FACTORS FOR E-LEARNING ADOPTION IN TANZANIA THE CASE … · the Mzumbe University, a...
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i FACTORS FOR E-LEARNING ADOPTION IN TANZANIA THE CASE OF HIGHER LEARNING INSTITUTIONS IN MWANZA REGION By Tale Shunashu Ndonje A Thesis Submitted in Partial Fulfillment of the requirements for the Award of the Degree of Master of Business Administration (MBA-Corporate Management) of Mzumbe University AUGUST, 2013
Transcript of FACTORS FOR E-LEARNING ADOPTION IN TANZANIA THE CASE … · the Mzumbe University, a...
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FACTORS FOR E-LEARNING ADOPTION IN TANZANIA
THE CASE OF HIGHER LEARNING INSTITUTIONS IN MWANZA
REGION
By
Tale Shunashu Ndonje
A Thesis Submitted in Partial Fulfillment of the requirements for the Award of the
Degree of Master of Business Administration (MBA-Corporate Management) of
Mzumbe University
AUGUST, 2013
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CERTIFICATION
We, the undersigned, certify that we have read and hereby recommend for acceptance by
the Mzumbe University, a dissertation/thesis entitled Factors for e-Learning adoption
in Tanzania, The case of Higher learning Institution in Mwanza region, in
partial/fulfillment of the requirements for award of the degree of Master of Business
Administration of Mzumbe University.
Signature
___________________________
Major Supervisor
Signature
___________________________
Internal Examiner
Accepted for the Faculty Board
……………………
Signature
____________________________________________
DEAN/DIRECTOR, FACULTY/DIRECTORATE/SCHOOL/BOARD
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DECLARATION AND COPYRIGHT
I, Tale Shunashu Ndonje, declare that this thesis is my own original work and that it has
not been presented and will not be presented to any other university for a similar or any
other degree award.
Signature ___________________________
Date________________________________
COPYRIGHT
© Tale S. Ndonje, 2013
This dissertation is a copyright material protected under the Berne Convention, the
Copyright Act 1999 and other international and national enactments, in that behalf, on
intellectual property. It may not be reproduced by any means in full or in part, except for
short extracts in fair dealings, for research or private study, critical scholarly review or
discourse with an acknowledgement, without the written permission of Mzumbe
University, on behalf of the author.
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ACKNOWLEDGEMENTS
Many people supported me during the completion of this thesis with criticism, helpful
assistance and references. This thesis would have never been possible without them.
First, I am very much grateful to my supervisor, Dr. Albogast Musabila of the School of
Business Administration, Mzumbe University (MU) Tanzania for his constructive
suggestions, guidance and valuable time devoted throughout this research work.
I would like to thank Dr. Kapaya, Mr. Edgar and Ms. Asha of The Open University of
Tanzania for their sincere cooperation during my work of data collection. I appreciate
special efforts of Mr. Kimambo of the College of Business Education (CBE) for the
assistance he gave me when I needed during data collection. My thanks are extended to
Mr. Andrew J. Jisaba of St Augustine University of Tanzania, Mr. Ernest Kasheshe of
Mzumbe University ( Mwanza Centre), Mr. Lemama of Tanzania Institute of
Accountancy (TIA) for their effort to facilitate data collection at their respective
Universities and Higher Learning Institution.
I acknowledge contributions from all stakeholders who participated in this research.
These include Lecturers, Tutors, students, member of staff and management for St
Augustine University of Tanzania, Open University of Tanzania, Mzumbe University-
Mwanza Centre, Tanzania Institute of Accountancy (TIA), and College of Business
Education (CBE). Thanks very much for your inputs and cooperation during
participatory activities.
Finally, it is my pleasure to thank my lovely wife Gaudencia Medard for her love,
encouragement, tolerance, and assistance. My sincere thanks are extended to my
children: Medard Ndonje, Joseph Ndonje, Christina Ndonje and Albert Ndonje for their
moral support, understanding and patience during the whole period of my research
study, especially during the period I spent away from home. Above all, I thank God for
the blessings which made this research successfully completed. Otherwise, I would have
not reached this point.
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DEDICATION
This research work is dedicated to:
My late Father Mr. Joseph Ndonje and My Mother Christina Kija
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LIST OF ACRONYMS
CBE : College of Business Education
CD-ROM : Compact Disc- Read only memory
CDs : Compact Discs
CMS : Content Management Systems
CAI : Computer-Aided Instruction
CSF : Critical Success Factors
DVD-ROM : Digital Versatile Disk – Read Only Memory
DOI : Diffusion of Innovation theory
e-Learning : electronic Learning
HEELAM : Higher Education E-Learning Adoption Model
HEFCE : Higher Education Funding Council of England
ICT : Information and Communication Technology
IS/IT : Information System/ Information Technology
LMS : Learning Management System
LAN : Local Area Network
NACTE : National Council for Technical Education
OUT : Open University of Tanzania
SCT : Social Cognitive Theory
SAUT : St Augustine University of Tanzania
SPSS : Statistical Packages for Social Scientists
TRA : Theory of Reasoned Action
TAM : Technology Acceptance Model
TCU : Tanzania Commission for Universities
TIA : Tanzania Institute of Accountancy
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ABSTRACT
The aim of this research study was to assess Tanzanian lecturers‘ and students‘ attitudes
towards the adoption and usage of e-Learning system. A number of hypotheses were
formulated for this purpose based on the model called Higher Education E-Learning
Adoption Model (HEELAM); derived from four theories namely: Theory of reasoned
action (TRA),Technology acceptance Model (TAM), Diffusion of innovation theory
(DOI) and social Cognitive Theory (SCT).
The model highlights the factors that influence the teachers and students‘ adoption of an
e-Learning in higher learning education .These factors are divided into four main
categories: the learner characteristics, characteristics of the e-Learning itself,
institutional factors, and instructors‘ characteristics.
Methodology involved five Institutions from various Universities and Higher learning
Institutions present in Mwanza region in Tanzania; the study was conducted in Mwanza
region and the number of respondents who participated in this study was 210. In order to
accomplish the study, several procedures were carried out, such as data collection which
was done using Questionnaire as the main tool for data collection. The main analysis
method used was multiple Regression analysis to observe the associations of proposed
constructs which was preceded by descriptive statistics, factor analysis, scale analysis,
and transformation of variables using Statistical Package for Social Sciences (SPSS).
Two dependent variables were involved in this study; Intention to adopt e-Learning and
the actual use of e-Learning which were tested against independent variables.
The results findings suggest that institutional policy, complexity, openness to change,
instructor timely response and Training on the use of e-Learning are positively related
to high level of e-Learning usage .Again, Institutional policy was also positively related
to intention to adopt e-Learning in the future.
Originality/value: These findings indicated that DOI and SCT have been partially
supported by the study, because, when measured intention to adopt e-Learning they were
not significant, but significantly supported e-Learning usage. This research also
contributes to the foundation for future research to improve e-Learning adoption.
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TABLE OF CONTENTS
DECLARATION AND COPYRIGHT ................................................................................ ii
Acknowledgements ......................................................................................................... iv
Dedication ....................................................................................................................... v
List of Acronyms ............................................................................................................ vi
Abstract ........................................................................................................................ vii
CHAPTER ONE .............................................................................................................. 1
1.0 Introduction .......................................................................................................... 1
1.1 Background ................................................................................................................ 2
1.1.1 e-Learning adoption – A global perspective .................................................................. 2
1.1.2 Mobile learning (m-Learning) ....................................................................................... 4
1.1.3 e-Learning implementation trends in Africa .................................................. 5
1.2 e-Learning in Tanzania .................................................................................................... 6
1.2.1 Factors for e-Learning adoption ..................................................................... 7
1.3 Statement of the problem ................................................................................................. 8
1.3.1 Gap in previous studies ................................................................................. 8
1.4 General Research Objectives ........................................................................................... 9
1.5Specific Research Objectives……………………………………………………………….9
1.6General Research Questions ............................................................................................. 9
1.7Scope and Limitation of the Study .................................................................................... 9
1.8 Significance of the study................................................................................................ 10
1.8.1 Importance of ICT in teaching and learning .................................................. 10
CHAPTER TWO ........................................................................................................... 12
2.0 Literature review....................................................................................................... 12
2. 1 Introduction .................................................................................................................. 12
2. 2 e-Learning – An overview ............................................................................................ 12
2.2.1 e-Learning technologies ................................................................................... 13
2.2.2 e-Learning delivery approaches ................................................................... 13
2.2.2.1 Asynchronous Mode (self study learning) .............................................. 13
2.2.2.2 Synchronous Mode ............................................................................... 14
2.2.2.3 Blended Learning .................................................................................. 15
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2.2.3 e-Learning in university teaching .................................................................. 15
2.3 Theoretical backgrounds ................................................................................................ 16
2.3.1 Learning theories ......................................................................................... 16
2.3.2 Innovation adoption theories ........................................................................ 18
2.3.2.1 Theory of a reasoned action (TRA) ................................................................. 18
2.3.2.1 The need for additional features on TAM ............................................... 20
2.3.2.2 Diffusion of innovation (DOI) theory .......................................................... 21
2.3.2.3 Social Cognitive Theory (SCT) .................................................................. 22
2.4 Empirical review ........................................................................................................... 24
2.4.1 Integration of e-Learning in Africa including Tanzanian higher education. .... 24
2.4.2 e-Learning factors .................................................................................... 25
2.4.3. Students Factors ..................................................................................... 25
2.4.4 Instructors factors ..................................................................................... 25
2.4. 5 Institutional factors ...................................................................................... 26
2.4.6 Intention to adopt e-Learning ....................................................................... 26
2.4.7 e-Learning actual use .................................................................................. 26
2.5 Conceptual framework and research model .................................................................... 27
2.5.1 Learner characteristics ................................................................................. 28
2.5.1.1Self efficacy ............................................................................................ 28
2.5.1.2 Openness to change ............................................................................. 28
2.5.2 E-Learning characteristics ............................................................................ 29
2.5.2.1Authenticity ............................................................................................. 29
2.5.2.2 Complexity ............................................................................................ 30
2.5.3 Instructors’ characteristics ............................................................................ 31
2.5.4.1 Organizational support .............................................................................. 32
2.5.4.2 ICT Infrastructure ...................................................................................... 32
2.5.4.3 Institutional policy...................................................................................... 33
2.5.4.4 Training in e-Learning Techniques ............................................................ 33
2.5.4.5 Management support ................................................................................ 34
2.6 Hypotheses Summary .................................................................................................... 35
2.6.1 Learner characteristics ................................................................................................ 35
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2.6.2 e-Learning characteristics ............................................................................ 35
2.6.3 Instructor characteristics .............................................................................. 35
2.6.4 Institutional factors ....................................................................................... 35
CHAPTER THREE ........................................................................................................ 37
3.0Research Methodology ............................................................................................... 37
3.1 Type of the study ........................................................................................................... 37
3.2 Study Area .................................................................................................................... 37
3.4 Units of analysis ............................................................................................................ 38
3.5 Variables and their Measurements ................................................................................. 39
3.5.1 Dependent Variable .................................................................................................... 39
3.5.2 Independent variable........................................................................................ 39
3.5.3Measurement Scales .................................................................................... 40
3.5.4 Data variable codification ............................................................................. 41
3.6 Sample size and sampling techniques ............................................................................. 41
3.6.1Sample size ................................................................................................................. 41
3.6.2 Sampling technique ..................................................................................... 42
3.8 Data collection method .................................................................................................. 44
3.8.1 Questionnaire ................................................................................................. 44
3.9 Validity issues ............................................................................................................... 45
3.10 Data analysis methods ................................................................................................. 46
3.12.1 Basis for the Budget .................................................................................................. 47
3.12.2 Units Costs Bases for the Budget .............................................................................. 47
3.12.3 The Budget Estimates (in T.shs)................................................................................ 48
CHAPTER FOUR .......................................................................................................... 49
4.0 Presentation of findings ............................................................................................. 49
4.1 Data preparation ............................................................................................................ 49
4.1.1Data editing................................................................................................... 49
4.1.2 Coding and Transcription ............................................................................. 49
4.1.3 Data cleaning ............................................................................................... 50
4.2 Preliminary data analysis ............................................................................................... 50
4.2.1 Introduction .................................................................................................. 50
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4.2.2 Respondents’ Gender .................................................................................. 50
4.2.4 Respondents’ Age........................................................................................ 51
4.2.4 Respondents’ e-Learning experience ........................................................... 52
4.2.5 Presence of e-Learning in Universities/Higher Learning Institutions ............. 53
4.3 Hypothesis Testing ........................................................................................................ 54
4.3.1 Dependent variable ...................................................................................... 54
4.3.1.1 Intention to adopt e-Learning .................................................................... 54
4.3.1.2 Actual use of e-Learning ........................................................................... 55
4.3.2 Factor analysis ............................................................................................. 56
4.3.3 Scale analysis .............................................................................................. 58
4.3.4 Scale transformation ........................................................................................ 58
4.3.5 Multiple regressions ........................................................................................ 59
4.3.5.1 Intention to adopt e-Learning .................................................................... 59
4.3.5.2 Actual use ................................................................................................. 61
4.4 Hypothesis testing ......................................................................................................... 63
4.4.1 Learner characteristics ................................................................................................ 64
4.4.3 Instructor characteristics .............................................................................. 65
4.4.4 Institutional factors ....................................................................................... 66
4.4.5 Hypothesis conclusion ..................................................................................... 67
5.0 Discussion of the study findings.................................................................................. 70
5.1 Introduction ................................................................................................................... 70
5.2 Measuring dependent variable ....................................................................................... 70
5.3 Hypothesis testing ......................................................................................................... 70
5.3.1 Learner characteristic ....................................................................................... 70
5.3.2 e-Learning characteristic .............................................................................. 71
5.3.3 Instructor characteristic ................................................................................ 72
5.3.4 Institutional factors .......................................................................................... 73
5.3.4.1 ICT infrastructure ......................................................................................... 73
5.3.4.2 Institutional policy...................................................................................... 73
5.3.4.3 Training in e-Learning techniques ............................................................. 74
5.3.4.4 Management support ................................................................................ 75
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CHAPTER SIX .............................................................................................................. 76
6.0 Summary, Conclusions, and Policy implications ........................................................... 76
6.1 Summary ....................................................................................................................... 76
6.2 Practical implications and Conclusion ........................................................................ 78
6.2.1 Implications .................................................................................................. 78
6.2.1.1 Policy makers in Tanzania ........................................................................ 78
6.2.1.2 Universities in Tanzania ............................................................................ 78
6.2.2 Conclusion ................................................................................................... 79
6.3 Recommendations........................................................................................................ 79
6.3.1 Limitations and Future Research Directions ................................................. 80
6.3.2 Suggestions for future research ................................................................... 80
References..................................................................................................................... 81
Appendix 1: Research Work Plan (in months, 2012/2013) ............................................. 91
Appendix 2: Research study Budget (in T.shs.) ................................................................. 91
Appendix 3: Research Questionnaire. .............................................................................. 92
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List of Tables
Table 1-2:Proposed Financial Budget ………………………………………… 91
Table 3-2: Operational definition of research model ……………………… 40
Table 3-3: Respondents forming sample size ..……………………………… 42
Table 3-4: cronbach's alpha ………………………………………………… 45
Table 4-1: Gender …………………………………………………………… 51
Table 4-2: Educational level ……………………………………………….. 51
Table 4-3: Presence of e-Learning in Universities …………………………. 53
Table 4-4: Validity testing …………………………………………………. 56
Table 4-5 KMO and Bartlett's Test …………………………………………. 57
Table 4-6: Rotated Component Matrix ……………………………………… 58
Table 4-7: Variable transformation …….…………………………………… 59
Table 4-8: Model summary ………………………………………….……… 60
Table 4-9: ANOVA …..………………………………………………………… 60
Table 4-10: coefficient - Intention to adopt………………………………….. 60
Table 4-11: Model summary2 ………………………………………………. 61
Table 4-12: ANOVA2 ………………………………………………………. 62
Table 4-13: Actual use coefficient ………………………………………….. 62
Table 4-14: Complexity 1…………………………………………………… 64
Table 4-15: complexity2……………………………………………………… 64
Table 4-16: Openness to change1 ……………………………………………… 65
Table 4-17: openness to change 2……………………………………………….. 65
Table 4-18:instructor timely response1 …………………………………………. 66
Table 4-19:instructor timely response2 …………………………………………. 66
Table 4-20: institutional factors1………………………………………………….. 67
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Table 4-21: institutional factors2………………………………………………….. 67
Table 4-22:Result summary1……………………………………………………… 68
Table 4-23: Result summary2 ……………………………………………………. 69
LIST OF FIGURES
Figure 2-1: Technology acceptance Model ……………………………….. ...…….21
Figure 2-2: Higher Education E-Learning adoption Model (HEELAM) ………… 27
Figure 3-1: Educational level ………………………………………………………43
Figure 4-1: Respondents‘ age………………………………………………………52
Figure 4-2: e-Learning usage experience………………………………………… 53
Figure 4-3:Intention to adopt e-Learning………………………………………… 55
Figure 4-4: e-Learning usage frequency ………………………………………… 55
Figure 4-5: Complexity …………………………………………………………… 64
Figure 4-6: openness to change …………………………………………………….65
Figure 4-7:Instructor timely response ……………………………………………...65
Figure 4-8: Institutional factors ……………………………………………………67
1
CHAPTER ONE
1.0 Introduction
Higher education institutions all over the world are challenged to become more
competitive on a global level. This can be seen as a part of a globalization process,
which includes a reshaping of higher education where networked learning, e-
Learning, and the formation of virtual institutions are important; The widespread of
internet technologies and applications provides incredible opportunities for the
delivery of education and training, and with rapidly increasing internet usage e-
Learning has now become a portable and flexible new method for learners to gain
essential knowledge. Students having access to an e-Learning system can now
interact with instructional materials in various formats (text, pictures, sound, video
on demand, and so on anywhere and at any time, as long as they can log on to the
internet (Bhuasiri et al.2012). Likewise, given the functionality of message boards,
instant message exchanges and video conferencing, they can even interact with
teachers and classmates both individually and on a simultaneous basis. They can also
engage in self-paced learning, taking control over both the process and the content of
their learning (Zhang & Zhou, 2003). This situation requires higher learning
institutions to accept and implement the new technology of e-Learning and can be
applied to Tanzanian Higher learning Institutions.
