Measuring Tourists’ Emotional Solidarity with One Another—A Modification of the Emotional Solidarity Scale

Abstract

Although tourists’ affective bonds with one another are essential to their travel experiences as well as their pretrip anticipations, it nevertheless remains as an underexplored topic in tourism research. Responding to this research gap, this study modified the Emotional Solidarity Scale (ESS), which is originally intended to measure affective bonds between tourists and residents and developed new scales for its theoretical antecedents (i.e., shared beliefs, shared behaviors, and propensity to interact), using potential faith-based tourists in the United States as the target population. For scale modification and development, both qualitative and quantitative research methods were used. The modified ESS (resulting in two unique factors—communality and fairness) along with the new scales for its antecedents demonstrated strong psychometric properties. Further studies are suggested to confirm the two-dimensional structure of the modified ESS and the relationships between the three antecedents in tourists’ intragroup relationship settings.

Keywords

emotional solidarity faith-based tourism interaction shared beliefs shared behaviors

Introduction

Individuals travel for various reasons, and one of which is to experience affective bonds with others whom they anticipate encountering while away. When traveling, tourists may anticipate in engaging in three distinct relationship forms: relationships with other tourists, with residents, and with service personnel in a destination (Pearce 2005). Given that service personnel are often residents of the destination as well, tourists’ relationships can either be with other tourists or residents. Of these two types, tourism research to date has primarily focused on how tourists and residents interact with one another (Huang and Hsu 2010), leaving few studies on tourists’ intragroup relationships (e.g., Murphy 2001; Sørensen 2003) or their influences (e.g., Huang and Hsu 2010; Mossberg 2007; Prebensen and Foss 2011; Wu 2007).

Despite the minimal scholarly attention paid to relationships among tourists, this is a topic that should not be overlooked. In the marketing and management literature, customer–customer relationships are viewed crucial to cocreating customer satisfaction and enhancing consumer loyalty (Clark and Martin 1994; Grove and Fisk 1997; Guenzi and Pelloni 2004; Martin 1996). In tourism, the intragroup relationship often casts a substantial influence on the quality of tourism experiences (Mossberg 2007; Rihova et al. 2015) and dominates much of tourists’ experiences during cruising (Huang and Hsu 2010), backpacking (Murphy 2001), and spectating at sporting events (Fairley 2003).

Such significance is likely because tourism experiences are typically socially shaped and occur in a setting exclusively for tourists. Tourists usually share a set of expectations and engage in similar activities. This is evident among tourists to popular destinations like Rome; throwing a coin into the Trevi Fountain and enjoying gelato are prime examples of “must do” activities for many tourists to Rome. Because of such collective expectations and common activities of tourists, destinations often have tourist areas where the interaction among tourists—albeit somewhat transient in nature—is as common as the interaction between tourists and residents. Such shared beliefs and behaviors accompanied by interaction can lead to affective bonds among tourists who meet in a destination, which have the potential to develop into a sense of solidarity or friendship in certain circumstances.

Affective bonds among tourists become more prominent in certain forms of tourism, and the anticipation for such a feeling can operate as a powerful pull factor (Fairley 2003; Huang and Hsu 2010; Jacobs 2006; Murphy 2001). For instance, observing hikers along the Pacific Crest Trail in the western United States, Lum, Keith, and Scott (2015) reported,

The social hiker is one who is motivated to hike primarily for the relationships within the hiking community. Much like the purist, the way in which the affinity for social interaction is manifested greatly depends on the hiker and was found to be diverse throughout the hiking community (10).

This anticipated feeling of affective bonds among tourists can be effectively explained by Durkheim’s ([1912] 1995) theory of emotional solidarity. The theory suggests that affective bonds or emotional solidarity among individuals is a result of individuals sharing beliefs and behaviors with one another while interacting (Collins 1975; Woosnam, Norman, and Ying 2009). In the tourism literature, Woosnam, Norman, and Ying (2009) first applied this theory to show that the relationship between residents and tourists is not as transient or transactional as other scholars (e.g., Aramberri 2001) viewed it. However, despite its utility in tourism research, the theory has yet to be applied in examining relationships among tourists or in an anticipated travel setting that constitutes substantial parts of one’s travel experience.

It seems timely to consider the emotional solidarity theory in studying tourists’ intragroup relationships and pretrip anticipations, which has received minimal scholarly attention despite its indicated importance. Thus, this study explored if potential tourists can develop emotional solidarity toward other tourists whom they expect to encounter. Addressing this research goal, Woosnam and Aleshinloye’s (2013) original scale for tourists’ emotional solidarity toward residents was modified, using potential faith-based tourists as the target population. Along with the modification, scales for potential faith-based tourists’ shared beliefs, shared behaviors, and propensity to interact were also formulated. Consequentially, the scales devised in this study will provide grounds to extend the applicability of the theory in tourism research.

Literature Review

Emotional Solidarity Theory

The intragroup relationship that tourists experience while traveling coincides well with Durkheim’s ([1912] 1995) emotional solidarity theory. After observing religions of aboriginal tribes in Australia, Durkheim ([1912] 1995) posited that affective bonds emerge among individuals who share sacred beliefs and ritualistic behaviors, and it is these beliefs and behaviors that unite the individuals. Later, Collins (1975) called such affective bonds emotional solidarity and added interaction, which was more implicit in Durkheim’s ([1912] 1995) theory, as the third antecedent of emotional solidarity. Granted that emotional solidarity is what holds society together (Durkheim [1912] 1995), the theory has been applied in studying a wide range of relationships among prison inmates (Street 1965), teenage students (Rosengren 1959), or family members (Gronvold 1988; Klapp 1959; Lowenstein and Daatland 2006).

However, it was not until Woosnam, Norman, and Ying’s (2009) study that the emotional solidarity theory (Durkheim [1912] 1995) made its way into tourism research. Departing from conventional views that disregarded the relationship between tourists and residents as transient and transactional (Harrill 2004), Woosnam, Norman, and Ying (2009) found emotional solidarity existing between the two groups in a destination and claimed that they can develop sustained and intimate relationships with one another. In this study, the terms “shared beliefs” and “shared behaviors” first appeared to substitute what Durkheim ([1912] 1995) called sacred beliefs and ritualistic behaviors, making the theory more relevant to tourism. Thus, a proper interpretation of the theory in tourism contexts is that tourists and residents can form emotional solidarity with one another when their interaction is accompanied by shared beliefs and behaviors.

Following Woosnam, Norman, and Ying’s (2009) qualitative study, Woosnam and Norman (2010) undertook a quantitative approach in applying the emotional solidarity theory. This included developing scales for residents’ shared beliefs, shared behaviors, interaction, and emotional solidary with tourists. Woosnam and Norman (2010) not only confirmed the applicability of the theory in tourism research but also developed a 10-item Emotional Solidarity Scale (ESS) composed of three distinct factors: emotional closeness, sympathetic understanding, and welcoming nature. Soon, the scale was also employed and validated in measuring tourists’ emotional solidarity toward residents (Woosnam and Aleshinloye 2013); however, the factor welcoming nature was named feeling welcomed to account for the context of tourists.

Since then, extensive efforts have been made to test the validity and the reliability of the ESS (e.g., Ribeiro et al. 2018; Woosnam 2011a, 2011b, 2012; Woosnam and Aleshinloye 2013), and the scale has been found appropriate in measuring emotional solidarity of both residents (Woosnam 2011b, 2012) and tourists (Woosnam and Aleshinloye 2013; Woosnam, Dudensing, and Walker 2015; Woosnam et al. 2015) within the United States. More recently, studies outside of the United States, in nations like Cape Verde (Ribeiro et al. 2018), Japan (Woosnam, Maruyama, and Boley 2016), Macau (Lai and Hitchcock 2017; Li and Wan 2017), Malaysia (Hasani, Moghavvemi, and Hamzah 2016), Nigeria (Woosnam, Aleshinloye, and Maruyama 2016; Woosnam et al. 2016), and South Korea (Joo, Cho, and Woosnam 2019), have demonstrated the cross-cultural applicability of the scale as well.

