Eduvest ï¿½ Journal of Universal Studies

Volume 4 Number 11, November, 2024

p- ISSN 2775-3735- e-ISSN 2775-3727

DYNAMICS OF LIVE-STREAM COMMERCE: UNVEILING EXTERNAL AND INTERNAL FACTORS IN IMPULSE BUYING DECISIONS IN LIVE SHOPPING

Ronny Albar Mahendra¹, Yudhistira Bawa Yusha², Maria Anisa Naulita³, Evi Rinawati Simanjuntak⁴

Bina Nusantara University, Indonesia

Email: ¹[email protected], ²[email protected], ³[email protected], ⁴[email protected]

ABSTRACT
The development of internet technology has encouraged innovation in digital commerce, especially e-commerce. One of the rapidly growing trends is live shopping, which combines social media interactivity with a real-time shopping experience. This study aims to explore the influence of internal and external factors on impulse purchase decisions in the context of live shopping. Using a quantitative approach and modeling of structural equations (Partial Least Squares-Structural Equation Modeling/PLS-SEM - Smart PLS 4.0.), data was collected from 300 respondents in Indonesia through an online survey. The results showed that external factors, such as streamer attractiveness, content quality, and perceived interactivity, significantly increased perceived enjoyment. However, perceived enjoyment has not been proven to significantly encourage impulse purchasing decisions. Conversely, internal factors, such as hedonistic motivation, viewing frequency, and impulse buying tendencies, have been shown to have a strong influence on impulse purchases during live shopping sessions. The study concluded that while external factors can create a pleasurable experience, they do not directly trigger impulsive buying behavior in the absence of internal impulses. These findings provide new insights for marketers to design effective strategies that focus more on the psychological aspects of consumers, such as hedonistic motivation and increased frequency of interactions, to encourage engagement and impulse purchases in live shopping platforms.
KEYWORDS	E-commerce, Internal and External Factors, Live Shopping, Impulse Purchases. Live Shopping, Impulse Purchases, Hedonic Motivation, Perceived Enjoyment, Interactivity, Streamer Attractiveness.
	*This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International*

INTRODUCTION

The rapid development of internet technology is an innovation in the world of commerce, the most significant progress in online shopping is the expansion of e-commerce. Live streaming shopping has emerged as a prominent feature in the multimedia realm, especially in the context of social media (Cai et al., 2018). This innovative concept has gained recognition and adoption relatively recently inï¿½ Indonesia's digital platforms. Distinguished by its real-time and authentic nature, live streaming shopping combines various aspects of social commerce and media attributes.

In live streaming shopping, there is the cultivation of different familiarity and a deep sense of relationship between consumers and the vendor or product offered (Hu et al., 2017).

The growth rate of live shopping has also increased significantly in India, reaching 21.5%, with a projection of US$111 billion by 2024. The same thing happened inï¿½ live streaming in China, which reached US$200 billion in 2023, with a projected growth of 19.5% (Chen et al., 2023). The rapid acceptance of live streamï¿½ trading is driven by the ease of broadcasting sales promotions via mobile devices, providing self-employed traders with the convenience of increasing revenue and profits (Kang et al., 2021). This growth is attributed to lifestyle changes during the pandemic in a technology-driven business environment, which accelerated the popularity of live streaming commerce.

Live streaming is a popular form of user-generated content (Chan et al., 2017), where streamersï¿½ upload real-time video content on various topics such as gaming, talent shows, and everyday life. Among them, live streaming shopping has developed as a significant innovation inï¿½ the e-commerce industryï¿½ (Qu et al., 2023). It happened during a live streaming sessionï¿½ on an e-commerce platformï¿½ and quickly gained popularity. According to a Statista report, the Gross Merchandise Value (GMV)ï¿½ of Indonesia's e-commerce marketï¿½ reached over $62 billion in 2023, with around $5 billion attributed to live shopping (Cho et al., 2019). With nearly 234 million active internet users,ï¿½ Indonesia's e-commerce marketï¿½ is expected to grow to around $120 billion by 2025 and $200 billion by 2028 (Uzunoglu, 2024). Due to its substantial commercial potential, conducting an in-depth study of live shopping is worthwhile.