Tanzania is located in the Eastern part of Africa and has two categories of
institutions of higher learning: accredited universities managed by the Tanzania
Commission for Universities (TCU), and technical colleges managed by National
Council for Technical Education (NACTE). e-Learning has penetrated most of these
institutions, although at low levels (Ndume et al., 2008). Institutions such as the
University of Dar Es Salaam (UDSM), Sokoine University of Agriculture,
Muhimbili University of Health and Allied Sciences, Open University of Tanzania,
Ardhi University and Mzumbe University use ICT-supported learning, but it is
generally yet to be fully adopted (Munguatosha et al.,2011).
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This study examined the roles of learner characteristics, e-Learning characteristics,
Instructor‘s characteristics and Institutional factors on e-Learning adoption; it builds
a model to support the adoption of e-Learning in African countries, based on a study
conducted in Tanzania.
1.1 Background
1.1.1 e-Learning adoption – A global perspective
e-Learning is now a major player in all areas of the educational system. Most
governments are addressing themselves to the issue of how to take advantage of new
technologies in education, and how to implement e-Learning. In the European Union,
the status of e-Learning has grown extremely in the past few years. It now forms an
important element of the practice of transnational institutional collaboration within
Europe (Hodgson, 2002). A major concern is the establishment of ‗best practice‘ in
the field of education, training and distance learning so as to ensure that citizens of
the European Union can play an active role in the knowledge economy. In Asia,
Japan has also changed the operational agenda of its national universities from
Government funded bodies to independent business entities, each competing for a
position within the higher education sector (Nguyen et al., 2005).
In a similar context, Australia has implemented the Learning and Teaching
Performance Fund. Access to this pool of Government funding is based on the
demonstration and achievement of teaching and learning performance measures
(such as student evaluations, employment and attrition rates, etc.) Therefore
competition in the market of Higher Education has pushed universities towards the
adoption of sophisticated organizational practices to ensure effectiveness.
Mac Keogh (2009) emphasize on new institutional models that requires changing
traditional functions and roles, as online education does not usually fit into the
existing university structure. The transition from on campus to online education
develops in new roles, either in the pedagogical or in the administration domains.
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Organizational factors, more than teachers and students attitudes or technological
features seem to mark the differences in the general perception about technology
mediated education getting successfully embedded in institutional new programs,
roles, procedures, culture and structures.
In recent years, pressures have emerged from policymakers and other stakeholders to
embed e-Learning technologies in ordinary higher education. The interest in
implementing e-Learning in higher education systems throughout the world has been
influenced by a number of pressures and drivers. According to Hammond (2003)
higher education institutions exist within political, cultural and social contexts which
shape policy and practice. Within this context the main drivers are national policies
and priorities with regard to economic and social development, beliefs and
expectations of the role of education in terms of supporting those priorities, and
developments in educational technologies which have the potential to enable the
system to achieve these objectives. These three drivers are interdependent, and
influence the adoption of learning technologies in the institutions through the role of
funding and support agencies (Hammond, 2003).
A number of countries have developed national e-Learning strategies for the higher
education sector which aim to meet needs for lifelong learning, up skilling, and
quality improvement. For example, Higher Education Funding Council of England
(HEFCE) has adopted a strategy to embed e-Learning in all higher education
institutions, ‗in a sustainable way, by 2010‘ (HEFCE, 2005); another driving factor
according to Mac Keogh (2009), is the pressure to adopt e-Learning which is seen in
the context of the pressure on European higher education systems to reform and
modernize in terms of curricula, teaching methods, expanded learning outcomes, new
types of students, qualifications frameworks, quality assurance, research and
innovation (CEC, 2003).
According to (Unwin et al., 2010), the adoption of various learning technologies in
developing countries, has shown a gain in recognition. For instance, a study
regarding the status of e-Learning in Africa from 25 African countries revealed that
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(49 per cent of the total sample) had used a learning management system (LMS) for
teaching in the previous 12 months. It is clear that most African universities have
established e-Learning systems in their institutions. Within this context, the
increasing availability of handheld and wireless devices prompts consideration of
their application and benefit in the curriculum and whether this is marginal activity
or ‗core business.‘ These handheld devices are utilized in the form of m-Learning as
can be seen in section 1.1.2.
1.1.2 Mobile learning (m-Learning)
A wireless device, such as a personal digital assistant (PDA), has the potential to
give instant satisfaction to students by allowing them to interact with the Internet
access, course contents, and retrieve information from anywhere at any time. Thus,
there has been a great change in education recently, especially in some of higher
education. With a mobile or handheld device, the relationship between the device
and its owner becomes one-to-one interaction. Despite the tremendous growth and
potential of the wireless devices and networks, mobile e-Learning or mobile learning
(m-learning) is still in its infancy and in an embryonic stage (Motiwalla, 2007).
Indeed, m-learning is a relatively new tool in the pedagogical store to support
students and teachers as they navigate the options available in the expanding world
of distance learning. M-learning is the learning accomplished with the use of small,
portable computing devices such as smart phones, PDAs and similar handheld
devices (McConatha & Praul, 2008).
With a mobile or handheld device, the relationship between the device and its owner
becomes one-to-one interaction. Mobile devices have the potential to change the way
students behave, the way students interact with each other and their attitude towards
learning (Homan & Wood, 2003). The key features of using mobile devices for m
learning are one-to-one interaction place and time independence, capability of
personalization, and extended reach. These features have a potential to attract more
and more learners, especially adult learners (Motiwalla, 2007).
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1.1.3 e-Learning implementation trends in Africa
The rapid development of each new technological advance brought new functionality
and hence a new way of supporting learning. Despite this high rate of adoption, the
actual level of usage is distinctly low across the continent. Literatures indicates that
the usage of e-Learning in Africa is still low; For example, a study of 25 African
countries revealed that about 46 per cent (out of 358 responses) of respondents used
LMSs for teaching and uploaded material less frequently than once a month, and
only 9 per cent claimed to do so on a daily basis (Unwin et al., 2010). A study of
Egyptian tourism higher education also showed that most universities had established
the required infrastructure for e-Learning. However, e-Learning was applied in only
a limited way in the universities surveyed due to inadequate numbers of qualified
Egyptian academics being available to participate efficiently in the e-Learning
process (Afifi, 2011).
The study conducted by Dadzie, (2009). Show that two-thirds (66.2 per cent) of
lecturers out of 74 lecturers from the University of Ghana did not have knowledge of
the e-Learning facility. And those who knew about it (33.8 per cent), only 10.8 per
cent knew how to access it, due to lack of awareness, skills and time (Dadzie, 2009).
The study finding show that in spite of improved investment in e-Learning systems,
actual usage of these technologies for teaching and learning is quite low in Africa
due many factors, such as lack of training, time and resistance to change towards e-
Learning issues. Limited ICT infrastructure and low internet bandwidth may have
prevented most African universities from high usage of e-Learning systems. The
level of internet usage in Africa is less (10.9 per cent) than the rest of the world (31.8
per cent). In Tanzania, usage is even less, amounting to 1.6 per cent (Internet World
Stats, 2011).
Therefore, the use of technologies to support teaching and learning varies across the
continent. According to (Mugwanya et al., 2011), the use of e learning technologies
to support learning and teaching activities is very low in Africa including Tanzania.
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The reasons behind this could be resistance to change, and lack of knowledge, skills
and awareness of the importance of e-Learning in teaching and learning practices.
Other factors could be lack of speedy and reliable internet connectivity, lack of e-
Learning policy, and lack of ICT facilities such as computers. This finding is also
similar to other studies in the developed world (Conole & Alevizou, 2010). Other
observations were made in Libya; most students and faculty had little or even no
experience in using ICTs, and those who were familiar with ICT generally used it as
a tool for entertainment and communication (Rhema & Miliszewska, 2010). One
must ask himself why this technology is not effectively used for learning/Teaching
purpose. The answers to this question require a research study in developed countries
like Tanzania.
1.2 e-Learning in Tanzania
The developing countries of Africa are characterized by limited access to ICT-
supported learning facilities, limited bandwidth, high ICT illiteracy levels, high
poverty levels, lack of or intermittent power supply and lack of appropriate ICT-
supported learning policies and sustainability plans (Farrell & Isaacs, 2007). Most of
the literature on e-Learning is from developed countries whose technological,
economical, social, political and cultural setup are quite different from those in
developing countries like Tanzania. As agued by (Bakari et al., 2010; Ndume et al.,
2008), the attendance of academic staff and students at e-Learning workshops has
not been encouraged, as noted by some respondents in the surveyed universities. As a
result, only few faculty members have been trained in the use of e-Learning systems
and services in their universities. Bakari et al. (2010) comment that, understanding
the human complexities, both of lecturers and learners can enhance the acceptance
and use of e-Learning systems and services in Tanzania.
By borrowing from e-Learning literature and undertaking research on e-Learning in
Tanzania, this study has developed a model for e-Learning adoption for Tanzanian
Higher Education Institutions known as Higher Education E-Learning Adoption
7
Model (HEELAM). Through this model, Students can be able to apply to join
universities and higher learning from anywhere in the country as no boundary is
experienced due to the use of ICT. Teachers and students can easily access teaching
and learning material easily respectively. In spite of these notable achievements,
there are still lacks of empirical findings on the extent to which Tanzanian
universities have integrated this e -Learning technology into their existing curricula.
1.2.1 Factors for e-Learning adoption
The curriculum is one of the challenges that face Tanzanian universities in deploying
ICT applications. The curriculum and pedagogical methods need to be revised and
developed to deploy ICT applications effectively, and they should be specifically
designed to fit the e-Learning setting because e-Learning is different from traditional
learning (Anderson & Gro¨nlund, 2009). This means that traditional pedagogy needs
to be adapted to pedagogy relating to a technology-based learning environment,
which promotes and facilitate constructivist, interactive, and collaborative learning
(Damoense, 2003). Other challenges are related to limited security and privacy which
are lack of a centralized system for storage of data, and inappropriate use of content.
It is thus clear that, issues regarding infrastructure and power distribution, funding,
human resources, awareness and attitudes towards e-Learning of both student and
Instructor, and curriculum development should be addressed for the effective
implementation of e-Learning activities in Tanzanian universities. Most faculty
members are reluctant to use ICT for teaching purposes. The literature shows that
resistance to change, more than infrastructure issues, is the most difficult part of
implementing a new technology like e-Learning (Njenga & Fourie, 2010).
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1.3 Statement of the problem
1.3.1 Gap in previous studies
Regardless of the increase in e-Learning adoption in organizations, the rate of
failures and abandonment continues to exist (Guri-Rosenblit, 2006) little is known
about why some users stop engaging e-Learning after their initial experience.
Research has found that the implementation of e-Learning in its various forms can be
costly to an organization due to the relatively low adoption rate among users. Indeed,
recent research has indicated that most e-Learning programs exhibit higher failure
rates when compared with traditional instructor-led courses (Zaharias &
Poylymenakou, 2009). These lead organizations to spend a lot of money investing in
e-Learning which is eventually not well utilized.
These being the focal points of this study, and given the increasing reliance and
availability of technology in the modern world, it is very important to understand the
factors that might lead to an increase in adoption of e-Learning in an organizational
context. Therefore, the study aimed to examine important factors which may increase
learners‘ intention to adopt and use more e-Learning in the future.
Up to this moment, there are no technology acceptance studies of e-Learning that
includes factors related to Students, Lecturers and Technology in the same model. As
a way to address these concerns, the researcher of this study is motivated to find out
ways through which a successful adoption of e learning can be undertaken in higher
learning institution in Tanzania. This study proposes a model (HEELAM), which
will explain adoption and use of e-Learning as governed by attitudes of students,
lecturers and institutional support. This includes assessment of the e-Learning factors
which are: Computer self-efficacy, Openness to change, Authenticity, Complexity,
Timely response, Self-efficacy and Organizational support toward e-Learning.
Others include ICT infrastructure, Institutional policy, management support and
training. The study finally proposes concrete ways to integrate online teaching tools
in face-to-face learning with the aim of getting students involved in the teaching-
learning process with the expectation to enhance student success.
9
1.4 General Research Objectives
The general objective of this research was to assess the key factors that determine the
adoption of e-Learning for higher learning institution in Tanzania.
1.5 Specific Research Objectives
As per the general research objective stated above, its translation leads to the
following specific objectives:
i. To assess the extent to which actual use of e-Learning has been achieved in
Higher learning institutions in Tanzania.
ii. To identify factors determining behavioral intention to adopt e-Learning in
Tanzania.
1.6 General Research Questions
In order to fulfill the above mentioned objectives the following questions arose:-
i) To what extent the e-Learning systems of Higher Learning Institutions‘ are
adopted and used?
ii) What are the factors determining the e-Learning system in Tanzania?
1.7 Scope and Limitation of the Study
The scope of this study was limited to the assessment and identification of factors
facing e-Learning implementation at the Open University of Tanzania, CBE-
Mwanza Campus, TIA Mwanza Campus, Mzumbe University-Mwanza centre and
SAUT. Here the assumption was that there are key issues/ factors affecting the
successful implementation of e-Learning management systems and that
implementation of e-Learning systems lead to improvement of learning -teaching
delivery to the students. Hence these factors both technical and non-technical do
affect the success of e-Learning implementation.
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The study was done only in Mwanza and at the selected universities and higher
learning institutions present in Mwanza region because of the convenience for the
researcher to easily collect data at a minimum cost while in Mwanza region, unlike
other regions where is far away from the researcher‘s residence, which could lead to
increase in cost of conducting the research study. The presence of the intended
respondents, time limit and financial problems, were the reasons why the selected
area was important and necessary area for this study.
1.8 Significance of the study
1.8.1 Importance of ICT in teaching and learning
There have been many studies on the importance of ICT in teaching and learning.
(Louw et al., 2008) argue that ICT holds much promise for use in curriculum
delivery. Thus, technology can effectively improve teaching and learning abilities,
hence increasing learners‘ performances. It is also believed that the use of ICTs in
education could promote ‗deep‘ learning and allow educators to respond better to the
different needs of different learners (Lau & Sim, 2008). E-Learning is being
recognized as having the power to transform the performance, knowledge and skills
landscape (Louw et al., 2008). Some of the important potential contributions of e-
Learning programs in such educational systems include:
Addresses the shortage of teachers, especially science and other specialty
teachers. It can do this by providing high quality teaching materials, such as
videos, interactive software or information from a ―cloud‖ on the Internet or a
local computer.
Addresses the shortage of learning material such as textbooks for students.
The material could be made available on hand-held devices such as e-readers
or mobile phones. Interactive features such as quizzes or games could
improve the level of learning and understanding.
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Despite vast number of researches done on e-Learning in the world, there is a
limited number of research work done for the case of Tanzania. There is no
specific research work done in relation to e-Learning adoption in Tanzanian
higher learning environment, hence this research provides an analytical
foundation for all stakeholders concerned as follows:
i. To practitioners:-This research will provide a foundation work upon which
implementers of e-Learning integration in the education system can consider during
actual implementation to ensure success in the process. Implementers in this case
include IT professionals, organization planners such as managers as well as the users
of such systems, which at large are the citizens of Tanzania.
ii To academicians:-This research work will add to the general understanding of e-
Learning implementation in the context of the Tanzanian environment. Also the
research work will provide useful knowledge to academicians for development of
models for e-Learning implementation for Tanzanian Universities. Moreover, the
results of the research are expected to open up new ideas for further research to be
done to enhance the understanding of e-Learning practices in the Tanzanian context.
iii. To Policy Makers:- The research results are expected to shed some light for
the improvement of the current National ICT policy so that it can take on
broad policy statements to guide and support the move towards e-Learning
based on the analytical fact.
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CHAPTER TWO
2.0 Literature review
2. 1 Introduction
This chapter provides a thorough definition of terms and concepts used in this
research. It provides a literature base, both theoretical and empirical perspectives
upon which the conceptual framework to guide the study was developed.
2. 2 e-Learning – An overview
E-Learning sometimes known as ICT supported learning, represents an alternative
way of teaching and learning into knowledge-economy environment, and the number
of organizations using these learning strategies for employee development has
progressively increased (Hill & Wouters, 2010).While definitions of e-Learning
broadly cover computer technology, there exist a number of approaches. For
instance, Fry (2001), described e-Learning as the ―delivery of training and education
via networked interactivity and a range of other knowledge collection and
distribution technologies.‖ (p. 234). Other researchers have defined e-Learning as
distance education that uses computer-based technologies, information
communication technologies (ICTs), and learning management systems
(Govindasamy, 2002). Although there is a range of e-Learning definitions, the
common elements are ―instructional content or learning experiences delivered or
enabled by electronic technology‖ (Servage, 2005, p. 306).
