*Article* **Creative Economy and Sustainable Development: Shaping Flexible Cultural Governance Model for Creativity**

**Wen-Jie Yan <sup>1</sup> and Shu-Tang Liu 2,3,\***


**Abstract:** With the development of cultural democratization, countries have attached increasing importance to the protection of cultural rights and the promotion of sustainable cultural development. The establishment of a flexible cultural governance model may release the transformative force of culture and creativity, gradually spread cultural values and ideas into governance, and shift activities to more sustainable behavior. This research was divided into two stages. In the first stage, CiteSpace was used to conduct a co-citation analysis of documents published between 2013 and 2022 in the Web of Science database. The results were combined with existing cultural development and value indicators from many countries to design cultural impact indicators suitable for evaluating the sustainable development of creative industries. In the second stage, a questionnaire survey was conducted on the cultural industry, the creative economy, and cultural consumption. Through statistical analysis, six dimensions were obtained, and 20 indicators were cultural sustainability, cultural democracy, cultural innovation, cultural industrialization, cultural vitality, and cultural policy systematization. The cultural governance framework of the creative economy and sustainable development was established through AMOS software. This study found that the humanistic rationality of cultural governance has a significant improvement and stable role in promoting the governance of cultural policies. Adjustable cultural impact indicators are effective cultural practices for shaping and framing creative industries, which should be invented, stabilized and improved.

**Keywords:** cultural governance; creative economy; cultural policy; regional development; cultural impact assessment

#### **1. Introduction**

In the sustainable development goals (SDGs) adopted by the United Nations in September 2015, culture is first mentioned in the international development agenda. UNESCO believes that the protection and promotion of culture is an end goal and directly contributes to many sustainable development goals: safe and sustainable cities, work and economic growth, reducing inequality, protecting the environment, promoting gender equality, and a peaceful and inclusive society. Sustainable development is a conceptual change beyond economic development and growth. If the economic, social and environmental goals are regarded as the three pillars of sustainable development, UNESCO believes that culture and creativity offer a horizontal contribution to each of these pillars. In turn, the economic, social, and environmental dimensions of sustainable development aid in protecting cultural heritage and fostering creativity. In the cultural development reports and documents of all the countries in the world, focus has consistently been placed on elements such as the cultural economy, economic statistics, and industrial statistics. However, the analysis of the cultural side is still lacking [1,2]. This paper examines the sustainable development of cultural and creative industries from a cultural perspective.

**Citation:** Yan, W.-J.; Liu, S.-T. Creative Economy and Sustainable Development: Shaping Flexible Cultural Governance Model for Creativity. *Sustainability* **2023**, *15*, 4353. https://doi.org/10.3390/ su15054353

Academic Editors: Justin Lewis and Marlen Komorowski

Received: 5 February 2023 Revised: 23 February 2023 Accepted: 27 February 2023 Published: 28 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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Presenting cultural changes and evaluating the effectiveness of cultural policies has become a topic of culturally sustainable development. At present, the key performance indicators (KPIs) widely used by the government are not wholly suitable for measuring cultural development. The government should establish an evaluation system and investigation method to observe cultural development over an extended period. In addition to building 'cultural basic indicators' based on the current level of cultural development, the government should also refer to the "culture for development indicators" of international organizations. To provide a test basis for the implementation of policies, cultural development indicators suitable for the national conditions should be proposed.

Cultural power is the soft power of a country, as well as the key driving force toward an era of innovation. Contemporary cultural governance is no longer simply the distribution and management of artistic and cultural resources and power. It should neither be seen as the inculcation and discipline of artistic and cultural concepts by administrators to ordinary people [3]. The rise of concepts such as creative cities, cities of art and design, and cultural capitals, as well as the emerging planning discourse such as art intervention space, citizens' cultural rights, cultural capital, and creative economy, refers to a "cultural turn" in contemporary cultural governance strategies [4]. Cultural governance should consider how to realize governance practices rooted in art and cultural logic, flexible governance strategies, and a "governance mentality" that may affect the mood of ordinary people.

Landry [5] advocates that the creative city is a new, strategic urban planning method establishing an interactive and cooperative relationship by connecting culture and other urban strategies. By gathering talent and organizations, and by fostering a creative atmosphere, such a city may become a hub for innovation and provide momentum for development. The UNESCO Creative Cities Network [6] encourages global cities to develop on a foundation of cultural and creative industries and to implement "cultural diversity" as the main axis of cultural governance. Culture is regarded as an important tool for economic revitalization and sustainable urban development. Economic and cultural value should be evaluated in the overall framework. The cultural statistical indicators proposed by UNESCO in 1986 were later revised in response to the trend of cultural concepts and the importance of cultural value awareness under globalization. With the 2014 publication of Culture for Development Indicators (CDIS), UNSECO attempted to establish relevance indicators of different cultural aspects and social development strategies in order to measure cultural values and the diffusion benefits of culture in other fields in more depth [7]. In 2016, the British Department for Digital, Culture, Media and Sport and the Arts and Humanities Research Council (AHRC) analyzed cultural value more thoroughly in their research study titled "Understanding the Value of Arts & Culture: The AHRC Cultural Value Project". It was concluded that such evaluations should not only be for the endorsement of public resource allocation but should also explore the true value of culture and art [8].

Similar systems to assess cultural value and conduct have been promoted internationally for many years. Numerous international organizations have conducted qualitative and quantitative research on both domestic and international cultural values. The overall evaluation framework is formulated for cultural identity, cultural diversity, cultural asset preservation proximity, social harmony, social happiness, cultural interaction and participation, cultural and economic development, as well as cultural industry benefits [3]. For example, the Taiwan Cultural Policy Research Institute, acting as the third sector of civil society, has carried out an early investigation and research on Taiwan's Cultural Values project [9] to explore the trends and connotations of cultural values in Taiwanese society. This was done by collecting and analyzing objective questionnaire data. The government wishes to use the survey results as a guide for the cultural and third sectors in order to formulate artistic and cultural policies, thus triggering a new discussion regarding cultural ontology in Taiwan's society.

As the driving force of economic growth, development, and regeneration, CCI has a significant impact on the social and cultural aspects of welfare, site creation, inclusiveness, sustainability, diversity, and culture. CCI development models include resource activation;

industrial upgrading; technology-driven, urban transformation; and policy guidance [10]. According to the policy guidance model, the government promotes the rapid formation and development of cultural and creative industries in a region by formulating industrial development strategies, policies, and laws; building financial and tax systems; and implementing talent training programs. These actions aid in realizing the leapfrog development of CCI (Figure 1). sustainability, diversity, and culture. CCI development models include resource activation; industrial upgrading; technology-driven, urban transformation; and policy guidance [10]. According to the policy guidance model, the government promotes the rapid formation and development of cultural and creative industries in a region by formulating industrial development strategies, policies, and laws; building financial and tax systems; and implementing talent training programs. These actions aid in realizing the leapfrog development of CCI (Figure 1).

As the driving force of economic growth, development, and regeneration, CCI has a significant impact on the social and cultural aspects of welfare, site creation, inclusiveness,

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 3 of 30

**Figure 1.** Creative and cultural industry development model integrating policy guidance and resource activation. **Figure 1.** Creative and cultural industry development model integrating policy guidance and resource activation.

