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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).

Drawing upon the natural resource-based view (NRBV) and social cognitive theory (SCT), the present study explores the role of green learning orientation (GLO) and green creativity (GC) as a mediating variable in the relationship between green transformational leadership (GTL) and green innovation (GI) in the Italian hotel industry. The research further assesses environmental performance (EP) and corporate green image (CGI) as a resultant factor of GI.

Two studies were conducted in Italy to evaluate theoretical models with workers in the lodging industry. Study 1 employed a three-wave, two-week time-lagged design with a total sample size of 303. Study 2 utilized a two-wave (four-week apart) design, with 349 participants using structural equation modeling.

The research findings emphasize that the enhancement of employees’ GLO and GC can be facilitated by providing GTL. This, in turn, may lead to the enhancement of GI, which improves the EP and CGI of a hotel.

The study comprehensively analyzes the previously unexamined relationships of employee-driven factors associated with GLO and GC. These factors are essential for promoting GI through GTL, ultimately enhancing EP and CGI. Therefore, it contributes by explaining previously unexplored employee and organizational factors in a unified model, utilizing time-lagged data, and enhancing the understanding of how organizations can elevate EP and CGI, particularly within the Italian hospitality sector.

In conventional discourses on sustainability, the relationship between economics and ecology is central. A number of nations' economies benefited from responsible tourism following these conferences. By supporting local businesses and attractions, ‘green’ tourism helps communities achieve their natural and cultural objectives while also preserving their limited resources. In terms of sustainable travel, Kerala was an early leader. This study looks at RT initiatives in various stages, with an emphasis on green tourism's sustainable responsible travel practises. The green economic development bottom line method was used for this descriptive research. These results highlight the difficulties inherent with RT implementation. Our review of secondary data shows that the first rollout of RT was unsuccessful, but that subsequent stages showed great promise. In order to create sustainable tourism on a worldwide scale, the study also highlights the necessity for more research in other culturally distant places.

This study investigates despotic leadership (DL) as an antecedent to bullying behavior with a mediating role of moral emotions at work. Another aim is to study the moderating role of self-concordance to buffer the relationship between DL and arousal of moral emotions.

The authors collected two-source (self-reported and supervisor reported) time-lagged data in the shape of a three-wave survey (i.e. one month time interval for each time) from 242 dyads in the health sector of Pakistan.

The results revealed that moral emotions mediated the relationship between DL and bullying behavior. Furthermore, self-concordance moderates the relationship between DL and moral emotions, such that the relationship will be stronger in the case of low self-concordance.

Managers need to promote a culture that accommodates diversity of opinion at the organization so that everyone is able to express and share their views openly. Organizations should encourage supervisors to participate in leadership development programs aimed at eliminating DL.

This study establishes the role of self-concordance and moral emotions in the relationship between despotic leadership DL and bullying behavior.

This study aims to critically analyze existing research on blockchain technology adoption, examining the dominant models and methodologies used, the primary domains where blockchain is applied and the emerging opportunities across various sectors.

Using a methodical systematic review approach, the authors meticulously examined a pool of 1,322 collected articles, subjecting 38 studies to rigorous assessment. Through this comprehensive analysis, the authors unveiled the key models and influential factors that intricately shape the trajectory of blockchain adoption.

The primary models identified for investigating blockchain adoption were the technology acceptance model and technology–organization–environment. Apart from the core variables within these models, the pivotal determinants influencing various blockchain applications include perceived trust, perceived cost and social influence. In addition, this study highlights supply chain management as a prominent domain for blockchain application adoption.

Understanding these influential factors and models can guide practical decisions and aid stakeholders in formulating effective strategies for blockchain adoption in diverse sectors.

This study contributes to advancing the understanding of blockchain adoption dynamics by unveiling the prevalent models and determinants shaping adoption. This study offers valuable insights into the factors influencing the use and adoption of blockchain technologies across diverse sectors.