Volume 3, No. 4 April 2024 (0000-0000)

p-ISSN 2980-4868 | e-ISSN 2980-4841

https://ajesh.ph/index.php/gp

 

The Effect of E-WOM, Lifestyle on Purchasing Decisions with Brand Image as a Mediating Variable

(Case Study on Compass Official Shopee Shoes)

 

Hilyatul Aulia1*, Meisha Gia Tiara Praliana2, Yono Maulana3

1,2,3Universitas Swadaya Gunung Jati, Cirebon, West Java, Indonesia
Email: hilyatulaulia6262@gmail.com1*, yono.maulana@ugj.ac.id2

 

 

ABSTRACT

This research aims to explore how electronic word of mouth (E-WOM) and lifestyle impact buying choices, with brand image playing a role as a mediator. The study surveyed 140 individuals residing in Ciayumajakuning, selected using a purposive sampling method, and employed quantitative analysis. The target population comprised the Ciayumajakuning community who had purchased official Shopee compass shoes. The findings show that E-WOM and lifestyle significantly influence brand image and purchasing decisions. Additionally, brand image was identified as a mediator between E-WOM, lifestyle, and purchasing decisions. This investigation enhances comprehension of the factors influencing purchasing decisions regarding compass shoe products on Shopee's official platform, thus contributing significantly to understanding the interplay among E-WOM, lifestyle, brand image, and purchasing decisions in the context of compass shoes.

Top of Form

Keywords: E-WOM, Lifestyle, Brand Image, Purchasing Decision.

 

 

INTRODUCTION

At this time, technology has developed rapidly, one of which is in the field of marketing, starting with the existence of internet technology that markets goods and services that can be done online (Lestari & Widjanarko, 2023). One of the technological developments that Indonesia has experienced is in the field of e-commerce at this time compared to previous years (Putri & Iriani, 2019). So that this phenomenon can open opportunities for online business owners, one of which is in the e-commerce category Marketplace (Rahmawati et al., 2022). This news is reinforced by Similarweb data on website databoks.katadata.co.id, shopee site e-commerce category Marketplace the most visited in Indonesia in 2023. In December 2023, Shopee was recorded to receive 242.2 million site visits, 41.39% higher than at the beginning of last year. Then, the second place is Tokopedia with 101.1 million visits, followed by Lazada occupying the third position with 48.6 million visits, and in fourth place, there is Blibli with 35.8 million visits, while the fifth is Bukalapak, which received 8.7 million visits (Annur, 2024). 

The development that occurred resulted in changes in people's tastes. Hence, several trends and fashions emerged to meet the changing needs of society, and one of the businesses that experienced development was the fashion business (Putra & Mukaromah, 2023). So, the number of online business actors that exist today makes economic competition in Indonesia very tight, one of which is in the shoe business, which is marked by the increasing emotional needs of someone (Fadhillah & Hendra, 2023). Shoes are not only a foot protector but have become one of the fashion products, resulting in an increase in demand for shoes (Yusditara et al., 2022). One Brand of shoe that is in great demand by the community, especially among young people in Indonesia, is Brand Local shoes originating from the city of Bandung are Brand Compass shoes with design Classic vintage and Modern Pop, which has a checklist sideline as a characteristic of each product (Fedriana & Jumhur, 2022).

Purchasing decision-making on a consumer product requires a process in which a purchase decision is influenced by desire and the environment (Dewi et al., 2020) (Utami & Jeanne, 2021). Purchasing decisions are one of the main components of consumer behavior that lead to the purchase of a product or service (Ramli, 2020) (Rahmawati et al., 2022). For consumers, purchasing is not just one action (for example, because of the product) but consists of several actions that are one and interrelated (Kotler and Armstrong, 2012) (Priansa, 2017). The consumer will choose one of the alternative options that he thinks is in accordance with what he wants and arrive at a decision to buy the product (Utami & Jeanne, 2021). There are so many elements that drive consumers to make purchasing decisions (Curatman et al., 2023). Purchasing decisions, fundamental to consumer behavior, are influenced by various factors such as electronic word of mouth (E-WOM), lifestyle, and brand image. These elements play crucial roles in guiding consumers when making purchasing choices for products (Pratiwi et al., 2022).

