1. Introduction

Over recent years, online social networks (OSN) (also known as social networking services, social networking sites, social media, social media sites, social media platforms) turns in to one of the technology applications of interest, which, in turn, constitutes a phenomenon that remains under debate (Cocosila & Igonor, 2015). OSN has become increasingly among the fast-growing internet platforms. In the literature, there are studies which link the internet and OSN to organization performance (Wang & Hou, 2003; Chen, 2008), and others examine how internet platforms improve knowledge acquisition, task efficiency, communication quality, and decision quality in organizations (Isaac et al., 2016). The growth number of OSN users worldwide (such as Facebook, Twitter, WhatsApp, and LinkedIn) needs initiatives and investigations to clarify and understand how organizations benefit from such platforms. Extant sources (Arab Social Media Report, 2015 ;Ameen & Ahmad, 2011) indicate that OSN in the Arab world are perceived as having numerous positive aspects that enhance the quality of life of individuals, business profitability, and governmental interaction with the public.

As organizations around the world struggle to keep pace with emerging technology, changes in Information Technology (IT) grows substantially and itself becomes a driver of further change (Kassim, Ramayah, & Kurnia, 2012). These changes are considered as one of the most salient and active factors influencing employees and organizations today. The public sector is among the main segments affected by the growth of IT. Online and internet technology applications and services have rapidly become indispensable in the daily life of most individuals and significantly impacted every facet of operations in organizations (Greengard, 2015; Annunziata, 2013;Mutahar et al., 2018). Such applications have also become an essential platform for knowledge management systems that improve knowledge acquisition, task efficiency, communication quality, and decision quality (Mukred, M. Yusof, Mokhtar, & Abdul Manap, 2016).

Comparison of the number of active accounts on the top social networks to the population shows that the United Arab Emirates (UAE) ranked first with a social network usage penetration of 99 percent (see Figure 1) (Statista, 2017), indicating a huge opportunity be exploited in both public and private sectors to improve professional practice, personal development, and quality of working life (Al-Obthani & Ameen, 2018).

Tourism is an important economic activity in most countries around the world. As well as its direct economic impact, the industry has significant indirect and induced impacts. The total contribution of the tourism sector to the UAE’s GDP was AED 159.1 billion (USD 43.3 billion) which is 12.1 per cent of GDP. It is forecast to rise by 4.9 per cent per annum to AED 264.5 billion (USD 72 billion) which would be 12.4 per cent of GDP in 2027 (Government.ae 2018).

The Tourism Development & Investment Company (TDIC) is the master developer of major tourism cultural and residential destinations in Abu Dhabi. TDIC was created to be the dedicated tourism asset management and development in order to drive the investment, growth and development of the Emirate's tourism sector. However, TDIC faces the challenges of promotion and attracting visitors from all over the world.

The adoption of OSN is considered to be quite important for improving the status of tourism (Milano, Baggio et al. 2011, Zeng and Gerritsen 2014, Matikiti, Mpinganjira et al. 2017). However, there is a lack of studies investigating OSN use effects on the tourism industry in UAE.

Further, good governance goes beyond the frameworks, policies and procedures built into organization structure; it needs to be instilled into the daily attitudes and activities of employees. Thus, this study examines the use of OSN in the context of tourism industry from the perspective of employees’ attitude. Mainly, this study targets the employees of TDIC, a public organization in Abu Dhabi. It aims to investigate the influence of performance expectancy, effort expectancy, social influence, and facilitating conditions on the actual usage of OSN in the TDIC in Abu Dhabi.

Why using UTAUT?

UTAUT is the most suitable model for conducting this research, investigating the key influence factors of online social network use in public sector employees in the UAE. UTAUT was developed by Venkatesh et al., (2003) to explain computer usage behaviour, has achieved wide acceptance and been validated by numerous empirical studies as an accurate predictor of system usage and acceptance. UTAUT has unified 8 theories and models by bringing together a range of theoretical frameworks and ideas; Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980), Theory of Planned Behavior (TPB) (Ajzen, 1985), Technology Acceptance Model (TAM) (Davis, 1989), Social Cognitive Theory (SCT) (Bandura, 1986), Combined TAM & TPB (C-TAM-TPB) (Todd & Taylor, 1995), Diffusion of Innovation Theory (DOI) (Rogers, 2003), Motivational Model (MM) (J.Vallerand, 1997), Model of PC Utilization (MPCU) (Chang & Cheung, 2001).

