The impact of eWOM information in social media on the online purchase intention of Generation Z

Abstract

This research aims to investigate the impact of characteristics of electronic word-of-mouth (eWOM), including Information Quality, Information Credibility, Information Quantity; and consumer behavior, including Needs of Information, and Attitudes towards Information, on the online purchase intention of Generation Z consumers in the context of social media. The research used a quantitative approach with a Likert scale questionnaire to collect the data from 280 Vietnamese Generation Z individuals. Statistical techniques, including Cronbach’s Alpha Test, Exploratory Factor Analysis, Confirm Factor Analysis, and Structural Equation Modeling, were used for data analysis. The findings indicated that Information Quality, Information Quantity, and Needs of Information significantly influenced Information Usefulness. Moreover, Information Quality, Needs of Information, and Attitudes towards Information had a significant impact on Information Adoption. Notably, Information Quality exhibited the strongest positive influence on both Information Usefulness and Information Adoption. The study found that Information Usefulness had a significant impact on Information Adoption and on Information Adoption had a significant impact on purchase intention. This result illustrated that information adoption and information usefulness act as partial mediators in the relationships between the independent variables and purchase intentions. This research distinguishes itself from previous studies by simultaneously identifying independent factors that affect Information Usefulness and Information Adoption. This provides marketers with better understanding the important of eWOM information on consumer purchase intention on social media. To address limitations and enhance the overall understanding of the research area, future studies should increase the sample size, diversify geographical representation, and conduct comparative analyses across different generational cohorts.

Impact STATEMENT

In the digital age, the rise of electronic word-of-mouth (eWOM) significantly influences consumer behavior, especially regarding online purchase intentions. Consumers increasingly rely on social media platforms for product information, and this reliance on eWOM has intensified after the COVID-19 pandemic. Despite the growing importance of eWOM on social media in shaping consumer purchase intentions, there is a lack of research in this area, particularly concerning its impact on specific generational groups like Generation Z. Given the evolving of Generation Z consumers, understanding the impact of eWOM on social media becomes vital for marketers. This study aims to explore the impact of specific eWOM characteristics (Information Quality, Credibility, Quantity) and consumer behavior (Needs and Attitudes towards Information) on Generation Z consumers’ purchase intentions through social media. The study offers valuable insights for marketers, enabling them to comprehend the importance of eWOM in shaping consumer decisions on social media and thus aiding in the formulation of effective marketing strategies.

Keywords:

• Electronic word-of-mouth
• eWOM information
• social media
• online purchase intention
• Generation Z

REVIEWING EDITOR:

• Len Tiu Wright, De Montfort University Faculty of Business and Law, United Kingdom

SUBJECTS:

• Marketing Research
• Internet / Digital Marketing / e-Marketing
• Marketing Communications
• Marketing Management

1. Introduction

The impact of word-of-mouth (WOM) on consumer purchasing decisions has been widely recognized by researchers and marketers (Dellarocas, 2003). With the advancement of the Internet era, a distinct type of WOM communication called electronic word-of-mouth (eWOM) has emerged (Cheung & Thadani, 2012). Huete-Alcocer (2017) highlights that eWOM sets itself apart from traditional WOM by facilitating rapid information dissemination through online communication channels, which are accessible and retrievable at any time.

In the digital era, individuals increasingly rely on online platforms, including social media, websites, and blogs, to access information. Social media platforms provide users with the ability to generate and share their own content, including photos and videos. According to Chivandi et al. (2020), consumers increasingly rely on social media platforms as a primary source for seeking product information, including details about the brand, manufacturer’s background, and available retailers, to inform their purchasing decisions. Recent research has highlighted that consumers increasingly turn to social media to gather information about brands, products, and services (Erkan & Evans, 2018). These platforms have opened up new possibilities for electronic word of mouth (eWOM), allowing users to engage with their network of followers and friends, sharing their opinions and experiences. Notably, the Digital 2020 Global Overview reports an impressive count of 3.8 billion active social media users (We Are Social, 2020).

Recent developments in eWOM highlight social media as the most popular channel for customers to engage in and seek eWOM (Khwaja & Zaman, 2020). Marketers highly regard social media as a crucial transmitter of eWOM, allowing them to swiftly disseminate information to their target audience (Bashir et al., 2019; Hussain et al., 2018). Moreover, social media has gained significance for marketers in influencing their customers through eWOM, offering a cost-effective and convenient means of communication (Alghizzawi, 2019). This phenomenon has opened up new avenues for communication and engagement between businesses and consumers, shaping the landscape of eWOM in the modern era.

The impact eWOM on social media platforms is highly influential in shaping consumers’ purchase decisions. According to Chivandi et al. (2020), consumers are increasingly turning to social media as a primary source of product information, including details about brands, manufacturers, and retailers, in their decision-making process. Recognizing the persuasive effect of eWOM, businesses are employing various methods to leverage social media to engage with consumers (Khwaja & Zaman, 2020). Moreover, after the COVID-19 pandemic, reliance on social media, and dependence on eWOM information have seen a significant rise (Bashir et al., 2021; Hall et al., 2020). It is worth noting that despite the increasing significance of eWOM, there remains a limited amount of research conducted in this field (Le-Hoang, 2020).

Despite the growing number of research, the association between eWOM in social media and consumer purchase intentions remains partially understood (Erkan & Evans, 2018). Tien et al. (2019) stated that empirical findings are limited, making it challenging to ascertain the specific link between customers’ adoption of eWOM in social networks and their purchase intentions. Moreover, previous studies on eWOM have primarily focused on consumers in general, without specifically examining the effects within a particular generational or age group (Erkan & Evans, 2018). As Generation Z grows up, marketers face the task of adapting their communication strategies to the evolving purchasing behaviors of this demographic (Ismail et al., 2021). Consequently, exploring the impact of eWOM in social media specifically within a particular generation becomes necessary. Therefore, this research aims to examine the influence of eWOM in social media on the online purchase intentions of Generation Z consumers.

Furthermore, this research integrates both aspects by developing a comprehensive research framework that combines the Information Acceptance Model (IACM) and the Information Adoption Model (IAM). This approach builds upon the findings of Yones and Muthaiyah (2022), which go beyond considering the impact of independent factors in the IAM model solely on IU but also examine their effect on IA. Additionally, this research incorporates factors from the IACM model into the research framework to provide a more comprehensive analysis.

