City and state where it is located

  • Category:
    Management
  • Document type:
    Essay
  • Level:
    Masters
  • Page:
    7
  • Words:
    5096

City and state where it is located

Contents

3Abstract

3Introduction 1.0

4Research Questions 2.0

4 Significance of the Study 3.0

5 Literature review 4.0

5 Social media marketing 4.1

7Technological factors: Perceived usefulness and Perceived Ease of Use 4.2

8Non-technological Factors: Personal, Organizational, and Social 4.3

8 Personal factors 4.3.1

9Organizational factors 4.3.2

10Social factors 4.3.3

11Methodology 5.0

11Data collection process 5.1

12Sample frame 5.2

12Data analysis process 5.3

12Demographic data analysis 6.1

14Data analysis concerning research objectives 6.2

18Summary of results 6.3

19Conclusion and implications 7.0

19Conclusions 7.1

19Implications 7.2

20Limitations and future research 8.0

21References

Acceptance and use of social media as a marketing platform: A case of insurance sales executives use of Facebook and Twitter

Abstract

Most retailers are turning to social media marketing due to the competitive advantage it offers their products or brands. While studies on social media acceptance and adoption have increased, few focus on social media marketing (SMM). This study examines acceptance and use of SMM (through Facebook and Twitter) among insurance sales executives (ISEs) in Canberra, Australia. The study sought to determine the influence of two technological factors (perceived usefulness and perceived ease of use) and three non-technological factors (organizational, personal and social) on SMM’s use and adoption. 134 of 300 participants returned completed questionnaires which were used in the survey. The data was analysed using logistic regression with the help of SPSS. The results indicate positive and significant relationship between the five factors and the ISEs acceptance and use of SMM. The study also found a positive correlation between perceived ease of use and personal factors. The study, therefore recommends that organizations should not only endeavor to increase their social media presence but also improve employees’ self-efficacy in as many social networks as possible if they are to optimize competitive advantage associated with SMM.

1.0 Introduction

Social media marketing has been gaining ground in the recent past due to the increasing number of social network users. Monthly active user (MAU) counts for the top three networks cumulatively account for over 2.3 billion hits with Facebook recording 1.59 billion in December, 2015; Instagram, 400 million in September, 2015; and Twitter, 320 million in March, 2016 (Social Times, 2016). Similarly, Instragram and Twitter registered 200, 000 and 130, 000 open advertisers in September, 2015. These trends clearly point to social media as a major marketing domain for promoting company brands and products. Research indicates that 64% of marketers spend five or more hours while 39% of them spend ten or more hours weekly on social media respectively (Stelzner, 2009).

There is growing notable interest by marketing researchers in various aspects of social media marketing (SMM) (Pradiptarini, 2011; Sago, 2013; Paquette, 2013; Lorenzo-Romero, Alarcon-del-Amo and Constantinides, 2014; Dahnil, Marzuki and Fabeil ,2014). However, there is paucity of studies on SMM in the insurance industry. This study examines acceptance and use of SMM among insurance sales executives (ISEs) with a view to gain insight into the factors that influence ISEs to accept and use specific social media networks.

2.0 Research Questions

1. How do technological factors affect acceptance and use of social media marketing?

2. What is the influence of organizational factors on acceptance and use of social media marketing?

3. How do personal factors influence one’s acceptance and use of social media marketing?

4. What is the influence of social factors on acceptance and use of social media marketing?

3.0 Significance of the Study

Acceptance and use of social media marketing would give a company competitive advantage and improve sales and brand/[product market dominance. This is because social media marketing is one of the most efficient and cost-effective media of connecting with the market in real time. By engaging its current and potential customers through social media, a company effectively promotes its brand and widely circulates its products. Insurance products (covers) require a high level of trust for a customer to purchase and social media provides an ideal environment for building intimacy and trust. Acceptance of new technology, like its use, may meet resistance from the intended users due to technological and non-technological factors. Gaining insight into these factors would; therefore, guide the organizations’ training and motivation of staff in the domain of social media marketing.

4.0 Literature review

Relevant literature on social media marketing and factors that influence social media marketing acceptance and adoption is reviewed. Two technological factors and three non-technological factors are considered in this study. Literature on the four factors forms the basis for the study hypothesis formulation.

