Customer engagement with tourism social media ... - Journal Papers

Tourism Management 59 (2017) 597e609

Contents lists available at ScienceDirect

Tourism Management journal homepage: www.elsevier.com/locate/tourman

Customer engagement with tourism social media brands Paul Harrigan a, Uwana Evers a, Morgan Miles b, *, Timothy Daly c a

The University of Western Australia, UWA Business School M263, 35 Stirling Highway, Crawley, WA 6009, Australia University of Canterbury, Department of Management, Marketing and Entrepreneurship, Private Bag 4800, Christchurch, New Zealand c United Arab Emirates University, Business Administration Department, PO BOX 15551, Al Ain, Saudi Arabia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 April 2016 Received in revised form 18 September 2016 Accepted 19 September 2016

In tourism, customer engagement has been found to boost loyalty, trust and brand evaluations. Customer engagement is facilitated by social media, but neither of these phenomena is well-researched in tourism. This research contributes in two ways. First, we validate the Customer Engagement with Tourism Brands (CETB) 25-item scale proposed by So, King & Sparks (2014) in a social media context, and offer an alternative three-factor 11-item version of the scale. Second, we replicate their proposed structural model, and test our alternative model, to predict the behavioural intention of loyalty from engagement, and to test customer involvement as an antecedent to engagement. Ultimately, we propose a customer engagement scale and a nomological framework for customer engagement, both of which can be applied in both tourism and non-tourism contexts. Managers of tourism brands on social to better assess the nature of customer engagement with the parsimonious 11-item scale. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Customer engagement Social media Tourism Brand loyalty Customer engagement scale

1. Introduction Customer engagement has gained much attention in the recent literature. This is due to engagement being linked with numerous important brand performance indicators including sales growth, customer involvement in product development, customer feedback, and referrals (Bijmolt et al., 2010; Bowden, 2009; Kumar et al., 2010; Nambisan & Baron, 2007; Sawhney, Verona, & Prandelli, 2005; Van Doorn et al., 2010). Not surprisingly, much of this brand engagement occurs online through social media (Malthouse & Hofacker, 2010). Customers engaged with brand communities online feel more connected to their brands, trust their preferred brands more, are more committed to their chosen brands, have higher brand satisfaction, and are more brand loyal (Brodie, Ili
c, Juri
c, & Hollebeek, 2013; Jahn & Kunz, 2012). In the tourism context, customer engagement has been found to boost loyalty, trust and brand evaluations (So, King & Sparks, 2014).

* Corresponding author. E-mail addresses: [email protected] (P. Harrigan), uwana.evers@uwa. edu.au (U. Evers), [email protected] (M. Miles), [email protected] (T. Daly). http://dx.doi.org/10.1016/j.tourman.2016.09.015 0261-5177/© 2016 Elsevier Ltd. All rights reserved.

Social media facilitate customer engagement, but neither of these phenomena are well researched in the tourism context. This has resulted in a need for practical social media recommendations for tourism organizations (Cabiddu, Carlo, & Piccoli, 2014; Hudson, Roth, Madden, & Hudson, 2015; Mistilis & Gretzel, 2013). Social media use is high among tourism organizations, particularly Facebook and Twitter (Leung, Bai, & Stahura, 2015); Instagram and other social media like TripAdvisor, Airbnb and Booking.com are growing in popularity and influence (Cabiddu et al., 2014; Filieri, 2014; Munar & Jacobsen, 2014). TripAdvisor is the world's largest travel review company and turned over $1.246 billion in 2014, up 32 percent from the previous year (Forbes, 2015). The goal of this research is to investigate the nature of customer engagement with tourism social media brands. We contribute to the tourism literature in two ways. First, we test the Customer Engagement with Tourism Brands (CETB) scale proposed by So et al. (2014) in a social media context. Further, we offer a psychometrically sound, concise eleven-item version of the scale. The social media context is very different to the offline hospitality brands (hotels and airlines) context in which the CETB scale was originally developed. Social media are driving fundamental business change, where they enable interactive, two-way communications between customers and organizations (Dijkmans, Kerkhof, & Beukeboom,

598

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

2015; Hollebeek, Glynn, & Brodie, 2014; Lee & Choi, 2014; Vivek, Beatty, & Morgan, 2012). Social media allow customers to comment, review, create and share content across online networks. They also allow customers direct access to organizations, brands and marketers (Chau & Xu, 2012). This creates challenges and opportunities for marketers, where they must engage with customers in real-time and manage the significant amounts of incoming customer data (Cui, Lui, & Guo, 2012; Hennig-Thurau et al., 2010; Pagani & Mirabello, 2012). The disruptive nature of the social media context means that it is valuable to test if a scale developed to measure engagement in an offline context performs similarly on social media. Second, we empirically replicate So et al.'s (2014) proposed conceptual model and test the nomological framework that incorporates customer involvement as an antecedent of customer engagement and behavioural intention of loyalty as an outcome of customer engagement. The next sections in this paper discuss the conceptualisation of customer engagement, its dimensions, and possible antecedents and consequences. The method, a survey of U.S. consumers, is outlined before the presentation of the scale validation and model testing results. Finally, there is a discussion around the implications for theory and practice. 2. Literature Customer engagement is characterised by repeated interactions between a customer and an organization that strengthen the emotional, psychological or physical investment a customer has in the brand and the organization (Hollebeek et al., 2014; Phang, Zhang, & Sutanto, 2013). Social exchange theory underpins this notion of investment, which holds that individuals evaluate the tangible and intangible costs and benefits of engaging in relationships (Thibaut & Kelley, 1959). For customer-brand engagement to persist, customers must at least achieve a balance in these costs and benefits over time (Brodie, Hollebeek, Juri
c, & Ili
c et al., 2011; Hollebeek, 2011). For example, consumers may invest enthusiasm and attention in engaging with a brand to receive benefits such as product news, offers, through to a sense of belonging (Blau, 1964; Foa & Foa, 1980). Social media are the dominant enablers of customer engagement, and these technologies are very different from previous marketer-customer technology platforms. They are owned by the customer but are transparent, and facilitate two-way interactions between customers and organizations (e.g. Deighton & Kornfeld, 2009; Dwyer, 2007; Hennig-Thurau et al., 2010; Vivek et al., 2012). Goh, Heng, and Lin's (2013) finding that engaged customers' messages were 22 times more valuable than those of marketers underlines the importance of understanding customer engagement. Social media are 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 UserGenerated Content’ (Kaplan & Haenlein, 2010, p. 61). This definition means that tourism sites like TripAdvisor, Booking.com, Airbnb, and Lonely Planet are considered as social media (Cabiddu et al., 2014; Munar & Jacobsen, 2014). They allow customers to comment, review, spread and even create content online that now even appears in search engine results. The importance of social media as a means for customer engagement within the tourism industry cannot be ignored (Cabiddu et al., 2014; Cheng & Edwards, 2015; Dijkmans et al., 2015; Hudson et al., 2015; Munar & Jacobsen, 2014). Customer engagement has been conceptualised in different ways (see Table 1). The majority of customer engagement research

