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St. Petersburg State University
Graduate School of Management
Master in International Business
FACTORS AFFECTING CONSUMER BEHAVIOR IN
CONTEXT OF SERVICE USING AFTER ITS
Master’s Thesis by the 2nd year student
Olga N. Alkanova
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ
Я, Титов Антон Станиславович, студент 2 курса магистратуры направления 080200
- «Менеджмент», заявляю, что в моей магистерской диссертации на тему «Факторы,
влияющие на поведение потребителя, в случае пользования сервисом после его
дигитализации», представленной в ГАК для публичной защиты, не содержится элементов
Все прямые заимствования из печатных и электронных источников, а также из
защищенных ранее выпускных квалификационных работ, магистерских, кандидатских и
докторских диссертаций имеют соответствующие ссылки.
Я ознакомлен с действующим в Высшей школе менеджмента СПбГУ регламентом
учебного процесса, согласно которому обнаружение плагиата (прямых заимствований из
других источников без соответствующих ссылок) является основанием для выставления за
магистерскую диссертацию оценки «неудовлетворительно».
STATEMENT ABOUT THE INDEPENDENT CHARACTER
OF THE MASTER THESIS
080200 «Management», state that my master thesis on the topic «Factors affecting consumer
behavior in context of service using after its digitalization», which was presented for the public
defense, does not contain any elements of plagiarism.
All direct borrowings from printed and electronic sources, as well as from master theses,
PhD and doctorate theses that were defended earlier, have appropriate references.
I am familiar with the study process regulations at Graduate School of Management of
Saint-Petersburg State University, according to which finding of plagiarism (direct borrowings
from other sources without appropriate references) can be the reason for master thesis to be
evaluated as «unsatisfactory».
ФИО научного руководителя
Описание целей, задач и
Титов Антон Станиславович
Факторы, влияющие на поведение потребителя, в
случае пользования сервисом после его дигитализации
Высшая школа менеджмента,
Санкт-Петербургский государственный университет
Алканова Ольга Николаевна
Цель исследования состоит в выявлении факторов,
влияющих на использование мобильных приложений
для потребления услуг, а также изменение частоты
Факторы, которые имеют потенциальное влияние
на использование мобильных приложений для
потребления услуг, а также изменение частоты
использования услуги были частично заимствованы из
третьей теоретической модели принятия технологий,
теории о диффузии инноваций и единой теории принятия
и использования технологий. В качестве статистического
метода был применен регрессионный анализ для
определения факторов, влияющих на использование
мобильных приложений для потребления услуг, а также
изменение частоты использования услуги. Результаты
статистического анализа, показали статистически
мобильного приложения для потребления услуг и
получением услуги, субъективное удовольствие от
В случае оценки взаимосвязи на изменение в
частоте использования, статистически значимая
зависимость была найдена между зависимой переменной
и контролем над получением услуги, наличием
триальной версии, отношением к использованию.
Мобильные приложения, дигитализация, единая теория
принятия и использования технологий, диджитал
Master Student’s Name
Master Thesis Title
Academic Advisor’s Name
Factors affecting consumer behavior in context of service
using after its digitalization
Graduate School of Management,
Alkanova N. Olga, Senior Lecturer
Description of the goal, tasks
and main results
The objective of the current research is to identify the
factors that determine the usage of mobile applications for
service consumption and change in usage frequency .
The factors that can potentially influence the mobile
applications usage were derived from the Technology
Acceptance Model 3, the Diffusion Theory and the Unified
Theory of Acceptance and Use of Technology. The
regression analysis was used in order to find corresponding
The analysis showed the strongest significant positive
relationship between the mobile application use and
facilitating conditions, service delivery control and perceived
Concerning change in usage frequency, significant
factors are service delivery control, trialability (reverse
relationship) and attitude towards using.
Digitalization, mobile applications, UTAUT model,
TABLE OF CONTENTS
1 LITERATURE REVIEW .............................................................................................................8
1.1 Digitalization .............................................................Ошибка! Закладка не определена.
1.2 Consumer behavior ....................................................Ошибка! Закладка не определена.
1.3 Switching behavior ..............................................................................................................14
1.5 Digitalization of products and services: shift to e-commerce..............................................21
1.6 Acceptance behavior models ...............................................................................................27
2 RESEARCH METHODOLOGY ...............................................................................................40
2.1 Research approach ...............................................................................................................40
2.2 Sample strategy ....................................................................................................................40
2.3 Data collection methods and procedures .............................................................................40
2.4 Data processing and analysis ...............................................................................................40
3 FACTORS OF SERVICE USAGE AFTER ITS DIGITALIZATION ......................................51
3.1 Descriptive analysis of digitalized services users ................................................................51
3.2 Determinants of digitalized service usage ...........................................................................51
3.3 Determinants of digitalized service usage frequency change ..............................................51
4 DISCUSSIONS AND CONCLUSIONS ....................................................................................63
4.1 Analysis of theoretical base .................................................................................................63
4.2 Explicit answers to research questions ................................................................................63
4.3 Managerial implications ......................................................................................................63
4.4 Limitations and future research ...........................................................................................63
Today, many traditional services as banking, retail, transportation, etc. are transforming
into online variations. Companies are stating that online channel is becoming one of the most
priority area of development (McKinsey, 2015). With the rise of touchphones, this trend
experiences new boost. It leads to digitalization of customer experience by existing players in
certain industries (traditional banks develop their online and mobile services) or even to emergence
of new, most likely, IT companies entering traditional industries (Uber, Airbnb or Skyscanner)
and reshaping their standards (Forbes, 2016). Some relevant fields as online banking and online
retail have extensive research in background. However, there are not so many researches of online
channel penetration into other industries. Moreover, the impact of mobile devices usage expansion
on consumer behavior is currently continued. According to Statista research (2016), the overall
number of smartphone users worldwide is going to reach 2 billion in 2016 and continues growing
quite rapidly. The growth exceeds 10-15% per year until 2019 (statista.com, 2016; emarketer.com,
2014). Thus, studies on this topic become outdated very fast.
New tech start-ups emerge, new services appear, new consumer behavior forms. In
addition, available papers are aimed more on specific companies and their newly developed
products or services. Hence, this study is aiming to analyzing the impact of certain services
technological transformation (without company focus) on consumer behavior. In other words, this
thesis is an attempt to understand whether current innovative technological products which,
basically, provide online (mobile) access to a traditional service, reshape consumer behavior,
particularly, form new habit to use them in the new way and more frequently.
This situation on the market has been formed recently, consequently, the research of this
problem is limited. Usually, switching behavior is examined either within one offline service or
online service (Keaveney, 2001; Chiu et al, 2005). However, the phenomenon of switching from
offline version of service to its digitalized (mobile) version is not well studied, hence this area is
determined as research gap which is going to be filled in this study.
In legacy understanding, the concept consumer behavior is based solely on the act of
purchase (Laudon, Bitta 1993). Contemporary works determine Consumer Behavior as “behavior
that consumers display in searching for, purchasing, using, evaluating and disposing of products
and services that they will expect satisfy their needs” (Schiffman, Kanul,2007). It could be inferred
from the definition, that frequency of purchase of certain service could be treated as a part of
consumer behavior. Therefore, the part which is going to be in focus of this work is relevant to the
consumer behavior concept.
The problem is going to be studied on the example of two services, which are:
1. Mobile banking
2. Taxi ordering
Each service has been chosen for several reasons. First, according to Gartner (2015)
recently, many companies have started providing these services via mobile applications. Second,
the penetration of mobile versions of these services continues to grow. Third, both services are
different and do not have a lot in common, so, analyzing this set of services, we mitigate try to
mitigate bias arising from specificity of certain service.
Potentially, the results of this study could shed light on determination factors which lead
to successful service technological transformation, hence, to define possible services that are not
yet massively transformed but have great potential in that perspective. Thus, this goal could be
possible direction of future research.
Research goal of the current study is to define factors that lead to consumer behavior
change, in particular, using the certain service more frequently in case of service digitalization,
and statistically test the influence of those factors on the frequency of the service usage.
The research object is consumers in context of banking and taxi services. The research
subject is factors affecting consumer behavior in the process of using digitalized services.
First chapter of this paper is devoted to observation of main terms and concepts from the
topic of the study. As a result, first, specifics of research object and subject are going to be
determined, second, research design and methodology is going to be defined, which is described
in chapter 2. Chapter 3 contains interpretation of statistical analysis and primary conclusions. The
last chapter is devoted to the discussion of the results, describing practical and theoretical
1 LITERATURE REVIEW
The following chapter contains review of main concepts related to the topic of the study.
The purpose of this review is to identify peculiarities of consumer switch from offline to digitalized
version of service from different angles. These peculiarities could be a basis for forming factors
influencing the consumer decision to use digitalized service for further statistical testing. First, as
digitalization concept is not obvious, we are going to review it first. The important thing is to
observe the case in different dimensions, thus, second, the concept of service is going to be
analyzed in order to find possible service-related specifics which could influence consumer
decision. The third step is observation of consumer behavior and switching behavior, in particular,
for the purpose of narrowing research focus to specific aspects of consumer behavior which are
relevant to research topic. Also this chapter will help to identify the research gap which is going
to be filled in with this research. Next is digitalization in context of e-commerce analysis performed
for seeking possible reasons of switch from the background of digitalized (mobile) service
specifics. The last part of the chapter is devoted to revising acknowledged technology adoption
models in order to first, find possible generalized appropriate factors for digital service usage
context and second, to lean on verified methodology for new model development.
We start this chapter with the analysis of digitalization concept. We will consider
digitalization of traditional “offline” services as well as such a term as a pure digital company
which provides online services from the beginning.
To move further with discussion on digitalization of services or digital services we first
consider “digitalization” concept. In this part we have a closer look to the meaning of
“digitalization” concept. Beforehand we need to make clear that two concepts ‘digitization’ and
‘digitalization’ are not mixed up.
‘Digitization’ and ‘digitalization’ are two concepts that are closely associated and often
used interchangeably in different ranges of research works. Although there is a clear distinction
between these terms.
The Oxford English Dictionary (OED) refers to the first uses of the terms ‘digitization’
and ‘digitalization’ in connection with computers in the mid-1950s. In the OED, digitization means
“The action or process of digitizing, and the conversion of analogue data (especially in later use
images, video, and text) into digital form.” Digitalization in the meantime refers to “the adoption
and/or increase in use of computer or digital technology by an industry, organization, country,
Thus in our paper we use this distinction and define digitization as “material process of
converting individual analogue streams of information into digital bits”. In contrast, we refer
to digitalization as the way in which many spheres of social life are restructured around digital
communications and media infrastructures. Below we discuss digitalization concept in detail.
The first use of the term “digitalization’ in conjunction with computerization refers to the
year 1971. The essay written by Robert Wachal (Tate et al, 2014) was published in the North
American Review. In this essay, the author discusses the social implications of the “digitalization
of society” in the context of considering objections to, and potentials for, computer-assisted
humanities research. From that point research works about digitalization have grown into a
massive literature. The researchers are concerned less with the specific process of converting
analogue data streams into digital bits but more the ways that digital media structured, shaped, and
influenced the contemporary world. In this sense, digitalization has come to refer to the structuring
of many and diverse spheres of social life around digital communication and media infrastructures.
We further focus on a few prominent works that touch the topic of digitalization implications that
scholars have traced across some of the many different spheres of social life.
Manuel Castells (Tate et al, 2014) at his work observes the digitalization of “the new
economy, society, and culture”. He views “digitalization” as one of the defining characteristics of
the new era. Castells is part of a broader group of scholarship that points out the underlying media
and communications system as a way to explain or understand most of the aspects of contemporary
social life. According to van Dijkargues (Tate et al, 2014) – “we will have a single communications
infrastructure that links all activities in society for the first time in history”. This communication
system is fully characterized as “new media”. It is often defined as “old media that have been
transformed through their reconfiguration into devices capable of managing digital signals”.
There are some ways that researches have analyzed how digitalization shapes the
contemporary world. Authors have focused on globalization rise – a process that has both
facilitating and been facilitated by the expansion of the economy beyond national borders via
digitalization. Both digitalization and globalization of the economy have subsequently eroded
national sovereignty, reshaped conceptions of place and materiality, and facilitated new
circulations of culture, commodities, capital, and people. For example only in the field of finance,
many scholars have shown how digital media are now central to global capital flows.
Although there are a lot of different researches and publications on the topic of
“information society” – most of them trace their roots to the early work by Daniel Bell and Fritz
Machlup (Tate et al, 2014). In their work the authors mentioned global shifts in national economies
patterns. Many scholars would have agreed with Bell and Machlup that “computer technology is
just the same to the information age, what was mechanization to the Industrial Revolution”.
Some other researchers have identified “digitalization” as bringing about convergence
across the media, which drives many of the broader social and technical changes outlined below.
For example ability of digitization to produce a medium that simulates or consolidates all other
media – means that the digital must ultimately be percept as a “generalized medium” that
consolidates “diverse forms of information”, or that is ultimately “mediumlessness”. Thus, the rise
of digital media “has entailed a reconsideration of what a medium is, because the digital computer
can reproduce or simulate all other known media”.
Researchers have explored the idea of convergence across a number of different processes
and spheres of social life. They identified a number of different forms of convergence. For the
clarity sake, we summarized existing information into four key dimensions of convergence which
is related to digitization and digitalization. These are the following types of convergence:
infrastructural, terminal, functional and rhetorical, as well as market convergence (Tate et al,
We referring to infrastructural convergence in this paper as to perhaps the most common
form of convergence discussed in the literature. Scholars describe how digitization brings about
the convergence of the material infrastructures to communication. There are two main forms of
this type of convergence. First, network or “infrastructure” convergence refers to the physical
network of wires and tubes that undergird the communication infrastructure. Because digitized
information can be manipulated and understood nearly by any digital system, “any network can
be used to transmit all kinds of digital signals”. This means that “a single physical device – be it
wires, cables, or airwaves – able to deliver services that in the past were provided separately”.
