{"id":"W2028550405","doi":"10.7545/ajip.2013.2.2.150","title":"Research Trends and Its Determinants in Mobile Commerce Research (1999-2012)","year":2013,"lang":"en","type":"article","venue":"Asian Journal of Innovation and Policy","topic":"ICT Impact and Policies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mobile commerce; Business; Data science; Computer science; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003682774,0.000248669,0.0003399766,0.01434899,0.0007255991,0.003920039,0.0004433304,0.0006159258,0.002882193],"category_scores_gemma":[0.0150973,0.000246223,0.0006318393,0.03034537,0.0007453443,0.003951565,0.0007317945,0.001021371,0.0009133958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002243833,"about_ca_system_score_gemma":0.002893838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009855573,"about_ca_topic_score_gemma":0.01520224,"domain_scores_codex":[0.9975302,0.0003863379,0.0005568521,0.0004664509,0.00077219,0.0002879806],"domain_scores_gemma":[0.9661419,0.01417188,0.00762749,0.0007465234,0.01006506,0.00124715],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002253123,0.0001274397,0.837258,0.003067702,0.000227359,0.0007132352,0.004107448,0.0003445177,0.0008848279,0.01131583,0.01079103,0.1309373],"study_design_scores_gemma":[0.000005977861,0.0001105247,0.9161223,0.002168228,0.0001803639,0.0007998843,0.008053795,0.0005220441,0.0009176484,0.0008616563,0.07022765,0.00002995688],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7625127,0.1531368,0.001502251,0.01598338,0.0007074024,0.0001955123,0.01260329,0.0000810936,0.05327746],"genre_scores_gemma":[0.9130428,0.07365517,0.001861993,0.0009662519,0.000773191,0.0001204758,0.004609867,0.00004422611,0.00492605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9963172,"threshold_uncertainty_score":0.0195964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0738933553023304,"score_gpt":0.4161092375201721,"score_spread":0.3422158822178417,"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."}}