Host–Tourist Relationship Quality in Evaluating B&B: The Impacts of Personality Traits and Emotional Labor

Abstract

With the development of Taiwanese society, the tourist B&B industry has become particularly important, marking the origin of the significant development of the tourism industry. This study focuses on the quality of host–tourist relationships in B&Bs. It is proposed that tourists “discover” the emotions and feelings of B&B hosts through service contact processes. Although researchers have pointed out that frontline service employee personality traits affect the quality of interactions and satisfaction from the consumers’ point of view, very few studies have investigated the relationships between tourists and B&B hosts, the latter playing a double role—both as a host and a service worker. Data were collected from 422 tourists who had utilized B&B services. A quantitative analysis of the questionnaires was conducted through descriptive statistics, K-means clustering, one-way ANOVA and structural equation modeling (SEM), in order to determine the relationships among the three sets of variables. The results of this study reveal that the personality traits of B&B hosts directly affect their emotional labor and the quality of their relationships with tourists. However, the emotional labor of B&B hosts is found not to affect the quality of relationships; in this respect, our findings go counter to those of previous studies.

1. Introduction

1.1. Research Background and Motivation

In an effort to create economic opportunities, Taiwan’s central government promoted rural tourism in 1981. As a result, over the past 30 years, there have been many changes in the lifestyles of residents of rural areas. In particular, the importance of agriculture has decreased greatly in Taiwan’s rural areas over the past 10 years, compared with other industries, with this situation worsening since Taiwan’s entry into the World Trade Organization in 2002. The period from 2008 to 2009 marked the beginning of innovation for Taiwan’s tourism industry. In 2007, the Tourism Administration of the Ministry of Transportation and Communications, Taiwan, launched the “Tour Taiwan Years 2008–2009” initiative, demonstrating its determination to promote the tourism sector. Tourism has grown to be viewed as universally acceptable, especially in rural areas, which are replete with natural resources and are thus suitable for tourism activities. In an effort to solve the problems faced by rural citizens caused by the decline in agriculture, the government has encouraged the development of B&Bs. In 2006, an article was published by H. J. Kim et al. (2006), indicating that Taiwan’s tourism receipts accounted for 4.2% of the gross domestic product (GDP) in 1996 (Huang, 2008). This figure exceeded the contribution of the agricultural sector to GDP, thereby making tourism one of the major industries in Taiwan. Following the development of rural tourism, the B&B industry has been expanding at a rapid pace in rural areas. According to monthly statistics for B&Bs (Taiwan Tourism Bureau, 2011), the number of B&Bs in Taiwan reached 3158 in 2010, seeing an increase of 3093 from 2003 for an annual growth rate of 7.4% (Figure 1). Furthermore, the residents of rural tourism areas provide tourist B&Bs to earn money, meaning that B&B income is often the main source of household income in these areas (e.g., in Sun Moon Lake, Nantou County, and Nanjuang, Miaoli County).

With more and more money being invested into the B&B economy, in 2002, the government introduced legislation to improve the management of the B&B industry. According to the “2003 Survey of Travel by R.O.C. Citizens”, 2.4% of citizens stayed at B&Bs, while 17.6% stayed at hotels. However, the “2009 Survey of Travel by R.O.C. Citizens” showed that 5.1% of citizens stayed at B&Bs and 12.4% stayed at hotels. This difference proves that B&Bs are playing a greater role in the tourism industry over time. With the change in accommodation trends, tourists are becoming more concerned about the quality of their accommodation, considering factors such as customer confidence, guest contact service, and effective communication. However, overall, there has been relatively little discussion about the B&B industry until recently. Most B&B-based research is related to B&B management strategies and marketing, website and internet marketing, or the areas of tourism development. Unfortunately, there have been few attempts to study the impacts of B&B host–guest relationships (Xing et al., 2022, 2024).

Recent years have seen a wealth of research into the relationships between frontline service employees and customers, especially in emotional labor-related studies. The interpersonal interactions between customers and service employees have a great effect on perceived service quality. An increasing number of recent publications and empirical studies have addressed the positive contribution that employee behavior and relationship quality can make to service quality. However, within the extensive literature on personality traits of service employees, comparatively little research has focused on the relationship between the personality traits of B&B hosts and the quality of their relationships with customers. Personality traits play a primary role in one’s emotional modulation and greatly influence the behaviors of service employees. Recent management-related literature has taken emotional labor into consideration in an effort to better understand how service organizations can manage employees’ positive behaviors with respect to customers. Service employees are expected to align their displayed emotions with organizationally desired emotions through their choice of emotional labor strategies. Furthermore, it has been mentioned that emotional stability has an important influence on the surface acting of service employees, while deep acting was positively correlated with Agreeableness and Extraversion traits. Based on these studies, it was assumed that personality traits could stimulate employees’ service behaviors and consequently increase the quality of relationships with customers (Tao et al., 2024).

