Fome de tecnologia: retenção de usuários do m-commerce no mercado de delivery de alimentos
DOI:
https://doi.org/10.5585/remark.v22i4.23354Palavras-chave:
Mobile marketing, M-commerce, Tecnologia no varejo, Transformação digitalResumo
Objetivo: Desenvolver um modelo de retenção de usuários de m-commerce para a indústria de aplicativos de entrega de alimentos, combinando aceitação de tecnologia e elementos fundamentais de m-commerce para explicar a retenção no m-commerce.
Método: Um modelo de seis fatores (conveniência percebida, contexto dependente da situação, facilidade de uso, dimensão hedônica, dimensão utilitária e preocupações com a privacidade) foi testado em uma amostra de 282 usuários de um aplicativo de entrega de comida no Brasil. As hipóteses foram testadas utilizando Modelagem de Equações Estruturais.
Resultados: A conveniência percebida é o principal antecedente da retenção no m-commerce, abrangendo características de personalização e ubiquidade, gerando um construto ampliado. Os aspectos utilitários estão ligados à conveniência trazida pelo m-commerce, uma vez que os consumidores tendem a valorizar a dimensão utilitária em comparação com a dimensão hedônica do app. A segurança da privacidade percebida pelos consumidores é outro antecedente da retenção no m-commerce.
Contribuições teóricas/metodológicas: Apresentamos o modelo de retenção de m-commerce para aplicativos de entrega de comida, combinando a aceitação da tecnologia e os elementos fundamentais do m-commerce. Conveniência percebida, aspectos utilitários e segurança da privacidade compõem nosso modelo final, conformando os principais drivers tecnológicos de retenção do m-commerce. Mostramos também a relevância da dimensão utilitária em detrimento da dimensão hedônica.
Originalidade/Relevância: O estudo apresenta contribuições para o campo de estudo que envolvem o comportamento do consumidor e o ambiente de varejo m-commerce, em especial a retenção de usuários de m-commerce em aplicativos de entrega de alimentos.
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Referências
Akram, U., Ansari, A. R., Fu, G., & Junaid, M. (2020). Feeling hungry? let's order through mobile! examining the fast food mobile commerce in China. Journal of Retailing and Consumer Services, 56, 102142. https://doi.org/10.1016/j.jretconser.2020.102142
Al-Amin, M., Arefin, M., Sultana, N., Islam, M., Jahan, I., & Akhtar, A. (2021), Evaluating the customers’ dining attitudes, e-satisfaction and continuance intention toward mobile food ordering apps (MFOAs): evidence from Bangladesh, European Journal of Management and Business Economics, 30(2), 211-229. https://doi.org/10.1108/EJMBE-04-2020-0066
Alnawas, I., & Aburub, F. (2016). The effect of benefits generated from interacting with branded mobile apps on consumer satisfaction and purchase intentions. Journal of Retailing and Consumer Services, 31, 313-322. https://doi.org/10.1016/j.jretconser.2016.04.004
Anuar, J., Musa, M., & Khalid, K. (2014). Smartphone's application adoption benefits using mobile hotel reservation system (MHRS) among 3 to 5-star city hotels in Malaysia. Procedia-Social and Behavioral Sciences, 130, 552-557. https://doi.org/10.1016/j.sbspro.2014.04.064
Belk, R. W. (2020). Post-pandemic consumption: portal to a new world?. Cadernos EBAPE. BR, 18, 639-647. https://doi.org/10.1590/1679-395120200175x
Bellman, S., Potter, R. F., Treleaven-Hassard, S., Robinson, J. A., & Varan, D. (2011). The effectiveness of branded mobile phone apps. Journal of interactive Marketing, 25(4), 191-200. https://doi.org/10.1016/j.intmar.2011.06.001
Bilgihan, A., Kandampully, J., & Zhang, T. C. (2016). Towards a unified customer experience in online shopping environments. International Journal of Quality and Service Sciences, 8(1), 102-119. https://doi.org/10.1108/IJQSS-07-2015-0054
Blumtritt, C. (2020). Online food delivery report 2020.
