Jurnal Ilmiah Pertanian dan Peternakan ISSN 3031-5883.
Vol 2.
No 1, 2024, 23-45 https://doi.
org/10.
35912/jipper.
Analysis of acceptance and use of the agree mart mobile groceries marketplace application using the UTAUT-3 Model in Indonesia Irfan Nurdin Salman1* Telkom University.
Indonesia1* nusairfan@gmail.
Article History:
Received on 1 June 2024 1st Revision 10 June 2024 2nd Revision 25 June 2024 3rd Revision 1 July 2024 Accepted on 5 July 2024 Abstract Purpose: This study aims to analyze the factors that influence user acceptance and usage of the Agree Mart mobile groceries marketplace application by applying the Unified Theory of Acceptance and Use of Technology 3 (UTAUT-.
model in the Indonesian context.
Research methodology: This research method uses a mixed method by taking a sample of the population of active users in Indonesia and using samples from several respondents, and registered users who have not made transactions Results: It is hoped that this research will provide benefits to add insight and as a digital business reference and material for consideration for meeting daily needs in Indonesia in determining the right strategy both in business and application.
Conclusion: The UTAUT-3 model is a useful framework for understanding grocery app adoption in Indonesia.
Personal innovativeness plays a dominant role in shaping behavioral intention, while habit and infrastructure support actual usage Limitations: This study focuses only on one marketplace application (Agree Mar.
and does not explore external market or competitor effects.
Contribution: This research contributes to digital business strategy literature by extending the applicability of UTAUT-3 to the Indonesian e-groceries sector and providing actionable insights for platform managers to enhance user adoption and retention.
Keywords: Business Model Canvas.
Business Strategy Formulation.
Digital Business Strategy.
E-Groceries.
Marketplace.
Mix Method Analysis.
Platform.
UTAUT.
How to cite: Salman.
Analysis of acceptance and use of the agree mart mobile groceries marketplace application using the UTAUT-3 Model in Indonesia.
Jurnal Ilmiah Pertanian dan Peternakan, 2.
, 23-45.
Introduction The use of the internet in the digital era is very important to support people's lives.
There are many things that can be done through the use of the internet, such as searching for information, finding inspiration, connecting with relatives, entertainment and even carrying out online transactions of buying and selling goods or services which can be done through the platform (Dwivedi et al.
, 2.
In the midst of the digital era and the rapid development of the internet today, many aspects of human life are being helped.
Including meeting daily needs e-groceries developed very rapidly during the pandemic because their presence provided very useful assistance to the community(Li.
Hallsworth, & Coca Stefaniak, 2.
Reporting from IGI Global, e-groceries is a business model or online daily grocery shopping service for consumers with the main aim of selling groceries online.
This platform concentrates on customers, shopping convenience and fast delivery (Billman et al.
, 2024.
Dhian, 2023.
Hawa.
Setyorini, & Nabyla, 2.
Some of the advantages of this platform include ease of shopping, saving time and money, many products available, accommodating large-scale purchases and there are promotions.
The commodities offered are usually vegetables, fruit, meat, basic necessities and other daily necessities (Mashur.
Yakubi, & Riswandi, 2024.
Pitts.
Ng.
Blitstein.
Gustafson, & Niculescu.
There are several things that make digitalization in the food sector, especially e-groceries platforms.
There is a very big challenge where fresh products are not products that are easy to Fresh products are products that are quite vulnerable both in terms of price .
rice sensitiv.
and durability .
erishable good.
(Mkansi.
Eresia-Eke, & Emmanuel-Ebikake, 2018.
Nur Susilowati et al.
, 2.
This price change is caused by a mismatch in supply and demand, this is because cultivators generally follow the trend of a commodity, causing oversupply for commodities that are currently trending, but on the one hand, it causes scarcity of some commodities.
This is what causes the price of a commodity to plummet when there is too much of it and become expensive when the commodity is experiencing a shortage (Atamer Balkan, 2019.
Kamilla.
Arumsari.
Nugraha, & Prasetiyo, 2.
Apart from that, there is a very difficult problem, namely product durability.
Where fresh products really need to be treated more than other products.
A product storage and delivery method is needed that can keep the commodity being bought and sold fresh(Shi.
Zhang, & Qin, 2.
This is made even more difficult because business actors need to secure the supply chain of the commodities to be sold.
Starting from the producers, namely cultivators, whether farmers, breeders, fishermen or fish farmers, their stock must be secured.
It requires cultivators who have products of appropriate quality and price and are committed in the long term to providing supplies(Ogutu.
Ochieng, & Qaim, 2.
There are still challenges such as the uncertainty of natural conditions which can affect cultivation results.
Apart from that, there is a challenge where it is possible that these cultivators are spread across various regions, because a commodity often requires certain environmental conditions for its cultivation(Mehmood.
Bashir, & Verma, 2.
In terms of customer retention, there are 2.
30% of customers who only make transactions once, and 70% of customers make repeat orders.
Based on this information, it is known that the majority of Agree Mart orders are orders generated from retained users.
Where half of the repeat orders use promotional vouchers which require large promotional costs .
ased on the previous slid.
(Chatzoglou.
Chatzoudes.
Savvidou.
Fotiadis, & Delias, 2.
The problem is seen when order data is displayed in monthly retention form.
Monthly retention displays the number of new users in a particular month, and the percentage of those who return to shop again a few months later.
Data shows that Agree Mart retention is still very good one month after buyers make their first purchase, but when they enter the second month, most of them stop shopping at Agree Mart(Fedushko & Ustyianovych, 2022.
Jimad.
Roslina.
Aviati Syarif, & P Wahono, 2.
This indicates that the tendency of Agree Mart users is still to depend on promotional vouchers.
Then the Digital Product Manager who is responsible for Agree Mart product development also explained that e-commerce competition in Indonesia is very tight, especially e-groceries.
This competition is marked by the number of new e-groceries players and is directly proportional to the number of egroceries players who have closed.
