Organizations and Markets in Emerging Economies ISSN 2029-4581 eISSN 2345-0037
2019, vol. 10, no. 2(20), pp. 227–256 DOI: https://doi.org/10.15388/omee.2019.10.12

The Influence of Recommendations in Social Media on Purchase Intentions of Generations Y and Z

Rasa Pauliene (Corresponding author)
Associate Professor, Vilnius University
rasa.pauliene@evaf.vu.lt

Karina Sedneva
MSc in Business Management, Vilnius University

Abstract. The aim of this study is to explore how the impact of recommendations in social media on intention to purchase varies between generations Y and Z. The research focuses on two types of online recommendations, namely online reviews and opinion leaders’ recommendations, and e-WOM, which refers to recommendations made by followers. It also aims to examine which of the two types predominates among generations. Based on various studies, a theoretical research model was developed as well as quantitative and qualitative research was employed. The research findings supported the idea that social media recommendations have an influence on purchasing intentions of consumers, however, the main managerial applications of this study are connected with the differences among consumers. Online reviews had been an influential source of information for Generation Y; however, it is losing its influential power towards shaping purchasing intentions. E-WOM is still important, thus brands and retailers are advised to develop and maintain branded communities in social media, encourage their consumers to share feedback not only in social media, but also in rating websites, apps and services. Retailers are advised to segment their target audience very carefully, as differences in generations’ social media habits and information adoption exist.

Keywords: e-WOM recommendations, social media usage, online reviews, opinion leaders, purchase intentions, generations Y and Z.

Received: 4/28/2019. Accepted: 11/11/2019
Copyright © 2019 Rasa Pauliene, Karina Sedneva. Published by Vilnius University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

Consumer communication through social media has become an important issue for marketing specialists around the world. Product image and reputation can now be built or destroyed without brand officials, instantly. Customers can quickly share their shopping experience on any social media as well as make recommendation regarding latest purchase to a follower through an immediate status update (Forbs & Vespol, 2013). This creates challenges as well as opportunities for modern businesses. Social media create a type of electronic word-of-mouth (hereinafter, e-WOM) activity with customer’s access to uploading links and photos, allowing share of emotions, habits, interests and findings with other participants of the network. They act as e-WOM because the ideas are repeated in social media by followers and opinion leaders (Zhaveri, 2013). Social media influence has been remarkably discussed in scientific literature recently, however, there are a lot of uncovered issues regarding this topic. The latest research of Forbs and Vespol (2013), Gunavan and Huang (2015), Wang et al. (2012), Zhaveri (2013) shows the potential of social media in shaping customer behavior. Most of researchers agree that social media infrastructure encourages consumers to share their experience and recommend products to their friends, relatives, or similar customers (Gunawan & Huang, 2015), however, the question of differences between consumer segments in evaluating social media recommendations is still vague.

The problem of the paper lies in different perception of recommendations in social media in different consumer segments. This is reasonable, as age, income, social status, education and gender influence how the person perceives oneself, uses social media and what content he or she absorbs there. According to the theory of generations, generational cohorts share life experiences which cause them to develop similar attitudes and beliefs (Lazarevic, 2012; Meriac et al., 2010). These shared life experiences and social contexts cause each generational cohort to develop different beliefs, expectations and views regarding their lives and consequently, different behaviors (Dries et al., 2008; Lancaster & Stillman, 2002). This results in generational cohorts developing distinct characteristics (Kupperschmidt, 2000). Therefore, these cohorts capture not only differences in age but also differences in values (Schewe & Meredith, 2004), and in attitudes and beliefs (Meriac et al., 2010).

The theory of generations provides a broad sociocultural approach rather than an individual focus on the consumer (Pendergast, 2007). Instead of competing with other paradigms of understanding groups, it complements them by clarifying how social context helps to create some homogeneous traits among generations (Pendergast, 2009). Utilizing generational cohorts allows marketers to create familiarity and personal appeals within marketing communications and, therefore, be more likely to target products and promotions more effectively (Meredith & Schewe, 2002).

For research purposes, it seems useful to divide social media users into two segments, also known as generations Y and Z. While the core of such differentiation is age, these segments also vary in terms of motives, triggers, world perception, income, education, types of social media they use, and time they spend on it. Moreover, these 2 generations will constitute the main purchasing power in the near future (by 2025, Generations Y and Z will constitute more than 75% of labor force – EY, 2015), which makes them targets for marketers (Apresley, 2010; Bolton et al., 2013), however, research on this topic is still limited.

The aim of the paper is to explore how the impact of recommendations in social media on intention to purchase varies between generations Y and Z. Considering previous research of Rapp, Beitelspacher, Grewal and Hughes (2013), Bambauer-Sachse and Mangold (2011), Hsu, Lin and Chiang (2013), Yoo and Donthu (2001), this research focuses on two types of online recommendations: online reviews and opinion leaders’ recommendations on the one hand, and e-WOM, which refers to recommendations made by followers, on the other hand; it also examines the dominance of these types among generations. To achieve this aim, a series of objectives was set: to define how social media recommendations contribute to purchase intentions; to explore the main factors of adopting recommendations; to examine whether usage and engagement in social media affects the perception of recommendation in social media and, therefore, intention to buy; to compare the influence of social media recommendation on Generations Y and Z intention to buy. Such researchers as R. Bolton, P. Gupta, K. Gwinner, J. Harris, T. Hennig-Thurau, etc. significantly contributed to the science providing new models and concepts in understanding consumer behavior. Based on general theories of communication within the frameworks of the theory of information adoption (Sussman & Siegal, 2003), the fundamental interpersonal relations orientation theory (Schutz, 1966) and the theory of planned behavior (Ajzen, 1991), which explain the motives of people sharing content online, this research expands understanding of the concept of social media e-WOM and attempts to provide useful insights for business practitioners.

In the Inclusive Development Index published by the World Economic Forum, Lithuania remains at the top of the list of the world’s emerging economies1. According to Castaño and Flores (2019), emerging markets are substantially different from markets in high-income, industrialized societies. While many aspects of consumer behavior are the result of inherent psychological processes and thus generalizable across countries and cultures, the specific contextual characteristics of emerging markets can significantly influence other aspects of consumer behavior. Despite the fact that Lithuania has been running private businesses for nearly three decades, customer behavior in the country is similar to that in emerging markets, therefore, research on Generations Y and Z as consumers is relevant and timely.