4.1 Social media marketing

Social media has been defined as a category of Internet-based applications that run on the Web 2.0 (interactive Internet) platform to enable creation and sharing user-generated content as opposed to Web 1.0 (informational Internet) interface (Kaplan and Haenlein, 2010). Sharing content and the possibility for interacting with other users qualify social media as an ideal marketing tool for companies and organization. The media exposes customers formally through company virtual advertisements and informally through customer reactions (comments, re-tweets, forwards, followings, and likes). It has been noted that customer reaction to formal product/brand information reaches a wider population of social media users due to the hype and buzz it creates over the original information (Koshy, 2013). Customer reactions also serve as feedback to marketers and other parties involved with the product/brand in question.

One crucial contribution of social media to the marketing industry is its instrumentality in building and boosting product/brand trust and loyalty (Laroche, Habibi & Richard, 2013). Loyal relationship between customers and the company grows as information sharing between the two parties become more open and intimate. The result is the emergence of brand communities as people talking about a given product converge on a (Facebook) page or a (Twitter) handle. Those who become consumers of the products widen the community by conveying their candid opinions of the products to their virtual friends. Two cohorts of social media users with respect to a product have been identified: fans of the trending brand (users who explicitly like the brand), and friends of fans (Lipsman, Mudd, Rich and Bruich, 2012). Social media marketing turns these two groups of fans into brand consumers and users.

In practice, a study found that between 80% and 88% of US firms with over 100 employees would use social media marketing between 2010 and 2012 respectively (Stelzner, 2009). Since online social contacts and product content play a decisive role on a customer’s preference for a product, retailing firms are beginning to report increased turnover accruing from online activities (Lorenzo-Romero, Alarcon-del-Amo and Constantinides, 2014). Traditional mass media outlets like television, radio, and print media are losing ground to the interactive online media that empower customer hence informing their purchasing decisions (Kaplan and Haenlein, 2010). It is the new trend of customer empowerment, and shift of power to the direction of consumers that push retailers to adopt social media as a marketing strategy (Constantinides, 2002).

4.2 Technological factors: Perceived usefulness and Perceived Ease of Use

The last two decades have witnessed research on the determinants of acceptance of Information and Communication Technologies (ICT). Consequently different models and theories have emerged to explain, describe or predict this phenomenon. The Technology Acceptance Model (TAM) (Davis, Bagozzi and Warshaw, 1989) has gained wide acceptance in explaining adoption of any technology. This model posits that perceived usefulness (PU) and perceived ease of use (PEOU) of new technology are the main predictors of a user’s attitude towards using that technology. PU refers to the user’s belief that a particular technology improves work performance while PEOU is the extent to which a person perceives that using a given system would be without effort. Castañeda, Muñoz-Leiva and Luque (2007) describe PU and PEOU in terms of instrumentality and self-efficacy respectively. The instrumental aspect means that one considers that using a given technology improves performance of tasks. The user hence becomes more productive by having more work done at no extra effort. Self-efficacy, on the other hand, is closely related to efficiency in handling the system which is considered a vital intrinsic motivation for individuals. People are more at ease with technologies they find themselves efficient to work with.

Research has demonstrated that PU and PEOU are useful constructs for explaining technology adoption and use (Ma and Liu, 2004). Applied to social media technology, perceptions of usefulness and ease of use vary with individuals and social networks. This partially explains the variance in the monthly account users (MAU) count for different networks. The two technological factors have been associated with uneven trends in adoption and use of Twitter, Facebook, and blog (Young, 2016), frequency and nature of use of Facebook and Twitter (Greenwood, 2013), and adoption and use of SMM by SMEs (Dahnil et al., 2014). The centrality of PU and PEOU in technology acceptance leads to the proposition of this study’s first hypothesis.

H1: Technological factors (PU and PEOU) have a positive and significant influence on acceptance and use of social media marketing among ISEs.

