The role of social media marketing in Ethiopian tourism and hospitality organizations: Applying the unified theory of acceptance and use of technology model

Abstract

Being one of the modern marketing technologies, social media marketing is effective to reach the right target market and it is widely utilized by business entities and tourism destinations to promote products and services. Through applying the Unified Theory of Acceptance and Use of Technology model, this research is designed to examine how tourism principal service providers use social media marketing. SPSS version 23 was employed to compute mean, one-way Analysis of variance and multiple regressions. The effective sample size is 176 tourism and hospitality organizations. The findings revealed that performance expectancy, effort expectancy, and facilitating conditions have significant impacts on the use of social media marketing. However, social influence has not a significant effect on the outcome variable. The major implication drawn from the study is that the need to pay due attention to provide appropriate training for tourism and hospitality sectors regarding usefulness and easiness of e-marketing innovations and technologies which helps to devise and implement creative management and marketing strategies that in turn contribute to tourism destinations and organizations to become competitive enough.

Keywords:

• Tourism and hospitality
• social media marketing
• Ethiopia
• UTAUT model

REVIEWING EDITOR:

• Pier Luigi Sacco, Humanities, IULM University, Milano, Italy

SUBJECTS:

• Information Technology
• Hospitality
• Tourism

1. Introduction

Tourism is one of the sectors which need a close relationship with information and communication technologies. It is also a competitive and information-intensive industry (Khatri, 2018). Thus, the implementation of technologies for transformations, better accessibility, and distribution of travel-related information to customers is crucial (Xiang & Gretzel, 2010). In this technologically dominated era, social media has become a real essential technique in today’s marketing mix in general and in promotional tools in particular (Bashar et al., 2012; Minazzi, 2015). The era of digital society allows people to join virtual communities through social media (Zamrudi & Wicaksono, 2018). During the Covid-19 pandemic, social media marketing is indispensable for small and medium enterprises to promote and survive their businesses. Throughout the social or physical distancing time, social media marketing provides advantageous public service for customers and organizations which enable people to work and communicate with less contagious risks (Winarko et al., 2020). Social media is a system that offers a chance to destination marketing organizations (DMOs) with limited human and financial resources to reach visitors at the global level (Buhalis et al., 2012; Hays et al., 2013).

Maintaining a presence on social media platforms has become inevitable for tourism destinations (Tiago et al., 2019). Social media campaigns help destinations to be competitive as it creates awareness of the destination and strengthens the destination image and reputation; reaching global publicity; encouraging visitors to plan their journey and targeting new/niche market that aimed at increasing the number of visitors as an outcome (Kiráľová & Pavlíčeka, 2015). Fetherstonhaugh (2010) indicated that “most successful salespeople, over two-thirds confirmed that social media is integral to their sales success” because social media has enabled marketers to engage with customers and facilitate their buying decision process. Social media contributes a significant impact on entrepreneurial sectors concerning business-to-business (B2B) marketing relationships, creating networks with new business partners and customers as well as to effectively communicate with industrial partners (Kaplan & Haenlein, 2010) and enhancing customer relationships (Drummond et al., 2018).

According to the DataReportal, 2021, the number of internet users are 4.66 billion (59.5% of total world population), and 4.20 billion active social media users (53.6% of world’s total poloualtion). Of the social media outlets, the three top platforms are Facebook, YouTube, and WhatsApp with the active number of users 2.74 billion, 2.30 billion and 2 billion respectively.

Due to the unprecedented growth of social media, several organizations adopt and use social media tools such as Facebook and Twitter to maintain a positive relationship with customers and to provide them a variety of services and products promptly. Though there is a dramatic growth of social media marketing globally, as noted in the work of Afolabi (2015) online marketing is affected by major problems (e.g. lack of internet connectivity, costs of broadband connections, shortage of laptops/desktops/smartphones, lack of skill or knowledge to customize online marketing).

Although Ethiopia has authentic and wonderful tourism endowments, the country is one of the least beneficiaries of the African tourism industry due to past western negative images as a land of war, starvation, and drought (Derseh, 1992; Gill, 2010) and poor promotion among other obstacles. From promotion and marketing perspectives, conventional communication methods dominate African emerging economies (Mosweunyane et al., 2019). According to Mango et al. (2020), most sub-Saharan Africa nations face challenges of destination promotions as they are dependent on static techniques such as traditional media (printed materials, radio and television) as well as websites that don’t have interactive maps.

Low internet connectivity, poor website presence and inactive social media marketing are some of the promotional gaps that exist at both national and regional Ethiopian tourism organizations (Ethiopia Ministry of Culture and Tourism (MoCT), 2015). Unless the tourist attractions are exposed and promoted to potential visitors through different marketing approaches, a country like Ethiopia could not benefit from tourism development. Hence, to attract a reasonable number of visitors to a given destination, a country needs to pay attention to better planning, developing and managing tourism destinations, ensuring peace and security followed by appropriate means of promotions.

Among various marketing strategies, social media is one of the most effective and least expensive platforms for destination marketing. To increase the number of tourist arrivals and the resulted revenue that will be generated from tourism, devising strategic destination marketing is one of the pillars among others. Tourism marketing is really a tough struggle for Ethiopia, and it trails far behind its neighbors like Kenya and Tanzania (Shanka & Frost, 1999). Moreover, according to Mekonnen and Feven (2018), the provision of adequate information to visitors and the degree of promotion using new media and modern Information and Communication Technology (ICT) services is very poor. Furthermore, Wassie and Ran Singh (2017) indicated that the performance of Ethiopian tourism marketing practice is low.

