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Article

Effect of Social Loneliness on Tourist Happiness: A Mediation Analysis Based on Smartphone Usage

by
Xuejiao Chen
1,*,
Kai Zhang
2 and
Yanting Huang
3
1
School of Business Administration, Zhongnan University of Economic and Law, Wuhan 430070, China
2
School of Information and Security Engineering, Zhongnan University of Economic and Law, Wuhan 430070, China
3
College of Tourism and Physical Health, Hezhou University, Hezhou 542899, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(11), 8760; https://doi.org/10.3390/su15118760
Submission received: 9 February 2023 / Revised: 7 May 2023 / Accepted: 23 May 2023 / Published: 29 May 2023
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Abstract

:
Smartphone usage affects the relationship between social loneliness in tourism and tourist happiness. This study discusses the effect of social loneliness on tourist happiness by considering three aspects of smartphone usage—habitual smartphone behaviors, smartphone communication, and smart tourism applications—as mediating variables. Based on stimulus–organism–response theory, this study collected data through questionnaires, analyzed the data using SPSS and Amos, and reached three findings, as follows: (1) Social loneliness affects tourist happiness either directly or indirectly. (2) Habitual smartphone behaviors not only directly affect tourist happiness but also affect tourist happiness as a mediating variable and multiple mediating variables. (3) Smartphone communication does not affect tourist happiness either directly or indirectly as a mediating variable or as one of multiple mediating variables of social loneliness. (4) Smart tourism applications not only directly affect tourist happiness but also affect tourist happiness indirectly as one of multiple mediating variables. This study is not only conducive to exploring social loneliness and the influence mechanism of social loneliness on tourist happiness, but it is also conducive to suggesting that scenic spots should add interesting group activities in project development to reduce social loneliness. Attention should also be paid to social loneliness in destination marketing.

1. Introduction

Smartphone usage is not only important in daily life [1,2,3] but is also indispensable in tourism activities [4,5]. Smartphone usage plays an important role in tourism activities [6,7], such as booking tickets, making hotel reservations, navigation, etc. [8,9,10]. Studies have found that smartphone usage enhances tourist happiness (TH) [10,11]. Some scholars have also found that social loneliness (SL) in tourism activities increases smartphone usage [12,13]. Meanwhile, other scholars have found that SL weakens TH [5,14]. However, whether smartphone usage is a mediating variable between SL and TH in tourism activities is still unclear. This study discusses smartphone usage in three aspects: habitual smartphone behaviors, smartphone communication, and smart tourism applications. Some studies have focused on SL in daily life [15,16,17], but few have discussed SL in tourism. Studies of the relationship between SL and TH have mainly focused on the indirect relationship between SL and TH [18,19], but seldom have studies discussed the influence of smartphone usage (habitual smartphone behaviors, smart tourism applications, and smartphone communication) on the relationship between SL and TH or the direct relationship between SL and TH. Therefore, this study discusses whether smartphone usage (habitual smartphone behaviors, smart tourism applications, and smartphone communication) can be used as a mediating variable to influence the relationship between SL and TH and whether SL and TH have a direct impact. In addition, exploring the relationships among the three not only enhances TH but also reduces death and other health risks posed by SL, thus contributing to human well-being.
SL exists not only in daily life [3,20] but also in tourism activities [21], being inextricably linked with smartphone usage. In daily life, SL may cause increased smartphone usage, even smartphone addiction [12,22,23], or reduced happiness with life. In tourism activities, SL indirectly weakens TH [18,21]. However, there is a lack of research on whether SL directly affects TH. Smartphones are used anytime and anywhere [24], and a great variety of powerful smartphone applications provide conveniences for tourists in their travels [25,26,27]. Smartphone usage is deeply embedded in all aspects of tourism activities, such as information queries [9], bookings [28], and AI experiences [29]. Moreover, the conveniences provided by smartphone usage in travel enhance TH [8,30]. Therefore, it is necessary to discuss the direct relationship between SL and TH in tourism and the role of smartphone usage between them.
Different scholars have classified smartphone usage from different perspectives. Taking adolescents as the target group, smartphone usage is classified into four categories, namely (1) studying, (2) social-networking services, (3) games, and (4) entertainment [31]. Depending on its relationships with other variables, smartphone usage is classified into habitual and addictive smartphone usage [32]. Tan and Lu classified smartphone usage into three categories: functional usage, entertainment usage, and communicative usage [18]. Based on the classification of smartphone usage by Tan and Lu, this study classifies smartphone usage in tourism into three categories. The first category is smart tourism applications, i.e., the functional usage of smartphones in tourism [10,33,34]. The second is smartphone communication, i.e., the communicative usage of smartphones in tourism [35,36,37]. The third is habitual smartphone behaviors [38,39,40]. Tan and Lu believed that the entertaining usage of smartphones mainly includes listening to music, watching movies, and playing games [18]. However, with the rise in short video platforms and the development of shopping software, the term “entertaining usage” no longer covers such smartphone usage behaviors. Based on the classification of smartphone usage of existing studies, this study categorizes such smartphone usage behaviors as habitual smartphone behaviors. Therefore, this study divides smartphone usage into three categories: habitual smartphone behaviors, smart tourism applications, and smartphone communication.
This study handles the discussion by taking the following three main categories of smartphone usage as mediating variables: habitual smartphone behaviors [30,41], smartphone communication [35,37], and smart tourism applications [9,19], all of which are associated with SL and TH. According to statistics, 96% of people in China own smartphones, and about 90% of hotel reservations are made online. Smartphone usage is especially important during travel. Many habitual smartphone behaviors involve smartphone communication, such as chatting and other social behaviors [32,41]. Habitual smartphone behaviors enhance users’ information search ability. The information searchability in smart tourism applications is the application of daily information searchability in tourism [42]. Therefore, habitual smartphone behaviors may affect tourists’ smart tourism applications. Moreover, the three aspects of smartphone usage all directly or indirectly affect TH [18,30,43]. SL affects smartphone usage and indirectly weakens TH [28]. However, few studies have been conducted thus far exploring whether SL affects TH via habitual smartphone behaviors, smartphone communication, and smart tourism applications as mediating variables or multiple mediating variables. Therefore, the three aspects of smartphone usage should be taken as mediating variables to explore the relationship between SL and TH.
Relying on stimulus–organism–response theory, this study discusses three issues by taking SL as the independent variable, habitual smartphone behaviors, smartphone communication, and smart tourism applications as the mediating variables, and TH as the dependent variable. The three issues are (1) whether SL directly or indirectly affects TH; (2) whether habitual smartphone behaviors affect TH directly or indirectly as a mediating variable or affect the relationship between SL and TH as one of multiple mediating variables together with smartphone communication and smart tourism applications; and (3) whether smartphone communication and smart tourism applications directly affect TH or serve as mediating variables or two of multiple mediating variables between SL and TH. The effect mechanism of SL on TH is analyzed and discussed based on data collected from a questionnaire survey. This approach not only clarifies the mechanism through which SL affects TH via smartphone usage (habitual smartphone behaviors, smartphone communication, and smart tourism applications) but also promotes the development of the stimulus–organism–response theory in the direction of tourist psychological experiences. In addition, discussing this effect mechanism also offers novel ideas for the development of new tourism projects and contributes to the enhancement of TH for tourists and the benign development of the tourism industry by adding all kinds of projects intended to reduce SL.

