OPEN ACCESS
Intl.
Journal on ICT Vol.
No.
Jun 2024.
doi: doi.
org/10.
21108/ijoict.
ISSN 2356-5462
http://socj.
id/ijoict/ Analysis of Factors Affecting the Use of Digital Paylater Transactions Using the Hedonic-Motivation System Adoption Model
(HMSAM)
Muhammad Zahwan Latif 1*.
Rio Guntur Utomo 2 School of Computing.
Telkom University Bandung Indonesia * mzahwanlatif@student.
Abstract The use of digital transaction methods paylater is one of the trends in the current era of digitalization transactions.
Innovation in the application of the use of digital transaction technology can attract public interest because it makes it easier to make digital To increase user satisfaction, which will affect continuance intention, service providers must also improve the quality of service provided.
This study aims to analyze the factors that influence the continuance intention of digital paylater transactions, which focus on individuals aged 18 years and over who have made digital paylater transactions.
The research uses a quantitative approach with Structural Equation Modeling with Partial Least Squares (SEM-PLS).
The model used is the modified Hedonic-Motivation System Adoption Model (HMSAM The six main variables include perceived ease of use, curiosity, joy, control, satisfaction, and continuance intention.
The results revealed that the hypothesis test conducted for 6 out of 7 hypotheses showed the T-Statistics value >1.
96, the P-Values value <0.
05, and the R-Square value in the low and medium classifications showed a medium and small classification of the influence of Factors Affecting the Use of Digital Paylater Transactions Using the Hedonic-Motivation System Adoption Model (HMSAM).
Keywords: Continuance Intention.
Digital Transaction.
Hedonic-Motivation System Adoption Model (HMSAM).
Paylater INTRODUCTION echnological developments have a major impact on business and online transactions, especially digital Digital transactions are conducted electronically through an internet-connected computer or mobile device.
The term is becoming more commonly used as e-commerce and digital financial services become more popular.
These transactions include online payments for goods/services, fund transfers, bill payments, and more through e-commerce/online banking platforms.
The main advantage of digital transactions is the ease and convenience they offer, allowing customers to access products and services online from anywhere at any time without having to visit a physical store or bank .
Received on 29 Dec 2023.
Revised on 13 Feb 2024.
Accepted and Published on 30 Jun 2024.
INTL.
JOURNAL ON ICT VOL.
NO.
JUNE 2024
For this reason, e-commerce payment systems, with the growing popularity of e-commerce, encourage entrepreneurs and consumers to choose a payment system that is safe, fast, and convenient.
One of the increasingly popular payment systems in the e-commerce era is the digital paylater or payment later transaction.
This transaction allows consumers to make purchases and pay later at a predetermined time.
Paylater is driving sales in e-commerce and many consumers are opting for it to buy items online and pay later to get more time to accumulate money.
Examples of paylater services are Kredivo.
Akulaku, and Tunaiku.
In its development, the concept of Paylater has become one of the popular payment methods in various transactions, including online shopping, airline ticket purchases, digital products, and more.
Consumers are given paylater payment options when shopping, which gives them time to pay their bills within weeks or months after the transaction is made .
According to the Research Institute of Socio-Economic Development (RISED) survey, the use of the paylater feature in Shopee is the highest with 52.
06%, followed by Gojek .
28%),
Tokopedia .
35%).
Traveloka .
08%), and .
23%) other applications .
This shows the increasing acceptance and usage of paylater in Indonesia.
Like research .
, which analyzes interest in using Gopay with the Technology Acceptance Model (TAM).
Or in research .
, which analyzes the acceptance and targets that will use OVO paylayer using the Unified Theory of Acceptance and Use of Technology (UTAUT).
Based on Lowry.
, et al.
in 2013 to measure the level of pleasure in using the system, the HedonicMotivation System Adoption Model (HMSAM) theory was proposed.
And the Hedonic-Motivation System (HMS) theory outperformed the utilitarian-motivation system (UMS) theory for more than a decade.
Where UMS focuses on measuring perceived benefits.
HMS measures perceived pleasure .
