# Research on the Complex Mechanism of Placeness, Sense of Place, and Satisfaction of Historical and Cultural Blocks in Beijing’s Old City Based on Structural Equation Model

Authors: Jing Zhang; Qiang Li
Journal: Complexity
 (2021)

---

## Abstract

Using a structural equation model, this study explores the complex influence mechanism between the place experience and satisfaction of the historical and cultural blocks in the old city of Beijing and the mechanism differences between different types. Based on the data obtained in the questionnaire survey, this study uses the structural equation model method to propose a theoretical model of the relationship between place experience and satisfaction, and through path analysis, the theoretical model of the path relationship between the dimensions of placeness, sense of place, and satisfaction is estimated and tested. Through the mathematical verification of the structural model, on the basis of establishing the final theoretical model, the hypothesis to be proved is further verified. This study also uses the bootstrap method to test the significance of the mediating effect of place experience and uses multiple-group analysis to try to explore the moderating role of residents’ and tourists’ identity types in the model. The study found that there are multiple correlations among placeness, sense of place, and place satisfaction in the historical and cultural blocks in the old city of Beijing. The placeness is the foundation and the sense of place is the intermediary variable, which both affect satisfaction; furthermore, tourists and residents have differences in the mechanism of placeness and sense of place on satisfaction. On the one hand, the perception of placeness directly affects satisfaction, and on the other hand, the sense of place has an indirect effect on satisfaction. The positive effects of tourists' placeness on sense of place and sense of place on satisfaction are greater than that of residents. However, the positive effect of residents' placeness on satisfaction is greater than that of tourists.


---

## Body

## 1. Introduction

Place experience is the process of interaction and construction between people as a subject and the place as an object. Existing domestic and foreign related studies have proved that place experience has been studied from multiple angles, but it mainly contains two aspects. One is the research of people’s cognition of place environmental elements, or the study of placeness; the second is the study of the emotional relationship between people and place, or the study of the sense of place. At the same time, studies have shown that environmental satisfaction is indeed closely related to relevant place experience [1]. However, the existing problems in related studies at home and abroad are as follows. First of all, the study on place experience, in terms of study content, either focuses on the attribute of placeness or the sense of place, and the study on place experience lacks completeness. Moreover, existing study perspectives generally start from place users (such as residents, immigrants, real estate owners, tourists, recreationists, and tourism operators, resp.), focusing on one of them. It is rare to conduct simultaneous surveys on residents and tourists, which brings about one-sidedness in the study content and study results. Secondly, the relationship between environmental satisfaction and place experience has no consistent results. In addition, as the most unique areas in the city, the historical and cultural blocks are not only the material carrier of the history and culture of the place, but also a lively stage of social life. This is a collection of place complex subjects and rich connotations. The contradiction between its protection and development has always been a hot topic for many scholars. However, at present, there are still few comprehensive researches from the multiple subjects of residents and tourists, as well as the multiple dimensions of placeness, sense of place, and satisfaction.Beijing is an ancient capital with a history of more than 3,000 years. Since the early years of the Western Zhou Dynasty (c.11th century–711 BC), there have been many relics left around the city, especially the 33 historical and cultural blocks distributed in the old city as the most representative. However, in recent years, the placeness of historical and cultural blocks in the old city of Beijing has suffered a greater impact driven by globalization and modernity, and these blocks are facing place restructuring in the process of protection and development. Therefore, this article intends to conduct a multidimensional and multisubject survey and analysis of the place experience in these blocks in the old city of Beijing, tries to portray place experience from both placeness and sense of place, and explores its complex relationship with the survey subject’s 20 satisfaction. At the same time, through group analysis, the mechanism differences of the model in different subjects are further revealed, to provide guidance and reference for the targeted protection and renewal of the historical and cultural blocks in the old city of Beijing.


## 2. Definition of Related Concepts

At present, there is no clear definition of place experience in the academic circle. It involves two dimensions: placeness and sense of place. Tuan [2] earlier proposed that place experience is closely related to the place. The related concepts have gradually deepened from place [2–4] to placeness [3, 5, 6] and sense of place [7–11]. The sense of place has derived place dependence, place attachment, and place identity [12–21]. These three concepts are often mixed whether in empirical or theoretical research. Each concept may involve other concepts [22]. Lalli [23] thinks that place identity is the main concept and place attachment is a subset of it. Place attachment is widely considered as a synonym for sense of place, and place identity and place dependence are its two subconcepts [14, 15] in the research of environmental psychology.Placeness refers to the physical environmental elements of a place and people’s cognition of them [24]. It is the quality of coming from a place, or placeness [25]. Sense of place refers to people’s emotional response to place and the emotional relationship between them [6–8], and it is people’s overall perception of local environment. From the perspective of the man–land relationship, placeness is more from the perspective of “place” of the object, and the sense of place is more from the perspective of “man” of the subject.This paper divides the place experience of historical and cultural blocks into two dimensions: placeness and sense of place. The placeness of these blocks is defined as local elements and people’s cognition of local elements. The local elements of these blocks refer to relevant studies [26–33], mainly including elements of natural environment, elements of historical and cultural value, elements of architecture and space, and local service elements. The sense of place of these blocks is defined as people’s overall perception of these blocks and the emotional connection with these blocks. Based on the classical theoretical framework of place attachment proposed by Williams [14, 15] and the dimensional research on sense of place by some scholars [11, 34–38], this paper divides the sense of place into three dimensions: place dependence, place attachment, and place identity. Place dependence emphasizes people’s functional attachment to place; place attachment emphasizes the emotional connection between people and place; and place identity emphasizes the relationship between people and place, which is the dimension of people and place’s related identities (Figure 1).Figure 1
Conceptual map of place experience in historic and cultural blocks.


## 3. Literature Review

Place experience research since the 1990s has focused on the relationship among placeness, sense of place, and satisfaction. Its research methods have gradually expanded from qualitative analysis to principal component analysis, regression analysis, structural equation modeling, and other quantitative research. The research mainly focuses on the following three aspects:First, the correlation between sense of place and satisfaction: Some researches in the field of tourism have confirmed that the sense of place positively affects satisfaction. If tourists have a stronger sense of identity with the destination, they are more likely to evaluate the destination positively, or the emotion between people and places plays an important role in the formation of tourist satisfaction [39–43]. In the field of residential satisfaction research, some scholars believe that identifying with a place will lead to positive evaluation and attitude towards the place [20, 23, 44, 45], or the sense of place has a positive impact on residential satisfaction [46–49]. However, some scholars believe that satisfaction is an influencing factor of place identity, and satisfaction tends to identify [50–52].Second, the correlation between placeness (objective attribute of place or environment) and satisfaction: In the study on residents, some scholars think that residential satisfaction is a complex phenomenon, which depends on a series of factors. Residents’ cognition of community is based on physical [53, 54], social [54–56], and personal factors [47, 57], which are intertwined and affect residents’ satisfaction [58]. Amerigo and Aragones [59] constructed a theoretical model of residential satisfaction based on the interaction between the objective attributes of the environment and people through qualitative analysis. The model reveals three paths of the objective attribute of environment affecting residential satisfaction: directly acting on residential satisfaction; turning into subjective after the valuator’s evaluation and resulting in a certain degree of satisfaction; and filtering the objective attributes through individual personality characteristics, thus affecting residents’ satisfaction. However, some scholars believe that the objective characteristics of the place do not determine the satisfaction, but also refer to the subjective interpretation of these objective characteristics [45, 60–64]. In the research on tourists, some scholars [25, 65] have also proved that some local factors of tourist destinations have a significant positive impact on tourists’ satisfaction through quantitative analysis methods.Third, the relationship between placeness and sense of place: Some scholars have established conceptual models of the relationship between placeness and sense of place. They think that sense of place is the reaction of people stimulated by environmental factors such as material environment, social environment [3, 8, 9], natural environment, and management environment [10], or placeness is the key factor to form sense of place. Other scholars have studied the relationship between placeness and sense of place through empirical analysis. Wardhani and Kusumowidagdo [32] used qualitative interviews to reveal the influence of physical factors on the formation of place sense. Özkan and Yilmaz [33] used regression analysis to verify that there is a significant positive correlation between environmental attributes of open space and place dependence. Zhang and Duan [29], Kyle et al. [66], and Tang [67] used structural equation models to verify the causal relationship between tourists’ or residents’ placeness and sense of place.According to the review, relevant studies have proved that there are correlations between sense of place and satisfaction, placeness and satisfaction, and placeness and sense of place, but there is no consistent conclusion on the causal relationship among them. What is more, there are few studies on the mechanism of action among placeness, sense of place, and satisfaction. Besides, the existing researches only start from residents or tourists unilaterally, which are also one-sided. Taking the historical and cultural blocks in Beijing’s old city as the research object, this paper attempts to explore the causal relationship and influencing mechanism among placeness, sense of place, and satisfaction from the perspective of residents and tourists.


