Share:


Exploring location determinants of Asia’s unique beverage shops based on a hybrid MADM model

    Sheng-Hau Lin   Affiliation
    ; Chih-Chen Hsu Affiliation
    ; Taiyang Zhong Affiliation
    ; Xiwei He Affiliation
    ; Jia-Hsuan Li Affiliation
    ; Gwo-Hshiung Tzeng Affiliation
    ; Jing-Chzi Hsieh Affiliation

Abstract

Identifying relevant location determinants is a good starting point for shop operators, help to increase profitability and, thus, avoiding business failure. Traditional Analytic Hierarchy Process (AHP) or the Analytic Network Process (ANP) have shortages that require improvement. Herein, Decision-Making Trial and Evaluation Laboratory (DEMATEL), ANP based on DEMATEL (DANP), and modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (modified VIKOR) are used to construct a hybrid multiple-attribute decision making (MADM) model, encompassing three dimensions and thirteen criteria in exploring the location determinants of Asia’s unique Bubble Tea Shops (BTSs) and to evaluate three preselected alternatives in Nanjing, China. The empirical findings of the DEMATEL method reveal that traffic traits (D1) and site traits (D2) are critical to BTSs, and that once these are enhanced, shop traits (D3) are also improved. Criteria deemed as important, based on the DEMATEL and DANP methodology, are (in descending order): proximity to a street corner (C2), proximity to public transportation systems (C1), road width (C3), proximity to communities (C5), proximity to commercial areas (C6), types of shop (C9), and proximity to schools (C7). Different decision-making rankings among alternatives are indicated based upon the modified VIKOR method and corresponding strategies for improvement are presented.

Keyword : Bubble Tea Shop (BTS), location determinants, Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process based on DEMATEL (DANP), Modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (modified VIKOR)

How to Cite
Lin, S.-H., Hsu, C.-C., Zhong, T., He, X., Li, J.-H., Tzeng, G.-H., & Hsieh, J.-C. (2021). Exploring location determinants of Asia’s unique beverage shops based on a hybrid MADM model. International Journal of Strategic Property Management, 25(4), 291-315. https://doi.org/10.3846/ijspm.2021.14796
Published in Issue
May 27, 2021
Abstract Views
1171
PDF Downloads
846
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adjoian, T., Dannefer, R., Sacks, R., & Van Wye, G. (2014). Comparing sugary drinks in the food retail environment in six NYC neighborhoods. Journal of Community Health, 39, 327–335. https://doi.org/10.1007/s10900-013-9765-y

Alonso, W. (1964). Location and land use: toward a general theory of land rent. Harvard University Press.
https://doi.org/10.4159/harvard.9780674730854

Austin, S. B., Melly, S. J., Sanchez, B. N., Patel, A., Buka, S., & Gortmaker, S. L. (2005). Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments. American Journal of Public Health, 95(9), 1575–1581.
https://doi.org/10.2105/AJPH.2004.056341

Brown, S. (1993). Retail location theory: evolution and evaluation. International Review of Retail Distribute Consumer Research, 3(2), 185–229. https://doi.org/10.1080/09593969300000014

Chen, J., Wang, J., Baležentis, T., Zagurskaitė, F., Streimikiene, D., & Makutėnienė, D. (2018). Multicriteria approach towards the sustainable selection of a teahouse location with sensitivity analysis. Sustainability, 10, 2926.
https://doi.org/10.3390/su10082926

Chen, L. F., & Tsai, C. T. (2016). Data mining framework based on rough set theory to improve location selection decisions: a case study of a restaurant chain. Tourism Management, 53, 197–206. https://doi.org/10.1016/j.tourman.2015.10.001

Chen, T. C., Lin, C. L., & Tzeng, G. H. (2019). Assessment and improvement of wetlands environmental protection plans for achieving sustainable development. Environmental Research, 169, 280–296. https://doi.org/10.1016/j.envres.2018.10.015