has been based on a multidimensional conceptualisation, encompassing some form of cognitive, emotional, and behavioural components (Bowden, 2009; Brodie et al. 2013; Cheung, Lee, & Jin, 2011, pp. 1e8; Dessart, Veloutsou, & Morgan-Thomas, 2015; Dwivedi, 2015; Hollebeek, 2011; Hollebeek et al., 2014; Patterson, Yu, & De Ruyter, 2006; So et al., 2014). The broader conceptualisation of customer engagement behaviours proposed by Van Doorn et al. (2010) encompasses valence, form, scope, impact of engagement, and the customers' goals. All of these conceptualisations assert that customer engagement is discriminately different from involvement, a construct with which it is frequently compared. As So et al. (2014) state, involvement tends to be limited to a cognitive component, whereas engagement incorporates cognitive, emotional, and behavioural components (Hollebeek, 2011; Mollen & Wilson, 2010; Vivek et al., 2012). A recent analysis of customer engagement dimensionality concluded that customer engagement is a multi-dimensional construct consisting of three dimensions: cognitive (customer focus and interest in a particular brand), emotional (feelings of inspiration or pride caused by a particular brand) and behavioural (customer effort and energy necessary for interaction with a particular brand) (Kuvykaite_ & Tarute, 2015). Conceptualisations of engagement that do not explicitly refer to underlying cognitive, affective, and behavioural components are still likely to encompass these dimensions. The proposed dimensions of customer engagement with tourism brands (So et al., 2014) and Dwivedi's (2015) customer brand engagement conceptualisation can be mapped, largely, onto the customer brand engagement dimensions offered by Hollebeek et al. (2014) (Table 2). Interaction is similar to Activation and Vigor, representing the behavioural component of customer engagement; Identification relates to Affection and Dedication as the emotional component of customer brand engagement, while Attention and Absorption, the cognitive component. The definitions of the Absorption, Enthusiasm and Attention dimensions (So et al., 2014) have both affective and cognitive elements. 2.1. Dimensions of customer engagement This research builds on So et al.'s (2014) conceptualisation of customer engagement, which incorporates five dimensions, identification, enthusiasm, attention, absorption, interaction, and identification. As So et al. (2014) undertake a comprehensive discussion around these dimensions, the purpose of this paper is best served by briefly introducing each dimension. 2.1.1. Enthusiasm Enthusiasm represents an individual's “strong level of excitement or zeal” and interest in a brand (Vivek, 2009, p. 60). So et al. (2014, p. 308) note that the dimension of enthusiasm “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.” 2.1.2. Attention Attention refers to a customer's level of focus, consciously or sub-consciously, on the brand. Persistent attention towards a brand is likely to lead to higher levels of engagement (Lin, Gregor, & Ewing, 2008; Scholer & Higgins, 2009). 2.1.3. Absorption Absorption goes further than attention, where it refers to a customer's high level of concentration and engrossment in a brand

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

599

Table 1 Examples of conceptualisations of customer engagement. Construct

Dimensions of engagement

Author(s)

Customer brand engagement Consumer brand engagement Consumer brand engagement Consumer brand engagement Consumer brand engagement Consumer engagement Customer engagement Customer engagement Customer engagement Customer engagement (process of)

(1) (1) (1) (1) (1) (1) (1) (1) (1) (1)

So, Sparks & King (2014) Hollebeek (2011) Hollebeek et al. (2014) Dessart et al. (2015) Dwivedi (2015) Brodie et al. (2013) Patterson et al. (2006) Cheung et al. (2011) Enginkaya and Esen (2014) Bowden (2009)

Identification (2) Enthusiasm (3) Attention (4) Absorption (5) Interaction Cognitive (2) Emotional (3) Behavioural Cognitive processing (cognitive) (2) Affection (emotional) (3) Activation (behavioural) Cognitive (2) Emotional (3) Behavioural Vigor (2) Dedication (3) Absorption Cognitive (2) Emotional (3) Behavioural Vigor (2) Dedication (3) Absorption (4) Interaction Vigor (2) Dedication (3) Absorption Trust (2) Dedication (3) Reputation Involvement (behavioural) (2) Commitment (cognitive and affective)

Table 2 Merging conceptualisations of customer engagement. Customer engagement with tourism brands So et al. (2014)

Consumer brand engagement Dwivedi (2015)

Consumer brand engagement Hollebeek et al. (2014)

Dimension

Definition

Dimension

Dimension

Definition

Behavioural

Interaction

“Various participation (both online and offline) that a customer has with a brand organization or other customers outside of purchase” (p.311)

Vigor

“Vigor denotes high levels of energy and mental resilience when interacting with a brand, and the consumer willingness and the ability to invest effort in such interactions” (p. 100)

Activation

“A consumer's level of energy, effort and time spent on a brand in a particular consumer/brand interaction” (p. 154)

Emotional

Identification

Cognitive

Absorption

“The degree of a consumer's perceived oneness with or belongingness to the brand” (p.311) “A pleasant state which describes the customer as being fully concentrated, happy and deeply engrossed while playing the role as a consumer of the brand” (p. 311) “The degree of excitement and interest that a consumer has in the brand” (p. 311) “The degree of attentiveness, focus and connection that a consumer has with a brand” (p. 311)

Dedication

“In the context of consumer-brand relationships … dedication refers to a sense of significance, enthusiasm, inspiration, pride and challenge” (p. 100)

Affection

“A consumer's degree of positive brand-related affect in a particular consumer/brand interaction” (p.154)

Absorption

“Absorption refers to the sense of being fully concentrated and happily engrossed in brand interactions and in which time passes quickly” (p. 101)

Cognitive Processing

“A consumer's level of brand-related thought processing and elaboration in a particular consumerbrand interaction” (p. 154)

Enthusiasm

Attention

(Csikszentmihalyi, 1990; Schaufeli, Salanova, Gonzalez-Roma, & Bakker, 2002). Absorption is a positive trait, where customers will be contently absorbed in or with the brand, most likely unaware of how much time they are devoting to the brand (Patterson et al., 2006; Scholer & Higgins, 2009). 2.1.4. Interaction Interaction is fundamental to customer engagement, and involves sharing and exchanging ideas, thoughts, and feelings about experiences with the brand and other customers of the brand (Vivek, 2009). Interaction between customers of the brand is supported by the brand community literature (e.g. Muniz & O'Guinn, 2001). This interaction, as well as direct brand interaction, is a behavioural element of customer engagement. 2.1.5. Identification Customers will identify more with certain brands over others, particularly with those that match their self-image (Bagozzi &

Dholakia, 2006). This notion draws on social identity theory, where individuals have both a personal identity and a social identity. The groups one is a member of, in this context the brands with which one engages, are a manifestation of the brand's social identity function (Mael & Ashforth, 1992; Tajfel & Turner, 1985). These five dimensions of customer engagement are readily applicable to tourism brands on social media. Where social media are generally a powerful enabler of customer engagement, it follows that tourism social media brands like TripAdvisor, Booking. com, Airbnb, and Lonely Planet will seek to inspire customer engagement in each of the five dimensions. Following So et al. (2014), we treat customer engagement as a second-order, reflective construct. Covin and Wales (2012, p. 682) note “in the reflective measurement model the latent construct is modeled as producing its measures.” This means that the five dimensions above are likely to be caused by customer engagement, and to be inter-correlated (Hair, Ringle, & Sarstedt, 2011).

600

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

Fig. 1. Conceptual model of customer engagement. Note. The latent factor labels represent the following: INV ¼ customer involvement with a tourism social media brand; CE ¼ customer engagement with a tourism social media brand; BIL ¼ behavioural intention of loyalty toward a tourism social media brand.

