Knowledge Mapping to Understand Corporate Value: Literature Review and Bibliometrics

Abstract

The purpose of this study is to summarize the research results on corporate value published from 2000 to 2022; show the research overview, hot trends, and topic evolution of this research field; provide new ideas for the mining of the research frontiers of corporate value and a summary of the change rules of research hotspots; and describe prospects for the evolution direction and path of future research. Combining the bibliometric research method with a literature review, the research results on corporate value were analyzed quantitatively by querying the WOS database from 2000 to 2022; the analysis tool was CiteSpace. This study has five findings. First, researchers are paying increasing attention to the study of corporate value, and most of the research results are obtained by independent authors. Second, Chinese research institutions rank among the top three in publication volume. However, their research results have had little impact, with Univ Penn and Peking Univ having the most significant impact. Third, the top three keywords that scholars pay attention to are performance, impact, and corporate governance. Keyword burst analysis, CSR, value reliability, and sustainability are the latest research frontiers. Fourth, evolutionary trends are divided into three stages: research on the influencing factors of corporate value, research on the impact of corporate behavior on corporate value, and research on the evaluation and growth of corporate value. Fifth, knowledge domains include corporate value research methods, the factors influencing corporate value, and corporate behavior. The aims of this study are to provide a new perspective for researchers to study corporate value, provide new ideas for enterprise managers to manage corporate value, and achieve the sustainable development of corporate value. At the same time, the scientific knowledge graph method is applied in corporate value research, adding a new research path for corporate value.

1. Introduction

The fundamental goal of an enterprise is to maximize corporate value (Michael and Meckling 2000), which is also the core of corporate management. Corporate value differs from profits. It includes not only the value of new creation but also the potential or expected profitability of the company (Zhang and Du 2020). Corporate value reflects the present value of expected future income from corporate assets; this is the best criterion for measuring corporate performance (Chi et al. 2021). Nevertheless, only a few researchers have comprehensively reviewed documents related to corporate value published over the past 20 years. With the knowledge, informatization, and globalization trends, enterprises face more complex environments and significant risks. Only by continuously creating value can enterprises achieve sustainable development. Do listed companies have the potential to continue creating value? What financial decisions should business managers make to increase existing value? Can the transaction between the two parties in a merger and acquisition achieve fair trade? How can a bankrupt enterprise liquidate its assets at a reasonable price? The enterprise value directly affects the investment decisions of investors, the value management of enterprise managers, the merger-and-acquisition decisions of both parties, and the bankruptcy liquidation decisions of bankrupt enterprises. Investors make business decisions, such as those related to acquisitions, mergers, and sales, by reasonably predicting the development prospects based on changes in enterprise value over different periods. Enterprise managers can use the enhancement of enterprise value as a benchmark for management actions, enhance the awareness of enterprise value management, and improve management efficiency and effectiveness. Therefore, corporate value’s definition, measurement, and influencing factors have always been hot topics in economic research.

The purposes of this study are to (1) summarize the research on corporate value published from 2000 to 2022; (2) learn from the perspectives of institutional cooperation, literature, and keywords; and (3) explore the trends and knowledge fields related to corporate value.

The value of this study is as follows: (1) it applies scientific econometrics, represented by the mapping of knowledge domains, to the field of corporate value research, highlighting the interdisciplinary nature between disciplines and using a new visual perspective to analyze the research and institutional collaboration, document co-citations, evolutionary trends, and knowledge domains of the enterprise value literature, which can expand the application scope of knowledge graph methods; (2) by revealing the influencing factors and future development trends of corporate value, it provides a reference for researchers to study corporate value and provides new ideas for enterprise managers to manage corporate value, which is conducive to enhancing corporate value.

The research limitations of this study are that (1) all the literature used in this study is sourced from the WOS, and essential literature from other databases may be overlooked; (2) literature updates have a lag, and the results of bibliometric research may have a lag.

2. Literature Review

2.1. Definition of Corporate Value

Capital value theory was established by the relationship between income and capital under the condition of future certainty (Fisher 1906). Modigliani and Miller (1958), meanwhile, proposed the Modigliani–Miller theorem, arguing that the capital structure of a company is independent of its market value, but they did not take its income tax and running risks into account, only different capital structures. Further, corporate value is the market value of the company, the sum of the market capitalization of stocks, and the market capitalization of debt. Corporate value is the relationship between the nature and functions of a firm that can satisfy its requirements and is the utility of the enterprise to society (Zheng et al. 2014). Corporate value is the discounted present value of future cash flows, which is directly related to future operating performance, especially in the long term (Zhan and Wang 2013). Corporate value includes both the book and market value, affected by factors such as corporate reputation and development potential, and the market value may exceed the book value (Zhang and Du 2020).

