Electronic Word-of-Mouth Analysis and its Impact on Purchase Decisions: Studies on “Millennial and Z” Generation



Abstract Views
2022

Downloads
488

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Oct 28, 2023
Published Dec 23, 2023
10.24018/ejbmr.2023.8.6.2203

Abstract viewed = 2022 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

The consumer purchase decision is a significant factor for a company as it concerns revenue and long-term operational continuity. This study aimed to measure the factors of electronic word-of-mouth (e-WOM) and its impact on consumer purchase decisions in the marketplace Shopeefood. It employed a quantitative approach paradigm. The research instrument was a questionnaire containing 15 items measured with the Likert scale model. The samples were selected using a purposive sampling approach covering millennial and Z-generation consumers. The primary data were obtained through surveys. A total of 107 respondents contributed to this study. The collected data were analyzed using descriptive analysis and multiple regression. The results indicated that most (71.40%) of consumers open the Shopeefood application, and 91.34% access information about the products, reviews, and ratings before making a transaction. A total of 72.66% of consumers have a positive experience from previous transactions. Hypothesis testing partially showed that the intensity parameter does not affect the purchase decision. Meanwhile, the content parameter and the valence of opinion simultaneously affect and contribute 30.30% to the purchase decision.

Keywords: Electronic word-of-mout marketplace millennial and Z generation purchase decision

Downloads

Download data is not yet available.

Introduction

In the era of digitalization, smartphone technology stands at the peak of change in all business sectors. Most potential consumers (especially the millennial or Z generations) are increasingly aware of online transactions. They are the ones who master information technology and other social media. They have an awareness of making purchase decisions in the marketplace. In a marketplace-based business, customer reviews become more important than the statements in the company’s advertisement. Consumers trust the previous consumers’ stories more, or what is called Word-of-Mouth (WOM) (Chatterjee, 2001). However, along with the rapid development of information available on the internet, allowing consumers to access information individually with less effort and low cost (Park & Lee, 2009), the impact is that consumers can freely access and collect information about products based on other consumers’ comments posted on the internet or known as electronic-WOM (e-WOM; Hennig-Thurauet al., 2004). Everyone can share their thoughts with millions of other internet users through e-WOM (Duanet al., 2008). Therefore, e-WOM in the digital age is fundamental information for potential buyers.

E-WOM has come to the attention of researchers in the field of marketing. Studies on e-WOM investigated the influence of e-WOM and brand image moderated by price discounts (Slametet al., 2022), e-WOM perception with purchase intention mediated by corporate image (Bataineh, 2015), the impact of e-WOM on buying interest along with brand image variables and product (Imbayani & Gama, 2018) and purchase intention online performed in Islamic countries (Nuseir, 2019), e-WOM against assessments and product choices moderated by a sense of virtual community (Huanget al., 2012). On the other hand, some studies focused on examining the influence of e-WOM on purchase decisions of DSRL camera products (Wijaya & Paramita, 2014), in the users of Surveymonkey.com in Turkey (Yaylı & Bayram, 2012), in potential consumers in Samarahan City, Sarawak, Malaysia (Sa’aitet al., 2016), in the consumers in social commerce (Yusufet al., 2018), and in restaurant consumers (Firdaus & Abdullah, 2017). Sa’aitet al. (2016) examined e-WOM’s influence on the purchase interests represented by relevance, accuracy, timeliness, and completeness. This study tested e-WOM on the purchase decision represented by intensity, content, and valence of opinion. Differences in subject and object may result in different studies.

This study aimed to examine e-WOM with three measurement parameters of purchase decisions on potential buyers of food and beverage products on Shopeefood. It was grounded in the argument that e-WOM is a variable that can affect purchase decisions, especially online purchases among millennials or Generation Z. In the digital and social media era, this generation often considers e-WOM before making a purchase decision. According to MarkPlus, Inc., Marketing Consultant, 46.10% of millennials have online shopping habits through smartphones, and the largest number of internet users are of millennials (Haryanto, 2019). Therefore, this new phenomenon describes consumer behavior that differs from previous generations. To answer the purpose of the study, the research hypothesis is formulated as follows:

H1: E-WOM directly affects the online purchase decisions of millennial and Z generations on food and/or beverage products.

This hypothesis is based on the notion of another study that e-WOM (also called online review) affects purchase decision (Arsyalan & Ariyanti, 2019; Imbayani & Gama, 2018; Linet al., 2013; Mighfaret al., 2020; Nuseir, 2019; Sa’aitet al., 2016; Sariet al., 2017; Tataet al., 2020; Yaylı & Bayram, 2012; Yusufet al., 2018), and also affects human behavior (Filieriet al., 2018).