So HMSAM is a model used to understand and analyze the factors that influence user acceptance of entertainment-based technologies or those that have entertainment value.
HMSAM was developed as a development of TAM which was first developed .
From the above research.
HMSAM is more suitable than models such as TAM.
UTAUT, or UMS because it considers pleasure and comfort for users.
In the study .
, it was found that analyzing the use of video on demand applications using HMSAM highlighted that factors such as perceived ease of use, control, and joy have a significant impact on user satisfaction and fulfill intrinsic needs which are hedonic needs.
HMSAM recognizes that emotional factors and hedonic motivations also play an important role in technology adoption, along with well-known factors such as perceived usefulness and perceived ease of use.
HMSAM then calculates user satisfaction, which affects continuance intention.
In HMSAM, satisfaction with using the system can be measured by how the system generates curiosity and pleasure in using the system.
In addition, it is also calculated how easy it is for users to use the system .
erceived ease of us.
Then, how satisfied users are in using the system .
, is influenced by curiosity, joy, and user control .
To evaluate the assessment of the public the level of satisfaction with using digital paylater transactions using the HMSAM method, a survey will be conducted to obtain test data.
II.
LITERATURE REVIEW
Digital transaction Digital transactions, encompassing online or electronic payments, have become a prevalent method for financial interactions, allowing users to conduct transactions without the need for cash or credit cards.
Utilizing platforms such as websites, mobile applications, or specialized software, these transactions involve activities like bill payments, fund transfers, and online purchases .
Key factors, including security, user-friendliness, and accessibility, contribute to the popularity of digital transactions, offering potential time and cost savings.
Despite these advantages, concerns about security risks, such as data breaches and online fraud, persist, emphasizing the need for secure platforms .
In Indonesia, the government and businesses actively promote the adoption of digital transactions to enhance efficiency and accessibility across various sectors, including finance, trade, and public services.
The characteristics of digital transactions, conducted through electronic networks, in real-time, location-independent, with 24/7 availability, and utilizing security technologies, ensure their efficiency, convenience, and security .
These characteristics contribute to the numerous advantages of MUHAMMAD ZAHWAN LATIF ET AL.
ANALYSIS OF FACTORS AFFECTING THE USE OF DIGITAL PAYLATER TRANSACTIONS USING THE HEDONIC MOTIVATION.
digital transactions, including convenience, real-time efficiency, lower costs, enhanced security, userfriendliness, 24/7 availability, and dedicated customer support, making them a preferred and beneficial choice for users.
Paylater Paylater, a term referring to online credit services without the need for credit cards or cash loans, has gained popularity in various sectors, initially within the e-commerce market and now expanding to areas such as food and beverage and airline ticket purchases.
Users of paylater services, offered by fintech companies like Gopay.
Shopee.
OVO.
Kredivo.
Akulaku, and Kredit Pintar in Indonesia, can make online or offline purchases and defer payment within a specified period, with some platforms allowing installment options .
While the convenience and speed of transactions are primary advantages, users enjoy flexibility in payment terms, avoiding the need for collateral, and access to enticing discounts and promotions.
However, the use of paylater is not without risks.
Users face the burden of high interest rates, potential late payment fees, and the risk of accumulating excessive debt if not managed carefully.
Discipline and financial responsibility are crucial to avoiding these risks.
Additionally, there's a potential threat of identity theft and online fraud, emphasizing the importance of cautious data sharing and choosing reputable paylater services to protect users from significant financial losses .
Hedonic motivation Hedonic motivation, as outlined by Utami .
, refers to the inclination of individuals to derive pleasure or emotional satisfaction from their activities, especially evident in the joy of shopping irrespective of the functional benefits of purchased products.
In the context of technology use, hedonic motivation extends to users seeking enjoyable experiences from their interactions with technology.
This motivation is categorized into three types: Affiliation, associated with the pleasure derived from specific activities.
Escapism, offering a refreshing break from routine activities and novelty, emphasizing the pursuit of novelty and new experiences.
Its influence on technology acceptance is significant, impacting user perceptions of technology's usefulness and relative usefulness in both TAM and UTAUT frameworks.