The paper is structured in the following way. It begins by approaching social media recommendations from scientific perspective outlining E-WOM communication as a part of general social communication that can be divided into two kinds of the main mode: shared experience (online reviews) and opinion leaders. Then the theory of generations is overviewed and the existing literature regarding differences between generation Y and generation Z is examined. This leads to section three, which proposes the theoretical model of influence of recommendations in social media on purchase intentions. The model is based on the theories on the impact of recommendations on social behavior by Katz and Lazarfeld (1955) and Arndt (1967), followed by social media studies by Abubakar et al. (2016), Hsu et al. (2013), etc. The key elements in the model are: social media recommendations represented by opinion leaders’ recommendations and online reviews, belonging to generations, usage and engagement in social media and purchasing intention. The existence of their connection is a subject to test in this empirical research. Generation Y has been recognized as a new major consumer group for almost a decade, which plays a growing and very important role in the global economy. Its general population in the world is nearly 2 billion, however, brands are still exploring how to approach and engage them in marketing activities. At the same time, Generation Z is a very new consumer group, which is currently undergoing the process of becoming individual consumers after leaving family’s budgets. However, together with generation Y, they will constitute the majority of modern consumers with increasing purchasing power in the near future. Although these generations share some habits and interests, they cannot be marketed and engaged in the same way. To reach Generation Z, companies must understand where they get information, how they absorb it, how they communicate through technologies, internet and social media. This is useful to marketers as they can consider social media usage and online recommendations to increase the likelihood of purchasing and the relationship between Generation Y and Generation Z consumers. Section four suggests conclusions and practical recommendations for marketers for targeting Generation Y and Generation Z. Then, the limitations of this study are discussed, followed by the directions for future research.

1. Approaching social media recommendations from a scientific perspective

1.1. E-WOM as a part of general social communication

Nowadays marketers become particularly interested in better understanding e-WOM, because traditional WOM as well as previous forms of communication and advertising appear to be losing their effectiveness (Abubakar et al., 2016). Therefore, it is important to understand the salient differences between electronic and traditional WOM (Gupta & Harris, 2010). E-WOM communication through social media allows consumers not only to obtain information related to goods and services from the few people they know, but also from a vast geographically dispersed group of people, who have experience with relevant products or services (Ratchford et al., 2001; Lee, 2009). Thus the diffusion pattern of e-WOM is much more dispersive, engaging not only closest friends or relatives or neighbors, but large numbers of unknown people located in great social distances (Abubakar et al., 2016).

To better understand the e-WOM mechanism, the research utilizes traditional communication theories adapted to the new paradigm. According to the communication theory, the major elements in social communication are as follows: the communicator (sender), the stimulus (message), the receiver, and the response (Xiaobo, 2014). The communicator is assigned to the individual who broadcasts the message. While WOM introduces only consumers as communicators, e-WOM includes companies that hope to be heard and participate in the network communication, too. The stimulus points out to the message broadcasted by the communicator. Social media become platforms for revealing true insights from consumer experiences, including the negative ones, with minimum costs, regardless of companies’ will (Childers & Rao, 1992). The receiver refers to the person who responds to the communication. The actual impact of the received information varies from person to person (Xiaobo, 2014) as the response stands for the result of the communication process and is made by the receiver. Regarding the e-WOM, customer’s purchase intention, attitude, information adoption, and trust are the most commonly investigated outcomes.

E-WOM communication should be considered as a part of general social communication that obeys the same rules. According to Xiaobo (2014), four aspects based on the pattern of cooperation and information transmission in various ways can be divided into two kinds of the main mode: share comments and opinion leaders (Xiaobo, 2014). However, the role of the former aspects differs, and the idea of communication varies; both modes positively influence consumer behavior. These two modes can be named as “shared experience” and “opinion leaders”.

1.2. Shared experience – online reviews

Evidence shows that consumers consider e-WOM as information more credible than corporate driven information which basically can be found on company’s websites or delivered through official representatives (Christodoulides et al., 2012). Abubakar et al. (2016) state that consumers find the information obtained from internet forums as more trustworthy than corporate web pages. Therefore, receivers do not feel compelled to recommend or receive manipulated information. The respondents also marked that obtaining information directly from the consumers similar to them is an important factor of gaining trustworthiness regardless of the product type reviewed.

Other scholars claim that purchase intention is influenced not only by the product value and benefits or national characteristics, but also by the type of testimonies from other consumers (Zhang et al., 2017). Lepkowska‐White (2013) indicates that multimedia factors, including video, audio and visual displays, affect the vividness of a message and its adoption. However, past studies highlight importance of the type of product being reviewed (Park & Lee, 2009). There is a lot of research proving the fact that consumers’ behavioral changes in response to recommendations may depend on the type of goods (Aggarwal & Vaidyanathan, 2005; Senecal & Nantel, 2004). The factor of the product type becomes even more important in recommendation theory as literature provides evidence of the effectiveness of different multimedia which varies with different types of goods (Hennig-Thurau et al., 2004).

1.3. Opinion leaders

Information overload is a huge issue online (Prassas et al., 2001; Zahir, 2002), which spoils shopping experience and makes consumer behavioral choices daunting. The internet offers vast seas of information which are sometimes impossible to sort through. Therefore it is important that recommendations are used as advancing tools that help consumers easily find the most consistent information. In situation like this, people tend to approach opinion leaders (Hsu et al., 2013). Previous studies have shown the source credibility as an important factor for the level of influence on intentions of a customer (Park & Lee, 2009). Internet opinion leaders, called bloggers, possess huge trustworthiness and credibility due to their popularity and public avowal. Bloggers’ recommendations constitute a kind of informal communication channel which allows consumers to observe products and brands chosen by an opinion leader and interact with the referent regarding it (Childers & Rao, 1992; Hsu et al., 2013). Empirically, many scholars verified that bloggers’ involvement significantly affects consumer’s choice regarding a certain product or brand (Bernoff & Li, 2008).

In regard to human psychology, consumers will arouse their strong desire to imitate when someone offers a specific mode to form a positive attitude (Xiaobo, 2014). The question is what drives a potential opinion leader to broadcast a message to the receiver? Understanding social media users’ motivation can help marketers to grow bloggers for their needs, appealing to the hidden triggers of their clients. This is crucial since the decision to transmit the content along is absolutely voluntary (Ho & Dempsey, 2010). Schutz (1966) introduced FIRO - a three dimensional theory of interpersonal behavior. The idea of this theory is that a person in a position of communicator is driven by three triggers: inclusion (a demand to be a member of a group and gain attention), affection (a need to demonstrate appreciation and concern for others), and control (a demand to exercise power in somebody’s social environment) (Schutz, 1966). Later, Flanagin and Metzger (2001) and Phelps et al. (2004) expanded the concept proposed by Schutz with additional motives, such as a need to share and demand in relationship maintenance (Flanagin & Metzger, 2001; Phelps et al., 2004).

Another theory which should be considered to understand consumer’s willingness to follow social media recommendation is the theory of planned behavior (TPB) (Ajzen, 1991). This theory conceptualizes human social behavior to attitudes towards behavior, subjective norms and behavioral intentions, and explains variances of human behavior (Ajzen, 1991). Following footsteps of Ajzen (1991), this research assumes that recommendations can act as variables shaping and influencing human behavior, creating behavioral beliefs in the paradigm of planned actions. This theory will be approached later in the research to examine relations between social media recommendations and purchase intentions among consumers of Generations Z and Y.

To generalize, it should be noted that since the development of social media platforms introduced a new way of communication between consumers, the maintenance of marketing tools became crucial (Abubakar et al., 2016). The companies understood necessity to monitor customers sharing their experience and to get online feedback on their products, and individuals started to satisfy their social needs to share, control and be included in social environment through creating content for masses of online users and sharing their experiences in purchasing different goods. E-WOM via social media occurred enormously influential on consumers’ intentions (Kudeshia et al., 2016).

