Keywords

Customer Engagement, Participation, Social Media, Tourism Brands, Mediation, Loyalty.

Introduction

Travel & Tourism is one the fastest growing sector across the globe. According to (WTTC, 2017b), the total contribution of tourism to world GDP was USD 7,613.3 billion and is expected to grow by 3.6% in 2017 and 3.9% per annum by 2027. Tourism in India is one of the key contributing sectors to the economy and ranks 7th in the world regarding total contribution to GDP (WTTC, 2017a). Tourism contributes a total of 9.6% to the GDP of India and is predicted to increase by 10.0% by 2027 (WTTC, 2017a). The increase in tourism in India is growing due to several reasons like robust demand for foreign tourists, attractive opportunities, diverse attractions, and supportive tourism policies.

The developments in Information Technology (IT) has brought dramatic changes in tourism and the behavior of the consumer in tourism decisions (Sheldon, 1997; Werthner & Klein, 1999). IT pursue to grow and influence the way tourist use and gain access to travel-related information (Xiang et al., 2015). The prodigious growth of social media is changing the landscape of online communications (Sigala et al., 2012). It is essential for tourism firms to understand the impact of social media on tourist’s behavior so that tourism firms can cater better services. Consequently, the use of social media like Facebook and Twitter is high among tourism firms (Leung et al., 2013) to engage tourists participating in online brand communities and fan pages.

Social media provided new opportunities for tourism firms to connect and interact with the customers regarding reviews, recommendations, and references, etc., which are beyond just service transactions (So et al., 2014). In the similar lines, consumers are also manifesting their behavior with their preferred tourism brand, in terms of interaction with the brand and another consumer to share or/and exchange their experience & information on social media (Verhoef et al., 2010). Thereby empowering the firms to encourage their consumers to become effective advocates of their brands (Malthouse et al., 2013). Such behavioral manifestations which are beyond service transactions is Customer Engagement (CE) (Bowden, 2009; Roderick et al., 2011; Van Doorn et al., 2010).

Customer Engagement has proved to be an effective enhancer of customer satisfaction, trust and loyalty in the tourism context (So et al., 2014). And the customers who engage with their preferred brands on online brand communities, exhibit more commitment and trust; and have experience higher satisfaction (Roderick et al., 2013). Simultaneously much of the customer engagement is happening online through social media (Malthouse & Hofacker, 2010). However, neither of these two concepts in combination is much studied in the tourism context (Harrigan et al., 2017).

The main aim of the study is to investigate the role of customer engagement with tourism brands; in enhancing customer loyalty towards the brands when customers participate on Facebook and Twitter. The study also attempts to validate the Customer Engagement with Tourism Brands (CETB) scale in the Indian online tourism brands context, developed by So et al. (2014). The study finally tries to highlight the importance of CE both conceptually and theoretically in the tourism context.

Literature Review

Customer Engagement (CE) is defined as the behavioral manifestation by the customer towards a brand/firm/service which enhances the emotional and psychological relationship with the brand (Roderick et al., 2011; Hollebeek, 2011a; Hollebeek et al., 2014). The Social Exchange Theory (SET) emphasizes this kind of engagement activity by the customer to assess the tangible and intangible benefits associated with the brands (Thibaut & Kelley, 1959). The customer is said to be engaged with the brand, when he finds a balance between their investment and benefits they seek for being engaged (Roderick et al., 2011; Hollebeek, 2011a). For instance, customers who invest their attention and enthusiasm in engaging with a brand expect such as product/service related information and offers from the brands (Blau, 1964; Foa & Foa, 1980).

Social media is an effective facilitator of customer engagement which is significantly different from the previous marketer-customer communication platforms. Social Media is a transparent medium often owned by customers than marketers; and provide two-way interaction between the customers and marketers (Dwyer, 2007; Hennig-Thurau et al., 2010; Shiri et al., 2012). According to Goh et al. (2013), the information generated by the customers is more valuable than the marketer's generated content on social media. Social media is defined as the "group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of User Generated Content” (Kaplan & Haenlein, 2010).

Travel & tourism websites (agencies) like MakeMyTrip, Goibibo making continuous efforts to generate content to engage customers through their fan pages/online brand communities on social media (Cabiddu et al, 2014; Munar & Jacobsen, 2014). Tourism and travel agencies promoting their brand to their potential consumer’s thorough various travel deals (Gupta, 2019). Information related to these deals is to be spread at a large scale and social medium is one such tool. These sites allow tourists to review, comment, share and exchange information and experience; and also create useful content online which can be used by other stakeholders (Samala et al., 2018). The customers also effectively share and spread their information on social media as they identify themselves with others in online communities (Prentice et al., 2019). Hence the role of social media as an effective facilitator of customer engagement in the tourism context cannot be disregarded (Cabiddu et al., 2014; Hudson et al., 2015; Munar & Jacobsen, 2014).

