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

ii

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

iii

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

iv

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.

viii

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

xi

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

xiii

LIST OF ABBREVIATIONS

KRA: Kenya Revenue Authority

SPSS: Statistical Package for Social Sciences

1

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

2

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.

3

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.

4

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

5

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

6

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.

7

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

8

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.

9

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

10

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

11

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