{"id":"W4412127868","doi":"10.64251/ijmmi.22","title":"The Effects of Pros and Cons of Applying Big Data Analytics to Enhance Consumers' Responses","year":2024,"lang":"en","type":"article","venue":"International Journal of Management and Marketing Intelligence","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Yorkville University","funders":"","keywords":"cons; Big data; Analytics; Data science; Computer science; Business; Data mining","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":[0.001933865,0.00006871868,0.0001240712,0.0003287171,0.00004803042,0.0001217779,0.001318736,0.00002201931,0.00000149142],"category_scores_gemma":[0.00111273,0.00004926354,0.00003085501,0.0002561141,0.0001492125,0.0002041565,0.001054177,0.0001052904,7.853899e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001045794,"about_ca_system_score_gemma":0.00002607199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006244934,"about_ca_topic_score_gemma":0.000006856166,"domain_scores_codex":[0.9989893,0.0000822831,0.0003759601,0.0001791023,0.0002879779,0.00008536382],"domain_scores_gemma":[0.9974419,0.001878345,0.0002043799,0.0002865732,0.0001584198,0.00003033322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001223826,0.00002126661,0.002314512,0.0001879532,0.0005694978,0.00008336052,0.0001641169,0.0000107221,0.0007739245,0.01583954,0.0006985743,0.9792141],"study_design_scores_gemma":[0.001176268,0.001719896,0.05773665,0.02020194,0.002353295,0.001047813,0.00496805,0.2235194,0.2265163,0.05382925,0.4051981,0.001733082],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05364048,0.005721272,0.9369955,0.002314907,0.0008745175,0.0001718401,0.00001194237,0.00001776785,0.0002517537],"genre_scores_gemma":[0.9809914,0.005418469,0.01329287,0.00004764237,0.00002499966,0.000002957815,8.240942e-7,0.000002778182,0.0002180608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9774811,"threshold_uncertainty_score":0.2450562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0250253348023789,"score_gpt":0.3176478680253585,"score_spread":0.2926225332229797,"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."}}