2.2. Measurement of Corporate Value

Measures of corporate value in the literature include Tobin’s Q (Dahya et al. 2008; Cao 2021; Xu and Liu 2022), return on total assets (Du et al. 2021), stock yield (Zheng et al. 2014), and return on equity (Ruan et al. 2015; Pan et al. 2018).

2.3. Factors Influencing Corporate Value

Cash flow hedging has a significant positive impact on company value (Disatnik et al. 2014). There is a positive correlation between corporate social responsibility and value, while corporate social responsibility weakens the negative correlation between management status and value (Ferrell et al. 2016). Shareholder participation in company compensation policies can affect the quality of corporate governance (Gulen and O’Brien 2017). Patents can increase a company’s customer resources, positively impacting company performance and stock valuation (Ertugrul et al. 2023) The CEO succession plan can increase the value of large companies, but it will reduce the value of small businesses (McConnell and Qi 2022). Employee flexibility is a valuable intangible asset for enterprises, which can help them cope with external shocks and enhance their value (Au et al. 2021). Companies with higher R&D intensity can achieve higher stock returns in the stock market, enhancing their corporate value (Hou et al. 2022). Companies with high employee satisfaction can better resist COVID-19 and have better business performance (Shan and Tang 2023).

2.4. The Application of CiteSpace

CiteSpace is an abbreviation for citation space, developed by Professor Chaomei Chen, a computer and intelligence professor at Drexel University in the United States. He developed CiteSpace information visualization software in 2003 (Chen 2004), which has been shared for free and continuously updated (Chen 2006, 2012, 2017; Chen and Song 2019; Chen et al. 2010). The development concept of CiteSpace is to use visualization methods to mine the domain literature and discover phenomena in scientific research (Chen 2017). It can be translated as “citation space”. CiteSpace presents the structure, patterns, and distribution of scientific knowledge through data visualization, so the visualization graphics obtained through such methods are usually referred to as “scientific knowledge graphs” or “scientific maps”. A scientific knowledge graph is an image that displays the development process and structural relationship of scientific knowledge based on a knowledge domain (Shiffrin and Börner 2004).

In the Web of Science core dataset (Timespan: All years; Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESCI), data retrieval was conducted with CiteSpace as the theme (TOPIC: CiteSpace), resulting in a total of 919 papers related to the CiteSpace theme, covering research directions such as General Internal Medicine (172), Environmental Sciences Ecology (170), Information Science Library Science (160), Business Economics (78), etc., published in 2022 (247) and 2023 (266). There are six highly cited papers. A comprehensive knowledge graph of hotel research was created using CiteSpace, providing relevant topics, contemporary research topics, and a list of the most influential researchers in the hotel discipline (Li et al. 2017). Through the evolutionary analysis of CiteSpace and the symbolic analysis of VOS Viewer, the evolutionary process of platform research was identified, and future development trends were anticipated. CiteSpace is used to scientifically measure and visually analyze the literature on affected areas and identify core hotspots, research frontiers, emerging research areas, and ICT trends (Sood et al. 2022). Using CiteSpace to analyze the quality of the impact of the digital economy era on corporate innovation behavior, it was found that research on corporate innovation behavior in the digital economy era has formed eight research directions (Yu et al. 2023). CiteSpace’s application areas in China mainly focus on library and archive management, management science and engineering, and education. The primary data sources for analysis are the WoS, CSSCI, and CNKI (Chen 2017).

2.5. Source of Literature Collection

Familiar sources of literature collection include the Web of Science (WoS), CNKI, Google Scholar, SSRN, Researcher, SCOPUS, etc. Each database has advantages and disadvantages, including analysis methods, coverage differences, and linking references (Li et al. 2010). The Web of Science is the web version of the three major citation databases of the Institute for Scientific Intelligence (ISI) in the United States: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). Among scientific databases, the Web of Science (WoS) is the central database for observing global academic information, covering approximately 34,000 of the most common high-impact research journals (Birkle et al. 2020). The WoS is the most comprehensive global literature database (He et al. 2017). The Core Collection database of the WoS also includes the world’s highest-quality journals (de Castro e Silva Neto et al. 2016), and articles published in these journals have undergone rigorous peer review (Falagas et al. 2008). When comparing Google Scholar with the Web of Science, it was found that the Web of Science is the more recognized peer-reviewed content database, well known to libraries and the research community (Mikki 2009). For citation analysis, Google Scholar offers results with inconsistent accuracy. Scopus covers a wide range of journals, but compared to the Web of Science, it is currently limited to recent articles (Falagas et al. 2008). When comparing Web of Science, Scopus, and Google Scholar, the WOS performed the best regarding the overall coverage of the journal sample population and retrieved the most unique items (Adriaanse and Rensleigh 2013). Given the breadth and impact of the WoS, many scholars use it as a data source for bibliometric analysis, such as Qiu and Lv (2014), Si et al. (2019), Zhou et al. (2022), and Khaled et al. (2021), who used the core collection of the WoS for data collection.

