The impact of COVID-19 pandemic on financial reporting delay. Evidence from Ghana

Abstract

The widespread coronavirus (COVID-19) has sparked considerable worry among businesses worldwide, including Ghana. Despite a growing body of information on market and business reactions to the COVID-19 pandemic, there are few to no studies that have empirically examined the direct impact of COVID-19 on a firm’s financial reporting. The paper employed a panel regression model to examine COVID-19 impacts on a firm’s financial reporting delay over 3 years for 100 firms in Ghana comprising 30 listed firms and 70 private firms. The study’s empirical findings indicate COVID-19 and financial report delays have a positive and significant relationship. The study also established that the positive relationship is more pronounced for low-performing and private firms. The study recommended that a firm’s operational strategies be improved and well-coordinated during pandemics to avoid reporting and subsequent operational delays.

Keywords:

• COVID-19 pandemic
• financial reporting quality
• audit timeliness
• report delay
• Ghana

Reviewing Editor:

• Collins Ntim, University of Southampton, United Kingdom of Great Britain and Northern Ireland

JEL CLASSIFICATIONS:

• G30

1. Introduction

The timeliness of financial reporting is a fundamental characteristic that underpins the reliability and relevance of financial information in the global business landscape. It serves as a cornerstone for decision-makers, investors, and various stakeholders, ensuring the transparency, integrity, and efficiency of financial markets (Daoud et al., 2014; Drake et al., 2019). However, the imperative of timely financial reporting is not uniform across diverse economic landscapes, and the challenges in emerging economies underscore the need for a nuanced understanding of the factors influencing reporting timelines (Albu et al., 2014; Errunza & Losq, 1985; Nurunnabi, 2015).

Extensive scholarly inquiry has explored the determinants of financial report timeliness, investigating variables such as business size, profitability, internal controls, and auditing standards (Afenya et al., 2022; Albitar et al., 2020; Çelik et al., 2023; Yeboah et al., 2023). Yet, despite these efforts, a critical gap persists in the literature—specifically, a comprehensive empirical evaluation of the direct impact of COVID-19 infection on firms’ financial reporting timeliness which is deeply enshrined in an accounting theoretical evaluation remains absent.

This study aims to fill this void by adopting a theoretical lens, specifically linking agency theory with the dynamics of COVID-19 and financial reporting delay. While some prior research has examined the repercussions of the pandemic on business and audit functions (Bajary et al., 2023; Ding et al., 2020; Ruiz et al., 2020), our research uniquely contributes by integrating agency theory into the analysis. This theoretical framework allows us to discern the intricate relationship between COVID-19 infection and financial report delays, providing a nuanced understanding of the underlying mechanisms governing reporting timelines. In doing so, our study not only addresses the current gap but also adds depth to the scholarly discourse by establishing a theoretical foundation for exploring the impact of global events on financial reporting within the agency theory framework.

As the world grapples with the aftermath of the COVID-19 pandemic, businesses face unparalleled challenges in navigating the complexities of financial reporting. The pandemic, with its far-reaching consequences on human life, economies, and societal norms, has triggered an upheaval in the functioning of firms across sectors (Castañeda‐Navarrete et al., 2021; Mackenzie & Smith, 2020). Travel restrictions, remote work mandates, and supply chain disruptions have added layers of complexity to routine business processes, including the financial closing procedures crucial for timely reporting (Rababah et al., 2020).

Amid the challenges posed by the COVID-19 pandemic, our research occupies a pivotal position where the intricacies of the pandemic intersect with the priorities of financial reporting. Departing from conventional studies, our study uniquely contributes by conducting a meticulous analysis deeply enshrined on an accounting theoretical evaluation. This departure aims to carve a niche in the literature, offering empirical insights into an unexplored facet of the pandemic’s repercussions on corporate reporting. Also, unlike existing studies that have explored market and business reactions to the pandemic (Decker & Haltiwanger, 2022; Klöckner et al., 2023; Xue et al., 2021), our research stands out by delving into the heart of financial reporting timeliness, adding granularity to the existing knowledge base. By empirically exploring the interplay between COVID-19 infection and financial report delays, our study not only enhances empirical understanding but also establishes a robust theoretical foundation, providing a nuanced perspective for guiding future policy decisions.

In elucidating the relationship between COVID-19 infection and financial report delays, our study navigates through the intricate web of factors that contribute to reporting timelines. The study indicates that COVID-19 altered financial reporting patterns. Specifically, the study provides evidence that COVID-19 has impacted the elements of a company’s reporting timeliness. The study shows evidence of a positive and significant relationship with financial report delay COVID-19, implying that the higher the COVID-19 infection in a company, the longer the financial report delay days. This evidence is chiefly attributed to the late filing of records and the auditor’s inability to access the firm’s financial report books quickly, unlike the normal times before the covid pandemic audit. Furthermore, the research discerns variations in the impact across different categories of firms, unraveling disparities in reporting delays based on performance and corporate structure.