#### **2. Literature Review 2. Literature Review**

#### *2.1. Theoretical Research on Cultural and Creative Industries 2.1. Theoretical Research on Cultural and Creative Industries*

In recent years, research on the cultural and creative industries has focused on the qualitative analysis of the connotation and categories of CCI, as well as the empirical study of individual cases. Daubaraite and Startiene [11] clarified the impact of creative industries on the national economy and conducted a systematic evaluation of the subsectors of creative industries. Pappalesore [12] found that the agglomeration of creative industry space provided opportunities for consumption and cultural capital accumulation, and promoted the development of creative tourism. Yu and Liu [13] used the TOPSIS comprehensive evaluation method to build the quality index system of cultural and creative industries, and point out that the improvement of marketization may aid the efficiency of cultural and creative industries. Wang et al. [14] used the Malmquist index to measure the CCI development efficiency and regional differences between provinces in terms of dynamic development, index decomposition, and provincial efficiency. Pan [15] pointed out that cultural innovation, when represented by cultural creative production, causes the agglomeration and diffusion of economic innovation. Agglomeration is the process of cultural creativity's self-multiplication, ultimately forming the cultural creativity class. Diffusion is the process of blending the two dimensions of communication and economy. Liao and Li [16] adopted the CiteSpace research method to conduct data visualization analysis on the integration of China's tourism industry and CCI. These authors found that research in China pertained to local economic benefits and mainly focused on specific cases, and that the smaller body of macro policy research on the overall development of CCI mainly focused on qualitative analysis. Wang [17] pointed out that "cultural products" mainly carry certain concepts and content that are traditionally recognized, while "cultural creativity" should be suitable for bearing certain social effects of "enlightened politics, ideology, customs, and aesthetic guidance". At present, the research on cultural and creative industries ranges from the theoretical category to regional development and the industrial economy. However, deficiencies include the inconsistent statistical cal-In recent years, research on the cultural and creative industries has focused on the qualitative analysis of the connotation and categories of CCI, as well as the empirical study of individual cases. Daubaraite and Startiene [11] clarified the impact of creative industries on the national economy and conducted a systematic evaluation of the subsectors of creative industries. Pappalesore [12] found that the agglomeration of creative industry space provided opportunities for consumption and cultural capital accumulation, and promoted the development of creative tourism. Yu and Liu [13] used the TOPSIS comprehensive evaluation method to build the quality index system of cultural and creative industries, and point out that the improvement of marketization may aid the efficiency of cultural and creative industries. Wang et al. [14] used the Malmquist index to measure the CCI development efficiency and regional differences between provinces in terms of dynamic development, index decomposition, and provincial efficiency. Pan [15] pointed out that cultural innovation, when represented by cultural creative production, causes the agglomeration and diffusion of economic innovation. Agglomeration is the process of cultural creativity's self-multiplication, ultimately forming the cultural creativity class. Diffusion is the process of blending the two dimensions of communication and economy. Liao and Li [16] adopted the CiteSpace research method to conduct data visualization analysis on the integration of China's tourism industry and CCI. These authors found that research in China pertained to local economic benefits and mainly focused on specific cases, and that the smaller body of macro policy research on the overall development of CCI mainly focused on qualitative analysis. Wang [17] pointed out that "cultural products" mainly carry certain concepts and content that are traditionally recognized, while "cultural creativity" should be suitable for bearing certain social effects of "enlightened politics, ideology, customs, and aesthetic guidance". At present, the research on cultural and creative industries ranges from the theoretical category to regional development and the industrial economy. However, deficiencies include the inconsistent statistical caliber and the isolated consideration of economic indicators.

iber and the isolated consideration of economic indicators. This paper analyses the co-citation of literature in the core database of the Web of Science from 2013 to 2022. The following two main thematic trend paths have been This paper analyses the co-citation of literature in the core database of the Web of Science from 2013 to 2022. The following two main thematic trend paths have been identified: citing region "Economics, Economic, Political" to cited region "Psychology, Education, Social"; and citing region "Psychology, Education, Health" to cited region "Psychology, Education, Social" (Figure 2).

chology, Education, Social" (Figure 2).

**Figure 2.** Domain-level citation patterns in CCI research (2013–2022, in Web of Science): The cluster on the left indicates the retrieved research frontier, while the cluster on the right indicates the location of their references; Citation tracks and reference tracks are distinguished by the color of the reference area; The thickness of these tracks is proportional to the reference frequency of the z-score. **Figure 2.** Domain-level citation patterns in CCI research (2013–2022, in Web of Science): The cluster on the left indicates the retrieved research frontier, while the cluster on the right indicates the location of their references; Citation tracks and reference tracks are distinguished by the color of the reference area; The thickness of these tracks is proportional to the reference frequency of the z-score.

identified: citing region "Economics, Economic, Political" to cited region "Psychology, Education, Social"; and citing region "Psychology, Education, Health" to cited region "Psy-

#### *2.2. Cultural Development Indicators 2.2. Cultural Development Indicators*

As with other developing countries, the key performance indicators widely used by the Chinese government are not entirely suitable for measuring the development of culture. Therefore, the country's presentation of cultural changes and assessments of the effectiveness of cultural policies should establish an evaluation system and investigation method that may observe cultural development over an extended time frame. In addition to the basic cultural indicators based on the current situation of cultural development, we should also refer to UNESCO's CDIS to propose standards suitable for a developing country as a test benchmark for policy implementation. Different countries and international organizsations present different situations when surveying cultural values and constructing national cultural indicators (Table 1). As with other developing countries, the key performance indicators widely used by the Chinese government are not entirely suitable for measuring the development of culture. Therefore, the country's presentation of cultural changes and assessments of the effectiveness of cultural policies should establish an evaluation system and investigation method that may observe cultural development over an extended time frame. In addition to the basic cultural indicators based on the current situation of cultural development, we should also refer to UNESCO's CDIS to propose standards suitable for a developing country as a test benchmark for policy implementation. Different countries and international organizsations present different situations when surveying cultural values and constructing national cultural indicators (Table 1).

**Table 1.** Cultural indicators and cultural value survey. **Table 1.** Cultural indicators and cultural value survey.


**Table 1.** *Cont*.


The contents of the above-mentioned cultural surveys may be divided into two distinct measurement methods: the measurement of cultural indicators and the measurement of cultural values. The reports on cultural indicators in Australia, New Zealand, and Taiwan mainly focus on the measurement of statistics. Numerical values, such as those of population, output value, and the number of activities involved, are taken as benefit analysis. The systems may be regarded as assessment tools for the allocation of national resources. The evaluation of cultural indicators in these countries and regions, although committed to the quantification of social environments, may highlight the significance of cultural values. However, from the setting of its evaluation indicators, it also brings to light social integration, cultural diversity, and identity attribution, and emphasizess the value of cultural identity. Economic development is listed as the final consideration of several evaluation indicators. However, culture valuation cannot rely solely on quantitative values such as cultural surveys and statistics. Relevant cultural value surveys show that the top five cultural values shared by the world are "democracy and civic awareness (71.3%), inclusion and diversity (69.5%), human rights and the rule of law (58.8%), fairness and justice (42.7%), and care and public welfare (42.7%)" [9]. Varying opinions regarding the connotation of cultural values in various societies exist. Therefore, ensuring the continuous and open dialogue of the core cultural values of a country is currently an indispensable cultural policy mechanism for all countries.

