CULTIVATING USAGE AND WORD-OF-MOUTH IN FOOD DELIVERY APPLICATIONS: A CONCEPTUAL STUDY
DOI:
https://doi.org/10.24191/VoA.v20i2.11875Keywords:
Food Delivery Applications, Technology Acceptance Model, Electronic Word-of-Mouth, Visibility, Affordance ValueAbstract
Food delivery applications (FDAs) have surged in popularity in recent years, gaining widespread acceptance among consumers in both developed and developing countries. The appeal of these applications stems from their user-friendly interfaces and efficient food delivery services to people's homes. FDAs have also offered restaurants a unique opportunity to boost revenue without expanding seating capacity. The food sector transformed as busy consumers increasingly use FDAs due to hectic schedules, impacting dining and cooking habits. Despite the escalating popularity of FDAs, a need remains to comprehend the drivers of their usage and electronic word-of-mouth across various age groups and generations. Hence, this study seeks to conduct an in-depth literature review to explore the potential factors influencing FDA usage and eWOM. The Technology Acceptance Model (TAM) is utilised as a guiding framework to underpin the rationale behind potential predictors, providing insights to practitioners and academics in the food delivery industry, thereby enhancing urban landscape planning and management efficiency. Additionally, the study aims to conceptually investigate potential connections between FDA users and demographic characteristics, uncovering potential differences. The study's findings are intended to offer valuable insights to practitioners and researchers within the food delivery sector, fostering a deeper understanding of the drivers behind food delivery app adoption and eWOM, with a clear focus on demographic distinctions.
References
Acumen Research and Consulting (2019, April 17). Malaysia online food delivery market size worth around USD 319.1 million by 2026. GlobeNewswire. https://www.globenewswire.com/news-release/2019/04/18/1806184/0/en/Malaysia-Online-Food-Delivery-Market-Size-Worth%20-Around-USD-319-1-Million-by-2026-Acumen-Research-and-Consulting.html
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (11-39). Springer-Verlag New York Inc.
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28–44. https://doi.org/10.1016/j.ijinfomgt.2019.04.008
Alshammari, S. H., & Rosli, M. S. (2020). A review of technology acceptance models and theories. Innovative and Learning Journal, 4(2), 12-22.
Antara, P. M., Mohamad, A., Othman, K., Wahab, M. Z., Ibrahim, N. A., Musa, R., & Ningrum, N. K. (2022). Turning crisis into opportunity in the gig economy-acceptance of e-hailing food delivery applications in Malaysia. Academia Journal e-Academia Journal of UiTM Cawangan Terengganu, 11(2). http://journale-academiauitmt.uitm.edu.mye
Arora, M., Singh, H., & Gupta, S. (2021). What drives e-hailing apps adoption? An analysis of behavioral factors through fuzzy AHP. Journal of Science and Technology Policy Management, 13(2), 382- 404.
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, Article e101473. doi: 10.1016/j.tele.2020.101473
Assaker, G. (2020). Age and gender differences in online travel reviews and user-generated- content (UGC) adoption: Extending the technology acceptance model (TAM) with credibility theory. Journal of Hospitality Marketing and Management, 29(4), 428-449.
Bae, S. Y., & Chang, P.-J. (2021). The effect of coronavirus disease-19 (COVID-19) risk perception on behavioural intention towards ‘untact’ tourism in South Korea during the first wave of the pandemic (March 2020). Current Issues in Tourism, 24(7), 1017-1035.
Belanche, D., Flavián, M., & Pérez-Rueda, A. (2020). Mobile apps use and WOM in the food delivery sector: the role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability, 12(10), Article e4275.
Bettman, J. R., & Zins, M. A. (1979). Information format and choice task effects in decision making. Journal of Consumer Research, 6(2), 141-153.
Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187-217.
Bezovski, Z. (2016). The future of the mobile payment as electronic payment system. European Journal of Business and Management, 8(8), 127-132.
Brem, A., Viardot, E. & Nylund, P. A. (2021). Implications of the coronavirus (COVID-19) outbreak for innovation: Which technologies will improve our lives? Technological Forecasting and Social Change, 163, Article e120451. https://doi.org/10.1016/j.techfore.2020.120451
Cameron, C. A. (2006). Examining the relationship that age, gender, experience and communication technology has on acceptance and use of information technology: Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. [Doctoral Dissertation, Touro University International]. ProQuest Information and Learning Company.