Customer engagement is conceptualized as a multidimensional construct circumscribed to cognitive, emotional or behavioral dimension (Roderick et al., 2013; Gambetti et al., 2012; Hollebeek, 2011a; Hollebeek et al., 2014; Patterson et al., 2006; Shiri et al., 2014). For example, Dwivedi (2015), conceptualized customer engagement broadly a behavior exhibiting vigor, absorption and dedication dimensions. Following these conceptualizations, we can posit that CE is different for other traditional relations like involvement and participation (Roderick et al., 2011). According to So et al. (2014), participation is a uni-dimension of behavioral activities indicating the level of interest in interaction, whereas CE encompasses the multidimensionality of cognition, emotion, and behavior (Mollen & Wilson, 2010; Shiri et al., 2012).

Customers participate and interact with the brands to co-create new products/services, share new ideas and express their feelings (Chen et al., 2011). Co-creation is found to be a positive predictor of loyalty and satisfaction (Flores & Vasquez-Parraga, 2015; Ranjan & Read, 2016). Hence the relationship of participation, CE and brand loyalty as a behavioral intention is considered as an interesting concept to empirically evaluate, especially in the context of tourism with the unique scale designed by (So et al., 2014) to measure the CE. The present attempts to study this relationship between participation, loyalty and CE as a multidimensional scale in the context of tourism in India.

Participation

Customer participation is defined as “the degree to which the customer is involved in producing and delivering the service” (Dabholkar, 2015). The role of a customer as a co-producer (Prahalad & Ramaswamy, 2002) is highlighted in the literature in the recent past. The customer takes an active part in producing and consuming products and service of value (Nysveen & Pedersen, 2014). According to Ranjan & Read (2016), the customer should be considered as an essential element of co-production. In the value co-creation perspective, customer participates to enhance their satisfaction (Ranjan & Read, 2016), further affecting their loyalty positively.

The concept of customer participation by Vivek (2009) states the difference between CE and participation. Participation is more of a behavioral component comprising of interaction and co-creative experience with a specific focal object (e.g., brand) for the purpose of engagement. Hence participation can be viewed as an antecedent to CE than consider it as a dimension (Roderick et al., 2011). Customers who participate actively on a social media attach themselves with the brand emotionally over some time. From the above literature, we can conclude that participation has a direct effect on CE and brand loyalty.

H1: Customer participation positively affects brand loyalty (BIL).

H2: Customer participation positively affects CE with tourism brands (CETB) online.

Customer Engagement

The present research is primarily based on the CE scale developed by (So et al., 2014), in the tourism context. The present study aims at validating the scale in the tourism context in India. The scale is developed as a multi-dimensional scale comprising of enthusiasm, attention, interaction, identification and absorption (So et al., 2014). In this section, an attempt is made to briefly introduce the dimensions of CETB.

According to (So et al., 2014) enthusiasm is a dimension defined as "represents an individual's strong level of excitement and interest regarding the focus of engagement and differentiate the construct of engagement from other similar constructs such as satisfaction." (pp. 308). It is also explained as an individual's interest in brand and "strong level of excitement or zeal" (Shiri, 2009). Attention as a dimension of CE referred to as a customer's level of focus (consciously or subconsciously) on a brand. Constant and purposeful attention towards a brand would likely lead to a higher level of customer engagement.

Interaction is the fundamental component of CE, which involves the exchange and sharing of ideas, feelings, and experiences with a brand (Shiri, 2009). Interaction with the brands happens online through social media or online brand community (Muniz & O'guinn, 2001). This kind of interaction with the brand on social media is a behavioral component of CE. Customers identify themselves with a specific brand over other brands, especially with those which match their self-image (Bagozzi & Dholakia, 2006). This dimension is drawn from the social identity theory, which explains that individuals have both social and personal identity. The individual engaging with a focal brand in a brand community is a manifestation of social identity (Mael & Ashforth, 1992). Absorption refers to a customer’s high level of engrossment and concentration in a brand (Schaufeli et al., 2002). It is a positive trait, in which customers constantly absorbed with or in the brand, and are more likely not aware of the time devoted (Patterson et al., 2006).

These five dimensions of CETB constitute three broad dimensions of CE, i.e., Cognitive, emotional and behavioral components. The act of engagement would directly influence Brand Loyalty (BIL) (Zeithaml et al., 1996). And in the earlier review in this section, we hypothesized participation as an antecedent of CE. Hence, the study would attempt to study the direct and indirect effect of participation on BIL thorough CETB. The conceptual model of the hypothesized relationships is represented in Figure 1.

H3: CETB positively affects the brand loyalty.

