An Overview of Impulse Purchase Behavior on F-Commerce Platform in Sumatera, Indonesia
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Fadilla Riska Rahayu, An Overview of Impulse Purchase Behavior…
MATRIK: JURNAL MANAJEMEN, STRATEGI BISNIS DAN KEWIRAUSAHAAN
Homepage: https://ojs.unud.ac.id/index.php/jmbk/index
Vol. 16 No. 1, Februari (2022), 99-114
An Overview of Impulse Purchase Behavior on F-Commerce
Platform in Sumatera, Indonesia
Fadilla Riska Rahayu1), Rina Suthia Hayu2), Effed Darta Hadi3)
123) Department of Management, University of Bengkulu
Email: [email protected], [email protected]
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SINTA 2
DOI : https://doi.org/10.24843/MATRIK:JMBK.2022.v16.i01.p08
ABSTRACT
This study aims to analysis how Indonesian impulse purchase on the F-Commerce platform through examine the effect of f-commerce browsing and f-commerce usage intensity of f-commerce impulse purchase mediated by urge to purchase on users of the Facebook. The data was collected from 197 respondents who had made impulsive purchases using the Facebook. The data collection technique used a questionnaire that was distributed online through social media. The research measurement in this questionnaire consists of 16 questions to the respondent. Data analysis in this study used SEM-AMOS to see the relationship between variables. the research showed that; (1) f-commerce browsing has a positive effect on f-commerce impulse purchase; (2) f-commerce usage intensity has a positive effect on f-commerce impulse purchase; (3) f-commerce browsing has a positive effect on urge to purchase; (4) f-commerce usage intensity has a positive effect on f-commerce urge to purchase; (5) urge to purchase has a positive effect on f-commerce impulse purchase; (6) urge to purchase mediates the effect of f-commerce browsing on f-commerce impulse purchase; (7) urge to purchase mediates the f-commerce usage intensity on f-commerce impulse purchases.
Keyword: Browsing, Usage Intensity, Urge To Purchase, Impulse Purchase, F-Commerce
Gambaran Perilaku Impulse Purchase Pengguna Platform F-Commerce di Sumatera, Indonesia
ABSTRAK
Penelitian ini bertujuan untuk menganalisis bagaimana perilaku impulse purchase masyarakat Indoensia melalui pengujian pengaruh F-commerce browsing dan intensitas penggunaan F-commerce terhadap pembelian impulsif F-commerce yang dimediasi oleh urge to purchase pada pengguna Facebook. Data dikumpulkan dari 197 responden yang pernah melakukan pembelian impulsif menggunakan Facebook. Teknik pengumpulan data menggunakan kuesioner yang disebarkan secara online melalui media sosial. Pengukuran penelitian dalam kuesioner ini terdiri dari 16 pertanyaan kepada responden. Analisis data dalam penelitian ini menggunakan SEM-AMOS untuk melihat hubungan antar variabel. penelitian menunjukkan bahwa; (1) F-commerce browsing berpengaruh positif terhadap F-commerce impulse purchase; (2) F-commerce usage intensity berpengaruh positif terhadap F-commerce impulse purchase; (3) F-commerce browsing berpengaruh positif terhadap F-commerce urge to purchase; (4) F-commerce usage intensity berpengaruh positif terhadap F-commerce urge to purchase; (5) F-commerce urge to purchase berpengaruh positif terhadap F-commerce impulse purchase; (6) F-commerce browsing memediasi F-commerce usage intensity terhadap F-commerce impulse purchase; (7) F-commerce urge to purchase memediasi F-commerce usage intensity terhadap F-commerce impulse purchase.
Keyword: Browsing, Usage Intensity, Urge to Purchase, Impulse Purchase, F-Commerce
INTRODUCTION
The rapid development of Web 2.0 technology as well as the rise of social networking sites (SNS) including Facebook have created a new genre f-commerce (e-commerce) or so-called Facebook commerce (f-commerce). F-commerce is defined as commercial and business activities that Facebook uses to facilitate social interaction and encourage consumer contributions in facilitating business transactions (Chen et al., 2016). However, there are significant differences between f-commerce and e-commerce. From a marketing perspective, the focus of e-commerce is to maximize shopping efficiency by offering product catalogs, advanced searches, product recommendations, and one-click purchases while f-commerce focuses on direct social activities such as networking, collaboration, and sharing with attention to companion needs outside of the basic needs of online shopping sites(Huang & Benyoucef, 2015).
