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Journal of Entrepreneurship & Business Journal homepage: https://journal.
id/index.
php/jerb Understanding Behavioral Intentions in Online Food Delivery Services:
The Role of E-SELFQUAL and Food Quality Jovanka Kristy Department of Management.
University of Surabaya.
Indonesia Fitri Novika Widjaja* Department of Management.
University of Surabaya.
Indonesia Prita Ayu Kusumawardhany Department of Management.
University of Surabaya.
Indonesia Article Info Keyword:
Behavioral Intentions.
Customer Satisfaction.
Customer Service.
Food Quality Received: 15-12-2025 Revised: 02-02-2026 Accepted: 03-02-2026 Published: 09-02-2026 JEL Classification Code:
M31.
L86.
Z13
Corresponding author:
fitri@staff.
DOI: 10.
24123/jeb.
Abstract Purpose: This study aims to examine the effect of perceived control, service convenience, customer service, service fulfillment, food quality on customer satisfaction and customer satisfaction on behavioral intentions in online food delivery service (OFDS) users.
Method: Respondents in this study were users of OFDS in Surabaya.
The data taken for this study were 200 respondents in Surabaya who had made transactions more than 2 times in the last 3 months on the OFDS provider application and respondents who had used the customer service feature.
The data that has been collected is then analyzed with the Structural Equation Modeling (SEM) measurement model using IBM SPSS version 24 and Smart PLS 4 Result: After the data processing process, the results show that the perceived control variable does not have a significant effect on customer satisfaction.
Meanwhile, the variables of service convenience, customer service, service fulfillment, and food quality have a positive and significant effect on customer satisfaction and the variable customer satisfaction has a positive and significant effect on behavioral intentions in OFDS users in Surabaya.
INTRODUCTION
In recent years, the OFDS industry has experienced remarkable growth in popularity, with an increasing number of consumers choosing to order food from home without having to visit restaurants in person.
This surge in OFDS usage has been driven by advancements in internet technology and changes in modern lifestyles, where consumers increasingly prioritize convenience and efficiency in food ordering.
With just a few clicks on their smartphones, customers can order desired food and beverages from various types of restaurants anytime and anywhere without needing to leave their homes or workplaces.
Increased internet access, availability of smart devices, improved purchasing power, and increasingly complex consumer lifestyle demands are compelling conventional businesses to continuously innovate and adapt to modern business models that utilize technology in fulfilling consumer needs and desires (Chowdhury, 2.
According to Statista .
in March, the OFDS industry has experienced significant With a CAGR (Compound Annual Growth Rat.
16% from 2024 to 2029, this Copyright A 2025 by Authors.
Published by University of Surabaya.
This is an open-access article under the CC BY License .
ttps://creativecommons.
org/licenses/by/4.
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industry promises revenues reaching US$20.
59 billion in 2024 and a market volume of US$41.
billion by 2029.
The development of smartphone technology, expanding internet connectivity, and rapid growth of the middle class in Indonesia represent significant macroeconomic components driving this growth.
Additionally, because Indonesia comprises thousands of islands and faces geographical challenges along with intense market competition, food distribution becomes more difficult and expensive.
To attract consumers, conventional business models and services must be transformed.
It is also known that the OFDS industry in Indonesia is not only experiencing significant growth but also faces specific challenges that require innovative strategies and rapid adaptation to changing consumer behaviors and market conditions.
Based on a Momentum Works report.
Grab Food dominates as the OFDS with the highest transaction value in Indonesia, accounting for 50% of Indonesia's GMV.
This percentage is estimated at US$2.
3 billion in 2023.
In second place.
Go Food's OFDS successfully captured 38% of sales with a transaction value of US$1.
74 billion.
Meanwhile, in third place is Shopee's food delivery service.
Shopee Food, which successfully captured 12% of sales with an estimated transaction value of US$552 million in 2023 in Indonesia (Goodstats.
id, 2.
Consumers' decisions to use OFDS are influenced by the level of satisfaction obtained from their experience using these services.
Consumers feel satisfied because the quality of service meets their expectations, both in terms of application quality and food quality provided by the application partners (Annaraud & Berezina, 2.
