{"id":"W2942686481","doi":"10.1111/poms.13035","title":"Monetization on Mobile Platforms: Balancing in‐App Advertising and User Base Growth","year":2019,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Digital Platforms and Economics","field":"Business, Management and Accounting","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Monetization; Advertising; Revenue; Computer science; Android (operating system); App store; Revenue sharing; Payment; Online advertising; Order (exchange); Contextual advertising; Search advertising; Planner; Business; Mobile apps; World Wide Web; The Internet; Economics; Operating system","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":[0.006394156,0.00149402,0.002176775,0.002338671,0.001927125,0.007177817,0.003079958,0.003247707,0.007259835],"category_scores_gemma":[0.01976523,0.001501891,0.001136125,0.001674411,0.002799882,0.008173432,0.004301264,0.00374276,0.0006624004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00753336,"about_ca_system_score_gemma":0.004053453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01086564,"about_ca_topic_score_gemma":0.01256968,"domain_scores_codex":[0.996336,0.001294258,0.0001004905,0.0006082645,0.00038313,0.001277842],"domain_scores_gemma":[0.9812352,0.0117181,0.002431261,0.001077252,0.0008881792,0.002650064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002006765,0.001154145,0.02562698,0.000464769,0.000351064,0.001920564,0.001319425,0.5085135,0.01631698,0.3239837,0.003568992,0.1147733],"study_design_scores_gemma":[0.0002029443,0.0007470787,0.01635639,0.00009508454,0.0002253917,0.0006513725,0.001850593,0.8420073,0.00338336,0.1269348,0.007418451,0.0001272083],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7574751,0.001186438,0.1813026,0.003809562,0.0001116628,0.0005571569,0.0002811072,0.0003297122,0.05494663],"genre_scores_gemma":[0.9926566,0.0001747038,0.003173424,0.00007553605,0.00003629413,0.00005146692,0.00002868349,0.00001874901,0.003784417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01086564,"threshold_uncertainty_score":0.05465859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005815103460808936,"score_gpt":0.1814704592630809,"score_spread":0.1756553558022719,"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."}}