The impact of travel motivation on emotions: A longitudinal study

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Research paper

The impact of travel motivation on emotions: A longitudinal study Yeqiang (Kevin) Lina,∗, Jeroen Nawijnb a b

Department of Experience Industry Management, California Polytechnic State University, San Luis Obispo, USA Academy for Tourism, NHTV Breda University of Applied Sciences, the Netherlands

ARTICLE INFO

ABSTRACT

Keywords: Motivation Emotions Tourist experience Longitudinal

Previous work on tourists' positive and negative affect has mainly used cross-sectional data. Consequently, little is known about how motivations are related to tourists' emotions over an extended period of time. The purpose of this study was to understand the impact of travel motivation on tourists' emotions and whether the impact would remain the same across different time points. The sample consisted of a panel of approximately 2000 leisure travelers in the Netherlands. After eliminating missing data, 412 panelists completed all seven questionnaires over the nine months of the study. The results indicated that motivation does not have a significant impact on tourists' emotions over a relatively long period of time. Specifically, the study found that travel motivations or a cluster of travel motivations do not seem to have significant within-subject or between-subject impacts on tourists' emotions over a nine-month period. The findings demonstrate the complex relationships between tourists’ travel motivation and emotions and highlight the importance of a longitudinal approach to studying emotions in the tourism context. Managerial implications for destination marketers are discussed.

1. Introduction Tourists’ emotions are increasingly recognized within the tourism literature as important components of the (remembered) tourist experience (Knobloch, Robertson, & Aitken, 2017; Prayag, Hosany, Muskat, & Del Chiappa, 2017; Tung & Ritchie, 2011). Emotions of tourists are studied in relation to behavioral consequences (Breitsohl & Garrod, 2016; Lee, 2016; Nawijn & Fricke, 2015) and on-site experiences (Lin, Kerstetter, Nawijn, & Mitas, 2014; Nawijn, Isaac, Van Liempt, & Gridnevskiy, 2016), while often taking into account managerial consequences (Bastiaansen, Straatman, Driessen, Stekelenburg, & Wang, 2016; Prayag, Hosany, & Odeh, 2013). Tourist experiences have cognitive and affective components (Chen, Lehto, & Choi, 2009; Walls, Okumus, & Wang, 2011). Regarding affective components, in their study on destination slogans, Galí, Camprubí, and Donaire (2016) recently identified a trend among destinations to evoke emotional attachment to a destination. According to these authors, destinations should aim to provoke positive emotions via slogans. Kotsi, Balakrishnan, Michael, and Ramsøy (2016) added that destinations should not only evoke positive emotions but also “create emotional bonds with customers that result in a positive attitude” (p. 3). Destination management organizations (DMOs) appear to adopt a hedonic approach in their communication, focusing mainly on evoking positive associations with their destinations. This approach corresponds

∗

with findings on tourists’ emotions, which indicate that tourists enjoy the on-site phase of the tourist experience the most (Mitas, Yarnal, Adams, & Ram, 2012; Nawijn, 2011). In addition to the affective component of emotions, destinations also highlight cognitive aspects of their tourism offer through their marketing. An important factor here is the motivation of tourists (Pearce & Lee, 2005), which ideally should match the destination attributes. The potential relationship between emotions and motivations is especially important for destination promotion and the segmentation of visitors. Motivation and emotions are central concepts in psychology (Weiner, 1985). Only recently have a few researchers adopted this psychological interpretation of motivation in relation to emotional experience (Cini, Kruger, & Ellis, 2013; Goossens, 2000; Jang, Bai, Hu, & Wu, 2009). These researchers viewed affective states as energizers for travel behavioral intentions and argued that tourists are pushed by their emotional needs, which are related to their pleasure-seeking motivation and actual behavior (Jang et al., 2009). However, there is a clear gap in the research in regard to empirical evidence on the motivations generally identified within tourism experiences (Pearce & Lee, 2005) and their relationship with emotional experience over an extended period of time. The current study intends to address this gap in the research by exploring the potential relation between motivation categories and emotions by using seven waves of panel data (n = 412) over a ninemonth period.

Corresponding author. E-mail addresses: [email protected] (Y.K. Lin), [email protected] (J. Nawijn).

https://doi.org/10.1016/j.jdmm.2019.05.006 Received 29 September 2017; Received in revised form 4 April 2019; Accepted 9 May 2019 2212-571X/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Yeqiang (Kevin) Lin and Jeroen Nawijn, Journal of Destination Marketing & Management, https://doi.org/10.1016/j.jdmm.2019.05.006

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2. Literature review

throughout vacations (Lin et al., 2014), except in very specific contexts, such as in darker forms of dark tourism (Nawijn & Fricke, 2015). Tourists generally remember their vacations in a positive manner, which is associated with the positive emotional experience during vacation (Tung & Ritchie, 2011). Tourists' motivations to vacation are likely to affect their emotional responses but this has rarely been studied. The following subsection discusses the nature of motivations in tourism in more detail and connects it to the scant research available on the relation between tourists’ emotions and motivations.

2.1. The tourist experience Traditionally, the tourist experience was a concept that separated the various phases of the vacation ‘experience’ tourists go through (Clawson & Knetsch, 1966). Additionally, the emphasis of early work on the tourist experience dealt with identifying differences and similarities between vacation and everyday life (Uriely, 2005). However, according to Uriely (2005), the focus of the work on the tourist experience has shifted from a study of toured objects to a study of subjective experiences. Today, the traditionally distinguished phases of the tourist experience are still valid, but the current model of the tourist experience is much more detailed. Tourists currently use multiple channels to, for instance, gather information and share experiences with others. Similarly, the supply side of the tourist experience has increasing means available to design and manage experiences for tourists (Tussyadiah, 2014). In terms of the subjective experiences of tourists, it is currently a common practice to differentiate between real-time/lived and remembered experiences, which is in line with contemporary views in psychology and economics (Kahneman & Krueger, 2005). Most work in tourism addresses the remembered experience, often referred to as ‘memorable’ experiences (Tung & Ritchie, 2011). Lived experiences are much more difficult to study for practical reasons, as measuring these experiences with rigor involves the use of equipment that hampers the lived tourist experience itself (cf. Li, Scott, & Walters, 2015).

