Research Trends in Going Concern Assessment and Financial Distress in Last Two Decades: A Bibliometric Analysis

Abstract

Over time, companies have faced many crises that have impacted their capacity to remain operational in the market. When going through periods of financial distress, companies must find solutions to face the risks and to fulfill the going concern assumption, which is an essential principle in accounting and also of major importance in financial-statements auditing. The research objective is to identify the research trends and approaches in the fields of going concern and financial distress. To achieve this purpose, a bibliometric analysis was carried out of the articles published in the journals indexed in Web of Science Clarivate Analytics (WoS) for the period 2004–2023. The selection criteria was based on instances of the keywords “going concern” and “financial distress” appearing in a sample of 2.859 articles. The results highlighted that these fields represent a domain of interest in the research, with the trend being most pronounced in the area of financial distress, since 2018. Based on the content analysis of the most influential articles, the main topics addressed were related to audit quality and aspects related to management and corporate governance. Thus, it is confirmed that the area of financial distress and going concern assessment is a widely studied one, and the studies made can provide essential information to overcome the difficult periods that a company can go through.

1. Introduction

When a company is established, the founders set up some goals including the going concern assessment which means that the company will not cease its activity but keep operating in the foreseeable future or at least until the subsequent reporting period. Furthermore, the entity’s goals can assure good performance, so the company will keep growing and developing in the future.

In accordance with McCloskey (1983) and Tomek (1992), in terms of long-term stability and the existence of an enterprise, the going concern assumption is the most important goal and the most frequently discussed theme in the literature dedicated not only to business management and accounting but also to pricing policy. One of the most cogent points of view on profit maximization from a long-term perspective can be found in the literature where McCloskey (1983) offers reasons for such profit maximization. Varian (1995) addresses this topic by considering the business risks. He affirms that in a world of certainty it is obvious that the maximization of the current value of profits represents a similar goal as the maximization of enterprise value in a long-term perspective.

The idea of the long-term reporting, measuring and analysis of economic phenomena in practice is established on a certain assumption about the period of future enterprise existence. In accounting, the going concern assumption refers to the fact that an entity will permanently remain in business for the foreseeable future. The assumption of continuous business existence is included in multiple accounting standards; in Europe, we can refer to International Accounting Standards (IAS) 1 (IFRS 2001) Presentation of Financial Statements or International Standard of Audit (ISA) 570 (Revised) (IFAC 2015) Going concern, and in the United States Generally Accepted Accounting Principles (GAAP) 1 Presentation of Financial Statements (FASB 2015). It is one of the fundamental presumptions that outline the content and form of information coming from accounting.

The objective of this research is to examine the trends and approaches in the field of going concern assessment and the symptoms of financial distress in the entity’s performance. In terms of research methodology, the study is based on the bibliometric analysis of the research publications in the domain of going concern and financial distress, using a sample of 2859 papers published between 2004 and 2023 in several journals. This technique is useful in identifying influential publications, networks of collaboration, thematic clusters and the trends and patterns in the research domain using the VOSviewer software. To deepen the research, a content analysis of the most relevant articles is also carried out.

The research contains an analysis of the available data on going concern assessment and financial distress prediction. It can be a relevant and useful source of information for researchers in business continuity, also for insolvency and bankruptcy practitioners and for the company’s management, to make informed decisions regarding the risks faced by the company. Moreover, the going concern assumption is important when valuing the company because of its perspectives. If the company accomplishes this assumption, it means that it will not cease its activity in the foreseeable future and it will keep developing. This principle is also taken into account when the entity is applying for loans, leasing or any other forms of credit. When this assumption is not respected, the reputation of the entity is also affected and because of this, there may be many problems with company’s business partners. Research confirms the relevance of the financial statements/data for the investors in early-stage business ventures (Foster and Shastri 2016). Armstrong et al. (2006) found that investors considered many reported expenses and sales relevant to assess the value of companies prior to Initial Public Offerings. The nature of reported expenses was important because investors viewed some of early-stage companies’ incurred costs as investments made for a foundation, with the potential to increase future revenue. Potential investors’ perceptions may be influenced by whether the independent accountant’s report accompanying the financial information is unqualified or contains a going concern modification. Moreover, auditor quality, as highlighted by audit firm size, may impact potential investors’ perceived reliability of the financial statements (DeFond and Lennox 2011).

This paper contributes to the existing research by synthesizing the information regarding the above-mentioned fields of study. Also, it highlights the links existing between the studied topics and the accounting and auditing sector, and the factors influencing the two concepts that make the object of the research. This paper advances the academic research by containing up-to-date research of the existing literature for the last 20 years and synthetizing which are the latest trends and patterns regarding the studied topics. Also, this kind of bibliometric analysis is relevant because it concludes if the fields of study are still representing a concern in the research domain or if the subjects are old and ignored by the researchers and practitioners today. Moreover, the bibliometric analysis helps researchers and academics focus on the most relevant articles form one domain, instead of reading many other irrelevant articles in order to find some useful papers for their research. The originality of this work lies in studying two concepts, so that from the evidential papers one can understand which factors influence the going concern assumption and the risk of financial crisis, in order to develop strategies to avoid facing this situation. Some researchers, using U.S. data, have shown that companies for which auditors expressed an uncertain going concern opinion are more prone to bankruptcy, but the results can be mixed (Sun 2007; Gissel 2010; Carson et al. 2013). Smolarski et al. (2011) concluded that investors treat audited financial statements as valuation tools. Francis (2004) found evidence that the going concern opinion helps investors anticipate bankruptcy because the market response to a bankruptcy announcement is less negative (by 13%) when the auditor has previously issued a going concern report. Other studies observed negative market reactions when firms received an unexpected going concern opinion from auditors (F. L. Jones 1996; Blay and Geiger 2001; Menon and Williams 2010).

On the other hand, practitioner studies, such as those published by professional bodies and large audit firms, are useful for researchers in writing articles. Given that these studies are not included in databases, citations also make them visible in academia. A hotly debated topic in practice has been the guides and studies published by practitioners related to the impact of COVID-19 on financial statements during the pandemic, documents that have subsequently been cited in many publications (Hategan et al. 2022; Kend and Nguyen 2022).

