Introduction

The expansion of the digital economy has resulted in noticeable shifts in consumer behavior, particularly in how social media is used for online shopping activities [1]. In Indonesia, the rapid growth in internet users has driven the development of social commerce, allowing people not only to communicate but also to find information and make purchasing decisions within the same platform [1].

Figure 1. Active TikTok Users in Indonesia

Figure 1 illustrates that the total number of inidividuals actively using TikTok in Indonesia has surpassed 150 million, underscoring the country’s position as one of the largest TikTok markets globally [3]. The substantial user base also reflects a high level of engagement, indicating that TikTok has become an important part of everyday digital activities. As a result, this situation provides strong opportunities for businesses to take advantage of TikTok as a social commerce platform [2].

In the context of TikTok GO, purchasing decisions are shaped by several important factors. Discount offerings function as economic stimuli that elevate consumers perception of product value, thereby prompting more immediate purchasing behavior [4]. Moreover, electronic word of mouth (e-WOM) functions as a core mechanism in establishing consumer trust, given that individuals frequently base their judgments on opinions and suggestions shared by other users or digital content creators [5]. Another important aspect is perceived ease of use (PEOU), reflecting the extent to which a platform can be used with ease, consequently enriching the user experience and lowering friction throughout the purchasing process [6]. The combination of these elements creates a distinctive online shopping experience that blends pricing appeal, social influence, and technological convenience. Nevertheless, there is still limited research examining these variables within the TikTok GO context. Accordingly, this research seeks to examine how discounts, electronic word of mouth, and perceived ease of use influence the purchasing decisions of TikTok GO users in Surabaya.

Literature Review

1. Digital Consumer Behavior in Social Commerce

The transformation of social commerce has profoundly altered the manner in which consumers connect and engage with digital platforms. Rather than simply receiving information, consumers now take an active role in exploring, assessing, and sharing their experiences with products. This change emphasizes the need to consider economic, social, and technological aspects when analyzing purchasing decisions [7]. A growing body of previous research has extensively investigated various factors that influence and shape consumer behavior, particularly within the rapidly evolving landscape of digital environments. For example, it was revealed that price perception, perceived ease of use, and electronic word of mouth all affect purchase decisions on TikTok Shop. Even so, the impact of e-WOM was identified as limited in its significance. The findings imply that pricing tactics and the ease of system operation continue to exert the strongest influence on consumers digital purchasing decisions [7].

2. Discount

Discount is widely recognized as a key promotional strategy in digital marketing. It provides economic incentives that increase perceived value and encourage consumers to make quicker purchasing decisions. According to previous findings, discounts not only affect rational evaluation but also trigger psychological responses such as fear of missing out (FOMO), leading to impulsive buying behavior [8]. It is confirmed that price discounts significantly influence consumer buying interest in digital marketplaces. These findings indicate that discounts are one of the most dominant factors in shaping consumer decisions due to their direct impact on perceived benefits [8].

3. Electronic Word of Mouth (e-WOM)

Electronic word of mouth (e-WOM) can be understood as the process of sharing information among consumers through digital platforms, such as reviews, comments, and recommendations. Unlike traditional advertising, e-WOM is often viewed as more trustworthy since it originates from actual user experiences [9]. It is demonstrated that electronic word of mouth (e-WOM) exerts a positive and statistically considerable influence on purchase decisions, as it enhances consumer trust and reduces uncertainty during product evaluation [10]. Additionally, it is found that user experience and satisfaction on TikTok Shop contribute significantly to the creation of word of mouth, which subsequently affects consumer behavior [11].

4. Perceived Ease of Use

Perceived ease of use (PEOU) is an essential concept adapted from the Technology Acceptance Model (TAM), which describes how individuals accept and utilize digital technologies. When a system is simple and user-friendly, it enhances user comfort while minimizing the effort required, which in turn encourages ongoing use and transaction activities [12]. It is found that perceived ease of use has a positive and statistically significant impact on consumer decisions when using digital service platforms. These findings suggest that system usability is essential in strengthening consumer confidence and motivating them to engage in digital transactions [12].

5. Purchase Decision

A purchase decision constitutes the ultimate step during the consumer purchasing decision process, where consumers ultimately ascertaintheir choice of a product after carefully assessing and weighing various available alternatives. In digital contexts, this process is influenced by multiple factors, including price incentives, social influence, and system usability [13]. It is shown that digital marketing, service quality, and consumer trust simultaneously influence purchase decisions. These findings emphasize that purchase decisions are multidimensional and require a combination of marketing strategies and user experience factors [13].

