Effect of SNS on Purchasing Intention for Sport Product

Authors: Seok Pyo Hong*(1) & Yong-Chae Rhee(2)
(1) Seok Pyo Hong is a full-time professor at the Kangwon National University, Korea and concentrating on sport consumer behaviors for his research.
(2) Washington State University

*Corresponding Author:
Seok Pyo Hong, Ph.D
Gangwon Daihak-gil 1
Division of Sport Science, Kangwon National University
Chuncheon, Gangwon, Korea
uconnhong@kangwon.ac.kr
82-10-6211-5308

ABSTRACT
The purpose of this study was to better understand the influence of exchange of information and opinion about sports product on sport consumers’ buying intention. This study also examined the moderating role of sport identification on the relationships among peer communication, perceived usefulness, attitude, and online sport product purchase intention. Two hundred and seventy-nine samples who use social network service regularly and have purchased sport product online were used for this study. A self-administrated questionnaire consisting 34 questions was used based on previous research. Structural equation modeling and multiple group analysis were used to test hypothesis of the study. Results showed that peer communication through Social Network System (SNS) about sport product influenced perceived usefulness of the information from peers which also positively influenced the attitude toward buying sport product based on information from peers. Attitude also positively influenced buying intention for sport product. The level of identification with certain sport in SNS setting was a matter for deciding whether purchasing sport product.

KEYWORDS: SNS, purchase intention, social capital, TAM, sport identification

INTRODUCTION
The total number of Facebook users worldwide reached to 1.4 billion in 2013 and 98% of people aged between 18-24 years old already use social network service (37). Americans spend an average of 37 minutes daily on social media, more than any other major Internet activity (1). Along with the ever more increasing popularity, social network service (SNS) has greatly extended its role in connecting people, sharing information, managing organizations, and expressing one’s self. The emergence of SNS enables interactive communication instead of traditional one-way communication. SNS users integrate their life and work through SNS and they even make new friends through it. In addition, a certain social community created in SNS functions as a mechanism for the socialization process of its norms (38). Socialization process via SNS can be influenced by not only traditionally significant others such as family and friends but also by strangers as new significant others. For marketing, this aspect of SNS leads to changes in consumer behaviors (26, 30, 33, 41). For example, user-based information in social media like user reviews have dramatically changed the process of information communication between consumers and firms, consumers’ decision making rules, and firms’ communication strategy (33,41).

Despite its prevalence and importance, there is little research on how social community in SNS influences buying decision making process, particularly for sports product. Sport was the one of the most frequently discussed subjects in Facebook in 2013 (10). Sport is distinctive in that fans often develop strong psychological attachment and identify with as a certain sport, which has considerable impact on consumer behaviors in various ways. For instance, members who share common interest for and identify with a certain sport exchange a variety of information and opinions in SNS and this should influence consumer buying behaviors.

Due to the paucity of research, the purpose of this study is to understand the influence of exchange of information and opinion about sports product on sport consumers’ buying intention. Specifically, this study examines how the level of social communication about sports product through SNS can influence on buying intention of sports product online by testing the mediating role of perceived usefulness of the information from SNS and mediating role of attitude toward buying sport product based on information from SNS. This study also examines the moderating role of sport identification on the relationships among peer communication, perceived usefulness, attitude, and purchasing intention of sport product online.

Theoretical Background
Based on theory of social capital, technology acceptance model, and sport identification theory, we develop a conceptual model for this study as illustrated in Figure 1. Depicted in the framework are the relationships among the research constructs. The nomological network of the identified constructs in this study is derived from theory and the frequency of placement in the existing body of scholarly research. Multiple steps were used to justify the inclusion of antecedent, mediator, moderator, and outcome constructs in the conceptual model. Theory and research evidence are woven together in the following sections to provide justification for each of the hypothesized relationships in the conceptual model.

