{"id":"W4391358403","doi":"10.1002/joom.1294","title":"Vendor selection in the wake of data breaches: A longitudinal study","year":2024,"lang":"en","type":"article","venue":"Journal of Operations Management","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Wake; Vendor; Selection (genetic algorithm); Business; Longitudinal data; Computer science; Operations management; Operations research; Marketing; Data mining; Economics; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.009533001,0.0002446841,0.0004904147,0.001445507,0.002961673,0.002987791,0.001163819,0.001576129,0.004199745],"category_scores_gemma":[0.02617279,0.0006531986,0.0008096629,0.002047583,0.0009750605,0.003200099,0.002280581,0.004884961,0.00113908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271127,"about_ca_system_score_gemma":0.002296151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01502035,"about_ca_topic_score_gemma":0.01799772,"domain_scores_codex":[0.9958877,0.001886881,0.0003372799,0.0004512716,0.000647847,0.0007890002],"domain_scores_gemma":[0.9691117,0.008542418,0.01279772,0.002494904,0.003190806,0.00386233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009746095,0.0006676165,0.9921163,0.00001213725,0.00004471685,0.000105188,0.003480524,0.00006043241,0.00005124339,0.0002481522,0.000650345,0.002465943],"study_design_scores_gemma":[0.0000228918,0.0007206612,0.9742939,0.00008848983,0.00005567412,0.0002656375,0.02069992,0.001031777,0.000144922,0.0004913545,0.002133809,0.00005099579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985117,0.0001302193,0.0002036616,0.0003484699,0.00001116204,0.00002773697,0.0003423838,0.000002984461,0.0004216323],"genre_scores_gemma":[0.9982881,0.0001219402,0.000210263,0.0001910605,0.00001645632,0.00005069905,0.0005104551,0.000004761243,0.0006062807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01502035,"threshold_uncertainty_score":0.05041593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4863857178479917,"score_gpt":0.523023495877093,"score_spread":0.03663777802910123,"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."}}