UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA SUMMER …
Transcript of UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA SUMMER …
FACTORS AFFECTING ADOPTION OF TECHNOLOGICAL
INNOVATION IN KENYA: A CASE OF KENYA REVENUE
AUTHORITY MEDIUM TAXPAYERS OFFICE
BY
EMMA MWAMBIA
UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA
SUMMER 2015
FACTORS AFFECTING ADOPTION OF TECHNOLOGICAL
INNOVATION IN KENYA: A CASE OF KENYA REVENUE
AUTHORITY MEDIUM TAXPAYERS OFFICE
BY
EMMA MWAMBIA
A Research Project Report Submitted to the Chandaria School of
Business in Partial Fulfilment of the Requirement for the Degree of
Executive Master of Science in Organizational Development (EMOD)
UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA
SUMMER 2015
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STUDENT’S DECLARATION
I, the undersigned, declare that this is my original work and has not been submitted to any
other college, institution or university other than the United States International
University in Nairobi for academic credit.
Signed: ________________________ Date: _________________________
Emma Mwambia (ID: 611469)
This research project proposal has been presented for examination with my approval as
the appointed supervisor.
Signed: ________________________ Date: _________________________
Dr. Joseph Ngugi
Signed: _______________________ Date: _________________________
Dean, Chandaria School of Business
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COPYRIGHT
All rights reserved; no part of this work may be reproduced, stored in a retrieval system
or transmitted in any form or by any means, electronic, mechanical, photocopying,
recording or otherwise without the express written authorization from the writer.
Emma Mwambia © 2015
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ABSTRACT
The purpose of the study was to determine the factors that affect adoption of technology
innovation in Kenya. The study was guided by the following research questions. What are
the technology innovation characteristics that affect adoption of iTax technology by
medium taxpayers at KRA? What are the organizational factors that affect adoption of
iTax technology by medium taxpayers at KRA? What are the individual factors that affect
adoption of iTax technology by medium taxpayers at KRA?
A case study research design was used. The design was descriptive in nature. The
population for this study involved the 1,630 medium taxpayers to the Kenya Revenue
Authority. The study used a stratified sampling design to draw a sample size of 200. The
study used a survey instrument to collect primary data from the respondents. Data
analysis involved frequencies, percentages, correlations and regression analysis to
determine the relationship between the dependent variable and the independent variables
of the study. Statistical significance level was used to infer deductions from the study to
the entire population. Findings were presented using tables and figures.
On the first specific study objective the study showed a strong positive relationship
between innovation characteristics and adoption of iTax technology. The study indicated
that considered singularly, Fifty-Seven percent of the variance in adoption of iTax
technology can be predicted by the independent variables of innovation characteristics.
The most significant innovation characteristics that affect positively the adoption of the
iTax technology by medium taxpayers in Kenya are; system compatibility; visibility of
results; clarity of advantages; and user friendliness.
The second specific study objective showed a strong positive relationship between
organizational factors and adoption of iTax technology. It showed that considered
singularly, Sixty-Seven percent of the variance in adoption of iTax technology can be
predicted by organizational factors. The study illustrated that top management’s support
and top management’s commitment to adoption of iTax technology are significant in
positively influencing adoption of iTax technology.
The third specific study objective illustrated a strong positive relationship between
individual factors and adoption of technology. Considered singularly, Sixty-Eight percent
of the variance in adoption of technology can be predicted by individual factors. The
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study showed that there is a significant positive influence on adoption of iTax technology;
when individuals consider the technology to be useful; when the individuals are well
trained on how to use the technology; when individuals take personal initiatives to use the
technology; when individuals consider the technology to be a sophistication; and when
there is support from colleagues on how to use the technology. The other factors that
affect adoption of iTax technology negatively were, lack of knowledge on the use of iTax
technology, the system being complicated, lack of top management support, lack of staff
awareness on the existance of the technology, lack of system support, iTax technology
cannot handle huge traffic especially during deadlines, and poor internet connectivity.
The study recommends that KRA needs to redesign or develop upgraded versions of the
systems so that it is compatible to most technology platforms, show clearly visible results
and be more user friendly. Furthermore, KRA needs to institute a more elaborate
promotional campaign to ensure its clients clearly understand the advantages of using the
system. The study also recommends clear top management’s support and commitment to
motivate them to adopt new technologies. This can be enhanced through adequate
resource allocation, clear policy directions and employee reward systems. Further, it is
significant for organizations to set aside resources to train their staff on how to use the
platform. Finally, studies on other categories of taxpayers and at different times zones
would be welcome.
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ACKNOWLEDGEMENT
I express gratitude to all people whose contributions have made this research project
report a success. I would also thank lectures at USIU for the vast knowledge they
impacted in me that has broadened my perspective of the world. Particular appreciation
goes to Dr. Joseph Ngugi for taking time to guide me through the research process.
Above all, I thank Almighty God for the ability and opportunity to complete my project.
Finally yet importantly, I thank my employer, Kenya Revenue Authority.
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DEDICATION
I dedicate this project to my husband, Ernest, our children Gabriella and Jenaya and my
parents Mr. and Mrs. Mwambia who have supported me through the entire process.
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TABLE OF CONTENTS
STUDENT’S DECLARATION ........................................................................................ ii
COPYRIGHT ................................................................................................................... iii
ABSTRACT ....................................................................................................................... iv
ACKNOWLEDGEMENT ................................................................................................ vi
DEDICATION.................................................................................................................. vii
TABLE OF CONTENTS .............................................................................................. viii
LIST OF TABLES ............................................................................................................. x
LIST OF FIGURES ......................................................................................................... xii
LIST OF ABBREVIATIONS ....................................................................................... xiii
CHAPTER ONE ................................................................................................................ 1
1.0 INTRODUCTION........................................................................................................ 1
1.1 Background of the Problem........................................................................................ 1
1.3 Purpose of the Study .................................................................................................. 6
1.4 Research Questions .................................................................................................... 6
1.6 Scope of the Study...................................................................................................... 7
1.7 Definition of Terms .................................................................................................... 7
1.8 Chapter Summary ....................................................................................................... 7
CHAPTER TWO ............................................................................................................... 9
2.0 LITERATURE REVIEW ........................................................................................... 9
2.1 Introduction ................................................................................................................ 9
2.2 Technological Innovation Characteristics that Affect Innovation Adoption ............. 9
2.3 Organizational Factors that Affect Innovation Adoption ......................................... 14
2.4 Individual Factors that Affect Innovation Adoption ................................................ 19
2.5 Chapter Summary ..................................................................................................... 23
CHAPTER THREE ......................................................................................................... 24
3.0 RESEARCH METHODOLOGY ............................................................................. 24
3.1 Introduction .............................................................................................................. 24
3.2 Research Design ....................................................................................................... 24
3.3 Population and Sampling Design ............................................................................. 25
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3.4 Data Collection Method ........................................................................................... 27
3.5 Research Procedure .................................................................................................. 27
3.6 Data Analysis Method .............................................................................................. 28
CHAPTER FOUR ............................................................................................................ 29
4.0 RESULTS AND FINDINGS ..................................................................................... 29
4.1 Introduction .............................................................................................................. 29
4.2 Reliability of the Survey Instrument ........................................................................ 29
4.3 Demographic Characteristics of the Respondents .................................................... 32
4.4 Innovation Characteristics ........................................................................................ 35
4.5 Organizational Factors ............................................................................................. 38
4.6 Individual Factors ..................................................................................................... 40
4.7 Adoption of iTax Technology .................................................................................. 42
4.8 Bivariate Analysis .................................................................................................... 45
4.9 Single and Multiple Regression Analysis ................................................................ 46
4.10 Chapter Summary ................................................................................................... 51
CHAPTER FIVE ............................................................................................................. 52
5.0 DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS ...................... 52
5.1 Introduction .............................................................................................................. 52
5.2 Summary .................................................................................................................. 52
5.3 Discussions ............................................................................................................... 54
5.4 Conclusions .............................................................................................................. 59
5.5 Recommendations .................................................................................................... 60
REFERENCES ................................................................................................................. 62
APPENDICES .................................................................................................................. 68
Appendix A: Cover Letter .............................................................................................. 68
Appendix B: Questionnaire ............................................................................................ 69
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LIST OF TABLES
Table 3.1: Study Population…………………………………………………………..….25
Table 3.2: Sample Size………………………………………………………………...…26
Table 4.1: Cronbach’s Alpha Analysis for Innovation Characteristics Test Items ............ 30
Table 4.2: Cronbach’s Alpha Analysis for Organizational Factors Test Items ................. 30
Table 4.3: Cronbach’s Alpha Analysis for Individual Factors Test Items ........................ 31
Table 4.4: Cronbach’s Alpha Analysis for Adoption of iTax Test Items .......................... 32
Table 4.5: Duration worked in the Current Organization .................................................. 34
Table 4.6: Highest Education Level ................................................................................... 34
Table 4.7: Management Position ....................................................................................... 35
Table 4.8: Innovation Characteristics ................................................................................ 36
Table 4.9: Correlation Matrix of Innovation Characteristics ............................................. 36
Table 4.10: Organizational Factors .................................................................................... 39
Table 4.11: Correlation Matrix of Organizational Factors ................................................ 40
Table 4.12: Individual Characteristics ............................................................................... 41
Table 4.13: Correlation Matrix of Individual Factors ........................................................ 41
Table 4.14: Adoption of iTax Technology ........................................................................ 43
Table 4.15: Correlations of Adoption of iTax Technology ............................................... 44
Table 4.16: Correlation Matrix of the core constructs ...................................................... 45
Table 4.17: Model Summary for Innovation Characteristics as Predictor of Adoption .. 46
Table 4.18: Coefficients for Innovation Characteristics as Predictor of iTax Adoption .. 47
Table 4.19: Model Summary for Organizational Factors as Predictor of iTax Adoption 47
Table 4.20: Coefficients for Organizational Factors as a Predictor of iTax Adoption ..... 48
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Table 4.21: Model Summary for Individual Factors as a Predictor of Adoption ............. 48
Table 4.22: Coefficients for Individual Factors as Predictor of iTax Technology ........... 49
Table 4.23: Model Summary for Combined Innovation Characteristics, Adoption .......... 50
Table 4.24: Coefficients for Combined Characteristics, Organizational Factors Factors as
Predictor of Technology Adoption ........................................................................... .50
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LIST OF FIGURES Figure 4.1: Response Rate……………………………………………………………..32
Figure 4.2: Gender……………………………………………………………………….33
Figure 4.3: Age Group…………………………………………………………………33
Figure 4.4: Sector………………………………………………………………………..34
Figure 4.5: Cross tabulation of Age versus Innovation Characteristics………………….37
Figure 4.6: Cross tabulation of Sector versus Innovation Characteristics……………….37
Figure 4.7: Other Factors that Affect Adoption of iTax Technology………....................41
Figure 4.8: Cross tabulation of Duration Served versus Adoption of iTax……………....43
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LIST OF ABBREVIATIONS
KRA: Kenya Revenue Authority
SPSS: Statistical Package for Social Sciences
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CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Problem
Today’s economic environment has been characterised by continues turbulence and
uncertainty to an extent that the only thing which is certain is the rapid environmental
changes themselves. Literature has left no doubt that innovation is a critical element for
organizations to meet the ever growing changes in customer preferences and to gain
competitive advantage, as well as develop products for the future (Zailani, Iranmanesh,
Nikbin, & Jumadi, 2014). Innovation is noted to be a necessary ingredient for the
sustained success of firms as it protects both tangible and intangible assets against the
erosion of the market (Ongong’a & Ochieng, 2013). In fact, according to Sumiyu (2013),
the ability to innovate is increasingly viewed as the single most important factor in
developing and sustaining competitive advantage. He contends that it is no longer
adequate to do things better; it’s about “doing new and better things
Innovation may be considered to involve acting on the creative ideas to make some
specific and tangible difference in the domain in which the innovation occurs (Ngugi &
Karina, 2013). Likewise, innovation may also be seen to consist of any practice that is
new to organizations, including equipments, products, services, processes, policies, and
projects (Zailani, Iranmanesh, Nikbin, & Jumadi, 2014). Thus, Ngugi and Karina (2013)
defined the term innovation as the successful implementation of creative ideas within an
organization. Okiro and Ndungu (2013) expanded the concept to mean any idea, object or
practice that is perceived as new by members of the social system. The social systems in
this context defines the organization.
In this paper the term innovation was used to mean technological innovation. According
to Letangule and Letting (2012), technological innovation is considered as the process of
introducing something new or a new idea, method or device which is science, technology
and system based. They contend that the process includes several factors affecting and
affected by the firm’s internal capabilities, its networking and its technological learning
ability and influenced by its environmental factors.
According to Talukder (2012), despite an organization’s decision to adopt an innovation,
its actual usage depends on how members of the organization and the organization’s
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customers implement the usage of the innovation. He continues that it is therefore,
important to examine the adoption of innovations by members of the organization
because if there is no acceptance among general staff and management, the desired
benefits cannot be realized and the organization may eventually abandon the innovation.
Since innovation comes with changes, Talukder (2012) entrenches the general assumption
that naturally people will resist change and unless they can be convinced that they can
directly benefit from the change. This argument is extended by Okiro and Ndungu (2013)
who indicated that resistance to change may be a hindrance to diffusion of innovation
although it might not stop the innovation, it will slow it down. They further stated that
not all innovations are adopted even if they are good it may take a long time for an
innovation to be adopted.
The foregoing study is guided by the innovation diffusion theory. Okiro and Ndungu
(2013) define diffusion of innovation as the process by which the innovation is
communicated through certain channels over time among members of social systems.
They therefore opined that diffusion of innovation theory attempts to explain and describe
the mechanisms of how new inventions are adopted and becomes successful. The linear
view of innovation adoption process takes a sequential approach. In this view, innovation
adoption is a process through which an individual or other decision making unit passes
from first knowledge of an innovation, forming an attitude toward the innovation, to a
decision to adopt or reject, to implementation of the new idea, and to confirmation of this
decision (Kundu & Roy, 2010). This is what is summarised into the five stages of
innovation adoption as; the awareness; consideration; intention; decision;
implementation; and confirmation stages.
Okiro and Ndungu (2013) looking at the innovation characteristics identified five critical
attributes that greatly influence the rate of innovation adoption. These include relative
advantage, compatibility, complexity, triability and observability. Thus, the rate of
adoption of new innovations will depend on how an organization perceives the
innovation’s relative advantage, compatibility, triability, observability and complexity
(Okiro & Ndungu, 2013). It is worth noting that the initiation stage of the innovation
adoption evaluates the innovation in terms of its relative advantage, compatibility,
triability, observability and complexity for the innovation to be accepted by its users.
