Open AccessArticle

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

Wen-Jie Yan

and

Shu-Tang Liu

^2,3,*

Fujian Key Laboratory of Novel Functional Textile Fibers and Materials, Minjiang University, Fuzhou 350108, China

Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China

Academic Affairs Office, Minjiang University, Fuzhou 350108, China

Author to whom correspondence should be addressed.

Sustainability 2023, 15(5), 4353; https://doi.org/10.3390/su15054353

Submission received: 5 February 2023 / Revised: 23 February 2023 / Accepted: 27 February 2023 / Published: 28 February 2023

(This article belongs to the Special Issue The Creative and Cultural Industries towards Sustainability and Recovery)

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Figure 1
Creative and cultural industry development model integrating policy guidance and resource activation. "> 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 3
Research Process and Design. "> 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 5
A landscape view of the co-citation network: (a) Timespan: 2013; (b) Timespan: 2022; (c) Timespan: 2023. "> Figure 5 Cont.
A landscape view of the co-citation network: (a) Timespan: 2013; (b) Timespan: 2022; (c) Timespan: 2023. "> Figure 6
A timeline visualisation of 20 clusters. "> Figure 7
City heat map. "> Figure 8
Research model. "> Figure 9
Cultural governance model of a creative economy. "> Figure 10
Flexible cultural governance model of creative economy: (a) self-management; (b) legality; (c) policy; (d) democracy. "> Figure 11
Path coefficient values of 20 indicators. "> Figure 12
Sustainable cultural and economic ecological system. ">

Versions Notes

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.

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).

2. Literature Review

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 sub-sectors 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.

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).

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).

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.

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).

3. Materials and Methods

3.1. Science Mapping

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).

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.

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 references is shown in Figure 4. One node represents a keyword, and the larger node represents a higher citation frequency.

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)

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.

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 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.

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.

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%).

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. Table 8 and Table 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.

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.

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).

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 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.

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:

The total effect value of CCI on CP was 0.396, the direct effect value was 0.396, the indirect effect value was 0.185, the mediating interval [0.205,0.626] did not include 0, and the p-value was 0.012. CCI ← CP had a complete mediating effect;
The total effect value of CCI on CD was 0.37, the direct effect value was 0.37, the mediating interval [0.105,0.535] did not include 0, and the p-value was 0.05. CCI ← CD had a partial mediating effect;
The total effect value of CD on CP was 0.499, the direct effect value was 0.05, the mediating interval [0.349,0.618] did not include 0, and the p-value was 0.016. CD ← CP had a partial mediating effect. The mediating effect of CCI ← CD ← CP was established;
The direct effect value of CI on CP was 0.34, and the direct effect value of CV on CI was 0.679. The indirect effect value of CV on CP was 0.45, and the indirect effect value of CI on CP was 0.34. The mediating effect of CP ← CI ← CV was established.

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 self-management-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.

4.3. 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].

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.

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.

5.3. Research Limitations and Future Work

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, co-citation 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).