While various studies have reviewed corporate value using different definitions and measurements, further research is still needed. For example, some studies have small samples and are highly subjective in their screening processes (de Andrade Guerra et al. 2021). Moreover, the research content is often limited, and only the methods or models are summarized (Urbinatt et al. 2020). In some studies, the research object was limited to a particular aspect, and the retrieval time was out of date (Hameed et al. 2019). In traditional review methods, it is difficult to conduct quantitative analysis and visually display the data for large samples. CiteSpace can not only control the citation space but also provide co-occurrence analysis functions for other technological text knowledge units, such as the author, institution, country, region cooperation, etc. This study, therefore, used the Web of Science (WoS) database to retrieve the literature spanning 2000–2022. Studies of corporate value were analyzed by combining the research methods and knowledge maps in the literature.

3. Data Sources and Methods

3.1. Data Sources

Data were retrieved from the WoS Core Collection database, which includes more than 34,000 authoritative and influential international academic journals in science, art, and the humanities. The WoS Core Collection contains the references cited in papers and is prepared as an index according to the author, source, and publishing age to facilitate visual analysis. Through the advanced search function of the WoS, a theme search was conducted in the core database in the form of strings. Given the different expressions of corporate value by scholars from various countries, the content of the string search was set to TS = (“Corporate value” OR “Firm value” OR “Enterprise Value”). The retrieval period was 2000–2022, and the retrieval time was 21 November 2022. The retrieved documents were sorted and screened. Conference papers, annual meeting reports, and other documents not relevant to the study purpose were removed. Finally, 4312 documents were obtained as the research sample.

3.2. Research Method

Based on the literature measurement method, this study quantitatively analyzed corporate value research using visual analysis software. Visual analysis enables complex data to be displayed intuitively. Current map-drawing software is diverse. CiteSpace is Java-based software for literature measurement visualization using dynamic citations. This study used CiteSpace version 5.7.R1 (64 BIT). The CiteSpace visualization process includes three main steps: similarity standardization, clustering, and visualization.

3.2.1. Similarity Standardization

CiteSpace similarity standardization includes the cosine-similarity, Dice, and Jaccard algorithms. The cosine-similarity algorithm evaluates the similarity between two vectors by calculating the cosine value of the included angle. Cosine similarity draws a vector in the vector space according to the coordinate value and obtains the cosine value corresponding to the included angle. The cosine value represents the similarity between the two vectors. The similarity between the vectors is determined by the size of the included angles. The smaller the included angle is, the more similar they are.

The Dice similarity coefficient can be used to calculate the similarity between two strings. Equation (1) is expressed as follows:

$$\mathrm{Dice}=\frac{2\times \mathrm{comm}\left(A_1,A_2\right)}{\mathrm{leng}\left(A_1\right)+\mathrm{leng}\left(A_2\right)},$$

(1)

where comm($A_1$, $A_2$) is the number of characters that are the same characters of $A_1$ and $A_2$; leng($A_1$) and leng($A_2$) are the strings of $A_1$ and $A_2$, respectively.

The Jaccard coefficient was used to compare the probabilities of similarity and dispersion in the sample set. The Jaccard coefficient is equal to the intersection of the common words in two texts divided by the set of all words in the two texts; this determines the similarity between the two texts. The closer the Jaccard coefficient value is to 1, the more similar the two texts are.