## 4. Theoretical Model and Data Collection

### 4.1. Construction of the Proposed Model

Based on the review of relevant research at home and abroad, this study finally determined three variables of placeness, sense of place, and satisfaction (Figure2), and the following hypotheses were proposed: H1: placeness has a significant positive impact on the sense of place; H2: placeness has a significant positive impact on satisfaction; H3: sense of place has a significant positive impact on satisfaction; H4: sense of place has a significant mediating effect on the relationship between placeness and satisfaction; and H5: when identity type is used as a moderating variable, the correlation strength between variables will change significantly.Figure 2
Hypothesis structure relationship model.


### 4.2. Research Design and Data Collection

The study uses a combination of structured questionnaire surveys and observation interviews to obtain basic sample data. The content of the questionnaire consists of four parts: a measurement table of placeness and sense of place (Table1), satisfaction, and demographic characteristics. Moreover, this study is formed on the basis of soliciting the opinions of relevant experts, using the five-point Likert scale method, with “strongly disagree-strongly agree” or “Very dissatisfied-very satisfied” corresponding to an evaluation scale of 1 to 5 points. Among them, the scale of placeness and sense of place is based on relevant research and has been modified as necessary to adapt to the current survey. Therefore, exploratory factor analysis (EFA) was conducted on the placeness scale and the sense of place scale by principal component analysis, and the observed variables X18, X22, X12, X13, and Y8 were deleted on this basis. The investigation time was from September 25, 2017, to October 6, 2017, and from March 26, 2018, to April 6, 2018, the survey was conducted in Dashilan, Nanluoguxiang, Fuchengmen Inner Street, and Dongsi North Santiao to Batiao in the historical and cultural districts of Beijing’s old city. And the random sampling surveys of tourists on the streets and random household surveys are conducted on residents. The survey actually distributed 1,300 questionnaires, of which 1,224 were recovered, with a recovery rate of 94.15%. Excluding 87 invalid questionnaires, 1137 valid questionnaires were finally obtained, with an effective rate of 92.89%. Among them, a total of 563 questionnaires were returned by tourists and 574 questionnaires were returned by residents. SPSS 22.0 statistical software was used to process the sample data for the recovered data and combined with the use of AMOS 22.0 for structural equation model verification analysis.Table 1
Measurement index system.
Basic dimensionsObservation variablesReference SourcesPlacenessElements of historical and cultural valueX1, characteristics of traditional culture; X2, folk custom and intangible cultural heritage; X3, cultural value; X4, era characteristics; X5, historical remains; X6, historical character events or historical background[28, 29]Elements of architecture and spaceX7, street system; X8, courtyard space; X9, building features; X10, space layout; X11, overall landscape; X12, landscape sketch; X13, overall styleElements of local serviceX14, public service management; X15, environmental sanitation facilities; X16, infrastructure; X17, identification and interpretation system; X18, external transportation; X19, public service facilitiesElements of natural environmentX20, overall natural environment; X21, plant landscape; X22, animal landscape; X23, courtyard natural environmentSense of placePlace dependenceY1, this is the best place for me to do my hobby[14, 15]Y2, there is nowhere comparable to thisY3, I will come here often/live here whenever possibleY4, this is a good place for work, travel, and lifeY5, this place can meet my needs better than other places (get relaxed, gain knowledge, get friends, etc.)Place attachmentY6, this place makes me happyY7, this place makes me feel relaxedY8, this is my favorite placeY9, I will miss this place when I cannot come for a long timePlace identityY10, the scenery and life atmosphere here make me feel that I belong to this placeY11, this place means a lot to meY12, my personality and characteristics can be fully displayed hereY13, I feel like this is a part of my lifeY14, I can be my true selfY15, this place is very special to meY16, I quite agree with this place


### 4.3. Construction of Structural Model

The causal relationship model between placeness, sense of place, and satisfaction constructed in this study includes two latent variables of placeness, sense of place, and one observed variable of satisfaction. According to the results of exploratory factor analysis, the observed variables of placeness and sense of place are represented by the arithmetic mean of the scores of the items contained in each factor by the internal consistency approach of item packaging. Therefore, the observed variables of latent variable of placeness include elements of historical and cultural value, elements of architecture and space, elements of natural environment, and local service elements; the observed variables of latent variable of sense of place include place dependence, place attachment, and place identity.


## 4.1. Construction of the Proposed Model

Based on the review of relevant research at home and abroad, this study finally determined three variables of placeness, sense of place, and satisfaction (Figure2), and the following hypotheses were proposed: H1: placeness has a significant positive impact on the sense of place; H2: placeness has a significant positive impact on satisfaction; H3: sense of place has a significant positive impact on satisfaction; H4: sense of place has a significant mediating effect on the relationship between placeness and satisfaction; and H5: when identity type is used as a moderating variable, the correlation strength between variables will change significantly.Figure 2
Hypothesis structure relationship model.


