Business Strategy and Auditor Report Lag: Do Board Characteristics Matter? Evidence from an Emerging Market

Abstract

This study investigates the association between business strategy and audit report lag (ARL). In addition, it reveals the moderating influence of board characteristics on this relationship. We used data collected from Egyptian firms listed on EGX100 during the period from 2014 to 2019, which were analyzed using ordinary least squares and binary logistic regression models. Our study revealed a decrease in ARL for firms adopting cost leadership or differentiation strategies. In addition, we found that ARL decreased for cost leadership firms with a higher percentage of non-executive director and board meetings. Moreover, ARL decreased for firms adopting a differentiation strategy with a higher percentage of non-executive directors. This study contributes to the literature on the potential factors affecting the link between business strategy and the quality of financial reporting by focusing on ARL, which is rarely examined in the literature, especially in emerging markets such as Egypt. The findings of this study are valuable to investors, auditors, corporate management, and other stakeholders, who should consider particular board attributes to better predict ARL and ensure the effective adoption and implementation of business strategies.

1. Introduction

Previous studies have noted that the business strategies presented according to the conceptual framework by Porter (1980) and methodology by Miles and Snow (1978) (i.e., cost leadership and differentiation strategies or defenders, and prospectors strategies) are still applicable to competition in the modern digital age (Kim et al., 2004; Banker et al., 2014). By reviewing the literature, we found that many existing studies have examined the influence of business strategies on various issues of financial reporting quality, such as disclosure (Bentley-Goode et al., 2019), earnings management practices (Wu et al., 2015; Wang & Zhao, 2018; Robiansyah et al., 2019; Purba et al., 2022), financial reporting irregularities (Bentley et al., 2013), financial reporting readability (Lim et al., 2018; Habib & Hasan, 2020), the quality of internal control systems (Bentley-Goode et al., 2017), and auditors’ reports (Chen et al., 2017). These studies highlighted the significant impact of business strategies on financial reporting quality (Wang & Zhao, 2018; Habib & Hasan, 2020; Purba et al., 2022).

This above-mentioned wide influences of business strategies explain the potential impacts of these strategies concerning business complexity and business risks (Bentley et al., 2013; Chen et al., 2017), tax avoidance (Higgins et al., 2015; Martinez & Ferreira, 2019), the cost of capital (Khedmati et al., 2019), investment efficiency (Navissi et al., 2017), and, hence, the risk of stock price collapse (Safi et al., 2022) and market share and firm financial performance (Thornhill & White, 2007; Banker et al., 2014). However, very few studies have examined the possible impact of business strategies on ARL (Choi & Park, 2021; Kim et al., 2022). This is unfortunate, because addressing the ARL issue is critical to enhance the usefulness of financial reporting to its various users (Rusmin & Evans, 2017; Endri et al., 2024). This gap motivated us to examine such a relationship in emerging markets, which suffer from weak governance and investor protection systems (Nasr & Ntim, 2018; Amara et al., 2023; Gontara et al., 2023; Khelil et al., 2023; Hessian et al., 2024).

Additionally, due to the above-mentioned critical impacts of business studies concerning corporate various issues, many scholars and professional organizations have emphasized the governance role of corporate boards in monitoring and supervising the implementation of business strategies (Stiles, 2001; Hendry & Kiel, 2004; Kim et al., 2012; Jaggi et al., 2015; Hernawati, 2020). Having an effective corporate board structure is vital to enhance audit quality (O’sullivan, 2000) and support business strategy (Choi & Park, 2021; Kim et al., 2022). However, to our knowledge, so far, the impact of board characteristics on the effectiveness of business strategy in achieving financial reporting quality, including reducing audit report lag (ARL), has not been tested in the literature. Thus, in this study, we are also concerned with addressing the potential moderating influence of board attributes concerning the relationship between business strategy and ARL.

To test the study hypotheses, the researchers relied on data from Egyptian listed firms on the EGX100 index during the period from 2014 to 2019. The final sample size was composed of 426 observations. Our data analyses revealed a decrease in ARL for firms adopting cost leadership or differentiation strategies. Further, we found that ARL decreased for cost leadership firms with a higher percentage of non-executive director and board meetings, and for firms adopting differentiation strategy with a higher percentage of non-executive directors. This study contributes to previous studies in some respects. Firstly, it contributes to the existing dearth of research on the potential moderating factors affecting the relationship between business strategy and the quality of financial reporting (e.g., Choi & Park, 2021; Kim et al., 2022) by focusing on ARL.

Secondly, to the best of the researchers’ knowledge, this is first study to examine the impact of business strategy on ARL in the Egyptian emerging market, with a focus on the governance role of corporate boards of directors. Focusing on the Egyptian emerging market is important because of its unique attributes in a developing context with emerging governance structures and audit regulations, in contrast to developed contexts such as the USA and UK (Saleh & Ragab, 2023). For instance, in 2016, the Egyptian Financial Regulatory Authority (EFRA) updated the governance code with comprehensive governance regulations to be followed by listed companies. These regulations stressed the importance of disclosing governance information, including board characteristics (Diab & Eissa, 2024). However, the Egyptian business context still suffers from weak legal enforcement, undermining its monitoring and compliance with existing regulations (Mostafa, 2016). These characteristics could influence the quality of financial and auditing reporting (see El-Sayed Ebaid, 2012).