OFDS provides several positive impacts for society, including transportation cost savings, time efficiency, ease of ordering, safety during travel, and good service.
According to Zeithaml et al.
as cited in Suhartanto et al.
, online self-service quality describes the level of an online platform's ability to provide a smooth and satisfying shopping experience.
This includes customers' overall assessment of a website's effectiveness and efficiency in facilitating the purchasing process, from product browsing to delivery.
Although prior studies have extensively examined OFDS, empirical findings regarding the effects of eservice quality dimensions on customer satisfaction remain inconsistent.
Previous research reports contradictory results, particularly for service convenience and food quality, suggesting that their impacts are highly context dependent.
This research refers to the study by Annaraud and Berezina .
, which includes five independent variables: perceived control, service convenience, customer service, service fulfillment, and food quality, as well as two dependent variables: customer satisfaction and behavioral intentions.
Their research found that the service convenience variable does not significantly influence customer satisfaction.
Meanwhile, research conducted by McNeil and Young .
found that the service convenience variable significantly influences customer satisfaction.
In addition, the role of perceived control inshaping customer satisfaction is still unclear, as earlier studies suggest positive effects, while its relevance in highly digitalized and promotion-driven markets has not been sufficiently examined.
Moreover, most existing studies are conducted in developed countries, limiting the generalizability of findings to emerging markets such as Indonesia, where OFDS adoption is rapid, and competition among platforms is intense.
This study contributes to the literature by providing empirical evidence from Surabaya.
Indonesia, one of the most active OFDS markets in Southeast Asia.
It offers new insights by demonstrating that perceived control does not significantly influence customer satisfaction, challenging findings from prior studies conducted in developed economies.
This study adopts the e-SELFQUAL framework as operationalized by Annaraud and Berezina .
, who extended the e-service quality model to the context of OFDS.
This research aims to determine the relationship between e-SELFQUAL variables: perceived control, service convenience, customer service, service fulfillment, and food quality on customer satisfaction and their impact on behavioral intentions among OFDS users in Surabaya.
This research also aims to determine customer intentions and satisfaction in using OFDS through evaluation of service satisfaction, food quality, and platform quality available within OFDS
This research is expected to provide practical benefits for restaurants and OFDS
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providers by providing an understanding of the most influential factors in driving customer intentions to use OFDS.
The Influence of Perceived Control on Customer Satisfaction Based on research conducted by Bolt et al.
, perceived control is defined as an individual's personal assessment derived from their personal experiences.
Research conducted by Ryu.
Lee, and Kim .
shows that customer satisfaction, which involves a comprehensive evaluation of the perceived difference between prior expectations and actual consumption experience, has become a central point in research on the restaurant industry due to its positive correlation with customer behavioral intentions, including the desire to return and the dissemination of positive information through direct communication.
H1: Perceived control positively influences customer satisfaction.
The Influence of Service Convenience on Customer Satisfaction Research conducted by Berry et al.
in Annaraud and Berezina .
states that service convenience represents the consumer's perspective regarding time and effort associated with the transaction process or service utilization.
According to research conducted by Djan & Burhanudin .
, satisfaction is defined as the consumer's assessment process after a purchase is made.
if consumers choose a substitute product, that product minimally provides results comparable to what was expected or even exceeds consumer desires.
Conversely, dissatisfaction or discomfort with the service can occur when the received goods do not align with consumer H2: Service convenience positively influences customer satisfaction.
The Influence of Customer Service on Customer Satisfaction Based on research conducted by Annaraud and Berezina .
, the success of a service industry heavily depends on the company's ability to provide customer service that satisfies or even exceeds customer expectations.
According to research conducted by Meuter et al.
and Scherer et al.
, one of the main challenges faced by service providers is creating the right balance between cost savings and the quality of service provided.
Academics and other related parties have found that self-service can save time, reduce costs, and provide customers with a better experience when utilizing such services.
H3: Customer service positively influences customer satisfaction.
The Influence of Service Fulfillment on Customer Satisfaction Research conducted by Parasuraman et al.
in Annaraud and Berezina .
states that fulfillment is the level of compliance of a website or online service with the promises made regarding food order delivery and product or item availability.
