{"id":"W3089426712","doi":"10.1080/10696679.2020.1812084","title":"Knowledge-based resources in explaining service recovery performance: a multilevel investigation","year":2020,"lang":"en","type":"article","venue":"The Journal of Marketing Theory and Practice","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Business; Service (business); Service recovery; Resource (disambiguation); Orientation (vector space); Knowledge management; Test (biology); Multilevel model; Marketing; Computer science; Service quality","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07431011,0.0001228699,0.0001784155,0.0001277628,0.0002415015,0.0001566424,0.0002602991,0.00005311812,0.00006614945],"category_scores_gemma":[0.02313325,0.00008995676,0.00003406245,0.0004754922,0.00004786615,0.001943165,0.0001073062,0.0004840145,0.0000233858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001495746,"about_ca_system_score_gemma":0.00004608337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005344069,"about_ca_topic_score_gemma":0.0000231311,"domain_scores_codex":[0.9948191,0.004258073,0.0004494437,0.0001087923,0.0002065878,0.0001579745],"domain_scores_gemma":[0.97392,0.02481253,0.0008593453,0.0001068889,0.0002746535,0.00002661603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.6194639,0.0006023157,0.03462831,0.01016825,0.0004220547,0.0001038755,0.1121052,0.0188356,0.00532257,0.003546232,0.004641018,0.1901607],"study_design_scores_gemma":[0.008836013,0.0003941317,0.3590049,0.005023016,0.001458831,0.0002941637,0.1352969,0.1730369,0.0003894641,0.004561877,0.3102991,0.001404659],"study_design_candidate":"randomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768491,0.0005286573,0.0001748872,0.01573933,0.0001274451,0.00008778812,4.132999e-7,0.00001946404,0.006472947],"genre_scores_gemma":[0.9762407,0.00007782122,0.0003123636,0.02259093,0.0007425516,0.000001378681,0.000001111937,0.00001407783,0.00001906472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6106279,"threshold_uncertainty_score":0.9850953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04995188821833248,"score_gpt":0.2746383438441443,"score_spread":0.2246864556258118,"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."}}