CLC number: U491
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-02-07
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Yi-lin Sun, Ari Tarigan, Owen Waygood, Dian-hai Wang. Diversity in diversification: an analysis of shopping trips in six-week travel diary data[J]. Journal of Zhejiang University Science A, 2017, 18(3): 234-244.
@article{title="Diversity in diversification: an analysis of shopping trips in six-week travel diary data",
author="Yi-lin Sun, Ari Tarigan, Owen Waygood, Dian-hai Wang",
journal="Journal of Zhejiang University Science A",
volume="18",
number="3",
pages="234-244",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1500198"
}
%0 Journal Article
%T Diversity in diversification: an analysis of shopping trips in six-week travel diary data
%A Yi-lin Sun
%A Ari Tarigan
%A Owen Waygood
%A Dian-hai Wang
%J Journal of Zhejiang University SCIENCE A
%V 18
%N 3
%P 234-244
%@ 1673-565X
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1500198
TY - JOUR
T1 - Diversity in diversification: an analysis of shopping trips in six-week travel diary data
A1 - Yi-lin Sun
A1 - Ari Tarigan
A1 - Owen Waygood
A1 - Dian-hai Wang
J0 - Journal of Zhejiang University Science A
VL - 18
IS - 3
SP - 234
EP - 244
%@ 1673-565X
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1500198
Abstract: Diversification in shopping, a long-pursued subject in consumer behavior analysis, is approached from a broad perspective of the diversity in daily travel patterns, which may or may not involve shopping trips, as well as the diversity in shopping locations and frequency. The focus of this analysis is on the heterogeneity across individuals in the ways in which they each diversify their respective shopping behavior. This study explores differences across individuals in the variations of their shopping travel patterns across days. Treating the day-of-the-week evolution of shopping travel patterns as a stochastic process, characteristics of diversification are quantified for respective individuals. Finally, heterogeneity across individuals is identified using an array of statistical methods. The analysis, based on results of a six-week travel diary survey in Germany with geo-coded activity locations, reveals the effects of individual, household, and urban attributes on diversification in shopping behavior, including that full-time workers with medium incomes (4000–4999 Deutsche Mark per month) tend to have more variations in their shopping engagement.
The paper investigates the end consumers' attitudes and preferences in shopping activities. In particular, the effects due to living in different urban areas are also pointed out. The paper is well written and the analysis are well described. The results are very interesting and useful for understanding and improving urban planning.
[1]Arentze, T.A., Timmermans, H.J.P., 2001. Deriving performance indicators from models of multipurpose shopping behavior. Journal of Retailing and Consumer Services, 8(6):325-334.
[2]Arentze, T.A., Oppewal, H., Timmermans, H.J.P., 2005. A multipurpose shopping trip model to assess retail agglomeration effects. Journal of Marketing Research, 42(1):109-115.
[3]Axhausen, K.W., Zimmermann, A., Schonfelder, S., et al., 2002. Observing the rhythms of daily life: a six-week travel diary. Transporation, 29(2):94-124.
[4]Bacon, R.W., 1995. Combined trips and the frequency of shopping. Journal of Retailing and Consumer Services, 2(3):175-183.
[5]Barone, V., Crocco, F., Mongelli, D.W.E., 2014. Models of choice between shopping and e-shopping. Applied Mechanics and Materials, 442:607-616.
[6]Bhat, C.R., 2001. Modeling the commute activity-travel pattern of workers: formulation and empirical analysis. Transportation Science, 35(1):61-79.
[7]Bhat, C.R., Steed, J.L., 2002. A continuous-time model of departure time choice for urban shopping trips. Transportation Research Part B: Methodological, 36(3):207-224.
[8]Blaylock, J.R., 1989. An economic model of grocery shopping frequency. Applied Economics, 21(6):843-852.
[9]Boerkamps, J.H., van Binsbergen, A.J., Bovy, P.H.L., 2000. Modeling behavioral aspects of urban freight movement in supply chains. Transportation Research Record, 1725: 17-25.
[10]Cao, X., Chen, Q., Choo, S., 2013. Geographical distribution of e-shopping: an application of structural equations models in the twin cities. Proceedings of the 92nd Transportation Research Board Annual Meeting.
[11]Comi, A., Nuzzolo, A., 2014. Simulating urban freight flows with combined shopping and restocking demand models. Procedia-Social and Behavioral Sciences, 125:49-61.
[12]Comi, A., Rosati, L., 2015. CLASS: a DSS for the analysis and the simulation of urban freight systems. Transportation Research Procedia, 5:132-144.
[13]Crocco, F., Eboli, L., Mazzulla, G., 2013. Individual attitudes and shopping mode characteristics affecting the use of e-shopping and related travel. Transport and Telecommunication, 14(1):45-56.
[14]Dellaert, B.G.C., Arentze, T.A., Bierlaire, M., et al., 1998. Investigating consumers’ tendency to combine multiple shopping purposes and destinations. Journal of Marketing Research, 35(2):177-188.
[15]Doti, J.L., Sharir, S., 1981. Households’ grocery shopping behavior in the short-run: theory and evidence. Economic Inquiry, 19(2):196-208.