H4: CETB plays a mediating role between participation and loyalty.

Methodology

This study followed a descriptive and cross-sectional survey research method to test the relationship hypothesized between the constructs. The study considered the customers following some of the major online travel & tourism brands on Facebook and Twitter as the sample population. The online tourism brands which are actively engaging their customers and followers on Facebook and Twitter were selected for the study. Makemytrip.com, Goibibo.com, Cleartrip.com, Yatra.com are the top Indian online travel & tourism agencies that were selected for the study based on the most number of followers on Facebook and Twitter. The respondents who are actively following these brands on their fan pages are sent structured questionnaire online to their Facebook and Twitter accounts. The selection of the respondents is not random. The most actively participating (tweeting, commenting, liking) followers of the brands are identified according to their activities and are considered for the study, to get an appropriate response out of self-interest and also in have a good response rate. Hence, the sampling technique followed is non-probable purposive sampling. All the respondents are Indians who are customer and followers of the selected brands. Therefore, the respondents are Indian tourist seeking travel & tourism services for either domestic or foreign.

A total of 386 responses were received, out of which 319 were used for the final data analysis after eliminating the non-usable and incomplete responses. The structured instrument for the study was designed by adapting the already developed scales of the constructs considered for the study. Four items for measurement of Participation (PAR) in brand communities is adapted from the scale developed by Eisingerich et al. (2014). The outcome variable Brand Loyalty (BIL) is measured using the scale developed by Zeithaml et al. (1996). The major construct for the study Customer Engagement in tourism context is adapted from the 25 item scale with five dimensions as a higher order construct developed by So et al. (2014). Since this is the latest scale validated in the tourism context defining CE as a cognitive, emotional and behavioral phenomenon. The questionnaire followed a five-point Likert scale; ranging from 1=“Strongly Disagree” to 5=“Strongly Agree”.

Further, the preliminary data analysis for demographic details of the respondents, data normality and reliability; and for conducting Exploratory Factor Analysis (EFA), SPSS 20.0 is used. For performing the Confirmatory Factor Analysis (CFA) both first and second-order measurement model; and testing the hypothesized relations between the variables, AMOS 20.0 is used. The sample size of 319 is considered appropriate according to the Hair et al. (1998), recommending a 5:1 ratio of responses to an item when we perform a Structural Equation Modelling (SEM) statistical technique. The total number of items used to test the structural model is 33. Hence 319 is well above the recommended range.

To test the indirect and direct effects of X, Y and M CB-SEM (Covariance based Structural Equation Modelling) is used. Direct effect of independent variable X on Y is considered as c^ and indirect of X on Y through M is (a×b), where

1. X is in independent variable “Participation”.

2. Y is dependent variable “Brand Loyalty”.

3. M is mediator variable “Customer Engagement with Tourism Brands (CETB)”.

4. ‘a’ is estimate of X on M.

5. ‘b’ is estimate of M on Y.

6. ‘c^’ is estimate of X on Y.

Data Analysis

The demographic details of the respondents show that majority of the respondents are graduates under the age group of 25-35. 68% of the respondents are working professionals who frequently travel for pleasure. 57% of the male and 43% of females have responded the questionnaire send to them directly on their Facebook or Twitter page.

The analysis of the data is conducted by first checking for the reliability and validity of the measurement scale used for the study. The Cronbach's alpha for the variables of CEB, Participation, and BIL are well above 0.70 standards suggested by (Nunnally, 1991) providing support for the reliability of the scale as shown in Table 1. The Exploratory Factor Analysis (EFA) is conducted through dimension reduction technique using the IBM SPSS 20.0 software. The sampling adequacy measure of Kaiser-Meyer-Olkin is 0.877 and is well above the recommended threshold. The seven factors are extracted out of which five are the dimensional factors of CETB, one factor for participation and the one for BIL. The factor loadings of the seven components according to the rotated component matrix are shown in Table 1 and are above 0.50 value as recommended by Guadagnoli & Velicer (1988). One item from each dimension of CETB (Identification, Enthusiasm, Attention, Absorption, and Interaction) and one from Participation are discarded for further analysis due to poor factor loadings.