The value propositionï¿½ of live streaming shopping lies in its ability to provide authenticity, immersive visualization, and increased interactivity, all of which contribute significantly to the user experience (Hu & Chaudhry, 2020). In addition, distinguished by its credibility, live streaming shopping allows consumers to engage directly with sellers and negotiate information, thereby increasing their attention to the promoted product (Todd & Melancon, 2017). The social presence of online sellers, facilitated through live streaming, bridges the gap between customer interaction and traditional offline sales approaches, bringing these interactions into the digital realm. This level of social media presence and interactionï¿½ not only enriches the shopping experience but also serves to eliminate customer doubts, thereby strengthening trust in sellers (Hajli, 2015).

The combination of online media and commercial activities is a phenomenon that contributes to the increase in impulsive buying behavior (Abdelsalam et al., 2020). This shopping purchase occurs because of incentives provided by online stores through promotions at shopping festivals, free shipping, discounts, ease of payment processing, and others (Tumanggor et al., 2022). The results of a survey conducted (Populix, 2022) regarding the underlying reasons for online unplanned shopping are dominated by, among others, the opportunity to buy the desired item (40%), as a self-reward (39%), tempted by attractive promotions from sellers (35%), tempted by twin number shopping festival discounts (34%), free shipping (31%), cashback (31%), shopping coupons (25%). IT elements such as visibility, metavoicing, and interactivity can create a deep flow experience during live streaming shopping, which in turn encourages impulsive buying behavior (Simanjuntak & Pratama, 2024).

In research conducted by (Febrilia & Warokka, 2021), consumers doing online impulse buying are driven by impulse buying tendency & consumer mood which is part of consumer traits or can also be called internal factors. Internal factors related to emotions are important determinants in a person's purchasing decision-making process.

Meanwhile, external factors, according to Febrilia & Warokka (2021), are situational factors or do not come from consumers. In his research, the situational factors that encourage consumers to do online impulse buying are not so dominant or only supported by one variable, namely motivational activities. research conducted by (Ahn et al., 2019) suggests that the compatibility between products and streamers is a key factor in regulating impulse buying behavior in the context of live shopping. These findings show that when consumers feelï¿½ the streamer'sï¿½ ability to represent a product, this makes the tendency to make impulse purchases increase. This refers to the state of mind created by the environment and specific personality traits in impulsive consumers.

Referring to previous research, this study aims to answer questions and gaps in previous research, by setting the goal of researching more deeply related to consumer behavior in live shopping in Indonesia and the extent to which impulse buying impulses are influential in it. The objective of this study is to build a model that tracks external and internal factors on impulse buying tendencies withï¿½ the Latent Trait Theory approach, which is to examine the mediating role of perceived enjoyment in watching live shopping in the relationship between external factors and their tendency to make impulsive purchases in live shopping sessions, and understanding these motivations is essential for businesses and marketers to maximize customer engagement and loyalty in the live shopping platform, as well as create a growing commerce ecosystem with long-term growth in the e-commerce world.

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RESEARCH METHOD

This study adopts a post-positivismï¿½ approach to identify factors that influence purchase decisions in the contextï¿½ of live streaming shopping (Sekaran & Bougie, 2016). This research method uses a survey with a questionnaire to collect quantitative data related to consumer knowledge, attitudes, and behaviors related to impulse purchases. Impulse buying is described as a purchase decision that is sudden, unplanned, and hedonistic. Inï¿½ today's era of e-commerce, impulse purchases increasingly pose challenges, as they are triggered by consumer emotions, spontaneous behavior, object attraction, and low cognitive control, which can lead to purchases without considering financial factors and other aspects (Jang et al., 2018).

The study was conducted without intervention or artificial conditions, by testing several independent variables on consumer repurchase behavior. Usingï¿½ a cross-sectional approachï¿½ on individual units of analysis, the purpose of the study was to investigate how internal and external variables, as well as the perception of pleasure, affect impulse purchases (Lin et al., 2022).

