{"id":"W3026917702","doi":"10.5267/j.msl.2020.5.024","title":"Factors affecting acceptance and use of online technology in Thai people during COVID-19 quarantine time","year":2020,"lang":"en","type":"article","venue":"Management Science Letters","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarantine; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Business; Internet privacy; Computer science; Virology; Medicine; Outbreak; Infectious disease (medical specialty); Internal medicine","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":[0.000433118,0.0001381293,0.0003148385,0.0008676231,0.00012162,0.0000712581,0.0004158851,0.00003741141,0.00005014063],"category_scores_gemma":[0.0008528553,0.0001531052,0.00003316556,0.002000145,0.0002517686,0.0005621958,0.0003596364,0.0001349237,0.00001613045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001931497,"about_ca_system_score_gemma":0.00001026756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001954924,"about_ca_topic_score_gemma":0.00003672746,"domain_scores_codex":[0.9985748,0.000009745374,0.0003810974,0.0005646228,0.00008062428,0.000389101],"domain_scores_gemma":[0.9992781,0.00008767613,0.0002565594,0.0002568533,0.000004040781,0.0001167129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008509618,0.00002221321,0.9895056,0.0001462261,0.000009010195,0.000009009478,0.001746255,0.002395272,0.004971738,0.0009916829,0.00008755176,0.0001069064],"study_design_scores_gemma":[0.0006426384,0.00002897132,0.9909478,0.00002213523,0.000004094566,9.05471e-7,0.000547674,0.00621277,0.000580312,0.0001965519,0.0006024558,0.0002137251],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964007,0.00004052715,0.002234515,0.033236,0.00005812877,0.0002861473,0.00001941252,0.00006250179,0.0000557594],"genre_scores_gemma":[0.9935352,0.00002724418,0.000729312,0.005647535,0.00001310183,0.000003353739,0.00000219307,0.0000103302,0.00003169739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02952822,"threshold_uncertainty_score":0.624345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05175989914161154,"score_gpt":0.2647962249354205,"score_spread":0.213036325793809,"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."}}