{"id":"W2908813984","doi":"10.3846/ijspm.2019.7437","title":"CUSTOMER ORIENTATION AND OFFICE SPACE PERFORMANCE: ASSESSING THE MODERATING EFFECT OF BUILDING GRADE USING PLS-MGA","year":2019,"lang":"en","type":"article","venue":"International Journal of Strategic Property Management","topic":"Facilities and Workplace Management","field":"Psychology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Quest University Canada","funders":"","keywords":"Customer satisfaction; Structural equation modeling; Customer orientation; Business; Orientation (vector space); Marketing; Loyalty business model; USable; Market orientation; Space (punctuation); Loyalty; Partial least squares regression; Computer science; Knowledge management; Service quality; Mathematics; Service (business); Machine learning","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":[0.001052156,0.0001481261,0.0002028548,0.0001841717,0.00008568409,0.000190292,0.0003213694,0.00003521566,0.0001567894],"category_scores_gemma":[0.000007559613,0.00008046658,0.00007305801,0.0001144542,0.00005584808,0.0003579094,0.0001311391,0.0001976356,0.00000970392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111101,"about_ca_system_score_gemma":0.0000189898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008219774,"about_ca_topic_score_gemma":8.263824e-7,"domain_scores_codex":[0.9984894,0.0001413268,0.0004601528,0.0001794296,0.0005530457,0.0001766223],"domain_scores_gemma":[0.9991225,0.0000885116,0.000454844,0.0001481445,0.0001533594,0.00003265406],"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":[0.004690028,0.001105568,0.1170512,0.003032272,0.0102326,0.0005613583,0.01240037,0.3686561,0.02647264,0.2480178,0.002843964,0.2049361],"study_design_scores_gemma":[0.03621065,0.007217733,0.1072681,0.009197866,0.002803335,0.001819068,0.1541414,0.6328487,0.01183558,0.003941404,0.02965763,0.003058642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746366,0.0001811838,0.00169827,0.0005017251,0.001363685,0.0003446181,0.00000145255,0.000007769855,0.02126474],"genre_scores_gemma":[0.9970834,0.00006667079,0.0008859352,0.00008682034,0.0001214084,0.000005775664,0.000001648897,0.00001228951,0.001736055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2641926,"threshold_uncertainty_score":0.3281332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03895664284668582,"score_gpt":0.334929255732976,"score_spread":0.2959726128862902,"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."}}