{"id":"W3185753751","doi":"10.1108/itp-03-2020-0105","title":"New product success through big data analytics: an empirical evidence from Iran","year":2021,"lang":"en","type":"article","venue":"Information Technology and People","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Leverage (statistics); Big data; New product development; Product innovation; Business; Knowledge management; Analytics; Product (mathematics); Business value; Marketing; Process management; Computer science; Data science","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":[0.006044183,0.0003797468,0.0004777641,0.00373939,0.00116315,0.002772539,0.001012019,0.0008025198,0.00326213],"category_scores_gemma":[0.01825996,0.0004390079,0.0007034463,0.006148585,0.002497047,0.002140414,0.001447748,0.001688719,0.0006830241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002022081,"about_ca_system_score_gemma":0.004069293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01781889,"about_ca_topic_score_gemma":0.01513624,"domain_scores_codex":[0.9967933,0.0008928608,0.0002661448,0.0002699081,0.00143668,0.0003409923],"domain_scores_gemma":[0.9617903,0.02103948,0.008961186,0.00148027,0.00524427,0.001484497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002286227,0.001671981,0.9187407,0.0009381576,0.0002565933,0.001554892,0.02473824,0.0004682688,0.0003191766,0.002748021,0.003768062,0.04456727],"study_design_scores_gemma":[0.00007377436,0.0004905557,0.9299356,0.0007009229,0.00017783,0.0008110577,0.054669,0.001318965,0.0004436216,0.0009251397,0.01040236,0.00005122218],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927937,0.001063486,0.0003157701,0.0008656908,0.00001158932,0.00009660238,0.0002395012,0.000006563425,0.0046071],"genre_scores_gemma":[0.9968615,0.001523628,0.000487214,0.0003446958,0.00002975773,0.00005203637,0.0003352575,0.000008031388,0.0003580617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01781889,"threshold_uncertainty_score":0.03543037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2077186257237886,"score_gpt":0.3449760022487097,"score_spread":0.1372573765249211,"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."}}