# An Empirical Study on the Digital Display Design of Intangible Cultural Heritages Based on Audience Satisfaction

Authors: Lei Zhang; Di Fu; Jinlong Zhao
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

How the audience perceive the display of intangible cultural heritages (ICHs) and what are their psychological needs of ICH display are of great value to the display design of ICHs. This article carries out a questionnaire survey on the audience of the “Splendid China” ICH costume show and empirically analyzes the survey results. After constructing the American Customer Satisfaction Index (ACSI), the authors classified the psychological needs of customers for the on-site display of ICHs through analytical hierarchy process (AHP) and factor analysis, conducted the theoretical discussion, and verified the results with actual data. The research explores how should the digital display of ICHs be designed to satisfy the audience and provides a reference for China’s ICH display designers. The research shows that the construction of a public satisfaction evaluation model for the digital display of ICHs is the key to satisfaction evaluation, there is ample room to improve public satisfaction with the digital display of ICHs, the dissemination of ICH can be strengthened through model construction, and personalized service is an effective way to promote the digital development of ICHs.


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## Body

## 1. Introduction

The display design of intangible cultural heritages (ICHs) fully reflects China’s ICH protection principle of inheriting and representing vitality. It provides an important way to promote ICHs, making the public understand and love ICHs. However, the actual display effect of the “Splendid China” costume show and related textile and apparel ICH exhibitions was not desirable. Due to the lack of scientific management and effect feedback mechanism, some ICH exhibitions fail to attract the audience, albeit incurring a high cost. The audience are not very impressed, and some exhibitions are way to commercialized.During ICH displays, the audience do not merely glance over the exhibits but go through a complex psychological process. The visit helps the audience form an overall cognition of ICHs through reasonable imagination, as their memories are invoked by the visual, auditory, and tactile perceptions of ICHs [1]. In essence, visiting an ICH display is about the mutual influence and interaction between ICH skills and the audience’s psychological activities [2]. To inherit and disseminate ICH culture, it is crucial to explore the ICH display design based on the audience's perception and preference.The significance of this research is to expand the research fields of ICH, design, and communication and to provide theoretical reference for promoting the creative transformation and innovative development of excellent traditional culture, at the same time promote the systematic protection of ICHS, provide consultation for the competent department of ICH exhibition and display, and provide a reference for the design practice of ICH display. As a combination of human knowledge and skills and aesthetic taste, the dissemination of ICH is facing the dilemma of “difficult to enter and difficult to exit.” In order to greatly improve the dissemination effect of ICH, it is necessary to focus on audience satisfaction and actively use artificial intelligence technologies such as computer recognition, human-computer interaction, and simulation scene construction, in the digitization of dissemination subjects, the correlation of dissemination objects, and the liveliness of dissemination content. Innovate the ways of communication of ICHs from the expansion and diversification of dissemination channels.


## 2. Theoretical and Technological Overview

### 2.1. Overview of ICH Digital Display Technologies

Digital display compiles and reorganizes the contents of the exhibit in a way to form a complete work of digital display, using digital technology as the main technical means [3]. Currently, the traditional protection and inheritance methods of ICH can no longer meet the needs of development, which requires the continuous development of ICH display towards the digital display. The digital display of ICHs relies heavily on new media technologies, such as virtual reality, human-computer interaction, and augmented reality, to make the display authentic, complete, and interactive [4].In the field of digital display, the ingenious application and combination of different technical means can stimulate the vitality of ICHs and improve the effectiveness of digital display, promoting innovation in the field [5]. However, advanced technology is not necessarily suitable. The digital display effect of ICHs can only be maximized by fully stimulating the audience’s senses, which calls for mining the inherent elements of different kinds of ICHs, and utilizing scientific and reasonable digital technology [6].Figure1 shows the flow of ICH digital display [7]. Starting from the ICH contents, ICH digital display involves content elements, database classes, display methods, and the perception experience. Based on the dissemination features of ICHs and appropriate digital technology, this article analyzes the display methods of different types of ICHs, with the aim to extract differentiated features of each type, and leaves the audience an excellent impression [8].Figure 1
Flow of ICH digital display.The research on ICH digitization technology started early in Europe and North America and has achieved outstanding results in theory and application. For example, Rossella Cafib expounded the opportunities and challenges of digital cultural heritage protection in ethnic cultural areas and gave examples of its application in Italian libraries, cultural protection departments, and tourism departments [9]. Hong [10] studied the strategies and methods of digital recording of ICHs and enumerated the advantages and disadvantages of digital recording, providing a guide for digital acquisition. Idris et al. [11] clarified the relationship between cultural diversity and ICH digitization and discussed the strengths and weaknesses of the application of ICH digitization technology as well as related issues. Using the open data resources of social media, Kyriacos [12] carried out a three-dimensional (3D) visual reconstruction of cultural heritages. Suarez et al. [13] promoted the protection and inheritance of cultural contents, using computer modeling tools and virtual reality, combined with the sensory experience of archaeological acoustics.The technology application in China is as follows: Focusing on the ICHs of ethnic minorities in western Hunan province, Dong [14] and Zhang and Zhao [15] proposed a digital protection strategy for digital museums and other entities. Song et al. [16] investigated the display of domestic digital websites and presented the construction strategy and model for creating a comprehensive network platform. Meanwhile, some progress has been achieved by ICH researchers in the digital protection of traditional handicrafts with regional features. The relevant researchers have constructed ICH digital collection standards, the reproduction methods of ICH digital storage, the general architecture of ICH database construction, and other digital strategies [17–21].With the advent of the era of digital communication, the characteristics of the Internet, such as interactivity, speed, convenience, openness, and comprehensiveness, have profoundly affected the way of digital display and dissemination of ICHs. Therefore, the dissemination characteristics of China’s ICHs in the digital ecological environment have also occurred. Great changes and presents the following five new characteristics: plasticity, rheology, unboundedness, sharing, and interactivity. (1) Plasticity – showing the state of active display; (2) rheology – from invisible bearing to polymorphic rheology; (3) unbounded – from local cognition to wide-area communication; (4) sharing – across regional boundaries; and (5) interactivity – multidirectional interaction and “circle layering.” Finally, based on the integration of digital technology and media, the advantages of digital display and dissemination will be brought into play, and intelligent and innovative digital display and dissemination methods of China’s ICHs will be developed.At present, the wide application and popularization of 5G technology in China is bound to bring about innovative changes in various industries. With the continuous development and large-scale application of AI technology, the digital communication of ICH has also been pushed into the era of application scenario experiential communication. This requires making full use of modern digital technology and AI technology to enhance the scene-based experience of ICH, which is embodied in two aspects: first, content experience, building an ICH virtual experience center, and comprehensively using VR and augmented reality, 3D dynamic, and other digital technologies, to show the audience the content of ICH culture, inheritance history, craftsmanship, and other contents from multiple dimensions and enhance the scene interaction and experience of ICHs.


### 2.2. Overview of Satisfaction Index Research

In 1965, American scholar Cardozo proposed customer satisfaction—the very first concept of satisfaction. Since then, the meaning of satisfaction, the evaluation method of satisfaction, and the analysis model of satisfaction have been widely studied and used. At present, business and academic circles commonly define satisfaction as a person’s feeling of pleasure or disappointment, which resulted from comparing a product’s perceived performance or outcome against his/her expectations, following the definition given by Philip Kotler, father of modern marketing. Satisfaction measures the level of his/her satisfaction.Domestic and foreign scholars and institutions have established customer satisfaction evaluation models and utilized them to measure customer satisfaction, for example, Fornell and Larcker’s [22] normal quality (NQ) standard model, Churchill and Supernant’s [23] evaluated performance (EP) behavior evaluation model, and Parasuraman et al.’s [24] SERVQUAL model. In addition, Sweden established the Sweden Customer Satisfaction Barometer (SCSB) in 1989, based on which Europe constructed the European Customer Satisfaction Index (SCSI) [25]. Since 1999, China started to build China Customer Satisfaction Index (CCSI) and has achieved certain results.The current research on satisfaction, especially customer satisfaction, is relatively systematic and mature in China. But the domestic research on ICH display satisfaction has just started. Chen Bin, a pioneer in this field [26], examined the quadrant graph model based on importance and satisfaction and discovered that professional visitors and exhibitors have obviously different evaluations of the exhibition objectives and indices. Ju et al. [27] used factor analysis to analyze the exhibition features valued by exhibitors and the exhibitors’ perception of each characteristic index. But they did not directly measure or consider the core of audience satisfaction. He [28] adopted the structural equation model to construct a model of factors affecting the Chinese audience’s satisfaction of international ICHs.Later, domestic scholars have combined the quadrant graph model with factor analysis and structural equation model to study audience satisfaction. For instance, Xu et al. [29] first carried out an exploratory factor analysis on 21 original indices and then performed structural equation modeling. Later, scatterplots were drawn and analyzed according to the importance rated by the organizers and audience satisfaction. Wu and Zhang [30] successively analyzed 21 main factors affecting tourists' destination choice through factor analysis, structural analysis model, and quadrant graph model.During the survey, satisfaction is actually a multivariate selection variable for ranking. The domestic research on ranking qualitative variables is relatively mature. For example, Lin and Ai [31] established an ordered probit regression model to examine the influence of main individual demographic features and social-economic status variables (e.g., age, income, gender, and education) over display demand. Zhao and Miao [32], Jin et al. [33], and Guo [34] also constructed and applied the probit model to systematically analyze the ranking qualitative variables in their research. Some domestic scholars have also used the probit model to analyze satisfaction.On this basis, the United States constructed the American Customer Satisfaction Index (ACSI) evaluation model in 1994, which is a customer-based organizational performance evaluation system and a market- or customer-oriented performance evaluation method [35]. There are many researches on ACSI at home and abroad [36–39]. The ACSI model is a national-level global customer satisfaction evaluation model that takes the quality of products or services as the evaluation object and including four levels: department, enterprise, industry, and country. The US government takes the ACSI model as the foundation, refers to its own domestic government service status and characteristics of public services, and finally obtains the US government public satisfaction index evaluation model, which is an innovative application of the customer satisfaction model in the government public service level. Figure 2 presents the final improvement of the ACSI model [40], which covers public expectations, perceived quality, public satisfaction, public complaints, and public trust [41].Figure 2
ACSI model.ACSI is a macro-index to measure the quality of economic output. It is a comprehensive evaluation index of audience satisfaction level based on the process of product and service consumption. It is a national audience satisfaction theory with the most complete system and the best application effect model. The biggest feature of ACSI is that audience satisfaction can not only be compared between different products and industries but also between different customers of the same product, reflecting the differences between people. Compared with other models, the biggest advantage of the ACSI model is that it can carry out cross-industry comparisons, as well as vertical and cross-time comparisons, which has become a barometer of the US economy. At the same time, ACSI is a very effective management tool, which can help companies compare with competitors and evaluate their competitive position. It should be pointed out that ACSI is an accurate quantitative economic model established based on advanced consumer behavior theory. It is used to monitor the macroeconomic operation, and the main consideration is the comparison of audience satisfaction across cross-industry and cross-industry sectors, rather than firm-specific diagnostic guidance, so this model is rarely used in micro-level satisfaction surveys.Based on the above discussion, this article takes the theory of ICH digital display service and satisfaction as the basis, and by sorting out and analyzing the related theories of public satisfaction in the digital display of ICH, it creates conditions for the subsequent theoretical integration. (1) The digital dissemination of ICH should not be bound by the boundaries of traditional communication methods and continue to lead the innovation of ICH dissemination through its own technological advantages so as to help the continuous advancement of ICH in terms of protection and inheritance. At the same time, it also provides a new model for the inheritance of the current ICHs. (2) The research on satisfaction (especially customer satisfaction) provides an important reference for the display of ICHs.