Although social media plays a profound role in destinations, tourism and hospitality sectors (Moro & Rita, 2018), it is scarcely studied in tourism marketing domains in the context of African countries. The effect of the Unified Theory of Acceptance and Use of Technology (UTAUT) on tourism and hospitality organizations of developing nations has not been sufficiently examined. Little is known about how tourism principal service providers are using social media to promote Ethiopia. Apart from politics and activism, the role of social media in travel and tourism is not recognized and studied in Ethiopia. The main goal of this study is to shed a light on role of social media marketing from tourism and hospitality perspectives. Specifically, the research tries to address a research question “Which factors significantly affect the use of social media marketing in tourism and hospitality sectors?”

2. Literature review

2.1. Unified theory of acceptance and use of technology (UTAUT)

Before the UTAUT, many theories had been developed by scholars regarding technology acceptance. For instance, in 1975, Fishbein and Ajzen (1975) developed the Theory of Reasoned Action and confirmed that our behavioural intentions were determined by subjective norms and attitudes. Later on, in 1989, the Technology Acceptance Model (TAM) was developed by Davis et al. (1989). According to TAM, perceived usefulness and perceived ease of use predict intention to use technology. And, actual use of a technology is determined by an intention to use it. Then, in 1995, Goodhue and Thompson (1995) introduced a theory called Task Technology Fit Theory which states that information technology will have a positive impact on individual performance provided that it is compatible with the tasks or duties of users. More importantly, the Unified Technology Acceptance Theory (UTAUT) was developed by Venkatesh et al. in 2003. The UTAUT model provides a handy tool to measure the prospect of success for the adoption of new technology/systems. It helps to understand the drivers of acceptance, and take proactive interventions such as training and marketing strategies could be devised accordingly (Thas Thaker et al., 2020).

In 2003, Venkatesh built the model entitled “the Unified Theory of Acceptance and Use of Technology (UTAUT)” which states that performance expectancy, effort expectancy, social influence, and facilitating conditions determined technology acceptance and usage behaviour (see Figure 1 and Table 1).

2.2. The impact of UTAUT variables on social media usage

According to Foon and Yin Fah (2011) performance expectancy, effort expectancy, social influence, facilitating condition and trust explained 56.6% of the variance of behaviour intention to adopt internet banking.

The empirical results of research entitled “Acceptance and impact of social networks marketing using extended technology acceptance model” conducted by Mulero (2012) confirmed that perceived credibility and perceived usefulness are the strongest factors in forecasting user’s intention to use social networks marketing. Similarly, Rahi and Ghani (2018) elucidated that performance expectancy and effort expectancy had a positive and significant effect on customer’s intention to adopt internet banking.

Xu et al. (2019) confirmed that product awareness, perceived ease of use, perceived usefulness, convenience conditions and individual innovativeness positively influence customers’ willingness to purchase products online using the internet technology. The result also revealed that perceived risks had a negative effect on users’ intention to purchase products online and, social influences did not have an impact on customers’ purchase intentions. Analogous to this, Tang and Wu (2015) stated that the higher the performance expectancy and effort expectancy, the more likely the customers will adopt online shopping

Workman’s (2014) study on “New media and the changing face of information technology use: The importance of task pursuit, social influence, and experience” confirmed that performance expectancy was not a significant determinant in social media use. However, effort expectancy, social influence and facilitating conditions were significant factors for social media use. Empirical findings by Rozmi et al. (2019) revealed that three out of five factors in UTAUT had a significant effect on the intention to adopt ICT in small and medium enterprises, namely effort expectancy, social influence and facilitating conditions. Using the UTAUT model, Zamrudi and Wicaksono (2018) found that performance expectancy, entrepreneurial expectancy, social influence, and facilitating conditions have a positive and significant effect on the intention to use social commerce in the context of logistics.

The work of El Ouirdi et al. (2016) showed that performance expectancy, effort expectancy, facilitating conditions and social influence have a positive impact on behavioral intention on the usage of social media to recruit employments.

The analytical results of Thas Thaker et al. (2020) showed that perceived relevance (performance expectancy), informativeness and perceived expectancy were found to have a significant statistical relationship with the purchase intention of Islamic banking products through social media platforms. As noted by Yap and Tan (2017), social interactive, cognitive needs, and training and support (facilitating condition) are positively related to sellers’ intention to adopt mobile social media marketing. On the other hand, performance expectancy, effort expectancy, and compatibility don’t have any significant relationship with the sellers’ intention to adopt mobile social media marketing. Bogéa (2018) confirmed that effort expectancy and performance expectancy have no positive impact on intention to adopt social media marketing.

A study by Gupta et al. (2018) on tourist adoption of smartphone apps using the UTAUT model indicated customers’ adoption of smartphone apps was significantly affected by price saving orientation, performance expectancy, social influence, perceived risk, perceived trust, and habit. Analogously, Oliveira et al. (2016) confirmed a significant positive relationship between performance expectancy and behaviour intentions regarding online travel purchasing. As noted by Palau-Saumell et al. (2019) the drivers of intentions to use mobile apps for restaurants searches and/or reservations (MARSR) are, in order of effect: habit, perceived credibility, hedonic motivation, price-saving orientation, effort expectancy, performance expectancy, social influence, and facilitating conditions.

A study conducted by Mekonnen and Feven (2018) confirmed that social influence, perceived usefulness, perceived ease of use, cost-effectiveness, competitive advantage, and facilitating conditions significantly affect behavioural intention to use ICT in the tourism sector of Ethiopia.

2.2.1. Performance expectancy

Performance expectancy is the perception of employing the system or technology will aid to enhance job performance. In other models, it is associated with perceived usefulness, extrinsic motivation, job-fit, relative advantage and outcome expectancy (Venkatesh et al., 2003). According to Yap and Tan (2017) performance expectancy includes three dimensions, namely, job effectiveness, the significance of the system towards the job and productivity. Scholarly works revealed that perceived usefulness has a significant impact on the adoption of internet banking (Foon & Yin Fah, 2011; Rahi & Ghani, 2018), online shopping (Tang & Wu, 2015; Xu et al., 2019), social e-commerce (Zamrudi & Wicaksono, 2018), social media adoption to recruit employee (El Ouirdi et al., 2016), tourist adoption of smartphone apps (Gupta et al., 2018), mobile applications for restaurants (Palau-Saumell et al., 2019) and ICT in the tourism (Mekonnen & Feven, 2018). Based on the empirical findings and theoretical reviews of the preceding academic works, this study posits the following hypothesis.