2. Literature Review

2.1 Performance Expectancy (PE)

Performance expectancy refers to the degree to which an individual believes using the system will help him/her to attain gains in job performance (Venkatesh, Morris, Davis, & Davis, 2003b). Many studies have proven that performance expectancy or perceived usefulness plays a major role in the context of information systems (IS) (Zhou, Lu, & Wang, 2010; Venkatesh, Thong, Chan, Hu, & Brown, 2011; Ramayah, 2006). Other studies claim that the higher the performance expectancy, the higher the actual system usage (Zhou, Lu, & Wang, 2010; Al-Qeisi, Dennis, Alamanos, & Jayawardhena, 2014; Faaeq, Alqasa, & Al- Matari, 2014; Wu, Tao, & Yang, 2007). This finding is echoed by other results which found a positive relationship between performance expectancy and usage behavior (Al-Gahtani, Hubona, & Wang, 2007; Tan & Lau, 2016; Raman & Don, 2013; Moghawemi, Salleh, Zhao, & Mattila, 2012; Raman et al., 2014; Datta, 2011; Lin & Anol, 2008; Ramayah, Ignatius, & Aafaqi, 2005). However, several investigations (Lian, 2015; Yueh, Huang, & Chang, 2015; Faraliza, Noor, Azmi, & Ramalingam, 2014; Singeh, Abrizah, & Karim, 2013) found that usage behavior was not influenced by performance expectancy. This research argues that the more employees in the public sector in UAE believe that online social networks are useful in their job and increases their productivity, the more they use the online social networks. Therefore, the following hypothesis is proposed:

H1: Performance expectancy positively affects online social network usage in the context of tourism industry.

2.2 Effort Expectancy (EE)

As it was defined by Venkatesh et al., (2003), effort expectancy is the degree of ease associated with the use of the system. It is one of the fundamental antecedent factors of technology usage and adoption (Mutahar et al., 2016; Seppo Pahnila, 2011; Ramayah & Lo, 2007). Many investigations in different contexts and technological applications have emphasized such relationship (Im, Hong, & Kang, 2011; Fang, 2014; Venkatesh, Thong, Chan, Hu, & Brown, 2011; Boonsawat & T.Naennab, 2014; Escobar- Rodríguez & Carvajal-Trujillo, 2014; Chia- Lin Hsu, Chen, Lin, Chang, & Hsieh, 2014). Martins, Oliveira, and Popovič (2014) indicated that there is a positive relationship between effort expectancy and system usage in the context of internet banking. By contrast, some researchers found that effort expectancy does not predict usage behavior (Guo, 2015; Zhou, 2008; Zhou, Lu, & Wang, 2010; Toh, 2013; Yang, 2013; Ayankunle Adegbite Taiwo, Ahmad Kamil Mahmood, 2012). This research argues that the more employees in the public sector in UAE find that online social networks easy to use, flexible to interact with, and understandable, the more they use the online social networks. Therefore, the following hypothesis is proposed:

H2: Effort expectancy positively influences online social network usage in the context of tourism industry.

2.3 Social Influence (SI)

Social influence is broadly described as the degree to which an individual perceives that important others believe he or she should use the new system (Venkatesh et al., 2003b). The social influence factor is substantial in the context of technology success (Martins et al., 2014). According to Venkatesh, Thong, and Xu (2012), in the context of information systems usage, a positive relationship exists between social influence and usage behavior. This claim that social influence appears to have a positive effect on usage behavior is supported by several previous research (AbuShanab & Pearson, 2007; Gonzalez, Sharma, & Galletta, 2012; Umak, Polancic, & Hericko, 2010; Martins, Oliveira, & Popovič, 2014; Escobar-Rodríguez & Carvajal-Trujillo, 2014; Wang, 2005; Hendarman & Tjakraatmadja, 2012). However, other studies did not find such effect (Nysveen & Pedersen, 2014; Nassuora, 2013; Faaeq, Alqasa, & Al- Matari, 2014; Addo, 2014; Faraliza, Noor, Azmi, & Ramalingam, 2014). This research argues that the more that supervisor, colleagues, close friends, and family of the employees in the public sector in UAE think that they the employees should use the online social networks, the more the employees use the online social networks. Therefore, the following hypothesis is proposed:

H3: Social influence has a positive effect on online social network usage in the context of tourism industry.

