Evaluation of collaborative consumption of food delivery services throughweb mining techniques

Online food delivery services rely on urban transportation to alleviate customers' burden of traveling in highly dense cities. As new business models, these services exploit user-generated contents to promote collaborative consumption among its members. This study aims to evaluate the impact of...

Full description

Autores:
Correa, Juan C.
Garzón, Wilmer
Brooker, Phillip
Sakarkar, Gopal
Carranza, Steven A.
Yunado, Leidy
Rincón, Alejandro
Tipo de recurso:
Article of investigation
Fecha de publicación:
2019
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
eng
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/1458
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/1458
https://doi.org/10.1016/j.jretconser.2018.05.002
https://www.sciencedirect.com/science/article/pii/S0969698918302339
Palabra clave:
Aplicaciones web
Software de aplicación
Negocios
Redes sociales en línea en los negocios
Online social networks in business
Collaborative consumption
Traffic conditions
Google maps
Online food ordering
Rights
openAccess
License
https://creativecommons.org/licenses/by/4.0/
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repository_id_str
dc.title.spa.fl_str_mv Evaluation of collaborative consumption of food delivery services throughweb mining techniques
title Evaluation of collaborative consumption of food delivery services throughweb mining techniques
spellingShingle Evaluation of collaborative consumption of food delivery services throughweb mining techniques
Aplicaciones web
Software de aplicación
Negocios
Redes sociales en línea en los negocios
Online social networks in business
Collaborative consumption
Traffic conditions
Google maps
Online food ordering
title_short Evaluation of collaborative consumption of food delivery services throughweb mining techniques
title_full Evaluation of collaborative consumption of food delivery services throughweb mining techniques
title_fullStr Evaluation of collaborative consumption of food delivery services throughweb mining techniques
title_full_unstemmed Evaluation of collaborative consumption of food delivery services throughweb mining techniques
title_sort Evaluation of collaborative consumption of food delivery services throughweb mining techniques
dc.creator.fl_str_mv Correa, Juan C.
Garzón, Wilmer
Brooker, Phillip
Sakarkar, Gopal
Carranza, Steven A.
Yunado, Leidy
Rincón, Alejandro
dc.contributor.author.none.fl_str_mv Correa, Juan C.
Garzón, Wilmer
Brooker, Phillip
Sakarkar, Gopal
Carranza, Steven A.
Yunado, Leidy
Rincón, Alejandro
dc.contributor.researchgroup.spa.fl_str_mv CTG-Informática
dc.subject.armarc.spa.fl_str_mv Aplicaciones web
Software de aplicación
Negocios
Redes sociales en línea en los negocios
topic Aplicaciones web
Software de aplicación
Negocios
Redes sociales en línea en los negocios
Online social networks in business
Collaborative consumption
Traffic conditions
Google maps
Online food ordering
dc.subject.armarc.eng.fl_str_mv Online social networks in business
dc.subject.proposal.eng.fl_str_mv Collaborative consumption
Traffic conditions
Google maps
Online food ordering
description Online food delivery services rely on urban transportation to alleviate customers' burden of traveling in highly dense cities. As new business models, these services exploit user-generated contents to promote collaborative consumption among its members. This study aims to evaluate the impact of traffic conditions (through the use of Google Maps API) on key performance indicators of online food delivery services (through the use of web scraping techniques to retrieve customer's ratings and the physical location of restaurants as provided by Facebook). From a collection of 19,934 possible routes between the physical location of 787 online providers and 4296 customers in Bogotá city, we found that traffic conditions exerted no practical effects on transactions volume and delivery time fulfillment, even though early deliveries showed a mild association with the number of comments provided by customers after receiving their orders at home.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2021-05-20T22:12:57Z
2021-10-01T17:22:48Z
dc.date.available.none.fl_str_mv 2021-05-20T22:12:57Z
2021-10-01T17:22:48Z
dc.type.spa.fl_str_mv Artículo de revista
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identifier_str_mv 0969-6989
url https://repositorio.escuelaing.edu.co/handle/001/1458
https://doi.org/10.1016/j.jretconser.2018.05.002
https://www.sciencedirect.com/science/article/pii/S0969698918302339
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationedition.spa.fl_str_mv Volume 46, January 2019, Pages 45-50
dc.relation.citationendpage.spa.fl_str_mv 50
dc.relation.citationstartpage.spa.fl_str_mv 45
dc.relation.citationvolume.spa.fl_str_mv 46
dc.relation.indexed.spa.fl_str_mv N/A
dc.relation.ispartofjournal.spa.fl_str_mv Journal of Retailing and Consumer Services
dc.relation.references.spa.fl_str_mv Barnes, S.J., Mattsson, J., 2017. Understanding collaborative consumption: test of a theoretical model. Technol. Forecast. Soc. Change 118, 281–292. http://dx.doi.org/ 10.1016/j.techfore.2017.02.029
Belk, R., 2014. You are what you can access: sharing and collaborative consumption online. J. Bus. Res. 67 (8), 1595–1600.