It is well-recognized in service industry research that a link exists between employee behaviors and consumer behaviors. The early theorization of relationship marketing can be traced back to Berry’s service marketing research in 1983. Relationship marketing strategies can promote customer loyalty, and the quality of relationships is regarded in the context of relationship marketing as a measurable factor. It is very important to create good relationships between service employees and customers in the service industry (Y. Li et al., 2024; Liu et al., 2025).

Therefore, understanding how to build a good relationship between B&B hosts and tourists is expected to directly affect the B&B industry’s revenue. It should be noted, however, that there have been few attempts that consider the notion that B&B hosts play a double role simultaneously: both as an operator and as a frontline service employee. While considerable attention has been paid in the past to research issues related to personality traits, the literature on issues relating to these double roles and associated traits is still critically lacking. While common sense seems to indicate its importance, there remains a lack of empirical support.

1.2. Research Purpose

The purpose of the research presented in this article is to examine the relationships between personality traits and emotional labor, and how they influence the quality of host–tourist relationships in the context of B&Bs. This study selected major cities in Taiwan with the highest number of B&Bs, then identified famous tourist attractions within these cities for the distribution of questionnaires.

B&Bs are a vital economic sector in Taiwan, promoting the development of the tourism industry; therefore, building good relationships between B&B hosts and tourists is expected to directly affect the B&B industry’s revenue. However, very few studies have considered that B&B hosts are both business managers and frontline service employees. Despite the considerable attention given to research questions related to personality traits in the past, there is still a significant lack of literature on B&B hosts who simultaneously serve as frontline service employees. The results and contributions of this study are expected to have a substantial positive impact on Taiwan’s overall B&B tourism industry, filling the research gap on the relationship between the personality traits of B&B hosts and the quality of their relationships with customers. The results of this study could have a considerable impact on small sectors of the tourism industry, such as B&B providers or local snack/product stores.

2. Literature Review

2.1. Personality Traits and Emotional Labor

Most research on service provider–customer relationships has involved adopting a unit-level framework. Past studies on service provider–customer encounters have provided significant data for discussion, which is a key aspect of services as contact employees are boundary spanners who interact with customers on an individual basis (Chung & Schneider, 2002; Payne & Webber, 2006); however, increased attention has recently been given to personality traits in the personality psychology literature. For example, Hurley (1998) noted that personality traits are dispositional forces that relate to various behaviors or behavioral syndromes, such as the need to be with people and interact with them. Ekinci and Dawes (2009) showed that, if employees have certain personality traits (e.g., agreeableness, openness), they will display the appropriate consumer service behavior routines to reveal successful service interactions with customers, especially in service organizations. In addition, a previous investigation has mentioned that service enterprises (e.g., the Disney Company) commonly recruit employees based on personality traits in order to predict their aptitude to serve (Henkoff, 1994; Taiwan Tourism Bureau, 2004, 2010, 2011).

Personality is defined as abstractions of behavior (or traits such as agreeableness) that determine an individual’s pattern of interaction with the environment (McKenna, 2000). Funder (2001) provided an excellent review of the dimensions and frameworks related to personality trait use (e.g., the ‘Five Factor Model’ or ‘Big Five’). In addition to concepts such as locus of control, self-esteem, aggression–withdrawal or challenge–affiliation, the Five Factor Model (Saucier, 1994; McCrae & Costa, 1996, 1999; Mark & Jenifer, 2000; Hao & Scott, 2006), as a unifying framework, has been used to describe an individual’s personality. According to the Five Factor Model, one’s personality can be linked to one of the following five basic characteristics: Openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism (or emotional instability). In fact, service firms (e.g., Disney or MacDonald’s) commonly recruit employees based on personality traits predicting their aptitude to serve (Black, 1994; Henkoff, 1994). Furthermore, scholars have also performed short studies of personality traits as predictors of employee service behavior and emotional labor (Hochschild, 1983; Henkoff, 1994; Kruml & Geddes, 2000; Brown et al., 2002; Grandey, 2003; Barger & Grandey, 2006; Ekinci & Dawes, 2009). As this review has shown, most trait-based research on the extent to which emotional labor-based traits are beneficial to service quality has already been undertaken. However, little research has focused on service providers functioning with double roles, as is common in the case of B&B hosts. More specifically, in this study, we aim to answer the following research question: “What is the magnitude of the overall effect of the five employee personality traits of B&B hosts on emotional labor?”

