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Intershopping duration: an analysis using multiweek data

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  • 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.

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Bibliographic Info

Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

Volume (Year): 38 (2004)
Issue (Month): 1 (January)
Pages: 39-60

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Handle: RePEc:eee:transb:v:38:y:2004:i:1:p:39-60

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References

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  1. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
  2. Bruce D. Meyer, 1988. "Unemployment Insurance And Unemployment Spells," NBER Working Papers 2546, National Bureau of Economic Research, Inc.
  3. Kay Axhausen & Andrea Zimmermann & Stefan Schönfelder & Guido Rindsfüser & Thomas Haupt, 2002. "Observing the rhythms of daily life: A six-week travel diary," Transportation, Springer, vol. 29(2), pages 95-124, May.
  4. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-79, June.
  5. Bhat, Chandra R., 1996. "A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 30(3), pages 189-207, June.
  6. Ram Pendyala & Toshiyuki Yamamoto & Ryuichi Kitamura, 2002. "On the formulation of time-space prisms to model constraints on personal activity-travel engagement," Transportation, Springer, vol. 29(1), pages 73-94, February.
  7. Ram Pendyala & Konstadinos Goulias, 2002. "Time use and activity perspectives in travel behavior research," Transportation, Springer, vol. 29(1), pages 1-4, February.
  8. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
  9. Bhat, Chandra R. & Singh, Sujit K., 2000. "A comprehensive daily activity-travel generation model system for workers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(1), pages 1-22, January.
  10. Han, Aaron & Hausman, Jerry A, 1990. "Flexible Parametric Estimation of Duration and Competing Risk Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(1), pages 1-28, January-M.
  11. Bhat, Chandra, 1999. "An analysis of evening commute stop-making behavior using repeated choice observations from a multi-day survey," Transportation Research Part B: Methodological, Elsevier, vol. 33(7), pages 495-510, September.
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Citations

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Cited by:
  1. 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.
  2. André De Palma & Fay Dunkerley & Stef Proost, 2005. "Trip chaining - who wins, who loses?," ERSA conference papers ersa05p496, European Regional Science Association.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  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. 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.
  10. 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.

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