Vacations have long been considered an avenue for travelers to escape their everyday life and pursue rest, relaxation and rejuvenation. In addition to the goal of rest and relaxation, travel and tourism has also been considered a promising opportunity for individuals to pursue higher levels of life satisfaction [1]. Hobson and Dietrich [2] argued that there is an “underlying assumption in our society that tourism is a mentally and physically healthy pursuit to follow in our leisure time” (p. 23). More recently, this has been conceptualized through studies investigating the contribution of travel and tourism to quality of life (QOL) by tourism scholars around the world [3-8].

Unfortunately, these views of traditional tourism experiences leading to healthier lives is becoming outdated with new evidence revealing that the reality of today’s vacation environment does not always lead to positive effects. According to the Global Wellness Institute [9], travel and tourism actually has the capability to decrease overall levels of health. Combining the stresses of airport hassle along with the interference of exercise routines, overindulgence in food, alcohol and sun exposure often times leaves travelers needing another vacation after their vacation [9]. While some researchers have argued vacations contribute positively to tourists’ overall well-being and quality of life [3], there is an opposing argument linking tourism to decreased levels of well-being, particularly those in relation to physical health [10]. These divergent viewpoints make it unclear whether vacations do in fact increase or decrease one’s overall well-being and quality of life.

In response to the growing concern for these pervasive issues, an alternative niche market, ‘wellness tourism’, has begun to emerge. Although travelling for wellness can be traced back hundreds of years, the current conceptualization and growth of this market in the western world is fairly new [9]. Overarching issues driving the wellness tourism market include an increasingly chaotic life environment, high stress work conditions and a decrease in social and community structures. Coupled with an increasing longing to simply slow down and focus on the deeper meaning, the growth of wellness tourism addresses a clear need amongst travelers [11-14].

Wellness tourism has been defined as a subset of health tourism including travel for the purposes of maintaining or enhancing one’s personal wellbeing [9]. More specifically, Voigt et al. [15] defined wellness tourism as “the sum of all the relationships resulting from a journey by people whose motive, in whole or in part, is to maintain or promote their health and well-being, and who stay at least one night at a facility that is specifically designed to enable and enhance people’s physical, psychological, spiritual and/or social well-being” (p. 17).

Within the wellness tourism market, travelers are further classified as either primary or secondary. Primary wellness travelers are those people who travel with wellness as the sole purpose for their trip while secondary wellness travelers are those people who seek to maintain wellness while taking any type of trip. For the purposes of this study, wellness tourism is delineated as a subset, housed under the larger umbrella of health tourism while wellness travelers are defined as those people who travel with wellness as their primary or secondary purpose [9,15-17]. In contrast to wellness travelers, non-wellness travelers are those people who travel for either business or leisure without participating in any wellness activities during travel.

Although there is some research to suggest that wellness tourism can positively impact tourists’ well-being [14,18], there is insufficient evidence to draw any firm conclusions. Furthermore, while there have been a handful of studies investigating the contribution of travel experiences to tourists QOL [19-22], an examination of the impact of different types of tourism on QOL is not evident in the literature. Consequently, this study aims to investigate the relationship between wellness tourism, overall well-being, and quality of life, compared to that of non-wellness tourism.

Tourism and quality of life

With over 100 definitions and models of QOL in existence, defining this concept has proven to be a difficult task [23]. The main debate regarding QOL lies in whether it should be measured objectively or subjectively, uni-dimensionally or multi-dimensionally. Inherent to the objective line of thought is the judgment of an elite person or group who has identified specific standards that must be met in order to lead to ultimate satisfaction with life. Subjectively, individuals are the tool by which QOL is measured [24]. Uni-dimensionally, QOL is determined by a set of survey items developed to measure ones satisfaction with life as a whole. For example one of the items from Diener et al.’s [25] satisfaction with life scale reads “the conditions of my life are excellent”. Contrarily, the multi-dimensional perspective measures QOL within numerous different life domains, for example social life, family life, and work life [8]. In the current study, QOL is understood from the subjective, uni-dimensional perspective and described broadly as one’s personal report of life satisfaction, including levels of gratification and contentment with regards to their life experiences [23].