## 2.1. Overview of ICH Digital Display Technologies

Digital display compiles and reorganizes the contents of the exhibit in a way to form a complete work of digital display, using digital technology as the main technical means [3]. Currently, the traditional protection and inheritance methods of ICH can no longer meet the needs of development, which requires the continuous development of ICH display towards the digital display. The digital display of ICHs relies heavily on new media technologies, such as virtual reality, human-computer interaction, and augmented reality, to make the display authentic, complete, and interactive [4].In the field of digital display, the ingenious application and combination of different technical means can stimulate the vitality of ICHs and improve the effectiveness of digital display, promoting innovation in the field [5]. However, advanced technology is not necessarily suitable. The digital display effect of ICHs can only be maximized by fully stimulating the audience’s senses, which calls for mining the inherent elements of different kinds of ICHs, and utilizing scientific and reasonable digital technology [6].Figure1 shows the flow of ICH digital display [7]. Starting from the ICH contents, ICH digital display involves content elements, database classes, display methods, and the perception experience. Based on the dissemination features of ICHs and appropriate digital technology, this article analyzes the display methods of different types of ICHs, with the aim to extract differentiated features of each type, and leaves the audience an excellent impression [8].Figure 1
Flow of ICH digital display.The research on ICH digitization technology started early in Europe and North America and has achieved outstanding results in theory and application. For example, Rossella Cafib expounded the opportunities and challenges of digital cultural heritage protection in ethnic cultural areas and gave examples of its application in Italian libraries, cultural protection departments, and tourism departments [9]. Hong [10] studied the strategies and methods of digital recording of ICHs and enumerated the advantages and disadvantages of digital recording, providing a guide for digital acquisition. Idris et al. [11] clarified the relationship between cultural diversity and ICH digitization and discussed the strengths and weaknesses of the application of ICH digitization technology as well as related issues. Using the open data resources of social media, Kyriacos [12] carried out a three-dimensional (3D) visual reconstruction of cultural heritages. Suarez et al. [13] promoted the protection and inheritance of cultural contents, using computer modeling tools and virtual reality, combined with the sensory experience of archaeological acoustics.The technology application in China is as follows: Focusing on the ICHs of ethnic minorities in western Hunan province, Dong [14] and Zhang and Zhao [15] proposed a digital protection strategy for digital museums and other entities. Song et al. [16] investigated the display of domestic digital websites and presented the construction strategy and model for creating a comprehensive network platform. Meanwhile, some progress has been achieved by ICH researchers in the digital protection of traditional handicrafts with regional features. The relevant researchers have constructed ICH digital collection standards, the reproduction methods of ICH digital storage, the general architecture of ICH database construction, and other digital strategies [17–21].With the advent of the era of digital communication, the characteristics of the Internet, such as interactivity, speed, convenience, openness, and comprehensiveness, have profoundly affected the way of digital display and dissemination of ICHs. Therefore, the dissemination characteristics of China’s ICHs in the digital ecological environment have also occurred. Great changes and presents the following five new characteristics: plasticity, rheology, unboundedness, sharing, and interactivity. (1) Plasticity – showing the state of active display; (2) rheology – from invisible bearing to polymorphic rheology; (3) unbounded – from local cognition to wide-area communication; (4) sharing – across regional boundaries; and (5) interactivity – multidirectional interaction and “circle layering.” Finally, based on the integration of digital technology and media, the advantages of digital display and dissemination will be brought into play, and intelligent and innovative digital display and dissemination methods of China’s ICHs will be developed.At present, the wide application and popularization of 5G technology in China is bound to bring about innovative changes in various industries. With the continuous development and large-scale application of AI technology, the digital communication of ICH has also been pushed into the era of application scenario experiential communication. This requires making full use of modern digital technology and AI technology to enhance the scene-based experience of ICH, which is embodied in two aspects: first, content experience, building an ICH virtual experience center, and comprehensively using VR and augmented reality, 3D dynamic, and other digital technologies, to show the audience the content of ICH culture, inheritance history, craftsmanship, and other contents from multiple dimensions and enhance the scene interaction and experience of ICHs.


## 2.2. Overview of Satisfaction Index Research

In 1965, American scholar Cardozo proposed customer satisfaction—the very first concept of satisfaction. Since then, the meaning of satisfaction, the evaluation method of satisfaction, and the analysis model of satisfaction have been widely studied and used. At present, business and academic circles commonly define satisfaction as a person’s feeling of pleasure or disappointment, which resulted from comparing a product’s perceived performance or outcome against his/her expectations, following the definition given by Philip Kotler, father of modern marketing. Satisfaction measures the level of his/her satisfaction.Domestic and foreign scholars and institutions have established customer satisfaction evaluation models and utilized them to measure customer satisfaction, for example, Fornell and Larcker’s [22] normal quality (NQ) standard model, Churchill and Supernant’s [23] evaluated performance (EP) behavior evaluation model, and Parasuraman et al.’s [24] SERVQUAL model. In addition, Sweden established the Sweden Customer Satisfaction Barometer (SCSB) in 1989, based on which Europe constructed the European Customer Satisfaction Index (SCSI) [25]. Since 1999, China started to build China Customer Satisfaction Index (CCSI) and has achieved certain results.The current research on satisfaction, especially customer satisfaction, is relatively systematic and mature in China. But the domestic research on ICH display satisfaction has just started. Chen Bin, a pioneer in this field [26], examined the quadrant graph model based on importance and satisfaction and discovered that professional visitors and exhibitors have obviously different evaluations of the exhibition objectives and indices. Ju et al. [27] used factor analysis to analyze the exhibition features valued by exhibitors and the exhibitors’ perception of each characteristic index. But they did not directly measure or consider the core of audience satisfaction. He [28] adopted the structural equation model to construct a model of factors affecting the Chinese audience’s satisfaction of international ICHs.Later, domestic scholars have combined the quadrant graph model with factor analysis and structural equation model to study audience satisfaction. For instance, Xu et al. [29] first carried out an exploratory factor analysis on 21 original indices and then performed structural equation modeling. Later, scatterplots were drawn and analyzed according to the importance rated by the organizers and audience satisfaction. Wu and Zhang [30] successively analyzed 21 main factors affecting tourists' destination choice through factor analysis, structural analysis model, and quadrant graph model.During the survey, satisfaction is actually a multivariate selection variable for ranking. The domestic research on ranking qualitative variables is relatively mature. For example, Lin and Ai [31] established an ordered probit regression model to examine the influence of main individual demographic features and social-economic status variables (e.g., age, income, gender, and education) over display demand. Zhao and Miao [32], Jin et al. [33], and Guo [34] also constructed and applied the probit model to systematically analyze the ranking qualitative variables in their research. Some domestic scholars have also used the probit model to analyze satisfaction.On this basis, the United States constructed the American Customer Satisfaction Index (ACSI) evaluation model in 1994, which is a customer-based organizational performance evaluation system and a market- or customer-oriented performance evaluation method [35]. There are many researches on ACSI at home and abroad [36–39]. The ACSI model is a national-level global customer satisfaction evaluation model that takes the quality of products or services as the evaluation object and including four levels: department, enterprise, industry, and country. The US government takes the ACSI model as the foundation, refers to its own domestic government service status and characteristics of public services, and finally obtains the US government public satisfaction index evaluation model, which is an innovative application of the customer satisfaction model in the government public service level. Figure 2 presents the final improvement of the ACSI model [40], which covers public expectations, perceived quality, public satisfaction, public complaints, and public trust [41].Figure 2
ACSI model.ACSI is a macro-index to measure the quality of economic output. It is a comprehensive evaluation index of audience satisfaction level based on the process of product and service consumption. It is a national audience satisfaction theory with the most complete system and the best application effect model. The biggest feature of ACSI is that audience satisfaction can not only be compared between different products and industries but also between different customers of the same product, reflecting the differences between people. Compared with other models, the biggest advantage of the ACSI model is that it can carry out cross-industry comparisons, as well as vertical and cross-time comparisons, which has become a barometer of the US economy. At the same time, ACSI is a very effective management tool, which can help companies compare with competitors and evaluate their competitive position. It should be pointed out that ACSI is an accurate quantitative economic model established based on advanced consumer behavior theory. It is used to monitor the macroeconomic operation, and the main consideration is the comparison of audience satisfaction across cross-industry and cross-industry sectors, rather than firm-specific diagnostic guidance, so this model is rarely used in micro-level satisfaction surveys.Based on the above discussion, this article takes the theory of ICH digital display service and satisfaction as the basis, and by sorting out and analyzing the related theories of public satisfaction in the digital display of ICH, it creates conditions for the subsequent theoretical integration. (1) The digital dissemination of ICH should not be bound by the boundaries of traditional communication methods and continue to lead the innovation of ICH dissemination through its own technological advantages so as to help the continuous advancement of ICH in terms of protection and inheritance. At the same time, it also provides a new model for the inheritance of the current ICHs. (2) The research on satisfaction (especially customer satisfaction) provides an important reference for the display of ICHs.


## 3. Model Construction

### 3.1. Evaluation Model

The original ACSI model contains five structural variables, while the optimized ACSI model has four structural variables (Figure3). In order to further optimize the structure, the research is guided by the public satisfaction index structure model constructed by the US government and further improved. Positive sentiment is relative, so incorporating the structural variable of public complaints into public trust is more conducive to building a public satisfaction evaluation model for the digital display of ICHs:(1)
Public expectations: the public’s pre-event expected service level for the digital display service provided by the ICH digitization service provider. As the object of ICH digital services, the public must have expectations for the services they enjoy. Its structural variables include the following three observation variables: the overall expectation of ICH digital display service, the ICH new media communication service expectation, and the ICH venue digital service expectation.(2)
Service perception: the public’s actual perception of the digital display service provided by the ICH service provider. Due to the difference between each person’s subjective ideology and the actual external environment, they will also have differentiated psychological perceptions of the same service they enjoy. The evaluation variables include the service perception of the digital dissemination of ICH and the digital service of ICH venues.(3)
Public satisfaction: the public’s post-event satisfaction with the digital display service provided by the ICH digitization service provider. The observed variables include the public’s overall satisfaction with ICH digital services and the public’s satisfaction with new media dissemination of ICH digital services and venues.(4)
Public trust: the public’s psychological response, emotional response, and complaints to/about the digital display service provided by the ICH digitization service provider [42, 43]. The high level of satisfaction brought by a good digital display service of ICH can convey more positive emotions; otherwise, it will bring the opposite result. The observed variables are divided into the following three aspects: the public’s reuse of ICH services, the public’s recommendation of ICH services, and the public’s complaints about services.Figure 3
ACSI-based public satisfaction index model for ICH digital display.


### 3.2. Calculation Principle

Our discussion and empirical analysis mainly utilize analytic hierarchy process (AHP) and factor analysis method (FAM), aiming to provide a guide for future calculation of satisfaction indices. The research mainly refers to the calculation principle of Cheng [44]. The calculation principle of public satisfaction index for ICH digital display is as follows:(1)
Letξ denote public satisfaction; x1,x2,…,xm denote the observed variables of public satisfaction; λ1,λ2,…,λm denote the standardized path coefficients of x1,x2,…,xm relative to ξ, then public satisfaction can be calculated as(1)publicsatisfaction=Eξ−MinξMaxξ−Minξ∗100,whereEξ=∑i=1mλix¯i;Maxξ=∑i=1mλi Max xi; and Minξ=∑i=1mλi Min xi. Eξ,Maxξ, and Minξ are the mean, maximum, and minimum of public satisfaction, respectively; x¯i, Max xi, and Min xi are the mean, maximum, and minimum of xi, respectively, i∈Ωm.(2)
Formula (1) can be properly simplified as(2)publicsatisfaction=∑i=1mλix¯i,whereλ1,λ2,…,λm are the standardized path coefficients of x1,x2,…,xm relative to ξ; and x¯i is the mean of xi, i∈Ωm. The relevant indices of the observed variables can be weighted by(3)yi=1n∑j=1nwiyij,whereyi is the evaluation item; yij is the score of item yi against an index; wi is the weight (importance) of that index; and n is the number of indices for evaluating item yi.(3)
The FAM measures the degree of influence of each variable on the structural model, in the light of the correlation between variables, and then describes the model generally with a few common factors.In the null hypothesis model, there arek measurement indices and n statistical variables:(4)Xi=αi1λ1+αi2λ2+⋯+αipλm+εi,where λ1,λ2,…,λm are the common factors of the model (these factors are related to all statistical variables; their correlation coefficients are 0, a sign of the independence between them); εi is the unique special correlation factor of each statistical variable Xi; and coefficient αi1 is the weight index of the jth common factor relative to the ith statistical variable, reflecting the explanatory power and effect of the common factor on that variable [43]. Because in factor analysis, only jth main factors are usually selected, that is, the first main factor is selected according to the correlation of variables, so that its contribution to the variance of the common factor variance of each variable is the largest, and then the variance of this factor is eliminated. Therefore, αi1 > 0.5.