Table 1 Exploratory Factor Analysis (Efa) Results
Constructs and their items.	Factor Loadings.	Cronbach's Alpha.
Identification (IDF)		0.842
IDF1. When someone criticizes this tourism brand, it feels like a personal. .insult.	0.789
IDF2. I am very interested in what others think about this tourism brand.	0.765
IDF3. When I talk about this tourism brand, I usually say we rather than they".	0.748
IDF4. When someone praises this tourism brand, it feels like a personal. .compliment.	0.724
Enthusiasm (ENT)		0.802
ENT1. I am heavily into this tourism brand.	0.774
ENT2. I am passionate about this tourism brand.	0.761
ENT3. I am enthusiastic about this tourism brand.	0.737
ENT4. I feel excited about this tourism brand.	0.706
Attention (ATT)		0.811
ATT1. I like to learn more about this tourism brand.	0.766
ATT2. Anything related to this tourism brand grabs my attention.	0.726
ATT3. I concentrate a lot on this tourism brand.	0.725
ATT4. I focus a great deal of attention on this tourism brand.	0.721
Absorption (ABP)		0.793
ABP1. Time flies when I am interacting with this tourism brand.	0.786
ABP2. When I am interacting with this tourism brand, I get carried away.	0.710
ABP3. In my interaction with this tourism brand, I am immersed.	0.659
ABP4. When interacting with this tourism brand intensely, I feel happy.	0.652
Interaction (INT)		0.818
INT1. In general, I like to get involved in this tourism brand community. discussions.	0.713
INT2. I am someone who enjoys interacting with like-minded others in the. .brand community.	0.695
INT3. I am someone who likes actively participating in brand community. discussions.	0.684
INT4. I thoroughly enjoy exchanging ideas with other people in this tourism. brand community.	0.668
Participation (PAR)		0.821
PAR1. I let this tourism brand know of ways that it can better serve my needs.	0.863
PAR2. I make constructive suggestions to this tourism brand on how to. improve its offering.	0.833
PAR3. I spend time sharing information with others about this tourism brand.	0.776
Brand Loyalty (BIL)		0.749
BIL1. I would say positive things about this tourism site to other people.	0.751
BIL2. I would recommend this tourism site to someone who seeks my advice.	0.709
BIL3. I would encourage friends and relatives to do business with this tourism. .site.	0.697
BIL4. I would do more business with this tourism site in the next few years.	0.478

In the next step of data analysis, Confirmatory Factor Analysis (CFA) is conducted for the seven-factor (of which five are first-order dimensions of CETB) to determine the discriminant and convergent validity of the measurement model. CFA is conducting for both first-order and second-order measurement models.

First-Order Measurement Model

CFA for the seven factors is conducted using SPSS AMOS 20.0. First order measurement is conducted initially to validate the second order construct in the nest step i.e., CETB later in the analysis, according to Marsh (1991) and is shown in Figure 2. The first-order measurement model satisfied with the minimum threshold values required for considering the model to be fit. The fit indices of the model are χ²=538.276; df=278; p<0.05; χ2/df=1.936; GFI=0.891; AGFI=0.861; CFI=0.928; TLI=0.915; IFI=0.929; RMSEA=0.054 and PCLOSE=0.151. All then values are well above the recommended minimum indices values requited for a model fit (Bentler, 1990; Bentler & Bonett, 1980; Hu & Bentler, 1999; Joreskog & Sorbom, 1988; MacCallum et al., 1977).

The constructs are tested for the convergent and discriminant validities along with CFA. The values obtained from the results are greater than the recommended values by Hair et al. (2006), provide support for the measurement model to be valid. The validity test results are shown in Table 2.

Table 2 Validity Measures Of The First-Order Measurement Model
	CR	AVE	MSV	ASV	PAR	IDF	ATT	ENT	ABP	INT	BIL
PAR	0.824	0.610	0.166	0.107	0.781
IDF	0.845	0.579	0.394	0.254	0.283	0.761
ATT	0.815	0.526	0.339	0.251	0.317	0.547	0.725
ENT	0.803	0.505	0.386	0.195	0.190	0.503	0.453	0.711
ABP	0.802	0.506	0.424	0.285	0.375	0.494	0.555	0.452	0.711
INT	0.821	0.537	0.424	0.312	0.342	0.628	0.507	0.621	0.651	0.733
BIL	0.770	0.532	0.391	0.255	0.407	0.505	0.582	0.293	0.625	0.545	0.729

Second-Order Measurement Model:

After testing the first-order measurement model for the validity concerns, a second-order model is tested for the validity measures, as the higher-order models needs the use of a hierarchical analysis (Byrne, 2016; Kline, 2011). This is conducted using AMOS 20.0 and is shown in the Figure 3. The model fit indices of the second-order construct are as follows, χ2=580.597, df=291, p<0.05; χ2/df=1.995; GFI=0.881; AGFI=0.856; CFI=0.921; TLI=0.911; IFI=0.921; RMSEA=0.056 and PCLOSE=0.071. All the indices are within the limits of the suggested minimum values while fitting a model (Bentler, 1990; Bentler & Bonett, 1980; Hu & Bentler, 1999; Joreskog & Sorbom, 1988; MacCallum et al., 1996; Wheaton et al., 1977).