Theory of social capital
According to Bourdieu (1986), social capital is ‘the aggregate of the actual or potential resources which are linked to the possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition or, in other words, to membership in a group—which provides each of its members with the backing of the collectively owned capital, a “credential” which entitles them to credit, in the various senses of the word (p.21).’ That is, social capital consists of resources acquired by social interaction and it can be engendered and transferred by social network. Social capital includes reciprocity and trust which enable successful life and work (20). Psychological resources such as emotional support and sense of belonging developed by social network can help to maintain happy life. Ellison, Heino, and Gibbs (2006) insisted that SNS had a critical role in developing and maintaining social capital. Through SNS, people feel a sense of belonging and support from their peer group for various aspects of their lives.
Important for marketers, consumers develop social group in SNS, learn the norms of the group, and shape their attitude and behaviors (2, 38). Individuals develop conformance to the SNS group’s norms, and they would feel pressure to abide by the group norms when buying products. Trusov, Bodapati, and Bucklin (2010) insisted that individuals in SNS conformed toward group norms by indicating their preference for a certain product. Therefore, learned norms of a group and information about the product through SNS can influence attitude toward the product or buying behavior.

Technology Acceptance Model
Davis (1986) proposed the Technology Acceptance Model (TAM) based on the theory of Reasoned Action to explain why users accept a new technology. TAM would be a useful framework to explain extrinsic and intrinsic motivation for online transactions because users in online transactions voluntarily employ information technology. In particular, TAM emphasizes perceived usefulness and perceived ease of use in order to explain users’ willingness to accept information system.

Perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance (8, p.320).” The degree of perceived usefulness can be decided by amount of perceived risk in accepting the new technology. Perceived ease of use, on the other hand, refers to “the degree to which a person believes that using a particular system would be free of effort (8, p.320).” Perceived ease of use in TAM is an instrumental concept to explain the perceived usefulness (16). For example, if online consumers feel a certain online site is easy to use, they tend to consider the site as useful. In the TAM, individual’s behavioral intention can be determined by his/her perception of usefulness and attitude. In turn, perceived ease of use indirectly influence behavior intention by mediating through the perceived usefulness. TAM has been tested in various fields and shown its robustness (7, 8, 9, 15, 16, 24, 34, 36, 40). Given that this study focuses on the degree of communication among peers in SNS, perceived ease of use may be highly overlapped with perceived usefulness of the information acquired from SNS. We, therefore, have decided to omit perceived ease of use as a variable for this study.

Sport identification
Sport identification refers to the degree to which a person possesses feeling of attachment and like toward a certain sport (42). The degree of sport identification is an important factor that influences attitude, thought, and behavior of the sports fan. For example, a fan who has relatively higher degree of identification would attend more games, know more information about players or teams, and feel more sensitive about wins or losses. From the consumer behavior perspective, sports identification is a critical factor because highly identified fans express their willingness of support, increase their involvement, and buy more products (e.g. uniform, hat, ticket, and licensed merchandise) and services (25, 45).

Research Hypotheses
Two-way communications through SNS enhance consumers’ feeling about ease of the site usage, which may result in the improvement of usefulness for the site in terms of product purchases. Many studies have shown that information provided by online media such as SNS reduces consumers’ physical and emotional efforts in making a decision to purchase certain product purchasing decisions (15, 13). Because communications among peers in SNS reduce social uncertainty and risk by reducing the difficulty of vendor verification, accessibility of information and the ease of search to acquire product information can be enhanced.
In turn, consumer attitude could be favorably formed by the knowledge resulted from reciprocal actions between the consumer and significant others. Consumer can learn about the product based on information acquired from peers in SNS and this information will influence the decision making process to buy a product and its post-purchase evaluation (41). Based on previous research, hypotheses were developed as follows.

H1. The degree of communication about a sport product through SNS will influence consumers’ perceived usefulness of the sport product information.

H2. The degree of communication about a sport product through SNS will affect consumers’ attitude for purchasing sport product based on information acquired from SNS.

Perceived usefulness in this study refers to how much information exchange through SNS about sports product was perceived as useful. Previous studies indicated that perceived usefulness was a key factor to form attitude toward an object. Lin and Lu (2000) insisted that perceived usefulness for a certain website positively influenced preference for the site. This result pointed out that the degree of usefulness consumer feels in web site determines the degree of favorable attitude about the site. Na and Hong (2008) also supported the influential effect of perceived usefulness on attitude. In their study, perceived usefulness toward internet shopping malls was shown to be a significant factor to create positive attitude for the sites. Based on previous research, hypothesis was developed as follows.

H3. Consumers’ perceived usefulness of the information acquired from SNS will influence the attitude on purchasing sport product based information acquired from SNS.