When considering ICT-supported learning, one should be specific about the sector
for which it is being defined. Generally, ICT-supported learning is regarded as a new
form of learning which utilizes the internet and other ICTs for content access and
delivery of a wide range of digital materials, communication, interaction and
collaboration across distant communities (Prensky, 2010). In this research study e-
Learning is defined as learning facilitated and supported through the utilization of
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information and communication technologies (ICTs). It includes use of ICT based
tools (e.g. Internet, computer, telephone, radio, video, and others) and content
created with technology (e.g. animations) to support teaching and learning activities
2.2.1 e-Learning technologies
There are several e-Learning technologies in use that dictate how actual learning will
take place depending on the environment in which they are implemented. These
technologies include Television (TV), CD ROMs, Learning Management Systems
(LMS), CMS, and virtual worlds as well as collaborative technologies, but the most
used e-Learning tools are Blackboard vista and Moodle (Mazman & Usluel, 2009).
Content Management Systems (CMS) such as Moodle are developed to facilitate the
collaborative creation of content, organization, control and to manage the publication
of documents in a centralized environment.
2.2.2 e-Learning delivery approaches
Various approaches can be used to make learning objects available over the web. The
simplest approach is to generate web pages containing these resources and make the
web pages available through a web site for the course. The other approach is to use a
full-fledged course management system such as a Learning Content Management
System (LCMS). Other approaches may include CD-ROM; print based material,
presentational slides etc. e-Learning materials can be delivered using different
modes; asynchronous or self study learning, synchronous leaning and blended
learning (Singh, 2003). So, e-Learning system can either operate in asynchronous or
synchronous mode.
2.2.2.1 Asynchronous Mode (self study learning)
Asynchronous or self study learning; consists of content that is available online at
any time that the student wants to access it (Singh, 2003). Is where communication,
collaboration and learning can occur in different time and different place, and users
can select when they wish to communicate. Based on the developed techniques of
14
networking, asynchronous learning is split up into on-line and off-line status (Lujara,
2008).
Off-line Learning
Computer-Aided Instruction (CAI) is a typical method of off-line learning. In
general, the content of CAI — text, graphs, pictures, audio and video are stored in a
CD-ROM. Once the contents have been stored, editing is not allowable. Hence, it is
suitable to construct the core courses that are well developed basic curriculum.
On-line Learning
The content of on-line learning is built by the hypermedia technique, which is stored
in the network computer server. Students can study or review the contents from the
web site at anytime. There are two types of data sources, the static type based on text,
graphs and pictures combined as the auxiliary parts of the resources to provide the
leaner a complete concept. The second is dynamic involve motion pictures,
associated texts, matched sounds etc. The static resources require less bandwidth
than the dynamic content; however, it lacks sense of reality that enables the learners
get a whole picture of the subject (Lujara, 2008). On the other hand, the latter type
enables the learners‘ to feel the sense of reality. Students would pay more attention
on the subjects due to the colourful and diversified environment; hence the outcome
is better than the former one (Fang et al., 2006).
2.2.2.2 Synchronous Mode
Synchronous learning; is generally occurring in real-time with highly interactive and
is led by the instructor (Singh, 2003). Allows people to interact with each other at the
same time in different places, synchronous e-Learning imitates a classroom, which
means classes take place in real-time and connect instructors and students via
streaming audio or video or through a conference room. Synchronous learning
requires the presence of both parties at the same time for the learning to take place.
Therefore, it is also referred to as live or real-time interaction (Harriman, 2005).
Discussion between students and instructor is ongoing in real time via the system
equipment. Instructor and students may not meet each other face-to-face. According
15
to Lujara (2008), the common source of content is distributed to learners at the same
time in different places, that avoiding repeating work of the lecture. The environment
is named Videoconference Classroom. The most important advantages of
synchronous learning are immediate feedbacks and more motivation and obligation
to be present and participate (Harriman, 2005).
2.2.2.3 Blended Learning
Blended learning also called hybrid learning, is the mixing and integration of
different learning delivery approaches including classroom and e-Learning to create
a single learning programme. This mode meets the needs of larger numbers of
students and teachers, and seems to be a key component of the more successful uses
of ICT (Smith, 2001). The term blended learning is used to describe a solution that
combines several different delivery methods. These can be a mix of various event-
based activities such as face to face classrooms, self-paced learning (asynchronous),
and synchronous.
The real situation for Higher learning Institutions in Tanzania is that there is very
limited use of e-Learning systems, and therefore, there is a need to look for better
ways of delivering the instruction to students in order to improve the
learning/Teaching process.
2.2.3 e-Learning in university teaching
The use of ICT in higher education makes it possible for universities to offer students
much more flexible access to learning resources. But when considering the use of an
e-Learning system, students may choose to receive instructional material and
education within classroom (face-to-face) settings, or to make use of educational
videos or educational CD-ROMs. e-Learning has been used very effectively in
university teaching for enhancing the traditional forms of teaching and
administration. Students on many courses in many universities now find they have
web access to the lecture notes and selected digital resources in support of their
study, they can be able to personalize web environments in which they can join
16
discussion forums with their class or group, and this new kind of access gives them
much greater flexibility of study. By participating in e-Learning, learners are actively
engaged in the learning process and experience flexible environments for
communication, global information sharing, personalized learning and independent
learning with respect to place and time (Mazman & Usluel, 2009). e-Learning
enables borderless learning, and its focus is toward learner-centered.
Therefore because of the rich functionality provided by e-Learning tools, Higher
Education Institutions need to ensure that how to use these tools effectively is clear
to both learners and educators.
2.3 Theoretical backgrounds
The e-Learning literature indicates that Learner characteristics, e-Learning
characteristics, Instructor characteristics and Institutional factors are dimensions
crucial for e-Learning adoption. Previous e-Learning studies applied various theories
to examine the determinants of e-Learning adoption and effectiveness. This research
studies identifies major theoretical perspectives related to e-Learning, namely social
Cognitive Theory, Theory of Reasoned Action (TRA), Technology Acceptance
Model, and Diffusion of Innovation theory.
2.3.1 Learning theories
Learning theories are concerned with the actual process of learning, not with the
value of what is being learned. The central ideology of learning theories is that
learning occurs inside a person (Koohang et al., 2005). There are basically three
main perspectives in learning theories that provide an understanding of a natural
learning process through which learners can construct knowledge within a particular
environment; these are constructivism, Cognitive theories and Behaviorism.
Constructivism: This type of learning facilitates critical thinking and problem
solving. The learner actively constructs or builds new ideas using previous
knowledge and experience attained. During the learning process, the teacher
17
takes on a facilitator role focusing on making corrections, fostering new
understandings, and creating social disclosure. The learners take on the
responsibility of learning by actively participating in the learning activities
placed at the centre of the learning process. This learning theory has guided many
educationists in providing education encouraging hands on for learners. To this
effect, Koohang and Harman (2005), confirm that in a constructivist
environment, learning situations represent the normal complexities of the real
world. As a result, multiple perspectives and representations that promote
cooperative and collaborative learning are encouraged.
Cognitive theories: This describes learning as involving the attainment of the
cognitive structures through which human beings process and store information
(Koohang and Harman (2005), They demonstrate how a student perceives,
processes, interprets, stores, and retrieves information and are mainly concerned
with the changes in a student‘s understanding that results from learning. The
student is involved in the learning process, so the teachers have to present
organized information in a way the student can relate to.
Behaviourism: Behaviorists define learning as an observable change in
behaviour. (Koohang et al., 2005) indicate that learning occurs as a result of
positive reinforcement leading to old patterns being abandoned as a result of
negative reinforcement. The learning activities carried out during teaching are
arranged contingencies of reinforcement under which learners construct
knowledge. Thus, learning theories explain the learning process through which
learners are able to acquire knowledge, but there is no single learning theory that
can fully explain all types of learning. Consequently, several theories coexist and
complement each other during a learning process. It should be kept in mind, that
the attainment of the learning concepts varies from one learner to another and the
learning methods dictate the level of knowledge to be attained.
Regardless of the reason for the investment decisions, much of the activity in e-
Learning takes place at the level of development of courses and their resources.
18
Higher learning institutions have to recognize that successful e-Learning takes place
within a complex system composed of many interrelated parts, where failure of only
one part of that system can cause the entire initiative to fail. e-Learning course
creation is complex and time-consuming because instructors must reevaluate their
courses and choose the most appropriate technical and pedagogical tools for e-
Learning applications related to the learning and teaching style in so doing e-
Learning adoption become easy.
2.3.2 Innovation adoption theories
Cognitive learning models were adopted in this study as the theoretical foundation to
explain behavioral intention in the technology context as they enable human beings
to influence their consumer environment through acknowledging that learning
involves processing a large amount of information and is not always a direct
response to external stimuli.
Cognitive learning models include the theory of reasoned action (TRA), the theory of
planned behavior (TPB) which does not apply in this study, the technology
acceptance model (TAM) and social cognitive theory.
2.3.2.1 Theory of a reasoned action (TRA)
Theory of a reasoned action (TRA) was originally proposed by Fishbein & Ajzen in
1975 to understand behavior and predict outcomes. The main assumption of TRA is
that a person takes into consideration the implications of his/her action before she/he
decides to actually engage or not in certain behaviour. It also posits that the main
determinant of a person's behavior is behavior intent. Ajzen & Fishbein (1975) point
out that a person's attitude is determined by his/her perception about the expected
consequences of performing the behavior and the assessment of those consequences,
and hence, if a person's intent is strong, then it is expected that the behavior will be
actually performed. Therefore, the primary concern is to identify the underlying
19
factors of the formation and change of behavioral intent. Based on TRA we can
measure intention to adopt e-Learning in the future.
2.3.2.2 Technology Acceptance Model (TAM)
Davis (1989) proposed a technology acceptance model (TAM) which is based on
TRA. The premise of TAM is that people behavioral intention to accept and actually
use a certain technology is determined by two constructs namely; perceived
usefulness and perceived ease of use. User's attitude and belief as proposed by TAM
is perceived to be an important factor which influences the use of new technology.
People who have positive attitudes toward information technology will have higher
acceptance of the use of e-Learning , compared to people who have negative
attitudes toward that technology.
Since e-Learning systems are a technological information system, their adoption
and/or diffusion should also be addressed from an information systems point of view
(Abbad et al., 2009). The literature (for example, Abbad et al., 2009; Lee, 2010)
indicates that the Technological Acceptance Model (TAM) has been widely used to
support the adoption and utilization of information systems. Similarly, the adoption
of e-Learning systems can be understood by application of the TAM.
According to Mazman and Usluel (2009) and Davis et al. (1989), the main idea
behind the TAM is that, people tend to accept or reject technology to the extent they
believe it is helpful in performing their job better (i.e. perceived usefulness) and if a
user believes that learning to use that technology in place is free of effort (i.e. ease of
use).
The reason TAM is chosen for this research is because TAM has been tested
empirically and supported through validations, applications, and replications (Lee,
2010; Venkatesh, 2000). TAM is one of the most powerful, robust and economical
model for predicting user acceptance especially in IS context. According to
Venkatesh (2000), ―the cost-cutting of TAM combined with its predictive power
makes it easy to apply to different situations‖. Perceived usefulness is defined by
20
Davis as ―the degree to which a person believes that using a particular system would
enhance his or her job performance‖ (Davis, 1989). Perceived ease of use is defined
as ―the degree to which a person believes that using a particular system would be free
from effort‖ (p. 320).
2.3.2.1 The need for additional features on TAM
Although a large body of research supports the TAM as a good model to explain the
acceptance of Information system/ Information Technology (IS/IT), Many studies
extend TAM with additional constructs (Venkatesh and Bala, 2008; Venkatesh,
2000) examined citizen‘s adoption of e-government in different countries by
integrating TAM with trust, perceived risk, perceived behavior control, and culture.
Also, Ilias et al. (2009) extended TAM with perceived credibility, information
system quality, as well as information quality and investigated the differences in the
demographics of taxpayers in Malaysia. Lee (2010) integrated TAM with the
expectation-confirmation model, theory of planned behavior, and flow experience to
investigate e-Learning in Taiwan.
TAM is questionable whether the model is applicable to analyze every IS/IT
adoption and implementation. To explain the adoption of e-Learning, the TAM is a
good base model, but it may not be able to fully capture all the important factors that
influence the adoption of an e-Learning for the following reasons. First, people may
choose to adopt e-Learning not merely because of technology. An e-Learning is an
integration of IT, innovation and pedagogy. Second, an e-Learning cannot exist
without people, so social factors may play an important role in the adoption of e-
Learning. Third, the special characteristics of e-Learning make them different from
any other information system.
Therefore, in trying to understand user adoption of e-Learning, the theory of
diffusion of innovation, which is also widely accepted in the IS field is used. A new
Information system/ Information Technology (IS/IT), such as e -Learning can be
regarded as an innovation, and the adoption of the innovation by people typically
takes time.
21
Figure 2-1: Technology acceptance Model
Source: Davis, F. (1989) Perceived Usefulness, Ease of Use, and User Acceptance of
Information Technology, MIS Quarterly, 13 (3), 319- 339
2.3.2.2 Diffusion of innovation (DOI) theory
DOI was first formalized by Rogers (1995). He defined diffusion as the process by
which an innovation is communicated through certain channels over time among the
members of a social system. It has been shown that DOI is suitable for investigating
IT diffusion for individual use (Attewell, 1992). Venkatesh and Bala (2008) suggest
a more consistent process arguing that ―it may be preferable to consider the process
as a whole rather than a series of discrete stages, with innovation being viewed as a
complex, iterative, and continuous process.
Among the various innovation diffusions, this study focuses specifically on the e-
Learning, and based on the above study of innovation diffusion, with the concept of
innovation being viewed as a complex, iterative, and continuous process‖. it is
reasonable to say that, implementing or adopting an e-Learning environment
requires many organizational changes within institutions including staff
organizational integration, flexible delivery to students (on/off campus), and new
concepts of teaching (Sife et al.,2007 ). This theory therefore applies to the
successful adoption of e learning by considering the organization as the whole and
not individual adoption.
22
Since the early applications of DOI to IS research, the theory has been applied and
adapted in numerous ways, such as for internet use and e-business adoption (Rogers,
1995), therefore e-Learning can be regarded as an innovative use of the internet
because they represent a new concept and a new phenomenon.
Both TAM and DOI have some obvious similarities in examining IS/IT adoption.
Technical complexity relates to the ease of use, while relative advantage relates to
perceived usefulness. Complexity and relative advantage are derived from DOI,
while perceive ease of use and perceived usefulness are from TAM. Because of the
common factors of both theories, some research has integrated these two theories in
order to investigate IT/IS adoption (Arbaugh & Duray, 2002). When using these two
theories to explain the adoption of e-Learning , there is still something missing since
e-Learning cannot only be regarded as a technology or an innovation because the e-
Learning itself has special features such as pedagogical issues, learning and
Teaching styles.
In order to fully capture the factors influencing the adoption of e-Learning, Social
Cognitive Theory should also be considered.
2.3.2.3 Social Cognitive Theory (SCT)
The theory presumes that higher outcome expectations and self-efficacy determine
individual decisions, actions, level of effort to invest, and strategies to use in any
situation (Yi & Hwang, 2003). Outcome expectations and self-efficacy are key
elements of SCT that influence human behavior. Self-efficacy refers an individual‘s
belief in their own abilities (Bandura, 1986) while outcome expectations refer to an
individual‘s belief that he/she will receive a desired outcome after accomplishing a
task. Self-efficacy is important as many studies found that it significantly affects
outcome expectations, behavior intention to use, and actual technology usage
(Bhuasiri et al., 2012) it is built upon the foundations of individual and group
psychological behavior, and is also referred to as social learning theory. Social
cognitive theory in contrast to TAM acknowledges the complex nature of behavior
23
intention which is influenced by the reciprocal interaction between the environment
in which an individual operates and their behavior (Bandura, 1986).
Thus, in this study Social Cognitive Theory is adopted as it involves an analysis of
behavioral intention as compared to the other cognitive learning models. Social
cognitive theory is a widely accepted model of individual behavior as it examines the
reasons why individuals adopt certain behaviors (Bandura, 1986). It proposes that
behavior is evaluated through an individual‘s expectation of the outcome of their
behavior, expectation of their direct experience and can be mediated through the
observations of others. Thus, the major premise of social cognitive theory is that
individuals can influence their actions (Bandura, 1986).
Social cognitive theory has been utilized in a number of disciplines due to its
dynamic nature as it considers human behavior to constantly change. It has been
applied in business through the analysis of organizational management (Bandura,
1997), and technological innovation adoption (Compeau et al., 1999).
The rapid changing technological environment has meant that social cognitive theory
is a useful theoretical framework to understand human behavior. Social cognitive
theory emphasizes that the adoption process of technology involves encouraging
individuals to ensure that they will have the necessary skills and confidence to use a
new or existing technology (Compeau et al., 1999), because, the range and scale of
possible applications of new technologies in higher education is almost beyond
imagining because, while we try to cope with what is possible now, another
technological application is becoming available that will extend those possibilities
even further.
The model is consistent with the foundations of social cognitive theory in that it
explains about the complex nature of behavior intention. Likewise, the Theory of
reasoned action indicates that if a person's intent is strong, then it is expected that the
behavior of adopting e-Learning will actually be performed. With TAM, it is
obviously that perceived usefulness and perceived ease of use influence the usage of
e-Learning. And with diffusion of innovation theory (DOI), we can say that
24
complexity of e-Learning can negatively affect the adoption and finally usage of e-
Learning systems.
