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\title{A Study of User-Generated Content on Social Networking Sites and its Impact on Consumer-Based Brand Equity Constructs}
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\begin{document}

             \author[1]{Iesha  Khajuria}

             \author[2]{Dr.  Rachna}

             \affil[1]{  University of Jammu}

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\date{\small \em Received: 16 December 2016 Accepted: 4 January 2017 Published: 15 January 2017}

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\begin{abstract}
        


The present study aims to understand the brand related content generated by internet users on social media and its influence on the consumer-based brand equity constructs. The dimensions of consumer-based brad equity considered in the study are based on the Aaker Brand Equity model, and Facebook, being the mostly accessed site, is considered for study. For the above mentioned objective, the data has been gathered from 500 respondents, using a wellstructured questionnaire. The respondents comprise people within the age group of 18-35 years. The findings reveal a significant impact of user-generated content on brand equity constructs and have implications for brand managers and media planners for administering the usergenerated content on social media, and also for various researchers and academicians towards examining the effects of such social interactions on brand elements.

\end{abstract}


\keywords{consumer-based brand equity, facebook, social media, social networking sites, usergenerated content.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
he advancements in web technologies, over the past few years, have brought about a significant change in our lives. Almost every sphere of life, from socialising to travelling, shopping, entertainment, has seen a shift from real life to a virtual one. shifted a common man to these spaces, but various brands, marketers, advertisers, national and international organisations have also moved their commerce, branding and customer relations to the internet spaces. Marketing also, with the invent of web 2.0 technologies, has evolved from one-to-many to many-to-many marketing. Media for many-to-many marketing communication include wikis, blogs, online forums, file sharing sites (photo and video sharing sites), and also Facebook, Twitter, and LinkedIn networks as well. The web has enhanced the already existing power of word-of-mouth, exponentially. In today's marketing, brands and branding are of great importance. Traditional forms of brand communication via public relations, television advertising etc., have no doubt, achieved a lot of success, but in today's customer-dominated business environment, their effectiveness is declining dramatically \hyperref[b35]{(Kotler et al., 2008}; Wright, Khanfar, Harrington \& Kizer, 2010). Social media networks are able to target comparatively much more audience, at a lesser cost, and that too with consumer involvement.\par
Word-of-mouth communication in Social Media platforms is highly powerful and less expensive \hyperref[b15]{(Bruyn, 2008;}\hyperref[b27]{Hennig-Thurau et al., 2004;}\hyperref[b36]{Kozinets et al., 2010)}. Social media can offer numerous benefits to brands. According to Fournier \& Avery (2011), \hyperref[b40]{Muniz \& Schau (2011)}, and Ulusu (2010), social media helps enhancing sales, involve consumers in brand creation process, expand brand awareness, provide more positive associations and increase consumer loyalty to a brand. Companies using such power of communication can, in a very short span of time, attract a lot of consumers who being impressed by a viral message might join the process of dissipation of information or will be willing to support the brand or the organisation. Social media, thus, enables consumers to create positive as well as negative influence on brand equity. Therefore, it is important for the companies to know, how to manage the communication process in social media seeking to build brand equity, thereby affecting consumer purchase decisions.\par
With the emergence of social media in recent times, the main aim of the present study is to create knowledge about the impact of social media communication (Facebook, under study) on consumerbased brand equity (CBBE), in terms of user-generated content (UGC) about the brands. 