Research findings, exemplified by studies such as .
, demonstrate that hedonic motivation plays a crucial role in influencing user intentions to adopt technology, with factors like enjoyment/affiliation and social enhancement being key contributors.
A comprehensive understanding of these dimensions of hedonic motivation is essential for grasping its impact on shaping user attitudes and behaviors in the realm of technology adoption.
Hedonic-Motivation System Adoption Model (HMSAM) The Hedonic-Motivation System Adoption Model (HMSAM), introduced by Lowry and colleagues in 2013, represents a noteworthy advancement in predicting the adoption of Hedonic Motivation Systems (HMS).
Going beyond existing models.
HMSAM integrates intrinsic motivational factors and elucidates the relationship between motivation and traditional technology acceptance factors.
This study found that the factors that encourage the use of HMS are Control.
Curiosity.
Joy, and Immersion in the game as the object under study .
HMSAM stands out as a technology adoption model that seeks to amalgamate hedonic and motivational factors in predicting technology adoption behavior.
It posits that technology adoption behavior is not solely influenced by instrumental factors related to technology's utility and ease of use but is also shaped by hedonic factors linked to user satisfaction and motivation compelling users to embrace technology.
The model's characteristics include its consideration of emotional and motivational factors, integration of hedonic aspects emphasizing pleasure in system use, accounting for users' previous experiences with similar systems or technologies, emphasizing the importance of user-system interaction, and identifying pivotal factors such as user trust, system usability, hedonic needs, and user motivation.
The comprehensive HMSAM thus serves as a valuable framework for understanding the complex interplay of emotional, hedonic, and motivational elements in technology adoption.
INTL.
JOURNAL ON ICT VOL.
NO.
JUNE 2024
RESEARCH METHOD
This research employs a quantitative associative approach .
to examine the relationships between variables influencing Continuance Intention in digital paylater transactions.
The research process, illustrated in Fig.
Fig.
Research Model Flowchart The research began with problem identification, followed by an in-depth literature search through problem review and literature study.
Proposed variables and hypotheses are developed, and research questions are formulated in the question development stage.
Data is collected through questionnaires at the data collection stage and then tested for validity.
If valid, data analysis was conducted, but if not, revisions were made by deleting invalid questions .
If it is still invalid, it will return to data collection.
After that, the data analysis stage and concluding the research results, which ends with the end stage as the completion of the entire research Research Hypothesis This study uses the proposed HMSAM model as the basis for research analyzing the use of digital paylater transactions based on hedonic motivation.
This model will be modified by combining two HMSAM models developed from the HMSAM model developed by Imen Elmezni and Jamel-Eddine Gharbi in 2010 and the HMSAM model developed by Lowry & Roberts in 2013 .
The following key variables are examined within this model:
Perceived Ease of Use: Measures the extent to which users find paylater digital transactions easy to use, influenced by simplicity, navigability, and availability of information.
Control: Refers to the user's perception of the level of control over the paylater digital transaction, including the ability to understand, organize, and control the process according to preference.
Joy: Reflects the emotional satisfaction that users feel when using paylater digital transactions, which can stem from a pleasant shopping experience.
Curiosity: Indicates the level of user curiosity towards the features, benefits, and experience of paylater digital transactions, which contributes to active user interaction.
Satisfaction: Measures the level of user satisfaction with the digital paylater transaction experience, including emotional and cognitive aspects related to the quality and benefits obtained.
Continuance Intention: Reflects the user's tendency to continue using paylater digital transactions in the future, influenced by the level of satisfaction with the previous usage experience.
The hypothesis is shown in Fig.
MUHAMMAD ZAHWAN LATIF ET AL.
ANALYSIS OF FACTORS AFFECTING THE USE OF DIGITAL PAYLATER TRANSACTIONS USING THE HEDONIC MOTIVATION.
Fig.
2 Hypothesis Research H1: Perceived Ease of Use has a positive effect on Control in using digital transactions.
H2: Perceived Ease of Use has a positive effect on Joy in using digital transactions.