Literature Review

Electronic Word-of-Mouth

The term e-WOM appeared along with the development of the marketplace. It was popularized by shopping activities online, which continues to increase because online shopping is considered more convenient and accessible from home (Bhatti & Ur Rehman, 2020). E-WOM is an extension of the concept of WOM (Word-of-Mouth). WOM is conceptualized and explored as the exchange of information between individuals or those who are familiar with each other (Brown & Reingen, 1987). It is a type of person-to-person verbal communication between a receiver and transmitter involving a product, service, or brand. For the recipient, the message does not have a strong commercial intention (Anderson, 1998), so consumer trust in WOM is higher than in commercial advertising (Laberet al., 1987), that interpersonal communication affects the decisions of marketers (Hennig-Thurauet al., 2004). Along with advances in information technology and its supporting facilities, including the internet network, consumers can explore diverse information or comments of others in the marketplace or other sites, so the term e-WOM becomes popular. This term is an extension of the concept of WOM.

E-WOM represents the continuous and dynamic exchange of information between actual and potential consumers about products, services, brands or companies available on the internet (Ismagilovaet al., 2017), a primary source of information for consumers and a significant and vital source of value for businesses as well as website owners (Namet al., 2020), positive or negative statements made by potential, actual, or former customers about a product or company that are available to large groups of people and institutions via the Internet (Hennig-Thurauet al., 2004), and communication media among consumers to share information about products/services they have consumed, even though they do not know one another (Gruenet al., 2006). E-WOM is a form of communication for marketing that contains positive or negative statements made by prospective or former consumers about a product available on the internet (Hennig-Thurauet al., 2004), which refers to any statement based on a positive, neutral, or negative experience by an actual or passive consumer about a product or service available on the internet (website, social network, messaging, instant media, and others; Kietzmann & Canhoto, 2013).

E-WOM does not describe any intimate relationship between individuals (Gupta & Harris, 2010). Therefore, Subramani and Rajagopalan (2003) suggest that the company needs to have a variety of platforms that facilitate e-WOM, such as discussion boards or other online communication tools. These media are increasingly recognized for their influence on the reception and use of products and services. Although e-WOM is a follow-up of WOM, both are applicably different. The differences are available in Table I.

Dimension WOM E-WOM
Source-receiver relationship Known and established Potentially unknown sources and recipients
Channel variations Usually by telephone or face-to-face Mediated by technology and across different online communities
Request for information Depending on known sources and existing source profiles Wider coverage for unknown sources and source profile ranges
Message retention Based on the ability to remember Representation is stored online
Motivation to disclose information Support in making decisions based on information In addition to decision-making, it offers the opportunity to socialize
Table I. Differences between WOM and e-WOM (Source: Thamet al., 2013)

Therefore, e-WOM, along with information technology and the internet, allows consumers to create information messages to other parties based on their experience of consuming products in the marketplace.

Purchase Decision

Purchase decision determines the purchase, starting with selection, acquisition, and evaluation (Rossiter, 2003). The decision maker is the one who determines part or all of the purchase decisions—what to buy, when to buy, and where to buy (Marin, 2015). There are five stages in decision-making for consumers: (1) Problem identification, (2) information retrieval, (3) evaluation of alternatives, (4) purchase decisions, and (5) post-purchase behavior (Kotler & Keller, 2009). A lot of studies explain that purchase decision is influenced by some factors, including brand image (Djatmiko & Pradana, 2016; Novansa & Ali, 2017; Rianto, 2014; Suhaily & Darmoyo, 2017), discount (Rianto, 2014), product quality (Nguyen & Gizaw, 2014; Rachmawatiet al., 2019; Suhaily & Darmoyo, 2017), price and price perception (Rachmawatiet al., 2019; Suhaily & Darmoyo, 2017), promotion (Rachmawatiet al., 2019), and e-WOM (Abd-Elazizet al., 2015; Almana & Mirza, 2013; Cheunget al., 2009; Prasadet al., 2017; Yaylı & Bayram, 2012). Therefore, the purchase decision is conceptually influenced by a lot of variables.

Marketplace

The term marketplace is new, following the development of businesses mediated by the internet. According to Cambridge Dictionary (n.d.), a marketplace is a set of trade media or business environments, a website designed to buy and sell products, a place (usually in the open) as a product facility for sale, or a place where certain products are traded. Vidal (2019) noted that the marketplace is a platform where vendors can come together to sell their products to a customer base. The marketplace owner unites vendors and customers as a medium of transaction through a multi-vendor platform. Sellers can have a place to gain visibility and sell their products, and the marketplace owner receives a commission from every sale. Further, Vidal (2019) mentioned three fundamental keys of the marketplace: The marketplace has no inventory, more customer satisfaction to build, and a streamlined and scalable business model. Therefore, the electronic marketplace is divided into several groups (Soh & Markus, 2002). For example, classify it into value proposition, product-market focus, and value creation based on its strategic position in the value chain. Thus, in simple language, the marketplace is an internet or website-based media for business and transactions between sellers and buyers.

