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This tech can tap every collectible coin
No full textThe electronic tax invoice management system (e-Tims) that the Kenya Revenue Authority recently rolled out is touted to be the game-changer in tax administration. It allows VAT-registered traders to connect real-time to the KRA database using any technology, including mobile phones.
By pushing for the introduction of this new technology, President William Ruto has stood by his clarion call: to collect every collectible shilling. Technology with the potential to double the VAT revenue yield is most welcome, but there is more to this technology. Taxes impose some incidental costs on society, making them even more punitive
Cybersecurity Assessment Model for Small and Medium Enterprises (Smes) E-commerce in Kenya
Get PDFIn Kenya, there has been a remarkable surge in the adoption of e-Commerce among Small and Medium Enterprises (SMEs), especially in the aftermath of the COVID-19 pandemic. This has seen several online stores developed and launched for Business-to-Consumer(B2C) e-Commerce. However, the rise of e-Commerce platforms has also led to an increased number of cyber-attacks and data breaches, leading to financial losses, damage to reputation, and decreased market share for SMEs with no cybersecurity strategies. This phenomenon is primarily caused by a lack of cybersecurity assessment models that are adapted to the specific demands of SMEs e-Commerce platforms. This study sought to assess e-Commerce's cybersecurity level and maturity for SMEs in Kenya by developing assessment model.
This study reviewed some theoretical framework, standards, models, and empirical study to guide the development of the model. A tailored cybersecurity assessment model was developed specifically for SMEs operating in the e-Commerce sector in Kenya. The study assessed e-Commerce cybersecurity maturity level by using mixed methods approach with a focus on experimental research design, and determined the most common cyberattacks in relation to e-Commerce operations.
The assessment included various metrics such as access control mechanisms, software, data security, authorization and authentication, and network, and was rated quantitatively on a scale of 1-100 with critical, moderate, and highly secured as qualitative output of the model. The study involved 40 SMEs, all of whom responded to a semi structured questionnaire, and 36 agreed to have their e-Commerce platforms assessed using the model developed. Out of 36 e-Commerce assessed, 34 were found to be most vulnerable to cyberattacks, with a critical rating and assessment falling below 50 points, while only two were considered moderate or secure for online operations. The study concluded that SMEs must implement robust cybersecurity measures and standards, including access controls, authentication and authorization, data security, software security and network security since the study discovered that all the variables in the model are contributing to overall security. The study recommends specific policy formulation on cybersecurity to control deployment of e-Commerce. The study further recommends a study on user behaviour and online trends in managing e-Commerce cybersecurity
Factors Affecting Financial Sustainability Of Local Non- Governmental Organization In Nairobi County, Kenya
Get PDFThe study's overarching goal was to identify the factors affecting financial sustainability of local non-governmental organization in Kenya. The specific objectives factors affecting financial sustainability of local non-governmental organization in Kenya. The benefactor relationship management, income divergence, financial managements and staff management skills. The NGO council's jurisdiction extends to 12 regions across Kenya. The study adopted stratified random sampling. According to Sekaran and Bougie (2019) sampling refers to process of selecting a group of participants from the target population to take part in a survey. Mugenda and Mugenda (2003) justified the sample of 10% where the sample frame is large (and exceeding a minimum sample size of 30 respondents). Therefore, the sample was made up of 114 NGOs, where one individual was picked to assist fill the questionnaires from each NGO. To gather primary data, self-administered structured questionnaires were employed. Cronbach's alpha coefficient was used in the study to determine dependability. An alpha coefficient of 0.75 or higher indicated that the gathered data is reliable as it has a relatively high internal consistency and can be generalized to reflect opinions of all respondents in the target population. Techniques for descriptive and inferential statistical analysis were both used. To guarantee that the study