The Impact Buy Now Pay Later Feature Towards Online Buying Decision in E-Commerce Indonesia
Muhammad Rafidarma K.1*, Fitri Aprilianty1
1 School of Business and Management, Bandung Institute of Technology, Bandung, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 August 2022 | Published: 1 September 2022
DOI:https://doi.org/10.55057/ijbtm.2022.4.3.13
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Abstract: The COVID-19 pandemic has had a variety of effects on people, ranging from a change in human behavior to a substantial influence on economic situations. E-commerce capitalizes on this shift in consumer behavior by offering a "Buy Now, Pay Later" payment option during periods of diminishing purchasing power. This research was undertaken to investigate the influence of the Buy Now Pay Later option on the E-Commerce Indonesia platform's online purchasing decisions. This research employs a survey technique in the form of a Google forms-distributed questionnaire. All data comes from 240 samples consisting of 80 users of Shopee Pay Later, 80 users of Tokopedia Pay Later, and 80 users of Lazada Pay Later residing in Jakarta, Bogor, Depok, Tangerang, Bekasi, and Bandung. Perceived usefulness and perceived ease of use have a positive influence on the intention to use Buy Now Pay Later, but impulsive buying, materialism, budget constraints, and compulsive purchasing do not have a positive effect on Buy Now Pay Later intentions. Online buying decision is influenced positively by the intention to use Buy Now Pay Later. This study's findings were derived via data processing using Structural Equation Modeling (SEM).
Keywords: Perceived Usefulness, Perceived Risk, Perceived Ease of Use, Online Purchase Decision, E-Commerce
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1. Introduction
E-commerce is now one of the most popular platforms. E-commerce offers many items that may satisfy all human requirements and desires. E-Commerce grows along with online purchasing and internet expansion. Michael Aldrich pioneered online purchasing in 1979 when he converted their television and linked it to real-time, multi-user telephone transactions (Miva.com, 2011). These innovations mark the beginning of E-Commerce, which facilitates our everyday lives. There are several sorts of electronic commerce, including B2B (business to business), B2C (business to consumer), and C2C (consumer to consumer) (Khurana, 2019).
The expansion of e-commerce is sufficiently encouraging. According to McKinsey &
Company's digital archipelago report, the internet commerce industry will approximately eightfold between 2017 and 2022. The estimate is based on the fact that expenditures in 2017 were around 8 billion dollars and would reach 65 billion dollars in 2022. Potentially increasing online commerce penetration to 83 percent of internet users by 2022.
Figure 1: E-Commerce Ecosystem in Indonesia (Source: Techinasia.com)
Figure 1 displays the classification of E-Commerce by class type. E-Commerce is no longer new for the people of Indonesia. The existence of E-Commerce is very beneficial to many individuals in their daily lives. E-commerce will be helpful for both the seller and the consumer.
According to globaldata.com, Indonesia has become one of Asia-most Pacific's potential E- Commerce markets. It is the rise in internet penetration that is driving the expansion of e- commerce (globaldata.com, 2021). According to statista.com, E-Commerce revenue in Indonesia is projected to reach $38.19 billion. Compared to 2019's sales of $30.31 billion, E- Commerce revenue in Indonesia will increase by 26% in 2021. This data indicates that digitization in Indonesia has been quite successful (statista.com, 2021).
The payment mechanism is an element of electronic commerce. Customers may pay their bills using a number of E-Commerce-supported payment options. Buy Now, Pay Later is one of the accessible payment methods on leading e-commerce websites such as Shopee, Tokopedia, and Lazada. In addition, underlying the increase of E-Commerce sales, customer demands indicate a growing desire for alternate payment methods. (Kemppainen in Pratika et al., 2021). Buy Now, Pay Later is often compared to credit cards. This payment method enables consumers to make payments with the option to pay at the end of the term. Numerous payment options make the site accessible for users that utilize it. (Pratika et al., 2021). According to recent study by Backman (2021), 62% of Buy Now Pay Later customers in the United States believe that Buy Now Pay Later may replace credit cards.