Sampling Method

This study aims to find the influence of internal and external customer factorsï¿½ mediated by the influence of pleasure perception to create impulse purchases in live shopping. In determining the sample size, a strategy is needed to determine the number of samples needed in a population (Sekaran & Bougie, 2019). Sample size can be defined as the part of a population that is required to ensure that the number is sufficient to obtain information that can be used to draw conclusions (Sekaran & Bougie, 2019).

According to (Joe et al., 2017), the determination of the number of samples is determined using a formula where the sample can be calculated based on the number of indicators multiplied by 5 to 10. Based on these guidelines, the number of samples for this study is: n = number of indicators ï¿½ 10. The number of indicators in this study is 30. Based on this formula, the sample obtained is as follows: n = 30 ï¿½ 10 = 300 citizen respondents in Indonesia.

Method and Technique of Data Collection

A simple random samplingï¿½ method through an onlineï¿½ survey was used to measure the research variables, including impulse purchase propensity, viewing frequency, emotional anticipation, and the influence of scarcity (Qu et al., 2023). The purposive samplingï¿½ method was applied to select samples based on the criteria of the type of social platform and product relevant to the research objectives, in accordance with the approach proposed by Abdelsam et al. (2020). Based on the literature, it was determined that the target sample of this study wasï¿½ productive age e-commerceï¿½ consumers who had made transactions on live shopping, with a population size that was not known for certainty (non-probabilistic sampling) (Sekaran & Bougie, 2019).

Data collection was conducted using a survey technique withï¿½ a 5-point Likert scaleï¿½ , as used in the research of Qu et al. (2023) and Lee et al. (2022 & 2023). Survey data is disseminated through Google Form. As for the measurement method for 8 variables consisting of 30 constructs which can be seen in Table 1.

Table 1 Variable name, number of indicators, & research adopted.

It	Variable	Number of Indicators	Research
1	Streamer Atractiveness (SA)	3	Zhao, Q., et al. (2023)
2	Content Quality (CQ)	4	Gulfraz, M. B., et al. (2022);
2	Content Quality (CQ)	4	(Saffanah et al., 2023)
3	Perceived Interactivity (PI)	4	(Saffanah et al., 2023)
3	Perceived Interactivity (PI)	4	Hwang, J., & Youn, S. Y. (2023)
4	Hedonic Shoping Motivation (HSM)	4	Arnold, M. J., & Reynolds, K. E. (2003)
5	Viewing Frequency (VF)	4	Lin, T.-C., & Huang, S.-L. (2014)
6	Impulse Buying Tendency (IBT)	4	Qu, Y., et al. (2023)
7	Perceived Enjoyment (PE)	4	Do, H.-N., et al. (2020);
7	Perceived Enjoyment (PE)	4	Parboteeah, D. V., et al. (2009)
8	Online Impulse Buying (OIB)	3	Gulfraz, M. B., et al. (2022)

Analysis Method

The data collected through filling out the questionnaire was then analyzed using the PLS-SEM (Partial Least Squares Structural Equation Modeling) method to test the validity of the hypothesis proposed. This approach, as proposed by Hair et al. (2019), involves two main stages, namely measurement and structural modeling. The PLS-SEM method, emphasized in the work of Hair et al. (2021), has been shown to be effective in explanatory research and has a strong ability to make statistical conclusions.

The use of PLS-SEM in this study aims to evaluate the quality of indicators used to measure certain constructs. This evaluation includes the validity and reliability of the indicators, allowing researchers to test the seven hypotheses proposed. The reliability scale is checked by considering items that have a load above the threshold (Rho: Correlation Coefficient) of 0.70, in accordance with the recommendations of Qu et al. (2023).

AVEï¿½ (Average Variance Extracted) measurements were used, with the threshold recommended by Lund (2021) of 0.50 or higher. After the validity and reliability testing, this study continues with the Path Coefficient Resultï¿½ test based on the methodology of Qu et al. (2023) to measure the structural model or theoretical framework. In the resultsï¿½ of Path Coefficient, standard deviation, P-value, and T-statistic calculations were carried out to evaluate the significance of the variable correlation. P-value assesses the significance of the correlation, while T-statistic helps determine the significance of the correlation to the variation and size of the sample. A high P-value indicates insignificance, and T-statistic helps determine the significance of the correlation (Sekaran & Bougie, 2019).

