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American Journal of  Economics and 
Business Innovation (AJEBI)

Perceived Online Tax Compliance Measures on Tax Compliance among Online Traders 
in Kenya

Eric Kiprono1*, Edwin Kimitei2, Collins Kapkiyai3

Volume 2 Issue 3, Year 2023
ISSN: 2831-5588 (Online), 2832-4862 (Print)

DOI: https://doi.org/10.54536/ajebi.v2i3.2041
https://journals.e-palli.com/home/index.php/ajebi

Article Information ABSTRACT

Received: September 15, 2023

Accepted: October 13, 2023

Published: October 20, 2023

In several developing countries, tax collection by government relied on manual taxation, 
but there is now more drive towards introduction of  electronic online filling of  taxes. 
In Kenya, online tax system relies on integrated tax system known as iTax system. This 
study determined the relationship between online tax system and tax compliance among 
online traders in Uasin Gishu in Kenya. The study is based on three theories as part of  the 
theoretical framework, that is, the Technology Acceptance Model (TAM) Theory and Game 
Theory Model of  Equilibrium in Tax Compliance. The research study adopted a positivist 
research paradigm and explanatory research design. Primary data were collected using online 
questionnaires from a sample of  160 online traders respondents in Uasin Gishu. The study 
found that during the response to five attributes of  tax compliance, the there was low levels 
of  tax compliance. The study also reported low levels of  perceived iTax security concerns as 
well as perceived tax system stability. The multiple linear regression coefficient (R2 = 0.863, 
P < 0.001) was positive indicating a positive correlation between perceived iTax security and 
perceived tax system stability with tax compliance. We conclude that perceived security risks 
concerns and perceived tax system stability affected tax compliance among online traders 
in Uasin Gishu County. Based on the findings, it is suggested that policy makers should not 
just place emphasis on technology but pay more attention on the characteristics of  potential 
iTax users, e.g. their perceived security and system stability while implementing iTax service.

Keywords
Tax Compliance, Small and 
Medium Enterprises, Online Tax 
Filing, Online Business, Kenya

1 Department of  Custom and Border Control, Kenya Revenue Authority, P. O. Box 48240–00100, Nairobi, Kenya
2 Department of  Marketing & Logistics, Moi University, PO Box 3900, Eldoret, Kenya
3 Department of  Accounting and Finance, Moi University, PO Box 3900, Eldoret, Kenya
* Corresponding author’s e-mail: erickiprono88@gmail.com

INTRODUCTION
In the contemporary world, increasing number of  
business people relate with internet for economic 
purposes (Alzoubi et al., 2022; Beniiche et al., 2022). 
Indeed this interaction with the internet has created the 
basis for clients or consumers, product and market, access 
and distribution of  information, and low-cost delivery of  
documents, business opportunities, consultancy services, 
online avenues for trading on various products, including 
engaging in online trades on various digital assets (Li 
and Zhang, 2022; Peters, 2023). Over the last 10 years, 
there has been more online users moving away from 
the ‘brochure websites’ into new generation websites 
which offer better platform for interaction between 
the users and a variety of  online businesses platforms 
(Murray et al., 2023). Kenya is one of  the countries that 
has made tremendous strides in the adoption internet 
being at 89.5% penetrability among the population as 
at January 2021 (Kapkai et al., 2021). This translated to 
internet coverage of  approximately 17.86 million users 
at the start of  2021. This unprecedented growth of  
internet has enabled many Kenyans to view internet as a 
platform where they can earn their living though online 
trades (Ndung’u, 2018; Wilson and Makau, 2018; Nyaga, 
2023). This enables business to be conducted without 
physical presence. While earning a living online used to 
be for some selected few in the past, the government 
of  Kenyan has gradually recognized the importance of  
internet business and ecommerce as an economic activity 