\section[{II.}]{II.} 
\section[{Literature Review a) Brand related User-Generated Content (UGC) on Social Media}]{Literature Review a) Brand related User-Generated Content (UGC) on Social Media}\par
Based on \hyperref[b29]{Kaplan \& Haenlein (2010)} and Solis (2011), social media can be defined as a comprehensive term for web-based applications which enable the internet users, online customers to be more precise, to exchange as well as create information, share views and experiences with friends, relationships, colleagues etc. Since the upsurge of the Internet as a business/commerce medium, one of its basic applications has been into the marketing practices.\par
Advertising on social networking sites (SNSs) is a great way to promote a company's offerings. In addition to having their own corporate websites, many companies find it worthwhile to have a social media existence through Facebook, Twitter, etc., as this allows their visibility even to those who might be unaware of the company existence. These social networking sites (SNSs) have become interactive platforms for the marketers and the audience. People have lately become more used to communicate and socialise via Internet \hyperref[b17]{(Carlsson, 2011)}. Many studies (e.g., Hennig-Thurau et al. 2004; Karakaya \& Barnes 2010; \hyperref[b34]{Kietzmann et al. 2011}) have proposed that people find social media to be much more reliable sources of information in comparison to the traditional marketing communication tools employed by the companies. They get more attached to the brands in social networks than the usual pop-up advertisements and banners. The brand communication on social media, however, can be broadly classified into two, firstly from marketers' end and the other being done on consumers' part in terms of conversations they engage themselves on the social networking sites like Facebook, twitter, etc. \hyperref[b25]{Godes \& Mayzlin (2009)} suggest that, to better understand the influence of social media communications, it is important to differentiate between the two forms, i.e., firm-generated and user-generated social media communication.\par
Firm-created WOM may be characterised as being firm actuated but consumer implemented \hyperref[b25]{(Godes \& Mayzlin, 2009)}. User-generated content on the other hand, is independent of the firm's control \hyperref[b11]{(Bergh et al., 2011)}. Of the two forms, however, the present study concentrates on the user-generated content (UGC).\par
Social media, today, has enabled the consumers with much more possibilities to generate content \hyperref[b29]{(Kaplan \& Haenlein, 2010;}\hyperref[b40]{Muniz \& Schau, 2011)}. Thus, consumers are no longer passive receivers of product(s) information via press releases or some other such tool used by the companies  {\ref (Li \& Bernhoff, 2008)}. \hyperref[b24]{Gangadharbatla (2008)} asserts that the proliferation of online brand communities and social networking sites has backed the increase in brandrelated user-generated social media communication. User-generated content (UGC) is a swiftly spreading avenue for brand conversations and customer insights \hyperref[b18]{(Christodoulides et al., 2012)}. Many studies (e.g., \hyperref[b10]{Barwise \& Meehan, 2010;}\hyperref[b12]{Beuker \& Abbing, 2010;}\hyperref[b23]{Fournier \& Avery, 2011;} {\ref Patterson, 2011)} propound that consumers affect the brands not only by directly reciprocating a message directly, but also by interpreting or/and communicating it, passing the message further to other consumers, who can either react to the sender, display no reaction or pass it to some other consumer with their own expositions. It is perceived that the information that is shared online by various consumers among each other can be positive, as well as negative. Further, it has been mentioned in a study by Bambauer-Sachse \& Mangold (2010) that as compared to satisfied people sharing their positive experiences, dissatisfied ones have more inclination towards voicing their negative experiences. And such negative comments online tend to develop a negative image about the brand, which ultimately has a fatal effect on brand equity. Thus, it can be assumed that the internet users get highly influenced by the information that appears on the social media platforms \hyperref[b43]{(Poynter, 2008)}. Further, DEI Worldwide (2008) found that 70\% of the customers embark on social networking sites to gather products and/or brands related information and 49\% of them seal their buying decisions based on such information.\par
In an effort to add to the present literature on the topic, the current study concentrates on brandrelated user-generated content, focusing solely on content created by Facebook users. 
