{"id":"W4415265136","doi":"10.1108/jhtt-11-2024-0777","title":"Crossing the innovation chasm: when and how to deploy service robots and facilitate customer adoption at restaurants","year":2025,"lang":"en","type":"article","venue":"Journal of Hospitality and Tourism Technology","topic":"AI in Service Interactions","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Context (archaeology); Service (business); Sample (material); Robot; Variance (accounting); Perception; Thematic analysis; Customer satisfaction","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004638167,0.0001111017,0.0001823406,0.0004386531,0.0003950439,0.0002898415,0.0003319422,0.0001378108,9.88608e-7],"category_scores_gemma":[0.0001275087,0.00008346377,0.00001808221,0.0008088922,0.0001496362,0.0006880941,0.0004767031,0.0003269448,0.000001761617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005746425,"about_ca_system_score_gemma":0.00004420468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000538641,"about_ca_topic_score_gemma":0.0001050617,"domain_scores_codex":[0.9991596,0.00004797834,0.0002856073,0.0002053241,0.000137469,0.0001640375],"domain_scores_gemma":[0.9989607,0.00009253509,0.0002440828,0.0002630613,0.0004007815,0.00003877635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003470263,0.0003891709,0.1338856,0.0005833516,0.0006336318,0.0002804854,0.0286447,0.0003844894,0.03085392,0.1532397,0.02626523,0.6244928],"study_design_scores_gemma":[0.003216541,0.001322575,0.6083683,0.001028218,0.0001900688,0.002376804,0.01122773,0.01104454,0.009085849,0.280684,0.07048006,0.000975243],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8013856,0.0004183083,0.03511234,0.1626631,0.0002086699,0.0001050731,0.000001339409,0.00005159608,0.00005400214],"genre_scores_gemma":[0.9885886,0.00008600967,0.009835479,0.001296984,0.0000289885,0.000004844363,1.954041e-7,0.000005072933,0.0001538316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6235175,"threshold_uncertainty_score":0.3403554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889258412803942,"score_gpt":0.2687172344839593,"score_spread":0.2498246503559199,"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."}}