The effect of social media marketing on brand loyalty in the hospitality industry in Zimbabwe: the moderating role of age

Abstract

Building and maintaining brand loyalty is crucial for every organisation in such competitive business environment and organisations in the hospitality industry are not spared. Hotels have made many efforts on branding to gain brand loyalty from their customers and recently they have carried their efforts to social media to survive in online environment as well. The growth in social media marketing is attributed to the increase in social networking users, internet users, mobile phone users and digital content consumption. Therefore, the aim of this study was to establish the effect of social media marketing on brand loyalty in the hospitality industry in Zimbabwe, with age as a moderator. Data was gathered from 223 hotel customers using a structured questionnaire with Likert-type questions. The findings show that social media marketing positively influences brand loyalty in emerging market. Also, the study findings show that age moderate the effect of social media marketing on brand loyalty. The study contributes to studies that proved a significant relationship between social media marketing and brand loyalty by including the moderating role of age. Thus, hospitality organisations are advised to utilise social media marketing in order to improve brand loyalty. In addition, they must consider the moderating role of age when formulating social media marketing strategies to enhance brand loyalty.

IMPACT STATEMENT

Social media marketing contributes a crucial role in the success of firms in the hospitality industry. This study examines effect of social media marketing on brand loyalty within the hospitality industry in Zimbabwe and the moderating role of age. The study specifically tests the effect of social media, media sharing networks and consumer review networks on brand loyalty. The study established that social media marketing positively influences brand loyalty in emerging market. Also, the study established that age moderates the relationship between social media marketing and brand loyalty. Thus, hospitality organisations are encouraged to utilise social media marketing in order to improve brand loyalty. Also, they are advised to consider age when formulating strategies to enhance brand loyalty.

Keywords:

• Brand loyalty
• consumer review networks
• hospitality
• media sharing networks
• social media marketing
• Zimbabwe

REVIEWING EDITOR:

• Ediz Akcay, Bournemouth University Talbot Campus, Bournemouth University, United Kingdom

SUBJECT:

• Consumer Behaviour

1. Introduction

World over, the advancements in the internet have opened up the way business is being conducted, social media being one of the advancements which has altered the way companies view and interconnect with their customers (Kaondera et al., 2023; Koay et al., 2021; Nguyen et al., 2021; Tatar & Eren-Erdogmus, 2016). Social media has given customers the power to analyse services before they decide on acquiring the product or service (Manyanga et al., 2022). Social media marketing has given individuals and brands a chance to communicate without the need for physical meetings (Alves et al., 2016; So et al., 2018). As a result of increased competition, companies are now considering social media as a proactive strategy to attract and retain customers, develop brand, market products and increase their sales volumes (Chikazhe & Makanyeza, 2022; Tam & Kim, 2019). Building and maintaining brand loyalty is crucial for every organisation in such competitive business environment and organisations in the hospitality industry are not spared (Tatar & Eren-Erdogmus, 2016). The growth in social media marketing is attributed to the increase in social networking users, internet users, mobile phone users and digital content consumption (Ibrahim & Aljarah, 2023). Hotels worldwide yearn for brand loyalty from their customers and recently they have carried their efforts to social media to survive in online environment as well (Njeri, 2021). This has been further prompted by the outbreak of Covid 19 which has pushed businesses to consider social media marketing as a leverage to attain brand loyalty (Perera et al., 2022).

Although Zimbabwe is an emerging country, the need of social media marketing is undisputable due to high levels of mobile penetration and adoption of social media in daily lives of most individuals (Ibrahim & Aljarah, 2018; Manyanga et al., 2022). Data from Zimbabwe Tourism Authority (ZTA) confirm a drop in tourist arrivals by 11% in 2019 to 2.29 million from 2.57 million in 2018 (Zimbabwe Tourism Authority, 2019). In 2020 tourist arrivals declined by 72% to 0.63 million (Zimbabwe Tourism Authority, 2020). Total tourist arrivals rose by 174% in 2022 (1.04 million) compared to 2021 (0.38 million) as the global travel and tourism continues to show strong recovery post-COVID 19 (Zimbabwe Tourism Authority, 2022). According to ZimStats (2022), Zimbabwe’s population increased by 16% between 2012 and 2022, then internet users increased by 4.2%. Zimbabwe also realized an increase in social media users by 320 thousand (33%) between 2020 and 2021 (ZimStats, 2022). There were 1.30 million social media users in Zimbabwe in January 2021 (Zim Digital, 2021). The number of social media users in Zimbabwe was equivalent to 8.7% of the total population in January 2021 (ZimStats, 2022). Given these statistics it shows an upsurge in the users of social media, in this regard hotels are encouraged to invest in social media marketing to influence brand loyalty (Kumar et al., 2022). Previous studies have looked at the general outlook of social media on brand loyalty and consumer buyer behaviour (Aberdeen et al., 2016; Chege, 2017; Mbetu, 2018; Musungwini et al., 2014) rather than explicit media channels like media sharing networks, consumer review networks. Most of these studies focused on direct relationship between social media or its sub variables and brand loyalty. This study is unique as it adds the moderating role of age on the relationship between social media marketing and brand loyalty.