\section[{b) Consumer-Based Brand Equity (CBBE) \& its Constructs}]{b) Consumer-Based Brand Equity (CBBE) \& its Constructs}\par
In the literature, the brand equity concept has been considered from various perspectives \hyperref[b13]{(Boo et al., 2009)}. Two major viewpoints, however, have been proposed that conceptualise brand equity \hyperref[b37]{(Lassar, Mittal and Sharma, 1995)}. One is the financial perspective \hyperref[b44]{(Simon \& Sullivan, 1993}) that defines brand equity as the incremental cash flows which accrue to branded products over unbranded products. And the other is the consumer-based \hyperref[b31]{Keller, 1993;} {\ref Kim, Kim \& An, 2003;} {\ref Leone et al., 2006;}\hyperref[b48]{Yoo \& Donthu, 2001}) that emphasises consumers' mindset; that is to say that the real brand power lies in the minds of the consumers. The current study, however, concentrates on the customer-based perspective and focuses on the measures pertaining to the consumer mindset like the evaluations, associations, and relationships customers tend to have towards the brand.\par
Two main frameworks find their unique place in the literature of brand equity, that operationalise the concept of customer-based brand equity. One of them has been proposed by \hyperref[b31]{Keller (1993)} that defines consumer-based brand equity as the differential effect of brand knowledge on consumer response towards the marketing of the brand. In order to understand the way customer based brand equity can be built, managed and measured, Keller (1998) further defines brand knowledge in terms of brand awareness and brand image. In this sense, \hyperref[b31]{Keller (1993)} observes brand equity in terms of brand awareness and perceptions about a brand as demonstrated by the strength, favourability and uniqueness of brand associations that are held in consumers' memory.\par
The other framework for consumer-based brand equity, as proposed by    \hyperref[b49]{Yoo et al., 2000)}. In this framework, Aaker (1991) defines brand equity as "a set of brand assets and liabilities linked to a brand, its name and symbol that add to or subtract from the value provided by a product or service to a firm and / or to that firm's customers". The set of brand assets / liabilities are categorised into five types: brand awareness, brand associations, brand loyalty, perceived brand quality, and other proprietary brand assets. Other proprietary brand assets include patents, trademarks and channel relationships, which are irrelevant to consumer perception. Thus, only the first four dimensions are essential to the measuring of customer based brand equity \hyperref[b48]{(Yoo \& Donthu 2001)}. Of the two frameworks mentioned, the present study, however, considers the four dimensions as suggested by Aaker's framework. \hyperref[b2]{Aaker (1996b)} asserts that brand awareness plays a very signifiacnt role in the study of branding and is an essential part of brand equity. Aaker (1991) defines brand awareness as, "the ability of a buyer to recognize that a brand is a member of a certain product category". The extent of brand awareness can be measured in terms of customer's ability to spot and recognise the brand in different circumstances \hyperref[b7]{Atilgan, Aksoy \& Akinci, 2005)}. Brand awareness should, ideally, occur irrespective of environmental conditions such as time and place.\par
Brand association may be defined as positive feelings of customers towards the brand based on the comparative degree of brand strength  {\ref (Lasser et al., 1995)}. Keller (1998) also mentions that based on the relative degree of brand strength, brand association can influence consumers' buying decisions. Buyers usually are ready to purchase branded goods at premium costs just because of the emotional bonding they share with the reputable brands \hyperref[b26]{(Hamann et al., 2007)}. James  {\ref (2005)} claims that the higher level of brand association will enhance brand equity.\par
One of the most important ingredients in marketing, branding to be specific, is the brand loyalty. Brand Loyalty can be measured in a behavioural sense in terms of number of repeat purchases \hyperref[b32]{(Keller, 1998)}.  defines brand loyalty as "a measure of the attachment of a customer with a particular brand". Mark et al. (2007) also emphasises upon the fact that the customers who are loyal to some specific brand will always consider that brand as their preferred selection, do not shift easily to other brands, and have little chances of getting affected by the price wars. Thus, increased brand loyalty definitely increases brand equity.  defines perceived brand quality as the customer's perception of products' superior quality in comparison to other offerings. \hyperref[b21]{Dean and Biswas (2001)} state that to what extent a customer knows about the quality of a product depends either on his/her past experience with the product use or/and possible comments/feedbacks from others in their acquaintances and the like. 
