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Evaluation of collaborative consumption of food delivery services throughweb mining techniques

dc.contributor.authorCorrea, Juan C.
dc.contributor.authorGarzón, Wilmer
dc.contributor.authorBrooker, Phillip
dc.contributor.authorSakarkar, Gopal
dc.contributor.authorCarranza, Steven A.
dc.contributor.authorYunado, Leidy
dc.contributor.authorRincón, Alejandro
dc.date.accessioned2021-05-20T22:12:57Z
dc.date.accessioned2021-10-01T17:22:48Z
dc.date.available2021-05-20T22:12:57Z
dc.date.available2021-10-01T17:22:48Z
dc.date.issued2019
dc.identifier.issn0969-6989
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/1458
dc.description.abstractOnline 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.eng
dc.description.abstractLos 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.spa
dc.format.extent6 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherElsevierspa
dc.rightsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).spa
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S0969698918302339spa
dc.titleEvaluation of collaborative consumption of food delivery services throughweb mining techniquesspa
dc.typeArtículo de revistaspa
dc.description.notesReceived 26 March 2018, Revised 30 April 2018, Accepted 5 May 2018, Available online 29 May 2018.spa
dc.description.notesFaculty 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, Indiaspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
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dc.contributor.researchgroupCTG-Informáticaspa
dc.identifier.doihttps://doi.org/10.1016/j.jretconser.2018.05.002
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0969698918302339
dc.publisher.placeReino Unidospa
dc.relation.citationeditionVolume 46, January 2019, Pages 45-50spa
dc.relation.citationendpage50spa
dc.relation.citationstartpage45spa
dc.relation.citationvolume46spa
dc.relation.indexedN/Aspa
dc.relation.ispartofjournalJournal of Retailing and Consumer Servicesspa
dc.relation.referencesBarnes, 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.2017.02.029spa
dc.relation.referencesBelk, R., 2014. You are what you can access: sharing and collaborative consumption online. J. Bus. Res. 67 (8), 1595–1600.spa
dc.relation.referencesBenoit, 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.spa
dc.relation.referencesBotsman, 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.spa
dc.relation.referencesÇavuşoğlu, M., 2012. Electronic commerce and turkish patterns of online food delivery system. J. Internet Appl. Manag. 3 (1), 45–62.spa
dc.relation.referencesChen, E.E., Wojcik, S.P., 2016. A practical guide to big data research in psychology. Psychol. Methods 21 (4), 458–474.spa
dc.relation.referencesCheung, M.W.-L., Jak, S., 2016. Analyzing big data in psychology: a split/analyze/metaanalyze approach. Front. Psychol. 7, 738.spa
dc.relation.referencesCollier, A., Wu, A., 2017. ubeR: Interface to the Uber API, R package version 0.1.4. URL ⟨https://CRAN.R-project.org/package=ubeR⟩.spa
dc.relation.referencesCorrea, 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.spa
dc.relation.referencesCorrea, 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.spa
dc.relation.referencesCorrea, 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.spa
dc.relation.referencesde 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.spa
dc.relation.referencesFire, 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.spa
dc.relation.referencesFishbein, 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.spa
dc.relation.referencesGefen, D., Karahanna, E., Straub, D.W., 2003. Trust and TAM in online shopping: an integrated model. MIS Q. 27 (1), 51–90.spa
dc.relation.referencesGoes, 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.spa
dc.relation.referencesGupta, 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.spa
dc.relation.referencesHamari, J., Sjöklint, M., Ukkonen, A., 2016. The sharing economy: why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol. 67 (9), 2047–2059.spa
dc.relation.referencesHong, 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.spa
dc.relation.referencesHuang, Z., Benyoucef, M., 2013. From e-commerce to social commerce: a close look at design features. Electron. Commer. Res. Appl. 12 (4), 246–259.spa
dc.relation.referencesJaffe, 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).spa
dc.relation.referencesJiang, B., Tian, L., 2016. Collaborative consumption: strategic and economic implications of product sharing. Manag. Sci. 1–19.spa
dc.relation.referencesKahle, D., Wickham, H., 2013. ggmap: Spatial visualization with ggplot2. R. J. 5 (1), 144–161.spa
dc.relation.referencesLanders, 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.spa
dc.relation.referencesLang, 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⟩.spa
dc.relation.referencesLindblom, A., Lindblom, T., 2017. De-ownership orientation and collaborative consumption during turbulent economic times. Int. J. Consum. Stud. 41 (4), 431–438.spa
dc.relation.referencesMunzert, 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.spa
dc.relation.referencesOstrom, 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.spa
dc.relation.referencesPigatto, 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.spa
dc.relation.referencesRoos, 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/⟩.spa
dc.relation.referencesRussell, 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.spa
dc.relation.referencesSilge, 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.spa
dc.relation.referencesSivarajah, U., Mustafa Kamal, M., Irani, Z., Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. J. Bus. Res. 70, 263–286.spa
dc.relation.referencesYeo, 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.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.subject.armarcAplicaciones webspa
dc.subject.armarcSoftware de aplicaciónspa
dc.subject.armarcNegociosspa
dc.subject.armarcRedes sociales en línea en los negociosspa
dc.subject.armarcOnline social networks in businesseng
dc.subject.proposalCollaborative consumptioneng
dc.subject.proposalTraffic conditionseng
dc.subject.proposalGoogle mapseng
dc.subject.proposalOnline food orderingeng
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dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa


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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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