2. Theoretical framework

2.1. Theory underpinning the study

Social exchange theory (SET) lies in the notion that individuals participate in a series of mutually dependent interactions that create obligations among the exchange parties (Lee et al., 2014). However, individuals are likely to disassociate themselves from a relationship if they discern it as being unprofitable to them (Lai et al., 2014). The social behaviour in social media marketing (SMM) is a culmination of an exchange process where each party, in either a (customer-firm) or (customer-customer) on-line interaction, seeks to maximize benefits and minimize costs (Charlesworth, 2014). Both SMM and SET depend on a network of acquaintances among which social exchanges take place (Charlesworth, 2014). In today’s business environment, these relationships manifest through various social media applications such as Facebook, Twitter, Instagram and YouTube among others.

2.2. Social media

Social media as a set of internet-based systems which permit formation and chat of consumer-generated information which is created, initiated, circulated and used by customers with the intention of educating and engaging with other customers (Blackshaw (2016; Godey et al., 2016). Social media also refer to online setting with intention to exchange views, remark and information (Alawadhi & Al-Daihani, 2019; Seo & Park, 2018). Munnukka et al. (2015) posit that social media involve the use of internet websites to foster collaboration, participation and community building. Therefore, social media refer to communication means that is utilised to make available information to customers using the internet and making use of user generated information.

Social media has an ability to reach millions of customers with brand-related content and engage them in conversations (Lacka & Wong, 2021; Matanhire et al., 2021). Social media has become crucial in the hospitality industry due to its ability to connect with the target audience instantly (Peruta & Shields, 2018; Yadav & Rahman, 2017). Tatar and Eren-Erdogmus (2016) posit that social media has grown to become a pioneering means to interact and firms have also taken advantage of that since it reassures communication with customers.

Social media has given birth to technology-based solutions globally (Alalwan et al., 2017). Hotel industry is not spared in its quest to understand consumer behaviours issues (Ibrahim & Aljarah, 2023). Having online presence in hotel industry helps to create competitive advantage (Ding & Keh, 2016). Social media has influenced many aspects of the consumer behaviour such as awareness, information acquisition and sharing, opinions, attitudes, purchase and postpurchase behaviour as well as brand loyalty (Tatar & Eren-Erdogmus, 2016).

2.2.1. Facebook

Facebook is one of the largest social networks accessible to all brands all over the world (Jeon et al., 2016). Facebook allows information of interest to be shared faster and become viral to all potential customers globally (Le, 2018). All liked and commented post will also be visible on timelines of friends hence creates a web so that exchange and interactivity using posts spread the brand to all users of Facebook (Jeon et al., 2016; Kumar et al., 2022). Facebook is widely used for promotion, consumer research and customer service, which makes it the foremost choice for destination promotion (Kaiser et al., 2020). Facebook is used as a supplement to traditional marketing tools and rarely for customer service and research (Kumar, 2018). Hotels benefit from the contents of scenic beauty, culture and cuisine which appeal more to engage users on Facebook pages (Ben-Shaul & Reichel, 2018).

Facebook attracts the largest number of followers (Ibrahim et al., 2021). Facebook had 1.82 billion daily active users at the end of the third quarter of 2020 and 2.7 billion monthly active users by the second quarter of 2020 (Statista, 2020a, 2020b). With such a reach, the platform enables its users to create an online profile and invite friends who can post, write comments on walls and watch each other’s activities (Kumar, 2018). Facebook is the most common social networking site (Kaiser et al., 2020), and the most acceptable social media platform (Ben-Shaul & Reichel, 2018) among users. It is also a tool for destination promotion (Le, 2018) as it has become the first choice to create a destination’s brand page (Kumar, 2018). Hotels make use of these brand pages in disseminating new product information, interacting with customers and handling complaints (Kumar et al., 2022; Vinh et al., 2019). Amaro et al. (2016) reported that Facebook users falling within the age ranges of less than 29 years and 30–40 years show high engagement while travelling. They post their experiences, comments and offer opinions related to travel, which provide information to other users (Kumar et al., 2022).

2.2.2. Twitter

Twitter curated images creates an opinion of community, hence reinforce loyalty to a brand and improve distinguishability to potential customers (Lee et al., 2015). The Twitter platform has generated revenue over five hundred and seventy-five million globally in adverts and it is regarded as a microblogging social platform and has over three hundred and thirty-five million users as at the first quarter of 2018 (Statista, 2018a, 2018b). Native advertising is most common on Twitter through tweets which are promoted where firms pay for their tweets which are shared by common users, the company itself and influencers to reach more people globally (Kumar et al., 2022; Lalicic et al., 2019). Twitter usage increased because it spreads content faster and improves brand interaction (Hayes et al., 2020).