Thus, a strategy is needed to change user habits regarding the use of promotional vouchers while continuing to increase the number of transactions to gain significant revenue amidst competition in the e-commerce sector, especially e-groceries which has too many There are also several other problems faced by the Agree Mart platform, such as the addition of very few purchasing users, which is an indication that the business is very unhealthy.
It can be concluded that this means Agree Mart's business is not growing.
Apart from that, too many buyers are retained solely because of promotional vouchers.
This illustrates that Agree Mart buyers are not loyal.
And also Agree Mart's current Cost to GMV ratio is still very large.
Agree Mart burns too much money to uplift GMV which is not worth the promotional costs.
This condition makes Agree Mart's business 2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 The transaction phenomenon that occurs at Agree Mart indicates that the Agree Mart groceries marketplace application is not running optimally.
It is important to know what factors have a significant influence on the intention to use Agree Mart so that it can run better.
This research will use mixed research methods, which are expected to combine elements of quantitative and qualitative approaches in one research, which can provide more comprehensive and in-depth insight into the phenomenon being researched.
Quantitative data can provide a general idea of the relationships between variables, while qualitative data can reveal the nuances, context, and reasoning behind quantitative findings.
If the findings from quantitative and qualitative analyzes confirm each other, this can strengthen confidence in the results of existing research.
Based on the description above, the author is interested in conducting research on "Analysis of Acceptance and Use of the Mobile Groceries Marketplace Agree Mart Application Using the UTAUT-3 Model in Indonesia".
Literature Review 1 Technology Adoption Model The technology adoption model is an approach to individual evaluative assessment of information technology .
and beliefs regarding technology adoption in companies(MaranguniN & GraniN.
Previous research has formulated models that can be used to measure technology adoption.
This model has of course been tested for its validity by paying attention to individual psychological and sociological aspects in using technology(Lai, 2.
In determining the theoretical model used for technology adoption using digital platforms in the digitalization of public services, there are ten theoretical models that are considered.
These models include Theory and Reasoned Action (TRA).
Technology Acceptance Model (TAM).
Motivational Model (MM).
Theory of Planned Behavior (TPB).
Combination of TAM and TPB theoretical models.
Model of PC Utilization (MPCU).
Innovation Diffusion Theory (IDT).
Social Cognitive Theory (SCT).
Unified of Acceptance and Use of Technology (UTAUT).
Unified of Acceptance and Use of Technology 2 (UTAUT .
and Unified of Acceptance and Use of Technology 3 (UTAUT .
The UTAUT theoretical model developed by Venkatesh.
Thing, and XU in 2012 is a combination of the eight theoretical models mentioned previously.
UTAUT is a comprehensive theoretical model that integrates the construction of factors that determine individuals or organizations adopting new In line with research that continues to develop.
UTAUT was developed into UTAUT 2 with the addition of 3 variables, namely price value, hedonic motivation, and habit (Venkatesh et al, 2.
In the latest research results.
UTAUT was re-developed into UTAUT 3 with the addition of 1 variable, namely personal innovativeness (Venkatesh et al, 2.
The theoretical model used to measure public acceptance of the use of technology continues to be refined from time to time.
This is inseparable from renewed research which has discovered that human psychological factors in using technology are constantly changing so that the relevant models and related variables are also undergoing updates.
The theoretical models used include theoretical models with improvements to previous models supported by references from previous research.
2 Unified Theory of Acceptance and Use of Technology 3 (UTAUT .
UTAUT 3 includes eight variables that influence technology acceptance and use:
Performance Expectancy: Performance expectancy refers to users' perceptions of how effectively technology will help them in carrying out their tasks.
Performance expectations are a strong contraction of the intention to use information technology so it can be concluded that the information technology system can concretely help one's work so that the use of technology can be used in the long term.
In this concept, there is a combination of variables which are a combination of previous research models and the use of technology (Venkatesh, 2.
Effort Expectancy: Effort expectancy refers to the user's perception of how easy the technology is to use.
Information technology that is easy to use can create the perception that the system brings benefits to users and is comfortable to use.
On the other hand, if the system is found to be difficult to use, a feeling of comfort in working with the system will not be created and furthermore, the intention to use and utilize the system will decrease.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 Social Influence: Social norm factors and pressure felt by users have an impact on individual behavior to use or not use technology.
There are three mechanisms, namely compliance, internalization and identification (Venkatesh & Davis, 2.
Social factors have direct implications for the adoption of information technology.
The more influence the environment has on potential users of developing information technology, the greater the interest that will arise in using that information technology.
Hedonic Motivation: Hedonic motivation is the user's desire to use technology to gain personal satisfaction or pleasure through the use of information technology.
This variable has been shown to have an important role in determining the adoption of technology use (Brown & Venkatesh, 2.
This variable is the motivation for pleasure obtained from using a system or technology (Vanketesh.
From research conducted by Van der Heijden .
Thong et al .
, and Venkatesh .
it has been found that hedonic motivation directly influences the acceptance and use of Habit: Habits operate as three levels based on the passage of time: .
post-training is when the system is initially available for use.
one month later.
three months later.
Habit refers to the extent to which people tend to behave automatically (Limayem et al, 2.
Apart from that, habits can also refer to automatic processes (Kim et al, 2.
Facilitating Conditions: There are conditions that affect the user's ability and opportunity to use This variable can be defined as the extent to which potential information technology users believe that the organizational and technical infrastructure can support system use (Venkatesh.
This variable has a direct influence on system use.
In general, users with lower levels of facilitating conditions will have lower intentions towards adopting the use of information Price Value: Price value gives the user's perception of the value of the technology and whether it is worth the costs incurred to obtain it.
Price value has been tested from Venkatesh's .
patented research as a variable that influences the adoption of information technology.
The greater the satisfaction with using the system regarding the monetary aspects that must be spent by individuals, the greater the intention to use information technology.
