{"id":"W2968643969","doi":"10.1177/0266382119868082","title":"The impact of business intelligence through knowledge management","year":2019,"lang":"en","type":"article","venue":"Business Information Review","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Competitive intelligence; Competition (biology); Multinational corporation; Order (exchange); Variable (mathematics); Knowledge management; Set (abstract data type); Business; Business intelligence; Structural equation modeling; Computer science; Measure (data warehouse); Dissemination; Competitive advantage; Marketing; Data mining","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":[0.003766237,0.0004258133,0.0002992649,0.003864351,0.001836044,0.01080325,0.0008295837,0.0009292331,0.003362665],"category_scores_gemma":[0.01111514,0.0002257663,0.0004070528,0.004093041,0.003145831,0.006629242,0.003985612,0.001042399,0.0005132259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002875692,"about_ca_system_score_gemma":0.005438251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004430132,"about_ca_topic_score_gemma":0.004630898,"domain_scores_codex":[0.9942795,0.002416699,0.0001893621,0.0003795607,0.002022686,0.0007120932],"domain_scores_gemma":[0.9848228,0.008918832,0.002493679,0.0007384859,0.001693897,0.001332351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001624186,0.001025931,0.1753007,0.001221132,0.0005261644,0.00123636,0.007912087,0.01017419,0.003621518,0.2341652,0.01074948,0.5539047],"study_design_scores_gemma":[0.00009783229,0.0009664093,0.402986,0.001923985,0.0006536649,0.00115537,0.04071257,0.02482153,0.008544774,0.2770511,0.2408147,0.0002720435],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5225446,0.005523185,0.01104401,0.01504194,0.0001075378,0.0001731378,0.0001734285,0.0001448918,0.4452472],"genre_scores_gemma":[0.99356,0.001390337,0.002620093,0.0003338815,0.00005050279,0.00002100078,0.00004604075,0.00001182625,0.001966417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01080325,"threshold_uncertainty_score":0.02086467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05278371659448555,"score_gpt":0.3285990838062484,"score_spread":0.2758153672117628,"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."}}