Based on the latest report we are social in 2019 it was mentioned that there are 175.4 million internet users in Indonesia. Compared to the previous year 2018 there were 150 million internet users, there was an increase of 17% or 25 million internet users in Indonesia. Based on Indonesia total population of 272.1 million people, it means that 64% of the population of Indonesia has felt access to cyberspace. The most visited social media by Indonesian internet users from the top are Youtube, Whatsapp, Facebook, Instagram, Twitter, Line, Facebook messenger, Linkedn, Pinterest, Wechat, Snapchat, Skype, Tik-tok, Tumblr, Reddit, Weibo. Total Facebook users in Indonesia in 2019 reached 130 million users. This number ranked Indonesia 4th in the country with the most Facebook users (wearesocial, 2020). With so many Facebook users, it can allow users when using Facebook there is a sudden purchase impulse purchase or impulse buying (Leong et al., 2017)
Impulse purchase refers to the act of buying spontaneously or suddenly and outright to buy without considering what was desirable before (Beatty & Elizabeth Ferrell, 1998). Consumers often act impulsively when making decisions online. Triggered by easy access to products, easy purchases (e.g., 1-Click bookings), lack of social pressure, and no attempts to make purchases and payments. Impulse buying occurs when consumers suddenly experience a strong and strong desire to buy something immediately. Impulse to buy is a hendonis complex thing that will stimulate emotional conflict. Impulsive purchases are also likely to be made by ignoring consideration of the consequences (Utami, 2012). The results of previous research stated that one of the factors that affect impulse purchase is urge to purchase so that users who have high urge to purchase behavior can also cause high impulse purchase. (Leong et al., 2017; Leong et al., 2018; Zafar et al., 2020)
Urge to Purchase is a drive to buy that is described as a complex, fun, suddenpurchase, and happens instantly without thinking long and there are other measures of consideration in making a purchase (Sharma et al., 2010). The urge to buy occurs because it is influenced by market or store stimuli, including visual elements, advertisements or gifts that encourage customers to spend a lot of time shopping and in searching for commodities in the store and often show a desire or drive to buy impulsively (Foroughi et al., 2013). Situational factors and position factors can make individuals grow a sense of comfort that can give rise to the urge to purchase (Kazempour & Lotfizadeh 2017). Urge to purchase can make individuals have a stronger confidence to make purchases, both planned purchases and unplanned purchases, it can make individuals who are initially still considering buying, become more confident to buy a product. Khan et al,. (2020) in his study explained that consumers with a high level of urge to purchase will show impulse purchase,so that consumers will make purchases suddenly or
spontaneously. Strong push to make purchases is indirectly influenced by Browsing activity (Khan et al., 2020)
Browsing is an activity of surfing or browsing the internet (Prastia, 2013), meaning to travel the world of the internet or explore information on the internet. Consumers who are more rational and non-emotional in shopping, have considerations before buying a product so often do the process of browsing the product in advance to get more information about the product, about the price, specifications, product details and things related to the product before deciding to make a purchase. Consumers who tend to pay attention to the attributes of a product will browse the desired product as a first step to finding information about the product.(Chung et al., 2017)
Khan et al., (2020) When consumers spend more time browsing Facebook pages with high frequency will lead to high urge to purchase behavior as well. In addition to browsing other factors that affect urge to purchase and impulse purchase is usage intensity. Usage intensity is the intensity of use which means the magnitude or strength of behavior, the amount ofphysical energy used in stimulating one's senses. (Kartono & Gulo 2003). So intensity is summed up as the frequency and how often a person engages in an activity and behavior (Chaplin, 2008). According to Horrigan (2002) there are two things that must be observed to know the intensity of one's internet use, namely the frequency of internet use and how long internet use is done by internet users.
Wilcox & Stephen, (2013) Asserts that higher levels of self-esteem stemming from the use of SNS (social networking services) such as Facebook can decrease user self-control which ultimately leads to more indulgent or impulsive behaviors. In addition, Wilcox et al., (2011) Also found that previous self-esteem tended to encourage more indulgent behaviors such as when users felt comfortable about themselves when their self-esteem levels were increased, they tended to be unable to control themselves and behave on the basis of irrational heart and pleasure. When the intensity of use is increased then the rate of impulsive purchase will also increase (Leong et al., 2017).
In this study, researchers chose the social networking site (SNS) Facebook because Facebook has grown rapidly and continues to experience a rise in users (wearesocial). The company makes Facebook as one of the tools to market and advertise a product that can increase brand awareness of a product Jin, (2003). According to McKinsey, 70% of business organizations use SNS as a tool to improve their business and 90% of those business organizations successfully benefit from promoting through SNS (Chen et al., 2016). According to Akman & Mishra, (2017) businesses in Asia choose social media as a means of promotion and some businesses benefit from lowering spending spent through social media, and there are swordsmen large and small who join social media to promote sales. Investments made by business organizations in the U.S. are estimated to reach 4.6 billion USD in 2013 (Braceweel et al., 2008). Where the world's profits from social media (SNS) reached 30 billion USD and 80 billion in 2015 (Chen et al., 2016).
Indonesia ranked 4th as the world's largest Facebook user, with 130 million people (wearesocial, 2020). Research conducted by Facebook with PT PricewaterhouseCoopers (PwC) Indonesia and the Institute for Development of Economics and Finance (INDEF) involving 1,220 individuals from 34 provinces in Indonesia where 89% of small and medium enterprises (SMEs) use four applications acquired by Facebook to be able to grow their business (Facebook, Messenger, Instagram, Whatsapp) the results of his research showed that the four platforms also help SMEs access new opportunities in business by using Facebook SME players can reduce marketing costs, increase the number of customers, reduce growth barriers and efficiency of staff management and operations in his research also stated that the
Facebook platform helps SMEs in expanding the market and can penetrate the international market (Katadata, 2019)
Research on impulse purchase has been much researched and discussed, but the research is still limited in examining the influence of browsing, usage intensity, urge to purchase on impulse purchase. Research conducted by (Verhagen & Van Dolen, 2011) only examined the influence of urge to buy and browsing on other impulse buy research Park et al., (2012) focusing only on the influence of utilitarian browsing and hedonic browsing on eimpulse buying. Both researchers (Verhagen &van dolen 2011; Park et al., 2012) in his research focused on online stores and e-commerce. While there is a lot of research being done on e- commerce, very little attention is paid to the new context of f-commerce. Therefore, this study aims to examine empirical models on social media platforms especially Facebook and try to modify previous research to become a whole unity regarding F-commerce impulse purchases.This research is expected to encourage to improve the strategies used by businesses and think about what measures are used during the covid-19 pandemic. Peneliti wants to conductresearch on the influence of f-commerce browsing and f-commerce usage intensity on f- commerce impulse purchase mediated by urge to purchase.
