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. 2023 Jan 20:1–12. Online ahead of print. doi: 10.1007/s12144-023-04263-3

Fatigue or satisfaction at crowded attractions?

Jie Yin 1, Yingchao Ji 2, Paoyu Huang 3, Yensen Ni 4,✉
PMCID: PMC9857920  PMID: 36713624

Abstract

Based on arousal theory, we argue that clarifying the mechanism of tourist fatigue on tourist satisfaction is critical for better understanding tourists visiting crowded attractions. A field survey was conducted in Zengcuoan, China’s most artistic fishing village. We revealed that, contrary to expectations, tourist fatigue does not always have a negative impact on tourist satisfaction, implying that, similar to the contrast phenomenon of “poor but happy”, “fatigue but satisfaction” may exist in tourism because there must be something to entice tourists in congested areas. Furthermore, we demonstrated that tourists with high experience quality may mitigate the negative effects of tourist crowding on tourist satisfaction from the theoretical perspective of arousal theory. We then propose that tourism authorities improve experience quality by creating a high-quality tourism experience, and even a one-of-a-kind and unforgettable experience for tourists. Nonetheless, we argue that finding more creative ways to reduce “fatigue” and increase “satisfaction” for tourists is critical for the tourism industry’s success, especially given the industry’s current competitive conditions. As a result, we believe there is still room for further research into these methods.

Keywords: Tourist crowding (TC), Tourist fatigue (TF), Tourist satisfaction (TS), Experience quality (EQ), Fatigue but satisfaction, Arousal theory

Introduction

Recent research has focused on tourist crowding (hereafter referred to as TC), particularly during holidays and festivals (Hou & Zhang, 2020). Because of the centralization of travel time and homogenization of destination selection, TC phenomena such as highly aggregated tourist crowds, tourist congestion, and overflowing in scenic spots are relatively common (Yin et al., 2020). Furthermore, TC may cause not only noise pollution and road congestion but also negative behaviors such as obstructing tourists from viewing the beautiful scenery and even causing tourism conflicts (Rathnayake, 2015). Thus, previous research has demonstrated that TC has a negative impact on tourist satisfaction (hereafter referred to as TS). For example, Klçarslan and Caber (2018) discovered that low TS is associated with crowding in cultural heritage sites; Luque-Gil et al. (2018) confirmed the existence of an inverse relationship between TC and TS in Mediterranean mountains. To our knowledge, however, the effects of TC on tourists’ physical and mental states, which are crucial for tourists’ intention to return, are rarely addressed in the existing literature.

Tourist fatigue (hereafter referred to as TF) is defined as a decline in physical function, a decrease in tourists’ emotions, and the occurrence of cognitive impairment, all of which contribute to a decrease in tourism motivation (Sun et al., 2020). Walking, waiting, weight-bearing, and recreational activities during a tour may result in TF. According to Teichert et al. (2021), TF is worth investigating because it may result in a decrease in TS. However, whether or not TC affects TF remains an open question for tourism research. Furthermore, experience quality (hereafter referred to as EQ) is important for business success (Chen & Chen, 2010; Suhartanto et al., 2020), as it influences customers’ brand identity and word of mouth (Shafieizadeh et al., 2021; Taheri et al., 2021). According to the research of Del Barrio-Garca and Prados-Pea (2019), the prior experience of tourists with destinations moderated the relationship between brand authenticity and brand equity. As per Wu et al. (2018), EQ perception plays a positive moderating role in the relationship between social and hedonic/utilitarian values. As a result, the tourist experience may be critical for TC because it has a significant impact on tourist satisfaction, loyalty, and revisit intention (Jones et al., 2010; Milman et al., 2020).

As a result, we address the relationship between TC, TF, TS, and EQ as follows. The first is to investigate whether TC affects TF, which includes four aspects of measuring fatigue (physical fatigue, motivational fatigue, affective fatigue, and cognitive fatigue) (hereafter referred to as PF, MF, AF, and CF). The other is to see if these various facets of measuring TF affect TS. To the best of our knowledge, both concerns appear to have been overlooked in tourism research.

