{"id":"W4395666704","doi":"10.5267/j.msl.2024.3.005","title":"Influential factors of cybersecurity investment: A quantitative SEM analysis","year":2024,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Information and Cyber Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer security; Investment (military); Computer science; Business; Sample (material); Process management; Politics; Political science; Law; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008005141,0.0008556074,0.0007773967,0.003875638,0.0009876047,0.002352794,0.0006234664,0.0006080157,0.006396431],"category_scores_gemma":[0.01713085,0.0003867091,0.001487512,0.003828517,0.001191188,0.001171201,0.00158352,0.0009375513,0.0004694011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197761,"about_ca_system_score_gemma":0.00233065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006528135,"about_ca_topic_score_gemma":0.004900983,"domain_scores_codex":[0.9956266,0.002959651,0.0002669852,0.0004161579,0.0004882644,0.0002424015],"domain_scores_gemma":[0.9683732,0.02729107,0.001685043,0.0008793821,0.001386976,0.0003842542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001916313,0.0006675973,0.9235404,0.0002347896,0.0006540826,0.0004321528,0.009464599,0.01385885,0.001271001,0.008652334,0.001893795,0.03913876],"study_design_scores_gemma":[0.0000523692,0.001048471,0.7240928,0.0002683704,0.0005692699,0.0006415222,0.0341286,0.2185114,0.001749215,0.01142159,0.007389131,0.0001273449],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829376,0.00004765252,0.01258073,0.0002390472,0.00001267712,0.0001966817,0.001228144,0.00007671466,0.00268068],"genre_scores_gemma":[0.991232,0.00004244492,0.007325123,0.00002501739,0.000006120278,0.0002858751,0.0005874566,0.00001242455,0.0004835538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008005141,"threshold_uncertainty_score":0.04233575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01288214063281724,"score_gpt":0.261288003110159,"score_spread":0.2484058624773418,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}