Developing a Multi-Dimensional Measure of Hotel Brand Customers’ Online Engagement Behaviors to Capture Non-Transactional Value

Abstract

While the historical focus of customer value research has been on transactional values, it is important to also understand the non-transactional values co-created by customer online engagement behaviors. However, online engagement research in tourism has focused almost exclusively on experience sharing behaviors via online review websites. This research is purposed to develop a multi-dimensional measure of non-transactional values created via online brand community members’ engagement behaviors. Focusing on Marriott Bonvoy Insider Brand Community members, scale development procedures were used to identify four dimensions of non-transactional value: influential-experience value, C-to-B innovation value, relational value, and functional value. The scale achieves good validity and reliability. Relationships with both antecedents (e.g., internal and external motivations) and consequences (e.g., brand attachment and brand loyalty) were examined to assess nomological validity. Tourism researchers and managers can employ this scale to diagnose the non-transactional values co-created by customer online engagement behaviors.

Keywords

non-transactional value customer engagement behaviors online brand community value co-creation measure development multi-dimensional scale

Introduction

Understanding the value of customers, and more importantly the value of different segments of the customer market, has long been a key tourism research focus. Historically, when examining the value of customers and the value of different market segments, both the tourism and marketing literatures have focused on conceptualizing and measuring transactional values, such as actual spending, purchase intentions, destination/brand loyalty, and, most recently, brand equity (e.g., Berger and Nasr 1998; Hwang, Baloglu, and Tanford 2019; Kumar et al. 2010; Nam, Ekinci, and Whyatt 2011; Wang et al. 2020). However, customers can also co-create non-transactional value based on engagement behaviors (Jaakkola and Alexander 2014; Kumar and Pansari 2016). For example, customers create experience value via experience sharing and innovation value by sharing service opinions or ideas through engagement via online customer brand communities (Kumar et al. 2019; Verhoef, Reinartz, and Krafft 2010).

More specifically, it is important to recognize that customers can create various types of non-transactional value. Given that customers develop active and dynamic engagements with other customers, with, tourism businesses and with destinations both during and after their travel experiences, customers can co-create not only experience values, but also customer to business (C-to-B) innovation values, relational values, functional values, and influence values (Shin and Perdue 2021). While the unique aspect of customer non-transactional values (e.g., relational and functional value) has been conceptually explored in previous research (Shin and Perdue 2021; So, Li, and Kim 2020), there is a lack of both conceptual clarity and measurement of these various types of values.

The exponential growth of online travel review websites, online brand, destination, and activity communities, and social media has greatly increased the importance of understanding the non-transactional values co-created via online customer engagement in tourism (Füller, Matzler, and Hoppe 2008; Kim, Yoo, and Yang 2020; So, Li, and Kim 2020). Along with most businesses, both destinations and many tourism brands have dramatically increased online technology and social media investments with the goal of building customer relationships and growing various non-transactional values (de Oliveira Santini et al. 2020; Hughes, Swaminathan, and Brooks 2019). Being able to conceptually categorize and effectively measure these values is critical to the management of these investments and to understanding the value of alternative market segments. While there is a large body of social media research in tourism, some of which focuses on understanding customer engagement issues (e.g., Casaló, Flavián, and Guinalíu 2010; Shin, Perdue, and Pandelaere 2020; Xiang et al. 2017), there are three key, related limitations with the existing research. First, the research has been overwhelmingly conducted in “general” engagement contexts or in the context of online engagement using online review websites. Second, in part as a result of this online review website focus, the existing research has largely focused heavily on experience sharing value via referral/word-of-mouth (WOM) behaviors. Third, it is unclear how various types of behavioral engagement co-creates different types of non-transactional value.

More specifically, most online engagement research in tourism has focused heavily on one format; experience sharing via communications posted to online review platforms (e.g., online travel agency websites—TripAdvisor, etc.) (e.g., Casaló, Flavián, and Guinalíu 2010; Lee, Law, and Murphy 2011; Wen et al. 2021; Xiang et al. 2017). These review platforms are mainly designed to support indirect, static customer to customer (C-to-C) interactions for experience sharing communications. To broaden the analysis of customer online engagement, the current study examines online brand communities wherein community members can engage in dynamic, direct, and sustained interactions both with brand managers and with other community members. For example, Marriott has operated an online brand community since 2008, called Marriott Bonvoy Insider Brand Community, where their hotel brand customers can interact with both other customers and brand managers by sharing service experiences, travel information, service recommendations, and brand knowledge. As an important venue for non-transactional values, a growing number of tourism brands and destinations are investing in similar online brand communities for the purpose of supporting customer engagement (Touni et al. 2020), but there is little research on the conceptualization and measurement of the non-transactional values created by such engagement.

Existing research has analyzed the multi-dimensional nature of non-transactional values (e.g., Jaakkola and Alexander 2014; Kumar and Pansari 2016; Kumar and Reinartz 2016). For example, Hollebeek, Juric, and Tang (2017) proposed multiple types of non-transactional value, such as purposive value, self-discovery value, interpersonal connectivity value, entertainment value, and social enhancement value. More recent non-transactional research using a qualitative netnography of online brand communications (Shin and Perdue 2021) has proposed five types of value co-created by online hotel brand community engagement behaviors: experience value, C-to-B innovation value, relational value, functional value, and influence value. Building on Shin and Perdue (2021), further quantitative research is necessary to assess the framework’s validity and to develop empirical measures of these various engagement behaviors.

Finally, the existing research on customer engagement has tended to mix both psychological and behavioral dimensions of engagement in a single framework (e.g., Hollebeek, Glynn, and Brodie 2014; Mirbagheri and Najmi 2019; Rather, Hollebeek, and Rasoolimanesh 2021; Vivek et al. 2014). As with many attitude—behavior constructs, the linkage of psychological engagement to actual behaviors which create non-transactional value is unclear; even if someone is psychologically engaged with a brand, without behavioral actions toward the brand, the associated non-transactional value is limited (Boulstridge and Carrigan 2000; Harmeling et al. 2017). Consequently, this research focuses exclusively on conceptualizing and measuring the behavioral dimensions of customer engagement and co-created value in an online brand community.