• H1: Performance expectancy has a significant effect on the use of social media marketing.

2.2.2. Effort expectancy

Effort expectancy is the perceived easiness as well as complexity associated with the use of a technology (Venkatesh et al., 2003). It is emphasized that a system or technology with an easy navigation structure and simple design leads to the easiness of learning and encourages the adoption of the new technology. Empirical findings showed that effort expectancy has a significant effect on the adoption of new technology or system: on internet banking adoption (Foon & Yin Fah, 2011; Rahi & Ghani, 2018); social networks marketing (Mulero, 2012); online purchasing (Tang & Wu, 2015; Xu et al., 2019); the adoption of ICT in Small Micro and Medium Enterprises (Rozmi et al., 2019); social e-commerce (Zamrudi & Wicaksono, 2018), social media adoption in employee recruitment (El Ouirdi et al., 2016); social media marketing in Islamic banks (Thas Thaker et al., 2020), and mobile applications for restaurants (Palau-Saumell et al., 2019). Accordingly, the following research hypothesis is proposed.

• H2: Effort expectancy has a significant impact on the usage of social media marketing.

2.2.3. Facilitating conditions

Facilitating conditions are the degree to which organizational and technical facilities exist to support the use of the technology or the system (Venkatesh et al., 2003). Evidence from scholarly works indicated that the capacity of organizational and technical support such as training and access to ICT facilities is one of the predominant factors to adopt technology. For instance, Bogéa (2018) corroborated that facilitating conditions have a significant and positive impact on the adoption of social media marketing. Similarly, facilitating conditions have a significant effect on online banking adoption (Foon & Yin Fah, 2011); online shopping (Xu et al., 2019); adoption of ICT in SME (Rozmi et al., 2019); social media adoption in employee recruitment (El Ouirdi et al., 2016); mobile social media marketing (Yap & Tan, 2017); integration of ICT for tourism promotion (Mekonnen & Feven, 2018), and adoption of mobile apps to search restaurants (Palau-Saumell et al., 2019). Accordingly, the following research hypothesis is established.

• H3: Facilitating condition has a significant effect on tourism and hospitality social media marketing.

2.2.4. Social influence

Social influence is the degree to which customers believe that organizations should use the technologies or systems to keep smooth interactions between customers and significant persons working in the organizations (Brown & Venkatesh, 2005). Alternative constructs such as subjective norms, social factors and images can be used (Bogéa, 2018). Empirical research findings revealed that social influence has a significant impact on intention to adopt social media marketing (Bogéa, 2018); customers’ use of online banking (Foon & Yin Fah, 2011); on the use of new media (Workman, 2014); ICT adoption in Small Micro and Medium Enterprises (Rozmi et al., 2019); social e-commerce (Zamrudi & Wicaksono, 2018); employment recruitment using social media (El Ouirdi et al., 2016); purchase intention of Islamic banking products through social media platforms (Yap & Tan, 2017); tourist adoption of smartphone apps (Gupta et al., 2018); use of mobile applications for restaurants (Palau-Saumell et al., 2019), and use of ICT for tourism promotion (Mekonnen & Feven, 2018). Based on the literature review, the research hypothesis is developed as follows.

• H4: Social influence has a significant impact on the usage of social media marketing in tourism and hospitality.

3. Methods and materials

3.1. Description of the study area

Ethiopia formerly known as Abyssinia is one of the oldest and independent nations in the world throughout history. Ethiopia is a country where three ancient religions of Judaism, Christianity, and Islam are found. The first church on African soil (Axum Zion), the largest medieval rock-hewn church of Lalibela (House of Saviour of the world), and the fourth holiest Islamic city (Harrar) are found in Ethiopia (Carillet & Phillips, 2006). The country is topographically land of extremes and natural contrasts encompassing the marvellous rugged Semien Mountains National Park with the highest peak of Ras Dejen (4620 m) as well as the Bale Mountains National Park (Tulu Dimtu with the elevation of 4370 m being the second-highest tip), and Dallol depression, with an altitude of 120 meters below sea level, having an active volcano called Ertalle is one of the hottest and inhospitable places on the earth (Briggs, 2005; Finneran, 2013).

Data were collected from Ethiopian popular tourist destinations (Addis Ababa, Debre Zeyit/Bishofitu, Hawassa, Mekele, Bahir Dar, Gondar, and Lalibela). Data was collected from Tourism Ethiopia, ministry of culture and tourism, regional tourism bureaus, five and four-star hotels and resorts, tour operators and Ethiopian airlines because they are responsible to promote tourism attractions of Ethiopia as well as to provide services to international visitors.

3.2. Research design and approach

Descriptive and explanatory research methods were employed. And, multiple cross-sectional research design was used (data collections had been undertaken only at one time (snapshot)).

A descriptive method is best suitable to investigate existing situations, problems and relevant aspects of the phenomenon (Malhotra & Briks, 2007). The explanatory research approach has been also applied to study the effect of UTAUT variables on social media usage to promote Ethiopia. The study entirely employed the quantitative research approach which encompasses both descriptive statistics (mean, standard deviations), and inferential statistics (One way ANOVA, Pearson correlation and multiple regression).

3.3. Sampling methods and sample size determination

Simple random sampling was applied to choose the samples from tour operators. A random number generator in Excel (the RANDBETWEEN Function) was applied to select samples randomly.