We thus argue that this study may contribute to the existing literature. First, our investigated issue, which focuses on the physical and mental state of tourists in crowded tourism, is rarely addressed in relevant studies, as previous studies focused on the effects of TC on emotion, attitude and evaluation, and loyalty (Milman et al., 2020). Second, to the best of our knowledge, we may be the first to investigate the relationship between TC and TF, which is critical for tourism development, potentially enriching the relevant literature and the application of arousal theory. Third, by demonstrating that physical fatigue, one type of tourist fatigue, does not always have a negative impact on TS in this study, we infer that “fatigue but satisfaction” may exist in crowded tourism, which is also rarely investigated in the existing literature. Fourth, we demonstrated that tourists with EQ would mitigate the negative effect of tourist crowding on tourist satisfaction, which is consistent with the arousal theory. As a result, we propose that tourism authorities may improve experience quality by providing tourists with a high-quality tourism experience, and even a unique and unforgettable experience for tourists, thereby being beneficial to tourism planning and development.

Literature review and hypotheses

Arousal theory

Arousal theory, first proposed by Murray (1938), stated that individuals are motivated to seek out stimulation when their arousal levels are low, but they become bored and unmotivated when their arousal levels are too high. In addition, the interpretation of Zajonc (1965) for arousal theory introduced the concept of challenge and threat as opposing variables; challenge is characterized by a positive emotional state and a desire to engage with the environment, whereas threat is characterized by a negative emotional state and a desire to avoid or withdraw from the environment (Zuckerman, 1984; Feinberg & Aiello, 2010). Furthermore, there is some evidence to support the arousal theory of motivation. For example, relevant research has shown that people are more likely to engage in risk-taking behaviors when they are bored or under-stimulated, but less likely to engage in these behaviors when they are highly aroused (Revelle et al., 1980; Zuckerman, 1984).

Moreover, arousal theory was primarily concerned with physiological and psychological arousal caused by excessive stimulation (Jeong & Biocca, 2012). Physiological arousal is defined by high-intensity activation of the autonomic nervous system, whereas psychological arousal is defined as a change in an individual’s psychological response caused by external information and environmental factors (Leonidou & Panayiotou, 2022; McCluskey et al., 2020). Previous studies showed that crowding is an important antecedent influencing individuals’ arousal. For example, Wohlwill (1974) exhibited that negative emotion affected by crowding may be associated with high arousal levels; Evans and Lepore (1992) pointed out that excessive crowding would lead to overstimulation, thereby resulting in high arousal levels. Furthermore, arousal theory indicates that a specific environment would stimulate individuals’ perceptions and make them aroused, thus affecting their response (Jung et al., 2021).

Consequently, based on arousal theory, we investigate whether tourists’ perceived crowding (i.e. environmental stimuli) causes psychological and physiological stress (i.e. physiological and psychological arousal), triggering their emotional response (i.e. response). Previous research has also discovered that TC can cause high-level arousal, decreased perceived control, and even pleasure (Liu & Ma, 2019), which could be the result of an external stimulus factor (i.e., TC) that causes physiological and psychological arousal.

Furthermore, TF is thought to be the result of excessive interaction between tourists and their destinations, which can impair physical function, weaken motivation, affect emotions, and even cause cognitive impairment in tourists (Sun et al., 2020). Previous research has also shown that fatigue is negatively related to satisfaction (Gupta et al., 2007; Sun et al., 2020), and satisfaction is frequently used as an emotional response in previous studies (Jung et al., 2021). We then assess whether TS as an emotional response would be influenced by TF (i.e., physiological and psychological arousal in tourism) that could be induced by an external stimulus (i.e., tourist crowding).

Accordingly, this study used arousal theory to develop the conceptual model for understanding how these factors are related, as shown in Fig. 1. That is, this study investigates whether TC (as an environmental stimulus) influences TF, which includes PF, MF, AF, and CF (as physiological and psychological arousal), thereby likely influencing TS (as a response), Based on concerns that the impact of TC on TS may differ depending on whether the tourists have a high or low EQ (subjective perceived experience), this study further investigates whether EQ would moderate the relationship between TC and TS. As a result of the above inference, we proposed several hypotheses, as shown below.

Fig. 1.

