Introduction and Annotated Bibliography

Forster 17

Advertising on Social Network Sites: A Structural Equation Modelling Approach

Anant Saxena Uday Khanna

Abstract Social networking sites (SNSs) emerged as one of the most powerful media for advertising across the globe. Globally, companies are shifting a larger pie of their advertising budgets towards social networking sites for better reach and interactive platform. The companies are also looking at it as a low-cost model, which could reap results in minimum time possible for the targeted ‘Facebook generation’. These very facts motivate researchers to study the value of advertisements on social networking sites like Facebook, LinkedIn, Twitter and others. The article is an empirical study to understand the implications of different variables in advertisements on the delivery of advertising value to the respondents. Confirmatory factor analysis (CFA) has been conducted to test the reliability of instrument being used for data collection. Further, a model has been proposed for measuring advertising value through structural equation modelling. The predicted results confirm the roles of different variables, namely, information, entertainment and irritation, in accessing value of advertisements displayed on social networking sites.

Key Words Advertising Value, Social Networking Sites, Structural Equation Modelling

Introduction Social networking websites (SNSs) have emerged as the ‘need of an hour’. Their journey started with the launch of sixdegrees.com in the year 1997, which attracted millions of users at that time. The site allowed the users to create profiles listing their friends with the ability to surf the friends list (Boyd and Ellison, 2007). This has been followed by an array of SNSs like Facebook, Orkut, Linkedin and MySpace in the year 2003–2004. Within a short span of time, these websites become an addiction for youngsters as these give them opportunity and platform to express their feelings and emotions in the society. Websites like Facebook, Orkut, Twitter and MySpace have become household names and an integral part of people’s life so much that it has become tough for regular users to imagine a life without them. Globally, Internet users spend more than four and a half hours per week on SNSs, more time than they spend on e-mail (Anderson et al., 2011). As more and more of what people think and do ends up getting expressed on SNSs, it is expected that SNSs affect the buying decisions greatly. In addition, the huge viewer’s base of these websites makes them a favourable media for advertisements by different companies. According to a study done by comScore, Inc., a market research firm, SNSs accounted for more than 20 per cent, that is, one in

five, display ads of all display ads viewed online, with Facebook and MySpace combining to deliver more than 80 per cent of ads among sites in the social networking category (comScore, 2009). According to Rizavi et al. (2011) social networking websites act as a good platform for advertising that attract millions of users from different countries, speaking multiple languages belonging to different demographics. According to Trusov et al. (2009) referrals and recommendations on SNSs have a significant impact on new customer acquisition and retention. This fact led marketers to turn to Internet platforms like SNSs, blogs and other social media as an avenue for cost-effective marketing, employing e-mail campaigns, website adver- tisements and viral marketing. Also from a marketing perspective, these websites give potential customers the opportunity to virtually explore a business, encourage them to visit and at last share their views and experiences with their friends (Phillips et al., 2010). Understanding the effectiveness of SNSs in promoting product and services through advertisements, companies across the globe have increased their advertising budget for SNSs which has led to increase in revenue generation for social networking website companies. According to a report released by Interactive Advertising Bureau (IAB), Internet advertising revenues totaled $14.9 billion in 2011, up 23 per cent from the $12.1 billion reported in 2010

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Article

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(PricewaterhouseCoopers LLP., 2011). India shares the same story in terms of Internet advertising revenues.

According to a report, the size of Internet advertising industry was INR 7.7 billion in 2010 registering a growth of 28.3 per cent over INR 6 billion in 2009 (PricewaterhouseCoopers Private Limited, 2011). The same report highlights that in India SNSs have shown a remarkable growth of 43 per cent in 2010 over 2009, with a 54 per cent growth in advertising on SNSs in 2010– 2011 (PricewaterhouseCoopers Private Limited, 2011). Considering the fact that advertising on SNSs is on a new high, this research focus on studying the value of advertisements being displayed on SNSs.