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Therefore, Kundu and Roy (2010) posit that the innovation process can only be
considered a success when the innovation is accepted and integrated into the organization
and the target adopters demonstrate commitment by continuing to use the product over a
period of time. This implies that adoption should also occur at the individual level i.e. the
organizational innovations have to be incorporated in the work processes and the
members comply with it if they are to add value to the organization.
Studies have also shown that people adopt innovations at different rates. There are five
established categories of adopters. Ndungu and Njeru (2014) present the categories to
include; first are the innovators who want to be the first to try the innovation. They are
venturesome and interested in new ideas, they are very willing to take risks, and are often
the first to develop new ideas. Then there are the early adopters who represent opinion
leaders who enjoy leadership roles, and embrace change opportunities. Third are the early
majority who are rarely leaders, but do adopt new ideas before the average person. Fourth
are the late majority who are skeptical of change, and will only adopt an innovation after
it has been tried by the majority. Finally, there are the laggards who are bound by
tradition and very conservative and very skeptical of change (Ndungu & Njeru, 2014).
In its major strategic plan dubbed Vision 2030, the Kenyan Government aims to establish
a nation that harnesses science, technology and innovation to foster global
competitiveness for wealth creation, national prosperity and a high quality of life for its
people. The government has gone ahead to instituted fiscal and taxation measures to
support innovation. A report by IST-Africa (2015) indicate that the launch of e-
Government services in Kenya is one of the main priorities of the Government of Kenya
towards the realization of national development goals and objectives for Wealth and
Employment Creation, as outlined in the Kenya Vision 2030. Since the launch of e-
Government in June 2004, the government has committed itself towards achieving an
effective and operational e-Government to facilitate better and efficient delivery of
information and services to the citizens, promote productivity among public servants,
encourage participation of citizens in Government and empower all Kenyans. Key among
the online services available through the e-government initiative is the online submission
of tax returns. These underscore the importance of identifying the factors that influence
the adoption of technological innovation in Kenya.
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The Kenya revenue Authority (KRA) was established by an Act of parliament, chapter
468 of the laws of Kenya, which became effective on 1st July 1995. The authority is
charged with the responsibility of collecting revenue on behalf of the Government of
Kenya and since 2000, KRA has been undergoing several reforms all geared towards
enhancing tax collection (Weru, Kamaara, & Weru, 2013). Some of the changes include
use of technological innovation to enhance tax collection and limit tax evasions. The
Authority has successful rolled out the use of electronic tax registers and online
submission of tax returns dubbed the iTax. Tax collection is critical in Kenyan’s economy
as KRA funds over 70% of the Kenya National Budget and hence the need to increase
revenue through continuous improvement on tax collection processes (Weru, Kamaara, &
Weru, 2013).
Despite this, the medium taxpayers office at KRA reports that only a limited number of
their clients use the online based tax submission platform (KRA, 2015). This raises the
question as to why the low adoption of a system envisioned to enhance efficient tax
submission. The current study therefore explored the factors that influence the adoption
of technological innovation at KRA. The study focused on online tax return submission
platform. The study explored the technology characteristics, organizational factors and
the individual factors.
1.2 Statement of the Problem
Innovation has been strongly and positively linked to the performance of organizations.
According to Ongong’a and Ochieng (2013), positive influence on performance is
ascribed to innovations that solve and accommodate the uncertainties (market and
technological turbulence) a firm faces in its environment. Despite this, literature
documents instances of low uptake of technological innovations attributable to a number
of factors. The factors may be individual based such a lack of training; individuals’
cognitive interpretations of innovation and themselves; individual’s perception of
innovation usefulness; personal innovativeness; prior experience; image; enjoyment and
social environment (Talukder, 2012).
Low adoptions have also been linked to the characteristics of technological innovation
such as the innovation’s relative advantage, compatibility, complexity, triability and
observability (Okiro & Ndungu, 2013). Still there are organizational factors such as
managerial support and incentives (Talukder, 2012). There are also social influence from
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peers and other social networks. The assumption is that the sum effect of these factors on
innovation adoption is subject to the context. Various organizations have unique
characteristics and environments. Similarly, different innovations are perceived
differently by different adopters. Therefore, the current study seeks to identify the factors
that affect adoption of iTax technology at Kenya Revenue Authority.
Several studies have been carried out at Kenya Revenue Authority to try to determine the
factors that influence adoption of technological innovations. One of such studies by
Kisang and Rotich (2014) looked at innovation adoption at Kenya Revenue Authority but
with a focus on electronic procurement system. The study found out that like other public
institutions, Kenya Revenue Authority has not fully adopted electronic procurement and
therefore continue to miss the benefits associated with adoption of the technology.
Further, a study by Weru, Kamaara and Weru (2013) sought to establish the effects of the
introduction of the new electronic tax register at Kenya Revenue Authority to enhance tax
collection. The study only targeted traders operating along Luthuli Avenue in Nairobi.
On a postive note, the study indicated that electronic tax register system had enhanced
tax collection in business premises in Nairobi and that the system had to a great extent
assisted in sealing loopholes of tax evasion in Nairobi and improved on tax compliance.
However, the study noted that the system is yet to be fully institutionalized in the KRA
system. Furthermore, stakehloders have not been trained effectively on the use of
electronic tax regiter machines and the Authority is still experiencing some resistance to
change from both internal and external customers. Even though the study captures the
factors affecting innovation adoption at KRA, the iTax system to be investigated in this
study is unique from the electronic tax register as it is exclusively internet based.
An earlier study by Obae (2009) acknowledged that Kenya Revenue used technological
innovation to pursue its turnaround strategy initiated in 2003. The study indicates that the
organization systemetically introduced new technologies in assessment, collection and
accounting for all revenues; administration and collection of revenue; enhancing
efficiency and effectiveness of tax administration by eliminating bureaucracy; and
eliminating tax evasion by simplifying and streamlining procedures and improving
taxpayer service thereby increasing the rate of compliance.
The studies do not expressly identify the factors that affect adoption of the internet based
iTax system by the medium taxpayers at Kenya Revenue Authority. This presented a
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knowledge gap. The current study sought to fill this gap by exploring the factors that
affect adoption of iTax technology by the medium taxpayers at Kenya Revenue
Authority. The study looked at individual, organizational and technology characteristics
that affect the adoption.
1.3 Purpose of the Study
The purpose of the study was to determine the the factors that affect adoption of
technology innovation in Kenya.
1.4 Research Questions
1.4.1 What are the technology innovation characteristics that affect adoption of iTax
technology by medium taxpayers at KRA?
1.4.2 What are the organizational factors that affect adoption of iTax technology by
medium taxpayers at KRA?
1.4.3 What are the individual factors that affect adoption of iTax technology by medium
taxpayers at KRA
1.5 Importance of the Study
Innovation has been strongly and positively linked to the performance of organizations.
The findings of the study will be significant to the following stakeholders;
1.5.1 Kenya Revenue Authority
The findings will provide the Authority with useful information as to the factors that
influence the adoption of technologies introduced by the organization. The information
will be critical in making strategic decisions for the organization.
1.5.2 Medium Taxpayers
From the study, medium taxpayers will understand more about those factors with the
greatest impact on their adoption of technologies. This is a valuable information in
coming up with measures to deepen the use of technology for organizational success
1.5.3 Academicians
The findings will be a reference point for other scholars interested in understanding
factors that affect adoption of technology especially among taxpayers. The findings will
also go a long way in beefing up empirical evidence on the subject.
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1.6 Scope of the Study
The study focused on the medium taxpayers in Kenya. The study population was 1,630
firms in the Agriculture and Manufacturing; Service industry; Distributors; Finance and
construction; and the High Net Worth Individuals. The respondents were the finance
managers in these organizations. The study was conducted in the month of April and
May, 2015.
Financial matters are normally considered sensitive by many organizations. Therefore,
getting information relating to tax is expected to be difficult. To mitigate this, the study
provided a cover letter and introductory letter from the Chandaria School of Business to
affirm to the respondents that the information sought was purely for academics and shall
not be used for any other reasons without expressly indicating so.
1.7 Definition of Terms
1.7.1 Innovation
Any idea, object or practice that is perceived as new by members of the social system
(Okiro & Ndungu, 2013).
1.7.2 Technological Innovation
The process of introduction of something new or a new idea, method or device which is
science, technology and system based (Letangule & Letting, 2012).
1.7.3 Innovation Diffusion
The process by which innovation is communicated through certain channels over time
among members of social systems (Okiro & Ndungu, 2013).
1.7.4 Adoption
The process through which an individual or other decision making unit passes from first
knowledge of an innovation, to forming an attitude toward the innovation, to a decision to
adopt or reject, to implementation of the new idea, and to confirmation of this decision
(Kundu & Roy, 2010).
1.8 Chapter Summary
Chapter One presented the concept of technological innovation. It has also highlighted the
processes of technological innovation diffusion and the factors that affect the innovation
diffusion from other studies. The chapter further highlighted the knowledge gap and
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presented the scope and terminologies used in this study. Chapter two presents the
literature review on the subject of technology innovation adoption. Chapter three gives
the study methodology while Chapter four presents the study findings. Finally, Chapter
five offers the study summary, discussions, conclusions and recommendations.
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CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Introduction
This chapter is a presentation of literature review on technology adoption. It is divided
into four sections. First section discusses the characteristics of the innovation, which may
influence adoption. The second section discusses the organizational factors while the third
section discusses the individual factors that affect adoption of technological innovations.
2.2 Technological Innovation Characteristics that Affect Innovation Adoption
The high failure rates and low adoption of substantial number of innovations is of
concern to both researchers and industry practitioners. Tolba and Mourad (2011) link this
to the inappropriate application of innovation diffusion models and the difficulty to
evaluate the factors associated with accelerating the rate of diffusion. Therefore, a better
understanding of the factors influencing innovation diffusion is becoming a top priority
for researchers and managers, particularly (Tolba & Mourad, 2011). The first section of
this review will provide the technology characteristics that could possibly influence the
rate of adoption of technological innovations. The review will discuss the technology’s
relative advantage, complexity, compatibility and observability of the innovation’s
outcomes.
2.2.1 Relative Advantage
According to Robinson (2012), relative adavantage of an innovation is the degree to
which an innovation is perceived as better than the idea it supersedes by a particular
group of users, measured in terms that matter to those users, like economic advantage,
social prestige, convenience, or satisfaction. He opines that the greater the perceived
relative advantage of an innovation, the more rapid its rate of adoption is likely to be.
On the other hand, Chigona and Licker (2008) put it that relative advantage is the degree
to which an innovation is perceived as being superior to its precursor, which is either the
previous way of doing things (if there is no current way), the current way of doing things,
or doing nothing. They note that perceived relative advantage of an innovation involves
both perception (i.e., evaluation) of the proposed innovation as well as perceptions of
other candidates and the status quo. They explain that this is not uniquely tied to objective
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characteristics of the innovation although, of course, perceptions usually, but not always,
are influenced by objective reality (Chigona & Licker, 2008).
The general assumption according to Mndzebele (2013) is that organisations must
recognise that the adoption of innovation will either offer solutions to existing problems
or present new production opportunities, such as increased productivity and improved
operational efficiency. This is in line with arguments by Al-Jabri and Sohail (2012) who
indicated that relative advantage results in increased efficiency, economic benefits and
enhanced status hence relative advantage of an innovation is positively related to the rate
of adoption.
Mndzebele (2013) explains that technological innovation adoption process involves a
rational decision in an organisation, which requires that one assess the potential benefits
of the new technology to the business. Therefore, organisations adopt a technology when
they see a need for that technology, believing it will either take advantage of a business
opportunity or close a suspected performance gap. This means that when a user perceives
relative advantage or usefulness of a new technology over an old one, they tend to adopt
it (Al-Jabri & Sohail, 2012).
Online tax submission offers benefits such as, faster tax filing, ease of tracing taxes,
better organization of tax information, reduced cost of filing taxes, increased productivity
over the manual tax filing (Weru, Kamaara, & Weru, 2013; Obae, 2009; IST-Africa,
2015, KRA, 2015). Thus, in this study, it is hypothesized that, when taxpayers perceive
distinct advantages offered by technological innovation, they are more likely to adopt it.
2.2.2 Compatibility
Lee, Hsieh and Hsu (2011) define compatibility as the degree to which innovation is
regarded as being consistent with the potential end-users’ existing values, prior
experiences, and needs. According to Dzogbenuku (2013), compatibility refers to the
degree to which a service is perceived as consistent with users’ existing values, beliefs,
habits and present and previous experiences. Mndzebele (2013) explains that if previous
technological ideas were introduced and were not accepted then the new ideas will be
judged based on the performance of the previous ideas.
Therefore, when an organisation perceives that the technology they want to adopt is
consistent with their beliefs, culture and values and there is no resistance to change from
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the staff, they will adopt that technology (Mndzebele, 2013). Hence, the greater the
compatibility with the felt needs, the greater the diffusion rate. The assumption is that
conformance with user’s lifestyle can propel a rapid rate of adoption (Dzogbenuku,
2013).
Chigona and Licker (2008) give an example of developing countries, where cellular
telephony is directly compatible with the need for mobility for the urban poor, who often
do not have the luxury of long-term fixed addresses and whose lifestyles dictate that they
are often in transit and do not have access to fixed lines. This is further well demostrated
by the rapid penetration of mobile money transfer in Kenya. The historical exclusion of
majority smallholder businesses and individuals who would want to transfer and transact
little amounts by the large banks, has seen the rapid adoption of mobile money transfers
as an alternative means of money transfer in Kenya (Kirui, Okello, & Nyikal, 2012). The
platform is compatible to the economic status of most Kenyan. It allows the users to make
transfers of less than 1 US dollar to 760 US dollars depending on one’s economic income.
The small amounts are compatibility to the income of the majority poor in the coutry. The
small amounts do not make economic sense to the traditionally large banks. On the other
hand, the large transfer figures is compatibility to the medium class and small to medium
enterprises which also form a significant percentage of the population.
A part from compatibility of the innovation to users’ experiences and needs, the
compatibility is also associated with the fit of the new innovation to existing technology
within the organization. In this context Ramazani and Allahyari (2013) define
compatibility as a steady working system which is aligned with operations, employees
and organizational structure. They posit that compatibility is the salability of any
innovation and technology across the organizations. Thus, a system not compatible with
the organization is doomed to failure.