#### *2.3. Study Design*

CiteSpace (abbreviation of Citation Space) is a visual citation analysis tool used to analyze the potential knowledge contained in the scientific literature [30]. CiteSpace is used to draw visual "Mapping Knowledge Domains" (MKD), which may present the structure, rules, and distribution of scientific knowledge in multiple, time-sharing, and dynamic manners. Since the relevant research on cultural and creative industries is biased towards economic research, MKD was used in the first stage of this paper to draw the trend map of CCI development research over the past 10 year, and to collect hot keywords. *Sustainability* **2023**, *15*, x FOR PEER REVIEW 7 of 30

> The structural equation model is a statistical technique to test the fitness of a theoretical or hypothetical model, which can simultaneously handle multiple variables in the causal model [31]. In order to propose cultural impact indicators with flexible characteristics, we sieved the variables collected in the first stage and added them to the questionnaire of the cultural and creative consumption market in the second stage. The data analysis of the questionnaire was aided by SPSS factor analysis technology and was constructed according to the AMOS model (Figure 3). The structural equation model is a statistical technique to test the fitness of a theoretical or hypothetical model, which can simultaneously handle multiple variables in the causal model [31]. In order to propose cultural impact indicators with flexible characteristics, we sieved the variables collected in the first stage and added them to the questionnaire of the cultural and creative consumption market in the second stage. The data analysis of the questionnaire was aided by SPSS factor analysis technology and was constructed according to the AMOS model (Figure 3).

**Figure 3.** Research Process and Design. **Figure 3.** Research Process and Design.

#### **3. Materials and Methods 3. Materials and Methods**

#### *3.1. Science Mapping 3.1. Science Mapping*

#### 3.1.1. Data Collection

3.1.1. Data Collection In this article, the scientific literature used may be found in the Web of Science Core Collection. The terms to the query are "(((TS = (cultural industry)) AND TS = (creative industry)) AND TS = (creative economy)) AND TS = (cultural heritage)". This query retrieved 5443 bibliographical records. The timespan is from January 2013 to December 2022. The document type is "article". The number of articles related to CCI increased from 404 to 731 between 2013 and 2019, and then remained stable. Between 2021 and 2022, the number of articles fell to a nadir of 290 (Table 2). In this article, the scientific literature used may be found in the Web of Science Core Collection. The terms to the query are "(((TS = (cultural industry)) AND TS = (creative industry)) AND TS = (creative economy)) AND TS = (cultural heritage)". This query retrieved 5443 bibliographical records. The timespan is from January 2013 to December 2022. The document type is "article". The number of articles related to CCI increased from 404 to 731 between 2013 and 2019, and then remained stable. Between 2021 and 2022, the number of articles fell to a nadir of 290 (Table 2).

**Records** 404 427 489 515 556 642 731 692 697 290

velopment of CCI has shifted from an instrumental to a humanistic rationality.

Thirty-one top words featured the strongest citation bursts (Table 3). These highly cited keywords are creative cla, cultural industry, economic geography, music, enterprise, value creation, amenity, university, creative field, contextual factor, work environment, film industry, service innovation, search, cultural diversity, United States, creative process, workplace, creative practice, smart city, history, globalization perception, student, law, care, video game, creative self-efficacy, digital economy, social innovation, and entrepreneurial orientation. A comparison of cluster words and keywords shows that most of these words are related to humanity, value and co-creation. This indicates that the de-

**Table 2.** The distribution of the bibliographic records in the dataset.


**Table 2.** The distribution of the bibliographic records in the dataset.

Thirty-one top words featured the strongest citation bursts (Table 3). These highly cited keywords are creative cla, cultural industry, economic geography, music, enterprise, value creation, amenity, university, creative field, contextual factor, work environment, film industry, service innovation, search, cultural diversity, United States, creative process, workplace, creative practice, smart city, history, globalization perception, student, law, care, video game, creative self-efficacy, digital economy, social innovation, and entrepreneurial orientation. A comparison of cluster words and keywords shows that most of these words are related to humanity, value and co-creation. This indicates that the development of CCI has shifted from an instrumental to a humanistic rationality. *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30 *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30 *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30 *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30 *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30 *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30


In this study, CiteSpace was used to create a co-citation network of selected files in the CCI research area. CiteSpace was chosen due to its visual flexibility, advanced filtering capabilities, and several built-in network analysis toolkits. The co-citation network of 5443 references is shown in Figure 4. One node represents a keyword, and the larger node rep-

**Table 3.** Keywords with the Strongest Citation Bursts during (2013–2022). **Table 3.** Keywords with the Strongest Citation Bursts during (2013–2022). *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30 *Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30

resents a higher citation frequency.

resents a higher citation frequency.


**Table 3.** *Cont*. history 2018 2.99 2018 2020 ▂▂▂▂▂▃▃▃▂▂ law 2019 4.14 2019 2022 ▂▂▂▂▂▂▃▃▃▃ workplace 2017 2.71 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative practice 2017 2.41 2017 2019 ▂▂▂▂▃▃▃▂▂▂ globalisation 2013 2.51 2018 2020 ▂▂▂▂▂▃▃▃▂▂

social innovation 2019 2.76 2019 2022 ▂▂▂▂▂▂▃▃▃▃ video game 2019 3.59 2019 2022 ▂▂▂▂▂▂▃▃▃▃ entrepreneurial Note: Colors (light blue-blue-red) represent the strength of keyword bursts (low-medium-high).

creative self-efficacy 2019 3.23 2019 2022 ▂▂▂▂▂▂▃▃▃▃

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 8 of 30

**Table 3.** Keywords with the Strongest Citation Bursts during (2013–2022).

**Table 3.** Keywords with the Strongest Citation Bursts during (2013–2022).

**Table 3.** Keywords with the Strongest Citation Bursts during (2013–2022).

**Table 3.** Keywords with the Strongest Citation Bursts during (2013–2022).

**Keywords Year Strength Begin End 2013–2022**  creative cla 2013 12.95 2013 2015 ▃▃▃▂▂▂▂▂▂▂ cultural industry 2013 5.88 2013 2015 ▃▃▃▂▂▂▂▂▂▂ economic geography 2013 4.66 2013 2016 ▃▃▃▃▂▂▂▂▂▂ music 2013 4.44 2013 2015 ▃▃▃▂▂▂▂▂▂▂ enterprise 2013 3.3 2013 2015 ▃▃▃▂▂▂▂▂▂▂ value creation 2013 2.9 2013 2015 ▃▃▃▂▂▂▂▂▂▂ amenity 2014 4.18 2014 2017 ▂▃▃▃▃▂▂▂▂▂ university 2014 3.95 2014 2018 ▂▃▃▃▃▃▂▂▂▂ creative field 2014 3.65 2014 2016 ▂▃▃▃▂▂▂▂▂▂ contextual factor 2014 3.16 2014 2017 ▂▃▃▃▃▂▂▂▂▂ work environment 2014 2.8 2014 2017 ▂▃▃▃▃▂▂▂▂▂ film industry 2015 2.93 2015 2017 ▂▂▃▃▃▂▂▂▂▂ service innovation 2015 2.64 2015 2017 ▂▂▃▃▃▂▂▂▂▂ search 2016 4.03 2016 2018 ▂▂▂▃▃▃▂▂▂▂ cultural diversity 2016 2.93 2016 2018 ▂▂▂▃▃▃▂▂▂▂ united states 2017 4.22 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative proce 2017 3.01 2017 2019 ▂▂▂▂▃▃▃▂▂▂ workplace 2017 2.71 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative practice 2017 2.41 2017 2019 ▂▂▂▂▃▃▃▂▂▂ smart city 2018 6.53 2018 2020 ▂▂▂▂▂▃▃▃▂▂