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of cross-cultural psychology, 1(3), 185-216. https://doi.org/10.1177/135910457000100301
Bruner II, G. C., & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of business research, 58(5), 553-558. https://doi.org/10.1016/j.jbusres.2003.08.002
Chauhan, S., Kumar, P., & Jaiswal, M. (2021). A meta-analysis of M-commerce continuance intention: moderating impact of culture and user types. Behaviour & Information Technology, 1-19. https://doi.org/10.1080/0144929X.2021.1960607
Chan, J. O. P. (2020). Digital transformation in the era of big data and cloud computing. International Journal of Intelligent Information Systems, 9(3), 16-23. https://pdfs.semanticscholar.org/bdbd/d0c75a304a536277e815a4e5ca1b1f7eaa95.pdf
Chong, A. Y. L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240-1247. https://doi.org/10.1016/j.eswa.2012.08.067
Chau, N. T., & Deng, H. (2018). Critical determinants for mobile commerce adoption in Vietnamese SMEs: A conceptual framework. Procedia computer science, 138, 433-440. https://doi.org/10.1016/j.procs.2018.10.061
Cho, M., Bonn, M. A., & Li, J. J. (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management, 77, 108-116. https://doi.org/10.1016/j.ijhm.2018.06.019
Chopdar, P. K., & Balakrishnan, J. (2020). Consumers response towards mobile commerce applications: SOR approach. International Journal of Information Management, 53, 102106. https://doi.org/10.1016/j.ijinfomgt.2020.102106
Clarke III, I. (2001). Emerging value propositions for m-commerce. Journal of business strategies, 18(2), 133-148.
Cohen, J. (2013), Statistical power analysis for the behavioral sciences, Routledge.
Daily Mail (2023), More than 17,000 shops shut in 2022 in worst year for retail in five years: Nearly 50 stores closed every day with 150,000 jobs lost from High Street and out-of-town shopping centres, available at: https://www.dailymail.co.uk/news/wechat/article-11590941/Nearly-50-shops-closed-doors-day-UK-year-survey.html (acessed 2 August 2023).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Eger, L., Komárková, L., Egerová, D., & Mičík, M. (2021). The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective. Journal of Retailing and consumer services, 61, 102542. https://doi.org/10.1016/j.jretconser.2021.102542
Fang, Y. H. (2017). Beyond the usefulness of branded applications: Insights from consumer–brand engagement and self‐construal perspectives. Psychology & Marketing, 34(1), 40-58. https://doi.org/10.1002/mar.20972
Figge, S. (2004). Situation-dependent services—a challenge for mobile network operators. Journal of Business Research, 57(12), 1416-1422. https://doi.org/10.1016/S0148-2963(02)00431-9
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis: Pearson new international edition. Essex: Pearson Education Limited.
Harris, M. A., Brookshire, R., & Chin, A. G. (2016). Identifying factors influencing consumers’ intent to install mobile applications. International Journal of Information Management, 36(3), 441-450. https://doi.org/10.1016/j.ijinfomgt.2016.02.004
Holmes, A., Byrne, A., & Rowley, J. (2014). Mobile shopping behaviour: insights into attitudes, shopping process involvement and location. International Journal of Retail & Distribution Management. https://doi.org/10.1108/IJRDM-10-2012-0096
Huang, M. H. (2005), Web performance scale. Information & Management, 42(6), 841-852. https://doi.org/10.1016/j.im.2004.06.003
Hung, M. C., Yang, S. T., & Hsieh, T. C. (2012). An examination of the determinants of mobile shopping continuance. International Journal of Electronic Business Management, 10(1), 29.
