{"id":"W4388095418","doi":"10.1002/mar.21935","title":"What is driving consumer resistance to crypto‐payment? A multianalytical investigation","year":2023,"lang":"en","type":"article","venue":"Psychology and Marketing","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Cryptocurrency; Payment; Mainstream; Context (archaeology); Resistance (ecology); Business; Consumer behaviour; Empirical research; Marketing; Advertising; Computer security; Computer science; Law; Biology; Ecology; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003906018,0.0003753303,0.0004355286,0.00193798,0.001357924,0.004556704,0.0007894856,0.001360665,0.006957359],"category_scores_gemma":[0.01368107,0.0002916483,0.0009724272,0.001377131,0.004679832,0.003300154,0.002207896,0.001771865,0.0002786366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002952901,"about_ca_system_score_gemma":0.00122803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002370589,"about_ca_topic_score_gemma":0.001494479,"domain_scores_codex":[0.998428,0.0008344735,0.00004756738,0.0002087366,0.0002409305,0.0002403659],"domain_scores_gemma":[0.9807083,0.0143514,0.002715755,0.0007433707,0.001131669,0.0003494897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008233304,0.001490883,0.6763765,0.0006232616,0.0004542464,0.001460294,0.07292445,0.003291664,0.00402559,0.1768401,0.001284105,0.06040568],"study_design_scores_gemma":[0.00008755349,0.0007748023,0.6394035,0.0003737069,0.0005851095,0.0008261184,0.1879305,0.05470997,0.003255969,0.09995431,0.01193637,0.0001622174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837298,0.0002004589,0.003291443,0.001319269,0.00001053039,0.0000643073,0.00002750509,0.000009525241,0.01134718],"genre_scores_gemma":[0.9991629,0.00006676355,0.0003415444,0.0000500114,0.000005009081,0.00001477165,0.000007730564,0.000003149273,0.000348206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006957359,"threshold_uncertainty_score":0.02327466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0318568846867352,"score_gpt":0.3598542198324827,"score_spread":0.3279973351457475,"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."}}