In online transactions, fulfillment is an essential part because it comprises a series of actions to fulfill orders placed by customers, where the fulfillment of promises made by a service can significantly influence customer satisfaction (Wulandari & Istiyanto, 2.
H4: Service fulfillment positively influences customer satisfaction.
The Influence of Food Quality on Customer Satisfaction Research conducted by Namkung & Jang .
states that food quality encompasses various components, such as presentation, mix, nutritional choices, taste, satisfaction, and temperature of a food product.
According to research conducted by Zhong and Moon .
, food product quality is identified as one of the main selling procedures capable of fulfilling customer satisfaction, maintaining the customer base, and providing information about transactions that can satisfy customers.
H5: Food quality positively influences customer satisfaction.
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The Influence of Customer Satisfaction on Behavioral Intentions Based on research conducted by Suhartanto.
Helmi Ali.
Tan.
Sjahroeddin, and Kusdibyo .
it was found that in service businesses, service quality is crucial for achieving customer Previous studies have shown that online services from OFDS have measurement uniformity, and there is support between online services and customer satisfaction.
Tran & Vu .
define behavioral intentions as an important indicator for management to determine whether customers will maintain or reject a service or product.
H6: Customer satisfaction positively influences behavioral intentions.
RESEARCH METHODS
This research is classified as a type of basic research, where the research aim to develop previous research.
The research uses quantitative research methods to identify the extent to which independent variables (X) comprising dimensions of perceived control, service convenience, customer service, fulfillment, and food quality influence the mediating variable (Z) of customer satisfaction, and the dependent variable (Y) of behavioral intentions.
The research methodology used in this study includes a quantitative approach.
Furthermore, this research is classified as causal research since there are cause-and-effect relationships between variables in the The type of data used in this research is primary data, as it is obtained directly from respondents who have used OFDS.
Research data is collected through questionnaires by distributing Google form links.
The research questionnaire includes respondent identities such as name, gender, age, educational background, current occupation, total purchase transactions, and which features the respondents have used.
The measurement level used in this research is an interval scale using a seven-point Likert scale, where a score of 1 indicates that respondents strongly disagree and a score of 7 indicates that respondents strongly agree with the statements presented in the questionnaire.
The target population in this research is users of OFDS in Surabaya.
The characteristics of respondents in this study are those who have the Gojek application with Go Food feature.
Grab with Grab Food feature, and Shopee with Shopee Food Additionally, respondents must have used these services within the last 3 months with a minimum of 3 transactions, be Indonesian citizens residing in Surabaya, be at least 17 years old, and have used customer service features within the OFDS provider applications.
Sampling is conducted using non-probability sampling technique, which is a sampling technique that provides equal opportunity for all members of the population to form the sample.
Furthermore, purposive sampling is used in the method of determining the sample.
Purposive sampling is a technique used for sampling based on certain specific considerations.
Data testing in this research will be carried out using processing through Smart PLS 4 software.
Outer Model (Measurement Mode.
tests will be conducted, including Convergent Validity.
Discriminant Validity.
Composite Reliability tests, and Inner Model (Structural Mode.
tests which include measurements of R-Square/Coefficient of Determination and Estimate for Path Coefficients.
RESULTS & DISCUSSION
In the first phase of questionnaire distribution, 30 respondent data points were obtained that met the research criteria, which had previously been distributed through Google Forms.
After obtaining these 30 data points, validity and reliability tests were subsequently conducted.
In performing the validity test, this research employed the correlation method used by Pearson product moment, with an indicator considered valid if it has a significance level of O 0.
1 ( 10%) with a correlation value of Ou 0.
Meanwhile, the reliability test was conducted using Cronbach's alpha Ou 0.
These validity and reliability tests were performed utilizing IBM SPSS Statistics 24 software.
After the 30 respondent data points were declared valid and reliable, the second phase of questionnaire distribution was conducted through Google Forms until 200 respondents were collected.
The data obtained had previously been filtered to conform to the established research criteria.