2. Relevance of the theory of generations

As early as the middle of the 20th century, Karl Mannheim (1952) developed the core tenets of the theory of generations that remain relevant even today (Pendergast, 2009). As Mannheim explains “belonging to the same generation or age group endows the individuals sharing in [it] with a common location in the social and historical process, and thereby limits them to a specific range of potential experiences, predisposing them for a certain characteristic mode of thought and experience, and a characteristic type of historically relevant action’’ (Mannheim, 1952, p. 291). Lazarevic (2012) states that the theory of generations differs from other theories such as life span theory or developmental psychology theories (Bates et al., 1998) that look at specific characteristics of people within a specific age group. Generational theory complements other paradigms of understanding groups by helping us to understand how social context helps to create some homogeneous traits within and among generations (Pendergast, 2009).

According to the theory of generations, generational cohorts share life experiences which cause them to develop similar attitudes and beliefs (Lazarevic, 2012; Meriac et al., 2010). These shared life experiences and social contexts cause each generational cohort to develop different beliefs, expectations and views regarding their lives and consequently, different behaviors (Lancaster & Stillman, 2002). This results in generational cohorts developing distinct characteristics (Kupperschmidt, 2000), moreover, these cohorts capture not only differences in age but also differences in values (Schewe & Meredith 2004), and in attitudes and beliefs (Meriac et al., 2010). Considering that these cohort effects are lifelong effects (Berkowitz & Schewe, 2011), the theory of generations provides researchers with a broad sociocultural approach rather than an individual focus on the consumer (Pendergast, 2007).

While such observations are valid and useful, the theory of generations posits that behaviour is not only shaped by age, but also by the social context a generation is brought up in (Berkowitz & Schewe, 2011). Lazarevic (2012) suggests that utilizing generational cohorts allows marketers to create familiarity and personal appeals within marketing communications and therefore, be more likely to target products and promotions more effectively (Meredith & Schewe, 2002). Therefore, by looking at traits of Generation Y and Generation Z, consumer marketers will be better equipped to appeal to the generations.

Major studies have shown that social media and Internet have become key channels of information exchange before purchases (Abubakar, 2016; Hsu et al., 2012; Kudeshia et al., 2016), and online communication is a crucial element of today’s customer experience (Lepkowska‐White, 2013), however, evidence points that these tendencies result differently with different customer segments. Christodoulides et al. (2012) investigated e-WOM adoption differences among nationalities. He notices that although the internet is a global space with open information access for consumers from different countries, online behavior customers show is not homogeneous (Christodoulides et al., 2012). Another research explored variables influencing media usage patterns among different genders. The results have also shown that behavior of men and woman in consuming information from media, and especially new media, differs (Shephard et al., 2016). However, the scope of research focusing on the difference in customer segments remains limited.

One of the most important customer segmentations is generation segmentation. By belonging to a specific generation, an individual also gains such characteristics as average income and education, world perception, social status and media usage patterns, which influences his perception of e-WOM and willingness to follow the recommendations. For this study, two main generations engaged with eWOM and active usage of social media, generations Y and Z, are defined. Generation Y, or Millennials, as they are also called, are defined as the people born between 1982 and 1994 (Duffett, 2017). Yet, there is still no general consensus regarding their birth period, other researchers suggest a marginally different time interval: 1980–1995 (Edelman/StrategyOne, 2010); 1981–1995 (Lafayette, 2011); 1980–2000 (Miller & Associates, 2011). They are also called as echo boomers, hip-hop, kwaito or Facebook generation, and refer to consumers who are the children of the Baby Boomers or Generation X (Berndt, 2007; Dotson & Hyatt, 2005).

Consumers from Generation Y tend to communicate since they freely express themselves and support freedom of speech, positively react to changes and are considered to be trendsetters (Bolton et al., 2013; Lingelbach et al., 2012; Moore, 2012). Members of this generation were characterized as individualistic, well-educated, familiar with technologies, sophisticated, mature, and structured (Gurău, 2012; Syrett & Lammiman, 2003). They are oriented to be a part of group and see themselves as “cool”, holding clear sense of identity (Pesquera, 2005; Peterson, 2004). Generation Y adores self-relevant products which will act as a form of self-presentation (Gupta et al., 2010).

Many scholars agree that Generation Y is consumers which are heavily influenced by technology and the internet (Bolton et al., 2013, Lingelbach et al., 2012; Valentine & Powers, 2013). Therefore, millennials’ usage of social media also became of peculiar interest to organizations and managers since it indicates how these consumers will behave in purchase situations (Bolton et al., 2013). The message to attract a Generation Y member should be clean, short, direct and honest (Pesquera, 2005).

Previous studies highlight significant role of online reviews as online posts and e-WOM impact purchase decisions of consumers that are supposed to be members of Generation Y (Priyanka, 2013). This generation does not like being an advertising target, thus they rely on their friends’ and relative’s thoughts and e-WOM when making purchase decisions and value general advertising channels less (Peterson, 2004). They trust user-generated content more than family recommendations (Sollis, 2012), which differs notably from their parents’ generation. Generation Y will constitute over 75% of labor force worldwide by 2025 (Sollis, 2012). This makes them the main target for marketers as the major purchasing power replacing their parents of Generation X. Speaking about their social media usage patterns, Sollis (2012) indicates that Millennials are 3 times as likely to follow a brand over a family member, and 66% of them will look up a store if they saw a friend checked in.

In the countries where internet penetration allows, generations Y and Z are the most active social media users, which means they consider social media as a key feature that influences their lifestyles and follows their daily routine, including home, working or leisure activities (Apresley, 2010). Generations Y and Z are skeptical towards traditional advertising and traditional media, which results in seeking information about products online and more trust and tolerance to WOM, or, in their case, e-WOM (Valentine & Powers, 2013). Generation Z, also referred to as iGeneration, Plurals and Generation Next, is defined as people who were born in the decade after the fast growing emergence of social media, in the period of 1997-2005 (Wood, 2013; Duffet, 2017), or 1995–2010 (Seemiller & Grace, 2017). As this is a relatively new cohort of consumers, the number of studies focusing on characteristics of this generation is extremely small. In general, Generation Y and Generation Z often share some attributes. For instance, like Millennials, Generation Z is highly proficient with new technologies and internet-dependent. Generation Z has a special connection with the World Wide Web, as the Internet has always existed for them (Wood, 2013). These are consumers most focused on innovation (Priporas et al., 2015). One of the predicted consumer-perspective behavioral tendencies is that Generation Z is more willing to spend money on technological and design-based innovation than anything else and expect technologies help them to make wise purchasing decisions (Priporas et al., 2015).

One of the crucial marketing technologies for Generation Z is influencer marketing. The demand taught opinion leaders to create content which is already outperforming the one brands create; they care about their audience and produce relevant ideas (Wood, 2013). The most important sources of information for Generation Z appear YouTube and Instagram. Influencers from these platforms are considered to be more reliable than traditional celebrities. This generation is brand savvy, prefers short and full of multimedia messages and respects videos more than pictures (Seemiller & Grace, 2017). Moreover, they are consuming information faster than anyone ever before (Lanier, 2017). Approaching Generation Z is challenging, since it occurs that they behave differently to other generations, and this behavior can lead to changes in consumer behavior. For instance, they have higher assumptions, no brand loyalty and care more about the experience (Priporas et al., 2015). Wood (2013) points out that Generation Z characteristics as consumers will consist of 4 major points: 1) Passion for new technologies; 2) Emphasis on fluency of use; 3) Aspiration to feel safe; 4) Eagerness to temporarily break out the routine they see.

