The ease use of Online Food Delivery

1. 1
No
Author/
Date
Country Variable Method Sample/Respondent Title
1.
Ngyuyen et al.,
(2023)
Taiwan Limited time and space Quantitative
Cross-sectional Survey
Reliability Investigate exact
reliability under limited
time and space of a
multistate online food
delivery network
2. Liboredo et al.,
(2022)
Brazil
Behavior
Mix mode
Qualitative Interview
Quantitative survey
Consumer (purchase
meal)
Food Delivery before
and during COVID
19 Pandemic in Brazil
3. Lee et al.,(2022) Korea Technology
Acceptance
Usefulness
Ease of use
Intention of use
Quantitative Food Delivery Apps
User
Application of the
technology acceptance
model to food delivery
apps.
4. Lee et al.(2022) Macau Customer brand loyalty Quantitative Food Delivery Apps
User
Food delivery
application quality in
customer brand loyalty
formation: Identifying
its antecedent and
outcomes
5. Traynor et al.,
(2022)
US Adoption Motivation
Operational Changes
Impact
Future Strategies
Quantitative Employee Investigating the
emergence of third-
party online food
delivery in the U.S.
restaurant industry: A
grounded theory
approach
6. Pitchay et
al.,(2022)
Malaysia Customer intention
Attitude
Performance Expectancy
Effort Expectancy
Social Influence
Information Quality
Price Saving Orientation
Qualitative
(existing theory)
Consumer Determinants of
customers' intention to
use online food delivery
application through
smartphone in Malaysia

2. Time Saving Orientation
7. Kwon J et al.,
(2022)
Korea Perceived Equity
Loyalty
Consumer Behavior
Sustainability
Value
Brand
Relationship
Qualitative Consumer Role of customers’
multidimensional
perceived equity in the
mobile food delivery
industry
8. Kwon J et.al.,
(2022)
Korea Customers behavior
Perceived value
Positive attitude
Satisfaction
Attitudinal loyalty
Service Value
Process Value
Relationship Value
Qualitative Consumer Multidimensional value
of customers' mobile
service experiences in
the food service context
9. Li et al., (2022) Taiwan Demand
Behavior
Price Variation
Quantitative Consumer Service pricing strategy
of food delivery
platform operators: A
demand-supply
interaction model
10. Bivona (2022) Italy Performance Management
Growth strategies
Attractiveness
Mix Method Food Delivery
Platforms
Determinants of
performance drivers in
online food delivery
platforms: a dynamic
performance
management
perspective
11. Shankar et
al.,(2022)
India Systematic Quantitative NA - Literature
Review
Online food delivery: A
systematic synthesis of
literature and a
framework
development
12. Wang et
al.,(2022)
China Quantify the nature
Performance
Quantitative Cities
Different Lockdown
How online food
delivery platforms

3. Positive Contribution Reopening measures contributed to the
resilience of the urban
food system in China
during the COVID-19
pandemic
13. Chan and Gao
(2021)
China Service
E-Quality
Food Quality
Mixed method
Quantitative
Qualitative
Consumer Measuring the up-to-
date quality of online
food delivery:
formative index
construction
14. Sharma et al.,
(2021)
India Consumer Behavior
Usage
Attitude
Consciousness Impacts
Trust
Price
Consciousness
Perceived Severity
Interface issue and Quality
Qualitative Consumer Over-ordering and food
waste: The use of food
delivery apps during a
pandemic
15. Kumar et al.,
(2021)
India Aesthetic Appeal
Formality
Dominance
Pleasure
Arousal
Continued Usage Intentions
Quantitative Independent Expert Revisiting food delivery
apps during COVID-19
pandemic?
Investigating the role of
emotions
16. Gavilan et
al.,(2021)
Spain Experiential Value
Willingness
Fear
Perceived
Utilitarian Value
Qualitative Consumer (standard
delivery, unboxing
,home chef, and diy
meal kit)
Innovation in online
food delivery:
Learnings from
COVID-19
17. Tandon et al.,
(2021)
Finland Visibility
Attitude
Functional, Social,
Conditional Value
Quantitative Consumer Why do people
purchase from food
delivery apps? A
consumer value
perspective

4. Purchase Intention
18. Annaraud et al.,
(2020)
US Increase
Customer Intentions
Satisfaction
Quality
Perceived
Satisfaction
Behavioral Intentions
Quantitative Consumer Predicting satisfaction
and intentions to use
online food delivery:
What really makes a
difference?
19. Zhao and Bacao
(2020)
Portugal Performance Expectancy
Effort
Social Influence
Trust
Perceived task
Satisfaction
Intention
Quantitative Consumer What factors
determining customer
continuingly using food
delivery apps during
2019 novel coronavirus
pandemic period?
20. Kaur et al., (2020) Finland Consumer Behavior
Value
Mixed Method Consumer The value proposition
of food delivery apps
from the perspective of
theory of consumption
value
21. Li et al., (2020) New Zealand Positive
Negative
Impact
Sustainability
Influence
Quantitative Literature Review
Document
Review of online food
delivery platforms and
their impacts on
sustainability
22. Chai and Yat
.,(2019)
Malaysia Perceived
Ease of Use
Time Saving
Motivation
Privacy & Security
Behavioral Intention
Quantitative Consumer Online Food Delivery
Services: Making Food
Delivery the New
Normal

5. 23. Griesbach et al.,
(2019)
US Satisfaction
App is Fair
Qualitative Employee Algorithmic Control
in Platform Food
Delivery Work
24. Suhartanto et
al.,(2018)
Loyalty
Influence
Satisfaction
Perceived Value
Quality
Quantitative Consumer Loyalty toward online
food delivery Service
: the role e-service
quality and food
quality
25.
26.
27.
28.
29.
30.