In summary, the purpose of this research was to develop a valid multi-dimensional measure of customer non-transactional values created via online engagement behaviors. This study focuses on members of an online hotel corporation brand community. The study followed widely accepted scale development procedures (DeVellis 2003; Hyun and Perdue 2017; Worthington and Whittaker 2006) consisting of phase 1—initial item development (e.g., item generation, content adequacy assessment, data collection), phase 2—exploratory factor analysis (EFA), phase 3—confirmatory factor analysis (CFA), and phase 4—nomological validity testing. Nomological validity was tested via both antecedent measures (e.g., motivations) and consequence measures (e.g., brand attachment, brand loyalty) of customer engagement behaviors. It is proposed that this measure will contribute to improving understanding of customer online engagement behaviors and provide tourism practitioners with a practical tool to comprehensively measure customer non-transactional value both overall and by segment.

Literature Review

The ability of customers to engage in various brand activities that go beyond transactional behaviors in an increasingly networked society prompts firms to pursue strategies steering customer non-transactional value co-creation (Verhoef, Reinartz, and Krafft 2010). In particular, the interactive nature of hospitality and tourism services suggests that customer engagement is a critical component of hospitality and tourism service management. Thus, measuring customer engagement is a forward-looking metric to capture non-transactional value co-created by engagement (Wei, Miao, and Huang 2013).

Customer Engagement in Online Brand Communities

Customer engagement refers to customers’ interactions with a brand that go beyond transactional behaviors (e.g., purchasing products or services) (Vivek, Beatty, and Morgan 2012). Due to the continuing growth of online platforms and social media, customers have more and more opportunities to interact with brands (de Oliveira Santini et al. 2020; Hughes, Swaminathan, and Brooks 2019). Included among these online platforms, online brand communities are an ideal place for customers to engage in a variety of brand activities (Romero and Molina 2011). “Online brand community” refers to an online community of brand customers who engage in repeated interactions with both the brand and other brand customers (Kim, Lee, and Hiemstra 2004). Online brand community members can take an active role in various engagement behaviors, such as experience sharing, making suggestions, and offline gatherings. Importantly, brand community members can also provide ideas and suggestions, which can be a critical source of service innovation (Füller et al. 2006; Lusch and Nambisan 2015).

In the last decade, customer engagement has become a central tourism and marketing research topic. Still, the existing research has concentrated heavily on conceptual frameworks (e.g., Brodie et al. 2011; Dessart, Veloutsou, and Morgan-Thomas 2015); further research is needed to measure and understand the effects of customer engagement behaviors (Harmeling et al. 2017; Kumar and Reinartz 2016; Maslowska, Malthouse, and Collinger 2016). In tourism, examining how destinations and brands promote customer online engagement has been an important research question (Harrigan et al. 2017). However, most studies have focused primarily on one specific form of customer engagement behavior—experience sharing via online review communications (e.g., Casaló, Flavián, and Guinalíu 2010; Kim, Lee, and Hiemstra 2004; Lee, Law, and Murphy 2011; Xiang et al. 2017; Zhang et al. 2021b). Given that online brand community members are, in general, loyal brand customers who engage in various brand activities, this study focuses on member engagement behaviors that co-create non-transactional values in the context of an online hotel brand community.

Customer engagement is a multi-dimensional concept (Rather, Hollebeek, and Rasoolimanesh 2021). Specifically, Dessart, Veloutsou, and Morgan-Thomas (2015) suggested three dimensions including affective, cognitive, and behavioral engagement, and seven sub-dimensions including enthusiasm, enjoyment, attention, absorption, sharing, learning, and endorsing. Affective engagement is based on enduring levels of emotions or feelings, such as enthusiasm or enjoyment. The cognitive dimension represents a set of enduring and active mental states, such as sustained attention and absorption dedicated to interacting with stakeholders in a community. Lastly, the behavioral dimension refers to behaviors associated with knowledge or experience sharing, learning through asking and receiving answers, and endorsing (Dessart, Veloutsou, and Morgan-Thomas 2015). Most research indicates that customer engagement reflects a psychological state, which occurs by virtue of interactions between customers and brands (e.g., Brodie et al. 2011; Sashi 2012; So, King, and Sparks 2014).

However, it is important to consider that the behavioral aspect of customer engagement makes a clear distinction from other dimensions (Romero 2017). An individual’s psychological state can be better understood as antecedents or outcomes of engagement behaviors. For example, while enthusiasm is originally suggested as a psychological dimension of customer engagement (So, King, and Sparks 2014), interactions with a brand enable customers to be more enthusiastic. Customer engagement behaviors function as an indicator of psychological engagement (Van Doorn et al. 2010). Furthermore, even if customers are psychologically engaged in a brand, it does not always lead to creating value for the brand due to the attitude-behavior gap (Boulstridge and Carrigan 2000); customer psychological engagement is not always correlated with behavioral engagement and customer value co-creation. Thus, engagement behaviors most effectively capture the implicit and explicit meaning of customer engagement; measuring engagement behaviors is the best way to examine the value of customer engagement (Harmeling et al. 2017). Consequently, this research focuses on the behavioral dimension of customer engagement.

Table 1 provides an overview of existing customer engagement research. In the broader marketing and business research, Vivek et al. (2014) developed a customer engagement scale composed of conscious attention, enthused participation, and social connection. Hollebeek, Glynn, and Brodie (2014) proposed a customer engagement measure consisting of cognitive processing, affection, and activation. Baldus, Voorhees, and Calantone (2015) operationalized 11 dimensions of customer engagement motivations in an online brand community (e.g., brand passion, self-expression, helping, etc.). Lastly, Dwivedi (2015) suggested a higher-order model of consumer brand engagement consisting of affective and cognitive dimensions including vigor, dedication, and absorption. In hospitality and tourism, So, King, and Sparks (2014) initially developed a customer engagement scale focusing on five dimensions including identification, attention, enthusiasm, absorption, and interaction. This scale was validated by following research (e.g., Harrigan et al. 2017; So et al. 2016). Most recently, Huang and Choi (2019) developed a customer engagement scale comprising four dimensions, such as interactions with employees, activity-related customer engagement, social interaction, and relatedness. Importantly, this previous research has focused on the levels and types of engagement as opposed to the non-transactional values created via the engagement. This study is purposed to develop a multi-dimensional scale of the non-transactional values created via customer engagement behaviors in the context of online hotel brand communities.

Table 1.

Customer Engagement Scales in Business and Hospitality & Tourism Research.