The key element of differences between e-WOM adoption among generations is perceived usefulness of information they receive. Perceived usefulness refers to one’s perception of enhancing individual’s performance by means of using the information (Cheung et al., 2008). In the existing literature, dual process theories are frequently used to explain how people adopt information and become influenced by different thoughts and ideas (Sussman & Siegal, 2003; Bhattacherjee & Sanford, 2006). This study attempts to assess the effects of social media recommendations within the framework of the theoretical model of information adoption by Sussman and Siegal (2003). The crucial idea expressed by scholars is based on the concept of usefulness, which becomes a mediator in information adoption processes. Individuals vary in their adoption of some informational piece in terms of perception of the usefulness of this piece. This conclusion is followed by the belief in the importance of external validity of shared knowledge, which in fact is more important than internal one (Sussman & Siegal, 2003). External validity of the message refers to how useful the knowledge was to solving the existing problem, while internal validity is defined by real importance of that knowledge. This idea fully reflects the phenomenon of social media influencers and their popularity, especially among Generation Z (Hsu et al., 2013).

Considering behavioral, mental, age- and status-related characteristics of Generations Y and Z, their perception of utility of different messages could vary. From this point of view, approaching consumers in terms of the marketing perspective from different generations should consist of distinctive mechanisms related to their peculiarities in information adoption. Marketers need to understand what is important for the consumer of the generation they are targeting and specify their message.

Characteristics of Generations Y and Z are summarized in Table 1.

To sum up, it should be noted that social media development grows up to constant brand-related communication even when the brand does not participate (Abubakar et al., 2016). Feedback and recommendations constitute e-WOM from satisfied and disappointed consumers. Today’s customers mostly belong to Generations Y and Z, who share a lot of same characteristics in the context of consumer behavior, yet significantly differ (Priporas et al., 2015). Both these generations are technically-savvy, prefer user-generated content to traditional advertisement and do not like to be marketing targets (Hsu et al., 2013). Still, it is possible for marketers to reach them by means of new technologies, e-WOM and different approaches.

TABLE 1. Characteristics of Generations Y and Z

	Generation Y	Generation Z
Age	23-36 (born between 1984 and 1995)	12-22 (born between 1996 and 2005)
Population	24.5%	19.4 %
Social media usage patterns	Generation of Facebook. 63% asked a Facebook friend for a brand advice; 6/10 bought a product after a recommendation from Facebook friend; 57% wrote about a brand on Facebook profile. Share experiences, communicate in social networks sites, write and read online reviews and blog posts.	Generation of Snapchat and YouTube. Dependent on social media influencers. Follow bloggers and opinion leaders, seek to become influencers.
Information adoption patterns	Believe friends’ and relative’s thoughts and e-WOM. Prefer user-generated content over professionally created. Need diversity to choose what to read and what to believe as a consequence of freedom of speech support.	Prefer multimedia content over text, video content over photo. Select an influencer and follow his lifestyle – consume the content he produces.
Level of technological advancement	High. Enjoy technological advances and seek to improve their life by means of technologies.	Very high. They were born when Internet already existed. Smartphone constitutes their daily routine.
Attitude to e-WOM	Very positive.	Positive.
Consumer behavior	Brand-loyal; Dependent on e-WOM; Trendsetters.	Lost brand-loyalty; Mass-market lovers; Follow trends; Care about experience and fluency.

Source: Based on Duffet (2017), Hsu et al. (2013), Priporas et al. (2015), Valentine & Powers (2013), Wood (2013) and Zhang et al. (2017).

3. Research design

The objective of this empirical research is to define how Generation Y responds to recommendations in social media, determine their impact on purchasing intentions and how, if ever, it is different from Generation Z. Based on various studies, quantitative and qualitative research was employed as well as a theoretical research model was developed.

The research model conceptualizes the influence of recommendations in social media on purchasing intention. The model itself is based on the theories of the impact of recommendations on social behavior by Katz and Lazarfeld (1955) and Arndt (1967), followed by social media studies by Abubakar et al. (2016), Hsu et al. (2013), etc. The key elements in the model are social media recommendations (hereinafter, SMR), represented by opinion leaders’ recommendations and online reviews; belonging to a generation; social media usage; and purchasing intention. This empirical research attempts to identify possible relationships between the key elements. For research purposes, the variable of “purchase intention” was conceptualized to the “intention to book a restaurant for the upcoming birthday”. Such behavioral intention fits with the general idea of the research, as intention to book a restaurant will be likely influenced by social media recommendations in terms of the theory of planned behavior (Ajzek, 1991).

FIGURE 1. Theoretical model of the influence of recommendations in social media on purchase intentions

Based on literature analysis and the theoretical model, 7 hypotheses were formulated. Assumption can be made that customers’ purchasing intentions are influenced by what they see in social networks.

Consumer purchase intentions are the signal of their actual purchasing behavior, which is why it is important to study how consumer purchase intentions are influenced by external factors. For research purposes, the variable of “purchase intentions” was narrowed to “intention to book a restaurant” and conceptualized for the respondents as “intention to find a restaurant for the upcoming birthday party”. Following the framework of TBP (Ajzek, 1991), it is assumed that behavioral intention must be affected by behavioral beliefs and experiences. Still, these intentions must be different in terms of recommendation adoption. The existence of relationships between information adoption and purchase intention had been suggested by several researchers (Cheung et al., 2009). The information adoption was proved to be one of the causes of social media recommendations which influence consumers’ purchase intentions. However, the information adoption process can be different in varied recommendations types (Cheung et al., 2009). Hence, the potential influence of social media recommendations and their differences among generations are the subject to test in the following hypotheses:

H1. Consumers of Generation Y adopt online reviews in a greater proportion than consumers of Generation Z.

H2. Consumers of Generation Z adopt opinion leaders’ recommendations in a greater proportion than consumers of Generation Y.

H3. For Generation Y, the influence of online reviews on intention to visit a restaurant is stronger than for Generation Z.

H4. For Generation Z, the influence of opinion leaders recommendations on intention to visit a restaurant is stronger than for Generation Y.

Several researchers indicated the importance of such a factor as social media usage as well as engagement in the context of adopting SMR (Baird& Parasnis, 2011; Brown et al., 2007; Erkan &Ewans, 2016; Kaplan & Haenlein, 2010). The direction of their relation remains unclear, however, if the existence of relationship is proved, major scientific and managerial implications must be developed to better understand how social media recommendations work. Hence, the following hypotheses were developed:

H5. There is a significant relationship between social media usage and adoption of the opinion leaders’ recommendation.

H6. There is a significant relationship between social media usage and adoption of online reviews.

What is more, younger consumers are more likely to have higher usage and engagement with social media due to psychographic characteristics (Wood, 2013). Thus, the following hypothesis was developed:

H7. Social media usage is higher for Generation Z consumers than for Generation Y consumers.