Overall, this research will be of great help to the marketing department of brands or individuals who want to do business on social networks. The research results will help businesses or marketers find out which factors of eWOM affect purchasing decisions. From there, businesses come up with a suitable and optimal strategy.

2. Literature review

2.1. Theoretical background

2.1.1. Electronic word of mouth (eWOM) in social media

The term ‘eWOM information’ refers to any kind of feedback or opinion about a product or service that is readily accessible to multiple individuals on internet-based platforms (Gvili & Levy, 2018). In the modern era, more customers turn to social media platforms to seek information (Erkan & Evans, 2018). Therefore, social media users quickly share their opinions through various forms such as images, text, or videos by utilizing mobile applications or reposting (Erkan & Evans, 2016). For this reason, businesses widely adopt social networking as a suitable platform for eWOM to disseminate information about their products or services (Erkan & Evans, 2018). However, the credibility of eWOM can be negatively affected due to the anonymity of both the sender and the recipient (Huete-Alcocer, 2017).

The eWOM information can manifest in various ways on social media. Alboqami et al. (2015) revealed that some content went viral and reached a larger audience, while others received only mediocre responses. This demonstrates the variability of information impact on individuals, as the identical content can evoke diverse perceptions among different recipients (Erkan & Evans, 2018). Erkan and Evans (2016) emphasize that individuals encountering eWOM on social media should thoroughly evaluate the information before relying on it for their purchasing decisions. Therefore, this research applied the IACM model to further investigate consumers’ intentions to seek information on social media platforms with the aim of making informed purchase decisions (Erkan & Evans, 2016).

2.1.2. Online purchase intention

Purchase intention indicates an individual’s consideration of buying a product (Spears & Singh, 2004) and is considered the main predictor of actual behavior (Peña-García et al., 2020). This implies that understanding purchase intention is crucial in predicting consumer buying behavior. Today, an increasing amount of consumers express their opinions in social media, and eWOM has gained significant influence on purchase intentions of consumers (Cham et al., 2021). Furthermore, Bronner and De Hoog (2011) demonstrates that eWOM assists individuals in making informed purchasing decisions. The research findings confirm that eWOM information directly impacts purchase intention (Bronner & De Hoog, 2011). Hence, it is essential to identify the specific characteristics or attributes of eWOM information that influence customers’ purchase decision-making processes on social media platforms.

2.1.3. Generation Z

‘Generation Z’ refers to individuals born between 1995 and 2010, as identified by Priporas et al. (2017). This group is different from previous generations due to their extensive exposure and familiarity with modern technology. Their inclination towards embracing novelty and extensive engagement with social media platforms (Sun et al., 2022) endows them with a commendable ability to embrace new ideas. Generation Z, known for their education and sophistication, is the most digitally connected generation, actively engaging with digital tools and various social communities (Kunja & Gvrk, 2020).

As Generation Z continues to evolve, marketers will encounter new challenges in adapting their communication strategies to cater to the constantly changing purchasing habits of this generation (Ismail et al., 2021). Previous research on eWOM has predominantly focused on the general consumer rather than specific age groups or generations (Tien et al., 2019). Consequently, the influence of eWOM in social media within a particular generation has been overlooked. Therefore, this research aims to explore the impact of eWOM in social media on the purchase intentions of Generation Z.

2.1.4. Information Adoption Model (IAM)

The Information Adoption Model (IAM) comprises four elements: Information Usefulness (IU), Information Adoption (IA), source credibility (representing the peripheral route), and argument quality (representing the central route). This model explains how individuals embrace information through computer-based communication, influencing their intentions and behavior. It integrates concepts from the Elaboration Likelihood Model (ELM) and the Technology Acceptance Model (TAM) (Sussman & Siegal, 2003). The IAM model is particularly suitable for studying electronic word-of-mouth (eWOM) because it provides insights into computer-mediated communication platforms (Erkan & Evans, 2016). Numerous studies have extensively assessed the impact of eWOM on purchase intention using the IAM model (Erkan & Evans, 2016; Leong et al., 2021; Yones & Muthaiyah, 2022). Therefore, utilizing the IAM model is appropriate for investigating the influence of eWOM on purchase intention (Figure 1).

Figure 1. Information Adoption Model (Sussman & Siegal, 2003).

2.1.5. Information Acceptance model (IACM)

Although the IAM model is widely employed, this research does not only focus on information characteristics. To fully explore the influence of information, it is necessary to go beyond its characteristics and also examine consumer behavior towards it. In this context, Erkan and Evans (2016) developed the Information Acceptance Model (IACM) as an extension of the IAM model, which takes into account consumer behaviors related to information. The IACM model suggests that consumer behavior toward eWOM information affects eWOM in social media in addition to the qualities of eWOM information. More specifically, the IACM incorporates consumer behavioral components such as NI and ATI into the analysis of eWOM information.

According to Abedi et al. (2020), future studies on IA should explore peripheral aspects of information, including its quantity. Similarly, Yones and Muthaiyah, (2022) consider argument quantity to be a key aspect of peripheral cues. Research by Ismagilova et al. (2021) demonstrates that the amount of eWOM influences its usefulness. Therefore, to incorporate these findings, we introduce the IQT factor into this research framework (Figure 2).

Figure 2. Information Acceptance Model (Erkan & Evans, 2016).

2.2. Research framework and hypothesis development

2.2.1. Information quality (IQL)

IQL can be defined as the effectiveness of a message in persuading customers to make a purchase (Yeap et al., 2014). Many researchers have concluded that some traits that have been linked to the quality of eWOM information include understanding, clear, high quality, detail, based on facts, and relevance to the need (Cheung et al., 2008; Filieri, 2015; Park et al., 2007). It has been observed that through eWOM, IU has been positively correlated with IQL (Xue et al., 2018). Hence, the hypothesis can be formulated as follows:

H1: eWOM IQL positively and significantly affects eWOM IU.

The previous study has found that IQL plays a critical role in consumer decisions to utilize eWOM information (Cheung & Thadani, 2012). Sussman and Siegal (2003) have demonstrated that information quality has a significant impact to adopt information on consumers’ decisions. When consumers seek information, the quality of the information can influence their acceptance of eWOM communication channels as reliable sources (Shankar et al., 2020). According to Sardar et al. (2021), the higher quality and quantity of eWOM information, the higher of eWOM information adoption rate. Hence, the hypothesis can be formulated as follows:

H2: eWOM IQL positively and significantly affects eWOM IA.