Castillo S, M. J., & Bigne, E. (2021). A model of adoption of AR-based self-service technologies: a two country comparison. International Journal of Retail and Distribution Management, 49(7), 875–898. https://doi.org/10.1108/IJRDM-09-2020-0380
Chakraborty, D. (2022). Exploring the meteoric rise of online food ordering apps (OFOAs): The moderating role of visibility. British Food Journal, 124(11), 3871–3887. https://doi.org/10.1108/BFJ-08-2021-0906
Chan, J. (2021). Hunger for profit: How food delivery platforms manage couriers in China. Sociologias, 23(57), 58-82.
Chandrasekhar, N., Gupta, S., & Nanda, N. (2019). Food delivery services and customer preference: A comparative analysis. Journal of Foodservice Business Research, 22(4), 375-386.
Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT Model [Original Research]. Frontiers in Psychology, 10, Article e01652. https://doi.org/10.3389/fpsyg.2019.01652
Chen, C., Fan, Y., & Farn, C. (2007). Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior. Transportation Research Part C: Emerging Technologies, 15(5), 300-311.
Chen, Y.-T., & Chou, T.-Y. (2012). Exploring the continuance intentions of consumers for B2C online shopping: Perspectives of fairness and trust. Online Information Review, 36(1), 104–125. doi: 10.1108/14684521211209572
Cho, M., Bonn, M. A., & Li, 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
Chotigo, J., & Kadono, Y. (2021). Comparative analysis of key factors encouraging food delivery app adoption before and during the COVID-19 pandemic in Thailand. Sustainability, 13(8), Article e4088.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end user information systems: Theory and results [Doctoral Dissertation, Massachusetts Institute of Technology]. DSpace@MIT.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Department of Statistics Malaysia. (2020, April). ICT use and access by individuals and households survey report, Malaysia, 2019. https://www.dosm.gov.my/portal-main/release-content/ict-use-and-access-by-individuals-and-households-survey-report-malaysia-2019
Dirsehan, T., & Cankat, E. (2021). Role of mobile food-ordering applications in developing restaurants’ brand satisfaction and loyalty in the pandemic period. Journal of Retailing and Consumer Services, 62, Article e102608.
Dong, D., Malik, H. A., Liu, Y., Elashkar, E. E., Shoukry, A. M., & Khader, J. A. (2021). Battling for consumer’s positive purchase intention: A comparative study between two psychological techniques to achieve success and sustainability for digital entrepreneurships. Frontiers in Psychology, 12, Article e665194. doi: 10.3389/fpsyg.2021.665194
Food on Demand. (2019). Survey identifies big delivery gripes, driver’s dirty secrets. https://foodondemandnews.com/07252019/survey-identifies-big-delivery-gripes-drivers-dirtysecrets/
Gafni, R., & Nissim, D. (2014). To social login or not login? Exploring factors affecting the decision. Issues in Informing Science and Information Technology, 11(1), 57-72.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
Ghose, A., & Han, S. P. (2014). Estimating demand for mobile applications in the new economy. Management Science, 60(6), 1470–1488. https://doi.org/10.1287/mnsc.2014.1945
Ghozali, I., & Latan, H. (2015). Partial least squares: Konsep, teknik dan aplikasi menggunakan Program SmartPLS 3.0 untuk penelitian empiris. Badan Penerbit UNDIP.
Guo, C., Kulviwat, S., Zhu, J., & Wang, Y. J. (2019). Competing in an emerging market: Antecedents and consequences of market orientation and the role of environmental factors. Journal of Strategic Marketing, 27(3), 248–267. https://doi.org/10.1080/0965254X.2017.1411386
Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565-571.
Hajli, M. N. (2020). The impact of positive valence and negative valence on social commerce purchase intention. Information Technology and People, 33(2), 774-791, doi: 10.1108/ITP-02- 2018-0099
Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers use of social media for transactions. Computers in Human Behavior, 80(1), 197-206.
Hong, C., Choi, E.-K., & Joung, H.-W. (2023). Determinants of customer purchase intention toward online food delivery services: The moderating role of usage frequency. Journal of Hospitality and Tourism Management, 54, 76–87. https://doi.org/10.1016/j.jhtm.2022.12.005
Hong, C., Choi, H., Choi, E.-K., & Joung, H.-W. (2021). Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. Journal of Hospitality and Tourism Management, 48, 509–518. https://doi.org/10.1016/j.jhtm.2021.08.012
Howard, J. A., & Sheth, J. N. (1969). The theory of buyer behavior. John Wiley and Sons.
