Factors Influencing Customer Decisions to Use Online Food Delivery Service during the COVID-19 Pandemic
Abstract
:1. Introduction
2. Literature Review
2.1. Online Food Delivery Service
2.2. Technology Acceptance Model
2.3. Perceived Usefulness (PU) and Ease of Use (EOU)
2.4. Enjoyment (EJM)
2.5. Trust (TR)
2.6. Social Influence (SI)
3. Methodology
3.1. Measurement
3.2. Sample and Data Collection
3.3. Data Analysis
4. Results
4.1. Descriptive Analysis
4.2. Validity and Reliability of Measurements
4.3. Hypotheses Testing
5. Discussion
6. Implications and Future Research
6.1. Theoretical Implications
6.2. Practical Implications
6.3. Limitations and Future Study
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- World Health Organization. Coronavirus. 2020. Available online: https://www.who.int/health-topics/coronavirus#tab=tab_1 (accessed on 27 November 2021).
- Restaurant Law Center. Official Orders Were Closing or Restricting Foodservice Establishments in Response to COVID-19. 2020. Available online: https://restaurant.org/downloads/pdfs/business/covid19-official-orders-closing-or-restricting.pdf (accessed on 21 November 2021).
- Statista. 2020. Available online: https://www.statista.com (accessed on 27 November 2021).
- Ray, A.; Dhir, A.; Bala, P.K.; Kaur, P. Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. J. Retail. Consum. Serv. 2019, 51, 221–230. [Google Scholar] [CrossRef]
- Yeo, V.C.S.; Goh, S.-K.; Rezaei, S. Customer experiences, attitude and behavioral intention toward online food delivery (OFD) services. J. Retail. Consum. Serv. 2017, 35, 150–162. [Google Scholar] [CrossRef]
- Nguyen, T.T.H.; Nguyen, N.; Nguyen, T.B.L.; Phan, T.T.H.; Bui, L.P.B.; Moon, H.C. Investigating customer attitude and intention towards online food purchasing in an emerging economy: An extended tam approach. Foods 2019, 8, 576. [Google Scholar] [CrossRef] [Green Version]
- Zhao, Y.; Bacao, F. What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? Int. J. Hosp. Manag. 2020, 91, 102683. [Google Scholar] [CrossRef]
- Kang, J.W.; Numkung, Y. The information quality and source credibility matter in customers’ evaluation toward food O2O commerce. Int. J. Hosp. Manag. 2019, 78, 189–198. [Google Scholar] [CrossRef]
- Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User acceptance of computer technology: A comparison of two theoretical models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef] [Green Version]
- Hong, C.; Choi, H.; Choi, E.; Joung, H. Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. J. Hosp. Tour. Manag. 2021, 48, 509–518. [Google Scholar] [CrossRef]
- Gu, S.; Slusarczyk, B.; Hajizada, S.; Kovalyova, I.; Sakhbieva, A. Impact of the COVID-19 Pandemic on online customer purchasing behavior. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 125. [Google Scholar] [CrossRef]
- Kimes, S.E. The current state of online food ordering in the US restaurant industry. Cornell Hosp. Rep. 2011, 11, 6–18. [Google Scholar]
- Statista. eServices Report 2019–Online Food Delivery. Available online: https://www.statista.com/study/40457/food-delivery/ (accessed on 15 July 2020).