Research Method

The current study employed a quantitative research paradigm. Aligned with the objectives of the study, it belongs to associative research. The data are quantitative, with primary data obtained through direct surveys of the subjects. The research instrument was a questionnaire containing 15 items of statements distributed into two variables, namely e-WOM variables represented by dimensions Intensity, Content, and Valence of Opinion (Goyetteet al., 2010), as well as purchase decision (Kotler & Keller, 2009). The measurement was referred to a Likert scale from 1 (strongly disagree) to 5 (strongly agree). The questionnaires had been tested for validity and reliability before being used as an actual research instrument. Statistically, the validity test of all items was declared valid as the coefficient r count was 0.34–0.85 > r table (0.26). The reliability test proved the reliability of the instrument with Cronbach’s Alpha = 0.88 > 0.60.

The survey was conducted in Surabaya with Millennial and Generation “Z” subjects aged 15 to 41 years. The type of population in the study was classified as an indefinite population. The number of Shopeefood users in Surabaya was not known. The sampling technique used non-probability sampling and determination of the number of samples using the Cochran equation (Sugiyono, 2017) as follows: (1)n=Z2pqe2where:

Z = 1.96 based on the value of Z at α = 95%,

p = 50% (true chance),

q = 50% (false chance),

e (margin of error) = 10%.

Therefore, the result of the calculation of the sample size required is given in (2). (2)n=(1.96)2×(0.5×0.5)(0.1)2=96.04

The data analysis adopted three analyses, namely:

Descriptive Analysis

The descriptive analysis refers to the percentage analysis developed by Sugiyono (2002), and it is given as follows:

  1. – Very important: n1×5=5n1
  2. – Important: n2×4=4n2
  3. – Less important: n3×3=3n3
  4. – Not important: n4×2=2n4
  5. – Very unimportant: n5×1=1n5
  6. – n=n1+n2+n3+n4+n5=Σxxx

Multiple Linear Regression Analysis

The multiple linear regression analysis is done by using the following formula:

(3)Y=a+β1X1+β2X2+β3X3where:

Y = predicted value (dependent variable),

a = constant number,

β1, β2, β3 = coefficients of the independent variable,

X1 = Intensity,

X2 = Content,

X3 = Valence of opinion.

Hypothesis Testing (t-Test and F-Test)

The t-test criteria (partial test) use a two-sided test with α = 5% and n = 107 so that ttable = 1.98. Ho is accepted if ttable = −1.983 ≤ tcount ≤ ttable = 1.984 or prob./Sig. > 0.05 (meaning that the independent variable does not affect the dependent variable). On the other hand, Ho is rejected if tcount < −1.983 or tcount > −1.983 or prob./Sig. > 0.05 (meaning that the independent variable affects the dependent variable).

As for the F-test (simultaneous test) with α = 5%, DF1 = k − 1 (4 − 1) = 3, DF2 = n − k (107 − 4) = 103, hence Ftable = 2.463. Test criteria: Ho is accepted if Fcount ≤ 2.463 or Prob./Sig. > 0.05 (simultaneously, the independent variable does not affect the dependent variable). Ho is rejected if Fcount > 2.463 or Prob./Sig. < 0.05 (simultaneously, the independent variable affects the dependent variable).

Results

The consumer assessment of the e-WOM of Shopeefood is presented in Table II.

E-WOM dimension Consumer action % Approval
Intensity Opening Shopeefood application 62.24%
Communicating with sellers (via chat) 61.12%
Accessing other users’ comments 90.84%
Average 71.40%
Content Accessing product variations 92.71%
Considering product quality 92.71%
Considering the product price 92.15%
Reading other consumer testimonials 88.97%
Average 91.64%
Valence of opinion Giving positive reviews if the products and services meet expectations 87.85%
Giving recommendations to other consumers 78.32%
Writing negative reviews if the products and services are less/not as expected 65.23%
Not giving recommendations to other consumers 59.25%
Average 72.66%
Table II. Consumer Assessment on the E-Wom of Shopeefood

Table II demonstrated that e-WOM, represented by the three dimensions of measurement, is vital for consumers who make transactions online via Shopeefood.

Before linear regression analysis and hypothesis testing, we first conducted classical assumption tests, which covered normality, linearity, and multicollinearity tests. The results are presented in Table III.

Classical assumption test Significance Decision Criteria Source
Normality test 0.45 Normal distribution Sig. ≥ 0.05 One-Sample Kolmogorov–Smirnov Test
Linearity test 0.41 Linear Sig. ≥ 0.05 Anova table
Multicollinearity test and Does not happen
Tolerance value X.1 = 0.56 Multicollinearity does not happen Tolerance ≥ 0.10 Table of coefficients (Collinearity statistics)
X.2 = 0.67
X.3 = 0.65
Variance inflating factor (VIF) X.1 = 1.04 VIF < 10
X.2 = 1.49
X.3 = 1.55
Table III. Classical Assumption Test Results

Based on Table III, all classical assumption tests (normality test, linearity test, and multicollinearity test) meet the requirements, so that linear regression analysis and hypothesis testing can be conducted.

