{"id":"W4416778868","doi":"10.1080/0965254x.2025.2595087","title":"Let’s Get Phygital: The Bright and Dark Sides of Generative AI for Phygital Customer Experience","year":2025,"lang":"en","type":"article","venue":"Journal of Strategic Marketing","topic":"AI in Service Interactions","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research","funders":"","keywords":"Customer experience; Generative grammar; Customer advocacy; Customer intelligence; Generative model; Customer to customer; Customer retention","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":[0.007386322,0.0005888652,0.0006114297,0.002019877,0.002107298,0.01606465,0.001400358,0.002712245,0.004153713],"category_scores_gemma":[0.01919938,0.0003535094,0.0008218537,0.002154196,0.01293055,0.01778231,0.00449757,0.004698486,0.001229877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002029736,"about_ca_system_score_gemma":0.003615655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008401417,"about_ca_topic_score_gemma":0.001493556,"domain_scores_codex":[0.9914983,0.005772211,0.0003453422,0.0004450787,0.001621883,0.0003171481],"domain_scores_gemma":[0.9809657,0.01542116,0.0007356267,0.00103781,0.001453795,0.0003857585],"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":[0.0001585657,0.000113019,0.002940676,0.01005944,0.0001677413,0.001957544,0.1332744,0.0006042594,0.00567712,0.4655019,0.01875212,0.3607931],"study_design_scores_gemma":[0.00004248409,0.0002718791,0.004755482,0.01551736,0.0001948495,0.002672433,0.1056237,0.001137614,0.00416993,0.2543718,0.6110987,0.0001436865],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1416113,0.2626576,0.1316878,0.1883383,0.005257926,0.000447492,0.0002451754,0.0006640046,0.2690903],"genre_scores_gemma":[0.7907826,0.1136597,0.04183554,0.03191238,0.001423603,0.0004745238,0.0001486143,0.0003485162,0.01941441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01606465,"threshold_uncertainty_score":0.0390631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02902949583315084,"score_gpt":0.3110509336174073,"score_spread":0.2820214377842564,"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."}}