**Keywords Year Strength Begin End 2013–2022**  creative cla 2013 12.95 2013 2015 ▃▃▃▂▂▂▂▂▂▂ cultural industry 2013 5.88 2013 2015 ▃▃▃▂▂▂▂▂▂▂ economic geography 2013 4.66 2013 2016 ▃▃▃▃▂▂▂▂▂▂ music 2013 4.44 2013 2015 ▃▃▃▂▂▂▂▂▂▂ enterprise 2013 3.3 2013 2015 ▃▃▃▂▂▂▂▂▂▂ value creation 2013 2.9 2013 2015 ▃▃▃▂▂▂▂▂▂▂ amenity 2014 4.18 2014 2017 ▂▃▃▃▃▂▂▂▂▂ university 2014 3.95 2014 2018 ▂▃▃▃▃▃▂▂▂▂ creative field 2014 3.65 2014 2016 ▂▃▃▃▂▂▂▂▂▂ contextual factor 2014 3.16 2014 2017 ▂▃▃▃▃▂▂▂▂▂ work environment 2014 2.8 2014 2017 ▂▃▃▃▃▂▂▂▂▂ film industry 2015 2.93 2015 2017 ▂▂▃▃▃▂▂▂▂▂ service innovation 2015 2.64 2015 2017 ▂▂▃▃▃▂▂▂▂▂ search 2016 4.03 2016 2018 ▂▂▂▃▃▃▂▂▂▂ cultural diversity 2016 2.93 2016 2018 ▂▂▂▃▃▃▂▂▂▂ united states 2017 4.22 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative proce 2017 3.01 2017 2019 ▂▂▂▂▃▃▃▂▂▂ workplace 2017 2.71 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative practice 2017 2.41 2017 2019 ▂▂▂▂▃▃▃▂▂▂ smart city 2018 6.53 2018 2020 ▂▂▂▂▂▃▃▃▂▂ history 2018 2.99 2018 2020 ▂▂▂▂▂▃▃▃▂▂

**Keywords Year Strength Begin End 2013–2022**  creative cla 2013 12.95 2013 2015 ▃▃▃▂▂▂▂▂▂▂ cultural industry 2013 5.88 2013 2015 ▃▃▃▂▂▂▂▂▂▂ economic geography 2013 4.66 2013 2016 ▃▃▃▃▂▂▂▂▂▂ music 2013 4.44 2013 2015 ▃▃▃▂▂▂▂▂▂▂ enterprise 2013 3.3 2013 2015 ▃▃▃▂▂▂▂▂▂▂ value creation 2013 2.9 2013 2015 ▃▃▃▂▂▂▂▂▂▂ amenity 2014 4.18 2014 2017 ▂▃▃▃▃▂▂▂▂▂ university 2014 3.95 2014 2018 ▂▃▃▃▃▃▂▂▂▂ creative field 2014 3.65 2014 2016 ▂▃▃▃▂▂▂▂▂▂ contextual factor 2014 3.16 2014 2017 ▂▃▃▃▃▂▂▂▂▂ work environment 2014 2.8 2014 2017 ▂▃▃▃▃▂▂▂▂▂ film industry 2015 2.93 2015 2017 ▂▂▃▃▃▂▂▂▂▂ service innovation 2015 2.64 2015 2017 ▂▂▃▃▃▂▂▂▂▂ search 2016 4.03 2016 2018 ▂▂▂▃▃▃▂▂▂▂ cultural diversity 2016 2.93 2016 2018 ▂▂▂▃▃▃▂▂▂▂ united states 2017 4.22 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative proce 2017 3.01 2017 2019 ▂▂▂▂▃▃▃▂▂▂

**Keywords Year Strength Begin End 2013–2022**  creative cla 2013 12.95 2013 2015 ▃▃▃▂▂▂▂▂▂▂ cultural industry 2013 5.88 2013 2015 ▃▃▃▂▂▂▂▂▂▂ economic geography 2013 4.66 2013 2016 ▃▃▃▃▂▂▂▂▂▂ music 2013 4.44 2013 2015 ▃▃▃▂▂▂▂▂▂▂ enterprise 2013 3.3 2013 2015 ▃▃▃▂▂▂▂▂▂▂ value creation 2013 2.9 2013 2015 ▃▃▃▂▂▂▂▂▂▂ amenity 2014 4.18 2014 2017 ▂▃▃▃▃▂▂▂▂▂ university 2014 3.95 2014 2018 ▂▃▃▃▃▃▂▂▂▂ creative field 2014 3.65 2014 2016 ▂▃▃▃▂▂▂▂▂▂ contextual factor 2014 3.16 2014 2017 ▂▃▃▃▃▂▂▂▂▂ work environment 2014 2.8 2014 2017 ▂▃▃▃▃▂▂▂▂▂ film industry 2015 2.93 2015 2017 ▂▂▃▃▃▂▂▂▂▂ service innovation 2015 2.64 2015 2017 ▂▂▃▃▃▂▂▂▂▂ search 2016 4.03 2016 2018 ▂▂▂▃▃▃▂▂▂▂ cultural diversity 2016 2.93 2016 2018 ▂▂▂▃▃▃▂▂▂▂ united states 2017 4.22 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative proce 2017 3.01 2017 2019 ▂▂▂▂▃▃▃▂▂▂ workplace 2017 2.71 2017 2019 ▂▂▂▂▃▃▃▂▂▂ creative practice 2017 2.41 2017 2019 ▂▂▂▂▃▃▃▂▂▂ smart city 2018 6.53 2018 2020 ▂▂▂▂▂▃▃▃▂▂ history 2018 2.99 2018 2020 ▂▂▂▂▂▃▃▃▂▂ globalisation 2013 2.51 2018 2020 ▂▂▂▂▂▃▃▃▂▂ perception 2014 2.48 2018 2020 ▂▂▂▂▂▃▃▃▂▂ student 2019 5.25 2019 2022 ▂▂▂▂▂▂▃▃▃▃

#### entrepreneurial orientation 2018 2.62 2019 2022 ▂▂▂▂▂▂▃▃▃▃ 3.1.2. Visualization and Analysis orientation 2018 2.62 2019 2022 ▂▂▂▂▂▂▃▃▃▃ 3.1.2. Visualization and Analysis

Note: Colors (light blue-blue-red) represent the strength of keyword bursts (low-medium-high). 3.1.2. Visualization and Analysis In this study, CiteSpace was used to create a co-citation network of selected files in the CCI research area. CiteSpace was chosen due to its visual flexibility, advanced filtering In this study, CiteSpace was used to create a co-citation network of selected files in the CCI research area. CiteSpace was chosen due to its visual flexibility, advanced filtering capabilities, and several built-in network analysis toolkits. The co-citation network of 5443 references is shown in Figure 4. One node represents a keyword, and the larger node represents a higher citation frequency. digital economy 3.17 social innovation 2019 2.76 2019 2022 ▂▂▂▂▂▂▃▃▃▃ entrepreneurial orientation 2018 2.62 2019 2022 ▂▂▂▂▂▂▃▃▃▃ Note: Colors (light blue-blue-red) represent the strength of keyword bursts (low-medium-high). 3.1.2. Visualization and Analysis Note: Colors (light blue-blue-red) represent the strength of keyword bursts (low-medium-high). 3.1.2. Visualization and Analysis In this study, CiteSpace was used to create a co-citation network of selected files in the CCI research area. CiteSpace was chosen due to its visual flexibility, advanced filtering capabilities, and several built-in network analysis toolkits. The co-citation network of 5443 In this study, CiteSpace was used to create a co-citation network of selected files in the CCI research area. CiteSpace was chosen due to its visual flexibility, advanced filtering capabilities, and several built-in network analysis toolkits. The co-citation network of 5443 references is shown in Figure 4. One node represents a keyword, and the larger node represents a higher citation frequency. *Sustainability* **2023**, *15*, x FOR PEER REVIEW 9 of 30

**Figure 4.** A landscape view of the co-citation network. (LRF = 3, LBY = 5, and e = 1.0; Timespan:2013– 2022; Slice Length = 1). **Figure 4.** A landscape view of the co-citation network. (LRF = 3, LBY = 5, and e = 1.0; Timespan: 2013–2022; Slice Length = 1).

