{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T19:35:30Z","timestamp":1778182530696,"version":"3.51.4"},"reference-count":118,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ITP"],"published-print":{"date-parts":[[2025,5,19]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>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) \u2013 explanatory analysis was used in understanding the relationships. ML algorithms were employed to predict the accuracy of the PMT model in an experimental evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>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%.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>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\u2019s theory and practice.<\/jats:p><\/jats:sec>","DOI":"10.1108\/itp-11-2022-0872","type":"journal-article","created":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T08:10:06Z","timestamp":1716711006000},"page":"2041-2068","source":"Crossref","is-referenced-by-count":5,"title":["Explanatory and predictive analysis of smartphone security using protection motivation theory: a hybrid SEM-AI approach"],"prefix":"10.1108","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9631-6517","authenticated-orcid":false,"given":"Naurin Farooq","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9516-9110","authenticated-orcid":false,"given":"Hajra","family":"Murtaza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Komal","family":"Malik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muzammil","family":"Mahmood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Aslam","family":"Asadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"key2025051605570135800_ref001","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1016\/j.future.2018.06.053","article-title":"Identifying cyber threats to mobile-IoT applications in edge computing paradigm","volume":"89","year":"2018","journal-title":"Future Generation Computer Systems"},{"key":"key2025051605570135800_ref002","doi-asserted-by":"publisher","first-page":"162687","DOI":"10.1109\/access.2021.3132574","article-title":"Exploring the influence of direct and indirect factors on information security policy compliance: a systematic literature review","volume":"9","year":"2021","journal-title":"IEEE Access"},{"issue":"3","key":"key2025051605570135800_ref003","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10494820.2020.1826982","article-title":"Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms","volume":"31","year":"2020","journal-title":"Interactive Learning Environments"},{"key":"key2025051605570135800_ref004","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115584","article-title":"Data-driven modeling of technology acceptance: a machine learning perspective","volume":"185","year":"2021","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"key2025051605570135800_ref005","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s10209-019-00685-8","article-title":"Extending UTAUT2 toward acceptance of mobile learning in the context of higher education","volume":"18","year":"2019","journal-title":"Universal Access in the Information Society"},{"key":"key2025051605570135800_ref006","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.chb.2018.09.005","article-title":"A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education","volume":"90","year":"2019","journal-title":"Computers in Human Behavior"},{"issue":"2","key":"key2025051605570135800_ref007","doi-asserted-by":"publisher","DOI":"10.1504\/ijmc.2020.105847","article-title":"What drives students' online self-disclosure behaviour on social media? 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