1 | International InformationSystem System Computer Engineering 4.
International Journal Journal of of Informatics Informatics Information Computer Engineering 4.
International Journal of Informatics.
Information System and Computer Engineering Combination of Technology Acceptance Model and Decision-making Process to Study Retentive Consumer Behavior on Online Shopping Edwin Rudini 1* .
Dwiza Riana2 .
Sri Hadianti 3 1,2 Program Studi Ilmu Komputer.
Universitas Nusa Mandiri 3 Program Studi Informatika.
Universitas Nusa Mandiri Jln.
Jatiwaringin No.
2 Cipinang Melayu.
Makasar Jakarta Timur.
Kota Jakarta, 13620 DKI Jakarta *Corresponding Email: edwinrudini98@gmail.
ABSTRACTS
ARTICLE INFO
During the spread of the Covid-19 virus, generally the Indonesian people began to switch from conventional markets to buying and selling goods and services online with various features and conveniences offered to users.
The purpose of this study is to find out the extent to which indicators of satisfaction and trust influence consumer attitudes and behavior when deciding to make transactions at online shops.
The study method uses a combination of TAM (Theory Acceptance Mode.
and DMP (Decision Making Proces.
models using a sampling of 110 student respondents and the public who have made transactions in online shops.
Data analysis using SEM (Structural Equation Modelin.
The results showed that satisfaction and trust will influence consumers in shaping.
Article History:
Submitted/Received 03 Dec 2022
First Revised 31 Dec 2022
Accepted 20 Feb 2023
First Available Online 10 April 2023
Publication Date 01 June 2023 8
INTRODUCTION
Changing people's behavior within the framework of online stores is a big challenge for companies to be able to serve all people's needs and wants.
Information released by the Ministry of Communication Information explained that the value of online shopping transactions in 2021 will reach DOI: https://doi.
org/10.
34010/injiiscom.
p-ISSN2810-0670 e-ISSN2775-5584
__________________
Keywords:
TAM.
DMP Model.
Trust.
Satisfaction.
Repurchase.
IDR 337 trillion and the number of Internet users will reach 210 million (Rose et al.
, 2.
Therefore, it can be concluded that the possibility of development in online trading is very This encourages several large companies to invest in the advancement of online business in Indonesia.
The growing potential of online commerce is Rudini et al.
Combination of Technology Acceptance Model and Decision-making A | 24 expected to produce more technology entrepreneurs and encourage the growth of MSMEs according to the characteristics of each company to utilize every potential they have (Haryanti & Subriadi, 2.
ICD Research Foundation estimates the growth potential of online shop business in Indonesia at 33.
2% during 2020-2021 and is one of the countries with the fastest and largest online shopping-based business growth rate in the Asia-Pacific region (Setyowati et al, 2.
The increase in online commerce activity is not in accordance with the growth of online shop buyers.
This is due to a number of barriers, including low credit and debit card accessibility as well as shoppers' reluctance to shop online (Nagy & Hajdy, 2.
Then based on Nielsen statistical surveys it is known that buyers will look for data on the Internet before choosing to buy a product they need.
The trust to buy products in online shopping is a barrier difficult to because it is related to customer views and behavior (Dennis et al.
, 2.
Therefore, to study the attitude and behavior of buyers towards online shopping so that business organizations can take advantage of existing It is possible to measure buyer behavior with a social behavior approach that acts as a variable that influences customer views and behavior in online shopping.
To measure buyer behavior must be possible with a social behavior approach that acts as a variable that influences customer perspective and behavior in shopping at online shops (Petcharat & Leelasantitham, 2.
estimating the use of data innovation, there are several models that can be used, such as the technology acceptance model (TAM) and the decision making process (DMP), which states that Individual DOI: https://doi.
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p-ISSN2810-0670 e-ISSN2775-5584 behavior is a measure of power and activity whereby individuals will leverage data frameworks and data innovations if it is beneficial (Wei et al.
Customer behavior in online commerce is also influenced by the satisfaction of making transactions on the Internet and is a major factor that makes buyers prefer online stores.
Furthermore, transactions is proven to affect customer confidence, which in turn will affect buyers' views on repurchases.
Given the problems, obstacles and difficulties, as well as the potential created by online merchants, the online shopping sector is expected to encourage the improvement of the Indonesian economy (Kim, 2.
METHOD
The study method uses a combination of TAM (Theory Acceptance Mode.
and DMP (Decision Making Proces.
Due to the large population and limited time and cost to 110 respondents from the population studied.
In addition, research methods can be used to evaluate and compare results and draw conclusions.