Chiang, P. H., Wahlqvist, M. L., Lee, M. S., Huang, L. Y., Chen, H. H., & Huang, T. Y. (2011). Fast-food outlets and walkability in school neighbourhoods predict fatness in boys and height in girls: a Taiwanese population study. Public Health Nutrition, 14(9), 1601–1609.
https://doi.org/10.1017/S1368980011001042

Chou, T. Y., Hsu, C. L., & Chen, M. C. (2008). A fuzzy multi-criteria decision model for international tourist hotels location selection. International Journal of Hospitality Management, 27(2), 293–301. https://doi.org/10.1016/j.ijhm.2007.07.029

Christaller, W. (1933). Die zentralen Orte in Süddeutschland. Gustav Fischer.

Christaller, W. (1966). Central places in Southern Germany. Prentice-Hall Press.

Chathoth, P. K., & Olsen, M. D. (2007). The effect of environment risk, corporate strategy, and capital structure on firm performance: an empirical investigation of restaurant firms. International Journal of Hospitality Management, 26(3), 502–516. https://doi.org/10.1016/j.ijhm.2006.03.007

Cutumisu, N., Traoré, I., Paquette, M. C., Cazale, L., Camirand, H., Lalonde, B., & Robitaille, E. (2017). Association between junk food consumption and fast-food outlet access near school among Quebec secondary-school children: findings from the Quebec Health Survey of High School Students (QHSHSS) 2010–11. Public Health Nutrition, 20(5), 927–937. https://doi.org/10.1017/S136898001600286X

Day, P. L., Pearce, J. R., & Pearson, A. L. (2015). A temporal analysis of the spatial clustering of food outlets around schools in Christchurch, New Zealand, 1966 to 2006. Public Health Nutrition, 181, 135–142.
https://doi.org/10.1017/S1368980013002863

Day, P. L., & Pearce, J. (2011). Obesity-promoting food environments and the spatial clustering of food outlets around schools. American Journal of Preventive Medicine, 40, 113–121. https://doi.org/10.1016/j.amepre.2010.10.018

Dehe, B., & Bamfor, D. (2015). Development, test and comparison of two multiple criteria decision analysis (MCDA) models: a case of healthcare infrastructure location. Expert Systems with Applications, 42(19), 6717–6727.
https://doi.org/10.1016/j.eswa.2015.04.059

Dock, J. P., Song, W., & Lu, J. (2015). Evaluation of dine-in restaurant location and competitiveness: applications of gravity modeling in Jefferson County, Kentucky. Applied Geography, 60, 204–209. https://doi.org/10.1016/j.apgeog.2014.11.008

dos Santos, P. H., Neves, S. M., Sant’Anna, D. O., de Oliveira, C. H., & Carvalho, H. D. (2019). The analytic hierarchy process supporting decision making for sustainable development: an overview of applications. Journal of Cleaner Production, 212, 119–138.
https://doi.org/10.1016/j.jclepro.2018.11.270

Duran, A. C., de Almeida, S. L., do Rosario DO Latorre, M., & Jaime, P. C. (2016). The role of the local retail food environment in fruit, vegetable and sugar-sweetened beverage consumption in Brazil. Public Health Nutrition, 19(6), 1093–1102. https://doi.org/10.1017/S1368980015001524

Eaton, B. C., & Lipsey, R. G. (1975). The principle of minimum differentiation reconsidered: some new developments in the theory of spatial competition. Review of Economic Studies, 42, 27–49. https://doi.org/10.2307/2296817

Engler-Stringer, R., Shah, T., Bell, S., & Muhajarine, N. (2014). Geographic access to healthy and unhealthy food sources for children in neighbourhoods and from elementary schools in a mid-sized Canadian city. Spatial and Spatio-temporal Epidemiology, 11, 23–32. https://doi.org/10.1016/j.sste.2014.07.001