While focusing on information technology systems, a study by Chapman and Kihn (2009)
further asserts that an organization with incompatible information technology systems
will fail in data focusing. Therefore, most organizations focus on applicable technologies
that are aligned with the requirements of their systems (Ramazani & Allahyari, 2013) and
give little thoughts if any to those technologies, which are not compatible to their
operating systems. The assumption here is that compatible technological innovation
systems create the needed synergies within aligned activities, employees and firm
12
structure. For example, technological innovations compatible with specific requirements
of a firm unit prevent time wasting, ease operations, increase productivity and help obtain
favourable goals based on firm requirements and objectives.
2.2.3 Complexity
Complexity defines the extent to which an innovation can be considered relatively
difficult to understand and use i.e. it’s the opposite of ease of use (Al-Jabri & Sohail,
2012). Lee, Hsieh and Hsu (2011) posit that new ideas that are simpler to understand are
adopted more rapidly than innovations that require the adopter to develop new skills and
understandings. According to Dzogbenuku (2013), much of the extant literature on
barriers of technology adoption is predominantly related to technical complexity. He
contends that complexity in use, technical infrastructure, and design of technology are
some of the reported individual barriers in a number of studies. He further posits that
users will be inhibited to use technological innovations if they find it requires more
mental effort, is time-consuming or frustrating.
Therefore, complexity is negatively correlated with the rate of adoption i.e. excessive
complexity of an innovation is an important obstacle in its adoption (Sahin, 2006).
Moghavvemi, Hakimian and Feissal (2012) posit that the absence of ease of use of
technological innovation has a negative impact on perceptions of the technology which
leads to decreased adoption and usage of the technology. In other words, a technological
innovation might confront challenge where the systems are complex to the users but if
hardware and software are user-friendly, then they might be adopted faster and
successfully (Sahin, 2006).
An empirical study by Lee, Hsieh and Hsu (2011) to determine the factors that influence
employees’ intentions to use technology in form of electronic learning systems found out
that complexity had significant effects on the employees’ behavioural intention of using
electronic learning systems. A study by Mndzebele (2013) to determine the effects of
complexity in the adoption of electronic commerce in the Hotel industry also indicated
that complexity is positively correlated to the adoption of electronic commerce
technology. The correlation analysis indicated that there is a positive association between
the extent of adoption of electronic commerce and the manager’s perception of the
innovation’s complexity. Hence, in order to promote the intention to use a technological
innovation, designers should pay attention to the development of innovative
13
characteristics and content of the systems to make them easier to use by the potential
users (Lee, Hsieh, & Hsu, 2011).
Therefore, given the well established rationale and empirical support for an effect of
complexity on technological adoption, in this study it is hypothesized that if technological
innovation is easy to use then users would have a higher intention to adopt and use the
technological innovation. This means that it is assumed that the intention of the adoption
of iTax technology by the medium taxpayers in Kenya is influenced by the extent to
which the users perecieve the ease of use of the technology.
2.2.4 Observability
Observablity is defined as the degree to which the results of the innovation are visible to
others (Moghavvemi, Hakimian, & Feissal, 2012). Al-Jabri and Sohail (2012) expanded
this definition to describe the extent to which an innovation is visible to the members of a
social system, and the benefits can be easily observed and communicated. The attribute of
observability is divided into two constructs; result demonstrability (the tangibility of the
results of using an innovation); and visibility which refers to the extent to which potential
adopters see the innovation as being visible in the adoption context (Moghavvemi,
Hakimian, & Feissal, 2012).
Chigona and Licker (2008) posit that in some innovation, it is easy for others to see the
results of adoptions from those who have already adopted the technology. However, this
is not the case with all innovations. They indicate that observability is positively
correlated with the rate of adoption e.g. to the extent that something has to be explained in
complicated ways to others (i.e., complexity), it becomes less “observable,” too.
Chigona and Licker (2008) furtther explains that language and culture might also affect
observability for text-oriented technologies e.g. abstract or ambiguous innovations are
generally difficult to observe and therefore diffuse slowly. They give an example of safe
sex as an example of innovations with low observability due to its ambiguity.
Empirical study by Al-Jabri and Sohail (2012) to determine the how mobile banking is
adopted by banks in Saudi Arabia indicated that observability have positive significant
effect on mobile banking adoption. The observability in the mobile banking context is the
ability to see the beneficial results like immediate access to transactions anytime and
anywhere e.g. from the customers’ perspective, mobile banking offers a very convenient
14
and effective way to manage one’s financial transactions as it is easily accessible around
the clock.
2.3 Organizational Factors that Affect Innovation Adoption
The organizational factors comprise the structure, climate, and culture of the organization
that will influence the adoption of innovation (Zailani, et. al., 2014). Therefore,
organizations should develop some facilitators, so that workers do not perceive any threat
but rather assume the innovation as their own (Kundu & Roy, 2010). According to
Talukder (2012), there is a general agreement in literature that organizations need to
provide facilitating conditions which include the extent and type of support provided to
individuals that influence their use of technology. He opines that facilitating conditions
such as positive training, managerial support and incentives affect an individual’s
awareness of the functioning and application of an innovation, its usefulness and fit with
the job which leads to its adoption. Therefore, organizational influence can motivate
individual employees adopting an innovation.
The current study looked at training, management support, incentives and organizational
readiness/infrastructure as measures of organizational support. The study hypothesise that
with proper training, management support, incentives and organizational readiness, newly
introduced innovations are more likely to be adopted.
2.3.1 Training
To explain the role of training on adoption of new technological innovations, Kundu and
Roy (2010) uses an analogy where Company A purchases computer but very few people
use it. On the other hand, Company B purchases the same and trains its people about
computer application and finally all the members use computer in all sorts of activities.
They explain that in this case, company B adopts the computer technology as it develops
favorable attitude trough training towards the technology.
Talukder (2012) supports the idea by contending that training promotes greater
understanding, favourable attitude, more frequent use, and more diverse use of
applications. He explains further that, by training, educating and assisting employees
when they encounter difficulties, some of the potential barriers to adoption can be
reduced or eliminated. Thus, individual adoption of innovation is positively influenced by
the amount of relevant formal training because such training enhances individual’s belief,
15
possession of skills and knowledge that permit successful task performance (Talukder,
2012).
The general presumption in this case is that attained education level is correlated with
cognitive ability. Machogu (2012) posits that higher levels of education should be
associated with an individual’s ability to generate and implement creative solution to
complex problems. He further asserts that their ability to generate creative solutions
explains why people who are more educated have more receptive attitudes towards
innovation, and therefore the association between education and both cognitive abilities
and attitudes towards innovation suggest that more innovative firms are those that have
more highly educated teams.
Machogu (2012) suggests that to gain benefits from technologies, there is a need to invest
not only on physical technologies but also capacity-building, and skills. In this case, he
sees training as a primary organizational factor that helps users to understand how to best
use and adopt technological innovations and lack of training plays a key role as a barrier
to the adoption of technological innovations. These sentiments were earlier fronted by
Barba-Sánchez, Pilar and Jiménez-Zarco (2007) who indicated that the main difficulties
for exploiting the potential of information communication technology innovations is the
lack of awareness of the benefits to be derived coupled with little or no specific training
on information communication technology (both at application and methodological
levels).
Empirically, an earlier study by Fishbein and Ajzen (2005) which evaluated the adoption
of information communication technology in Malaysian SMEs, showed a link between
employee training and technology adoption rate. The study found out that in more than 70
percent of the companies which did not have formal information communication
technology training for their employees, adoption for information communication
technologies was at its lowest. Lack of formal training in this case resulted in lack of
trained personnel in information communication technology, which further hindered the
adoption of the technology.
Another study by Zailani, et. al. (2014) to determine the factors that affect the adoption of
green technology innovation in the transportation industry in Malaysia indicated that a
company with high-quality professional development, such as better education or
training, will be more capable of adopting and implementating innovation. For example,
16
employees with competent learning capabilities will be easily involved in training
programmes that can advance green practice adoption (Zailani, et. al., 2014). The study
opines that the degree to which an organization is receptive to new ideas can be attributed
to having personnel with higher education and better training which will influence the
company’s propensity towards adopting new technologies.
Machogu (2012) therefore suggests that since highly educated workers are more likely to
adopt and implement new technologies, the adoption of continuous training solutions can
play an important role in increasing the awareness of the huge potentialities of
technological innovations for concrete situations. He continues that in these way
employees and managers can acquire a learning culture, integrating the training in their
work activities and understanding in depth the potentialities of technological innovation
tools.
2.3.2 Management Support
Management support encompasses the extent to which a company’s management helps
employees using a particular technology or system (Weng & Lin, 2011). The
management support is considered essential because it motivates employees to implement
new ideas. Ahmer (2013) posits that the understanding of innovation, attitudes toward
innovation, extent of involvement in adoption process could influence top management
support as they play a critical role in creation of a supportive climate and provision of
adequate resource to adopt and implement new technology.
Weng and Lin (2011) stresses the role of top management in ensuring the management
support. They contend that, as many innovations require the collaboration and
coordination of different departments and divisions during adoption, to successful
adoption, new initiatives are usually endorsed and encouraged from the top management.
The central role played by the top management is to mobilize resources and allocate them
in a manner that promotes the adoption of the new technology.
In a study to determine the determinants of Radio Frequency Identification [RFID]
technology adoption in supply chain among manufacturing companies in China, Wen,
Zailani and Fernando (2009) found that top management support measured by the level of
funding/ resources and effective management control had the impact on the adoption of
RFID in China.
17
Still, a study by Bazurli, Cucciniello, Mele and Nasi (2014) to identify the determinants
and barriers of adoption, diffusion and upscaling of information communication
technology driven social innovation in the public sector indicated a relationship between
lack of top management support/ vision and innovation adoption. The study contends that
resistance to change from the top management is one of the biggest barriers to the
introduction of electronic procurement within the public sector and this cannot be simply
solved by a fast Internet connection or yet another departmental reorganization.
Further, a similar study by Ahmer (2013) to identify factors that influence adoption of
Human Resource Information Systems [HRIS] innovation in Pakistani organizations
ranked top management support as the biggest contributor towards adoption of HRIS
innovations in any organization. The study contends that with active involvement and
support, the top management could foster right direction for adoption of innovation.
Likewise,visible top management support could signal the importance of innovation, lead
to positive attitudes from users towards the innovation, and smoothen the conversion
from existing work procedures to the new. Ahmer (2013) adds that with their leadership
role, top management could ensure allocation of required capital and human resource for
adoption of innovation and help in overcoming user resistance and resolving probable
conflicts.
2.3.3 Incentives
Yusof, Abu-Jarad and Badree (2012) define the term incentive as an inducement that is
deployed as a motivational mechanism to encourage a desired action or simply as
something that encourages someone to do something. According to Talukder, Harris and
Mapunda (2008) incentives are considered powerful motivators of employee behaviour in
adopting an innovation. They posit that managers must provide individual employees
either incentives such as commissions, recognition and praise for adoption and penalties
such as threat and demotion for non-adoption of innovation. The incentives may also
come in form of benefits that employees may receive by using the technology such as,
increased autonomy and greater job security (Talukder, et. al, 2008).
In a study to highlight how to enhance adoption of technology in the education system,
Sahin (2006) suggested that to increase the rate of adopting innovations and to make
relative advantage more effective, direct or indirect financial payment incentives may be
18
used to support the individuals of a social system in adopting an innovation. He contends
that incentives are part of support and motivation factors.
The incentive to adopt any given innovation is not only domiciled on the emplyees’
motivation but may also exist at the organizational level. While giving an example of the
housing industry, Yusof, et. al. (2012) reports that financial incentives in form of rebates,
direct and indirect tax benefits are some of the most important drivers of innovative
sustainable construction. Hence, companies only develop new technologies if they enjoy
additional support either in the form of grants from the government or in terms of a
favourable environment for innovation.
2.3.4 Organizational Readiness/Infrastructure
Organizational readiness can be described as the level of preparedness of a firm for
adopting and implementing innovation (Martin, Beimborn, Parikh, & Weitzel, 2008).
Accrording to Panuwatwanich and Stewart (2012) the organisational readiness for
innovation and change may include such factors as existing staff skill and knowledge,
availability of resource, innovation-supportive values and goals, innovation-system fit
and tension for change.
A study by Ruikar, Anumba and Carrillo (2006) proposed an assessment model to
evaluate electronic readiness for construction companies in adopting e-commerce
technology and came up with four variables for testing organizational readiness. The
model established that for an organisation to be e-ready, it must have (1) management
that drives the adoption, implementation and usage of the technology; (2) processes that
are favourable to the successful adoption of the technology; (3) people who have belief,
knowledge, skills and abilities in the technology; and (4) technology necessary to support
the business functions.
Alam, Ali and Jani (2011) contend that organisational readiness reflects a firm’s
technological capabilities, or the level of use of innovative knowledge and skills. For
example, access to adequate equipment in the organization is a major determinant of the
adoption of new technologies. Likewise, introduction and implementation of innovation
depend on the firms’ pre-existing knowledge in areas relating to the intended innovation
(Alam, Ali & Jani 2011). Hence, an organisation without such capacity lacks readiness
and will be less likely to adopt innovation.
19
2.4 Individual Factors that Affect Innovation Adoption
It is generally agreed that personal characteristics such as educational level, age, gender,
educational experience, and attitude towards technology can influence the decision by an
individual to make use of an innovation as the best course of action available or the
decision that individuals make each time that they consider taking up an innovation
(Buabeng-Andoh, 2012).
It is in no doubt that an individual’s preparedness and attitude greatly influences the
adoption and integration of technological innovation at the work place. Therefore, an
understanding of personal characteristics that influence innovation adoption and
integration is relevant. It is hypothesised that the degree of an individual’s preparedness
and an individual’s attitude has a correlation with adoption of technological innovation.
The current study looks at four individual characteristics; personality, personal
innovativeness, enjoyment of the innovation and social networks.
2.4.1 Personality
Being cognizant to the fact that people react differently to new ideas, practice, or object
based on differences in their attitudes toward innovations, Lo (2014) sees personality
traits as a characteristic of a person that has a pervasive influence over a broad range of
different behaviors relevant to that trait i.e. the tendency to behave in a certain way. He
proposes a two prong model to measure personality influence on the adoption of
innovation.