2.2. Relationship Quality

In the highly competitive environment of today, losing customers is very costly. For this reason, customer retention and loyalty have become possible through the development of long-term, mutually beneficial relationships with customers (Athanasopoulou, 2009). Relationship quality can be seen as a customer’s perception of how well the whole relationship fulfils their expectations, predictions, goals and desires (Jarvelin & Lehtinen, 1996; W. A. Smith, 2003; Jeong, 2004; Simon & David, 2006; Beatson et al., 2008). Early theories of relationship quality can be traced back to Crosby et al. (1990) and Dwyer and Oh (1987). The concept of relationship quality arises from theory and research in the field of relationship marketing, in which the ultimate goal is to strengthen already existing relationships and to convert indifferent customers into loyal ones (Dwyer & Oh, 1987; Crosby et al., 1990; Berry & Parasuraman, 1991). In order to create long-lasting customer relationships, relationship quality includes the following aspects: Trust, Satisfaction, and Commitment. These characteristics have been utilized in a wide variety of industries (Crosby et al., 1990; Kumar et al., 1995; J. B. Smith, 1998; Brady & Cronin, 2001; Bienstock et al., 2003; Rauyruen & Miller, 2007; Huang, 2008; Athanasopoulou, 2009; W. G. Kim et al., 2001). Crosby et al. (1990) have pointed out that customers care about the relationship as a whole and judge the relationship according to past experiences, expectations, predictions, goals, and desires. The importance of relationship quality in relationship marketing has been well-documented; however, very little attention has been paid to the issues of relationship quality in the context of B&B consumer behaviors. Furthermore, several studies have shown that relationship quality may even affect performance negatively (Dowling & Uncles, 1997; Colgate & Danaher, 2000; Sharpley & Vass, 2006; Hall & Page, 2009). While considerable attention has been paid to research issues related to relationship marketing in the past, the literature on the issue of relationship quality has emerged only very slowly and in a more scattered manner. The purpose of this study is to examine the quality of the relationships between B&B hosts and tourists.

2.3. The Relationships Between Personality Traits, Emotional Labor, and Relationship Quality

Sharp and Sharp (1997) have pointed out that the effectiveness of relationship marketing tactics can be evaluated in terms of the behavioral changes they create. Numerous studies (Kruml & Geddes, 2000; Brown et al., 2002; Grandey, 2003; Hennig-Thurau et al., 2002, 2006; Ekinci & Dawes, 2009) have stressed that the service behaviors of employees are contingent on their personality traits (Diefendorff et al., 2005). Additionally, service behaviors influence how customers evaluate the service provided and respond to relationship quality (Crosby et al., 1990; J. B. Smith, 1998; Rauyruen & Miller, 2007; Athanasopoulou, 2009). Figure 2 shows the model that was developed and tested in this study. The model was developed by integrating the existing literature on relationship quality (Crosby et al., 1990; Kumar et al., 1995; J. B. Smith, 1998; Rauyruen & Miller, 2007; Athanasopoulou, 2009), and personality traits (Costa & McCrae, 1987; Saucier, 1994; Mark & Jenifer, 2000; Hao & Scott, 2006) with that focused on emotional labor (Kruml & Geddes, 2000; Grandey, 2003).

Tourists, as consumers, prioritize the emotional experiences gained from B&B hosts (R. Li et al., 2023; Y. Li et al., 2024). Amenity theory holds that elements providing pleasant experiences can fulfill emotional needs, attract people, and foster regional development. The emotional experiences of tourists positively influence their behavioral intentions, serving as a key mediating factor that links rural B&B amenities to behavioral intentions. While emotional experiences can enhance overall satisfaction and service quality, optimize the emotional connection between B&B hosts and tourists, and promote sustainable development, improving the quality of B&Bs can also contribute in these aspects (Y. Li et al., 2024; Liu et al., 2025). Therefore, ensuring that tourists achieve the most enjoyable living experience at B&Bs, including positive emotions, nostalgia, and happiness, is a key factor for the sustainable operation of B&Bs (J. J. Kim & Han, 2022; Xing et al., 2022, 2024; Tao et al., 2024).