Method

The research implements a quantitative strategy alongside a descriptive–associative approach to investigate the extent to which discounts, electronic word of mouth (e-WOM), and perceived ease of use influence the purchasing decisions of TikTok GO users in Surabaya. The use of a quantitative method facilitates statistical testing of relationships between variables, whereas the associative design aims to examine the causal relationships in relation to the variables being investigated.

(1)

In this context, n refers to the number of samples, N indicates the total population, and e represents the margin of error. By using a margin of error of 0.08, the calculation shows that the minimum required sample size is approximately 156 respondents. To improve the representativeness and reliability of the data, this study involves 160 respondents. This study utilizes a non-probability sampling approach, employing purposive sampling to deliberately choose respondents in accordance with predetermined criteria, where participants are selected according to predetermined criteria, specifically individuals who are part of Generation Z, live in Surabaya, and have experience using TikTok GO for purchasing purposes. Furthermore, proportional area sampling was applied to ensure that respondents are evenly distributed across different areas in Surabaya. The proportion of respondents in each region was calculated using the formula :

(2)

where proportion area sampling is the number of samples in each region, Total populasion Area is the population in each region, Total population is the total population, and Total respondent the total sample size. Based on this calculation, the sample distribution consists of 41 respondents from East Surabaya, 39 from South Surabaya, 34 from North Surabaya, 28 from West Surabaya, and 18 from Central Surabaya.

This research is based on primary data gathered from 160 respondents through online questionnaires, alongside secondary data derived from pertinent literature sources. The analytical process is initiated through the examination of data validity and reliability, and subsequently continues with classical assumption testing, including assessments of normality, multicollinearity, and heteroscedasticity [15] [16]. Furthermore, a multiple linear regression model is utilized to evaluate how discounts, electronic word of mouth, and perceived ease of use affect purchase decisions. In addition, the evaluation of hypotheses is carried out through the use of the F-test and t-test, along with the R² value to appraise the model’s capability to represent the observed variability.

Result And Discussion

A. Validity Test

To give a comprehensive description of the respondents involved in the research, the distribution of their demographic characteristics is presented in Table 1 below:

Table 1. Validity Test Results

The Pearson Product Moment correlation technique was employed to evaluate data validity, employing a 0.05 significance level and an r-table benchmark of 0.155. As presented in Table 1, all questionnaire items show correlation coefficients (r-count) that exceed the r-table value. The obtained correlation values range between 0.715 and 0.865 for all variables, namely discount (X1), electronic word of mouth (X2), perceived ease of use (X3), and purchase decision (Y). Furthermore, all items show significance values below 0.05. The findings demonstrate that all measurement items are valid and appropriate for subsequent analysis.

B. Reability Test

The results obtained from the validity test, which aim to assess the degree to which each measurement item accurately represents the intended construct, are as shown in Table 2 below:

Table 2.Reability Test Results

The reliability analysis of the instrument was conducted using Cronbach’s Alpha, where a value of 0.60 was adopted as the minimum standard for determining acceptable reliability. As indicated in Table 2, all variables exhibit Cronbach’s Alpha values above the specified threshold. Specifically, the discount variable (X1) records a value of 0.861, electronic word of mouth (X2) reaches 0.803, perceived ease of use (X3) shows 0.773, and purchase decision (Y) has a value of 0.863. These results confirm that all variables are reliable, indicating that the research instrument is consistent and appropriate for further analysis.

C. Classical Assumption Test

1. Normality Test

The results obtained from the reliability test, reflecting the internal consistency and stability of the research instrument, as shown in Table 3 below:

Table 3.Normality Test Results

Figure2.Normal Probability Plot Graph

2. Multicolinearity Test

To determine the existence of multicollinearity among the independent variables in the regression model, the examination findings are presented in Table 4 below:

Table 4.Multicollinearity Test Results

The the multicollinearity test was administered to identify the presence of relationships or overlap among the independent variables included in the regression model, through the use of tolerance values and the Variance Inflation Factor (VIF) as benchmarks. According to the Table 4, all variables exhibit tolerance values exceeding the 0.10 threshold, specifically 0.989 for discount, 0.989 for e-WOM, and 0.999 for perceived ease of use. Likewise, the VIF values for each variable are far below the critical limit of 10, recorded at 1.011, 1.012, and 1.001, respectively. The findings confirm that there is no multicollinearity among the independent variables, confirming the suitability of the regression model for subsequent analysis.