2.4. Motivations Early work adopted a sociological perspective and mainly studied the mechanisms of push and pull factors that push people away from their usual environment and pull them towards certain tourism destinations (e.g. Dann, 1981). The question of why people travel was one of the first questions addressed in tourism research (e.g. Crompton, 1979; Dann, 1981; Iso-Ahola, 1983) and has received wide attention in the tourism literature ever since (cf. Goossens, 2000; Pearce, 1993). Recent work has continued to address the motivations of tourists, but mainly in specific contexts, such as dark tourism contexts (e.g. Dunkley, Morgan, & Westwood, 2011; Isaac, Nawijn, Van Liempt, & Gridnevskiy, 2019) or volunteer tourism (Knollenberg, McGehee, Bynum, & Clemmons, 2014). Important general motivational categories for tourists to vacation are escape, novelty, relationship, and self-development (Pearce & Lee, 2005). The relationship between tourists’ motivations and emotions is rarely addressed, perhaps because the study of motivation in tourism has relied heavily on sociological theories, while the study of emotion in tourism has been based largely on psychological insights. Recently, Weaver and Jin (2016) reviewed the existing work on compassion as a motivator for the sustainable behavior of tourists. Thus, they regarded the emotional response as a potential cause of behavior. Similar work was undertaken empirically by Nawijn, Isaac, Gridnevskiy, and Van Liempt (2018), who investigated the role of the expected emotional response as a motivator to visit concentration camp memorials. Cini et al. (2013) used a psychological approach to motivations and studied the extrinsic and intrinsic motivations of visitors to Kruger National Park in South Africa. They adopted a dichotomous approach to study motivation (i.e. intrinsic and extrinsic motivations). Intrinsic motivations refer to doing something for its own sake and personal rewards, while extrinsic motivations refer to doing something to earn a reward or avoid punishment that is separable from the activity itself (Deci & Ryan, 1985). Such an approach to motivation is more common in psychological studies but does not correspond with motivation categories typically identified in tourism studies (Crompton, 1979; Dann, 1981; Pearce & Lee, 2005). Cini et al. (2013) collected data over a one-week period in the winter, with their sample consisting of 389 overnight visitors to the park. Their findings indicated that overnight visitors who are more intrinsically motivated experience more intense positive feelings and less intense negative feelings. Meanwhile, it was found that overnight visitors who are less intrinsically motivated tend to have lower life satisfaction levels, less intense positive feelings and more intense negative feelings. Studies that more generally address the relation between multiple tourist motivation categories (e.g. Pearce & Lee, 2005) and emotional responses over a relatively longer period are, however, lacking. This paper therefore sets out to study this gap in the research. Next, the methodological background of the study is described in more detail.

2.2. Emotions In consumer research and tourism, emotions are generally conceptualized via the discrete approach. Rather than the dimensional approach, which views emotions in terms of their activation and intensity (e.g. Russell, 1980), the discrete approach acknowledges the valence of emotions (i.e. positive or negative) and regards emotions as being different from one another (Izard, 1977). As specific emotions have specific consequences (Baumeister, Vohs, DeWall, & Zhang, 2007; Zeelenberg, Nelissen, Breugelmans, & Pieters, 2008), the discrete approach is often more useful than the dimensional approach in tourism contexts. Emotions consist of five main components, namely, cognitive appraisal of an event, a neurophysiological component (i.e. bodily symptoms), a motor expression component (e.g. facial and vocal expression), subjective feelings, and a motivational component (i.e. action tendencies) (Scherer, 2005). Most studies in tourism address the component of subjective feelings and use surveys to measure these feelings (Li et al., 2015). Other methods to measure emotions exist and are used but are mostly useful in very specific contexts (e.g. Bastiaansen et al., 2016). Next, the paper discusses the study of emotions in tourist experiences in more detail. 2.3. Emotions in the tourist experience Earlier work on emotions related to the tourist experience addressed the post-trip phase (e.g. Gilbert & Abdullah, 2004; Nawijn, Marchand, Veenhoven, & Vingerhoets, 2010). Later work also studied the emotional experiences during (Nawijn, Mitas, Lin, & Kerstetter, 2013) and before vacations (Nawijn, De Bloom, & Geurts, 2013). Specific causes and effects of emotions in the tourist experience have also been studied, such as the impact of personality on the emotional experience during vacation (Lin et al., 2014) or the effect of the emotional experience on the frequency of taking photos during vacation (Gillet, Schmitz, & Mitas, 2016). Recent work has also addressed non-Western populations, such as Chinese tourists (Chen, Lehto, & Cai, 2013). Generally, it is now known that positive emotions fluctuate during different phases of the tourist experience, peaking during vacation (Mitas et al., 2012), while negative emotions remain low in intensity and rather constant

3. Methodology 3.1. Data collection The sample used in this study consisted of members of a panel of approximately 2000 leisure travelers in the Netherlands. The 2

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item-total correlation (CITC) and Cronbach's alpha were used to ensure the reliability of each subdimension. A corrected item-total correlation (CITC) of 0.30 was used as the cutoff value, and Cronbach's alpha was used to ensure the reliability of each dimension of travel motivations (Devellis, 2012). AMOS 24 was used to conduct confirmatory factor analysis (CFA) through structural equation modeling (SEM) with the maximum likelihood method of estimation on the nine travel motivation items. CFA was applied to verify the unidimensionality and validity of the four travel motivation dimensions. The root mean square error of approximation (RMSEA), the normed fit index (NFI), the Tucker-Lewis index (TLI), the comparative fit index (CFI) and the item regression coefficients were examined. A good model fit requires the ratio of chi square to degrees of freedom to be lower than 5; NFI, TLI, and CFI to be higher than 0.90; and RMSEA to be lower than 0.10 (Bentler, 1990; Hu & Bentler, 1998; Steiger, 1990; Tucker & Lewis, 1973). Because of the longitudinal nature of the data set and the complexity of the constructs, the researchers used three approaches to examine the impact of travel motivation on tourists’ emotions and whether the impact would remain the same across different time points. The first approach was to recode each of the four motivation dimensions into three groups by motivation intensity (i.e. high, medium, and low) and then conduct a series of repeated-measures analysis of variance (ANOVA) to explore the impact of these motivation dimensions on the change in affect balance (i.e. PA/NA) over the nine-month period. The repeated measures analysis was adopted to account for the similarity in error structures resulting from the longitudinal design of the study. The second approach was to use the four motivation indices as covariates and conduct repeated-measures analysis of covariance (ANCOVA) on affect balance. The sample size of this study (n = 412) was acceptable for repeated-measures ANOVA and ANCOVA (Kim, Kim, & Bolls, 2011; Olejnik & Algina, 1984); this approach has been used in previous studies on changes in emotions in the tourism context (e.g. Lin et al., 2014; Nawijn, De Bloom et al., 2013). The third approach was to conduct cluster analysis to examine whether a certain combination of motivation categories would have any impact on changes in emotions. The purpose of this approach was to examine whether a certain combination of motivation categories would have any impact on either the baseline or changes in emotions. ANOVA of emotions was conducted on the distinct groups identified as a result of the cluster analysis.