3.2.2. Clustering

CiteSpace clustering includes clustering, log-likelihood ratio (LLR), and MI algorithms. This study introduced an LLR clustering algorithm. The LLR algorithm is commonly used in the field of communication. The likelihood ratio is defined as the ratio of the maximum value of the likelihood function under constrained conditions to that under unconstrained conditions. The idea behind the likelihood ratio test is that if parameter constraints are effective, they should not cause a significant reduction in the maximum value of the likelihood function (Yang 2022). The same is true when the test algorithm is applied to clustering, assuming that for category $(S_j)$, the frequency of words ($V_i)$, concentration, and dispersion constitute a vector $\left(W_{ij}\right)$. Whether $V_i$ can be used as a characteristic word of $S_j$ is determined according to $W_{ij}$ (Li and Chen 2016). Thus, Equation (2) is expressed as follows:

$$\mathrm{LLR}=\log \frac{p\left(S_j\mid W_{ij}\right)}{p\left({\overline{S}}_j\mid W_{ij}\right)},$$

(2)

where LLR is the log-likelihood ratio of word $V_i$ to category $S_j$, and $p\left(S_j\mid W_{ij}\right)$ and $p\left({\overline{S}}_j\mid W_{ij}\right)$ are the density functions of categories $S_j$ and $\overline{S_j}$ respectively (Chen et al. 2010).

3.2.3. Visual Presentation

Visual presentation methods include distance-, graph-, and time-sequence-based methods (Van Eck and Waltman 2010). The visual presentation methods in CiteSpace include the cluster view, timeline view, and time area view. The CiteSpace clustering algorithm was used to detect mutation words in the literature, detect and visualize trends and changes over time, and further predict the development trends of the subject (Chen and Song 2019).

3.3. Research Framework

The data obtained from CiteSpace were stored in text format and set to 2000–2022 for the time-slicing period. Then, CiteSpace’s author, keyword, and organization analysis functions were used to visually analyze the research dynamics and development processes to determine academic hotspots and frontiers in corporate value research. This helped to grasp research on corporate value at the macro level and also provided a reference for in-depth research in the field. Figure 1 shows the details of the framework.

4. Results

4.1. Literature Publishing Time

Figure 2 shows the annual volume of corporate-value-related research obtained after screening. From 2000 to 2008, there was a rapid growth trend. While only nine documents were published in 2000, there was a steady upward trend from 2000 to 2020. Since the statistical time for 2022 was slightly less than one year, the number decreased from 2021. The trend in the number of papers published from 2000 to 2022 indicates that research on corporate value will continue to increase.

4.2. Analysis of Researcher Collaboration and Institutional Collaboration

4.2.1. Analysis of Researcher Collaboration

Scientific collaboration can be seen as the product of scientific development after a certain “stable period”. After entering the “stable period”, the cooperation effect plays an increasingly important role in improving the output of scientific knowledge (AlShebli et al. 2018). The attribution information for scientific research cooperation comes mainly from the authors’ research. Therefore, the data were introduced into CiteSpace with the researchers as the nodes. The period was 2000–2022, with time slices of five years. The threshold was the top 50 nodes at each stage. Figure 3 depicts the visualization analysis.

Table 1. Researchers with the top 10 publications.

S/N	Number of Publications	Betweenness Centrality	Time of Initial Publication (Year)	Researcher
1	8	0	2012	Pedro Ruivo
2	8	0	2006	Xueming Luo
3	8	0	2014	Stefan Cristian Gherghina
4	8	0	2014	Georgeta Vintila
5	8	0	2012	Tiago Oliveira
6	7	0	2018	Yue Lu
7	7	0	2016	Kam C Chan
8	7	0	2006	Wang Xiaowei
9	6	0	2017	Oksana Pirogova
10	6	0	2017	Vladimir Plotnikov

lists the researchers with the highest number of publications. In Figure 3, the node represents the author, the connection line represents a cooperative relationship, and the thickness of the connection line represents the strength of the relationship.

The betweenness centrality column in Table 1 indicates that researchers in the field of corporate value have not received much assistance. Combined with Figure 3, we can see that the connections and crossovers between researchers are very small, indicating a lack of cooperation or mutually referenced literature among researchers. The cooperative relationships are closer between Pedro Ruivo and Tiago Oliveira, Stefan Cristian Gherghina and Georgeta Vintila, and Oksana Pirogova and Vladimir Plotnikov. Among them, Pedro Ruivo, Xueming Luo, Stefan Cristian Gherghina, Georgeta Vintila, and Tiago Oliveira contributed the most to corporate value research. Still, they accounted for less than 0.1% of the total number of issued documents. This shows that the number of papers from the author group is low.

4.2.2. Analysis of Institutional Collaboration

Cooperation among research institutions can promote in-depth research. To analyze the degree of collaboration among research institutions, the node was set to the institution’s operation. Figure 4 shows the institution collaboration map, and

Table 2. Top 10 institutions in terms of publications.