Author (year)	Context	Dimensions	Focus
Vivek et al. (2014)	Retail industry offline context	Conscious attention Enthused Participation Social connection	Cognitive engagement Affective engagement Behavioral engagement
Hollebeek, Glynn, and Brodie (2014)	Social media community context	Cognitive processing Affection Activation	Cognitive engagement Affective engagement Behavioral engagement
Baldus, Voorhees, and Calantone (2015)	Online brand community context	Brand influence Brand passion Connecting Helping Like-minded discussion Rewards (hedonic) Rewards (utilitarian) Seeking assistance Self-expression Up-to-date information Validation	Cognitive engagement motivation Affective engagement motivation
Dwivedi (2015)	Mobile phone industry offline context	Vigor Dedication Absorption	Cognitive engagement Affective engagement
Kumar and Pansari (2016)	Manufacturing and service industry offline context	Purchase Referrals Influence Knowledge	Cognitive engagement Behavioral engagement
So, King, and Sparks (2014)	Hotel and airline industry offline context	Identification Enthusiasm Attention Absorption Interaction	Cognitive engagement Affective engagement Behavioral engagement
Harrigan et al. (2017)	Tourism brand social media context	Identification Attraction Absorption Interaction	Cognitive engagement Affective engagement Behavioral engagement
Huang and Choi (2019)	Tourism destination offline context	Social interaction Interaction with employees Relatedness Activity-related tourist engagement	Cognitive engagement Affective engagement Behavioral engagement
Mirbagheri and Najmi (2019)	Social media activation campaign context	Attention Interest and enjoyment Participation	Cognitive engagement Affective engagement Behavioral engagement

Non-Transactional Value Co-Creation

Understanding and measuring customer value has long been a focus of tourism marketing research both for the overall evaluation of brands and strategies and, even more importantly, for effective market segmentation and target market identification. Historically, this research has focused heavily on measuring transactional values associated with purchase behavior and their impact on business success: (i.e., purchase value, volume, and frequency, brand loyalty, brand switching, brand equity, etc.) (e.g., Berger and Nasr 1998; Kumar et al. 2010; Pansari and Kumar 2017; Verhoef, Reinartz, and Krafft 2010). While transactional values are clearly critical, the increasing growth of online and social media technologies has greatly increased the impact of the non-transactional values created via customer engagement via these technologies (Hughes, Swaminathan, and Brooks 2019). Clearly, customers contribute to destination and brand success through their online non-purchasing engagement behaviors. For example, customers’ experience and information sharing WOM behaviors influence potential customers’ perceptions, preferences, and purchasing behaviors (e.g., Liu, Wu, and Li 2019; Xiang and Gretzel 2010; Xiang et al. 2017). Further, customers’ online destination and brand improvement recommendations contribute to product or service innovation (Shin, Perdue, and Pandelaere 2020). From a long-term perspective, understanding these non-transactional values creates opportunities for identifying key market segments, building customer relationships, and growing destination transactional value (Kumar and Reinartz 2016; Verhoef, Reinartz, and Krafft 2010).

While value co-creation has been widely adopted as an important tourism research construct, most existing research has focused on the experience/referral/WOM values co-created by indirect and static C-to-C interactions via online review platforms (e.g., Liu, Wu, and Li 2019; Xiang et al. 2017; Zhang et al. 2022). Further, most existing studies have conceptually and empirically examined value co-creation in terms of static C-to-B interactions (e.g., Casaló, Flavián, and Guinalíu 2010; Xiang et al. 2017). There has been limited research examining value co-creation processes in terms of dynamic interactions including both C-to-B and C-to-C interactions (Romero and Molina 2011). Further research needs to analyze how brand community members engage via sustained and dynamic interactions for non-transactional value co-creation (Brodie et al. 2019).

Some recent studies focus on the relationship between customer engagement and value co-creation (Rather, Hollebeek, and Rasoolimanesh 2021). Kumar and Reinartz (2016) found that customer engagement behaviors co-create several non-transactional values, such as customer referral value (e.g., customers recommend a brand or service to others) and customer knowledge value (e.g., customers provide firms with critical or important knowledge). In hospitality and tourism, Christina, Zhang, and Lu (2020) proposed functional, emotional, social, and epistemic values associated with customer online engagement behaviors, such as online ratings, online blogging, and customer to customer interactions. Shin, Perdue, and Pandelaere (2020) propose that customer engagement behaviors for experience sharing can empower them, resulting in innovation value. Most recently, Shin and Perdue (2021) used a netnography of customer engagement behaviors to identify five types of customer non-transactional value (e.g., experience value, C-to-B innovation value, relational value “also called social value,” functional value, and influence value) in an online hotel brand community (See Table 2). Building on this earlier qualitative work, this study develops an empirical measure of customer engagement behaviors in terms of the five non-transactional values in the online hotel brand community context.

Table 2.

Dimensions of Non-Transactional Value and Associated Engagement Behaviors.

Non-transactional value	Explanation	Engagement behaviors
Experience value	Information and experiential value co-created by C-to-C engagement behaviors to share their service experiences of others.	Sharing service experiences (e.g., online review), service issues, and other information (e.g., hotel news).
C-to-B innovation value	Innovative knowledge for service enhancement, new service development, and service innovation.	Making a critical suggestion, evaluating hotel program or policy, and proposing a new service idea.
Relational value	Social bond build by C-to-C interactions of community members.	Participating in offline gatherings, sharing personal hobbies, and interacting in an emotional way.
Functional value	Value co-created by customers’ voluntary, helpful, and constructive engagement behaviors that are beneficial for the operation of online brand community.	Revising/updating threads, making technical suggestions, or providing correct information for other community members.
Influence value	Value co-created by customers’ influential engagement behaviors that potentially affect prospective customers’ decision-making behaviors.	Sharing important tip, making direct recommendation, and providing useful advice.

Source: Shin and Perdue (2021).

Nomological Validity of Customer Non-Transactional Value

The nomological validity of the proposed measure was assessed via both theoretical antecedents and consequences. In terms of antecedents, both intrinsic and extrinsic motivations are known to drive customer engagement behaviors for non-transactional value (Kumar et al. 2010; Van Doorn et al. 2010). Previous research argued that community members participate in brand activities because they are intrinsically motivated. Intrinsic motivation refers to the internal motivational drivers (e.g., enjoyment, fun, satisfaction), for doing an activity without expecting separable consequences (Antikainen and Vaataja 2010; de Oliveira Santini et al. 2020). In addition, community members can be extrinsically motivated; extrinsic motivation refers to an individual’s motivational driver for doing an activity for external rewards (e.g., money, praise, social recognition, etc.). For example, they engage in brand activities to get social recognition from other customers or community members (Bock, Eastman, and Eastman 2018). In other words, they co-create non-transactional value by sharing their experiences or knowledge with others because they care about how their behaviors and content are recognized by others. In this regard, this study proposes to examine the positive relationships between customer intrinsic/extrinsic motivations and non-transactional engagement behaviors. The following hypotheses are suggested as nomological validity tests of the proposed scale.

