Effects of apps on consumer behavior of smartphones and

Eindhoven University of Technology

MASTER

Effects of apps on consumer behavior of smartphones and telecommunicationprovidersfeature fatigue vs. mass customization

Agterhuis, D.J.

Award date:2012

DisclaimerThis document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Studenttheses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the documentas presented in the repository. The required complexity or quality of research of student theses may vary by program, and the requiredminimum study period may vary in duration.

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.

Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain

Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.

Download date: 15. Feb. 2018

Student identity number: 0722219

In partial fulfillment of the requirements for the degree of

Master of Science in Innovation Management

First supervisor: dr. ing. J.P.M. Wouters

Second supervisor: dr. J.J.L. Schepers

Company Supervisor: S. Balkenende

Publishing date: 9th

of August, 2012

University: Eindhoven University of Technology

Faculty: Industrial Engineering & Innovation

Sciences

Department: Innovation, Technology,

Entrepreneurship and Marketing

(ITEM)

Effects of apps on consumer behavior of

smartphones and telecommunication

providers: Feature fatigue vs. mass

customization

by D.J. Agterhuis

2

Series Master Theses Innovation Management

Subject headings: Apps, Smartphones, Feature Fatigue, Mass Customization, Customer

Satisfaction, Technology Acceptance, Consumer Behavior,

Telecommunication

3

Abstract

The use of apps on smartphones raises questions about how apps affect usage and

satisfaction with the device and with telecom operators, and which of two concepts feature

fatigue or mass customization is applicable to model these effects. The number of apps

installed on the smartphone is proposed to predict three aspects of value: usefulness,

usability, and effort. In turn, this value in turn is proposed to predict usage of both the device

itself and of services provided by the operating company, and satisfaction with the device and

the operator.

The model is tested using survey results of 252 customers of a Dutch

telecommunication provider that are combined with objective usage data. Results indicate

that mass customization applies to the use of apps on smartphones since the number of apps

positively relates to usability. In turn, usability is related to utility, which affects mobile data

usage through the daily frequency of use of the smartphone. Satisfaction with the smartphone

is directly affected by the number of apps and through value. Implications are that a high

number of apps is not related to usability issues and that apps facilitate the transition in the

telecom world of voice and SMS services to mobile data.

4

Acknowledgements

Naturally, I would like to thank my first supervisor Joost Wouters of Eindhoven

University of Technology for guiding the search towards a suitable topic for graduation, for

steering the project into the right direction and for his thoughts on the theories, which were a

useful input. I want to thank my second supervisor Jeroen Schepers for his creative

contribution to the model, for answering my long e-mails and for his support in the statistical

analysis.

Much appreciation is also attributed to Sanne, Caspar, Frederieke, Jan Z., Joppe,

Michel, Nathalie and Unni for the fantastic and educating experience in the past six months at

the large Dutch telecom company. In particular, I would like to thank Sanne for her

contributions to the research. Also the funding of the market research and the access to the

customer database of the company deserves recognition.

Last but not least, much is obliged to Esmee, my family, the sponsors down under,

my friends and Douwe Egberts for the (mental) support and the mere reaching of the

graduation thesis, and for the creative discussions which definitely shaped it. Also many

thanks go out to Tim and Jan A. for their detailed reviews.

I hope you enjoy reading this report, and that you look differently at the palm of your

hand after reading it.

Dirk

5

Contents

SUMMARY 7

1 INTRODUCTION 10

2 THEORY AND HYPOTHESIS DEVELOPMENT 11

2.1 THEORY AND RECENT RESEARCH 12

2.1.1 APPS AND SMARTPHONES: MODULES FOR MODULAR PRODUCTS 12

2.1.2 TOO MANY FEATURES: FEATURE FATIGUE 12

2.1.3 MASS CUSTOMIZATION: ADAPTING FUNCTIONALITY TO MEET SPECIFIC NEEDS 13

2.1.4 CUSTOMER SATISFACTION 14

2.1.5 TECHNOLOGY ACCEPTANCE MODEL (TAM) 14

2.2 CONCEPTUAL FRAMEWORK AND HYPOTHESES 15

2.2.1 NUMBER OF APPS ON PERCEIVED VALUE AND CUSTOMER SATISFACTION 17

2.2.2 PERCEIVED EASE OF USE ON PERCEIVED USEFULNESS 19

2.2.3 ASPECTS OF VALUE ON USAGE OF THE SMARTPHONE, SERVICES PROVIDED

BY THE OPERATOR AND CUSTOMER SATISFACTION 19

2.2.4 USAGE OF THE SMARTPHONE ON USAGE OF TELECOMMUNICATION

PROVIDER SERVICES 20

2.2.5 CUSTOMER SATISFACTION OF THE SMARTPHONE ON CUSTOMER

SATISFACTION OF THE TELECOM PROVIDER 21

2.2.6 MODERATING VARIABLES 21

3 METHOD 22

3.1 DATA SAMPLE 22

3.2 MEASUREMENT INSTRUMENT 23

3.3 DATA COLLECTION PROCEDURE 25

3.4 ANALYTICAL METHOD 25

4 RESULTS 27

4.1 SAMPLE AND DATA CHARACTERISTICS 27

4.1.2 COMMON METHOD BIAS 28

4.1.3 MISSING DATA, OUTLIERS AND MULTIVARIATE ASSUMPTIONS 28

4.2 MEASUREMENT MODEL 29

4.2.1 CONSTRUCT RELIABILITY 29

4.2.2 CONSTRUCT VALIDITY AND MULTICOLLINEARITY 30

4.3 STRUCTURAL MODEL 30

4.3.2 MODERATING VARIABLES 33

6

4.3.3 CONTROL VARIABLES 34

4.4 POST-HOC ANALYSES 34

4.4.2 DIFFERENCES BETWEEN OPERATING SYSTEMS 36

5 DISCUSSION 37

5.1 FINDINGS 38

5.2 SCHOLARLY IMPLICATIONS 41

5.3 MANAGERIAL IMPLICATIONS 41

5.4 LIMITATIONS AND AVENUES FOR FURTHER RESEARCH 43

6 REFERENCES 45

6.1 LITERATURE REFERENCES 45

6.2 WEB REFERENCES 49

APPENDIX A: MEASUREMENT INSTRUMENT 50

APPENDIX B: QUESTIONNAIRE 51

APPENDIX C: DATA MANIPULATION 56

APPENDIX D: GENERALIZATION OF THE SAMPLE 58

APPENDIX E: DATA EXPLORATION: OUTLIERS AND NORMALITY 59

APPENDIX F: FACTOR LOADINGS AND CROSS LOADINGS OF THE

FIRST CFA 61

APPENDIX G: FACTOR LOADINGS AND CROSS LOADINGS OF THE

DEFINITE CFA 62

APPENDIX H: STATISTICAL VALIDITY OF THE BASE MODEL 63

APPENDIX I: POST-HOC MEDIATION ANALYSIS 64

APPENDIX J: MANOVA POST-HOC ANALYSIS 65

7

Summary

The revolution of apps and smartphones demands insights in how these apps affect

customer satisfaction and usage behavior of smartphone consumers with respect to the device

itself and usage of services offered by telecommunication providers such as voice, SMS and

mobile data. Two concepts compete in modeling these effects: feature fatigue and mass

customization. Apps are assumed to be features since the use of apps offers functionality in

addition to the hard technical specifications of smartphones. Consumers suffering from

feature fatigue have purchased a feature-packed product which is high in utility but low in

usability because of that high number of features (Thompson et al., 2005). Because

consumers can modulate the functionality of their device by adding or deleting apps, it is

interesting to assess whether the phenomenon is suitable to model effects of apps on

consumer behavior of smartphones. A competing concept is mass customization, stating that

manufacturers meet market needs by allowing consumers to adapt the functionality of

products until the latest possible moment in the supply chain (Chase et al., 2006). For

smartphones, this can be done with apps even after purchase of the device.

This study contributes by adding to the sparse body of research in smartphones and

apps. Moreover, because of the potential negative effects of feature fatigue, it is useful for

managers to know whether this phenomenon applies to smartphones. Studying the effects of

apps on smartphone usage & satisfaction is also deemed useful for practitioners in the

telecom industry due to the transition of voice and SMS to mobile data usage.

Theory and conceptual framework

A literature review was conducted, revealing (recent) research on smartphones and

apps, feature fatigue, customer satisfaction and the Technology Acceptance Model (TAM).

The basis for the theoretical framework is that apps affect the value of the smartphone as

perceived by consumers, which in turn affects usage and satisfaction. For modeling customer

satisfaction, a transaction-specific and postpurchase view of customer satisfaction is used in

combination with the comparison-standards paradigm in which perceived value is an

experience-based standard for comparison (Cadotte et al, 1987).

