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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

by Xuejiao Chen^1,*

, Kai Zhang² and Yanting Huang³

Reviewer 1:

Asad Javed

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Reviewer 4:

Davide Provenzano

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

Round 1

Reviewer 1 Report

please find attached file.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 1 Comments

Thank you very much for your constructive and helpful comments on our manuscript. With a concerted effort to address all of the valuable suggestions, we have revised the paper mainly: 1) the introduction is modified to increase the discussion of the relationship between research variables and emphasize the research gaps; 2) add at least one line between the two headings and correct errors in the theoretical background; 3) theoretical and practical implications, literature, and the importance of research results and discussion are added to the discussion section. The green, italicized and bold contents are our changes to the manuscript, and the text in blue font marks the position of the changes in the draft. We believe that those changes have enhanced the manuscript’s quality and contribution, and we hope you agree.

Below, we provide our detailed responses in tabular form to explain how your points have been included in the revision. As you will see, we have made every possible attempt to address your concerns, and we hope you will find our revision acceptable. Once again, thank you for helping us to improve the manuscript significantly.

Point 1: Topic: The topic looks good

Response 1: Thank you for your recognition of the research topic, which we have put a lot of effort into.

Point 2: Abstract: The abstract is well written but can be improved by adding more detail about the

methodology of the study. Also, add a couple of implications.

Response 2: This is a constructive suggestion. The abstract should explain the research methods clearly. In the process of revision, we added a sentence about the data sources and data analysis methods of this study. At the same time, we condensed the revised "implication" into one sentence and revised the last sentence of the abstract. All changes are as follows:

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 social loneliness on tourist happiness, but 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.

#Please also refer to Page 1 (Line 15-17, 23-26) in the revised manuscript.

Point 3: Keywords: Appropriate but remove the short form (abbreviations).

Response 3: It's a good idea to remove keyword abbreviations. We have removed relative abbreviations.

Keywords: social loneliness, tourist happiness, smartphone usage, habitual smartphone behaviors, smartphone communication, smart tourism applications

#Please also refer to Page 1 (Line27-28) in the revised manuscript.

Point 4: Introduction

On lines 29 to 31, two contradictory matters are given. It should be corrected.
Author should also answer the “why” related to the concept of this study. Otherwise, the

research importance is negatively affected.

In the introduction section, normally the author provides detail about the gap(s) in the literature that this study will fill, the objectives of the study, and then provides a breakdown of the current manuscript. Currently, the gap(s) is missing. The author should add research in this section.
The novelty needs to be strengthened by using the latest studies.

Response 4: We were very inspired by the suggestion about the introduction. We revised the draft according to your comments.

The comments on lines 29 to 31 is professional and detailed. A close reading of the draft reveals that these three lines are indeed reversed. Some scholars believe that smartphone usage can increase tourism happiness, some scholars believe that social loneliness can increase smartphone usage, and others find that social loneliness can reduce tourism happiness. This is something that we didn't write exactly. It has been corrected:

Smartphone usage is not only important in daily life [1-3] 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. [8-10]. 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 [15-17], 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; neither has 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.

#Please also refer to Page 1-2 (Line 33-38) in the revised manuscript.

It is a professional suggestion. We thought long and hard about how to fix it. Finally, we decided to revise the first paragraph of the introduction mainly. The original first paragraph emphasized the role of smartphone usage as an intermediary. The first revised paragraph discusses not only the mediating role of smartphones but also the relationship between social loneliness and tourism happiness. Strengthen the relationship between variables in this way. The first paragraph of the revised paragraph is the general paragraph, introducing smartphone usage, social loneliness and tourism happiness. The second paragraph is about the direct relationship between social loneliness and travel happiness. The third paragraph introduces the classification of smartphone usage in this study. The fourth paragraph introduces the relationship between smartphone as intermediaries. The fifth paragraph is the conclusion of the section, highlighting the research questions, theories used and possible research contributions.

Smartphone usage is not only important in daily life [1-3] 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. [8-10]. 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 [15-17], 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; neither has 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.

#Please also refer to Page 1-2 (Line 36-48) in the revised manuscript.

This is also professional advice. Research gaps is in the introduction part must be seriously introduced. We reread and reread the introduction and did find some problems. The second paragraph of the introduction mainly introduces the existing relationship and research gap between social loneliness and trourism happiness. The third paragraph describes the types of smartphones useage. The fourth paragraph introduces the existing research results and research gaps on three types of smartphone usage as mediating variables of social loneliness and tourism happiness. However, there is some wrong for the research gapsi of the entire study in the first paragraph . It mainly introduces the research gaps of smartphone usage as a mediating variable, and lacks the close elaboration that emphasizes social loneliness and tourism happiness. Therefore, we added the gap study on social loneliness and tourism happiness, and again emphasized the gap study of the whole study.

Smartphone usage is not only important in daily life [1-3] 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. [8-10]. 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 [15-17], 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; neither has 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.

#Please also refer to Page 1-2 (Line 36-48) in the revised manuscript.

Adding the latest relevant research literature is a good suggestion so that our research can keep in going with the latest research results. In response, we added more than two dozen recent studies to our draft. There are many references added to the whole introduction, so it is not included here. All references added are marked in the draft, please refer to the draft.

Point 5: Theoretical Background

Write at least a line between two headings.
While writing hypotheses, the full form of all the abbreviations should be given.
Line 192, reference is missing due to a software error.
All the similar hypotheses should be clubbed together like H1a…d should be given with H1.

Response 5: Thank you for your suggestions on our theoretical background. According to your suggestions, we have made corresponding modifications.

Adding at least one line between the two headings is a thoughtful suggestion to make reading smoother. So, we added at least one line between all two headings in the draft. The result of the change is as follows:

Theoretical Background

The stimulus-organism-response theory used in this study is introduced in the theoretical background. Meanwhile, this section introduces TH, smartphone usage (habitual smartphone behaviors, smartphone communication and smart tourism applications), and SL. In this part, the relationship between variables is discussed, and the research hypotheses are proposed.

2.1. Stimulus-Organism-Response Theory

2.3. Smartphone Usage in Tourism

Smartphone usage mainly includes three aspects: habitual smartphone behaviors, smart tourism applications, and smartphone communication. This part not only introduces what the three variables are but also discusses the relationship between the variables and puts forward the corresponding hypotheses.

2.3.1. Habitual Smartphone Behaviors

Research Design

The research design includes two parts: questionnaire design and data collection. The questionnaire design mainly introduces the questionnaire sources of all variables in this study. According to the designed questionnaire, this study mainly collected questionnaires through the snowball sampling method.

3.1. Questionnaire Design

Results

On the basis of the 417 valid questionnaires, descriptive statistics, exploratory factor analysis, reliability and validity tests, and direct effect and mediating effect test were conducted.

4.1. Descriptive Statistics

Discussion

Based on the data analysis results, this discussion is divided into two parts. The first part discusses the relationship between SL and other variables. The second part discusses the mediation effect of smartphone usage.

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

5.2. Discussions about the Mediating Effects of Smartphone Usage

Three aspects of smartphone usage (including smartphone communication, habitual smartphone behaviors and smart tourism applications), as mediating variables or multiple mediating variables, have an impact on the relationship between SL and TH.

5.2.1. Discussions about Smartphone Communication

Conclusions, Implications, Limitations, and Prospects

This is the last part of the study, and it is a meaningful part, where the main results of this study are presented. Theoretical and practical implications are the significance of this study. The limitations and prospects of this study are the deficiencies of this study and the inspirations for future researchers.

6.1. Conclusions

6.2. Implications

The implications of this study are divided into two parts: theoretical implications and practical implications.

6.2.1 Theoretical Implications

#Please also refer to Page 3 (Line 116-120), Page 4 (Line 153-156), Page 8 (Line 347-350), Page 9 (Line 389-391), Page 14 (Line 487-489), Page 16 (Line 580-582), Page 17 (Line 641-644), and Page 18 (Line 665-666) in the revised manuscript.

