Keywords

Social Media Marketing, Online Customer Review, Brand Awareness, Purchase Decision

Introduction

The purchase decision is a process of evaluating a product that consumers carry out as a material for consideration before buying a product or service. Researchers conducted by Liana & Oktafani (2020); Mustafa & Al-Abdallah (2020) found that purchasing decisions were processes where consumers assessed various choices from one or more of the alternatives needed based on specific considerations. Unlike previous studies, this study discusses purchasing decisions as measured by media marketing, online customer reviews, and brand awareness.

Concise and clear information is indispensable for consumers because it can help consumers consider choosing the company's products (Ahmed et al., 2017). Decision making occurs when the consumer is faced with several choices, and he has to select the most appropriate one to meet his wants and needs.

In short, a purchasing decision is a process used to select action as a way to solve a problem (Pratama, 2020; Rachmawati et al., 2019). To market the products offered, the company can do several ways to convey information about the products offered to consumers. One thing that can be done is to take advantage of increasingly developing technology by utilizing social media. At this time, in people's daily activities, it is inseparable from social media.

Marketing carried out with social media or what is often referred to as social media marketing is delivering information about the products offered by the company to consumers using internet-based applications, such as Facebook, Instagram, Tiktok, Twitter, etc.

According to Vinerean (2017); Knoblich, et al., (2017); Ali, et al., (2016),social media marketing is defined as a facilitator of connectivity and interaction with customers or prospective customers in meeting business goals related to consumer equity, purchase intention, satisfaction, and loyalty. Social media are fundamentally changing the way we communicate, collaborate, consume, and create. Social media affect various business processes, ranging from marketing, operations, finance, and human resource management.

In the context of marketing, social media are seen as fundamentally different from other forms of digital media and have the potential to herald a paradigm shift in marketing (Keegan & Rowley, 2017). Another thing that consumers can consider when choosing to buy a product is online customer reviews. Online customer reviews are review comments given by other consumers who have used and felt the benefits of a company's products or services sent directly to the company's social media.

According to Shoja & Tabrizi (2019); Li, et al., (2019), online customer reviews are among the most important resources in developing a recommendation system. The written section of the rating review includes important information about what customers think about the product. It will be very easy for consumers to get important information about a product with online customer reviews.

The rapid growth of virtual communities has introduced a new type of e-Wom, namely online customer reviews. Online customer reviews are defined as product evaluations produced by colleagues that facilitate the consumer purchasing decision process (Erkan & Elwelda, 2018). Brand awareness is a brand that appears in the minds of consumers and thoughts about certain product categories that have convenience when the brand is raised (Novansa & Ali, 2017). According to Shabbir, et al., (2017); Ahmed, et al., (2017), brand awareness is defined as the first and basic attribute of the customer's brand. Brand awareness leads to the construction of brand equity in the mindset that affects consumer perceptions and attitudes.

Literature Reviews

The purchase decision is a product introduction process carried out by consumers with various choices before consumers decide to buy products that suit the wishes and needs of these consumers. Purchasing decisions are the process of identifying problems, seeking information, evaluating and selecting alternative products, establishing distribution channels, and implementing decisions on products to be used or purchased by consumers (Tamara et al., 2021; Suharto et al., 2019). Individual decision making has a significant influence, directly or indirectly, followed by external variables with personal habits that can affect the decision-making process. Companies must understand how consumers will make purchase decisions, adjust the mix in the value chain, and operate and build their competitive brands through their retail operations or outlets (Widyastuti & Said, 2017; Muslichah et al., 2019; Rayi & Aras, 2021).

Social media marketing refers to any form of direct or indirect marketing using social media to build awareness, recognition, memory, and action for brands, businesses, products, people, or other entities. Unlike personal users, businesses using social media for advertising and marketing report that social media offer advantages such as strengthening business-consumer relationships, fostering timely relationships, and building long-term relationships at low costs (Chiang et al., 2019; kumar & Lakshmi, 2012; Bilgin, 2018).

Furthermore, Ismail (2017) said that social media marketing was a broad category of advertising expenditure, including advertising using social networks, virtual worlds, user-generated product reviews, support for bloggers and social news sites, podcasts, games, and consumer-generated advertising. Social media in some way turn consumers into marketers and advertisers who generate and share online information about companies, products, and services that can attract attention and encourage online users to share it with their social networks.