researchers asked the panelists to complete an initial questionnaire before the start of the summer holiday in June 2013. Starting in September, the researchers conducted a series of monthly surveys to measure panelists’ positive and negative emotions at each time point and asked the panelists whether they had booked their summer vacations for the next year. If this was not the case, the panelists were asked a list of questions on their information-gathering behaviors and motivations. If the vacation had already been booked, the panelists were asked a list of questions on their vacations for the next year. After eliminating missing data, 412 panelists completed all seven questionnaires across nine months of the study. 3.2. Instrumentation Based on the previous literature and a series of interviews with the panel of leisure travelers, nine motivation items were included in the study to measure the four dimensions (i.e. Escape, Novelty, Relationship, and Self-development) of travel motivation (Pearce & Lee, 2005). Panelists were asked to rate each of the motivation items on a five-point Likert scale, with 1 being strongly disagree and 5 being strongly agree. For emotions, the researchers adopted 16 items developed by Cohn, Fredrickson, Brown, Mikels, and Conway (2009), which were based on Fredrickson, Tugade, Waugh, and Larkin's (2003) modified Differential Emotions Scale (mDES). These emotion items have been tested in previous tourism studies, and they exhibited satisfactory reliability and validity (e.g. Lin et al., 2014; Nawijn, De Bloom et al., 2013). Individuals were asked to rate their strongest experiences with each emotion item using a five-point Likert scale (e.g. very little, little, moderate, quite a bit, extreme). The researchers followed Veenhoven's (1984) approach and grouped nine items into positive affect (i.e. interested, amused, loving, proud, happy, grateful, hopeful, content, and awed) and seven items into negative affect (i.e. ashamed, contemptuous, embarrassed, guilty, sad, afraid, and disgusted). As a result, the researchers created an index of how emotionally positive each participant's experience was at a given time point (affect balance; Veenhoven, 1984) by generating an average positive emotions score (i.e. PA), an average negative emotions score (i.e. NA), and the ratio between PA and NA. Fig. 1 provides a visual interpretation of the framework. The researchers also included demographic questions in the survey that elicit information on age, gender, education, type of household, and employment.

4. Results

3.3. Data analysis

4.1. Sample characteristics

The data were entered into IBM SPSS 24 and screened for typos, missing values, multivariate normality, and outliers that could impair the following analyses. Considering the longitudinal nature of the data set, complexity of the model, and sample size (West, Finch, & Curran, 1995), no serious violations of statistical analysis were found. Analyses were conducted to examine the consistency of the items comprising the motivation dimensions (Churchill, 1979). Corrected

The sample was evenly split between males (50.9%) and females (49.1%). More than half of the respondents (54.4%) were between the ages of 45 and 64, and over 80% had a university education or higher. Nearly half of the respondents were cohabiting without children, and approximately one third of the respondents (39.4%) worked 30 h or more per week. Table 1 lists the frequency and percentage of each of the responses on sociodemographic characteristics in the study sample. Table 2 lists the mean scores and standard deviations of the 16 emotion items across seven time points. Overall, the survey participants exhibited higher positive affect (average PA range: 3.52–3.66) than negative affect (average PA range: 1.79–1.91) during the nine-month period. The affect balance ranged from 2.05 to 2.27. Notably, there was an increase in the affect balance from June 2013 (i.e. 2.05) to the months following the summer break (i.e. 2.21 to 2.27). Furthermore, the range of the affect balance was quite small after the summer break, especially between Oct. 2013 and Feb. 2014 (i.e. 2.25–2.27). The following analyses will examine whether the change in affect balance was statistically significant and whether travel motivation had any impact on the change in affect balance over the nine-month period.

Fig. 1. Study framework: The impact of travel motivation on emotions. 3

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using standardized factor loadings, the composite reliability, and the average variance extracted (AVE; Table 4). All factor loadings were significant at the 0.001 level. The composite reliability of all four latent variables was above 0.60, and the AVE values for all of the factors except Novelty were above the 0.50 cutoff value. Considering the limited number of items within the Novelty dimension and its satisfactory standardized factor loading and composite reliability, it can be inferred that the proposed model fits the data. Indices were generated for each of the dimensions for subsequent analyses. The mean scores and standard deviations (in parentheses) for the four indices were 4.03(0.73) for Escape, 3.94(0.67) for Novelty, 2.74(1.21) for Relationship, and 3.43(0.90) for Self-development.

Table 1 Sociodemographic characteristics of study sample. Variables

Response

Frequency

(%)

Age

18–34 35–44 45–54 55–64 65–74 Over 75 Male Female Elementary Preparatory secondary vocational Senior general secondary school/pre-university Professional low Professional high Academic Single Cohabiting without children Cohabiting with children Single with children Other N/A does not work 4 h or less per week Between 5 and 12 h per week Between 12 and 30 h per week 30 h or more per week

35 39 88 136 103 11 209 202 5 25 41 75 132 120 75 204 113 7 11 152 13 23 61 162

(8.5) (9.5) (21.4) (33.0) (25.0) (2.7) (50.9) (49.1) (1.3) (5.3) (9.6) (18.5) (33.1) (32.1) (18.3) (49.8) (27.6) (1.7) (2.7) (37.0) (3.2) (5.6) (14.8) (39.4)

Gender Education

Household

Work time

4.3. Travel motivation and emotion The researchers used three approaches to examine the impact of travel motivation on tourists’ emotions and whether the impact would remain the same across different time points. For the results from the first approach, Table 5 lists the F value, p value, and partial eta squared values for tests on both the within-subject and between-subject effects. The results indicated that there was indeed a significant change in PA/ NA over time. The partial eta squared value for time was above .07, indicating a medium to large effect size ( Cohen, 1988). In particular, pairwise comparison results indicated that there was a significant increase in PA/NA after the summer break (i.e. from June 2013 to September 2013; mean difference = 0.183; std. error = 0.042). However, none of the motivation dimensions had a significant impact on either the within-subject or the between-subject differences over the ninemonth period. In other words, travel motivation did not have a significant impact on affect balance at any given time point during the nine-month period. Furthermore, travel motivation also did not have a significant impact on the change in affect balance from June 2013 to Feb. 2014. To further explore the impact of motivation on emotions, the second approach was to use raw scores of the four motivation indices as covariates and conduct ANCOVA on PA/NA across the seven time points. Table 6 lists the F value, p value, and partial eta squared value for tests on both the within-subject and between-subject effects. The results showed that all effects were insignificant. Again, motivation does not seem to have any significant impact on the baseline of or changes in emotions.

