{"id":"W2128959702","doi":"10.1111/deci.12057","title":"The Importance of Social Embeddedness: Churn Models at Mobile Providers","year":2014,"lang":"en","type":"article","venue":"Decision Sciences","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Advanced Institute of Science and Technology; Technische Universität Berlin; York University","keywords":"Embeddedness; Snowball sampling; Vendor; Social network (sociolinguistics); Computer science; Sampling (signal processing); Nonprobability sampling; Sample (material); Node (physics); Marketing; Business; Social media; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004092183,0.0005070345,0.0007142866,0.00138595,0.0009601562,0.002053791,0.001150397,0.001081386,0.002587641],"category_scores_gemma":[0.01345057,0.000406944,0.0008101371,0.0009426528,0.002272556,0.002349385,0.001717368,0.001560207,0.0002562428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679146,"about_ca_system_score_gemma":0.0005874688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065008,"about_ca_topic_score_gemma":0.007806284,"domain_scores_codex":[0.9981555,0.001057614,0.0000434981,0.0002540977,0.0001787095,0.0003104564],"domain_scores_gemma":[0.9830041,0.01208417,0.002673018,0.0008046469,0.0007319315,0.0007020487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006024061,0.0004948282,0.130196,0.00008515563,0.0002837324,0.0007547407,0.002558254,0.7283928,0.001513204,0.1150753,0.0009411716,0.01910233],"study_design_scores_gemma":[0.00001484657,0.00009979197,0.01264713,0.00001605985,0.00003088184,0.00007966586,0.000411348,0.9608386,0.0001566596,0.02542018,0.0002669395,0.0000178767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431773,0.0001517658,0.05213501,0.00054125,0.00001273332,0.0000539096,0.00009538655,0.00003074613,0.003801828],"genre_scores_gemma":[0.9980452,0.000041589,0.001205538,0.00001759727,0.00001084591,0.00001456429,0.00002351242,0.000004951999,0.0006362379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01065008,"threshold_uncertainty_score":0.02164179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04475123478257822,"score_gpt":0.3091663634987999,"score_spread":0.2644151287162217,"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."}}