2.2.2. Information credibility (IC)

According to Eagly and Chaiken (1993), credible information sources consistently deliver persuasive and optimistic messages that motivate customers to develop a positive perception of the evaluated products or services. IC possesses qualities such as being convincing, credible, true, and trustworthy (Erkan & Evans, 2018; Weitzl, 2017). In the IAM model, source credibility serves as a peripheral route through which informational influence can spread (Sussman & Siegal, 2003). Filieri (2015) suggests that IC, particularly its accuracy, impacts the ability to convince consumers and establish trust in the information. In another research by Ngarmwongnoi et al. (2020), the results of interviews indicate that eWOM credibility significantly affects eWOM usefulness. Hence, the hypothesis can be formulated as follows:

H3: eWOM IC positively and significantly affects eWOM IU.

The relationship between the IC and IA is confirmed by empirical evidence from Liang et al. (2021). Teng et al. (2017) define IC as online suggestions perceived as reputable sources, which enhances the acceptance of online information. The literature on eWOM has indicated that information credibility is a critical factor in promoting the adoption of relevant information (Tien et al., 2019). Moreover, Sussman and Siegal (2003) found that individuals who accept authentic eWOM information are more likely to use it compared to those who do not. Hence, the hypothesis can be formulated as follows:

H4: eWOM IC positively and significantly affects eWOM IA.

2.2.3. Information quantity (IQT)

IQT is defined as the frequency or volume of eWOM information or reviews provided to consumers (Filieri, 2015). Lee and Chen (2021) proposed that the extent of customer reviews can be determined by evaluating the level of involvement of each consumer in the market. López and Sicilia (2014) highlight that individuals rely on the amount of information available to comprehend the product performance. Moreover, Yones and Muthaiyah (2022) and Verma et al. (2023) demonstrated IQT in social networks has a significantly positive influence on the IU. Hence, the hypothesis can be formulated as follows:

H5: eWOM IQT positively and significantly affects eWOM IU.

According to Park et al. (2007), an increased quantity of eWOM leads to greater adoption of eWOM information by customers. Previous research has also shown that IQT influences IA (Yones & Muthaiyah, 2022). Abedi et al. (2020) further propose that increasing the quantity of information contributes to the construction of IA. Hence, the hypothesis can be formulated as follows:

H6: eWOM IQT positively and significantly affects eWOM IA.

2.2.4. Needs of information (NI)

Erkan and Evans (2016) illuminate that the desire for information is a primary incentive for eWOM activities. When considering and selecting a product, individuals rely on information, including their own experiences (Chu & Kim, 2011). Phung et al. (2020) found that NI is a significant factor impacting IU. Furthermore, individuals can provide or seek opinions to address problems by sharing eWOM information, thereby obtaining more specific and helpful feedback (Ismagilova et al., 2021). Hence, the hypothesis can be formulated as follows:

H7: eWOM NI positively and significantly affects eWOM IU.

The NI in social network users regarding eWOM information has a positive impact on the adoption of that information (Chu & Kim, 2011). Prior research has demonstrated a significant relationship between NI and eWOM adoption (Peres & Silva, 2021). Individuals who seek information in social networks are more probably to discover valuable resources and adopt them (Sardar et al., 2021). Hence, the hypothesis can be formulated as follows:

H8: eWOM NI positively and significantly affects eWOM IA.

2.2.5. Attitude towards information (ATI)

ATI was developed from the Theory of Reasoned Action (TRA) by Fishbein and Ajzen (1975) which indicates the relationship between attitudes and behavioral intentions. The Theory of Planned Behavior (TPB) (Ajzen, 1991) and the Technology Acceptance Model (TAM) (Bagozzi et al., 1992) also demonstrate similarities to the TRA theory. Moreover, individuals increasingly rely on information about products when they make a purchase (Park et al., 2007). Erkan and Evans (2016) accommodated ATI into the IACM and suggested that attitudes of social media towards information can have a positive effect on IU. Moreover, numerous studies show that attitude towards eWOM information has been positively related with information usefulness (Leong et al., 2021; Wan & Shen, 2015). Hence, the hypothesis can be formulated as follows:

H9: eWOM ATI positively and significantly affects eWOM IU.

Sardar et al. (2021) consider ATI as one of the critical factors influencing consumer reception of eWOM information. According to Verma et al. (2023), attitude towards eWOM has a direct impact on eWOM. Additionally, a study conducted by Sardar et al. (2021) supports these findings by demonstrating the substantial and positive impact of ATI, indicating that consumers who possess a positive attitude towards eWOM information are more inclined to adopt such information. Hence, the hypothesis can be formulated as follows:

H10: eWOM ATI positively and significantly affects eWOM IA.

2.2.6. Information Usefulness (IU) and information adoption (IA)

According to Yeap et al. (2014), IU refers to an individual’s awareness regarding the improvement of their performance through the utilization of new technologies. This perspective is supported by previous studies by Cheung et al. (2008), Erkan and Evans (2018), and Gökerik et al. (2018), who define IU as a valuable, informative, and supportive indicator. Sardar et al. (2021) suggest that individuals are more likely to adopt information if they perceive it to be relevant and valuable to their specific needs and goals. In the context of social networks, where individuals are exposed to a substantial volume of eWOM information, the presence of IU plays an important role in its acceptance. The previous studies have argued that the perceived IU of eWOM information positively influences individuals’ likelihood to adopt it (Erkan & Evans, 2016; Kohler et al., 2023). Hence, the following hypothesis is proposed:

H11: eWOM IU positively and significantly affects eWOM IA.

2.2.7. Information adoption (IA) and online purchase intention (PI)

Information Adoption (IA), is the cognitive process through which individuals internalize and accept information from external sources, thereby enhancing their understanding and aiding in decision making (Shen et al., 2014). The conscious adoption of information is demonstrated in the process of personal behavioral intention (Wang, 2016). In addition, IA has been identified as a factor influencing customers’ purchasing intentions (Erkan & Evans, 2016; Kohler et al., 2023). Torres et al. (2018) highlight a positive correlation between purchase intention and information adoption, with information adoption through social media platforms exerting a significant impact on individual purchase intentions. Hence, the following hypothesis is proposed:

H12: eWOM IA positively and significantly affects eWOM PI.