Subsequently, the 200 respondent data points that met these criteria
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were analyzed for their measurement model and structural model, and hypotheses were also tested through Smart PLS 4 software.
The Outer Model (Measurement Mode.
testing was conducted through measurements of convergent validity, discriminant validity, and composite reliability.
Convergent validity can be observed based on the loading factor/outer loading value and Average Variance Extracted (AVE).
Table 1.
Loading Factor Value Perceived Control
Service Convenience Customer Service Service Fulfillment Food Quality Customer Satisfaction Behavioral Intentions Indicator PC1
PC2
PC3
SC1
SC2
SC3
CSR1
CSR2
CSR3
SF1
SF2
SF3
SF4
FQ1
FQ2
FQ3
FQ4
CS1
CS2
CS3
CS4
CS5
BI1
BI2
Loading Factor
0,823
0,847
0,792
0,717
0,852
0,860
0,753
0,866
0,811
0,719
0,750
0,743
0,802
0,700
0,740
0,755
0,807
0,748
0,775
0,811
0,776
0,712
0,933
0,942
Note Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Table 1 shows that the loading factor/outer loading value of each variable indicator has met the requirements, where the loading factor value Ou 0.
7 so that each variable indicator can be said to be valid.
Table 2.
Average Variance Extracted (AVE) Results Perceived Control Service Convenience Customer Service Service Fulfillment Food Quality Customer Satisfaction Behavioral Intentions E-ISSN 2721-706X Average Variance Extracted 0,674 0,660 0,658 0,569 0,565 0,585 0,879 Note Valid Valid Valid Valid Valid Valid Valid Journal of Entrepreneurship & Business.
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Table 2 shows that the Average Variance Extracted (AVE) value of each research variable has an AVE value Ou 0.
5 so that the research variables can be said to be valid and qualified.
The discriminant validity test is carried out based on the Fornell-Larcker Criterion value and cross loading.
The Fornell-Larcker value can be said to be qualified if the resulting value of each latent variable is greater than the correlation of the latent variable with other variables.
addition, the cross loading value can be qualified when the value of the indicators in each variable is greater than the indicators of the other variables.
The Fornell-Larcker Criterion and cross loading values are shown in the following table.
Table 3.
Fornell-Larcker Criterion Value CSR
0,938
0,328
0,240
0,269
0,296
0,163
0,212
CSR
0,765
0,581
0,683
0,524
0,545
0,630
0,811
0,569
0,566
0,407
0,616
0,752
0,470
0,479
0,573
0,821
0,542
0,589
0,812
0,385
0,754
Table 3 shows that all variables in the Fornell-Larcker Criterion test have qualified values.
As a marker, the value of the latent variable with the bolded number must be greater than the correlation value of the variable below it.
Table 4.
Cross Loading Value BI1
BI2
CS1
CS2
CS3
CS4
CS5
CSR1
CSR2
CSR3
FQ1
FQ2
FQ3
FQ4
PC1
PC2
PC3
SC1
SC2
SC3
SF1
SF2
SF3
SF4
0,933
0,942
0,310
0,177
0,255
0,327
0,168
0,194
0,215
0,173
0,241
0,091
0,212
0,245
0,206
0,291
0,230
0,192
0,051
0,158
0,204
0,163
0,117
0,158
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0,297
0,318
0,748
0,775
0,811
0,776
0,712
0,409
0,539
0,455
0,492
0,425
0,546
0,571
0,423
0,459
0,406
0,381
0,427
0,508
0,436
0,463
0,434
0,553
CSR
0,200
0,249
0,421
0,478
0,507
0,416
0,392
0,753
0,866
0,811
0,546
0,314
0,382
0,456
0,388
0,451
0,560
0,365
0,286
0,347
0,348
0,408
0,518
0,567
0,219
0,284
0,535
0,479
0,607
0,549
0,419
0,416
0,491
0,473
0,700
0,740
0,755
0,807
0,314
0,425
0,420
0,372
0,396
0,401
0,403
0,376
0,467
0,479
0,231
0,321
0,373
0,409
0,468
0,391
0,351
0,513
0,461
0,415
0,408
0,291
0,292
0,414
0,823
0,847
0,792
0,449
0,429
0,449
0,500
0,331
0,464
0,484
0,120
0,185
0,319
0,427
0,530
0,452
0,329
0,296
0,350
0,343
0,324
0,328
0,346
0,431
0,451
0,450
0,436
0,717
0,852
0,860
0,370
0,192
0,335
0,279
0,182
0,215
0,463
0,483
0,556
0,427
0,479
0,515
0,517
0,473
0,528
0,399
0,331
0,470
0,465
0,461
0,529
0,284
0,313
0,339
0,719
0,750
0,743
0,802
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Table 4 shows that all variables in the cross loading test have a qualified value, where each indicator in the research variable has a greater cross loading value when compared to indicators from other variables.