Igbaria, M., Schiffman, S. J., & Wieckowski, T. J. (2007). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Taylor and Francis Group, 13(6), 349-361, doi: 10.1080/01449299408914616
Inthong, C., Champahom, T., Jomnonkwao, S., Chatpattananan, V., & Ratanavaraha, V. (2022). Exploring factors affecting consumer behavioral intentions toward online food ordering in Thailand. Sustainability, 14(14), Article e8493. https://doi.org/10.3390/su14148493
Itani, O. S., & Hollebeek, L. D., 2021. Consumers’ health-locus-of-control and social distancing in pandemic-based e-tailing services. Journal of Services Marketing, 35(8), 1073–1091. https://doi.org/10.1108/JSM-10-2020-0410
Javed, M. K., & Wu, M. (2020). Effects of online retailer after delivery services on repurchase intention: An empirical analysis of customers’ past experience and future confidence with the retailer. Journal of Retailing and Consumer Services, 54, Article e101942. https://doi.org/10.1016/j.jretconser.2019.101942
Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of m-payment services: Understanding the impact of privacy risk on m-payment services. Computers in Human Behavior, 79(3), 111-122.
Jun, K., Yoon, B., Lee, S., & Lee, D.-S. (2021). Factors influencing customer decisions to use online food delivery service during the COVID-19 pandemic. Foods, 11(1), Article e64.
Jung, J., Park, E., Moon, J., & Lee, W. S. (2021). Exploration of sharing accommodation platform Airbnb using an extended technology acceptance model. Sustainability, 13(3), Article e1185.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, Article e101212.
Kang, J.-W., & Namkung, Y. (2019). The information quality and source credibility matter in customers’ evaluation toward food O2O commerce. International Journal of Hospitality Management, 78, 189-198.
Kaur, P., Dhir, A., Ray, A., Bala, P. K., & Khalil, A. (2021). Innovation resistance theory perspective on the use of food delivery applications. Journal of Enterprise Information Management, 34(6), 1746–1768. https://doi.org/10.1108/JEIM-03-2020-0091
Kaur, P., Dhir, A., Talwar, S., & Ghuman, K. (2020). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, 33(4), 1129–1159. https://doi.org/10.1108/IJCHM-05-2020-0477
Kim, J.J., Kim, I., & Hwang, J. (2021). A change of perceived innovativeness for contactless food delivery services using drones after the outbreak of COVID-19. International Journal of Hospitality Management, 93, Article e102758.
Kim, T., Lee, J., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500-513.
Kim, Y., Wang, Q., & Roh, T. (2021). Do information and service quality affect perceived privacy protection, satisfaction, and loyalty? Evidence from a Chinese O2O-based mobile shopping application. Telematics and Informatics, 56, Article e101483. doi: 10.1016/j.tele.2020.101483
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
Kumar, S. & Shah, A. (2021). Revisiting food delivery apps during COVID-19 pandemic? Investigating the role of emotions. Journal of Retailing and Consumer Services, 62, Article e102595.
Lai, Y.-H., Huang, H.-C., Lu, R.-S., & Chang, C.-M. (2013). The effects of website trust, perceived ease of use, and perceived usefulness on consumers’ online booking intention: Evidence from Taiwan B&B sector. Life Science Journal, 10(2), 1516–1523.
Lau, T. C., & Ng, D. C. Y. (2019). Online food delivery services: Making food delivery the new normal. Journal of Marketing advances and Practices, 1(1), 62-77.
Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: Extending UTAUT2 with information quality. Sustainability, 11(11), Article e3141.
Lee, W. S., Song, M., Moon, J., & Tang, R. (2023). Application of the technology acceptance model to food delivery apps. British Food Journal, 125(1), 49–64. https://doi.org/10.1108/BFJ-05-2021-0574
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
Li, C., Mirosa, M., & Bremer, P. (2020). Review of online food delivery platforms and their impacts on sustainability. Sustainability, 12(14), Article e5528.