It is a good idea to add the full names of the variables in the hypothesis, to increase the readability of the article. Other reviewer thought there were too many abbreviations and removed of habitual smartphone behaviors, smartphone communication, smart tourism applications. Therefore, we made the following modifications:

H1: Habitual smartphone behaviors affect smartphone communication.

H2: Habitual smartphone behaviors affect tourist happiness (TH).

H3: Habitual smartphone behaviors affect smart tourism applications.

H4: Habitual smartphone behaviors play a mediating role between social loneliness (SL) and tourist happiness (TH).

H5: Smartphone communication affects tourist happiness (TH).

H6: Smartphone communication plays a mediating role in the relationship between social loneliness (SL) and tourist happiness (TH).

H7: Habitual smartphone behaviors and smartphone communication play multiple mediating roles in the relationship between social loneliness (SL) and tourist happiness (TH).

H8: Smart tourism applications affect tourist happiness (TH).

H9: Smart tourism applications play a mediating role in the relationship between social loneliness (SL) and tourist happiness (TH).

H10: Habitual smartphone behaviors and smart tourism applications play multiple mediating roles in the relationship between social loneliness (SL) and tourist happiness (TH).

H11: Social loneliness (SL) affects habitual smartphone behaviors.

H12: Social loneliness (SL) affects smartphone communication.

H13: Social loneliness (SL) affects tourist happiness (TH).

H14: Social loneliness (SL) affects smart tourism applications.

#Please also refer to Page 4 (Line 175, 189), Page 5 (Line 201, 214-215, 234, 244-245), Page 5 (Line 256-258, 271, 282-283, 298-300), Page 7 (Line 313, 328, 343) in the revised manuscript.

This is a very detailed opinion, alerting us to a software error. We have made corresponding modifications for Line 162, 191 and 191.

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 show 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).

The influence of habitual smartphone behaviors and smart tourism applications on SL and TH is not obvious as smartphone communication and smart tourism applications. 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 families 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 no correlation between smartphone communication and smart tourism applications and thus makes no assumption about their relationship. Also, 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):

Few studies have examined on 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 smart phones [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):

#Please also refer to Page 5 (Line 233), Page 6 (Line 297), and Page 7 (Line 343) in the revised manuscript.

Hypotheses starts with H1, which is not only concise and beautiful but also can increase the readability of the article. We modified all the hypothetical headings, as shown below:

H1: Habitual smartphone behaviors affect smartphone communication.

H2: Habitual smartphone behaviors affect tourist happiness (TH).

H3: Habitual smartphone behaviors affect smart tourism applications.

H4: Habitual smartphone behaviors play a mediating role between social loneliness (SL) and tourist happiness (TH).

H5: Smartphone communication affects tourist happiness (TH).

H6: Smartphone communication plays a mediating role in the relationship between social loneliness (SL) and tourist happiness (TH).

H7: Habitual smartphone behaviors and smartphone communication play multiple mediating roles in the relationship between social loneliness (SL) and tourist happiness (TH).

H8: Smart tourism applications affect tourist happiness (TH).

H9: Smart tourism applications play a mediating role in the relationship between social loneliness (SL) and tourist happiness (TH).

H10: Habitual smartphone behaviors and smart tourism applications play multiple mediating roles in the relationship between social loneliness (SL) and tourist happiness (TH).

H11: Social loneliness (SL) affects habitual smartphone behaviors.

H12: Social loneliness (SL) affects smartphone communication.

H13: Social loneliness (SL) affects tourist happiness (TH).

H14: Social loneliness (SL) affects smart tourism applications.

#Please also refer to Page 4 (Line 175, 189), Page 5 (Line 201, 214, 234, 244), Page 5 (Line 256, 271, 282, 298), Page 7 (Line 313, 328, 343) in the revised manuscript.

Point 6: Research Design

What was the total population and how the sample of 511 was drawn from it?

Response 6: Your comments on data representativeness have made us pay great attention to an issue in the process of sample collection. China's total population is about 1.4 billion. We do not have the time, energy or ability to survey all residents over the age of 18, so we use a snowball sampling method. We increase the representativeness of the sample in two main ways. On the one hand, we are concerned about the number of questionnaires collected. We processed and analyzed the questionnaire again. The original questionnaire on the questionnaire star contains 541 copies. On the basis of 541 original questionnaires, 417 valid questionnaires were obtained by excluding those whose answer time was lower than 40S and those with identical answers. The 417 valid questionnaires were more than ten times the total number of questions (24*10=240) and greater than the threshold of statistical significance of 300. Therefore, we believe that the questionnaire is representative.

On the other hand, we are concerned about the quality of questionnaire retrieval. In the process of questionnaire distribution, we paid attention to the balance of gender, age, educational background, job and income of respondents, so as to cover as many respondents as possible and increase the representativeness of the sample. Based on the collected questionnaires, we analyzed the personal information of the interviewees (Table 1). There is a better balance of female (51.32%) and male (48.68%). In terms of age distribution, the age group of 18–50 contained 395 samples (94.72%), consisting of 164 in the age group of 18–30, 134 in 31–40, and 97 in 41-50. The age group between 18 and 50 accounts for a large proportion of all respondents. In terms of education level, a majority of respondents have a college (24.70%), Bachelor (44.36%), or Master's degree (23.50%), while those with a high school degree or below accounted for only 7.43%. With the expansion of university en-rollment in China and different universities offering different ways to improve their degrees, respondents have relatively high degrees. Regarding job categories, company employees, employees of government agencies and public institutions accounted for a high proportion of about 47.72%. Some of the interviewees were students (22.78%), as well as self-employed (8.87%), freelancer (7.91%), and other professionals (12.71%). Due to the COVID-19, income and employment levels generally declined, with the in-come ranges of ¥ 3,001–5,000 and ¥ 5,001–8,000 jointly accounting for about 52.52%. Not many earn more than ¥ 8,000 (21.34%), and not many earn less than ¥ 3,000 (26.14%). Therefore, we believe that the sample data is representative.

Table 1. Characteristics of the Participants.

Variables		Frequency	Percentage
Gender	Female	214	51.32%
Gender	Male	203	48.68%
Age	18~30	164	39.33%
	31~40	134	32.13%
	41~50	97	23.26%
	51~60	17	4.08%
	≥61	5	1.20%
Level of Education	High school graduate or less	31	7.43%
	Associate degree	103	24.70%
	Undergraduate	185	44.36%
	Postgraduate degree	98	23.50%
Work	Employees of government agencies and public institutions	94	22.54%
	Self-employed	37	8.87%
	Company employee	105	25.18%
	Freelancer	33	7.91%
	Student	95	22.78%
	Else	53	12.71%
Monthly Income	≤¥3000	109	26.14%
	¥3001-5000	114	27.34%
	¥5001-8000	105	25.18%
	¥8001-10000	46	11.03%
	>¥10001	43	10.31%

#Please also refer to Page 9-10 (Line 414) in the revised manuscript.

Point 7: Results

Explain all the values like what data mean or age of respondents means for this research?
The value of P is normally less than 0.001, its not =0.000. correct it throughout the manuscript.
Along with results, give detail about hypothesis acceptance and rejection.

Response 7: For your comments on the results, we have made the following responses.

As for your opinion that you need to explain the meaning of the data or explain the information of the interviewees, we think it is very meaningful and can make the data clearer to the readers. Therefore, we revised the interviewees' age, education background, job and income. This is shown below.

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 (94.72%), consisting of 164 in the age group of 18–30, 134 in 31–40, and 97 in 41-50. 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. Fewer respondents were over 51 years old, accounting for about 5.28% of the total respondents. In terms of education level, a majority of respondents have a college (24.70%), Bachelor’s (44.36%), or Mas-ter's degree (23.50%), while those with a high school degree or below accounted for only 7.43%. With the expansion of university enrollment in China and different uni-versities offering different ways to improve their degrees, respondents had relatively high degrees. Regarding job categories, company employees, employees of government agencies and public institutions accounted for a high proportion of respondents (47.72%). Some of the interviewees were students (22.78%), self-employed (8.87%), freelancers (7.91%), and other professionals (12.71%). Due to COVID-19, income and employment levels had generally declined, with the income ranges of ¥ 3,001–5,000 and ¥ 5,001–8,000 jointly accounting for about 52.52% of respondents. Not many earned more than ¥ 8,000 (21.34%), and not many earned less than ¥ 3,000 (26.14%). In brief, the samples were relatively ideal and suitable for analysis, laying a solid founda-tion for follow-up analysis.

