2018
DOI: 10.1016/j.jairtraman.2018.02.008
|View full text |Cite
|
Sign up to set email alerts
|

Accounting for the impact of variety-seeking: Theory and application to HSR-air intermodality in China

Abstract: While variety-seeking has been analysed intensively in consumer marketing, little is known about its impact in the transport world where many novel travel services have emerged in recent years. In this paper, we investigate how variety-seeking could influence intercity travellers' mode choice decisions in the new context of HSR (high-speed rail)-air intermodality in China. The study is based on data collected in Shanghai, including responses to stated choice tasks and attitudinal statements on variety-seeking.… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
12
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
6
3

Relationship

3
6

Authors

Journals

citations
Cited by 22 publications
(16 citation statements)
references
References 44 publications
1
12
0
Order By: Relevance
“…Due to this variation in number of responses collected by each survey distributor, unbalanced panel effects may be present across the survey responses (Choo and Oum, 2013;Chow, 2014;Yokomi et al, 2017;Sarwar et al, 2017a;Sarwar et al, 2017b;Asahi and Murakami, 2017;Fountas et al, 2018b;Fountas et al, 2018c;Fountas et al, 2018d;Fountas et al, 2019). Besides, the effect of the explanatory variables may vary across observations due to the presence of unobserved heterogeneity, i.e., the effect of unobserved characteristics on the respondents' opinions (Hainen et al, 2013;Chu, 2014;Anastasopoulos et al, 2017;Fountas and Anastasopoulos, 2017;Mathew et al, 2017;Brueckner and Abreu, 2017;Song et al, 2018). To account for these two misspecification issues, grouped random parameters are introduced in the bivariate probit modeling framework, which allows the parameter estimates to vary across the groups of observations.…”
Section: Methodsmentioning
confidence: 99%
“…Due to this variation in number of responses collected by each survey distributor, unbalanced panel effects may be present across the survey responses (Choo and Oum, 2013;Chow, 2014;Yokomi et al, 2017;Sarwar et al, 2017a;Sarwar et al, 2017b;Asahi and Murakami, 2017;Fountas et al, 2018b;Fountas et al, 2018c;Fountas et al, 2018d;Fountas et al, 2019). Besides, the effect of the explanatory variables may vary across observations due to the presence of unobserved heterogeneity, i.e., the effect of unobserved characteristics on the respondents' opinions (Hainen et al, 2013;Chu, 2014;Anastasopoulos et al, 2017;Fountas and Anastasopoulos, 2017;Mathew et al, 2017;Brueckner and Abreu, 2017;Song et al, 2018). To account for these two misspecification issues, grouped random parameters are introduced in the bivariate probit modeling framework, which allows the parameter estimates to vary across the groups of observations.…”
Section: Methodsmentioning
confidence: 99%
“…Based on the theoretical taxonomy from McAlister and Pessemier (1982), a number of studies empirically validated various driving factors of variety seeking behaviors (also see Appendix 1). As for direct variety seeking, individual traits such as the need for variety (Sharma et al, 2010;Song et al, 2018;Van Trijp et al, 1996), curiosity (Martenson, 2018) and Big Five personality traits (Olsen et al, 2016) -chou, 2012;Zhao et al, 2019), store environment (Mohan et al, 2012), opinions of others (Chuang et al, 2013) and weather conditions (Tian et al, 2018). Though the variety seeking perspective is wildly applied to investigate brand/product switching in consumer behavior research, the findings might not be applicable to explain consumers' variety seeking in IT goods/services.…”
Section: Direct Variety Seeking Versus Derived Variety Seekingmentioning
confidence: 99%
“…SEM is widely used to explore the linear relationships between endogenous and exogenous variables in relevant studies (Gao et al 2020;Jia et al 2018;Mijares et al 2016). Alongside numerous applications of SEM, HCM has been employed to account for a variety of attitudes in research on key decisions in the public transport context, such as mode choice (Atasoy et al 2013;Hess et al 2018;Kamargianni et al 2014;Roberts et al 2018;Song et al 2018;Tran et al 2020) and departure time choice (Thorhauge et al 2016). Additionally, substantial effort has been devoted to further refinement of the HCM framework in studies exploring the proper way to accommodate latent variables in choice models (Bahamonde-Birke et al 2017), testing non-linearity and distributional assumptions (Kim et al 2016), and seeking to improve estimation techniques (Bhat and Dubey 2014;Daziano 2015;Raveau et al 2012).…”
Section: Literature Reviewmentioning
confidence: 99%