{"id":"W4411121473","doi":"10.1108/mip-07-2024-0525","title":"Insights from customers’ chats with bots and human agents","year":2025,"lang":"en","type":"article","venue":"Marketing Intelligence & Planning","topic":"AI in Service Interactions","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Business; Advertising; Marketing; Computer science; Internet privacy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002580609,0.0002194839,0.0001996602,0.0002566356,0.0007127548,0.0002981614,0.0008107912,0.00007900605,0.00002665251],"category_scores_gemma":[0.0001115166,0.00019902,0.00003263326,0.0005344086,0.00007343816,0.0005480034,0.000628343,0.0003358427,0.00002831237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006854117,"about_ca_system_score_gemma":0.00003896462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002742384,"about_ca_topic_score_gemma":0.0000475035,"domain_scores_codex":[0.9983843,0.0001529932,0.0003090033,0.0006060319,0.0002516984,0.0002959761],"domain_scores_gemma":[0.9984229,0.0007401237,0.0001441456,0.0005035526,0.00009713425,0.00009221012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005318926,0.0005507508,0.6789871,0.0004737494,0.0009857079,0.001243041,0.07688923,0.01338446,0.0166197,0.0730627,0.007398302,0.1298734],"study_design_scores_gemma":[0.0006292601,0.0003128182,0.6547215,0.008437584,0.0001317398,0.00008112376,0.008910467,0.2474409,0.01998557,0.01013734,0.04714374,0.002067916],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7689902,0.0003442928,0.2069487,0.0002988201,0.0004458023,0.0001538482,0.000001385385,0.0002571119,0.02255979],"genre_scores_gemma":[0.9884546,0.000008356119,0.01030296,0.0006642175,0.00004241571,0.00001935305,0.000004969642,0.00001222621,0.0004909217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2340565,"threshold_uncertainty_score":0.8115798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194269425937359,"score_gpt":0.3003298644966668,"score_spread":0.2783871702372932,"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."}}