{"id":"W2168315542","doi":"10.1089/cyber.2014.0675","title":"Can Coolness Predict Technology Adoption? Effects of Perceived Coolness on User Acceptance of Smartphones with Curved Screens","year":2015,"lang":"en","type":"article","venue":"Cyberpsychology Behavior and Social Networking","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Attractiveness; Usability; Technology acceptance model; Structural equation modeling; Originality; Psychology; Computer science; Aesthetics; Human–computer interaction; Advertising; Social psychology; Business; Art; Machine learning; Creativity","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":[],"consensus_categories":[],"category_scores_codex":[0.0005887104,0.0002090244,0.0005453442,0.00014762,0.0004449505,0.00003201074,0.0002875489,0.0004701531,0.000007018264],"category_scores_gemma":[0.0001805419,0.0002007652,0.00007524331,0.000601046,0.002023909,0.00009983741,0.00005256246,0.0003085491,0.000001161128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006863619,"about_ca_system_score_gemma":0.0001612165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000504751,"about_ca_topic_score_gemma":0.001676083,"domain_scores_codex":[0.9979951,0.0004477981,0.0003264786,0.0003609659,0.0003940459,0.000475637],"domain_scores_gemma":[0.998702,0.0003413826,0.000306162,0.0001758845,0.000328017,0.0001465793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005893291,0.0004974426,0.7831469,0.00003067659,0.00008174611,0.0000439454,0.04193642,0.000005135724,0.0001562022,0.0072921,0.0004824071,0.1657377],"study_design_scores_gemma":[0.001764007,0.0004395889,0.9747448,0.0002373485,0.0001394175,0.000002800884,0.02032219,0.000001394118,0.00004093306,0.000356375,0.001634209,0.0003169267],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919207,0.0001752728,0.00002627141,0.0004588238,0.001318534,0.0004864163,0.00001324587,0.000105755,0.005494988],"genre_scores_gemma":[0.9989352,0.00006714555,0.00007953612,0.00008445848,0.0005008668,0.0000909763,0.00001179397,0.00002831239,0.0002017745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.191598,"threshold_uncertainty_score":0.8186967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02426396384087866,"score_gpt":0.3010847156968076,"score_spread":0.276820751855929,"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."}}