Analysis of tweets from food retailers operating in Spain and the UKHow usergenerated content on Twitter can help agrifood cooperatives build better relationships with their customers

Referencias bibliográficas

• Aggrawal, N. and Arora, A. (2016) Visualization, analysis and structural pattern infusion of DBLP co-authorship network using Gephi. IEEE 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 494-500. DOI: 10.1109/NGCT.2016.7877466.
• Alaparthi, S. and Mishra, M. (2021) BERT: A sentiment analysis odyssey. Journal of Marketing Analytics, Vol. 9, Nº 2, pp. 118-126. DOI:10.1057/s41270-021-00109-8.
• Andrews, R.L. and Currim, I.S. (2004) Behavioural differences between consumers attracted to shopping online versus traditional supermarkets: implications for enterprise design and marketing strategy. International Journal of Internet Marketing and Advertising, Vol. 1, Nº 1, pp. 38-61. DOI: 10.1504/IJIMA.2004.003689
• Angelopoulos, S. and Merali, Y. (2017) Sometimes a cigar is not just a cigar: Unfolding the transcendence of boundaries across the digital and physical. ICIS international conference in information systems. Vol. 17. https://aisel.aisnet.org/icis2017/SocialMedia/Presentations/17.
• Araújo Britto Sass, C., Colombo Pimentel, T., Gonzalez Barandela Aleixo, M., Mendes Dantas, T., Cyrino Oliveira, F.L., Queiroz de Freitas, M., Gomes da Cruz, A. and Almeida Esmerino, E. (2020) Exploring social media data to understand consumers’ perception of eggs: a multilingual study using Twitter. Journal of Sensory Studies, Vol. 35, Nº 6. DOI: 10.1111/joss.12607.
• Barabási, A. L. (2016) Network science. Cambridge University Press.
• Bastian, M., Heymann, S. and Jacomy, M. (2009) Gephi: an open source software for exploring and manipulating networks. Association for the Advancement of Artificial Intelligence (www.aaai.org). Extracted from https://gephi.org/publications/gephi-bastian-feb09.pdf (accessed 23 February 2021).
• Bisogni, C.A., Winter Falk, L., Madore, E., Blake, C.E., Jastran, M., Sobal, J. and Devine, C.M. (2007) Dimensions of everyday eating and drinking episodes. Appetite, Vol. 48, Nº 2, pp. 218–231. DOI: 10.1016/j.appet.2006.09.004.
• Blondel, V.D., Guillaume, J.L., Lambiotte, R. and Lefebvre, E. (2008) Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, Vol. 2008, Nº 10, P10008. DOI: 10.48550/arXiv.0803.0476.
• Borgatti, S.P. (2005) Centrality and network flow. Social Networks, Vol. 27, Nº, pp. 55–71. DOI: 10.1016/j.socnet.2004.11.008.
• Boyd, D.M. and Ellison, N.B. (2007) Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, Vol. 13, Nº 1, pp. 210-230. DOI: 10.1111/j.1083-6101.2007.00393.x.
• Buskens, V. (2020) Spreading information and developing trust in social networks to accelerate diffusion of innovations. Trends in Food Science and Technology, Vol. 106, pp. 485-488. DOI: 10.1016/j.tifs.2020.10.040.
• Carr, J., Decreton, L., Qin, W., Rojas, B., Rossochacki, T. and Wen Yang, Y. (2015) Social media in product development. Food Quality and Preference, Vol. 40, Part B, pp. 354–364. DOI: 10.1016/j.foodqual.2014.04.001.
• Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R. and Hassanien, A.E. (2020) Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—a study to show how popularity is affecting accuracy in social media. Applied Soft Computing, Vol. 97, Nº A, p. 106754. DOI: 10.1016/j.asoc.2020.106754.
• Chen, M.-F. (2013) Influences of health consciousness on consumers’ modern health worries and willingness to use functional foods. Journal of Applied Social Psychology, Vol. 43, Nº S1, pp. E1–E12. DOI: https://doi.org/10.1111/jasp.12033.
• Congosto Martínez, M. L., Basanta Val, P., and Sánchez Fernández, L. (2017) T-Hoarder: A framework to process Twitter data streams. Journal of Network and Computer Applications, Vol. 83, pp. 28-39. DOI: 10.1016/j.jnca.2017.01.029.
• Crisci, A., Grasso, V., Nesi, P., Pantaleo, G., Paoli, I. and Zaza, I. (2018) Predicting TV programme audience by using twitter based metrics. Multimedia Tools and Applications, Vol. 77, Nº 10, pp. 12203–12232. DOI: 10.1007/s11042- 017-4880-x.