The value of each variable indicator can be seen in the bolded numbers in the table.
Composite reliability testing is seen based on the composite reliability and Cronbach's alpha values.
The value of composite reliability is acceptable if Ou 0.
Meanwhile, the Cronbach's alpha value can be said to be reliable if Ou 0.
Table 5.
Composite Reliability and CronbachAos Alpha Values Perceived Control
Service Convenience Customer Service Service Fulfillment Food Quality Customer Satisfaction Behavioral Intentions Composite Reliability 0,762 0,760 0,758 0,756 0,750 0,830 0,865 CronbachAos Alpha
0,759
0,740
0,741
0,748
0,744
0,823
0,862
Based on table 5, it is shown that each variable in this study is reliable.
This is shown in the composite reliability value of each variable Ou 0.
7 and the Cronbach's alpha value of each variable Ou 0.
In addition, there is Inner Model (Structural Mode.
testing which is carried out to determine the relationship between latent variables.
Inner model testing aims to determine the results of the research hypothesis whether it is supported or not supported.
Inner model testing is done through measuring R-Square and path coefficients.
Testing the R-Square value aims to check the strength level of the dependent variable.
The R^2 value has a range between 0 and 1, if the value is greater, it shows stronger power.
The following will show a table of R-Square values.
Table 6.
R-Square Result R-Square 0,603 0,108 Customer Satisfaction Behavioral Intentions Table 6 shows that the R-Square value on the customer satisfaction variable is 0.
603 so that it is included in the moderate category because it has an R-Square value > 0.
Meanwhile, the R-Square value on the behavioral intentions variable is 0.
108 so that it is included in the weak category because it has an R-Square value < 0.
Table 7.
Hypothesis Test Results Path
PC Ie CS
SC Ie CS
CSR Ie CS
SF Ie CS
FQ Ie CS
CS Ie BI
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Original Sample 0,019 0,216 0,123 0,257 0,354 0,328 Standard
Deviation 0,079 0,077 0,074 0,111 0,093 0,084 T-Statistics P-Value Result
0,246
2,789
1,655
2,313
3,803
3,890
0,805
0,005***
0,098*
0,021**
0,000***
0,000***
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Based on the results of hypothesis testing in table 7, there are several supported hypotheses, namely H2.
H3.
H4.
H5 and H6.
Meanwhile, there is one hypothesis that is not supported, namely H1.
Figure 1.
Research Model Result Based on the hypothesis testing results, hypothesis 1 indicates no significant influence between the perceived control variable and customer satisfaction, thus the hypothesis is declared This result differs from the research findings of Annaraud and Berezina .
, which stated that the perceived control variable significantly influences customer satisfaction.
This research finding is supported by Nichola Robertson et al.
, who stated that perceived control has no influence on customer satisfaction beca Use most customers expect more online interactions.
Another factor is that customers are still confused about the steps that must be taken in ordering food online through applications, where the process from selecting a restaurant, choosing food to order, processing food payment, until the food delivery process is quite lengthy.
Additionally, customers do not understand how long it takes for transactions to complete, as payment transactions may take a long time if customers are constrained by signal issues.
For customers trying OFDS for the first time, they might also not know what information will be provided on each page in the application, necessitating easy-to-remember and understand food ordering steps for customers.
Based on the hypothesis testing results, hypothesis 2 indicates a significant influence between the service convenience variable and customer satisfaction, thus the hypothesis is This result differs from the research findings of Annaraud and Berezina .