2. Theoretical Background

2.1. Stimulus–Organism–Response Theory

The stimulus–organism–response theory, based on the Watson stimulus response theory [44] (pp. 1–10), was first proposed by Woodworth [45] (pp. 103–118). Stimulus–organism–response theory includes three variables: stimulus, organism, and response, which are the processes that occur when people respond to stimuli. Stimulus is an antecedent variable, representing environmental factors that are important to the subject’s behavior [46,47,48]. Organism is a mediating variable, representing stimulus and response processes. Response is the result variable, representing the output of the agent. This theory is applied to many fields, such as employee behavior [49], online shopping [47,50], and tourist behavior [46,48,51].
Stimulus–organism–response theory applies to this study. The stimulus is the emotion generated during tourist activities. The organism is smartphone usage and is a mediator variable; it is used to determine how SL reacts with TH. The response is TH, the result variable, and the psychological state generated by SL after smartphone usage. Smartphone usage in this study is the organism and includes three variables, namely, habitual smartphone behaviors, smart tourism applications, and smartphone communication. When considering the stimulus–organism–response theory, this study discusses the effect of smartphone usage (habitual smartphone behaviors, smart tourism applications, and smartphone communication) on TH when stimulated by SL.

2.2. Tourist Happiness (TH)

In this study, TH is the subjective happiness in tourism [52,53,54]. The factors that affect TH are classified into two types. The first type includes tourist-related factors, age [55], quality of life [56], motivation and preference [55], habitual smartphone behaviors [57], and psychological or emotional states. The second involves destination-related factors, such as service quality [58,59], beliefs regarding hotel staff [60], types of tourist attractions [61,62,63], and development level of smart tourism applications [8,9]. In turn, TH can affect tourists’ loyalty [43,64], satisfaction [56,65,66], and happiness with life [67]. A review of existing literature reveals that the factors affecting TH have been explored from different perspectives, but existing literature rarely takes SL—a potential subjective mood of tourists—as the independent variable to study TH. In this context, this study explores the effect mechanism of SL on TH; that is, whether, and if so how, SL affects TH.

2.3. Smartphone Usage in Tourism

2.3.1. Habitual Smartphone Behaviors

Nowadays, habitual smartphone behaviors and smartphone communication, being highly correlated, are indispensable and indisputable elements of smartphone usage. In tourism, habitual smartphone behaviors constitute a spillover of the habitual smartphone behaviors in everyday life [24]. The most basic function of smartphones is smartphone communication. Both habitual smartphone behaviors and smartphone communication rely on smartphones as a carrier [39], and many habitual smartphone behaviors involve smartphone communication [1,41,68]. In addition, smartphone communication may be an important cause of habitual smartphone behaviors. Similar research has found that one type of habitual smartphone behaviors is smartphone communication [69]. People with higher habitual smartphone behaviors also result in higher smartphone communication [70]. In the elderly group especially, smartphone communication is an important aspect of habitual smartphone behaviors [6]. In addition, habitual smartphone behaviors and smartphone communication are equally essential in tourism activities. However, few studies have discussed whether habitual smartphone behaviors in tourism activities affect smartphone communication. Therefore, this study believes that habitual smartphone behaviors affect smartphone communication and obtains hypothesis H1 (as depicted in Figure 1).
H1. 
Habitual smartphone behaviors affect smartphone communication.
It is possible that habitual smartphone behaviors may increase TH; habitual smartphone behaviors are also unconscious behaviors of tourists in tourism activities. Tourism is an extension of daily life, and the habits of daily life often extend into tourism activities [24,41,71]. This explains why habitual smartphone behaviors also exist in tourism. The reflection of subjective happiness in tourism is TH [65,72], while habitual smartphone behaviors in travel include checking social media and sharing travel experiences on social media. Sharing the happy experiences of travel with others is also an important way to increase TH [73]. Being able to use smartphones according to the habits of daily life constitutes a part of TH. Although some studies have shown that habitual smartphone behaviors may be related to TH, fewer studies have discussed the relationship between habitual smartphone behaviors (as part of smartphone usage) and TH. Therefore, this study claims that habitual smartphone behaviors are related to TH, and thus, H2 is proposed (as depicted in Figure 1).
H2. 
Habitual smartphone behaviors affect tourist happiness (TH).
At first glance, habitual smartphone behaviors and smart tourism applications do not seem to have much to do with each other. Some studies have analyzed the influence of habitual smartphone behaviors on TH [30,73,74] and smart tourism applications on TH [8,10,26], but few studies have looked at the influence of habitual smartphone behaviors on smart tourism applications. In fact, people with more frequent habitual smartphone behaviors may have better learning abilities [1,41] and are likely to use more smart tourism applications. Tourists with strong habitual smartphone behaviors may have learned a lot about travel and may be more able to search for information. The ability to form habitual smartphone behaviors lays a good foundation for smart tourism applications. Therefore, this study holds that there is a correlation between habitual smartphone behaviors and smart tourism applications, as shown in H3 and depicted in Figure 1.
H3. 
Habitual smartphone behaviors affect smart tourism applications.
In reality, habitual smartphone behaviors are related to both SL and TH. Smartphone usage is now a habitual behavior [41]. In daily life, habitual smartphone behaviors are diverse [75], depending on the specific periods in which they occur (such as the period before sleep [76]) and the reasons they occur (such as socializing and playing games [1,18]). This study maintains that habitual smartphone behaviors are related to age, gender, and other factors [32]. In addition, one important reason for increased smartphone usage is SL [23,77]. As a part of smartphone usage, habitual smartphone behaviors affects TH [30]. Differences in SL may lead to differences in habitual smartphone behaviors [22,23,78], which may further affect TH [30]. Although few studies have discussed the influence of habitual smartphone behaviors on SL and TH, through the analysis of existing studies, this paper believes that habitual smartphone behaviors play an intermediary role in SL and TH. Therefore, Hypothesis H4 is proposed (as depicted in Figure 1).
H4. 
Habitual smartphone behaviors play a mediating role between social loneliness (SL) and tourist happiness (TH).

