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This research aims to understand the smartphone security behavior using protection motivation theory (PMT) and tests the current PMT model employing statistical and predictive analysis using machine learning (ML) algorithms.

This study employs a total of 241 questionnaire-based responses in a nonmandated security setting and uses multimethod approach. The research model includes both security intention and behavior making use of a valid smartphone security behavior scale. Structural equation modeling (SEM) – explanatory analysis was used in understanding the relationships. ML algorithms were employed to predict the accuracy of the PMT model in an experimental evaluation.

The results revealed that the threat-appraisal element of the PMT did not have any influence on the intention to secure smartphone while the response efficacy had a role in explaining the smartphone security intention and behavior. The ML predictive analysis showed that the protection motivation elements were able to predict smartphone security intention and behavior with an accuracy of 73%.

The findings imply that the response efficacy of the individuals be improved by cybersecurity training programs in order to enhance the protection motivation. Researchers can test other PMT models, including fear appeals to improve the predictive accuracy.

This study is the first study that makes use of theory-driven SEM analysis and data-driven ML analysis to bridge the gap between smartphone security’s theory and practice.

This study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information.

This study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach.

The results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70.

The findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.

This study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).