The other convergence type, device or terminal convergence, refers to how digitization
entails the consolidation of multiple media devices into a single one. The simple example here is
a smartphone, which now replaces number of “outdated” devices (telephone, computer, camera,
audio recorder, calendar, calculator, notepad, etc.).
After reviewing some of the works on digitalization concept, we now defined the
“digitalization” as the “adoption and/or increase in use of computer or digital technology by an
industry, organization, country, etc.” Although the term digitalization is went far beyond this
definition – we use and mean in our paper the definition provided by OED.
Contemporary market provides variety of goods and services to be consumed. In our study
we are going to focus on services, or on electronic services if to be more specific. However, before
deep dive in the specific topic of the study, it is required to observe concepts that are more abstract
in order to understand its nature and specifics.
The concept of service is going to be observed. First of all, it should be mentioned, that this
concept has a variety of definitions. The reason of that is extreme diverse of services as such
(Lovelock et al, 2010). Thus, it is tough task to combine such things as management consulting
and medical insurance within one common term.
One of widespread definitions reads as follows “activity or series of activities of a more or
less intangible nature that normally, but not necessarily, take place in the interaction between the
customer and service employees and/or physical resources or goods and/or systems of the service
provider, which are provided as solutions to customer problems” (Groenroos, 2001). Christopher
Lovelock et al (2011) outlined two definitions. The first is “Any act, performance or experience
that one party can offer to another and that is essentially intangible and does not result in the
ownership of anything, but nonetheless creates value for the recipient. Its production may or may
not be tied to physical product.” And the second is “Services are processes (economic activities)
that provide time, place, form, problem solving or experiential value to the receiver.” The first one
looks like edited version of service definition by Kotler (1999): “A service is any act or
performance that one party can offer to another that is essentially intangible and does not result in
ownership of anything. Its production may or may not be linked to a physical product.”
Thereby, it could be concluded that term “service” has been contradistinguished from term
“good” which is defined, as a material that satisfies human wants and provides utility. (Bannock
et al. (1998). Defining service term, scholars stress out intangibility as the main differentiator from
the “good” term. Vargo and Lusch suggested (2004) different approach: they define service as “the
application of specialized competences (knowledge and skills) through deeds, processes, and
performances for the benefit of another entity or the entity itself.” Therefore, they use service
provider perspective to describe the concept, putting differences between service and good on the
Edvardsson et al. (2005) conducted interesting research: they sent short text with statement
and question about “service” term to the experts in respective field. And then analyzed frequency
of keywords used by them. Hence, they received the following results: while commenting and
explaining “service” term, 6 experts out of 11 mentioned “performance” term, 5 experts mentioned
“processes” term and 3 of them mentioned “deeds”. Some experts also used “activities” and
“experience” for an explanation.
The researchers concluded that the most widespread definitions could be divided into two
groups. First group contains definitions which are focused on distinguishing services from
products. The other group focuses on service as a perspective to value creation. Edvardsson et al
(2005) drew the conclusion that the former approach is rather outdated and already lost its
usefulness, while the latter one shed the light on the most important part of ‘service’ concept.
Although, some scholars think that approach of opposing services to products is outdated,
for this study, in order to get bigger exposure, it is important to familiarize with it.
Classical explanation of the difference between product and service told us that goods
could be determined as physical objects, while services always include actions (Berry, 1980). One
of the primary goals of early stages of services studying was to distinguish between the concepts
of product and service. At that time 4 specific distinctive characteristics were found: intangibility
(impossible to touch services), variability (hard to meet two identical services), fragility (the effect
from service is not infinite), simultaneous creation, delivery and consumption (impossible to store
services) (Zeithaml, 1985). Contemporary studies provide more comprehensive analysis of this
topic (Lovelock, 2011).
1. Buyers are not becoming owners of the services. Service sale is more similar to rental of
any good, not the sale of one. Choosing criteria of a product to own are different to
choosing criteria of a product to rent.
2. Services are intangible. The intention to make the service as tangible as it is possible is
fair. To do so, it is better to describe each step of service delivery and manage each one
3. Intense consumer involvement into “production process”. Consumer behavior and
experience could help or prevent in the service delivery process. Sometimes, managing
customers as employees is necessary. Self-service option has to be considered. Location
and office hours have to be convenient for customers. A company has to manage its assets
to make customer experience more attractive and convenient.
4. Company’s employees and customers are sometimes considered as a part of delivered
service. It is crucial to manage employees and clients as their behavior could impact
experience of other clients. Hiring of employees with developed hard and soft skills is
required. Management has to maintain solid motivation of personnel. Serve customers
from different segments at the same time and location could decrease average satisfaction
from the service.
5. Impossible to ensure permanent resource and outputs quality. For services it is harder to
manage consistent quality control. As for goods, standardization might help to get more
consistent quality. Set up automated systems also could be helpful in achieving consistent
6. It is hard for consumers to evaluate service quality. Setting trustful atmosphere between
service company and the client is critical. Keeping clients informed could make them
confident about their choice.
7. Impossible to store. So, the company has to develop strategy of effective demand
management. It is required to manage “production capacity” in order to meet forecasted
level of demand.
8. Time factor. In contrast to products, consumer invest not only money to buy it but also his
time. Therefore, management has to be aware of time limits and priorities of clients. For
majority of services higher amount of time required to get a service perceived negatively.
A company could compete on the basis of faster service speed, longer office hours, lower
9. Services could be delivered through both offline and online channels. It is a good idea to
consider electronic delivery of some information pieces, make service delivery available
After service concept outline, we are going to review how customers actually consume
services. Again, what is the difference between product consumption and service consumption?
Christian Groenroos (2001) describeв the difference between product and service consumption the
following way: product consumption is consumption of a result, while service consumption is
consumption of a process. However, term “consumption” does not perfectly fit in service provision
process. For majority of services, we can capture that the only thing being consumed is time for
both sides, service provider and service buyer.
Often, customers are not paying only for a service to get one. Also, they are obliged to have
indirect costs in order to get a service. Nonfinancial costs could be structured in 4 precise
categories (Lovelock, 2011)
1. Cost of time is integral part of service process. Time spent in the service process could be
treated as opportunity cost, as customers could use this time earning additional money.
2. Physical effort, for instance, fatigue or discomfort, also, could be a part of receiving service
process. This happens when the customer has to be in specific place to receive a service or
in the case of self-service.
3. Psychological burden, for instance, intellectual effort, stressful conditions, or fear, usually
takes place in case of choosing service from a set of them. Or sometimes even in the process
of receiving a service.
4. Sensory stimuli which are uncomfortable feelings from each of 5 types of senses. For
instance: loud unexpected sound, inside cold, unpleasant taste or smell, or even
These are possible nonfinancial costs which occurred before or during receiving a service.
There are also costs which have to be paid after receiving a service. These costs are the most
undesirable ones. For instance, after taking a car to the auto repair, it turns out that after fixing one
issue another one comes up and this new issue cannot be fixed in this auto repair, so you have to
drive to another place that is far away from your current location. Hence, the customer has to spent
additional time and money to satisfy his need. Another case is that for updating international
passport you have to submit all your documents in one specific place and then you have to go to
another place to pay fee for it. These 2 cases are examples of costs after service. The first one,
concerning car repair, is post problem solving, the second one, concerning passport update, is post
We should not forget about financial costs. They are not as straightforward as they seem to
be. It is often the case when the customer pay more than he or she expected. Sometimes, companies
require some prerequisite before receiving specific service, or simply, the service turns out to be
more expensive than it was expected. Thus, we can divide financial costs as expected and
unexpected costs. Figure 1 represents costs structure.
Figure 1 – The service costs structure (Lovelock, 2011)
In the context of current study digitalized or electronic services is going to be observed.
Thus it is important to mention, that cost part is critical in the sense that in the case of electronic
service some part of costs are usually disappeared. This might be one of competitive advantages
toward traditional services. Hence, financial and nonfinancial costs might be a determination factor
of mobile application use for service consumption, consequently it is considered for empirical part
of the stydy.
1.3 Consumer behavior
Consumer behavior reflects the totality of consumers’ decisions with respect to the
acquisition, consumption, and disposition of goods, services, activities, experiences, people, and
ideas by (human) decision-making process over time (Hoyer et al, 2012).
Consumer behavior is the behavior that consumers display in searching for, purchasing,
using, evaluating and disposing of the products and services that they expect will satisfy their
needs. Consumer behavior is focused on how individuals, households or families make decisions
to spend their available resources as time, money and effort on consumption related items. Within
consumer behavior discipline researchers learn how consumer decides about whether to buy or not
to buy certain goods or services, how they feel during this process, and how they actually behave
physically during searching, choosing, purchasing and using goods or services (Shiffman et al,
Actual study is an attempt to observe several aspects of consumer behavior. Particularly,
the focus is on switching from traditional way of certain service consumption to digital or mobile
means. Beside this, we are going to examine the presence of interconnection between such decision
and change in usage frequency of those certain services.
Figure 2 – Consumer behavior possible aspects (Hoyer et al, 2012)
According to Hoyer et al (2012), figure 2 shows all possible aspects of consumer behavior.
The marked aspects are those aspects which are going to be explored in current study. At the
beginning of observable consuming behavior comes service acquisition. We are interested how
people are going to acquire a service: in traditional or digital (mobile) way and why they make
such decision. After the fact of acquisition of the offering consumers start using it, which is at the
very core of consumer behavior, according to Krugman et al, 1995. Hereby, as for “how often”
aspect, we are going to verify the idea that consumers tend to increase the service usage frequency
in case of consumption via digital channel.
In order to understand the concept of consumer behavior Pieters and Vernblanken suggest
distinguishing several aspects of behavior (Pieters and Verplanken, 1991).
1. Control of behavior. People tend to control their temporary environment. This is
obvious desire, as human beings cannot survive in pure chaotic environment, so it
subconsciously gravitates to systematize things around it. Moreover, constant changes
entail stress for an individual. Thus, behavior is often directed towards individual’s
2. Objective versus subjective. External part of behavior is measurable and might be
considered as objective. Consumers execute some visible actions: they walk around
book shelfs, for instance, they take books in their hand, read titles, contents of a book,
look at book’s cover. Nevertheless, when an observer is going to get the intention of
one specific consumer, its goals and reasons why he or she acts that certain way, he
faces an internal part of his behavior. Behavioral analysis cannot be complete without
understanding of hidden part – consumer’s motives. However, all attempts to describe
internal part of behavior, or interpret it are going to be subjective. The most adequate
way to get full interpretation to ask subject of behavior to share his or her intensions,
even though such interpretation might be biased as well. Anyway, for a full explanation
and interpretation of behavior subjective self-reporting insights are needed (Wells et al,
3. Scripts. Behavior does not stand on its own, it consists of connected sequence of
behaviors. For instance, in case of online shopping consumer realize the set of actions:
turns on his laptop, tablet or smartphone, puts search inquiry into search engine,
chooses one of online stores provided, looks for desired good in its catalog, looks for
its price and other specifics, fills in respective forms with payment information, waits
for deliver, receives the product and starts using it. This is the example of ‘script’ of
behaviors, which form entire ‘online shopping’. A script is a schema of the order in
which certain behaviors or acts and conversations in general (and sometimes in a
stereotypical way) take place.
4. Feedback. Behavior usually provides feedback to goals, needs, values, knowledge, etc.
People tend to receive external assessment of their performance, whether they are in
right way to reach their goals or not. Feedback provides them this assessment. On the
basis of this information they can modify their behavioral patterns in order to achieve
what they want to achieve. The following feedback intensions could be outlined:
Learning function. While getting the results of certain behavior, it is possible to
determine what previous actions are correct and what are not. Thus, by means
of feedback, consumers could learn the consequences of their behavior and
redirect its vector towards their goals.
Habit formation. Feedback can help in habit formation and its strengthening. In
case when consumer deviate from their regular routines and this brings negative
outcome, they are more likely to get back to their previous routines and keep
Internalization. Feedback throws light on consequences of consumer’s
behavior. From this perspective, existing attitudes of an individual could be
replaced with new ones. Thereby, positive or negative outcomes from consumer
behavior could impact their convictions. (Antonides et al, 1998)
Gerrit Antonides and W. Fred van Raaij (1998) outline hierarchy of behavior. This
framework helps to structure all components of behavior. Framework includes 4 degrees in which
the person thinks about his/her own behavior. First level is single act, the basic part of behavior
(example: I turned the light off). Second level is so called behavioral domain which is a whole
group of connecting behaviors that in most cases lead to a certain goal in other words a set of
behaviors frequently organized around a common goal. Example: I do not use electricity for no
purpose. The goal is the next level of behavior hierarchy. Example: I cut my electricity bills by, at
least, 10 percent. The value is the last and the most abstract category. Example: I want to use as
much resources as I need, in sustainable manner (Antonides et al, 1998).
Another way to structure the content of behavior is divide it with the help of questions:
Question ‘What?’ addresses the level of behavioral domain. Example: I am trying
to find cheapest way to spend 6 nights in Greece during my holiday.
Question ‘Why?’ addresses the level of goals and values. Example: I want to save
more money for entertaining during the trip.
Question ‘How?’ address the basic level of simple actions. Example: I use hotel
booking websites which provides options with lowest prices.
The ‘how’- question refers to specific explanation of behavior. It requires explanation of
how specifically a person is going to reach his goal in terms of basic actions. For instance in order
to lower costs for living, one can look for a hostel, or even try to find host for several nights for
free using couchserfing.com. Thereby, there are many more acts by which living costs could be
The ‘why’- question asks for an abstract explanation of behavior. It addresses the reasons,
goals and values. It is more likely that for certain person the answer to ‘why’ question determines
the answer on ‘how’- question. For an individual, his values and goals are primary. That means
that one chooses means of achieving his goals, finding the answer on ‘why’- question before. At
the same time, for external observer it is unclear what ‘why’ underlies the basic acts. Thus, for the
observer for each act there are many possible reasons, goals and values.
