Volume 3, No. 6 June 2024 (
1359-1376)
p-ISSN 2980-4868 | e-ISSN 2980-4841
https://ajesh.ph/index.php/gp
Craving
Continuity: Unveiling the Impact of Integrating Information
System
Success and Expectation Confirmation Models on Sustained Use of Food Delivery
Apps
Enjelita Pasaribu1*, Daniel Tumpal Hamonangan Aruan2
Universitas
Indonesia, Central Jakarta, DKI Jakarta, Indonesia
Email:
enjelita.pasaribu@ui.ac.id1*, dtumpal@ui.ac.id2
ABSTRACT
This
study aims to investigates the complex dynamics of users' intention to continue
using food delivery applications, examining the interplay between information
system success and expectation confirmation models. Additionally, it explores
the influence of perceived promotion, perceived time saving, and the moderating
effect of trust on users' inclination to persist with these applications. To gather data, a quantitative survey was conducted among
462 users of food delivery applications, selected through non-probability sampling
methods. The collected data was then analyzed using Structural Equation
Modeling (SEM), employing the PLS-SEM technique. The findings reveal that all
the examined variables confirmation, perceived usefulness, system quality,
information quality, service quality, perceived enjoyment, perceived promotion,
perceived time saving, and satisfaction have a positive influence on the
intention to continue using food delivery apps. Moreover, the study highlights
the crucial role of trust in amplifying the relationship between user
satisfaction and their decision to continue using these services. This research
offers valuable insights into the complex dynamics of consumer behavior in the
digital age, particularly within the context of food delivery app usage. By
understanding the factors that drive user satisfaction and continued
engagement, industry practitioners and policymakers can develop more effective
strategies to improve service quality, optimize promotional efforts, and
ultimately enhance user experiences within this rapidly evolving landscape.
Keywords: Success
model, Expectation Confirmation Model, User Satisfaction, Food Delivery Apps
(FDAs), Indonesia, User Persistence, Trust.
INTRODUCTION
The
digital revolution has irrevocably reshaped consumer behavior, with the food
industry undergoing a particularly transformative shift
The Indonesian food delivery industry
has emerged as a dynamic and fiercely competitive sector characterized by rapid
platform proliferation and consumer behaviors marked
by low brand loyalty. Despite generating significant revenues—rising from $12.2
million in 2022 to an anticipated $33.2 million by 2027—the market exhibits a
concentration of power, with substantial players Grab and Gojek
dominating nearly 93% of the total Gross Merchandise Value, which was valued at
$4.5 billion in 2022
Despite extensive research on
continued usage intention within various digital contexts
Furthermore, the scope of existing
research often narrows to prominent platform-toconsumer
services like GoFood, GrabFood,
and ShopeeFood, while restaurant-to-consumer
platforms that operate their applications are less studied
This research focuses on consumer
perceptions and behaviors influenced by perceived
promotions and time savings
The main goal of the study is to
explore two key research questions: 1. How does user satisfaction with app
features, as defined by the ISS Model, combined with the fulfillment
of user expectations, as outlined in the ECM, influence the continued use of
food delivery apps? 2. What role does trust play as a moderating factor in the
sustained use of these apps, particularly when considering the integration of
the ISS Model with the ECM? This paper makes significant contributions to
information systems and service management. First, it extends the application
of the ISS Model (DeLone and McLean, 2003) and the ECM (Bhattacherjee,
2001) to the context of food delivery apps. This area has seen exponential
growth, yet there is limited scholarly examination of this theoretical
intersection.
Our results provide new insights into
how user satisfaction and service quality perceptions influence the continued
use of these platforms, filling a crucial gap in contemporary service
management literature. Secondly, by integrating these models, this study offers
a more comprehensive framework for understanding the drivers of sustained app
engagement. We empirically demonstrate that both perceived system quality and
confirmation of expectations are critical in shaping user retention decisions,
an essential factor for the economic viability of app-based services. This
enhanced understanding aids practitioners in designing more effective user
engagement strategies, which are vital for maintaining competitive advantage in
the fast-paced digital marketplace.
Lastly, our research methodologies
adapt and validate measurement scales in mobile commerce, contributing robust
tools for the future. In summary, this study investigates the impact of
combining ISS and ECM Models on the prolonged use of food delivery applications.
It seeks to determine the crucial factors contributing to ongoing user
engagement and satisfaction with these platforms.
