All IJMKL articles are:
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Peer-reviewed (rigorous, timely, constructive).
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Open access on the journal website.
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DOI-registered via Crossref and discoverable in Google Scholar.
Badges: Crossref · Open Access · DOI · Google Scholar · DOAJ · ULRICHSweb
Author
Oriana Valentina Rodríguez Guedes
Abstract
This study evaluates an end-to-end hiring pipeline that aligns Artificial Intelligence (AI) with
Customer Experience (CX) to accelerate high-volume recruitment across sales, retention, support, and operations.
With human-in-the-loop (HITL) controls and fairness monitoring, the pipeline improves speed, candidate experience,
and first-year retention using mixed-methods and quasi-experimental analyses (ITS/DiD).
Key outcomes: time-to-hire reduced from 28.4 to 14.7 days (−48%); candidate satisfaction (CSAT-10) increased from 6.2 to 8.7 (+40%);
first-year retention rose from 72.5% to 86.3% (+13.8 percentage points, +19% relative).
Governance-by-design includes HITL decision gates, transparency notes, and selection-rate parity monitoring (FPRP 0.8–1.25).
Methods & stack: multi-model AI ranking (logistic baseline, boosting, NN), NLP skill/sentiment extraction, chatbot prescreen with engagement signals,
predictive analytics for success/attrition, and an epsilon-greedy bandit policy to rebalance sourcing spend. Findings indicate faster cycles without quality loss,
higher offer acceptance via CX instrumentation, and measurable retention lift. :contentReference[oaicite:1]{index=1}
Suggested Citation (APA 7th)
Rodriguez Guedes, O. V. (2025). AI-Optimized, CX-Driven: High-Volume Hiring for Sales, Retention, Support and Operations.
International Journal of Management, Knowledge and Learning, 14, 369–386.
https://doi.org/10.53615/2232-5697.14.369-386
Published by ToKnowPress. (Slovenija )