H3: Perceived Ease of Use has a positive effect on Curiosity in using digital transactions.
H4: Control has a positive effect on Satisfaction in using digital transactions.
H5: Joy has a positive effect on Satisfaction in using digital transactions.
H6: Curiosity has a positive effect on Satisfaction in using digital transactions.
H7: Satisfaction has a positive effect on Continuance Intention in using digital transactions.
Data Collection According to Sugiyono .
, the population is a generalized area .
consisting of objects or subjects with specific qualities and characteristics set by the researcher for the study and subsequent In this study, the population refers to the group of users employing digital paylater transactions as their payment method.
This group comprises individuals aged 18 and above with experience in using digital paylater transactions, respondents are classified based on age .
: Generation Z for ages above 18 to 26.
Generation Y .
for ages 27 to 42, and Generation X for ages above 42.
As this age range is considered capable of making independent decisions and being responsible for their actions, with a minimum of 384 respondents.
The sample size was determined using the Lemeshow formula for an unknown population.
, as shown in the formula .
ycu= ycs 2 ycuycE.
Oe ycE) ycc2 Here n is the sample amount.
Z is the Z score at confidence 95%.
P is the maximum estimate, and d is the sampling error.
This research involves 14 indicators of questions relating to the six variables being tested.
The data collection method uses a questionnaire as a standardized instrument that will be distributed to respondents through Google Forms.
This approach facilitates online data collection, obtaining the required information efficiently and effectively.
The measurement of the questionnaire is done using five indicators based on a Likert scale where 1 strongly disagrees and 5 strongly agrees.
Data Analysis In the process of data analysis and measurement, the tested questionnaire results were classified and analyzed using Partial Least Square (PLS), a Structural Equation Model (SEM) technique.
PLS, as a regression-based approach, aims to minimize residual variance.
The advantages of PLS include its ability to test complex relationships simultaneously, use relatively small samples, and assess relationships between constructs .
, .
Evaluation of the measurement model .
uter mode.
involves examining convergent and discriminant validity, as well as reliability.
The measurements will be tested for convergent validity and discriminant validity, involving loading factor values.
Average Variance Extracted (AVE), and Heterotrait-Monotrait Ratio (HTMT).
INTL.
JOURNAL ON ICT VOL.
NO.
JUNE 2024
Reliability tests use Cronbach's Alpha and composite reliability values to assess the stability and consistency of the instrument.
Furthermore, the structural model was analyzed using F-square.
R-square, and Model Fit to evaluate explanatory power, predictive relevance, and overall model fit.
IV.
RESULTS AND DISCUSSION
Of the total 412 respondents, the majority were female with 274 people or 66.
Respondents were divided into three age groups, namely 18-26 years, 27-42 years, and above 42 years.
The age group of 18-26 years is the largest with a percentage of 70.
9%, followed by the age group of 27-42 years with 15.
8%, and the age group above 42 years with 13.
Outer Model .
Convergent Validity Test The convergent validity test plays an important role in ensuring the accuracy and quality of the measurement instrument which is a strong basis for interpreting the overall research results.
The test is carried out by checking the outer loading value of each question with a value above 0.
7 and the AVE value for each variable above 0.
By having an outer loading value above 0.
7 and an AVE above 0.
5, it is considered to meet convergent validity.
The following are the results of the validity test in Table I TABLE I CONVERGENT VALIDITY TESTING
Variable Indicator Ease to learn
PEOU
Understable Curious
CUR
Improve Imagination JOY Pleasurable A lot of Control
CTR
SAT
Choose freely Expectation Confirmation Intention to continue Willingness to Plan to expand
Question
PEOU1
Code
PEOU2
PEOU3
PEOU4
CUR1
CUR2
CUR3
CUR4
JOY3
JOY4
JOY5
JOY6
CTR2
CTR3
CTR4
SAT3
SAT4
CI1
CI2
CI3
CI4
CI5
CI6
Outer Loading AVE Description Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid Valid MUHAMMAD ZAHWAN LATIF ET AL.