Sample and Data Collection
This study utilizes a quantitative,
descriptive, and cross-sectional survey method. A
non-probability purposive sample of 462 participants over 18 years old, who had
used food delivery apps and made transactions within the last three months, was
recruited via online platforms. Data were collected using a rigorously validated
questionnaire, comprising 41 items (Table 1) on a 7-point Likert scale, distributed
through Google Forms. The questionnaire underwent wording tests and pre-tests
with five active users, followed by a pilot test with 30 respondents, ensuring
content validity and clarity. Participants who failed the screening question or
exhibited straight-lining response patterns were excluded, ensuring data
quality. The final sample of 462 valid responses was subjected to further
analysis to examine the specific research objectives.
Table
1. Construct and Measurement Items
|
Construct |
Citation |
Items |
|
Confirmation Satisfaction Perceived Usefulness Perceived Enjoyment System Quality Information Quality Service Quality Perceived Promotion Perceived Time
Saving Trust Continuance Intention |
(Foroughi
et al., 2023) (Zhao & Bacao, 2020) (Yen,
2023) (Yen,
2023) (Wang
et al., 2019) (Hoang & Le
Tan, 2023) (Zhong & Chen,
2023) (Yao & Li,
2024) (Yao & Li,
2024) (Zhao & Bacao, 2020) (Yao & Li,
2024) |
C1: My experience with using
Food Delivery Apps was better than I expected. C1: My experience with using
Food Delivery Apps was better than I expected. C3: Overall, most of the
expectations from using Food Delivery Apps were confirmed. S1: I am very satisfied that
Food Delivery Apps meet my requirements. S2: I am satisfied with Food
Delivery Apps S3: My interaction with the
Food Delivery Apps is very satisfying. S4: I think I did the right
thing by using Food Delivery Apps PU1: I can efficiently use
the Food Delivery Apps at anytime and anywhere PU2: I can find restaurant
information and use Food Delivery Apps anytime and anywhere PU3: It is convenient to
find restaurant information and use Food Delivery Apps PU4: Using the Food Delivery
Apps is useful to my daily life PE1: Using the Food Delivery
Apps is truly fun. PE2: I kill time by finding
restaurant information by using the Food Delivery Apps PE3: I use the Food Delivery
Applications not because I have to but because want to PE4: Compared to other
platforms, the time spent using the Food Delivery Apps is truly enjoyable. PE5: I enjoy reading about
the restaurant by using the Food Delivery Apps PE6: Using the Food Delivery
Apps itself is enjoyable to me. SQ1: This app is
user-friendly. SQ2: This app is easy to
use. SQ3: This app has high
reliability without errors. SQ4: This app has high
efficiency IQ1: Food Delivery Apps
allow me to search and get the information I need quickly and efficiently. IQ2: The information
provided by Food Delivery Apps is accurate and reliable. IQ3: The Information
provided by Food Delivery Apps is easy to understand and clear IQ4: Food Delivery Apps
helps me get real-time updates SEQ1: Food Delivery Apps
quickly respond to my needs SEQ2: The Food Delivery Apps
has the knowledge to answer my questions SEQ3: The Food Delivery Apps
understand my specific needs PPT1: Discounts on the Food
Delivery Apps are encouraging me to place orders PPT2: Coupons on the Food
Delivery Apps are important to my decision to place orders. PPT3: Promotions on the Food
Delivery Apps are attractive when I place orders. PPT1: Discounts on the Food
Delivery Apps are encouraging me to place orders PPT2: Coupons on the Food
Delivery Apps are important to my decision to place orders. PPT3: Promotions on the Food
Delivery Apps are attractive when I place orders. T1: I believe Food Delivery
Apps are trustworthy. T2: I believe Food Delivery
Apps keep customer’s interests in mind. T3: I felt secure in
ordering and receiving delivery food through the Food Delivery Apps T4: The information provided
by Food Delivery Applications is reliable. CI1: I intend to continue
using Food Delivery Apps in the future. CI2: I will always try to
use Food Delivery Apps in my daily life. CI3: I plan to continue to
use Food Delivery Apps frequently. |
|
|
|
|
Measures
In this comprehensive research endeavor, we leveraged established scales from existing literature to thoroughly assess each variable
within our proposed conceptual model.
Following meticulous data collection, rigorous preliminary analyses were
conducted, encompassing outlier screening and
normality assessment. This meticulous approach ensured data integrity and
minimized potential statistical errors, bolstering the robustness of our
findings. To validate the research constructs, we employed SmartPLS-4
(v.4.1.02) to perform comprehensive tests for construct, convergent, and
discriminant validity.
