{"id":"W2894513301","doi":"10.29333/ejmste/97192","title":"The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students","year":2018,"lang":"en","type":"article","venue":"Eurasia Journal of Mathematics Science and Technology Education","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Technology acceptance model; Leverage (statistics); Situational ethics; Variance (accounting); Psychology; Explanatory power; Situated; Cognitive dimensions of notations; Sample (material); Usability; Dimension (graph theory); Cognition; Social cognitive theory; Social psychology; Knowledge management; Computer science; Artificial intelligence; Mathematics; Human–computer interaction","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.002711333,0.0001257571,0.0002380495,0.001262924,0.001798123,0.0001065842,0.001786968,0.000192018,0.00000758302],"category_scores_gemma":[0.01411268,0.00006795875,0.00003884739,0.004072688,0.009306568,0.0003698234,0.0004593078,0.0004447017,0.000006918159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005092546,"about_ca_system_score_gemma":0.0006484786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.514235e-7,"about_ca_topic_score_gemma":0.0000175452,"domain_scores_codex":[0.9973911,0.00005441256,0.0007532277,0.0002608635,0.001330595,0.0002098053],"domain_scores_gemma":[0.9942542,0.0007046704,0.001129406,0.0004158461,0.003437852,0.00005800462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002612354,0.0006369564,0.6580417,0.000003968256,0.00003427192,0.000001940689,0.001383972,0.000001848324,0.009123131,0.246341,0.001379673,0.08302535],"study_design_scores_gemma":[0.0002867728,0.000187357,0.7872683,0.0001660621,0.00003030003,0.0003328991,0.02118906,0.00002740184,0.004831385,0.1852318,0.0003638384,0.00008485788],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898571,0.00009810833,0.0001253648,0.009193494,0.000397205,0.0002101323,0.000006464689,0.00001892649,0.00009324283],"genre_scores_gemma":[0.9970648,0.00007761,0.002626782,0.00008512856,0.00002324112,0.000009468021,4.989123e-8,0.000005371957,0.0001076041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1292266,"threshold_uncertainty_score":0.9995014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06557084213544466,"score_gpt":0.4084725913807914,"score_spread":0.3429017492453468,"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."}}