2.2.3. LinkedIn

Quinton and Wilson (2016) postulates that LinkedIn is a business-related social networking mostly utilised for proficient interaction. Witzig et al. (2012) posit that LinkedIn is an online professional network which permits people to link with reliable associates to discuss information, thoughts and prospects. Financial organisers discovered that LinkedIn is useful for linking with potential customers (Alalwan et al., 2017). The site permits individuals to look for business contacts, deal with their expert personality, research firms, join industry gatherings and recognize wanted vocation openings (Deng & Tavares, 2013).

2.3. Media sharing networks

Media sharing networks are social networks that allows users to share information via pictures or photos, audios, videos and a combination with text (Hayes et al., 2020). These platforms include Instagram and YouTube (Peruta & Shields, 2018). Media sharing networks has delivered a worldwide network for interactions, permitting people to show what they are able to do including giving and obtain information concerning the brand’s offerings (Quinton & Wilson, 2016).

2.3.1. Instagram

Instagram is one of the most efficient platforms under media sharing networks used to interact with clients in a unique way that influence consumer’s purchase intention (We Are Social, 2015). Members can see what others send and comment and like (Peruta & Shields, 2018). Instagram gives an opportunity to know consumer attitudes, perceptions and behaviour towards brands (Le, 2018). According to We Are Social (2015), the number of people using Instagram is approximately 29% of the global population. Instagram is an indispensable marketing tool for brands, as it relies on visual content through photo and video-sharing (Ibrahim & Aljarah, 2023). Over 500 million Instagram accounts use stories, including photos and videos, every day and four million businesses use story ads every month (Instagram for Business, 2021). In addition, with one billion active users monthly (Statista, 2020a), Instagram is a powerful marketing tool for establishing customer–brand relationships. Over 90% of the one hundred and fifty million users on Instagram falls below 30-year old, hence, it is the ideal media for selling hotel related services (Mancuso & Stuth, 2015). Instagram is ideal for generation of young people and female customers (Smith, 2014). These groups spend more due to influence from other users (Ibrahim & Aljarah, 2023). Also, these groups follow the trends in hotel and they want to move with the wave (Roncha & Radclyffe-Thomas, 2016).

2.3.2. YouTube

Media sharing networks also include YouTube (Wu, 2016). YouTube endorsement marketing is sometimes referred to as native advertising and is a form of marketing where advertisements are seamlessly incorporated into the video content unlike traditional commercials (Ilich & Hardey, 2020). Reino and Hay (2011) noted that YouTube is a site where users can upload, share and watch videos and is the global leader in the video streaming market, with over a billion videos viewed every day. Duffett et al. (2019) claims that people have used YouTube as a platform to step into the spotlight, however, most brands have been left behind or in the shadows. Through YouTube visitors can get an instantaneous appeal of tourism places (Madzharov et al., 2015; Salem & Chaichi, 2018). Duffett et al. (2019) posits that digital video is one of the fastest growing social media, especially among Millennials (18- to 34-year olds), with an estimated 4.5 billion global users and an advertising spending forecast of $37 billion by 2022. YouTube is now crucial communication channel since it provides a better platform for reaching customers via videos, hence firms cannot overlook YouTube since it is used to target very profitable segment of Millenials (Duffett et al., 2019).

2.4. Consumer review networks

Online consumer reviews have become a standard for new consumers to try out a business or a new product (Goswami et al., 2017). The reviews provide a quick overview into the application and experience of the business/product and advertise it to new customers (So et al., 2018). Gafni and Golan (2016) posit that the number of online social users grows very fast and socialization became most important activity.

2.4.1. TripAdvisor

TripAdvisor reviews can help destination marketers in designing the marketing strategies destination (Ramanujam & Kumar, 2022). TripAdvisor aims to assist in travel data and advisory e-channel (Müller & Christandl, 2019). TripAdvisor features guesthouses, attractions, capitals, cafeterias and travellers’ pictures (Gafni & Golan, 2016). Many consumers have resorted to TripAdvisor for travel bookings (Huang, 2018). TripAdvisor content is separately created by tourists (Raji et al., 2018). Users post reviews, comments and ratings on a destination, a hotel, an attraction or any other tourism related ‘object’ or service. (Müller & Christandl, 2019). Trip advisor attracts nearly 30 million monthly visitors making it one of the most popular sources of travel information on the web (Amaral et al., 2014). About 8% of all leisure travellers who used the web for travel research visit TripAdvisor (Kladou & Mavragani, 2015). TripAdvisor’s primary functions are the collection and dissemination of user-generated content reviews, ratings, photos and videos on travel (Gafni & Golan, 2016).

2.5. Age

Brand loyalty in the hospitality sector is also affected by demographic factors such as age (Kim & Ko, 2010). However, other scholars postulate that consumers are the same despite their age (Vukasovic, 2013). Chawla and Joshi (2017) argued that the respondents’ demographics like age, income, gender and education impact buying decision-making process. Consumer demographics such as age were found to impact satisfaction and loyalty (Shaikh & Karjaluoto, 2015). Similarly, demographic and psychographic factors such as income, age, education and gender influence consumer decisions (Olasina, 2015; Shaikh & Karjaluoto, 2015). Furthermore, demographic aspects like age, income, residential area, gender, employment status and marital status impact on customers’ behavioural intentions (Lee et al., 2015). Also, Manyanga et al. (2022) established that age moderate the effect of customer satisfaction on loyalty within the banking sector in Zimbabwe.