Furthermore,
we utilized partial least squares structural equation modeling (PLS-SEM) to
delve into the intricate direct and indirect relationships within our research
model and hypotheses. This sophisticated statistical technique allowed us to
uncover the underlying mechanisms and complex interactions between variables,
providing a deeper understanding of the phenomena under investigation. By
rigorously examining these relationships, we gained valuable insights into the
dynamics that drive our model, ultimately contributing to a more comprehensive
and nuanced interpretation of our research findings.
Figure
1. Research framework
RESULTS
AND DISCUSSION
Data for this study was collected through a
questionnaire distributed via Google Forms. Before the full distribution, a
pilot test with five individuals was conducted to assess the clarity and
comprehensibility of the questionnaire items. The pre-test phase involved 30
respondents and included both validity and reliability testing of the
questionnaire. Validity tests confirmed that all indicators met the required
criteria (KMO, Anti-Image Correlation, and Component Matrix values exceeding
0.5) and Bartlett’s Test Sphericity <0.05, ensuring the questionnaire's suitability
for the main study. Reliability tests further demonstrated the questionnaire's
consistency, with all variables achieving a Cronbach's Alpha value above 0.5.
These results validate the reliability of the measurement instrument and allow
the study to proceed to the main testing phase.
Main-Test
Assessment of Measurement Model
We assessed the reliability and validity of our
eight reflective measurement scales using composite reliability (CR) and
Cronbach’s alpha (α). Convergent validity was evaluated with average variance
extracted (AVE), while discriminant validity was assessed using the Fornell-Larcker criterion and Heterotrait-Monotrait
(HTMT) ratio (Hair et al., 2019, 2022; Henseler et
al., 2015). As detailed in Table 1, all
CR and α values surpassed the 0.7 threshold, confirming the reliability of our
latent constructs. Additionally, significant t-statistics and AVE values above
0.5 indicate convergent validity. Discriminant validity results (Tables 2 and
3) show that AVE values (diagonal) are greater than squared correlations
(off-diagonal) with any other construct. Furthermore, HTMT ratios fall below
0.90, with no cross-loadings
Table 2. Composite Reliability &
Convergent Validity
|
Construct |
Outer loadings |
Cronbach's alpha |
Composite reliability (rho_a) |
Composite reliability (rho_c) |
(AVE) |
|
|
Confirmation |
C1 |
0.897 |
0.875 |
0.876 |
0.923 |
0.800 |
|
Confirmation |
C2 |
0.906 |
||||
|
Confirmation |
C3 |
0.879 |
||||
|
Continuance
Intention |
CI1 |
0.894 |
0.879 |
0.880 |
0.925 |
0.805 |
|
Continuance
Intention |
CI2 |
0.911 |
||||
|
Continuance
Intention |
CI3 |
0.888 |
||||
|
Information Quality |