Multiple linear regression analysis in research is intended to measure the extent to which the dimensions/variables of intensity (x1), content (x2), and valence of opinion (x3) influence purchase decisions on Shopeefood. The results of the multiple linear regression test are presented in Table IV.

Coefficient Anova Model summary
Variable Regression coefficient tcount Sig. (Prob.)
Constant 6.458 Fcount = 14.949Sig. < 0.001 R-squared = 0.303
Intensity (x1) −0.001 −0.161 0.872
Content (x2) 0.580 3.994 0.000
Valence of opinion (x3) 0.207 2.058 0.042
Table IV. Multiple Linear Regression Test Results

Intensity (x1) negatively affects purchase decisions by 0%. Content (x2) positively affects purchase decisions by 58%, and valence of opinion (x3) also positively affects purchase decisions by 20.70%. If the consumers do not act on intensity, content, and valence of opinion, they make a purchase decision worth 6.458.

Table IV shows that:

  1. Intensity (x1) brings no effect on purchase decision in Shopeefood. Based on the hypothesis test, the decision to accept Ho is statistically indicated by Sig./Prob. (0.872) > 0.050. Based on normal curve analysis, ttable (−1.983) < tcount (−0.161) < ttable (1.984).
  2. Content (x2) has a positive effect on purchase decision in Shopeefood. This is evidenced by the hypothesis test rejecting Ho, statistically indicated by Sig./Prob. < 0.001, and based on normal curve analysis, tcount (3.994) > ttable (1.984).
  3. Valence of opinion (x3) positively affects purchase decision in Shopeefood. Hypothetically, it rejects Ho since Sig./Prob. (0.042) < 0.050, and based on the normal curve, tcount (2.058) > ttable (1.984).

Simultaneously, it indicates that the dimensions intensity (x1), content (x2), and valence of opinion (x3) exert a positive effect but are less significant. Statistically, Sig./Prob. < 0.001, and based on normal curve analysis, Fcount (14.949) > Ftable (2.463). The coefficient of determination (R-squared) = 0.303 indicates a less significant effect. It means that the three variables intensity (x1), content (x2), and valence of opinion (x3)—contribute only to making purchase decisions on Shopeefood by 30.30%.

Discussion

The findings of this study suggested that e-WOM, represented by dimensions intensity, content, and valence of opinion, makes only a small contribution to someone making a purchase decision on a marketplace that offers this type of food product. The level of intensity or frequency (intensity) in accessing the marketplace has no impact on an individual to make a purchase decision. Meanwhile, the content or information in the marketplace and other consumers’ experiences or comments affect purchase decisions. Therefore, e-WOM on the marketplace still influences one’s purchase decision. In particular, complete information about products and comments based on previous consumer experiences are fundamental for consumers to make purchase decisions.

Based on the findings of this study, a person accessing various marketplaces does not necessarily make purchase decision but sometimes solely to spend the free time, to dabble, to look around, and so forth. Meanwhile, cautious consumers who intend to make purchase decisions on the marketplace first look closely at the information about the product itself. For example, price, product texture, quality assumptions, food substance content, and packaging. In addition to looking at information about the product, comments on previous consumer experiences are also considerations for consumers in making purchase decisions on the marketplace. Therefore, although consumers have accessed the marketplace, looked closely at the product information, and comments of other consumers do not necessarily let them make purchase decisions.

Some reasons for the low effect of e-WOM on the purchase decision of food products in the marketplace are as follows. Millennial and Z generations already have networked smartphones, and most of their smartphones have been installed by applications marketplace from various vendors. Accessing the marketplace is a part of their daily lives. A study by Wijayaet al. (2020) suggested that the motive e-lifestyles of Generation Z are associated with factors like e-activities, e-interests, e-opinions, and e-values. The purposes of information access and use of social media are communication, entertainment, shopping, and community activities. Millennials have more affinity and rely more on information online from the previous generation (Dabijaet al., 2018). The second reason is that a person buys food through the marketplace only as an alternative due to several factors, such as feeling lazy or having a tight schedule that does not allow him/her to leave the house. Further, Monsuwéet al. (2004) mention that online shopping affects exogenous factors, such as consumer properties, situational, product characteristics, previous experience of online shopping, and confidence.