The co-citation network is clustered by means of a modular approach, and the clustering is marked by LSI (Latent Semantic Indexing) and log-likelihood ratio techniques. Through the clustering analysis of keywords, 20 clusters are obtained (Table 4). The keywords cluster includes political economy, creative performance, creative economy, social networks, creative industries, human resource management, creative construction, product development, transformation leadership, impact, gender new economy, creative tourism, creative firms, cultural production, knowledge economy, value co-creation, creative labor, cultural policy, and human capital. (Complete data in Appendix A.) The co-citation network is clustered by means of a modular approach, and the clustering is marked by LSI (Latent Semantic Indexing) and log-likelihood ratio techniques. Through the clustering analysis of keywords, 20 clusters are obtained (Table 4). The keywords cluster includes political economy, creative performance, creative economy, social networks, creative industries, human resource management, creative construction, product development, transformation leadership, impact, gender new economy, creative tourism, creative firms, cultural production, knowledge economy, value co-creation, creative labor, cultural policy, and human capital. (Complete data in Appendix A)

**(Log-Likelihood Ratio,** *p* **= 0.0001)**

(79.42, 0.0001) Transformational leadership

(58.65, 0.0001Employee creativity (38.48, 0.0001) Knowledge sharing (35.7, 0.0001) Creative self-efficacy

(30.06, 0.0001) Creativity

(17.58, 0.0001) Precariat (16.58, 0.0001) Work (16, 0.0001) Digital media

(50.44, 0.0001) Creative labor (17.58, 0.0001) Creative thinking

(28.82, 0.0001) Product development (24.53, 0.0001) Design thinking (21.04, 0.0001) Competitive advantage (20.31, 0.0001) Information technology (20.31, 0.0001) Absorptive capacity

**ID Size Silhouette Mean (Year) Label by LLR** 

3 32 0.965 2015 (103.54, 0.0001) Creative economy

**Table 4.** The 20 LLR clusters sorted by size (2013–2022).

0 46 0.882 2016

1 41 0.915 2015

2 36 0.823 2016

**Cluster** 


**Table 4.** The 20 LLR clusters sorted by size (2013–2022).


**Table 4.** *Cont*.

Figure 5 presents a co-citation network clustering view of CCI research in 2013, 2021, and 2022. The ranking of clusters is based on their size, which represents the number of cited publications in a cluster. The two largest clusters (#0 and #1) are Transformational Leadership and Creative Labor. These are followed by three similar clusters, namely Product Development (#2), Creative Economy (#3), and Cultural Industries (#4). The clustering path in 2013 clearly shows that the clusters established by keywords are related to each other (Figure 5a). The fragmentation between clusters begins in 2021. By 2023, there are also links between the following clusters: Political Economy (#15), Creative Self-Efficacy (#16), Creative Destruction (#13), and Digital Economy (#14). Therefore, the keywords of 2021 and 2022 are focused on the field of cultural policy (#13, #14, #15, #16), and are seen to represent the main factor affecting the development of CCI.

**Figure 5.** *Cont*.

**Figure 5.** A landscape view of the co-citation network: (**a**) Timespan: 2013; (**b**) Timespan: 2022; (**c**) Timespan: 2023. **Figure 5.** A landscape view of the co-citation network: (**a**) Timespan: 2013; (**b**) Timespan: 2022; (**c**) Timespan: 2023.

#### 3.1.3. Timeline View 3.1.3. Timeline View

The timeline visualization in CiteSpace depicts clusters along horizontal timelines (Figure 6). Each cluster is arranged vertically according to its size, with the largest cluster displayed at the top of the view. Color curves represent the co-reference links added in the corresponding color years. Large nodes, or nodes with red tree rings, are highly referenced. Under each timeline, the keywords with the highest frequency in a specific year are displayed. The most referenced tags are located at the lowest position of the timeline. The clusters are numbered from 0, with cluster #0 being the largest cluster, cluster #1 is the second-largest, and so forth. Some clusters last more than 10 years, whereas others are relatively short. The largest cluster lasted for 10 years and remains active. Clusters #8, #9, #12, and #13 span 10 years and are still active. In contrast, Cultural Policy (#6) ends in 2020, indicating that relevant research has found its own direction in new professional The timeline visualization in CiteSpace depicts clusters along horizontal timelines (Figure 6). Each cluster is arranged vertically according to its size, with the largest cluster displayed at the top of the view. Color curves represent the co-reference links added in the corresponding color years. Large nodes, or nodes with red tree rings, are highly referenced. Under each timeline, the keywords with the highest frequency in a specific year are displayed. The most referenced tags are located at the lowest position of the timeline. The clusters are numbered from 0, with cluster #0 being the largest cluster, cluster #1 is the second-largest, and so forth. Some clusters last more than 10 years, whereas others are relatively short. The largest cluster lasted for 10 years and remains active. Clusters #8, #9, #12, and #13 span 10 years and are still active. In contrast, Cultural Policy (#6) ends in 2020, indicating that relevant research has found its own direction in new professional fields.

#### fields. *3.2. Cultural Values and Cultural Consumption Market Survey*

#### 3.2.1. Data Collection

The questionnaire covers cultural identity, cultural diversity, the preservation of cultural assets, cultural interaction and participation, cultural and economic development, and cultural policy evaluation. This research questionnaire is divided into two parts. The first part investigates the attitude of cultural and creative consumption, the intention of cultural heritage protection, and the perception of cultural policy. The second part pertains to basic information of the research object, such as gender, age, and occupation. The questionnaire variables used the Likert 7-point scale, with options ranging from "completely disagree" (1 point) to "completely agree" (5 points). The latent and observation variables of the questionnaire refer to the research of relevant scholars and are modified according to this study. The specific scale settings are shown in Table 5.


**Figure 6.** A timeline visualisation of 20 clusters. **Figure 6.** A timeline visualisation of 20 clusters.

*3.2. Cultural Values and Cultural Consumption Market Survey*  **Table 5.** Measurement items and sources.


The subjects of this survey are mainly young and middle-aged groups in China, and questionnaires are distributed through the Credamo platform. The formal questionnaire was distributed using the Credamo data mart between 12 and 14 December 2022. A total of 635 questionnaires were collected, with 500 rated as valid. There were 308 female respondents and 192 male respondents. All responses to the questionnaire were anonymous, and participants were required to be over 18 years old. In terms of data quality control, the questionnaire restricted repeated answers from the same IP address; only one person per five-kilometres range could respond. Users who had already answered were filtered out, and the author was required to authorize each IP location. Table 6 records the time and quantity of questionnaires.