S/N	Number of Publications	Centrality	Time of Initial Publication (year)	Institution
1	49	0.06	2003	Harbin Inst Technol
2	43	0	2005	Wuhan Univ Technol
3	29	0	2007	Beijing Jiaotong Univ
4	25	0.06	2006	Univ Illinois
5	22	0	2006	Texas A&M Univ
6	22	0.26	2006	University of Pennsylvania
7	21	0.04	2011	Hong Kong Polytech Univ
8	20	0.01	2006	Univ Elect Sci & Technol China
9	19	0.01	2006	Korea Univ
10	19	0.11	2008	Peking Univ

shows the top 10 institutions. Figure 4 and Table 2 show that Harbin Institute of Technology ranks first in the field and has published the most papers (49 in total). Wuhan University of Technology published 43 articles, ranking second, and Beijing Jiaotong University published 29, ranking third. The fourth to tenth places are University of Illinois (25 articles), Texas A&M University (22), University of Pennsylvania (22), Hong Kong Polytech University (21), University of Electronic Science and Technology of China (20), Korea University (19), and Peking University (19). The centrality of the University of Pennsylvania is 0.26, and that of Peking University is 0.11, both exceeding 0.1. This indicates that the research work of these two institutions is important and has a significant influence.

4.3. Keyword Co-Occurrence

Keywords are descriptive words representing the core content of an article. Analyzing keywords helps identify the main topics in corporate value research. The keyword network comprised 108 nodes and 137 links. In this study, the node was set to keywords, covering 2000–2022, with a time slice of five years. The most frequently appearing top 50 keywords in each stage were selected. Figure 5 shows the top 50 keywords. The size of the keywords is proportional to their frequency of appearance. The top keywords in the figure include “performance”, “firm value”, “impact”, “corporate governance”, “management”, and “corporate social responsibility”.

Table 3. Top 20 most-frequent keywords.

S/N	Amount	Centrality	Year	Keyword
1	857	0.17	2003	performance
2	654	0.33	2003	firm value
3	469	0	2003	impact
4	462	0.32	2003	corporate governance
5	376	0	2002	management
6	328	0.18	2007	corporate social responsibility
7	297	0.17	2003	governance
8	294	0	2003	ownership
9	291	0.36	2003	determinant
10	286	0.18	2003	innovation
11	281	0.28	2006	financial performance
12	247	0.1	2003	investment
13	230	0.04	2003	market
14	220	0.26	2003	strategy
15	209	0.18	2003	information
16	191	0	2003	model
17	185	0.25	2003	valuation
18	184	0	2003	cost
19	169	0.16	2003	firm
20	162	0.54	2002	agency cost

lists the top 10 keywords in terms of frequency. “Performance” had the highest frequency (857 occurrences), referring to the association between corporate value and performance. “Impact” occurred 469 times; this refers to the factors influencing corporate value. “Corporate governance” appeared 462 times, referring to the association between corporate governance and corporate value. “Management” appeared 376 times; this refers to the role of management in corporate value. Lastly, “corporate social responsibility” appeared 328 times, indicating the interaction between corporate value and corporate social responsibility (CSR).

A time zone map is a collection of keywords that first appear in the same year in the same time zone, representing keywords with a significant increase in frequency in that year. It indicates that a given keyword represents a new research frontier and hotspot in that year. This helps to show the evolution process of the knowledge field more clearly in the time dimension (Zhang et al. 2022). The extracted keywords can accurately express the core content and essence of the literature, reflecting hotspots and development trends in corporate value research. In Figure 6, the larger the size of the keyword, the higher its frequency of occurrence. We can see that performance, enterprise, and impact have always been the focus of research. With changes in economic development and social environments, the impact of COVID-19 on corporate value has also been studied over the last few years.

4.4. Document Co-Citation

Document co-citation means that two papers are cited in the same record. Based on the key nodes of the document co-location diagram, researchers can learn about the important documents and knowledge bases in specific research fields. Figure 7 shows the entire citation network of the selected 4312 articles, with 296 nodes and 299 links. Each node represents a cited document, and the links between nodes represent the co-citation relationships. The link color represents the color of the time slice and the reference relationship at different times. The larger the node shape in the figure, the higher the citation frequency of the document, indicating that it is highly recognized in the field. A node is an important hub in a network, and its corresponding document is also of great significance (Chen 2017).

As indicated in Figure 7 and

Table 4. Top 10 papers in the field of corporate value.

