University of Amsterdam Faculty of Economics and Business
How perceived credibility of UGC on YouTube affects purchase
intentions: the moderating role of consumers’ impulsiveness.
Student: Paola Gambuzza (11085762)
MSc Business Administration -Marketing Track Supervisor: Tom Paffen
Statement of Originality
This document is written by Paola Gambuzza who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
With this research, I am finishing one of the most important and educational experiences of my life. I decided to start this Master to improve myself and, and at the end it fully met my expectations. I could not have done this without the help and support of many people. First of all, I would like to thank Mr. Tom Paffen for his commitment and availability. He always showed interest and involvement in my research.
My special thanks go to my family, that allows me to attend this master; without them, nothing of this would have been possible. My love goes also to the beautiful friends I’ve made, like Ale, Benny e Giulia who supported me in every step of the way and became my family during the past months. Finally, and most important I would like to thank my boyfriend Antonio, who has always been by my side and has always been my first supporter.
The advent of the internet originated a shift from traditional offline word-of-mouth (WOM) to electronic word-of mouth (eWOM). People can now converse and exchange information potentially with thousands and millions of people around the world. Product reviews on YouTube are a form of user generated eWOM, that differently from traditional online product reviews take the form of a video. Studies about user generated content (UGC) flourished during the last decades, but only few studies have taken into account the persuasive powers of this new, very particular form of online reviews. The main goal of this study is to investigate the effect of perceived credibility of UGC on purchase intentions and to find out whether consumers’ buying impulsiveness has a role in this model. Using an online survey, data was gathered from 123 respondents, mostly European and in particular Dutch YouTube users. The results show that there is a positive relation between perceived credibility of UGC and purchase intentions, and that this effect is mediated by attitude towards UGC. Concerning consumers’ buying impulsiveness, this variable has no effect in the model; it does not moderate the relationship between perceived credibility of UGC and purchase intentions, as hypothesised. The findings of this research give understanding of the aspects of UGC which affect purchase intentions of YouTube reviews’ viewers. The outcomes of this research will help practitioners design effective marketing strategies which can include YouTube product reviews performed by influencers and opinion leaders, and consequently increase revenues coming from an additional marketing channel.
TABLE OF CONTENTS
1.1 Problem Definition and Research Objective……….8
1.2 Thesis Overview ……….………..9
2. LITERATURE REVIEW AND HYPOTHESIS………9
2.2 Opinion Leaders and Online Influencers………13
2.3 Social Media….………..…15
2.4 User Generated Content……….……….16
2.5 Online Reviews ………..17
2.5.1 YouTube Product reviews ………,,,18
2.7 Theory of Reasoned Action (TRA)……….20
2.8 Purchase Intentions……….21
2.9 Online Reviews and Purchase Intentions………22
2.10 Consumers’ Lifestyle………24
2.10.1 Buying Impulsiveness………24
2.11 Research Objective………25
3. CONCEPTUAL MODEL……….26
3.1 Hypothesis ………..……26
4. RESEARCH DESIGN AND METHODOLOGY ………..…..27
4.2 Research Design………..…28
4.3 Measures ………29
4.4 Data Collection………..….30
4.5 Pilot Study……….….30
5. RESULTS AND ANALYSIS ………..……31
5.1 Results from Survey………..….31
5.1.1 Missing Data………31 5.1.2 Sample………..32 5.2 Error Check……….……32 5.3 Reliability………..….33 5.4 Descriptive Statistics………..……….33 5.5 Correlations ………35 5.6 Hypothesis Testing ……….………36 5.6.1 Linear Regression………..…..36
5.6.2 Mediating and Moderating Effects………..37
6.1 Interpreting the Research Results………..…….41
6.2.1 Theoretical Implications……….….43
6.2.2 Managerial Implications ……….…44
6.3 Limitations and Future Research………45
LIST FIGURES AND TABLES
Fig 1 - Two Step Flow Model. (Lazarsfeld & Katz,1955) Fig 2 - Multi Step Flow Model (Watts & Dodds, 2007)
Fig 3 - Theory of Reasoned Actions (TRA) - (Fishbein and Ajzen, 1980) Fig 4 - Conceptual Model
Fig 5 - Statistical Model 8 with PROCESS Fig 6 - Mediation Effect
Fig 7 - Moderation Effect
Table 1 - Respondents’ Characteristics
Table 2 - Skewness, Kurtosis, Mean, SD, Cromlech Alpha Table 3 - Correlation’s Matrix
Table 4 - Linear Regression perceived credibility of UGC and attitude towards UGC Table 5 - Linear Regression attitude towards UGC and purchase intentions
Table 6 - Linear Regression perceived credibility of UGC and purchase intentions Table 7 - Linear Regression buying impulsiveness and purchase intentions
The usage of the internet increased exponentially over the past decade. This phenomenon originated new ways of interacting and communicating with others, in particular through Web platforms and social networks. Traditional offline word-of-mouth (WOM), that is a face-to-face interaction or conversation between people, has been partly converted in electronic word-of-mouth (eWOM). In other words, through Web-based platforms, the Internet gives consumers the opportunity to share their opinions and experiences about goods and services with a multitude of other consumers; the result of this, is that more and more people are engaging electronic word-of-mouth (eWOM) (Hennig-Thurau et al., 2004). According to Cheung & Thadani (2012), there are numerous types of eWOM, such as blogs, social networking sites, online reviews sites, online discussion forums, boycott Web sites, blogs, etc. Online reviews sites, or Web-based consumer-opinion platforms (Hennig-Thurau et al. , 2004 p.39) “are the most widely used of the existing eWOM formats”; it is in fact estimated that nine to ten million product related reviews are available on the Internet on Web-based consumer-opinion platforms (Hennig-Thurau et al. , 2004) and an increasing number of people are enthusiastic about sharing and exchanging their experience and opinion with their peers on the Internet.
In the last years an exponential quantity of eWOM has been exchanged on social networks, such as Facebook, Twitter and YouTube. Specifically, YouTube has lately become a very varied and diversified platform, extremely rich of user generated content (UGC). According to Molyneaux et al. (2009) a new media format, namely online video sharing has grown exponentially lately, especially in platforms like YouTube. Thousands of videos are uploaded daily by YouTube users, that with their vlogs, unboxing videos, product reviews, hauls, “how’s to” and tutorials, contribute to the increasing amount of user generated content
available on the platform. YouTube hosts mostly self-promotional and brand related-content, and its slogan — “broadcast yourself” — allows normal individuals to become micro celebrities and online influencers (Smith et al., 2012). Due to the characteristics of YouTube, the platform appears to be the ideal place to share opinions and experiences with peers, to evaluate products and to communicate with millions of people worldwide. The users who decide to broadcast themselves on Youtube, are usually called video bloggers, or — to be more accurate — vloggers; they perform different types of videos and they have a certain amount of subscribers to their channel.
