{"id":"W3122547185","doi":"10.1287/msom.2015.0532","title":"Newsvendor Selling to Loss-Averse Consumers with Stochastic Reference Points","year":2015,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Newsvendor model; Loss aversion; Economics; Microeconomics; Profit (economics); Economic order quantity; Product (mathematics); Risk aversion (psychology); Valuation (finance); Expected utility hypothesis; Business; Marketing; Financial economics; Supply chain","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002305429,0.0009887519,0.001860039,0.0006039652,0.0006837128,0.00271525,0.001377567,0.002884834,0.004521067],"category_scores_gemma":[0.009004463,0.0008024031,0.001230159,0.0006576091,0.002007338,0.003390444,0.001250332,0.002600587,0.0003905067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001605959,"about_ca_system_score_gemma":0.0005534292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004349593,"about_ca_topic_score_gemma":0.002088641,"domain_scores_codex":[0.999241,0.0003087409,0.00003469525,0.0001379151,0.00008157852,0.0001961343],"domain_scores_gemma":[0.9888221,0.007951332,0.001944842,0.000357522,0.0003844191,0.0005397958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00113267,0.000822846,0.01314932,0.0003265065,0.0004002832,0.00491547,0.001606861,0.6110026,0.005065916,0.3397891,0.004701019,0.01708732],"study_design_scores_gemma":[0.0001502963,0.0002176916,0.002055363,0.00003088649,0.00007741763,0.0002361065,0.0005013405,0.9136324,0.0004280646,0.08158983,0.001000394,0.0000801697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8197643,0.001546877,0.1533328,0.003259952,0.0001239169,0.00009876811,0.0002486942,0.0001517448,0.021473],"genre_scores_gemma":[0.9909968,0.0005547662,0.003715665,0.0001113615,0.00006913747,0.00002879175,0.00004828903,0.00001775418,0.004457376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004521067,"threshold_uncertainty_score":0.0151245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03963132261225225,"score_gpt":0.2335301830168793,"score_spread":0.193898860404627,"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."}}