S/N	Citations	Centrality	Year	Literature
1	64	0.75	2013	Servaes, Henri, and Ane Tamayo. 2013. The impact of corporate social responsibility on firm value: The role of customer awareness. Management science, 59(5): 1045–1061. doi:10.1287/mnsc.1120.1630
2	55	0	2009	Mitchell A. Petersen.2009. Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches, The Review of Financial Studies, 22(1): 435–480. doi:10.1093/rfs/hhn053
3	53	0.21	2014	Cheng, Beiting, Ioannis Ioannou, and George Serafeim. 2014. Corporate social responsibility and access to finance. Strategic Management Journal, 35 (1): 1–23. doi:10.1002/Smj.2131
4	44	0.02	2011	Kramer, Mark R., and Michael Porter. 2011. Creating shared value. Harvard Business Review, 89(1–2): 62–77.
5	43	0.02	2009	Bebchuk, Lucian, Alma Cohen, and Allen Ferrell. 2009. What matters in corporate governance? The Review of Financial Studies, 22 (2): 783–827. doi:10.1093/Rfs/Hhn099
6	40	0.05	2003	Gompers, Paul, Joy Ishii, and Andrew Metrick. 2003. Corporate governance and equity prices. The Quarterly Journal of Economics 118(1): 107–156. doi:10.1162/00335530360535162
7	40	0.33	2017	Lins, Karl V., Henri Servaes, and Ane Tamayo. 2017. Social capital, trust, and firm performance: The value of corporate social responsibility during the financial crisis. The Journal of Finance, 72 (4): 1785–1824. doi:10.1111/Jofi.12505
8	38	0.78	2011	Dan S. Dhaliwal, Oliver Zhen Li, Albert Tsang, Yong George Yang. 2011. Voluntary nonfinancial disclosure and the cost of equity capital: The initiation of corporate social responsibility reporting. The Accounting Review, 86 (1): 59–100. doi:10.2308/Accr.00000005
9	34	0.08	2006	Villalonga, Belen, and Raphael Amit. 2006. How do family ownership, control and management affect firm value?. Journal of financial Economics 80 (2): 385–417. doi:10.1016/J.Jfineco.2004.12.005
10	33	0.7	2009	Srinivasan, Shuba, and Dominique M. Hanssens.2009. Marketing and firm value: Metrics, methods, findings, and future directions. Journal of Marketing Research 46 (3): 293–312. doi:10.1509/Jmkr.46.3.293

, which lists the top 10 most-cited articles, the higher the citation frequency, the higher the centrality (equal to or greater than 0.1), indicating that they are more important in the corporate value knowledge field. The centralities of Servaes et al. (2013), Dhaliwal et al. (2011), and Lins et al. (2017) reached 0.75, 0.78, and 0.33, respectively. Servaes et al. (2013) had the highest number of citations (64). That study argues that corporate value is positively related to CSR. CSR, which has long been a popular economic research topic, increases corporate value. The top 10 most-cited articles mainly analyze the influencing factors of corporate value, including CSR, manager ethics, manager power, corporate governance level, and corporate marketing methods, reflecting that scholars are most concerned about seeking ways to enhance corporate value, which is also the same as the focus of corporate managers. It is worth noting that although China’s Harbin Institute of Technology, Wuhan University of Technology, and Beijing Jiaotong University had the most related literature, the top 10 articles with the most citations have no Chinese authors.

4.5. Keyword Cluster Analysis

Although the primary research content and evolution trends of corporate value are analyzed through keywords and time-zone charts, there is no clear description of themes in the field. Keyword cluster analysis can help overcome this problem. CiteSpace provides two indicators—the module value (Q value) and average contour value (S value)—as the basis for measuring the clustering effect of the atlas. Q > 0.3 means the clustering structure is significant. When S > 0.7, the clustering is highly convincing (Li et al. 2018).

Applying CiteSpace’s clustering function to the keywords in the 4312 selected papers, 11 clear cluster modules with 11 boundaries were obtained, as shown in Figure 8. Modularity Q = 0.7369 (>0.3), indicating that the overall cluster result was significant. The mean silhouette was 0.7284 (>0.5), indicating that the cluster members had certain similarities and homogeneities.

Table 5. Eleven research clusters in corporate value research.