Fig. 1

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Research conceptual model

Tourist crowding and satisfaction

Tourist Crowding (TC) is an important predictor of tourist intention and tourism destination success (Marques et al., 2021). Recent research has looked into the effects of destination image, tourist experience, service quality, and tourist behavioral intentions on tourist satisfaction (Carreira et al., 2022; Marques et al., 2021). However, as tourism attractions have grown in popularity, TC has had a significant impact on TS (Brown et al., 2013), particularly during holidays and festivals. For example, Kılıçarslan and Caber (2018) asserted that tourists would often feel dissatisfied once they have had a high crowding perception at cultural heritage sites. Thus, based on arousal theory, excessive environmental stimulation (e.g., TC) might elicit an emotional response such as satisfaction (Rivera et al., 2019). We then infer that TC might hurt TS by proposing H1.

  • H1: TC negatively affects TS.

Tourist crowding, fatigue, and satisfaction

Pathological fatigue and nonpathological fatigue are the two types of fatigue. Pathological fatigue is primarily associated with physical diseases (e.g., cancer and heart disease), whereas nonpathological fatigue is primarily associated with high-intensity activities. As a result, TF (generally classified as nonpathological fatigue) may be the result of excessive interaction between tourists and their destinations, resulting in a decline in physical function, a loss of motivation, and the occurrence of cognitive impairment (Sun et al., 2020).

Sun et al. (2020) divided TF into four dimensions: PF, MF, AF, and CF. Physical fatigue (PF) is defined as a tourist losing physical function during a tour, most likely as a result of walking, weight-bearing, and playing (Matteucci, 2014). Motivational fatigue (MF) manifests as a decline in tourist motivation and demand. MF appears to act as a decreasing novelty perception of the destination as visiting time and frequency of travel activities increase. Affective fatigue (AF) is the loss of enthusiasm for various aspects of tourism activities. For example, frequent interaction between tourists and destinations would diminish the perception of novelty and excitement, resulting in AF. Cognitive fatigue (CF) is the impairment of a tourist’s cognitive ability that occurs when they store, extract, and even process information that exceeds a certain limit.

Tourist crowding (TC) is not only reflected in the density exceeding the maximum capacity of the environment but also manifested as the interaction and even conflict among tourists. Previous studies proved that a negative external stimulus is an important antecedent of fatigue. Rahman et al. (2017) revealed that work-family conflict would lead to stress and physical fatigue for critical care nurses. Lee et al. (2016) pointed out that communication technology overload would result in social network fatigue. Based on arousal theory, when tourists experience the stimulation of the external environment, they may feel physiological and psychological arousal of individuals, thereby likely resulting in TF classified as four dimensions (i.e., PF, MF, AF, and CF). We thus propose H2a-H2d.

  • H2a: TC positively affects PF.

  • H2b: TC positively affects MF.

  • H2c: TC positively affects AF.

  • H2d: TC positively affects CF.

Tourist fatigue (TF) is accompanied by the decline of physical function, the weakness of tourist motivation, the abatement of tourist emotion, and the impairment of cognitive ability, which also indicates that TF is a common but complex state in tourism activities (Izard, 2007). As revealed that TF was correlated with TS in the recent study by Sun et al. (2020), it might indicate that tourism research on TF might be still in its infancy (Sun et al., 2020) of tourism studies. According to arousal theory, TF incorporating PF, MF, AF, and CF might be the reflection of psychological and physical arousal for tourists, thereby likely affecting their emotional arousal (i.e., TS). We thus proposed H3a-H3d.

  • H3a: PF negatively affects TS.

  • H3b: MF negatively affects TS.

  • H3c: AF negatively affects TS.

  • H3d: CF negatively affects TS.

The moderating effect of experience quality

In addition, experience quality (EQ) refers to subjective perception and emotional response to overall service as a component of the emotional experience (Burmeister et al., 2022; Wei et al., 2022). In previous studies, Wu et al. (2018) presented that perceived experience played a positively moderating role in the relationship between social value and hedonic/utilitarian value. Yin et al. (2020) also showed that high EQ might enhance the positive impact of tourists’ perceived crowding on destination attractiveness. The previous studies on TC mainly focused on the effects of TC on TS (Kılıçarslan & Caber, 2018; Luque-Gil et al., 2018), while few of them explored how to effectively moderate the effect of TC on TS. Based on the above, we argue that EQ might be able to moderate the negative impact of TC on TS for tourists with high EQ, We thus propose the H4.