Fisher, D. P. (1997). Location, location, location: ensuring a franchisee’s success. Hospitality Review Journal, 15(1), 4.
https://digitalcommons.fiu.edu/hospitalityreview/vol15/iss1/4

Fleischhacker, S. E., Evenson, K. R., Rodriguez, D. A., & Ammerman, A. S. (2013). A systematic review of fast food access studies. Obesity Reviews, 12, e460–e471.
https://doi.org/10.1111/j.1467-789X.2010.00715.x

Fortune Business Insights. (2019). Bubble tea: global market analysis, insights and forecast, 2019–2026.
https://www.fortunebusinessinsights.com/v

Fraser, L. K., Edwards, K. L., Cade, J., & Clarke, G. P. (2010). The geography of fast food outlets: a review. International Journal of Environmental Research and Public Health, 7, 2290–2308. https://doi.org/10.3390/ijerph7052290

Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Centre Press.

Gabus, A., & Fontela, E. (1973). Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility. Battelle Geneva Research Centre Press.

Gölcük, I., & Baykasoĝlua, A. (2016). An analysis of DEMATEL approaches for criteria interaction handling within ANP. Expert Systems with Applications, 46, 346–366.
https://doi.org/10.1016/j.eswa.2015.10.041

He, M., Tucker, P., Gilliland, J., Irwin, J. D., Larsen, K., & Hess, P. (2012). The influence of local food environments on adolescents’ food purchasing behaviors. International Journal of Environmental Research and Public Health, 9, 1458–1471. https://doi.org/10.3390/ijerph9041458

Ho, H. P., Chang, C. T., & Ku, C. Y. (2013). On the location selection problem using analytic hierarchy process and multi-choice goal programming. International Journal of Systems Science, 44(1), 94–108.
https://doi.org/10.1080/00207721.2011.581397

Hotelling, H. (1929). Stability in competition. The Economic Journal, 39(153), 41–57. https://doi.org/10.2307/2224214

John, T. S., Jones, M. F., & Botieff, M. (2015). Where restaurants fail: a longitudinal study of micro locations. Journal of Foodservice Business Research, 18(4), 328–340.
https://doi.org/10.1080/15378020.2015.1068670

Kheybari, S., Rezaie, F. M., & Farazmand, H. (2020). Analytic network process: an overview of applications. Applied Mathematics and Computation, 367, 124780.
https://doi.org/10.1016/j.amc.2019.124780

Kuo, M. (2011). Optimal location selection for an international distribution center by using a new hybrid method. Expert Systems with Applications, 38(6), 7208–7221. https://doi.org/10.1016/j.eswa.2010.12.002

Kuo, R. J., Chi, S. C., & Kao, S. S. (1999). A decision support system for locating convenience store through fuzzy AHP.
Computers & Industrial Engineering, 37(12), 323–326.
https://doi.org/10.1016/S0360-8352(99)00084-4

Kuo, R. J., Chi, S. C., & Kao, S. S. (2002). A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network. Computers in Industry, 47(2), 199–214.
https://doi.org/10.1016/S0166-3615(01)00147-6

Lan, Y. H. (2017). The sweet taste of familiar: Taipei pearl milk tea consumer and identity. Department of Sociology, Soochow University (in Chinese).

Lin, C. Y., & Tzeng, H. Y. (2010). Research on local food globalization in Taiwan: the meaning of pearl milk tea among college students. Cross-cultural Studies, 1(4), 37–61 (in Chinese).