• Cucagna, M.E. and Goldsmith, P.D. (2018) Value adding in the agri-food value chain. International Food and Agribusiness Management Review, Vol. 21, Nº 3, pp. 293-316. DOI: 10.22434/IFAMR2017.0051.
• Degeratu, A.M., Rangaswamy, A. and Wu, J. (2000) Consumer choice behavior in online and traditional supermarkets: The effects of brand name, price, and other search attributes. International Journal of research in Marketing, Vol. 17, Nº 1, pp. 55-78. DOI: 10.1016/S0167-8116(00)00005-7.
• Deloitte TTL (2018) Global Powers of Retailing 2018. Deloitte Touche Tohmatsu, London. Extracted from https://www2.deloitte.com/ni/es/pages/consumer-business/articles/global-powers-of-retailing-2018.html (accessed 30 July 2021).
• Ducange, P., Fazzolari, M., Petrocchi, M. and Vecchio, M. (2019) An effective Decision Support System for social media listening based on cross-source sentiment analysis models. Engineering Applications of Artificial Intelligence, Vol. 78, pp. 71–85. DOI: 10.1016/j.engappai.2018.10.014.
• Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., … Wang, Y. (2020) Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, Vol. 59. DOI: 10.1016/j.ijinfomgt.2020.102168.
• EAE Business School (2018) La situación de la gran distribución en el mercado de consumo. https://www.eae.es/ (accessed 26 May 2021).
• Elghannam, A., Mesias, F.J., Escribano, M., Fouad, L., Horrillo, A. and Escribano, A.J. (2020) Consumers’ perspectives on alternative short food supply chains based on social media: a focus group study in Spain. Foods, Vol. 9, Nº 1, p. 22. https://doi.org/10.3390/foods9010022.
• Erdogmus, D.E. and Tatar, S.B. (2015) Drivers of Social Commerce through Brand Engagement. Procedia-Social and Behavioral Sciences, Vol. 207 (20 October), pp. 189-195. DOI: 10.1016/j.sbspro.2015.10.087.
• Fleming-Milici, F. and Harris, J.L. (2020) Adolescents’ engagement with unhealthy food and beverage brands on social media. Appetite, Vol. 146, p. 104501. DOI: 10.1016/j.appet.2019.104501.
• Freeman, L.C. (1979) Centrality in social networks: Conceptual clarification. Social Networks, Vol. 1, Nº 3, pp. 215– 239. DOI: 10.1016/0378-8733(78)90021-7.
• Fried, D., Surdeanu, M., Kodbourov, S., Hingle, M. and Bell, D. (2014) Analyzing the language of food on social media. In: 2014 IEEE International Conference on Big Data, October 27–30th, 2014, Washington, EEUU.
• Food Retail (2021). Lidl abandona Aces y se une a Asedas https://www.foodretail.es/retailers/cadena-supermercadosLidl-socio-Asedas_0_1555344472.html (accessed 8 May 2022).
• Gentry, J. (2015) twitteR. R package version 1.1.9. Extracted from https://www.rdocumentation.org/packages/twitteR/versions/1.1.9 (accessed 1 May 2021).
• Go, E., and You, K. H. (2016) But not all social media are the same: Analyzing organizations’ social media usage patterns. Telematics and Informatics, Vol. 33, Nº 1, pp. 176–186. DOI: 10.1016/j.tele.2015.06.016.
• Grewal, D., Levy, M. and Kumar, V. (2009) Customer experience management in retailing: An organizing framework. Journal of Retailing, Vol. 85, Nº 1, pp. 1-14. DOI: 10.1016/j.jretai.2009.01.001.
• Grewal, D. and Roggeveen, A.L. (2020) Understanding retail experiences and customer journey management. Journal of Retailing, Vol. 96, Nº 1, pp. 3-8. DOI: 10.1016/j.jretai.2020.02.002.
• Hayawi, K., Mathew, S., Venugopal, N. et al. (2022) DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data. Social Network Analysis and Mining, Vol. 12, Nº 43. DOI: 10.1007/s13278- 022-00869-w.
• Ibrahim, N. F., Wang, X. and Bourne, H. (2017) Exploring the effect of user engagement in online Brand communities: Evidence from Twitter. Computers in Human Behavior, Vol. 72, pp. 321-338. DOI: 10.1016/j.chb.2017.03.005.
• Imhof, G. and Klaus, P. (2020) The dawn of traditional CX metrics? Examining satisfaction, EXQ, and WAR. International Journal of Market Research, Vol. 62, Nº 6, pp. 673-688. DOI: 10.1177/1470785319848955.