for which the citizens actively participate and contribute 
to the development of  the country (David and Grobler, 
2020; Myovella et al., 2020).
Economic growth and development of  a counties pivots 
on the revenues obtained by the government from the 
citizens and investments (Christensen and Hearson, 2019; 
Allayarov, 2020). The governments also need revenue to 
funds the growth of  public expenditure, infrastructure, 
education, public healthcare services, public services, and 
pay debts among others (Kimaro et al., 2017; Bausch, 
2019). The main instruments that a government may use 
to obtain funds for public expenditure is mainly through 
taxation (Ndajiwo, 2020; Olawole et al., 2022). The tax 
system is expected to mobilize revenues, reduce economic 
distortions, improve resource allocation, and improve 
productivity and growth prospects. In the past several 
years, there have been elaborate plans by governments 
to increase tax revenue generated (Habibov et al., 2018; 
Deojain and Lindequist, 2019) and this extend to tax 
from online business platforms.
Taxes collected by governments rely on voluntary 
compliance by taxpayers in fulfilling their tax obligation 
freely and completely without coercion (Slemrod, 2019; 
Okwara, 2020). Here, tax compliance will enable the 
citizens to pay taxes on time and timely reporting of  the 
correct tax information (McGill, 2019; De Neve et al., 
2021). In this case, governments require all citizens in the 
respective jurisdiction to encompass voluntary obligation 
to pay taxes as set out in law (Hofmann et al., 2017; Guerra 



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and Harrington, 2018). However, countries in developing 
economies have difficulties of  raising adequate tax revenue 
to finance the ever increasing public expenditures due to 
tax non-compliance (Rosid et al., 2019). An increase in tax 
compliance would reasonably enhance tax revenue level 
(Rosid et al., 2018; Tilahun, 2019). Subsequently, ways of  
improving tax compliance have been suggested in several 
countries. The rapidly increasing pace of  technological 
change has significantly impacted tax compliance (Cotton 
and Dark, 2017; Gangodawilage et al., 2021). Through 
technological innovations, tax systems and databases 
may be integrated to facilitate tax compliance (Qadri 
and Darmawan, 2021; Qi and Azmi, 2021). There are a 
number of  methods employed today by tax agencies to 
capture tax return and payment data electronically thus 
likely to affect tax compliance.
The electronic tax filing is a major form of  electronic 
government services (Bassey et al., 2022). The Kenyan 
government introduced electronic tax filing in order 
to achieve greater tax administrative and compliance 
efficiency (Ng’ong’o, 2021). This system of  taxation has 
an immense advantage including the convenience of  
taxpayers who can file tax returns at home or cybercafés, 
and minimization of  errors associated with manual 
filing as the system automatically check the application 
(Bassongui and Houngbédji, 2021; Do et al., 2022). In 
Kenya, the earliest form of  the online filing of  tax returns 
was through the implementation of  the Integrated Tax 
Management System (ITMS) in 2013. The Kenya Revenue 
Authority (KRA) later phased out the ITMS and replaced 
it with the integrated tax management (iTax) system. The 
iTax was more efficient to the taxpayer in filling their tax 
returns, undertake internet based registration, paying and 
status inquiries with real time monitoring of  the accounts 
(Bett and Yudah, 2017; Opiyo, 2022). While there is an 
obvious advantage of  iTax system, success of  the system 
may rely on a number of  factors including perception on 
the security of  the system, and to some extent technical 
knowledge of  system stability by the taxpayers (Migot and 
Paul, 2019). However, to the best of  our knowledge, there 
is no study that has empirically tested these assertions. 
Therefore the aim of  this study was to establish the 
impact of  adoption of  iTax on tax compliance among 
online traders in Uasin Gishu, Kenya.