2.4 Empirical review
2.4.1 Integration of e-Learning in Africa including Tanzanian higher education.
The adoption of various learning technologies in developing countries, and Africa in
particular, has indicated a gain in reputation. For instance, a study regarding the
status of e-Learning in Africa based on 358 responses from 25 African countries
revealed that 174 respondents (49 per cent of the total sample) had used a learning
management system (LMS) for teaching in the previous 12 months, and 185
respondents (52 per cent) had used for learning (Unwin et al., 2010). Similarly, a
previous study of 54 tertiary institutions from 27 African countries revealed that only
47 per cent of respondents had installed e-Learning applications. It is clear that most
African universities have established e-Learning systems in their institutions.
According to Samuel et al., (2004), who did a study of fourth-year medical students
with 92 attending Muhimbili University of Health and Allied Sciences in Tanzania
also showed that most students had the highest levels of competence in e-mail,
internet and file management. The main reasons for using a computer were to
communicate by e-mail (75 per cent), internet navigation (33 per cent), learning
purposes (27 per cent), and to prepare reports (22 per cent) .This fact shows that
African universities should take a lead in using e-Learning to enhance learning and
teaching activities.
A study of Egyptian tourism higher education also showed that most universities had
established the required infrastructure for e-Learning. However, e-Learning was
applied in only a limited way in the universities surveyed due to inadequate numbers
of qualified Egyptian academics being available to participate efficiently in the e-
Learning process (Afifi, 2011).
25
Another study of 74 lecturers from the University of Ghana showed that two-thirds
(66.2 per cent) of lecturers did not have knowledge of the e-Learning facility
(Dadzie, 2009).
2.4.2 e-Learning factors
The rapid growth of e-Learning courses at academic institutions has brought about a
big change for students and tutors. Students may demonstrate their learning efforts
via different types of technology such as text, video or audio devices. Instructors
often need to restructure their courses to successfully incorporate e-Learning (Pirani,
2004). These activities represent challenges that all groups of users must overcome to
succeed in e-Learning.
2.4.3. Students Factors
During the implementation of e-Learning activities, students often encounter several
problems. Students need the necessary hardware and skills to progress access online
information appropriately. Some students may lack experience and confidence in
using technology. Not all students have the required skills to participate and succeed
in e-Learning. Lwoga (2012), declare that a student‘s technical limitations including
hardware and bandwidth issues must be considered by instructors when designing
online courses. Some instructors might add complex web pages or multimedia
components to their courses, which require proper network access to be viewed.
2.4.4 Instructors factors
One of the biggest challenges for instructors is the amount of time needed to deal
with e-Learning requirements (Smith & Taveras, 2005). Instructors need to develop
and restructure their courses in a way that suits online requirements. These activities
often require more time and increase workload. Moreover, there is often an
expectation that tutors will respond to their student‘s comments as soon as possible.
Consequently, it is important that appropriate procedures are implemented so that
realistic expectations are set so that student receives a positive learning experience.
26
2.4. 5 Institutional factors
Adopting e-Learning in Higher education institutions raises many financial and
strategic challenges (Levine & Sun, 2002). Financial problems push institutions to
find adequate resources to develop and maintain proper equipment, provide static
technical support, fund training courses and hire support staff. Many institutions
underestimate the costs associated with designing and administrating online courses.
Institutions need to urgently convince academic staff to engage with and accept the
use of technology in their teaching.
Generally the study findings show that the actual usage of e-Learning for teaching
and learning is quite low in Africa; For instance, the level of internet usage in Africa
is less (10.9 per cent) than the rest of the world (31.8 per cent). In Tanzania, usage is
1.6 per cent (Internet World Stats, 2011). However, the situation is different in South
Africa, where the use of e-Learning technologies for teaching and learning is quite
high. A study in Western Cape University showed that most students (98 per cent)
and lecturers (97 per cent) used computers for teaching or learning (Brown et al.,
2007). In spite of the high use of ICTs for teaching, the use of ICTs for this activity
was lower in frequency compared to other activities.
2.4.6 Intention to adopt e-Learning
This is the decision to use a system before you actually do it and it is predicted to
happen in future (Hassanzadeh et al. 2012).
2.4.7 e-Learning actual use
The use of e-Learning technologies to support learning and teaching activities is very
low in Africa (Mugwanya et al., 2011). The reasons behind this could be resistance
to change, and lack of knowledge, skills and awareness of the importance of e-
Learning in teaching and learning practices. Other factors could be lack of speedy
and reliable internet connectivity, lack of e-Learning policy, and lack of ICT
facilities such as computers. According to Njenga & Fourie (2010) who identified
27
factors for poor usage of e-Learning in Tanzania include; Low awareness of e-
Learning issues and most faculty members are reluctant to use ICT for teaching
purposes. Understanding these barriers is important for effective adoption and use of
e-Learning in Tanzanian universities and higher learning institutions.
2.5 Conceptual framework and research model
Based on the literature review, a conceptual model is presented (Figure 2-2). The
model highlights the factors that influence the Lecturers and students‘ adoption of an
e-Learning in Higher learning education .These factors are divided into four main
categories: Learner characteristics, characteristics of the e-Learning, instructors‘
characteristics and Institutional factors.
Figure 2-2: Higher Education E-Learning adoption Model (HEELAM)
Source: Researcher‘s synthesis from literatures
Instructors’ characteristic
cccccccccccharacteristics Timely response (H5)
· Teaching style
Institutional factors
· Organizational support (H6)
· ICT Infrastructure (H7)
· Institutional policy (H8)
.Training (H9)
. Management support (H10)
E– Learning characteristics
· Authenticity (H3)
· Complexity (H4)
Intention to
adopt e-
Learning
Learner characteristics
· Self efficacy (H1)
· Openness to change (H2)
Actual
use
28
2.5.1 Learner characteristics
Individual characteristics of the learner are a key area of research regarding
successful e-Learning implementation. Indeed, several studies have linked various
learner characteristics with e-Learning satisfaction or dissatisfaction (e.g. Piccoli et
al., 2001; Sun et al., 2008). Learner characteristic involve two factors; these are self
efficacy and openness to change.
2.5.1.1Self efficacy
The concept of self-efficacy is derived from Bandura, (1986) social learning theory
which explains that efficacy expectations can affect intrinsic motivation for
performing a task. In an e-Learning context, confidence in one‘s ability to complete a
task using technology is defined as technological efficacy (Sawang et al., 2013).
Efficacy also plays a major role in adoptive behavior. For instance, computer
efficacy has been found to be a significant predictor of adoption of technologies such
as the internet and web-based information systems (Yi & Hwang, 2003). Self-
efficacy and technological self-efficacy in particular, are important factors in
determining which employees will effectively adopt a technology (Bandura, 1997).
According to self-efficacy theory, individuals evaluate their ability to cope with a
new challenge (i.e. e-Learning) and, based on this judgment, individuals initiate and
continue with behavioral strategies to manage the challenge (i.e. e-Learning
adoption). Hence, the following hypotheses were put forward:
H1a: Higher levels of self efficacy are positively related to intention to adopt
e-Learning.
H1b: Higher levels of self efficacy are positively related to actual use of e-
Learning.
2.5.1.2 Openness to change
Another individual-level learner characteristic that can be related to higher levels of
adoption of e-Learning is openness to change (i.e. being open to new ways of doing
things and experiences). Openness to change has been demonstrated to significantly
29
influence adoption behavior. For instance, Baylor and Ritchie (2002) found that
individuals who scored highly on openness to change were also more willing to try
new ideas in the work environment as well as in their personal life. As agued by Al-
Ahmad, (2010), that faculty staff prefers to use pen and paper and be in front of their
students. So when new technology is introduced in higher learning education to be
used for learning/teaching process, administrators/ management should advice their
employee to be transparent on their likes and dislikes to avoid rejection of the
technology in this case (e-Learning ). So it was postulated that:
H2a: Openness to change will be related to higher levels of intention to adopt
e-Learning in the future
H2b: Openness to change will be related to higher levels of actual use of e-
Learning
2.5.2 E-Learning characteristics
Another major factor that can be linked to successful e-Learning implementation
relates to the characteristics of e-Learning itself. Two key aspects of e-Learning
characteristics involve the authenticity and the complexity of the e-Learning.
2.5.2.1Authenticity
The term authentic activities are defined as tasks that are relevant and useful to the
real world, and provide learners with a scenario to identify the questions and
activities that are logically related to the scenario (Sawang et al., 2013). Authentic
activities in e-Learning have been shown to have many benefits for learners. One
such outcome is satisfaction (Meyers & Nulty, 2009). They also suggested that
learners were more satisfied with their online course when the problems were
presented in a relevant and realistic context that resulted in the gaining of new
knowledge that helped them to solve problems in their professional lives. Hence,
authentic learning within e-Learning design can also be linked to adoption of e-
Learning. For instance, employees such as lecturers and tutors may be more
30
motivated to use e-Learning due to the authentic activities which they can apply in
their work situation.
This link between authenticity and adoption of e-Learning is supported by the
Diffusion of Innovation (DOI) theory (Rogers, 1995). For example, DOI theory
states that one of the key factors that influence individuals to adopt innovation (such
as e-Learning in the present context) is compatibility – the extent to which an
innovation can be assimilated into an individual‘s life. If learners have negative
experiences with e-Learning (e.g. the content is not related to their real life or
working situation), they may not want to adopt further e-Learning as a part of their
learning and development. Therefore, it was hypothesized that:
H3a: Authenticity will be positively related to intention to adopt e-Learning
in the future
H3b: Authenticity will be positively related to actual use of e-Learning
2.5.2.2 Complexity
A second e-Learning characteristic that is important to implementation success is
complexity. ICT diffusion research frequently investigated the external variables
related to the technology itself such as compatibility, relative advantage, complexity,
trialability, and observability (Rogers, 1995). In this research complexity is chosen
because users are regarded to perceive e-Learning Technology as complex and
difficult to learn. For instance, e-Learning that is perceived as relatively difficult to
use can lead to learners‘ disengagement and dissatisfaction (Davis, 1989).
The broad body of research relating to innovation diffusion supports the close
relationship between complexity and ease of use, and if one of these factors was
found to be significant, the other would also be significant (Rogers, 1995).
Therefore, e-Learning that requires a high level of learners‘ effort will negatively
impact on e-Learning satisfaction. A review of literature also supports the notion that
complexity of use of an e-Learning system will relate to its adoption. Again, drawing
on DOI theory (Rogers, 1995), if an innovation (or e-Learning system in this case) is
31
too difficult to use or takes too much time to use; individuals will be less likely to
adopt that innovation. Therefore, e-Learning that is complex may receive negative
response from users; Based on DOI, relative advantage is aligned with perceived
usefulness. This indicator is integrated into perceived usefulness. Complexity is
aligned (in the opposite direction) with perceived ease of use. The following
hypotheses were put forward:
H4a: Complexity will have a negative effect on intention to adopt e-Learning.
H4b: Complexity will have a negative effect on actual use of e-Learning.
2.5.3 Instructors’ characteristics
Instructors‘ characteristics also play an important role in the perception of the
effectiveness of learning management systems (Selim, 2007), States that both
technology as well as the implementation of technology impacts educational learning
outcomes. Attitude toward technology, teaching styles, and technology control also
influence learning outcomes. Previous studies found that an instructor‘s control of
technology along with providing enough time to interact with students impacts
learning outcomes (Arbaugh, 2002). Relevant instructor characteristics include
timely response, self-efficacy, technology control, focus on interaction, and attitude
toward e-Learning, distributive fairness, procedural fairness, and interaction fairness
(Arbaugh, 2002; Sun et al., 2008; Bhuasiri et al.,2012). So, it was hypothesized that:
H5a: instructors’ timely response toward e-Learning, will positively influence
students’ intention to adopt e-learning
H5b: instructors’ timely response toward e-Learning will positively influence
students’ perceived actual usage of e-Learning.
2.5.4 Institutional factors
These include five factors which are: organizational support, ICT infrastructure,
Institutional policy, Training and Management support.
32
2.5.4.1 Organizational support
Organizational support refers to the degree to which an individual believes that an
organizational infrastructure supports the use of e-Learning (Thompson et al., 1991).
In the education sector in particular, successful implementation of e-Learning
requires institutional support (Selim, 2007). This support is not limited to the
provision of an e-Learning platform, technical assistance, and troubleshooting but
also includes information availability.
The issue of organizational support has also been highlighted in the technology
adoption literature (Agarwal & Karahanna, 2000), sufficient support helps
individuals become comfortable with systems and software which then leads to
learners‘ satisfaction with e-Learning and finally adopt the technology.
The support in this regard include: student support, teachers support, technical
assistance support and pedagogical support. Hence the following hypotheses were
put forward:
H6a: Organizational support for e-Learning will be related to higher levels
of intention to adopt e-Learning in the future.
H6b: Organizational support for e-Learning will be related to higher levels
of actual use of e-Learning
2.5.4.2 ICT Infrastructure
Appropriate infrastructure for ICT development, (i.e. internet, extranet, intranet and
LAN networks) is considered one of the biggest challenges in the implementation of
e-Learning in higher education institutions, particularly in developing countries
(Fares, 2007). He argues that an e-Learning environment must provide students and
teachers with a high degree of reliability and accessibility. There is a considerable
technological infrastructure difficulty, which limit developments (Lwoga, 2012),
Technological obstacles in an e-Learning environment often occur in one of three
basic components, namely hardware, software and bandwidth capacity. This strongly
affects the process of e-Learning adoption. Higher education institutions need to
33
provide wireless and wired networks with high connectivity ―bandwidth‖ to avoid
higher education e-Learning initiatives being negatively affected (Kunaefi, 2006).
Therefore, higher education institutions should invest in the right ICT infrastructure
that allows students and teachers to easily access the ICT hardware, using friendly
software and provide fixed technical support. Hypotheses were put forward as:
H7a: There is a positive relationship between Instructor’s intention to adopt
e-Learning and ICT infrastructure.
H7b: There is a positive relationship between Instructor’s usage of e-
Learning and ICT infrastructure.
2.5.4.3 Institutional policy
Generally, the literature shows that the adoption and implementation of e-Learning in
developed countries is also affected by lack of institutional policy and strategies. The
use of these technologies is mainly driven by individual efforts rather that
institutional policies and strategies, which limits the wide utilization of these
technologies to support learning and teaching in higher learning institution, (Lwoga,
2012). So it seems unavoidable that, starting from the basis of the motivations and
values of individuals, we need supportive institutional and national policies that
encourage them in the desired directions, institutional policies and strategies need to
think about creative ways to motivate staff, (Rosenberg, 2006). It is thus important to
assess the extent to which these technologies are deployed in Tanzanian public and
private universities for effective and efficient teaching and learning in higher
learning institution. Hence it was postulated that:
H8a. Lack of institutional policies is related to poor users’ intention to adopt
e-Learning in the future.
H8b. Lack of institutional policies is related to users’ poor usage of e-
learning
2.5.4.4 Training in e-Learning Techniques
According to Volery (2000), Training transfer generally refers to the use of trained
knowledge and skill back on the job. Literature indicate that the success of e-
34
Learning methods in higher education can only be measured according to the
effectiveness of delivery, training staff may be regarded as a major challenge in the
adoption of e-Learning initiatives. It is acknowledged that some academics working
in higher education are reluctant in accepting aspects of technology in their teaching
and learning, (Charlesworth, 2002). The evidence suggests that staff training is a
central concern for universities implementing distance learning methods. Shapiro
(2000) argues that, inadequately trained lecturers using e-Learning in educational
environments can become an obstacle in a finely balanced learning process and can
lead to problems in application use and in the perception of students. Therefore it
was hypothesized that:
H9a: There is a positive relationship between training and user’s attitude on
intention to adopt e-Learning.
H9b: There is a positive relationship between training and users’ attitude on
actual use of e-Learning.
2.5.4.5 Management support
Management support is defined as the extent to which a person "believes that
organizational and technical resources exist to support the use of the system"
(Venkatesh et al., 2003). Venkatesh & Bala (2008) demonstrate that when users hold
a strong believe with regard to the availability of organization resources, technical
and managerial support, then, that will facilitate the adoption of technology in
question. it is expected that in the e-Learning environment, educators who believe
that they will have a management support with regard to the implementation of e-
Learning system, which requires changes in university structures and educators roles,
will have a positive effect on the adoption of e-Learning system, and hence, the
following hypotheses were formulated:
H10a: There is a positive relationship between management support and
intention to adopt e-Learning system.
35
H10b: There is a positive relationship between management support and
actual use of e-Learning system.
2.6 Hypotheses Summary
2.6.1 Learner characteristics
H1a: Higher levels of self efficacy are positively related to intention to adopt
H1b: Higher levels of self efficacy are positively related to actual use of e-
Learning.
H2a: openness to change will be related to higher levels of intention to adopt
e-Learning in the future
H2b: openness to change will be related to higher levels of actual use of e-
Learning
2.6.2 e-Learning characteristics
H3a: Authenticity will be positively related to intention to adopt e-Learning
in the future
H3b: Authenticity will be positively related to actual use of e-Learning
H4a: Complexity will have a negative effect on intention to adopt e-Learning.
H4b: Complexity will have a negative effect on actual use of e-Learning.
2.6.3 Instructor characteristics
H5a: instructors‘ timely response toward e-Learning, will positively influence
students‘ intention to adopt e-learning
H5b: instructors‘ timely response toward e-Learning will positively influence
students‘ perceived actual usage of e-Learning.
2.6.4 Institutional factors
H6a: Organizational support for e-Learning will be related to higher levels of
intention to adopt e-Learning in the future.