Table 2 contains all the major variables used in the research, such as Recommendations in social media (SMR); Social media usage; Online reviews (e-WOM); Opinion Leaders; Purchase intention, which are operationalized based on the concepts in previous studies.

TABLE 2. List of concepts

Construct	Definition	Author
Recommendations in social media (SMR)	“Recommendations published in social media which are used as advancing tools that help consumers easily find the most consistent information and make purchasing decision”.	(Hsu et al. (2012), Forbes & Vespol) (2013)
Social media usage	Customers’ enablement to speak up on social media platforms because of ease to express opinions, to raise complaints and compliments. “Social media extends opportunities to strengthen the relationships with consumers by facilitating them so that they engage with the products and services through interaction (Doorn et al., 2010) and by fostering user communities and online brand (Goldenberg et al. 2009), which improve brand equity”. Particularly, firms are expected to participate on such platforms because consumers’ social media usage as a place for complaints is increasing rapidly.	(Prasad et al. (2017) Doorn et al. (2010) Goldenberg et al. (2009)
Online reviews (e-WOM)	Consumers’ interactions with each other on a “wide range of online channels, such as blogs, emails, consumer review websites and forums, virtual consumer communities, and social network sites.”	Trusov et al. (2009)
Opinion Leaders	“An individual who is known to the public for his or her achievements in areas other than that of the product endorsed”, “a famous person who uses public recognition to recommend or co-present with a product in an ad”	Gupta & Haris (2010), Prasad et al. (2017)
Generation	“A group of individuals with shared similar experiences and unique common characteristics around these experiences”, divided by “life stage, current conditions and cohort experiences”.	Wolburg (2014)
Purchase intention	“Intention is the factor that motivates consumers and in turn influences their behavior”, “antecedents that stimulate and drive consumers’ purchases of products and services”. When the intentions of performing certain behavior are strong, there are higher likelihoods that the respective behavior will be performed.	Ajzen (1991), Hawkins & Mothersbaugh (2010)

Source: compiled by the authors based on systematic scientific literature review

To develop scales for measuring constructs such as social media usage, opinion leaders’ recommendation adoption, online reviews adoption and intention to purchase, measures adapted from past research were utilized (Bambauer-Sachse & Mangold, 2011; Hsu et al., 2013; Rapp et al., 2013; Yoo & Donthu, 2001), with minimal modifications to suit the social media recommendation context and general idea of the research. Each item was measured on a five-point Likert scale, ranging from “totally disagree” (1) to “totally agree” (5).

Social media usage (SMU) is operationalized using 8 items. The scale was adopted from Rapp et al. (2013) without modifications. It asks whether the respondent uses social media to monitor sales and promotions, events, friends’ activity, to be reached by friends or communicate with brands and enhance relationships with brands. Cronbach’s alpha of the scale in the research equals 0.89.

Opinion leaders’ recommendations adoption (OL) is operationalized using 3 items. The scale was adopted from Hsu et al. (2013) minimally modifying it to suit the general idea of the research. It asks whether the respondent believes that opinion leader’s recommendation can improve his online searching performance or enhance effectiveness. Cronbach’s alpha of the scale in the research is 0.75.

Online reviews adoption (OR) is operationalized using 6 items. The scale was adopted from Bambauer-Sachse and Mangold (2011) minimally modifying the wording to suit the idea of intention to choose a restaurant. It asks whether the respondent reads and searches for online reviews before choosing a product or service and whether he/she worries making purchase without online consultation. Cronbach’s alpha of the scale in the research is 0.788.

Purchase intention (PI) is operationalized using 4 items. The scale was adopted from Yoo and Donthu (2001) with small modifications in the wording to fit in the context of intention to choose a restaurant for the upcoming birthday. This scale was presented after a small introduction to the situation, where the respondent must imagine himself/herself choosing a restaurant for the upcoming birthday. The questions were designed to ask if the respondent expects to book a restaurant based on online recommendation. Cronbach’s alpha of the scale in this research is 0.97.

Demographic part consists of three questions, which measure age, gender and education. While the question measuring age is required for research purposes, other demographic questions contribute to sample diversity control. To increase instrument validity, pre-test of draft research tool was conducted. In pre-test, the draft questionnaire was revised for formal validation of the research tool for qualitative research. The main goal of this phase was to obviate ambiguous terms, double-barreled questions and poorly formulated ideas. There were 10 participants in the pre-test. First, 5 respondents were asked to complete the survey. After they finished, feedback from each respondent was collected to modify the questionnaire. Subsequently, the procedure was repeated with the rest 5 respondents, which were given the modified questionnaire. Additionally, the time required for filling in the questionnaire was measured. The average time of completing the questionnaire appeared between 7 and 10 minutes.

The sample size for quantitative research – 287 respondents - is based on the average of the samples of previously performed similar studies (see Table 3 below).

TABLE 3. List of relevant studies and sizes of the sample

No	Title of article, year	Author	Sample
1	“The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities”, 2008	Cheung Ch.M.K., Lee M.K.O., Rabjohn N.	154
2	“Role of different electronic-commerce (EC) quality factors on purchase decision: a developing country perspective”, 2008	Shareef, M., Kumar U., Kumar V.	370
3	“The influence of online product recommendations on consumers‘ online choices”, 2004	Senecal S., Nantel J.	487
4	“eWOM, eReferral and gender in the virtual community”, 2016	Abubakar M., Ilkan M., Sahin P.	308
5	“Cross‐national differences in e‐WOM influence”, 2012	Christodoulides G., Michaelidou N. , Argyriou E.	209
6	“The effects of blogger recommendations on customers’ online shopping intentions”, 2013	Chin‐Lung Hsu, Judy Chuan‐Chuan Lin, Hsiu‐Sen Chiang	327
7	“Are they listening? Designing online recommendations for today‘s consumers”, 2013	Lepkowska‐White E.	202
8	“Social eWOM: does it affect the brand attitude and purchase intention of brands?”, 2016	Kudeshia Ch., Kumar A.	311
9	“A structural model of the antecedents and consequences of Generation Y luxury fashion goods purchase decisions”, 2017	Qian Ying Soh C., Rezaei S., Man-Li Gu	384
10	“Viral effects of social network and media on consumers’ purchase intention”, 2015	Gunawan D.D, Huarng K.	118
Average sample size: 287

The questionnaire was distributed using a non-probability convenience sapling method. Due to the time constraints, the questionnaire was distributed through social media (Facebook) and email, and targeted international students and workers in Vilnius, capital of Lithuania. The total of 292 complete questionnaires were received. There is no data regarding incomplete questionnaires due to survey software peculiarities, therefore the response rate could not be calculated. 8 questionnaires were deleted from the research as the respondents were above the age of the target audience – representatives of Generation Y and Generation Z. In total, 284 questionnaires were valid for the research. It corresponds positively with comparative analysis from previous research, which showed that the average sample size must be around 287 respondents, therefore, it allows conducting a representative research with minimal error in terms of the sample.

All scales were previously validated and adopted from previous research. The list of items and constructs is presented in detail in Table 4.