LITERATURE REVIEW
Theoretical Review
The first theory that was used to explain the tax compliance 
behaviour in relation to technology is the technology 
acceptance model (TAM) developed by Davis and 
Venkatesh in 1989 (Tarhini et al., 2015; Lai, 2017).  The 
authors argue that “when users are presented with a new 
technology; its perceived usefulness (PU) and Perceived 
ease-of-use (PEOU) will influence their decisions on how 
and when they will use it.” The PU will allow the user to 
depict how using the technology will enhance their job 
performance while PEOU enable the user to perceive a 
particular system as free from effort. This implies that user 
acceptance of  a technology will be pivotal in shaping the 
success or failure of  adopting any system (Lippert and 
Davis, 2006; Hooks et al., 2022). The theory allows for the 
users to successful adopt the technology and have positive 
attitudes towards using the technology for their maximum 
benefits. The attitudes towards usage may be not be there 
but this attitude can increase if  the user learn the basic of  
using the technology. There are however weaknesses of  
the theory one being that TAM has managed to divert the 
attention of  the researchers from other significant research 
themes by creating an illusion of  progress in knowledge 
accumulation. Secondly another weakness of  the theory is 
that attempts at expanding TAM to enable it to adapt the 
dynamic IT environments have caused more theoretical 
chaos and confusion. The second theory that was used 
in this study is the Game Theory Model of  Equilibrium 
in Tax Compliance (Geckil and Anderson, 2016). This 
group of  model that try to explain compliance behaviour 
among taxpayers are generally based on the standard game-
theory concept of  equilibrium. Assuming linear tax rate 
and penalties, risk neutral taxpayer, no additional cost of  
an audit other than penalties levied on underreporting of  
income and that the objective of  the tax authority is to 
maximize revenue. A tax authority will pursue the strategy 
that provides optimum audit revenue subject to a fixed 
audit budget by setting up a cut-off, whereby all declarations 
below the set level are audited with a probability p but leave 
out all declarations above the cut-off. 

Conceptual Framework
This study conceptualizes that tax compliance could be 

Figure 1: Conceptual framework linking the attributes of  iTax management in tax



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affected by online tax registration, tax liability verification, 
tax law enforcements. The purpose of  this study will 
therefore be to test the nature and the strength of  these 
relationships.

METHODOLOGY 
Research paradigm and design
The underlying issue in research paradigm is to 
understand the ontology, epistemology and methodology 
of  research (Rahi, 2017). This research study adopted a 
positivist research paradigm. The  ontology of  positivist 
paradigm judge that the world is independent, external 
and there exist a single objective reality to any situation 
regardless of  the researcher’s viewpoint (Rehman and 
Alharthi, 2016). The epistemology of  positivism relies 
on observable and measurable facts with a presumption 
of  causal explanation and prediction. The methodology 
of  the said paradigm state that the best way to solve a 
problem involves quantification and statistical probability. 
Here, a hypothesis is put forward in propositional 
or question form about the causal relation between 
phenomena. Empirical evidence is gathered; the mass 
of  empirical evidence is then analyzed and formulated 
in the form of  a theory that explains the effect of  the 
independent variable on the dependent variable.
This study adopted explanatory research design. The 
explanatory research design analyzes the cause-effect 
relationship between two or more variables (Casey et 
al., 2022). This design was adopted since the analysis 
investigated the cause-effect relationship between the 

independent and the dependent variables. The cause was 
iTax measures whereas the effect was tax compliance.

Study Area, Population and Sampling
The study was carried out in Uasin Gishu County in 
Kenya. Uasin Gishu is one of  the 47 counties in Kenya 
lying between longitudes 34º50’ East to 35º37’ East and 
latitudes 0º03’ South to 0º 55’ North. The County shares 
common borders with Trans Nzoia County to the North, 
Elgeyo Marakwet County to the East, Baringo County 
to the South East, Kericho County to the South, Nandi 
County to the South West and Kakamega County to the 
North West. It covers a total area of  3,345.2 km2. 
The population for this study were online traders. The 
online traders are registered at the Department of  Trade 
in Uasin County offices. During the time of  study, there 
were 459 individuals with registration status of  business 
on online platform in any form.  The sample size was 
derived from the population and the information used to 
generalize the findings within the limit of  random error. 
The sample size of  the study was calculated by using the 
Slovins formula (Tejada and Punzalan, 2012) with a 95% 
confidence level as:

Table 1: Socio-economic characteristics of  the respondents (n = 160)
Socio-economic attributes (n = 160) Variable attributes Freq. Percent

Gender Male 100 62.5
Female 60 37.5

 Age 

18-25 years 48 30.0
26–35 years 60 37.5
36–55 years 44 27.5
> 55 years 8 5.0

Level of  Education

Primary school 20 12.5
Secondary school 68 42.5
College 42 26.3
Bachelor degree 22 13.8
Master degree 8 5.0

Business age 

<1 years 48 30.0
2-5 years 92 57.5
5-10 years 16 10.0
>10 years 4 2.5

Source: Data Analysis (2023)

Where: n = Sample size required
N = Number of  people in the population
e = Allowable error (5%)
Thus the sample size was 214 online traders.
A total of  214 self-administered questionnaires were 

distributed and a total of  160 were returned resulting in 
a response rate of  84.1%. The overall response rate was 
found to be suitable for analysis and making interpretations 
since response rate of  60-100% is considered adequate 
to validate any survey based studies (Meyer et al., 2022). 