Lin, J. J., & Yang, S. H. (2019). Proximity to metro stations and commercial gentrification. Transport Policy, 77, 79–89. https://doi.org/10.1016/j.tranpol.2019.03.003

Lin, S. H., Wang, D., Huang, X., Zhao, X., Hsieh, J. C., Tzeng, G. H., Li, J. H., & Chen, J. T. (2021). A multi-attribute decision-making model for improving inefficient industrial parks. Environment, Development and Sustainability, 23, 887– 921. https://doi.org/10.1007/s10668-020-00613-4

Liu, K. M., Lin, S. H., Hsieh, J. C., & Tzeng, G. H. (2018a). Improving the food waste composting facilities site selection for sustainable development using a hybrid modified MADM model. Waste Management, 75, 44–59.
https://doi.org/10.1016/j.wasman.2018.02.017

Liu, Y., Wang, H., & Tzeng, G. H. (2018b). From measure to guidance: galactic model and sustainable development planning toward the best smart city. Journal of Urban Planning and Development, 144(4), 04018035. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000478

Maimaiti, M., Ma, X., Zhao, X., Jia, M., Li, J., Yang, M., Ru, Y., Yang, F., Wang, N., & Zhu, S. (2020). Multiplicity and complexity of food environment in China: full-scale field census of food outlets in a typical district. European Journal of Clinical Nutrition, 74, 390–408.
https://doi.org/10.1038/s41430-019-0462-5

Maimaiti, M., Zhao, X., Jia, M., Ru, Y., & Zhu, S. (2018). How we eat determines what we become: opportunities and challenges brought by food delivery industry in a changing world in China. Journal of Clinical Nutrition, 72, 1282–1286. https://doi.org/10.1038/s41430-018-0191-1

Ministry of Finance, R.O.C. (2018). https://findbiz.nat.gov.tw/fts/query/QueryBar/queryInit.do

Murphy, M., Badland, H., Jordan, H., Koohsari, M. J., & GilesCorti, B. (2018). Local food environments, suburban development, and BMI: a mixed methods study. International Journal of Environmental Research and Public Health, 15, 1392. https://doi.org/10.3390/ijerph15071392

Neumüller, C., Kellner, F., Gupta, J. N. D., & Lasch, R. (2015). Integrating three-dimensional sustainability in distribution centre selection: the process analysis method-based analytic network process. International Journal of Production Research, 53(2), 409–434. https://doi.org/10.1080/00207543.2014.939241

Oh, S. J., Lee, J. H., Kim, H. K., & Shin, J. (2015). Sales determinants of restaurant chain business: focused on family restaurants in Korea. Indian Journal of Science and Technology, 8(23), 1–7. https://doi.org/10.17485/ijst/2015/v8i23/79228

Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1

Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514–529.
https://doi.org/10.1016/j.ejor.2006.01.020

Ou Yang, Y. P., Shieh, Y. P., & Tzeng, G. H. (2013). A VIKOR technique based on DEMATEL and ANP for information security risk control assessment. Information Sciences, 232, 482–500. https://doi.org/10.1016/j.ins.2011.09.012

Özcan, T., Çelebi, N., & Esnaf, S. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773–9779.
https://doi.org/10.1016/j.eswa.2011.02.022

Park, K., & Khan, M. A. (2006). An exploratory study to identify the site selection factors for US franchise restaurants. Journal of Foodservice Business Research, 8(1), 97–114.
https://doi.org/10.1300/J369v08n01_07

Parsa, H. G., Self, J. T., Njite, D., & King, T. (2005). Why restaurants fail. Cornell Hotel and Restaurant Administration Quarterly, 46(3), 304–322. https://doi.org/10.1177/0010880405275598

Peng, K. H., & Tzeng, G. H. (2019). Exploring heritage tourism performance improvement for making sustainable development strategies using the hybrid-modified MADM model. Current Issues in Tourism, 22(8), 921–947.
https://doi.org/10.1080/13683500.2017.1306030

Prayag, G., Landré, M., & Ryan, C. (2012). Restaurant location in Hamilton, New Zealand: clustering patterns from 1996 to 2008. International Journal of Contemporary Hospitality Management, 24(3), 430–450. https://doi.org/10.1108/09596111211217897

Qin, X., Zhen, F., & Gong, Y. (2019). Combination of big and small data: empirical study on the distribution and factors of catering space popularity in Nanjing, China. Journal of Urban Planning and Development, 145(1), 05018022.
https://doi.org/10.1061/(ASCE)UP.1943-5444.0000489

Ran, L. N. (2018). Běi jīng năi chá xiāo fèi diào chá: 80hòu 90hòu gòng xiàn jìn 9chéng pái zhăng duì chéng zuì dà cáo diăn. http://www.sohu.com/a/249614350_393779 (in Chinese).