There is extensive literature about traditional online product reviews but only a few studies have investigated this new form or reviews, which are — differently from the traditional ones — performed by users and take the form of a video. Due to the numerous differences, it is clear that this reviews have different characteristics and more importantly, different effects on consumers purchase intentions. Several studies demonstrated that traditional online reviews are positively related to consumers’ purchase intentions, but very little research has taken into account video reviews on YouTube. In order to assess the persuasive effect of YouTube product reviews we will take in consideration a very important characteristic of them, namely credibility of UGC. We will analyse the effect of this variable on attitude towards UGC and lastly on purchase intentions. Ultimately also the effect of a consumers’ lifestyle variable, namely consumers’ impulsiveness, will be taken into account.
1.2 Problem Definition and Research Objectives
As anticipated in the previous chapter, the main goal of this study is to dig more into a low-investigated and fast-growing type of product reviews that thousands of consumers worldwide consult on a daily basis, in order to gather information about product they want to
buy. This research will analyse the influence of perceived credibility of UGC and attitude towards UGC on consumers’ purchase intentions, and the relationship of these variables with consumer’s buying impulsiveness. First the mediating role of attitude towards UGC will be tested, then we will investigate whether consumers’ buying impulsiveness moderate the relationship between perceived credibility of UGC and consumers’ purchase intentions. The main research question is therefore: Do the perceived credibility of UGC enhance the attitude towards UGC and do the consumers’ impulsiveness moderate the direct and indirect effect of perceived credibility of UGC on purchase intentions?
1.2 Thesis Overview
This research is structured as follows: the study begins with an extensive literature review about the main concepts which are relevant to the research; then, the conceptual model and the hypotheses will be presented. Chapter 4 will present the analysis and results of the study, while chapter 5 will be dedicated to the discussion. Finally, the implications — managerial and theoretical — limitations, and conclusion will be presented.
2. LITERATURE REVIEW AND HYPOTHESES
The main purpose of this chapter is to provide a review of the main concepts that might be relevant to this study. First, some preliminary information about word-of-mouth (WOM) and electronic word-of-mouth (eWOM) is given. Then we describe the role of opinion leaders –- and in particular online influencers — on the Internet; this will be followed by the definition of the concept of social media, and YouTube will be examined more in depth. Consequently a short paragraph explains what UGC is; following this, purchase intentions and online reviews
are reviewed. Previous literature about attitude and the theory of reasoned action (TRA) are also taken into account, as well as the analysis of consumers’ buying impulsiveness.
Online reviews are a specific form of electronic word-of-mouth (eWOM), therefore a short literature analysis about this phenomenon is given. In order to explore eWOM, we firstly have to define what offline word-of-mouth (WOM) is. WOM has been studied extensively during the past years and its definitions are numerous and various. Lazarfed & Katz (1955) were the first scholars to coin the term WOM; thenceforth, numerous researches on this topic have been conducted. Arnd (1967, p.190) explained WOM as a “oral, person-to-person communication between a perceived non-commercial communicator and a receiver, concerning a brand, a product, or a service offered for sale” while Westbrook (1987, p.261) defines it as “informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services and/or their sellers”. More recent studies defines WOM as a face-to-face two-way communication within a social relationship (Prendergast et al. 2010). Lau & Ng (2011) state that information through offline WOM can hypothetically reach many receivers, but usually it is needed for it to pass through a chain of physical correspondents. According to the literature, WOM is a powerful influence in the consumer’s decision-making processes (Silverman 1997) with great effectiveness and persuasive power (Bristor 1990). Lazarfed & Katz (1955) show that direct WOM is more effective in influencing consumer purchase decision-making than traditional advertising or direct selling. According to this Sundaram et al. (1998) demonstrated that consumers purchase behaviour is influenced by both positive and negative WOM, inasmuch positive WOM is
likely to increase purchase intention, while the opposite effect appears to happen in presence of negative WOM.
2.1.1. Electronic word-of-mouth (eWOM)
Preceding the advent of the Internet, consumers took advantages from WOM sharing product-related experiences and opinions with friends and acquaintances (Jalilvand et al. 2011). Now, with the Internet consumers have a lot more options for gathering product information from other consumers and there is the opportunity for consumers “to offer their own consumption-related advice by engaging electronic word-of-mouth” (Hennig Thurau et al., 2004). Henning Thurau et al. (2004, p.39) refer to eWOM as “any positive or negative statement made by potential, actual, or former customers about a product or service, which is made available to a multitude of consumers via the Internet”. Henning-Thurau et al. (2004) also investigated the reasons why people share their experiences and interact and exchange information with other users on Web-based platforms. They found that people have a natural desire for social interaction, they have concerns for other consumers, and above all they find chance to enhance their self-esteem and self-worth in giving advice to others; these are the primary drivers that lead to eWOM behaviour and diffusion.
There are three dimensions that contribute to the uniqueness of eWOM compared to WOM. eWOM (Cheung & Thadani, 2012) specifically:
•Carries extraordinary scalability and it spreads rapidly. The information is exchanged multi-way and not at the same time.
•Are more accessible and persistent. The explanation for this construct lies in the fact that most of the time text-based information on the Internet are archived but they remain available on the Web for an indefinite period of time.
•They can be measured more efficiently than traditional WOM. This occurs because the format, quantity, and persistence of eWOM communications made them more observable and measurable; the volume of eWOM is larger in quantity compared to offline WOM but thanks to their intrinsic characteristics eWOM messages can be easily retrieved in the Internet.
Thanks to these three dimensions eWOM can reduce some significant limitations of traditional WOM (Godes and Mayzlin, 2004). In their study Godes and Mayzlin (2004) explain that in traditional WOM communication, the information is exchanged in private and physical conversations, so direct observation has been difficult; today eWOM allows consumers to obtain information from a broad, geographically variegated group of people, who have experience with relevant products or services. Cheung and Thadani (2012) assert that a number of studies have identified five types of eWOM in the online environment namely: blogs, social networking sites, online consumer review sites, online discussion forums, and online brand/shopping sites. There are numerous studies that investigates the influence of eWOM on consumers’ behaviour, such as the study by Chen et al. (2011), Hennig-Thurau et al., 2004, Jalilvand and Samiei (2012), Jiménez and Mendoza (2013), and Chen et al. (2011). These studies on eWOM focus on the causes for writing and reading online reviews and the consumer’s reactions to the eWOM messages. Little is known about other forms of eWOM; Lee and Youn (2009) study is indeed the only one that focuses on the effect of eWOM in the form of personal blogs. For this reason this study wants investigate more about a form of eWOM — in particular a specific emerging type of online reviews — which have had very little attention from researchers and scholars; specifically this study focuses on online video reviews on YouTube. the main aim of this study is to understand which variables influence consumer’s purchase intentions when they
watch a product review on YouTube and whether consumers’ impulsiveness has a relevant role in the persuasion process. You Tube product reviews are usually performed by video bloggers, namely vloggers, that can be considered influencers, or — to us a term related to WOM diffusion — opinion leaders.