Online customer reviews are defined as product or service evaluations made by customers on third-party websites, which directly affect the company's image. Information received by consumers has become a strategic factor in corporate communications as it shapes public evaluations of the perceived quality and value of services (Rodriguez-Diaz et al., 2018; Zhou et al., 2018). Online customer reviews, according to Kim, et al., (2018); Guo, et al., (2018), can be described in terms of quantitative and qualitative features. The quantitative aspect is the review which is often expressed as a numerical summary such as the average star rating and the number of reviews. Qualitative aspects are often displayed above or beside the product description, which the customer will read without reading the textual part of the review.

Brand awareness is the level of brand knowledge that involves identifying the brand name or structure that has been developed on detailed information. Brand awareness is fundamental and the main limitation in brand-related searches and consumers' ability to recognize and remember brands in different situations (Shahid et al., 2017; Bilgin, 2018). According to Switala, et al., (2018), brand awareness is the capacity of certain customers to recognize or remember that a given brand belongs to a certain product category. Brand awareness is very strongly related to the strength of the presence and imprint of the brand in the minds of customers resulting in their capacity to recognize the brand in various market conditions.

Based on the description of the literature above, the following is an explanation of the hypotheses one by one:

Social Media Marketing and Purchase Decisions

Social media marketing refers to the process of getting website traffic or attention through social media sites. Social media marketing programs are usually centered on making the content interesting and encouraging readers to share it with their social networks (Bajpai et al., 2012). Purchase decisions are consumer actions to form preferences between brands in the choice group and buy which brand is preferred. The decision to buy a product made by consumers does not only occur but requires a processor stage (Ayuningsih & Maftukhah, 2020; Dapas et al., 2019).

H1: There is a direct positive effect of social media marketing on purchase decisions.

Online Customer Reviews and Purchase Decisions

Online customer reviews are recognized as the most accessible and valuable feedback platform in the business environment because previous customers tend to list their experiences, which potential customers review before purchasing a product (Kim et al., 2016; Clare et al., 2018). Purchasing decisions are why consumers determine the choice of purchasing a product according to their needs, desires, and expectations to lead to satisfaction or dissatisfaction with the product (Harahap & Amanah, 2020).

H2: There is a direct positive effect of online customer reviews on purchase decisions.

Brand Awareness and Purchase Decisions

Brand awareness is the ability of customers to remember or identify a brand. One of the important aspects that help customers remember and choose a brand is their direct experience (El Naggar & Bendary, 2017). Purchase decisions are taken through a consumer decision process to buy products/services offered through a purchase decision process which includes consumers before making a purchase decision and at the time of making a purchase. The purpose of every marketing of a product is to satisfy the needs and desires of consumers or target consumers. Therefore, consumers have an important meaning in a company, namely product buyers (Sembiring, 2020; Shajrawi, 2020).

H3: There is a direct positive effect of brand awareness on purchase decisions.

Social Media Marketing and Brand Awareness

Social media marketing is understood as the use of social media to facilitate exchanges between consumers and companies. For marketers, social media are valuable tools for creating stakeholder value as they provide an economical way to reach, interact, and engage with different consumers both internally and externally and with clients at various points in the buying process (Silva et al., 2020; Alves et al., 2016). Brand awareness is to choose a brand from among product categories given to consumers, so that they can remember it (Dülek & Saydan, 2019).

H4: There is a direct positive effect of social media marketing on brand awareness.

Online Customer Reviews and Brand Awareness

Online customer reviews are text written in natural language. These reviews provide relatively reliable information, continuously updated customer feedback that engages the emotions and sincere opinions of the customer. Consumers always consult the opinions and experiences of others through online customer reviews (Li et al., 2019; Phillips et al., 2017). Brand awareness is defined as a consumer's ability to identify and remember a brand in various situations, and it also plays an important role in a consumer’spurchasedecisions. Brand awareness has contributed to brand equity for experienced customers but not to the same extent (Hoang et al., 2020).

H5: There is a direct positive effect of online customer reviews on brand awareness.

Research Method

This study uses a quantitative descriptive approach using a survey method. The study was conducted in a shopping center on cellular products in the province of Lampung and used a non-probability method of 180 respondents. The response rate is done using a closed design and instrument and sent using email. It is hoped that this design can increase the response rate. In addition, the authors took several steps, namely preparation and finalization, and calculating whether the respondents were interested in being involved in the research and could fill out the answers to the questionnaires sent. After sending via email, of the 245 questionnaires sent, the authors obtained192 (78.3%) respondents, but the ones who could be processed were180 respondents (73.4%) of all respondents.