Cluster	Cluster label	Size	s	Concentration
#0	segmentation	16	0.825	cost, investment, foreign board membership, additional evidence
#1	performance	12	0.782	strategy, organizational culture, competing values framework, comparative management, reciprocal opposition
#2	value relevance	11	1	book value, earnings persistence, net asset value, financial market performance, residual income
#3	CSR	11	1	sustainability, financial performance, performance, corporate social responsibility
#4	global value chains	10	0.967	ownership, corporate social responsibility, China, earnings, information technology, manufacturing firm
#5	capital structure	10	0.909	enterprise value, corporate governance, firm value, board committees, nature of assets
#6	COVID-19	10	0.948	risk, banks, eco-label, debt, tourism enterprises
#7	entrenchment	8	0.949	board of directors, agency cost, emerging markets, auditing, corporate performance
#8	incentive	7	0.892	strategic choice, announcement, firm network, behavior, value network behavior
#9	research and development	6	1	technology transfer, countermeasures, identity theft, television, centrality
#10	value chain	4	0.925	construction enterprise, business opportunities and value creation functions, value-added, value innovation, innovation net

lists the 11 largest clusters by group size. The silhouette scores for the cluster ranged from 0.825 to 1.00, indicating that the members of the cluster fell well within their groups. The largest cluster, #0 (segmentation), refers to a common method for studying economic problems. Cluster #0 includes cost, investment, foreign board membership, and additional evidence, all of which are used in segmentation research methods. Other clusters highlight the improvement of enterprise performance, the analysis of the relationship between CSR and corporate value, the impact of global value chains on corporate value, the impact of COVID-19 on corporate value, the advancement of corporate value, incentives for corporate value, and research and development on corporate value.

4.6. Keyword Burst Analysis

Keyword bursts refer to high-frequency keywords that occur within a certain period, reflecting research hotspots and development trends in specific periods to a certain extent (Zhang et al. 2017). As shown in Figure 9, regarding mutation keywords in the corporate value literature, the research in this field has shown diversified features, and different mutant keywords have appeared in different periods. From 2000 to 2022, there were 20 emerging keywords in the corporate value literature. Regarding emerging keywords, the five keywords with the highest exposure intensity were value chain (22.8581), competitive advantage (20.4566), CSR (12.1896), company (10.9787), and competition (10.0817). These represent crucial hotspots in corporate value research, consistent with changes in economic development. Second, from the perspective of duration, the top keywords were investor protection and competitive advantage, lasting for a duration of 12 years, and value chain, lasting 11 years. Researchers have focused on those keywords over the past decade. Finally, CSR, value relevance, and sustainability have become prominent and continue to be the latest research frontiers. Cheng et al. (2014) found that enterprises with better CSR performance faced significantly lower capital constraints and suggested that better stakeholder participation and transparency regarding CSR performance are important for reducing capital constraints Lins et al. (2017), meanwhile, suggested that high CSR is associated with higher profitability, growth rates, and employee sales, and trust between a company and its stakeholders and investors (established through investment in social capital) will be rewarded.

5. Discussion and Conclusions

5.1. Discussion

The literature review shows that researchers have mainly focused on defining and measuring corporate value and its relationship with other economic variables. Given the limitations of existing research, it may be helpful to give a comprehensive overview of the development trends and content of corporate value research. Based on the results of the CiteSpace analysis, knowledge domains are further discussed.

The keyword clustering analysis showed that there were multiple clustering modules. However, these classifications need to reflect related research knowledge fields better. By combining the clustering results and high-frequency keywords, this section further divides the knowledge fields of corporate value.

The first concerns research methods for corporate value (#0 segmentation). Whether it is the composition, influencing factors, or behavior of corporate value, it is necessary to use the segmentation method for analysis. Domestic prejudice affects corporate valuation at the national and enterprise levels. Enhancing domestic investors’ preferences for housing rights and interest at the national level reduces the market valuation of housing rights and interest (Chan et al. 2009). At the company level, a company’s value is invested at home and abroad. The composition of local stocks people hold increases the weight of a company’s global market value, but this decreases as the weight deviates from global weights. A 4V model revolves around the global brand value chain organization, which segments value creation activity into value brands, sources, delivery, and value (Steenkamp 2014).

The second category includes the factors influencing corporate value, including #4 (global value chains), #5 (capital structure), #6 (COVID-19), #7 (entrenchment), #8 (incentive), #9 (research and development), and #10 (value chains). Higher insider ownership, lower outer block ownership, and fewer independent boards hurt corporate value (Brook et al. 1998). Diversified operations of Indian group companies can enhance corporate value when they exceed a certain level (Khanna and Palepu 2000)—linking organizational behavior, marketing, and financial functions through the marketing value chain to overcome organizational inertia and create enterprise value (Palomino-Tamayo and Timana 2022). Staggered boards promote firm value creation by committing the firm to holding long-term projects and linking the benefits to specific investors (Cremers et al. 2017). The effect of capital structure on profitability is negative and insignificant. Enterprise size and growth have positive but insignificant effects on profitability; profitability has a positive but insignificant effect on company value (Irawan et al. 2022). The Effect of Supply Chain Disruptions on Stock Returns in the Context of COVID-19 found that such disruptions led to negative stock returns, thus affecting enterprise value (Xia et al. 2022). During COVID-19, enterprises’ maintenance of economic output could enhance the enterprise value (Bizjak et al. 2022). This literature supports the results of this study.

