THE INFLUENCE OF BRAND EXPERIENCE TOWARDS BRAND TRUST ON NEW ENTRY SPORT PRODUCT

The purpose of this research is to determine the impact of brand experience towards brand trust, particularly in new entry products with the new entry product in the sports industry as a proxy, particularly in golf balls. The objective of this study is to research how a new entry product (in the sports market) could gain its brand trust based on its brand experience. This research collected and analyzed using numerical data through questionnaires and a total of 87 responses were considered valid for data analysis using SPSS. The hypothesis testing is done using linear regression, using brand experience as independent variable and brand trust as dependent one. The result shows that there is a linear correlation and positive influence from brand experience towards brand trust in the new entry golf product. Thus, it could be concluded that brand experience, especially in new entry products, is essential, since it could lead to customer trust towards the brand. Impllication of this research is findings emphasize the significance of brand experience as a driver of brand trust for new entry products. This suggests that companies entering competitive markets should prioritize creating positive brand experiences to establish trust among consumers. Managers can now tailor their strategies to enhance brand experiences, ultimately boosting brand trust and customer loyalty.


INTRODUCTION
Studying brand experience and its relation to brand trust is important, especially for enabling the new entry product to enter the existing market. A successful brand creates a bond with their consumers, which will differentiate them from another similar brand (Akoglu & Özbek, 2022). To do so, firstly, the consumers have to experience the brand, both directly or indirectly. Direct experience with the product could be initiated by searching information about the product, making a purchase and also using the products, meanwhile the indirect experience could be initiated by product advertisement (Akoglu & Özbek, 2022) (Huaman-Ramirez & Merunka, 2019).
The sports equipment sector consists of businesses that produce and sell various types of athletic gear, including footwear, apparel, and accessories. People's enthusiasm for athletics suggests a bright future for the sporting goods market. Extreme supporters may be found in any sport. Play whatever sport you choose, from football to basketball to futsal to badminton to golf and beyond. The sports equipment market is expanding fast in step with the advancement of technology. The current market for sports equipment is more diverse and high-tech. In recent years, competition has only heated up in the sports equipment market. This is due to the effects of globalization and advances in technology, particularly in the realm of digital media. on creating positive experiences, companies can build stronger connections with consumers, leading to higher brand trust and loyalty. And A brand that has successfully established trust through positive experiences with a new entry sport product can leverage that trust when introducing related products or expanding into different segments of the market.

METHOD
This research used model of Brand experience and model of Brand Trust as their main foundation. Thus, it can be written that brand experience is expressed as an independent variable (X). For purposes of experimentation or scientific study, the phrase "independent variable" is used to describe a variable over which the researcher has some influence. The dependent variables are the variables that are measured or monitored for change, while the independent variables are the variables that their leaders intentionally manipulate to see how they affect their dependent variables. The independent variable is the dependent variable which is displayed on the y axis. In short, in a study or experiment, independent variables are variables that are changed or controlled to see how they affect the dependent variables.
Meanwhile, brand trust can be identified as a dependent variable (Y). In a scientific study or experiment, the dependent variable is a variable that is monitored for shifts in response to changes in the independent variable. Independent variables are variables that are manipulated, while independent variables are variables whose values or behavior are observed. During a study or experiment, their leader will usually focus on their dependent variables to see what they are and how their independent variables affect them. Dependent variables are those that are being analyzed or realized visually, and are often shown on the y-axis of a graph or used as outcome variables in statistical models, while independent variables are shown visually on the x-axis and their sequences as predictors. In conclusion, in a research study or experiment, a dependent variable is a variable that is measured or observed to understand the impact of changes in the independent variables.

