{"id":"W4225907991","doi":"10.5267/j.ijdns.2022.3.009","title":"The impact of artificial intelligence, big data analytics and business intelligence on transforming capability and digital transformation in Jordanian telecommunication firms","year":2022,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Organizational and Employee Performance","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Applied Science Private University","keywords":"Digital transformation; Big data; Business intelligence; Transformation (genetics); Data science; Computer science; Analytics; Process (computing); Sample (material); Business analytics; Knowledge management; Data mining; Business; Business model; World Wide Web; Electronic business; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001654479,0.0001803502,0.0001295945,0.001315328,0.001144365,0.003603493,0.0002871964,0.0003861142,0.002140352],"category_scores_gemma":[0.005941593,0.00009193207,0.0002166882,0.001841439,0.001101993,0.00144246,0.001755763,0.0009445961,0.0001814257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002549907,"about_ca_system_score_gemma":0.005104691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01113483,"about_ca_topic_score_gemma":0.0231426,"domain_scores_codex":[0.9983953,0.0005864931,0.00009627475,0.0000859299,0.0003766646,0.0004593327],"domain_scores_gemma":[0.9908714,0.003740267,0.001863834,0.000214481,0.001427974,0.001882086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001167097,0.000717776,0.9224576,0.0001247197,0.00007301373,0.001255143,0.01627883,0.001973938,0.0009494825,0.007301088,0.0009544535,0.04779719],"study_design_scores_gemma":[0.00001032914,0.0002421424,0.8970163,0.0001465103,0.00004459386,0.0002391413,0.09196516,0.00239954,0.0011981,0.001422673,0.005290075,0.00002537699],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940979,0.0001057421,0.00004657332,0.0003583955,0.000002561992,0.000007407934,0.00001279871,0.000001312423,0.005367227],"genre_scores_gemma":[0.9994678,0.0001072129,0.00004375642,0.00002082929,0.000002351946,0.000002557119,0.00001369151,4.017988e-7,0.0003413161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01113483,"threshold_uncertainty_score":0.02214003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07254390404266649,"score_gpt":0.321638291165604,"score_spread":0.2490943871229375,"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."}}