There is the Novelty seeking personality trait, which is associated with sensory seeking for
or exploratory activity in novel stimulation, impulsive decision making, and
extravagance. This trait, or the predisposition to look for new products and services,
involves differences in one’s motivation to seek out originality and thus determines the
adoption of innovative products (Lo, 2014). Then there is the desire for uniqueness. Lo (
2014) explains that according to uniqueness theory, people find high levels of similarity
and dissimilarity unpleasant and therefore seek to be moderately distinct from others. The
more they perceive that they are similar to others, the more unique they seek to be and
tend to go for new ideas. Therefore, employees who exhibit more of these traits, novelty
seeking and desire to be unique, tend to adopt new innovations faster than those who
exhibit less.
20
The effect of personality may also be viewed in line with self-efficacy. According to
Buabeng-Andoh (2012) self-efficacy is defined as a belief in one’s own abilities to
perform an action or activity necessary to achieve a goal or task. This means that self-
efficacy is the confidence that individual has in his/her ability to do the things that he/she
strives to do. Therefore, employee’s innovation self-efficacy encompasses the perception
of the individual on the likelihood of using technological innovation and on how far the
employee perceives success as being under his or her control.
An empirical study by Peralta and Costa (2007) on teachers’ competences and confidence
regarding the use of information communication technology in Italy, Greece and Portugal
revealed a number of intresting findings. The study revealed that in in Italy, teachers’
technical competence with technology is a factor of improving higher confidence in the
use of information communication technology. Secondly, the study showed that, teachers
in Greece reported pedagogical and personal factors as those which mostly contribute to
their confidence in information communication technology use (Peralta & Costa, 2007).
In Portugal, the teachers linked the perception of confidence in using information
communication technology with the loss of fear of damaging the computer and at the
same possessing absolute control over the computer. They also reported availability of
practice time and support from peers as favourable conditions for gaining confidence in
information communication technology usage. Thus, the confidence level in using
technological innovations depended on personal factors.
2.4.2 Personal Innovativeness
According Jianlin and Qi (2010), personal innovativeness is considered an inherent
feature of all individuals with respect to new ideas, products and innovations. As a
personal trait, Xu and Gupta (2009) indicate that personal innovativeness differs among
individuals and is likely to influence their technology adoption decisions. Since the term
is individual based, personal innovativeness has drawn a number of varied definitions.
Earlier, Hirunyawipada and Paswan (2006) defined the term as the degree to willingly
increase the chance to try new products or services. Huang, Hsieh and Chang (2011) saw
personal innovativeness as the tendency for an individual to have extensive technical
knowledge and willingness to understand technological innovations.
On the other hand, Hung, Chen, Hung and Ho (2013) indicated that personal
innovativeness is a person’s general willingness to make changes to do things better or
21
differently. They explain that the degree of change can be conceptualized based on an
individual’s characteristics and behaviour e.g. an innovator typically thinks and acts
outside existing perceptual frames when trying to solve problems while an adaptor feels
uncomfortable veering away from existing perceptual frames.
Jianlin and Qi (2010) opine that the concept is related to individual attitude towards new
ideas and innovative decisions regardless of other people’s experience and it has been
proven to be extremely relevant for explaining the adoption of new innovations. Infact,
the study by Hirunyawipada and Paswan (2006) found that domain-specific
innovativeness enhances the actual adoption of the high-tech products by individuals. The
rational is that the more a user shows signs of innovativeness to use new technologies and
loves everything new, the more he will enjoy its use (Rouibah & Abbas, 2010).
Jianlin and Qi (2010) links personal innovativeness to risk-taking tendencies, since an
innovative behaviour involves unavoidable risk and uncertainty. Hung, et, (2013) further
supported this idea by indicating that in response to new technology adoption, individuals
who have a higher degree of personal innovativeness are more willing to take risks and
tends to be innovators and quick adopters while those with a stable trait or predisposition
are slower tending to lag behind.
2.4.3 Enjoyment of the Innovation
Previous studies suggested that perceived enjoyment is one of the most important types of
user needs in technological products and services. Shen (2012) defines the concept of
perceived enjoyment as the extent to which the activity of using a specific system is
perceived to be enjoyable in its own right, aside from any performance consequences
resulting from system use. It describes a state in which people are so involved in an
activity that nothing else seems to matter (Shen, 2012).
Hill and Troshani (2010) suggested that intuitively, intrinsic motivators such as
enjoyment might be the primary need when consumers adopt personalised innovations.
They contend that, play or fun, enjoyment, escapism and aesthetic value gained by
participating in service experiences satisfy pleasure-oriented or hedonic needs and operate
outside extrinsic motivations, such as enhanced job performance and increased pay.
An empirical study by Marez, Evens and Stragier (2011) to determine evaluate diffusion
theory agaisnt the today’ information communication technology environment found that
22
innovation adoption decisions seem to be determined by more factors than the original
five initiated by Rogers’ diffusion theory. The study affirmed the additional determinants
to include, perceived enjoyment, perceived cost, reliability (innovation-related
characteristics), voluntariness, image among other factors.
According to Ong, Poong and Ng (2008) perceived enjoyment is theorised to influence
usage intention directly. It intrinsically influences an individual’s motivation, liking,
enjoyment, joy and pleasure associated with technology use. The current study therefore
hypothesises that the more the users perceive an innovation to be enjoyable, the more
likely they will adopt its use.
2.4.4 Social Networks
According to MacVaugh and Schiavone (2010), new technology adoption can be said to
take place within three domains due to the threefold nature of most economic phenomena.
They describe these domains as the market/industry domain (macro domain) of new
technology adoption. The second domain is the meso type of dimension which relates to
the set of relationships shaping the social system in which the potential adopters are
located and the last domain is the individual (micro) dimension.
The social networking falls in the second domain. This borrows from the reality that
people do not exist in exclusion. Individuals are surrounded by communities and other
social networks. Therefore, there is the general agreement that people are influenced by
others within theirs societies/communities. Lekhanya (2013) when considering the use of
new technologies argues that one’s community shapes their attitude towards the usage of
new systems. He attributes this to the fact that peoples’ decision to adopt a technology
includes the external impressions, such as cultural values and norms that people are
subject to.
While considering the domain of community of users, MacVaugh and Schiavone (2010)
posit that in communities, the benefits and costs of change or adopting new way of doing
things are evaluated according to their impact on social relationships between community
members. For example, introduction of technologies within a community of workers may
change power relationships within the workforce. Therefore, social networks which prefer
stability my discourage their members from adopting new innovations while a more risk
taking and open social networks may promote introductions of new innovations.
23
This fact is supported by Di Pietro, Di Virgilio and Pantano (2012) who indicated that
individual behaviours are influenced by reference groups such as friends, superiors, and
experts in their field. This in turn plays a major role in influencing the adoption of new
technologies. Lekhanya (2013) also points out that, individuals consider people who are
close to him/her, such as family, friends and relatives to him/her, when thinking of using
new technology. for example, social networks and cultural factors have been found to
influence the adoption of electronic commerce (Kenneth, Rebecca, & Eunice, 2012).
According to Mazman, Usluel and Çevik (2009), the diffusion innovation theory as is
grounded on four main elements. These are, innovation, communication channels, time
and social system. They see social influence as social factors which are the individual's
internalization of the reference groups' subjective culture, and specific interpersonal
agreements that the individual has made with others, in specific social situations. They
posit that individuals are influenced by their social environment under three basic
conditions; when an individual accepts influence because he hopes to achieve a favorable
reaction from another person or group (social approval/disapproval from others)
[Compliance]; when an individual accepts influence because he wants to establish or
maintain a satisfying self defining relationship with others [Identification]; and when an
individual accepts influence because it is congruent with her value system
[Internalization] (Mazman, Usluel, & Çevik, 2009).
The study hypothesise that individuals are externally influenced by their social
sorrounding in the process of being informed about innovation as well as at the point of
deciding to adopt the use of the innovation. Therefore, the degree to which an individual
perceives that others believe he or she should use the new system partly determines the
actual decision for the adoption of the innovation by the individual.
2.5 Chapter Summary
The current Chapter dwelt on the literature review on the factors that affect adoption of
innovations. The review discussed innovation characteristics such as relative advantage,
compatibility, complexity and observability. The review also looked at organizational
factors such as training, management support, incentives and organizational
readiness/infrastructure. Lastly it explored literature on individual characteristics which
included individual’s personality, personal innovativeness, enjoyment and social
networks. Chapter three will discuss the research methodology adopted for this study.
24
CHAPTER THREE
3.0 RESEARCH METHODOLOGY
3.1 Introduction
This chapter is blue print on how the study was carried out. It offers a systematic flow of
how the study was conducted. It covers the research design used and its appropriateness.
It also presents the study population, sampling design, data collection method, research
procedure and the analysis methods.
3.2 Research Design
According to Okiro and Ndungu (2013), a research design is a plan for selecting subjects,
research sites and data collection procedures to answer the research questions. They
contend that it forms the conceptual framework within which research is conducted and
constitutes the blueprint for the collection of data and the analysis thereof of the collected
data.
A case study research design was used in this study to explore the factors that affect the
adoption of online tax submission systems by the medium taxpayers at Kenya Revenue
Authority. KRA is the government revenue collection department. Obae (2009)
acknowledged that a case study is a powerful form of qualitative analysis that involves a
careful and complete observation of a social unit, irrespective of what type of unit is
under study.
The design involved descriptive studies using a survey to establish relationship between
the dependant variable and the independent variable. A descriptive study was appropriate
for this study as it sought to portray accurately the characteristics of a particular situation
or a group (Kothari, 2004). For descriptive studies, the high levels of accuracy is
achievable through use of systematic research methods for collecting data from a
representative sample of individuals using structured instruments composed of closed-
ended and/or open-ended questions, observations, and interviews (Gakure & Ngumi,
2013).
The dependent variable in this study was the adoption of online tax submission system at
KRA while the independent variables was the technological innovation characteristics;
organizational factors and individual factors that affect the adoption of the iTax system.
25
3.3 Population and Sampling Design
3.3.1 Population
A population is the total collection of elements about which the researcher wishes to
make some inferences (Okiro & Ndungu, 2013). An element in this context is the subject
on which the measurement is being taken and is the unit of the study. The population for
this study involved the 1630 taxpayers ranked as medium taxpayers by the Kenya
Revenue Authority. The Kenya Revenue Authority was chosen for the case given that it is
the sole government mandated body to collect all revenues in the country. The population
is categorised in to sectors by the KRA as shown in Table 3.1.
Table 3.1: Study Population
Sector Sample Frame
[Total number of
taxpayers]
Total Percentage
Agriculture & Manufacturing 352 22%
Distributors 331 20%
Finance Construction 328 20%
Services 474 29%
High Net Worth Individuals 145 9%
Total 1630 100%
Source: KRA (2015)
3.3.2 Sampling Design
According to Cooper and Schindler (2008), a sampling design is the procedure by which
a particular sample is drawn from a population. It therefore, shows systematically how a
study sample was arrived at.
3.3.2 .1 Sampling Frame
A sampling frame constitutes all the study elements accessible to the researcher at the
time of carrying out the study i.e. it may comprise the entire population or a section of it
(Cooper & Schindler, 2008). All the 1630 medium taxpayers constituted the sample frame
for this study since they are all active legal businesses that submit taxes to KRA on a
regular basis.
3.3.2.2 Sampling Technique
Cooper and Schindler (2008) contended that the sampling technique involves selecting a
group of people, events or behaviour with which to conduct a study. In the process, a
26
portion of the population is selected for the study and the findings used to generalise
about the entire population’s characteristics.
3.3.2.3 Sample Size
Garson (2012) indicates that a sample size represents a subset (any combination of
sampling units that does not include the entire set of sampling units that has been defined
as the population) of a sampling units from a population. The population of medium
taxpayers was 1,630. Yamane (1967:886) provided a simplified formula to calculate
sample sizes. He posits that at 95% confidence level and p =0.5, the formula below was
assumed for calculating sample size;
Where:
n = sample size
N = population size
e = acceptable sample error=0.05
Based on this formula, a sample size of 321 is required. Due to time and cost factors, a
sample size of 200 was selected for this study. Furthermore, Blanche, Durrheim and
Painter (2008) provided that for small populations of up to 1,000 a sample size of 30% is
sufficient while for populations between 1,000 and 10,000 a sample size of 10% is
sufficient while 1% for populations of up to 150,000 and 0.025% for large populations
such as 10 million. Therefore, a sample size of 200 representing 12% of 1630 was
considered sufficient for this study.
Table 3.2: Sample Size
Category Sample
Frame
Percentage Sample
Size
Percentage
of Total
population
Agriculture & Manufacturing 352 22% 44 2.6%
Distributors 331 20% 40 2.4%
Finance and Construction 328 20% 40 2.4%
Services 474 29% 58 3.5%
High Net Worth Individuals 145 9% 18 1.1%
Total 1630 100% 200 12.2%
27
3.4 Data Collection Method
A survey instrument was used to collect primary data from the respondents. This tool was
appropriate since responses were gathered in a standardised way (Harris & Brown, 2010).
Survey instruments also enabled faster collection of large data within a limited time
frame. The survey instrument was divided into three sections. The first section aimed to
collect the respondent’s general information. The findings in this section acted as part of
the independent variables that affect the adoption of technological innovations. The
second section, also presented questions on the independent variables such as the
organizational factors, individual factors and the technological innovation characteristics
that affect adoption of technological innovations. The third section of the questionnaire
aimed to gather information about the dependent variable. That is, the adoption of
technological innovation by medium taxpayers in Kenya.
The questionnaire was structured into closed ended questions and open ended questions.
The closed ended questions were in the form of a five point Likert scale. The respondents
were expected to indicate their level of agreement to the statements provided. The scale
was such that; [1] was strongly disagree; [2] disagree; [3] neutral; [4] agree; and [5]
strongly agree. The Likert scale limited the respondents to the choices provided for
uniformity and easy of data analysis. The open ended questions on the other hand had no
restriction whatsoever on how the questions were answered. This created room for
respondents to offer more insights beyond what was limited by the closed ended
questions.
3.5 Research Procedure
First a pilot test was carried on 15 members of the study population. The piloting was
critical in identifying the reliability and validity of the test items. It was used to identify
and correct questions which presented ambiguity to the respondents. The views of an
expert [project supervisor] were also employed to determine whether the questions
elicited the intended information. The respondents who participated in the pilot were
excluded in the final study to eliminate bias.
Finance managers of the target organizations distributed the questions in hard copies for
filling. The study adopted a drop and pick strategy where the questionnaires were
delivered and the respondents given time to complete the questions and the filled
28
questionnaires picked at a later date after a week. A research assistant was involved in the
process of data collection.