\section[{c) Development of Research Hypotheses}]{c) Development of Research Hypotheses}\par
As mentioned before, the present study considers user-generated content on social media and attempts to understand and analyse its impact on consumer-based brand equity in terms of its four dimensions (namely, Brand Awareness, Brand Associations, Brand Loyalty, and Perceived Brand Quality) as proposed by Aaker's framework. \hyperref[b6]{Anindya et al. (2012)}, in their study, indicate that user-generated content on social media sites and various search engines is influencing to a great extent the way customers buy online.\par
Anzmac (2010), in his article, proposed a relationship between social media participation and engagement, and behavioural outcomes. Also, these relationships were proposed to have a mediating effect of brand engagement and motivation. Ligita Zailskaite-Jakste and Rita Kuvykaite (2013) showed through their empirical research on a coffee brand that a proper management of communication in social media (SM) aids in building brand awareness and a positive brand image. The study proposed that igniting communication in social media through consumer engagement into brand building can enhance a brand's likeability. Many studies indicate that social media marketing as a tool for a company's marketing activities revolves around six major dimensions: Online Communities, Interaction, Sharing of Content, Accessibility, and credibility. As'ad, H. Abu-Rumman and Anas Y. Alhadid (2014) in their empirical study on mobile service providers in Jordan concluded that there is a statistically significant impact of these dimensions of social media marketing on the brand equity of the mentioned service providers. Jennifer Bonhomme, George Christodoulides, and Colin Jevons (2010) studied the effect of consumer's involvement in user-generated content (UGC) on brand equity. Their empirical research on 201 consumers through an online survey provided evidence that an overall UGC involvement has a positive impact on CBBE. And thus, the study gave a strong message to brand managers that UGC campaigns may indeed enhance their brand equity. \hyperref[b49]{Yoo et al. (2000)} suggest that a message contained in the brand communication process, if triggers a positive and satisfactory customer reaction, increases the possibility that a brand will be included in the customer's consideration set. This will further shorten the decision-making process, converting that choice into a habit and hence improving the brand equity. Also,  {\ref Keller (2008)} explains that the consumers' favourable reaction towards a product/brand and its easy identification leads to a positive customer-based brand equity.\par
In view of all the above following hypotheses have been stated: H1: User-generated content on Facebook has a significant impact on brand awareness. H2: User-generated content on Facebook has a significant impact on brand associations. H3: User-generated content on Facebook has a significant impact on brand loyalty. H4: User-generated content on Facebook has a significant impact on perceived brand quality. 
\section[{III. Method a) Data Collection \& Sampling}]{III. Method a) Data Collection \& Sampling}\par
For data collection, a self-structured questionnaire was used as the research instrument. And a combination of convenience and snowball sampling technique was employed. 400 questionnaires were initially distributed personally to the known people in the age group 18-35 years, and 150 given to some of them for further referring to their acquaintances for filling up the questionnaires. Thus, a total of 550 questionnaires were distributed initially, out of which 500 were found to serve the purpose of the study (after excluding the ones that did not return or were found incomplete). Thus, the sample size was 500. The data collection was conducted for about 2 months (December, 2016-January, 2017). As per the findings, out of 500 credible respondents, there were 65\% males and 35\% females. And average age was found to be 25 years. 
\section[{b) Measure}]{b) Measure}\par
A little screening was done prior to the distribution of the questionnaires. The individuals were enquired to see if they did in fact use the social media (Facebook) of interest to the study and whether they could serve the purpose relevant for the analysis. These questions were, "Do you use Facebook?", "Do you follow some retail brand on Facebook", and "Have you ever seen or gone through some product/brand reviews on Facebook?" (Yes or No). Only after getting a favourable response for these, the questionnaires were handed over to the respondents. The questionnaire was divided into two sections. First section referred to demographic information (such as age, gender, occupation etc.) related with the sample and also questions about social media usage. Second section contained scales for measuring the impact on CBBE dimensions (viz., brand awareness, brand associations, brand loyalty, and perceived quality). Respondents were instructed beforehand, "Think of one retail brand of your interest that you follow on Facebook. Consider that brand and indicate your level of agreement or disagreement with the following statements." The statements for the variable brand awareness (5-item index) were pulled from a scale by \hyperref[b7]{Atilgan et al. (2005)} and \hyperref[b49]{Yoo et al. (2000)}; those for brand association and brand loyalty (5-items each) were taken from Kim \& Kim (2005) and \hyperref[b49]{Yoo et al. (2000)}; whereas those for perceived quality (3-item index) were adopted from \hyperref[b49]{Yoo et al. (2000)}. Further, the items for the variable usergenerated content (3-item index) on social media were obtained from \hyperref[b39]{Mägi (2003)}, \hyperref[b47]{Tsiros et al. (2004)}, and \hyperref[b14]{Bruhn et al. (2012)}. All the statements were measured on a 5-point Likert scale (1= strongly disagree to 5=strongly agree). 
