Machine Learning and Survey-based Predictors of InfoSec Non-Compliance

Survey items developed in behavioral Information Security (InfoSec) research should be practically useful in identifying individuals who are likely to create risk by failing to comply with InfoSec guidance. The literature shows that attitudes, beliefs, and perceptions drive compliance behavior and has influenced the creation of a multitude of training programs focused on improving ones’ InfoSec behaviors. While automated controls and directly observable technical indicators are generally preferred by InfoSec practitioners, difficult-to-monitor user actions can still compromise the effectiveness of automatic controls. For example, despite prohibition, doubtful or skeptical employees often increase organizational risk by using the same password to authenticate corporate and external services. Analysis of network traffic or device configurations is unlikely to provide evidence of these vulnerabilities but responses to well-designed surveys might. Guided by the relatively new IPAM model, this study administered 96 survey items from the Behavioral InfoSec literature, across three separate points in time, to 217 respondents. Using systematic feature selection techniques, manageable subsets of 29, 20, and 15 items were identified and tested as predictors of non-compliance with security policy. The feature selection process validates IPAM's innovation in using nuanced self-efficacy and planning items across multiple time frames. Prediction models were trained using several ML algorithms. Practically useful levels of prediction accuracy were achieved with, for example, ensemble tree models identifying 69% of the riskiest individuals within the top 25% of the sample. The findings indicate the usefulness of psychometric items from the behavioral InfoSec in guiding training programs and other cybersecurity control activities and demonstrate that they are promising as additional inputs to AI models that monitor networks for security events.

Impact of tax fairness and tax knowledge on tax compliance behavior of listed manufacturing companies in Nigeria

Tax compliance is a major contemporary debate surrounding corporate taxation in the business world. The tax avoidance issue, which remains an ethical problem for companies, has been a general concern in developed and developing countries alike. The main problem of this study is a non-tax compliance behavior of the corporate organization taxpayers in Nigeria. This study examined the influence of tax fairness on the tax compliance behavior of listed manufacturing companies in Nigeria. The paper adopted a survey research method, and four hundred (400) copies of the questionnaire were administered to the selected manufacturing companies of both consumer and industrial goods sectors. The Laffer Curve Theory underpinned this study and Correlation Analysis, Analysis of Variance (ANOVA), and Multiple Regression Analysis were also employed. The study found that there is a significant level of tax compliance among the listed manufacturing companies in Nigeria. The study also shows that the corporate taxpayer’s perception of fairness of –2.765 (0.006) has a significant impact on corporate taxpayers’ willingness to pay taxes and tax knowledge of 4.601 (0.000) significantly influenced tax compliance. Based on tax knowledge, the study recommends that tax authorities must improve the knowledge of taxpayers and tax collection agents through programs, initiatives, and training on tax awareness.

Effect of Religiosity, Perceived Risk, and Attitude on Tax Compliant Intention Moderated by e-Filing

This research examined the effect of Religiosity, Perceived Risk, and Attitude on Tax Compliant Intention, moderated by e-Filing. This research used a quantitative approach which involved the Structural Equation Model (SEM). Large taxpayers are generally in the form of agencies and individuals, so the population of this study comprised of companies that are in the Directorate General of Taxation of Large Taxpayers Jakarta, Large Tax Service Offices 1 and 2, totaling 529 companies. Religiosity (X1) and Perceived Risk (X2) significantly influence Attitude (Y1). Furthermore, Attitude (Y1) has a positive and significant effect on Tax Compliant Intention (Y2). e-Filing showed an insignificant moderating effect on the research model. The novelties are the development of the Theory of Planned Behavior by involving other variables that affect taxpayer compliance behavior, namely Religiosity and the perceived risk of taxpayers. In addition, this research involves the e-Filing variable as a moderating variable.

Factors Influencing Information Security Policy Compliance Behavior

One of the major concerns of organizations in today's networked world is to unravel how employees comply with information security policies (ISPs) since the internal employee has been identified as the weakest link in security policy breaches. A number of studies have examined ISP compliance from the perspective of deterrence; however, there have been mixed results. The study seeks to examine information security compliance from the perspective of the general deterrence theory (GDT) and information security climate (ISC). Data was collected from 329 employees drawn from the five top-performing banks in Ghana and analyzed with PLS-SEM. Results from the study show that security education training and awareness, top-management's commitment for information security, and peer non-compliance behavior affect the information security climate in an organization. Information security climate, punishment severity, and certainty of deterrent were also found to influence employees' intention to comply with ISP. The implications, limitations, and directions for future research are discussed.