• Jacobsen, L.F., Stancu, V., Wang, Q.J., Aschemann-Witzel, J. and Lähteenmäki, L. (2021) Connecting food consumers to organisations, peers, and technical devices: the potential of interactive communication technology to support consumers’ value creation. Trends in Food Science and Technology, Vol. 109, pp. 622-631. DOI: 10.1016/j.tifs.2021.01.063.
• Kantar WorldPanel (2018) Tendencias en la distribución 2018. Extracted from https://www.kantarworldpanel.com/es/Noticias/el-gran-consumo-crece-un-1porciento-en-los-8-primeros-meses (accessed 8 June 2021).
• Kaplan, A.M. and Haenlein, M. (2010) Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, Vol. 53, pp. 59–68. DOI: 10.1016/j.bushor.2009.09.003.
• Kar, A.K. and Dwivedi, Y.K. (2020) Theory building with big data-driven research – Moving away from the “What” towards the “Why”. International Journal of Information Management, Vol. 54, pp. 1-10. DOI: 10.1016/j.ijinfomgt.2020.102205.
• Kim, A. J. and Ko, E. (2012) Do social media marketing activities enhance customer equity? An empirical study of luxury fashion Brand. Journal of Business Research, Vol. 65, Nº 10, pp. 1480-1486. DOI: 10.1016/j.jbusres.2011.10.014.
• Kwak, H., Lee, C., Park, H. and Moon, S. (2010) What is Twitter, a Social Network or a News Media? In: Proceedings of the 9th International World Wide Web (WWW) Conference, April 26–30, 2010, Raleigh, North Carolina, USA.
• Ladhari, R., Rioux, M.-C., Souiden, N. and Chiadmi, N.-E. (2019) Consumers’ motives for visiting a food retailer's Facebook page. Journal of Retailing and Consumer Services, Vol. 50, pp. 379-385. DOI: 10.1016/j.jretconser.2018.07.013,
• Liere-Netheler, K., Gilhaus, L., Vogelsang, K., and Hoppe, U. (2019) A literature review on application areas of social media analytics. In: Abramowicz, W. and Corchuelo, R. (Eds.). Business Information Systems. Cham: Springer, pp. 38-49. DOI: 10.1007/978-3-030-20482-2_4.
• Liu, X., Burns, A.C. and Hou, Y. (2017) An investigation of brand-related user-generated content on Twitter. Journal of Advertising, Vol. 46, Nº 2, pp. 236-247. DOI: 10.1080/00913367.2017.1297273.
• Meiselman, H.L. (2008) Experiencing food products within a physical and social context, in H.N.J. Schifferstein and P. Hekkert (Eds.), Product experience, Oxford, UK: Elsevier, pp. 559-580.
• Messe Berlin GmbH (2021) Fruit Logistica's annual European Statistics Handbook 2021. Retrieved from https://www.fruitlogistica.com/fruit-logistica/downloads-alle-sprachen/auf-einenblick/european_statistics_handbook_2021.pdf (accessed 3 may 2021).
• Mishra, M. (2021) Customer Experience: Extracting Topics From Tweets. International Journal of Market Research, Vol. 64, Nº 3, pp. 334-353. DOI: 10.1177/14707853211047515.
• Moreno-Sandoval L.G., Sánchez-Barriga C., Buitrago, K.E., Pomares-Quimbaya A. and Garcia J.C. (2018) Spanish Twitter Data Used as a Source of Information About Consumer Food Choice. En: Holzinger A., Kieseberg P., Tjoa A., Weippl E. (Eds.). Machine Learning and Knowledge Extraction. CD-MAKE 2018. Lecture Notes in Computer Science. Cham: Springer, Cham, p. 11015.
• Mostafa, M.M. (2019) Clustering halal food consumers: A Twitter sentiment analysis. International Journal of Market Research, Vol. 61, Nº 3, pp. 320-337. DOI: 10.1177/1470785318771451.
• Nobre, H. and Silva, D. (2014) Social network marketing strategy and SME strategy benefits. Journal of Transnational Management, Vol. 19, Nº 2, pp. 138-151. DOI: 10.1080/15475778.2014.904658.
• Pew Research Center (2019) Use of smartphones and social media is common across most emerging economies. Available at: https://www.pewresearch.org/internet/2019/03/07/use-of-smartphones-and-social-media-is-commonacross-most-emerging-economies/ (accessed 27 April 2022).
• R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/ (accessed 25 April 2022).