Finally, the results of this study are useful to investors, auditors, corporate management, and other stakeholders, who should consider board characteristics, especially board independence and frequent board meetings, to better predict ARL and ensure the effective adoption and implementation of business strategies.

The remainder of the paper is arranged as follows. Section 2 introduces the theoretical background and hypotheses’ development. Section 3 presents research methods. Section 4 displays and discusses the empirical results. Finally, Section 5 and Section 6 discuss the findings and conclude the paper, respectively.

2. Literature Review and Hypotheses Development

2.1. Business Strategy Defined

Business strategies aim to achieve a distinct position for an organization in its industry by offering unique activities, implementing operations differently from competitors, and achieving consistency between all the organization’s activities to achieve strategic objectives (Porter, 1996). According to Porter (1980), organizations may rely on cost leadership and differentiation strategies. A cost leadership strategy can be attained through using a firm’s unique capabilities to produce products at the lowest possible cost while maintaining a certain level of quality. Cost leadership organizations work to benefit from economies of scale because of large investments in tangible assets, the relentless pursuit of cost reduction, tight control over direct and indirect costs, and reducing spending in some areas such as research and development, services, salespeople, and advertising (Jermias, 2008; Fernando et al., 2016). In contrast, differentiation strategies are based on developing unique characteristics for the products or services offered to customers, which allows organizations to obtain a price premium. A differentiation strategy is achieved by investing in intangible assets such as research and development, advertising, and services (Banker et al., 2014; Fernando et al., 2016).

2.2. Business Strategy and ARL

According to the resource dependency theory, a competitive advantage can be achieved when an organization exploits its unique resources to adopt successful business strategies (Andrews, 1997). This view is supported by previous studies that suggested the positive impact of business strategies on market share and financial performance (Thornhill & White, 2007; Banker et al., 2014), investment efficiency (Navissi et al., 2017), tax avoidance practices (Martinez & Ferreira, 2019), internal control systems’ quality, and business risks (Bentley-Goode et al., 2019). These broad impacts of business strategies invited recent researchers to examine their influence on the quality of financial reporting (Chun & Cho, 2017; Widuri & Sutanto, 2018; Purba et al., 2022).

Thus, it is anticipated that business strategies may have a role to play concerning auditor report lag (Choi & Park, 2021). This is based on the view that business strategies can affect the degree of uncertainty, level of risk acceptance, and degree of complexity of organizational structures and the complexity of firms’ operations (Bentley et al., 2013). This, in turn, could affect auditors’ assessments of audit risks and the scope of audit work, and, hence, it might result in delaying audit reports (Choi & Park, 2021; Kim et al., 2022; Endri et al., 2024). In this regard, focusing on South Korea, Choi and Park (2021) noted a significant negative effect of the efficiency strategy on the probability of delaying audit reports. However, they did not find any significant effect of the innovation strategy on the probability of delaying audit reports. In the same context, Kim et al. (2022) found a significant negative effect of the efficiency strategy on the probability of delaying auditor reports, while they found a significant positive effect of the innovation strategy on the probability of delaying auditor reports. Considering the existing scarce and mixed evidence regarding the possibility of an association between business strategies and ARL, we examine such a relationship in a novel emerging context. Consequently, H1 is formulated as follows:

Hypothesis 1 (H1). 

Having a business strategy is likely to be negatively associated with ARL.

Hypothesis 1a (H1a). 

Having a product differentiation strategy is likely to be negatively associated with ARL.

Hypothesis 1b (H1b). 

Having a cost leadership strategy is likely to be negatively associated with ARL.

2.3. The Moderating Influence of Board Characteristics

The above-mentioned advantages resulting from adopting an appropriate business strategy might hinge on some external factors specific to the industry and competitors, and how firms possess the resources necessary to achieve a sustainable competitive advantage, as informed by the resource dependency theory (Barney, 2002; Coeurderoy & Durand, 2004; Banker et al., 2014). Such advantages may also depend on the tendency of corporate management to engage in opportunistic practices that can undermine the quality of financial information, which necessitates the existence of effective monitoring and governance mechanisms, as informed by the agency theory (Castañer & Kavadis, 2013).

In other words, from the agency theory perspective, the existence of an effective corporate board is crucial to reduce conflicts of interest between shareholders and management, reduce agency costs, and protect shareholders’ rights (Zahra & Pearce, 1989). Along with this view, recent studies have started to emphasize the role of corporate governance in achieving business strategies and maximizing the awaited positive effects of their implementation (Effiok et al., 2012; Novatiani et al., 2018; Hernawati, 2020; Habib et al., 2024). For instance, Hernawati (2020) emphasized the important role of the board of directors in choosing a business strategy, by providing executive management with useful insights into environmental conditions. These insights are anticipated to reduce psychological biases and opportunistic motives made by management, contributing to an effective choice and implementation of business strategy.

Therefore, it is believed that the governance role of the board of directors might affect the quality of financial and auditing reports (Tauringana et al., 2008; Mathuva et al., 2019). Considering the above, we expect the characteristics of the board of directors to affect the relationship between business strategy and ARL (Basuony et al., 2016; Eissa & Hashad, 2021), as further explained below.

