{"id":"W2025896074","doi":"10.1108/09593841011069149","title":"Building institutional trust through e‐government trustworthiness cues","year":2010,"lang":"en","type":"article","venue":"Information Technology and People","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Development Research Centre","funders":"","keywords":"Public relations; Originality; Public sector; Government (linguistics); Agency (philosophy); Business; Value (mathematics); Service (business); Institutional theory; Private sector; Marketing; Empirical research; Knowledge management; Sociology; Political science; Economics; Qualitative research; Computer science; Management; Economic growth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005471934,0.00030652,0.0003892491,0.001598932,0.003230692,0.007510112,0.0006604544,0.0009341901,0.005171483],"category_scores_gemma":[0.03465785,0.0003654334,0.0003055172,0.001204556,0.007761352,0.006357842,0.007062073,0.001689291,0.000260565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004757811,"about_ca_system_score_gemma":0.004360496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01068081,"about_ca_topic_score_gemma":0.009216582,"domain_scores_codex":[0.9905544,0.00604935,0.0005436215,0.0005695784,0.001382543,0.0009005334],"domain_scores_gemma":[0.969699,0.01529822,0.007526684,0.003364203,0.002894358,0.001217579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008787256,0.0006726877,0.3241091,0.001094086,0.0003033227,0.004088724,0.210205,0.006982997,0.007296158,0.2590742,0.003567437,0.1817276],"study_design_scores_gemma":[0.0001965925,0.000866499,0.2853154,0.002150473,0.0004611908,0.001652003,0.3491063,0.02885209,0.009976634,0.2336176,0.08739637,0.0004089068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8943633,0.0003695172,0.01480198,0.003589511,0.00003546751,0.0001075161,0.00007099759,0.00008301045,0.08657867],"genre_scores_gemma":[0.9993777,0.00003118882,0.000333569,0.00002717314,0.000002027262,0.000006354353,0.000005219895,0.000003147962,0.0002134626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01068081,"threshold_uncertainty_score":0.03452045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006668608809638303,"score_gpt":0.2580128526198168,"score_spread":0.2513442438101784,"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."}}