The socio-economic background of  the respondents is 
presented in Table 1.

Research Instruments and Data Collection
Data from the online traders were collected using 



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questionnaires. The questionnaire was divided into 
three parts; section A had the background information 
of  the online traders, section B gathered information 
about the security and stability of  itax while section C 
gathered information on tax compliance measurements. 
The questionnaires were self-administered by giving out 
in person to the respondents and could be completed at 
the informant’s own time. The questionnaire was used 
to collect quantitative data determined by a Likert scale 
consisting of  5 items. All items were positively scored. 
A five-point Likert scale ranging from Strongly Disagree 
(1), Disagree (2), Neither Agree nor Disagree (3), Agree 
(4) and Strongly Agree (5) was employed.
The researcher used expert judgement (Demirpence and 
Putnam, 2020) of  subject matter specialists to evaluate 
validity of  test items. Experts also made suggestions on 
the effectiveness of  the questionnaire by checking whether 
it was related to the research questions or not and gave 
feedback. Their recommendations were incorporated in 
the final questionnaire. 
For this analysis, reliability was evaluated using the 
Cronbach alpha test. For Cronbach’s alpha the commonly 
agreed lower limit is=>0.70, however in explanatory 
research it may decrease to =>0.60 and increase up to 
≥0.80 in studies requiring more stringent reliability 
(Alkhadim, 2022). The alpha coefficient results of  the 
reliability tests are provided in Table 2 The reliability 
of  the perceived stability of  iTax was the highest (α = 
0.8426), followed by perceived security of  iTax (α = 
0.8222), and finally, tax compliance had a lower reliability 
score (α = 0.7862). Reliability coefficient were above 0.7 
which is acceptable (Amirrudin et al., 2021).

upon expiration of  the allocated duration and kept in safe 
custody awaiting analysis.

Model Development, Measures and Data Analysis
Data collected were checked for errors and cleaned 
before analysis using Statistical Package for Social 
Sciences (SPSS 23.0). The research instruments were 
further edited for completeness and consistency. Data 
were then coded before statistical analysis. Descriptive 
statistics was used to summarize the data and included 
percentages, frequencies, means and standard deviations. 
Quantitative data evaluating the relationship between the 
independent and dependent variable were analyzed using 
Multiple Linear regression model of  the form:
Yi= β0 + β1X1 + β2X2 + βnXn + εi for i = 1,2,3,....n; n = 2
Where Yi = Dependent variable, 
β0 = Y-intercept (constant term)
X1 = Perceived security concern and X2 = Perceived 
system stability
β1, β2, β3, β4 and β5 = Regression Coefficients        
Xi = predictor variables for the independent variables and 
ε = error
The assumptions of  multiple regression analysis were 
strictly adhered to so as to control bias and they included:

Linearity
The linear regression needs the relationship between the 
independent and dependent variables to be linear. This 
was tested with the use of  scatter plots.

Normality
The linear regression assumes that all variables have 
normal distributions. This assumption was checked with 
the use of  goodness of  fit test

Multicollinearity in the Data
Multicollinearity occurs when the independent variables 
are highly correlated with each other. This was checked 
using Tolerance and Variance Inflation Factor (VIF). 
Tolerance measures the influence of  one independent 
variable on all other independent variables. VIF values of  
less than 10 and tolerance value of  more than 0.2 signifies 
absence of  multicollinearity (Lavery et al., 2019).

Homoscedasticity (constant variance) of  the Errors
This was checked by looking at a plot of  residuals versus 
predicted values.