Note. N = 412

4.2. CFA of travel motivation Table 3 highlights the descriptive statistics as well as the CITC and alpha coefficients for the four motivation dimensions. The alpha coefficients or Escape and Relationship were higher than the cutoff value of 0.65. Although the alpha coefficients for Novelty and Self-development were slightly lower than 0.65, these coefficients are justifiable when there are fewer items in the dimensions (Cortina, 1993). All CITCs were higher than the cutoff value of 0.30. Thus, the reliability of the travel motivation constructs was established. The GOF statistics for the CFA model were satisfactory, and all path coefficients were significant and in the expected direction (χ2(21, N = 312) = 54.179, CMIN/DF = 2.580, CFI = 0.956, NFI = 0.932, TLI = 0.905, RMSEA = 0.071). After assessing the overall fit of the measurement model, the four motivation dimensions were examined Table 2 Mean scores and standard deviations of emotion items. Variablesa

Jun. 13b

Sept. 13

Oct. 13

Nov. 13

Dec. 13

Jan. 14

Feb. 14

Content Interested Loving Happy Grateful Amused Proud Hopeful Awed Average PA Sad Disgusted Afraid Guilty Contemptuous Embarrassed Ashamed Average NA PA/NA

4.10(.67) 4.19(.69) 3.91(.87) 3.92(.69) 3.68(.84) 3.87(.67) 3.23(.91) 3.37(.87) 3.00(.80) 3.52(.46) 2.37(.94) 1.50(.75) 1.81(.89) 1.95(.84) 1.73(.79) 2.10(.93) 1.94(.83) 1.91(.58) 2.05(.64)

4.12(.67) 4.16(.68) 3.89(.85) 3.92(.67) 3.70(.80) 3.80(.72) 3.29(.92) 3.36(.86) 2.94(.88) 3.69(.50) 2.27(.92) 1.50(.76) 1.76(.91) 1.94(.84) 1.62(.75) 2.09(.94) 1.89(.82) 1.87(.56) 2.21(.75)

4.08(.71) 4.13(.69) 3.89(.82) 3.85(.70) 3.64(.85) 3.80(.66) 3.28(.86) 3.34(.87) 2.97(.82) 3.66(.51) 2.25(.94) 1.42(.73) 1.70(.86) 1.86(.82) 1.63(.80) 2.05(.91) 1.77(.75) 1.81(.57) 2.27(.77)

4.05(.72) 4.12(.66) 3.85(.89) 3.83(.73) 3.62(.91) 3.73(.70) 3.19(.90) 3.34(.87) 2.98(.86) 3.63(.55) 2.29(.93) 1.41(.70) 1.66(.86) 1.88(.82) 1.56(.75) 2.04(.94) 1.80(.80) 1.80(.58) 2.26(.75)

4.03(.71) 4.05(.70) 3.85(.84) 3.80(.72) 3.63(.85) 3.76(.68) 3.26(.95) 3.28(.90) 2.94(.82) 3.58(.54) 2.30(.91) 1.45(.74) 1.76(.92) 1.86(.82) 1.58(.79) 1.98(.91) 1.73(.81) 1.81(.58) 2.25(.75)

4.06(.71) 4.06(.65) 3.86(.87) 3.81(.72) 3.62(.84) 3.74(.76) 3.26(.91) 3.40(.82) 2.90(.88) 3.61(.55) 2.26(.89) 1.41(.71) 1.75(.90) 1.95(.85) 1.53(.72) 1.96(.90) 1.71(.73) 1.80(.56) 2.26(.73)

4.03(.68) 4.05(.71) 3.87(.88) 3.85(.72) 3.57(.83) 3.76(.77) 3.26(.89) 3.35(.87) 2.95(.83) 3.61(.56) 2.30(.93) 1.46(.72) 1.73(.84) 1.84(.79) 1.53(.69) 1.96(.90) 1.71(.75) 1.79(.56) 2.26(.75)

a Individuals were asked to “… rate [their] strongest experience of each emotion” at each time point. They did this using a 5-point Likert scale that ranged from 1 “Very little,” to 3 “Moderate,” to 5 “Extreme.” b Standard deviations are in the parentheses.

4

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Table 3 Reliability test results on travel motivation. Dimension

Item

Mean (SD)

CITC

Alpha Coefficient

Escape

To To To To To

relax restore be away from daily hassles see attractions experience the destination

4.433 3.870 3.794 3.987 3.968

(.699) (.930) (1.027) (.933) (.833)

.512 .596 .599 .424 .424

.733

To To To To

spend time with family share the experience with my family learn something new experience other cultures

2.782 2.702 3.236 3.623

(1.346) (1.244) (.975) (1.098)

.720 .720 .461 .461

Novelty Relationship Self-development

Table 4 Confirmatory factor analysis results. Dimension Item Escape To relax To restore To be away from daily hassles Novelty To see attractions To experience the destination Relationship To spend time with family To share the experience with my family Self-development To learn something new To experience other cultures

Composite Reliability

Average Variance Extracted

.691 .910 .621

.790

.564

.743 .569

.605

.438

Between-Subjects Effects

.722

.859

.758

.663

.507

.997

.843

Variable

F

Sig.

Partial Eta Squared

Time Time * Escape Time * Novelty Time * Relationship Time * Self-development Escape Novelty Relationship Self-development

2.114 .480 .569 .578 .624 .199 .458 .265 .244

.049 .927 .868 .861 .823 .820 .633 .767 .784

.014 .007 .008 .008 .009 .003 .006 .004 .003

Variable

F

Sig.