## 4.2. Research Design and Data Collection

The study uses a combination of structured questionnaire surveys and observation interviews to obtain basic sample data. The content of the questionnaire consists of four parts: a measurement table of placeness and sense of place (Table1), satisfaction, and demographic characteristics. Moreover, this study is formed on the basis of soliciting the opinions of relevant experts, using the five-point Likert scale method, with “strongly disagree-strongly agree” or “Very dissatisfied-very satisfied” corresponding to an evaluation scale of 1 to 5 points. Among them, the scale of placeness and sense of place is based on relevant research and has been modified as necessary to adapt to the current survey. Therefore, exploratory factor analysis (EFA) was conducted on the placeness scale and the sense of place scale by principal component analysis, and the observed variables X18, X22, X12, X13, and Y8 were deleted on this basis. The investigation time was from September 25, 2017, to October 6, 2017, and from March 26, 2018, to April 6, 2018, the survey was conducted in Dashilan, Nanluoguxiang, Fuchengmen Inner Street, and Dongsi North Santiao to Batiao in the historical and cultural districts of Beijing’s old city. And the random sampling surveys of tourists on the streets and random household surveys are conducted on residents. The survey actually distributed 1,300 questionnaires, of which 1,224 were recovered, with a recovery rate of 94.15%. Excluding 87 invalid questionnaires, 1137 valid questionnaires were finally obtained, with an effective rate of 92.89%. Among them, a total of 563 questionnaires were returned by tourists and 574 questionnaires were returned by residents. SPSS 22.0 statistical software was used to process the sample data for the recovered data and combined with the use of AMOS 22.0 for structural equation model verification analysis.Table 1
Measurement index system.
Basic dimensionsObservation variablesReference SourcesPlacenessElements of historical and cultural valueX1, characteristics of traditional culture; X2, folk custom and intangible cultural heritage; X3, cultural value; X4, era characteristics; X5, historical remains; X6, historical character events or historical background[28, 29]Elements of architecture and spaceX7, street system; X8, courtyard space; X9, building features; X10, space layout; X11, overall landscape; X12, landscape sketch; X13, overall styleElements of local serviceX14, public service management; X15, environmental sanitation facilities; X16, infrastructure; X17, identification and interpretation system; X18, external transportation; X19, public service facilitiesElements of natural environmentX20, overall natural environment; X21, plant landscape; X22, animal landscape; X23, courtyard natural environmentSense of placePlace dependenceY1, this is the best place for me to do my hobby[14, 15]Y2, there is nowhere comparable to thisY3, I will come here often/live here whenever possibleY4, this is a good place for work, travel, and lifeY5, this place can meet my needs better than other places (get relaxed, gain knowledge, get friends, etc.)Place attachmentY6, this place makes me happyY7, this place makes me feel relaxedY8, this is my favorite placeY9, I will miss this place when I cannot come for a long timePlace identityY10, the scenery and life atmosphere here make me feel that I belong to this placeY11, this place means a lot to meY12, my personality and characteristics can be fully displayed hereY13, I feel like this is a part of my lifeY14, I can be my true selfY15, this place is very special to meY16, I quite agree with this place


## 4.3. Construction of Structural Model

The causal relationship model between placeness, sense of place, and satisfaction constructed in this study includes two latent variables of placeness, sense of place, and one observed variable of satisfaction. According to the results of exploratory factor analysis, the observed variables of placeness and sense of place are represented by the arithmetic mean of the scores of the items contained in each factor by the internal consistency approach of item packaging. Therefore, the observed variables of latent variable of placeness include elements of historical and cultural value, elements of architecture and space, elements of natural environment, and local service elements; the observed variables of latent variable of sense of place include place dependence, place attachment, and place identity.


## 5. Validation Analysis of Empirical Results

### 5.1. Structural Characteristics of the Sample

The proportion of female tourists (58.1%) is slightly higher than that of males (41.9%). The age distribution is dominated by young and middle-aged under 44, with a cumulative percentage of 80.5%; among them, young people under 34 account for 62.2%. Their occupations are widely distributed, with a relatively large proportion of students, accounting for 30.4%, followed by business/service personnel and enterprise and institution management personnel, accounting for 15.2% and 14%, respectively. The average monthly income is not high, and those with a monthly income of less than 10,000 yuan or a low-income group (including current students) account for 91.6%. Highly educated people, with a college degree or above, account for 83.9%. The source of tourists is mainly domestic, with tourists from other provinces accounting for 55.7% and local tourists from Beijing accounting for 41.2%. The degree of involvement of tourists was not high before they came, and the percentage of tourists who did not understand and only knew a little reached 69.9%. The proportion of first-time visitors and revisiting tourists is the same, and the proportion of tourists who visit twice or more is 55.2%. Sightseeing and leisure and entertainment are the main motives for traveling, accounting for 79.5% of the total. Tourists stay mainly within half a day, with a cumulative percentage of 89.6%.The proportion of female residents (55.7%) is slightly higher than that of men (44.3%). The age distribution is dominated by middle-aged and elderly people over 45 years old. The cumulative proportion of people over 45 years old accounts for 82.1%; among them, the proportion of people over 55 years old reaches 63.2%. The occupation distribution is dominated by retirees, with a proportion of 68.9%, followed by business/service personnel with a proportion of 14.2%. The average monthly income is low, and those with a monthly income of less than 5,000 yuan (excluding current students) account for 89.2%. The education level is not high, and the accumulative proportion of college degrees (excluding colleges) is 77.8%. The main types of residents are aboriginals, with a total of 77.8% of people living for more than 20 years, followed by new immigrants, with 14.2% of people living for less than 5 years. The type of property ownership is mainly public rental housing, which accounts for 67.9% of the total, while people with private property rights and those who rent property rights to others each account for 16%.


### 5.2. Reliability and Validity Testing of the Scale

Use SPSS 22.0 statistical software to calculate the Cronbachα of latent variables such as elements of historical and cultural value, elements of architecture and space, local service elements, natural environment elements, place dependence, place attachment, and place identity. The results show (Table 2) that the Cronbach α of the above latent variables is between 0.737 and 0.934, which has good internal consistency and stability.Table 2
Reliability test results of the observed variables and latent variables∗.
The name of the factorReliability (∗coefficient)Composite reliability (CR)Average variance extracted (AVE) valuesElements of historical and cultural value0.8550.8930.584Elements of architecture and space0.8780.9110.673Elements of local service0.7370.8810.598Elements of natural environment0.8000.8840.717Place dependence0.9070.9310.729Place attachment0.8400.9070.764Place identity0.9340.9470.719∗represents the measured value after eliminating the observed variables X18, X22, X12, X13, and Y8.The comprehensive reliability (CR) of each scale is greater than 0.7, and the average variance extracted (AVE) is greater than 0.5, indicating that the scale has good internal reliability and discriminant validity.


### 5.3. Inspection and Evaluation of the Measurement Model

#### 5.3.1. Testing of the Measurement Model

Using AMOS22.0 to test the degree of fit of the measurement model, the study obtains the index value of the degree of fit, shown in Table3. It can be seen from Table 3 that the RMSEA (0.082) in the absolute fitting index is slightly greater than 0.08 and the PGFI (0.452) in the simple fitting index is slightly less than 0.5, which does not meet the fitting requirements; the other indicators meet the fitting requirements. It can be seen that the degree of fitting between the measurement model and the data are at a better level, the external quality of the model is good, and no further correction is needed.


#### 5.3.2. Evaluation of the Measurement Model

Using the confirmatory factors to analyze and evaluate the measurement model, Table4 lists the path coefficients of the measurement model; among them, the standardized coefficients are all above 0.6 and the standard deviations are all less than 0.1, all of which have passed the significance test. The load value of the standardized factor of each measurement index in the latent variable is between 0.626 and 0.877, which meets the standard of factor load greater than 0.5; each research variable has no negative measurement error, and the standard deviation is relatively small, lower than 0.05; the critical ratios are all greater than 3.29, and the parameter estimates all reach the significant level of 0.001 (P<0.001, represented by the ∗∗∗symbol), indicating that each factor index has a strong explanatory power for the measurement model, and the basic fitness of the model is good.Table 3
Overall measurement model indices.
IndexAbsolute fitting indexValue-added fitting indexSimple fitting indexSpecific classificationχ2/dfGFIRMRRMSEANFITLICFIPGFIPNFIJudgment criteria<3>0.9<0.08<0.08>0.9>0.9>0.9>0.5>0.5Fitting results8.689∗0.9660.0290.0820.9580.9410.9620.4520.616Fitting evaluationGoodVery goodGood∗The sample size in this paper is n = 1137, so the χ2/df index is not referenced. Note: when the sample size is greater than 1,000, the index has no reference significance [68].