Cluster ID	Size	Silhouette	Mean (Year)	Label by LSI (Latent Semantic Indexing)	Label by LLR (Log-Likelihood Ratio, p = 0.0001)
0	46	0.882	2016	transformational leadership; transactional leadership; dialectical thinking; leadership comparison; green process innovation \| employee creativity; hindrance-related stress; challenge-related stress; supervisory task feedback; voice theory	(79.42, 0.0001) transformational leadership (58.65, 0.0001) employee creativity (38.48, 0.0001) knowledge sharing (35.7, 0.0001 creative self-efficacy (30.06, 0.0001) creativity
1	41	0.915	2015	creative industries; creative city; city branding; tourism labour migrant; pharmaceutical industry \| creative labor; creative city policy; tourism labour migrant; pharmaceutical industry; creative cluster	(50.44, 0.0001) creative labour (17.58, 0.0001) creative thinking (17.58, 0.0001) precariat (16.58, 0.0001) work (16, 0.0001) digital media
2	36	0.823	2016	creative industries; manufacturing industries; developing country; information technology; cultural diversity \| design thinking; product development; stakeholders engagement; green innovation; medium size enterprises	(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
3	32	0.965	2015	creative economy; human capital; regression analysis; intellectual activity; adaptive resilience \| creative industries; creative city; city branding; adaptive resilience; night-time economy	(103.54, 0.0001) creative economy (50.42, 0.0001) creative city (42.06, 0.0001) economic development (29.49, 0.0001) higher education (28.79, 0.0001) creative cities
4	29	0.87	2016	creative industries; entertainment industries; urban growth machine; text analysis; planning culture \| creative economy; creative city; cultural economy; economic development; cultural policy	(38.85, 0.0001) cultural industries (34.49, 0.0001) urban development (27.63, 0.0001) cultural industry (22.2, 0.0001) urban (19.17, 0.0001) city
5	28	0.964	2016	creative industries; social networks; informal economy; radical innovation; creativity method \| city; place; economy; geography; neighborhood	(21.88, 0.0001) gender (13.05, 0.001) gender inequality; (13.05, 0.001) production; (13.05, 0.001) intersectionality; (13.05, 0.001) feminism
6	27	0.933	2014	creative industries; emotional labour; urban growth machine; night-time economy; pink accounts \| cultural policy; creative ecosystem; developing countries; digital industries; inter-organizational learning	(42.41, 0.0001) cultural policy (31.1, 0.0001) south Africa (30.16, 0.0001) Cultural production (21.9, 0.0001) policy mobilities (18.94, 0.0001) politics
7	27	0.878	2017	creative performance; gender difference; innovation speed; sensing capability; organizational resources \| social media; dynamic capabilities; advertising agency; innovation speed; sensing capability	(33.34, 0.0001) entrepreneurial orientation (29.32, 0.0001) creative performance (22.58, 0.0001) business performance (22.56, 0.0001) social media (21.26, 0.0001) big data
8	24	0.957	2016	creative class; public policy; location factors; tourism labour migrant; management model \| creative industries; labour precarity; primary school age; knowledge-based development; knowledge-intensive businesses	(32.5, 0.0001) creative class (19.82, 0.0001) creative workers (19.82, 0.0001) social networks (14.39, 0.001) digital transformation (12.74, 0.001) multivariate linear regression
9	24	0.864	2014	creative industries; social networks; informal economy; firm characteristics; techno-creative innovation \| creative industry; creative class; creative clusters; creative spaces; mental maps	(188.43, 0.0001) creative industries (53.14, 0.0001) cultural and creative industries (27.55, 0.0001) creative industry (27.21, 0.0001) sustainable development (20.04, 0.0001) Fashion
10	23	0.938	2015	creative industries; creative work; career management; arts management; team composition \| innovation; ecosystem; disruption; strategy; technology	(34.82, 0.0001) creative work (25.81, 0.0001) cultural work (17.8, 0.0001) energy transition (16.35, 0.0001) research and development (11.86, 0.001) O31
11	23	0.904	2015	creative industries; cultural tourism; cultural heritage; historical centre; network \| regional development; creative class; regional policy; high-growth firms; knowledge base	(36.59, 0.0001) regional development (20.38, 0.0001) creative tourism (19.36, 0.0001) system (19.29, 0.0001) cultural tourism (16.5, 0.0001) cultural heritage
12	23	0.953	2014	creative industries; information technology; manufacturing industries; developing country; cultural regeneration model \| creative economy; policy; city; rethinking; entrepreneurship	(17.53, 0.0001) capability (12.48, 0.001) product (12.48, 0.001) everyday life (12.48, 0.001) technology transfer (12.48, 0.001) tradition
13	22	0.905	2016	creative destruction; factor reallocation; digital delivery; news production; magazine revenue \| business model; digital distribution; decorative marks; symbolic production; process innovations	(58.11, 0.0001) creative destruction (22.38, 0.0001) sharing economy (22.38, 0.0001) disruptive innovation (19.62, 0.0001) law (19.62, 0.0001) intellectual property
14	22	0.86	2016	knowledge economy; innovation district; place quality; delphi method; artificial neural network \| knowledge-based urban development; knowledge industry; knowledge worker; urban competitiveness; artificial neural network	(26.56, 0.0001) digital economy (25.71, 0.0001) knowledge economy (25.15, 0.0001) knowledge-based urban development (22.79, 1.0 × 10⁻⁴) artificial intelligence (22.64, 1.0 × 10⁻⁴) industry 4.0
15	22	0.934	2014	political economy; climate change; emissions trading; instrument choice; environmental economics \| creative industries; cultural industries; cultural economics; innovation policy; soft innovation	(85.98, 0.0001) political economy (13.35, 0.001) climate change (13.27, 0.001) firm (13.17, 0.001) new media (13.17, 0.001) outsourcing
16	21	0.929	2017	creative self-efficacy; job satisfaction; hotel industry; organizational support; knowledge transfer \| innovative behavior; task interdependence; resources theory; in-role behavior; three-way interaction	(37.78, 0.0001) creative self-efficacy (27.32, 0.0001) creative industries (14.51, 0.001) human resource management (14.08, 0.001) phenomenology (14.08, 0.001) servant leadership
17	21	0.865	2014	creative industries; creative work; music journalism; unpaid work; survie des nouvelles entreprises \| music industry; social network service; music journalism; unpaid work; survie des nouvelles entreprises	(64.67, 0.0001) music industry (29.39, 0.0001) popular music (26.78, 0.0001) new economy (18.8, 0.0001) music industries (13.37, 0.001) digital technology
18	20	0.94	2017	open innovation; knowledge-based engineering; manufacturing process innovation; knowledge management; collective intelligence \| satisfaction; performance; mediating role; physical environment; innovation	(20.99, 0.0001) open innovation (19.41, 0.0001) value co-creation (14.97, 0.001) service design (14.39, 0.001) tourist experience (13.81, 0.001) creative industries
19	20	0.883	2015	human capital; creative class; regional economics; labour market; entrepreneurial discovery process \| economic growth; reflexive capitalism; smart specialisation; entrepreneurial discovery process; secular stagnation	(43.32, 0.0001) human capital (17.14, 0.0001) economic growth (15.51, 0.0001) 21st-century skills (13.76, 0.001) creative industries (13.36, 0.001) intellectual capital