[16]Farag, S., Schwanen, T., Dijst, M., et al., 2007. Shopping online and/or in-store A structural equation model of the relationships between e-shopping and in-store shopping. Transportation Research Part A: Policy and Practice, 41(2):125-141.
[17]Ganesh, J., Reynolds, K.E., Luckett, M., et al., 2010. Online shopper motivations, and e-store attributes: an examination of online patronage behavior and shopper typologies. Journal of Retailing, 86(1):106-115.
[18]Ghosh, A., McLafferty, S., 1984. A model of consumer propensity for multipurpose shopping. Geographical Analysis, 16(3):244-249.
[19]Gonzalez-Feliu, J., Toilier, F., Routhier, J.L., 2010. End consumer goods movement generation in French medium urban areas. Social and Behavioral Sciences, 2(3):6189-6204.
[20]Handy, S.L., 1996. Understanding the link between urban form and non-work travel behavior. Journal of Planning Education and Research, 15(3):183-198.
[21]Hirsh, M., Prashker, J.N., Ben-Akiva, M., 1986. Day-of-the-week models of shopping activity patterns. Transportation Research Record, 1085:63-69.
[22]Hsiao, M.H., 2009. Shopping mode choice: physical store shopping versus e-shopping. Transportation Research Part E: Logistics and Transportation Review, 45(1):86-95.
[23]Kim, B.D., Park, K., 1997. Studying patterns of consumer’s grocery shopping trip. Journal of Retailing, 73(4):501-517.
[24]Kitamura, R., 1988. An analysis of weekly activity patterns and travel expenditure. In: Golledge, R.G., Timmermans, H. (Eds.), Behavioral Modeling Approaches in Geography and Planning. Croom Helm, London, UK, p.399-423.
[25]Levinson, D.M., 1999. Space, money, life-stage and the allocation of time. Transportation, 26(2):141-171.
[26]Messinger, P.R., Narasimhan, C., 1997. A model of retail formats based on consumer’s economizing on shopping time. Marketing Science, 16(1):1-23.
[27]Miodonski, D., Kawamura, K., 2012. Effects of built environment on freight consumption. Procedia-Social and Behavioral Sciences, 39:74-88.
[28]Mokhtarian, P.L., 2004. A conceptual analysis of the transportation impacts of B2C e-commerce. Transportation, 31(3):257-284.
[29]Narula, S.C., Harwitz, M., Lentnek, B., 1983. Where shall we shop today A theory of multiple-stop, multiple-purpose shopping trips. Papers of the Regional Science Association, 53(1):159-173.
[30]Nuzzolo, A., Comi, A., 2014. A system of models to forecast the effects of demographic changes on urban shop restocking. Research in Transportation Business & Management, 11:142-151.
[31]O’Kelly, M.E., 1981. A model of the demand for retail facilities, incorporating multistop, multipurpose trips. Geographical Analysis, 13(2):134-148.
[32]O’Kelly, M.E., 1983. Multipurpose shopping trips and the size of retail facilities. Annals of the Association of American Geographers, 73(2):231-239.
[33]Popkowski Leszczyc, P.T.L., Timmermans, H., 2001. Experimental choice analysis of shopping strategies. Journal of Retailing, 77(4):493-509.
[34]Popkowski Leszczyc, P.T.L., Sinha, A., Sahgal, A., 2004. The effect of multi-purpose shopping on pricing and location strategy for grocery stores. Journal of Retailing, 80(2):85-99.
[35]Robinson, R.V.F., Vickerman, R.W., 1976. The demand for shopping travel: a theoretical and empirical study. Applied Economics, 8(4):267-281.
[36]Russo, F., Comi, A., 2010. A modelling system to simulate goods movements at an urban scale. Transportation, 37(6):987-1009.
[37]Smith, M.F., Carsky, M.L., 1996. Grocery shopping behavior a comparison of involved and uninvolved consumers. Journal of Retailing and Consumer Services, 3(2):73-80.
[38]Susilo, Y.O., Hanks, N., Ullah, M., 2013. An exploration of shoppers travel mode choice in visiting convenience stores in the UK. Transportation Planning and Technology, 36(8):669-684.
[39]Thill, J.C., 1985. Demand in space and multipurpose shopping: a theoretical approach. Geographical Analysis, 17(2):114-129.
[40]Thill, J.C., Thomas, I., 1987. Toward conceptualizing trip-chaining behavior: a review. Geographical Analysis, 19(1):1-17.
[41]Vickerman, R.W., Barmby, T.A., 1984. The structure of shopping travel: some developments of the trip generation model. Journal of Transport Economics and Policy, 18(2):109-121.
[42]Yun, D.S., O’Kelly, M.E., 1997. Modeling the day-of-the-week shopping activity and travel patterns. Socio-Economic Planning Sciences, 31(4):307-319.
[43]Zhou, Y., Wang, X., 2014. Explore the relationship between online shopping and shopping trips: an analysis with the 2009 NHTS data. Transportation Research Part A: Policy and Practice, 70:1-9.
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