This research paper is structured as follows: in the next Section, there is a literature review on the studied topic; in Section 3, the research methodology is explained; in Section 4, we will present the results obtained and in the final section, point out the main conclusions, limitations of the research and future directions.

2. Literature Review

The subject of going concern assumption is a subject widely studied among professionals in the field because it represents a sensitive point in a business. Companies are established to maintain the business going concern. Going concern is an important issue to be addressed because it determines the way the business will survive. Higher performance of management brings higher possibilities to ensure companies will survive. Sometimes, going concern problems are caused by economic conditions. Economic performance can sustain a company to reach a better performance (Hardi et al. 2020).

The evaluation of a company’s continuity perspective is a fundamental decision-making task for each possible investor or accounting information user. And because of this, all the information must be neutral and presented in a way so that the going concern/liquidation assumption outcomes from decisions are made on the basis of the data available. In no case should the data be prepared based on assumptions. Evaluations of a company’s existence perspectives are founded not only on information from accounting, but also on financial analysis methods. Quite often, several methods cause opposite results, and it can be said that the future of an entity is often affected by facts and decisions that do not come out from accounting or financial analysis. An important subject is referring to the factors that influence the quality of financial reporting, a topic that was studied by many researchers who reached the same consensus, that this may be influenced on the one side by the specific factors of the internal environment of the company (for example, the degree of indebtedness, level of profitability, field of activity, the size of the entity), by the corporate governance system or by the activity of auditors, and on the other side, by macroeconomic factors, for example, the legal and political system specific to a country or community or certain accounting/fiscal policies, and also the specifics of the accounting standards used when preparing the financial statements, particularly, the International Financial Reporting Standards (Ciocan et al. 2021).

The going concern principle is one of the most important concepts applied by the accountants while preparing the financial statements. IAS 1 requires management to assess the entity’s ability to continue as a going concern. The Standard defines going concern by explaining that financial statements are prepared on a going concern basis unless management either intends to liquidate the entity or to cease trading or has no realistic alternative but to do so (https://www.ifrs.org/content/dam/ifrs/news/2021/going-concern-jan2021.pdf, accessed on 24 August 2024). The Standards request that the management needs to take into consideration all available data about the future. Therefore, management may need to examine this wide range of factors before it can conclude whether preparing financial statements on a going concern basis is proper.

While formulating an opinion about this assumption, the company management examines future outcomes in accordance with external or internal conditions and, mainly, the probable risks. The principal disadvantage of such an examination is the fact that it is mostly based on the data accessible at the time of the evaluation that refers to size, complexity, area and nature of business.

ISA 570 gives examples of circumstances or situations that, apart or in common, may cause serious uncertainty regarding the going concern assumption. They are grouped into three categories: financial factors, operating factors, and other factors (non-financial factors). Examples for each category are presented in

Table 1. Factors threatening the going concern assumption.

Classification	Examples
Financial factors	net liability or net current liability position (compared with assets); excessive or long-term use of short-term borrowings to finance long-term assets; negative operating cash flows; unfavorable key financial ratios; significant operating losses or worsening of the value of assets used to generate cash flows; incapacity to pay creditors on due dates; change from credit to cash-on-delivery transactions with suppliers.
Operating factors	management intentions to close down the entity or to stop operations; loss of key management without substitution; loss of an important market or major supplier(s); labor problems; emergence of a highly successful competitor.
Non-financial factors	changes in legislation or state policy with a negative impact on the company; pending legal or regulatory proceedings against the entity that may, if successful, result in demands that the company is improbable to be able to satisfy; uninsured or underinsured catastrophes when they occur, etc.

Source: own work based on (Wójcik-Jurkiewicz and Karczewska 2019) and (Hammond et al. 2022).

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The factors presented in Table 1 are not appearing all simultaneously; some other elements may also appear but not all of them need certainly operate in the same direction. The weight of some of these factors can be decreased by the impact of other factors. Moreover, not all these elements can be evaluated using the available financial statements only. In an entrepreneurial environment, the consequences that a company may face when passing financial distress periods may refer to the credibility of its creditors, investors and partners. They may require more guarantees and should be more reluctant when talking about the beginning or continuing the partnership. Moreover, going through periods of financial difficulty within a company can affect society, through problems related to the labor market, because one of the first ways used to reduce costs is to reduce staff. Because of this, the company’s image on the market is affected, and its credibility is also called into question. Precisely for that reason, it is essential for a company to develop methods to avoid this financial situation and to take into account the factors that can lead to periods of financial difficulty.

Financial distress prediction has become a topic of great interest for most decision-makers over the last decades, especially because of the valuable insights and effective early warnings of potential bankruptcy yielded by such prediction models. Therefore, discovering a suitable model for predicting financial distress is likely to be of great significance to global investors (Tudor et al. 2015). Financial difficulty and companies’ failure have always been a complicated and intriguing problem for businesses. Because of the unfavorable impacts of financial distress on companies and societies, accounting and finance researchers around the world are thinking of ways to anticipate corporate financial distress. Several models are provided in the literature for predicting financial distress such as nonlinear decision tree and linear discriminant analysis models (Tahmasebi et al. 2020). Also, financial distress may influence the performance of the companies by modifying the cost of stakeholder relationships. Opler and Titman (1994) concluded that the performance of the companies in financial distress can be affected by the reluctance of customers to have deals with distressed entities, the offensive responses from the concurrence and due to the mobility in downsizing the more leveraged companies.

S. Jones (2023) conducted a survey of corporate-failure-prediction models and concluded that machine-learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there is now a much wider range of variables being used to model and predict firm failure. The implications of the financial difficulties on many variables of performance are of huge importance to the management of the company and to researchers as well. During the high-performance periods, companies increase their trade receivables; however, when facing a period of reduced cash-flow, trade receivables decline (Ak et al. 2013). Moreover, the authors argued that stock returns and sales suffered a decline when entities have less trade receivables during financial distress periods.

