delivering value through mobile apps by mark hoskam

Master thesis

An Exploration of Delivering Value

through Mobile Apps

by

Mark Hoskam

2013

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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

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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),

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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).

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:



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

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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;



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

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.

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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

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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,

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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.

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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;



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

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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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