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Abstract

In order to promote the prosperity and development of the research on “regional tourism performance” and better guide the practice of regional tourism development, this paper gives a basic and comprehensive review of the research activities on “regional tourism performance.” Data were collected from 418 English papers (2004–2020) collected from the Web of Science database. This study uses CiteSpace and Gephi to analyze the development of the thematic research from four dimensions: research overview, knowledge base, discipline structure, and research frontier. The study found that “regional tourism performance” is still a hot spot of the future. The existing literature on “regional tourism performance” mainly focuses on constructing models, exploring influencing factors, and innovating management models to improve tourist satisfaction, enhance regional tourism competitiveness, and promote regional economic growth. Panel data, entropy index, data envelopment analysis, bootstrap truncated regression models, coupling coordination degree, and spatial variation are the main research methods. Since 2016, cultural tourism, heritage tourism, rural tourism, tourism destinations competitiveness, and regional tourism governance have become hot topics in the thematic research. This paper is helpful to improve the research efficiency of the thematic research, promote the theoretical results to better guide the practice, and improve the level of regional tourism performance. However, this paper has limitations in terms of concept differentiation and data accuracy.

Keywords

regional tourism performance literature review bibliometric analysis complex network analysis CiteSpace

Introduction

Tourism has become one of the most influential participants in the global economy (Shahin & Dabestani, 2010), and the development of tourism is very dependent on global trade. Affected by COVID-19, the development of the global tourism market has been frustrated, and forces the development strategy of tourism to change from globalization to regionalization. Therefore, the development performance of regional tourism is bound to be paid more attention. In social practice, it needs the coordination and cooperation of multiple interest groups such as government departments, tourism enterprises, tourism researchers, and residents in the region to improve the performance level of tourism in the region. In terms of theoretical research, it is necessary for tourism experts and scholars to measure the comprehensive strength of regional tourism industry by building a regional tourism performance index system (Sainaghi et al., 2017) and put forward corresponding suggestions.

This paper reviews the literature on the topic of “regional tourism performance,” which helps to reflect the historical accumulation, current situation, and latest progress of the current research topic of “regional tourism performance,” and can reflect the new trends, new trends, new levels, new principles, and new technologies of relevant research (Zhou, 2021). Thus, the research objective of this paper is to preliminarily describe the research overview, knowledge base, discipline structure, and research frontier of the topic of “regional tourism performance,” so as to provide reference to scholars engaged in the topic of “regional tourism performance.” Then it is more scientifically and effectively applied to the development practice of regional tourism.

Before the literature review, the paper needs to describe and define the concept of “regional tourism performance.” At the same time, “tourism performance” is distinguished from “tourism competitiveness,” “tourism driving force” and other concepts, so as to make the problem analysis more clear. As for the discussion on the concept of “tourism performance,” some scholars believe that tourism performance achievements involve many aspects such as economy, society, culture, and ecology (Ma & Liu, 2016). Some scholars define “regional tourism performance” as the economic benefits generated through a series of tourism economic activities in a tourism space (Zhou, 2017). This paper holds that “tourism performance” refers to the economic, institutional, technological, and living space benefits of production and reproduction with characteristics of tourism industry in the original material environment by means of capital, rights, innovation, and living habits. The “regional tourism performance” is a supplement to specific spatial constraints on the basis of the concept of tourism performance.

Compared with tourism competitiveness and tourism driving force, “regional tourism performance” is a comprehensive index to measure regional tourism competitiveness. It ensures the correctness of special policies and business decisions, and stimulates the interest of stakeholders in improving tourism performance (Assaf & Josiassen, 2012; Hanafia et al., 2016). Tourism destination competitiveness mainly refers to “the ability of tourism destinations to continuously provide tourists with satisfactory tourism experience and various benefits for other stakeholders” (Zang & Huang, 2006). Previous studies have used “importance performance analysis (IPA)” to analyze and evaluate the competitiveness of tourism destinations (Tian et al., 2009; Zang & Ge, 2014). Tourism driving force refers to one or several factors that can promote the development of tourism, and cannot comprehensively evaluate the development status of regional tourism. Tourism performance can be used as a tool to evaluate tourism destination competitiveness, and tourism destination competitiveness is the embodiment of regional tourism performance. Tourism destination competitiveness focuses more on describing the ability of tourism destination to provide tourism benefits for tourists and relevant stakeholders, while tourism performance focuses more on the output of benefits brought by people in the process of tourism production and reproduction. It can be seen that there are significant differences between regional tourism performance and tourism destination competitiveness and tourism destination driving force.

The second part is “literature review,” which discusses the previous research results and the differences between this paper and the previous research. The third part explains the bibliometric method, introduces the bibliometric analysis visualization software CiteSpace, the complex network visualization analysis tool Gephi, and the use strategies of the two sorts of software. The third and fourth parts review the research overview and knowledge foundation of the topic of “regional tourism performance” from the five dimensions of author, institution, journal source, references, and keywords, summarize the discipline structure of the topic of “regional tourism performance” and explore the research frontier. The fifth part expounds the research conclusions and prospects of this paper.

Literature Review

At present, the research objects and methods for the topic of “tourism performance” are rich and diverse. Some scholars take tourism landscapes as the research object and comprehensively evaluate the environmental, social and economic performance from the perspective of sustainable development (Cao & Wu, 2020; Zhao et al., 2018). Ham et al. (2020) took 141 countries as the research object to explore how the factors affecting the competitive advantage of smart tourism are configured in countries with high tourism performance. Yadegaridehkordi et al. (2019) takes tourism enterprises in Malaysia as the research object to conclude that technology is the most important factor affecting the effect of big data on the performance of tourism enterprises.

Furthermore, the research on the topic of “regional tourism performance” has achieved rich results. Wang and Lv (2008) evaluated the tourism image performance of provincial administrative regions in the Yangtze River basin by constructing the regional tourism image performance evaluation index system. Zheng (2008) constructed the performance evaluation model of nature reserves and standardized the operation process of performance evaluation. Lu (2011) constructed a performance evaluation system based on connection density, effect intensity, and information measurement for the performance of regional tourism cooperation, which provided methodological experience in the study of regional tourism collaborative performance. Wang and Yi (2013) studied the relationship between regional tourism industry clusters and industrial performance, and took profit, employment, and labor effectiveness as the indicator sources of performance measurement. Huang et al. (2018) studied the dynamic evolution and synergy characteristics of regional tourism development pattern and evaluated the quality performance of tourism development process and the total performance of development results from the perspectives of “efficiency” and “performance.”