3.6 Data Analysis Method
According to Cooper and Schindler (2008), data analysis involves editing and reducing
accumulated data to a manageable size, developing summaries and seeking for patterns
using statistical methods. The data collected was first coded and entered into Statistical
Package for Social Sciences [SPSS] for analysis. The data was checked for completeness,
consistence and reliability before analysis.
The analysis was both descriptive and inferential. The descriptive analysis involved
frequencies and percentages. Regression analysis and correlations were conducted to
determine the relationship between the dependent variable and the independent variables
of the study. Statistical significance level was used to infer deductions from the study to
the entire population. Findings were presented using tables and figures.
3.7 Chapter Summary
Chapter Three presented a systematic way of carrying out the study. It gave the research
design adopted. It also illustrated the study population, sampling design, data collection
method, research procedure and the analysis methods. Chapter Four presents the study
findings in line with the study objectives.
29
CHAPTER FOUR
4.0 RESULTS AND FINDINGS
4.1 Introduction
The main objective of the study was to determine the factors that affect adoption of
technology innovation in Kenya. The study focused on the effects of the technology
innovation characteristics, the organizational factors and the individual factors that affect
adoption of iTax technology by medium taxpayers at KRA. Chapter 4 is a presentation of
the findings in line with the specific research questions. Section 1 presents the reliability
and validity tests. Section 2 presents the demographic characteristics of the study
population. Section 3 presents findings on the independent variables; technology
innovation characteristics, organisational factors and the individual factors. Section three
presents the results on the depended variable; adoption of the iTax. Section four presents
the chapter summary.
4.2 Reliability of the Survey Instrument
A construct composite reliability co-efficient (Cronbach alpha) was used to determine
reliability of the survey instrument i.e. was used to determine whether the instrument
consistently measured what it was intended to measure. Cronbach’s Alpha of less than
0.5 indicated unreliability of the variables hence could be used to deduce findings but a
Cronbach alpha of 0.6 or above, was considered reliable (Makgosa, 2006). The
Cronbach’s Alpha values ranged from 0.948 to 0.971 for the four Likert Scales used as
indicated in Tables 4.1; 4.2; 4.3; and 4.4. This registered a high level of reliability for the
constructs.
4.2.1 Reliability of Innovation Characteristics Test Items
Table 4.1 indicates that Cronbach's alpha is 0.945, which indicates a high level of internal
consistency for the scale. Table 4.1 also shows that the item-total correlation ranges from
0.661 to 0.865 and that removal of any question would result in a lower Cronbach's alpha
or the alpha remains the same. Therefore, we would not want to remove any of these
questions as they improve on the reliability of the constructs.
30
Table 4.1: Cronbach’s Alpha Analysis for Innovation Characteristics Test Items
Scale Sub
Scale
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
Cronb
ach’s
Alpha
Innovation
characteristics
A1 Systems is fully compatible
with other systems .809 .937
.945
A2 Systems is easy to
implement .834 .936
A3 Results are clearly visible .865 .934
A4 More advantageous to use
the systems .676 .945
A5 Systems is easy to test .842 .935
A6 External support is
available .780 .939
A7 System is user friendly .755 .940
A8 System is secure .853 .935
A9 System is cost effective .661 .945
4.2.2 Reliability of Organizational Factors Test Items
Table 4.2 indicates that Cronbach's alpha is 0.955, which indicates a high level of internal
consistency for the scale. Table 4.2 also shows that the item-total correlation ranges from
0.649 to 0.858 and that removal of any question would result in a lower Cronbach's alpha.
Therefore, we would not want to remove any of these questions from the construct.
Table 4.2: Cronbach’s Alpha Analysis for Organizational Factors Test Items
Scale Sub
Scale
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
Cronb
ach’s
Alpha
Organizational
Factors
B10 Management encourages
use of the system .831 .950
.955
B11 Resources allocated for
training on the system .856 .949
B12 Policy for system use .797 .951
B13 Availability of internet .755 .953
B14 Reward for use .649 .954
B15 Employees views are taken
into account .858 .948
B16 Positive perception .850 .949
B17 Clear commitment from
CEO .855 .949
B18 System investment .836 .949
B19 Management familiar with
the system .808 .950
31
4.2.3 Reliability of Individual Factors Test Items
Table 4.3 indicates that Cronbach's alpha is 0.948, which indicates a high level of internal
consistency for the scale. Table 4.3 also shows that the item-total correlation ranges from
0.703 to 0.862 and that removal of any question would result in a lower Cronbach's alpha.
Therefore, we would not want to remove any of these questions as they improve on the
reliability of the construct.
Table 4.3: Cronbach’s Alpha Analysis for Individual Factors Test Items
Scale Sub
Scale
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
Cronb
ach’s
Alpha
Individual
Factors C20
Online tax submission
considered e very useful .750 .944
.948
C21 Sufficient training on how
to use iTax platform .813 .940
C22 Taking of personal initiative
to file taxes online .861 .938
C23 Find filing taxes online easy
due to previous experience .862 .938
C24
Filing taxes online shows
sophistication level of the
organization .764 .943
C25 Enjoy filing taxes online
.800 .941
C26 Colleagues at work
encourage use of iTax .831 .939
C27 All firms within category
use iTax platform .768 .943
C28 Prefer computer based
systems .703 .946
4.2.4 Reliability of Adoption of iTax Test Items
Table 4.4 indicates that Cronbach's alpha is 0.971, which indicates a high level of internal
consistency for the scale. Table 4.4 also shows that the item-total correlation ranges from
0.638 to 0.882 and that that removal of any question would result in a lower Cronbach's
alpha. Therefore, we would not want to remove any of these questions as they improve on
the reliability of the construct.
32
Table 4.4: Cronbach’s Alpha Analysis for Adoption of iTax Test Items
Scale Sub
Scale
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
Cronb
ach’s
Alpha
Adoption of
iTax D1
The system simplifies
routine work .841 .968
.971
D2 The system is faster
.861 .968
D3 The system integrates with
other business units .833 .968
D4 The system betters tax
submission .865 .968
D5 The system leads to
efficient coordination .822 .968
D6 The system lead to better
returns .857 .968
D7 The system lead to meeting
of deadlines .857 .968
D8 The system improves equity
owners satisfaction .855 .968
D9
The system lead to
reduction of operational
cost .831 .968
D10 The system lead to faster
tax refund .638 .970
D11 The system lead to
increased productivity .877 .967
D12 The system lead to more
accurate tax filing .882 .967
D13 The system lead to easy
tracking of tax records .830 .968
4.3 Demographic Characteristics of the Respondents
4.3.1 Response Rate
Two hundred questionnaires were distributed for filling. Figure 4.1 shows that the study
achieved 80% response rate.
Figure 4.1: Response Rate
33
4.3.2 Gender
Majority of the respondents were female [52%] while males 48% were as shown in
Figure 4.2.
Figure 4.2: Gender
4.3.3 Age Group
Majority of the respondents were falling between 26-35 years [46%] followed by 36-45
years [28%], Less than 25 years [12%], 46-55 years [9%] and 56 years and above [5%] as
shown in Figure 4.3.
Figure 4.3: Age Group
4.3.4 Sector
Figure 4.4 shows 23% of the respondents were drawn from the Agriculture and
Manufacturing sector, 23% from finance and constructions, 22% were distributors, 19%
from Service sector and 13% were High Net worth Individuals.
34
Figure 4.4: Sector
4.3.5 Duration worked in the Organization
Table 4.5 indicates that majority of the respondents had with their current organizations
for more than 9 years [44%]. Seventeen percent had served in their organizations for 7 to
9 years, 16% for 1 to 3 years, 15% for less than 1 year and 8% for 4 to 6 years
Table 4.5: Duration worked in the Current Organization
Number of years Percentage [%]
Less than 1 year 15
1-3 years 16
4-6 years 8
7-9 years 17
More than 9 years 44
Total 100
4.3.6 Highest Education Level
Table 4.6 shows that degree holders were 51%, Masters Degree holders 18%, Diploma
14%, Certificate 14% and Postgraduate diploma 3%.
Table 4.6: Highest Education Level
Highest Education Level Percentage [%]
Certificate 14
Diploma 14
Degree 51
Postgraduate Diploma 3
Masters 18
Total 100
4.3.7 Management Position
Table 4.7 shows that majority were Junior Managers [43%], followed by middle level
managers [35%], top level managers [21%] and Directors [1%].
35
Table 4.7: Management Position
Management Position Percentage [%]
Junior Manager 43
Middle level manager 35
To level manager 21
Director 1
Total 100
4.4 Innovation Characteristics
The first specific study objective was to determine the effects of innovation
characteristics in adoption of technological innovations. Descriptive analysis using
frequencies and percentages was used to determine the innovative characteristics of the
iTax technology. Table 4.8 shows that a third of the respondents [33%] where neutral on
the characteristics of the iTax technology. This neutrality arose from the fact that their
organizations have not adopted the iTax technology for submitting taxes.
Comparing opinions of those who adopted the system, majority agreed [35% agreed; 7 %
strongly agreed] that iTax technology have favourable innovation characteristics. This
scored strongly compared to those who disagreed that iTax system has unfavourable
characteristics [25% disagreed].
Table 4.8 also shows that compared to those in disagreement, majority agreed that iTax
technology is fully compatible with their organizations systems [40% agreed; 6% strongly
agreed]; iTax submission system is easy to implement [42% agreed; 4% strongly agreed];
the positive results of using iTax is clearly visible [37% agreed; 11 strongly agreed]; it is
advantageous to use iTax system [34% agreed; 18% strongly agreed]; iTax system is easy
to test [31% agreed; 9% strongly agreed]; external support for iTax system is available
[30% agreed]; iTax system is secure [30% agreed; 12% strongly agreed]; iTax system is
cost effective [37% agreed; 23 strongly agreed].
36
Table 4.8: Innovation Characteristics
Variable Percentage [%]
SD
(1)
D
(2)
N
(3)
A
(4)
SA
(5)
1. The KRA’s online tax submission systems is fully
compatible with our organization systems
12 22 20 40 6
2. The KRA’s online tax submission systems is easy to
implement
8 27 19 42 4
3. The positive results of using iTax is clearly visible 9 20 23 37 11
4. It is more advantageous to use iTax than the manual
systems
8 17 22 34 18
5. The KRA’s online tax submission systems is easy to test
before implementation
13 25 21 31 9
6. External support for online tax submission is available 11 28 22 30 8
7. KRA’s iTax system is user friendly 15 27 21 29 8
8. KRA’s iTax system is secure 7 17 34 30 12
9. Use of KRA’s iTax system is cost effective 3 11 25 37 23
Summated [Favourability of Innovation characteristics] 0 25 33 35 7
The study then sought to find out whether the findings are influenced by the demographic
characteristics. To determine this, bivariate correlation analysis was carried out. Table 4.9
shows that at 95% level of confidence, there is significant relationship between age group
[.189*] and sector [-.165
*] and the innovation characteristics.
Table 4.9: Correlation Matrix of Innovation Characteristics
Correlation on Innovation characteristics
Gender Pearson Correlation -.029
Sig. (2-tailed) .728
N 148
Age group Pearson Correlation .189*
Sig. (2-tailed) .020
N 150
Sector Pearson Correlation -.165*
Sig. (2-tailed) .043
N 150
Duration served in the organization Pearson Correlation .101
Sig. (2-tailed) .220
N 150
Highest Education Level Pearson Correlation .352**
Sig. (2-tailed) .000
N 150
Management Position Pearson Correlation .495**
Sig. (2-tailed) .000
N 150 **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
37
The study then sought to identify the nature of the relationship between the innovation
characteristics and age/ sector. Cross tabulation was carried out. Figure 4.5 shows that as
age increases from 26 years the level of agreement that iTax characteristics are favourable
increase. For age 26 to 35 years [34% agreed], 36 to 45 years [36% agreed], 46 to 55
[61% agreed], and above 56 years [73% agreed]. This means that the older respondents
where more confident that innovation characteristics of iTax technology are favourable.
13%
37%
17% 14% 13%
40%30%
48%
14% 13%
47%
27% 31%
57%63%
0%7% 5%
14% 13%
Less than 25 years 26-35 years 36-45 years 46-55 years 56 years and above
Disgaree Neutral Agree Strongly Agree
Figure 4.5: Cross tabulation of Age versus Innovation Characteristics
Figure 4.6 shows that while majority in Agriculture and Manufacturing [53%],
Distributors [46%], Service [47%] and High Net worth Individuals [52%] agreed that the
characteristics of iTax is favourable, majority in Finance and Construction sector [46%]
disageed.
38
10%
14%
46%
40%
11%
32%
40%
34%
23%
37%35%
46%
20%
40%
47%
23%
0% 0%
7%5%
Agriculture and Manufacturing
Distributors Finance and Construction
Service High Net worth Individuals
Disgaree Neutral Agree Strongly Agree
Figure 4.6: Cross tabulation of Sector versus Innovation Characteristics
4.5 Organizational Factors
The second specific study objective was to determine the effects of organizational factors
in adoption of technological innovations. Table 4.10 shows that the summated scale of the
organizational factors indicates that close to a third [31%] were neutral on the matter.
This was attributed to low up take of the technology. Comparing those who agreed
against those who disagreed, majority agreed [33% agreed; 5% strongly agreed] that their
organisations have favourable organizational factors for the adoption of iTax technology.
This represented 53% of those who expressed their opinions on the influence of
organizational factors. The majority who agreed that their organizations have favourable
organizational factors for iTax adoption indicated that top management in their
organizations encourage use of iTax [38% agreed; 8% strongly agreed]; resources are
provided for staff to learn online tax submission [38% agreed; 7% strongly agreed];
management ensures internet connectivity [29% agreed; 14% strongly agreed];
employees’ suggestions on technology are taken into account [31% agreed; 8% strongly
agreed]; top management have positive attitudes towards adoption of technology [31%
agreed; 13% strongly agreed]; clear commitment from top management [27% agreed;
12% strongly agreed]; investment in information technology [32% agreed; 13% strongly
39
agreed]; and that top management are familiar with on line business activities [33%
agreed; 17% strongly agreed].