2.6. Brand loyalty

Brand loyalty is a commitment to make repurchase of a particular service/product/brand from the same company despite changes or situations that might push him or her to switch to another service provider (Kotler & Keller, 2012). Kabiraj and Shanmugan (2011) defined brand loyalty as the integration of attitudes, emotions and behaviours to continue buying a brand based on previous experience because the brand offers the right image, price, quality and attributes. Brand loyalty is an intensely held assurance to buy again same brand in future (Oliver, 1999; Salem & Salem, 2018). Aaker (1991) defines brand loyalty as a customer’s consistent repurchase of one brand out of a set of alternative brands. Thus, brand loyalty is articulated repetitive purchase of the same brand (Gaura et al., 2021).

Brand loyalty has a cognitive aspect as well as being the first to come to mind and price tolerance (Tweneboah-Kodual & Farley, 2015). Conversely, Bilgin (2018) states that behavioural loyalty to the brand provides direct income to the business, while attitudinal and cognitive loyalty enhances the tendency to give reliable recommendations to people in their environment and plays a crucial role in catching new customers. Brand loyalty shows customer intention to move to other brands once there is an alteration on product/service price (Ebrahim, 2020).

3. Development of research hypotheses and research model

It has been established in literature that social media has a positive effect on brand loyalty (Aghakhani et al., 2018; Aji et al., 2020; Danniswara et al., 2020; Ebrahim, 2020; Huang et al., 2018; Hughes et al., 2016; Ibrahim &Aljarah, 2023; Kumar, 2020; Le, 2018; Mavenga, 2013; Salem & Salem, 2021; Van Asperen et al., 2018; Vinh et al., 2019). Also, Bilgin (2018) in Turkey established that social media positively influence brand loyalty. Furthermore, in a study by Munnukka et al. (2015) in Finland it was confirmed that social media through Facebook positively influences hotels to gain brand loyalty. In India, Kumar et al. (2022) confirmed that social media through Facebook positively influence brand loyalty. In Thailand, Muangmee (2021) confirmed that that social media through Facebook significantly influence sustainable brand loyalty. In addition, Novotová (2018) established that social media through Facebook positively influence customer loyalty. Also, Ibrahim et al. (2021) confirmed a significant positive influence of social media marketing activities on brand loyalty. Furthermore, Ismail (2017) found that social media has a positive effect on brand loyalty. Similarly, Tatar and Eren-Erdogmus (2016) established that social media activities in hospitality business positively affect brand loyalty. Also, Njeri (2021) established that social media networks positively affect brand loyalty. Likewise, Tapfumaneyi (2015) established that social media positively influence brand loyalty. From the above studies it is empirically evident that social media has a positive effect on brand loyalty. Therefore, it is hypothesised that;

H1:

Social media has a positive effect on brand loyalty in emerging market.

Previous studies established that media sharing networks has a positive effect on brand loyalty (Aji et al., 2020; Duffett et al., 2019; Murwaningtyas et al., 2020; Njeri, 2021; Roncha & Radclyffe-Thomas, 2016; Salem & Salem, 2021; Tatar & Eren-Erdogmus, 2016). Ebrahim (2020) established that media sharing networks through instagram and YouTube positively influence brand loyalty. Additionally, Ibrahim and Aljarah (2023) confirmed that media sharing networks through instagram had a positive effect on brand loyalty. Similarly, Mhlanga and Maloneytichaawa (2017) established that media sharing networks such as Instagram and YouTube positively influence the customers’ experiences and brand loyalty. Likewise, Ibrahim (2021) confirmed that media sharing networks through Youtube and Instagram has a significant positive effect on brand loyalty. From the studies above it is clear that media sharing networks has a positive effect on brand loyalty. Therefore, it is hypothesised that;

H2:

Media sharing networks has a positive effect on brand loyalty in emerging market.

Extant literature confirms that consumer review networks have a positive effect on brand loyalty (Amaral et al., 2014; Ebrahim, 2020; Guo et al., 2021; Njeri, 2021; Ramanujam & Kumar, 2022; Salem & Salem, 2021). In addition, Goswami et al. (2017) established that online consumer reviews positively influence brand loyalty. Similarly, Song et al. (2021) established that consumer review networks such as TripAdvisor has a significant positive effect on brand loyalty. Also, Tatar and Eren-Erdogmus (2016) confirmed that user-generated websites from consumer review networks such as TripAdvisor positively influence brand loyalty. Likewise, Nowacki and Niezgoda (2020) confirmed that consumer review networks such as TripAdvisor has a positive effect on brand loyalty. In addition, consumer review network positively influences brand loyalty (Lee et al., 2022). Furthermore, Vassiliadis et al. (2021) confirmed that consumer review networks have a positive effect on brand loyalty. From studies above it has been established that consumer review networks has a positive effect on brand loyalty. Therefore, it is hypothesised that;

H3:

Consumer review networks has a positive effect on brand loyalty in emerging market.