It can be seen from the above statement that the research on the topics of “tourism performance” and “regional tourism performance” has a long history and rich achievements. However, the empirical research on the topic of “regional tourism performance” is far from enough. It is necessary to guide the relevant research by systematically and comprehensively summarizing the existing literature. In order to avoid repeated research on “regional tourism performance” and point out the research trend and cutting-edge hot spots, this paper uses the bibliometric method to summarize the special research on “regional tourism performance.” This paper studies the co-occurrence frequency, co-citation frequency, cluster analysis, and burst analysis of existing literature authors, supporting institutions, journal sources, references, and keywords, and summarizes the special research on “regional tourism performance” from four perspectives: research overview, knowledge base, discipline structure, and research frontier. Through these analyses, we can understand the achievements, authoritative scholars, high-frequency vocabulary, cutting-edge hot spots, and other information in the research field of “regional tourism performance.” Under the guidance of the above information, it will help to open up new ideas, find collaborators and sort out research ideas for scholars engaged in the special research on “regional tourism performance” in the future. To sum up, the research objective and core value of this paper are to point out the research foundation, trend, and hot spots for scholars engaged in the special research of “regional tourism performance,” so as to improve the research efficiency in this field.

Method

This section first explains the bibliometric methods mainly used in this paper, and then introduces the bibliometric visualization software CiteSpace and the complex network visualization analysis tool Gephi.

In 1923, bibliometrics came from the statistical bibliography of the British scholar E. W. Hulme. Pritchard (1969), a British scholar, proposed to replace documentary statistics with bibliometrics, and expand the research object of documentary statistics from journals to all books and journals, and called it “the application of mathematical and statistical methods in books and other means of communication.” Bibliometrics can present the knowledge base and cooperative network structure conducive to the research field through the interpretation and inference of correlation analysis, and describe the overall picture of scientific knowledge in a specific research field in detail, so as to promote the progress of knowledge in this field. It also offers useful information for practitioner sand academics in their endeavor to identify gaps in the extant literature and future trends (Mulet-Forteza, 2021). Merigó et al. (2019) used the bibliometric method to evaluate the articles and authors contributing to tourism geography, highlighting the main trends and topics covered by the journal. The vocabulary involved includes author, article, institution, country, citation, and key words.

This paper uses the bibliometric method to discuss the existing literature of the special research on “regional tourism performance,” which can give full play to the advantages of bibliometrics. With the help of professional bibliometric visualization software, we can clearly describe the research overview, knowledge base, cooperation network, discipline structure, and cutting-edge hot spots of the special research on “regional tourism performance.” Therefore, this paper selects the bibliometric research method and its visualization software CiteSpace.

In the process of writing this paper, it is mainly applied to CiteSpace 5.7 R1. CiteSpace software was developed by Dr. Chen (2006), lifelong professor of Drexel University. It is one of the most popular knowledge mapping tools and is widely used in literature review research. It can focus the research process of a knowledge field on a citation network map, and automatically identify the citation nodes and research frontiers on the map. It can visually display the development trend of research in this field, cutting-edge hot spots, knowledge correlation, important authors, institutions, and their cooperation in a period of time. This paper uses several functions of CiteSpace 5.7 software, including author co-occurrence analysis, institution co-occurrence analysis, author co-citation analysis, journal co-citation analysis, reference co-citation analysis, keywords co-occurrence analysis, journal double graph superposition analysis, reference cluster analysis, keywords cluster analysis, burst analysis of co-citation of authors burst analysis of co-citation of references, burst analysis of co-occurrence of keywords, etc. Then it systematically and comprehensively analyzes the research overview, knowledge base, discipline structure, and cutting-edge hot spots of the topic of “regional tourism performance.”

Each time CiteSpace is used, some parameters need to be set to ensure that the measurement results are more in line with expectations and the generated images are clearer and more concise. The time slice mentioned below refers to the initial literature collection divided and calculated by time period. If the time slice is 1, it means Top N literature is used to extract data every year; if the time slice is 2, it means Top N data is extracted every 2 years. For example, the purpose of keywords co-occurrence analysis is to observe the occurrence of keywords in existing literature in all years, so the time slice needs to be set to the full period, that is, the initial year of the retrieved literature. Top N: The system set N = 30, which means that N references with the highest citation times are extracted from each time slice. The larger N is, the more comprehensive the network will be. Top N%: The cited references in each time slice are sorted by citation times, and the highest N% is reserved as the node. Threshold interpolation: Threshold interpolation setting, with values of three time slices and values of the remaining time slices assigned by linear Interpolation. The three groups that need to be configured are the first, the middle, and the last. The three values in each group are C, CC, and CCV. C (Citation) is the lowest number of Citation. Only literatures that meet this condition can participate in the following operations. CC (Cocitation) is the number of co-citations in this slice. Cosine coefficient is the number of co-citations after normalization (0–100).

Because the parameters set are different when using each function of CiteSpace software, the detailed application of the research method in different situations cannot be explained uniformly. This paper describes the setting of CiteSpace parameters in detail before each function use below. Including the length of time slice, Top N, Top N%, and the first part of the threshold value.

Research Strategy

In order to improve the relevance and effectiveness of the literature review of the special research on “regional tourism performance,” this paper uses the data from Web of Science, which is considered to be the most influential database among scholars (Merigó et al., 2015). In addition, this paper further narrows the search scope, limits the language of the retrieved literature to English, the types of retrieved literature are articles and reviews, the search time is all years, and the deadline for retrieval is September 26, 2020.

