Two-adults households work schedule preference under social influence

Bilin Han, Harry Timmermans

Research output: Contribution to conferencePaperAcademic

Abstract

Work schedules with their start and end times substantially affect traffic flows, particularly during peak hours. Therefore, the study of work schedules is of great importance to understand daily activity travel patterns. Particularly adults in households with children need to decide on the amount of work to do vis-à-vis their time expenditure related to household tasks. This choice may depend on the attributes of work schedules, etc. Work schedule arrangement preferences are reflected in job application decisions in the sense that people will not apply if the job profile is incongruent with such preferences beyond some threshold. This paper reports some findings of a study on preference of work schedule arrangement in two-adults households with children. The aim of this study is to estimate the effects of work schedule on people’s decisions to apply for jobs. To that end, a stated choice experiment is constructed and implemented. A latent class mixed logit model with two classes is used to estimate the effects of work schedule attributes, social influence and socio-demographic characteristics on the probability of applying for a particular job. Results show that the effects of work attributes are more significant than social influence attributes in both classes, and the effects of working time and salary differ much across different people.
Original languageEnglish
Publication statusPublished - Jul 2015
Event14th International Conference on Travel Behaviour Research (IATBR 2015) - Beaumont Estate, Winsor, United Kingdom
Duration: 19 Jul 201523 Jul 2015

Conference

Conference14th International Conference on Travel Behaviour Research (IATBR 2015)
Abbreviated titleIATBR 2015
Country/TerritoryUnited Kingdom
CityWinsor
Period19/07/1523/07/15

Keywords

  • work schedule preference
  • job application
  • social influence
  • children-related activities
  • latent class mixed logit model

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