Do Perceived Riders’ Conditions Influence Online Food Delivery? Investigating Determinants of Online Food Delivery during COVID-19 Outbreak
DOI:
https://doi.org/10.19245/25.05.pij.7.2.4Keywords:
Online food delivery, consumer behavior, technology acceptance model, Covid-19Abstract
The purpose of this paper is to investigate how the COVID-19 pandemic and consumers’ perception of riders’ conditions influence the adoption of online food delivery. This research tries to extend the current literature on online food delivery (OFD) by adding two new perspectives not yet extensively investigated, namely COVID-19 pandemic and perceived riders’ conditions. We extend the technology acceptance model (TAM) with COVID-19 and perceived riders’ condition to empirically evaluate consumers’ behaviours in the OFD context. This paper adopts a quantitative and exploratory approach. Specifically, the study leverages the PLS approach to SEM using SmartPLS for model evaluation. The final sample of this study consists of 492 consumers in Italy. Our research shows that both COVID-19 and perceived riders’ condition negatively influence the adoption of online food delivery.
References
Adnkronos (2020), “Sarzana (Deliveroo): Nel 2021 valore food delivery a 1,45 mld”. Available online at https://www.adnkronos.com (last accessed: May 25, 2022).
Alagoz, S.M., and Hekimoglu, H. (2012), “A Study on TAM: Analysis of Customer Attitudes in Online Food Ordering System”, Procedia-Social and Behavioral Sciences, 62: 1138–1143.
Barnes, S.J. (2020), “Information Management Research and Practice in the Post-COVID-19 World”, International Journal of Information Management, 55: 102–175.
Belanche, D., Flavián, M., and 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): 42–75.
Belanche, D., Casaló, L.V., Flavián, C., and Pérez-Rueda, A. (2021), “The Role of Customers in the Gig Economy: How Perceptions of Working Conditions and Service Quality Influence the Use and Recommendation of Food Delivery Services”, Service Business, 15 (1): 45–75.
Belk, R.W. (1974), “An Exploratory Assessment of Situational Effects in Buyer Behavior”, Journal of Marketing Research, 11 (2): 156–163.
Chin, W.W. (1998), “The Partial Least Squares Approach to Structural Equation Modeling”, Modern Methods for Business Research, 295 (2): 295–336.
Henseler, J., Ringle, C.M., and Sinkovics, R.R. (2009), “The Use of Partial Least Squares Path Modeling in International Marketing”, in R.R. Sinkovics and P.N. Ghauri (eds), New Challenges to International Marketing, pp. 277–319, Bingley: Emerald Group Publishing Limited.
Cho, M., Bonn, M.A., and 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.
Costa Jr, P.T., and McCrae, R.R., (2008), “The Revised NEO Personality Inventory (NEO-PI-R)”, in G.J. Boyle, G. Matthews, and D.H. Saklofske (eds), The SAGE Handbook of Personality Theory and Assessment: Vol. 2 – Personality Measurement and Testing, pp. 179–198, Thousand Oaks, CA: SAGE.
Creyer, E.H. (1997), “The Influence of Firm Behavior on Purchase Intention: Do Consumers Really Care about Business Ethics?”, Journal of Consumer Marketing, 14 (6): 421–432.
Cummins, S., Berger, N., Cornelsen, L., Eling, J., Er, V., Greener, R., Kalbus, A., Karapici, A., Law, C., Ndlovu, D., and Yau, A. (2020), “COVID-19: Impact on the Urban Food Retail System, Diet and Health Inequalities in the UK”, Cities & Health, 5 (S1): 119–122.
Dang, V.T., Nguyen, N., and Wang, J. (2020), “Consumers’ Perceptions and Responses Towards Online Retailers’ CSR”, International Journal of Retail & Distribution Management, 48 (12): 1277–1299.
Davis, F.D. (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology”, MIS Quarterly, 13 (3): 319–340.
DeSimone, J.A., Harms, P.D., and DeSimone, A.J. (2015), “Best Practice Recommendations for Data Screening”, Journal of Organizational Behavior, 36 (2): 171–181.
Dupta, V., and Duggal, S., (2020), “How the Consumer’s Attitude and Behavioural Intentions Are Influenced: A Case of Online Food Delivery Applications in India”, International Journal of Culture, Tourism and Hospitality Research, 15 (1): 77–93.
Ajzen, I., and Fishbein, M. (1975), “A Bayesian Analysis of Attribution Processes”, Psychological Bulletin, 82 (2): 261–277.
Folkes, V.S., and Kamins, M.A. (1999), “Effects of Information about Firms’ Ethical and Unethical Actions on Consumers’ Attitudes”, Journal of Consumer Psychology, 8 (3): 243–259.