ANALYSIS OF FACTORS AFFECTING THE USE OF DIGITAL PAYLATER TRANSACTIONS USING THE HEDONIC MOTIVATION.
Table I indicates that each indicator exhibits an outer loading above 0.
7, along with an Average Variance Extracted (AVE) value surpassing 0.
5, signifying their validity.
Consequently, all constructs fulfill the criteria for convergent validity.
Divergent Validity Test Discriminant validity testing is done using the Heterotrait-Monotrait Ratio (HTMT), where measurements of different constructs should not show a high correlation.
Validity is tested using HTMT where the discriminant value is met if the value <0.
Discriminant validity testing in Table II below:
TABLE II
DISCRIMINANT VALIDITY TESTING
Continuance Intention Perceived Control Curiosity Joy Ease of Satisfaction Use Continuance Intention Control Curiosity Joy Perceived Ease of Use Satisfaction Table II shows good discriminant validity results.
Judging from the correlation value between variables Then the constructs are valid in terms of discriminant validity.
Reliability Test To determine the reliability of a variable, it is expected that the composite reliability value and Cronbach's alpha coefficient are > 0.
Reliability Testing Table i below:
TABLE i RELIABILITY TESTING Cronbach's Composite Alpha Reliability PEOU Reliable CUR Reliable Variable Description JOY Reliable CTR Reliable SAT Reliable Reliable From Table i.
CronbachAos Alpha and Composite Reliability values are also obtained, except for Cronbach's Alpha values on Control and Satisfaction which only have values above 0.
6 which can be accepted, all other values have good values above 0.
Which shows a good level of consistency between INTL.
JOURNAL ON ICT VOL.
NO.
JUNE 2024
Inner Model .
F Square F Square is used to test the effect of certain variables on other variables in the model structure.
Commonly used threshold values are around 0.
02 for a small effect, 0.
15 for a medium effect, and 0.
35 for a large effect in the model structure.
TABLE IV
F SQUARE
Continuance Intention Continuance Intention Control Control Curiosity Joy Perceived Ease of Use Satisfaction Curiosity Joy Perceived Ease of Use Satisfaction Table IV reveals three types of F Square impacts: negligible, small, and moderate.
Notably, small impacts are observed in the relationships between curiosity and joy on satisfaction, each ranging between 0.
Additionally, moderate impacts are identified in associations such as Perceived Ease of Use with Control.
Curiosity, and Joy, as well as the connection between Satisfaction and Continuance Intention, with F Square values ranging from 0.
15 to 0.
Conversely, the influence of Control on Satisfaction is negligible, indicated by an F Square value of 0.
No influences exceeding 0.
35 are .
R Square R Square gauges the model's ability to elucidate variations in the dependent variable, ranging from 0 to A higher value signifies better explanatory power.
Strength is classified as approximately 0.
moderate at about 0.
333, and weak at 0.
190 or below.
TABLE V
R SQUARE
R Square R Square Adjusted Continuance Intention Control Curiosity Joy Satisfaction Table V indicates that Continuance Intention.
Control, and Curiosity exhibit a weak R Square below On the other hand.
Joy and Satisfaction both demonstrate moderate R Square values, with 0.
253, respectively.
Fit Model Model Fit .
assesses the model's quality through the Standardized Root Mean Square Residual (SRMR), where a lower value signifies a better fit.
Additionally, the Normed Fit Index (NFI) is MUHAMMAD ZAHWAN LATIF ET AL.
ANALYSIS OF FACTORS AFFECTING THE USE OF DIGITAL PAYLATER TRANSACTIONS USING THE HEDONIC MOTIVATION.
considered, with values approaching 1 indicating a good fit compared to a poor null model.
Evaluation of the fit model in this study was carried out as shown in the table below:
TABLE VI
FIT MODEL
Saturated Model
Estimated Model
SRMR
NFI
Table VI indicates a marginal fit for the model, with an SRMR value of 0.
lose to 0.
and an NFI value of 0.
lose to 0.
These results suggest that the model effectively explains the data, meeting the required criteria for a reasonable fit.
Hypothesis Measurement In hypothesis testing with a 95% confidence interval, a T-test value exceeding 1.
t a significance level of .
is considered significant.
