{"id":"https://openalex.org/W7160923769","doi":"https://doi.org/10.1109/access.2026.3691944","title":"Predicting Users\u2019 Privacy Protection Behavior in Mobile Applications: An Empirical Study Using Machine Learning and Explainable AI","display_name":"Predicting Users\u2019 Privacy Protection Behavior in Mobile Applications: An Empirical Study Using Machine Learning and Explainable AI","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7160923769","doi":"https://doi.org/10.1109/access.2026.3691944"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3691944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3691944","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3691944","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135980237","display_name":"Yunfei Wang","orcid":"https://orcid.org/0009-0005-1767-4428"},"institutions":[{"id":"https://openalex.org/I118987531","display_name":"Anhui Jianzhu University","ror":"https://ror.org/0108wjw08","country_code":"CN","type":"education","lineage":["https://openalex.org/I118987531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunfei Wang","raw_affiliation_strings":["School of Economics and Management, Anhui Jianzhu University, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0009-0005-1767-4428","affiliations":[{"raw_affiliation_string":"School of Economics and Management, Anhui Jianzhu University, Hefei, Anhui, China","institution_ids":["https://openalex.org/I118987531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5135980237"],"corresponding_institution_ids":["https://openalex.org/I118987531"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.57586909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"77931","last_page":"77943"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.24539999663829803,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.24539999663829803,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11045","display_name":"Privacy, Security, and Data Protection","score":0.1655000001192093,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.08990000188350677,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.7627999782562256},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.5896999835968018},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4050999879837036},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.3440999984741211},{"id":"https://openalex.org/keywords/mobile-computing","display_name":"Mobile computing","score":0.28519999980926514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7998999953269958},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.7627999782562256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6043000221252441},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.5896999835968018},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5641999840736389},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C144543869","wikidata":"https://www.wikidata.org/wiki/Q2738570","display_name":"Mobile computing","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.25189998745918274}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3691944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3691944","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:92e24785cc984bd8bab873a496f7b6f0","is_oa":true,"landing_page_url":"https://doaj.org/article/92e24785cc984bd8bab873a496f7b6f0","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 77931-77943 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3691944","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3691944","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4464207326","display_name":null,"funder_award_id":"2024QDHR03","funder_id":"https://openalex.org/F4320329077","funder_display_name":"Anhui Jianzhu University"}],"funders":[{"id":"https://openalex.org/F4320329077","display_name":"Anhui Jianzhu University","ror":"https://ror.org/0108wjw08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,118],"rapid":[1],"proliferation":[2],"of":[3,47,115],"mobile":[4,53,207],"applications":[5],"has":[6],"intensified":[7],"concerns":[8],"regarding":[9],"user":[10],"privacy":[11,21,49,96,133,149,165,199],"and":[12,30,90,124,148,177,194,202,210],"personal":[13],"data":[14,69],"protection.":[15],"Although":[16],"users":[17],"can":[18],"adopt":[19],"various":[20],"protection":[22,50,97,134,166,200],"behaviors":[23,35],"to":[24,93,110],"mitigate":[25],"potential":[26],"risks,":[27],"the":[28,112,128],"psychological":[29,175],"contextual":[31,64,139,178],"factors":[32,131,140],"influencing":[33],"such":[34,141],"remain":[36],"insufficiently":[37],"understood.":[38],"This":[39],"study":[40,46,191],"presents":[41],"an":[42],"empirical,":[43],"application-oriented":[44],"prediction":[45,184],"users\u2019":[48,95],"behavior":[51,167,201],"in":[52],"application":[54,208],"environments":[55],"by":[56,137,170],"integrating":[57],"Protection":[58],"Motivation":[59],"Theory":[60],"(PMT)":[61],"with":[62,160,185],"application-level":[63,138],"variables.":[65],"Using":[66],"cross-sectional":[67],"survey":[68],"collected":[70],"from":[71],"987":[72],"respondents,":[73],"seven":[74],"machine":[75,182],"learning":[76,153,183],"models\u2014including":[77],"Logistic":[78],"Regression,":[79],"Decision":[80],"Tree,":[81],"Random":[82],"Forest,":[83],"K-Nearest":[84],"Neighbors,":[85],"Support":[86],"Vector":[87],"Machine,":[88],"XGBoost,":[89],"LightGBM\u2014are":[91],"employed":[92],"predict":[94],"behavior.":[98],"To":[99],"enhance":[100],"model":[101],"interpretability,":[102],"SHAP-based":[103],"explainable":[104,186],"artificial":[105,187],"intelligence":[106,188],"techniques":[107],"are":[108,127],"applied":[109],"quantify":[111],"relative":[113],"importance":[114],"different":[116],"predictors.":[117],"results":[119],"show":[120],"that":[121,164],"perceived":[122,125],"severity":[123],"vulnerability":[126],"most":[129],"influential":[130],"driving":[132],"behavior,":[135],"followed":[136],"as":[142],"electronic":[143],"word-of-mouth,":[144],"permission":[145],"request":[146],"depth,":[147],"policy":[150],"readability.":[151],"Ensemble":[152],"models":[154],"demonstrate":[155],"superior":[156],"predictive":[157],"performance":[158],"compared":[159],"traditional":[161],"models,":[162],"indicating":[163],"is":[168],"shaped":[169],"complex":[171],"nonlinear":[172],"relationships":[173],"among":[174],"perceptions":[176],"factors.":[179],"By":[180],"combining":[181],"techniques,":[189],"this":[190],"provides":[192],"empirical":[193],"practically":[195],"interpretable":[196],"insights":[197],"into":[198],"offers":[203],"implications":[204],"for":[205],"privacy-aware":[206],"design":[209],"digital":[211],"platform":[212],"governance.":[213]},"counts_by_year":[],"updated_date":"2026-05-29T06:17:43.578629","created_date":"2026-05-13T00:00:00"}