Personal Innovativeness: Individual abilities and interests are important for users in adopting and using new technology.
These variables are interrelated and influence each other, and can be used as a basis for building better and more accurate prediction models of technology acceptance and use.
3 Marketplace E-commerce is an electronically mediated buying and selling service where the goods sold are limited only to the e-commerce owner.
This is different with the marketplace, where this platform is a forum that can accommodate many online shop sellers with various kinds of merchandise.
This is a good opportunity to pursue business digitally.
Quoting a study conducted by Mc.
Kinsey Institute in a journal written by (Husnurrosyidah, 2.
: Mc.
The Kinsey Institute states that "MSMEs that use emarketplaces as an alternative to selling can be said to grow twice as fast as without selling on emarketplaces.
Markets with their advantages can create economic value for buyers, sellers, market intermediaries for the wider community (Bakos, 1.
However, in recent years, the use of Information Technology (IT) in traditional markets has begun to move to electronic markets, such as internet-based online markets.
The three functions of markets according to Turban et al, .
, namely as a meeting place for sellers and buyers, exchanging information for goods, services, payment methods, and how to transact in the market, as well as providing institutional infrastructure that can enable the market to function The use of the internet in recent years has increased market efficiency and its function.
The emergence of e-marketplaces or marketspaces can change several processes in traditional markets by utilizing IT and producing greater economic efficiency.
Similar to physical markets, online markets or marketspaces have important components, such as customers, sellers, goods, infrastructure, a frontend, a back-end, intermediaries, other business partners and support services.
Electronic Marketplace (EM) is a virtual location for consumers and producers to meet to conduct commercial transactions 2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 (Gulledge, 2.
These transactions can be public or private.
Turban et al.
, .
stated that the difference between malls and markets is not very significant on websites.
4 Framework The thinking framework is a conceptual model that describes the conceptual model of the relationship between theory and factors identified as important problems to be resolved (Widayat & Amirullah.
Through a thinking framework, a comprehensive picture of the research concept can be depicted more clearly.
Thus, the framework of thought can be used as a basis for formulating hypotheses.
As explained in the previous section, the UTAUT theoretical model is a refinement of eight theoretical models for measuring technology adoption, namely Theory and Reasoned Action (TRA).
Technology Acceptance Model (TAM).
Motivational Model (MM).
Theory of Planned Behavior (TPB).
Combination of TAM and TPB theoretical models.
Model of PC Utilization (MPCU).
Innovation Diffusion Theory (IDT) and Social Cognitive Theory (SCT).
UTAUT 3 is a development of the theoretical models UTAUT and UTAUT 2 to analyze technology adoption with the addition of the variable personal innovativeness.
With the addition of these variables, the UTAUT 3 theoretical model is a relevant model in discussing this research, namely Acceptance of the Groceries Marketplace Agree Mart Application.
Figure 1.
Framework (Source: Processed researcher data, 2.
5 Research Hypothesis The hypothesis was tested with the aim of analyzing the impact of each variable in UTAUT 3 on the adoption of the use of the agree mart groceries marketplace by the Indonesian people.
Performance expectancy refers to the extent to which someone believes that using a system can help them to achieve good performance at work (Venkatesh et al, 2.
Performance expectancy is the strongest predictor in determining individual habits to adopt technology (Venkatesh et al, 2.
The hypothesis prepared for this variable is:
H1: Performance expectancy will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Effort expectancy is a variable that has a construct as a strong predictor at the beginning of the technology adoption phase, but becomes an insignificant predictor after technology adoption has lasted 2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 for some time.
Effort expectancy can be defined as the extent of ease associated with using technology.
The hypothesis prepared for this variable is:
H2: Effort expectancy will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Social influence can be defined as the influence of the surrounding environment .
amily or relative.
influencing the use of the agree mart groceries marketplace application.
Previous research states that social influence has direct implications for technology adoption at the individual level (Al Sobhi, 2011.
Vanketersh & Brown, 2001.
Fulk & Boyd, 1991.
Fulk et al, 1.
The hypotheses prepared for this variable include:
H3: Social influence will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Facilitating conditions are defined as infrastructure that supports individuals to be able to use the system and eliminates obstacles to use (Vanketash et al, 2.
Facilitating conditions will have significance on behavioral intention and use behavior if they are moderated by demographic variables such as age, education level and experience using the internet (Al Sobhi, 2.
The hypothesis prepared for this variable is:
H4: Facilitation conditions will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
H5: Facilitation conditions will have a significant influence on the use behavior of the groceries marketplace agree mart application by Indonesian people.
Behavioral intention is a construct that is found to have a direct influence on individuals in using various types of technology (Venkatersh et al, 2004.
Ajzen.
In this research, behavioral intention can be defined as the extent to which individuals will adopt technology, which refers to the use of the internet and the groceries marketplace agree mart application.
The hypothesis prepared for this variable is:
H6: Behavioral intention to use the groceries marketplace agree mart application will have a significant influence on use behavior of the groceries marketplace agree mart application.
Hedonic motivation in this research explains that using the groceries marketplace agree mart application can provide pleasure or entertainment for individuals in enjoying public services online.
Previous research states that this variable has a significant influence on behavioral intention (Hasrono & Suryana.
The hypothesis prepared for this variable is:
H7: Hedonic motivation will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Price value explains the suitability of the costs incurred with the benefits received by individuals in accessing public services online.
Previous research states that this variable has a significant influence on behavioral intention (Roriguez & Trulijo, 2.
The following is a hypothesis prepared for this H8: Price value will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Habit refers to an individual's habit of using groceries marketplace agree mart to access public service needs in daily life.
Previous research states that this variable has a significant influence on behavioral intention and use behavior(Huang, 2023.
Zheng.
Han.
Huang.
Wu, & Wu, 2.