To assess a firm valuation of equity in order to explore its performance over time and during various economic conditions, several models are used, such as the Divided Discount Model (DDM), the Gordon Growth Model (GGM), the Fed Model or the Residual Income Model (RIM). The DDM uses perfect foresight, referring to future dividend payments, and uses the capital asset pricing model (CAPM) in order to estimate the discount rate. The GGM involves forecasting the future growth rate of dividends. The Fed Model compares the firm’s earnings yield (the level of earnings in comparison with the price) with the yield in a long-term government bond. It is concluded that dividend-based models perform best (Foerster and Sapp 2005). Because of the failure of the DDM to explain stock price fluctuations, researchers pursued alternative models of stock valuation. So, in accounting literature, an alternative valuation model is considered to be the Residual Income Model (RIM). This model derived from the DDM replaces dividends with earnings and book value, and because of this, the model may be able to explain volatile stock price movements (Jiang and Lee 2005).

Blay et al. (2011) concluded that the market changes its valuation procedures in response to a going concern opinion. Before the going concern opinion, the market focuses mainly on the net income. But after receiving a going concern opinion, the market’s valuation focus is on balance sheet assets and liabilities. So, in conclusion, the going concern assessment’s opinion is providing data to the marker regarding the company beyond what is publicly available.

Considering the importance of going concern assumptions for the company, and to prevent the risk of financial distress, it is vital to comprehend the factors contributing to a higher risk of bankruptcy or financial distress periods and to elaborate strategies to avoid them. Because of this, the research aims to review the existing literature on this topic (trends and patterns) to provide insights into the intellectual structure regarding the risk of bankruptcy and going concern assessment and the underlying nuances. It is intended to reach this objective by conducting a bibliometric analysis and by giving answers to the following research questions (RQs):

RQ1: What are the publication trends and patterns, the articles with most citations, most cited authors, and journals in the topics of going concern assessment and financial distress?

RQ2: What important themes (structure of knowledge) exist within the area of going concern assessment and financial distress?

3. Materials and Methodology

Bibliometric analysis uses quantitative and qualitative methods in order to identify, select, collect, categorize and synthetize substantial volumes of textual information and determine specific indicators (Chersan and Mironiuc 2015). This bibliometric analysis was carried out to identify the trends and approaches regarding the going concern assumption and financial distress, being a systematic literature review, with a theory-based approach. The data processing was generated by the VOSviewer software 1.6.20, to identify the connections between the most usual keywords in these fields of research, as well as the relationships between the authors, and their research, but also, the citations related to the subject of the study. The data used were downloaded from Web of Science (WoS)—Clarivate Analytics on 24 April 2024; the papers were selected for a period of 20 years, more exactly 2004–2023.

The Web of Science is a paid-access platform that offers access to multiple international databases that provide reference and articles from academic journals, conference proceedings and many papers in several academic disciplines. This database delivers information or a comprehensive overview of many research results in the world in science, technology, medicine, social sciences, arts and humanities. An advantage of this platform is the citation index because citations in science work as linkages between similar research items and conduct to matching or related scientific literature, such as journal articles, conference proceedings, abstracts, etc. Moreover, the literature that reveals the most considerable impact in a particular field, or more than one discipline, can be located through a citation index. For example, a paper’s influence can be determined by linking to all the papers that have cited it. In this way, current trends, patterns and emerging fields of research can be assessed. Also, another advantage is that it offers the possibility to analyze the results of the search and see the most important fields of study for your research, and the dynamics of the publications in the studied domain.

VOSviewer is a software tool for constructing and visualizing bibliometric networks. These networks may, for instance, include journals, researchers or individual publications, and they can be constructed based on citation, bibliographic coupling, co-citation or co-authorship relations. VOSviewer also offers a text-mining function that should be used to build and visualize co-occurrence networks of important terms extracted from a body of the scientific literature. The maps displayed after importing the data can be seen in many ways, such as network visualization, overlay visualization and density visualization, and because of this, the user can map articles or publications in more detail. Bibliometric analyses in information sciences are studies that can disclose models of document use, literature development or sources of information in a specific field. Subjects in the bibliometric analysis are carried out qualitatively and quantitatively (Velasco et al. 2012).

Van Eck and Waltman (2014) demonstrated that VOSviewer by default also assigns the nodes in a network to clusters. A cluster is a set of closely related nodes. Each node in a network is assigned to exactly one cluster. The number of clusters is determined by a resolution parameter. The higher the value of this parameter, the larger the number of clusters. In the visualization of a bibliometric network, VOSviewer uses colors to indicate the cluster to which a node has been assigned. The clustering technique used by VOSviewer is discussed by Waltman et al. (2010), stating that the technique requires an algorithm for solving an optimization problem. For this purpose, VOSviewer uses the smart local moving algorithm introduced by Waltman and Van Eck (2013).

Performance analysis (to answer RQ1) and scientific mapping (to answer RQ2) were performed using bibliometric analysis tools. Also, to complete the answer to RQ2, a content analysis of the most cited articles will be performed.

To identify the papers that refer to the topic of going concern assumption, expressions that contained the “going concern” and “financial distress” keywords were selected. The database initially highlighted 5195 papers belonging to all document categories. The filters applied on the papers returned are presented in the following: the document type (article), Web of Science categories (business finance, management, business, economics, law) with minimum 50 record count, research areas (business economics or government law). In the analyzed period, 2004–2023, after applying the selection criteria, 2859 papers were identified, as Figure 1 shows.

The criteria used for the final sample were based on selecting only the articles from the initial sample because they were considered most relevant for the research and also this document category was predominant in comparison with the other categories (proceeding papers, book chapters, review articles, etc.). Also, because of the type of the study, in the domain of economics, it was considered most relevant to take into account the articles from the category of business finance, management, business, economics and law. The period chosen is of the last 20 years because it was considered a representative period to make relevant conclusions regarding the evolution of the studies made in the field of going concern assessment and financial distress. The data were downloaded from the WoS database because it is a representative database, with many articles and it provides more study materials in comparison with other databases. Also, Scopus is a good option but taking into consideration that both databases provide similar content, WoS has been chosen because it is more analytical in the author’s opinion.