The relationship between travel and quality of life has recently attracted significant attention from scholars in the tourism field [5,19,20,26]. QOL studies in tourism can be identified from two perspectives, that of the host community and that of the tourist [7,14,18,21,22]. The current study operationalizes QOL from the perspective of the tourist. Uysal et al. [8] conducted an extensive review of the literature revealing a fairly equal split between the two groups. Neal et al. [6] employed three subjective overall life satisfaction measures to reveal that tourism services contributed to overall life satisfaction. Following this study, in 2007, the authors set out to further examine this model by testing the moderating effect of length of stay. Results indicated that overall life satisfaction is higher for those tourists who stay for longer periods of time [7]. More recently, Dolnicar et al. [3] employed eight life domains (vacations, health, money, family, leisure, people, work and spiritual life) to investigate the contribution of vacations to people’s quality of life. Results from this study revealed that vacations do in fact contribute to QOL, however, this happens at different levels for different people. In contrast to these findings, some studies found that vacations do not significantly contribute to QOL. For example, Michalko et al. [28] found that vacation experiences did not affect the overall QOL of Hungarian tourists. Additionally, Kroesen and Handy [29] discovered that, while holidays can increase short-term happiness, they are unable to enduringly raise tourist happiness. While there have been numerous studies done regarding traveler vacation experience and QOL, there has only been one study that attempted to look at the link between wellness tourism and travelers’ quality of life [30]. Their study revealed that variables relating to intrinsic reward and treating dermatitis were the only two indicators that affected the overall QOL of wellness tourism patrons at resorts in Taiwan. Although the positive relationship between vacations and QOL seems evident, it is unclear as to whether or not QOL can be increased based on the specific type of tourism being experienced by the traveler.

Theoretical background

In order to delve into the theoretical background of this study, we must first visit the notion of happiness. Travelers’ happiness can be described as “a psychological state of fulfillment and well-being that is experienced in anticipatory, on site and reflective travel phases” [4]. Substantiating a link between happiness and tourism can aid in a more explanatory understanding of the positive psychological benefits of travel. While there has been a steady increase in research addressing the psychological benefits that may lead to happiness while traveling, an extensive review of the literature revealed that tourist happiness has been predominantly understood through the lens of subjective well-being (SWB) [19,27], thus overlooking the eudemonic nature of tourism experiences [4]. Subjective well-being measures encompass positive affect components such as joy, elation, contentment and ecstasy [31]. Gilbert and Abdullah [19] conducted a study to examine whether or not holiday-taking has a positive impact on both life satisfaction and subjective well-being. Comparing a holiday-taking group with a non-holiday-taking control group revealed that holidays do in fact result in a higher sense of subjective well-being. More recently, Kroesen and Handy [29] examined the extent to which holiday behavior and happiness influence each other over time. These authors also examined happiness from a subjective perspective, revealing that holiday-taking does have a positive long term relationship with the cognitive component of happiness, but does not have a positive long term relationship with the affective component. In response to the continuous use of SWB in tourism happiness studies, Filep [4] argued that this is problematic, due to the fact that SWB is largely based on hedonic measures, it is missing the mark in understanding meaningful vacation experiences. Expanding on SWB to include more eudemonic measures, Seligman [32] refined his original theory of authentic happiness advancing into what is now known as the PERMA model of well-being. Filep [4] argued that this model is better suited to explain the phenomenon of happiness within the tourism field.

Developed by Seligman [32] the PERMA model of well-being delineates the key domains which need to be satisfied in order to achieve the ultimate form of well-being – that is, happiness. Operationalized as ‘The PERMA Profiler’, this model measures the five key pillars of well-being namely, positive emotion, engagement, relationships, meaning and accomplishment, in addition to health and negative emotions [33]. The profiler also includes general measures of physical health and vitality, happiness as well as tendencies towards feeling sad, angry and anxious. The five key domains of the PERMA model were operationalized to represent well-being, while health, happiness and negative emotions were maintained as intermediate factors to help explain how or why the PERMA model may influence QOL within the tourism context.

