{"id":"W2946224702","doi":"10.1016/j.jbusres.2019.04.019","title":"Connecting with consumers using ubiquitous technology: A new model to forecast consumer reaction","year":2019,"lang":"en","type":"article","venue":"Journal of Business Research","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Université du Québec à Montréal; Université du Québec en Outaouais","funders":"Fonds de Recherche du Québec - Santé; Social Sciences and Humanities Research Council of Canada; Fonds de Recherche du Québec-Société et Culture","keywords":"Purchasing; Marketing; Business; Consumer behaviour; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001289701,0.0009229269,0.0006013718,0.001377288,0.0004441745,0.002129237,0.001066156,0.002018867,0.003868571],"category_scores_gemma":[0.007228492,0.0003629927,0.0008234112,0.00104249,0.0006871,0.003512064,0.0007103095,0.001143016,0.0009524708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009305882,"about_ca_system_score_gemma":0.0005322744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006237566,"about_ca_topic_score_gemma":0.005614449,"domain_scores_codex":[0.9992293,0.0002847418,0.00003066058,0.0002422281,0.0001233239,0.0000898332],"domain_scores_gemma":[0.9962225,0.002782736,0.0003241954,0.0001991971,0.0002784298,0.0001929407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002330725,0.002390869,0.289884,0.000341408,0.0008347777,0.0007706775,0.002605136,0.4260023,0.009487652,0.05447578,0.006717511,0.2041592],"study_design_scores_gemma":[0.00002787044,0.0002979548,0.01352191,0.00001694472,0.00008105923,0.0001169359,0.0002501137,0.9737041,0.0004476117,0.0108598,0.0006439434,0.00003185652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7540929,0.0004180406,0.2305205,0.00190234,0.0003906524,0.0001989481,0.0006981954,0.0005107498,0.01126759],"genre_scores_gemma":[0.9857839,0.0001166103,0.01143712,0.0001223843,0.00008401098,0.00008932198,0.0001915654,0.00001801643,0.002157047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006237566,"threshold_uncertainty_score":0.01294172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.131441703770956,"score_gpt":0.4110385257162076,"score_spread":0.2795968219452515,"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."}}