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Business disruption from cyberattacks is a growing concern, yet cyberinsurance uptake remains low. Using an online behavioural economics experiment with 4800 participants across four EU countries, this study tests a predictive model of cyberinsurance adoption, incorporating elements of Protection Motivation Theory (PMT) and the Theory of Planned Be...
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Effective information technology governance (ITG) is vital for managing risks and ensuring proper oversight of IT in government organizations and enterprises. However, many organizations struggle with implementing effective ITG strategies, resulting in a higher likelihood of cybersecurity breaches, operational inefficiencies, and financial losses....
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... There are several traditional psychological theories that allow us to understand insurance policy buying behavior: the Theory of Planned Behavior (TPB), the Protection Motivation Theory (PMT), the Technology Acceptance Model (TAM), and the General Deterrence Theory (GDT). Previous studies investigating the purchase of nondemand products such as life insurance (Masud et al., 2021;Nasir et al., 2017;Brahmana et al., 2018) or cyber insurance (Branley-Bell et al., 2021) have used these theoretical models, but we do not have a clear view of their applicability in interpreting black box technology car insurance purchase intention. Nevertheless, the present paper draws on these theoretical models to provide a comprehensive understanding of consumer attitudes, awareness, and purchasing propensity towards telematics. ...
Disruptive technologies are changing the car insurance sector, with behavioral and adaptive impacts for individuals as well as organizations. An innovative factor in this industry is connected to telematics and concerns the installation of a small device called a ‘black box’, which is becoming more and more widespread, with consequent financial impacts on the insurance policy market. However, the psychological drivers that underpin consumers' intentions to purchase black box auto insurance are scarcely researched. To fill this gap, we used a mixed-methods sequential exploratory design to analyze a sample of 757 consumers. Our results, obtained through PLS-SEM, highlight that attitude, awareness, subjective norms, risk perception, and trust have a significant positive influence on consumers' intentions to purchase black box technology auto insurance, while the effect of perceived behavioral control is not supported. Furthermore, the blindfolding analysis underscores the predictive relevance of the model. Our results have important implications for auto insurance companies interested to better understand consumers' needs and motives in relation to the purchase of black box insurance.
... presented inBranley-Bell et al. (2021). This experiment was designed and implemented to measure participants' cybersecurity decisions in a controlled situation and was mainly composed of two tasks: (i) Purchase decisions about cyberprotection measures products (cyber-security strategy), in particular the adoption or not of advanced security measure at a given costa and capable to reduce the risk of suffering a cyberattack; and (ii) online behavior whilst performing an online task. ...
... This experiment was designed and implemented to measure participants' cybersecurity decisions in a controlled situation and was mainly composed of two tasks: (i) Purchase decisions about cyberprotection measures products (cyber-security strategy), in particular the adoption or not of advanced security measure at a given costa and capable to reduce the risk of suffering a cyberattack; and (ii) online behavior whilst performing an online task. The instructions clearly explained all tasks and decisions to be made during the experiment and their implications.Figure 2shows the experiment blueprint, which is described in detail inBranley-Bell et al. (2021). ...
This paper presents a sandbox example of how the integration of models borrowed from Behavioral Economic (specifically Protection-Motivation Theory) into ML algorithms (specifically Bayesian Networks) can improve the performance and interpretability of ML algorithms when applied to Behavioral Data. The integration of Behavioral Economics knowledge to define the architecture of the Bayesian Network increases the accuracy of the predictions in 11 percentage points. Moreover, it simplifies the training process, making unnecessary training computational efforts to identify the optimal structure of the Bayesian Network. Finally, it improves the explicability of the algorithm, avoiding illogical relations among variables that are not supported by previous behavioral cybersecurity literature. Although preliminary and limited to 0ne simple model trained with a small dataset, our results suggest that the integration of behavioral economics and complex ML models may open a promising strategy to improve the predictive power, training costs and explicability of complex ML models. This integration will contribute to solve the scientific issue of ML exhaustion problem and to create a new ML technology with relevant scientific, technological and market implications.
Following the Asian Financial Crisis, South Korea, Hong Kong, and Taiwan experienced card debt crisis in 2001, 2002 and 2005, respectively. Various countries have studied and tried to find the factors that lead to the card debt crisis, hoping that the proposed countermeasures can effectively solve the problem. However, these are only practical operations and observations. Therefore, through information asymmetry, this article constructs a model of card debt crisis from adverse selection and moral hazard, and theoretically provides the government or competent authority with a policy basis. This article employs document analysis, combined with qualitative and quantitative data, to test the research hypotheses. The verification result is supported regardless of hypotheses tests for adverse selection, or moral hazard and confirms that information asymmetry and market failure do exist in the Taiwan credit card market. The policy implication of the article is that the government or competent authority should stop the illusion of free market mechanism and have to be responsible for employing countermeasures to face the crisis. JEL classification numbers: G01,G21,G28. Keywords: Card debt crisis, Information asymmetry, Adverse selection, Moral hazard , Document analysis, Market failure.
In this digitized world, everything is changing from offline to online. Data plays a vital role in this digital network. The theft or loss of USB devices, computers, or mobile devices by an unauthorized person who gains access to your mobile or laptop devices, email account, or network is generally termed as a data breach. Securing data from theft and breaches is a challenging issue. It is very hard to identify data breaches in complex networks. Adding extra intelligence using machine learning (ML) approaches will be efficient in identifying such attackers. In this chapter, various ML techniques to identify data breaches such as malware attack, man in the middle (MIM), spear phishing attack, eavesdropping attack, password attack, cross-site scripting attack will be depicted with suitable case studies.