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

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 3. Research Process and Design.

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

Figure 6. A timeline visualisation of 20 clusters.

Figure 7. City heat map.

Figure 8. Research model.

Figure 9. Cultural governance model of a creative economy.

Figure 10. Flexible cultural governance model of creative economy: (a) self-management; (b) legality; (c) policy; (d) democracy.

Figure 11. Path coefficient values of 20 indicators.

Figure 12. Sustainable cultural and economic ecological system.

Table 1. Cultural indicators and cultural value survey.

Cultural Indicator	Report
Reflective individuals, Civic engagement, The self, Communities, Regeneration, Space, Economy, Health, aging, Wellbeing, Arts in education	Understanding the Value of Arts & Culture: The AHRC Cultural Value Project (2016) [8]
Economy: GDP, Employment, Household expenditure Education: Inclusive education, Multilingual education, Arts education, Professional training Governance: Standard-setting framework, Policy and institutional framework, Arts education, Professional training Social: Going-out participation, Identity-building participation, Intercultural trust, Interpersonal trust, Self-determination Gender: Gender equality outputs, Perception of gender equality Communication: Freedom of expression, Internet use, Diversity of media content Heritage: Heritage sustainability	UNESCO. Culture for development indicators: methodology manual (2014) [18]
Arm’s-length governance: Delegated models at the national level Multistakeholder governance: Civil society, Non-government actors, and the Private sector Interministerial governance: Cross-portfolio engagement Multilevel governance: Decentralized models at all government tiers	UNESCO. Building resilient and sustainable cultural and creative sectors (2022) [19]
Citizenship, Equalities, Education, Innovation, Local government, Justice	BOP. Cultural and Creative Industries in the Face of COVID-19: An Economic Impact Outlook (2021) [20]
Professional and formal cultural and creative sectors; Education and training: retaining talent; Arts education	UNESCO. Culture and sustainable development: a still untapped potential (2022) [21]
Economic development: Cultural employment, Government support for culture, Voluntary work in arts and culture, Economic contribution of cultural industries Cultural value: Cultural assets, Talent (human capital), Cultural identity, Innovation (new work/companies), Global reach Engagement and social impact: Cultural attendance, Cultural participation, Access, Education in arts and culture	Vital Signs: cultural indicators for Australia (2011) [22]
Cultural vitality: the presence of opportunities for cultural participation, cultural participation itself, and support for arts and cultural activities.	Cultural Vitality in Communities: Interpretation and Indicators (2006) [23]
Engagement: Cultural employment, Heritage protection, Access to arts, culture, and heritage activities and events Cultural Identity: Local Content on television, The importance of culture to national identity Diversity: Cultural grants to minority ethnic groups, Attendance at and participation in ethnic cultural activities, Minority cultural activities Social Cohesion: Other-ethnicities attendance, Community cultural experiences Economic development: Income of the cultural industries, Value-added contributed by the creative industries, The creative industries’ proportion of total industry value-added	Cultural indicators for New Zealand (2009) [24]
Self-expression values, Survival values, Secular-rational values, Traditional values	World values survey. Values change the world (2008) [25]
Deeply cultivate cultural “sense of history”, Shaping the “international sense” of culture, Cultural diversity and freedom as a “sense of value” Stimulate “cultural creativity”	TAICCA. 2021 Taiwan Cultural & Creative industries Annual Report (2021) [26]
Cultural democracy: Promoting cultural governance reform and organizational reengineering Cultural creativity: Support the freedom of artistic creation and cultivate aesthetic cultivation Cultural vitality: Cultural preservation and rooting, linking land and people’s historical memory Cultural sustainability: The sustainability of the cultural economy and the ecosystem “cultural and creative industries” Cultural inclusiveness: Promoting the development and exchange of cultural diversity Cultural transcendence: Carry out cultural future, Create cultural science and technology, Cross-domain co-creation and sharing	Ministry of culture. 2018 culture policy the white paper (2018) [27]
Place and identity, Engagement, Identity, Social cohesion, Diversity	Carol Scott (2014) [28]
Cultural consistency, Diversity, Justice, Inclusive perception bias	PWC. Global Culture Survey 2021 (2021) [29]