, which stated that the service convenience variable does not significantly influence customer satisfaction.
This research finding is supported by Yo et al.
, who stated that convenience has a significant influence on customer satisfaction measured through three elements: time required, location, and purchasing process.
According to Chen.
Hsu, and Lin .
as cited in Yo et al.
, convenience when shopping online can help customers save time and effort when making Research results by Ing et al.
suggest that service convenience becomes an important non-monetary benefit and proves to play a significant role in improving overall customer satisfaction.
Ali & Naushad .
also state that customers typically prefer shopping experiences that are easy and do not require additional effort.
Customers prefer to reduce time
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and costs.
However, sometimes customers can change their minds about the food they want to Through OFDS, customers can update their orders easily without wasting much time.
Such easy and quick ordering certainly affects customer satisfaction.
Based on the hypothesis testing results, hypothesis 3 indicates a significant influence between the customer service variable and customer satisfaction, thus the hypothesis is This result aligns with the research findings of Annaraud and Berezina .
, which stated that the customer service variable significantly influences customer satisfaction.
This research finding is supported by Annaraud and Berezina .
, showing an influence between customer service and customer satisfaction among OFDS users, meaning that customer service can affect customer satisfaction where when customers experience an obstacle or problem, they certainly want that obstacle or problem to end quickly by reporting it or asking the customer service in the OFDS provider application first.
This research finding is also supported by Giao .
, who stated that to increase customer satisfaction, a business is responsible for protecting the rights of sellers and buyers to avoid losses.
Additionally, research conducted by Hasan et al.
states that the nature of service typically depicts customer satisfaction, such as helping sales teams select service providers and support, assisting in answering customer questions through provided media, providing necessary information, processing payments and completing transactions, and handling post-sales deliveries, making customer service quite influential on customer satisfaction.
This indicates that OFDS providers are also responsible and need to pay attention to problems or obstacles faced by customers and provide the best solutions so that customer satisfaction with the services provided can increase, and OFDS providers do not experience losses due to losing customers.
Research findings by Jaiswal & Singh .
also state that customer service influences customer satisfaction, where customer satisfaction with a service is shown in the reviews given by customers after using the service.
Before utilizing OFDS, customers who will use the service will certainly first look at reviews from previous customers who have used OFDS before deciding whether to use the service or not.
Based on the hypothesis testing results, hypothesis 4 indicates a significant influence between the service fulfillment variable and customer satisfaction, thus the hypothesis is This result aligns with the research findings of Annaraud and Berezina .
, which stated that the service fulfillment variable significantly influences customer satisfaction.
This research finding is supported by Annaraud and Berezina .
, showing an influence between service fulfillment and customer satisfaction among OFDS users, meaning that customer satisfaction can be fulfilled if OFDS such as Grab Food.
Go Food, and Shopee Food deliver customer orders on time and provide accurate information regarding services as promised.
Nhung & Ngan .
state that fulfillment is defined as a website's ability to deliver goods that match customer expectations in terms of condition and delivery time.
Research conducted by Karim .
also suggests that customers generally pay close attention to the convenience and ease of use of service applications, the ability to fulfill service needs on time at appropriate hours supported by multiple payment options, and quick service in problem resolution.
According to San et al.
, fulfillment is a promise made by websites to satisfy customers.
According to research conducted by Shergill and Chen .
in San et al.
, one aspect that can increase customer satisfaction in online shopping is fulfillment.
This is evidenced by other research conducted by Kandulapati and Bellamkonda .
in San et al.
where fulfillment has a significant and positive influence on customer satisfaction.
This demonstrates that the fulfillment of promises made by OFDS providers can impact customer satisfaction, where fulfilling customer orders, on-time delivery, and providing accurate information can increase customer satisfaction, which can then encourage customers to use OFDS again in the future.
Based on the hypothesis testing results, hypothesis 5 indicates a significant influence between the food quality variable and customer satisfaction, thus the hypothesis is supported.
This result aligns with the research findings of Annaraud and Berezina .