Autocorrelation
This study used Durbin-Watson test to check for 
autocorrelation. A value of  between 1.5 and 2.5 is deemed 
appropriate to show lack of  serial correlation among the 
errors (Ding, 2019).

RESULTS AND DISCUSSION
Tax Compliance Levels
The dependent variable for this study was tax compliance 
levels. The metric score for the tax compliance levels 

Table 2: Reliability statistics of  items in the questionnaire
Variables No. of  

items
Cronbach’s 
alpha

Remark

Perceived security 
of  iTax

211 0.8222 Reliable

Perceived stability 
of  iTax

208 0.8426 Reliable

Tax compliance 221 0.7862 Reliable
Source: Data Analysis (2023)

Before data collection, relevant documentation and 
permissions were sought and granted. First approval 
of  the research was granted by Moi University School 
of  Business. Then a research permit was obtained from 
the National Commission for Science, Technology 
and Innovation (NACOSTI). Data were collected at 
designated times in sampling units through the “drop-
and-pick-later” method of  questionnaire administration. 
In some instances the data collection were done at the 
convenience of  the respondents. The respondents were 
assured that strict confidentiality would be maintained in 
dealing with the responses. Each of  the respondents were 
given about 30 to 45 minutes to fill in the questionnaires 
after which the filled-in questionnaires were collected 



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is shown in Table 3 Based on five attributes of  tax 
compliance, the overall mean of  1.91 ± 0.10/5.00 
indicated low levels of  tax compliance. Tax compliance 
has remained a pervasive issue in developing countries 
(Okpeyo et al., 2019; Musimenta, 2020). It is however 
very hard to prove tax noncompliance as taxpayers 
always acknowledge the fact that they are tax compliant. 
In this study we maintained strict anonymity and 
insisted on seeing any prove of  tax compliance and 
surprisingly the result was that most online traders were 
actually non-compliant on payment of  taxes. A previous 
study in Uganda established a significant difference 
between taxpayers and tax authorities in regards to tax 
compliance (Musimenta, 2020). The baseline is the tax 
compliance can only be proved through physical checks 

of  the taxpayers accounting records and tax authorities’ 
records which enable us to get the actual tax compliance 
situation. There are a number of  extant studies that have 
investigated various explanations for low tax compliance 
in developing counties with varied results such as high 
compliance costs (Musimenta, 2020), low level of  tax 
fairness (Khamis and Mastor, 2021; Oladipo et al., 2022), 
isomorphic forces (Sadress et al., 2019; Nartey, 2023); tax 
morale (Hardika et al., 2021), complexity of  the tax system 
(Musimenta, 2020), tax literacy (Nichita et al., 2019; Twum 
et al., 2020), fines and penalties (Mustapha et al., 2021) 
as well as poor taxpayers attitudes (Wijaya, 2019) among 
others. Our study did not get in details of  what caused 
the low tax compliance and therefore we cannot pursue 
to answer such as question in the current study.

Table 3: Metrics and Score (means, Std. Dev. and distribution) for tax compliance among online traders
Tax compliance among 
online traders

SD D NS A SA   
Freq. % Freq. % Freq. % Freq. % Freq. % Mean SD

Timely filing of  tax returns 45 28.1 53 33.1 50 31.3 12 7.5 0 0.0 2.18 0.12
Accurate filling of  tax returns 25 15.6 56 35.0 34 21.3 13 8.1 32 20.0 1.82 0.11
Timeline payments of  taxes 44 27.5 46 28.8 38 23.8 15 9.4 17 10.6 1.94 0.09
Voluntary tax compliance 39 24.4 53 33.1 38 23.8 13 8.1 17 10.6 1.94 0.10
No tendency to evade tax 36 22.5 31 19.4 40 25.0 12 7.5 41 25.6 1.66 0.08
Mean ± SD 1.91 0.10
Kurtosis 0.28
Skewness           0.80  

N=160; 5-point Likert scale: 1= strongly disagree; 5=strongly agree; Freq. = Frequency
Source: Data Analysis (2023)

Among the attributes of  tax compliance that attracted 
moderate score was timely filing of  tax returns only while 
other attributes such as tendency of  the online traders 
to file of  tax returns on time, accuracies in filling of  tax 
returns, voluntary tax compliance and tendency to evade 
tax scored low. 