\section[{c) Analysis}]{c) Analysis}\par
SPSS Version 21 was employed for all the analysis purposes. Besides the descriptive statistics mentioned previously, a correlation was carried out to check the relationship between user-generated brand content on Facebook and the four CBBE constructs (brand awareness, brand associations, brand loyalty, and perceived brand quality). Also, a regression was run to see the impact on these CBBE constructs by UGC on Facebook on the basis of gender. Results are reported further. An alpha level of .05 was considered for all the statistical tests. 
\section[{IV. Results}]{IV. Results} 
\section[{a) Reliability of Scales}]{a) Reliability of Scales}\par
The reliability of the variables under examination was tested using Cronbach's Alpha coefficient for the internal consistency of the scale. The following table \hyperref[tab_1]{(Table 1}) illustrates the reliability of all the variables. All the scales produced an alpha value in the range 0.70-0.88, thus, passing the reliability test, and finding their way towards inclusion in the study. 
\section[{b) Testing the Research Hypotheses}]{b) Testing the Research Hypotheses}\par
The correlation for Facebook users, who follow some retail brand on Facebook and see or engage themselves in UGC, was run to see the relationship between UGC and CBBE constructs. UGC was found to correlate significantly with brand awareness, r (500) = 0.214, brand associations, r (500) = 0.810, brand loyalty, r (500) = 0.467, and perceived brand quality, r (500) = -0.447.\par
Next, four regression tests were carried out to test the hypotheses. A regression was run because it allowed the simultaneous analysis of the influence of multiple independent variables (gender and user-generated content) on the dependent variables (brand awareness, brand associations, brand loyalty, and perceived brand quality). By administering this kind of analysis, a better representation of, how multiple variables were affecting and interacting with the dependent variables, could be seen.\par
The first regression was administered to find out the impact on brand awareness by these variables: gender and UGC. With an Adjusted R 2 = 0.151, the regression model was found to be significant enough in explaining the variance in brand awareness. Within the regression model, with gender (Beta = 0.000, p > .05) and UGC (Beta = 0.214, p < .05), only UGC surfaced as the significant predictor for brand awareness. Thus, H1 was supported (Table \hyperref[tab_2]{2}). The second regression was administered to find out the impact on brand association by these variables: gender and UGC. With an Adjusted R 2 = 0.636, the regression model was found to be significant enough in explaining the variance in brand association. Within the regression model, out of the two independent variables, viz., gender (Beta = -0.033, p > .05), and UGC (Beta = 0.813, p < .05), only UGC was found to be the significant predictor for rand associations. Thus, H2 was supported (see Table \hyperref[tab_3]{3}). The third regression was administered to find out the impact on brand awareness by these variables: gender and UGC. With an Adjusted R 2 = 0.208, the regression model was found to be significant enough in explaining the variance in purchase intentions. Here again, out of gender (Beta = -0.084, p > .05) and UGC (Beta = 0.473, p < .05), UGC alone emerged as a significant predictor of brand loyalty. Thus, H3 was also supported (see Table \hyperref[tab_4]{4}). 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XVII Issue I Version I Year ( ) Finally, the last regression was administered to find out the impact on brand awareness by these variables: gender and UGC. With an Adjusted R 2 = 0.209, the regression model was found to be significant enough in explaining the variance in perceived brand quality. Here again, among the two independent variables, gender (Beta = -0.103, p > .05) and UGC (Beta = -0.440, p < .05), only UGC can be considered as a significant predictor of perceived brand quality. Thus, H4 was also supported (see Table \hyperref[tab_5]{5}). All the above regression analyses reveal that while UGC has a significant impact on CBBE dimensions (brand awareness, brand associations, brand loyalty, perceived quality), gender does not play a significant role while studying the impact of UGC on CBBE constructs.\par
V. 