is conducted in compliance with the university's norms and regulations, the researcher adhered to the rules established by the KCAU University. Conclusions are that the NGOs have established networks with donors and communicate regularly for funding. Despite the NGOs having donor tracking systems they are unable to meet the conditions set by donors for funding like accountability of funding. The study concludes income divergence affect financial sustainability of NGOs to a great extent. Financial management affect financial sustainability NGOs in Kenya through various practices with compliance to NGO conditions being the main factor. The study concludes that that staff management skills is perceived to affect financial sustainability of NGOs in Kenya; the study further concludes that the management of local NGOs in Kenya are not experienced enough to hold their positions as per donor’s required standards. Recommendations are that NGOs should lay emphasis on hiring management staff who are competent this is because competence of staff significantly affects the financial sustainability of NGOs. Therefore, NGOs should not only prepare strategic plans but also periodically review the strategic plans. Staff participation and proper communication in decision making should be highly encouraged in all NGOs. NGOs should adopt proper accounting knowledge and techniques as required by international accounting standards such as preparation of relevant financial reports supported by proper documentation
A Regression Model To Predict The Risk Of Incomplete Grading Of Student Assessments In Higher Education Institutions
Get PDFThe accuracy and completeness of student assessment data are paramount in higher education
institutions, serving as a cornerstone for informed decision-making, equitable education, and
student success. However, the issue of incomplete grading, where grades for assessments are
missing or inaccurate, poses a significant challenge. This research presents a regression model
designed to predict the risk of incomplete grading of student assessments in higher education
institutions. By leveraging historical data, the model identifies factors contributing to incomplete
grading, such as grading errors, data entry issues, and technological challenges. Moreover, it
examines the consequences of incomplete grading, encompassing student well-being, academic
performance, and institutional accountability. The model, built using a comprehensive dataset
and machine learning techniques, serves as a valuable tool for educational institutions to
proactively address and mitigate the issue of incomplete grading. The research targeted a
population of 367 and higher education students from Kenyan universities. Online questionnaires
were used to get data from the respondents and SPSS was used to convert data into numerical
values. The data collected was analyzed using python data analysis tool to identify patterns and
generate the model. The source-code was written in Python. The ANOVA statistic showed that
the independent variables are significant to the dependent variable. Subsequently, the
independent variables in the study have a significant impact on the dependent variable of
Sustainable prediction of incomplete grading. The findings of the research are significant to the
education sector as it adds knowledge that will help guide the institutions on how to manage missing marks
Do income diversification and capital adequacy affect liquidity creation? A case study of commercial banks in Kenya.
No full textThe paper investigates how income diversification and capital adequacy affect the liquidity creation of banks in Kenya. We employed unbalanced panel data from 36 commercial banks from 2001 to 2020. We extracted data from published banks’ financial reports and statements. The study used the broad and narrow measures to measure liquidity creation. Owing to the persistent nature of liquidity creation, we used a dynamic panel model and a two-step system Generalized.
Method of Moments (SYS GMM) in the analysis. The findings suggest a positive linkage exists between income diversification and the liquidity creation of commercial banks, implying that well-diversified banks have a high level of liquidity creation.
and vice versa. However, the study discovered a negative relationship between capital adequacy and liquidity creation, supporting the financial fragility-crowding out hypothesis. Consequently, the study suggests that the diversification drive in
banks must be reinforced to enhance their liquidity creation. Additionally, due to the tradeoff between capital adequacy and liquidity creation, an optimal level of capital is required to provide a buffer against shocks without negatively impacting liquidity.