LITERATURE REVIEW
F-commerce Impulse Purchase
Impulse Purchase is a sudden and complex purchasing behavior, hedonistically where impulsive purchasing speed precludes thoughtful and deliberate consideration of alternative or future implications(Sharma et al., 2010). This definition has evolved from previous research on impulsive purchases. Rook, (1987) defines impulsive purchases as astrong and persistent urge to buy something immediately. Early research sometimes showed impulsive purchases as unplanned purchases and often used the term synonymously in literature (Stern, 1962). Beatty & Ferrell, (1998) describes impulsive purchases as spontaneous or sudden and direct urges to buy without considering what you wanted beforehand. Impulsive purchases have the characteristics of affective components and reactive components (Huang & Benyoucef, 2015). Actual impulsive purchasing behavior will still occur after someone experiences a urge to buy (Floh & Madlberger, 2013). Emarketing stimuli have made impulsive purchases easier and reduced risk avoidance (Park et al., 2012).
Urge to Purchase
Urge to Purchase is an urge to buy that is described as a complex, fun, sudden purchase, and happens instantly without thinking long and there are other measures of consideration in making a purchase (Sharma et al., 2010). Byun, S. E, (2011) A strong push from a product that has a relatively short life or has a perceived and enhanced resilience to the desire to own a product tends to encourage buyers to engage with the product, urges them to try new trends or immediately own the product, and by building a competitive retail environment can make buyers feel a strong urge to buy and take immediate action. Lee & Kacen, (2008), the impulse to buy impulsively is defined as a series of sudden, coercive, and enjoyable purchases that quickly involve a mindless decision-making process, and Generally, all information and selective choices are blocked. It is interpreted that the urge to buy occurs spontaneously. The urge to buy impulsively is a driving force to make impulsive purchasing behaviors. Therefore, it can be said that the urge to buy occurs before the actual purchase of animpulsive purchase product.
F-commerce Browsing
Browsing or surfing is an activity of surfing the internet (Taslim et al., 2011), meaning to travel the internet or explore information on the internet. This activity can be interpreted asa walk in the mall while looking into the shops without buying anything. Overby & Lee, (2006) argues that the main understanding of web browsers is to purchase products in a timely and efficient manner to achieve convenience goals and price savings with little effort. In fact, web browsing is the initial phase of online buying involving buyers searching for information and making selections through websites (Rowley, 2002). Bloch et al., (1989) States that consumers who browse have more knowledge in terms of product categories compared to non- browsers. In addition, due to increased knowledge of browsers or consumers in products and market motives consumer curiosity is fulfilled (Moe, 2003).
F-commerce Usage Intensity
Usage intensity is an intense consumer behavior using social media. Facebook usage can be measured on a variety of self-reported scales and from Initially, it concentrates on the frequency and duration of time users spend on Facebook (daily or weekly) According to Horrigan, (2002) there are two things that must be observed to know the intensity of a person's internet use, namely the frequency of frequent internet use and how long internet use is done by internet users.
Effect of F-commerce Browsing on F-commerce Impulse Purchase
Madhavaram & Laverie (2004) found that web browsing had a significant effect on impulsive purchases. They argue that browsing is a situational factor that acts as a predictor of purchases among consumers. Various researchers such as (Prihatini & Susanto, 2015), Gultekin &Ozer., (2012), Park et al., (2012), Khan et al., (2020)) found that impulsive purchases are mainly related to product searches. Lee & Lee (2003) found that hedonistic exploration positively influenced impulsive purchasing behavior. Park et al., (2012), found that web browsing had a significant influence on impulsive purchases on online websites of clothing- related products.
H1: f-commerce browsing effect f-commerce impulse purchase
Effect of F-commerce Usage Intensity on Impulse Purchase F-commerce
Wilcox & Stephen, (2013) Asserts that higher levels of self-esteem stemming from the use of SNS (social networking services) such as Facebook can decrease user self-control which ultimately leads to more indulgent or impulsive behaviors. Leong et al., (2017) and Khan et al (2020) state that when a person's usage intensity increases it will lead to impulse purchase behavior on Facebook users.
H2: F-commerce usage intensity affects F-commerce impulse purchase
Effect of F-commerce Browsing on Urge to Purchase
Consumers who tend to pay attention to the attributes of a product will browse the desired product as a first step to finding information about the product. (Chung et al., 2017). If consumers browse longer, they will face a lot of stimulation, and may increase the likelihood of impulsive purchases (Park et al., 2012). Khan et al,. (2020) When consumers spend more time browsing Facebook pages with high frequency will lead to high urge to purchase behavior as well. Verhagen & Van Dolen, (2011) In his research also found that browsing has a positive and significant effect on impulsive urge to buy in online store
customers. Xu et al., (2020) in his research also found similar results that browsing has a positive and significant effect on urge to buy impulsivee influenced by hedonic and utilitarian value properties.
H3: f-commerce browsing affects urge to purchase
Effect of F-commerce Usage Intensity on F-commerce Urge to purchase
Usage intensity is defined as peril I'm an intense consumer of nature using social media.Usage intensity can be seen from how often a person's frequency and duration in performingan activity and behavior (Chaplin, 2008). There is a positive relationship between the frequency of browsing Facebook pages and the attractiveness of consumers with certain items.So, it can be said that the possibility of development of encouragement or feeling to buy increases due to the higher intensity of Facebook (Leong et al., 2017). Various researchers such as (Leong et al., 2017), Khan et al., (2020), Zhang et al (2019), Verhagen & Van Dolen, (2011) found that f-commerce usage intensity directly and significantly affects urge to purchase.