### 3.3. Evaluation Method

The public satisfaction evaluation model for ICH digital display consists of four structural variables, namely public expectations, service perception, public satisfaction, and public trust; each structure contains one or more observed variables. The variables interact with each other to form a whole. The author mainly refers to the evaluation methods and models of scholar Cheng [44] and recollects the data for calculation. The specific variables are defined in Table 1.Table 1
Measurement indices.
Structural variablesObserved variablesPublic expectationsξOverall expectation of ICH digital display service (x1)Expectation of ICH new media communication service (x2)Expectation of ICH venue digital display service (x3)Service perceptionη1Perception off ICH new media communication service (y1)Perception of ICH venue multimedia service (y2)Perception of ICH venue human-machine interaction service (y3)Public satisfaction (η2)Overall satisfaction with ICH digital display service (y4)Overall satisfaction with ICH new media communication (y5)Overall satisfaction with ICH venue digital display (y6)Public trustη3Complaints (y7)Public reuse of ICH digitization service (y8)Public recommendation of ICH digitization service (y9)The public satisfaction evaluation model for ICH digital display assumes that there is a linear relationship between variables. The structural variable of public expectations is the only exogenous causal variable in the model, while the other structural variables are the endogenous structural variables. The relationship can be represented by three matrix equations, including one structural equation and two measurement equations:Structural equation:(5)η=Aη+Bξ+m.Measurement equations:(6)Y=Λ1η+n,X=Λ2η+p,where A and B are the coefficient matrices reflecting the structural variable relationship in the structural model (A can be regarded as the relationship matrix of the mutual influence between all endogenous structural variables, and B can be understood as the relationship matrix of the influence of the exogenous structural variable “public expectations” on all endogenous variables); Λ1andΛ2 are the relationship matrices of the mutual influence between observed variables, and that between structural variables; and m, n, and p are the error vectors of the three equation models, respectively.The three matrix equations are as follows:The structural model, that is, the relationship between structural variables can be calculated as(7)η1η2η3=000β21000β320∗η1η2η3+γ11γ120∗ξ+m1m2m3,where η1,η2,andη3 are service perception, public satisfaction, and public trust, respectively; βij is the degree of influence of endogenous structural variable j on endogenous structural variable i; γij is the degree of influence of endogenous structural variable ξ on exogenous structural variable ηj; and m is the error vector of the model.(8)y1y2y3y4y5y6y7y8y9=φ1100φ2100φ31000φ4200φ5200φ62000φ7300φ8300φ93∗η1η2η3+n1n2n3n4n5n6n7n8n9,(9)x1x2x3=ω1ω2ω3∗ξ+ρ1ρ2ρ3,where x1,x2, andx3 are the observed variables of exogenousstructural variable public expectations (ξ); y1,y2,y3⋯y8,andy9 are the observed variables of service perception (η1), public satisfaction (η2), and public trust (η3);φijandωi are the coefficients of the coefficient matrices Λ1andΛ2 of the measurement models, respectively (the two coefficients reflect the degree of influence of each structural variable over its own observed variables); and niandρi are the error vectors of the two models, respectively.This article will use the Amos Graphics tool in the statistical software SPSS to construct and analyze the structural model in order to derive the structural equations of the measurement model of public satisfaction of the digital display of ICH and to verify and optimize the path coefficients of the measurement equations in order to obtain the optimal fitting model. Common model validation fit indicators areP-value, confidence level, significance level, chi-square value, chi-square ratio, canonical fit index, etc. Among them, P-value is generally applied together with the significance level for true-false speculation of the hypothesized model. And it is generally considered that if P<0.10, it can indicate that the model test variable is significant and the original hypothesis is not valid.The specific index criteria are as follows [44].


#### 3.3.1. Chi-Squared Test

To test the reasonableness of the model, or to verify the consistency of the theoretical model with the empirical data, some scientific indicators are needed. One of the most commonly used evaluation indicators is the chi-squared statistic. Its derived formula is as follows:(10)x2=N−1Fmin,where x2 represents the check value of the model fit and N is the number of samples, and Fmin indicates the minimum value of the test equation derived by different parameter estimation methods (maximum likelihood estimation and generalized least squares method).At the significance level, ifx2 < xα2, it indicates that the original hypothesis is approved and the model fit meets the requirements. Otherwise, the original hypothesis is rejected and the alternative hypothesis is accepted. From the above, it can be seen that the chi-squared test statistic is smaller, and the data is the better. It can also be seen that when its value is 0, it indicates that the measured data fit the hypothesized model perfectly.


#### 3.3.2. Goodness-of-Fit Index

GFI (goodness-of-fit index) is the division of the explanatory strength of the hypothetical model based on the theory of the overall variability of the measured data. The formula is as follows:(11)GFI=trσwσtrsws.AGFI (adjusted goodness-of-fit index) will be combined with the number of parameters of the model itself for a comprehensive analysis. The greater the number of parameters, the greater the value of the AGFI, and the more favorable it is to obtain a model with a good fit. The formula is as follows:(12)AGFI=1−GFI1−the number of estimated paramters/the number of observations.When normalized values are taken, the value of AGFI is between 0 and 1. The difference between its value and 1 is inversely related to the degree of fit of the model. In general, when its value is not lower than 0.90, it indicates that the fitting model is acceptable.PGFI (parsimony goodness-of-fit index) can reflect the number of parameters to be estimated in the fitting model from the side, and its formula is as follows:(13)PGFI=1−the number of estimated paramtersthe number of observations∗GFI.In general, according to (12), the PGFI is related to the number of estimated parameters and the number of observations, so it is not much less than, but there is a definite coefficient relationship between them.NFI (normed fit indices) and NNFI (non-normed fitindices) can both be interpreted as the level of difference between the fitting model and the independent model [44].(14)NFI=xindep2−xtest2xindep2,NNFI=xindep2−dfindepxtest2/dftestxindep2−dfindep,where xtest2 and dftest denote the chi-square value and degrees of freedom of the fitting model, and xindep2 and dfindep denote the chi-square value and degrees of freedom of the independent model. Practice has proved that when the amount of data obtained is insufficient and there are many model parameters. The NFI can be misjudged in the evaluation of a well-fitted model, while the NNFI compensates for its deficiency to some extent. A value of not less than 0.90 for both marks an acceptable fitting model.


## 3.1. Evaluation Model

The original ACSI model contains five structural variables, while the optimized ACSI model has four structural variables (Figure3). In order to further optimize the structure, the research is guided by the public satisfaction index structure model constructed by the US government and further improved. Positive sentiment is relative, so incorporating the structural variable of public complaints into public trust is more conducive to building a public satisfaction evaluation model for the digital display of ICHs:(1)
Public expectations: the public’s pre-event expected service level for the digital display service provided by the ICH digitization service provider. As the object of ICH digital services, the public must have expectations for the services they enjoy. Its structural variables include the following three observation variables: the overall expectation of ICH digital display service, the ICH new media communication service expectation, and the ICH venue digital service expectation.(2)
Service perception: the public’s actual perception of the digital display service provided by the ICH service provider. Due to the difference between each person’s subjective ideology and the actual external environment, they will also have differentiated psychological perceptions of the same service they enjoy. The evaluation variables include the service perception of the digital dissemination of ICH and the digital service of ICH venues.(3)
Public satisfaction: the public’s post-event satisfaction with the digital display service provided by the ICH digitization service provider. The observed variables include the public’s overall satisfaction with ICH digital services and the public’s satisfaction with new media dissemination of ICH digital services and venues.(4)
Public trust: the public’s psychological response, emotional response, and complaints to/about the digital display service provided by the ICH digitization service provider [42, 43]. The high level of satisfaction brought by a good digital display service of ICH can convey more positive emotions; otherwise, it will bring the opposite result. The observed variables are divided into the following three aspects: the public’s reuse of ICH services, the public’s recommendation of ICH services, and the public’s complaints about services.Figure 3
ACSI-based public satisfaction index model for ICH digital display.


## 3.2. Calculation Principle

Our discussion and empirical analysis mainly utilize analytic hierarchy process (AHP) and factor analysis method (FAM), aiming to provide a guide for future calculation of satisfaction indices. The research mainly refers to the calculation principle of Cheng [44]. The calculation principle of public satisfaction index for ICH digital display is as follows:(1)
Letξ denote public satisfaction; x1,x2,…,xm denote the observed variables of public satisfaction; λ1,λ2,…,λm denote the standardized path coefficients of x1,x2,…,xm relative to ξ, then public satisfaction can be calculated as(1)publicsatisfaction=Eξ−MinξMaxξ−Minξ∗100,whereEξ=∑i=1mλix¯i;Maxξ=∑i=1mλi Max xi; and Minξ=∑i=1mλi Min xi. Eξ,Maxξ, and Minξ are the mean, maximum, and minimum of public satisfaction, respectively; x¯i, Max xi, and Min xi are the mean, maximum, and minimum of xi, respectively, i∈Ωm.(2)
Formula (1) can be properly simplified as(2)publicsatisfaction=∑i=1mλix¯i,whereλ1,λ2,…,λm are the standardized path coefficients of x1,x2,…,xm relative to ξ; and x¯i is the mean of xi, i∈Ωm. The relevant indices of the observed variables can be weighted by(3)yi=1n∑j=1nwiyij,whereyi is the evaluation item; yij is the score of item yi against an index; wi is the weight (importance) of that index; and n is the number of indices for evaluating item yi.(3)
The FAM measures the degree of influence of each variable on the structural model, in the light of the correlation between variables, and then describes the model generally with a few common factors.In the null hypothesis model, there arek measurement indices and n statistical variables:(4)Xi=αi1λ1+αi2λ2+⋯+αipλm+εi,where λ1,λ2,…,λm are the common factors of the model (these factors are related to all statistical variables; their correlation coefficients are 0, a sign of the independence between them); εi is the unique special correlation factor of each statistical variable Xi; and coefficient αi1 is the weight index of the jth common factor relative to the ith statistical variable, reflecting the explanatory power and effect of the common factor on that variable [43]. Because in factor analysis, only jth main factors are usually selected, that is, the first main factor is selected according to the correlation of variables, so that its contribution to the variance of the common factor variance of each variable is the largest, and then the variance of this factor is eliminated. Therefore, αi1 > 0.5.


## 3.3. Evaluation Method

The public satisfaction evaluation model for ICH digital display consists of four structural variables, namely public expectations, service perception, public satisfaction, and public trust; each structure contains one or more observed variables. The variables interact with each other to form a whole. The author mainly refers to the evaluation methods and models of scholar Cheng [44] and recollects the data for calculation. The specific variables are defined in Table 1.Table 1
Measurement indices.
Structural variablesObserved variablesPublic expectationsξOverall expectation of ICH digital display service (x1)Expectation of ICH new media communication service (x2)Expectation of ICH venue digital display service (x3)Service perceptionη1Perception off ICH new media communication service (y1)Perception of ICH venue multimedia service (y2)Perception of ICH venue human-machine interaction service (y3)Public satisfaction (η2)Overall satisfaction with ICH digital display service (y4)Overall satisfaction with ICH new media communication (y5)Overall satisfaction with ICH venue digital display (y6)Public trustη3Complaints (y7)Public reuse of ICH digitization service (y8)Public recommendation of ICH digitization service (y9)The public satisfaction evaluation model for ICH digital display assumes that there is a linear relationship between variables. The structural variable of public expectations is the only exogenous causal variable in the model, while the other structural variables are the endogenous structural variables. The relationship can be represented by three matrix equations, including one structural equation and two measurement equations:Structural equation:(5)η=Aη+Bξ+m.Measurement equations:(6)Y=Λ1η+n,X=Λ2η+p,where A and B are the coefficient matrices reflecting the structural variable relationship in the structural model (A can be regarded as the relationship matrix of the mutual influence between all endogenous structural variables, and B can be understood as the relationship matrix of the influence of the exogenous structural variable “public expectations” on all endogenous variables); Λ1andΛ2 are the relationship matrices of the mutual influence between observed variables, and that between structural variables; and m, n, and p are the error vectors of the three equation models, respectively.The three matrix equations are as follows:The structural model, that is, the relationship between structural variables can be calculated as(7)η1η2η3=000β21000β320∗η1η2η3+γ11γ120∗ξ+m1m2m3,where η1,η2,andη3 are service perception, public satisfaction, and public trust, respectively; βij is the degree of influence of endogenous structural variable j on endogenous structural variable i; γij is the degree of influence of endogenous structural variable ξ on exogenous structural variable ηj; and m is the error vector of the model.(8)y1y2y3y4y5y6y7y8y9=φ1100φ2100φ31000φ4200φ5200φ62000φ7300φ8300φ93∗η1η2η3+n1n2n3n4n5n6n7n8n9,(9)x1x2x3=ω1ω2ω3∗ξ+ρ1ρ2ρ3,where x1,x2, andx3 are the observed variables of exogenousstructural variable public expectations (ξ); y1,y2,y3⋯y8,andy9 are the observed variables of service perception (η1), public satisfaction (η2), and public trust (η3);φijandωi are the coefficients of the coefficient matrices Λ1andΛ2 of the measurement models, respectively (the two coefficients reflect the degree of influence of each structural variable over its own observed variables); and niandρi are the error vectors of the two models, respectively.This article will use the Amos Graphics tool in the statistical software SPSS to construct and analyze the structural model in order to derive the structural equations of the measurement model of public satisfaction of the digital display of ICH and to verify and optimize the path coefficients of the measurement equations in order to obtain the optimal fitting model. Common model validation fit indicators areP-value, confidence level, significance level, chi-square value, chi-square ratio, canonical fit index, etc. Among them, P-value is generally applied together with the significance level for true-false speculation of the hypothesized model. And it is generally considered that if P<0.10, it can indicate that the model test variable is significant and the original hypothesis is not valid.The specific index criteria are as follows [44].