2.2.8. The mediating effect of information usefulness and information adoption

In addition to the direct relationships among the variables, this research additionally examines the mediating of IU and IA between the relationships of independent and dependent variables. In several studies, researchers have delved into the complex connections between IU and IA in relation to eWOM and its impact on PI. For instance, Erkan and Evans (2016) highlighted the role of eWOM adoption in aiding suppliers by transforming recommendations from social networking sites into tangible purchases. Tien et al. (2019) explored the effects of customer-to-customer eWOM on purchase intention within social networking sites, indicating that IA acts as a mediator between IU and PI. In another study, Sardar et al. (2021) revealed that factors like IQL, IC, ATI, and NI have indirect effects on PI, where IA plays a mediating role. Additionally, eWOM IU is shown to empower consumers, influencing their attitudes and purchase decisions (Wang et al., 2012). Furthermore, consumers benefit from higher levels of eWOM credibility, impacting their consumption choices and decisions. Based on the literature, the following hypotheses were proposed:

H12a: IU and IA mediate the impact between IQL and PI.

H12b: IU and IA mediate the impact between IC and PI.

H12c: IU and IA mediate the impact between IQT and PI.

H12d: IU and IA mediate the impact between NI and PI.

H12e: IU and IA mediate the impact between ATI and PI.

2.2.9. Research framework

The research framework has been constructed by integrating relevant literature and formulating hypotheses, as demonstrated in Figure 3. It investigates the impacts of Information Quality (IQL), Information Credibility (IC), Information Quantity (IQT), Needs of Information (NI), and Attitude towards Information (ATI) as independent variables on the dependent variable Information Usefulness (IU), Information Adoption (IA), and Online Purchase Intention (PI). The mediating roles of Usefulness (IU), and Information Adoption (IA) are also explored in these relationships.

Figure 3. The proposed research framework.

3. Method

3.1. Research design

The research used a quantitative method to examine the correlation between different aspects of eWOM information, consumer behavior, and the online purchase intentions of Generation Z in Vietnam. Google Forms served as the survey tool for data collection. Target respondents needed to satisfy two criteria: having used social media and having participated in online shopping.

The research consisted of two phases: a pilot study and an empirical study. The pilot study served as a crucial step to assess the feasibility of the research before proceeding to a greater scale (Cope, 2015). In the Empirical study, a total of 280 responses were used to analyze the constructs and model to demonstrate the relationship between eWOM in social media and online purchase intention. Both pilot study and empirical study in the research used quantitative research methods.

3.2. Questionnaire and sample

There are two main sessions in the questionnaire. The first session collected demographic information of the participants, including gender, age, income, education, major, and duration of social media usage (Sheard, 2018). The second session assessed all variables which comprise multiple items to enhance reliability. All items were designed as closed-ended questions using a 5-point Likert scale (ranging from 1- strongly disagree to 5- strongly agree), inherited from previous studies by Erkan and Evans (2016), Leong et al. (2021), and Yones and Muthaiyah, (2022).

The research applied a non-probability sampling method, specifically adopting convenience sampling for data collection. Convenience sampling was chosen as it is a widely used method due to its practicality, efficiency, and cost-effectiveness in research (Acharya et al., 2013). This sampling technique is particularly suitable when obtaining a complete population list is unfeasible, as highlighted by Wiśniowski et al. (2020). The decision to employ convenience sampling allowed the researchers to select participants based on accessibility and availability within a specified time frame. This strategic choice enables to simplify the data collection, address the practical challenges in the research environment, and most important improving the study’s effectiveness (Stratton, 2021). The survey had a total of 350 responses between April 01, 2023, and May 15, 2023. Among these, 280 responses were deemed valid and included in the subsequent analysis. The sample size was determined based on the expectation of at least 200 responses for exploratory factor analysis (EFA) (Gorsuch, 1983), as well as the guideline of having a sample size at least five times the total number of observed factors (Hair et al., 1998). With 28 observed variables in the research, the sample size was calculated as 28 * 10 = 280.

3.3. Data collection and analysis

The utilization of Google Forms facilitated the collection and management of data for both online and offline surveys. In the case of online surveys, the Google Forms questionnaire was disseminated through email and various social media platforms, including Facebook, Zalo, and Instagram. Furthermore, to conduct offline surveys, researchers visited universities, schools, and offices to administer the Google Forms questionnaire. The adoption of this dual approach in data collection modes was deliberate, aiming to enhance the reliability of the respondents’ input.

The dataset was screened for outliers (Hoaglin & Iglewicz, 1987) and was analyzed using SPSS 26.0 software in two stages: the pilot study and the empirical study. In the pilot study, Cronbach’s Alpha Reliability Coefficient and EFA were conducted to assess the reliability of the variables and exclude any unreliable ones. Subsequently, CFA and SEM were applied to examine the factor structure and correlation model of the variables. Prior to conducting the large-scale survey, the variables were adjusted to align with the research model. In the Empirical study, frequency analysis, Cronbach’s Alpha Reliability Coefficient, EFA, and SEM were applied. SEM represents multivariate quantitative technique that facilitate in-depth explanatory analysis with the neccesary statistical efficiency. This analysis tool empowers researcher to ‘test or validate theoretical models for theory testing and extension’ (Thakkar, 2020). The application of this advanced approach proved particularly suitable for this study, enabling the examination of intricate relationships. It provided a comprehensive investigation into the impact of independent variables on PI through IU and IA.

4. Results

4.1. Respondents’ profile

The research participants’ characteristics were presented in Table 1. The survey targeted individuals who engage in eWOM activities on social media platforms, such as uploading, commenting, sharing, and interacting with various types of content such as statuses, blogs, videos, and images. Additionally, individuals who watch, read, share, and provide comments or reviews on products or brands were also included in the survey.

Table 1. Respondents’ profile.

Respondents’ characteristics		Frequency	Percent
Gender	Male	135	48.2
	Female	138	49.3
	Other	7	2.5
Age	14-16	6	2.1
	17-19	97	34.6
	20-22	114	40.7
	23-25	51	18.2
	26-28	12	4.3
Use Time	More than 3 hours a day	78	27.9
	From 2 hours to 3 hours a day	71	25.4
	From 1 hour to 2 hours a day	85	30.4
	Less than 1 hour a day	46	16.4
Income	Under 5 million VND	129	46.1
	From 5 million to 10 million VND	93	33.2
	Over 10 million VND	58	20.7

The respondents consisted of both male and female participants from Generation Z, specifically individuals between the ages of 14 and 28. Out of the total of 280 respondents, 135 were male, accounting for 48.2% of the participants, while 138 were female, making up 49.3% of the participants. In terms of age distribution, the majority of respondents fell within the 20-22 age group, comprising 40.7% of the sample. The second and third largest age groups were the 17-19 age group, representing 34.6% of the respondents, and the 23-25 age group, accounting for 18.2% of the participants. Among the survey respondents, 83.7% reported using social media for more than one hour per day, while 53.3% indicated spending at least two hours per day on social media.