36
H6b. Organizational support for e-Learning will be related to higher levels of
actual use of e-Learning
H7a: There is a positive relationship between Instructor‘s intention to adopt
e-Learning and ICT infrastructure.
H7b: There is a positive relationship between Instructor‘s usage of e-
Learning and ICT infrastructure.
H8a: Lack of institutional policies is related to poor users‘ intention to adopt
e-Learning in the future.
H8b: Lack of institutional policies is related to users‘ poor usage of e-
learning
H9a: There is a positive relationship between training and students attitude on
intention to adopt e-Learning.
H9b: There is a positive relationship between training and students attitude on
actual use of e-Learning.
H10a: There is a positive relationship between management support and
intention to adopt e-Learning system.
H10b: There is a positive relationship between management support and
actual use of e-Learning system.
37
CHAPTER THREE
3.0Research Methodology
3.1 Type of the study
The nature and purpose of the study required multi-method research design, which
combined both survey research and descriptive research design. The descriptive
research design was considered due to its ability of describing the characteristics of a
particular individual or of a group (Kothari, 2004), with the aim of seeking detail of
individual factor and organizational factors of e-Learning adoption. Furthermore a
survey study research design was chosen due to its ability to make comparison of
behavioral or attitudinal groups. It could also provide results that can allow
generalizations about large population on the basis of studies of representative
sample easily. In this study, a survey was conducted using a questionnaire to collect
the data.
3.2 Study Area
This study was conducted at the selected branches of higher learning institutions
present in Mwanza region, these include: Open University of Tanzania, SAUT, CBE,
Mzumbe University and TIA; They all have branches in Mwanza city. There were
several reasons for selecting Mwanza city. First, most of the Universities and Higher
learning Institutions in Tanzania have their branches in Mwanza. It is also one of the
fastest growing cities in Tanzania and Africa in general; therefore most of the ICT
advancements and e-Learning centres in future are expected to be concentrated in
Mwanza. Hence the Mwanza city offered a good study area for exploring the various
challenges that implementers of e-Learning have to address so that various factors
that hindered the adoption of e-Learning can now are minimized.
38
3.3 Study of sampling frame
In this study, the sampling frame consisted of: (1) students as main user of e-
Learning (2) instructors are other main e-Learning system users and).
3).administrators that have authority in the Universities as well as IT experts that
have knowledge and experience in the Technology. In the first and second category
which is the main target of the study, the total volume of the sample was 204 users
which among them, 191 persons were students, and 13 were teachers as indicated in
table 3-1 below. In the third category, 02 experts in the field of e-Learning in
Tanzania were identified and questionnaire given to them, and 04 were given to
administrators. According to the study objectives, students of higher learning
institutions, tutors, Lecturers were chosen as the main source of information for the
study. The undergraduate Students from SAUT and OUT were only third year, with
the focus that they had enough experience with the university learning system. The
sampling frame covered all the five selected branches of higher learning institutions
in Mwanza region.
Table 3-1: Sampling frame and Sample size.
S/NO University/Institution Category
Teachers students Administrators/Expert
Sample
frame
Sample
size
Sample
frame
Sample
size
Sample
frame
Sample
size
1 CBE 16 2 1895 46 05 0
2 SAUT 32 6 2564 60 12 04
3 TIA 04 3 381 45 03 0
4 OUT 5 2 450 46 04 02
5 MZUMBE (MU) 4 0 101 34 02 0
TOTAL 61 13 4941 191 26 06
Source: Author‘s construction from secondary data
3.4 Units of analysis
The unit of analysis was the major entity that was being analyzed in the study. From
the Theory of reasoned action (TRA) and Technology acceptance Model (TAM)
point of view, as indicated in chapter two, that, peoples‘ behavioral intention to
39
accept and then use e-Learning system in Higher learning institution is the basis for
Students and Lecturers to adopt and use e-Learning system in their respective
Universities (Hassanzadeh et al., 2012). Hence the main unit in this case was the
individual Students and teachers since the researcher had the value recorded for each
student and each teacher as respondents. And, since the data that went into the
analysis was the value obtained from the respondents, the unit of analysis was
actually the students, teachers, administrators and IT expert.
3.5 Variables and their Measurements
3.5.1 Dependent Variable
The dependent variable is what is affected by the independent variable (Kothari,
2004) it is the variable which you observe and measure to determine the effect of the
independent variable. Two dependent variables were involved in this study; these
are:
Intention to adopt e-Learning in the future.
Actual use of e-Learning.
3.5.2 Independent variable
The independent variable is the major variable which you hope to investigate. It is
the variable which is selected, manipulated, and measured (its effect) by the
researcher. (Kothari, 2004), the concern of independent variable is with their direct
relationship to the dependent variable these were defined for each factor as shown in
table 3-2 below.
Learner characteristic: included factor are Self efficacy and openness to change
E-Learning characteristic with two factors : included factors are Authenticity and
complexity
Instructor characteristic: with one factor; instructor timely response.
Organizational factors. Included factors are Organizational support, ICT
Infrastructure, Institutional policy and Training
40
Table 3-2: Operational definition of research model
Dimension Factor Operational definitions Learners‘
characteristics Computer self-
efficacy
Openness to
change
One‘s perceptions of his or her ability to use
computer to complete a specific tasks
being open to new ways of doing things and
experiences Instructors‘
characteristics Timely response
Self-efficacy
Whether students perceive that instructors
responded promptly to their problems
One‘s belief about the ability to perform certain
tasks successfully
E-Learning
characteristics Authenticity
Complexity
authentic activities are defined as tasks that are
relevant and useful to the real world
e-Learning that is perceived as relatively
difficult to understand and use
Organizational
factor Organizational
support toward
e-Learning
ICT
infrastructure
Institutional
policy
Training
The degree to which an individual believes
that an organizational infrastructure supports
the use of e-Learning . The support include:
student support, teachers support, technical assistance support and pedagogical support
infrastructure that allows students and teachers
to easily access the ICT hardware, using
friendly software and provide fixed technical support
the policies that limits the wide utilization of
e-Learning to support learning and teaching in
higher learning institution
Provision of ICT knowledge through
workshops and seminar implementation.
Source: researcher‘s own synthesis
3.5.3Measurement Scales
Questionnaire items which were looking for answer to build the conceptual Model
started at Q501, Q601, Q602, Q701, Q801, Q802, Q803, Q804, and Q805 (these
were independent variable which each variable carried 5 items, a, b, c, d and e). And
two dependent variables Q900 with five items that measured intention to adopt e-
Learning and Q201c which measured the actual use of e-learning.
41
The scale used to measure those variables was a 5-point Likert scale; the scale was
selected to reduce measurement error when compared to other measures such as 3
and 7 likert scale.
3.5.4 Data variable codification
Coding of data was done following the items in the questionnaire. The variable
names were written starting with letter Q, for example Q101a to Q101e were
questions about respondent‘s personal detail. All variables were entered and given
variable name, values, and label. Setting of data type was done and all were numeric.
Also scale measures were assigned to be nominal, scale or ordinal depending on the
type of data.
The problem encountered was that some of respondents did not complete the
questions, they left others unanswered. This was a major challenge to the researcher
as it was difficult to predict what the respondent would say. The solution for such a
problem was to eliminate the respondent from the list.
Another problem was researcher‘s mistake of entering a wrong coded value. This
was solved by carefully crosschecking the entered data and making the right
correction.
3.6 Sample size and sampling techniques
3.6.1Sample size
The study being a survey used samples that were representatives of the sampling
frame from a sample of 05 branches of Tanzanian Universities and Higher learning
Institutions from both public and private Institutions, these were; St Augustine
University of Tanzania (SAUT), Open University of Tanzania (OUT), College of
Business Education (CBE), Mzumbe University (MU), and Tanzania Institute of
Accountancy (TIA). The study sampling frame of this research was the sample from
42
students, Teachers and Administrators of the selected higher learning institution as
shown in table 3-3 below.
Table 3-3: Respondents forming sample size 1
Name of higher learning institution
Total
POSITION SAUT OUT CBE MZUMBE TIA
Management 3 1 0 0 0 4
IT expert 1 1 0 0 0 2
Lecturer/Tutor 5 3 2 0 3 13
Student 51 43 44 32 21 191
Total 60 48 46 32 24 210
Source: Research findings (2013)
3.6.2 Sampling technique
In selecting the sample both purposive and non-purposive sampling methods were
used to pick the sample from the sampling frame which constituted higher learning
institutions in Tanzania, The sample comprised of Certificate/ Diploma,
undergraduate, Postgraduate diploma and master‘s students enrolled in public
universities (Open university of Tanzania, Mzumbe University, College of Business
Education and Tanzania Institute of Accountancy) and a private university (St
Augustine University of Tanzania). The purposive selection of the university was
done because Higher learning selected were those which have branches in other
regions in the country to make easy generalization of the results. The Higher
education Institution was considered as a stratum. Simple random sampling was then
used to select the sample from each stratum. The analysis of the selection in figures
from each University/ Higher Learning Institution was as follows:
SAUT: 13 certificate, 40 third year bachelor degree, 07 Masters (all were
Lecturer and administrators) making a total of 60 respondents
OUT: 18 Certificate and Diploma, 24 Third year Bachelor degree and 6 Masters;
making a total of 48 respondents
43
CBE: 33 certificate and diploma, 01 advanced diploma, 10 postgraduate diploma
and 2 masters (Lecturers) making a total of 46 respondents
Mzumbe : 32 all were masters students
TIA: 8 Diploma, 03 Advanced diploma, 10 postgraduate, 03 Masters(Lectures),
making a total of 24 respondents
For Bachelor/Advanced diploma selected were all third year students; because the
researcher had a notion that these group of respondents would have enough
experience of using e-Learning, since they had stayed at the university for a longer
period .The sample sizes of respondents were again selected depending on their total
number in each University/Higher Learning institution. The larger the samplings
frame from each university, the larger the sample size and vice versa.
The figure 3-1below indicates the exact number of respondents from each Institution.
Figure 3-1: Educational level
Source: Research findings (2013)
44
3.7 Types and sources of data
For this study both secondary and primary data were collected for analysis. The
Primary data was collected by means of a self-administered questionnaire; the
questionnaires served to both staff and students at the respective universities and
higher learning institutions in which tutors and lecturers constituted the personnel
responsible for teaching process and students responsible for learning process.
3.8 Data collection method
Questionnaires were used to collect data from respondents in all five branches of
Universities and Higher Learning Institutions.
3.8.1 Questionnaire
In this study, widely accepted and recognized survey questionnaires were reviewed
and integrated for the survey, including the e-Learning characteristics, learner
characteristics, instructor characteristics and organizational factor dimension. The
questionnaire asked students and Teachers the questions covering authenticity and
complexity for e-Learning characteristic dimension, openness to change and self
efficacy for learner characteristic dimension, computer self efficacy and timely
response for instructor characteristic and organizational factors dimension with
Organizational support, ICT Infrastructure and Institutional policy. The required
primary data was collected through a self administrated questionnaire which was
originally developed and employed for the purpose of the study. Questions asked
respondents to rate their degree of agreement using a 5-point Likert scale; the scale
was selected to reduce measurement error. To achieve the purpose of the study, 81
questionnaires were sent to students and lecturers, who work in different Tanzanian
universities using a systematic random sampling process, where every member of the
sample frame had an equal chance of being selected, produced a sample of 210 e-
Learning users. The research questionnaire used for collection of data is attached as
appendix 3.
45
3.9 Validity issues
Validity is an indication of how sound one‘s research is. It represents whether the
survey actually measured what the questionnaire meant to measure. This was
measured using Cronbach's alpha, which is a measure of internal consistency. And, it
is one of many tests of reliability. Cronbach's alpha comprises a number of items that
make up a scale designed to measure a single construct and determines the degree to
which all the items are measuring the same construct. Cronbach‘s α is widely used to
estimate the internal reliability of multi-items and its rate of 0.70 or higher is
considered acceptable. In this study each variable had item questions as shown table
3-4 below.
Table 3-4: Cronbach's alpha
Variable label Code name Cronbach‘alpha (α)
Authenticity Q501a, Q501b, Q501c, Q501d, Q501e 0.251
Complexity Q502a, Q502b, Q502c, Q502d ,Q502e 0.739
Self efficacy Q601a,Q601b, Q601c, Q601d, Q601e 0.363
Openness to change Q602a, Q602b, Q602c, Q602d, Q602e 0.678
Instructor response Q701a, Q701b, Q701c, Q701d, Q701e 0.584
Organizational support Q801a, Q801b, Q801c, Q801d, Q801e 0.362
ICTI infrastructure Q802a, Q802b, Q802c, Q802d, Q802e 0.874
Institutional policy Q803a, Q803b, Q803c, Q803d, Q803e 0.813
Training Q804a, Q804b, Q804c, Q804d, Q804e 0.714
Management support Q805a, Q805b, Q805c, Q805d, Q805e 0.883
Intention to use : (Q900a, Q900b, Q900c, Q900d, Q900e 0.804
Actual use Q201a, Q201b, Q201c, Q201d, Q201e 0.810
The variable with α = 0.7 or higher including those which were close to 0.7 were
used as input in the other data analysis methods. Cronbach‘s α with the lowest values
such as: Authenticity with α = 0.251, self efficacy with α = 0.363 and Organizational
support with α = 0.362 were considered unacceptable because they have very high
internal inconsistency with other variables and were not included for further analysis.
Openness to change with α = 0.678 and instructor timely response with α = 0.584
46
were included because they were close to the cut-off point of 0.7. But the reason why
they had low cronbach‘s alpha might be due to the respondents‘ low knowledge of e-
Learning adoption which resulted into being unable to understand the questions
clearly when responding to the questionnaire.
3.10 Data analysis methods
Statistical Package for Social Sciences (SPSS) was applied as an analysis tool.
Descriptive statistical was done then, scale analysis which measured whether the
questions were consistent before Factor analysis was done. Data reduction by factor
analysis was carried out in order to extract principal components. Then a rotated
component matrix was identified using varimax with Kaiser Normalization (Table 4-
6 in chapter 4).Factor analysis was also used to screen variables for subsequent
analysis for example, to identify collinearity prior to performing a multiple
regression analysis.
Before factor analysis, some of the variables were removed from the analysis,
because, after making scale analysis 3 variables were found to have Cronbach‘ alpha
below the required one which is 0.7. The variables that were removed are:
Authenticity with its Item (Q501a, Q501b, Q501c, Q501d, Q501e), Self efficacy
with items (Q601a, Q601b, Q601c, Q601d, Q601e) and Organizational support with
items (Q801a, Q801b, Q801c, Q801d, Q801e) their respective Cronbach's α were
0.251,0.363 and 0.362. They were removed to avoid internal inconsistency. The
remaining variables with their item as identified in table 3-4 of section 3.9 were
considered for other types of analysis, these variable include the following:
Complexity with α = 0.739, Openness to change with α = 0.678(close to 0.7),
Instructor response with α = 0.584 (was considered to be close to 0.7), ICT
Infrastructure with α = 0.874, Institutional policy with α = 0.813, Training with α =
0.714, Management support with α = 0.883, Intention to use with α = 0.804 and
actual use with α = 0.810.
47
The result obtained from factor analysis was taken for transformation analysis to
reduce the number of item questions which was finally used for regression analysis
in section 4.3.5
3.11 Study Time Plan
The study was expected to be accomplished within six (6) months starting 1st
October 2012 to the end of March 2013. Table 1-1 given in appendix 1 features the
activities breakdown. It should be noted that as indicated in the chart (Table 1-2) the
researcher was constantly keeping in touch with supervisor all along the research
process in order to be guided and for improvement purposes.
3.12 Study Budget
3.12.1 Basis for the Budget
The accomplishment of this study involved a lot of activities such as data collection,
printing of questionnaires, photocopy, travelling, meals and accommodation and
others. All these activities required some expenses that were met.
3.12.2 Units Costs Bases for the Budget
The budget for the study aimed at accomplishing the following activities:
The total cost of data collection while the researcher was in Mwanza was Tsh.
300,000/=, Travelling expenses go and return from Mwanza to Morogoro= 270,000/,
Meal and accommodation cost was 450,000/=and Report writing cost was
361,500/=Tanzanian shillings (Tshs). The detailed analysis of the budget is shown in
Table 1-2 (appendix2)
It was not an easy task to meet the above mentioned cost because sometimes the cost
was beyond budget. For example accommodation cost was not uniform sometimes
was higher than it was expected, also the number of days the researcher planned to
48
stay in Morogoro for consultation with his supervisor increased due to unavoidable
circumstances. The researcher had to incur extra cost to meet the requirement of the
study.
3.12.3 The Budget Estimates (in T.shs)
In order to accomplish activities in this study such as data collection, printing of
questionnaires, photocopy and others, there were expenses that might be met. Due to
these expenses the financial budget for this study was T.shs. 1,511,500/=only as per
the breakdown shown in the table 1-2 given in appendix 2.
49
CHAPTER FOUR
4.0 Presentation of findings
This chapter include data preparation, data editing, coding and cleaning; it also have
descriptive statistics, scale analysis, factor analysis scale transformation, multiple
regression and hypothesis testing.
4.1 Data preparation
The data preparation process started by making data editing, followed by coding then
data classification and finally tabulation before analyzing the data.
4.1.1Data editing
Is the process of examining the collected raw data (specifically in surveys) to detect
errors and omissions and to correct these when possible (Kothari, 2004). Editing was
done by making a carefully scrutiny of the completed Questionnaires in order to
ensure that the data are accurate, some question were mistakenly entered for
instance, 22 which was not in the list of values to be entered. The researcher
corrected by writing 2 and hence data were uniformly entered and completed and
then well arranged to facilitate coding and tabulation.
