{"id":"W4407103082","doi":"10.1080/23270012.2025.2455550","title":"The adoption of human resources analytics in construction projects in Jordan: antecedents and consequences","year":2025,"lang":"en","type":"article","venue":"Journal of Management Analytics","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Analytics; Knowledge management; Business; Data science; Computer 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":[0.005069983,0.0003160097,0.0002570969,0.001775922,0.001362714,0.002651757,0.0005954792,0.0007126912,0.001738404],"category_scores_gemma":[0.01468545,0.0003780956,0.0003027918,0.002507242,0.002016689,0.001496056,0.002422377,0.001497653,0.0002613771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003472005,"about_ca_system_score_gemma":0.006049566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01307178,"about_ca_topic_score_gemma":0.02494158,"domain_scores_codex":[0.9950171,0.002196016,0.0003042444,0.0003641058,0.001243922,0.0008746297],"domain_scores_gemma":[0.9709976,0.01007443,0.009356318,0.0009122847,0.005475065,0.003184279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009014921,0.0008509416,0.9431678,0.0001080747,0.00003348221,0.000505369,0.01638174,0.001281975,0.0007278142,0.00121261,0.0003978294,0.03524229],"study_design_scores_gemma":[0.000008985537,0.0004167428,0.9226269,0.0001672595,0.00002218696,0.0002284895,0.06999052,0.003245598,0.0005200859,0.0008141003,0.001926568,0.00003248289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985172,0.00008292709,0.0001355684,0.0001939366,0.000001476237,0.00001502201,0.00001296118,0.000003041838,0.001037796],"genre_scores_gemma":[0.9993334,0.0001475061,0.0002205321,0.000029142,0.000002379525,0.00001206596,0.00001707181,0.000001592175,0.0002363617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01307178,"threshold_uncertainty_score":0.02681297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218353666992485,"score_gpt":0.2642648503919301,"score_spread":0.2424294836926816,"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."}}