IQ1 |
0.833 |
0.865 |
0.867 |
0.908 |
0.712 |
|
Information Quality |
IQ2 |
0.873 |
||||
|
Information Quality |
IQ3 |
0.867 |
||||
|
Information Quality |
IQ4 |
0.799 |
||||
|
Perceived Enjoyment |
PE1 |
0.745 |
0.883 |
0.889 |
0.911 |
0.630 |
|
Perceived Enjoyment |
PE2 |
0.784 |
||||
|
Perceived Enjoyment |
PE3 |
0.752 |
||||
|
Perceived Enjoyment |
PE4 |
0.819 |
||||
|
Perceived Enjoyment |
PE5 |
0.834 |
||||
|
Perceived Enjoyment |
PE6 |
0.825 |
||||
|
Perceived Promotion |
PPT1 |
0.888 |
0.855 |
0.855 |
0.912 |
0.775 |
|
Perceived Promotion |
PPT2 |
0.881 |
||||
|
Perceived Promotion |
PPT3 |
0.873 |
||||
|
Perceived Time
Saving |
PTS1 |
0.895 |
0.876 |
0.878 |
0.924 |
0.801 |
|
Perceived Time
Saving |
PTS2 |
0.903 |
||||
|
Perceived Time
Saving |
PTS3 |
0.887 |
||||
|
Perceived Usefulness |
PU1 |
0.880 |
0.887 |
0.888 |
0.922 |
0.748 |
|
Perceived Usefulness |
PU2 |
0.878 |
||||
|
Perceived Usefulness |
PU3 |
0.869 |
||||
|
Perceived Usefulness |
PU4 |
0.830 |
||||
|
Satisfaction |
S1 |
0.876 |
0.885 |
0.887 |
0.921 |
0.744 |
|
Satisfaction |
S2 |
0.887 |
||||
|
Satisfaction |
S3 |
0.856 |
||||
|
Satisfaction |
S4 |
0.831 |
||||
|
Service Quality |
SEQ1 |
0.875 |
0.851 |
0.852 |
0.910 |
0.771 |
|
Service Quality |
SEQ2 |
0.895 |
||||
|
Service Quality |
SEQ3 |
0.864 |
||||
|
System Quality |
SQ1 |
0.823 |
0.858 |
0.859 |
0.904 |
0.702 |
|
System Quality |
SQ2 |
0.824 |
||||
|
System Quality |
SQ3 |
0.852 |
||||
|
System Quality |
SQ4 |
0.851 |
||||
|
Trust |
T1 |
0.844 |
0.886 |
0.888 |
0.921 |
0.745 |
|
Trust |
T2 |
0.857 |
||||
|
Trust |
T3 |
0.889 |
||||
|
Trust |
T4 |
0.863 |
||||
|
Trust x Satisfaction |
T x S |
1.000 |
|
|
|
|
Table 3. Discriminant validity: Heterotrait-Monotrait Ratio (HTMT).
|
|
C |
CI |
IQ |
PE |
PPT |
PTS |
PU |
S |
SEQ |
SQ |
T |
TxS |
|
C |
|
|
|
|
|
|
|
|
|
|
|
|
|
CI |
0.778 |
|
|
|
|
|
|
|
|
|
|
|
|
IQ |
0.749 |
0.875 |
|
|
|
|
|
|
|
|
|
|
|
PE |
0.754 |
0.674 |
0.751 |
|
|
|
|
|
|
|
|
|
|
PPT |
0.711 |
0.752 |
0.685 |
0.590 |
|
|
|
|
|
|
|
|
|
PTS |
0.715 |
0.815 |
0.730 |
0.610 |
0.707 |
|
|
|
|
|
|
|
|
PU |
0.717 |
0.859 |
0.770 |
0.667 |
0.645 |
0.774 |
|
|
|
|
|
|
|
S |
0.775 |
0.860 |
0.853 |
0.727 |
0.627 |
0.734 |
0.793 |
|
|
|
|
|
|
SEQ |
0.632 |
0.761 |
0.681 |
0.608 |
0.522 |
0.660 |
0.661 |
0.717 |
|
|
|
|
|
SQ |
0.731 |
0.843 |
0.751 |
0.665 |
0.656 |
0.694 |
0.806 |
0.827 |
0.637 |
|
|
|
|
T |
0.624 |
0.772 |
0.672 |
0.555 |
0.640 |
0.650 |
0.715 |
0.651 |
0.597 |
0.707 |
|
|
|
TxS |
0.192 |
0.316 |
0.341 |
0.238 |
0.308 |
0.297 |
0.293 |
0.267 |
0.278 |
0.325 |
0.510 |
|
Note: C=Confirmation,
CI=Continuance Intention; IQ=Information Quality; PE=Perceived Enjoyment;
PPT=Perceived Promotion; PTS=Perceived Time Saving; PU=Perceived Usefulness;
S=Satisfaction; SEQ=Service Quality; SQ=System Quality; T=Trust.
Table 4. Discriminant validity:
Fornell-Larcker Criterion.
