چکیده مقاله
This article develops and tests a KPI driven, Balanced Scorecard aligned framework that links AI modalities to enterprise outcomes across the passenger journey The primary objective is to quantify and explain how dynamic offers, recommender systems, conversational AI, biometrics, and predictive IRROPS affect customer experience NPS, CSAT, CES , monetization ARPP, conversion , operations IRROPS time, OTP , and finance/ESG RASK, CASK, CO₂/ASK Using a mixed methods, explanatory sequential design, we analyze multi system KPIs and A/B or difference in differences rollouts, followed by executive interviews on governance privacy, fairness, robustness, explainability Findings show dynamic ancillary pricing delivers 17–58% conversion and 10–43% revenue per offer; conversational AI reduces waiting time up to 80% for routine intents but requires hybrid escalation to sustain NPS; recommenders raise CTR ~ 15% yet need stronger causal links to repeat booking and CLV; revenue management accuracy improves 14–22%, supporting yield stability Evidence remains limited on causal bridges to RASK/CASK and CO₂/ASK Theoretically, we formalize a five layer KPI architecture and position digital maturity as a measurable moderator Practically, we recommend a Foundations → Pilot → Scale → Optimize roadmap with per pax/per ASK denominators, instrumentation A/B, DiD, uplift , and responsible AI gates consent, bias, robustness, model cards The framework enables airline leaders to convert AI initiatives into decision grade, auditable value
کلیدواژهها
نویسندگان
مهدی نیک منش
کارشناسی ارشد مدیریت صنعتی، دانشگاه آزاد اسلامی، واحد کرج، کرج، ایران
SeyyedAbdolHojjat MoghadasNian
Tarbiat Modares University
Farzaneh GhajarGar
Payame Noor University Damavand Branch
شیوه ارجاع
MoghadasNian, SeyyedAbdolHojjat and GhajarGar, Farzaneh,1404,AI-Powered Passenger Experience in Airlines: A KPI-Driven Framework with Mixed-Methods Evidence,11th International Conference on Management, Tourism and Technology,Tehran
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