{"id":"W3081261125","doi":"10.1177/0022242920953847","title":"Consumers and Artificial Intelligence: An Experiential Perspective","year":2020,"lang":"en","type":"article","venue":"Journal of Marketing","topic":"Consumer Behavior in Brand Consumption and Identification","field":"Business, Management and Accounting","cited_by":1016,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Marketing Science Institute","keywords":"Value (mathematics); Experiential learning; Scholarship; Perspective (graphical); Bridge (graph theory); Marketing; Computer science; Knowledge management; Sociology; Business; Artificial intelligence; Economics; Medicine","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":[0.005852759,0.0004559363,0.0002132351,0.001246498,0.003240787,0.009715681,0.0009280372,0.003046456,0.002977234],"category_scores_gemma":[0.007445684,0.0002768243,0.0002445539,0.001004189,0.02752506,0.008220719,0.004121212,0.004241432,0.0002600885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002682246,"about_ca_system_score_gemma":0.001343149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001660232,"about_ca_topic_score_gemma":0.001223425,"domain_scores_codex":[0.9935415,0.005222793,0.0000859276,0.0001714872,0.0005610182,0.0004172741],"domain_scores_gemma":[0.9915481,0.006695716,0.0003833866,0.0004045272,0.000435565,0.0005327087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004084633,0.0001979711,0.003065452,0.0001337728,0.00001429881,0.0004617817,0.3611947,0.0002680959,0.0005806267,0.619125,0.002710255,0.01220714],"study_design_scores_gemma":[0.00003195717,0.0002097233,0.003291698,0.0003573156,0.00002414733,0.001076541,0.5048537,0.001971323,0.0009499689,0.2837333,0.2034427,0.00005750711],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3916644,0.003878047,0.02235251,0.08260385,0.0003557619,0.0001188657,0.00005916277,0.00006063733,0.4989067],"genre_scores_gemma":[0.9879649,0.0009080424,0.001869467,0.002522013,0.00009301769,0.00003388982,0.00001171581,0.00001444405,0.006582541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009715681,"threshold_uncertainty_score":0.03095269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05997483709654781,"score_gpt":0.2936334813755446,"score_spread":0.2336586442789968,"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."}}