|
|
C |
CI |
IQ |
PE |
PPT |
PTS |
PU |
S |
SEQ |
SQ |
T |
|
C |
0.894 |
|
|
|
|
|
|
|
|
|
|
|
CI |
0.684 |
0.897 |
|
|
|
|
|
|
|
|
|
|
IQ |
0.650 |
0.764 |
0.844 |
|
|
|
|
|
|
|
|
|
PE |
0.668 |
0.601 |
0.662 |
0.794 |
|
|
|
|
|
|
|
|
PPT |
0.616 |
0.652 |
0.589 |
0.513 |
0.880 |
|
|
|
|
|
|
|
PTS |
0.626 |
0.716 |
0.636 |
0.544 |
0.611 |
0.895 |
|
|
|
|
|
|
PU |
0.633 |
0.760 |
0.674 |
0.595 |
0.563 |
0.683 |
0.865 |
|
|
|
|
|
S |
0.682 |
0.760 |
0.748 |
0.651 |
0.546 |
0.647 |
0.703 |
0.863 |
|
|
|
|
SEQ |
0.546 |
0.659 |
0.585 |
0.535 |
0.446 |
0.571 |
0.575 |
0.622 |
0.878 |
|
|
|
SQ |
0.634 |
0.734 |
0.646 |
0.584 |
0.561 |
0.602 |
0.704 |
0.722 |
0.546 |
0.838 |
|
|
T |
0.550 |
0.682 |
0.590 |
0.492 |
0.557 |
0.574 |
0.636 |
0.578 |
0.518 |
0.618 |
0.863 |
Note: C=Confirmation,
CI=Continuance Intention; IQ=Information Quality; PE=Perceived Enjoyment;
PPT=Perceived Promotion; PTS=Perceived Time Saving; PU=Perceived Usefulness; S=Satisfaction;
SEQ=Service Quality; SQ=System Quality; T=Trust.
Assessment of Structural Model
We
assessed the suggested relationships using multiple criteria, including VIF,
standardized path coefficient, t-statistics, coefficient of determination (R2),
predictive relevance (Q2), effect size (f2), and
standardized root mean square residual (SRMR). As shown in Table 5 and Table 6,
the R2 values for CI, PE, PU, and S are 0.782, 0.446, 0.400, and
0.708, respectively. The Q2 values for CI, PE, PU, and S are 0.0751,
0.441, 0.397, and 0.679, respectively, demonstrating the inner model's
predictive relevance. F2 of each predictor in the inner model ranged
from 0.007 to 0.805, while the SRMR value was 0.097, i.e., less than the
threshold value of 0.10. Therefore, the results still indicate a good model fit
for the structural model.
Table 5. Collinearity and F-square
|
|
VIF |
f-square |
|
Confirmation Perceived Enjoyment |
1.000 |
0.805 |
|
Confirmation Perceived Usefulness |
1.000 |
0.667 |
|
Confirmation Satisfaction |
2.384 |
0.025 |
|
Information Quality Continuance Intention |
2.833 |
0.066 |
|
Information Quality Satisfaction |
2.566 |
0.107 |
|
Perceived Enjoyment Satisfaction |
2.246 |
0.011 |
|
Perceived Promotion Continuance Intention |
1.925 |
0.031 |
|
Perceived Time
Saving Continuance Intention |
2.449 |
0.025 |
|
Perceived Usefulness
Continuance Intention |
2.929 |
0.036 |
|
Perceived Usefulness
Satisfaction |
2.568 |
0.023 |
|
Satisfaction Continuance Intention |
3.297 |
0.020 |
|
Service Quality Continuance Intention |
1.875 |
0.040 |
|
Service Quality Satisfaction |
1.775 |
0.033 |
|
System Quality Continuance Intention |
2.695 |
0.030 |
|
System Quality Satisfaction |
2.407 |
0.081 |
|
Trust Continuance Intention |
2.364 |
0.038 |
|
Trust x Satisfaction
Continuance Intention |
1.319 |
0.007 |
Table
6. Coefficient determination
|
Variabel |
R-square |
R-square adjusted |
|
Continuance
Intention |
0.782 |
0.778 |
|
Perceived Enjoyment |
0.446 |
0.445 |
|
Perceived Usefulness |
0.400 |
0.399 |
|
Satisfaction |
0.708 |
0.703 |
Table 7. Predictive relevance (Q²)
|
Q²predict |
RMSE (Root Mean Square Error) |
MAE (Mean Absolute Error) |
|
|
Continuance
Intention |
0.751 |
0.501 |
0.376 |
|
Perceived Enjoyment |
0.441 |
0.750 |
0.597 |
|
Perceived Usefulness |
0.397 |
0.780 |
0.598 |
|
Satisfaction |
0.679 |
0.569 |
0.403 |
Table 8. Model fit
|
Saturated model |
Estimated model |
|
|
SRMR |
0.046 |
0.097 |
|
Chi-square |
2.697.277 |
2.877.722 |
|
NFI |
0.821 |
0.809 |
We
investigated the relationships between the constructs within our proposed
model; we employed a bootstrapping procedure with 10,000 resamples, utilizing a
sample of 462 cases. We used the standardized path coefficients and their
statistical significance as primary indicators for evaluating these
relationships. A path coefficient was considered significant if its empirical
t-value surpassed 1.96 at a 5% significance level. As shown in Table 8 and Fig.