2.2. Customer engagement antecedents As noted earlier in the paper, engagement is conceptually different from involvement. Based on this premise, it may be that involvement is an antecedent to customer engagement (Hollebeek et al., 2014). This relationship was proposed by So et al. (2014) in their conceptual model, that we test (Fig. 1). Zaichkowsky (1994) defines customer involvement as a customer's perceived relevance of an object (brand) based on their needs, values, and interests. Zaichkowsky (1985, p. 342) states that involvement is “independent of the behavior that results from involvement” and reflects an object's relevance in meeting a customer's value based needs. There are certain tourism brands on social media that may elicit higher involvement than others may, for different types of customers. For example, Airbnb's #mankind campaign seeks to involve the consumer at a cognitive level through focusing on positive news stories and the trust and kindness aspect of opening one's home to strangers. Other sites like Booking.com or TripAdvisor, may elicit involvement through different strategies, such as ease of use, soliciting reviews, transparency or social influence. 2.3. Customer engagement consequences Building on So et al. (2014), we test the relationship proposed between customer engagement and behavioural intention of loyalty (BIL), which is a widely used outcome variable. BIL, as operationalised by Zeithaml, Berry, and Parasuraman (1996), measures a customer's intention to say positive things about a brand, to recommend a brand generally and to friends, and to purchase this brand in the near future. As illustrated in Fig. 1 customer involvement with a tourism social media brand is an antecedent of customer engagement; while customer's behavioural intention of loyalty is a consequence of customer engagement. It is important, theoretically and managerially, that customer engagement is not treated as an outcome but rather a process that leads to more measurable outcomes such as customer satisfaction or loyalty. For example, Hudson et al. (2015) considered word-ofmouth as an outcome in their research on social media interaction. For engagement, there is evidence to support that it is a predictor of loyalty (Bowden, 2009; Hollebeek, 2011; Patterson et al., 2006). For tourism social media brands, such as Lonely Planet, Travelocity or Expedia, the relationship between engagement and loyalty is essential. The fragmentation of the tourism market, particularly online, has led to hyper-competition. In turn, this has led to these brands using social media to try to increase customer engagement, at cognitive, affective and behavioural levels, with the principal aim of encouraging higher customer retention. This paper contributes to tourism research, first, by validating a customer engagement scale previously developed by So et al. (2014). This scale consists of five dimensions, identification, enthusiasm, attention, absorption, and interaction. We take these

dimensions, and the scale as whole, and test it in a social media context among U.S. customers. Second, we develop a concise 11item version of the So et al. (2014) scale. Third, we examine the behavioural loyalty intention as a consequence of customer engagement (Bowden, 2009; Brodie, Hollebeek, Juri
c, & Ili
c, 2011; De Villiers, 2015; De Vries & Carlson, 2014; Dwivedi, 2015; Hollebeek, 2011; So et al., 2014). Fourth, we also examine customer involvement as an antecedent to customer engagement (Hollebeek et al., 2014; So et al., 2014).

3. Method This study extends recent work by Evers, Harrigan, and Daly (2015) and uses the dataset developed for that study. The data were gathered from Amazon Mechanical Turk (MTurk) marketplace during the first half of 2015 using an online survey about social media use in tourism-related decisions. There were three constructs of interest. First, consumer engagement with tourism brands was measured using the original 25- item So et al. (2014) CETB scale. This scale included five subscales, namely (1) identification; (2) enthusiasm; (3) attention; (4) absorption; and (5) interaction (items in Table 5). Second, behavioural intention of loyalty was measured using the 4-item Zeithaml et al. (1996) behavioural intention of loyalty (BIL) scale (items reported in Table 3). Third, customer involvement was measured using Zaichkowsky's (1994) 10-item scale (items reported in Table 3). Finally, basic demographics were collected. MTurk is Amazon's crowdsourcing employment website where anonymous participants find and complete tasks (HITs) posted by employers. Studies by Buhrmester, Kwang, and Gosling (2011) and Paolacci, Chandler, and Ipeirotis (2010) found that MTurk users were as representative of the population as online panels. In order to reduce potential fatigue effects, the current survey was split into two 15-min surveys. Using the method outlined by Daly and Nataraajan (2015) the 300 respondents who completed the first survey were invited to complete the second approximately two weeks later. In total 195 respondents completed both surveys, resulting in an attrition rate of 35%. Respondents were compensated $1.50 per completed survey. A HIT0 was posted on MTurk to invite individuals registered on the U.S.-based site to participate in two surveys over two weeks. The survey was separated into two 15min phases due to length (Daly & Nataraajan, 2015). Respondents were matched across both phases with 195 respondents completed both survey phases (see Daly & Nataraajan, 2015). Three hundred completed the first phase but not the second phase, resulting in an attrition rate of 35%. Respondents were paid a monetary inducement of $1.50 USD. Only respondents located in the United States were eligible to complete the surveys (this was verified by IP geolocation). Factor analysis of the customer engagement with tourism brands scale and descriptive statistics were carried out using SPSS (22.0) and the structural equation modeling in AMOS (22.0).

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

601

Table 3 Items used to measure behavioural intention of loyalty and customer involvement. Items for consumer involvement (INV) (Adapted from Zaichkowsky, 1994) 10 items presented on a 7-point semantic differential scale Thinking about your favourite tourism site, please indicate your attitudes toward the site from the descriptive words below: INV1 INV2 INV3 INV4 INV5 INV6 INV7 INV8 INV9 INV10

Important e Unimportant Boring e Interesting Relevant e Irrelevant Exciting - Unexciting Means nothing - Means a lot to me Appealing - Unappealing Fascinating - Mundane Worthless e Valuable Involving e Uninvolving Not needed e Needed

Items for Behavioural Intention of Loyalty (BIL) (adapted from Zeithaml et al., 1996) 4 items presented on a 7-point Likert scale (strongly disagree to strongly agree) Thinking of your favourite tourism site, indicate the extent to which you agree or disagree with the following statements: BIL1 I would say positive things about this tourism site to other people. BIL2 I would recommend this tourism site to someone who seeks my advice. BIL3 I would encourage friends and relatives to do business with this tourism site. BIL4 I would do more business with this tourism site in the next few years.

Table 4 The regional dispersion of the sample compared with 2015 U.S. Census estimates. Region

% of sample in region

% of U.S. households in regiona

Northeast Midwest West South

21.9% 19.9% 15% 43.4%

17.5% 21.1% 23.7% 37.7%

a

www.census.gov/popclock/data_tables.php?component¼growth.

4. Results It is not possible to estimate a response rate from a sampling frame based on MTurk (Evers et al., 2015). The sample was split evenly across the genders (50.8% male), and the average age of the respondents was 36 years (SD ¼ 11). The majority of respondents had completed university education (67.8%) and currently worked outside of the home (70.3%); the average annual household income bracket was $50,000-$59,999 (Evers et al., 2015). In addition, the sample was also broadly representative of the regional geographic dispersion of the U.S. population (Table 4). Importantly, 42 out of the 50 U.S. states were represented in the final data set. Overall these results suggest that the sampling should have limited exposure to geo-demographic based biases. Questions for involvement, engagement, and behavioural intention of loyalty were answered in relation to respondents' favourite tourism social media site; the top five favourite tourism brand sites specified by respondents were TripAdvisor (29%, n ¼ 56), Expedia (19%, n ¼ 37), Priceline (14%, n ¼ 27), Kayak (9%, n ¼ 18), and Orbitz (9%, n ¼ 18). A one-way ANOVA confirmed that there were no differences in the level of brand engagement across the nominated tourism brands (F (13, 181) ¼ 1.370, p ¼ 0.178). 4.1. Study 1. the customer engagement with tourism brands (CETB) factor structure To identify the underlying factor structure of So et al.'s (2014) 25-item customer engagement scale, data collected from 195 respondents were subjected to exploratory factor analysis (maximum likelihood estimation); oblique rotation (promax) was chosen over orthogonal (varimax) because the factors were highly correlated

