significance), speed (0,001 significance) and security (0,000
significance). Thus, hypothesis 2c is supported; education does significantly influence
the app attributes’ Kano classification.
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Figure 17: Education vs. app attribute classification
5.4 Behavioural characteristics and perceived value
The second set of hypotheses concern the effects of behavioural characteristics ‘usage
experience’ and ‘user status’ on the perceived value of a mobile service app within the
overall value proposition and the perceived value of the app’s different attributes.
First, we analyze the effects of the behaviour characteristic ‘usage experience’ on the
level of the mobile service app’s Kano classification. A two-‐tailed Kendall’s tau test is
used to find if there is the correlation. Figure 18 shows the correlation coefficients for
the app’s classification and smartphone usage experience. Kendall’s tau shows there is
a significant correlation, with 0,004 significance. The more experienced a user is with
his or her smartphone, the more the app shifts towards Kano’s must-‐have classification.
Thus, hypothesis 3a is supported; smartphone usage experience directly influences the
app attributes’ Kano classification.
Figure 18: Smartphone usage experience vs. app classification
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Second, we analyze the effects of the behaviour characteristic ‘usage experience’ in
terms of ‘frequency of app usage’ on the level of the mobile service app’s Kano
classification. The two-‐tailed Kendall’s tau test is used to find if there is the correlation.
Figure 19 shows the correlation coefficients for the app’s classification and app usage
frequency. Kendall’s tau shows there is a significant correlation, with 0,043 significance.
The more a customer uses apps on his or her smartphone, the more the mobile service
app shifts towards Kano’s must-‐have classification. Thus, hypothesis 3b is supported;
app usage frequency directly influences the app attributes’ Kano classification.
Figure 19: Frequency of app usage vs. app classification
Third, we analyze the effects of the behaviour characteristic ‘usage experience’ in terms
of ‘amount of apps in use’ on the level of the mobile service app’s Kano classification.
The two-‐tailed Kendall’s tau test is used to find if there is the correlation. Figure 20
shows the correlation coefficients for the app’s classification and the number of apps in
use. Kendall’s tau shows there is a significant correlation, with 0,324 significance. The
higher the number of apps used, the more the mobile service app shifts towards Kano’s
must-‐have classification. Thus, hypothesis 3c is not supported; the number of apps in
use does not influence the app attributes’ Kano classification.
59
Figure 20: Number of apps in use vs. app classification
However, when analyzing importance ratings on a 1-‐10 scale with Spearman’s
correlation coefficient, figure 21 shows a significant (0,000 significance) correlation
between number of apps used and importance rating. Thus, the more apps in use, the
more important a mobile service app is perceived.
Figure 21: Number of apps in use vs. app importance
Finally, we analyze the effects of the behaviour characteristic ‘user status’, in terms of
‘existing or non-‐existing My service app user’, on the level of the mobile service app’s
Kano classification. The two-‐tailed Kendall’s tau test is used to find if there is the
correlation. Figure 22 shows the correlation coefficient for the app’s classification and
user status. Kendall’s tau shows there is a significant correlation, with 0,000
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significance.
Figure 22: User status vs. app classification
As figure 23 shows, existing users of the My service app consider the app more as a
one-‐dimensional attribute, where non-‐users value it more as indifferent and thus less
important. In addition, figure 23 shows that the more the My service app is used, the
more the app shifts towards the must-‐have Kano category and thus, the more
important it becomes. Hypothesis 3d is supported; existing My app users classify the
mobile service app more as a must-‐have attribute, where non-‐users consider a mobile
service app irrelevant.
Figure 23: User status vs. app classification
5.5 Main findings
The research results reveal that different customer segments have different attitudes
towards mobile apps, with a significant number of customers regarding a mobile app a
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must-‐have element of the overall value proposition offered. Education, usage
experience and user status are found to significantly influence a customer’s perceived
value regarding a mobile app. On the other hand, age was found to be not significantly
influencing an app’s perceived value.
First, education is found to influence an app’s perceived value; higher educated
customers regard an app more must-‐have compared to lower educated customers.