TABLE 4. Constructs and items

Author	Construct	Items
Rapp A., Beitelspacher L.S., Grewal D., Hughes D.E. (2013)	Social media usage	(SM1) My relationship with the brand is enhanced by the social media. (SM2) I use social media to monitor other members in the community. (SM3) I use social media to follow sales and promotions. (SM4) I use social media to monitor events. (SM5) People use social media to reach me. (SM6) I use social media to improve my relationship with different brands. (SM7) I use social media to keep current on events and trends. (SM8) I use social media to communicate with firms.
Bambauer-Sachse S., Mangold S. (2011)	Online reviews adoption	(OR1) I often read other consumers’ online reviews to know what products or services make good impressions on others. (OR2) To make sure I choose the right product or service, I often read other consumers’ online reviews.
Bambauer-Sachse S., Mangold S. (2011)	Online reviews adoption	(OR3) I often consult other consumers’ online reviews to help choose the right product or service. (OR4) I frequently gather information from online consumers’ reviews before I choose a product or service. (OR5) If I don’t read consumers’ online reviews when I choose a product or service, I worry about my decision. (OR6) When I choose a product or service, consumers’ online reviews make me confident choosing it.
Hsu C.L., Chiang H.S. (2013)	Opinion leaders’ recommendation adoption	(OL1) Opinion leaders’ recommendations will improve my online searching performance. (OL2) Opinion leaders’ recommendations will enhance my online searching effectiveness. (OL3) Opinion leaders’ recommendations can increase my productivity when searching online.
Yoo B., Donthu N. (2001)	Purchase intention	(PI1) It is likely that I will book this restaurant in the near future. (PI2) I expect to book this restaurant in the near future. (PI3) I intend to book this restaurant in the near future. (PI4) I will definitely book this restaurant in the near future.

The demographic profile of the respondents: 53.5% of respondents belong to Generation Z, which means they are between 18 and 22 years old; 152 filled questionnaires in total. 46.5% (132 respondents) belong to Generation Y, with the age range between 23 and 35 years. Frequencies showed that the biggest percentage of respondents was 21 years old, born between 1997 and 1998. The modification of the variable was needed for research grouping purposes. The variable “age” was modified to contain only two groups of respondents: Generation Y and Generation Z. According to statistical data for 2018 from the World Population Review2, Lithuanian population consisted of 53.9% of female and 46.1% of male population. The research sample includes 59.9% female and 40.1% male respondents, with a slightly bigger proportion of females in comparison with the general population.

Statistical analysis of data was conducted in SPSS program software. All constructs had Cronbach’s Alpha above the conventional level of 0.7 (Nunnally, 1987).

TABLE 5. Descriptive statistics

	Cronbach‘s α	Mean	Variance	Std. deviation	N. items
Social media usage	0.855	28.332	46.683	6.8325	8
Online reviews adoption	0.853	20.062	4.337	4.933	5
Opinion leaders’ recommendation adoption	0.91	9.709	12.887	3.5899	3
Purchase intention	0.867	14.308	13.753	3.7086	4

Cronbach’s Alpha of social media usage construct is 0.855, which shows sufficient level of internal reliability. Opinion leaders’ recommendation adoption Cronbach’s Alpha is 0.91, which is even higher than in the research of Hsu et al. (2013) from which it was adopted. Online reviews adoption Cronbach’s alpha is 0.817, which is high, however, the descriptive analytics showed that elimination of one item will increase internal consistency of the scale to 0.853; that is why one item “If I don’t read other consumers’ online reviews when I choose products or services, I worry about my decision” was excluded from this construct. Purchase intention Cronbach’s Alpha is 0.867, which indicates a good internal validity of the scale (Table 5).

The aim of this research is to explore the differences in the impact of social media recommendations on intention to buy in generations Y and Z. It is also interesting whether social media usage is related to recommendation adoption of either e-WOM or opinion leaders. To achieve this aim, several hypotheses were developed. To explore hypotheses H5 and H6, correlation analysis must be conducted through SPSS software. To compare the differences in adoption levels of recommendations (H1, H2, H7), parametric Independent Samples t-test was used for statistical analysis of data through SPSS Software. Finally, multiple regression analysis was conducted to compare the impacts of recommendations on purchasing intentions (H3, H4).

4. Research findings

The importance of online reviews (e-WOM) for generation Y consumers was tested in Hypothesis 1. Independent Samples T-test showed that there is difference in adopting online reviews by consumers of generations Y and Z (p<0.001): M (Gen Y) = 4.16, while M (Gen Z) = 3.9. t (280.045) = -2.883. It proves that Generation Y is more willing to accept and follow recommendations made by other users online than Generation Z consumers, therefore H1 is proved. Such findings correspond to general conclusions of several studies regarding e-WOM impacts on intention to purchase (Cheung et al., 2009; Jalilvand & Samiei, 2012; Jiménez et al., 2013; Leung et al., 2015). It also follows the psychographic image of Generation Y, drawn by several researchers (Bolton, 2013; Duffet, 2017; Sollis, 2012), who call Generation Y individuals who prefer SNS to live human interaction, friends’ online recommendations to parents’ advice and online content to that offline (Sollis, 2012).

For Generation Z consumers, online reviews do not play such an important role in the process of decision-making on purchasing. Thus, Hypothesis 2 was tested by conducting an Independent Samples T-test on the construct of adoption of opinion leaders’ recommendation as a dependent variable and the age of the respondents as an independent variable. Statistical comparison means showed that there is significant difference in adopting opinion leaders’ recommendations by Generation Y and Z consumers (p<0.001). M (Gen Z) = 3.6206; M (Gen Y) = 2.8535; t (5.720) = 282, which means H2 is proved. Generation Z consumers adopt opinion leaders’ recommendations more than Generation Y consumers. These findings are consistent with the findings of Park et al. (2007) and Hsu et al. (2013). Hsu et al. (2013) verify that bloggers and opinion leaders provide useful information to consumers before the purchase, which is perceived differently in terms of consumer segments. The results of analysis are presented in Table 6.

TABLE 6. Descriptive statistics

		N	Mean	Std. Deviation	Std. Error Mean	t	df	Mean Difference	Std. Error Difference	Sig.
Online reviews	Gen Y	132	4.16	0.66	0.057	-2.883	280.045	-0.255	0.088	0.004
	Gen Z	152	3.9	0.84	0.067
Opinion leaders’ recommendations	Gen Y	132	2.85	1.093	0.95	282	5.720	0.767	0.134	0.000
	Gen Z	152	3.62	1.155	0.093

The next objective of this research was to explore differences in the impact of recommendations through social media on intention to buy between generations Y and Z, thus hypotheses H3 and H4 were tested. Both hypotheses were assessed with multiple regression analysis. Multiple regression is used when the value of one variable needs to be predicted by several variables. Such methods allow seeing how much the variance is explained by the predictor and what the impact of each predictor is. Multiple regression estimates the β’s in the equation:

y_j =β₀+β₁x1_j +β₂x2_j + ... +β_px_pj +ε_j

where x represents independent variables, y is a dependent variable, the subscript j represents the observation number, and the β’s are the regression coefficients. In this research the regression model is:

Y = β₀ + β₁ X₁ + β₂ X₂

where X₁ is opinion leader’s recommendations, X₂is online reviews, and Y is intention to book a restaurant for the upcoming birthday.