2.3.1. The Influence of Board Size

It is expected that large boards of directors will include a variety of experiences, which can improve the process of oversight and supervision over executive management (Basuony et al., 2016). From this view, it is suggested that board size would be positively associated with better corporate transparency, and, hence, an improved financial reporting quality (Elamer et al., 2020). In contrast, other scholars have argued that larger boards can result in difficulties in coordination and communication, which increase agency conflicts and undermine corporate performance (Eisenberg et al., 1998; Haque & Ntim, 2018). Thus, we believe that board size can play a part concerning the association between business strategy and financial reporting quality (Alzoubi, 2014), and hence, we set H2 as follows:

Hypothesis 2 (H2). 

Board size is likely to affect the relationship between business strategy and ARL.

Hypothesis 2a (H2a). 

Board size is likely to affect the relationship between differentiation strategy and ARL.

Hypothesis 2b (H2b). 

Board size is likely to affect the relationship between cost leadership strategy and ARL.

2.3.2. The Influence of Board Meetings

The frequency of board meetings indicates the level of board activity, and, thus, it might enable the board of directors to have effective oversight over executive management (Botti et al., 2014). From this perspective, it is believed that the frequency of board meetings could support board directors in carrying out their duties according to shareholders’ interests (Salim et al., 2016; Diab et al., 2023). In contrast, another view suggests that the frequency of board meetings might not allow much time for independent directors to better oversee managerial activities. This, in turn, would cause a time shortage for directors to exchange meaningful views (Jensen, 1993). This issue could result in a communication problem in a way that might undermine governance and corporate performance (Vafeas, 1999). Thus, we believe that the frequency of board meetings might affect the business strategy–ARL relationship, and hence, we set H3 as follows:

Hypothesis 3 (H3). 

The frequency of board meetings is likely to affect the relationship between business strategy and ARL.

Hypothesis 3a (H3a). 

The frequency of board is likely to affect the relationship between differentiation strategy and ARL.

Hypothesis 3b (H3b). 

The frequency of board is likely to affect the relationship between cost leadership strategy and ARL.

2.3.3. The Influence of Board Independence

While executive directors have in-depth knowledge of the competitive position of their organization, non-executive directors provide an external perspective that can enrich corporate decision making. Outside or independent directors are in a better position to protect their reputation compared to insiders, which induces them to effectively oversee managerial issues. From this perspective, it is believed that the inclusion of more independent directors in corporate boards could enhance the monitoring of corporate activities, which, in turn, might affect financial reporting quality (Elamer et al., 2020; Eissa & Hashad, 2021). In contrast, other scholars have noted that independent directors’ representation on corporate boards might bring negative effects, because their existence could undermine CEOs’ inclination to share information with other board members. This, in turn, is anticipated to increase uncertainty in firms in a way that might affect their financial reporting quality (Porter & Sherwood, 2023) and strategic direction in the long run (Sarkar & Selarka, 2021). Considering these views, we believe in the significant influence of board independence on the business strategy–ARL relationship, and, hence, we set H4 as follows:

Hypothesis 4 (H4). 

Board independence is likely to affect the relationship between business strategy and ARL.

Hypothesis 4a (H4a). 

Board independence is likely to affect the relationship between differentiation strategy and ARL.

Hypothesis 4b (H4b). 

Board independence is likely to affect the relationship between cost leadership strategy and ARL.

2.3.4. The Influence of CEO Duality

Some studies support the value of CEO duality concerning corporate various issues, including financial reporting quality. For instance, Klein (1998) noted that CEO duality can enhance corporate disclosure. Gerged (2021) revealed that CEO duality may result in better disclosures of nonfinancial performance. However, numerous studies have argued against CEO duality. This is mainly based on the view that it maximizes CEO power, which may negatively influence directors’ capability to effectively oversee managerial activities (Haniffa & Cooke, 2005; Hussain et al., 2018). According to this view, the combination of the function of the CEO and the chairmanship of the board creates a conflict of interest, which increases the concentration of power and influence of the executive director. This situation harms the independence of the board and limits its supervisory and monitoring capacity (Afify, 2009; Khlif & Samaha, 2014; Basuony et al., 2016). The aforementioned studies imply a significant effect of CEO duality concerning business performance and reporting quality. Consequently, the last hypothesis is formulated as follows:

Hypothesis 5 (H5). 

CEO duality is likely to affect the relationship between business strategy and ARL.

Hypothesis 5a (H5a). 

CEO duality is likely to affect the relationship between differentiation strategy and ARL.

Hypothesis 5b (H5b). 

CEO duality is likely to affect the relationship between cost leadership strategy and ARL.

3. Research Methods

3.1. Sample and Data Collection

This study is based on a sample of firms listed on the EGX100 index during the period from 2014 to 2019. The data were collected from financial statements and corporate governance reports obtained from the Egyptian Information Dissemination Company. We exclude data related to banks and other financial institutions, as well as institutions with missing data during the study period. In total, 29 companies were excluded, resulting in a final sample of 71 companies, with 426 observations.

Table 1. The sample of the study.

Panel A: Sample Selection Methodology
	No. of Firms	Observations
Firms listed on EGX100 (2014–2019)	100	600
(−) Firms belonging to Financial and Banking	(22)	(132)
(−) Firms with missing data	(7)	(42)
Final sample	71	426
Panel B: Sample Distribution by Sector
	%	Observations
Food and Drinks	15.49%	66
Construction and Building Materials	12.68%	54
Industrial Products and Services Automotive	9.86%	42
Tourism and Recreation	7.04%	30
Housing and Real Estate	15.49%	66
Home and Personal Products	8.45%	36
Basic Resources	8.45%	36
Chemical Industries	9.86%	42
Telecommunications and Technology	4.22%	18
Health Care and Pharmaceuticals	2.82%	12
Media	1.41%	6
Distributors and Retail	1.41%	6
Gas and Mining	2.82%	12
Total	100%	426

displays the sample selection methodology in Panel A and sample distribution by sector in Panel B. All the continuous variables were winsorized at 5% tails to lessen the effect of outliers.