2.3.2. Smartphone Communication

Globally, smartphone communication has become a common means of communication [37,79,80]. Smartphones affect communication skills [81,82]. Verduyn et al. even discussed whether smartphone communication would displace face-to-face interactions [37]. Being a convenient mode of communication, smartphone communication relieves loneliness [83,84] and helps human beings with special needs [27]; smartphone communication also makes tourism activities richer and more interesting [4]. Therefore, smartphone communication, as one of the most original functions of smartphones, has created clearer and more diversified modes of communication, making communication smoother. In addition, smartphone usage mediates the relationship between SL and TH [18]. Therefore, it is worth exploring whether smartphone communication affects TH and whether smartphone communication also affects the relationship between SL and TH as a mediating variable or as one of multiple mediating variables. An analysis of the mediating effect of smartphone communication shows that smartphone communication has dramatically transformed the way people communicate with each other [37,79,80] and that smartphone communication positively affects subjective happiness [85]. As tourism is also a part of life, this study argues that smartphone communication affects TH, as prosed in Hypothesis H5 (as depicted in Figure 1).
H5. 
Smartphone communication affects tourist happiness (TH).
Smartphone communication has an influence on the relationship between SL and TH: an increase in SL may lead to an increase in smartphone usage [86], and smartphone communication is part of smartphone usage [31]. Thus, SL may be related to smartphone communication. Smartphone usage can increase TH [4,87]. Therefore, smartphone communication can also be considered to increase TH. Simultaneously, Tan and Lu found that SL affects smartphone communication, which in turn affects TH [18]. Referring to their findings, this study suggests that smartphone communication plays a mediating role between the two. Therefore, Hypothesis H6 is proposed (as depicted in Figure 1).
H6. 
Smartphone communication plays a mediating role in the relationship between social loneliness (SL) and tourist happiness (TH).
Habitual smartphone behaviors and smartphone communication also have an influence on the relationship between SL and TH: SL affects habitual smartphone behaviors [88], which are correlated with smartphone communication [39]. In turn, smartphone communication in turn affects TH [14]. Studies have explored the relationship between SL, habitual smartphone behaviors, smartphone communication, and TH, but the relationship between SL and TH has not been discussed with regard to habitual smartphone behaviors and smartphone communication. However, based on existing research findings, this study believes that habitual smartphone behaviors and smartphone communication can be used as multiple mediating variables in the relationship between SL and TH. Therefore, the following Hypothesis H7 is proposes (as depicted in Figure 1).
H7. 
Habitual smartphone behaviors and smartphone communication play multiple mediating roles in the relationship between social loneliness (SL) and tourist happiness (TH).

2.3.3. Smart Tourism Applications

Without question, smart tourism applications bring conveniences to tourism activities [8,34], especially as smartphone functions are becoming increasingly powerful. Today’s smart tourism applications are involved in many aspects of tourism activities and make it convenient to search for information about tourism destinations [89,90], prepare tourism strategies [9], and book hotels and entrance tickets [91]. Some smart tourism applications also offer high-tech, immersive experiences, such as the augmented reality (AR) experiences available at heritage sites [92]. The development of smartphone functions has built a platform for smart tourism applications [68]. When tourists experience smart tourism applications with smartphones and other electronic devices [93,94], their happiness increases [42,43,92,95]. Therefore, this study believes that smart tourism applications can increase TH, thereby forming Hypothesis H8 (as depicted in Figure 1):
H8. 
Smart tourism applications affect tourist happiness (TH).
Analysis of the mediating effect of smart tourism applications: SL affects smartphone usage [18], which partly involves smart tourism applications [4,93]. This study believes that SL affects smart tourism applications. In addition, smart tourism applications positively affect TH [90,92,96]. Moreover, SL affects smartphone usage [13,88] and weakens TH [5]. Thus, it is worth exploring whether smart tourism applications, as a part of smartphone usage in tourism, further enhance TH and whether they affect the relationship between SL and TH as a mediating variable or as one of multiple mediating variables. This study assumes that smart tourism applications play a mediating role in the relationship between SL and TH. Therefore, Hypothesis H9 is proposed in this study (as depicted in Figure 1).
H9. 
Smart tourism applications play a mediating role in the relationship between social loneliness (SL) and tourist happiness (TH).
The influence of habitual smartphone behaviors and smart tourism applications on SL and TH is not obvious. There is no significant relationship between smart tourism applications and smartphone communication; smartphone communication is used in tourism mainly to fill in blank time by communicating with family or friends [24]. However, blank time is rare in tourism, and smart tourism applications are intended to serve tourism as a whole [42], so there may be circumstances where communication is inconvenient due to the use of smart tourism applications. Therefore, this study maintains that there is no correlation between smartphone communication and smart tourism applications and thus makes no assumption about their relationship. In addition, SL affects habitual smartphone behaviors [13,32], which are correlated with smart tourism applications [42], and in turn, smart tourism applications are correlated with TH [90,92,96]. Therefore, this study believes that habitual smartphone behaviors and smart tourism applications play multiple mediating roles in the relationship between SL and TH. This study proposes hypothesis H10 (as depicted in Figure 1).
H10. 
Habitual smartphone behaviors and smart tourism applications play multiple mediating roles in the relationship between social loneliness (SL) and tourist happiness (TH).

