{"id":"W4400474378","doi":"10.5267/j.uscm.2024.5.005","title":"Utilizing Artificial Intelligence (AI) in enhancing customer-supplier relationship: An exploratory study in the banking industry","year":2024,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Organizational and Employee Performance","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Banking industry; Exploratory research; Industrial organization; Supplier relationship management; Process management; Computer science; Operations management; Marketing; Finance; Supply chain management; Supply chain; 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":[],"consensus_categories":[],"category_scores_codex":[0.001959781,0.0002177904,0.000154995,0.0006737829,0.0002126738,0.0005224982,0.001102532,0.00009518024,0.00005658869],"category_scores_gemma":[0.00003096285,0.0001817503,0.0000318524,0.002830563,0.00004222116,0.001117648,0.0003546702,0.0007360617,0.0001270749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002401253,"about_ca_system_score_gemma":0.00008517298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007419366,"about_ca_topic_score_gemma":0.0009601227,"domain_scores_codex":[0.9975296,0.0002861474,0.0005428898,0.0006465449,0.0005677746,0.0004270513],"domain_scores_gemma":[0.9991024,0.0002044852,0.00004569207,0.0005616763,0.00003376461,0.00005204687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001844127,0.0007983116,0.08602858,0.0001988966,0.00003968025,0.0007496563,0.0926839,0.04725648,0.00008847054,0.6956212,0.0004816102,0.07603476],"study_design_scores_gemma":[0.0004134333,0.0003520461,0.1377927,0.0007800884,0.00004455341,0.00002058985,0.1298428,0.5969703,0.0005787092,0.1263057,0.005529738,0.001369411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8263516,0.0002828925,0.1613217,0.005840604,0.001075222,0.001880929,0.000003172525,0.0003890138,0.002854859],"genre_scores_gemma":[0.9971538,0.00001372725,0.001661943,0.00069787,0.0001319536,0.0001883479,0.00001002273,0.00001995605,0.0001224035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5693156,"threshold_uncertainty_score":0.7411562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04773534530649241,"score_gpt":0.2988426381306817,"score_spread":0.2511072928241893,"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."}}