{"id":"W4362590534","doi":"10.1093/jcr/ucad023","title":"Understanding and Improving Consumer Reactions to Service Bots","year":2023,"lang":"en","type":"article","venue":"Journal of Consumer Research","topic":"AI in Service Interactions","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Erasmus Research Institute of Management; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Service (business); Business; Service provider; Marketing; Automation; Service delivery framework; Service guarantee; Service level objective; Service quality; Service design; Engineering","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":[0.002728491,0.0001058173,0.0001897022,0.001258989,0.000460176,0.0004698599,0.0008031889,0.00006555839,0.00003920655],"category_scores_gemma":[0.0006265364,0.00009677165,0.00005330844,0.002463245,0.00007252864,0.001043684,0.0006097914,0.0008471424,0.0004032299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002715002,"about_ca_system_score_gemma":0.0003618696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000235457,"about_ca_topic_score_gemma":0.0001481252,"domain_scores_codex":[0.9976516,0.0002582721,0.0004529796,0.0002410974,0.0009098562,0.0004861314],"domain_scores_gemma":[0.9963557,0.001610014,0.0001598413,0.0004072023,0.00109785,0.0003694253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005935442,0.0007018176,0.03506887,0.001234509,0.001533584,0.002734242,0.03974358,0.001540445,0.3301359,0.1168937,0.3510734,0.1187464],"study_design_scores_gemma":[0.004199575,0.001428732,0.0356329,0.00213977,0.0001398973,0.006218621,0.0283312,0.1038747,0.01049871,0.0603082,0.745559,0.001668649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.655432,0.0009079179,0.2215622,0.1124598,0.002965168,0.000811133,0.00001203831,0.0003267704,0.005522993],"genre_scores_gemma":[0.9912491,0.0002460682,0.007285481,0.0004865696,0.0001113952,0.00001009419,4.059099e-7,0.00002029885,0.0005905943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3944856,"threshold_uncertainty_score":0.5182838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3554261394342855,"score_gpt":0.4380558881015402,"score_spread":0.08262974866725464,"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."}}