Hypothesis 1: Intrinsic motivation will have positive effects on engagement behaviors for non-transactional value.

Hypothesis 2: Extrinsic motivation will have positive effects on engagement behaviors for non-transactional value.

Customer engagement behaviors for co-creating non-transactional value have psychological consequences as well. Generally, previous research focuses on the positive brand impact of customer engagement, such as brand attitude, behavioral intention, brand attachment, and brand loyalty (e.g., de Oliveira Santini et al. 2020; Li, Teng, and Chen 2020; So et al. 2016). This study focuses on brand attachment and brand loyalty as indicators for the long-term and sustainable success of brands (Li, Teng, and Chen 2020). Specifically, when customers engage in brand activities, they are likely to feel attachment to the brand community. Previous research argued that community members are assumed to have a higher level of brand attachment (Blanchard 2008). In addition, customer non-transactional behaviors are known to be positively associated with transactional value, such as brand loyalty or purchase intentions (e.g., Li, Teng, and Chen 2020; So et al. 2016; Zheng et al. 2015). Brand loyalty refers to customers’ dedication to purchase the same products or services in the future from the same brand even if competitors’ new products or environment changes (Jacoby and Kyner 1973). Zhang et al. (2021a) found that hotel customers’ engagement behaviors (e.g., feedback, cross-buying, and mobilizing behaviors) have positive effects on hotel brand loyalty. In addition, travelers’ WOM behaviors are likely to have a positive impact on the destination/brand loyalty or purchasing behaviors (e.g., Liu, Wu, and Li 2019; So, Wei, and Martin 2021; Xiang et al. 2017). For example, Zheng et al. (2015) found that user engagement through WOM behaviors in online brand communities enhanced their brand loyalty. Theoretically, when customers co-create non-transactional value by engaging in brand activities, they are likely to feel attached to the brand, resulting in a higher sense of brand loyalty (Li, Teng, and Chen 2020). The following two hypotheses were developed to test the relationships between customer non-transactional value and brand effects as evidence of the nomological validity of the proposed scale.

Hypothesis 3: Engagement behaviors for non-transactional value will have positive effects on brand attachment.

Hypothesis 4: Engagement behaviors for non-transactional value will have positive effects on brand loyalty.

Methodology and Results

The initial issue for multidimensional scale development is to select the measurement model format. For two key reasons, a reflective model was adopted for this research. First, according to Coltman et al. (2008), the indicators of reflective measures share a common theme, resulting in high levels of intercorrelations, whereas the indicators of formative measures do not necessarily share a similar theme and hence, there are low levels of intercorrelations between them. Given that the proposed scale consists of indicators that share similar themes (e.g., engagement behaviors for C-to-B innovation value, engagement behaviors for functional value), there are presumably high levels of intercorrelations between indicators for a given construct. Second, Edwards (2011) questioned the validity of formative measurement models in terms of dimensionality, causality, and measurement errors and argued that multidimensional measurement models are better designed in a reflective format.

This study followed widely accepted scale development procedures (DeVellis 2003; Hyun and Perdue 2017; Worthington and Whittaker 2006). Specifically, the first phase was to develop a set of customer engagement behavior items that representing the various dimensions of non-transactional values. In this phase, content adequacy assessment was conducted to test content validity. The second phase was to conduct an exploratory factor analysis (EFA) of these initial items. The third phase involved validity and reliability checks using confirmatory factor analysis (CFA). The fourth phase examined the nomological validity of the developed measure by analyzing correlations between the developed measure and (intrinsic/extrinsic) motivation, brand attachment, and brand loyalty.

The study subjects were members of Marriott Bonvoy Insider Brand Community. This online brand community was created by Marriott hotel corporation in 2008 to promote Marriott brand customers’ online engagement behaviors (e.g., experience sharing, Q&A, and making suggestions, etc.). The community members call each other “insiders.” In 2020, the community moved to the Facebook-based online brand community.

Phase 1: Initial Item Development

The first phase involved initial item generation, content adequacy assessment to examine the face validity of the items, and data collection. The detailed steps are described in the following sections.

Initial item generation

To develop initial sets of items, an extensive review of the hospitality and marketing literatures on customer engagement and non-transactional value was performed (Baldus, Voorhees, and Calantone 2015; Chen et al. 2018; Kumar and Pansari 2016; Ranjan and Read 2016; So, King, and Sparks 2014). Importantly, the study findings by Shin and Perdue (2021) were carefully reviewed to categorize the nature of engagement behaviors and co-created non-transactional value. A list of 38 items measuring five types of engagement behaviors in terms of non-transactional value were generated: eight items for experience value, eight items for C-to-B innovation value, seven items for relational value, seven items for functional value, and eight items for influence value.

Content adequacy assessment

Following the procedures proposed by Hinkin, Tracey, and Enz (1997), content adequacy assessment was conducted using informed judges with expertise in hospitality and tourism. A total of 15 judges were selected based on academic experience (e.g., at least three publications on the topic of hospitality marketing and management), industry experience (e.g., more than three years of hospitality industry experience), and/or teaching experience (e.g., at least five years of teaching experience on hospitality management and marketing at the university level) (Hyun and Perdue 2017). In a content assessment questionnaire, the dimensions of co-created value were defined and the initial items were listed. The judges both qualitatively (e.g., clarity, potential problems, etc.) and quantitatively (e.g., evaluation of content adequacy of each item on a 5-point Likert-type scale) assessed each of the initial.

The results of this assessment were screened based on a standard-agreement index of 6.0 (Clemenz and Weaver 2003). Specifically, at least 60% of the judges (at least 9 out of 15) needed to agree that the engagement behavior item reflected a specific value dimension. In addition, potential problems associated with questionnaire design, clarity, description, and wording were discussed based on judges’ comments. As a result of this process, 30 items were chosen (See Table 3).

Table 3.

Initial Items for Measuring Customer Engagement Behaviors.

