Enhanced Buying Experiences in Smart Cities: The SMARTBUY Approach

  • Lorena Bourg
  • Thomas Chatzidimitris
  • Ioannis Chatzigiannakis
  • Damianos GavalasEmail author
  • Kalliopi Giannakopoulou
  • Vlasios Kasapakis
  • Charalampos Konstantopoulos
  • Damianos Kypriadis
  • Grammati Pantziou
  • Christos Zaroliagis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)


The establishment of shopping malls and the growth of online shopping increasingly diminishes the turnover of “small”, independent retailers in urban environments. However, retailers could reverse this trend through complementing the offline experiences they already offer with online offerings and establishing business “alliances” to achieve economies of scale and enable the provision of innovative digital services. The EU-funded project SMARTBUY aims at realizing the concept of a “distributed shopping mall” ecosystem which allows retailers to band together in a large commercial coalition which generates added-value for its retailers-members and customers: centralized products and services inventory management; geo-located marketing of products/services; location-based search for products offered by nearby retailers; personalized recommendations for purchasing products based on innovative recommendation systems. In effect, SMARTBUY proposes a blended shopping paradigm, wherein the benefits of online shopping are combined with the appeal of traditional store shopping. The article provides an overview of the main outcomes and achievements of SMARTBUY. It also reports on conclusions drawn in the context of the project’s official pilot execution in four European cities.


e-commerce Retailer Shopping Inventory management Product Service Smart cities Smart retailing Geo-located marketing Location-based search Recommendation 



This work has been partly supported by the University of Piraeus Research Center. The research has also been supported by the EU H2020 Programme under grant agreement no. 687960 (SMARTBUY). The research work of D. Gavalas and T. Chatzidimitris has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-01572).