This paper uses advanced retrieval mode to obtain literature, and the advanced search formula is as follows: (1) (TS = “tourism performance”) AND LANGUAGES: (English) AND DOCUMENT TYPES: (Article or Review) AND TIMESPAN: (All years) AND Indexes: (SCI-EXPANDED, SSCI) = 3,069; (2) (TS = “region*tour*”) AND LANGUAGES: (English) AND DOCUMENT TYPES: (Article or Review) AND TIMESPAN: (All years) AND INDEXES: (SCI-EXPANDED, SSCI) = 8,983. Then the two groups of retrieval conditions were matched, and finally 418 literature retrieval results were obtained. In this paper, the filtering and cleaning of literature collection are mainly carried out from the following aspects. (1) The author’s name and journal name in Chinese form in the literature set are modified to English form. (2) The author’s name and journal name in capital form in the literature set are modified to initial capital form, and the abbreviation of journal name is modified to full writing form. (3) The citations that do not correspond to the author in DOI in each literature are eliminated. (4) Citations with incomplete information in various literature were eliminated. (5) The abbreviation of the names of various organizations and groups was modified to full write format. The purpose of this is to identify the same object expressed in different forms as much as possible and combine them to avoid dilution of research results. (6) Query the institution of each author in each literature one by one, and modify the short form of the name of the institution to the full form. However, there are many difficulties in cleaning up the literature set. There are some books and periodicals whose names can only be found in their original capital form. Because the citations and their authors in this situation appear less frequently, they cannot have a significant impact on the results of co-occurrence and co-citation, so they can be ignored.

The main content of this paper is divided into four parts: research overview, knowledge foundation, discipline structure, and research frontier. By using CiteSpace bibliometric visualization software and Gephi complex network visualization analysis tool, this paper analyzes the selected literature set of “regional tourism performance” from five dimensions: author, institution, journal, reference, and keywords. The research framework of this paper is shown in Figure 1.

Figure 1.

The research framework of literature research on “regional tourism performance.”

Research Overview and Knowledge Foundation

Research Overview

Total frequency of literature published and citation

Figure 2 shows the development trend of the published number and cited frequency of literature related to “regional tourism performance” from 2004 to 2020. It can be seen that the development trend of the number of documents published is similar to that of the cited frequency, and the cited frequency curve lags slightly, indicating that it takes a certain time from published to cited. From 2004 to 2007, the research on “regional tourism performance” was in its infancy, and related literatures were published and cited less than 10 times. From 2008 to 2015, “regional tourism performance” research has increased year by year. The number of published papers and the frequency of citations increased by nearly 3 times and 14 times, respectively. From 2016 to 2018, the research on “regional tourism performance” entered a stage of rapid development, with the number of published literatures reaching a peak and the frequency of citations reaching a peak in 2019. By 2020, although both numbers are falling, remain at a high level. This indicates that “regional tourism performance” research is still a hot topic in the future.

Figure 2.

Publication and citation frequency of papers from 2004 to 2020.

Co-occurrence analysis of authors

CiteSpace software was used to calculate the number of papers published by each author in the research area of “regional tourism performance.” By running CiteSpace 5.7 software, the number of papers published on “regional tourism performance” by each scholar was obtained, and authors who published four or more papers were selected (Table 1). Among them, Romao J, Kallmuenzer A, and Guccio C ranked the top 3 in the number of papers published, reflecting that they are relatively active researchers in the thematic research of “regional tourism performance.”

Table 1.

Number of Papers Published by the Author on the Topic of “Regional Tourism Performance” ≥4.

Rank	Author	Frequency	Year
1	Joao Romao	6	2013
2	Andreas Kallmuenzer	5	2018
3	Calogero Guccio	5	2016
4	Mike Peters	5	2018
5	Salvador Torra	4	2014
6	Manuela Pulina	4	2014
7	Peter Nijkamp	4	2016
8	Oscar Claveria	4	2014
9	Dimitrios Buhalis	4	2008
10	Ilde Rizzo	4	2016
11	Rob Hallak	4	2012

Furthermore, Gephi0.9.2 was used to analyze the cooperative network among authors who published papers on this topic, as shown in Figure 3. In the images generated by Gephi, the larger the nodes are, the more authors cooperate with a particular author, indicating that the author is more important in the thematic study of “regional tourism performance.” The thicker the line between the two nodes, the greater the amount of collaboration between the authors.

Figure 3.

Cooperation network of authors on the theme of “regional tourism performance.”

Compared Table 1 to Figure 3, the number of papers published by the authors of the “regional tourism performance” monograph is inconsistent with the number of collaborations between the authors. Guccio C and Rizzo I have the largest nodes, reflecting the largest number of authors working with them. Guccio C, Rizzo I, Mignosa A, Cuccia T, and Lisi D collaborated closely, forming a relatively stable research team. In addition, Romao J, Guerrero J, Hallak R, and Buhalis D each have three collaborators, playing a irreplaceable role in their respective research teams. Therefore, in future researches on “regional tourism performance,” we can seek cooperation with Guccio C, Rizzo I and Romao J, Guerrero J or refer to their research results.

Co-occurrence analysis of institutions

When CiteSpace software was used to measure the publication frequency of scholastic institutions, the length of time slice was limited to 17 years. Research institutions with five or more papers published in the thematic study of “regional tourism performance” are put into Table 2, and a total of 10 research institutions are obtained. As shown in Table 2, the Algarve University and the Hong Kong Polytechnic University are the two most active research institutions publishing papers in the thematic study of “regional tourism performance,” with significantly more papers than other institutions. These institutions are leaders in the field of research. University of Innsbruck, Bournemouth University, the University of Alicante, Chinese Academy of Sciences, Università degli Studi di Catania, Universitat de Barcelona, National Taiwan University, and University of Sassari have published five to seven papers on the topic of “regional tourism performance.” These research institutions are the backbone of the research on the topic. The Chinese Academy of Sciences and the University of Chinese Academy of Sciences are two different research institutions. The Chinese Academy of Sciences is the Comprehensive Research and Development Center of Natural Science and high technology. The University of Chinese Academy of Sciences was formerly known as the Graduate School of the Chinese Academy of Sciences. The University of Chinese Academy of Sciences is just a part of the Chinese Academy of Sciences. The research institutions listed above have made important contributions to the research on “regional tourism performance,” providing strong support for further research on this topic.

Table 2.

Research Institutions That Have Published Five or More Papers on “Regional Tourism Performance.”.