Customers perceived value of the smartphone is a tradeoff between benefits and

sacrifices perceived by consumers (Eggert & Ulaga, 2002), in which the benefits are adopted

from the Technology Acceptance Model: perceived ease of use and perceived usefulness. In

the TAM, perceived ease of use and perceived utility are the main variables that indirectly

explain the actual use of a product. The sacrifice is consumers perceived effort exerted to

install, update and to learning to use the apps.

These aspects of value were hypothesized to be predicted by the number of apps on

the smartphone of a consumer. In turn, these aspects of value predict the usage of a

smartphone and of services provided by the telecommunication operator. Usage of the

smartphone was broken down into daily frequency of use, daily duration of use and the

perceived usage level. Services of the telecom provider that were adopted in the model were

voice, SMS and mobile data. The number of apps installed on the smartphone and the aspects

of value were also predicted to affect customer satisfaction with both the smartphone and the

telecom provider.

8

Moderating effects were also proposed, which were adopted from the consumer

decision making process: consumers hedonic or utilitarian attitude towards smartphones, the

brand image as perceived by consumers and the subjective knowledge about smartphones

were all expected to moderate the relation between the number of apps installed on the device

and the aspects of perceived value.

Method

The model is tested with an online survey among 252 customers of a Dutch

telecommunication provider. The customers in this population were postpaid smartphone

users in possession of a smartphone of the brand Apple, Blackberry, HTC or Samsung in the

consumer segment. Results of the survey are combined with objective data of voice, SMS and

mobile data usage. The measurement instrument used to measure the concepts in the

hypothesized model was based on prior research. Control variables are age, gender,

education, duration of smartphone possession, number of smartphones owned, smartphone

brand and type of use (business or consumer. The model was tested using Structural Equation

Modeling (SEM) with Partial Least Squares (PLS) as estimation method.

Results

The data sample appeared to reflect the customer base of the telecom operator. After

deletion of a low number of items with insufficient loadings and cross-loading items, the

constructs in the model were measured reliably and convergent and discriminant validity

were warranted. Most variables or constructs have an R2 which is moderate to high for

consumer behavior studies, but the perceived effort, SMS usage and voice usage were weakly

explained.

Of the 40 hypotheses in the base model, 13 were confirmed. The number of apps on

consumers smartphones was positively related to the perception of usability of the device

and satisfaction with the device, but negatively to the effort perceived to download, update

and learning how to use the apps. Perceived usability was positively related to the perception

of usefulness of the smartphone. Regarding the aspects of value, all three constructs were

positively related to the perceived usage level, but only the perceived usefulness of the

smartphone was positively associated to the daily frequency of use of the smartphone. In turn,

consumers that use their device more often per day, often have a higher mobile data usage.

Moreover, consumers perceiving their smartphone useful, tend to have a lower SMS

usage, albeit that only 4% of the variance of SMS usage is explained. Besides the number of

apps, customer satisfaction with the smartphone was related negatively to the perceived

effort, but positively to the perceived usefulness and the perceived usability. In turn,

consumers that are satisfied with their smartphone are disposed to be satisfied with the

telecommunication company. Of the moderating effects, only a negative moderating effect of

subjective knowledge was confirmed on the relation between the number of apps installed

and the perceived usability of the smartphone.

The results gave rise to two post-hoc analyses: A one-way MANOVA for assessing

differences between operating systems, and the analysis of mediating effects of subjective

knowledge, utilitarian attitude, hedonic attitude and the perceived brand image. It appeared

9

that Blackberry users have less apps installed than users of other operating systems, and that

Apple users are generally more satisfied than Blackberry or Android users. Finally,

consumers utilitarian attitude mediates the effect of the number of apps on the perception of

usability.

Discussion

Because the number of apps installed on the smartphone positively related to the

perceived usability and negatively to the perceived effort for installing, updating and learning

to use apps, the concept of mass customization applies to apps on smartphones and no

support is found for feature fatigue. The utility of the smartphone as perceived by consumers

plays a key role in predicting the usage: it is directly associated with decreases in SMS usage,

and through an increased frequency of daily use it is positively related to mobile data usage.

The number of apps and all aspects of value related to satisfaction with the smartphone, the

only negative relation being the perceived effort. No direct effects of apps or value of the

smartphone predict satisfaction with the operator, albeit that users that are satisfied with their

smartphone do tend to be satisfied with their telecom provider. This implies that no cross-

over effects exist for the value of the smartphone or the number of apps towards the operating

company.

A main implication for scholars is that they can apply mass customization to model the

use of apps on smartphones , and that feature fatigue does not apply to modular products.

Integrating customer satisfaction and technology acceptance proved to provide a model with

which effects on apps could be adequately predicted. Managerial implications are that

managers need not worry about negative effects of a high number of apps. In particular,

usability issues are not associated with a high number of apps, on the contrary: mass

customization provides opportunities for app developers and for managers in the telecom

sector. Moreover, apps can be used as leverage in the transition from voice and SMS to data

usage due to the indirect negative associations with SMS usage and positive relations to

mobile data usage. To improve satisfaction, managers could consider automating the

updating process of apps on the device, and spend more effort on promoting Apples high end

devices since these customers are generally more satisfied. Finally, since expert users benefit

less from increases in perceived usability due to increases in the number of apps installed,

self-service programs should aim at usability issues for expert users.

Limitations of the study are that no distinction is made between different categories of

apps; the aggregation of all apps on the smartphone and satisfaction of the apps to a central,

absolute number of apps; the omitting of psychometric differences based on the moment of

adoption; a weak theoretical basis for the mediation analysis; and the fact that the study does

not differentiate between the number of apps installed and the number of apps actually used.

Research design-related issues are the sample size when considering the serious non-normal

distribution of the data, using single-item constructs, and the nature of the data which does

not allow causal inferences. These issues are directions for further research.

10

1 Introduction

With the convergence of communication technologies and innovative product features

in the past decade (Arruda-Filho et al., 2010) the smartphone has arisen: a combination of

handheld computers and mobile phones accommodated with a wide variety of features such

as cameras, organizers, web browsers, media players and navigation. The smartphone has

put the world in the palm of your hand (Madison, 2011 on dailymail.co.uk). In the first

quarter of 2012, smartphones sales accounted for 34% of total mobile phone sales

(Gartner.com, 2012). In the Netherlands, smartphone penetration has reached 53% in January

2012 (Telecompaper, 2012). It is predicted that smartphone sales will approach one billion

units in 2015 (Idc.com, 2011).

With this revolution of smartphones, a new way of consuming software applications

has surfaced in the form of apps. This study assumes that apps are features. In the context of

software, features are defined as a distinguishing characteristic of a software item such as

performance, portability or functionality (IEEE, 1998). Apps combine hard technical features

such as the camera and a connection to the internet in order to provide additional

functionality, which is the basis for the assumption. Since apps can be added and deleted, this

assumption results in viewing smartphones as modular products products of which the

functionality can be altered (Stone, 2000).

The use of apps raises two important questions about the use of smartphones. For one,

what are the effects of the use of apps on the behavior of smartphone consumers? Two very

different concepts can be used for modeling the use of apps: feature fatigue and mass

customization. Consumers that suffer from feature fatigue have purchased a feature-packed

product that is complex in use, while they actually only want a simple, easy to use product

(Thompson et al., 2005). Concerns of the phenomenon have been even expressed for all-in-

one portable devices (Taliuaga et al., 2009 on rockresearch.com) and in particular for app

fatigue (Kendrick, 2011 on Zdnet.com, 2012). On the other hand, mass customization posits

that manufacturers can meet specific needs of consumers by providing flexible market

offerings (Anderson & Narus, 1998). Apps allow smartphones to be products of which the

functionality is customizable after purchase. This thesis provides an answer to the question

which of these theories is best applicable to model the effects of apps on behavior of

smartphone consumers. Additionally, it is interesting to assess whether external factors

influence the effects of apps on behavior of smartphone consumers.

A second question stemming from the use of apps is: What is the effect of apps on the

use of services offered by telecommunications providers? A transition is in process in which

the more traditional services provided by operator companies (or opcos) such as voice

(calling) and SMS (short message service) are moving more towards the consumption of

mobile data (Tilson & Lyytinen, 2006). Apps facilitate this transition since apps exist that

replace the use of SMS and voice with the use of mobile data, such as Whatsapp or Skype.

