{"id":"W2521764869","doi":"10.5539/ass.v12n10p208","title":"Structural Relationships between Disruptive Attributes and Women Consumers’ Attitude when Using Mobile Retailing","year":2016,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Mobile marketing; Nonprobability sampling; Affect (linguistics); Personalization; Marketing; Business; Advertising; Sample (material); Positive attitude; Psychology; Social psychology; Sociology; Mathematics; Digital marketing; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009568612,0.0001511428,0.00013461,0.0006373795,0.0003663494,0.0008604435,0.0001416248,0.0002581169,0.002506555],"category_scores_gemma":[0.00547496,0.0001259459,0.0003459199,0.0005825071,0.0005741606,0.0003564045,0.0005109904,0.0004457411,0.0001511295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005253394,"about_ca_system_score_gemma":0.0006128507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004999117,"about_ca_topic_score_gemma":0.008319004,"domain_scores_codex":[0.9993376,0.0002232429,0.0000735694,0.00006517042,0.0001990735,0.0001012263],"domain_scores_gemma":[0.9944726,0.002095872,0.002155768,0.0002604863,0.0006241208,0.0003911321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003967572,0.00005600625,0.9884396,0.00002289697,0.00002495812,0.00008255255,0.004302192,0.00003936753,0.0005630739,0.0002084193,0.00005556118,0.006165719],"study_design_scores_gemma":[0.000001661398,0.0001037543,0.9897103,0.00001452443,0.00002064184,0.000115469,0.008954991,0.0002814629,0.0001760624,0.0001573497,0.0004578499,0.000005899059],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984396,0.00004433507,0.0001883111,0.0001060573,0.00000325846,0.000007768052,0.00002957698,0.000001653045,0.001179351],"genre_scores_gemma":[0.9995584,0.00004573553,0.0001261909,0.00001883601,0.00000314168,0.000004240243,0.00002425015,6.435029e-7,0.0002185723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004999117,"threshold_uncertainty_score":0.009940088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1750124482562866,"score_gpt":0.4007573545273449,"score_spread":0.2257449062710583,"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."}}