Graduate School of Management
Master in International Business Program
Factors influencing intention to use massive open online-course
on German market
Master's Thesis by the 2nd year student:
Associate professor, Sergey A. Starov
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ
ВЫПОЛНЕНИЯ ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЫ
Я, Хан Екатерина Михайловна, студентка второго курса магистратуры направления
тему «Факторы, влияющие на намерение потребителя использовать он-лайн курсы:
изучение рынка Германии», представленной в службу обеспечения программ
магистратуры для последующей передачи в государственную аттестационную комиссию
для публичной защиты, не содержится элементов плагиата.
Все прямые заимствования из печатных и электронных источников, а также из
защищенных ранее выпускных квалификационных работ, кандидатских и докторских
диссертаций имеют соответствующие ссылки.
Мне известно содержание п. 9.7.1 Правил обучения по основным образовательным
программам высшего и среднего профессионального образования в СПбГУ о том, что
«ВКР выполняется индивидуально каждым студентом под руководством назначенного
ему научного руководителя», и п. 51 Устава федерального государственного бюджетного
высшего образования «Санкт-Петербургский
государственный университет» о том, что «студент подлежит отчислению из СанктПетербургского университета за представление курсовой или выпускной
квалификационной работы, выполненной другим лицом (лицами)».
STATEMENT ABOUT THE INDEPENDENT CHARACTER
OF THE MASTER THESIS
I, Khan Ekaterina Mikhailovna, second year master student, program «Management»,
state that my master thesis on the topic «Factors influencing intention to use massive open
online-course on German market », which is presented to the Master Office to be submitted to
the Official Defense Committee for the public defense, does not contain any elements of
All direct borrowings from printed and electronic sources, as well as from master theses,
PhD and doctorate theses which were defended earlier, have appropriate references.
I am aware that according to paragraph 9.7.1. of Guidelines for instruction in major
curriculum programs of higher and secondary professional education at St.Petersburg University
«A master thesis must be completed by each of the degree candidates individually under
the supervision of his or her advisor», and according to paragraph 51 of Charter of the Federal
State Institution of Higher Education Saint-Petersburg State University «a student can be
expelled from St.Petersburg University for submitting of the course or graduation qualification
work developed by other person (persons)».
Master Student’s Name
Master Thesis Title
Ekaterina M. Khan
Factors influencing intention to use massive
open online-course on German market
Graduate School of Management
Sergey A. Starov
Academic Advisor’s Name
Description of the goal, tasks and main
The goal of current study is to identify what are
the main factors affecting the intention to use
massive open online-course.
In order to achieve the main goal the author
analyses the e-learning market, MOOCs
particularly, the theoretical background of
technology adoption and the models widely used
by scholars. For the study the author uses and
modifies UTAUT model of technology adoption,
which serves as a foundation for the hypothesis
To tests the hypothesis the survey was created
and 500 respondents from Germany interviewed.
The collected data was used for quantitative
Based on the empirical results of the study it was
identified that such UTAUT moderators such as
age, gender, and internet experience do not
influence an intention to use MOOC.
Among the core determinants simplicity of
MOOC usage was also proven insignificant
variables, whereas the perceived usefulness,
independence of usage and social influence have
positive relationship with the intention to use
Хан Екатерина Михайловна
магистерской Факторы, влияющие на намерение потребителя
использовать он-лайн курсы (изучение рынка
Высшая Школа Менеджмента
Сергей Александрович Старов
Описание цели, задач и Цель данного исследования – идентификация и
изучение факторов, влияющих на намерение
В ходе работы был проведен анализ рынка
электронного обучения, в особенности онлайнкурсов, был проведен анализ теоретических
моделей принятия и использования технологий.
На основании анализа была отобрана единая
теория принятия и использования технологии,
эмпирической части исследования.
Также была разработана регрессионная модель
для тестирования сформулированных гипотез.
Был проведен опрос респондентов в Германии,
которые были использованы в эмпирическом
В результате исследования были получены
- контрольные переменные: возраст, пол,
опыт не взаимосвязаны с намерением
эффективность, влияние социльного окружения и
ожидаемое независимое использование системы
взаимосвязана с намерением пользователя.
Онлайн-курсы, единая теория принятия и
использования технологии, принятие технологий
TABLE OF CONTENT
INTRODUCTION ........................................................................................................................ 6
THEORETICAL FOUNDATION OF E-LEARNING TOOLS ..................................... 10
1.1 E-Learning: development overview and modern trends ................................................. 10
1.2 MOOC as a sub-segment of e-learning ................................................................................14
1.3 Technology acceptance theories ..............................................................................................15
1.3.1 Theory of Reasoned Action ...........................................................................................17
1.3.2 Technology Acceptance model .....................................................................................18
1.3.3 Theory of Planned Behavior ..........................................................................................19
1.3.4 Triandis model ...............................................................................................................21
1.3.5 Diffusion of Innovation .................................................................................................23
1.3.6 Social cognitive theory ..................................................................................................24
1.3.7 Unified theory of user acceptance of technology ..........................................................25
1.3.8 Summary of the adoption of technology theoretical frameworks .................................28
2. EMPIRICAL PART: DEFINITION AND EVALUATION OF FACTORS
INFLUENCING ADOPTION OF THE ONLINE-COURSES .............................................. 30
2.1 Research methodology and framework ................................................................................30
2.2 Research hypothesis .............................................................................................................32
2.3 Research design .......................................................................................................................34
2.4 Data analysis ............................................................................................................................35
2.4.1 Descriptive statistics ......................................................................................................35
2.4.2 Reliability analysis ........................................................................................................37
2.4.4 Hypothesis testing and results interpretation .................................................................38
2.5 Analysis of the obtained results ...............................................................................................41
CONCLUSION ........................................................................................................................... 43
REFERENCES ........................................................................................................................... 46
Emerging technologies are technologies that create new industries and transform
existing ones (Day, Schoemaker, & Gunther, 2004). E-learning is an emerging technology that
makes an impact and reshapes the relationship between students and teachers, employees and
organizations. The rise of e-learning for the last decades was huge. The European commission
describes e-learning as the use of the Internet and new multimedia technologies to advance the
quality of learning by providing access to resources and services, as well as enabling remote
exchange and collaboration (Dominici & Palumbo, 2013). E-learning offers the online delivery
of information, communication, education and training (Sloman, 2001). Main advantages like
cost and time saving, independence from physical space limitation made it popular and
important. Additionally it is mentioned that effectiveness and usefulness of education can be
enhanced by content customization in accordance to the learners needs.
To sum up we can say that the main change occurred in education in the information
age is the shift from teacher centered to a learner – centered educational process (B. C. Lee,
Yoon, & Lee, 2009).
According to Androulla Vassiliou (2014) - European Commissioner for Education,
Culture, Multilingualism and Youth “the online and open education world is changing how
education is resourced, delivered and taken up. Over the next 10 years, E-learning is projected
to grow fifteen-fold, accounting for 30% of all educational provision” (European Commission,
2014). Among the instruments of e-learning, online courses are considered as a subsector with
particularly strong growth. The dream of the democratization of knowledge might soon be
As e-learning becomes more and more pervasive in institutions, it is imperative to
research learners acceptance of such technology as it is a critical factor to success in the
implementation (Roca, Chiu, & Martinez, 2006). As at the end of 2015 more than 140
universities worldwide are offering online-degree programs, expanding without time and
geographical limits, as well as combining and completing traditional offline classes with online
replacing/combining traditional offline office job training with online trainings. Total flow of
investments to e-learning market amounted to $ 6,000 million for the last five years. E-learning
market is driven by start-up dot-com entrepreneurs as well as by big corporations. Thus the
company management should address the questions of the service adoption from both business
and technological perspectives.
The success of e-learning depends on several factors such as implementation, the
educational model, the way of distribution and the degree of technology adoption of the
targeted segments. There are many of studies examining success and motivational factors, but
there is still a lack of empirical studies that explore and explain the interrelation of technology
adoption and behavioral intention of a potential learner (B. C. Lee et al., 2009).
The research goal and objectives
The issue of e-learning adoption is not studied deeply because of its relative novelty.