Literature Review and Hypothesis Web advertising continues to be a major area of advertising research from a long time. A number of studies have been done discussing advertisements on the Web and their effects. Berthon et al. (1996) have discussed the role of World Wide Web as an advertising medium in the mar- keting communication mix and proved that World Wide Web is a new medium for advertising characterized by ease-of-entry, relatively low set-up costs, globalness, time independence and interactivity. In spite of the acceptance of World Wide Web as an effective media for advertising, few studies have focused on the value of advertisements displayed on this medium. R.H. Ducoffe introduced the concept of advertisement value in 1995. According to Ducoffe (1995) advertising value is defined as the utility or worth of the advertisement. Ducoffe (1996), in his another study on World Wide Web, proved the significant impact (either +ve or –ve) of entertainment, information and irritation on advertisement value. Brackett and Carr (2001) in their study on cyberspace advertising reports that information, entertainment, irritation and credibility significantly affect advertisement value which in turn affects attitude towards advertisements. Discussion on different predictors of advertisement value with reference to SNSs advertisements is hereby illustrated:

1. Information: Information content is an important determinant of advertisement effectiveness. Comp- anies advertise for one main reason—providing information about their product, services and brand to consumers. Consumers reported that supplying information is the primary reason why they approve advertising (Bauer et al., 1968). According to Norris (1984) information in advertisements enables the customers to evaluate the products more rationally leading to improved markets with low prices and high quality of the product. Information content on Internet can be delivered better in comparison to

television medium, reason being short time span of television advertisements. Yoon and Kim (2001) mentioned that Internet advertising differs from tra- ditional advertising as it delivers unlimited informa- tion beyond time and space and it gives unlimited amount and sources of information. Web advertise- ments provide information and generate awareness without interactive involvement (Berthon et al., 1996). On the contrary, information delivered through SNSs advertisements is different from tra- ditional Web advertisements because SNSs provide a medium that is interactive in nature. A person could scan and share information with online friends and followers, thus making the advertisement infor- mation viral in nature. Large media companies have realized the potential of SNSs to reach and deepen relationships with the ‘subscribed’ audience (Jhih- Syuan and Pena, 2011). This specialty of SNSs advertisement makes it the most competitive plat- form for sharing information about products and services. As the delivery and importance of infor- mation for SNSs advertisements is different from other forms of advertisements, it is important to note its effect on advertisement value. Based on this rationale, the hypothesis tested is:

H1: There is a significant positive impact of infor- mation content of advertisements on the value of advertisements displayed on social networking websites.

2. Entertainment: An advertisement that is full of information but nil in entertainment content is not worthy. According to McQuail (1994) an advertise- ment entertains when it fulfils the audience needs for escapism, diversion, aesthetic enjoyment or emotional release. The ability of advertising to entertain can enhance the experience of advertising. In addition, an advertisement could be information for one and entertainment for other person at the same time (Alwitt and Prabhaker, 1992). Consumers who found advertising to be entertaining also evalu- ate it as informative (Ducoffe, 1995). This shows that entertainment and information are interrelated concepts when talking about advertisements. SNSs platform is interactive in nature and display banner advertisements of different brands at the same platform and same time; they have the power to entertain the audience. Kim and Lee (2010) noted that college students use SNSs for six main reasons: entertainment, passing time, social interaction, information seeking, information provi- ding, and professional advancement. According to

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Taylor et al. (2011) SNSs advertisements provide entertainment value to the audience. The same study reported that entertainment exhibits almost four times more strength of influence on favourable con- sumers’ attitude towards advertisements than infor- mation. With reference to the existing literature, it is important to find the impact of entertainment on advertisement value of SNSs advertisements. In the same vein, the hypothesis tested is:

H2: There is a significant positive impact of enter- tainment content of advertisements on the value of advertisements displayed on social networking websites.

3. Irritation: Irritation from advertisements arises when we feel discomfort in watching advertisement due to any reason. The reason can be personal or social. A personal reason could be distraction while focusing on a particular task on World Wide Web. According to Wells et al. (1971) irritation is one amongst six dimensions of personal reactions towards advertising. It is the degree to which the viewer disliked the contents that he had seen. The words that came into the mind of the viewer at time of getting irritated from an advertisement are ‘terrible’, ‘stupid’, ‘ridiculous’, ‘irritating’ and ‘phony’. An advertisement can be rewarding for some viewers and yet be an irritant and unrewarding for others (Alwitt and Prabhaker, 1992). According to Aaker and Bruzzone (1985), increase in irritation can lead to general reduction in the effectiveness of advertisement. In case of Internet advertising, it also generates considerable irritation (Schlosser et al., 1999). As online behaviour including use of SNSs is highly goal oriented, advertisements on SNSs might irritate the user (Taylor et al., 2011). The lit- erature suggested that irritation has a negative effect on the effectiveness of advertisement irrespective of the media. Based on this rationale the hypothesis tested is:

H3: There is a significant negative impact of irrita- tion content of advertisements on the value of adver- tisements displayed on social networking websites.

