{"id":"W3003443137","doi":"10.5539/mas.v14n2p71","title":"The Moderating Role of Business Intelligence in the Impact of Big Data on Financial Reports Quality in Jordanian Telecom Companies","year":2020,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Variable (mathematics); Variety (cybernetics); Quality (philosophy); Business intelligence; Variables; Moderation; Online analytical processing; Regression analysis; Business; Sample (material); Data warehouse; Computer science; Database; Data mining; Mathematics; Artificial intelligence","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":[0.002785947,0.0001927627,0.0003116592,0.0001685365,0.0002893549,0.0003135674,0.002454304,0.0000486453,0.000007100721],"category_scores_gemma":[0.001288742,0.0001147978,0.00003643772,0.003028798,0.0006200879,0.0007831982,0.0008237337,0.0002723591,0.000005623167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003126565,"about_ca_system_score_gemma":0.0002439938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003491619,"about_ca_topic_score_gemma":0.001342705,"domain_scores_codex":[0.9975046,0.00002368259,0.0007768388,0.0005979557,0.000719467,0.0003774646],"domain_scores_gemma":[0.997985,0.0001939957,0.0005390393,0.001058341,0.000208667,0.00001493936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008140326,0.0009566014,0.06305431,0.0004756286,0.00001784582,0.00003160029,0.005889765,0.1512093,0.1499302,0.08237131,0.0003899816,0.5448595],"study_design_scores_gemma":[0.0001145674,0.00001590344,0.1630435,0.00008946899,0.00000686369,0.00000251802,0.001143016,0.8077168,0.001897607,0.02546856,0.0002376665,0.0002634602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952673,0.000107787,0.04023251,0.0007430713,0.0001553291,0.0005586121,0.00002096014,0.0000266086,0.005482148],"genre_scores_gemma":[0.9993523,0.00001199435,0.0001303719,0.0002959159,0.0001695704,0.00001295698,0.00001672773,0.000008902783,0.000001247372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6565076,"threshold_uncertainty_score":0.5278307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1410322628888354,"score_gpt":0.3338814135839434,"score_spread":0.192849150695108,"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."}}