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

Time	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Records	404	427	489	515	556	642	731	692	697	290

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

Keywords	Year	Strength	Begin	End
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
law	2019	4.14	2019	2022
precarity	2019	3.59	2019	2022
video game	2019	3.59	2019	2022
creative self-efficacy	2019	3.23	2019	2022
digital economy	2018	3.17	2019	2022
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).

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

Cluster ID	Size	Silhouette	Mean (Year)	Label by LLR (Log-Likelihood Ratio, p = 0.0001)
0	46	0.882	2016	(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
1	41	0.915	2015	(50.44, 0.0001) Creative labor (17.58, 0.0001) Creative thinking (17.58, 0.0001) Precariat (16.58, 0.0001) Work (16, 0.0001) Digital media
2	36	0.823	2016	(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
3	32	0.965	2015	(103.54, 0.0001) Creative economy (50.42, 0.0001) Creative city (42.06, 0.0001) Economic development (29.49, 0.0001) Higher education (28.79, 0.0001) Creative cities
4	29	0.87	2016	(38.85, 0.0001) Cultural industries (34.49, 0.0001) Urban development (27.63, 0.0001) Cultural industry (22.2, 0.0001) Urban (19.17, 0.0001) City
5	28	0.964	2016	(21.88, 0.0001) Gender (13.05, 0.001) Gender inequality; (13.05, 0.001) Production; (13.05, 0.001) Intersectionality; (13.05, 0.001) Feminism
6	27	0.933	2014	(42.41, 0.0001) Cultural policy (31.1, 0.0001) South Africa (30.16, 0.0001) Cultural production (21.9, 0.0001) Policy mobilities (18.94, 0.0001) Politics
7	27	0.878	2017	(33.34, 0.0001) Entrepreneurial orientation (29.32, 0.0001) Creative performance (22.58, 0.0001) Business performance (22.56, 0.0001) Social media (21.26, 0.0001) Big data
8	24	0.957	2016	(32.5, 0.0001) Creative class (19.82, 0.0001) Creative workers (19.82, 0.0001) Social networks (14.39, 0.001) Digital transformation (12.74, 0.001) Multivariate linear regression
9	24	0.864	2014	(188.43, 0.0001) Creative industries (53.14, 0.0001) Cultural and creative industries (27.55, 0.0001) Creative industry (27.21, 0.0001) Sustainable development (20.04, 0.0001) Fashion
10	23	0.938	2015	(34.82, 0.0001) Creative work (25.81, 0.0001) Cultural work (17.8, 0.0001) Energy transition (16.35, 0.0001) Research and development (11.86, 0.001) O31
11	23	0.904	2015	(36.59, 0.0001) Regional development (20.38, 0.0001) Creative tourism (19.36, 0.0001) System (19.29, 0.0001) Cultural tourism (16.5, 0.0001) Cultural heritage
12	23	0.953	2014	(17.53, 0.0001) Capability (12.48, 0.001) Product (12.48, 0.001) Everyday life (12.48, 0.001) Technology transfer (12.48, 0.001) Tradition
13	22	0.905	2016	(58.11, 0.0001) Creative destruction (22.38, 0.0001) Sharing economy (22.38, 0.0001) Disruptive innovation (19.62, 0.0001) Law (19.62, 0.0001) Intellectual property
14	22	0.86	2016	(26.56, 0.0001) Digital economy (25.71, 0.0001) Knowledge economy (25.15, 0.0001) Knowledge-based urban development (22.79, 0.0001) Artificial intelligence (22.64, 0.0001) Industry 4.0
15	22	0.934	2014	(85.98, 0.0001) Political economy (13.35, 0.001) Climate change (13.27, 0.001) Firm (13.17, 0.001) New media (13.17, 0.001) Outsourcing
16	21	0.929	2017	(37.78, 0.0001) Creative self-efficacy (27.32, 0.0001) Creative industries (14.51, 0.001) Human resource management (14.08, 0.001) Phenomenology (14.08, 0.001) Servant leadership
17	21	0.865	2014	(64.67, 0.0001) Music industry (29.39, 0.0001) Popular music (26.78, 0.0001) New economy (18.8, 0.0001) Music industries (13.37, 0.001) Digital technology
18	20	0.94	2017	(20.99, 0.0001) Open innovation (19.41, 0.0001) Value co-creation (14.97, 0.001) Service design (14.39, 0.001) Tourist experience (13.81, 0.001) Creative industries
19	20	0.883	2015	(43.32, 0.0001) Human capital (17.14, 0.0001) Economic growth (15.51, 0.0001) 21st-century skills (13.76, 0.001) Creative industries (13.36, 0.001) Intellectual capital