The remaining portion of the paper is organized as follows: Section 2 provides a literature review and hypothesis development on financial report delays. The articulation of hypotheses is informed by the intricate interplay between agency theory, financial report timeliness, and the unprecedented challenges posed by the COVID-19 pandemic. Section 3 describes research data and methodology; Section 4 provides empirical results and interpretations; and Section 5 offers a conclusion and makes a policy recommendation.

2. Literature review

2.1. Key observations regarding the COVID-19 pandemic in Ghana

Ghana is being affected by the Coronavirus pandemic like other countries around the globe since the World Health Organization (WHO) confirmed it as a global pandemic on 11th March 2020 (Lone & Ahmad, 2020). Ghana has suffered under this pandemic as COVID-19 has impacted many economies and claimed millions of lives as the virus spread rapidly all over the world. Before recording the first case on 12th March 2020 (Aduhene & Osei-Assibey, 2021), the government of Ghana took prudent steps to respond to any expected cases. Based on flights and passenger traffic, Ghana evaluated its ability to respond to any suspected or confirmed cases of COVID-19.

The Government of Ghana pledged initial funding of 2.8 million Ghanaian cedis to assist in disaster preparedness in the country. In addition, the health authorities have carried out various activities to strengthen systems along the theme lines. In this regard, the government adopted and announced a raft of measures before and after Ghana recorded its first case in line with the objectives of the country’s response. The prevention and control measures include mandatory 14-day quarantine for Ghanaian citizens and persons with residence permits arriving in the country, ban of points of entry, the ban on travel into Ghana by foreigners from countries with at least 200 cases, international travel by public officials except for special cases was suspended, suspension of all public gatherings for four (4) weeks, closure of all schools except final year students who were preparing for their examinations and many others. Regardless of the measures above, the number of positive cases recorded rose dramatically within 15 days from just two (2) cases on 12th March 2020 to 137 cases on 27th March 2020.

Ghana was the second country with the highest number of confirmed cases of coronavirus (COVID-19) in the West African region, after Nigeria, and the seventh country across Africa, behind South Africa, Egypt, Ethiopia, Kenya, Algeria, and Nigeria as of 3rd December 2020. The pandemic has impacted and affected all groups of persons across the Ghanaian social level. As of 3rd December 2020, 51,667 confirmed cases had been reported, with 50,547 recoveries and 323 deceases. The astronomic increase within 15 days proved to be a forerunner of COVID-19 incidence in Ghana and prompted the imposition of restrictions on the movement of persons (lockdown) in the Greater Accra Metropolitan Area and Greater Kumasi Metropolitan Area by the president from 30th March 2020 to 20th April 2020.

The COVID-19 pandemic has severely affected Ghana in a multidimensional way. Growth has slowed, financial conditions have tightened, and exchange rates have come under pressure. For example, GDP growth was projected to drop from 6.8% to 0.9% by the end of 2020. A rise in unplanned social and health expenditures, foreign direct investment, linear growth in the hospitality industry, decrease in revenues due to severe shortfalls in tax revenues, oil revenues, and import tariffs. The COVID-19 pandemic has resulted in significant adverse economic and operational impacts on numerous organizations and has presented personal challenges for individuals in accomplishing their tasks amidst this period of uncertainty (Metwally et al., 2021), especially in financial reporting.

2.2. Theoretical analysis and hypothesis development

The study draws on agency theory to elucidate the intricate dynamics between management and shareholders, unraveling the inherent challenges arising from information asymmetry (Bendickson et al., 2016). This theory posits that when agents possess valuable information sought by principals, it may lead to a misuse of obligations. Berle and Means (1991) underscored the propensity for managers to pursue self-interests due to the separation of ownership and control in large corporations. The consequence often manifests as conflicting goals with the principle of maximizing shareholders’ wealth (Shleifer & Vishny, 1989), resulting in information imbalances.

Regulations mandating timely submission of financial statements serve as a corrective measure to mitigate these information imbalances (Mathuva et al., 2019). However, the advent of the COVID-19 pandemic has introduced unprecedented challenges. The ensuing preventive measures, including business closures and remote work, disrupted the conventional flow of information. This disruption, coupled with the government-imposed restrictions, has a profound impact on the statutory reporting timelines.