H4: F-commerce usage intensity affects urge to purchase
Urge to Purchase on Impulse Purchase F-commerce
Urge to Purchase is a drive to buy that is described as a complex, fun, suddenpurchase, and happens instantly without thinking long and there are other measures of consideration in making a purchase (Sharma et al., 2010). Khan et al,. (2020) in his study explained that consumers with a high level of urge to purchase will show impulse purchase,so that consumers will make purchases suddenly or spontaneously. The relationship between urge to purchase and f-commerce impulse purchase is related among researchers Leong et al., (2017), Leong et al., (2018), Zafar et al., (2020).
H5: Urge to purchase affects f-commerce impulse purchase
Urge to Purchase as a Mediation Variable between F-commerce Browsing and F-commerce Usage Intensity against F-commerce Impulse Purchase
Previous research conducted by Leong et al., (2017) and Verhagen & Van Dolen, (201 1) has tested the effect of browsing mediated by urge to purchase on impulse purchases. Research conducted by Leong et al., (2017) was conducted on 800 samples in Malaysia, using variable f-commerce impulse purchase, browsing, usage intensity, urge to purchase. The findings of this study show the results that browsing is able to influence impulse purchase through urge to purchase as mediation. The findings made by Verhagen & Van Dolen, (2011) also found similar results that browsing was able to influence impulse purchase through urge to purchase as mediation. In previous studies researchers such as Leong et al., (2017) have tested about the influence of usage intensity mediated by urge to purchase on impulse purchase. The results of research conducted by Leong et al., (2017) suggest that usage intensity is able to affect impulse purchase through urge to purchase as mediation.
H6: Effect of f-commerce browsing mediated urge to purchase on f-commerce impulsepurchase
H7: Effect of f-commerce usage intensity mediated urge to purchase on f-commerceimpulse purchase
Figure 1. Research Framework
RESEARCH METHODS
This research is included in quantitative research, namely the existence of hypothesis testing. Testing this hypothesis is usually to test a particular relationship or determine differences between groups or the interpedence of two or more factors in a situation (Now, 2006). The sampling technique used in this study is a non probability sampling technique with a snowball sampling. Sampling approach where the initial sample determination is small, then enlarged. Like a rolling snowball that has long been great (Sugiyono, 2001). The population in this study was active users of Facebook in Sumatera, Indonesia. Samples make up part of the population. The sample consists of a select number of members from the population (Sekaran, 2006). The samples used in this study were respondents who had made spontaneous/sudden purchases through Facebook at least once in 6 months. A total of 197 respondents. Data analysis such as validity and reliability tests; goodness of fit and hypothesis testing is done with the SEM-AMOS program. Operational variables of this research can be seen in Table 1, the following:
Table 1. Variable Operations
Variable |
Measurement Item ValidityReliability (Sources Adapted fro |
FIP 1 F-commerce Impulse FIP 2 Purchase |
I buy stuff quickly in f-commerce 0.738** 0.848 When I find something I like in f- 0.788** Park et al., (2012); Floh, commerce,then I buy it right away A.&Madlberger, M. (201 even though it’s not on my shoppi list |
FIP 3 |
During shopping in f-commerce I bought a product without long thought |
0.743** | |||
Urge To |
FIP 4 UTP 1 |
It’s fun for me when I can make a spontaneous purchase I tend to experience a sudden urge |
0.785** 0.799** |
0.864 |
Coley, A. & Amp; Burge |
Purchase F-commerce |
UTP 2 UTP 3 UTP 4 FB 1 |
buy something I immediately bought any product that looked interestingwhen sellin the product I have difficulty controlling the de to make spontaneous purchases in commerce The emergence of products with a new trend, was able to encourage purchases in f-commerce I'm looking for products in f- |
0.780** 0.836** 0.806** 0.820** |
0.869 |
B. (2003); Foroughi et al (2013); Byun, S. E,(2011 Zheng et al., (2018) |
Browsing F-commerce |
FB 2 FB 3 FB 4 FUI 1 |
commerce that have more rational pricing I often compare existing products f-commerce to find better Quality I searched f-commerce for produc that offer better benefits and usabi I browsed through the various stor in f-commerce to find an efficient online shopping store I access f-commerce more tha |
0.788** 0.801** 0.753** 0.802** |
0.850 |
Horrigan J.B. (2002); |
Usage Intensity |
FUI 2 FUI 3 FUI 4 |
times a week I access f-commerce at least once day I access f-commerce for more than hours a week I access f-commerce for at least 3 minutes a day |
0.745** 0.779** 0.809** |
Sa'adah (2018) |
Source: Research Results, 2021
This study uses confirmatory factor analysis (CFA) to show the relationship between variables. According to Hair et al., (2010), the minimum number of loading factors is ≥0.5 or ideally ≥0.7. The results of the study in the Table showed that the overall results of each indicator contained in this study have a good convergent validity of > 0.7. It can be concluded that every indicator in this study is valid, for it can be used in this study. Reliability test in Amos can be seen based on the value of cronbach's alpha or construct reliability By using standardized regression weight estimates. Cronbach's alpha Indicates how much the relationship factor between a variable and another variable The value of cronbach's alpha reference is >0.70 (Hair et al., 2010). According to Hair et al., (2010), the minimum value of cronbach's alpha is ≥0.5 or ideally ≥0.7. Based on the reliability test found in Table 1 shows that each variable in this study has good reliability, namely cronbach's alpha value > 0.7 which means that the test is trusted to produce a consistent score, relatively unchanged even when performed in different situations.