creation, a crucial channel through which banks contribute to the economy
Effect Of Forensic Accounting Practices On Claim Settlement Efficiency In Health Insurance Sector In Kenya
Get PDFThe vast majority of insurance plans include common statement indicating that in the event
of a fraudulent claim, all policy benefits are null and void. The fraudulent insurance claims
have the ability to destabilize the economy, bankrupt financial and banking institutions,
damage government operations, disrupt tax-funded development efforts, and erode public
trust in government administration. The purpose of this research was to determine the effect
of forensic accounting techniques on the efficiency of claim settlement in Kenya's health
insurance sector. Among the specific goals was to investigate the effect of fraud detection
technologies, litigation support strategies, and dispute resolution practices on claim
settlement efficiency in Kenya's health insurance sector. The study is significant to
insurance companies, Insurance Regulatory Authority of Kenya and future, academics and
researchers. The study was anchored on Occupational Fraud Theory, Moral Hazard Theory
and White-Collar Crime Theory. The study adopted descriptive research design. The
population of interest constituted administrative staff from head offices of medical
insurance companies. The study adopted census design whereby all the 32 medical
insurance providers were involved in the study. The questionnaire was the main primary
data collection method. The data was quantitatively analyzed aided by SPSS package to
generate percentage ratings, frequencies inferential statistics. Presentation was done in
form of tables. The study found that in fraud investigation, it could be easier to monitor
and keep trail of any fraud if it arises considering that systems of claims settlement are in
place. In litigation support, clients might receive assistance from insurance companies to
reduce the cost effect of the risk of legal action while others claim that insurance companies
may not necessarily engage in helping clients on various legal engagements. Lastly, in
dispute resolution, settling a dispute about claims can be done through court of laws, thus,
providing documented evidence for claim processing. However, this may amount to
notable inefficiency as court proceedings may last longer than the claimants anticipate. The
study recommends that policy makers in the insurance companies should ensure that fraud
investigations are properly documents and be carried out in the shortest time possible to
fulfill expected efficiency in claim settlement. Still, the companies should be obligated
enough to invest own funds to take care of legal claims in relations to litigations. The
management of the insurance firms should ensure that they put in place dispute resolution
mechanism that is workable and that can support quick settlement of complaints on claims
Effect of Leverage on Social- Environmental Responsibilities Disclosures in Financial Reports of Kenyan Listed Firms
No full textThe purpose of this study was to determine the effect leverage on social-environmental responsibilities disclosures in Annual financial reports of Kenyan listed firms. Descriptive research design was used and secondary data was collected from 2009 to 2018 annual reports of 45 companies listed prior to 2009. Content analysis was used to determine the quality of disclosure guided by Global Reporting Initiative index. Using random regression analysis the study showed that leverage were positively significant disclosure of social environmental responsibilities information on financial reports of Kenyan listed firms. This study deviates from previous studies done in Kenya by exploring one factor of leverage and extents the quality score from just 0 and 1 to 0 to 3 to measure quality. This study informs the need for companies in Kenya to voluntary disclose social environmental issues in their financial reports to attract capital providers and to contribute the understanding of determinant of leverage on SER in theory and practice from its findings
Effect Of Intangible Resources On Firm’s Competitive Advantage In The Telecommunication Industry In Kenya
No full textThe study sought to determine the effect of
intangible resources on a firm’s competitive
advantage in the telecommunications
industry in Kenya. The specific objectives
were to evaluate the influence of intellectual
property, goodwill, intellectual capital as
well as corporate culture on the competitive
advantage of telecommunication firms in
Kenya. The study utilized an explanatory
research design. The study focused on four
telecommunication companies in Kenya,
which included Safaricom, Airtel, Finserve,
and Telkom. The target population was
therefore 153 staff working in enterprise
business, finance, human resource, and
corporate affairs departments in
telecommunication companies in Kenya.
Slovin's Formula was employed to determine
the sample size. This study used stratified
random sampling in the selection of the
sample size from the target population. The
study used primary data, which was collected
by the use of semi-structured questionnaires.
A pilot test was conducted to test the validity
and reliability of the research instrument. The
semi-structured questionnaire produced both
quantitative and qualitative data. The
qualitative data were analyzed using content
analysis, and the findings were presented in a
narrative form. With the help of SPSS
version 24, quantitative data was analyzed
using descriptive and inferential statistics.
Descriptive statistics comprised of frequency
distribution, mean, percentages, and standard
deviation. Inferential statistics including
correlation as well as multivariate regression
analysis then followed. Tables and figures
(bar charts as well as pie charts) were
employed to present the results. The study
found that intellectual property has a positive
and significant effect on the competitive
advantage of Kenya's telecommunications
industry. Moreover, the study found that
goodwill has a positive and significant effect
on the competitive advantage of Kenya's
telecommunications industry. Further, the
study found that intellectual capital has a
positive and significant effect on the
competitive advantage of Kenya's
telecommunications industry. The study also
found that corporate culture has a positive
and significant effect on the competitive
advantage of Kenya's telecommunications
industry. This study, therefore, recommends
that the management of Kenya's
telecommunications industry should
motivate the employees by rewarding them,
acknowledging their achievements, sharing
positive feedback, offering flexible
scheduling, and providing a conducive
working environment to help strengthen the
competitive advantage of the organizations.