Besides, the census sampling method was applied to take all 42 five and four-star rated hotels and resorts. According to Ethiopian Culture and Tourism Minister, there are 481 travel and tour operators in Ethiopia. Applying Yamane’s formula (Yamane, 1967): n= $\frac{N}{1+N{(E)}^2}$; where, N = the total population (481 tour operators), n = the required sample size, e = the precision level which is ± 5% at 95% Confidence Level, the sample size was determined to be 218 tour operators. Besides, the questionnaires were disseminated for all 30 marketing experts working in regional and federal government-owned tourism organizations. Hence, a total of 290 tourism and hospitality organizations were selected as subjects of the study.

3.4. Data gathering instruments and measurement

The research encompasses primary sources of data gathering instruments (questionnaire with 5 points Likert scale). After intensively reviewed related works, the questionnaire was prepared and disseminated for principal tourism service providers. Items regarding the impact of UTAUT variables on social media use were adapted from (Alshehri, 2012; Brown & Venkatesh, 2005; Venkatesh et al., 2003, 2012).

3.5. Validity and reliability

Before conducting the main survey, a pilot study was carried out to mitigate potential errors and check the reliability as well as to ascertain whether the survey covers appropriate items to measure the issues under-investigated (i.e. content validity). To do so, 10 questionnaires were disseminated for tourism principal service providers (tour operators, hotels and resorts, government-owned tourism organizations). The output of the pilot study affirmed that items in all variables have good internal consistency (a Cronbach’s alpha greater than .70). According to Rovai et al. (2013), a Cronbach’s alpha between .70 and .90 shows high reliability (See Table 2).

3.6. Sample characteristics

290 questionnaires were disseminated for government-owned tourism organizations, hotels and resorts and tour operators. Of which, 30 organizations didn’t fill the questionnaire since they don’t use social media for promotion; 50 questionnaires were not properly filled (most of the responses were incomplete), and 34 questionnaires were not returned. Hence, 114 questionnaires were not usable for analysis. The remaining 176 questionnaires (61% response rate) were properly filled and considered for analysis. Among 176 tourism principal service providers, 59% of them were tour operators, 24% and 17% were hotels & resorts, and government-owned tourism organizations respectively.

3.7. Ethical considerations

Before undertaking investigations and data collections through questionnaires, the full consent of the tourism principals was asked humbly to keep the ethical considerations. Most importantly, participants were briefly informed about the aim of the research, and finally, they can access the findings of the study. Furthermore, the researchers have properly cited and duly acknowledged all sources that were used throughout the research.

4. Results and discussions

4.1. The role of social media in travel and tourism

The result indicated that social media offers multiple advantages to tourism and hospitality organizations. Marketing activities supported by social media tools helps to overcome communication obstacles such as distance barrier (M = 4.3, SD= .88); to reach global audience (M = 4.4, SD= .79); to disseminate information quickly (M = 4.4, SD= .88); to analyse feedbacks that can help tourism and related establishments to be competitive (M = 4.2, SD= .89); to share customers’ personal experiences and comments (M = 4.2, SD= .91); to improve customer service (M = 4.0, SD= .89); to increase number of customers and raise sales volume (M = 4.1, SD= .77); to supplement the use of traditional media (M = 3.9, SD= .90). A similar research result by Mosweunyane et al. (2019) showed that the majority of tourism businesses used social media outlets to retain customers, solicit feedback, increase market dominance, maintain connection and facilitate information sharing among fellow workers.

A study on “Social media marketing in wine tourism” by Canovi and Pucciarelli (2019) stated that the majority of winery owners concede the social, economic and emotional values of social media. The social benefits include building friendship and interactions, experience sharing, direct communication and follow-up, repeat visits, e-WOM, creating and improving images and reputation. The economic values are e-commerce and direct sales, attracting more visitors to destinations, free publicity, repeat sales and seasonal online promotions. Social media also offers emotional benefits such as emotive, happiness and enjoyment. To sum up, being relatively cost-effective, social media marketing help to disseminate information swiftly at a global level to reach targeted customers, to improve customer service and boost sales volume as well as to supplement traditional media.

4.2. Usage of social media by tourism principals to promote Ethiopia

The findings indicated that tourism principal service providers’ actual usage of social media (mean = 3.39) is higher than the cut-off point (3.2) (see Table 3 and 4). The composite mean of 3.2 is a cut-off point suggested by Castro and Martins (2010).

The one-way ANOVA result pointed out a significant means difference between categories of tourism organizations (F (2, 173) = 3.249, p=.041; assumption regarding homogeneity of variances is fulfilled (Leven static (2, 173) =.471, Sig=.772)). The mean value of hotels is the highest (M = 3.48, SD=.93) regarding actual social media usage, and followed by tour operators (M = 3.46, SD=.85), whereas the actual usage of social media by government organizations is indicated by the lowest mean value (M = 3.02, SD= .85) (see Table 5). The mean difference between hotels and government organizations is significant (mean difference=.46, Sig= .041). The mean value of tour operators is also significantly greater than government organizations (mean difference=.44, Sig= .043) (See Tables 2 and 6).

4.3. The effect of UTAUT variables on the use of social media

Effort Expectancy (EE), Performance Expectancy (PE), Social influence (SI) and Facilitating Condition (FC) were identified as predictor variables whereas actual usage of social media (AU) is the outcome variable, and linear multiple regression analysis was employed. Furthermore, one way ANOVA was performed to know if significant means differences exist among types of tourism organizations.

4.4. One way ANOVA results of UTAUT variables

1. Social influence

Social influence is measured by items related to the influence and encouragement of customers on the organizations’ behaviour to use social media and competitors’ social media marketing practices. The one way ANOVA result (see Table 7) confirmed that there is no significant mean difference among tourism organizations concerning social influence (F (2, 173) = 1.299, Sig =.275). The assumption of homogeneity of variance is satisfied (Levene’s statistics (2,173) = 2.312, Sig= .102). Though it is not significant, the mean score of government organization is relatively high (M = 4.03, SD= .66) followed by hotels that have a higher mean score (M = 3.83, SD=.61) than tour operators (M = 3.78, SD=.79).