Table 4.1: Organizational Factors
Variable Percentage [%]
SD
(1)
D
(2)
N
(3)
A
(4)
SA
(5)
1. Top management encourages employees to use iTax 8 18 27 38 8
2. Resources provided for staff to learn online tax submission 10 22 22 38 7
3. Policy in place for submitting taxes online 9 25 29 29 7
4. Management ensures internet connectivity 13 16 27 29 14
5. There is a reward for successfully online filing of taxes 25 20 20 21 12
6. Employees suggestion are considered on how to improve
online tax submission
13 23 15 31 8
7. Top management has a positive perception and attitudes
towards adoption of new technologies
8 25 21 31 13
8. There is clear commitment from my CEO for the adoption of
online tax submission system
10 21 31 27 12
9. Substantial investments in information technology to support
online business activities
10 17 29 32 13
10. Top management in my organization are well familiar with the
online business activities in my organization
11 15 25 33 17
Summated [Organizational Factors] 10 22 31 33 5
The study then sought to find out whether the findings are influenced by the demographic
characteristics. Table 4.11 shows that at 95% level of confidence, there lacked statistical
relationship between the demographic characteristics and findings on the organisational
factors. This means that the findings on Table 4.10 can be generalised to the entire
studied population.
40
Table 4.2: Correlation Matrix of Organizational Factors
Correlations on Organizational Factors
Gender Pearson Correlation .085
Sig. (2-tailed) .294
N 155
Age group Pearson Correlation .285**
Sig. (2-tailed) .000
N 157
Sector Pearson Correlation -.031
Sig. (2-tailed) .696
N 157
Duration served in the organization Pearson Correlation .218**
Sig. (2-tailed) .006
N 157
Highest Education Level Pearson Correlation .434**
Sig. (2-tailed) .000
N 157
Management Position Pearson Correlation .516**
Sig. (2-tailed) .000
N 157
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
4.6 Individual Factors
The third specific study objective was to determine the effects of individual factors on the
adoption of iTax technology. Table 4.12 shows that on a summated scale, majority [41%
agreed; 5% strongly agreed] that there are favourable individual factors for the adoption
of iTax technology in their organizations. Comparing those who agreed against those who
disagreed, majority agreed that they consider online tax to be very useful [39% agreed;
11% strongly agreed]; they are sufficiently trained on how to use the iTax platform [30%
agreed; 7% strongly agreed]; they take personal initiatives to use the online platform for
submitting taxes [32% agreed; 12% strongly agreed]; they have past experiences on how
to submit taxes online [31% agreed; 11% strongly agreed]; they consider online tax
submission a sophistication [29% agreed; 22% strongly agreed]; they enjoy online tax
submission [27% agreed; 11% strongly agreed]; colleagues at work encourage use of
online tax platform [32% agreed; 9% strongly agreed]; encouraged by the fact that firms
in their category use online tax platform [27% agreed; 10% strongly agreed]; and that
they prefer computer based systems [30% agreed; 40% strongly agreed].
41
Table 4.3: Individual Characteristics
Variable Percentage [%]
SD
(1)
D
(2)
N
(3)
A
(4)
SA
(5)
1. I consider online tax submission to be very useful 6 22 22 39 11
2. I am sufficiently trained on how to use iTax platform 13 21 28 30 7
3. I take personal initiative to file my organization taxes online 10 20 28 31 12
4. I find filing taxes online easy due to my previous experience 13 21 25 31 11
5. Filing taxes online shows sophistication 8 25 17 29 22
6. I enjoy filing taxes online 12 26 25 27 11
7. My colleagues at work encourage me to use iTax platform 11 20 29 32 9
8. All firms within our category use iTax platform 9 20 34 27 10
9. I prefer computer based systems more than manual entries 5 12 20 30 40
Summated [Individual characteristics] 6 20 28 41 5
The study then sought to find out whether the findings are influenced by the demographic
characteristics. Table 4.13 does not show any significant relationship between the
individual factors and the demographic characteristics, thus the findings in Table 4.12 is
generalised to the entire population.
Table 4.1 3: Correlation Matrix of Individual Factors
Correlations on Individual Characteristics
Gender Pearson Correlation .135
Sig. (2-tailed) .093
N 155
Age group Pearson Correlation .247**
Sig. (2-tailed) .002
N 157
Sector Pearson Correlation -.021
Sig. (2-tailed) .790
N 157
Duration served in the organization Pearson Correlation .210**
Sig. (2-tailed) .008
N 157
Highest Education Level Pearson Correlation .429**
Sig. (2-tailed) .000
N 157
Management Position Pearson Correlation .540**
Sig. (2-tailed) .000
N 157 **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
The respondents where then asked to indicated other factors that affect adoption of iTax
technology in their organiations. Twenty four respondes expressed their opinions and Figure 4.7
shows that the respondents indicated lack of knowledge on the use of iTax technology [45.8%],
the system being complicated [20.8%], lack of top management support [12.5%], lack of staff
42
awareness on the existance of the technology [8.4%], lack of system support [4.2%], iTax
technology cannot handle huge traffic especially during deadlines [4.2%], and poor internet
connectivity [4.2%].
Figure 4.7: Other Factors that Affect Adoption of iTax Technology
4.7 Adoption of iTax Technology
The then sought to identify the level of adoption of iTax technology. Table 4.14 indicated
that on a summated scale, majority [38% agreed; 3% strongly agreed] agreed experiencing
positive impacts of iTax technology. Still about a third [34%] was neutral on adoption of
the technology. Table 4.14 further shows that adoption of iTax technology leads to
simplification of work routines [36% agreed; 6% strongly agreed]; faster filing of taxes
[41% agreed; 6% strongly agreed]; integration of business units [34% agreed; 7% strongly
agreed]; better organization of tax records [38% agreed; 12% strongly agreed]; efficient
coordination of departments [32% agreed; 12% strongly agreed]; better returns [38% agreed;
8% strongly agreed]; meeting of deadlines [48% agreed; 8% strongly agreed]; improved
43
equity owners satisfactions [36% agreed; 8% strongly agreed]; reduced operational costs
[33% agreed; 9% strongly agreed]; improve productivity [28% agreed; 8% strongly agreed];
accurate tax filing [39% agreed; 5% strongly agreed]; and ease of tax tracking [35% agreed;
11% strongly agreed].
Table 4.4: Adoption of iTax Technology
Variable Percentage [%]
SD
(1)
D
(2)
N
(3)
A
(4)
SA
(5)
1. Online tax submission has lead to simplification of work
routines in my organization
6 22 30 36 6
2. Online tax submission has lead to faster filing of tax returns
for my organization
6 19 27 41 6
3. Online tax submission has lead to integration of business units
in my organization
6 20 32 34 7
4. Online tax submission has lead to better organization of tax
records in my organization
6 16 28 38 12
5. Online tax submission has lead to efficient coordination of
departments in my organization
5 24 28 32 12
6. Online tax submission has lead to better returns for my
organization
6 18 36 33 8
7. Online tax submission has lead to meeting of tax submission
deadlines for my organization
8 15 30 48 8
8. Online tax submission has improved equity owners
satisfaction in my organization
9 13 34 36 8
9. Online tax submission has lead to reduction of operational
cost at my organization
5 24 29 33 9
10. Online tax submission has lead to faster tax refund for my
organization
13 22 34 26 5
11. Online tax submission has lead to increased productivity in
my organization
8 22 34 28 8
12. Online tax submission has lead to more accurate tax filing for
my organization
10 19 28 39 5
13. Online tax submission has lead to easy tracking of tax records
in my organization
7 22 25 35 11
Summated [Adoption of iTax Technology] 5 21 34 38 3
The study then sought to find out whether the findings are influenced by the demographic
characteristics. Table 4.15 shows that at 95% level of confidence, there is a significant
[.188*] relationship between duration served within the organization and the beneficial
effects of adopting iTax technology. Cross tabulation expressed in Figure 4.8 was then
carried out to determine the nature of the relationship.
44
Table 4.15: Correlations of Adoption of iTax Technology
Correlations on iTax Adoption
Gender Pearson Correlation .080
Sig. (2-tailed) .325
N 153
Age group Pearson Correlation .227**
Sig. (2-tailed) .005
N 155
Sector Pearson Correlation -.108
Sig. (2-tailed) .181
N 155
Duration served in the organization Pearson Correlation .188*
Sig. (2-tailed) .019
N 155
Highest Education Level Pearson Correlation .364**
Sig. (2-tailed) .000
N 155
Management Position Pearson Correlation .484**
Sig. (2-tailed) .000
N 155 **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Figure 4.8 shows that compared to those who disagreed, majority in each category agreed
that their organizations have adopted iTax and they are experiencing positive results.
Despite this, those who have stayed in their organization for more than 9 years were more
assertive [49%] followed by less than 1 years [46%], 7-9 years [34%], 4-6 years [33%],
1-3 years [22%].
Figure 4.8: Cross tabulation of Duration Served versus Adoption of iTax
45
4.8 Bivariate Analysis
Bivariate analysis was conducted to examine the relationship between the core constructs.
The correlation indicated the direction, strength and significant of the bivariate
relationships of the variable. Correlation matrix of the core constructs was required since
regression could not be conducted for the correlation within and among the predictors
themselves.
Table 4.16 shows that all variables (innovation characteristics, organizational factors, and
individual factors) displayed statistically significant positive correlation with adoption of
technology. Innovation characteristics had a strong positive correlation with
organizational factors (r=0.77; p<0.001), individual factors had a strong positive
correlation with innovation characteristics (r=0.725; p<0.001) while it had a strong
positive correlation with organizational factors (r=0.860; p<0.001). Adoption of
innovation had a strong positive correlation with innovation characteristics (r=0.760;
p<0.001), organizational factors (r=0.819; p<0.001) and individual factors (r= 0.828;
p<0.001). These results show that there is significant relationship among antecedents’
variables (innovation characteristics, organizational factors and individual factors).
Therefore, hypotheses were accepted indicating the innovation characteristics,
organizational factors and individual factors significantly correlate with one another in
the model and there were no multi-collinearity among them.
Table 4.16: Correlation Matrix of the core constructs
Innovation
Characteristics
Organiz
ational
Factors
Individual
Factors
Adoption of
iTax
Innovation
Characteristics
Pearson
Correlation
1
Sig. (2-tailed)
N 150
Organizational
Factors
Pearson
Correlation
.777**
1
Sig. (2-tailed) .000
N 150 157
Individual
Factors
Pearson
Correlation
.725**
.816**
1
Sig. (2-tailed) .000 .000
N 150 157 157
Adoption of
iTax
Pearson
Correlation
.760**
.819**
.828**
1
Sig. (2-tailed) .000 .000 .000
N 148 155 155 155
**. Correlation is significant at the 0.01 level (2-tailed).
46
4.9 Single and Multiple Regression Analysis
The general assumption of this study was that the adoption of technology [iTax] is
affected by innovation characteristics; organizational factors; and individual factors.
Single and multiple regression analysis were carried out to predict the impact of the
independent variables [innovation characteristics; organizational factors; and individual
factors] on the depended variable [adoption of technology]. To achieve this, the
independent variable test items and the dependent test items were first expressed into
single summated scale for carrying out the regression.
4.9.1 Innovation Characteristics as Predictor of Technology Adoption
Table 4.17 shows a positive strong relationship between innovation characteristics and
adoption of iTax technology (R) is (0.760), R square is (0.577) and adjusted R square is
(0.574), meaning that considered singularly, (57.4%) of the variance in adoption of iTax
technology can be predicted by the independent variables of innovation characteristics.
Table 4.17: Model Summary for Innovation Characteristics as Predictor of iTax
Technology Adoption
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .760a .577 .574 .544
a. Predictors: (Constant), innovation characteristics
The result of regression analysis in Table 4.18 shows that system compatibility, visibility
of results, clarity of advantages and user friendliness are significant in influencing
adoption of iTax technology as shown by p-values, which are smaller than alpha value of
(0.05). Therefore, the hypotheses were accepted.
On the other hand, the results of the regression analysis in Table 4.18 shows that there is
no significant impact of ease of use, ease of testing, availability of the external support
system, system security and cost effectiveness on the adoption of iTax technology as the
significant level is above (5%), therefore the hypotheses were rejected.
47
Table 4.18: Coefficients for Innovation Characteristics as Predictor of iTax
Technology Adoption
Model T Calculated Sig. Results
(Constant) 3.470 .001
Systems compatibility 2.191 .030 Accept
Ease of use 1.546 .124 Reject
Visibility of results 3.353 .001 Accept
Clarity of advantages 3.512 .001 Accept
Ease of testing -.519 .604 Reject
External support availability -.209 .835 Reject
User friendliness 2.996 .003 Accept
System security -1.471 .143 Reject
Cost effectiveness 1.680 .095 Reject
a. Dependent Variable: Adoption of iTax Technology
4.9.2 Organizational Factors as Predictor of Technology Adoption
Table 4.19 shows a strong positive relationship between organizational factors and
adoption of iTax technology (R) is (0.819), R square is (0.671) and adjusted R square is
(0.669), meaning that considered singularly, (66.9%) of the variance in adoption of iTax
technology can be predicted by the independent variables of organizational factors.
Table 4.19: Model Summary for Organizational Factors as Predictor of iTax
Technology Adoption
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .819a .671 .669 .533
a. Predictors: (Constant), organizational characteristics
The result of regression analysis in Table 4.20 shows that top management’s support
[ensure there is internet connectivity] and top management’s commitment to adoption of
iTax technology are significant in influencing adoption of iTax technology as shown p-
values, which are smaller than alpha value of (0.05) in Table 4.20. Thus, the hypotheses
were accepted.
On the other hand, the results of the regression analysis in Table 4.20 shows that there is
no significant impact of management direction setting [management encouraging staff to
use iTax], resources allocation [resources for staff training on iTax], policy on use of
48
iTax, reward system for use of iTax, consideration of employee views on iTax adoption,
top management’s perception on the adoption of iTax, level of investment on information
technology and top management’s interest on iTax [familiarity with the system] on the
adoption of technology as the significant level is above (5%), therefore this hypothesis is
rejected.
Table 4.20: Coefficients for Organizational Factors as a Predictor of iTax
Technology Adoption
Model T Calculated Sig. Results
(Constant) 5.129 .000
Management direction setting .660 .511 Reject
Resources allocation 1.712 .089 Reject
Policy in place 1.299 .196 Reject
Management support 2.617 .010 Accept
Reward system .146 .884 Reject
Employees views considered .437 .663 Reject
Top management perception 1.952 .053 Reject
To management commitment 2.279 .024 Accept
Substantial investments .578 .564 Reject
Top management interest -.597 .552 Reject
b. Dependent Variable: Adoption of iTax Technology
4.9.3 Individual Factors Characteristics as Predictor of Technology Adoption
Table 4.21 shows a strong positive relationship between individual factors and adoption
of technology (R) is (0.828), R square is (0.685) and adjusted R square is (0.683),
meaning that considered singularly, (68.3%) of the variance in adoption of technology
can be predicted by the independent variables of individual factors.