  • H4: EQ moderates the relationship between TC and TS.

In summary, this study not only tested a structural model integrating constructs about the effects of TC on TF and TS based on arousal theory but also explored the moderating effect of EQ between TC and TS, all of which seem ignored in the existing literature.

Data and methodology

Measurement terms

To measure the essential terms employed in this study—including TC TF, EQ, and TS—as shown in Fig. 1, we then referred to existing validated and reliable multi-item scales. TC was measured with 8 items proposed by Jones et al. (2010). TF was measured with 16 items, including 4 items of PF, 4 items of MF, 4 items of AF, and 4 items of CF, formulated by Sun et al. (2020). EQ was measured with 4 items formulated by Yin et al. (2020). TS was measured with 4 items developed by Kim et al. (2015). All of these items were measured using a 7-point Likert-type scale from strongly disagree to strongly agree respectively.

Data collection and sample

Study site

This study takes a tourist attraction, Zengcuoan located in Xiamen City, as the study site to test our model based on two reasons. First, Zengcuoan was praised as the most Artistic Fishing Village in China because of its diverse cultures, numerous coastal delicacies, and unique style of overseas Chinese culture. Second, this attraction site covers only 0.33 square kilometers with 5 streets and 18 lanes only and each street and alley is very narrow within 2 m. With the increase of tourists, the streets and alleys of Zengcuoan become crowded such as the congestion of shops and homestay inns, thereby resulting in such a typical phenomenon, tourist crowding. According to data released from the Zengcuoan tourism website (https://www.zgzca.com/), there were 803,000 tourists in total and 114,700 tourists per day on average in Zengcuoan during the National Day Golden Week of 2019. We thus conducted our questionnaires at Zengcuoan during this holiday week (Fig. 2).

Fig. 2.

Fig. 2

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Tourism crowding at Zengcuoan

Data collection

Based on the structural equation modeling (SEM), the minimum sample size is suggested as 150 samples (Hair et al., 2010). Thus, during the 2020 National Day Golden Week (i.e., from Oct. 1 to Oct. 7, 2020), a well-trained research team distributed the questionnaires to tourists in the Zengcuoan community since 738,500 tourists visited Zengcuoan during the National Day Golden Week. A convenience sampling method was employed for the on-site fieldwork. The field research team distributed and collected the questionnaire to those who are willing to participate in this survey after explaining to them the purpose of this research. To ensure at least 150 valid samples, we distributed 500 questionnaires with a 95.6% recovery rate. A total of 420 valid samples were collected after discarding the incomplete questionnaires, with a valid rate of 84%, which is much higher than the minimum sample size suggested by Hair et al. (2010). Thus, Table 1 showed the demographic characteristics of the participants.

Table 1.

The demographic characteristics of the sample (N = 420)

Sample characteristics N % Sample characteristics N %
Gender Male 154 36.7% Job Government employees 12 2.9%
Female 266 63.3% Business employees 102 24.3%
Age Under 18 42 10.0% Teacher 20 4.8%
18–30 345 82.1% Student 191 45.5%
31–40 25 6.0% Freelance 44 10.5%
41–50 6 1.4% Retired 1 0.2%
51–60 1 0.2% Others 50 11.9%
Over 60 1 0.2% Income Less than ¥ 2500 186 44.3%
Education Junior high school and below 26 6.2% ¥2500–4999 65 15.5%
Senior high school 75 17.9% ¥5000–7499 86 20.5%
College or university graduates 295 70.2% ¥7500–9999 39 9.3%
Post-graduates 24 5.7% Over ¥ 10,000 44 10.5%
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Table 1 illustrated that most of the participants were females (63.3%), aged 18–30 (82.1%), university graduates (70.2%), ranging less than ¥ 2500 for their monthly income (52.42%), and students (45.5%) for these participants. These data were analyzed using Mplus 8.0 and Process 3.4. following the suggestion of Anderson and Gerbing (1988) to test the conceptual model with two stages. In the first stage, we employed the measurement model to confirm whether the constructs and items adopted were valid and reliable by conducting confirmatory factor analysis (CFA). As for the second stage, we employed Process 3.4 to clarify the causal relationships between the constructs and examine moderating effects (Hayes, 2013), widely accepted in the field of tourism research (Lombardi et al., 2019; Kalyar et al., 2021).