2. Conceptual Framework
Figure 2.1 Conceptual Framework (Source: Hoang et al., 2021; Omar et al., 2014)
From the framework, the proposed hypothesis is as follow:
H1: Perceived Usefulness has a positive effect on Intention to Use H2: Perceived Ease of Use has a positive effect on Perceived Usefulness H3: Perceived Ease of Use has a positive effect on Intention to Use H4: Perceived Risk has a negative effect on Perceived Usefulness H4a: Transaction Risk has a negative effect on Perceived Usefulness H4b: Payment Risk has a negative effect on Perceived Usefulness H4c: Credit Risk has a negative effect on Perceived Usefulness H5: Perceived Risk has a negative effect on Perceived Ease of Use H5a: Transaction Risk has a negative effect on Perceived Ease of Use H5b: Payment Risk has a negative effect on Perceived Ease of Use H5c: Credit Risk has a negative effect on Perceived Ease of Use H6: Perceived Risk has a negative effect on Intention to Use
H6a: Transaction Risk has a negative effect on Intention to Use H6b: Payment Risk has a negative effect on Intention to Use H6c: Credit Risk has a negative effect on Intention to Use H7: Materialism has a positive effect on Intention to Use H8: Budget Constraint has a positive effect on Intention to Use H9: Impulsive Buying has a positive effect on Intention to Use H10: Compulsive Buying has a positive effect on Intention to Use H11: Intention to Use has a positive effect on Online Buying Decision
3. Research Methodology
This study applied a quantitative methodology to verify the impact of the Buy Now, Pay Later feature towards online buying decision in Indonesian e-commerce. In this research, the questionnaire will serve as the instrument, with online surveys prepared using Google Form.
This questionnaire's survey style makes it easy to contact a diverse group of rapid respondents.
This questionnaire is in Likert-scale style and comprises numerous items. On a range from 1 to 5, respondents may respond to Likert-Range questions, with 1 indicating strong disagreement and 5 indicating strong agreement. This research is shaped by a multitude of things, which will be asked about on the questionnaire. This study's demographic is comprised of Indonesians who have used the Buy Now, Pay Later feature on E-Commerce Shoppe, Tokopedia, or Lazada. This study requires 240 respondents, consisting of 80 Shopee Paylater users, 80 Tokopedia Paylater users, and 80 Lazada Paylater users.
4. Data Analysis
The researcher evaluates and analyzes the acquired data using descriptive statistics and PLS- SEM. Google Form was used to collect all data gathered through online surveys. The purpose of descriptive statistics is to prepare data for analysis by applying codes and filters. PLS-SEM is also used to survey data analysis and interpretation. In addition, the PLS-SEM evaluation will include a Reliability Analysis, Validity Analysis, Collinearity Test, Coefficient of Determination (R2), Stone-Giesser (G), and F Square Effect Size (F2).
5. Result
5.1 Descriptive Statistics of Research Variables
a. The lowest value of the transaction risk variable is 1, signifying "strongly disagree," and the highest value is 5, representing "strongly agree." The average score is 2.7285, which
is close to 3, suggesting that the majority of respondents chose "Neutral" when asked about the transaction risk statement indicators. The standard deviation is 0.64433, which is smaller than the mean, indicating that the distribution of transaction risk data is acceptable.
b. Minimum value for the payment risk variable is 1, signifying "strongly disagree," and maximum value is 5, representing "strongly agree." The mean value of the payment risk variable is 3.0192, which is close to 3, suggesting that the majority of respondents selected
"Neutral" in response to the statement about payment risk. The standard deviation is 0.54131, which is smaller than the mean, indicating that the distribution of data regarding payment risk is good.