Now the three main constructs (CETB, Participation, and BIL) of the conceptual model are tested for the validity measures. The results of the validity indices are shown in Table 3. The results provide support for the construct to be convergent and discriminately valid and can be considered further for the structural model to be tested. The Average Variance Extracted (AVE) of the constructs is greater than the correlation values of the other constructs (Fornell & Larcker, 1981). AVE value of each construct is above 0.50 (Fornell & Larcker, 1981) and Composite Reliability (CR) values are above 0.70 as recommended by Hair et al. (2006).

Table 3 Validity Measures Of The Second-Order Measurement Model.
	CR	AVE	MSV	ASV	BIL	PAR	CETB
BIL	0.770	0.531	0.500	0.333	0.729
PAR	0.825	0.611	0.174	0.170	0.408	0.782
CETB	0.855	0.544	0.500	0.337	0.707	0.417	0.737

Resultsf

The proposed structural model for the study, to test the relationship between Participation, CETB, and BIL, is performed using structural equation modeling as shown in Figure 4 with the standard loading values. The model exhibits satisfactory fit indices recommended (Bentler, 1990; Bentler & Bonett, 1980; Hu & Bentler, 1999; Joreskog & Sorbom, 1988; MacCallum et al., 1996; Wheaton et al., 1977), and are as follows, χ²=71.462.

df=41; p<0.05; χ2/df=1.743; GFI=0.959; AGFI=0.934; CFI=0.975; TLI=0.967; IFI=0.975; RMSEA=0.048 and PCLOSE=0.535.

According to the results from the structural model, all the four hypothesized relations are accepted with significant p-values and are shown in Table 4. The results provide the significant effect of direct and indirect effect of participation of customer on their brand loyalty. Hence, we can conclude that CETB acts a mediator between the participation and customer brand loyalty. Since the effect of participation without the presence of the mediator and with the presence mediator (CETB), is significant and is in the same direction, the mediation is complementary (Zhao et al., 2010), i.e., the direct and indirect effects are both in the same direction. Though the mediator is identified consistent with the hypothesized model, there might be an omitted mediator who would explain the role in a more comprehensive way. This complementary mediation might be due to the incomplete theoretical hypothesized framework (Zhao et al., 2010).

Table 4 Results Of The Hypothesized Relationships
Effect (s) of Independent Variable on Dependent Variable
Hypothesis	Standardized Regression Estimate	Standard Error	Critical Ratio	Probability (Significance)
H1: PAR->BIL	0.404	0.073	5.444	***
H2: PAR->CETB	0.427	0.231	5.819	***
H3: CETB->BIL	0.649	0.028	7.334	***
H4: PAR->CETB->BIL	0.131	0.064	2.006	*

Consideration of the other mediators like satisfaction, trust, and commitment in the further studies would better explain the role of CETB. However, this study concludes that CETB acts a mediator and effects the relationship by 0.131 and is significant at p ≤ 0.05. And the relationship between PAR and BIL without the inclusion CETB has an impact of 0.404 and is significant at 0.001. This result provides insights into the important role played by CETB in complementing the relationship between PAR and BIL.

Discussion

The one the main objective of the research was to validate the CETB (So et al., 2014) scale in the tourism context in India. The scale originally has five dimensions Identification, Enthusiasm, Attention, Absorption, and Interaction with 5-items for each dimension to measure customer engagement. The scale is validated through a proper statistical procedure and found valid to measure the customer engagement in the tourism sector. However, for further research, the five dimensions can be reduced to three or four dimension scale in a more parsimonious method reducing item redundancy (Streiner, 2003). A successful attempt is made in the same direction by (Harrigan et al., 2017).

The results strongly provide support of participation as an antecedent of customer engagement even in the tourism context. It has been proved as an effective driver of engagement on social media. Customer engagement on social media by brands encourage the firms outcomes in terms of profitability (Kumar, 2013), boost customer loyalty and satisfaction (Roderick et al., 2013). This study provides support for the same argument as true in the tourism context also. CE effects the behavioral intention of the customers to be loyal to brands of their choice.

The other major main of the study was to examine the role of CETB as a mediator between participation (PAR) and Brand Loyalty (BIL). The results provide support the role of CETB as a mediator. However, the direct effect of participation without the engagement variable is higher than the effect of participation on BIL in the presence of CE. This result suggests the complementary relationship between participation and CE. A more comprehensive framework, by including some relevant consequences of CE, like satisfaction, commitment and trust in the brand would provide more vivid details about the role of CE in enhancing customer loyalty.

The participation of customers on social media pages of their chosen brands would directly impact their purchase and loyalty decision. However, customer, while participating and interacting with the brands, if engaged actively, would exhibit more loyalty towards the brand. Hence the marketing practitioners should encourage taking up more engagement activities on social media to keep their customers delighted and loyal.