  1. 1.
    Amaxilatis, D., Giannakopoulou, K.: Evaluating retailers in a smart-buying environment using smart city infrastructures. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 284–288 (2018)Google Scholar
  2. 2.
    Chatzigiannakis, I., Mylonas, G., Vitaletti, A.: Urban pervasive applications: challenges, scenarios and case studies. Comput. Sci. Rev. 5(1), 103–118 (2011)CrossRefGoogle Scholar
  3. 3.
    Chen, B.W., Ji, W.: Intelligent marketing in smart cities: crowdsourced data for geo-conquesting. IT Prof. 18(4), 18–24 (2016)CrossRefGoogle Scholar
  4. 4.
    Gavalas, D., Kenteris, M.: A web-based pervasive recommendation system for mobile tourist guides. Pers. Ubiquitous Comput. 15(7), 759–770 (2011)CrossRefGoogle Scholar
  5. 5.
    Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333 (2014)CrossRefGoogle Scholar
  6. 6.
    Gensler, S., Neslin, S.A., Verhoef, P.C.: The showrooming phenomenon: it’s more than just about price. J. Interact. Mark. 38, 29–43 (2017)CrossRefGoogle Scholar
  7. 7.
    Giffinger, R., Fertner, C., Kramar, H., Meijers, E.: City-ranking of European medium-sized cities. Cent. Reg. Sci. Vienna UT, 1–12 (2007)Google Scholar
  8. 8.
    Hsiao, M.H.: Shopping mode choice: physical store shopping versus e-shopping. Transp. Res. Part E: Logistics Transp. Rev. 45(1), 86–95 (2009)CrossRefGoogle Scholar
  9. 9.
    Kowatsch, T., Maass, W.: In-store consumer behavior: how mobile recommendation agents influence usage intentions, product purchases, and store preferences. Comput. Hum. Behav. 26(4), 697–704 (2010)CrossRefGoogle Scholar
  10. 10.
    Vinod Kumar, T.M., Dahiya, B.: Smart economy in smart cities. In: Vinod Kumar, T.M. (ed.) Smart Economy in Smart Cities. ACHS, pp. 3–76. Springer, Singapore (2017). Scholar
  11. 11.
    Lin, C., Hong, C.: Using customer knowledge in designing electronic catalog. Expert Syst. Appl. 34(1), 119–127 (2008)CrossRefGoogle Scholar
  12. 12.
    OrganiCity project.
  13. 13.
    Pantano, E., Timmermans, H.: What is smart for retailing? Procedia Environ. Sci. 22, 101–107 (2014)CrossRefGoogle Scholar
  14. 14.
    Pantano, E., Priporas, C.V.: The effect of mobile retailing on consumers’ purchasing experiences: a dynamic perspective. Comput. Hum. Behav. 61, 548–555 (2016)CrossRefGoogle Scholar
  15. 15.
    Pantano, E., Rese, A., Baier, D.: Enhancing the online decision-making process by using augmented reality: a two country comparison of youth markets. J. Retail. Consum. Serv. 38, 81–95 (2017)CrossRefGoogle Scholar
  16. 16.
    Piotrowicz, W., Cuthbertson, R.: Introduction to the special issue information technology in retail: toward omnichannel retailing. Int. J. Electron. Commer. 18(4), 5–16 (2014)CrossRefGoogle Scholar
  17. 17.
    Sanyal, P., Ghosh, A.: Attractiveness of retail agglomeration based on product type: an experimental study. Available at SSRN 2989281 (2017)Google Scholar
  18. 18.
    Sapiezynski, P., Stopczynski, A., Gatej, R., Lehmann, S.: Tracking human mobility using WiFi signals. PLoS ONE 10(7), e0130824 (2015)CrossRefGoogle Scholar
  19. 19.
    Sassi, I.B., Mellouli, S., Yahia, S.B.: Context-aware recommender systems in mobile environment: on the road of future research. Inf. Syst. 72, 27–61 (2017)CrossRefGoogle Scholar
  20. 20.
    SMARTBUY Deliverable 2.8: SMARTBUY system – final prototypes (2019)Google Scholar
  21. 21.
    SMARTBUY Deliverable 3.2: Wireless geo-located marketing tool (2017)Google Scholar
  22. 22.
    SMARTBUY Deliverable 4.4: Integration of advanced tools for products digitalization and monitoring (2017)Google Scholar
  23. 23.
    SMARTBUY Deliverable 5.4: Deliverable 5.4 report on feedback from real-life customers and retailers (2019)Google Scholar
  24. 24.
    Theodoridis, E., Mylonas, G., Chatzigiannakis, I.: Developing an IoT smart city framework. In: IISA 2013, pp. 1–6 (2013)Google Scholar
  25. 25.
    Yang, W.S., Cheng, H.C., Dia, J.B.: A location-aware recommender system for mobile shopping environments. Expert Syst. Appl. 34(1), 437–445 (2008)CrossRefGoogle Scholar
  26. 26.
    Yuan, S.T., Tsao, Y.W.: A Recommendation mechanism for contextualized mobile advertising. Expert Syst. Appl. 24(4), 399–414 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lorena Bourg
    • 1
  • Thomas Chatzidimitris
    • 2
    • 8
  • Ioannis Chatzigiannakis
    • 3
    • 8
  • Damianos Gavalas
    • 4
    • 8
    Email author
  • Kalliopi Giannakopoulou
    • 5
    • 8
  • Vlasios Kasapakis
    • 2
    • 8
  • Charalampos Konstantopoulos
    • 6
    • 8
  • Damianos Kypriadis
    • 6
    • 8
  • Grammati Pantziou
    • 7
    • 8
  • Christos Zaroliagis
    • 5
    • 8
  1. 1.Planet Media StudiosMadridSpain
  2. 2.Department of Cultural Technology and CommunicationUniversity of the AegeanMytileneGreece
  3. 3.Department of Computer, Control and Informatics EngineeringSapienza University of RomeRomeItaly
  4. 4.Department of Product and Systems Design EngineeringUniversity of the AegeanSyrosGreece
  5. 5.Department of Computer Engineering and InformaticsUniversity of PatrasPatrasGreece
  6. 6.Department of InformaticsUniversity of PiraeusPiraeusGreece
  7. 7.Department of Informatics and Computer EngineeringUniversity of West AtticaAthensGreece
  8. 8.Computer Technology Institute and Press (CTI)PatrasGreece

Personalised recommendations