Current studies are usually very fragmented and focused on a specific subject and usually are
aimed to investigate if there is an interrelation between e-learning presence/absence during the
study process and a rate of student’s success, drop rates (Levy, 2007), motivation (Hew &
Cheung, 2014) and satisfaction(Name et al., 2014). Additionally some researchers studied a
separate factors interrelation with e-learning successful outcomes such as readiness factor,
technology acceptance stage and others (Sun, Tsai, Finger, Chen, & Yeh, 2008). Mainly
researches do not take into consideration the level of technology adoption on the market of a
particular country; the process of adoption and factors interrelated with the intention to use elearning instruments in future.
It has been a great number of studies and great number of papers published which
confirmed an intention as a good predictor of actual behavior. Actually an intention is often
called as a starting point of an action ((Bird, 1988; Locke & Latham, 2002; Ramayah, Lee, &
Mohamad, 2010). Social psychology scholars refer to an intention as a cognitive state of mind,
which usually precedes to a decision and to an act (Ajzen, 1991; Ajzen and Fishbein, 1980).
Moreover among a wide range of different behavior the behavioral intention was confirmed as
the “most immediate predictor of actual behavior” (Ramayah et al., 2010).
Still not all intentions are transformed into actual behavior. Empirical studies of
intention – behavior relationship have identified that the gap between an intention and the
potentially consequent action is mainly attributed to the person intending to perform an action,
but are not successful in realizing their intentions into actions (Orbell & Sheeran, 1998;
Ramayah et al., 2010) Additionally the actual behavior is influenced by the perceived level of
efforts necessary to conduct the behavior (Bagozzi, Yi, & Baumgartner, 1990). The degree of
efforts needed was also incorporated into the attitudinal measure of individual behavioral
consequences (Sidique, Lupi, & Joshi, 2010).
Nevertheless there is a solid evidence confirmed by many scholars in several research
fields of the high level of intention – behavior correlation (Ajzen, Czasch, & Flood, 2009).
Thus the general research question can be stated as following:
“What are the main factors affecting the intention to use an online-course?”
The main objective is to understand what factors influence an intention of a new
student to select an online-course. In order to achieve the main goal the following objectives
should be fulfilled:
To define special characteristics of an online-course as a subcategory of e-
To provide current market overview and the last years trends;
To give an overview of the technology adoption theoretical frameworks with an
emphasis on the online-course peculiarities;
To analyze general factors influencing adoption;
To derive recommendations for the strategic management of the online-course
Study and thesis structure
In order to ensure in research quality and efficiency the design of the study if focusing
on systematic, integrated process as follows:
Literature review – the main goal of the stage is to explore existing studies, do
define the research gap and to construct the theoretical foundation;
Theoretical modeling – after thoughtful and detailed analysis of the existing
models, the most efficient and applicable is selected and then adopted. The model should
correspond the key requirements of the study and help to highlight the main concepts and
implications. All hypothesis proposed will be validated by the experts of the e-learning sector;
Development of questionnaire – for the purpose of the study widely accepted,
recognized survey questionnaire will be reviewed, analyzed, adapted and integrated into the
survey. The questionnaire will be reviewed by the experts to ensure the quality and feasibility
of the survey;
Statistical Analysis – the data gathered with the survey will be analyzed
statistically with the help of the recognized statistical tool such as IBM SPSS.
The thesis is structured in the following way: an introduction, two chapters,
conclusion, references and appendixes.
In the introduction the relevance of the research is explained, research goal, objectives,
purposes and strategies are presented.
In the first chapter, author present the literature review of historical overview of elearning emerge and expansion, overview of different e-learning instruments and detailed
description of online-courses as a on of the most spread, analysis of current trends in the
market, description of current technology acceptance models taking into account the specific of
online-course as a service.
In the second chapter the empirical study is conducted, based on the results the authors
made recommendations with managerial implications.
In the conclusion the recommendations are summarized, and limitation of the study
and the scope for further research are defined.
THEORETICAL FOUNDATION OF E-LEARNING TOOLS
E-Learning: development overview and modern trends
Welsh et al. (2003) define e-learning as “usage of computer network technology to
deliver information and instruction to individuals”. Similarly, an e-learning system is defined as
“an information system that can integrate a wide variety of instructional material (via audio,
video, and text mediums) conveyed through e-mail, live chat sessions, online discussions,
forums, quizzes and assignments”(Abdullah & Ward, 2016). E-learning got significant
attention from various stakeholders last decades, such as educational institutions, business
organizations, program software developers and current and potential customers.
Many practitioners and researchers agree that technological progress significantly
changed education, training and development landscape. Particularly increasing share of usage
of Internet technologies has been named as “e-learning revolution” (Welsh, Wanberg, Brown,
& Simmering, 2003). E-learning market started developing shortly after the Internet disrupted
the education industry in the late of 90s’.
Figure 1. The Technology Environment Allows e-Learning to Flourish//IBIS Capital, Learning Light,
The market is still growing actively: e-learning expenditure is projected to grow to
$255.5bln by 2017 with CAGR of 23%. North America is the biggest market, followed by
Europe and Asia. However, growth rates in Asia and Eastern Europe (42-45%) are three times
higher than ones in Western Europe and the USA (12-15%) (Other, 2014).
According to several studies e-learning education is continuously increasing its share
in total education expenditures around the world. Average projected CAGR of 23% for elearning subsector is 15.5 p.p. higher than projected growth rate of educational expenditures on
average for all the subsectors.
No wonder that geographical structure of e-learning market resembles structure of
global education market. At the same time some distinctions need to be mentioned, e.g., it is
projected that by 2017 USA will occupy 52% of the market, meanwhile Europe and Asia
(combined) will have 20% and 22.23% share of the market respectively. Not necessary to say
that the USA and Europe are the biggest markets, but projected growth rates are comparatively
slow – 13% and 15% correspondingly. Meanwhile projected growth rate for Asian market is
expected to be equal 45% per annum and by the end of 2017 the market can become the second
It is not considered as possible to define similar patterns and trends and value drivers
common for the whole world, mainly because of differences of the cultures and of stages of
developments of the regions. The only trend can be identified is a trend of huge investments
into digitization. In particular digitization of the education system is not only driven by market
but also actively supported by governmental and non-profit organizations.
Table 1. Regional trends on e-learning market (Docebo, 2014)
adoptions of new technologies,
shortage of quality education
Mass Digitalization process
Turkey, Kuwait, Qatar
Focus on SME and outsource of eNo data
North America 52,43%
Leverage internal knowledge in order
to make LMS a revenue generating No data
system aimed at target audience
Brazil, Argentina, Chile
E-learning in Germany
Germany has a reputation of economically stable and developed country. Currently it
is the largest consumer market in Western Europe and is also characterized by low
unemployment rate and economic overperformance over its peers.
If to talk about e-learning and its development in the country for the last years, it is
necessary to say that the state outperforms the neighbors once again. Revenue in the e-learning
sector amounted to €582m in 2013, demonstrating 13% increase in comparison with the previous
year. Bitkom published these findings basing a recent nation wide study performed by MBB
Institute. Necessary to mention that e-learning sector employes more than 9,000 people, showing
a significant increase by 700 employees from 2012.
The abovementioned facts and numbers are also supplemented by a survey results
(Bitcom, 2014) that more than 67% of German IT companies are actively using e-learning, the
rest of the companies are intending to implement e-learning systems and tools in the near future.
Moreover more than 50% of Germans, aged from 14 till 44 have e-learning experience
at least once, and approximately 33% of these people had an education application installed to
their device. In accordance with the Docebo report (2014) Germany constant growth of the elearning revenue exceeded the average growth of Western European countries by 7,2%.
It is necessary to mention that the German government recognized the trend from the
beginning and launched the first German comprehensive website aggregating e-learning
opportunities in July 2000. The initiative was supported by Bund-Länder Commission for
Educational Planning and Research Promotion.
Investments and M&A
Current situation on the market of e-learning is highly favorable for investors and
software developers. E-learning is driven not only universities and dot-com startups, but also
more and more big corporations, venture capitalists enters the market.
There are three major types of investments in e-learning business:
mergers & acquisitions (M&A);
The USA is leading in venture deals, presenting more than 60% of the global venture
investments made since 2007 in e-learning. For comparison, Europe represents only 6% for the
same period. Top US deals are on average 8x times greater than the deals in Europe(Other,
As the market is still actively developing an increase in venture investment is
expected. The venture market in the US is significantly larger than in Europe; $48.5bn in the
US in 2014 compared to $7.1bn in Europe. It is the most active global fundraising market for elearning, accounting for 59.7% of total deals. High-growth markets (India and China) trail with
11.3% and 8.4% respectively(Other, 2014).