Age has been found to influence consumer decisions and choices (Dutta & Bhatt, 2016; Kim & Ko, 2010). The older and younger consumers have different purchase behaviour, young consumers consider different brands whereas older consumers are more loyalty to more established brands (Chikazhe et al., 2021; Manyanga et al., 2022). Thus, social media relationship with brand loyalty is stronger on older consumers than young ones. Hence, it can be proposed that:

H4a:

The effect of social media on brand loyalty is stronger in older than younger consumers

Age influence positively the connection between media sharing networks and brand loyalty (Kim & Ko, 2010). Chawla and Joshi (2017) confirm that age influence buying decision-making process. Existing literature confirm distinguished variances in consumer buying behaviour between adult and young consumers (Chikazhe et al., 2021). Younger consumers are more energetic and consider more brands when making purchase decisions than older consumers consider (Chikazhe et al., 2021). Older consumers usually choose well-established brands, avoiding newer brands because the ability to process purchasing information decreases as consumers grow older (Manyanga et al., 2022). Similarly, the connection between media sharing networks and brand loyalty is robust in older than young consumers (Quinton & Wilson, 2016). Therefore, it is hypothesised that:

H4b:

The effect of media sharing networks on brand loyalty is stronger in older than younger consumers.

Age has been found to impact on consumer decisions (Chawla & Joshi, 2017). Consumer demographics such as age were found to influence consumer review networks and brand loyalty (Lee et al., 2015; Shaikh & Karjaluoto, 2015). Younger consumers tend to shift from one brand to the other, whereas older consumers tend to stick to their chosen brands and become loyal over time (Chikazhe et al., 2021). Correspondingly, the connection between consumer review networks and brand loyalty is stronger in older than younger consumers (Olasina, 2015). Therefore, it is postulated that:

H4c:

The effect of consumer review networks on brand loyalty is stronger in older than younger consumers.

Based on the forgoing hypotheses, the following research model in Figure 1 is suggested.

Figure 1. Research model.

4. Research methods

4.1. Questionnaire design and measures

Data was gathered using a structured questionnaire with Likert type questions. The Likert scale that ranged from 1 (Strongly disagree) to 5 (Strongly agree) was used to measure items under each construct. Items scales used in the study were borrowed from existing previous related studies (Bilgin, 2018; Kim & Ko, 2012; Laroche et al., 2012, 2013; Oliver, 1999; Salem & Salem, 2021) and they were modified to suit the requirements of this study. Five sections of the questionnaire comprised these sections; demographics, social media (SOMED), media sharing networks (MSNET), consumer review networks (CRNET) and brand loyalty (BRLOY).

4.2. Sampling and data collection

The target population comprised of 20,000 clients from the hospitality industry in Harare, Zimbabwe (Hospitality Association of Zimbabwe Annual Report, 2022). Harare was chosen because most hospitality operators are in Harare. Convenience sampling was employed in selecting the sample for the study. A cross-section survey of 223 clients was done. The sample size was obtained using a formula by Kombo and Tromp (2009). The respondents were conveniently selected by intercepting them as they left the hotels. Out of a total of 223 questionnaires, 220 (98.7%) were usable. Table 1 shows demographic data gathered during the study.

Table 1. The study demography.

Characteristic		Frequency	Percent (%)
Age	18–20	20	9.1
	21–30	36	16.4
	31–40	30	13.6
	41–50	48	21.8
	51–60	40	18.2
	61+	46	20.9
Gender	Male	142	64.5
	Female	78	35.5
Occupation	Self-Employed	40	18.2
	Employed	82	37.3
	Unemployed	12	5.4
	Student	46	20.9
	Retired	40	18.2
Nationality Nationality	Zimbabwe	71	32.3
	Zambia	35	15.9
	Malawi	25	11.4
	Mozambique	17	7.7
	Botswana	9	4.1
	South Africa	32	14.5
	Other	31	14.1

The majority (90.9%) of hotel customers in this study were aged between 21 and 61 and above. Male participants constituted the majority (64.5%) with female making up 35.5%. The majority of respondents are those employed (37.3%) and students (20.9%) which both account for 58.2% of the respondents. The majority of customers are from Zimbabwe (32.3%), Zambia (15.9%), South Africa (14.5%) and others outside Africa (14.1%) which all these account for 76.8% of respondents.

5. Analysis and results

5.1. Scale validation

Before structural equation modelling was performed in AMOS, scale validation was conducted using common method bias (CMB), exploratory factor analysis (EFA), discriminant validity and convergent validity.

5.1.1. Common method bias

CMB is an outcome of differences in responses that stem from the instrument itself instead of biases of the respondents that the instrument tries to reveal (Siemsen et al., 2010). Also, the CMB expounds the measurement error that is brought in by the friendliness of participants who aim to give positive answers (MacKenzie & Podsakoff, 2012). CMB impacts item validity and reliability and the covariation between hidden constructs (Min et al., 2016). Harman’s single factor assessment was used in measuring CMB. Also, exploratory factor analysis was performed in SPSS version 21, fixing the number of factors at 1. Prior studies such as Kim et al. (2013) recommend that the existence of a single factor with a variance explained greater than 50% suggests that there is CMB. The solution gave a factor with a variance explained of 34.819%. This implies that CMB did not affect this research.