c. Minimum value for the credit risk variable is 1, signifying "strongly disagree," and maximum value is 5, representing "strongly agree." The mean value of the credit risk variable is 3.0222, which is close to 3, suggesting that the average respondent answers
"Neutral" to the credit risk statement suggestions. The standard deviation is smaller than the mean, indicating that the distribution of credit risk data is appropriate.
d. The lowest value for the perceived ease of use variable is 1, which corresponds to "strongly disagree," and the highest value is 5, which corresponds to "strongly agree." The mean score is 3.5150, which is close to 4, suggesting that the usual respondent selects "Agree"
for the perceived ease of use criteria. The standard deviation is 0.40964, which is smaller than the mean, indicating that the data distribution on perceived ease of use is good.
e. The lowest value for the perceived usefulness variable is 1, signifying "strongly disagree,"
and the highest value is 5, representing "strongly agree." The mean value of the payment risk variable is 3.4219, which is close to 3, suggesting that the average respondent chooses
"Neutral" for the perceived usefulness indicators. The standard deviation of the perceived usefulness data is 0.56704, which is smaller than the mean, indicating that the distribution is good.
f. The smallest value for the materialism variable is 1, which corresponds to "strongly disagree," and the highest value is 5, which corresponds to "strongly agree." The mean value of the credit risk variable is 3.2708, which is close to 3, suggesting that the average respondent to the materialism indicator phrases choose "Neutral." The standard deviation is 0.90919, which is smaller than the mean, indicating that the distribution of the materialistic data is good.
g. The variable budget restriction has a minimum value of 1, which corresponds to "strongly disagree," and a maximum value of 5, which corresponds to "strongly agree." The typical response to the budget constraint indicator phrases was "Neutral," as shown by the mean result of 3.0729, which is close to 3. The standard deviation is 0.67697, which is smaller than the mean, indicating that the distribution of budget constraint data is acceptable.
h. The impulse buying variable has a minimum value of 1, which corresponds to "strongly disagree," and a maximum value of 5, which corresponds to "strongly agree." The mean value of the impulse purchasing variable is 3.3424, which is close to 3, suggesting that the average answer to the impulse buying indicator statement was "Neutral." The standard deviation is 0.88693, which is smaller than the mean, indicating that the distribution of data on impulsive buying is adequate.
i. Minimum value for the variable compulsive buying is 1, which corresponds to "strongly disagree," and maximum value is 5, which corresponds to "strongly agree." The mean value of the variable measuring obsessive buying is 2.92500, which is close to 3, suggesting that the typical respondent's reaction to the indicators of compulsive shopping is "Neutral." The standard deviation is 0.74187, which is smaller than the mean, indicating that the distribution of the data about compulsive buying is good.
j. The intention to use Buy Now Pay Later variable has a minimum value of 1, which corresponds to "strongly disagree," and a maximum value of 5, which corresponds to
"strongly agree." The mean score is 3.8208, which is close to 4, suggesting that the normal answer to the signals of the intention to use Buy Now Pay Later is "Agree." The standard deviation is 0.50367, which is smaller than the mean, indicating that the distribution of data on intention to use is good.
k. The online buying decision variable has a minimum value of 1, which corresponds to
"strongly disagree," and a maximum value of 5, which corresponds to "strongly agree."
The mean value of the online buying decision variable is 3.45, which is close to 3, suggesting that the majority of respondents selected "Neutral" in response to the online buying decision signals. The standard deviation is 0.62141, which is smaller than the mean, indicating that the distribution of the data about online buying decision is appropriate.
5.2 PLS-SEM Analysis Result 5.2.1 Reliability Analysis
A loading factor value greater than 0.70 is regarded as valid. Nevertheless, according to Hair et al. (2017), for an initial examination of the loading factor matrix, a loading factor of approximately 0.3 is considered to have met the minimum level, a loading factor of approximately 0.4 is considered to be better, and a loading factor of greater than 0.5 is typically considered to be significant. After processing the data using SmartPLS 3.0, the loading factor values of the indicators for each research variable satisfy the requirements, enabling the conclusion that the data are trustworthy.