The emphasis is on P-Values: A P-Value < 0.
05 rejects the hypothesis, while PValues Ou 0.
05 accepts the hypothesis.
TABLE VII
HYPOTHESIS TESTING
Original Sample T Statistics P Values Status Control -> Satisfaction Rejected Curiosity -> Satisfaction Accepted Joy -> Satisfaction Accepted Perceived Ease of Use -> Control Accepted Perceived Ease of Use -> Curiosity Accepted Perceived Ease of Use -> Joy Accepted Satisfaction -> Continuance Intention Accepted Table VII of the 7 hypotheses that Control paylater has a positive effect on satisfaction of digital transaction users, it is rejected because it has a P value above 0.
05, namely, it has a P value with a value of 0.
addition, this hypothesis has a T value of less than 1.
Discussion The study, involving 412 respondents, aims to assess the impact of indicators on the use of Paylater Digital Transactions using Google Form questionnaires and analysis with SmartPLS.
Hypotheses are considered valid if the P-value is less than 0.
05 and the T-statistic exceeds the T-table value of 1.
H1: Perceived Ease of Use (PEOU) has a positive effect on Control (CTR).
Structural Model Analysis shows a significant positive effect with a path coefficient = 0.
P Value = 0.
and T-Test = 6.
678 from PEOU on CTR in paylater users.
This shows that an easier understanding and use of paylater services strengthens user control over digital transactions.
The P value is below 0.
05 and the T-statistic 96 confirming the validity of the hypothesis.
The F Square value of 0.
159 indicates a moderate impact, in line with previous studies .
, .
, .
Therefore, to increase user control in using paylater digital transaction services, service providers must continue to optimize their service quality.
Whether it's optimizing INTL.
JOURNAL ON ICT VOL.
NO.
JUNE 2024
the registration process, using easy-to-understand language, and providing responsive customer service that increases ease of use is essential to instill trust in paylater digital transaction services.
This indicates that control is influenced by Perceived Ease of Use, because the ease of using the service increases control in using the paylater digital transaction service.
H2: Perceived Ease of Use (PEOU) has a positive effect on fun (Jo.
The Structural Model analysis indicates a significant positive influence with path coefficient = 0.
P-value = 0.
000, and T-test = 8.
944 from PEOU on JOY in paylater users.
This suggests that an easier understanding and utilization of paylater services contribute to a higher level of joy in digital transactions.
The validity of the hypothesis is confirmed by a P-value below 0.
05 and a T-statistic exceeding the T-table value .
The F Square value of 0.
328 indicates a moderate impact, aligning with previous research .
, .
, .
To enhance user satisfaction, digital paylater service transactions should create an attractive and interactive impression.
Implementing these recommendations can increase PEOU and JOY, encouraging users to utilize these services more frequently.
H3: Perceived Ease of Use (PEOU) has a positive effect on Curiosity (CUR) The results of the Structural Model analysis show a significant positive effect with a path coefficient = 0.
P-value = 0.
000, and T-test = 7.
863 from PEOU on CUR in paylater users.
Easier understanding and use of paylater services correlates with higher levels of curiosity in digital transactions.
The validity of the hypothesis is confirmed by the P-value below the 0.
05 significance level and the T-statistic exceeding the T-table value .
The F Square value of 0.
188 indicates a moderate impact, in line with previous studies .
, .
, .
To increase user curiosity, service providers implement various suggestions to attract user attention such as implementing reward programs, competitions, or promos can increase satisfaction and motivate users to advocate others to use paylater services.
H4: Control (CTR) has a positive effect on Satisfaction (SAT) The Structural Model analysis reveals that the path coefficient value of 0.
054, which is below the threshold 1, results in a non-significant impact of user control on satisfaction with P-value = 0.
265 and T-test = 1.
The hypothesis is rejected as the P-value exceeds 0.
05, indicating insignificance.
The low path coefficient .
signifies a weak effect.
Despite the analysis showing that easier use of paylater services relates to CTR, this effect on user SAT is deemed not significant.