The aim of this research is to answer these two questions, thereby shedding light on

the effect of apps on the behavior of smartphone consumers. The answers to these questions

have both theoretical and practical implications. For scholars, this thesis adds to the existing

body of empirical research on smartphones and apps, which is sparse according to Peslak et

al. (2011). The research also adds smartphones and apps as a case study to the existing

11

research on consumer behavior, such as customer satisfaction research and technology

acceptance research, and it is assessed which of two concepts is applicable to modular

products such as smartphones and apps: mass customization versus feature fatigue. A final

unique aspect of this study is the use of objective data of usage of telecom operator services

in order to study effects of apps on consumer behavior of smartphones. Practically, it is

useful for managers to know whether feature fatigue is applicable to smartphones since the

phenomenon affects consumer behavior, with reductions in customer satisfaction levels and

usage levels as a consequence (Thompson et al., 2005). Along with the positive impact of

customer satisfaction on market performance indicators (Anderson & Sullivan, 1993; Fornell

et al, 1996; Szymanski & Henard, 2001), customer retention through customer satisfaction is

especially relevant in the telecommunications industry in which drop-out rates are high (Lee

et al., 2001). Therefore, cross-over effects of satisfaction of the smartphone to satisfaction of

the opco are also assessed. Moreover, practitioners working in the telecommunications

industry will gain insights in the effects of the use of apps on the use of services provided by

telecommunication providers, which is important in the context of the transition of voice and

SMS towards mobile data. Additionally, findings of this study are relevant for practitioners in

the telecommunications industry who want consumers to have an optimal experience with

their handset, which is a general strategic trend in telecommunications (Haverila, 2011b;

Ojiako & Maguire, 2009).

The remainder of this writing is organized as follows: In the following section,

theories and prior research of relevant concepts will be discussed, thereby elaborating upon

smartphones and apps, feature fatigue, mass customization, customer satisfaction and

technology acceptance. This is followed by the formulation of a conceptual framework and

the development of hypotheses. Section 3 will discuss the methodology that is used to

empirically test the model, followed by Section 4 in which the results of the empirical testing

will be presented. Finally, section 5 will discuss the findings, theoretical and practical

implications, and the limitations and directions for further research.

2 Theory and hypothesis development

This chapter will present results of a literature review, thereby elaborating on

concepts that are relevant to the construction of the conceptual framework with which the use

of apps and smartphones can be analyzed. The chapter starts by discussing smartphones and

smartphone apps, followed by a brief discussion of feature fatigue, mass customization,

customer satisfaction and the technology acceptance model (TAM). Subsequently, the

theoretical framework is presented and the hypotheses are developed.

The literature review was conducted according to the guidelines of Randolph (2009);

the focus was on research outcomes and theories and the goal was to develop a model with

which effects of apps on consumer behavior could be modeled. The covered literature was a

purposive sample of the available literature, and the review was conceptually organized.

Literature was searched by prompting (combinations of) key concepts into several academic

search engines such as Google Scholar, Wiley, JSTOR, ABI/Inform, ISI Web of Knowledge

and Sciencedirect. Literature was selected based on scanning titles and abstracts for

relevance, and was evaluated according to Google Scholar citations and appearance of the

publication source in the Harzing Journal Ranking (Harzing, 2011).

12

2.1 Theory and recent research

2.1.1 Apps and smartphones: modules for modular products

Various but similar definitions are used for the smartphone. This research uses the

following: a smartphone is a mobile phone that includes software that a user is able to

modify and update (Tyssy & Helenius, 2006, p. 110). Smartphone apps are computer

programs that can be installed on a smartphone. Different categories of smartphone

applications can be identified, such as communication, browsing, media playing,

productivity, system, gaming and map apps (Falaki et al., 2010). The apps result in numerous

possibilities for smartphone use such as in healthcare (Park & Chen, 2007), mobile commerce

(Chang & Chen, 2004), logistic services (Chen et al., 2007), mobile-based corporate intranet

systems (Funk, 2006) and sustainable technology (Pitt et al., 2011). Most smartphone users

have around 50 applications installed on their smartphone, while they use only 8-12

applications regularly and on a daily basis (Falaki et al., 2010).

An important assumption of this study is that apps are viewed as features because

apps provide additional functionality in addition to the hard technical features of

smartphones. In the context of software, features are defined as a distinguishing characteristic

of a software item such as performance, portability or functionality (IEEE, 1998).

Smartphones have hard features, such as a camera, which can in turn be used by software

(apps) in order to provide additional functionality. An example is the Sleep Cycle Alarm

Clock app, which uses the accelerometer and the microphone in order to analyze sleep

patterns and to wake you up in the lightest sleep phase within the half hour before you have

set the alarm (iTunes.apple.com, 2012). The author believes that this is an adequate example

of why apps can be assumed to be features.

The assumption leads to the observation that smartphones are modular products.

Modular products are machines, assemblies or components that accomplish an overall

function through combination of distinct building blocks or modules (Pahl & Beitz, 1998, in:

Stone, 2000). The number of hard technological features does not differ greatly between

different smartphone types because most smartphones have similar designs. However, in the

set of apps that users have installed in their device, variation can be expected, which supports

the need to assume that apps are features.

2.1.2 Too many features: Feature fatigue

As technology advances, it becomes more feasible to load products with a large

number of features or functions. Adding features makes products more appealing for

consumers due to increased expected utility of a product, which is in turn appealing for

businesses due to the predicted market share increase. However, too many features can make

a product overwhelming for consumers and difficult to use, resulting in consumers

dissatisfaction with their purchase and a state of frustration which is called feature fatigue

(Thompson et al., 2005). The existence of this post-purchase phenomenon is underlined by an

increase in product returns for products that turn out not to have any technical malfunction

(Den Ouden et al., 2005).

Feature fatigue can be explained by a shift in consumer preferences before and after

the use of a product, illustrated in Figure 1. Before purchase, consumers believe each feature

13

Figure 1 Shift in preference for utility to usability.

Adapted from Gaigg (2012) on Michealgaigg.com

to add to the utility of a product, knowing that more features make products more difficult to

use. However, after having used the product, a shift occurs in consumer preferences towards

a simple product with basic functionality which is easy to use. This structural change can be

explained by different considerations being salient in expected and experienced utility (Hsee

et al., 2009; Thompson et al., 2005). Economic theory models consumers preferences with

an additive utility function (Lancaster, 1971 in: Thompson et al., 2005): adding attributes to a

product or service that consumers evaluate

as positive increases the perceived utility of

that product or service. Market research

techniques such as conjoint analysis or

discrete choice analysis model products as

bundles of attributes in which each attribute

has value (Srinivasan et al., 1997 in:

Thompson et al., 2005). Hence, products

are bundles of features, and each feature

adds value to the products utility. A

tradeoff is postulated between an increase

in this utility perception of consumers and

a decrease in the perceived usability.

Feature fatigue has been found to

affect consumer behavior, or more specific: satisfaction and usage (Thompson et al., 2005).

Consumer behavior encompasses the acquisition, consumption, and disposition of goods,

services, time, and ideas by decision making units (e.g. individuals, families, organizations,

etc) (Jacobi, 1978, p. 87). Various theories are used to model consumer behavior, varying

from consumer choice theories such as the Black Box model on the consumer decision

making process, to models on the adoption of innovations by consumers such as the

Technology Acceptance model (Davis, 1989) or other diffusion models and consumer

decision making models. Two major streams of research on consumer behavior are discussed

later in this chapter: Customer Satisfaction and Technology Acceptance.

2.1.3 Mass customization: adapting functionality to meet specific needs

By varying in the set of product features, each consumers specific desire in product

capability can be adhered to. Suppliers can meet these specific desires by providing flexible

market offerings (Anderson & Narus, 1998), which corresponds to mass customization.

Originally, mass customization has been interpreted to create value based on customer-

company interaction at the manufacturing and assembly stage of operations, thereby creating

customized products (Kaplan & Haenlein, 2006). However mass customization is more

recently conceptualized as postponing the task of differentiating a product for a specific

customer until the latest possible point in the supply network (Chase et al., 2006). This is

certainly the case for smartphones for which apps are obtained after purchase of the

smartphone. This interpretation is facilitated by the assumption that apps are features, also

since modularity is often viewed as a key factor for mass customization since modularity

provides the flexibility for quick and inexpensive customization (Feitzinger & Lee, 1997).

14

The concepts of mass customization and feature fatigue lead to two different

viewpoints of how consumers use the apps on their smartphone: applying mass customization

results in the view that consumers adapt the functionality of their device according to their

preferences. On the other hand, feature fatigue indicates that consumers install a large

number of apps because of the expected utility, but end up being frustrated because their

device is complex in use.

In the following sections, customer satisfaction and technology acceptance are

described, which are two types of consumer behavior that can be affected by apps on

smartphones.

2.1.4 Customer satisfaction

Customer satisfaction is an attitude judgment following a purchase act or a series of

consumer-product interactions (Yi, 1990 in: Fournier & Mick, 1999) and is often about

evaluation as a result of comparison (Cadotte et a., 1987; Oliver, 1993), which is the basis for

the comparison standards paradigm. In this paradigm, different norms are used by consumers

in order to form standards for comparison. Such norms can be expectations, experiences,

desires or equity (Cadotte et al., 1987; Eggert & Ulaga, 2002; Fournier & Mick, 1999;

Halstead, 1999).