Japutra, A., Molinillo, S., Utami, A. F., & Ekaputra, I. A. (2022). Exploring the effect of relative advantage and challenge on customer engagement behavior with mobile commerce applications. Telematics and Informatics, 101841. https://doi.org/10.1016/j.tele.2022.101841
Kamiya, A. S. M., & Branisso, D. S. P. (2021). In the right place at the right time: a review of mobile location-based marketing and a research agenda. REMark, 20(2), 199. https://doi.org/10.5585/remark.v20i2.18713
Kim, S., & Garrison, G. (2009). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323-333. https://doi.org/10.1007/s10796-008-9073-8
Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & security, 64, 122-134. https://doi.org/10.1016/j.cose.2015.07.002
Lee, J. M., & Rha, J. Y. (2016). Personalization–privacy paradox and consumer conflict with the use of location-based mobile commerce. Computers in Human Behavior, 63, 453-462. https://doi.org/10.1016/j.chb.2016.05.056
LI, J., & Mo, W. (2015). The O2O Mode in Electronic Commerce. Proceedings of theInternational Conference on Education, Management, Commerce and Society, 17, 238–241. https://doi.org/10.2991/emcs-15.2015.50
Limayem, M., Khalifa, M., & Frini, A. (2000). What makes consumers buy from Internet? A longitudinal study of online shopping. IEEE Transactions on systems, man, and Cybernetics-Part A: Systems and Humans, 30(4), 421-432. https://doi.org/10.1109/3468.852436
Liu, W., & Huang, J. (2017). Adaptive leader-following consensus for a class of higher-order nonlinear multi-agent systems with directed switching networks. Automatica, 79, 84-92. https://doi.org/10.1016/j.automatica.2017.02.010
Longaray, A. A., Castelli, T. M., Maia, C. R., & Tondolo, V. G. (2021). Study about the Evaluation of Internet Banking and Mobile Banking Users’ Satisfaction in Brazil. REMark, 20(1), 27. https://doi.org/10.5585/remark.v20i1.14590
Luo, X., Andrews, M., Fang, Z., & Phang, C. W. (2014). Mobile targeting. Management Science, 60(7), 1738-1756. https://doi.org/10.1287/mnsc.2013.1836
Magnusson, J., Elliot, V., & Hagberg, J. (2022). Digital transformation: why companies resist what they need for sustained performance. Journal of Business Strategy, 43(5), 316-322. https://doi.org/10.1108/JBS-02-2021-0018
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information systems research, 15(4), 336-355. https://doi.org/10.1287/isre.1040.0032
Marriott, H. R., & Williams, M. D. (2016). Developing a theoretical model to examine consumer acceptance behavior of mobile shopping. In Conference on e-Business, e-Services and e-Society (pp. 261-266). Springer, Cham. https://doi.org/10.1007/978-3-319-45234-0_24
Marriott, H. R., Williams, M. D., & Dwivedi, Y. K. (2017). What do we know about consumer m-shopping behaviour?. International Journal of Retail & Distribution Management. 45( )6, 568-586. https://doi.org/10.1108/IJRDM-09-2016-0164
Marriott, H. R., & Williams, M. D. (2018). Exploring consumers perceived risk and trust for mobile shopping: A theoretical framework and empirical study. Journal of retailing and consumer services, 42, 133-146. https://doi.org/10.1016/j.jretconser.2018.01.017
McKinsey & Company (2019), Brazil Digital Report, available at: https://www.mckinsey.com/br/our-insights/blog-made-in-brazil/brazil-digital-report (accessed 4 October 2023).
McLean, G. (2018). Examining the determinants and outcomes of mobile app engagement-A longitudinal perspective. Computers in Human Behavior, 84, 392-403. https://doi.org/10.1016/j.chb.2018.03.015
McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining consumer attitudes towards retailers' m-commerce mobile applications–An initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139-157. https://doi.org/10.1016/j.jbusres.2019.08.032
Morosan, C. (2014). Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel. International journal of contemporary hospitality management. 26(2), 246-271. https://doi.org/10.1108/IJCHM-11-2012-0221
Morosan, C., & DeFranco, A. (2016). Modeling guests’ intentions to use mobile apps in hotels: The roles of personalization, privacy, and involvement. International Journal of Contemporary Hospitality Management. 28(9), 1968-1991. https://doi.org/10.1108/IJCHM-07-2015-0349
Oberlo (2022). MOBILE COMMERCE SALES IN 2022 , available at: https://www.oberlo.com/statistics/mobile-commerce-sales (accessed 4 August 2023).