In TAM, attitude was considered as a factor that mediates perceived usefulness and purchasing intention. Previous research also confirmed the role of attitude on purchasing intention of products. Na & Hong (2008) indicated that attitude toward certain internet shopping site positively influenced purchasing intention in the site. In addition, Zhang & Won (2010) found that attitude toward online sports product site was a factor that discriminated actual buyer and just visitor.

H4. Attitude for purchasing sports product based on information acquired from SNS will positively impact on intention to purchasing sports product online.

Sports fans tend to have a desire to express their supports, involvement, and attachment to the team with which they identify (25, 28, 45). This desire is expressed by purchasing products or services related to the team (44). Kwon and Armstrong (2002) suggested that sports identification had a crucial role as an antecedent factor to influence buying sports product and determine the willingness to pay more for products or services. Kim et al. (2013) examined the moderating effect of identification in the relationship between the motives and attendance to understand better the disconnection between sports fans’ high level of motives and actual sporting event attendance. They reported that along with increasing team identification the relationship between motives and attendance was getting stronger and thus, team identification should be considered as a moderator in examination of the relationship between the motives and sport consumer behaviors. Madrigal (2001) also suggested the role of the identification as a moderator on the effect of attitude toward purchasing products from a corporate sponsor. More specifically, he suggested that individuals who were highly identified with sport team were more likely to purchase sponsor’s products regardless of their evaluation about the sponsor, taken together, it could be inferred from previous research that identification has moderating effects on the relationship between various drivers and sports consumption behaviors.

H5. The level of sport identification will moderate the relationship among peer communication, perceived usefulness, attitude, and purchasing intention of sport product online.

Figure 1
Figure 1. Research Model

METHODS
Sample and Procedure
The data were collected using 526 undergraduate and graduate students from multiple universities in the United States. To reduce social desirability bias and to encourage candor, the participants were assured that individual responses would remain confidential and that they could withdraw at any time. Among the 526 students, 352 students completed the survey. After sorting out the unusable data, results from 279 subjects who had been using social network service and had bought sport product online were used for this study. Demographic information of the participants are reported in Table 1. The survey was conducted online in early 2014 with an Internet survey tool. First, the URL of survey questionnaire was sent to the contacted instructors by e-mail. Next, the instructors were asked to send the online survey link via e-mail to their students and post the link on the course websites. The contacted instructors were guided to ask their students to participate in the online survey and also share the survey with other undergraduate or graduate students not enrolled in their classes. To mitigate potential common method variance (CMV), we took several steps suggested by Podsakoff et al (2003). First, web-based survey was used to reduce the chance of bias due to social desirability and evaluation apprehension by ensuring anonymity of the responses. Second, the items and overall questionnaire were designed as unambiguously and succinctly as possible. Third, to induce a psychological separation between measurement of the predictor and criterion variables, the measures of the independent and dependent variables in the questionnaire were separated. Finally, Harmon’s one-factor test (18) was conducted. No single factor emerged to account for the majority of variance. Taken together, CMV was not likely a serious concern in this study.

Instrument
A self-administrated questionnaire that consisted of 34 questions was used in the study. The instrument began with five qualifying questions to check if the participant had been using any kind of social network service. The first question asked participants whether the participant has used SNS. The second question asked participants to specify the name of the most frequently used SNS followed by average hour per day using SNS. Fourth question asked participants whether the participant has ever searched for information about sport product using SNS. The last qualifying question asked participants to list the sport products they have searched using SNS.

After five qualifying questions, measures of degree of the Peer Communication in SNS for sport product, Perceived Usefulness, Attitude, Purchase Intention, and Sport Identification from previous research were employed and modified for our context. An expert review was conducted to ensure face and content validity of the items. Degree of the peer communication in SNS was measured through five items modified from Wang et al.’s (2012). Perceived Usefulness of information from SNS (3items) and Purchase Intention of sport product (3items) were used from Jin (2013). Four items for Sport identification were modified from Dees, Bennett, & Villegas’s (2008). All items were rated with a Likert-type scale. Attitude was measured using eight items from Davis’ (1993) that examined how information system could be accepted at workplace. For example, participants were asked to rate the following statement on a evaluative seven-point semantic differential scale ranging from +3 (Extremely) to -3 (Extremely): “In your opinion, purchasing a sport product based on information acquired from social media was good/bad.” Seven related statements were used to address the degree of attitude.