RESEARCH RESULTS
The data in this study was collected using questionnaires distributed to 197 respondentsonline, the following characteristics of respondents in this study are shown in table 2.
Table 2. Characteristics of Respondents
No. |
Demographic Characteristics |
Frequenc |
Percentage | |
1 |
Gender |
Man |
43 |
21.8% |
Woman |
154 |
78.2% | ||
Total |
197 |
100% | ||
2 |
Age |
16-25 Years Old |
82 |
41.6% |
26-35 Years Old |
66 |
33.5% | ||
36-45 Years Old |
34 |
17.3% | ||
46-55 Years Old |
14 |
7.1% | ||
>55 Years |
1 |
0.5% | ||
Total |
197 |
100% | ||
3 |
Last |
Elementary school |
1 |
0.5% |
Education |
Junior high school |
1 |
0.5% | |
Senior high school |
87 |
44.2% | ||
D3 |
9 |
4.6% | ||
Bachelor |
95 |
48.2% | ||
Master |
4 |
2% | ||
Doctor |
- |
- | ||
Total |
197 |
100% | ||
4 |
Job |
Student |
69 |
35% |
Civil Servants/TNI/PORLI |
46 |
23.4% | ||
Self employed |
19 |
9.6% | ||
Housewife |
20 |
10.2% | ||
Private Employees |
30 |
15.2% | ||
Etc |
13 |
6.6% | ||
Total |
197 |
100% | ||
5 |
Earn Per |
<Rp. 1,000,000 |
48 |
24.4% |
Month |
Rp. 1.000.000 - Rp. 2.500.000 |
55 |
27.9% | |
Rp. 2.500.001 - Rp. 5.000.000 |
79 |
40.1% | ||
Rp. 5.000.001 - Rp. 7.500.000 |
15 |
7.6% | ||
Rp. 7.500.001 - Rp. 10.000.000 |
- |
- | ||
>Rp. 10.000.000 |
- |
- | ||
Total |
197 |
100% |
Source: Research Results, 2021
Based on Table 2, it can be seen that the majority of respondents in this study were women. This is possible because the more frequent shopping behaviors are female than male because of their penchant for it. According to Campbell et al., (2000) that women have a lot of actions and traits in tracing, shopping and consider purchasing behavior as a fun activity compared to men. Based on the age range, the majority of respondents aged 16-25 years are asmany as 82 respondents. In the last education category, the respondents were dominated by S1and SMA as many as 182 respondents. Respondents in this study were mostly students with a total of 69 respondents. However, if accumulated as many as 115 respondents who have a job and have the most income on the amount of income Rp. 2.500.001 - Rp. 5.000.000 as 107
many as 79 respondents, this is because some respondents who filled out this questionnaire already have a job so that the income earned is used to make purchases through Facebook.
This study uses Structural Equation Model (SEM) analysis using AMOS software. Before forming a sem model in its entirety, tests will be conducted on the factors that make up each variable contained in this study. Testing will be conducted using confirmatory factor analysis (CFA) model. In Confirmatory Factor Analysis (CFA) there is goodness of fit that will be duji to obtain a measure of conformity between the hypothetical model and the observed covariance matrix (Baumgartner &Hombur, 1996).
Table 3. Goodness of Fit (Model Conformity Test)
Type |
Default Mode |
Standard |
Ket. |
Chi-Square |
111.266 |
Expected Smal |
Fit |
RMSEA |
0.026 |
<0.08 |
Good Fit |
GFI |
0.937 |
>0.9 |
Good Fit |
AGFI |
0.913 |
>0.9 |
Good Fit |
PGFI |
0.675 |
0-1 |
Good Fit |
CMIN/DFI |
1.135 |
<2 |
Good Fit |
NFI |
0.960 |
>0.9 |
Good Fit |
IFI |
0.995 |
>0.9 |
Good Fit |
Source : Research Results, 2021
Table 3 shows that based on the results of sem analysis, in this study has met thecriteria of goodness of fit test. The model in this study was accepted because the default results of the model already met the criteria that have been determined. The value of ChiSquare in this study was 111.266. The Root Mean Square Error of Approximation (RMSEA) represents the numbers 0.026 (<0.08), the Goodness of Fit Index (GFI) with the numbers 0.937 (>0.9) and the Adjusted Goodness of Fit Index (AGFI) with a value of 0.913 (>0.9) has shown a recommended value or good fit. Parsimony Goodness of Fit (PGFI) value in this study was 0.675 (ranging between 0 and 1) which means the research model was accepted. The model and data in this study also indicate good fit with CMIN/DF value of 1.135. Normed Fit Index (NFI) values of 0.960 (≥0.90) and Incremental Fit Index (IFI) values of 0.995 (≥0.09) are within the recommended value range which means that this research model is accepted because it shows a good fit value.