Okazaki, S., & Mendez, F. (2013a). Exploring convenience in mobile commerce: Moderating effects of gender. Computers in Human Behavior, 29(3), 1234-1242. https://doi.org/10.1016/j.chb.2012.10.019
Okazaki, S., & Mendez, F. (2013b). Perceived ubiquity in mobile services. Journal of Interactive marketing, 27(2), 98-111. https://doi.org/10.1016/j.intmar.2012.10.001
Okazaki, S., Molina, F. J., & Hirose, M. (2012). Mobile advertising avoidance: exploring the role of ubiquity. Electronic Markets, 22(3), 169-183. https://doi.org/10.1007/s12525-012-0087-1
Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36(6), 1350-1359. https://doi.org/10.1016/j.ijinfomgt.2016.04.005
Pigatto, G., Machado, J. G. D. C. F., dos Santos Negreti, A., & Machado, L. M. (2017). Have you chosen your request? Analysis of online food delivery companies in Brazil. British Food Journal, 119(3), 639-657. https://doi.org/10.1108/BFJ-05-2016-0207
Ray, A., Dhir, A., Bala, P. K., & Kaur, P. (2019). Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. Journal of retailing and consumer services, 51, 221-230. https://doi.org/10.1016/j.jretconser.2019.05.025
Roy, S., & Moorthi, Y. L. R. (2017). Technology readiness, perceived ubiquity and M-commerce adoption: The moderating role of privacy. Journal of Research in Interactive Marketing. 11(3), 268-295. https://doi.org/10.1108/JRIM-01-2016-0005
Semblante, C. J., Catanduanes, R., Martin, A., Radaza, K. J. I., Bokingkito Jr, P., & Velasco, L. C. (2023). Food Delivery Service Applications in Highly Urbanized Cities: A Scoping Review. International Journal of Computing and Digital Systems, 14(1), 1-12.
Seuwou, P., Banissi, E., & Ubakanma, G. (2016). User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. In Global Security, Safety and Sustainability-The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings 11 (pp. 230-251). Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_19
Shankar, V., Kleijnen, M., Ramanathan, S., Rizley, R., Holland, S., & Morrissey, S. (2016). Mobile shopper marketing: Key issues, current insights, and future research avenues. Journal of Interactive Marketing, 34(1), 37-48. https://doi.org/10.1016/j.intmar.2016.03.002
Shankar, A., Rishi, B. (2020). Convenience matter in mobile banking adoption intention? Australasian Marketing Journal, 28(4): 273–285. https://doi.org/10.1016/j.ausmj.2020.06.008
Shen, X. L., Wang, N., Sun, Y., & Xiang, L. (2013). Unleash the power of mobile word‐of‐mouth: An empirical study of system and information characteristics in ubiquitous decision making. Online Information Review. 37(1), 42-60. https://doi.org/10.1108/14684521311311621
Soares, J. C., Limongi, R., De Sousa Júnior, J. H., Santos, W. S., Raasch, M. and Hoeckesfeld, L. (2022), Assessing the effects of COVID-19-related risk on online shopping behavior. Journal of Marketing Analytics, 1-13. https://doi.org/10.1057/s41270-022-00156-9
Spiekermann, S., Rothensee, M., & Klafft, M. (2011). Street marketing: how proximity and context drive coupon redemption. Journal of Consumer Marketing. 28(4), 280-289. https://doi.org/10.1108/07363761111143178
Statista (2023a), Forecast number of mobile devices worldwide from 2020 to 2025 (in billions), available at: https://www.statista.com/statistics/245501/multiple-mobile-device-ownership-worldwide (accessed 2 August 2023).
