{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01222003,0.00007932474,0.0001844517,0.0006250224,0.00009377544,0.0006865687,0.001694417,0.00001350024,0.0001620313],"category_scores_gemma":[0.0002300662,0.00004527985,0.00005954715,0.001259372,0.00003121405,0.00135968,0.0003927535,0.0001631403,0.00004309307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004431827,"about_ca_system_score_gemma":0.00004566071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009342134,"about_ca_topic_score_gemma":0.00110369,"domain_scores_codex":[0.9969144,0.0004176361,0.0009946107,0.0002501033,0.001319987,0.0001032905],"domain_scores_gemma":[0.9988227,0.0002773435,0.0001430126,0.0005995289,0.0001337869,0.0000236268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001633365,0.006306164,0.01390023,0.0001992851,0.00142375,0.0008265911,0.02261303,0.02634127,0.00008956216,0.1203965,0.3309315,0.4768088],"study_design_scores_gemma":[0.002122432,0.001642972,0.3116259,0.0003258443,0.0008420998,0.0002214298,0.1488558,0.04529419,0.00002219469,0.008756889,0.4799314,0.0003588458],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5421041,0.001671437,0.41095,0.02262577,0.002162628,0.003150929,0.0001634413,0.00004026642,0.01713147],"genre_scores_gemma":[0.9955401,0.00006025089,0.003307552,0.0001098759,0.0000979706,0.00001043132,0.000007310315,0.000004140631,0.000862348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4764499,"threshold_uncertainty_score":0.6620598,"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."}}