2. Facilitating condition

The facilitating condition was measured by the availability of resources such as ICT facilities, social media experts, and online marketing training. The mean scores of tourism organizations with respect to facilitating conditions did not differ significantly at p<.05 but significant at P<.1 (F (2,173) = 3.052, p= .05). The mean score of hotels (M = 4.17, SD= .56, mean difference= .3933, p= .043, 95% CI: .0105 to .7762) is significantly higher than government organizations (M = 3.77, SD=.87) (See Tables 8, 9 and 10).

3. Performance Expectancy - indicates the extent to how social media enables employees to accomplish job tasks more quickly; the degree of how social media helps staff to obtain work-related information and knowledge; and how it helps to meet more potential customers. The one-way ANOVA indicated that there is no significant mean difference between groups (F (2, 173) = 1.125, p=.327).

4. Effort Expectancy- It is the perceived easiness of social media in tourism and hospitality promotion/marketing tasks. There is a significant difference (see Table 11) concerning to effort expectancy of social media among types of tourism organizations (F (2, 173) = 4.005, p= .020). The post hoc comparisons (see Table 12) revealed that the mean score of hotels (M = 3.89, SD = 0.51, mean difference= .38, p = 0.03) is significantly greater than governmental tourism organizations (M = 3.51, SD= .71). There is also is a significant mean difference between tour operators (M = 3.84, SD= .63) and governmental organizations (mean difference= .34, p = 0.026). The mean score is high for hotels followed by tour operators, and the lowest mean score is reported by governmental tourism organizations (see Table 13).

4.5. Analysis of multiple regression outputs of UTAUT variables

Multiple regressions have been performed to determine the impact of explanatory variables (social influence (SI), effort expectancy (EE), facilitating condition (FC) and performance expectancy (PE)) on the dependent variable which is the actual use of social media. Assumptions of correlation (see Table 14), multi-collinearity (see Table 15), normality (see Figure 2), linearity (see Figure 3), and homoscedasticity (see Figure 4) were checked and satisfied.

The results of the regression revealed that the predictor variables explained 29.4% of the variability in the dependent variable namely actual usage of social media (R²=.294, F (4, 171) = 17.384, p= .000). (See Tables 16 and 17). Although the model or the coefficients of determination (R²) significantly explain the outcome variable (actual use of social media) at p< .01, it is evident that 70.6% of the dependent variable has remained unexplained

The one-way ANOVA (see Table 16) provides a significant F statistic that shows the model’s fitness of the explanatory variables to explain the outcome variable (actual usage of social media) which had a significant result (P = .000). Accordingly, the ANOVA table indicates that using the model is better than estimating the mean. In other words, the ANOVA table F statistic indicates the model is a good fit for the data.

The regression coefficients indicate the extent that how each independent variable can explain the dependent variable. The coefficients of the determination table give unstandardized beta coefficients which can help us to formulate the regression equation (see Table 17).

The equation can be written as: $$Y={\beta }_o+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3+{\beta }_4{X}_4+\epsilon ,$$ where β_o= -.041 is the intercept or the value of dependent variable (Y) when the value of predictors (X) are zero

Predicted Actual use of social media = -.041 + .222(FC) +.333(PE) +.249(EE) + .071(SI) + ε

Of the four predictor variables, performance expectancy makes the largest unique contribution (beta (β₁) =.333, t = 3.391, p=.001 which is significant at p<.01), followed by effort expectancy (beta (β₃) =.249, t = 2.445, p= .016), and the third one is facilitating condition that makes significant contribution in explaining the outcome variable (beta (β₂) = .222, t = 2.124, p=.047). However social influence has no significant effect in explaining the dependent variable (beta (β₄) = .071, t= .738, p= .461). The result supports the research hypotheses: H1 (Performance expectancy has a significant effect on the use of social media); H2 (Effort expectancy has a significant impact on the usage of social media); and H3 (Facilitating condition has a significant effect on social media use). However, the findings do not support the research hypothesis: H4 (Social influence has a significant impact on the usage of social media).

The intercept/constant or the value of the dependent variable (Y or use of social media in this case) when the value of predictors = zero, is not significant (β_o= -.041, t (-.089), p=.930).

5. Conclusions, industrial Implications and limitations of the research

5.1. Conclusions

The main inferences drawn from the study is that social media plays a significant role to promote tourism and hospitality organizations as well as tourist destinations and services. Digital marketing provides several advantages for instance lower promotional costs, brand awareness and growth sales volume to organizations (Dwivedi et al., 2021). If social media is properly managed and leveraged, beyond promoting products and services, it can enable firms to maximize the positive role of corporate social responsibility (CSR) activities on corporate reputation (Benitez et al., 2018).

Information technology is the life-blood of the travel and hospitality industry where timely and accurate information is indispensable to customers’ needs. The internet technology provides a chance for tourism and hospitality companies to adopt the new media (social media) marketing strategies including online bookings, and business to business (B2B), business to consumer (B2C), consumer to consumer (C2C), consumer to business (C2B), and business to government (B2G) transactions, to upgrade the service quality at all levels of customer interaction (in pre traveling (pre-sale), during visiting and after visiting (during and post-sale) (Buhalis & Law, 2008; Alisha & Andrew, 2014).

It is apparent that online social networks marketing outwit traditional marketing because millions of prospective customers can be reached worldwide within a minute (Minazzi, 2015). Information on social media can be updated regularly, stored for a long time, and helps tourists to see a real sense of landmarks, panoramic views, accommodations, and transportation services through videos on YouTube or other social media (Hays et al., 2013). It becomes a paradigm shift from ‘read and listens -only’ format to ‘speak and interact’ way.

