{"id":"W4392175645","doi":"10.1109/upcon59197.2023.10434540","title":"An Empirical Examination of the Factors of Big Data Analytics Implementation in Supply Chain Management and Logistics","year":2023,"lang":"en","type":"article","venue":"","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Big data; Supply chain; Supply chain management; Computer science; Analytics; Empirical research; Data science; Process management; Business; Data mining; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01281058,0.0002132595,0.0001953824,0.001617945,0.001206121,0.003162733,0.0007131192,0.0008061708,0.004507565],"category_scores_gemma":[0.06550392,0.0003930572,0.0003706955,0.003042279,0.001656443,0.003300872,0.001423083,0.001527846,0.0003527834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002037353,"about_ca_system_score_gemma":0.004390277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006034824,"about_ca_topic_score_gemma":0.008784609,"domain_scores_codex":[0.9893039,0.006458279,0.000797166,0.0005924927,0.00180903,0.001039118],"domain_scores_gemma":[0.8338999,0.1157221,0.03283684,0.003611611,0.008618069,0.005311457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007596127,0.0006624152,0.9738825,0.00008731897,0.00005003301,0.0001420292,0.009260462,0.000306364,0.0002737939,0.002050345,0.0001616983,0.013047],"study_design_scores_gemma":[0.000008749971,0.0002795843,0.9431441,0.0001287337,0.00003497359,0.0001102656,0.05200664,0.001704291,0.0003754902,0.0005188774,0.001669272,0.00001887103],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967134,0.00008539026,0.0004024311,0.0003663608,0.000003410295,0.00004561084,0.00003183181,0.000003612389,0.002348001],"genre_scores_gemma":[0.9992806,0.00006843998,0.0003066138,0.00002533274,0.000002855459,0.00001817116,0.0000293742,0.000001771521,0.0002667486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01281058,"threshold_uncertainty_score":0.06774962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2668363285873043,"score_gpt":0.3830585571851115,"score_spread":0.1162222285978072,"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."}}