A considerable amount of research on determinants of Web advertising effectiveness and value has been done (Berthon et al., 1996; Brown et al., 2007; Ducoffe, 1995; Lei, 2000; Schlosser et al., 1999; Yoon and Kim, 2001); however, these studies were more focused on traditional websites rather than SNSs. Advertising through SNSs is different from traditional websites due to several reasons.

First, advertisements on SNSs are different not only in form and substance but also in delivery method. Some of the messages are ‘pushed’ upon consumers while others rely on consumers to ‘pull’ content; some generate revenue whereas some are non-paid content delivered through media content (Taylor et al., 2011). Second, SNSs have their own unique user-to-user interface (Safko and Brake, 2009). Third, SNSs users are increasing day by day all over the world, which makes this medium suitable for advertising. As SNSs advertising is different from traditional Web advertising and a little is known about value of SNSs advertisements, this study tries to fill this research gap by providing a model, which tests the interrelationships between different determinants of advertisement value.

Model Testing The importance of advertisements displayed on SNSs is increasing day by day. According to Stelzner (2011) 88 per cent of the marketers have reported that their social media advertisements have generated more exposure for their businesses. This leads the authors to test a model for accessing the value of advertisements displayed on SNSs by employing structural equation modelling (SEM) approach. Use of SEM technique gives us the opportunity to examine multiple dependence techniques simultaneously. SEM approach is a statistical methodology that combines the strength of factor analysis and path analysis. According to Singh (2009) SEM is considered as a more advanced technique than other multivariate techniques because it can estimate a series of interrelated dependence relationship simultaneously. According to Byrne (1998) SEM technique is better because:

1. It accounts for measurement errors in course of model testing.

2. It can incorporate observed (indicator) variables as well as latent (unobserved) variables at same time during model testing,

3. It tests a priori relationships rather than allowing the technique or data to define the nature of relationship between the variables.

In the present study, SEM analysis is conducted in two major steps; first, to test the measurement model and second, a structural model. Measurement model provides the series of relationships that suggests how observed variables represent latent variables (Figure 1), tested by means of confirmatory factor analysis (CFA). Structural model tests the conceptual representation of the relationships between the latent variables. It tells whether the proposed model is eligible to represent a proposed concept and conceptual relationships between the variables or not (Figure 2).

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Figure 1. Measurement Model

ENTERTAINMENT

INFORMATION

IRRITATION

ADD.VALUE

ENTERTAIN 3

ENTERTAIN 2

ENTERTAIN 1

INFO 1

INFO 2

INFO 3

IRRITATION 1

IRRITATION 2

IRRITATION 3

ADDVALUE 1

ADDVALUE 2

ADDVALUE 3

0.57

0.17

0.19

0.47

0.40

0.51

0.45

0.80

0.39

0.80

0.86

0.52

0.23

0.75

0.65

0.76

0.77

0.73

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Figure 2. Structural Model

Method Sample Design

The research focuses on social networking websites with college students as sample respondents. The college students were selected as sample for two basic reasons. First, student sample is more homogeneous (less variable) in terms of socio-economic background, demographics and education (Peterson, 2001). Second, a number of studies have reported that students are the main users of social networking websites (Dwyer et al., 2007; Pempek et al., 2009; Subrahmanyam et al., 2008). With this rationale, present study sample includes postgraduate management students of a reputed college based in India. 276 students have responded to an online questionnaire mailed to 300 students. The questionnaires were mailed with Google documents facility to form and mail online forms/ questionnaires. After removing incomplete questionnaires, only 189 questionnaires were found to be useable for analysis and further study. Resulting sample consists of 71 per cent males and 29 per cent females. Subjects were asked to report their reactions to instrument statements by considering their

perceptions of advertisements on SNSs in general, not a single advertisement or advertisement for any particular product. The objective of this generalization is to assess the value of advertisement on social networking websites across different advertisements of product and service categories.