Partial Eta Squared

Time Time * Escape Time * Novelty Time * Relationship Time * Self-development Escape Novelty Relationship Self-development

.759 .945 .518 1.279 .507 .481 1.129 .008 .018

.602 .462 .795 .264 .803 .489 .289 .930 .894

.004 .005 .003 .007 .003 .003 .006 .000 .000

Between-subjects effects

Cluster

1

2

3

Number of cases Center values Escape Novelty Relationship Self-development

120

135

57

3.91 4.15 1.71 3.69

4.22 4.10 3.85 3.70

3.84 3.08 2.31 2.25

5. Discussion and implications 5.1. Discussion This study measured a comprehensive list of emotions among 412 people at seven time points over a period of nine months (i.e. 2884 data points), which allowed a longitudinal examination of the impact of travel motivation on tourists' emotions and whether the impact would remain the same across different time points. Regarding changes in emotions over the nine-month period, the results showed a significant increase in affect balance (i.e. PA/NA) between June and September. In other words, tourists' affect balance was significantly elevated immediately after they returned from their summer vacations. This result confirmed previous studies on the short-term impact of a vacation on subjective well-being (e.g. Nawijn, 2010). No significant changes in

Table 6 ANCOVA using motivation as covariance.

Within-subjects effects

.629

The third approach was to conduct ANOVA on distinct groups identified from a cluster analysis on travel motivation. After seven iterations, convergence was achieved due to no or small changes in the cluster centers, and the minimum distance between initial centers was 4.955. Three groups were identified as a result of the cluster analysis and added to the data set as a grouping variable (Table 7). Group 1 consists of participants with low motivation scores for Relationship and high scores for the other three dimensions. Group 2 consists of participants with high motivation scores for all four dimensions, and participants in Group 3 have relatively low motivation scores for relationship and self-development but high scores for escape and novelty. ANOVA was then conducted on the three groups (Table 8). However, the three clusters did not differ significantly in terms of the baseline of or changes in emotions. Once again, the results indicated that travel motivation does not have a significant impact on tourists’ emotions over a nine-month period.

Table 5 ANOVA using motivation as factors.

Within-Subjects Effects

.836

Table 7 Final cluster centers.

Standardized Factor Loading

.551

.593

Table 8 ANCOVA using three motivation clusters.

Within-subjects effects Between-subjects effects

5

Variable

F

Sig.

Partial Eta Squared

Time Time * Clusters Clusters

4.275 .841 .635

.000 .608 .531

.022 .009 .007

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affect balance were detected between the six months from September 2013 to February 2014. In particular, there was little change in affect balance between October 2013 and February 2014, which indicated that the PA/NA ratio remained elevated and did not exhibit any significant change after the increase during the summer. The relatively stable (i.e. little variation) affect balance after summer break partially demonstrated to the insignificant impact of travel motivation on tourists’ emotional experiences. This study also explored the impact of travel motivation on tourists' emotions and whether the impact would remain the same across different time points. Considering the longitudinal nature of the data set and the complexity of the constructs, this study adopted three different statistical analysis approaches. All three approaches pointed to the same result: motivation does not have a significant impact on tourists' emotions over a relatively long period of time. Specifically, it was found that travel motivations or a cluster of travel motivations did not seem to have significant within-subject or between-subject impacts on tourists' emotions over a nine-month period. In other words, travel motivation did not have a significant impact on: (1) tourists' emotions at any given time point, or (2) a change in affect balance over the nine-month period from June 2013 to Feb. 2014. The nonsignificant impact of motivation on emotions is inconsistent with the findings of Cini et al. (2013). A number of reasons could explain this discrepancy. First, the study setting and sample characteristics are different. Cini et al. (2013) focused on overnight visitors to a national park and examined their emotions over a short period of time. The present study focused on general holidaymakers and tracked their emotions for nine months. Furthermore, the inherent complexity (e.g. types and intensity) of tourists' emotional experiences may have also led to these diverging findings (Prayag et al., 2017). Second, Cini et al. (2013) adopted a dichotomy of motivations, while the present study used a four-dimensional travel motivation scale. Different motivation categories could account for different impacts on tourists’ emotional experiences. Third, other variables (e.g. high travel experience level vs. low travel experience level) confound the relationship between motivation and emotions (Pearce & Lee, 2005). Future studies should adopt a stratified sampling method and incorporate a diverse pool of panelists to study the interactions between motivation, emotions, and potential moderating variables.

the reasons people travel, which can be identified based on the motivational factors illustrated in this study. Of the four motivation dimensions used in this study, escape had the highest value of 4.03, while relationship had the lowest value of 2.74. Previous studies have found that the motivational dimensions of escape, novelty, relationship, and self-development are the most important factors contributing to the reasons why people travel (Pearce & Lee, 2005). Mannell and Iso-Ahola (1987) argued that escape from routine responsibilities and stress is a major motivation for recreational travel, and the results confirmed that escape remains one of the most important reasons why people vacation. The relationship-seeking motive received the lowest ratings among the four dimensions, demonstrating a decrease compared with previous studies (Jang et al., 2009; Pearce & Lee, 2005; Tinsley, Barrett, & Kass, 1977). A potential cause for this finding is that the component of social bonding is not well addressed by destination marketers. Destination marketers generally promote 2spectacular scenery, superb attractions, friendly people, and a unique cultural and heritage” (Hudson & Ritchie, 2009, p. 217), as these features are likely to elicit positive emotions and generate positive attitudes towards a destination. The component of social bonding with loved ones is thus often neglected and requires more attention in promotion strategies. Thus, destination marketers should highlight the social bonding opportunities at the destination in their promotional messages and convince potential travelers to consider relationship-seeking travel to enhance their positive affect or escape from a negative affective state. The three identified clusters of travel motivation could offer destination marketers some insights on the multidimensionality of travel motivation. Although the three clusters did not differ significantly in terms of the baseline of or changes in emotions, tourist profiling has been adopted by some DMOs and used as the basis for targeted marketing and itinerary recommendation (e.g. Utrip, a personalized trip planner). The fact that this study did not find significant effects of motivation on emotions after a vacation confirms earlier work on vacation after-effects that generally find either no effects or only shortlived effects (e.g. De Bloom et al., 2010; Nawijn et al., 2010). This finding, however, does not imply that marketers should neglect motivation and emotional responses in their marketing strategies. On the contrary, emotions constitute a large part of memorable tourist experiences (Tung & Ritchie, 2011), as emotions provide meaning to experiences and are consequently remembered better than non-emotional experiences. As vacations are generally pleasurable (Nawijn, 2011), the emotional component of such memories is also positive. When asked to recall a vacation, such positive recollections then translate to general life satisfaction through life domain satisfactions (Sirgy, Kruger, Lee, & Yu, 2011). Thus, the focus of destination managers and marketers should be twofold. First, experience design should target creating positive emotional connections to on-site experiences (Tussyadiah, 2014). Second, once travelers have returned from vacation, marketing should appeal to these memories and use them to create anticipation for future trips (cf. Wirtz, Kruger, Scollon, & Diener, 2003). Appealing to memories is key, as the present study found that the link between motivations and emotions is not present when not specifically triggered, as was done in other studies (Sirgy et al., 2011; Tung & Ritchie, 2011).