Theoretical Contributions

Theoretically, the study primarily contributes to the emerging concept of customer engagement to the tourism research. Though studies on the role of social media in the tourism sector are adequate, due to increased use of social media by customers and its applications in the tourism sector, there is still a broad scope to explore. Hence, this study directly contributes to the tourism literature in terms of the role of social media and its implications on marketing communication activities of the brands. The research has contributed to the tourism literature by validating the 25 item original scale of customer engagement developed by So et al. (2014) in the tourism context.

In the theoretical context, the study confirms the multi-dimensionality nature of customer engagement (Gambetti et al., 2012). The study also contributes to providing support for participation on social media as an antecedent to customer engagement, which in turn drives customer’s loyalty.

The study contributes to the literature of CE concept which is based on Service-Dominant (S-D) logic (Vargo & Lusch, 2004) and Social Exchange (SET) theoretical frameworks. It supports the S-D logic concept of explaining the role of the customer becoming active players from a passive audience in the process of co-creation, when they participate, interact and explicitly dialogue with stakeholders of the brand. The study reveals that the effect of participation in increasing engagement with the tourism brand. And also provides support for the Social Exchange theory that customer gets more obliged, thus become loyal then they interact with the brand.

Managerial Implications

The advent of social media demanding relationship managers to keep engaging the customers. The online tourism brands breaking the conventional modes to travel and tourism decision making of the tourists. The social media provide sophisticated ways of gaining information on tourism and destinations for the customers. Tourists participating in social media to exchange, share and discuss tourism-related information and experiences on social media, and use the same for decision making.

Tourism marketers should understand this emerging mode of communication and its effects on consumer behavior. Active tourists should be engaged by the tourism brands more effectively to create new tourists and retain loyal customers. The interaction of the active tourist with the brands and other customers on social media should be used by the tourism brands to create new products and services which delight the customers. Engagement should be effectively utilized by the brands to co-create customer-oriented services.

Conclusion

The five dimensional scale of CETB has been validated with proper statistical procedure in the Indian tourism context. The scale can be recommended as a comprehensive tool to measure the customer engagement with tourism brands. The study also finds the mediating role of CETB in the relationship between participation and brand loyalty. Customers participate on the social media brand pages of tourism and travels brands to share, express and receive information and experience related to these brands. By which the members participating benefit with the information which positive improves their loyalty towards the brand.

In this process the brands also benefit by understanding the needs of the customers through their comments, likes and recommendations. These cues help marketers to understand the strengths and weakness of their service offering. This will help brands serve better according to the expectations of the customers. Hence, it can be concluded that it is very important to engagement customer with appropriate and relevant information to increase brand loyalty as mere participation drive loyalty, it is through customer engagement the participation of customer leads to brand loyalty.

Limitations

The primary limitation of the study is to consider the final behavioral outcome of customers, i.e., loyalty as a consequence of CE. The study did not consider the immediate consequences of CE like satisfaction, commitment and trust defined in the literature. The future research should attempt to include the possible and relevant antecedents and consequences of CE within the tourism context to have a comprehensive study of the role of CE.

Though the sample size is justified to be adequate in terms of the statistical technique used, it is more appropriate to increase the sample size to generalize the results; considering the vast population of customer's participation on social media with tourism brands.

The study followed a non-probable sampling technique to collect the data, which we expect would bring more generalizable results if a probability-based sampling technique is followed. The selection of online tourism brands is based on the most number of followers of the brands on social media (Facebook and Twitter). However, a considering a single firm and a case study based method would provide greater insights into the engagement concept.

References

 