#Please also refer to Page 9 (Line 394-413) in the revised manuscript.

Advice on p-values is professional and detailed. It is not appropriate to use P=0.000 in this paper, and we have modified it.

The EFA is an important method for testing the structural validity of an entire scale and eliminating unqualified items. In our EFA, the main factors were extracted by combining principal component analysis with the maximum variance method [118] (pp. 612-680), and the eigenvalues were required to be above 1. The Kaiser–Meyer–Olkin (KMO) value of the questionnaire was 0.886, and the significance level in the Bartlett test was p<0.000, suggesting that EFA was suitable. Tabachnick & Fidell sug-gested that, in EFA, only items with a factor loading value of above 0.4 are included [118]. 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.

#Please also refer to Page 10 (Line 422) in the revised manuscript.

Thank you for reminding us that we need to present all hypothetical proposals to accept or reject. Explicit display of hypothetical results increases the readability of the article. Table 5 shows the results of hypothesis testing for direct effects and Figure 2 shows the results of all hypotheses. We added the indirect effect test results in the last column of Table 6, as shown in Table 6.

Table 2. Mediating Effect Tests.

Path relationship	Point estimate	Product of coefficient		Bootstrapping 1000 times 95%				Results
		Product of coefficient		Bias-corrected		Percentile
		SE	Z	Lower	Upper	Lower	Upper
Indirect effects
H4: Social loneliness→ habitual smartphone behaviors→ tourist happiness	-0.037	0.016	-2.313	-0.076	-0.012	-0.071	-0.009	*Yes*
H6: Social loneliness→ smartphone communication→ tourist happiness	0.000	0.003	0.000	-0.008	0.006	-0.007	0.007	No
H7: Social loneliness→ habitual smartphone behaviors→ smartphone communication→ tourist happiness	-0.005	0.006	-0.833	-0.027	0.002	-0.020	0.004	No
H9: Social loneliness→ smart tourism applications→ tourist happiness	-0.018	0.012	-1.500	-0.046	0.002	-0.044	0.003	No
H10: Social loneliness→ habitual smartphone behaviors→ smart tourism applications→ tourist happiness	-0.015	0.007	-2.143	-0.037	-0.005	-0.033	-0.004	*Yes*
Direct effects
Social loneliness→ tourist happiness	-0.091	0.039	-2.333	-0.174	-0.018	-0.175	-0.021	*Yes*
Total effects
Social loneliness→ tourist happiness	-0.166	0.049	-3.388	-0.271	-0.074	-0.264	-0.071	*Yes*
Note: SE= standard error.

#Please also refer to Page 13-14 (Line 483-484) in the revised manuscript.

Point 8: Discussion

In the discussion section, before presenting results, the authors should provide detail of the study along with its importance.
Add literature support with your findings.
Add the implications in detail. Currently, this section is missing. Theoretical and practical implications should be provided separately and in detail.

Response 8: Three comments on the discussion could add to the completeness of the draft. Thank you very much for your suggestions on the discussion part. We have made corresponding modifications.

Thank you for reminding us of the need to add research importance before the discussion begins. This will make the discussion more logical. Therefore, we preface each discussion with a sentence about the importance of the discussion and add the value of the research results to the discussion. The modification result is shown as follows:

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 [19,117,126]. A study by Lee & Hyun focused on two direct effects to demonstrate that SL and user satisfaction are related in some way [19]. Patterson & Balderas-Cejudo and Karagöz & Ramkissoon found that SL is related to TH and advocated reducing SL through tourism activities [5,126]. 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 former involved no mediating effect test while the latter has performed mediating effect tests and obtained significant results. This study believes that different ways 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). Also, habitual smartphone behaviors, as an element of smartphones, are the overflow of daily behaviors and the involuntary behaviors in tourism activities. This paper also finds that SL affects TH through smartphones, which is consistent with Kamboj & Joshi's study [117]. Kamboj & Joshi discussed the influence of SL on TH through smartphone apps [117], while this study focuses on one aspect of SL that can influence TH through smartphone usage. This approach is different from Kamboj & Joshi's study. Some studies different from Kamboj & Joshi's study. Studies have demonstrated that SL affects smartphone usage [13,77] and have held discussions on the categories of smartphone usage[31,127]. However, few of them have taken smartphone usage as a mediation 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 [13,77,128,129] and a study by Lee & Hyun [19]. Consistent with studies by Bian & Leung, Enez Darcin et al., and Meng et al. [13,77,128], 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 & 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 & 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 [130,131]. 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 [132,133], 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 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 [32,134]. This study shows that SL affects habitual smartphone behaviors, which is the development of the 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 & Lu [18]. This may be because SL is caused by a lack of social networks or collective members [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. Also, 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 [18,117]. Consistent with Tan & Lu and Kamboj & Joshi, this study finds that SL is related to TH [18,117]. Also, SL affects the use of smartphone applications, which in turn affects TH. In other cases, existing studies have only suggested that tourism reduces SL [21,126,135], 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.

5.2.1. Discussions about Smartphone Communication

Not only is smartphone communication one of the most original functions of mobile phones, but 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 finding 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 finding 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 [35,136]. By contrast, this study suggests that there is no long period of free time to communicate or do other things in tourism (not during a stay at the hotel [137]), 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 & Anglim, Rotondi et al., and Twenge et al. [30,57,138]. The studies by Horwood & Anglim, Rotondi et al., and Twenge et al. also revealed that habitual smartphone behaviors affect subjective happiness [30,57,138]. This study finds that habitual smartphone behaviors are related to subjective happiness in tourism. This paper differs from the studies by Horwood & Anglim, Rotondi et al., and Twenge et al., however, in that this paper focuses on TH rather than happiness with life [30,57,138]. 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 [30,57,138]. 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. Also, smart tourism applications positively affect TH (0.290, ***), a finding which is consistent with the findings of existing studies [42,90,92,96]. 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 [91,139] 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 experience [34], virtual reality experience [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 & Lu [18]. Additionally, the relationship between SL and TH is also significant [18,117] when mediated by smartphone communication and smart tourism applications as two multiple mediating variables. Moreover, the relationship is inverse in each case.

#Please also refer to Page 15 (Line 496-497,507-509, 525-527, 541-543), Page 16 (Line 550-552, 561-563, 570-572, 585-589), and Page 17 (Line 607-610, 625-628) in the revised manuscript.

The addition of a discussion section of the findings to the literature is an insightful suggestion. The addition of literature support can make the theoretical basis of part of the discussion more sufficient and clarify the similarities and differences with previous studies. We added literature support from two aspects. First, add references to the existing literature in this paper, and keep relevant to the use of literature in the preface and literature review. Secondly, new literature is added. More than 20 latest research results are added to the whole draft. The specific additions are in the draft.

It is wise to remind us to write implications in detail. The implication section of the draft is not clear enough. Therefore, we made the corresponding modification. Write the theoretical and practical implications separately. Two aspects of the theoretical implications are discussed: the direct and indirect relationship between social loneliness and travel happiness; and the implications for smartphone usage as a mediator variable. Practical implications are also divided into two aspects. On the one hand, the implications of scenic spots project development (increase group experience project). On the other hand, there are implications of destination marketing (focusing on the existence of social loneliness, and targeted marketing). The following changes have been made to 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. Further, SL mainly exists in daily life [5,116], 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. Further, 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 accommodation, 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. On 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 of 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 scenic spot check-in activities. 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, make full use 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. Avoiding words, pictures and videos should be used that may give tourists the illusion of “Second Life”. Such a marketing method is more likely to make potential tourists more willing to travel to the marketed tourist destination.