### 5.4. Correlation Analysis and Discriminant Validity

It can be seen from Table5 that the correlation coefficients between the variables are all lower than 0.7, and there is no multicollinearity. The bold numbers are the square root of the AVE of the corresponding variables. The nonbold numbers are the correlation coefficients of the corresponding variables. The bold numbers are all greater than the absolute value of the nonbold numbers, indicating that the discriminant validity is better; that is, there is a certain degree of discrimination between variables.


### 5.5. Inspection and Evaluation of Structural Model

#### 5.5.1. Testing of the Structural Model

Using AMOS22.0 to test the degree of fit of the conceptual model, the study obtains the index value of the degree of fit shown in Table6. It can be seen from Table 6 that the RMSEA (0.084) in the absolute fitting index is slightly greater than 0.08 and the PGFI (0.483) in the simple fitting index is slightly less than 0.5, which does not meet the fitting requirements; the other indicators meet the fitting requirements. It can be seen that the degree of fitting between the measurement model and the data is at a better level, the external quality of the model is good, and no further correction is needed.Table 4
Estimation of path coefficients for measurement models.
Relationship between potential variables and observed variablesUnstandardized estimationS.E.C.R.PStandardized estimationSMC1-SMCCRAVEElements of historical and cultural value<---Placeness10.7910.6260.3740.8090.517Elements of architecture and space<---Placeness1.0020.04323.378∗∗∗0.7800.6080.392Elements of natural environment<---Placeness0.8600.04419.503∗∗∗0.6260.3920.608Elements of local service<---Placeness0.7720.03720.723∗∗∗0.6660.4440.556Place identity<---Sense of place1∗∗∗0.8770.7700.2300.8810.712Place attachment<---Sense of place0.7770.02630.180∗∗∗0.7780.6050.395Place dependence<---Sense of place0.9350.02833.612∗∗∗0.8720.7600.240∗∗∗At the 0.001 level (2-tailed), the correlation is significant.


#### 5.5.2. Evaluation of the Structural Model

The software AMOS22.0 is used to evaluate the structural relationship between the latent variables of the initial conceptual model, and the specific results are shown in Table7. From Table 7, the absolute value of the critical value (CR) of each coefficient is greater than 3.29, and the parameter estimates are all up to 0.001 (P value is ∗∗∗, representing significant at the 0.001 level) significance level, that is, all passing the significance test. Among them, the standardized path coefficient of placeness and sense of place is 0.212, indicating that the respondents’ perception of placeness has a significant positive impact on the formation of sense of place; the standardized path coefficient of placeness and satisfaction is 0.366, indicating that the respondents’ perception of placeness has a significant positive impact on their satisfaction; the standardized path coefficient between place and satisfaction is 0.382, indicating that the sense of place formed by the survey has a significant positive impact on satisfaction. Through the comparison of the size of the path coefficient, it can be seen that the degree of influence of the respondents’ sense of place on satisfaction is greater than the influence of their perception of placeness on satisfaction (Figure 3).Table 5
Discriminant validity matrix.
PlacenessSense of placeSatisfactionPlaceness0.719Sense of place.207∗∗0.844Satisfaction.413∗∗.446∗∗1∗∗At the 0.01 level (2-tailed), the correlation is significant.Figure 3
Ultimate structural equation modeling.


### 5.6. Analysis of Mediating Effect

This study uses the bias-corrected percentile bootstrap method to test the significance of the mediating effect. First, the study uses the method of repeated random sampling to extract 2,000 bootstrap samples from the original data (N = 1137), and then fit the model based on these samples, generate and save the estimated value of 1,000 mediation effects, from an approximate sampling distribution, and calculate the average path value of the mediation effect, and these effect values are sorted by numerical value. Finally, the study uses the 2.5th percentile and the 97.5th percentile to estimate the 95% confidence interval of the mediation effect. If the 95% confidence interval of these path coefficients does not include 0, it indicates that the mediating effect is significant. Then, look at whether the confidence interval (or significance) of the standardized direct effect contains 0, if it contains 0 (that is, it is not significant), it is full mediation; otherwise, it is partial mediation.It can be seen from Table8 that the 95% confidence interval of the indirect effect of placeness on satisfaction is [0.049, 0.115], which does not contain 0, so the mediating effect is significant. The 95% confidence interval of the direct effect of placeness on satisfaction is [0.308, 0.427], which does not include 0, so partial mediating effects are significant. The indirect effect value was 0.081, while the direct effect value was 0.366, and the total effect value was 0.447. All passed the test of significance level (0.001). The ratio of indirect effect to total effect was 0.081/0.447 = 0.181. That is to say, 18.1% of the variation was caused by the sense of place when placeness affected overall satisfaction.Table 6
Structural equation model indices.
IndexAbsolute fitting indexValue-added fitting indexSimple fitting indexSpecific classificationχ2/dfGFIRMRRMSEANFITLICFIPGFIPNFIJudgment criteria<3>0.9<0.08<0.08>0.9>0.9>0.9>0.5>0.5Fitting results9.069∗0.9660.0290.0840.9580.9410.9620.4830.616Fitting evaluationGoodVery goodGood∗The sample size in this paper is n = 1137, so the χ2/df index is not referenced. When the sample size is greater than 1,000, this index has no reference significance [68].It can be proved that the respondents’ perception of placeness can not only directly affect satisfaction, but also form an indirect effect on satisfaction through the sense of place. The sense of place can be used as an intermediary variable for the influence of placeness on satisfaction.