Sample Size and SEM Analysis

Sample size is a key issue when performing SEM analysis. According to Bentler and Bonett (1980) and Hair et al. (2007) chi-square value is sensitive to increase in sample size, while it lacks power to discriminate between good fit and poor fit models with small sample size (Kenny and McCoach, 2003). Hair et al. (2007) mentioned that 15 res- ponses per parameter is an appropriate ratio for sample size. Going on with this approach a sample size of 189 res- pondents for measuring 12 parameters was appropriate.

Research Instrument

For measuring the advertisement value of advertisements displayed on social media, a 12 item scale developed by

ADD. VALUE

ADDVALUE 3

ADDVALUE 2

ADDVALUE 1

IN1 IN2 IN3

e3 e2 e1

IRR1 IRR2 IRR3

e9 e8 e7

EN1 EN2 EN3

e6 e5 e4

INFORMATION

IRRITATION

ENTERTAINMENT

e10

e11

e13

e12

0.55 0.84 0.38

0.27

0.15

0.87 0.58

0.77 0.90 0.51

0.40

0.36 0.38

0.75

0.76

0.72

0.25

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Ducoffe (1995) was used. The instrument was modified as per the need of the study. A five-item Likert scale was used as a response scale, from strongly disagrees to strongly agree.

Measurement Model Measurement model is a specification of the measurement theory that shows how constructs are operationalized by sets of measured items. Confirmatory factor analysis is used to test the reliability of a measurement model. Unlike exploratory factor analysis, CFA allows the researcher to tell the SEM programme which variable belongs to which factor before the analysis (Hair et al., 2007). According to Salisbury et al. (2001) CFA allows the researcher to specify the actual relationship between the items and factors as well as linkages between them.

Construct Validity

According to Hair et al. (2007) construct validity is the extent to which a set of measured items actually represents theoretical latent construct; those items are designed to measure. The reliability of advertisement value scale was examined by specifying a model in CFA using AMOS 19. Reliability of an instrument can also be calculated by Cronbach’s alpha, but use of SEM technique makes such a practice unnecessary and redundant (Bagozzi and Yi, 2012). The results (see Table 1) confirm the overall fit of a measurement model when employed to CFA.

According to Hair et al. (2007) one incremental fit index (CFI), one goodness of fit index (GFI), one absolute fit index (GFI, SRMR) and one badness of fit index (SRMR), with chi-square statistic should be used to assess a model’s goodness of fit. Our study results show all the different types of indices in the acceptable range.

Convergent and Discriminant Validity

Convergent validity exists when the items that are indicators of a specific construct converge or share a high proportion of variance in common. In general, ‘factor loading’ and ‘variance extracted’ measures are used to measure convergent validity. We have used factor loading measure in our study to measure convergent validity (Hair et al., 2007; Salisbury et al., 2001). All the factor loadings are statistically significant, a minimum requirement for convergence (Hair et al., 2007). Furthermore, except items ‘Info 3’ and ‘Irritation 1’ all factor loadings are in the range of 0.50 to 0.80, which is more than acceptable value of 0.50 (Hair et al., 2007) (see Figure 1). According to Chin et al. (1997) discriminant validity exists if the correlation between the constructs is not equal to 1. Following the rule, our study shows the discriminant validity between the constructs (see Figure 1).

Structural Model After assessing the eligibility of scale for measuring different variables in the study, the next step is to test the hypothesized relationships in a structural model. Ducoffe (1996) has proved the respective role of information, entertainment and irritation on advertisement value for the advertisements on the Web. In our study, we try to explore the impact of these respective variables on advertisement value vis-à-vis SNSs.

Performance of the Model

Hypothesized relationships are supported by the overall model fit indices obtained. All of the fit indices are above the recommended values. The c2/df value 2.31 met the recommended value of less than 3 (Carmines and McIver, 1981). Hair et al. (2007) argues that chi-square value is sensitive to sample size and number of variables; therefore, c2/df value is not taken as a sole indicator of model fit. Other model fit indicators taken are also within the recommended range (see Table 2). In sum, various model fit indices indicates that the proposed model fitted well with the present data set.