**Table 6.** Time and quantity of questionnaires.


#### 3.2.2. Descriptive Statistics and Analysis

In the effective proportion sample, women accounted for 61.6%, which is consistent with the fact that women form the main user group in cultural and creative consumption [10]. The age range is mainly between 21 and 40 years old, accounting for 86.4% in total (Table 7). This shows that the young and middle-aged group is the most important group pertaining to cultural and creative consumption. From the perspective of occupational distribution, private enterprise practitioners account for more than half of the total, and students account for 22.2%. The composition of these groups is similar to that of Chinese netizens. From the perspective of the urban heat map (Figure 7), the average distribution of respondents among cities is mainly as follows: Shandong (15.4%), Guangdong (15%), Hebei (7.4%), Jiangsu (6.8%), and Shanxi (5.4%).

**Table 7.** Frequency of response statistics by age and occupation type.


**Table 7.** Frequency of response statistics by age and occupation type.

**Items Category Number of Responses Percentage** 

18–20 21 4.2% 21–30 243 48.6% 31–40 189 37.8% 41–50 29 5.8% 51–60 17 3.4% Over 60 1 0.2%

Student 67 13.4% state-owned enterprise 111 22.2% government-affiliated institutions 32 6.4% civil servant 10 2% private enterprise 264 52.8% foreign enterprise 16 3.2%

**Figure 7.** City heat map. **Figure 7.** City heat map.

Age group

Occupation type

The validity of the questionnaire should generally be tested by reliability and validity analyses. A reliability analysis is mainly used to test the internal stability and consistency of the questionnaire scale, which is judged by Cronbach's α coefficient and composite reliability (CR). Cronbach's α ≥ 0.7 and CR > 0.7 were considered good reliability. Tables 8 and 9 show that Cronbach's α coefficient in this study's questionnaire is greater than 0.7, and the CR value is above 0.7, indicating that the reliability of the questionnaire is good. A validity analysis is mainly used to test the reliability of the scale, observe the degree of latent variables reflected in the scale, and use aggregation validity and discriminant validity to judge. The aggregation validity was assessed by average variance extracted (AVE) and factor loading. Table 9 demonstrates that the factor loading value of each item is greater than 0.6 (higher than the threshold value of 0.5), and the AVE values are all greater than 0.7 (higher than the threshold value of 0.5), indicating that the aggregation The validity of the questionnaire should generally be tested by reliability and validity analyses. A reliability analysis is mainly used to test the internal stability and consistency of the questionnaire scale, which is judged by Cronbach's α coefficient and composite reliability (CR). Cronbach's α ≥ 0.7 and CR > 0.7 were considered good reliability. Tables 8 and 9 show that Cronbach's α coefficient in this study's questionnaire is greater than 0.7, and the CR value is above 0.7, indicating that the reliability of the questionnaire is good. A validity analysis is mainly used to test the reliability of the scale, observe the degree of latent variables reflected in the scale, and use aggregation validity and discriminant validity to judge. The aggregation validity was assessed by average variance extracted (AVE) and factor loading. Table 9 demonstrates that the factor loading value of each item is greater than 0.6 (higher than the threshold value of 0.5), and the AVE values are all greater than 0.7 (higher than the threshold value of 0.5), indicating that the aggregation validity of the scale is good.

**Table 8.** Reliability Statistics.

validity of the scale is good.



**Table 9.** *Cont*.

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. Rotation converged in six iterations.

This paper was adjusted using the Varimax with Kaiser Normalization method of factor analysis, with factor rotation excluding factor coefficients less than or equal to 0.4. The Kaiser–Meyer–Olkin (KMO) value was 0.942 (Table 10), and the significance index was 0.000. As this was less than 0.05, the questionnaire was found to be suitable for factor analysis.

**Table 10.** KMO and Bartlett's Test.


Through multiple-factor convergences, a total of 6 dimensions and 20 indicators were obtained after 6 factor rotations. The overall explained variation was found to be 73.361% (Table 11).

**Table 11.** Total Variance Explained.


Extraction Method: Principal Component Analysis.

#### 3.2.3. AMOS Fitness Analysis

This study used AMOS software to verify the theoretical model and tested whether the hypothesis is tenable by means of the path coefficient and significance level. The path coefficient mainly shows the relationship between various variables and the significance of the impact. The Bootstrapping sampling method was repeated 5000 times and was used to solve the path coefficient and test the significance level of the model path. The results are shown in Table 12. The standardized path coefficient values of H1, H2, H3, H4, H6, and H7 were 0.679, 0.340, 0.450, and 0.499, respectively, with a value of about 0.5. Considering that the *p*-value was less than 0.05, it may be concluded that the research hypothesis had statistical differences. The normalized path coefficient values of H5, H8, and H9 were 0.405, 0.334, and 0.488, respectively, which are close to 0.5. Considering that their *p*-values were all less than 0.01, the research hypothesis has significant statistical differences.


**Table 12.** Results of structural equation modelling analysis.

Note: The data listed are standard coefficients.

Table 12 shows a positive initial model data fit; all evaluation indicators were within an acceptable range, so there was no need to modify the MI index. The model fitness test results are shown in Table 13. The CN value = 320.177 > 200, meeting the model adaptation standard. From other overall fitness indexes, the chi-square degree of freedom ratio was 1.989 < 3.00, and the root mean square error of approximation (RMSEA) value was 0.045 < 0.05. The GFI value was 0.943, the NFI value was 0.943, the RFI value was 0.932, the IFI value was 0.971, the TLI value was 0.965, and the CFI value was 0.971. These were all greater than 0.09. The fitness of the overall model was therefore ideal. The Consistent Akaike's Information Criterion (CAIC) value of the theoretical model was equal to 673.693, less than that of the independent model value (1515.068), and less than the Expected Cross-Validation Index value of the saturated model (5732.911), indicating that the model is acceptable. The relationship and path coefficient values of each dimension in the model are shown in Figure 8.



model is acceptable. The relationship and path coefficient values of each dimension in the

Residual) <0.05 0.045 <sup>√</sup> GFI (Goodness-of-Fit Index) >0.90 0.943 √

NFI (Normed Fit Index) >0.90 0.943 √ RFI (Relative Fit Index) >0.90 0.932 √ IFI (Incremental Fit Index) >0.90 0.971 √ TLI (Tucker–Lewis Coefficient) >0.90 0.965 √ CFI (Comparative Fit Index) >0.90 0.971 √

Index) >0.50 0.723 <sup>√</sup> PNFI (Parsimony-Adjusted NFI) >0.50 0.799 √ PCFI (Parsimony-Adjusted CFI) >0.50 0.822 √ CN (Critical N) >200 320.177 √

Freedom) <3.00 1.989 <sup>√</sup>

model value.

The theoretical model value is less than the independent model value, and at the same time less than the saturated

**Adaptation Test Result Data Model Fit** 

673.693 < 1515.068 673.693 < 5732.911 <sup>√</sup>

**Judgement** 

**Statistical Test Quantity Criterion or Threshold for** 

model are shown in Figure 8.

**Table 13.** Model fit summary.

Absolute Fit Measures RMSEA (Root Mean Square

Baseline Comparisons

Parsimony-Adjusted

CAIC

Criterion)

PGFI (Parsimony Goodness-of-Fit

CMIN/DF (Chi-Square/Degrees of

(Consistent Akaike's Information

**Figure 8.** Research model. **Figure 8.** Research model.

#### **4. Results**

#### *4.1. The Mediating Effect of Cultural Innovation on Cultural Sustainability*

In this study, the Bootstrap method was used to repeatedly sample the original data, forming a new sample with a capacity of 500 in order to evaluate the relationship between the paths. The test results may be seen below.