Results

Measurement model validation

Williams and Brown (1994) indicated that common method variance is a systematic error variance among variables, which is caused by the similarity in methods used for collecting data (Hsiao et al., 2020). We thus carried out exploratory factor analysis (EFA) for all of the items using a rotation-free principal component analysis method according to the suggestion of Podsakoff et al. (2003). The result showed that the single-factor model could explain 37.685%, less than 50%, of the observed variance, indicating that common method bias was within the acceptable range (Hsiao et al., 2020), thereby indicating that the results of this study would not bias.

We also employed Mplus 8.0 to conduct EFA to eliminate items whose factor loads are less than 0.5. Accordingly, items TC1 and TC6-7 of TC and items PF1, MF1, AF1, and CF4 of TF were eliminated with the factor loads of these items less than 0.5 in Table 2. Additionally, according to the suggestion of Fornell and Larcker (1981), the standardized factor loading of each item for its corresponding construct and each construct’s average variance extracted (AVE) should both be higher than 0.5. We then presented the standardized factor loading of each item and the AVE in Table 2. In addition, the AVEs of all dimensions and CR values of latent constructs were higher than 0.5 and 0.7, respectively (Nunnally, 1994) and the AVE was higher than the squared correlations between variables, indicating that the discriminative validity between variables was qualified. We thus argue that our samples exhibited good construct validity and consistency.

Table 2.

Confirmatory factor analysis: items and factor loadings

Dimensions Items Standardized loading AVE CR
TC TC2 The destination appears to be crowded. 0.844 0.897 0.639
TC3 I believe the entire tour is very limited. 0.780
TC4 For me, the entire tour area is crowded. 0.902
TC5The visit site appears to be very crowded to me. 0.839
TC8 This location attracts a large number of tourists. 0.598
PF PF2 My steps and movements are becoming slower. 0.834 0.904 0.759
PF3 I’m exhausted. 0.949
PF4 I’d like to sit down and relax. 0.826
MF MF2 My interest in the remaining attractions is dwindling. 0.949 0.954 0.874
MF3 My desire to return to visit is dwindling. 0.957
MF4 I am not interested in visiting the remaining attractions. 0.897
AF AF2 The destination’s freshness is dwindling. 0.931 0.964 0.899
AF3 My preference for this location is dwindling. 0.972
AF4 My enthusiasm is waning. 0.941
CF CF1 My thinking has slowed down. 0.945 0.947 0.856
CF2 My focus is gradually waning. 0.970
CF3 My reaction to the outside world is becoming monotonous. 0.857
EQ Equation 1 In this location, I can enjoy the natural scenery. 0.871 0.894 0.680
Equation 2 Visiting this location has the potential to broaden my horizons. 0.944
Equation 3 Visiting this location may enable me to make a new friend. 0.732
Equation 4 This location can help me relax. 0.731
TS TS1 Overall, I am pleased with my trip. 0.874 0.951 0.829
TS2 I made a wise decision to visit this location. 0.943
TS3 I have positive feelings about my trip experience. 0.938
TS4 In terms of my expectations, I am pleased with this trip. 0.885
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CR = Composite Reliability, and AVE = Average Variance Extracted

Subsequently, we validated the measure using CFA and justified that the seven-factor model fit indices in the SEM met the acceptable criteria (Baumergartner & Homburg, 1996), as shown that χ2 = 556.312 (df = 254, χ2/df = 2.190 < 3, P < .001), RMSEA = 0.053 < 0.08, SRMR = 0.049 < 0.08, CFI = 0.971 > 0.9, and TLI = 0.966 > 0.9 (Table 3).

Table 3.