The financial audit report delay, defined as the duration between the mandatory date of audited financial statement publication and the actual issuance of the annual report (Khaksar et al., 2022), becomes exacerbated in the backdrop of the pandemic. The multifaceted reasons behind financial report delays encompass the size and profitability of the business, accuracy of financial data, internal controls, and, notably, epidemics (Fatmawati & Rohimah, 2022; Johnson, 1996). The unexpected nature of the COVID-19 pandemic amplifies these challenges, making it arduous for businesses to generate financial statements and auditors to conduct audits.

The pandemic introduces complexities, making it harder for businesses to compile financial statements (Tibiletti et al., 2021). Its ripple effect across various sectors, from hospitality to finance (Aharon et al., 2021), amplifies uncertainties and delays. Reduced service delivery and layoffs, resulting from the pandemic, further impede financial report creation due to personnel infections, communication challenges, inventory counting difficulties, and technological obstacles.

Intercommunication hurdles triggered by the pandemic and related restrictions, compounded by the shift to remote work, impede physical inventory counts and affect audit procedures. Hence, building upon the interplay elucidated by agency theory and the challenges posed by the COVID-19 pandemic, the study hypothesizes a positive relationship between COVID-19 infection and financial reports delay.

3. Research methodology

This subsection provides information on the methodological approach adopted in the study. The specific areas include sample selection, variable definitions and measurement, etc.

3.1. Sample selection

As of the end of 2020, the study’s target sample included all corporate organizations in Ghana (both listed and unlisted). Given the study’s aim, we employed the purposive sampling technique (Guarte & Barrios, 2006). The data spans the years 2018–2020 and includes up to 300 observations. The study’s data was gathered from two main sources. First, the annual financial statements of 30 Ghana stock exchange-listed companies were gathered. Second, the study data is gathered from the annual financial reports of 70 unlisted enterprises. The companies are from 10 sectors. These sectors are Agriculture, Health and Beauty, Food and Beverages, Retail, Investment and Education, Manufacturing, Mining, Oil and Gas, Print and ICT, and Telecommunication.

3.2. Dependent variable

Financial report delay is the dependent variable in this chapter. Reports delays are occasioned by firms’ reports being published matching the current publishing date with the average dates in previous years and finding the difference in days as employed by Lukason and Camacho-Miñano (2019). In Ghana, firms are mandated to publish their report at least 90 days before the closure of their accounting year (Appiah-Konadu et al., 2022). As such, delay days are calculated as the published financial reports later than 90 days (3 months) after the close of the financial year.

3.3. Independent variable

COVID-19 infection is the study’s key independent or explanatory variable. A dummy variable is formed using the number of infected persons in each region. A good design instrument was utilized in collecting data on firms that were double-checked with the number of infections data as ascertained from the Ghana Health Service for each region. Therefore, this variable is estimated as a dummy variable where ‘1’ is indicated if COVID-19 infects a person of each region within a year, and ‘0’ if otherwise.

3.4. Control variables

Numerous control variables were used in the research on the impact of COVID-19 infection on firm financial report delays based on reviews of the existing literature on the subject. Even though they are not the subject of this study, these control variables were used since they have been shown in the literature to have a possible influence on the response variable. The control variables used are firm growth, firm size, BIG4, return on assets, board size, firm age, board independence, and leverage. These are adapted from the works of (Jaggi & Tsui, 1999; Leventis & Weetman, 2004; Owusu-Ansah, 2000; Sengupta, 2004) and have all been identified as predicting financial report timeliness (Musah et al., 2022).

3.5. Empirical strategy and model

The study emulates (Afenya et al., 2022; Benedict et al., 2021) and used a multivariate standard panel-data technique regression with multiple fixed effect settings to compensate for various business and industry-level heterogeneity factors that could potentially alter the empirical relationship between the major variables of interest (Lawal & Shinozawa, 2022). The baseline model specification is written as follows: (1) $${\mathit{\text{DELAY}}\_\mathit{\text{DAYS}}}_{\mathit{\text{it}}}=\alpha +{\beta }_1\left({\mathit{\text{COVID}}\_\mathit{\text{INF}}}_{\mathit{\text{it}}}\right)+{\beta }_2\left({\mathit{\text{FRM}}\_\mathit{\text{SIZE}}}_{\mathit{\text{it}}}\right)+{\beta }_3\left({\mathit{\text{ROA}}}_{\mathit{\text{it}}}\right)+{\beta }_4\left({\mathit{\text{LEV}}}_{\mathit{\text{it}}}\right)+{\beta }_5\left({\mathit{\text{GROWTH}}}_{\mathit{\text{it}}}\right)+{\beta }_6\left({\mathit{\text{BRDSIZE}}}_{\mathit{\text{it}}}\right)+{\beta }_7\left({\mathit{\text{BIG}}4}_{\mathit{\text{it}}}\right)+{\beta }_8\left({\mathit{\text{AGE}}}_{\mathit{\text{it}}}\right)+{\beta }_9\left({B\mathrm{R}D\_\mathit{\text{IND}}}_{\mathit{\text{it}}}\right)+\mathrm{\text{yea}}{r}_{\mathit{\text{dummy}}}+\mathit{\text{fir}}{m}_{\mathit{\text{dummy}}}+{\epsilon }_{\mathit{\text{it}}}\dots \dots \dots \dots \left(1\right)$$(1)