This study corresponds to some of the findings of previous studies, such as those by Sa’aitet al. (2016), that all elements of e-WOM (relevance, accuracy, timeliness, and completeness) impact the purchase intentions of potential consumers. Also, Bataineh (2015) shows that e-WOM quality, e-WOM credibility, and e-WOM quantity significantly and positively influence purchase intention. Similarly, Firdaus and Abdullah (2017) suggested that e-WOM significantly affects restaurant customers’ purchase decisions. Partially, this study reinforces the previous finding that consumer reviews (content) have an impact on product selection (Yaylı & Bayram, 2012), and valence of opinion exerts a positive effect on purchase decisions (Istnaini, 2019). On the other hand, Sindunata (2018) and Prastuti and Karyanti (2020) mentioned that intensity against purchase decision, and I Gustiet al. (2022) demonstrated that content brings no effect on purchase decision. Therefore, the similarity between this study and other studies lies in the subject and object of the study. The “millennial and Z” generations certainly differ in understanding the variables of e-WOM when it comes to making purchase decisions, The product to purchase also determines the level of foresight upon observing the aspects of e-WOM.

E-WOM on various marketplaces has become a consideration for potential consumers, especially millennial, Z, and next-generation consumers. This generation has mastered information technology and social media. The media are the means of their lives, including businessmen. Many studies suggested that most potential consumers pay attention to previous comments on certain e-commerce or marketplace systems. They easily access their positive or negative comments for consideration in purchasing decisions. This condition encourages businessmen in the digital era to consider the tendency of potential consumers.

On the other hand, they need to consider using internet-based systems and business models that are designed in the form of e-commerce and marketplace. Businesses that have entered this area are required to provide spaces for comments from consumers or customers. This comment is intended to provide consideration information for potential consumers and as an ongoing evaluation material. Thus, the digital age and the development of diverse social media, e-commerce, and marketplaces are sources of pressure for businessmen today and in the future.

Conclusion

The most prominent finding of this study is that e-WOM has become one of the considerations for potential consumers in online business transactions, especially in the marketplace that accommodates food and beverage sellers. Potential consumers often consider the comments of the previous consumers (content aspect) since humans directly consume food and beverages. Therefore, they are highly interested in the consumers’ comments. According to Dataindonesia.id, millennials contribute about 48% of all transactions online. The millennial generation is the starting point for the next generation in accessing the marketplace or any other business site. This phenomenon is a picture of the prospective trend of business transaction patterns, so online transactions are expected to dominate the business market. Therefore, e-WOM becomes a fundamental activity for potential consumers before they make a purchase decision.

This study has provided a scientific horizon in the field of marketing. For business owners, understanding e-WOM is significant, especially the aspects of content and valence of opinion. However, in this study, e-WOM contributes less to the purchase decision. The consequence of e-WOM is that it risks low-quality businesses. Business owners are dependent variables because they can only control consumer comments. To receive positive comments, they should maintain and improve product quality continuously. For academics, this study proves that e-WOM is a growing field of study that can be viewed from diverse perspectives.

The current study is limited to measuring e-WOM on marketplace Shopeefood in Surabaya, represented by three parameters: intensity, content, and valence of opinion. The use of different e-WOM measurement parameters may result in different information. Likewise, different subjects and objects might generate different study results. Accordingly, studies from a comparative perspective are necessary, especially those that accommodate more comprehensive measurement parameters and are adapted to the marketplace model. Further studies also need to be directed at the broader subjects and objects, for example, testing different marketplaces.