5.1.2. Exploratory factor analysis

Kaiser–Meyer Olkin (KMO) measure and Bartlett’s Test of Sphericity were used to test sample adequacy. The sample satisfied minimum requirements as recommended (KMO = .801, Approx. Chi-Square = 2043.836, Degrees of Freedom = 276, p < .001) (Field et al., 2012; Pallant, 2005). Yong and Pearce (2013) recommended that the Bartlett’s Test of Sphericity should be significant at p < .05 while the KMO statistic should be at least 0.5. Varimax Rotation was used to conduct factor analysis. Bagozzi and Yi (1988) recommended that acceptable factor loadings should be more than 0.5. All items had factor loadings above 0.6.

5.1.3. Convergent validity

Maximum Likelihood Estimation (MLE) was used to estimate the measurement model as commended by Field (2009). Convergent validity was measured using measurement model fit indices, reliability, standardised factor loadings, critical ratios and average variance extracted (AVE). Minimum conditions for convergent validity conditions were fulfilled. Thus, the measurement model indicated a good fit (CMIN/DF 3.172; GFI .903; AGFI .927; NFI .931; TLI .923; CFI .961; RMSEA .051). Reisinger and Mavondo (2007) recommended that a satisfactory good model should exhibit a χ2/DF that falls within the scale of 0–5 with lesser values indicating a better fit. Additionally, Hooper et al. (2008) emphasised that values of NFI, TLI, GFI, AGFI and CFI specify a good fit when they are closer to 1, and RMSEA must be between 0.05 and 0.10 for it to be satisfactory.

As shown in Table 2, all constructs had Cronbach’s alpha (α)’s and composite (CRel) reliabilities with a cut-off point of above 0.6 (Leech et al., 2014). Additionally, all items had standardised factor loadings (λ) above the recommended cut-off point of 0.6 (Pallant, 2005). Critical ratios (CRs) were suitably large and significant at p < .001. Furthermore, all individual item reliabilities (IIRs) were greater than 0.5 as commended by Leech et al. (2014).

Table 2. λ, IIR, CR, α and CRel.

Constructs	Code	Item	Λ	IIR	CR	α	CRel
Social Media	SOMED1	I like the adverts that this brand has published on social media	.632	.601	–	.749	.815
	SOMED2	The adverts that this brand has released on social media are interesting	.842	.709	8.550***
	SOMED3	Social media adverts of this brand positively affect my attention for the brand	.843	.711	8.270***
	SOMED4	The social media account of this brand is enjoyable	.751	.664	6.313***
	SOMED5	The information that I need can be found on social media account of this brand	.672	.652	15.110***
Media Sharing Networks	MSNET1	Information sharing is possible on media sharing networks of this brand	.679	.661	–	.713	.821
	MSNET2	The discussion and exchange of opinions is possible on media sharing networks media page of this brand	.816	.666	15.143***
	MSNET3	The expression of opinions is easy on media sharing networks of this brand	.829	.687	10.714***
	MSNET4	The content shared by media sharing networks of this brand is enjoyable	.812	.659	17.219***
	MSNET5	The sharings of this brand on media sharing networks are interesting	.645	.616	17.216***
Consumer Review Networks	CRNET1	The information shared on consumer review networks of this brand is up to date	.727	.629	–	.784	.806
	CRNET2	The use of consumer review networks by this brand is trendy	.796	.634	12.398***
	CRNET3	The consumer review networks of this brand provided the information that I needed	.818	.669	16.161***
	CRNET4	I can easily obtain information of reviews by other customers from the consumer review networks of this brand	.729	.631	16.391***
	CRNET5	It is easy to deliver my opinion about the brand I am considering buying through consumer review networks	.613	.616	17.002***
Brand Loyalty	BRLOY1	I am satisfied with brand that appeared on social media	.826	.682	–	.766	.859
	BRLOY2	I usually use this brand as my first choice in comparison with the other brands	.832	.692	13.292***
	BRLOY3	This brand provides me superior product quality as compared to other competitors in marketplace	.664	.641	14.419***
	BRLOY4	I would recommend this brand to others through the social media	.839	.704	16.283***
	BRLOY5	I will continuously use services from brand in future	.887	.787	17.318***

Note: - CR is fixed; *** p < .001.