4.2. Cronbach’s Alpha analysis

To assess the reliability of each element within the proposed theoretical model, Cronbach’s Alpha analysis was employed in this research. The factors were evaluated based on the test conditions, in which the Total Correlation Value needed to be higher than or equal to 0.3 for the acceptance of items. Additionally, the Cronbach’s Alpha coefficient should be greater than or equal to 0.6, as proposed by George and Mallery (2003). In the research, a pilot was carried out including 65 test samples, the results showed that the Crobach’s Alpha reliability of the factors ranged from 0.700 to 0.856 (greater than 0.6). The reliability of the questionnaire is accepted to continue to collect the survey sample. The findings demonstrated that all variables satisfied the specified criteria, indicating their fulfilment of the requirements. Consequently, the factors examined in the research exhibited reliability and were deemed suitable for further testing. As presented in Table 2, the Cronbach’s Alpha coefficients for the factors were notably high, ranging from 0.749 to 0.824. These coefficients affirm the reliability of all items included in the research and their suitability for research purposes.

Table 2. Cronbach’s alpha analysis.

Code	Standard Deviation (SD)	Corrected Item-Total Correlation	Mean	Cronbach’s Alpha
IC (Information credibility)			3.289	0.824
IC1	0.989	0.676	4.02
IC2	0.682	0.642	3.47
IC3	0.816	0.660	3.29
IC5	0.870	0.653	3.63
NI (Need of information)			3.984	0.749
NI1	0.902	0.599	4.08
NI2	0.872	0.599	3.89
IQL (Information quality)			3.696	0.822
IQL1	1.012	0.659	4.01
IQL2	0.863	0.694	3.71
IQL5	0.673	0.618	3.76
IQL6	0.894	0.650	3.30
ATI (Attitude towards information)			4.143	0.811
ATI1	0.894	0.654	4.018
ATI2	0.920	0.649	4.10
ATI3	0.922	0.677	4.15
IQT (Information quantity)			4.020	0.818
IQT1	0.983	0.692	4.07
IQT2	1.019	0.692	3.97
IA (Information adoption)			4.061	0.765
IA1	0.930	0.603	4.05
IA2	0.861	0.616	4.02
IA3	0.835	0.577	4.11
IU (Information usefulness)			4.181	0.782
IU1	0.890	0.642	4.21
IU2	0.919	0.583	4.14
IU3	0.914	0.635	4.19
PI (Purchase intention)			4.007	0.806
PI1	0.857	0.667	3.98
PI2	0.944	0.659	4.05
PI4	0.884	0.639	3.99

4.3. Exploratory factor analysis (EFA)

Exploratory Factor Analysis (EFA) was a widely used method for determining the underlying structure of a set of observed variables in quantitative research (Hair et al., 2014). In this research findings were obtained through EFA, with the removal of item IQL5 (0.465) and items IC2 due to concerns regarding unidirectionality when included in both groups. A total of eight factors were extracted based on the criterion of Eigenvalue 1.056 greater than 1, indicating a high loading factor, as presented in Table 3.

Table 3. Exploratory factor analysis.

Items	Factor
	PI	ATI	IC	IQL	IU	IA	IQT	NI
PI2	0.906
PI1	0.875
PI4	0.740
ATI1		0.870
ATI3		0.856
ATI2		0.815
IC5			0.888
IC3			0.809
IC1			0.793
IQL6				0.863
IQL2				0.843
IQL1				0.729
IU3					0.847
IU1					0.844
IU2					0.798
IA2						0.889
IA3						0.808
IA1						0.751
IQT1							0.926
IQT2							0.891
NI1								0.892
NI2								0.885
Eigenvalue	5.549	2.214	2.136	1.627	1.472	1.123	1.111	1.056
Total variance extracted (%)	74.037%

To assess the appropriateness of the EFA analysis, Hair et al. (2010) provided standards to follow. The Kaiser-Meyer-Olkin (KMO) measure, an indicator of sampling adequacy, produced a value of 0.810, exceeding the suggested threshold of 0.5. Additionally, the Bartlett test, evaluating the null hypothesis of no relationship between variables, yielded a statistically significant result with a significance level of lower than 0.05. Subsequently, Confirmatory Factor Analysis (CFA) was performed, resulting in eight factors that explained 74.037% of the variation in the data. This percentage exceeds the minimum threshold of 50% required for satisfactory explanation of the observed variables (Hair et al., 2010).

4.4. Confirmatory factor analysis (CFA)

The Confirmatory Factor Analysis (CFA) results provide compelling evidence regarding the convergent and discriminant validity of the theoretical structures. The CFA analysis model met the requirements based on various analytical indicators. The CMIN/DF ratio was 1.107, which is smaller than 5. The CFI (Comparative Fit Index) value was 0.991, surpassing the minimum acceptable threshold of 0.9. The GFI (Goodness of Fit Index) value was 0.939, exceeding the recommended value of 0.8. The RMSEA (Root Mean Square Error of Approximation) value was 0.02, falling below the critical value of 0.08. Lastly, the PCLOSE value was 1.000, which is greater than the significance level of 0.05. Therefore, the factors of the model were a good fit to the data, as indicated by Hu and Bentler (1999) (Table 4).

Table 4. Measurement model.

Goodness-of-fit indices	Value Obtained	Recommend Value
χ2 /df	1.107	< 5
GFI	0.939	> 0.8
TLI	0.988	> 0.9
CFI	0.991	> 0.9
RMSEA	0.020	< 0.08
PCLOSE	1.000	> 0.05

Furthermore, all observed variables demonstrated standardized regression weights that were at least 0.7 times greater than 0.5, indicating a strong agreement between the variables (Hair et al., 2010). This provides additional support for the reliability and validity of the observed variables in relation to the theoretical model.