Competition model fitting indexes

Factor model χ2 df χ2/df RMSEA SRMR CFI TLI
Seven-factor (TC, PF, MF, AF, CF, EQ, TS) 556.312 254 2.190 0.053 0.049 0.971 0.966
Four-factor (TC, PF + MF + AF + CF, EQ, TS) 2740.494 269 10.188 0.148 0.087 0.764 0.737
Three-factor (TC, PF + MF + AF + CF, EQ + TS) 3421.205 272 12.578 0.166 0.099 0.700 0.669
Two-factor (TC + PF + MF + AF + CF, EQ + TS) 4420.948 274 16.135 0.190 0.124 0.604 0.567
One-factor (TC + PF + MF + AF + CF + EQ + TS) 6310.216 275 22.946 0.229 0.176 0.424 0.372
Standard — — < 3 < 0.08 < 0.08 > 0.9 > 0.9
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TC = Tourist crowding, PF = Physical fatigue, MF = Motivational fatigue, AF = Affective fatigue, CF = Cognitive fatigue, EQ = Experience quality, TS = Tourist satisfaction

By presenting the descriptive statistics and associated measures for these constructs, Table 4 showed that TC is positively associated with PF (γ = 0.390, p < .05), MF (γ = 0.433, p < .05), AF (γ = 0.399, p < .05), and CF (γ = 0.394, p < .05) as well as negatively associated with TS (γ= − 0.256, p < .01). These results might provide initial support for our proposed hypotheses.

Table 4.

Descriptive statistics and associated measures

Dimension M SD Discriminatory validity
TC PF MF AF CF EQ TS
TC 4.900 1.157 0.799
PF 4.551 1.388 0.390** 0.871
MF 3.713 1.398 0.433** 0.672** 0.935
AF 3.852 1.321 0.399** 0.449** 0.730** 0.948
CF 3.520 1.274 0.394** 0.523** 0.666** 0.765** 0.925
EQ 4.787 1.193 − 0.142 0.042 − 0.142** − 0.299** − 0.176** 0.825
TS 4.899 1.115 − 0.256** − 0.122 − 0.351** − 0.424** − 0.375** 0.529 0.910
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**P < 0.05, ***P < 0.01, M = Mean, SD = Standard Deviation,. Correlations are shown below the diagonal. The diagonal represents the discriminant validity

Direct effect testing

As per the conceptual framework in Fig. 1; Table 5 showed that TC had no significant effect on TS (β = − 0.0055, P > .1), not supporting H1. TC has a positive effect on PF (β = 0.3104, P < .001), MF (β = 0.3083, P < .001), AF (β = 0.2932, P < .001), and CF (β = 0.2645, P < .001), respectively, supporting H2a, H2b, H2c, and H2d. PF had a positive effect on TS (β= 0.0847, P < .1), not supporting H3a, while MF (β = − 0.1110, P < .1), AF (β = − 0.1212, P < .1), and CF (β = − 0.1268, P < .05) negatively affect TS, respectively, supporting H3b, H3c, and H3d.

Table 5.

Standardized parameter estimates for the structural model and hypothesis testing

Hypotheses (H) β SE P- values Support
H1: TC → TS − 0.0055 0.0423 0.8968 No
H2a: TC → PF 0.3104*** 0.0465 0.0000 Yes
H2b: TC → MG 0.3083*** 0.0465 0.0000 Yes
H2c: TCg → AF 0.2932*** 0.0468 0.0000 Yes
H2d: TC → CF 0.2645*** 0.0472 0.0000 Yes
H3a: PF → TS 0.0847* 0.0498 0.0899 No
H3b: MF → TS − 0.1110* 0.0622 0.0752 Yes
H3c: AF → TS − 0.1212* 0.0667 0.0699 Yes
H3d: CF → TS − 0.1268** 0.0584 0.0305 Yes
H4: TC*EQ→ TS − 0.0822** 0.0353 0.0204 Yes
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*p < .1; **p < .05; ***p < .001

Moderating effect testing

We employed the PROCESS macro Model 5 (Hayes, 2013) to investigate the moderation of EQ. Table 4 showed that the interaction term, TC*EQ, has a significantly negative impact on TS (β = − 0.0822, P < .05), indicating that high EQ might mitigate the negative effect of TC on TS, supporting H4. To further illustrate that EQ may moderate the negative effect of TC on TS, we plotted predicted TS against either higher or lower EQ with 1 standard deviation (SD) below and above the mean in Fig. 3, indicating the lower and higher levels respectively (Aiken & West, 1991). We thus reveal that with the high level of EQ, the negative effect of TC on TS would considerably decrease compared with the low level of EQ in Fig. 3.