Figure 3 – Consumer behavior levels (Wells and Foxall, 2014)
Means-end hierarchy of single acts, behavioral domains, goals and values is shown in
figure 3. When behavior is defined as single act, in order to receive comprehensive view external
observer could ask the question ‘Why do you do that?’, thus, one has to follow top-down approach
to explain his behavior, starting with behavioral domain, and finishing with goals and values. In
case when the observer managed to identify values of behavior subject, it is a good intension to
ask the question ‘How do you do that’ to get full understanding.
In many cases it is crucial to outline costs and benefits of behavior. This kind of costs and
benefits distinction gives better understanding of why consumers act in certain ways and do not
act in other ways. Most of the time consumers act economically in the sense that they try to
maximize benefits and minimize costs. Basically, an action is undertaken in case the benefits of it
outweigh its cost from the behavior subject point of view. Thus, the perception of costs and
benefits is subjective and therefore might differ. It depends on benefits and costs precision and
time gap between action and consequence. Sometimes, characteristic of application to the society
or individual emphasizes separately, however, it might be interpreted as part of precision level.
(Rex and Homans, 1962).
The benefits of the behavior are usually defined at quite high level for instance, one could
think, ‘How happy would I be, if I only got master degree’. The same person most likely thinks
about the cost at much more precise level ‘I paid money for it. Also, I have to stop all alternative
activities, both business and fun and put so much effort to study’. Thereby, the individual considers
financial and opportunity costs. T. Verhallen classified time and effort as behavioral costs. In order
to make an individual behave in certain way it is ideal to set cost perception at abstract level before
he/she begins it. During its realization one becomes aware at operational level of acts, efforts and
time expenses (Verhallen, 1984).
Time difference between costs and benefits significantly influences consumers’ behavior.
A good example is smoking. For a smoker instant (short-term) benefit of relaxation from smoking
outweigh long-term consequence as poor health. This is called positive time preference.
Individual benefits sometimes comes with societal costs. Take an example of answering to
a phone call in a library. In this case individual benefit might be receiving some urgent information,
while societal cost is disturbing people round due to out loud speech.
Thus, taking into account all stated above, consumer high level behavior algorithm could
be derived. Before the fact of the purchase the consumer makes a decision about possible purchase,
this decision is based on consumer’s goals and values which contains the individual’s motivation,
thus address questions what, why, how, when, how often, etc. Consumer compares costs and
benefits of the purchase, understands whether the purchase is beneficial or not and depending on
his subjective assessment performs behavioral act. After the fact of first purchase habit might be
formed, so the consumer does not evaluate costs and benefits until something significant occurs,
as change in product/service price, change in consumer’s goals, etc. Thereby, such algorithm might
be considered in the case when individual did not use traditional services previously, so, the first
interaction with the service happened online with digitalized version of it. However, it is less likely
to happen often. More frequently, before starting using digitalized service, consumers already uses
traditional one, so the fact of switching takes place. In order to review the latter case, the
observation of concept of switching behavior is going to be performed.
1.4 Switching behavior
One of the basic definition of ‘consumer switching behavior’ is the behavior of consumers
in shifting their attitude from one brand (product) to another brand (product) (Zkiene and
Bakanauskas, 2006). However, it is possible to find more detailed and at the same time, more
generalized one, for instance, consumer switching behavior is referred to the times when consumer
chooses a competing choice rather than the previously purchased choice on the next purchase
occasion (Rabin et al, 2014). In other words, switching behavior reflects the decision that a
consumer makes to stop purchasing a particular service (Boote, 1998).
According to Roos and Gustafsson (2007), customers might switch the service provider
firm for different reasons, for instance, existing service provider no longer meets customers’
requirements due to their changing circumstances or customers receive more beneficial offers from
other market players or customers just want to see variety in front of them.
The other researchers derive 4 categories of factors which highly affect consumer switching
1) Cultural factors. Probably, the major influencing factors, which are related to the
cultural environment, which surrounds the customer and forms and forms his living,
needs and wants referred to his culture, sub-culture and social class.
2) Social factors. Societal norms and values, affecting the behavior, shared with the
closest, reference groups, as family members and friends.
3) Personal factors. Personal characteristics like age, life cycle, occupation, income and
4) Psychological factors. Factors as motivation, perception, learning, beliefs, attitudes,
and thinking could also affect consumer decision whether to buy or not to buy certain
service. (Zeeshan et al, 2015)
Keaveney (1995) has conducted research which identifies more than 800 critical behaviors
of service firms that forced customers to switch services. Derived reasons that push customers for
switching were classified into eight categories. These categories worth to be observed in order to
gain deeper understanding of switching behavior concept.
1) Pricing factor includes all critical switching behaviors that involve prices, price deals, price
promotions, rates, fees, charges, service charges, surcharges, coupons and penalties.
Customers might switch services in case when:
actual prices exceed reference prices;
prices are considered as too high relative to internal normative price;
prices are considered as too high relative to the services received;
prices are considered as too high relative to competitive prices.
2) Inconvenience factor includes critical cases when feel inconvenienced by the service
provider’s working hours, location, waiting time before being served. Customers might
switch services when:
customer discovers another service provider option with more suitable schedule;
customer discovers another service provider option with more suitable location;
getting an appointment takes too long relative to customer’s internal, normative point;
service delivery takes too much time relative to normative reference point or to service
3) Core service failures factor includes critical cases that happens when some mistake or
technical problem takes place. Customers might switch services when:
core service mistakes have longitudinal nature and mistakes series leads to service
single serious mistake occurs which completely denies service delivery;
multiple mistakes occur during one service encounter;
mistakes concerning billing occur (incorrect or not timely billing);
damage is caused to customer’s side while serving.
4) Service encounter failures factor includes critical cases of unpleasant personal interaction
with service firm’s employees. Customers might switch services when:
service personnel does not listen to a customer;
service personnel rushes when it is inappropriate;
service personnel ignore customer’s requests;
service personnel is not successful in execution of their obligations.
5) Employee responses to service failure factor includes critical cases of inappropriate
reaction to occurred issue of service provider employees. Customers might switch services
they face reluctant response;
employees fail in managing the issue;
they face patently negative responses, blaming them back.
6) Attraction by competitors factor reflects cases when customers switch because they find
more attractive option, regardless of satisfaction level previous service. Customers might
switch services when:
they find more personable offering;
they find more reliable offering;
they find option with higher quality.
7) Ethical problems factor includes cases which seem immoral, illegal, unsafe, etc. for
customer. Customers might switch services when:
they reveal dishonesty in service providers activities;
they face overly aggressive selling behavior from employees;
they receive unsafe or unhealthy offerings.
8) Last category is involuntary switching and seldom-mentioned incidents. It includes cases
which are basically out of control of either the customer or the service company. Such
switch might be a cause when:
the customer had moved;
the service provider had moved;
insurance or other third-party payer had changed alliances.
According to the study, failed service encounters, responses to failed service and pricing
are top reasons why customers tend to switch between services.
Another study done by Lee and Murphy (2005) states the other set of switching
determinants in case of cellular network service. However, the results might be relevant to the
wider set of services.
The top reason from the study results is price. Price fluctuations might cause the loyaltyswitching transition. Authors suppose that such effect arises especially with regard to
commoditized products and services.
Service quality is top2 switching determinant. Thus, perceived decline in services quality
level might also cause the loyalty-switching transition. Customers tend to consider good service
quality as unconditional event. Thereby, they always expect good service and do not tolerate poor
one. Also, the researchers divide quality into two parts: technical and functional. The first group
means technical execution of declared service characteristics, while the second one means entire
interaction with the provided service. Unsatisfying technical quality is likely to be a reason for
switching than unsatisfying functional quality.
The third ranked reason is loyalty programs. Attractive loyalty program of the competitive
service provider might be a reason to switch current provider.
Next comes behavioral factors. By them, the authors mean such behaviors as variety
seeking or impulse buying. This factor is extremely irritating for managers as effort, which they
put for customer retention maximization, might be helpless due to behavioral factor.
Top5 factor is brand trust. This factor is particularly important in case of service
consumption as customers have no chance to evaluate the offering prior to paying for it (Liljander
and Strandvik,1995). The exception here is giving customer a trial version. However, in case of
services it is not always possible. The authors of the research suppose that low rank of the factor
in their study is commoditized observed service.
The last factor is reference group influence. This factor reflects social influence from
relatives, friends or mates. Often, this factor plays more significant role than in observed study
(Bearden and Etzel, 1982). The researches use the same explanation of such phenomenon – cellular
service is perceived as commodity from the side of consumers. (Lee and Murphy, 2005)
Bruhn and Georgi (2006) suggest reasons for service switching behavior classification in a
different dimension. They divide these reasons into 3 groups:
1) Customer-related switching reasons are those reasons which concern customer
characteristics as age, sex, lifestyle, preferences, etc. They have more or less direct
connection with the service provider and are directly connected to customers’ needs.
2) Provider-related switching reasons are those reasons which concern perceived service
quality and customer satisfaction. In order to manage customer retention executives have
to deal with this category of reasons.
3) Competition-related switching reasons are those reasons which concern actions from
current service provider competitors. This action might include marketing efforts affecting
customer’s decision to use specific company as a provider or new offering which is treated
by consumer as more favorable one.
Thus, we should state that current study is focused on competition-related and customerrelated switching factors. In other words we are interested in those reasons which attract consumer
to try a new service, not those which repulse customer from service in use. To this group of factors
we could refer costs, which we already outlined, loyalty programs, and reference group influence.
We are not going to focus on brand factor as the purpose of the study is to examine switching from
digital to non-digital service regardless brand names of its providers.
As we see, switching behavior is studied in the context of either non-digital services or
digital ones (Keaveney and Parthasarathy, 2001). The latter studies specifically focuses on
consumers’ characteristics which differentiate ‘switchers’ from ‘continuers’. The fact of switching
is stated in the case when online service consumer just stops using it. For instance, one has e-mail
box of the online service provider, is subscribed to their newsletters, he also check the on their
web portal. Suddenly, this person cease to use any service from that provider. In observed study,
such case is interpreted as service switch. However, it is not obvious whether this lost client
actually changes the provider or stops use any online services to satisfy specific needs.
Consequently, it is possible to conclude that the consumer switch from non-digital to digital
service is limited and could be treated as research gap which is going to be approached with the
The current study is an attempt to partly explain consumer decision to switch from nondigital to digital (online, electronic, mobile) service which satisfy common needs. In order to
1.5 Digitalization of products and services: shift to e-commerce
By digital or online product and services we understand the products and services being
sold by using digital technologies, e.g. via Internet.
Online product and services sales can affect both demand and supply – market
fundamentals. As for the demand side, it precludes potential customers from inspecting goods
before the purchase. Moreover, online sellers tend to be newly formed firms and may have less
reputation or brand capital to signal or bond quality. These factors may create information
asymmetries between sellers and buyers that are not present in services and products purchased
offline. Also sales via Internet often involve a delay between purchase and consumption when a
product should be physically delivered. However, in the same time e-commerce technologies
reduce consumer search costs; make the search easier compare to different producers’ products
and prices (Lieber and Syverson, 2011).
As for the supply side, selling online enables new distribution technologies that lead to
reducing supply chain costs, improving of service, or sometimes both. Both the new distribution
technologies and the reduction in consumer search costs combine to change the geography of
markets; geographical space may matter less online. Finally, combining further both sides of the
market we might notice that online sales face different tax treatment comparing to the offline sales.
We discuss each of these factors in this section further.
The information asymmetries appear in a more obvious way when purchasing online for
a several reasons (Lieber and Syverson, 2011). The most obvious one is: consumer does not have
the opportunity to physically examine the good at the point of buying. There potential lemons
problem arise where unobservable varieties are selected into the online market. Another reason is
that online retailing is relatively new; retailers have less brand capital than the established
traditional retailers. Also another factor is that some consumers’ concerns about the security of
Due to information asymmetries can lead to market inefficiencies, both consumers and
sellers (especially sellers of high quality goods) have incentives to structure transactions and form
market institutions to alleviate lemons problems. Some examples of such efforts on the part of
online sellers exist. Some firms such Zappos offer free shipping on purchases and returns, which
moves closer to making purchases conditional upon inspection. Although, a delay between
ordering and consumption inherent to online commerce still can create a wedge.
As an alternative approach a convey prior to purchase the information that would be
gleaned by inspecting the product could be in place. Garicano and Kaplan (2001) examining used
cars which were selling via online auction – Autodaq, and physical auctions. Researchers found
little evidence of adverse selection or informational asymmetries. They referred this to actions that
Autodaq has taken so that to reduce information asymmetries. Besides offering extensive
information on each car’s condition and attributes, something that the tools of e-commerce could
make easier, Autodaq brokers arrangements between potential buyers and third-party inspection
services. As an example, Jin and Kato (2010) examine the market for the collections of baseball
cards and showed how the use of third-party certification has alleviated information asymmetries.
They find a big increase in use of professional grading services when eBay began being used by
customers for buying and selling baseball cards. Another form of disclosure is described by Lewis
(2009). Using data from eBay Motors, he discovers positive correlation between the number of
pictures that the seller posts and the winning price of the auction. Although, he does not find
evidence that information voluntarily disclosed by seller affects the probability that the auction
listing results are in a sale.
Instead of telling consumers about the product itself, firms can try to establish a reputation
for quality or some other brand capital. Smith and Brynjolfsson (2001) use data from online price
comparison site to study an online book market. They found that brand has a significant effect on
buyers demand. Consumers are ready to pay an extra $1.72 (the normal item price in the sample
was about $50) to purchase from one of the big three book online retailers: Amazon, Borders, and
Barnes & Noble. We have an evidence that the premium is due to perceived reliability of the
quality of services in bundles, and particular shipping times. In online auction markets, rating
systems allow even not big sellers to build reputations, although Hortaçsu and Bajari (2011)
(conclude that the evidence about if the premium accrues to sellers with high ratings is ambiguous.