4.1.2 Coding and Transcription
As agued by Kothari, 2004 that Coding is the process of assigning numerals or other
symbols to answers so that responses can be put into a limited number of categories
or classes. Basically coding decision in this study was taken at the designing stage of
the questionnaire. In this study SPSS was used to make coding by assigning
variables a names such as Q501a, Q501b to Q501e to represent variable
Authenticity, then all variables were assigned, values, and label. Setting of data type
50
was done and all were numeric. Also scale measures were assigned to be nominal,
scale or ordinal depending on the type of data. Finally data were entered in SPSS.
4.1.3 Data cleaning
In data entry process there were minor errors such as entering for example; number 2
twice and become 22 instead of 02 or, entering number 4 in the first column and then
3 in the next column of the same raw to become 43 instead of 4 and 3 to two
different column of the same raw. All these were detected and corrected accordingly.
There were 04 item questions which were wrongly entered these include: Q401a,
Q402a, Q402b, Q501c. Also item Q801a and 801c were left unanswered. The
solution for these problem involved removing from the log file every line (record)
that contained one or more null values in relevant attributes used, because nulls do
not identify any kind of profile So, 06 questionnaires were considered invalid
because respondents skipped many items. The data obtained from the survey were
analyzed for descriptive frequency analysis.
4.2 Preliminary data analysis
4.2.1 Introduction
Out of the 240 distributed questionnaires a total of 216 or a response rate of 90% was
returned. The strategy used to collect questionnaire was to take the respondents
mobile phone number at the time of giving him/her the questionnaire. Then after a
certain period (two to three days), respondents were reminded by requesting them to
return the questionnaire after they had completed answering them. After removing
the invalid questionnaires, 210 questionnaires were used in the analytical stage.
4.2.2 Respondents’ Gender
Among respondents, 152 (72.4%) were males and 58(27.6%) were females. The
weighting of this sample does not equate to an equal distribution between males and
51
females. This indicates that there is high possibility of an effect from gender bias to
occur as it can be seen in table 4-1
Table 4-1: Gender
Frequency Percent
Female 58 27.6
Male 152 72.4
Total 210 100.0
Source: Research findings (2013)
4.2.3 Respondents Educational level
With most of these respondents 72 (34.3 %) being Certificate/ Diploma Students,
68(32.4 %) Bachelors/Advanced Diploma in which 4 were from management and 2
IT expert; 20 (9.5%) Postgraduate Diploma who study at two Higher learning
Institutions namely CBE and TIA; and 50 (23.8%) were masters degree in which 13
were lecturers and 36 were students from various Universities (most of them were
Students of Mzumbe university which have only Masters Students in Mwanza
Centre), as shown in Table 4-2
Table 4-2: Educational level
Current position Total
Management IT expert Lecturer/Tutor Student
Highest educational
level
Certificate/Diploma 0 1 0 71 72
Bachelor degree/Advanced
Diploma 3 1 0 64 68
Postgraduate diploma 0 0 0 20 20
Masters degree 1 0 13 36 50
Total 4 2 13 191 210
Source: Research findings (2013)
4.2.4 Respondents’ Age
In term of respondents‘ ages: (33.8%) were under 25, (36.7%) were between 25-35,
(4.8%) between the age of 46-55, and 1% above 55 years old of total sample
52
attending in the surveyed universities in Tanzania. The results show that, most of the
respondents were below the age of 45 as shown in figure 4-1
Figure 4-1: Respondents’ age
Source: Research findings (2013)
4.2.4 Respondents’ e-Learning experience
Participants were asked four questions relating to their e-Learning knowledge,
experience, frequency and understanding of using e-Learning tools. Each question
had five rating scales from 1 to 5, for example a question asking: my knowledge of e-
Learning is: and 18.1% said ―No knowledge , (35.7%) said ―I have little experience ,
(31.9%), ―I have some experience‖, (5.2%)said ―I have considerable experience‖,
and ―(9%) said ―I have a lot of experience‖
Responses demonstrated that the majority of users in this study indicated that they
had little experience followed by those with some experience as shown in figure 4-2
below.
53
Figure 4-2: e-Learning usage experience
Source: Research findings (2013)
4.2.5 Presence of e-Learning in Universities/Higher Learning Institutions
Of the 210 respondents, when asked whether they have e-Learning in their
University/Institute; 94 said YES and 116 said NO. The result indicate that Mzumbe
University and OUT have e-Learning and the rest have no e-Learning system in their
Institutions.
Table 4-3: Presence of e-Learning in Universities
Institution name
Total SAUT OUT CBE MZUMBE TIA
Presence of e-Learning
system
YES 20 37 5 29 3 94
NO 40 11 41 3 21 116
Total 60 48 46 32 24 210
Source: Research findings (2013)
54
4.3 Hypothesis Testing
4.3.1 Dependent variable
There were two dependent variables in this study; the first was Intention to adopt e-
Learning and the second was Actual use of e-Learning. The assumption as they can
be seen in the model (figure 2-2 of chapter two) was that, there are Lecturers and
students who are intending to adopt e-Learning in the future, and that, there are also
others who are actually using e-Learning at the moment (actual use of e-learning).
4.3.1.1 Intention to adopt e-Learning
Out of 210 respondents when asked if they expected their use of e-Learning to
continue in the future; their answers were: 62.86% said I strongly agree, 34.76% said
I agree, 1.9%said I can‘t decide and 1.48% said I strongly disagree. These statistics
show that the majority of respondents have higher intention to adopt e-Learning in
the future as shown in figure 4-3 below.
55
Figure 4-3: Intention to adopt e-Learning
Source: Research findings (2013)
4.3.1.2 Actual use of e-Learning
About the usage of e-Learning per month, Responses demonstrated that the
62(29.5%) used once, 69(32.9%) used 2-5 times and 6-10 times had 34(16.2%) but
those who used 11-15 times were only 20(9.5%). Therefore most of them used 2-5
times per month as indicated in figure 4-4.which is very poor use of e-Learning.
Figure 4-4: e-Learning usage frequency
56
Source: Research findings (2013)
4.3.2 Factor analysis
Factor analysis was undertaken to prepare data for other analysis method.The output
were as follows: KMO and Bartlett's Test: The result of Bartlett's Test of Sphericity was
significant at p value = .000 (Chi-square = 4988.829, DF =741. The sampling adequacy was
found to be 0.844 which was appropriate for further multivariate analysis, Extraction and
Method used was Principal Component Analysis. Rotation Method:Varimax with Kaiser
Normalization as shown in table 4-4
Table 4-4 KMO and Bartlett's Test
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .844
Bartlett's Test of Sphericity Approx. Chi-Square 4988.829
df 741
Sig. .000
57
Table 4-5: Rotated Component Matrix
Component
1 2 3 4 5 6 7 8 9
Q804a: .748 .182 .109 -.020 .091 .067 .252 .110 .067
Q804d: .731 .108 .158 -.067 -.021 .264 -.271 .055 -.018
Q803d: .717 .238 .445 .015 -.033 -.120 .040 .118 -.027
Q804b: .698 .170 .181 -.006 .123 .120 .248 .169 .029
Q803c: .653 .276 .433 -.028 .001 -.126 .182 .086 .062
Q502e: .496 -.217 .350 -.020 .039 -.234 -.153 .076 .022
Q805d: 0.821 .101 .275 -.036 .033 -.062 .034 .057 .025
Q805e: 0.787 .094 .114 -.084 .092 .196 .091 .075 -.018
Q805a: 0.775 -.100 .234 -.048 .040 .337 .062 -.079 .021
Q805b: 0.757 .227 .039 -.142 .006 .031 .150 .030 .095
Q805c: 0.743 .293 .277 -.029 .016 -.155 .051 .103 -.058
Q803e: .422 .447 .284 .057 .005 .082 .374 -.173 -.325
Q802c: .093 0.836 .170 .062 -.031 .032 .086 .121 .155
Q802d: .142 0.797 .221 .037 -.080 .141 .097 .090 .194
Q802b: .201 0.723 .243 .127 .109 .038 .129 .008 -.183
Q802e: .340 0.659 .189 .101 -.070 .034 .108 .009 -.044
Q802a: .353 0.567 .277 .143 .112 -.045 .193 .013 -.261
Q502b: .017 -.091 0.850 -.086 .049 -.041 .130 -.020 -.193
Q502a: -.091 -.077 0.813 .064 -.068 .130 -.002 -.081 .020
Q502c: .316 -.057 0.747 .078 -.021 -.221 -.208 .154 -.035
Q502d: -.013 -.042 0.665 .121 .075 .096 .149 -.096 .111
Q602b: -.256 .052 .053 .630 .093 .164 .076 .059 .320
Q602c: -.325 -.028 .200 .565 -.082 -.075 .008 .087 .388
Q602e: .290 -.161 .151 .554 -.089 -.420 -.025 .286 .144
Q900b: .053 .053 .072 .066 .795 .007 .077 -.133 -.091
Q900e: -.191 .028 -.005 -.069 .793 .144 .148 .017 .115
Q900c: .067 .045 -.016 .009 .792 -.002 .051 .005 -.161
Q900d: .209 -.022 .009 .045 .671 -.041 -.024 .023 -.084
Q900a: -.073 .067 -.112 -.079 .665 .117 .113 -.078 .325
Q804e: -.031 .059 -.009 .051 .014 .826 .180 -.127 -.107
Q804c: .241 .167 .173 .000 .173 .754 .201 -.253 -.012
Q803a: .183 .162 .179 .119 .177 .100 .799 -.083 .008
Q803b: .094 .259 .228 .036 .181 .334 .739 -.042 .116
Q701e: -.354 .056 .276 .090 .178 .293 .517 .367 .011
Q701c: .142 -.059 .308 -.093 -.124 -.071 .015 .751 .111
Q701d: .338 .261 -.007 .006 .024 -.163 .053 .643 -.041
Q701b: -.056 -.035 -.072 .445 -.119 -.225 -.058 .596 .025
58
Q701a: .510 .128 .031 -.100 -.006 -.148 -.242 .543 -.032
Q602d: .207 .071 .050 .346 -.078 -.217 .090 .058 .686
Extraction Method: Principal Component
Analysis. Rotation Method: Varimax with Kaiser
Normalization.
a. Rotation converged in 11
iterations.
4.3.3 Scale analysis
Cronbach's alpha test was used to measure internal consistency, because it is one of
many tests of reliability. The results were as shown table 4-6
Table 4-6: Validity testing
Factor
code
Component factor(Variable) Question codes Cronbach‘
s alpha
Q501 Authenticity Q501a,Q501b, Q501c,Q501d,Q501e 0.251
Q502 Complexity Q502a, Q502b,Q502c,Q502d ,Q502e 0.739
Q601 Self efficacy (Q601a,Q601b, Q601c, Q601d, Q601e 0.363
Q602 Openness to change (Q602a,Q602b, Q602c, Q602d, Q602e 0.678
Q701 Instructor timely response Q701a,Q701b,Q701c, Q701d,Q701e 0.584
Q801 Organizational support Q801a, Q801b, Q801c, Q801d, Q801e 0.362
Q802 ICT infrastructure (Q802a, Q802b, Q802c, Q802d, Q802e 0.874
Q803 Institutional policy (Q803a, Q803b, Q803c, Q803d, Q803e 0.813
Q804 Training (Q804a, Q804b, Q804c, Q804d, Q804e 0.714
Q805 Management support (Q805a, Q805b, Q805c, Q805d, Q805e 0.883
Q900 Intention to adopt e-Learning (Q900a, Q900b, Q900c, Q900d, Q900e 0.804
Q102 Actual use Q102a, Q102b, Q102c, and Q102d 0.810
Three variables with the lowest Cronbach‘s alpha were removed from the analysis
because they did not meet the required value which is 0.7, these included authenticity
(α =0.251), self efficacy (α=0.363) and Organizational support (α=0.362) as shown
in table 4-6.the details of the reasons for the inconsistency is shown in section 3.9.
4.3.4 Scale transformation
Before multiple regressions were done, computation through transformation analysis
was employed to make variables from the questionnaire relate to the hypothesis as
shown table 4-7 below:
59
Table 4-7: Variable transformation
Variable label Computation
Complexity Q502a + Q502b + Q502c + Q502d / 4
Openness to change Q602b + Q602c + Q602e / 3.
Instructor response Q701a + Q701b + Q701c + Q701d / 4.
ICTI infrastructure Q802a + Q802b + Q802c + Q802d + Q802e / 5.
Institutional policy Q803a + Q803b / 2.
Training Q804c + Q804e / 2
Management support Q805a + Q805b + Q805c + Q805d + Q805e / 5.
Intention to use Q900a + Q900b + Q900c + Q900d + Q900e / 5.
Actual use Q102c.
The variables mentioned above were used as inputs for regression analysis and the
result of regression analysis is shown in section 4.3.5
4.3.5 Multiple regressions
The multiple linear regression analysis allows the prediction of one variable from
several other variables. It has three main components of the output. The first is called
the Model Summary, The second part of the output that we were interested at, is the
ANOVA summary table and the final section of the output is the table of
coefficients.
This study involves multi-measurement approach because two dependent variables
(a) intention to adopt e-Learning and (b) actual use of e-Learning were measured.
This is because we were measuring the relationship between independent variables
and dependent variables as shown in conceptual model in chapter two figures 2-2.
4.3.5.1 Intention to adopt e-Learning
Model summary
The result indicates that, 9.2% of the variation in intention to adopt e-Learning can
be explained by those seven independent variables as shown in Table 4-8 below. The
value of R square is 0.092 which is very low because the Students and lectures in
Higher learning Institutions in Tanzania had very low knowledge of e-Learning
because it is a new Technology in Tanzania. And therefore they could not real
60
understand the meaning of Intention to adopt e-Learning as a result they came up
with answers that were not expected. Another reason is that, there might be other
factors which are not in the model, which can explain better the factors for e-
Learning adoption in Tanzania. These should be addressed in future research.
Table 4-8: Model summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .304a .092 .061 1.94963
a. Predictors: (Constant), management Support, Openness to change,
instructor Timely response, institutional Policy, Training, Complexity,
ICT Infrastructure
ANOVA
Since the p value is less than 0.05, then we have a significant multiple regression.
Table 4-9: ANOVA
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 78.114 7 11.159 2.936 .006a
Residual 767.810 202 3.801
Total 845.924 209
a. Predictors: (Constant), management Support, Openness to change, instructor Timely response,
institutional Policy, Training, Complexity, ICT Infrastructure
b. Dependent Variable: Intention to adopt
e-Learning
Coefficient:
Table 4-10 show that only one independent variable is significant and the rest were
not significant because the skills of e-Learning for our respondents in relation to their
intention to adopt it was still low, therefore they couldn‘t provide realistic answers.
Table 4-10: coefficient: Intention to adopt e-Learning
61
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 5.752 .880 6.537 .000
Complexity .022 .052 .036 .428 .669
Openness -.047 .072 -.054 -.653 .514
Timely response -.055 .068 -.062 -.808 .420
ICT Infrastructure -.025 .041 -.051 -.598 .551
Institutional Policy .361 .115 .256 3.151 .002
Training .113 .130 .071 .870 .385
Management Support .008 .043 .016 .193 .847
a. Dependent Variable: Intention to adopt
e-Learning
4.3.5.2 Actual use
Three main component of output were found
Model summary
R Square (called the coefficient of determination) gave the proportion of the variance
of the dependent variable (actual use of e-Learning) that can be explained by
variation in the independent variables (which are: complexity, openness to change,
Instructor timely response, ICT infrastructure, Institutional policy, Training and
management support.
The result indicates that, 16.9% of the variation in actual use of e-Learning can be
explained by those seven independent variables as shown in table 4-11. The value of
R square is a little bit better because the dependent variable (Actual use of e-
Learning) was simple and easily understood by respondents and hence the realistic
answers given gave a better result.
Table 4-11: Model summary
62
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .411a .169 .140 .46113
a. Predictors: (Constant), management Support, Openness to change,
instructor Timely response, institutional Policy, Training, Complexity,
ICT Infrastructure
ANOVA
The second part of the output that the researcher was interested at is the ANOVA
Since the p value is less than 0.05, then we had a significant multiple regression.
Table 4-12: ANOVA 2
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 8.742 7 1.249 5.873 .000a
Residual 42.953 202 .213
Total 51.695 209
a. Predictors: (Constant), management Support, Openness to change, instructor Timely response,
institutional Policy, Training, Complexity, ICT Infrastructure
b. Dependent Variable: Actual use
Coefficients
This is where the actual prediction equation can be found.
The result indicate that a multiple linear regression was calculated predicting actual
use of e-Learning based on complexity, openness to change, Instructor timely
response, ICT infrastructure, Institutional policy, Training and management support.
All independent variables were significant predictor for actual usage of e-Learning
except ICT infrastructure and management support as shown in table 4-13. Majority
of variables were significant because respondents understood the question and
answered correctly.