### 3.3.1. Chi-Squared Test

To test the reasonableness of the model, or to verify the consistency of the theoretical model with the empirical data, some scientific indicators are needed. One of the most commonly used evaluation indicators is the chi-squared statistic. Its derived formula is as follows:(10)x2=N−1Fmin,where x2 represents the check value of the model fit and N is the number of samples, and Fmin indicates the minimum value of the test equation derived by different parameter estimation methods (maximum likelihood estimation and generalized least squares method).At the significance level, ifx2 < xα2, it indicates that the original hypothesis is approved and the model fit meets the requirements. Otherwise, the original hypothesis is rejected and the alternative hypothesis is accepted. From the above, it can be seen that the chi-squared test statistic is smaller, and the data is the better. It can also be seen that when its value is 0, it indicates that the measured data fit the hypothesized model perfectly.


### 3.3.2. Goodness-of-Fit Index

GFI (goodness-of-fit index) is the division of the explanatory strength of the hypothetical model based on the theory of the overall variability of the measured data. The formula is as follows:(11)GFI=trσwσtrsws.AGFI (adjusted goodness-of-fit index) will be combined with the number of parameters of the model itself for a comprehensive analysis. The greater the number of parameters, the greater the value of the AGFI, and the more favorable it is to obtain a model with a good fit. The formula is as follows:(12)AGFI=1−GFI1−the number of estimated paramters/the number of observations.When normalized values are taken, the value of AGFI is between 0 and 1. The difference between its value and 1 is inversely related to the degree of fit of the model. In general, when its value is not lower than 0.90, it indicates that the fitting model is acceptable.PGFI (parsimony goodness-of-fit index) can reflect the number of parameters to be estimated in the fitting model from the side, and its formula is as follows:(13)PGFI=1−the number of estimated paramtersthe number of observations∗GFI.In general, according to (12), the PGFI is related to the number of estimated parameters and the number of observations, so it is not much less than, but there is a definite coefficient relationship between them.NFI (normed fit indices) and NNFI (non-normed fitindices) can both be interpreted as the level of difference between the fitting model and the independent model [44].(14)NFI=xindep2−xtest2xindep2,NNFI=xindep2−dfindepxtest2/dftestxindep2−dfindep,where xtest2 and dftest denote the chi-square value and degrees of freedom of the fitting model, and xindep2 and dfindep denote the chi-square value and degrees of freedom of the independent model. Practice has proved that when the amount of data obtained is insufficient and there are many model parameters. The NFI can be misjudged in the evaluation of a well-fitted model, while the NNFI compensates for its deficiency to some extent. A value of not less than 0.90 for both marks an acceptable fitting model.


## 3.3.1. Chi-Squared Test

To test the reasonableness of the model, or to verify the consistency of the theoretical model with the empirical data, some scientific indicators are needed. One of the most commonly used evaluation indicators is the chi-squared statistic. Its derived formula is as follows:(10)x2=N−1Fmin,where x2 represents the check value of the model fit and N is the number of samples, and Fmin indicates the minimum value of the test equation derived by different parameter estimation methods (maximum likelihood estimation and generalized least squares method).At the significance level, ifx2 < xα2, it indicates that the original hypothesis is approved and the model fit meets the requirements. Otherwise, the original hypothesis is rejected and the alternative hypothesis is accepted. From the above, it can be seen that the chi-squared test statistic is smaller, and the data is the better. It can also be seen that when its value is 0, it indicates that the measured data fit the hypothesized model perfectly.


## 3.3.2. Goodness-of-Fit Index

GFI (goodness-of-fit index) is the division of the explanatory strength of the hypothetical model based on the theory of the overall variability of the measured data. The formula is as follows:(11)GFI=trσwσtrsws.AGFI (adjusted goodness-of-fit index) will be combined with the number of parameters of the model itself for a comprehensive analysis. The greater the number of parameters, the greater the value of the AGFI, and the more favorable it is to obtain a model with a good fit. The formula is as follows:(12)AGFI=1−GFI1−the number of estimated paramters/the number of observations.When normalized values are taken, the value of AGFI is between 0 and 1. The difference between its value and 1 is inversely related to the degree of fit of the model. In general, when its value is not lower than 0.90, it indicates that the fitting model is acceptable.PGFI (parsimony goodness-of-fit index) can reflect the number of parameters to be estimated in the fitting model from the side, and its formula is as follows:(13)PGFI=1−the number of estimated paramtersthe number of observations∗GFI.In general, according to (12), the PGFI is related to the number of estimated parameters and the number of observations, so it is not much less than, but there is a definite coefficient relationship between them.NFI (normed fit indices) and NNFI (non-normed fitindices) can both be interpreted as the level of difference between the fitting model and the independent model [44].(14)NFI=xindep2−xtest2xindep2,NNFI=xindep2−dfindepxtest2/dftestxindep2−dfindep,where xtest2 and dftest denote the chi-square value and degrees of freedom of the fitting model, and xindep2 and dfindep denote the chi-square value and degrees of freedom of the independent model. Practice has proved that when the amount of data obtained is insufficient and there are many model parameters. The NFI can be misjudged in the evaluation of a well-fitted model, while the NNFI compensates for its deficiency to some extent. A value of not less than 0.90 for both marks an acceptable fitting model.


## 4. Empirical Analysis

### 4.1. Basic Data Analysis

#### 4.1.1. Index Determination

The research team consulted 21 experts in relevant research directions. Among them, 11 are experts in planning cultural exhibitions, 5 are professors from cultural heritage research institutes of relevant universities, and 5 are ICH protection experts. These experts were invited to evaluate the correlation between indices. According to their opinions, the audience mainly focus on 16 aspects of ICH display design (Table2).Table 2
Key aspects of audience satisfaction with ICH display design.
Serial numberName1Favorability and intelligibility of the subject2Representativeness of craftsmanship3Rationality of the visiting route4Creation of atmosphere (sound and light)5Instructions for texts and images6Communication between artisans and audience7Space design8Audience engagement9Construction of relevant cultural scenes10Purchase of souvenirs11Relevance to life12Combination of high technology such as Internet and virtual reality13Entertaining design14Rest facilities for audience15ICH video introduction16Quality of display service


#### 4.1.2. Data Collection

From June 1, 2018 to July 1, 2022, four sessions of the “Splendid China” ICH costume show were hosted by Prince Kung’s Palace Museum of the Ministry of Culture and Tourism. The subjects were extracted from all the audience interested in the show. A total of 400 questionnaires were distributed, and 395 were collected. Among them, 97% (390) responses were valid. The statistical results show that the respondents are largely aged between 18 and 60, including 51.2% males and 48.8% females. The audience are roughly made up of employees of enterprises and public institutions (44%), college students (25%), and the retired (31%). The random sampling of the subjects assures the representativeness and reliability of the questionnaire survey.


### 4.2. Model Calculation and Empirical Analysis

#### 4.2.1. Model Calculation

Tables3 and 4 show the parameter estimations based on the model structure in Figure 4, through the statistical analysis on Amos Graphics. In the two tables, x1, x2, x3, y1, y2, y3, …, y8, y9 represent the 12 observed variables; p1, p2, p3, n1, n2, n3, …, n8, n9 represent their error terms; m1, m2, and m3 are the disturbance terms of the three endogenous structural variables, namely service perception, public satisfaction, and public trust, respectively. A path coefficient can be considered significant, when its confidence is 96% and P value is smaller than 0.01.Table 3
Statistical estimates of path coefficients.
Nonstandardized path coefficientsS.E.C.R.PLabelStandardized path coefficientsfwgz←gzqw0.7110.0897.887∗∗∗par_90.642gzmy←fwgz0.6470.0719.997∗∗∗par_100.728gzmy←gzqw0.2810.0594.209∗∗∗par_110.289gzxr←gzmy1.1290.08911.502∗∗∗par_120.791y1←fwgz1.0000.856y2←fwgz0.8990.04917.494∗∗∗par_10.891y3←fwgz1.0050.06117.098∗∗∗par_20.802x3←gzqw0.9710.08910.611∗∗∗par_30.799x2←gzqw0.6670.0809.037∗∗∗par_40.611x1←gzqw1.0000.776y4←gzmy1.0000.751y5←gzmy1.0790.07912.989∗∗∗par_50.799y6←gzmy1.1390.07813.798∗∗∗par_60.873y7←gzxr−0.1080.040−3.087∗∗∗par_7−0.198y8←gzxr1.0000.947y9←gzxr0.9410.06117.571∗∗∗par_80.895Note:∗∗∗significance at the level of 0.01; S.E. and C.R. represent standard error and Cronbach’s alpha, respectively.Table 4
Variance estimates.
Variance estimatesS.E.C.R.PLabelgzqw0.4560.0716.399∗∗∗par_13m10.3680.0516.876∗∗∗par_14m20.0370.0193.4110.001par_15m30.3410.0526.897∗∗∗par_16n10.2340.0198.610∗∗∗par_17n20.1300.0217.576∗∗∗par_18n30.1730.0288.311∗∗∗par_19p30.2680.0427.209∗∗∗par_20p20.3140.0519.596∗∗∗par_21p10.3270.0497.410∗∗∗par_22n40.3350.0429.945∗∗∗par_23n50.2640.0379.311∗∗∗par_24n60.1860.0398.210∗∗∗par_25n70.2460.01911.219∗∗∗par_26n80.1370.0413.809∗∗∗par_27n90.2400.0386.298∗∗∗par_28Note:∗∗∗significance at the level of 0.01.Figure 4
Structural equation model for public satisfaction with ICH digital display.As shown in Tables3 and 4, every model parameter was significant at the level of 0.05, which is in line with reality. Table 5 displays the common fitness indices solved by the model.Table 5
Common fitness indices.
Fitting indexX2 (chi-squared)/degree of freedomX2/df valueAGFIRMRRMSEAIFICFIAICResults152.421/503.0480.8640.0310.0910.8950.931202.412Note:X2, df, AGFI, RMR, RMSEA, IFI, CFI, and AIC represent chi-squared, degree of freedom, adjusted goodness-of-fit index, root mean square residual, root mean square error of approximation, incremental fit index, comparative fit index, and Akaike’s information criterion (AIC), respectively.The results in Table5 reflect a good fitness of our model. However, the fitness indies have not reached the ideal values.Next, the model was modified by the modification index (MI) obtained through Amos fitting. Tables6 and 7 show the MI results for covariance and regression weight, respectively, which were fitted through the structural equation model. Note that the sign ⟷ indicates the measuring results on the relationship between the error term and disturbance term of the structural equation model, that is, how much the chi-squared of the model is improved by adding the bidirectional relationship between indicator variables. The sign ← indicates the measuring results on the relationship between observed variables in the structural equation model, that is, how much the chi-squared of the model is improved by adding the unidirectional relationship between indicator variables.Table 6
MI values of covariance.
M.I.Par changen9↔m14.1980.036n9↔m210.159−0.043n8↔m26.6110.041n7↔n85.1290.034n6↔m38.2130.059n5↔m34.412−0.048n5↔n917.798−0.079n4↔n54.7120.051p2↔n84.7120.053p2↔n65.312−0.052n3↔n99.5130.063n3↔n57.312−0.039n3↔n46.899−0.038n2↔n98.9100.051n2↔n59.812−0.037n1↔n94.212−0.041n1↔n535.4210.122Table 7
MI values of regression weight.
M.I.Par changey9←y56.511−0.112y9←y34.2410.097y8←y74.7980.162y6←x24.396−0.087y5←y97.298−0.087y5←y16.8990.111y4←x24.4110.105y3←y44.399−0.087y1←y513.0190.147Before optimizing the model with the MI, it is important to consider the rationality and comprehensibility of each index. For each revision, the largest value should be selected from the obtained index values and analyzed first. The modification starts from the covariance part. It can be seen from Table6 that the path parameter between variables n1 and n5 led to the greatest decline of chi-squared (MI)—35.421. In other words, the chi-squared of the model declined the deepest, after the path related to the error between n1 and n5 was added. In addition, there must be a correlation between the perception of ICH new media communication and the overall satisfaction of ICH new media service. Therefore, the null hypothesis model could be optimized by adding the path between n1 and n5.After optimizing the null hypothesis model, the relevant parameters were recalculated, and the MI was selected in the same way. The largest chi-squared (MI) value (12.369) was found between n5 and n9. But no clear correlation was observed between the overall satisfaction of ICH new media service and the public recommendation of ICH digitization service. Moreover, the MI between other variables was small and fell short of fitting and optimization requirements. Thus, the model improved from the null hypothesis model is displayed in Figure5.Figure 5
Optimized structural equation model for public satisfaction with ICH digital display.Table8 shows the parameter estimations based on the model structure in Figure 5 through the statistical analysis on Amos Graphics.Table 8
Common fitness indices.
Fitting indexX2 (chi-squared)/degree of freedomX2/df valueAGFIRMRRMSEAIFICFIAICResults108.126/492.2070.9160.0180.0720.9710.971167.421Comparing Tables5 and 8, it is clear that the fitness indices were optimized to different degrees, and the correlation paths all passed the significance test. The final optimal estimates of path coefficients and variances are shown in Tables 9 and 10, respectively.Table 9
Final estimates of path coefficients.
Nonstandardized path coefficientsS.E.C.R.PLabelStandardized path coefficientsfwgz←gzqw0.6920.0897.856∗∗∗par_90.637gzmy←fwgz0.6310.05910.011∗∗∗par_100.721gzmy←gzqw0.2910.0584.741∗∗∗par_110.302gzxr←gzmy1.1520.10111.554∗∗∗par_120.789y1←fwgz1.0000.828y2←fwgz0.9320.06117.542∗∗∗par_10.889y3←fwgz1.0340.07117.095∗∗∗par_20.881x3←gzqw0.9720.09810.547∗∗∗par_30.781x2←gzqw0.6870.0819.121∗∗∗par_40.629x1←gzqw1.0000.771y4←gzmy1.0000.739y5←gzmy1.0710.08912.823∗∗∗par_50.791y6←gzmy1.1620.09113.771∗∗∗par_60.858y7←gzxr−0.1190.042−3.1310.002par_7−0.211y8←gzxr1.0000.931y9←gzxr0.9410.06117.590∗∗∗par_80.882Note:∗∗∗significance at the level of 0.01.Table 10
Final variance estimates.
EstimateS.E.C.R.PLabelgzqw0.4510.0706.447∗∗∗par_14m10.3190.0516.988∗∗∗par_15m20.0560.0173.982∗∗∗par_16m30.3340.0516.798∗∗∗par_17n10.2410.0298.977∗∗∗par_18n20.1150.0197.098∗∗∗par_19n30.1810.0237.969∗∗∗par_20p30.2700.0387.109∗∗∗par_21p20.3240.0349.622∗∗∗par_22p10.3100.0477.348∗∗∗par_23n40.3310.0349.792∗∗∗par_24n50.2960.0329.413∗∗∗par_25n60.1890.0317.781∗∗∗par_26n70.2410.02811.177∗∗∗par_27n80.1490.0413.932∗∗∗par_28n90.2410.0416.337∗∗∗par_29Note:∗∗∗significance at the level of 0.01.Referring to formulas (8)–(10), the relationship between structural variables and observed variables can be described by the following three equations, where the coefficients are standardized coefficients:(15)η1η2η3=0000.7210000.7890∗η1η2η3+0.6370.3020∗ξ+m1m2m3,y1y2y3y4y5y6y7y8y9=0.828000.889000.8810000.739000.791000.858000−0.211000.931000.882∗η1η2η3+n1n2n3n4n5n6n7n8n9,x1x2x3=0.7710.6290.781∗ξ+ρ1ρ2ρ3.


