{"id":"W4292954651","doi":"10.5267/j.ijdns.2022.4.012","title":"Continuance intention to use smartwatches: An empirical study","year":2022,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Smartwatch; Wearable computer; Context (archaeology); Expectancy theory; Continuance; Construct (python library); Structural equation modeling; Habit; Wearable technology; Psychology; Empirical research; Computer science; Applied psychology; Human–computer interaction; Social psychology; Mathematics; Machine learning; Statistics; Geography","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":[0.00838615,0.00007605593,0.0001662947,0.0004806964,0.0003574277,0.0005839272,0.005007008,0.00001838395,0.0001787713],"category_scores_gemma":[0.001460741,0.00005826637,0.00002957059,0.001068814,0.0002146813,0.002771913,0.002395021,0.0002924029,0.000008892866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006021146,"about_ca_system_score_gemma":0.0001403763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002118215,"about_ca_topic_score_gemma":0.00007987737,"domain_scores_codex":[0.9961843,0.0001944137,0.0006204221,0.0004344163,0.002389338,0.0001771058],"domain_scores_gemma":[0.9977531,0.0002631471,0.000391704,0.0005667807,0.0008519287,0.0001732708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001162107,0.0002463953,0.9538553,5.241591e-8,0.00001015279,0.00005440814,0.0006168477,0.0002658051,0.0001663148,0.0001217268,0.01012816,0.03441862],"study_design_scores_gemma":[0.0004352047,0.0005943439,0.9379933,0.000006688304,0.00001039165,0.0002795376,0.004201358,0.002873544,0.00001005456,0.001463105,0.0520414,0.00009109369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866081,0.0000275775,0.008638863,0.002736124,0.001817803,0.00009319516,0.00004717024,0.00001278865,0.00001837054],"genre_scores_gemma":[0.9946235,0.000009245095,0.004177687,0.0009196926,0.0001092734,0.00000246707,0.000004221737,0.000003348884,0.0001505218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04191323,"threshold_uncertainty_score":0.9304351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2774950386622274,"score_gpt":0.4926217006064464,"score_spread":0.215126661944219,"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."}}