Table 4 Hypothesis Testing Results
Hyphothesis |
Relationship |
Direct Effect |
C.r. |
P. |
Ket |
H1 |
FB => FIP |
0.364 0.113 |
3.225 |
0.001 |
Accepted |
H2 |
FUI => FIP |
0.190 0.07 |
2.595 |
0.009 |
Accepted |
H3 |
FB =>UTP |
0.672 0.104 |
6.442 |
0.000 |
Accepted |
H4 |
FUI => UTP |
0.410 0.084 |
4.901 |
0.000 |
Accepted |
H5 |
UTP => FIP |
0.316 0.099 |
3.206 |
0.001 |
Accepted |
Indirect Effect | |||||
H6 |
FB – UTP – FIP |
0.213 |
2.835 |
Accepted | |
H7 |
FUI – UTP – FIP |
0.130 |
2633 |
Accepted |
Source: Research Results, 2021
Based on the results of the calculation of regression weight in the hypothesis test table is done with direct effect value and compare the t-count value that should be greater than the t-table value of 1.96 and P < 0.05. The first test results showed that there was an influence
between f-commerce browsing on f-commerce impulse purchase with a direct effect value of 0.364, P value of 0.001 (P < 0.05), and C.R value of 3.225 (>1.96), thus it can be concluded that f-commerce browsing has a positive and significant effect on f-commerce impulse purchase. The results of the second test showed that there is an influence between f-commerce usage intensity on f-commerce impulse purchase with direct effect value of 0.190, Pvalue of 0.009 (P < 0.05), and C.R value of 2.595 (>1.96), It can be concluded that f-commerce usage intensity has a positive and significant effect on f-commerce impulse purchase.
The results of the third test showed that there is an influence between f-commerce browsing on urge to purchase with a direct effect value of 0.672, A value of 0.000 (P < 0.05), and A.R. value of 6.442 (>1.96), It can be concluded that f-commerce browsing has a positive and significant effect on urge to purchase. The results of the fourth test showed that there is an influence between f-commerce usage intensity on urge to purchase with a direct effect value of 0.410, P value of 0.000 (P < 0.05), and C.R value of 4.901 (>1.96), It can be concluded that f-commerce usage intensity has a positive and significant effect on urge to purchase. The results of the fifth test showed that there is an influence between urge to purchase on f-commerce impulse purchase with a direct effect value of 0.316, the value of P of 0.001 (P < 0.05), and the value of C.R of 3.206 (>1.96), It can be concluded that urge to purchase has a positive and significant effect on f-commerce impulse purchase. Following Baron &Kenny, (1986) proposed four stages of such an approach to conduct regression analysis testing in the testing of the role of mediated variables. Judging from Table 4 it is known that the direct influence of f-commerce browsing variables on f-commerce impulse purchase is 0.364, while for indirect influence of f-commerce browsing variables against f-commerce impulse purchase through urge to purchase is 0.213. Furthermore, the direct influence of variable f-commerce usage intensity on f-commerce impulse purchase is 0.190 and the indirect influence of f-commerce usage intensity on f-commerce impulse purchase through urge to purchase is 0.130. Based on mediation testing, this type of mediation is partial mediation this is because independent variables (f-commerce browsing and f-commerce usage intensity) are able to directly affect dependent variables (f-commerce impulse purchase) without involving mediator variables (urge to purchase).
The results of the first hypothesis testing that has been conducted state that proven -commerce browsing has a significant positive effect on f-commerce impulse purchase. This shows that the higher f-commerce browsing that consumers have, the higher their tendency to make f-commerce impulse purchases. Supported by research conducted by Betty & Ferrell, (1998) which stated impulse purchase is related to browsing. When consumers spend more time exploring a product, it's possible that they become fond of the product so impulse purchase based on the feeling of excitement they experience at the time. The statement was supported by Lee & Yi, (2008) the moderate relationship between pleasure and impulse purchase Testing the second hypothesis, it found that f-commerce usage intensity had a significant positive effect on f-commerce impulse purchases on Facebook users. The results of this study show the stronger the f-commerce usage intensity owned by consumers, the more flexibility to make f-commerce impulse purchases higher. In previous studies, it was shown that impulse purchasing behavior can occur when a person increases the intensity of their use Leong et al., (2017). Wilcox &Stephen (2013) States that when the intensity of Facebook usage increases consumers feel a higher level of self-esteem so as to decrease self-control of Facebook users which ultimately leads to more indulgent or impulsive behavior.
F-commerce browsing has a significant positive effect on urge to purchase on Facebook platform users. This indicates that the higher one's browsing activity will increase
the urge to purchase the person. The browsing effect has been said to be a major component in the unplanned purchasing process. If consumers venture longer, they will face more stimulation (Park et al., 2011). In previous research it was proven that browsing will cause an urge to purchase someone (Verhagen &Van Dolen, 2011; Leong et al., 2017; Zhang et al 2018; Khan et al., 2020) his research found that browsing activities have a positive and significant effect on the urge to purchase conducted on online stores.
The results of the fourth hypothesis test show that f-commerce usage intensity has a significant positive effect on urge to purchase. The results showed the stronger the f-commerce usage intensity that consumers have, the higher the tendency to urge to purchase. This study supports researchers Leong et al., ( 2017), Khan et al., ( 2020). According to, Leong et al., (2017) Because the frequency of browsing this Facebook page increases the likelihood that consumers are interested in certain products will also increase. Therefore, the higher the intensity of consumer Facebook, the possibility of feeling or encouragement to buy will also increase. When consumers develop a positive mood towards f-commerce, their chances of using Facebook pages more intensively will be higher.
The results of the fifth hypothesis test show that urge to purchase has a significant positive effect on f-commerce impulse purchase. Urge to purchase can make individuals have a stronger confidence to make purchases, both planned purchases and unplanned purchases, it can make individuals who are initially still considering buying, become more confident to buy a product. The motivation or desire that arises from a product is sometimes unbearable and therefore consumers may feel uncontrollable so that an impulse purchase occurs. This research is proven to support research conducted by Leong et al., (2017), Leong et al., (2018), Zafar et al., (2020) which states that consumers who have a strong boost at the time ofpurchase, the higher their impulse purchase behavior.