Apart from shared beliefs, shared behaviors, and interaction, studies have identified other constructs predicting emotional solidarity. Frequently, individuals’ attachment to a destination has been found to be positively related to emotional solidarity with others (Li and Wan 2017; Woosnam et al. 2014; Woosnam et al. 2016). For instance, in Woosnam et al.’s (2014) study, residents’ length of residence successfully predicted their emotional closeness and sympathetic understanding experienced with tourists. Li and Wan (2017) considered how residents’ community attachment was related to their emotional solidarity and confirmed a positive relationship between the two. Likewise, tourists with greater place attachment to a UNESCO World Heritage Site demonstrated stronger emotional solidarity toward residents (Woosnam et al. 2016). Other predictors of emotional solidarity that have been considered so far include the travel distance (Joo et al. 2017) or effectiveness of the safety force (Simpson and Simpson 2017).

Although evidence for applying the emotional solidarity theory (Durkheim [1912] 1995) in tourism research has been increasing, some limitations need to be addressed in future studies. First of all, there has been little recognition of emotional solidarity among individuals in the same group (i.e., residents or tourists). The only exception to this is Woosnam, Maruyama, and Boley’s (2016) study in Japan, where they found that Japanese and Brazilian residents showed different degrees of emotional solidarity with one another, resulting in divergent views concerning ethnic neighborhood tourism focused on Brazilian culture. Although the findings acknowledged the potential for intragroup solidarity to exist, the groups were not truly homogenous in their cultural backgrounds and roles in ethnic neighborhood tourism. That is, Japanese could only be tourists to ethnic enclaves where Brazilians resided.

Furthermore, it is unclear if individuals can form emotional solidarity toward others whom they anticipate encountering in tourism contexts. To date, only tourists or residents who were physically present in destinations were asked about their feelings toward others with whom they had already interacted. While it is challenging to ask tourists or residents about their anticipated relationships, studies (e.g., Hsu, Cai, and Li 2010) have nevertheless endorsed the importance of such anticipation in shaping actual perceptions and behaviors. Given that tourists’ intragroup relationships account for much of their experience and satisfaction related to the trip (Fairley 2003; Murphy 2001; Rihova et al. 2015), a logical supposition is that individuals may expect to experience affective bonds with others who hold similar beliefs and similar behaviors.

Emotional Solidarity and Travel Decision

Given that the emotional solidarity theory has exclusively been applied to understand intergroup relationships between residents and tourists, how emotional solidarity affects travel decision remains as a topic for further research. However, studies have constantly supported the positive relationship between intergroup emotional solidarity and how individuals think or act with respect to tourism, both from residents’ and tourists’ point of view. That is, regardless of which group (i.e., residents or tourists) they belong, emotional solidarity toward the other group is likely to result in positive attitudes (Hasani, Moghavvemi, and Hamzah 2016; Li and Wan 2017; Woosnam 2011a, 2011b, 2012; Woosnam et al. 2015; Woosnam, Maruyama, and Boley 2016) or favorable behavioral outcomes (Ribeiro et al. 2018; Woosnam et al. 2015) with respect to tourism in the destination.

To date, a host of studies have been undertaken examining the connection between residents’ emotional solidarity toward tourists and their approval of tourism in their community. Woosnam (2012) was the first to look into such possibility and demonstrated that the ESS factors were significantly related to perceived community benefits and support for tourism development. Since then, the findings were replicated by other scholars who revisited the idea in different cultures (e.g., Hasani, Moghavvemi, and Hamzah 2016) or settings (e.g., festival tourism, urban tourism) (e.g., Lai and Hitchcock 2017; Li and Wan 2017). Although some ESS factors failed to present significant outcome at times, the central and dominant findings have been that more positive social feelings toward tourists leads to more amicable views of tourism.

Relatively few studies have considered how emotional solidarity predicts individuals’ behavioral outcomes in tourism settings. Most notably, Woosnam et al. (2015b) undertook an interesting study on the linkage between nature tourists’ emotional solidarity toward residents and their expenditure patterns. Although their effect sizes were limited, the ESS factors were meaningful predictors in five expenditure categories of the eight that were considered. To date, this study by Woosnam et al. (2015b) remains as the sole instance where an actual behavior was tested as others have mostly looked at behavioral intentions. For instance, Joo et al. (2018) analyzed how residents’ emotional solidarity is associated with their willingness to interact with tourists. The findings showed that higher emotional solidarity promoted approaching and reduced avoidance.

The study of Ribeiro et al. (2018) is most relevant to the current study. Looking at international tourists to a sun-and-sand destination, it investigated how emotional solidarity explains travel satisfaction and destination loyalty. Results were that all three ESS factors significantly contributed to travel satisfaction, which then mediated their influences on destination loyalty. Of the three ESS factors, feeling welcomed and sympathetic understanding were also directly associated with destination loyalty, thus suggesting a partial mediating role of travel satisfaction. Since destination loyalty, as discussed by Ribeiro et al. (2018), consists of willingness to revisit and recommend, their findings can serve as a reference for a similar relationship between tourists’ emotional solidarity with one another and their intention to travel.

Research Objectives

In recognition of the noted research gaps, the primary objective of this study was to develop scales for individuals’ emotional solidarity toward others whom they anticipate encountering. Despite the increasing research on emotional solidarity between individuals from two distinctive groups (i.e., tourists and residents), little effort has been made to see how individuals in the same group feel about one another. Consequentially, no scale exists to measure intragroup emotional solidarity. Also attempted in this study was to invent scales for potential tourists’ shared beliefs, shared behaviors, and propensity to interact with one another in an anticipated travel setting. Note that “interaction” in the emotional solidarity theory has been rephrased to “propensity to interact” to account for an anticipated travel setting. Given that the three constructs are common antecedents to emotional solidarity, developing scales for them is essential in validating the theory in intragroup relationship settings as well.

Addressing these research objectives, this study considered potential faith-based tourists as the target population. The decision was based on a few rationales. First, the emotional solidarity theory was initially conceived in a religious context (Durkheim [1912] 1995), but faith-based tourism has not been considered in studies to date. More importantly, given faith-based tourism usually entails a set of shared beliefs and shared behaviors among its participants, this study proposed that potential faith-based tourists can more easily imagine themselves in an anticipated travel setting. Lastly, an abundance of qualitative statements exists illustrating how faith-based tourists think and behave while traveling. Granted that qualitative statements serve as building blocks (i.e., items) of a scale, faith-based tourism was viewed as fertile grounds for the scale development.

Methods

Data Collection

Data collection was undertaken in two iterations. First, to develop scales for potential faith-based tourists’ shared beliefs and shared behaviors regarding their anticipated trip (hereafter called “shared beliefs” and “shared behaviors”) and their propensity to interact with other faith-based tourists (hereafter called “propensity to interact”), qualitative statements were drawn from the existing literature. This literature involved tourism studies on emotional solidarity and faith-based tourism as well as nonscholarly sources such as travel diaries and video clips. This qualitative data collection continued until the authors encountered a data saturation point where little new information was gained from subsequent investigation.