Data Collection
The research method used in this study is a quantitative research method. This is used because the data is from this group of researchers and analyzed using numerical data through surveys or questionnaires. The weaknesses of using this research method are: (1) data collection will be better (2) can obtain more responses (3) data collection time is flexible and efficient.
This quantitative method only focuses on numerical data, which is then realized only by numbers, unlike qualitative methods which offer in-depth analysis. However, this can be done first by analyzing the respondents' answers to their like-scale questionnaires, to find out the respondents' opinion of the questionnaire.
The data obtained from this researcher is about their perceptions/opinions about their brand and the brand trust of their new golf ball product. Perceptions were collected from respondents through survey/questionnaire techniques. This technique is used because it saves time and is an effective way to collect data from a large number of respondents over a large area, if the authors know exactly what their variables are in software. After they made a questionnaire, it was distributed to their responses offline, through golf events at a golf course in Bandung. They are replying to those who have played golf using this new ball golf product. Overall, there were 88 surveys collected and there weirei 87 valid answers, therefore the effective sample rate is 98.86%.

Variable and Measurement
After the data is collected, then the data is analyzed using linear regression with the help of SPSS. Linear regression is used to determine the effect of brand experience (X) on brand trust (Y). The main framework/theory that we will adopt is from Brakus, et al. (2009) for brand experience and (Chaudhuri and Holbrook (2001) for brand trust. These theories have been used in similar research on the effect of brand experience on brand trust conducted by (Akoglu & Özbek, 2022;Khan & Fatma, 2017). Based on these theories, their views, the Survey I propose is:

Seinsory
The visual or other sensory impact of this brand is significant for mei.
Likert Scale 1-6 In terms of my senses, I like this brand. I just can't get behind this product line.

Affective
Emotions and emotions are evoked by this brand. This is not a brand that inspires any strong feelings in mei. This is a deeply felt brand.

Behavioral
When I use this product, I do a number of activities and behaviors. This product line produces tangible outcomes.
The products of this company lack initiative.
Intellectual Every time I come across this brand, I find myself deep in thought. I don't get any deep ideas from this product. This product line encourages mei to think critically and creatively.
Brand Trust (Y) Chaudhuri & Holbrook (2001) I trust this brand I rely on his brand This is a genuine brand. When I purchase this brand, I never have to worry since I know it will always deliver.
The variable is weirei measured using 6 points Likert scale. The 6 points Likert scale was used to encourage their respondents in considering the question more carefully, and perform higher reliability and decrease the bias of their respondents' answers. Before conducting the linear regression test, the validity and reliability test will be performed to ensure the data is valid and reliable. Therefore, we will conduct the classical assumption test, such as normality test, multicollinearity test, heteroscedasticity test. And the last step is establishing the linear regression test using SPSS.

RESULTS AND DISCUSSION Analysis of sample structure
From the results of the questionnaire, the demographics of their answers are as follows. They were 63.2% male dan 36.8% female, who age less than and up to 20 years old were 2.3%, 21-30 years old were 32.2%, 31-40 years old were 19.5%, 41-50 years old were 18.4% and over 50 years old were envy 27.6%. For their work, 36.8% of their respondents are office workers, 26.4% of respondents are hard workers, 16.1% are university students, 9.2% of respondents are hard workers and 11.5% of respondents have other jobs.
As for their income, 17.5% of respondents had incomes of up to 10 million rupiahs, 16.1% had incomes ranging from more than 10 to 20 million rupiahs, 10.3% had incomes of more than 20 to 30 million rupiahs, 14.9% had incomes of more than 30 to 40 million rupiahs, and retainers of 41.4% had incomes of more than 40 million rupiahs. And for their experience in playing golf, most of the respondents (48.3%) have played golf for more than 6 years, 28.7% have played for more than a year and up to 3 years, 16.1% have played for less Ars.
From the demographics of the respondents above, it is similar to the characteristics of golfers in Indonesia. Most of their players were men, and their ages are mostly in their upper 50s, and their jobs are mostly their employee or entrepreneur. As golf is their prestige sport and usually a sport for the upper class, there is no doubt that their response is also with their income mostly hovering above 40 million rupiah. Regarding the age of their respondents, almost half of their respondents have experience playing golf more than 6 years. Table 2 summarizes their response profiles.