The AVE and CR values were shown in Table 5. Accordingly, CR values were greater than 0.7 and AVE values were above 0.5, indicating that the scales are all convergent (Hair et al., 2010). Discriminant validity was examined using Fornell and Larcker (1981) criteria. The square root of the Average Variance Extracted (AVE) for each construct should be higher than the correlation coefficients between constructs for adequate discriminant validity. In Table 5, all eight factors are distinct from each other, and the diagonal elements (representing the AVE) exceed the interconstruct correlation coefficients, fulfilling Fornell and Larcker (1981) criteria for discriminant validity.

Table 5. AVE and CR results.

	CR	AVE	MSV	IA	IU	PI	IQL	ATI	IC	IQT	NI
IA	0.766	0.522	0.318	0.723
IU	0.784	0.548	0.285	0.464	0.740
PI	0.806	0.582	0.318	0.564	0.242	0.763
IQL	0.800	0.571	0.285	0.446	0.534	0.340	0.756
ATI	0.811	0.589	0.129	0.327	0.211	0.165	0.135	0.767
IC	0.794	0.562	0.285	0.358	0.341	0.202	0.534	0.186	0.750
IQT	0.819	0.693	0.177	0.338	0.404	0.107	0.420	0.360	0.354	0.833
NI	0.753	0.605	0.073	0.270	0.226	0.174	0.048	0.228	0.100	0.072	0.778

Note: Correlations below the diagonal; Squared root of AVE on the diagonal.

Overall, the CFA results indicate strong support for the convergent and discriminant validity of the theoretical structures, as demonstrated by the fulfilment of the requirements and the high agreement among the observed variables.

4.5. Structural Equation Modelling (SEM)

Structural Equation Modelling (SEM) was conveyed in this research to assess the correlation among the components of the model. Figure 4 presents the findings obtained from the SEM analysis. The evaluation of the model’s goodness of fit revealed that the χ2/df value was 1.101, which is below the threshold of 5. The RMSEA was 0.019, lower than the recommended value of 0.08, the GFI value exceeded 0.8 and the TLI (Tucker-Lewis Index) was 0.989, higher than 0.9. Moreover, the CFI value was 0.939, which exceeded the minimum threshold of 0.8. These findings indicate that the model accurately describes the data, and the scales used in the study are appropriate, as indicated by Byrne (1998) (Table 6).

Figure 4. Structural Equation Model (SEM).

Table 6. Measurement model.

Goodness-of-fit indices	Value Obtained	Recommend Value
χ2 /df	1.101	< 5
GFI	0.938	> 0.8
TLI	0.989	> 0.9
CFI	0.991	> 0.8
RMSEA	0.019	< 0.08
PCLOSE	1.000	> 0.05

The results of the SEM analysis revealed the following relationships: ATI (0.59) and IC (0.825) did not significantly affect IU, with Sig values greater than 0.05. Similarly, IC (0.305) and IQT (0.818) showed no significant influence on IA, as indicated by Sig values greater than 0.05. On the other hand, three factors - IQL, IQT, and NI - displayed significant effects on IU, with Sig values less than 0.05 (0.000, 0.022, and 0.013, respectively). Similarly, IQL, ATI, and NI, with Sig values less than 0.05 (0.010, 0.015, 0.030, respectively), significantly influenced IU. Furthermore, IU, with Sig value of 0.025, had an impact on IA, while IA, with Sig value of 0.000, had a significant effect on PI (Table 7).

Table 7. Hypotheses testing results (direct relationships).

Direct Relationships	Coefficient	SE	P	Result
IU ← IQL	0.418	0.107	***	Accepted
IU ← ATI	0.043	0.079	0.59	Rejected
IU ← IC	0.020	0.091	0.825	Rejected
IU ← IQT	0.171	0.074	0.022	Accepted
IU ← NI	0.196	0.079	0.013	Accepted
IA ← IQL	0.290	0.113	0.010	Accepted
IA ← ATI	0.191	0.079	0.015	Accepted
IA ← IC	0.091	0.089	0.305	Rejected
IA ← IQT	0.017	0.073	0.818	Rejected
IA ← NI	0.172	0.079	0.030	Accepted
IA ← IU	0.204	0.091	0.025	Accepted
PI ← IA	0.535	0.076	***	Accepted

Note: (*P < 0.1; **P < 0.05, ***P < 0.01).

Table 8 presents the results of hypothesis testing for indirect effects within the research model. The analysis supported the idea that IU and IA mediated the relationship between certain independent variables and PI, although with their mediating roles considered as partly mediating. Specifically, ATI has an impact on IA but not on IU. However, the overall mediating role of IU and IA in the relationship between ATI and PI is supported (β = 0.109, p = 0.010). Factors NI (β = 0.113, p = 0.007) and IQL (β = 0.200, p = 0.006) exerted influences on both IU and IA, and the mediating role of IU and IA in the relationship between NI, IQL, and PI was supported, indicating a full mediation effect of IU and IA. Notably, among these three variables, IQL exhibited the most significant indirect influence on PI through IU and IA. Conversely, the results indicate that two independent variables, IQT (β = 0.033, p = 0.484) and IC (β = 0.052, p = 0.368), were not significantly mediated by IU and IA in their relationship with PI, as evidenced by p > 0.05. In other words, IU and IA did not play a significant mediating role in the connection between IQT and PI, or between IC and PI. Overall, it can be concluded that IU and IA act as partial mediators in the relationships between the independent variables and PI. The findings revealed that information quality is most strongly influenced indirectly in shaping purchase intention.

Table 8. Hypotheses testing results (indirect effects).

Indirect Relationships	Beta (β)	P	Result
NI → IU → IA → PI	0.113	0.007	Accepted
IQT → IU → IA → PI	0.033	0.484	Rejected
IC→ IU → IA → PI	0.052	0.368	Rejected
ATI→ IU → IA → PI	0.109	0.010	Accepted
IQL→ IU → IA → PI	0.200	0.006	Accepted

In summary, the SEM analysis results demonstrate that the model fits the data well, with specific variables significantly affecting one another, thus enhancing our comprehension of the interconnections among the variables under investigation (Table 9).

Table 9. Measurement of variables in the research model.