Table4-13: Actual use coefficient
63
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95% Confidence
Interval for B
B Std. Error Beta
Lower
Bound
Upper
Bound
1 (Constant) .936 .208 4.496 .000 .525 1.346
Complexity .026 .012 .167 2.084 .038 .001 .050
Openness -.048 .017 -.220 -2.776 .006 -.081 -.014
Timely response .042 .016 .192 2.612 .010 .010 .073
ICT Infrastructure .009 .010 .078 .946 .345 -.010 .028
Institutional Policy .094 .027 .269 3.457 .001 .040 .147
Training -.067 .031 -.171 -2.187 .030 -.128 -.007
Management Support .006 .010 .044 .558 .577 -.014 .026
a. Dependent Variable: Actual use
Source: Research findings (2013)
4.4 Hypothesis testing
As it is shown in chapter one, that the general objective of this study was to assess
the key issues/ factors that determine e- Learning adoption for higher learning
Institution in Tanzania; The formulated hypothesis were in four groups: Learner
characteristics (self efficacy and openness to change) referred from section 2.5.1 in
chapter two.
e-Learning characteristics, (Authenticity and complexity) referred from
section 2.5.2 in chapter two.
Instructor characteristic (Instructor timely response) referred from section
2.5.3 in chapter two.
Institutional characteristics (organizational support, Infrastructure,
Institutional policies, Training and Management support) referred from
section 2.5.4 in chapter two
But three variables and their hypothesis were deleted (H1, H3 and H6) because of
internal inconsistency; the remaining Hypotheses were as follows:
64
4.4.1 Learner characteristics
H2a Complexity will have a negative effect on intention to adopt e-Learning.
H2b Complexity will have a negative effect on actual use of e-Learning.
Figure 4-5: Complexity 1
Based on the result from (Table 4-10-Intention to adopt and Table 4-13- Actual use) the
following were the result
Intention to adopt e-Learning result
Table4-14: Complexity 2
Hypothesis variable Std. Error Beta t Sig. Interp.
H4b Complexity .052 .036 .428 .669 NS
Actual use of e-Learning result
Table4-15: complexity2
Hypothesis variable Std. Error Beta t Sig. Interp.
H4b Complexity .012 .167 2.084 .038 S
NS= not significant, S = significant and Interp. = Interpretation
4.4.2 e-Learning characteristics
H2a: openness to change will be related to higher levels of intention to adopt e-
Learning in the future
H2b: openness to change will be related to higher levels of actual use of e-Learning
Complexity
Intention to
adopt e-Learning
Actual use of
e-Learning
H4a
H4b
65
Figure 4-6: openness to change 1
Intention to adopt e-Learning result
Table 4-16: Openness to change 1
Hypothesis Variable Std. Error Beta t Sig. Interp.
H2a Openness to
change .072 -.054 -.653 .514 NS
Actual use of e-Learning result
Table 4-17: Openness to change 2
Hypothesis Variable Std. Error Beta t Sig. Interp.
H2b Openness to change .017 -.220 -2.776 .006 S
NS= not significant, S = significant and Interp. = Interpretation
4.4.3 Instructor characteristics
H5a instructors‘ timely response towards e-Learning will positively influence
students‘ intention to adopt e-learning
H5b: instructors‘ timely response towards e-Learning will positively influence
students‘ perceived actual usage of e-Learning
Figure 4-7: Instructor timely response 2
Intention to adopt e-Learning result
Intention to
adopt e-Learning
Openness to change
Actual use of e-
Learning
H2a
H2b
Instructor‘s timely
response
Actual use of e-
Learning
H5a
H5b
Intention to
adopt e-Learning
66
Table4-18: instructor timely response 1
Hypothesis Variable Std. Error Beta t Sig. Interp.
H5a Timely
response .068 -.062 -.808 .420 NS
Actual use of e-Learning result
Table 4-19: instructor timely response 2
Hypothesis Variable Std. Error Beta t Sig. Interp.
H5b Timely response .016 .192 2.612 .010 S
NS= not significant, S = significant and Interp. = Interpretation
4.4.4 Institutional factors
H7a: There is a positive relationship between Instructor‘s intention to adopt e-
Learning and ICT infrastructure.
H7b: There is a positive relationship between Instructor‘s usage of e-Learning and
ICT infrastructure.
H8a. Lack of institutional policies is related to poor users‘ intention to adopt e-
Learning in the future.
H8b. Lack of institutional policies is related to users‘ poor usage of e-learning
H9a: There is a positive relationship between training and users‘ attitude on intention
to adopt e-Learning.
H9b: There is a positive relationship between training and users‘ attitude on actual
use of e-Learning.
H10a: There is a positive relationship between management support and intention to
adopt e-Learning system.
H10b: There is a positive relationship between management support and actual use
of e-Learning system.
67
Figure 4-8: Institutional factors 1
The finding from regression analysis (Table 4-10-Intention to adopt and Table 4-13-
Actual use) give the following results
Intention to adopt e-Learning result
Table 4-20: institutional factors
Hypothesis Variable Std. Error Beta t Sig. Interp.
H7a ICT Infrastructure .041 -.051 -.598 .551 NS
H8a Institutional Policy .115 .256 3.151 .002 S
H9a Training .130 .071 .870 .385 NS
H10a Management support .043 .016 .193 .847 NS
Actual use of e-Learning result
Table4-21: institutional factors2
Hypothesis Variable Std. Error Beta t Sig. Interp.
H7b ICT infrastructure .010 .078 .946 .345 NS
H8b Institutional Policy .027 .269 3.457 .001 S
H9b Training .031 -.171 -2.187 .030 S
H10 Management Support .010 .044 .558 .577 NS
NS= not significant, S = significant and Interp. = Interpretation
4.4.5 Hypothesis conclusion
a) Intention to adopt e-Learning
H7 - ICT infrastructure
H8 - Institutional policy
H9 - Training
H10 - Management support
Intention to adopt
e-Learning
Actual use of e-
Learning
68
Hypothesis2a: examined the relationship between openness to change and
intention to adopt e-Learning, the result show that it was not significant with
p > 0.05
Hypotheses H4a examined the relationships that, Complexity will have a
negative effect on intention to adopt e-Learning the result is not significant
with P>0.05; therefore it is not supported.
Hypothesis5a: examined the relationship between Instructor timely response
towards e-Learning and intention to adopt e-Learning, the result show that it
was not significant with p >0.05
Hypotheses 7a, 9a and 10a were not supported, with p-values greater than
0.05.
Hypotheses 8a examined the relationship between the Institutional policy and
intention to adopt e-Learning it was significant with p < 0.5. (Shown in table
4-21)
Table4-22: Result summary
Dimension Hypothesis Factor/Variable alpha Sig. Interp. Result
Learner characteristic H2a Openness to change 0.678 .514 NS Not supported(p>0.050)
e-Learning characteristic H4a Complexity 0.739 .669 NS Not supported ( p > 0.05)
Instructor characteristic H5a Instructor timely
response 0.584 .420
NS Not supported ( p > 0.05)
Institutional
characteristics
H7a ICT infrastructure 0.874 .551 NS Not supported ( p > 0.05)
H8a Institutional Policy 0.813 .002 S Supported ( p < 0.05)
H9a Training 0.714 .385 NS Not supported ( p > 0.05)
H10a Management
Support 0.883 .847 NS Not supported ( p > 0.05)
Source: Research findings (2013)
(b) Actual use of e-Learning
Only two variables were rejected: ICT infrastructure (H7a) and Management support
(H10a) and the rest were accepted as shown in table 4-22
69
Table4-23: Result summary
Dimension Hypothesis Factor/Variable alpha Sig. Interp. Result
Learner characteristic H2b Openness to change 0.678 .006 S Supported(p<0.05)
e-Learning
characteristic
H4b Complexity
0.739
.038
S
Supported ( p < 0.05)
Instructor
characteristic
H5b Instructor timely
response 0.584 .010
S Supported ( p < 0.05)
Institutional
characteristics
H7b ICT infrastructure 0.874 .345 NS Not supported ( p > 0.05)
H8b Policy 0.813 .001 S Supported ( p < 0.05)
H9b Training 0.714 .030 S Supported ( p < 0.05)
H10 Management Support 0.883 .577 NS Not supported ( p > 0.05)
Source: Research findings (2013)
Hypothesis 2b: examined the relationship between openness to change and
actual use of e-Learning, the result show that it is significant with p < 0.05
Hypothesis H4b: examined the relationships between complexity and actual
use of e-Learning such that, Complexity will have a negative effect on Actual
use of e-Learning the result was significant with P < 0.05 therefore it is
supported
Hypothesis5b: examined the relationship between Instructor timely response
towards e-Learning and actual use of e-Learning, the result show that it is
significant with p = 0.010
Hypotheses 7b and 10b were not supported, with p-values greater than 0.05.
Hypotheses 8b and 9b were significant with p < 0.5. H8b examined the
relationship between the Institutional policy and actual use of, e-Learning and
H9b examined the relationship between Training and actual use of e-Learning
Therefore, Hypotheses 8b and 9b are supported.
70
CHAPTER FIVE
5.0 Discussion of the study findings
5.1 Introduction
The objective of this study was to assess on the key factors that determined e-
Learning adoption for higher learning institution in Tanzania.
It examined the behavioral intention of the adoption and actual usage of e-Learning
for Higher learning Institutions. There were four sections which included the
following: e-Learning characteristics, learner characteristics, Instructor
characteristics, Institutional characteristics with subsections (organizational support,
ICT infrastructure, Institutional policies, Training and Management support). From
the study findings, seven factors which included complexity, openness to change,
Instructor timely response, ICT Infrastructure, Institutional policy, Training, and
Management support, were identified.
5.2 Measuring dependent variable
In this study, the dependent variables were intention to adopt e-Learning and actual
use of e-Learning. These dependent variables were measured against the independent
variables (the seven factors mentioned in section 5.1 above).Each of the two
dependent variables was measured separately through the formulated hypothesis
which were tested whether they were significant or non significant.
5.3 Hypothesis testing
In this section four groups of hypothesis were discussed, compared with the literature
review and finally a researcher‘s view was presented.
5.3.1 Learner characteristic
The previous literature in section 2.5.1.2 indicate that when employees with high
openness to change and who perceived e-Learning to be less complex were more
71
likely to adopt e-Learning in the future (Sawang, S. and Cameron, 2013). In this
study therefore, it was hypothesized that:
H2a: Openness to change will be related to higher levels of intention to
adopt e-Learning in the future.
H2b: Openness to change will be related to higher levels of actual use of e-
Learning
The result for intention to adopt e-Learning show that H2a was not significant with p
> 0.5, Therefore, Theory of Reasoned Action (TRA) which measures behavioral
intention to adopt e-Learning was rejected. The reason for rejection could be lack of
e-Learning knowledge for most of our students and Lecturers in Tanzania which
resulted into being reluctance to openness to change.
The result for actual use indicated that H2b is significant with p < 0.05, suggesting
that the hypothesis is supported.
5.3.2 e-Learning characteristic
Referring to the theories in section 2.5.2.2 which indicated that ICT diffusion
research frequently investigated the external variables related to the technology itself
such as compatibility, relative advantage, complexity, trialability, and observability
(Rogers, 1995, e-Learning as one of ICT diffusion that is perceived as relatively
difficult to use can lead to learners‘ disengagement and dissatisfaction. Other
research such as Robinson et al. (2005) argues that technology learners expect and
desire the expenditure of minimal effort in dealing with a new technology .Also,
according to expectation-confirmation theory (Oliver, 1980), effort expectancy is a
determinant of satisfaction because it provides the baseline for individuals to form
evaluative judgments about the focal technology. Authenticity, the other variable in
this category was deleted due to internal inconsistency which was detected during
reliability and validity testing. Therefore, it was hypothesized that,
H4a: Complexity will have a negative effect on intention to adopt e-Learning.
H4b: Complexity will have a negative effect on actual use of e-Learning
72
The result finding of this study show that H4a was not significant with p value
greater than 0.05, it could not relate to the literature because respondents in this study
were not familiar with the term intention to adopt e-Learning due to the facts shown
in section 4.2.5 and table 4-3; that 55.2% (116 respondents) who were surveyed
declared that they had no e-Learning at their respective universities/Institutions.
Also, the result finding for the second dependant variable (actual use of e-learning)
in this study showed that: H4b was significant with p < 0.05 which suggest that
complexity of e-Learning will negatively affect its use. H4b was supported.
5.3.3 Instructor characteristic
Previous studies as indicated in section 2.5.3 of chapter two, found that an
instructor‘s control of technology along with providing enough time to interact with
students impacts learning outcomes (Arbaugh, 2002). Relevant instructor
characteristics include timely response, self-efficacy, technology control, focus on
interaction, and attitude toward e-Learning, distributive fairness, procedural fairness,
and interaction fairness (Arbaugh, 2002; Sun et al., 2008; Bhuasiri et al., 2012). It
was postulated that:
H5a: instructors’ timely response toward e-Learning will positively influence
students’ intention to adopt e-learning.
H5b: instructors’ timely response toward e-Learning will positively influence
students’ perceived actual usage of e-Learning.
This study show that H5a is not significant with p value greater than 0.05 and hence it was
rejected. The rejection might be due to the fact that most of the lecturers indicated to prefer
using pen rather than computers; in this study 17.6% strongly agreed and 47.6 agreed that
they preferred using pen than computers. But on the other hand the current results show
that: H5b is significant with p value less than 0.05, which is accepted. This means
that in Tanzania, there are some Universities which have e-Learning and are real
using it for teaching/learning purpose, so these are the one whose students usage of
e-Learning are positively influenced by their instructor‘s timely response.
73
5.3.4 Institutional factors
With reference to previous studies as shown in section 2.5.4 of chapter two, five
factors of e-Learning were identified. These include organizational support (which
was deleted due to detected internal inconsistency), ICT infrastructure, Institutional
policy, Training in e-Learning techniques, and management support.
5.3.4.1 ICT infrastructure
Literature recognizes that, there is a considerable technological infrastructure
difficulty, which limit developments (Lwoga, 2012), Technological obstacles in an e-
Learning environment often occur in one of three basic components, namely
hardware, software and bandwidth capacity. It was hypothesized that:
H7a: There is a positive relationship between Instructor’s intention to adopt
e-Learning and ICT infrastructure.
H7b: There is a positive relationship between Instructor’s usage of e-
Learning and ICT infrastructure.
Although the literature have shown ICT infrastructure as the key factor for e-
Learning usage according to Muyinda (2011); In this study the result show that H7a
and H7b were not significant both with p > 0.05 suggesting that H7a and H7b are not
supported. The reason for rejection is that, with the increase in number of laptops
possessed by students and lecturers and with the presence of internet service provider
(Telecommunication companies such as Tigo, Vodacom and Airtel), in Tanzania,
users perceive ICT infrastructure (computer Hardware) and network bandwidth as no
longer a problem to them. Hence ICT infrastructure is not a key factor for e-Learning
adoption in Tanzania.
5.3.4.2 Institutional policy
The prior literature indicate that, the use of e-Learning technologies is mainly driven
by individual efforts rather that institutional policies and strategies, which limits the
74
wide utilization of these technologies to support learning and teaching in higher
learning institution, (Lwoga, 2012). It was hypothesizes that;
H8a: Lack of institutional policies is related to poor users’ intention to adopt
e-Learning in the future.
H8b: Lack of institutional policies is related to users’ poor usage of e-Learning.
In this study both H8a and H8b were significant with p < 0.05, therefore they are
supported in line with the previous study. Hence, as argued by (Rosenberg, 2007)
that, institutional policies and strategies need to think about creative ways to
motivate staff. This is also an indication that institutional policies are either not
communicated to the e-Learning users or are completely not there in Tanzanian
higher learning institutions which results into users not ready to adopt the system.
5.3.4.3 Training in e-Learning techniques
The previous literature as argued by Charlesworth (2002) gives evidence that staff
training is a central concern for universities implementing distance learning methods.
(Shapiro, 2000) argues that, inadequately trained lecturers using e-Learning in
educational environments can become an obstacle in a finely balanced learning
process and can lead to problems in application, use and in the perception of
students. It was hypothesized that:
H9a: There is a positive relationship between training and users’ attitude on
intention to adopt e-Learning.
H9b: There is a positive relationship between training and users attitude on
actual use of e-Learning.
In this study H9a was not significant with p > 0.05 and therefore it was rejected. The
reason for rejection could again be the intention to adopt e-Learning was not clear to
respondents due to lack of e-Learning adoption knowledge. On the other dependant
variable, the result indicated in line with the previous study that, training is positively
influencing students and lecturers‘ attitude on the actual use of e-Learning because
75
H9b was significant with p value less than 0.05, hence it was accepted. This result
confirmed the theoretical expectation, because majority of Tanzanian are not familiar
to the usage of e-Learning and they hesitate to plan using e-Learning in the future.
5.3.4.4 Management support
From the previous literature, for instance, Venkatesh & Bala (2008) demonstrate that
when users hold a strong believe with regard to the availability of organization
resources, technical and managerial support, then, that will facilitate the adoption of
technology in question. It was postulated that:
H10a: There is a positive relationship between management support and
intention to adopt e-Learning system.
H10b: There is a positive relationship between management support and
actual use of e-Learning system.
This literature is not in the same line with the result of this study because both H10a
and H10b are not significant with P > 0.05. So the hypotheses were rejected. The
competition among universities might be the reason behind. Because the universities
in Tanzania are trying to adopt e-Learning just because other universities has
introduced it, they are introducing without having its policy to guide its
implementation.
While with intention to adopt e-Learning, only one hypothesis H8a is significant and
the rest (H2a, H4a, H5a, H7a, H9a and H10) were not significant, and the result of
actual use was that, only two hypothesis(H7b and H10) were not significant and were
rejected, the remaining five hypothesis (H2b, H4b, H5b, H8b and H9b) were
significantly supported. These results provide the indication that respondents knew a
little bit about actual use of e-Learning but they were not aware of the term intention
to adopt e-Learning because e-Learning Technology is still new in Tanzania.