In addition, the management should strive
towards recruiting as well as grooming the
best team as well as enhance the employees’
skills by conducting regular training,
coaching, mentorship, and workshops to get
a competitive advantage in the organization.
The management should also invest in their
employees through promotion, involving
employees in writing a mission statement,
conducting training programs, motivating the
employees through rewards, and creating a
feedback culture. In addition, the
management should include the invention of
products and services in their patents to stop
others from copying, manufacturing, selling,
or importing their invention without their
permission
Two Sample Approaches to Regression Calibration for Measurement Error Correction
No full textThe goal of this work is to create methods for enhancing measurement error using regression calibration as a
strategy by combining two samples, thereby increasing the relative efficiency of linear regression models. Because two or
more samples are more likely to provide an accurate representation of the population than a single sample under inquiry,
utilizing two samples in regression calibration is likely to produce a realistic depiction of what the actual population is when
error-free. This study has generated independent estimates from two samples and combined them with weights equal to the
inverse of their estimated probabilities of sample inclusion. It has also integrated two data sets into a single data set and
suitably adjusted the weights on each sampled unit. The regression calibration method is most commonly used to correct
predictor-response bias caused by variable measurement imperfections. Because of its simplicity, this method is often used.
The fundamental principle behind regression calibration is to estimate the conditional expectation of a genuine response, given
predictors measured with error and other covariates supposed to be measured without error. The predicted values are then
estimated and used to assess the relationship between the response and an outcome in place of the unknown genuine response.
Further information on the unobservable true predictors is required by the regression calibration program. This data is
frequently obtained from a validation study that employs unbiased measurements for genuine predictors. This study has
employed and compared the results obtained from the two sample approaches. Measuring errors can be produced by a variety
of sources, including instrument error, laboratory error, human error, problems in documenting or executing measurements,
self-reporting errors, and natural oscillations in the underlying amount. Covariate measurement error has three effects: In
addition to hiding the properties of the data, which makes graphical model analysis difficult, it produces bias in parameter
estimates for statistical models, resulting in a sometimes significant loss of power for detecting fascinating correlations
between variables. The two sample approaches employed by the study have yielded acceptable results
A comparison of two sample approaches to regression calibration for measurement error correction
No full textThis study compares ways for improving regression calibration. This is a method for combining two
samples in order to reduce measurement error and improve the relative efficiency of linear regression
models. Since two or more samples are more likely than a single sample to accurately represent the
population under study, two samples are used in regression calibration to produce a realistic picture of
the actual population. In this investigation, we compared independent estimates derived from two
samples using a weight equal to the reciprocal of the estimated sampling probability. The study also
examined the estimations produced after combining the two datasets into one, and modified the weight of
each sample unit accordingly. The most typical application of regression calibration methods is to
account for bias in projected responses induced by measurement inaccuracies in variables. Because of its
simplicity, this method is commonly utilized. The conditional expectation of the genuine response is
estimated using regression calibration, given that the predictor variables are measured with error and the
other covariates are assessed without error. Instead of the unknown genuine response, predictors are
estimated and used to examine the link between response and result. Regression calibration programs
necessitate extensive knowledge of unobservable true predictors. This information is frequently collected
from validation studies that employ unbiased measurements of true predictors. The results of two sample
strategies were employed and compared in this study. Device fault, laboratory mistake, human error,
difficulty documenting or completing measurements, self-reported errors, and intrinsic vibrations of the
underlying instrument can all cause measurement inaccuracies. Covariate measurement error has three
consequences: In addition to obscuring data features and making graphical model analysis more difficult,
estimates of statistical model parameters might be skewed, and effectiveness in detecting correlations
between variables can be severely impaired. This study's two sampling procedures produced satisfactory
results