Better metric of an effect of reputation in such markets comes from the field experiment provided
by Resnick et al. There, an experienced eBay seller with a very good feedback rating sold matched
lots of postcards. A randomized subset of the lots was sold by the experienced eBay seller, using
its own identity. The other subset was sold by the same seller, but using a new eBay identity
without any buyer feedback history. The lots sold using the experienced seller identity received
winning bids that were approximately eight percent higher. More recently, Adams et al (2006)
evaluate whether seller ratings affect how much buyers are willing to pay for Corvettes on eBay
Motors. Most of the previous research had dealt with items of small value where the role of
reputation might have a relatively modest influence. Collectable sports cars, however, are clearly
high value items. In that market, Adams et al. find very little (even negative) effect of seller ratings.
In another recent paper, Cabral and Hortaçsu (2010) use a different approach and find an
important role for eBay’s seller reputation mechanism. They first run cross-sectional regressions
of prices on seller ratings and obtain results similar to Resnick et al. Next, using a panel of sellers
to examine reputation effects over time, they find that sellers’ first negative feedback drops their
average sales growth rates from +5% to –8%. Further, subsequent negative feedback arrives more
quickly, and the seller becomes more likely to exit as her rating falls.
Outside of online auction markets, Waldfogel and Chen (2013) look at the interaction of
branding online and information about the company from a third party. They find that the rise of
information intermediaries such as BizRate leads to lower market shares for major branded online
sellers like Amazon. Thus other sources of online information may be a useful substitute for
branding in some markets.
While a lot of digital media that is purchased online can be used almost immediately after
purchase online purchases of physical goods typically involve delivery lags that can range from
hours to days and occasionally longer. Furthermore, these delayed-consumption items are the kind
of product most likely to be available in both online and brick-and-mortar stores, so the role of this
lag can be particularly salient when considering the interaction between online and offline market
The traditional view of a delay between choice and consumption is as a waiting cost. This
may be modeled as a simple discounted future utility flow or as a discrete cost. In either case, this
reduces the expected utility from purchasing the good’s online version. However, more behavioral
explanations hold out the possibility that, for some goods at least, the delay actually confers
benefits to the buyer in the form of anticipation of a pleasant consumption experience. This holds
out the possibility that the impact of delay on the relative advantage of online channels is
ambiguous. Though one might think that if delay confers a consistent advantage, offline sellers
should offer their consumers the option to delay consumption after purchase rather easily. This, to
say the least, is rarely seen in practice.
It is generally accepted that search costs online are lower than in offline markets. The rise
of consumer information sites, from price aggregation and comparison sites to product review and
discussion forums, has led to large decreases in consumers’ costs of gathering information. This
has important implications for market outcomes like prices, market shares, and profitability.
Online search is not fully free; several papers have estimated positive but not high costs.
Bajari and Hortaçsu (2011), for example, find the implied price of entering an eBay auction to be
$3.20. Brynjolfsson et al(2010) write that the maximum cost for viewing additional pages of search
results on a books shopbot is $6.45. Hong and Shum (2013) in their turn estimate the median
consumer search cost for textbooks as the less than $3.00. Nevertheless these costs are less for
most of the consumers than the value of the time it would take them to travel to the offline seller.
Online sales affect how goods get from producers to customers. In some industries, the
internet was the reason of disintermediation, a diminishment or sometimes the entire removal of
links of the supply chain. Thus, between 1997 and 2007, the number of travel agencies offices fell
by about half, from 29,500 to 15,700. This was accompanied by a large increase in consumers’
propensity to directly make travel arrangements – and buy airline tickets in particular – using
online selling sites.
E-commerce technologies have also brought changes in a way sellers worked out the
orders. Firms can easily assess the state of demand for their products and turn this information into
orders sent to manufacturers and wholesalers. This has reduced the need for inventory holding.
An example of how increased speed of communication along the supply chain affects
distribution costs is a practice referred to as “drop-shipping.” In drop-shipping, retailers transfer
orders to wholesalers who then ship directly to the consumer, bypassing the need for a retailer to
physically handle the goods. This reduces distribution costs. Online-only retailers in particular can
have a minimal physical footprint when using drop-shipping; they only need a virtual storefront to
inform customers and take orders (Lieber and Syverson, 2011).
The main effects of opening a concurrent online sales channel in an industry may have
implications for firms’ competitive strategies. These strategy choices may interact with the
A major factor that influences company’s’ joint strategies on offline and online markets
is the degree of connectedness between offline and online markets for the same product. This
connectedness might be multidimensional. It can include a demand side: how good customers view
the two channels as substitutes. It can include the supply side: if online and offline distribution
technologies are complementary.
The research findings of Michael Fitzgerald et al stated that according to 78% of
respondents, achieving digital transformation will become critical to their organizations within the
next two years. However, 63% said the pace of technology change in their organization is too slow.
The most frequently cited obstacle to digital transformation was “lack of urgency.” Only 38% of
respondents said that digital transformation was a permanent fixture on their CEO’s agenda. Where
CEOs have shared their vision for digital transformation, 93% of employees feel that it is the right
thing for the organization. But, a mere 36% of CEOs have shared such a vision (Fitzgerald et al,
Figure 4 – Attitude towards digital transformation (Fitzgerald et al, 2013).
One company that has succeeded is Starbucks. In 2009, after dismal performance cut the
company’s stock price in half, Starbucks looked to digital to help re-engage with customers. It
created a vice president of digital ventures, hiring Adam Brotman to fill the post. His first move
was to offer free Wi-Fi in Starbucks stores, along with a digital landing page with a variety of
digital media choices, including free content from publications like The Economist. It sounds
simple, but as Brotman says, “we were not just doing something smart around Wi-Fi, but we were
doing something innovative around how we were connecting with customers.” Brotman is now
chief digital officer at Starbucks, where he and Curt Garner, Starbucks’ chief information officer,
have formed a close working relationship, restructuring their teams so that they collaborate from
the very start of projects. Last year, they cut 10 seconds from every card or mobile phone
transaction, reducing time-in-line by 900,000 hours. Starbucks is adding mobile payment
processing to its stores, and is processing 3 million mobile payments per week. Soon, customers
will order directly from their mobile phones. Using social media, mobile and other technologies
to change customer relationships, operations and the business model has helped Starbucks reengage with customers and boosted overall performance. Its stock price has also bounced back up
from roughly $8 in 2009 to nearly $73 in July 2013 (Fitzgerald et al, 2013).
At Intel, there is no lack of a sense of urgency; the company knows mobile technology is
upending its market. The company has failed multiple times to become an important provider of
mobile processors, including turning down the opportunity to provide chips for the original iPhone.
Intel’s culture has long been built around maintaining market dominance through intense internal
competition, said Kim Stevenson, its chief information officer. Now, Intel believes it needs a more
collaborative culture to help it gain an edge in mobile processors. To start this cultural change,
Intel’s top 25 executives gathered for a strategy discussion led by Stevenson and the head of human
resources. First, the group had to agree on the overall vision, the need for cultural change in order
for Intel to compete effectively in the emerging mobile market. Then it had to create ways to bring
people together. That would mean breaking down barriers to communication that existed in the
company’s culture of rivalry. Among steps Intel took to improve communications were adding
220 video conferencing rooms, electronic white boarding, and adding search functions to its
SharePoint implementation. All company employees are now on an internal social network. Intel
has also set up teams based on accounts, not internal departments. Intel is taking small, concrete
steps towards changing its culture, rather than massive, risky leaps. The small-step strategy is one
many companies could adopt when trying to transform. As one survey respondent said, “The kind
of transformation being adopted does not give much leeway for failure and the cost to the
organization’s reputation and brand is great. A thoughtful and piloted approach needs to be
adopted.” Small steps do not mean companies lack urgency. According to Stevenson, “We had the
top 25 executives in the company buy in to the strategy. You have to admit that your competitive
culture needs to change to be successful in the future, and we want to change before it’s evident
on the outside that we need to change, right? And I think that’s a really key premise.” (Fitzgerald
et al, 2013).
Another important factor in creating an innovative way to communicate with customers
is mobile application channel. It allows to interact with company’s services or even consume them
and pay for them via mobile applications.
IBM IBV C-suite study stated mobile banking is considered as one of the most popular
services used via mobile application. Also, statistics on Techcrunch (2014) shows that among bank
account holders in US, almost 50% will use mobile applications by 2017.
The set of activities executed via mobile banking application has been increasing since first
mobile banking applications have arisen. In the beginning of mobile banking apps evolution bank
clients used them for the purposes of checking their bank account statement and transaction
history. Contemporary mobile banking applications allow to perform much broader set of actions
including cross-banking money transactions, payments for various services, deposits making,
loans requests, etc (Lardinois, 2012).
Obviously, bank account statement is the number which each bank client want to absolutely
control. In this case, the mobile application is the instrument giving the opportunity to increase
control over the service – saving client’s money. Prior to mobile banking applications, the way
how client could check his account statement or transaction history was very complex and time
consuming, confirms McKinsey (2014). While mobile banking was evolving, other more advanced
banking services were successfully adopted in mobile applications, allowing users to operate with
well-known services in more convenient way. Additional control provided by mobile app could
be related to other services operating through mobile application channel. This idea is reasonable
because the app is capable to deliver more detailed information in a given time frame than phone
call or in-person meeting.
Another good example of popular digitalized service is taxi ordering. The main player on
this market is American company – Uber. Uber operates the Uber mobile app which allows
consumers with smartphones to request a ride which is then routed to Uber driver who is signed
in Uber platform. In many countries there are local companies which replicates Uber business
model, Russia is not an exception in this case. According to the RBC (2016) estimations such
companies occupied more than 50% of taxi market in Moscow. Today, taxi aggregator service
companies continue to expand in Russian regions. However, the market share will hardly reach
the level of Moscow market share as the number of smartphones users in regions is significantly
lower than in other Russian regions.
Uber top management, Travis Kalanick and Ryan Graves, shared their ideas with CNBC
(2016) and RBC (2016) about the idea that reliability and high frequency of use are key success
determinants of service they provide. This statement could be potentially transferred to many other
services provided via mobile application. Firstly, reliability of applications was doubtful when the
speed of mobile internet was limited by 2G networks. Also, smartphones performance has
increased significantly since couple of years ago. These factors create an opportunity to develop
reliable service based on mobile app. Still, it is crucial to utilize these factors in right way in order
to develop demanding mobile application. Secondly, frequency of use determinant seems to be
relevant to other possible services because the mobility is the essence of smartphone, which means
that offering services via this channel, constant availability of one has to be considered or even
treated as an advantage.
1.3 Acceptance behavior models
Since 1960s substantial research has been done aiming to finding the determinants of the
technology acceptance among users. Today, numerous theories and models are developed to
explain IT adoption process.
The evolution of these theories is connected with the development of information
technologies and market trends. Early theories considered mainly technical features as the main
factors of the technology adoption, while the later theories included also the network-related
determinants such as social influence (Venkatesh, 2006). As there is the large number of theories
and models of technology adoption in the research literature, the focus in the current study will be
made on the most influential ones. The most influential theories and models based on the citation
analysis and qualitative content analysis (Korpelainen, 2011) are going to be reviewed.
Theory of Reasoned Action (TRA).
Theory of reasoned action (figure 5) is a widely studied model. Its development belongs to
social psychology field. While developing the model Ajzen and Fishbein (1976) was aiming to
distinguish the concepts of attitudes, beliefs, intensions and behaviors (Al-Gahtani and King,
1999). The theory of reasoned actions was introduced in 1967 and till present time has been
improved and reshaped by many researchers (Talukder, 2014). The main assumption, which
underlies TRA, is that human beings are rational as a rule and they can make systematic use of
information available to them (Ajzen and Fishbein, 1976). The name of the theory is justified with
the idea that in general people tend to be aware of reasons of their actions and think of them before
decide to act or not to act in certain way. The ultimate goal of the theory is to interpret and predict
an individual’s behavior (Ajzen and Fishbein, 1978).
Theory of reasoned action implies that there are two factors determining individual’s
behavioral intension to perform the behavior, which in its turn, determines the actual person’s
an individual’s attitude towards particular behavior
subjective norms concerning behavior to perform
The first determinant basically reflects individual’s judgement whether specific behavior
is positive or negative. While the second determinant reflects the perceived social pressure strength
arising in the case of behavior performing. These two factors are thought to form behavioral
intension which eventually leads to the behavior performing.
Figure 5 - Theoretical model of Reasoned Action (Fishbein and Fishbein, 1976)
As it was stated previously, during developing stage of the model the researchers were
aimed to create general behavioral model lacking any kind of context. Hereafter, TRA was used
as a basis for development other models connected with acceptance behavior regarding
Technology Acceptance Model (TAM)
As an adaptation of theory of reasoned action, Davis et al (1989) introduced the theory of
acceptance model, which happened in 1986. As distinct from TRA, the new model was aimed to
consider both internal and external factors influencing the behavior. Moreover, this model
narrowed behavior in general to the specific information technology acceptance.
6 - The
technology acceptance model (Davis,1989)
The TAM (figure 6) is designed to describe and explain acceptance of technology in
organizational context (Carlsson et al, 2006). According to the theory, the usage of technology is
affected by perceived ease of use and perceived usefulness of the technology. Perceived ease of
use was defined by Fred Davis as "the degree to which a person believes that using a particular
system would enhance his or her job performance". By perceived usefulness Davis means "the
degree to which a person believes that using a particular system would enhance his or her job
performance". Both these factors determine the attitude toward using the technology. The
behavioral intention to use the system is the function of perceived usefulness and attitude (positive
or negative) towards using the system. Behavioral intention, in its turn, influences the actual
system use (Davis, 1989).
Since it was developed, the theory has been expanded twice. The second version was
introduced in 2000 by Venkatesh and Davis. In that version one of two main factors was updated.
