The impact of airline service failures on travelers’ carrier choice

1. The impact of airline service failures on travelers’ carrier choice Yoshinori Suzuki

2. Background • Understanding airline choice behavior is important for airline managers • Pricing strategy • Marketing strategy • Yield management • Several discrete-choice studies have been conducted • Limitation = Ignored possible impact of airline service failures on future choices

3. Airline service failures • Types of service failures • Seat denials (bumping) • Flight delays • Baggage mishandling • Importance of investigating this issue • Overbooking policies • On-time targets • Some attempts, but service failures did not reflect the actual experiences of decision makers • The nature of the effects largely unknown

4. Study Hypotheses • Service-failure experiences adversely affect one’s future airline choices • Loss aversion theory • “Loss Aversion Hypothesis” • Service-failure experiences have no impact on one’s future airline choices • Random utility theory • “No-Service Carryover Hypothesis” • They are mutually exclusive hypotheses • The effects of service failures on choice probabilities are separately estimated by type of service failure (bump, delay, mishandling)

5. Discrete Choice Model • Multinomial Logit Model • Estimates the impact of utility variables on choice probabilities • Two models estimated • Loss Aversion Model • No-Service Carryover Model • If loss aversion hypo holds, the former model should explain the actual choices better • If no-service carryover hypo holds, the two models will be statistically indistinguishable

6. Sample Data • Survey of “recent” flyers in DSM service area (IA DOT, Travel and Transport) • Two data gathering methods • Mail survey (835 sent, 198 returned) • Intercept survey at DSM (331 collected) • Total sample = 635, usable sample = 529 • Summary statistics in Table 1

7. Utility function • Airfare (perceived fare) • Service frequency (OAG Flight Guide) • Flight miles (DB1A) • Frequent Flyer Program (active members) • Direct flight availability (DB1A) • Service failure experiences • Airline constants

8. Measuring service failure experiences • 3 Variables • BUMP – involuntary denied boarding • DELAY – “substantial” arrival delay • BAG – lost, damaged, delayed, or pilfered • Separate impacts for business and leisure • Do not include the experience at time t • Out-dated experiences deleted (> m months) • “m” is estimated by testing variety of values

9. Results • Duration of service carryover (Table 2) • Comparison of the two models (Table 3) • Coefficients are generally in line with theory • Service-failure variables not statistically significant or have incorrect signs • Two models are statistically indistinguishable • Favors the “No-Carryover” hypothesis • Cross validation shown in Table 4

10. Conclusions and Implications • Air travelers may not be loss averse with respect to service failure experiences • Air travelers may be “rational” decision makers • Airline choices may be made without regard to the past service-failure experiences • May maximize utility on each trip occasion using the traditional framework

11. Discussion questions • What are implications of this study to airlines? (Overbooking policies?) • Are the study results counter-intuitive to you? Why? • Are the study results generalizable? • Do airlines lose “goodwill” by service failures? • What other service-failure experiences can you think of? Do you think they will affect future choices of travelers?