Table 1. Model Fit Indices for Measurement Model

Statistic Recommended

Value Obtained Value

Chi-square c2 92.616 Df 48 c2/df (Hinkin, 1995),

(Carmines and McIver, 1981)

< 3.00 1.93

GFI (Hooper et al., 2008), (Hair et al., 2007)

> 0.90 0.92

AGFI (Muenjohn and Armstrong, 2008)

> 0.80 0.88

SRMR (Hu and Bentler, 1999)

< 0.08 0.06

CFI (Watchravesringkan et al., 2008)

> 0.80 0.92

Note: AGFI: Adjusted goodness of fit index; SRMR: Standardized root mean square residual; CFI: Comparative fit index

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Table 2. Model Fit Indices for Structural Model

Statistic Recommended

Value Obtained Value

Chi-square c2 115.539 Df 50 c2/df (Hinkin, 1995),

(Carmines and McIver, 1981)

< 3.00 2.31

GFI (Hooper et al., 2008), (Hair et al., 2007)

> 0.90 0.91

AGFI (Muenjohn and Armstrong, 2008)

> 0.80 0.86

RMSEA (MacCallum et al., 1996)

< 0.10 0.08

CFI (Watchravesringkan et al., 2008)

> 0.80 0.88

Note: SMSEA: Root Mean Square Error of Approximation

Estimated Standardized Path Coefficients

Figure 2 shows the standardized path coefficients of the four constructs under investigation. All the path coefficients were significant at the level of 0.01 with the direction of influence as hypothesized (+ or −). Information and entertainment were positively associated with advertisement value whereas irritation is negatively asso- ciated with advertisement value; thus all the hypotheses framed are statistically supported. A significant correlation between information and entertainment also indicates that the consumers who find advertisement to be entertaining are more likely to evaluate it as informative. These results are consistent with another study (Ducoffe, 1995). Finally, the squared multiple correlations (R2) indicates that the present model explains 38 per cent of the variance in advertisement value.

Discussion and Implication The study yielded important new insights about a topic that is important for both industry practitioners and aca- demicians. The concept of advertisement value and factors affecting it had been widely tested for various types of advertisements in a number of studies but lack of work for advertisements displayed on social networking websites was the motivating factor to do research in the particular domain. The study tests the model to assess advertisement value by employing SEM approach. SEM combines the strength of factor analysis and path analysis. It enables us to test whether observed variables completely describes latent variables or not. In addition, SEM is a more successful technique than other multivariate techniques as it can estimate a series of interrelated dependence relationship simultaneously. It tells whether the proposed

model is eligible to represent a proposed concept and conceptual relationships between the variables or not. The results of CFA suggest that the observed variables are suitable enough to represent different latent variables, that is, information, entertainment, irritation and advertisement value in the particular domain of social networking advertising.

The findings of structural model analysis suggest that the proposed model for accessing the value of advertisements displayed on SNSs fits well. In addition, the proposed hypotheses assessing the relationships between the variables are statistically supported. The findings suggest that when advertisements displayed on SNSs provide entertainment and information content or impressions, it increases the worth of the advertisement. On the one hand, as has been proved true for other types of media advertising, consumers derive utility from advertisements that provide some useful or functional information and increase hedonic value by entertaining them. On the other hand, irritation decreases the net worth of the advertisements displayed on SNSs. This suggests that the companies using SNSs media for advertising their products and services should reduce the contents, which irritate the viewers’ base.

It is worth noting that ‘information’ exhibited around 1.6 times more strength of influence on advertisement value than entertainment. This suggests that companies should firstly focus on providing information content in their advertisements to make their advertisements worth for consumers. In addition, it is interesting to note that findings of this study show a significant correlation between information and entertainment, which indicates that consumers who find advertisement to be entertaining are more likely to evaluate it as informative.

Limitations Although the study has been done taking into account the methodological rigour, some limitations remain. First, the sampling used is convenience sampling. Second, exploration of other variables that affects the value of advertisement is needed.

References Aaker, D.A., & Bruzzone, D.E. (1985). Causes of irritation in

advertising. The Journal of Marketing, 49(2), 47–57. Alwitt, L.F., & Prabhaker, P.R. (1992). Functional and belief

dimensions of attitudes to television advertising: Implications for copytesting. Journal of Advertising Research, 32(5), 30–42.

