{"id":"W2531762111","doi":"10.5539/mas.v10n12p222","title":"Evaluating of the Effectiveness of Television Advertisement of Life Insurance and Investing in Ma Insurance Company","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Consumer Behavior in Brand Consumption and Identification","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Advertising; Marketing mix modeling; Marketing; Life insurance; Business; Cronbach's alpha; Test (biology); Sales promotion; Profit (economics); Marketing strategy; Marketing effectiveness; Actuarial science; Economics; Sales management; Return on marketing investment","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001702559,0.0002089598,0.0001933813,0.0008397247,0.0002913651,0.00064078,0.0002438796,0.0003355962,0.002530367],"category_scores_gemma":[0.008713896,0.00008383104,0.0002922425,0.0004216667,0.0002496425,0.0003725131,0.0002137677,0.0003703502,0.0002295931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005184571,"about_ca_system_score_gemma":0.0003996218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002353049,"about_ca_topic_score_gemma":0.003588799,"domain_scores_codex":[0.9990453,0.0003849997,0.00007098005,0.00007526014,0.0003310717,0.00009232901],"domain_scores_gemma":[0.9914368,0.005287909,0.001661097,0.0001958618,0.0007816465,0.0006366387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009926374,0.002386586,0.9224483,0.0003196259,0.0001712516,0.0002651865,0.003750982,0.0005090862,0.004525973,0.0005153628,0.0005737837,0.06354115],"study_design_scores_gemma":[0.00001178368,0.001935259,0.9920379,0.00003310211,0.0001684982,0.00009071227,0.002568325,0.0008852507,0.001381047,0.00006198796,0.0008128725,0.00001319807],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973929,0.00009714333,0.00004788108,0.00004646985,0.000006190161,0.00001319629,0.00002699392,0.000002394287,0.002366773],"genre_scores_gemma":[0.9989319,0.0001236908,0.0001885744,0.00001556695,0.00001431644,0.000009254282,0.00004259601,9.820005e-7,0.0006731353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002530367,"threshold_uncertainty_score":0.009004116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04688925951348886,"score_gpt":0.2859617801192792,"score_spread":0.2390725206057903,"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."}}