- Hwang, J.; Kim, D.; Kim, J.J. How to Form Behavioral Intentions in the Field of Drone Food Delivery Services: The Moderating Role of the COVID-19 Outbreak. Int. J. Environ. Res. Public Health 2020, 17, 9117. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
- Fishbein, M.; Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Philos. Rhetor. 1977, 10, 177–188. [Google Scholar]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Processes 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Fishbein, M. An investigation of the relationships between beliefs about an object and the attitude toward that object. Hum. Relat. 1963, 16, 233–239. [Google Scholar] [CrossRef]
- Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 1992, 22, 1111–1132. [Google Scholar] [CrossRef]
- Ingham, J.; Cadieux, J.; Berrada, A.M. e-Shopping acceptance: A qualitative and meta-analytic review. Inf. Manag. 2015, 52, 44–60. [Google Scholar] [CrossRef]
- Pavlou, P.A. Customer acceptance of electronic commerce: Integrating Trust and risk with the technology acceptance model. Int. J. Electron. Commer. 2003, 7, 101–134. [Google Scholar]
- Gefen, D.; Karahanna, E.; Straub, D.W. Trust and TAM in online shopping: An integrated model. MIS Q. 2003, 27, 51–90. [Google Scholar] [CrossRef]
- King, W.R.; He, J. A meta-analysis of the technology acceptance model. Inf. Manag. 2006, 43, 740–755. [Google Scholar] [CrossRef]
- Legris, P.; Ingham, J.; Collerette, P. Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag. 2003, 40, 191–204. [Google Scholar] [CrossRef]
- Yousafzai, S.Y.; Foxall, G.R.; Pallister, J.G. Technology acceptance: A meta-analysis of the TAM: Part 2. J. Model. Manag. 2007, 2, 281–304. [Google Scholar] [CrossRef]
- Henderson, R.; Rickwood, D.; Roberts, P. The beta test of an electronic supermarket. Interact. Comput. 1998, 10, 385–399. [Google Scholar] [CrossRef]
- Childers, T.L.; Carr, C.L.; Peck, J.; Carson, S. Hedonic and utilitarian motivations for online retail shopping behavior. J. Retail. 2001, 77, 511–535. [Google Scholar] [CrossRef]
- Koufaris, M. Applying the technology acceptance model and flow theory to online customer behavior. Inf. Syst. Res. 2002, 13, 205–223. [Google Scholar] [CrossRef] [Green Version]
- Cyr, D.; Hassanein, K.; Head, M.; Ivanov, A. The role of social presence in establishing loyalty in e-service environments. Interact. Comput. 2007, 19, 43–56. [Google Scholar] [CrossRef]
- Wen, C.; Prybutok, V.R.; Xu, C. An integrated model for customer online repurchase intention. J. Comput. Inf. Syst. 2011, 52, 14–23. [Google Scholar]
- Kim, J.; Ma, Y.J.; Park, J. Are US customers ready to adopt mobile technology for fashion goods? An integrated theoretical approach. J. Fash. Mark. Manag. 2009, 13, 215–230. [Google Scholar]
- Shen, J. Social comparison, social presence, and enjoyment in the acceptance of social shopping websites. J. Electron. Commer. Res. 2012, 13, 198–212. [Google Scholar]
- Hassanein, K.; Head, M. Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping. Int. J. Hum. Comput. Stud. 2007, 65, 689–708. [Google Scholar] [CrossRef]
- Ha, S.; Stoel, L. Customer e-shopping acceptance: Antecedents in a technology acceptance model. J. Bus. Res. 2009, 62, 565–571. [Google Scholar] [CrossRef]
- Oh, S.H.; Kim, Y.M.; Lee, C.W.; Shim, G.Y.; Park, M.S.; Jung, H.S. Customer adoption of virtual stores in Korea: Focusing on the role of Trust and playfulness. Psychol. Mark. 2009, 26, 652–668. [Google Scholar] [CrossRef]
- Van der Heijden, H. User acceptance of hedonic information systems. MIS Q. 2004, 28, 695–704. [Google Scholar] [CrossRef]