with one another (Tabachnick & Fidell, 2007), and it can better identify the ‘simple structure’ of factors (Finch, 2006). The data satisfied the factor analysis assumptions; the Kaiser-Meyer-Olkin measure of sampling adequacy was ideal at 0.951, and Bartlett's test of sphericity was significant (c2 (300) ¼ 6048.58, p < 0.001). Although So et al. (2014) proposed a five-factor structure, the current analysis only supported four-factors (based on eigenvalues greater than 1). Our subsequent analyses proceeded in three steps: first, we present the analysis with five-factors as per So et al. (2014), second we test a four-factor structure to better fit the data, and third, following further analysis, we propose a three-factor 11-item scale that best fits the data. 4.2. The five-factor CETB scale We initially extracted five factors from the data, though the last factor had an eigenvalue of only 0.701. The interaction factor alone accounted for 61.6% of variance, while the remaining four factors accounted for an additional 22% of variance. In total, the five factors accounted for 83.8% of variation in the scale data. The scale items loaded onto separate factors as indicated by the original sub-scales. The loadings onto each factor ranged as follows: identification (0.755-0.861), enthusiasm (0.544e1.015), attention (0.695-0.935), absorption (0.505-0.869), and interaction (0.8740.981). The analysis also supported the reliability and validity of the original CETB scale. The reliability of all factor scales was examined by internal consistency analyses; the Cronbach's alpha for interaction (0.977), absorption (0.934), enthusiasm (0.951), identification (0.910), attention (0.951), and overall customer engagement with brands (0.974) all indicated high internal consistency. Maximum shared variance (MSV) and average shared squared variance (ASV) were both lower than the average variance extracted (AVE) for all factors demonstrating discriminant validity of the scale (Table 5). 4.3. The four-factor CETB scale As a result of our initial analysis, we re-ran the exploratory factor analysis and extracted four factors from the data, each with an eigenvalue greater than 1. In this four-factor model, all

602

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

Table 5 Confirmatory factor analysis of the five-factor customer engagement with tourism brands scale (So et al., 2014). Factor and item description

Model and item indices SL

Identification ID1. When someone criticizes this tourism site, it feels like a personal insult. ID2. When I talk about this tourism site, I usually say ‘we’ rather than ‘they’. ID3. This tourism site's successes are my successes. ID4. When someone praises this tourism site, it feels like a personal compliment. Enthusiasm EN1. I am heavily into this tourism site. EN2. I am passionate about this tourism site. EN3. I am enthusiastic about this tourism site. EN4. I feel excited about this tourism site. EN5. I love this tourism site. Attention AT1. I like to learn more about this tourism site. AT2. I pay a lot of attention to anything about this tourism site. AT3. Anything related to this tourism site grabs my attention. AT4. I concentrate a lot on this tourism site. AT5. I like learning more about this tourism site. Absorption AB1. When I am interacting with the tourism site, I forget everything else around me. AB2. Time flies when I am interacting with the tourism site. AB3. When I am interacting with the tourism site, I get carried away. AB4. When interacting with the tourism site, it is difficult to detach myself. AB5. In my interaction with the tourism site, I am immersed. AB6. When interacting with the tourism site intensely, I feel happy. Interaction INT1. In general, I like to get involved in the tourism site community discussions. INT2. I am someone who enjoys interacting with like-minded others in the tourism site community. INT3. I am someone who likes actively participating in the tourism site community discussions. INT4. In general, I thoroughly enjoy exchanging ideas with other people in the tourism site community. INT5. I often participate in activities of the tourism site community.

CR

SMC

0.913 0.804 0.826 0.895 0.877

AVE

MSV

ASV

0.725

0.546

0.451

0.785

0.743

0.561

0.786

0.743

0.563

0.712

0.643

0.527

0.893

0.487

0.436

0.647 0.683 0.801 0.769 0.948

0.907 0.955 0.881 0.913 0.763

0.822 0.912 0.777 0.833 0.582 0.948

0.841 0.929 0.924 0.890 0.844

0.707 0.863 0.855 0.793 0.712 0.937

0.802 0.896 0.866 0.844 0.872 0.775

0.644 0.804 0.750 0.712 0.760 0.601 0.977

0.957 0.948 0.974 0.965 0.879

0.916 0.899 0.948 0.932 0.772

Note. c2 ¼ 407.410 (p < 0.001, df ¼ 252) c2/df ¼ 1.617; goodness-of-fit index (GFI) ¼ 0.857; adjusted GFI ¼ 0.816; comparative fit index (CFI) ¼ 0.974; normed fit index (NFI) ¼ 0.936; root mean square error of approximation (RMSEA) ¼ 0.056; PCLOSE ¼ 0.147; SL ¼ standardised loadings; CR ¼ composite reliability; AVE ¼ average variance extracted; SMC ¼ squared multiple correlation.

Table 6 Confirmatory factor analysis of the proposed four-factor 20-item customer engagement with tourism brands scale. Factor and item description

Model and item indices SL

Identification ID1. When someone criticizes this tourism site, it feels like a personal insult. ID2. When I talk about this tourism site, I usually say ‘we’ rather than ‘they’. ID3. This tourism site's successes are my successes. ID4. When someone praises this tourism site, it feels like a personal compliment. Attraction EN2. I am passionate about this tourism site. EN3. I am enthusiastic about this tourism site. EN4. I feel excited about this tourism site. AT1. I like to learn more about this tourism site. AT2. I pay a lot of attention to anything about this tourism site. AT3. Anything related to this tourism site grabs my attention. Absorption AB1. When I am interacting with the tourism site, I forget everything else around me. AB2. Time flies when I am interacting with the tourism site. AB3. When I am interacting with the tourism site, I get carried away. AB4. When interacting with the tourism site, it is difficult to detach myself. AB5. In my interaction with the tourism site, I am immersed. Interaction INT1. In general, I like to get involved in the tourism site community discussions. INT2. I am someone who enjoys interacting with like-minded others in the tourism site community. INT3. I am someone who likes actively participating in the tourism site community discussions. INT4. In general, I thoroughly enjoy exchanging ideas with other people in the tourism site community. INT5. I often participate in activities of the tourism site community.

CR

SMC

0.922 0.851 0.821 0.922 0.862

MSV

ASV

0.452

0.415

0.805

0.570

0.487

0.734

0.570

0.487

0.893

0.480

0.433

0.725 0.674 0.850 0.744 0.961

0.928 0.896 0.935 0.868 0.906 0.848

0.861 0.803 0.875 0.753 0.820 0.719 0.932

0.813 0.900 0.870 0.836 0.863

0.661 0.811 0.756 0.700 0.745 0.977

0.957 0.948 0.974 0.965 0.879

AVE 0.748

0.916 0.900 0.948 0.932 0.772

Note. c2 ¼ 194.834 (p ¼ 0.003, df ¼ 143) c2/df ¼ 1.362; goodness-of-fit index (GFI) ¼ 0.908; adjusted GFI ¼ 0.866; comparative fit index (CFI) ¼ 0.989; normed fit index (NFI) ¼ 0.960; root mean square error of approximation (RMSEA) ¼ 0.043; PCLOSE ¼ 0.765; SL ¼ standardised loadings; CR ¼ composite reliability; AVE ¼ average variance extracted; SMC ¼ squared multiple correlation.