2.4. Social Loneliness (SL) in Tourism and its Relationships with Other Variables

This study believes that SL may affect habitual smartphone behaviors. One study found that SL “has to do with the objective characteristics of a situation and refers to the absence of relationships with other people” [97]. Research on SL covers a wide range, extending from death [20,98] to other health conditions [99,100,101]; from elderly people [20,102,103,104] to other groups [105,106]; and from other branches to tourism [18,19,21,107]. SL has also emerged in tourism [18]. According to the social compensation hypothesis, people with a strong sense of SL tend to compensate for the deficiency in their offline life by going online [108,109]. Thus, SL is an important factor affecting smartphone usage [13,18,78]. A strong sense of SL may increase smartphone usage [22,23,78], leading to frequent habitual smartphone behaviors [88]. Therefore, SL may be related to habitual smartphone behaviors, as proposed in Hypothesis H11 (as depicted in Figure 1).
H11. 
Social loneliness (SL) affects habitual smartphone behaviors.
The impact of SL on smartphone communication is defined as the degree to which SL affects the frequency of smartphone communication [12,110]. In reality, SL is a kind of negative emotion [105,111], while smartphone communication is an important aspect of smartphone usage [35,37]. Studies believe that, in the elderly, SL can be reduced by communicating with others through smartphone communication [112]. During COVID-19, smartphone communication reduced SL as part of smartphone usage [113]. These studies tend to discuss the influence of smartphone communication on SL. The influence of SL on smartphone communication mainly focuses on the influence of SL on smartphone usage [12,36,86]. Undoubtedly, smartphone communication is a part of smartphone usage, and SL is considered to have an impact on smartphone communication. Few studies have focused on the direct effect of SL on smartphone communication. After a comprehensive analysis of the relationship between SL and smartphone communication, this study believes that SL affects smartphone communication, and hypothesis H12 is thereby obtained (as depicted in Figure 1).
H12. 
Social loneliness (SL) affects smartphone communication.
The relationship between SL and TH is interesting; TH can be increased by decreasing SL. Many studies have analyzed SL and happiness. Take the recent outbreak of COVID-19 as an example. During this period, residents’ activities had to be restricted to reduce mobility, resulting in an increase in SL and a decrease in life happiness [16,114,115]. At the same time, there are also studies and discussions that have found that SL has a negative impact on TH in tourism activities [5,116]. Therefore, SL is related to happiness, both in life and in travel activities. On this basis, Hypothesis H13 is proposed (as depicted in Figure 1).
H13. 
Social loneliness (SL) affects tourist happiness (TH).
Few studies have examined the direct relationship between SL and smart tourism applications, but many studies have discussed the relationship between SL and smartphone usage. Without question, SL increases smartphone usage and even leads to smartphone addiction [13,17,77,86]. In tourism activities, smart tourism applications are part of the use of smartphones [26,42]. Therefore, this study believes that SL is correlated with smart tourism applications. For the sake of validation, Hypothesis H14 it proposes (as depicted in Figure 1).
H14. 
Social loneliness (SL) affects smart tourism applications.

3. Research Design

3.1. Questionnaire Design

The scales were designed using five dimensions: SL, habitual smartphone behaviors, smartphone communication, smart tourism applications, and TH. The scales for SL and smartphone communication are based on the scales proposed by Lee and Hyun [19] and Tan and Lu [18,19]. The scale for habitual smartphone behaviors was compiled according to the scale proposed by van Deursen et al. [32]. The scale for smart tourism applications is based on the scale of Wang et al. [91] and Tavitiyaman et al. [42]. The scale for TH is based on the scales developed by Huta and Ryan [95] and Liu et al. [43] for reference. The questionnaire was divided into two parts. Part 1 describes variable measurements based on a 5-point Likert scale (1 = “strongly disagree” and 5 = “strongly agree”). Part 2 provides demographic information, which is based on the actual situations of the respondents.

3.2. Data Collection

Data were collected from April to May 2022. Questionnaires were distributed and recovered through both online (https://www.wjx.cn/, accessed on 1 March 2023) and offline channels. The online channel relied on snowball sampling for two main reasons. First, the strong infectivity of COVID-19 posed a great risk of infection, especially in crowded places, such as scenic spots and railway stations. This study adopted online collection as the dominant channel of data collection for everyone’s safety. Second, China’s strict COVID-19 policies and occasional traffic and flow control measures made it inconvenient to distribute and recover questionnaires outside, so online collection was also the last resort. During the questionnaire’s distribution, the authors first sent the link to their relatives’, friends’, and classmates’ WeChat groups, who would then forward it to their own WeChat groups, like snowballing. In China, where tourism has become a lifestyle, this method of questionnaire distribution is typical. In the offline channel, teachers and students of different universities (such as Zhongnan University of Economics and Law and Hubei University for Nationalities) were asked to collect questionnaires in public places, such as canteens and lecture rooms. Notably, even the offline collection of questionnaires proceeded with the aid of a contactless QR code or link.
In total, 541 questionnaires were collected in this round. Invalid questionnaires were eliminated according to two principles. First, questionnaires in which the same answer was given to all questions were rejected. This is because a respondent who gives the same answer to all questions can be considered as not taking the questionnaire survey seriously. Second, questionnaires filled out too quickly, e.g., in less than 40 s, were rejected. The reason for this is that filling out the questionnaire within less than 40 s means that the respondent did not read the questions carefully, and the answers given by the respondent cannot represent their real thoughts. After eliminating all invalid questionnaires based on these two principles, 417 valid samples were obtained, with a valid questionnaire rate of 77.08%.

4. Results

4.1. Descriptive Statistics

Statistics of the 417 valid samples are presented in Table 1. The gender ratio is 51.32% female to 48.68% male, meaning the gender ratio is balanced. In terms of age distribution, the age group of 18–50 contained 395 samples. The age group between 18 and 50 accounts for a large proportion of all respondents. Respondents in this age group usually have strong learning ability, are good at using smartphone functions, and are willing to explore new things related to smart tourism. In terms of education level, a majority of respondents have a college (24.70%), bachelor’s (44.36%), or master’s degree (23.50%). With the expansion of university enrollment in China and different universities offering different ways to improve their degrees, the respondents had relatively high-level degrees. Regarding job categories, company employees and employees of government agencies and public institutions accounted for a high proportion of respondents (47.72%). Due to COVID-19, income and employment levels had generally declined, with the income ranges of CNY 3001–5000 and CNY 5001–8000 jointly accounting for about 52.52% of respondents. Not many earned more than CNY 8000 (21.34%), and not many earned less than CNY 3000 (26.14%). In brief, the samples were relatively ideal and suitable for analysis, laying a solid foundation for follow-up analysis.

4.2. Exploratory Factor Analysis

Exploratory factor analysis is an important method for testing the structural validity of an entire scale and eliminating unqualified items. In exploratory factor analysis, the main factors were extracted by combining principal component analysis with the maximum variance method [117] (pp. 612–680), and the eigenvalues were required to be above 1. The Kaiser–Meyer–Olkin value of the questionnaire was 0.886, and the significance level in the Bartlett test was p < 0.000, suggesting that exploratory factor analysis was suitable. Tabachnick and Fidell suggested that, in exploratory factor analysis, only items with a factor loading value of above 0.4 are included [117]. According to this criterion, a total of 19 items passed the test, and five principal components were obtained through analysis, i.e., smart tourism applications, smartphone communication, habitual smartphone behaviors, SL, and TH (Table 2). The eigenvalues of the five principal components were all above 1, and the cumulative variance was 75.88%, suggesting that the scale has sound construct validity.