From that point of time perceived usefulness was determined by 5 factors which are:
Subjective norm - the degree to which an individual perceives that most people
who are important to him think he should or should not use the system
Image - the degree to which an individual perceives that use of an innovation
will enhance his or her status in his or her social
Job relevance - The degree to which an individual believes that the target system
is applicable to his or her job
Output quality - The degree to which an individual believes that the system
performs his or her job tasks well
Result demonstrability - The degree to which an individual believes that the
results of using a system are tangible, observable, and communicable
Also, 2 moderators of subjective norm were added:
Figure 7 -- Proposed TAM2—Extension of the Technology Acceptance Model
(Venkatesh and Davis, 2000)
The second extension (figure 7) of the technology acceptance model was introduced in
2008 by Venkatesh and Bala. The main improvement was incorporation anchors and adjustment
determinants of perceived ease of use into the model. Those determinants are:
Computer Self-Efficacy - The degree to which an individual believes that he or
she has the ability to perform a specific task/job using the computer
Perception of External Control - The degree to which an individual believes
that organizational and technical resources exist to support the use of the system
Computer Anxiety - The degree of “an individual’s apprehension, or even fear,
when she/he is faced with the possibility of using computers
Computer Playfulness - The degree of cognitive spontaneity in microcomputer
Perceived Enjoyment - The extent to which “the activity of using a specific
system is perceived to be enjoyable in its own right, aside from any performance
consequences resulting from system use”
Objective Usability - A “comparison of systems based on the actual level (rather
than perceptions) of effort required to completing specific tasks”
Figure 8 -- Proposed TAM3—Extension of the Technology Acceptance Model
(Venkatesh and Bala, 2008)
The Diffusion Theory was developed in 1962 by Everett Rogers to give explanations on
how new ideas and innovations spread in the society, define the role that the networks play in the
diffusion process and analyze the behaviors of different segments of users. Rogers (1962) argues
that diffusion is the process by which an innovation is communicated, the characteristics of the
innovations, the structure of the decision process that leads to either adoption or rejection of
Figure 9 – The theoretical model of diffusion of innovations (Rogers, 1962)
The most important finding of the theory is that all innovations must have specific set of
qualities to become successful in the community. Evaluation of these qualities might contribute to
the innovation fast adoption. According to the theory (1962), there are five main qualities of
innovations that define the rate of their adoption and as a result their success in the social group.
1) Relative advantage represents “the degree to which an innovation is perceived as better that the
idea it supersedes” and measured in social and economic gains, the convenience, the user’s
satisfaction level. In the essence of the factor there is an argument that the greater the relative
advantage of the innovation is, the more likely users will adopt it without any complications.
2) Compatibility defined as “the degree to which an innovation is perceived as being consistent
with the exiting values, past experiences and needs of potential adopters”. The innovation is less
likely being adopted, if it does not fit the social norms that exist in the community.
3) Complexity is the perceived ease of use of the innovation. The innovation will be adopted
quickly in the community if it is simple for the user’s perception and if it does not require additional
skills to be operated.
4) Trialabilty represents “the degree to which an innovation might be experimented with on a
limited basis”. The innovation that tried by the adopter is less likely to spread since the adopter
will associate more risks with its future use.
5) Observability represents the extent to which the adopter is able to observe the results of the
innovation adoption. The higher observability of the innovation adoption outcomes, the more
certain the user is and the more intensive the discussion is in the community.
The model has several limitations, in particular, Wolfe (1994) criticized the model for the
absence of the innovation characteristics changes which take place over time. Chatterjee (2012)
concluded that the assumed linearity of the stages in the adoption process is another limitation of
the diffusion of innovation model. The problem here is that there is a probability that adopters skip
one or more steps of the innovation adoption process.
Unified Theory of Acceptance and Use of Technology (UTAUT)
Venkatesh together with other scholars (2003) developed The Unified Theory of
Acceptance and Use of Technology (UTAUT) which was based on the analysis and empirical
comparison of popular user acceptance models including the innovation diffusion theory (Rogers,
1962) Theory of Reasoned Actions (Ajzen, 1980), the social cognitive theory (Bandura, 1986, the
Technology Acceptance Model (Davis, 1989), the theory of planned behavior (Ajzen, 1991), the
motivational model (Davis, 1992), a model comprising the technology acceptance model and the
theory of planned behavior (Taylor and Todd, 1995). This unified theory was proposed as the
attempt to remove the difficulties many researchers face while choosing among various models or
specific constructs across them for the research (Venkatesh et al, 2003).
Figure 9 – The Unified Theory of Acceptance and Use of Technology (Venkatesh et al
Venkatesh et al (2003) included two groups of constructs in his model – direct and indirect
determinants of the user acceptance behavior (Figure X). The constructs that reflect the direct
influence on the user behavior include performance expectancy, effort expectancy, social influence
and facilitating conditions. Indirect determinants are attitude toward using technology, selfefficacy and anxiety are considered to be indirect determinants. Also, the specific role in the model
is designated to key moderators as gender, age, experience and voluntariness of use.
The UTAUT model was applied and proved quite successful in various studies (Eckhardt
et al, 2009; Maldonado et al, 2010; Curtis et al, 2010) related to the technology acceptance theory.
All described models have their own context which makes incorporated factor relevant in
every single case. Current study is aiming to derive mobile application usage for service
consumption determination factors. Thus, several most relevant factors are going to be taken from
the models and adapted to the context of our research in the manner of many similar studies
(Moghavvemi, 2014; Cimperman et al, 2016; Qi Ma et al, 2015). These factors are effort
expectancy, performance expectancy, social influence, anxiety, facilitating conditions, trialability,
perceived enjoyment, attitude toward using. Context specific factors which were derived from the
analysis of previous chapters include service delivery control, regularity of service use, reliability,
financial costs decrease, nonfinancial costs decrease.
2 RESEARCH METHODOLOGY
In this chapter the methodological framework of the study is going to be described and
explained. It includes research approach, data collection and data analysis.
2.1 Research Approach
Prior to conducting the research, the main thing which has to be done is identification of
clear research problem.
The literature review has shown that services tend to continue its development in online.
According to Criteo (2015), during last several years the number of financial transactions made
via smartphones in Russia has significantly risen. This statistics means that the turnover from
smartphone money transactions has increased accordingly. McKinsey (2015) stated that generally
the number of offline services which somehow transform in order to serve clients online increases
from year to year. It is reasonable to assume that the audience of service users through smartphones
includes those consumers who switched from offline service analogs and those who previously
did not use any service analog, in other words, latent demand. Since this market situation is rather
new, the research of this problem is limited. However, large number of studies have already
explored technology adoption in different contexts. Switching behavior has been also examined,
within online service usage, for instance (Keaveney, 2001). Hence, in this case we could adapt and
partly use models and frameworks of applicable studies with similar goals but different contexts.
Thus, the main research problem of this study is to identify factors that have the strongest influence
on consumer behavior in the context of service usage after its digitalization.
It is crucial to formulate research questions in order to set the research design that
represents the framework for data collection and analysis accordingly and guides the entire
execution of research methods (Bryman and Bell, 2003).
Based on the research problem, the main research questions being formulated are the
RQ1 – What factors determine consumers’ decision to switch to digitalized service?
RQ2 – What factors determine the usage frequency change of a service after its
Depending on the type of required data for answering research questions, the specific
approach of a research conduction has to be chosen. Scientific research implies three main
approaches in order to get reliable results of a study. They are quantitative, qualitative and mixed
method that incorporates both qualitative and quantitative methods (Williams, 2007). For the
purpose of this study quantitative research was chosen.
Quantitative research represents the collection and analysis of data that can be quantified
and subjected to statistical treatment (Creswell, 2003). Quantitative method helps to establish and
verify the mathematical relationship between different variables. In some cases it is also possible
to generalize obtained results to the larger population (Leedy and Ormrod, 2014). Specifically, the
causal comparative research was utilized, since it allows verifying the nature of independent
variables influence to the dependent variables. Such approach was chosen in order to find the
factors that determine consumers’ decision to switch from offline services to digitalized ones.
Another research type classification is described by (Babbie, 2007). He derives three types
Exploratory research – is research usually conducted to cover problem which
is not clearly defined. It uses to gather preliminary information, when
researchers do not have enough verified information to make conceptual
distinctions or posit an explanatory relationship (Shields, 2013);
Descriptive research – is research conducted to describe characteristics of
studied phenomenon or population. While examining these characteristics, it
does not explain the existence of them (Shields, 2013);
Explanatory or causal research – is research which investigates cause-andeffect relationship and test hypotheses about it. Within this research type two
methods are outlines: experimentation and statistical research (Lynch, 2013).
The topic of this study is not well covered. Switching behavior from offline service to its
digitized mobile form is nor well studied so far, thus there is little theory available to guide the
development of hypotheses. Moreover, recognized models are going to be adapted in order to find
relations between studied variables. In other words, this study is aimed to develop better
understanding of the observed phenomenon. Thus, this research falls into the explanatory research
category (Hair et al, 2011)
The next crucial stage of scientific research is to detail research design. According to Selltiz
et al (1959), research design implies “the arrangement of conditions for collection and analysis of
data in a manner that aims to combine relevance to the research purpose with economy in
procedure”. Thereby, specific aspects of the research design as sample choice, data collection and
variables formulation are going to be described further.
2.2 Theoretical model
Based on theoretical literature review we formed the set of constructs, which are basically
factors, represents in figure 10.
Figure 10 – The theoretical model of the study
These constructs could be divided into two groups. The first group contains constructs that
could affect individual’s decision whether to use an abstract technology or not. They are taken
from Unified Theory of Acceptance and Use of Technology (Venkatesh et al, 2003), Technology
acceptance model 3 (Venkatesh and Bala, 2008) and Diffusion Theory (Rogers, 1962). The
purpose of these models were technology acceptance measurement in the organizational context.
Hence, the context of each construct has to be adapted to the mobile application use, despite the
fact that the core of the constructs remains the same. Most constructs are borrowed from UTAUT
model as its authors empirically justified outperformance relative to other models (Venkatesh et
The second group of constructs is designed specifically for the purpose of this study in a
manner of many other researches using technology acceptance models as the foundation for their
study (Arpaci, 2014, Moghavvemi, 2014; Cimperman et al, 2016) These constructs reflects the
specific relevant peculiarities of service usage or mobile application usage which are inferred from
literature review described in chapter 1. The constructs are represented in Table 1.
Table 1 – The constructs incorporated in the theoretical model
Theory of technology Definition
of The degree of ease associated with
Acceptance and Use of the use of a mobile application for
Performance expectancy Unified
of The degree to which an individual
Acceptance and Use of believes that using a mobile
application for service consumption
will help him or her to attain gains
in job performance.
of The degree of the social pressure
Acceptance and Use of experienced by the adopter
regarding mobile application usage
for service consumption.
acceptance The degree of an individual’s
apprehension, or even fear, when
she/he is faced with the possibility
of using mobile application for
of The degree to which an individual
Acceptance and Use of believes that he has an access to
required resources to support the use
of a mobile application for service
Service delivery control
The degree to which a mobile
application increase the level of
user’s service delivery process
Regularity of service use The degree to which a regular need
of service is required for mobile
Diffusion of Innovation
The degree to which a mobile
application for service consumption
may be experimented with on a
acceptance The extent to which the activity of
using a mobile application for
service consumption is perceived to
be enjoyable in its own right, aside
Financial costs decrease
Attitude toward using
The degree to which a mobile
application seems to be reliable
instrument for service consumption
The degree to which usage of a
mobile application for service
consumption decreases financial
costs for a user
The degree to which usage of a
mobile application for service
consumption decreases nonfinancial
costs for a user
acceptance The degree to which usage of a
mobile application for service
consumption is assessed positively
Corresponding assumption are being outlined:
Effort expectancy has positive interconnection with mobile application usage for
service consumption and change in usage frequency
Performance expectancy has positive interconnection with mobile application
usage for service consumption and change in usage frequency
Social influence has positive interconnection with mobile application usage for
service consumption and change in usage frequency
Anxiety has positive interconnection with mobile application usage for service
consumption and change in usage frequency
Facilitating conditions has positive interconnection with mobile application usage
for service consumption and change in usage frequency
Service delivery control has positive interconnection with mobile application usage
for service consumption and change in usage frequency
Regularity of service use has positive interconnection with mobile application
usage for service consumption and change in usage frequency
Trialability has positive interconnection with mobile application usage for service
consumption and change in usage frequency
Perceived enjoyment has positive interconnection with mobile application usage
for service consumption and change in usage frequency
Reliability has positive interconnection with mobile application usage for service
consumption and change in usage frequency
Financial costs decrease has positive interconnection with mobile application usage
for service consumption and change in usage frequency
Nonfinancial costs decrease has positive interconnection with mobile application
usage for service consumption and change in usage frequency
Attitude toward using has positive interconnection with mobile application usage
for service consumption and change in usage frequency
2.3 Sample strategy
Sample selection is generic step of the entire research. From the type of chosen sample
depends the direction of possible analysis outcomes and overall results of the study. The concept
of population is also important. It represents the group of individuals that possess required
characteristics and information for the research. For consistent results of the study, the researchers
has to consider in advance the availability accessibility of the population’s information (Philips et
Information gathering from the entire population is always impossible, thus in qualitative
research the sample or subset is taken in order to collect required information from individuals in
it and make further conclusions about entire population. Due to the fact that the sample represents
the population, the individuals within the selected subset have to have similar characteristics as
individuals from entire population. Thus, the consistent patterns identified during data analysis can
be attributed to the entire population (Philips et al, 2013).
The significance of choosing the right method of sampling for the study should not be
underestimated. Wrong sampling method might result in the poor representativeness of the subset
for the entire population (Carver, 2010).
During sampling we tried to avoid sampling errors which occurred when the parameters of
the sample are different from the parameters of the population (Harry, 2010).Target sample of the
research are Moscow and Saint Petersburg residents aged from 18 to 30. Such sample was chosen
for several reasons. First, we study the switching behavior from offline services to the digital
services, particularly those which are available via smartphone. Thus, the selected sample has to
be composed of individuals who are able to perform such switch. Obviously, owning a smartphone,
in this case, is prerequisite for the service switching.
According to GFK (2016) research 16-29 years old age group has the highest metrics in
terms of mobile internet usage. In Russia 70% from this age group uses mobile internet, 40% of
population aged from 30 to 54 uses mobile internet, and only 5% of population aged 55 and higher
uses it. We assume that those who use mobile internet use mobile applications as well.