2.2. Opinion Leaders and Online Influencers
Where does WOM come from? Jalilvand et al. (2011) state that the key WOM player is the opinion leader. The construct of opinion leadership has its roots in the work of Lazarsfeld et al. (1948) who used the term “opinion leader” for the first time, defining them as “the individuals who were likely to influence others persons in their immediate environment”. Lazarsfeld & Katz (1955) expanded this work and described opinion leaders like individuals that can influence consumers over the purchase decisions of products or services; according to them, information diffusion follows a “two step
flow” (Fig.1 ) model, following a process that goes from the media to the opinion leader and consequently from the opinion leader to their followers or “large majority”. In other words this model asserts that opinion leaders are essential to allow the flow and the diffusion of information, opinions, innovations and trends; according to this study, without these unique individuals all the processes underlined above would not arise.
Fig.1 Two Step Flow. (Lazarsfeld &
Watts & Dodds (2007) propose a new model (Fig.2) namely multi-step flow, that differs from the two-step flow in several ways. First of all, in the multi-step model, influence can be propagated for many step, while in the preceding one it can propagate only through two steps. Second thing, according to the two-step model,
information can exclusively flow from opinion leaders to followers, while in the multi step model it can flow in any direction. Therefore, the more recent model states that opinion leaders are important for the diffusion of information, but they are neither sufficient nor necessary in order for this process to occur.
More recent studies define opinion leaders as individuals “interested in particular product fields, that make an effort to expose themselves to mass media sources, and are trusted by opinion seekers to provide knowledgeable advice” (Jalivand et al., 2011). Following the same line of thought, Walker (1995) asserts that only a few opinion leaders spread information within their own spheres, which in the same way spread it to other spheres and so on. Walker (1995) states that opinion leaders have great potential to create positive WOM around a product or service and on the same line of thought, a recent survey identify that most consumers perceive online opinions to be as relevant and reliable as brand web sites; these studies show how considerable is the impact eWOM can employ on the consumer decision process (Nielson, 2007).
Several studies have identified variables that mediate WOM. We find tie strength (Frenzen & Nakamoto, 1993), source credibility (Bansal & Voyer, 2000), perceptual affinity (Gilly et al., 1998), and demographic similarity (Brown & Reingen, 1987) as the most Fig.2 Multi-Step flow (Watts & Dodds,
important antecedents of WOM effect. Cheung & Thadani (2012) observe that source of source credibility is among the most frequently investigated factors regarding eWOM influence. Source credibility indicates the message receiver’s perception of the credibility of a message source (Chaiken, 1980) and the extent to which a source is perceived as believable and trustworthy (Cacioppo et al. 1986). In this study opinion leaders are of great importance, especially because they are the individuals who create content on YouTube and prompt products to their followers, or in general to YouTube Users.
2.3 Social Media
In the past years, social media have become a new crucial mean of integrated marketing communications especially because it allows companies to build durable relationship with their consumers (Mangold & Faulds 2009). Kaplan and Haenlein (2016) define social media as ‘a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user generated content’.
Social media is a vast concept that consists in rating sites (e.g. TripAdvisor), blogs (e.g. TMZ), social networks (e.g. Facebook, LinkedIn), wiki (e.g. Wikipedia), multimedia sharing sites (e.g. YouTube), bookmarking sites (e.g. Digg), photo sharing (e.g. Instagram, Flickr), microblogging sites (e.g. Twitter), and virtual games worlds (e.g. World of warcraft) (Mir & Rehman, 2013). Thanks to the collaborative and social nature of social network sites, these platforms represent a perfect setting for consumer-to-consumer interaction and conversation, specifically brand related eWOM (Chu & Kim, 2011).
As already said, Jalilvand et al. (2011) identify opinion leaders as the main players in the WOM diffusion. Today, with the increase in the usage of Internet and social media, opinion leaders can be identified with online influencers like bloggers, vloggers, or
instagrammers, that with a network of millions of followers have the opportunity to reach a large and heterogeneous audience. Many studies have investigated different aspects about social media, however very little research has been focused on YouTube, which is an extensive source of UGC.
YouTube is a content community and web-based platform that was founded in 2005. It gives users the possibility to post, view, link to and comment on videos that are present on the website (Smith, et al. 2012). According to Kavoori (2011, p.3) YouTube is a “modern-day bard, a storyteller for the digital age, a provider of modern-day myths” but most importantly the “poster child for consumer-generated content”. Through YouTube, a lot of UGC can be created; users can in fact arrange personal profiles, post their own videos, acquire subscribers, comments and shares. Smith, et al (2012) state that “the most commented-on videos tend to be user-generated, while the most viewed videos tend to be professionally produced”.
Several studies have analysed the overall content of YouTube, which appears to consist mostly in music videos, live material, product reviews, vlogs and script performances (Burgess & Green, 2009). According to Blythe & Cairns (2009) UGC on YouTube take the form of product advertisement, demonstrations of functionality and hacks, “unboxing” of new products, reviews, satires, and daily storytelling (vlogs). Borghol et al. (2012) assert that content on YouTube can have a broad effect on cultures, opinions and thoughts and it can shape and transform attitudes, public views and ideas. For this reason, this study has the aim to investigate the effects of UGC on YouTube — in particular of product reviews — on attitudes and purchase intentions of the viewers.
Another relevant concept when talking about reviews and eWOM and product reviews on YouTube is user generated content (UGC). Although the concept of eWOM and UGC might seem similar and closely related, there is a difference based on whether the content is generated or transmitted by the user (Cheong & Morrison, 2008). UGC is referred to media content which is created and generated by the user of a web-based platform who has experienced the product in first person and is eager to let other consumers know what he or she thinks about it (Daugherty et al., 2008). According to Mir & Rehman (2013) users and consumers have in general a high level of trust towards UGC because it is believed that other users are willing to share both their positive and negative experience in order to help their peers in the decision process. It has indeed been demonstrated that UGC on YouTube is one of the most popular type of videos in terms of comments received and users interaction (Smith, et al. 2013). Smith et al. (2013) also assert that the strongest site influences in brand-related UGC arises from YouTube’s culture of self-promotion and self-broadcasting.
