{"id":"W2029168398","doi":"10.1016/j.dss.2015.02.016","title":"Getting the most out of third party trust seals: An empirical analysis","year":2015,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Business; Exploit; Value (mathematics); Third party; Seal (emblem); Marketing; Empirical evidence; Advertising; Internet privacy; Computer security; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01435902,0.0002527904,0.000932943,0.0009384055,0.0002455901,0.0003481246,0.002019486,0.0003660777,0.0004616853],"category_scores_gemma":[0.004205464,0.0001387398,0.0003616411,0.002857323,0.000246446,0.0004547607,0.0003375035,0.0003483387,0.001013478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006027538,"about_ca_system_score_gemma":0.0002506588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008582999,"about_ca_topic_score_gemma":0.0001432544,"domain_scores_codex":[0.9926139,0.0007530627,0.002131722,0.0008055132,0.003286373,0.0004095032],"domain_scores_gemma":[0.993481,0.001402788,0.0009402056,0.002444637,0.001320109,0.0004112813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005926677,0.0001342661,0.8323475,0.000001503147,0.00008843862,0.00003655873,0.00215711,0.0005743561,0.00004897332,0.0003693478,0.154506,0.009676704],"study_design_scores_gemma":[0.001441329,0.0004618673,0.4847506,0.00003073583,0.0005440597,0.0001108807,0.04068714,0.03586954,0.0001621036,0.005264878,0.4300416,0.0006352985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9626253,0.0001458425,0.03110833,0.001131081,0.001652176,0.0003477812,0.00008154225,0.0001692298,0.002738715],"genre_scores_gemma":[0.9953042,0.000003964893,0.0008982848,0.000306077,0.0001062459,0.00002599262,0.00002314903,0.00001695567,0.003315191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3475969,"threshold_uncertainty_score":0.9997643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2514834593939663,"score_gpt":0.4545668100572395,"score_spread":0.2030833506632732,"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."}}