Research Results

Requirements Analysis

Normality Test

The normality test is one part of the data analysis requirements test, which aims to determine whether the data are normally distributed or not. Based on the calculation of the normality test using SPSS, the following results were obtained:

Table 1 Normality Test Results
Variable	α value	Sig. value	Conclusion
ξ1	0.05	0.140	Normal
ξ2	0.05	0.050	Normal
η1	0.05	0.071	Normal
η2	0.05	0.272	Normal

Table 1 shows that all variables have a sig. value greater than 0.05, so that it can be concluded that all variables are normally distributed.

Homogeneity Test

A homogeneity test is used to test whether the variable data are homogeneous or heterogeneous in a population.

Table 2 Homogeneity Test Results
Variable	α value	Sig. value	Conclusion
η2 over ξ1	0.05	0.058	Homogeneous
η2 over ξ2	0.05	0.104	Homogeneous
η2 over η1	0.05	0.094	Homogeneous
η1 over ξ1	0.05	0.256	Homogeneous
η1 over ξ2	0.05	0.330	Homogeneous

Table 2 shows that η2 over ξ1, η2 over ξ2, η2 over η 1, η1 over ξ1, and η1 over ξ2 have sig. values greater than 0.05, so that it can be stated that all the data come from homogeneous variance.

Tests for Linearity and Significance of Regression

Linearity and regression tests have the aim of knowing the relationship between variables, with the requirement that each variable forms a significant linear and regression line or not.

Table 3 Linearity and Regression Test Results
Variable	Sig. Regression		Regression Significance	Lin. Regression		Regression linearity
	fvalue	ftable		tvalue	ttable
η2 over ξ1	1.11	2.65	Significant	4.54	1.65	Linear
η2 over ξ2	1.62	2.65	Significant	5.07	1.65	Linear
η2 over η1	1.43	2.65	Significant	4.05	1.65	Linear
η1 over ξ1	2.11	2.65	Significant	6.72	1.65	Linear
η1 over ξ2	1.44	2.65	Significant	6.26	1.65	Linear

The criteria for linearity and regression testing in this study are significant regressions having f value<f table, so that it can be assumed that the data are meaningful or significant. As for the regression linearity criteria, if it has a value of t value> t table, then it can be concluded that the data have a linear relationship.

Construct Reliability and Variance Extracted Tests

This calculation was carried out to determine the construct's ability to measure exogenous (ξ) and endogenous (η) latent variables as follows:

Table 4 Construct Reliability and Variance Extracted Tests Results
Constructs	Indicator	Std. Loading	Std.Loading²	Error	CR	AVE	Conclusion
ξ1	X1	0,81	0,66	0,34	0,70	0,90	Consistent
	X2	0,90	0,81	0,20
	X3	0,90	0,81	0,18
ξ2	X4	0,67	0,56	0,23	0,72	0,87	Consistent
	X5	0.68	0.54	0.14
	X6	0.70	0.52	0.39
	X7	0.43	0.81	0.19
η1	Y1	0.80	0.37	0.36	0.61	0.88	Consistent
	Y2	0.90	0.10	0.11
	Y3	0.60	0.64	0.24
η2	Y4	0.87	0.25	0.23	0.55	0.99	Consistent
	Y5	0.89	0.22	0.14
	Y6	0.74	0.45	0.39
	Y7	0.88	0.23	0.19

 

Based on the summary of the calculation results in table 4, it shows: construct reliability value of ξ1 is 0.70, which is smaller than 0.70 (CR<0.70) and the variance extracted value is 0.90, which is greater than 0.50 (VE>0.50). This means that the four manifest constructs have consistency in measuring the latent variable ξ1.The construct reliability value ξ2 is 0.72, which is greater than 0.70 (CR>0.70) and the variance extracted value is 0.87, which is greater than 0.50 (VE>0.50). This means that the four manifest constructs have consistency in measuring the latent variable ξ2. The construct reliability value ofη1 is 0.61, which is greater than 0.70 (CR>0.70) and the variance extracted value is 0.88, which is greater than 0.50 (VE>0.50). This means that the three manifest constructs have consistency in measuring the latent variable η1. The construct reliability value ofη2 is 0.55, which is smaller than 0.70 (CR<0.70) and the variance extracted value is 0.99,which is greater than 0.50 (VE>0.50). This means that the four manifest constructs have consistency in measuring the latent variable η2.