Data Analysis
Data screening and test of assumptions
Descriptive statistics such as minimum, maximum, means, standardized deviations, skewness, and kurtosis values were examined to check for outliers, missing, and unreliable data. To detect extreme multicollinearity in the data, the determinant of the input matrix was used. The univariate normality was assessed with the skewness and kurtosis and multivariate normality was evaluated using Mardia’s multivariate kurtosis.

Analytical procedures
Cronbach’s alpha coefficient was calculated to test internal consistency of the constructs using SPSS 22.0. A general guideline of .70 for the Cronbach’s alpha for each construct was used to assess the internal consistency (32). For examination of discriminant and convergent validity, average variance extracted (AVE), average shared variance (ASV), and maximum shared variance (MSV) were used (17). A confirmatory factor analysis (CFA) was conducted to evaluate the measurement model using AMOS 22.0. A structural regression analysis was conducted to test the proposed model and research hypotheses. Lastly, a multi-group analysis was performed to test the moderating effect of Sport Identification on the proposed model.

Goodness-of-fit indices, such as χ 2/df, Bollen-Stine bootstarp p value, Comparative Fit Index (CFI), Tucker Luis Index (TLI), Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA) were used to evaluate the global fit of both measurement and research model. The recommended values to determine goodness-of-fit for research model are as follows: NC (less than 3.0), CFI and NFI (greater than .90), TLI (greater than .90), SRMR (less than .05), and RMSEA (less than .08).

RESULTS
Data screening and test of assumptions
None of the research variables had greater than 15% missing data, nor did any case have greater than 10% missing data. As for linearity, scatter plots showed that there was no evidence of non-ignorable data patterns. The skewness ranged from -1.31~ .15 and kurtosis ranged from -1.08~ 1.70, which indicates normal distribution of the data. Univariate normality is a prerequisite, though not sufficient, condition for multivariate normality (43). Mardia’s multivariate kurtosis suggested that multivariate non-normality exists (Mardia’s coefficient = 180.51, c.r. = 42.67). To address the non-normality issues, Bollen-Stine bootstarps (n=1000) were performed, which adjusts the p value for the χ2 test. To test the multicollinearity, tolerance and variance inflation factor (VIF) were obtained. Results (R2=.16; Tolerance = .59 ~ .82; VIF = 1.22 ~ 1.71) indicate that extreme level of multicollinearity did not exist (23).

Table 2. Mean, Standard Deviation, Correlation among Latent Factors
table 2

Psychometric properties of the measurement scale
Table 3 presents Cronbach alpha, composite reliability, AVE, MSV, and average ASV for each factor. Values of selected fit indices from CFA indicated acceptable overall fit of the measurement model to the data (χ2/df = 605.81/242 = 2.50, Bollen-Stine bootstrapped p-value = 0.001, CFI = .93, TLI = .93, SRMR = .04, RMSEA = .07). All Cronbach’s α coefficients of the constructs exceeded .70, ranging from .92 to .94. All factor loadings were significant in the predicted direction (p less than .001; loadings ranging from .69 to .93). Composite reliability values for all constructs were higher than .70 (ranging from .91 to .94) and all of the average variance extracted (AVE) values were greater than .50 (ranging from .61 to .79). Thus, the measures demonstrated good convergent validity and reliability.

Table 3. Psychometric Properties of the Measurement Scale
Table 3

Discriminant validity was examined for each construct by performing multiple χ2 difference tests of unity between all pairs of constructs. The unconstrained model (correlation estimated freely) was significantly better than the constrained model (correlation between a pair of latent factors constrained as 1) in all comparisons. In addition, AVE values for all constructs were larger than the corresponding squared inter-construct correlations, providing additional support for discriminant validity (14). In aggregate, the results indicated that the measures possess adequate psychometric properties.

Test of the proposed model
Based on the criteria discussed in the previous section, the model showed moderate fit to the data (χ2/df = 574.92/166 = 3.46, Bollen-Stine bootstrapped p = 0.001, CFI = .91, TLI =.90, SRMR = 0.11, RMSEA = .09). The path coefficients estimates are reported in Table 4 and Figure 2.