Quantitative data were collected from residents in the United States who self-identified as Christians (i.e., Catholics, Protestants, and Orthodox) and expressed their interest in visiting religious destinations for purely or partially religious reasons. Of major religious populations in the United States, Muslims and Mormons were not considered in this study, given their relatively weak presence in the United States or weak ties to religious destinations abroad. According to the Pew Research Center (2014), only 5.9% of the US population confessed non-Christian faiths. In addition, Mormons typically place little importance on religious destinations outside the United States (Jackson and Henrie 1983).

To approach the target population, a survey respondent panel was used. A survey respondent panel is composed of individuals who agreed to complete online surveys either voluntarily or for small compensation (Pew Research Center, n.d.). Using a survey respondent panel may entail the risks of undersampling certain populations (Dillman, Smyth, and Christian 2014; Fulgoni 2014), exploiting the panel (Query Group 2014), or facing fraudulent individuals (Yoro 2016). However, there have been constant efforts to counter such risks via sample matching, sample blending (Pew Research Center, n.d.), or sample quota (Yoro 2016). As a result, using a survey respondent panel has become increasingly popular among scholars (Fulgoni 2014; Query Group 2014).

Notwithstanding the risks, this study considered that benefits of using a survey respondent panel outweighed its costs, given the nature of its target population. Unlike other tourism studies, this study focused on individuals with a specific religious faith (i.e., Christianity) who were not confined to a geographic area. They were not easily identifiable in a face-to-face setting. Although recruiting Christians through religious organizations might have made them more identifiable, doing so would have undermined the representativeness of the sample, since individuals in the same religious organization were likely to share social, economic, and racial backgrounds. On the other hand, a survey respondent panel could reduce the risk of undersampling certain populations, as individuals were from various backgrounds.

With this being said, two survey respondent panels were provided by a survey company, one each for a pilot study and the main study. To be a part of the survey respondent panels, individuals not only had to have matching religious and demographic backgrounds but had also expressed their clear interest in a faith-based tourism trip to destinations within or outside the United States. To that end, a categorical format question with four categories (i.e., yes, within the United States; yes, outside the United States; no; and maybe) was used in the pilot survey, whereas in the main survey a five-point Likert-type scale question (1 = not interested at all, 5 = extremely interested) was employed. Those who answered “no” or “maybe” in the pilot survey or chose “1” in the main survey were excluded in the data collection. Also, to ensure data quality, some restrictions were put in place. First, individuals who had participated in the pilot survey were prohibited from joining the main survey. Second, the pilot survey required participants be at least 40 years of age, given the high percentage of older women comprising the actual faith-based tourists in the United States (Fleischer 2000; Kaell 2014). Lastly, responses that took less than one-third of the median time to complete were eliminated.

Survey Instrument

Scales for potential faith-based tourists’ shared beliefs and shared behaviors as well as their propensity to interact with other faith-based tourists were developed by analyzing the content of qualitative statements collected in the data collection stage (DeVellis 2017). As for emotional solidarity among tourists, this study modified the ESS for tourists (Woosnam and Aleshinloye 2013). Acknowledging that the existing ESS has only been applied in the contexts of experienced relationships between two distinctive groups, each of its ten original items was rephrased to reflect the context of this study. Also, four additional items that had been discarded in the development process of the ESS (Woosnam 2008) were considered in this study. This resulted in 14 ESS items prepared for the pilot survey (Table 1). The main survey instrument contained all the items that appeared in the pilot survey except one that had been eliminated in the exploratory factor analysis (EFA) stage.

Table 1.

Measurement Scale for Emotional Solidarity.

ESS Factor	Original ESS Item	Modified ESS Item
Emotional closeness	I feel close to some visitors I have met in [Destination].	I feel close to other faith-based tourists I expect to meet in my destination.
Emotional closeness	I have made friends with some visitors in [Destination].	I expect to make friends with other faith-based tourists in my destination.
Sympathetic understanding	I identify with visitors in [Destination].	I identify with other faith-based tourists I expect to meet in my destination.
	I have a lot in common with [Destination] visitors.	I have a lot in common with other faith-based tourists I expect to meet in my destination.
	I feel affection towards visitors in [Destination].	I feel affection towards other faith-based tourists I expect to meet in my destination.
	I understand visitors in [Destination].	I understand other faith-based tourists I expect to meet in my destination.
Feeling welcomed	I am proud to have visitors come to [Destination]	I will be proud to be welcomed as a fellow faith-based tourist to my destination.
	I feel the community benefits from having visitors in [Destination].	I feel the community will benefit from having us (me and other faith-based tourists) in my destination.
	I appreciate visitors for the contribution they make to the local economy.	I appreciate other faith-based tourists I expect to meet for the contribution they make to the local economy.
	I treat visitors fair in [Destination].	I will treat other faith-based tourists I expect to meet in my destination fairly.
New addition	I get along with [Destination] visitors.	I will get along with other faith-based tourists I expect to meet in my destination fairly.
	I can trust visitors to [Destination].	I can trust other faith-based tourists I expect to meet in my destination fairly.
	I have respect for [Destination] visitors.	I have respect for other faith-based tourists I expect to meet in my destination fairly.
	I share ideas with visitors to [Destination].	I share similar views with other faith-based tourists I expect to meet in my destination fairly.

Note: The original scale is from Woosnam and Aleshinloye (2013), and the additional items are from Woosnam (2008). Changes in the modified scales are underlined.

Data Analysis

Content analysis of qualitative data

When analyzing qualitative data, this study followed the procedure used by Carney (1972), Busch et al. (2012), and DeVellis (2017). Two independent coders coded each qualitative statement by exact phrases and for three parental nodes of potential faith-based tourists’ shared beliefs, shared behaviors, and propensity to interact with other faith-based tourists. To ensure consensus between the two coders, interrater reliability (IRR) (DeVellis 2017; Holsti 1969) was calculated. An IRR value of 0.80 or higher (Miles and Huberman 1994) was set as the threshold, where any value higher than that did not require additional discussion and re-coding. Once solid IRR values were established across all the parental nodes, the authors searched for recurring themes (i.e., child nodes) within each parental node. When there was disagreement regarding under which theme a code should be placed, the authors revisited the code later. On completion of the coding procedure, each theme was turned into an item to measure potential faith-based tourists’ shared beliefs, shared behaviors, or propensity to interact. These items went through additional reviews by experts external to this study to ensure content and face validity, clarity, and redundancy (DeVellis 2017).

EFA of pilot survey data

Data from the pilot study were analyzed in SPSS 23.0. As for each construct (i.e., shared beliefs, shared behaviors, propensity to interaction, and emotional solidarity), EFA was undertaken separately. EFA is usually employed when there is no a priori knowledge regarding how a construct (i.e., latent variable) can be represented by a set of items (i.e., observed variable) that are relatively independent from one another (DeVellis 2017; Tabachnick and Fidell 2013). Given that ESS items considered in this study had never been tested in the context of tourists’ intragroup relationships, EFA was deemed essential (Choi and Sirakaya 2005; Woosnam and Norman 2010).

Prior to EFA for each construct, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (Kaiser 1974) and Bartlett’s (1954) test of sphericity were assessed to determine if there was enough variation in the data to allow factors to emerge. Once the use of EFA was justified, the number of factors to be extracted from each construct was decided based on Kaiser–Guttman criterion (i.e., maintaining factors whose eigenvalue is greater than 1.0), scree plot, and Monte-Carlo simulation (i.e., bootstrapping) (DeVellis 2017). If there was any inconsistency among the results of the three criteria, the authors undertook EFA with every result and looked for one that provided the clearest fit.

The actual factor extraction and rotation were conducted using principal axis factoring (PAF) and promax rotation. Since PAF assumes the common variance between items and takes measurement error into account (Hair et al. 2010), it is usually more preferred to principal components analysis when a researcher intends to identify the structure of a construct (Hair et al. 2010). When interpreting the results, promax rotation, which allows factors to correlate (Schmitt 2011; Tabachnick and Fidell 2013), was used.