4.3 Non-technological Factors: Personal, Organizational, and Social

4.3.1 Personal factors

User trust of the technology is one of the factors that characterize technology acceptance and use (Tang and Liu, 2015). Applied to IT, trust refers to the belief that data content constructed and relayed online should meet both the sender’s and the receiver’s threshold of confidence (Pavlou and Gefen, 2004). Three concepts construct the multidimensional trust model in online contexts: dispositional, interpersonal and institutional trust (Tan and Sutherland, 2004). Dispositional trust applies to personal traits which include expectancy, belief, and feeling accumulated from personal experiences of using a system. Interpersonal trust is defined by the level of confidence between individuals sharing online information while institutional trust is confidence that is built by groups in the course of their interaction. Factors that enhance trust in a user of an IT system include the user’s perception of the system to deliver expected results, accuracy of the content conveyed, and conformance between what is said about the system and what it can actually do (Koufaris and Hampton-Sosa, 2004). The proposed study identifies perception of a system’s trustworthiness as a major predictor of user acceptance of SMM as a system. This leads to the first study hypothesis:

H2: Personal factors significantly influence acceptance and use of social media as a marketing tool.

The study will evaluate the level of trust that the sale representatives place on social media and asses how it affects their willingness to adopt it for marketing. In essence, the study seeks to establish the relationship between user trust and technology acceptance. Other personal factors previously studied in relation to technology acceptance and/or adoption include enjoyment or the entertainment element (Kim et al., 2009), user involvement (Shu and Chuang, 2011), user awareness (Abdullah and Gibb, 2006) and gender (Goh, 2007).

4.3.2 Organizational factors

Organizational characteristics that determine the extent of technology acceptance and use include infrastructure, support and involvement. Bruque and Moyano (2007) observe that management provides structure for facilitating and rationalizing new information structure. An organization’s top management influences resource, finance, and human talent mobilization for initiating and continuing new technology. It is worth to note that if the management is enthusiastic about the existence and capabilities of a system, positive influence will be noted in other employees (Tarafdar and Vaidya, 2006). Key components in a model for organizational adoption of social media include organizational influence, identification, and presence (Bhattacherjee, 2001). The model defines organizational influence as the degree of pressure on an individual from significant members of an organization to behave in a certain way; for instance, use social media for communication with the organization and its members. Organizational identity refers to the perception of belongingness or oneness to an organization such that members define themselves in terms of the organization. Therefore, when an organization adopts a new communication technology, members view it as an added element to their organizational identity. Presence of an organization in relation to technology is the degree of activity of its members and the strength of relationships based on that technology. On the basis of this perspective, the third hypothesis is stated thus:

H3: Organizational factors positively and significantly influence acceptance and use of social media marketing.

4.3.3 Social factors

In a study, Guo, Shim, and Otondo (2010) considered the influence of three social network determinants on technology acceptance. These were the tie strength, centrality and density. This study examined how technology acceptance by an individual is influenced by others rather than the technology per se. The study hence described social characteristics that predict other people’s influence on a user’s acceptance of technology. Tie strength refers to the closeness between individuals created by time, emotional intensity, intimacy and reciprocal interactions (Granovetter, 1973). As such, the stronger the social ties, the higher the influence –the reverse also applies. The centrality characteristic refers to the number of direct ties or connections an individual has with others; the more the ties, the higher the centrality of the individual within that network, and the greater the influence (Scott, 2000). The third characteristic, density, defines the degree of connectedness of individuals within a network, and the denser the ties, the higher the conformity, solidarity, cooperation and degree of trust (Sparrowe, Liden, Wayne and Kraimer, 2001). The proposed study adopts social network as one of the predictors of acceptability of new ICT by employees in the marketing industry and the findings from this study provide useful insights into this factor. It forms the basis for the fourth study hypothesis:

H4: Acceptance of SMM is dependent upon social factors – tie strength, tie centrality, and tie density. These factors are non-technology-related since they have more to do with significant others than with the user’s interaction with the technology.

FCity and state where it is located igure 1.0: Conceptual Model

5.0 Methodology

5.1 Data collection process

The purpose of the study was to examine the acceptance and use of social media as a marketing platform among the insurance sales executives in using of Facebook and Twitter. Data was mainly obtained by the descriptive research method (survey design). Survey design was deemed appropriate for this particular research because it sought to gain insight into related phenomenon as a means of providing information in the area of Social Media Marketing (SMM).