Table 4.21: Model Summary for Individual Factors as a Predictor of Technology
Adoption
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .828a .685 .683 .521
a. Predictors: (Constant), individual factors
The result of regression analysis in Table 4.22 shows that are significant influence on
adoption of iTax technology when individuals consider the technology to be useful, when
49
they are well trained on how to use the technology, when they take personal initiatives to
use the technology, when they consider the technology to be a sophistication, and when
there is support from colleagues on how to use the technology as shown by p-values,
which are smaller than alpha value of (0.05). Therefore, the hypotheses were accepted.
On the other hand, the results of the regression analysis in Table 4.22 shows that there is
no significant impact of an individual’s past experiences, enjoyment of using the
technology, whether the technology is used by peers or whether the individual prefers
computer systems on the adoption of iTax technology as the significant level is above
(5%). Therefore, this hypothesis is rejected.
Table 4.22: Coefficients for Individual Factors as Predictor of iTax Technology
Adoption
Model T
Calculated
Sig. Results
(Constant) 4.053 .000
I consider online tax submission to be very useful 3.008 .003 Accept
I am sufficiently trained on how to use iTax platform 2.017 .046 Accept
I take personal initiative to file my organization taxes online 2.352 .020 Accept
I find filing taxes online easy due to my previous experience .528 .598 Reject
Filing taxes online shows sophistication 2.761 .007 Accept
I enjoy filing taxes online -1.436 .153 Reject
My colleagues at work encourage me to use iTax platform 2.229 .027 Accept
All firms within our category use iTax platform 1.297 .197 Reject
I prefer computer based systems more than manual entries .538 .592 Reject
c. Dependent Variable: Adoption of iTax Technology
4.9.4 Innovation Characteristics, Organizational Factors and Individual Factors as
Combined Predictor of Technology Adoption
Multiple regressions were used to examine the ability of the model to predict adoption of
innovation among medium taxpayers at KRA. The regression analysis also tested the
direct path hypotheses; H1a, H1b and H1c. Table 4.23 shows a strong positive
relationship between independent variables [innovation characteristics; organizational
factors; individual factors] and the adoption of technology. (R) is (0.853), R square is
(0.728) and adjusted R square is (0.723), meaning that considered collectively, (72.3%) of
the variance in adoption of technology can be predicted by the independent variables of
innovation characteristics, organizational factors and individual factors.
50
Table 4.23: Model Summary for Combined Innovation Characteristics,
Organizational Factors and Individual Factors as Predictor of Technology Adoption
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .853a .728 .723 .439
a. Predictors: (Constant), innovation characteristics, organizational factors, individual factors
The result of regression analysis in Table 4.24 shows that innovation characteristics,
organizational factors and individual factors are significant in influencing adoption of
iTax technology as shown by p-values, which are smaller than alpha value of (0.05).
Therefore, the hypotheses were accepted.
Table 4.24: Coefficients for Combined Innovation Characteristics, Organizational
Factors and Individual Factors as Predictor of Technology Adoption
Model Unstandardized
Coefficients
Standardized
Coefficients
T
Calculated
Sig. Results
B Std. Error Beta
(Constant) .569 .143 3.973 .000
Innovation
Characteristics
.249 .066 .270 3.770 .000 Accept
Organizational
Factors
.217 .067 .257 3.238 .001 Accept
Individual Factors .360 .065 .403 5.546 .000 Accept
a. Dependent Variable: Adoption of iTax Technology
Regression Model
For modelling the relationship between the depended variable [Adoption of iTax] and
independent variables [innovation characteristics, organizational factors, and individual
factors], the following multiple regression equation is applied;
γ=α + β1x1 + β2x2 +β3x3 +ε
Where γ= Dependent variable [Adoption of iTax technology]
α=Constant i.e. the y intercept or the average response when both predictor
variables are 0
x1= Independent variable 1 [Innovation Characteristics]
x2= Independent variable 2 [Organizational Factors]
x3= Independent variable 3 [Individual Factors]
ε=Random Component/standard error
51
β1= Coefficient of innovation characteristics.
β2= Coefficient of organizational factors.
β3= Coefficient of individual factors
Hence;
γ=0.569 +0.249 x1 + 0.217 x2 +0.360 x3
4.10 Chapter Summary
The chapter presented the study findings. The chapter is organized according to the study
objectives. The chapter first presents the findings on the demographics of the population
followed by the descriptive analysis of the relationship between the depended and
independent variables. The chapter then presented the findings of the hypothesis testing
and development of the study model. Chapter five presents the study summary,
discussions, conclusions and recommendations.
52
CHAPTER FIVE
5.0 DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS
5.1 Introduction
The purpose of this study was to determine the factors that affect adoption of technology
innovation in Kenya. The study used iTax technology at Kenya Revenue Authority as a
case study. Chapter five offers the summary of the findings, discussions, conclusions and
recommendations.
5.2 Summary
The study focused on innovation characteristics, organizational factors and individual
factors as the predictors of adoption of iTax technology by medium taxpayers at KRA.
The study adopted a case study research design. The design was descriptive in nature. The
population for this study involved the 1,630 medium taxpayers to the Kenya Revenue
Authority. The study used a stratified sampling design to draw a sample size of 200. The
study used a survey instrument to collect primary data from the respondents. Data
analysis involved frequencies, percentages, correlations and regression analysis to
determine the relationship between the dependent variable and the independent variables
of the study. Statistical significance level was used to infer deductions from the study to
the entire population. Findings were presented using tables and figures.
The first specific study objective was to determine the technology innovation
characteristics that affect adoption of iTax technology by medium taxpayers at KRA. The
study showed a strong [R=0.76] positive and statistically significant [p<0.001]
relationship between innovation characteristics and adoption of iTax technology. The
study indicated that considered singularly, (57.4%) of the variance in adoption of iTax
technology can be predicted by the independent variable of innovation characteristics.
The study further showed that system compatibility [40% agreed; 6% strongly agreed],
visibility of results [37% agreed; 11 strongly agreed], clarity of advantages [34% agreed;
18% strongly agreed] and user friendliness [29% agreed; 8% strongly agreed] are
significant in influencing adoption of iTax technology as shown by p-values, which are
smaller than alpha value of (0.05).
The second specific study objective was to determine the organizational factors that affect
adoption of iTax technology by medium taxpayers at KRA. The study showed a strong
53
[R=0.819] positive and statistically significant [p<0.001] relationship between
organizational factors and adoption of iTax technology. It showed that considered
singularly, (66.9%) of the variance in adoption of iTax technology can be predicted by
organizational factors. The study illustrated that top management’s support [29% agreed;
14% strongly agreed] and top management’s commitment [27% agreed; 12% strongly
agreed] to adoption of iTax technology are significant in influencing adoption of iTax
technology as shown p-values, which are smaller than alpha value of (0.05).
The third specific study objective was to determine the individual factors that affect
adoption of iTax technology by medium taxpayers at KRA. The study illustrated a strong
[R=0.828] positive and statistically significant [p<0.001] relationship between individual
factors and adoption of technology. Considered singularly, (68.3%) of the variance in
adoption of technology can be predicted by individual factors. The study further showed
that there is a significant influence on adoption of iTax technology; when individuals
consider the technology to be useful [39% agreed; 11% strongly agreed]; when the
individuals are well trained on how to use the technology [30% agreed; 7% strongly
agreed]; when individuals take personal initiatives to use the technology [32% agreed;
12% strongly agreed]; when individuals consider the technology to be a sophistication
[29% agreed; 22% strongly agreed]; and when there is support from colleagues on how to
use the technology [32% agreed; 9% strongly agreed] as shown by p-values, which are
smaller than alpha value of (0.05).
The other factors that affect adoption of iTax technology negatively were, lack of
knowledge on the use of iTax technology [45.8%], the system being complicated
[20.8%], lack of top management support [12.5%], lack of staff awareness on the
existance of the technology [8.4%], lack of system support [4.2%], iTax technology
cannot handle huge traffic especially during deadlines [4.2%], and poor internet
connectivity [4.2%].
The adoption of iTax technology was then subjected to multiple regression analysis with
the three independent variables. The study showed a strong [R=0.853] positive and
statistically significant [p<0.05] relationship between the independent variables and the
dependent variable with (72.3%) of the variance in adoption of technology being
predicted by the independent variables of innovation characteristics; organizational
factors; and individual factors. The study showed that innovation characteristics [x1],
54
organizational factors [x2] and individual factors [x3] are significant in influencing
adoption of iTax technology as shown by p-values, which are smaller than alpha value of
(0.05). The model was thus expressed as;
γ=0.569 +0.249 x1 + 0.217 x2 +0.360 x3
5.3 Discussions
5.3.1 Innovation Characteristics
The first specific study objective was to determine the technology innovation
characteristics that affect adoption of iTax technology by medium taxpayers at KRA. The
study showed a strong [R=0.76] positive and statistically significant [p<0.001]
relationship between innovation characteristics and adoption of iTax technology. This
means that the more the technology is perceived to be beneficial and compatible to the
users systems, beliefs and values the more likely the technology will be adopted. The
study indicated that considered singularly, (57.4%) of the variance in adoption of iTax
technology can be predicted by the independent variables of innovation characteristics.
The first innovation characteristics identified by the study to affect adoption of iTax
technology was the technology’s compatibility with the organization’s systems. In this
context, compatibility was considered as the degree to which the technology is perceived
as consistent with other technologies used by the organization (Dzogbenuku 2013). This
finding is in line Ramazani and Allahyari (2013) assertion that most organizations focus
on applicable technologies that are aligned with the requirements of their systems and
give little thoughts if any to those technologies which are not compatible to their
operating systems. This is based on the assumption that compatible technological
innovation systems create the needed synergies within aligned activities, employees and
firm structure. This is further in line with Chapman and Kihn (2009) argurment that
organization with incompatible information technology systems will fail in data focusing.
The second innovation characteristic that was identified to have an effect on the adoption
of iTax technology is the visibility of the results. This supports findings by Chigona and
Licker (2008) which indicated that in some innovation, it is easy for others to see the
results of adoptions from those who have already adopted the technology. They indicate
that observability is positively correlated with the rate of adoption e.g. to the extent that
55
something has to be explained in complicated ways to others (i.e., complexity), it
becomes less “observable,” too.
The third innovation characteristics which was shown to affect iTax innovation adoption
was the clarity of advantages. This is in line with Mndzebele (2013) explanation that
technological innovation adoption process involves a rational decision in an organisation,
which requires that one assesses the potential benefits of the new technology to the
business. Therefore, organisations adopt a technology when they see a need for that
technology, believing it will either take advantage of a business opportunity or close a
suspected performance gap. This means that when a user perceives relative advantage or
usefulness of a new technology over an old one, they tend to adopt it (Al-Jabri & Sohail,
2012). This further supports arguments that online tax submission offers benefits such as,
faster tax filing, ease of tracing taxes, better organization of tax information, reduced cost
of filing taxes, increased productivity over the manual tax filing (Weru, Kamaara, &
Weru, 2013; Obae, 2009; IST-Africa, 2015, KRA, 2015).
The fourth characteristic of innovation that was demonstrated to affect the adoption of
iTax technology is the user friendliness of the innovation. This is in line with Hakimian
and Feissal (2012) arguments that the absence of ease of use of technological innovation
has a negative impact on perceptions of the technology, which leads to decreased
adoption, and usage of the technology. This is further supported by Sahin (2006) who
indicated that a technological innovation might confronted with challenges where the
systems are complex to the users but if hardware and software are user-friendly, then they
might be adopted faster and successfully.
5.3.2 Organizational Factors
The second specific study objective was to determine the organizational factors that affect
adoption of iTax technology by medium taxpayers at KRA. The study showed a strong
[R=0.819] positive and statistically significant [p<0.001] relationship between
organizational factors and adoption of iTax technology. This means that the supportive
organisational environment influences the extent of technology adoption. The more the
organizational system supports the adoption of technology the more likely that the
technology will have a higher uptake within the organization. The study showed that
considered singularly, (66.9%) of the variance in adoption of iTax technology can be
predicted by organizational factors.
56
The study first organisational factor identified to influence the adoption of iTax
technology is the top management’s support. This is in line with Weng and Lin (2011)
arguments that since many innovations require the collaboration and coordination of
different departments and divisions during adoption, to successful adoption, new
initiatives are usually endorsed and encouraged from the top management. Therefore the
central role played by the top management is to mobilize resources and allocate them in a
manner that promotes the adoption of the new technology. This is further in line with a
study by Wen, Zailani and Fernando (2009) which focused on determining the
determinants of Radio Frequency Identification [RFID] technology adoption in supply
chain among manufacturing companies in China. The study found that top management
support measured by the level of funding/ resources and effective management control
had the impact on the adoption of RFID in China. Similarly, a study by Bazurli,
Cucciniello, Mele and Nasi (2014) to identify the determinants and barriers of adoption,
diffusion and upscaling of information communication technology driven social
innovation in the public sector indicated a relationship between lack of top management
support/ vision and innovation adoption. The study contends that resistance to change
from the top management is one of the biggest barriers to the introduction of electronic
procurement within the public sector and this cannot be simply solved by a fast Internet
connection or yet another departmental reorganization.
The second organizational factor identified to affect the adoption of iTax technology is
the top management’s commitment to adoption of technology. This supports arguments
by Ahmer (2013) that the understanding of innovation, attitudes toward innovation, extent
of involvement in adoption process could influence top management support as they play
a critical role in creation of a supportive climate and provision of adequate resource to
adopt and implement new technology. Similarly, a study by Ahmer (2013) to identify
factors that influence adoption of Human Resource Information Systems [HRIS]
innovation in Pakistani organizations identified top management involvement and
commitment as some of the biggest contributors towards adoption of HRIS innovations in
any organization. The study contended that with active involvement and commitment, the
top management could foster right direction for adoption of innovation.
Likewise, visible top management commitment could signal the importance of innovation
and lead to positive attitudes from users towards the innovation, and smoothen the
conversion from existing work procedures to the new. The commitment would also ne
57
demosttrated through resources allocation as indicated by Ahmer (2013) that with their
leadership role, top management could ensure commitment through allocation of required
capital and human resource for adoption of innovation and help in overcoming user
resistance and resolving probable conflicts.
