{"id":"W4410512025","doi":"10.33423/jabe.v27i3.7644","title":"Enhancing Employee Retention: Predicting Attrition Using Machine Learning Models","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Attrition; Employee retention; Computer science; Machine learning; Artificial intelligence; Knowledge management; Business; Marketing; Medicine","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":[0.005763352,0.0006031564,0.0006306532,0.001555625,0.0006457801,0.001527588,0.001004545,0.000954231,0.001423785],"category_scores_gemma":[0.01649865,0.0002044907,0.00071963,0.0009388344,0.000246157,0.001478917,0.0009762695,0.001570998,0.0006773366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008998776,"about_ca_system_score_gemma":0.001629527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007954282,"about_ca_topic_score_gemma":0.01082872,"domain_scores_codex":[0.9984331,0.000812778,0.00008126853,0.0001473497,0.0002689829,0.00025647],"domain_scores_gemma":[0.9873114,0.008449835,0.001413043,0.0005921006,0.001767975,0.0004657319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006802093,0.001555724,0.4336649,0.0001969319,0.0002180467,0.0001292249,0.0004855619,0.2539078,0.0008963265,0.001977252,0.007976877,0.2983111],"study_design_scores_gemma":[0.0000288958,0.000405579,0.0366318,0.0001251162,0.00007078684,0.00004031317,0.0004563903,0.9542078,0.001181008,0.004904583,0.001910433,0.00003732172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9102036,0.001084567,0.08049729,0.002602361,0.0001074926,0.0001511842,0.001281069,0.0007784221,0.003293875],"genre_scores_gemma":[0.9871337,0.000192877,0.01058939,0.0001429846,0.00005325993,0.00005050919,0.0009802902,0.00002276228,0.0008342299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007954282,"threshold_uncertainty_score":0.03047991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03216298367089593,"score_gpt":0.2102283357084472,"score_spread":0.1780653520375512,"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."}}