{"id":"W3026844958","doi":"10.5430/rwe.v11n2p98","title":"The Effects of Obstructive Factors and Rewards Related to Technological Innovation on Management Performance: Focusing on Chinese Companies","year":2020,"lang":"en","type":"article","venue":"Research in World Economy","topic":"Organizational Leadership and Management Strategies","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Computer science; Knowledge management; Marketing; Business; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004842072,0.0001223407,0.0001447248,0.0008396194,0.0002330788,0.000185212,0.0002201425,0.00003192607,0.00001568192],"category_scores_gemma":[0.000258084,0.0000838049,0.00001491322,0.002765985,0.0001542052,0.0003685558,0.0002520024,0.0002625115,0.00004217702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005624488,"about_ca_system_score_gemma":0.000007006603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001085929,"about_ca_topic_score_gemma":0.00001083836,"domain_scores_codex":[0.999025,0.00002337236,0.0002375601,0.0002523214,0.0002169546,0.0002447813],"domain_scores_gemma":[0.9994154,0.0002730542,0.00007480467,0.000132292,0.00009287852,0.0000115866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001059871,0.00004338117,0.1238121,0.0006623578,0.00004343988,0.000004887148,0.0001483093,0.0003578242,0.00005477697,0.8680879,0.0005554285,0.006123552],"study_design_scores_gemma":[0.0009201753,0.000181587,0.917174,0.0003899586,0.00001098337,1.42613e-7,0.003302394,0.001397426,0.0006539067,0.04745711,0.02822142,0.0002908905],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359319,0.00001493981,0.000007380771,0.009946622,0.00006255628,0.0006222901,3.363228e-7,0.00004799013,0.05336597],"genre_scores_gemma":[0.9992517,0.00001993117,0.0000254703,0.0004381107,0.00006764183,0.0000304705,0.000006765206,0.00001210933,0.0001478243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8206308,"threshold_uncertainty_score":0.3417465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0441171947504258,"score_gpt":0.2909109806869688,"score_spread":0.246793785936543,"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."}}