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

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

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

Study Hypotheses  Service-failure experiences adversely affect one’s future airline choices Loss aversion theory Loss aversion theory “Loss Aversion Hypothesis” “Loss Aversion Hypothesis”  Service-failure experiences have no impact on one’s future airline choices Random utility theory Random utility theory “No-Service Carryover Hypothesis” “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)

Discrete Choice Model  Multinomial Logit Model Estimates the impact of utility variables on choice probabilities Estimates the impact of utility variables on choice probabilities  Two models estimated Loss Aversion Model Loss Aversion Model No-Service Carryover 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

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) Mail survey (835 sent, 198 returned) Intercept survey at DSM (331 collected) Intercept survey at DSM (331 collected)  Total sample = 635, usable sample = 529  Summary statistics in Table 1

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

Measuring service failure experiences  3 Variables BUMP – involuntary denied boarding BUMP – involuntary denied boarding DELAY – “substantial” arrival delay DELAY – “substantial” arrival delay BAG – lost, damaged, delayed, or pilfered 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

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

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

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?