Rank	Institution	Frequency	Year
1	University Algarve	14	2009
2	The Hong Kong Polytechnic University	11	2007
3	University of Innsbruck	7	2018
4	Bournemouth University	6	2008
5	The University of Alicante	6	2014
6	Chinese Academy of Sciences	6	2016
7	Università degli Studi di Catania	5	2016
8	Universitat de Barcelona	5	2014
9	National Taiwan University	5	2008
10	University of Sassari	5	2014

Knowledge Foundation

Co-citation analysis of authors

Through co-citation analysis and co-occurrence analysis, this paper reflects the knowledge flow and integration of the research on “regional tourism performance,” and reveals the “prior knowledge” of the research. By summing up these “prior knowledge,” the knowledge base of the special research on “regional tourism performance” is formed. In the first place, CiteSpace software was utilized for analyze the co-citation and centrality of authors. Parameters of the software were configured: The length of time slice was 5 years, TOP N was 10, TOP N% was 10, and the first part of the threshold was set to (3, 3, 20). The results showed that N = 111, E = 128, Density = 0.021, Q = 0.7574, and S = 0.9518, which showed that the team structure and clustering effect were significant. Based on the network structure and clustering clarity, CiteSpace provides two indicators, module value (Q value) and average contour value (S value), which can be used to judge the mapping effect. In general, Q is in the interval [0, 1]. When $Q > 0.3$ means that the segmented group structure is significant. When $S > 0.7$ , clustering is efficient and convincing. If greater than 0.5, clustering is generally considered reasonable. Figure 4 showed the results of the software run. In order to present clear images as much as possible, 53 authors were selected who were cited for eight times or more in total. Excluding five international organizations including UN WTO, European Commission, Organization for Economic Co-operation and Development, World Economic Forum, and WTTC, the remaining 48 samples form author co-cited images of the “regional tourism performance” thematic study.

Figure 4.

Co-citation of authors of the study on “regional tourism performance.”

The larger the size of the node and author name in Figure 4, the more times the author is co-referenced. The thicker the outermost purple ring of the node is, the stronger the author’s co-citation intermediation centrality is. It is observed that Dwyer L has the largest node, followed by Hall AM, Barros CP, Porter ME, and Assaf AG. In addition, the nodes of Hair JF, Buhalis D, Getz D, Charnes A, Crouch GI, Bramwell B, and Butler RW were significantly larger than those of other authors. It can be concluded that the authors listed above have a high number of co-citations, which reflects that they are scholars with great influence in the thematic study of “regional tourism performance.” The links between nodes reveal which authors have similar research topics. The observation results showed that Dwyer L was related to Kozak M, Assaf AG was related to Botti L, Barros CP was in contact with Brida JG, Botti L, and Charnes A. The above analysis indicates that the research fields of these related authors are similar.

The purple ring at the outermost layer of the node represents the intermediate centrality of author co-citation. Intermediate centrality refers to the number of times a node acts as the shortest bridge between two other nodes. The more times a node acts as a mediator, the greater its mediation centrality. The mediating centrality in CiteSpace can reflect which authors are the liaisons of different research projects, that is, which authors have a multidisciplinary background or whose research projects are interdisciplinary. According to this rule, it can be found that Novelli M, Hjalager AM, Thomas R, Morrison A, and other authors have strong mediation centrality, but due to the small nodes, their influence on the thematic study of “regional tourism performance” is small. Buhalis D, Crouch GI, Bramwell B, Butler RW, Cracolici MF, and other authors have high co-citation frequency and mediating centrality, indicating that they are prominent contributors to the research on “regional tourism performance” and Bridges to other research fields. Future studies can refer more to the research results of these authors to understand the knowledge foundation in the early stage.

Co-citation analysis of journals

CiteSpace software was used to analyze the co-citation and mediating centrality of journals. This review article adjusted the running parameters of the software, and set the length of the time slice to 5 years, TOP N was 10; TOP N% was 10, the first part of the threshold was (3, 3, 20). The results showed that N = 154, E = 134, Density = 0.0114, Q = 0.8383, and S = 0.9511, indicating that the journal team structure and clustering effect in this research field were significant.

Through CiteSpace calculation, the top 10 journals of co-citation frequency and intermediary centrality for the thematic study of “regional tourism performance” are extracted, and Table 3 is formed. Tourism Management and Annals Of Tourism Research have published more than 200 papers on the topic of “regional tourism performance,” reflecting their importance in the field of research. Meanwhile, the mediating centrality of Tourism Management and Annals Of Tourism Research ranked No. 2 and No. 7, respectively, reflecting the strong ability of the two journals to connect different research fields and their interdisciplinary nature. It reflects the status of the two journals as the top journals of tourism discipline. Journal Of Travel Research, International Journal Of Hospitality Management and International Journal Of Contemporary Hospitality Management published more than 100 papers on the “regional tourism performance” project, and they were ranked No. 6, No. 3, and No. 4 in mediating centrality. This reflects Journal Of Travel Research, International Journal Of Hospitality Management and International Journal Of Contemporary Hospitality Management is second only to Tourism Management and Annals Of Tourism Research in the comprehensive influence of “regional tourism performance.” The number of papers published in Journal Of Sustainable Tourism, Tourism Economics, and Current Issues In Tourism has exceeded 100. But their mediating centrality ranked lower or did not make the top 10. This reflects that they have certain influence and reference value in the research of this topic, but the subject field and research background are relatively narrow. In addition, The Academy Of Management Journal, The Cornell Hotel And Restaurant Administration Quarterly, Journal Of Marketing Research, Journal Of Management, and Journal Of Applied Economics have strong mediating centrality, but the number of published papers is relatively small. This reflects that although these journals are highly connected and interdisciplinary, their reference value is reduced due to the small number of papers published in the thematic study of “regional tourism performance.”

Table 3.

Top 10 Journals in Co-citation Frequency and Mediating Centrality for the Thematic Study on “Regional Tourism Performance.”