While positive psychological theories have been applied in the tourism context [5,21,34,35], there have been no empirical investigations using The PERMA Profiler in tourism research. Although Dolnicar’s study did draw from the precepts of Seligman’s theory, linking travel experiences to the elements of authentic happiness (positive emotions, engagement and meaning), it was published prior to the current development of The PERMA Profiler. Unlike comparable theories such as Subjective Well-being theory [25] and Self Determination Theory [36], The PERMA Profiler merges divergent viewpoints by incorporating both hedonic and eudemonic characteristics of wellbeing. Although this model has never been used within the tourism field, it is argued that Seligman’s ideas are relevant to deepen the explanation of powerful tourism experiences [4]. Therefore, the current study adopts the PERMA model, for the first time in tourism research, to examine the relationships between tourism, well-being and quality of life (Figure 1).

Figure 1: Hypothesized Model.

In addition to the PERMA model, the Satisfaction with Life Scale (SwLS) will be employed in order to measure quality of life, [25]. This 5-item instrument was designed to measure global cognitive judgments of satisfaction with one’s QOL. The SwLS has been used in hundreds of studies and has established good psychometric properties [37]. In line with previous studies on tourism and QOL [6], the hierarchy model of life satisfaction is used to explain the relationship between tourism, well-being, and quality of life [38]. The underlying premise of this model suggests that life satisfaction is determined by satisfaction within different life domains and thus a ‘spillover’ effect occurs linking satisfaction within one domain with QOL [38]. In addition to investigating these relationships, length of time spent travelling will also be examined within the model. Previous research has revealed that individuals who travel for longer periods of time are more likely to have a higher satisfaction with life [7]. More specifically, length of stay has been examined as a moderating variable to determine whether or not travelers who stay for longer periods of time are more likely to have a higher satisfaction with life than those who stay for shorter periods of time [7]. The goal of the current study is to examine this effect as it is related to both wellness travel and non-wellness travel. To this end, the following research questions and hypotheses are posited:

• What positive psychological benefits derived from wellness/ non-wellness tourism predict an increased satisfaction with quality of life?

• Do happiness, health and negative emotions play a mediating role between the PERMA model of psychological well-being and quality of life?

• Does length of stay play a moderating role in determining how wellness/non-wellness tourism contributes to quality of life?

• What differences exist amongst the types of travelers (wellness vs. non) as it relates to quality of life?

• What are the most important positive psychological benefits from wellness tourism the perspective of wellness travelers?

• Based on importance, how well does wellness tourism satisfy the positive psychological needs of wellness travelers?

Main hypotheses

H₁ Happiness is a positive function of the PERMA model of wellbeing.

H₂ Health is a positive function of the PERMA model of well-being.

H₃ Negative emotion is a negative function of the PERMA model of well-being.

H₄ Quality of life is a positive function of the PERMA model of well-being

H_5a Quality of life is a positive function of happiness.

H_6a Quality of life is a positive function of health.

H_7a Quality of life is a negative function of negative emotion.

Mediation hypotheses

H_5b Happiness mediates the positive relationship between the PERMA model of well-being and quality of life.

H_6b Health mediates the positive relationship between the PERMA model of well-being and quality of life.

H_7b Negative emotion mediates the negative relationship between the PERMA model of well-being and quality of life.

Moderation hypotheses

H₈ Length of stay strengthens the effect of the PERMA model on quality of life (mediated by happiness, health & negative emotions).

Multi group hypotheses

H₉ The effect of the positive psychological well-being on quality of life is stronger for wellness travelers than for non-wellness travelers.

H₁₀ The effect of happiness on quality of life is stronger for wellness travelers than for non-wellness travelers.

H₁₁ The effect of health on quality of life is stronger for wellness travelers than for non-wellness travelers.

H₁₂ The effect of negative emotions on quality of life is stronger for non-wellness travelers than for wellness travelers.

In order to adequately answer the research questions posed, this study employed a series of quantitative methods over five phases: (1) exploratory factor analysis (the pilot study) (2) analysis of descriptive statistics and frequencies (3) confirmatory factor analysis (4) structural equation modeling testing for mediation, moderation and multi-group differences and (5) importance performance analysis.

