Abstract
The most important strategic decision in retailing is location. The process of selecting a proper place is a complex and multidimensional problem. A relevant factor that must be taken into account in the decision is the existence of an appropriate commercial ecosystem for the type of business to be located. There are different network-based quality indices to quantify the fitness of each location. In this paper, we show that the combined use of all the primary quality indices through generalized linear models and the aggregation of the information through consensus techniques allow improving the assessment of the different locations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zentes, J., Morschett, D., Schramm-Klein, H.: Strategic Retail Management. Gabler Verlag, Wiesbaden (2012). https://doi.org/10.1007/978-3-8349-6740-4
Shaikh, S.A., Memon, M.A., Prokop, M., Kim, K.S.: An AHP/TOPSIS-based approach for an optimal site selection of a commercial opening utilizing geospatial data. In: Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, pp. 295–302 (2020). https://doi.org/10.1109/BigComp48618.2020.00-58
Çoban, V.: Solar energy plant project selection with AHP decision-making method based on hesitant fuzzy linguistic evaluation. Complex Intell. Syst. 6(3), 507–529 (2020). https://doi.org/10.1007/s40747-020-00152-5
Sánchez-Saiz, R.M., Ahedo, V., Santos, J.I., Gómez, S., Galán, J.M.: Identification of robust retailing location patterns with complex network approaches. Complex Intell. Syst. 8(1), 83–106 (2021). https://doi.org/10.1007/s40747-021-00335-8
Jensen, P.: Network-based predictions of retail store commercial categories and optimal locations. Phys. Rev. E 74, 035101 (2006). https://doi.org/10.1103/PhysRevE.74.035101
Ahedo, V., Santos, J.I., Galan, J.M.: Knowledge transfer in commercial feature extraction for the retail store location problem. IEEE Access 9, 132967–132979 (2021). https://doi.org/10.1109/ACCESS.2021.3115712
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009). https://doi.org/10.1007/b94608
Tibshirani, R.: Regression selection and shrinkage via the lasso. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 58, 267–288 (1996). https://doi.org/10.2307/2346178
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 67, 301–320 (2005). https://doi.org/10.1111/j.1467-9868.2005.00503.x
Sánchez-Saiz, R.M., Ahedo, V., Santos, J.I., Gómez, S., Galán, J.M.: Dataset of the retailing location networks in the cities of Castile-Leon, Madrid and Barcelona (2021). http://hdl.handle.net/10259/5585. https://doi.org/10.36443/10259/5585
Statistics Canada Government of Canada: NAICS 2012 – 44-45 - Retail trade (2011). http://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=118464&CVD=118465&CPV=44-45&CST=01012012&CLV=1&MLV=5
Gómez, S., Jensen, P., Arenas, A.: Analysis of community structure in networks of correlated data. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 80, 16114 (2009). https://doi.org/10.1103/PhysRevE.80.016114
Jensen, P.: Analyzing the localization of retail stores with complex systems tools. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 10–20. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03915-7_2
Craswell, N.: Mean reciprocal rank. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 1703–1703. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9_488
Carrasco, J., García, S., Rueda, M.M., Das, S., Herrera, F.: Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol. Comput. 54, 100665 (2020). https://doi.org/10.1016/j.swevo.2020.100665
Benavoli, A., Corani, G., Demšar, J., Zaffalon, M.: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. J. Mach. Learn. Res. 18, 1–36 (2017)
Acknowledgements
The authors acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities (Excellence Network RED2018‐102518‐T), the Spanish State Research Agency (PID2020-118906GB-I00/AEI/10.13039/501100011033) and the Fundación Bancaria Caixa D. Estalvis I Pensions de Barcelona, La Caixa (2020/00062/001). In addition, we acknowledge support from the Santander Supercomputación group (University of Cantabria), that provided access to the Altamira Supercomputer—located at the Institute of Physics of Cantabria (IFCA-CSIC) and member of the Spanish Supercomputing Network—to perform the different simulations/analyses.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ahedo, V., Santos, J.I., Galán, J.M. (2023). Combining Quality Indexes in the Retail Location Problem Using Generalized Linear Models. In: García Márquez, F.P., Segovia Ramírez, I., Bernalte Sánchez, P.J., Muñoz del Río, A. (eds) IoT and Data Science in Engineering Management. CIO 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 160. Springer, Cham. https://doi.org/10.1007/978-3-031-27915-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-27915-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27914-0
Online ISBN: 978-3-031-27915-7
eBook Packages: EngineeringEngineering (R0)