RESULT AND DISCUSSION

After the dissemination of the survey form through google form, 300 respondents were obtained who had been summarized in Table 1. Of the total respondents who have watched, the majority of 300 respondents carried out live shopping activitiesï¿½ onï¿½ the Instagram platform as many as 41 people (13.67%), Shopee as many as 144 people (48%), then Tiktok 92 people (30.67%) and Tokopedia as many as 23 people (7.67%), and were dominated by women as many as 180 (60%) compared to men as many as 120 (40%). Of the 300 respondents, the majority made live shoppingï¿½ purchases 1 to 3 times (66.67%). The age characteristics that are quite frequent for live shopping are 16-20 years old as many as 18 (6%), with the majority at the age of 21-40 years as many as 124 (41.33%), at the age of 31-40 years as many as 116 (38.67%) who are millennials and at the age of 41-50 years as many as 24 (8%) then at the age of 50 years as many as 18 (6%). Based on the level of income, the most are respondents who have an income of 5-10 million/month as many as 99 (33%) and an income of 10-20 million/month as much as 90 (30%), with the data obtained first tested for validity and reliability using the Smart PLS4 applicationï¿½ on the PLS-SEM Algorithm function.

Table 2. Characteristic Respondents

CHARACTERISTIC RESPONDENTS
ITEMS	TOTAL COMPOSITION	%
Live Shopping Viewing Platform
Instagram	41	13.67%
Shopee	144	48.00%
TikTok	92	30.67%
Tokopedia	23	7.67%
Grand Total	300	100.00%
Frequency of Shopping through LS in 1 month
1 SD 3x	200	66.67%
2 SD 3x	1	0.33%
3 SD 3x	1	0.33%
3 SD 5x	68	22.67%
More than 5x	30	10.00%
Grand Total	300	100.00%
Gender
Man	120	40.00%
Woman	180	60.00%
Grand Total	300	100.00%
Age
16 - 20 Years	18	6.00%
21 - 30 Years	124	41.33%
31 - 40 Years	116	38.67%
41 - 50 Years	24	8.00%
> 50 Years	18	6.00%
Grand Total	300	100.00%
Total income
< IDR 5 million	75	25.00%
Rp 5 Million - Rp 10 Million	99	33.00%
ï¿½iRp 10 Million - Rp 20 Million	90	30.00%
Rp 20 Million - Rp 30 Million	25	8.33%
Rp 30 Million - Rp 40 Million	5	1.67%
> Rp 40 Million	6	2.00%
Total	300	100.00%

Table 3. Reliability & Validity Analysis

Variable	Items	*Factor Loading*	CA	CR (rho_a)	CR (rho_c)	AVE
Streamer Attractiveness (SA)	SA1	0.85	0.72	0.75	0.84	0.64
	SA2	0.80
	SA3	0.74
Content Quality (CQ)	CQ1	0.82	0.81	0.81	0.87	0.63
	CQ2	0.79
	CQ3	0.80
	CQ4	0.78
Perceived Interactivity (PI)	PI1	0.82	0.79	0.80	0.86	0.61
	PI2	0.78
	PI3	0.80
	PI4	0.73
Perceived Enjoyment (PE)	PE1	0.85	0.86	0.86	0.91	0.71
	PE2	0.82
	PE3	0.85
	PE4	0.84
Viewing Frequency (VE)	VF1	0.89	0.88	0.88	0.91	0.73
	VF2	0.83
	VF3	0.84
	VF4	0.85
Hedonic Shoping Motivation (HSM)	HSM1	0.85	0.85	0.86	0.90	0.69
	HSM2	0.84
	HSM3	0.82
	HSM4	0.82
Impulse Buying Tendency (IBT)	IBT1	0.84	0.84	0.85	0.89	0.68
	IBT2	0.82
	IBT3	0.84
	IBT4	0.80
Online Impulse Buying (OIB)	OIB1	0.90	0.88	0.88	0.92	0.80
	OIB2	0.90
	OIB3	0.88