The data was collected by using online questionnaires. The questionnaires were developed objectively to ascertain data that would generate valid and reliable information from the population under study. The questionnaires were structured to two sections: demographic data and data concerning the four research objectives. The questionnaires were checked for validity and reliability by use of test and pretest methods, piloting and revision methods. The questions set in the questionnaires were specific, measurable, reliable, brief and grammatically correct to avoid ambiguity. The responses to some of the questions had choices scaled using the 5-point Likert scale.

5.2 Sample frame

The research was targeting all Insurance Sales Executives (ISEs) of Canberra Australia. Due to the large population of ISEs found in Canberra, Australia and due to lack of enough time and resources, a sample size of 300 was deemed fit for this research. Simple random sampling design was used because any member of the ISEs in Canberra had an equal chance to be selected to form the sample. From the 300 persons who were sent the questionnaires as anticipated sample units, 134 respondents turned up and filled the questionnaires. This represents 45% response turn up.

5.3 Data analysis process

The collected data was cleaned/ edited to check missing values or abnormal values then coded and entered in SPSS interface for analysis. The demographic data was first organized using frequency tables, cross tabulations, pie charts and bar graphs.

The data for each research objective were analyzed using correlations and logistic regression. The correlation coefficient (r), the beta values (β) and their respective p-values were used to test the research hypothesis. The results of data analysis were interpreted in order to come up with findings of the study that will generate the study conclusions.

6.0 DATA ANALYSIS RESULTS AND INTERPRETATION OF RESULTS

6.1 Demographic data analysis

Table 1.1: Frequency distribution of demographic data

Frequency

Valid Percent

Cumulative Percent

Education Level

High school

Bachelors

Work experience

0-4 years

5-9 years

10-14 years

15+ years

According to table 1.1, 51.1% respondents were males while 48.9% were females. This shows that there is gender parity among the ISEs of Canberra, Australia. The results also indicate that most of the ISEs are aged between 30 and 49 years (39.8% are 30-39 years and 38.3% are 40-49 years). This shows that most of the ISEs are middle-aged. Concerning the education level, 63.2% of the respondents have Bachelors level of education, 33.8% have High school education and 3.0% have obtained post-graduate education. Lastly, 42.1% of the respondents have a working experience of more than 15 years while 38.3% have less than 5 years working experience. The percent frequencies of other working experience cadres are: 16.5% and 3.0% for 10-14 years and 5-9 years working experience respectively.

6.2 Data analysis concerning research objectives

Table 1.2: Correlations

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

**. Correlation is significant at the 0.01 level (2-tailed).

PU=Perceived Usefulness, PEOU=Perceived Ease Of Usefulness, OP=Organizational Participation,

PT=Personal Trust, SST=Strength of Social Ties

Table 1.2 shows the correlations between Social Media Marketing (SMM) and the media acceptance and use dimensions. For SMM verses Perceived Usefulness (PU), the correlation coefficient was 0.593. This indicates a moderate positive correlation between the SMM and media acceptance and use dimension characterized by Perceived Usefulness (PU). The correlation coefficient of SMM verses Perceived Ease of Use (PEOU) is 0.921. This is a strong and positive correlation between SMM and media acceptance and use characterized by Perceived Ease of Use (PEOU). The correlation coefficient of SMM verses Organizational Participation (OP) is 0.821 indicating a strong and positive correlation between SMM and media acceptance and use characterized by Organizational Participation (OP). The correlation coefficient of SMM verses Personal Trust (PT) is 0.817. This is equally a strong and positive correlation between SMM and media acceptance and use characterized by Personal Trust (PT). This is a strong and positive correlation between SMM and media acceptance and use characterized by Personal Trust (PT).

Conversely, the correlation of SMM verses Strength of Social Ties (SST) is 0.477. This is a moderate relationship between SMM and media acceptance and use characterized by SST. Therefore, these results indicate that there exists a mutual relationship between SMM and media acceptance and use. Having realized that there exist some relationships between SMM and the independent variables, the relationships can then be analyzed using logistic regression. The logistic regressions for the two respective relationships will be analyzed independently and then their results compared to draw conclusions based on the research objectives.

