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Intershopping duration: an analysis using multiweek data

Author

Listed:
  • Bhat, Chandra R.
  • Frusti, Teresa
  • Zhao, Huimin
  • Schönfelder, Stefan
  • Axhausen, Kay W.
Abstract
This study examines the rhythms in the shopping activity participation of individuals over a multiweek period by modeling the duration between successive shopping participations. A hazard-based duration model is used to model intershopping duration, and a latent segmentation method is applied to distinguish between erratic shoppers and regular shoppers. The paper applies the methodology to examine the regularity and frequency of shopping behavior of individuals using a continuous six-week travel survey collected in the cities of Halle and Karlsruhe in Germany in the fall of 1999. The empirical results underscore the need to adopt a flexible hazard model form for analyzing intershopping durations. The results also provide important insights into the determinants of the regularity and frequency of individuals' shopping activity participation behavior.

Suggested Citation

  • Bhat, Chandra R. & Frusti, Teresa & Zhao, Huimin & Schönfelder, Stefan & Axhausen, Kay W., 2004. "Intershopping duration: an analysis using multiweek data," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 39-60, January.
    • Handle: RePEc:eee:transb:v:38:y:2004:i:1:p:39-60
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    References listed on IDEAS

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    Cited by:

    1. Sharman, Bryce W. & Roorda, Matthew J., 2013. "Multilevel modelling of commercial vehicle inter-arrival duration using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 94-107.
    2. Mohammadian, Abolfazl & Doherty, Sean T., 2006. "Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 475-490, July.
    3. Andre De Palma & Fay Dunkerley & Stef Proost, 2010. "Trip Chaining: Who Wins Who Loses?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 19(1), pages 223-258, March.
    4. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    5. Steven Farber & Antonio Páez & Ruben Mercado & Matthew Roorda & Catherine Morency, 2011. "A time-use investigation of shopping participation in three Canadian cities: is there evidence of social exclusion?," Transportation, Springer, vol. 38(1), pages 17-44, January.
    6. Lee, Backjin & Timmermans, Harry J.P., 2007. "A latent class accelerated hazard model of activity episode durations," Transportation Research Part B: Methodological, Elsevier, vol. 41(4), pages 426-447, May.
    7. Lee, J.F. Jennifer & Kwok, Peter K. & Williams, Jeffrey, 2014. "Heterogeneity among motorists in traffic-congested areas in southern California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 281-293.
    8. Erika Spissu & Abdul Pinjari & Chandra Bhat & Ram Pendyala & Kay Axhausen, 2009. "An analysis of weekly out-of-home discretionary activity participation and time-use behavior," Transportation, Springer, vol. 36(5), pages 483-510, September.
    9. Tai-Yu Ma & Iragaël Joly & Charles Raux, 2010. "A shared frailty semi-parametric markov renewal model for travel and activity time-use pattern analysis," Working Papers hal-00477695, HAL.
    10. Jara-Díaz, Sergio & Rosales-Salas, Jorge, 2015. "Understanding time use: Daily or weekly data?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 38-57.
    11. La Paix Puello, Lissy & Chowdhury, Saidul & Geurs, Karst, 2019. "Using panel data for modelling duration dynamics of outdoor leisure activities," Journal of choice modelling, Elsevier, vol. 31(C), pages 141-155.
    12. Tri Basuki Joewono & Ari K. M. Tarigan & Muhamad Rizki, 2019. "Segmentation, Classification, and Determinants of In-Store Shopping Activity and Travel Behaviour in the Digitalisation Era: The Context of a Developing Country," Sustainability, MDPI, vol. 11(6), pages 1-23, March.
    13. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan & Axhausen, Kay W., 2005. "An analysis of multiple interepisode durations using a unifying multivariate hazard model," Transportation Research Part B: Methodological, Elsevier, vol. 39(9), pages 797-823, November.
    14. Kang, Hejun & Scott, Darren M., 2010. "Exploring day-to-day variability in time use for household members," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 609-619, October.
    15. Geoffrey Fairchild & Kyle S. Hickmann & Susan M. Mniszewski & Sara Y. Del Valle & James M. Hyman, 2014. "Optimizing human activity patterns using global sensitivity analysis," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 394-416, December.
    16. Jiahang He & Toshiyuki Yamamoto & Tomio Miwa & Takayuki Morikawa, 2020. "Hazard Duration Model with Panel Data for Daily Car Travel Distance: A Toyota City Case Study," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    17. Kitamura, Ryuichi & Yamamoto, Toshiyuki & Susilo, Yusak O. & Axhausen, Kay W., 2006. "How routine is a routine? An analysis of the day-to-day variability in prism vertex location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 259-279, March.
    18. Ke Wang & Xin Ye & Jie Ma, 2018. "An empirical analysis of post-work grocery shopping activity duration using modified accelerated failure time model to differentiate time-dependent and time-independent covariates," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-17, November.
    19. Esra Suel & Nicolò Daina & John W. Polak, 2018. "A hazard-based approach to modelling the effects of online shopping on intershopping duration," Transportation, Springer, vol. 45(2), pages 415-428, March.

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