2.5 Online Product Reviews
As specified before, online product reviews are a particular type of eWOM, that instead of being verbal face-to-face communication, are in a written for and “contain graphical and textual elements” (Jiménez & Mendoza, 2013). Particularly Jiménez & Mendoza (2013) state that consumers assess online product reviews using number of likes, number of positive and negative reviews, ratings, text-rating congruence and source. Mudambi & Schuff (2010) assert that online product reviews are “peer-generated product evaluations posted on company or third party websites” that are able to enhance customers’ perception of the usefulness of products and services. This type of user generated content also facilitates the consumers’ purchase decision process, because it allows them to get
knowledge about other consumers’ opinion and it gives the opportunity to acquire indirect experiences (Mudambi & Schuff, 2010). Consumers in fact, prior to purchasing a product, seek information and recommendations about it, in order to improve the quality of the decision that will be made (Mir & Rehman, 2013); in other words, consumers browse different web platforms to gather information to support their purchase decisions. According to MacKinnon (2012, p) “66.3% of consumers rely heavily on UGC when attempting to make purchasing decisions” and “65% of consumers trust word of mouth on the Internet more than content produced by advertisers”.
According to what has been said, users can benefit from the social nature of YouTube to share their opinions with other users in the form of videos.
2.5.1 YouTube Product Reviews
Most of eWOM studies focus on online graphic and/or textual product reviews, rating sites and discussion forums (Cheung & Thadani, 2012), while other forms of eWOM, like blogs and social networking websites have received less attention. What online influencers — and in particular YouTube influencers — create is a different form of review, that instead of being graphic and/or textual, it takes the form of a “video review” posted on YouTube user’s channel. Thanks to the social nature of YouTube, this can easily reach an extensive number of people and influence their behaviour.
When the same information about a product is shared by a lot of users on the social media this will increase the usefulness and credibility of that information (Mir & Zaheer, 2012). There are several factors that make a review on YouTube credible and useful to other consumers, namely the quality of the video, source credibility, the length of the video, number of followers of the reviewer, number of views of the video, number of comments, the valence of these comments. Mir & Rehman (2013) found out that quantity of posts, views and
reviews have a positive effect on perceived credibility and on perceived usefulness of the review, so that the higher the number of posts, views and reviews about a product, the greater perceived credibility and usefulness of UGC will be observed. Similarly, according to Vickery & Wunsch-Vincent (2007) when a substantial number of users view and comment UGC, its perceived usefulness and credibility increase.
Attitude is a relevant notion in the marketing research inasmuch several studies have demonstrated it is a secure and solid predictor of consumer behaviour (Olson & Mitchell, 2000). One of the first studies about attitude (Allee et al., 1935) states that attitude is the most indispensable concept in contemporary social psychology, in fact over the past century, a vast literature about attitude has been developed. Several scholars investigated antecedents of attitude and different definitions have been proposed; Fishbein & Azjen (1975, p.6) have defined attitude as a “learned predisposition to respond in a consistently favorable or unfavorable manner with respect to a given object” and a few years later the same authors have argued that a person’s attitude toward an object influences the overall sequence of his responses to the object (Fishbein & Azjen, 1977). In their several studies about attitude-behaviour’s relation Fishbein & Ajzen have developed the expectancy-value theory, which states that distinct consequences need to be taken into account when attitude is measured; in other words they assert that attitude is an association of affect and cognition (Fishbein & Azjen, 1977). Following this line of thought Fazio (1986) asserts that together with affect and cognition, also behavioural intentions can be considered as an important aspect strongly related to attitude. The same author a few years later defined attitude as “an association between a given object and a given evaluation” (Fazio, 1989 p.155) while According to Eagly
& Chaiken, (1993) attitude is a psychological tendency that is revealed when an individual evaluate a particular matter with degree of favour or disfavour. A recent research (Glasman & Albarracín, 2006) investigated more in depth the attitude-behaviour relation, finding that individuals “form attitudes more predictive of behaviour when they are motivated to think about the object they are considering, have direct experience with the attitude object, report their attitudes frequently, construct their attitudes on the basis of information that is relevant to the behaviour, receive or generate either positive or negative information about the object, and believe that their attitudes are correct”. Perloff (2014 p.71) defines attitude as a “learned, global evaluation of an object (person, place, or issue) that influences thoughts and actions”; in other words it’s not a behaviour, but it consists of learned patterns of reacting to social stimuli. Recent studies (Mir & Rehman, 2013 and Wang, 2015) investigated the aspects that influence consumers’ attitude towards YouTube UGC, and they found out that perceived credibility of UGC, and perceived usefulness of UGC have a positive effect on it. Furthermore According to (Xie et al., 2011) more credible content compared to less credible content results in stronger persuasion and more attitude change. Accordingly, this study hypothesise that:
H1. There’s a positive relation between perceived credibility of UGC and attitude towards UGC
2.7 Theory of reasoned actions (TRA)
TRA is an extensively employed framework that finds its roots in social psychology (Talukder & Quazi, 2011). The theory is based on the assumption that individuals are naturally rational and can make regular use of information available to them (Azjen and Fishbein, 1980). TRA (Fishbein 1979) proposes that two factors “arouse a person’s intention
to perform a behaviour: the personal factor (personal interest) and the subjective norm (social influence)”. Fig.3 show the model firstly developed by Fishbein and Ajzen (1980), that in numerous studies has been used to predict most human behaviours and it has been demonstrated to have an extremely strong predictive utility, especially when it is applied to purchase intentions (Sheppard et al. 1988).
Fig 3. Theory of Reasoned Actions (TRA) - (Fishbein and Ajzen, 1980)
2.8 Purchase Intentions
According to Spears and Singh (2004, p.56) purchase intention describes “an individual’s conscious plan to make an effort to purchase a brand”, while Ling et al. (2010) assert that purchase intention can be “classified as one of the components of consumer cognitive behaviour on how an individual intends to buy a specific brand” . Similarly, Pavlou (2003) states that purchase intentions occur when an individual is inclined and intends to get involved in a monetary transaction to buy a product or a service; this means that purchase intention can be considered a valid indicator to see if a consumer will buy a product, so that, as Schiffman & Kanuk (2000) assert, “the higher the purchase intention expressed, the greater the probability that the consumer will buy the product”.
Nowadays, when individuals have to make a purchase decision, they face a wide of range of choices, which make the process difficult and long; accordingly, over the past decade
many researchers investigated the factors that influence people in the purchase decision process. For example, Engel et al. (1995), studied several factors affecting the process of purchase decision making, namely personal factors, psychological factors and social factors. More recently, Jalalkamali & Nikbin (2010, p.235) state that “the key stimuli that lead consumers to make their purchase decisions in the complex business environment are prices, quality, brands of products, advertisements, friends'/families' recommendations and disqualifications and consumers' previous purchase experiences”; According to this the consumer purchase motivation model consists of two parts, internal stimuli and external stimuli, which means that the buying behaviour is influenced by both internal and external aspects. Internal stimuli arises from within an individual, such as the past experiences, while the external stimuli mostly comes from the environment like social class and reference group (Jalalkamali and Nikbin, 2010).