- Ahn, T.; Ryu, S.; Han, I. The impact of web quality and playfulness on user acceptance of online retailing. Inf. Manag. 2007, 44, 263–275. [Google Scholar] [CrossRef]
- Gefen, D.; Straub, D. Managing user trust in B2C e-services. e-Serv J. 2003, 2, 7–24. [Google Scholar] [CrossRef]
- Jarvenpaa, S.L.; Tractinsky, N.; Vitale, M. Customer trust in an Internet store. Inf. Technol. Manag. 2000, 1, 45–71. [Google Scholar] [CrossRef]
- Kim, D.J.; Ferrin, D.L.; Rao, H.R. A trust-based customer decision-making model in electronic commerce: The role of Trust, perceived risk, and their antecedents. Decis. Support. Syst. 2008, 44, 544–564. [Google Scholar] [CrossRef]
- McCloskey, D.W.; Leppel, K. The impact of age on electronic commerce participation: An exploratory model. J. Electron. Commer. Organ. 2010, 8, 41–60. [Google Scholar] [CrossRef]
- Wei, T.T.; Marthandan, G.; Chong, A.Y.; Ooi, K.; Arumugam, S. What drives Malaysian m-commerce adoption? An empirical analysis. Ind. Manag. Data Syst. 2009, 109, 370–388. [Google Scholar]
- Dash, S.; Saji, K.B. The role of customer self-efficacy and website social-presence in customers’ adoption of B2C online shopping: An empirical study in the Indian context. J. Int. Consum. Mark. 2008, 20, 33–48. [Google Scholar] [CrossRef]
- Chiu, C.M.; Lin, H.Y.; Sun, S.Y.; Hsu, M.H. Understanding customers’ loyalty intentions towards online shopping: An integration of technology acceptance model and fairness theory. Behav. Inf. Technol. 2009, 28, 347–360. [Google Scholar] [CrossRef]
- Chai-Har, L.; Eze, U.C.; Ndubisi, N.O. Analyzing key determinants of online repurchase intentions. Asia Pac. J. Mark. Logist. 2011, 23, 200–221. [Google Scholar]
- Van Slyke, C.; Lou, H.; Belanger, F.; Sridhar, V. The influence of culture on customer-oriented electronic commerce adoption. J. Electron. Commer. Res. 2010, 11, 30–40. [Google Scholar]
- Lee, M.C. Predicting and explaining the adoption of online trading: An empirical study in Taiwan. Decis. Support Syst. 2009, 47, 133–142. [Google Scholar] [CrossRef]
- Izquierdo-Yusta, A.; Calderon-Monge, E. Internet as a distribution channel: Empirical evidence from the service sector and managerial opportunities. J. Internet Commer. 2011, 10, 106–127. [Google Scholar] [CrossRef]
- Zhu, D.S.; Lee, Z.C.; O’Neal, G.S.; Chen, Y.H. Mr. Risk! Please trust me: Trust antecedents that increase online customer purchase intention. J. Internet Bank Commer. 2011, 16, 1–23. [Google Scholar]
- Featherman, M.S.; Pavlou, P.A. Predicting e-services adoption: A perceived risk facets perspective. Int. J. Hum. Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef] [Green Version]
- Chang, H.H. Intelligent agent’s technology characteristics applied to online auctions’ task: A combined model of TTF and TAM. Technovation 2008, 28, 564–577. [Google Scholar] [CrossRef]
- Pavlou, P.A.; Fygenson, M. Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Q. 2006, 30, 115–143. [Google Scholar] [CrossRef]
- Kim, H.B.; Kim, T.T.; Shin, S.W. Modeling roles of subjective norms and eTrust in customers’ acceptance of airline B2C eCommerce websites. Tour. Manag. 2009, 30, 266–277. [Google Scholar] [CrossRef]
- Benamati, J.; Fuller, M.A.; Serva, M.A.; Baroudi, J. Clarifying the integration of Trust and TAM in e-commerce environments: Implications for systems design and management. IEEE Trans. Eng. Manag. 2009, 57, 380–393. [Google Scholar] [CrossRef]
- Yang, S.; Park, J.; Park, J. Customers’ channel choice for university-licensed products: Exploring factors of customer acceptance with social identification. J. Retail. Consum. Serv. 2007, 14, 165–174. [Google Scholar] [CrossRef]
- Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef] [Green Version]
- Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
- Vijayasarathy, L.R. Predicting customer intentions to use online shopping: The case for an augmented technology acceptance model. Inf. Manag. 2004, 41, 747–762. [Google Scholar] [CrossRef]
- Bosque, I.R.D.; Crespo, A.H. How do internet surfers become online buyers? An integrative model of e-commerce acceptance. Behav. Inf. Technol. 2011, 30, 161–180. [Google Scholar] [CrossRef]
- Barkhi, R.; Wallace, L. The impact of personality type on purchasing decisions in virtual stores. Inf. Technol. Manag. 2007, 8, 313–330. [Google Scholar] [CrossRef]
- Suh, B.; Han, I. Effect of Trust on customer acceptance of Internet banking. Electron. Commer. Res. Appl. 2002, 1, 247–263. [Google Scholar] [CrossRef]
- Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
- Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2016. [Google Scholar]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Yuan, S.; Liu, Y.; Yao, R.; Liu, J. An investigation of users’ continuance intention towards mobile banking in China. Inf. Dev. 2016, 32, 20–34. [Google Scholar] [CrossRef]
- Kim, M.J.; Hall, C.M. A hedonic motivation model in virtual reality tourism: Comparing visitors and non-visitors. Int. J. Inf. Manag. 2019, 46, 236–249. [Google Scholar] [CrossRef]
- Heritage Radio Network the World’s Pioneer Found Radio Station. How Do You Produce Successful Virtual Events? 2021, Volume 34. Available online: https://heritageradionetwork.org/episode/how-do-you-produce-successful-virtual-events (accessed on 27 November 2021).
Demographic Characteristics | Frequency | Percentage | |
---|---|---|---|
Gender | Male | 232 | 54.5 |
Female | 194 | 45.5 | |
Ethnicity | White/Caucasian | 318 | 74.6 |
African American | 43 | 10.1 | |
Hispanic or Latino | 23 | 5.4 | |
Asian American | 32 | 7.5 | |
Native American or American Indian | 3 | 0.7 | |
Others | 7 | 1.6 | |
Educational level | High school diploma and under | 120 | 28.2 |
Associate degree | 63 | 14.8 | |
Bachelor’s degree | 180 | 42.4 | |
Graduate degree (Master or Doctoral) | 62 | 14.6 | |
Annual household income | Less than $20,000 | 47 | 11.0 |
$20,000~$39,999 | 94 | 22.1 | |
$40,000~$59,999 | 76 | 17.8 | |
$60,000~$79,999 | 85 | 20.0 | |
$80,000~$99,999 | 50 | 11.7 | |
$100,000 or more | 74 | 17.4 | |
Living area | Urban | 158 | 37.1 |
Suburban | 221 | 51.9 | |
Rural | 47 | 11.0 | |
Frequency of use | Several times a day | 4 | 0.9 |
Once a day | 4 | 0.9 | |
Several times a week | 68 | 16.0 | |
Once a week | 105 | 24.6 | |
At least once a month | 148 | 34.7 | |
At least once every two months | 40 | 9.4 | |
At least once every three months | 37 | 8.7 | |
Only used once | 20 | 4.7 |
Constructs and Measurement Items | Standardized Loading | CR | AVE |
---|---|---|---|
Perceived Usefulness (PU, Cronbach’s Alpha = 0.871) | |||
Online food delivery platform makes my food ordering efficient | 0.853 | 0.874 | 0.698 |
Online food delivery platform enhances my effectiveness in food ordering | 0.814 | ||
Online food delivery platform is useful in food ordering | 0.840 | ||
Perceived ease of use (EOU, Cronbach’s Alpha = 0.894) | |||
Learning to operate the online food delivery platform is easy for me | 0.844 | 0.896 | 0.743 |
The online food delivery platform is clear and understandable | 0.834 | ||
The online food delivery platform is easy to use | 0.906 | ||