2, all paths exhibited statistical significance. This result indicates a strong
alignment between our collected data and the theoretical expectations outlined
in our hypotheses. This robust empirical support significantly bolsters the
credibility and validity of our findings, suggesting that our research is
firmly grounded in a solid theoretical foundation.
Moreover,
the acceptance of all hypotheses underscores the methodological rigor employed
in this study and the representativeness of our sample. Interestingly, our
analysis revealed that information quality (IQ) had a greater impact on
satisfaction (S) than either service quality (SEQ) or system quality (SQ), with
path coefficient values of 0.283, 0.240, and 0.130, respectively. Additionally,
we identified that information quality (IQ) and perceived usefulness (PU) were
the strongest predictors of continued intention to use (CI), with path
coefficient values of 0.202 and 0.152, respectively. This influence surpassed
the effects observed for service quality (SEQ), system quality (SQ), perceived
promotion (PPT), perceived time saving (PTS), and satisfaction (S).
Furthermore,
our results demonstrate that confirmation (C) exerted the most substantial
impact on perceived enjoyment (PE), with a path coefficient value of 0.668.
This was followed closely by perceived usefulness (PU) with a value of 0.633.
These comprehensive findings contribute significantly to our understanding of
the intricate relationships between the various constructs within the proposed
model. They also provide valuable insights for practitioners seeking to enhance
customer satisfaction and foster long-term loyalty.
Table 9. Hypothesis testing results
|
Hypothesis |
Structural Paths |
Standardized Path Coefficient |
T statistics |
P values |
Supported |
|
H1 |
Confirmation Perceived Usefulness |
0.633 |
18.020 |
0.000 |
Yes |
|
H2 |
Confirmation Satisfaction |
0.133 |
2.941 |
0.002 |
Yes |
|
H3 |
Confirmation Perceived Enjoyment |
0.668 |
20.137 |
0.000 |
Yes |
|
H4 |
Perceived Enjoyment Satisfaction |
0.086 |
1.910 |
0.028 |
Yes |
|
H5 |
Service Quality Satisfaction |
0.130 |
3.456 |
0.000 |
Yes |
|
H6 |
System Quality Satisfaction |
0.240 |
4.629 |
0.000 |
Yes |
|
H7 |
Information QualitySatisfaction |
0.283 |
5.596 |
0.000 |
Yes |
|
H8 |
Perceived Usefulness
Satisfaction |
0.133 |
2.986 |
0.001 |
Yes |
|
H9 |
Perceived Usefulness
Continuance Intention |
0.152 |
3.101 |
0.001 |
Yes |
|
H10 |
Service Quality Continuance Intention |
0.127 |
3.367 |
0.001 |
Yes |
|
H11 |
System QualityContinuance Intention |
0.134 |
2.534 |
0.006 |
Yes |
|
H12 |
Information QualityContinuance Intention |
0.202 |
3.938 |
0.000 |
Yes |
|
H13 |
Perceived PromotionContinuance Intention |
0.115 |
2.944 |
0.002 |
Yes |
|
H14 |
Perceived Time
SavingContinuance Intention |
0.116 |
3.027 |
0.001 |
Yes |
|
H15 |
Trust x Satisfaction Continuance Intention |
0.042 |
1.870 |
0.031 |
Yes |
|
H16 |
Satisfaction Continuance Intention |
0.119 |
2.190 |
0.014 |
Yes |
Figure 2. Structural Model
DISCUSSION
This
study aimed to investigate the effects of predictors (i.e., satisfaction,
perceived enjoyment, perceived usefulness, confirmation, system quality,
service quality, information quality, moderating effects of trust on continued
intention to use food delivery applications. The results indicate that
satisfaction positively affected continued intention. This result collaborates
with findings of many previous studies on food delivery appplications
The
results indicate a strong positive relationship between confirmation and
perceived usefulness, corroborating previous research in the technology
acceptance domain. When user expectations about a food delivery app are met or
exceeded, this confirmation significantly enhances their perception of the
app's utility and value, aligning with the core tenets of the Expectation
Confirmation Model. Previous study has indicated that confirmation positively
affects perceived usefulness
The
findings reveal that confirmation not only influences perceived usefulness but
also plays a pivotal role in shaping overall satisfaction and perceived
enjoyment. This is consistent with the concept of expectation disconfirmation,
where positive disconfirmation (exceeding expectations) leads to heightened
satisfaction and enjoyment (Bhattacherjee, 2001).