### 5.7. Analysis of Moderating Effect

To test whether the constructed model has cross-group stability, this study uses the model to perform multiple-group path analysis with different identity type (resident or tourist) variables. If there is no significant difference, it means that the identity type variable has no effect on the model; if there is a significant difference, it means that the identity type variable has a moderating effect.When the multiple-group structural equation model is running, Amos 22.0 sets five parameter restriction models, so the multiple-group model includes six models: unrestricted model, measurement weighted model, structure weighted model, structure covariance model, structure residual error model, and measurement residual model. Two judgments are required for multigroup analysis: whether each model is recognized and whether the recognized model meets the standard. If the sample data of each group is large, the chi-square value that limits the overall fitness statistics of the model should be used as one of the reference standards, and the overall judgment needs to be made from the fitness situation of each fitness statistics [69]. If there are more than two identified models that meet the standard, the optimal model needs to be judged based on AIC and ECVI. The model with the smallest value of AIC and ECVI is the best model. The overall fitness test results of the multiple-group model are shown in Table 9. Through the comparison and analysis of the fitness of the six hypothetical models, the multigroup model with identity type as moderator variables after comparison, the measurement weighted model is determined as the best model.Table 7
Path coefficient estimation of the structural equation model.
Path relationship among potential variablesUnstandardized estimationStandardized estimationStandard errorCritical valueP valueSense of place<---Placeness0.2860.2120.0476.102∗∗∗Satisfaction<---Placeness0.5730.3660.04712.263∗∗∗Satisfaction<---Sense of place0.4430.3820.03313.61∗∗∗∗∗∗At the 0.001 level (2-tailed), the correlation is significant.Table 8
Bootstrap analysis for the significance test of mediation effect.
Effect value95% confidence intervalPIntermediary judgmentLower limitUpper limitIndirect effect0.0810.0490.1150.001Intermediary existedDirect effect0.3660.3080.4270.001Part of the intermediaryTotal effect0.4470.3880.5060.001Table 9
Multigroup modeling indicators comparison of the overall adaptation.
Test statisticReference standardUnrestricted modelMeasurement weighting modelStructure weighted modelStructural covariance modelStructural residual modelMeasurement residual modelCMIN/DF<34.2824.4295.6175.5265.5247.506P>0.05≤0.001≤0.001≤0.001≤0.001≤0.001≤0.001RMR<0.050.0230.0300.0610.0590.0680.073RMSEA<0.050.0540.0550.0640.0630.0630.076GFI>0.90.9680.9600.9510.9510.9500.923NFI>0.90.9590.9490.9340.9340.9320.892CFI>0.950.9680.9600.9450.9450.9440.905PNFI>0.50.6170.7290.7340.7500.7660.860PGFI>0.50.4840.5740.5810.5940.6070.692AICThe smaller the better226.148248.449303.159302.676306.109441.309ECVIThe smaller the better0.1990.2190.2670.2670.2700.389According to the model comparison in Table10, there are significant differences between the unrestricted model and the restricted model. The p value was less than 0.05, which passed the significance test. Therefore, there are significant differences in the models of different identity types, that is, identity types play a moderating role.By sorting out the data calculation results, the estimation results of the multiple-group structural equation model of two different identity types are obtained, as shown in Table11, and the specific path diagrams are shown in Figures 4 and 5.The analysis results of the multiple-group structural equation model show that in the path H1 of the positive influence of placeness on the sense of place, the influence of tourists and local residents is very significant (P<0.001), and the influence effects of the two types of identity are quite different. Among them, the impact effect of tourists (0.601) is greater than that of local residents (0.257). In the path H2 of the positive influence of placeness on satisfaction, the influence of tourists and local residents is very significant (P<0.001), and the difference is large. Among them, the influence of tourists (0.289) is smaller than that of local residents (0.306). In path H3, where the sense of place has a positive impact on satisfaction, the impact of tourists and local residents is very significant (P<0.001), and there are large differences. Among them, the impact effect of tourists (0.454) is greater than that of local residents (0.334).Table 10
Comparison between unrestricted model and restricted model (measurement weighted model).
ModelDFCMINPNFI Delta-1IFI Delta-2RFI rho-1TLI rho2Measurement weights736.3010.0000.0100.0100.0020.002Table 11
Estimated results of multigroup structural equation modeling based on tourists and residents.
RouteRespondentsTouristResidentH1: sense of place<---placeness0.601∗∗∗0.257∗∗∗H2: satisfaction <--- placeness0.289∗∗∗0.306∗∗∗H3: satisfaction <--- sense of place0.454∗∗∗0.334∗∗∗∗denotes P<0.05, ∗∗denotes P<0.01, ∗∗∗ indicates P<0.001; paths H1, H2, and H3 are consistent with the previous hypothesis.Figure 4
Standardized path coefficient of multi-group structural equation modeling for tourists.Figure 5
Standardized path coefficient of multi-group structural equation modeling for residents.


## 5.1. Structural Characteristics of the Sample

The proportion of female tourists (58.1%) is slightly higher than that of males (41.9%). The age distribution is dominated by young and middle-aged under 44, with a cumulative percentage of 80.5%; among them, young people under 34 account for 62.2%. Their occupations are widely distributed, with a relatively large proportion of students, accounting for 30.4%, followed by business/service personnel and enterprise and institution management personnel, accounting for 15.2% and 14%, respectively. The average monthly income is not high, and those with a monthly income of less than 10,000 yuan or a low-income group (including current students) account for 91.6%. Highly educated people, with a college degree or above, account for 83.9%. The source of tourists is mainly domestic, with tourists from other provinces accounting for 55.7% and local tourists from Beijing accounting for 41.2%. The degree of involvement of tourists was not high before they came, and the percentage of tourists who did not understand and only knew a little reached 69.9%. The proportion of first-time visitors and revisiting tourists is the same, and the proportion of tourists who visit twice or more is 55.2%. Sightseeing and leisure and entertainment are the main motives for traveling, accounting for 79.5% of the total. Tourists stay mainly within half a day, with a cumulative percentage of 89.6%.The proportion of female residents (55.7%) is slightly higher than that of men (44.3%). The age distribution is dominated by middle-aged and elderly people over 45 years old. The cumulative proportion of people over 45 years old accounts for 82.1%; among them, the proportion of people over 55 years old reaches 63.2%. The occupation distribution is dominated by retirees, with a proportion of 68.9%, followed by business/service personnel with a proportion of 14.2%. The average monthly income is low, and those with a monthly income of less than 5,000 yuan (excluding current students) account for 89.2%. The education level is not high, and the accumulative proportion of college degrees (excluding colleges) is 77.8%. The main types of residents are aboriginals, with a total of 77.8% of people living for more than 20 years, followed by new immigrants, with 14.2% of people living for less than 5 years. The type of property ownership is mainly public rental housing, which accounts for 67.9% of the total, while people with private property rights and those who rent property rights to others each account for 16%.


## 5.2. Reliability and Validity Testing of the Scale

Use SPSS 22.0 statistical software to calculate the Cronbachα of latent variables such as elements of historical and cultural value, elements of architecture and space, local service elements, natural environment elements, place dependence, place attachment, and place identity. The results show (Table 2) that the Cronbach α of the above latent variables is between 0.737 and 0.934, which has good internal consistency and stability.Table 2
Reliability test results of the observed variables and latent variables∗.
The name of the factorReliability (∗coefficient)Composite reliability (CR)Average variance extracted (AVE) valuesElements of historical and cultural value0.8550.8930.584Elements of architecture and space0.8780.9110.673Elements of local service0.7370.8810.598Elements of natural environment0.8000.8840.717Place dependence0.9070.9310.729Place attachment0.8400.9070.764Place identity0.9340.9470.719∗represents the measured value after eliminating the observed variables X18, X22, X12, X13, and Y8.The comprehensive reliability (CR) of each scale is greater than 0.7, and the average variance extracted (AVE) is greater than 0.5, indicating that the scale has good internal reliability and discriminant validity.


## 5.3. Inspection and Evaluation of the Measurement Model

### 5.3.1. Testing of the Measurement Model

Using AMOS22.0 to test the degree of fit of the measurement model, the study obtains the index value of the degree of fit, shown in Table3. It can be seen from Table 3 that the RMSEA (0.082) in the absolute fitting index is slightly greater than 0.08 and the PGFI (0.452) in the simple fitting index is slightly less than 0.5, which does not meet the fitting requirements; the other indicators meet the fitting requirements. It can be seen that the degree of fitting between the measurement model and the data are at a better level, the external quality of the model is good, and no further correction is needed.


### 5.3.2. Evaluation of the Measurement Model

Using the confirmatory factors to analyze and evaluate the measurement model, Table4 lists the path coefficients of the measurement model; among them, the standardized coefficients are all above 0.6 and the standard deviations are all less than 0.1, all of which have passed the significance test. The load value of the standardized factor of each measurement index in the latent variable is between 0.626 and 0.877, which meets the standard of factor load greater than 0.5; each research variable has no negative measurement error, and the standard deviation is relatively small, lower than 0.05; the critical ratios are all greater than 3.29, and the parameter estimates all reach the significant level of 0.001 (P<0.001, represented by the ∗∗∗symbol), indicating that each factor index has a strong explanatory power for the measurement model, and the basic fitness of the model is good.Table 3
Overall measurement model indices.
IndexAbsolute fitting indexValue-added fitting indexSimple fitting indexSpecific classificationχ2/dfGFIRMRRMSEANFITLICFIPGFIPNFIJudgment criteria<3>0.9<0.08<0.08>0.9>0.9>0.9>0.5>0.5Fitting results8.689∗0.9660.0290.0820.9580.9410.9620.4520.616Fitting evaluationGoodVery goodGood∗The sample size in this paper is n = 1137, so the χ2/df index is not referenced. Note: when the sample size is greater than 1,000, the index has no reference significance [68].