4.3. Reliability and Validity Tests

The reliability and discriminant validity of scales were analyzed using SPSS 26.0. The results are provided in Table 3 and Table 4. The Cronbach’s α values of SL, smartphone communication, habitual smartphone behaviors, smart tourism applications, and TH were all within the range of 0.852–0.923, and the overall KMO value was 0.886, with sound reliability, indicating satisfactory internal reliability [118] (pp. 816). According to the results of confirmatory factor analysis using Amos, the standardized coefficients (factor loadings) of various items were all above 0.5. The composite reliability values all fell within the range of 0.859–0.928, i.e., >0.7 [119,120], and the average variance extracted (AVE) values all exceeded 0.5, with all standardized coefficients being above 0.5 [121]. Therefore, the analysis results were relatively ideal. It is generally believed that discriminant validity exists when the correlation coefficient between a latent variable and another latent variable is less than the square root of AVE. As presented in Table 4, the square root of AVE (the bold value on the diagonal) was greater than the correlation coefficient between latent variables in each case, suggesting that the discriminant validity was satisfactory and met the analysis requirements [122].

4.4. Model Goodness-of-Fit and Structural Model Validation

Data were calculated by the maximum likelihood method using Amos 26.0, and the following indices were obtained: χ2 = 230.834, df = 143, χ2/df = 1.614, SRMR = 0.041, RMSEA = 0.038, CFI = 0.982, and TLI = 0.979. The main fitting indices (CFI and TLI) were all above 0.9, suggesting that the model fitted well with the data [118]. The results of the hypothesis analysis using the structural model are provided in Table 5. The habitual smartphone behaviors exert a significant positive influence on smartphone communication (λ = 0.634, p < 0.001), smart tourism applications (λ = 0.468, p < 0.001), and TH (λ = 0.324, p < 0.001), supporting H1, H2, and H3. However, H5 (λ = 0.062, p > 0.01) is not supported, meaning that smartphone communication has no significant effect on TH. H8 (λ = 0.290, p < 0.001) is supported, which means that smart tourism applications significantly affect TH. Two of the four hypotheses about SL are supported, and two are not supported. Specifically, H11 (λ = −0.129, p < 0.05) and H13 (λ = −0.103, p < 0.05) are supported, meaning that SL has a significant effect on habitual smartphone behaviors and TH. Meanwhile, H12 (λ = −0.001, p > 0.01) and H14 (λ = −0.069, p > 0.01) are not supported, meaning that SL has no effect on smartphone communication and smart tourism applications. The test results of all hypotheses are depicted in Figure 2, where the bold solid lines and bold hypothesis numbers indicate that the hypotheses are true, while the dotted lines and non-bold hypothesis numbers mean that the hypotheses are false.

4.5. Mediating Effect Test

The bootstrap method was employed to test the mediating roles of habitual smartphone behaviors, smartphone communication, and smart tourism applications in SL and TH [123]. Following the advice of Hayes [124], the test under a bootstrap sample size of 1000 and a confidence level of 95% is performed. Under the confidence level of 95%, 0 was not contained in either the confidence interval of the bias-corrected method or the percentile method, which indicates the presence of a significant effect. According to the results of the mediating effect test (Table 6 and Figure 2), this study finds that H4 and H10 are supported, whereas H6, H7, and H9 are not supported. Specifically, SL can affect TH through habitual smartphone behaviors; SL can also affect TH through habitual smartphone behaviors and smart tourism applications.

5. Discussion

5.1. Relationships between Social Loneliness (SL) and Other Variables

5.1.1. Indirect Effect of Social Loneliness (SL) on Tourist Happiness (TH)

This study finds that SL affects TH through intermediaries, which indicates that SL can form different paths and increase TH through different factors in tourism activities. Essentially, SL affects TH via mediation, which is consistent with the findings of previous research [19,116,125]. A study by Lee and Hyun focused on two direct effects to demonstrate that SL and user satisfaction are related in some way [19]. Patterson and Balderas-Cejudo and Karagöz and Ramkissoon found that SL is related to TH and advocated reducing SL through tourism activities [5,125]. This study reveals that SL and TH can have a significant relationship via mediation. The difference between other studies and this paper is that the previous studies involved no mediating effect test, while this study has performed mediating effect tests and obtained significant results. This study believes that different strategies can be used to reduce SL to achieve the purpose of increasing TH.
On the one hand, SL affects TH via habitual smartphone behaviors (−0.076, −0.012; −0.071, −0.009). In addition, habitual smartphone behaviors, as an element of smartphone use, are the overflow of daily behaviors and involuntary behaviors into tourism activities. This paper also finds that SL affects TH through smartphone use, which is consistent with Kamboj and Joshi’s study [116]. Kamboj and Joshi discussed the influence of SL on TH through smartphone apps [116], while this study focuses on one aspect of SL that can influence TH through smartphone usage. This approach is different from Kamboj and Joshi’s study. Some studies are different from Kamboj and Joshi’s study. Studies have demonstrated that SL affects smartphone usage [13,77] and have held discussions on the categories of smartphone usage [31,35]. However, few of them have taken smartphone usage as a mediating variable to explore the effects of SL on other variables [18]. In view of this, three aspects of smartphone usage (habitual smartphone behaviors, smartphone communication, and smart tourism applications) are used in this study as mediating variables in order to clarify the effect of SL on TH. This study finds that habitual smartphone behaviors play a significant mediating role, whereas neither smart tourism applications nor smartphone communication alone exert any significant mediating effect. This suggests that when SL is taken as the independent variable, habitual smartphone behaviors significantly affect TH and play a significant mediating role in the relationship between SL and TH.
On the other hand, as two elements of smartphones, habitual smartphone behaviors and smart tourism applications can exert an influence on the relationship between SL and TH as multiple mediating variables (−0.037, −0.005; −0.033, −0.004). This finding is consistent with existing literature on smartphone usage [13,77,86,126] and a study by Lee and Hyun [19]. Consistent with studies by Bian and Leung, Enez Darcin et al., and Meng et al. [13,77,126], SL is found to have effects on smartphone usage. However, few studies have taken different categories of smartphone usage as mediating variables to analyze TH. The finding of this study is consistent with the study by Lee and Hyun about the possible effect of SL on user experiences [19]. The difference is that this study has not only discussed the direct effect of SL on user experiences but also explores the possible mediating effects involved. The analysis results of this study indicate that SL affects TH via habitual smartphone behaviors and smart tourism applications, so the presence of multiple mediating effects is established. Thus, this study not only discusses the mediating effect of either function alone but also explores the effects of the two functions as mediating variables on TH.