Statista (2023b), Online food delivery – statistics & facts, available at: https://www.statista.com/topics/9212/online-food-delivery/#topicOverview (accessed 2 August 2023).
Statista (2023c), Online food delivery users worldwide 2022-2027, by region, available at: https://www.statista.com/forecasts/1358171/online-food-delivery-users-by-region-worldwide#:~:text=Online%20food%20delivery%20users%20worldwide%202022%2D2027%2C%20by%20region&text=Approximately%202.5%20billion%20people%20worldwide,percent)%20of%20all%20users%20worldwide (accessed 15 August 2023).
Statista (2023d), Key figures on food delivery company iFood in Brazil as of April 2022, available at: https://www.statista.com/statistics/1051639/brazil-key-figures-food-delivery-app-ifood (accessed 2 August 2023).
Stocker, V., Lehr, W., & Smaragdakis, G. (2023). COVID-19 and the Internet: Lessons learned. In Beyond the Pandemic? Exploring the Impact of COVID-19 on Telecommunications and the Internet (pp. 17-69). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80262-049-820231002
Tandon, A., Kaur, P., Bhatt, Y., Mäntymäki, M., & Dhir, A. (2021). Why do people purchase from food delivery apps? A consumer value perspective. Journal of Retailing and Consumer Services, 63, 102667. https://doi.org/10.1016/j.jretconser.2021.102667
Timur, B., Oguz, Y. E., & Yilmaz, V. (2023). Consumer behavior of mobile food ordering app users during COVID-19: dining attitudes, e-satisfaction, perceived risk, and continuance intention. Journal of Hospitality and Tourism Technology, 14(3), 460-475. https://doi.org/10.1108/JHTT-04-2021-0129
United Nations (UN) (2022), World Population Prospects 2022, available at: https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf (accessed 2 August 2023).
Vandana, V., Kumar, S., Kumar, V., & Goyal, P. (2023). Investigating the Impact of Online Service Convenience on Customer Engagement, Attitude and Intention to Use Food Delivery Apps. International Journal on Food System Dynamics, 14(3), 331-344. https://doi.org/10.18461/ijfsd.v14i3.G6
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178. https://doi.org/10.2307/41410412
Vinerean, S., Budac, C., Baltador, L. A., & Dabija, D. C. (2022). Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach. Electronics, 11(8), 1269. https://doi.org/10.3390/electronics11081269
Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of marketing research, 40(3), 310-320. https://doi.org/10.1509/jmkr.40.3.310.19238
Xu, X., Huang, Y., 2019. Restaurant information cues, diners’ expectations, and need for cognition: experimental studies of online-to-offline mobile food ordering. J. Retailing Consum. Serv. 51, 231–241. https://doi.org/10.1016/j.jretconser.2019.06.010
World Trade Organization (WTO). E-Commerce, Trade and the COVID-19 Pandemic; World Trade Organization: Geneva, Switzerland, 2020; Volume 5.
Yang, Y., Asaad, Y., & Dwivedi, Y. (2017). Examining the impact of gamification on intention of engagement and brand attitude in the marketing context. Computers in Human Behavior, 73, 459-469. https://doi.org/10.1016/j.chb.2017.03.066
Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer services, 35, 150-162. https://doi.org/10.1016/j.jretconser.2016.12.013
Zanetta, L. D. A., Hakim, M. P., Gastaldi, G. B., Seabra, L. M. A. J., Rolim, P. M., Nascimento, L. G. P., ... & da Cunha, D. T. (2021). The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects. Food Research International, 149, 110671. https://doi.org/10.1016/j.foodres.2021.110671
Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period?. International journal of hospitality management, 91, 102683. https://doi.org/10.1016/j.ijhm.2020.102683
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