5.3.4 Individual Factors
The third specific study objective was to determine the individual factors that affect
adoption of iTax technology by medium taxpayers at KRA. The study illustrated a strong
[R=0.828] positive and statistically significant [p<0.001] relationship between individual
factors and adoption of technology. This means that positive individual factors would
positively influence the uptake of iTax technology e.g. the more favourable the individual
factors, the more likely the innovation will be adopted. Considered singularly, (68.3%) of
the variance in adoption of technology can be predicted by individual factors.
The first individual factor identified to have an influence on the adoption of iTax
technology is the individual user’s perception on the usefulness of the technology. This is
supported by arguments by Chigona and Licker (2008) that perceived relative advantage
or usefulness of an innovation involves both perception (i.e., evaluation) of the proposed
innovation as being superior to its precursor, as well as perceptions of other candidates
and the status quo. Hence, the perceived level of usefulness of the innovation
economically, socially, or in terms of convenience and satisfaction while influence the
rate at which the technology is adopted (Robinson, 2012).
The second individual factor that influence the adoption of iTax technology is the
individual’s training on how to use the technology. This is a competency issue. This
finding supports an empirical study by Peralta and Costa (2007) on teachers’ competences
and confidence regarding the use of information communication technology in Italy,
Greece and Portugal. The study revealed that in in Italy, teachers’ technical competence
with technology is a factor of improving higher confidence in the use of information
communication technology and promote ease of adoption of the technology.
The third individual based factor that affects adoption of iTax technology is an
individual’s drive to take personal initiatives to use the technology. This is linked to the
personal innovativeness, which is considered an inherent feature of all individuals with
respect to new ideas, products and innovations (Jianlin & Qi, 2010). Thie represents the
58
the degree to willingly increase the chance to try new products or services
(Hirunyawipada & Paswan, 2006). The findings therefore supports assertion by Rouibah
and Abbas (2010) that the more a user shows signs of innovativeness to use new
technologies and loves everything new, the more he will enjoy its use. This further
supports arguments by Hung, et. al. (2013) that in response to new technology adoption,
individuals who have a higher degree of personal innovativeness are more willing to take
risks and tends to be innovators and quick adopters while those with a stable trait or
predisposition are slower tending to lag behind.
The third individual factor identified to influence the adoption of iTax technology is the
consideration of the technology as a sign of sophistication. This confers with the social
network theory of technology adoption. The social construct borrows from the reality that
people do not exist in exclusion and communities and other social networks influence
actions of individuals. The findings are supported by Lekhanya (2013) arguments that
when considering the use of new technologies, one’s community shapes their attitude
towards the usage of new systems. He attributes this to the fact that peoples’ decision to
adopt a technology includes the external impressions, such as cultural values and norms
that people are subject to. The findings are also in line with Mazman, Usluel and Çevik
(2009) who indicated that individuals are influenced by their social environment under
three basic conditions. First, when an individual accepts influence because he hopes to
achieve a favorable reaction from another person or group (social approval/disapproval
from others) [Compliance]. Second, when an individual accepts influence because he
wants to establish or maintain a satisfying self defining relationship with others
[Identification]. Third, when an individual accepts influence because it is congruent with
her value system [Internalization]. Therefore, if the feeling of sophistication would be a
trigger for an individual to adopt a technology for them to be viewed favourably among
the social networks.
The other factors that hinder adoption of iTax technology identified by the respondents
were, lack of knowledge on the use of iTax technology, the system being complicated,
lack of top management support, lack of staff awareness on the existance of the
technology, lack of system support, iTax technology cannot handle huge traffic especially
during deadlines, and poor internet connectivity.
59
5.4 Conclusions
5.4.1 Innovation Characteristics
The first specific study objective was to determine the technology innovation
characteristics that affect adoption of iTax technology by medium taxpayers at KRA. The
study showed a strong positive relationship between innovation characteristics and
adoption of iTax technology. The study indicated that considered singularly, Fifty-Seven
percent of the variance in adoption of iTax technology can be predicted by the
independent variables of innovation characteristics.
The most significant innovation characteristics that affect adoption of the iTax technology
by medium taxpayers in Kenya are; system compatibility; visibility of results; clarity of
advantages; and user friendliness. The relationship is such that the more compatible an
organization’s system to iTax system; the more visible the results of iTax; the more clear
the advantages of the iTax system to the organization; and the more the users perceive the
system to be user friendly, the more likely that they will adopt the use of iTax technology.
5.4.2 Organizational Factors
The second specific study objective was to determine the organizational factors that affect
adoption of iTax technology by medium taxpayers at KRA. The study showed a strong
positive relationship between organizational factors and adoption of iTax technology. It
showed that considered singularly, Sixty-Seven percent of the variance in adoption of
iTax technology can be predicted by organizational factors.
The study illustrated that top management’s support and top management’s commitment
to adoption of iTax technology are significant in positively influencing adoption of iTax
technology. The more the top management’s support and commitment to adoption of the
technology, the more likely the organization will adopt the system as a way of filing their
taxes.
5.4.3 Individual Factors
The third specific study objective was to determine the individual factors that affect
adoption of iTax technology by medium taxpayers at KRA. The study illustrated a strong
positive relationship between individual factors and adoption of technology. Considered
singularly, Sixty-Eight percent of the variance in adoption of technology can be predicted
by individual factors.
60
The study further showed that there is a significant positive influence on adoption of iTax
technology; when individuals consider the technology to be useful; when the individuals
are well trained on how to use the technology; when individuals take personal initiatives
to use the technology; when individuals consider the technology to be a sophistication;
and when there is support from colleagues on how to use the technology.
The other factors that affect adoption of iTax technology negatively were, lack of
knowledge on the use of iTax technology, the system being complicated, lack of top
management support, lack of staff awareness on the existance of the technology, lack of
system support, iTax technology cannot handle huge traffic especially during deadlines,
and poor internet connectivity.
5.5 Recommendations
5.5.1 Recommendations for Improvement
5.5.1.1 Innovation Characteristics
Since, the innovation characteristics is more of design issue, KRA needs to redesign or
develop upgraded versions of the systems so that it is compatible to most technology
platforms as possible to address the concerns of those medium taxpayers who indicated
lack of system compatibilities. The design should also be such that results are clearly
visible and the platform be more user friendly. Furthermore, KRA needs to institute a
more elaborate promotional campaign to ensure its clients clearly understand the
advantages of using the system.
5.5.1.2 Organizational Factors
It is resoundingly clear that organisational support is required for effective and faster
technology diffusion, staffs require clear top management’s support and commitment to
motivate them to adopt a new technology. This support and motivation can be enhanced
through adequate resource allocation, clear policy directions and employee reward
system. Each organization should ensure proper and clear organisational support to
promote adoption of the technology.
5.5.1.3 Individual Factors
Awareness, competencies and personal innovativeness are critical elements in enhancing
the adoption of innovation. It is therefore significant for organizations to set aside
61
resources to train their staff on how to use the platform. Furthermore, the training should
be instrumental in creating awareness and to motivate the employees to try out the new
technology.
On the other hand, the following need to be addressed to ensure high adoption rates of the
iTax technology. Lack of knowledge on the use of iTax technology, the system being
complicated, , lack of staff awareness on the existance of the technology, lack of system
support, iTax technology cannot handle huge traffic especially during deadlines, and poor
internet connectivity.
5.5.2 Recommendations for Further Studies
The study only focused on Medium Taxpayers Office at KRA. Studies on other categories
of taxpayers would be welcome to ensure conclusively of the subject. Furthermore,
technological environment is highly dynamic and a study under different time zones
would be welcome to corroborate the findings.
62
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APPENDICES
Appendix A: Cover Letter
Respondent,
Dear Sir/Madam,
RESEARCH QUESTIONNAIRE
I am a graduate student at United States International University pursuing Executive
Master of Science in Organizational Development (EMOD). I am conducting a research
on the factors that affect the adoption of technological innovation in Kenya in partial
fulfilment of the degree. My study uses medium taxpayers at Kenya Revenue Authority
as a case study.
The findings of this study will provide both management of KRA, Government and the
taxpayers with an understanding on the factors that affect adoption of technological
innovations. The information will be important for these organizations in making
decisions on the best strategies to use to enhance the innovation adoption process.
The information provided will be held in confidence and for academic purpose only. The
questionnaire takes approximately 20 minutes to complete.
Yours faithfully,
Emma
P.O. Box 14634, 00800
NAIROBI
DATE:
69
Appendix B: Questionnaire
Answer the following questions by ticking or marking the boxes using X or √ or by filling
the empty boxes.
PART I: GENERAL DEMOGRAPHICS
1. What is your gender?
Male ☐ Female ☐
2. What is your age range
Less than 25 years ☐ 26-35 years ☐ 36-45 years ☐ 46-55 years ☐
56 years and above ☐
3. Under which sector do you submit your tax returns a KRA
Agriculture and Manufacturing ☐ Distributors ☐
Finance and Construction ☐ Services ☐ High Net Worth Individual ☐
4. How long have you been with you organization
Less than one year ☐ 1-3 years ☐ 4-6 years ☐
7-9 years ☐ More than 9 years ☐
5. What is your highest education level
Certificate ☐ Diploma ☐ Degree ☐ Post graduate Diploma ☐
Masters ☐
6. What is your position at your organization
Junior Manager ☐ Middle level Manager ☐ Top level Manager ☐
Director ☐
70
PART II: Independent Variables
Please indicate the degree to which you agree or disagree that the following statements.
Use a scale of 1-5 where; [1] is strongly disagree; [2] disagree; [3] neutral; [4] agree; and
[5] strongly agree.
A. Characteristics of Technological Innovation
Strongly
disagree
(1)
Disagree
(2)
Neutral
(3)
Agree
(4)
Strongly
agree
(5)
10. The KRA’s online tax
submission systems is fully
compatible with our
organization systems
( ) ( ) ( ) ( ) ( )
11. The KRA’s online tax
submission systems is easy to
implement
( ) ( ) ( ) ( ) ( )
12. The positive results of using
iTax is clearly visible ( ) ( ) ( ) ( ) ( )
13. It is more advantageous to use
iTax than the manual systems ( ) ( ) ( ) ( ) ( )
14. The KRA’s online tax
submission systems is easy to
test before implementation
( ) ( ) ( ) ( ) ( )
15. External support for online tax
submission is available ( ) ( ) ( ) ( ) ( )
16. KRA’s iTax system is user
friendly ( ) ( ) ( ) ( ) ( )
17. KRA’s iTax system is secure ( ) ( ) ( ) ( ) ( )
18. Use of KRA’s iTax system is
cost effective ( ) ( ) ( ) ( ) ( )
B. Organizational Factors
Strongly
disagree
(1)
Disagree
(2)
Neutral
(3)
Agree
(4)
Strongly
Agree
(5)
19. In my organization, top management
encourages employees to use iTax ( ) ( ) ( ) ( ) ( )
20. Our company provides resources for
staff to learn online tax submission ( ) ( ) ( ) ( ) ( )
21. It is a policy at my organization to
file taxes online ( ) ( ) ( ) ( ) ( )
22. In my company, the management
ensures there is internet connectivity
for filing taxes online
( ) ( ) ( ) ( ) ( )
71
23. There is a reward for successfully
online filing of taxes in my
organization
( ) ( ) ( ) ( ) ( )
24. My organization takes into account
employees suggestion on how to
improve online tax submission
25. At my organization, top
management has a positive
perception and attitudes towards
adoption of new technologies
26. There is clear commitment from my
CEO for the adoption of online tax
submission system
27. My organization has invested
substantially in information
technology to support online
business activities
28. Top management in my organization
are well familiar with the online
business activities in my
organization
C. Individual Factors
Strongly
disagree
(1)
Disagree
(2)
Neutral
(3)
Agree
(4)
Strongly
Agree
(5)
29. I consider online tax submission to be
very useful ( ) ( ) ( ) ( ) ( )
30. I am sufficiently trained on how to use
iTax platform ( ) ( ) ( ) ( ) ( )
31. I take personal initiative to file my
organization taxes online ( ) ( ) ( ) ( ) ( )
32. I find filing taxes online easy due to
my previous experience ( ) ( ) ( ) ( ) ( )
33. Filing taxes online shows
sophistication level of the organization ( ) ( ) ( ) ( ) ( )
34. I enjoy filing taxes online ( ) ( ) ( ) ( ) ( )
35. My colleagues at work encourage me
to use iTax platform ( ) ( ) ( ) ( ) ( )
36. All firms within our category use iTax
platform ( ) ( ) ( ) ( ) ( )
37. I prefer computer based systems more
than manual entries ( ) ( ) ( ) ( ) ( )
38. In your opinion, what other factors affect the adoption of innovation technologies at
your organization?
…......................................................................................................................................
..........................................................................................................................................
72
PART III: Dependent Variable-iTax Technology Adoption
Indicate the degree to which you agree or disagree to the following statements regarding
the adoption of iTax system in your organization. Use a scale of 1-5 where; [1] is strongly
disagrees; [2] disagree; [3] neutral; [4] agree; and [5] strongly agree.
Strongly
disagree
(1)
Disagree
(2)
Neutral
(3)
Agree
(4)
Strongly
Agree
(5)
14. Online tax submission has lead to
simplification of work routines in my
organization ( ) ( ) ( ) ( ) ( )
15. Online tax submission has lead to faster
filing of tax returns for my organization ( ) ( ) ( ) ( ) ( )
16. Online tax submission has lead to
integration of business units in my
organization
( ) ( ) ( ) ( ) ( )
17. Online tax submission has lead to better
organization of tax records in my
organization
( ) ( ) ( ) ( ) ( )
18. Online tax submission has lead to
efficient coordination of departments in
my organization
( ) ( ) ( ) ( ) ( )
19. Online tax submission has lead to better
returns for my organization ( ) ( ) ( ) ( ) ( )
20. Online tax submission has lead to
meeting of tax submission deadlines for
my organization
( ) ( ) ( ) ( ) ( )
21. Online tax submission has improved
equity owners satisfaction in my
organization
( ) ( ) ( ) ( ) ( )
22. Online tax submission has lead to
reduction of operational cost at my
organization
( ) ( ) ( ) ( ) ( )
23. Online tax submission has lead to faster
tax refund for my organization ( ) ( ) ( ) ( ) ( )
24. Online tax submission has lead to
increased productivity in my organization ( ) ( ) ( ) ( ) ( )
25. Online tax submission has lead to more
accurate tax filing for my organization ( ) ( ) ( ) ( ) ( )
26. Online tax submission has lead to easy
tracking of tax records in my organization ( ) ( ) ( ) ( ) ( )
THANK YOU