Gefen, D., and Straub, D.W. (2000), “The Relative Importance of Perceived Ease of Use in Is Adoption: A Study of E-Commerce Adoption”, Journal of the Association for Information Systems, 1 (1): 1–28.
Gillett, P.L. (1976), “In-Home Shoppers: An Overview”, Journal of Marketing, 40 (4): 81–88.
Gwozdz, W., Nielsen, K.S., and Müller, T. (2017), “An Environmental Perspective on Clothing Consumption: Consumer Segments and Their Behavioral Patterns”, Sustainability, 9 (5): 1–27.
Hair, J.F., Ringle, C.M., and Sarstedt, M. (2011), “PLS-SEM: Indeed a Silver Bullet”, Journal of Marketing Theory and Practice, 19 (2): 139–152.
Hair Jr, J.F., Hult, G.T.M., Ringle, C., and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Thousand Oaks, CA: SAGE.
Hulland, J. (1999), “Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent Studies”, Strategic Management Journal, 20 (2): 195–204.
Joergens, C. (2006), “Ethical Fashion: Myth or Future Trend?”, Journal of Fashion Marketing and Management: An International Journal, 10 (3): 360–371.
Kaine, S., and Josserand, E. (2019), “The Organisation and Experience of Work in the Gig Economy”, Journal of Industrial Relations, 61 (4): 479–501.
Kapoor, A.P., and Vij, M. (2018), “Technology at the Dinner Table: Ordering Food Online through Mobile Apps”, Journal of Retailing and Consumer Services, 43: 342–351.
Kazancoglu, I., and Yarimoglu, E.K. (2018), “How Food Retailing Changed in Turkey: Spread of Self-Service Technologies”, British Food Journal, 120 (2): 290–308.
Keeble, M., Adams, J., Sacks, G., Vanderlee, L., White, C.M., Hammond, D., and Burgoine, T. (2020), “Use of Online Food Delivery Services to Order Food Prepared Away-From-Home and Associated Sociodemographic Characteristics: A Cross-Sectional, Multi-Country Analysis”, International Journal of Environmental Research and Public Health, 17 (14): 51–90.
King, W.R., and He, J. (2006), “A Meta-Analysis of the Technology Acceptance Model”, Information & Management, 43 (6): 740–755.
Kline, T.J., Sulsky, L.M., and Rever-Moriyama, S.D. (2000), “Common Method Variance and Specification Errors: A Practical Approach to Detection”, The Journal of Psychology, 134 (4): 401–421.
Kock, N. (2015), “Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach”, International Journal of E-Collaboration, 11 (4): 1–10.
Kurnia, S., and Chien, J.A.W. (2003), “The Acceptance of the Online Grocery Shopping”, in Proceedings of the 16th Bled Electronic Commerce Conference, pp. 219–233, Bled, Slovenia.
Lee, E.Y., Lee, S. B., and Jeon, Y.J.J. (2017), “Factors Influencing the Behavioral Intention to Use Food Delivery Apps”, Social Behavior and Personality: An International Journal, 45 (9): 1461–1473.
Lee, M.C. (2009), “Factors Influencing the Adoption of Internet Banking: An Integration of TAM and TPB with Perceived Risk and Perceived Benefit”, Electronic Commerce Research and Applications, 8 (3): 130–141.
Lee, S.W., Sung, H.J., and Jeon, H.M. (2019), “Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality”, Sustainability, 11 (11): 31–41.
Liébana-Cabanillas, F., Marinković, V., and Kalinić, Z. (2017), “A SEM-Neural Network Approach for Predicting Antecedents of M-Commerce Acceptance”, International Journal of Information Management, 37 (2): 14–24.
López-Nicolás, C., Molina-Castillo, F.J., and Bouwman, H. (2008), “An Assessment of Advanced Mobile Services Acceptance: Contributions from TAM and Diffusion Theory Models”, Information & Management, 45 (6): 359–364.
Munoz-Leiva, F., Climent-Climent, S., and Liébana-Cabanillas, F. (2017), “Determinants of Intention to Use the Mobile Banking Apps: An Extension of the Classic TAM Model”, Spanish Journal of Marketing-ESIC, 21 (1): 25–38.
Natarajan, T., Balasubramanian, S.A., and Kasilingam, D.L. (2017), “Understanding the Intention to Use Mobile Shopping Applications and Its Influence on Price Sensitivity”, Journal of Retailing and Consumer Services, 37: 8–22.
OECD (2020a), OECD Policy Responses to Coronavirus (COVID-19): E-commerce in the time of COVID-19. Available online at http://www.oecd.org/coronavirus/policy-responses/e-commerce-in-the-time-ofcovid-19-3a2b78e8/ (last accessed: May 25, 2022).