Reilly, W. J. (1929). Methods for the study of retail relationships. University of Texas, Bureau of Business Research Press.

Reilly, W. J. (1931). The law of retail gravitation. Knickerbrocker Press.

Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill Press. https://doi.org/10.21236/ADA214804

Saaty, T. L. (1996). The analytic network process. RWS Publications Press.

Sevtsuk, A. (2014). Location and agglomeration: the distribution of retail and food businesses in dense urban environments. Journal of Planning Education and Research, 34(4), 374–393. https://doi.org/10.1177/0739456X14550401

Shahi, E., Alavipoor, F. S., & Karimi, S. (2018). The development of nuclear power plants by means of modified model of fuzzy DEMATEL and GIS in Bushehr, Iran. Renewable and Sustainable Energy Reviews, 83, 33–49.
https://doi.org/10.1016/j.rser.2017.10.073

Shen, K. Y., & Tzeng, G. H. (2018). Advances in multiple criteria decision making for sustainability: modeling and applications. Sustainability, 10(5), 1600.
https://doi.org/10.3390/su10051600

Shi, G., Shan, J., Ding, L., Ye, P., Li, Y., & Jiang, N. (2019). Urban road network expansion and its driving variables: a case study of Nanjing City. International Journal of Environmental Research and Public Health, 16, 2318. https://doi.org/10.3390/ijerph16132318

Smith, D., Cummins, S., Clark, C., & Stansfeld, S. (2013). Does the local food environment around schools affect diet? Longitudinal associations in adolescents attending secondary schools in East London. BMC Public Health, 13, 70.
https://doi.org/10.1186/1471-2458-13-70

Smith, S. L. J. (1983). Restaurants and dining out: geography of a tourism business. Annals of Tourism Research, 10(4), 515–549.
https://doi.org/10.1016/0160-7383(83)90006-3

Smith, S. L. J. (1985). Location patterns of urban restaurants. Annals of Tourism Research, 12(4), 581–602.
https://doi.org/10.1016/0160-7383(85)90079-9

Subramanian, N., & Ramanathan, R. (2012). A review of applications of analytic hierarchy process in operations management. International Journal of Production Economics, 138(2), 215–241. https://doi.org/10.1016/j.ijpe.2012.03.036

Tolga, A. C., Tuysuz, F., & Kahraman, C. (2013). A fuzzy multicriteria decision analysis approach for retail location selection. International Journal of Information Technology and Decision Making, 12(4), 729–755.
https://doi.org/10.1142/S0219622013500272

Trivedi, A. (2018). A multi-criteria decision approach based on DEMATEL to assess determinants of shelter site selection in disaster response. International Journal of Disaster Risk Reduction, 31, 722–728. https://doi.org/10.1016/j.ijdrr.2018.07.019

Tsai, P. M. (2019). Yĭn liào diàn 10nián chéng zhăng 9000jiā yè zhě zuì pà liú bú zhù rén. https://www.cna.com.tw/news/firstnews/201907060122.aspx (in Chinese).

Tseng, H. C., Wang, C. J., Cheng, S. H., Sun, Z. J., Chen, P. S., Lee, C. T., Lin, S. H., Yang, Y. K., & Yang, Y. C. (2014). Teadrinking habit among new university students: associated factors. Kaohsiung Journal of Medical Sciences, 30, 98–103. https://doi.org/10.1016/j.kjms.2013.08.004

Tzeng, G. H., & Shen, K. Y. (2017). New concepts and trends of hybrid multiple criteria decision making. CRC Press.
https://doi.org/10.1201/9781315166650