Supported by a P-value above 0.
05 and a T-statistic below the T-table value .
, the F Square value of 0.
003 confirms a negligible influence of Control on Satisfaction in the model structure, contrary to .
Regarding the control factor, the findings of this analysis show that there is no significant effect on user satisfaction using paylater services.
This indicates that control is not the main factor influencing the level of user satisfaction with paylater services.
H5: Joy (JOY) has a positive effect on Satisfaction (SAT) The Structural Model analysis indicates a path coefficient value of 0.
341, exceeding the 0.
1 threshold, with a P-value of 0.
000 and a T-test of 5.
These results confirm a significant and positive influence of paylater user joy on digital transaction satisfaction.
Higher user JOY corresponds to increased SAT levels with a P-value < 0.
05 and T-statistic > 1.
96, validating the hypothesis.
The F Square value of 0.
108 suggests a modest influence of Joy on Satisfaction in the model structure.
This aligns with existing research .
Therefore, paylater services should prioritize creating a positive user experience through responsive customer service and efficient transaction processes.
Because increasing satisfaction is influenced by the joy experienced by users.
H6: Curiosity (CUR) has a positive effect on Satisfaction (SAT) The Structural Model analysis reveals a path coefficient value of 0.
205, surpassing the 0.
1 threshold, with a P-value of 0.
000 and a T-test of 3.
These findings confirm a positive and significant influence of paylater users' CUR on digital transaction SAT.
Higher CUR levels correspond to increased SAT, especially when expectations are met (P-value < 0.
T-statistic > 1.
, validating the hypothesis.
The F Square value of 0.
indicates a minor influence of Curiosity on Satisfaction in the model structure, aligning with existing research MUHAMMAD ZAHWAN LATIF ET AL.
ANALYSIS OF FACTORS AFFECTING THE USE OF DIGITAL PAYLATER TRANSACTIONS USING THE HEDONIC MOTIVATION.
, .
In the context of increasing user curiosity regarding paylater services, user engagement through competitions or promotional events can be an effective strategy.
This is because competitions and promotional events have the potential to capture users' attention and encourage active participation.
When users feel intrigued about the service they are using, they are more likely to be actively engaged and exploratory.
H7: Satisfaction (SAT) has a positive effect on Continuance Intention (CI) The Structural Model analysis reveals a path coefficient value of 0.
418, surpassing the 0.
1 threshold, with a P-value of 0.
000 and a T-test of 8.
These findings affirm a positive and significant influence of paylater users' SAT on their intention to continue digital transactions.
Increased SAT correlates with a stronger intention to use paylater services continuously, particularly when aligned with expectations P-value < 0.
T-statistic > 96, validating the hypothesis.
The F Square value of 0.
212 indicates a minor influence of Satisfaction on Continuance Intention in the model structure, consistent with prior research .
, .
, .
To facilitate user preferences, paylater services can customize offerings based on user needs and previous usage experience.
Because continuance intention is influenced by user satisfaction using paylater.
CONCLUSION
The study, which involved 412 respondents, aimed to assess the impact of various factors on the intrinsic elements of Continuance Intention in digital payment transactions through HMSAM.
The findings show that perceived ease of use significantly affects control, joy, and curiosity.
User joy and curiosity have a positive impact on satisfaction, thereby influencing the intention to continue using the service, which indicates that it fulfills hedonic needs.
Notably, the positive effect of perceived ease of use on user control contrasts with the insignificant impact of control on user satisfaction.
Six out of seven hypotheses showed a significant and positive influence on the intention to continue using paylater digital transactions, with T-statistic values > Ttable .
and P-value <0.
However, the analysis revealed that user control does not have a significant impact on satisfaction, showing a path coefficient of 0.
054, below the threshold of 0.
1, with a P-value of 0.
and a T-Test of 1.
The F Square value of 0.
003 further confirms the negligible influence of control on satisfaction in the model structure.
Based on the research.
Joy, which is influenced by perceived ease of use, has fulfilled the intrinsic aspects that have a significant positive correlation with user satisfaction towards continued use.
REFERENCES