Note: Cronbach's Alpha (CA), Composite Reliability (CR), and Average Variance

Extracted (AVE)

Table 4 Discriminant Validity

	CQ	HSM	IBT	OIB	PE	PI	SA	VF
CQ
HSM	0,516
IBT	0,375	0,672
OIB	0,194	0,441	0,473
PE	0,531	0,629	0,701	0,427
PI	0,499	0,320	0,264	0,056	0,422
SA	0,537	0,485	0,616	0,172	0,614	0,564
VF	0,358	0,609	0,633	0,429	0,659	0,228	0,520

Figure 2. Full Model

Table 5. Path Coefficients

Hypothesis	Code	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics (\|O/STDEV\|)	P values	Hypothesis Test
Extenal Factor
Streamer Attractiveness (SA) → Perceived Enjoyment (PE)	H1	0.191	0.188	0.055	3.479	0.000	Accepted
Content Quality (CQ)→ Perceived Enjoyment (PE)	H2	0.196	0.197	0.056	3.527	0.000	Accepted
Perceived Interactivity (PI) → Perceived Enjoyment (PE)	H3	0.108	0.112	0.051	2.123	0.017	Accepted
Perceived Enjoyment (PE)→ Online Impulse Buying (OIB)	H4	0.094	0.092	0.076	1.235	0.108	Rejected
Internal Factor
Vieweing Frequency (VF)→ Perceived Enjoyment (PE)	H5	0.414	0.416	0.047	8.724	0.000	Accepted
Vieweing Frequency (VF)→ Online Impulse Buying (OIB)	H6	0.134	0.133	0.076	1.760	0.039	Accepted
Hedonic Shoping Motivation (HSM) → Online Impulse Buying (OIB)	H7	0.147	0.151	0.076	1.941	0.026	Accepted
Impulse Buying Tendency (IBT)→ Online Impulse Buying (OIB)	H8	0.201	0.203	0.071	2.832	0.002	Accepted

The results of the reliability test showed that all items met the criteria of Cronbach's Alpha (CA) and Composite Reliability (CR), both greater than 0.7.ï¿½ The validity test showed that all items had a factor load exceeding 0.7 and was statistically significant with an Average Variance Extracted (AVE) ≥ 0.5. These results confirm that all variables meet the reliability and validity testing requirements (Hair Jr. et al., 2021), as shown in Table 2.

Discrimination was assessed, indicating that the Heterotrait-Monotraite (HTMT) value did not exceed the threshold of 0.9 (Henseler et al., 2015), as detailed in Table 3. The results confirm the validity of the discrimination of the measurement items of each construction. Figure 2 shows the full PLS-SEM model (outside and inside) used in the study.

Seeing the significance of the influence between constructs can be seen from the path coefficient (path coefficient). The marks inï¿½ the coeffecient path must be in accordance with the hypothetical theory, to assess the significance of the coeffecient path can be seen from the t test (critical ratio) obtained from the bootstrapping process (resampling method). The following Path Coefficients testï¿½ results on each variable are shown in Table 4.

The results of Smart PLS analysisï¿½ using Bootstrapping show that several factors contribute significantly to Perceived Enjoyment and online impulse buying. In external factors: Streamer attractiveness (H1), Content Quality (H2), and Perceived Interactivity (H3) all have a positive relationship with Perceived Enjoyment.

This is because H1, H2, H3 have a P value < 0.05 & a positive Path Coefficient . The R Square Adjusted value on H1, H2, H3 to Perceived enjoyment is 0.394 or 39.4%. It can be interpreted that Streamer Attractiveness, Content Quality, and Perceived Interactivity can explain the relationship with the dependent variables, namely perceived enjoyment of 39.4% & 60.6% explained by other variables. Based on (Hair et al., 2019) because the R Square value > 0.25 and is still < 0.5, it can be categorized that the relationship is still moderate.