From Table 14, it can be concluded that:


**Table 14.** Summary table of mediation effects.



**Table 14.** *Cont*.

#### *4.2. Flexible Cultural Development Impact Indicators*

The 6 dimensions and 20 indicators proposed by the research institute were designed by combining the bibliometric analysis and cultural development indicators of various countries. The six dimensions are named according to the content, as shown in Table 15. The complete cultural governance model of the creative economy may be divided into four separate models to adapt to different situations (Figure 9). These models are selfmanagement-oriented (Figure 10a), legality-oriented (Figure 10b), policy-oriented (Figure 10c), and democracy-oriented (Figure 10d). Cultural validity (CV) was an independent variable. The variables of CS, CI, CCI, CP, and CD were both dependent and independent. The distribution of variable scores shows that the relationship between them is parallel and almost equally important (Figure 11). The basic logic of cultural governance points to different cultural governance models under specific historical conditions, and these different cultural governance models have their own political and economic dimensions [32]. Liu [33] proposed to analyze the possibility of cultural governance from three perspectives: cultural governance as the regulation of the system of public culture, cultural governance as self-regulation and self-reflection of rulers and ruled, and cultural governance regarded as governance by culture.

Cultural pluralism or diversity has almost become the contemporary universal value. In the practice of cultural policy, what is important may not be the single or diverse form itself, but rather the value concept and means behind the realization of single and diverse forms. This also shows the importance of seeking a problem consciousness and a method of rethinking contemporary cultural policy and governance. Between the value tradition of the cultural economy and the modernity of cultural policy governance, a local, unique, reflexive, autonomous, and dynamic cultural governance and cultural self-management model should be sought.

*4.2. Flexible Cultural Development Impact Indicators*

*4.2. Flexible Cultural Development Impact Indicators*

*Sustainability* **2023**, *15*, x FOR PEER REVIEW 21 of 30

regarded as governance by culture.

regarded as governance by culture.

The 6 dimensions and 20 indicators proposed by the research institute were designed by combining the bibliometric analysis and cultural development indicators of various countries. The six dimensions are named according to the content, as shown in Table 15. The complete cultural governance model of the creative economy may be divided into four separate models to adapt to different situations (Figure 9). These models are selfmanagement-oriented (Figure 10a), legality-oriented (Figure 10b), policy-oriented (Figure 10c), and democracy-oriented (Figure 10d). Cultural validity (CV) was an independent variable. The variables of CS, CI, CCI, CP, and CD were both dependent and independent. The distribution of variable scores shows that the relationship between them is parallel and almost equally important (Figure 11). The basic logic of cultural governance points to different cultural governance models under specific historical conditions, and these different cultural governance models have their own political and economic dimensions [32]. Liu [33] proposed to analyze the possibility of cultural governance from three perspectives: cultural governance as the regulation of the system of public culture, cultural governance as self-regulation and self-reflection of rulers and ruled, and cultural governance

The 6 dimensions and 20 indicators proposed by the research institute were designed by combining the bibliometric analysis and cultural development indicators of various countries. The six dimensions are named according to the content, as shown in Table 15. The complete cultural governance model of the creative economy may be divided into four separate models to adapt to different situations (Figure 9). These models are selfmanagement-oriented (Figure 10a), legality-oriented (Figure 10b), policy-oriented (Figure 10c), and democracy-oriented (Figure 10d). Cultural validity (CV) was an independent variable. The variables of CS, CI, CCI, CP, and CD were both dependent and independent. The distribution of variable scores shows that the relationship between them is parallel and almost equally important (Figure 11). The basic logic of cultural governance points to different cultural governance models under specific historical conditions, and these different cultural governance models have their own political and economic dimensions [32]. Liu [33] proposed to analyze the possibility of cultural governance from three perspectives: cultural governance as the regulation of the system of public culture, cultural governance as self-regulation and self-reflection of rulers and ruled, and cultural governance

**Figure 9.** Cultural governance model of a creative economy. **Figure 9.** Cultural governance model of a creative economy. **Figure 9.** Cultural governance model of a creative economy.

ity; (**c**) policy; (**d**) democracy. **Figure 10.** Flexible cultural governance model of creative economy: (**a**) self-management; (**b**) legality; (**c**) policy; (**d**) democracy. **Figure 10.** Flexible cultural governance model of creative economy: (**a**) self-management; (**b**) legality; (**c**) policy; (**d**) democracy. *Sustainability* **2023**, *15*, x FOR PEER REVIEW 22 of 30

**Figure 11.** Path coefficient values of 20 indicators. **Figure 11.** Path coefficient values of 20 indicators.


Cultural Democracy

Cultural Innovation

Industrialization

Cultural Vitality

Cultural Policy Systematisation (CP)

model should be sought.

(CD)

(CCI)

(CI)

(CV)

Culture


CD2 Implement Arm's-Length governance

CCI3 Product sustainable innovation design

CP1 Professional management and preservation CP2 Establish a cultural impact assessment mechanism CP3 Continuously improving cultural regulations

Cultural pluralism or diversity has almost become the contemporary universal value. In the practice of cultural policy, what is important may not be the single or diverse form itself, but rather the value concept and means behind the realization of single and diverse forms. This also shows the importance of seeking a problem consciousness and a method of rethinking contemporary cultural policy and governance. Between the value tradition of the cultural economy and the modernity of cultural policy governance, a local, unique, reflexive, autonomous, and dynamic cultural governance and cultural self-management

*4.3. Comparison of Cultural Impact Indicators under the Framework of Cultural Governance* 

The white paper on cultural policy issued by Taiwan in 2018 still affects the strategy and direction of its cultural governance. The six proposed cultural forces (cultural sustainability, cultural democracy, cultural innovation, cultural vitality, cultural tolerance, and cultural transcendence) cover the family and policy direction of cultural policy. These

CI1 Protect the right to cultural work

CI3 Establish a national cultural brand

CV1 Citizen cultural participation

CV3 Local cultural consciousness

CD3 Implement cultural equality and cultural citizenship

CI2 Intangible cultural heritage as a cultural intermediary

CV2 Multiple aesthetic education and local cultural experience

nationalization

CCI2 Industry digital innovation



#### *4.3. Comparison of Cultural Impact Indicators under the Framework of Cultural Governance*

The white paper on cultural policy issued by Taiwan in 2018 still affects the strategy and direction of its cultural governance. The six proposed cultural forces (cultural sustainability, cultural democracy, cultural innovation, cultural vitality, cultural tolerance, and cultural transcendence) cover the family and policy direction of cultural policy. These six forces also respond to the diversified development of social groups, the trend of cultural ecological diversification, the change of cultural science and technology, and the demand of cultural democracy. It is of great help for this paper to explore the cultural value impact indicators of CCI. This study integrates the six aspects proposed by TAICCA into four aspects while adding two new aspects ("culture industrialisation" and "cultural policy systematization") (Table 16). Although the language used is the same, the geographical and social environment is different. Therefore, Taiwan's cultural values are not entirely applicable to all Chinese-speaking areas. China's mainland should develop more flexible, applicable, and local value impact indicators to implement the democratization of cultural resources, discourse, and participation rights. The scope of this study is limited to the field of CCI development and aims to "fully integrate and culture creativity into local development", as proposed in the mission statement of the Creative Cities Network [6].