No	Variables	Measurement
I.	Information Quality (Cheung et al., 2008; Filieri, 2015; Park et al., 2007)
1.		‘I can understand eWOM information shared in social media’.
2.		‘I think eWOM information shared in social media is clear’.
3.		‘I think eWOM information shared in social media is based on facts’.
4.		‘I find eWOM information in social media relevant to my needs’.
5.		‘I think eWOM information in social media is detailed’.
6.		‘In summary, I think eWOM information in social media is of high quality’.
II.	Information Credibility (Erkan & Evans, 2018; Filieri, 2015; Weitzl, 2017)
1.		‘I think eWOM information in social media is convincing’.
2.		‘I think eWOM information in social media is credible’.
3.		‘I think eWOM information in social media is believable’.
4.		‘I think eWOM information in social media is true’.
5.		‘I think eWOM information in social media is trustworthy’.
III.	Information Quantity (López & Sicilia, 2014)
1.		‘I can rely on the amount of eWOM information in social media’.
2.		‘The amount of eWOM information in social media can help me understand the product performance’.
IV.	Needs of Information (Chu & Kim, 2011)
1.		‘I like to apply eWOM information in social media when I concern new products’.
2.		‘If I have little experience with a product, I often find eWOM information in social media about them’.
V.	Attitude towards Information (Park et al., 2007)
1.		‘I always review eWOM information in social media when I want buy a product’.
2.		‘I find eWOM information in social media helpful for me to make decision when buying a product’.
3.		‘I feel eWOM information in social media make me more confident in purchasing product’.
VI.	Information Usefulness (Bailey & Pearson, 1983; Cheung et al., 2008; Erkan & Evans, 2018; Gökerik et al., 2018)
1.		‘eWOM information in social media is generally useful for me to evaluate the product’.
2.		‘eWOM information in social media is generally helpful for me to evaluate the product’.
3.		‘eWOM information in social media is generally informative for me to evaluate the product’.
VII.	Information Adoption (Shen et al., 2014)
1.		‘I learn something new of brands through eWOM information in social media’.
2.		‘I accept the eWOM information of brands in social media’.
3.		‘I accept the eWOM recommendation of brands in social media’.
VIII.	Purchase intention (Coyle & Thorson, 2001)	After reviewing the eWOM information spread in social media about the products:
1		‘It is very likely that I will buy the product’.
2		‘I will buy the product next time I need a product’.
3		‘I will definitely try the product’.
4		‘I will recommend the product to my friends’.

5. Discussion

The results of the research indicate that IU, IA in the IAM model are acceptable in the research context on the impact of independent factors including IQL, IC, IQT, NI, ATI on purchase intention. In consistent with previous studies (Erkan & Evans, 2016; Kohler et al., 2023; Torres et al., 2018), the findings demonstrated a significantly positive impact of eWOM IA on PI. In fact, PI increases when the respondents accept eWOM information and recommendations regarding brands; as well as if they learn something new of brands through eWOM information. Additionally, this study found a significantly positive relationship between IU and IA, which is supported by several research (Chu & Kim, 2011; Kohler et al., 2023; Sardar et al., 2021). This suggests that customers are more likely to accept information and make purchase decisions when they find it useful, helpful, and informative. Particularly in the context of social media, individuals exposed to extensive eWOM information tend to accept it when they perceive it as useful. Overall, these findings suggest that the IU and IA of the IAM model are appropriate in the context of social networks eWOM and that these factors have significant effects on Generation Z’s purchasing intention.

The discussion also focuses on the factors related to eWOM information characteristics within the Information Acceptance Model (IAM). The IQL has a significantly positive impact on IU, as found in a study by Xue et al. (2018) and Yones and Muthaiyah, (2022). Social network users tend to perceive information as high quality when it is clear, understandable, and reliable. Similarly, IQL has a significantly positive impact on IA, which is consistent with studies by Shankar et al. (2020) and Sardar et al. (2021). This implies that users are more likely to accept information as credible and useful if they perceive it to be high quality. Overall, when consumers seek and access information through eWOM, the quality of the information can influence their adoption and perception of its usefulness in meeting their needs. These findings align with the theories presented in the literature review that support the relationships between IQL and IU, IA in the research framework. However, the results also reveal some inconsistencies with previous studies. The relationship between IC and IU was found to be insignificant, which contradicts the findings of Park et al. (2007), Ngarmwongnoi et al. (2020) but aligns with Huete-Alcocer (2017), who suggested that eWOM information, being anonymous, can negatively impact reliability. This implies that IC may not be perceived as useful by users in social networks. Furthermore, IC had no significant impact on IA, inconsistent with the findings of Liang et al. (2021), and Tien et al. (2019). These differences in results may be associated with the research context, which focused exclusively on Generation Z in Vietnam, as opposed to the broader scope of Liang et al. (2021). Although there are similarities with the study conducted by Tien et al. (2019) in terms of the age demographics of participants, it’s essential to note that Tien et al. (2019) focused on understanding purchase intentions related to skincare products. In contrast, the present research differs by not restricting its scope to a particular product category or industry.

Concerning the quantitative factor of eWOM information, IQT was found to have a significantly positive impact on IU, in accordance with studies conducted by Ngarmwongnoi et al. (2020), Yones and Muthaiyah, (2022), and Verma et al. (2023). This indicates that consumers benefit from having more quantity-based information to evaluate product performance and quality. In contrast, the impact of IQT on IA was found to be statistically insignificant, which contradicts the findings of Abedi et al. (2020) and Yones and Muthaiyah, (2022). One possible explanation for this disparity in results lies in the variation of respondents. This research specifically targeted the Generation Z demographic in the cultural context of Vietnam, whereas Abedi et al. (2020) had a broader scope and a different context. In a recent study, Yones and Muthaiyah, (2022) explored the impact of eWOM on the purchase intention specifically related to a beauty brand on the social networking platform TikTok. It is worth noting that responses do not rely on the quantity of information as a shortcut to determining product popularity. This observation may help elucidate the lack of predictive value associated with this construct.

