{"id":"W1936423809","doi":"10.1287/mksc.2015.0944","title":"Matching Value and Market Design in Online Advertising Networks: An Empirical Analysis","year":2015,"lang":"en","type":"article","venue":"Marketing Science","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Hong Kong University of Science and Technology; Chinese University of Hong Kong; University of Hong Kong","keywords":"Counterfactual thinking; Matching (statistics); Profit (economics); Online advertising; Microeconomics; Incentive; Revenue; Advertising; Two-sided market; Mechanism design; Value (mathematics); Incentive compatibility; Economics; Network effect; Computer science; Business; The Internet","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":[0.008786975,0.0003177946,0.0007708869,0.001884934,0.00104833,0.002337943,0.001074946,0.00152869,0.009399146],"category_scores_gemma":[0.04807157,0.0003715467,0.0008036795,0.00277067,0.001406531,0.004815935,0.00104638,0.001689973,0.001085868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001560831,"about_ca_system_score_gemma":0.0007065604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003579205,"about_ca_topic_score_gemma":0.003078818,"domain_scores_codex":[0.996786,0.001970842,0.0001238541,0.0003622628,0.0004039205,0.0003531611],"domain_scores_gemma":[0.9099454,0.072857,0.01096256,0.002845198,0.001436503,0.001953288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001461167,0.003016533,0.8917609,0.000180886,0.0004149242,0.0004147502,0.001341208,0.01992406,0.001023947,0.03055315,0.003200653,0.04670789],"study_design_scores_gemma":[0.0004597168,0.0009811248,0.6775233,0.0000778342,0.000349359,0.0007677673,0.00370768,0.2791707,0.001246462,0.03051683,0.00511484,0.00008452078],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929193,0.0002745467,0.00236925,0.0003219258,0.000006716982,0.00009829463,0.0002598333,0.00002223041,0.003727847],"genre_scores_gemma":[0.9982346,0.00008036843,0.0006554268,0.00002975615,0.0000145117,0.00004802864,0.0001818795,0.000005412908,0.0007499414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009399146,"threshold_uncertainty_score":0.04647052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04097693701520671,"score_gpt":0.3046892982724157,"score_spread":0.263712361257209,"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."}}