5.1.2. The Direct Effects of Social Loneliness (SL) on Other Variables

First, this study finds that SL does not affect smartphone communication (−0.004, p > 0.5), meaning that the level of SL does not affect the degree of smartphone communication. This finding differs from the findings of existing studies. A study by Tan and Lu revealed that SL affects smartphone communication. The SL in this study is caused by the lack of communication with peers [18] and is reduced by interactions with others via smartphones [127,128]. In contrast, this study focuses on the SL generated in tourism, rather than the relationship between tourists and peers. Tourists are often busy with sightseeing or experiencing activities [129,130], which reduces the possibility of smartphone communication. Therefore, SL does not affect smartphone communication.
Second, this study discovers that SL directly affects habitual smartphone behaviors (−0.004, *), meaning that the level of SL will affect the degree of habitual smartphone behaviors. This finding is consistent with findings of Koban et al. [88] and Chen et al. [22]. The similarities with these studies lie in that SL indeed affects habitual smartphone behaviors. The difference is that the latter study emphasized the effect of smartphone gaming [22,88], whereas this study focuses on the effect of habitual smartphone behaviors. It is true that not everyone has the habit of playing games, but most people have the habit of using smartphones [32,131]. This study shows that SL affects habitual smartphone behaviors, which is a development of the existing research on the relationship between SL and smartphones [13,17]. At the same time, discussing only one function of smartphones (habitual smartphone behaviors) is a further development of previous research.
Third, this study also finds that the relationship between SL and smart tourism applications is non-significant (−0.069, p > 0.5), meaning that the degree of SL does not affect smart tourism applications in this study. This finding differs from the finding of Tan and Lu [18]. This may be because SL is caused by a lack of social networks or collective membership [105,107]. Tourists with a stronger sense of SL are less willing to take part in group activities in collective travel, such as making travel plans [9] and booking hotels. When traveling alone, they also tend to stay away from new things [105,107] and lack the enthusiasm to explore new things in smart tourism. Therefore, the relationship between SL and smart tourism applications is non-significant.
Finally, this study discovers that the direct relationship between SL and TH is significant (−0.103, *). An increased degree of SL decreases TH perception. In addition, SL exists in tourism, while TH is the goal of tourism. However, previous studies have rarely explored the relationship between the SL directly generated in tourism and TH; the relationship is often discussed indirectly [18,116]. Consistent with Tan and Lu and Kamboj and Joshi, this study finds that SL is related to TH [18,116]. In addition, SL affects the use of smartphone applications, which in turn affects TH. In other cases, existing studies have only suggested that tourism reduces SL [21,125,132], but those studies offer few discussions about the effect of SL on TH. This study shows that SL negatively affects TH, which means that SL does exist in tourism; reducing the degree of SL can also improve TH.

5.2. Discussions about the Mediating Effects of Smartphone Usage

5.2.1. Discussions about Smartphone Communication

Not only is smartphone communication one of the most basic functions of mobile phones, but it is also the most important function of smartphones. Undoubtedly, smartphone communication is affected by habitual smartphone behaviors (0.634, ***), but the path through which smartphone communication serves as a mediating variable or as one of multiple mediating variables is non-significant. Firstly, smartphone communication is affected by habitual smartphone behaviors, which is consistent with the findings of Park et al. [41]. The study by Park et al. demonstrated that smartphone communication is related to habitual smartphone behaviors, and smartphone communication affects TH [41]. This reveals that smartphone communication is related to habitual smartphone behaviors, but the former is affected by the latter. Secondly, smartphone communication does not directly affect TH in this study, which differs from the findings of Chen et al. [14]. The study by Chen et al. highlighted the need to deal with work-related matters during holidays [14]. As most people are not in the office during holidays, smartphones are naturally needed as an important medium of communication to avoid work mistakes due to a lack of communication [35,133]. In contrast, this study suggests that there is no long period of free time to communicate or do other things in tourism (except during a stay at a hotel [134]), so the effect of smartphone communication on TH is non-significant. Finally, smartphone communication does not affect the relationship between SL and TH as a mediating variable, possibly because the relationship itself is non-significant. As a result, the mediating effect of smartphone communication is non-significant, whether it is a mediating variable or one of multiple mediating variables.

5.2.2. Discussions about Habitual Smartphone Behaviors

Nowadays, habitual smartphone behaviors have become a part of life, and forcing them out of travel may reduce TH. This study finds that habitual smartphone behaviors positively affect TH (0.324, ***), which is consistent with the findings of Horwood and Anglim, Rotondi et al., and Twenge et al. [30,57,135]. The studies by Horwood and Anglim, Rotondi et al., and Twenge et al. also revealed that habitual smartphone behaviors affect subjective happiness [30,57,135]. This study finds that habitual smartphone behaviors are related to subjective happiness in tourism. This paper differs from the studies by Horwood and Anglim, Rotondi et al., and Twenge et al., however, in that this paper focuses on TH rather than happiness with life [30,57,135]. The effect of SL on TH via habitual smartphone behaviors is significant; habitual smartphone behaviors and smart tourism applications also jointly significantly affect the relationship between SL and TH as two multiple mediating variables. In addition, habitual smartphone behaviors, being the continuation of tourism experiences in the relationship between SL and subjective happiness serve as one of multiple mediating variables between SL and TH [57]. Moreover, existing studies have revealed that a correlation exists between habitual smartphone behaviors and subjective happiness [30,57,135]. This study finds that the relationship between habitual smartphone behaviors and TH is significant and positive in tourism activities.

5.2.3. Discussions about Smart Tourism Applications

Smart tourism applications involve many aspects, such as accommodation, transportation, food, and shopping, bringing convenience to tourism activities. In addition, smart tourism applications positively affect TH (0.290, ***), a finding which is consistent with the findings of existing studies [42,90,92,96]. A prior information query is the best way to learn about a tourist destination [42], as it not only reduces the prejudice that may exist in obtaining information through smart tourism applications but also increases the understanding of the tourist destination. Booking hotels and passenger tickets [91,136] in advance reduces uncertainties in tourism and even lowers the chance of being ripped off. It is a desirable way to maximize economic benefits and enhance TH. Smart experience activities in tourism, such as smart scenic spot experiences [34], virtual reality experiences [87], and other smart tourism technologies [34], are all ways to heighten TH. When the relationship between SL and TH is mediated by smart tourism, it is non-significant. This finding is different from the findings of Tan and Lu [18]. Additionally, the relationship between SL and TH is also significant [18,116] when mediated by smartphone communication and smart tourism applications as two multiple mediating variables. Moreover, the relationship is inverse in each case.