3. Method

3.1. Data Collection and Sample

A two-phase study was designed to explore the relationship quality between B&B hosts and tourists. This study selected the cities in Taiwan with the highest number of B&Bs, then identified famous tourist attractions within those cities. As shown in Figure 3, Miaoli (Nanzhuang), Nantou (Sun Moon Lake), Chiayi (Alishan), Pingtung (Kenting), Hualien (Qixingtan), and Yilan (Dongshan River) were selected. This study distributed questionnaires based on the number of B&Bs in each city. A total of 500 questionnaires were distributed, of which 422 were valid. In Miaoli, 60 questionnaires were distributed (41 valid); in Nantou, 90 questionnaires (78 valid); in Chiayi, 35 questionnaires (30 valid); in Pingtung, 50 questionnaires (36 valid); in Hualien, 170 questionnaires (151 valid); and in Yilan, 95 questionnaires (86 valid).

The method of carrying out this study involved the use of a survey, which included questions and statements to which the participants were expected to respond anonymously. With the development of Taiwanese society, the tourist B&B industry has become an important industry. Taking the 2008–2009 Tourism Taiwan Year as an important starting point for the plan (Tourism Administration, 2007), data were collected from B&B tourists who visited famous tourism sites in Taiwan over a period of two months between April 2009 and June 2009. The selected sites were comparatively well-developed and provided famous locations for tourists. In cases where the B&B host agreed to collect data for the study, the survey questionnaires were distributed to the survey sites, and respondents freely participated in answering the survey questionnaire after they had stayed in the village for at least one night. Then, the researchers visited and collected the survey questionnaires from each B&B. Figure 3 describes the survey sites and valid questionnaires obtained in this study. Of the 500 questionnaires distributed, 422 were deemed valid, for a response rate of 84.4%. The final sample comprised slightly more men (57.3%) than women (42.7%). The two major demographic categories were those under 30 or age 30 (63.7%) and those in the 31–40 (19.4%) age group, with at least a college degree (60.7%).

3.2. Variables Operational Definition

Personality Traits. The scale of personality traits was adapted from Mark and Jenifer (2000), Hao and Scott (2006), and Saucier (1994) with some modifications and revision of wording to fit our empirical case. After some procedures of exploratory and confirmatory factor analysis, a scale consisting of 40 items was proposed. These personality traits included: Agreeableness (eight items), Extraversion (eight items), Conscientiousness (eight items), Openness (eight items), and Neuroticism (eight items). All measures were scored on a seven-point Likert scale, with anchors of “strongly disagree” (1) to “strongly agree” (7). The scale was tested and was proven to be acceptable in terms of its reliability and validity. Most pre-test participants noted that the scale of personality traits used too many items. In order to clarify the personality trait factors, some items were deleted through factor analysis. The final scale consisted of 20 items: Agreeableness (four items), Extraversion (four items), Conscientiousness (four items), Openness (four items), and Neuroticism (four items).

Emotional Labor. To measure emotional labor, we developed 10 items based on two studies (Grandey, 2003; Kruml & Geddes, 2000). In this study, emotional labor was divided into Deep Acting (four items) and Surface Acting (six items). All measures were scored on a seven-point Likert scale, with anchors of “strongly disagree” (1) to “strongly agree” (7). The scale has been tested and proven to be acceptable in terms of reliability and validity. The respondents filled out the questionnaires after the service offering.

Relationship Quality. The relationship quality scale was designed according to Crosby et al. (1990), Kumar et al. (1995), and J. B. Smith (1998), with some modifications according to tourists’ suggestions in order to better fit the research. The scale includes three factors: Trust (eight items), Satisfaction (four items), and Commitment (four items). This scale measures the relationship quality between hosts and tourists, using a seven-point Likert scale ranging from “strongly disagree” to “strongly agree” for evaluation. This scale was tested and was proven to be acceptable in terms of reliability and validity.

3.3. Data Analysis

For analysis, the SPSS 23 statistical software package was used. First, descriptive statistics were computed. Next, reliability (as a measure of internal consistency) was calculated. In order to separate the B&Bs from personality traits, cluster analysis was conducted. ANOVA was performed to detect significant differences among variables. The next step of the analysis involved calculating Pearson product moment correlations to examine the relationships among personality traits, emotional labor, and relationship quality. Using AMOS 5.0, confirmatory factor analysis (CFA) was performed to assess the measurement model of the items on our survey, following which the proposed hypothesized model (Figure 2) and research hypotheses were tested via structural equation modeling (SEM).