T-value Coefficient Calculation Results

After testing the requirements analysis, the next step is to calculate and test each path coefficient as presented in the following table:

Table 5Path Coefficient Results
No.	Variable	Path Coefficient		Result	Conclusion
		SLF*	tvalue
1	η2 over ξ1	0.26	2.77	H0 rejected	Significant
2	η2 over ξ2	0.51	3.94	H0 rejected	Significant
3	η2 over η1	-0.02	-1.14	H0 accepted	Not Significant
4	η1 over ξ1	0.34	3.90	H0 rejected	Significant
5	η1 over ξ2	0.60	5.51	H0 rejected	Significant

Path Coefficient in Sub-Structure 1

The path coefficient analysis model found, namely sub-structure 1, is expressed in the form of the equation η1=γ11ξ1+γ12ξ2+ζ1. This test will provide decision making for testing hypotheses 1 and 2.

Based on the results of testing sub-structure 1, the path coefficient of γη1ξ1 is 0.34 and the t value=3.90>t table (0.05: 180)=1.65, then Ho is rejected and the path coefficient of γη1ξ1 is significant. The path coefficient of γη1ξ2 is 0.49 and t value=5.51>t table (0.05: 180)=1.65, then Ho is rejected and the path coefficient of γη1ξ2 is significant.

Path Coefficient in Sub-Structure 2

The path coefficient analysis model found, namely sub-structure 1, is expressed in the form of the equation η2=γ21ξ1+ γ22ξ2+β21η1+ζ2. This test will provide decision making for testing hypotheses 3, 4, and 5.

Based on the results of testing sub-structure 2, the path coefficient of γη2ξ1is 0.26 and the t value=3.90>t table (0.05: 180)=1.65, then Ho is rejected and the path coefficient of γη2ξ1 is significant. The path coefficient of γη2ξ2 is 0.51 and the t value=3.94>t table (0.05: 180)=1.65, then Ho is rejected and the path coefficient of γη2ξ2 is significant. The path coefficient of βη2η1is -0.02 and the t value=-1.14<t table (0.05: 180)=1.65, then Ho is accepted and the path coefficient of βη2η1 is not significant.

The calculation of the path coefficient and t-value for hypothesis testing purposes shows that all path coefficients' standardized loading factor values are more significant than 0.05, and the t values is 1.65, so Ho is rejected and significant. Overall, the standardized solution diagram for each variable through the linear program structural relationship is described as follows:

Based on Figure 3, the standardized solution path diagram, in addition to the direct effect, there is a total and indirect effect between the exogenous variable (ξ) and the endogenous variable (η). Based on the output of the linear structural relationship regarding the total standardized effect, it shows that: (1) the effect value of variables ξ1, ξ2, and η1to η2 is the same as the direct effect value of each of these variables because it is not mediated by other variables (intervening variables), (2) the value of the influence (total effect) of variables ξ1 and ξ2 to η1 is also the same as the value of the direct effect of each of these variables because it is not mediated by other variables (intervening variables), (3) indirect effect of variable ξ1 to η2 is 0.59 x 0.16=0.094 because there is another variable (intervening variable), namely η1 of 0.51, while the total effect is 0.51+0.094=0.604, and (4) indirect effect of variable ξ2 to η2 is 0.14 x 0.16=0.022 because there is another variable (intervening variable), namely η1 of 0.23, while the total effect is 0.23+0.22=0.252.