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

Enthusiasm and Attention items, plus one Absorption item (AB6), loaded onto the same factor. This result suggested that the Enthusiasm and Attention items measure the same underlying construct. This factor was renamed ‘Attraction’ to better represent the measurement items. The factor loadings for Attraction ranged from 0.504 to 1.02. The other five Absorption items (0.792-0.867) loaded onto one factor as expected. All five Interaction items (0.879-0.991) and all four Identification items (0.745-0.878) loaded onto separate factors. Five items (EN1, EN5, AT4, AT5, AB6) were removed from the model based upon inspection of the standardised loadings and model fit. The four factors of the 20-item scale accounted for a cumulative 83.4% of variation in the data. Therefore, in terms of explained variance, the 20-item CETB scale performs equally as well as the original 25-item scale. The reduced 20-item CETB scale also demonstrates good reliability and validity. The reliability of the four factors individually and the scale as a whole were examined by internal consistency analyses; the Cronbach's alpha for interaction (0.977), absorption (0.931), identification (0.910), attraction (0.952), and overall customer engagement with brands (0.967) all indicated high internal consistency. A confirmatory factor analysis was conducted in AMOS to examine the validity of the scale. Convergent validity was demonstrated by the average variance extracted (AVE) exceeding 0.5 for all constructs (Fornell & Larcker, 1981; Hair, Black, Babin, & Anderson, 2010). Further, the maximum shared variance (MSV) and average shared squared variance (ASV) were both lower than the AVE for all factors demonstrating discriminant validity of the scale (Gaskin, 2016; Hair et al., 2010) (Table 6).

4.4. The three-factor CETB scale High inter-item correlations in the 20-item scale suggested potential item redundancy, and the factor loadings pointed to an overlap, whereby some items were loading onto multiple factors (see Streiner, 2003). We therefore removed the highly correlated items (ID3, EN3, AT1, AT2, AB2, AB3, AB4, INT3, INT4) to reduce redundancy and move toward a more parsimonious version of the CETB scale. We re-ran the factor analysis on the remaining 11 items and discovered that the items were loading onto three factors. Identification and Interaction remained independent factors, while the items from the CETB's three factors Absorption, Enthusiasm, and Attention all collapsed into one factor measuring a single construct - Absorption. The three factors of the 11-item scale

603

accounted for a cumulative 80.5% of variation in the data, explaining almost as much variance in the data as the original 25item scale. The 11-item CETB scale also demonstrated reliability and validity. The Cronbach's alpha for interaction (0.948), absorption (0.906), identification (0.869), and overall customer engagement with brands (0.936) all indicated high internal consistency. Lastly, a confirmatory factor analysis examined the validity of the scale. Convergent validity was demonstrated by the average variance extracted (AVE) exceeding 0.5 for all constructs (Fornell & Larcker, 1981; Hair et al., 2010) and discriminant validity was shown as the maximum shared variance (MSV) and average shared squared variance (ASV) were both lower than the AVE for all factors (Gaskin, 2016; Hair et al., 2010) (Table 7).

4.5. Study 2: an antecedent and consequence of customer engagement We tested the predictive validity of the five-factor CE scale with a structural model, placing a path from CE to BIL (Fig. 2). The fit indices suggested that the model fit the data fairly well; (c2 (346) ¼ 559.375, p < 0.001), c2/df ¼ 1.617, GFI ¼ 0.829; AGFI ¼ 0.785; CFI ¼ 0.968; NFI ¼ 0.922; RMSEA ¼ 0.056; PCLOSE ¼ 0.111. The results suggest that original 25-item CETB scale is a significant predictor of BIL (b ¼ 0.648, CR ¼ 7.668, p < 0.001) and explained 42% of the variance in BIL. We tested the predictive validity of the 20-item CETB scale with a structural model, placing a path from CE to BIL (Fig. 3). The fit indices suggested that the model fit the data well; (c2 (224) ¼ 323.499, p < 0.001), c2/df ¼ 1.444, GFI ¼ 0.874; AGFI ¼ 0.832; CFI ¼ 0.982; NFI ¼ 0.943; RMSEA ¼ 0.048; PCLOSE ¼ 0.611. The results suggest that the four-factor CE scale is a significant predictor of BIL (b ¼ 0.625, CR ¼ 7.493, p < 0.001) and explained 39% of the variance in BIL. The final examination was of the predictive ability of the 11-item three-factor CETB scale. As with the previous two structural models, we tested the three-factor model by placing a path from CE to BIL (Fig. 4). The fit indices suggested that the model fit the data moderately well; (c2 (82) ¼ 172.101, p < 0.001), c2/df ¼ 2.10, GFI ¼ 0.890; AGFI ¼ 0.839; CFI ¼ 0.965; NFI ¼ 0.935; RMSEA ¼ 0.075; PCLOSE ¼ 0.005. The results suggest that the three-factor CE scale is a significant predictor of BIL (b ¼ 0.635, CR ¼ 7.364, p < 0.001) and explained 40% of the variance in BIL.

Table 7 Confirmatory factor analysis of the proposed three-factor 11-item customer engagement with tourism brands scale. Factor and item description

Model and item indices SL

Identification ID1. When someone criticizes this tourism site, it feels like a personal insult. ID2. When I talk about this tourism site, I usually say ‘we’ rather than ‘they’. ID4. When someone praises this tourism site, it feels like a personal compliment. Absorption EN2. I am passionate about this tourism site. EN4. I feel excited about this tourism site. AT3. Anything related to this tourism site grabs my attention. AB1. When I am interacting with the tourism site, I forget everything else around me. AB5. In my interaction with the tourism site, I am immersed. Interaction INT1. In general, I like to get involved in the tourism site community discussions. INT2. I am someone who enjoys interacting with like-minded others in the tourism site community. INT5. I often participate in activities of the tourism site community.

CR

SMC

0.874 0.832 0.809 0.878

MSV

ASV

0.697

0.569

0.501

0.663

0.569

0.530

0.860

0.491

0.462

0.696 0.640 0.757 0.906

0.920 0.930 0.858 0.642 0.675

0.850 0.843 0.712 0.436 0.472 0.948

0.949 0.956 0.874

AVE

0.904 0.909 0.767

Note. c2 ¼ 172.101 (p ¼ 0.000, df ¼ 82) c2/df ¼ 2.10; goodness-of-fit index (GFI) ¼ 0.890; adjusted GFI ¼ 0.839; comparative fit index (CFI) ¼ 0.965; normed fit index (NFI) ¼ 0.935; root mean square error of approximation (RMSEA) ¼ 0.075; PCLOSE ¼ 0.005; SL ¼ standardised loadings; CR ¼ composite reliability; AVE ¼ average variance extracted; SMC ¼ squared multiple correlation.

604

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

Fig. 2. The five-factor model of CE structural model with behavioural intention of loyalty. Note. The latent factor labels represent the following: INT ¼ interaction; AB ¼ absorption; EN ¼ enthusiasm; ID ¼ identification; AT ¼ attention; CE ¼ customer engagement; BIL ¼ behavioural intention of loyalty.