Table 5. Measurement items and sources.

Measuring Items	Sources
The role of cultural heritage as a cultural intermediary.	[22,24]
The role of CCI in local, regional, national, and supranational economies.	[20]
Cultural governance strategy.	[19,26,28]
Art education and professional training.	[18]
Cultural assets preservation strategy.	[18,22,24]
Sustainable development strategy of the cultural economy and the CCI ecosystem.	[28]
Policies to promote the development and exchange of cultural diversity.	[23]
Creating culture, technology, cross-regional co-creation and sharing, and cultural governance strategies.	[18,22,24]
Ways to participate in the protection of cultural heritage.	[19,20,22]
Cultural consumption attitudes towards intangible cultural heritage and cultural and creative products.	[23,24]

Table 6. Time and quantity of questionnaires.

No.	Date	Name of Questionnaire	Distribution Channels	Number of Releases	Total Answers
1	12 December 2022	Formal questionnaire	Credamo data mart	100	130
2	13 December 2022	Formal questionnaire	Credamo data mart	100	126
3	13 December 2022	Formal questionnaire	Credamo data mart	100	130
4	14 December 2022	Formal questionnaire	Credamo data mart	200	249
Number of questionnaires answered					635
Number of valid questionnaires				500
Female				308	61.6%
Male				192	38.4%

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

Items	Category	Number of Responses	Percentage
Age group	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%
Occupation type	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%

Table 8. Reliability Statistics.

Cronbach’s Alpha	Cronbach’s Alpha Based on Standardised Items	N of Items
0.905	0.887	51

Table 9. Reliability Statistics.

Variance	Item	Factor Loadings	Cronbach’s Alpha	AVE	CR
CS	CS1	0.800	0.865	0.559	0.835
	CS2	0.756
	CS3	0.735
	CS4	0.697
CD	CD1	0.799	0.859	0.527	0.816
	CD2	0.716
	CD3	0.707
	CD4	0.678
CCI	CCI1	0.795	0.832	0.582	0.806
	CCI2	0.768
	CCI3	0.724
CI	CI1	0.844	0.824	0.576	0.802
	CI2	0.727
	CI3	0.700
CV	CV1	0.790	0.830	0.566	0.796
	CV2	0.737
	CV3	0.730
CP	CP1	0.817	0.811	0.563	0.794
	CP2	0.731
	CP3	0.699

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

Table 10. KMO and Bartlett’s Test.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy.		0.942
Bartlett’s Test of Sphericity	Approx. Chi-Square	5504.622
	df	190
	Sig.	0.000

Table 11. Total Variance Explained.

