{"id":"W4390478595","doi":"10.1108/mip-07-2023-0319","title":"The influence of quality of big data marketing analytics on marketing capabilities: the impact of perceived market performance!","year":2024,"lang":"en","type":"article","venue":"Marketing Intelligence & Planning","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Thompson Rivers University","funders":"","keywords":"Marketing; Structural equation modeling; Business; Quality (philosophy); Sample (material); Big data; Marketing research; Marketing management; Marketing strategy; Analytics; Population; Quantitative marketing research; Relationship marketing; Computer science; Data science; Data mining; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.007729589,0.0002459557,0.0002674846,0.001382636,0.0005752229,0.004105351,0.0003078426,0.0005007932,0.004077726],"category_scores_gemma":[0.04257715,0.0001588693,0.0005089745,0.001480292,0.001316763,0.002819683,0.001361861,0.001167514,0.0002964451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009367727,"about_ca_system_score_gemma":0.001739965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002294311,"about_ca_topic_score_gemma":0.002863017,"domain_scores_codex":[0.9953153,0.002118041,0.0003381566,0.0003148172,0.001444786,0.000468875],"domain_scores_gemma":[0.8672491,0.09360033,0.02367523,0.003162057,0.005920173,0.006393052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002211723,0.0004686973,0.9534737,0.0001954659,0.0001714154,0.0001128097,0.002235994,0.0005683449,0.0008956675,0.00152024,0.0004511712,0.03968537],"study_design_scores_gemma":[0.00001216444,0.0004291388,0.9876748,0.0001521915,0.00007159117,0.0001157854,0.005340869,0.002336651,0.0009494213,0.001396959,0.001489575,0.00003078987],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916978,0.0003794745,0.0008354819,0.001011232,0.00001595732,0.0000379778,0.0001092666,0.00001360752,0.00589926],"genre_scores_gemma":[0.9993389,0.0000687034,0.0003453247,0.00005083603,0.00001103292,0.000007384501,0.00002991519,0.000003087535,0.0001447227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007729589,"threshold_uncertainty_score":0.04087847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1266042136869369,"score_gpt":0.3559250266265576,"score_spread":0.2293208129396207,"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."}}