To improve the selection of the words, the expressions that were similar or abbreviations were replaced and meaningless words were removed. The final database was imported in VOSviewer software 1.6.20, being subsequently processed, resulting in an analysis of the keywords and citations based on the distribution of the papers by the individual authors and the countries, carried out after their affiliation.

To deepen the research, a content analysis was also carried out, resulting in the top-10 most relevant articles, based on the average number of citations per year accumulated since publication.

4. Results

4.1. Performance Analysis

In the first stage of the research, performance analysis was carried out to identify the answers to RQ1.

Figure 2 shows the yearly distribution of the articles published on the studied subject.

Figure 2 shows an increasing evolution of the number of articles published during the analyzed period. The publications increased during the studied period, the highest point being in 2021 with 290 articles. A possible reason could be the COVID-19 pandemic in 2020, when most companies faced a huge challenge regarding their capacity to continue the activity or not. In the period 2004–2011, the two concepts were studied at the same scale, approximately, but since 2012, the attention has moved onto the concept of “financial distress”, as a change in terminology of the studies made. In comparison with the first decade of the studied period, in the second decade, the number of the studies increased significantly, a possible explanation being the fact that the studied topic came to the attention of many authors and researchers, a fact that strengthened the importance of the subject of the study.

To support the previous affirmation, Figure 3 presents the evolution of the published articles in the two fields studied.

Figure 3 shows that during the studied period, the concept of “financial distress” was a more studied one, but since 2015, it became more and more studied, while the concept of “going concern” maintained its trend in studies. This is the main reason why this research is carried out with the two keywords in parallel.

For a detailed analysis,

Table 2. Web of Science category.

WoS Categories	Record Count	% of 2859
Business Finance	1547	54.11
Economics	1102	38.54
Management	450	15.74
Business	380	13.29
Law	167	5.84
Operations Research Management Science	63	2.20

Source: own work based on WoS data.

documents were grouped according to the domains classified in WoS.

Based on the settings presented in Section 3, six categories were highlighted. Thus, according to Table 2, the Business Finance research area has the largest weight, followed by Economics and Management fields.

A performance indicator highlighted by the bibliometric analysis is the individual co-author network, thus Figure 4 highlights the individual co-authorship network, which was grouped into four clusters, detailed in

Table 3. Distribution of authors in clusters.

Cluster 1 (Red)	Doc.	Citations	Avg. Citations	Cluster 2 (Green)	Doc.	Citations	Avg. Citations
Campello, M	5	474	94.80	Zhang, J	4	1275	318.75
Li, C	6	259	43.17	Huang, X	3	412	137.33
Krishnan, J	4	161	40.25	Zhu, H	3	412	137.33
Gregoriou, A	3	106	35.33	Zhou, H	4	476	119.00
Tashjian, E	3	105	35.00	Al-Hadi, A	3	171	57.00
Singhal, R	3	102	34.00	Wang, C	4	212	53.00
Li, H	8	249	31.13	Hasan, I	5	224	44.80
Zhou, J	4	117	29.25	Krishnan, Gv	4	176	44.00
Eshleman, Jd	4	114	28.50	Cui, X	3	124	41.33
Gupta, J	6	140	23.33	Wang, K	3	124	41.33
Sun, J	10	213	21.30	Wang, H	8	304	38.00
Sun, Y	3	60	20.00	Richardson, G	4	143	35.75
Zhang, Y	11	217	19.73	Thorburn, Ks	4	136	34.00
Amin, K	3	59	19.67	Taylor, G	7	228	32.57
Zhu, Y	5	87	17.40	Eckbo, Be	5	137	27.40
Xing, Y	3	884	294.67	Francis, Jr	5	807	161.40
Strebulaev, Ia	5	591	118.20	Lim, Cy	3	473	157.67
Rodgers, Kj	4	472	118.00	Simnett, R	4	591	147.75
Li, Z	7	412	58.86	Tan, Ht	3	360	120.00
Wilson, N	5	257	51.40	Defond, Ml	3	351	117.00
Xu, J	3	143	47.67	Lennox, Cs	5	492	98.40
Ansell, J	3	106	35.33	Willenborg, M	3	247	82.33
Andreeva, G	3	105	35.00	Raghunandan, K	5	387	77.40
Zhao, L	4	137	34.25	Knechel, Wr	5	368	73.60
Wu, W	3	91	30.33	Bruynseels, L	4	280	70.00
Zhang, Z	5	144	28.80	Vanstraelen, A	4	266	66.50
White, Mj	3	67	22.33	Carson, E	8	501	62.63
Wang, M	5	99	19.80	Willekens, M	6	343	57.17
Chen, H	5	83	16.60	Blay, Ad	4	200	50.00
Li, W	3	47	15.67	Geiger, Ma	11	549	49.91

Source: own work based on WoS data.

. A co-authorship map was used to build collaboration networks and find partners for research projects (De Jong and Bus 2023). Co-authorship is measuring the most efficient set of documents and the ones with a maximum degree of mutual publications. In this kind of analysis, a bibliometric network consists of the links between scholars, research organizations and countries based on the quantity of journals they have authored conjointly. For the data selection and thresholds, we chose a minimum of three documents for an author, with a minimum of three citations for an author and a maximum of 25 authors per document.

Among the 5542 authors in total, only 332 met the thresholds and the largest set of connected items consisted of 149 items, presented in Figure 4, where each node represents an author, and the lines and distances indicate the relations between them. The distance between two nodes reflects the intensity of the relation; this means that if two nodes are closer to each other, they tend to have a stronger relation. Also, authors who have higher weight, referring to citations and publications, are represented as bigger nodes. Each link has a strength, indicating the number of publications that two researchers have coauthored (Van Eck and Waltman 2019). The link strength can be used as a quantitative index to depict the relationship between two items and the total link of a node is the sum of the link strengths of this node over all the other nodes (Pinto et al. 2014). The words (names) are assigned to a cluster, based on a computer algorithm. Each cluster has its own color. When the colors are mixed, then the algorithm could not make clear distinctions between the clusters (De Jong and Bus 2023).