The following is a hypothesis prepared for this variable:
H9: Habit will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
H10: Habit will have a significant influence on the use behavior of the groceries marketplace agree mart application by Indonesian people.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 Personal Innovativeness refers to the level of innovation or an individual's tendency to adopt and use groceries marketplace agree mart.
In previous research, it was found that this variable had a significant influence in understanding the adoption of educational technology by teachers in previous research.
hin & Dursun, 2.
The following is a hypothesis prepared for this variable:
H11: Personal innovativeness will have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
H12: Personal innovativeness will have a significant influence on the use behavior of the groceries marketplace agree mart application by Indonesian people.
Research methodology 1 Types of Research The approach used in this research is a quantitative method (Dawadi.
Shrestha, & Giri, 2.
The data collection method used in this research is a survey using a questionnaire.
A questionnaire is a method of collecting data through a series of structured questions addressed to respondents.
Surveys are more effective because the variables to be discussed and measured have been identified beforehand(Arundel.
Apart from that, interviews will also be conducted with resource persons who have not made transactions on the Agree Mart application to find out what factors cause users not to use the application for shopping.
Researchers did not intervene with the data obtained from the survey.
Therefore, this research is classified as non-interventional research(AlArfaj & Solaiman, 2.
The research environment or context of this research is non-contrived.
This research was carried out in a natural environment with the aim of seeing the actual situation.
In terms of the time of conducting the research, the type used is cross-sectional, which means that data is collected once in a certain period to answer research questions with a predetermined sample(Sharaf, 2.
Cross-sectional is a research method with a large dataset to look at many cases and the relationship between the variables studied.
2 Operational Variable 1 Performance Expectancy (PE) This variable measures how much an individual's intention to use Agree Mart will increase performance and effectiveness in meeting daily needs.
This variable measurement item uses 7 question items.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly agree.
2 Effort Expectancy (EE) This variable measures the extent to which individuals believe that using Agree Mart will help them meet their daily needs.
the extent to which individuals believe that using Agree Mart will help them meet their daily needs.
The measurement items for this variable are 6 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5:
Strongly agree.
3 Social Influence (SI) This variable measures how the influence of the surrounding social environment .
amily, colleagues, friends, superior.
influences the intention to use Agree Mart.
The measurement items for this variable are 3 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly agree.
4 Facilitating Conditions (FC) This variable measures the support of the Agree Mart application infrastructure in terms of smooth use without usage problems.
The measurement items for this variable are 6 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5:
Strongly agree.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 5 Hedonic Motivation (HM) Measuring feelings of pleasure and enjoyment from using Agree Mart technology.
The measurement items for this variable are 6 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly agree.
6 Price Value (PV) Measuring whether the material value spent to be able to access the Agree Mart application technology is commensurate with the benefits obtained.
The measurement items for this variable are 6 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly agree.
7 Habit (H.
Measuring an individual's tendency to adopt the use of Agree Mart application technology into automatic behavior.
The measurement items for this variable are 7 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly 8 Personal Innovativeness (PI) Measuring the level of innovation or individual tendency to adopt the use of Agree Mart to meet daily The measurement items for this variable are 5 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly agree.
9 Behavioural Intention (BI) Measuring how much an individual intends to use Agree Mart to meet daily needs.
The measurement items for this variable are 8 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly agree.
10 Use Behaviour (UB) Actual use of Agree Mart by individuals in meeting daily needs digitally.
The measurement items for this variable are 8 questions.
Respondents were asked to fill out this questionnaire using a 5 Likert scale starting from 1 = Strongly disagree to 5: Strongly agree.
Results and analysis Respondent Characteristics This section explains the characteristics of research respondents who are buyers of the Agree Mart marketplace application obtained through a questionnaire.
Characteristics are important for knowing the picture of all the respondents who have been studied.
Some of the respondent characteristic data used in this research include those who have downloaded the application, used the application and shopped in the Agree Mart application.
Table 1.
Respondent Characteristics Characteristics Have you downloaded the Agree Mart application? Yes Have you ever used the Agree Mart application? Yes 2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 No Have you ever shopped using the Agree Mart application? Yes Male Female Your gender Source: Processed researcher data .
SmartPLS Testing 1 Measurement Model Test At the test and analysis stage of the measurement model .
uter mode.
, there are two things that will be analyzed, namely validity which consists of construct validity, discriminant validity and convergent Then there is reliability which consists of Cronbach's alpha and composite reliability.
The application used to carry out this test is SmartPLS 3.
The diagram or Outer Model in this research can be seen in the following picture:
Figure 2.
Initial Model Outer Diagram Source: Processed researcher data .
1 Convergent Validity Convergent validity is used to show indicators that are positively and significantly correlated with other indicators on the same construct.
In SmartPLS, convergent validity testing for reflective indicators is evaluated based on loading factors .
he relationship between item scores or component scores and construct score.
on the indicators that measure the construct.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 Minimum value according to Barclay et al.
in (Santosa, 2.
, that the minimum outer loading value of an indicator is 0.
However, according to Chin .
in (Santosa, 2.
also stated that the outer loading value between 0.
6 - 0.
7 is still acceptable but with the caveat that this indicator is not the only indicator of the construct, so there are comparative indicators.
The following are the results of the convergent validity test with loading factor values.
Table 2.