\section[{Limitations}]{Limitations}\par
The results of the current study might be considered under certain limitations. Firstly, the sample size was rather small (comparing with huge population of Jammu city, which runs in lakhs) with 500 being the final sample size. As a consequence, the potency of the findings is limited. Secondly, since convenience and snowball sampling technique were used, generalizing the results to whole population would be somewhat unfair. Further, the respondents lied between 18-35 years of age group, and thus, the involvement in UGC on Facebook could not be analyzed and hence, its impact on CBBE constructs might not represent other age groups. Also, the responses were analyzed only on the basis of gender and as such other demographic variables (such as age, income, education, occupation, etc.) were not considered as a part of impact analysis, which otherwise could have given better insights. Moreover, there always lies the chance for self-reporting biases, as in there can be no guarantee that individuals report their level of brand engagement on Facebook (or social media) genuinely. In addition to this, the CBBE measurement scales used were approximately only 3-5 items, and hence may not have entirely encapsulated the very fruitful effects. Last but not the least, a particular product/brand was not chosen for the study and the respondents were free to choose any of the brands they follow. So there are chances that different brands have different impact on CBBE.\par
IV. 
\section[{Conclusions \& Future Scope}]{Conclusions \& Future Scope}\par
Changes in the socialising and buying behaviour of people due to social media, is one of the most intriguing aspects in the contemporary marketing. Noting the paucity of research examining the involvement and engagement of people in brand communication on social media (Facebook, under study), the present study aims to investigate the impact on CBBE by user-generated content on Facebook regarding various brands they come across intentionally or unintentionally. While first examining the correlation analysis, a lesser significant correlation was seen between UGC and CBBE constructs. These relationships present a room for further researches into what is influencing (more/less) and yielding those relationships. Further, as the study considered people only in the age-group of 18-35 years, it may be intriguing to understand the behavior in little higher age-groups. However, analysis did not consider age as an influencing factor, assuming similar characteristics of age-group under study, in terms of social media usage. Further, comparative studies can be conducted to distinguish the impacts on CBBE among different agegroups, occupations, males and females, between metropolitan and non-metropolitan consumers, and for different brands.\par
An investigation of the results of the study presents certain eye-openers to the marketing managers. With the help of user-generated content (UGC) via social networks, organisations can generate and increase brand equity of products/services and eventually pull customers. However, marketers need to always keep in mind the power of electronic word of mouth (e-WOM) in a sense that it can equally be fatal as it is useful. People on social media tend to express their negative experiences more often than do they share positive ones, therefore marketers need to make sure that they manage the UGC well, and that the target population receives the right communications at right time and right place. In nutshell, the marketers must prioritise UGC as their marketing and branding strategy, as the people engaged in UGC serve as opinion leaders, and keep a close watch on it too, so that things can be managed before they get worse.\begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.3030737704918033\textwidth}P{0.34487704918032785\textwidth}P{0.05922131147540983\textwidth}P{0.14282786885245902\textwidth}}