Where DELAY_DAYS is the Published financial report later than 3 months after the close of the financial year. COVID_INF is a dummy variable where 1 represents confirmed COVID-19 infection in each region in a year and 0 if otherwise. GROWTH is the natural log of change in annual revenue.

AGE is the number of years a company has been operating. BIG4 is a dummy variable ‘1’ if the firm is audited by Big4, and ‘0’ if otherwise. ROA is the ratio of net income to total assets. LEV is the ratio of total debt to total assets. Board independence is the number of board members who are independent from the firm executives. The number of board directors is BRDSIZE. FRM_SIZE is the size of the firm, estimated as the Log of Total Assets.

4. Results and discussion

4.1. Summary statistics

The study investigated the important patterns in the variables (mean, standard deviation, maximum and minimum values, and the number of observations) using summary statistics, which provides a clear picture of the sample’s characteristics. The descriptive data for COVID-19 infections, financial report delays days, and a set of control variables suggested to influence financial report delay in the theoretical and empirical literature are provided in Table 1.

Table 1. Descriptive statistics.

Variable	Obs	Mean	Median	Std. dev	Min	Max
DELAY_DAYS	300	41.475	40.112	14.85	0.000	83.00
COVID_INF	300	0.271	0.000	0.114	0.000	1.000
FRM_SIZE	300	17.983	22.354	2.434	12.586	23.187
ROA	300	0.09	0.044	0.408	−5.650	0.825
LEV	300	0.765	0.628	1.218	0.021	21.126
BRDSIZE	300	8.03	9.251	1.813	4.000	16.000
BRD_IND	300	0.731	0.307	0.145	0.291	0.910
GROWTH	300	0.256	0.114	0.461	−0.847	5.754
BIG4	300	0.835	1.000	0.372	0.000	1.000
AGE	300	25.938	30.00	18.763	1.000	58.00

DELAY_DAYS is the Published financial report later than 3 months after the close of the financial year. COVID_INF is a dummy variable where 1 represent confirm COVID-19 infection in a region in a year and 0 if otherwise. GROWTH is the natural log of change in annual revenue. AGE is the number of years a Company has been in operation. BIG4 is a dummy variable where ‘1’ if the firm is audited by Big4, ‘0’ if otherwise. ROA is the net income /total asset. Leverage is the Total Debt to total assets. BRDSIZE is the number of board directors. FRM_SIZE is the size of the firm estimated as the Log of total Assets. BRD_IND is the percentage of non-executive board members to total board.

The study employed a multivariate conventional panel-data approach regression with several fixed effect settings to account for the company and industry-level heterogeneity factors that could potentially influence the empirical relationship between the primary variables of interest. Additionally, the investigation discovers a large difference in the dependent variable’s values; financial reports delays days from their mean value. The standard deviation of 14.85 indicates that there is considerable variation around the mean of 41.475. Financial report delay days have a minimum and maximum value of 0 and 83, respectively. These are the minimum and maximum values for Fanmilk Ltd and Unilever Ltd. This indicates that Ghana’s average audited financial report is longer than the 90-day reporting period (Owusu-Sekyere & Kotey, 2019).

The control variables include growth, firm age, Big4, firm size, leverage, return on assets, board size, and board independent. The study discovers a considerable range in firm size values relative to their mean value. The standard deviation of 2.434 indicates that there is considerable variation around the mean of 17.983. The mean exceeds the standard deviation by seven times. Firm size ranges between 12.586 and 23.187. The study found a standard deviation of 1.218 and a mean value of 0.765 for firm leverage. This association indicates that the values are substantially spread around their mean, resulting in a large standard deviation. The company leverage has a minimum and maximum value of 0.021 and 21.126, respectively.

Additionally, the ROA observations exhibit a high degree of dispersion from their mean. The mean and standard deviation of ROA are respectively 0.09 and 0.408. The ROA has a range of values between −5.650 and 0.825. Additionally, the study discovers a large range in the values of company growth relative to their mean value. The standard deviation of 0.461 indicates that there is considerable variation around the mean of 0.256. Firm growth has a minimum and maximum value of −0.847 and 5.754, respectively. The study found a standard deviation of 18.763 and a mean value of 25.938 for firm age. This association indicates that the values are substantially spread around their mean, resulting in a large standard deviation. Firm age has a minimum and maximum value of 1 and 58, respectively.