The result of the study indicates a significant means difference between tourism and hospitality organizations regarding the use of social media marketing. As a result, the use of social media marketing by hotels and resorts is the highest followed by tour operators. However, the actual usage of social media marketing by government organizations is low. Among the four UTAUT variables, a significant mean difference is noted on effort expectancy and facilitating conditions. In this regard, the mean score of resorts and hotels is higher than that of tour operators while the government tourism and hospitality sectors or organizations have lowest mean score. Through applying the UTAUT model, the study revealed that performance expectance has a greater contribution to explain the dependent variable (use of social media) followed by effort expectancy and then facilitating condition. However, social influence is not making a significant contribution in explaining the outcome variable. The work of Herrero et al. (2018) on "Market orientation and SNS adoption for marketing purposes in hospitality microenterprises" revealed that performance expectancy, effort expectancy and social influence, as perceived by managers or owners, have a significant influence on the intention to use social networking sites as a communication tool in their business. It is also indicated that facilitating conditions has no significant influence on the intention to use social networking sites as promotion techniques in their enterprises. However, in the present study, social influence has no significant impact on the use of social media sites for tourism marketing.

A study by Humaid and Sabri (2019) concluded that small business entrepreneurs use social media in their businesses considering its easiness (effort expectancy), usefulness (performance expectancy), social influence, and the facilitating conditions. More importantly, their study revealed that social influence and facilitating conditions have a significant positive effect on behavioural intention and use behaviour.

Research on “Factors affecting the adoption of social media as a business platform” by Nawi et al. (2019) showed that performance expectancy, perceived trust, perceived enjoyment and perceived risk have a significant positive impact on the adoption of social media. The result indicated that entrepreneurs are interested to adopt social media as a business communication platform provided that social media improves work performance and creates enjoyment or entertainment. Hence, in terms of performance expectancy, the present research is consistent with the work of Nawi, Mamun, Nasir, and Muniady.

5.2. Practical implications

The results of the study contribute some insights or practical implications for practitioners, managers and marketers working in destination management organizations (DMOs), tourism and hospitality organizations. Tourism and hospitality is a highly competitive and information-intensive industry. To be competitive enough, tourism and hospitality principal service providers need to adopt smart tourism. It is noted that almost all tourists use ICT to get updated information about tourism attractions, accommodations, transportation and other services and facilities (Ramos et al., 2020). Hence, the implementation of smart technologies in tourism and hospitality management and marketing enhances competitiveness that can attract more customers. The findings contribute useful insights to tourism and hospitality organizations with respect to the impacts of the UTAUT variable on the use of social media marketing. In this perspective, due attention should be paid to provide appropriate training regarding the usefulness and applications of e-marketing technologies for staff working in marketing and promotion areas of tourism and hospitality. Besides, it is also crucial to assign professionals and allocate adequate ICT facilities to run social media marketing successfully. Zamrudi and Wicaksono (2018) posit that the role of government is crucial in providing an adequate information and technology infrastructure to disseminate the use of technology for business and logistics. Due attention shall be paid to provide appropriate training or workshops to employees working in sales and marketing sections so that they can get experiences and awareness on every feature of the system or technology. Organizational infrastructure and support (ICT facilities, training) help to create good communication between employees and customers and provides sufficient assistance to customers as well as to utilize the social media platforms to brainstorm useful marketing insights (Yap & Tan, 2017). Training should validate how adopting the new technology (social media marketing) is useful, needs less effort and compatible with the aim and objectives of the organizations.

Proper implementations of innovations and technologies in areas of tourism management and marketing help tourist destinations as well as tourism and hospitality organizations to reach and influence customers. As such, scholars increasingly urge tourism and hospitality businesses to not solely depend on traditional marketing approaches but to explore and apply interactive marketing systems to create better communication with visitors (Chan & Denizci Guillet, 2011).

Maintaining quality and competitive tourist destination requires the art of devising creative management and marketing strategies that can catch the attention of prospective and actual visitors. To this end, the Ministry of Culture and Tourism, Tourism Ethiopia and other tourism stakeholders should provide training and technical assistant to tour operators, hotels, destination managers and guides regarding applications of innovations and technologies in tourism management and marketing.

Tourism and hospitality organizations should craft strategies of social media campaigns to create and increase destination awareness, reaching global publicity, encouraging visitors to plan a holiday to the destinations, maintaining a favourable destination brand and image, targeting new market, increasing number of customer, increasing the number of email and website subscribers, and increasing the number of followers, likes, shares, comments on social media outlets (Kiráľová, Pavlíčeka, 2015). Marketers and managers in the tourism and hospitality sectors should appreciate customers for their contribution of sharing experience, feedbacks, photos and videos about products and services. Besides, tourism and hospitality companies may face negative comments or posts, and managers should critically and efficiently manage negative contents to improve service quality or to mitigate product defect.

Destination Management Organization needs to launch an online application that can integrate information on tourism routes, shopping facilities, cafes and restaurants, public transportation, hotels, fitness centres, and places to visit, and options to display tourist comments on specific tourist products such as destinations, services, and facilities (He et al., 2015).

According to Peters et al. (2013) contents of posts on social media pages should have some sufficiently distinct features. Namely, content quality (interactivity, vividness); content domain (contents related to education, entertainment, information, narrative styles); content valence and tonality – the content in line with emotions (e.g., joy, positive); content volume such as length of text, number of figures, pictures and videos. Interactivity mostly depends on the message’s ability to call action (encouraging users to participate in online games, contest), phrase style (whether the message includes expressions in affirmative, interrogative, or exclamatory), sentences traceability meaning the degree to which the message is easily searchable online (Pino et al., 2018). On the other hand, vividness depends on the presence of photos and/or videos or even links in the message, the languages used in the messages (local language versus foreign language), and the length of the message in terms of the number of words and characters.