6. Conclusions, Implications, Limitations, and Prospects

6.1. Conclusions

Relying on the stimulus–organism–response theory, this study develops a model that takes SL as the independent variable; habitual smartphone behaviors, smartphone communication, and smart tourism applications as the mediating variables; and TH as the dependent variable. By conducting a questionnaire survey and empirically analyzing survey data, the following conclusions are drawn:
  • SL affects TH either directly or indirectly. This study finds that SL has a significant negative effect on TH; SL also significantly affects habitual smartphone behaviors. Meanwhile, SL affect TH through habitual smartphone behaviors. SL also affects TH through habitual smartphone behaviors and smart tourism applications.
  • Habitual smartphone behaviors not only directly affect TH but also affect TH as a mediating variable and multiple mediating variables.
  • This study also finds that smartphone communication does not affect TH, either directly or indirectly, as a mediating variable or as one of multiple mediating variables of SL.
  • STAs not only directly affect TH but also have an indirect effect as one of multiple mediating variables. The analysis results of this study indicate that smart tourism applications affect TH. When mediated by smart tourism, the relationship between SL and TH is significant.

6.2. Implications

6.2.1. Theoretical Implications

This study has two theoretical implications. On the one hand, this study attempts to explore whether SL (as a negative emotion) exists in travel and how TH can be influenced through smartphone usage. Data analysis shows that SL is an emotion that exists in tourism activities, and SL affects TH either directly or indirectly. Furthermore, SL mainly exists in daily life [5], so few studies have discussed SL in tourism. Studies often focus solely on the relationship between SL and smartphone usage [13,77] but rarely take smartphone usage as a mediation variable to explore the relationships between SL and other variables. This study reveals that the relationship between SL and TH is significant when examined using smartphone usage as a mediating variable. Specifically, SL affects TH via habitual smartphone behaviors, and SL also affects TH via habitual smartphone behaviors and smart tourism applications. Furthermore, smartphone communication does not affect TH in tourism. Therefore, habitual smartphone behaviors and smart tourism applications play important roles in the effect mechanism of SL on TH.
On the other hand, this study tries to discuss the role of smartphone usage (including habitual smartphone behaviors, smart tourism applications, and smartphone communication) in travel. With the development of technology and smartphones, the influence of smart tourism applications is being more frequently discussed in tourism activities [8,29], including with regard to booking accommodations, transportation, tickets, etc. [10,87,93]. Less attention is paid, however, to the impact of the other two aspects of smartphone usage (habitual smartphone behaviors and smartphone communication) in travel. In effect, habitual smartphone behaviors are the overflow of daily behaviors and habits [6,41], which are also essential behaviors in tourism. This study finds that habitual smartphone behaviors can be used as a mediator between SL and TH or, together with smart tourism applications, as a multiple mediator between SL and TH. Meanwhile, this study has also made an interesting finding, namely that smartphone communication is one of the most basic and important functions of smart phones [37,113]. Importantly, smartphone communication has no direct or indirect influence on TH. Studies have shown that smartphone communication plays an important role in daily life and is an important communication tool [35,81]. However, the leisure and entertainment characteristics of tourism activities weaken smartphone communication and do not affect TH. This is a further exploration of smartphone communication in tourism activities.

6.2.2. Practical Implications

This study also has two practical implications. One is the implication for the project development of scenic spots. Practical implications include setting up diversified collective interactive projects in scenic spots, reducing SL and improving TH. The reduction in SL and the enhancement of TH are essential considerations in the development of new scenic spot projects. For example, experience-based activities could be added to the development of scenic spot projects, especially collective experience activities, such as bonfire parties and group-based check-in activities at scenic spots. Fun team events could be added, in small groups. In this way, tourists are more likely to cooperate and participate together, thus enhancing the emotion between tourists and their companions. Such activities enhance active or passive interactions between tourists, thereby reducing SL and enhancing TH. In turn, TH increases the probability of scenic spots being revisited and recommended to others, thus promoting the benign development of the tourism industry.
On the other hand, when a company or management department conducts tourism destination marketing, full consideration should be given to the characteristics of SL that are prevalent in people’s emotions. In the process of destination marketing, full use should be made of words, pictures, and videos that can eliminate SL from everyday life. At the same time, the marketing process should highlight that the destination is harmonious and beautiful. Words, pictures, and videos that may give tourists the illusion of “Second Life” should be avoided. Such a marketing method is more likely to make potential tourists more willing to travel to the marketed tourist destination.

6.3. Limitations and Prospects

This study also has its limitations. First, both online and offline data collection was conducted in the form of online questionnaires, which resulted in a low valid questionnaire rate. The advantage of filling out the questionnaire online was that there were no missing values in the questionnaire, but some respondents did not take it seriously. As a result, a substantial proportion of questionnaires had to be eliminated (mainly because some respondents gave the same answer to all questions in the questionnaire or filled it out too quickly). Affected by COVID-19 and strict prevention and control measures, online questionnaire collection was the last resort, which lowered the quality of the recovered questionnaires. In follow-up research, offline questionnaire collection should be strengthened to control questionnaire quality. Second, tourism is a way to escape from routine life and to relax. Many tourists tend to reduce smartphone usage in travel. Therefore, this study has included the entertainment functions in smartphone usage for the convenience of analysis. In the future, a customized study could be dedicated to research on smartphone entertainment functions. Third, smartphone communication is non-significant in most of the paths investigated in this study, which means that smartphone communication neither weakens nor enhances TH. In future research, the expansion or conversion of research scenarios may produce other results. This is a promising research direction, worthy of further exploration.