4. Results

4.1. Descriptive Results

The descriptive statistics and scale internal reliabilities for the scales used in this study are provided in

Table 1. Descriptive statistics and internal reliabilities for personality traits, emotional labor, and relationship quality scales.

		M	SD	Cronbach’s α
Favor	A	6.18	0.86	0.80
	E	5.98	0.94	0.79
	C	6.09	0.92	0.81
	O	5.77	1.06	0.81
	N	5.80	1.07	0.80
Feel	A	5.47	1.15	0.94
	E	5.49	1.04	0.90
	C	5.32	1.15	0.92
	O	5.22	1.17	0.86
	N	5.28	1.15	0.88
Favor	DA	4.88	1.31	0.71
	SD	5.90	0.99	0.79
Feel	DA	4.84	1.28	0.75
	SD	5.58	1.10	0.82
T		5.37	1.09	0.94
S		5.39	1.10	0.89
Co		5.42	1.15	0.88

Note: A = agreeableness; E = extraversion; C = conscientiousness; O = openness; N = neuroticism; DA = deep acting; SD = surface acting; T = trust; S = satisfaction; Co = commitment.

. We computed Cronbach’s alpha to examine scale reliabilities. All scales included in the study demonstrated high reliabilities, with values ranging from 0.71 to 0.94. The means of the five personality traits (Favor) ranged from 5.77 for openness to experience to 6.18 for agreeableness, while the means of the five personality traits (Feel) ranged from 5.22 for openness to experience to 6.18 for extraversion. These results are consistent with those of Ekinci and Dawes (2009). The results indicated that “tourists favor personality traits of B&B hosts” on average more than “tourists have a real feeling for the personality traits of B&B hosts”. The more likely explanation for this rests in the personality traits of hosts falling short of tourists’ expectations. The same results were also observed with respect to emotional labor as perceived by tourists. Clearly, the findings indicate that tourists care more about the surface acting of B&B hosts than about their deep acting.

4.2. Cluster Analysis and ANOVA

Table 2. Tourists favor/feel the traits of B&B hosts clustered by self-assessed traits.

Tourists Favor the Traits of B&B Hosts	N	(%)	Tourists Feel the Traits of B&B Hosts	N	(%)
Cluster 1	58	13.74	Cluster 1	152	36.02
Cluster 2	168	39.81	Cluster 2	181	42.89
Cluster 3	196	46.45	Cluster 3	89	21.09
Total	422	100	Total	422	100

shows the results of the cluster analysis. We conducted a one-way ANOVA to determine whether the three clusters exhibited significant differences in terms of personality trait variables (

Table 3. ANOVA results for hosts’ trait-based clusters.

		Mean			F-Value	Sheffe
		Cluster 1	Cluster 2	Cluster 3
Tourist favor the traits of B&B hosts	Agreeableness	−1.612	0.725	−0.145	286.160 ***	2 > 3 > 1
	Conscientiousness	−1.441	0.750	−0.216	238.273 ***	2 > 3 > 1
	Extraversion	−1.639	0.756	−0.163	328.668 ***	2 > 3 > 1
	Neuroticism	−1.354	0.871	−0.346	328.674 ***	2 > 3 > 1
	Openness	−1.428	0.857	−0.312	341.015 ***	2 > 3 > 1
Tourist feel the traits of B&B hosts	Agreeableness	0.909	−0.123	−1.303	413.325 ***	1 > 2 > 3
	Conscientiousness	0.927	−0.189	−1.200	356.848 ***	1 > 2 > 3
	Extraversion	0.950	−0.114	−1.391	596.872 ***	1 > 2 > 3
	Neuroticism	0.971	−0.250	−1.150	384.038 ***	1 > 2 > 3
	Openness	0.939	−0.197	−1.203	373.122 ***	1 > 2 > 3

Note: *** p < 0.001.