Halstead (1999) offers a typology of customer satisfaction models based on 1) the

level of aggregation of the comparison standard; 2) the stage of the comparison process, and

3) the level of abstraction of the comparison. Customer satisfaction can be measured for

individuals per transaction, or all individual satisfaction levels of consumers over a series of

transactions can be aggregated to a cumulative level (Anderson & Sullivan, 1993). The stage

of the comparison process refers to the stages in the consumer decision process such as need

recognition or post-purchase evaluation. Finally, for assessing the level of abstraction of the

comparison, the hierarchy of value of Gardial et al. (1994) can be used, stating that

satisfaction studies focus on 1) satisfaction with product attributes; 2) satisfaction with

products overall; or 3) on global satisfaction of consumers.

Customer satisfaction is often a performance indicator for companies. Increased

satisfaction leads to positive word of mouth and increased customer loyalty, which is the

ultimate dependent variable of customer satisfaction because of its value for actual

customer retention and profitability (Johnson et al., 2001, p. 222).

2.1.5 Technology Acceptance Model (TAM)

Originally introduced by Davis (1989), the Technology Acceptance Model posits that

external variables influence a technologys ease of use (usability) and usefulness (utility) as

experienced by users of that technology. Both ease of use and usefulness influence

consumers attitude towards using a technology, which in turn affects the behavioral intention

to use it, which finally affects actual use of a technology and the continued use thereof

(Davis, 1989).

The TAM is based on the Theory of Reasoned Action (TRA). The TRA stems from

social psychology and states that ones behavioral intention depends on the persons attitude

about that behavior and subjective norms, and that if one intends to conduct a certain

15

behavior, it is likely that he or she will do so (Fishbein & Ajzen, 1975). Later variations of

the TAM leave out the attitude construct (Venkatesh et al., 2003).

The theoretical foundation for using the perceived ease of use and usefulness as

predictors for usage behavior can be found in self-efficacy theory and the cost-benefit

paradigm. Self-efficacy is defined as judgments of how well one can execute courses of

action required to deal with prospective situations (Bandura, 1982, p. 122 in Davis, 1989).

The cost-benefit paradigm stems from behavioral decision theory and describes decision

making strategies in terms of a cognitive tradeoff between the effort required and the quality

of the resulting decision (Payne, 1982). Based on the cost-benefit paradigm and self-efficacy

theory, Davis (1989) distinguishes between outcomes judgments and self-efficacy judgments,

in which the outcomes relate to usefulness of using a technology, and self-efficacy relates to

how easy that technology is to use. Both these judgments, or perceptions, of consumers

determine the acceptance and usage of a technology by consumers.

The Technology Acceptance Model has been extensively tested in research and

numerous variations and extensions of the model have been developed. An overview of

several variations and extensions is provided by Venkatesh et al. (2003).

2.2 Conceptual framework and hypotheses This section will first describe the theoretical lens with which the questions posed in

the introduction were approached, followed by a discussion of the concepts and the relations

between them. The conceptual model and the proposed hypotheses can be viewed in Figure 2.

It should be noted that, based on the initial literature review, an elaborate model was

developed initially. However, only part of that model is discussed here in order to present a

simplified model.

The framework in Figure 2 can be used to analyze the effects of apps on behavior of

smartphone consumers. The number of apps installed on the smartphone predicts the value

perceived by consumers. Derived from customer satisfaction research, value is a standard for

comparison which consumers use to assess their satisfaction. The majority of customer

satisfaction research focuses on expectations as standard for comparison, which is however

not relevant for post-purchase processes (Halstead, 1999) and durable goods (Churchill &

Surprenant, 1982). Since smartphones are durable goods and apps are acquired after purchase

of the smartphone, value as an experience-based norm is used as a standard of comparison.

Satisfaction models based on experience-norms use the confirmation/disconfirmation

paradigm in combination with brand attribute beliefs and the experience of using a product

(Cadotte et al., 1987). Moreover, satisfaction is viewed here as transaction-specific (and not

cumulative) since this view is more suitable for assessing individual differences between

consumers (Halstead, 1999).

Value is based on the exchange theory of marketing (Eggert & Ulaga, 2002), cognitive

psychology and economic theory (Thaler, 1985 in: Gallarza & Saura, 2006): perceived value

relates to consumer behavior based on the concept of value transaction (Gallarza & Saura,

2006). Value is defined as the benefits customers receive in relation to total costs or

sacrifice (McDougal & Levesque, 2000). Sacrifices can be monetary and non-monetary

(time and effort) costs associated with acquiring and using a product or service (Cronin et al.,

16

Figure 2: the hypothesized model

Usage of the smartphone

Usage Opco services

Satisfaction

Moderators

Customer

satisfaction

smartphone

Number of

apps

Perceived

usefulness

Perceived

usability

Perceived

effort

Telecom

provider

satisfaction

Mobile data

Perceived value of

the smartphone

H2(+)

Hedonic

attitude

Utilitarian

attiude

Subjective

knowledgeSMSVoice

Daily

frequency of

use

Daily usage

time

Perceived

usage

Perceived

brand

image

H1a-c

H9a-c H10a-cH8a-c

H1d,e(+)

H3a-g

H4a-h

H5a-f

H6a-i

H7(+)

H11a-c

2000). The benefits that a consumer perceives from product features are adopted from the

acceptance Model: perceived ease of use and perceived usefulness, which both predict usage

(Davis, 1989). In the original TAM, the intention to use is an intermediate variable between

usefulness & usability, and usage behavior. However, the intention to use is not adopted in

the conceptual framework in order to simplify the model. As in a later extension of the TAM,

the Unified Theory of Acceptance and Use of a Technology (Venkatesh & Davis, 2003), also

the attitude towards using a technology is not adopted.

Perceived usefulness is defined as the belief that using a product or service positively

relates to job performance (Davis, 1989), which can be extended to performance in everyday

life for consumer products. Perceived ease of use is defined as the degree to which a person

believes that using a particular product or service would be free of effort (Davis, 1989), in

which effort is conceptualized to be a finite resource that a person may allocate to the various

activities that he or she is responsible for (Radner & Rothschild, 1975, in: Davis, 1989).

Outcomes of perceived value in this framework are actual usage levels and customer

satisfaction. Value and satisfaction are related since they are both evaluative judgments about

products or services (Woodruff, 1997). However, they are two distinct constructs due to

satisfaction being the result of an affective comparison process and value being the result of a

cognitive comparison process. In the model, satisfaction stems from value being an

experienced-based standard for comparison which consumers use to assess their satisfaction.

Usage is also related to value as it is the result of the value-variables perceived ease of use

and perceived usefulness in the Technology Acceptance Model. In the outcome variables

usage and satisfaction, distinction is made between the smartphone and the

telecommunication operator. For satisfaction, this means that customer satisfaction towards

both the telecom provider and the smartphone manufacturer is assessed. Usage is divided into

usage of the smartphone, and usage of services provided by the operating company: voice

17

(calling), SMS and mobile data. Usage of the smartphone is broken down into daily usage

time, daily frequency of use and the perceived usage level (Al-Gahtani & King, 1999).

Recent research has found that the frequency of use and the average duration of usage varies

greatly among users of smartphones (Falaki et al., 2010), which is why usage of the

smartphone is divided into three separate variables.

Finally, variables influencing the relations between the number of apps and the aspects

of value are derived from the consumer decision making process (Lamb et al., 2012). Klein

(1998) proposes that several characteristics of consumers shape the information-search phase

in this process. The following consumer characteristics are adopted in this study: subjective

knowledge, attitude towards the smartphone and the perception of the brands image in the

mind of the consumer. In the following sections, the proposed relations between the concepts

are described.

2.2.1 Number of apps on perceived value and customer satisfaction

A central predictor in the model for the value of the smartphone as perceived by

consumers is the number of apps. Based on the assumption that apps are features, this study

focuses on the number of apps installed on the smartphone as the number of features. The

number of apps is proposed to predict value and satisfaction. Two competing theories, each

leading to a different set of hypotheses, are used to predict the relations between the number

of apps and the aspects of value: feature fatigue and mass customization.