3. Heteroscedasticity Test

Figure3.Scatterplot Hasil Uji Heterokedastisitas

Heteroscedasticity was evaluated using a scatterplot to assess whether the regression model residuals display heteroscedasticity. The plot indicates that the data points appear to be randomly scattered around the zero axis, both above and below, with no identifiable pattern. This suggests that heteroscedasticity is not present, indicating that the residuals have constant variance and the regression model is fit for continued analytical procedures.

4. Multiple Linear Regression Analysis

The outcomes of the heteroscedasticity test, which are used to ascertain whether the variance of residuals remains constant across observations, can be observed in Table 5 below:

Table 5.Multiple Linear Regression Analysis Test Results

This study employs multiple linear regression analysis to assess the effect of discounts, e-WOM, and perceived ease of use on purchase decisions. As illustrated in Table 5, the regression model is formulated as follows:

Y = -2.907 + 0.705X₁ + 0.392X₂ + 0.276X₃

The intercept value of -2.907 represents the initial level of purchase decisions assuming no variation in the independent variables. In contrast, the discount variable has a coefficient of 0.705, suggesting that any increase in discount will lead to a corresponding increase of 0.705 in purchase decisions. The coefficient for electronic word of mouth (X₂) is 0.392, indicating that higher levels of e-WOM contribute to an increase of 0.392 in purchase decisions. Correspondingly, perceived ease of use (X₃) shows a coefficient of 0.276, demonstrating a positive relationship with purchase decisions. The positive coefficients and significance levels below 0.05 across all independent variables indicate that discounts, e-WOM, and perceived ease of use significantly and positively influence purchase decisions.

D. Hypothesis Testing

1. F Test (Simultaneous)

To measure the overall effect exerted by all independent variables on the dependent variable, the findings of the simultaneous hypothesis test (F-test) are shown in Table 6 below:

Table 6.F Test Results

The F-test was carried out to evaluate the simultaneous impact of the independent variables on purchase decisions. Based on Table 6, the obtained F-value of 64.603 considering the obtained significance level of 0.000 falls under the 0.05 benchmark, it can be inferred that discounts, e-WOM, and perceived ease of use together exert a beneficial and statistically a significant role in shaping purchase decisions.

2. t Test (Partial)

The outcomes of the partial hypothesis testing (t-test), aimed at determining the partial influence of each explanatory variable on the outcome variable is reported in Table 7 below:

Table 7.T Test Results

The t-test was employed to assess the separate impact of each predictor on buying decisions. As indicated in Table 7, all variables have t-statistics that surpass the critical threshold, accompanied by significance values below 0.05. In detail, discount yields a t-value of 11.688, e-WOM reaches 6.839, and perceived ease of use records 4.593, with all significance levels at 0.000. These results confirm that each independent variable, namely discount, e-WOM, and perceived ease of use, individually contributes a supportive and statistically has a pronounced effect on purchase decisions.

3. Coefficient of Determination Test (R 2 )

The R-squared value (R²), indicating the degree to which the independent variables explain the variance in the outcome variable, is displayed in Table 8 below:

Table 8.Coefficient of Determination Test Results (R2)

The coefficient of determination was employed to assess the explanatory power of the independent variables in relation to purchase decisions. As shown in Table 8, the R-squared value of 0.554 denotes that 55.4% of the differences in purchasing choices is determined by discount, e-WOM, and perceived ease of use. Approximately 44.6% of the variance is described by external variables not incorporated in this research model.

Conclusion

This study culminates in the finding that discount, e-WOM, and perceived ease of use have a positive and significant impact on purchase decisions among TikTok GO users in Surabaya, both partially and simultaneously, with discount identified as the most dominant factor. These findings highlight that pricing strategies, supported by social influence and ease of platform usage, play an instrumental part in directing consumers’ decision-making processes in social commerce. Practically, this implies that businesses should focus on optimizing promotional strategies while maintaining platform usability to enhance customer engagement and conversion. For the next study, it is recommended to incorporate additional variables such as trust, perceived value, or brand image, as well as to expand the research scope to different regions or digital platforms to obtain more comprehensive insights.

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