References

  1. Abd-Elaziz, M. E., Aziz, W. M., Khalifa, G. S., & Abdel-Aleem, M. (2015). Determinants of electronic word of mouth (EWOM) influence on hotel customers’ purchasing decision. Journal of Faculty of Tourism and Hotels, Fayoum University, 9(2), 194–223.
     Google Scholar
  2. Almana, A. M., & Mirza, A. A. (2013). The impact of electronic word of mouth on consumers’ purchasing decisions. International Journal of Computer Applications, 82(9), 23–31.
     Google Scholar
  3. Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5–17. https://doi.org/10.1177/109467059800100102.
     Google Scholar
  4. Arsyalan, A. G., & Ariyanti, M. (2019). Pengaruh electronic word of mouth (eWOM) Terhadap Keputusan Pembelian Masyarakat Bandung di Shoppe [The influence of electronic word of mouth (eWOM) on Bandung people’s purchasing decisions at the shoppe]. Jurnal E-Proceding of Management, 6(3), 55–66.
     Google Scholar
  5. Bataineh, A. Q. (2015). The impact of perceived e-WOM on purchase intention: The mediating role of corporate image. International Journal of Marketing Studies, 7(1), 126–137. https://doi.org/10.5539/ijms.v7n1p126.
     Google Scholar
  6. Bhatti, A., & Ur Rehman, S. (2020). Perceived benefits and perceived risks effect on online shopping behavior with the mediating role of consumer purchase intention in Pakistan. International Journal of Management Studies, 26(1), 33–54. https://doi.org/10.32890/ijms.26.1.2019.10512.
     Google Scholar
  7. Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350–362. https://doi.org/10.1086/209118.
     Google Scholar
  8. Cambridge Dictionary (n.d.). Dictionary. https://dictionary.cambridge.org/dictionary/english.
     Google Scholar
  9. Chatterjee, P. (2001). Online reviews: Do consumers use them? Association for Consumer Research, 18(2), 129–134.
     Google Scholar
  10. Cheung, C. M. K., Lee, M. K. O., & Thadani, D. R. (2009). The impact of positive electronic word-of-mouth on consumer online purchasing decision. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 219, 501–510. https://doi.org/10.1007/978-3-642-04754-1_51.
     Google Scholar
  11. Dabija, D. C., Bejan, B. M., & Tipi, N. (2018). Generation X ver- sus millennials communication behaviour on social media when purchasing food versus tourist services. E a M: Ekonomie a Manage- ment, 21(1), 191–205. https://doi.org/10.15240/tul/001/2018-1-013.
     Google Scholar
  12. Djatmiko, T., & Pradana, R. (2016). Brand image and product price; Its impact for Samsung smartphone purchasing decision. Procedia—Social and Behavioral Sciences, 219, 221–227. https://doi.org/10.1016/j.sbspro.2016.05.009.
     Google Scholar
  13. Duan, W., Gu, B., & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales-an empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242. https://doi.org/10.1016/j.jretai.2008.04.005.
     Google Scholar
  14. Filieri, R., Raguseo, E., & Vitari, C. (2018). When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type. Computers in Human Behavior, 88, 134–142. https://doi.org/10.1016/j.chb.2018.05.042.
     Google Scholar
  15. Firdaus, T., & Abdullah, T. (2017). E-WoM: Pengaruhnya terhadap Keputusan Pembelian Tamu Restoran di Bandung Utara. The Journal: Tourism and Hospitality Essentials Journal, 7(1), 21–26. https://doi.org/10.17509/thej.v7i1.6843.
     Google Scholar
  16. Goyette, I., Richard, L., Bergeron, J., & Marticotte, F. (2010). Word-of-mouth measurement scale for eservice context. Canadian Journal of Administrative Sciences, 27(1), 5–23.
     Google Scholar
  17. Gruen, T. W., Osmonbekov, T., & Czaplewski, A. J. (2006). eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty. Journal of Business Research, 59(4), 449– 456. https://doi.org/10.1016/j.jbusres.2005.10.004.
     Google Scholar
  18. Gupta, P., & Harris, J. (2010). How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective. Journal of Business Research, 63(9– 10), 1041–1049. https://doi.org/10.1016/j.jbusres.2009.01.015.
     Google Scholar
  19. Haryanto, A. T. (2019, November). Pengguna internet Indonesia Didominasi Milenial. DetikNet.Com.
     Google Scholar
  20. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52. https://doi.org/10.1002/dir.10073.
     Google Scholar
  21. Huang, J. H., Hsiao, T. T., & Chen, Y. F. (2012). The effects of electronic word of mouth on product judgment and choice: The moderating role of the sense of virtual community. Journal of Applied Social Psychology, 42(9), 2326–2347. https://doi.org/10.1111/j.1559-1816.2012.00943.x.
     Google Scholar
  22. I Gusti, N. S. W., Ni Wayan, C. A. P., Ni Nyoman, M., & I Gede, D. Y. (2022). Pengaruh electronic word of mouth (E-Wom), Persepsi Risiko, Kepercayaan Pelanggan, Dan Keputusan Pembelian E-Commerce Tokopedia [The influence of electronic word of mouth (E-Wom), risk perception, customer trust, and tokopedia E-commerce purchase decisions]. E-Jurnal Manajemen, 11(1), 107–115.