Nowadays consumers search information, advice their peers and share their experiences using the internet and the social media; user generated reviews are a wide source of information for consumers, and it has been demonstrated that they can considerably affect consumers’ purchase decisions (Zhu & Zhang, 2010)
2.9 Online Reviews and Purchase Intentions
Several studies proved that a high number of consumers take purchase decisions based on online WOM. Godes & Mayzlin (2004) investigate the relationship between online WOM and television show viewerships which it revealed to be positive, while another study (Liu, 2006) found a positive relationship between movie reviews on the internet and box office revenues. On the same line of thought Senecal and Nantel (2004) prove that consumers select products two times more often when they had consulted online product reviews. According to
Zhang et al. (2014) a consumer survey from 2011 by Channel Advisor found that 90% of online shoppers consult online reviews and 83% think that product reviews affect their purchases. Conformly to all these studies we assume that a vlogger, when making a product review on YouTube, might influence his/her followers/viewers purchase intentions. Wang (2015) investigated the relationship between attitude towards UGC on YouTube and Purchase intentions, finding a positive relationship between the two variables. In other words when there’s a positive attitude towards UGC then the intentions to purchase the product reviewed should increase. Therefore:
H2. There is a positive relationship between attitude towards UGC and Purchase Intentions. Recent study assert that 66.3% of consumer often rely on UGC when they face a purchase decision and 65% of consumers trust more on WOM than company/advertisers created content (MacKinnon, 2012). Furthermore Wang (2015), found out that there is a positive relationship between perceived credibility of UGC and purchase intentions. On the same line of thought, Xie et al. (2011) demonstrated that more credible content compared to less credible content results in stronger persuasion and more intentions and behaviour change. According to this:
H3. There is a positive relationship between perceived credibility of UGC and purchase intentions.
2.10 Consumers’ lifestyle
In addition to the variables already discussed that affect consumers’ purchase intentions, also a consumers’ lifestyle variable will be considered. The moderating impact consumers’ impulsiveness between perceived credibility of UGC and consumer’s purchase intentions will be investigated. Consumers’ impulsiveness is a variable which is related to the
consumer lifestyle. Lifestyle has been defined commonly as “how one lives”; in the marketing context, lifestyle however, “describes the behavior of individuals, a small group of interacting people, and large groups of people (e.g. market segments) acting as potential consumers” (Kucukemiroglu, 1999 p.473). Accordingly, the concept of lifestyle shows fairly different attributes from the concept of personality; lifestyle indeed represents the “economic level at which people live, how they spend their money, and how they allocate their time” (Kucukemiroglu, 1999 p.473). According to Kim et al. (2000) “individual characteristics such as a consumer’s lifestyle need to be emphasised as key determinants of purchasing decisions”.
Little research has been done about this particular variable of consumers’ lifestyle, especially when it is related to purchase behaviours. This study wants to investigate the potential moderating role of buying impulsiveness between credibility of UGC and purchase intentions.
2.10.1 Buying Impulsiveness
Impulsive behaviour in past studies has always been associated with immaturity, primitivism, foolishness, lower intelligence, and even social deviance and criminality (Rook & Fisher, 1995). According to Rook (1987), when we talk about impulsiveness in the consumption field, we always refer to a negative impulsive behaviour that causes negative consequences in the personal finances, post-purchase satisfaction, social reactions, and overall self-esteem. We define buying impulsiveness as a “consumer's tendency to buy spontaneously, unreflectively, immediately, and kinetically” (Rook & Fisher, 1995 p.306); In other words, impulsive buyers are more “likely to experience spontaneous buying stimuli”. As a consequence of this impulsive buyers will respond promptly to their purchase impulses.
Existing literature demonstrates that impulsive purchase will positively affect online purchase intention (Zhang et al., 2007). Very few studies investigate the role of consumers’ impulsiveness when related to online reviews and purchase intentions, while no researches at all have investigated the role of purchase impulsiveness in relation to YouTube product reviews and buying intentions. What we want to investigate in this study is whether consumer impulsiveness has a moderating effect on the relationship between perceive credibility of UGC and purchase intention. Therefore we hypothesise:
H4. Impulsiveness moderates the relationship between credibility of UGC and Purchase Intentions so that the effect of credibility of UGC on purchase intentions will be greater for impulsive consumers.
2.11 Research objectives
The main object of this study is to investigate the relationship between credibility of UGC, attitude towards UGC and purchase intentions, and to research whether consumers’ impulsiveness has a moderating impact on the relationship between credibility of UGC and purchase intentions.
The main research questions of this study, is:
In what ways and to what extent does credibility of UGC on YouTube affect consumers’ buying intentions?
• Does attitude towards UGC mediate the relationship between attitude towards UGC and consumers’ buying intentions?
• Do consumers’ impulsiveness moderate the relationship between credibility of UGC and consumers’ purchase intentions?
3. CONCEPTUAL MODEL
Fig 4. - Conceptual Model
H1. There’s a positive relationship between perceived credibility of UGC and Attitude towards UGC
H2. There’s a positive relationship between attitude toward UGC and purchase intentions H3. There is a positive relationship between perceived credibility of UGC and purchase intentions.
H5. Consumers’ Impulsiveness moderate the direct and indirect effect between perceived credibility of UGC and purchase intentions, so that the effect of perceived credibility of UGC on purchase intentions will be greater for impulsive buyers.
4. RESEARCH DESIGN AND METHODOLOGY
Perceived Credibility of UGC Attitude towards UGC Purchase Intentions Buying Impulsiveness
To be able to understand the relationship between credibility of UGC, attitude towards UGC and purchase intentions, to investigate the moderating role of impulsiveness, and to test the hypotheses proposed, data is collected through an online survey. This chapter will describe how the data was gathered. The sample of this study will be described in the first section of the chapter; the second part will define the research design including the measures and the measures items; Finally the procedure how data was collected will be described.
