TY - JOUR
T1 - Social-psychological determinants and nonlinear thresholds
T2 - behavioral insights into urban air mobility adoption as an airport shuttle
AU - Zhang, Kaihan
AU - Liu, Xiang
AU - Cui, Qinyu
AU - Gao, Xing
AU - Cao, Mengqiu
AU - Kim, Inhi
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2026/3
Y1 - 2026/3
N2 - Urban Air Mobility (UAM) is an emerging mobility service increasingly proposed by cities worldwide. Among its various applications, UAM as an airport shuttle offers particularly strong early-stage commercial potential. However, understanding of the key factors influencing the adoption of UAM as an airport shuttle service remains limited, particularly regarding the role of social-psychological factors and their tolerance thresholds from a nonlinear perspective, where critical points in factors such as time or cost may shift the decision from declination to acceptance. Using a stated-preference survey of 1250 respondents from South Korea, this study identifies the primary determinants of UAM adoption and examines their decision thresholds using a newly proposed hybrid approach that combines automated machine learning (AutoML) and statistical models in a complementary manner. The results show thatt: (1) Previously overlooked social psychological factors, such as individuals seeking time savings, environmental benefits, and openness to new technologies, play adominant role, accounting for 55.4 % of explanatory power in predicting adoption decisions. (2) Threshold effects emerge in airport trip chains, with first-mile and in-vehicle durations under 15 min or over one hour marking critical adoption points; and (3) UAM holds strong substitute potential for car use for long-distance airport access. These findings provide actionable insights for policymakers and service providers aiming to promote UAM adoption, emphasizing the need to align service design and marketing strategies with users’ psychological motivations and to improve access environments for UAM connectivity within urban areas.
AB - Urban Air Mobility (UAM) is an emerging mobility service increasingly proposed by cities worldwide. Among its various applications, UAM as an airport shuttle offers particularly strong early-stage commercial potential. However, understanding of the key factors influencing the adoption of UAM as an airport shuttle service remains limited, particularly regarding the role of social-psychological factors and their tolerance thresholds from a nonlinear perspective, where critical points in factors such as time or cost may shift the decision from declination to acceptance. Using a stated-preference survey of 1250 respondents from South Korea, this study identifies the primary determinants of UAM adoption and examines their decision thresholds using a newly proposed hybrid approach that combines automated machine learning (AutoML) and statistical models in a complementary manner. The results show thatt: (1) Previously overlooked social psychological factors, such as individuals seeking time savings, environmental benefits, and openness to new technologies, play adominant role, accounting for 55.4 % of explanatory power in predicting adoption decisions. (2) Threshold effects emerge in airport trip chains, with first-mile and in-vehicle durations under 15 min or over one hour marking critical adoption points; and (3) UAM holds strong substitute potential for car use for long-distance airport access. These findings provide actionable insights for policymakers and service providers aiming to promote UAM adoption, emphasizing the need to align service design and marketing strategies with users’ psychological motivations and to improve access environments for UAM connectivity within urban areas.
KW - Airport shuttle
KW - Machine learning
KW - Nonlinear relationship
KW - Social psychology
KW - Stated preference survey
KW - Urban air mobility
UR - https://www.scopus.com/pages/publications/105028305598
U2 - 10.1016/j.tra.2025.104856
DO - 10.1016/j.tra.2025.104856
M3 - Article
AN - SCOPUS:105028305598
SN - 0965-8564
VL - 205
JO - Transportation Research Part A: Policy and Practice
JF - Transportation Research Part A: Policy and Practice
M1 - 104856
ER -