     Google Scholar
  23. Imbayani, I., & Gama, A. (2018). The influence of electronic word of mouth (E-Wom), brand image, product knowledge on purchase intention. Product Knowledge on Purchase Intention Jurnal Ekonomi Dan Bisnis Jagaditha, 5(2), 145. https://ejournal.warmadewa.ac.id/index.php/jagaditha.
     Google Scholar
  24. Ismagilova, E., Dwivedi, Y. K., Slade, E., & Williams, M. D. (2017). Electronic word of mouth (eWOM) in the marketing context a state of the art analysis and future directions. In SpringerBriefs in Business. Springer Nature.
     Google Scholar
  25. Istnaini, N. R. (2019). Electronic Word of Mouth dan Minat Beli (Studi Kuantitatif Pengaruh Intensity, Content, dan Valence of Opinion dalam Electronic Word of Mouth (e-WOM) di Media Sosial Insta- gram terhadap Minat Beli di Kalangan Followers @fitsaria.id Tahun 2018) [Electronic Word of Mouth and Purchase Interest (Quantitative Study of the Influence of Intensity, Content, and Valence of Opinion in Electronic Word of Mouth (e-WOM) on Instagram Social Media on Purchase Interest among @fitsaria.id Followers in 2018)] (Unpublished undergraduate thesis). Sebelas Maret University. https://digilib.uns.ac.id/dokumen/detail/70916/.
     Google Scholar
  26. Kietzmann, J., & Canhoto, A. (2013). Bittersweet! understanding and managing electronic word of mouth. Journal of Public Affairs, 13(2), 146–159. https://doi.org/10.1002/pa.1470.
     Google Scholar
  27. Kotler, P., & Keller, K. L. (2009). Manajemen pemasaran [Marketing management]. Erlangga.
     Google Scholar
  28. Laber, C. A., Rahim, C. F., Dreyer, S. F., Uehara, G. T., Kwok, P. T., & Gray, P. R. (1987). Design considerations for a high-performance 3-μm CMOS analog standard-cell library. IEEE Journal of Solid-State Circuits, 22(2), 181–189. https://doi.org/10.1109/JSSC.1987.1052700.
     Google Scholar
  29. Lin, C., Wu, Y.-S., & Chen, J.-C. V. (2013). Electronic word-of-mouth: the moderating roles of product involvement and brand image. Proceedings of 2013 International Conference on Technology Innovation and Industrial Management, pp. 29–47.
     Google Scholar
  30. Marin, D. (2015). Research regarding the purchase decision process of consumer of food products. Scientific Papers: Animal Science & Biotechnologies, 48(1), 328–332.
     Google Scholar
  31. Mighfar, S., Sukaris, S., Saleh, M., & Dewantoro, A. Q. (2020). The effect of electronic word of mouth (EWOM), product quality and price on purchase decisions. Innovation Research Journal, 1(2), 99. https://doi.org/10.31933/DIJMS.
     Google Scholar
  32. Monsuwé, T. P. Y., Dellaert, B. G. C., & De Ruyter, K. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102–121. https://doi.org/10.1108/09564230410523358.
     Google Scholar
  33. Nam, K., Baker, J., Ahmad, N., & Goo, J. (2020). Dissatisfaction, disconfirmation, and distrust: An empirical examination of value co-destruction through negative electronic word-of-mouth (eWOM). Information Systems Frontiers, 22, 113–130. https://doi.org/10.1007/s10796-018-9849-4.
     Google Scholar
  34. Nguyen, T. H., & Gizaw, A. (2014). Factors that influence consumer purchasing decisions of private label food products a case study of ICA basic. In School of Business, Society and Engineering. vol. 1, Issue 13, School of Business, Society and Engineering.
     Google Scholar
  35. Novansa, H., & Ali, H. (2017). Purchase decision model: Analysis of brand image, brand awareness and price (case study SMECO Indonesia SME products). Saudi Journal of Humanities and Social Sciences, 2(8), 621–632.
     Google Scholar
  36. Nuseir, M. T. (2019). The impact of electronic word of mouth (e- WOM) on the online purchase intention of consumers in the Islamic countries—a case of (UAE). Journal of Islamic Marketing, 10(3), 759–767. https://doi.org/10.1108/JIMA-03-2018-0059.
     Google Scholar
  37. Park, C., & Lee, T. M. (2009). Information direction, website reputation and eWOM effect: A moderating role of product type. Journal of Business Research, 62(1), 61–67. https://doi.org/10.1016/j.jbusres.2007.11.017.
     Google Scholar
  38. Prasad, S., Gupta, I. C., & Totala, N. K. (2017). Social media usage, electronic word of mouth and purchase-decision involvement. Asia-Pacific Journal of Business Administration, 9(2), 134–145. https://doi.org/10.1108/apjba-06-2016-0063.
     Google Scholar
  39. Prastuti, D. I., & Karyanti, Y. (2020). Pengaruh electronic word of mouth terhadap Keputusan Pembelian Tiket Kereta api Daring [The influence of electronic word of mouth on online train ticket purchase decisions]. Faktor Exacta, 13(3), 176–184. https://doi.org/10.30998/faktorexacta.v13i3.7071.
     Google Scholar
