{"id":"W4385641594","doi":"10.1007/978-3-031-34821-1_13","title":"Adoption Potentials of Metaverse Omnichannel Retailing and Its Impact on Mass Customization Approaches","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Metaverse; Omnichannel; Purchasing; Context (archaeology); Marketing; Personalization; Enthusiasm; Business; Computer science; Virtual reality; Psychology; Human–computer interaction; Geography","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":[0.001585228,0.0002547134,0.0002110752,0.001482549,0.0006339258,0.005515953,0.0006897949,0.0007608167,0.01493003],"category_scores_gemma":[0.00516753,0.0001839562,0.0004477646,0.002647943,0.001148011,0.004579464,0.001927651,0.0008342313,0.0009033203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001907683,"about_ca_system_score_gemma":0.0009054387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003629758,"about_ca_topic_score_gemma":0.006428919,"domain_scores_codex":[0.9990523,0.00028289,0.0000330892,0.0001430512,0.0003265566,0.0001620081],"domain_scores_gemma":[0.9947236,0.00336508,0.0005256048,0.0004932192,0.000542837,0.0003497251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005547497,0.0004211191,0.1037707,0.0002329246,0.00005971274,0.0006181333,0.007969893,0.003535902,0.007253799,0.5688286,0.003109428,0.303645],"study_design_scores_gemma":[0.0000854989,0.001502299,0.3930867,0.000848009,0.0005662483,0.002126286,0.04418715,0.08087982,0.02101345,0.3106717,0.1448313,0.0002015124],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6994705,0.0009494112,0.01027429,0.001319909,0.00002700265,0.00004242922,0.0001745976,0.000145306,0.2875966],"genre_scores_gemma":[0.9857113,0.000318986,0.003020518,0.00006917112,0.00001955801,0.00001470407,0.00009105779,0.00003492335,0.01071982],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01493003,"threshold_uncertainty_score":0.04994595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04175085784442325,"score_gpt":0.2347080405002057,"score_spread":0.1929571826557824,"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."}}