Determinants of Tax Compliance Behaviour among Rental Income Earners in Pokhara

Rental tax is an important source of revenue for local government units. After Nepal adopted fiscal federalism, concerns have been shown by policymakers as well as academicians on the fiscal sustainability of the local government units. In this context, this study attempts to explore tax compliance behaviour among rental income owners in Pokhara. The study is based on a survey of 196 households located in wards 9, 11 and 17 of Pokhara Metropolitan City. The wards represent commercial, semi-urban, and densely populated areas of the city respectively. The required data were gathered employing a self-administered questionnaire comprising Likert scales to measure tax awareness, attitude towards tax, cost of non-compliance, peer influence for compliance, government incentives, and tax compliance behavior of the rental income earner. The data collected were analyzed employing ANOVA, correlation and multiple regression. The research revealed a positive relationship between tax awareness, attitude towards tax, peer influence, and government incentives with tax compliances. The study findings depict that the taxpayers’ awareness and perception towards tax are important variables that affect the compliance behavior of rental taxpayers. Moreover, the results reveal that the rental income earner in Pokhara does not comply with the tax rules due to the lower cost of non-compliance as negative relationship established was found between tax compliance and the cost of non-compliance. Metropolitan tax authority needs to focus on awareness and government incentives rather than the cost of non-compliance for better adherence to tax rules among rental taxpayers.

Development of Nonparametric Path Model using Multivariate Adaptive Regression Spline (MARS) Method In Compliance Behavior of Paying Credit In Bank

The purpose of this study is to develop a Non-parametric Path with the MARS (Multivariate Adaptive Regression Spline) approach which is applied to the behavior of paying credit compliance at Bank. prospective debtor by a Bank. The data used in this study is primary data using a research instrument in the form of a questionnaire. There are 7 variables, namely 5 exogenous variables in the form of 5C variables (Character (X1), Capacity (X2), Capital (X3), Collateral (X4), Condition of Economy (X5)), and two endogenous variables, namely Punctual Payment (Y1), Obedient Paying Behavior (Y2). Variable measurement technique is done by calculating the average score on the items. Sampling in this study used a purposive sampling technique with the criteria of respondents in the study were mortgage debtors (House Ownership Credit) at Bank X. Respondents obtained in this study were 100 respondents. The analysis used is nonparametric path with Multivariate Adaptive Regression Spline (MARS) approach. The result of this research is the estimation of nonparametric Path function using MARS approach on various interactions. The best estimate of the function of obedient behavior in paying credit is when it involves 4 variables, namely Character (X1), Capacity (X2), Conditions of economy (X5), and On time pay (Y1) with a value of generalized cross-validation The smallest (GCV) obtained is 0.2496. The originality of this research is the development of a nonparametric path with the MARS approach that is able to capture interactions between existing variables and is also able to handle the limitations of the truncated spline to determine the position and number of knot points used when involving many predictor variables. There has been no previous research that has examined the development of a nonparametric path with the MARS approach.

Fatigue during the COVID-19 pandemic: Evidence of social distancing adherence from a panel study of young adults in Switzerland

In this paper we analyze panel data (N = 400) to investigate the change in attitudes towards the Covid-19 measures and the change in compliance behavior between the first and second lockdowns in a sample of young adults from the University of Bern, Switzerland. We find considerable fatigue. While respondents expressed high acceptance of and compliance with the Covid-19 measures during the first lockdown, both acceptance and compliance behavior decreased substantially during the second lockdown. Moreover, we show via a structural equation model that respondents’ compliance behavior is largely driven by the perception of how others behave and by the acceptance of the Covid-19 measures. All other effects scrutinized e.g., individual and social risk perception, trust in politics, and pro-social orientations affect compliance behavior via the acceptance of Covid-19 measures. We also conduct two tests of causality of the estimated relation between attitudes towards the measures and social distancing behavior. The first test incorporates the effect of compliance behavior reported during the first lockdown on attitudes during the second lockdown. The second test involves estimating a first difference panel regression model of attitudes on compliance behavior. The results of both tests suggest that the effect of Covid-19 attitudes on social distancing behavior can be interpreted causally.