{"id":"W7104180107","doi":"10.5267/j.ijdns.2025.10.007","title":"The big data analytics to digital marketing path strengthened by knowledge management","year":2025,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Al-Imam Muhammad Ibn Saud Islamic University","keywords":"Big data; Digital marketing; Analytics; Marketing research; Marketing management; Competitive advantage; Marketing strategy; Path analysis (statistics); Relationship marketing","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":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.00280086,0.0001092045,0.000115184,0.0002206417,0.000356779,0.002381196,0.006372364,0.0000185731,0.000008689286],"category_scores_gemma":[0.0006200807,0.00007311084,0.00001764237,0.001012812,0.0002281059,0.003470658,0.006062214,0.0001169619,0.00001298165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002622957,"about_ca_system_score_gemma":0.00007951134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007800997,"about_ca_topic_score_gemma":0.00003488767,"domain_scores_codex":[0.9984445,0.000008896662,0.0003984601,0.0003396854,0.0005690689,0.0002393913],"domain_scores_gemma":[0.9983036,0.0002172281,0.0002649505,0.0007129736,0.0004754784,0.00002580595],"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":[0.00008386279,0.00004465814,0.005037305,0.00001391222,0.00006763286,0.000008161902,0.000005267781,0.00003206933,0.00001601531,0.004308818,0.2070286,0.7833537],"study_design_scores_gemma":[0.0001667689,0.000004699688,0.003655174,0.0003057174,0.00004361685,0.000007265523,0.0002247864,0.02508777,0.000005668862,0.001002308,0.9693864,0.0001098327],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.143172,0.01951826,0.493666,0.05656414,0.05910993,0.001478787,0.002612968,0.00018548,0.2236925],"genre_scores_gemma":[0.9935414,0.001197691,0.0009974714,0.001040859,0.002391934,0.0000010152,0.0002022773,0.000007509449,0.0006198151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8503695,"threshold_uncertainty_score":0.9990036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0835496485562388,"score_gpt":0.3363218663237651,"score_spread":0.2527722177675263,"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."}}