Multiple studies have recognized the concept of confirmation as a significant
determinant of perceived enjoyment
The
positive relationship between perceived enjoyment and satisfaction suggests
that users who derive pleasure and enjoyment from using the app are more likely
to be satisfied with their overall experience. This aligns with the hedonic
aspect of technology adoption, emphasizing the importance of creating a
user-friendly and enjoyable interface. Studies
have consistently found that enjoyment positively impacts satisfaction with
technology usage
The empirical findings
unequivocally affirm the pivotal role of service quality in shaping user
satisfaction and fostering continued engagement within the food delivery
landscape. Consistent with seminal research
Similarly, the technical
performance and reliability of the app, as reflected in system quality,
significantly influences user satisfaction and continued usage. DeLone &
McLean's (2003) emphasis on a well-functioning and user-friendly interface, the
positive relationship between system quality and satisfaction underscores the
importance of a seamless user experience. Additionally, the positive impact of
system quality on continuance intention demonstrates that a reliable and
efficient app is instrumental in retaining users, as it facilitates a
frictionless and enjoyable interaction with the platform
The
results indicate that information quality positively affected satisfaction and continuance
intention as the strongest predictor of continued intention in the current
model. While many previous studies on FDAs have confirmed the effects of
information quality on relevant consumer behavior
The
empirical findings of this study confirm the positive impact of perceived
usefulness on both satisfaction and continuance intention in the context of
food delivery apps. This corroborates existing research
The empirical findings of
this study reveal that perceived promotion is a key driver of continued
intention to use food delivery apps. This corroborates previous research
highlighting the influence of price-related factors on consumer behavior in
this context
Additionally, perceived time
saving was identified as another significant extrinsic motivator for continued
intention, particularly among employed individuals with limited time for food
preparation or dining out
Furthermore,
this study delved into the moderating effect of trust on the relationship
between satisfaction and continuance intention. The findings suggest that trust
acts as a catalyst, amplifying the positive impact of satisfaction on users'
intention to continue using the food delivery app. In essence, when users have
a high level of trust in the platform, their satisfaction becomes an even
stronger predictor of their continued engagement
This research contributes to the
growing body of knowledge on user behavior in food
delivery applications by demonstrating the critical role of user confirmation,
perceived usefulness, and service, system, and information quality in shaping
user satisfaction and continuance intention. These findings align with the
Information Systems Success Model and Expectation Confirmation Theory, further
emphasizing the importance of meeting user expectations and delivering a
seamless, enjoyable, and valuable user experience. The study's results
highlight the multifaceted nature of user satisfaction in the context of food
delivery apps. Beyond the functional aspects of service quality and system quality,
perceived enjoyment and time-saving benefits also play significant roles in
fostering user engagement and loyalty. This suggests that food delivery app
providers must adopt a holistic approach to app development and management,
addressing both the functional and hedonic aspects of the user experience.
While this research offers valuable insights, it is not without limitations.
The reliance on self-reported survey data may introduce biases, and the
specific demographic and cultural context of the study may limit the
generalizability of the findings. Future research could address these
limitations by employing diverse methodologies and expanding the sample to
include a wider range of users. Despite these limitations, the theoretical and
practical implications of this study are significant. By confirming and
extending existing theories on user behavior, this
research contributes to a deeper understanding of the factors that drive user
satisfaction and continuance intention in the context of food delivery
applications. From a practical standpoint, this research provides actionable
insights for food delivery app providers. It underscores the importance of
prioritizing user satisfaction through continuous improvement in service
quality, system functionality, and information accuracy. Moreover, it
highlights the need to effectively communicate the app's benefits, focusing on
convenience, time saving, and enjoyment, to enhance user engagement and
loyalty. By fostering a culture of trust and transparency, food delivery app
providers can further strengthen the relationship between user satisfaction and
continued app use. In conclusion, this study offers a comprehensive and nuanced
understanding of the factors influencing user behavior
in food delivery applications. By addressing the identified key drivers of user
satisfaction and continuance intention, food delivery app providers can enhance
their competitiveness, foster a loyal user base, and achieve sustained success
in this rapidly evolving market.
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Copyright
holder: Enjelita Pasaribu,
Daniel Tumpal Hamonangan Aruan (2024) |
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