Items that failed to load sufficiently onto a single factor (i.e., clear primary loading) or loaded significantly onto multiple factors were eliminated in this stage. This study demanded that primary loadings be above 0.55 and that cross loadings be at least 0.20 smaller than the primary loading. The 0.55 cutoff is equal to what Comrey and Lee (1992) considered good loading and is a rather conservative cutoff (Matsunaga 2010).

CFA of main survey data

To establish a measurement model, this study conducted a series of CFA in AMOS 22.0. When an initial measurement model was deemed inappropriate, the model was respecified by consulting modification index (MI) values. Although any MI values greater than 3.84 imply some room for improvement, Byrne (2016) suggested using 10.00 for greater efficiency. However, given the complexity of the initial measurement model in this study, an even more stringent cutoff of 40.00 was used.

While this study considered all options to improve a model fit (i.e., covarying error terms, adding a regression path, or deleting an item) (Byrne 2016), each decision was reviewed with respect to its theoretical grounds and the model parsimony. Byrne (2016) warned against making a model too complicated where each parameter makes trivial contribution to the fit and the model becomes difficult to replicate in other studies. Thus, the authors stopped respecifying the model when no further options remained that could be justified. For instance, dropping an item was avoided when the item was believed to represent an idea essential to its corresponding factor; an error covariance term was added only between items belonging to the same factor and it was reasonable to suspect correlation between the items; and adding a regression path between an item and factors other than its theoretically corresponding factor was avoided.

The validity and the reliability of the model was estimated by consulting the values of average variance explained (AVE), composite reliability (CR), and interfactor correlation. To support the convergent validity (i.e., extent to which items within a factor share variance) of the model, AVE values should be at least 0.5 (Hair et al. 2010). To demonstrate the discriminant validity (i.e., extent to which items in different factors differ from one another), it was established that a factor should have a higher AVE value than its squared interfactor correlation values. Lastly, the internal consistency of a factor was estimated by CR. Hair et al. (2010) claimed that CR values need to be 0.7 or higher for good reliability, where values between 0.6 and 0.7 are acceptable.

Results

Qualitative Study

Between July and September 2017, the authors identified 201 qualitative statements from scholarly and nonscholarly sources on emotional solidarity and faith-based tourism. However, 10 of the 201 were found less relevant to the purpose of this study and discarded in the data screening stage. Of the remaining 191, which were subject to coding, the first coder found 93 that illustrated shared beliefs (45) or shared behaviors (48). The second coder found 52 for shared beliefs and 55 for shared behaviors. IRR values were 0.825 (shared beliefs) and 0.796 (shared behaviors), with the overall IRR (0.810) exceeding the 0.80 cutoff (Miles and Huberman 1994). It should be noted that no statement was coded for propensity to interact, presumably because of its implicit nature.

When the authors looked for recurring themes across the codes, 26 themes emerged for shared beliefs, with the three most frequent themes being anticipation or appreciation for “religious experience” (21), “physical or mental endurance” (16), and “religious rejuvenation” (15) that the trip will entail. With respect to shared behaviors, 23 themes were found, which include “visiting Biblical holy sites” (23), “traveling in organized groups” (15), or “visiting institutionalized holy sites (e.g., martyr’s tombs or churches)” (9). Since no code or theme was generated for propensity to interact, the authors inferred them from the results obtained for shared behaviors. Consequently, 19 themes were generated for propensity to interact, which were mostly centered around different types of activities in a religious designation. Later, these themes were turned into items and reviewed by external experts for content validity, clarity, and redundancy. Consequentially, 24, 20, and 17 items formed preliminary scales for shared beliefs, shared behaviors, and propensity to interact, respectively.

Pilot Study

The pilot survey took place in November 2017. During that period, 148 respondents, all from a survey respondent panel, participated in the online survey, but only 124 completed it (completion rate of 83.78%). Because of technical limitations, it is unknown how many individuals opened the link but left without any click. Hence, only completion rates, instead of response rates, are reported in this study. Of these 124 respondents, 5 provided nondifferentiated responses (i.e., straight-lined responses) and were discarded from further analysis. Consequentially, only 119 responses were used in EFA. While this sample size fell short of 10 times of a number of items in a scale (Nunnally 1978) or 200 (Comrey and Lee 1992), it nevertheless satisfied a ratio of five-to-one (Hatcher 1994).

Values of the KMO measure ranged between 0.892 (shared behaviors) and 0.945 (emotional solidarity) which were in the range of marvelous (i.e., 0.90 ≤ KMO) and meritorious (i.e., 0.80 ≤ KMO < 0.90) (Kaiser 1974). Bartlett’s test also yielded a solid p-value of less than 0.05 for each construct. Confirming the factorability of each construct, the authors moved onto actual EFA using the methods and cut-off criteria illustrated above.

The inclusion and exclusionary criteria (i.e., 0.55 ≤ primary loading; cross loading < primary loading - 0.2) for items were generally upheld, but the following items were retained regardless of their failure, as the items were considered indispensable: “visiting Biblical holy sites” (shared behaviors) and “at institutionalized holy places” (propensity to interact). Resultantly, 19 items were discarded across all four constructs.

Within shared beliefs, there were 14 items scattered across the following three factors: devotion (seven items), concerns (four items), and entertainment (four items) (Table 2). Together, these three factors accounted for 70.69% of the variance in respondents’ shared beliefs about their anticipated trip.

Table 2.

EFA Loading and Reliability Statistics.

Construct / Factor / Items	Loading	Cronbach’s Alpha	% Variance Explained
Shared beliefs (3 factors / 14 items) (KMO = 0.934^a)			70.685
Devotion (7 items)		0.926	55.022
Anticipation that the trip will strengthen my religious identity	0.977
Anticipation that the trip will help me understand myself better	0.875
Anticipation that the trip will help me experience a sense of religious belonging	0.836
Anticipation that the trip will help me achieve personal growth and self-actualization	0.765
Anticipation that the trip will provide me religious recognition	0.748
Anticipation that the trip will be a religious experience	0.656
Appreciation for physical and mental endurance that the trip requires	0.585
Concerns (4 items)		0.883	11.349
Concerns for degree of commercialization we may encounter	0.894
Concerns for degree of authenticity we may encounter	0.885
Concerns for degree of crowding we may encounter	0.696
Concerns for degree of safety we may encounter	0.675
Entertainment (3 items)		0.931	4.315
Appreciation for the history of the destination	0.996
Appreciation for the culture of the destination	0.939
Anticipation that the trip will be a fresh experience	0.601
Shared behaviors (3 factors / 15 items) (KMO = 0.892^a)			65.170
Cultural behaviors (7 items)		0.922	45.635
Visiting non-religious historical sites	0.986
Visiting non-religious cultural sites	0.964
Visiting non-religious natural sites	0.919
Visiting holy places of other religions	0.659
Participating in a guided tour during the trip	0.661
Visiting institutionalized holy places	0.609
Visiting Biblical holy places	0.491
Devotional behaviors (4 items)		0.850	13.831
Praying during the trip	0.961
Participating in religious celebrations during the trip	0.827
Participating in volunteer or charitable works during the trip	0.641
Planning and preparing for the trip several months in advance	0.550
Touristic behaviors (4 items)		0.812	5.704
Traveling during religious holidays or anniversaries	0.831
Shopping for souvenirs during the trip	0.708
Wearing clothes or accessories that represent my religion or denomination during the trip	0.621
Traveling with a spiritual leader	0.585
Propensity to interact (2 factors / 14 items) (KMO = 0.945^a)			75.934
Touristic occasions (7 items)		0.955	69.493
While taking photographs	0.955
While at restaurants	0.870
While at accommodations	0.886
While shopping for souvenirs	0.871
While at non-religious cultural sites	0.650
While at non-religious historical sites	0.737
While at non-religious natural sites	0.701
Religious occasions (7 items)		0.950	6.441
While participating in religious celebrations	0.971
While participating in volunteer or charitable works	0.920
While participating in religious conferences	0.910
That are members of your similar religious faith	0.742
On more of a personal level	0.669
While at institutionalized holy places	0.584
While at Biblical holy places	0.501
Emotional solidarity (2 factors / 13 items) (KMO = 0.942^a)			75.423
Communality (10 items)		0.962	68.480
Feel affection towards other faith-based tourists	0.970
Feel close to other faith-based tourists	0.967
Have a lot common with other faith-based tourists	0.848
Will be proud to be welcomed as a fellow faith-based tourist	0.834
Understand other faith-based tourists	0.795
Expect to make friends with other faith-based tourists	0.792
Identify with other faith-based tourists	0.768
Share similar views with other faith-based tourists	0.718
Feel the community will benefit from having us (me and other faith-based tourists) in my destination	0.658
Can trust other faith-based tourists	0.652
Fairness (3 items)		0.930	6.944
Will treat other faith-based tourists fairly	0.979
Plan to get along with other faith-based tourists	0.952
Have respect for other faith-based tourists	0.684