  40. Rachmawati, D., Shukri, S., Azam, S. M. F., & Khatibi, A. (2019). Factors influencing customers’ purchase decision of residential property in Selangor, Malaysia. Management Science Letters, 9(2019), 1341– 1348. https://doi.org/10.5267/j.msl.2019.5.016.
     Google Scholar
  41. Rianto, A. (2014). Pengaruh merek dan kebijakan harga terhadap keputusan pembelian (Survei pada pengunjung yang membeli dan mendapatkan diskon musiman terhadap produk Inspired27 di Kota Malang) [The influence of brands and pricing policies on pur- chasing decisions (Survey of visitors who purchased and received seasonal discounts on Inspired27 products in Malang City)]. Jurnal Administrasi Bisnis S1 Universitas Brawijaya, 14(1), 1–9.
     Google Scholar
  42. Rossiter, J. R. (2003). How to construct a test of scientific knowledge in consumer behavior. Journal of Consumer Research, 30(2), 305–310. https://doi.org/10.1086/376799.
     Google Scholar
  43. Sari, N., Saputra, M., & Husein, J. (2017). Pengaruh electronic word of mouth Terhadap Keputusan Pembelian Pada Toko online Bukala- pak.com [The influence of electronic word of mouth on purchasing decisions at the bukalapak.com online store]. Jurnal Manajemen Magister, 3(1), 96–106.
     Google Scholar
  44. Sa’ait, N., Kanyan, A., & Nazrin, M. F. (2016). The effect of E-WOM on customer purchase intention. International Academic Research Journal of Social Science, 2(1), 73–80.
     Google Scholar
  45. Sindunata, I. (2018). Pengaruh electronic word of mouth terhadap Keputusan Pembelian di agoda.com [The influence of electronic word of mouth on purchasing decisions on agoda.com]. Hospitality Dan Manajemen Jasa, 6(1), 128–138.
     Google Scholar
  46. Slamet, S., Prasetyo, B. P. W., & Azmala, I. (2022). The impact of electronic word of mouth and brand image on online purchase decisions moderated by price discount. European Journal of Business and Management Research, 7(2), 139–148. https://doi.org/10.24018/ejbmr.2022.7.2.1340.
     Google Scholar
  47. Soh, C., & Markus, M. L. (2002). Business-to-business electronic marketplaces: A strategic archetypes approach. Proceedings of the International Conference on Information Systems, ICIS 2002, 835–845.
     Google Scholar
  48. Subramani, M. R., & Rajagopalan, B. (2003). Knowledge-sharing and influence in online social networks via viral marketing Mani R. Subramani and Balaji Rajagopalan. Communications of the ACM, 46(12), 300–307.
     Google Scholar
  49. Sugiyono, S. (2002). Metode penelitian administrasi [Administrative Research Methods]. CV Alfabeta.
     Google Scholar
  50. Sugiyono, S. (2017). Metode penelitian kuantitatif, kualitatif, dan R&D [Quantitative, Qualitative and R&D Research Methods]. CV Alfabeta.
     Google Scholar
  51. Suhaily, L., & Darmoyo, S. (2017). Effect of product quality, perceived price and brand image on purchase decision mediated by customer trust (study on japanese brand electronic product). Jurnal Manaje- men, 21(2), 179–194. https://doi.org/10.24912/jm.v21i2.230.
     Google Scholar
  52. Tata, S. V., Prashar, S., & Gupta, S. (2020). An examination of the role of review valence and review source in varying consumption contexts on purchase decision. Journal of Retailing and Consumer Services, 52, 1–10. https://doi.org/10.1016/j.jretconser.2019.01.003.
     Google Scholar
  53. Tham, A., Croy, G., & Mair, J. (2013). Social media in destination choice: Distinctive electronic word-of-mouth dimensions. Journal of Travel and Tourism Marketing, 30(1–2), 144–155. https://doi.org/10.1080/10548408.2013.751272.
     Google Scholar
  54. Vidal, F. (2019). What is a marketplace? Our understanding of multi-seller businesses. Shopery.Com. https://www.shopery.com/insights/what-is-a-marketplace.
     Google Scholar
  55. Wijaya, T., Darmawati, A., & Kuncoro, A. M. (2020). e-lifestyle confirmatory of consumer generation Z. International Journal of Advanced Computer Science and Applications, 11(10), 27–33. https://doi.org/10.14569/IJACSA.2020.0111004.
     Google Scholar
  56. Wijaya, T., & Paramita, E. L. (2014). Pengaruh electronic word of mouth (E-WoM) terhadap Keputusan Pembelian Kamera DSLR [The influence of electronic word of mouth (E-WoM) on DSLR camera purchasing decisions]. Seminar Nasional Dan Call for Paper, 12–19. https://publikasiilmiah.ums.ac.id/xmlui/handle/11617/4729.
     Google Scholar
  57. Yaylı, A., & Bayram, M. (2012). E-WOM: The effects of online consumer reviews on purchasing decisions. International Journal of Internet Marketing and Advertising, 7(1), 51–64.
     Google Scholar
  58. Yusuf, A. S., Che Hussin, A. R., & Busalim, A. H. (2018). Influence of e-WOM engagement on consumer purchase intention in social commerce. Journal of Services Marketing, 32(4), 493–504. https://doi.org/10.1108/JSM-01-2017-0031.
     Google Scholar