## 5.3.1. Testing of the Measurement Model

Using AMOS22.0 to test the degree of fit of the measurement model, the study obtains the index value of the degree of fit, shown in Table3. It can be seen from Table 3 that the RMSEA (0.082) in the absolute fitting index is slightly greater than 0.08 and the PGFI (0.452) in the simple fitting index is slightly less than 0.5, which does not meet the fitting requirements; the other indicators meet the fitting requirements. It can be seen that the degree of fitting between the measurement model and the data are at a better level, the external quality of the model is good, and no further correction is needed.


## 5.3.2. Evaluation of the Measurement Model

Using the confirmatory factors to analyze and evaluate the measurement model, Table4 lists the path coefficients of the measurement model; among them, the standardized coefficients are all above 0.6 and the standard deviations are all less than 0.1, all of which have passed the significance test. The load value of the standardized factor of each measurement index in the latent variable is between 0.626 and 0.877, which meets the standard of factor load greater than 0.5; each research variable has no negative measurement error, and the standard deviation is relatively small, lower than 0.05; the critical ratios are all greater than 3.29, and the parameter estimates all reach the significant level of 0.001 (P<0.001, represented by the ∗∗∗symbol), indicating that each factor index has a strong explanatory power for the measurement model, and the basic fitness of the model is good.Table 3
Overall measurement model indices.
IndexAbsolute fitting indexValue-added fitting indexSimple fitting indexSpecific classificationχ2/dfGFIRMRRMSEANFITLICFIPGFIPNFIJudgment criteria<3>0.9<0.08<0.08>0.9>0.9>0.9>0.5>0.5Fitting results8.689∗0.9660.0290.0820.9580.9410.9620.4520.616Fitting evaluationGoodVery goodGood∗The sample size in this paper is n = 1137, so the χ2/df index is not referenced. Note: when the sample size is greater than 1,000, the index has no reference significance [68].


## 5.4. Correlation Analysis and Discriminant Validity

It can be seen from Table5 that the correlation coefficients between the variables are all lower than 0.7, and there is no multicollinearity. The bold numbers are the square root of the AVE of the corresponding variables. The nonbold numbers are the correlation coefficients of the corresponding variables. The bold numbers are all greater than the absolute value of the nonbold numbers, indicating that the discriminant validity is better; that is, there is a certain degree of discrimination between variables.


## 5.5. Inspection and Evaluation of Structural Model

### 5.5.1. Testing of the Structural Model

Using AMOS22.0 to test the degree of fit of the conceptual model, the study obtains the index value of the degree of fit shown in Table6. It can be seen from Table 6 that the RMSEA (0.084) in the absolute fitting index is slightly greater than 0.08 and the PGFI (0.483) in the simple fitting index is slightly less than 0.5, which does not meet the fitting requirements; the other indicators meet the fitting requirements. It can be seen that the degree of fitting between the measurement model and the data is at a better level, the external quality of the model is good, and no further correction is needed.Table 4
Estimation of path coefficients for measurement models.
Relationship between potential variables and observed variablesUnstandardized estimationS.E.C.R.PStandardized estimationSMC1-SMCCRAVEElements of historical and cultural value<---Placeness10.7910.6260.3740.8090.517Elements of architecture and space<---Placeness1.0020.04323.378∗∗∗0.7800.6080.392Elements of natural environment<---Placeness0.8600.04419.503∗∗∗0.6260.3920.608Elements of local service<---Placeness0.7720.03720.723∗∗∗0.6660.4440.556Place identity<---Sense of place1∗∗∗0.8770.7700.2300.8810.712Place attachment<---Sense of place0.7770.02630.180∗∗∗0.7780.6050.395Place dependence<---Sense of place0.9350.02833.612∗∗∗0.8720.7600.240∗∗∗At the 0.001 level (2-tailed), the correlation is significant.


### 5.5.2. Evaluation of the Structural Model

The software AMOS22.0 is used to evaluate the structural relationship between the latent variables of the initial conceptual model, and the specific results are shown in Table7. From Table 7, the absolute value of the critical value (CR) of each coefficient is greater than 3.29, and the parameter estimates are all up to 0.001 (P value is ∗∗∗, representing significant at the 0.001 level) significance level, that is, all passing the significance test. Among them, the standardized path coefficient of placeness and sense of place is 0.212, indicating that the respondents’ perception of placeness has a significant positive impact on the formation of sense of place; the standardized path coefficient of placeness and satisfaction is 0.366, indicating that the respondents’ perception of placeness has a significant positive impact on their satisfaction; the standardized path coefficient between place and satisfaction is 0.382, indicating that the sense of place formed by the survey has a significant positive impact on satisfaction. Through the comparison of the size of the path coefficient, it can be seen that the degree of influence of the respondents’ sense of place on satisfaction is greater than the influence of their perception of placeness on satisfaction (Figure 3).Table 5
Discriminant validity matrix.
PlacenessSense of placeSatisfactionPlaceness0.719Sense of place.207∗∗0.844Satisfaction.413∗∗.446∗∗1∗∗At the 0.01 level (2-tailed), the correlation is significant.Figure 3
Ultimate structural equation modeling.


## 5.5.1. Testing of the Structural Model

Using AMOS22.0 to test the degree of fit of the conceptual model, the study obtains the index value of the degree of fit shown in Table6. It can be seen from Table 6 that the RMSEA (0.084) in the absolute fitting index is slightly greater than 0.08 and the PGFI (0.483) in the simple fitting index is slightly less than 0.5, which does not meet the fitting requirements; the other indicators meet the fitting requirements. It can be seen that the degree of fitting between the measurement model and the data is at a better level, the external quality of the model is good, and no further correction is needed.Table 4
Estimation of path coefficients for measurement models.
Relationship between potential variables and observed variablesUnstandardized estimationS.E.C.R.PStandardized estimationSMC1-SMCCRAVEElements of historical and cultural value<---Placeness10.7910.6260.3740.8090.517Elements of architecture and space<---Placeness1.0020.04323.378∗∗∗0.7800.6080.392Elements of natural environment<---Placeness0.8600.04419.503∗∗∗0.6260.3920.608Elements of local service<---Placeness0.7720.03720.723∗∗∗0.6660.4440.556Place identity<---Sense of place1∗∗∗0.8770.7700.2300.8810.712Place attachment<---Sense of place0.7770.02630.180∗∗∗0.7780.6050.395Place dependence<---Sense of place0.9350.02833.612∗∗∗0.8720.7600.240∗∗∗At the 0.001 level (2-tailed), the correlation is significant.


## 5.5.2. Evaluation of the Structural Model

The software AMOS22.0 is used to evaluate the structural relationship between the latent variables of the initial conceptual model, and the specific results are shown in Table7. From Table 7, the absolute value of the critical value (CR) of each coefficient is greater than 3.29, and the parameter estimates are all up to 0.001 (P value is ∗∗∗, representing significant at the 0.001 level) significance level, that is, all passing the significance test. Among them, the standardized path coefficient of placeness and sense of place is 0.212, indicating that the respondents’ perception of placeness has a significant positive impact on the formation of sense of place; the standardized path coefficient of placeness and satisfaction is 0.366, indicating that the respondents’ perception of placeness has a significant positive impact on their satisfaction; the standardized path coefficient between place and satisfaction is 0.382, indicating that the sense of place formed by the survey has a significant positive impact on satisfaction. Through the comparison of the size of the path coefficient, it can be seen that the degree of influence of the respondents’ sense of place on satisfaction is greater than the influence of their perception of placeness on satisfaction (Figure 3).Table 5
Discriminant validity matrix.
PlacenessSense of placeSatisfactionPlaceness0.719Sense of place.207∗∗0.844Satisfaction.413∗∗.446∗∗1∗∗At the 0.01 level (2-tailed), the correlation is significant.Figure 3
Ultimate structural equation modeling.