The population for this study consists of YouTube users in Europe. Regarding the sample selection method, a non-probability sampling was used, because no sampling frame can be retrieved for this large research population. A convenience sampling via the snowballing technique (asking participants to spread the questionnaire in their own social network) was used and it was distributed via e-mail and social media. The main goal for data collection was to gather as many responses as possible, in order to increase the probability of having a representative sample and to be able to generalise conclusions over the population. The minimum sample size will be represented by at least one hundred (Saunders & Lewis, 2012) shopping consumers in the Netherlands. On June 4, 2016 the survey was sent and it was active for a total of 10 days, until June 14th. In total 247 respondents have started the survey. 47 of these left the survey before the first question. 67 respondents did not pass the first question, where respondents were asked if they had ever watched a user generated product review on YouTube. When the answer was “no”, they were automatically redirected to the last page of the survey. Out of the 133 who responded “yes” to the first question, 10 did not completed the survey or left some questions not responded.
In order to test and identify which variables affect consumers’ purchase intentions when they watch a product review on YouTube, a quantitative research through a digital survey was carried. This design was chosen because it is the most adequate for using statistics to test hypotheses and discern consistent findings out of them. Furthermore through a digital survey, it was possible to gain results from a diversified and large sample of respondents. The survey, contains an explanatory, structured web questionnaire for collecting and analysing the data consistently (Saunders & Lewis, 2012). The study is cross-sectional, as the respondent fill out the questionnaire at one moment. A pilot test was done on June 2, 2016 with a small number of people to check whether the survey was clear and complete. The survey measures credibility towards UGC on YouTube, perceived credibility of UGC, consumers’ buying impulsiveness, and purchase intentions. Finally, participants were asked to specify their gender, their age and the highest level of education they had completed. The questions are shown in the Appendix I
Perceived credibility of UGC
In order to assess the independent variable of the model, namely perceived credibility of UGC on YouTube, a scale from Chi (2011) was used. The scale is composed of a total of five items and participants gave responses on a five-point Likert-type scale from “strongly disagree” to “strongly agree”. Concerning the reliability of the scale a Cronbach’s Alpha of . 94 was reported. The items of the scale are: “User generated product content on YouTube is dependable”, “User generated product content on YouTube is unbiased”, “User generated product content on YouTube is honest”, “User generated product content on YouTube is reliable,” and “User generated product content on YouTube is truthful”.
Attitude towards UGC
The mediator of this study, namely attitude towards UGC, was measured on a five-point Likert-type scale with an adapted scale from Betra & Ray (1986). The five items are: “User generated product content on YouTube is unbiased”, User generated product content on YouTube is dependable”, “User generated product content on YouTube is truthful”, “User generated product content on YouTube is honest”, “User generated product content on YouTube is reliable”. This scale reported a Crombach of .80
Concerning the moderator of this research namely consumers’ impulsiveness a scale from Rook & Fisher (1995) was used. The 5 items picked from the larger scale are: “I often buy things without thinking”, “I often buy things spontaneously”, “I carefully plan most of my purchases”, Sometimes I feel like buying things on the spur of the moment” and, “I buy things according to how I feel at the moment”. The Crombach of this scale is .88 .The items were assessed on a five-point Likert-type scale, from strongly disagree to strongly agree. Purchase Intentions
An intention scale adapted from Mortazavi et al. (2014) was used to assess consumers’ purchase intentions. Items were measured on a five-point Likert-type scale. Crombach was . 87. The 3 items were “I will definitely buy products recommended by UGC in the near future,” “I intend to purchase a product recommended by UGC in the near future,” and “It is likely that I will purchase a product recommended by UGC in the near future.”
Gender, Age, and Education
The highest level of education achieved was asked on a multiple choices question that included High school, bachelor’s degree, master’s degree, HBO, PhD degree, or other. Gender
was asked with a multiple choice with two categories (male or female).
4.4 Data Collection
In order to create the survey www.qualtrics.com was used. Qualtricsis a very flexible and powerful tool that allows to create surveys in an easy and fast way. Furthermore, Qualtrics allows you to export the data into different formats, of which one is .sav, format to open and analyse data with IBM SPSS.
4.5 Pilot study
The main purpose of the pilot test is to assess whether the survey was understandable and clear; in fact according to van Teijlingen & Hundley (2002) pilot tests are of great importance because they can provide indications about whether determined questions are complicated or not responded.
5. RESULTS AND ANALYSIS
In the following chapter the relation between perceived credibility of UGC, attitude towards UGC, consumers’ impulsiveness and purchase intentions is analyzed. Perceived credibility of UGC is the independent variable of the model (X), purchase intentions is the dependent variable (Y), attitude towards UGC is the mediator between them (M), and consumers’ buying impulsiveness is the moderator between perceived credibility of UGC and consumers’ purchase intentions (W). Finally, XW represent the interaction between independent variable and moderator. The following paragraph illustrates the analysis of the quantitative methodology.
In this section, the results of the survey are presented. The first paragraph shows a short presentation of the respondents, then the descriptive and reliability analysis are examined. The hypotheses testing will explain the analyses performed to validate this research.
5.1.2 Missing Data
In total 247 respondents have started the survey. 47 of these left the survey before the first question. As mentioned before, 67 respondents (33.5%) did not pass the first question, where respondents were asked if they had ever watched a user generated product review on YouTube. When the answer was “no”, they were automatically redirected to the last page of the survey. Out of the 133 who responded “yes” to the first question, 10 did not completed the survey or left some questions not answered. The problem with the missing data was dealt by executing the cases list-wise; in this way only the cases with no missing data were analyzed.
The final sample includes 123 participants. 65 of them are males (52.8%) and 94% of the respondents are between 18 and 29 years old. Finally, 39.8% of the respondent had completed their bachelor degree, while for the 43.9% a master’s degree had been completed. Table 1 describes the characteristics of the respondents.
Variable Frequency Percent
Gender Male Female 65 58 52,8 47,2
Table 1 - Respondents Characteristics
5.2 Error Checks
A check of the frequencies to examine whether there were any errors in the data was performed. Then descriptive analysis has been done to check if there were errors in the data. Accidentally, the scale of the attitude towards UGC and impulsiveness were coded with the numbers from 18 (strongly disagree) to 22 (strongly agree); these items were recoded with the number from 1 (strongly disagree to 5 (strongly agree). This allowed to have a consistent scale ranging from 1 (strongly disagree) to 7 (strongly agree). Only one item of the impulsiveness scale (Imp3) showed to be counter-indicative, and for this reason it was recoded.
The Cronbach’s alpha allows to study the consistency of the scales. The Cronbach’s alpha was examined for all the variables of the model, namely perceived credibility of UGC,
Age 18 – 24 25 – 29 30 – 45 > 46 74 42 6 1 60,2 34,1 4,9 0.8 Highest Level of Education
High School Diploma HBO Bachelor’s Degree Master’s Degree Phd Other 13 3 49 54 3 1 10,6 2,4 39,8 43,9 2,4 0.8
attitude towards UGC, consumers’ impulsiveness, and purchase intentions. A Cronbach’s alpha above 0.7 can be interpreted as a sign of a good internal consistency of the scale (Nunnally, 1978). Lower values than that indicate that the scales are measuring different constructs, while values above 0.95 can indicate that there are possible redundancies between the questions.All the variables have a Cronbach’s Alpha > 0.7 and < 0.95, and the Corrected Item-Total Correlation shows that all the items have a good correlation with the total score of the scale. No items of the scale were deleted, because their removal didn’t assure a higher Crombach’s Alpha.
