{"id":"W4254032788","doi":"10.32920/ryerson.14650116","title":"Using dynamic pricing to target off-peak hours in a brick-and-mortar service environment to increase revenue","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dynamic pricing; Revenue; Interpretation (philosophy); Revenue management; Brick; Brick and mortar; Service (business); Pricing strategies; Business; Computer science; Industrial organization; Environmental economics; Operations research; Economics; Marketing; Engineering; Finance; Civil engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002222122,0.0002492759,0.0004473114,0.0004102134,0.0001547043,0.0002528376,0.0006800981,0.0001535545,0.000744922],"category_scores_gemma":[0.0005384008,0.0002339995,0.00008752506,0.0009100561,0.00003125193,0.0001239948,0.001889904,0.0003654427,0.0003115909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002406294,"about_ca_system_score_gemma":0.0001505874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001903242,"about_ca_topic_score_gemma":0.001243758,"domain_scores_codex":[0.9968569,0.0003350917,0.0007367039,0.001116984,0.0006795808,0.0002747435],"domain_scores_gemma":[0.9979314,0.000320741,0.0002384901,0.001101053,0.00009676324,0.000311529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001336747,0.0005437152,0.0120965,0.00008818807,0.00008963446,0.0001439432,0.01297497,0.8931862,0.01593844,0.001879789,0.001605151,0.06131981],"study_design_scores_gemma":[0.001877442,0.0001904732,0.3917809,0.002398268,0.0003469356,0.0004797776,0.05019752,0.2232388,0.004025183,0.185245,0.134475,0.005744706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8524253,0.0001445673,0.1433986,0.002534276,0.000139656,0.0006488596,0.0000789683,0.00002361222,0.0006061433],"genre_scores_gemma":[0.9334297,0.00003310764,0.06126393,0.002814201,0.00006555183,0.000123103,0.00002707029,0.00002574464,0.002217587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6699474,"threshold_uncertainty_score":0.9542223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0781600265571569,"score_gpt":0.381296798076922,"score_spread":0.303136771519765,"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."}}