Initial Convergent Validity Test Results Variable Indicator Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions Hedonic Motivation Loading Factor
Conclusion PE1
0,814
Valid
PE2
0,786
Valid
PE3
0,828
Valid
PE4
0,726
Valid
PE5
0,775
Valid
PE6
0,841
Valid
PE7
0,792
Valid
EE1
0,838
Valid
EE2
0,884
Valid
EE3
0,708
Valid
EE4
0,880
Valid
EE5
0,742
Valid
EE6
0,728
Valid
SI1
0,741
Valid
SI2
0,807
Valid
SI3
0,854
Valid
FC1
0,827
Valid
FC2
0,802
Valid
FC3
0,762
Valid
FC4
0,812
Valid
FC5
0,804
Valid
FC6
0,793
Valid
HM1
0,819
Valid
HM2
0,813
Valid
HM3
0,860
Valid
HM4
0,859
Valid
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45
Variable Price Value Habit
Personal Innovativeness Behavioural Intention
Use Behaviour
Indicator Loading Factor
Conclusion HM5
0,833
Valid
HM6
0,868
Valid
PV1
0,809
Valid
PV2
0,866
Valid
PV3
0,764
Valid
PV4
0,628
Invalid
PV5
0,366
Invalid
PV6
0,678
Invalid
Hb1
0,909
Valid
Hb2
0,885
Valid
Hb3
0,892
Valid
Hb4
0,897
Valid
Hb5
0,786
Valid
Hb6
0,751
Valid
Hb7
0,794
Valid
PI1
0,804
Valid
PI2
0,814
Valid
PI3
0,748
Valid
PI4
0,877
Valid
PI5
0,850
Valid
BI1
0,844
Valid
BI2
0,849
Valid
BI3
0,833
Valid
BI4
0,836
Valid
BI5
0,827
Valid
BI6
0,842
Valid
BI7
0,818
Valid
BI8
0,655
Invalid
UB1
0,697
Invalid
UB2
0,756
Valid
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45
Variable Indicator Loading Factor
Conclusion UB3
0,848
Valid
UB4
0,880
Valid
UB5
0,865
Valid
UB6
0,820
Valid
UB7
0,837
Valid
UB8
0,875
Valid Source: Processed researcher data .
Based on Table 2 above, it can be seen that there are 5 indicator items that have a factor loading value of <0.
700, which means they are invalid, so they need to be eliminated and the convergent loading factor algorithm retested.
Convergent validity results were obtained with factor loading values in Table 3, as follows:
Table 3.
Final Convergent Validity Test Results Variable Indicator Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions Loading Factor
Conclusion PE1
0,815
Valid
PE2
0,787
Valid
PE3
0,830
Valid
PE4
0,725
Valid
PE5
0,774
Valid
PE6
0,842
Valid
PE7
0,791
Valid
EE1
0,839
Valid
EE2
0,884
Valid
EE3
0,707
Valid
EE4
0,880
Valid
EE5
0,742
Valid
EE6
0,728
Valid
SI1
0,756
Valid
SI2
0,799
Valid
SI3
0,846
Valid
FC1
0,827
Valid
FC2
0,801
Valid
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45
Variable Hedonic Motivation Price Value Habit
Personal Innovativeness Behavioural Intention
Indicator Loading Factor
Conclusion FC3
0,761
Valid
FC4
0,812
Valid
FC5
0,804
Valid
FC6
0,794
Valid
HM1
0,818
Valid
HM2
0,814
Valid
HM3
0,859
Valid
HM4
0,858
Valid
HM5
0,833
Valid
HM6
0,869
Valid
PV1
0,866
Valid
PV2
0,907
Valid
PV3
0,787
Valid
Hb1
0,909
Valid
Hb2
0,885
Valid
Hb3
0,892
Valid
Hb4
0,897
Valid
Hb5
0,786
Valid
Hb6
0,751
Valid
Hb7
0,793
Valid
PI1
0,803
Valid
PI2
0,813
Valid
PI3
0,748
Valid
PI4
0,878
Valid
PI5
0,851
Valid
BI1
0,840
Valid
BI2
0,855
Valid
BI3
0,846
Valid
BI4
0,844
Valid
BI5
0,826
Valid
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45
Variable Indicator Loading Factor
Conclusion BI6
0,846
Valid
BI7
0,821
Valid
UB2
0,727
Valid
UB3
0,862
Valid
UB4
0,884
Valid
UB5
0,880
Valid
UB6
0,831
Valid
UB7
0,852
Valid
UB8
0,878
Valid Use Behaviour Source: Processed researcher data .
Based on Table 3 above, it can be seen from the results of the final evaluation of convergent validity with loading factor, by deleting several indicators that have a value of less than 0.
7 and re-estimating them, the result is that all indicators have a loading factor value of more than 0.
7, so it can be stated Apart from that, convergent validity can also be measured by calculating each indicator in the average variance extracted (AVE).
The indicator for calculating AVE, if the AVE value is more than 5, then the items in this variable are considered to have sufficient convergent validity.
The results of the AVE value can be seen in Table 4 as follows:
Table 4.
Average Variance Extracted (AVE) Value Results Variable Average Variance Extracted (AVE) Performance Expectancy 0,633 Effort Expectancy 0,640 Social Influence 0,642 Facilitating Conditions 0,640 Hedonic Motivation 0,709 Price Value 0,731 Habit
0,717
Personal Innovativeness 0,672 Behavioural Intention
0,705
Use Behaviour
0,716
Source: Processed researcher data .
Based on Table 4, the results of the convergent validity calculation with AVE can be seen, showing that the AVE value of each variable has a value > 0.
So it can be stated that the data in this study has met the criteria for convergent validity.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 2 Discriminant Validity An indicator is declared to have discriminant validity if the loading factor of each indicator that measures the latent variable is greater than the cross loading value .
orrelation of the indicator with other latent variable.
Then the results of the discriminant validity test can be seen in Table 4.
16, as Table 5.
Discriminant Validity Test Results (Cross Loadin.