5.2.2 Discriminant Validity
Cross-loading findings indicate that the construct's correlation value with its indicators is stronger than its correlation value with other constructs. Thus, all constructs and latent variables show strong discriminant validity, with the indicators in the construct indicator block being superior to those in other blocks.
5.2.3 Validity Analysis
Calculations indicate that all constructions have AVE values larger than 0.50, with the objective to use variable with the least value at 0.527. The defined minimum AVE value limit of 0.50 has been fulfilled by this value.
5.2.4 Reliability Analysis
Based on the output of SmartPLS, the composite reliability value for all constructions is more than 0.50. In line with the specified minimum value, all structures exhibit excellent dependability with the resultant value. According to Hair et al. (2017), the composite reliability value is deemed trustworthy if it falls within the range of ≥ 0,60 – 0,90, ≤ 0,60 if the value is 0,60 dan ≥ 0,95 if the item is unexpected since it implies that each indicator may measure the same thing.
5.2.5 Collinearity Test
The purpose of the collinearity test is to determine the presence of collinearity issues and the variables that influence each variable such that it must be deleted, merged into one, or larger than the latent variable. The collinearity test is determined by the VIF value based on the lack of collinearity when the VIF value is less than 5. According to the computations, the VIF value is less than 5, hence there is no collinearity issue.
5.2.6 Variance Analysis (R2) or Determination Test
According to the r-square value, transaction risk, payment risk, and credit risk are only capable of influencing the perceived ease of use by 0.131 or 13.1%; the remaining 86.9% is impacted
by factors that were not investigated in this research. The transaction risk, payment risk, and credit risk factors may only impact the perceived usefulness by 0.238%, or 23.8 %; the remaining 76.2 % is affected by variables not addressed in this research. The transaction risk, payment risk, credit risk, perceived ease of use, and perceived usefulness variables may only impact the intention to use variable by 0.332 or 33.2%; the remaining 66.8% is influenced by factors that were not addressed in this research. The factors transaction risk, payment risk, credit risk, perceived ease of use, and perceived usefulness as measured by intention to use impact the online purchasing decision variable by 0.17 %; the remaining 83 % is affected by additional variables not investigated in this research. The R Square values for the four variables indicate that their influence is insignificant.
5.2.7 Stone-Geisser Test (Q2)
The calculation results for Q2 indicate that its value is 0.198871. According to Ghozali (2014), the value of Q2 may be used to determine how effectively the observed values and estimated parameters are created by the model. A Q2 score larger than 0 indicates that the model is deemed adequate, but a Q2 value below 0 suggests that the model lacks predictive significance.
In this research model, the construct or endogenous latent variable has a Q2 value larger than 0 (zero), indicating that the model's predictions are valid
5.2.8 F Square Effect Size
According to the table, the transaction risk variable has a f value of 0.028, the payment risk variable has a f value of 0.027, and the credit risk variable has a f value of 0.025 for perceived ease of use. This number suggests that transaction risk, payment risk, and credit risk have a minor impact on perceived ease of use. On perceived usefulness, the transaction risk variable has a f value of 0.025, payment risk is 0.029, credit risk is 0.025, and perceived ease of use is 0.130. This number implies that transaction risk, payment risk, credit risk, and perceived ease of use have a minor impact on perceived usefulness.
The transaction risk variable has a f value of 0.075, the payment risk variable has a value of 0.029, the credit risk variable has a value of 0.057, perceived ease of use has a value of 0.032, perceived usefulness has a value of 0.087, materialism has a value of 0.063, budget constraint has a value of 0.091, compulsive buying has a value of 0.030, and impulsive buying has a value of This number indicates that transaction risk, payment risk, credit risk, perceived ease of use, perceived usefulness, materialism, budget constraints, compulsive buying, and impulsive buying have minimal influence on the intention to use Buy Now Pay Later. Meanwhile, the f value of the intention to use Buy Now Pay Later variable for online buying decisions is 0.204, which indicates that this variable has a significant impact on online buying decisions.