Enjoyment (EJM, Cronbach’s Alpha = 0.896) | |||
I have fun using the online food delivery platform | 0.823 | 0.896 | 0.683 |
Using the online food delivery platform is exciting | 0.813 | ||
Using the online food delivery platform is enjoyable | 0.854 | ||
Using the online food delivery platform is interesting | 0.816 | ||
Trust (TR, Cronbach’s Alpha = 0.899) | |||
The online food delivery platform is trustworthy | 0.889 | 0.900 | 0.751 |
The online food delivery platform keeps promises and commitments | 0.833 | ||
I trust in the online food delivery platform | 0.876 | ||
Social influence (SI, Cronbach’s Alpha = 0.902) | |||
People who influence my behavior think that I should use the online food delivery platform | 0.870 | 0.904 | 0.759 |
People who are important to me think that I should use the online food delivery platform | 0.944 | ||
My friends want me to use the online food delivery platform | 0.793 | ||
Attitude (AT, Cronbach’s Alpha = 0.921) | |||
Using the online food delivery platform is a pleasant idea | 0.889 | 0.921 | 0.795 |
Using the online food delivery platform is a positive idea | 0.905 | ||
Using the online food delivery platform is an appealing idea | 0.881 | ||
Behavior Intention (BI, Cronbach’s Alpha = 0.967) | |||
I intend to continue using the online food delivery platform in the future | 0.959 | 0.967 | 0.880 |
I predict I would use the online food delivery platform in the future | 0.932 | ||
I plan to use the online food delivery platform in the future | 0.934 | ||
I expect my use of the online food delivery platform to continue in the future | 0.927 |
Variable | Perceived Usefulness | Perceived Ease of Use | Enjoyment | Trust | Social Influence | Attitude | Behavior Intention |
---|---|---|---|---|---|---|---|
Perceived usefulness | 0.836 | ||||||
Perceived ease of use | 0.783 | 0.862 | |||||
Enjoyment | 0.637 | 0.475 | 0.827 | ||||
Trust | 0.799 | 0.724 | 0.675 | 0.866 | |||
Social influence | 0.357 | 0.230 | 0.464 | 0.476 | 0.871 | ||
Attitude | 0.809 | 0.642 | 0.728 | 0.801 | 0.466 | 0.892 | |
Behavior intention | 0.763 | 0.615 | 0.499 | 0.725 | 0.406 | 0.763 | 0.938 |
Hypotheses | Beta | S.E. | Critical Ratio | p-Value | Decision | |
---|---|---|---|---|---|---|
H1 | AT -> BI | 0.499 *** | 0.092 | 5.413 | 0.000 | Supported |
H2a | PU -> AT | 0.461 *** | 0.086 | 5.366 | 0.000 | Supported |
H2b | PU -> BI | 0.475 *** | 0.098 | 4.864 | 0.000 | Supported |
H3 | EOU -> AT | −0.031 | 0.075 | −0.419 | 0.675 | Not supported |
H4a | EJM -> AT | 0.238 *** | 0.047 | 5.052 | 0.000 | Supported |
H4b | EJM -> BI | 0.241 *** | 0.061 | −3.968 | 0.000 | Supported |
H5a | TR -> AT | 0.302 *** | 0.077 | 3.929 | 0.000 | Supported |
H5b | TR -> BI | 0.240 ** | 0.092 | 2.602 | 0.009 | Supported |
H6a | SI -> AT | 0.062 | 0.032 | 1.942 | 0.052 | Not supported |
H6b | SI -> BI | 0.084 * | 0.039 | 2.143 | 0.032 | Supported |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jun, K.; Yoon, B.; Lee, S.; Lee, D.-S. Factors Influencing Customer Decisions to Use Online Food Delivery Service during the COVID-19 Pandemic. Foods 2022, 11, 64. https://doi.org/10.3390/foods11010064
Jun K, Yoon B, Lee S, Lee D-S. Factors Influencing Customer Decisions to Use Online Food Delivery Service during the COVID-19 Pandemic. Foods. 2022; 11(1):64. https://doi.org/10.3390/foods11010064
Chicago/Turabian StyleJun, Kyungyul, Borham Yoon, Seungsuk Lee, and Dong-Soo Lee. 2022. "Factors Influencing Customer Decisions to Use Online Food Delivery Service during the COVID-19 Pandemic" Foods 11, no. 1: 64. https://doi.org/10.3390/foods11010064