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The purpose of this research is to explain the influence of information security monitoring and other social learning factors on employees’ security assurance behaviour. Security assurance behaviour represents employees’ intentional and effortful actions aimed towards protecting information systems. The behaviour is highly desired as it tackles the human factor within the information security framework. The authors posited that security assurance behaviour is a learned behaviour that can be enhanced by the implementation of information security monitoring.

Theoretical framework underlying this study with six constructs, namely, subjective norm, outcome expectation, information security monitoring, information security policy, self-efficacy and perceived inconvenience, were identified as significant in determining employees’ security assurance behaviour (SAB). The influence of these constructs on SAB could be explained by social cognitive theory and is empirically supported by past studies. An online questionnaire survey as the main research instrument is adopted to elicit information on the six constructs tested in this study. Opinion from industry and academic expert panels on the relevance and face validity of the questionnaire were obtained prior to the survey administration.

Findings from this research indicate that organisations will benefit from information security monitoring by encouraging security behaviours that extend beyond the security policy. This study also demonstrates that employees tend to abandon security behaviour when the behaviour is perceived as inconvenient. Hence, organisations must find ways to reduce the perceived inconvenience using various security automation methods and specialised security training. Reducing perceived inconvenience is a challenge to information security practitioners.

There are some limitations in the existing work that could be addressed in future studies. One of them is the possible social desirability bias due to the self-reported measure adopted in the study. Even though the authors have made every effort possible to collect representative responses via anonymous survey, it is still possible that the respondents may not reveal true behaviour as good conduct is generally desired. This may lead to a bias towards favourable behaviour.

In general, the present research provides a number of significant insights and valuable information related to security assurance behaviour among employees. The major findings could assist security experts and organisations to develop better strategies and policies for information security protection. Findings of this research also indicate that organisations will benefit from information security monitoring by encouraging security behaviours that extend beyond the security policy.

In this research, the social cognitive learning theory is used to explain the influence of information security monitoring and other social learning factors on employees’ security assurance behaviour; the finding implies that monitoring emphases expected behaviours and helps to reinforce organisational norms. Monitoring may also accelerate learning when employees become strongly mindful of their behaviours. Hence, it is important for organisations to communicate the monitoring practices implemented, even more imperative whenever security monitoring employed is unobtrusive in nature. Nonetheless, care must be taken in this communication to avoid resentment and mistrust among employees.

This study is significant in a number of ways. First, this study highlights significant antecedents of security assurance behaviour, which helps organisations to assess their current practices, which may nurture or suppress information security. Second, using users’ perspective, this study provides recommendations pertaining to monitoring as a form of information security measure. Third, this study provides theoretical contribution to the existing information security literature via the application of the social cognitive learning theory.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

This paper aims to address some of the practical and security problems when using fingerhash to secure biometric key for protecting digital contents.

Study the two existing directions of biometric‐based key generation approach based on the usability, security and accuracy aspects. Discuss the requisite unresolved issues related to this approach.

The proposed Fingerhashing approach transforms fingerprint into a binary discretized representation called Fingerhash. The Reed Solomon error correction method is used to stabilize the fluctuation in Fingerhash. The stabilized Fingerhash is then XORed with a biometric key. The key can only be released upon the XOR process with another Fingerhash derived from an authentic fingerprint. The proposed method could regenerate an error‐free biometric key based on an authentic fingerprint with up to 99.83 percent success rate, leading to promising result of FAR = 0 percent and FRR = 0.17 percent. Besides, the proposed method can produce biometric keys (1,150 bit length) which are longer in size than the other prevailing biometric key generation schemes to offer higher security protection to safeguard digital contents.

Outlines a novel solution to address the issues of usability, security and accuracy of biometric based key generation scheme.