Elements that influence purchases in e-commerce, especially Shopee, are Electronic word of mouth, where consumers will first find out the truth about a product or service they will buy; this can be seen through E-WOM. Electronic word of mouth, seen as an advancement from conventional in-person interactions, has embraced modernity through the utilization of cyberspace or electronic platforms within computer networks, extensively employed for online communication, whether unidirectional or interactive (Priansa, 2017). E-WOM can make it easier for consumers to influence other consumers positively in making purchasing decisions on a product or service (Rahmawati et al., 2022). So, the e-WOM provided by previous consumers related to products or services can influence potential consumers to make purchase intentions for these products or services (Yulindasari & Fikriyah, 2022). E-WOM is carried out through social media networks so that consumers can experience more effective and practical communication that can influence consumers positively in making purchasing decisions (Pristiwasa & Widodo, 2019) (Rahmawati et al., 2022). According to (Sari, 2019) his research, E-WOM (X1) positively and significantly affects the purchase decision. According to (Putra & Mukaromah 2023) research, it can be concluded that E-WOM has a positive but not significant effect on the Purchase Decision.

Another element that influences purchasing decisions is lifestyle (Kadeari & Heryanda, 2021). In this modern era, Lifestyle What happens to society cannot be separated from technology or the digital world because it can be done effectively and understandably so that consumers who previously made purchases with How to Face to Face Switch can purchase on a regular basis Online (Sari, 2019). Lifestyle also becomes valuable and often becomes a place to show one's identity (Rahmi et al., 2023). Lifestyle arises when there is encouragement from people, family, education, and the nature of work (Swatama & Warmika, 2022). (Fallo, 2018) (Pratyaharani et al., 2022) found that lifestyle has a real influence on consumers' purchasing decisions. Lifestyle captures the interaction of "a person as a whole" with his environment. A person's lifestyle has differences, where this lifestyle leads to consumption culture and the elements they often use, so marketers can take advantage of these differences to develop their marketing efficiency and effectiveness (Purwati et al., 2019). Lifestyle is relevant to the way a person lives, how he uses his money, and how he allocates his time (Basallama & Ariyanti, 2023). According to (Gin et al., n.d.) research, it can be concluded that lifestyle positively affects brand image. According to (Trihudiyatmanto et al., 2023) the study, it can be concluded that Ha was rejected and Ho was accepted, so it can be said that lifestyle has no effect on brand image.

Brand image is one of the targets of the product marketing strategy (Pratyaharani et al., 2022). Brand Image is the collective impression held by the public regarding the identity of a brand or company (Priansa, 2017). In the business world, companies must create brands that are different from competitors and can be remembered by consumers (Utami & Jeanne, 2021). Products that have Brand Image The goodwill influence consumers to buy the product (Hidayat, 2023). One way to get a brand image is to make a strong plan in advance in marketing the product so that it can create a product with unique characteristics and advantages over other products (Narayana & Rahanatha, 2020). According to (Pandiangan et al., 2021), the Brand image refers to how consumers perceive and believe in a brand, demonstrated through the associations stored in their memory, which are immediately recalled upon hearing slogans and deeply ingrained in consumer consciousness. According to (Putra & Mukaromah, 2023), Based on their study, it can be inferred that brand image exerts a favorable and noteworthy influence on purchase choices. Then, according to (Yuriananda & Mahargiono, 2023) In his research, it can also be concluded that brand image has a positive and significant effect on purchasing decisions.

This study aims to determine the influence of E-WOM and lifestyle on purchasing decisions with the brand image as a mediating variable (Case Study on Shopee Official Compass Shoes).

 

RESEARCH METHODS

This research focuses on e-WOM, Lifestyle, Brand Image, and purchasing decisions, employing quantitative research methods. Primary data was collected through online questionnaires distributed via Google Forms, utilizing the Likert scale to gauge attitudes, opinions, and perceptions. The scale ranges from 1 (Strongly Disagree) to 5 (Strongly Agree), with intermediate points representing varying degrees of agreement (Sugiyono, 2012). Sampling methods like Non-Probability Sampling entail selecting elements from a population without ensuring equal chances for each element to be included, often accomplished through purposive sampling.

Purposive sampling is a sampling technique that requires certain considerations (Sugiyono, 2012). Certain considerations in this study are criteria that have been set. The population in this study is the Ciayumajakuning community, which has purchased official Shopee compass shoes, but the population is unknown. The sampling technique in this study was determined by the requirements (Hair JR et al., 2009), which state then multiplied by the number of indicators used in the study. This study used 20 indicators, so the number of samples used was 7 x 20 = 140 respondents. Hair JR et al., 2009) also mentioned that the number of representative samples to use SEM analysis techniques is 100-200.

