{"id":"W3087407537","doi":"10.1111/isj.12310","title":"Possible negative effects of big data on decision quality in firms: The role of knowledge hiding behaviours","year":2020,"lang":"en","type":"article","venue":"Information Systems Journal","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Big data; Variety (cybernetics); Information hiding; Quality (philosophy); Data quality; Computer science; Data science; Resource (disambiguation); Knowledge management; Data mining; Artificial intelligence; Business; Marketing; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001184888,0.0001065724,0.0002348067,0.0002454331,0.00010778,0.0002586875,0.0007141777,0.0000560547,0.0000116522],"category_scores_gemma":[0.001114044,0.00006940171,0.00004116377,0.0006806316,0.00003866602,0.003750197,0.0002579696,0.0002131647,0.00006113333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002525949,"about_ca_system_score_gemma":0.00004971793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005515429,"about_ca_topic_score_gemma":0.00002918078,"domain_scores_codex":[0.9984463,0.00004211295,0.0008935913,0.00008546891,0.0004045014,0.0001280498],"domain_scores_gemma":[0.9979988,0.0002866561,0.001044922,0.0002895278,0.0003649553,0.00001514475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008572791,0.0003456185,0.1706545,0.003675788,0.000112371,0.000007109547,0.01013086,0.00473493,0.001265737,0.02162469,0.01119181,0.7753993],"study_design_scores_gemma":[0.007820815,0.0003331159,0.4544579,0.01617643,0.0003325811,0.00008693943,0.04106021,0.2295624,0.01661203,0.004775612,0.2271481,0.001633809],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747023,0.0007588926,0.01374809,0.0003525913,0.002112848,0.0006239573,0.0000646453,0.00002914953,0.007607502],"genre_scores_gemma":[0.9993175,0.00002132336,0.0000279448,0.0001201635,0.0004679854,0.000003255946,0.00003420709,0.000004928759,0.000002753102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7737655,"threshold_uncertainty_score":0.283012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.121112930908302,"score_gpt":0.3322024642750019,"score_spread":0.2110895333666999,"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."}}