Engagement behaviors for experience value (EBEX)

I share my hotel experiences to help other Insiders.

I write online reviews to share my service experiences with other Insiders.

I post my travel experiences in the Marriott Bonvoy Insiders community.

I post hotel reviews onto the Marriott Bonvoy Insiders message board to share my service experiences with other Insiders.

If I have positive or negative hotel service experiences, I share them with Insiders.

I keep other Insiders updated with my hotel service experiences.

Engagement behaviors for C-to-B innovation value (EBIN)

I contribute ideas and thoughts to Marriott by engaging in the Marriott Bonvoy Insiders community.

I help Marriott improve its services by sharing my knowledge in the Marriott Bonvoy Insiders community.

I improve Marriott through my participation and expression in the Marriott Bonvoy Insiders community.

I spend the time and effort to share my ideas and suggestions in order to help Marriott improve its services.

I provide feedback/suggestions for developing new services for Marriott.

I evaluate hotel services or programs and share them in order to help Marriott.

Engagement behaviors for relational value (EBRE)

When possible, I try to meet other Insiders in person.

When there is a social gathering of Insiders, I try to participate in it.

I personally interact with other Insiders in the Marriott Bonvoy Insiders community.

I bond with other Insiders by sharing my personal stories.

I react emotionally to other Insiders’ good or bad experiences.

I interact with other Insiders to share personal preferences and interests.

Engagement behaviors for influence value (EBIF)

I post content to help Insiders make better hotel and destination selection decisions.

I make recommendations of hotel services to Insiders.

I give advice about hotel or travel to Insiders.

When other Insiders ask about hotel services, I give an answer to help their decisions.

I provide Insiders with my personal opinions to help their decisions.

I share my experiences and knowledge to help Insiders’ decision-making behaviors.

Engagement behaviors for functional value (EBFU)

I volunteer to organize information and update it in the Insiders message board.

I spontaneously update information in the Insiders message board.

If I see incorrect information, I point it out or modify the information.

I spend time creating a certain thread by organizing information (e.g., updating a list, creating a new discussion, etc.).

If I know the best Insiders to answer a question, I introduce him/her to help solve the question.

I help other Insiders get correct information by referring to relevant posts updated in the community message board.

Questionnaire development and data collection

An online questionnaire was developed including the selected engagement behavior items. Additionally, to evaluate nomological validity, items measuring intrinsic and extrinsic motivations as antecedents and brand attachment and brand loyalty as consequences were included in the questionnaire. Intrinsic motivation for enjoyment was measured by four items adopted from Kim and Drumwright (2016) and extrinsic motivation for social recognition was measured by four items adopted from Helm et al. (2013). Brand attachment was measured by four items adopted from Hwang, Baloglu, and Tanford (2019). Brand loyalty was measured by three items adopted from Nam, Ekinci, and Whyatt (2011) and Rather (2018). All measures were highly reliable in terms of Cronbach’s alpha: intrinsic motivation = 0.90, extrinsic motivation = 0.92, brand attachment = 0.86, brand loyalty = 0.82. Combining with the engagement behavior items, an online Qualtric survey comprising 45 items was developed.

To facilitate data collection from Marriott Bonvoy Insider Community members, the lead author became a community member in August 2018 and had engaged in community activities by creating content and interacting with other community members. Importantly, connections with active community members were made to get their assistance in collecting data from the community members. With the support of two active community members, two survey recruitment threads with the survey link were posted in the community message board during September 18th–November 29th, 2019. In addition, a private recruiting message was sent by the author and some community members to 80 community members via the community’s personal messenger system. A drawing for one of fifteen $50 gift cards was an incentive for survey participation.

Phase 2: Exploratory Factor Analysis (EFA)

Exploratory factor analysis (EFA) was conducted of the resulting data. In total, 113 community members completed the survey. Of the respondents, 71.1% were male, and 28.1% were female. In terms of age, respondents were primarily between 40 and 49 (25.4%), 50 and 59 (24.6%), and 30 and 39 (22.8%). Most respondents had been members of the community for one to three years (29.8%) and three to five years (21.9%) (See Table 4).

Table 4.

Profile of Exploratory Survey Respondents.

Variable	N	Percentage
Gender
Male	81	71.7
Female	32	28.3
Age
20–29	10	8.8
30–39	26	23.0
40–49	29	25.7
50–59	28	24.8
60–69	12	10.6
70–79	8	7.1
Community membership period
1 year or less	20	17.5
1–3 years	34	29.8
3–5 years	25	21.9
5–8 years	18	15.8
8–10 years	7	6.1
More than 10 years	10	8.8

Before conducting the EFA, all items’ skewness and kurtosis were reviewed to examine the normality of the data. All items had skewness and kurtosis values between −1 and 1, except for four items (e.g., relational value—first and second items, functional value—first and fourth items). However, the data appeared to meet the normality assumption since their values were slightly different from the absolute 1 ranging from 1.19 to 1.46 (Hair et al. 1998).

To ensure the adequacy of the sample size (n = 113) and the appropriateness of the factor analysis, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were performed. The KMO value was 0.929 and Bartlett’s test of sphericity value was 3,794.796 (p < .001), suggesting that the factor analysis was appropriate (Tabachnick and Fidell 2001). Using principal component extraction and varimax rotation, EFA was conducted to assess the dimensionality of the scale and to further reduce the number of items. As an orthogonal approach, varimax rotation yields similar factor analysis results as oblique rotation approaches (e.g., promax rotation) and provides accurate factor structures even for the case of high inter factor correlations (Finch 2006).

Four constructs (factors) with an eigenvalue greater than 1.0 were identified (Hair et al. 1998). The four factors accounted for 75.9% of the total variance. In the item purification process, several standards were employed. First, items with low factor loadings (<0.5) were deleted for their lack of significant contribution. Second, items with cross-loadings that negatively affect scale dimensionality were deleted. Third, items with a negative impact on reliability (Cronbach’s alpha) were deleted. Based on these standards, eight items were deleted. A final principal factor analysis of the reduced set of 22 items revealed a clear four-factor pattern that explained 78.3% of the total variance. In Table 5, the bold values indicate factor loadings of the four factors. The internal consistency of the four dimensions was assessed by estimating Cronbach’s alpha; all scales exceeded 0.7 (e.g., experience + influence value: 0.95, C-to-B innovation value: 0.89, relational value: 0.92, functional value: 0.94) (Hair et al. 1998) (See Table 5).