Figure 2. Test of Research Hypothesis
The direct path from Peer Communication (PC) to Perceived Usefulness (USE) was significant (standardized γ = .64, S.E.boot =.05, p less than .01). The direct path from PC to Attitude (ATT) was not significant (standardized β = .15, S.E.boot =.08, p = .07). Direct path from Perceived Usefulness to Attitude was significant (standardized β = .31, S.E.boot =.10 p less than .01). Lastly, the pass from ATT to Intention (INT) was significant (standardized β = .40, S.E.boot =.07, p < .01). Table 4. Test of the Proposed Model Table 4

Test of moderation effect of Team Identification.
A multiple-group analysis was used to test the moderation effect of Sport Identification (SI) on the proposed model. Standard econometric conventions (23) suggest that the top and the bottom 35 percent of the cases could be selected to obtain two subgroups, which resulted in high level of SI group (N= 131) and low level of SI group (N=84).

The objective of multigroup analysis was to determine if the path coefficients for the relationships among peer communication, perceived usefulness, attitude, and intention vary across the high and the low groups of Sport Identification. Group comparisons of paths can be meaningfully interpreted only when metric invariance (equal factor loadings) of the measurement model across groups was established. Measurement invariance of the scales was tested using stepwise procedure whereby the starting model contained the least restrictive constraints (configural invariance, meaning that the number of factors and the pattern of indicator-factor loading is identical across groups), and subsequent models entailing increasingly restrictive constraints were evaluated in comparison with less constrained models (using χ2 difference test of the nested models).

The first stage in the main equivalence tests was the configural invariance test. The models for the two groups demonstrated configural invariance when the model specification for both group were identical. The test revealed that the baseline model represented a moderate fit across the groups, χ2/df = 719.69/332 = 2.17, CFI =.89, TLI = .87, RMSEA = .07, SRMR = .09. Next, a model with factor loadings constrained to be equal across the two groups did not fit significantly worse than the configural invariance model (Δχ2 (df) = 23.06(16), p is great than .05), thus supporting metric invariance. The hypothesized effect of the moderator was tested at both the overall and the individual path level. At the overall level, comparison between unconstrained model and fully constrained model suggested that the groups were different at the model level (Δχ2 (df) = 39.30(21), p less than .01). We, therefore, tested moderating effect on individual paths by constraining one path (i.e., from Peer Communication/ Perceived Usefulness) to be the same across both groups and then compared this model to the metric invariant model. Table X shows the results of multiple group comparison test. The results showed that the path from Peer Communication to Perceived Usefulness was statistically different between a high (λ = .52, p less than .001) and a low sport identity group (λ = .61, p less than .001). The result of χ2 difference comparison provided evidence that there is significant difference between a high- and a low-sport identity group in the relationship between Peer Communication and Perceived Usefulness (Δχ2 (df) = 4.51(1), p=0.03) suggesting significant moderating effects of the Sport Identity. The path from Usefulness to Attitude was statistically different between a high-(β=.23, p=.003) and a low-sport identity group (β = .30, p = 0.003). The result of χ2 difference comparison suggested that there was significant difference in the relationship between Perceived Usefulness and Attitude between a high- and a low-sport identity group (Δχ2 (df) = 5.38(1), p=0.02) suggesting significant moderating effects of the Sport Identity. However, the moderating effect of the relationship between Peer Communication and Attitude was not supported based on insignificant differences in χ2 over the change of degree of freedom (Δχ2 (df) = .639(1), p=0.42). Also the relationship between Attitude and Intention was not significantly different across the groups (Δχ2 (df) = 2.47 (1), p=0.12).

Table 5. Test of moderation effect of Team Identification
Table 5

Figure 3. Test of Moderating Effect of Sport Identification
Figure 3

DISCUSSION
The results of this study provide unique insights into peer communication and its impacts on purchase intention of sport product through SNS. Peer communication in SNS positively influences attitude mediated through perceived usefulness with which attitude directly influence purchase intention of sport product. This study provides evidence that TAM can be applied effectively to a social media setting for sport product. The findings provide both theoretical and managerial insights.

Theoretical Implications
This study shows that peer communication through SNS can establish a certain form of social network among members of the group and can influence decision making process on buying sport product. The primary contribution of the findings to theory lies in the findings that consumption related to peer communications through SNS on sport product is becoming increasingly a sport consumer behavior issues and can significantly influence purchase intention of sport product. Specially, this study shows the adaptability of TAM in sport settings which has shown the robustness of the TAM in sport product consumption. TAM was originally developed to explain how individuals adopt information systems to enhance work performance (9, 6). Although the TAM was initially employed in workplace settings, researchers have applied and examined the extended TAM to explain how consumers use SNS for shopping due to the growth of SNS and its impact on e-commerce (e.g. 20).

