{"id":"W4379280496","doi":"10.5267/j.uscm.2023.3.022","title":"Critical success factors for business intelligence and bank performance","year":2023,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business intelligence; Sample (material); Order (exchange); Knowledge management; Business; Work (physics); Process (computing); Business activity monitoring; Business process; Marketing; Process management; Computer science; Business process modeling; Work in process; Finance; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007063073,0.0004107295,0.0003362193,0.0007179298,0.0005234657,0.0006641466,0.0007286711,0.0001084781,0.0003850511],"category_scores_gemma":[0.0003882617,0.0003629639,0.00007701872,0.002083466,0.0002685315,0.001589738,0.0007205207,0.0001357792,0.0003138605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004614543,"about_ca_system_score_gemma":0.00001521449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003015912,"about_ca_topic_score_gemma":0.00005262486,"domain_scores_codex":[0.9974783,0.00001153648,0.0004557909,0.0007746651,0.0004621956,0.0008175176],"domain_scores_gemma":[0.9986571,0.0003147145,0.0001190376,0.00049539,0.0003743431,0.00003942754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003339135,0.0003484032,0.169754,0.01235436,0.0001968431,0.0001032891,0.0003783011,0.005639777,0.0001012196,0.4812531,0.05792961,0.2716072],"study_design_scores_gemma":[0.0007652347,0.00005961655,0.237135,0.0007793842,0.0002913207,0.000006012333,0.003574821,0.2005232,0.0003731337,0.02462632,0.5299603,0.00190575],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7882075,0.0005300065,0.1563946,0.01880889,0.006197035,0.004781255,0.0001953554,0.00234377,0.02254161],"genre_scores_gemma":[0.9944293,0.0004323665,0.000730394,0.001024919,0.0006795988,0.000281224,0.0004696038,0.00006267142,0.001889888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4720307,"threshold_uncertainty_score":0.9998822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06355167500962419,"score_gpt":0.3021732878237659,"score_spread":0.2386216128141417,"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."}}