1. Bagozzi, R.P., & Dholakia, U.M. (2006). Antecedents and purchase consequences of customer participation in small group brand communities. International Journal of Research in Marketing, 23(1), 45-61.
2. Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
3. Bentler, P.M., & Bonett, D.G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606.
4. Blau, P.M. (1964). Exchange and power in social life, (Second Edition). New York: John Wiley & Sons.
5. Bowden, J.L.H. (2009). The process of customer engagement: A conceptual framework. Journal of Marketing Theory and Practice, 17(1), 63-74.
6. Brodie, R.J., Hollebeek, L.D., Juri?, B., & Ili?, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252-271.
7. Brodie, R.J., Hollebeek, L.D., & Smith, S.D. (2011). Engagement: An important bridging concept for the emerging SD logic lexicon. Paper Presented at the University of Auckland Business School.
8. Brodie, R.J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66(1), 105-114.
9. Byrne, B.M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming, (Third Edition). New York: Routledge.
10. Cabiddu, F., Carlo, M.D., & Piccoli, G. (2014). Social media affordances: Enabling customer engagement. Annals of Tourism Research, 48(3), 175-192.
11. Chen, J.S., Tsou, H.T., & Ching, R.K. (2011). Co-production and its effects on service innovation. Industrial Marketing Management, 40(8), 1331-1346.
12. Dabholkar, P.A. (2015). How to improve perceived service quality by increasing customer participation. Paper Presented at the Proceedings of the 1990 Academy of Marketing Science (AMS) Annual Conference.
13. Dwivedi, A. (2015). A higher-order model of consumer brand engagement and its impact on loyalty intentions. Journal of Retailing and Consumer Services, 24(1), 100-109. 14
14. Dwyer, P. (2007). Measuring the value of electronic word of mouth and its impact in consumer communities. Journal of Interactive Marketing, 21(2), 63-79.
15. Eisingerich, A.B., Auh, S., & Merlo, O. (2014). Acta non verba? The role of customer participation and word of mouth in the relationship between service firms’ customer satisfaction and sales performance. Journal of Service Research, 17(1), 40-53.
16. Flores, J., & Vasquez-Parraga, A.Z. (2015). The impact of choice on co-produced customer value creation and satisfaction. Journal of Consumer Marketing, 32(1), 15-25.
17. Foa, E.B., & Foa, U.G. (1980). Resource theory of social exchange, (First Edition). Springer.
18. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research (JMR), 18(1), 39-50.
19. Gambetti, R.C., Graffigna, G., & Biraghi, S. (2012). The grounded theory approach to consumer-brand engagement. International Journal of Market Research, 54(5), 659-687.
20. Goh, K.Y., Heng, C.S., & Lin, Z. (2013). Social media brand community and consumer behavior: Quantifying the relative impact of user- and marketer-generated content. Information Systems Research, 24(1), 88-107.
21. Guadagnoli, E., & Velicer, W.F. (1988). Relation to sample size to the stability of component patterns. Psychological Bulletin, 103(2), 265-275.
22. Gupta, G. (2019). Inclusive use of digital marketing in tourism industry information systems design and intelligent applications (First Edition). Springer.
23. Hair, J., Black, W., Babin, B., & Anderson, R. (2006). Multivariate data analysis, (Sixth Edition). New Jersey: Pearson Prentice Hall.
24. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (1998). Multivariate data analysis, (Sixth Edition). Prentice hall Upper Saddle River, NJ.
25. Harrigan, P., Evers, U., Miles, M., & Daly, T. (2017). Customer engagement with tourism social media brands. Tourism Management, 59(2), 597-609.
26. Hennig-Thurau, T., Malthouse, E.C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., & Skiera, B. (2010). The impact of new media on customer relationships. Journal of Service Research, 13(3), 311-330.
27. Hollebeek, L.D. (2011). Demystifying customer brand engagement: Exploring the loyalty nexus. Journal of Marketing Management, 27(8), 785-807.
28. Hollebeek, L.D. (2011b). Exploring customer brand engagement: Definition and themes. Journal of Strategic Marketing, 19(7), 555-573.
29. Hollebeek, L.D., Glynn, M.S., & Brodie, R.J. (2014). Consumer Brand Engagement in Social Media: Conceptualization, Scale Development and Validation. Journal of Interactive Marketing (Mergent, Inc.), 28(2), 149-165.
30. Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
31. Hudson, S., Roth, M.S., Madden, T.J., & Hudson, R. (2015). The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees. Tourism Management, 47(4), 68-76.
32. Joreskog, K.G., & Sorbom, D. (1988). LISREL 7: A guide to the program and its applications. Chicago: SPSS Inc.
33. Kaplan, A.M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68.
34. Kline, R.B. (2011). Principles and practice of structural equation modeling, (Third Edition). New York, NY: Guilford publications.
35. Leung, X.Y., Bai, B., & Stahura, K.A. (2013). The marketing effectiveness of social media in the hotel industry: A comparison of Facebook and Twitter. Journal of Hospitality & Tourism Research, 39(2), 147-169.
36. MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-145.
37. Mael, F., & Ashforth, B.E. (1992). Alumni and their alma mater: A partial test of the reformulated model of organizational identification. Journal of Organizational Behavior, 13(2), 103-123.