#### 4.2.2. Empirical Analysis

The main function of the structural equation model is to reveal the structural relationships between the structural variables. These relationships are reflected by path coefficients in the model. Combined with the empirical research of scholar Cheng [44], the optimal structural equation model for public satisfaction with ICH digital display was analyzed on Amos Graphics. Table 11 shows the estimated standardization effects between the structural variables.Table 11
Estimates of standardization effect.
Public expectationsService perceptionPublic satisfactionPublic trustService perception (total effect)0.6370.0000.0000.000(Direct effect)0.6370.0000.0000.000(Indirect effect)0.0000.0000.0000.000Public satisfaction (total effect)0.7530.7170.0000.000(Direct effect)0.3020.0000.0000.000(Indirect effect)0.4510.0000.0000.000Public trust (total effect)0.5890.5650.7900.000(Direct effect)0.0000.0000.7900.000(Indirect effect)0.5890.5650.0000.000The direct effect refers to the direct influence of the cause variable on the result variable in all paths of the structural equation model. Generally, the direct effect is measured by the standardized path coefficient from the cause variable to the result variable, as shown in the last column of Table9. For example, the normalized path coefficient from public expectations (gzqw) to public satisfaction (gzmy) is 0.302, indicating that the direct effect of public expectations on public satisfaction through the path relationship is 0.302, that is, when other conditions remain unchanged in the structural equation model, the structural variable “public expectations” will increase by 1 standardized coefficient and the structural variable “public satisfaction” will correspondingly increase by 0.302 of a standardized coefficient.The indirect effect means that in all paths of the structural equation model, the cause variable and the result variable are not directly connected, but are influenced by the mutual conduction of one or more intermediary variables between them. The value of the indirect effect is the product between the standardized path coefficients of connected paths. As shown in Table9, the standardized path coefficient from public expectations (gzqw) to service perception (fwgz) was 0.637 and that from service perception (fwgz) to public satisfaction (gzmy) was 0.721. Then, the indirect effect from public expectations to public satisfaction was 0.637 ∗ 0.721 = 0.459. When the other conditions in the structural equation model remain unchanged, if the structural variable “public expectations” increases by 1 standardized coefficient, then the structural variable “public satisfaction” will indirectly increase by 0.459 of a standardized coefficient.The total effect refers to the overall influence of the cause variable on the result variable in all paths of the structural equation model. It consists of direct effect and indirect effect. For example, the direct effect from public expectations (gzqw) to public satisfaction (gzmy) was 0.302 and the indirect effect was 0.451. Then, the total effect was 0.302 + 0.451 = 0.753. When the other conditions in the structural equation model remain unchanged, if the structural variable “public expectations” increases by 1 standardized coefficient, then the structural variable “public satisfaction” will increase by 0.753 of a standardized coefficient.Tables11 and 12 show the structural relationship between each structural variable and each observed variable, indicating the total effect of each structural variable on the observed variable. It can be found that almost all structural variables have a positive effect or no effect on the observed variable. The only exception is the negative effect of the structural variable public trust (gzxr) on the observed variable public complaints (y7). Therefore, the increase of the structural coefficient by each standardized coefficient can enhance each observed variable. The factor weights are shown in Table 13.Table 12
Estimates of standardized total effect.
Public expectationsService perceptionPublic satisfactionPublic trusty10.5210.8280.0000.000y20.5600.8890.0000.000y30.5450.8810.0000.000y40.5560.5330.7390.000y50.5850.5620.7910.000y60.6480.6220.8690.000y7−0.121−0.117−0.163−0.211y80.5450.5230.7320.931y90.5160.4950.6920.882x10.7710.0000.0000.000x20.6290.0000.0000.000x30.7810.0000.0000.000Table 13
Final factor weights.
x1x2x3y1y2y3y4y5y6y7y8y9Public expectations0.250.160.28−0.010.020.010.030.050.080−0.000.020.01Service perception0.000.000.000.180.340.240.04−0.010.1004−0.000.020.01Public satisfaction0.040.020.040.000.130.090.100.120.226−0.000.060.03Public trust0.010.000.010.000.030.020.020.030.057−0.030.510.30Following the calculation principle, public satisfaction was solved by formulas (1) and (2) based on the data in Table 13.For the structural variable public satisfaction (gzmy), the sum of factor weights was 0.856. Thus, the MI of the structural variable wasψ=1/0.856.By formula (1), we have(16)publicsatisfactioncs1=Eξ−MinξMaxξ−Minξ∗100=1/12∑i=13wix¯i+∑j=19φjy¯j−1/12∑i=13wiMinxi+∑j=19φjMinyi1/12∑i=13wiManxi+∑j=19φjMaxyi−1/12∑i=13wiMinxi+∑j=19φjMinyi∗100,ψ=3.54−1.034.47−1.03∗100∗ψ=2.513.44∗100∗ψ=85.24,where Eξ,Maxξ), and Minξ are the mean, maximum, and minimum of public satisfaction, respectively; x¯i, Max(xi), and Min(xi) are the mean, maximum, and minimum of xi; y¯j, Maxyj, and Minyj are the mean, maximum, and minimum of yj; wi and φj are the weights of each factor; and ψ is the correction index, i∈Ω3, j∈Ω9.By formula (2), we have(17)publicsatisfactioncs2=∑j=13wix¯i+∑j=19ϕjy¯j∗ψ=3.54∗ψ=4.14.The above result can be expressed as a percentage:(18)publicsatisfactioncs3=publicsatisfactioncs2∗1005=82.8.With reference to the relevant literature on satisfaction evaluation standards, combined with the characteristics of ICH digital display services, the evaluation standards for this evaluation model are set as follows:When CS < 60, it indicates that the quality of the digital display service of ICH is not good, and the public feels very bad about the quality of the digital display service of ICH; when 60 ≤ CS < 70, it indicates that the service quality of the digital display of ICH is average, and the public has a general perception of the quality of the digital display service of ICH; when 70 ≤ CS < 80, it shows that the service quality of the digital display of ICH is good, and the public feels good about the service quality of the digital display of ICH; and when CS ≥ 80, it shows that the quality of the digital display service of ICH is very good, and the public feels very good about the quality of the digital display service of ICH.


### 4.3. Countermeasures

The ICH digitization should be service-oriented and emphasize the digitization service quality of ICH works. According to the results of the valid responses, the digital forms of the ICH works visited by the public and favored by the public are ranked in Tables14 and 15, respectively. In the structural equation model of public satisfaction with the digital display of ICH, the structural variable service perception (fwgz) has the highest direct impact on the structural variable public satisfaction (gzmy), reaching 0.715, which shows that improving the service quality of ICH digital display is also the top priority for improving public satisfaction with the digital display of ICH.Table 14
Digital forms of the ICH works visited by the public.
ResponseCase percentageNPercentageForm of digital worksImage47725.977.3Audio31016.951.6Video49827.185.0Animation26614.541.6Interactive game1216.617.4Human-computer interaction784.211.1Integrated display of sound and light894.814.8In total1839100.0298.8Table 15
Digital forms of the ICH works favored by the public.
MeanRanking by the degree of preferenceCharacter1.64Image2.37Audio1.88Video3.44Animation2.21Interactive game1.76Human-computer interaction1.02Integrated display of sound and light1.65Note: the greater the value, the stronger the degree of preference.As shown in Tables14 and 15, the public mainly visited digital works in the form of video, image, and audio, but rarely visited those in the form of interactive game, human-computer interaction, and integrated display of sound and light. The top 3 most preferred forms are video, image, and animation.The survey shows that 42.4% of the respondents have visited digital ICH exhibits. Hence, digital display means have not been widely utilized. The public has not comprehensively learned the display forms of ICHs. Table16 shows the degree of preference of the public for different display forms of ICHs.Table 16
Degree of preference for different ICH display forms.
Display formMeanPurely physical display2.88Purely digital display1.76Physical display + digital display2.89Field participation of inheritor + digital display2.43Note: the greater the value, the stronger the degree of preference.Through the empirical research on the Splendid China costume show, it can be seen that the different ICH display design elements act differently on audience satisfaction. This requires that ICH display designers not only have rich ICH knowledge, display theory, and practical skills but also need to study the psychology of ICH audience to understand how the audience’s psychology will be affected by different ICH display strategies and feel and develop different design strategies for different types of elements. The following countermeasures were therefore designed:(1)
Application of conventional display media. Based on the research on the ICH project itself, we can understand the way its elements are displayed. In other words, these elements have begun to have the nature of exhibits and can be managed through digital display schemes. No matter what kind of media tool is used, the act of dissemination itself has no value, only when the work obtains useful information in the process of dissemination and affects people's behavior, it has meaning. Therefore, in the process of displaying the existing ICH projects, it is necessary to screen reasonable media and obtain key information from them before spreading.(2)
Emotional positioning in ICH projects. In terms of display form, it is necessary to combine physical display with digital display. Using the art elements of visual language (e.g., equipment modeling; matching lighting and color), the scene needs to be creatively processed according to a certain theme and rationality, creating a cultural atmosphere that matches the ICHs, arouses the interest of the audience, and provides a pleasant experience of the ICHs.(3)
The construction of interactive mode. The operation interaction mode is a multidimensional interactive experience design through multimedia technology, supplemented by computer program processing solutions. The core of ICHs lies in the activation of people's participation. Therefore, ICH designers should not only consider the contents and form of ICHs but also fully explore the interactive features of ICHs with the aid of online multimedia technology. The intricate technical contents of ICHs should be organized into a rich experience effect so that the audience become more satisfied with the experience of ICHs.