, which stated that the food quality variable significantly influences customer satisfaction.
This research finding is supported by Annaraud and Berezina .
, showing an influence between food quality and customer satisfaction among OFDS users.
According to Clark & Wood .
in Annaraud and E-ISSN 2721-706X Journal of Entrepreneurship & Business.
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Berezina .
, food quality is considered one of the elements that influence customers' desire in determining the desired restaurant.
If the taste of the food tried is delicious and meets customer expectations, then customer satisfaction will increase, and customers will reorder from the same restaurant.
OFDS are known to pay attention to food quality before delivering to customers, by selecting restaurant partners that serve fresh food, attractive food, offer a variety of menus, and have delicious food taste.
Restaurants that have proven their food quality will appear in the restaurant recommendation list and have a 5-star rating.
This rating is obtained from reviews of many customers who have previously ordered food at certain restaurants and shared their respective experiences.
This research finding is also supported by Serhan & Serhan .
, who stated that food quality is a special characteristic of food quality that can be accepted by According to Zhong andin Yo et al.
Moon .
, food quality encompasses various aspects such as food appearance, taste, menu variety, healthiness, and freshness.
High food quality becomes an important strategy in promotion to satisfy and retain customers, while providing a pleasant transaction experience.
Based on observational results, it is known that OFDS such as Grab Food.
Go Food, and Shopee Food select restaurant partners that serve fresh, attractive food with many menu variations and have good taste, thus affecting customer satisfaction with the services provided.
Based on the hypothesis testing results, hypothesis 6 indicates a significant influence between the customer satisfaction variable and behavioral intentions, thus the hypothesis is This result aligns with the research findings of Annaraud and Berezina .
, which stated that the customer satisfaction variable significantly influences behavioral intentions.
This research finding is supported by Annaraud and Berezina .
, showing an influence between customer satisfaction and behavioral intentions among OFDS users, where service provision is very important in achieving customer satisfaction.
Customer satisfaction with the services provided can influence customers' behavioral intentions to use OFDS again in the future because customers have had good experiences with previous transactions.
Research findings by Ge et al.
also state that changes in customer satisfaction levels can directly impact customers' desire to revisit, repurchase, and share their experiences with other customers.
In OFDS provider applications such as Grab Food.
Go Food, and Shopee Food, they compete to attract many customers to use their respective services by providing various promotions or discounts with purchase transactions in certain amounts, where the provision of promotions and discounts can attract customers' interest to use OFDS.
According to research conducted by Tran & Le .
, when the performance or result of a product or service is matched with customer expectations, it is called customer satisfaction.
Additionally.
Muskat et al.
also state that customers who are satisfied with goods or services tend to return, while disappointed customers tend to spread negative reviews.
Behavioral intentions include encouraging customers to return, providing recommendations, and generally informing positive things about restaurants.
Based on observational results, it is known that OFDS such as Grab Food.
Go Food, and Shopee Food have paid attention to their service quality and the food quality of each of their restaurant partners, resulting in overall customer satisfaction with the services provided.
CONCLUSION
Based on the results of hypothesis testing on the research model, it was concluded that Customer Satisfaction is significantly influenced by Service Convenience.
Customer Service.
Service Fulfillment, and Food Quality, but was not proven to be caused by Perceived Control.
The test results also prove that Grab Food.
Go Food, and Shopee Food customers who feel satisfied with OFDS will have the intention to use OFDS again in the future.
Recommendations can be given to OFDS providers to pay attention to the factors of convenience, service, order fulfillment, and the quality of food delivered, so that customer satisfaction can be maintained and will have the intention to use their services again.
The practical implications of online service delivery can be seen in the key aspects of service convenience, customer service, service fulfillment, and food quality, which have been shown to have a positive and significant impact on customer satisfaction.
This means that E-ISSN 2721-706X Journal of Entrepreneurship & Business.
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No.
ordering features, payment methods, ease of access to OFDS, customer problem resolution, and special requests are all important.
Furthermore, food quality must be ensured to meet the promised value or match all reviews given by previous customers.
These aspects will strengthen customer satisfaction and foster a desire to purchase again using OFDS.
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