## 5.6. Analysis of Mediating Effect

This study uses the bias-corrected percentile bootstrap method to test the significance of the mediating effect. First, the study uses the method of repeated random sampling to extract 2,000 bootstrap samples from the original data (N = 1137), and then fit the model based on these samples, generate and save the estimated value of 1,000 mediation effects, from an approximate sampling distribution, and calculate the average path value of the mediation effect, and these effect values are sorted by numerical value. Finally, the study uses the 2.5th percentile and the 97.5th percentile to estimate the 95% confidence interval of the mediation effect. If the 95% confidence interval of these path coefficients does not include 0, it indicates that the mediating effect is significant. Then, look at whether the confidence interval (or significance) of the standardized direct effect contains 0, if it contains 0 (that is, it is not significant), it is full mediation; otherwise, it is partial mediation.It can be seen from Table8 that the 95% confidence interval of the indirect effect of placeness on satisfaction is [0.049, 0.115], which does not contain 0, so the mediating effect is significant. The 95% confidence interval of the direct effect of placeness on satisfaction is [0.308, 0.427], which does not include 0, so partial mediating effects are significant. The indirect effect value was 0.081, while the direct effect value was 0.366, and the total effect value was 0.447. All passed the test of significance level (0.001). The ratio of indirect effect to total effect was 0.081/0.447 = 0.181. That is to say, 18.1% of the variation was caused by the sense of place when placeness affected overall satisfaction.Table 6
Structural equation model indices.
IndexAbsolute fitting indexValue-added fitting indexSimple fitting indexSpecific classificationχ2/dfGFIRMRRMSEANFITLICFIPGFIPNFIJudgment criteria<3>0.9<0.08<0.08>0.9>0.9>0.9>0.5>0.5Fitting results9.069∗0.9660.0290.0840.9580.9410.9620.4830.616Fitting evaluationGoodVery goodGood∗The sample size in this paper is n = 1137, so the χ2/df index is not referenced. When the sample size is greater than 1,000, this index has no reference significance [68].It can be proved that the respondents’ perception of placeness can not only directly affect satisfaction, but also form an indirect effect on satisfaction through the sense of place. The sense of place can be used as an intermediary variable for the influence of placeness on satisfaction.


## 5.7. Analysis of Moderating Effect

To test whether the constructed model has cross-group stability, this study uses the model to perform multiple-group path analysis with different identity type (resident or tourist) variables. If there is no significant difference, it means that the identity type variable has no effect on the model; if there is a significant difference, it means that the identity type variable has a moderating effect.When the multiple-group structural equation model is running, Amos 22.0 sets five parameter restriction models, so the multiple-group model includes six models: unrestricted model, measurement weighted model, structure weighted model, structure covariance model, structure residual error model, and measurement residual model. Two judgments are required for multigroup analysis: whether each model is recognized and whether the recognized model meets the standard. If the sample data of each group is large, the chi-square value that limits the overall fitness statistics of the model should be used as one of the reference standards, and the overall judgment needs to be made from the fitness situation of each fitness statistics [69]. If there are more than two identified models that meet the standard, the optimal model needs to be judged based on AIC and ECVI. The model with the smallest value of AIC and ECVI is the best model. The overall fitness test results of the multiple-group model are shown in Table 9. Through the comparison and analysis of the fitness of the six hypothetical models, the multigroup model with identity type as moderator variables after comparison, the measurement weighted model is determined as the best model.Table 7
Path coefficient estimation of the structural equation model.
Path relationship among potential variablesUnstandardized estimationStandardized estimationStandard errorCritical valueP valueSense of place<---Placeness0.2860.2120.0476.102∗∗∗Satisfaction<---Placeness0.5730.3660.04712.263∗∗∗Satisfaction<---Sense of place0.4430.3820.03313.61∗∗∗∗∗∗At the 0.001 level (2-tailed), the correlation is significant.Table 8
Bootstrap analysis for the significance test of mediation effect.
Effect value95% confidence intervalPIntermediary judgmentLower limitUpper limitIndirect effect0.0810.0490.1150.001Intermediary existedDirect effect0.3660.3080.4270.001Part of the intermediaryTotal effect0.4470.3880.5060.001Table 9
Multigroup modeling indicators comparison of the overall adaptation.
Test statisticReference standardUnrestricted modelMeasurement weighting modelStructure weighted modelStructural covariance modelStructural residual modelMeasurement residual modelCMIN/DF<34.2824.4295.6175.5265.5247.506P>0.05≤0.001≤0.001≤0.001≤0.001≤0.001≤0.001RMR<0.050.0230.0300.0610.0590.0680.073RMSEA<0.050.0540.0550.0640.0630.0630.076GFI>0.90.9680.9600.9510.9510.9500.923NFI>0.90.9590.9490.9340.9340.9320.892CFI>0.950.9680.9600.9450.9450.9440.905PNFI>0.50.6170.7290.7340.7500.7660.860PGFI>0.50.4840.5740.5810.5940.6070.692AICThe smaller the better226.148248.449303.159302.676306.109441.309ECVIThe smaller the better0.1990.2190.2670.2670.2700.389According to the model comparison in Table10, there are significant differences between the unrestricted model and the restricted model. The p value was less than 0.05, which passed the significance test. Therefore, there are significant differences in the models of different identity types, that is, identity types play a moderating role.By sorting out the data calculation results, the estimation results of the multiple-group structural equation model of two different identity types are obtained, as shown in Table11, and the specific path diagrams are shown in Figures 4 and 5.The analysis results of the multiple-group structural equation model show that in the path H1 of the positive influence of placeness on the sense of place, the influence of tourists and local residents is very significant (P<0.001), and the influence effects of the two types of identity are quite different. Among them, the impact effect of tourists (0.601) is greater than that of local residents (0.257). In the path H2 of the positive influence of placeness on satisfaction, the influence of tourists and local residents is very significant (P<0.001), and the difference is large. Among them, the influence of tourists (0.289) is smaller than that of local residents (0.306). In path H3, where the sense of place has a positive impact on satisfaction, the impact of tourists and local residents is very significant (P<0.001), and there are large differences. Among them, the impact effect of tourists (0.454) is greater than that of local residents (0.334).Table 10
Comparison between unrestricted model and restricted model (measurement weighted model).
ModelDFCMINPNFI Delta-1IFI Delta-2RFI rho-1TLI rho2Measurement weights736.3010.0000.0100.0100.0020.002Table 11
Estimated results of multigroup structural equation modeling based on tourists and residents.
RouteRespondentsTouristResidentH1: sense of place<---placeness0.601∗∗∗0.257∗∗∗H2: satisfaction <--- placeness0.289∗∗∗0.306∗∗∗H3: satisfaction <--- sense of place0.454∗∗∗0.334∗∗∗∗denotes P<0.05, ∗∗denotes P<0.01, ∗∗∗ indicates P<0.001; paths H1, H2, and H3 are consistent with the previous hypothesis.Figure 4
Standardized path coefficient of multi-group structural equation modeling for tourists.Figure 5
Standardized path coefficient of multi-group structural equation modeling for residents.