5.2.9 Hypothesis Testing
Table 1: Summary of Hypothesis Testing Results
Hypothesis Result Description
H1: Perceived usefulness has a positive effect on intention to use
Sample mean: 0,290 T Statistics: 4,600
P Values: 0,000
Accepted
H2: Perceived ease of use has a positive effect on perceived
usefulness
Sample mean: 0,333 T Statistics: 4,772
P Values: 0,000
Accepted
H3: Perceived ease of use has a positive effect on intention to use
Sample mean: 0,157 T Statistics: 2,195
P Values: 0,015
Accepted
H4a: Transaction risk has a negative effect on perceived
usefulness
Sample mean: -0,169 T Statistics: 1,924
P Values: 0,023
Accepted
H4b: Payment risk has a negative effect on perceived usefulness
Sample mean: -0,190 T Statistics: 1,900
P Values: 0,029
Accepted
H4c: Credit risk has a negative effect on perceived usefulness
Sample mean: 0,089 T Statistics: 1,257
P Values: 0,105
Rejected
H5a: Transaction risk has a negative effect on perceived ease
of use
Sample mean: -0,155 T Statistics: 1,668
P Values: 0,043
Accepted
H5b: Payment risk has a negative effect on perceived ease of use
Sample mean: -0,106 T Statistics: 0,910
P Values: 0,184
Rejected
H5c: Credit risk has a negative effect on perceived ease of use
Sample mean: -0,198 T Statistics: 1,739
P Values: 0,042
Accepted
H6a: Transaction risk has a negative effect on intention to use
Buy Now Pay Later
Sample mean: -0,079 T Statistics: 1,081
P Values: 0,140
Rejected
H6b: Payment risk has a negative effect on intention to use Buy
Now Pay Later
Sample mean: -0,181 T Statistics: 2,537
P Values: 0,006
Accepted
H6c: Credit risk has a negative effect on intention to use Buy
Now Pay Later
Sample mean: -0,084 T Statistics: 1,207
P Values: 0,114
Rejected
H7: Materialism has a positive effect on intention to use Buy
Now Pay Later
Sample mean: 0,034 T Statistics: 0,651
P Values: 0,258
Rejected
H8: Budget constraint has a positive effect on intention to use
Buy Now Pay Later
Sample mean: 0,064 T Statistics: 0,585
P Values: 0,280
Rejected
H9: Impulsive buying has a positive effect on intention to use
Buy Now Pay Later
Sample mean: -0,011 T Statistics: 0,068
P Values: 0,473
Rejected
H10: Compulsive buying has a positive effect on intention to use
Buy Now Pay Later
Sample mean: -0,005 T Statistics: 0,163
P Values: 0,435
Rejected
H11: Intention to use Buy Now Pay Later has a positive effect on
online buying decision
Sample mean: 0,415 T Statistics: 7,962
P Values: 0,000
Accepted
6. Conclusion
According to the findings of the research, the desire to intention to use buy now, pay later had a beneficial impact on online buying decision. These findings indicate that the increased desire of users to use the buy now, pay later function will enhance their propensity to make online buying decision. This is also connected to the Buy Now, Pay Later feature's simplicity and usability, which encourages people to embrace the technology. Buy Now, Pay Later is convenient since it is immediate and does not waste a great deal of time. Moreover, the Buy Now, Pay Later function is currently accessible on a large number of online buying sites, hence enhancing its perceived use. When someone chooses to utilize buy now pay later, they are already aware of the dangers involved, thus they are able to make their own judgments when
utilizing buy now pay later to make online purchases. In addition, the user's inclination to use this function in the future is influenced by their credit limit, allowing them to make judgments based on their emotions while making online purchases.
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