Rank	Name of journal	Frequency of co-citation	Name of journal	Centrality
1	Tourism Management	282	The Academy of Management Journal	0.95
2	Annals of Tourism Research	220	Tourism Management	0.92
3	Journal of Travel Research	173	International Journal of Hospitality Management	0.78
4	International Journal of Hospitality Management	131	International Journal of Contemporary Hospitality Management	0.75
5	Journal of Sustainable Tourism	118	The Cornell Hotel and Restaurant Administration Quarterly	0.73
6	Tourism Economics	118	Journal of Travel Research	0.62
7	Current Issues in Tourism	112	Annals of Tourism Research	0.61
8	International Journal of Contemporary Hospitality Management	106	Applied Economics	0.49
9	International Journal of Tourism Research	103	Tourism Economics	0.48
10	Journal of Business Research	84	Journal of Management	0.42

Co-citation analysis of references

CiteSpace software is used to analyze the co-citation and mediating centrality of references in the study of “regional tourism performance.” Before running the software, you need to set the parameters. This paper adjusted the time slice length to 6 years, TOP N was 20, TOP N% was 10, and adjust the first part of the threshold value to (3, 3, 20). The results showed that N = 95, E = 180, Density = 0.0411, Q = 0.7541, and S = 0.9292, which indicated that the community structure was significant and the clustering effect was reliable. After the background operation of CiteSpace software, the top 10 references of co-citation frequency were extracted to form Table 4.

Table 4.

Top 10 Co-Citations of the Research on “Regional Tourism Performance.”.

Rank	Frequency of co-citation	Title	Author
1	18	Performance of French destinations: Tourism attraction perspectives	Barros et al. (2011)
2	18	Determinants of Spanish regions’ tourism performance: a two-stage, double-bootstrap data envelopment analysis	Benito et al. (2014)
3	18	The effects of UNESCO World Heritage List inscription on tourism destinations performance in Italian regions	Cuccia et al. (2016)
4	17	Identifying and ranking the determinants of tourism performance: A global investigation	Assaf and Josiassen (2012)
5	15	UNESCO sites and performance trend of Italian regional tourism destinations: A two-stage DEA window analysis with spatial interaction	Cuccia et al. (2017)
6	13	Networks, clusters, and innovation in tourism: A UK experience	Novelli (2006)
7	12	The role of individual entrepreneurs in the development of competitiveness for a rural tourism destination e A case study	Komppula (2014)
8	12	Frontier analysis: A state-of-the-art review and meta-analysis	Assaf and Josiassen (2016)
9	11	Cultural resources as a factor in cultural tourism attraction	Herrero-Prieto (2017)
10	9	Efficiency and its determinants in Portuguese hotels in the Algarve	Oliveira (2013)

Table 4 respectively list the top 10 references for co-citation times and the top 10 references for intermediary centrality strength of the thematic research on “regional tourism performance.” These cited papers need to be carefully analyzed to understand their main idea. The papers in Table 4 are all about the thematic research on “regional tourism performance” in general, but there are differences in research focus and research methods. Research types include empirical studies, case studies, and literature reviews, among which two-stage data envelopment analysis (DEA) and bootstrap truncated regression model are the most used. The countries involved include France, Spain, Italy, the United Kingdom, Finland, and Portugal, with Spain and Italy being more used as case studies, as well as research and review studies on the global tourism industry.

To be specific, on the basis of the expected decline in French tourism competitiveness, Barros et al. (2011) took 22 regions of France as samples, and used two-stage DEA method to have evaluated and compared their performance according to their input and output levels. Benito et al. (2014) changed the study location to 17 autonomous regions in Spain, and based on the data from 2002 to 2010, and analyzed the determinants of tourism performance. In order to explore whether the World Heritage List has an impact on regional tourism attraction, Cuccia et al. (2016) took Italy as an example and used the two-stage DEA method to study the regional tourism competitive from 1995 to 2010. Then, the semi-parametric regression method was introduced to explore the determinants of tourism performance value. Meanwhile, Cuccia et al. (2017) put forward suggestions to improve public decision-making process and optimize local policies. From a global perspective, Assaf et al. (2012) adopted the DEA model to evaluate tourism performance based on industry data of 120 countries from 2005 to 2008. In addition, other references integrated destination quality into tourism performance estimation (Komppula, 2014).

As scholars engaged in the thematic study of “regional tourism performance” are more concerned with which papers need to be referred to during the research period and which papers are more valuable in the thematic study. Therefore, the co-citation times of references can affect the judgment of other scholars, while the research on the mediating centrality of references is not the focus. Therefore, the papers with the top 10 mediating centrality in references are no longer elaborated in detail.

Co-occurrence analysis of keywords

In this paper, the keywords co-occurrence analysis method makes use of the common occurrence of noun phrases in the literature to determine the relationship between the various topics in the thematic study of “regional tourism performance” represented by the literature collection. A keywords co-occurrence network composed of these word pairs can be formed by counting the frequency of the subject words of a group of documents appearing in the same document. Using CiteSpace bibliometrics software, this paper analyzes the intensity of keywords co-occurrence network and intermediary centrality in the study of “regional tourism performance.”

Before running the software, this review article set the length of time slice was 17 years, TOP N was 50, and other parameters were default. The running results showed that N = 103, E = 112, Density = 0.0213, Q = 0.7877, and S = 0.9347. As showed in Figure 5, the more obvious the node sizes, the more frequent the keywords appeared, and the thicker the purple band at the node boundary, indicating the stronger centrality, the more keywords associated with it.

Figure 5.

Co-occurrence frequency and centrality of keywords.

A total of 103 noun phrases were obtained from keywords co-occurrence analysis of the thematic study “regional tourism performance.” In order to make the keywords co-occurrence image more concise and clear, this paper extracted the top 20 keywords in co-occurrence frequency. The lowest value of these keywords was 17 times.

As shown in Figure 5, the node and size of “performance” are the largest, and the purple outer ring is more prominent. This reflects the high frequency of “performance” in the study of “regional tourism performance,” which has a strong importance. The “tourism” is second only to “performance” in node size and font size, but there is no purple ring. Reflecting that “tourism” in the “regional tourism performance” thematic research has a very important position, the use of high frequency, but rarely as the shortest bridge between the other two keywords. It can be seen that “performance” and “tourism” are most used in the research on “regional tourism performance.” The node and font size of “impact,” “model,” “management,” and “industry” are similar to that of “tourism,” reflecting that the thematic study of “regional tourism performance” is more closely related to the exploration of influencing factors, the construction of evaluation model, the strengthening of management, and the tourism industry. The nodes and size of “innovation,” “destination,” “data envelopment analysis,” “network,” and “region” are at the same level. As these keywords are more specific in their directivity, their co-occurrence frequency is lower than that of “performance,” “impact,” “model,” and “industry.” Although their co-occurrence frequency is low, their mediating centrality is strong, which reflects that innovation, destination and DEA are closely related to other research directions. The “DEA” and “data envelopment analysis” are different expressions of the same research method, which reflects that data envelopment analysis is very popular in regional tourism performance evaluation research.

