IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v144y2022icp416-427.html
   My bibliography  Save this article

Search well and be wise: A machine learning approach to search for a profitable location

Author

Listed:
  • Han, Shuihua
  • Jia, Xinyun
  • Chen, Xinming
  • Gupta, Shivam
  • Kumar, Ajay
  • Lin, Zhibin

Abstract

A good location is critical for the sales performance of a hotel or a restaurant. This study proposes a machine learning-based model for location selection that focuses on the estimation of sales potential of a prospective site and helps to overcome the lack of historical data for the prospective site and the subjective criteria used in conventional models. The proposed model involves three major steps. First, we use an attribute selection algorithm to identify the key factors that contribute to the profitability of a specific location. Second, we evaluate the similarity between the candidate site and the existing stores by using an improved grey comprehensive evaluation method. Finally, we use a kernel regression model to predict the sales potential of the candidate site. A case study of a well-known international restaurant chain is used to illustrate the application of the proposed data-driven model. The results indicate that our proposed model helps to accurately select the most profitable locations.

Suggested Citation

  • Han, Shuihua & Jia, Xinyun & Chen, Xinming & Gupta, Shivam & Kumar, Ajay & Lin, Zhibin, 2022. "Search well and be wise: A machine learning approach to search for a profitable location," Journal of Business Research, Elsevier, vol. 144(C), pages 416-427.
    • Handle: RePEc:eee:jbrese:v:144:y:2022:i:c:p:416-427
    DOI: 10.1016/j.jbusres.2022.01.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296322000601
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2022.01.049?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Jenhung & Tsai, Ching-Hui & Lin, Pei-Chun, 2016. "Applying spatial-temporal analysis and retail location theory to public bikes site selection in Taipei," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 45-61.
    2. Abdullah Dasci & Gilbert Laporte, 2005. "A Continuous Model for Multistore Competitive Location," Operations Research, INFORMS, vol. 53(2), pages 263-280, April.
    3. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    4. Ajay Kumar & Ram D. Gopal & Ravi Shankar & Kim Hua Tan, 2022. "Fraudulent review detection model focusing on emotional expressions and explicit aspects : investigating the potential of feature engineering," Post-Print hal-03630420, HAL.
    5. Karande, Kiran & Lombard, John R, 2005. "Location strategies of broad-line retailers: an empirical investigation," Journal of Business Research, Elsevier, vol. 58(5), pages 687-695, May.
    6. Merino, María & Ramirez-Nafarrate, Adrian, 2016. "Estimation of retail sales under competitive location in Mexico," Journal of Business Research, Elsevier, vol. 69(2), pages 445-451.
    7. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    8. Sengupta, Pooja & Biswas, Baidyanath & Kumar, Ajay & Shankar, Ravi & Gupta, Shivam, 2021. "Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities," Journal of Business Research, Elsevier, vol. 137(C), pages 538-554.
    9. Chen, Li-Fei & Tsai, Chih-Tsung, 2016. "Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 53(C), pages 197-206.
    10. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
    11. Assaf, A. George & Josiassen, Alexander & Agbola, Frank W., 2015. "Attracting international hotels: Locational factors that matter most," Tourism Management, Elsevier, vol. 47(C), pages 329-340.
    12. Tomoki Nakaya & A. Fotheringham & Kazumasa Hanaoka & Graham Clarke & Dimitris Ballas & Keiji Yano, 2007. "Combining microsimulation and spatial interaction models for retail location analysis," Journal of Geographical Systems, Springer, vol. 9(4), pages 345-369, December.
    13. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Tan, Kim Hua & Gupta, Shivam, 2021. "Validating the impact of accounting disclosures on stock market: A deep neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    14. Hui-Ping Ho & Ching-Ter Chang & Cheng-Yuan Ku, 2013. "On the location selection problem using analytic hierarchy process and multi-choice goal programming," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(1), pages 94-108.
    15. Phillips, Paul & Moutinho, Luiz, 2014. "Critical review of strategic planning research in hospitality and tourism," Annals of Tourism Research, Elsevier, vol. 48(C), pages 96-120.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Blasco-Arcas, Lorena & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2023. "Leveraging user behavior and data science technologies for management: An overview," Journal of Business Research, Elsevier, vol. 154(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yeo, Sook Fern & Tan, Cheng Ling & Kumar, Ajay & Tan, Kim Hua & Wong, Jee Kit, 2022. "Investigating the impact of AI-powered technologies on Instagrammers’ purchase decisions in digitalization era–A study of the fashion and apparel industry," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Muñoz de Prat, Javier & Delen, Dursun, 2022. "Can customer sentiment impact firm value? An integrated text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Yıldız, Nurdan & Tüysüz, Fatih, 2019. "A hybrid multi-criteria decision making approach for strategic retail location investment: Application to Turkish food retailing," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    4. Arpan Kumar Kar & Shweta Kumari Choudhary & P. Vigneswara Ilavarasan, 2023. "How can we improve tourism service experiences: insights from multi-stakeholders’ interaction," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 73-89, March.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Yuan Yuan & Tianhui You & Tian’ai Xu & Xun Yu, 2022. "Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    7. Derya Celik Turkoglu & Mujde Erol Genevois, 2020. "A comparative survey of service facility location problems," Annals of Operations Research, Springer, vol. 292(1), pages 399-468, September.
    8. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    9. İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    10. A. George Assaf & Alexander Josiassen & Haemoon Oh, 2016. "Internationalization and hotel performance," Tourism Economics, , vol. 22(3), pages 572-592, June.
    11. Halkin Andrii, 2020. "Assessing the Utility of Retailer Based on Generalized Costs of End-Consumers," Foundations of Management, Sciendo, vol. 12(1), pages 31-42, January.
    12. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    13. Hu, Yujie & Zhang, Yongping & Lamb, David & Zhang, Mingming & Jia, Peng, 2019. "Examining and optimizing the BCycle bike-sharing system – A pilot study in Colorado, US," Applied Energy, Elsevier, vol. 247(C), pages 1-12.
    14. Carmela Iorio & Giuseppe Pandolfo & Antonio D’Ambrosio & Roberta Siciliano, 2020. "Mining big data in tourism," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1655-1669, December.
    15. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
    16. Papavasileiou, Emmanouil & Lyons, Sean & Shaw, Gareth & Georgiou, Anthimos, 2017. "Work values in tourism: Past, present and future," Annals of Tourism Research, Elsevier, vol. 64(C), pages 150-162.
    17. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    18. Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    19. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.
    20. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:144:y:2022:i:c:p:416-427. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.