In the sixth hypothesis of mediation effect testing showed that there was a significant positive influence of f-commerce browsing on impulse purchase mediated by urge to purchase. This research shows that the role of urge to purchase to be a mediator between f-commerce browsing against f-commerce impulse purchase is proven to have a significant influence, but mediating is partial (partial mediation). This is because f-commerce browsing against can affect f-commerce impulse purchases directly without the role of mediator. On the table it is known that variable f-commerce browsing has a higher direct influence to the mediation variable urge to purchase with an influence of 0.672. Compared to the direct influence of f-commerce browsing on f-commerce impulse purchase with a magnitude of 0.364 influence. From the results of the study it is known that when a person searches through Facebook, with a strong driving factor will be more convincing consumers to make purchase decisions. Compared to the behavior of consumers who suddenly make purchases without any driving factors.
The results of the seventh test of mediation effect stated that there is a significant positive influence of f-commerce usage intensity on f-commerce usage intensity mediated by urge to purchase. The results of this study also support research conducted by Leong et al., (2017) that f-commerce usage intensity can affect f-commerce impulse purchase by urge to purchase as a mediation variable. The nature of mediation is partial (partial mediation). On the table it is known that the influence of f-commerce usage intensity on urge to purchase has a higher direct influence with the magnitude of influence 0.410. Compared to the direct influence of f-commerce usage intensity on f-commerce impulse purchase with the magnitude of influence 0.190. It can be concluded that when the intensity of a person's Facebook usage is high, the variable that allows a person to make a purchase decision is the
mediation variable urge to purchase compared to the dependent variable impulse purchase. So this is a conceptual model of research result:
Source: Research Results, 2021
Figure 2. Structural Equation Model
CONCLUSION
Based on the results of hypothesis testing, it can be concluded that all seven hypotheses in this study were accepted. The conclusions obtained in this study are that urge to purchase is partially able to mediate the influence of f-commerce browsing and f-commerce usage intensity on f-commerce impulse purchase on Facebook users. Based on the results of the study, it is recommended that businesses should pay more attention to what behavior arises when a person experiences impulse purchase. Therefore, businesses can pay attention to variables that can simultaneously improve one's impulse purchase behavior. The study found that the greatest influence was on variable f-commerce browsing. Businesses are also advised to offer convenience in Facebook's online store to make consumers feel comfortable and make it easier for them to make purchases and payment transactions. So that the browsing activities of consumers can behave impulse purchase. In an effort to maintain usage intensity consumers can regularly post useful products and information related to their business on Facebook pages for example by building conversations, establishing contacts and communities between businesses and consumers, updating product posts. This is one of the efforts that companies can make to make consumers feel attached, bound and difficult to switch to a competitor's brand. In addition, this urge to purchase can be a factor in helping companies to retain and attract consumers. Furthermore, to increase again urge to purchase should businesses be advised to focus on trends that can last for a long period of time and in accordance with the needs and desires of consumers businesseson Facebook and can focus on selling products that are trending, or businesses themselves who becomeTrendsetters of a product. For further research, we recommend expanding the sample to a specific brand community, thereby strengthening the theory and results of the research. The addition of variables and indicators is recommended for future researchers to make the future research even better.
REFERENCES
Akman, I., & Mishra, A. (2017). Factors influencing consumer intention in social commerce adoption. Information Technology and People, 30(2), 356–370.
https://doi.org/10.1108/ITP-01-2016-0006
Baron, R. M. & Kenney, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Beatty, S. E., & Ferrell, E. M. (1998). Impulse buying: Modeling its precursors. Journal of Retailing, 74(2), 161–167. https://doi.org/10.1016/s0022-4359(98)90009-4
Bloch, P. H., Ridgway, N. M., & Sherrell, D. L. (1989). Extending the concept of shopping: An investigation of browsing activity. Journal of the Academy of Marketing Science, 17(1), 13–21. https://doi.org/10.1007/BF02726349
Byun, S. E. (2011). Consumer Response to Seller-Induced Perishability. International Journal of Costume and Fashion,1(2), 53–64.
Chen, J. V., Su, B. C., & Widjaja, A. E. (2016). Facebook C2C social commerce: A study of online impulse buying. Decision Support Systems, 83, 57–69.
https://doi.org/10.1016/j.dss.2015.12.008
Chung, N., Song, H. G., & Lee, H. (2017). Consumers’ impulsive buying behavior of restaurant products in social commerce. International Journal of Contemporary Hospitality Management, 29(2), 709–731. https://doi.org/10.1108/IJCHM-10-2015-0608
Floh, A., & Madlberger, M. (2013). The role of atmospheric cues in online impulse-buying behavior. Electronic Commerce Research and Applications, 12(6), 425–439.
https://doi.org/10.1016/j.elerap.2013.06.001
Foroughi, A., Buang, N. A., Senik, C. Z., & Hajmisadeghi, S. R. (2013). Impulse Buying Behaviour and Moderating Role of Gender among Iranian Shoppers. Journal of Basic and Applied Scientific Research, 3(4), 760–769.
Hair, J., Black, F., C, W., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7thEditio). Pearson Prentice Hall.
Horrigan, J.B. (2002). “New Internet Users: What They Do Online, What They Don’t andImplications for the Net’s Future.” Pew internet and American life project. pg. 1-27.