Second, usage experience is found to influence an app’s perceived value; the more
smartphone and app experience a customer has, the more must-‐have an app is
perceived. Third, user status is found to influence an app’s perceived value; existing app
users regard an app as must-‐have, while non-‐users see an app as irrelevant. Fourth, on
the app level, security was found to be a must-‐have attribute amongst all customer
segments. Finally, also on the app’s attributes level different customer segments are
found to have different needs and expectations. Age and education are significantly
influencing needs and expectations regarding the app’s attributes; younger
respondents perceive availability of the app as more important than do older
respondents, while older respondents perceive providence of a user’s manual more
important than do younger respondents.
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6. DISCUSSION AND CONCLUSIONS
In this chapter, we summarize the research’s aim and objectives, the research process
and the main findings of our study. In addition, we reflect on the use of Kano’s model for
measuring perceived value and on the research project as a whole.
6.1 Main conclusions
This study contributes to research on service quality and value delivery in the context of
online and mobile services, with emphasis on services delivered through mobile apps
(software applications for smartphones and tablets). It aimed to increase the
understanding of value creation through the development of mobile apps from the
theoretical perspective of perceived value, customer satisfaction and customer loyalty
and from the perspective of different customer segments having different needs,
preferences and expectations. First, we reviewed existing theories regarding value-‐
adding strategies, perceived value, customer satisfaction and customer loyalty and
reviewed the measurement models available to measure perceived value. Second, we
formulated the hypotheses which help us find the answers to the main research
questions of this study;
• What is the value of a mobile service app within the overall value proposition
offered to the customer, and how does the value differ between customer
segments?
• What are the key attributes to the app's perceived value, and how do these
attributes differ between customer segments?
To answer these hypotheses, we have developed and conducted an online
questionnaire amongst users of a smartphone service application (app). Analysis of the
questionnaire’s responses helped us answer the main research questions.
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Based on an extensive literature review on perceived value we found Zeithaml’s
customer perspective (1988) on value to best fit the research: ‘Value is the customer’s
overall assessment of the utility of a product based on perceptions of what is received
and what is given.’ The overall perceived value of a customer is found to be dynamic
and is influenced by demographic variables like age and education, psychographic
variables like social class and lifestyle and behavioral variables like usage experience
user status (Zeithaml, 1988; Webster, 1989; Ravald and Gronroos, 1996; Mittal and
Katrichis, 2000; Kano, 2001; Witell and Fundin, 2005; Zhao and Dholakia, 2009; Kim and
Hwang, 2012). Therefore, Kano’s methodology for measuring customer value is used as
analysis tool; it helped us determine the value of a mobile service app and its attributes
from a customer’s point of view and it captures the dynamic nature of perceived value
(Matzler and Hinterhuber, 1998; Zhang and Von Dran, 2002). This, in order to find
answer’s to our main research questions. After developing and conducting the online
questionnaire, we have analyzed the results and looked for insights on the value
proposition level and on the app’s attributes level.
What is the value of a mobile service app within the overall value proposition offered to
the customer, and how does the value differ between customer segments?
On the overall value proposition level, we found customers generally perceive a mobile
service app as a one-‐dimensional Kano attribute, contributing positively to customer
satisfaction when present, but with relative low-‐added value compared to other
attributes. However, significant differences were found between customers regarding
the perceived value of an app within the total value proposition. A significant number of
customers (13%) perceived the app as must-‐have attribute, followed by one-‐
dimensional (23%) and attractive classification (13%). Education was found to influence
a customer’s perceived value of a mobile service app, with higher educated customer
segments regarding a mobile service app as more must-‐have compared to lower
educated customer segments. Additionally, usage experience and user status strongly
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influence the customer’s perceived value of a mobile service app. The more years of
smartphone experience and the more frequent apps are used, the more must-‐have a
mobile service app becomes for a customer. In line with these findings, existing users of
a mobile app service perceive this service as must-‐have while non-‐users perceive it as
irrelevant. These findings match with earlier studies regarding the impact of usage
experience (Zeithaml, 1988; Kano, 2001; Witell and Fundin, 2005; Zhao and Dholakia,
2009) and user status on the perceived value of services and technologies (Woodruff,
1997; Mittal and Katrichis, 2000).
What are the key attributes to the app's perceived value, and how do these attributes
differ between customer segments?