Fig. 3.

Fig. 3

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Moderated effect of experience quality

We thus summarize the overall results regarding whether or not our proposed hypotheses would be supported in Fig. 4.

Fig. 4.

Fig. 4

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Results of model

Concluding remarks

Conclusion and discussion

This study not only tested a structural model integrating constructs about the effects of TC on TF and TS based on arousal theory but also explored the moderating effect of EQ between TC and TS, all of which seem ignored in the existing literature. Given that our proposed issues essential for tourism management seem rarely concerned in previous studies, we then examined our proposed hypotheses and revealed several essential conclusions as follows.

First, we stated that TC may not have a significant effect on TS because such an effect is dependent on tourism environments, types, and cultures (Klçarslan & Caber, 2018). People with different characteristics may perceive crowding differently (Zehrer & Raich, 2016), resulting in different TC perceptions. Bultena et al. (2009) discovered that TC may not affect TS among hikers in National Parks; Milman et al. (2020) discovered that TC may not always be negative for popular tourism destinations. As the study site of Zengcuoan Community, Chian’s most artistic fishing village, is full of shops and homestay hotels, the community may attract many tourists who come to enjoy delicious food, relax, and even find happiness. We infer that the crowded Zengcuoan community may provide them with a boisterous atmosphere rather than an unpleasant one, reducing the negative effect of TC on TS.

Second, previous research has shown that external stimuli such as work-family conflict (Abdul Rahman et al., 2017) and communication technology overload (Lee et al., 2016) can lead to fatigue. Because the effect of TC on TF appears to be understudied in the relevant studies, we investigated the effects of TC on different dimensions of TF, finding that TC has a positive effect on all of them (PF, MF, AF, and CF). Furthermore, we discovered that MF, AF, and CF all have a negative impact on TS, but PF has a particularly positive impact on TS. We argue that the effects of TF on TS appear to be very complex, which could be due to tourist perception, tourism activity types, and other factors. As a result, tourists with PF, primarily as a result of tiring activities (e.g., walking, waiting, queuing, and photographing), would provide a one-of-a-kind experience that gives tourists the feeling of “fatigue but satisfaction” on their tours. According to the embodied social presence theory (Wang et al., 2016), the final experience of tourists is the result of the interaction of a number of relevant factors (e.g., perception of body and environment). Xu et al. (2021) also stated that there are many elements of pain and pleasure during their tours, resulting in a mixed impact on TS.

Finally, despite the fact that previous studies have investigated the effects of TC on TS, there are few studies that address how to mitigate the effects of TC on TS (Kim et al., 2015). As a result, we attempted to demonstrate that the negative effect of TC on TS may not be exacerbated for tourists with high EQ. To some extent, our findings are consistent with the findings of Wu et al. (2018), who found that perceived experience plays a positive moderating role between social and hedonic/utilitarian values. As a result, even if a destination is overcrowded, tourists with high EQ may find the destination so appealing that their perception of TC is temporarily reduced, demonstrating the moderating effect of EQ in this study.

Theoretical implications

TC may lead to some changes in physical and psychological symptoms, as well as the decline of TS (Rathnayake, 2015). According to arousal theory, excessive environmental stimuli may cause physiological and psychological responses. Therefore, by exploring the effects of TC on TF and TS, we revealed the effects of TC (external stimulus) on TF (physiological and psychological arousal) and TS (emotional arousal) based on arousal theory. Because TF is rarely employed in arousal theory, this study may broaden the application of arousal theory. Moreover, we argue that how to mitigate the negative effect of TC on TS would be an essential issue beneficial to tourism development. As such, by revealing the moderation effect of EQ between TC and TS, this study provides a better understanding of how to moderate the negative effect of TC on TS, which seems rarely explored in the existing literature.