#Please also refer to Page 18 (Line 689-673, 677-699), Page 19 (Line 701-703, 707-709, 713-720) in the revised manuscript.

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Author Response File: Author Response.pdf

Reviewer 2 Report

Dear authors, the paper is focusing on a very relevant topic. The structure and flow of the paper is adequate as well as the literature used. The theoretical background is also matching the content of the paper and the study. Unfortunately the research process of your study is using a convenient sample. Although you name it snowball sampling, it is still necessary to name it convenience sample as it contains incoherent participants from social media channels of you the authors as well as students from the university. Therefore the results of this paper can in my perspective unfortunately just be pre-results used to conduct further research. As the study was also conducted in a very limited geographical and cultural space this has to be considered when interpreting the results. Therefore I would reject the paper at this very moment but reconsider it as a research note with preliminary results where additional data is collected that is more representative of the population. Despite the COVID situation, many researchers have conducted research online collecting data using random samples that (proven by literature) can in fact be seen as representative of the population.

Furthermore I would suggest you to reformat the tables to be more readable (APA format) and work on some mistakes and typos:
- Page 4, Line 162 & 191 & 210, Error in Source link, typos

- Line 204 missing space

Author Response

Response to Reviewer 2 Comments

Thank you very much for your careful and professional comments on our manuscript. With a concerted effort to address all of the valuable suggestions, we have revised the paper mainly: 1) the number of questionnaires was increased, and the questionnaires were processed and analyzed again; 2) changeed source link errors and increased a space. The green, italicized and bold contents are our changes to the manuscript, and the text in blue font marks the position of the changes in the draft. We believe that those changes have enhanced the manuscript’s quality and contribution, and we hope you agree.

Point 1: Dear authors, the paper is focusing on a very relevant topic. The structure and flow of the paper is adequate as well as the literature used. The theoretical background is also matching the content of the paper and the study. Unfortunately the research process of your study is using a convenient sample. Although you name it snowball sampling, it is still necessary to name it convenience sample as it contains incoherent participants from social media channels of you the authors as well as students from the university. Therefore the results of this paper can in my perspective unfortunately just be pre-results used to conduct further research. As the study was also conducted in a very limited geographical and cultural space this has to be considered when interpreting the results. Therefore I would reject the paper at this very moment but reconsider it as a research note with preliminary results where additional data is collected that is more representative of the population. Despite the COVID situation, many researchers have conducted research online collecting data using random samples that (proven by literature) can in fact be seen as representative of the population.

Response 1: Thank you very much for your approval of our draft structure, literature, and research conclusions. We spent a lot of time and effort on the draft. Your questions about our sample are serious. There are some flaws in the way we sample, but there are several reasons why we continue to collect data this way.

Frist, in the process of questionnaire distribution, we should pay attention to the group characteristics of respondents, such as gender, age, and occupation, and cover as many groups as possible to increase sample representativeness (shown as Table 1, results after adding questionnaire data and reprocessing). The gender ratio is 51.32% (female) to 48.68% (male), and the gender ratio is balanced. In terms of age distribution, the age group of 18–50 contained 395 samples (94.72%), consisting of 164 in the age group of 18–30, 134 in 31–40, and 97 in 41-50. 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. Fewer respondents were over 51 years old, accounting for about 5.28% of the total respondents. In terms of education level, a majority of respondents have a college (24.70%), Bachelor (44.36%), or Master's degree (23.50%), while those with a high school degree or below accounted for only 7.43%. With the expansion of university enrollment in China and different universities offering different ways to improve their degrees, respondents have relatively high degrees. Regarding job categories, company employees, employees of government agencies and public institutions accounted for a high proportion of about 47.72%. Some of the interviewees were students (22.78%), as well as self-employed (8.87%), freelancer (7.91%), and other professionals (12.71%). Due to the COVID-19, income and employment levels generally declined, with the income ranges of ¥ 3,001–5,000 and ¥ 5,001–8,000 jointly accounting for about 52.52%. Not many earn more than ¥ 8,000 (21.34%), and not many earn less than ¥ 3,000 (26.14%). In brief, the samples were relatively ideal and suitable for analysis, laying a solid foundation for follow-up analysis.

Table 1 Characteristics of the Participants.

Variables		Frequency	Percentage
Gender	Female	*214*	*51.32%*
Gender	Male	*203*	*48.68%*
Age	18~30	*164*	*39.33%*
	31~40	*134*	*32.13%*
	41~50	97	*23.26%*
	51~60	17	*4.08%*
	≥61	5	*1.20%*
Level of Education	High school graduate or less	31	*7.43%*
	Associate degree	*103*	*24.70%*
	Undergraduate	*185*	*44.36%*
	Postgraduate degree	98	*23.50%*
Work	Employees of government agencies and public institutions	94	*22.54%*
	Self-employed	37	*8.87%*
	Company employee	*105*	*25.18%*
	Freelancer	33	*7.91%*
	Student	95	*22.78%*
	Else	53	*12.71%*
Monthly Income	≤¥3000	*109*	*26.14%*
	¥3001-5000	*114*	*27.34%*
	¥5001-8000	*105*	*25.18%*
	¥8001-10000	46	*11.03%*
	>¥10001	43	*10.31%*

#Please also refer to Page 9-10 (Line 414) in the revised manuscript.

Second, the sampled addresses covered many provinces and cities in China, which is representative to a certain extent. Third, this study discussed the relationship between social loneliness, smartphone usage, and tourism happiness. Cultural and regional differences were not the focus of the study, so, these variables were not controlled for. Today, China is developing rapidly, with 96% smartphone ownership. Therefore, mobile phone ownership is not an important factor affecting this study.

Most importantly, we increased the number of questionnaires and re-analyzed the data. Revised research results, discussion, conclusion, and so on. Compared with the original data, the research results did not change greatly. Therefore, we believe that the data in this paper are representative to a certain extent. The following is an introduction to the data processing process. When we analyzed the data, there were only 511 original questionnaires. Now when we enter the questionnaire star, we find 541 questionnaires. As a result, the original questionnaire was increased. Based on 541 original questionnaires, we shorten the answering time for eliminating invalid questionnaires. In the beginning, we found that it took 48 seconds to complete the questionnaire. After the retest, we found that 40 seconds could complete the questionnaire. Therefore, we eliminated the answers with the same answers and less than 40 seconds and obtained 417 valid questionnaires. The 417 valid questionnaires meet two criteria: first, the number of valid questionnaires is 10 times of the questions (24*10=240); Secondly, the statistical significance of 300 valid questionnaires was reached.

Point 2: Furthermore I would suggest you to reformat the tables to be more readable (APA format) and work on some mistakes and typos:

- Page 4, Line 162 & 191 & 210, Error in Source link, typos

- Line 204 missing space

Response 2: Using APA citation formats does have some advantages, such as seeing the author and year of the literature directly. But the template provided by Sustainability suggests using the reference format used in this draft. After our discussion, we decided not to change the format of the reference. If you insist that APA format is more conducive to reading, please let us know in the next submission comments.

Thank you very much for reminding us of the errors in the details of the draft. .There is indeed a formatting error in the submitted version. We have made the correction.

#Please also refer to Page 5 (Line 233), Page 6 (Line 297), and Page 7 (Line 343) in the revised manuscript.

Thank you very much for pointing out the lack of space on draft line 204. We have overhauled the paper and the problem of missing spaces in line 204 has been solved. Meanwhile, we also checked the full text to reduce formatting errors.