In most of the cases e-learning is asynchronous, which is represented by e-learning
pre-recorded before and which usually available for a learner at any moment and from any
location. This type of e-learning can vary from very simple like slides uploaded to Internet site
to very complex and sophisticated programs and applications which require more engagement,
involvement and efforts from a learner. It is evident that learners prefer more interactive tools
and instruments in order to make training, educational and developing process more
entertaining and easier.
Synchronous e-learning or “live” e-learning requires learners to be present in front of
their computers at the same time. There are also various types of e-learning in that group: from
simple live chat which enables communication between trainer/teacher and learners to more
sophisticated type which allow learners and teachers communicate using slides, white board
and video streaming services. The main advantage of synchronous e-learning is an opportunity
to communicate personally, to collaborate with real person and to get support and feedback
Blended e-learning combines both asynchronous and asynchronous e-learning.
Different mixes of asynchronous, synchronous and classroom learning present blended elearning. As an example class room trainings with supporting materials for home works
(asynchronous) and live chat for communication and educational support (synchronous) can be
MOOC as a sub-segment of e-learning
Since the first times technologies were introduced into humans’ life academics started
sharing content (Lane & McAndrew, 2010). That so called tradition was a foundation of open
educational resources (OER). Mainly OER related to higher education, in years it became a
very important knowledge base for teachers and trainers as well for students and learners. In
the beginning of 2001 Massachusetts Institute of Technology (MIT) the project called as
OpenCourseWare (OCW) in order to make available all the published materials on a permanent
basis on the Web. Actually, many researchers name OER as a foundation for MOOCs
“Massive Open Online Courses” (MOOCs) are the online courses with scientific,
business or any other content with a large number of participants – in some cases tens of
thousands. Most typical MOOC incudes digital lectures with interactive elements such as
discussion at forums, video clips with lectures, mind maps, special tasks and assignments with
open or multiple-choice questions.
A MOOC is usually “massive, with theoretically no limit to enrollment; open,
allowing anyone to participate, usually at no cost; online, with learning activities typically
taking place over the web; and a course, structured around a set of learning goals in a defined
area of study” (Educause, 2013).
MOOCs stepped beyond the geographical borders several years ago, using famous and
prestigious university brands as main instrument for global expansion. Such partnership as
Coursera (www.coursera.org), a specifically purposed coalition of 78 world class universities
(as of April 11, 2014) led by Stanford University, and edX (www.edx.org) which includes the
Massachusetts Institute of Technology (MIT), École Polytechnique Fédérale de Lausanne, The
Hong Kong University of Science and Technology and other members can be considered as a
pioneers of international cooperation in MOOCs distribution. In December 2014 the number of
universities offering MOOCs has exceeded 400, and the cumulative number of courses offered
has reached 2400 to more than 18 million registered students worldwide (M. Zhou, 2016).
Venture capital firms, non-for-profit organizations, often sponsor MOOCs’ production
One of the main reasons of MOOCs’ popularity is a video component. By now video
is present in 4,5 out of 5 MOOCs released. The trend corresponds with growing interest in
video format. The average upload of videos to YouTube per minute, boosted from 8 hours in
2007 to 300 hours in 2014 (Statista, 2014).
E-learning formed firstly in academic field, but currently it has been playing a
significant role in other areas. It has a great advantage of substantial scalability, which is not
limited in comparison with traditional classes. The scalability provides the opportunity to offer
courses on various topics to a broad range of learners at low price or even for free. During the
last 3 years “… MOOCs have largely moved from pedagogy to promotion and are now more
used to advance institutional reputation than any serious drive to reinvent the institution”
(Stewart, Khare & Schatz, 2015).
The main features than make MOOCs unique are scalability, flexibility, distance
availability and international or nation wide learning communities. Many researchers consider
MOOCs as a solution to solve certain problems in education using competitive advantage of the
1.3 Technology acceptance theories
User acceptance of new technology is often described as one the most mature research
areas. Mainly studies aiming to explore innovativeness of the population apply ownership
surveys with cross-sectional samples (Ganglmair-Wooliscroft & Wooliscroft, 2014; Im, Bayus,
& Mason, 2003). The respondents are usually asked by the researchers to indicate which items
they are using at the moment within an existing list of the products. Comparison of level of
product ownership across the population is now the most reliable way to investigate the
consumers’ innovativeness and many researchers use the approach in the various context and
Rogers (1976, 1995) used an S-curve to illustrate the cumulative adoption process of
innovation over time. The cumulative distribution in S-shape is agreed with the normal
distribution curve, defining the percentage/share of the population adopting innovations in a
certain time period, see Fig. 2 below.
Figure 2. The diffusion of innovations (Rogers, 1995)
As can be seen from the Figure 2 the population can be divided into several groups in
accordance with the time element and relative view indexes:
innovators – 2,5% - represent the very first group to adopt an innovation;
early adopters – 13.5% - go second;
early majority – 34%;
late majority – 34%;
laggards – 16% - the group of people who adopt very slowly and in many cases
have to adopt more then want to (Rogers, 1995).
There are number of characteristics which influence the speed of adoption of
innovation such as: perceived relative advantage (in both economic and social prestige
context), the convenience of innovation and the future satisfaction to get, innovations’
observability and exciting values (which are strongly influenced by social norms) and
innovations’ trialability. If the actual and perceived complexity of use is increasing, it reduce
the adoption rates (Ganglmair-Wooliscroft & Wooliscroft, 2014).
As results of many studies in the field several technology acceptance models were
introduced. Further a brief overview of the main theoretical models is described (Vankatesh,
Morris, Davis, & Davis, 2003). Later the selected model for the empirical data analysis is
presented in details with additional explanation of model modification in accordance with the
specific of researched topics.
1.3.1 Theory of Reasoned Action
The Theory of Reasoned Action (TRA) developed by M. Fishbein and I. Ajzen and is
one of the most influential and fundamental theories of human behavior connected to the
determinants of consciously intended behavior (Ajzen, 1991, Ajzen and Fishbein, 1980 and
Fishbein and Ajzen, 1975). In accordance with the TRA a behavioral intention of a person to
perform a specific behavior and performance of it are jointly determined by an attitude of the
person towards the behavior and social influence associated with the behavior in question.
An attitude is defined as “an individual's positive or negative feelings (evaluative
affect) about performing the target behavior” (Fishbein & Ajzen, 1975, p. 216).
Social influence is defined as “the person's perception that most people who are
important to him think he should or should not perform the behavior in question” (Fishbein &
Ajzen, 1975, p. 302).
In scientific research practice there are two main rationales to use the TRA in order to
establish extended and modified theoretical framework aiming to explain and to predict user
First, an innovation model developed based on a reasoned action perspective is
perceived as having high potential mainly because it provides coherent and solid theoretical
foundation to unite both the cost – benefit framework and the community perspective of user
innovation (Bin, 2013). There are two most critical aspects of user’s attitude towards user
innovation: expected benefit and perceived cost(Mishra, Akman, & Mishra, 2014). Users are
tend to evaluate an expected benefit from innovative activities versus perceived costs. At the
same time there are several studies discus the influence of social communication and
interaction on user innovation (Franke and Shah, 2003, Füller et al., 2007 and Jeppesen and
Frederiksen, 2006). In most of the cases users are rarely innovative in isolation but they are in
interaction with the close circle of friends, relatives, colleagues and acquaintances. These
interactions usually motivate users to search for new ideas, knowledge and skills to implement
and realize their ideas(Bin, 2013). Based on the studies the intentional and behavioral aspects
are significantly affected by social influence (Franke and Shah, 2003, Füller et al., 2007).
Second, the studies on social behavior consider the TRA as an excellently applicable
in the context of voluntary behavior (Bin, 2013). Within the context the TRA got significant
attention in consumer behavior field as it allows to predict consumer intentions and behavior
and as well provide a basis for identification of how and where to target consumers’ behavior
attempts to change. Generally user innovations are characterized by voluntary basis. User
innovators are able to decide themselves whether to get involved into improvement,
development or modification of a product or not using their own judgment (Bin, 2013).