Survey items were adapted from previously validated positive psychological scales, however in order to evaluate clarity, content, reliability and the underlying dimensionality of the data within a tourism context, a pilot survey was deemed necessary. The pilot survey was tested among 144 travelers, 89 wellness travelers and 55 nonwellness travelers. Data from the pilot study were deemed to be normally distributed with no skewness (<3), kurtosis (<10) or multicolinearity (< .850) violations (Kline, 2015). The results of the initial EFA yielded five components with a KMO of 0.874, total variance explained of 69.94% and an overall Cronbach’s Alpha score of 0.89. All variables had sufficient factor loadings and was reviewed in order to ensure that the wording clearly reflected the purpose of each factor to measure the well-being and quality of life of participants as a result of travelling, as opposed to life in general. The final survey instrument consisted of 48 items, including two identifying questions to determine whether or not the participant was a wellness traveler or a non-wellness traveler, eight demographic questions, ten questions about travel behavior and 28 5-point Likert scale items (1=strongly disagree; 5=strongly agree) to measure the study variables.

The main study sample

Participants for this study were recruited through an online data collection platform, Amazon Mechanical Turk. Previous research supports the use of this platform as a valid method for collecting data as the participants recruited through this platform are demographically varied [39] and because online panels suffer from lower levels of sample bias compared to traditional mail surveys [40]. In order to participate, users had to have traveled for wellness (primary or secondary), business or leisure within the past year. A total of 888 survey responses were collected using the online research software company, Qualtrics. After screening for unengaged responses, twenty-six respondents were removed resulting in 862 two useable responses included for analysis. Participants in the study were fairly equally split between male (51%) and female (49%). The majority of the sample was between 25-34 years of age, held a bachelors degree and resided in the United States of America. Within the main sample, 42% identified as secondary wellness travelers, 34% as primary wellness travelers and 24% as nonwellness travelers.

Characteristics of the sample

Due the novelty of wellness tourism as a niche market and area of research, it was deemed appropriate to report on the characteristics of the sample. Mean scores and frequencies are reported on categories including length of stay, monetary commitment to travel, travel group, activities and accommodations. In general, across groups, people stayed about 4 nights while traveling and spent $1618 on their trip. Most people travelled with family (30%) or as a couple (28%) and chose to stay in a hotel (33%) for their accommodations. The majority of both primary and secondary wellness travelers participated in eco/adventure activities while traveling (38%) while the second largest group was spa and beauty (17%) (Table 1).

Table 1: Characteristics of the sample.

Confirmatory factor analysis

Data from the main study survey were deemed normally distributed with no skewness (<3), kurtosis (<10) or multicollinearity (< .850) violations (Kline, 2015). The next step in the analysis was to perform a confirmatory factor analysis (CFA) in order to verify the measurement scale properties as well as the reliability and validity of the indicators. An initial confirmatory factor analysis conducted in AMOS V. 22 used the a priori assumptions based on the hypothesized model. Maximum likelihood estimation procedure along with the covariance matrix method was considered suitable for latent structure analysis and convergent validity checks due to the continuous nature of the data. Maximum likelihood confirmatory factor analysis demands data have no missing cases in order to maintain the reliability of the data set. A total of 17 variables had missing data <5%. These scaled variables were replaced using the median of nearby points. No observations were removed during this process (Table 2).

Table 2: Confirmatory Factor Analysis Items.