• Recuero-Virto, N. and Valilla-Arróspide, C. (2022) Forecasting the next revolution: food technology’s impact on consumers' acceptance and satisfaction. British Food Journal, Vol. ahead-of-print Nº ahead-of-print. DOI: 10.1108/BFJ-07-2021-0803.
• Roggeveen, A. L. and Grewal, D. (2016) Engaging customers: The wheel of social media engagement. Journal of Consumer Marketing, Vol. 33, Nº 2. DOI: 10.1108/JCM-12-2015-1649.
• Ruggeri, A. and Samoggia, A. (2018) Twitter communication of agri-food chain actors on palm oil environmental, socio-economic, and health sustainability. Journal of Consumer Behaviour, Vol. 17, Nº 1, pp. 75-93. DOI: 10.1002/cb.1699.
• Samoggia, A., Riedel, B. and Ruggeri, A. (2020) Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data. British Food Journal, Vol. 122, Nº 12, pp. 3815-3835. DOI: 10.1108/BFJ-03-2019-0172.
• Samoggia, A., Bertazzoli, A. and Ruggeri, A. (2019) Food retailing marketing management: social media communication for healthy food. International Journal of Retail and Distribution Management, Vol. 47, Nº 9, pp. 928-956, DOI: 10.1108/IJRDM-08-2018-0178.
• Sashittal, H. and Jassawalla, A. (2019) Brand entification as a post-anthropomorphic attribution among twitter-using millennials. Marketing Intelligence and Planning, Vol. 37, Nº 2, pp. 741-753. DOI: 10.1108/MIP-10-2018-0446.
• Singha, A., Shuklab, N. and Mishrac N. (2018) Social media data analytics to improve supply chain management in food industries. Transportation Research Part E. Logistics and Transportation Review, Vol. 114, pp. 398-415, DOI: 10.1016/j.tre.2017.05.008.
• Sivarajah, U., Kamal, M. M., Irani, Z. and Weerakkody, V. (2017) Critical analysis of big data challenges and analytical methods. Journal of Business Research, Vol. 70, pp. 263-286. DOI: 10.1016/j.jbusres.2016.08.001.
• Smith, A. N., Fischer, E. and Yongjian, C. (2012) How does brand-related user-generated content differ across YouTube, Facebook, and Twitter. Journal of Interactive Marketing, Vol. 26, Nº 2, pp. 102-113. DOI: 10.1016/j.intmar.2012.01.002.
• Statista (2022). Global social networks ranked by number of users 2020. Retrieved from https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ (accessed 3 May 2022).
• Tao, D., Yang, P. and Feng, H. (2020) Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive Reviews in Food Science and Food Safety, Vol. 19, pp. 875-894. DOI: 10.1111/1541- 4337.12540.
• Taueja, S. and Toombs, L. (2014) Putting a face on small businesses: Visibility, viability, and sustainability the impact of social media on small business marketing. Academy of Marketing Studies Journal, Vol. 18, Nº 1, 249-260.
• Twitter, 2022 Timelines. Extracted from https://developer.twitter.com/en/docs/twitter-api/tweets/timelines/introduction (accessed 3 May 2022).
• Vidal, L., Ares, G., Machín, L. and Jaeger, S.R. (2015) Using Twitter data for food-related consumer research: A case study on ‘‘what people say when tweeting about different eating situations. Food Quality and Preference, Vol. 45, pp. 58-69. DOI: 10.1016/j.foodqual.2015.05.006.
• Yanai, K. and Kawano, Y. (2014) Twitter Food Photo Mining and Analysis for One Hundred Kinds of Foods. In: Advances in Multimedia Information Processing – PCM 2014: 15th Pacific-Rim Conference on Multimedia, Kuching, Malaysia, December 1-4, pp.22-32.
• Zachlod, C., Samuel, O., Ochsner, A. and Werthmüller, S. (2022). Analytics of social media data – State of characteristics and application. Journal of Business Research, Vol. 144, pp. 1064-1076. DOI: 10.1016/j.busres.2022.02.16.
• Zanini, M., Lima, V., Migueles, C., Reis, I., Carbone, D. and Lourenco, C. (2019) Soccer and twitter: virtual brand community engagement practices. Marketing Intelligence and Planning, Vol. 37, Nº 7, pp. 791-805. DOI: 10.1108/MIP-08-2018-0371.
• Zuo, M. Z., Angelopoulos, S. A., Ou, C. X., Carol, X. C., Liu, H. L. and Liang, Z. L. (2020) Optimizing product offerings to enhance competitive sector on online marketplaces (April 22, 2020). SSRN. DOI: 10.2139/ssrn.3598889.