Table 5.

Exploratory Factor Loadings.

Items	Factors
Items	1	2	3	4
EBEX 5	0.816	0.262	0.243	0.154
EBEX 4	0.790	0.196	0.307	0.056
EBIF 3	0.778	0.281	0.157	0.336
EBIF 2	0.713	0.369	0.140	0.368
EBIF 1	0.705	0.250	0.189	0.326
EBEX 6	0.688	0.225	0.321	0.367
EBIF 6	0.657	0.447	0.225	0.340
EBFU 1	0.129	0.847	0.243	0.099
EBFU 3	0.240	0.821	0.165	0.139
EBFU 5	0.266	0.820	0.218	0.158
EBFU 4	0.214	0.810	0.255	0.104
EBFU 6	0.412	0.721	0.257	0.183
EBFU 2	0.332	0.717	0.279	0.224
EBRE 1	0.188	0.434	0.790	0.078
EBRE 2	0.265	0.344	0.788	0.044
EBRE 4	0.449	0.342	0.665	0.198
EBRE 5	0.094	0.072	0.643	0.431
EBRE 6	0.405	0.292	0.595	0.395
EBRE 3	0.402	0.473	0.580	0.281
EBIN 5	0.278	0.170	0.121	0.836
EBIN 6	0.258	0.194	0.189	0.830
EBIN 4	0.419	0.127	0.163	0.674

While the existing theoretical background suggests engagement behaviors for experience value are distinct from engagement behaviors for influence value, the two dimensions are highly correlated; customers’ experience sharing behaviors can influence other customers’ decision-making behaviors (Shin and Perdue 2021). The EFA results suggest that both behaviors be categorized as a same construct. This merged construct was named as “influential experience value.” Figure 1 reflects these differences.

Figure 1.

Theoretical dimensions of non-transactional value versus dimensions derived from EFA.

Phase 3: Confirmatory Factor Analysis (CFA)

For the confirmatory factor analysis (CFA), 219 community members participated in an online survey; a reasonable sample size for a CFA model is about N = 150 and an even larger sample size for SEM (N = 200) is recommended (Hoogland and Boomsma 1998; Kline 2005). Among the participants, 68.8% were male. With regard to age, 30.3% were between 30 and 39, 18.3% were between 40 and 49, and 17.4% were between 50 and 59. In terms of community membership period, 36.1% were members for one to three years, followed by three to five years (21.5%) and one year or less (19.6%) (See Table 6).

Table 6.

Profile of Confirmatory Survey Respondents.

Variable	N	Percentage
Gender
Male	141	69.8
Female	60	29.7
Age
20–29	20	9.9
30–39	59	29.2
40–49	38	18.8
50–59	36	17.8
60–69	31	15.3
70–79	17	8.4
Community membership period
1 year or less	39	19.3
1–3 years	70	34.7
3–5 years	46	22.8
5–8 years	21	10.4
8–10 years	14	6.9
More than 10 years	12	5.9

The data met the normality assumption; all items’ values were within absolute 1 ranging from −0.81 to 0.94 (Hair et al. 1998). The third phase involved validity and reliability checks using CFA. The CFA results of 22 items (four factors) showed a model fit: χ² = 815.733, p < .001, Comparative Fit Index (CFI) = 0.861, Tucker Lewis Index (TLI) = 0.841, Standardized Root Mean Square Residual (SRMR) = 0.066; Root Mean Square Error of Approximation (RMSEA) = 0.122 (Hooper, Coughlan, and Mullen 2008). These results showed that some indices are below the acceptable threshold values. Using modification indices, items with high residual covariance were reviewed to improve model fit (Anderson and Gerbing 1988). Theoretical basis was considered to determine the items to be deleted. For example, first and second items for relational value were deleted due to high residual covariance; the nature of these items was different from the nature of others since both items focused on actual meeting behaviors while the other items focused on personal interactions with community members on the brand community. Following this approach, seven items were deleted, resulting in an improved model fit: χ² = 212.662, p < .001, CFI = 0.949, TLI = 0.936, SRMR = 0.045; RMSEA = 0.087. The results of a combination of other indices including Normed χ², CFI, TLI, and SRMR met the standards for acceptable fit (Kenny, Kaniskan, and McCoach 2015).

All standardized factor loadings were greater than 0.6 (p < .001) ranging from 0.62 to 0.91, and the composite reliabilities (CR) of the four constructs were all greater than 0.7 ranging from 0.85 to 0.92. Cronbach alpha (α) coefficients were calculated for each construct, ranging from 0.84 to 0.92. All values indicated well-structured items and good reliabilities (Bagozzi and Yi 1988; Hair et al. 2006) (See Table 7).

Table 7.

Confirmation Factor Analysis, Descriptive Analysis, Reliability, and Item Loadings.

Variables and measurement item	Mean (SD)	Standardized factor loading
Engagement behaviors for influential experience value (CR = 0.92, α = 0.91)
I give advice about hotel or travel to Insiders.	3.38 (1.54)	0.916
I make recommendations of hotel services to Insiders.	3.51 (1.45)	0.894
I post content to help Insiders make better hotel and destination selection decisions.	3.45 (1.49)	0.878
I post hotel reviews onto the Marriott Bonvoy Insiders message board to share my service experiences with other Insiders.	3.12 (1.35)	0.737
Engagement behaviors for C-to-B innovation value (CR = 0.90, α = 0.89)
I provide feedback/suggestions for developing new services for Marriott.	3.16 (1.38)	0.869
I evaluate hotel services or programs and share them in order to help Marriott.	3.49 (1.43)	0.853
I spend the time and effort to share my ideas and suggestions in order to help Marriott improve its services.	3.52 (1.45)	0.821
Engagement behaviors for relational value (CR = 0.85, α = 0.84)
I interact with other Insiders to share personal preferences and interests.	3.18 (1.49)	0.898
I bond with other Insiders by sharing my personal stories.	2.78 (1.45)	0.896
I react emotionally to other Insiders’ good or bad experiences.	3.33 (1.43)	0.619
Engagement behaviors for functional value (CR = 0.92, α = 0.91)
If I know the best Insiders to answer a question, I introduce him/her to help solve the question.	2.91 (1.62)	0.913
I help other Insiders get correct information by referring to relevant posts updated in the community message board.	3.04 (1.53)	0.905
I spend time creating a certain thread by organizing information (e.g., updating a list, creating a new discussion, etc.).	2.49 (1.43)	0.830
I spontaneously update information in the Insiders message board.	2.42 (1.37)	0.777
If I see incorrect information, I point it out or modify the information.	3.06 (1.53)	0.768

Average variance extracted (AVE) was calculated to assess convergent validity and discriminant validity. Since all AVE values for each construct exceeded 0.5, ranging from 0.66 to 0.74, convergent validity was confirmed. To assess discriminant validity, the AVE for each construct was compared with the squared correlation coefficients between constructs; the AVE was greater than the squared correlation of each construct, indicating acceptable discriminant validity (Bagozzi and Yi 1988) (See Table 8).