Year\tabcellsep \tabcellsep \tabcellsep \\
4\tabcellsep \tabcellsep \tabcellsep \\
Volume XVII Issue I Version I\tabcellsep \tabcellsep \tabcellsep \\
( )\tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Management and Business Research\tabcellsep Variable\tabcellsep No. of Items\tabcellsep Cronbach's Alpha\\
\tabcellsep Brand Awareness\tabcellsep 5\tabcellsep 0.707\\
\tabcellsep Brand Associations\tabcellsep 5\tabcellsep 0.796\\
\tabcellsep Brand Loyalty\tabcellsep 5\tabcellsep 0.721\\
\tabcellsep Perceived Brand Quality\tabcellsep 3\tabcellsep 0.798\\
\tabcellsep User-Generated Content\tabcellsep 3\tabcellsep 0.853\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.14539473684210527\textwidth}P{0.16030701754385965\textwidth}P{0.15285087719298246\textwidth}P{0.13421052631578947\textwidth}P{0.13793859649122808\textwidth}P{0.05964912280701754\textwidth}P{0.05964912280701754\textwidth}}
\tabcellsep \tabcellsep \tabcellsep Coefficients a\tabcellsep \tabcellsep \\
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\
1\tabcellsep (Constant)\tabcellsep 1.017\tabcellsep .187\tabcellsep \tabcellsep 5.432\tabcellsep .000\\
\tabcellsep Gender\tabcellsep .000\tabcellsep .095\tabcellsep .000\tabcellsep -.004\tabcellsep .997\\
\tabcellsep User-Generated Content\tabcellsep .147\tabcellsep .068\tabcellsep .214\tabcellsep 2.155\tabcellsep .034\\
\multicolumn{3}{l}{a. Dependent Variable: Brand Awareness}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.16301369863013698\textwidth}P{0.16689497716894977\textwidth}P{0.1591324200913242\textwidth}P{0.08538812785388127\textwidth}P{0.14748858447488583\textwidth}P{0.06598173515981734\textwidth}P{0.06210045662100456\textwidth}}
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\
1\tabcellsep (Constant)\tabcellsep .644\tabcellsep .149\tabcellsep \tabcellsep 4.324\tabcellsep .000\\
\tabcellsep Gender\tabcellsep -.041\tabcellsep .075\tabcellsep -.033\tabcellsep -.548\tabcellsep .585\\
\tabcellsep User-Generated Content\tabcellsep .740\tabcellsep .054\tabcellsep .813\tabcellsep 13.632\tabcellsep .000\\
\multicolumn{3}{l}{a. Dependent Variable: Brand Associations}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.1469626168224299\textwidth}P{0.17079439252336448\textwidth}P{0.16682242990654206\textwidth}P{0.08738317757009345\textwidth}P{0.15093457943925231\textwidth}P{0.06355140186915888\textwidth}P{0.06355140186915888\textwidth}}
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\
1\tabcellsep (Constant)\tabcellsep 2.947\tabcellsep .300\tabcellsep \tabcellsep 9.824\tabcellsep .000\\
\tabcellsep Gender\tabcellsep -.143\tabcellsep .152\tabcellsep -.084\tabcellsep -.941\tabcellsep .349\\
\tabcellsep User-Generated Content\tabcellsep .577\tabcellsep .109\tabcellsep .473\tabcellsep 5.279\tabcellsep .000\\
\multicolumn{2}{l}{a. Dependent Variable: Brand Loyalty}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.1470464135021097\textwidth}P{0.15421940928270042\textwidth}P{0.15421940928270042\textwidth}P{0.1291139240506329\textwidth}P{0.13987341772151898\textwidth}P{0.06814345991561181\textwidth}P{0.057383966244725734\textwidth}}
\tabcellsep \tabcellsep \tabcellsep Coefficients a\tabcellsep \tabcellsep \\
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\
1\tabcellsep (Constant)\tabcellsep 4.464\tabcellsep .402\tabcellsep \tabcellsep 11.098\tabcellsep .000\\
\tabcellsep Gender\tabcellsep -.231\tabcellsep .203\tabcellsep -.103\tabcellsep -1.137\tabcellsep .258\\
\tabcellsep User-Generated Content\tabcellsep -.712\tabcellsep .147\tabcellsep -.440\tabcellsep -4.860\tabcellsep .000\\
\multicolumn{3}{l}{a. Dependent Variable: Perceived Quality}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 5 :}\end{figure}
 			\footnote{© 2017 Global Journals Inc. (US)} 			\footnote{A Study of User-Generated Content on Social Networking Sites and its Impact on Consumer-Based Brand Equity Constructs} 			\footnote{© 2017 Global Journals Inc. (US) 1} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