On the app’s attributes level, customers perceive security as must-‐have and critical
attribute. In other words, the reliability of a mobile service app is a critical success
factor. These findings match with earlier research regarding the perceived value of
reliability and security in services contexts like online shopping and mobile banking
(Aladwani and Palvia, 2002; Bauer et al, 2006; Choi et al, 2008; Lee and Lin, 2005; Lin
and Wang, 2006; Parasuraman et al, 2005; Sahadev and Purani, 2008; Santos, 2003; San
Martin Gutierrez et al, 2012; Wu and Wang, 2005; Yang et al, 2003; Yen and Lu, 2008;
Zhang and Von Dran, 2002). When the mobile app service is perceived by its customer
as not reliable, the service will not be used at all, or existing users will stop using the
service. Ease of use is considered an attractive attribute and high-‐value added. This
feature is not explicitly demanded by customers, but when present it will result in
delight and increase customer satisfaction. Availability (ease of access), actuality
(usefulness), relevance (usefulness) and speed (ease of access) are regarded as one-‐
dimensional and high-‐value added. The more these attributes are fulfilled, the more
satisfied the customer will be with the mobile app service. Also on the app’s attributes
level, differences between customer segments are found. Younger respondents
perceive availability of the app as more important than do older respondents, while
65
older respondents perceive providence of a user’s manual more important than do
younger respondents. Moreover, the level of education not only influences the
perceived value of the mobile service app within the overall value proposition, but also
the perceived value of the different attributes of the app. In other words, customers
have varying needs and expectations regarding the features offered in a mobile service
app.
6.2 Reflection on Kano’s model for measuring perceived value
Kano’s model (Matzler and Hinterhuber, 1998) was used for measurement of the
perceived value of the app within the overall value proposition offered and for the
perceived value of the different attributes of the app. First, because Kano’s model
enables measurement of perceived value at the attribute level (Matzler and
Hinterhuber, 1998), in order to answer our research questions. Second, since the model
was found to be able to capture the dynamic nature of customer perceptions,
identifying changes in customers’ perceptions and expectations over time, based on
variables like usage experience and a user’s status in the product lifecycle (Zhang and
Von Dran, 2002).
We found the Kano model to be capable of delivering both objectives, measuring
perceived value at the attribute level and measuring the dynamics of perceived value.
Although, developing a survey based on Kano’s measurement model was found to be
difficult and time consuming. First, we needed some rounds of pilot questionnaires
before developing the final questionnaire, in order to stimulate response volumes and
completion rates. Second, the method of asking functional and dysfunctional questions
felt strange for most respondents, which asked for extra explanation in the introduction
text of the questionnaire. Third, the method of asking functional and dysfunctional
questions results in a lengthy questionnaire, which gave limited room for questions if
we wanted to pursue a high number of responses and a high completion rate. Finally,
the manual coding of the questionnaires answers into Kano categories is time
66
consuming and due to its manual processing, prone to errors. Despite these remarks,
we found the Kano model to be a very strong tool for measuring perceived value,
delivering findings which are easy to interpret and use.
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7. RECOMMENDATIONS
In this final chapter of our research, we describe the limitations of our study and give
suggestions for further research, which can build upon our findings. To summarize, we
explain the managerial implications of the findings of our study. We stress the
importance for businesses to understand the value of mobile app’s and explain how
businesses could developed real and sustainable added value for their customers by
providing apps which fulfil customer needs and expectations, adding real value for the
customer.
7.1 Limitations and recommendations for future research
Some limitations of this study must be kept in mind to put its findings in perspective.
First, the product lifecycle for smartphones and apps is still in its growth phase,
especially in emerging markets like Asia and Africa, but also in Western countries. Based
on the dynamics of perceived value of new services and technologies, with time
influencing customers’ perceptions, we expect the value of mobile apps to have just
passed its infancy stage and that it will be rising over the coming years. Therefore, we
recommend to further investigate the potential changes in customers’ perceived value
of mobile apps over time. Second, the research focuses on mobile service apps
delivering added value to customers in the context of the telecom industry. To be more
precise, it studied the perceived value of a mobile service app amongst customers of a
Dutch mobile telephony and internet network provider. In that sense, findings should
not be generalized to other service businesses like banking, insurance agencies and
utility providers. Further research on the use of mobile service apps in other service
industries is recommended, in order to see if there is consistency with our findings and
if our conclusions can be applied to the service industry in general. Moreover, findings
should not be generalized on a global scale. Additional research on the value of mobile
service apps in other countries, regions and cultures is recommended in order to find
potential differences in perception based on these geographic and cultural dimensions.
