{"id":"W2130667797","doi":"10.2139/ssrn.2367836","title":"Dynamic Commercialization Strategies for Disruptive Technologies: Evidence from the Speech Recognition Industry","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Baycrest Hospital; Quest University Canada","funders":"","keywords":"Commercialization; Speech recognition; Business; Dynamic capabilities; Computer science; Industrial organization; Marketing","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":[0.002685018,0.0004180597,0.0003587631,0.001982547,0.0008927308,0.003653768,0.0006870565,0.001854359,0.007092089],"category_scores_gemma":[0.01694861,0.000201064,0.0003757029,0.001822016,0.001402087,0.00323134,0.00111912,0.001630834,0.001481947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001574193,"about_ca_system_score_gemma":0.001017285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01342157,"about_ca_topic_score_gemma":0.01331893,"domain_scores_codex":[0.9989741,0.0002404373,0.00007160783,0.000179905,0.000340371,0.0001936763],"domain_scores_gemma":[0.9696828,0.01976764,0.005985041,0.001088543,0.002581063,0.0008949784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002778033,0.003303804,0.6542113,0.0005731834,0.0004031661,0.001748071,0.004535994,0.009402067,0.005772464,0.02622464,0.007313338,0.2837338],"study_design_scores_gemma":[0.0003390234,0.001876431,0.9035087,0.0003567013,0.0004986225,0.0005655562,0.01939272,0.02443483,0.007071854,0.02390978,0.0179037,0.0001420265],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977368,0.001898111,0.001002325,0.001712769,0.0000167911,0.00003320712,0.0002054813,0.00001880787,0.01774452],"genre_scores_gemma":[0.9969319,0.001099525,0.0002027412,0.0001055073,0.00002035855,0.000006369999,0.0001303509,0.000004101747,0.00149915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01342157,"threshold_uncertainty_score":0.02668691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1105236002906724,"score_gpt":0.3759994821268656,"score_spread":0.2654758818361931,"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."}}