BI1
0,840
0,542
0,603
0,653
0,612
0,590
0,749
0,688
0,438
0,745
BI2
0,855
0,535
0,547
0,653
0,727
0,559
0,764
0,627
0,551
0,696
BI3
0,846
0,490
0,535
0,691
0,749
0,566
0,684
0,616
0,582
0,724
BI4
0,844
0,569
0,592
0,695
0,665
0,596
0,683
0,674
0,525
0,764
BI5
0,826
0,713
0,675
0,691
0,590
0,601
0,716
0,681
0,491
0,766
BI6
0,846
0,699
0,638
0,676
0,508
0,648
0,735
0,694
0,444
0,770
BI7
0,821
0,686
0,683
0,696
0,512
0,577
0,687
0,645
0,460
0,704
EE1
0,551
0,839
0,606
0,506
0,342
0,590
0,516
0,483
0,263
0,541
EE2
0,634
0,884
0,651
0,578
0,398
0,591
0,584
0,599
0,317
0,631
EE3
0,429
0,707
0,568
0,403
0,227
0,488
0,435
0,436
0,205
0,415
EE4
0,616
0,880
0,716
0,656
0,468
0,714
0,551
0,592
0,380
0,644
EE5
0,493
0,742
0,641
0,482
0,371
0,598
0,459
0,420
0,319
0,517
EE6
0,671
0,728
0,641
0,696
0,612
0,710
0,614
0,639
0,503
0,726
FC1
0,586
0,632
0,827
0,547
0,438
0,529
0,569
0,585
0,349
0,564
FC2
0,442
0,612
0,801
0,476
0,255
0,539
0,488
0,535
0,304
0,468
FC3
0,483
0,604
0,761
0,501
0,380
0,552
0,416
0,542
0,347
0,534
FC4
0,520
0,615
0,812
0,489
0,281
0,545
0,543
0,600
0,287
0,502
FC5
0,638
0,688
0,804
0,660
0,581
0,612
0,566
0,599
0,473
0,647
FC6
0,728
0,664
0,794
0,751
0,570
0,744
0,696
0,702
0,575
0,733
HM1
0,763
0,688
0,713
0,818
0,602
0,682
0,682
0,690
0,552
0,731
HM2
0,718
0,652
0,639
0,814
0,541
0,680
0,620
0,689
0,421
0,713
HM3
0,681
0,635
0,617
0,859
0,622
0,661
0,553
0,657
0,449
0,676
HM4
0,624
0,492
0,589
0,858
0,641
0,599
0,497
0,629
0,497
0,651
HM5
0,592
0,510
0,523
0,833
0,670
0,545
0,501
0,545
0,631
0,599
HM6
0,676
0,561
0,592
0,869
0,674
0,579
0,548
0,628
0,595
0,659
Hb1
0,674
0,501
0,516
0,664
0,909
0,568
0,555
0,537
0,582
0,674
Hb2
0,619
0,438
0,491
0,639
0,885
0,508
0,486
0,532
0,607
0,649
Hb3
0,651
0,441
0,460
0,654
0,892
0,526
0,514
0,489
0,582
0,663
Hb4
0,662
0,415
0,459
0,650
0,897
0,506
0,541
0,507
0,612
0,660
Hb5
0,675
0,572
0,565
0,677
0,786
0,602
0,565
0,582
0,619
0,685
Hb6
0,537
0,359
0,338
0,506
0,751
0,411
0,413
0,460
0,445
0,496
Hb7
0,557
0,330
0,365
0,569
0,793
0,414
0,454
0,466
0,538
0,534
PE1
0,546
0,579
0,646
0,578
0,544
0,815
0,552
0,528
0,448
0,651
PE2
0,643
0,706
0,652
0,637
0,522
0,787
0,581
0,580
0,453
0,631
PE3
0,551
0,597
0,540
0,554
0,452
0,830
0,560
0,553
0,386
0,651
PE4
0,512
0,513
0,504
0,639
0,488
0,725
0,509
0,488
0,389
0,600
PE5
0,466
0,592
0,602
0,528
0,344
0,774
0,552
0,539
0,288
0,597
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45
PE6
0,592
0,624
0,619
0,566
0,458
0,842
0,628
0,546
0,424
0,672
PE7
0,579
0,587
0,583
0,645
0,520
0,791
0,568
0,545
0,540
0,673
PI1
0,665
0,576
0,547
0,509
0,417
0,580
0,803
0,627
0,372
0,628
PI2
0,651
0,517
0,627
0,533
0,433
0,655
0,813
0,602
0,416
0,659
PI3
0,554
0,448
0,458
0,384
0,323
0,448
0,748
0,454
0,282
0,509
PI4
0,781
0,578
0,572
0,605
0,583
0,593
0,878
0,586
0,487
0,702
PI5
0,810
0,602
0,636
0,703
0,641
0,621
0,851
0,629
0,629
0,744
PV1
0,639
0,541
0,630
0,618
0,472
0,557
0,615
0,866
0,490
0,638
PV2
0,738
0,661
0,719
0,733
0,560
0,664
0,661
0,907
0,500
0,731
PV3
0,635
0,515
0,571
0,600
0,516
0,513
0,543
0,787
0,392
0,622
SI1
0,508
0,368
0,439
0,533
0,675
0,458
0,429
0,455
0,756
0,543
SI2
0,380
0,277
0,304
0,396
0,454
0,330
0,333
0,332
0,799
0,381
SI3
0,513
0,365
0,447
0,536
0,473
0,464
0,526
0,485
0,846
0,531
UB2
0,672
0,442
0,425
0,633
0,836
0,540
0,543
0,519
0,547
0,727
UB3
0,772
0,715
0,707
0,730
0,633
0,676
0,662
0,711
0,571
0,862
UB4
0,783
0,624
0,597
0,710
0,680
0,701
0,693
0,697
0,544
0,884
UB5
0,746
0,628
0,643
0,654
0,562
0,685
0,686
0,662
0,514
0,880
UB6
0,704
0,686
0,680
0,602
0,454
0,687
0,712
0,688
0,393
0,831
UB7
0,761
0,625
0,622
0,695
0,591
0,748
0,723
0,674
0,524
0,852
UB8
0,764
0,653
0,674
0,719
0,640
0,722
0,707
0,654
0,560
0,878
Source: Processed researcher data .
Based on Table 5 above, it is known that each indicator in each research variable has a cross loading value that is greater than the correlation value of the indicator with indicators in other variables, so that each indicator used in this research has good discriminant validity.