Table 8.

Correlations Matrix Among the Latent Constructs (Squared).

Construct	EBIE	EBIN	EBRE	EBFU
Engagement behaviors for influential experience value (EBIE)	1.00
Engagement behaviors for C-to-B innovation value (EBIN)	0.49	1.00
Engagement behaviors for relational value (EBRE)	0.66	0.38	1.00
Engagement behaviors for functional value (EBFU)	0.40	0.25	0.50	1.00
AVE	0.74	0.74	0.66	0.71
Mean	3.36	3.39	3.10	2.78
Standard deviation	1.30	1.29	1.27	1.31

All values were significant (p < .001).

To identify the best fit model for the scale, four alternative models were compared. Model 1 estimated one first-order factor model with 15 observable variables. Model 2 contained the four first-order factors without correlations among latent variables. Model 3 posited the four first-order factors with correlations. Lastly, model 4 estimated the second-order factor model with covariance among the four latent variables. The model fit indices of each model were compared to determine the best fitting model. While models 1 and 2 showed unacceptable model fit, models 3 and 4 provided an acceptable model fit. Although model 3 was slightly better than model 4, model 4 provided the evidence of a second-order factor model for the developed scale with acceptable model fit indices χ² = 220.632, p < .001, CFI = 0.946, TLI = 0.935, SRMR = 0.052, RMSEA = 0.088 (See Table 9 and Figure 2).

Table 9.

Model Comparisons for Dimensionality.

Model fit indices	Model 1: One first order factor	Model 2: Four first order factors without correlation	Model 3: Four first order factors with correlation	Model 4: Second order factor
χ²	878.853	603.248	212.662	220.632
df	90	90	84	86
Normed χ²	9.765	6.703	2.531	2.565
CFI	0.690	0.796	0.949	0.946
TLI	0.638	0.762	0.936	0.935
SRMR	0.102	0.401	0.045	0.052
RMSEA	0.208	0.168	0.087	0.088

Figure 2.

Second order factor model.

Phase 4: Nomological Validity

Structural equation modeling (SEM) was conducted to test the four nomological validity hypotheses. Using the same data as was used for the CFA, the analysis was conducted to test the relationships between the two motivational constructs and the four engagement constructs, and between the engagement constructs and the two consequence constructs. The model fit was acceptable: χ² = 288.831, p < .001, CFI = 0.918, TLI = 0.911, SRMR = 0.049; RMSEA = 0.088. We found that intrinsic motivation positively influences all engagement behaviors for non-transactional values; intrinsic motivation to engagement behaviors for influential experience value (β = 0.336, p < .001), for C-to-B innovation value (β = 0.227, p < .001), for relational value (β = 0.448, p < .001), and for functional value (β = 0.390, p < .001). Thus, hypothesis 1 was supported. Regarding the impact of extrinsic motivation on engagement behaviors for non-transactional value, there was a significant and positive impact on engagement behaviors for functional value (β = 0.304, p < .001). However, no significant impact was found on other engagement behaviors for influential experience value (β = −0.224, p = .10), for C-to-B innovation value (β = −0.122, p = .31), and for relational value (β = 0.025, p = .74). Thus, hypothesis 2 was partially supported.

The impacts of engagement behaviors for non-transactional value on brand attachment and brand loyalty were also analyzed. Significant and positive impacts of engagement behaviors for C-to-B innovation value (β = 0.119, p < .05), relational value (β = 0.151, p < .05), and functional value (β = 0.236, p < .001) on brand attachment were found. However, no significant impact of engagement behaviors for influential experience value on brand attachment was found (β = 0.112, p = .12). Consequently, hypothesis 3 was partially supported. Lastly, significant and positive impacts of engagement behaviors for influential and experience value (β = 0.375, p < .001), and C-to-B innovation value (β = 0.208, p < .001) on brand loyalty were found. On the other hand, no significant relationship was found with engagement behaviors for relational value (β = 0.019, p = .43) and for functional value (β = −0.158, p = .11). Hypothesis 4 was partially supported.

Discussion and Conclusions

Summary of Findings

This study contributes to a better understanding of various customer non-transactional values co-created by engagement behaviors in a tourism online brand community by developing and validating a multi-dimensional scale of non-transactional values. Fifteen items capturing engagement behaviors for influential experience value, C-to-B innovation value, relational value, and functional value showed high validity and reliability. In addition, the engagement process, including motivations as antecedents and brand attachment and loyalty as consequences of engagement behaviors, provides further insights into value co-creation mechanism. The developed scale may serve as a key tool to capture customer non-transactional value in the hospitality industry. The next section reports on the theoretical and practical implications of the proposed scale. In addition, limitations of the current research and potential research opportunities are presented.

Theoretical Implications

First, this study contributes to the conceptual and empirical understanding of customer engagement and non-transactional value co-creation. Although promoting customer engagement has become a widely used service management strategy (Brodie et al. 2011), the inconsistent definitions and dimensions of customer engagement and associated values required further empirical assessment to establish the dimensionality of the phenomenon (Dessart, Veloutsou, and Morgan-Thomas 2015; Van Doorn et al. 2010; Verhoef, Reinartz, and Krafft 2010). The existing hospitality and tourism research (e.g., Casaló, Flavián, and Guinalíu 2010; Lee, Law, and Murphy 2011; Xiang et al. 2017) focuses heavily on static customer experience sharing via online review processes. Given that the pattern of customer engagement differs between static and dynamic contexts (So, Wei, and Martin 2021), this study empirically explored online engagement behaviors in a dynamic setting involving repeated interactions between the customer and both other customers and the brand managers. Building on Shin and Perdue (2021), the current study provides operational knowledge on customer engagement behaviors in terms of non-transactional value in online hotel brand community contexts.