4.6. A test of the conceptual model Finally, we examined the initial conceptual model, where customer involvement predicts customer engagement, which in turn, predicts behavioural intentions of loyalty. We tested the predictive validity of the proposed three-factor 11-item CETB scale with a structural model, placing paths from INV to CE and CE to BIL (Fig. 4). The fit indices suggested that the model fit the data moderately well; (c2 (248) ¼ 390.581, p < 0.001), c2/df ¼ 1.575, GFI ¼ 0.859; AGFI ¼ 0.815; CFI ¼ 0.962; NFI ¼ 0.903; RMSEA ¼ 0.054; PCLOSE ¼ 0.234. The results suggest that customer involvement can predict customer engagement (b ¼ 0.694, CR ¼ 6.781, p < 0.001), accounting for 48% of the variance in CE. Importantly, the three-factor CE scale remained a significant predictor of BIL (b ¼ 0.663, CR ¼ 7.505, p < 0.001) and explained 44% of

the variance in BIL when testing the relationship between customer involvement, customer engagement and loyalty proposed in So et al.'s (2014) nomological framework (see Fig. 5). 5. Discussion The first objective of our research was to examine and validate the CETB scale proposed by So et al. (2014). The initial analyses found that the original scale has four instead of five underlying factors. In this phase of our study, the original items for the factors Enthusiasm and Attention loaded together onto the same factor. Therefore, for So et al.'s (2014) original CETB scale it is proposed these factors be merged into one factor named Attraction. The results demonstrate that the proposed four-factor, twenty-item scale has better structural model fit than the original five-factor

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

605

Fig. 3. The four-factor model of CE structural model with behavioural intention of loyalty, Note. The latent factor labels represent the following: ID ¼ identification; ATT ¼ attraction; AB ¼ absorption; INT ¼ interaction; CE ¼ customer engagement; BIL ¼ behavioural intention of loyalty.

twenty-five-item scale (So et al., 2014). In addition, the 20-item four-factor scale demonstrated a similar ability to predict the behavioural intention of loyalty. However, additional analysis revealed high inter-item correlations in the four-factor, twentyitem scale that suggested item redundancy (Streiner, 2003). Highly correlated items were removed from the twenty-item scale. This resulted in a psychometrically sound three-factor, eleven-item scale that is more parsimonious and better fits the data. This finding has implications for future research into customer engagement with tourism brands. Empirically, the results strongly support the 11-item reduced scale. Conceptually, the collapsing of Enthusiasm and Attention into Absorption is supported by considering Hollebeek's et al.’s (2014, p. 160) “attitudinal CBE factors”. These three factors map against Hollebeek's et al.'s (2014) factors of cognitive processing and affection, which they label attitudinal. Looking beyond the definitions into the items, and thus what the constructs are measuring, we can see that there is also conceptual argument for construct validity. Absorption is described as “(a) pleasant state with which describes the customer as being fully concentrated, happy, and deeply engrossed while playing the role as a consumer of the brand,” (So et al. 2014, p. 311) which involves customer passion

with, excitement about, and attraction to the tourism site. In this way, absorption encompasses enthusiasm and attention. This leads to the second objective of this research, which was to investigate the nature of customer engagement within So et al.'s (2014) proposed nomological framework. Specifically, with customer involvement as an antecedent and behavioural intention of loyalty as a consequence. Providing empirical support for So et al.'s (2014) nomological framework, the current research finds that customer engagement is a predictor of brand loyalty using the 11-item CETB scale. This is a relationship proposed by many other researchers (e.g. De Villiers, 2015; De Vries & Carlson, 2014; Dwivedi, 2015; Hollebeek, 2011; Hollebeek et al., 2014; Vivek et al., 2012). The findings also build on Hudson et al.'s (2015) parallel work on the effects of social media interaction on word-ofmouth, which did not explicitly consider the role of customer engagement. The finding that involvement is a predictor of engagement with tourism brands on social media is important. Brands must use social media, among other channels, to elicit involvement with their brand if they seek to engage with consumers effectively. Involvement is characterised, for example, by the brand's level of appeal, meaning, and value to customers (Zaichkowsky, 1994). By placing

606

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

Fig. 4. The proposed three-factor model of CE structural model with behavioural intention of loyalty. Note. The latent factor labels represent the following: ID ¼ identification; AB ¼ absorption; INT ¼ interaction; CE ¼ customer engagement; BIL ¼ behavioural intention of loyalty.

r2=.48 .69 INV

CE

r2=.44 .66

BIL

Note. The latent factor labels represent the following: INV = customer involvement; CE = customer engagement; BIL = behavioural intention of loyalty; the 11-item scale was used to measure CE. Fig. 5. The relationship between customer involvement, engagement and loyalty. Note. The latent factor labels represent the following: INV ¼ customer involvement; CE ¼ customer engagement; BIL ¼ behavioural intention of loyalty; the 11-item scale was used to measure CE.

and testing customer engagement as part of So et al.'s (2014) nomological framework, we emphasise its interdependence on existing constructs. 6. Conclusion and implications This research contributes to tourism research, first, by validating a customer engagement scale previously developed by So et al. (2014). The original scale consists of five dimensions, identification, enthusiasm, attention, absorption, and interaction. We take these dimensions, and the scale as whole, and test it in a social media context among U.S. customers. Second, we offer a psychometrically sound, parsimonious 11-item version of the CETB. Third, we examine the behavioural loyalty intention as a consequence of customer engagement (Bowden, 2009; Brodie et al., 2011; De Villiers, 2015; De Vries & Carlson, 2014; Dwivedi, 2015; Hollebeek, 2011; So et al. 2014). Fourth, we examine customer involvement as an antecedent to customer engagement (Hollebeek et al., 2014; So et al., 2014). Customer engagement is an area of significant theoretical and practical relevance. How brands can utilise social media to increase engagement among their customers is a key question in the hypercompetitive tourism sector. This research extends the excellent

work by Cabiddu et al. (2014) on how organizations engage with customers by articulating how customers engage with the organization's brands. Another key question is around the outcomes of customer engagement. This paper has addressed these questions with two specific contributions. First, we validate and revise a customer engagement scale developed by So et al. (2014) specifically for the tourism sector. We applied this scale to tourism brands on social media. Second, we place customer engagement within a meaningful nomological framework for researchers and practitioners, where customer involvement leads to customer engagement, which in turn leads to brand loyalty. The multi-dimensionality of customer engagement is confirmed by our analysis. We find there to be three dimensions of customer engagement in the 11-item scale that can be used for future research within and beyond the tourism sector. These dimensions are Absorption, Identification and Interaction. Previous research, notably by Dwivedi (2015) and Hollebeek et al. (2014), has similarly conceptualised and operationalised dimensions of customer engagement. This research builds on So et al. (2014) and provides a conceptualisation of customer engagement, operationalised in the tourism sector. This study's theoretical contributions include: (1) the development of a parsimonious 11-item customer engagement scale that

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

can be taken and tested in other tourism or non-tourism contexts; and (2) an empirically tested nomological framework that places customer engagement as a consequence of involvement and as an antecedent of behavioural intention of loyalty that can be applied to and assessed in other tourism or non-tourism contexts. The focus on tourism brands on social media is an important contribution that adds theoretical weight to the social media marketing literature. Although the tourism sector is unique, findings may be applied to other sectors. We believe social exchange theory is an appropriate theoretical underpinning for customer engagement research, no matter the sector. It is clear that exchange between consumer and marketer is essential for consumers to identify with, absorb themselves in, and interact with brands. Managerially, tourism brand managers using social media will be able to better understand and shape the nature of customer engagement and the nuances of its dimensions using a concise 11item CETB scale. Social media is the ideal channel through which to inspire customers' absorption, identification and interaction with a brand. However, these are complex cognitive, affective and behavioural components. Brands must understand how to effectively use various functions of social media, such as pictures, videos, polls, reviews, comments, blogs, all of which can be both marketerand user-generated, to foster these three different dimensions of engagement with their brand over another. For example, brands can provide entertaining or educational content through blogs to absorb customers. Through these activities and others, brands can develop a unique image on social media that can enable customers to identify with their brand over others. A final example would be brands that provide honest and transparent responses to customer reviews can experience positive interaction with their customers. Managers must also understand the ecosystem within which customer engagement exists and functions. We have illustrated that involvement is an antecedent to customer engagement, which means that brand managers are particularly responsible for