OECD (2020b), Food Supply Chains and COVID-19: Impacts and Policy Lessons – OECD Policy Responses to Coronavirus (COVID-19). Available online at http://www.oecd.org/coronavirus/policy-responses/food-supply-chains-and-covid-19-impacts-and-policy-lessons-71b57aea/ (last accessed: May 25, 2022).
Okumus, B., and Bilgihan, A. (2014), “Proposing a Model to Test Smartphone Users’ Intention to Use Smart Applications When Ordering Food in Restaurants”, Journal of Hospitality and Tourism Technology, 5 (1): 31–49.
Pigatto, G., Machado, J.G.D.C.F., dos Santos Negreti, A., and Machado, L.M. (2017), “Have You Chosen Your Request? Analysis of Online Food Delivery Companies in Brazil”, British Food Journal, 119 (3): 639–657.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., and Pahnila, S. (2004), “Consumer Acceptance of Online Banking: An Extension of the Technology Acceptance Model”, Internet Research, 14 (3): 224–235.
Podsakoff, N.P. (2003), “Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies”, Journal of Applied Psychology, 885 (879): 10- 879–903.
Polytechnic of Milan Observatory (2019), Osservatorio E-Commerce B2c. Available online at https://www.osservatori.net/it_it/osservatori/comunicati-stampa/food-grocery-online-crescitavalore-2019 (last accessed: May 25, 2022).
Quevedo-Silva, F., Freire, O., de Oliveira Lima-Filho, D., Brandão, M.M., Isabella, G., and Moreira, L.B. (2016), “Intentions to Purchase Food through the Internet: Developing and Testing a Model”, British Food Journal, 118 (3), 572–587.
Rafique, H., Almagrabi, A.O., Shamim, A., Anwar, F., Bashir, A.K. (2020), “Investigating the Acceptance of Mobile Library Applications with an Extended Technology Acceptance Model (TAM)”, Computers & Education, 145, 1–22. DOI: 10.1016/j.compedu.2019.103732.
Ray, A., Dhir, A., Bala, P.K., and 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.
Rezaei, S., Shahijan, M.K., Amin, M. and Ismail, W.K.W. (2016), “Determinants of App Stores Continuance Behavior: A PLS Path Modelling Approach”, Journal of Internet Commerce, 15 (4): 408–440.
Ringle, C.M., Wende, S., and Becker, J.M. (2015), SmartPLS 3, Boenningstedt: SmartPLS GmbH. Available online at http://www.smartpls.com (last accessed: May 25, 2022).
Saunders, M.N., and Lewis, P. (2012), Doing Research in Business and Management: An Essential Guide to Planning Your Project, Harlow: Pearson.
Sen, S., and Bhattacharya, C.B. (2001), “Does Doing Good Always Lead to Doing Better? Consumer Reactions to Corporate Social Responsibility”, Journal of Marketing Research, 38 (2): 225–243.
Stewart, A., and Stanford, J. (2017), “Regulating Work in the Gig Economy: What Are the Options?”, The Economic and Labour Relations Review, 28 (3): 420–437.
Sundararajan, A. (2016), The Sharing Economy: The End of Employment and the Rise of Crowd‐Based Capitalism, Cambridge, MA: MIT Press.
Tan, G.W.H., and Ooi, K.B. (2018), “Gender and Age: Do They Really Moderate Mobile Tourism Shopping Behavior?”, Telematics and Informatics, 35 (6): 1617–1642.
Troise, C., O’Driscoll, A., Tani, M., and 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.
Vahdat, A., Alizadeh, A., Quach, S., and Hamelin, N. (2021), “Would You Like to Shop via Mobile App Technology? The Technology Acceptance Model, Social Factors and Purchase Intention”, Australasian Marketing Journal, 29 (2): 187–197.
Veen, A., Barratt, T., and Goods, C. (2020), “Platform-Capital’s ‘App-Etite’ for Control: A Labour Process Analysis of Food-Delivery Work in Australia”, Work, Employment and Society, 34 (3): 388–406.
Venkatesh, V., and Davis, F.D. (2000), “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies”, Management Science, 46 (2): 186–2004.
Yeo, V.C.S., Goh, S.K., and Rezaei, S. (2017), “Consumer Experiences, Attitude and Behavioral Intention toward Online Food Delivery (OFD) Services”, Journal of Retailing and Consumer Services, 35: 150–162.
Zhao, Y., and Bacao, F. (2020), “What Factors Determining Customer Continuingly Using Food Delivery Apps during 2019 Novel Coronavirus Pandemic Period?”, International Journal of Hospitality Management, 91: 1–12. DOI: 10.1016/j.ijhm.2020.102683.
