Electronic Word-of-Mouth Analysis and its Impact on Purchase Decisions: Studies on “Millennial and Z” Generation
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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.
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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 |
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:
- – Very important: n1×5=5n1
- – Important: n2×4=4n2
- – Less important: n3×3=3n3
- – Not important: n4×2=2n4
- – Very unimportant: n5×1=1n5
- – 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 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 |
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 |
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:
- 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).
- 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).
- 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.
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