5.2. Theoretical and managerial implications This study found that affect balance increased significantly after the summer break and remained relatively stable afterwards, providing additional support for previous reports of the short-term positive effect of vacationing on people's wellbeing as well as the transient nature of this positive effect. This finding validates the tourism industry's use of emotions and experiences in travel marketing (cf. De Bloom et al., 2009). Furthermore, the results also indicated that motivation did not have a significant impact on tourists' emotions over a relatively long period of time, which was inconsistent with some previous studies that adopted a shorter timeframe when studying travel motivation and emotions. Thus, this study highlighted the importance of a longitudinal approach to studying emotions in the tourism context. The three different approaches used in this study also demonstrated the complexity of travel motivation and the effects of treating motivation indices as covariates or clusters. To be successful in the ever-changing travel industry, destination marketers and researchers need to work together to adopt different approaches to track motivational changes and travel trends and enhance their understanding of modern-day travelers. Periodic surveys, longitudinal panel studies, or social media sentiment analysis of the same market could be effective in capturing trends, adjusting advertising messages, and subsequently matching the travelers' motivations with service offerings and physical facilities in destinations (Cha, McCleary, & Uysal, 1995; Jang & Wu, 2006). The results of the study also offer important implications for destination marketers. It is imperative for destination marketers to pinpoint

6. Limitations and recommendations for future research There are several limitations to this study. First, this study used longitudinal measurement of a comprehensive list of tourists’ emotions with a panel of approximately 2000 tourists. After deleting panelists who dropped out and panelists with incomplete data, the final sample size of 412 allowed us to detect between-participant effects as well as within-participant development. However, the fact that a large portion of the panelists either submitted incomplete data or dropped out during the nine-month period limits the generalizability of the results. Future studies should explore incentives and/or innovative approaches to increase the panel retention rate and the overall panel data quality. 6

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Second, the mDES Scale (Cohn et al., 2009) was used to capture people's emotions over a 9-month period. Although the mDES covers a full range of positive and negative emotions and has been applied in the tourism setting, recent studies have questioned the applicability, reliability, and validity of psychological emotion scales in tourism studies (Hosany & Gilbert, 2010; Lee & Kyle, 2013). Furthermore, although self-report emotion measures have been widely applied to understand tourist experiences, there are inherent drawbacks to this approach. Previous research has shown that affective responses often change after the consumption experience, and relying on retrospective evaluations can be problematic in capturing tourists' emotional responses (Cutler, Larsen, & Bruce, 1996; Hosany & Gilbert, 2010). More recently, Li, Walters, Packer, and Scott (2018) employed skin conductance and facial electromyography to measure emotional responses to tourism advertising and found that psychophysiological measures, compared with self-report measures, were better at distinguishing between different dimensions of emotion. With recent technological developments in facial recognition (e.g. Face ID from Apple), a less intrusive data collection method of tourists' emotional experiences might be possible. Last, this study did not address actual behaviors in the relationship between motivation and emotions. The quality and length of the vacation affect people's short-term and long-term emotions (Nawijn, De Bloom, et al., 2013). Furthermore, whether the actual vacation was a true reflection of motivation would also affect tourists' experiences. For example, although a family of four spends the same vacation together, the travel motivation of each family member might differ significantly. Thus, future studies should explore the dynamics of travel motivation within a travel group and how actual travel behavior might mediate the relationship between travel motivation and the consequent emotional impact. Despite these limitations, the present study is the first in travel and tourism research to adopt a long-term longitudinal approach to study travel motivation and emotions on a large sample of leisure travelers. The study found that travel motivations or a cluster of travel motivations do not seem to have significant within-subject or between-subject impacts on tourists' emotions over a nine-month period. The findings have demonstrated the complex relationships between tourists’ travel motivations and emotions and highlighted the importance of a longitudinal approach to studying emotions in the tourism context.