Measurement model
This study uses the SPSS program. 26 to test the reliability and validity of the data. From their validity test, their question is an invalid question about the dimensions of behavior in Brand Experience (question no. 9). Therefore, this question will not be used in quantitative measurement. The questionnaire used in this study will not be tested for validity and reliability so that it can be used as a measurement instrument for future studies (Malhotra, 2010). According to the definition given by (Hair et al. (2010), validity is "degree whose measurement device can be measured reliably is the target variable." The research instrument of this research should be able to measure factors such as customer satisfaction with their brand and their level of trust in their brand. The reliability of the tist is determined by its correlation coefficient. Using a sample size of 87 ratio and a significance threshold of 5%, this correlation coefficient is derived from the correlation coefficient table (r table) for each of the 12 brand variable questions and 4 trust variable questions According to table 3, their correlation coefficient is larger than their r-table, indicating that the questions they used to collect data can be considered reliable. Reliability testing claims on the process of assessing the consistency, stability, and accuracy of measurement results obtained from measuring instruments or methods. It is an important aspect of measuring quality in research and is used to determine the points at which measurement instruments produce a consistent and reproducible measure over time and under different conditions. Reliability testing involves the use of various statistical methods or techniques to evaluate the consistency of measurement results. These methods of assessing the quality produced by their main instrument are consistent and stable results under different conditions or across various administrations. The aim of reliability testing is to detect those whose resolution can actually be related to the correct construct and is not fundamental to being measured, rather than measuring errors or inconsistencies.
Reliability testing is important in research because it ensures that the measurement tool it is used for is producing reliable and trustworthy results. If a measurement instrument is unreliable, the results obtained may be inconsistent, unstable, and not reflect the actual construction of the measurement. Reliability testing provides the reliability of consistency and stability of assessment of the accuracy of results, which are essential for making valid beliefs, drawing accurate conclusions, and ensuring the quality and rigor of research findings (Hair et al, 2010). Cronbach's alpha, also known as Cronbach's coefficient alpha or simply alpha, is a statistical measure used to assess the reliability of the internal consistency of a measurement instrument, such as a scale or questionnaire. It is one of the most commonly used methods for assessing the reliability of multi-item scales or questionnaires designed to measure a single construct or concept.
Cronbach's alpha is a coefficient that ranges from 0 to 1, with a high value indicating a high quality of internal consistency reliability. It is calculated on the basis of the correlation among their items in the measurement instrument, and it reflects the points at which their items correlate with one another and measures them with the same construct and indirectly. Cronbach's alpha is based on the idea that a measuring instrument with good internal consistency reliability must have items that are highly correlated with each other, meaning that they measure the same construct consistently. It is calculated as an overview of all possible split-half coefficients, when the measurement instrument is divided into two parts and the correlation between the parts is calculated. They are often responsible for reliability in testing reliability, including Cronbach's Alpha when the method has a value of 0.6 or above this value. Based on table 4 it is known that the value of Cronbach alpha on dependent and independent variables is greater than 0.6. Before them, it was concluded that their questions were asked by their reliable leader.

Hypothesis Testing
To determine the effect of Brand Experience on Brand Trust, their linear regression was used in this study. Thus, before they could test their regression, there were statistical requirements that had to be performed. The advantage of linear regression is that the data must be normally distributed or close to normal (Ghozali, 2009). Therefore, the theist normality must be carried out. In this research, the normal distribution theory is carried out by looking at the output of the P-Plot. The observed data shows that the distribution is close to normal, this can be seen from the line that describes the actual data, following the diagonal line.

Figure 1. Normality Test (P-Plot)
Also, the heteroscedasticity theory must be done before regression. From theist heteroscedasticity, we can see that they have no clear pattern between their points, nor do their points spread out and leave them 0 on their Y-axis. What shows them is not Heteroscedasticity in this regression model.