Furthermore, the observations for Big4 demonstrate a high degree of dispersion from their mean. Big4’s mean and standard deviation are 0.835 and 0.372, respectively. The mean implies that 84 percent of the enterprises under consideration were audited in Ghana during the study period by one of the Big4 audit firms. Big4 has a maximum and a minimum value of 0 and 1, respectively. The board size (BRDSIZE) observations demonstrate a high degree of dispersion from their mean. Board size (BRDSIZE) has a mean of 8.03 and a standard deviation of 1.813. The board size (BRDSIZE) has a minimum and maximum value of four and sixteen, respectively. Individual board independence observations (BRD_IND) also demonstrate a high degree of dispersion. Board Independence (BRD_ IND) has a mean and standard deviation of 0.731 and 0.145, respectively. This means that, on average, 73.1% members on the board are independent from the firm executive members. Board Independence (BRD_ IND) has a minimum and maximum value of 0.291 and 0.910, respectively.

4.2. Regression results of COVID-19 infection and financial reports delay

Although the regression findings in Table 2 include control variables, model 1 column 1 has no control variables, but model 2 column 2 includes both firm and year fixed effects as well as control variables. Robust error was used to account for any concerns about heteroscedasticity and autocorrelation. The regression findings indicate that when fixed factors are included in model 2, the R-squared value improves dramatically (Nyantakyi et al., 2023). This conclusion shows that some unobserved time-invariant firm-level characteristics have a significant effect on the relationship and are hence necessary to control for.

Table 2. Regression results of COVID-19 and financial reports delay.

DELAY_DAYS	Model 1	Model 2
COVID_INF	0.530***	0.391***
	(5.78)	(8.22)
FRM_SIZE		0.020***
		(5.01)
BRDSIZE		−0.012**
		(−2.01)
AGE		−0.006
		(−0.75)
GROWTH		0.131***
		(7.28)
LEV		0.028**
		(2.40)
BRD_IND		−0.004**
		(−2.10)
BIG4		−0.555**
		(−2.63)
ROA		−0.006
		(−0.46)
Constant	0.140**	0.172**
	(2.14)	(2.53)
Obs.	300	300
R-squared	0.383	0.501
Year FE	NO	YES
Firm FE	NO	YES

^{*, **, and ***} indicate significance at the 10%, 5%, and 1% levels, respectively. T-statistics in parenthesis. DELAY_DAYS is the Published financial report later than 3 months after the close of the financial year. COVID_INF is an indicator variable where 1 represents confirmed COVID-19 infection in a region in a year and 0 if otherwise. GROWTH is the natural log of change in annual revenue, AGE is the number of years a Company has been in operation. BIG4 is a dummy variable ‘1’ if the firm is audited by Big4, ‘0’ if otherwise. ROA is the net income/total asset. LEV is the Total Debt to total assets. BRDSIZE is the number of board directors. FRM_SIZE is the size of the firm estimated as the Log of total Assets. BRD_IND is the percentage of non-executive board members to total board.

It can be seen from the outcomes in Table 2 that both models show the impact of COVID-19 on reporting delay days in the same direction. COVID-19 infection has a positive significant impact on firms’ financial reporting delay days which suggests that COVID-19 increases the firm’s financial reporting delay days. Conferring to model 2, a unit increase in COVID-19 will result in a 0.391 unit increase in firms’ financial reporting delay days. The results support hypothesis 1, which states that COVID-19 infection is positively correlated with financial reporting delay. By implication, if the company has a higher rate of COVID-19 infection, it will also have more reporting delay days. This aligns with the findings of the study conducted by Lawal and Shinozawa in 2022, demonstrating that COVID-19 induced operational complexities, and subsequent earnings news elucidates the delays in financial reporting. The study of Bajary et al. (2023) also give credence to our findings. The study using internal audit reports reveals auditors take took longer than expected duration of time to complete their task during the COVID-19 era. Inconsistent with our study findings is the research undertaken by Harymawan and Putri (2023) which states what firm that has good corporate governance in place will have no COVID-19 era reporting lag.