The number of clusters is determined by a resolution parameter (Van Eck and Waltman 2014), so that based on the settings applied, four co-authorship clusters resulted, as shown in Figure 4. It can be noted that there is no significant node between authors that can be seen, according to Table 3. Campello M, found in cluster 1, has one link, one strength and five documents, with a number of citations of 474. Also, Zhang, J, found in cluster 2, has the largest number of citations, respectively, 1275, and the highest number of average citations per year, of 318.75, with two links, two strengths and four documents. Xing Y belonging to cluster 3, has a total of two links, two strengths and three documents, a total number of citations of 884. Francis Jr. from the last cluster, has one link, one strength and five documents, with a total number of citations of 807. The rest of the authors show significantly lower weight of citations and publications and less collaboration links because the clusters are distributed separately and are barely interconnected.

From the Table 3 results in the first cluster, the most cited author is Campello, with an average citation of 94.80, from which, the most cited article refers to the topic of customer concentration and loan contract terms (Campello and Gao 2017). In cluster 2, the most cited author is Zhang, with an average citation of 318.75, whose most cited article is referring to the various methods used to measure the quality of an audit engagement, in collaboration with DeFond and Zhang (2014). In the third cluster, the author that registered most citations is Xing, accumulating an average of citations of 294.67. The most cited article of Xing is studying the default risk in equity returns (Vassalou and Xing 2004). In the last cluster, the most cited author is Francis, with an average of 161.40 citations, whose most cited article is analyzing the differences between the audit missions performed by Big 4 companies and other any other small audit offices (Francis and Yu 2009).

The articles written by the authors mentioned in Table 3 were published in many journals whose distribution, in descending order, is detailed in

Table 4. Distribution of papers by publication titles.

Publication Titles	Record Count	% of 2859
Journal of Banking Finance	73	2.55
Journal of Corporate Finance	62	2.16
Auditing A Journal of Practice Theory	49	1.71
Journal of Financial Economics	46	1.60
International Review of Financial Analysis	39	1.36
Review of Financial Studies	38	1.32
Contemporary Accounting Research	31	1.08
International Review of Economics Finance	31	1.08
Managerial Auditing Journal	31	1.08
Review of Quantitative Finance and Accounting	31	1.08
Accounting and Finance	30	1.04
Managerial Finance	30	1.04
Other publications with less 30 papers	2368	82.90
Total	2859	100

Source: own work based on WoS data.

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In Table 4, the journals that published at least 30 articles from the selected sample were included. Most papers were published in Journal of Banking Finance (73), Journal of Corporate Finance (62) and Auditing a Journal of Practice Theory (49). Those three journals are indexed in the Business Finance category, a topic which is increasingly published by journals in this field, which also leads to a higher number of citations of the published articles. The number of publications is a sign that this subject is of wide interest, being published by many journals.

4.2. Science Mapping

To answer RQ2, the most used method is the analysis of links between keywords. Keywords are an essential part of bibliometric analysis because they facilitate the identification of the topics studied in a set of research papers. The keyword occurrence analysis reveals how the most common keywords were found together in the analyzed studies, in order to define the popularity, age and impact of topics (De Jong and Bus 2023). In order to highlight the transition from the concept of going concern, used in the research papers of the first decade of the analyzed period, to the concept of financial distress, used in the second decade, Figure 5 shows the map of keywords as a result of searches for “going concern”.

From Figure 5, the results show that a total of 1880 keywords were identified in VOSviewer, from which, those that registered at least five occurrences were picked in the analysis and resulted in a selection of 131 words, which were grouped into four clusters, with a minimum of fifteen per cluster, as shown in

Table 5. Distribution of keywords related to going concern in clusters.

Cluster 1 (Red)	No.	Cluster 2 (Green)	No.
Independence	113	Going concern (GC)	117
Audit fee	96	Bankruptcy	76
Non audit fees	78	Company	70
Audit quality	66	Financial distress	64
Earnings management	62	Performance	52
Corporate governance	58	Market	47
Earnings	46	Prediction	43
Litigation	40	Cost	32
Industry expertise	39	Management	29
Determinants	31	Debt	22
Cluster 3 (Blue)	No.	Cluster 4 (Yellow)	No.
Going concern opinion	121	Quality	88
Information	70	Impact	55
Audit report	51	Opinion	48
Decision	47	Litigation risk	33
Disclosure	40	Size	22
Risk	38	Business risk	13
Auditing	27	Reputation	10
Audit opinion	22	Services	9
Information-content	22	Investment	8
Conservatism	21	Lawsuits	6

Source: own work based on WoS data.

.

The distribution of the keywords presented in Figure 5 by clusters is highlighted in Table 5.

As can be seen in the Table 5 results, cluster 1 contains words corresponding to the audit area, like “independence” with 113 occurrences, 102 links and a total link strength of 858, or “audit quality”, with 66 occurrences; words that introduce the connection between going concern assessment and the audit sector. Cluster 2 focuses on the main keyword “going concern”, with 117 occurrences, 119 links and a total link strength of 639, and refers also to the concept of “financial distress”, with 64 occurrences. Cluster 3 also contains keywords from the audit field, like “going concern opinion”, with 121 occurrences, 115 links and a total link strength of 755, or “audit report”, with 51 occurrences. Finally, cluster 4 refers to the areas of “impact”, with 55 occurrences, 87 links and a total link strength of 372 and “litigation risk”, with 33 occurrences, mostly referring to the “business risk” and similar terms.

Also, when talking about the concept of financial distress, a total of 6789 words were found, and those which registered at least 20 occurrences were picked for the analysis, which resulted in a sample of 139 keywords (Figure 6).

Based on the settings made in the software, in Figure 4 it can be seen that four clusters of words correlated with “financial distress” resulted, with the distribution by clusters being presented in

Table 6. Distribution of keywords related to financial distress in clusters.