 

RESULTS AND DISCUSSION

This study uses the Partial Least Square (PLS) data analysis technique with the SmartPLS 4.1.0.1 program The following is the PLS program model scheme tested:

 

Figure 1. Outer Model

Source: Primary Analysis Data, 2024

 

Outer model testing is employed to establish the characteristics of the connection between underlying variables and indicators. This examination encompasses validity, reliability, and multicollinearity. Following that is the evaluation of convergent validity. Convergent validity implies that the items in a particular construct should converge or display a substantial amount of shared variance (Ghozali, 2016). According to (Hussein, 2015) Convergent validity is expected to be > 0.70. The following is the outer loading value of each indicator on the research variable.

 

Table 1. Outer Loading Value

Variable

Indicators

Outer Loading

 

 

 

E-WOM (X1)

X1.1

0,718

X1.2

0,731

X1.3

0,791

X1.4

0,711

X1.5

0,728

X1.6

0,736

X1.7

0,814

X1.8

0,781

 

Lifestyle (X2)

X2.1

0,793

X2.2

0,874

X2.3

0,813

X2.4

0,832

 

 

 

Brand Image (Z)

Z.1

0,841

Z.2

0,850

Z.3

0,810

Z.4

0,843

Z.5

0,755

Z.6

0,799

Z.7

0,786

 

 

 

Purchase Decision (Y)

Y1.1

0,811

Y1.2

0,777

Y1.3

0,830

Y1.4

0,850

Y1.5

0,856

Y1.6

0,868

Y1.7

0,704

 

The provided table displays each indicator for numerous research variables, all of which possess an outer loading value exceeding 0.70. The data indicates that there are no indicators or variables with an outer loading value below 0.70, thus affirming the suitability and validity of all indicators for research purposes. Consequently, these indicators can be utilized for subsequent analysis.

Discrimination Validity Test

Next, assess the discriminant validity. The expected AVE value is > 0.5 (Hussein, 2015). So, it can be valid in terms of discriminant validity. Here are the AVE values of each research variable:

 

Table 2. AVE (Average Variance Extracted) Value

Variable

AVE (Average Variance Extracted)

Information

E-WOM (X1)

0,566

Valid

Lifestyle (X2)

0,687

Valid

Brand Image (Z)

0,660

Valid

Purchase Decision (Y)

0,665

Valid

Source: Primary Analysis Data, 2024

 

The provided table indicates that all variables examined in this study possess an AVE (Average Variance Extracted) value exceeding 0.50. Each variable in the study exhibits distinct values for E-WOM (Electronic Word-of-Mouth) at 0.566, lifestyle at 0.687, brand image at 0.660, and purchase decision at 0.665. These figures demonstrate that each variable in the study can be considered valid in terms of discriminant validity.

Reliability Test

Moreover, reliability examinations assess the dependability and consistency of measurement instruments or research tools in capturing a concept or construct. In this study, reliability testing was conducted using Composite Reliability and Cronbach's Alpha. A variable is considered to meet the criteria for composite reliability if it obtains a value exceeding 0.70. Provided below are the composite reliability values for each variable examined in this study:

Table 3. Composite Reliability

Variable

Composite Reliability

E-WOM

0,912

Lifestyle

0,898

Brand Image

0,931

Purchasing Decision

0,933

Source: Primary Analysis Data, 2024

 

Based on the data provided in the table, it is evident that the composite reliability values for all the research variables exceed 0.70. Specifically, the E-WOM variable has a value of 0.912, lifestyle 0.898, brand image 0.931, and purchase decision 0.933. These results indicate that each variable demonstrates satisfactory composite reliability, leading to the conclusion that all variables exhibit a high degree of reliability.

 

Table 4. Cronbachs Alpha

Variable

Crombachs Alpha

E-WOM

0,890

Lifestyle

0,848

Brand Image

0,914

Purchasing Decision

0,915

Source: Primary Analysis Data, 2024

 

According to Nunnaly, 1994 in (Ghozali, 2016) provides a benchmark of 0.70 for the internal consistency scale. The Cronbach's alpha coefficient displayed in the table indicates that all variables in this study possess a Cronbach alpha exceeding 0.70, indicating their reliability. Additionally, multicollinearity was assessed through tolerance and variance inflation factor (VIF) values, with values above 0.10 for tolerance or below 5 for VIF indicating absence of multicollinearity. The following presents the VIF values obtained in this study.