## 4.1. Basic Data Analysis

### 4.1.1. Index Determination

The research team consulted 21 experts in relevant research directions. Among them, 11 are experts in planning cultural exhibitions, 5 are professors from cultural heritage research institutes of relevant universities, and 5 are ICH protection experts. These experts were invited to evaluate the correlation between indices. According to their opinions, the audience mainly focus on 16 aspects of ICH display design (Table2).Table 2
Key aspects of audience satisfaction with ICH display design.
Serial numberName1Favorability and intelligibility of the subject2Representativeness of craftsmanship3Rationality of the visiting route4Creation of atmosphere (sound and light)5Instructions for texts and images6Communication between artisans and audience7Space design8Audience engagement9Construction of relevant cultural scenes10Purchase of souvenirs11Relevance to life12Combination of high technology such as Internet and virtual reality13Entertaining design14Rest facilities for audience15ICH video introduction16Quality of display service


### 4.1.2. Data Collection

From June 1, 2018 to July 1, 2022, four sessions of the “Splendid China” ICH costume show were hosted by Prince Kung’s Palace Museum of the Ministry of Culture and Tourism. The subjects were extracted from all the audience interested in the show. A total of 400 questionnaires were distributed, and 395 were collected. Among them, 97% (390) responses were valid. The statistical results show that the respondents are largely aged between 18 and 60, including 51.2% males and 48.8% females. The audience are roughly made up of employees of enterprises and public institutions (44%), college students (25%), and the retired (31%). The random sampling of the subjects assures the representativeness and reliability of the questionnaire survey.


## 4.1.1. Index Determination

The research team consulted 21 experts in relevant research directions. Among them, 11 are experts in planning cultural exhibitions, 5 are professors from cultural heritage research institutes of relevant universities, and 5 are ICH protection experts. These experts were invited to evaluate the correlation between indices. According to their opinions, the audience mainly focus on 16 aspects of ICH display design (Table2).Table 2
Key aspects of audience satisfaction with ICH display design.
Serial numberName1Favorability and intelligibility of the subject2Representativeness of craftsmanship3Rationality of the visiting route4Creation of atmosphere (sound and light)5Instructions for texts and images6Communication between artisans and audience7Space design8Audience engagement9Construction of relevant cultural scenes10Purchase of souvenirs11Relevance to life12Combination of high technology such as Internet and virtual reality13Entertaining design14Rest facilities for audience15ICH video introduction16Quality of display service


## 4.1.2. Data Collection

From June 1, 2018 to July 1, 2022, four sessions of the “Splendid China” ICH costume show were hosted by Prince Kung’s Palace Museum of the Ministry of Culture and Tourism. The subjects were extracted from all the audience interested in the show. A total of 400 questionnaires were distributed, and 395 were collected. Among them, 97% (390) responses were valid. The statistical results show that the respondents are largely aged between 18 and 60, including 51.2% males and 48.8% females. The audience are roughly made up of employees of enterprises and public institutions (44%), college students (25%), and the retired (31%). The random sampling of the subjects assures the representativeness and reliability of the questionnaire survey.


## 4.2. Model Calculation and Empirical Analysis

### 4.2.1. Model Calculation

Tables3 and 4 show the parameter estimations based on the model structure in Figure 4, through the statistical analysis on Amos Graphics. In the two tables, x1, x2, x3, y1, y2, y3, …, y8, y9 represent the 12 observed variables; p1, p2, p3, n1, n2, n3, …, n8, n9 represent their error terms; m1, m2, and m3 are the disturbance terms of the three endogenous structural variables, namely service perception, public satisfaction, and public trust, respectively. A path coefficient can be considered significant, when its confidence is 96% and P value is smaller than 0.01.Table 3
Statistical estimates of path coefficients.
Nonstandardized path coefficientsS.E.C.R.PLabelStandardized path coefficientsfwgz←gzqw0.7110.0897.887∗∗∗par_90.642gzmy←fwgz0.6470.0719.997∗∗∗par_100.728gzmy←gzqw0.2810.0594.209∗∗∗par_110.289gzxr←gzmy1.1290.08911.502∗∗∗par_120.791y1←fwgz1.0000.856y2←fwgz0.8990.04917.494∗∗∗par_10.891y3←fwgz1.0050.06117.098∗∗∗par_20.802x3←gzqw0.9710.08910.611∗∗∗par_30.799x2←gzqw0.6670.0809.037∗∗∗par_40.611x1←gzqw1.0000.776y4←gzmy1.0000.751y5←gzmy1.0790.07912.989∗∗∗par_50.799y6←gzmy1.1390.07813.798∗∗∗par_60.873y7←gzxr−0.1080.040−3.087∗∗∗par_7−0.198y8←gzxr1.0000.947y9←gzxr0.9410.06117.571∗∗∗par_80.895Note:∗∗∗significance at the level of 0.01; S.E. and C.R. represent standard error and Cronbach’s alpha, respectively.Table 4
Variance estimates.
Variance estimatesS.E.C.R.PLabelgzqw0.4560.0716.399∗∗∗par_13m10.3680.0516.876∗∗∗par_14m20.0370.0193.4110.001par_15m30.3410.0526.897∗∗∗par_16n10.2340.0198.610∗∗∗par_17n20.1300.0217.576∗∗∗par_18n30.1730.0288.311∗∗∗par_19p30.2680.0427.209∗∗∗par_20p20.3140.0519.596∗∗∗par_21p10.3270.0497.410∗∗∗par_22n40.3350.0429.945∗∗∗par_23n50.2640.0379.311∗∗∗par_24n60.1860.0398.210∗∗∗par_25n70.2460.01911.219∗∗∗par_26n80.1370.0413.809∗∗∗par_27n90.2400.0386.298∗∗∗par_28Note:∗∗∗significance at the level of 0.01.Figure 4
Structural equation model for public satisfaction with ICH digital display.As shown in Tables3 and 4, every model parameter was significant at the level of 0.05, which is in line with reality. Table 5 displays the common fitness indices solved by the model.Table 5
Common fitness indices.
Fitting indexX2 (chi-squared)/degree of freedomX2/df valueAGFIRMRRMSEAIFICFIAICResults152.421/503.0480.8640.0310.0910.8950.931202.412Note:X2, df, AGFI, RMR, RMSEA, IFI, CFI, and AIC represent chi-squared, degree of freedom, adjusted goodness-of-fit index, root mean square residual, root mean square error of approximation, incremental fit index, comparative fit index, and Akaike’s information criterion (AIC), respectively.The results in Table5 reflect a good fitness of our model. However, the fitness indies have not reached the ideal values.Next, the model was modified by the modification index (MI) obtained through Amos fitting. Tables6 and 7 show the MI results for covariance and regression weight, respectively, which were fitted through the structural equation model. Note that the sign ⟷ indicates the measuring results on the relationship between the error term and disturbance term of the structural equation model, that is, how much the chi-squared of the model is improved by adding the bidirectional relationship between indicator variables. The sign ← indicates the measuring results on the relationship between observed variables in the structural equation model, that is, how much the chi-squared of the model is improved by adding the unidirectional relationship between indicator variables.Table 6
MI values of covariance.
M.I.Par changen9↔m14.1980.036n9↔m210.159−0.043n8↔m26.6110.041n7↔n85.1290.034n6↔m38.2130.059n5↔m34.412−0.048n5↔n917.798−0.079n4↔n54.7120.051p2↔n84.7120.053p2↔n65.312−0.052n3↔n99.5130.063n3↔n57.312−0.039n3↔n46.899−0.038n2↔n98.9100.051n2↔n59.812−0.037n1↔n94.212−0.041n1↔n535.4210.122Table 7
MI values of regression weight.
M.I.Par changey9←y56.511−0.112y9←y34.2410.097y8←y74.7980.162y6←x24.396−0.087y5←y97.298−0.087y5←y16.8990.111y4←x24.4110.105y3←y44.399−0.087y1←y513.0190.147Before optimizing the model with the MI, it is important to consider the rationality and comprehensibility of each index. For each revision, the largest value should be selected from the obtained index values and analyzed first. The modification starts from the covariance part. It can be seen from Table6 that the path parameter between variables n1 and n5 led to the greatest decline of chi-squared (MI)—35.421. In other words, the chi-squared of the model declined the deepest, after the path related to the error between n1 and n5 was added. In addition, there must be a correlation between the perception of ICH new media communication and the overall satisfaction of ICH new media service. Therefore, the null hypothesis model could be optimized by adding the path between n1 and n5.After optimizing the null hypothesis model, the relevant parameters were recalculated, and the MI was selected in the same way. The largest chi-squared (MI) value (12.369) was found between n5 and n9. But no clear correlation was observed between the overall satisfaction of ICH new media service and the public recommendation of ICH digitization service. Moreover, the MI between other variables was small and fell short of fitting and optimization requirements. Thus, the model improved from the null hypothesis model is displayed in Figure5.Figure 5
Optimized structural equation model for public satisfaction with ICH digital display.Table8 shows the parameter estimations based on the model structure in Figure 5 through the statistical analysis on Amos Graphics.Table 8
Common fitness indices.
Fitting indexX2 (chi-squared)/degree of freedomX2/df valueAGFIRMRRMSEAIFICFIAICResults108.126/492.2070.9160.0180.0720.9710.971167.421Comparing Tables5 and 8, it is clear that the fitness indices were optimized to different degrees, and the correlation paths all passed the significance test. The final optimal estimates of path coefficients and variances are shown in Tables 9 and 10, respectively.Table 9
Final estimates of path coefficients.
Nonstandardized path coefficientsS.E.C.R.PLabelStandardized path coefficientsfwgz←gzqw0.6920.0897.856∗∗∗par_90.637gzmy←fwgz0.6310.05910.011∗∗∗par_100.721gzmy←gzqw0.2910.0584.741∗∗∗par_110.302gzxr←gzmy1.1520.10111.554∗∗∗par_120.789y1←fwgz1.0000.828y2←fwgz0.9320.06117.542∗∗∗par_10.889y3←fwgz1.0340.07117.095∗∗∗par_20.881x3←gzqw0.9720.09810.547∗∗∗par_30.781x2←gzqw0.6870.0819.121∗∗∗par_40.629x1←gzqw1.0000.771y4←gzmy1.0000.739y5←gzmy1.0710.08912.823∗∗∗par_50.791y6←gzmy1.1620.09113.771∗∗∗par_60.858y7←gzxr−0.1190.042−3.1310.002par_7−0.211y8←gzxr1.0000.931y9←gzxr0.9410.06117.590∗∗∗par_80.882Note:∗∗∗significance at the level of 0.01.Table 10
Final variance estimates.
EstimateS.E.C.R.PLabelgzqw0.4510.0706.447∗∗∗par_14m10.3190.0516.988∗∗∗par_15m20.0560.0173.982∗∗∗par_16m30.3340.0516.798∗∗∗par_17n10.2410.0298.977∗∗∗par_18n20.1150.0197.098∗∗∗par_19n30.1810.0237.969∗∗∗par_20p30.2700.0387.109∗∗∗par_21p20.3240.0349.622∗∗∗par_22p10.3100.0477.348∗∗∗par_23n40.3310.0349.792∗∗∗par_24n50.2960.0329.413∗∗∗par_25n60.1890.0317.781∗∗∗par_26n70.2410.02811.177∗∗∗par_27n80.1490.0413.932∗∗∗par_28n90.2410.0416.337∗∗∗par_29Note:∗∗∗significance at the level of 0.01.Referring to formulas (8)–(10), the relationship between structural variables and observed variables can be described by the following three equations, where the coefficients are standardized coefficients:(15)η1η2η3=0000.7210000.7890∗η1η2η3+0.6370.3020∗ξ+m1m2m3,y1y2y3y4y5y6y7y8y9=0.828000.889000.8810000.739000.791000.858000−0.211000.931000.882∗η1η2η3+n1n2n3n4n5n6n7n8n9,x1x2x3=0.7710.6290.781∗ξ+ρ1ρ2ρ3.