Cluster 1 (Red)	No.	Cluster 2 (Green)	No.
Risk	319	Bankruptcy	411
Market	205	Company	359
Liquidity	95	Determinants	283
Equity	92	Capital structure	246
Crise	85	Investment	222
Default risk	84	Debt	218
Credit	82	Cost	206
Leverage	81	Agency costs	115
Return	78	Reorganization	95
Trade credit	73	Policy	75
Cluster 3 (Blue)	No.	Cluster 4 (Yellow)	No.
Financial distress	1155	Prediction	388
Performance	394	Ratios	262
Corporate governance	316	Model	212
Impact	181	Discriminant analysis	128
Information	155	Neural-networks	95
Ownership	151	Default	87
Management	113	Financial ratios	82
Earnings management	97	Credit risk	79
Ownership structure	69	Failure	71
Quality	59	Financial distress prediction	55

Source: own work based on WoS data.

, in which they are grouped by of keywords, in descending order.

As Table 6 reveals, there is much more interest in the subject of financial distress, in comparison with the going concern concept, even referring to the total number of keywords found. Also, the clusters do not contain the keyword “going concern”, in the top ten keywords; a reason for this may be the fact that, as previously highlighted, this concept was replaced by “financial distress” in studies over time. Cluster 1 has the word “risk” with 319 occurrences, 136 links and a total link strength of 1567 and “liquidity”, with 95 occurrences, which are some significant factors when talking about financial distress influence factors. Also, the cluster contains the word “market”, with 205 occurrences, a word that has a significant connection between a financially distressed company and its position on the market. Cluster 2 focuses on words in the sphere of “bankruptcy”, with words like “reorganization” and “debt”, also referring to the “determinants” of that state of crisis, like “capital structure” with 246 occurrences, 125 links and a total link strength of 1440. Cluster 3 pays attention to the most important word in the analysis, “financial distress”, with 1155 occurrences, 138 links and a total link strength of 5574, highlighting the relevance of this topic in the studies made in the selected period. Moreover, it makes a connection to the importance of “corporate governance” and “performance” when talking about the financial distress of entities. Cluster 4 focuses on words in the sphere of “prediction”, highlighting some words from this sphere, like “discriminant analysis”, with 128 occurrences, 85 links and a total link strength of 825, or “neural-networks” with 95 occurrences.

When considering both terms, a total of 7747 keywords were highlighted in the software used. From these, those that registered at least 20 occurrences were picked for the analysis and this resulted in a selection of 172 words. Finally, those 172 words were considered for the analysis and set in the VOSviewer software to a minimum of 20 per cluster, as reflected in Figure 7.

As can be seen in Figure 7, the keywords were grouped into four clusters. In

Table 7. Distribution of keywords in clusters.

Cluster 1 (Red)	No.	Cluster 2 (Green)	No.
Financial distress	1171	Information	210
Bankruptcy	469	Earnings management	150
Company	414	Quality	138
Risk	352	Independence	130
Determinants	311	Going concern opinion	121
Capital structure	249	Going concern	117
Market	245	Audit fee	113
Debt	236	Earnings	98
Cost	232	Non audit fees	88
Investment	229	Decision	86
Cluster 3 (Blue)	No.	Cluster 4 (Yellow)	No.
Prediction	412	Performance	439
Ratios	263	Corporate governance	368
Model	227	Impact	229
Discriminant analysis	134	Ownership	161
Financial ratios	101	Management	138
Neural-networks	100	Agency costs	119
Default	89	Ownership structure	70
Credit risk	79	Incentives	59
Failure	79	Directors	45
Financial distress prediction	55	Corporate social responsibility	44

Source: own work based on WoS data.

, there are presented the clusters of words that interacted one with another, grouped in descending order, in accordance with the number of occurrences. The first cluster comprises the keywords referring to “financial distress” and “bankruptcy”, essential words in the analysis made, the expression “financial distress” having a total of 171 links, a total link strength of 5915 and a total of 1171 occurrences. Cluster 2 makes the connection with the audit sphere, with words like “going concern opinion” with 121 occurrences, 103 links and a total link strength of 683, and “independence” with 130 occurrences, 100 links and an 876-total-link strength. Also, the third cluster highlight keywords like “prediction”, with 412 occurrences, 159 links and a total link strength of 2461 and “ratios with 263 occurrences, 140 links and a total link strength of 1632, both, words from the area of predictability, also referring to the “financial distress prediction” and the methods used, making the connection with the last cluster that refers to the “corporate governance”, recording a total of 368 occurrences, 165 links and a total link strength of 2178, and “ownership” with 142 links, 161 occurrences and a total link strength of 1046, highlighting the connection with the “management” when measuring the risks faced by a company.

In Table 7, it is observed that cluster 1 contains a group of keywords related to the topic of the study, the “financial distress”, with 1171 links, followed by other words like “bankruptcy” and “risk”, words that confirm that the topic of this research was studied before on a large scale and remains a subject of interest, over time. Cluster 2 refers to the audit sector with words like “independence” and “going concern opinion”, a word with 121 links, demonstrating the link between going concern assessment, financial distress, and the audit sphere. Cluster 3 contains a group of words referring to methods and “ratios” used in “bankruptcy prediction”, like “discriminant analysis” or “neural networks”. Cluster 4 focuses on keywords from a company’s management area, like “corporate governance”, with 368 links, or “management”, with 138 links, words also found in the analysis of keywords from the topic of financial distress.

Analyzing the links between the above-mentioned keywords, it can be concluded that the studied topics are connected with many fields from the economics domain. From the first cluster, the connection with the bankruptcy, risk and cost areas is highlighted, the second one reveals the connection with the audit sector and the third one refers to the methods used when measuring the risk of bankruptcy and financial difficulty. The fourth cluster highlights the connection with the management of the company. So, in conclusion, the studied topics are essential when analyzing the risks faced by a company, how it influences the audit opinion and the decisions made by the corporate governance and also some methods to prevent and measure those risks.

4.3. Content Analysis

In order to spot the most significant publications on this topic,

Table 8. Top 10 most-cited articles.