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	9.214	46.070	46.070	9.214	46.070	46.070	2.907	14.537	14.537
2	1.282	6.411	52.482	1.282	6.411	52.482	2.766	13.828	28.365
3	1.156	5.780	58.262	1.156	5.780	58.262	2.339	11.696	40.060
4	1.118	5.589	63.851	1.118	5.589	63.851	2.236	11.181	51.242
5	0.952	4.761	68.612	0.952	4.761	68.612	2.222	11.110	62.351
6	0.950	4.748	73.361	0.950	4.748	73.361	2.202	11.009	73.361

Extraction Method: Principal Component Analysis.

Table 12. Results of structural equation modelling analysis.

Hypothesis	Standardized Path Coefficient	Standard Error	Bias-Corrected 95%CI		p Value	Support
Hypothesis	Standardized Path Coefficient	Standard Error	Lower	Upper	p Value	Support
H1: CI ← CV	0.679	0.052	0.594	0.752	0.012	Yes
H2: CP ← CI	0.340	0.068	0.193	0.440	0.020	Yes
H3: CP ← CV	0.450	0.064	0.318	0.580	0.011	Yes
H4: CD ← CP	0.499	0.069	0.349	0.618	0.016	Yes
H5: CD ← CI	0.405	0.066	0.277	0.575	0.004	Yes
H6: CCI ← CP	0.396	0.094	0.205	0.626	0.012	Yes
H7: CCI ← CD	0.370	0.081	0.105	0.535	0.050	Yes
H8:CS ← CCI	0.334	0.057	0.190	0.463	0.009	Yes
H9:CS ← CD	0.488	0.060	0.373	0.621	0.008	Yes

Note: The data listed are standard coefficients.

Table 13. Model fit summary.

Statistical Test Quantity	Criterion or Threshold for Adaptation	Test Result Data	Model Fit Judgement
Absolute Fit Measures
RMSEA (Root Mean Square Residual)	<0.05	0.045	√
GFI (Goodness-of-Fit Index)	>0.90	0.943	√
Baseline Comparisons
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	√
Parsimony-Adjusted
PGFI (Parsimony Goodness-of-Fit Index)	>0.50	0.723	√
PNFI (Parsimony-Adjusted NFI)	>0.50	0.799	√
PCFI (Parsimony-Adjusted CFI)	>0.50	0.822	√
CN (Critical N)	>200	320.177	√
CMIN/DF (Chi-Square/Degrees of Freedom)	<3.00	1.989	√
CAIC (Consistent Akaike’s Information Criterion)	The theoretical model value is less than the independent model value, and at the same time less than the saturated model value.	673.693 < 1515.068 673.693 < 5732.911	√

Table 14. Summary table of mediation effects.

	95% Confidence Interval
	Estimate	BC/PC p Value	BC	PC
Total Effect
CI ← CV	0.679	0.012/0.010	0.594~0.752	0.596~0.756
CP ← CI	0.340	0.020/0.010	0.193~0.440	0.198~0.469
CP ← CV	0.450	0.015/0.010	0.612~0.749	0.614~0.759
CD ← CP	0.499	0.016/0.010	0.349~0.618	0.362~0.626
CD ← CI	0.405	0.009/0.010	0.466~0.704	0.198~0.697
CCI ← CD	0.370	0.050/0.042	0.105~0.535	0.123~0.538
CCI ← CP	0.396	0.006/0.010	0.473~0.741	0.459~0.722
CS ← CCI	0.334	0.009/0.010	0.190~0.463	0.189~0.457
CS ← CD	0.488	0.012/0.010	0.471~0.709	0.478~0.713
Direct Effect
CI ← CV	0.679	0.012/0.010	0.594~0.752	0.596~0.756
CP ← CI	0.340	0.020/0.010	0.193~0.440	0.198~0.469
CP ← CV	0.450	0.015/0.010	0.612~0.749	0.614~0.759
CD ← CP	0.499	0.016/0.010	0.349~0.618	0.362~0.626
CD ← CI	0.405	0.009/0.010	0.466~0.704	0.198~0.697
CCI ← CD	0.370	0.050/0.042	0.105~0.535	0.123~0.538
CCI ← CP	0.396	0.006/0.010	0.473~0.741	0.459~0.722
CS ← CCI	0.334	0.009/0.010	0.190~0.463	0.189~0.457
CS ← CD	0.488	0.012/0.010	0.471~0.709	0.478~0.713
Indirect Effect
CP ← CV	0.231	0.007/0.010	0.146~0.343	0.138~0.323
CD ← CI	0.170	0.011/0.010	0.093~0.248	0.094~0.251
CD ← CV	0.616	0.025/0.010	0.543~0.671	0.551~0.681
CCI ← CP	0.185	0.021/0.043	0.101~0.297	0.071~0.282
CCI ← CI	0.347	0.007/0.010	0.256~0.455	0.247~0.452
CCI ← CV	0.497	0.014/0.010	0.433~0.571	0.436~0.580
CS ← CD	0.123	0.018/0.042	0.061~0.207	0.038~0.193
CS ← CP	0.437	0.016/0.010	0.330~0.530	0.349~0.541
CS ← CI	0.396	0.009/0.010	0.310~0.506	0.303~0.506
CS ← CV	0.466	0.007/0.010	0.411~0.545	0.406~0.543

