{"id":"W2074827511","doi":"10.1287/opre.2014.1305","title":"Supporting New Product or Service Introductions: Location, Marketing, and Word of Mouth","year":2014,"lang":"en","type":"article","venue":"Operations Research","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Marketing; Word of mouth; Profit (economics); Time horizon; Computer science; Product (mathematics); Digital marketing; Service (business); Marketing mix; Business; Marketing strategy; Multi-level marketing; Phone; New product development; Economics; Mathematics","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":[0.00183934,0.001081663,0.001537241,0.0008071031,0.0008101063,0.002890762,0.001717186,0.003555644,0.007841375],"category_scores_gemma":[0.008139382,0.001093381,0.0009344554,0.001255643,0.001118966,0.004748953,0.001477413,0.001306505,0.0006345483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001872024,"about_ca_system_score_gemma":0.002378057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006704114,"about_ca_topic_score_gemma":0.006214715,"domain_scores_codex":[0.9984086,0.0006903231,0.00007638705,0.0003637015,0.0002029946,0.0002579209],"domain_scores_gemma":[0.9942696,0.003841063,0.001098735,0.0001785503,0.0002500962,0.000361961],"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":[0.0005003173,0.0004491251,0.003141753,0.0005564305,0.00007870285,0.0004620563,0.0002112133,0.8914444,0.005856315,0.03546684,0.001558827,0.06027401],"study_design_scores_gemma":[0.0001118999,0.0004269128,0.001580208,0.00003680533,0.00009966005,0.0001749964,0.0002436365,0.9761548,0.001755848,0.0172317,0.002136401,0.00004705812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3431217,0.001407369,0.629556,0.001724463,0.0001246081,0.0006826049,0.0004917294,0.0003498909,0.02254166],"genre_scores_gemma":[0.9444873,0.0006329112,0.04891711,0.0000599348,0.00004396531,0.000238455,0.000114904,0.00003357693,0.005471835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007841375,"threshold_uncertainty_score":0.026232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2366794999603054,"score_gpt":0.4884130249851267,"score_spread":0.2517335250248214,"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."}}