{"id":"W2549266666","doi":"10.5430/ijba.v7n6p91","title":"Big Data as a Customer Management Relationship Tool","year":2016,"lang":"en","type":"article","venue":"International Journal of Business Administration","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Relation (database); Big data; Computer science; Relevance (law); Context (archaeology); Customer relationship management; The Internet; Data science; Value (mathematics); Key (lock); Marketing; Competitive advantage; Volume (thermodynamics); Globalization; Knowledge management; Business; World Wide Web; Data mining; 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":[0.0175715,0.0007597582,0.0007111385,0.009378416,0.001736031,0.01074542,0.001878118,0.00160996,0.006524913],"category_scores_gemma":[0.04105112,0.0007560668,0.0006600611,0.01135224,0.001872237,0.01186014,0.005429986,0.003078999,0.002235976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416478,"about_ca_system_score_gemma":0.003303925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001006373,"about_ca_topic_score_gemma":0.001150282,"domain_scores_codex":[0.9891492,0.005658367,0.001102333,0.0007371528,0.003047114,0.0003059529],"domain_scores_gemma":[0.9283313,0.05142678,0.00322251,0.008796575,0.00643218,0.001790756],"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":[0.0006354565,0.0004921712,0.01950137,0.003016971,0.0002707545,0.001246981,0.02462579,0.003817777,0.007072537,0.3735276,0.08301936,0.4827732],"study_design_scores_gemma":[0.0001045745,0.0003382383,0.01310664,0.00250746,0.000198449,0.001867039,0.02337441,0.0498359,0.01502205,0.2801183,0.6132435,0.0002834087],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06101734,0.003916847,0.7740083,0.03425831,0.001585587,0.001840474,0.01969221,0.01580959,0.08787131],"genre_scores_gemma":[0.3959319,0.002613058,0.5798484,0.003049019,0.0008955191,0.001548757,0.007401959,0.001341156,0.007370373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0175715,"threshold_uncertainty_score":0.09292805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002074070853698,"score_gpt":0.374408718571691,"score_spread":0.2742013114863212,"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."}}