Despite some researchers’ attempts to apply the TAM in the sport context, only a few researchers have applied the TAM and empirically tested it to explain the factors that influence sport product purchasing behavior. For example, Hur et al. (2012) studied sports fans’ use of websites in general and not just for shopping and suggested that sports fans might use websites for both information seeking and commercial purposes. To our knowledge, no research has been conducted on how TAM can be applied in SNS setting for the sport product. Specially, some of the issues consumers may consider for e-commerce are fiduciary, security, and privacy risks when engaging in online monetary transactions because they need to depend on unseen and unknown vendors for their purchases (13). This study has shown that peer communication for sport product can reduce the risk issues in online transaction because it may increase the perceived usefulness of the information about the sport product from peers.

Attitude was not directly influenced by peer communication but mediated by perceived usefulness of the information from the peer group. It means that peer communication does not guarantee the formation of positive attitude regarding purchasing a sport product based on information from peers but can foster a positive attitude if the information from peers is viewed as useful. For example, Lin & Lu (2000) showed that perceived usefulness toward a web site positively influenced the preference of the site. It indicates that the degree of usefulness about information consumer perceived from peers has a crucial role to form favorable attitude toward purchasing sport product based on information from peers.

Based on previous research, we hypothesized that sport identification would moderate the decision making process of purchasing intention for sport product in SNS setting. As we expected, sport identification has moderating effect on the process. In particular, sport identification influenced the relationships between peer communication and perceived usefulness, and perceived usefulness and attitude. Our finding suggests that the level of identification with certain sport in SNS setting is important in deciding whether or not to purchase a sport product. More specifically, highly identified individuals are more likely to perceive information from their peers as useful and have positive attitude to accept the information for purchase intention. This finding supports results from previous research. For example, Terry and Hogg (1996) stated that a type of depersonalization occurs for high identifiers such that their behavioral intentions are guided more by a group prototype or norm than by personal factors. Madrigal (2001) found that the critical personal determinant of intentions is the favorability of the person’s attitude toward the behavior for the low identifiers. Madrigal (2001) also found the moderating effect of team identification on the relationship between attitudes toward purchasing products from a corporate sponsor and purchase intentions for the sponsor’s products.

Practical Implications
There are practical implications based on the findings of this study. SNS is a new form of word-of-mouth platform and the trustworthiness of the information is one of the most crucial factors in decision making process in SNS. Brown, Broderick, and Lee (2007) pointed out that trustworthiness is determined by the receiver’s belief that the sender’s opinions are unbiased, credible, and generated by someone having no self-interest in pushing a product. Peers in SNS usually consist of people who already have emotional and attitudinal attachment based on common interests or backgrounds and may be considered as a trustful resource. Sport marketers should, therefore, pay special attention to building and maintaining trust from buyers to cultivate favorable thought of their product. It is also important for marketers to monitor online reviews about their product among consumers.

ACKNOWLEDGMENTS
This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A2A1A01034201)

REFERENCES
1. Adler, E. (2013). A portrait of time-spend and activity on the top social network. Retrieved from http://itsworldwar.blogspot.kr/2014/01/a-portrait-of-time-spend-and-activity.html.

2. Bearden, R. P., Netemeyer, R. G., & Teel J. E. (1989). Measurement of consumer susceptibility to interpersonal influence. Journal of Consumer Research, 15(4), 473-481.

3. Bourdieu, P. (1986). The forms of capital. J.G. Richardson (Ed.), Handbook of theory and research for the sociology of education, Greenwood:NY .

4. Byrne, B. M. (2001), Structural equation modeling with AMOS. London: Lawrence Erlbaum.

5. Casteleyn, J., Mottart, A,. & Rutten, K. (2009). How to use Facebook in your market research. International Journal of Market Research, 51(4), 439-447.

6. Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.

7. Davis, F. D.(1986). A technology acceptance model for empirically testing new end-use information systems: Theory & results (Doctoral dissertation, Massachusetts Institute of Technology). Retrieved from http://hdl.handle.net/1721.1/ 15192.

8. Davis, F. D.(1989). Perceived usefulness, perceived ease of use, & user acceptance of information technology. MIS Quarterly, 13(3),319-340.

9. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R.(1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

10. D’Onofrio, R.(2013). 2013 year in review. Retrieved from http://newsroom.fb.com/news/2013/12/2013-year-in-review/

11. Dees, W., Bennett, G., & Villegas, J. (2008). Measuring the effectiveness of sponsorship of an elite intercollegiate football program. Sport Marketing Quarterly, 17(2), 79-89.

12. Ellison, N., Heino, J., & Gibbs, J. (2006). Managing impression online: Self-presentation processes in the online dating environment. Journal of Computer-Mediated Communication, 11(2), 415-441.

13. Everard, A., & Galletta, D. (2006). How presentation flaws affect perceived site quality, trust, and intention to purchase from an online store. Journal of Management Information Systems, 22(3), 55-95.

14. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.

15. Gefen, D., Karahanna, E., & Straub, D. W. (2003).Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.

16. Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model .Journal of Business Research, 62(5), 565-571.

17. Hair, J., Black, W., Babin, B., and Anderson, R. (2010). Multivariate data analysis (7th ed.): Prentice-Hall, Inc. Upper Saddle River, NJ, USA.

18. Harman, H. H. (1976). Modern factor analysis (3rd. ed.). Chicago, IL: University of Chicago Press.

19. Hu, L., & Bentler, P. (1999), “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Structural Equation Modeling, 6, 1-55.

20. Jin, C. H. (2013). The perspective of a revised TRAM on social capital building: the case of Facebook usage. Information & Management, 50(4), 162-168.

21. Kim, Y. K., Trail, G. T., & Magnusen, M. J. (2013). Transition from motivation to behaviour: Examining the moderating role of Identification (ID) on the relationship between motives and attendance. International Journal of Sports Marketing & Sponsorship, 14(3), 190-211.

22. Kline, R. B. (2011). Principles and practice of structural equation modeling. New York, NY: The Guilford Press.

23. Kline, R. B. (2005). Principles and practices of structural equation modeling. New York: Guilford.

24. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223.

25. Kwon, H. H., & Armstrong, K. L. (2002). Factors influencing impulse buying of sport team licensed merchandise. Sport Marketing Quarterly, 11(3), 151-163.

26. Leug, J. E., Ponder, P., Beatty, S. E., & Capella, M. L. (2006). Teenager’s use of alternative shopping channels: A consumer socialization perspective. Journal of Retailing, 82(2), 137-153.

27. Lin, J. C., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20(3), 197-208.

28. Madrigal, R. (2001). Social identity effects in a belief-attitude-intentions hierarchy: Implications for corporate sponsorship. Psychology & Marketing, 18(2), 145-165.

29. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological bulletin, 103(3), 391.

30. Muratore, I. (2008). Teenagers, blogs and socialization: A case study of young French bloggers. Young Consumers, 9(2), 131-142.

31. Na, Y. K., & Hong, B. S. (2008). The effect of perceived risk, trust of internet shopping on the perceived usefulness, attitude, and purchase intention of the fashion merchandise. Journal of the Korean Society of Clothing and Textiles, 32(5), 834-845.

32. Nunnally, J. C. (1978). Psychometric theory. New York, NY: McGraw Hill.

33. Okazaki, S. (2009). The taqctical use of mobile marketing: How adolescents’ social networking can best shape brand extensions. Journal of Advertising Research, 49(1), 12-26.

34. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134.

35. Podsakoff, P. M., MacKenzie, S. B., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 807-903.

36. Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999-1007.

37. Statistical Brain Research Institute, 2014

38. Trusov, M., Bodapati, A. V., & Bucklin, R. E. (2010). Determining influential users in internet social networks. Journal of Marketing Research, 47(August), 643-658.

39. Tuten, T., & Solomon, M. (2013). Social media marketing. Englewood Cliffs, NJ: Prentice Hall.

40. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.

41. Wang, X., Yu, C., & Wei Y. (2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26, 198-208.

42. Wann, D. L., & Branscombe, N. R. (1993). Sports fans: Measuring degree of identification with their team. International Journal of Sport Psychology. 24(1), 1-17.

43. West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies.

44. Zhang, Z., & Won, D. (2010). Buyer or browser? An analysis of sports fan behavior online. International Journal of Sports Marketing & Sponsorship, 11(2), 124-139.

45. Zhang, Z., & Won, D., & Pastore, D. L. (2005). The effects of attitudes toward commercialization on college students’ purchasing intentions of sponsors’ products. Sport Marketing Quarterly, 14(3), 177-187.

Print Friendly