Overall Model Fit Test

Based on the results of the SEM test with LISREL, the results of the goodness of fit test in Structural Equation Modeling (SEM) can be seen in the following table:

Table 6 Summary of Goodness of Fit Test Results
No.	Index	Results	Recommended Value	Conclusion
1.	Probability X2	0.0021	<0.05	Marginal Fit
2.	X2/df	1.74	<5	Good fit
3.	RMSEA	0.063	<0.08	Good fit
4.	AGFI	0.87	<0.90	Marginal fit
5.	GFI	0.91	>0.90	Good fit
6.	CFI	0.98	>0.90	Good fit
7.	NFI	0.96	>0.90	Good fit
8.	NNFI	0.98	>0.90	Good fit
9.	IFI	0.98	>0.90	Good fit
10.	RFI	0.95	≥0.90	Good fit
11.	ECVI	1.06	<5	Good fit

Based on the results from Lisrel's output, the overall fittest of the model uses the χ2 test (chi-square) obtained from the Weighted Least Squares chi-square value of 165.39 with p-value of 0.0000<0.05, so that it can be concluded that the overall χ2 test results are not suitable. In addition, the comparison of the value of χ2 with degree of freedom (χ2/df) is 123.96/71=1.74<0.05. So, it can be concluded that by controlling for the complexity of the model (which is proxied by the number of freedom pressures),the model has a reasonably good fit.

The next test is that the RMSEA shows that it is smaller than 0.08, so that it can be concluded that the model has a good fit. Furthermore, AGFI shows test results of less than 0.90, so that it can be concluded that the model has a poor fit, while GFI, CFI, NFI, NNFI, IFI, RFI, and ECVI show test results of more than 0.90, so that it can be concluded that the model has a good match.

Positive Direct Effect of Variable ξ1 on Variable η2

Hypothesis 1 has a positive direct effect of ξ1 (social media marketing) on η2 (purchase decision). This study indicates a direct positive effect of variable ξ1 on variable η2 with a value of t value>t table, which is 2.77>1.65, so that it can be concluded that hypothesis 1 is accepted. The findings of this study are relevant to the conclusions put forward by Alalwan, et al., (2017).

Positive Direct Effect of Variable ξ2 on Variable η2

Hypothesis 2 has a positive direct effect of ξ2 (online customer reviews) on η2 (purchase decision). This study indicates a direct positive effect of variable ξ2 on variableη2 with a value of t value> t table, which is 3.94>1.65, so that it can be concluded that hypothesis 2 is accepted. The findings of this study are relevant to the conclusions put forward by Manner (2017).

Positive Direct Effect of Variable η1 on Variable η2

In hypothesis 3, there is no direct positive effect of η1 (brand awareness) on η2 (purchase decision). This study indicates that there is no direct positive effect of variable η1 on variable η2 with a value of t value<t table, which is -1.14<1.65, so that it can be concluded that hypothesis 3 is rejected. The findings of this study are relevant to the conclusions put forward by Ilyas, et al., (2020).

Positive Direct Effect of Variable ξ1 on Variable η1

Hypothesis 4 has a positive direct effect of ξ1 (social media marketing) on η1 (brand awareness). This study indicates a direct positive effect of variable ξ1 on variable η1 with a value of tvalue>ttable,which is 3.90>1.65, so that it can be concluded that hypothesis 4 is accepted. The findings of this study are relevant to the conclusions put forward by Vinerean, et al., (2013).

Positive Direct Effect of Variable ξ2 on Variable η1

Hypothesis 5 has a positive direct effect of ξ2 (online customer reviews) on η1 (brand awareness). This study indicates a direct positive effect of variableξ2 on variable η1 with a value of t value>t table, which is 5.51>1.65, so that it can be concluded that hypothesis 5 is accepted. The findings of this study are relevant to the conclusions put forward by Ofosu-Boateng & Agyei (2020).

Conclusions and Suggestions

Based on the study results, it can be concluded that social media marketing has a positive direct effect on purchase decisions. Online customer reviews have a positive direct effect on purchase decisions. Brand awareness has a positive direct effect on purchase decisions. Social media marketing has a positive direct effect on brand awareness. Online customer reviews have a positive direct effect on brand awareness.

This means that with today's rapid technological developments, people cannot be separated from the use of social media, which companies can utilize as a facilitator of connectivity and interaction with customers by offering company products. By using social media as marketing media, it will be easier for consumers to find information and find alternatives before deciding to buy the needed products in accordance with their expectations.

In addition, consumers in determining purchasing choices and seeking information about products can see from the reviews of consumers who have used these products that are available on the company's social media pages. Social media as promotional media will significantly facilitate the company to market the products offered and help the customers remember or identify a brand of the product quickly.

New consumers will use reviews that are written opinions and experiences of others written by online customers as references. If online customers provide positive and trustworthy reviews, it will increase the level of consumer knowledge of brands that have been identified and developed on detailed information.

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