Title	Authors	Journal	IF	Year	Citations	Avg/Year
A review of archival auditing research	DeFond, M.; Zhang, J.	Journal of Accounting & Economics	3.535	2014	1266	115.09
Do Managers Withhold Bad News?	Kothari, S. P.; Shu, S.; Wysocki, P. D.	Journal of Accounting Research	3.346	2009	941	58.81
In Search of Distress Risk	Campbell, J. Y.; Hilscher, J.; Szilagyi, J.	Journal of Finance	3.764	2008	937	55.12
Default risk in equity returns	Vassalou, M; Xing, YH	Journal of Finance	2.549	2004	810	38.57
Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z Score Model	Altman, E. I.; Iwanicz-Drozdowska, M.; Laitinen, E. K.; Suvas, A.	Journal of International Financial Management & Accounting	1.478	2017	296	37
National and Office-Specific Measures of Auditor Industry Expertise and Effects on Audit Quality	Reichelt, K. J.; Wang, D.	Journal of Accounting Research	2.378	2010	543	36.2
Big 4 Office Size and Audit Quality	Francis, J. R.; Yu, M. D.	Accounting Review	2.488	2009	535	33.44
Tracing the Impact of Bank Liquidity Shocks: Evidence from an Emerging Market	Khwaja, A. I.; Mian, A.	American Economic Review	2.531	2008	567	33.35
Systemic risk measurement: Multivariate GARCH estimation of CoVaR	Girardi, G.; Erguen, A. Tolga	Journal of Banking & Finance	1.299	2013	375	31.25
Firing Costs and Capital Structure Decisions	Serfling, M.	Journal of Finance	5.397	2016	229	25.44

Source: own work based on WoS data.

presents the top 10 most relevant articles, descending by the number of average citations per year accumulated since publication.

Table 8 shows that the most relevant articles were published in the first decade of the analysis, namely between 2004 and 2014. Thus, the article “A review of archival auditing research”, written by DeFond and Zhang (2014) accumulated the most citations (1266) and the highest annual citations (115.09). This result is also confirmed by the information in Table 3, where Zhang was the most cited author (1275 citations) along with DeFond (351 citations).

From the Table 8 results showing that the articles were published in many journals, the ones in which the most articles were published were Journal of Finance (three articles) indexed in SSCI (Business, Finance), publisher WILEY, with an impact factor of 7.6 based on the Journal Citation Report (JCR) 2023. The ranking is followed by Journal Accounting Research (two articles) indexed in SSCI (Business, Finance), publisher WILEY, with an impact factor of 4.9 based on the JCR 2023. The other five articles were published in five different journals.

Table 8 also includes the journal’s impact factor in the year of publication. In the case of the three articles published in the Journal of Finance, the impact factor increased from 2.549 in 2004 to 5.397 in 2016, but the average number of citations is lower for the article published in the year in which the impact factor is higher, namely 25.44 (article published in 2016) compared with 38.57 (article published in 2004). Indeed, after a longer period of time from the date of publication, more citations can be accumulated, but in the case of the selected articles, the time interval is over 5 years; therefore, it is not possible to know how many citations each article will accumulate in the future. Thus, we cannot assess that the number of citations would be influenced by the impact factor of the journal, but rather by the topic of the article and also by the research interest existing at a given time in economic and business research. There are also other criteria that establish the relevance of a journal, such as the article influence score, but we do not believe that this would significantly influence the number of citations.

Indeed, it would be useful to identify in bibliometric analyses other factors that would show influence on the accumulation of citations but considering that bibliometric analyses are carried out based on information downloaded from databases, there is a condition that this additional information can be easily downloaded in order to be included in the analyses.

The research objective of the articles presented in Table 8 will be detailed in the following.

DeFond and Zhang (2014) studied different methods used to measure audit quality, relating an analysis of their strengths and weaknesses. The close connection between the quality of financial reporting and the quality of audit processes is highlighted, this being the starting point in the study carried out by the authors, with the aim of bringing clarifications regarding the necessary guidelines for carrying out a quality audit.

Kothari et al. (2009) conducted a study regarding managers’ inclination to reveal bad news to company management later, and good news as soon as possible. They affirm this hypothesis, and through the conducted study they try to prove it, bringing evidence to correlate this attitude with the fluctuation of the stock price, related to the company’s prestige.

Campbell et al. (2008) conducted a study on the factors that contribute to establishing the state of corporate failure and financial distress, related to certain accounting and market variables. The research was carried out in two stages, the first being an empirical study regarding financial distress, and the second being based a study case to measure the probability that a company will go bankrupt based on an econometric model, concluding that the companies in financial distress have low average returns, not high, highlighting a series of anomalies related to the subject of predicting financial distress.

Vassalou and Xing (2004) used Merton’s option pricing model in order to estimate indicators of probability of non-fulfilment of obligations by individual firms, using capital data. Thus, it was found that the risk of loan default is closely correlated with the size and book-to-market characteristics of a company. These can be seen as implicit effects, especially the size effect. From this point of view, the difference in profitability between small and large companies is 45%/year.

Altman et al. (2017) evaluated the Z-score model’s ability to predict bankruptcy or other types of financial distress that companies may face to assess its usefulness to stakeholders. These are considered in the special banking units that must evaluate the bankruptcy risk of the companies they collaborate with. The conclusion of the study was that the model developed in 1983 still works reasonably with a prediction accuracy of 75%, an accuracy that can reach 90% if the country-specific estimates incorporating additional variables are used.

Reichelt and Wang (2010) conducted a study on audit quality, viewed from the auditor’s perspective. So, it was found that the auditors who are both national and city-specific industry specialists have the highest quality audit engagements. Evidence was found that abnormal accruals of firms audited only by city industry specialists (without also being national industry-specific specialists) are lower than those audited by non-industry specialists. In conclusion, it highlighted that auditors’ national positive network synergies, as well as the detailed knowledge of the industry by individual auditors, offer a superior audit quality.