5.3. Limitations of the study

While this research contributes to the existing body of knowledge concerning the role of social media marketing in hospitality and tourism organizations, it is rational to recognize and explain the limitations of the study. It is evident that every research has defects, and there is no perfect research design. In this regard, the present research faced some limitations. This research employed a cross-sectional research design, and the result is specific to the time when data is collected. Future studies may adopt a time series or longitudinal approach to investigate e-marketing practices of tourism and hospitality organizations to promote Ethiopian tourist attractions and their organizations. The subjects of the study include principal tourism service providers: four and five-star hotels and resorts, tour operators, and government-owned tourism organizations. Due to time and financial constraints, the study doesn’t include samples from small and medium tourism and hospitality enterprises. Based on the limitations of the study, future research may give prioritization on the impact of social media marketing in hospitality and tourism, evaluating tourism organizations’ social media pages and websites, developing social media application that can incorporate important information about Ethiopian tourist destinations and services as well as devising an application that can integrate major social media platforms, and how to integrate social media marketing with the traditional media marketing approach. Furthermore, the multiple regression model indicated that the predictor variables explained 29.4% of the variability in the outcome variable namely actual usage of social media as explained by the model (R²=.294, F (4, 171) = 17.384, P = .000). Consequently, the model directs that 70.6% of the dependent variable is left unexplained, and future studies need to incorporate more variables for a better estimate of the dependent variable (Use of social media marketing).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Kassegn Berhanu

Kassegn Berhanu Melese (PhD) is an assistant professor in Department of Tourism and Hotel Management, at Debre Berhan University, Ethiopia. He has published a number of articles in tier journals. His research areas include social media marketing, tourism management, tourism product development, and heritage and wildlife conservation. Dr Kassegn is serving the scientific and reputable journals as a volunteer and anonymous reviewer. Besides, Dr Kassegn shares his research output with the academic community at national and international research conferences and symposiums.

Sahil Raj

Dr Sahil Raj (PhD, Assistant professor) - is a senior researcher and instructor at Punjabi University, School of Management Studies. His research domains include but not limited to business analytics, social media analytics, sentiment analytics, and management information system. He has published research articles and books in ABDC, Scopus and Web of Science indexed journals. Dr Sahil is participating in different international research conferences as a keynote speaker and research presenter.

Appendix

Figure 1. Factors affecting the use of social media.

Figure 4. Homoscedasticity of data).

Figure 3. Linearity of data (for online publication only).

Figure 2. Normal distribution of data.

Table 1. UTAUT constructs or variables and their meanings.

Constructs	Meanings or definitions	Adapted from Authors
Performance expectancy	The degree to which using social media will provide benefits of promoting a destination or organization.	Venkatesh et al., 2003
Effort expectancy	The extent to which the use of social media is an easy system.	Venkatesh et al., 2012
Social influence	Customers perceive that organizations should use social media.	Brown & Venkatesh, 2005
Facilitating conditions	The degree to which an organizational and technical infrastructure exists to support the use of the system.	San Martín & Herrero, 2012
Use behaviour	The degree how organizations exerted to use SM sites, spends time and intends to use	Venkatesh et al., 2003

Table 3. Actual use of social media by tourism principals.

Actual usage of social media (items)	M	SD	Skewness	Kurtosis
The organization exerted itself to SM sites.	3.32	1.02	−.313	−.276
The organization spends a lot of time on SM	3.10	1.09	.032	−.626
The organization intends to use the SM sites frequently.	3.76	1.07	−.912	.397
Valid N (176)

Table 4. Usage of social media among tourism organizations (for online publication only).

Types of organization	N	M	SD	Std. Error
Governmental	30	3.02	.85	.1557
Hotel	42	3.48	.93	.1442
Tour Operator	104	3.46	.85	.0838
Total	176	3.39	.88	.0667

Table 5. ANOVA and homogeneity of variance of actual use of social media.

Actual Use	Sum of Squares	df	Mean Square	F	Sig	Leven statistic	sig
Between Groups	4.961	2	2.481	3.249	.041	.259	.772
Within Groups	132.099	173	.764
Total	137.060	175

Table 6. Actual use of social media (post hoc results/ multiple comparisons).

(I) Type of Organization	(J) Type of Organization	Mean Difference (I-J)	Std. Error	Sig
Governmental	Hotel	−.46190*	.2088	.041
	Tour Operator	−.43932*	.1811	.043
Hotel	Governmental	.46190*	.2088	.041
	Tour Operator	.02259	.1597	.989
Tour Operator	Governmental	.43932*	.1811	.043
	Hotel	−.02259	.1597	.989

* Significant at the 0.05 level. (Source: Survey result).

Table 2. UTAUT variables reliability, normality of data and mean score.

Variables	No items	M	SD	Skewness	Kurtosis	Reliability (a)
EE (IV)	3	3.80	.63	−.358	.397	.760
PE (IV)	4	4.01	.82	−1.000	1.001	.828
FC (IV)	6	4.03	.58	−.353	−.187	.727
SI (IV)	3	3.84	.73	−.506	−.042	.738
AU (DV)	3	3.39	.88	−.470	.435	.779

Table 7. ANOVA and test of homogeneity of variances of social influence (for online publication only).

Groups	Sum Squares	df	Mean Square	F	Sig	Levene statistics	Sig
Between Groups	1.386	2	.693	1.299	.275	2.312	.102
Within Groups	92.306	173	.534
Total	93.692	175

Table 8. Descriptive result of the variable “Facilitating condition” (for online publication only).

Organizations	N	M	SD	Std. Error
Governmental	30	3.773	.869	.15865
Hotel	42	4.167	.564	.08700
Tour Operator	104	3.956	.657	.06444
Total	176	4.03	.58	.05166

Table 9. ANOVA and test of homogeneity of variance regarding facilitating condition.