Anderson, M., Brusa, J., Price, J., & Sims, J. (2011). Turning ‘Like’ to ‘Buy’ social media emerges as a commerce

24 Advertising on Social Network Sites

Vision, 17, 1 (2013): 17–25

channel (pp. 1–9). Booz & Company. Available at http:// www.booz.com/media/uploads/BaC-Turning_Like_to_Buy. pdf

Bagozzi, R., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34.

Bauer, R.A., Greyser, S.A., Kanter, D.L., & Weilbacher, W.M. (1968). Advertising in America: The consumer view. Division of Research, Graduate School of Business Administration, Harvard University Boston.

Bentler, P.M., & Bonett, D.G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606.

Berthon, P., Pitt, L.F., & Watson, R.T. (1996). The World Wide Web as an advertising medium: Toward an understanding of conversion efficiency. Journal of Advertising Research, 36(1), 43–54.

Boyd, Danah M., & Ellison, N.B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer- Mediated Communication, 13(1), 210–230.

Brackett, L.K., & Carr, B.N. (2001). Cyberspace advertising vs. other media: Consumer vs. mature student attitudes. Journal of Advertising Research, 41(5), 23–32.

Brown, J., Broderick, A.J., & Lee, N. (2007). Word of mouth communication within online communities: Conceptualizing the online social network. Journal of Interactive Marketing, 21(3), 2–20.

Byrne, B.M. (1998). Structural equation modeling with Lisrel, Prelis, and Simplis: Basic concepts, applications, and pro- gramming. New Jersey: Routledge.

Carmines, E.G., & McIver, J.P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. In Social measurement: Current issues (pp. 65–115). Beverly Hills: SAGE.

Chin, W.W., Gopal, A., & Salisbury, W.D. (1997). Advancing the theory of adaptive structuration: The development of a scale to measure faithfulness of appropriation. Information Systems Research, 8(4), 342–367.

Ducoffe, R.H. (1995). How consumers assess the value of adver- tising. Journal of Current Issues & Research in Advertising, 17(1), 1–18.

———. (1996). Advertising value and advertising the Web. Journal of Advertising Research, 36(5), 21–35.

Dwyer, C., Hiltz, S.R., & Passerini, K. (2007). Trust and privacy concern within social networking sites: A compari- son of Facebook and MySpace. AMCIS 2007 Proceedings (pp. 1–12). Presented at the Thirteenth Americas Conference on Information Systems, 9–12 August 2007, Colorado, USA. Retrieved from http://aisel.aisnet.org/amcis2007/339

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2007). Multivariate data analysis (6th edn). New Delhi: Pearson Education India.

Hinkin, T.R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967–988.

Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Articles, 6(1), 53–60.

Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

Jhih-Syuan Lin, & Peña, J. (2011). Are you following me? A content analysis of TV networks’ brand communication on Twitter. Journal of Interactive Advertising, 12(1), 17–29.

Kenny, D.A., & McCoach, D.B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling, 10(3), 333–351.

Kim, M., & Lee, M. (2010). Why do college students use Twitter? Presented at the Association for Education in Journalism and Mass Communication Annual Conference, 4–7 August 2007, Denver, CO.

Lei, R.M. (2000). An assessment of the World Wide Web as an advertising medium. Social Science Journal, 37(3), 465.

MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covari- ance structure modeling. Psychological Methods, 1(2), 130.

McQuail, D. (1994). Mass communication theory: An introduc- tion. London, UK: SAGE.

Muenjohn, D.N., & Armstrong, P.A. (2008). Evaluating the structural validity of the multifactor leadership questionnaire (MLQ), capturing the leadership factors of transformational- transactional leadership. Contemporary Management Research, 4(1), 3–14.

Norris, V.P. (1984). The economic effects of advertising: A review of the literature. Current Issues & Research in Advertising, 7(2), 39.

Pempek, T.A., Yermolayeva, Y.A., & Calvert, S.L. (2009). College students’ social networking experiences on Facebook. Journal of Applied Developmental Psychology, 30(3), 227–238.