607

importance of, one, being on social media, and two, developing strategies for customer engagement on social media. There are limitations to the research, which can be mitigated by future research. For example, this study is based on one-country (the U.S.) non-random sample and therefore the findings cannot be generalized beyond the sample. Future research should validate the customer engagement scale and model using random samples in countries with varying cultures. A second, related point is that the United States itself is extremely large and diverse both from a tourism accessibility perspective and overall with state and regional cultural differences. It is potentially important to investigate if the results reported in this research vary depending on state or regional comparisons. As discussed in the results, the sample of this study is broadly representative of the major U.S. regions e however the sample size is not large enough to do this fine-grained intra-national investigation. Third, we looked at the most popular tourism brands on social media. It would be useful for future research to assess the scale and model on other brands' social media, such as tourism organizations (e.g. Tourism Australia, Discover America), major attractions, and small and large hotels. Funding This research was funded by a BHP Billiton Distinguished Research Award. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.tourman.2016.09.015. Appendix

Consumer engagement with tourism brand items

N

Mean

SD

Skewness

Kurtosis

ID1. CBE - Identification -When someone criticizes this tourism site, it feels like a personal insult. ID2. CBE - Identification -When I talk about this tourism site, I usually say ‘we’ rather than ‘they’. ID3. CBE - Identification -This tourism site's successes are my successes. ID4. CBE - Identification -When someone praises this tourism site, it feels like a personal compliment. EN1. CBE - Enthusiasm -I am heavily into this tourism site. EN2. CBE - Enthusiasm -I am passionate about this tourism site. EN3. CBE - Enthusiasm -I am enthusiastic about this tourism site. EN4. CBE - Enthusiasm -I feel excited about this tourism site. EN5. CBE - Enthusiasm -I love this tourism site. AT1. CBE - Attention -I like to learn more about this tourism site. AT2. CBE - Attention -I pay a lot of attention to anything about this tourism site. AT3. CBE - Attention -Anything related to this tourism site grabs my attention. AT4. CBE - Attention -I concentrate a lot on this tourism site. AT5. CBE - Attention -I like learning more about this tourism site. AB1. CBE - Absorption -When I am interacting with the tourism site, I forget everything else around me. AB2. CBE - Absorption -Time flies when I am interacting with the tourism site. AB3. CBE - Absorption -When I am interacting with the tourism site, I get carried away. AB4. CBE - Absorption -When interacting with the tourism site, it is difficult to detach myself. AB5. CBE - Absorption -In my interaction with the tourism site, I am immersed. AB6. CBE - Absorption -When interacting with the tourism site intensely, I feel happy. INT1. CBE - Interaction -In general, I like to get involved in the tourism site community discussions. INT2. CBE - Interaction -I am someone who enjoys interacting with like-minded others in the tourism site community. INT3. CBE - Interaction -I am someone who likes actively participating in the tourism site community discussions. INT4. CBE - Interaction -In general, I thoroughly enjoy exchanging ideas with other people in the tourism site community. INT5. CBE - Interaction -I often participate in activities of the tourism site community.

195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195

1.72 1.63 1.79 2.02 2.73 2.79 3.36 3.31 4.09 3.96 3.55 3.31 3.32 3.77 2.89 3.51 3.00 2.45 3.24 3.73 2.58 2.81 2.64 2.76 2.46

1.225 1.166 1.308 1.489 1.717 1.709 1.938 1.934 1.747 1.870 1.845 1.827 1.765 1.816 1.755 1.898 1.830 1.557 1.790 1.720 1.775 1.856 1.851 1.852 1.750

2.108 2.463 1.769 1.452 0.612 0.608 0.160 0.248 -0.254 -0.210 0.111 0.264 0.143 -0.155 0.573 0.155 0.418 0.892 0.122 -0.267 0.851 0.584 0.868 0.732 1.046

4.519 6.450 2.412 1.220 -0.785 -0.680 1.326 1.213 -0.720 1.066 1.144 1.094 1.027 -0.999 -0.790 1.149 1.171 -0.138 1.234 -0.983 -0.451 1.026 -0.484 -0.680 -0.070

developing a brand that inspires involvement. Such high level branding decisions will influence customer engagement on social media. We have also illustrated that loyalty is an outcome of customer engagement, which emphasises to managers the

References 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, 45e61.

608

P. Harrigan et al. / Tourism Management 59 (2017) 597e609

Bijmolt, T. H. A., Leeflang, P. S. H., Block, F., Eisenbeiss, M., Hardie, B. G. S., Lemmens, A., et al. (2010). Analytics for customer engagement. Journal of Service Research, 13(3), 341e356. Blau, P. M. (1964). Exchange and power in social life. New York: John Wiley & Sons. Bowden, J. L. (2009). The process of customer engagement: A conceptual framework. Journal of Marketing Theory and Practice, 17(1), 63e74. Brodie, R. J., Hollebeek, L. D., Juri
c, B., & Ili
c, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252e271. Brodie, R. J., Ili
c, A., Juri
c, B., & Hollebeek, L. D. (2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66, 105e114. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon's Mechanical Turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3e5. Cabiddu, F., Carlo, M. D., & Piccoli, G. (2014). Social media affordances: Enabling customer engagement. Annals of Tourism Research, 48, 175e192. Chau, M., & Xu, J. (2012). Business intelligence in Blogs: Understanding consumer interactions and communities. MIS Quarterly, 36(4), 1189e1216. Cheng, M., & Edwards, D. (2015). Social media in tourism: A visual analytic approach. Current Issues in Tourism, 18, 1080e1087. Cheung, C. M. K., Lee, M. K. O., & Jin, X.-L. (2011). Customer engagement in an online platform: A conceptual model and scale development. In The proceedings of the thirty-second international conference on information systems (ICIS), Shanghai, China. Covin, J. G., & Wales, W. J. (2012). The measurement of entrepreneurial orientation. Entrepreneurship Theory and Practice, 36(4), 677e702. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper & Row. Cui, G., Lui, H.-K., & Guo, X. (2012). The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17(1), 39e57. Daly, T., & Nataraajan, R. (2015). Swapping bricks for clicks: Crowdsourcing longitudinal data on Amazon Turk. Journal of Business Research, 68(12), 2603e2609. De Villiers, R. (2015). Consumer brand enmeshment: Typography and complexity modeling of consumer brand engagement and brand loyalty enactments. Journal of Business Research, 68(9), 1953e1963. De Vries, N. J., & Carlson, J. (2014). Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. Journal of Brand Management, 21, 495e415. Deighton, J., & Kornfeld, L. (2009). Interactivity's unanticipated consequences for marketers and marketing. Journal of Interactive Marketing, 23(1), 4e10. Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in online brand communities. The Journal of Product and Brand Management, 24(1), 28e42. Dijkmans, C., Kerkhof, P., & Beukeboom, C. J. (2015). A stage to engage: Social media use and corporate reputation. Tourism Management, 47, 58e67. Dwivedi, A. (2015). A higher-order model of consumer brand engagement and its impact on loyalty intentions. Journal of Retailing and Consumer Services, 24, 100e109. Dwyer, P. (2007). Measuring the value of electronic word of mouth and its impact in consumer communities. Journal of Interactive Marketing, 21(2), 63e79. Enginkaya, E., & Esen, E. (2014). Dimensions of online customer engagement. Journal of Business, Economics & Finance, 3(1), 106e114. Evers, U., Harrigan, P., & Daly, T. (2015). Brand engagement with tourism organisations on social media. In Innovation, and growth strategies in marketing conference proceedings of the Australian and New Zealand marketing association conference, Sydney, Australia, 2e4 December (pp. 898e904). Filieri, R. (2014). Why do travellers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Management, 51, 174e185. Finch, H. (2006). Comparison of the performance of Varimax and Promax rotations: Factor structure recovery for dichotomous items. Journal of Educational Measurement, 43(1), 39e52. Foa, E. B., & Foa, U. G. (1980). Resource theory: Inter-personal behavior as social exchange. In K. J. Gergen, M. S. Greenberg, & R. H. Willis (Eds.), Social Exchange: Advances in theory and research. New York: Plenum Press. Forbes. (2015). TripAdvisor's growth plans for 2015 and beyond. Forbes.com. Available at: http://www.forbes.com/sites/greatspeculations/2015/03/06/tripadvisorsgrowth-plans-for-2015-and-beyond/ (Accessed 04.08.15). Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39e50. Gaskin, J. (2016). ValidityMaster, stats tools package. http://statwiki.kolobkreations. com. 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), 88e107. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Upper Saddle River, NJ: Pearson. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139e151. Hennig-Thurau, T., Malthouse, E. C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., et al. (2010). The impact of new media on customer relationships. Journal of Service Research, 13(3), 311e330. Hollebeek, L. D. (2011). Demystifying customer brand engagement: Exploring the loyalty nexus. Journal of Marketing Management, 27(7e8), 785e807.