Benoit, S., Baker, T.L., Bolton, R.N., Gruber, T., Kandampully, J., 2017. A triadic framework for collaborative consumption (cc): motives, activities and resources & capabilities of actors. J. Bus. Res. 219–227.
Botsman, R., Rogers, R., 2010. What's Mine is Yours: The Rise of Collaborative Consumption. Harper Business, New York. Côrte-Real, N., Oliveira, T., Ruivo, P., 2017. Assessing business value of big data analytics in european firms. J. Bus. Res. 70, 379–390.
Çavuşoğlu, M., 2012. Electronic commerce and turkish patterns of online food delivery system. J. Internet Appl. Manag. 3 (1), 45–62.
Chen, E.E., Wojcik, S.P., 2016. A practical guide to big data research in psychology. Psychol. Methods 21 (4), 458–474.
Cheung, M.W.-L., Jak, S., 2016. Analyzing big data in psychology: a split/analyze/metaanalyze approach. Front. Psychol. 7, 738.
Collier, A., Wu, A., 2017. ubeR: Interface to the Uber API, R package version 0.1.4. URL 〈https://CRAN.R-project.org/package=ubeR〉.
Correa, J.C., Forero, D.E., 2017. The Relevance of Urban Mobility for Consumer Research: an Interdisciplinary perspective. In: In: Becerra, E.P., Chitturi, R., Henriquez Daza, M.C., Londoño Roldan, J.C. (Eds.), LA-Latin American Advances in Consumer Research 4. Duluth, MN, pp. 101–104.
Correa, J.C., 2017. Exploring the synergy between motorists and motorcyclists in urban mobilization. In: Proceedings of the First Complex Systems Digital Campus World EConference 2015, Springer, pp. 291–295.
Correa, J.C., 2018. Urban mobility social networks as valid sources for collaborative consumption research (Mar). 〈http://dx.doi.org/10.17605/OSF.IO/TWHD4〉. 〈psyarxiv.com/twhd4〉. Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 319–340.
de Rivera, J., Gordo, Á., Cassidy, P., Apesteguía, A., 2017. A netnographic study of p2p collaborative consumption platforms' user interface and design. Environ. Innov. Soc. Transit. 23, 11–27.
Fire, M., Kagan, D., Puzis, R., Rokach, L., Elovici, Y., 2012. Data Mining Opportunities in Geosocial Networks for Improving Road Safety. In: Electrical & Electronics Engineers in Israel (IEEEI), 2012 IEEE 27th Convention of, IEEE, pp. 1–4.
Fishbein, M., 1979. A Theory of Reasoned Action: Some Applications and Implications. University of Nebraska Press. Garrett, A., Straker, K., Wrigley, C., 2017. Digital channels for building collaborative consumption communities. J. Res. Interact. Mark. 11 (2), 160–184. http://dx.doi. org/10.1108/JRIM-08-2016-0086.
Gefen, D., Karahanna, E., Straub, D.W., 2003. Trust and TAM in online shopping: an integrated model. MIS Q. 27 (1), 51–90.
Goes, P.B., Lin, M., Au Yeung, C.-m., 2014. popularity effect? In user-generated content: evidence from online product reviews. Inf. Syst. Res. 25 (2), 222–238.