Additionally, the data indicate that firm size has a positive effect on financial reporting delay at the 1% level of significance. This indicates that reporting delays will increase if the organization grows in size. This is consistent with the study of Harjoto and Laksmana (2022). In terms of leverage, organizations that are highly leveraged are likely to experience a longer reporting delay (Aksoy et al., 2021). This is because high-leverage firms have a greater tendency to manipulate their financial statements to appear trustworthy and earn the trust of creditors to get larger loans. Similarly, the results indicate that enterprises experiencing rapid expansion have a higher rate of reporting delay, which is statistically significant. Firms reporting days tend to reduce as return on asset grows, while the effect is not statistically significant. When board size and independence are increased, there is a strong probability that a company’s reporting delay days will decrease (Aksoy et al., 2021). Thus, at the 5% level of significance, a unit increase in the size and independence of the firm’s board results in a 0.014 and 0.006 decrease in reporting days, respectively. Additionally, it was discovered that firm age has a detrimental effect on firms’ reporting delays. Finally, BIG4 was found to have a substantial negative effect on firms’ reported delay days in the study sample. Consistent with our study findings on the BIG4 is the outcome of the works of Chen et al. (2022).

4.3. Robustness test

To check the robustness and sensitivity of the results obtained from the baseline regression of COVID-19 infection on reporting delay days, an alternative measure of COVID-19 was used. Unlike the baseline estimation where COVID-19 infection was treated as an indicator/dummy variable of ‘1’ and ‘0’. The confirmed total number of COVID-19 infection cases recorded in each region of Ghana in a year was utilized. This alternate measure was deployed to check whether the results were sensitive to the precise definition of the key variable used in the study. As such, it is projected that if a company has a higher rate of COVID-19 infection, it will also have high reporting delay days. The results of the robustness test are shown in Table 3.

Table 3. Robustness test of COVID-19 and financial reports delay.

DELAY_DAYS	Model 1	Model 2
NUM_CASES	0.811***	0.672***
	(7.26)	(6.45)
FRM_SIZE	0.023***	0.031***
	(4.87)	(5.67)
BRDSIZE	−0.010**	−0.016**
	(−2.15)	(−3.08)
AGE	−0.007	−0.008
	(−1.45)	(−1.03)
GROWTH	0.115**	0.201**
	(3.10)	(2.36)
LEV	0.004**	0.008**
	(2.22)	(2.15)
BRD_IND	0.013**	0.012**
	(3.16)	(2.40)
BIG4	−0.213**	−0.421**
	(−2.09)	(−3.35)
ROA	−0.006	−0.008
	(−0.15)	(−0.57)
Constant	0.234***	0.343***
	(4.96)	(5.91)
Obs.	300	300
R-squared	0.409	0.581
Year FE	NO	YES
Firm FE	NO	YES

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. T-statistics in parenthesis DELAY_DAYS is the Published financial report later than 3 months after the close of the financial year. NUM_CASES is the total number confirmed COVID-19 infection cases in each region of Ghana. GROWTH is the natural log of change in annual revenue.AGE is the number of years a Company has been in operation. BIG4 is a dummy variable ‘1’ if the firm is audited by Big4, ‘0’ if otherwise. ROA is the net income/total asset. LEV is the Total Debt to total assets. BRDSIZE is the number of board directors. FRM_SIZE is the size of the firm estimated as the Log of total Assets. BRD_IND is the percentage of non-executive board members to total board.

The results found from the robustness test using an alternate measure of COVID-19 infection are consistent with the main outcomes stated, validating that COVID-19 infection indeed upsurges the financial reports delay days because COVID-19 infection affects all lines of accounting reporting (Velayutham et al., 2021), especially accounting personnel (Dyczkowska, 2021) which in turn affects reporting timelines and cause a massive delay in reporting of financials by firms. The baseline regression result and the robustness test result both show that there is a positive and significant relationship between COVID-19 infection and firm financial reports delay. However, comparably to the baseline model results, the robustness test results of total COVID-19 infection in each region in a year have a bigger coefficient. This mirrors exactly the expectation that as the number of COVID-19 infections increases, the impact of COVID-19 infection on firm reports days delay increases further accordingly. The consistency of the coefficient signs and the significance of the robustness check results validate the robustness of the main or baseline regression results. Hence, the result of the study is robust even to an estimation of alternate proxy for COVID-19 infection.

4.4. Further analysis

4.4.1. Financial reports delays based on firms’ financial performance (low performance vs high performance)

To further investigate the magnitude to which COVID-19 infection affects organizations reporting delay, the study conducted two primary subsample studies to evaluate how the connection may change in a cross-section of enterprises based on financial performance and ownership.