Figure 3 Theory of Reasoned Actions (Ajzen, 1991)
1.3.2 Technology Acceptance model
Technology Acceptance Model, see Figure 3 below, was developed from the TRA
(Fishbein & Ajzen, 1975) in 1986 by Davis. The main purpose of the theory is to explain
technology adoption behavior. In accordance with TAM there are two main perception of user:
perceived ease of use (PEOU) and perceived usefulness (PU).
PEOU is defined as "the degree to which a person believes that using a particular
system would be free of physical and mental effort” and directly influences PU, which is "the
degree to which a person believes that using a particular system would enhance his/her job
These two main perceptions influence and define users attitude towards using
technology. Attitude in most of the cases defines and affects behavioral intention (BI) to use the
technology. In it’s turn the intention to use technology determines an actual use (Abdullah &
The TAM was widely used in many studies related to e-learning acceptance and use
(Al-Gahtani, 2014; T. G. Kim, Lee, & Law, 2008; Motaghian, Hassanzadeh, & Moghadam,
2013; Padilla-Melendez, Del Aguila-Obra, & Garrido-Moreno, 2013; Wu & Zhang, 2014).
The TAM is widely applied to a great range of technological systems. Last years it is
been actively used in studies devoted to e-commerce and Internet technologies. The main goal
of the early researches was to replicate the study to test scales validity (Ha & Stoel, 2009;
Polancic, Hericko, & Rozman, 2010; Yi, Liao, Huang, & Hwang, 2009). There are more than
100 studies applying and validating the TAM (Ma & Liu, 2009). Most of these studies proved
the reliability and validity of OU and PEOU in predicting BI to use technology, although it is
necessary to say that some conflicting evidence still exist. Šumak et al. (2011) systematically
reviewed 42 e-learning acceptance studies, it showed that the TAM is the most commonly used
theory. More than 86% of the studies used TAM as a ground theory (Šumak et al. 2011).
In addition to that many pervious e-learning studies showed that extended TAM
provides good explanatory power, with total variance ranging from 53% to 70% (including
Ifinedo, 2006, p.12; Lee et al., 2014, p.572; Lee et al., 2013, p.182; Liu, Li, & Carlsson, 2010,
p.1217; Shen & Chuang, 2010, p.205).
Figure 4 Technology Acceptance Model (Davis, 1986)
1.3.3 Theory of Planned Behavior
The Theory of Planned Behavior (TPB) allows to map the process of forming
intentions to conduct the behavior consistent with their self-determined motivation (Sicilia,
Saenz-Alvarez, Gonzalez-Cutre, & Ferriz, 2015).
The main assumption of the theory is than an intention of an individual’s intention to
conduct a behavior is a key determinant of its execution (Ajzen & Madden, 1986). In
accordance with the theory can be determined by three sets of beliefs:
1. Beliefs about the most likely outcomes of the behavior;
2. Beliefs about expectations other people have and motivation to fulfill
these expectations. Than can lead to perceived social pressure (subjective norms) to
conduct specific behavior or nor;
3. Beliefs about factors which can facilitate/impede behavior to be
conducted and the perceived power of those factors (M. Zhou, 2016).
These three sets of beliefs together affect an intention to carry out specific behavior.
An attitude of technology user towards the behavior can be defined as ““degree to
which a person has a favorable or unfavorable evaluation of the behavior in question” (Ajzen,
1991). Additionally, the attitude includes judgment whether the behavior under consideration
is good or bad and whether technology user wants to carry out the behavior (Leonard, Graham,
& Bonacum, 2004). In further studies Ramayah et al. (2010) highlighted that an attitude
includes with the judgement also potential consequences following the behavior.
In accordance with studies of Kotchen and Reiling (2000) an attitude is the most
important determinant of behavioral intention. The TPB uses subjective norm as the second
most important determinant of behavioral intention. The subjective norm is defined as “the
perceived social pressure to perform or not to perform the behavior ” (Han, Hsu, & Sheu,
2010). In most of the cases the users are influenced by those who are close to them e.g. family
members, close friends, colleagues and business partners. Sometimes people who are not close
to a potential user, but have a professional reputation and credibility in a specific industry may
influence on the attitude and consequently on an intention. Subjective norm captures persons’
feelings about social norms and pressure. Also the studies confirmed that consumers who have
positive subjective norm towards given behavior in most of the cases are more likely to have
positive intentions (Paul, Modi, & Patel, 2016). Several studies in the marketing and consumer
behavior fields confirmed subjective norm to be an important determinant of participation
intention (M. J. Lee, 2005) and intention to use technology (White Baker, Al‐Gahtani, &
Hubona, 2007). The studies documented a positive interrelation between intention and
It’s necessary to mention that PBC becomes the most influential and important when
behaviors are partially conducted under volitional control. The term “perceived behavioral
control” refers to “the perceived ease or difficulty of performing the behavior” (Ajzen, 1991)
and is closely connected to user’s past experience and anticipated obstacles on his way.
The TPB was proven valid and reliable by a number of reviews and experimental
studies in such fields as physical activity (Luszczynska, Schwarzer, Lippke, & Mazurkiewicz,
2011), e-learning in higher education using mobile applications (Cheon, Lee, Crooks, & Song,
2012), internet banking (Nasri, 2011) proving its validity and reliability in explaining
technology users’ intention and behavior. On the other side there are much variances that still
remain unexplained in the TPB variables (Hagger, Chatzisarantis, & Biddle, 2002).
The relationship between attitude and intention, in accordance with the TPB, defines
that attitude serves as “an evaluative predisposition to behavior” (Ajzen, 1985).
The great scope of researches has repeatedly confirmed that attitude is the most
powerful predictor of intention to use technology (e.g., Park et al., 2012, Teo and Noyes, 2011
and Teo and Zhou, 2014).
Figure 5 Theory of Planned Behavior (M. Zhou, 2016)
1.3.4 Triandis model
In 1980 Triandis proposed a theoretical model presupposed the attitude - behavior
relationship with the following constructs: culture, genetic or biological factors and social
situation. In accordance with the framework the constructs can potentially influence the
behavior, see Figure 6 below.
Figure 6. Triandis model (Chang and Cheung, 2001)
The Triandis model, often referred to as the theory of interpersonal behavior,
complements TRA and provide norms, with the help of which human social behavior can be
explained and understood. The Triandis models assumes an “attitude – intention – behavior”
relationship as well as TRA and TPB do (T. (Terry) Kim & Lee, 2012).
Nevertheless the Triandis model additionally takes into account such relevant
variables as habits, social factors (close to subjective norms), affect, the consequence perceived
and facilitating conditions (the term is similar to perceived behavioral control) in order to
understand behavioral intention and actual behavior.
In accordance with the Traindis framework behavior can be defined as a function of
the habit strength (1) in conducting the behavior, an intention to conduct the behavior (2) and
facilitating conditions (3). Additionally it is stated that an intention depends on such factors as
social factors (1), affect towards conducting certain behavior (2) and the perceived
consequences and a desire to conduct the behavior.
Triandis (1980) named geographic and resource limitations as facilitating conditions.
The importance of these two factors is supported by the argument that the behavior can not be
conducted if the environmental conditions prevent it or make it difficult. The statement is valid
even in case the intention to perform the behavior is strong and habit is already established (T.
(Terry) Kim & Lee, 2012; Bergeron, Raymond, Rivard, & Gara, 1995).
The Triandis model differs from TRA, TAM, TPB and IDT as it uses different
determinants to explain human behavior. Still all these theories have something in common,
e.g. the TRA, TAM, TPB, IDT and Triandis model all assume an “attitude-intention-behavior”
relationship. To break the concept into details it’s assumed that normative and cognitive beliefs
are forming an attitude, consequently it has an influence on intention to behave a certain way
and on actual behavior later on. Also the PE in TAM is similar to the definition of relative
advantage used in the IDT and to a certain extent to the perceived consequences in the Triandis
model. Scholars also say that facilitating conditions in the Triandis model is closely related to
the perceived behavioral controls in the TPB (T. (Terry) Kim & Lee, 2012). The main different
in the constructs is as follows: the facilitating conditions in the Triandis framework influence
only on actual behavior, when the perceived behavior makes an impact on both an intention and
an actual behavior.