Third, the research focused on mobile service apps as part of the augmented product,
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delivering supporting services to an intangible core service offering. Further research on
mobile apps as part of the augmented product of a tangible product offering is
recommended. This, in order to investigate if there are differences between the added
value of a mobile app as tangible extension of an intangible service offering (f.e. mobile
telephony subscription) and the added value of a mobile app as extension of a tangible
product (f.e. Nike+ mobile app, linked to running shoes). Fourth, this study investigated
the value of a utilitarian and productivity-‐oriented service app. Future research should
investigate the perceived value of hedonistic and more pleasure-‐oriented mobile apps.
Finally, this study focused around an evolutionary mobile app services, offering
traditional services digitalized in the form of a mobile app, delivering value based on
relative benefit compared to traditional service channels. Therefore, we recommend
the investigation of revolutionary mobile app services, services which did not exist
before and which are designed around the unique characteristics of mobile
technologies (f.e. Nike+ mobile app). This, to find if evolutionary mobile service apps
deliver different value to customers than do revolutionary service apps.
7.2 Managerial implications
Results of this study provide several implications for businesses. When managers are
developing value-‐adding strategies and are considering the development of a mobile
app to deliver added value to its customers, they must be aware of the different
attitudes of their different customer segments regarding mobile app services and must
base their value-‐adding strategies on thorough understanding and analysis of their
different customer segments’ needs and expectations. This, in line with earlier research
on value creation, relationship marketing and customer loyalty (Ravald and Gronroos,
1996; Riel et al, 2004). In general, apps can be considered as irrelevant or of low-‐added
value, but when zooming onto specific customer segments like higher-‐educated
customers, a mobile service app is perceived as must-‐have element within the overall
value proposition. Moreover, customers who are more mature in terms of smartphone
69
and app usage consider a mobile app as a must-‐have element, adding high value, in
contrast to less mature users, which consider app’s as irrelevant. This, in line with
earlier studies regarding the perceived value of new services and technologies
(Zeithaml, 1988; Kano, 2001; Witell and Fundin, 2005; Zhao and Dholakia, 2009). For
these customer segments, the higher-‐educated and more mature smartphone and app-‐
experienced customers, offering a mobile service app as part of the total value
proposition could make or break the purchase of the product or service, or the
customer’s loyalty to the product or service (Ravald and Gronroos, 1996). In other
words, for these customers, a mobile service app can be used as strategic weapon,
creating competitive advantage to fight of competitors’ offerings and stimulating the
acquisition of new customers and the loyalty of existing customers (Yang, 2005). In line
with the findings on a value proposition level, not only the perceived value of the app
itself varies amongst customers, but also the perceived value of the app’s attributes
varies. In general, reliability of the app can be seen as a critical success factor. But, for
other attributes, their perceived value varies between customer segments based on
factors like age and education. Therefore, it is important for companies to analyze and
understand the needs and expectations of their different customer segments regarding
the app’s attributes in other to develop a successful mobile app service, adding real and
long-‐term value for the customer.
Finally, the adoption of smartphones, tablets and mobile internet and the usage of apps
is still increasing year by year, especially in developing markets like Asia and Africa
where smartphone usage is exceeding PC usage, but also in Western-‐Europe and the US
(Canalys, 2011; ITU, 2012; Telecompaper, 2012; eMarketer, 2013; TNS Mobile Life,
2013). Emerging technology is continuously creating new opportunities for both
smartphone and app developers as well as their customers. Consumers (B2C) are using
their smartphones and apps to fulfill a wide variety of needs for information,
entertainment, communication, productivity, socialization and instant messaging.
Businesses (B2B) are using smartphones, tablets and apps for tasks like customer
70
relationship management (CRM), warehouse management and sales-‐force
management (Cortimiglia et al, 2011). The combination of increasing adoption rates of
mobile devices and mobile internet and emerging technologies is expected to push
boundaries for smartphone and app functionalities and increase maturity of both B2C
and B2B customers regarding the usage of smartphones and apps. In line with earlier
studies on the change of perceived value of new services and products over time (Kano,
2001; Zhao and Dholakia, 2009), this curve in the product lifecycle of smartphones and
apps will definitely push the perceived value of service apps to more and more must-‐
have, offering businesses growing opportunities for the delivery of value through
mobile apps.
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