Table 15. Variable and dimension naming.

Dimension	Variable	Content
Cultural Sustainability (CS)	CS1	Promoting urban competitiveness
	CS2	Construct the space environment of virtual and real integration
	CS3	Cultural inclusion
	CS4	Stimulate community vitality and develop local creation
Cultural Democracy (CD)	CD1	Expand the content of native culture and promote cultural internationalization
	CD2	Implement Arm’s-Length governance
	CD3	Implement cultural equality and cultural citizenship
	CD4	Smooth reward and grants mechanism for arts and culture
Cultural Innovation (CCI)	CCI1	Equality and accessibility of art culture
	CCI2	Industry digital innovation
	CCI3	Product sustainable innovation design
Culture Industrialization (CI)	CI1	Protect the right to cultural work
	CI2	Intangible cultural heritage as a cultural intermediary
	CI3	Establish a national cultural brand
Cultural Vitality (CV)	CV1	Citizen cultural participation
	CV2	Multiple aesthetic education and local cultural experience
	CV3	Local cultural consciousness
Cultural Policy Systematisation (CP)	CP1	Professional management and preservation
	CP2	Establish a cultural impact assessment mechanism
	CP3	Continuously improving cultural regulations

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

	Taicca [27]	This Study
Cultural Sustainability	Cultural economy and the sustainability of cultural and creative industry ecosystem	● Promoting urban competitiveness ● Construct the space environment of virtual and real integration ● Cultural inclusion ● Stimulate community vitality and develop local creation
Cultural Democracy	Promote cultural governance reform and organizational restructuring	● Expand the content of native culture and promote cultural internationalization ● Implement Arm’s-Length governance ● Implement cultural equality and cultural citizenship ● Smooth reward and grants mechanism for arts and culture
Cultural Innovation	Support the freedom of artistic and literary creation and cultivate the sense of beauty	● Equality and accessibility of art culture ● Industry digital innovation ● Product sustainable innovation design
Cultural Vitality	Cultural preservation and rooting, linking land and people’s historical memory	● Citizen cultural participation ● Multiple aesthetic education and local cultural experience ● Local cultural consciousness
Cultural Tolerance	Promote the development and exchange of cultural diversity:
Cultural Transcendence	Carry out cultural future, create cultural technology, and create and share across regions
Culture Industrialisation	-	● Protect the right to cultural work ● Intangible cultural heritage as a cultural intermediary ● Establish a national cultural brand
Cultural Policy Systematisation	-	● Professional management and preservation ● Establish a cultural impact assessment mechanism ● Continuously improving cultural regulations

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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

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Yan W-J, Liu S-T. Creative Economy and Sustainable Development: Shaping Flexible Cultural Governance Model for Creativity. Sustainability. 2023; 15(5):4353. https://doi.org/10.3390/su15054353

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Yan, Wen-Jie, and Shu-Tang Liu. 2023. "Creative Economy and Sustainable Development: Shaping Flexible Cultural Governance Model for Creativity" Sustainability 15, no. 5: 4353. https://doi.org/10.3390/su15054353

APA Style

Yan, W. -J., & Liu, S. -T. (2023). Creative Economy and Sustainable Development: Shaping Flexible Cultural Governance Model for Creativity. Sustainability, 15(5), 4353. https://doi.org/10.3390/su15054353

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