Table 1.3: Logistic regression analysis results

Facebook

Hypothesis

Variables

Constant

Constant

Constant

Constant

Variable(s): PU=Perceived Usefulness, PEOU=Perceived Ease Of Usefulness, OP=Organizational Participation, PT=Personal Trust, SST=Strength of Social Ties

The beta statistic (B) and its respective p-value are used to determine statistical significance for each of the independent variables. From table 1.3, results show that Perceived Usefulness (PU) with
City and state where it is located 1, Organizational Participation (OP) with
City and state where it is located 2 and Personal Trust (PT) with
City and state where it is located 3 have p-values (statistical significance) which are less than 0.05. These results show that PU, OP and PT are significant in predicting Facebook’s SMM. While Perceived Ease of Use (PEOU) with
City and state where it is located 4 and p-value of 0.178 and Strength of Social Ties (SST) with
City and state where it is located 5 and p-value of 0.618 were not significant in the modeling Facebook SMM since their p-values are more than 0.05. Therefore, PU, OP and PT dimensions of Facebook media are statistically significant in predicting the outcome of SMM.

On the other hand, results in table 1.3 also show that Perceived Ease of Use (PEOU) with
City and state where it is located 6, Personal Trust (PT) with
City and state where it is located 7 and Strength of Social Ties (SST) with
City and state where it is located 8 have p-values (statistical significance) which are less than 0.05. These results indicate that Perceived Ease of Use (PEOU), PT and SST are significant in predicting Twitter’s SMM. On the other hand Perceived Usefulness (PU) with
City and state where it is located 9 and p-value of 0.189 and Organizational Participation (OP) with
City and state where it is located 10 and p-value of 0.494 were not significant in the model since their p-values are more than 0.05. Therefore, PEOU, PT and SST dimensions of twitter media were generally significant in predicting the outcome of SMM when using twitter.

Secondly, the model depending on its significance can be used to predict the outcome of Facebook’s SMM outcome based on a unit change in one of the variables among: PU, OP and PT provided the other independent variables are kept constant. This can be done using Exp(B) as the odds ratio. Table 1.3 shows that the odds ratio of having high SMM is 1.492 times better for ISEs who agree on PU as opposed to those who do not agree with it. Similarly, the odds ratio of having high SMM is 1.501 times better for ISEs who agree on OP as opposed to those who do not agree with it. Lastly, the odds ratio of having high SMM is 1.456 times better for ISEs who agree on PT as opposed to those who do not agree with it. The other variables (PEOU and OP) cannot be used to predict the outcome of SMM since they are not significant.

Similarly, the information about the variables in the model and their significance can be used to predict the outcome of Twitter’s SMM outcome based on a unit change in one of the variables among: PEOU, PT and SST provided the other independent variables are kept constant. Table 1.3 shows that the odds ratio of having high SMM outcome is 1.719 times better for ISEs who agree on PEOU as opposed to those who do not agree with it. Similarly, the odds ratio of having high SMM is 2.025 times better for ISEs who agree on PT as opposed to those who do not agree with it. Lastly, the odds ratio of having high SMM is 0.578 times better for ISEs who agree on SST as opposed to those who do not agree with it. The other variables (PU and OP) cannot be used to predict the Twitter’s SMM outcome since they are not significant.

In conclusion, it was realized that the logistic regression model obtained by modeling Facebook’s SMM outcome using PU, OP and PT media dimensions is a strong significant model since it contains three significant predictor variables (PU, OP and PT) and these predictor variables regresses the Facebook’s SMM outcome directly. On the other hand, the PEOU, PT and SST media dimensions are significant in modeling and predicting Twitters SMM outcome. The three predictor variables impact directly on twitter’s SMM of any particular company.

6.3 Summary of results

Table 1.4 shows the data analysis results summary.

Table 1.4: Summary of results

HYPOTHESIS

Perceived usefulness (PU) and perceived ease of use (PEOU) influence acceptance and use of SMM among ISEs.