2.4 Facilitating Conditions (FC)

According to (Venkatesh et al., 2003b), facilitating conditions are is the degree to which an individual believes organizational and technical infrastructure support the use of the system. Facilitating conditions are essential for individuals to use the system (Guo, 2015). To guarantee that the technology is utilized fully, appropriate introduction and back-up are required before an organization can expect its employees to adopt any innovations (Al- Maamari et al., 2018). Raman & Don, (2013) showed that facilitating conditions had a positive influence on usage behavior within the context of learning management software in Malaysia. This finding is in line with other research that confirmed that facilitating conditions can predict usage behavior (Guo, 2015; Moghawemi, Salleh, Zhao, & Mattila, 2012; Ameen & Ahmad, 2013). However, contrasting studies revealed facilitating conditions do not influence usage behavior (Lian, 2015; Yueh, Huang, & Chang, 2015; Martins, Oliveira, & Popovič, 2014; Fang, 2014; Singeh, Abrizah, & Karim, 2013; Singeh, Abrizah, & Karim, 2013). This research argues that the more hardware, software, and knowledge are available and accessible in the public sector organizations in UAE, the more the employees use the online social networks. Therefore, the following hypothesis is proposed:

H4: Facilitating conditions positively influence online social network usage in the context of tourism industry.

2.5 Actual Usage (USE)

Actual usage refers to the degree and manner in which users utilize the capabilities of a system. Actual usage relates to the amount, frequency, nature, appropriateness, extent, and purpose of use (DeLone & McLean, 2016). Kim et al. (2007) suggested actual usage reflects the usage frequency of the technology and usage times. Actual use is defined as the consumption of an IS or its output described in terms of actual or self- reported usage (Petter & McLean, 2009). Actual usage is considered one of the core constructs in the IS field, and literature is full investigations wherein actual usage is used in numerous contexts and applications because it is the ultimate goal of any technology or application (Ameen & Ahmad, 2013a; Aldholay, Isaac, Abdullah, & Ramayah, 2018).

3. Research Methodology

3.1 Proposed Conceptual Model

The relationships between the constructs hypothesized in the conceptual model have been adapted from relevant literature. Figure 2 shows the proposed model containing performance expectancy, effort expectancy, social influence, and facilitating conditions to predict actual usage. These relationships are taken from Venkatesh, Thong, and Xu (2016). The said model examines the relationship between the constructs among employees in the TDIC of Abu Dhabi in the UAE. Four hypotheses are tested with the suggested conceptual framework.

3.2 Research Instruments

Developing the instrument for this study involved a 17-item questionnaire applying a multi-item Likert scale based on the information systems literature (Lee, Yoon, & Lee, 2009). Constructs were measured using a Likert scale recommended in the previous studies (Isaac, Abdullah, Ramayah, & Mutahar, 2017; Ameen & Ahmad, 2014) with 5 being “Strongly Agree” and 1 being “Strongly Disagree.” Because the respondents were Arabic speakers, questionnaires had to be translated from English to Arabic in a precise manner. Thus, a back translation was applied, which is a procedure used widely in cross-cultural surveys (Brislin, 1970; Ameen & Ahmad, 2013a). Extant research was used to validate the measurement of the variables in this study as shown in Appendix A. The number of items for each construct is estimated based on guidelines of Hayduk and Littvay (2012) who recommended the use of a few optimal items.

3.3 Data Collection

Data were collected by conducting self-administered distribution of questionnaires to employees within TDIC from July to November 2017. A total of 750 questionnaires were distributed, and 443 were returned, among which 401 were considered suitable for the analysis. According to Tabachnick and Fidell (2012) and Krejcie and Morgan (1970) the sample size was sufficient. Compared with relevant literature, the 59.06% response rate of this study is considered highly satisfactory (Baruch & Holtom, 2008; Cable & Derue, 2002). This study used a personally administered questionnaire (in- person) for data collection. Overall the average response rates for this technique is 62.4% (Baruch & Holtom, 2008), because this technique can motivate respondent and doubts can be clarified (Sekaran & Bougie, 2012).

Excluded questionnaires totaled to 42, including 26 cases with missing data for more than 15% of the questions, 5 cases as outliers, and 11 cases with straight lining. Table 1 represents the demographic profile of respondents based on the valid questionnaires.