The Triandis model was successfully adopted in various researches after the author
had introduced the framework. It was applied in such contexts as consumer behavior by
Domarchi et al. (2008) and Lee (2000); as social and health behavior by Lulseged and D’Este
(2002), Milhausen et al. (2006) and Yuldirim et al. (2009). Lately the model is widely applied
in the studies related to usage behaviors of PC (T. (Terry) Kim & Lee, 2012), the users’
behavior in the Internet (e.g. Chang & Cheung, 2001; Cheung, Chang, & Lai, 2000; Ramayah,
Ahmad, Chin, & Lo, 2009).
1.3.5 Diffusion of Innovation
Diffusion of Innovation is now one of the most influential theories in marketing
communications, thus the main focus of the theory is on the means by which the information
about innovations are spread within the population (H. C. Chang, 2010).
Rogers defines an innovation as “an idea, practice, object that is perceived as new by an
individual or a group of individuals or any other unit of adoption” (Değerli, Aytekin, & Değerli,
2015). It is much less important if an object or idea is actually new or it is just the unit of
adoption only perceives it as new.
The newness perceived determines the reaction following the moment of “discovery”,
so if the idea is new to an individual, so he perceives it as an innovation (Rogers, 1995).
Diffusion is defined as a spread of the innovation, the way or the process the innovation is
communicated to society or target audience, it is also determines the channels of communication
(Değerli et al., 2015; Rogers, 1995).
Rogers assumes that a decision about innovation is a process which occurs over time
and includes a consequent series of actions, see Figure 7 below.
Figure 7. Innovation-decision process phases
This Innovation – decision process includes five stages or actions such as knowledge,
persuasion, decision, implementation and confirmation. The process starting point is knowledge
phase, which is considered by Rogers as decision-making unit exposed to innovation existence
and is oriented to gain some information and understanding about it.
The knowledge stage is followed by a persuasion stage where and when favorable or
unfavorable attitude towards innovation is formed. The decision is the stage where a decisionmaking unit involved into some activities that lead to choice of adoption or rejection of the
innovation considered during the process. The next stage is an implementation which occurs
only if the user selected an adoption of innovation on the previous stage. The overall process
ends with the confirmation which is aimed to reinforcement of the decision already made.
Nevertheless a user can change his decision in case he or she is exposed to conflicting and
confusing messages about the innovation (Değerli et al., 2015; Rogers, 1995).
Usually the diffusion process includes mass media and interpersonal communication
channels of informational spread. In current conditions social networks united these two
channels together, e.g. Facebook represents mass media as well as an interpersonal channel of
communication (Robinson, 2009). The dynamics of the process resulted in new norms,
institutions and a great variety of social technological ways of innovation spread within the
population. Using networks an individual may interact independently of their geographical
location and physical proximity (Montanari & Saberi, 2010).
Besides the innovation decision process and Rogers defined attributes of unnovation
that influence the innovation adoption process. He pointed out that scholar in the past treated all
innovations as equivalent and equal from the study and analysis point of view. The simplification
could be dangerous. At least the fact that some innovations fail and some succeed proves that not
all the innovations are the same. He defines five main attributes of an innovation:
Compatibility – the innovation should be compatible with skills, values and
practices of potential users;
Complexity – the innovation is relatively difficult to understand and use;
Observability – the benefits of usage should be easily found out and observed;
Relative advantage – an innovation should be or or least should be perceived as
technically superior than the its predecessors;
Trialability – the trail use of the innovation can be experimented without
excessive efforts and expenses.
These five attributes can be considered as one of the main contributions of the theory of
Diffusion of Innovations (Aizstrauta, Ginters, & Piera Eroles, 2015).
1.3.6 Social cognitive theory
Bandura proposed a social cognitive theory (SCT), which discusses changes in social
behavior based on the interaction concept of reciprocal determinism (Bandura, 2005). He defines
three main factors, which have reciprocal relationship to name all three: behavior, environment
and personal, see Figure 8. These factors operate as determinants and influence each other.
Figure. 8. Albert Bandura’s Social Cognitive Theory Reciprocal Model
In accordance with Bandura (1986) behavior is formed through the reinforcement of
social context. It is assumed that people may think and perform certain behavior without being
influenced by the social environment. The factors surrounding a person do not cause any changes
in behavioral patterns and trends just because there is proven interrelation between the factors.
In 2001 Bandura explains and describes personal factors as ones that cover cognition,
emotions, perceptions and internal knowledge. All these influence self-efficacy by intervening
Environment factors, according to Bandura (1986, 2001), are forming an interaction
with the involvement of the source of model representation and social norms, which can
influence people operating within.
The behavior factors include the variety of actions, choices, decisions and verbal
expressions of a person through his/her experience, skills and practice (Bandura, 2001; Antley,
SCT assumes that a person acquires knowledge and accumulates experience and
develops skills through role modes. The concept of role models provides a human who become
an example and learning process is executed through looking at someone and imitating his/her
actions and behavior (Severin & Tankard, 2010).
The theory is widely used in such fields of research as communications, education,
business and health.
1.3.7 Unified theory of user acceptance of technology
The Unified Theory of User Acceptance of technology was developed in 2003 by
Venkatesh, Morris, Davis and Davis based on TAM. The model is used to predict an acceptance
of information technology by a person, which means both an intention and actual behavior.
The UTAUT has four main constructs:
Performance expectancy (PE) – perceived usefulness of an innovative
Effort expectancy (EE) – perceived ease of use of the technology;
Social influence (SE) – an indicator of the influence of social members;
Facilitating conditions (FC) – relates to technological support, see Figure 9
Figure 9. Unified Theory of Acceptance and Use of Technology (Vankatesh et al., 2003)
As it can be concluded from the framework illustration the three main constructs are
anticipated to influence the behavioral intention directly (with approximately 70% of variance in
intention) and one construct determines an actual behavior.
Venkatesh (2003) assumes that the higher are the values of the four constructs the
higher is the value of behavioral intention, and consequently the higher is the level of acceptance
of the technology of a person. So behavioral intention of individual defines and determines the
acceptance of technology.
In addition previous studies allow to highlight the role of age, gender and technology
usage experience, as these factors were not taken into account in TRA, TPB, TAM an others
(Min, Ji, & Qu, 2008).
UTAUT was originally developed in order to define and explain the factors affecting
technology acceptance and use of ICT by employees. Since then various studies applied the
model in the consumer context e.g. an adoption by users the following technologies: mobile
phone technologies (Lu, Yao, & Yu, 2005; Park, Yang, & Lehto, 2007; Wang & Wang, 2010);
internet banking (AbuShanab, 2007; Martins, Oliveira, & Popovi??, 2014; Riffai, Grant, &
Edgar, 2012), mobile banking (T. Zhou, Lu, & Wang, 2010), e-learning (Chiu & Wang, 2008).
The UTAUT represents a synthesis of eight theoretical models taken from sociological and
psychological theories, for details see the Table 2 below.
Table 2. Similarity of constructs with those of the UTAUT (Escobar-Rodriguez & CarvajalTrujillo, 2014)
Theory of Reasoned Action
(Fishbein & Ajzen, 1975)
1989 and Davis
et al., 1989)
Similar UTAUT constructs
Attitude towards behavior
Perceived ease of use
Motivational model (MM)
Theory of planned behavior
Attitude towards behavior
(Azjen, 1991 and Schifter
and Ajzen, 1985)
Perceived behavioral control
Decomposed theory of
Attitude towards behavior
(Taylor & Todd, 1995)
Perceived behavioral control
Model of PC utilization
(Thompson, Higgins, &
Affect towards use
Innovation diffusion theory
(Moore & Benbasat, 1991)
Ease of use
Voluntariness of use
The model has been tested empirically in numerous studies and has outperformed all
eight separate models, which were used to construct the UTAUT, including TAM.
1.3.8 Summary of the adoption of technology theoretical frameworks
In order to summarize the overview of the main theoretical models of adoption of
technology and to provide the arguments for the UTAUT application for the study we prepared a
brief table where the evaluation of applicability is conducted.
As far as the main focus of the study is the behavioral intention to use the technology the
TPB was selected for the research. The application of UTAUT was approved by two experts in
e-learning sector (Udemy.com business analyst and Simpleshow Gmbh market researcher) who
were left anonymous.