Meanwhile, perceived enjoyment was not proven to significantly encourage online impulse buying (H4), it was due to a P Value > 0.05 & a negative Path Coefficient . Interestingly, the condition of Indonesian customers who experience enjoyment is not significant in online impulse buying. There are differences with the research conducted by (Ninh Do et al., 2020), & (Lee et al., 2022 & 2023).

Discussions

Based on the results shown in Table 5, this study tests various hypotheses regarding internal and external factors that affect perceived enjoyment and online impulse buying in the contextï¿½ of live shopping.

The first hypothesis (H1), which tests the effect of Streamer Attractiveness (SA) on Perceived Enjoyment (PE), proved significant. These results support research by Song & Liu (2021), Xu et al. (2019), and Lou and Yuan (2019), which found that streamers' physical attractiveness and personalityï¿½ can influence consumer engagement and satisfaction during live shopping sessions. In addition, research by (Basch et al., 2022) also confirms that content generated by influencers, including streamers, has a great influence on the positive consumer experience and their engagement with the content.

The second hypothesis (H2), which investigates the relationship between Content Quality (CQ) and Perceived Enjoyment (PE), has also proven significant. These findings are consistent with research by Cho et al. (2019), Ramadhan et al. (2021), and Seol et al. (2016), both of which emphasized that quality content, both in terms of completeness of information and relevance to consumer needs, can increase the pleasure felt during the shopping experience. In addition, research by (Carlson et al., 2018) also supports that the quality of informative and engaging content can encourage a positive experience, which then has an effect on consumer behavior.

The third hypothesis (H3), regarding the influence of Perceived Interactivity (PI) on Perceived Enjoyment, shows a significant relationship. These findings are supported by research by Zhao et al. (2021), Rubio et al. (2019), and (Karampela et al., 2020), which found that increased interactivity within shopping platforms, especially through interactive features such as live chat or real-time feedback, can improve user engagement and enjoyment. In addition, Lee et al. (2022) also revealed that higher interactivity strengthens consumers' emotional responses, which can increase their satisfaction during shopping.

However, the fourth hypothesis (H4), which tests the effect of Perceived Enjoyment (PE) on Online Impulse Buying (OIB), has not been proven significant. This is in contrast to the results of research by Siregar & Firdausy (2024), Karahan (2024), and Lin et al. (2022), all of which found that pleasure felt by consumers directly can encourage impulsive buying behavior. This mismatch may be due to differences in the context or platform used by the respondents, as well as other variables that may not have been identified in this study.

The fifth hypothesis (H5), which tests the relationship between Viewing Frequency (VF) and Perceived Enjoyment, proved significant. This supports the results of research by Sun et al. (2019), which showed that the more often consumers watch a live streaming session, the more engaged they are in the experience and the higher the level of pleasure perceived. This finding is also reinforced by the study of (Gabler et al., 2017), which showed that viewing frequency was positively related to consumer pleasure when shopping.

Furthermore, the sixth hypothesis (H6), which tests the effect of Viewing Frequency (VF) on Online Impulse Buying, is also significant. These findings are supported by research by Yi Qu et al. (2023), which shows that the frequency of watching live streaming directly affects the tendency to impulse purchases. In addition, research by Sun et al. (2019) and (Gabler et al., 2017) also found that the more often consumers watch live shopping content, the more likely they are to engage in impulse purchases.

The seventh hypothesis (H7), which tests the effect of Hedonic Shopping Motivation (HSM) on Online Impulse Buying, proved to be significant. This is in line with the results of research by Widagdo & Roz (2020), (M Ruby EVANGELIN et al., 2021), and (Santini et al., 2019), which stated that hedonistic shopping motivation, which involves the urge to seek pleasure and emotional satisfaction during shopping, is significantly related to impulsive purchasing behavior. In addition, research by (Sari & Hermawati, 2020) shows that consumers with hedonistic motivations are more likely to make impulsive purchases to meet their emotional needs.