In order to compare impacts, two regressions were run. The first regression assessed the impact of social media recommendations on Generation Z. Statistical analysis showed significant (ANOVA p<0.001) influence of opinion leaders’ recommendations on generation Z consumers’ intention to book a restaurant for the upcoming birthday (std. b. = 0.548), however, online reviews have no compelling impact on intention to buy of the consumers from younger generation (p=0.186). Overall impact is moderate, as only 31% of dependent variable is explained by predictors (R2 = 0.31). Thus, only one dependent variable – opinion leaders’ recommendation - has significant direct influence on intention to buy with regard to Generation Z. Correlation coefficient = 0.556 reveals moderate positive correlation between variables, supporting the assumption of direct positive influence of opinion leaders’ recommendations on intention to book a restaurant for the upcoming birthday. The results for generation Y differ from those of generation Z, however, are quite expected. Generation Y consumers’ purchasing intention is also exposed to the influence of social media recommendations (ANOVA p<0.001), and both independent variables have significant impact (p(OL)<0.001; p(OR)<0.006), however, their impact is lower, as only 29 percent of the observed variance explained the intention to book a restaurant based on online recommendation in generation Y consumers. Correlation coefficient R equals 0.543, which corresponds to existence of direct positive influence of online recommendations on intention to book a restaurant for the upcoming birthday among Generation Y. Unexpectedly, the impact of opinion leaders’ recommendation on purchasing intentions is bigger than the one from online reviews: OL (stand. b) = 0.512, while OR (stand. b) = 0.207. Detailed results of multiple regressions are presented in Table 7.

TABLE 7. Multiple regression

		Sig	R2	R	Std b	df	F
Opinion leaders (OL)	Gen Y	0.000	0.295	0.543	0.512	2	27.011
	Gen Z	0.000	0.309	0.556	0.548	2	33.323
Online recommendations (OR)	Gen Y	0.006	0.295	0.543	0.207	2	27.011
	Gen Z	0.186	0.309	0.556	0.09	2	33.323

Thus, both H3 and H4 are supported. H4 is accepted in terms of regression coefficients, as generation Z showed bigger exposure to the influence of opinion leaders’ recommendations. H3 is accepted, as online reviews have no impact on intention to buy among Generation Z, but have a relatively small influence on decision to buy among consumers of Generation Y.

These results correspond with the findings of several scholars. Hsu et al. (2013) examined bloggers’ influence on purchasing intentions in terms of facts and proved that bloggers have significant impact on consumer behavior. The core goal of that research was to explore factors influencing adoption of bloggers’ recommendations, where trust and credibility of the source appeared to play the major role in the level of adoption of the recommendation by consumer and the following influence on intention to buy. The results also correspond to Park et al. (2007), who proved the importance of informant role in the decision-making process of consumer, which exceeds the importance of the recommender role.

This study regression model has an R2 on the level of 0.3, which is rather low considering 0< R2<1, however, still significant. R squared explains how much of predicted variable could be predicted by independent variables, in other words, how much of purchasing intention (intention to book a restaurant for the upcoming birthday) is predicted by opinion leaders’ recommendations and online reviews. The result of 0.3 shows that these predictors contribute to shaping purchasing intention, however, do not shape it entirely. It partly corresponds with other scholars’ findings, who revealed that the content of recommendation (Sollis, 2012), credibility of the source (Lepkowska‐White, 2013), type of the purchase (Hsu et al., 2013) and other factors contribute to the influence on purchasing intentions of the consumers.

The model of this research proposes a link between social media usage and adoption of recommendations. Thus, the next objective of the research is to explore the relationship between consumer’s social media usage and one’s adoption of recommendations while testing hypotheses 5 and 6. To explore the relationships between two scale variables, the Pearson’s correlation was chosen as a method for analysis. The first test between social media usage and opinion leaders’ recommendations showed significant level of correlation (p<0.001), with Pearson’s coefficient of correlation on a moderate level (Pearson’s R= 0.571), which shows that there is significant positive relationship between social media usage and opinion leader’s recommendation. Thus, H5 is supported. Correlation matrix 1 is presented in Table 8.

TABLE 8. Correlation matrix 1

		Social media usage	Opinion leaders’ recommendations
Social media usage	Pearson correlation	1	0.579
	Sig. (2-tailed)		0.000**
	N	284	284
Opinion leaders’ recommendations	Pearson correlation	0.579	1
	Sig. (2-tailed)	0.000**
	N	284	284
** Correlation is significant at the 0.01 level (2-tailed).

The second correlation test between social media usage and online reviews did not show a significant level of correlation between variables (p=0.974), which means there is no direct relationship between social media usage and adoption of online reviews. Thus, H6 is not supported. Correlation matrix 2 is presented in Table 9.

TABLE 9. Correlation matrix 2

		Social media usage	Online reviews
Social media usage	Pearson correlation	1	-0.002
	Sig. (2-tailed)		0.974
	N	284	284
Online reviews	Pearson correlation	0.974	1
	Sig. (2-tailed)	0.000
	N	284	284

These results make sense, as online reviews include not only recommendations in social media, but also e-WOM in general, including review web-sites (e.g., Foursquare, TripAdviser, etc.), restaurant web-sites with comments section, ratings and comments in affiliated web-sites such as Google Maps, etc. Taking into consideration the restaurant context, it is assumed that social media does not play the major role in collecting and distributing online recommendations, as it happens with grocery or beauty products (Hsu et al., 2013).

For deeper development of comparative analysis of generations in terms of recommendation adoption and social media behavior as well as for deeper understanding of causal links, one more test should be performed. The last hypothesis (H7) is based on the findings of research dedicated to generation Z and their comparison with previous generations (Lanier, 2017; Seemiller & Grace, 2017; Wood, 2013); they highlight its passion for social media, new technologies and more digitalized approach to shopping (Wood, 2013).

The hypothesis was tested with Independent Samples T test with age as an independent variable and social media usage as a dependent variable. The test showed that the differences between social media usage of generations Y and Z exist (p=0.043). For Generation Z, usage level is higher (M=3.64) than for Generation Y (M=3.43). It reveals bigger social media consumption by Generation Z and supports the findings of Seemiller and Grace (2017), and Lanier (2017). Detailed statistics are presented in Table 10.

TABLE 10. Descriptive statistics

		N	Mean	Std. Deviation	Std. Error Mean	t	df	Mean Difference	Std. Error Difference	Sig.
Social Media usage	Gen Y	132	3.43	0.78	0.068	2.034	281.997	0.204	0.1	0.04
	Gen Z	152	3.64	0.9	0.073

According to the results of Independent Samples T test, H7 is supported.

Additionally, differences in the adoption of social media recommendations and the level of social media usage among Generations Y and Z were tested taking into consideration the gender variable. However, no significant difference was ever noted (p>0.05), which means that both women and men of these generations behave according to their generational patterns in terms of recommendation adoption and social media usage.