38. Malthouse, E., & Hofacker, C. (2010). Looking back and looking forward with interactive marketing. Journal of Interactive Marketing, 24(3), 181-184.
39. Malthouse, E.C., Haenlein, M., Skiera, B., Wege, E., & Zhang, M. (2013). Managing customer relationships in the social media era: Introducing the social CRM house. Journal of Interactive Marketing, 27(4), 270-280.
40. Marsh, H.W. (1991). Multidimensional students' evaluations of teaching effectiveness: A test of alternative higher-order structures. Journal of Educational Psychology, 83(2), 285-296.
41. Mollen, A., & Wilson, H. (2010). Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives. Journal of Business Research, 63(9), 919-925.
42. Munar, A.M., & Jacobsen, J.K.S. (2014). Motivations for sharing tourism experiences through social media. Tourism Management, 43(3), 46-54.
43. Muniz, A.M., & O'guinn, T.C. (2001). Brand community. Journal of Consumer Research, 27(4), 412-432.
44. Nunnally, J.C. (1991). Psychometric theory, (First Edition). New York, NY.: McGraw.
45. Nysveen, H., & Pedersen, P.E. (2014). Influences of co-creation on brand experience: The role of brand engagement. International Journal of Market Research, 56(6), 807-832.
46. Patterson, P., Yu, T., & De-Ruyter, K. (2006). Understanding customer engagement in services. Paper Presented at the Advancing Theory, Maintaining Relevance, Proceedings of ANZMAC 2006 Conference, Brisbane.
47. Prahalad, C.K., & Ramaswamy, V. (2002). The co-creation connection. Strategy and Business, 27(2) 50-61.
48. Prentice, C., Han, X.Y., Hua, L.L., & Hu, L. (2019). The influence of identity-driven customer engagement on purchase intention. Journal of Retailing and Consumer Services, 47(2), 339-347.
49. Ranjan, K.R., & Read, S. (2016). Value co-creation: Concept and measurement. Journal of the Academy of Marketing Science, 44(3), 290-315.
50. Samala, N., & Sapna, S. (2018). Investigating the role of customer brand engagement and relationship quality on brand loyalty: An empirical analysis. International Journal of E-Business Research (IJEBR), 14(3), 34-53.
51. Samala, N., & Sapna, S. (2018). Millennial’s engagement with fashion brands: A moderated-mediation model of brand engagement with self-concept, involvement and knowledge. Journal of Fashion Marketing and Management: An International Journal.
52. Schaufeli, W.B., Salanova, M., González-romá, V., & Bakker, A.B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71-92.
53. Sheldon, P.J. (1997). Tourism information technology. Wallingford, UK: CAB International.
54. Sigala, M., Christou, E., & Gretzel, U. (2012). Social media in travel, tourism and hospitality: Theory, practice and cases. Ashgate Publishing, Ltd.
55. So, K.K.F., King, C., & Sparks, B. (2014). Customer engagement with tourism brands scale development and validation. Journal of Hospitality & Tourism Research, 38(3), 304-329.
56. So, K.K.F., King, C., Sparks, B.A., & Wang, Y. (2014). The role of customer engagement in building consumer loyalty to tourism brands. Journal of Travel Research, 55(1), 64-78.
57. Streiner, D.L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99-103.
58. Thibaut, J.W., & Kelley, H.H. (1959). The social psychology of groups. New York: John Wiley & Sons.
59. Kumar, V. (2013). Profitable customer engagement: Concepts, metrics, and strategies. New Delhi, India: Sage Publications.
60. Van-Doorn, J., Lemon, K.N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P.C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253-266.
61. Vargo, S.L., & Lusch, R.F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1-17.
62. Vargo, S.L., & Lusch, R.F. (2008). Service-dominant logic: Continuing the evolution. Journal of the Academy of Marketing Science, 36(1), 1-10.
63. Verhoef, P.C., Reinartz, W.J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13(3), 247-252.
64. Vivek, S.D. (2009). A scale of consumer engagement. The University of Alabama TUSCALOOSA.
65. Vivek, S.D., Beatty, S.E., Dalela, V., & Morgan, R.M. (2014). A Generalized multidimensional scale for measuring customer engagement. Journal of Marketing Theory and Practice, 22(4), 401-420.
66. Vivek, S.D., Beatty, S.E., & Morgan, R.M. (2012). Customer engagement: Exploring customer relationships beyond purchase. The Journal of Marketing Theory and Practice, 20(2), 122-146.
67. Werthner, H., & Klein, S. (1999). Information technology and tourism: A challenging ralationship. Vienna: Springer-Verlag Wien.
68. Wheaton, B., Muthen, B., Alwin, D.F., & Summers, G.F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8(1), 84-136.
69. WTTC. (2017). Travel & tourism economic impact 2017: India. London, United Kingdom. World Travel & Tourism Council (WTTC).
70. WTTC. (2017b). Travel & tourism economic impact 2017: World. London, United Kingdom. World Travel & Tourism Council (WTTC).
71. Xiang, Z., Magnini, V.P., & Fesenmaier, D.R. (2015). Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. Journal of Retailing and Consumer Services, 22(3), 244-249.
72. Zeithaml, V.A., Berry, L.L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.
73. Zhao, X., Lynch-Jr, J.G., & Chen, Q. (2010). Reconsidering baron and kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206.