### 4.2.2. Empirical Analysis

The main function of the structural equation model is to reveal the structural relationships between the structural variables. These relationships are reflected by path coefficients in the model. Combined with the empirical research of scholar Cheng [44], the optimal structural equation model for public satisfaction with ICH digital display was analyzed on Amos Graphics. Table 11 shows the estimated standardization effects between the structural variables.Table 11
Estimates of standardization effect.
Public expectationsService perceptionPublic satisfactionPublic trustService perception (total effect)0.6370.0000.0000.000(Direct effect)0.6370.0000.0000.000(Indirect effect)0.0000.0000.0000.000Public satisfaction (total effect)0.7530.7170.0000.000(Direct effect)0.3020.0000.0000.000(Indirect effect)0.4510.0000.0000.000Public trust (total effect)0.5890.5650.7900.000(Direct effect)0.0000.0000.7900.000(Indirect effect)0.5890.5650.0000.000The direct effect refers to the direct influence of the cause variable on the result variable in all paths of the structural equation model. Generally, the direct effect is measured by the standardized path coefficient from the cause variable to the result variable, as shown in the last column of Table9. For example, the normalized path coefficient from public expectations (gzqw) to public satisfaction (gzmy) is 0.302, indicating that the direct effect of public expectations on public satisfaction through the path relationship is 0.302, that is, when other conditions remain unchanged in the structural equation model, the structural variable “public expectations” will increase by 1 standardized coefficient and the structural variable “public satisfaction” will correspondingly increase by 0.302 of a standardized coefficient.The indirect effect means that in all paths of the structural equation model, the cause variable and the result variable are not directly connected, but are influenced by the mutual conduction of one or more intermediary variables between them. The value of the indirect effect is the product between the standardized path coefficients of connected paths. As shown in Table9, the standardized path coefficient from public expectations (gzqw) to service perception (fwgz) was 0.637 and that from service perception (fwgz) to public satisfaction (gzmy) was 0.721. Then, the indirect effect from public expectations to public satisfaction was 0.637 ∗ 0.721 = 0.459. When the other conditions in the structural equation model remain unchanged, if the structural variable “public expectations” increases by 1 standardized coefficient, then the structural variable “public satisfaction” will indirectly increase by 0.459 of a standardized coefficient.The total effect refers to the overall influence of the cause variable on the result variable in all paths of the structural equation model. It consists of direct effect and indirect effect. For example, the direct effect from public expectations (gzqw) to public satisfaction (gzmy) was 0.302 and the indirect effect was 0.451. Then, the total effect was 0.302 + 0.451 = 0.753. When the other conditions in the structural equation model remain unchanged, if the structural variable “public expectations” increases by 1 standardized coefficient, then the structural variable “public satisfaction” will increase by 0.753 of a standardized coefficient.Tables11 and 12 show the structural relationship between each structural variable and each observed variable, indicating the total effect of each structural variable on the observed variable. It can be found that almost all structural variables have a positive effect or no effect on the observed variable. The only exception is the negative effect of the structural variable public trust (gzxr) on the observed variable public complaints (y7). Therefore, the increase of the structural coefficient by each standardized coefficient can enhance each observed variable. The factor weights are shown in Table 13.Table 12
Estimates of standardized total effect.
Public expectationsService perceptionPublic satisfactionPublic trusty10.5210.8280.0000.000y20.5600.8890.0000.000y30.5450.8810.0000.000y40.5560.5330.7390.000y50.5850.5620.7910.000y60.6480.6220.8690.000y7−0.121−0.117−0.163−0.211y80.5450.5230.7320.931y90.5160.4950.6920.882x10.7710.0000.0000.000x20.6290.0000.0000.000x30.7810.0000.0000.000Table 13
Final factor weights.
x1x2x3y1y2y3y4y5y6y7y8y9Public expectations0.250.160.28−0.010.020.010.030.050.080−0.000.020.01Service perception0.000.000.000.180.340.240.04−0.010.1004−0.000.020.01Public satisfaction0.040.020.040.000.130.090.100.120.226−0.000.060.03Public trust0.010.000.010.000.030.020.020.030.057−0.030.510.30Following the calculation principle, public satisfaction was solved by formulas (1) and (2) based on the data in Table 13.For the structural variable public satisfaction (gzmy), the sum of factor weights was 0.856. Thus, the MI of the structural variable wasψ=1/0.856.By formula (1), we have(16)publicsatisfactioncs1=Eξ−MinξMaxξ−Minξ∗100=1/12∑i=13wix¯i+∑j=19φjy¯j−1/12∑i=13wiMinxi+∑j=19φjMinyi1/12∑i=13wiManxi+∑j=19φjMaxyi−1/12∑i=13wiMinxi+∑j=19φjMinyi∗100,ψ=3.54−1.034.47−1.03∗100∗ψ=2.513.44∗100∗ψ=85.24,where Eξ,Maxξ), and Minξ are the mean, maximum, and minimum of public satisfaction, respectively; x¯i, Max(xi), and Min(xi) are the mean, maximum, and minimum of xi; y¯j, Maxyj, and Minyj are the mean, maximum, and minimum of yj; wi and φj are the weights of each factor; and ψ is the correction index, i∈Ω3, j∈Ω9.By formula (2), we have(17)publicsatisfactioncs2=∑j=13wix¯i+∑j=19ϕjy¯j∗ψ=3.54∗ψ=4.14.The above result can be expressed as a percentage:(18)publicsatisfactioncs3=publicsatisfactioncs2∗1005=82.8.With reference to the relevant literature on satisfaction evaluation standards, combined with the characteristics of ICH digital display services, the evaluation standards for this evaluation model are set as follows:When CS < 60, it indicates that the quality of the digital display service of ICH is not good, and the public feels very bad about the quality of the digital display service of ICH; when 60 ≤ CS < 70, it indicates that the service quality of the digital display of ICH is average, and the public has a general perception of the quality of the digital display service of ICH; when 70 ≤ CS < 80, it shows that the service quality of the digital display of ICH is good, and the public feels good about the service quality of the digital display of ICH; and when CS ≥ 80, it shows that the quality of the digital display service of ICH is very good, and the public feels very good about the quality of the digital display service of ICH.


## 4.2.1. Model Calculation

Tables3 and 4 show the parameter estimations based on the model structure in Figure 4, through the statistical analysis on Amos Graphics. In the two tables, x1, x2, x3, y1, y2, y3, …, y8, y9 represent the 12 observed variables; p1, p2, p3, n1, n2, n3, …, n8, n9 represent their error terms; m1, m2, and m3 are the disturbance terms of the three endogenous structural variables, namely service perception, public satisfaction, and public trust, respectively. A path coefficient can be considered significant, when its confidence is 96% and P value is smaller than 0.01.Table 3
Statistical estimates of path coefficients.
Nonstandardized path coefficientsS.E.C.R.PLabelStandardized path coefficientsfwgz←gzqw0.7110.0897.887∗∗∗par_90.642gzmy←fwgz0.6470.0719.997∗∗∗par_100.728gzmy←gzqw0.2810.0594.209∗∗∗par_110.289gzxr←gzmy1.1290.08911.502∗∗∗par_120.791y1←fwgz1.0000.856y2←fwgz0.8990.04917.494∗∗∗par_10.891y3←fwgz1.0050.06117.098∗∗∗par_20.802x3←gzqw0.9710.08910.611∗∗∗par_30.799x2←gzqw0.6670.0809.037∗∗∗par_40.611x1←gzqw1.0000.776y4←gzmy1.0000.751y5←gzmy1.0790.07912.989∗∗∗par_50.799y6←gzmy1.1390.07813.798∗∗∗par_60.873y7←gzxr−0.1080.040−3.087∗∗∗par_7−0.198y8←gzxr1.0000.947y9←gzxr0.9410.06117.571∗∗∗par_80.895Note:∗∗∗significance at the level of 0.01; S.E. and C.R. represent standard error and Cronbach’s alpha, respectively.Table 4
Variance estimates.
Variance estimatesS.E.C.R.PLabelgzqw0.4560.0716.399∗∗∗par_13m10.3680.0516.876∗∗∗par_14m20.0370.0193.4110.001par_15m30.3410.0526.897∗∗∗par_16n10.2340.0198.610∗∗∗par_17n20.1300.0217.576∗∗∗par_18n30.1730.0288.311∗∗∗par_19p30.2680.0427.209∗∗∗par_20p20.3140.0519.596∗∗∗par_21p10.3270.0497.410∗∗∗par_22n40.3350.0429.945∗∗∗par_23n50.2640.0379.311∗∗∗par_24n60.1860.0398.210∗∗∗par_25n70.2460.01911.219∗∗∗par_26n80.1370.0413.809∗∗∗par_27n90.2400.0386.298∗∗∗par_28Note:∗∗∗significance at the level of 0.01.Figure 4
Structural equation model for public satisfaction with ICH digital display.As shown in Tables3 and 4, every model parameter was significant at the level of 0.05, which is in line with reality. Table 5 displays the common fitness indices solved by the model.Table 5
Common fitness indices.
Fitting indexX2 (chi-squared)/degree of freedomX2/df valueAGFIRMRRMSEAIFICFIAICResults152.421/503.0480.8640.0310.0910.8950.931202.412Note:X2, df, AGFI, RMR, RMSEA, IFI, CFI, and AIC represent chi-squared, degree of freedom, adjusted goodness-of-fit index, root mean square residual, root mean square error of approximation, incremental fit index, comparative fit index, and Akaike’s information criterion (AIC), respectively.The results in Table5 reflect a good fitness of our model. However, the fitness indies have not reached the ideal values.Next, the model was modified by the modification index (MI) obtained through Amos fitting. Tables6 and 7 show the MI results for covariance and regression weight, respectively, which were fitted through the structural equation model. Note that the sign ⟷ indicates the measuring results on the relationship between the error term and disturbance term of the structural equation model, that is, how much the chi-squared of the model is improved by adding the bidirectional relationship between indicator variables. The sign ← indicates the measuring results on the relationship between observed variables in the structural equation model, that is, how much the chi-squared of the model is improved by adding the unidirectional relationship between indicator variables.Table 6
MI values of covariance.
M.I.Par changen9↔m14.1980.036n9↔m210.159−0.043n8↔m26.6110.041n7↔n85.1290.034n6↔m38.2130.059n5↔m34.412−0.048n5↔n917.798−0.079n4↔n54.7120.051p2↔n84.7120.053p2↔n65.312−0.052n3↔n99.5130.063n3↔n57.312−0.039n3↔n46.899−0.038n2↔n98.9100.051n2↔n59.812−0.037n1↔n94.212−0.041n1↔n535.4210.122Table 7
MI values of regression weight.
M.I.Par changey9←y56.511−0.112y9←y34.2410.097y8←y74.7980.162y6←x24.396−0.087y5←y97.298−0.087y5←y16.8990.111y4←x24.4110.105y3←y44.399−0.087y1←y513.0190.147Before optimizing the model with the MI, it is important to consider the rationality and comprehensibility of each index. For each revision, the largest value should be selected from the obtained index values and analyzed first. The modification starts from the covariance part. It can be seen from Table6 that the path parameter between variables n1 and n5 led to the greatest decline of chi-squared (MI)—35.421. In other words, the chi-squared of the model declined the deepest, after the path related to the error between n1 and n5 was added. In addition, there must be a correlation between the perception of ICH new media communication and the overall satisfaction of ICH new media service. Therefore, the null hypothesis model could be optimized by adding the path between n1 and n5.After optimizing the null hypothesis model, the relevant parameters were recalculated, and the MI was selected in the same way. The largest chi-squared (MI) value (12.369) was found between n5 and n9. But no clear correlation was observed between the overall satisfaction of ICH new media service and the public recommendation of ICH digitization service. Moreover, the MI between other variables was small and fell short of fitting and optimization requirements. Thus, the model improved from the null hypothesis model is displayed in Figure5.Figure 5
Optimized structural equation model for public satisfaction with ICH digital display.Table8 shows the parameter estimations based on the model structure in Figure 5 through the statistical analysis on Amos Graphics.Table 8
Common fitness indices.
Fitting indexX2 (chi-squared)/degree of freedomX2/df valueAGFIRMRRMSEAIFICFIAICResults108.126/492.2070.9160.0180.0720.9710.971167.421Comparing Tables5 and 8, it is clear that the fitness indices were optimized to different degrees, and the correlation paths all passed the significance test. The final optimal estimates of path coefficients and variances are shown in Tables 9 and 10, respectively.Table 9
Final estimates of path coefficients.
Nonstandardized path coefficientsS.E.C.R.PLabelStandardized path coefficientsfwgz←gzqw0.6920.0897.856∗∗∗par_90.637gzmy←fwgz0.6310.05910.011∗∗∗par_100.721gzmy←gzqw0.2910.0584.741∗∗∗par_110.302gzxr←gzmy1.1520.10111.554∗∗∗par_120.789y1←fwgz1.0000.828y2←fwgz0.9320.06117.542∗∗∗par_10.889y3←fwgz1.0340.07117.095∗∗∗par_20.881x3←gzqw0.9720.09810.547∗∗∗par_30.781x2←gzqw0.6870.0819.121∗∗∗par_40.629x1←gzqw1.0000.771y4←gzmy1.0000.739y5←gzmy1.0710.08912.823∗∗∗par_50.791y6←gzmy1.1620.09113.771∗∗∗par_60.858y7←gzxr−0.1190.042−3.1310.002par_7−0.211y8←gzxr1.0000.931y9←gzxr0.9410.06117.590∗∗∗par_80.882Note:∗∗∗significance at the level of 0.01.Table 10
Final variance estimates.
EstimateS.E.C.R.PLabelgzqw0.4510.0706.447∗∗∗par_14m10.3190.0516.988∗∗∗par_15m20.0560.0173.982∗∗∗par_16m30.3340.0516.798∗∗∗par_17n10.2410.0298.977∗∗∗par_18n20.1150.0197.098∗∗∗par_19n30.1810.0237.969∗∗∗par_20p30.2700.0387.109∗∗∗par_21p20.3240.0349.622∗∗∗par_22p10.3100.0477.348∗∗∗par_23n40.3310.0349.792∗∗∗par_24n50.2960.0329.413∗∗∗par_25n60.1890.0317.781∗∗∗par_26n70.2410.02811.177∗∗∗par_27n80.1490.0413.932∗∗∗par_28n90.2410.0416.337∗∗∗par_29Note:∗∗∗significance at the level of 0.01.Referring to formulas (8)–(10), the relationship between structural variables and observed variables can be described by the following three equations, where the coefficients are standardized coefficients:(15)η1η2η3=0000.7210000.7890∗η1η2η3+0.6370.3020∗ξ+m1m2m3,y1y2y3y4y5y6y7y8y9=0.828000.889000.8810000.739000.791000.858000−0.211000.931000.882∗η1η2η3+n1n2n3n4n5n6n7n8n9,x1x2x3=0.7710.6290.781∗ξ+ρ1ρ2ρ3.