In addition, although numerous studies have examined the effects of TC on TS in different tourist attractions, such as National parks (Rathnayake, 2015), shopping stores (Jones et al., 2010), rivers (Manning & Ciali, 1980) cruises (Xu & Liu, 2022), we revealed that few studies focused on the TC issues that may exist in community tourism since community tourism, consisting of tourists, staff, shops, residents, and other stakeholders, may be different from other tourism types. Thus, the TC perception of Zengcuoan (i.e., our study site) might not be the same as that of other attractions (e.g., National parks, zoos, and beaches). Moreover, because few studies take the issue of how to mitigate the negative effect of TC on TS into account (Rathnayake, 2015; Zehrer & Raich 2016), we thus not only reveal the EQ would moderate the negative effect of TC on TS but also provide a better understanding of how to moderate such a negative effect in this study.

Furthermore, previous studies mainly focused on the effects of TC on tourists’ emotions, attitudes, and evaluation (Manning & Ciali, 1980), but few of them were concerned about the effect of TC on either tourists’ physical state or psychological state. Based on arousal theory, we explored the effect of TC on physical and psychological arousal, incorporating different dimensions of TF (i.e., PF, MF, AF, and CF), which might not only better understand whether the different dimensions of TF might not have the same impact on TS but also enrich the relevant studies related to TF.

Managerial implications

Based on our research findings, we thus provide the following managerial and practical implications in this study. First, by revealing the positive effects of TC on four dimensions of TF (i.e., PF, MF, AF, and CF) for tourists, we suggest that managers of tourism destinations should take reasonable measures to effectively manage TC and keep TC within acceptable limits. For example, authorities of destination attractions should set up a flow monitoring system for scenic spots, adopt measures for limiting the tourist flow, and evacuate the crowd once the number of tourists exceeds the maximum environmental capacity. In addition, security staff should inspect scenic spots in the destination, reserve emergency routes in advance, find out diverse highly aggregated tourist crowds situations, and use emergency routes to evacuate crowds, thereby likely solving the issues caused by TC.

Second, we found that TF has different impacts on TS. That is, PF has a positive impact on TS, whereas MF, AF, and CF have negative impacts on TS. Tourists might immerse in experiential tourism activities, thereby likely generating mixed effects (e.g., they might generate higher destination memory accompanied by the feeling of PF). Thus, we suggest that authorities should create a comfortable atmosphere for tourism attractiveness, such as a gentle attitude, bright smile, and a friendly environment full of tourism attractiveness, to prevent tourists from MF, AF, and CF. Because PF has a positive impact on TS, we thus state that “fatigue but satisfaction” may indeed exist for tourists in crowded tourism, which is somewhat like mountain tourism. That is, tourists may not able to enjoy the spectacular sight from a mountain’s peak without becoming fatigued from climbing the mountain hill (i.e., fatigue but satisfaction).

Finally, tourists with high EQ may mitigate the negative effect of TC on TS, thus being beneficial to the development of tourism. We thus state that EQ can be improved by linking with key attributes of a specific destination (Wong & Li, 2015). For example, we suggest that tourism authorities should enhance EQ by creating a high-quality tourism experience and even a unique and unforgettable experience for tourists from various aspects, such as special and delicious food, cozy accommodation, amazing tours, as well as memorable shopping experiences.

Limitation and further research

In this study, EQ, as an internal factor, was the moderating variable between TC and TS in this research, but other external variables, such as service recovery (Amoako et al., 2021) and service convenience (Kumar et al., 2018), may provide additional moderation effects of TC on TS. As a result, we may employ other external variables instead of EQ as moderating variables for future studies. In addition, TF might be an important variable to predict the behavior intention of tourists. We then state that future studies may put more stress on the exploration of its antecedents, which may be beneficial to searching for factors reducing TF. Furthermore, we argue that it is essential for the success of the tourism industry to discover more creative ways to reduce “fatigue” and increase “enjoyment” for tourists, particularly given the industry’s current competitive conditions as a result of COVID-19. As a result, we believe that there is still room for further research into these methods.

Funding

Jie Yin has really appreciated the financial support from Nature Science Foundation Project of Fujian Province, China (2020J01076).

Data availability

The datasets used and/or analyzed during the current study are available from the first author on reasonable request at 15,980,301,687@163.com.

Declarations

Conflict of interest

The authors declare that they have no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

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Data Availability Statement

The datasets used and/or analyzed during the current study are available from the first author on reasonable request at 15,980,301,687@163.com.

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