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The impact of the residential built environment on work at home adoption frequency: An example from Northern California

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Abstract

Working at home is widely viewed as a useful travel-reduction strategy, and it is partly for that reason that considerable research related to telecommuting and home-based work has been conducted in the last two decades. This study examines the effect of residential neighborhood built environment (BE) factors on working at home. After systematically presenting and categorizing various relevant elements of the BE and reviewing related studies, we develop a multinomial logit (MNL) model of work-at-home (WAH) frequency using data from a survey of eight neighborhoods in Northern California. Potential explanatory variables include sociodemographic traits, neighborhood preferences and perceptions, objective neighborhood characteristics, and travel attitudes and behavior. The results clearly demonstrate the contribution of built environment variables to WAH choices, in addition to previously-identified influences such as sociodemographic predictors and commute time. BE factors associated with (neo)traditional neighborhoods were associated both positively and negatively with working at home. The findings suggest that land use and transportation strategies that are desirable from some perspectives will tend to weaken the motivation to work at home, and conversely, some factors that seem to increase the motivation to work at home are widely viewed as less sustainable. Accordingly, this research points to the complexity of trying to find the right balance among demand management strategies that sometimes act in competition rather than in synergy.

Suggested Citation

  • Tang, Wei (Laura) & Mokhtarian, Patricia L. & Handy, Susan L., 2011. "The impact of the residential built environment on work at home adoption frequency: An example from Northern California," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 4(3), pages 3-22.
    • Handle: RePEc:ris:jtralu:0066
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    Cited by:

    1. Yuan Gao & Kun Liu & Peiling Zhou & Hongkun Xie, 2021. "The Effects of Residential Built Environment on Supporting Physical Activity Diversity in High-Density Cities: A Case Study in Shenzhen, China," IJERPH, MDPI, vol. 18(13), pages 1-16, June.
    2. Wu, Guoqiang & Hong, Jinhyun, 2022. "An analysis of the role of residential location on the relationships between time spent online and non-mandatory activity-travel time use over time," Journal of Transport Geography, Elsevier, vol. 102(C).
    3. Blumenberg, Evelyn & Paul, Julene & Pierce, Gregory, 2021. "Travel in the digital age: Vehicle ownership and technology-facilitated accessibility," Transport Policy, Elsevier, vol. 103(C), pages 86-94.
    4. Khan, Nazmul Arefin & Habib, Muhammad Ahsanul & Jamal, Shaila, 2020. "Effects of smartphone application usage on mobility choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 932-947.
    5. Jinjia Liang & Tomio Miwa & Takayuki Morikawa, 2023. "Preferences and Expectations of Japanese Employees toward Telecommuting Frequency in the Post-Pandemic Era," Sustainability, MDPI, vol. 15(16), pages 1-16, August.
    6. Georges A. Tanguay & Ugo Lachapelle, 2019. "Potential Impacts of Telecommuting on Transportation Behaviours, Health and Hours Worked in Québec," CIRANO Project Reports 2019rp-07, CIRANO.
    7. Nicholas S. Caros & Jinhua Zhao, 2022. "Preparing urban mobility for the future of work," Papers 2201.01321, arXiv.org.

    More about this item

    Keywords

    Work at home; telecommuting; teleworking; multinomial logit; residential location; built environment;
    All these keywords.

    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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