According to feature fatigue, each additional feature is expected to increase perceived

usefulness, to decrease usability, and to increase perceived sacrifice. Due to the added utility

of each app, apps are expected to increase perceived usefulness. Moreover, each additional

feature is one more thing to learn, one more thing to possibly misunderstand, and one more

thing to search through when looking for the thing you want (Nielsen, 1993, p. 155 in

Thompson et al., 2005). Therefore, a negative relation is expected between the number of

apps and the perceived usability. This increase in utility and decrease in usability due to an

increase in the number of apps corresponds to feature fatigue. Then there is the cost-aspect of

value: applications require resources in the form of time and effort to install and update the

applications and to learn to use the applications effectively. Barriers for customer satisfaction

of software products that have been found in previous research are effort for installation and

maintenance (Kekre et al., 1995) which correspond to the effort of customers required to

install and maintain (update) apps. Because a large number of applications is offered free of

charge and because the price of apps differs per operating system, monetary costs are not

adopted in the model. However, apps are expected to require effort and time since users are to

download, install and regularly update the apps and since users are to learn how to use the

apps on their smartphone. Therefore, based on the perspective of feature fatigue, smartphone

users with a high number of apps are expected to be more likely to perceive a high degree of

effort.

On the other hand, the concept of mass customization contradicts the expectations that

are based on feature fatigue regarding usability and effort. The modular property of

smartphones can be considered an anomaly of feature fatigue: consumers can modify the

functionality of their device after purchase by adding or deleting apps according to their

preferences. Based on this anomaly, mass customization is proposed as a theory competing

18

with feature fatigue as an explanation of the effects of apps on behavior of smartphone

consumers. Applying mass customization creates value (Kaplan & Haenlein, 2006) which

can be attributed to the increased added utility of each app or feature and also to increased

perceptions of usability (Kamis et al., 2008). A positive relation can be proposed between the

number of apps and usability based on mass customization: smartphone users with a high

number of apps have customized the utility of their smartphone and are also expected to have

customized the usability of their smartphone, e.g. by configuring different home-screens and

shortcuts that make their device more easy to use. The number of apps installed on

consumers smartphone may well contribute to a users control over the functionality and

usability of their device. Therefore, based on the view of mass customization, a positive

association between the number of apps and the perception of usability can be expected.

Also, a negative association between the number of apps and the effort perceived by

consumers is expected: based on mass customization, it can be expected that users of modular

products adapt the functionality of their device according to their preferences. Those users

will associate a higher number of apps on their smartphone with a desirable degree of effort

exerted into downloading, updating and learning how to use those apps. Moreover, a learning

curve can be expected in which consumers that have a high number of apps, perceive less

effort for the high number of apps because they are have developed an aptitude for using the

apps. This lower perception can be especially expected when they have installed a number of

apps conform to their need and have customized their product.

Moreover, the number of apps is expected to relate to customer satisfaction directly,

which is in line with previous research findings of products attributes and customer

satisfaction: according to the hierarchy of value by Gardial et al. (1994), customer

satisfaction stems from product attributes. In the context of this research, smartphone users

with a high number of apps are expected to be more likely to be satisfied with their

smartphone, based on this hierarchy of value. Additionally, cross-over effects are expected

between the number of apps on the smartphone and the satisfaction with the

telecommunication provider. Mittal et al. (1998) find that users of consumption systems (a

combination of products and services) attribute satisfaction of products to service providers

over time. Since telecommunication operators deliver smartphones to consumers and provide

the services necessary to operate smartphones, they form a consumption system and

consumers can be expected to relate their satisfaction with their handset to their satisfaction

with the operating company.

The following relations are hypothesized between the number of applications that

consumers have installed on their smartphone, and the aspects of value and customer

satisfaction (note that the original hypotheses reflect feature fatigue and that the alternative

hypotheses reflect mass customization): H1a-e The number of apps installed on the smartphone is (a) positively related to perceived

usefulness, (b) negatively related to perceived usability, (c) positively related to perceived

effort and is positively related to customer satisfaction of (d)the smartphone and (e) the

telecom provider

H1b,c,alt The number of apps installed in the smartphone is (b,alt) positively related to perceived

usability and (c,alt) negatively related to perceived effort

19

2.2.2 Perceived ease of use on perceived usefulness

Extensive empirical evidence has been generated in previous research for the relation

between perceived usability and perceived usefulness, which is why this relation is not

widely elaborated. The underlying explanation is that products that are easy to use increase

the utility that consumers perceive to enjoy from the product in use (Davis, 1989; Venkatesh

& Davis, 2000). Consumers are more likely to perceive a product as useful when they

perceive that product to be easy to use, than when they experience difficulties in operating the

product. This is especially true for products in the information systems category (Venkatesh

et al., 2003), such as smartphones. The following relation is hypothesized: H2 Perceived usability is positively related to perceived usefulness

2.2.3 Aspects of value on usage of the smartphone, services provided by the

operator and customer satisfaction

In the Technology Acceptance Model and variants thereof, perceived usefulness and

perceived usability are positively related to (attitude towards) usage (e.g. Davis, 1989;

Venkatesh & Davis, 2000). Smartphone consumers that believe their device is useful are

expected to indicate to 1) use it for more hours per day 2) more often per day, 3) and also to

actually say to use it more, when compared to users that perceive their device to be useless.

This is expected because they see more purposes for using their smartphone. Additionally,

users that perceive their device as easy to use are also expected to use it longer, more often

and to perceive to use it more: when the smartphone is easy to handle, there are less barriers

to use the device for a longer period of time or more frequent, which also explains why those

users believe they have a high degree of usage. Also users that perceive to exert a high degree

of effort into their smartphone for installing, updating and learning how to use the apps on

their smartphone are expected to use it more often, longer and to perceive a higher degree of

usage. This expectation is a direct consequence from the concept of effort, i.e. people that

perceive to spend time on updating, installing and learning how to use the apps on their

smartphone probably tend to use it more.

Besides the positive associations expected between the value aspects of the smartphone

and the usage aspects of the smartphone, associations are also expected between the value

aspects of the smartphone and the usage of the services provided by telecommunication

providers: SMS and voice services and mobile data. Usability and usefulness are expected to

relate negatively to SMS and voice usage, but positively to data usage, based on the

following rationale: people that perceive their device as easy to use have low barriers to using

the apps on their device. Moreover, if consumers believe that their smartphone is useful, they

will be expected to use more apps in order to make use of the full potential of their device.

Since the barriers to use their smartphone is low and they use more apps, consumers are also

expected to use more mobile data since many apps require a connection to the internet.

However, with the transition of SMS and voice usage towards mobile data usage in mind, it

may be expected that apps which use mobile data are replacements for the more traditional

phone services. Therefore, it is expected that consumers that perceive their device as useful,

make less use of voice and SMS. The same relation is expected for usability because of the

lower barriers for the interaction with the smartphone: consumers that consider that

20

interaction as easy, have more interactions with the apps on their smartphone and

consequently often use more mobile data and use less traditional telecom services.

Finally, the effort that consumers perceive to have put into the smartphones apps are

only expected to relate positively to mobile data usage, since this is the only service provided

by opcos that is needed to use apps.

The value perceived by consumers is also expected to relate to the degree of

satisfaction with both the smartphone and the provider since in the model it is the standard

that consumers use to assess their satisfaction. Customer perceived value has been found to

directly affect satisfaction (Eggert & Ulaga, 2002; McDougal & Levesque, 2000; Spiteri &

Dion, 2004) and especially usability has been found to be an important driver of overall

customer satisfaction for software products (Flavian et al, 2005; Kekre et al., 1995).

Consumers that perceive their smartphone as useful and easy to use are expected to rate their

satisfaction level with the smartphone as higher than consumers who believe otherwise. On

the other hand, effort is a negative aspect of value, and therefore effort is expected to be

negatively related to satisfaction. Based on the cross-over effects between products and

services (Mittal et al., 1998) discussed in section 2.2.1, the aspects of value are also expected

to be associated with the satisfaction that customers assign to the provider of the services that

are needed to use their smartphones. Based on the above, the following relations are

hypothesized: H3a-c Perceived usefulness is negatively related to (a) SMS usage and (b) voice usage but is

positively related to (c) mobile data usage

H3d-f Perceived ease of use is negatively related to (d) SMS usage and (e) voice usage but is

positively related to (f) mobile data usage

H3g Perceived effort is positively related to mobile data usage

H4a-c Perceived usefulness is positively related to (a) daily usage duration, (b) daily frequency of

usage and (c) perception of usage

H4d-f Perceived usability is positively related to (d) daily usage duration, (b) daily frequency of

usage and (c) perception of usage

H4g-h Perceived effort is positively related to (a) daily usage duration, (b) daily frequency of

usage and (c) perception of usage.