As a result, the complete sample was divided into two groups, high and low performance, based on the company’s financial performance. The study divided the complete sample based on the median value of the firm’s return on equity. Low-performing firms are those whose returns fall below the median quintile, whereas high-performing companies are those whose returns fall above the median quintile. With the year and firm fixed effects for the relevant groups, the baseline estimation model (Equation 1(1) $${\mathit{\text{DELAY}}\_\mathit{\text{DAYS}}}_{\mathit{\text{it}}}=\alpha +{\beta }_1\left({\mathit{\text{COVID}}\_\mathit{\text{INF}}}_{\mathit{\text{it}}}\right)+{\beta }_2\left({\mathit{\text{FRM}}\_\mathit{\text{SIZE}}}_{\mathit{\text{it}}}\right)+{\beta }_3\left({\mathit{\text{ROA}}}_{\mathit{\text{it}}}\right)+{\beta }_4\left({\mathit{\text{LEV}}}_{\mathit{\text{it}}}\right)+{\beta }_5\left({\mathit{\text{GROWTH}}}_{\mathit{\text{it}}}\right)+{\beta }_6\left({\mathit{\text{BRDSIZE}}}_{\mathit{\text{it}}}\right)+{\beta }_7\left({\mathit{\text{BIG}}4}_{\mathit{\text{it}}}\right)+{\beta }_8\left({\mathit{\text{AGE}}}_{\mathit{\text{it}}}\right)+{\beta }_9\left({B\mathrm{R}D\_\mathit{\text{IND}}}_{\mathit{\text{it}}}\right)+\mathrm{\text{yea}}{r}_{\mathit{\text{dummy}}}+\mathit{\text{fir}}{m}_{\mathit{\text{dummy}}}+{\epsilon }_{\mathit{\text{it}}}\dots \dots \dots \dots \left(1\right)$$(1) ) is re-run. The same control variables are used in all regressions. However, they are not given for the sake of brevity. Table 4 shows the results for the impact of COVID-19 infections on corporate reporting days for low- and high-performance organizations. COVID-19 infections increase companies’ financial report delay days, according to the results based on firm financial performance subsamples. This effect is especially pronounced for companies with poor financial performance because they are unable to find the right personnel replacement and have difficulty disclosing their poor financial performance. Low-performing companies are more than prone to postpone their reports.

Table 4. Further analysis of financial reports delays based on firms’ financial performance.

	Low-performance firms		High-performance firms
DELAY_DAYS	Model 1	Model 2	Model 1	Model 2
COVID_INF	0.331***		0.048
	(7.69)		(0.86)
NUM_CASES		0.517***		0.110*
		(6.54)		(1.95)
CONTROLS	YES	YES	YES	YES
Obs.	135	135	165	165
R-squared	0.342	0.438	0.482	0.561
Year FE	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Coefficient test between the low-performance firm and high-performance firms COVID_INF chi2 (1) = 8.23 Prob > chi2 = 0.0013**
NUM_CASES chi2 (1) = 7.19 Prob > chi2 = 0.0021**

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. robust errors are in parenthesis.

4.4.2. Financial reports delays based on firms’ ownership (public firms vs private firms)

Similarly, the complete sample was divided into two categories, specifically listed and unlisted firms, in terms of firm ownership. The baseline estimate model (Equation 1(1) $${\mathit{\text{DELAY}}\_\mathit{\text{DAYS}}}_{\mathit{\text{it}}}=\alpha +{\beta }_1\left({\mathit{\text{COVID}}\_\mathit{\text{INF}}}_{\mathit{\text{it}}}\right)+{\beta }_2\left({\mathit{\text{FRM}}\_\mathit{\text{SIZE}}}_{\mathit{\text{it}}}\right)+{\beta }_3\left({\mathit{\text{ROA}}}_{\mathit{\text{it}}}\right)+{\beta }_4\left({\mathit{\text{LEV}}}_{\mathit{\text{it}}}\right)+{\beta }_5\left({\mathit{\text{GROWTH}}}_{\mathit{\text{it}}}\right)+{\beta }_6\left({\mathit{\text{BRDSIZE}}}_{\mathit{\text{it}}}\right)+{\beta }_7\left({\mathit{\text{BIG}}4}_{\mathit{\text{it}}}\right)+{\beta }_8\left({\mathit{\text{AGE}}}_{\mathit{\text{it}}}\right)+{\beta }_9\left({B\mathrm{R}D\_\mathit{\text{IND}}}_{\mathit{\text{it}}}\right)+\mathrm{\text{yea}}{r}_{\mathit{\text{dummy}}}+\mathit{\text{fir}}{m}_{\mathit{\text{dummy}}}+{\epsilon }_{\mathit{\text{it}}}\dots \dots \dots \dots \left(1\right)$$(1) ) is re-run for each group with the year and company fixed effects. The same control variables are included in all regressions, although they are not presented for the sake of convenience. The impact of COVID-19 infections on corporate reporting delay days for both public and unlisted companies is shown in Table 5.