Yandex published (2015) another research which stated that Moscow and Saint Petersburg
are among those regions in Russia which has highest penetration of mobile internet, 57% and 53%
respectively. There are the other two regions which are among the leaders in terms of that metrics,
however, the average price of owned smartphones in these regions is significantly lower than its
price in Moscow and St. Petersburg. Consequently, we can assume that large number of mobile
internet users in those regions most likely do not acquire services via them due to poor technical
characteristics of gadgets. Moreover, in Russian regions the penetration of banking cards and other
electronic payment services is much lower than in Moscow and St. Petersburg (GFK, 2015).
Hence, this might be one of the reasons, why service providers are not willing to provide their eservices in such regions.
Therefore, the information was collected from the sample of the population which more
likely has an access to digitalized form of tradition services as taxi ordering or banking. This
population is individuals living in St. Petersburg and Moscow aged from 18 to 30. Thus, for the
purpose of this study residents of St. Petersburg and Moscow cities aged from 18 to 30 represent
the whole population.
Hence, stratified random sampling would be the best fit in order to meet the goals of the
study. Stratified sampling is the method of sampling wherein the entire population is divided into
non-overlapping subsets called strata and then the random sampling is applied within strata
(Cochran, 1978, Särndal et al., 2003). The stratified sampling has to be applied in the case when
representatives of all subsets must be presented in the sample for the study. In the case of the study
the population is going to be divided into two subsets to respondents’ gender. According to the
information about gender distribution of Moscow and St. Petersburg within targeted age range,
provided by Russian Federal State Statistics Service (2014), it was decided to include equal
number of male and female respondents. However, in the case of limited resources it is hard to
conduct required procedures to select random sampling. Thus, convenience sampling has been
done. Convenience sampling involves the sample being taken from the part of population which
is easy to approach (Boxill et al, 1997). However, randomization of the sample gained was applied
in order to increase the validity of data. Hence, the set of respondents from each of gender group
was picked randomly in such a way that the final sample contained 270 respondents, 135 male and
135 female responses.
2.4 Data collection methods and procedures
Self-administered questionnaires were chosen as the instrument for data collection because
this instrument provides the researcher with the possibility to collect the huge amount of data at
the low cost of data collection, simplicity of administration, relatively high level of responses
quality and during the short period time of data collecting (Schmee and Oppenlander, 2010).
Survey is one of the most popular methods of data collection to verify individuals’ attitudes and
explain their behavior (Fink, 2003).
The questionnaire was prepared in both the electronic and paper-based forms. The
responses of paper-based questionnaire were collected in crowded places in the streets of St.
Petersburg and Moscow. Each respondent was asked how old he is, in the case of target age group
match the questionnaire was given to the respondent.
The Google Forms service was used in order to make the survey available for online
audience. Social network website vk.com was used is order to distribute the questionnaire. Such
approach was selected due to several reasons:
According to GFK 97% of Russian population aged 16-30 (target population)
are using internet which approves electronic form usage in the study.
The portion of internet users located in St. Petersburg and Moscow within 1830 age which uses vk.com is close to 80%, according to Brand Analytics (2015).
Google Forms usage ensures avoiding missing data problem described by
Goldstein et al (2007), so that each respondents cannot finish the survey until
he answers each question in the survey.
The last reason of the chosen approach is the low time cost of the data collection
due to the fact that the survey distribution does not require personal
The time frame in which questionnaire responses were collected lasted 14 days, from 26th
of April till 9th of May. Overall, more than 120 paper-based questionnaires and 310 online
questionnaires were collected.
The introduction is considered to have a great influence on the response rate, so the most
important information about the study has to be clearly described in the introduction (Bauman,
2000). Moreover, the respondents were informed that the data obtained via the questionnaire is
confidential and is going to be used only for the purpose of the study.
Comprehensibility of questions and information retrieval are the main components of
adequate service design (Tourangeau and Rassinski, 1988). This means that proper vocabulary has
to be used for question formation in order to maximize clarity of the questions. Questions were
tried to be formulated in the simplest way avoiding difficult and technical terms where it is
possible. Each type of question was followed with appropriate instruction. Finally, questions were
designed in the common format in order to minimize the cognitive effort (Graf, 2002).
The designed survey consists of 3 blocks of questions. First block contains two types of
questions. First is about the fact of mobile banking and taxi ordering via mobile application usage.
This type of questions has ‘yes’ and ‘no’ response options. The second type of questions concerns
service usage frequency in case of mobile application usage versus service usage frequency before
start of mobile application usage. Respondents evaluated the service usage frequency subjectively
using the Likert scale from 1 to 7 where 1 is ‘very rarely’ and 7 is ‘very often’.
The second and the main block of questions contains 26 questions representing 13
constructs to be evaluated. Those constructs are: effort expectancy, performance expectancy,
social influence, anxiety, facilitating conditions, service delivery control, attitude towards mobile
services usage, regularity of service use, trialability, perceived enjoyment, reliability, financial
costs decrease and nonfinancial costs decrease.
It was offered to the respondents to answer to each questions assessing mentioned
constructs using 7 points Likert scale. All questions were formulated in the form of statement.
Answering the question from this block respondents measured their level of agreement where 1 is
strongly disagree and 7 is strongly agree.
The last block of questions contains questions about demographic data of respondents
including the gender, age, city of residence.
The survey was conducted considering main possible errors which could be occurred
during data collection stage. Usually, four main types of possible errors in data collection with the
help of surveys are outlined: sampling error, coverage error, nonresponse error and measurement
error (Dillman et al, 2009).
Sampling error was avoided as relevant proportions within the subset reflect entire
population characteristics. Adequate conditions were organized in order to eliminate coverage
error. One of the useful actions was utilization both paper-based and online versions of
questionnaire. The non-response error was managed as first, online forms which restricted
inappropriate survey filling were used, second, in case of paper-based survey distribution personal
contact with each of respondent helped control correctness of survey filling. Only the measurement
error could be occurred as in case of online survey, as there was no chance to explain the question,
if it is required for a specific respondent. Thus, some questions formulation or measurement scale
might be misinterpreted.
2.5 Data processing and Analysis
Due to the fact that information was collected by the means of survey and thus has a
quantitative nature, statistical analysis has to be run in order to gain insights from the data and
explain possible relationships (Hays, 1973).
As the aim of the study is to find factors affecting consumer behavior, the regression
analysis could be the instrument to find the relationship between factors and consumer behavior
criteria. Regression analysis is used to verify which among the independent variables are
interrelated with the dependent variables (Gordon, 2015).
As it was already stated, exploratory research is conducted. Thus, no preliminary
hypotheses about possible interrelation were developed.
In our case, the dependent variable represents the fact of digitalized service usage via
mobile application in terms of taxi ordering and banking services. Consequently, based on
responses, users might be categorized into 4 groups:
People who use none of these two services
People who use only mobile banking
People who use only taxi service via mobile app
People who use both services
From the theoretical point of view it does matter whether each respondent use specifically
mobile banking or taxi ordering via app. Hence, it is reasonable to combine second and third
categories into common one – people who use only one of two services. Thereby, 3 categories are
formed in ordinal manner. They might be interpreted as follows:
people who tend to ignore mobile applications as the instrument of traditional
people who tend to consider mobile application as the instrument of traditional
people who tend to prefer mobile application as the instrument of traditional
When the purpose of the analysis is to verify how well dependent variable representing
ordinal outcomes, can be predicted by the responses to other questions, which might be
quantitative, ordinal logistic regression could be applied (McCullagh and Agresti, 1985). Thus,
ordinal logistic regression analysis was conducted in IBM SPSS Statistics tool.
Before running a regression, first, it is required to find Cronbach’s alpha for the sets of
units forming each construct/factor. This check allows to verify validity of the questionnaire
testing reliability of designed scale. (Kaplan and Saccuzzo, 2012). Second, presence of collinearity
between constructs has to be tested. This step has special importance when a large number of
explanatory variable are included in a model (Hill and Adkins, 2001).
If there is no collinearity found between independent variables, then regression analysis
can be run with entire set of 13 variables described above.
In order to find what factors determine the usage frequency of the service before and after
switching to digitalized version the same approach was used. One important difference is that it
was decided to run separate regression analyses for each service as way of using them differs
significantly. Thus, samples for each regression compose of individuals who are actually users of
the digitalized version of each service.
3 FACTORS OF SERVICE USAGE AFTER ITS DIGITALIZATION
In this chapter empirical results derived from regression analysis will be discussed. The
entire process of data analysis is going to be described. Factors which determine mobile apps usage
and change in its usage frequency are going to be defined.
3.1 Descriptive analysis of digitalized services users
The final sample contains 270 respondents from St. Petersburg and Moscow cities aged
from 18 to 30. The gender distribution simulates the objective distribution of males and females
in the targeted population.
All the following analysis basically was conducted in IBM SPSS Statistics tool with little
use of Microsoft Excel program for sampling formation.
First, it is good intension to describe data in the context of the stratification pattern. For
this study the gender distribution within the sample is important, thus gender stratification was
conducted. Table 2 represents the number and proportion of service consumption via mobile
application usage responses with the respect to respondent’s gender. Where ‘0’ usage level means
person consumes neither of services via mobile app, ‘1’ usage level means person consumes at
least one of two services via mobile app and ‘2’ usage level means person consumes both services
via mobile app.
Table 2 – Usage level variable Crosstabulation
It can be inferred from the table 2, that overall proportion of males consumes at least one
service is large than related female proportion. More significant difference is observed in the case
of both services consumption via mobile apps. Hereby, the number of males in this group consists
62% from entire male respondents, while related female number is only 42%. This kind of
distribution was expected as males are tend to adopt technologies at higher extent than females.
Additionally, this table shows the distribution of usage level outcome responses which
close to the entire population characteristics and also is a good fit for further statistical analysis
conduction as each of outcome category is represented in a decent number of responses.
3.2 Determinants of digitalized service usage
According to our research design, the first step is scale validation using Cronbach’s alpha
for each set of units. Table 3 represents values for each construct.
Table 3 – Reliability statistics
Name of construct
Number of items
Service delivery control
Regularity of use
Attitude toward using
The next step is multicollinearity check. As we stated previously, this is required procedure
for the models with big number of covariates. Table 3 represents collinearity statistics for one of
Table 3 – Collinearity statistics
All VIF values are lower than 3 which means no correlation between Effort variable and
other variables. All combinations of the multicollinearity analysis was run. The great majority of
VIF values was lower than 3.
The next step of analysis is checking whether the assumption of proportional odds is met
or not. For this purpose it is needed to stress attention on the test of parallel lines which is presented
in the figure X.
Table 4 – Test of Parallel lines
If the assumption of proportional odds is met than the difference in fit between assessed
odds models can be interpreted as not statistically significant. In this case p-value is going to be
less than .05. As we can see the p-value for our model is greater than .05. Consequently, due to
the positive outcomes of multicollinearity check and full likelihood test the ordinal logistic
regression could be applied to analyze the collected data.
Next, ordinal logistic regression was conducted with ‘Usagelevel (VARD)’ as dependent
variable and 13 following dependent variables: effort (VAR1), perform (VAR2), social (VAR3),
risks (VAR4), facility (VAR5), control (VAR6), regular (VAR7), trial (VAR8), enjoy (VAR9),
reliable (VAR10), fincost (VAR11), othercost (VAR12).
After running the regression the following table represented in figure 11 came out.
Figure 11 – Missing values combination warning
This means 538 different possible combinations of explanatory variables are not presented
in the data, which could be the negative sign. In order to verify whether this issue is significant for
quality of the model, overall goodness-of-fit tests should be conducted. First table represented in
figure X below, contains Pearson and Deviance tests which measure how poorly the model fits the
data. Consequently, we want this test not to have statistically significant results.
Table 5 – Goodness-of-Fit
In our case both Pearson and Deviance tests have results (.685 and .687 respectively) far
from statistical significance level.
The next model fitting information is shown in tables 5 and 6. However, according to Laerd
expertise (2016), those measures represent the portion of variance explained not well, in any case,
the values in these table goes along with the majority of similar studies.
Table 6 – Pseudo R-Square
The likelihood ratio test is the best tool to verify the ordinal logistic model fitting
characteristics. Its results presented in the table 7.
Table 7 – Likelihood ratio test
According to the results, it might be concluded that the final model statistically
significantly predicted the dependent variable over and above the intercept-only model, ChiSquare equals to 79,868, p < .05.
As the overall model is statistically significant, it is possible to form logistic regression
equations using values from the table 8.
Table 8 – Parameter estimates
The first cumulative logit equation is the following:
Another logistic regression equation for this dataset is
Due to proportional odds assumption, the slope coefficients are the same, only the value of
According to overall results it might be reported that a cumulative odds ordinal logistic
regression with proportional odds was conducted in order to determine the the effect of 13
variables which are: effort expectancy (Var 1), performance expectancy (Var 2), social influence
(Var 3), anxiety (Var 4), facilitating conditions (Var 5), service delivery control (Var 6), regularity
of service use (Var 7), trialability (Var 8), perceived enjoyment (Var 9), reliability (Var 10),
fnancial costs decrease (Var 11), nonfinancial costs decrease (Var 12), attitude toward usage (Var
13), on the dependent variable – the extent to which each person tends to consume services via
mobile applications. The final model statistically significantly predicted the dependent variable
over and above the intercept-only model.
It might be concluded that 3 explanatory variables have statistically significant effect on
the dependent variable. These are VAR 5 - the degree to which an individual believes that he has
an access to required resources to support the use of a mobile application for service consumption,
VAR 6 - the degree to which an individual believes that a mobile application increase the level of
user’s service delivery process control and VAR 9 - the degree to which an individual believes
that using a mobile application for service consumption is enjoyable, aside from any performance
consequences resulting from application use.
Determinants of digitalized service usage frequency change
The next step is factors determination influencing the frequency of service consumption
after the start using mobile application. Actually, the following analysis is an attempt to find
answer on Research Question 2.
As it was described in methodology chapter, the sample for second regression constitutes
taxi ordering service via mobile application users only. First, in order to decide about the coding
pattern for usage frequency dependent variable, we have to analyze frequency of each value of
that metrics in the data we have. For this purpose we ran frequency of relative responses analysis.