3 Reliability Test (CronbachAos Alpha dan Composite Reliabilit.
In carrying out reliability testing in SmartPLS, there are two methods, namely Cronbach's Alpha and Composite Reliability.
Cronbach's Alpha measures the lower limit of the reliability value of an item, while composite reliability measures the actual value of the reliability of a construct.
Therefore, a reliability test must be carried out to find out whether each item on the questionnaire meets the reliability A research instrument is said to have good reliability if the composite reliability value is Ou 0.
or the Cronbah's Alpha value is Ou 0.
Based on the results of the researcher's data processing, reliability results were obtained with Cronbach's Alpha and Composite Reliability which can be seen in Table 4.
17, as follows:
Table 6.
Reliability Test Results CronbachAos Variable Alpha
Composite Reliability Recommended Information 0,903 0,923 >0,6 Reliabel
0,886
0,914
>0,6
Reliabel
0,723
0,843
>0,6
Reliabel
0,889
0,914
>0,6
Reliabel
0,918
0,936
>0,6
Reliabel
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45
0,814
0,890
>0,6
Reliabel
0,934
0,946
>0,6
Reliabel
0,878
0,911
>0,6
Reliabel
0,930
0,944
>0,6
Reliabel
0,933
0,946
>0,6
Reliabel Source: Processed researcher data .
From Table 6, it can be seen that the results of the latent variable reliability test in this study have Cronbach's alpha and composite reliability values above 0.
In other words, the measuring instrument used in this research provides consistent and reliable results for measuring concepts that are not directly 2 Structural Model Test (Inner Mode.
In testing the structural model or inner model, there are several tests carried out such as: R square.
Q square, and f square analysis.
1 R Square Analysis (RA) According to Ghozali .
R square analysis was carried out to measure the ability of the model used in this research to explain the dependent variable.
The greater the R square value, the better the model used in explaining how much influence the dependent variable receives.
The R square value in this research can be seen in the table below.
Table 7.
Results of R Square Value Dependent Variable Behavioural Intention (BI) Use Behaviour (UB) Source: Processed researcher data .
R-Square 0,864 0,817 R-Square Adjusted 0,857 0,812 The R square value ranges from 0 Ae 1 with a value closer to 1, meaning it shows a prediction with greater accuracy.
However, according to Cohen .
in (Santosa, 2.
states that R square with a value greater than or equal to 0.
25 already shows a high influence.
Based on the R square test results, the R square value for BI is 0.
This shows that the abilities of the independent variables in this research are Performance Expectancy (PE).
Effort Expectancy (EE).
Social Influence (SI).
Facilitating Conditions (FC).
Hedonic Motivation (HM).
Price Value (PV).
Habit (Hb ), and Personal Innovativeness (PI) influences Behavioral Intention (BI) by 86.
This means that this independent variable has a big influence on respondents' desire to use the Agree Mart application.
Then, looking at the R-square value for the Use Behavior (UB) variable, it is 0.
This explains that Facilitating Conditions (FC).
Habit (H.
Personal Innovativeness (PI), and Behavioral Intention (BI) are quite large factors in influencing the adoption or use of the Agree Mart application, namely 81.
2 Analysis of Q Square (QA) In SmartPLS 3, the Q square value is obtained using PLSpredict/CVPAT.
This Q square value analysis was carried out to test the predictive relevance of the model used.
Predictive relevance testing is carried out to show how good the resulting observation value is.
The results of the Q square calculation can be seen in the following table.
Table 8.
Q-Square Results Dependent Variable Behavioural Intention (BI) Use Behaviour (UB) Q-Square 0,593 0,569 2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 Source: Processed researcher data .
In Q square analysis, if the Q square value > 0, then it shows the model has predictive relevance and if Q square < 0, it shows the model does not have predictive relevance.
Based on the Q square results displayed in the table above, the Q square value of the Behavioral Intention (BI) variable is 0.
593 and Use Behavior (UB) is 0.
So it can be seen that the values of the Behavioral Intention (BI) and Use Behavior (UB) variables have predictive relevance.
3 Analysis of f Square .
A) F-square analysis is used to assess the influence of the independent variable on the dependent variable.
The following are the values of f square which can be seen in the following table.
Table 9.
Results of f-Square Values Variable Behavioural Intention (BI) Use Behaviour (UB) Performance Expectancy 0,037 Effort Expectancy 0,046 Social Influence 0,003 Facilitating Conditions 0,000 0,077 Hedonic Motivation 0,077 Price Value 0,061 Habit
0,144
0,104
Personal Innovativeness 0,613 0,033 0,205 Behavioural Intention Source: Processed researcher data .
The f square value can be said to have a small influence if the f square value Ou 0.
02, f square Ou 0.
15 is a medium influence, and f square Ou 0.
35 has a large influence (Santosa, 2.
Based on the table above, the effect size (F.
results show that the influence of Personal Innovativeness (PI) on Behavioral Intention (BI) has the largest contribution with an f-square value of 0.
Meanwhile, other variables that influence Behavioral Intention (BI) have a relatively small influence because they have an f-square value <0.
Among the variables that influence Use Behavior (UB).
Behavioral Intention (BI) is the variable that has the greatest influence with an f-square value of 0.
205 with medium influence criteria.
Meanwhile, other variables that influence Behavioral Intention (BI) have a relatively small influence because they have an f-square value <0.
3 Hypothesis testing In this section, hypothesis testing is carried out to find out whether the independent variable has an influence on the dependent variable.
In SmartPLS to test the path coefficient using bootstrapping testing with a significance level of 5% .
and a two-tailed significance level, with the hypothesis:
If the t-statistic Ou 1.
96, then H0 is rejected and H1 is accepted If the t-statistic < 1.
65, then H0 is rejected and H1 is accepted The calculation results from hypothesis testing are displayed in the form of figures and tables as follows.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 Figure 3.
Test results using SmartPLS Source: Processed researcher data .