The UTAUT is considered as the most suitable model for current study because of the
The model proved to outperform all the models applicable to technology adoption
field of research in 70% of the cases;
The model takes into account demographic factors such as age and gender, which
were previously ignored in other models. We consider inclusion of these factors as helpful for
the segmentation and developing more precise recommendations for managers;
It allows to identify whether the Internes Experience affect the intention to use
MOOCs, which was proven influential in previous studies of PayPal system (2014);
The core constructs of the models are comprehensive for the main goal of the
research about MOOCs and further can be broken into several subconstructs to test.
Table 3. Analysis of applicability of theoretical models
Attitude towards behavior
Perceived ease of use
Attitude towards behavior
Applicability to the study
Not applicable. Personal characteristics are not
taken into account
Applicable, similar to social influence
Applicable as similar to performance
Applicable as similar to efforts expectancy
Applicable, similar to social influence
Applicable, similar to social influence
Ease of use
Voluntariness of use
Applicable as similar to efforts expectancy
Applicable as similar to efforts expectancy
Applicable under performance expectancy
Applicable under performance expectancy
Applicable, similar to social influence
Applicable as similar to user previous
Applicable as similar to Performance
Applicable but was not the focus of the study
Applicable but was not the focus of the study
Applicable but was not the focus of the study
Not applicable as usage of MOOC is
voluntary for learners
EMPIRICAL PART: DEFINITION AND EVALUATION OF FACTORS
INFLUENCING ADOPTION OF THE ONLINE-COURSES
2.1 Research methodology and framework
The current study has two main purposes: exploratory and explanatory.
An exploratory study is aimed to find out “what is happening; to seek new insights; to
ask questions and to assess phenomena in a new light” (Robson, 2002).
And an explanatory study in its turn is aimed to “studying a situation or a problem in
order to explain the relationships between variables” (Robson, 2002, p. 140).
A research strategy is the way the researcher achieves the main goal of the study and
answer a key question. In other words the strategy is the way of collecting and examining
empirical evidence. There is no single unified and widely accepted strategy to be used by every
scholar. Each research strategy has both advantages and disadvantages, which makes it more or
less applicable depending on the research goal, the questions, and data availability and time
Table 4. Relevant situations for different research strategies (Yin, 1994)
Who, What, Where, How many, How much
Who, What, Where, How many, How much
Form of research question
The correct strategy selection defines the success of the research and therefore the
selection process should be conducted with consideration of not only the set of the objectives and
the key research questions, but also of the information and time available and the existing
The research strategy selected for the current Master Thesis is a survey strategy. It’s
widely used for business and management researches and it is traditionally used for exploratory
and explanatory search. The main argument for survey strategy is the collection of a large data
amount in a short period and at low cost. In addition to that is easy to explain and to
understand because of being widely used.
Beside these two factors the survey allows a researcher to collect quantitative data for
consecutive quantitative analysis such as descriptive statistics. The data collected with the help
of the survey can be also used to suggest particular relationship between variables (Saunders,
Lewis, & Thornhill, 2009).
The deductive approach will be used for the current master study. It was selected as it
allows to derive a particular conclusions based on existing theories. It provides an opportunity
to analyze the results both quantitatively and qualitatively, and to give an interpretation of
relationship between variables.
Data analysis is mainly presented by a quantitative analysis. It is planned to use the
method for factors’ analysis and deriving recommendations. Quantitative data is to be collected
by using a questionnaire and analyzed using IBM SPSS statistical package.
It was previously mentioned that the basic theoretical model of the research is the
UTAUT model. Still we would like to modify the model for the specific purpose of the study
and in accordance with the needs determined by the research subject.
First of all we would like to simplify the model by excluding the constructs and units
irrelevant for the study.
First of all we eliminated “Use behavior” as an actual behavior is not the subject of the
study, as far as the main focus is on behavioral intention. Nevertheless, current research can be
then used as a foundation for further studies devoted to interrelation between an intention and
behavior. Consequently, “Facilitating conditions (FC)” construct should also be excluded from
the UTAUT, as it influences the actual behavior and not the behavioral intentions. Moreover in
2003 Venkatesh et al. defined FC as “degree to which organizational and technical
infrastructure exists to support the system”, which is not relevant for MOOCs topic, as the very
technological development of educational platforms made MOOCs existent.
The third unit to be eliminated is “Voluntariness of use” moderator. In most of the
cases MOOCs use is not obligatory, it is not a pre-installed software and the basis of the
MOOCs itself assumes some willing to learn and acquire knowledge without an order or
conditions to do so.
After the exclusion of the three units off the model we have:
three determinants left: Performance Expectancy, Efforts Expectancy, Social
three moderatos: Gender, Age and Experience.
Figure 10. Modified Unified Theory of Acceptance and Use of Technology
2.2 Research hypothesis
Performance acceptance was defined as a certain extent to which a persons believes that
using a specific technology will benefit him/her in terms of job performance. Venkatesh et al.
(2003) defined five constructs derived from previous models that can be referred to performance
Perceived usefulness (TAM/TAM2, C-TAM-TPB);
Extrinsic motivation (MM);
Relative advantage (IDT);
Outcome expectations (SCT).
Additionally it is indicated that performance expectancy is the strongest predictor of behavioral
intention to use technology. In 1989 Davis proved that perceived usefulness was the most
frequent factor used to decide a higher or lower rate of adoption of technology.
The assumption to use usefulness expected (perceived) was also supported by two experts
engaged to the current study, this the following hypothesis was developed:
Hypothesis 1: Usefulness expected has a positive relationship with users’ intentions to use
Effort expectancy was defines as “degree of ease that individuals think they will have
when using an information system” (Venkatesh et al., 2003). There are three main constructs
derived from previous frameworks that relate to the effort expectancy concept: perceived ease of
use (TAM, TAM2), complexity (MPCU) and ease of use (IDT). Wu et al. (2008) defined an ease
of use as one of the key factors of technology acceptance. Previous researches suggested an idea
that individuals expectation may vary depending on gender, age and experience. Moreover
several studies proved the effort expectations will be more influential determinant of an intention
for female users (Venkatesh & Morris, 2000; Venkatesh, Morris, & Ackerman, 2000; Venkatesh
et al., 2003), especially for those ones who are older (Morris & Venkatesh, 2000) and have little
experience (Venkatesh et al., 2003). The effort expectancy usually is broken down to simpler
construct such as simplicity of use, independence of use (Venkatesh et al., 2003). As far as these
constructs are easier to understand by the respondents of the survey, it was decided to break the
effort expectancy based hypothesis into two:
Hypothesis 2.1: Simplicity of usage expected has a positive relationship with users’ intentions to
Hypothesis 2.2: Independence of usage expected has a positive relationship with users’
intentions to use MOOCs.
Social influence is defined as “the extent to which a person perceives it is important that
other believes he/she should use the new information system” (Venkatesh et al., 2003). In
accordance to the results of prevous studies social influence is a direct determinants of
behavioral intention to use new technology (Thompson, Higgins, & Howell, 1991; Mathieson,
1991; Moore & Benbasat, 1991; Harrison, Mykytyn, & Riemenschneider, 1997; Venkatesh &
Davis, 2000). Social influence is usually divided for two constructs: influence of superior people
and influence of the peers. For the purposes of this study these two constructs were incorporated
into one group “of people who influence individual’s behavior”.
Hypothesis 3: Social influence has a positive relationship with users’ intentions to use MOOCs.
Demographic factors and experience
Additionally we would like to test of the demographic factors and previous experience
influence the intention to use MOOCs:
Hypothesis 4: There is a positive relationship between age and users’ intentions to use MOOCs.
Hypothesis 5: There is a positive relationship between gender and users’ intentions to use
Hypothesis 6: There is a positive relationship between previous Internet experience and users’
intentions to use MOOCs.
2.3 Research design
Survey design and submission of sample size
The modified UTAUT model was used as basis for the questions development to be
used in the survey. Setting the right questions for each of the determinant and the moderator is
critical for the research success and results objectivity.
In order to investigate the moderators the following questions were asked:
3. Years of active Internet usage;
In order to gather knowledge about the key determinants of the behavioral intention to
use MOOCs the following questions were developed:
MOOCs would improve my knowledge in the areas
interesting to me.
MOOCs are easy and flexible to use.