## 6. Conclusion and Discussion

### 6.1. Conclusion

In summary, based on relevant studies at home and abroad and previous studies, this paper constructs a causal model of placeness, sense of place, and satisfaction in the historical and cultural blocks in the old city of Beijing, and then, the model and research hypothesis proposed in this study were tested through the analysis of the covariance structure provided by AMOS. The test results show that all five hypotheses proposed in this study are valid.First, direct effect: The two dimensions of place experience (placeness and sense of place) of residents and tourists in historical and cultural blocks of Beijing’s old city have a remarkable positive impact on their satisfaction. Placeness has a significant positive impact on sense of place, hypothesis 1 is tenable; placeness has a significant positive impact on satisfaction, hypothesis 2 is tenable; sense of place has a significant positive impact on satisfaction, hypothesis is tenable. Moreover, the sense of place formed by the respondents has a slightly greater impact on satisfaction than placeness.Second, indirect effect: Sense of place has a prominent mediating effect on the relationship between placeness and satisfaction. From the research results, we can see that placeness not only has a significant direct effect on satisfaction, but also has a significant indirect effect on satisfaction through sense of place.Thirdly, when identity type is used as a moderating variable, the correlation strength among placeness, sense of place, and satisfaction will change significantly. The results show that the causalities of the three for residents and tourists are consistent, but the influence intensities are significantly different.


### 6.2. Discussion

This study aims to take Beijing’s historical and cultural district as the object, organize the three variables of placeness, sense of place, and satisfaction into a model by the structural equation, build the causal model of placeness, sense of place, and satisfaction, and compare the differences of causal mechanism for residents and tourists. The following results are obtained:Firstly, it eliminates the differences of causality between placeness and satisfaction and between sense of place and satisfaction based on regression analysis and other research methods and obtains the definite causality among placeness, sense of place, and satisfaction.Secondly, it reveals that the influence mechanisms of residents’ and tourists’ sense of place and satisfaction in historical and cultural blocks originate from placeness. Placeness is the foundation, and sense of place is the intermediary variable, and they affect satisfaction jointly. However, in the age of globalization and modernization, the landscape of these blocks in various places gradually tends to be similar, and the historical authenticity and placeness are gradually lost. The results of this study show that improving the local characteristics of these blocks in the old city of Beijing and accelerating sense of place for residents and tourists are the core of improving the satisfaction.Thirdly, it reveals the direct and indirect effects of placeness on satisfaction, and the effect intensity varies on residents and tourists. The results show that the residents’ satisfaction with Beijing’s historical and cultural blocks is more directly influenced by placeness and less by local emotional links. Tourists’ satisfaction is rooted in the indirect influence of placeness, or the emotional connection between tourists and places. The authors think that this may be because the local characteristics of Beijing’s historical and cultural blocks have not been effectively protected. Tourists conduct local experience activities in a particular way and time constraints. In the process of tourism, tourists form sense of place through mutual confirmation and stimulus of the on-the-spot feelings based on the objective elements and the memory and imagination before tourism, and then their satisfaction evaluations are affected. However, residents’ living environment has changed from the original pure living environment into tourist destinations under tourism development. This change often disrupts residents’ place dependence and place attachment, thus affecting the promotion effect of sense of place on satisfaction.


## 6.1. Conclusion

In summary, based on relevant studies at home and abroad and previous studies, this paper constructs a causal model of placeness, sense of place, and satisfaction in the historical and cultural blocks in the old city of Beijing, and then, the model and research hypothesis proposed in this study were tested through the analysis of the covariance structure provided by AMOS. The test results show that all five hypotheses proposed in this study are valid.First, direct effect: The two dimensions of place experience (placeness and sense of place) of residents and tourists in historical and cultural blocks of Beijing’s old city have a remarkable positive impact on their satisfaction. Placeness has a significant positive impact on sense of place, hypothesis 1 is tenable; placeness has a significant positive impact on satisfaction, hypothesis 2 is tenable; sense of place has a significant positive impact on satisfaction, hypothesis is tenable. Moreover, the sense of place formed by the respondents has a slightly greater impact on satisfaction than placeness.Second, indirect effect: Sense of place has a prominent mediating effect on the relationship between placeness and satisfaction. From the research results, we can see that placeness not only has a significant direct effect on satisfaction, but also has a significant indirect effect on satisfaction through sense of place.Thirdly, when identity type is used as a moderating variable, the correlation strength among placeness, sense of place, and satisfaction will change significantly. The results show that the causalities of the three for residents and tourists are consistent, but the influence intensities are significantly different.


## 6.2. Discussion

This study aims to take Beijing’s historical and cultural district as the object, organize the three variables of placeness, sense of place, and satisfaction into a model by the structural equation, build the causal model of placeness, sense of place, and satisfaction, and compare the differences of causal mechanism for residents and tourists. The following results are obtained:Firstly, it eliminates the differences of causality between placeness and satisfaction and between sense of place and satisfaction based on regression analysis and other research methods and obtains the definite causality among placeness, sense of place, and satisfaction.Secondly, it reveals that the influence mechanisms of residents’ and tourists’ sense of place and satisfaction in historical and cultural blocks originate from placeness. Placeness is the foundation, and sense of place is the intermediary variable, and they affect satisfaction jointly. However, in the age of globalization and modernization, the landscape of these blocks in various places gradually tends to be similar, and the historical authenticity and placeness are gradually lost. The results of this study show that improving the local characteristics of these blocks in the old city of Beijing and accelerating sense of place for residents and tourists are the core of improving the satisfaction.Thirdly, it reveals the direct and indirect effects of placeness on satisfaction, and the effect intensity varies on residents and tourists. The results show that the residents’ satisfaction with Beijing’s historical and cultural blocks is more directly influenced by placeness and less by local emotional links. Tourists’ satisfaction is rooted in the indirect influence of placeness, or the emotional connection between tourists and places. The authors think that this may be because the local characteristics of Beijing’s historical and cultural blocks have not been effectively protected. Tourists conduct local experience activities in a particular way and time constraints. In the process of tourism, tourists form sense of place through mutual confirmation and stimulus of the on-the-spot feelings based on the objective elements and the memory and imagination before tourism, and then their satisfaction evaluations are affected. However, residents’ living environment has changed from the original pure living environment into tourist destinations under tourism development. This change often disrupts residents’ place dependence and place attachment, thus affecting the promotion effect of sense of place on satisfaction.


## 7. Research Deficiency and Prospect

Firstly, the research subject is not extensive enough. The development of a historical and cultural district involves a wide range of participants, including residents, tourists, business operators, developers, and local governments. Different subjects have different experiences and interests, as well as different perceptions of the place experience of historical and cultural blocks, resulting in different appeals and satisfaction. This study only focuses on residents and tourists, which has certain limitations. In the future, we can further expand research subjects and make a comparative study among multiple subjects, so as to more comprehensively reflect the local characteristics of historical and cultural districts and the place significance given by different subjects.Secondly, the research variables can be further expanded. This study provides a basic research framework for the formation of place experience and its impact on the satisfaction of residents and tourists in historical and cultural blocks. Through the model analysis, it can be concluded that for different subjects, residents and tourists, the effects of placeness and sense of place on satisfaction are different. This shows that the formation of sense of place and satisfaction not only comes from placeness, but may also be affected by other variables. For example, Tang [67] thinks that the formation of sense of place is an evaluation process based on environmental perception combined with personal behavior goals, and the level of personal knowledge and experience is a vital factor in this evaluation process. Therefore, demographic factors affect the formation of sense of place in many cases. In addition, some studies have shown that there is a relationship between satisfaction and future behavior intention [70–73]. Therefore, variables such as demographic characteristics and place revisit intention can be added to expand the model variables in the future, so as to further reveal the complex mechanism between place experience and satisfaction.



---

*Source: 6673158-2021-07-05.xml*