Data analysis indicates that generation Y consumers’ adoption of online reviews is higher than that of Generation Z consumers. These results are partially consistent with Zhang et al. (2017) findings of e-WOM influence among generation Y consumers, which revealed that the younger the respondent of Generation Y, the smaller impact e-WOM has on their purchasing intentions. Taking into the consideration that the cohort of Generation Z was not assessed in their research, it may be assumed that the tendency continues in younger generation as well. The results also correspond with several studies about e-WOM explanatory power regarding purchasing intentions of consumers (Priyanka, 2013; Gvili & Levy, 2018; Prasad et al., 2017). Facebook as a primary source of e-WOM for Millennials (Priyanka, 2013) is proved to influence positively and significantly on intention to buy a product or service.

Generation Z consumers’ adoption of opinion leaders’ recommendations is higher than Generation Y consumers. The findings revealed correspond to a small number of works about Generation Z (Wood, 2013; Lanier, 2017; Seemler & Grace, 2017). Consumers of this generation are relatively young to constitute significant purchasing power; therefore, they are not as interesting for marketers yet as Generation Y. Thus the number of studies covering their peculiarities and relationship with social media is relatively low. Still, the data analysis results correlate with the descriptive characteristics of the internet habits of Generation Z (Wood, 2013) and research on their information adoption habits (Lanier, 2017; Seemiller & Grace, 2017).

For Generation Y, online reviews work better than for generation Z when making them come to a decision to book a restaurant. This conclusion was expected from the previous two hypotheses; however, multiple regression analysis gave an insight on real impact of online reviews on Generation Y consumers’ purchasing intentions. The results indicated that while purchasing intentions of Generation Y are to some extent influenced by online reviews, reviews do not have a significant impact on intention to book a restaurant for the upcoming birthday among Generation Z. It was noted in previous studies (Christodoulides et al., 2012; Bambauer-Sachse & Mangold, 2011; Kudeshia & Kumar, 2017) that online reviews can influence purchase intention, while others (Abubakar et al., 2016) argued that reviews do not always influence purchase intention. Relying on the current findings and provided analysis we conclude that e-WOM does not have enough influential power to impact intention to book a restaurant for some important event in the near future among Generation Z, and has a small impact on intention to book (a restaurant) among Generation Y. It can be reasonable in a way that this research model describes differences between consumer segments in adopting recommendations and their influence on purchasing intentions, however, does not cover factors influencing those decisions, which can be an object for further research and analysis.

For Generation Z, opinion leaders’ recommendations are more influential than for Generation Y in the context of booking a restaurant for the upcoming birthday. There is also direct positive relationship between social media usage and adoption of opinion leaders’ recommendations. The more engaged and active in social media the consumer is, the more likely one reacts to the opinion leaders’ endorsement of the product or service. Similar results were established by previous research (Zhang et al., 2017; Hsu et al., 2013) which supported the fact that Generation Y and Z consumers are more likely to engage in e-WOM and opinion leaders’ activities in social media if they are active and sophisticated users of social media technology (Zhang et al., 2017). However, even though technological sophistication is expected to be constant among Generation Y and Z (Sollis, 2012), they still differ in terms of what they do in social media, how they engage with friends, brands, events, how much time they spend in social media daily and what content they prefer to read (Pesquera, 2005).

Contrary to our prediction, there is no relationship between social media usage and online reviews adoption. Some previous studies (e.g., Park & Lee, 2009) indicated the importance of social media proficiency of consumer while advocating the importance of online reviews; however, others (Ochi et al., 2010) did not find any connection between these two variables. The research findings contribute to the theory that there is no connection between online reviews adoption and social media usage. The scale used for the measurement of adoption of online reviews did not specify the source of online review: either social media or website, which gives a respondent freedom to include every online review experience in the answer. Thus, the respondent could be inactive in social media, however, use rating websites that affect his choice of a restaurant for the upcoming important party.

Social media usage is higher for Generation Z consumers than for Generation Y consumers. The prediction within this hypothesis was formed based on the research of internet behavior of consumers of Generations Y and Z (Sollis, 2012; Wood, 2013; Soh et al., 2017). Statistical comparison means for the dependent variable of social media usage proved the assumption that Generation Z consumers are more engaged in different activities in social media and thus could be targeted differently by marketers.

5. Conclusions

This research was carried out on the basis of the theoretical model which explores the effect of social media recommendations on intention to book a restaurant for the upcoming birthday. It is quite valuable for researchers and practitioners working in the field of social media marketing and exploring the possibilities of brand endorsement on social media. Several implications can be readily obtained from the findings of our study.

First of all, it contributes to the theory of social media as an important communication channel for brands nowadays. The findings support the idea that social media recommendations have an influence on purchasing intentions of consumers, which means that there is one more reason for practitioners and brands to develop their communities in social media. However, the main managerial applications of our study are connected with the differences among consumers. Nowadays the consumer behavior of Generation Y is quite well researched from different angles, but there is obvious lack of research in the field of generation Z consumer behavior. Retailers are advised to segment their target audience very carefully, as differences in generations’ social media habits and information adoption exist. Online reviews had been an influential source of information for Generation Y; however, it is losing its influential power towards shaping purchasing intentions. E-WOM is still important, and brands and retailers are advised to develop and maintain branded communities in social media, encourage their consumers to share feedback not only in social media, but in rating websites, apps and services as well in order to increase market exposure and to generate public interest and raise brand awareness.

Generation Y has been recognized as a new major consumer group for almost a decade. This generation plays a growing and very important role in the global economy. Its general population in the world is nearly 2 billion, however, brands are still exploring how to approach and engage them in marketing activities. At the same time, Generation Z is a very new consumer group, which currently are in the middle of the process of becoming individual consumers after leaving family’s budgets. However, in the near future, together with generation Y, they will constitute the majority of modern consumers with increasing purchasing power. Although these generations share some habits and interests, they cannot be marketed and engaged in the same way. To reach Generation Z, companies must understand where they get information, how they absorb it, how they communicate through technologies, internet and social media.

6. Limitations and further research

This research, as well as every other scientific work, has its own limitations. Its results should be interpreted and accepted with caution for the following reasons. First of all, the research has been conducted in the sphere of the restaurant business, which means that the results are primarily representative in this field. Generalization of the results in another setting should be made with care, and additional research and validation of the results may be needed. Secondly, this study employed internet users as respondents to an online survey. Although internet proficiency of consumers is expected to be high in analyzing social media marketing influence, a bias may exist because the sample was self-selected. However, consistency of the results with previously obtained results and theories was checked, which increases reliability of our findings. Thus, the present study does not only contribute to deeper understanding of the social media recommendations theory but also provides a starting point for future research.

Although general research of this topic was conducted, there is a big scope of further research. Other researchers interested in differences between generations might explore the factors influencing different ways of adoption of information as well as mechanics for engaging generations in different activities. The most important idea that companies and especially restaurants must consider is that social media marketing strategy for targeting consumers from Generation Y and Z must differ. It is essential to find the most popular social media among brand consumers, taking into consideration that Generation Y consumers will be the main audience willing to share and follow user generated content. After determining the top social media, several campaigns encouraging users to give recommendations must be set up. Such campaigns might include contests, giveaways and interactive shareable content in the brand’s own media. Thus, both generations – Y and Z – will consume information from different sources, which will result in increased brand awareness and loyalty.

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