## 4.2.2. Empirical Analysis

The main function of the structural equation model is to reveal the structural relationships between the structural variables. These relationships are reflected by path coefficients in the model. Combined with the empirical research of scholar Cheng [44], the optimal structural equation model for public satisfaction with ICH digital display was analyzed on Amos Graphics. Table 11 shows the estimated standardization effects between the structural variables.Table 11
Estimates of standardization effect.
Public expectationsService perceptionPublic satisfactionPublic trustService perception (total effect)0.6370.0000.0000.000(Direct effect)0.6370.0000.0000.000(Indirect effect)0.0000.0000.0000.000Public satisfaction (total effect)0.7530.7170.0000.000(Direct effect)0.3020.0000.0000.000(Indirect effect)0.4510.0000.0000.000Public trust (total effect)0.5890.5650.7900.000(Direct effect)0.0000.0000.7900.000(Indirect effect)0.5890.5650.0000.000The direct effect refers to the direct influence of the cause variable on the result variable in all paths of the structural equation model. Generally, the direct effect is measured by the standardized path coefficient from the cause variable to the result variable, as shown in the last column of Table9. For example, the normalized path coefficient from public expectations (gzqw) to public satisfaction (gzmy) is 0.302, indicating that the direct effect of public expectations on public satisfaction through the path relationship is 0.302, that is, when other conditions remain unchanged in the structural equation model, the structural variable “public expectations” will increase by 1 standardized coefficient and the structural variable “public satisfaction” will correspondingly increase by 0.302 of a standardized coefficient.The indirect effect means that in all paths of the structural equation model, the cause variable and the result variable are not directly connected, but are influenced by the mutual conduction of one or more intermediary variables between them. The value of the indirect effect is the product between the standardized path coefficients of connected paths. As shown in Table9, the standardized path coefficient from public expectations (gzqw) to service perception (fwgz) was 0.637 and that from service perception (fwgz) to public satisfaction (gzmy) was 0.721. Then, the indirect effect from public expectations to public satisfaction was 0.637 ∗ 0.721 = 0.459. When the other conditions in the structural equation model remain unchanged, if the structural variable “public expectations” increases by 1 standardized coefficient, then the structural variable “public satisfaction” will indirectly increase by 0.459 of a standardized coefficient.The total effect refers to the overall influence of the cause variable on the result variable in all paths of the structural equation model. It consists of direct effect and indirect effect. For example, the direct effect from public expectations (gzqw) to public satisfaction (gzmy) was 0.302 and the indirect effect was 0.451. Then, the total effect was 0.302 + 0.451 = 0.753. When the other conditions in the structural equation model remain unchanged, if the structural variable “public expectations” increases by 1 standardized coefficient, then the structural variable “public satisfaction” will increase by 0.753 of a standardized coefficient.Tables11 and 12 show the structural relationship between each structural variable and each observed variable, indicating the total effect of each structural variable on the observed variable. It can be found that almost all structural variables have a positive effect or no effect on the observed variable. The only exception is the negative effect of the structural variable public trust (gzxr) on the observed variable public complaints (y7). Therefore, the increase of the structural coefficient by each standardized coefficient can enhance each observed variable. The factor weights are shown in Table 13.Table 12
Estimates of standardized total effect.
Public expectationsService perceptionPublic satisfactionPublic trusty10.5210.8280.0000.000y20.5600.8890.0000.000y30.5450.8810.0000.000y40.5560.5330.7390.000y50.5850.5620.7910.000y60.6480.6220.8690.000y7−0.121−0.117−0.163−0.211y80.5450.5230.7320.931y90.5160.4950.6920.882x10.7710.0000.0000.000x20.6290.0000.0000.000x30.7810.0000.0000.000Table 13
Final factor weights.
x1x2x3y1y2y3y4y5y6y7y8y9Public expectations0.250.160.28−0.010.020.010.030.050.080−0.000.020.01Service perception0.000.000.000.180.340.240.04−0.010.1004−0.000.020.01Public satisfaction0.040.020.040.000.130.090.100.120.226−0.000.060.03Public trust0.010.000.010.000.030.020.020.030.057−0.030.510.30Following the calculation principle, public satisfaction was solved by formulas (1) and (2) based on the data in Table 13.For the structural variable public satisfaction (gzmy), the sum of factor weights was 0.856. Thus, the MI of the structural variable wasψ=1/0.856.By formula (1), we have(16)publicsatisfactioncs1=Eξ−MinξMaxξ−Minξ∗100=1/12∑i=13wix¯i+∑j=19φjy¯j−1/12∑i=13wiMinxi+∑j=19φjMinyi1/12∑i=13wiManxi+∑j=19φjMaxyi−1/12∑i=13wiMinxi+∑j=19φjMinyi∗100,ψ=3.54−1.034.47−1.03∗100∗ψ=2.513.44∗100∗ψ=85.24,where Eξ,Maxξ), and Minξ are the mean, maximum, and minimum of public satisfaction, respectively; x¯i, Max(xi), and Min(xi) are the mean, maximum, and minimum of xi; y¯j, Maxyj, and Minyj are the mean, maximum, and minimum of yj; wi and φj are the weights of each factor; and ψ is the correction index, i∈Ω3, j∈Ω9.By formula (2), we have(17)publicsatisfactioncs2=∑j=13wix¯i+∑j=19ϕjy¯j∗ψ=3.54∗ψ=4.14.The above result can be expressed as a percentage:(18)publicsatisfactioncs3=publicsatisfactioncs2∗1005=82.8.With reference to the relevant literature on satisfaction evaluation standards, combined with the characteristics of ICH digital display services, the evaluation standards for this evaluation model are set as follows:When CS < 60, it indicates that the quality of the digital display service of ICH is not good, and the public feels very bad about the quality of the digital display service of ICH; when 60 ≤ CS < 70, it indicates that the service quality of the digital display of ICH is average, and the public has a general perception of the quality of the digital display service of ICH; when 70 ≤ CS < 80, it shows that the service quality of the digital display of ICH is good, and the public feels good about the service quality of the digital display of ICH; and when CS ≥ 80, it shows that the quality of the digital display service of ICH is very good, and the public feels very good about the quality of the digital display service of ICH.


## 4.3. Countermeasures

The ICH digitization should be service-oriented and emphasize the digitization service quality of ICH works. According to the results of the valid responses, the digital forms of the ICH works visited by the public and favored by the public are ranked in Tables14 and 15, respectively. In the structural equation model of public satisfaction with the digital display of ICH, the structural variable service perception (fwgz) has the highest direct impact on the structural variable public satisfaction (gzmy), reaching 0.715, which shows that improving the service quality of ICH digital display is also the top priority for improving public satisfaction with the digital display of ICH.Table 14
Digital forms of the ICH works visited by the public.
ResponseCase percentageNPercentageForm of digital worksImage47725.977.3Audio31016.951.6Video49827.185.0Animation26614.541.6Interactive game1216.617.4Human-computer interaction784.211.1Integrated display of sound and light894.814.8In total1839100.0298.8Table 15
Digital forms of the ICH works favored by the public.
MeanRanking by the degree of preferenceCharacter1.64Image2.37Audio1.88Video3.44Animation2.21Interactive game1.76Human-computer interaction1.02Integrated display of sound and light1.65Note: the greater the value, the stronger the degree of preference.As shown in Tables14 and 15, the public mainly visited digital works in the form of video, image, and audio, but rarely visited those in the form of interactive game, human-computer interaction, and integrated display of sound and light. The top 3 most preferred forms are video, image, and animation.The survey shows that 42.4% of the respondents have visited digital ICH exhibits. Hence, digital display means have not been widely utilized. The public has not comprehensively learned the display forms of ICHs. Table16 shows the degree of preference of the public for different display forms of ICHs.Table 16
Degree of preference for different ICH display forms.
Display formMeanPurely physical display2.88Purely digital display1.76Physical display + digital display2.89Field participation of inheritor + digital display2.43Note: the greater the value, the stronger the degree of preference.Through the empirical research on the Splendid China costume show, it can be seen that the different ICH display design elements act differently on audience satisfaction. This requires that ICH display designers not only have rich ICH knowledge, display theory, and practical skills but also need to study the psychology of ICH audience to understand how the audience’s psychology will be affected by different ICH display strategies and feel and develop different design strategies for different types of elements. The following countermeasures were therefore designed:(1)
Application of conventional display media. Based on the research on the ICH project itself, we can understand the way its elements are displayed. In other words, these elements have begun to have the nature of exhibits and can be managed through digital display schemes. No matter what kind of media tool is used, the act of dissemination itself has no value, only when the work obtains useful information in the process of dissemination and affects people's behavior, it has meaning. Therefore, in the process of displaying the existing ICH projects, it is necessary to screen reasonable media and obtain key information from them before spreading.(2)
Emotional positioning in ICH projects. In terms of display form, it is necessary to combine physical display with digital display. Using the art elements of visual language (e.g., equipment modeling; matching lighting and color), the scene needs to be creatively processed according to a certain theme and rationality, creating a cultural atmosphere that matches the ICHs, arouses the interest of the audience, and provides a pleasant experience of the ICHs.(3)
The construction of interactive mode. The operation interaction mode is a multidimensional interactive experience design through multimedia technology, supplemented by computer program processing solutions. The core of ICHs lies in the activation of people's participation. Therefore, ICH designers should not only consider the contents and form of ICHs but also fully explore the interactive features of ICHs with the aid of online multimedia technology. The intricate technical contents of ICHs should be organized into a rich experience effect so that the audience become more satisfied with the experience of ICHs.


## 5. Conclusions and Suggestions

(1)
The construction of a public satisfaction evaluation model for the digital display of ICHs is the key to satisfaction evaluation. Our public satisfaction evaluation model for the digital display of ICHs is in line with China’s national conditions. With a strong practical value, the model creates favorable conditions for the research on public satisfaction for ICH digital display.(2)
There is ample room to improve public satisfaction with the digital display of ICHs. According to the ACSI-based public satisfaction evaluation model for ICH digital display, the ICH-related service provider should strengthen the construction of ICH digital service-related projects, understand the public’s expectations of and emphasis on ICH digital-related projects, and improve the key links and weak points.(3)
The digital display design of ICH is the product of the era combining ICH protection and digital technology. As an information processing technology, digital technology is the foundation of computer technology, multimedia technology, intelligent technology, and information dissemination technology. It provides various technical support for the display of ICHs. No matter how the digital display design of ICH is carried out, the owner and inheritor of ICH should be the main body. From the perspective of the development trend of ICH digital display design, combined with the theory of audience satisfaction, the ultimate goal of ICH digital display design is to ignore digital technology, that is, to integrate digital technology into ICHs and become a part of its life.(4)
Promoting the dissemination of ICHs is an important measure to further enhance ICH protection. The main weakness lies in the brand image of ICH display. ICH curators should actively improve their own brand awareness; increase publicity through television, radio, Internet, and other channels; and expand the influence of ICHs by hosting large-scale events.(5)
Personalized service is an effective way to promote the digital development of ICHs. Only by understanding the preference of different groups of the public for different types of ICHs, and digitizing ICHs by the digital service methods favored by the public, can we achieve targeted development of ICHs and improve the efficiency and effectiveness of ICH display.



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*Source: 9501415-2022-10-05.xml*