Discipline Structure and Research Frontier

Discipline Structure

Cluster analysis of references

Reference cluster analysis is a common method to sort out the discipline composition of “regional tourism performance.” CiteSpace software is made available for references cluster analysis in this review article. The parameter setting and index calculation results of the software are the same as the co-citation analysis of references described in Section 3.2. This paper adjusted the time slice length to 6 years, TOP N was 20, TOP N% was 10, and adjust the first part of the threshold value to (3, 3, 20). The results showed that N = 95, E = 180, Density = 0.0411, Q = 0.7541, S = 0.9292. The clustering rule chose “K-cluster”; and the algorithm chose “LLR.” K-means clustering algorithm is a cluster analysis algorithm for iterative solution. The procedure is to divide the data into K groups before randomly selecting K objects as the initial cluster center, calculate the distance between each object and each seed cluster center, and assign each object to the cluster center nearest to it. Cluster centers and the objects assigned to them represent a cluster. K-means clustering is the most famous partition clustering algorithm, and it has become the most widely used among all clustering algorithms due to its simplicity and efficiency. LSI, LLR, and MI provide three different algorithms for CiteSpace to extract cluster labels from different locations of references. Research terms extracted by LLR algorithm emphasize research characteristics. In the actual research process, users can use labels extracted by LLR algorithm in the visual network to display cluster naming, and interpret the research in combination with the results obtained by different methods in the clustering interpretation of the paper.

Figure 6 is generated by running CiteSpace software, and cluster analysis is conducted on references of the research topic “regional tourism performance.” Five noun phrases were extracted from the clustering results: “panel data,” “entropy index,” “coupling coordination degree,” “museum management assessment,” and “coupling coordination relationship.” From the clustering results, the noun phrases extracted from the “regional tourism performance” thematic study mainly involve the choice and application of research methods. It includes empirical research based on panel data, index data calculated by entropy method, and coupling coordination degree model to evaluate the coupling coordination relationship between tourism and other industries. In addition, it also involves the management and evaluation of the museum industry.

Figure 6.

Cluster analysis of references.

The number of papers belonging to the “panel data” module is the largest. The research topics are mainly related to sustainable development, competitiveness, and economic growth. Khan et al. (2017) formed a panel data set by taking 19 countries as research objects and 1990 to 2014 as the research period and the tourism competitiveness index of inbound and outbound tourism is constructed. Chou (2013) investigated the causal relationship between tourism expenditure and economic growth in 10 countries in transition from 1988 to 2011. This is a typical analysis of panel data. Martín et al. (2017) established a comprehensive index of tourism competitiveness based 139 countries as the research object in 2011. Romao and Nijkamp (2019) analyzed the effects of traditional factors of production, productivity, specialization, and other environmental variables on “regional tourism performance” of 237 European regions by taking 8 years as the research period. Cvelbar et al. (2016) investigated the driving factors of tourism destination competitiveness from a global perspective. The study period was from 2007 to 2011, and 139 destinations were selected as the study subjects. In addition, Mendola and Volo (2017) and Webster et al. (2014) also measure and evaluate the competitiveness index of tourism destinations by constructing panel data sets. At present, some scholars are keen to equate tourism destination competitiveness with “regional tourism performance” and to show regional tourism performance by measuring tourism destination competitiveness index. This paper argues that these two concepts cannot be simply understood as equivalent. Regional tourism performance is a broader concept than tourism destination competitiveness, and competitiveness is a component of performance level. If the competitiveness of a tourist destination is enhanced, its performance level will also be improved.

The thesis topic in this module is not closely related to “entropy index,” and is not even mentioned in the thesis content. However, a summary of the similarities between the various papers shows that the construction and measurement of the sustainability index is favored by scholars (Agyeiwaah et al., 2017; Torres-Delgado & Palomeque, 2014; Torres-Delgado & Saarinen, 2014). In addition, two-stage data envelopment analysis (Cuccia et al., 2017), and DEA-Malmquist index method (Sun et al., 2015) is also used to evaluate and measure the economic growth index of tourist destinations and regional tourism performance level. Entropy index borrows the concept of entropy in information theory and has the meaning of average amount of information. When scholars calculate tourism destination growth index or regional tourism performance index, they usually use entropy index and index weight to get a comprehensive index.

The clustering blocks “coupling coordination degree” and “coupling coordination relation” have great similarity, both of which emphasize the coupling and coordination between two or more elements. The coordination between driving factors such as sea, sunshine, and beach and tourism performance is discussed (Barros et al. 2011). Fernandez et al. (2018) considered the relationship between the development of tourism and the improvement of the operational efficiency of air transport. Assaf et al. (2012) discussed the coordination relaxship among the factors such as tourism price competitiveness, economic conditions, and labor skills and training. Cuccia et al. (2016) discussed the coordination between cultural endowments and environmental endowments.

As for the content of “museum,” it is more about taking the relevant data of the museum as a part of the indicator system when constructing the tourism performance indicator system. Guccio et al. (2017) used data from 2004 to 2010 in Italy to explore the role of cultural participation in improving tourism performance. The “Museums” as a series of indexes in the index construction. Figueroa et al. (2018) took Chile as a case country and regarded “Regional state heritage (Museums and listed National monuments)” as one of the variables.

Cluster analysis of keywords

Through one or more keywords of the paper, you can initially master the main research methods and core research direction of the paper. This section uses CiteSpace software to conduct cluster analysis of keywords co-occurrence in the thematic study of “regional tourism performance.” Keywords cluster analysis is a more microscopic and specific analysis method to sum up the subject structure of the research on “regional tourism performance.” The clustering of words can more accurately depict the core areas involved in the thematic study of “regional tourism performance.” CiteSpace keywords clustering analysis is to use the algorithm provided by CiteSpace software to extract text according to certain rules and features in the large-scale hierarchical classification corpus of the classification system, and then classify the words by modules. This review article used CiteSpace software for keywords cluster analysis and set the running parameters of the software: the length of time slice was 1 year, the TOP N was 50, the TOP N% was 10, and the threshold value was (2, 2, 20). The results showed that: N = 37, E = 45, Density = 0.0676, Q = 0.6959, and S = 0.9328. On this basis, this paper clustered the keywords co-occurrence graph and selected the K-cluster method and the LLR algorithm.

