{"id":"W2052426618","doi":"10.5539/mas.v7n8p78","title":"Analysis of Cost Overrun Factors for Small Scale Construction Projects in Malaysia Using PLS-SEM Method","year":2013,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Tun Hussein Onn Malaysia","keywords":"Cost overrun; Structural equation modeling; Scale (ratio); Variance (accounting); Multivariate statistics; Operations management; Questionnaire; Business; Computer science; Statistics; Construction industry; Mathematics; Engineering; Construction engineering; Accounting; Geography","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":[0.004077236,0.0006762446,0.0003734594,0.002055763,0.0004894919,0.001307322,0.0003043358,0.0003159272,0.001479651],"category_scores_gemma":[0.007639484,0.0003824459,0.0006395397,0.002285849,0.0004965641,0.001007877,0.0005944239,0.0004457403,0.000143325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001595478,"about_ca_system_score_gemma":0.002397893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005510562,"about_ca_topic_score_gemma":0.008761375,"domain_scores_codex":[0.9974263,0.0009373093,0.0002191933,0.0002309458,0.001002458,0.0001837452],"domain_scores_gemma":[0.9932857,0.003488094,0.001472606,0.000246515,0.001354131,0.0001529113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002404125,0.0004948166,0.8043633,0.0006708471,0.0002432042,0.0007014733,0.0103413,0.02949936,0.005566904,0.00291597,0.0008020349,0.1441604],"study_design_scores_gemma":[0.00001556424,0.0005417401,0.905888,0.0002235714,0.0001054629,0.0002731772,0.01788402,0.0671235,0.003655954,0.001521254,0.00269454,0.00007314321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931954,0.00005548128,0.005167244,0.00007309059,0.000002762756,0.00009774465,0.0001294864,0.00001727517,0.001261571],"genre_scores_gemma":[0.9942815,0.0000973569,0.004927373,0.000007792255,0.000001750702,0.00007540196,0.0001609301,0.000006802557,0.0004410689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005510562,"threshold_uncertainty_score":0.02156276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1472763951030907,"score_gpt":0.3822230107428794,"score_spread":0.2349466156397887,"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."}}