  • Perceived usefulness has influence on Facebook’s SMM

  • Perceived usefulness has no influence on Twitter’s SMM

  • Perceived ease of usefulness has influence on Twitter’s SMM

  • Perceived ease of usefulness has no influence on Facebook’s SMM

Organizational participation in social media platforms positively influence employees’ acceptance and use of social media as a marketing tool

  • Organization participation has positive influence on use of Facebook’s SMM

  • Organization participation has no influence on use of Twitter’s SMM

Personal trust and past experience affects an individual’s acceptance and use of SMM.

  • Personal trust has influence on use of Facebook on SMM.

  • Personal trust has influence on use of Twitter on SMM.

The strength of ties with other users of social media determines their influence on an individual’s acceptance and use of SMM.

  • Strength of social ties has influence on Twitter’s SMM

  • Strength of social ties has no influence on Facebook’s SMM

7.0 Conclusion and implications

7.1 Conclusions

Media acceptance and use characterized by perceived usefulness, perceived ease of use, organizational participation, personal trust and strength of social ties are necessary for social media marketing.

Social media marketing in Facebook platform is positively influenced by perceived usefulness, organizational participation and personal trust dimensions of media acceptance and use.

Social media marketing in Twitter platform is positively influenced by perceived ease of use, strength of social ties and personal trust dimensions of media acceptance and use.

7.2 Implications

For improved social media marketing, the following factors should be in place:

The ISEs should embrace and practice more on use of social media in marketing their products and services.

The ISEs should accept and use more frequently both Facebook and Twitter platforms to increase the frequency of perceived usefulness and perceived ease of use in social media marketing.

The management and owners of insurance companies in Canberra Australia should provide more trainings, seminars, workshops and consultancies about social media marketing on their ISEs.

The government of Australia should provide favorable policies on social media marketing to foster the use and acceptance of both Facebook and Twitter in marketing.

8.0 Limitations and future research

The inclusion of the TAM in the research model may raise application issues. Lucas and Spitler (2000) found that the model cannot be applied to all technology possibilities and concluded that it might fail to work for complex technologies. Web 2.0 technologies are more complex than Web 1.0 technologies which the proponents of TAM had in mind at formulation. Another limitation of the study is that it does not take cultural dimension into account. An individual’s cultural background influences decision making process including the process of accepting and using information systems (Myers and Tan, 2002). Similarly, as noted earlier, gender differences have also been found to affect the process IT adoption (Gefen and Straub, 1997).

Future research on the social media marketing acceptance and use should use integrated models of technology acceptance and adoption so as to eliminate biases that TAM may occasion. Secondly, the influence of other factors like cultural and other personal factors not considered in this study should also be investigated. Finally, it is important to evaluate the effect technology acceptance on use/adoption. It is presumptuous to believe that acceptance of technology automatically translates into its use. There could be intervening or mediating factors between acceptance and actual use that future research could reveal.

References

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation- confirmation model. MIS Quarterly, 25, (3), 351-370.

Bruque, S. and Moyano, J. (2007). Organisational determinants of information technology adoption and implementation in SMEs: The case of family and cooperative firms. Technovation, 27(5), 241–253.

Castañeda, J., Muñoz-Leiva, F. and Luque, T. (2007). Web Acceptance Model (WAM): Moderating effects of user experience. Information & Management, 44(4), pp.384-396.

Constantinides, E. (2002). The 4S Web-Marketing Mix model. Electronic Commerce Research and Applications, 1(1), pp.57-76.

Dahnil, M., Marzuki, K., Langgat, J. and Fabeil, N. (2014). Factors Influencing SMEs Adoption of Social Media Marketing. Procedia — Social and Behavioral Sciences, 148, pp.119-126.

Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(4), pp 1111-1132.

Gefen, D. and Straub, D.W. (1997). Gender differences in the perception and use of email: An extension to the Technology Acceptance Model. MIS Quarterly, 21(4), pp 389- 400.

Goh, T. (2011). Exploring gender differences in SMS-based mobile library search systems adoption. Educational Technologies & Society, 14(4), pp 192-206.

Guo, C., Shim, J.P. and Otondo, R. (2010) ‘Social network services in china: An integrated model of centrality, trust, and technology acceptance’, Journal of Global Information Technology Management, 13(2), pp. 76–99. doi: 10.1080/1097198x.2010.10856515.