). When we found significant ANOVA results, we conducted further pairwise comparisons using Sheffe tests to determine which differences between clusters were responsible for the overall significant ANOVA results. Regardless, the goal of the cluster analysis performed in this study was to group individual respondents into clusters based on their characteristics, such as personality traits. Cluster 1 features mostly had lower scores in each trait variable, when compared to the other clusters. The results indicated that the considered B&B hosts had low enthusiasm for providing service to their customers and had very few interactions with the customers: we call these “Uninterested Hosts”. Cluster 2 features mostly had higher scores in each trait variable than in the other clusters. These B&B hosts like to serve and interact with their customers, and we call these “Versatile Hosts”. Cluster 3 had higher scores than Cluster 2 but lower scores than Cluster 1. These hosts satisfied the requests of customer incompletely, so we call these “Conventional Hosts”.

4.3. Measurement Model

Table 4. Correlation matrix.

Tourist Favor the Traits of B&B Hosts	DA	SA	T	S	Co
A	0.35 **	0.07	0.31 **	0.25 **	0.22 **
E	0.26 **	0.15 **	0.26 **	0.26 **	0.24 **
C	0.34 **	0.06	0.26 **	0.22 **	0.21 **
O	0.31 **	0.14 **	0.22 **	0.25 **	0.21 **
N	0.36 **	0.13 **	0.32 **	0.28 **	0.28 **

Note: A = agreeableness; E = extraversion; C = conscientiousness; O = openness; N = neuroticism; DA = deep acting; SA = surface acting; T = trust; S = satisfaction; Co = commitment. ** p < 0.01.

lists the Pearson product moment correlations, which indicate a strong relationship between personality traits and deep acting (p < 0.05). Meanwhile, no significant correlation was uncovered between agreeableness, conscientiousness, and surface acting (p > 0.05). The results demonstrate a clear and strong relationship between personality traits and relationship quality (p < 0.05). We utilized confirmatory factor analysis (CFA) techniques to evaluate the construct validity for all constructs, and the results of the construct validity assessments are presented in

Table 5. Model fit statistics of tourists favoring the traits of B&B hosts.

Fit Indices	Cluster 1	Cluster 2	Cluster 3
chi-square	42.624	74.275	65.205
chi-square/df (≤3)	1.332	2.321	2.038
GFI (>0.9)	0.867	0.914	0.941
RMSEA (<0.05)	0.076	0.089	0.073
AGFI (>0.9)	0.772	0.852	0.898
PGFI (>0.5)	0.505	0.532	0.547
NFI (>0.9)	0.874	0.940	0.873
CFI (>0.9)	0.964	0.965	0.946

. The CFA models provided acceptable overall fit. Figure 4, Figure 5 and Figure 6 illustrate all model paths. The SEM results revealed that H1 and H2 were significant; however, H3 was insignificant.

In this study, the five factors extracted from the factor analysis of the self-assessed traits of B&B hosts were used as the basis for segmentation. The non-hierarchical K-means cluster analysis method was performed to conduct segmentation analysis of B&B hosts, allowing them to be divided into three groups. The clustering results are presented in Table 2.

The differences in personality traits among the preferred/actual perceived B&B host groups by tourists were analyzed by examining whether the three clusters showed significant differences in the preference/actual perception dimensions. Each of the three clusters was treated as an independent variable, while the five preference/actual perception factors were treated as dependent variables. A one-way ANOVA was conducted, and the results provided in Table 3 indicated that all five factors differed among the three clusters, demonstrating the effectiveness of the clustering results.

A correlation analysis was conducted on the characteristics and emotional labor load of B&B hosts that were “preferred” by tourists. It was found that the characteristics of B&B hosts “preferred” by tourists—including agreeableness, extraversion, conscientiousness, openness, and neuroticism—were significantly correlated with deep acting (p < 0.01); meanwhile, extraversion, openness, and neuroticism were significantly correlated with surface acting (p < 0.05). A correlation analysis was conducted on the characteristics of B&B hosts “preferred” by tourists and the quality of B&B relationships. It was found that the characteristics of B&B hosts “preferred” by tourists—including agreeableness, extraversion, conscientiousness, openness, and neuroticism—were significantly correlated with trust and satisfaction in terms of relationship quality (p < 0.01).

The fit testing results of the B&B host trait model for the tourists’ preferred cluster 1 in this study were as follows: chi-square = 42.624 (p = 0.099), chi-square/df = 1.332, GFI = 0.867, RMSEA = 0.076, AGFI = 0.772, PGFI = 0.505, NFI = 0.874, and CFI = 0.964, all of which meet the standards. The fit test results of the B&B host trait model in cluster 2 preferred by tourists were: chi-square = 74.275 (p = 0.000), chi-square/df = 2.321, GFI = 0.914, RMSEA = 0.089, AGFI = 0.852, PGFI = 0.532, NFI = 0.940, CFI = 0.965, which all met the agreeable standards except for the chi-square result.

