{"id":"W7108335857","doi":"10.2139/ssrn.5849331","title":"Modeling Cross-Channel Return–Exchange Strategies and E-Coupon promotion Design in Omnichannel Retailing","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Omnichannel; Economic surplus; Coupon; Rate of return; Profit (economics); Robustness (evolution); Risk–return spectrum; Promotion (chess)","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":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.01186653,0.00156895,0.001680757,0.002217199,0.001566745,0.004702923,0.001183653,0.0009882342,0.00006817134],"category_scores_gemma":[0.0004591816,0.0016751,0.000508482,0.001156007,0.0002723782,0.004484975,0.002172944,0.01096863,0.00002673544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00251758,"about_ca_system_score_gemma":0.004539562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002201844,"about_ca_topic_score_gemma":0.01401812,"domain_scores_codex":[0.9885508,0.0003007578,0.002297991,0.001873062,0.001004475,0.005972936],"domain_scores_gemma":[0.9964389,0.0001354633,0.001379097,0.0007130903,0.001260454,0.00007298628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00617161,0.003206282,0.2091142,0.0188634,0.007096314,0.0006056198,0.009593162,0.251828,0.001563533,0.09582911,0.00004560617,0.3960832],"study_design_scores_gemma":[0.004120376,0.0002970347,0.002317324,0.005232014,0.001745558,0.0002339226,0.02000522,0.6544238,0.00002346909,0.3089015,0.0001045397,0.002595209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7952549,0.09499629,0.101685,0.001705277,0.002441323,0.002733199,0.00001081996,0.0001984444,0.0009747965],"genre_scores_gemma":[0.9467167,0.05019294,0.0001574245,0.00003952956,0.0008434663,0.000178498,0.00003324258,0.0001343878,0.001703794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4025958,"threshold_uncertainty_score":0.9997331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04793217229378479,"score_gpt":0.2926530604120673,"score_spread":0.2447208881182825,"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."}}