3.2. Variables Measurement

3.2.1. Board Characteristics

In previous studies, the researchers relied on some generally accepted board characteristics (e.g., Botti et al., 2014; Basuony et al., 2016), namely, the size of the board of directors (BSize_it), the number of board meetings (BMeetings_it), the combination of the function of the CEO and the chairmanship of the board (Duality_it), and the percentage of non-executive members on the board (NonExecutive_it). It is expected that large boards of directors, as well as the existence of more independent directors, would result in a variety of experiences and improve the process of oversight over executive management (Afify, 2009; Basuony et al., 2016; Eissa & Hashad, 2021). Further, the number of board meetings also provides an indicator to measure the degree of board activity, and, thus, the frequency of meetings enables the board of directors to achieve good oversight of executive management (Botti et al., 2014). On the other hand, CEO duality (i.e., the combination of the CEO’s and the chairmanship of the board’s functions) creates a conflict of interest, which increases the concentration of power and the influence of the executive director. This harms the independence of the board and limits its supervisory role and monitoring capacity (Afify, 2009; Khlif & Samaha, 2014; Basuony et al., 2016).

3.2.2. Business Strategy

Cost leadership strategy. To measure the extent to which a firm adopts a cost leadership strategy, the researchers depended on the asset turnover ratio, which is calculated as the ratio of outputs (sales revenue) to inputs (average operating assets). An increase in this ratio indicates the optimal use of the firm’s resources to achieve operational efficiency (Wu et al., 2015; Agustia et al., 2020; Dalwai & Salehi, 2021; Purba et al., 2022).

$$ATO={\displaystyle \frac{Sales}{Average Operating Assets}}$$

(1)

where, Sales—net revenue from sales and Average Operating Assets—total assets minus cash and short-term financial investments.

Differentiation strategy. To measure the extent to which a firm adopts a differentiation strategy, the researchers used the ratio of total operating income and research and development expenses to net sales revenue. This is because differentiation is closely linked to the goal of maximizing profit, based on providing products with unique characteristics and a superior quality. To achieve this, firms exert more efforts in research and development (Wu et al., 2015; Agustia et al., 2020; Dalwai & Salehi, 2021; Purba et al., 2022).

$$Profit Margin \left(PM\right)={\displaystyle \frac{Operating Income+R\&D EXP}{Sales}}$$

(2)

where, Operating Income—net operating income, R&D Exp—research and development expenses, and Sales—net revenue from sales.

3.2.3. Auditor’s Report Lag

ARL is measured by the length of time between the end of the fiscal year and the date of issuing the audit report. The longer this period, the more the delay in issuing audit reports (Afify, 2009; Khlif & Samaha, 2014; Abernathy et al., 2018; Eissa & Hashad, 2021).

3.3. Research Models

To test the first hypothesis, we measured business strategy and ARL (Section 3.2.2). Several control variables were considered, because they may have a significant impact on ARL to neutralize their effect. The most important of these variables are firm size (FSize) and financial leverage (LEV). The probability of ARL decreases with an increasing firm size, while it increases with increasing financial leverage (Abernathy et al., 2018). Return on assets (ROA) and a dummy variable of carried forward losses (Loss) were also included, because a decrease in the probability of ARL has been noticed in firms with a history of good financial performance (Khlif & Samaha, 2014). Moreover, inherent risk (RecInv) was included, because the likelihood of ARL increases with the degree of this risk (Khlif & Samaha, 2014; Abernathy et al., 2018). Further, a dummy variable for the end of the fiscal year (YEnd) was included because of a noticed increase in the probability of late financial reporting for entities whose fiscal year ends on December 31 (Abernathy et al., 2018). The fiscal year for most entities ends on this date, which puts pressure on all audit firms at that time. We also controlled for audit firm size (ASize), because the probability of ARL might decrease for firms audited by a Big Four auditor. Big Four auditors have sufficient resources and capabilities to carry out high-quality, more efficient, and timely audits (Afify, 2009; Khlif & Samaha, 2014; Abernathy et al., 2018; Eissa & Hashad, 2021). Finally, the type of industry and years under examination were used as control variables. We analyzed data depending on the OLS regression model, after ensuring that our data met OLS regression assumptions, as follows:

$$\begin{gathered} {ARL}_{it}=\alpha +B_1\left({ATO}_{it}\right)+B_2\left({PM}_{it}\right)+B_3\left({FSize}_{it}\right)+B_4\left({LEV}_{it}\right)+B_5\left({ROA}_{it}\right)+B_6\left({Loss}_{it}\right) \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+B_7\left({RecInv}_{it}\right)+B_8\left({YEnd}_{it}\right)+B_9\left({ASize}_{it}\right)+B_{10}\left(Industries\right)+B_{11}\left(Years\right) \end{gathered}$$

(3)

