{"id":"W4394895965","doi":"10.5267/j.uscm.2024.2.017","title":"Optimizing supply chain excellence: Unravelling the synergies between IT proficiencies, smart supply chain practices, and organizational culture","year":2024,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Supply chain; Excellence; Business; Organizational culture; Process management; Supply chain management; Chain (unit); Knowledge management; Information technology; Industrial organization; Operational excellence; Operations management; Marketing; Management; Computer science; Economics","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":[0.007638963,0.000578431,0.0005143733,0.00251988,0.001359702,0.00689746,0.0006336947,0.0006604109,0.001399546],"category_scores_gemma":[0.01457024,0.0002946154,0.00045501,0.002076366,0.003089147,0.005501437,0.005463118,0.001101334,0.0001769222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003283448,"about_ca_system_score_gemma":0.006488992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002957731,"about_ca_topic_score_gemma":0.005227805,"domain_scores_codex":[0.9938464,0.003522322,0.0004511887,0.0005372379,0.00109818,0.0005447508],"domain_scores_gemma":[0.9838997,0.007371151,0.003973479,0.001084708,0.002388442,0.001282535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001298691,0.0006385446,0.6559377,0.00127809,0.0005101502,0.0004584131,0.03266266,0.008497288,0.00571916,0.03307706,0.0005808468,0.2605101],"study_design_scores_gemma":[0.00004796755,0.001522686,0.7422322,0.001765087,0.0005122045,0.0004375502,0.1492218,0.02823714,0.009662914,0.04900781,0.01715223,0.0002004984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706004,0.0008957834,0.01456033,0.001426201,0.00001840751,0.00008900699,0.00002762377,0.00002672609,0.01235564],"genre_scores_gemma":[0.9965374,0.0003090037,0.002849852,0.00006830737,0.000005220003,0.00001885039,0.00001314973,0.000003403562,0.0001947968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007638963,"threshold_uncertainty_score":0.04039919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04021006238076281,"score_gpt":0.2779347391595989,"score_spread":0.2377246767788361,"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."}}