Lock, S. (2020). Amount of smartphone food delivery app users in the U.S. 2019-2023. Statista. Retrieved June 14, 2021, from https://www.statista.com/statistics/1189404/food-delivery-app-users-us/
Malaquias, R. F., & Hwang, Y. (2016). An empirical study on trust in mobile banking: A developing country perspective. Computers in Human Behavior, 54, 453-461. https://doi.org/10.1016/j.chb.2015.08.039
Marinković, V., & Lazarević, J. (2021). Eating habits and consumer food shopping behaviour during COVID‐19 virus pandemic: Insights from Serbia. British Food Journal, 123, 3970–3987. https://doi.org/10.1108/BFJ‐11‐2020‐1072
Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24), Article e81.
McCloskey, D. (2006). The importance of ease of use, usefulness, and trust to online consumers: An examination of the technology acceptance model with older customers. Journal of Organizational and End User Computing, 18(3), 47-65.
Min, S., So, K., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel and Tourism Marketing, 36(7), 770-783.
Mohammed, A., & Ferraris, A. (2021). Factors influencing user participation in social media: Evidence from twitter usage during COVID-19 pandemic in Saudi Arabia. Technology in Society, 66(2), Article e101651.
Morosan, C. (2011). Customers, adoption of biometric systems in restaurants: An extension of the technology acceptance model. Journal of Hospitality Marketing and Management, 20(6), 661-690.
Muangmee, C., Kot, S., Meekaewkunchorn, N., Kassakorn, N., & Khalid, B. (2021). Factors determining the behavioral intention of using food delivery apps during COVID-19 pandemics. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1297–1310. https://doi.org/10.3390/jtaer16050073
Naeem, M. (2021). Understanding the customer psychology of impulse buying during COVID-19 pandemic: Implications for retailers. International Journal of Retail and Distribution Management, 49(3), 377-393.
Nguyen, T. T. H., Nguyen, N., Nguyen, T. B. L., Phan, T. T. H., Bui, L. P., & Moon, H. C. (2019). Investigating consumer attitude and intention towards online food purchasing in an emerging economy: An extended TAM approach. Foods, 8(11), Article e576.
Pandey, S., Chawla, D., & Puri, S. (2022). Food delivery apps (FDAs) in Asia: An exploratory study across India and the Philippines. British Food Journal, 124(3), 657–678. https://doi.org/10.1108/BFJ-01-2020-0074
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275
Pitchay, A. A., Ganesan, Y., Nurul Syifa Zulkifli, & Ahmad Khaliq. (2022). Determinants of customers’ intention to use online food delivery application through smartphone in Malaysia. British Food Journal, 124(3), 732–753. https://doi.org/10.1108/BFJ-01-2021-0075
Ray, A., & Bala, P. (2021). User generated content for exploring factors affecting intention to use travel and food delivery services. International Journal of Hospitality Management, 92(4), Article e102730.
Ray, A., Dhir, A., Bala, P., & 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.
Rivera, M. (2019). Online delivery provider (ODP) services: Who is getting what from food deliveries? International Journal of Hospitality Management, 80(3), A1–A2.
Roggeveen, A. L., & Sethuraman, R. (2020). Customer-interfacing retail technologies in 2020 and beyond: an integrative framework and research directions. Journal of Retailing, 96(3), 299-309.
Roh, M., & Park, K. (2019). Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation. International Journal of Information Management, 47, 262–273. https://doi.org/10.1016/j.ijinfomgt.2018.09.017
Rondi, E., Sciascia, S. and De Massis, A. (2020). Innovation in small family firms. Piccola Impresa/ Small Business, 1(1), 10-19.
Saad, A. T. (2020). Factors affecting online food delivery service in Bangladesh: An empirical study. British Food Journal, 123(2), 535-550.
Salehi-Esfahani, S., & Kang, J. (2019). Why do you use Yelp? Analysis of factors influencing customers’ website adoption and dining behavior. International Journal of Hospitality Management, 78, 179-188.
Selvi, B. N. M., & Thomson, J. E. (2016). An exploratory study on the electronic word of mouth communication in promoting brands in the online platforms. Intelligent Information Management, 8(5), 115–141. https://doi.org/10.4236/iim.2016.85010
Shao, Z., Zhang, L., Li, X., & Guo, Y. (2019). Antecedents of trust and continuance intention in mobile payment platforms: The moderating effect of gender. Electronic Commerce Research and Applications, 33, Article e100823. https://doi.org/10.1016/j.elerap.2018.100823
Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44(5), 65-75. https://doi.org/10.1016/j.ijinfomgt.2018.09.013
Shim, J., Moon, J., Song, M., & Lee, W. (2021). Antecedents of purchase intention at Starbucks in the context of Covid-19 pandemic. Sustainability, 13(4), Article e1758.