5.2. Discriminant validity

To measure discriminant validity, AVEs were compared against squared inter construct correlations (SICCs). Table 3 shows that minimum conditions to fulfil the requirements were achieved since all AVEs were greater than their corresponding SICCs (Leech et al., 2014). Also, all constructs had averages (AVEs) greater than 0.5 (Fornell & Larcker, 1981). Heterotrait-monotrait ratio of correlations (HTMT) was introduced by Henseler et al. (2015) as an estimator for the correlation between two latent variables. It is based on the multitrait-multimethod (MTMM) matrix, in which correlations are compared to assess discriminant validity. To establish discriminant validity, the HTMT value should be different from 1 because the HTMT is an estimator for the inter-construct correlation; if the correlation between two constructs is 1, they cannot be discriminated properly (Henseler et al., 2015). The computed HTMT was at 0.65. Recommended threshold values range from 0.85, which is considered a conservative benchmark (Henseler et al., 2015), to a more liberal cut-off value of 0.9 (Henseler et al., 2015) or higher. Hence, based on the heuristic rules, the HTMT value below the two threshold values indicates presence of discriminant validity.

Table 3. Mean (M), standard deviation (SD), AVEs and SICCs.

Construct	M	SD	SOMED	MSNET	CRNET	BRLOY
Social Media	4.171	.819	.687
Media Sharing Networks	4.126	.863	.487	.694
Consumer Review Networks	4.182	.827	.482	.456	.676
Brand Loyalty	4.134	.813	.472	.429	.551	.742

Note: Diagonal elements in bold represent AVEs.

5.3. Research hypotheses tests

5.3.1. Structural equation modelling

Structural equation modelling (SEM) technique in AMOS Version 20 was used to test the hypotheses (H₁, H₂ and H₃). Maximum likelihood estimation (MLE) was used to estimate the structural model (Pallant, 2005). The structural equation modelling technique was adopted since it has the ability to determine relationships while at the same time determining whether or not there is a general fit between the research model and observed data (Leech et al., 2014). The structural model showed suitable model fit indices (CMIN/DF 3.172; GFI .903; AGFI .927; NFI .931; TLI .923; CFI .961; RMSEA .051). Results of hypotheses testing are presented in Table 4. The results confirm that H₁, H₂ and H₃ were all supported. This confirms that social media, media sharing networks and consumer review networks have a positive effect on brand loyalty in the hospitality industry in emerging market.

Table 4. Hypotheses tests results.

Hypothesis	Hypothesised relationship	SRW	CR	Remark
H1	Social Media → Brand Loyalty	.469	11.815***	Supported
H2	Media Sharing Networks → Brand Loyalty	.236	19.613***	Supported
H3	Consumer Review Networks → Brand Loyalty	.238	8.037***	Supported

Notes: SRW standardised regression weight, CR critical ratio, *** significant at p < .001.

5.3.2. Moderated regression

In testing H4(a–d), a moderated regression analysis was performed. Results are summarised in Table 5.

Table 5. Coefficients of moderated regression model.

Variable	Beta	t-statistic	p value
Social media	.012	.288	.761
Media sharing networks	.113	2.755	.003
Consumer review networks	.238	6.631	.000
Age	.001	.028	.978
Social media*Age	.082	2.113	.032***
Media sharing networks*Age	.085	2.125	.035***
Consumer review networks*Age	.088	2.139	.039***

Notes: R Square = 0.112, F = 22.453, significant at p < .05.

Results show that coefficients for the interaction terms (Social media *Age, Media sharing networks*Age and Consumer review networks *Age) were significant (p < .05). Hence H4(a–d) was supported. This suggests that age moderate the effect of social media, media sharing networks and consumer review networks on brand loyalty. When age of respondents is high the relationship among social media, media sharing networks and consumer review networks on loyalty becomes stronger and vice versa. As such, older consumers are more loyal than younger consumers are.

6. Discussion and implications

6.1. Theoretical implications

In the marketing literature, despite of the call for enhancement of social media to increase brand loyalty (Ebrahim, 2020; Ibrahim, 2021; Ibrahim & Aljarah, 2023; Kumar et al., 2022). There is a necessity to include other variables to further reinforce this relationship. Media sharing networks and consumer review networks are also important in marketing particularly in the hospitality industry (Aji et al., 2020; Danniswara et al., 2020; Duffett, 2020). There is inconclusive empirical literature about the moderating effect of age on the relationship among social media, media sharing networks and consumer review networks on brand loyalty. Accordingly, the current research was done to reduce the current knowledge gap in marketing literature. Hence, the study results corroborate the social exchange theory proposed by Homans (1958), which stresses that there is a significant link between social media marketing and brand loyalty.

The study reveals that social media, media sharing networks and consumer review networks are crucial aspects that impact on brand loyalty within the hospitality industry. As anticipated, the study established that social media has a positive effect on brand loyalty and these findings confirm previous studies (Aghakhani et al., 2018; Aji et al., 2020; Danniswara et al., 2020; Ebrahim, 2020; Salem & Salem, 2021; Van Asperen et al., 2018; Vinh et al., 2019). Also, the study findings reveal that media sharing networks has a positive effect on brand loyalty and these results confirm extant literature (Duffett et al., 2019; Murwaningtyas et al., 2020; Njeri, 2021; Roncha & Radclyffe-Thomas, 2016; Salem & Salem, 2021; Tatar & Eren-Erdogmus, 2016). In addition, the study reveals that consumer review networks have a positive effect on brand loyalty and these findings corroborates previous studies (Amaral et al., 2014; Ebrahim, 2020; Guo et al., 2021; Njeri, 2021; Ramanujam & Kumar, 2022; Salem & Salem, 2021).

