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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 discover the complex relationships between individual factors (IF), organizational culture (OC) and leadership styles that impact employee mental health (MH) in the post-pandemic age. Considering the changing nature of the workforce, which has been made worse by the COVID-19 epidemic, the research attempts to clarify the complex interactions between these components.

This research uses the structural equation modeling (SEM) methodology. The authors collected data from 383 information technology sector employees and used the partial least squares SEM tool to analyze. The SEM analysis models the relationships between IF, OC and organizational leadership (OL), examining how these factors collectively influence employee MH. In addition, the study explores the mediating effects of organizational interventions (OI) to assess the pathways through which these interventions impact the observed relationships.

OL and OC significantly impact employees’ MH. Also, OI plays a role in mediating variables in fortifying this relationship; one of the viable explanations for this may be that unlike IF, OL and OC are more comprehensive in coverage and influence the overall organization.

The present study suggests the crucial role of OL and the OC in ensuring better employee MH, emphasizing how organizations navigate these transformative shifts, which are critical for realizing their full potential professionally and personally.