	Sum of Squares	df	Mean Square	F	Sig	Levene statistic	Sig
Between Groups	2.801	2	1.401	3.052	.050	[.855	.427
Within Groups	79.409	173	.459
Total	82.210	175

Table 10. Multiple comparisons results of facilitating condition.

(I) Type of Organization	(J) Type of Organization	Mean difference (I-J)	Std. Error	Sig
Governmental	Hotel	−.3933*	.16195	.043
	Tour Operator	−.1824	.14041	.397
Hotel	Governmental	.3933*	.16195	.043
	Tour Operator	.2109	.12386	.207
Tour Operator	Governmental	.1824	.14041	.397
	Hotel	−.2109	.12386	.207

* Significant at the 0.05 level.

Table 13. Descriptive results of effort expectancy (for online publication only).

Organizations	N	M	SD	Std. Error
Governmental	30	3.51	.71	.12881
Hotels	42	3.89	.51	.07864
Tour Operators	104	3.84	.63	.06198
Total	176	3.80	.63	.04743

Table 11. ANOVA and test of homogeneity of variances about effort expectancy.

	Sum Squares	df	Mean Square	F	Sig	Leven Statistic	Sig
Between Groups	3.067	2	1.533	4.005	.020	1.625	[.200
Within Groups	66.234	173	.383
Total	69.301	175

Table 12. Multiple comparisons of effort expectancy.

(I) Type of organization	(J) Type of organization	Mean difference (I-J)	Sig
Governmental	Hotel	−.37857*	.030
	Tour Operator	−.33542*	.026
Hotel	Governmental	.37857*	.030
	Tour Operator	.04315	.923
Tour Operator	Governmental	.33542*	.026
	Hotel	−.04315	.923

* Significant at p = 0.05.

Table 14. Correlations among independent variables and dependent variable (for online publication only).

Correlation		SM usage	FC	SI	PE	EE
Pearson Correlation	(SM usage)	1.000	.421	.327	.440	.346
	Facilitating condition (FC)	.421	1.000	.423	.535	.284
	Social influence (SI)	.327	.423	1.000	.553	.236
	Performance expectancy (PE)	.440	.535	.553	1.000	.399
	Effort expectancy (EE)	.346	.284	.236	.399	1.000
Sig. (1-tailed)	(SM usage)	.	.000	.000	.000	.000
	Facilitating condition (FC)	.000	.	.000	.000	.000
	Social influence (SI)	.000	.000	.	.000	.001
	Performance expectancy (PE)	.000	.000	.000	.	.000
	Effort expectancy (EE)	.000	.000	.001	.000	.
N	SM Usage, FC, SI, PE, EE	176	176	176	176	176

Table 16. Multiple regression model summary of UTAUT.

Model^b	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.542^a	.294	.277	.73682

^a Predictors: (Constant), Facilitating condition, Effort Expectancy, Social influence, Performance expectancy.

^b Dependent Variable: Social media usage.

Table 17. ANOVA^a model fitness of UTAUT variables.

Model		Sum of Squares	df	Mean Square	F	Sig
1	Regression	37.751	4	9.438	17.384	.000^b
	Residual	90.665	171	.543
	Total	128.416	175

^a Dependent variable: Social media usage.

^b Predictors: (Constant), Facilitating condition, Effort expectancy, social influence, Performance expectancy.

Table 15. Regression coefficients^a of UTAUT.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig	Collinearity Statistics
		B	Std. Err	Beta			Tolerance	VIF
1	(Constant)	−.041	.466		−.089	.930
	EE	.249	.102	.178	2.445	.016	.797	1.26
	PE	.333	.098	.301	3.391	.001	.536	1.87
	SI	.071	.096	.059	.738	.461	.665	1.51
	FC	.222	.120	.148	2.124	.047	.655	1.53

^a Dependent variable: Social media usage.

Questionnaire to be filled by tourism and hospitality organizations

A. Below are statements describing the role of social media. Please indicate your level of agreement in the given box (1 = Strongly disagree, 2= Disagree, 3= Neutral, 4= Agree, 5= Strongly agree)

Table

The Roles of Social Media		1	2	3	4	5
1	Social media helps to overcome communication obstacles like distance barriers.
2	Social media helps tourism and hospitality organizations to be competitive by analyzing the feedbacks and employing constructive advice.
3	Social media is a popular tool among consumers in sharing personal experiences and comments.
4	Social media can be an advantage to reach global audience.
5	Social media helps a tourism destinations and organizations to disseminate information quickly
6	Social media can increase number of customer and hence sales volume rise.
7	Through social media we can improve customer service.
8	With free or little internet and transport cost, social media is relatively cheap
9	Social media supplements the use of traditional media.

B. Variables that determine the usage of tourism social media marketing. Kindly indicate your level of agreement. (1 = Strongly disagree, 2= Disagree, 3= Neutral, 4= Agree, 5= Strongly agree)

Table

Social Influence		1	2	3	4	5
1	Customers who influence our organization behaviour think that we should use social media.
2	Customers who are important to our company think that we should use social media.
3	Customers encourage us to use social media and share the information as much as possible.
Facilitating Conditions		1	2	3	4	5
1	In general, the organization has supported the use of social media.
2	The organization has the resources necessary to use social media.
3	The company has employees with knowledge necessary to use social media.
4	Using social media fits in to my work activities.
5	An expert is available for assistance with social media difficulties.
6	The government controls social media during public unrest.
Perceived Usefulness		1	2	3	4	5
1	Social media enables me to accomplish job tasks more quickly.
2	Social media is more useful to promote Ethiopia to the world more quickly than traditional media.
3	I use social media to obtain work related information and knowledge.
4	Social networking sites help meet more number of potential customers.
Effort Expectancy/Perceived Ease of Use		1	2	3	4	5
1	Learning to use social networking sites is easy for me.
2	It is easy for me to become skilful at social media.
3	Using social media consumes much time from my normal duties.