Figure 7 is generated by CiteSpace software, and cluster analysis is conducted on keywords of the research topic “regional tourism performance.” The clustering results showed that six noun phrases were extracted by induction: “spatial variation,” “Kish Island,” “data envelopment analysis,” “Sydney,” “satisfaction,” and “tourism impact.” Keywords clustering results show that the words or phrases extracted from the “regional tourism performance” thematic research mainly involve research methods, research objectives, and case sites. The “spatial variation,” “satisfaction,” and “tourism impact” reflect that the research on “regional tourism performance” is more related to the research purposes of describing spatial characteristics, improving tourist satisfaction, and enhancing tourism influence. Data envelopment analysis (DEA) is the most commonly used research method in the study of “regional tourism performance.” Sydney and Kish Island are the case studies of “regional tourism performance.”

Figure 7.

Keywords clustering timeline graph.

Research Frontier

This section uses the burst detection function of CiteSpace to obtain the cutting-edge hot issues in the study of “regional tourism performance.” It includes authors with high burst intensity, literature with high burst intensity, and keywords with high burst intensity, and shows the start time and end time when authors, references, and keywords become research hotspots. To get to the research frontier, click on Noun Phrases and select Create POS Tags.

Burst analysis of co-citation of references

This section made a burst analysis of the references of “regional tourism performance research” built on the co-citation analysis. After running CiteSpace, parameters of “Control Panel” (Burstness) was set. “The Number of States” is set to 3. On the one hand, references who appear less frequently are excluded; on the other hand, references which study this topic are covered as much as possible to avoid contingency and one-sidedness. The “Minimum Duration” is set to 2, which can effectively screen out references whose research popularity lasts for the Minimum of 2 years. This section selects the top 5 in the emergent analysis list to make the research results more representative. Figure 8 shows five heat authors and the start and end times of their heat bursts.

Figure 8.

Top 5 of burst analysis of co-citation of references.

Figure 8 lists the references’s name, the year in which it was first cited, the intensity of the burst, and when the references popularity began and ended. From Figure 9, we can see that the time when the five references in the list were first cited is not exactly the same. In addition, the first reference was widely cited in 2006, and the intensity of citation decreased in 2015. The other four widely cited references all started in 2016 and continued until 2020. The references covered include “Networks, Clusters and Innovation in Tourism: A UK Experience,” “The Effects of UNESCO World Heritage List Inscription on Tourism Destinations Performance in Italian Regions,” “Determinants of Spanish Regions’ Tourism Performance: a Two-stage, Double-bootstrap Data Envelopment Analysis,” “The Role of Individual Entrepreneurs in the Development of Competitiveness for a Rural Tourism Destination—a Case Study,” and “Performance of French Destinations: Tourism Attraction Perspectives” . By analyzing and summarizing the title and content of the above five references, it can be found that since 2016, the hot issues in the thematic research of “regional tourism performance” include “cultural tourism,” “heritage tourism,” “tourism destinations and their competitiveness,” “data enveloping analysis,” and “rural tourism destinations.” In the future, scholars engaged in the thematic study of “regional tourism performance” can start from the above hot issues.

Figure 9.

Top 7 keywords with the strongest co-occurrence burst.

Burst analysis of co-occurrence of keywords

This section made a burst analysis on the keywords co-occurrence network of “regional tourism performance” research. The keywords burst analysis can more specifically and correctly reveal the research direction of the topic in the future. This paper made a burst analysis of the keywords of “regional tourism performance” research built on the co-citation analysis. The running parameters of the software was set: the length of time slice was 1 year, the TOP N was 50, the TOP N% was 10, and the threshold value was (2, 2, 20). After running CiteSpace, parameters of “Control Panel” (Burstness) was set CiteSpace. “The Number of States” is set to 3. On the one hand, keywords with low frequency were excluded; On the other hand, we should cover as many keywords as possible in the study of “regional tourism performance” to avoid contingency and one-sidedness. The “Minimum Duration” is set to 2, which can effectively screen out keywords whose research popularity lasts for the Minimum of 2 years. This section selects the top 5 in the emergent analysis list to make the research results more representative. Figure 9 shows four heat keywords and the start and end times of their heat bursts.

From Figure 9, we can see that the four keywords in the list were all cited for the first time in 2004. However, the four keywords became hot topics in different periods of time. The “system” became widely used in 2015, and its popularity continued until 2017. The “data envelopment analysis (DEA)” was widely used in 2016 and continued into 2018. The “determinant” started off with loud strains in 2016, lasted a year, heat waned in 2017. The “governance” began to be used frequently in 2018, and continues to be used today.

The “system” has been cited frequently from 2015 to 2017. Šťastná and Vaishar (2017) noted a relationship between public transport systems and rural economic development. They believe that integrated public transport systems can play an important role in the development of rural areas. The “data envelopment analysis” is the most commonly used research method to measure industrial production efficiency and development performance. DEA has been extended and expanded from the most basic forms of CRS and VRS to two-stage DEA, three-stage DEA, EBM-DEA, SBM-DEA, SE-DEA, SE-SBM-DEA, and DEA-Malmquist. Fragoudaki et al. (2016) used data envelopment analysis (DEA) and Manquist productivity index (MPI) to assess the changes in operating efficiency and productivity of Greek airports during the severe economic crisis in Greece (2010–2014). The “determinant” burst fever appeared relatively late and lasted only a year. When estimating regional tourism performance or efficiency, scholars are no longer just looking at what the measured efficiency score is, but more on the criteria that determine the current level of performance. The “governance” boom came late, but it continued until the deadline for the study to collect data. This reflects that scholars are currently paying more attention to the governance issues in the special study of “regional tourism performance.” After four links: building a measurement model, estimating performance scores, and determining influencing factors and determinants, how to improve the performance level of regional tourism, optimize governance schemes, and put forward governance suggestions has become a problem worth discussing at this stage. At the same time, it shows that the research on the topic of “regional tourism performance” is becoming more and more mature and closer to the real problem. This rule is also reflected in the results of the keywords burst analysis shown in Figure 9.