Using MOOCs is benefitial because oft he absence of physical
and time limitations
People who influence my behaviour think that I should use
In order to measure adoption readiness variables 5-level Linkert – type scale level of
agreement was used:
Neither Disagree Nor Agree;
We also included the most important question about behavioral intention into the survey, and it
is ranked by 1-5 scale from “I do not consider using MOOCs in the future” to “Yes, definitely I
“Do you plan to use MOOCs in next 12 month?”
The questionnaire was designed as a result of theoretical research during summer
internship of the author at Simpleshow Gmbh, Germany. As the company was considering the
launch of MOOCs production the analysis of technology adoption was actual for primary market
research. With the help of external marketing agency the survey was distributed widely across
Germany in order to gather representative primary data for analysis.
Venkatesh et al. (2003) proposed the UTAUT theoretical framework, which was tested
by the researchers on three samples, all the samples consisted of 215 respondents. Thus for the
purpose of current Master Thesis we needed to obtain a sample no less than 215. With the help
of agency in relation of survey distribution the sample size of the study consist of 491
respondents. The questionnaire was designed to be short (5 minutes to complete) in order to get
more honest and sincere responses and not making respondents tired and inattentive. The survey
was distributed via Survey Monkey online survey tools.
2.4 Data analysis
2.4.1 Descriptive statistics
At first we would like to perform analysis of the results of descriptive statistics for dependent
variables covering demographic factors and previous Internet experience.
Firstly we analyzed the sample by gender, as it can be seen from the Figure 11, the genders
are equally presented in the sample. The sample distribution by gender is considered as representative
Germany, as in accordance with the official statistics there were 51% females and 49% males living in
Germany as at 30.09.2015. (Destatis, 2016).
Figure 11. Distribution of sample by gender
Next we have Figure 12 illustrating sample distribution by age groups. As can be seen from
the illustration respondents aged 60 years and older present the biggest share equal to 27%. As the
main purpose of the study was to get the representative data for analysis we consider that the sample
corresponds with the age distribution within the population of Germany, as the share of population
over 60 years equals to 30% of total population of the country.
Under 18 years
Figure 12. Distribution of sample by age
The results of the Internet experience distribution across the sample correspond to the age
distribution presented above. As far as only 12% of the respondents are 24 years old or younger, the
proportion of the respondents with Internet usage experience less than 3 years equals to 11%. Most of
respondents – 89% - have been using Internet actively for more than 3 years.
< 1 year
1 – 3 years
3 – 7 years
7 – 10 years
> 10 years
Figure 13. Distribution of sample by Internet experience
Additionally we have studied the means of the core constructs, on average the mean index is
allocated closer to 1-2 points, which can be a sign of positive attitude to MOOCs as to an educational
Table 5. Summary statistics (n=491)
2.4.2 Reliability analysis
To ensure and to measure the internal consistency reliability of the data we conducted
Cronbach’s Alpha reliability tests. Cronbach’s Alpha index varies from 0 (no similarities) to 1
(maximum similarities). As the result (see Table 6 below) all the coefficients exceed 0,70 which is
recommended minimum level for confirmatory research (Churchill Jr, 1979).
Table 6. Reliability analysis (n=491)
Number of items
2.4.3 Correlation analysis
Correlation analysis was performed in order to investigate whether the independent variable
in the model are interrelated. As it can bee seen from the Table 7 variables presenting demographic
factors and Internet usage experience are not interrelated with any others. All the correlation
coefficients of the variables are significantly less than 0,05.
Meanwhile the variables related to the core construct of the modified UTAUT model are
positively interrelated, having correlation indexes equal or close to the benchmark – 0,5.
Table 7. Correlation analysis
0,584*** 0,564*** 1
0,469** 0,479** 0,652*** 1
***p > 0,5; **p ≈ 0,5
Along with correlation analysis we performed collinearity analysis, see the result in the Table 8
below. As VIF index for all variables is significantly less than 5, there is no indication of
multicollinearity in the model used.
Table 8. Collinearity analysis
2.4.4 Hypothesis testing and results interpretation
The next stage after reliability, correlation and collinearity tests is the test of hypotheses
proposed. In order to conduct the test of hypotheses we used Multiple Liner Regression method. The
stage is the most important of the research as it allows investigating and identifying whether
performance expectancy, effort expectancy and social influence significantly affect an intention to use
We also included three moderators selected into regression analysis in order to answer the
question if the demographic factors such as age and gender, and previous internet experience have any
influence on the behavioral intention to use the MOOCs.
The regression analysis was conducted three times for three various models testing:
PE, EE and SI and the effect on the BI;
Three moderators: Age, Gender, Internet Experience and the effect on the BI;
Three core constructs + three moderators and their effect on the BI,
the results of the tests are presented below.
Test of Model 1
R-sqared of the tested model is equal to 0, 464 which falls into the initial UTAUT model
testing interval from 0,4 to 0,51 conducted by Venkatesh et al. in 2003.
The model is significant and it can be concluded that all variables except the simplicity of
MOOCs usage affect the behavioral intention (see Table 9.1).
Table 9.1 Results of models’ testing
P > |t|
Test of model 2
R-sqared of the tested model is equal to 0, 021 which does not fall into the initial UTAUT
model testing interval from 0,4 to 0,51 conducted by Venkatesh et al. in 2003.
The model is not significant and it can be concluded that age, gender and previous Internet
experience do not affect the behavioral intention (see Table 9.2).
Table 9.2 Results of models’ testing
P > |t|
Test of Model 3
R-sqared of the tested model is equal to 0, 462 which falls into the initial UTAUT model
testing interval from 0,4 to 0,51 conducted by Venkatesh et al. in 2003.
The model is significant and usefulness, independence and social influence affect the
behavioral intention to use the MOOCS, while the simplicity, age, gender and internet experience do
not, see the Table 9.3.
Table 9.3 Results of models’ testing
P > |t|
To get into details after the tests of the three models were conducted we analyzed if the
hypothesis proposed was supported or not and how we should interpret the result of the test. The short
summary is presented in the Table 10 below, additionally each hypothesis was also analyzed and
results were explained.
Table 10. Testing of hypotheses
Hypothesis 1: Usefulness expected has a positive relationship with users’ intentions to
use MOOCs. The results showed that a core construct “Performance Expectancy”, measured for
the purpose of the study via “Usefulness expected” positively affects behavioral intention of a
user to use MOOCs (β=0,286, p<0,001). Therefore H1 is supported. That means that when a
potential learner expects using MOOCs is useful for him he/she increases the intention to use it.
The core construct “Effort expectancy” was broken down to two units: “Simplicity” and
“Independence”, therefore it is correct to analyze the hypotheses separately. Moreover it is
necessary to mention that the construct was proven significant partly, thus the divided analysis
allows identifying the relevant determinant out of two.
Hypothesis 2.1: Simplicity of usage expected has a positive relationship with users’
intentions to use MOOCs. The results showed that simplicity does not positively affects an
intention of a user to use MOOCs (β=0,034, p>0,5). Therefore H2.1 is not supported. That
means that when a potential user expects using MOOCs to be a simple process it does not
positively affect his/her intention to use it.
Hypothesis 2.2: Independence of usage expected has a positive relationship with users’
intentions to use MOOCs. The results showed that independent usage of MOOCs positively
affects an intention of a potential user to use MOOCs (β=0,102, p≤0,05). Therefore, H2.2 is
supported. That means when a potential user expects using MOOCs independently without any
external help and support he/she increases the intention to use it.
Hypothesis 3: Social influence has a positive relationship with users’ intentions to use
MOOCs. The results showed that social influence positively affects user’s intention to use
MOOCs (β=0,198, p<0,001). Therefore H3 is supported. That means when user’s peers, friends,
colleagues or someone important to him/her suggest that they use MOOCs, the user increase the
intention to use it.
Hypothesis 4: There is a positive relationship between age and users’ intentions to use
MOOCs. The results showed that age does not affect an intention of a potential user to use
MOOCs (β=0,007, p>0,7). Therefore H4 is not supported. That means that age of a potential user
does not interrelate with the intention to use MOOCs.
Hypothesis 5: There is a positive relationship between gender and users’ intentions to use
MOOCs. The results showed that gender does not affect an intention of a potential user to use
MOOCs (β=0,038, p>0,6). Therefore H5 is not supported. That means that gender of a potential
user does not interrelate with the intention to use MOOCs.
