{"id":"W4416301380","doi":"10.1108/jsm-05-2025-0362","title":"Reading between the lines: AI and customer acquisition in professional services","year":2025,"lang":"en","type":"article","venue":"Journal of Services Marketing","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Personalization; Service (business); Reading (process); Customer relationship management; Resource (disambiguation); Customer intelligence; Customer retention; Customer service","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003075802,0.000351283,0.0001930187,0.0008346042,0.0006914124,0.002622883,0.0004747355,0.0007259412,0.003337602],"category_scores_gemma":[0.03179434,0.0002311101,0.000233203,0.0007330725,0.001280444,0.002226291,0.001307031,0.001277711,0.0006367599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009036526,"about_ca_system_score_gemma":0.0008377379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004503873,"about_ca_topic_score_gemma":0.005498336,"domain_scores_codex":[0.9978599,0.001333359,0.0001005569,0.0001868548,0.0004069982,0.0001124569],"domain_scores_gemma":[0.9581799,0.03287735,0.005393844,0.0009415341,0.001661009,0.0009462744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006632726,0.0005101595,0.8809532,0.0001577728,0.00006712658,0.0009081702,0.03128467,0.003958799,0.003915787,0.002782586,0.000953453,0.07384495],"study_design_scores_gemma":[0.00002657039,0.001098073,0.8341733,0.000204485,0.0000858581,0.001445893,0.04585759,0.09859431,0.005164263,0.009546838,0.003675421,0.0001274576],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953389,0.00004046187,0.002139983,0.0002755646,0.00000382516,0.00001869496,0.00003361109,0.00001707159,0.002131819],"genre_scores_gemma":[0.997898,0.00002707983,0.001419312,0.0000629882,0.000005959478,0.00001225253,0.00004672701,0.000009201569,0.000518568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004503873,"threshold_uncertainty_score":0.01626664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006507116549359087,"score_gpt":0.2946145630173553,"score_spread":0.2881074464679962,"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."}}