H5a, b Perceived usefulness is positively related to customer satisfaction of (a) the smartphone and

(b) the telecom provider

H5c,d Perceived ease of use is positively related to customer satisfaction of (a) the smartphone

and (b) the telecom provider

H5e,f Perceived effort is negatively related to customer satisfaction of (a) the smartphone and (b)

the telecom provider

2.2.4 Usage of the smartphone on usage of telecommunication provider services

The aspects of usage of the smartphone are expected to be associated with the usage of

the services provided by telecom operators. The top three features that are used on phones are

the phone, SMS and internet (Haverila, 2011b). For making calls and for texting messages,

smartphone users have to make use of the services provided by telecom operators. However,

it is expected that smartphone users make less use of the traditional telecommunication

services for texting and calling, which is based on the transition in the telecoms industry from

these traditional services to mobile data (Tilson & Lyytinen, 2006). Therefore, users that

indicate to use their smartphone more often or longer per day, and/or perceive to have a high

level of usage are expected to make more use of mobile data and less use of SMS and voice

services. For use of mobile internet services, this relation is less straightforward since heavy

21

users of apps can use a WiFi network, thereby circumventing the costs associated with using

mobile data provided by their operator. Nevertheless, consumers that say to be heavy users of

smartphones, that use it often and for a long duration on a daily basis, are expected to make

more use of mobile data. The following associations are expected: H6a-c The daily usage duration of the smartphone is negatively related to (a) SMS usage and (b)

voice usage but (c) positively to mobile data usage

H6d-f The daily frequency of use of the smartphone is negatively related to (a) SMS usage and

(b) voice usage but (c) positively to mobile data usage

H6g-i The perception of the usage level of the smartphone is positively negatively related to (a)

SMS usage and (b) voice usage but (c) positively to mobile data usage

2.2.5 Customer satisfaction of the smartphone on customer satisfaction of the

telecom provider

The telecommunication provider is a value added reseller of the smartphone because it

often provides the handset itself and the services that are necessary to use the smartphone

and, such as mobile internet. Again, cross-over effects can be expected between satisfaction

that the users of smartphones attribute to their device, and the satisfaction that they attribute

to the provider of the device and the services necessary to operate it. For instance, if a

customer is in possession of a smartphone that frequently drops calls because of an error in

the design of the antenna, it is likely that his or her dissatisfaction with the device is also

projected on the provider of the services necessary to make the call. Therefore, it is expected

that customer satisfaction with the smartphone and customer satisfaction with the operator are

related: H7 Customer satisfaction of the smartphone is positively related to customer satisfaction of the

telecommunication provider

2.2.6 Moderating variables

Finally, variables that influence the relation between the number of apps on consumers

smartphones and the perception of value are discussed. These are subjective knowledge,

hedonic attitude or utilitarian attitude towards the smartphone and perceived brand image.

However, the directions of the moderating effects are not established in the hypotheses

because these are differential, depending on which of the either concepts of feature fatigue or

mass customization is supported.

Consumers that are knowledgeable about smartphones have been found to be better

able to exploit mobile services (Deng et al., 2010). Consumers subjective knowledge is

defined as a consumers perception of the amount of information they have stored in their

memory (Flynn & Goldsmith, 1999, p. 59), which describes the consumers knowledge

associated with the product in general. It is plausible to believe that users that consider

themselves adequate users of smartphones have a different perception of the usability and

utility of their smartphone than novice users. It is also likely that experienced users will

benefit more from increases in usefulness and usability, and will suffer less from decreases in

perceived usability and increases of sacrifice which are related to the number of apps. The

latter can be expected because they take less time and effort to install, learn to use and

maintain applications, when compared to average or beginning users of applications.

In the TAM, a consumers attitude towards a product or technology has been adopted

as a moderating variable (Venkatesh & Davis, 2000). Consumers can have different attitudes

22

towards products in which two major categories can be distinguished: a utilitarian and a

hedonic attitude (Childers et al., 2001). Consumers with a hedonic attitude find pleasure in

using a product, while consumers with a utilitarian attitude use a product to achieve certain

outcomes (Batra & Athola, 1990). Based on these different attitudes that consumers can have

towards smartphones as a product category, differences in the perception of value are

expected because people that experience smartphone as hedonic are more likely to enjoy the

range of functionality offered by different applications. On the other hand, consumers with an

utilitarian attitude can be expected to be more fatigued by functionality of their smartphone

which they do not make use of. It should be noted that consumers could also have both

attitudes towards smartphones and that they are not mutually exclusive.

The perception of the brand by consumers is expected to affect the extent to which

they perceive the aspects of value, e.g. consumers that are biased by a positive brand

perception are expected to be positively inclined from functionality of applications and

negatively biased from perceived effort increases. Conversely, consumers with a low brand

perception could be prone to being irritated from decreases in usability from the high number

of apps or perceive more effort required for installing, updating and learning to use apps.

The following relations are hypothesized: H8a-c Consumers subjective knowledge moderates the relation between the number of apps and

(a) perceived usefulness, (b) perceived usability and (c) perceived effort

H9a-d Consumers hedonic attitude towards the smartphone moderates the relation between the

number of apps and (a) perceived usefulness (b) perceived usability and (c) perceived effort

H10a-d Consumers Utilitarian attitude towards the smartphone moderates the relation between the

number of apps (a) perceived usefulness, (b) perceived usability and (c) perceived effort

H11a-c Consumers perception of the image of the smartphone brand moderates the relation

between the number of apps and (a) perceived usefulness, (b) perceived usability and (c)

perceived effort

The model that has been developed in this section should provide an adequate basis

for answering the research questions posed in the introduction about the effects of apps on

consumer behavior of smartphones and telecommunication provider services. In the

following section, the methods used to empirically test the model will be addressed.

3 Method The model that is developed in the previous section was tested with an online survey.

This section will discuss the data sample, the measurement instrument, the data collection

procedure and the methods used to analyze the data and to test the model.

3.1 Data sample Data was collected from customers of a telecommunication provider in the

Netherlands. The respondents had to be in possession of a smartphone of the brands Apple,

Blackberry, HTC or Samsung. These brands accounted for 90.6% of smartphone sales in

March 2012 (GfK, 2012). Only postpaid consumers (i.e. with a monthly subscription) were

approached because smartphone apps have a higher penetration rate among postpaid users

compared to prepaid users (Telecompaper, 2011). In addition, the respondents were to have a

subscription at the operator under focus for at least one month in order to ensure that their

smartphone is out of the box and in use. Finally, a selection criterion was that the

23

respondents were to have installed at least one app themselves such that they could answer

questions about perceived effort of apps.

3.2 Measurement instrument

Several variables in the conceptual model are measured with multiple items.

Measuring constructs with multiple items can capture the complexity of a theoretical

construct or a latent variable that is not directly measurable (Fornell et al., 1996). To facilitate

construct validity and face validity, the constructs were measured with items from previous

studies (Flavian et al., 2005; Hair et al., 2010).

The variables and constructs in the conceptual model were measured with the

measurement instrument in Appendix A. Information on usage of services offered by the

telecom provider were extracted from the customer database. The other constructs and

variables were measured with an online survey. This survey, which was in Dutch, can be

found in Appendix B. The design of the website for the online survey was such that it could

easily be completed on desktop or laptop PCs, smartphones and tablets. The answer options

of the questions of the online survey appeared in a random order. The survey and the

translation were reviewed by a marketing analyst at the telecom operator and two assistant

professors of Marketing at Eindhoven University of Technology. It was also pre-tested by

five people.

The constructs and variables that are measured by the instrument are the following:

Number of apps installed on the smartphone The number of installed apps was used

to measure the number of features that consumers have installed on their smartphone. The

consumers were asked for the total number of apps on their smartphone, including pre-

installed features, in intervals of 10 applications. Above 100 apps, the answer option was

open such that the scale could be extended if need be. The pre-installed applications were

required because all applications that consumers had installed on their smartphone could

provide functionality that pre-installed applications also offer.

Perceived usefulness This measure was adapted from a study assessing a product for

business use and was adapted such that the usage situation reflected situations in everyday

life. The item Using this technology improves my job performance was not adopted, since

smartphones were assessed for consumer use and since translating the job performance to

performance in everyday life could lead to ambiguity when the other three items are taken

into consideration.

Perceived ease of use Is measured by asking respondents for the clearness and

understandability of the interaction with the smartphone, the degree of mental effort required

for operation, the easiness of use and the degree to which consumers can get their device to

do what they want it to do, as in Venkatesh & Davis (2000).

Perceived effort and perceived monetary costs In previous research the sacrifice

aspects of value were measured under one construct: perceived sacrifice. In this research

however, only the perceived effort or time was measured because the price paid for

applications is different for different smartphone operating system, i.e. numerous apps that

are freely available in the Android app market are charged for in Apples Appstore. Because

24

of possible confusion of effort with the construct perceived ease of use, the effort was

measured in perceived time. The measures were adopted from Deng et al. (2010).