Specimen collection techniques through targeted sampling of students and In determining research specimens based on several criteria, namely students who are active, willing to answer surveys distributed by researchers, a minimum sample size of 15% of the total population, and have already done a set online shopping transaction .
ee Tables 1 and .
The method of collecting data is carried out by distributing questionnaires through google forms to respondents with specified randomization criteria and observing directly the object to be In this study the variables used are independent variables, moderator 25 | International Journal of Informatics Information System and Computer Engineering 4.
23-30 variables and dependent variables.
The independent variables used are Perceived Usefulness (X.
Perceived Ease of Use (X.
Evaluation Alternative (X.
Information Search (X.
, moderator variables namely trust (Y.
, satisfaction (Y.
and the dependent variable used is purchase (Z).
Table 1.
Respond Total (N-.
Demographics Gender Nationality Age Frequency percentage (%) Male Female Indonesia Table 2.
Variable and Indicators Variable Perceived (X.
Indicators Online shopping platforms help you search and buy products faster than offline shopping Online shopping platforms help you buy products cheaper than offline shopping You can use online shopping platforms with your own gadgets Perceived easy use (X.
Evaluation alternative (X.
Information Search (X.
Perceived Trust (Y.
Online shopping platforms have clear functions and are easy to understand The search function in online shopping platforms is necessary and benefits Intend to buy products before going to the shopping cart Functions in online shopping platforms help you to compare a product The search function on online shopping platforms is quite Comparing the quality and price of similar products will make it easier for you to make a decision to buy Online shopping platforms have accurate and clear results such as product details and prices You are sure to get the purchased products from the online shopping platform Online shopping platforms have accurate and clear results such as product details and prices Satisfaction (Y.
You are sure to get the purchased products from the online shopping platform An online shopping platform is required for you You can buy back from online shopping platforms Repurchase (Z) You have repurchased the same product from an online shopping platform DOI: https://doi.
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p-ISSN2810-0670 e-ISSN2775-5584 Rudini et al.
Combination of Technology Acceptance Model and Decision-making A | 26
RESULTS AND DISCUSSION
This analysis was used to describe the results of a survey consisting of the number of students and the general public who answered a questionnaire that measured their trust and satisfaction in using online shopping (Bhatti et al.
The data was processed using Smart Partial Least Squares (PLS) software version 3.
9 and Microsoft Excel Windows 2016 version.
The variables analyzed in this study include satisfaction before and after transactions (X1.
X2.
X3 and X.
, trust (Y.
, and satisfaction of online shopping repurchases (Y.
and purchases (Z) (Riantini.
The instrumentation uses Likert scale The Likert scale consists of two types of statements, positive and negative, with positive statements scoring 4 points for strongly agreeable responses and strongly disagree responses as 1 point using Structural Equation Modeling (SEM) and PLS data analysis techniques to develop predictive theories related to satisfaction and trust in the use of online shopping in students and the community (Fedorko et al.
, 2.
PLS model analysis is based on predictive measures with non-parametric properties due to Here measurement of individual reflections correlates with discriminant validity values comparing loading values of > 0.
and squared values.
Root of extracted mean variance (AVE) for each component with correlation between components in the model (Sheth, 2.
Discriminant validity is good if the AVE value is greater than the correlation value between the component and the model.
Structural models are tested using R squared for dependent structures.
Stone Geyser Q-squared test to test predictive associations, t tests and significance for structural path parameters.
Data analysis was carried out by entering all validity and significance (Cai et al.
, 2.
The results of the calculation explain that all indicators meet a construct loading value of >0.
5 so that all indicators can be used in tests using the PLS model.
Referring to the results of the calculation of convergence validity determined loading values per indicator is shown in Table 3.
Table 3.
Convergent Validity Value Evaluation Alternative EA1
0,879
EA2
0,830
IS2
IS3
PEU1
PS1
PS2
PT1
PT2
PU2
RP2
Uses Facilities Belief Satisfaction Information Search Purchase 0,737 0,941 1,000 0,899 0,897 0,920 0,880 1,000 DOI: https://doi.
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1,000
27 | International Journal of Informatics Information System and Computer Engineering 4.
23-30 Table 4.
Discriminant Validity Evaluation Alternativ e Uses Facilities Satisfaction Informatio n Search Purchase Evaluation Alternative 0,855 Uses Facilities Belief Satisfaction Information 0,283 0,314 0,344 0,497 -0,099 1,000 0,496 0,377 0,455 0,167 1,000 0,152 0,510 0,272 0,900 0,353 -0,191 0,898 0,145 0,845 0,041 0,065 0,192 -0,069 -0,109 0,177 Purchase 1,000 Table 5.