Figure 2. Heteroscedasticity Test
The linearity theory must be conductive because the data for the linear regression must have a linear correlation between X and Y, which can be seen from the ANOVA table in Figure 3. The linearity can be derived from the Significance Value and Fi Value i. Based on the significance value, the deviation from the linearity sig. is 0.410, which is more than 0.05. It means their relationship is a significant linear correlation between Brand Experience and Brand Trust. As from their F value, the calculated F is 1.059, which is smaller than their table F (1.81), their precedent, a significant linear correlation between Brand Experience and Brand Trust was found. Linearity testing is a type of statistical analysis used to evaluate their degree for which two or more variables are more or less linearly relevant. It is commonly used in regression analysis and other statistical methods that assume a linear relationship between variables, such as linear regression, analysis of variance (ANOVA), and correlation analysis.
According to the Simultaneous Test Analysis (F test), it is known that the calculated F is 193.517, with a significance value of <0.005. F table is 3.95, which shows that F count > F table. Thus, their H0 is rejected, which means that they are the influence from X to Y or from Brand Experience to Brand Trust. Simultaneous theist analysis, also known as theist F or the ANOVA (Analysis of Variance) F theist, is a statistical test used to compare the means of three or more groups simultaneously. Oneway analysis of variance, often known as ANOVA, is a standard instrument intended for use in research and data analysis. This is a parametric thesis used to compare averages across various conditions, attitudes, or groups.
F theory is based on the F statistic, which is calculated by comparing the variability between their group means (call it "within-group variability" or "within-group sum of squares") with the within-group variability (call it "within-group variability" or "within-group sum of squares"). The F statistic is obtained by dividing the total variance by the normally distributed within-group variance of each. From the results of their Deiteration Coefficient (R2), their R2 is 0.695, this shows that their endogenous constructs can be explained by their exogenous. And from the R2 adjustment it can be seen that Brand Experience (X) contributes or influences 69.1% to Brand Trust (Y). R-squared, or R2, is a statistical measure that reflects how much variation in the total internal variable can be explained by the independent variable (or variables) in a regression model. R2 is also known as iteration co-efficiency, which is another name for this metric. It is a measure of the goodness of their regression model and indicates how much their independent variable is a variation in the variable being modeled. The R squared may have any value between 0 and 1, where 0 indicates that the model does not account for any of the variations in the dependent variable and 1 indicates that model i explains all the variances in the dependent variable. R squared is a measure of how one model can explain another.
As for their regression model (figure 6), we found information for their constant and effective values to be included in their regression equation: Y = a + bX, where Y = 7.511 + 1.674 X. If they had no brand experience, the constant value of brand trust was 7.511. And if brand experience increases for one unit, brand trust will increase by 1,674 or 167.5%, the coefficient is positive, meaning they have a positive influence from Brand Experience on Brand Trust. According to the findings of the research described above, it was found that the level of consumer trust in the brand for new entry sport items was found. This shows that the level of brand experience customers have in relation to new entry sports items is directly related to the level of consumer trust in their brand related to their sports products. This is also in accordance with the findings of research conducted by (Akoglu & Özbek, 2022), who stated that the findings provide evidence supporting their hypothesis and highlight the importance of quality and trust in developing customer loyalty for businesses operating in the sports sector. They are the positive and direct influence that brand experience has on brand trust.
It has been revealed that they are significant managerial functions implemented by intelligent positions that address their relationship between consumer brand quality and service quality and brand trust. This shows that consumers' internal responsibilities (sensory, effective, behavioral, and intellectual) and consumer responsibilities are influenced by their brands, customers, and their brand environment that become marketer has influence is the perception of reliability from the consumer's point of view based on experience, or more on transaction orders or intentions characterized by them fulfilling the ex meaning of aspects of product performance and satisfaction (Brand Trust).
In a separate study conducted by (Hussain & Colleiagueis (2022), researchers came to the conclusion that brand experience and customer participation are the two most important factors driving purchases in India. It enables a business to stand out from its competitors. In terms of sporting goods, newcomers have to compete according to the needs of the clients, which is determined by their product objectives (Kim et al., 2020). encounter with new entry sports products that are memorable and favorite, they have confidence to recall their brand and have a strong level of trust in their brand. On the other hand, brand experience may be the essence explained by the nature of product quality, which influences the degree of relationship between customers and these brands to build brand trust. More innovative, the characteristic traits of their products may be able to explain why people trust certain brands more than others (Kim & Chao, 2019).