The results are represented in table 13.
Table 9 – Descriptive statistics of difference in usage frequency (taxi services users)
It was decided to ignore responses which indicate negative difference for two reasons: first,
the nature of such consumer behavior is not considered while design this research, thus it might
be related to negative experience with mobile application or even to misinterpretation of questions;
second, the number of this type of responses is quite low. Hence, values from 0 to 6 were combined
into 4 categories: the first category has 0 value and constitutes responses only with value 0, the
second category has value 1 and combines responses with values 1 and 2, the third category has
value 2 and combines responses with values 3 and 4 and the fourth category has value 3 and
combines responses with values 5 and 6.
It is not required to test variable for multicollinearity, as in this case the same variables are
used in order to predict the new dependent variable.
First what we receive after regression conduction is warning (represented in figure 12)
about the portion of unavailable values combination in the data set.
Figure 12 – Missing values combination warning (1)
Thus, the model fitting check has to be done. As we already stated, the way to verify the
model is to use Model Fitting Information, represented in table 10.
Table 10 – Likelihood ratio test
According to the results, it might be concluded that the final model statistically
significantly predicted the dependent variable over and above the intercept-only model, ChiSquare equals to 36,620, p < .05.
As the overall model is statistically significant, it is possible to form logistic regression
equations using values from the table 11.
Table 11 – Parameter estimates (1)
As dependent variable has 4 levels, 3 different logistic regression equations could be
derived. They are:
The final model statistically significantly predicted the dependent variable over and above
the intercept-only model.
Thus, it might be concluded that 3 explanatory variables have statistically significant effect
on the dependent variable. These are VAR 6 - the degree to which an individual believes that a
mobile application increase the level of user’s service delivery process control, VAR 8 - the degree
to which an individual believes that a mobile application for service consumption may be
experimented with on a limited basis and VAR 13 - the degree to which usage of a mobile
application for service consumption is assessed positively.
The next conducted analysis which is same as previous one but using those respondents,
who consume banking services via mobile application. Again, in order to decide about the coding
pattern for the new service usage frequency dependent variable, we have to analyze frequency of
each value of that metrics in the data set. The results are represented in figure X.
Table 12 – Descriptive statistics of difference in usage frequency (mobile banking users services
The number of respondents who did not change the frequency of service consumption after
start using mobile app represents 50% of entire set. Thus, in order to equalize the number of units
in each category, it was decided to divide frequency into two categories as 1 – there is no change
in frequency, 2 – there is a change in frequency. Consequently, binomial logistic regression was
However, the model does not fit the data in data set which is implied in the following results
in Table 13.
Table 13 – Omnibus Tests of Model Coefficients
For this type of logistic regression we can reference the “Model” row. P value is
significantly greater than 0,05, thus the model is not statistically significant.
4 DISCUSSIONS AND CONCLUSIONS
This chapter is devoted to the main results and its interpretation synthesis. Theoretical
contribution coming out from the quantitative analysis is explained in this chapter. Also, it is
explained how company’s management can use insights derived from the analysis. Finally, the
limitations of the research and reasonable future directions of it are described in the last part of the
4.1 Analysis of theoretical base
The first part was devoted to the literature review which helped to identify research gap.
The research switching behavior between online and offline services appeared to be limited. Also,
the topic of the research was observed from the perspective of service, consumer behavior,
switching behavior and digitalization concepts. This helped to determine relevant factors of mobile
application usage for service consumption. Then, the overview of acknowledged technology
adoption models was performed in order to select appropriate factors of technology adoption for
the context of mobile applications. Finally, the specific model for current research was defined.
4.2 Explicit answers to research questions
Taking this research gaps into consideration the following research questions were being
RQ1 – What factors determine consumers’ decision to switch to digitalized service?
RQ2 – What factors determine the usage frequency change of a service after its
The ordinal logistic regression was applied in order to identify those constructs/factors
which have the strongest influence on the consumers’ decision to switch to digitalized service. The
same type of analysis was used for identification of factors/constructs influencing the service use
frequency. The constructs of the model were partly taken from Technology Acceptance Model 3
(Venkatesh and Bala, 2008), the Diffusion Theory (Rogers, 1962) and the Unified Theory of
Acceptance and Use of Technology (Venkatesh et al 2003). These factors include effort
expectancy, performance expectancy, social influence, anxiety, facilitating conditions, trialability,
perceived enjoyment and attitude toward using. Also, according to literature review it was decided
to add the following factors: service delivery control, regularity of service use, financial costs
decrease, nonfinancial costs decrease and reliability.
Concerning Research Question 1, the statistical analysis has shown that 3 out of 13
constructs have statistically significant effect on dependent variable. Which means that facilitation
conditions, service delivery control and perceived enjoyment affect consumer decision to switch
to digitalized service. Also, regularity of use and financial cost decrease factors have close to
statistically significant effect on consumer decision to use digitalized service.
Concerning Research Question 2, the gathered data shows the increase of service frequency
usage in the case of using digitalized version of the service. Due to high specificity of services
used for analysis, it was decided to run separate regression for each service. In first case the
frequency of taxi service usage was taken for the analysis. The statistical analysis has shown that
3 out of 13 constructs have statistically significant effect on dependent variable. These 3 constructs
are service delivery control, trialability (negative relationship), attitude toward using. Another 3
constructs have close to statistically significant effect on usage frequency change. These are effort
expectancy, social influence and regularity of service use. The model for usage frequency banking
services turned out to be not statistically significant. It is assumed that such results are caused by
the difficulty to measure frequency of using digital banking services by the respondents, because
such actions as checking bank account statement and transaction history hardly perceived by
customers as significant to mention uses of service.
4.3 Managerial implications
The empirical results can bring benefits to various companies’ management. As we stated
in the introduction, many companies are considering the opportunity to create their own mobile
application for various purposes. One of the most popular is to provide or sell their services through
this channel. Current study is providing the feasibility verification of the idea to try to digitalize
services they provide into the form of mobile application.
In particular, company might conduct a survey among its customers in order to verify their
attitude toward relevant factors of service usage. In order to understand whether a company might
count on increase of their service usage frequency, another survey among the clients might be
conducted on corresponding 3 significant factors.
Thus, a company’s management might find out whether its customers have facilitation
conditions to use a service mobile app for provided service consumption, whether they believe that
they acquire additional control over the provided service in case of mobile app use, whether the
company is capable to create pleasant user experience in developed mobile app. Although, the
statistical results did not prove the significance of other factors, it might be worth for a company
to stress attention on perceived regularity of services provided and possibility to decrease financial
costs of the customers in case of service consumption via mobile app. The important note here is
that approvable results of the factors measurement by a company do not imply that digitalization
of their service is a good idea. However, in case when results of the factors measurement is not
satisfying, it might be a signal for a company that the mobile app as a channel to provide services
will not attract company’s customers.
4.4 Limitations and future research
There are some limitations of the study to be taken into consideration. First, the age range
of the survey participants has to be taken into account. Current research was focused on Moscow
and St. Petersburg residence aged from 18 to 30. This was done for the purpose to reach those
individuals who is owning a smartphone with the highest probability. Also, sample of surveyed
individuals was not perfectly randomized, although the attempt to randomize the sample at some
extent was made. Thus, the results of the study is hardly to be generalized to entire population of
targeted group of people. Consequently, companies which are going to apply results of the study
have to be aware of possible bias connected with difference between research sample and their
The second limitation relates to the approach of finding factors influencing the change in
usage frequency of the service. In current research design factors were assessed for each from two
services separately. Thus, the results of the regression are significantly depended on service
Consequently, in order to gain more reliable results, first, future studies should use
randomization for studied sample properly. Second, in order to provide generalized results of
factors measurement in context of affecting service usage frequency, more services has to be taken
for evaluating usage frequency pattern among them.
Adams, Christopher P., Laura Hosken, and Peter Newberry, 2006. “Vettes and Lemons on EBay.”
Available at SSRN: http://ssrn.com/abstract=880780
Ajzen, Icek and Martin Fishbein, (1980). Understanding Attitudes and Predicting Social Behavior:
Illustration of Applied Social Research, 0th ed. (Englewood Cliffs, NJ: Englewood Cliffs, New
Ajzen, Icek and Martin Fishbein, (1978). “Use and Misuse of Bayes’ Theorum in Causal
Attribution: Don’t Attribute It to Ajzen and Fishbein Either.” Psychological Bulletin 85, no. 2:
Al-Gahtani, Said S. and Malcolm King, (1999). “Attitudes, Satisfaction and Usage: Factors
Contributing to Each in the Acceptance of Information Technology.” Behaviour & Information
Technology 18, no. 4: 277–97.
Antonides, Gerrit, Fred W van Raaij, W. F. Van Raaij, and W. Fred Van Raaij Antonides.
Consumer Behaviour: A European Perspective. New York: Wiley, John & Sons, 1998.
Arpaci, Ibrahim. “A Comparative Study of the Effects of Cultural Differences on the Adoption of
Mobile Learning.” British Journal of Educational Technology 46, no. 4 (April 14, 2014): 699–712.
Bannock, Graham, R E Baxter, and Evan Davis. Economist Dictionary of Economics. New York:
John Wiley & Sons, 1998.
Bajari, Patrick and Ali Hortaçsu, 2003. “The Winner’s Curse, Reserve Prices, and Endogenous
Entry: Empirical Insights from eBay Auctions.” RAND Journal of Economics, 34(2), 329-355.
Bauman, S., Jobity, N., Airey, J. and Atak, H. (2000). Invites, Intros and Incentives: Lessons from
a Web Survey. In: The 55th Annual Conference of American Association for Public Opinion
Bearden, William O. and Michael J. Etzel. “Reference Group Influence on Product and Brand
Purchase Decisions.” Journal of Consumer Research 9, no. 2 (September 1982): 183.
Berry, Leonard L. (1980), “Services Marketing Is Different,” Business-. v30n3, 24-29.
Boote J. (1998). Towards a comprehansive taxanomy and model of consumer complaining
behavior. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 11, 141149.
Boxill, Ian, Claudia Chambers, and Eleanor Wint. Introduction to Social Research: With
Applications to the Caribbean. Kingston: Canoe Press Univ. of the West Indies, 1997.
Brand Analytics, 2015. Accessed May 26, 2016. http://br-analytics.ru/blog/socialnye-seti-v-rossiivesna-2015-cifry-trendy-prognozy/.
Bruhn, Manfred. Services Marketing: Managing the Service Value Chain. Edited by Dominck
Georgi. New York: Financial Times/ Prentice Hall, 2005.
Brynjolfsson, Erik, Astrid A. Dick, and Michael D. Smith, 2010. “A Nearly Perfect Market?
Differentiation vs. Price in Consumer Choice.” Quantitative Marketing and Economics, 8, 1-33.
Cabral, Luís and Ali Hortaçsu, 2010. “The Dynamics of Seller Reputation: Evidence from eBay.”
Journal of Industrial Economics, 58(1), 54-78.
Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., & Walden, P. (2006). Adoption
of Mobile Devices/Services – Searching for Answers with the UTAUT. Finland: Abo
Carver, Robert. Practical Data Analysis with JMP. United States: SAS Publishing, 2010.
Chiu, Hung-Chang, Yi-Ching Hsieh, Yu-Chuan Li, and Monle Lee. “Relationship Marketing and
Consumer Switching Behavior.” Journal of Business Research 58, no. 12 (December 2005): 1681–
Cimperman, Miha, Maja Makovec Brenčič, and Peter Trkman. “Analyzing Older Users’ Home
Telehealth Services Acceptance Behavior − Applying an Extended UTAUT Model.” International
Journal of Medical Informatics March 2016.
CNBC. “CNBC Transcript: Interview with Travis Kalanick, CEO and Co-Founder of Uber.”
March 29, 2016. Accessed May 25, 2016. http://www.cnbc.com/2016/03/28/cnbc-transcriptinterview-with-travis-kalanick-ceo-and-co-founder-of-uber.html.
Cochran, W. G. “Sampling Techniques.” Technometrics 20, no. 1 (February 1978): 104.
Creswell, J. (2003). Research design. Thousand Oaks, Calif.: Sage Publications.
Creswell, J. and Creswell, J. (2007). Qualitative Inquiry & Research Design. Thousand Oaks: Sage
Davis, Fred D. “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information
Technology.” MIS Quarterly 13, no. 3 (September 1989): 319.
Davis, Fred D., Richard P. Bagozzi, and Paul R. Warshaw. “User Acceptance of Computer
Technology: A Comparison of Two Theoretical Models.” Management Science 35, no. 8 (August
Dillman, D., Smyth, J., Christian, L. and Dillman, D. (2009). Internet, Mail, and Mixed-Mode
Surveys. Hoboken, N.J.: Wiley & Sons.
Earl R. Babbie. (2007). The Practice of Social Research, Cengage Learning.
Edvardsson, B. “Cocreating Customer Value Through Hyperreality in the Prepurchase Service
Experience.” Journal of Service Research 8, no. 2 (November 1, 2005): 149–61.
eMarketer Inc. “2 Billion Consumers Worldwide to Get Smart(phones) by 2016.” December 11,
2014. Accessed May 25, 2016. http://www.emarketer.com/Article/2-Billion-ConsumersWorldwide-Smartphones-by-2016/1011694.
Fink, A. (Ed.). (2003). How to Sample in Surveys. (2nd ed.). Thousand Oaks, CA: SAGE
Fishbein, Martin, Icek Ajzen, and M. Fishbein. Belief, Attitude, Intention, and Behavior: An
Introduction to Theory and Research. 4th ed. Reading, MA: Longman Higher Education, 1976.
Fitzgerald, Michael, Nina Kruschwitz, Didier Bonnet and Michael Welch, 2013. Embracing
Forbes. Accessed May 25, 2016. http://www.forbes.com/sites/jacobmorgan/2015/12/17/are-uberairbnb-and-other-sharing-economy-businesses-good-for-america/#61eddfdcf696.