Chen, Y., Lehto, X. Y., & Choi, S. (2009). Effect of experience on cognition, affect and satisfaction: The case of Japanese visitors to Macau. Journal of Hospitality Marketing & Management, 18(2), 273–293. Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. Cini, F., Kruger, S., & Ellis, S. (2013). A model of intrinsic and extrinsic motivations on subjective well-being: The experience of overnight visitors to a national park. Applied Research in Quality of Life, 8(1), 45–61. https://doi.org/10.1007/s11482-012-9173-y. Clawson, M., & Knetsch, J. L. (1966). Economics of outdoor recreation. Baltimore, MD: Johns Hopkins. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York, NY: Routledge Academic. Cohn, M. A., Fredrickson, B. L., Brown, S. L., Mikels, J. A., & Conway, A. M. (2009). Happiness unpacked: Positive emotions increase life satisfaction by building resilience. Emotion (Washington, D.C.), 9(3), 361–368. https://doi.org/10.1037/ a0015952. Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. Crompton, J. L. (1979). Motivations for pleasure vacation. Annals of Tourism Research, 6(4), 408–424. https://doi.org/10.1016/0160-7383(79)90004-5. Cutler, S. E., Larsen, R. J., & Bruce, S. C. (1996). Repressive coping style and the experience and recall of emotion: A naturalistic study of daily affect. Journal of Personality, 64(2), 379–405. Dann, G. M. S. (1981). Tourism motivation: An appraisal. Annals of Tourism Research, 8(2), 187–219. De Bloom, J., Geurts, S. A. E., Taris, T. W., Sonnentag, S., De Weerth, C., & Kompier, M. A. J. (2010). Effects of vacation from work on health and well-being: Lots of fun, quickly gone. Work & Stress, 24(2), 196–216. De Bloom, J., Kompier, M. A. J., Geurts, S. A. E., De Weerth, C., Taris, T., & Sonnentag, S. (2009). Do we recover from vacation? Meta-analysis of vacation effects on health and well-being. Journal of Occupational Health, 51(1), 13–25. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Devellis, R. F. (2012). Scale development: Theory and applications (3rd ed.). Los Angeles: Sage. Dunkley, R., Morgan, N., & Westwood, S. (2011). Visiting the trenches: Exploring meanings and motivations in battlefield tourism. Tourism Management, 32(4), 860–868. https://doi.org/10.1016/j.tourman.2010.07.011. Fredrickson, B. L., Tugade, M. M., Waugh, C. E., & Larkin, G. R. (2003). What good are positive emotions in crisis? A prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, 2001. Journal of Personality and Social Psychology, 84(2), 365–376. Galí, N., Camprubí, R., & Donaire, J. A. (2016). Analysing tourism slogans in top tourism destinations. Journal of Destination Marketing & Management, 6(3), 243–251. https:// doi.org/10.1016/j.jdmm.2016.04.004i. Gilbert, D., & Abdullah, J. (2004). Holidaytaking and the sense of well-being. Annals of Tourism Research, 31(1), 103–121. https://doi.org/10.1016/j.annals.2003.06.001. Gillet, S., Schmitz, P., & Mitas, O. (2016). The snap-happy tourist: The effects of photographing behavior on tourists' happiness. Journal of Hospitality & Tourism Research, 40(1), 37–57. https://doi.org/10.1177/1096348013491606. Goossens, C. (2000). Tourism information and pleasure motivation. Annals of Tourism Research, 27(2), 301–321. Hosany, S., & Gilbert, D. (2010). Measuring tourists' emotional experiences toward hedonic holiday destinations. Journal of Travel Research, 49(4), 513–526. https://doi. org/10.1177/0047287509349267. Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. Hudson, S., & Ritchie, J. R. B. (2009). Branding a memorable destination experience. The case of ‘Brand Canada’. International Journal of Tourism Research, 11(2), 217–228. https://doi.org/10.1002/jtr.720. Isaac, R. K., Nawijn, J., Van Liempt, A., & Gridnevskiy, K. (2019). Understanding Dutch visitors' motivations to concentration camp memorial sites. Current Issues in Tourism, 22(7), 747–762. https://doi.org/10.1080/13683500.2017.1310190. Iso-Ahola, S. E. (1983). Towards a social psychology of recreational travel. Leisure Studies, 2(1), 45–56. https://doi.org/10.1080/02614368300390041. Izard, C. E. (1977). Human emotions. New York, NY: Plenum Press. Jang, S., Bai, B., Hu, C., & Wu, C. E. (2009). Affect, travel motivation, and travel intention: A senior market. Journal of Hospitality & Tourism Research, 33(1), 51–73. Jang, S., & Wu, C. E. (2006). Seniors' travel motivation and the influential factors: An examination of Taiwanese seniors. Tourism Management, 27(2), 306–316. Kahneman, D., & Krueger, A. B. (2005). Developments in the measurement of subjective well-being. Journal of Economic Perspectives, 20(1), 3–24. Kim, S., Kim, D.-Y., & Bolls, P. D. (2011). An experimental investigation of cognitive response to advertising: A physiological perspective for tourism destination marketing. International Journal of Tourism Science, 11(2), 101–129. Knobloch, U., Robertson, K., & Aitken, R. (2017). Experience, emotion, and eudaimonia: A consideration of tourist experiences and well-being. Journal of Travel Research, 56(5), 651–662. https://doi.org/10.1177/0047287516650937. Knollenberg, W., McGehee, N. G., Bynum, B., & Clemmons, D. (2014). Motivation-based transformative learning and potential volunteer tourists: Facilitating more sustainable outcomes. Journal of Sustainable Tourism, 22(6), 922–941. https://doi.org/10. 1080/09669582.2014.902065. Kotsi, F., Balakrishnan, M. S., Michael, I., & Ramsøy, T. Z. (2016). Place branding: Aligning multiple stakeholder perception of visual and auditory communication elements. Journal of Destination Marketing & Management, 7, 112–130. https://doi. org/10.1016/j.jdmm.2016.08.006.

Acknowledgments We would like to thank Reiswerk, Netherlands for their financial support of this research project. We would also like to thank Deborah Kerstetter from The Pennsylvania State University and Ondrej Mitas from The Breda University of Applied Sciences for their expertise and assistance throughout all aspects of our study and for their help in writing the manuscript. References Bastiaansen, M., Straatman, S., Driessen, E., Mitas, O., Stekelenburg, J., & Wang, L. (2016). My destination in your brain: A novel neuromarketing approach for evaluating the effectiveness of destination marketing. Journal of Destination Marketing & Management, 7, 76–88. https://doi.org/10.1016/j.jdmm.2016.09.003. Baumeister, R. F., Vohs, K. D., DeWall, N., & Zhang, L. (2007). How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direct causation. Personality and Social Psychology Review, 11(2), 167–203. https://doi.org/10.1177/ 1088868307301033. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. Breitsohl, J., & Garrod, B. (2016). Assessing tourists' cognitive, emotional and behavioural reactions to an unethical destination incident. Tourism Management, 54, 209–220. https://doi.org/10.1016/j.tourman.2015.11.004. Cha, S., McCleary, K., & Uysal, M. (1995). Travel motivations of Japanese overseas travelers: A factor-cluster segmentation approach. Journal of Travel Research, 34(1), 33–39. Chen, Y., Lehto, X. Y., & Cai, L. (2013). Vacation and well-being: A study of Chinese tourists. Annals of Tourism Research, 42(1), 284–310. https://doi.org/10.1016/j. annals.2013.02.003.