Prior to conducting the initial first-order CFA, goodness of fit test statistics for evaluating CFA and SEM models were assessed. Thresholds for these statistics were adopted from Hair et al. [41] and Hu and Bentler [42]. The initial first-order CFA produced good model fit with tucker lewis index (TLI=0.961) and comparative fit index (CFI=0.967) indices exceeding the recommended 0.90 minimum. Additionally, the goodness of fit (GFI) and the adjusted goodness of fit (AGFI=0.916) also exceeded the recommendation of >0.90 and 0.80 respectively. Root mean square error of approximation (RMSEA=0.044) and standardized root mean square residual (SRMR=0.032) both indicated good model fit as well. Prior to assessing the final measurement model fit, tests for validity and reliability had to be conducted. Composite reliability scores were all above 0.70, except for Happiness (CR=0.587) [41]. Convergent validity was violated as determined by calculating the average variance extracted (AVE) which were all above .50 except for two factors (engagement and happiness), which fell just below this cut off point at .495 and 0.426 respectively. Discriminant validity was determined by the square root of the AVE being greater than any inter factor correlation. Each factor maintained good discriminant validity except for engagement and happiness. Based on these validity issues, the latent factors engagement and happiness were re-examined for cross-loading issues and low factor scores, thus resulting in removing the construct happiness and one of the variables for engagement (E3). The variable Hap 2 was removed due to low factor loadings and cross loading issues. Due to the fact that happiness only consisted of two variables that described the latent factor, the entire construct could not be included for further analysis (Tables 3 and 4).

Table 3: Validity & Reliability Scores.

Table 4: Model Fit Comparison between Initial CFA & Final CFA.

At this point, the final CFA model was assessed for overall measurement fit. Similar to the initial model, this final CFA produced good fit statistics (TLI=0.962, CFI=0.968, RMSEA=0.047, SRMR=0.0324, GFI=0.938 and AGFI=0.918). While these fit statistics are not much different than the initial model, the choice was made to remove E3 and happiness based on validity issues. Both models are represented below (Figure 2). Well-being related constructs included in the PERMA model revealed high correlations >0.70. The highest correlation occurred between the constructs positive emotion and engagement (r=.80). This was determined to be acceptable due to the interrelation between the constructs in the model (Table 2).

Figure 2: Initial (left) and Final (right) Confirmatory Factor Analysis Models.

Structural equation model

Structural equation modeling (SEM) was used to test the hypothesized model in Amos v. 22. However, because happiness was removed during the CFA process, it was also removed from the final structural model, therefore hypothesis 1 and 4b could not be tested. The structural equation model tested positive psychological wellbeing measures and their influence on quality of life. The estimated standardized coefficients for the hypothesized model indicate that only 2 paths in the model were significant at the p<0.001 level (Figure 3).

Figure 3: Standardized estimated structural model.

The only significant hypotheses (H_6a and H_7a) evaluated the predictive relationships between health and negative emotions with QOL. Health (β=0.44, p<0.001) was found to be a positive function of QOL while negative emotions (β=-0.28, p<0.001) were found to be a negative function of QOL.

Mediation and moderation

A mediation effect becomes apparent when a third construct intercedes the relationship between two other related constructs [41]. Mediation is determined by measuring the indirect effects between the anteceding variables and the consequential variables. Based on the results from the main hypotheses revealing that PERMA has no significant relationship with health or negative emotions, no mediation within the model was expected. However, because mediation was originally hypothesized, it was tested using a bootstrapping technique with user defined estimand for indirect effects. No significant mediation effects were found.

A moderating effect becomes apparent when a third construct changes (strengthens or weakens) the relationship between two other related constructs [41]. The potential moderating effect of length of stay was tested based on significant positive findings from previous research [7]. However, within this model, no significant effect was found for length of stay.

Multi group analysis

In order to test multi group effect differences within the structural model, a chi square difference test was conducted where both models were freely estimated, except for the significant paths found in the main hypothesis testing. The chi square difference test was found to be significant (p<0.05) indicating that there was a significant difference between groups. Further investigation into each individual path revealed that significant differences only existed between health and quality of life. The path between health and quality of life was found to be significantly (p =0.043) stronger for wellness travelers (β=0.44) than for non-wellness travelers (β=- .01).

Importance performance analysis

An importance/performance (IP) analysis was conducted in order to identify how well the positive psychological benefits sought by wellness travelers performed based on their importance ranking. Mean scores were computed in order to determine the most important positive psychological benefits to travelers. For the purposes of this study, only mean scores of “4” or “5” were considered to be of importance. Positive emotion (m=4.209; 4.154), engagement (m=4.069; 4.024) and happiness (m= 4.436; 4.401) all revealed mean scores above 4, indicating these positive psychological benefits to be the most important amongst both wellness and non-wellness travelers. Other variables included in the model all fell below the cutoff mean score of 4.