4. Data Analysis and Results

Structural Equation Modeling (SEM) was selected for data analyzing in this study, because it offers simultaneous analysis that leads to more accurate estimates (Isaac, Abdullah, Ramayah, & Mutahar, 2017).

4.1 Descriptive analysis

Table 3 lists the values of the mean (M, with maximum value of 5) and standard deviation (SD) as follows: performance expectancy (M = 3.405 and SD= 1.025) indicates the respondents agreed that OSN use benefitted their jobs, helped in their daily life, increased their productivity, and improved their performance. Effort expectancy (M = 3.395 and SD = 1.037) suggested the respondents agreed that OSN were easy to use, flexible to interact with, clear, and understandable. Social influence (M =3.259 and SD = 0.996) implied that the respondents agreed that supervisors, colleagues, close friends, and family think that they should use OSN. Facilitating conditions (M = 3.333 and SD = 1.091) signify the respondents agreed that they have the hardware, software, knowledge, and skills necessary to use the OSN. Actual usage (M = 3.201 and SD = 0.969) denotes the respondents agreed that they use regularly OSN, they prefer communication through OSN, and they promote the use of OSN to their colleagues.

4.2 Measurement Model Assessment and Confirmatory Factor Analysis (CFA)

As shown in Table 2, all goodness- of-fit indices surpassed acceptance levels as recommended by previous research, thereby indicating that the measurement model displayed a good fit with the data collected. The total fit indices reveal the chi-square is not significant (p-value should be > 0.5).

Despite the nonsignificant chi-square, the model still fits because the chi-square statistic nearly always rejects the model when large samples are used (Bentler & G.Bonnet, 1980; Jöreskog & Sörbom, 1993). Note that the chi-square is sensitive to sample size > 200 (Byrne, 2010), and the sample size for this study is 401. Therefore, we could proceed to evaluate the psychometric properties of the measurement model in terms of construct and indicator reliability, and convergent and discriminant validities.

- The indexes in bold are recommended because they are frequently reported in the literature (Awang, 2014)

As regards to construct reliability, the results show all Cronbach’s alpha coefficients are bigger than the suggested level of 0.7 (Kannana & Tan, 2005; Nunnally & Bernstein, 1994). Furthermore, all values of the composite reliability (CR) were greater than the recommended value of 0.7 (Kline, 2010; Gefen, Straub, & Boudreau, 2000). This result confirms that measurements of all construct are reliable (Table 3). Furthermore, to measure the indicator reliability, factor loadings were checked (J. F. J. Hair, Hult, Ringle, & Sarstedt, 2014). The loadings for all items surpassed the suggested value of 0.5 except item PE4, which was removed because of the low loading, thus the indicator reliability is fulfilled. Moreover, to examine the convergent validity, average variance extracted (AVE) criterion was applied. Results showed that all AVE values are greater than the suggested value of 0.50 (J. F. Hair, Black, Babin, & Anderson, 2010). Therefore, sufficient convergent validity was demonstrated successfully (Table 3).

Results for discriminant validity shows that the constructs are strongly related to their particular indicators (Table 4) (Fornell & Larcker, 1981; Chin, 1998), hence indicating good discriminant validity (J. F. J. Hair et al., 2014). Further, the correlation among exogenous constructs equals < 0.85 (Awang, 2014). Therefore, the discriminant validity of all constructs is achieved.

4.3 Structural Model Assessment

The goodness-of-fit of the structural model was comparable to the previous CFA measurement model. In this structural model, the values were recorded as X²/df = 1.176, CFI = 0.997, and RMSEA = 0.021. These fit indices provide indication of acceptable fit among the hypothesized model and observed data (Byrne, 2010). Thus, path coefficients of the structural model could now be examined.

4.3.1 Direct Hypotheses Tests

The hypotheses were tested using SEM via AMOS (Fig. 3). The structural model assessment results (shown in Table 5) provide indication of the hypotheses tests, so four out of the four hypotheses of this study being supported. With the results of Performance expectancy (and p=0.006), effort expectancy (and p=0.039), social influence (and p=0.010), and facilitating conditions (and p=0.017), H1, H2, H3, and H4 are supported. These results showed that all factors have a positive impact on actual usage of OSN. Furthermore, the path coefficients’ values indicate the strengths of the relationship between independent and dependent variables, and the direct effects of facilitating conditions on actual usage of OSN is stronger than those of the other independent variables.

