{"id":"W4415654810","doi":"10.1108/md-10-2024-2285","title":"Personnel capabilities and the quality of big data marketing analytics (BDMA)","year":2025,"lang":"en","type":"article","venue":"Management Decision","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Allison University; Thompson Rivers University","funders":"","keywords":"Quality (philosophy); Big data; Analytics; Context (archaeology); Sample (material); Marketing research; Information quality; Construct (python library); Sampling frame","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004373886,0.000159087,0.000285895,0.0003104529,0.0002445875,0.0003278006,0.001068903,0.00004439659,0.00009045289],"category_scores_gemma":[0.00131998,0.0001057251,0.00005587403,0.0008034733,0.0002971902,0.0005823136,0.002620864,0.00009413852,0.00001936372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000135666,"about_ca_system_score_gemma":0.000007167641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005012756,"about_ca_topic_score_gemma":0.0001755218,"domain_scores_codex":[0.9983789,0.0000454255,0.0005306725,0.0004356043,0.0004187256,0.000190639],"domain_scores_gemma":[0.9976172,0.0007512115,0.0002058499,0.001276544,0.0001431263,0.000006016728],"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":[0.001209517,0.0001311426,0.05462345,0.002335981,0.0002061329,0.000004651539,0.0001281565,0.00006988936,0.00001515767,0.2038676,0.03605469,0.7013536],"study_design_scores_gemma":[0.004289192,0.000007115034,0.3881963,0.001526283,0.0008425026,0.00000121725,0.01986097,0.05675061,0.00002548933,0.0942324,0.4335055,0.0007623496],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6531355,0.002628787,0.1264596,0.006929307,0.003146509,0.001627108,0.00007273742,0.0001821965,0.2058182],"genre_scores_gemma":[0.9962511,0.000381917,0.0008649754,0.0008212105,0.0002224314,0.000008983806,0.00005648426,0.0000097379,0.001383166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7005913,"threshold_uncertainty_score":0.4311346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1572514765427576,"score_gpt":0.344965155215453,"score_spread":0.1877136786726954,"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."}}