{"id":"W2534520787","doi":"10.5539/mas.v11n2p8","title":"Investigating the Relationship between Marketing Knowledge Sharing and Developing Competitive Advantage (Case Study: Arak Shazand Petrochemical)","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Knowledge Management and Sharing","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cronbach's alpha; Stratified sampling; Statistic; Knowledge sharing; Content validity; Competitive advantage; Univariate; Confirmatory factor analysis; Regression analysis; Reliability (semiconductor); Marketing; Variables; Structural equation modeling; Computer science; Statistics; Knowledge management; Mathematics; Business; Multivariate statistics; Service (business)","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":[0.001395618,0.0002223152,0.0002293276,0.001377983,0.002830564,0.001534057,0.0006662012,0.0008318008,0.003418533],"category_scores_gemma":[0.001729045,0.0001554747,0.0003013326,0.001206125,0.0009741641,0.00121857,0.001446071,0.0006629947,0.0002613032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001974936,"about_ca_system_score_gemma":0.002624436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007688131,"about_ca_topic_score_gemma":0.01493802,"domain_scores_codex":[0.9991334,0.000286297,0.00003512,0.00009109731,0.0002124596,0.0002416518],"domain_scores_gemma":[0.9982837,0.0007937074,0.0003591018,0.0000662467,0.00017523,0.0003220035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003894945,0.004723778,0.6298551,0.0006468295,0.000108448,0.06621391,0.147115,0.001521287,0.005353857,0.01200336,0.002749393,0.1293196],"study_design_scores_gemma":[0.00005916571,0.001418356,0.4596224,0.0002849068,0.0001327411,0.02312409,0.479674,0.006100961,0.005110458,0.003023674,0.02135863,0.00009070912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960409,0.00008368272,0.0001585005,0.0002034217,0.000002360615,0.00001821605,0.000009012799,0.000001703653,0.003482103],"genre_scores_gemma":[0.9983084,0.0001707687,0.0004239362,0.00002984202,0.000003134073,0.00001345354,0.00001477157,8.611348e-7,0.001034906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007688131,"threshold_uncertainty_score":0.01528674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08719340929987202,"score_gpt":0.3464536564217149,"score_spread":0.2592602471218429,"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."}}