To test the second hypothesis, the researchers added board characteristics, as independent variables, and as interactive variables with business strategies to model No. (3), as follows:

$$\begin{array}{c}{ARL}_{it}=\alpha +B_1\left({ATO}_{it}\right)+B_2\left({PM}_{it}\right)+B_3\left({FSize}_{it}\right)+B_4\left({LEV}_{it}\right)+B_5\left({ROA}_{it}\right)+B_6\left({Loss}_{it}\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+B_7\left({RecInv}_{it}\right)+B_8\left({YEnd}_{it}\right)+B_9\left({ASize}_{it}\right)+B_{10}\left({Bsize}_{it}\right)+B_{11}\left({BMeetings}_{it}\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+B_{12}\left({Duality}_{it}\right)+B_{13}\left({NonExec}_{it}\right)+B_{14}\left({Bsize}_{it}\times {ATO}_{it}\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+B_{15}\left({BMeetings}_{it}\times {ATO}_{it}\right)+B_{16}\left({Duality}_{it}\times {ATO}_{it}\right)+B_{17}\left({NonExec}_{it}\times {ATO}_{it}\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+B_{18}\left({Bsize}_{it}\times {PM}_{it}\right)+B_{19}\left({BMeetings}_{it}\times {PM}_{it}\right)+B_{20}\left({Duality}_{it}\times {PM}_{it}\right)\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+B_{21}\left({NonExec}_{it}\times {PM}_{it}\right)+B_{22}\left(Industries\right)+B_{23}\left(Years\right)\hfill \end{array}$$

(4)

Table 2. Variables’ definitions.

Abbreviation	Variable	Measurement
ARL	Auditor report lag	The length of time between the end of the fiscal year and the date of audit report.
ATO	Cost leadership	Asset turnover ratio
PM	Differentiation	The ratio of operating income and research and development expenses to net sales revenue.
FSize	Firm size	Natural logarithm of total assets
LEV	Firm leverage	Total debt to total assets ratio.
ROA	Return on assets	Ratio of net profit before taxes to total assets.
Loss	Last year losses	A dummy variable that takes one if there are stage losses and zero otherwise.
RecInv	Latent Risk Index	Total customer balance and inventory to total assets ratio.
YEnd	Year end	A dummy variable that takes one if the firm’s fiscal year ends on December 31 and zero otherwise.
ASize	Audit size	A dummy variable that takes one if the entity is audited by a Big Four audit firm, and zero otherwise.
BSize	Board size	Number of directors on the board.
BMeetings	Board meetings	Number of board meetings.
Duality	CEO duality	A dummy variable that takes one if the CEO is also the chairman of the board, and zero otherwise.
NonExec	Non-executive director	Proportion of non-executive members on the board.

defines the variables used in the previously mentioned models.

4. Empirical Results

4.1. Descriptive Statistics

Table 3. Descriptive statistics.

Variable	Mean	Median	SD	Minimum	Maximum
ARLit	76.725	71	24.227	15	135
ATOit	0.591	0.451	0.536	0	1.9
PMit	0.151	0.115	0.315	−0.44	0.76
FSizeit	20.925	20.978	1.665	16.96	25.5
LEVit	0.428	0.397	0.263	0	1
ROAit	0.056	0.04	0.11	−0.17	0.29
Lossit	0.22	0	0.415	0	1
RecInvit	0.256	0.211	0.205	0	0.84
YEndit	0.732	1	0.443	0	1
ASizeit	0.377	0	0.485	0	1
BSizeit	8.112	8	2.884	3	17
BMeetingsit	10.119	10	4.649	2	23
Dualityit	0.704	1	0.456	0	1
NonExecit	0.696	0.75	0.209	0.14	1

shows the descriptive statistics of the research variables. The mean value of ARL was 76.7 days, with a standard deviation 24.2 days. ARL varied between 15 and 135 days, which is consistent with some studies conducted in Egypt (e.g., Afify, 2009; Khlif & Samaha, 2014). The mean value of the ATO index was 0.591, with a standard deviation of 0.536 and minimum and maximum values of 0.00 and 1.90, respectively. This implies a high variation level in the cost leadership index, indicating that not all firms in our sample pursued a cost leadership strategy. The mean value of PM was 0.151, with a standard deviation of 0.315 and minimum and maximum values of −0.44 and 0.76, respectively, indicating a high variation level in the differentiation index. These values are consistent with previous studies that adopted the same methodology in measuring business strategies (Wu et al., 2015; Agustia et al., 2020; Purba et al., 2022). The mean value of BSize was 8.112, with a standard deviation of 2.884 and minimum and maximum values of 3 and 17, respectively. The mean value of BMeetings was 10.119, with a standard deviation of 4.649 and minimum and maximum values of 2 and 23 meetings, respectively. The percentage of firms with duality was 70.4%. Finally, the mean value of NonExec was 69.6%, with a standard deviation of 20.9% and minimum and maximum values of 14% and 100%, respectively. These results show the variation in board characteristics in our sample compared to some previous studies (e.g., Afify, 2009; Khlif & Samaha, 2014).