The third category is corporate behavior, including #1 (performance), #2 (value relevance), and #3 (CSR). An S-shaped relationship between the franchise degree and the financial performance of catering enterprises was found (Koh et al. 2009). The controlling shareholders’ expropriation incentives display a connection between corporate governance and corporate value (Bae et al. 2012). Compared with low-CSR acquirers, high-CSR acquirers achieved higher merger returns and more significant long-term business performance growth after the merger (Deng et al. 2013). Valuation plays a role in motivating mergers (Erel et al. 2012). The company’s performance will be more robust when senior management is trustworthy and ethical (Guiso et al. 2015). Disclosing social responsibility information is not conducive to enterprises’ short-term profits but can increase their long-term value. A high level of corporate governance is conducive to legitimate management and the disclosure of information on social responsibility (Liu and Zhang 2017). CSR has a positive indirect effect on corporate value through its effect on risk bearing and suggests that CSR performance is positively related to corporate value (Harjoto and Laksmana 2018). Excessive investment beyond a reasonable level reduces enterprise value; according to the analysis of investment tendencies, the value relevance of investment increases was the opposite of the enterprise’s investment tendency (Lee and Jeon 2021). Intense compensation can affect corporate culture, influencing financial decisions and risk preferences. Cultural norms are essential to enhance corporate value (Graham et al. 2022). Fulfilling corporate social responsibility enhances corporate value (Freund et al. 2023). These research findings demonstrate corporate behavior, including performance, value relevance, and CSR.

5.2. Conclusions

This study systematically reviewed the literature on corporate value based on a total of 4312 selected articles. The WoS database was searched, and after manual screening, corporate value research spanning 2000–2022 was summarized. Researchers, institutions, co-cited articles, keyword co-occurrence, and clusters were analyzed to help understand the overall research status. Finally, the evolutionary trends and knowledge domains of corporate value research were discussed.

One of the purposes of this study is to clearly and intuitively present an overview of the research and hotspots in the field. Identifying trends and the evolution of themes in corporate value research can provide new ideas for studying research frontiers, analyzing hotspots, and anticipating future research directions. Based on the research results, all research purposes have been achieved.

The results show the following: (1) The publication volume of the relevant literature on corporate value has been increasing year by year, indicating that this field has gradually received attention from scholars and academic institutions. However, most of the research results were independently obtained by researchers, and there is a lack of collaboration or mutual citation among researchers. (2) The top three research institutions in terms of publication volume are all Chinese research institutions, namely, Harbin Inst Technol, Wuhan Univ Technol, and Beijing Jiaotong Univ. However, the impact of the research results is not high, with Univ Penn and Peking Univ having the greatest impact. (3) The three words that researchers paid the most attention to are performance, impact, and corporate governance. The centrality of Servaes et al. (2013), Dhaliwal et al. (2011), and Lins et al. (2017) reached 0.75, 0.78, and 0.33. Servaes et al. (2013) achieved the highest citation frequency (64), and the top 10 articles with the highest citation frequency did not have Chinese authors. (4) Through keyword cluster analysis, 11 clustering modules were identified, namely, segmentation, performance, value release, CSR, global value chains, capital structure, COVID-19, enrollment, incentive, research and development, and value chain. Keyword burst analysis, CSR, value relevance, and sustainability have become the most recent research frontiers. (5) Evolutionary trends are divided into three stages. The first stage (2000–2005) is the study of the influencing factors of corporate value. The second stage (2006–2015) is the investigation of how corporate behavior affects corporate value. The third stage (2016 present) is the study of various hot topics related to corporate value. (6) Knowledge domains can be classified into three categories: first, the research methods for corporate value (#0 segmentation), and second, the influencing factors for corporate value, including #4 (global value chains), #10 (value chains), #5 (capital structure), #6 (COVID-19), #7 (introduction), #8 (active), and #9 (research and development). The third type is corporate behavior, including #1 (performance), #2 (value release), and #3 (CSR).

The data in this study were limited to the core dataset of the WOS. Thus, important research available in other databases may have been neglected. Additionally, because the literature itself has a lag, results based on bibliometric research may also have a lag. Therefore, future research could provide necessary supplements to the present results by expanding the database or using other research methods.