Table 5. Further analysis for reporting delays based on firms’ ownership.

	Public firms (listed)		Private firms (unlisted)
DELAY_DAYS	Model 1	Model 2	Model 1	Model 2
COVID_INF	0.031*		0.066***
	(1.95)		(4.86)
NUM_CASES		0.084**		0.213***
		(3.14)		(5.78)
CONTROLS	YES	YES	YES	YES
Obs.	90	90	210	210
R-squared	0.237	0.313	0.421	0.462
Year FE	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Coefficient test between the public firms and private firms
COVID_INF chi2 (1) = 21.01 Prob > chi2 = 0.0000**
NUM_CASES chi2 (1) = 8.29 Prob > chi2 = 0.0033**

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. robust errors are in parenthesis.

The impact on firm financial reporting delay is more pronounced in private firms, according to the findings. This means that COVID-19 infections in firms had adversely influenced financial reporting timeliness. The extent of the impact was more pronounced for private firms. This is because some private corporations try to get around the Securities and Exchange Commission’s deadlines. Furthermore, some private companies’ corporate governance structures are not as strong as publicly listed firms.

5. Conclusion and policy recommendations

The onset of this COVID-19 pandemic has impacted all businesses, either directly or indirectly. However, there is little empirical research on the direct impact of the pandemic on financial reporting delays. This study makes significant contributions both theoretically and practically by addressing the dearth of empirical research on the direct impact of the COVID-19 pandemic on financial reporting delays, particularly within the context of Ghana. The findings, derived from a comprehensive analysis using a panel regression model encompassing 100 firms over three years, reveal a substantial and pronounced positive relationship between COVID-19 infection and financial reporting delays. Besides, the study’s results revealed a more pronounced positive impact on low-performing companies and private firms. This not only fills a critical gap in the existing literature but also offers valuable insights into the nuanced dynamics of this relationship.

Theoretical contributions of this study extend beyond the immediate context, shedding light on the intricate interplay between a global crisis and financial reporting timelines. By employing robustness tests and alternative measures of COVID-19 infection, the study ensures the reliability and validity of its outcomes, providing a foundation for future research in similar domains. The identification of a more significant impact on low-performing companies and private firms adds granularity to our understanding, enriching theoretical frameworks in the field.

Practically, the study’s recommendations for firms to establish clear policy procedures, policymakers to provide explicit guidance, and leveraging emerging information technology offer actionable insights. These practical implications extend to organizational assessments, encouraging firms to develop effective procedures and systems capable of mitigating pandemic effects. While the study acknowledges limitations, such as potential small sample bias and the novelty of the pandemic, it opens avenues for future research. Suggestions for cross-country studies, a focus on Africa, and the use of primary data and different research techniques provide a roadmap for researchers to further explore and expand the intuitive dimension of the topic under consideration. Overall, this study contributes to advancing knowledge in the field and provides valuable guidance for both academia and industry stakeholders.

Authors contributions

Abel Obeng Amanfo Ofori: Idea conceptualization and full manuscript writing. Benedict Arthur: Formal Analysis and Data curation. Michael Asiedu: Literature, Theoretical Framework. 4George Nyantakyi, Opoku Pious: Revision, editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data for this original research work will be available by the corresponding author upon request.

Additional information

Notes on contributors

Abel Obeng Amanfo Ofori

Abel Obeng Amanfo Ofori is a Lecturer at Institute of Accounting and finance policy research with barely six year of industry experience. His main research interest’s area is Auditing and Corporate Governance.

Benedict Arthur

Benedict Arthur is a Lecturer at Ningxia Normal University. His main research interests lie in the area of international finance and monetary economics and financial accounting. He is interested in Auditing, determinants, and impact of FDI in host countries, Sustainable Development (SDGs), consequences of policy action of central banks and digital money.

Michael Asiedu

Michael Asiedu is a researcher in School of Engineering, Johns Hopkins Whiting USA. His research interest spans from securities analysis, asset valuation and pricing, financial innovation and inclusion, monetary policy and growth theories.

George Nyantakyi

George Nyantakyi is a PhD candidate in the School of Accounting at Zhongnan University of Economics and Law, Wuhan, spans corporate tax compliance, environmental sustainability, and sustainable accounting. China. He has 2 years of industry experience with the Ghana Revenue Authority, Ashtwown office. His research interest.

Pious Opoku

Pious Opoku is a Ph.D. student in Accounting at Jiangxi University of Finance and Economics in Nanchang- China. He holds master’s and bachelor’s degree in accounting at Zhongnan University of Economics and Law and university for development studies respectfully. His main research interest’s area is Auditing and Corporate Governance.