Reference:

Lancioni, G.E.; Singh, N.N.; O'Reilly, M.F.; Sigafoos, J.; Alberti, G.; Perilli, V.; Chiariello, V.; Buono, S. An Upgraded Smartphone-Based Program for Leisure and Communication of People With Intellectual and Other Disabilities. Front Public Health 2018, 6, 234. DOI: 10.3389/fpubh.2018.00234.
Verduyn, P.; Schulte-Strathaus, J.C.C.; Kross, E.; Hülsheger, U.R. When do smartphones displace face-to-face interactions and what to do about it? Comput. Hum. Behav. 2021, 114, 106550. DOI: 10.1016/j.chb.2020.106550.
Lapierre, M.A.; Custer, B.E. Testing relationships between smartphone engagement, romantic partner communication, and relationship satisfaction. Mob Media Commun 2021, 9, 155-176. DOI: 10.1177/2050157920935163.
Bae, S. The relationship between smartphone use for communication, social capital, and subjective well-being in Korean adolescents: Verification using multiple latent growth modeling. Child. Youth Serv. Rev. 2019, 96, 93-99. DOI: 10.1016/j.childyouth.2018.11.032.
Ayar, D.; Gürkan, K.P. The Effect of Nursing Students' Smartphone Addiction and Phubbing Behaviors on Communication Skill. Computers, informatics, nursing 2021, 40, 230. DOI:
Cerit, B.; Çıtak Bilgin, N.; Ak, B. Relationship between smartphone addiction of nursing department students and their communication skills. Contemporary nurse : a journal for the Australian nursing profession 2018, 54, 532-542. DOI: 10.1080/10376178.2018.1448291.
Cho, J. Roles of Smartphone App Use in Improving Social Capital and Reducing Social Isolation. Cyberpsychology, behavior and social networking 2015, 18, 350. DOI:
Kim, J. Smartphone-mediated communication vs. face-to-face interaction: Two routes to social support and problematic use of smartphone. Comput. Hum. Behav. 2017, 67, 282-291. DOI: 10.1016/j.chb.2016.11.004.
Li, L.; Lin, T.T.C. Examining how dependence on smartphones at work relates to Chinese employees’ workplace social capital, job performance, and smartphone addiction. Inform Dev 2018, 34, 489-503. DOI: 10.1177/0266666917721735.

Author Response File: Author Response.pdf

Reviewer 3 Report

1. To my opinion, it is more compelling to frame the research with the problem of smartphone use rather than social loneliness. Social loneliness is a latent (unobservable) construct, while the smartphone use is more observable (whether the tourists use smartphone as a habit or for functional purposes). Moreover, intuitively, the negative effect of social loneliness on total happiness is predicted. Thus, social loneliness is more appropriate to be placed as the mediating variable rather than independent variable. Please consider my suggestion.

2. Please provide some statistics that can demonstrate the substantial use of smartphone during the tourism activities.

3. The use of abbreviations throughout the article is excessive, please minimize the use of abbreviations only to terminologies that consist of more than three words.

4. There is a typo in line 57, the sentence is not readable.

5. Reinforcement theory doesn’t seem capable to explain the phenomenon being studied. This is also related to the position of smartphone usage behaviour as the mediating variable and the SL as the independent variable. As smartphone use affects social loneliness (as stated in line 147), it can be concluded that smartphone usage affects people psychological state. Probably a theory such as social cognitive theory or SOR (stimulus organism response) would be more suitable.

6. Please separate the hypotheses development section for each hypothesis. I am not convinced with the current hypotheses development section.

7. Collecting the data via online and offline is not well justified. Why only educators and students were collected from offline.

8. As 167 samples could not be used. This rate is too high, the validity of the data is questionable.

9. The sampling criteria is not accurate. What if the sample was never been engaged in tourism activities recently before? What can justify the context of the study?

10. Related to my feedback earlier (no.1), the practical implication of this study is rather weak. How can practitioners reduce tourists’ social loneliness?

Author Response

Response to Reviewer 3 Comments

Thank you for your positive comments and constructive data suggestions on our manuscript. With a concerted effort to address all of the valuable suggestions, we have revised the paper mainly: 1) the questionnaire data were reprocessed; 2) the study design, results, discussion, conclusions, implications, limitations, and prospects, abstact, and introduction were modified based on the results of the data analysis; 3)the number of abbreviations has been reduced and typos corrected.. The green, italicized and bold contents are our changes to the manuscript, and the text in blue font marks the position of the changes in the draft. We believe that those changes have enhanced the manuscript’s quality and contribution, and we hope you agree.

Point 1: To my opinion, it is more compelling to frame the research with the problem of smartphone use rather than social loneliness. Social loneliness is a latent (unobservable) construct, while the smartphone use is more observable (whether the tourists use smartphone as a habit or for functional purposes). Moreover, intuitively, the negative effect of social loneliness on total happiness is predicted. Thus, social loneliness is more appropriate to be placed as the mediating variable rather than independent variable. Please consider my suggestion.

Response 1: Yours comment coincides with ours. We designed the questionnaire with smartphone use as the independent variable original. Meanwhile, our articles on smartphone use and travel happiness have been successfully onlined. We choose to discuss social loneliness as the independent variable, smartphone usage as the intermediary variable, and tourism happiness as the dependent variable, because we find this is interesting in the process of discussion. And few studies on the relationship between negative emotions (social loneliness) and positive emotions (tourism happiness). Meanwhile, social loneliness can be measured using a variety of questionnaires, and previous studies have measured it using a variety of questions.

Point 2: Please provide some statistics that can demonstrate the substantial use of smartphone during the tourism activities.

Response 2: Adding statistics on smartphone usage in tourism activities is a suggestion to add credibility to the article. After careful consideration, we think it is more convincing to increase the smartphone ownership rate and hotel online booking data. Therefore, we added the corresponding data in the draft.

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 Chinese 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 search ability in smart tourism applications is the application of daily information search ability 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 yet to explore 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.

#Please also refer to Page 2 (Line 83-84 )in the revised manuscript.

Point 3: The use of abbreviations throughout the article is excessive, please minimize the use of abbreviations only to terminologies that consist of more than three words.

Response 3: Reducing abbreviations increases the readability of the text, which is a thoughtful suggestion. We found no abbreviations that were larger than three words. But, there are a lot of abbreviations, so we've replaced everything except social loneliness (SL) and tourism happiness (TH) with their full names. The acronyms for the three main aspects of smartphone usage -- habitual smartphone behavior, smart tourism applications, and smartphone communication -- were replaced with full names. These abbreviations have been modified in the full text, please refer to the draft. We also dropped the acronym for exploratory factor analysis, as shown below:

4.2. Exploratory Factor Analysis

Table 2. Exploratory Factor Analysis.

#Please also refer to Page 10 (Line 416, 429) in the revised manuscript.

Point 4: There is a typo in line 57, the sentence is not readable.

Response 4: Thank you very much for pointing out the spelling mistake. It is a very detailed suggestion. In the process of revision, a problem was found in the literature reference, which has been corrected. By the way, we also invited the language editing company to double-check the draft for language and grammar problems to reduce such errors.

Different scholars have classified smartphone usage from different perspectives. Taking adolescents as the target group, smartphone usage is classified into four categories, namely (1) study, (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 & Lu classified smartphone usage into three categories: functional usage, entertainment usage, and communicative usage [18]. Based on the classification of smartphone usage by Tan & 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 [35-37]. The third is habitual smartphone behaviors [38-40]. Tan & 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, smartphone communication.

#Please also refer to Page 2 (Line 72-74) in the revised manuscript.

Point 5: Reinforcement theory doesn’t seem capable to explain the phenomenon being studied. This is also related to the position of smartphone usage behaviour as the mediating variable and the SL as the independent variable. As smartphone use affects social loneliness (as stated in line 147), it can be concluded that smartphone usage affects people psychological state. Probably a theory such as social cognitive theory or SOR (stimulus organism response) would be more suitable.

Response 5: The suggestion of theory is an instructive one. We choose the theory of reinforcement because we think the theory of positive reinforcement and negative reinforcement combined with the actual research of this paper. However, your suggestion gives us an idea. We believe that both social cognition theory and SOR theory are suitable for this study. After consideration, we choose SOR theory and have made corresponding modifications to the theoretical basis, research conclusion, abstract, and introduction.