5.4 Descriptives statistics
Table 2 illustrates the skewness, kurtosis, mean, standard deviation and Cronbach’s alpha for each item. The independent variable, perceived credibility of UGC present a Skewness that varies from -1,009 and 0,121, while the kurtosis appears to be between -1,050 and 0,982. The variable attitude towards UGC present skewness that goes from -1,056 and -0,254, while kurtosis is between -0,106 and 1,817. The items of the moderator, namely consumers’ buying impulsiveness don’t present relevant skewness, while the kurtosis is between -1,095 and -0,670. The defendant variable, purchase intentions, present skewness between -0,514 and -0,181 while kurtosis is between -0,939 and -0,656. Tabachnick and Fidell (2000) state that skewness does not impact the analysis when the number of the sample is relevant. According to this, since the sample of this research is composed of 123 people, skewness and kurtosis should not affect the analysis. Overall of the means result around 3, which on a five-point Likert-type scale can be considered an expected value. Item one of attitude towards UGC (“UGC is useful”) shows to be slightly above the average (mean=4.12).
In conclusion, the scale means of every variable were computed in order to test the hypothesis.
Variable Structure Skewnes s
Mean SD Alpha
Perceived Credibility of UGC -,837 1,744 3,331 ,615 ,725 UGC on YouTube is unbiased UGC on YT is dependable UGC on YT is honest UGC on YT is reliable UGC on YT is truthful ,121 -,937 -,924 -,443 -1,009 -1,050 ,982 ,117 -,208 ,747 2,76 3,48 3,44 3,50 3,48 1,01 ,843 ,916 ,855 ,823
Attitude Towards UGC -,332 ,339 3,720 ,571 ,760 UGC is Useful UGC is Important UGC is Pleasant UGC is Nice UGC is Good -1,056 -,557 -,671 -,582 -,254 1,817 ,385 ,727 ,495 -,106 4,12 3,57 3,59 3,64 3,67 ,742 ,897 ,818 ,791 ,741 Consumers’ Impulsiveness -,008 -,843 2,860 ,866 ,837
Table 2 - Skewness, Kurtosis, Mean, SD, Cromlech Alpha
The correlation matrix in Table 3 represents the mean, standard deviation and correlations of the computed variables. Perceived credibility of UGC appears to be positively correlated with attitude towards UGC (r=0.268, p<0.01), while there is no correlation with impulsiveness (r= -0.196, p<0.05) and negative correlation with purchase intentions (r= -0.275, p<0.01). There is a slight tendency to a positive relation between attitude towards
I often buy things with thinking
I often buy thing spontaneously
I carefully plan most of my purchases
Sometimes I feel like buying things on the spur of the moment
I buy things according to how I feel at the moment
,486 ,033 ,427 -,512 -,486 -1,047 -1,095 -,730 -,910 -,670 2,40 2,97 2,50 3,21 3,22 1,240 1,180 ,970 1,081 1,098 Purchase Intentions -,371 -,553 3,054 0,973 ,888
I will definitely buy products reviewed on YouTube in the near future
I intend to purchase products reviewed on YouTube in the near future It is likely that I will purchase products reviewed on
YouTube in the near future
-,181 -,241 -,514 -,656 -,939 -,548 2,93 2,93 3,30 1,038 1,049 1,138
UGC and Impulsiveness (r=0.200, p<0.05), while there is a positive correlation between attitude towards UGC and purchase intentions (r=0.363, p<0.01). Finally, no correlation was found between purchase intentions and impulsiveness (r=0.006).
In the next chapter, the hypotheses presented in the literature review will be tested to identify potential patterns.
5.6 Hypotheses Testing
This chapter discusses the results of the analysis. First of all, the hypothesis will be presented. Then, the mediating role of attitude towards UGC will be investigated. Finally, the moderation effects for will be tested.
5.6.1 Linear Regression
Before running the tests to assess linearity, homoscedasicity was verified. All the variable presented a constant variance.
Relationship between the perceived credibility of UGC and attitude towards UGC
Regression analysis was executed to identify the potential relationship between credibility of UGC and attitude towards UGC.
Variables M SD 1 2 3 4 5
1. Gender 1,47 0,501
-2. Perceived Credibility UGC 3,33 0,615 -0,64 (0,725)
3. Attitude towards UGC 3,72 0,571 -0,022 0,268** (-0,760)
4.Buying Impulsiveness 2,86 0,870 0,374 -,196* -0,200* (-0,837 )
5. Purchase Intentions 3,05 0,973 0,009 -0,275** 0,363** 0,006 (0,888)
**. Correlation is significant at the .01 level (2-tailed). *. Correlation is significant at the .05 level (2-tailed).
There was a significant positive relationship of credibility of UGC on the attitude towards UGC.
F(1, 121) = 9.328, p < .05, beta= .268 . This model explained 7.2% of its variance
Table 4 - Linear regression perceived credibility of UGC and attitude towards UGC
Relationship between the attitude towards UGC and Purchase Intentions
There was a significant positive relationship between attitude towards UGC and purchase intentions.
F(1, 121) = 18.345, p < .001, beta= .363 . This model explained 13.2% of its variance
Table 5 - Linear regression attitude towards UGC and purchase intentions
Relationship between the perceived credibility of UGC and Purchase Intentions
There was a significant positive relationship between perceived credibility of UGC and purchase intentions.
F (1, 121) = 9.900 p < .05 beta=.275 . This model explained 7.6% of its variance
Table 6 - Linear regression perceived credibility UGC and Purchase intentions
R R2 R2 change p B SE t 0.363 0.132 0.124 0,000 0.618 0.144 4.283 R R2 R2 change p B SE t 0.268 0.072 0.064 0,003 0.288 0.094 3.054 R R2 R2 change p B SE t 0.275 0.76 0.068 0,002 0.435 0.138 3.146
Relationship between buying impulsiveness and purchase intentions
There is not significant relationship between consumers’ buying impulsiveness and purchase intentions.
F(1, 121)=0.004 p—- beta=0.006. This model explained 0% of its variance.