Regarding the consumer behavior factor of the Information Acceptance Consumer Model (IACM), the findings show that NI has a significantly positive impact on IU, as demonstrated by Erkan and Evans (2016), and Phung et al. (2020). This means individuals consider information useful when it serves their purpose. Additionally, NI also had a significantly positive impact on IA, in accordance with the findings of Erkan and Evans (2016), Sardar et al. (2021), and Peres and Silva (2021). This suggests that Generation Z consumers in Vietnam are more likely to accept information when they find it relevant to their needs or have prior experience with it. As proved in the study by Verma et al. (2023), Sardar et al. (2021) ATI had a significantly positive impact on the eWOM IA. Accordingly, customers exhibit increased attentiveness and careful absorption of information when they possess a favorable attitude towards it. However, contrary to the studies conducted by Leong et al. (2021), and Wan and Shen (2015), this research found an insignificant impact of ATI on IU. It should be noted that the former study (Leong et al., 2021) focuses on customers of bubble milk tea drinks, while the latter (Wan & Shen, 2015) explores the research context of urban green space. The variations in results may be attributed to differences in respondents and research contexts. In this research, the focus is specifically on Generation Z in Vietnam and the influence of eWOM information on purchase intention. Nonetheless, ATI remains an influential factor that affects consumers’ behavioral responses when utilizing eWOM information in their purchase intention. In the context of this research, it is observed that respondents tend to disregard information sources that have no impact on their attitudes.

6. Conclusion

This research investigates the influence of eWOM information on Information Usefulness (IU), Information Adoption (IA), and their indirect impact on Purchase Intention (PI) among Generation Z in the context of social media. The research employed EFA, CFA, and SEM analysis, utilizing a sample size of 280 participants. The results revealed three factors, namely IQL, IQT, and NI, significantly impacting IU. Likewise, three factors, IQL, NI, and ATI, were found to significantly influence IA. On the other hand, two factors, IC and ATI, did not demonstrate a significant impact on IU, while IC and IQT did not significantly affect IA. Notably, IQL emerged as the most influential factor, exerting a significantly positive impact on both IU and IA. Furthermore, IU displayed a positive influence on IA, which in turn positively affected the PI of Generation Z. Additionally, the result identified the partly mediating roles of IU and IA in the relationship between independent variables and online purchase intentions. These findings provide valuable insights for marketers, enabling them to enhance their eWOM communication strategies targeted at potential customers. Moreover, the exploration of the impact of eWOM information within social networks on the purchase intention of Generation Z contributes to a more accurate understanding and assessment for researchers and marketers seeking to engage this particular demographic segment.

6.1. Theoretical implications

This research significantly contributes to the existing literature on the online purchase intention of Generation Z by investigating the impact of eWOM in social media. While previous studies have recognized the validity of the Information Adoption Model (IAM) in the context of eWOM information, the specific relationship between eWOM information in social networks and consumer purchase intention remains inadequately understood (Erkan & Evans, 2018). Building upon the findings of Yones and Muthaiyah, (2022), this research explores the simultaneous impact of independent factors on both IU and IA. The innovative aspect of this research lies in the incorporation of factors from the Information Adoption and Consumer Behavior Model (IACM), and the inclusion of quantitative factors of information (IQT) within the conceptual framework.

6.2. Managerial implications

This research provides valuable insights for marketers seeking to understand how eWOM information in social media influences the purchase intentions of Generation Z consumers. The framework developed in this research offers meaningful managerial implications as it identifies the key elements of eWOM information. Considering the significant importance of social media platforms to marketers due to their extensive user base and suitability for eWOM (Canhoto & Clark, 2013), understanding eWOM characteristics, and consumer behavior factors that positively influence PI is crucial.

Firstly, marketers should focus on improving the quality of information to enhance its usefulness and adoption. Secondly, gaining a deep understanding of customers’ Needs for Information (NI) enables marketers to grasp market dynamics and develop tailored marketing campaigns. Thirdly, increasing the volume of information shared in social media platforms can contribute to creating brand awareness. Finally, improving the content of information to cater to the desired preferences of target customers will foster a positive attitude toward receiving information.

Overall, this research highlights essential aspects of eWOM information that are of particular interest to Generation Z consumers, providing substantial value to marketers. By leveraging the insights gained from this research, marketers can improve their comprehension of the dynamics of eWOM in social media and effectively engage with Generation Z, ultimately enhancing their marketing strategies and achieving greater success in targeting this demographic segment.

6.3. Limitation and recommendation

This research acknowledges certain limitations that should be considered in interpreting the findings. Firstly, the sample used in this research, consisting of 280 participants, primarily represents Generation Z in the southern region of Vietnam. Thus, the generalizability of the findings to the entire Generation Z population in Vietnam may be limited. Future research should aim to expand the sample size and include participants from diverse geographic locations across Vietnam. This would provide a more comprehensive understanding of the characteristics and consumer behavior of Generation Z in different regions.

Additionally, it is worth exploring the possibility of developing a research model that compares the characteristics and behaviors of different demographic generations. By including multiple generations in the analysis, researchers can gain insights into how eWOM information influences consumer behavior and purchase intention across various age groups. This comparative approach would contribute to a more comprehensive understanding of the dynamics of eWOM information in different generational contexts. Moreover, it is essential to explore additional demographic variables, including gender, age, and income, among others. These factors may exert a notable influence on the Information Usefulness and Information Adoption.

To address these limitations and enhance the overall understanding of the research area, future studies should focus on increasing the sample size, exploring more demographic factors, diversifying the geographical representation, and incorporating a comparative analysis across different generational cohorts. By undertaking these measures, researchers can strengthen the credibility and applicability of the findings, thereby establishing a more resilient foundation for marketers and decision-makers seeking to engage Generation Z consumers effectively.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Thi Thuy An Ngo

Thi Thuy An Ngo is a Business lecturer and the Head of Soft Skills Department at FPT University, Can Tho, Vietnam. She holds two master’s degrees from Ghent University, Belgium, one in Economic Management and the other in Aquaculture. Her research interests include contemporary issues in education, marketing, business management, entrepreneurship, innovation, and sustainability. She has published several research papers in both national and international journals.

Binh Long Vuong

Binh Long Vuong is a Bachelor in International Business from the Department of Business Administration at FPT University, Can Tho, Vietnam. Her major field of study is in Marketing.

My Dien Le

My Dien Le is a Bachelor in International Business from the Department of Business Administration at FPT University, Can Tho, Vietnam. Her major field of study is in Logistic.

Thanh Trung Nguyen

Thanh Trung Nguyen is a Bachelor in International Business from the Department of Business Administration at FPT University, Can Tho, Vietnam. His major field of study is in Marketing.

My My Tran

My My Tran is a Bachelor in International Business from the Department of Business Administration at FPT University, Can Tho, Vietnam. Her major field of study is in Logistic.

Quoc Khanh Nguyen

Quoc Khanh Nguyen is a Bachelor in International Business from the Department of Business Administration at FPT University, Can Tho, Vietnam. His major field of study is in Marketing.