Usage of the Smartphone Previous studies have measured usage with the self-

reported items daily duration of use, daily frequency of use, the number of applications used

and the perceived usage (Al-Gahtani & King, 1999; Kim, 2008). Since the present study

focused on usage and satisfaction of smartphones stemming from the number of applications

in use, the number of applications were dropped. Because this study expected differential

effects for frequency of use, duration of use and perception, these aspects were not used as

items but as variables. Previous studies such as Kim (2008) divided the frequency of

interaction and hours of use per time period in prefixed time intervals which are probably

different for smartphones. Therefore, the daily usage and frequency of interaction were stated

as open questions and recoded afterwards. The process of recoding is described in Appendix

C. Recoding the frequency and duration of use into ordinal scales should have resulted in

more reliable answers since the self-reported measures can be expected to be inaccurate.

SMS, Voice and Mobile data usage Information on SMS, voice and mobile data

usage was an objective measure that was extracted from the operators customer database and

is the average usage per month between December 2011 and February 2012. It should be

noted that data usage over WiFi networks cannot be registered by telecom providers. People

can use internet on their smartphone solely over their home WiFi network but not have a

mobile data subscription, meaning that they can be heavy users of internet on their

smartphone without using the mobile data services offered by operators.

Customer satisfaction Almost all studies that use a self-measurement of customer

satisfaction show a negatively skewed distribution of this variable (Peterson & Wilson,

1992), i.e. overall, customers are more satisfied than dissatisfied. Turel & Serenko (2006) use

a ten-point Likert scale to avoid skewness problems because it enables respondents to make

better discriminations (Andrews, 1984 in Turel & Serenko); this approach was adopted in the

measurement instrument with an eleven point likert scale in order to allow for a middle

answer option, which is coherent with the 7-point Likert scales used for the other items. A

single-item was used to measure customer satisfaction directly. Using single-item scales for

measuring customer satisfaction may decrease the quality of measurement (LaBarbera &

Mazursky, 1983 in: Mittal et al., 1998). Nevertheless, Bergkvist & Rossiter (2007) suggest

that single item constructs can be used in marketing for concrete objects and attributes such

as customer satisfaction. Using single-item constructs also decrease the completion time of

the survey.

Subjective knowledge One item was deleted from Flynn & Goldsmith (1999) since

the translation in Dutch language offered too little differentiation from another item

measuring the same construct. The measure then consisted of the items knowing much about

smartphones, how to operate them, whether the respondent believes that other people think he

or she is an expert. The respondent is also asked to compare him/herself to others regarding

smartphone knowledge.

Utilitarian and hedonic attitude towards smartphones The two dimensions of

attitude towards smartphones are adopted from Voss et al. (2003) who develop measurement

scales for the two dimensions of attitude towards product categories. The scales consist of 5

25

items per dimension regarding the hedonic and utilitarian attitude, and are adapted to relate to

enjoyment of smartphones and the utility of smartphones in general.

Perceived brand image The perception of the brands image by consumers is

measured by asking respondents for the attractiveness, reliability and quality of the brand, as

in Low & Lamb (2000). Low & Lamb (2000) find support for distinguishing between brand

image, brand attitude and perceived quality as three types of brand associations.

Control variables Age, gender, the education level, the number of smartphones

owned, the duration of smartphone possession, the smartphones brand, type of use (business

or consumer) and the device on which the survey was completed were measured as control

variables. The motivation for including each of these variables is discussed in the final

section of this chapter.

It should be noted that all the latent constructs are reflective constructs for which the

items are caused by the constructs, as opposed to formative constructs which are formed by

the items measuring the constructs (Hair et al., 2010, Ch. 12).

3.3 Data collection procedure The request to complete the online survey was administered via e-mail to 2446

consumer of the Dutch operator. The extraction of the respondents from the customer

database of the operator was at random except for the criteria discussed in Section 3.1. Data

collection, graphic design of the online survey and the contacting of the consumers was

conducted by a marketing research agency. As an incentive to boost the response rate, five

vouchers for an online store with a value of 20 were raffled among the respondents. The

survey request was issued on April 17th

2012. A reminder was issued after 4 days, and the

survey was closed on the 28th

of April and hence was available for 15 days.

8 invitations were bounced a non-delivery notification was received due to e.g. a

non-existing e-mail address. 18 people accessed the link of the survey, but did not start the

survey and 71 potential respondents started the survey without finishing it. A total of 271

surveys were fully completed, a response rate of 11.1%. On average, people took 11:34

minutes to complete the survey.

The manipulation of the original dataset is described in Appendix C. Three respondents

were deleted from the database because they had a completion time of four minutes or less

and because it was suspected that the answering of the questions was conducted at random.

Of the remaining 271 respondents, 19 did not meet the criteria of the data sample since four

respondents were not customers of the operator and 15 respondents did not download apps.

These respondents were deleted from the dataset, which left 252 usable respondents (10.3%).

3.4 Analytical method The dataset was delivered in Microsoft Excel 2010. SPSS 18 was used for the initial

processing of the data. A non-response analysis was conducted using a one-way MANOVA

for comparing early and late respondents. To test for common method bias, Harmans one

factor test was applied using principal components analysis in SPSS.

Structural equation modeling (SEM) is used to test the model developed in Section 2.

SEM consists of two steps: the confirmation of the measurement model and the estimation of

26

the path model or structural model. When the measurement model is analyzed, it is assessed

how well the different directly measured items reflect the indirectly measured constructs.

Subsequently, in the path analysis, it is assessed whether the predicted relations between the

latent constructs exist and to what extent (Hair et al., 2010, Ch. 11).

Structural equation models can be estimated by Maximum Likelihood (ML)

estimation or by Partial Least Squares (PLS) estimation methods, of which the former is a

covariance based approach and well known through software applications such as LISREL or

AMOS. The latter is a less-known, variance-based approach (Haenlein & Kaplan, 2004).

PLS is used as estimation technique for several reasons. Compared to other multivariate

techniques, PLS is robust to violations of distributional assumptions such as normality and

smaller sample sizes can be used (Fornell & Bookstein, 1982). In addition, PLS is less

sensitive to multicollinearity problems (Hair et al., 2010, Ch. 12), and more information of

the data is retained since PLS uses covariance instead of correlation, which is the

standardized covariance (Ringle et al., 2005 on smartpls.de). Finally, PLS is more suitable for

prediction rather than confirmation of existing theory (Hair et al., 2011).

The software package that was used for PLS is SmartPLS 2.0 M3 (Ringle et al., 2005

on Smartpls.de, 2012). An abort criterion of 10-5

was used to assure the PLS algorithms

convergence while minimizing computational requirements (Hair et al., 2011). A maximum

of 300 iterations was permitted. Cutoff values and test approaches for validity and reliability

are according to Hair et al. (2011). Bootstrapping with 500 resamples was used as a reactive

Monte Carlo resampling strategy in order to assess the significance of the estimates. In

bootstrapping, parameters values and standard errors are compared to repeated random

samples drawn with replacement from the original observed sample data, in order to assess

the significance of the estimated parameters (Marcoulides & Saunders, 2006). Effects were

deemed significant if the probability of erroneously detecting an effect was smaller than .05 .

Moderating effects were analyzed based on the product indicator approach (Henseler

& Fassot, 2010). For assessing the moderator effects in SmartPLS, the indicator values were

standardized (recomputed to have to a mean of 0 and a standard deviation of 1) before

multiplication. Hypotheses regarding moderating effects were significant if the coefficient of

the interaction term was significant (Henseler & Fassot, 2010). The strength of the

moderating effects was determined by calculating the effect size 2 which is an indicator for

the change in R2 due to adding the interaction term of the moderator.

1 Effect sizes of

respectively .02, .15 and .35 are weak, moderate and strong (Henseler & Fassot, 2010). The

moderator analysis was conducted separately per moderating effect, using 500 resamples for

the bootstrapping procedure.

To ensure correct model estimation, several control variables were included in the

model. Preferences for different features and the satisfaction caused by features have been

found to differ by demographic variables, such as age, gender and education (Haverila,

2011a, 2011b). In addition, the time of owning a smartphone and the number of smartphones

owned are also used because people that have owned multiple smartphones or that have

owned a smartphone for a long time may be more experienced with and knowledgeable about

1

2

(Cohen, 1988 in: Henseler & Fassot, 2010)

27

smartphones. The brand of the device used by the consumer is controlled for as well since

the different brands make use of different operating systems, have different apps available to

them and have different interfaces, leading to possible differences in e.g. the perception of

usability between the users of different smartphone brands. Finally, the type of use of the

smartphone (business, pleasure or both) is controlled for, since users that use their

smartphone for business purposes do not use it entirely voluntarily, and therefore the

evaluations of the device and the apps may differ from the evaluations offered by consumers.