76
CHAPTER SIX
6.0 Summary, Conclusions, and Policy implications
6.1 Summary
The objective of this study was to assess the key issues/factors that that determine the
adoption of electronic learning for higher learning institution in Tanzania, which
involved the measure of students‘ as well as lecturers' attitudes toward the adoption
of e-Learning system as a new way of learning/teaching. The theoretical basis of the
current research was derived from behavioural intention (TRA) and technology
acceptance models. The model has been adapted to reflect determinants relevant to
students and lecturers' attitudes to the adoption of e-Learning system. Factors like
complexity was derived from diffusion of innovation theory (DOI), and four factors
were institutional factors.
Three factors were removed from the study analysis because their Cronbach‘s alpha
which measured internal consistency of variables was below the required value.
These were: Authenticity which was hypothesizes H1, self efficacy (H3) and
organizational support (H6). The researcher was left with seven factors.
The findings of this study show that, for the case of intention to adopt e-Learning,
most of formulated hypotheses were not in the same direction as was hypothesized in
the study except H8a. For example H2a, which was complexity, was not supporting
the original theory; Diffusion of innovation theory but the same hypothesis H2b,
when measured the actual use of e-Learning it significantly supported Diffusion of
innovation theory.
Hypothesis 4a was about Openness to change effect: the result was also not
significant for the case of intention to adopt e-Learning, but significant to the actual
use of e-Learning. This means that Lecturers and Students may receive pressures
from a university as a de-motivation to them when they hear that they have to get
involved in e-Learning usage, since the concept of e-Learning is not well
conceptualized and understood within the Tanzanian university setting and hence,
77
lecturers and students may resist changing their work routines because the attained
benefits of e-Learning system are not realized. H2 partially supported social
cognitive theory (SCT) because intention to adopt was rejected and actual use was
accepted
Hypothesis 5 was about instructor timely response towards e-Learning, again Social
Cognitive Theory was partially rejected because on the side of intention to adopt e-
Learning the theory was not supported p >0.05 while at the same time when
measured the actual use of e-Learning it significantly supported SCT, indicating that
student need lecturer immediate and consistency support in using e-Learning
otherwise the implementation of e-Learning may result into failure.
The final group of factors (Institutional factors) had four hypotheses: H7, H8, H9 and
H10. When measured intention to adopt e-Learning three hypotheses were not
significant (H7, H9 and H10). The only significant hypothesis was H8, supporting
that the poor users‘ intention to adopt e-Learning in the future is because of Lack of
institutional policies. But when measured the actual use of e-Learning the result
show that, two hypothesis were not significant (H7 and H10) confirming that ICT
infrastructure in Tanzanian universities and Higher learning institutions is not a key
factor of e-Learning adoption and actual use. Likewise management support is also
not a key factor according to this study. Two other hypotheses (H8 and H9) were
significant and supported the literature review. These are training and institutional
policy.
Training is an important factor of e-Learning adoption and usage, it was the reason
why most of the hypotheses when measured intention to adopt e-Learning were not
significant because e-Learning is a new technology in Tanzanian Higher learning
institution to both students and lecturers/Tutors; as the results they are reluctant to
have behavioral intention to adopt e-learning. The findings from respondents‘
personal details demonstrated that students who suffered from a lack of ICT skills,
experience, and training on the use of e-Learning tools were not able to benefit or
78
engage with e-Learning opportunities whether these took place in classes or
elsewhere. This lack of ICT skills resulted in a type of resistance or lack of openness
to change among students and Lectures/Tutors which led to uncertainty about the
benefits of e-Learning
6.2 Practical implications and Conclusion
6.2.1 Implications
This research provides several important implications for various stakeholders
involved in building and promoting effective e-Learning systems in Tanzania.
6.2.1.1 Policy makers in Tanzania
As stakeholders for e-Learning , especially political readers should promote and
increase e-Learning awareness to society by communicating the national ICT policy
in order to make it known to all Tanzanian, which will decrease the fear of using e-
Learning, hence minimize the resistance to change among e-Learning system users.
6.2.1.2 Universities in Tanzania
Universities should encourage computer usage among stakeholders and promote
various applications of the Internet to develop computer skill and competency. This
may be done by providing support to users and setting up Internet access points or
computer rooms for users. Universities and system developers need to communicate
their Policy, especially ICT policy to facilitate e-Learning success by: (a)
disseminating up-to date and useful learning information; (c) continuing to establish
user-friendly websites and promoting the ease of use of electronic learning services
to minimize complexity of the systems (d) increasing technology awareness and
providing training to all types of e-Learning tool to users, both learners and faculty.
79
6.2.2 Conclusion
Organizations considering e-Learning adoption need not only be concerned that
possible users lack technical competency or confidence with computer technology.
Technical ability is not an obstacle when learners are provided with proper training
on the use of e-Learning. In other words, the Training of the e-Learning tools and
the proper content of the e-Learning system act as a bridge mechanism for perceived
benefit from e-Learning ; and therefore the system must be seen to be, a good way
of learning.
The findings of this research explain the reasons behind the failure to use e-Learning
in Tanzania, despite both teachers' and students‘ positive attitudes towards the
adoption of e-Learning. The justification for the reluctance to adopt is attributed to
(1) perceived complexity of e-Learning (2) lack of openness to change among
Lecturer and students which led them to prefer the old ways of doing things and fail
to accept the new technology.
(3) poor Instructor timely response towards e-Learning to their students (4)
Institutional policy towards e-Learning Technology which are either not
communicated or completely absent in most of the universities and Higher learning
Institutions in Tanzania and (5) lack of specific training at all levels particularly,
Lecturers/Tutors, students and Management
6.3 Recommendations
Several recommendations can be made to increase lecturers' and students‘ adoption
of e-Learning and its use. Firstly, in order to overcome e-Learning barriers, learners
need to be provided with support in term of user training, technical support, and
managerial encouragement to use e-Learning which may change the users‘ perceived
complexity of adoption and usage of e-Learning.
80
Secondly, educational institutions should make a systematic effort to provide
lecturers with training on how to use e-Learning system effectively, and then the
trained teachers will influence their adoption attitude to their students.
Thirdly, the integration of Courses (policy issue) in the e-Learning system should be
communicated to both lecturers and students to explain them the benefits of adopting
e-Learning system, and how such system can effectively support their educational
objectives; and since lack of skill has been found to have a strong and negative effect
on intention to adopt e-Learning system, training should be designed to increase
lecturers' and students‘ computer knowledge.
6.3.1 Limitations and Future Research Directions
There are few limitations about this study, amongst which was the sample size. The
participants in this study were drawn from Tanzanian Universities and higher
learning institutions which are one of developing countries that are in the early stages
of employing ICT in education. The influences and challenges affecting stakeholders
that are in the earlier stages of e-Learning systems diffusion will likely be different
than where e-Learning systems are mature.
The next one was the knowledge of the participants, who are not really considered as
expert in the field of e-Learning, for instance the behavioral intention to adopt e-
Learning was not clearly understood by respondents; with time as students and
lecturers gain knowledge of e-Learning, this model needs to be adjusted over time,
this means, the impact of some factors may change, hence future research is required.
6.3.2 Suggestions for future research
Further research should be carried out to identify other factors that may influence
lecturers' and students‘ attitudes toward the adoption of e-Learning system. Students‘
age and level of education was not examined to find their effect. Future research
needs to examine the effect of age and educational level on their adoption level
towards e-Learning system.
81
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APPENDICES
Appendix 1: Research Work Plan (in months, 2012/2013)
Table 1-1 Research activity schedule
Activity
Duration in Months
October
November December January February March
Proposal Development and
Submission
Data collection and Analysis
Report Writing and 1st Draft
Submission
Final Submission
Appendix 2: Research study Budget (in T.shs.)
Table 1-2: Proposed Financial Budget
ACTIVITY BREAKDOWN SUB-TOTALS
1.SHUTTLING COSTS IN
MWANZA
A: Shuttling expenses that covered the transport
expenses in Mwanza during data collections.
A: Data collection: 30 daysx10,000/=
300,000/=
2.TRAVELLING
EXPENSES
From Mwanza to Morogoro: 45,000 x6
That is to and fro to see the supervisor.
270,000/=
MEAL-AND
ACCOMODATION
22,500 X 20 meals and accomodation expenses
in Morogoro during consultation with the
supervisor.
450,000
3.REPORT WRITING Stationeries
Secretarial services
30,000/=
40,000/=
92
Photocopy:
Questionnaires: 210copiesx5x100/=
Other Documents:
Printing :
Questionnaires: 5pagesx500/=
Proposal:69pagesx 500/=
Binding expense: 4copiesx1,500/=
Report printing and binding : 4copiesx20,000/=
105,000/=
10,000/=
2,500/=
34,000/=
6,000/=
80,000/=
361,500/=
CONTIGENCIES 190,000/=
GRAND TOTAL 1,511,500/=
Appendix 3: Research Questionnaire.
Dear respondent,
Mr. Tale S. Ndonje is an MBA student from Mzumbe University School of Business
administration. As a requirement for the fulfillment of his studies he is conducting a
research on ―ADOPTION OF E-LEARNING IN HIGHER LEARNING
INSTITUTIONS. By e-Learning adoption, we imply the use of ICTs to enhance
and/or support learning activities. I assure you that, the contents of this
questionnaire are absolutely confidential; the answers will go only to the researcher
and information identifying respondents will not be disclosed in any way. Please,
tick the relevant boxes provided. Thank you in advance for your cooperation and
valuable time.
QUESTIONNAIRES PART1: GENERAL RESPONDENT’S INFORMATION
(100). Please put a tick [√] the most appropriate alternative in the box provided.
(101) PERSONAL DETAIL
(101a) Name of the Higher
Learning Institution you
belong
SAUT OUT CBE MZUMBE TIA
93
(101b) Please indicate your current
position in the university in
which you belong.
Management IT
expert
Lect
urer/T
utor
Student
(101c) Which of the following best
describe your age?
Under 25 25-35 36-
45
46-55 abov
e 55
(101d) What is your gender/Sex? Female Male
(101e) What is you highest educational
level? (For students: What is the level of your studies?)
certificate/di
ploma
Bachelo
r degree/
Adv
diploma
Post
gradua
te
diplom
a
masters
degree
PhD
(102) HOW E-LEARNING IS USED IN UNIVERSITIES FOR TEACHING/ LEARNING
PURPOSE
(102a) My knowledge of e
learning is: I have
some basic
ideas
I know
a little
about it;
Never
heard of
it
I know
it quite
well
I know
it very
well
(102b) My experience of using e
learning is:
No
experience
at all;
I have
a little
experien
ce;
I have
some
experien
ce
I have
considerab
le
experience
I have a
lot of
experience
(102c) My frequency of using e
learning (monthly) is: once 2-5
times
6-10
times
11-15
times
more
than 15
times
(102d) How best can you
describe your
understanding on e-
Learning ?
learned
by
experience
attende
d short
courses
I’ve
certificate
in IT
I’ve
Diploma in
IT
I’ve
degree and
above in IT
PART11: SUBJECT COURSES IN THE E LEARNING
(200) To what extent subject courses are in the e-Learning YES NO
(201a) Does this course employ eLearning elements? If YES, please answer the following questions below
(201b) It is easy to use the e-Learning elements
(201c) The e-Learning elements are well coordinated with the course
94
(201d) The e-Learning content is well organized.
(201e)
The e-Learning elements help me reach the learning objectives.
PART111: ACCESS OF THE INTERNET AS THE LEARNING/TEACHING DELIVERY METHOD
(300)To what extent can you access the internet?
(301) I have access to a
networked computer at:
Home/student residence
Work University/College
Internet cafe
Other Locations
(302) I normally access email and/or the Internet(please tick one)
Very
rarely, if ever.
Occasi
onally
A few
times a week
Every
day
I’m
addicted
(303) How often do you use your internet? (please tick one)
Every
day
A few
times a week
Occasio
nally
Rarel
y
never
(304) My purpose of accessing-to-the
internet/network system is:
learning Teachin
g
chatting e-
others
PART1V: INSTITUTIONAL CHARACTERISTICS (400). Please put a tick [√]one in the
box provided.
Item
Question
I st
ro
ng
ly a
gre
e
I a
gre
e
Ca
n’t
dec
ide
I d
isa
gre
e
I st
ro
ng
ly d
isag
ree
ICT COMPETENCY OF THE INSTITUTE/UNIVERSITY (401)
401a Our institute/University is located nearby Institutions that
provide IT support and training courses
401b The university employees are aware of benefits of e-Learning
401c Our university have already in-house IT expert and skills to
support e-Learning
401d Our university has enough financial resources to support e-
Learning adoption.
Please Tick [√] either YES or NO in the box provided.
(402) INSTITUTIONAL ICT COMPETENCY YES NO
402a In our institute/University we have e-Learning system
402b The university depends much on e-Learning in the learning/ teaching
process
402c Our university had already existing in-house IT infrastructure to support e-Learning
95
402d the access of our university website is free for students and teachers
PART V: Please Tick [√] the most appropriate alternative in the relevant boxes to indicate
your level of agreement or disagreement for each of the following statement.
(500) Innovation characteristics influencing the adoption of e-Learning (e-Learning
characteristics)
Item
Qu
est
ion
I st
ron
gly
agre
e
I a
gre
e
Ca
n’t
dec
ide
I d
isagre
e
I st
ron
gly
dis
agre
e
(501) Authenticity
501a I worked on activities that dealt with real world information
501b Using e-Learning helps me to learn the topic
501c Using e-Learning increases my chance of scoring higher marks
501d Using e-Learning in studies enables me to accomplish tasks (e.g. learn the
topic, complete assignment) more quickly
501e I find e-Learning useful in my studies
(502) Complexity
502a Doing the e-Learning was so complicated that it was difficult to follow
502b It is difficult for me to learn how to use e-Learning tools
502c It is difficult for me to become competent at using e-Learning
502d using e-Learning requires a lot of mental effort
502e My interaction with e-Learning is clear and understandable.
(601) Self efficacy (LEARNER CHARACTERISTICS)
601a I am able to operate the e-Learning system with less support and
assistance
601b I am confident that I can overcome any obstacles when using the e-
Learning system
601c I believe that I can use different e learning software and systems to receive
education
Item
Qu
est
ion
I st
ro
ng
ly a
gre
e
I a
gre
e
Ca
n’t
dec
ide
I d
isa
gre
e
I st
ro
ng
ly d
isagre
e
601d My teachers possess the skills to use e-Learning
96
601e I possess the skills necessary to use e-Learning tools
(602) Openness to change
602a I consider myself to be ‗open‘ to changes in my studies
602b I am reluctant to consider changing the way I do my studies
602c I prefer to use my pen rather than using a computer.
602d A change to online learning approach could cause difficulties for student
learning
602e I hate computer usage for learning/Teaching purpose
(700) Instructor timely response (INSTRUCTOR CHARACTERISTICS)
701a When I faced challenges in using e-Learning I reported on the technical
problems for assistance?
701b I did not get response or reply from my head of department when I
reported on technical problem in the above
701c when I got response, my problem(s) were solved
701d I do not have time to reply to all the enquiries of students. I prefer talking
to all students in the class which saves time and leads to better
understanding
701e I have enough time to interact with students/Teachers electronically
INSTITUTIONAL CHARACTERISTICS
(801) Organizational support: For both teachers and students
(801a) I‘ve heard of my university/institute‘s electronic Learning System
(801b) I have used my Electronic Learning System
(801c) My head of department is supportive to me on the use of e-Learning for
my work
(801d) There are technical help available if required while using e-Learning
(801e) When I encounter issues during my work, I am always given technological
and pedagogical support
(802) ICT infrastructure
(802a) My institute has provided me all the facilities I need for e-Learning
(802b) The ICT infrastructure such as internet, extranet, intranet and LAN
networks at my institute/University are available when needed
(802c) There are so many computer facilities available in my university/institute.
(802d) The computers facilities are mostly used for teaching purpose
(802e) The current bandwidth of the networked computers is sufficient in the university/Institute.
97
Item
Qu
est
ion
I st
ron
gly
agre
e
I agre
e
Can
’t d
ecid
e
I d
isagre
e
I st
ron
gly
dis
agre
e
(803) Institutional policies
(803a) I am aware of the current ICT policy
(803b) The ICT policy, addresses the issues regarding e-Learning
(803c) My institute/University provides incentives to Teachers who use e-
Learning
(803d) My institute/University provides incentives to students who use e-Learning
(803e) My institute/University promotes the adoption of e learning through proper
ICT policy implementation.
(804) Training
(804a) I have attended ICT training / workshop on the use of e-Learning tools
(804b) In my perception the trainings are sufficient in terms of facilities, materials
delivered and timings
(804c) I would like some more training into the usage of e-Learning technology
(804d) I possess limited basic skills in using computers and its
applications
(804e) I would like to have more in-service training whenever there is new
technology of e-Learning in the market
(805) Management Support
(805a) Top management considers e-Learning as important for our university
success
(805b) Top management allocates resources for e-Learning
(805c) Top management discuss with employees their support for e-Learning in
our university
(805d) Top management enthusiastically supports e-Learning adoption
(805e) Top management is aware of the benefits of e-Learning
(900)Behavioral Intention to use e-Learning
(900a) Using e-Learning is a good idea
(900b) I would continue to use e-Learning for my learning needs
(900c) I expect my use of the e learning to continue in the future
(900d) I plan to use e learning in the near future
(900e) In my view, using e-Learning is a wise idea intention
~ Thank you for your cooperation ~