The fit test results for the B&B host trait model in cluster 3 preferred by tourists were: chi-square = 65.205 (p = 0.000), chi-square/df = 2.038, GFI = 0.941, RMSEA = 0.073, AGFI = 0.898, PGFI = 0.547, NFI = 0.873, CFI = 0.946. Except for chi-square and CN, the other indicators all met the standards.

According to the research results, except for AGFI and PGFI values (which were lower than 0.8), the other values of the sampling data and the research model of this study were all above 0.8, indicating a good fit and an acceptable model. Therefore, the research model can appropriately explain and predict the causal relationships between the characteristics of tourists’ preferred B&B hosts and variables such as the emotional labor load of these B&B hosts and the quality of B&B relationships.

This study performed path analysis via structural equation modeling to verify the causal relationship between the trait emotional labor load of B&B hosts and the quality of B&B relationships (Figure 4, Figure 5 and Figure 6), and tested whether the t-value of the standardized estimated parameter was greater than 1.96 (p < 0.05); in particular, if the t-value is higher than the standard value, the hypothesis can be confirmed.

The research model path diagrams (Figure 4, Figure 5 and Figure 6) indicate that the t-values regarding the effect of the characteristics of the B&B host actually felt by tourists in clusters 1, 2, and 3 with respect to the emotional labor load of the B&B host actually felt by tourists were 2.979, 5.841, and 5.735 (p < 0.01), respectively, all reaching a significant level, indicating that the characteristics of the B&B host actually felt by tourists have a positive and significant impact on the emotional labor load actually felt by the B&B host.

The t-values regarding the effect of the B&B host characteristics actually felt by tourists on the relationship quality of the B&B were 3.050, 3.527, and 4.960 (p < 0.01), all reaching a significant level, indicating that the B&B host characteristics actually felt by tourists have a positive and significant impact on the host–tourist relationship quality.

The t-values regarding the effect of the emotional labor load of the B&B host actually felt by tourists on the relationship quality of the B&B were 1.917, 0.751, and 1.265 (p > 0.05), and none of the three clusters reached a significant level.

5. Conclusions

5.1. Theoretical Implications

The basic premise of our research was that each of the five personality traits is likely to have a positive relationship with emotional labor and relationship quality. As expected, all personality traits were found to be positively correlated with emotional labor. The results of this study clearly support the notion that the “Versatile Host” and “Conventional Host” types have an advantage over the “Uninterested Host” in terms of management. Nevertheless, emotional labor did not show any significant effect on relationship quality. One explanation for this is that B&B hosts are not professional service workers, like hotel or restaurant frontline employees. They do not understand how to display deep acting and surface acting, and thus, when they serve customers, they may not exhibit those qualities. More specifically, when B&B hosts play the double role of host and service worker simultaneously, they are more likely to be unsuccessful. Compared with professional service workers, B&B hosts have not been trained in these skills. Future research could re-examine B&B hosts who have been trained, in order to determine whether such training has a significant impact on their service or the success of their businesses.

5.2. Practical Implications

This study mainly revealed that the “Agreeableness” trait differs the most from customer perceptions, while “Conscientiousness” only slightly differs. In other words, it is very important that the host presents agreeable personality traits, directly affecting the success of their business. Furthermore, the scores for all personality traits are close to the highest marks. As such, host personality traits are important qualities for developing a favorable image and relationship quality for staff in the B&B industry. It is recommended that B&B hosts should always pay attention to their personality traits and make attempts to meet customer expectations regarding service quality.

5.3. Limitations and Direction for Research

Although this research makes important theoretical contributions to our understanding of the relationships between B&B host personality traits, emotional labor, and relationship quality, it has still some limitations. First, this investigation used a cross-sectional design, and thus causality among the variables could not be inferred. Although the model is based on existing theories and empirical research, thereby justifying the causal ordering in this research, future studies should consider that the data may be interpreted in different ways. Second, this research used a non-probability sampling method. Future research should consider using a more comprehensive sampling design. Third, although having an insignificant hypothesis in this study, future research could examine whether other moderating variables, such as interaction quality, have been ignored. Finally, while much remains to be done, we anticipate that this work will generate important findings in the fields of relationship quality.