Dummy variables were used for categorical variables with more than two categories (i.e.

smartphone brand, survey method and type of use). The coding of dummy variables is

described in Appendix C. As in prior research using PLS, age, number of smartphones

owned, usage time and education were treated as ordinally scaled variables (e.g. Venkatesh &

Davis, 2000).

4 Results This section will present the results of the data collection and the statistical analyses.

First, the sample will be described, followed by a non-response analysis and the cleaning of

the data. Subsequently, the measurement model will be tested, followed by several tests of

the structural model: the base model, moderating effects and control variables. The results

induced two post-hoc analyses which are also described: a comparison between the different

operating systems and the analysis of mediating variables.

4.1 Sample and data characteristics Several characteristics of the data sample (N=252) can be viewed in Table 1 (note that

this table was reconstructed after the deletion of outliers in section 4.1.3). Corresponding

characteristics of other customer groups and the Dutch population can be viewed in Appendix

D. The distribution of males and females in the population seems to be relatively even,

although the fraction of females is slightly higher than the fraction of males. This is also the

case for other customer groups and the Dutch population. The distribution of the different

types of education in the sample does not accurately reflect the average Dutch population in

the sense that the average Dutch population seems lower educated. Regarding age, the sample

does appear to reflect the operators customer base, which consists of younger people when

compared to the Dutch population.

Regarding the representation of the smartphone brands of the sample to the operators

customer base, Blackberry and Samsung users seem slightly underrepresented as opposed to

HTC users, which are overrepresented. Regarding the respondents, an equal proportion of the

four brands was present in the 2446 invitees, which is not the case for the actual sample:

more HTC and Samsung users responded than Apple and Blackberry users.

The majority of the respondents indicated to use their smartphone for consumer

purposes only while about one-third uses their smartphone for both business and consumer

purposes. Only one respondent indicated to use his or her smartphone solely for business,

which makes sense because the survey was issued to consumer users. Finally, the majority of

the respondents indicated that their current smartphone was their first, and the majority of the

respondents has owned a smartphone for less than two years.

28

Table 1 Descriptive statistics of control variables

Variable Label Frequency Percentage Variable Label Frequency Percentage

Gender Male 123 48.8 % Smartphone Brand

Apple 47 18.7 % Female 129 51.2 % Blackberry 34 13.5%

Age 15-25 46 18.3 % HTC 81 32.1%

26-35 46 18.3 % Samsung 86 34.1 % 36-45 63 25.0 % Other 4 1.6 %

46-55 64 25.4 % Type of use Consumer 160 63.5 %

56-65 26 10.3 % Business 1 0.4 % 66-75 3 1.2 % Both 91 36.1 %

75+ 2 0.8 % Number of

Smartphones owned

1 smartphone 153 60.7%

Education None 1 0.4 % 2 smartphones 44 17.5% Primary 1 0.4 % 3 smartphones 39 15.5%

VMBO/MBO1 30 11.9 % 4 or more smartphones 16 6.3%

HAVO/VWO 29 29 % Usage Time 0-8 months 52 20.6%

MBO 2-4 71 28.2 % 8-19 months 89 35.3%

HBO 79 31.3 % 19-31 months 41 16.3%

WO 34 13.5 % 31-43 months 27 10.7%

Other 7 2.8% 43-54 months 16 6.3%

More than 54 months 27 10.7%

4.1.1 Non-response analysis

Extrapolation is used for assessing non-response. The early and late respondents are

compared, since late respondents and non-respondents are both less readily in their response

(Scott Armstrong & Overton, 1977). The first and last quartile of respondents are compared

(n=63). A one-way MANOVA was conducted in SPSS with the response group as the

predictor variable and the measured constructs used in the structural model (as calculated by

SmartPLS) are used as the dependent variables. In the calculation of these variables, the items

that are deleted in section 4.2 are not used. The MANOVA revealed no significant

multivariate main effects for response group at the p < .05 level, Wilks = .868, F(16;105) =

1.000, partial eta squared = .132. Power to detect the effect was .631. Given the low power to

detect the effect (under .80), the tests of between subject effects were assessed for further

differences between the different response groups on the metric variables, but no significant

effects were observed. Based on these findings, it is concluded that there are no significant

differences between early and late respondents and that in turn it is not likely that there is a

non-response bias.

4.1.2 Common method bias

Harmans one factor test was used to test for common method bias. Using principal

component analysis, eight factors were extracted with an eigenvalue exceeding 1.0 (the

Kaiser criterion), accounting for 69.0% of the variance. The first and largest factor of the

unrotated solution accounted for 30.5% of the variance, indicating that common method bias

is probably not an issue since this is not the majority of the variance (Podsakoff et al., 2003).

4.1.3 Missing data, outliers and multivariate assumptions

The data did not cont

Effects of apps on consumer behavior of smartphones and

Text of Effects of apps on consumer behavior of smartphones and

smartphones for Large- scale Behavior Change interventionspejovicv/docs/Lathia2013IEEEPervasive.pdf · Large- scale Behavior Change interventions A ... For example, in the US, 26

The Usage of Smartphones Social Media Applications by ... · 2. 94.3% of social media apps’ users via smartphones aim to receive news, followed by 68.3% use it for chatting with

Wearables, smartphones and apps - do they really measure

Predicting personality from patterns of behavior collected with smartphones€¦ · 13-07-2020 · Smartphones enjoy high adoption rates around the globe. Rarely more than an arm’s

Detecting Potentially Malicious Behavior in Mobile Apps

Operational Effectiveness of Smartphones and Apps for ... · using smartphones, connected through commercial and private communication networks and coupled with different types of

Smartphones and Mobile Apps for Lawyers€¦ · With more than 800,000 apps currently available, discover how your smartphone or tablet can assist with running your law practice as

Aviation Apps for iPads and Smartphones Apps for... · Aviation Apps for iPads & Smartphones ... Final authorization ... party training materials are becoming available every year

On the Run: Using Smartphones to Track Millennials ...€¦ · On the Run: Using Smartphones to Track Millennials Purchase Behavior Jeff Scagnelli Research Methods Center of Innovation

On Mining Mobile Apps Usage Behavior for Predicting Apps Usage in Smartphones

A First Look at Deep Learning Apps on Smartphones · The huge smartphones market provides very promising platform for Deep Learning based apps Year 2017 marked noticeable increased

Apps, Podcasts & Smartphones: A Perfect Trinity?€¦ · Apps, Podcasts & Smartphones: A Perfect Trinity? Jaime Selwood Institute for Foreign Language Research & Education [email protected]

Infographic: Millennials & Social Media - Smart facts on smartphones, apps & m-commerce

O Impacto negativo dos tablets e smartphones no ... · The negative impact of tablets and smartphones on children’s behavior El impacto negativo de las tabletas y los smartphones

Examining the use of smartphones for travel behavior data ...yhu42/papers/SmartphoneTBR.pdfWith the ubiquity of personal, GPS enabled devices such as smartphones and tablets, and the

Examining the use of smartphones for travel behavior data ...hu/papers/SmartphoneTBR.pdf · Examining the use of smartphones for travel behavior data ... the use of smartphones for

How smartphones change human behavior ?

Smartphones apps for productivity

Smartphones, Tablets and Apps

Mobile Distance Learning with Smartphones and Apps in ... · Mobile Distance Learning with Smartphones and Apps ... complemented with text documents in different ... participating

Mobile, Smartphones, Wi-Fi, and Apps · Mobile, Wi-Fi, Apps. Lecture 04 Talking about tourism and mobility, let’s see now whether tourists’needs are expressed and addressed via

Hybrid Approach to extend your Web Apps to Tablets & Smartphones- Impetus Webinar

Smartphones Apps are not that smart: Insecure Development Practices … · 2012-04-07 · VULNEX Research Paper - Smartphone Apps Are Not That Smart: Insecure Development Practices

Effects of apps on consumer behavior of smartphones and ...alexandria.tue.nl/extra2/afstversl/tm/Agterhuis_2012.pdf · University of Technology for guiding the search towards a suitable

Smartphones with Smarter Apps

Behavior Change Techniques in Top-Ranked Mobile Apps for

Learning to Love Your Students Smartphones: How Mobile Apps Can Breathe New Life into Your Reading Class

The Operator as Innovator: Smartphones, Smart Apps and Smart Pipes

Characterizing Instant Messaging Apps on Smartphonesspirit.cs.ucdavis.edu/pubs/conf/li-pam15.pdf · Characterizing Instant Messaging Apps on Smartphones Li Zhang, Chao Xu, ... Messenger

Learning Driver Braking Behavior using Smartphones, Neural ...€¦ · conducted and vehicle motion was recorded using smartphones and a data acquisition system, comprising an IMU

Documents

Effects of apps on consumer behavior of smartphones and

Text of Effects of apps on consumer behavior of smartphones and