The theoretical background of this study has been modified.

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 [46-48]. 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. Stimulus is the emotion generated during tourist activities. Organism is smartphone usage and is a mediator variable; this usage is it's how SL reacts with TH. Response is TH, the result variable, and the psychological state generated by SL after the smartphone usage. The smartphone usage in this study was organism, which 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.

#Please also refer to Page 3 (Line 121-139) in the revised manuscript.

The conclusions of this study were modified.

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:

#Please also refer to Page 17 (Line 647) in the revised manuscript.

The abstract has been revised.

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 social loneliness on tourist happiness, but 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.

#Please also refer to Page 1 (Line 15-16) in the revised manuscript.

The introduction was revised.

Relying on stimulus-organism-response theory, this study discusses three issues by taking SL as the independent variable, smartphone usage (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.

#Please also refer to Page 2 (Line 109-111) in the revised manuscript.

Point 6: Please separate the hypotheses development section for each hypothesis. I am not convinced with the current hypotheses development section.

Response 6: Thank you for your constructive comments on the hypothesis. A separate paragraph for each hypothesis in this study does make the hypothesis more reasonable. Therefore, we write a separate paragraph for each hypothesis and modify the result as follows:

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 habitual smartphone behaviors of 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]. Also, 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 don't 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 look 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 better able to search for information. The ability of habitual smartphone behaviors’ formation 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 they occur (such as the period before sleep [76]) and reasons 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]. Also, 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 show 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).

The influence of smartphone communication on the relationship between SL and TH: an increase in SL may lead to an increase in smartphone usage [86], and smartphone communication is used as 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 & 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 inFigure 1).

H6: Smartphone communication plays a mediating role in the relationship between social loneliness (SL) and tourist happiness (TH).

The influence of habitual smartphone behaviors and smartphone communication 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 of SL to 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 involve 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]. In addition, SL affects smartphone usage [13,88] and weakens TH [5]. Then, 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 as smartphone communication and smart tourism applications. 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 families 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 no correlation between smartphone communication and smart tourism applications and thus makes no assumption about their relationship. Also, 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 [99-101]; from elderly people [20,102-104] 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: the degree of 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 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 influence of SL on the 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. 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 SL a negative impact on TH in tourism activities [116,117]. 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 on 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 smart phones [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.

#Please also refer to Page 4 (Line 158-200), Page 5 (Line 232-253), Page 6-8 (Line 254-344)in the revised manuscript.

Point 7: Collecting the data via online and offline is not well justified. Why only educators and students were collected from offline.

Response 7: You comments are professional about the source of data. There is something wrong with distinguishing between online and offline approaches to collecting questionnaires. In 2021, the COVID-19 situation in China will be replicated, with strict prevention and control policies everywhere, and those with close contact and sub-close contact will need to be isolated. Therefore, it is risky to collect data in scenic spots or elsewhere. It is safer for teachers and students to collect questionnaires offline. At the same time, college students have the ability to tourism consumption and are interested in new things (such as smart tourism applications). So, they are relatively effective survey objects. In the process of data collection, we pay attention to the group representation of the sample and the balance of gender, age, job, and income of the respondents. After increasing the number of original questionnaires and re-analyzing them, the personal characteristics of the interviewees (Table 1) are more reasonable. Therefore, we believe that the data collected online and offline, as well as by teachers and students, is reasonable and representative.

Table 1. Characteristics of the Participants.

Variables		Frequency	Percentage
Gender	Female	*214*	*51.32%*
Gender	Male	*203*	*48.68%*
Age	18~30	*164*	*39.33%*
	31~40	*134*	*32.13%*
	41~50	97	*23.26%*
	51~60	17	*4.08%*
	≥61	5	*1.20%*
Level of Education	High school graduate or less	31	*7.43%*
	Associate degree	*103*	*24.70%*
	Undergraduate	*185*	*44.36%*
	Postgraduate degree	98	*23.50%*
Work	Employees of government agencies and public institutions	94	*22.54%*
	Self-employed	37	*8.87%*
	Company employee	*105*	*25.18%*
	Freelancer	33	*7.91%*
	Student	95	*22.78%*
	Else	53	*12.71%*
Monthly Income	≤¥3000	*109*	*26.14%*
	¥3001-5000	*114*	*27.34%*
	¥5001-8000	*105*	*25.18%*
	¥8001-10000	46	*11.03%*
	>¥10001	43	*10.31%*

#Please also refer to Page 9-10 (Line 414) in the revised manuscript.

Point 8: As 167 samples could not be used. This rate is too high, the validity of the data is questionable.

Response 8: Your questioning of invalid samples once again proves that you are a professional and meticulous scholar. We only had 511 original data when we analyzed the questionnaires. After excluding 167 questionnaires with the same answers and less than 48 seconds, we got 344 valid questionnaires. The control value of 48 seconds is because we chose the minimum time for the testers to complete the questionnaire. Later, we tested again and found that respondents could complete the questionnaire in 40 seconds. The respondents' ability to read and complete the questions improved. At the same time, when we punched the link of the questionnaire star, we found that the number of questionnaires increased to 541. Therefore, we took 541 as the original questionnaires for analysis. 417 valid questionnaires were obtained after removing the questionnaires that took less than 40 seconds to answer and all the answers were the same. The analysis was based on 417 valid questionnaires. According to the changes to the data, we also made corresponding modifications to the draft.

In total, 541 questionnaires were collected in this round. Invalid questionnaires were eliminated according to two principles. First, a questionnaire in which the same answer was given to all questions was rejected. This is because a respondent who gave the same answer to all questions was not taking the questionnaire survey seriously. Second, a questionnaire filled out too quickly, e.g., in less than 40 seconds, was rejected. The reason is that filling out the questionnaire within less than 40 seconds meant that the respondent did not read the questions carefully, and the answers given by the respondent could not 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%.

#Please also refer to Page 9 (Line 380, 383-388) in the revised manuscript.

Point 9: The sampling criteria is not accurate. What if the sample was never been engaged in tourism activities recently before? What can justify the context of the study?

Response 9: The suggestion of a background study is insightful. Clear background means a clear premise. In order to obtain more accurate survey data, we should limit the travel to one trip in one or two years. However, we still feel that the background is sound. To obtain the questionnaire of respondents with a clear research background, we also did corresponding work. First, we prompted the public in the questionnaire: "Please answer questions according to a recent/impressive travel". Second, generally speaking, if the respondent finds that the content of the questionnaire is a question that she/he cannot answer, she/he will give up answering the questionnaire. Third, we believe that the most influential trip is also in line with our research requirements. On the one hand, due to the impact of COVID-19, the Chinese government strictly controls residents' travel, and it is normal for them not to travel in 2020-2021. So, stretch out the last time frame. On the other hand, China's smart tourism development and smartphone use have developed to a better degree. Online bookings for hotels reached 90%, railway 94.6 %, and air tickets 81 %. This level of smart tourism development is not the result of one day or two days or one year or two years. Therefore, we believe that the most impressive travel experience is also in line with our research scope. As described in the review, we chose the most recent or impressive trip to fit the research context and diversify the interviewees.

Point 10: Related to my feedback earlier (no.1), the practical implication of this study is rather weak. How can practitioners reduce tourists’ social loneliness?

Response 10: It is an inspiring suggestion for us. Let us think about the original intention and purpose of this study, and what kind of implications it can bring to practical activities. After thinking and analyzing, we believe that this study has two practical implications. On the one hand, it can make scenic spots pay attention to more projects involving group participation when developing new projects, so as to reduce the social loneliness during the travel. On the other hand, in the process of destination marketing, we should pay attention to the phenomenon that potential tourists may have social loneliness and social loneliness in the process of tourism. We should pay attention to the propaganda of beautiful, united, and harmonious pictures, videos, and texts to enhance the willingness of potential tourists to travel. Meanwhile, we have revised the practical implications of the draft accordingly.

6.2.2 Practical Implications

#Please also refer to Page 19 (Line 701-703, 707-709, 713-720) in the revised manuscript.