Note: Separate EFAs were run for each scale representing the construct.

KMO’s measure of sampling adequacy can have a value between 0 and 1 where a value closer to 1 indicates the data is appropriate for factor analysis. A value of 0.6 or higher is required for good factor analysis (Tabachnick and Fidell 2013).

Fifteen items were retained to represent respondents’ share behaviors (Table 2). These items belonged to the three factors of cultural behaviors, devotional behaviors, and touristic behaviors. They each explained 45.64%, 13.83%, and 5.70% of the variance, respectively, in the construct, amounting to a total of 65.17%.

Propensity to interact was also composed of two factors: touristic occasions made up of seven items and religious occasions with another seven items (Table 2). While the two factors explained a substantial portion (i.e., 75.93%) of the variance in respondents’ propensity to interact with other faith-based tourists, there was much imbalance between the contribution that each factor made.

Among 14 ESS items considered, only 1 was found to be problematic. Namely, the item “appreciate other faith-based tourists I expect to meet for the contribution they make to the local economy” was appropriate neither in its primary loading nor cross loading. Consequentially, 13 ESS items were retained, which belonged to two factors that were named communality with 10 items and fairness with 3 items (Table 2). A total of 75.43% of the variance in respondents’ emotional solidarity with other faith-based tourists was explained by these two factors.

Main Study

The main survey was undertaken throughout December of 2017 and January of 2018, using the same procedures as the pilot survey. Of 980 individuals that participated in the online survey, 439 completed the survey instrument in its entirety. This resulted in a significantly lower completion rate (i.e., 44.80%) compared with the pilot survey (i.e., 83.78%) or other studies (e.g., Bosnjak et al. 2016; Quintal et al. 2010) that also used a survey respondent panel. This might have resulted from the survey being undertaken over the Christmas and New Year’s holiday season.

To single out disengaged responses, this study examined if individuals’ responses yielded an extremely small or large standard deviation value. That is, an extremely high standard deviation value was considered as an indication of bipolar responses (i.e., alternating between 1 and 7), whereas the opposite was a sign of nondifferentiated response. From this, 10 responses were found disengaged. This study also looked for multivariate outliers by consulting Mahalanobis distance values and further eliminated 54 responses that had Mahalanobis distance values ranging from 94.64 to 200.61. Consequently, 375 responses were used in further analysis. Since all responses were completely provided, there was no need for imputation to address missing values.

Sample overview

The sample was composed of more women (n = 247, 65.9%) than men (n = 128, 34.1%) (Table 3). While few studies have provided a demographic overview of faith-based tourists, and even less so when it comes to gender, this finding is consistent with what Kaell’s (2014) ethnographic study of pilgrims to the Holy Land indicated. In Kaell’s (2014) study, most of the American Christians who visited the Holy Land for pilgrimage were elderly females.

Table 3.

Demographic Characteristics of the Main Survey’s Sample Population.

Variable	n	%	Mean	SD
Gender (N = 375)
Male	128	34.1
Female	247	65.9
Religious affiliation (N = 375)
Catholic	208	55.5
Evangelical protestant	103	27.5
Mainline protestant	64	17.1
Age (n = 374)			44.64	17.06
18–29	81	21.7
30–39	97	25.9
40–49	54	14.4
50–59	50	13.4
≥60	92	24.6
Education (N = 375)
Primary / elementary school	1	0.3
Secondary / high school certificate / diploma	95	25.3
Technical, vocational, or trade school	87	23.2
Four-year college	137	36.5
Master’s degree	45	12.0
Doctoral or professional degree	10	2.7
Marital status (N = 375)
Single	107	28.5
Married	212	56.5
Divorced/separated	39	10.4
Widowed	11	2.9
Other	6	1.6
Income (N = 375)
≤$25,000	66	17.6
$25,000–$49,999	105	28.0
$50,000–$74,999	86	22.9
$75,000–$99,999	63	16.8
$100,000–$149,999	36	9.6
≥$150,000	19	5.1

In terms of age, the mean of the sample was 44.64 years. The single largest age group was those in their 30s (n = 97, 25.9%), followed closely by others in the eldest age group (n = 92, 24.6%) (Table 3). Respondents were generally well educated, with slightly more than half (n = 192, 51.3%) of the sample having completed a four-year college education or beyond: 36.5% (n = 131) had a bachelor’s degree and an additional 15.3% (n = 55) had a master’s degree or higher (Table 5).

In regard to income, the sample conformed more to what had been reported by other scholars (e.g., Collins-Kreiner and Kliot 2000; Fleischer 2000; Kaell 2014) (Table 3). It mostly consisted of respondents from lower income ranges of $74,999 or less. The single largest-income group was those earning $25,000 to $49,999 (n = 105, 28.0%) and 22.9% (n = 86) were from the next highest income group (i.e., $50,000–$74,999). When the three lowest-income groups were combined, they made up 68.5% (n = 257) of the sample.

With respect to faith-based tourism, the sample was moderately experienced and interested in such an experience (Table 3). More than half (n = 202, 53.9%) of the sample said that they experienced faith-based tourism in the past but only to a destination within the United States, whereas 47 (12.5%) respondents visited a destination outside the United States. The remaining 126 (33.6%) respondents did not have any faith-based tourism experience. When asked about their interest in faith-based tourism, the sample was moderately interested in visiting a faith-based tourism destination within the United States (M = 3.77) as well as outside the United States (M = 3.53). Thus, respondents were more attracted to US destinations than non-US ones.

CFA

To build an initial measurement model, each of the 10 factors was added 1 at a time until the model included all items within corresponding factors. In the process of model building, each factor was allowed to covary with one another (Byrne 2016). This initial model showed an acceptable fit to the data but revealed much room for improvement (Table 4). By consulting regression coefficient and MI values, the authors eliminated four items that loaded poorly (i.e., λ < 0.7) (Fornell and Larcker 1981) onto their respective factor (i.e., shared behaviors) and added four error covariance terms (Table 5). This interim measurement model performed better in its fit to the data (Table 4); however, it demonstrated some discriminant validity issues.

Table 4.

Measurement Model Fit Indices in First-Order CFA.

