{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00250957,0.00009730318,0.0001552236,0.00008996333,0.001089191,0.0003190824,0.0007044107,0.00004135466,0.0001276411],"category_scores_gemma":[0.0001769656,0.00005875722,0.00008030733,0.0007184268,0.0003819753,0.00112815,0.0002789389,0.00005615368,0.0001138446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001263979,"about_ca_system_score_gemma":0.00002511567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001172718,"about_ca_topic_score_gemma":0.0007662809,"domain_scores_codex":[0.9984083,0.00001562027,0.0003435393,0.0002745113,0.0007215086,0.0002365917],"domain_scores_gemma":[0.9989787,0.000324544,0.0003118162,0.0002205951,0.0001540784,0.00001026254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002476202,0.0002076023,0.04174984,0.0002017426,0.00002475717,0.000002305874,0.002829093,0.006307935,0.0003857421,0.6111416,0.05714588,0.2797559],"study_design_scores_gemma":[0.0007197445,0.00004873622,0.02685702,0.00005749728,0.0000329631,0.000001625635,0.007801026,0.173744,0.0001355727,0.3112924,0.4788465,0.0004629658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756832,0.0001173194,0.001031256,0.001183586,0.0003841024,0.0001671071,0.000001046982,0.00003739406,0.02139497],"genre_scores_gemma":[0.997866,0.00000633733,0.0001606298,0.001448031,0.0002638269,0.00001791144,0.000001369013,0.00000550534,0.0002303882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4217006,"threshold_uncertainty_score":0.8377289,"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."}}