An IP grid was created using SPSS v. 22 based on grouping these positive psychological benefits (Figure 4). The vertical axis of the IP grid represents the travelers perceived importance of the benefit variables while the horizontal axis represents the performance of these benefits during their travel experiences. Quadrant A includes variables considered important by travelers that performed poorly. The variables in Quadrant B are representative of benefits that were rated of high importance as well as high performance. Quadrant C contains variables low in both importance and performance, while Quadrant D includes variables of low importance but high performance (Figure 4).

Figure 4: Importance performance analysis grid.

The IP grid illustrates that positive emotion, engagement and happiness were perceived as important and were also performing at a high level for both wellness and non-wellness travelers. On the other hand, relationships, meaning and accomplishments were not seen as important experiences to be had by travelers. Similarly, these items rated low on the performance scale and ended up in quadrant C.

Although the study of QOL boasts a long history in the social sciences, research on QOL within the tourism domain is still relatively new. More specifically, QOL research on tourism can be divided into two subgroups, host communities and tourists. However, an investigation of QOL across different types of tourism is not apparent in the literature. Wellness was chosen as the niche tourism market for this study due to its relationship to well-being [14,18] while traditional travelers were chosen as the comparison group, namely ‘non-wellness travelers’. The aim of this study was to investigate the relationship between wellness travelers, overall well-being and quality of life, in comparison to that of non-wellness travelers. This study makes a significant contribution to the extant literature on tourism and wellness tourism as it takes into consideration a never before used theory - the PERMA model of well-being [32].

The findings from this study suggest that the PERMA model of wellbeing does not contribute to travelers quality of life. It is unfortunate that happiness was not able to be included in the structural model, as this could have proven a positive relationship between PERMA and quality of life. Happiness was measured by two variables, one asking participants about their overall levels of happiness, and a second asking participants about their happiness while traveling. It is possible that the questions posited were too general in nature, and developed to simply be a benchmark for the more in depth PERMA constructs – this, happiness did not stand up in the structural model. However, this is only a theory of speculation and further research into developing a measure for happiness within the tourism domain is certainly warranted.

Divergent from the findings revealed in this study, previous research indicated that overall life satisfaction is impacted by travel and tourism [6,7]. However, these results may be explained in light of Kroesen and Handy’s [29] findings that demonstrated that vacations are unable to enduringly raise happiness, and thus do not effect quality of life in the long term. Additionally, this result also confirms Nawijn [21] concept that while vacations can increase happiness in the short term, there is no lasting impact on the long-term levels of happiness of vacationers. Another explanation of this anomaly may be supported by Dolnicar et al. [3] who found that while vacations do contribute to the QOL of the majority of people, QOL is an extremely dynamic and individual concept that may be different for different people at different times in their life. Another possible explanation for these findings is that the PERMA model needs further testing and validation within both wellness tourism and traditional tourism domains before it can be linked to QOL. Essentially, it is evident that the relationship between tourism and QOL remains unclear. Substantively, it would be interesting to explore well-being tourism as a subset within the leisure domain of quality of life.

Although this study did not reveal that the PERMA model of wellbeing within the tourism domain had a significant effect on Quality of Life, it did demonstrate that both overall health and negative emotions having significant impact on QOL. The latter finding is consistent with previous research. This result may be evidenced by Andrews and Whithey’s concept that personal heath is important to ones evaluation of life, therefore, this domain spills over into overall satisfaction with QOL. On the other hand, it was hypothesized that the PERMA model of well-being would also contribute to quality of life. One possible explanation of this may be the fact that happiness and well-being while traveling may be considered a fleeting emotion, holding little longevity within the larger concept of QOL. Therefore, travelers may not associate well-being on vacation as contributing to their quality of life. This however, does not explain why the significant eudemonic variables in the PERMA model (engagement and meaning) did not contribute to QOL. It would be interesting to investigate whether the PERMA model reveals the same results in a non-tourism setting in order to glean more insight into the relationships between well-being and QOL.