Tzeng, G. H., Teng, M. H., Chen, J. J., & Opricovic, S. (2002). Multicriteria selection for a restaurant location in Taipei. International Journal of Hospitality Management, 21, 171–187. https://doi.org/10.1016/S0278-4319(02)00005-1

Wibisono, Y. Y., & Marella, S. (2020). A decision making model for selection of café location: an ANP approach. Journal of Physics: Conference Series, 1477, 052030.
https://doi.org/10.1088/1742-6596/1477/5/052030

Widener, M. J., Minaker, L., Farber, S., Allen, J., Vitali, B., Coleman, P. C., & Cook, B. (2017). How do changes in the daily food and transportation environments affect grocery store accessibility? Applied Geography, 83, 46–62. https://doi.org/10.1016/j.apgeog.2017.03.018

Wu, S. S., Kuang, H., & Lo, S. M. (2018). Modeling shopping center location choice: shopper preference–based competitive location model. Journal of Urban Planning and Development, 145(1), 1–20.
https://doi.org/10.1061/(ASCE)UP.1943-5444.0000482

Wu, Y., Wu, C., Zhou, J., Zhang, B., Xu, C., Yan, Y., & Liu, F. (2019). A DEMATEL-TODIM based decision framework for PV power generation project in expressway service area under an intuitionistic fuzzy environment. Journal of Cleaner Production, 247, 119099.
https://doi.org/10.1016/j.jclepro.2019.119099

Yang, Y., Roehl, W. S., & Huang, J. H. (2017). Understanding and projecting the restaurantscape: the influence of neighborhood sociodemographic characteristics on restaurant location. International Journal of Hospitality Management, 67, 33–45. https://doi.org/10.1016/j.ijhm.2017.07.005

Yap, J. Y. L., Ho, C. C., & Ting, C. Y. (2019). A systematic review of the applications of multi-criteria decision-making methods in site selection problems. Built Environment Project and Asset Management, 9(4), 548–563. https://doi.org/10.1108/BEPAM-05-2018-0078

Yıldız, N., & Tüysüz, F. (2019). A hybrid multi-criteria decision making approach for strategic retail location investment: application to Turkish food retailing. Socio-Economic Planning and Science, 68, 100619.
https://doi.org/10.1016/j.seps.2018.02.006

Zhai, S., Xu, S., Yang, L., Zhou, M., Zhang, L., & Qiu, B. (2015). Mapping the popularity of urban restaurants using social media data. Applied Geography, 63, 113–120.
https://doi.org/10.1016/j.apgeog.2015.06.006

Zhuang, Z. Y., Chiang, I. J., Su, C. R., & Chen, C. Y. (2017). Modelling the decision of paper shredder selection using analytic hierarchy process and graph theory and matrix approach.
Advances in Mechanical Engineering, 9(12), 1–11.
https://doi.org/10.1177/1687814017737668

Zhuang, Z. Y., Lin, C. C., Chen, C. Y., & Su, C. R. (2018a). Rank-based comparative research flow benchmarking the effectiveness of AHP–GTMA on aiding decisions of shredder selection by reference to AHP–TOPSIS. Applies Science, 8(10), 1974. https://doi.org/10.3390/app8101974

Zhuang, Z. Y., Su, C. R., & Chang, S. C. (2019). The effectiveness of IF-MADM (intuitionistic-fuzzy multi-attribute decision-making) for group decisions: methods and an empirical assessment for the selection of a senior centre. Technological and Economic Development of Economy, 25(2), 322–364.
https://doi.org/10.3846/tede.2019.8399

Zhuang, Z. Y., Yang, L. W., Lee, M. H., & Wang, C. Y. (2018b). ‘MEAN+R’: implementing a web-based, multi-participant decision support system using the prevalent MEAN architecture with R based on a revised intuitionistic-fuzzy multiple attribute decision-making model. Microsystem Technologies, 24(3), 4291–43093. https://doi.org/10.1007/s00542-018-3755-z