Table 5. Colinearity Statistic (VIF)

Variable

Brand Image

Purchasing Decision

E-WOM

1,609

2,314

Lifestyle

1,609

2,528

Brand Image

 

3,615

Purchasing Decision

 

 

Source: Primary Analysis Data, 2024

 

The table reveals Collinearity Statistics (VIF) results indicating multicollinearity testing. Specifically, the VIF for the E-WOM variable reflects values of 1,609 for brand image and 2,314 for purchase decisions. Then, the value of the lifestyle variable on brand image was 1,609, and on purchasing decisions was 2,528. Furthermore, the brand image value of the purchase decision was 3,615. Each variable has a cut-off value of > 0.1 or equal to the value of VIF < 5, so it does not violate the multicollinearity test.

Inner Model Test

Table 6. R-Square

Variable

R-Square

Brand Image

0,723

Purchasing Decision

0,731

 

Table 6 gives a value for brand image of 0.723 which means that E-WOM and lifestyle variables influence brand image by 72.3%. Then, purchasing decisions have an R-Square value of 0.731, which means the variables E-WOM, lifestyle, and brand image influence purchasing decisions by 73.1%.

Test the hypothesis

The subsequent step in hypothesis testing involves examining the hypothesis in this research by utilizing the table of path coefficient values to assess direct influence, as well as determining the specific indirect effect of indirect influence (mediation).

Bootstrap Testing Methods

Afterward, employ the bootstrapping method to examine the path coefficient, thereby determining the t statistics or p values (critical ratio) and the original sample value derived from the procedure. A p-value below 0.05 implies a direct impact between variables, while a p-value above 0.05 suggests no direct influence between variables. For this investigation, the chosen significance level was a t-statistic of 1.96 (significance level = 5%). If the t-statistic value exceeds 1.96, it indicates a substantial effect. Presented below are the test results' path coefficient values.

 

Table 7. Path Coefficient (Direct Effect)

 

Hypothesis

Original

Sample

T-statistics

P Value

Information

E-WOM (X1) -> Purchase Decision (Y)

H1

0,219

2,270

0,023

Significant Positive

Lifestyle (X2) -> Purchase Decision (Y)

H2

0,376

4,997

0,000

Significant Positive

E-WOM (X1) -> Brand Image (Z)

H3

0,442

5,628

0,000

Significant Positive

Lifestyle (X2) -> Brand Image (Z)

H4

0,504

7,366

0,000

Significant Positive

Brand Image (Z) -> Purchase Decision (Y)

H5

0,349

3,113

0,002

Significant Positive

Source: Primary Analysis Data, 2024

 

Based on the table above, the interpretation is as follows:

1.    The first hypothesis (H1) is accepted, namely from the table above showing a t-statistic value of 2.270 with a large influence of 0.219 and a p-value of 0.023, with a t-statistic value of > 1.96 and a p-value of < 0.05, it can be concluded that the first hypothesis is accepted where there is a positive and significant influence between E-WOM on purchasing decisions.

2.    The second hypothesis (H2) is accepted, namely from the table above showing a t-statistic value of 4.997 with a large influence of 0.376 and a p-value of 0.000 with a t-statistic value of > 1.96 and a p-value of < 0.05, it can be concluded that hypothesis two is accepted where there is a positive and significant influence between lifestyle on purchasing decisions.

3.    The third hypothesis (H3) is accepted, namely from the table above shows a t-statistic value of 5.628 with an influence of 0.442 and a p-value of 0.000. With a t-statistic value of > 1.96 and a p-value of < 0.05, it can be concluded that the third hypothesis is accepted where there is a positive and significant influence between E-WOM and brand image.

4.    The fourth hypothesis (H4) is accepted, namely from the table above showing a t-statistic value of 7.366 with an influence of 0.504 and a p-value of 0.000. With a t-statistic value of > 1.96 and a p-value of < 0.05, it can be concluded that the fourth hypothesis is accepted where there is a positive and significant influence between lifestyle and brand image.

5.    The fifth hypothesis (H5) is accepted, namely from the table above shows a t-statistic value of 3.113 with an influence of 0.349 and a p-value of 0.002. With a t-statistic value of > 1.96 and a p-value of < 0.05, it can be concluded that the fifth hypothesis is accepted where there is a positive and significant influence between brand image and purchasing decisions.