Gupta, T., Paul, K., 2016. Consumer attitude towards quick service restaurants: a study across select quick service restaurants in Gurgaon. Indian J. Appl. Res. 6 (4), 639–641.
Hamari, J., Sjöklint, M., Ukkonen, A., 2016. The sharing economy: why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 67 (9), 2047–2059.
Hong, L., Li, Y., Wang, S., 2016. Improvement of online food delivery service based on consumers' negative comments. Can. Social. Sci. 12 (5), 84–88. http://dx.doi.org/10. 3968/8464.
Huang, Z., Benyoucef, M., 2013. From e-commerce to social commerce: a close look at design features. Electron. Commer. Res. Appl. 12 (4), 246–259.
Jaffe, K., 2017. The Scientific Roots of Synergy and How to Make Cooperation Successful. CreateSpace Independent Publishing Platform. Jia, S., 2018. Behind the ratings: text mining of restaurant customers' online reviews. Int. J. Mark. Res. http://dx.doi.org/10.1177/1470785317752048. (1470785317752048).
Jiang, B., Tian, L., 2016. Collaborative consumption: strategic and economic implications of product sharing. Manag. Sci. 1–19.
Kahle, D., Wickham, H., 2013. ggmap: Spatial visualization with ggplot2. R. J. 5 (1), 144–161.
Landers, R.N., Brusso, R.C., Cavanaugh, K.J., Collmus, A.B., 2016. A primer on theorydriven web scraping: automatic extraction of big data from the internet for use in psychological research. Psychol. Methods 21 (4), 475–492.
Lang, D.T., 2017. the CRAN Team, XML: Tools for Parsing and Generating XML Within R and S-Plus. r package version 3.98-1.9.URL 〈https://CRAN.R-project.org/package= XML〉.
Lindblom, A., Lindblom, T., 2017. De-ownership orientation and collaborative consumption during turbulent economic times. Int. J. Consum. Stud. 41 (4), 431–438.
Munzert, S., Rubba, C., Meißner, P., Nyhus, D., 2015. Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining. John Wiley & Sons ltd, West Sussex, UK. Nowak, M.A., 2006. Five rules for the evolution of cooperation. science 314 (5805), 1560–1563.
Ostrom, E., 1990. Governing the Commons: The Evolution of Institutions for Collective Actions. Cambridge university press. Pavlou, P.A., 2003. Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 7 (3), 101–134.
Pigatto, G., Machado, J.G., Negreti, A., Machado, L., 2017. Have you chosen your request? Analysis of online food delivery companies in Brazil. Br. Food J. 119 (3), 639–657.
Roos, D., Hahn, R., 2017. Understanding collaborative consumption: An extension of the theory of planned behavior with value-based personal norms. Journal of Business Ethics, 1–19. 〈http://dx.doi.org10.1007/s10551-017-3675-3/〉.
Russell, M.A., 2014. Mining the Social Web. 2nd Edition, O'Reilly, California. Scaraboto, D., 2015. Selling, sharing, and everything in between: the hybrid economies of collaborative networks. J. Consum. Res. 42 (1), 152–176. http://dx.doi.org/10.1093/ jcr/ucv004.
Silge, J., Robinson, D., 2017. Text Mining with R: A tidy approach, O'Reilly Media. Inc., Silva, T.H., de Melo, P.O.V., Viana, A.C., Almeida, J.M., Salles, J., Loureiro, A.A., 2013. Traffic Condition is More than Colored Lines on a Map: Characterization of Waze Alerts. In: International Conference on Social Informatics, Springer, pp. 309–318.
Sivarajah, U., Mustafa Kamal, M., Irani, Z., Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. J. Bus. Res. 70, 263–286.
Yeo, V.C.S., Goh, S.-K., Rezaei, S., 2017. Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. J. Retail. Consum. Serv. 35, 150–162.