Model	χ²_(df)	χ² / df	SRMR	CFI	TLI	RMSEA
Initial	3996.563₍₁₄₃₉₎	2.777	0.049	0.886	0.878	0.069
Interim	2778.893₍₁₂₂₅₎	2.268	0.042	0.926	0.920	0.058
Final	2091.539₍₉₈₇₎	2.119	0.041	0.940	0.935	0.055

Note: CFA = confirmatory factor analysis; df = degree of freedom; SRMR = standardized root mean square residual; CFI = confirmatory fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation.

Table 5.

Descriptive, Loading, and Reliability Statistics of the Final Measurement Model.

Scale and Items	Mean	SD	λ
Shared beliefs (3 factors / 12 items)
Devotion (5 items) (CR = 0.932; AVE = 0.733)
Anticipation that the trip will strengthen my religious identity	5.67	1.24	0.886
Anticipation that the trip will help me understand myself better	5.67	1.19	0.871
Anticipation that the trip will help me experience a sense of religious belonging	5.66	1.26	0.892
Anticipation that the trip will help me achieve personal-growth and self-actualization	5.72	1.22	0.852
Anticipation that the trip will provide me religious recognition	5.34	1.34	0.773
Anticipation that the trip will be a religious experience	Discarded for validity
Appreciation for physical and mental endurance that the trip requires	Discarded for validity
Concerns (4 items) (CR = 0.892; AVE = 0.675)
Concerns for degree of commercialization we may encounter	5.41	1.34	0.865
Concerns for degree of authenticity we may encounter	5.42	1.43	0.792
Concerns for degree of crowding we may encounter	5.41	1.33	0.866
Concerns for degree of safety we may encounter	5.57	1.38	0.758
Entertainment (3 items) (CR = 0.912; AVE = 0.776)
Appreciation for the history of the destination	5.98	1.19	0.912
Appreciation for the culture of the destination	5.93	1.19	0.878
Anticipation that the trip will be a fresh experience	5.94	1.15	0.813
Shared behaviors (3 factors / 10 items)
Cultural behaviors (4 items) (CR = 0.871; AVE = 0.628)
Visiting holy places of other religions	5.65	1.20	0.765
Participating in a guided tour during the trip	5.74	1.20	0.818
Visiting institutionalized holy places	5.63	1.13	0.813
Visiting Biblical holy places	5.95	1.14	0.773
Visiting non-religious natural sites	Discarded for model fit
Visiting non-religious cultural sites	Discarded for model fit
Visiting non-religious historical sites	Discarded for model fit
Devotional behaviors (3 items) (CR = 0.870; AVE = 0.690)
Praying during the trip	5.94	1.14	0.793
Participating in religious celebrations during the trip	5.79	1.14	0.902
Participating in volunteer or charitable works during the trip	5.65	1.27	0.788
Planning and preparing for the trip several months in advance	Discarded for validity
Touristic behaviors (3 items) (CR = 0.814; AVE = 0.593)
Traveling during religious holidays or anniversaries	5.17	1.48	0.745
Wearing clothes or accessories that represent my religion or denomination during the trip	5.10	1.48	0.738
Traveling with a spiritual leader	5.48	1.32	0.825
Shopping for souvenirs during the trip	Discarded for model fit
Propensity to interact (2 factors / 12 items)
Touristic occasions (6 items) (CR = 0.948; AVE = 0.753)
While taking photographs	4.51	1.63	0.913
While at restaurants	4.55	1.57	0.913
While at accommodations	4.50	1.58	0.926
While shopping for souvenirs	4.45	1.55	0.876
While at non-religious cultural sites	4.65	1.43	0.806
While at non-religious historical sites	4.64	1.43	0.759
While at non-religious natural sites	Discarded for validity
Religious occasions (6 items) (CR = 0.950; AVE = 0.761)
While participating in religious celebrations	4.95	1.52	0.909
While participating in volunteer or charitable works	495	1.49	0.884
While participating in religious conferences	4.90	1.57	0.921
That are members of your similar religious faith	5.00	1.46	0.878
While at institutionalized holy places	4.73	1.44	0.824
While at Biblical holy places	5.93	1.51	0.807
On more of a personal level	Discarded for validity
Emotional solidarity (2 factors / 13 items)
Communality (10 items) (CR = 0.964; AVE = 0.730)
Feel affection towards other faith-based tourists	5.43	1.25	0.827
Feel close to other faith-based tourists	5.48	1.22	0.871
Have a lot common with other faith-based tourists	5.47	1.23	0.877
Will be proud to be welcomed as a fellow faith-based tourist	5.80	1.09	0.862
Understand other faith-based tourists	5.55	1.10	0.860
Expect to make friends with other faith-based tourists	5.56	1.18	0.854
Identify with other faith-based tourists	5.56	1.13	0.881
Share similar views with other faith-based tourists	5.63	1.17	0.844
Feel the community will benefit from having us (me and other faith-based tourists) in my destination	5.50	1.20	0.836
Can trust other faith-based tourists	5.59	1.13	0.820
Fairness (3 items) (CR = 0.932; AVE = 0.820)
Will treat other faith-based tourists fairly	5.97	1.11	0.904
Plan to get along with other faith-based tourists	6.01	1.14	0.940
Have respect for other faith-based tourists	5.96	1.11	0.872

Note: CR = composite reliability; AVE = average variance extracted. “Discarded for model fit” indicates items discarded to improve the model fit. “Discarded for validity” indicates items discarded to resolve the discriminant validity issue. Solid [ indicates error covariance terms retained in the final measurement model. Dashed [ indicates error covariance terms added during the model respecification process but discarded in the final measurement model.

To identify the causes of the issue, another EFA was run only with items that belonged to the problematic factors. Consequentially, five additional items were discarded (Table 5). However, doing so did not fully remove the discriminant validity issues, as all three factors of shared behaviors showed high correlation with one another, so were the factors belonging to propensity to interact (Table 6). In other words, some of the factors were correlated but only with factors of the same construct. The final measurement model demonstrated a good fit to the data, satisfying all cut-off criteria for CFI, TLI, and RMSEA (Table 4).

Table 6.

Values of Each Factors’ AVE and Squared Interfactor Correlation in First-Order CFA.

	Shared Beliefs			Shared Behaviors			Propensity to Interact		Emotional Solidarity
	DV	CNC	ENT	CUBH	DVBH	TBH	TOCC	ROC	CMM	FAIR
DV	0.733
CNC	0.503	0.675
ENT	0.717	0.506	0.776
CUBH	0.444	0.265	0.476	0.628 ¹
DVBH	0.526	0.326	0.483	0.717	0.690 ¹
TBH	0.457	0.407	0.270	0.712	0.745	0.593 ¹
TOCC	0.294	0.236	0.177	0.306	0.365	0.517	0.753 ²
ROCC	0.388	0.216	0.334	0.353	0.484	0.441	0.762	0.761 ²
CMM	0.548	0.336	0.453	0.531	0.573	0.575	0.494	0.623	0.730
FAIR	0.403	0.240	0.584	0.514	0.466	0.282	0.221	0.392	0.711	0.820

Note: AVE = average variance extracted; CFA = confirmatory factor analysis; DV = Devotion; CNC = Concerns; ENT = Entertainment; CUBH = Cultural behaviors; DVBH = Devotional behaviors; TBH = Touristic behaviors; TOCC = Touristic occasions; ROCC = Religious occasions; CMM = Communality; FAIR = Fairness. AVE values for each construct are presented in the diagonal line. Superscripted numbers indicated factors that have discriminant validity issues with one another.

Though discarding additional items could have resolved the discriminant validity issues, the authors refrained from doing so for two reasons. First, as interfactor correlation remained within a construct, it was not obscuring the hypothesized relationships between the constructs. Second, the only possible change other than item elimination was to combine highly correlated factors into a single factor (Farrell 2010), but it was determined that conducting second-order CFA was the best course of action. In fact, when second-order CFA was run using the same items and factors, no discriminant validity issue was observed (Table 7).