Silva, G. M., Dias, Á., & Rodrigues, M. S. (2022). Continuity of use of food delivery apps: An integrated approach to the health belief model and the technology readiness and acceptance model. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), Article e114. https://doi.org/10.3390/joitmc8030114
Similarweb. (2019). The most popular food and drink Android apps in MY according to Google play. https://www.similarweb.com/apps/top/google/store-rank/my/food-and-drink/top-free
Song, H. J., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95(4), Article e102943. https://doi.org/10.1016/j.ijhm.2021.102943
Statista. (2022, October 5). Frequency of using food delivery apps in Malaysia 2021. https://www.statista.com/statistics/1143154/malaysia-ordering-food-from-food-delivery-apps-frequency/
Statista. (2023, August 29). Smartphone penetration in Malaysia 2010-2025. https://www.statista.com/statistics/625418/smartphone-user-penetration-in-malaysia/
Suh, B., & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce Research and Applications, 1(3), 247-263.
Suhartanto, D., Dean, D., Leo, G., & Triyuni, N. N. (2019). Millennial experience with online food home delivery: A lesson from Indonesia. Interdisciplinary Journal of Information, Knowledge, and Management, 14, 277–294. https://doi.org/10.28945/4386
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92.
Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Why do people purchase from online travel agencies (OTAs)? A consumption values perspective. International Journal of Hospitality Management, 88, Article e102534. https://doi.org/10.1016/j.ijhm.2020.102534
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, Article e102667. https://doi.org/10.1016/j.jretconser.2021.102667
Teng, C.-C., & Wang, Y.-M. (2015). Decisional factors driving organic food consumption: Generation of consumer purchase intentions. British Food Journal, 117(3), 1066–1081. https://doi.org/10.1108/BFJ-12-2013-0361
Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: Applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65-79.
Thomé, K.M., Pinho, G.M., & Hoppe, A. (2019). Consumption values and physical activities: Consumers’ healthy eating choices. British Food Journal, 121(2), 590-602. https://doi. org/10.1108/BFJ-12-2017-0683
tom Dieck, M.C., & Jung, T. (2018). A theoretical model of mobile augmented reality acceptance in urban heritage tourism. Current Issues in Tourism, 21(2), 154-174.
Tong, S., Luo, X., & Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64-78. https://doi.org/10.1007/s11747-019-00693-3
Troise, C., Corvello, V., Ghobadian, A., & O’Regan, N. (2022). How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era. Technological Forecasting and Social Change, 174, Article e121227.
Troise, C., O’Driscoll, A., Tani, M., & Prisco, A. (2020). Online food delivery services and behavioural intention - A test of an integrated TAM and TPB framework. British Food Journal, 123(2), 664-683. https://doi.org/10.1108/BFJ-05-2020-0418
Venkatesh, V., & Morris, M.G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.
Vlachos, P. A., & Vrechopoulos, A. P. (2008). Determinants of behavioral intentions in the mobile internet services market. Journal of Services Marketing, 22(4), 280–291. https://doi.org/10.1108/08876040810881687
Wang, J., Shen, X., Huang, X., & Liu, Y. (2021). Influencing factors of the continuous usage intention of consumers of online food delivery platform based on an information system success model. Frontiers in Psychology, 12, Article e716796. https://doi.org/10.3389/fpsyg.2021.716796
Wells, J., Valacich, J., & Hess, T. (2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly, 35(2), 373–396. doi: 10.2307/23044048
Wen, H., Pookulangara, S., & Josiam, B. M. (2022). A comprehensive examination of consumers’ intentions to use food delivery apps. British Food Journal, 124(5), 1737–1754. https://doi.org/10.1108/BFJ-06-2021-0655
Zeng, Y., Jia, F., Wan, L., & Guo, H. (2017). E-Commerce in agri-food sector: A systematic literature review. The International Food and Agribusiness Management Review, 20(4), 439-459.
Zhang, H., Wang, Z., Chen, S., & Guo, C. (2019). Product recommendation in online social networking communities: An empirical study of antecedents and a mediator. Information and Management, 56(2), 185–195. https://doi.org/10.1016/j.im.2018.05.001
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, Article e102683.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