Value and Limitations

Research Value

This paper conducts a comprehensive and systematic literature review study with “regional tourism performance” as the core topic. The study was based on 418 references from the Web of Science database from 2004 to 2020. The language type of the 418 references is English, and the document type is article and review. The full text mainly uses two sorts of software, CiteSpace 5.8 and Gephi 0.9.2, to conduct research from several links: cooperative network (author, institution), co-citation analysis (author, journal, and reference), co-occurrence analysis (keywords), cluster analysis (references, keywords), and burst analysis (references, keywords). The research results and values are mainly reflected in the following aspects.

First of all, this paper clarifies the definition of “tourism performance” and “regional tourism performance” by summarizing the literature. It points out that “tourism performance” refers to the economic, institutional, technological, and living space benefits with the characteristics of the tourism industry in the original material environment through capital, rights, innovation, and living habits. Regional tourism expression is based on the concept of tourism expression to supplement the specific spatial constraints. It also tries to distinguish the differences between “tourism performance” and “tourism competitiveness,” “tourism driving force” and other concepts, so as to provide reference for other scholars engaged in the above topics.

Second, from the most basic perspective, the development context of the special research on “regional tourism performance” is analyzed, and the research framework of this paper is clearly constructed. This paper does not use very advanced and complex bibliometric techniques, but in line with the most basic and important principles, from the “research overview,” “knowledge foundation,” “discipline structure,” and “research frontier” four aspects of the “regional tourism performance” research basis, status, structure, and trend.

Third, this paper conducts co-occurrence analysis, co-citation analysis, cluster analysis, and burst analysis of authors, supporting institutions, journal sources, references, and keywords of existing literature. Through these analyses, we have mastered the existing results of the “regional tourism performance” special research, authoritative scholars, relevant research directions, high-frequency vocabulary, cutting-edge hotspots, and other information. Under the guidance of the above information, it will help future scholars engaged in the special research of “regional tourism performance” to grasp the research hotspots, open up new ideas, and find co-authors and cooperative institutions, in order to improve the research efficiency of the special research.

Limitations

This paper has some limitations in the conceptual distinction and data accuracy of the special study of “regional tourism performance.” At present, some literatures choose to use tourism destination performance or efficiency to reflect the competitiveness of tourism destinations (Komppula, 2014). However, whether the performance score and efficiency score are completely equal to the level of competitiveness, and whether there is an inclusive relationship or an equivalent relationship between tourism destination competitiveness and regional tourism performance need to be further analyzed and discussed. Second, the data accuracy of this paper has limitations. The main reason for this difficulty is that there are a large number of documents obtained from the Web of Science database, and the paper format is complex and cannot be completely consistent, which greatly interferes with the accuracy of bibliometric analysis.

Conclusion and Implication

Conclusion

Based on 418 English articles and reviews (2004–2020) from the Web of Science, this paper has conducted a basic and comprehensive literature review on the special research on “regional tourism performance.” The research overview, knowledge foundation, discipline structure, and research frontier of the special research on “regional tourism performance” are understand.

From the research overview, from 2016 to 2019, the research on “regional tourism performance” entered a rapid development stage, and the number of literature publications and citations reached the peak one after another. In 2020, it is still at a high level. It reflects that the special research on “regional tourism performance” is still a hot spot in the future. In future studies, scholars can seek cooperation with Guccio C, Rizzo I, Romao J, the Algarve University, the Hong Kong Polytechnic University, and the Bournemouth University. From the perspective of knowledge foundation, future research can refer more to the research results of Buhalis D, Crouch GI, Bramwell B, Butler RW, as well as articles published in Tourism Management, Annals Of Tourism Research, and Journal Of Travel Research. The results of references co-citation analysis and keywords co-occurrence analysis reflect that the existing literature on “regional tourism performance” mainly constructs models, explores influencing factors, and innovates management models to improve regional tourism competitiveness and promote regional economic growth. From the perspective of discipline structure, panel data, entropy index, data envelopment analysis, coupling coordination degree, and spatial variation are the main research methods. Museums, Kishe Island, and Sydney are the main research objects, while improving tourist satisfaction and enhancing tourism influence are the main research objectives. From the perspective of research frontiers, Scholars pay more and more attention to governance issues in the research of “regional tourism performance.” Four steps, including establishing a measurement model, estimating performance scores, determining influencing factors and determining factors, and how to improve regional tourism performance and putting forward governance suggestions and schemes, have become issues worth discussing at the present stage. At the same time, the research on “regional tourism performance” is becoming more and more mature and closer to reality.

Implication

In this paper, the “research overview” will help future scholars understand the publishing trend of papers in the field of “regional tourism performance,” and the cooperation network between authors and institutions. The “knowledge foundation” will helpful for scholars to identify scholars who can learn, journal papers that can be referred to, and research ideas commonly used in this field. The “discipline structure” will helpful for scholars to understand the subdivided fields, research objects, research methods, and research objectives. The “research frontier” will helpful for scholars to clearly grasp the hot topics of “regional tourism performance” in each period. At present, the popular topics in this field are tourism destination competitiveness and tourism governance. Mastering these basic problems can promote the research efficiency of scholars engaged in the topic of “regional tourism performance.” So as to guide the practical development of regional tourism more scientifically.

Prospects

Optimize the effect of regional tourism governance and promote the improvement of tourism performance has become the focus of future research on “regional tourism performance,” reflecting the increasingly close combination of theory and practice, and the increasing importance of data calculation in guiding practical problems. Therefore, the construction of regional tourism performance index system to measure regional tourism development strength, guide government departments, enterprises, researchers, and residents and other groups, through cooperation to improve regional tourism performance level is an urgent need for future research.

Supplemental Material

sj-xlsx-1-sgo-10.1177_21582440221088013 – Supplemental material for Regional Tourism Performance Research: Knowledge Foundation, Discipline Structure, and Academic Frontier

Supplemental material, sj-xlsx-1-sgo-10.1177_21582440221088013 for Regional Tourism Performance Research: Knowledge Foundation, Discipline Structure, and Academic Frontier by Wei Guo and Xin Zhang in SAGE Open

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Xin Zhang

Supplemental Material

Supplemental material for this article is available online.

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