Table 7.

Values of Each Factors’ AVE and Squared Interfactor Correlation in in Second-Order CFA.

	Shared Beliefs	Shared Behaviors	Propensity to Interact	Emotional Solidarity
Shared beliefs	0.760
Shared behaviors	0.626	0.850
Propensity to interact	0.423	0.527	0.878
Emotional solidarity	0.612	0.663	0.632	0.854

Note: AVE = average variance extracted; CFA = confirmatory factor analysis; AVE values for each construct are presented in the diagonal line.

Besides the discriminant validity, all 10 factors were found sufficient in their composite reliability and convergent validity. All CR values were greater than 0.7 as recommended by Hair et al. (2010). The convergent validity was assessed by reviewing if there was any factor with an AVE value less 0.5 (Fornell and Larcker 1981). Again, all the factors exceeded the cutoff as their AVE values ranged from 0.593 to 0.820 (Table 6).

Discussion

The purpose of this study was to explore the possibility of emotional solidarity emerging among potential tourists to a destination. This required modifying the existing ESS for tourists (Woosnam and Aleshinloye 2013), so the scale can be used to quantify emotional solidarity among tourists in an anticipated travel setting. Furthermore, this study also developed scales for potential tourists’ shared beliefs, shared behaviors, and propensity to interact with others whom they expect to encounter. In so doing, qualitative data were used to furnish preliminary items for the newly developed scales. Then, for scale modification and purification, EFA and CFA were undertaken using the survey data from potential faith-based tourists in the United States.

This study discovered two factors of the modified ESS that were, respectively, named communality and fairness. Communality was mostly composed of the original 10 ESS items that addressed the feeling of similarity and intimacy, while fairness consisted of three items illustrating the feeling of respect that tourists hold toward other tourists. Only one item, namely, “appreciate other faith-based tourists I expect to meet for the contribution they make to the local economy,” was discarded through EFA. The exclusion made intuitive sense given it might have been difficult for respondents to gauge this in an anticipated travel setting. Or possibly, the original ESS item may be less valid or reliable. The item has traditionally performed poorly in previous studies when considering group differences (e.g., Woosnam et al. 2015) or factor loading (e.g., Woosnam and Aleshinloye 2013; Woosnam, Aleshinloye, and Maruyama 2016; Woosnam et al. 2016).

The two-factor structure of the modified ESS departs from what others have found. Studies have constantly identified the same three factors of emotional closeness, feeling welcomed (or welcoming nature), and sympathetic understanding whether the focus was on tourists (e.g., Joo et al. 2018; Joo, Cho, and Woosnam 2019; Ribeiro et al. 2018; Woosnam and Aleshinloye 2013) or residents (e.g., Lai and Hitchcock 2017; Li and Wan 2017; Woosnam 2012). This deviation from the three-factor structure can be attributed to at least two causes. First, this study looked at the intragroup relationship in an anticipated setting that had not been considered in other studies. Thus, it is logical to suppose that the difference in the study settings was reflected in the factor structure. Second, the ESS used in this study had four additional items that had not been considered in other studies. It is possible that the structural difference was in part driven by these four additional items.

The scales for the three antecedents to emotional solidarity also demonstrated multidimensional structures with each having two (propensity to interact) or three (shared beliefs and shared behaviors) factors jointly representing the construct. Although all factors were found reliable, almost half of the initial 61 items from the preliminary scales were eliminated. Some of the eliminations were unexpected, since the discarded items represented faith-based tourists’ shared beliefs or shared behaviors that were considered essential in other studies. For instance, interviewees in Kaell’s (2014) study frequently cherished their trip to the Holy Land as a once-in-a-lifetime experience that enhanced their religious faith or religious knowledge. However, items that were relevant to such religious and spiritual benefits were mostly eliminated in EFA. Presumably, some religious benefits might have been less conceivable to respondents who were interested but also inexperienced.

Other items that were unexpectedly discarded from EFA included traveling in an organized group or taking photographs during the trip. Both in scholarly and nonscholarly sources, it was suggested that faith-based tourists commonly travel together in an organized group, since such experiences usually include traveling to unfamiliar cultures abroad (Catholic Travel Centre 2012; Kaell 2014). Taking photographs is also another behavior that is shared across any type of tourists. Presumably, there are two possible explanations for such a large number of items eliminated. First, unlike studies (e.g., Kaell 2014; Lopez 2013) that examined actual faith-based tourists, the data for this study came from potential faith-based tourists. It is possible that respondents, although they were interested, were not aware of what it takes to participate in faith-based tourism or what faith-based tourism entails.

Despite some unanticipated results, all factors of the modified ESS, as well as those of the newly invented scales for the antecedents, were sufficient in their convergent validity and composite reliability. AVE values—which is an indication of whether the items are measuring the factor that they are intended to measure (i.e., convergent validity) (Churchill 1979)—were all above 0.5, which was the cutoff set by Fornell and Larcker (1981). Likewise, high CR values were all in support of solid composite reliability. However, as illustrated earlier, this study encountered discriminant validity issues in the factors of shared behaviors and propensity to interact. That is, the factors that addressed the same construct were highly correlated with one another and may only be a reflection of other factors. However, the discriminant validity issues were observed only among factors of the same construct; thus, they would not pose much problem when testing relationships between constructs in future studies. In fact, when second-order CFA was undertaken, no discriminant validity issues were observed (Table 6).

Theoretical Implications

Above all, the findings of this study contribute to expanding the applicability of the emotional solidarity theory in understanding tourists’ intragroup relationships. Tourism research to date has often neglected the importance of how tourists feel and think about other tourists they may encounter while traveling. There have been several studies that underscored the influential role that the intragroup relationship performs in motivating individuals to travel (e.g., Lum, Keith, and Scott 2015) and shaping tourists’ experience (e.g., Fairley 2003); however, the efforts have mostly remained qualitative. Given that quantitative research methods can help parse what appears to be a complex phenomenon into smaller components and test their relationships, they can complement qualitative findings and present a more complete picture of what is going on. The modified ESS invented in this study should enable exploring such quantitative domains of tourists’ intragroup relationships by serving as a valid and reliable tool for measuring their emotional solidarity with one another. Eventually, along with the newly invented scales of faith-based tourists’ shared beliefs, shared behaviors, and propensity to interact, the modified ESS can be used to test if the emotional solidarity theory truly holds in tourists’ intragroup relationships.

Furthermore, given this study assumed an anticipated travel setting, the scales established in this study can be used to illustrate tourists’ pretrip anticipation, especially in a faith-based tourism context. Because of inherent difficulties associated with identifying potential tourists and asking them about a hypothetical travel setting, only few studies (e.g., Chen, Shang, and Li 2014; Jalilvand et al. 2012; Ng, Lee, and Soutar 2007) have looked into how individuals feel and think prior to their actual travel, and the focus has been mostly on their intention to travel to a destination. In that sense, the scales from this study can be used not only to unveil potential tourists’ pretrip anticipations but also to fathom what beliefs and behaviors potential tourists expect to share with others, which have rarely been considered. Especially, the literature on faith-based tourism have existed in a relatively fragmented manner where multiple fields (e.g., religious studies, cultural studies, or tourism studies) supply qualitative illustrations of the phenomena. On that account, the findings of this study can be used to achieve a more comprehensive understanding of what faith-based tourists think and feel with respect to their anticipated travel experience by comparing quantitative evidence with qualitative clues.

