{"id":"W4392200366","doi":"10.18280/isi.290112","title":"Analyzing Benefits of Online Train Ticket Reservation App Using Technology Acceptance Model","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ticket; Reservation; Reservation system; Computer science; Technology acceptance model; Computer network; Human–computer interaction","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":[0.002783959,0.000553812,0.0004479102,0.001592514,0.0004658852,0.001455762,0.000454212,0.0008745864,0.004396619],"category_scores_gemma":[0.01603158,0.0001751151,0.001510559,0.001081453,0.0003393165,0.001538788,0.000691883,0.001093308,0.0004839302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000736551,"about_ca_system_score_gemma":0.001096455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003079287,"about_ca_topic_score_gemma":0.003848303,"domain_scores_codex":[0.997122,0.001214224,0.0001286307,0.0002073263,0.0009010878,0.0004266874],"domain_scores_gemma":[0.9705857,0.02245732,0.00182692,0.001043007,0.003118815,0.0009683365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003796248,0.005534402,0.8034313,0.0004904,0.0008887597,0.000495239,0.0009543087,0.03728786,0.00782835,0.003881728,0.001280782,0.1341306],"study_design_scores_gemma":[0.0001021931,0.004923122,0.8101414,0.00008932451,0.00121017,0.0002636622,0.003096431,0.1701552,0.005492695,0.002644519,0.001795324,0.00008603118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950067,0.000105693,0.00155795,0.0001204325,0.00001225553,0.00004371597,0.00008978262,0.00002551893,0.003037889],"genre_scores_gemma":[0.9986159,0.0000299573,0.0005902803,0.00001596854,0.000006781434,0.00002968607,0.00006994689,0.000004150083,0.0006373613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004396619,"threshold_uncertainty_score":0.01472312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1025730274461117,"score_gpt":0.3706502859797404,"score_spread":0.2680772585336286,"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."}}