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spelling Correa, Juan C.dbe7fa59a0ff15e6973e46936be7adb2600Garzón, Wilmer6b04a33a7db33dd5cc3491063fc48a95600Brooker, Phillipfd42d7eca8f86ca7514c9f9cd39a036e600Sakarkar, Gopal8d8f6955185e5fc23ae542b138b7d7e0600Carranza, Steven A.8a93e07cf61ef549572499d059efc48b600Yunado, Leidyf79c7622c6077dc927017db999758a4d600Rincón, Alejandro075f095a89ce745500e3ad7fe7779e0b600CTG-Informática2021-05-20T22:12:57Z2021-10-01T17:22:48Z2021-05-20T22:12:57Z2021-10-01T17:22:48Z20190969-6989https://repositorio.escuelaing.edu.co/handle/001/1458https://doi.org/10.1016/j.jretconser.2018.05.002https://www.sciencedirect.com/science/article/pii/S0969698918302339Online food delivery services rely on urban transportation to alleviate customers' burden of traveling in highly dense cities. As new business models, these services exploit user-generated contents to promote collaborative consumption among its members. This study aims to evaluate the impact of traffic conditions (through the use of Google Maps API) on key performance indicators of online food delivery services (through the use of web scraping techniques to retrieve customer's ratings and the physical location of restaurants as provided by Facebook). From a collection of 19,934 possible routes between the physical location of 787 online providers and 4296 customers in Bogotá city, we found that traffic conditions exerted no practical effects on transactions volume and delivery time fulfillment, even though early deliveries showed a mild association with the number of comments provided by customers after receiving their orders at home.Los servicios de entrega de alimentos en línea dependen del transporte urbano para aliviar la carga de los clientes de viajar en ciudades muy densas. Como nuevos modelos de negocio, estos servicios explotan contenidos generados por los usuarios para promover el consumo colaborativo entre sus miembros. Este estudio tiene como objetivo evaluar el impacto de las condiciones del tráfico (mediante el uso de la API de Google Maps) en los indicadores clave de rendimiento de los servicios de entrega de alimentos en línea (mediante el uso de técnicas de raspado web para recuperar las calificaciones de los clientes y la ubicación física de los restaurantes según lo proporcionado por Facebook ). A partir de una colección de 19,934 rutas posibles entre la ubicación física de 787 proveedores en línea y 4296 clientes en la ciudad de Bogotá, encontramos que las condiciones del tráfico no ejercieron efectos prácticos sobre el volumen de transacciones y el cumplimiento del tiempo de entrega, aunque las entregas tempranas mostraron una asociación leve con el número. de los comentarios aportados por los clientes tras recibir sus pedidos en casa.Received 26 March 2018, Revised 30 April 2018, Accepted 5 May 2018, Available online 29 May 2018.Faculty of Psychology, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia Escuela Colombiana de Ingeniería Julio Garavito, Bogotá, Colombia Department of Sociology, Social Policy, and Criminology at University of Liverpool, UK Department of Computer Applications, Raisoni College of Engineering, Nagpur, India6 páginasapplication/pdfengElsevierReino UnidoThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://www.sciencedirect.com/science/article/pii/S0969698918302339Evaluation of collaborative consumption of food delivery services throughweb mining techniquesArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Volume 46, January 2019, Pages 45-50504546N/AJournal of Retailing and Consumer ServicesBarnes, S.J., Mattsson, J., 2017. Understanding collaborative consumption: test of a theoretical model. Technol. Forecast. Soc. Change 118, 281–292. http://dx.doi.org/ 10.1016/j.techfore.2017.02.029Belk, R., 2014. You are what you can access: sharing and collaborative consumption online. J. Bus. Res. 67 (8), 1595–1600.Benoit, S., Baker, T.L., Bolton, R.N., Gruber, T., Kandampully, J., 2017. A triadic framework for collaborative consumption (cc): motives, activities and resources & capabilities of actors. J. Bus. Res. 219–227.Botsman, R., Rogers, R., 2010. What's Mine is Yours: The Rise of Collaborative Consumption. Harper Business, New York. Côrte-Real, N., Oliveira, T., Ruivo, P., 2017. Assessing business value of big data analytics in european firms. J. Bus. Res. 70, 379–390.