Tax Compliances Measures
There are various measures on the iTax that was put in 
place to ensure compliance. In this study the focus was on 
perceived security aspect and perceived system stability 
of  the iTax. The results are presented and discussed in 
subsequent sections.

Perceived iTax Security Concerns 
The first independent variable for this study was perceived 
iTax security measures by the online traders. The metric 
score for the perceived tax security concerns is shown in 
Table 3. Based on five attributes of  tax compliance, the 
overall mean of  1.91 ± 0.12/5.00 indicated low levels of  
perceived iTax security concerns. The low perceived iTax 
security measures may have occurred due to occurrence of  
several cybersecurity threats and hacks into the computer 
system (Abed and Anupam, 2023; AL-Hawamleh, 2023). 
This has seen large number of  people shying away from 
uploading large volumes of  personal data, such as their 
personal details like security numbers in cyberspace. With 

Table 4: Metrics and Score (means, Std. Dev. and distribution) for perceived online tax security to online traders
Perceived online tax 
security to online traders

SD D NS A SA  
Freq. % Freq. % Freq. % Freq. % Freq. % Mean SD

Enhanced business secrecy 44 27.5 67 41.9 38 23.8 9 5.6 2 1.3 2.11 0.15
That party has no access to 
business information

35 21.9 52 32.5 34 21.3 9 5.6 30 18.8 1.73 0.11

There is reduced business risk 61 38.1 46 28.8 38 23.8 11 6.9 4 2.5 1.94 0.13
There is reduced website hack 50 31.3 59 36.9 38 23.8 13 8.1 0 0.0 1.99 0.12
Passwords and keys are safe 31 19.4 36 22.5 40 25.0 12 7.5 41 25.6 1.69 0.08
Mean ± SD 1.91 0.12



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exception of  enhanced business secrecy, all the attributes 
of  perceived tax security concerns attracted low score 
meaning they were not highly rated by the online traders. 

Perceived Online Tax Returns System Stability 
The second independent variable for this study was 
perceived online tax returns system stability among the 
online traders. The metric score for the perceived online 
tax returns system stability is shown in Table 5. Based 
on five attributes of  was perceived online tax returns 
system stability, the overall mean of  2.01 ± 0.08/5.00 

indicated low to moderate level of  perceived online tax 
returns system stability. The current result imply that the 
online traders did not have confidence about the online tax 
returns system stability due to frequent hang ups, collapse, 
delays and slow at execution of  the tasks as well as several 
cyber security attacks. These problems reported here are 
common in many online setting where internet is used in 
performing some tax (Arora et al., 2022; Liu et al., 2022; 
Renaud and Coles-Kemp, 2022). We observed that during 
the last 3 days towards the deadline of  filing the returns, 
the system hanged or was too slow to execute any function.

Kurtosis -0.20
Skewness -2.89

N=160; 5-point Likert scale: 1= strongly disagree; 5=strongly agree; Freq. = Frequency
Source: Data Analysis (2023)

Table 5: Metrics and Score (means, Std. Dev. and distribution) for perceived tax system stability
Perceived tax system 
stability

SD D NS A SA
Freq. % Freq. % Freq. % Freq. % Freq. % Mean SD

System rarely hang up 
during filing

42 26.3 56 35.0 23 14.4 23 14.4 16 10.0 2.47 0.10

System rarely collapse 
during peak hours

30 18.8 43 26.9 21 13.1 21 13.1 45 28.1 1.64 0.07

System has no delays 
during tax filing

39 24.4 54 33.8 20 12.5 22 13.8 25 15.6 1.84 0.10

System is fast leading 
during tax filing 

31 19.4 52 32.5 32 20.0 26 16.3 19 11.9 2.09 0.07

System is rarely affected 
by cyber attacks

23 14.4 45 28.1 27 16.9 31 19.4 34 21.3 1.99 0.06

Mean ± SD 2.01 0.08
Kurtosis 0.66
Skewness 0.81

N=160; 5-point Likert scale: 1= Strongly Disagree; 5=Strongly Agree; Freq. = Frequency
Source: Data Analysis (2023)