Author Contributions

Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, visualization, project administration, and funding acquisition, X.C.; Writing: review and editing, X.C. and Y.H.; Supervision, K.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the School of Business Administration of the Zhongnan University of Economics and Law (28 September 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 08760 g001 550
Figure 1. Research model.
Figure 1. Research model.
Sustainability 15 08760 g001
Sustainability 15 08760 g002 550
Figure 2. Structural model assessment (note: *** p <0.001, * p <0.05).
Figure 2. Structural model assessment (note: *** p <0.001, * p <0.05).
Sustainability 15 08760 g002
Table
Table 1. Characteristics of the Participants.
Table 1. Characteristics of the Participants.
VariablesFrequencyPercentage
GenderFemale21451.32%
Male20348.68%
Age18~3016439.33%
31~4013432.13%
41~509723.26%
51~60174.08%
≥6151.20%
Level of EducationHigh school graduate or less317.43%
Associate degree10324.70%
Undergraduate18544.36%
Postgraduate degree9823.50%
WorkEmployees of government agencies and public institutions9422.54%
Self-employed378.87%
Company employee10525.18%
Freelancer337.91%
Student9522.78%
Other5312.71%
Monthly Income≤CNY 300010926.14%
CNY 3001–500011427.34%
CNY 5001–800010525.18%
CNY 8001–10,0004611.03%
>CNY 10,0014310.31%
Table
Table 2. Exploratory factor analysis.
Table 2. Exploratory factor analysis.
ItemsFactorsFactor LoadingCharacteristic RootCumulative Explained Variance (%)
Smart tourism applicationsS10.8323.91820.62%
S20.838
S30.856
S40.712
Smartphone communicationS10.8252.99515.76%
S20.798
S30.832
Habitual smartphone behaviorsH10.8222.83514.92%
H20.833
H30.843
H40.876
H50.754
Social lonelinessS10.7832.34612.35%
S20.820
S30.838
S40.892
Tourist happinessT10.8332.32412.23%
T20.827
T30.837
Notes: (1) Total variance explained: 75.88%. Extraction method: principal component analysis. (2) Rotation method: oblimin with Kaiser Normalization; rotation converged in five iterations.
Table
Table 3. Reliability and validity tests of the scale.
Table 3. Reliability and validity tests of the scale.
ItemsFactorsUnstd.S.E.Z-ValuePStd.Cronbach’s αCRAVE
Smart tourism
applications		S11.000——————0.8010.8740.8780.646
S21.0780.05619.241***0.854
S31.0850.05519.894***0.883
S40.8230.05913.973***0.659
Smartphone
communication		S11.000———— 0.7190.8520.8590.673
S21.0610.06316.888***0.937
S31.0500.06815.500***0.790
Habitual
smartphone
behaviors		H11.000———— 0.9050.9230.9280.720
H20.9900.03528.260***0.897
H31.0550.05020.924***0.778
H41.0940.03928.265***0.897
H50.9780.04919.767***0.753
Social
loneliness		S11.000———— 0.6770.8550.8600.607
S21.1490.08613.375***0.745
S31.0900.07813.901***0.780
S41.2440.08315.062***0.899
Tourist
happiness		T11.000———— 0.9300.8580.8700.692
T20.8450.04220.314***0.821
T30.9530.05517.419***0.733
Notes: (1) Unstd. = unstandardized coefficient; S.E. = standard error; Std. = standardized coefficient; CR = composite reliability, AVE = average variance extracted. (2) *** p < 0.001.
Table
Table 4. Results of the discriminant validity test.
Table 4. Results of the discriminant validity test.
Smart Tourism ApplicationsSmartphone CommunicationHabitual Smartphone BehaviorsSocial LonelinessTourist Happiness
Smart tourism applications0.804
Smartphone communication0.4280.820
Habitual Smartphone behaviors0.4690.6290.849
Social loneliness−0.130−0.082−0.1290.779
Tourist
happiness		0.4830.3950.512−0.1880.832
Note: The bold and italics numbers in the diagonal represent the square root of the average variances extracted (AVE).
Table
Table 5. Results of SEM.
Table 5. Results of SEM.
HypothesisPathS.E.Z-ValuePStd.Results
H1Habitual smartphone behaviors→Smartphone communication0.05911.381***0.634Yes
H2Habitual smartphone behaviors→Tourist happiness0.0874.831***0.324Yes
H3Habitual smartphone behaviors→Smart tourism applications0.0648.992***0.468Yes
H5Smartphone communication→Tourist happiness0.0751.0130.3110.062No
H8Smart tourism applications→Tourist happiness0.0575.327***0.290Yes
H11Social
loneliness		→Habitual smartphone behaviors0.037−2.400*−0.004Yes
H12Social
loneliness		→Smartphone communication0.032−0.0200.984−0.001No
H13Social
loneliness		→Tourist happiness0.041−2.211*−0.103Yes
H14Social
loneliness		→Smart tourism applications0.042−1.3860.166−0.069No
Notes: (1) S.E. = standard error; Std. = standardized coefficient; CR = composite reliability, and AVE = average variance extracted. (2) *** p < 0.001, * p < 0.05.
Table
Table 6. Mediating effect tests.
Table 6. Mediating effect tests.
Path RelationshipPoint EstimateProduct of CoefficientBootstrapping 1000 Times 95%Results
Bias-CorrectedPercentile
SEZLowerUpperLowerUpper
Indirect effects
H4: Social loneliness→ habitual smartphone behaviors→ tourist happiness−0.0370.016−2.313−0.076−0.012−0.071−0.009Yes
H6: Social loneliness→ smartphone communication→ tourist happiness0.0000.0030.000−0.0080.006−0.0070.007No
H7: Social loneliness→ habitual smartphone behaviors→ smartphone communication→ tourist happiness−0.0050.006−0.833−0.0270.002−0.0200.004No
H9: Social loneliness→ smart tourism applications→ tourist happiness−0.0180.012−1.500−0.0460.002−0.0440.003No
H10: Social loneliness→ habitual smartphone behaviors→ smart tourism applications→ tourist happiness−0.0150.007−2.143−0.037−0.005−0.033−0.004Yes
Direct effects
Social loneliness→ tourist happiness−0.0910.039−2.333−0.174−0.018−0.175−0.021Yes
Total effects
Social loneliness→ tourist happiness−0.1660.049−3.388−0.271−0.074−0.264−0.071Yes
Note: SE= standard error.
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Chen, X.; Zhang, K.; Huang, Y. Effect of Social Loneliness on Tourist Happiness: A Mediation Analysis Based on Smartphone Usage. Sustainability 2023, 15, 8760. https://doi.org/10.3390/su15118760

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Chen X, Zhang K, Huang Y. Effect of Social Loneliness on Tourist Happiness: A Mediation Analysis Based on Smartphone Usage. Sustainability. 2023; 15(11):8760. https://doi.org/10.3390/su15118760

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Chen, Xuejiao, Kai Zhang, and Yanting Huang. 2023. "Effect of Social Loneliness on Tourist Happiness: A Mediation Analysis Based on Smartphone Usage" Sustainability 15, no. 11: 8760. https://doi.org/10.3390/su15118760

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