Garicano, Luis and Steven N. Kaplan, 2001. “The Effects of Business-to-Business E-Commerce
on Transaction Costs,” Journal of Industrial Economics, 49(4), 463-485.)
Gartner. “Gartner Says Demand for Enterprise Mobile Apps Will Outstrip Available Development
Global C-Suite Study, 2016. Accessed May 25, 2016. http://www-935.ibm.com/services/csuite/study/study/.
Goldstein, Harvey, de Leeuw, and Meijer Jan. Handbook of Multilevel Analysis. Edited by Jan de
Leeuw and Erik Meijer. New York: Springer-Verlag New York, 2007.
Gordon, Rachel A. Regression Analysis for the Social Sciences. United Kingdom: Taylor &
Gräf, L. (2002). Assessing Internet Questionnaires: the Online Pretest Lab. Online social sciences,
Grönroos, Christian. “The Perceived Service Quality Concept – a Mistake?” Managing Service
Quality: An International Journal 11, no. 3 (June 2001): 150–52.
Hair, Joseph F, Arthur H. Money, Phillip Samouel, and Arthur H. M. Essentials of Business
Research Methods. 2nd ed. United States: John Wiley and Sons (WIE), 2003.
Harry, Mikel J, Prem S. Mann, Ofelia C. De Hodgins, Ofelia C. De Hodgins, Richard L. Hulbert,
and Christopher J. Lacke. The Practitioner’s Guide to Statistics and Lean Six Sigma for Process
Improvements. United Kingdom: Wiley-Blackwell (an imprint of John Wiley & Sons Ltd), 2010.
Hays, William L. Statistics for the Social Sciences. United Kingdom: Holt,Rinehart & Winston of
Hill, R. Carter; Adkins, Lee C., 2001. "Collinearity". In Baltagi, Badi H. A Companion to
Theoretical Econometrics. Blackwell.
Hong, Han and Matthew Shum, 2006. “Using Price Distributions to Estimate Search Costs.” The
RAND Journal of Economics, 37(2), 257-275
Hoyer, Wayne D, Deborah J MacInnis, Rik Pieters, and Hoyer Leon. Consumer Behavior. 6th ed.
United States: South Western Cengage Learning, 2012.
Jin, Ginger Zhe and Andrew Kato, 2006 “Price, Quality, and Reputation: Evidence from an Online
Field Experiment.” RAND Journal of Economics, 37(4), 983-1005.
Kaplan, Robert M. and Dennis P Saccuzzo. Psychological Testing: Principles, Applications, and
Issues. 8th ed. United States: Wadsworth, Cengage Learning, 2012.
Keaveney, S. M. and M. Parthasarathy. “Customer Switching Behavior in Online Services: An
Exploratory Study of the Role of Selected Attitudinal, Behavioral, and Demographic Factors.”
Journal of the Academy of Marketing Science 29, no. 4 (October 1, 2001): 374–90.
Keaveney, Susan M. “Customer Switching Behavior in Service Industries: An Exploratory Study.”
Journal of Marketing 59, no. 2 (April 1995): 71.
Korpelainen, E. (2011). Theories of ICT System Implementation and Adoption – A Critical
Review. Working Paper. Espoo: Aalto University, School of Science, Department of Industrial
Engineering and Management.
Kotler, Philip. Marketing Management: [analysis, Planning, Implementation, and Control]. United
States: Pearson Education (US), 1999.
Krugman, Dean M., Glen T. Cameron, and Candace McKearney White. “Visual Attention to
Programming and Commercials: The Use of in-Home Observations.” Journal of Advertising 24,
no. 1 (March 1995): 1–12.
Laerd, 2016. Accessed May, 26, 2016. https://statistics.laerd.com/premium/spss/olr/ordinallogistic-regression-in-spss.php
Lardinois, Frederic. “Report: 46% of U.S. Bank Account Holders Will Use Mobile Banking by
2017.” August 14, 2012. Accessed May 25, 2016. http://techcrunch.com/2012/08/14/report-46-ofu-s-bank-account-holders-will-use-mobile-banking-by-2017/.
Lee, R and Murphy, J, (2005). From loyalty to Switching: Exploring Determinants in the
Transition. ANZMAC, Perth Australia.
Leedy, Paul D and Jeanne Ellis Ormrod. Practical Research: Planning and Design (10th Edition).
10th ed. Boston: Addison Wesley, 2014.
Lewis, Gregory, 2009. “Asymmetric Information, Adverse Selection and Online Disclosure: The
Case of eBay Motors.” Available at SSRN: http://ssrn.com/abstract=1358341.
Lieber, Ethan, Chad Syverson, 2011. Online vs. Offline Competition, Oxford Handbook of the
Liljander, Veronica, Inger Roos, and Tore Strandvik (1998), “Quality of Loyalty—Switching
Alertness Is Customer Relationships,” paper presented at a Workshop on Quality Management in
Services VIII, EIASM, 20-21 April, Ingolstadt, Germany.
Lovelock, Christopher H., Jochen Wirtz, and Lovelock Christopher. Services Marketing People
Terminology Strategy. 7th ed. Harlow: Pearson Education, 2011.
Lovelock, Patterson, Christopher H. Lovelock, Paul G. Patterson, and Jochen Wirtz. Services
Marketing: An Asia Pacific and Australian Perspective. 5th ed. Australia: Pearson Education
Australia (TAFE), 2010.
Lynch, Scott M. Using Statistics in Social Research: A Concise Approach. New York, NY:
Springer-Verlag New York, 2013.
Ma, Qi, Alan H. S. Chan, and Ke Chen. “Personal and Other Factors Affecting Acceptance of
Smartphone Technology by Older Chinese Adults.” Applied Ergonomics 54 (May 2016): 62–71.
McCullagh, Peter and Alan Agresti. “Analysis of Ordinal Categorical Data.” Technometrics 27,
no. 3 (August 1985): 317.
McKinsey. “What ‘digital’ Really Means.” July 2015. Accessed May 25, 2016.
Moghavvemi, Sedigheh and Noor Akma Mohd Salleh. “Effect of Precipitating Events on
Information System Adoption and Use Behaviour.” Journal of Enterprise Information
Management 27, no. 5 (September 2, 2014): 599–622.
Moore, Gary C. and Izak Benbasat. “Development of an Instrument to Measure the Perceptions of
Adopting an Information Technology Innovation.” Information Systems Research 2, no. 3
(September 1991): 192–222.
Oliva, Rogelio and Robert Kallenberg. “Managing the Transition from Products to Services.”
International Journal of Service Industry Management 14, no. 2 (May 2003): 160–72.
Phillips, P., Phillips, J. and Aaron, B. (2013). Survey Basics. Alexandria, Va.: ASTD Press.
Pieters, R.G.M. and Verplanken, B., (1991), "Changing our minds about behavior", in: Antonides,
G., Arts, W. and van Raaij, W.F.(eds.), The Consumption of Time and the Timing of Consumption
- Toward a New Behavioral and Socio-Economics, Amsterdam: North-Holland,, 63-67.
Rabin, Barry J, Eric G Harris, and Barry J. Babin. CB: Consumer Behavior: Student Edition 6. 6th
ed. United States: South-Western College Publishing, 2014.
Rex, John and George C. Homans. “Social Behaviour, Its Elementary Forms.” The British Journal
of Sociology 13, no. 1 (March 1962): 75.
Rogers, Everett M. Diffusion of Innovations. 4th ed. New York, NY [u. a.]: Free Press, 1995.
Roos, I. and A. Gustafsson. “Understanding Frequent Switching Patterns.” Journal of Service
Research 10, no. 1 (August 1, 2007): 93–108.
Schiffman, Leon G., David Bednall, Aron O’Cass, Angela Paladino, Steve Ward, and Leslie Lazar
Kanuk. Consumer Behaviour. 4th ed. Australia: Pearson Education Australia, 2007.
Schmee, Josef Schmee. JMP Means Business: Statistical Models for Management. Edited by Jane
E. Oppenlander. n.p.: SAS Institute, 2010.
Selltiz, Claire, Marie Jahoda, Morton Deutsch, and Stuart W. Cook. “Research Methods in Social
Relations.” The American Catholic Sociological Review 20, no. 3 (1959): 264.
Shields, Patricia M and Nandhini Rangarajan. A Playbook for Research Methods: Integrating
Conceptual Frameworks and Project Management. Stillwater, OK: New Forum Press, 2013.
Smith, Michael D. and Erik Brynjolfsson, 2001.“Consumer Decision-Making at an Internet
Shopbot: Brand Still Matters,” Journal of Industrial Economics, 49(4), 541-58.
Särndal, Carl-Erik, Bengt Swensson, Jan Wretman, Carl-Erik Sarndal, and Carl-Erik S. Model
Assisted Survey Sampling (Springer Series in Statistics). New York, NY: Springer-Verlag New
Statista. “Number of Smartphone Users Worldwide from 2014 to 2019 (in Millions).” 2016.
Accessed May 25, 2016. http://www.statista.com/statistics/330695/number-of-smartphone-usersworldwide/.
Tate, Mary, Elfi Furtmueller, Hongzhi Gao, Guy Gable, 2014. Reconceptualizing digital service
quality: a call to action and research approach. PACIS 2014 Proceedings. Paper 11.
Talukder, Majharul. Managing Innovation Adoption: From Innovation to Implementation. United
Kingdom: Ashgate Publishing, 2014.
Tourangeau, R. and Rasinski, K. (1988). Cognitive Processes Underlying Context Effects in
Attitude Measurement. Psychological Bulletin, 103(3), pp. 299-314.
Vargo, Stephen L. and Robert F. Lusch. “Evolving to a New Dominant Logic for Marketing.”
Journal of Marketing 68, no. 1 (January 2004): 1–17.
Venkatesh, Viswanath. “Where to Go from Here? Thoughts on Future Directions for Research on
Individual-Level Technology Adoption with a Focus on Decision Making.” Decision Sciences 37,
no. 4 (November 2006): 497–518.
Venkatesh, Viswanath and Hillol Bala. “Technology Acceptance Model 3 and a Research Agenda
on Interventions.” Decision Sciences 39, no. 2 (May 2008): 273–315.
Venkatesh, Viswanath and Fred D. Davis. “A Theoretical Extension of the Technology
Acceptance Model: Four Longitudinal Field Studies.” Management Science 46, no. 2 (February
Venkatesh, V., Morris, M., Davis, G. and Davis, F. (2003). User Acceptance of Information
Technology: Toward a Unified View. MIS Quarterly, 27(3), pp. 425-478.
Verhallen, Theo M. M. and Rik G. M. Pieters. “Attitude Theory and Behavioral Costs.” Journal
of Economic Psychology 5, no. 3 (September 1984): 223–49.
Waldfogel, Joel and Lu Chen, 2006. “Does Information Undermine Brand? Information
Intermediary Use and Preference for Branded Web Retailers.” Journal of Industrial Economics,
Wells, Victoria and G R Foxall. Handbook of Developments in Consumer Behaviour. United
States: Not Avail, 2014.
Wells, William D. and Leonard A. Lo Sciuto. “Direct Observation of Purchasing Behavior.”
Journal of Marketing Research 3, no. 3 (August 1966): 227.
Williams, C. (2007). Research Methods. Journal of Business & Economic Research, 5(3), pp.6572.
Zeeshan Ahmed, Maleehah Gull, Usman Rafiq, (2015). “Factors Affecting Consumer Switching
Behavior: Mobile Phone Market in Manchester - United Kingdom.” IJSRP, Volume 5, Issue 7
Zeithaml, Valarie A., A. Parasuraman, and Leonard L. Berry. “Problems and Strategies in Services
Marketing.” Journal of Marketing 49, no. 2 (1985): 33.
Zikiene K, and Bakanauskas A (2006). Research of Factors influencing loyal customer switching
APPENDIX - Questionnaire units
Unit 1. I believe that it is convenient to use mobile app for service consumption.
Unit 16. I believe that mobile app use for service consumption is simple.
Unit 8. I believe that using mobile apps for service consumption frees additional time for other
Unit 24. I believe that mobile apps for service consumption gives an opportunity to be more
Unit 2. My friends approve using mobile apps for service consumption
Unit 17. I believe that using mobile app for service consumption is a good sign about a person.
Unit 4. I believe that the risk of personal data loosing is insignificant using mobile app for
Unit 10. I believe that the risk of payment data loosing is insignificant using mobile app for
Unit 7. I have required knowledge for using mobile apps for service consumption.
Unit 9. I have required resources for using mobile apps for service consumption.
Unit 12. I believe that mobile app can give additional control over the consuming service.
Unit 19. I believe that mobile app could provide additional information to follow the process of
Unit 14. I believe that the regularity of need could be a reason for using mobile app for service
Unit 20. I believe that mobile app for service consumption might be helpful in case of systematic
purchase of a service.
Unit 15. I believe that mobile application for service consumption has to have trial version.
Unit 21. I believe that before the start of service consumption via mobile app, free service
providing should be available.
Unit 11. I believe I would like using mobile app for service consumption.
Unit 13. I believe that using mobile app for service consumption is entertaining.
Unit 6. I believe that mobile apps for service consumption operate faultless.
Unit 18. I would rely on mobile app to consume a service.
Unit 3. I believe that service consumption via mobile app decrease user’s financial costs.
Unit 23. I believe that in case of service consumption via mobile app the price of service usually
Unit 22. I believe that using mobile app for service consumption user decrease his nonfinancial
Unit 26. I believe that using mobile app for service consumption gives some nonfinancial
Unit 5. I believe that mobile apps use for service consumption is advanced.
Unit 25. I believe that using mobile app for service consumption is a good idea.
Unit 27. Do you use mobile banking applications on Android, iOS, Windows Phone?
Unit 28. Do you use mobile application on Android, iOS, Windows Phone for taxi calling?
Unit 27. How often did you use banking services prior to using mobile app for this purpose?
Unit 28. How often did you use banking services since using mobile app for this purpose?
Unit 27. How often do you use taxi services prior to using mobile app for this purpose?
Unit 28. How often do you use taxi services since using mobile app for this purpose?
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