4.3.2 Coefficient of Determination R²: The Variance Explained

The R² from the structural model showed all R² values are sufficiently high such that the model can fulfill a satisfactory level of explanatory power (Urbach & Ahlemann, 2010) (see Table 6)

5. Discussion

This study proposed a model to improve the understanding of the role of performance expectancy and effort expectancy along with the impact of society and facilitating conditions in predicting the actual use of OSN among employees of the TDIC in Abu Dhabi. The results found that performance expectancy has a positive effect on the actual usage of OSN among respondents. This findings are supported by previous studies (Chang, 2013; Toh, 2013). This implies the fact that the more the user perceived OSN as useful for improvements in work, daily life, user productivity, and performance, the more likely he utilizes OSN regularly and employs them as communication platforms, which makes user more willing to promote OSN to his/her colleagues. Likewise, results showed that effort expectancy has a positive effect on actual usage of OSN among respondents. This outcome is supported by previous studies (Martins et al., 2014; Nysveen & Pedersen, 2014; Ameen, Almulla, Ali, Al-shibami, & Ghosh, 2018). This result implies the fact that the more user perceived OSN easy to use, flexible to interact with, and clear and understandable to engage with, the more likely he regularly uses OSN.

In addition, social influence was found to have a positive effect on the actual usage of OSN among respondents, which is consistent with the outcomes of previous studies (Gonzalez et al., 2012; Lian, 2015). This result is explained by the fact that the more supervisors promote the use of OSN among colleagues surrounding the employee, the more likely the employee use OSN and employ them in his work.

Finally, facilitating conditions also was found to have a positive influence on the actual usage of OSN among respondents. This finding is consistent with previous studies (Umak, Polancic, & Hericko, 2010; Aldholay, Isaac, Abdullah, Alrajawy, & Nusari, 2018). This implies that the more the organization is well equipped, dedicates resources to provide necessary hardware and software to use OSN, and provides training and guidelines for its employees to ensure they have the necessary knowledge and skills to use the technology, the more likely the employees utilize OSN.

6. Implications, Limitations and Future Directions

Unified theory of acceptance and use of technology (UTAUT) has played a vital role in the understanding of what affects the acceptance and use of different types of technology applications. This work successfully validated the UTAUT in a new context, namely, in the usage of OSN among employees in a public organization in the UAE. However, the examined relationships may be influenced by moderating variables such as experience, age and gender, thus testing these effects is suggested in the future studies in the same context.

The research prescribed here has implications for the improved understanding of the links between the various significant dimensions related to social networking as a technology innovation in the tourism sector. The results should be of interest to industry players, policymakers, and researchers. Given the fashionableness and reputation of OSN and mobile technology, it seems likely that OSN can enhance the promotion of the UAE in general and Abu Dhabi, in particular, to attract visitors from around the world. Therefore, the tourism authority of Abu Dhabi and government policymakers are advised to integrate OSN into their operating procedures. Such positive views are also reported in the literature (Amato et al., 2018). This study has the limitation of being conducted in only one organization of the public sector in the UAE, and thus its results should be taken with caution.

We live in a technology-driven world. Change is constant and overwhelming, raising the compelling call for continuous research effort regarding the use and adoption of OSN to leverage potential customers’ engagement in their various applications. Such engagement will no doubt improve the delivery of information in an innovative way and accommodate new directions in attracting numerous visitors (He, He, & Xu, 2018).

7. Conclusion

While the government institutions of the United Arab Emirates are ahead of its regional counterparts in terms of performance, the government is striving to enhance its public organizations’ productivity (Global Innovation Index, 2016). The application of innovative ICT platforms and Internet in the tourist sector make it easier, spacious, and more efficient.

The findings of this study could be considered as one of the initiatives toward achieving this aim. The main objective of this research was to determine factors influencing the use of OSN within the TDIC in Abu Dhabi. Despite various constraints to the study, the outcomes have been encouraging, as this work managed to shed some light on a new perspective. This research proposed a model which identifies performance expectancy, effort expectancy, social influence, and facilitating conditions as independent variables and actual usage of OSN as the dependent variable. Findings revealed the four independent variables significantly explain 50% of actual usage of OSN. The implications of this study were deliberated and some directions for future research were suggested.

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