Table 4. Correlation matrix.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1—ARLit	1
2—ATOit	−0.163 ***	1
3—PMit	−0.159 ***	−0.116 **	1
4—FSizeit	0.028	−0.095	0.236 ***	1
5—LEVit	0.144 ***	0.189 ***	−0.322 ***	−0.001	1
6—ROAit	−0.184 ***	0.333 ***	0.676 ***	0.257 ***	−0.295 ***	1
7—Lossit	0.184 ***	−0.008	−0.448 ***	−0.335 ***	0.331 ***	−0.467 ***	1
8—RecInvit	0.009	0.359 ***	−0.134 ***	−0.109 **	0.512 ***	−0.001	0.116 **	1
9—YEndit	−0.038	−0.324 **	−0.019	0.044	−0.229 ***	−0.069	−0.049	−0.316 ***	1
10—ASizeit	−0.033	−0.092	0.054	0.341 ***	−0.100 **	0.066	−0.146 ***	−0.171 ***	0.373 ***	1
11—BSizeit	−0.175 ***	0.050	0.128 ***	0.388 ***	−0.112 **	0.230 ***	−0.111 **	−0.109 **	0.219 ***	0.205 ***	1
12—BMeetingsit	0.052	0.228 ***	−0.036	0.185 ***	0.154 ***	0.179 ***	−0.065	0.168 ***	−0.287 ***	−0.136 ***	0.074	1
13—Dualityit	−0.039	0.086	−0.063	−0.077	−0.112 **	0.021	0.084 *	0.018	−0.148 ***	−0.280 ***	0.006	0.191 ***	1
14—NonExecit	−0.096 **	0.060	0.024	0.033	0.192 ***	0.102 **	−0.016	−0.189 ***	0.408 ***	0.250 ***	0.401 ***	−0.123 **	−0.275 ***	1

Where, *, **, and *** are significant at the 10, 5, and 1 percent levels, respectively.

shows the correlation matrix. The results revealed a significant negative relationship between ATO, PM, and ARL at the 1% significance level, which reflects a decrease in the likelihood of ARL for firms adopting cost leadership or differentiation strategies. Further, there was a significant positive relationship between LEV, Loss, and ARL at the 1% level. However, there was a significant negative relationship between ROA, BSize, NonExec, and ARL at the 1%, 1%, and 5% levels, respectively. There was an insignificant positive relationship between FSize, RecInv, BMeetings, and ARL. Finally, there was an insignificant negative relationship between ASize, Duality, YEnd, and ARL. The correlation results showed that the relationship between independent variables was under 0.50, and VIF (as shown in

Table 5. OLS regressions for models 3 and 4.

	Model (3)		Model (4)
	Coefficient	t-Value	Coefficient	t-Value
Constant	52.042 ***	3.126	42.893 **	2.359
ATOit	−9.826 ***	−2.999	−16.056 ***	−3.165
PMit	−10.209 *	−1.855	−2.154	−0.268
FSizeit	1.277	1.593	1.566*	1.804
LEVit	3.654	0.659	9.665*	1.707
ROAit	25.664	1.447	28.671	1.555
Lossit	8.932 ***	2.738	10.243 ***	3.249
RecInvit	−3.467	−0.477	−9.750	−1.337
YEndit	−2.955	−0.984	−2.519	−0.722
ASizeit	−5.306 *	−1.882	−5.189 *	−1.848
BSizeit			−1.444 ***	−2.958
BMeetingsit			0.269	0.953
Dualityit			4.535	1.558
NonExecit			−12.185 *	−1.848
BSizeit × ATOit			0.888	0.962
BMeetingsit × ATOit			−0.093 *	−1.792
Dualityit × ATOit			10.493 *	1.881
NonExecit × ATOit			−38.323 ***	−3.180
BSizeit × PMit			−1.025	−0.624
BMeetingsit × PMit			−0.398	−0.458
Dualityit × PMit			−10.534	−1.299
NonExecit × PMit			−72.990 ***	−3.283
Industries	Yes		Yes
Years	Yes		Yes
Observations (n)	426		426
F.test	5.328 ***		5.618 ***
R2	0.258		0.356
Adj. R2	0.209		0.292
VIF	<10		<10

Where, *,**, and *** are significant at the 10, 5, and 1 percent levels, respectively.

) was under 10, indicating that multicollinearity in the regression model was not an issue.

4.2. Regression Analysis

Table 5 shows the results of OLS regressions for models 3 and 4. R² ranged from 0.258 to 0.356 and adj. R² ranged from 0.209 to 0.292, reflecting that the independent variables interpreted 20.9% and 29.2% of the changes in ARL in model 3 and 4, respectively. The F-test resulted in values of 5.328 and 5.618 for models 3 and 4, respectively, and was significant at the 1% level. This reflects a high goodness of fit of the regression, and graphs for the goodness of fit of these regressions are shown in Appendix A. The results of model 3 revealed a significant negative relationship between ATO and ARL at the 1% significance level. This result is consistent with some previous studies (e.g., Choi & Park, 2021; Kim et al., 2022). This result reflects a higher likelihood of timely financial reporting in cost leadership firms. Likewise, the results showed a significant negative relationship between PM and ARL at the 10% level. This finding confirms a higher likelihood of timely financial reporting in differentiation firms. This result is different from Choi and Park (2021), who, using evidence from South Korea, did not find any significant effect of a differentiation strategy on ARL. It is also different from Kim et al. (2022), who indicated a higher likelihood of ARL in differentiation firms due to the complexity of their operations, organizational structures, and higher business risks. Considering the above discussion, our first hypothesis is accepted.