Peterson, A.R. (2001). On the use of college students in social science research: Insights from a second-order meta-analysis. Journal of Consumer Research, 28(3), 450–461.

Phillips, M., McFadden, D.T., & Sullins, M. (2010). How effec- tive is social networking for direct marketers? Journal of Food Distribution Research, 41(1), 96–100.

PricewaterhouseCoopers LLP. (2011). IAB Internet advertising revenue report (pp. 1–23). New York: Interactive Advertising Bureau.

PricewaterhouseCoopers Private Limited. (2011). India entertainment and media outlook (pp. 1–141). Kolkata: PricewaterhouseCoopers Private Limited.

Rizavi, S.S., Ali, L., & Rizavi, S.H.M. (2011). User perceived quality of social networking websites: A study of Lahore region. Interdisciplinary Journal of Contemporary Research in Business, 2(12), 902–913.

Safko, L., & Brake, D.K. (2009). The social media bible: Tactics, tools, and strategies for business success. New Jersey: John Wiley & Sons.

Salisbury, W.D., Pearson, R.A., Pearson, A.W., & Miller, D.W. (2001). Perceived security and World Wide Web purchase inten- tion. Industrial Management & Data Systems, 101(3/4), 165.

Schlosser, A.E., Shavitt, S., & Kanfer, A. (1999). Survey of Internet users’ attitudes toward Internet advertising. Journal of interactive marketing, 13(3), 34–54.

Anant Saxena and Uday Khanna 25

Vision, 17, 1 (2013): 17–25

Singh, R. (2009). Does my structural model represent the real phenomenon? A review of the appropriate use of structural equation modelling (SEM) model fit indices. The Marketing Review, 9(3), 199–212.

Social Networking Sites Account for More than 20 Percent of All U.S. Online Display Ad Impressions, According to com- Score Ad Metrix - comScore, Inc. (2009, 1 September). Retrieved from http://www.comscore.com/Press_Events/ Press_Releases/2009/9/Social_Networking_Sites_Account_ for_More_than_20_Percent_of_All_U.S._Online_Display_ Ad_Impressions_According_to_comScore_Ad_Metrix (accessed on 14 December 2012).

Stelzner, M. (n.d.). 2011 Social Media Marketing Industry Report | Social Media Examiner. Retrieved from http://www. socialmediaexaminer.com/social-media-marketing-industry- report-2011/

Subrahmanyam, K., Reich, S.M., Waechter, N., & Espinoza, G. (2008). Online and offline social networks: Use of social networking sites by emerging adults. Journal of Applied Developmental Psychology, 29(6), 420–433.

Taylor, D.G., Lewin, J.E., & Strutton, D. (2011). Friends, fans, and followers: Do ads work on social networks? How gender and age shape receptivity. Journal of Advertising Research, 51(1), 258–275.

Trusov, M., Bucklin, R.E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an Internet social networking site. Journal of Marketing, 73(5), 90–102.

Watchravesringkan, K.T., Yan, R.N., & Yurchisin, J. (2008). Cross-cultural invariance of consumers’ price perception

measures: Eastern Asian perspective. International Journal of Retail & Distribution Management, 36(10), 759–779.

Wells, W.D., Leavitt, C., & McConville, M. (1971). A reaction profile for TV commercials. Journal of Advertising Research, 11(6), 11–18.

Yoon Sung-Joon, & Kim Joo-Ho. (2001). Is the Internet more effective than traditional media? Factors affecting the choice of media. Journal of Advertising Research, 41(6), 53–60.

Anant Saxena ([email protected]) is working as a Research Associate at IMT Ghaziabad, UP, India. He is currently researching the role of common service center (CSC) project in Indian governance and also working on the impact of green marketing on consumer purchase decision in India. He has published research papers in national and international journals of repute. His research interests are marketing through social media, information technology & government policies and e-marketing.

Uday Khanna ([email protected]) is an Assistant Professor at the Faculty of Management Studies at Graphic Era University, Dehradun, India. His areas of interest are Marketing, Marketing Research and Sales and Distribution. He is currently researching the quality of corporate governance of Indian companies. He has published some good papers in national and international journals of repute. He has rich industry experience in FMCG companies of repute like Gillette India Ltd and Hindustan Pencils Ltd.