This study demonstrates that length of stay has no significant effect in predicting quality of life. While this result does contradict findings by Neal et al. [6] and Neal et al. [7], it supports the findings of Gilbert and Abdullah [19] and Nawijn [21]. The data from this study confirm that length of stay has no bearing on well-being, happiness or quality of life for wellness travelers and non-wellness travelers. It is possible that this finding was influenced by the fact that travelers were not asked about a specific trip, but about their travel experiences in general.

Results from this study partially support the multi-group hypothesis. No differences existed between groups with regards to positive psychological well-being or negative emotions, but significant differences between groups did exist based on overall health. It was shocking to see that no difference existed as this is not in line with some research that suggests wellness tourism can positively impact travelers’ well-being [14,18]. This may be explained in light of the assumption that wellness travelers may have an overall higher level of health and well-being in their everyday life, therefore, positive psychological wellbeing experienced while traveling would not necessarily have a stronger effect than it does for non-wellness travelers. It is important to note that there are no other studies comparing wellness travelers and nonwellness travelers. Therefore, this result should be taken with caution and explored further. The relationship between health and quality of life was found to be stronger for wellness travelers than for nonwellness travelers. This may be explained by taking into account that the overall health and well-being of the traditional wellness traveler may be higher than that of the non-wellness traveler.

Importance performance analysis revealed that the travel and tourism sector is performing up to expectations. More specifically, travelers (both wellness and non-wellness) found positive emotions, engagement and happiness to be most important to their travel experiences as well as the highest performers. This is in line with current marketing trends in the industry with tourism boards like Aruba promoting themselves as “One happy Island”, Vanatu’s slogan “Discover what matters” and Pennsylvania’s slogan “Pursue Your Happiness”. On the other hand, both groups of travelers found relationships, meaning and accomplishments to be of low importance and performance. It seems that this finding is in line with the traditional ideals of travel to relax and unwind, without aspects of relationship building and goal setting as a priority. Traditionally, variables of low importance and low performance would not pose a threat to the industry, however, it is suggested that these categories of well-being not be ignored as an opportunity for growth. Previous research suggests meaning making to be an important aspect of a memorable tourism experience [42-46], therefore this result should be taken with caution. Continued focus and differentiation on offering experiences conducive to engaging memorable tourism experiences could eventually provide a stronger link between wellness, tourism and overall satisfaction with QOL.

Findings from this study suggest several theoretical and managerial implications. From a theoretical point of view, this study confirms, for the first time within the tourism context, that the PERMA model of wellbeing does exist. Traditionally, tourist happiness and well-being has been explored through the lens of subjective well-being. Marrying both hedonic and eudemonic measures, this model introduces a new value to travel and tourism that did not exist previously. Moving beyond simply offering ‘happiness’ as an outcome of travel and tourism, this study extends the idea of happiness within the tourism context to include more eudemonic measures of overall well-being. Future research examining this model within other tourism contexts is warranted, in addition to further testing to validate a happiness measure within the tourism domain. Furthermore, studies comparing the constructs of the PERMA model between vacationers and non-vacationers would reveal whether or not vacations have the ability to increase overall well-being as opposed to those who do not take vacations. This study was limited in regards to the fact that it did not look at other domains of QOL, such as family, work and leisure life. Studies examining the PERMA model within each of these domains and then in relation to QOL would reveal more information about the holistic makeup of QOL as it relates to well-being.

From a managerial point of view, this study brings light to the fact that tourism is just scraping the surface of peoples happiness, but does not seem to be infiltrating into travelers QOL. It has been proven that loyal customers are made through memorable experiences. With this information, tourism providers need to do more to provide experiences with true meaning and lasting impact as an avenue for travel to impact quality of life. On a positive note, current tourism providers seem to be doing a good job in providing what travelers expect out of a vacation experience. However, providing deeper, more meaningful experiences could be advantageous to companies wishing to grow and differentiate themselves in the wellness tourism sector. Lastly, non-wellness tourism companies are encouraged to provide more wellness offerings to the traditional traveler. As this study revealed, wellness travel offerings may have a more significant impact on traditional travelers than on wellness travelers. Expanding offerings into this market could have positive implications for tourism companies around the globe.