Test for Relationships between Electronic Tax 
Measures on Tax Compliance
The multiple linear regressions was used to examine 
the cumulative effect of  electronic tax measures on tax 
compliance. The multiple correlation coefficient (R) 
was positive and of  a value of  0.663 indicating that 
there was a strong and positive correlation between 
the two independent variable cumulatively and the 
dependent variable. On the other hand, the coefficient 
of  determination (R Square) indicates the variance on tax 
compliance attributed to the two independent variables 
is 44.2%. The one way ANOVA was used to give an 
indication on whether the linear regression model was 
a good fit for the data or two independent variables 
were good predictor of  the independent variable. In this 
context, P < 0.05 indicates that the model was considered 
a good fit for the data.
The unstandardized coefficients of  the model were 
examined with a view of  giving security perceptions, 

and online returns system stability on the tax compliance 
levels at an independent level. The two independent 
variables had positive effect on the dependent variable 
as indicated by their coefficients in the below linear 
regression equation; Tax Compliance Levels = -0.377 + 
0.422 (X1) + 0.417 (X2) where X1 is security concerns and 
X2 = online tax returns system stability.
The coefficient of  intercept 0.377 indicates that any 
increase in the perceived online tax security and perceived 
tax system stability would increase the tax compliance by 
37.7%. Individually, perceived online tax security increase 
the compliance by 36.6% and perceived tax system 
stability increases it by 34.8%. It was also determined that 
the correlations between perceived online tax security 
and perceived tax system stability with tax compliance 
were found to be positive and significant. These results 
suggest that perceived online tax security and perceived 
tax system stability resulted in increased tax compliance 
among online traders.



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Table 6: Multiple regression analysis showing the relationship between tax compliance and perceived attributes of  
the iTax
Regression Statistics
Multiple R 0.6632
R Square 0.4424
Adjusted R Square 0.4332
Standard Error of  Estimate 0.6857
Durbin-Watson 1.6943
Dependent Variable Tax compliance
Predictors: (Constant), Perceived tax security for online business, perceived system stability
ANOVA TSS df MSS F P-value
Regression 57.937 2 28.969 61.596 <0.0001
Residual 73.838 157 0.470
Total 131.775 159

Unstandardized Coefficients Standardized Coefficients
B Std. Error Beta t Stat P value

(Constant) 0.377 0.161 2.343 0.020
Perceived online tax security 0.422 0.100 0.366 4.230 0.000
Perceived tax system stability 0.417 0.103 0.348 4.029 0.000
Correlations Zero-order Partial Part Collinearity Statistics
(Constant) Tolerance VIF
Perceived online tax security 0.618 0.320 0.253 0.467 2.196
Perceived tax system stability 0.613 0.306 0.241 0.477 2.096

The research hypothesis was tested using multiple linear 
regression analysis. In the context of  where the p value is 
less than 0.05 significance level then the null hypothesis 
was rejected. The following hypotheses were tested; H01: 
There is no significant relationship between perceived 
security risks concerns and perceived tax system stability 
on tax compliance among online traders in Uasin Gishu 
County. The P value of  perceived security risks was 
<0.001 leading to the rejection of  null hypothesis (H01). 

CONCLUSIONS
The iTax system has been in operation in Kenya for few 
years since 2018, with an aim to drive up tax compliance 
including the online traders who were not in the tax 
brackets previously. In the current study, there was a 
low level of  tax compliance among the online trader and 
the same group of  people indicated that they have low 
levels of  perceived system security and system stability. 
Moreover, the perceived system security and stability 
affected tax compliance. The findings of  the current 
study lend credence to the fact that policy makers should 
not just place emphasis on technology but pay more 
attention on the characteristics of  potential iTax users, 
e.g. their perceived security and system stability while 
implementing iTax service.
Based on the foregoing discussion, the government 
is challenged to develop e-filing systems that satisfy 
the curiosity, desires, and perceptions of  taxpayers. 
There is also need for further research on more factors 
affecting compliance in the realm of  acceptance models 

to determine how adoption factors interact, how 
antecedents of  salient predictors impact intentions, and 
how additional personal perceptions and abilities impact 
taxpayer intentions. 

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