Second, this study contributes to understanding hospitality and tourism customers’ non-transactional value co-creation in online contexts. Historically, customer value has been mainly measured and understood by transactional tools, such as purchase intention or brand loyalty; customer non-transactional value has received relatively little academic attention (Pansari and Kumar 2017; Venkatesan 2017; Verhoef, Reinartz, and Krafft 2010). In addition, while customer value co-creation has been an important research issue in hospitality and tourism research for the last decade, most research has examined customer value co-creation in the service design process, such as travel planning and self-service delivery (e.g., Prebensen and Xie 2017; Shin and Perdue 2019). In this sense, this study establishes a theoretical process of value co-creation in terms of customer engagement behaviors.

More specifically, this study demonstrates that customers can contribute to firms with various resources (e.g., customer network asset, persuasion capital, creativity, and knowledge) (Harmeling et al. 2017) and provides nuanced insights into how various customer online engagement behaviors lead to non-transactional values. For example, online brand community members not only influence other members’ decision-making by sharing their experiences or recommending service products but also provide new ideas and novel solutions which can be an external knowledge for developing service innovation (Lusch and Nambisan 2015). The study results demonstrate that online brand community members can be a critical external knowledge source for open innovation (Chesbrough 2011). In addition, it was found that brand community members voluntarily build C-to-C relationships, which can strengthen their brand identity and attachment. Lastly, the altruistic nature of customer engagement behaviors can help effectively operate tourism online brand communities, confirming the functional value of such behaviors.

Third, to the authors’ knowledge, this is the first research that exclusively focuses on developing a measure of actual online engagement behaviors in online hotel brand community contexts. Historically, most marketing and hospitality research proposed measures to capture customer attitude or perception (e.g., brand attitude, brand image, and brand satisfaction, etc.) and behavioral intention (e.g., buying intention, revisit intention, etc.) (e.g., Islam and Daud 2011; Manhas and Tukamushaba 2015; Yang and Mattila 2014). Given the importance of behavioral manifestations of customer engagement (Wei, Miao, and Huang 2013), measuring actual customer behaviors can have a stronger predictive power compared to attitudinal measures; attitude and intention do not always directly cause behaviors (Boulstridge and Carrigan 2000). In addition, previous hospitality and tourism research examined tenuous relationships between satisfaction and intention (Dolnicar, Coltman, and Sharma 2015) or between intention and behavior (McKercher and Tse 2012). In this regard, the developed behavioral measure can be a stronger predictor for capturing customer non-transactional value in hospitality and tourism.

Lastly, this study analyzed multi dimensions of engagement behaviors for non-transactional values as related to different antecedents and consequences. While most studies focused on analyzing the antecedent and consequence mechanism of unidimensional customer engagement, such as the impact of brand quality on customer engagement (France, Merrilees, and Miller 2016), the impact of engagement on brand loyalty (Li, Teng, and Chen 2020), and the impact of customer engagement on purchase intention (Chan et al. 2014), this study shows that the different nature of engagement behaviors result in a different nomological mechanism. For example, while all customer engagement behaviors are significantly associated with intrinsic motivation, extrinsic motivation is only correlated with engagement behaviors for functional value. This result supports the significant role of intrinsic motivation for customer engagement (e.g., Fernandes and Remelhe 2016; Kim, Kim, and Wachter 2013). On the other hand, the study finding explains that functional engagement behaviors supporting the operation of online brand communities are only externally motivated. In addition, unlike previous research (e.g., Li, Teng, and Chen 2020; So et al. 2016), this study found not all engagement behaviors are associated with brand effects (e.g., brand attachment and brand loyalty). This is an important theoretical finding, which provides a basis to better understand the distinct nature of engagement behaviors in terms of their non-transactional value.

Practical Implications

The study results can be employed for promoting customer non-transactional value co-creation in online hospitality and tourism brand communities. Most importantly, the developed measure can be used to fully capture customer value. Along with various tools to capture customer transactional value (e.g., purchasing behaviors, brand loyalty, etc.), hospitality and tourism practitioners can employ the proposed scale to systematically analyze their customers’ non-transactional value. Measuring both transactional and non-transactional value will be beneficial for hospitality and tourism firms to create successful customer management strategies. Given the importance of intrinsic motivation for engagement behaviors, brand community managers need to make sure that community members can be internally motivated to engage in brand activities by adopting enjoyable and fun factors in the management of online brand communities.

More specifically, this study demonstrates the strategic value of customers for open innovation. Given the nature of the hospitality and tourism industry, such as the lack of research and development (R&D) departments for internal innovation (Williams and Cothrel 2000) and the experiential nature of hospitality services (Den Hertog, Van der Aa, and De Jong 2010), open innovation based on customer knowledge co-creation can be a promising innovation process. Thus, hospitality and tourism firms need to operate online brand communities not only for experience sharing and distribution channel platforms but also for open innovation platforms where community members can freely share their brand knowledge and insight.

Study Limitations and Future Research

Future research directions are identified by acknowledging study limitations. First, this scale needs to be externally validated in other online brand community contexts as only a single brand community was analyzed in the study. Further research may need to test the validity of the developed measure in other customer online engagement and value co-creation contexts. Importantly, while this study considers several motivational drivers in the development of multi-dimensional engagement measures, further research needs to test the validity of the measurement model in a broader context.

Second, further research can explore additional antecedents and consequences of engagement behaviors. While the results of this study initially proposed the empirical linkage of different engagement behaviors with distinct motivations (intrinsic and extrinsic motivations) and brand effects (brand attachment and brand loyalty), future research needs to add further empirical knowledge on the process of customer engagement and non-transactional value co-creation.

Lastly, future research needs to examine customer engagement and value co-creation processes in different lodging contexts (e.g., peer-to-peer accommodations, homestays, urban/rural accommodations, etc.) since lodging sectors and property types can be an important factor that influences value co-creation.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hakseung Shin

Author Biographies

Hakseung Shin is an assistant professor in the Department of Tourism at the Hanyang University. His research interests include service management and marketing strategy in hospitality and tourism. Particular interests include service innovation, technology infusion, value co-creation, and service eco-system.

Richard R. Perdue is the R.B. Pamplin Emeritus Professor of Hospitality and Tourism Management in the Virginia Tech Pamplin College of Business. He is an elected fellow and former president of the International Academy for the Study of Tourism. He is also former president of the Travel and Tourism Research Association. His research focuses on destination strategy, revenue management, pricing, and destination marketing.

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