**Table 16.** Cultural value index design (compared with Taiwan, China).



**Table 16.** *Cont*.

#### **5. Conclusions and Discussion**

#### *5.1. The Sustainability of a Cultural Economic Ecosystem*

The presence of CCI enables individuals to rethink culture and industry. Culture and creativity improve the quality of the industry, while in turn, industrial development stimulates the accumulation of culture. The existence of cultural and creative industries has established the practical legitimacy of cultural industrialization and industrial culture [3]. However, the cultural field and industry often fall into the opposing categories of cultural connotation and economic development. The ecosystem of the cultural economy may be more compatible with the values and behaviors of various agents, so different agents in the ecosystem may find the position of symbiosis, co-prosperity, coexistence, interdependence, and cooperation, as well as the direction of mutual nourishment and value cycle. This means going beyond the current instrumental logic regarding mainstream cultural administration, government bureaucracy, market rules, and economic values, and rather emphasizing human rationality, such as cooperation, coordination, and symbiosis between different actors and ecological chains in the natural and human ecosystems [34,35].

With the consideration of a cultural, economic ecosystem in contemporary cultural governance (Figure 12), important implications include (1) evaluating cultural value to move beyond the narrow perspective of economic output; (2) measuring cultural value according to a framework that is beyond the management perspective of political and economic bureaucracy; and (3) maintaining close interaction between different departments and organizations within the culture to maintain diversity and preserve the vitality of the sustainable development of cultural values.

#### *5.2. Research Contribution*

A flexible cultural impact assessment framework is conducive to the realization of diversified CCI development. In terms of research methods, this study consisted of a hybrid quantitative analysis (the combination of bibliometric analysis and a questionnaire survey) to obtain indicators that affect the evaluation of cultural and creative consumption and cultural value. Regarding academic theory, the contribution of this study is its proposal of a flexible CCI cultural governance framework that may be aggregated or split. The indicators in the framework may be transformed into operational definitions and applied to both policy and cultural governance.

of the sustainable development of cultural values.

• Continuously improving cultural reg-

ulations

The presence of CCI enables individuals to rethink culture and industry. Culture and creativity improve the quality of the industry, while in turn, industrial development stimulates the accumulation of culture. The existence of cultural and creative industries has established the practical legitimacy of cultural industrialization and industrial culture [3]. However, the cultural field and industry often fall into the opposing categories of cultural connotation and economic development. The ecosystem of the cultural economy may be more compatible with the values and behaviors of various agents, so different agents in the ecosystem may find the position of symbiosis, co-prosperity, coexistence, interdependence, and cooperation, as well as the direction of mutual nourishment and value cycle. This means going beyond the current instrumental logic regarding mainstream cultural administration, government bureaucracy, market rules, and economic values, and rather emphasizing human rationality, such as cooperation, coordination, and symbiosis between different actors and ecological chains in the natural and human ecosystems

With the consideration of a cultural, economic ecosystem in contemporary cultural governance (Figure 12), important implications include (1) evaluating cultural value to move beyond the narrow perspective of economic output; (2) measuring cultural value according to a framework that is beyond the management perspective of political and economic bureaucracy; and (3) maintaining close interaction between different departments and organizations within the culture to maintain diversity and preserve the vitality

**Figure 12.** Sustainable cultural and economic ecological system. **Figure 12.** Sustainable cultural and economic ecological system.

**5. Conclusions and Discussion** 

*5.1. The Sustainability of a Cultural Economic Ecosystem* 

*5.2. Research Contribution*  A flexible cultural impact assessment framework is conducive to the realization of diversified CCI development. In terms of research methods, this study consisted of a hybrid quantitative analysis (the combination of bibliometric analysis and a questionnaire survey) to obtain indicators that affect the evaluation of cultural and creative consumption and cultural value. Regarding academic theory, the contribution of this study is its proposal of a flexible CCI cultural governance framework that may be aggregated or split. The indicators in the framework may be transformed into operational definitions and applied to both policy and cultural governance. To balance art and the cultural ecosystem, as well as to safeguard basic cultural human rights and national sustainable development, ideal cultural governance requires a To balance art and the cultural ecosystem, as well as to safeguard basic cultural human rights and national sustainable development, ideal cultural governance requires a more flexible framework from which to measure the vitality and value of culture. The government should actively absorb the wisdom of scholars, experts, and civil society, refer to the research results of cultural statistics and cultural indicators, reintroduce the methods and ideas of art and humanities into the economic and social science assessment model of cultural value, incorporate the qualitative and quantitative cultural value assessment and overall cultural impact assessment systems into planning, and gradually promote the practical operation. Society should recognize the current trend of international cultural policy, transcend the myth that the cultural economy is focused on output value and GDP growth, and move towards a new direction of cultural value evaluation.

#### more flexible framework from which to measure the vitality and value of culture. The *5.3. Research Limitations and Future Work*

[34,35].

government should actively absorb the wisdom of scholars, experts, and civil society, refer to the research results of cultural statistics and cultural indicators, reintroduce the In terms of research methods, this paper lacks qualitative investigation and analysis. Since the survey of cultural values is only collected through the quantitative questionnaire, it would be ideal to add qualitative interview data. In order to remedy this defect, cocitation analysis technology was used to capture the trend of research topics from specific time spans. This co-citation analysis method is based on the existing literature and can predict the trend of future CCI research within a certain range.

Another limitation of the article is that the questionnaire survey in the second stage of the research process was not used to carry out a comparative analysis of the population in different countries. The questionnaire was only distributed in China, and there is no sample survey of regions outside China, such as Taiwan and Macao. In order to improve the sample quality of the collected questionnaire, we manually rejected non-standard questionnaire answers. The proportion of questionnaire aggregation was 27%. In order to further reduce the impact of the unstable quality of questionnaire data recovery. In terms of the questionnaire setting, this paper sets the quality control of the corresponding sample requirements that include limiting the number of times the subjects answered to be greater than or equal to 50 (the subjects have less experience); the subject's credit score is greater than or equal to 80 (the higher the sample's credit score, the higher the quality of the questionnaire); the historical adoption rate of the subjects is greater than or equal to 80 (historical adoption rate = the number of questionnaires adopted/total number of questionnaires answered); intelligent behavioral verification (intelligent human-machine verification is carried out before answering to greatly improve data quality and safety); and the scope of the answering area is limited (only one person is allowed to answer in this area). A sample feature setting is limited education (undergraduate or above).

Future research may add questionnaires from different countries or conduct mixed comparative analyses of single or multiple cities. Countries with the same language or similar cultural backgrounds may also be grouped for comparative analysis. With the continuous integration, collision, and change of culture through the cross-border migration of citizens, citizens' perception of cultural rights has become more detailed. According to the knowledge map analysis of this study, the educational and social dimensions may direct future research on CCI.

**Author Contributions:** Conceptualisation, W.-J.Y.; methodology, W.-J.Y.; software, S.-T.L.; validation, W.-J.Y. and S.-T.L.; formal analysis, W.-J.Y.; investigation, W.-J.Y.; data curation, S.-T.L.; writing original draft preparation, W.-J.Y.; writing—review and editing, W.-J.Y.; visualization, W.-J.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by 2021 Fujian Young and Middle-aged Teacher Education and Research Project, grant number JAS21279, and was funded by Major Science and Technology Project of Fuzhou, grant number 2021-ZD-298, and was funded by Major Science and Technology Project of Nanping, grant number N2021A004.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to all the interviewees being older than 20 years old and the questionnaires being anonymous.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** The 20 LLR clusters sorted by size (2013–2022).



#### **Table A1.** *Cont*.


#### **Table A1.** *Cont*.


#### **Table A1.** *Cont*.

#### **References**


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