Çavuşoğlu, M., 2012. Electronic commerce and turkish patterns of online food delivery system. J. Internet Appl. Manag. 3 (1), 45–62.Chen, E.E., Wojcik, S.P., 2016. A practical guide to big data research in psychology. Psychol. Methods 21 (4), 458–474.Cheung, M.W.-L., Jak, S., 2016. Analyzing big data in psychology: a split/analyze/metaanalyze approach. Front. Psychol. 7, 738.Collier, A., Wu, A., 2017. ubeR: Interface to the Uber API, R package version 0.1.4. URL 〈https://CRAN.R-project.org/package=ubeR〉.Correa, J.C., Forero, D.E., 2017. The Relevance of Urban Mobility for Consumer Research: an Interdisciplinary perspective. In: In: Becerra, E.P., Chitturi, R., Henriquez Daza, M.C., Londoño Roldan, J.C. (Eds.), LA-Latin American Advances in Consumer Research 4. Duluth, MN, pp. 101–104.Correa, J.C., 2017. Exploring the synergy between motorists and motorcyclists in urban mobilization. In: Proceedings of the First Complex Systems Digital Campus World EConference 2015, Springer, pp. 291–295.Correa, J.C., 2018. Urban mobility social networks as valid sources for collaborative consumption research (Mar). 〈http://dx.doi.org/10.17605/OSF.IO/TWHD4〉. 〈psyarxiv.com/twhd4〉. Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 319–340.de Rivera, J., Gordo, Á., Cassidy, P., Apesteguía, A., 2017. A netnographic study of p2p collaborative consumption platforms' user interface and design. Environ. Innov. Soc. Transit. 23, 11–27.Fire, M., Kagan, D., Puzis, R., Rokach, L., Elovici, Y., 2012. Data Mining Opportunities in Geosocial Networks for Improving Road Safety. In: Electrical & Electronics Engineers in Israel (IEEEI), 2012 IEEE 27th Convention of, IEEE, pp. 1–4.Fishbein, M., 1979. A Theory of Reasoned Action: Some Applications and Implications. University of Nebraska Press. Garrett, A., Straker, K., Wrigley, C., 2017. Digital channels for building collaborative consumption communities. J. Res. Interact. Mark. 11 (2), 160–184. http://dx.doi. org/10.1108/JRIM-08-2016-0086.Gefen, D., Karahanna, E., Straub, D.W., 2003. Trust and TAM in online shopping: an integrated model. MIS Q. 27 (1), 51–90.Goes, P.B., Lin, M., Au Yeung, C.-m., 2014. popularity effect? In user-generated content: evidence from online product reviews. Inf. Syst. Res. 25 (2), 222–238.Gupta, T., Paul, K., 2016. Consumer attitude towards quick service restaurants: a study across select quick service restaurants in Gurgaon. Indian J. Appl. Res. 6 (4), 639–641.Hamari, J., Sjöklint, M., Ukkonen, A., 2016. The sharing economy: why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 67 (9), 2047–2059.Hong, L., Li, Y., Wang, S., 2016. Improvement of online food delivery service based on consumers' negative comments. Can. Social. Sci. 12 (5), 84–88. http://dx.doi.org/10. 3968/8464.Huang, Z., Benyoucef, M., 2013. From e-commerce to social commerce: a close look at design features. Electron. Commer. Res. Appl. 12 (4), 246–259.Jaffe, K., 2017. The Scientific Roots of Synergy and How to Make Cooperation Successful. CreateSpace Independent Publishing Platform. Jia, S., 2018. Behind the ratings: text mining of restaurant customers' online reviews. Int. J. Mark. Res. http://dx.doi.org/10.1177/1470785317752048. (1470785317752048).Jiang, B., Tian, L., 2016. Collaborative consumption: strategic and economic implications of product sharing. Manag. Sci. 1–19.Kahle, D., Wickham, H., 2013. ggmap: Spatial visualization with ggplot2. R. J. 5 (1), 144–161.Landers, R.N., Brusso, R.C., Cavanaugh, K.J., Collmus, A.B., 2016. A primer on theorydriven web scraping: automatic extraction of big data from the internet for use in psychological research. Psychol. Methods 21 (4), 475–492.Lang, D.T., 2017. the CRAN Team, XML: Tools for Parsing and Generating XML Within R and S-Plus. r package version 3.98-1.9.URL 〈https://CRAN.R-project.org/package= XML〉.Lindblom, A., Lindblom, T., 2017. De-ownership orientation and collaborative consumption during turbulent economic times. Int. J. Consum. Stud. 41 (4), 431–438.Munzert, S., Rubba, C., Meißner, P., Nyhus, D., 2015. Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining. John Wiley & Sons ltd, West Sussex, UK. Nowak, M.A., 2006. Five rules for the evolution of cooperation. science 314 (5805), 1560–1563.Ostrom, E., 1990. Governing the Commons: The Evolution of Institutions for Collective Actions. Cambridge university press. Pavlou, P.A., 2003. Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 7 (3), 101–134.Pigatto, G., Machado, J.G., Negreti, A., Machado, L., 2017. Have you chosen your request? Analysis of online food delivery companies in Brazil. Br. Food J. 119 (3), 639–657.Roos, D., Hahn, R., 2017. Understanding collaborative consumption: An extension of the theory of planned behavior with value-based personal norms. Journal of Business Ethics, 1–19. 〈http://dx.doi.org10.1007/s10551-017-3675-3/〉.Russell, M.A., 2014. Mining the Social Web. 2nd Edition, O'Reilly, California. Scaraboto, D., 2015. Selling, sharing, and everything in between: the hybrid economies of collaborative networks. J. Consum. Res. 42 (1), 152–176. http://dx.doi.org/10.1093/ jcr/ucv004.Silge, J., Robinson, D., 2017. Text Mining with R: A tidy approach, O'Reilly Media. Inc., Silva, T.H., de Melo, P.O.V., Viana, A.C., Almeida, J.M., Salles, J., Loureiro, A.A., 2013. Traffic Condition is More than Colored Lines on a Map: Characterization of Waze Alerts. In: International Conference on Social Informatics, Springer, pp. 309–318.Sivarajah, U., Mustafa Kamal, M., Irani, Z., Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. J. Bus. Res. 70, 263–286.Yeo, V.C.S., Goh, S.-K., Rezaei, S., 2017. Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. J. Retail. Consum. Serv. 35, 150–162.Aplicaciones webSoftware de aplicaciónNegociosRedes sociales en línea en los negociosOnline social networks in businessCollaborative consumptionTraffic conditionsGoogle mapsOnline food orderingLICENSElicense.txttext/plain1881https://repositorio.escuelaing.edu.co/bitstream/001/1458/1/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD51open accessORIGINALEvaluation of collaborative consumption of food delivery services through web mining techniques.pdfapplication/pdf1076246https://repositorio.escuelaing.edu.co/bitstream/001/1458/2/Evaluation%20of%20collaborative%20consumption%20of%20food%20delivery%20services%20through%20web%20mining%20techniques.pdf3778c4f3345006fe4862f4bf0d8d2642MD52metadata only accessTEXTEvaluation of collaborative consumption of food delivery services through web mining techniques.pdf.txtEvaluation of collaborative consumption of food delivery services through web mining techniques.pdf.txtExtracted texttext/plain35190https://repositorio.escuelaing.edu.co/bitstream/001/1458/3/Evaluation%20of%20collaborative%20consumption%20of%20food%20delivery%20services%20through%20web%20mining%20techniques.pdf.txt3cb402eec4b83d40c4de6844ce43fbfdMD53open accessTHUMBNAILEvaluation of collaborative consumption of food delivery services through web mining techniques.pdf.jpgEvaluation of collaborative consumption of food delivery services through web mining techniques.pdf.jpgGenerated Thumbnailimage/jpeg14742https://repositorio.escuelaing.edu.co/bitstream/001/1458/4/Evaluation%20of%20collaborative%20consumption%20of%20food%20delivery%20services%20through%20web%20mining%20techniques.pdf.jpgca85ae6e402603031e7685528844e335MD54open access001/1458oai:repositorio.escuelaing.edu.co:001/14582022-08-09 17:03:16.083metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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