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, 28(2), 149e165. 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, 68e76. Jahn, B., & Kunz, W. (2012). How to transform consumers into fans of your brand. Journal of Service Management, 23(3), 344e361. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59e68. Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010). Undervalued or overvalued customers: Capturing total customer engagement value. Journal of Service Research, 13(3), 297e310. _ R., & Tarute, A. (2015). A critical analysis of consumer engagement Kuvykaite, dimensionality [abstract]. Kaunas, Lithuania: International Scientific Conference Economics and Management (ICEM). Lee, J.-N., & Choi, B. (2014). Strategic role of IT and its impact on organizations. Information & Management, 51, 881e882. Leung, X. Y., Bai, B., & Stahura, K. A. (2015). The marketing effectiveness of social media in the hotel industry a comparison of Facebook and Twitter. Journal of Hospitality & Tourism Research, 39(2), 147e169. Lin, A., Gregor, S., & Ewing, M. (2008). Developing a scale to measure the enjoyment of Web experiences. Journal of Interactive Marketing, 22(4), 40e57. 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, 103e123. Malthouse, E., & Hofacker, C. (2010). Looking back and looking forward with interactive marketing. Journal of Interactive Marketing, 24(3), 181e184. Mistilis, N., & Gretzel, U. (2013). Tourism operators' digital uptake benchmark survey 2013. Research report http://www.tra.gov.au/documents/tourism_operators_ surveypdf (Accessed February 2015). Mollen, A., & Wilson, H. (2010). Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives. Journal of Business Research, 63(9e10), 919e925. Munar, A. M., & Jacobsen, J. K. S. (2014). Motivations for sharing tourism experiences through social media. Tourism Management, 43, 46e54. Muniz, J. A. M., & O'Guinn, T. C. (2001). Brand community. Journal of Consumer Research, 27(4), 412e432. Nambisan, S., & Baron, R. A. (2007). Interactions in virtual customer environments: Implications for product support and customer relationship management. Journal of Interactive Marketing, 21(2), 42e62. Pagani, M., & Mirabello, A. (2012). The influence of personal and social-interactive engagement in social TV web sites. International Journal of Electronic Commerce, 16(2), 41e67. Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5(5), 411e419. Patterson, P., Yu, T., & De Ruyter, K. (2006). Understanding customer engagement in services. In Advancing theory, maintaining relevance: Proceedings of ANZMAC 2006 conference, Brisbane, Australia (pp. 4e6). Phang, C. W., Zhang, C., & Sutanto, J. (2013). The influence of user interaction and participation in social media on the consumption intention of niche products. Information & Management, 50, 661e672. Sawhney, M., Verona, G., & Prandelli, E. (2005). Collaborating to create: The Internet as a platform for customer engagement in product innovation. Journal of Interactive Marketing, 19(4), 4e17. Schaufeli, W. B., Salanova, M., Gonzalez-Roma, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71e92. Scholer, A. A., & Higgins, E. T. (2009). Exploring the complexities of value creation: The role of engagement strength. Journal of Consumer Psychology, 19(2), 137e143. 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), 304e329. Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99e103. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed). Upper Saddle River, NJ: Pearson Allyn & Bacon. Tajfel, H., & Turner, J. C. (1985). The social identity theory of intergroup behaviour. In S. Worchel, & W. G. Austin (Eds.), Psychology of intergroup relations (pp. 7e24). Chicago, IL: Nelson-Hall. Thibaut, J. W., & Kelley, H. H. (1959). The social psychology of groups. New York: John Wiley & Sons. Van Doorn, J., Lemon, K. E., Mittal, V., Nab, S., Pick, D., Pirner, P., et al. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253e266. Vivek, S. D. (2009). A scale of consumer engagement, doctor of philosophy dissertation, department of management & marketing. The University of Alabama. http:// libcontent1.lib.ua.edu/content/u0015/0000001/0000096/u0015_0000001_ 0000096.pdf. Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer relationships beyond purchase. Journal of Marketing Theory and Practice, 20(2), 127e145. Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341e352. Zaichkowsky, J. L. (1994). The personal involvement inventory: Reduction, revision,

P. Harrigan et al. / Tourism Management 59 (2017) 597e609 and application to advertising. Journal of Advertising, 23(4), 59e70. Zeithaml, V., Berry, L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31e46. Dr. Paul Harrigan has been at UWA since July 2012. Before this, he was a Lecturer in Marketing at the University of Southampton in the UK from 2008 to 2012. PhD, from the University of Ulster in 2008, looked at customer relationship management (CRM) in SMEs. His current research interests lie in CRM, spanning the marketing and information systems literature. Social media is my specific expertise, with current projects looking at social CRM (i.e. the impact of social media on CRM) from business and consumer perspectives. He has published in journals such as the International Journal of Electronic Commerce, the Journal of Marketing Management, the Journal of Strategic Marketing and the Journal of Marketing Education and has close engagement with industry and marketing practitioners. Uwana Evers is a Research Fellow and Lecturer in Marketing at the UWA Business School, University of Western Australia. She is a BPS Chartered Psychologist, has a PhD in Psychology from the University of Wollongong and has worked at the University College London. Uwana currently teaches undergraduate Marketing Research, and has published and presented research in social marketing, behaviour change, personal values, and cross-cultural psychology.

609 Professor Morgan P. Miles is Miles is Professor of Entrepreneurship and Innovation at the University of Canterbury, Previously he had been the Tom Hendrix Chair of Excellence at the University of Tennessee Martin, Professor of Enterprise Development at the University of Tasmania, and Professor of Marketing at Georgia Southern University. He has been a visiting scholar at Georgia Tech, Cambridge University, University of Stockholm, the University of Otago, University of Auckland, and an Erskine Fellow at the University of Canterbury. He holds a D.B.A. in marketing from Mississippi State University.

Dr. Timothy Daly is Assistant Professor at United Arab Emirates University. Dr Daly graduated in 2010 from the University of Western Australia with a PhD in Marketing. Since then he has worked as an Assistant Professor at the University of Akron, Ohio (USA), the University of Western Australian and United Arab Emirates University. He currently teaches Consumer Behavior.