Hypothesis 6: There is a positive relationship between previous Internet experience and
users’ intentions to use MOOCs. The results showed that pervious Internet experience does not
affect an intention of a potential user to use MOOCs (β=-0,031, p>0,3). Therefore, H6 is not
supported. That means that there is not positive interrelation between user Internet experience
and his/her intention to use MOOCs.
2.5 Analysis of the obtained results
2.5.1 Interpretation Of Moderators
The results of hypotheses testing showed that an intention to use MOOCs is not affected
by age, gender and Internet experience of a potential user. Which means that people of different
ages, sexes and previous Internet experience can easily adopt such e-learning tools as MOOCs.
The sample population was proved to be representative comparing with the structure of the
whole Germany population in by age and gender; therefore the conclusion can be extrapolated.
Both companies’ representatives expected interdependence between age and behavioral
intention to use MOOCs, but the data did not validate the results expected. The results strongly
contradict the expectation, thus also should be take into account. Therefore it is not possible to
identify the behavioral pattern for the company to target a specific age group customers basing of
the findings of the research. Still others demographic factors can be taken into consideration for
testing such as education level, profession and occupation, income level etc.
2.5.2 Interpretation of determinants
As the results of the study showed 3 out of 4 proposed hypotheses related to core
determinants were supported, thus 3 determinants are significant and should be analyzed
separately each by each.
Performance expectancy is proved to be significant (β = 0,286) and is considered as one
of the most important factors of adoption of technology such as MOOCs. Performance
expectancy is closely related to the perceived usefulness an individual would get using the
technology, therefore it can be concluded that individuals consider MOOCs usage as an
instrument of development and improvement in terms of knowledge, skills, qualification etc.
Simplicity as a part of effort expectancy was expected to be significant factor influencing
the intention. Nevertheless the results showed that it does not affect an intention to use MOOCs
(β=0,034). In accordance to several studies simplicity does not play a significant role in an
educational process, as individuals perceive a process of acquiring knowledge and developing
skills as a complex and challenging (Tan, 2013).
At the same time the second part of the effort expectancy construct, which is
independence of use technology, is significant (β = 0,102). As far as all e-learning tools are
designed to ease the limitations such as time, geographical location and costs of education, the
users perceive almost all e-learning tools as a tool for individual work (V. Chang, 2016). Thus
the independence is now considered as one of the key attribute of MOOCs and significantly
influences individuals’ behavioral intention to use the technology.
As the analysis results showed social influence construct significantly influences the
intention to use MOOCs (β=0,198). So the adoption level of the peers and superior means a lot
for individuals, and stimulate them to adopt the technology. It is necessary to say that MOOCs
are usually advertised via social network using so called recommendation features, making the
MOOCs visible by the individuals.
Current study is devoted to the research and analysis of the factors influencing users’
intention to use such tool of e-learning as MOOCs in Germany. As the e-learning becomes more
and more important and wide spread in Europe it is vital for companies providing MOOCs and
other stakeholders interested in the segment to accumulate and to analyze information related to
user’s intentions and the factors influencing their intention. Commonly accepted approach to
study an intention to use technology is based on the technology adoption models, which allow to
measure and interpret the factors affecting the degree of acceptance of technology, readiness to
use it. We performed detailed theoretical analysis of theoretical frameworks currently existing
and based on this UTAU model was selected for foundation of empirical part of the research.
The model allowed the author to test whether such core constructs as performance expectancy,
effort expectancy and social influence affect the intention to use MOOCs. Additionally the
model included such moderators as age, gender and Internet experience, which gives opportunity
to test the impact of these attributes on the intention.
Based on the quantitative analysis we it was identified that age, gender, Internet
experience in the past and perceived simplicity do not influence user’s intention to use MOOC as
a learning instrument. At the same time usefulness expected, independence of usage and social
influence have significant relationship with the intention. The significance of the variable
allowed us to accept the hypotheses about the positive relationship with the intention. The results
of the current study enable to draw several important conclusions important for the companies
and entrepreneurs and other stakeholders involved into MOOC development and expansion.
The results prove the age to be irrelevant in terms of intention to use or not MOOC. It is
still widely discussed issue as there are studies confirmed the age as a significant variable
affecting technology adoption process of such technologies as mobile banking (Yu, 2012),
electronic medical record systems (Venkatesh, Sykes, & Zhang, 2011), e-government services
(Alshehri & Drew, 2012), social media (Salim, 2012) and others. It is necessary to mention that
one of the mostly used customer segmentation practice is based mainly on the age segmentation,
which is proven to be ineffective for the case of MOOC. It does not mean that the age should be
excluded for the analysis and segmentation, but clearly confirms that the age is not solid and
sufficient foundation for the primary market segmentation.
Also it was concluded that the simplicity of using MOOC expected does not correlate
with the intention. The result is considered as significant because it contradicts the last main
trend in IT, which has simplicity of use as a main goal (H. Lee et al., 2008; Madni, 2012; Maeda,
2006; Mayer, 2008). Mainly that finding can be explained by studies made in consumer behavior
in education as the educational process itself is not perceived as simple one, in most of the cases
it is expected to be challenging and complex at some level. As now many MOOC developers
invest time, funds and human resources into design and system simplification the results of the
study can be useful for further projects budget allocation, and can also cause deeper researches in
order to identify correctly users expectations and preferences.
At the same time based on the respondents’ answers we can confirm that independence
expected in MOOC usage positively affects users’ intention. Which also proves some benchmark
in technical and design attributes of the MOOCs and the system, which provides them. Mainly
the studies in this field proved that user friendly interface, system sufficiency and low rate of
system bags are three main dimensions an average user measures the degree of his/her
independence (Bai, Lin, Huang, Fei, & Floeter, 2010; Bonino, Corno, & De Russis, 2011; Kurdi,
Hamad, & Khalifa, 2014).
As far as perceived usefulness is confirmed as the most significant variable in the study
performed we consider it as the main point to focus on for the MOOC developers. The finding
was partly expected as it presents actually the basis of the e-learning as a whole (Davis, 1989; Ha
& Stoel, 2009; T. G. Kim et al., 2008; Motaghian et al., 2013; Sun et al., 2008). For practical
implementation the content and it’s usefulness for the user can be used as a key point of
promotion, it also can define the partnerships with the most trusted educational organizations,
trainers and tutors. As an example we can name successful business models of the leading
universities launching MOOCs on external or own platforms (Adams, 2012; Anderson, 2012;
Bates, 2012; Educause, 2013; Severance, 2012) . The names standing behind the MOOCs
usually imply high quality and great perceived usefulness in terms of theoretical knowledge or
special skills offered as well as brand recognition by others.
In addition to the last two points social influence also has positive relationship with the
intention to use MOOCs. As far as many MOOCs’ current and potential users have several years
of Internet experience in the past the social medias now represent one of the main channels of
promotion (H. C. Chang, 2010; Koutropoulos et al., 2014; Salim, 2012; Shen & Kuo, 2015;
Ternauciuc & Mihaescu, 2014). Still the main type of promotion and advertising of MOOC is a
context ad banners, whereas the results of the study proves that it’s is more important for
potential user to be aware that a person who is an opinion leader for him (friend, colleague,
family members, celebrity, businessman etc.) uses MOOC. Motivation by examples here works
the most effectively. The MOOC developers for promotion and brand awareness actions also can
use this finding. Just as an examples we can mention that there is not connecting links to
Facebook on courser.org. Usually that technical solution are not costly but as the author assumes
are effective in terms of promotion, users’ attraction and consequent revenue generation.
Discussing theoretical input and managerial implications of the current master thesis
study it’s necessary to outline several limitations applicable to the research.
Firstly, the focus of the study is the population of Germany, thus geographical limitation
Second, the main advantage and at the same time great disadvantage of the current study
is its sample size and structure. As far as the main goal was to make conclusions applicable to
the great part of the population, the sample size represents variety age groups and Internet
experience groups. That fact made it impossible to include very specific questions, as the sample
is not homogenous. Thus the main conclusions are perceived as common and to be used for
further researches as a foundation, a starting point. Beside that despite the size of the sample
group the study is limited as the survey was distributed through the Internet, thus the key
conclusions are applicable mainly for frequent Internet users.
Additionally among the various e-learning tools only MOOCs were studied, thus the
results of the study are strictly limited in practical application to e-learning.
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