Huang, Z., & Benyoucef, M. (2015). User preferences of social features on social commerce websites: An empirical study. Technological Forecasting and Social Change, 95, 57–72. https://doi.org/10.1016/j.techfore.2014.03.005
Jin, H. S. (2003). Compounding consumer interest: Effects of advertising campaign publicity on the ability to recall subsequent advertisements. Journal of Advertising, 32(4), 29–41. https://doi.org/10.1080/00913367.2003.10639145
Kazempour, Y. dan Lotfizadeh, F. (2017). The Impact of Situational Factors (Store, Personal) on Urge to Buy Impulsively and Impulsive Buying Behavior. European Journal of Business and Innovation Research, 5(4): 12–27
Khan, I., Ghazanfar, S & Abdullah, M. (2020). Does social media browsing and usage intensity impact impulse purchase behavior of consumer? a moderating role of gender in f-commerce impulse purchase. Hamdardslamicus, Vol. 43 No. 2, 60-75.
Lee, G. Y., & Yi, Y. (2008). The Effect of Shopping Emotions and Perceived Risk on Impulsive Buying : The Moderating Role of Buying Impulsiveness Trait. Seoul Journal of Business, 14(2), 67–92.
Lee, J. A., & Kacen, J. J. (2008). Cultural influences on consumer satisfaction with impulse and planned purchase decisions. Journal of Business Research, 61(3), 265–272. https://doi.org/10.1016/j.jbusres.2007.06.006
Leong et al., (2017). Understanding impulse purchase in Facebook commerce: does Big Five matter?. Internet Research, 27(4), 786–818. http://10.0.4.84/IntR-04-2016-
0107%0Ahttp://search.ebscohost.com/login.aspx?direct=true&db=llf&AN=124365795 &site=ehost-live
Leong, L. Y., Jaafar, N. I., & Sulaiman, A. (2018). What triggers impulse purchase in Facebook commerce?. International Journal of Mobile Communications, 16(4), 459– 492. https://doi.org/10.1504/IJMC.2018.092679
Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1–2), 29– 39. https://doi.org/10.1207/s15327663jcp13-1&2_03
Overby, J. W., & Lee, E. J. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10–11), 1160–1166. https://doi.org/10.1016/j.jbusres.2006.03.008
Park, E. J., Kim, E. Y., Funches, V. M., & Foxx, W. (2012). Apparel product attributes, web browsing, and e-impulse buying on shopping websites. Journal of Business Research, 65(11), 1583–1589. https://doi.org/10.1016/j.jbusres.2011.02.043
Prastia, F. E. (2013). Pengaruh Shopping Lifestyle, Fashion Involvement dan Hedonic Shopping Value terhadap Impulse Buying Behaviour pelanggan toko Elizabeth Surabaya. Jurnal Ilmiah Mahasiswa Manajemen, 2(4), 1–6.
http://journal.wima.ac.id/index.php/JUMMA/article/view/424
Prihatini, R., & Susanto, S. (2015). Pengaruh Motif Hedonis Dan Browsing Terhadap Pembelian Impulsif Pada Pembelian Online Produk Fashion. Jurnal Manajemen Bisnis, 6(2), 351–375.
Rook, D. W. (1987). The Buying Impulse. Journal of Consumer Research, 14(2), 189. https://doi.org/10.1086/209105
Rowley, J. (2002). ‘Window’ shopping and browsing opportunities in cyberspace. Journal of Consumer Behaviour, 1(4), 369–378. https://doi.org/10.1002/cb.81
Sharma, P., Sivakumaran, B., & Marshall, R. (2010). Impulse buying and variety seeking: A trait-correlates perspective. Journal of Business Research, 63(3), 276–283.
https://doi.org/10.1016/j.jbusres.2009.03.013
Stern, H. (1962). The Significance of Impulse Buying Today. Journal of Marketing, Vol. 26, April, pp. 59–63. http://dx.doi.org/10.2307/1248439
Taslim & Septiana. (2011). Pengaruh Shopping Lifestyle, Fashion Involvement dan Hedonic Shopping Value terhadap Impulse Buying Behaviour Pelanggan Toko Elizabeth Surabaya. Jurnal Ilmiah Mahasiswa Manajemen, 2 (4).
Verhagen, T., & Van Dolen, W. (2011). The influence of online store beliefs on consumer online impulse buying: A model and empirical application. Information and Management, 48(8), 320–327. https://doi.org/10.1016/j.im.2011.08.001
Wilcox, K., Kramer, T., & Sen, S. (2011). Indulgence or self-control: A dual process model of the effect of incidental pride on indulgent choice. Journal of Consumer Research, 38(1), 151–163. https://doi.org/10.1086/657606
Wilcox, K., & Stephen, A. T. (2013). Are close friends the enemy? online social networks, self-esteem, and self-control. Journal of Consumer Research, 40(1), 90–103.
https://doi.org/10.1086/668794
Xu, H., Zhang, K. Z. K., & Zhao, S. J. (2020). A dual systems model of online impulse buying. Industrial Management and Data Systems, 120(5), 845–861.
https://doi.org/10.1108/IMDS-04-2019-0214
Zafar, A. U., Qiu, J., & Shahzad, M. (2020). Do digital celebrities’ relationships and social climate matter? Impulse buying in f-commerce. Internet Research, 30(6), 1731–1762. https://doi.org/10.1108/INTR-04-2019-0142
Zhang, X., Jinqi, M., Feng, Y., Xiuyuan, G. (2019). Understanding impulsebuying in mobile commerce: An investigation into hedonic and utilitarian browsing. International Journal of Information Management, 48(), 151–160. Doi:10.1016/j.ijinfomgt.2019.02.010
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