Furthermore, the study established that age moderates the relationship between social media and brand loyalty. This imply that age play a crucial role on the relationship between social media and brand loyalty. This suggest that when formulating strategies for improving brand loyalty, hospitality organisations must take into cognisance the role of age. In addition, the study also ascertains that age moderate the relationship between media sharing networks and brand loyalty. This imply that age plays a crucial role on the relationship between media sharing networks and brand loyalty. Hence, organisations in the hospitality industry must consider age when formulating strategies to enhance brand loyalty. Finally, the study established that age moderates the relationship between consumer review networks and brand loyalty. So, age plays an important role on the relationship between consumer review networks and brand loyalty. Thus, the relationship between consumer review networks and brand loyalty may be predicted by considering the moderating role of age. These finding on the moderation role of age add to the growing body of knowledge about the relationship among social media, media sharing networks, consumer review networks and brand loyalty.

6.2. Managerial implications

Building and maintaining brand loyalty is crucial for every organisation in such competitive business environment and organisations in the hospitality industry are not spared. Hence, managers in the hospitality are encouraged to embrace social media marketing in their quest to enhance brand loyalty. To improve brand loyalty, management is encouraged to pay attention to issues to do with social media. Thus, management within the hospitality sector is recommended to consider posting their adverts on social media platforms such as Facebook, Twitter and LinkedIn, which are interesting, that capture the attention of consumers, enjoyable and relevant. It is also recommended that management within the hospitality sector should consider using media sharing networks such as Instagram and YouTube and ensure that there is discussion and exchange of opinions on such networks, allow consumers to express their views, share content that is enjoyable and interesting. Furthermore, to improve brand loyalty, management is encouraged to pay attention to issues to do with consumer review networks such as TripAdvisor by sharing up to date and trendy information about their brands, provide information needed by customers about their brands. In addition, they must ensure that it is easy for consumers to obtain information about a brand as well as ensure that it becomes easy for consumers to deliver their opinions about a brand through consumer review networks.

Furthermore, management is recommended to focus on designing social media content that captures the minds of customers. Also, social media sites of hotels should allow for user generated content to be posted on their social media platforms. Thus, designing social media content that provide an exciting social media experience to customers. Hotels are encouraged to have well planned media sharing networks to ensure brand loyalty. It is recommended that management should ensure that their hotels share their content on media sharing networks and allow customers to post their experiences in form of testimonials, pictures and videos. Management should consider using consumer review networks and ensure that customers are able to review the hotels’ offerings. Above all, managers are encouraged to ensure that they consider age of the targeted customers when making choices on social media marketing channels and platforms to use in order to enhance brand loyalty. The moderating role of age should not be neglected when making strategies to improve brand loyalty using social media marketing strategies particularly in the hospitality industry.

7. Conclusion

The purpose of the study was to examine the effect of social media, media sharing networks and consumer review networks on brand loyalty. Also, the study sought to understand whether age moderates the effect of social media, media sharing networks and consumer review networks on brand loyalty. The study established that social media, media sharing networks and consumer review networks influence brand loyalty. Furthermore, the study concluded that age moderates the effect of social media, media sharing networks and consumer review networks on brand loyalty.

8. Limitations of the study

The study has its own limitations, hence the need for further studies to be conducted. For instance, the study was conducted in one sector and in one country. This makes it difficult to generalise the findings. Therefore, it is recommended that future studies be carried out across other cities in Zimbabwe and other countries. Also, this study employed a quantitative strategy only and only hotel clients were respondents of the study. Therefore, it is also recommended that future studies employ a mixed-methods and utilise interviews by interviewing hotel managers and employees to get their perceptions. Also, the authors faced several challenges in conducting this research. The first challenge was lack of funding in conducting this research, hence all costs were borne by the researchers. Also, some participants withdrew from participating due to lack of time, since most of them had busy personal schedules. The researchers managed to approach more participants until the targeted sample size was reached.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Wilbert Manyanga

 Wilbert Manyanga holds a PhD in Marketing from Chinhoyi University of Technology. He is a marketing lecturer. His research areas of interest are on customer experience, social media, branding and finance.

James Kanyepe

 James Kanyepe holds a PhD in Supply Chain Management from Chinhoyi University of Technology. He is a lecturer at University of Botswana in the Department of Management. His research areas of interest are logistics and supply chain management.

Lovemore Chikazhe

 Lovemore Chikazhe holds a PhD in Marketing from Chinhoyi University of Technology. He is currently a lecturer at the same university in the Department of Retail and Consumer Science. His research area of interest is services marketing.

Tendai Manyanga

 Tendai Manyanga holds a Bachelor of Education honours degree in Educational Management from Zimbabwe Open University. Also, she holds a Diploma in Education as well as a Diploma in Executive Secretarial. Her research area of interest is educational management.