Although this study focused on a certain group and setting, its findings have broader applications. After some rephrasing, the modified ESS can be employed to elucidate the intragroup relationship of tourists in other settings. In any case, tourists—whether they are religious tourists, cruise tourists, or urban tourists—to the same destination share beliefs and behaviors to some extent and can develop affective bonds with one another in appropriate circumstances. Likewise, the scales for shared beliefs and shared behaviors can be useful in settings where commonness among tourists is more salient and influential, such as volunteer tourism (Zahra and McIntosh 2007), dark tourism (Buda, d’Hauteserre, and Johnston 2014), or heritage tourism (Park 2010). Especially, some of the items are likely to perform well in other contexts that are distinctive from the setting of this study. For instance, items for concerns, entertainment (factors of shared beliefs), or touristic behaviors (factor of shared behaviors) are less bound to a specific form of tourism, and hence can serve as references to other studies or scales.

Implications also exist for the modification of the ESS. Results demonstrated that through the inclusion of four additional items (i.e., “I share similar views . . . ,” “I can trust . . . ,” “I plan to get along. . .,” and “I have respect for . . . ”), a larger degree of variance was explained (75.42%) in emotional solidarity as compared to what Woosnam and colleagues had found (67.95%) in previous studies. This reveals evidence that the modified scale yields greater utility in additional contexts to capture the robustness of emotional solidarity between individuals. Furthermore, this amended scale more accurately represents Durkheim’s ([1912] 1995) original conceptualization of emotional solidarity, addressing more intimate degrees of the relationship between individuals. Nowhere is this clearer than in comparing the modified scale to studies undertaken by Gronvold (1988) and others within the sociology, social psychology, and intergenerational literature that included measures of sharing similar views, trusting, getting along, and respecting others to capture emotional solidarity.

Finally, the findings of this study provide quantitative insights into faith-based tourism. Although tourism and pilgrimage are intricately and intimately related to one another, the literature on faith-based tourism is mostly limited to qualitative accounts. Occasionally, there have been studies (e.g., Collins-Kreiner and Kliot 2000; Fleischer 2000) featuring quantitative analysis of who faith-based tourists are and what they wish to experience, but their findings remained mostly descriptive based on basic statistics. Arguably, the lack of quantitative research on faith-based tourism owes to its intricate nature encompassing multiple types and layers of beliefs and behaviors. Although by no means are quantitative research methods more capable of catching the complexity of faith-based tourism, they can complement qualitative findings and provide a more holistic understanding. In that respect, the methods and the findings of this study expand the knowledge on faith-based tourism.

Practical Implications

The modified ESS can help managers understand how their destinations are rated by potential tourists with respect to anticipated intragroup relationships. Low scores for the modified ESS items may suggest that tourists see little room for developing affective bonds with other tourists in the destination. Given emotional solidarity is essential to individuals’ perceptions and behaviors, and some travel decisions are encouraged by such a feeling, low or deteriorating emotional solidarity among tourists should be something that every destination wishes to avoid, especially destinations known for active tourist-to-tourist relationship. Thus, destination managers are advised to conduct a regular assessment of tourists’ emotional solidarity with one another to figure out the current status of relationships that exist within the destination.

On the flip side, the modified ESS can help to identify new marketing opportunities for destinations. Often destinations have a good understanding of their key attractions and activities but fail to recognize the social environment that tourists create among themselves. That is, outdoor destinations may be engrossed to promoting their natural environments, when affective bonds among tourists visiting the destinations may also contribute to travel satisfaction and destination loyalty. Thus, the modified ESS can inform destination managers if their destinations foster positive tourist-to-tourist relationships that were unknown to many and blend such strengths into their marketing messages.

Limitations and Suggestions

Above all, it should be noted that the qualitative data used in the scale development phase of this study was limited to archival data and did not include interview data. While the decision was to proceed without any interviews because of temporal and fiscal constraints, collecting primary data from interviews and observations would have enhanced the rigor in the scale development. Although archival data often provides proven and systematized knowledge of a topic, the knowledge is nevertheless presented in a summarized and refined form.

Furthermore, it is possible that respondents in the pilot and main surveys had difficulties in imagining themselves in an anticipatory travel setting and answering the survey instruments reliably. Unlike other studies (e.g., Hasani, Moghavvemi, and Hamzah 2016; Joo et al. 2018; Li and Wan 2017; Ribeiro et al. 2018; Woosnam 2011a, 2011b) concerning emotional solidarity, this study asked potential faith-based tourists about beliefs and behaviors they anticipated sharing with other faith-based tourists. Although the authors implemented measures to enhance the validity and the reliability of the items, the anticipatory nature of this study might have engendered a greater likelihood of error.

It should be noted that the discriminant validity issue still persisted even after eliminating a considerable number of items. Specifically, the three factors of shared behaviors (i.e., cultural behaviors, devotional behaviors, and touristic behaviors) were highly correlated with one another, so were the two factors of propensity to interact (i.e., touristic occasions and religious occasions). Although this study resolved the issue by undertaking second-order CFA, it would still prevent from presenting a more detailed understanding of the role that the highly correlated factors play in shaping emotional solidarity.

Finally, although tourists tend to share beliefs and behaviors with one another, they may not be fully recognized or appreciated in some circumstances and may not lead to emotional solidarity between tourists. Both shared beliefs and shared behaviors are likely to be more salient in special interest tourism settings—such as faith-based tourism as discussed in this study—than others. At the same time, studies (e.g., Ribeiro et al. 2018; Woosnam 2011a, 2011b, 2012) have confirmed the existence of emotional solidarity in non-urban general tourism settings, and it may be impetuous to limit the applicability of the modified ESS to special interest tourism.

As to overcome the limitations of this study, the following suggestions can be considered. Most importantly, using the scales developed in this study, future research should consider investigating if potential faith-based tourists’ shared beliefs, shared behaviors, and propensity to interact lead to their emotional solidarity, which in turn shapes their intention to travel. Likewise, one may examine how emotional solidarity emerges among current or past tourists and affects their travel satisfaction and revisit intention. This will not only establish the predictive validity of the scales but also demonstrate the applicability of the emotional solidarity theory in tourists’ intragroup relationships in diverse settings. Furthermore, in future studies, a distinction between beliefs and behaviors related to the trip and those related to religion can be drawn. While faith-based tourists’ shared beliefs and shared behaviors may stem from their collective identity as tourists, they may also come from the religious backgrounds the faith-based tourists share. In other words, multiple forms or layers of shared beliefs and shared behaviors may exist among faith-based tourists. In addition, it would be worthwhile to consider examining how actual faith-based tourists think about their relationships with others they have already interacted. By doing so, a comparison between potential and actual faith-based tourists can be achieved. This would also enhance the validity and the reliability of the scales modified and developed in this study as faith-based tourists with actual experiences are likely to present a clearer picture of the phenomena. Finally, additional research is needed to examine how the modified ESS performs in settings outside faith-based tourism. Applying the modified ESS (which sits at the core of this study) to explore the intragroup relationship among nature-based tourists, heritage tourists, or event tourists would be a good start. This would strengthen the theoretical implications of the findings from this study and provide valuable insight for practitioners who cater to specific groups of tourists.

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 iDs

Dongoh Joo

Kyle Maurice Woosnam

Author Biographies

Dongoh Joo is an Assistant Professor of Tourism Management at the University of Tennessee at Chattanooga, USA. His primary research interest is in investigating tourists’ and residents’ social interaction from economic and sociological viewpoints; more specifically, how emotion influences individual perception and behavior in event tourism, nature tourism, and community-based tourism settings.

Kyle Maurice Woosnam is an Associate Professor in Parks, Recreation and Tourism Management Program at the University of Georgia, USA. Kyle’s research interests concern socio-logical and economic impacts of tourism, resident-tourist interactions within destinations, and sustainable tourism planning and development.

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