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Tracy Lu, Y.; Wei, W.; Wu, L.; Swanson, J.; Getz, D. The Role of Meeting Size in Attendees' Psychological Response and Consequence Behavior: A Social Anxiety Approach. Event Manage. 2022, 26, 1623-1635. DOI: 10.3727/152599522X16419948694784.
Casper, J.M.; Bocarro, J.N.; Lothary, A.F. An examination of pickleball participation, social connections, and psychological well-being among seniors during the COVID-19 pandemic. World Leisure J. 2021, 63, 330-346. DOI: 10.1080/16078055.2021.1957708.
Karagöz, D.; Ramkissoon, H. Loneliness, travel nostalgia, subjective well-being and prevention regulatory focus: a moderated mediation model analysis. Curr Issues Tour 2023, ahead-of-print, 1-17. DOI: 10.1080/13683500.2023.2175201.
Kamboj, S.; Joshi, R. Examining the factors influencing smartphone apps use at tourism destinations: a UTAUT model perspective. Int J. Tour Cities 2021, 7, 135-157. DOI: 10.1108/IJTC-05-2020-0094.

Author Response File: Author Response.pdf

Reviewer 4 Report

Dear Author(s),

your study is interesting and well strucutured.

The purpose of the study is clearly stated and the analyses are carried out coherently with the research questions.

Yet, I have several concerns about the questionnaires and the valid samples.

Questions are too few as well as 344 valid questionnaires could not provide generalizable results. The snowball sampling does not ensure that the cohort of respondents is balanced. The Author(s) shoud discuss more these two aspects and provide evidence that their approach is suitable for the study proposed.

That's why I am asking for a major revision.

Author Response

Response to Reviewer 4 Comments

Thank you for your positive comments and constructive data suggestions on our manuscript. With a concerted effort to address all of the valuable suggestions, we have revised the paper mainly: 1) the questionnaire data were reprocessed; 2) the study design, results, discussion, conclusions, implications, limitations, and prospects, abstact, and introduction were modified based on the results of the data analysis. The green, italicized and bold contents are our changes to the manuscript, and the text in blue font marks the position of the changes in the draft. We believe that those changes have enhanced the manuscript’s quality and contribution, and we hope you agree.

Point: Dear Author(s),

your study is interesting and well strucutured.

The purpose of the study is clearly stated and the analyses are carried out coherently with the research questions.

Yet, I have several concerns about the questionnaires and the valid samples.

That's why I am asking for a major revision.

Response: Thank you for affirming the study topic and structure. We also spent a lot of time and energy discussing research questions, and thank you for your recognition of these content. Your attention to the sample size and validity of the questionnaire shows that you have a very rigorous attitude towards academia. We also think that further processing of the questionnaire data is needed to increase the persuasiveness of the research results. At that time, respondents with insufficient experience in processing questionnaire data may have errors in choosing the time to fill in the questionnaire. It takes a minimum of 48 seconds to complete the questionnaire. Each question takes about 2 seconds. With the increasing use of mobile phones, the time to complete the questionnaire is shorter, about 1.5 seconds to complete a question. Therefore, the entire questionnaire took about 40 seconds to complete. All questionnaires with the same answers and less than 40 seconds were excluded. The questionnaire data were processed again. In addition, we now open the questionnaire and find that the total number of questionnaires has increased from 511 to 541. Our analysis was based on 541 raw data, and 417 valid questionnaires were obtained. So, we made corresponding modifications to the research design, research results, discussion, and conclusions (evisions to implications, limitations, and prospects, abstact, and introduction in the draft).

Modification of the research design

3.2. Data Collection

#Please also refer to Page 9 (Line 380, 383-388) in the revised manuscript.

Revisions to the results of the study

4.1. Descriptive Statistics

Table 1 Characteristics of the Participants.

Variables		Frequency	Percentage
Gender	Female	*214*	*51.32%*
Gender	Male	*203*	*48.68%*
Age	18~30	*164*	*39.33%*
	31~40	*134*	*32.13%*
	41~50	97	*23.26%*
	51~60	17	*4.08%*
	≥61	5	*1.20%*
Level of Education	High school graduate or less	31	*7.43%*
	Associate degree	*103*	*24.70%*
	Undergraduate	*185*	*44.36%*
	Postgraduate degree	98	*23.50%*
Work	Employees of government agencies and public institutions	94	*22.54%*
	Self-employed	37	*8.87%*
	Company employee	*105*	*25.18%*
	Freelancer	33	*7.91%*
	Student	95	*22.78%*
	Else	53	*12.71%*
Monthly Income	≤¥3000	*109*	*26.14%*
	¥3001-5000	*114*	*27.34%*
	¥5001-8000	*105*	*25.18%*
	¥8001-10000	46	*11.03%*
	>¥10001	43	*10.31%*

#Please also refer to Page 9-10 (Line 394-414) in the revised manuscript.

4.2. Exploratory Factor Analysis

The 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 [118] (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 & Fidell suggested that, in exploratory factor analysis, only items with a factor loading value of above 0.4 are included [118]. 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.

Table 2. Exploratory factor analysis.

Items	Factors	Factor loading	Characteristic root	Cumulative explained variance (%)
*Smart tourism applications*	S1	*0.832*	*3.918*	*20.62%*
	S2	*0.838*
	S3	*0.856*
	S4	*0.712*
*Smartphone communication*	S1	*0.825*	*2.995*	*15.76%*
	S2	*0.798*
	S3	*0.832*
*Habitual smartphone behaviors*	H1	*0.822*	*2.835*	*14.92%*
	H2	*0.833*
	H3	*0.843*
	H4	*0.876*
	H5	*0.754*
*Social loneliness*	*SL1*	*0.783*	*2.346*	*12.35%*
	*SL2*	*0.820*
	*SL3*	*0.838*
	*SL4*	*0.892*
*Tourist happiness*	*TH1*	*0.833*	*2.324*	*12.23%*
	*TH2*	*0.827*
	*TH3*	*0.837*
*Notes: 1) Total variance explained: 75.88%. Extraction method: principal component analysis.* 2) Rotation method: oblimin with Kaiser Normalization; rotation converged in five iterations.

#Please also refer to Page 10 (Line 420-421, 427-428) in the revised manuscript.

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 [119] (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 [120,121], and the average variance extracted (AVE) values all exceeded 0.5, with all standardized coefficients being above 0.5 [122]. 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 Tables 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 [123].

Table 3 Reliability and Validity Tests of the Scale.

Items	Factors	Unstd.	S.E.	Z-Value	P	Std.	Cronbach's α	CR	AVE
*Smart tourism* *applications*	S1	*1.000*	——	——	——	*0.801*	*0.874*	*0.878*	*0.646*
	S2	*1.078*	*0.056*	*19.241*	*******	*0.854*
	S3	*1.085*	*0.055*	*19.894*	*******	*0.883*
	S4	*0.823*	*0.059*	*13.973*	*******	*0.659*
*Smartphone* *communication*	S1	*1.000*	——	——		*0.719*	*0.852*	*0.859*	*0.673*
	S2	*1.061*	*0.063*	*16.888*	*******	*0.937*
	S3	*1.050*	*0.068*	*15.500*	*******	*0.790*
*Habitual* *smartphone* *behaviors*	H1	*1.000*	——	——		*0.905*	*0.923*	*0.928*	*0.720*
	H2	*0.990*	*0.035*	*28.260*	*******	*0.897*
	H3	*1.055*	*0.050*	*20.924*	*******	*0.778*
	H4	*1.094*	*0.039*	*28.265*	*******	*0.897*
	H5	*0.978*	*0.049*	*19.767*	*******	*0.753*
*Social* *loneliness*	S1	*1.000*	——	——		*0.677*	*0.855*	*0.860*	*0.607*
	S2	*1.149*	*0.086*	*13.375*	*******	*0.745*
	S3	*1.090*	*0.078*	*13.901*	*******	*0.780*
	S4	*1.244*	*0.083*	*15.062*	*******	*0.899*
*Tourist* *happiness*	T1	*1.000*	——	——		*0.930*	*0.858*	*0.870*	*0.692*
	T2	*0.845*	*0.042*	*20.314*	*******	*0.821*
	T3	*0.953*	*0.055*	*17.419*	*******	*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 4 Results of the Discriminant Validity Test.

