{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02463529,0.0002608081,0.0003194267,0.002802323,0.001159176,0.005751528,0.0006118634,0.0005369689,0.002757992],"category_scores_gemma":[0.1298178,0.0002725701,0.0003698982,0.002685798,0.00274119,0.003156443,0.002483551,0.001134767,0.0002672414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002525161,"about_ca_system_score_gemma":0.006073033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005012552,"about_ca_topic_score_gemma":0.005441602,"domain_scores_codex":[0.9851872,0.006656511,0.001364639,0.0007785137,0.005092162,0.0009209285],"domain_scores_gemma":[0.7049854,0.2031072,0.0461434,0.01439047,0.02324771,0.008125917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001369331,0.0004292186,0.7911928,0.0009385735,0.0002261192,0.0002173506,0.01852691,0.002010905,0.00166454,0.01039137,0.001581962,0.1726833],"study_design_scores_gemma":[0.00003284248,0.0006687965,0.9056579,0.001280164,0.0001600086,0.0005275357,0.04364626,0.006718942,0.003345013,0.01714213,0.02070019,0.0001202723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620035,0.0008320679,0.0132072,0.003505799,0.00004503061,0.0001600106,0.0002847447,0.0001044649,0.01985719],"genre_scores_gemma":[0.9966357,0.0001543931,0.002748055,0.0001264883,0.00001310047,0.00002258444,0.00005335287,0.000005605286,0.0002408779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02463529,"threshold_uncertainty_score":0.1302854,"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."}}