Granovetter, M.S. (1973). The strength of weak ties, American Journal of Sociology, 78(6), 1360-1380.

Greenwood, D. (2013). Fame, Facebook, and Twitter: How attitudes about fame predict frequency and nature of social media use. Psychology of Popular Media Culture, 2(4), pp.222-236.

Hartzel, K. (2003). How self-efficacy and gender issues affect software adoption and use. Communications of the ACM, 46(9), pp 167-171.

Hung, S.Y., Ku, C.Y., Chang, C.M. (2003). Critical factors of WAP services adoption: An empirical study. Proceedings for the First Workshop on Knowledge Economy and Electronic Commerce. 253-262. Retrieved from http://moe.ecrc.nsysu.edu.tw/chinese/workshop/2003.pdf.

Kaplan, M.A. & Haenlein, M. (2010) Users of the world, unite! The challenges and opportunities of social media. Business Horizons 53 (1) 59—68.

Kim, B. (2011). Understanding antecedents of continuence intention in social-netowrking services. Cyberpsychology, Behavior, and Social Networking
, 14(4), pp199-205.

Kim, B., Choi, M., & Han, I. (2009). User behaviors toward mobile data services: The role of perceived fee and prior experience. Export Systems with Applications, 36(4), pp 8528- 8536.

Koshy, S. (2013) Factors that affect the use of Facebook and Twitter as marketing tools in the UAE’, The 18th Annual International Conference of the UK Academy for Information Systems, UK Academy for Information Systems, United Kingdom, pp. 1-7.

Koufaris, M. and Hampton-Sosa, W.(2004) The development of initial trust in an online company by new customers. Information & Management, 41(3): 377-397.

Laroche, M., Habibi, M.R, & Richard, M.(2013). To be or not to be in social media: How brand loyalty is affected by social media? International Journal of Information Management, 33 (1) pp 76–82.

Lipsman, A., Mudd, G., Rich, M. & Bruich, S. (2012). The power of ‘like’: how brands reach (and influence) fans through social-media marketing, Journal of Advertising Research, 52(1) 40-52.

Liu, C.C., Chuang, L.M., Huang, C.M., & Tsai, W.C. (2010). Construction of Index Weight for Organizational Innovation in Taiwanese High-tech Enterprises. Journal of Business (4) 594-598.

Ma, Q. and Liu, L. (2004). The Technology Acceptance Model. Journal of Organizational and End User Computing, 16(1), pp.59-72.

Pavlou, P. A. and Gefen, D. (2004) Building effective online marketplaces with institution-based trust, Information Systems Research, 15(1): 37-59.

Shu, W., & Chuang, Y. (2011). The behavior of Wiki users. Social Behavior and Personality, 39(6), pp. 851- 864.

Social Times (April 4, 2016) Here’s How Many People Are on Facebook, Instagram, Twitter and Other Big Social Networks. Retrieved from: <5http://www.adweek.com/socialtimes/heres- how-many- people-are-on-facebook-instagram-twitter-other-big-social- networks/63720> Retrieved on July 19, 2016.

Scott, J., (2000) Social network analysis. Thousand Oaks: Sage.

Sparrowe R.T, Liden, R.C., Wayne, S.J. and Kraimer, M.L. (2001), Social networks and the performance of individuals and groups, Academic Management Journal, 44(2), 316–325

Tan, F. B. and Sutherland, P. 2004. Online consumer trust: A multi-dimensional model. Journal
of Electronic Commerce in Organizations, 2(3): 40-5

Tang, J. and Liu, H. (2015) Trust in social media. http://dx.doi.org/10.2200/S00657ED1V01Y201507SPT013.

Tarafdar, M., and Vaidya, S. D. (2006). Challenges in the adoption of E-Commerce technologies in India: The role of organizational factors. International Journal of Information Management, 26(6), 428–441.

Young, J. (2016). Facebook, Twitter, and Blogs: The Adoption and Utilization of Social Media in Nonprofit Human Service Organizations. Human Service Organizations: Management, Leadership & Governance, pp.1-14.