	*Smart tourism applications*	*Smartphone communication*	*Habitual smartphone behaviors*	*Social loneliness*	*Tourist happiness*
*Smart tourism applications*	*0.804*
*Smartphone communication*	*0.428*	*0.820*
*Habitual Smartphone behaviors*	*0.469*	*0.629*	*0.849*
*Social loneliness*	*-0.130*	*-0.082*	*-0.129*	*0.779*
*Tourist* *happiness*	*0.483*	*0.395*	*0.512*	*-0.188*	*0.832*
Note: The bold and italics figures in the diagonal represent the square root of the average variances extracted (AVE).

#Please also refer to Page 11-12 (Line 433-440, 447-449) in the revised manuscript.

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 [119]. 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 affects 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 hypotheses numbers indicate that the hypotheses concerned are true, while the dotted lines and non-bold hypothesis numbers mean that the hypotheses concerned are false.

Table 5. Results of SEM.

Hypothesis	Path			S.E.	Z-Value	P	Std.	Results
H1	*Habitual smartphone behaviors*	→	*Smartphone communication*	*0.059*	*11.381*	*******	*0.634*	*Yes*
H2	*Habitual smartphone behaviors*	→	*Tourist happiness*	*0.087*	*4.831*	*******	*0.324*	*Yes*
H3	*Habitual smartphone behaviors*	→	*Smart tourism applications*	*0.064*	*8.992*	*******	*0.468*	*Yes*
H5	*Smartphone communication*	→	*Tourist happiness*	*0.075*	*1.013*	*0.311*	*0.062*	No
H8	*Smart tourism applications*	→	*Tourist happiness*	*0.057*	*5.327*	*******	*0.290*	*Yes*
H11	*Social* *loneliness*	→	*Habitual smartphone behaviors*	*0.037*	*-2.400*	*	*-0.004*	*Yes*
H12	*Social* *loneliness*	→	*Smartphone communication*	*0.032*	*-0.020*	*0.984*	*-0.001*	No
H13	*Social* *loneliness*	→	*Tourist happiness*	*0.041*	*-2.211*	*	*-0.103*	*Yes*
H14	*Social* *loneliness*	→	*Smart tourism applications*	*0.042*	*-1.386*	*0.166*	*-0.069*	No
Notes: 1) S.E.=standard error; Std.=standardized coefficient; CR=composite reliability, and AVE=average variance extracted. 2) **P<0.001, P<0.05.

#Please also refer to Page 12-13 (Line 454-446,470-471) in the revised manuscript.

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 [124]. Following the advice of Hayes [125], the test under a bootstrap sample size of 1,000 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.

Table 6. Mediating Effect Tests.

Path relationship	Point estimate	Product of coefficient		Bootstrapping 1000 times 95%				Results
		Product of coefficient		Bias-corrected		Percentile
		SE	Z	Lower	Upper	Lower	Upper
Indirect effects
*H4: Social loneliness* *→ habitual smartphone behaviors→ tourist happiness***	*-0.037*	*0.016*	*-2.313*	*-0.076*	*-0.012*	*-0.071*	*-0.009*	*Yes*
*H6: Social loneliness→ smartphone communication→ tourist happiness*	*0.000*	*0.003*	*0.000*	*-0.008*	*0.006*	*-0.007*	*0.007*	No
*H7: Social loneliness→ habitual smartphone behaviors→ smartphone communication→ tourist happiness***	*-0.005*	*0.006*	*-0.833*	*-0.027*	*0.002*	*-0.020*	*0.004*	No
*H9: Social loneliness→ smart tourism applications→ tourist happiness*	*-0.018*	*0.012*	*-1.500*	*-0.046*	*0.002*	*-0.044*	*0.003*	No
*H10: Social loneliness→ habitual smartphone behaviors→smart tourism applications→ tourist happiness***	*-0.015*	*0.007*	*-2.143*	*-0.037*	*-0.005*	*-0.033*	*-0.004*	*Yes*
*Direct effects*
*Social loneliness→ tourist happiness***	*-0.091*	*0.039*	*-2.333*	*-0.174*	*-0.018*	*-0.175*	*-0.021*	*Yes*
*Total effects*
*Social loneliness→ tourist happiness***	*-0.166*	*0.049*	*-3.388*	*-0.271*	*-0.074*	*-0.264*	*-0.071*	*Yes*
Note: SE= standard error.

Figure 2. Structural model assessment (Note: ***p <0.001, *p <0.05).

#Please also refer to Page 13-14 (Line 478-484) in the revised manuscript.

Revisions to disscutions

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

The discussion of the relationship between SL and other variables is also divided into two parts. The first part is the indirect effect of SL and TH. The second part discusses the direct relationship between 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 [19,117,126]. A study by Lee & Hyun focused on two direct effects to demonstrate that SL and user satisfaction are related in some way [19]. Patterson & Balderas-Cejudo and Karagöz & Ramkissoon found that SL is related to TH and advocated reducing SL through tourism activities [5,126]. 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 former involved no mediating effect test while the latter has performed mediating effect tests and obtained significant results. This study believes that different ways 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). Also, habitual smartphone behaviors, as an element of smartphones, are the overflow of daily behaviors and the involuntary behaviors in tourism activities. This paper also finds that SL affects TH through smartphones, which is consistent with Kamboj & Joshi's study [117]. Kamboj & Joshi discussed the influence of SL on TH through smartphone apps [117], while this study focuses on one aspect of SL that can influence TH through smartphone usage. This approach is different from Kamboj & Joshi's study. Some studies different from Kamboj & Joshi's study. Studies have demonstrated that SL affects smartphone usage [13,77] and have held discussions on the categories of smartphone usage[31,127]. However, few of them have taken smartphone usage as a mediation 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.

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 & 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 [130,131]. 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 [132,133], 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 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 [32,134]. This study shows that SL affects habitual smartphone behaviors, which is the development of the 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.

5.2. Discussions about the Mediating Effects of Smartphone Usage

5.2.1. Discussions about Smartphone Communication

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 & Anglim, Rotondi et al., and Twenge et al. [30,57,138]. The studies by Horwood & Anglim, Rotondi et al., and Twenge et al. also revealed that habitual smartphone behaviors affect subjective happiness [30,57,138]. This study finds that habitual smartphone behaviors are related to subjective happiness in tourism. This paper differs from the studies by Horwood & Anglim, Rotondi et al., and Twenge et al., however, in that this paper focuses on TH rather than happiness with life [30,57,138]. 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 [30,57,138]. 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

#Please also refer to Page 14 (Line 492-494), Page 15 (Line 496-499, 500-502, 505-511, 525-527, 541-543), Page 16 (Line 550-563, 570-572, 581-583, 585-589), Page 17 (Line 607-609, 626-628) in the revised manuscript.

Revisions to conclusions

(1) 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. Also, SL affects TH through habitual smartphone behaviors and smart tourism applications.

(2) Habitual smartphone behaviors not only directly affect TH but also affect TH as a mediating variable and multiple mediating variables.

(3) 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.

(4) 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.

#Please also refer to Page 18 (Line 652-664) in the revised manuscript.

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Author Response File: Author Response.pdf

Round 2

Reviewer 4 Report

The Author(s) have greatly improved the manuscript. I suggest publication

Author Response

Point: The Author(s) have greatly improved the manuscript. I suggest publication

Response: Thank you very much for your approval of our draft. Your comments on the number of questionnaires in the first round gave us great inspiration, which improved the scientific and rigorous draft. We also put a lot of time and effort into the conception and writing of the paper. Finally, thank you again for your help and approval of the draft.

Author Response File: Author Response.pdf

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

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