Table 7 - Linear regression buying impulsiveness and Purchase intentions
5.6.2 Mediation and Moderation Effect
The mediation analysis enables to establish whether the variable X determines Y through one mediator variable, while the moderation analysis enables to test whether the effect of the variable X on Y is stronger in the presence of W. The PROCESS model 8 was used to test these hypotheses in order to prove the moderated mediation: X is the independent variable (Perceived credibility of UGC), Y the dependent variable (Purchase intentions), M the mediator (attitude towards UGC), W the moderator (consumers’ buying impulsiveness). and XW the interaction effect between perceived credibility of UGC and Buying impulsiveness.
R R2 R2 change p B SE t
Conditional indirect effect of X on Y through Mj = (a1j + a3jW) b1j Conditional direct effect of X on Y = c'1 + c'3W
*Indirect effect of XW on Y through Mj = a3j b1j
Fig 5: Statistical Model 8 with PROCESS
According to Hayes (2012) the main goal of the mediation analysis is to assess the extent to which the independent variable X influences Y through M. The mediation exists if there is not a significant relationship between the independent variable X and the dependent variable Y when the mediator M is considered (c’1 in Figure 4). When the effect of the Independent variable on the dependent is significant, mediation is not present. Hayes (2012) states that a variable can be analysed as mediator if there is a correlation between X and Y, and X and M (a1 in the figure). Moreover, M has a significant relationship with Y (b1 in the figure) and the effect of X on Y has to be less after controlling the mediator.
On the other hand, a moderation analysis seeks to establish whether the extent of the effect of the independent variable X on the outcome Y depends on a moderator M (Hayes, 2012). The function, c1’ + c3’W, is the conditional direct effect of X on Y or simple slope for X. It assess
X (Perceived Credibility UGC) M (Attitude towards UGC) Y (Purchase Intentions) W (Impulsiveness) b1 a1 c’1 XW c2’ c3’ a2 a3
“how much two cases that differ by one unit on X are estimated to differ on Y when M equals some specific value” (Hayes, 2012 p.4). Mj = (a1j + a3jW) b1j is the conditional indirect effect of X on Y through, that is defined as “the magnitude of an indirect effect at a particular value of a moderator (or at particular values of more than one moderator)”. Preacher et al. (2007). What this research wants to demonstrate through this is, wether the indirect effect of credibility of UGC (independent variable) on purchase intentions, through attitude towards UGC is moderated by consumers’ buying impulsiveness.
Mediation effect on Purchase Intentions mediated by Attitude towards UGC
The results of the analysis show that attitude towards UGC mediates the effect of perceived credibility of UGC on purchase intentions. c1’ represent the direct effect, while the indirect effect is a1’b1’. Therefore the total effect will be represented by c1’ + a1b1.
Looking at the outcome attitude towards UGC (M) we can see that F (1,121) = 4.135, p<0.05. The model explained 9.4% of the variance.
Perceived credibility of UGC has no significant effect on the attitude towards UGC (beta=0.151, p>0.05). In the same way Impulsiveness has no significant effect on attitude towards UGC sine beta= -.1760 and p>0.05.
Moving to the second outcome, purchase intentions, the results shows a F(1,121) = 7.334, p< 0.001 and the model explained 19.91% of the variance. The attitude towards UGC significantly influences the purchase intentions (beta=0.566, p<0.001). There is a positive direct effect of perceived credibility of UGC on purchase intentions (beta=1.235, p<0.05). No indirect effect was found so that attitude towards UGC does not mediate the relationship between perceived credibility of UGC and purchase intentions. According to the results, this study asserts that the perceived credibility of UGC remain a significant predictor of purchase
intentions in the presence of the mediator variable, as shown by the direct effect (p=0.0207). This result does not support the fact and the expectations that attitude towards UGC fully mediated the relationship between perceived credibility of UGC and purchase intentions. Fig. 6 shows the mediation effect.
Notes: The coefficients are presented. Statistical significance: *p<.05; **p <.01; ***p <.001 Fig.6 Mediating effect of attitude towards UGC
Moderating effect of Impulsiveness
As already mentioned before the moderation analysis seeks to establish whether the extent of the effect of the perceived credibility of UGC on the outcome purchase intentions depends on a moderator, namely consumers’ buying impulsiveness.
Impulsiveness has no significant effect (c2’) on purchase intentions (beta=1.103, p>0.05) and no significant effect on attitude towards UGC (a2), since beta = -0.1760 and p>0.05.
The interaction (XW) between the independent variable and the moderator has not significant effect on purchase intentions since the p value is >0.05. In the same way the interaction has no Y (Purchase Intentions) M (Attitude towards UGC) Direct Effect= 1,235* Indirect Effect= 0,0128 Total Effect= 1,376 X (Perceived Credibility UGC) 0,151 0,566***
significative effect on attitude towards UGC, (p>0.05). According to this, the conditional direct effect of X on Y is not significant at high values of impulsiveness (effect=0.148, p>0.05, LLCI=-0,1912 and ULCI=0,4868), while it is significant for low levels of impulsiveness (effect=0,655, p<0.05). Same thing happens for the conditional effect on the indirect effect between perceived credibility of UGC and purchase intentions (
According to this we can state that consume’rs’ buying impulsiveness does not moderate the direct effect of perceived credibility of UGC on purchase intention, nor the indirect effect via attitude towards UGC.
Notes: The coefficients are presented. Statistical significance: *p<.05; **p <.01; ***p <.001 Fig. 7 Moderating effect of consumers’ impulsiveness X (Perceived Credibility M (Attitude towards UGC) Y (Purchase Intentions) 0,151 0,566*** Direct Effect= 1,235* Indirect Effect= 0,0128 W (Impulsiveness) XW 1,103 -0,292 -0,176 -0,023
The main aim of this research is to provide empirical findings about the impact of perceived credibility of UGC on the attitude towards UGC and purchase intentions, and the role of consumers’ buying impulsiveness in the model proposed. This chapter presents and discusses the important results and implications found in the survey.
6.1 Interpreting the research results
As mentioned before product reviews on YouTube gained relevant importance over the past decade. We examined different variables that can influence consumers’ purchase intentions when they watch a user generated product review on YouTube, namely perceived credibility of UGC, attitude towards UGC, consumers’ impulsiveness. This study tried to examine the effect of YouTube UGC on viewers’ purchase intention and test the potential moderating role of consumers buying impulsiveness.
From the analysis we performed on SPSS we state that not all the variables considered influence consumers’ purchase intentions. As illustrated in Table 8, hypothesis 1 and 5 is not supported by the results of this study. In other words there is not mediating and moderating effect of impulsiveness.
H1.There’s a positive relation between perceived credibility of UGC and
attitude towards UGC NOT SUPPORTED
H2. There is a positive relationship between attitude towards UGC and
Purchase Intentions. SUPPORTED
H3. There is a positive relationship between perceived credibility of UGC and