Francis and Yu (2009) studied the differences between audit missions performed by Big 4 auditors and small audit offices. Thus, by examining a sample of 6568 observations, it was found that the hypothesis from which the study starts, namely that the audit missions reported by Big 4 auditors are of higher quality than those carried out by auditors from smaller audit offices, is validated. It was concluded that larger offices are more likely to issue audit opinions regarding the going concern assessment, at the same time proving a less aggressive behavior in terms of earnings management.

Khwaja and Mian (2008) analyzed how liquidity shocks in banks affect large firms and small firms differently. The conclusion of the study was that large companies manage to compensate for these shocks through additional loans from the credit market, while small companies face significant decreases in loans and growing financial distress.

Girardi and Ergun (2013) carried out a study in which they brought something new to the field of predicting financial distress, thus changing the definition of financial distress within an existing variable, CoVaR (Conditional Value-at-Risk), a variable that appeared as a result of previous studies from another variable, VaR (Value at Risk). The authors took into account severe danger events that can occur within a company with the aim of facilitating the extrapolation of data at the level of several companies, by means of the previously mentioned variables.

Serfling (2016) addressed the issue of firing costs, which lower optimal debt ratios by increasing the costs of financial difficulties. Because companies facing financial difficulties are forced in some cases to reduce staff, in order to cover cash deficits, the additional costs of dismissal increase as well as the total cost of the danger faced by the entity. At the same time, he claims that the costs associated with the dismissal of workers affect decisions regarding the capital structure.

The most relevant articles previously presented can be grouped into two significant topics addressed, as shown in

Table 9. The research topics.

Topic	Article Title	Authors
Audit quality	A review of archival auditing research	DeFond, M.; Zhang, J.
	National and Office-Specific Measures of Auditor Industry Expertise and Effects on Audit Quality	Reichelt, K. J.; Wang, D.
	Big 4 Office Size and Audit Quality	Francis, J. R.; Yu, M. D.
Management and Corporate governance	Do Managers Withhold Bad News?	Kothari, S. P.; Shu, S.; Wysocki, P. D.
	In Search of Distress Risk	Campbell, J. Y.; Hilscher, Je.; Szilagyi, J.
	Default risk in equity returns	Vassalou, M; Xing, YH
	Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z Score Model	Altman, E.I.; Iwanicz-Drozdowska, M.; Laitinen, E. K.; Suvas, A.
	Tracing the Impact of Bank Liquidity Shocks: Evidence from an Emerging Market	Khwaja, A. I.; Mian, A.
	Systemic risk measurement: Multivariate GARCH estimation of CoVaR	Girardi, G.; Erguen, A. Tolga
	Firing Costs and Capital Structure Decisions	Serfling, Matthew

Source: own work based on WoS data.

.

As a complement to the answers to RQ2, Table 9 shows there is an important link between the going concern and financial distress topics and the audit sector, as mentioned in the Introduction, and the quality of the audit engagement depends on many factors, influencing even the going concern assumption and the financial distress risk.

Also, it reveals that the management of the company plays an essential role when talking about the capability of the entity to keep operating in the foreseeable future, as their decisions influence the operationality of the company. Moreover, the articles included in the second topic address some methods of predicting the financial distress and also many risks that a company may face in the event of applying for borrowings. These are just some of the risks and threats that management should take into consideration when analyzing the compliance with the going concern principle.

Previous bibliometric analyses related to going concern assessment and financial distress prediction have referred to intelligent techniques in bankruptcy prediction for corporate firms (Shi and Li 2019), relationships between going concern and investor confidence (Hammond and Opok 2023) and future trends, research directions and content analysis regarding financial distress (Sa’diyah et al. 2022). Also, Rashid et al. (2024) discussed the factors and consequences of bankruptcy in SMEs and conducted a bibliometric analysis that contains a cluster referring to financial difficulty, which is similar to the results of our research.

We have achieved our objective of summarizing the research undertaken in this field and the originality of this paper consists in the identified answers to the research questions so that the obtained results can provide additional contributions to the existing literature. Identifying the most cited articles on a topic helps identify gaps for further studies, so this bibliometric analysis can be used to discover research opportunities in this area (Ferreira 2018; Bota-Avram 2023).

5. Conclusions

The literature review, as well as the conducted research, highlighted that the accounting principle of “going concern” and the phenomenon of “financial distress” represent a large-scale topic for studies. The objective of this research was to identify the published articles on the fields of going concern and financial distress through a bibliometric analysis of the links between key concepts, authors, number of average citations per year and the journals in which those articles were published. Also, a detailed analysis of the 10 most relevant articles carried out. During the research, links were identified between the studied topics and other fields of study, such as accounting, audit or corporate governance.

The main conclusion is that there was a transition between these concepts over time, and the interest for the “financial distress” topic increased during the studied period. The studies carried out have highlighted the factors that lead to a high level of financial distress within a company that was facing the risk of bankruptcy or insolvency. A possible explanation can be the fact that, before reaching a state of insolvency or even bankruptcy, a company is facing financial distress problems and risks that lead to not respecting the principle of going concern. And because of this, it is more useful to study how to avoid this state in a company by identifying the factors, the risks and the ways to recovery from the company’s activity.

The results obtained can be useful to professionals in the field of accounting, insolvency, and bankruptcy practitioners and even for auditors, because of the analysis and synthesis carried out in this research. Accountants can use the information provided to understand how the data from the financial statements can help when identifying if the company is facing the risk of not respecting the going concern assessment or even the risk of bankruptcy. Also, managers should take into consideration that the way in which the company is conducted can lead to success or failure, and they bear the responsibility for the decisions and recommendations provided to the shareholders and to the ownership. Auditors can conclude that there is an essential link between the information from audited financial statements and the going concern assessment because it can provide signals regarding the company’s ability to remain operational on the market in the foreseeable future.

The limits of this study are due to the selected sample, with the articles being taken from a single database (WoS), which led to limitations in identifying any other publications that were not in that database. However, the research can be extended through a bibliometric analysis of the articles indexed in other databases’ data. This paper is also a premise for a quantitative analysis, which can include variables in an econometric model to highlight the factors influencing the going concern assessment and financial distress prediction. Also, it can serve as a base for a statistical analysis on the studied topics.