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Journal of Destination Marketing & Management xxx (xxxx) xxxx

Y.K. Lin and J. Nawijn Lee, Y.-J. (2016). The relationship amongst emotional experience, cognition, and behavioral intention in battlefield tourism. Asia Pacific Journal of Tourism Research, 21(6), 697–715. https://doi.org/10.1080/10941665.2015.1068195. Lee, J. J., & Kyle, G. T. (2013). The measurement of emotions elicited within festival contexts: A psychometric test of a Festival Consumption Emotions (FCE) scale. Tourism Analysis, 18(6), 635–649. https://doi.org/10.3727/ 108354213X13824558188541. Li, S., Walters, G., Packer, J., & Scott, N. (2018). Using skin conductance and facial electromyography to measure emotional responses to tourism advertising. Current Issues in Tourism, 21(15), 1761–1783. Lin, Y., Kerstetter, D., Nawijn, J., & Mitas, O. (2014). Changes in emotions and their interaction with personality in a vacation context. Tourism Management, 40, 416–424. https://doi.org/10.1016/j.tourman.2013.07.013. Li, S., Scott, N., & Walters, G. (2015). Current and potential methods for measuring emotion in tourism experiences: A review. Current Issues in Tourism, 18(9), 805–827. https://doi.org/10.1080/13683500.2014.975679. Mannell, R. C., & Iso-Ahola, S. E. (1987). Psychological nature of leisure and tourism experience. Annals of Tourism Research, 14(3), 314–331. https://doi.org/10.1016/ 0160-7383(87)90105-8. Mitas, O., Yarnal, C., Adams, R., & Ram, N. (2012). Taking a 'peak' at leisure travelers’ positive emotions. Leisure Sciences, 34(2), 115–135. https://doi.org/10.1080/ 01490400.2012.652503. Nawijn, J. (2010). The holiday happiness curve: A preliminary investigation into mood during a holiday abroad. International Journal of Tourism Research, 12(3), 281–290. https://doi.org/10.1002/jtr.756. Nawijn, J. (2011). Determinants of daily happiness on vacation. Journal of Travel Research, 50(5), 559–566. https://doi.org/10.1177/0047287510379164. Nawijn, J., De Bloom, J., & Geurts, S. (2013). Pre-vacation time: Blessing or burden? Leisure Sciences, 35(1), 33–44. https://doi.org/10.1080/01490400.2013.739875. Nawijn, J., & Fricke, M.-C. (2015). Visitor emotions and behavioral intentions: The case of concentration camp memorial Neuengamme. International Journal of Tourism Research, 17(3), 221–228. https://doi.org/10.1002/jtr.1977. Nawijn, J., Isaac, R. K., Gridnevskiy, K., & Van Liempt, A. (2018). Holocaust concentration camp memorial sites: An exploratory study into expected emotional response. Current Issues in Tourism, 21(2), 175–190. https://doi.org/10.1080/13683500.2015. 1058343. Nawijn, J., Isaac, R. K., Van Liempt, A., & Gridnevskiy, K. (2016). Emotion clusters for concentration camp memorials. Annals of Tourism Research, 61, 244–247. https://doi. org/10.1016/j.annals.2016.09.005. Nawijn, J., Marchand, M., Veenhoven, R., & Vingerhoets, A. (2010). Vacationers happier, but most not happier after a holiday. Applied Research in Quality of Life, 5(1), 35–47. https://doi.org/10.1007/s11482-009-9091-9. Nawijn, J., Mitas, O., Lin, Y., & Kerstetter, D. (2013). How do we feel on vacation? A closer look at how emotions change over the course of a trip. Journal of Travel Research, 52(2), 265–274. https://doi.org/10.1177/0047287512465961. Olejnik, S. F., & Algina, J. (1984). Parametric ANCOVA and the rank transform ANCOVA when the data are conditionally non-normal and heteroscedastic. Journal of Educational Statistics, 9(2), 129–149. https://doi.org/10.3102/10769986009002129. Pearce, P. L. (1993). Fundamentals of tourist motivation. In D. G. Pearce, & R. W. Butler (Eds.). Tourism research: Critiques and challenges (pp. 113–134). London and New

York: Routledge. Pearce, P. L., & Lee, U.-I. (2005). Developing the travel career approach to tourist motivation. Journal of Travel Research, 43(3), 226–237. https://doi.org/10.1177/ 0047287504272020. Prayag, G., Hosany, S., Muskat, B., & Del Chiappa, G. (2017). Understanding the relationships between tourists’ emotional experiences, perceived overall image, satisfaction, and intention to recommend. Journal of Travel Research, 56(1), 41–54. https://doi.org/10.1177/0047287515620567. Prayag, G., Hosany, S., & Odeh, K. (2013). The role of tourists' emotional experiences and satisfaction in understanding behavioral intentions. Journal of Destination Marketing & Management, 2(2), 118–127. https://doi.org/10.1016/j.jdmm.2013.05.001. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714. Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729. https://doi.org/10.1177/0539018405058216. Sirgy, M. J., Kruger, P. S., Lee, D.-J., & Yu, G. B. (2011). How does a travel trip affect tourists' life satisfaction? Journal of Travel Research, 50(3), 261–275. https://doi.org/ 10.1177/0047287510362784. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2), 173–180. Tinsley, H. E. A., Barrett, T. C., & Kass, R. A. (1977). Leisure activities and need satisfaction. Journal of Leisure Research, 9(2), 110–120. Tung, V. W. S., & Ritchie, J. R. B. (2011). Exploring the essence of memorable tourism experiences. Annals of Tourism Research, 38(4), 1367–1386. https://doi.org/10.1016/ j.annals.2011.03.009. Tussyadiah, I. P. (2014). Toward a theoretical foundation for experience design in tourism. Journal of Travel Research, 53(5), 543–564. https://doi.org/10.1177/ 0047287513513172. Uriely, N. (2005). The tourist experience: Conceptual developments. Annals of Tourism Research, 32(1), 199–216. https://doi.org/10.1016/j.annals.2004.07.008. Veenhoven, R. (1984). Conditions of happiness. Dordrecht, Boston: Kluwer Academic. Walls, A. R., Okumus, F., & Wang, Y. (2011). Cognition and affect interplay: A framework for the tourist vacation decision-making process. Journal of Travel & Tourism Marketing, 28(5), 567–582. https://doi.org/10.1080/10548408.2011.588121. Weaver, D. B., & Jin, X. (2016). Compassion as a neglected motivator for sustainable tourism. Journal of Sustainable Tourism, 24(5), 657–672. https://doi.org/10.1080/ 09669582.2015.1101130. Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548–573. https://doi.org/10.1037/0033-295X.92.4.54. West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.). Structural equation modeling: Concepts, issues, and applications (pp. 56–75). Thousand Oaks, CA, US: Sage Publications, Inc. Wirtz, D., Kruger, J., Scollon, C. N., & Diener, E. (2003). What to do on spring break? The role of predicted, on-line, and remembered experience in future choice. Psychological Science, 14(5), 520–524. https://doi.org/10.1111/1467-9280.03455. Zeelenberg, M., Nelissen, R., Breugelmans, S. M., & Pieters, R. (2008). On emotion specificity in decision making: Why feeling is for doing. Judgment and Decision Making, 3(1), 18–27.

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