Sobel Method Testing

The subsequent phase involves testing for indirect effects, as evidenced by the outcomes of the particular indirect effect analysis. If the p-value is less than 0.005, it indicates significance. This signifies that the intermediary variable facilitates the influence of an exogenous variable on an endogenous variable, implying an indirect influence. Conversely, if the p-value exceeds 0.05, it indicates insignificance. This suggests that the intermediary variable does not facilitate the influence of an exogenous variable on an endogenous variable, indicating a direct influence (Juliandi, 2018). Presented below are the specific values for indirect effects:

 

Table 8. Indirect Effect Test

Indirect Effect

Hypothesis

Original Sample

T-statistics

P value

Information

E-WOM (X1) -> Brand Image (Z) -> Purchase Decision (Y)

H6

0,154

2,714

0,007

Significant Positive

Lifestyle (X2) -> Brand Image (Z) -> Purchase Decision (Y)

H7

0,176

2,685

0,007

Significant Positive

Source: Primary Analysis Data, 2024

Based on the table above, the following results can be obtained:

1.     The sixth hypothesis examines the relationship between E-WOM and purchasing decisions through brand image mediating variables. The table above shows that the t-statistic value is 2.714 and the p value is 0.007. With a t-statistic value of > 1.96 and a p-value of < 0.05. So, it can be concluded that the sixth hypothesis is accepted because E-WOM has a positive and significant effect on purchasing decisions through brand image mediation variables.

2.     The seventh hypothesis examines the relationship between lifestyle and purchasing decisions through brand image mediating variables. The table above shows that the t-statistic value is 2.685 and the p value is 0.007. With a t-statistic value of > 1.96 and a p-value of < 0.05. So, it can be concluded that the seventh hypothesis is accepted because lifestyle positively and significantly affects purchasing decisions through brand image mediation variables.

 

Table 9. Mediation Testing with Variance Accounted For (VAF) Method

Indirect Effect

 

1.     E-WOM (X1) -> Brand Image (Z) -> Purchase Decision (Y)

0,154

2.     Lifestyle (X2) -> Brand Image (Z) -> Purchase Decision (Y)

0,176

Direct Influence

 

1.     E-WOM (X1) -> Purchase Decision (Y)

0,219

2.     Lifestyle (X2) -> Purchase Decision (Y)

0,376

3.     E-WOM (X1) -> Brand Image (Z)

0,442

4.     Lifestyle (X2) -> Brand Image (Z)

0,504

5.     Brand Image (Z) -> Purchase Decision (Y)

0,349

Total Influence

 

1.     E-WOM (X1) -> Brand Image (Z) -> Purchase Decision (Y) + E-WOM (X1) -> Purchase Decision (Y)

0,373

2.     Lifestyle (X2) -> Brand Image (Z) -> Purchase Decision (Y) + Lifestyle (X2) -> Purchase Decision (Y)

0,552

VAF = Indirect Influence / Total Influence

 

1.     VAF E-WOM (X1)

0,412

2.     VAF Lifestyle (X2)

0,318

Source: Primary Analysis Data, 2024

 

Based on the calculation of Variance Accounted For (VAF) above with the aim of testing the influence of brand image as a mediation variable. The table above shows that the influence of E-WOM on purchasing decisions with brand image as a mediating variable is 0,412 or 41,2%. Then, the influence of lifestyle on purchasing decisions with brand image as a mediating variable of 0,318 or 31,8%. So from these results, it can be concluded that the influence of brand image as a mediating variable on the e-wom variable on purchasing decisions and lifestyle variables on purchasing decisions has a partial mediation effect according to Hair et al., 2013 (Solihin & Ratmono, 2013).

 

CONCLUSION

This study discovered that while Electronic Word of Mouth (E-WOM) shows a positive and significant, as evidenced by t-statistical values of 2.270, exceeding 1.96, and p-values of 0.023, less than 0.05. Conversely, lifestyle was found to significantly and positively affect purchasing decisions, with t-statistic values of 4.997, surpassing 1.96, and p-values of 0.000, below 0.05. Similarly, E-WOM and lifestyle both significantly and positively impacted brand image, with t-statistic values of 5.628 and 7.366, respectively, and p-values of 0.000, less than 0.05. Moreover, brand image was identified to positively and significantly influence purchasing decisions, with t-statistic values of 3.113 and p-values of 0.002, below 0.05. Additionally, E-WOM and lifestyle were found to positively and significantly influence purchasing decisions through the mediation of brand image variables, as indicated by t-statistic values of 2.714 and 2.685, both exceeding 1.96, and p-values of 0.007, below 0.05.

 

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Copyright holder:

Hilyatul Aulia, Meisha Gia Tiara Praliana, Yono Maulana (2024)

 

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Asian Journal of Engineering, Social and Health (AJESH)

 

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