Finally, the eighth hypothesis (H8), regarding the influence of Impulse Buying Tendency (IBT) on Online Impulse Buying, has also proven to be significant. These findings are consistent with research by (Ahn et al., 2019), Febrilia & Warokka (2021), and (Iyer et al., 2020), which show that impulsivity is one of the main predictors of impulse buying behavior, both in the context of e-commerce and live-stream commerce. Individuals with high impulsivity tendencies are more susceptible to marketing stimuli such as promotions or discounts, which encourage them to make unplanned purchases.

Based on the results of the study, internal factors such as Viewing Frequency, Hedonic Shopping Motivation, and Impulse Buying Tendency are proven to have a significant influence on Online Impulse Buying. This shows that internal factors related to the frequency of engagement, emotional drive, and impulsive behavior tendency play a significant role in encouraging consumers to make impulsive purchases during live shopping. Conversely, external factors such as Streamer Attractiveness, Content Quality, and Perceived Interactivity, while having a significant influence on Perceived Enjoyment, do not directly impact Online Impulse Buying when mediated by Perceived Enjoyment. This indicates that while external factors can increase consumer pleasure, they are not strong enough to encourage impulsive buying behavior in the absence of an underlying internal impulse. This is in line with the findings of research by Febrilia & Warokka (2021) that the variable consumer traits (internal factors) significantly affect online impulse buying compared to situational factors (external factors). Some of the internal factors that show significant influence include Impulse Buying Tendency and Consumer Mood, which indicate a strong impulse behavior tendency among consumers when shopping online.

CONCLUSION

ï¿½ï¿½ This study introduces a new perspective in understanding the factors that affect impulse purchases in live shopping. The results of the study corroborate that internal factors have a more dominant influence than external factors in influencing consumers' impulsive behavior when shopping through live shopping. These findings make a unique contribution to the existing literature, as well as improve our understanding of the dynamics of live shopping trade.

External factors, such as streamer attractiveness, content quality, and perceived interactivity, can indeed create a pleasant shopping experience and improve customer convenience. However, the results of the analysis show that these factors do not have a significant influence on impulse purchases directly. Although they are able to induce a feeling of comfort (perceived enjoyment), their impact on impulse buying behavior remains limited.

In contrast, internal factors such as hedonistic shopping motivation, viewing frequency, and impulsive purchasing tendencies have a much greater influence in driving impulse buying behavior. High hedonistic motivation makes consumers more likely to make impulsive purchases as a form of fulfillment of personal pleasure. Consumers who have hedonistic motivations often shop for an emotionally satisfying experience, not just to fulfill functional needs. In the context of live shopping, visual appeal and direct interaction with streamers can indeed increase a sense of engagement and satisfaction, but the hedonistic urge to buy impulsively is much greater than that. Previous research supports these findings, showing that consumers with high hedonistic motivation tend to seek entertainment in the shopping process, which often leads to impulse purchases (Atulkar & Kesari, 2018).

The high frequency of watching live shopping also increases the tendency to make impulse purchases. Consumers who often watch live streaming tend to feel more connected to impulsively want to buy. They are already familiar with the format and styleï¿½ of live shopping. Studies show that the more often a person is exposed to live shopping content, the more likely they are to make impulse purchases, as they are more susceptible to promotions and special offers presented during live broadcasts (Sun et al., 2019). High viewing frequency is also often associated with the fear of missing out, which can drive quick and impulsive purchase decisions.

In addition, individuals with a high impulse purchasing tendency will be more easily tempted to make purchases without prior planning. This tendency reflects impulsive personality traits, where individuals have low self-control and are more likely to react spontaneously to live shopping stimuli. This study shows that consumers with high impulsivity tend to have a strong emotional response to offers and discounts, which can trigger a sudden impulse to buy (Rook & Fisher, 1995).

Thus, while external factors such as streamer attractiveness, content quality, and perceived interactivity can improve the convenience and shopping experience, they do not directly trigger impulse purchases. Internal factors, such as personal motivation and frequency of interaction with live shopping, play a more significant role in encouraging impulsive behavior. This research emphasizes the importance of understanding the role of internal factors in designing effective marketing strategies to maximize customer engagement and loyalty in live shopping platforms. Companies need to consider strategies that target hedonistic motivations and increase the frequency of interactions with consumers to encourage higher impulse purchases.

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