Furthermore, the results of model 4 showed a significant negative relationship between ATO and ARL at the 1% level. Further, the reported results revealed a significant effect of the board of director’s characteristics on the relationship between ATO and ARL. In particular, there was a significant negative effect of BMeetings × ATO on ARL at the 10% level. This finding implies that frequent board meetings increase the likelihood of timely financial reporting or reduce ARL in cost leadership firms, suggesting the acceptance of H3b. Likewise, we found a significant negative influence of NonExec × ATO on ARL at the 1% level. This finding implies that increasing the proportion of non-executive members on the board improves the likelihood of timely financial reporting or reduces ARL in cost leadership firms, suggesting the acceptance of H4b. However, our results showed a significant positive effect of Duality × ATO on ARL at the 10% level. This finding indicates that duality increases the likelihood of ARL in cost leadership firms, suggesting the acceptance of H5b. Finally, we did not observe any significant effect of BSize × ATO on ARL, indicating the rejection of H2.

Regarding the effect of a differentiation strategy on ARL, the results of model 3 showed a significant negative relationship between PM and ARL. However, the reported findings in model 4 showed that the negative relationship between PM and ARL disappeared as the board characteristics were inserted as moderators in the model, except for board independence. Particularly, concerning the existence of non-executive members on the board, the results revealed a significant negative effect of NonExec × PM on ARL at the 1% level, suggesting the acceptance of H4a. However, the results did not confirm a significant effect of the other interactive variables (BSize × PM & BMeetings × PM & Duality × PM) on ARL. Therefore, the negative effect of a differentiation strategy on ARL appears only in more independent boards, confirming the acceptance of H4a.

4.3. Additional Analysis

We re-estimated ARL using Abernathy et al.’s (2018) method, i.e., as a dummy variable that takes a value of one if a firm’s financial statements are issued after the deadline set by the Egyptian Financial Regulatory Authority (EFRA), which is three months from the end of the financial year (EFRA, 2021), and takes a value of zero otherwise. Therefore, models 3 and 4 were analyzed using Binary Logistic Regression (BLR). BLR models are useful when the dependent variable is dichotomous in nature and independent variables are categorical or continuous. The findings reported in Table 5, Panel A, indicate the significance of Chi-square values in Omnibus tests at the 1% level. This finding means that the business strategy and control variables in regression models 3 and 4 had a significant effect on ARL. Consequently, the two regression models are appropriate for predicting ARL. According to Hosmer–Lemeshow test results, the Chi-square values were 7.219 and 5.009 with a significance of 0.513 and 0.757 for models 3 and 4, respectively. These values are greater than the alpha value of 0.05, indicating that the BLR models were fitted with our data, confirming the quality of the two models in predicting the relationship between business strategy and ARL. The Nagelkerke R² values were 0.279 and 0.400 in model 3 and 4, respectively, which means that the independent variables explained 27.9% and 40% of the variations in ARL in models 3 and 4, respectively.

According to

Table 6. Binary logistic regression models 3 and 4.

Panel A, the Goodness-of-Fit Test of the Models:
Omnibus tests of model coefficients			Chi-Square		df		Sig.
Step 1 (Model 3)	Step		84.519		26		0.000
	Block		84.519		26		0.000
	Model		84.519		26		0.000
Step 2 (Model 4)	Step		42.894		12		0.000
	Block		42.894		12		0.000
	Model		127.413		38		0.000
Hosmer and Lemeshow test			Chi-Square		df		Sig.
Step 1 (Model 3)			7.219		8		0.513
Step 2 (Model 4)			5.009		8		0.757
The explanatory power of variables			−2Log likelihood		Cox & Snell R Square		Nagelkerke R Square
Step 1 (Model 3)			357.420		0.180		0.279
Step 2 (Model 4)			314.526		0.259		0.400
Panel B, Wald Test:
	Model (3)				Model (4)
	B	S.E	Wald	Exp(B)	B	S.E	Wald	Exp(B)
Constant	−21.015	0.115	0.000	0.000	−17.740	0.896	0.000	0.000
ATOit	−1.283 ***	0.463	7.694	0.277	−1.561 **	0.780	4.007	0.210
PMit	−1.744 **	0.683	6.524	0.175	−0.584	1.090	0.287	0.558
FSizeit	0.065	0.101	0.409	1.067	0.060	0.128	0.223	1.062
LEVit	1.004	0.685	2.148	2.730	1.944 **	0.829	5.506	6.988
ROAit	−5.363 **	2.329	5.305	0.213	−4.802 *	2.768	3.009	0.218
Lossit	0.499	0.403	1.533	1.648	0.691	0.441	2.453	1.966
RecInvit	−0.790	0.919	0.738	0.454	−2.043 *	1.099	3.457	0.130
YEndit	0.798 *	0.419	3.546	1.454	−0.355	0.554	0.411	0.701
ASizeit	0.253*	0.362	0.487	0.777	0.137	0.447	0.093	1.146
BSizeit					−0.233 ***	0.090	6.653	0.792
BMeetingsit					−0.011	0.042	0.069	0.989
Dualityit					0.127	0.456	0.077	1.135
NonExecit					−1.622	1.152	1.980	0.198
BSizeit × ATOit					−0.248	0.192	1.679	0.780
BMeetingsit × ATOit					−0.296 ***	0.105	7.940	0.743
Dualityit × ATOit					−0.429	0.917	0.219	0.651
NonExecit × ATOit					−7.133 ***	2.270	9.876	0.798
BSizeit × PMit					−0.091	0.230	0.157	0.913
BMeetingsit × PMit					−0.020	0.124	0.027	0.980
Dualityit × PMit					−1.192	1.123	1.127	0.304
NonExecit × PMit					−6.665 **	2.812	5.619	0.128
Industries	Yes				Yes
Years	Yes				Yes
Nagelkerke R2	0.279				0.400
Observations (n)	426				426

Where, *,**, and *** are significant at the 10, 5, and 1 percent levels, respectively.