Then the table below shows the results using bootstrapping calculations.
Table 10.
Path Coefficients Hypothesis Test Results Original Sample (O) T Statistics (|O/STDEV|) P Values BI UB
0,467
4,723
0,000
EE BI
0,151
2,250
0,025
FC BI
-0,015
0,270
0,787
FC UB
0,177
3,166
0,002
HM BI
0,210
3,042
0,002
Hb BI
0,231
4,040
0,000
Hb UB
0,207
3,516
0,000
PE BI
-0,134
2,332
0,020
PI BI
0,477
7,631
0,000
PI UB
0,153
1,708
0,088
PV BI
0,163
2,462
0,014
SI BI
-0,028
0,569
0,570
Source: Processed researcher data .
So based on Table 10, the interpretation of the process and results of hypothesis testing will be presented as follows:
Hypothesis Testing 1:
The first hypothesis (H.
is accepted, which means that performance expectancy has a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 2.
332 > 1.
96 and a p-value of 0.
< 0.
Hypothesis Testing 2:
The second hypothesis (H.
is accepted, which means that effort expectancy has a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 2.
250 > 1.
96 and a p-value of 0.
< 0.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 Hypothesis Testing 3:
The third hypothesis (H.
is rejected, which means that social influence does not have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 0.
569 < 1.
96 and a p-value of 570 > 0.
Hypothesis Testing 4:
The fourth hypothesis (H.
is rejected, which means that facilitating conditions do not have a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 0.
270 < 1.
96 and a p-value of 0.
787 < 0.
Hypothesis Testing 5:
The fifth hypothesis (H.
is accepted, which means that facilitating conditions have a significant influence on the use behavior of the groceries marketplace agree mart application by Indonesian This was proven by obtaining T-statistics of 3.
166 < 1.
96 and a p-value of 0.
002 < 0.
Hypothesis Testing 6:
The sixth hypothesis (H.
is accepted, which means that behavioral intention to use the groceries marketplace agree mart application has a significant influence on use behavior of the groceries marketplace agree mart application.
This was proven by obtaining T-statistics of 4.
723 > 1.
96 and a p-value of 0.
000 < 0.
Hypothesis Testing 7:
The seventh hypothesis (H.
is accepted, which means that hedonic motivation has a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 3.
042 > 1.
96 and a p-value of 0.
< 0.
Hypothesis Testing 8:
The eighth hypothesis (H.
is accepted, which means that price value has a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian This was proven by obtaining T-statistics of 2.
462 > 1.
96 and a p-value of 0.
014 < 0.
Hypothesis Testing 9:
The ninth hypothesis (H.
is accepted, which means that habit has a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 4.
040 > 1.
96 and a p-value of 0.
000 < 0.
Hypothesis Testing 10:
The tenth hypothesis (H.
is accepted, which means that habit has a significant influence on the use behavior of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 3.
516 > 1.
96 and a p-value of 0.
000 < 0.
Hypothesis Testing 11:
The eleventh hypothesis (H.
is accepted, which means that personal innovativeness has a significant influence on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 7.
631 > 1.
96 and a p-value of 0.
000 < 0.
Hypothesis Testing 12:
The twelfth hypothesis (H.
is rejected, which means that personal innovativeness does not have a significant influence on use behavior of the groceries marketplace agree mart application by Indonesian people.
This was proven by obtaining T-statistics of 1.
708 < 1.
96 and a p-value of 088 > 0.
2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 Conclusions Conclusions Based on the results of the research and discussion in the previous chapter, conclusions can be drawn from Agree Mart user respondents who have made the following transactions:
Performance expectancy has a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Effort expectancy has a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Social Influence does not have a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Facilitating conditions do not have a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Facilitating conditions have a significant effect on use behavior of the groceries marketplace agree mart application by Indonesian people.
Behavioral intention to use the groceries marketplace agree mart application has a significant effect on use behavior of the groceries marketplace agree mart application.
Hedonic motivation has a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Price value has a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Habit has a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Habit has a significant influence on use behavior of the groceries marketplace agree mart application by Indonesian people.
Personal innovativeness has a significant effect on the behavioral intention of the groceries marketplace agree mart application by Indonesian people.
Personal innovativeness does not have a significant effect on use behavior of the groceries marketplace agree mart application by Indonesian people.
2 Suggestions Based on the results of the research conducted, the following are suggestions for Agree Mart:
Optimize Availability: Pay attention to and increase the availability of sellers and goods around potential buyers to overcome obstacles that some users may experience.
Ensuring adequate stock will increase customer satisfaction and the possibility of adopting the Agree Mart application in accordance with the obstacles highlighted by several sources regarding the Performance Expectancy variable.
Strengthen Promotion and Discount Strategies: Continue to develop promotion strategies, discounts and additional prizes as elements that can increase the ease of transactions.
This can help motivate users to transact more and regularly.
Consider Product Diversification: Consider product diversification or adding new products that suit consumer preferences and needs.
This can increase the appeal of the application and meet the needs of diverse customers.
Focus on User Experience Consistency: Ensure consistency in providing a positive user experience, especially related to ease of category exploration, attractive image display, and uniqueness of local products.
User Engagement and Feedback: Actively engage users to get their feedback.
This can help companies in crafting policies and continuous improvements based on user needs and preferences.
Further research can be carried out in various ways.
Comparative Study with Other Platforms: Compare the user experience and preferences of Agree Mart with other e-commerce platforms or similar This can provide further context and help in understanding Agree Mart's competitiveness in the market.
Comparisons can provide an understanding of user preferences and competitive advantages in the market.
Qualitative Approach for Non-Significant Variables: Use a qualitative approach to better understand why variables such as Social Influence and Personal Innovativeness do 2024 | Jurnal Ilmiah Pertanian dan Peternakan | / Vol 2 No 1, 23-45 not have a significant effect on Behavioral Intention or Use Behavior.
This can provide deeper contextual insight.
References