Average Variance Extracted (AVE) and Composite Reability Cronbach's Alpha Composite Reliability 0,633 0,644 0,844 0,731 1,000 1,000 0,769 0,760 1,000 1,000 0,789 0,760 1,000 1,000 0,896 0,893 1,000 1,000 0,811 0,806 0,634 0,845 0,831 0,714 1,000 1,000 1,000 1,000 Evaluation of Alternativ e Uses Facilities Belief Satisfaction Information Search Purchase Average Variance Extracted (AVE) Table 6.
Path Coefficient and Decision Sample Mean (M) Standard
Deviation (STDEV) T Statistics (|O/STDEV|) P Value 0,232 0,257 0,099 2,331 0,020 0,320 0,314 0,113 2,836 0,005 0,111 0,131 0,144 0,775 0,439 0,371 0,143 0,051 -0,046 0,284 0,352 0,146 0,055 -0,054 0,288 0,104 0,094 0,158 0,125 0,120
3,554
1,511
0,325
0,367
2,373
0,000
0,132
0,745
0,714
0,018
0,305
0,315
0,135
2,258
0,025
Original Sample (O) Evaluation Alternative -> Belief Evaluation Alternativ e -> Satisfaction Evaluation of Alternativ e -> Purchase Uses -> Belief Uses -> Satisfaction Uses -> Purchase Facilities -> Belief Facilities -> Satisfaction Facilities -> Purchase The value discriminant validity.
AVE and composite reliability, and path coefficient on fornell-larcker are shown in Tables 4 Ae 6.
Table 6 shows path coefficients is also known as significance and a measure of This figure is used to interpret DOI: https://doi.
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p-ISSN2810-0670 e-ISSN2775-5584 the importance and strength relationships between concepts.
This table has a range of path factor values from Ae 0.
05 to 0.
05 for path Value greater than 0.
05 is considered a negative relationship, while a positive relationship is a value less than Rudini et al.
Combination of Technology Acceptance Model and Decision-making A | 28 05, which increases the strength of the Table 6 Gives a detailed description of the table of path coefficients:
H1A.
Evaluation of alternatives has an effect on trust.
H1B.
Evaluation of alternatives has a significant effect on satisfaction.
H1C.
Evaluation of alternatives has no effect on purchasing.
H2A.
Usability has a significant effect on H2B.
Usability on satisfaction.
H2C.
Usability on purchases.
H3A.
Convenience has a significant and positive effect on purchases.
H1B.
Evaluation of alternatives has a significant effect on satisfaction H2C.
Usability on purchases H3A.
Convenience has a significant and positive effect on purchasing H4A.
Trust has no effect on satisfaction H4B.
Trust has no effect on H4C.
Satisfaction affects purchases H5A.
Seeking information has no effect on trust H5B.
Seeking information has no effect on satisfaction.
Teory Acceptance Model (TAM) This model was originally created by Davis and has become one of the most widely used models to explain how users receive new technologies.
This model was developed from the Theory of Reasoned Action and provides a basis for identifying how external variables such as beliefs, attitudes, and intentions influence the acceptance of new technologies .
ee Fig.
(Dennis et , 2.
Perceived Usefulness Shows how far individuals will believe that utilizing technology improves the quality of their work (Moe & Fader, 2.
Perceived Easy Easy of Use Means that individuals believe that using information technology systems will not cause problems or require too much effort when used (Hernyndez et al.
, 2.
DMP (Decision Making Proces.
The purchase decision process used in this study is related to the theory that the process is an orderly action and Information Search.
Table 7.
R Square Adjust Value R Square R Square Belief Satisfaction Adjusted Purchase DOI: https://doi.
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p-ISSN2810-0670 e-ISSN2775-5584 29 | International Journal of Informatics Information System and Computer Engineering 4.
23-30 Fig.
Smart Pls Use TAM & DMA Model This process includes searching for and gathering information about relevant products or services.
It gives you a wide range of service products worth buying (Cai et al.
, 2.
Evaluation of Alternative Involves evaluating and comparing different products or services worth buying (Deananda et al.
, 2.
CONCLUSION
customer attitudes and behaviors when shopping online are influenced by the process of customer trust in online stores.
From this it follows that trust in online stores have a significant impact on customer attitudes and behavior.
In order for the online shop business to succeed optimally, the goal is to maintain customer trust well and protect MSME business actors who use information technology to further optimize sales.
Customer satisfaction is influenced by the trust process.
This research shows that REFERENCES