, panel B, model 3 confirmed a significant negative relationship between ATO and ARL at the 1% level, with an Exp (B) of 0.277, which means that an increase in the cost leadership orientation increases the likelihood of delivering financial statements within the timeframe specified by the EFRA by 0.277 times. The results also confirmed a significant negative relationship between PM and ARL at the 5% level, with an Exp (B) of 0.175. This means that an increase in the differentiation orientation increases the likelihood of delivering financial statements within the timeframe specified by the EFRA by 0.175 times. These results are consistent with the basic analysis, consequently, the first main hypothesis of the study (H1) will be fully accepted.

The findings related to model 4 in Table 6, panel B, showed a significant negative relationship between ATO and ARL at the 5% level, where Exp (B) was 0.210. The results also indicated that some board characteristics have a significant effect on the relationship between ATO and ARL. Particularly, BMeetings × ATO had a significant negative effect on ARL at the 1% level, where Exp (B) was 0.743. This finding implies that frequent board meetings enhance the likelihood of delivering financial reporting within the timeframe specified by the EFRA for cost leadership firms by 0.743 times. The results also showed a significant negative effect of NonExec × ATO on ARL at the 1% level, where Exp (B) was 0.798. This finding means that a rise in the level of non-executive directors improves the likelihood of delivering financial reporting within the timeframe set by the EFRA for cost leadership firms by 0.798 times. However, we did not find any significant effect of the other interactive variables (i.e., BSize × ATO & Duality × ATO) on ARL.

The findings related to model 4 in Table 6, panel B, showed an insignificant negative relationship between PM and ARL. However, the results revealed a significant negative effect of NonExec × PM on ARL at the 5% level, where Exp (B) was 0.12. This finding reveals that increasing the proportion of non-executive directors increases the likelihood of delivering financial reports within the timeframe specified by the EFRA for differentiation firms by 0.128 times. However, the results did not show any significant effect of the other interaction variables (BSize × PM & BMeetings × PM & Duality × PM) on ARL, indicating that the significant effect of a differentiation strategy on ARL appears only when the proportion of non-executive board members is increased. Overall, these results are consistent with the results of our main analysis reported in Table 5 (Section 4.2).

5. Discussion

Measuring the effect of business strategy on ARL is one of the recent research streams that previous research has not adequately addressed. Additionally, the literature has not examined the potential governance role of corporate boards in enhancing the effectiveness of business strategies and improving their positive effects on financial reporting quality, especially reducing ARL. Thus, the current study aimed at investigating the governance role of board attributes concerning strengthening the relationship between business strategy and ARL. In doing so, we depended on a sample of Egyptian listed firms on the EGX100 index during the period from 2014 to 2019. The results showed a significant effect of business strategy on ARL in the Egyptian audit market. In other words, the period between the end of the fiscal year and the issuance date of the auditor’s report decreased with the existence of an effective business strategy. This finding indicates the likelihood of delivering audit reports within the timeframe set by the EFRA in firms that are more oriented towards cost leadership or differentiation strategies (Choi & Park, 2021; Kim et al., 2022). This finding is consistent with the resource dependency theory, which implies that firms’ unique resources enable them to implement one of Porter’s generic strategies (Furrer et al., 2008), not only contributing to achieving a competitive advantage, but also enhancing their financial reporting (and auditing) quality through issuing timely financial and audit reports (Choi & Park, 2021; Kim et al., 2022).

Our findings also revealed that board characteristics have a significant effect on the relationship between business strategy and ARL. In particular, we found that ARL decreases for cost leadership firms with an increasing proportion of non-executive members and frequent board meetings. However, the results indicated that the proportion of non-executive members has a moderating effect on the relationship between a differentiation strategy and ARL, where ARL decreases only for differentiation firms with an increase in the proportion of non-executive directors. These results are consistent with the agency theory, which emphasizes the importance of the governance role of board directors in supporting business strategies (Castañer & Kavadis, 2013).

6. Conclusions

In this study, we sought to examine the link between business strategy and ARL in an emerging market, as well as the governance role of board attributes concerning such a relationship. The findings of this study contribute to the scant literature examining the relationship between business strategy and ARL in emerging markets (Choi & Park, 2021; Kim et al., 2022). Additionally, it provides novel evidence on the moderating influence of board characteristics on the relationship between business strategy and ARL in Egypt.

Therefore, this study provides important implications for corporate management, investors, analysts, auditors, and researchers. In line with the current findings, we recommend that firms should adhere to governance rules, in general, and board independence and frequent board meetings, in particular, to enhance the success of their business strategies and ensure the quality of financial reporting. We recommend that investors, analysts, and auditors should consider business strategies, board meetings, and the proportion of non-executive directors as indicators to predict ARL.

Finally, our study is not without limitations. In particular, we did not examine the possible moderating effects of other governance mechanisms such as audit committees’ and external auditors’ characteristics. Future research could examine the effects of such variables to provide a fuller understanding of the relationship between business strategy and financial reporting quality. Further, we did not examine the implications of governance on the relationship between business strategy and internal control systems. Future research can examine this issue in emerging markets. Moreover, considering the limitation of the study period from 2014 to 2019, it is suggested that future research can extend this period to beyond 2019, which could also be beneficial in capturing the possible influence of the recent COVID-19 pandemic on the factors investigated in the current study.